From 2f86d84193cb4825b547db809ff106972c2d4b4c Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 13 Jun 2023 08:58:47 +0200
Subject: [PATCH 001/167] add getting sgtarted in the docs, and add some
docstrings examples of code
---
pyPLNmodels/models.py | 18 ++++++++++--------
1 file changed, 10 insertions(+), 8 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 91b01402..0515e053 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -2248,12 +2248,19 @@ class PlnPCA(_model):
Examples
--------
- >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data, get_simulation_parameters, sample_pln
>>> counts, labels = get_real_count_data(return_labels = True)
>>> data = {"counts": counts}
>>> pca = PlnPCA.from_formula("counts ~ 1", data = data, rank = 5])
>>> print(pca)
>>> pca.viz(colors = labels)
+
+ >>> plnparam = get_simulation_parameters(n_samples =100, dim = 60, nb_cov = 2, rank = 8)
+ >>> counts = sample_pln(plnparam)
+ >>> data = {"counts": plnparam.counts, "cov": plnparam.covariates, "offsets": plnparam.offsets}
+ >>> plnpca = PlnPCA.from_formula("counts ~ 0 + cov", data = data, rank = 5)
+ >>> plnpca.fit()
+ >>> print(plnpca)
"""
def __init__(
@@ -2490,18 +2497,13 @@ class PlnPCA(_model):
def _get_max_components(self) -> int:
"""
- Get the maximum number of components.
-
- Returns
- -------
- int
- The maximum number of components.
+ Get the maximum number of components possible by the model.
"""
return self._rank
def _pring_beginning_message(self):
"""
- Print the beginning message.
+ Print the beginning message when fitted.
"""
print("-" * NB_CHARACTERS_FOR_NICE_PLOT)
print(f"Fitting a PlnPCAcollection model with {self._rank} components")
--
GitLab
From e3fdcdac79773ef8d1ae4bf031683f05965220e5 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 13 Jun 2023 12:06:38 +0200
Subject: [PATCH 002/167] add plot_expected_vs_true. Works only for Pln, not
yet PlnPCA. change defaults of get_simulation_parameters, add a add_const =
True by default.
---
pyPLNmodels/_utils.py | 33 ++++++++++++++++++++++++++-------
pyPLNmodels/models.py | 28 ++++++++++++++++++++++++----
2 files changed, 50 insertions(+), 11 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index e0179bdb..c4f8a73e 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -462,7 +462,7 @@ def _get_simulation_components(dim: int, rank: int) -> torch.Tensor:
def _get_simulation_coef_cov_offsets(
- n_samples: int, nb_cov: int, dim: int
+ n_samples: int, nb_cov: int, dim: int, add_const: bool
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Get offsets, covariance coefficients with right shapes.
@@ -472,9 +472,14 @@ def _get_simulation_coef_cov_offsets(
n_samples : int
Number of samples.
nb_cov : int
- Number of covariates. If 0, covariates will be None.
+ Number of covariates. If 0, covariates will be None,
+ unless add_const is True.
+ If add_const is True, then there will be nb_cov+1
+ covariates as the intercept can be seen as a covariates.
dim : int
Dimension required of the data.
+ add_const : bool, optional
+ If True, will add a vector of ones in the covariates.
Returns
-------
@@ -484,7 +489,10 @@ def _get_simulation_coef_cov_offsets(
prev_state = torch.random.get_rng_state()
torch.random.manual_seed(0)
if nb_cov == 0:
- covariates = None
+ if add_const is True:
+ covariates = torch.ones(n_samples, 1)
+ else:
+ covariates = None
else:
covariates = torch.randint(
low=-1,
@@ -493,7 +501,12 @@ def _get_simulation_coef_cov_offsets(
dtype=torch.float64,
device="cpu",
)
- coef = torch.randn(nb_cov, dim, device="cpu")
+ if add_const is True:
+ covariates = torch.cat((covariates, torch.ones(n_samples, 1)), axis=1)
+ if covariates is None:
+ coef = None
+ else:
+ coef = torch.randn(covariates.shape[1], dim, device="cpu")
offsets = torch.randint(
low=0, high=2, size=(n_samples, dim), dtype=torch.float64, device="cpu"
)
@@ -607,7 +620,7 @@ def _check_two_dimensions_are_equal(
def get_simulation_parameters(
- n_samples: int = 100, dim: int = 25, nb_cov: int = 1, rank: int = 5
+ n_samples: int = 100, dim: int = 25, nb_cov: int = 1, rank: int = 5, add_const=True
) -> PlnParameters:
"""
Generate simulation parameters for a Poisson-lognormal model.
@@ -619,9 +632,13 @@ def get_simulation_parameters(
dim : int, optional
The dimension of the data, by default 25.
nb_cov : int, optional
- The number of covariates, by default 1.
+ The number of covariates, by default 1. If add_const is True,
+ then there will be nb_cov+1 covariates as the intercept can be seen
+ as a covariates.
rank : int, optional
The rank of the data components, by default 5.
+ add_const : bool, optional
+ If True, will add a vector of ones in the covariates.
Returns
-------
@@ -629,7 +646,9 @@ def get_simulation_parameters(
The generated simulation parameters.
"""
- coef, covariates, offsets = _get_simulation_coef_cov_offsets(n_samples, nb_cov, dim)
+ coef, covariates, offsets = _get_simulation_coef_cov_offsets(
+ n_samples, nb_cov, dim, add_const
+ )
components = _get_simulation_components(dim, rank)
return PlnParameters(components, coef, covariates, offsets)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 0515e053..335d7ae9 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -1248,6 +1248,14 @@ class _model(ABC):
ax = plt.gca()
predictions = self._counts_predictions().ravel().detach()
sns.scatterplot(x=self.counts.ravel(), y=predictions, hue=colors, ax=ax)
+ max_y = int(torch.max(self.counts.ravel()).item())
+ y = np.linspace(0, max_y, max_y)
+ ax.plot(y, y)
+ ax.set_yscale("log")
+ ax.set_xscale("log")
+ ax.set_ylabel("Predicted values")
+ ax.set_xlabel("Counts")
+ ax.legend()
return ax
@@ -1317,10 +1325,7 @@ class Pln(_model):
def _counts_predictions(self):
return torch.exp(
- self._offsets
- + self._covariates @ self._coef
- + self._latent_mean
- + 1 / 2 * self._latent_sqrt_var**2
+ self._offsets + self._latent_mean + 1 / 2 * self._latent_sqrt_var**2
)
def _smart_init_latent_parameters(self):
@@ -2403,6 +2408,21 @@ class PlnPCA(_model):
"""
return self._latent_sqrt_var**2
+ def _counts_predictions(self):
+ # covariance_a_posteriori = self.latent_mean.unsqueeze(1)*self._components.unsqueeze(0)
+ # covariance_a_posteriori = covariance_a_posteriori @ self._components.T.unsqueeze(0)
+ covariance_a_posteriori = torch.sum(
+ (self._components**2).unsqueeze(0)
+ * (self.latent_sqrt_var**2).unsqueeze(1),
+ axis=2,
+ )
+ # print('latent_var', self.latent_variables)
+ # print('cov:', covariance_a_posteriori)
+ # x
+ return torch.exp(
+ self._offsets + self.latent_variables + 1 / 2 * covariance_a_posteriori
+ )
+
@latent_mean.setter
@_array2tensor
def latent_mean(self, latent_mean: torch.Tensor):
--
GitLab
From 1135888d5c98b07df6d03be709bc7e3cad3d7758 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 13 Jun 2023 15:52:57 +0200
Subject: [PATCH 003/167] add the plot_expected_vs_true funciton, and works
fine for Pln and PlnPCA
---
pyPLNmodels/models.py | 35 ++++++++++++++++++++++++++---------
1 file changed, 26 insertions(+), 9 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 335d7ae9..b4c57420 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -733,7 +733,7 @@ class _model(ABC):
axes : numpy.ndarray, optional
The axes to plot on. If None, a new figure will be created. Defaults to None.
"""
- print("Likelihood:", -self.loglike)
+ print("Likelihood:", self.loglike)
if self._fitted is False:
nb_axes = 1
else:
@@ -1242,15 +1242,34 @@ class _model(ABC):
return ""
def plot_expected_vs_true(self, ax=None, colors=None):
+ """
+ Plot the predicted value of the counts against the counts.
+
+ Parameters
+ ----------
+ ax : Optional[Any], optional
+ The matplotlib axis to use. If None, the current axis is used, by default None.
+
+ colors : Optional[Any], optional
+ The colors to use for plotting, by default None.
+
+ Returns
+ -------
+ Any
+ The matplotlib axis.
+ """
if self._fitted is None:
raise RuntimeError("Please fit the model before.")
if ax is None:
ax = plt.gca()
predictions = self._counts_predictions().ravel().detach()
- sns.scatterplot(x=self.counts.ravel(), y=predictions, hue=colors, ax=ax)
+ print("colors:", np.array(colors).ravel().shape)
+ print("pred", predictions.shape)
+ colors = np.repeat(np.array(colors), repeats=self.dim)
+ sns.scatterplot(x=self.counts.ravel(), y=predictions, hue=colors.ravel(), ax=ax)
max_y = int(torch.max(self.counts.ravel()).item())
y = np.linspace(0, max_y, max_y)
- ax.plot(y, y)
+ ax.plot(y, y, c="red")
ax.set_yscale("log")
ax.set_xscale("log")
ax.set_ylabel("Predicted values")
@@ -2409,18 +2428,16 @@ class PlnPCA(_model):
return self._latent_sqrt_var**2
def _counts_predictions(self):
- # covariance_a_posteriori = self.latent_mean.unsqueeze(1)*self._components.unsqueeze(0)
- # covariance_a_posteriori = covariance_a_posteriori @ self._components.T.unsqueeze(0)
covariance_a_posteriori = torch.sum(
(self._components**2).unsqueeze(0)
* (self.latent_sqrt_var**2).unsqueeze(1),
axis=2,
)
- # print('latent_var', self.latent_variables)
- # print('cov:', covariance_a_posteriori)
- # x
return torch.exp(
- self._offsets + self.latent_variables + 1 / 2 * covariance_a_posteriori
+ self._offsets
+ + self.covariates @ self.coef
+ + self.latent_variables
+ + 1 / 2 * covariance_a_posteriori
)
@latent_mean.setter
--
GitLab
From 662204d4f019be271e7624588bd9aae790817bb5 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 13 Jun 2023 15:55:25 +0200
Subject: [PATCH 004/167] add the plot_expected_vs_true inside the useful
properties
---
pyPLNmodels/models.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index b4c57420..ed4708ed 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -1163,7 +1163,7 @@ class _model(ABC):
str
The string representation of the useful methods.
"""
- return ".show(), .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix()"
+ return ".show(), .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix(), .plot_expected_vs_true()"
def sigma(self):
"""
--
GitLab
From bb61ac16bc8f448369c68d43dc9fd40d72c6f6fa Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 13 Jun 2023 15:55:56 +0200
Subject: [PATCH 005/167] test for pln.plot_expected_vs_true. Should not pass.
---
tests/test_viz.py | 6 ++++++
1 file changed, 6 insertions(+)
diff --git a/tests/test_viz.py b/tests/test_viz.py
index 13510d49..da9f05b4 100644
--- a/tests/test_viz.py
+++ b/tests/test_viz.py
@@ -81,3 +81,9 @@ def test_fail_plot_pca_correlation_graph_without_names(pln):
@filter_models(["Pln", "PlnPCA"])
def test_plot_pca_correlation_graph_without_names(pln):
pln.plot_pca_correlation_graph([f"var_{i}" for i in range(3)], [0, 1, 2])
+
+
+@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_sim_pln"])
+@filter_models(["Pln", "PlnPCA"])
+def test_expected_vs_true(pln):
+ pln.plot_expected_vs_true()
--
GitLab
From 5c8c2dc75e25f7125e4e36a4685c6afaba4c69f6 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 20 Jun 2023 17:52:20 +0200
Subject: [PATCH 006/167] add decorator for docstrings (example and params).
---
pyPLNmodels/models.py | 55 +++++++++++++++++++++++++------------------
1 file changed, 32 insertions(+), 23 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index ed4708ed..f2fe18ac 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -3,6 +3,7 @@ from abc import ABC, abstractmethod
import warnings
import os
from typing import Optional, Dict, List, Type, Any, Iterable
+import textwrap
import pandas as pd
import torch
@@ -54,7 +55,7 @@ class _model(ABC):
Base class for all the Pln models. Should be inherited.
"""
- _WINDOW = 15
+ _WINDOW: int = 15
_counts: torch.Tensor
_covariates: torch.Tensor
_offsets: torch.Tensor
@@ -66,6 +67,7 @@ class _model(ABC):
def __init__(
self,
counts: torch.Tensor,
+ *,
covariates: Optional[torch.Tensor] = None,
offsets: Optional[torch.Tensor] = None,
offsets_formula: str = "logsum",
@@ -2263,6 +2265,24 @@ class PlnPCAcollection:
return ".BIC, .AIC, .loglikes"
+def add_doc(parent_class, *, params=None, example=None):
+ def wrapper(fun):
+ doc = getattr(parent_class, fun.__name__).__doc__
+ if doc is None:
+ doc = ""
+ doc = textwrap.dedent(doc).rstrip(" \n\r")
+ if params is not None:
+ doc += textwrap.dedent(params.rstrip(" \n\r"))
+ if example is not None:
+ doc += "\n\nExamples"
+ doc += "\n--------"
+ doc += textwrap.dedent(example)
+ fun.__doc__ = doc
+ return fun
+
+ return wrapper
+
+
# Here, setting the value for each key in _dict_parameters
class PlnPCA(_model):
_NAME: str = "PlnPCA"
@@ -2287,9 +2307,20 @@ class PlnPCA(_model):
>>> print(plnpca)
"""
+ @add_doc(
+ _model,
+ params="""
+ rank : int, optional
+ The rank of the approximation, by default 5.
+ """,
+ example="""
+ totopassword mange des pates.
+ """,
+ )
def __init__(
self,
counts: torch.Tensor,
+ *,
covariates: Optional[torch.Tensor] = None,
offsets: Optional[torch.Tensor] = None,
offsets_formula: str = "logsum",
@@ -2298,28 +2329,6 @@ class PlnPCA(_model):
take_log_offsets: bool = False,
add_const: bool = True,
):
- """
- Initialize the PlnPCA object.
-
- Parameters
- ----------
- counts : torch.Tensor
- The counts tensor.
- covariates : torch.Tensor, optional
- The covariates tensor, by default None.
- offsets : torch.Tensor, optional
- The offsets tensor, by default None.
- offsets_formula : str, optional
- The offsets formula, by default "logsum".
- rank : int, optional
- The rank of the approximation, by default 5.
- dict_initialization : Dict[str, torch.Tensor], optional
- The dictionary for initialization, by default None.
- take_log_offsets : bool, optional
- Whether to take the log of offsets. Defaults to False.
- add_const: bool, optional
- Whether to add a column of one in the covariates. Defaults to True.
- """
self._rank = rank
super().__init__(
counts=counts,
--
GitLab
From 61e234b6d1c195827f0f2a1a2fdef95497ab730a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 22 Jun 2023 19:54:39 +0200
Subject: [PATCH 007/167] added keywords only and docs inherited with _add_doc
(add examples and see_also)
---
pyPLNmodels/_utils.py | 32 +++-
pyPLNmodels/models.py | 352 ++++++++++++++++++++++++++++--------------
2 files changed, 266 insertions(+), 118 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index c4f8a73e..ab09750a 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -1,6 +1,10 @@
import os
import math
import warnings
+import textwrap
+from typing import Optional, Dict, Any, Union, Tuple, List
+import pkg_resources
+
import numpy as np
import pandas as pd
import torch
@@ -9,8 +13,6 @@ from matplotlib import transforms
from matplotlib.patches import Ellipse
import matplotlib.pyplot as plt
from patsy import dmatrices
-from typing import Optional, Dict, Any, Union, Tuple, List
-import pkg_resources
torch.set_default_dtype(torch.float64)
@@ -993,3 +995,29 @@ def _handle_data(
)
_check_data_shape(counts, covariates, offsets)
return counts, covariates, offsets, column_counts
+
+
+def _add_doc(parent_class, *, params=None, example=None, returns=None, see_also=None):
+ def wrapper(fun):
+ doc = getattr(parent_class, fun.__name__).__doc__
+ if doc is None:
+ doc = ""
+ doc = textwrap.dedent(doc).rstrip(" \n\r")
+ if params is not None:
+ doc += textwrap.dedent(params.rstrip(" \n\r"))
+ if returns is not None:
+ doc += "\n\nReturns"
+ doc += "\n-------"
+ doc += textwrap.dedent(returns)
+ if see_also is not None:
+ doc += "\n\nSee also"
+ doc += "\n--------"
+ doc += textwrap.dedent(see_also)
+ if example is not None:
+ doc += "\n\nExamples"
+ doc += "\n--------"
+ doc += textwrap.dedent(example)
+ fun.__doc__ = doc
+ return fun
+
+ return wrapper
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index f2fe18ac..52e4fcb7 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -3,7 +3,6 @@ from abc import ABC, abstractmethod
import warnings
import os
from typing import Optional, Dict, List, Type, Any, Iterable
-import textwrap
import pandas as pd
import torch
@@ -30,6 +29,7 @@ from ._utils import (
_get_dict_initialization,
_array2tensor,
_handle_data,
+ _add_doc,
)
from ._initialization import (
@@ -82,17 +82,17 @@ class _model(ABC):
----------
counts : torch.Tensor
The count data.
- covariates : torch.Tensor, optional
+ covariates : torch.Tensor, optional(keyword-only)
The covariate data. Defaults to None.
- offsets : torch.Tensor, optional
+ offsets : torch.Tensor, optional(keyword-only)
The offsets data. Defaults to None.
- offsets_formula : str, optional
+ offsets_formula : str, optional(keyword-only)
The formula for offsets. Defaults to "logsum".
- dict_initialization : dict, optional
+ dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
- take_log_offsets : bool, optional
+ take_log_offsets : bool, optional(keyword-only)
Whether to take the log of offsets. Defaults to False.
- add_const: bool, optional
+ add_const: bool, optional(keyword-only)
Whether to add a column of one in the covariates. Defaults to True.
"""
(
@@ -113,13 +113,13 @@ class _model(ABC):
cls,
formula: str,
data: dict,
+ *,
offsets_formula: str = "logsum",
dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
):
"""
Create a model instance from a formula and data.
- See also :func:`~pyPLNmodels.PlnPCAcollection.__init__`
Parameters
----------
@@ -127,26 +127,21 @@ class _model(ABC):
The formula.
data : dict
The data dictionary.
- offsets_formula : str, optional
+ offsets_formula : str, optional(keyword-only)
The formula for offsets. Defaults to "logsum".
- dict_initialization : dict, optional
+ dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
- take_log_offsets : bool, optional
+ take_log_offsets : bool, optional(keyword-only)
Whether to take the log of offsets. Defaults to False.
-
- Returns
- -------
- _model
- The initialized _model instance.
"""
counts, covariates, offsets = _extract_data_from_formula(formula, data)
return cls(
counts,
- covariates,
- offsets,
- offsets_formula,
- dict_initialization,
- take_log_offsets,
+ covariates=covariates,
+ offsets=offsets,
+ offsets_formula=offsets_formula,
+ dict_initialization=dict_initialization,
+ take_log_offsets=take_log_offsets,
add_const=False,
)
@@ -341,6 +336,7 @@ class _model(ABC):
def fit(
self,
nb_max_iteration: int = 50000,
+ *,
lr: float = 0.01,
class_optimizer: torch.optim.Optimizer = torch.optim.Rprop,
tol: float = 1e-3,
@@ -348,30 +344,22 @@ class _model(ABC):
verbose: bool = False,
):
"""
- Fit the model.
+ Fit the model. The lower tol, the more accurate the model.
Parameters
----------
nb_max_iteration : int, optional
The maximum number of iterations. Defaults to 50000.
- lr : float, optional
+ lr : float, optional(keyword-only)
The learning rate. Defaults to 0.01.
class_optimizer : torch.optim.Optimizer, optional
The optimizer class. Defaults to torch.optim.Rprop.
- tol : float, optional
+ tol : float, optional(keyword-only)
The tolerance for convergence. Defaults to 1e-3.
- do_smart_init : bool, optional
+ do_smart_init : bool, optional(keyword-only)
Whether to perform smart initialization. Defaults to True.
- verbose : bool, optional
+ verbose : bool, optional(keyword-only)
Whether to print training progress. Defaults to False.
- .. code-block:: python
- Examples
- --------
- >>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts = get_real_count_data()
- >>> pln = Pln(counts,add_const = True)
- >>> pln.fit()
- >>> print(pln)
"""
self._pring_beginning_message()
self._beginning_time = time.time()
@@ -1303,6 +1291,106 @@ class Pln(_model):
"""
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts= get_real_count_data()
+ >>> pln = Pln(counts, add_const = True)
+ >>> print(pln)
+ """,
+ returns="""
+ Pln
+ """,
+ see_also="""
+ :func:`pyPLNmodels.Pln.from_formula`
+ """,
+ )
+ def __init__(
+ self,
+ counts: torch.Tensor,
+ *,
+ covariates: Optional[torch.Tensor] = None,
+ offsets: Optional[torch.Tensor] = None,
+ offsets_formula: str = "logsum",
+ dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
+ take_log_offsets: bool = False,
+ add_const: bool = True,
+ ):
+ super().__init__(
+ counts=counts,
+ covariates=covariates,
+ offsets=offsets,
+ offsets_formula=offsets_formula,
+ dict_initialization=dict_initialization,
+ take_log_offsets=take_log_offsets,
+ add_const=add_const,
+ )
+
+ @classmethod
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> pln = Pln.from_formula("counts ~ 1", data = data)
+ """,
+ returns="""
+ Pln
+ """,
+ see_also="""
+ :class:`pyPLNmodels.Pln`
+ :func:`pyPLNmodels.Pln.__init__`
+ """,
+ )
+ def from_formula(
+ cls,
+ formula: str,
+ data: Any,
+ offsets_formula: str = "logsum",
+ dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
+ ):
+ counts, covariates, offsets = _extract_data_from_formula(formula, data)
+ return cls(
+ counts,
+ covariates=covariates,
+ offsets=offsets,
+ offsets_formula=offsets_formula,
+ dict_initialization=dict_initialization,
+ take_log_offsets=take_log_offsets,
+ add_const=add_const,
+ )
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> pln = Pln(counts,add_const = True)
+ >>> pln.fit()
+ >>> print(pln)
+ """,
+ )
+ def fit(
+ self,
+ nb_max_iteration: int = 50000,
+ *,
+ lr: float = 0.01,
+ class_optimizer: torch.optim.Optimizer = torch.optim.Rprop,
+ tol: float = 1e-3,
+ do_smart_init: bool = True,
+ verbose: bool = False,
+ ):
+ super().fit(
+ nb_max_iteration,
+ lr=lr,
+ class_optimizer=class_optimizer,
+ tol=tol,
+ do_smart_init=do_smart_init,
+ verbose=verbose,
+ )
+
@property
def _description(self):
"""
@@ -1539,6 +1627,7 @@ class PlnPCAcollection:
def __init__(
self,
counts: torch.Tensor,
+ *,
covariates: Optional[torch.Tensor] = None,
offsets: Optional[torch.Tensor] = None,
offsets_formula: str = "logsum",
@@ -1554,20 +1643,27 @@ class PlnPCAcollection:
----------
counts : torch.Tensor
The counts.
- covariates : torch.Tensor, optional
+ covariates : torch.Tensor, optional(keyword-only)
The covariates, by default None.
- offsets : torch.Tensor, optional
+ offsets : torch.Tensor, optional(keyword-only)
The offsets, by default None.
- offsets_formula : str, optional
+ offsets_formula : str, optional(keyword-only)
The formula for offsets, by default "logsum".
- ranks : Iterable[int], optional
+ ranks : Iterable[int], optional(keyword-only)
The range of ranks, by default range(3, 5).
- dict_of_dict_initialization : dict, optional
+ dict_of_dict_initialization : dict, optional(keyword-only)
The dictionary of initialization, by default None.
- take_log_offsets : bool, optional
+ take_log_offsets : bool, optional(keyword-only)
Whether to take the logarithm of offsets, by default False.
- add_const: bool, optional
+ add_const: bool, optional(keyword-only)
Whether to add a column of one in the covariates. Defaults to True.
+ Returns
+ -------
+ A collection where item q corresponds to a PlnPCA object with rank q.
+ See also
+ --------
+ :class:`~pyPLNmodels.PlnPCA`
+ :func:`~pyPLNmodels.PlnPCAcollection.from_formula`
"""
self._dict_models = {}
(
@@ -1600,13 +1696,14 @@ class PlnPCAcollection:
The formula.
data : dict
The data dictionary.
- offsets_formula : str, optional
+ offsets_formula : str, optional(keyword-only)
The formula for offsets, by default "logsum".
- ranks : Iterable[int], optional
+ Overriden if data["offsets"] is not None.
+ ranks : Iterable[int], optional(keyword-only)
The range of ranks, by default range(3, 5).
- dict_of_dict_initialization : dict, optional
+ dict_of_dict_initialization : dict, optional(keyword-only)
The dictionary of initialization, by default None.
- take_log_offsets : bool, optional
+ take_log_offsets : bool, optional(keyword-only)
Whether to take the logarithm of offsets, by default False.
Returns
-------
@@ -1618,16 +1715,20 @@ class PlnPCAcollection:
>>> counts = get_real_count_data()
>>> data = {"counts": counts}
>>> pca_col = PlnPCAcollection.from_formula("counts ~ 1", data = data, ranks = [5,6])
+ See also
+ --------
+ :class:`~pyPLNmodels.PlnPCA`
+ :func:`~pyPLNmodels.PlnPCAcollection.__init__`
"""
counts, covariates, offsets = _extract_data_from_formula(formula, data)
return cls(
counts,
- covariates,
- offsets,
- offsets_formula,
- ranks,
- dict_of_dict_initialization,
- take_log_offsets,
+ covariates=covariates,
+ offsets=offsets,
+ offsets_formula=offsets_formula,
+ ranks=ranks,
+ dict_of_dict_initialization=dict_of_dict_initialization,
+ take_log_offsets=take_log_offsets,
add_const=False,
)
@@ -1868,7 +1969,8 @@ class PlnPCAcollection:
def fit(
self,
- nb_max_iteration: int = 100000,
+ nb_max_iteration: int = 50000,
+ *,
lr: float = 0.01,
class_optimizer: Type[torch.optim.Optimizer] = torch.optim.Rprop,
tol: float = 1e-3,
@@ -1876,21 +1978,21 @@ class PlnPCAcollection:
verbose: bool = False,
):
"""
- Fit the PlnPCAcollection.
+ Fit each model in the PlnPCAcollection.
Parameters
----------
nb_max_iteration : int, optional
- The maximum number of iterations, by default 100000.
- lr : float, optional
+ The maximum number of iterations, by default 50000.
+ lr : float, optional(keyword-only)
The learning rate, by default 0.01.
- class_optimizer : Type[torch.optim.Optimizer], optional
+ class_optimizer : Type[torch.optim.Optimizer], optional(keyword-only)
The optimizer class, by default torch.optim.Rprop.
- tol : float, optional
+ tol : float, optional(keyword-only)
The tolerance, by default 1e-3.
- do_smart_init : bool, optional
+ do_smart_init : bool, optional(keyword-only)
Whether to do smart initialization, by default True.
- verbose : bool, optional
+ verbose : bool, optional(keyword-only)
Whether to print verbose output, by default False.
"""
self._pring_beginning_message()
@@ -2265,30 +2367,10 @@ class PlnPCAcollection:
return ".BIC, .AIC, .loglikes"
-def add_doc(parent_class, *, params=None, example=None):
- def wrapper(fun):
- doc = getattr(parent_class, fun.__name__).__doc__
- if doc is None:
- doc = ""
- doc = textwrap.dedent(doc).rstrip(" \n\r")
- if params is not None:
- doc += textwrap.dedent(params.rstrip(" \n\r"))
- if example is not None:
- doc += "\n\nExamples"
- doc += "\n--------"
- doc += textwrap.dedent(example)
- fun.__doc__ = doc
- return fun
-
- return wrapper
-
-
# Here, setting the value for each key in _dict_parameters
class PlnPCA(_model):
- _NAME: str = "PlnPCA"
- _components: torch.Tensor
"""
- PlnPCA class.
+ PlnPCA object where the covariance has low rank.
Examples
--------
@@ -2305,17 +2387,33 @@ class PlnPCA(_model):
>>> plnpca = PlnPCA.from_formula("counts ~ 0 + cov", data = data, rank = 5)
>>> plnpca.fit()
>>> print(plnpca)
+
+ See also
+ --------
+ :class:`pyPLNmodels.Pln`
"""
- @add_doc(
+ _NAME: str = "PlnPCA"
+ _components: torch.Tensor
+
+ @_add_doc(
_model,
params="""
- rank : int, optional
+ rank : int, optional(keyword-only)
The rank of the approximation, by default 5.
""",
example="""
- totopassword mange des pates.
- """,
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> counts= get_real_count_data()
+ >>> pca = PlnPCA(counts, add_const = True)
+ >>> print(pca)
+ """,
+ returns="""
+ PlnPCA
+ """,
+ see_also="""
+ :func:`pyPLNmodels.PlnPCA.from_formula`
+ """,
)
def __init__(
self,
@@ -2341,6 +2439,26 @@ class PlnPCA(_model):
)
@classmethod
+ @_add_doc(
+ _model,
+ params="""
+ rank : int, optional(keyword-only)
+ The rank of the approximation, by default 5.
+ """,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> pca = PlnPCA.from_formula("counts ~ 1", data = data, rank = 5)
+ """,
+ returns="""
+ PlnPCA
+ """,
+ see_also="""
+ :class:`pyPLNmodels.Pln`
+ :func:`pyPLNmodels.PlnPCA.__init__`
+ """,
+ )
def from_formula(
cls,
formula: str,
@@ -2349,44 +2467,46 @@ class PlnPCA(_model):
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
):
- """
- Create a PlnPCA object from a formula.
-
- Parameters
- ----------
- formula : str
- The formula.
- data : Any
- The data.
- rank : int, optional
- The rank of the approximation, by default 5.
- offsets_formula : str, optional
- The offsets formula, by default "logsum".
- dict_initialization : Dict[str, torch.Tensor], optional
- The dictionary for initialization, by default None.
-
- Returns
- -------
- PlnPCA
- The created PlnPCA object.
- Examples
- --------
- >>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> pca = PlnPCA.from_formula("counts ~ 1", data = data, rank = 5)
- """
counts, covariates, offsets = _extract_data_from_formula(formula, data)
return cls(
counts,
- covariates,
- offsets,
- offsets_formula,
- rank,
- dict_initialization,
+ covariates=covariates,
+ offsets=offsets,
+ offsets_formula=offsets_formula,
+ rank=rank,
+ dict_initialization=dict_initialization,
add_const=False,
)
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> plnpca = PlnPCA(counts,add_const = True, rank = 6)
+ >>> plnpca.fit()
+ >>> print(plnpca)
+ """,
+ )
+ def fit(
+ self,
+ nb_max_iteration: int = 50000,
+ *,
+ lr: float = 0.01,
+ class_optimizer: torch.optim.Optimizer = torch.optim.Rprop,
+ tol: float = 1e-3,
+ do_smart_init: bool = True,
+ verbose: bool = False,
+ ):
+ super().fit(
+ nb_max_iteration,
+ lr=lr,
+ class_optimizer=class_optimizer,
+ tol=tol,
+ do_smart_init=do_smart_init,
+ verbose=verbose,
+ )
+
def _check_if_rank_is_too_high(self):
"""
Check if the rank is too high and issue a warning if necessary.
--
GitLab
From 2be156e5f9fedbdde3a34158a7d25e1eda3d587a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 23 Jun 2023 13:17:33 +0200
Subject: [PATCH 008/167] minor chagnes.
---
tests/conftest.py | 3 +--
1 file changed, 1 insertion(+), 2 deletions(-)
diff --git a/tests/conftest.py b/tests/conftest.py
index d87255cf..1713f658 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -53,9 +53,8 @@ def add_list_of_fixture_to_dict(
RANK = 8
-RANKS = [2, 6]
+RANKS = [2, 6, 8]
instances = []
-# dict_fixtures_models = []
def convenient_PlnPCA(*args, **kwargs):
--
GitLab
From 5bff03fbae38331be4d97bdbcee9fe1586efbaea Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 23 Jun 2023 13:17:59 +0200
Subject: [PATCH 009/167] add examples in docstrings.
---
pyPLNmodels/_utils.py | 45 +++++++++++++++++++++++++++++--
pyPLNmodels/models.py | 62 +++++++++++++++++++++++++++++++++++--------
2 files changed, 94 insertions(+), 13 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index ab09750a..b5d910a4 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -742,7 +742,7 @@ def get_real_count_data(
def load_model(path_of_directory: str) -> Dict[str, Any]:
"""
- Load model from the given directory for future initialization.
+ Load Pln or PlnPCA model (that has previously been saved) from the given directory for future initialization.
Parameters
----------
@@ -753,7 +753,26 @@ def load_model(path_of_directory: str) -> Dict[str, Any]:
-------
Dict[str, Any]
A dictionary containing the loaded model.
-
+ Examples
+ --------
+ >>> from pyPLNmodels import PlnPCA, Pln, get_real_count_data, load_model
+ >>> counts= get_real_count_data()
+ >>> pca = PlnPCA(counts, add_const = True)
+ >>> pca.fit()
+ >>> pca.save()
+ >>> dict_init = load_model("PlnPCA_nbcov_1_dim_200_rank_5")
+ >>> loaded_pca = PlnPCA(counts, add_const = True, dict_initialization = dict_init)
+ >>> print(loaded_pca)
+
+ >>> pln = Pln(counts, add_const = True)
+ >>> pln.fit()
+ >>> pln.save()
+ >>> dict_init = load_model("Pln_nbcov_1_dim_200")
+ >>> loaded_pln = Pln(counts, add_const = True, dict_initialization = dict_init)
+ >>> print(loaded_pln)
+ See also
+ --------
+ :func:`~pyPLNmodels.load_plnpcacollection`
"""
working_dir = os.getcwd()
os.chdir(path_of_directory)
@@ -779,6 +798,13 @@ def load_pln(path_of_directory: str) -> Dict[str, Any]:
return load_model(path_of_directory)
+def load_plnpca(path_of_directory: str) -> Dict[str, Any]:
+ """
+ Alias for :func:`~pyPLNmodels._utils.load_model`.
+ """
+ return load_model(path_of_directory)
+
+
def load_plnpcacollection(
path_of_directory: str, ranks: Optional[List[int]] = None
) -> Dict[int, Dict[str, Any]]:
@@ -802,6 +828,20 @@ def load_plnpcacollection(
ValueError
If an invalid model name is encountered and the rank cannot be determined.
+ Examples
+ --------
+ >>> from pyPLNmodels import PlnPCAcollection, get_real_count_data, load_plnpcacollection
+ >>> counts = get_real_count_data()
+ >>> pcas = PlnPCAcollection(counts, add_const = True, ranks = [4,5,6])
+ >>> pcas.fit()
+ >>> pcas.save()
+ >>> dict_init = load_plnpcacollection("PlnPCAcollection_nbcov_1_dim_200")
+ >>> loaded_pcas = PlnPCAcollection(counts, add_const = True, ranks = [4,5,6], dict_of_dict_initialization = dict_init)
+ >>> print(loaded_pcas)
+
+ See also
+ --------
+ :func:`~pyPLNmodels.load_model`
"""
working_dir = os.getcwd()
os.chdir(path_of_directory)
@@ -819,6 +859,7 @@ def load_plnpcacollection(
datas = {}
for rank in ranks:
datas[rank] = load_model(f"PlnPCA_rank_{rank}")
+ datas["ranks"] = ranks
os.chdir(working_dir)
return datas
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 52e4fcb7..4bfc626e 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -964,6 +964,7 @@ class _model(ABC):
"""
if path is None:
path = f"./{self._directory_name}"
+ print("paht:", path)
os.makedirs(path, exist_ok=True)
for key, value in self._dict_parameters.items():
filename = f"{path}/{key}.csv"
@@ -1279,14 +1280,18 @@ class Pln(_model):
--------
>>> from pyPLNmodels import Pln, get_real_count_data
>>> counts, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(counts,add_const = True, rank = 5])
+ >>> pln = Pln(counts,add_const = True)
+ >>> pln.fit()
>>> print(pln)
>>> pln.viz(colors = labels)
>>> from pyPLNmodels import Pln, get_simulation_parameters, sample_pln
>>> param = get_simulation_parameters()
>>> counts = sample_pln(param)
- >>> data = {}
+ >>> data = {"counts": counts}
+ >>> pln = Pln.from_formula("counts ~ 1", data)
+ >>> pln.fit()
+ >>> print(pln)
"""
@@ -1348,8 +1353,10 @@ class Pln(_model):
cls,
formula: str,
data: Any,
+ *,
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
+ take_log_offsets: bool = False,
):
counts, covariates, offsets = _extract_data_from_formula(formula, data)
return cls(
@@ -1359,7 +1366,7 @@ class Pln(_model):
offsets_formula=offsets_formula,
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
- add_const=add_const,
+ add_const=False,
)
@_add_doc(
@@ -1621,6 +1628,30 @@ class Pln(_model):
class PlnPCAcollection:
+ """
+ A collection where item q corresponds to a PlnPCA object with rank q.
+
+ Examples
+ --------
+ >>> from pyPLNmodels import PlnPCAcollection, get_real_count_data, get_simulation_parameters, sample_pln
+ >>> counts, labels = get_real_count_data(return_labels = True)
+ >>> data = {"counts": counts}
+ >>> pcas = PlnPCAcollection.from_formula("counts ~ 1", data = data, ranks = [5,8, 12]])
+ >>> print(pcas)
+ >>> pcas.show()
+
+ >>> plnparam = get_simulation_parameters(n_samples =100, dim = 60, nb_cov = 2, rank = 8)
+ >>> counts = sample_pln(plnparam)
+ >>> data = {"counts": plnparam.counts, "cov": plnparam.covariates, "offsets": plnparam.offsets}
+ >>> plnpcas = PlnPCAcollection.from_formula("counts ~ 0 + cov", data = data, ranks = [5,8,12])
+ >>> plnpcas.fit()
+ >>> print(plnpcas)
+ >>> pcas.show()
+ See also
+ --------
+ :class:`~pyPLNmodels.PlnPCA`
+ """
+
_NAME = "PlnPCAcollection"
_dict_models: dict
@@ -1659,11 +1690,11 @@ class PlnPCAcollection:
Whether to add a column of one in the covariates. Defaults to True.
Returns
-------
- A collection where item q corresponds to a PlnPCA object with rank q.
+ PlnPCAcollection
See also
--------
:class:`~pyPLNmodels.PlnPCA`
- :func:`~pyPLNmodels.PlnPCAcollection.from_formula`
+ :meth:`~pyPLNmodels.PlnPCAcollection.from_formula`
"""
self._dict_models = {}
(
@@ -1903,6 +1934,15 @@ class PlnPCAcollection:
"Please instantiate with either a list "
"of integers or an integer."
)
+ if dict_of_dict_initialization is not None:
+ if ranks != dict_of_dict_initialization["ranks"]:
+ msg = (
+ "The given ranks in the dict_initialization are loaded but"
+ " you should fit the model once again or instantiate the"
+ " model with the ranks loaded."
+ )
+ warnings.warn(msg)
+
elif isinstance(ranks, (int, np.integer)):
dict_initialization = _get_dict_initialization(
ranks, dict_of_dict_initialization
@@ -2000,11 +2040,11 @@ class PlnPCAcollection:
model = self[self.ranks[i]]
model.fit(
nb_max_iteration,
- lr,
- class_optimizer,
- tol,
- do_smart_init,
- verbose,
+ lr=lr,
+ class_optimizer=class_optimizer,
+ tol=tol,
+ do_smart_init=do_smart_init,
+ verbose=verbose,
)
if i < len(self.values()) - 1:
next_model = self[self.ranks[i + 1]]
@@ -2613,7 +2653,7 @@ class PlnPCA(_model):
str
The directory name.
"""
- return f"{self._NAME}_nbcov_{self.nb_cov}_rank_{self._rank}"
+ return f"{super()._directory_name}_rank_{self._rank}"
@property
def covariates(self) -> torch.Tensor:
--
GitLab
From 338707021440296f4db28b9a234abe5be3690fa8 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 23 Jun 2023 13:18:55 +0200
Subject: [PATCH 010/167] add titles for html and function load_plnpca.
---
docs/source/conf.py | 1 +
docs/source/load.rst | 5 ++++-
docs/source/pln.rst | 3 +++
docs/source/plnpca.rst | 3 +++
docs/source/plnpcacollection.rst | 3 +++
5 files changed, 14 insertions(+), 1 deletion(-)
diff --git a/docs/source/conf.py b/docs/source/conf.py
index f3a032af..091a047b 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -68,3 +68,4 @@ exclude_patterns = []
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
+html_title = "pyPLNmodels"
diff --git a/docs/source/load.rst b/docs/source/load.rst
index 6a3ea50c..a3e8a23b 100644
--- a/docs/source/load.rst
+++ b/docs/source/load.rst
@@ -1,3 +1,6 @@
-.. autofunction:: pyPLNmodels.load_pln
+Load saved models
+=================
.. autofunction:: pyPLNmodels.load_model
.. autofunction:: pyPLNmodels.load_plnpcacollection
+.. autofunction:: pyPLNmodels.load_pln
+.. autofunction:: pyPLNmodels.load_plnpca
diff --git a/docs/source/pln.rst b/docs/source/pln.rst
index bb4bf654..15b03926 100644
--- a/docs/source/pln.rst
+++ b/docs/source/pln.rst
@@ -1,3 +1,6 @@
+Pln
+===
+
.. autoclass:: pyPLNmodels.Pln
:members:
:inherited-members:
diff --git a/docs/source/plnpca.rst b/docs/source/plnpca.rst
index 1badbc4e..ac521272 100644
--- a/docs/source/plnpca.rst
+++ b/docs/source/plnpca.rst
@@ -1,3 +1,6 @@
+PlnPCA
+======
+
.. autoclass:: pyPLNmodels.PlnPCA
:members:
:inherited-members:
diff --git a/docs/source/plnpcacollection.rst b/docs/source/plnpcacollection.rst
index 7871a724..ef84ac05 100644
--- a/docs/source/plnpcacollection.rst
+++ b/docs/source/plnpcacollection.rst
@@ -1,3 +1,6 @@
+PlnPCAcollection
+================
+
.. autoclass:: pyPLNmodels.PlnPCAcollection
:members:
:show-inheritance:
--
GitLab
From 1bf37eb9724f143caa21e715913d31709f442ffe Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 23 Jun 2023 14:25:58 +0200
Subject: [PATCH 011/167] tests should pass.
---
pyPLNmodels/_utils.py | 74 +++++++++++++++++++++++++------------------
pyPLNmodels/models.py | 18 ++++++-----
tests/conftest.py | 14 ++++++--
tests/import_data.py | 4 +--
tests/test_common.py | 4 +++
5 files changed, 72 insertions(+), 42 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index b5d910a4..26189a9c 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -95,7 +95,7 @@ def _sigmoid(tens: torch.Tensor) -> torch.Tensor:
return 1 / (1 + torch.exp(-tens))
-def sample_pln(pln_param, seed: int = None, return_latent=False) -> torch.Tensor:
+def sample_pln(pln_param, *, seed: int = None, return_latent=False) -> torch.Tensor:
"""
Sample from the Poisson Log-Normal (Pln) model.
@@ -104,9 +104,9 @@ def sample_pln(pln_param, seed: int = None, return_latent=False) -> torch.Tensor
pln_param : PlnParameters object
parameters of the model, containing the coeficient, the covariates,
the components and the offsets.
- seed : int or None, optional
+ seed : int or None, optional(keyword-only)
Random seed for reproducibility. Default is None.
- return_latent : bool, optional
+ return_latent : bool, optional(keyword-only)
If True will return also the latent variables. Default is False.
Returns
@@ -517,21 +517,21 @@ def _get_simulation_coef_cov_offsets(
class PlnParameters:
- def __init__(self, components, coef, covariates, offsets, coef_inflation=None):
+ def __init__(self, *, components, coef, covariates, offsets, coef_inflation=None):
"""
Instantiate all the needed parameters to sample from the PLN model.
Parameters
----------
- components : torch.Tensor
+ components : torch.Tensor(keyword-only)
Components of size (p, rank)
- coef : torch.Tensor
+ coef : torch.Tensor(keyword-only)
Coefficient of size (d, p)
- covariates : torch.Tensor or None
+ covariates : torch.Tensor or None(keyword-only)
Covariates, size (n, d) or None
- offsets : torch.Tensor
+ offsets : torch.Tensor(keyword-only)
Offset, size (n, p)
- _coef_inflation : torch.Tensor or None, optional
+ _coef_inflation : torch.Tensor or None, optional(keyword-only)
Coefficient for zero-inflation model, size (d, p) or None. Default is None.
"""
@@ -540,9 +540,10 @@ class PlnParameters:
self.covariates = _format_data(covariates)
self.offsets = _format_data(offsets)
self.coef_inflation = _format_data(coef_inflation)
- _check_two_dimensions_are_equal(
- "components", "coef", self.components.shape[0], self.coef.shape[1], 0, 1
- )
+ if self.coef is not None:
+ _check_two_dimensions_are_equal(
+ "components", "coef", self.components.shape[0], self.coef.shape[1], 0, 1
+ )
if self.offsets is not None:
_check_two_dimensions_are_equal(
"components",
@@ -622,24 +623,29 @@ def _check_two_dimensions_are_equal(
def get_simulation_parameters(
- n_samples: int = 100, dim: int = 25, nb_cov: int = 1, rank: int = 5, add_const=True
+ *,
+ n_samples: int = 100,
+ dim: int = 25,
+ nb_cov: int = 1,
+ rank: int = 5,
+ add_const: bool = True,
) -> PlnParameters:
"""
Generate simulation parameters for a Poisson-lognormal model.
Parameters
----------
- n_samples : int, optional
+ n_samples : int, optional(keyword-only)
The number of samples, by default 100.
- dim : int, optional
+ dim : int, optional(keyword-only)
The dimension of the data, by default 25.
- nb_cov : int, optional
+ nb_cov : int, optional(keyword-only)
The number of covariates, by default 1. If add_const is True,
then there will be nb_cov+1 covariates as the intercept can be seen
as a covariates.
- rank : int, optional
+ rank : int, optional(keyword-only)
The rank of the data components, by default 5.
- add_const : bool, optional
+ add_const : bool, optional(keyword-only)
If True, will add a vector of ones in the covariates.
Returns
@@ -652,15 +658,19 @@ def get_simulation_parameters(
n_samples, nb_cov, dim, add_const
)
components = _get_simulation_components(dim, rank)
- return PlnParameters(components, coef, covariates, offsets)
+ return PlnParameters(
+ components=components, coef=coef, covariates=covariates, offsets=offsets
+ )
def get_simulated_count_data(
+ *,
n_samples: int = 100,
dim: int = 25,
rank: int = 5,
nb_cov: int = 1,
return_true_param: bool = False,
+ add_const: bool = True,
seed: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
@@ -668,17 +678,19 @@ def get_simulated_count_data(
Parameters
----------
- n_samples : int, optional
+ n_samples : int, optional(keyword-only)
Number of samples, by default 100.
- dim : int, optional
+ dim : int, optional(keyword-only)
Dimension, by default 25.
- rank : int, optional
+ rank : int, optional(keyword-only)
Rank of the covariance matrix, by default 5.
- nb_cov : int, optional
+ add_const : bool, optional(keyword-only)
+ If True, will add a vector of ones. Default is True
+ nb_cov : int, optional(keyword-only)
Number of covariates, by default 1.
- return_true_param : bool, optional
+ return_true_param : bool, optional(keyword-only)
Whether to return the true parameters of the model, by default False.
- seed : int, optional
+ seed : int, optional(keyword-only)
Seed value for random number generation, by default 0.
Returns
@@ -686,7 +698,9 @@ def get_simulated_count_data(
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
Tuple containing counts, covariates, and offsets.
"""
- pln_param = get_simulation_parameters(n_samples, dim, nb_cov, rank)
+ pln_param = get_simulation_parameters(
+ n_samples=n_samples, dim=dim, nb_cov=nb_cov, rank=rank, add_const=add_const
+ )
counts = sample_pln(pln_param, seed=seed, return_latent=False)
if return_true_param is True:
return (
@@ -700,18 +714,18 @@ def get_simulated_count_data(
def get_real_count_data(
- n_samples: int = 469, dim: int = 200, return_labels: bool = False
+ *, n_samples: int = 469, dim: int = 200, return_labels: bool = False
) -> np.ndarray:
"""
Get real count data from the scMARK dataset.
Parameters
----------
- n_samples : int, optional
+ n_samples : int, optional(keyword-only)
Number of samples, by default max_samples.
- dim : int, optional
+ dim : int, optional(keyword-only)
Dimension, by default max_dim.
- return_labels: bool, optional
+ return_labels: bool, optional(keyword-only)
If True, will return the labels of the count data
Returns
-------
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 4bfc626e..67e67ab4 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -1254,10 +1254,11 @@ class _model(ABC):
if ax is None:
ax = plt.gca()
predictions = self._counts_predictions().ravel().detach()
- print("colors:", np.array(colors).ravel().shape)
- print("pred", predictions.shape)
- colors = np.repeat(np.array(colors), repeats=self.dim)
- sns.scatterplot(x=self.counts.ravel(), y=predictions, hue=colors.ravel(), ax=ax)
+ print("pred shpae", predictions.shape)
+ print("counts shape", self.counts.ravel().shape)
+ if colors is not None:
+ colors = np.repeat(np.array(colors), repeats=self.dim).ravel()
+ sns.scatterplot(x=self.counts.ravel(), y=predictions, hue=colors, ax=ax)
max_y = int(torch.max(self.counts.ravel()).item())
y = np.linspace(0, max_y, max_y)
ax.plot(y, y, c="red")
@@ -2602,11 +2603,12 @@ class PlnPCA(_model):
* (self.latent_sqrt_var**2).unsqueeze(1),
axis=2,
)
+ if self.covariates is not None:
+ XB = self.covariates @ self.coef
+ else:
+ XB = 0
return torch.exp(
- self._offsets
- + self.covariates @ self.coef
- + self.latent_variables
- + 1 / 2 * covariance_a_posteriori
+ self._offsets + XB + self.latent_variables + 1 / 2 * covariance_a_posteriori
)
@latent_mean.setter
diff --git a/tests/conftest.py b/tests/conftest.py
index 1713f658..f983df5e 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -118,7 +118,12 @@ dict_fixtures = {}
@pytest.fixture(params=params)
def simulated_pln_0cov_array(request):
cls = request.param
- pln = cls(counts_sim_0cov, covariates_sim_0cov, offsets_sim_0cov, add_const=False)
+ pln = cls(
+ counts_sim_0cov,
+ covariates=covariates_sim_0cov,
+ offsets=offsets_sim_0cov,
+ add_const=False,
+ )
return pln
@@ -126,7 +131,12 @@ def simulated_pln_0cov_array(request):
@cache
def simulated_fitted_pln_0cov_array(request):
cls = request.param
- pln = cls(counts_sim_0cov, covariates_sim_0cov, offsets_sim_0cov, add_const=False)
+ pln = cls(
+ counts_sim_0cov,
+ covariates=covariates_sim_0cov,
+ offsets=offsets_sim_0cov,
+ add_const=False,
+ )
pln.fit()
return pln
diff --git a/tests/import_data.py b/tests/import_data.py
index 03930286..154f5644 100644
--- a/tests/import_data.py
+++ b/tests/import_data.py
@@ -12,14 +12,14 @@ from pyPLNmodels import (
offsets_sim_0cov,
true_covariance_0cov,
true_coef_0cov,
-) = get_simulated_count_data(return_true_param=True, nb_cov=0)
+) = get_simulated_count_data(return_true_param=True, nb_cov=0, add_const=False)
(
counts_sim_2cov,
covariates_sim_2cov,
offsets_sim_2cov,
true_covariance_2cov,
true_coef_2cov,
-) = get_simulated_count_data(return_true_param=True, nb_cov=2)
+) = get_simulated_count_data(return_true_param=True, nb_cov=2, add_const=False)
data_sim_0cov = {
"counts": counts_sim_0cov,
diff --git a/tests/test_common.py b/tests/test_common.py
index ccdd7b92..38711fef 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -46,6 +46,10 @@ def test_find_right_covariance(simulated_fitted_any_pln):
true_covariance = true_sim_0cov["Sigma"]
elif simulated_fitted_any_pln.nb_cov == 2:
true_covariance = true_sim_2cov["Sigma"]
+ else:
+ raise ValueError(
+ f"Not the right numbers of covariance({simulated_fitted_any_pln.nb_cov})"
+ )
mse_covariance = MSE(simulated_fitted_any_pln.covariance - true_covariance)
assert mse_covariance < 0.05
--
GitLab
From 695663af08fa2b06ff837398ac974d42df926797 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 25 Jun 2023 22:49:36 +0200
Subject: [PATCH 012/167] add scripts that tests the examples in the
docstrings. Running examples works fine but it does not throw an error when
the script fails.
---
.gitlab-ci.yml | 12 ++++++++
tests/create_example_files.py | 53 +++++++++++++++++++++++++++++++++++
tests/test_examples.sh | 5 ++++
3 files changed, 70 insertions(+)
create mode 100644 tests/create_example_files.py
create mode 100755 tests/test_examples.sh
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 6bccf9e4..d0f6a915 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -21,6 +21,18 @@ tests:
- pip install .
- cd tests
- pytest
+examples:
+ stage: checks
+ image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
+ before_script:
+ pip install '.[tests]'
+ script:
+ - pip install .
+ - cd tests
+ - python create_example_files.py
+ - ./test_examples.sh
+
+
build_package:
stage: build
diff --git a/tests/create_example_files.py b/tests/create_example_files.py
new file mode 100644
index 00000000..8469f58d
--- /dev/null
+++ b/tests/create_example_files.py
@@ -0,0 +1,53 @@
+import ast
+import os
+
+
+def get_lines(filename):
+ with open(f"../pyPLNmodels/{filename}.py") as file:
+ lines = [line.rstrip() for line in file]
+ return lines
+
+
+def get_examples(lines):
+ examples = []
+ in_example = False
+ example = []
+ for line in lines:
+ line = line.lstrip()
+ if len(line) > 3:
+ if line[0:3] == ">>>":
+ in_example = True
+ example.append(line[4:])
+ else:
+ if in_example is True:
+ examples.append(example)
+ example = []
+ in_example = False
+ return examples
+
+
+def write_examples(examples, prefix_filename):
+ for i in range(len(examples)):
+ example = examples[i]
+ nb_example = str(i + 1)
+ example_filename = f"examples/{prefix_filename}_example_{nb_example}.py"
+ try:
+ os.remove(example_filename)
+ except FileNotFoundError:
+ pass
+ with open(example_filename, "a") as the_file:
+ for line in example:
+ the_file.write(line + "\n")
+
+
+def filename_to_example_file(filename):
+ lines = get_lines(filename)
+ examples = get_examples(lines)
+ write_examples(examples, filename)
+
+
+# filename_to_example_file("models")
+os.makedirs("examples", exist_ok=True)
+filename_to_example_file("_utils")
+filename_to_example_file("models")
+filename_to_example_file("elbos")
diff --git a/tests/test_examples.sh b/tests/test_examples.sh
new file mode 100755
index 00000000..b8a32f31
--- /dev/null
+++ b/tests/test_examples.sh
@@ -0,0 +1,5 @@
+search_dir="examples"
+for entry in "$search_dir"/*
+do
+ python "$entry"
+done
--
GitLab
From e85ce4b1bee1993cffa7e4b9aebc7c204f1a3ccb Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 25 Jun 2023 22:56:13 +0200
Subject: [PATCH 013/167] print statement remove
---
pyPLNmodels/models.py | 1 -
1 file changed, 1 deletion(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 67e67ab4..9e84b16d 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -964,7 +964,6 @@ class _model(ABC):
"""
if path is None:
path = f"./{self._directory_name}"
- print("paht:", path)
os.makedirs(path, exist_ok=True)
for key, value in self._dict_parameters.items():
filename = f"{path}/{key}.csv"
--
GitLab
From 2d019c35bd15949960388f104febbe2d3137a11c Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 25 Jun 2023 23:08:43 +0200
Subject: [PATCH 014/167] began to move the loading functions in the loading
module.
---
pyPLNmodels/_utils.py | 124 ----------------------------------------
pyPLNmodels/load.py | 128 ++++++++++++++++++++++++++++++++++++++++++
2 files changed, 128 insertions(+), 124 deletions(-)
create mode 100644 pyPLNmodels/load.py
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 26189a9c..5945f5ed 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -754,130 +754,6 @@ def get_real_count_data(
return counts, labels
-def load_model(path_of_directory: str) -> Dict[str, Any]:
- """
- Load Pln or PlnPCA model (that has previously been saved) from the given directory for future initialization.
-
- Parameters
- ----------
- path_of_directory : str
- The path to the directory containing the model.
-
- Returns
- -------
- Dict[str, Any]
- A dictionary containing the loaded model.
- Examples
- --------
- >>> from pyPLNmodels import PlnPCA, Pln, get_real_count_data, load_model
- >>> counts= get_real_count_data()
- >>> pca = PlnPCA(counts, add_const = True)
- >>> pca.fit()
- >>> pca.save()
- >>> dict_init = load_model("PlnPCA_nbcov_1_dim_200_rank_5")
- >>> loaded_pca = PlnPCA(counts, add_const = True, dict_initialization = dict_init)
- >>> print(loaded_pca)
-
- >>> pln = Pln(counts, add_const = True)
- >>> pln.fit()
- >>> pln.save()
- >>> dict_init = load_model("Pln_nbcov_1_dim_200")
- >>> loaded_pln = Pln(counts, add_const = True, dict_initialization = dict_init)
- >>> print(loaded_pln)
- See also
- --------
- :func:`~pyPLNmodels.load_plnpcacollection`
- """
- working_dir = os.getcwd()
- os.chdir(path_of_directory)
- all_files = os.listdir()
- data = {}
- for filename in all_files:
- if filename.endswith(".csv"):
- parameter = filename[:-4]
- try:
- data[parameter] = pd.read_csv(filename, header=None).values
- except pd.errors.EmptyDataError:
- print(
- f"Can't load {parameter} since empty. Standard initialization will be performed for this parameter"
- )
- os.chdir(working_dir)
- return data
-
-
-def load_pln(path_of_directory: str) -> Dict[str, Any]:
- """
- Alias for :func:`~pyPLNmodels._utils.load_model`.
- """
- return load_model(path_of_directory)
-
-
-def load_plnpca(path_of_directory: str) -> Dict[str, Any]:
- """
- Alias for :func:`~pyPLNmodels._utils.load_model`.
- """
- return load_model(path_of_directory)
-
-
-def load_plnpcacollection(
- path_of_directory: str, ranks: Optional[List[int]] = None
-) -> Dict[int, Dict[str, Any]]:
- """
- Load PlnPCAcollection models from the given directory.
-
- Parameters
- ----------
- path_of_directory : str
- The path to the directory containing the PlnPCAcollection models.
- ranks : List[int], optional
- A List of ranks specifying which models to load. If None, all models in the directory will be loaded.
-
- Returns
- -------
- Dict[int, Dict[str, Any]]
- A dictionary containing the loaded PlnPCAcollection models, with ranks as keys.
-
- Raises
- ------
- ValueError
- If an invalid model name is encountered and the rank cannot be determined.
-
- Examples
- --------
- >>> from pyPLNmodels import PlnPCAcollection, get_real_count_data, load_plnpcacollection
- >>> counts = get_real_count_data()
- >>> pcas = PlnPCAcollection(counts, add_const = True, ranks = [4,5,6])
- >>> pcas.fit()
- >>> pcas.save()
- >>> dict_init = load_plnpcacollection("PlnPCAcollection_nbcov_1_dim_200")
- >>> loaded_pcas = PlnPCAcollection(counts, add_const = True, ranks = [4,5,6], dict_of_dict_initialization = dict_init)
- >>> print(loaded_pcas)
-
- See also
- --------
- :func:`~pyPLNmodels.load_model`
- """
- working_dir = os.getcwd()
- os.chdir(path_of_directory)
- if ranks is None:
- dirnames = os.listdir()
- ranks = []
- for dirname in dirnames:
- try:
- rank = int(dirname[-1])
- except ValueError:
- raise ValueError(
- f"Can't load the model {dirname}. End of {dirname} should be an int"
- )
- ranks.append(rank)
- datas = {}
- for rank in ranks:
- datas[rank] = load_model(f"PlnPCA_rank_{rank}")
- datas["ranks"] = ranks
- os.chdir(working_dir)
- return datas
-
-
def _check_right_rank(data: Dict[str, Any], rank: int) -> None:
"""
Check if the rank of the given data matches the specified rank.
diff --git a/pyPLNmodels/load.py b/pyPLNmodels/load.py
new file mode 100644
index 00000000..4b59d5e8
--- /dev/null
+++ b/pyPLNmodels/load.py
@@ -0,0 +1,128 @@
+import os
+from typing import Optional, Dict, Any, List
+
+import pandas as pd
+
+
+def load_model(path_of_directory: str) -> Dict[str, Any]:
+ """
+ Load Pln or PlnPCA model (that has previously been saved) from the given directory for future initialization.
+
+ Parameters
+ ----------
+ path_of_directory : str
+ The path to the directory containing the model.
+
+ Returns
+ -------
+ Dict[str, Any]
+ A dictionary containing the loaded model.
+ Examples
+ --------
+ >>> from pyPLNmodels import PlnPCA, Pln, get_real_count_data, load_model
+ >>> counts= get_real_count_data()
+ >>> pca = PlnPCA(counts, add_const = True)
+ >>> pca.fit()
+ >>> pca.save()
+ >>> dict_init = load_model("PlnPCA_nbcov_1_dim_200_rank_5")
+ >>> loaded_pca = PlnPCA(counts, add_const = True, dict_initialization = dict_init)
+ >>> print(loaded_pca)
+
+ >>> pln = Pln(counts, add_const = True)
+ >>> pln.fit()
+ >>> pln.save()
+ >>> dict_init = load_model("Pln_nbcov_1_dim_200")
+ >>> loaded_pln = Pln(counts, add_const = True, dict_initialization = dict_init)
+ >>> print(loaded_pln)
+ See also
+ --------
+ :func:`~pyPLNmodels.load_plnpcacollection`
+ """
+ working_dir = os.getcwd()
+ os.chdir(path_of_directory)
+ all_files = os.listdir()
+ data = {}
+ for filename in all_files:
+ if filename.endswith(".csv"):
+ parameter = filename[:-4]
+ try:
+ data[parameter] = pd.read_csv(filename, header=None).values
+ except pd.errors.EmptyDataError:
+ print(
+ f"Can't load {parameter} since empty. Standard initialization will be performed for this parameter"
+ )
+ os.chdir(working_dir)
+ return data
+
+
+def load_pln(path_of_directory: str) -> Dict[str, Any]:
+ """
+ Alias for :func:`~pyPLNmodels._utils.load_model`.
+ """
+ return load_model(path_of_directory)
+
+
+def load_plnpca(path_of_directory: str) -> Dict[str, Any]:
+ """
+ Alias for :func:`~pyPLNmodels._utils.load_model`.
+ """
+ return load_model(path_of_directory)
+
+
+def load_plnpcacollection(
+ path_of_directory: str, ranks: Optional[List[int]] = None
+) -> Dict[int, Dict[str, Any]]:
+ """
+ Load PlnPCAcollection models from the given directory.
+
+ Parameters
+ ----------
+ path_of_directory : str
+ The path to the directory containing the PlnPCAcollection models.
+ ranks : List[int], optional
+ A List of ranks specifying which models to load. If None, all models in the directory will be loaded.
+
+ Returns
+ -------
+ Dict[int, Dict[str, Any]]
+ A dictionary containing the loaded PlnPCAcollection models, with ranks as keys.
+
+ Raises
+ ------
+ ValueError
+ If an invalid model name is encountered and the rank cannot be determined.
+
+ Examples
+ --------
+ >>> from pyPLNmodels import PlnPCAcollection, get_real_count_data, load_plnpcacollection
+ >>> counts = get_real_count_data()
+ >>> pcas = PlnPCAcollection(counts, add_const = True, ranks = [4,5,6])
+ >>> pcas.fit()
+ >>> pcas.save()
+ >>> dict_init = load_plnpcacollection("PlnPCAcollection_nbcov_1_dim_200")
+ >>> loaded_pcas = PlnPCAcollection(counts, add_const = True, ranks = [4,5,6], dict_of_dict_initialization = dict_init)
+ >>> print(loaded_pcas)
+
+ See also
+ --------
+ :func:`~pyPLNmodels.load_model`
+ """
+ working_dir = os.getcwd()
+ os.chdir(path_of_directory)
+ if ranks is None:
+ dirnames = os.listdir()
+ ranks = []
+ for dirname in dirnames:
+ try:
+ rank = int(dirname[-1])
+ except ValueError:
+ raise ValueError(
+ f"Can't load the model {dirname}. End of {dirname} should be an int"
+ )
+ ranks.append(rank)
+ datas = {}
+ for rank in ranks:
+ datas[rank] = load_model(f"PlnPCA_rank_{rank}")
+ datas["ranks"] = ranks
+ os.chdir(working_dir)
+ return datas
--
GitLab
From 9c060b6b6c14d9428f06446b55be854c5c0069e4 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 26 Jun 2023 08:21:51 +0200
Subject: [PATCH 015/167] change import file for load_pln etc.
---
pyPLNmodels/__init__.py | 4 +---
pyPLNmodels/load.py | 4 ++--
2 files changed, 3 insertions(+), 5 deletions(-)
diff --git a/pyPLNmodels/__init__.py b/pyPLNmodels/__init__.py
index 992fcf19..2076e34f 100644
--- a/pyPLNmodels/__init__.py
+++ b/pyPLNmodels/__init__.py
@@ -4,13 +4,11 @@ from .elbos import profiled_elbo_pln, elbo_plnpca, elbo_pln
from ._utils import (
get_simulated_count_data,
get_real_count_data,
- load_model,
- load_plnpcacollection,
- load_pln,
sample_pln,
get_simulation_parameters,
PlnParameters,
)
+from load import load_model, load_plnpcacollection, load_pln
from ._initialization import log_posterior
diff --git a/pyPLNmodels/load.py b/pyPLNmodels/load.py
index 4b59d5e8..26871ae8 100644
--- a/pyPLNmodels/load.py
+++ b/pyPLNmodels/load.py
@@ -57,14 +57,14 @@ def load_model(path_of_directory: str) -> Dict[str, Any]:
def load_pln(path_of_directory: str) -> Dict[str, Any]:
"""
- Alias for :func:`~pyPLNmodels._utils.load_model`.
+ Alias for :func:`~pyPLNmodels.load.load_model`.
"""
return load_model(path_of_directory)
def load_plnpca(path_of_directory: str) -> Dict[str, Any]:
"""
- Alias for :func:`~pyPLNmodels._utils.load_model`.
+ Alias for :func:`~pyPLNmodels.load.load_model`.
"""
return load_model(path_of_directory)
--
GitLab
From 1c112b4cff42fc08253ca2157d77faedd5a17253 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 26 Jun 2023 08:55:39 +0200
Subject: [PATCH 016/167] add docstrings and examples and wrote test for viz.
---
pyPLNmodels/__init__.py | 2 +-
pyPLNmodels/models.py | 52 +++++++++++++++++++++++++++++------
tests/create_example_files.py | 1 +
tests/test_viz.py | 12 +++++++-
4 files changed, 57 insertions(+), 10 deletions(-)
diff --git a/pyPLNmodels/__init__.py b/pyPLNmodels/__init__.py
index 2076e34f..b379b8d2 100644
--- a/pyPLNmodels/__init__.py
+++ b/pyPLNmodels/__init__.py
@@ -8,7 +8,7 @@ from ._utils import (
get_simulation_parameters,
PlnParameters,
)
-from load import load_model, load_plnpcacollection, load_pln
+from .load import load_model, load_plnpcacollection, load_pln
from ._initialization import log_posterior
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 9e84b16d..9c04cc0e 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -1247,14 +1247,13 @@ class _model(ABC):
-------
Any
The matplotlib axis.
+ >>>
"""
if self._fitted is None:
raise RuntimeError("Please fit the model before.")
if ax is None:
ax = plt.gca()
predictions = self._counts_predictions().ravel().detach()
- print("pred shpae", predictions.shape)
- print("counts shape", self.counts.ravel().shape)
if colors is not None:
colors = np.repeat(np.array(colors), repeats=self.dim).ravel()
sns.scatterplot(x=self.counts.ravel(), y=predictions, hue=colors, ax=ax)
@@ -1302,6 +1301,7 @@ class Pln(_model):
>>> from pyPLNmodels import Pln, get_real_count_data
>>> counts= get_real_count_data()
>>> pln = Pln(counts, add_const = True)
+ >>> pln.fit()
>>> print(pln)
""",
returns="""
@@ -1398,6 +1398,23 @@ class Pln(_model):
verbose=verbose,
)
+ @_add_doc(
+ _model,
+ example="""
+ >>> import matplotlib.pyplot as plt
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(counts,add_const = True)
+ >>> pln.fit()
+ >>> pln.plot_expected_vs_true()
+ >>> plt.show()
+ >>> pln.plot_expected_vs_true(colors = labels)
+ >>> plt.show()
+ """,
+ )
+ def plot_expected_vs_true(self, ax=None, colors=None):
+ super().plot_expected_vs_true(ax=ax, colors=colors)
+
@property
def _description(self):
"""
@@ -1636,13 +1653,14 @@ class PlnPCAcollection:
>>> from pyPLNmodels import PlnPCAcollection, get_real_count_data, get_simulation_parameters, sample_pln
>>> counts, labels = get_real_count_data(return_labels = True)
>>> data = {"counts": counts}
- >>> pcas = PlnPCAcollection.from_formula("counts ~ 1", data = data, ranks = [5,8, 12]])
- >>> print(pcas)
- >>> pcas.show()
+ >>> plncas = PlnPCAcollection.from_formula("counts ~ 1", data = data, ranks = [5,8, 12])
+ >>> plncas.fit()
+ >>> print(plncas)
+ >>> plncas.show()
>>> plnparam = get_simulation_parameters(n_samples =100, dim = 60, nb_cov = 2, rank = 8)
>>> counts = sample_pln(plnparam)
- >>> data = {"counts": plnparam.counts, "cov": plnparam.covariates, "offsets": plnparam.offsets}
+ >>> data = {"counts":counts, "cov": plnparam.covariates, "offsets": plnparam.offsets}
>>> plnpcas = PlnPCAcollection.from_formula("counts ~ 0 + cov", data = data, ranks = [5,8,12])
>>> plnpcas.fit()
>>> print(plnpcas)
@@ -2417,13 +2435,14 @@ class PlnPCA(_model):
>>> from pyPLNmodels import PlnPCA, get_real_count_data, get_simulation_parameters, sample_pln
>>> counts, labels = get_real_count_data(return_labels = True)
>>> data = {"counts": counts}
- >>> pca = PlnPCA.from_formula("counts ~ 1", data = data, rank = 5])
+ >>> pca = PlnPCA.from_formula("counts ~ 1", data = data, rank = 5)
+ >>> pca.fit()
>>> print(pca)
>>> pca.viz(colors = labels)
>>> plnparam = get_simulation_parameters(n_samples =100, dim = 60, nb_cov = 2, rank = 8)
>>> counts = sample_pln(plnparam)
- >>> data = {"counts": plnparam.counts, "cov": plnparam.covariates, "offsets": plnparam.offsets}
+ >>> data = {"counts": counts, "cov": plnparam.covariates, "offsets": plnparam.offsets}
>>> plnpca = PlnPCA.from_formula("counts ~ 0 + cov", data = data, rank = 5)
>>> plnpca.fit()
>>> print(plnpca)
@@ -2547,6 +2566,23 @@ class PlnPCA(_model):
verbose=verbose,
)
+ @_add_doc(
+ _model,
+ example="""
+ >>> import matplotlib.pyplot as plt
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> counts, labels = get_real_count_data(return_labels = True)
+ >>> plnpca = Pln(counts,add_const = True)
+ >>> plnpca.fit()
+ >>> plnpca.plot_expected_vs_true()
+ >>> plt.show()
+ >>> plnpca.plot_expected_vs_true(colors = labels)
+ >>> plt.show()
+ """,
+ )
+ def plot_expected_vs_true(self, ax=None, colors=None):
+ super().plot_expected_vs_true(ax=ax, colors=colors)
+
def _check_if_rank_is_too_high(self):
"""
Check if the rank is too high and issue a warning if necessary.
diff --git a/tests/create_example_files.py b/tests/create_example_files.py
index 8469f58d..b575159f 100644
--- a/tests/create_example_files.py
+++ b/tests/create_example_files.py
@@ -51,3 +51,4 @@ os.makedirs("examples", exist_ok=True)
filename_to_example_file("_utils")
filename_to_example_file("models")
filename_to_example_file("elbos")
+filename_to_example_file("load")
diff --git a/tests/test_viz.py b/tests/test_viz.py
index da9f05b4..4cab97f9 100644
--- a/tests/test_viz.py
+++ b/tests/test_viz.py
@@ -83,7 +83,17 @@ def test_plot_pca_correlation_graph_without_names(pln):
pln.plot_pca_correlation_graph([f"var_{i}" for i in range(3)], [0, 1, 2])
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_sim_pln"])
+@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
@filter_models(["Pln", "PlnPCA"])
def test_expected_vs_true(pln):
pln.plot_expected_vs_true()
+ fig, ax = plt.figure()
+ pln.plot_expected_vs_true(ax=ax)
+
+
+@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_real_pln"])
+@filter_models(["Pln", "PlnPCA"])
+def test_expected_vs_true_labels(pln):
+ pln.plot_expected_vs_true(colors=labels_real)
+ fig, ax = plt.subplots()
+ pln.plot_expected_vs_true(ax=ax, colors=labels_real)
--
GitLab
From 0b0cc5345522b4f62970e4476190a21b34387a9d Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 26 Jun 2023 09:23:42 +0200
Subject: [PATCH 017/167] began to aggregate docstrings for fitting and added
docstrings for methods
---
pyPLNmodels/models.py | 189 ++++++++++++++++++++++++++++++++----------
1 file changed, 147 insertions(+), 42 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 9c04cc0e..d89a9bfe 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -49,6 +49,21 @@ else:
NB_CHARACTERS_FOR_NICE_PLOT = 70
+str_fit_plnpca_formula = (
+ ">>> from pyPLNmodels import PlnPCA, get_real_count_data"
+ ">>> counts = get_real_count_data()"
+ ">>> data = {'counts': counts}"
+ ">>> plnpca = PlnPCA.from_formula('counts ~ 1', data = data)"
+ ">>> plnpca.fit()"
+)
+str_fit_pln_formula = (
+ ">>> from pyPLNmodels import PlnPCA, get_real_count_data"
+ ">>> counts = get_real_count_data()"
+ ">>> data = {'counts': counts}"
+ ">>> plnpca = PlnPCA.from_formula('counts ~ 1', data = data)"
+ ">>> plnpca.fit()"
+)
+
class _model(ABC):
"""
@@ -174,17 +189,17 @@ class _model(ABC):
"""
return self._fitted
- def viz(self, ax=None, colors=None, show_cov: bool = False):
+ def viz(self, *, ax=None, colors=None, show_cov: bool = False):
"""
Visualize the latent variables with a classic PCA.
Parameters
----------
- ax : Optional[Any], optional
+ ax : Optional[Any], optional(keyword-only)
The matplotlib axis to use. If None, the current axis is used, by default None.
- colors : Optional[Any], optional
+ colors : Optional[Any], optional(keyword-only)
The colors to use for plotting, by default None.
- show_cov: bool, optional
+ show_cov: bool, optional(keyword-only)
If True, will display ellipses with right covariances. Default is False.
Raises
------
@@ -414,15 +429,6 @@ class _model(ABC):
------
ValueError
If the number of components asked is greater than the number of dimensions.
- Examples
- --------
- >>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> pln = Pln.from_formula("counts ~ 1", data = data)
- >>> pln.fit()
- >>> pca_proj = pln.pca_projected_latent_variables()
- >>> print(pca_proj.shape)
"""
pca = self.sk_PCA(n_components=n_components)
return pca.transform(self.latent_variables.cpu())
@@ -1415,6 +1421,70 @@ class Pln(_model):
def plot_expected_vs_true(self, ax=None, colors=None):
super().plot_expected_vs_true(ax=ax, colors=colors)
+ @_add_doc(
+ _model,
+ example="""
+ >>> import matplotlib.pyplot as plt
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(counts,add_const = True)
+ >>> pln.fit()
+ >>> pln.viz()
+ >>> plt.show()
+ >>> pln.viz(colors = labels)
+ >>> plt.show()
+ >>> pln.viz(show_cov = True)
+ >>> plt.show()
+ """,
+ )
+ def viz(self, ax=None, colors=None, show_cov: bool = False):
+ super().plot_expected_vs_true(ax=ax, colors=colors, show_cov=show_cov)
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> pln = Pln.from_formula("counts ~ 1", data = data)
+ >>> pln.fit()
+ >>> pca_proj = pln.pca_projected_latent_variables()
+ >>> print(pca_proj.shape)
+ """,
+ )
+ def pca_projected_latent_variables(self, n_components: Optional[int] = None):
+ super().pca_projected_latent_variables(n_components=n_components)
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> pln = Pln.from_formula("counts ~ 1", data = data)
+ >>> pln.fit()
+ >>> pln.scatter_pca_matrix(n_components = 5)
+ """,
+ )
+ def scatter_pca_matrix(self, n_components=None, color=None):
+ super().scatter_pca_matrix(n_components=n_components, color=color)
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> pln = Pln.from_formula("counts ~ 1", data = data)
+ >>> pln.fit()
+ >>> pln.plot_pca_correlation_graph(["a","b"], indices_of_variables = [4,8])
+ """,
+ )
+ def plot_pca_correlation_graph(self, variables_names, indices_of_variables=None):
+ super().plot_pca_correlation_graph(
+ variables_names=variables_names, indices_of_variables=indices_of_variables
+ )
+
@property
def _description(self):
"""
@@ -2583,6 +2653,70 @@ class PlnPCA(_model):
def plot_expected_vs_true(self, ax=None, colors=None):
super().plot_expected_vs_true(ax=ax, colors=colors)
+ @_add_doc(
+ _model,
+ example="""
+ >>> import matplotlib.pyplot as plt
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> counts, labels = get_real_count_data(return_labels = True)
+ >>> plnpca = PlnPCA(counts,add_const = True)
+ >>> plnpca.fit()
+ >>> plnpca.viz()
+ >>> plt.show()
+ >>> plnpca.viz(colors = labels)
+ >>> plt.show()
+ >>> plnpca.viz(show_cov = True)
+ >>> plt.show()
+ """,
+ )
+ def viz(self, ax=None, colors=None, show_cov: bool = False):
+ super().plot_expected_vs_true(ax=ax, colors=colors, show_cov=show_cov)
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> plnpca = PlnPCA.from_formula("counts ~ 1", data = data)
+ >>> plnpca.fit()
+ >>> pca_proj = plnpca.pca_projected_latent_variables()
+ >>> print(pca_proj.shape)
+ """,
+ )
+ def pca_projected_latent_variables(self, n_components: Optional[int] = None):
+ super().pca_projected_latent_variables(n_components=n_components)
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> plnpca = PlnPCA.from_formula("counts ~ 1", data = data)
+ >>> plnpca.fit()
+ >>> plnpca.scatter_pca_matrix(n_components = 5)
+ """,
+ )
+ def scatter_pca_matrix(self, n_components=None, color=None):
+ super().scatter_pca_matrix(n_components=n_components, color=color)
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> plnpca = PlnPCA.from_formula("counts ~ 1", data = data)
+ >>> plnpca.fit()
+ >>> plnpca.plot_pca_correlation_graph(["a","b"], indices_of_variables = [4,8])
+ """,
+ )
+ def plot_pca_correlation_graph(self, variables_names, indices_of_variables=None):
+ super().plot_pca_correlation_graph(
+ variables_names=variables_names, indices_of_variables=indices_of_variables
+ )
+
def _check_if_rank_is_too_high(self):
"""
Check if the rank is too high and issue a warning if necessary.
@@ -2943,35 +3077,6 @@ class PlnPCA(_model):
"""
return torch.linalg.qr(self._components, "reduced")[0]
- def pca_projected_latent_variables(
- self, n_components: Optional[int] = None
- ) -> np.ndarray:
- """
- Perform PCA on projected latent variables.
-
- Parameters
- ----------
- n_components : Optional[int]
- Number of components to keep. Defaults to None.
-
- Returns
- -------
- np.ndarray
- The transformed projected latent variables.
- Raises
- ------
- ValueError
- If the number of components asked is greater than the number of dimensions.
- """
- if n_components is None:
- n_components = self._get_max_components()
- if n_components > self.rank:
- raise ValueError(
- f"You ask more components ({n_components}) than maximum rank ({self.rank})"
- )
- pca = PCA(n_components=n_components)
- return pca.fit_transform(self.latent_variables.cpu())
-
@property
def components(self) -> torch.Tensor:
"""
--
GitLab
From d6e66a85897ab632099dee1370d532523415e1d9 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 26 Jun 2023 17:15:35 +0200
Subject: [PATCH 018/167] add some docstrings
---
pyPLNmodels/__init__.py | 3 +-
pyPLNmodels/models.py | 73 ++++++++++++++++++++---------------------
2 files changed, 37 insertions(+), 39 deletions(-)
diff --git a/pyPLNmodels/__init__.py b/pyPLNmodels/__init__.py
index b379b8d2..e785b288 100644
--- a/pyPLNmodels/__init__.py
+++ b/pyPLNmodels/__init__.py
@@ -8,7 +8,7 @@ from ._utils import (
get_simulation_parameters,
PlnParameters,
)
-from .load import load_model, load_plnpcacollection, load_pln
+from .load import load_model, load_plnpcacollection, load_pln, load_plnpca
from ._initialization import log_posterior
@@ -24,6 +24,7 @@ __all__ = (
"load_model",
"load_plnpcacollection",
"load_pln",
+ "load_plnpca",
"sample_pln",
"log_posterior",
"get_simulation_parameters",
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index d89a9bfe..335fa96b 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -49,21 +49,6 @@ else:
NB_CHARACTERS_FOR_NICE_PLOT = 70
-str_fit_plnpca_formula = (
- ">>> from pyPLNmodels import PlnPCA, get_real_count_data"
- ">>> counts = get_real_count_data()"
- ">>> data = {'counts': counts}"
- ">>> plnpca = PlnPCA.from_formula('counts ~ 1', data = data)"
- ">>> plnpca.fit()"
-)
-str_fit_pln_formula = (
- ">>> from pyPLNmodels import PlnPCA, get_real_count_data"
- ">>> counts = get_real_count_data()"
- ">>> data = {'counts': counts}"
- ">>> plnpca = PlnPCA.from_formula('counts ~ 1', data = data)"
- ">>> plnpca.fit()"
-)
-
class _model(ABC):
"""
@@ -878,7 +863,7 @@ class _model(ABC):
Returns
-------
torch.Tensor or None
- The latent mean or None.
+ The latent mean or None if it has not yet been initialized.
"""
return self._cpu_attribute_or_none("_latent_mean")
@@ -1297,8 +1282,6 @@ class Pln(_model):
>>> pln = Pln.from_formula("counts ~ 1", data)
>>> pln.fit()
>>> print(pln)
-
-
"""
@_add_doc(
@@ -1579,6 +1562,15 @@ class Pln(_model):
-------
torch.Tensor
The computed ELBO.
+ Examples
+ --------
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(counts,add_const = True)
+ >>> pln.fit()
+ >>> elbo = pln.compute_elbo()
+ >>> print("elbo", elbo)
+ >>> print("loglike/n", pln.loglike/pln.n_samples)
"""
return profiled_elbo_pln(
self._counts,
@@ -1643,16 +1635,18 @@ class Pln(_model):
"""
print(f"Fitting a Pln model with {self._description}")
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(counts,add_const = True)
+ >>> pln.fit()
+ >>> print(pln.latent_variables.shape)
+ """,
+ )
@property
def latent_variables(self):
- """
- Property representing the latent variables.
-
- Returns
- -------
- torch.Tensor
- The latent variables.
- """
return self.latent_mean.detach()
@property
@@ -1830,10 +1824,10 @@ class PlnPCAcollection:
The created PlnPCAcollection instance.
Examples
--------
- >>> from pyPLNmodels import PlnPCAcollection, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> pca_col = PlnPCAcollection.from_formula("counts ~ 1", data = data, ranks = [5,6])
+ >>> from pyPLNmodels import PlnPCAcollection, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> pca_col = PlnPCAcollection.from_formula("counts ~ 1", data = data, ranks = [5,6])
See also
--------
:class:`~pyPLNmodels.PlnPCA`
@@ -2730,16 +2724,19 @@ class PlnPCA(_model):
warnings.warn(warning_string)
self._rank = self.dim
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> plnpca = PlnPCA.from_formula("counts ~ 1", data = data)
+ >>> plnpca.fit()
+ >>> print(plnpca.latent_mean.shape)
+ """,
+ )
@property
def latent_mean(self) -> torch.Tensor:
- """
- Property representing the latent mean.
-
- Returns
- -------
- torch.Tensor
- The latent mean tensor.
- """
return self._cpu_attribute_or_none("_latent_mean")
@property
--
GitLab
From 03b7a82df9556ba38b59d75ad6e6c3a79a11a552 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 26 Jun 2023 19:44:22 +0200
Subject: [PATCH 019/167] @property was placed before @_add_doc
---
pyPLNmodels/models.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 335fa96b..92ffae7e 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -1635,6 +1635,7 @@ class Pln(_model):
"""
print(f"Fitting a Pln model with {self._description}")
+ @property
@_add_doc(
_model,
example="""
@@ -1645,7 +1646,6 @@ class Pln(_model):
>>> print(pln.latent_variables.shape)
""",
)
- @property
def latent_variables(self):
return self.latent_mean.detach()
@@ -2724,6 +2724,7 @@ class PlnPCA(_model):
warnings.warn(warning_string)
self._rank = self.dim
+ @property
@_add_doc(
_model,
example="""
@@ -2735,7 +2736,6 @@ class PlnPCA(_model):
>>> print(plnpca.latent_mean.shape)
""",
)
- @property
def latent_mean(self) -> torch.Tensor:
return self._cpu_attribute_or_none("_latent_mean")
--
GitLab
From 47316411dfdbc6f642bc1de7f10ada21d00b2fda Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 26 Jun 2023 23:41:28 +0200
Subject: [PATCH 020/167] add docstrings Union[tensor, np, pd], and fix bug in
test_viz
---
pyPLNmodels/_utils.py | 44 +++++++-----
pyPLNmodels/models.py | 155 +++++++++++++++++++++++-------------------
tests/test_viz.py | 2 +-
3 files changed, 113 insertions(+), 88 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 5945f5ed..50683989 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -232,7 +232,9 @@ def _raise_wrong_dimension_error(
raise ValueError(msg)
-def _format_data(data: pd.DataFrame) -> torch.Tensor or None:
+def _format_data(
+ data: Union[torch.Tensor, np.ndarray, pd.DataFrame]
+) -> torch.Tensor or None:
"""
Transforms the data in a torch.tensor if the input is an array, and None if the input is None.
Raises an error if the input is not an array or None.
@@ -266,9 +268,9 @@ def _format_data(data: pd.DataFrame) -> torch.Tensor or None:
def _format_model_param(
- counts: torch.Tensor,
- covariates: torch.Tensor,
- offsets: torch.Tensor,
+ counts: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame],
offsets_formula: str,
take_log_offsets: bool,
add_const: bool,
@@ -278,11 +280,11 @@ def _format_model_param(
Parameters
----------
- counts : torch.Tensor or None, shape (n, )
+ counts : Union[torch.Tensor, np.ndarray, pd.DataFrame], shape (n, )
Count data.
- covariates : torch.Tensor or None, shape (n, d) or None
+ covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None, shape (n, d) or None
Covariate data.
- offsets : torch.Tensor or None, shape (n, ) or None
+ offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None, shape (n, ) or None
Offset data.
offsets_formula : str
Formula for calculating offsets.
@@ -517,21 +519,29 @@ def _get_simulation_coef_cov_offsets(
class PlnParameters:
- def __init__(self, *, components, coef, covariates, offsets, coef_inflation=None):
+ def __init__(
+ self,
+ *,
+ components: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ coef: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ coef_inflation=None,
+ ):
"""
Instantiate all the needed parameters to sample from the PLN model.
Parameters
----------
- components : torch.Tensor(keyword-only)
+ components : : Union[torch.Tensor, np.ndarray, pd.DataFrame](keyword-only)
Components of size (p, rank)
- coef : torch.Tensor(keyword-only)
+ coef : : Union[torch.Tensor, np.ndarray, pd.DataFrame](keyword-only)
Coefficient of size (d, p)
- covariates : torch.Tensor or None(keyword-only)
+ covariates : : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None(keyword-only)
Covariates, size (n, d) or None
- offsets : torch.Tensor(keyword-only)
+ offsets : : Union[torch.Tensor, np.ndarray, pd.DataFrame](keyword-only)
Offset, size (n, p)
- _coef_inflation : torch.Tensor or None, optional(keyword-only)
+ _coef_inflation : : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None, optional(keyword-only)
Coefficient for zero-inflation model, size (d, p) or None. Default is None.
"""
@@ -778,7 +788,9 @@ def _check_right_rank(data: Dict[str, Any], rank: int) -> None:
)
-def _extract_data_from_formula(formula: str, data: Dict[str, Any]) -> Tuple:
+def _extract_data_from_formula(
+ formula: str, data: Dict[str, : Union[torch.Tensor, np.ndarray, pd.DataFrame]]
+) -> Tuple:
"""
Extract data from the given formula and data dictionary.
@@ -847,8 +859,8 @@ def _get_dict_initialization(
def _to_tensor(
- obj: Union[np.ndarray, torch.Tensor, pd.DataFrame, None]
-) -> Union[torch.Tensor, None]:
+ obj: Union[np.ndarray, torch.Tensor, pd.DataFrame]
+) -> Union[torch.Tensor]:
"""
Convert an object to a PyTorch tensor.
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 92ffae7e..f4389312 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -2,7 +2,7 @@ import time
from abc import ABC, abstractmethod
import warnings
import os
-from typing import Optional, Dict, List, Type, Any, Iterable
+from typing import Optional, Dict, List, Type, Any, Iterable, Union
import pandas as pd
import torch
@@ -66,10 +66,10 @@ class _model(ABC):
def __init__(
self,
- counts: torch.Tensor,
+ counts: Union[torch.Tensor, np.ndarray, pd.DataFrame],
*,
- covariates: Optional[torch.Tensor] = None,
- offsets: Optional[torch.Tensor] = None,
+ covariates: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
+ offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets_formula: str = "logsum",
dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
@@ -80,14 +80,15 @@ class _model(ABC):
Parameters
----------
- counts : torch.Tensor
+ counts : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The count data.
- covariates : torch.Tensor, optional(keyword-only)
+ covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The covariate data. Defaults to None.
- offsets : torch.Tensor, optional(keyword-only)
+ offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The offsets data. Defaults to None.
offsets_formula : str, optional(keyword-only)
- The formula for offsets. Defaults to "logsum".
+ The formula for offsets. Defaults to "logsum". Overriden if
+ offsets is not None.
dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
@@ -112,7 +113,7 @@ class _model(ABC):
def from_formula(
cls,
formula: str,
- data: dict,
+ data: dict[str : Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
offsets_formula: str = "logsum",
dict_initialization: Optional[dict] = None,
@@ -126,7 +127,8 @@ class _model(ABC):
formula : str
The formula.
data : dict
- The data dictionary.
+ The data dictionary. Each value can be either a torch.Tensor,
+ a np.ndarray or pd.DataFrame
offsets_formula : str, optional(keyword-only)
The formula for offsets. Defaults to "logsum".
dict_initialization : dict, optional(keyword-only)
@@ -180,11 +182,11 @@ class _model(ABC):
Parameters
----------
- ax : Optional[Any], optional(keyword-only)
+ ax : Optional[matplotlib.axes.Axes], optional(keyword-only)
The matplotlib axis to use. If None, the current axis is used, by default None.
- colors : Optional[Any], optional(keyword-only)
+ colors : Optional[np.ndarray], optional(keyword-only)
The colors to use for plotting, by default None.
- show_cov: bool, optional(keyword-only)
+ show_cov: bool, Optional(keyword-only)
If True, will display ellipses with right covariances. Default is False.
Raises
------
@@ -468,8 +470,8 @@ class _model(ABC):
If not specified, the maximum number of components will be used.
Defaults to None.
- color (str, optional): The name of the variable used for color coding the scatter plot.
- If not specified, the scatter plot will not be color-coded.
+ color (str, np.ndarray): An array with one label for each
+ sample in the counts property of the object.
Defaults to None.
Raises
------
@@ -707,7 +709,9 @@ class _model(ABC):
def show(self, axes=None):
"""
- Show plots.
+ Show 3 plots. The first one is the covariance of the model.
+ The second one is the stopping criterion with the runtime in abscisse.
+ The third one is the elbo.
Parameters
----------
@@ -881,13 +885,13 @@ class _model(ABC):
@latent_mean.setter
@_array2tensor
- def latent_mean(self, latent_mean):
+ def latent_mean(self, latent_mean: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the latent mean property.
Parameters
----------
- latent_mean : torch.Tensor
+ latent_mean : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The latent mean.
Raises
@@ -903,13 +907,15 @@ class _model(ABC):
@latent_sqrt_var.setter
@_array2tensor
- def latent_sqrt_var(self, latent_sqrt_var):
+ def latent_sqrt_var(
+ self, latent_sqrt_var: Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ ):
"""
Setter for the latent variance property.
Parameters
----------
- latent_sqrt_var : torch.Tensor
+ latent_sqrt_var : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The latent variance.
Raises
@@ -1005,13 +1011,13 @@ class _model(ABC):
@counts.setter
@_array2tensor
- def counts(self, counts):
+ def counts(self, counts: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the counts property.
Parameters
----------
- counts : torch.Tensor
+ counts : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The counts.
Raises
@@ -1029,13 +1035,13 @@ class _model(ABC):
@offsets.setter
@_array2tensor
- def offsets(self, offsets):
+ def offsets(self, offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the offsets property.
Parameters
----------
- offsets : torch.Tensor
+ offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The offsets.
Raises
@@ -1051,13 +1057,13 @@ class _model(ABC):
@covariates.setter
@_array2tensor
- def covariates(self, covariates):
+ def covariates(self, covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the covariates property.
Parameters
----------
- covariates : torch.Tensor
+ covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The covariates.
Raises
@@ -1070,13 +1076,13 @@ class _model(ABC):
@coef.setter
@_array2tensor
- def coef(self, coef):
+ def coef(self, coef: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the coef property.
Parameters
----------
- coef : torch.Tensor or None
+ coef : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The coefficients.
Raises
@@ -1157,13 +1163,13 @@ class _model(ABC):
"""
return self.covariance
- def predict(self, covariates=None):
+ def predict(self, covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None):
"""
Method for making predictions.
Parameters
----------
- covariates : torch.Tensor, optional
+ covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional
The covariates, by default None.
Returns
@@ -1174,16 +1180,16 @@ class _model(ABC):
Raises
------
AttributeError
- If there are no covariates in the model.
+ If there are no covariates in the model but some are provided.
RuntimeError
If the shape of the covariates is incorrect.
Notes
-----
- If `covariates` is not provided and there are no covariates in the model, None is returned.
+ If there are covariates in the model, then the mean covariates @ coef is returned.
- If `covariates` is provided, it should have the shape `(_, nb_cov)`, where `nb_cov` is the number of covariates.
- The predicted values are obtained by multiplying the covariates by the coefficients.
-
"""
if covariates is not None and self.nb_cov == 0:
raise AttributeError("No covariates in the model, can't predict")
@@ -1228,7 +1234,7 @@ class _model(ABC):
Parameters
----------
- ax : Optional[Any], optional
+ ax : Optional[matplotlib.axes.Axes], optional
The matplotlib axis to use. If None, the current axis is used, by default None.
colors : Optional[Any], optional
@@ -1236,7 +1242,7 @@ class _model(ABC):
Returns
-------
- Any
+ matplotlib.axes.Axes
The matplotlib axis.
>>>
"""
@@ -1261,8 +1267,6 @@ class _model(ABC):
# need to do a good init for M and S
class Pln(_model):
- _NAME = "Pln"
- coef: torch.Tensor
"""
Pln class.
@@ -1284,6 +1288,9 @@ class Pln(_model):
>>> print(pln)
"""
+ _NAME = "Pln"
+ coef: torch.Tensor
+
@_add_doc(
_model,
example="""
@@ -1302,10 +1309,10 @@ class Pln(_model):
)
def __init__(
self,
- counts: torch.Tensor,
+ counts: Optional[torch.Tensor, np.ndarray, pd.DataFrame],
*,
- covariates: Optional[torch.Tensor] = None,
- offsets: Optional[torch.Tensor] = None,
+ covariates: Optional[torch.Tensor, np.ndarray, pd.DataFrame] = None,
+ offsets: Optional[torch.Tensor, np.ndarray, pd.DataFrame] = None,
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
@@ -1341,7 +1348,7 @@ class Pln(_model):
def from_formula(
cls,
formula: str,
- data: Any,
+ data: Dict[str : Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
@@ -1421,7 +1428,7 @@ class Pln(_model):
""",
)
def viz(self, ax=None, colors=None, show_cov: bool = False):
- super().plot_expected_vs_true(ax=ax, colors=colors, show_cov=show_cov)
+ super().viz(ax=ax, colors=colors, show_cov=show_cov)
@_add_doc(
_model,
@@ -1499,14 +1506,14 @@ class Pln(_model):
return None
@coef.setter
- def coef(self, coef):
+ def coef(self, coef: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the coef property.
Parameters
----------
- coef : torch.Tensor
- The coefficients.
+ coef : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The regression coefficients of the gaussian latent variables.
"""
def _counts_predictions(self):
@@ -1698,7 +1705,8 @@ class Pln(_model):
@covariance.setter
def covariance(self, covariance):
"""
- Setter for the covariance property.
+ Setter for the covariance property. Only here for completeness, since
+ this function does nothing
Parameters
----------
@@ -1710,7 +1718,7 @@ class Pln(_model):
class PlnPCAcollection:
"""
- A collection where item q corresponds to a PlnPCA object with rank q.
+ A collection where value q corresponds to a PlnPCA object with rank q.
Examples
--------
@@ -1739,10 +1747,10 @@ class PlnPCAcollection:
def __init__(
self,
- counts: torch.Tensor,
+ counts: Union[torch.Tensor, np.ndarray, pd.DataFrame],
*,
- covariates: Optional[torch.Tensor] = None,
- offsets: Optional[torch.Tensor] = None,
+ covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
+ offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
offsets_formula: str = "logsum",
ranks: Iterable[int] = range(3, 5),
dict_of_dict_initialization: Optional[dict] = None,
@@ -1754,11 +1762,11 @@ class PlnPCAcollection:
Parameters
----------
- counts : torch.Tensor
+ counts :Union[torch.Tensor, np.ndarray, pd.DataFrame]
The counts.
- covariates : torch.Tensor, optional(keyword-only)
+ covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The covariates, by default None.
- offsets : torch.Tensor, optional(keyword-only)
+ offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The offsets, by default None.
offsets_formula : str, optional(keyword-only)
The formula for offsets, by default "logsum".
@@ -1794,7 +1802,8 @@ class PlnPCAcollection:
def from_formula(
cls,
formula: str,
- data: dict,
+ data: Dict[str : Union[torch.Tensor, np.ndarray, pd.DataFrame]],
+ *,
offsets_formula: str = "logsum",
ranks: Iterable[int] = range(3, 5),
dict_of_dict_initialization: Optional[dict] = None,
@@ -1808,7 +1817,8 @@ class PlnPCAcollection:
formula : str
The formula.
data : dict
- The data dictionary.
+ The data dictionary. Each value can be either
+ a torch.Tensor, np.ndarray or pd.DataFrame
offsets_formula : str, optional(keyword-only)
The formula for offsets, by default "logsum".
Overriden if data["offsets"] is not None.
@@ -1919,13 +1929,13 @@ class PlnPCAcollection:
@counts.setter
@_array2tensor
- def counts(self, counts: torch.Tensor):
+ def counts(self, counts: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the counts property.
Parameters
----------
- counts : torch.Tensor
+ counts : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The counts.
"""
for model in self.values():
@@ -1933,13 +1943,13 @@ class PlnPCAcollection:
@coef.setter
@_array2tensor
- def coef(self, coef: torch.Tensor):
+ def coef(self, coef: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the coef property.
Parameters
----------
- coef : torch.Tensor
+ coef : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The coefficients.
"""
for model in self.values():
@@ -1947,13 +1957,13 @@ class PlnPCAcollection:
@covariates.setter
@_array2tensor
- def covariates(self, covariates: torch.Tensor):
+ def covariates(self, covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the covariates property.
Parameters
----------
- covariates : torch.Tensor
+ covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The covariates.
"""
for model in self.values():
@@ -1973,13 +1983,13 @@ class PlnPCAcollection:
@offsets.setter
@_array2tensor
- def offsets(self, offsets: torch.Tensor):
+ def offsets(self, offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the offsets property.
Parameters
----------
- offsets : torch.Tensor
+ offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The offsets.
"""
for model in self.values():
@@ -2474,7 +2484,7 @@ class PlnPCAcollection:
str
The string representation of the useful methods.
"""
- return ".show(), .best_model()"
+ return ".show(), .best_model(), .keys(), .items(), .values()"
@property
def _useful_properties_string(self) -> str:
@@ -2540,10 +2550,10 @@ class PlnPCA(_model):
)
def __init__(
self,
- counts: torch.Tensor,
+ counts: Optional[torch.Tensor, np.ndarray, pd.DataFrame],
*,
- covariates: Optional[torch.Tensor] = None,
- offsets: Optional[torch.Tensor] = None,
+ covariates: Optional[torch.Tensor, np.ndarray, pd.DataFrame] = None,
+ offsets: Optional[torch.Tensor, np.ndarray, pd.DataFrame] = None,
offsets_formula: str = "logsum",
rank: int = 5,
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
@@ -2585,7 +2595,8 @@ class PlnPCA(_model):
def from_formula(
cls,
formula: str,
- data: Any,
+ data: Dict[str : Union[torch.Tensor, np.ndarray, pd.DataFrame]],
+ *,
rank: int = 5,
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
@@ -2663,7 +2674,7 @@ class PlnPCA(_model):
>>> plt.show()
""",
)
- def viz(self, ax=None, colors=None, show_cov: bool = False):
+ def viz(self, ax: matplotlib.axes.Axes = None, colors=None, show_cov: bool = False):
super().plot_expected_vs_true(ax=ax, colors=colors, show_cov=show_cov)
@_add_doc(
@@ -2706,7 +2717,9 @@ class PlnPCA(_model):
>>> plnpca.plot_pca_correlation_graph(["a","b"], indices_of_variables = [4,8])
""",
)
- def plot_pca_correlation_graph(self, variables_names, indices_of_variables=None):
+ def plot_pca_correlation_graph(
+ self, variables_names: List[str], indices_of_variables=None
+ ):
super().plot_pca_correlation_graph(
variables_names=variables_names, indices_of_variables=indices_of_variables
)
@@ -2837,13 +2850,13 @@ class PlnPCA(_model):
@covariates.setter
@_array2tensor
- def covariates(self, covariates: torch.Tensor):
+ def covariates(self, covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
Setter for the covariates.
Parameters
----------
- covariates : torch.Tensor
+ covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The covariates tensor.
"""
_check_data_shape(self.counts, covariates, self.offsets)
diff --git a/tests/test_viz.py b/tests/test_viz.py
index 4cab97f9..3cafe93f 100644
--- a/tests/test_viz.py
+++ b/tests/test_viz.py
@@ -87,7 +87,7 @@ def test_plot_pca_correlation_graph_without_names(pln):
@filter_models(["Pln", "PlnPCA"])
def test_expected_vs_true(pln):
pln.plot_expected_vs_true()
- fig, ax = plt.figure()
+ fig, ax = plt.subplots()
pln.plot_expected_vs_true(ax=ax)
--
GitLab
From c7c9cbbeb5efa258a6eddc782ab4958e5cdf3cc0 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 27 Jun 2023 08:35:38 +0200
Subject: [PATCH 021/167] bug in type hints and viz definition.
---
pyPLNmodels/_utils.py | 2 +-
pyPLNmodels/models.py | 21 +++++++++++----------
2 files changed, 12 insertions(+), 11 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 50683989..2e7f3ce3 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -789,7 +789,7 @@ def _check_right_rank(data: Dict[str, Any], rank: int) -> None:
def _extract_data_from_formula(
- formula: str, data: Dict[str, : Union[torch.Tensor, np.ndarray, pd.DataFrame]]
+ formula: str, data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]]
) -> Tuple:
"""
Extract data from the given formula and data dictionary.
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index f4389312..ac0ed77a 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -12,6 +12,7 @@ import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
import plotly.express as px
from mlxtend.plotting import plot_pca_correlation_graph
+import matplotlib
from ._closed_forms import (
_closed_formula_coef,
@@ -1309,10 +1310,10 @@ class Pln(_model):
)
def __init__(
self,
- counts: Optional[torch.Tensor, np.ndarray, pd.DataFrame],
+ counts: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- covariates: Optional[torch.Tensor, np.ndarray, pd.DataFrame] = None,
- offsets: Optional[torch.Tensor, np.ndarray, pd.DataFrame] = None,
+ covariates: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
+ offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
@@ -1348,7 +1349,7 @@ class Pln(_model):
def from_formula(
cls,
formula: str,
- data: Dict[str : Union[torch.Tensor, np.ndarray, pd.DataFrame]],
+ data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
@@ -1802,7 +1803,7 @@ class PlnPCAcollection:
def from_formula(
cls,
formula: str,
- data: Dict[str : Union[torch.Tensor, np.ndarray, pd.DataFrame]],
+ data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
offsets_formula: str = "logsum",
ranks: Iterable[int] = range(3, 5),
@@ -2550,10 +2551,10 @@ class PlnPCA(_model):
)
def __init__(
self,
- counts: Optional[torch.Tensor, np.ndarray, pd.DataFrame],
+ counts: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- covariates: Optional[torch.Tensor, np.ndarray, pd.DataFrame] = None,
- offsets: Optional[torch.Tensor, np.ndarray, pd.DataFrame] = None,
+ covariates: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
+ offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets_formula: str = "logsum",
rank: int = 5,
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
@@ -2595,7 +2596,7 @@ class PlnPCA(_model):
def from_formula(
cls,
formula: str,
- data: Dict[str : Union[torch.Tensor, np.ndarray, pd.DataFrame]],
+ data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
rank: int = 5,
offsets_formula: str = "logsum",
@@ -2675,7 +2676,7 @@ class PlnPCA(_model):
""",
)
def viz(self, ax: matplotlib.axes.Axes = None, colors=None, show_cov: bool = False):
- super().plot_expected_vs_true(ax=ax, colors=colors, show_cov=show_cov)
+ super().viz(ax=ax, colors=colors, show_cov=show_cov)
@_add_doc(
_model,
--
GitLab
From ed26e61a21c8fca147dda44febc02c4be51a744a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 27 Jun 2023 10:07:55 +0200
Subject: [PATCH 022/167] add docstrings in plnparam class
---
pyPLNmodels/_utils.py | 77 ++++++++++++++++++++++++++++++++++---------
tests/test_viz.py | 2 ++
2 files changed, 63 insertions(+), 16 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 2e7f3ce3..d8d027ac 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -545,37 +545,47 @@ class PlnParameters:
Coefficient for zero-inflation model, size (d, p) or None. Default is None.
"""
- self.components = _format_data(components)
- self.coef = _format_data(coef)
- self.covariates = _format_data(covariates)
- self.offsets = _format_data(offsets)
- self.coef_inflation = _format_data(coef_inflation)
- if self.coef is not None:
+ self._components = _format_data(components)
+ self._coef = _format_data(coef)
+ self._covariates = _format_data(covariates)
+ self._offsets = _format_data(offsets)
+ self._coef_inflation = _format_data(coef_inflation)
+ if self._coef is not None:
_check_two_dimensions_are_equal(
- "components", "coef", self.components.shape[0], self.coef.shape[1], 0, 1
+ "components",
+ "coef",
+ self._components.shape[0],
+ self._coef.shape[1],
+ 0,
+ 1,
)
- if self.offsets is not None:
+ if self._offsets is not None:
_check_two_dimensions_are_equal(
"components",
"offsets",
- self.components.shape[0],
- self.offsets.shape[1],
+ self._components.shape[0],
+ self._offsets.shape[1],
0,
1,
)
- if self.covariates is not None:
+ if self._covariates is not None:
_check_two_dimensions_are_equal(
"offsets",
"covariates",
- self.offsets.shape[0],
- self.covariates.shape[0],
+ self._offsets.shape[0],
+ self._covariates.shape[0],
0,
0,
)
_check_two_dimensions_are_equal(
- "covariates", "coef", self.covariates.shape[1], self.coef.shape[0], 1, 0
+ "covariates",
+ "coef",
+ self._covariates.shape[1],
+ self._coef.shape[0],
+ 1,
+ 0,
)
- for array in [self.components, self.coef, self.covariates, self.offsets]:
+ for array in [self._components, self._coef, self._covariates, self._offsets]:
if array is not None:
if len(array.shape) != 2:
raise RuntimeError(
@@ -587,7 +597,42 @@ class PlnParameters:
"""
Covariance of the model.
"""
- return self.components @ self.components.T
+ return self._components @ self._components.T
+
+ @property
+ def components(self):
+ """
+ Components of the model.
+ """
+ return self._components
+
+ @property
+ def offsets(self):
+ """
+ Data offsets.
+ """
+ return self._offsets
+
+ @property
+ def coef(self):
+ """
+ Coef of the model.
+ """
+ return self.coef
+
+ @property
+ def covariates(self):
+ """
+ Data covariates.
+ """
+ return self.covariates
+
+ @property
+ def coef_inflation(self):
+ """
+ Inflation coefficient of the model.
+ """
+ return self._coef_inflation
def _check_two_dimensions_are_equal(
diff --git a/tests/test_viz.py b/tests/test_viz.py
index 3cafe93f..be24fcf1 100644
--- a/tests/test_viz.py
+++ b/tests/test_viz.py
@@ -60,6 +60,8 @@ def test_viz_pcacol(plnpca):
colors = np.random.randint(low=0, high=2, size=n_samples)
model.viz(colors=colors)
plt.show()
+ model.viz(show_cov=True)
+ plt.show()
@pytest.mark.parametrize("pln", dict_fixtures["real_fitted_pln_intercept_array"])
--
GitLab
From 7f0e53d0dace96ca1f0d3294a282bf5803a68d51 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 27 Jun 2023 11:39:01 +0200
Subject: [PATCH 023/167] remove tests for debugging the publish_package ci.
---
.gitlab-ci.yml | 60 +++++++++++++++++++++++++-------------------------
1 file changed, 30 insertions(+), 30 deletions(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index d0f6a915..d51311ec 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -1,36 +1,36 @@
-stages:
- - checks
- - build
- - publish
+# stages:
+# - checks
+# - build
+# - publish
-black:
- stage: checks
- image: registry.gitlab.com/pipeline-components/black:latest
- script:
- - black --check --verbose -- .
- tags:
- - docker
+# black:
+# stage: checks
+# image: registry.gitlab.com/pipeline-components/black:latest
+# script:
+# - black --check --verbose -- .
+# tags:
+# - docker
-tests:
- stage: checks
- image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
- before_script:
- pip install '.[tests]'
- script:
- - pip install .
- - cd tests
- - pytest
-examples:
- stage: checks
- image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
- before_script:
- pip install '.[tests]'
- script:
- - pip install .
- - cd tests
- - python create_example_files.py
- - ./test_examples.sh
+# tests:
+# stage: checks
+# image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
+# before_script:
+# pip install '.[tests]'
+# script:
+# - pip install .
+# - cd tests
+# - pytest
+# examples:
+# stage: checks
+# image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
+# before_script:
+# pip install '.[tests]'
+# script:
+# - pip install .
+# - cd tests
+# - python create_example_files.py
+# - ./test_examples.sh
--
GitLab
From 715c96d44feb33abc0830274261b7a732df5aa35 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 27 Jun 2023 11:40:24 +0200
Subject: [PATCH 024/167] bug in the ci
---
.gitlab-ci.yml | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index d51311ec..05199a35 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -1,8 +1,8 @@
-# stages:
-# - checks
-# - build
-# - publish
+stages:
+ - checks
+ - build
+ - publish
# black:
# stage: checks
--
GitLab
From af8c718d82e3ace9f24696b742b0604f6f27a6c9 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 27 Jun 2023 11:41:24 +0200
Subject: [PATCH 025/167] not only on tags for debugging.
---
.gitlab-ci.yml | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 05199a35..be352639 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -57,8 +57,8 @@ publish_package:
- TWINE_PASSWORD=${pypln_token} TWINE_USERNAME=__token__ python -m twine upload dist/*
tags:
- docker
- only:
- - tags
+ # only:
+ # - tags
pages:
stage: publish
--
GitLab
From e8e53c2699b2c519df454b87a28e17b034451024 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 27 Jun 2023 11:43:37 +0200
Subject: [PATCH 026/167] debug.
---
.gitlab-ci.yml | 1 +
1 file changed, 1 insertion(+)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index be352639..c3935dbe 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -54,6 +54,7 @@ publish_package:
before_script:
- pip install twine
script:
+ - echo ${pypln_token}
- TWINE_PASSWORD=${pypln_token} TWINE_USERNAME=__token__ python -m twine upload dist/*
tags:
- docker
--
GitLab
From 26804397d30f8321d64f6c647ba9d38a75e903e2 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 27 Jun 2023 13:59:10 +0200
Subject: [PATCH 027/167] small change to try cicd
---
.gitlab-ci.yml | 1 +
1 file changed, 1 insertion(+)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index c3935dbe..2425e78f 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -4,6 +4,7 @@ stages:
- build
- publish
+#
# black:
# stage: checks
# image: registry.gitlab.com/pipeline-components/black:latest
--
GitLab
From 6895580667d30c746f942188fbe00bc22af84061 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 27 Jun 2023 14:04:08 +0200
Subject: [PATCH 028/167] right ci
---
.gitlab-ci.yml | 58 +++++++++++++++++++++++++-------------------------
1 file changed, 29 insertions(+), 29 deletions(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 2425e78f..c21b0e65 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -4,34 +4,34 @@ stages:
- build
- publish
-#
-# black:
-# stage: checks
-# image: registry.gitlab.com/pipeline-components/black:latest
-# script:
-# - black --check --verbose -- .
-# tags:
-# - docker
-# tests:
-# stage: checks
-# image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
-# before_script:
-# pip install '.[tests]'
-# script:
-# - pip install .
-# - cd tests
-# - pytest
-# examples:
-# stage: checks
-# image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
-# before_script:
-# pip install '.[tests]'
-# script:
-# - pip install .
-# - cd tests
-# - python create_example_files.py
-# - ./test_examples.sh
+black:
+ stage: checks
+ image: registry.gitlab.com/pipeline-components/black:latest
+ script:
+ - black --check --verbose -- .
+ tags:
+ - docker
+
+tests:
+ stage: checks
+ image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
+ before_script:
+ pip install '.[tests]'
+ script:
+ - pip install .
+ - cd tests
+ - pytest
+examples:
+ stage: checks
+ image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
+ before_script:
+ pip install '.[tests]'
+ script:
+ - pip install .
+ - cd tests
+ - python create_example_files.py
+ - ./test_examples.sh
@@ -59,8 +59,8 @@ publish_package:
- TWINE_PASSWORD=${pypln_token} TWINE_USERNAME=__token__ python -m twine upload dist/*
tags:
- docker
- # only:
- # - tags
+ only:
+ - tags
pages:
stage: publish
--
GitLab
From a9695db5c58dbd5b1c6ab63bda47bb42512022e7 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 27 Jun 2023 14:58:13 +0200
Subject: [PATCH 029/167] small docstrings for PlnPCAcollection
---
pyPLNmodels/models.py | 10 ++++++----
1 file changed, 6 insertions(+), 4 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index ac0ed77a..e0fd0444 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -1726,10 +1726,12 @@ class PlnPCAcollection:
>>> from pyPLNmodels import PlnPCAcollection, get_real_count_data, get_simulation_parameters, sample_pln
>>> counts, labels = get_real_count_data(return_labels = True)
>>> data = {"counts": counts}
- >>> plncas = PlnPCAcollection.from_formula("counts ~ 1", data = data, ranks = [5,8, 12])
- >>> plncas.fit()
- >>> print(plncas)
- >>> plncas.show()
+ >>> plnpcas = PlnPCAcollection.from_formula("counts ~ 1", data = data, ranks = [5,8, 12])
+ >>> plnpcas.fit()
+ >>> print(plnpcas)
+ >>> plnpcas.show()
+ >>> print(plnpcas.best_model())
+ >>> print(plnpcas[5])
>>> plnparam = get_simulation_parameters(n_samples =100, dim = 60, nb_cov = 2, rank = 8)
>>> counts = sample_pln(plnparam)
--
GitLab
From d216bd108ea139ff492f3c7621ee64b98a5023e5 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 16:20:33 +0200
Subject: [PATCH 030/167] update README for JOSS.
---
README.md | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 6ca8d043..9a8a2e18 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,9 @@
> The Poisson lognormal model and variants can be used for analysis of mutivariate count data.
> This package implements
-> efficient algorithms to fit such models.
+> efficient algorithms extracting meaningful data from difficult to interpret
+> and complex count data. It has been built to scale on large datasets even
+> though it has memory limitations.
<!-- accompanied with a set of -->
<!-- > functions for visualization and diagnostic. See [this deck of -->
<!-- > slides](https://pln-team.github.io/slideshow/) for a -->
--
GitLab
From eff91f372eaa26a11087a77a5d5a011f2d981851 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 16:30:05 +0200
Subject: [PATCH 031/167] test for README.
---
README.md | 12 +++++++-----
1 file changed, 7 insertions(+), 5 deletions(-)
diff --git a/README.md b/README.md
index 9a8a2e18..fe7f0650 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,13 @@
# PLNmodels: Poisson lognormal models
-> The Poisson lognormal model and variants can be used for analysis of mutivariate count data.
-> This package implements
-> efficient algorithms extracting meaningful data from difficult to interpret
-> and complex count data. It has been built to scale on large datasets even
-> though it has memory limitations.
+The Poisson lognormal model and variants can be used for analysis of mutivariate count data.
+This package implements
+efficient algorithms extracting meaningful data from difficult to interpret
+and complex count data. It has been built to scale on large datasets even
+though it has memory limitations. Possible fields of applications are
+- test
+- test bis
<!-- accompanied with a set of -->
<!-- > functions for visualization and diagnostic. See [this deck of -->
<!-- > slides](https://pln-team.github.io/slideshow/) for a -->
--
GitLab
From 4322d781ad253f62fc78fc3c7499d55190efe108 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 16:31:09 +0200
Subject: [PATCH 032/167] other test
---
README.md | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/README.md b/README.md
index fe7f0650..fd5b82a9 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,13 @@
# PLNmodels: Poisson lognormal models
-The Poisson lognormal model and variants can be used for analysis of mutivariate count data.
-This package implements
-efficient algorithms extracting meaningful data from difficult to interpret
-and complex count data. It has been built to scale on large datasets even
-though it has memory limitations. Possible fields of applications are
-- test
-- test bis
+> The Poisson lognormal model and variants can be used for analysis of mutivariate count data.
+> This package implements
+> efficient algorithms extracting meaningful data from difficult to interpret
+> and complex count data. It has been built to scale on large datasets even
+> though it has memory limitations. Possible fields of applications are
+> - test
+> - test bis
<!-- accompanied with a set of -->
<!-- > functions for visualization and diagnostic. See [this deck of -->
<!-- > slides](https://pln-team.github.io/slideshow/) for a -->
--
GitLab
From 5fb53379211501f6c61870d7f6d9ed0037e99493 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 16:32:10 +0200
Subject: [PATCH 033/167] test on README joss
---
README.md | 2 --
1 file changed, 2 deletions(-)
diff --git a/README.md b/README.md
index fd5b82a9..12664535 100644
--- a/README.md
+++ b/README.md
@@ -6,8 +6,6 @@
> efficient algorithms extracting meaningful data from difficult to interpret
> and complex count data. It has been built to scale on large datasets even
> though it has memory limitations. Possible fields of applications are
-> - test
-> - test bis
<!-- accompanied with a set of -->
<!-- > functions for visualization and diagnostic. See [this deck of -->
<!-- > slides](https://pln-team.github.io/slideshow/) for a -->
--
GitLab
From 846bef1c36cefaec567d0af577ceea3351c0cf49 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 16:49:32 +0200
Subject: [PATCH 034/167] change to README
---
README.md | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index 12664535..b38549bb 100644
--- a/README.md
+++ b/README.md
@@ -4,8 +4,10 @@
> The Poisson lognormal model and variants can be used for analysis of mutivariate count data.
> This package implements
> efficient algorithms extracting meaningful data from difficult to interpret
-> and complex count data. It has been built to scale on large datasets even
-> though it has memory limitations. Possible fields of applications are
+> and complex multivariate count data. It has been built to scale on large datasets even
+> though it has memory limitations. Possible fields of applications include
+> - Genomics (number of times a gene is expressed in a cell)
+> - Ecology (number of individuals of some species in a specific site)
<!-- accompanied with a set of -->
<!-- > functions for visualization and diagnostic. See [this deck of -->
<!-- > slides](https://pln-team.github.io/slideshow/) for a -->
--
GitLab
From e22511f7faba480d6dcacc1e1887c68a6251ff72 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 16:50:59 +0200
Subject: [PATCH 035/167] typo in the README.
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index b38549bb..411029ef 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ The getting started can be found [here](https://forgemia.inra.fr/bbatardiere/pyp
## Installation
-**PLNmodels** is available on
+**pyPLNmodels** is available on
[pypi](https://pypi.org/project/pyPLNmodels/). The development
version is available on [GitHub](https://github.com/PLN-team/pyPLNmodels).
--
GitLab
From dda16e27490492d7588c365b7ca0f30e1c3d0038 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 16:52:45 +0200
Subject: [PATCH 036/167] add index.html in the .gitignore
---
.gitignore | 1 +
1 file changed, 1 insertion(+)
diff --git a/.gitignore b/.gitignore
index 89d5095d..c73ed815 100644
--- a/.gitignore
+++ b/.gitignore
@@ -151,3 +151,4 @@ test.py
## directories that outputs when running the tests
tests/Pln*
slides/
+index.html
--
GitLab
From 996f7af5da8358404a2c57884901da154ef41f64 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 16:55:46 +0200
Subject: [PATCH 037/167] add transform functionality in the README.
---
README.md | 3 +++
1 file changed, 3 insertions(+)
diff --git a/README.md b/README.md
index 411029ef..93e01027 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,7 @@
> though it has memory limitations. Possible fields of applications include
> - Genomics (number of times a gene is expressed in a cell)
> - Ecology (number of individuals of some species in a specific site)
+> One main functionality is to normalize the data to obtain more valueable data.
<!-- accompanied with a set of -->
<!-- > functions for visualization and diagnostic. See [this deck of -->
<!-- > slides](https://pln-team.github.io/slideshow/) for a -->
@@ -44,6 +45,7 @@ oaks = load_oaks()
pln = Pln.from_formula("counts ~ 1 + tree + dist2ground + orientation ", data = oaks, take_log_offsets = True)
pln.fit()
print(pln)
+transformed_data = pln.transform()
```
@@ -53,6 +55,7 @@ print(pln)
pca = PlnPCAcollection.from_formula("counts ~ 1 + tree + dist2ground + orientation ", data = oaks, take_log_offsets = True, ranks = [3,4,5])
pca.fit()
print(pca)
+transformed_data = pln.transform()
```
--
GitLab
From 484319576ca83e759235cc8d2be8cc2651dd1312 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 17:16:02 +0200
Subject: [PATCH 038/167] add docs for transform variables.
---
pyPLNmodels/models.py | 55 +++++++++++++++++++++++++++++++------------
1 file changed, 40 insertions(+), 15 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index e0fd0444..7176bbd3 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -1476,6 +1476,25 @@ class Pln(_model):
variables_names=variables_names, indices_of_variables=indices_of_variables
)
+ @_add_doc(
+ _model,
+ returns="""
+ torch.Tensor
+ The transformed counts (latent variables of the model).
+ """,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> counts = get_real_count_data()
+ >>> data = {"counts": counts}
+ >>> pln = Pln.from_formula("counts ~ 1", data = data)
+ >>> pln.fit()
+ >>> transformed_counts = pln.transform()
+ >>> print(transformed_counts.shape)
+ """,
+ )
+ def transform(self):
+ return super().transform()
+
@property
def _description(self):
"""
@@ -1671,12 +1690,7 @@ class Pln(_model):
def transform(self):
"""
- Method for transforming the model.
-
- Returns
- -------
- torch.Tensor
- The transformed model.
+ Method for transforming the counts. Can be seen as a normalization of the counts.
"""
return self.latent_variables
@@ -2542,6 +2556,7 @@ class PlnPCA(_model):
>>> from pyPLNmodels import PlnPCA, get_real_count_data
>>> counts= get_real_count_data()
>>> pca = PlnPCA(counts, add_const = True)
+ >>> pca.fit()
>>> print(pca)
""",
returns="""
@@ -3124,20 +3139,30 @@ class PlnPCA(_model):
)
self._components = components
- def transform(self, project: bool = True) -> torch.Tensor:
- """
- Transform the model.
-
+ @_add_doc(
+ _model,
+ params="""
Parameters
----------
project : bool, optional
Whether to project the latent variables, by default True.
-
- Returns
- -------
+ """,
+ returns="""
torch.Tensor
- The transformed model.
- """
+ The transformed counts (latent variables of the model).
+ """,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> counts= get_real_count_data()
+ >>> pca = PlnPCA(counts, add_const = True)
+ >>> pca.fit()
+ >>> transformed_counts_low_dim = pca.transform()
+ >>> transformed_counts_high_dim = pca.transform(project = False)
+ >>> print(transformed_counts_low_dim.shape)
+ >>> print(transformed_counts_high_dim.shape)
+ """,
+ )
+ def transform(self, project: bool = True) -> torch.Tensor:
if project is True:
return self.projected_latent_variables
return self.latent_variables
--
GitLab
From bb63c878c339a6c5a0f7aea3ea3957c7ccac7b34 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 17:21:22 +0200
Subject: [PATCH 039/167] add other functionalities of the package.
---
README.md | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 93e01027..2398c85b 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,9 @@
> though it has memory limitations. Possible fields of applications include
> - Genomics (number of times a gene is expressed in a cell)
> - Ecology (number of individuals of some species in a specific site)
-> One main functionality is to normalize the data to obtain more valueable data.
+> One main functionality is to normalize the count data to obtain more valuable
+> data. It also analyse the significance of each variable and their correlation as well as the weight of
+> covariates (if available).
<!-- accompanied with a set of -->
<!-- > functions for visualization and diagnostic. See [this deck of -->
<!-- > slides](https://pln-team.github.io/slideshow/) for a -->
--
GitLab
From 3794206fe67cd196618cbdaf060b6e37df072698 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 28 Jun 2023 18:01:24 +0200
Subject: [PATCH 040/167] changed individuals of species to species abundances.
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 2398c85b..9401cfe6 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@
> and complex multivariate count data. It has been built to scale on large datasets even
> though it has memory limitations. Possible fields of applications include
> - Genomics (number of times a gene is expressed in a cell)
-> - Ecology (number of individuals of some species in a specific site)
+> - Ecology (species abundances)
> One main functionality is to normalize the count data to obtain more valuable
> data. It also analyse the significance of each variable and their correlation as well as the weight of
> covariates (if available).
--
GitLab
From 7bbdea9eca7f0771d624bb5ce5cb410c51e6de39 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 12:04:29 +0200
Subject: [PATCH 041/167] add qq_plots on Pln. does not work.
---
pyPLNmodels/models.py | 22 ++++++++++++++++------
1 file changed, 16 insertions(+), 6 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 7176bbd3..091afe0b 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -13,6 +13,7 @@ from sklearn.decomposition import PCA
import plotly.express as px
from mlxtend.plotting import plot_pca_correlation_graph
import matplotlib
+from scipy import stats
from ._closed_forms import (
_closed_formula_coef,
@@ -400,6 +401,21 @@ class _model(ABC):
self._update_closed_forms()
return loss
+ def transform(self):
+ """
+ Method for transforming the counts. Can be seen as a normalization of the counts.
+ """
+ return self.latent_variables
+
+ def qq_plots(self):
+ centered_latent = self.latent_variables - torch.mean(
+ self.latent_variables, axis=0
+ )
+ chol = torch.linalg.cholesky(torch.inverse(self.covariance))
+ residus = torch.matmul(centered_latent.unsqueeze(1), chol.unsqueeze(0))
+ stats.probplot(residus.ravel(), plot=plt)
+ plt.show()
+
def pca_projected_latent_variables(self, n_components: Optional[int] = None):
"""
Perform PCA on the latent variables and project them onto a lower-dimensional space.
@@ -1688,12 +1704,6 @@ class Pln(_model):
"""
return self.dim * (self.dim + self.nb_cov)
- def transform(self):
- """
- Method for transforming the counts. Can be seen as a normalization of the counts.
- """
- return self.latent_variables
-
@property
def covariance(self):
"""
--
GitLab
From f2fcb191a172475e550d7407b0bfee7ca12f3a2c Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 15:00:58 +0200
Subject: [PATCH 042/167] changed all counts occurences for endog
---
.gitignore | 2 +
pyPLNmodels/_initialization.py | 38 ++--
pyPLNmodels/_utils.py | 98 +++++-----
pyPLNmodels/elbos.py | 48 ++---
pyPLNmodels/load.py | 16 +-
pyPLNmodels/models.py | 320 ++++++++++++++++-----------------
pyPLNmodels/oaks.py | 8 +-
tests/conftest.py | 38 ++--
tests/import_data.py | 14 +-
tests/test_common.py | 10 +-
tests/test_pln_full.py | 2 +-
tests/test_plnpcacollection.py | 2 +-
tests/test_setters.py | 20 +--
13 files changed, 308 insertions(+), 308 deletions(-)
diff --git a/.gitignore b/.gitignore
index c73ed815..fb7059f0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -152,3 +152,5 @@ test.py
tests/Pln*
slides/
index.html
+
+tests/examples/*.py
diff --git a/pyPLNmodels/_initialization.py b/pyPLNmodels/_initialization.py
index 2510b963..ea22106b 100644
--- a/pyPLNmodels/_initialization.py
+++ b/pyPLNmodels/_initialization.py
@@ -10,16 +10,16 @@ else:
def _init_covariance(
- counts: torch.Tensor, covariates: torch.Tensor, coef: torch.Tensor
+ endog: torch.Tensor, covariates: torch.Tensor, coef: torch.Tensor
) -> torch.Tensor:
"""
- Initialization for the covariance for the Pln model. Take the log of counts
- (careful when counts=0), and computes the Maximum Likelihood
+ Initialization for the covariance for the Pln model. Take the log of endog
+ (careful when endog=0), and computes the Maximum Likelihood
Estimator in the gaussian case.
Parameters
----------
- counts : torch.Tensor
+ endog : torch.Tensor
Samples with size (n,p)
offsets : torch.Tensor
Offset, size (n,p)
@@ -33,15 +33,15 @@ def _init_covariance(
torch.Tensor
Covariance matrix of size (p,p)
"""
- log_y = torch.log(counts + (counts == 0) * math.exp(-2))
+ log_y = torch.log(endog + (endog == 0) * math.exp(-2))
log_y_centered = log_y - torch.mean(log_y, axis=0)
- n_samples = counts.shape[0]
+ n_samples = endog.shape[0]
sigma_hat = 1 / (n_samples - 1) * (log_y_centered.T) @ log_y_centered
return sigma_hat
def _init_components(
- counts: torch.Tensor, covariates: torch.Tensor, coef: torch.Tensor, rank: int
+ endog: torch.Tensor, covariates: torch.Tensor, coef: torch.Tensor, rank: int
) -> torch.Tensor:
"""
Initialization for components for the Pln model. Get a first guess for covariance
@@ -49,7 +49,7 @@ def _init_components(
Parameters
----------
- counts : torch.Tensor
+ endog : torch.Tensor
Samples with size (n,p)
offsets : torch.Tensor
Offset, size (n,p)
@@ -65,13 +65,13 @@ def _init_components(
torch.Tensor
Initialization of components of size (p,rank)
"""
- sigma_hat = _init_covariance(counts, covariates, coef).detach()
+ sigma_hat = _init_covariance(endog, covariates, coef).detach()
components = _components_from_covariance(sigma_hat, rank)
return components
def _init_latent_mean(
- counts: torch.Tensor,
+ endog: torch.Tensor,
covariates: torch.Tensor,
offsets: torch.Tensor,
coef: torch.Tensor,
@@ -86,7 +86,7 @@ def _init_latent_mean(
Parameters
----------
- counts : torch.Tensor
+ endog : torch.Tensor
Samples with size (n,p)
offsets : torch.Tensor
Offset, size (n,p)
@@ -109,7 +109,7 @@ def _init_latent_mean(
torch.Tensor
The initialized latent mean with size (n,rank)
"""
- mode = torch.randn(counts.shape[0], components.shape[1], device=DEVICE)
+ mode = torch.randn(endog.shape[0], components.shape[1], device=DEVICE)
mode.requires_grad_(True)
optimizer = torch.optim.Rprop([mode], lr=lr)
crit = 2 * eps
@@ -117,7 +117,7 @@ def _init_latent_mean(
keep_condition = True
i = 0
while i < n_iter_max and keep_condition:
- batch_loss = log_posterior(counts, covariates, offsets, mode, components, coef)
+ batch_loss = log_posterior(endog, covariates, offsets, mode, components, coef)
loss = -torch.mean(batch_loss)
loss.backward()
optimizer.step()
@@ -155,14 +155,14 @@ def _components_from_covariance(covariance: torch.Tensor, rank: int) -> torch.Te
def _init_coef(
- counts: torch.Tensor, covariates: torch.Tensor, offsets: torch.Tensor
+ endog: torch.Tensor, covariates: torch.Tensor, offsets: torch.Tensor
) -> torch.Tensor:
"""
Initialize the coefficient for the Pln model using Poisson regression model.
Parameters
----------
- counts : torch.Tensor
+ endog : torch.Tensor
Samples with size (n, p)
covariates : torch.Tensor
Covariates, size (n, d)
@@ -178,12 +178,12 @@ def _init_coef(
return None
poiss_reg = _PoissonReg()
- poiss_reg.fit(counts, covariates, offsets)
+ poiss_reg.fit(endog, covariates, offsets)
return poiss_reg.beta
def log_posterior(
- counts: torch.Tensor,
+ endog: torch.Tensor,
covariates: torch.Tensor,
offsets: torch.Tensor,
posterior_mean: torch.Tensor,
@@ -195,7 +195,7 @@ def log_posterior(
Parameters
----------
- counts : torch.Tensor
+ endog : torch.Tensor
Samples with size (batch_size, p)
covariates : torch.Tensor or None
Covariates, size (batch_size, d) or (d)
@@ -229,7 +229,7 @@ def log_posterior(
- 1 / 2 * torch.norm(posterior_mean, dim=-1) ** 2
)
second_term = torch.sum(
- -torch.exp(log_lambda) + log_lambda * counts - _log_stirling(counts), axis=-1
+ -torch.exp(log_lambda) + log_lambda * endog - _log_stirling(endog), axis=-1
)
return first_term + second_term
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index d8d027ac..ba6b2a39 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -112,7 +112,7 @@ def sample_pln(pln_param, *, seed: int = None, return_latent=False) -> torch.Ten
Returns
-------
tuple[torch.Tensor, torch.Tensor, torch.Tensor] if return_latent is True
- Tuple containing counts (torch.Tensor), gaussian (torch.Tensor), and ksi (torch.Tensor)
+ Tuple containing endog (torch.Tensor), gaussian (torch.Tensor), and ksi (torch.Tensor)
torch.Tensor if return_latent is False
See also :func:`~pyPLNmodels.PlnParameters`
@@ -143,12 +143,12 @@ def sample_pln(pln_param, *, seed: int = None, return_latent=False) -> torch.Ten
else:
ksi = 0
- counts = (1 - ksi) * torch.poisson(parameter)
+ endog = (1 - ksi) * torch.poisson(parameter)
torch.random.set_rng_state(prev_state)
if return_latent is True:
- return counts, gaussian, ksi
- return counts
+ return endog, gaussian, ksi
+ return endog
def _log_stirling(integer: torch.Tensor) -> torch.Tensor:
@@ -176,13 +176,13 @@ def _trunc_log(tens: torch.Tensor, eps: float = 1e-16) -> torch.Tensor:
return torch.log(integer)
-def _get_offsets_from_sum_of_counts(counts: torch.Tensor) -> torch.Tensor:
+def _get_offsets_from_sum_of_endog(endog: torch.Tensor) -> torch.Tensor:
"""
- Compute offsets from the sum of counts.
+ Compute offsets from the sum of endog.
Parameters
----------
- counts : torch.Tensor
+ endog : torch.Tensor
Samples with size (n, p)
Returns
@@ -190,8 +190,8 @@ def _get_offsets_from_sum_of_counts(counts: torch.Tensor) -> torch.Tensor:
torch.Tensor
Offsets of size (n, p)
"""
- sum_of_counts = torch.sum(counts, axis=1)
- return sum_of_counts.repeat((counts.shape[1], 1)).T
+ sum_of_endog = torch.sum(endog, axis=1)
+ return sum_of_endog.repeat((endog.shape[1], 1)).T
def _raise_wrong_dimension_error(
@@ -268,7 +268,7 @@ def _format_data(
def _format_model_param(
- counts: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ endog: Union[torch.Tensor, np.ndarray, pd.DataFrame],
covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame],
offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame],
offsets_formula: str,
@@ -280,7 +280,7 @@ def _format_model_param(
Parameters
----------
- counts : Union[torch.Tensor, np.ndarray, pd.DataFrame], shape (n, )
+ endog : Union[torch.Tensor, np.ndarray, pd.DataFrame], shape (n, )
Count data.
covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None, shape (n, d) or None
Covariate data.
@@ -300,29 +300,29 @@ def _format_model_param(
Raises
------
ValueError
- If counts has negative values or offsets_formula is not None and not "logsum" or "zero"
+ If endog has negative values or offsets_formula is not None and not "logsum" or "zero"
"""
- counts = _format_data(counts)
- if torch.min(counts) < 0:
+ endog = _format_data(endog)
+ if torch.min(endog) < 0:
raise ValueError("Counts should be only non negative values.")
covariates = _format_data(covariates)
if add_const is True:
if covariates is None:
- covariates = torch.ones(counts.shape[0], 1)
+ covariates = torch.ones(endog.shape[0], 1)
else:
if _has_null_variance(covariates) is False:
covariates = torch.concat(
- (covariates, torch.ones(counts.shape[0]).unsqueeze(1)), dim=1
+ (covariates, torch.ones(endog.shape[0]).unsqueeze(1)), dim=1
)
if offsets is None:
if offsets_formula == "logsum":
- print("Setting the offsets as the log of the sum of counts")
+ print("Setting the offsets as the log of the sum of endog")
offsets = (
- torch.log(_get_offsets_from_sum_of_counts(counts)).double().to(DEVICE)
+ torch.log(_get_offsets_from_sum_of_endog(endog)).double().to(DEVICE)
)
elif offsets_formula == "zero":
print("Setting the offsets to zero")
- offsets = torch.zeros(counts.shape, device=DEVICE)
+ offsets = torch.zeros(endog.shape, device=DEVICE)
else:
raise ValueError(
'Wrong offsets_formula. Expected either "zero" or "logsum", got {offsets_formula}'
@@ -331,7 +331,7 @@ def _format_model_param(
offsets = _format_data(offsets).to(DEVICE)
if take_log_offsets is True:
offsets = torch.log(offsets)
- return counts, covariates, offsets
+ return endog, covariates, offsets
def _has_null_variance(tensor: torch.Tensor) -> bool:
@@ -352,27 +352,27 @@ def _has_null_variance(tensor: torch.Tensor) -> bool:
def _check_data_shape(
- counts: torch.Tensor, covariates: torch.Tensor, offsets: torch.Tensor
+ endog: torch.Tensor, covariates: torch.Tensor, offsets: torch.Tensor
) -> None:
"""
Check if the shape of the input data is valid.
Parameters
----------
- counts : torch.Tensor, shape (n, p)
+ endog : torch.Tensor, shape (n, p)
Count data.
covariates : torch.Tensor or None, shape (n, d) or None
Covariate data.
offsets : torch.Tensor or None, shape (n, p) or None
Offset data.
"""
- n_counts, p_counts = counts.shape
+ n_endog, p_endog = endog.shape
n_offsets, p_offsets = offsets.shape
- _check_two_dimensions_are_equal("counts", "offsets", n_counts, n_offsets, 0, 0)
+ _check_two_dimensions_are_equal("endog", "offsets", n_endog, n_offsets, 0, 0)
if covariates is not None:
n_cov, _ = covariates.shape
- _check_two_dimensions_are_equal("counts", "covariates", n_counts, n_cov, 0, 0)
- _check_two_dimensions_are_equal("counts", "offsets", p_counts, p_offsets, 1, 1)
+ _check_two_dimensions_are_equal("endog", "covariates", n_endog, n_cov, 0, 0)
+ _check_two_dimensions_are_equal("endog", "offsets", p_endog, p_offsets, 1, 1)
def _nice_string_of_dict(dictionnary: dict) -> str:
@@ -751,21 +751,21 @@ def get_simulated_count_data(
Returns
-------
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
- Tuple containing counts, covariates, and offsets.
+ Tuple containing endog, covariates, and offsets.
"""
pln_param = get_simulation_parameters(
n_samples=n_samples, dim=dim, nb_cov=nb_cov, rank=rank, add_const=add_const
)
- counts = sample_pln(pln_param, seed=seed, return_latent=False)
+ endog = sample_pln(pln_param, seed=seed, return_latent=False)
if return_true_param is True:
return (
- counts,
+ endog,
pln_param.covariates,
pln_param.offsets,
pln_param.covariance,
pln_param.coef,
)
- return pln_param.counts, pln_param.cov, pln_param.offsets
+ return pln_param.endog, pln_param.cov, pln_param.offsets
def get_real_count_data(
@@ -799,14 +799,14 @@ def get_real_count_data(
f"\nTaking the whole max_dim variables. Requested:dim={dim}, returned:{max_dim}"
)
dim = max_dim
- counts_stream = pkg_resources.resource_stream(__name__, "data/scRT/counts.csv")
- counts = pd.read_csv(counts_stream).values[:n_samples, :dim]
- print(f"Returning dataset of size {counts.shape}")
+ endog_stream = pkg_resources.resource_stream(__name__, "data/scRT/endog.csv")
+ endog = pd.read_csv(endog_stream).values[:n_samples, :dim]
+ print(f"Returning dataset of size {endog.shape}")
if return_labels is False:
- return counts
+ return endog
labels_stream = pkg_resources.resource_stream(__name__, "data/scRT/labels.csv")
labels = np.array(pd.read_csv(labels_stream).values[:n_samples].squeeze())
- return counts, labels
+ return endog, labels
def _check_right_rank(data: Dict[str, Any], rank: int) -> None:
@@ -849,16 +849,16 @@ def _extract_data_from_formula(
Returns
-------
Tuple
- A tuple containing the extracted counts, covariates, and offsets.
+ A tuple containing the extracted endog, covariates, and offsets.
"""
dmatrix = dmatrices(formula, data=data)
- counts = dmatrix[0]
+ endog = dmatrix[0]
covariates = dmatrix[1]
if covariates.size == 0:
covariates = None
offsets = data.get("offsets", None)
- return counts, covariates, offsets
+ return endog, covariates, offsets
def _is_dict_of_dict(dictionary: Dict[Any, Any]) -> bool:
@@ -946,7 +946,7 @@ def _array2tensor(func):
def _handle_data(
- counts,
+ endog,
covariates,
offsets,
offsets_formula: str,
@@ -958,7 +958,7 @@ def _handle_data(
Parameters
----------
- counts : The counts data. If a DataFrame is provided, the column names are stored for later use.
+ endog : The endog data. If a DataFrame is provided, the column names are stored for later use.
covariates : The covariates data.
offsets : The offsets data.
offsets_formula : The formula used for offsets.
@@ -967,22 +967,22 @@ def _handle_data(
Returns
-------
- tuple: A tuple containing the processed counts, covariates, offsets, and column counts (if available).
+ tuple: A tuple containing the processed endog, covariates, offsets, and column endog (if available).
Raises
------
- ValueError: If the shapes of counts, covariates, and offsets do not match.
+ ValueError: If the shapes of endog, covariates, and offsets do not match.
"""
- if isinstance(counts, pd.DataFrame):
- column_counts = counts.columns
+ if isinstance(endog, pd.DataFrame):
+ column_endog = endog.columns
else:
- column_counts = None
+ column_endog = None
- counts, covariates, offsets = _format_model_param(
- counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
+ endog, covariates, offsets = _format_model_param(
+ endog, covariates, offsets, offsets_formula, take_log_offsets, add_const
)
- _check_data_shape(counts, covariates, offsets)
- return counts, covariates, offsets, column_counts
+ _check_data_shape(endog, covariates, offsets)
+ return endog, covariates, offsets, column_endog
def _add_doc(parent_class, *, params=None, example=None, returns=None, see_also=None):
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index a2fa1b2f..b0298a6a 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -6,7 +6,7 @@ from typing import Optional
def elbo_pln(
- counts: torch.Tensor,
+ endog: torch.Tensor,
offsets: torch.Tensor,
covariates: Optional[torch.Tensor],
latent_mean: torch.Tensor,
@@ -19,7 +19,7 @@ def elbo_pln(
Parameters:
----------
- counts : torch.Tensor
+ endog : torch.Tensor
Counts with size (n, p).
offsets : torch.Tensor
Offset with size (n, p).
@@ -39,11 +39,11 @@ def elbo_pln(
torch.Tensor
The ELBO (Evidence Lower Bound), of size one.
"""
- n_samples, dim = counts.shape
+ n_samples, dim = endog.shape
s_rond_s = torch.square(latent_sqrt_var)
offsets_plus_m = offsets + latent_mean
if covariates is None:
- XB = torch.zeros_like(counts)
+ XB = torch.zeros_like(endog)
else:
XB = covariates @ coef
m_minus_xb = latent_mean - XB
@@ -52,18 +52,18 @@ def elbo_pln(
)
elbo = -0.5 * n_samples * torch.logdet(covariance)
elbo += torch.sum(
- counts * offsets_plus_m
+ endog * offsets_plus_m
- 0.5 * torch.exp(offsets_plus_m + s_rond_s)
+ 0.5 * torch.log(s_rond_s)
)
elbo -= 0.5 * torch.trace(torch.inverse(covariance) @ d_plus_minus_xb2)
- elbo -= torch.sum(_log_stirling(counts))
+ elbo -= torch.sum(_log_stirling(endog))
elbo += 0.5 * n_samples * dim
return elbo / n_samples
def profiled_elbo_pln(
- counts: torch.Tensor,
+ endog: torch.Tensor,
covariates: torch.Tensor,
offsets: torch.Tensor,
latent_mean: torch.Tensor,
@@ -76,7 +76,7 @@ def profiled_elbo_pln(
Parameters:
----------
- counts : torch.Tensor
+ endog : torch.Tensor
Counts with size (n, p).
covariates : torch.Tensor
Covariates with size (n, d).
@@ -92,7 +92,7 @@ def profiled_elbo_pln(
torch.Tensor
The ELBO (Evidence Lower Bound) with size 1.
"""
- n_samples, _ = counts.shape
+ n_samples, _ = endog.shape
s_squared = torch.square(latent_sqrt_var)
offsets_plus_mean = offsets + latent_mean
closed_coef = _closed_formula_coef(covariates, latent_mean)
@@ -101,16 +101,16 @@ def profiled_elbo_pln(
)
elbo = -0.5 * n_samples * torch.logdet(closed_covariance)
elbo += torch.sum(
- counts * offsets_plus_mean
+ endog * offsets_plus_mean
- torch.exp(offsets_plus_mean + s_squared / 2)
+ 0.5 * torch.log(s_squared)
)
- elbo -= torch.sum(_log_stirling(counts))
+ elbo -= torch.sum(_log_stirling(endog))
return elbo / n_samples
def elbo_plnpca(
- counts: torch.Tensor,
+ endog: torch.Tensor,
covariates: torch.Tensor,
offsets: torch.Tensor,
latent_mean: torch.Tensor,
@@ -124,7 +124,7 @@ def elbo_plnpca(
Parameters:
----------
- counts : torch.Tensor
+ endog : torch.Tensor
Counts with size (n, p).
covariates : torch.Tensor
Covariates with size (n, d).
@@ -145,7 +145,7 @@ def elbo_plnpca(
torch.Tensor
The ELBO (Evidence Lower Bound) with size 1, with a gradient.
"""
- n_samples = counts.shape[0]
+ n_samples = endog.shape[0]
rank = components.shape[1]
if covariates is None:
XB = 0
@@ -153,7 +153,7 @@ def elbo_plnpca(
XB = covariates @ coef
log_intensity = offsets + XB + latent_mean @ components.T
s_squared = torch.square(latent_sqrt_var)
- counts_log_intensity = torch.sum(counts * log_intensity)
+ endog_log_intensity = torch.sum(endog * log_intensity)
minus_intensity_plus_s_squared_cct = torch.sum(
-torch.exp(log_intensity + 0.5 * s_squared @ (components * components).T)
)
@@ -161,20 +161,20 @@ def elbo_plnpca(
mm_plus_s_squared = -0.5 * torch.sum(
torch.square(latent_mean) + torch.square(latent_sqrt_var)
)
- log_stirling_counts = torch.sum(_log_stirling(counts))
+ log_stirling_endog = torch.sum(_log_stirling(endog))
return (
- counts_log_intensity
+ endog_log_intensity
+ minus_intensity_plus_s_squared_cct
+ minus_logs_squared
+ mm_plus_s_squared
- - log_stirling_counts
+ - log_stirling_endog
+ 0.5 * n_samples * rank
) / n_samples
## should rename some variables so that is is clearer when we see the formula
def elbo_zi_pln(
- counts,
+ endog,
covariates,
offsets,
latent_mean,
@@ -189,7 +189,7 @@ def elbo_zi_pln(
See the doc for more details on the computation.
Args:
- counts: torch.tensor. Counts with size (n,p)
+ endog: torch.tensor. Counts with size (n,p)
0: torch.tensor. Offset, size (n,p)
covariates: torch.tensor. Covariates, size (n,d)
latent_mean: torch.tensor. Variational parameter with size (n,p)
@@ -204,8 +204,8 @@ def elbo_zi_pln(
if torch.norm(pi * dirac - pi) > 0.0001:
print("Bug")
return False
- n_samples = counts.shape[0]
- dim = counts.shape[1]
+ n_samples = endog.shape[0]
+ dim = endog.shape[1]
s_rond_s = torch.square(latent_sqrt_var)
offsets_plus_m = offsets + latent_mean
m_minus_xb = latent_mean - covariates @ coef
@@ -213,9 +213,9 @@ def elbo_zi_pln(
elbo = torch.sum(
(1 - pi)
* (
- counts @ offsets_plus_m
+ endog @ offsets_plus_m
- torch.exp(offsets_plus_m + s_rond_s / 2)
- - _log_stirling(counts),
+ - _log_stirling(endog),
)
+ pi
)
diff --git a/pyPLNmodels/load.py b/pyPLNmodels/load.py
index 26871ae8..6abeffd1 100644
--- a/pyPLNmodels/load.py
+++ b/pyPLNmodels/load.py
@@ -20,19 +20,19 @@ def load_model(path_of_directory: str) -> Dict[str, Any]:
Examples
--------
>>> from pyPLNmodels import PlnPCA, Pln, get_real_count_data, load_model
- >>> counts= get_real_count_data()
- >>> pca = PlnPCA(counts, add_const = True)
+ >>> endog= get_real_count_data()
+ >>> pca = PlnPCA(endog, add_const = True)
>>> pca.fit()
>>> pca.save()
>>> dict_init = load_model("PlnPCA_nbcov_1_dim_200_rank_5")
- >>> loaded_pca = PlnPCA(counts, add_const = True, dict_initialization = dict_init)
+ >>> loaded_pca = PlnPCA(endog, add_const = True, dict_initialization = dict_init)
>>> print(loaded_pca)
- >>> pln = Pln(counts, add_const = True)
+ >>> pln = Pln(endog, add_const = True)
>>> pln.fit()
>>> pln.save()
>>> dict_init = load_model("Pln_nbcov_1_dim_200")
- >>> loaded_pln = Pln(counts, add_const = True, dict_initialization = dict_init)
+ >>> loaded_pln = Pln(endog, add_const = True, dict_initialization = dict_init)
>>> print(loaded_pln)
See also
--------
@@ -95,12 +95,12 @@ def load_plnpcacollection(
Examples
--------
>>> from pyPLNmodels import PlnPCAcollection, get_real_count_data, load_plnpcacollection
- >>> counts = get_real_count_data()
- >>> pcas = PlnPCAcollection(counts, add_const = True, ranks = [4,5,6])
+ >>> endog = get_real_count_data()
+ >>> pcas = PlnPCAcollection(endog, add_const = True, ranks = [4,5,6])
>>> pcas.fit()
>>> pcas.save()
>>> dict_init = load_plnpcacollection("PlnPCAcollection_nbcov_1_dim_200")
- >>> loaded_pcas = PlnPCAcollection(counts, add_const = True, ranks = [4,5,6], dict_of_dict_initialization = dict_init)
+ >>> loaded_pcas = PlnPCAcollection(endog, add_const = True, ranks = [4,5,6], dict_of_dict_initialization = dict_init)
>>> print(loaded_pcas)
See also
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 091afe0b..62351a03 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -58,7 +58,7 @@ class _model(ABC):
"""
_WINDOW: int = 15
- _counts: torch.Tensor
+ _endog: torch.Tensor
_covariates: torch.Tensor
_offsets: torch.Tensor
_coef: torch.Tensor
@@ -68,7 +68,7 @@ class _model(ABC):
def __init__(
self,
- counts: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ endog: Union[torch.Tensor, np.ndarray, pd.DataFrame],
*,
covariates: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
@@ -82,7 +82,7 @@ class _model(ABC):
Parameters
----------
- counts : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ endog : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The count data.
covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The covariate data. Defaults to None.
@@ -99,12 +99,12 @@ class _model(ABC):
Whether to add a column of one in the covariates. Defaults to True.
"""
(
- self._counts,
+ self._endog,
self._covariates,
self._offsets,
- self.column_counts,
+ self.column_endog,
) = _handle_data(
- counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
+ endog, covariates, offsets, offsets_formula, take_log_offsets, add_const
)
self._fitted = False
self._plotargs = _PlotArgs(self._WINDOW)
@@ -138,9 +138,9 @@ class _model(ABC):
take_log_offsets : bool, optional(keyword-only)
Whether to take the log of offsets. Defaults to False.
"""
- counts, covariates, offsets = _extract_data_from_formula(formula, data)
+ endog, covariates, offsets = _extract_data_from_formula(formula, data)
return cls(
- counts,
+ endog,
covariates=covariates,
offsets=offsets,
offsets_formula=offsets_formula,
@@ -231,26 +231,26 @@ class _model(ABC):
@property
def n_samples(self) -> int:
"""
- The number of samples, i.e. the first dimension of the counts.
+ The number of samples, i.e. the first dimension of the endog.
Returns
-------
int
The number of samples.
"""
- return self._counts.shape[0]
+ return self._endog.shape[0]
@property
def dim(self) -> int:
"""
- The second dimension of the counts.
+ The second dimension of the endog.
Returns
-------
int
- The second dimension of the counts.
+ The second dimension of the endog.
"""
- return self._counts.shape[1]
+ return self._endog.shape[1]
@property
def nb_cov(self) -> int:
@@ -270,7 +270,7 @@ class _model(ABC):
"""
Initialize coefficients smartly.
"""
- self._coef = _init_coef(self._counts, self._covariates, self._offsets)
+ self._coef = _init_coef(self._endog, self._covariates, self._offsets)
def _random_init_coef(self):
"""
@@ -403,7 +403,7 @@ class _model(ABC):
def transform(self):
"""
- Method for transforming the counts. Can be seen as a normalization of the counts.
+ Method for transforming the endog. Can be seen as a normalization of the endog.
"""
return self.latent_variables
@@ -488,7 +488,7 @@ class _model(ABC):
Defaults to None.
color (str, np.ndarray): An array with one label for each
- sample in the counts property of the object.
+ sample in the endog property of the object.
Defaults to None.
Raises
------
@@ -530,12 +530,12 @@ class _model(ABC):
A list of variable names to visualize.
indices_of_variables : Optional[List[int]], optional
A list of indices corresponding to the variables.
- If None, indices are determined based on `column_counts`, by default None
+ If None, indices are determined based on `column_endog`, by default None
Raises
------
ValueError
- If `indices_of_variables` is None and `column_counts` is not set.
+ If `indices_of_variables` is None and `column_endog` is not set.
ValueError
If the length of `indices_of_variables` is different from the length of `variables_names`.
@@ -544,16 +544,16 @@ class _model(ABC):
None
"""
if indices_of_variables is None:
- if self.column_counts is None:
+ if self.column_endog is None:
raise ValueError(
"No names have been given to the column of "
- "counts. Please set the column_counts to the"
+ "endog. Please set the column_endog to the"
"needed names or instantiate a new model with"
"a pd.DataFrame with appropriate column names"
)
indices_of_variables = []
for variables_name in variables_names:
- index = self.column_counts.get_loc(variables_name)
+ index = self.column_endog.get_loc(variables_name)
indices_of_variables.append(index)
else:
if len(indices_of_variables) != len(variables_names):
@@ -835,7 +835,7 @@ class _model(ABC):
The dictionary of data.
"""
return {
- "counts": self.counts,
+ "endog": self.endog,
"covariates": self.covariates,
"offsets": self.offsets,
}
@@ -991,16 +991,16 @@ class _model(ABC):
)
@property
- def counts(self):
+ def endog(self):
"""
- Property representing the counts.
+ Property representing the endog.
Returns
-------
torch.Tensor or None
- The counts or None.
+ The endog or None.
"""
- return self._cpu_attribute_or_none("_counts")
+ return self._cpu_attribute_or_none("_endog")
@property
def offsets(self):
@@ -1026,29 +1026,29 @@ class _model(ABC):
"""
return self._cpu_attribute_or_none("_covariates")
- @counts.setter
+ @endog.setter
@_array2tensor
- def counts(self, counts: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
+ def endog(self, endog: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
- Setter for the counts property.
+ Setter for the endog property.
Parameters
----------
- counts : Union[torch.Tensor, np.ndarray, pd.DataFrame]
- The counts.
+ endog : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The endog.
Raises
------
ValueError
- If the shape of the counts is incorrect or if the input is negative.
+ If the shape of the endog is incorrect or if the input is negative.
"""
- if self.counts.shape != counts.shape:
+ if self.endog.shape != endog.shape:
raise ValueError(
- f"Wrong shape for the counts. Expected {self.counts.shape}, got {counts.shape}"
+ f"Wrong shape for the endog. Expected {self.endog.shape}, got {endog.shape}"
)
- if torch.min(counts) < 0:
+ if torch.min(endog) < 0:
raise ValueError("Input should be non-negative only.")
- self._counts = counts
+ self._endog = endog
@offsets.setter
@_array2tensor
@@ -1086,9 +1086,9 @@ class _model(ABC):
Raises
------
ValueError
- If the shape of the covariates or counts is incorrect.
+ If the shape of the covariates or endog is incorrect.
"""
- _check_data_shape(self.counts, covariates, self.offsets)
+ _check_data_shape(self.endog, covariates, self.offsets)
self._covariates = covariates
@coef.setter
@@ -1247,7 +1247,7 @@ class _model(ABC):
def plot_expected_vs_true(self, ax=None, colors=None):
"""
- Plot the predicted value of the counts against the counts.
+ Plot the predicted value of the endog against the endog.
Parameters
----------
@@ -1267,11 +1267,11 @@ class _model(ABC):
raise RuntimeError("Please fit the model before.")
if ax is None:
ax = plt.gca()
- predictions = self._counts_predictions().ravel().detach()
+ predictions = self._endog_predictions().ravel().detach()
if colors is not None:
colors = np.repeat(np.array(colors), repeats=self.dim).ravel()
- sns.scatterplot(x=self.counts.ravel(), y=predictions, hue=colors, ax=ax)
- max_y = int(torch.max(self.counts.ravel()).item())
+ sns.scatterplot(x=self.endog.ravel(), y=predictions, hue=colors, ax=ax)
+ max_y = int(torch.max(self.endog.ravel()).item())
y = np.linspace(0, max_y, max_y)
ax.plot(y, y, c="red")
ax.set_yscale("log")
@@ -1290,17 +1290,17 @@ class Pln(_model):
Examples
--------
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(counts,add_const = True)
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(endog,add_const = True)
>>> pln.fit()
>>> print(pln)
>>> pln.viz(colors = labels)
>>> from pyPLNmodels import Pln, get_simulation_parameters, sample_pln
>>> param = get_simulation_parameters()
- >>> counts = sample_pln(param)
- >>> data = {"counts": counts}
- >>> pln = Pln.from_formula("counts ~ 1", data)
+ >>> endog = sample_pln(param)
+ >>> data = {"endog": endog}
+ >>> pln = Pln.from_formula("endog ~ 1", data)
>>> pln.fit()
>>> print(pln)
"""
@@ -1312,8 +1312,8 @@ class Pln(_model):
_model,
example="""
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts= get_real_count_data()
- >>> pln = Pln(counts, add_const = True)
+ >>> endog= get_real_count_data()
+ >>> pln = Pln(endog, add_const = True)
>>> pln.fit()
>>> print(pln)
""",
@@ -1326,7 +1326,7 @@ class Pln(_model):
)
def __init__(
self,
- counts: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
+ endog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
covariates: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
@@ -1336,7 +1336,7 @@ class Pln(_model):
add_const: bool = True,
):
super().__init__(
- counts=counts,
+ endog=endog,
covariates=covariates,
offsets=offsets,
offsets_formula=offsets_formula,
@@ -1350,9 +1350,9 @@ class Pln(_model):
_model,
example="""
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> pln = Pln.from_formula("counts ~ 1", data = data)
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> pln = Pln.from_formula("endog ~ 1", data = data)
""",
returns="""
Pln
@@ -1371,9 +1371,9 @@ class Pln(_model):
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
):
- counts, covariates, offsets = _extract_data_from_formula(formula, data)
+ endog, covariates, offsets = _extract_data_from_formula(formula, data)
return cls(
- counts,
+ endog,
covariates=covariates,
offsets=offsets,
offsets_formula=offsets_formula,
@@ -1386,8 +1386,8 @@ class Pln(_model):
_model,
example="""
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts = get_real_count_data()
- >>> pln = Pln(counts,add_const = True)
+ >>> endog = get_real_count_data()
+ >>> pln = Pln(endog,add_const = True)
>>> pln.fit()
>>> print(pln)
""",
@@ -1416,8 +1416,8 @@ class Pln(_model):
example="""
>>> import matplotlib.pyplot as plt
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(counts,add_const = True)
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(endog,add_const = True)
>>> pln.fit()
>>> pln.plot_expected_vs_true()
>>> plt.show()
@@ -1433,8 +1433,8 @@ class Pln(_model):
example="""
>>> import matplotlib.pyplot as plt
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(counts,add_const = True)
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(endog,add_const = True)
>>> pln.fit()
>>> pln.viz()
>>> plt.show()
@@ -1451,9 +1451,9 @@ class Pln(_model):
_model,
example="""
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> pln = Pln.from_formula("counts ~ 1", data = data)
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> pln = Pln.from_formula("endog ~ 1", data = data)
>>> pln.fit()
>>> pca_proj = pln.pca_projected_latent_variables()
>>> print(pca_proj.shape)
@@ -1466,9 +1466,9 @@ class Pln(_model):
_model,
example="""
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> pln = Pln.from_formula("counts ~ 1", data = data)
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> pln = Pln.from_formula("endog ~ 1", data = data)
>>> pln.fit()
>>> pln.scatter_pca_matrix(n_components = 5)
""",
@@ -1480,9 +1480,9 @@ class Pln(_model):
_model,
example="""
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> pln = Pln.from_formula("counts ~ 1", data = data)
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> pln = Pln.from_formula("endog ~ 1", data = data)
>>> pln.fit()
>>> pln.plot_pca_correlation_graph(["a","b"], indices_of_variables = [4,8])
""",
@@ -1496,16 +1496,16 @@ class Pln(_model):
_model,
returns="""
torch.Tensor
- The transformed counts (latent variables of the model).
+ The transformed endog (latent variables of the model).
""",
example="""
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> pln = Pln.from_formula("counts ~ 1", data = data)
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> pln = Pln.from_formula("endog ~ 1", data = data)
>>> pln.fit()
- >>> transformed_counts = pln.transform()
- >>> print(transformed_counts.shape)
+ >>> transformed_endog = pln.transform()
+ >>> print(transformed_endog.shape)
""",
)
def transform(self):
@@ -1552,7 +1552,7 @@ class Pln(_model):
The regression coefficients of the gaussian latent variables.
"""
- def _counts_predictions(self):
+ def _endog_predictions(self):
return torch.exp(
self._offsets + self._latent_mean + 1 / 2 * self._latent_sqrt_var**2
)
@@ -1608,15 +1608,15 @@ class Pln(_model):
Examples
--------
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(counts,add_const = True)
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(endog,add_const = True)
>>> pln.fit()
>>> elbo = pln.compute_elbo()
>>> print("elbo", elbo)
>>> print("loglike/n", pln.loglike/pln.n_samples)
"""
return profiled_elbo_pln(
- self._counts,
+ self._endog,
self._covariates,
self._offsets,
self._latent_mean,
@@ -1683,8 +1683,8 @@ class Pln(_model):
_model,
example="""
>>> from pyPLNmodels import Pln, get_real_count_data
- >>> counts, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(counts,add_const = True)
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(endog,add_const = True)
>>> pln.fit()
>>> print(pln.latent_variables.shape)
""",
@@ -1748,9 +1748,9 @@ class PlnPCAcollection:
Examples
--------
>>> from pyPLNmodels import PlnPCAcollection, get_real_count_data, get_simulation_parameters, sample_pln
- >>> counts, labels = get_real_count_data(return_labels = True)
- >>> data = {"counts": counts}
- >>> plnpcas = PlnPCAcollection.from_formula("counts ~ 1", data = data, ranks = [5,8, 12])
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> data = {"endog": endog}
+ >>> plnpcas = PlnPCAcollection.from_formula("endog ~ 1", data = data, ranks = [5,8, 12])
>>> plnpcas.fit()
>>> print(plnpcas)
>>> plnpcas.show()
@@ -1758,9 +1758,9 @@ class PlnPCAcollection:
>>> print(plnpcas[5])
>>> plnparam = get_simulation_parameters(n_samples =100, dim = 60, nb_cov = 2, rank = 8)
- >>> counts = sample_pln(plnparam)
- >>> data = {"counts":counts, "cov": plnparam.covariates, "offsets": plnparam.offsets}
- >>> plnpcas = PlnPCAcollection.from_formula("counts ~ 0 + cov", data = data, ranks = [5,8,12])
+ >>> endog = sample_pln(plnparam)
+ >>> data = {"endog":endog, "cov": plnparam.covariates, "offsets": plnparam.offsets}
+ >>> plnpcas = PlnPCAcollection.from_formula("endog ~ 0 + cov", data = data, ranks = [5,8,12])
>>> plnpcas.fit()
>>> print(plnpcas)
>>> pcas.show()
@@ -1774,7 +1774,7 @@ class PlnPCAcollection:
def __init__(
self,
- counts: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ endog: Union[torch.Tensor, np.ndarray, pd.DataFrame],
*,
covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
@@ -1789,8 +1789,8 @@ class PlnPCAcollection:
Parameters
----------
- counts :Union[torch.Tensor, np.ndarray, pd.DataFrame]
- The counts.
+ endog :Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The endog.
covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The covariates, by default None.
offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
@@ -1815,12 +1815,12 @@ class PlnPCAcollection:
"""
self._dict_models = {}
(
- self._counts,
+ self._endog,
self._covariates,
self._offsets,
- self.column_counts,
+ self.column_endog,
) = _handle_data(
- counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
+ endog, covariates, offsets, offsets_formula, take_log_offsets, add_const
)
self._fitted = False
self._init_models(ranks, dict_of_dict_initialization)
@@ -1862,17 +1862,17 @@ class PlnPCAcollection:
Examples
--------
>>> from pyPLNmodels import PlnPCAcollection, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> pca_col = PlnPCAcollection.from_formula("counts ~ 1", data = data, ranks = [5,6])
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> pca_col = PlnPCAcollection.from_formula("endog ~ 1", data = data, ranks = [5,6])
See also
--------
:class:`~pyPLNmodels.PlnPCA`
:func:`~pyPLNmodels.PlnPCAcollection.__init__`
"""
- counts, covariates, offsets = _extract_data_from_formula(formula, data)
+ endog, covariates, offsets = _extract_data_from_formula(formula, data)
return cls(
- counts,
+ endog,
covariates=covariates,
offsets=offsets,
offsets_formula=offsets_formula,
@@ -1895,16 +1895,16 @@ class PlnPCAcollection:
return self[self.ranks[0]].covariates
@property
- def counts(self) -> torch.Tensor:
+ def endog(self) -> torch.Tensor:
"""
- Property representing the counts.
+ Property representing the endog.
Returns
-------
torch.Tensor
- The counts.
+ The endog.
"""
- return self[self.ranks[0]].counts
+ return self[self.ranks[0]].endog
@property
def coef(self) -> Dict[int, torch.Tensor]:
@@ -1954,19 +1954,19 @@ class PlnPCAcollection:
"""
return {model.rank: model.latent_sqrt_var for model in self.values()}
- @counts.setter
+ @endog.setter
@_array2tensor
- def counts(self, counts: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
+ def endog(self, endog: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
- Setter for the counts property.
+ Setter for the endog property.
Parameters
----------
- counts : Union[torch.Tensor, np.ndarray, pd.DataFrame]
- The counts.
+ endog : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The endog.
"""
for model in self.values():
- model.counts = counts
+ model.endog = endog
@coef.setter
@_array2tensor
@@ -2042,7 +2042,7 @@ class PlnPCAcollection:
rank, dict_of_dict_initialization
)
self._dict_models[rank] = PlnPCA(
- counts=self._counts,
+ endog=self._endog,
covariates=self._covariates,
offsets=self._offsets,
rank=rank,
@@ -2067,7 +2067,7 @@ class PlnPCAcollection:
ranks, dict_of_dict_initialization
)
self._dict_models[rank] = PlnPCA(
- self._counts,
+ self._endog,
self._covariates,
self._offsets,
ranks,
@@ -2534,17 +2534,17 @@ class PlnPCA(_model):
Examples
--------
>>> from pyPLNmodels import PlnPCA, get_real_count_data, get_simulation_parameters, sample_pln
- >>> counts, labels = get_real_count_data(return_labels = True)
- >>> data = {"counts": counts}
- >>> pca = PlnPCA.from_formula("counts ~ 1", data = data, rank = 5)
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> data = {"endog": endog}
+ >>> pca = PlnPCA.from_formula("endog ~ 1", data = data, rank = 5)
>>> pca.fit()
>>> print(pca)
>>> pca.viz(colors = labels)
>>> plnparam = get_simulation_parameters(n_samples =100, dim = 60, nb_cov = 2, rank = 8)
- >>> counts = sample_pln(plnparam)
- >>> data = {"counts": counts, "cov": plnparam.covariates, "offsets": plnparam.offsets}
- >>> plnpca = PlnPCA.from_formula("counts ~ 0 + cov", data = data, rank = 5)
+ >>> endog = sample_pln(plnparam)
+ >>> data = {"endog": endog, "cov": plnparam.covariates, "offsets": plnparam.offsets}
+ >>> plnpca = PlnPCA.from_formula("endog ~ 0 + cov", data = data, rank = 5)
>>> plnpca.fit()
>>> print(plnpca)
@@ -2564,8 +2564,8 @@ class PlnPCA(_model):
""",
example="""
>>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts= get_real_count_data()
- >>> pca = PlnPCA(counts, add_const = True)
+ >>> endog= get_real_count_data()
+ >>> pca = PlnPCA(endog, add_const = True)
>>> pca.fit()
>>> print(pca)
""",
@@ -2578,7 +2578,7 @@ class PlnPCA(_model):
)
def __init__(
self,
- counts: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
+ endog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
covariates: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
@@ -2590,7 +2590,7 @@ class PlnPCA(_model):
):
self._rank = rank
super().__init__(
- counts=counts,
+ endog=endog,
covariates=covariates,
offsets=offsets,
offsets_formula=offsets_formula,
@@ -2608,9 +2608,9 @@ class PlnPCA(_model):
""",
example="""
>>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> pca = PlnPCA.from_formula("counts ~ 1", data = data, rank = 5)
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> pca = PlnPCA.from_formula("endog ~ 1", data = data, rank = 5)
""",
returns="""
PlnPCA
@@ -2629,9 +2629,9 @@ class PlnPCA(_model):
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
):
- counts, covariates, offsets = _extract_data_from_formula(formula, data)
+ endog, covariates, offsets = _extract_data_from_formula(formula, data)
return cls(
- counts,
+ endog,
covariates=covariates,
offsets=offsets,
offsets_formula=offsets_formula,
@@ -2644,8 +2644,8 @@ class PlnPCA(_model):
_model,
example="""
>>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts = get_real_count_data()
- >>> plnpca = PlnPCA(counts,add_const = True, rank = 6)
+ >>> endog = get_real_count_data()
+ >>> plnpca = PlnPCA(endog,add_const = True, rank = 6)
>>> plnpca.fit()
>>> print(plnpca)
""",
@@ -2674,8 +2674,8 @@ class PlnPCA(_model):
example="""
>>> import matplotlib.pyplot as plt
>>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts, labels = get_real_count_data(return_labels = True)
- >>> plnpca = Pln(counts,add_const = True)
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> plnpca = Pln(endog,add_const = True)
>>> plnpca.fit()
>>> plnpca.plot_expected_vs_true()
>>> plt.show()
@@ -2691,8 +2691,8 @@ class PlnPCA(_model):
example="""
>>> import matplotlib.pyplot as plt
>>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts, labels = get_real_count_data(return_labels = True)
- >>> plnpca = PlnPCA(counts,add_const = True)
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> plnpca = PlnPCA(endog,add_const = True)
>>> plnpca.fit()
>>> plnpca.viz()
>>> plt.show()
@@ -2709,9 +2709,9 @@ class PlnPCA(_model):
_model,
example="""
>>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> plnpca = PlnPCA.from_formula("counts ~ 1", data = data)
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> plnpca = PlnPCA.from_formula("endog ~ 1", data = data)
>>> plnpca.fit()
>>> pca_proj = plnpca.pca_projected_latent_variables()
>>> print(pca_proj.shape)
@@ -2724,9 +2724,9 @@ class PlnPCA(_model):
_model,
example="""
>>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> plnpca = PlnPCA.from_formula("counts ~ 1", data = data)
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> plnpca = PlnPCA.from_formula("endog ~ 1", data = data)
>>> plnpca.fit()
>>> plnpca.scatter_pca_matrix(n_components = 5)
""",
@@ -2738,9 +2738,9 @@ class PlnPCA(_model):
_model,
example="""
>>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> plnpca = PlnPCA.from_formula("counts ~ 1", data = data)
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> plnpca = PlnPCA.from_formula("endog ~ 1", data = data)
>>> plnpca.fit()
>>> plnpca.plot_pca_correlation_graph(["a","b"], indices_of_variables = [4,8])
""",
@@ -2770,9 +2770,9 @@ class PlnPCA(_model):
_model,
example="""
>>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts = get_real_count_data()
- >>> data = {"counts": counts}
- >>> plnpca = PlnPCA.from_formula("counts ~ 1", data = data)
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> plnpca = PlnPCA.from_formula("endog ~ 1", data = data)
>>> plnpca.fit()
>>> print(plnpca.latent_mean.shape)
""",
@@ -2804,7 +2804,7 @@ class PlnPCA(_model):
"""
return self._latent_sqrt_var**2
- def _counts_predictions(self):
+ def _endog_predictions(self):
covariance_a_posteriori = torch.sum(
(self._components**2).unsqueeze(0)
* (self.latent_sqrt_var**2).unsqueeze(1),
@@ -2887,7 +2887,7 @@ class PlnPCA(_model):
covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The covariates tensor.
"""
- _check_data_shape(self.counts, covariates, self.offsets)
+ _check_data_shape(self.endog, covariates, self.offsets)
self._covariates = covariates
print("Setting coef to initialization")
self._smart_init_coef()
@@ -2943,7 +2943,7 @@ class PlnPCA(_model):
super()._smart_init_coef()
if not hasattr(self, "_components"):
self._components = _init_components(
- self._counts, self._covariates, self._coef, self._rank
+ self._endog, self._covariates, self._coef, self._rank
)
def _random_init_model_parameters(self):
@@ -2969,7 +2969,7 @@ class PlnPCA(_model):
if not hasattr(self, "_latent_mean"):
self._latent_mean = (
_init_latent_mean(
- self._counts,
+ self._endog,
self._covariates,
self._offsets,
self._coef,
@@ -3007,7 +3007,7 @@ class PlnPCA(_model):
The ELBO value.
"""
return elbo_plnpca(
- self._counts,
+ self._endog,
self._covariates,
self._offsets,
self._latent_mean,
@@ -3159,17 +3159,17 @@ class PlnPCA(_model):
""",
returns="""
torch.Tensor
- The transformed counts (latent variables of the model).
+ The transformed endog (latent variables of the model).
""",
example="""
>>> from pyPLNmodels import PlnPCA, get_real_count_data
- >>> counts= get_real_count_data()
- >>> pca = PlnPCA(counts, add_const = True)
+ >>> endog= get_real_count_data()
+ >>> pca = PlnPCA(endog, add_const = True)
>>> pca.fit()
- >>> transformed_counts_low_dim = pca.transform()
- >>> transformed_counts_high_dim = pca.transform(project = False)
- >>> print(transformed_counts_low_dim.shape)
- >>> print(transformed_counts_high_dim.shape)
+ >>> transformed_endog_low_dim = pca.transform()
+ >>> transformed_endog_high_dim = pca.transform(project = False)
+ >>> print(transformed_endog_low_dim.shape)
+ >>> print(transformed_endog_high_dim.shape)
""",
)
def transform(self, project: bool = True) -> torch.Tensor:
@@ -3199,13 +3199,13 @@ class ZIPln(Pln):
super()._smart_init_model_parameters()
if not hasattr(self, "_covariance"):
self._covariance = _init_covariance(
- self._counts, self._covariates, self._coef
+ self._endog, self._covariates, self._coef
)
if not hasattr(self, "_coef_inflation"):
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
def _random_init_latent_parameters(self):
- self._dirac = self._counts == 0
+ self._dirac = self._endog == 0
self._latent_mean = torch.randn(self.n_samples, self.dim)
self._latent_sqrt_var = torch.randn(self.n_samples, self.dim)
self._pi = (
@@ -3215,7 +3215,7 @@ class ZIPln(Pln):
def compute_elbo(self):
return elbo_zi_pln(
- self._counts,
+ self._endog,
self._covariates,
self._offsets,
self._latent_mean,
diff --git a/pyPLNmodels/oaks.py b/pyPLNmodels/oaks.py
index 6676d572..c2e126ba 100644
--- a/pyPLNmodels/oaks.py
+++ b/pyPLNmodels/oaks.py
@@ -15,7 +15,7 @@ def load_oaks():
For each sample, 3 additional covariates (tree, dist2ground, orientation) are known.
The data is provided as dictionary with the following keys
- counts a 114 x 116 np.array of integer (counts)
+ endog a 114 x 116 np.array of integer (endog)
offsets a 114 x 116 np.array of integer (offsets)
tree a 114 x 1 vector of character for the tree status with respect to the pathogen (susceptible, intermediate or resistant)
dist2ground a 114 x 1 vector encoding the distance of the sampled leaf to the base of the ground
@@ -30,16 +30,16 @@ def load_oaks():
Pathogen Erysiphe alphitoides . Microb Ecol 72, 870–880 (2016).
doi:10.1007/s00248-016-0777-x
"""
- counts_stream = pkg_resources.resource_stream(__name__, "data/oaks/counts.csv")
+ endog_stream = pkg_resources.resource_stream(__name__, "data/oaks/counts.csv")
offsets_stream = pkg_resources.resource_stream(__name__, "data/oaks/offsets.csv")
covariates_stream = pkg_resources.resource_stream(
__name__, "data/oaks/covariates.csv"
)
- counts = pd.read_csv(counts_stream)
+ endog = pd.read_csv(endog_stream)
offsets = pd.read_csv(offsets_stream)
covariates = pd.read_csv(covariates_stream)
oaks = {
- "counts": counts.to_numpy(),
+ "endog": endog.to_numpy(),
"offsets": offsets.to_numpy(),
"tree": covariates.tree.to_numpy(),
"dist2ground": covariates.distTOground.to_numpy(),
diff --git a/tests/conftest.py b/tests/conftest.py
index f983df5e..e7a23ddd 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -25,17 +25,17 @@ from tests.import_data import (
)
-counts_sim_0cov = data_sim_0cov["counts"]
+endog_sim_0cov = data_sim_0cov["endog"]
covariates_sim_0cov = data_sim_0cov["covariates"]
offsets_sim_0cov = data_sim_0cov["offsets"]
-counts_sim_2cov = data_sim_2cov["counts"]
+endog_sim_2cov = data_sim_2cov["endog"]
covariates_sim_2cov = data_sim_2cov["covariates"]
offsets_sim_2cov = data_sim_2cov["offsets"]
-counts_real = data_real["counts"]
-counts_real = pd.DataFrame(counts_real)
-counts_real.columns = [f"var_{i}" for i in range(counts_real.shape[1])]
+endog_real = data_real["endog"]
+endog_real = pd.DataFrame(endog_real)
+endog_real.columns = [f"var_{i}" for i in range(endog_real.shape[1])]
def add_fixture_to_dict(my_dict, string_fixture):
@@ -119,7 +119,7 @@ dict_fixtures = {}
def simulated_pln_0cov_array(request):
cls = request.param
pln = cls(
- counts_sim_0cov,
+ endog_sim_0cov,
covariates=covariates_sim_0cov,
offsets=offsets_sim_0cov,
add_const=False,
@@ -132,7 +132,7 @@ def simulated_pln_0cov_array(request):
def simulated_fitted_pln_0cov_array(request):
cls = request.param
pln = cls(
- counts_sim_0cov,
+ endog_sim_0cov,
covariates=covariates_sim_0cov,
offsets=offsets_sim_0cov,
add_const=False,
@@ -144,7 +144,7 @@ def simulated_fitted_pln_0cov_array(request):
@pytest.fixture(params=params)
def simulated_pln_0cov_formula(request):
cls = request.param
- pln = cls("counts ~ 0", data_sim_0cov)
+ pln = cls("endog ~ 0", data_sim_0cov)
return pln
@@ -152,7 +152,7 @@ def simulated_pln_0cov_formula(request):
@cache
def simulated_fitted_pln_0cov_formula(request):
cls = request.param
- pln = cls("counts ~ 0", data_sim_0cov)
+ pln = cls("endog ~ 0", data_sim_0cov)
pln.fit()
return pln
@@ -162,7 +162,7 @@ def simulated_loaded_pln_0cov_formula(simulated_fitted_pln_0cov_formula):
simulated_fitted_pln_0cov_formula.save()
return generate_new_model(
simulated_fitted_pln_0cov_formula,
- "counts ~ 0",
+ "endog ~ 0",
data_sim_0cov,
)
@@ -172,7 +172,7 @@ def simulated_loaded_pln_0cov_array(simulated_fitted_pln_0cov_array):
simulated_fitted_pln_0cov_array.save()
return generate_new_model(
simulated_fitted_pln_0cov_array,
- counts_sim_0cov,
+ endog_sim_0cov,
covariates=covariates_sim_0cov,
offsets=offsets_sim_0cov,
add_const=False,
@@ -217,7 +217,7 @@ dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "sim_pln_0cov", sim_p
def simulated_pln_2cov_array(request):
cls = request.param
pln_full = cls(
- counts_sim_2cov,
+ endog_sim_2cov,
covariates=covariates_sim_2cov,
offsets=offsets_sim_2cov,
add_const=False,
@@ -235,7 +235,7 @@ def simulated_fitted_pln_2cov_array(simulated_pln_2cov_array):
@cache
def simulated_pln_2cov_formula(request):
cls = request.param
- pln_full = cls("counts ~ 0 + covariates", data_sim_2cov)
+ pln_full = cls("endog ~ 0 + covariates", data_sim_2cov)
return pln_full
@@ -250,7 +250,7 @@ def simulated_loaded_pln_2cov_formula(simulated_fitted_pln_2cov_formula):
simulated_fitted_pln_2cov_formula.save()
return generate_new_model(
simulated_fitted_pln_2cov_formula,
- "counts ~0 + covariates",
+ "endog ~0 + covariates",
data_sim_2cov,
)
@@ -260,7 +260,7 @@ def simulated_loaded_pln_2cov_array(simulated_fitted_pln_2cov_array):
simulated_fitted_pln_2cov_array.save()
return generate_new_model(
simulated_fitted_pln_2cov_array,
- counts_sim_2cov,
+ endog_sim_2cov,
covariates=covariates_sim_2cov,
offsets=offsets_sim_2cov,
add_const=False,
@@ -303,7 +303,7 @@ dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "sim_pln_2cov", sim_p
@cache
def real_pln_intercept_array(request):
cls = request.param
- pln_full = cls(counts_real, add_const=True)
+ pln_full = cls(endog_real, add_const=True)
return pln_full
@@ -317,7 +317,7 @@ def real_fitted_pln_intercept_array(real_pln_intercept_array):
@cache
def real_pln_intercept_formula(request):
cls = request.param
- pln_full = cls("counts ~ 1", data_real)
+ pln_full = cls("endog ~ 1", data_real)
return pln_full
@@ -331,7 +331,7 @@ def real_fitted_pln_intercept_formula(real_pln_intercept_formula):
def real_loaded_pln_intercept_formula(real_fitted_pln_intercept_formula):
real_fitted_pln_intercept_formula.save()
return generate_new_model(
- real_fitted_pln_intercept_formula, "counts ~ 1", data=data_real
+ real_fitted_pln_intercept_formula, "endog ~ 1", data=data_real
)
@@ -340,7 +340,7 @@ def real_loaded_pln_intercept_array(real_fitted_pln_intercept_array):
real_fitted_pln_intercept_array.save()
return generate_new_model(
real_fitted_pln_intercept_array,
- counts_real,
+ endog_real,
add_const=True,
)
diff --git a/tests/import_data.py b/tests/import_data.py
index 154f5644..c44353bb 100644
--- a/tests/import_data.py
+++ b/tests/import_data.py
@@ -7,14 +7,14 @@ from pyPLNmodels import (
(
- counts_sim_0cov,
+ endog_sim_0cov,
covariates_sim_0cov,
offsets_sim_0cov,
true_covariance_0cov,
true_coef_0cov,
) = get_simulated_count_data(return_true_param=True, nb_cov=0, add_const=False)
(
- counts_sim_2cov,
+ endog_sim_2cov,
covariates_sim_2cov,
offsets_sim_2cov,
true_covariance_2cov,
@@ -22,7 +22,7 @@ from pyPLNmodels import (
) = get_simulated_count_data(return_true_param=True, nb_cov=2, add_const=False)
data_sim_0cov = {
- "counts": counts_sim_0cov,
+ "endog": endog_sim_0cov,
"covariates": covariates_sim_0cov,
"offsets": offsets_sim_0cov,
}
@@ -31,11 +31,9 @@ true_sim_2cov = {"Sigma": true_covariance_2cov, "beta": true_coef_2cov}
data_sim_2cov = {
- "counts": counts_sim_2cov,
+ "endog": endog_sim_2cov,
"covariates": covariates_sim_2cov,
"offsets": offsets_sim_2cov,
}
-counts_real, labels_real = get_real_count_data(
- return_labels=True, n_samples=100, dim=50
-)
-data_real = {"counts": counts_real}
+endog_real, labels_real = get_real_count_data(return_labels=True, n_samples=100, dim=50)
+data_real = {"endog": endog_real}
diff --git a/tests/test_common.py b/tests/test_common.py
index 38711fef..5904857d 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -6,7 +6,7 @@ import pytest
from tests.conftest import dict_fixtures
from tests.utils import MSE, filter_models
-from tests.import_data import true_sim_0cov, true_sim_2cov, counts_real
+from tests.import_data import true_sim_0cov, true_sim_2cov, endog_real
@pytest.mark.parametrize("any_pln", dict_fixtures["loaded_and_fitted_pln"])
@@ -60,8 +60,8 @@ def test_find_right_covariance(simulated_fitted_any_pln):
@filter_models(["Pln", "PlnPCA"])
def test_right_covariance_shape(real_fitted_and_loaded_pln):
assert real_fitted_and_loaded_pln.covariance.shape == (
- counts_real.shape[1],
- counts_real.shape[1],
+ endog_real.shape[1],
+ endog_real.shape[1],
)
@@ -81,9 +81,9 @@ def test_find_right_coef(simulated_fitted_any_pln):
@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
@filter_models(["Pln", "PlnPCA"])
def test_fail_count_setter(pln):
- wrong_counts = torch.randint(size=(10, 5), low=0, high=10)
+ wrong_endog = torch.randint(size=(10, 5), low=0, high=10)
with pytest.raises(Exception):
- pln.counts = wrong_counts
+ pln.endog = wrong_endog
@pytest.mark.parametrize("instance", dict_fixtures["instances"])
diff --git a/tests/test_pln_full.py b/tests/test_pln_full.py
index db0185f1..2d61befd 100644
--- a/tests/test_pln_full.py
+++ b/tests/test_pln_full.py
@@ -14,4 +14,4 @@ def test_number_of_iterations_pln_full(fitted_pln):
@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
@filter_models(["Pln"])
def test_latent_var_full(pln):
- assert pln.transform().shape == pln.counts.shape
+ assert pln.transform().shape == pln.endog.shape
diff --git a/tests/test_plnpcacollection.py b/tests/test_plnpcacollection.py
index ac77b442..6634f2d2 100644
--- a/tests/test_plnpcacollection.py
+++ b/tests/test_plnpcacollection.py
@@ -33,7 +33,7 @@ def test_number_of_iterations_plnpca(fitted_pln):
@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_pln"])
@filter_models(["PlnPCA"])
def test_latent_var_pca(plnpca):
- assert plnpca.transform(project=False).shape == plnpca.counts.shape
+ assert plnpca.transform(project=False).shape == plnpca.endog.shape
assert plnpca.transform().shape == (plnpca.n_samples, plnpca.rank)
diff --git a/tests/test_setters.py b/tests/test_setters.py
index d5716fc2..6814e842 100644
--- a/tests/test_setters.py
+++ b/tests/test_setters.py
@@ -8,7 +8,7 @@ from tests.utils import MSE, filter_models
@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
def test_data_setter_with_torch(pln):
- pln.counts = pln.counts
+ pln.endog = pln.endog
pln.covariates = pln.covariates
pln.offsets = pln.offsets
pln.fit()
@@ -27,13 +27,13 @@ def test_parameters_setter_with_torch(pln):
@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
def test_data_setter_with_numpy(pln):
- np_counts = pln.counts.numpy()
+ np_endog = pln.endog.numpy()
if pln.covariates is not None:
np_covariates = pln.covariates.numpy()
else:
np_covariates = None
np_offsets = pln.offsets.numpy()
- pln.counts = np_counts
+ pln.endog = np_endog
pln.covariates = np_covariates
pln.offsets = np_offsets
pln.fit()
@@ -58,13 +58,13 @@ def test_parameters_setter_with_numpy(pln):
@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
def test_data_setter_with_pandas(pln):
- pd_counts = pd.DataFrame(pln.counts.numpy())
+ pd_endog = pd.DataFrame(pln.endog.numpy())
if pln.covariates is not None:
pd_covariates = pd.DataFrame(pln.covariates.numpy())
else:
pd_covariates = None
pd_offsets = pd.DataFrame(pln.offsets.numpy())
- pln.counts = pd_counts
+ pln.endog = pd_endog
pln.covariates = pd_covariates
pln.offsets = pd_offsets
pln.fit()
@@ -90,17 +90,17 @@ def test_parameters_setter_with_pandas(pln):
@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
def test_fail_data_setter_with_torch(pln):
with pytest.raises(ValueError):
- pln.counts = pln.counts - 100
+ pln.endog = pln.endog - 100
- n, p = pln.counts.shape
+ n, p = pln.endog.shape
if pln.covariates is None:
d = 0
else:
d = pln.covariates.shape[-1]
with pytest.raises(ValueError):
- pln.counts = torch.zeros(n + 1, p)
+ pln.endog = torch.zeros(n + 1, p)
with pytest.raises(ValueError):
- pln.counts = torch.zeros(n, p + 1)
+ pln.endog = torch.zeros(n, p + 1)
with pytest.raises(ValueError):
pln.covariates = torch.zeros(n + 1, d)
@@ -116,7 +116,7 @@ def test_fail_data_setter_with_torch(pln):
@filter_models(["Pln", "PlnPCA"])
def test_fail_parameters_setter_with_torch(pln):
n, dim_latent = pln.latent_mean.shape
- dim = pln.counts.shape[1]
+ dim = pln.endog.shape[1]
with pytest.raises(ValueError):
pln.latent_mean = torch.zeros(n + 1, dim_latent)
--
GitLab
From 672eef3e486a6181279f8d81466be3ac50b8a76e Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 16:19:20 +0200
Subject: [PATCH 043/167] changed the occurences of covariates by exog.
---
pyPLNmodels/_closed_forms.py | 24 ++--
pyPLNmodels/_initialization.py | 56 ++++-----
pyPLNmodels/_utils.py | 138 +++++++++++----------
pyPLNmodels/elbos.py | 32 ++---
pyPLNmodels/models.py | 212 ++++++++++++++++-----------------
pyPLNmodels/oaks.py | 14 +--
tests/conftest.py | 18 +--
tests/import_data.py | 8 +-
tests/test_common.py | 2 +-
tests/test_setters.py | 28 ++---
10 files changed, 260 insertions(+), 272 deletions(-)
diff --git a/pyPLNmodels/_closed_forms.py b/pyPLNmodels/_closed_forms.py
index ff99eae3..b57e7850 100644
--- a/pyPLNmodels/_closed_forms.py
+++ b/pyPLNmodels/_closed_forms.py
@@ -4,7 +4,7 @@ import torch # pylint:disable=[C0114]
def _closed_formula_covariance(
- covariates: torch.Tensor,
+ exog: torch.Tensor,
latent_mean: torch.Tensor,
latent_sqrt_var: torch.Tensor,
coef: torch.Tensor,
@@ -15,7 +15,7 @@ def _closed_formula_covariance(
Parameters:
----------
- covariates : torch.Tensor
+ exog : torch.Tensor
Covariates with size (n, d).
latent_mean : torch.Tensor
Variational parameter with size (n, p).
@@ -31,10 +31,10 @@ def _closed_formula_covariance(
torch.Tensor
The closed-form covariance with size (p, p).
"""
- if covariates is None:
+ if exog is None:
XB = 0
else:
- XB = covariates @ coef
+ XB = exog @ coef
m_minus_xb = latent_mean - XB
closed = m_minus_xb.T @ m_minus_xb + torch.diag(
torch.sum(torch.square(latent_sqrt_var), dim=0)
@@ -43,14 +43,14 @@ def _closed_formula_covariance(
def _closed_formula_coef(
- covariates: torch.Tensor, latent_mean: torch.Tensor
+ exog: torch.Tensor, latent_mean: torch.Tensor
) -> Optional[torch.Tensor]:
"""
Compute the closed-form coef for the M step of the Pln model.
Parameters:
----------
- covariates : torch.Tensor
+ exog : torch.Tensor
Covariates with size (n, d).
latent_mean : torch.Tensor
Variational parameter with size (n, p).
@@ -58,11 +58,11 @@ def _closed_formula_coef(
Returns:
-------
Optional[torch.Tensor]
- The closed-form coef with size (d, p) or None if covariates is None.
+ The closed-form coef with size (d, p) or None if exog is None.
"""
- if covariates is None:
+ if exog is None:
return None
- return torch.inverse(covariates.T @ covariates) @ covariates.T @ latent_mean
+ return torch.inverse(exog.T @ exog) @ exog.T @ latent_mean
def _closed_formula_pi(
@@ -70,7 +70,7 @@ def _closed_formula_pi(
latent_mean: torch.Tensor,
latent_sqrt_var: torch.Tensor,
dirac: torch.Tensor,
- covariates: torch.Tensor,
+ exog: torch.Tensor,
_coef_inflation: torch.Tensor,
) -> torch.Tensor:
"""
@@ -86,7 +86,7 @@ def _closed_formula_pi(
Variational parameter with size (n, p).
dirac : torch.Tensor
Dirac tensor.
- covariates : torch.Tensor
+ exog : torch.Tensor
Covariates with size (n, d).
_coef_inflation : torch.Tensor
Inflation coefficient tensor.
@@ -97,4 +97,4 @@ def _closed_formula_pi(
The closed-form pi with the same size as dirac.
"""
poiss_param = torch.exp(offsets + latent_mean + 0.5 * torch.square(latent_sqrt_var))
- return torch._sigmoid(poiss_param + torch.mm(covariates, _coef_inflation)) * dirac
+ return torch._sigmoid(poiss_param + torch.mm(exog, _coef_inflation)) * dirac
diff --git a/pyPLNmodels/_initialization.py b/pyPLNmodels/_initialization.py
index ea22106b..e0c3f47e 100644
--- a/pyPLNmodels/_initialization.py
+++ b/pyPLNmodels/_initialization.py
@@ -10,7 +10,7 @@ else:
def _init_covariance(
- endog: torch.Tensor, covariates: torch.Tensor, coef: torch.Tensor
+ endog: torch.Tensor, exog: torch.Tensor, coef: torch.Tensor
) -> torch.Tensor:
"""
Initialization for the covariance for the Pln model. Take the log of endog
@@ -23,7 +23,7 @@ def _init_covariance(
Samples with size (n,p)
offsets : torch.Tensor
Offset, size (n,p)
- covariates : torch.Tensor
+ exog : torch.Tensor
Covariates, size (n,d)
coef : torch.Tensor
Coefficient of size (d,p)
@@ -41,7 +41,7 @@ def _init_covariance(
def _init_components(
- endog: torch.Tensor, covariates: torch.Tensor, coef: torch.Tensor, rank: int
+ endog: torch.Tensor, exog: torch.Tensor, coef: torch.Tensor, rank: int
) -> torch.Tensor:
"""
Initialization for components for the Pln model. Get a first guess for covariance
@@ -53,7 +53,7 @@ def _init_components(
Samples with size (n,p)
offsets : torch.Tensor
Offset, size (n,p)
- covariates : torch.Tensor
+ exog : torch.Tensor
Covariates, size (n,d)
coef : torch.Tensor
Coefficient of size (d,p)
@@ -65,14 +65,14 @@ def _init_components(
torch.Tensor
Initialization of components of size (p,rank)
"""
- sigma_hat = _init_covariance(endog, covariates, coef).detach()
+ sigma_hat = _init_covariance(endog, exog, coef).detach()
components = _components_from_covariance(sigma_hat, rank)
return components
def _init_latent_mean(
endog: torch.Tensor,
- covariates: torch.Tensor,
+ exog: torch.Tensor,
offsets: torch.Tensor,
coef: torch.Tensor,
components: torch.Tensor,
@@ -90,7 +90,7 @@ def _init_latent_mean(
Samples with size (n,p)
offsets : torch.Tensor
Offset, size (n,p)
- covariates : torch.Tensor
+ exog : torch.Tensor
Covariates, size (n,d)
coef : torch.Tensor
Coefficient of size (d,p)
@@ -117,7 +117,7 @@ def _init_latent_mean(
keep_condition = True
i = 0
while i < n_iter_max and keep_condition:
- batch_loss = log_posterior(endog, covariates, offsets, mode, components, coef)
+ batch_loss = log_posterior(endog, exog, offsets, mode, components, coef)
loss = -torch.mean(batch_loss)
loss.backward()
optimizer.step()
@@ -155,7 +155,7 @@ def _components_from_covariance(covariance: torch.Tensor, rank: int) -> torch.Te
def _init_coef(
- endog: torch.Tensor, covariates: torch.Tensor, offsets: torch.Tensor
+ endog: torch.Tensor, exog: torch.Tensor, offsets: torch.Tensor
) -> torch.Tensor:
"""
Initialize the coefficient for the Pln model using Poisson regression model.
@@ -164,7 +164,7 @@ def _init_coef(
----------
endog : torch.Tensor
Samples with size (n, p)
- covariates : torch.Tensor
+ exog : torch.Tensor
Covariates, size (n, d)
offsets : torch.Tensor
Offset, size (n, p)
@@ -172,19 +172,19 @@ def _init_coef(
Returns
-------
torch.Tensor or None
- Coefficient of size (d, p) or None if covariates is None.
+ Coefficient of size (d, p) or None if exog is None.
"""
- if covariates is None:
+ if exog is None:
return None
poiss_reg = _PoissonReg()
- poiss_reg.fit(endog, covariates, offsets)
+ poiss_reg.fit(endog, exog, offsets)
return poiss_reg.beta
def log_posterior(
endog: torch.Tensor,
- covariates: torch.Tensor,
+ exog: torch.Tensor,
offsets: torch.Tensor,
posterior_mean: torch.Tensor,
components: torch.Tensor,
@@ -197,7 +197,7 @@ def log_posterior(
----------
endog : torch.Tensor
Samples with size (batch_size, p)
- covariates : torch.Tensor or None
+ exog : torch.Tensor or None
Covariates, size (batch_size, d) or (d)
offsets : torch.Tensor
Offset, size (batch_size, p)
@@ -218,10 +218,10 @@ def log_posterior(
components.unsqueeze(0), posterior_mean.unsqueeze(2)
).squeeze()
- if covariates is None:
+ if exog is None:
XB = 0
else:
- XB = torch.matmul(covariates, coef)
+ XB = torch.matmul(exog, coef)
log_lambda = offsets + components_posterior_mean + XB
first_term = (
@@ -245,7 +245,7 @@ class _PoissonReg:
Methods
-------
- fit(Y, covariates, O, Niter_max=300, tol=0.001, lr=0.005, verbose=False)
+ fit(Y, exog, O, Niter_max=300, tol=0.001, lr=0.005, verbose=False)
Fit the Poisson regression model to the given data.
"""
@@ -256,7 +256,7 @@ class _PoissonReg:
def fit(
self,
Y: torch.Tensor,
- covariates: torch.Tensor,
+ exog: torch.Tensor,
offsets: torch.Tensor,
Niter_max: int = 300,
tol: float = 0.001,
@@ -270,8 +270,8 @@ class _PoissonReg:
----------
Y : torch.Tensor
The dependent variable of shape (n_samples, n_features).
- covariates : torch.Tensor
- The covariates of shape (n_samples, n_covariates).
+ exog : torch.Tensor
+ The exog of shape (n_samples, n_exog).
offsets : torch.Tensor
The offset term of shape (n_samples, n_features).
Niter_max : int, optional
@@ -285,13 +285,13 @@ class _PoissonReg:
"""
beta = torch.rand(
- (covariates.shape[1], Y.shape[1]), device=DEVICE, requires_grad=True
+ (exog.shape[1], Y.shape[1]), device=DEVICE, requires_grad=True
)
optimizer = torch.optim.Rprop([beta], lr=lr)
i = 0
grad_norm = 2 * tol # Criterion
while i < Niter_max and grad_norm > tol:
- loss = -compute_poissreg_log_like(Y, offsets, covariates, beta)
+ loss = -compute_poissreg_log_like(Y, offsets, exog, beta)
loss.backward()
optimizer.step()
grad_norm = torch.norm(beta.grad)
@@ -309,7 +309,7 @@ class _PoissonReg:
def compute_poissreg_log_like(
- Y: torch.Tensor, O: torch.Tensor, covariates: torch.Tensor, beta: torch.Tensor
+ Y: torch.Tensor, O: torch.Tensor, exog: torch.Tensor, beta: torch.Tensor
) -> torch.Tensor:
"""
Compute the log likelihood of a Poisson regression model.
@@ -320,10 +320,10 @@ def compute_poissreg_log_like(
The dependent variable of shape (n_samples, n_features).
O : torch.Tensor
The offset term of shape (n_samples, n_features).
- covariates : torch.Tensor
- The covariates of shape (n_samples, n_covariates).
+ exog : torch.Tensor
+ The exog of shape (n_samples, n_exog).
beta : torch.Tensor
- The regression coefficients of shape (n_covariates, n_features).
+ The regression coefficients of shape (n_exog, n_features).
Returns
-------
@@ -331,5 +331,5 @@ def compute_poissreg_log_like(
The log likelihood of the Poisson regression model.
"""
- XB = torch.matmul(covariates.unsqueeze(1), beta.unsqueeze(0)).squeeze()
+ XB = torch.matmul(exog.unsqueeze(1), beta.unsqueeze(0)).squeeze()
return torch.sum(-torch.exp(O + XB) + torch.multiply(Y, O + XB))
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index ba6b2a39..805c9dca 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -102,7 +102,7 @@ def sample_pln(pln_param, *, seed: int = None, return_latent=False) -> torch.Ten
Parameters
----------
pln_param : PlnParameters object
- parameters of the model, containing the coeficient, the covariates,
+ parameters of the model, containing the coeficient, the exog,
the components and the offsets.
seed : int or None, optional(keyword-only)
Random seed for reproducibility. Default is None.
@@ -124,10 +124,10 @@ def sample_pln(pln_param, *, seed: int = None, return_latent=False) -> torch.Ten
n_samples = pln_param.offsets.shape[0]
rank = pln_param.components.shape[1]
- if pln_param.covariates is None:
+ if pln_param.exog is None:
XB = 0
else:
- XB = torch.matmul(pln_param.covariates, pln_param.coef)
+ XB = torch.matmul(pln_param.exog, pln_param.coef)
gaussian = (
torch.mm(torch.randn(n_samples, rank, device=DEVICE), pln_param.components.T)
@@ -136,9 +136,7 @@ def sample_pln(pln_param, *, seed: int = None, return_latent=False) -> torch.Ten
parameter = torch.exp(pln_param.offsets + gaussian)
if pln_param.coef_inflation is not None:
print("ZIPln is sampled")
- zero_inflated_mean = torch.matmul(
- pln_param.covariates, pln_param.coef_inflation
- )
+ zero_inflated_mean = torch.matmul(pln_param.exog, pln_param.coef_inflation)
ksi = torch.bernoulli(1 / (1 + torch.exp(-zero_inflated_mean)))
else:
ksi = 0
@@ -269,7 +267,7 @@ def _format_data(
def _format_model_param(
endog: Union[torch.Tensor, np.ndarray, pd.DataFrame],
- covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ exog: Union[torch.Tensor, np.ndarray, pd.DataFrame],
offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame],
offsets_formula: str,
take_log_offsets: bool,
@@ -282,7 +280,7 @@ def _format_model_param(
----------
endog : Union[torch.Tensor, np.ndarray, pd.DataFrame], shape (n, )
Count data.
- covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None, shape (n, d) or None
+ exog : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None, shape (n, d) or None
Covariate data.
offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None, shape (n, ) or None
Offset data.
@@ -291,7 +289,7 @@ def _format_model_param(
take_log_offsets : bool
Flag indicating whether to take the logarithm of offsets.
add_const: bool
- Whether to add a column of one in the covariates.
+ Whether to add a column of one in the exog.
Returns
-------
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
@@ -305,14 +303,14 @@ def _format_model_param(
endog = _format_data(endog)
if torch.min(endog) < 0:
raise ValueError("Counts should be only non negative values.")
- covariates = _format_data(covariates)
+ exog = _format_data(exog)
if add_const is True:
- if covariates is None:
- covariates = torch.ones(endog.shape[0], 1)
+ if exog is None:
+ exog = torch.ones(endog.shape[0], 1)
else:
- if _has_null_variance(covariates) is False:
- covariates = torch.concat(
- (covariates, torch.ones(endog.shape[0]).unsqueeze(1)), dim=1
+ if _has_null_variance(exog) is False:
+ exog = torch.concat(
+ (exog, torch.ones(endog.shape[0]).unsqueeze(1)), dim=1
)
if offsets is None:
if offsets_formula == "logsum":
@@ -331,7 +329,7 @@ def _format_model_param(
offsets = _format_data(offsets).to(DEVICE)
if take_log_offsets is True:
offsets = torch.log(offsets)
- return endog, covariates, offsets
+ return endog, exog, offsets
def _has_null_variance(tensor: torch.Tensor) -> bool:
@@ -352,7 +350,7 @@ def _has_null_variance(tensor: torch.Tensor) -> bool:
def _check_data_shape(
- endog: torch.Tensor, covariates: torch.Tensor, offsets: torch.Tensor
+ endog: torch.Tensor, exog: torch.Tensor, offsets: torch.Tensor
) -> None:
"""
Check if the shape of the input data is valid.
@@ -361,7 +359,7 @@ def _check_data_shape(
----------
endog : torch.Tensor, shape (n, p)
Count data.
- covariates : torch.Tensor or None, shape (n, d) or None
+ exog : torch.Tensor or None, shape (n, d) or None
Covariate data.
offsets : torch.Tensor or None, shape (n, p) or None
Offset data.
@@ -369,9 +367,9 @@ def _check_data_shape(
n_endog, p_endog = endog.shape
n_offsets, p_offsets = offsets.shape
_check_two_dimensions_are_equal("endog", "offsets", n_endog, n_offsets, 0, 0)
- if covariates is not None:
- n_cov, _ = covariates.shape
- _check_two_dimensions_are_equal("endog", "covariates", n_endog, n_cov, 0, 0)
+ if exog is not None:
+ n_cov, _ = exog.shape
+ _check_two_dimensions_are_equal("endog", "exog", n_endog, n_cov, 0, 0)
_check_two_dimensions_are_equal("endog", "offsets", p_endog, p_offsets, 1, 1)
@@ -476,29 +474,29 @@ def _get_simulation_coef_cov_offsets(
n_samples : int
Number of samples.
nb_cov : int
- Number of covariates. If 0, covariates will be None,
+ Number of exog. If 0, exog will be None,
unless add_const is True.
If add_const is True, then there will be nb_cov+1
- covariates as the intercept can be seen as a covariates.
+ exog as the intercept can be seen as a exog.
dim : int
Dimension required of the data.
add_const : bool, optional
- If True, will add a vector of ones in the covariates.
+ If True, will add a vector of ones in the exog.
Returns
-------
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
- Tuple containing offsets, covariates, and coefficients.
+ Tuple containing offsets, exog, and coefficients.
"""
prev_state = torch.random.get_rng_state()
torch.random.manual_seed(0)
if nb_cov == 0:
if add_const is True:
- covariates = torch.ones(n_samples, 1)
+ exog = torch.ones(n_samples, 1)
else:
- covariates = None
+ exog = None
else:
- covariates = torch.randint(
+ exog = torch.randint(
low=-1,
high=2,
size=(n_samples, nb_cov),
@@ -506,16 +504,16 @@ def _get_simulation_coef_cov_offsets(
device="cpu",
)
if add_const is True:
- covariates = torch.cat((covariates, torch.ones(n_samples, 1)), axis=1)
- if covariates is None:
+ exog = torch.cat((exog, torch.ones(n_samples, 1)), axis=1)
+ if exog is None:
coef = None
else:
- coef = torch.randn(covariates.shape[1], dim, device="cpu")
+ coef = torch.randn(exog.shape[1], dim, device="cpu")
offsets = torch.randint(
low=0, high=2, size=(n_samples, dim), dtype=torch.float64, device="cpu"
)
torch.random.set_rng_state(prev_state)
- return coef, covariates, offsets
+ return coef, exog, offsets
class PlnParameters:
@@ -524,7 +522,7 @@ class PlnParameters:
*,
components: Union[torch.Tensor, np.ndarray, pd.DataFrame],
coef: Union[torch.Tensor, np.ndarray, pd.DataFrame],
- covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame],
+ exog: Union[torch.Tensor, np.ndarray, pd.DataFrame],
offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame],
coef_inflation=None,
):
@@ -537,7 +535,7 @@ class PlnParameters:
Components of size (p, rank)
coef : : Union[torch.Tensor, np.ndarray, pd.DataFrame](keyword-only)
Coefficient of size (d, p)
- covariates : : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None(keyword-only)
+ exog : : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None(keyword-only)
Covariates, size (n, d) or None
offsets : : Union[torch.Tensor, np.ndarray, pd.DataFrame](keyword-only)
Offset, size (n, p)
@@ -547,7 +545,7 @@ class PlnParameters:
"""
self._components = _format_data(components)
self._coef = _format_data(coef)
- self._covariates = _format_data(covariates)
+ self._exog = _format_data(exog)
self._offsets = _format_data(offsets)
self._coef_inflation = _format_data(coef_inflation)
if self._coef is not None:
@@ -568,24 +566,24 @@ class PlnParameters:
0,
1,
)
- if self._covariates is not None:
+ if self._exog is not None:
_check_two_dimensions_are_equal(
"offsets",
- "covariates",
+ "exog",
self._offsets.shape[0],
- self._covariates.shape[0],
+ self._exog.shape[0],
0,
0,
)
_check_two_dimensions_are_equal(
- "covariates",
+ "exog",
"coef",
- self._covariates.shape[1],
+ self._exog.shape[1],
self._coef.shape[0],
1,
0,
)
- for array in [self._components, self._coef, self._covariates, self._offsets]:
+ for array in [self._components, self._coef, self._exog, self._offsets]:
if array is not None:
if len(array.shape) != 2:
raise RuntimeError(
@@ -618,14 +616,14 @@ class PlnParameters:
"""
Coef of the model.
"""
- return self.coef
+ return self._coef
@property
- def covariates(self):
+ def exog(self):
"""
- Data covariates.
+ Data exog.
"""
- return self.covariates
+ return self._exog
@property
def coef_inflation(self):
@@ -695,13 +693,13 @@ def get_simulation_parameters(
dim : int, optional(keyword-only)
The dimension of the data, by default 25.
nb_cov : int, optional(keyword-only)
- The number of covariates, by default 1. If add_const is True,
- then there will be nb_cov+1 covariates as the intercept can be seen
- as a covariates.
+ The number of exog, by default 1. If add_const is True,
+ then there will be nb_cov+1 exog as the intercept can be seen
+ as a exog.
rank : int, optional(keyword-only)
The rank of the data components, by default 5.
add_const : bool, optional(keyword-only)
- If True, will add a vector of ones in the covariates.
+ If True, will add a vector of ones in the exog.
Returns
-------
@@ -709,13 +707,11 @@ def get_simulation_parameters(
The generated simulation parameters.
"""
- coef, covariates, offsets = _get_simulation_coef_cov_offsets(
+ coef, exog, offsets = _get_simulation_coef_cov_offsets(
n_samples, nb_cov, dim, add_const
)
components = _get_simulation_components(dim, rank)
- return PlnParameters(
- components=components, coef=coef, covariates=covariates, offsets=offsets
- )
+ return PlnParameters(components=components, coef=coef, exog=exog, offsets=offsets)
def get_simulated_count_data(
@@ -742,7 +738,7 @@ def get_simulated_count_data(
add_const : bool, optional(keyword-only)
If True, will add a vector of ones. Default is True
nb_cov : int, optional(keyword-only)
- Number of covariates, by default 1.
+ Number of exog, by default 1.
return_true_param : bool, optional(keyword-only)
Whether to return the true parameters of the model, by default False.
seed : int, optional(keyword-only)
@@ -751,7 +747,7 @@ def get_simulated_count_data(
Returns
-------
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
- Tuple containing endog, covariates, and offsets.
+ Tuple containing endog, exog, and offsets.
"""
pln_param = get_simulation_parameters(
n_samples=n_samples, dim=dim, nb_cov=nb_cov, rank=rank, add_const=add_const
@@ -760,7 +756,7 @@ def get_simulated_count_data(
if return_true_param is True:
return (
endog,
- pln_param.covariates,
+ pln_param.exog,
pln_param.offsets,
pln_param.covariance,
pln_param.coef,
@@ -799,7 +795,7 @@ def get_real_count_data(
f"\nTaking the whole max_dim variables. Requested:dim={dim}, returned:{max_dim}"
)
dim = max_dim
- endog_stream = pkg_resources.resource_stream(__name__, "data/scRT/endog.csv")
+ endog_stream = pkg_resources.resource_stream(__name__, "data/scRT/counts.csv")
endog = pd.read_csv(endog_stream).values[:n_samples, :dim]
print(f"Returning dataset of size {endog.shape}")
if return_labels is False:
@@ -849,16 +845,16 @@ def _extract_data_from_formula(
Returns
-------
Tuple
- A tuple containing the extracted endog, covariates, and offsets.
+ A tuple containing the extracted endog, exog, and offsets.
"""
dmatrix = dmatrices(formula, data=data)
endog = dmatrix[0]
- covariates = dmatrix[1]
- if covariates.size == 0:
- covariates = None
+ exog = dmatrix[1]
+ if exog.size == 0:
+ exog = None
offsets = data.get("offsets", None)
- return endog, covariates, offsets
+ return endog, exog, offsets
def _is_dict_of_dict(dictionary: Dict[Any, Any]) -> bool:
@@ -947,7 +943,7 @@ def _array2tensor(func):
def _handle_data(
endog,
- covariates,
+ exog,
offsets,
offsets_formula: str,
take_log_offsets: bool,
@@ -959,30 +955,30 @@ def _handle_data(
Parameters
----------
endog : The endog data. If a DataFrame is provided, the column names are stored for later use.
- covariates : The covariates data.
+ exog : The exog data.
offsets : The offsets data.
offsets_formula : The formula used for offsets.
take_log_offsets : Indicates whether to take the logarithm of the offsets.
- add_const : Indicates whether to add a constant column to the covariates.
+ add_const : Indicates whether to add a constant column to the exog.
Returns
-------
- tuple: A tuple containing the processed endog, covariates, offsets, and column endog (if available).
+ tuple: A tuple containing the processed endog, exog, offsets, and column endog (if available).
Raises
------
- ValueError: If the shapes of endog, covariates, and offsets do not match.
+ ValueError: If the shapes of endog, exog, and offsets do not match.
"""
if isinstance(endog, pd.DataFrame):
column_endog = endog.columns
else:
column_endog = None
- endog, covariates, offsets = _format_model_param(
- endog, covariates, offsets, offsets_formula, take_log_offsets, add_const
+ endog, exog, offsets = _format_model_param(
+ endog, exog, offsets, offsets_formula, take_log_offsets, add_const
)
- _check_data_shape(endog, covariates, offsets)
- return endog, covariates, offsets, column_endog
+ _check_data_shape(endog, exog, offsets)
+ return endog, exog, offsets, column_endog
def _add_doc(parent_class, *, params=None, example=None, returns=None, see_also=None):
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index b0298a6a..6dcda361 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -8,7 +8,7 @@ from typing import Optional
def elbo_pln(
endog: torch.Tensor,
offsets: torch.Tensor,
- covariates: Optional[torch.Tensor],
+ exog: Optional[torch.Tensor],
latent_mean: torch.Tensor,
latent_sqrt_var: torch.Tensor,
covariance: torch.Tensor,
@@ -23,7 +23,7 @@ def elbo_pln(
Counts with size (n, p).
offsets : torch.Tensor
Offset with size (n, p).
- covariates : torch.Tensor, optional
+ exog : torch.Tensor, optional
Covariates with size (n, d).
latent_mean : torch.Tensor
Variational parameter with size (n, p).
@@ -42,10 +42,10 @@ def elbo_pln(
n_samples, dim = endog.shape
s_rond_s = torch.square(latent_sqrt_var)
offsets_plus_m = offsets + latent_mean
- if covariates is None:
+ if exog is None:
XB = torch.zeros_like(endog)
else:
- XB = covariates @ coef
+ XB = exog @ coef
m_minus_xb = latent_mean - XB
d_plus_minus_xb2 = (
torch.diag(torch.sum(s_rond_s, dim=0)) + m_minus_xb.T @ m_minus_xb
@@ -64,7 +64,7 @@ def elbo_pln(
def profiled_elbo_pln(
endog: torch.Tensor,
- covariates: torch.Tensor,
+ exog: torch.Tensor,
offsets: torch.Tensor,
latent_mean: torch.Tensor,
latent_sqrt_var: torch.Tensor,
@@ -78,7 +78,7 @@ def profiled_elbo_pln(
----------
endog : torch.Tensor
Counts with size (n, p).
- covariates : torch.Tensor
+ exog : torch.Tensor
Covariates with size (n, d).
offsets : torch.Tensor
Offset with size (n, p).
@@ -95,9 +95,9 @@ def profiled_elbo_pln(
n_samples, _ = endog.shape
s_squared = torch.square(latent_sqrt_var)
offsets_plus_mean = offsets + latent_mean
- closed_coef = _closed_formula_coef(covariates, latent_mean)
+ closed_coef = _closed_formula_coef(exog, latent_mean)
closed_covariance = _closed_formula_covariance(
- covariates, latent_mean, latent_sqrt_var, closed_coef, n_samples
+ exog, latent_mean, latent_sqrt_var, closed_coef, n_samples
)
elbo = -0.5 * n_samples * torch.logdet(closed_covariance)
elbo += torch.sum(
@@ -111,7 +111,7 @@ def profiled_elbo_pln(
def elbo_plnpca(
endog: torch.Tensor,
- covariates: torch.Tensor,
+ exog: torch.Tensor,
offsets: torch.Tensor,
latent_mean: torch.Tensor,
latent_sqrt_var: torch.Tensor,
@@ -126,7 +126,7 @@ def elbo_plnpca(
----------
endog : torch.Tensor
Counts with size (n, p).
- covariates : torch.Tensor
+ exog : torch.Tensor
Covariates with size (n, d).
offsets : torch.Tensor
Offset with size (n, p).
@@ -147,10 +147,10 @@ def elbo_plnpca(
"""
n_samples = endog.shape[0]
rank = components.shape[1]
- if covariates is None:
+ if exog is None:
XB = 0
else:
- XB = covariates @ coef
+ XB = exog @ coef
log_intensity = offsets + XB + latent_mean @ components.T
s_squared = torch.square(latent_sqrt_var)
endog_log_intensity = torch.sum(endog * log_intensity)
@@ -175,7 +175,7 @@ def elbo_plnpca(
## should rename some variables so that is is clearer when we see the formula
def elbo_zi_pln(
endog,
- covariates,
+ exog,
offsets,
latent_mean,
latent_sqrt_var,
@@ -191,7 +191,7 @@ def elbo_zi_pln(
Args:
endog: torch.tensor. Counts with size (n,p)
0: torch.tensor. Offset, size (n,p)
- covariates: torch.tensor. Covariates, size (n,d)
+ exog: torch.tensor. Covariates, size (n,d)
latent_mean: torch.tensor. Variational parameter with size (n,p)
latent_sqrt_var: torch.tensor. Variational parameter with size (n,p)
pi: torch.tensor. Variational parameter with size (n,p)
@@ -208,8 +208,8 @@ def elbo_zi_pln(
dim = endog.shape[1]
s_rond_s = torch.square(latent_sqrt_var)
offsets_plus_m = offsets + latent_mean
- m_minus_xb = latent_mean - covariates @ coef
- x_coef_inflation = covariates @ _coef_inflation
+ m_minus_xb = latent_mean - exog @ coef
+ x_coef_inflation = exog @ _coef_inflation
elbo = torch.sum(
(1 - pi)
* (
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 62351a03..db42daeb 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -59,7 +59,7 @@ class _model(ABC):
_WINDOW: int = 15
_endog: torch.Tensor
- _covariates: torch.Tensor
+ _exog: torch.Tensor
_offsets: torch.Tensor
_coef: torch.Tensor
_beginning_time: float
@@ -70,7 +70,7 @@ class _model(ABC):
self,
endog: Union[torch.Tensor, np.ndarray, pd.DataFrame],
*,
- covariates: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
+ exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets_formula: str = "logsum",
dict_initialization: Optional[dict] = None,
@@ -84,7 +84,7 @@ class _model(ABC):
----------
endog : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The count data.
- covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
+ exog : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The covariate data. Defaults to None.
offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The offsets data. Defaults to None.
@@ -96,15 +96,15 @@ class _model(ABC):
take_log_offsets : bool, optional(keyword-only)
Whether to take the log of offsets. Defaults to False.
add_const: bool, optional(keyword-only)
- Whether to add a column of one in the covariates. Defaults to True.
+ Whether to add a column of one in the exog. Defaults to True.
"""
(
self._endog,
- self._covariates,
+ self._exog,
self._offsets,
self.column_endog,
) = _handle_data(
- endog, covariates, offsets, offsets_formula, take_log_offsets, add_const
+ endog, exog, offsets, offsets_formula, take_log_offsets, add_const
)
self._fitted = False
self._plotargs = _PlotArgs(self._WINDOW)
@@ -138,10 +138,10 @@ class _model(ABC):
take_log_offsets : bool, optional(keyword-only)
Whether to take the log of offsets. Defaults to False.
"""
- endog, covariates, offsets = _extract_data_from_formula(formula, data)
+ endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
endog,
- covariates=covariates,
+ exog=exog,
offsets=offsets,
offsets_formula=offsets_formula,
dict_initialization=dict_initialization,
@@ -255,22 +255,22 @@ class _model(ABC):
@property
def nb_cov(self) -> int:
"""
- The number of covariates.
+ The number of exog.
Returns
-------
int
- The number of covariates.
+ The number of exog.
"""
- if self.covariates is None:
+ if self.exog is None:
return 0
- return self.covariates.shape[1]
+ return self.exog.shape[1]
def _smart_init_coef(self):
"""
Initialize coefficients smartly.
"""
- self._coef = _init_coef(self._endog, self._covariates, self._offsets)
+ self._coef = _init_coef(self._endog, self._exog, self._offsets)
def _random_init_coef(self):
"""
@@ -836,7 +836,7 @@ class _model(ABC):
"""
return {
"endog": self.endog,
- "covariates": self.covariates,
+ "exog": self.exog,
"offsets": self.offsets,
}
@@ -1015,16 +1015,16 @@ class _model(ABC):
return self._cpu_attribute_or_none("_offsets")
@property
- def covariates(self):
+ def exog(self):
"""
- Property representing the covariates.
+ Property representing the exog.
Returns
-------
torch.Tensor or None
- The covariates or None.
+ The exog or None.
"""
- return self._cpu_attribute_or_none("_covariates")
+ return self._cpu_attribute_or_none("_exog")
@endog.setter
@_array2tensor
@@ -1072,24 +1072,24 @@ class _model(ABC):
)
self._offsets = offsets
- @covariates.setter
+ @exog.setter
@_array2tensor
- def covariates(self, covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
+ def exog(self, exog: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
- Setter for the covariates property.
+ Setter for the exog property.
Parameters
----------
- covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame]
- The covariates.
+ exog : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The exog.
Raises
------
ValueError
- If the shape of the covariates or endog is incorrect.
+ If the shape of the exog or endog is incorrect.
"""
- _check_data_shape(self.endog, covariates, self.offsets)
- self._covariates = covariates
+ _check_data_shape(self.endog, exog, self.offsets)
+ self._exog = exog
@coef.setter
@_array2tensor
@@ -1180,14 +1180,14 @@ class _model(ABC):
"""
return self.covariance
- def predict(self, covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None):
+ def predict(self, exog: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None):
"""
Method for making predictions.
Parameters
----------
- covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional
- The covariates, by default None.
+ exog : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional
+ The exog, by default None.
Returns
-------
@@ -1197,29 +1197,29 @@ class _model(ABC):
Raises
------
AttributeError
- If there are no covariates in the model but some are provided.
+ If there are no exog in the model but some are provided.
RuntimeError
- If the shape of the covariates is incorrect.
+ If the shape of the exog is incorrect.
Notes
-----
- - If `covariates` is not provided and there are no covariates in the model, None is returned.
- If there are covariates in the model, then the mean covariates @ coef is returned.
- - If `covariates` is provided, it should have the shape `(_, nb_cov)`, where `nb_cov` is the number of covariates.
- - The predicted values are obtained by multiplying the covariates by the coefficients.
- """
- if covariates is not None and self.nb_cov == 0:
- raise AttributeError("No covariates in the model, can't predict")
- if covariates is None:
- if self.covariates is None:
- print("No covariates in the model.")
+ - If `exog` is not provided and there are no exog in the model, None is returned.
+ If there are exog in the model, then the mean exog @ coef is returned.
+ - If `exog` is provided, it should have the shape `(_, nb_cov)`, where `nb_cov` is the number of exog.
+ - The predicted values are obtained by multiplying the exog by the coefficients.
+ """
+ if exog is not None and self.nb_cov == 0:
+ raise AttributeError("No exog in the model, can't predict")
+ if exog is None:
+ if self.exog is None:
+ print("No exog in the model.")
return None
- return self.covariates @ self.coef
- if covariates.shape[-1] != self.nb_cov:
- error_string = f"X has wrong shape ({covariates.shape}). Should"
+ return self.exog @ self.coef
+ if exog.shape[-1] != self.nb_cov:
+ error_string = f"X has wrong shape ({exog.shape}). Should"
error_string += f" be ({self.n_samples, self.nb_cov})."
raise RuntimeError(error_string)
- return covariates @ self.coef
+ return exog @ self.coef
@property
def _directory_name(self):
@@ -1328,7 +1328,7 @@ class Pln(_model):
self,
endog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- covariates: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
+ exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
@@ -1337,7 +1337,7 @@ class Pln(_model):
):
super().__init__(
endog=endog,
- covariates=covariates,
+ exog=exog,
offsets=offsets,
offsets_formula=offsets_formula,
dict_initialization=dict_initialization,
@@ -1371,10 +1371,10 @@ class Pln(_model):
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
):
- endog, covariates, offsets = _extract_data_from_formula(formula, data)
+ endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
endog,
- covariates=covariates,
+ exog=exog,
offsets=offsets,
offsets_formula=offsets_formula,
dict_initialization=dict_initialization,
@@ -1533,11 +1533,7 @@ class Pln(_model):
torch.Tensor or None
The coefficients or None.
"""
- if (
- hasattr(self, "_latent_mean")
- and hasattr(self, "_covariates")
- and self.nb_cov > 0
- ):
+ if hasattr(self, "_latent_mean") and hasattr(self, "_exog") and self.nb_cov > 0:
return self._coef.detach().cpu()
return None
@@ -1617,7 +1613,7 @@ class Pln(_model):
"""
return profiled_elbo_pln(
self._endog,
- self._covariates,
+ self._exog,
self._offsets,
self._latent_mean,
self._latent_sqrt_var,
@@ -1645,7 +1641,7 @@ class Pln(_model):
torch.Tensor
The coefficients.
"""
- return _closed_formula_coef(self._covariates, self._latent_mean)
+ return _closed_formula_coef(self._exog, self._latent_mean)
@property
def _covariance(self):
@@ -1658,7 +1654,7 @@ class Pln(_model):
The covariance matrix or None.
"""
return _closed_formula_covariance(
- self._covariates,
+ self._exog,
self._latent_mean,
self._latent_sqrt_var,
self._coef,
@@ -1717,7 +1713,7 @@ class Pln(_model):
if all(
hasattr(self, attr)
for attr in [
- "_covariates",
+ "_exog",
"_latent_mean",
"_latent_sqrt_var",
"_coef",
@@ -1759,7 +1755,7 @@ class PlnPCAcollection:
>>> plnparam = get_simulation_parameters(n_samples =100, dim = 60, nb_cov = 2, rank = 8)
>>> endog = sample_pln(plnparam)
- >>> data = {"endog":endog, "cov": plnparam.covariates, "offsets": plnparam.offsets}
+ >>> data = {"endog":endog, "cov": plnparam.exog, "offsets": plnparam.offsets}
>>> plnpcas = PlnPCAcollection.from_formula("endog ~ 0 + cov", data = data, ranks = [5,8,12])
>>> plnpcas.fit()
>>> print(plnpcas)
@@ -1776,7 +1772,7 @@ class PlnPCAcollection:
self,
endog: Union[torch.Tensor, np.ndarray, pd.DataFrame],
*,
- covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
+ exog: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
offsets_formula: str = "logsum",
ranks: Iterable[int] = range(3, 5),
@@ -1791,8 +1787,8 @@ class PlnPCAcollection:
----------
endog :Union[torch.Tensor, np.ndarray, pd.DataFrame]
The endog.
- covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
- The covariates, by default None.
+ exog : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
+ The exog, by default None.
offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The offsets, by default None.
offsets_formula : str, optional(keyword-only)
@@ -1804,7 +1800,7 @@ class PlnPCAcollection:
take_log_offsets : bool, optional(keyword-only)
Whether to take the logarithm of offsets, by default False.
add_const: bool, optional(keyword-only)
- Whether to add a column of one in the covariates. Defaults to True.
+ Whether to add a column of one in the exog. Defaults to True.
Returns
-------
PlnPCAcollection
@@ -1816,11 +1812,11 @@ class PlnPCAcollection:
self._dict_models = {}
(
self._endog,
- self._covariates,
+ self._exog,
self._offsets,
self.column_endog,
) = _handle_data(
- endog, covariates, offsets, offsets_formula, take_log_offsets, add_const
+ endog, exog, offsets, offsets_formula, take_log_offsets, add_const
)
self._fitted = False
self._init_models(ranks, dict_of_dict_initialization)
@@ -1870,10 +1866,10 @@ class PlnPCAcollection:
:class:`~pyPLNmodels.PlnPCA`
:func:`~pyPLNmodels.PlnPCAcollection.__init__`
"""
- endog, covariates, offsets = _extract_data_from_formula(formula, data)
+ endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
endog,
- covariates=covariates,
+ exog=exog,
offsets=offsets,
offsets_formula=offsets_formula,
ranks=ranks,
@@ -1883,16 +1879,16 @@ class PlnPCAcollection:
)
@property
- def covariates(self) -> torch.Tensor:
+ def exog(self) -> torch.Tensor:
"""
- Property representing the covariates.
+ Property representing the exog.
Returns
-------
torch.Tensor
- The covariates.
+ The exog.
"""
- return self[self.ranks[0]].covariates
+ return self[self.ranks[0]].exog
@property
def endog(self) -> torch.Tensor:
@@ -1982,19 +1978,19 @@ class PlnPCAcollection:
for model in self.values():
model.coef = coef
- @covariates.setter
+ @exog.setter
@_array2tensor
- def covariates(self, covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
+ def exog(self, exog: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
- Setter for the covariates property.
+ Setter for the exog property.
Parameters
----------
- covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame]
- The covariates.
+ exog : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The exog.
"""
for model in self.values():
- model.covariates = covariates
+ model.exog = exog
@property
def offsets(self) -> torch.Tensor:
@@ -2043,7 +2039,7 @@ class PlnPCAcollection:
)
self._dict_models[rank] = PlnPCA(
endog=self._endog,
- covariates=self._covariates,
+ exog=self._exog,
offsets=self._offsets,
rank=rank,
dict_initialization=dict_initialization,
@@ -2068,7 +2064,7 @@ class PlnPCAcollection:
)
self._dict_models[rank] = PlnPCA(
self._endog,
- self._covariates,
+ self._exog,
self._offsets,
ranks,
dict_initialization,
@@ -2117,12 +2113,12 @@ class PlnPCAcollection:
@property
def nb_cov(self) -> int:
"""
- Property representing the number of covariates.
+ Property representing the number of exog.
Returns
-------
int
- The number of covariates.
+ The number of exog.
"""
return self[self.ranks[0]].nb_cov
@@ -2543,7 +2539,7 @@ class PlnPCA(_model):
>>> plnparam = get_simulation_parameters(n_samples =100, dim = 60, nb_cov = 2, rank = 8)
>>> endog = sample_pln(plnparam)
- >>> data = {"endog": endog, "cov": plnparam.covariates, "offsets": plnparam.offsets}
+ >>> data = {"endog": endog, "cov": plnparam.exog, "offsets": plnparam.offsets}
>>> plnpca = PlnPCA.from_formula("endog ~ 0 + cov", data = data, rank = 5)
>>> plnpca.fit()
>>> print(plnpca)
@@ -2580,7 +2576,7 @@ class PlnPCA(_model):
self,
endog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- covariates: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
+ exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets_formula: str = "logsum",
rank: int = 5,
@@ -2591,7 +2587,7 @@ class PlnPCA(_model):
self._rank = rank
super().__init__(
endog=endog,
- covariates=covariates,
+ exog=exog,
offsets=offsets,
offsets_formula=offsets_formula,
dict_initialization=dict_initialization,
@@ -2629,10 +2625,10 @@ class PlnPCA(_model):
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
):
- endog, covariates, offsets = _extract_data_from_formula(formula, data)
+ endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
endog,
- covariates=covariates,
+ exog=exog,
offsets=offsets,
offsets_formula=offsets_formula,
rank=rank,
@@ -2810,8 +2806,8 @@ class PlnPCA(_model):
* (self.latent_sqrt_var**2).unsqueeze(1),
axis=2,
)
- if self.covariates is not None:
- XB = self.covariates @ self.coef
+ if self.exog is not None:
+ XB = self.exog @ self.coef
else:
XB = 0
return torch.exp(
@@ -2865,30 +2861,30 @@ class PlnPCA(_model):
return f"{super()._directory_name}_rank_{self._rank}"
@property
- def covariates(self) -> torch.Tensor:
+ def exog(self) -> torch.Tensor:
"""
- Property representing the covariates.
+ Property representing the exog.
Returns
-------
torch.Tensor
- The covariates tensor.
+ The exog tensor.
"""
- return self._cpu_attribute_or_none("_covariates")
+ return self._cpu_attribute_or_none("_exog")
- @covariates.setter
+ @exog.setter
@_array2tensor
- def covariates(self, covariates: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
+ def exog(self, exog: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
"""
- Setter for the covariates.
+ Setter for the exog.
Parameters
----------
- covariates : Union[torch.Tensor, np.ndarray, pd.DataFrame]
- The covariates tensor.
+ exog : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The exog tensor.
"""
- _check_data_shape(self.endog, covariates, self.offsets)
- self._covariates = covariates
+ _check_data_shape(self.endog, exog, self.offsets)
+ self._exog = exog
print("Setting coef to initialization")
self._smart_init_coef()
@@ -2943,7 +2939,7 @@ class PlnPCA(_model):
super()._smart_init_coef()
if not hasattr(self, "_components"):
self._components = _init_components(
- self._endog, self._covariates, self._coef, self._rank
+ self._endog, self._exog, self._coef, self._rank
)
def _random_init_model_parameters(self):
@@ -2970,7 +2966,7 @@ class PlnPCA(_model):
self._latent_mean = (
_init_latent_mean(
self._endog,
- self._covariates,
+ self._exog,
self._offsets,
self._coef,
self._components,
@@ -3008,7 +3004,7 @@ class PlnPCA(_model):
"""
return elbo_plnpca(
self._endog,
- self._covariates,
+ self._exog,
self._offsets,
self._latent_mean,
self._latent_sqrt_var,
@@ -3198,9 +3194,7 @@ class ZIPln(Pln):
def _smart_init_model_parameters(self):
super()._smart_init_model_parameters()
if not hasattr(self, "_covariance"):
- self._covariance = _init_covariance(
- self._endog, self._covariates, self._coef
- )
+ self._covariance = _init_covariance(self._endog, self._exog, self._coef)
if not hasattr(self, "_coef_inflation"):
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
@@ -3216,7 +3210,7 @@ class ZIPln(Pln):
def compute_elbo(self):
return elbo_zi_pln(
self._endog,
- self._covariates,
+ self._exog,
self._offsets,
self._latent_mean,
self._latent_sqrt_var,
@@ -3232,9 +3226,9 @@ class ZIPln(Pln):
return [self._latent_mean, self._latent_sqrt_var, self._coef_inflation]
def _update_closed_forms(self):
- self._coef = _closed_formula_coef(self._covariates, self._latent_mean)
+ self._coef = _closed_formula_coef(self._exog, self._latent_mean)
self._covariance = _closed_formula_covariance(
- self._covariates,
+ self._exog,
self._latent_mean,
self._latent_sqrt_var,
self._coef,
@@ -3245,7 +3239,7 @@ class ZIPln(Pln):
self._latent_mean,
self._latent_sqrt_var,
self._dirac,
- self._covariates,
+ self._exog,
self._coef_inflation,
)
diff --git a/pyPLNmodels/oaks.py b/pyPLNmodels/oaks.py
index c2e126ba..bfdbad97 100644
--- a/pyPLNmodels/oaks.py
+++ b/pyPLNmodels/oaks.py
@@ -12,7 +12,7 @@ def load_oaks():
found in each sample, which depend on the technology used for either
bacteria (16S) or fungi (ITS1).
- For each sample, 3 additional covariates (tree, dist2ground, orientation) are known.
+ For each sample, 3 additional exog (tree, dist2ground, orientation) are known.
The data is provided as dictionary with the following keys
endog a 114 x 116 np.array of integer (endog)
@@ -32,17 +32,15 @@ def load_oaks():
"""
endog_stream = pkg_resources.resource_stream(__name__, "data/oaks/counts.csv")
offsets_stream = pkg_resources.resource_stream(__name__, "data/oaks/offsets.csv")
- covariates_stream = pkg_resources.resource_stream(
- __name__, "data/oaks/covariates.csv"
- )
+ exog_stream = pkg_resources.resource_stream(__name__, "data/oaks/covariates.csv")
endog = pd.read_csv(endog_stream)
offsets = pd.read_csv(offsets_stream)
- covariates = pd.read_csv(covariates_stream)
+ exog = pd.read_csv(exog_stream)
oaks = {
"endog": endog.to_numpy(),
"offsets": offsets.to_numpy(),
- "tree": covariates.tree.to_numpy(),
- "dist2ground": covariates.distTOground.to_numpy(),
- "orientation": covariates.orientation.to_numpy(),
+ "tree": exog.tree.to_numpy(),
+ "dist2ground": exog.distTOground.to_numpy(),
+ "orientation": exog.orientation.to_numpy(),
}
return oaks
diff --git a/tests/conftest.py b/tests/conftest.py
index e7a23ddd..3a072f20 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -26,11 +26,11 @@ from tests.import_data import (
endog_sim_0cov = data_sim_0cov["endog"]
-covariates_sim_0cov = data_sim_0cov["covariates"]
+exog_sim_0cov = data_sim_0cov["exog"]
offsets_sim_0cov = data_sim_0cov["offsets"]
endog_sim_2cov = data_sim_2cov["endog"]
-covariates_sim_2cov = data_sim_2cov["covariates"]
+exog_sim_2cov = data_sim_2cov["exog"]
offsets_sim_2cov = data_sim_2cov["offsets"]
endog_real = data_real["endog"]
@@ -120,7 +120,7 @@ def simulated_pln_0cov_array(request):
cls = request.param
pln = cls(
endog_sim_0cov,
- covariates=covariates_sim_0cov,
+ exog=exog_sim_0cov,
offsets=offsets_sim_0cov,
add_const=False,
)
@@ -133,7 +133,7 @@ def simulated_fitted_pln_0cov_array(request):
cls = request.param
pln = cls(
endog_sim_0cov,
- covariates=covariates_sim_0cov,
+ exog=exog_sim_0cov,
offsets=offsets_sim_0cov,
add_const=False,
)
@@ -173,7 +173,7 @@ def simulated_loaded_pln_0cov_array(simulated_fitted_pln_0cov_array):
return generate_new_model(
simulated_fitted_pln_0cov_array,
endog_sim_0cov,
- covariates=covariates_sim_0cov,
+ exog=exog_sim_0cov,
offsets=offsets_sim_0cov,
add_const=False,
)
@@ -218,7 +218,7 @@ def simulated_pln_2cov_array(request):
cls = request.param
pln_full = cls(
endog_sim_2cov,
- covariates=covariates_sim_2cov,
+ exog=exog_sim_2cov,
offsets=offsets_sim_2cov,
add_const=False,
)
@@ -235,7 +235,7 @@ def simulated_fitted_pln_2cov_array(simulated_pln_2cov_array):
@cache
def simulated_pln_2cov_formula(request):
cls = request.param
- pln_full = cls("endog ~ 0 + covariates", data_sim_2cov)
+ pln_full = cls("endog ~ 0 + exog", data_sim_2cov)
return pln_full
@@ -250,7 +250,7 @@ def simulated_loaded_pln_2cov_formula(simulated_fitted_pln_2cov_formula):
simulated_fitted_pln_2cov_formula.save()
return generate_new_model(
simulated_fitted_pln_2cov_formula,
- "endog ~0 + covariates",
+ "endog ~0 + exog",
data_sim_2cov,
)
@@ -261,7 +261,7 @@ def simulated_loaded_pln_2cov_array(simulated_fitted_pln_2cov_array):
return generate_new_model(
simulated_fitted_pln_2cov_array,
endog_sim_2cov,
- covariates=covariates_sim_2cov,
+ exog=exog_sim_2cov,
offsets=offsets_sim_2cov,
add_const=False,
)
diff --git a/tests/import_data.py b/tests/import_data.py
index c44353bb..9ef5ef7e 100644
--- a/tests/import_data.py
+++ b/tests/import_data.py
@@ -8,14 +8,14 @@ from pyPLNmodels import (
(
endog_sim_0cov,
- covariates_sim_0cov,
+ exog_sim_0cov,
offsets_sim_0cov,
true_covariance_0cov,
true_coef_0cov,
) = get_simulated_count_data(return_true_param=True, nb_cov=0, add_const=False)
(
endog_sim_2cov,
- covariates_sim_2cov,
+ exog_sim_2cov,
offsets_sim_2cov,
true_covariance_2cov,
true_coef_2cov,
@@ -23,7 +23,7 @@ from pyPLNmodels import (
data_sim_0cov = {
"endog": endog_sim_0cov,
- "covariates": covariates_sim_0cov,
+ "exog": exog_sim_0cov,
"offsets": offsets_sim_0cov,
}
true_sim_0cov = {"Sigma": true_covariance_0cov, "beta": true_coef_0cov}
@@ -32,7 +32,7 @@ true_sim_2cov = {"Sigma": true_covariance_2cov, "beta": true_coef_2cov}
data_sim_2cov = {
"endog": endog_sim_2cov,
- "covariates": covariates_sim_2cov,
+ "exog": exog_sim_2cov,
"offsets": offsets_sim_2cov,
}
endog_real, labels_real = get_real_count_data(return_labels=True, n_samples=100, dim=50)
diff --git a/tests/test_common.py b/tests/test_common.py
index 5904857d..b1a6837c 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -98,7 +98,7 @@ def test__print_end_of_fitting_message(instance):
@pytest.mark.parametrize("pln", dict_fixtures["fitted_pln"])
@filter_models(["Pln", "PlnPCA"])
-def test_fail_wrong_covariates_prediction(pln):
+def test_fail_wrong_exog_prediction(pln):
X = torch.randn(pln.n_samples, pln.nb_cov + 1)
with pytest.raises(Exception):
pln.predict(X)
diff --git a/tests/test_setters.py b/tests/test_setters.py
index 6814e842..828989e8 100644
--- a/tests/test_setters.py
+++ b/tests/test_setters.py
@@ -9,7 +9,7 @@ from tests.utils import MSE, filter_models
@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
def test_data_setter_with_torch(pln):
pln.endog = pln.endog
- pln.covariates = pln.covariates
+ pln.exog = pln.exog
pln.offsets = pln.offsets
pln.fit()
@@ -28,13 +28,13 @@ def test_parameters_setter_with_torch(pln):
@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
def test_data_setter_with_numpy(pln):
np_endog = pln.endog.numpy()
- if pln.covariates is not None:
- np_covariates = pln.covariates.numpy()
+ if pln.exog is not None:
+ np_exog = pln.exog.numpy()
else:
- np_covariates = None
+ np_exog = None
np_offsets = pln.offsets.numpy()
pln.endog = np_endog
- pln.covariates = np_covariates
+ pln.exog = np_exog
pln.offsets = np_offsets
pln.fit()
@@ -59,13 +59,13 @@ def test_parameters_setter_with_numpy(pln):
@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
def test_data_setter_with_pandas(pln):
pd_endog = pd.DataFrame(pln.endog.numpy())
- if pln.covariates is not None:
- pd_covariates = pd.DataFrame(pln.covariates.numpy())
+ if pln.exog is not None:
+ pd_exog = pd.DataFrame(pln.exog.numpy())
else:
- pd_covariates = None
+ pd_exog = None
pd_offsets = pd.DataFrame(pln.offsets.numpy())
pln.endog = pd_endog
- pln.covariates = pd_covariates
+ pln.exog = pd_exog
pln.offsets = pd_offsets
pln.fit()
@@ -93,17 +93,17 @@ def test_fail_data_setter_with_torch(pln):
pln.endog = pln.endog - 100
n, p = pln.endog.shape
- if pln.covariates is None:
+ if pln.exog is None:
d = 0
else:
- d = pln.covariates.shape[-1]
+ d = pln.exog.shape[-1]
with pytest.raises(ValueError):
pln.endog = torch.zeros(n + 1, p)
with pytest.raises(ValueError):
pln.endog = torch.zeros(n, p + 1)
with pytest.raises(ValueError):
- pln.covariates = torch.zeros(n + 1, d)
+ pln.exog = torch.zeros(n + 1, d)
with pytest.raises(ValueError):
pln.offsets = torch.zeros(n + 1, p)
@@ -137,10 +137,10 @@ def test_fail_parameters_setter_with_torch(pln):
with pytest.raises(ValueError):
pln.components = torch.zeros(dim + 1, dim_latent)
- if pln.covariates is None:
+ if pln.exog is None:
d = 0
else:
- d = pln.covariates.shape[-1]
+ d = pln.exog.shape[-1]
with pytest.raises(ValueError):
pln.coef = torch.zeros(d + 1, dim)
--
GitLab
From 9840eda7cd521d35f1fcafd6116a64ecf034fede Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 17:10:19 +0200
Subject: [PATCH 044/167] began to add tests for the readme file.
---
..._files.py => create_docstrings_example.py} | 34 ++++++++++++-------
tests/create_readme_examples.py | 2 ++
tests/test_docstrings_example.sh | 5 +++
...st_examples.sh => test_readme_examples.sh} | 2 +-
4 files changed, 29 insertions(+), 14 deletions(-)
rename tests/{create_example_files.py => create_docstrings_example.py} (50%)
create mode 100644 tests/create_readme_examples.py
create mode 100755 tests/test_docstrings_example.sh
rename tests/{test_examples.sh => test_readme_examples.sh} (65%)
mode change 100755 => 100644
diff --git a/tests/create_example_files.py b/tests/create_docstrings_example.py
similarity index 50%
rename from tests/create_example_files.py
rename to tests/create_docstrings_example.py
index b575159f..6ebf37d7 100644
--- a/tests/create_example_files.py
+++ b/tests/create_docstrings_example.py
@@ -2,13 +2,13 @@ import ast
import os
-def get_lines(filename):
- with open(f"../pyPLNmodels/{filename}.py") as file:
+def get_lines(filename, filetype=".py"):
+ with open(f"../pyPLNmodels/{filename}{filetype}") as file:
lines = [line.rstrip() for line in file]
return lines
-def get_examples(lines):
+def get_examples_docstring(lines):
examples = []
in_example = False
example = []
@@ -26,11 +26,11 @@ def get_examples(lines):
return examples
-def write_examples(examples, prefix_filename):
+def write_examples(examples, filename, dirname):
for i in range(len(examples)):
example = examples[i]
nb_example = str(i + 1)
- example_filename = f"examples/{prefix_filename}_example_{nb_example}.py"
+ example_filename = f"{dirname}/{prefix_filename}_example_{nb_example}.py"
try:
os.remove(example_filename)
except FileNotFoundError:
@@ -40,15 +40,23 @@ def write_examples(examples, prefix_filename):
the_file.write(line + "\n")
-def filename_to_example_file(filename):
+def filename_to_docstring_example_file(filename, dirname):
lines = get_lines(filename)
- examples = get_examples(lines)
- write_examples(examples, filename)
+ examples = get_examples_docstring(lines)
+ write_examples(examples, filename, dirname)
+
+
+def filename_to_readme_example_file(dirname):
+ lines = get_lines("README", filetype=".md")
+ examples = get_examples_docstring(lines)
+ write_examples(examples, "readme")
# filename_to_example_file("models")
-os.makedirs("examples", exist_ok=True)
-filename_to_example_file("_utils")
-filename_to_example_file("models")
-filename_to_example_file("elbos")
-filename_to_example_file("load")
+os.makedirs("docstrings_examples", exist_ok=True)
+filename_to_docstring_example_file("_utils", "docstrings")
+filename_to_docstring_example_file("models", "docstrings")
+filename_to_docstring_example_file("elbos", "docstrings")
+filename_to_docstring_example_file("load", "docstrings")
+
+filename_to_readme_example_file("docstrings")
diff --git a/tests/create_readme_examples.py b/tests/create_readme_examples.py
new file mode 100644
index 00000000..c27d00ef
--- /dev/null
+++ b/tests/create_readme_examples.py
@@ -0,0 +1,2 @@
+import ast
+import os
diff --git a/tests/test_docstrings_example.sh b/tests/test_docstrings_example.sh
new file mode 100755
index 00000000..a11209ac
--- /dev/null
+++ b/tests/test_docstrings_example.sh
@@ -0,0 +1,5 @@
+search_dir="docstrings_examples"
+for entry in "$search_dir"/*
+do
+ python "$entry"
+done
diff --git a/tests/test_examples.sh b/tests/test_readme_examples.sh
old mode 100755
new mode 100644
similarity index 65%
rename from tests/test_examples.sh
rename to tests/test_readme_examples.sh
index b8a32f31..4af8b766
--- a/tests/test_examples.sh
+++ b/tests/test_readme_examples.sh
@@ -1,4 +1,4 @@
-search_dir="examples"
+search_dir="readme_examples"
for entry in "$search_dir"/*
do
python "$entry"
--
GitLab
From 7cdfe9b687e3e4ae07386ac47a26b9e800d6f9b4 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 20:01:19 +0200
Subject: [PATCH 045/167] rewrite how we create the tests for docstrings (and
added for the readme). Now they will al be performed by pytest.
---
.gitignore | 3 +-
... create_readme_and_docstrings_examples.py} | 36 ++++++++++---------
tests/create_readme_examples.py | 2 --
tests/test_docstrings_example.sh | 5 ---
tests/test_readme_examples.sh | 5 ---
5 files changed, 22 insertions(+), 29 deletions(-)
rename tests/{create_docstrings_example.py => create_readme_and_docstrings_examples.py} (56%)
delete mode 100644 tests/create_readme_examples.py
delete mode 100755 tests/test_docstrings_example.sh
delete mode 100644 tests/test_readme_examples.sh
diff --git a/.gitignore b/.gitignore
index fb7059f0..aaf6fd98 100644
--- a/.gitignore
+++ b/.gitignore
@@ -153,4 +153,5 @@ tests/Pln*
slides/
index.html
-tests/examples/*.py
+tests/test_models*
+tests/test_load*
diff --git a/tests/create_docstrings_example.py b/tests/create_readme_and_docstrings_examples.py
similarity index 56%
rename from tests/create_docstrings_example.py
rename to tests/create_readme_and_docstrings_examples.py
index 6ebf37d7..a1a8a20a 100644
--- a/tests/create_docstrings_example.py
+++ b/tests/create_readme_and_docstrings_examples.py
@@ -2,8 +2,12 @@ import ast
import os
-def get_lines(filename, filetype=".py"):
- with open(f"../pyPLNmodels/{filename}{filetype}") as file:
+dir_docstrings = "docstrings_examples"
+dir_readme = "readme_examples"
+
+
+def get_lines(path_to_file, filename, filetype=".py"):
+ with open(f"{path_to_file}{filename}{filetype}") as file:
lines = [line.rstrip() for line in file]
return lines
@@ -26,11 +30,11 @@ def get_examples_docstring(lines):
return examples
-def write_examples(examples, filename, dirname):
+def write_examples(examples, filename):
for i in range(len(examples)):
example = examples[i]
nb_example = str(i + 1)
- example_filename = f"{dirname}/{prefix_filename}_example_{nb_example}.py"
+ example_filename = f"test_{filename}_example_{nb_example}.py"
try:
os.remove(example_filename)
except FileNotFoundError:
@@ -41,22 +45,22 @@ def write_examples(examples, filename, dirname):
def filename_to_docstring_example_file(filename, dirname):
- lines = get_lines(filename)
+ lines = get_lines("../pyPLNmodels/", filename)
examples = get_examples_docstring(lines)
- write_examples(examples, filename, dirname)
+ write_examples(examples, filename)
-def filename_to_readme_example_file(dirname):
- lines = get_lines("README", filetype=".md")
- examples = get_examples_docstring(lines)
+def filename_to_readme_example_file():
+ lines = get_lines("../", "README", filetype=".md")
+ examples = get_examples_readme(lines)
write_examples(examples, "readme")
-# filename_to_example_file("models")
-os.makedirs("docstrings_examples", exist_ok=True)
-filename_to_docstring_example_file("_utils", "docstrings")
-filename_to_docstring_example_file("models", "docstrings")
-filename_to_docstring_example_file("elbos", "docstrings")
-filename_to_docstring_example_file("load", "docstrings")
+# os.makedirs(dir_readme, exist_ok=True)
+# filename_to_readme_example_file(dir_readme)
-filename_to_readme_example_file("docstrings")
+os.makedirs("docstrings_examples", exist_ok=True)
+filename_to_docstring_example_file("_utils", dir_docstrings)
+filename_to_docstring_example_file("models", dir_docstrings)
+filename_to_docstring_example_file("elbos", dir_docstrings)
+filename_to_docstring_example_file("load", dir_docstrings)
diff --git a/tests/create_readme_examples.py b/tests/create_readme_examples.py
deleted file mode 100644
index c27d00ef..00000000
--- a/tests/create_readme_examples.py
+++ /dev/null
@@ -1,2 +0,0 @@
-import ast
-import os
diff --git a/tests/test_docstrings_example.sh b/tests/test_docstrings_example.sh
deleted file mode 100755
index a11209ac..00000000
--- a/tests/test_docstrings_example.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-search_dir="docstrings_examples"
-for entry in "$search_dir"/*
-do
- python "$entry"
-done
diff --git a/tests/test_readme_examples.sh b/tests/test_readme_examples.sh
deleted file mode 100644
index 4af8b766..00000000
--- a/tests/test_readme_examples.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-search_dir="readme_examples"
-for entry in "$search_dir"/*
-do
- python "$entry"
-done
--
GitLab
From 5aac26014e342bc1116b1674c05fcadf47473593 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 20:03:44 +0200
Subject: [PATCH 046/167] renaming.
---
...cstrings_examples.py => create_readme_and_docstrings_tests.py} | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename tests/{create_readme_and_docstrings_examples.py => create_readme_and_docstrings_tests.py} (100%)
diff --git a/tests/create_readme_and_docstrings_examples.py b/tests/create_readme_and_docstrings_tests.py
similarity index 100%
rename from tests/create_readme_and_docstrings_examples.py
rename to tests/create_readme_and_docstrings_tests.py
--
GitLab
From 7207ee4857f8a92ae7546a04410d3221eeb9f380 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 20:04:00 +0200
Subject: [PATCH 047/167] rewrite the ci with appropriate names and remove the
examples job.
---
.gitlab-ci.yml | 44 +++++++++++++++++---------------------------
1 file changed, 17 insertions(+), 27 deletions(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 2425e78f..13a9c1dc 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -4,35 +4,25 @@ stages:
- build
- publish
-#
-# black:
-# stage: checks
-# image: registry.gitlab.com/pipeline-components/black:latest
-# script:
-# - black --check --verbose -- .
-# tags:
-# - docker
-# tests:
-# stage: checks
-# image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
-# before_script:
-# pip install '.[tests]'
-# script:
-# - pip install .
-# - cd tests
-# - pytest
-# examples:
-# stage: checks
-# image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
-# before_script:
-# pip install '.[tests]'
-# script:
-# - pip install .
-# - cd tests
-# - python create_example_files.py
-# - ./test_examples.sh
+black:
+ stage: checks
+ image: registry.gitlab.com/pipeline-components/black:latest
+ script:
+ - black --check --verbose -- .
+ tags:
+ - docker
+tests:
+ stage: checks
+ image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
+ before_script:
+ pip install '.[tests]'
+ script:
+ - pip install .
+ - cd tests
+ - python create_readme_and_docstrings_test.py
+ - pytest .
build_package:
--
GitLab
From e30fe36c291ab9a7f26c19573201a914f789248d Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 20:21:53 +0200
Subject: [PATCH 048/167] create the right examples. Does not take the pip
install pyPLNmodels
---
tests/create_readme_and_docstrings_tests.py | 24 +++++++++++++++++++--
1 file changed, 22 insertions(+), 2 deletions(-)
diff --git a/tests/create_readme_and_docstrings_tests.py b/tests/create_readme_and_docstrings_tests.py
index a1a8a20a..56e6cdf7 100644
--- a/tests/create_readme_and_docstrings_tests.py
+++ b/tests/create_readme_and_docstrings_tests.py
@@ -30,6 +30,26 @@ def get_examples_docstring(lines):
return examples
+def get_examples_readme(lines):
+ examples = []
+ example = []
+ in_example = False
+ for line in lines:
+ line = line.lstrip()
+ if len(line) > 2:
+ if line[0:3] == "```":
+ if in_example is False:
+ in_example = True
+ else:
+ in_example = False
+ examples.append(example)
+ example = []
+ elif in_example is True:
+ example.append(line)
+ examples.pop(0) # The first is pip install pyPLNmodels which is not python code.
+ return examples
+
+
def write_examples(examples, filename):
for i in range(len(examples)):
example = examples[i]
@@ -56,8 +76,8 @@ def filename_to_readme_example_file():
write_examples(examples, "readme")
-# os.makedirs(dir_readme, exist_ok=True)
-# filename_to_readme_example_file(dir_readme)
+os.makedirs(dir_readme, exist_ok=True)
+filename_to_readme_example_file()
os.makedirs("docstrings_examples", exist_ok=True)
filename_to_docstring_example_file("_utils", dir_docstrings)
--
GitLab
From e7760d71752ea9cdf97245f0e67b14b48467a27a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 20:22:43 +0200
Subject: [PATCH 049/167] add tests/test_readme in .gitignore since they are
auto-generated.
---
.gitignore | 1 +
1 file changed, 1 insertion(+)
diff --git a/.gitignore b/.gitignore
index aaf6fd98..a0185fcb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -155,3 +155,4 @@ index.html
tests/test_models*
tests/test_load*
+tests/test_readme*
--
GitLab
From aa89da92184b1134d3f326b1c4b23351f4279ea4 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 20:25:27 +0200
Subject: [PATCH 050/167] wrong ci.
---
.gitlab-ci.yml | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 13a9c1dc..9da1a847 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -49,8 +49,8 @@ publish_package:
- TWINE_PASSWORD=${pypln_token} TWINE_USERNAME=__token__ python -m twine upload dist/*
tags:
- docker
- # only:
- # - tags
+ only:
+ - tags
pages:
stage: publish
--
GitLab
From a8dd8d31f1b6b91f4733eac7fc4ff391915a04d5 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 20:26:24 +0200
Subject: [PATCH 051/167] forgot s in tests
---
.gitlab-ci.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 9da1a847..d8c20b0f 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -21,7 +21,7 @@ tests:
script:
- pip install .
- cd tests
- - python create_readme_and_docstrings_test.py
+ - python create_readme_and_docstrings_tests.py
- pytest .
--
GitLab
From 756015dae220bd92c36354b0c15175cb4b332ba5 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 20:46:22 +0200
Subject: [PATCH 052/167] rewrite counts for oaks instead of endog
---
pyPLNmodels/oaks.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/pyPLNmodels/oaks.py b/pyPLNmodels/oaks.py
index bfdbad97..ab59a3fd 100644
--- a/pyPLNmodels/oaks.py
+++ b/pyPLNmodels/oaks.py
@@ -37,7 +37,7 @@ def load_oaks():
offsets = pd.read_csv(offsets_stream)
exog = pd.read_csv(exog_stream)
oaks = {
- "endog": endog.to_numpy(),
+ "counts": endog.to_numpy(),
"offsets": offsets.to_numpy(),
"tree": exog.tree.to_numpy(),
"dist2ground": exog.distTOground.to_numpy(),
--
GitLab
From fa95906b69d8b06714701b4d995cabfd26e47169 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 20:46:40 +0200
Subject: [PATCH 053/167] fix bugs since it was output more than one file but
it is not a bunch of cells.
---
tests/create_readme_and_docstrings_tests.py | 11 ++++-------
1 file changed, 4 insertions(+), 7 deletions(-)
diff --git a/tests/create_readme_and_docstrings_tests.py b/tests/create_readme_and_docstrings_tests.py
index 56e6cdf7..d9f27aeb 100644
--- a/tests/create_readme_and_docstrings_tests.py
+++ b/tests/create_readme_and_docstrings_tests.py
@@ -30,8 +30,7 @@ def get_examples_docstring(lines):
return examples
-def get_examples_readme(lines):
- examples = []
+def get_example_readme(lines):
example = []
in_example = False
for line in lines:
@@ -42,12 +41,10 @@ def get_examples_readme(lines):
in_example = True
else:
in_example = False
- examples.append(example)
- example = []
elif in_example is True:
example.append(line)
- examples.pop(0) # The first is pip install pyPLNmodels which is not python code.
- return examples
+ example.pop(0) # The first is pip install pyPLNmodels which is not python code.
+ return [example]
def write_examples(examples, filename):
@@ -72,7 +69,7 @@ def filename_to_docstring_example_file(filename, dirname):
def filename_to_readme_example_file():
lines = get_lines("../", "README", filetype=".md")
- examples = get_examples_readme(lines)
+ examples = get_example_readme(lines)
write_examples(examples, "readme")
--
GitLab
From c5f70d4d084a72c4fecfeba06586a20a8d5944de Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 23:10:37 +0200
Subject: [PATCH 054/167] add the notebook in the Getting started. Should be
automated but it needs an image of jupyter notebook and a little
preprocessing to remove the pip install line.
---
Getting_started.py | 139 +++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 139 insertions(+)
create mode 100644 Getting_started.py
diff --git a/Getting_started.py b/Getting_started.py
new file mode 100644
index 00000000..4fcc9552
--- /dev/null
+++ b/Getting_started.py
@@ -0,0 +1,139 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+
+# ## pyPLNmodels
+
+# We assume the data comes from a PLN model: $ \text{counts} \sim \mathcal P(\exp(\text{Z}))$, where $Z$ are some unknown latent variables.
+#
+#
+# The goal of the package is to retrieve the latent variables $Z$ given the counts. To do so, one can instantiate a Pln or PlnPCA model, fit it and then extract the latent variables.
+
+# ### Import the needed functions
+
+from pyPLNmodels import (
+ get_real_count_data,
+ get_simulated_count_data,
+ load_model,
+ Pln,
+ PlnPCA,
+ PlnPCAcollection,
+)
+import matplotlib.pyplot as plt
+
+
+# ### Load the data
+
+counts, labels = get_real_count_data(return_labels=True) # np.ndarray
+
+
+# ### PLN model
+
+pln = Pln(counts, add_const=True)
+pln.fit()
+
+
+print(pln)
+
+
+# #### Once fitted, we can extract multiple variables:
+
+gaussian = pln.latent_variables
+print(gaussian.shape)
+
+
+model_param = pln.model_parameters
+print(model_param["coef"].shape)
+print(model_param["covariance"].shape)
+
+
+# ### PlnPCA model
+
+pca = PlnPCA(counts, add_const=True, rank=5)
+pca.fit()
+
+
+print(pca)
+
+
+print(pca.latent_variables.shape)
+
+
+print(pca.model_parameters["components"].shape)
+print(pca.model_parameters["coef"].shape)
+
+
+# ### One can save the model in order to load it back after:
+
+pca.save()
+dict_init = load_model("PlnPCA_nbcov_1_rank_5")
+loaded_pca = PlnPCA(counts, add_const=True, dict_initialization=dict_init)
+print(loaded_pca)
+
+
+# ### One can fit multiple PCA and choose the best rank with BIC or AIC criterion
+
+pca_col = PlnPCAcollection(counts, add_const=True, ranks=[5, 15, 25, 40, 50])
+pca_col.fit()
+
+
+pca_col.show()
+
+
+print(pca_col)
+
+
+# ### One can extract the best model found (according to AIC or BIC criterion).
+
+# #### AIC best model
+
+print(pca_col.best_model(criterion="AIC"))
+
+
+# #### BIC best model
+
+print(pca_col.best_model(criterion="BIC"))
+
+
+# #### Visualization of the individuals (sites) with PCA on the latent variables.
+
+pln.viz(colors=labels)
+plt.show()
+
+
+best_pca = pca_col.best_model()
+best_pca.viz(colors=labels)
+plt.show()
+
+
+# ### What would give a PCA on the log normalize data ?
+
+from sklearn.decomposition import PCA
+import numpy as np
+import seaborn as sns
+
+
+sk_pca = PCA(n_components=2)
+pca_log_counts = sk_pca.fit_transform(np.log(counts + (counts == 0)))
+sns.scatterplot(x=pca_log_counts[:, 0], y=pca_log_counts[:, 1], hue=labels)
+
+
+# ### Visualization of the variables
+
+pln.plot_pca_correlation_graph(["var_1", "var_2"], indices_of_variables=[0, 1])
+plt.show()
+
+
+best_pca.plot_pca_correlation_graph(["var_1", "var_2"], indices_of_variables=[0, 1])
+plt.show()
+
+
+# ### Visualization of each components of the PCA
+#
+
+pln.scatter_pca_matrix(color=labels, n_components=5)
+plt.show()
+
+
+best_pca.scatter_pca_matrix(color=labels, n_components=6)
+plt.show()
--
GitLab
From 9ae89155e9fd150ebcc6b7f85245e6904806d743 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 23:11:57 +0200
Subject: [PATCH 055/167] renaming
---
Getting_started.py => tests/test_getting_started.py | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename Getting_started.py => tests/test_getting_started.py (100%)
diff --git a/Getting_started.py b/tests/test_getting_started.py
similarity index 100%
rename from Getting_started.py
rename to tests/test_getting_started.py
--
GitLab
From 3b09353ef2753b485ed29b036c59a9be0f18543a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 23:19:24 +0200
Subject: [PATCH 056/167] fix bugs in tests.
---
pyPLNmodels/models.py | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index db42daeb..492c9106 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -1460,7 +1460,7 @@ class Pln(_model):
""",
)
def pca_projected_latent_variables(self, n_components: Optional[int] = None):
- super().pca_projected_latent_variables(n_components=n_components)
+ return super().pca_projected_latent_variables(n_components=n_components)
@_add_doc(
_model,
@@ -1759,7 +1759,7 @@ class PlnPCAcollection:
>>> plnpcas = PlnPCAcollection.from_formula("endog ~ 0 + cov", data = data, ranks = [5,8,12])
>>> plnpcas.fit()
>>> print(plnpcas)
- >>> pcas.show()
+ >>> plnpcas.show()
See also
--------
:class:`~pyPLNmodels.PlnPCA`
@@ -2671,7 +2671,7 @@ class PlnPCA(_model):
>>> import matplotlib.pyplot as plt
>>> from pyPLNmodels import PlnPCA, get_real_count_data
>>> endog, labels = get_real_count_data(return_labels = True)
- >>> plnpca = Pln(endog,add_const = True)
+ >>> plnpca = PlnPCA(endog,add_const = True)
>>> plnpca.fit()
>>> plnpca.plot_expected_vs_true()
>>> plt.show()
@@ -2714,7 +2714,7 @@ class PlnPCA(_model):
""",
)
def pca_projected_latent_variables(self, n_components: Optional[int] = None):
- super().pca_projected_latent_variables(n_components=n_components)
+ return super().pca_projected_latent_variables(n_components=n_components)
@_add_doc(
_model,
--
GitLab
From ef7903ff8c9d2342ca107f5f1dea450ac3ca334b Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 29 Jun 2023 23:47:41 +0200
Subject: [PATCH 057/167] fix notebook
---
Getting_started.ipynb | 5812 +++++++++++++++++----------------
pyPLNmodels/load.py | 7 +-
tests/test_getting_started.py | 4 +-
3 files changed, 2919 insertions(+), 2904 deletions(-)
diff --git a/Getting_started.ipynb b/Getting_started.ipynb
index 28c863c3..f5f30e8d 100644
--- a/Getting_started.ipynb
+++ b/Getting_started.ipynb
@@ -74,7 +74,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"id": "5a8e0140",
"metadata": {},
"outputs": [],
@@ -93,7 +93,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"id": "cee623e0",
"metadata": {},
"outputs": [
@@ -119,7 +119,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"id": "ef9f6c70",
"metadata": {},
"outputs": [
@@ -127,7 +127,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Setting the offsets as the log of the sum of counts\n",
+ "Setting the offsets as the log of the sum of endog\n",
"Fitting a Pln model with full covariance model.\n",
"Initialization ...\n",
"Initialization finished\n",
@@ -142,7 +142,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"id": "ac28955b",
"metadata": {},
"outputs": [
@@ -158,7 +158,7 @@
"* Useful properties\n",
" .latent_variables, .model_parameters, .latent_parameters, .optim_parameters\n",
"* Useful methods\n",
- " .show(), .coef() .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix()\n",
+ " .show(), .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix(), .plot_expected_vs_true()\n",
"* Additional properties for Pln\n",
" None\n",
"* Additional methods for Pln\n",
@@ -180,7 +180,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"id": "182e24a2",
"metadata": {},
"outputs": [
@@ -199,7 +199,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"id": "b8e7b983",
"metadata": {},
"outputs": [
@@ -228,7 +228,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"id": "0a4fe7a4",
"metadata": {
"scrolled": true
@@ -238,12 +238,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Setting the offsets as the log of the sum of counts\n",
+ "Setting the offsets as the log of the sum of endog\n",
"----------------------------------------------------------------------\n",
"Fitting a PlnPCAcollection model with 5 components\n",
"Initialization ...\n",
"Initialization finished\n",
- "Tolerance 0.001 reached in 2902 iterations\n"
+ "Tolerance 0.001 reached in 4204 iterations\n"
]
}
],
@@ -254,7 +254,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"id": "93db535a",
"metadata": {},
"outputs": [
@@ -265,12 +265,12 @@
"A multivariate Poisson Lognormal with 5 principal component. \n",
"======================================================================\n",
" Loglike Dimension Nb param BIC AIC\n",
- " -264802.03 200 1190 268461 265992\n",
+ " -264724.62 200 1190 268384 265914\n",
"======================================================================\n",
"* Useful properties\n",
" .latent_variables, .model_parameters, .latent_parameters, .optim_parameters\n",
"* Useful methods\n",
- " .show(), .coef() .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix()\n",
+ " .show(), .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix(), .plot_expected_vs_true()\n",
"* Additional properties for PlnPCA\n",
" .projected_latent_variables\n",
"* Additional methods for PlnPCA\n",
@@ -284,7 +284,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"id": "c8321820",
"metadata": {},
"outputs": [
@@ -302,7 +302,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"id": "b3e7db70",
"metadata": {},
"outputs": [
@@ -330,7 +330,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 13,
"id": "f81ccaf7",
"metadata": {},
"outputs": [
@@ -338,16 +338,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Setting the offsets as the log of the sum of counts\n",
+ "Setting the offsets as the log of the sum of endog\n",
"A multivariate Poisson Lognormal with 5 principal component. \n",
"======================================================================\n",
" Loglike Dimension Nb param BIC AIC\n",
- " -264800.09 200 1190 268459 265990\n",
+ " -264723.8 200 1190 268383 265913\n",
"======================================================================\n",
"* Useful properties\n",
" .latent_variables, .model_parameters, .latent_parameters, .optim_parameters\n",
"* Useful methods\n",
- " .show(), .coef() .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix()\n",
+ " .show(), .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix(), .plot_expected_vs_true()\n",
"* Additional properties for PlnPCA\n",
" .projected_latent_variables\n",
"* Additional methods for PlnPCA\n",
@@ -357,7 +357,7 @@
],
"source": [
"pca.save()\n",
- "dict_init = load_model(\"PlnPCA_nbcov_1_rank_5\")\n",
+ "dict_init = load_model(\"PlnPCA_nbcov_1_dim_200_rank_5\")\n",
"loaded_pca = PlnPCA(counts, add_const = True, dict_initialization= dict_init)\n",
"print(loaded_pca)"
]
@@ -372,7 +372,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 14,
"id": "084290d5",
"metadata": {},
"outputs": [
@@ -380,36 +380,36 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Setting the offsets as the log of the sum of counts\n",
+ "Setting the offsets as the log of the sum of endog\n",
"----------------------------------------------------------------------\n",
"Fitting a PlnPCAcollection model with 5 components\n",
"Initialization ...\n",
"Initialization finished\n",
- "Tolerance 0.001 reached in 2871 iterations\n",
+ "Tolerance 0.001 reached in 2849 iterations\n",
"----------------------------------------------------------------------\n",
"Fitting a PlnPCAcollection model with 15 components\n",
"Initialization ...\n",
"Initialization finished\n",
- "Tolerance 0.001 reached in 6305 iterations\n",
+ "Tolerance 0.001 reached in 6459 iterations\n",
"----------------------------------------------------------------------\n",
"Fitting a PlnPCAcollection model with 25 components\n",
"Initialization ...\n",
"Initialization finished\n",
- "Tolerance 0.001 reached in 5134 iterations\n",
+ "Tolerance 0.001 reached in 5989 iterations\n",
"----------------------------------------------------------------------\n",
"Fitting a PlnPCAcollection model with 40 components\n",
"Initialization ...\n",
"Initialization finished\n",
- "Tolerance 0.001 reached in 4695 iterations\n",
+ "Tolerance 0.001 reached in 3915 iterations\n",
"----------------------------------------------------------------------\n",
"Fitting a PlnPCAcollection model with 50 components\n",
"Initialization ...\n",
"Initialization finished\n",
- "Tolerance 0.001 reached in 1660 iterations\n",
+ "Tolerance 0.001 reached in 1717 iterations\n",
"======================================================================\n",
"\n",
"DONE!\n",
- " Best model(lower BIC): 40\n",
+ " Best model(lower BIC): 25\n",
" \n",
" Best model(lower AIC): 50\n",
" \n",
@@ -425,13 +425,13 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 15,
"id": "65f42831",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -446,7 +446,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 16,
"id": "7d0d778f",
"metadata": {},
"outputs": [
@@ -461,20 +461,20 @@
" - Ranks considered:[5, 15, 25, 40, 50]\n",
" - BIC metric:\n",
" rank 5 15 25 40 50\n",
- " criterion 268485 202026 191816 191814 194678\n",
+ " criterion 268473 201896 191725 191802 194696\n",
"\n",
- " Best model(lower BIC): 40\n",
+ " Best model(lower BIC): 25\n",
" \n",
" - AIC metric:\n",
" rank 5 15 25 40 50\n",
- " criterion 266015 195603 181647 176415 176053\n",
+ " criterion 266004 195472 181556 176403 176070\n",
"\n",
" Best model(lower AIC): 50\n",
"----------------------------------------------------------------------\n",
"* Useful properties\n",
" .BIC, .AIC, .loglikes\n",
"* Useful methods \n",
- " .show(), .best_model()\n",
+ " .show(), .best_model(), .keys(), .items(), .values()\n",
"----------------------------------------------------------------------\n",
"\n"
]
@@ -502,7 +502,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 17,
"id": "a4b86b44",
"metadata": {},
"outputs": [
@@ -513,12 +513,12 @@
"A multivariate Poisson Lognormal with 50 principal component. \n",
"======================================================================\n",
" Loglike Dimension Nb param BIC AIC\n",
- " -167078.03 200 8975 194678 176053\n",
+ " -167095.75 200 8975 194696 176070\n",
"======================================================================\n",
"* Useful properties\n",
" .latent_variables, .model_parameters, .latent_parameters, .optim_parameters\n",
"* Useful methods\n",
- " .show(), .coef() .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix()\n",
+ " .show(), .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix(), .plot_expected_vs_true()\n",
"* Additional properties for PlnPCA\n",
" .projected_latent_variables\n",
"* Additional methods for PlnPCA\n",
@@ -540,7 +540,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 18,
"id": "abace013",
"metadata": {},
"outputs": [
@@ -548,15 +548,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "A multivariate Poisson Lognormal with 40 principal component. \n",
+ "A multivariate Poisson Lognormal with 25 principal component. \n",
"======================================================================\n",
" Loglike Dimension Nb param BIC AIC\n",
- " -168995.39 200 7420 191814 176415\n",
+ " -176656.73 200 4900 191725 181556\n",
"======================================================================\n",
"* Useful properties\n",
" .latent_variables, .model_parameters, .latent_parameters, .optim_parameters\n",
"* Useful methods\n",
- " .show(), .coef() .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix()\n",
+ " .show(), .transform(), .sigma(), .predict(), .pca_projected_latent_variables(), .plot_pca_correlation_graph(), .viz(), .scatter_pca_matrix(), .plot_expected_vs_true()\n",
"* Additional properties for PlnPCA\n",
" .projected_latent_variables\n",
"* Additional methods for PlnPCA\n",
@@ -578,7 +578,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 19,
"id": "8be11bac",
"metadata": {},
"outputs": [
@@ -600,13 +600,13 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 20,
"id": "212f3777",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -631,7 +631,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 21,
"id": "20407247",
"metadata": {},
"outputs": [],
@@ -643,7 +643,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 22,
"id": "29830551",
"metadata": {},
"outputs": [
@@ -653,7 +653,7 @@
"<AxesSubplot: >"
]
},
- "execution_count": 20,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
@@ -684,7 +684,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 23,
"id": "18bde994",
"metadata": {},
"outputs": [
@@ -706,13 +706,13 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 24,
"id": "fda1732e",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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btnleftiwYQr3L168mOdl8/qX+pw4OzujWrVqeZo3JiYGgYGB8vtdu3ZFiRIllNpehw4d5F8nJCTg5s2bSi2vap9ey0zsLJ+6cOGCwv3PXyO5qVWrFpo2bSq/n5aWhmvXruVpWUdHR9SpUydP8zo4OCj8keLFixd5zpgXYryXVUGZ9+SpU6cU7k+cOFHVcfLk/PnzCkcQDBo0SKnl9fX10bp1a/n9K1euyAdWIqLCxwJHRBrN3Nxc4f6nf90uLNevX1e437p1a6UO22zZsqXCB+GbN28iLS0tT8s2adIkz9sp6PKXLl2CIAjy+40bN1Z6exUrVlS4f//+faXX8SURERFYuHAhevfujdq1a6N06dIwNDSERCLJcrt69ap8uXfv3qk8S359/ppS9qLTn/8BIa8FTpn/U319fYW9Ysrs5csLMd7LBWVubo7atWvnad6MjAyF/xcdHR107NixsKLl6vMRPwv63o6Pj8erV68KnIuI8oaXESAijVayZEmF+6r+UJmdz4far1evnlLLGxoaolatWvJzyzKHvK9QocIXl61cubJS2yrI8p+Xre+//x7ff/99gbb/+WFyBfHs2TN8++238Pf3VyiaefXp8PhiEgRBYW+WhYWFwp7CvKhfv77C/efPn+dpOWXPMzM1NZUX388vKVFQJUuWVPg/KYr3ckFVqlQpz3+8iYqKUiilNWvWzNN5c4Xh8/d2Qf8wBPz33s7LzzAiKjjugSMijfZ5gXv79m2hb/Pza3RZW1srvY7Pl/l8nTlRZuCOgi4fExNToG1lR1UfygMDA1G/fn34+fnlq7wByPNez8IWFxencPhZqVKllF5Hfl9PBbk2X36/7zn5/L386WHK6kqZ95M6DdKizu9tIvoy7oEjIo32+WiMHz9+xJMnT/J8nld+JCQkKNzPz7k6ny/z+fk/OdHX11d6W/ldvjD2UKniPJmYmBh07tw5ywfGevXqoUWLFqhWrRrs7OxgbGwMIyMjhT0k3333He7cuVPgDKok5utJndSqVQtBQUHy++p0jmJOlHk/ff5/ItbeN0B939tElDcscESk0b766ivo6ekhIyNDPi0oKKhQC9znH7zyc67O58t8fv6POjAxMVG4P2nSJHTp0qVA66xSpUqBlgeAefPmKexBqF69Onbs2JGnw8A+f07qoLi8nr6kRYsWCsPR37x5E1KpFLq6uiKmUp3P/08+L+5F6fP3wZYtW1C+fPkCrfPzw3iJqPCwwBGRRjM1NUWjRo0URkv09/dXakhsZX0+EmN+Dkf6fAANZUd3LAqfH5ZXtmxZtGvXTqQ0/7Nnzx7510ZGRjhx4kSei6Eqz8FTFUtLS+jo6Mj3YGjr6+lLPr++WVJSEs6cOaMwkqkmU6dDRD9/b9epU0cl58ERUdHgOXBEpPG+/vprhfsHDhwolHM8MlWqVEnh/u3bt5VaPjU1FQ8fPpTfNzQ0RJkyZVSSTZU+H/DkyZMnIiX5n+fPnyMyMlJ+v2PHjnkub8nJyQgPDy+saPkmkUgUBn+Ij49HRESEUuv4/DX4+WtUE9SuXTvLIdGfXnxd09na2irsbX348KFoe+HU8b1NRHnHAkdEGm/UqFEKH4xSU1OxYMGCQtveV199pXD//PnzSg3oEBAQgPT0dPn9Ro0awcDAQGX5VOXT6zwBwNmzZ0VK8j9v3rxRuF+zZs08L/v59/1LdHQUf0WqetCOT33+mlL2e/35/J+vT1NMmTJF4f6hQ4eU/gOJutLT04Orq6v8vkwmw4kTJ0TJoo7vbSLKOxY4ItJ4JUqUwPDhwxWmLV26NMu1tfLj2LFjWabVrFlTYY/Zs2fPcO7cuTyvc/PmzQr3W7Vqlf+AhahcuXJwcHCQ3w8LC8Px48dFTJS1RCkzkuTq1auV2tbnA4MkJSUptbwyPn8NbN26Nc/LPnz4EJcvX5bfNzQ0hIuLi6qiFSlvb2+F91ZGRgaGDBmiVPHOTlJSUpaLpYvh8+u+rVixQpQc7dq1U7gW5V9//VWoRy0QkWqxwBGRVpgzZ47CtbOkUim6d++Ou3fv5mt96enpmDp1KsaNG5flMYlEkqUw+vj4QCqVfnG9gYGB+OuvvxTWNWLEiHxlLAo+Pj4K9ydNmiTqcOG2trYK9y9dupSn5Y4dOwY/Pz+ltvX5OUuFefhl//79FQpjQEAADh06lKdlv/vuO4X7ffr0gaWlpSrjFRlDQ0OsW7dOYdqtW7fQr1+/fF/2ISwsDC1atFDqjyyFZdiwYQqDmQQEBIhymGiZMmUwaNAg+f3ExMRsf9YRkXpigSMirWBlZYU9e/YoDOsdFRWFVq1aYffu3Uod/nbhwgU0btwYS5YsyXG5MWPGwNjYWH4/JCQEo0ePznU7z58/R69evRTm8fT0RNWqVfOcrah5eXmhbt268vuPHj1Cp06dFM5D+5L09HT8+eefKjmstWLFiihXrpz8/o0bNxQGNclOYGAgBg4cqPS2Pn3ewH+D4xR0T1BOrKysMGzYMIVpw4YN++IlD2bPno2jR4/K7+vo6GDy5MmFkrGoeHp6YuLEiQrTDhw4gA4dOuDBgwd5Xk9KSgoWL16MBg0aICQkRNUx88XKygrffvutwrTx48cr/FEnN+np6QgICFBJlp9++klhNMo9e/Zg1KhRShXl9+/f49dff8Xhw4dVkomI8oYFjoi0RpMmTbBhwwaFc5c+fPiAAQMGwMXFBevWrcPz58+zXfbx48dYunQpmjZtCjc3ty9+cC5fvjwWLlyoMG3jxo1o06YNrl27pjA9MTERmzZtgpOTE168eCGfXrJkSaxatUrZp1mkdHV18ffffyvs0bl69SocHBwwe/ZsPHr0KNvl3rx5gyNHjmDUqFEoV64chgwZgvv376skk7e3d5b78+fPR3x8vML0ly9f4qeffkLLli3x4cMHGBkZKeyl/RIbGxuFodEfP34MV1dXLF++HEeOHMHp06cVbikpKQV6XvPmzVPI9+HDBzRt2hRLlizJcmHu0NBQ9OzZE3PnzlWY7uPjg4YNGxYohzpYtGgROnfurDDtwoULcHR0xLBhw3DkyJFsD2lNT0/H2bNnMXnyZFSuXBk+Pj6iDtefnVmzZimco5iWlob+/fujZ8+euHz5cpY9+RkZGQgODsZPP/2EqlWrYubMmSrJUaVKFWzatElh2vr16+Ho6IgNGzZkOd8U+O8Q5rCwMGzfvh09evRA+fLlMXPmTB5+SVTUBCIiLXPw4EHBwsJCAJDtrWTJkkLNmjWFJk2aCFWrVhVMTExynPerr77KcTsymUwYNGhQtsuVLl1aaNSokVCnTh3B2Ng4y+PGxsbCiRMnvvhcWrVqpbCcsrZs2aKw/JYtW5RehyAIwtmzZ4USJUpk+1ytra0FBwcHwcXFRahdu7ZQunTpbOcbPHiwSnLGxMQI5cuXz7J+PT09oW7dukKTJk2EypUrCxKJROHx9evXK/393LZtW46vjc9v4eHhWZb/9PFWrVp9cXs3btzI9vusr68v1KhRQ2jcuLFQrly5bLffsWNHITU1Ndf1nzt3TmGZ2bNnfzHTpypVqiRftlKlSkotq6zU1FRhxIgROX6/9fT0BDs7O6F+/fpCo0aNBDs7O0FPTy/H+deuXZvtdpR9jyj7f5qdly9fCnXr1s02p7m5uVCnTh3BxcVFqFWrlmBkZJTnbebn58WSJUsEHR2dbLNUqFBBaNiwoeDs7CxUr15dMDc3z3a+/P5cIaL8YYEjIq0UHh4ueHp65vnD9+e3UqVKCX/88YeQlpaW63ZkMpng4+OT4weg7G62trZCQEBAnp6HuhQ4QRCEJ0+eCM7Ozvn6fkokEuGnn35SWc6QkBChTJkyedq2jo6OsHTpUkEQ8vf9/P7777OUwcIqcIIgCHfv3hWqVKmi1Pd3yJAhX3ytCoJmFbhMBw8eFOzt7fP9XnZ1dRUuX76c4/rFKHCCIAixsbFCt27dlH4+qi5wgiAIJ06cEMqWLZuv76+hoaFw9OjRfH8fiEh5PISSiLSSvb09Dh06hJs3b2Ls2LFZBr/IjqmpKTp16oS//voLr169wqRJkxTOqcuORCLBwoULERISgm7dusHQ0DDHee3s7DBz5kw8fvwYzZs3V/o5ia1q1aoIDAyEv78/2rRp88VLH+jq6sLV1RVz587FkydP8Msvv6gsS8OGDREcHIyBAwdCV1c323kkEgnat2+Pa9euFei8sAULFuDWrVuYOnUqmjdvDhsbGxgZGeV7fV/i4OCAf//9F4sWLcr1Gnd6enpo27YtAgICsGXLli++VjVV9+7d8fjxY+zatQudOnXK9T2WqXz58hg3bhyCgoJw5coVNG3atAiSKsfS0hJ+fn44d+4cOnTo8MX3U82aNfHTTz9h27ZtKs/i7u6Op0+fYsWKFahXrx4kEkmu85uZmaFLly5Ys2YNXr9+neVwVyIqXBJBKMQL2xARqZGwsDCEhobixYsX+PjxIwRBgJWVFUqWLIk6deqgbt26OZaBvEpKSsKlS5fw/PlzvHv3DoaGhrCxsUHdunXRoEED1TwRNZGUlIRr167hxYsXiImJQXJyMszMzGBtbY2aNWuidu3aWYbiLwzv37/HxYsX8ezZM3z8+BGmpqaoXLkymjZtChsbm0LffmF78OABbt68iejoaCQlJaFUqVIoV64cmjdvrrGjTRZEamoqbt++jcePH+PNmzdISkqCoaEhSpYsiTJlysDJyQlly5YVO6bSEhMTcfnyZbx8+RLv3r2DVCqFhYUFKleujHr16qF8+fJFluXt27e4fv06oqKiEBMTA5lMBgsLC9ja2qJ27dqoXr261v7BgEgTsMARERERERFpCB5CSUREREREpCFY4IiIiIiIiDQECxwREREREZGGYIEjIiIiIiLSECxwREREREREGkJP7ADFmUwmQ2RkJMzNzb94zRUiIiIiItJegiDg48ePsLOzg45OzvvZWOBEFBkZiQoVKogdg4iIiIiI1MSLFy9yvfYjC5yIzM3NAfz3n2RhYSFyGiIiKmqJiYmws7MD8N8f9YriwudERKSe4uPjUaFCBXlHyAkLnIgyD5u0sLBggSMiKoZ0dXXlX1tYWLDAERHRF0+t4iAmREREREREGoIFjoiIiIiISEOwwBEREREREWkIFjgiIiIiIiINwQJHRERERESkITgKJRERkUiMjY0RHh4u/5qIiOhLWOCIiIhEoqOjA3t7e7FjEBGRBuEhlERERERERBqCBY6IiEgkaWlp8PHxgY+PD9LS0sSOQ0REGkAiCIIgdojiKj4+HpaWloiLi4OFhYXYcYiIqIglJibCzMwMAJCQkABTU1ORExERkVjy2g24B46IiIiIiEhDsMARERERERFpCBY4IiIiIiIiDcECR0REREREpCFY4IiIiIiIiDQECxwREREREZGG0BM7ABERUXFlbGyM0NBQ+ddERERfwgJHREQkEh0dHdStW1fsGEREpEF4CCUREREREZGG4B44IiIikaSlpeG3334DAMyYMQMGBgYiJyIiInUnEQRBEDtEcRUfHw9LS0vExcXBwsJC7DhERFTEEhMTYWZmBgBISEiAqampyImIiEgsee0GPISSiIiIiIhIQ7DAERERERERaQgWOCIiIiIiIg3BAkdERERERKQhWOCIiIiIiIg0BAscERERERGRhuB14IiIiERiZGSEwMBA+ddERERfUiz2wF28eBEeHh6ws7ODRCLBoUOHvrjM+fPn0ahRIxgaGqJatWrYunVrlnlWrVoFe3t7GBkZwcXFRf5LmIiIKC90dXXh7OwMZ2dn6Orqih2HiIg0QLEocImJiahfvz5WrVqVp/nDw8PRpUsXtG7dGrdu3cKkSZMwYsQInDx5Uj7Pnj17MGXKFMyePRshISGoX78+3N3dER0dXVhPg4iIiIiIijmJIAiC2CGKkkQiwcGDB9G9e/cc55k2bRqOHj2K0NBQ+bR+/fohNjYWJ06cAAC4uLjA2dkZvr6+AACZTIYKFSpgwoQJ+OGHH/KUJa9XWyciIu2UlpaG5cuXAwC+/fZbGBgYiJyIiIjEktduwHPgsnH16lW0a9dOYZq7uzsmTZoE4L9fuMHBwZg+fbr8cR0dHbRr1w5Xr17Ncb2pqalITU2V34+Pj1dtcCIiUhmpVIq4uDh8/PgRCQkJCv8qMy0pKQlSqRQymQwymSzL1ykpKQCAmTNnQk9PDzo6Olluurq6MDQ0hLm5OczNzWFmZpbl67xMs7S0hKGhocjfWSIiKggWuGxERUWhTJkyCtPKlCmD+Ph4JCcn48OHD5BKpdnO8+DBgxzXO3/+fPz888+FkpmIiPImPT0dUVFReP36tfwWGRmZ5X50dDRkMlmO69HX18+xOFlbW8unmZiY5FjM0tPT8f333wMAfv31V+jr6+da9D4viG/evMkyLTk5OdfnX6JECZQtW1Z+s7Ozy/a+qampSr/vRESkGixwRWj69OmYMmWK/H58fDwqVKggYiIiIu2SkpKCsLAwPH78GC9fvsy2oL19+1ZhGV1dXdja2srLi7Ozs/zrUqVK5bhHSxWHOyYmJsoL3JgxY1RSmjIyMpCYmJjt3sEPHz7Iy2tkZCQiIiJw9epVREZGZil+5ubmORa8qlWrokaNGihRokSB8xIRkXJY4LJha2uLN2/eKEx78+YNLCwsYGxsDF1dXejq6mY7j62tbY7rNTQ05KErREQFJJVK8fz5czx69CjL7dmzZ8g8tVtfX1+hgDRv3jxLIbGzs4O1tTV0dLRnTC89PT1YWlrC0tIyz8sIgoD4+Phc90gGBwfj9evX+Pjxo3w5a2tr1KhRI8utWrVqMDY2LoynR0RU7LHAZcPV1RXHjh1TmPbPP//A1dUVAGBgYAAnJyecOXNGPhiKTCbDmTNnMH78+KKOS0SkdQRBwJs3b/D48eMsJe3JkydIS0sD8N/P4+rVq6N69ero06ePvEBUr14dZcqUgUQiEfmZaAaJRCIvfbVq1cp13vj4eISFhSn8n/z77784ePAg4uLi5PNVrFgx23JXqVIl6Onx4wcRUX4Vi5+gCQkJePLkifx+eHg4bt26hZIlS6JixYqYPn06Xr16hW3btgEARo8eDV9fX3z//fcYNmwYzp49i7179+Lo0aPydUyZMgWDBw9G48aN0aRJEyxbtgyJiYkYOnRokT8/IiJNlpqairt37yI4OBghISG4efMmHjx4IN/TI5FIYG9vjxo1aqBt27YYM2aMvAxUqFCB108rYhYWFmjYsCEaNmyoMF0QBLx79y5L4b548SI2bdokH8RLX18fVatWRb169eDk5AQnJyc0atSIh2MSEeVRsShwQUFBaN26tfx+5nlogwcPxtatW/H69Ws8f/5c/njlypVx9OhRTJ48GcuXL0f58uWxceNGuLu7y+fp27cv3r59i1mzZiEqKgoNGjTAiRMnsgxsQkRE/5OSkiIva5m30NBQpKenQ1dXF3Xq1EGjRo3Qq1cveUmrUqUKjIyMxI5OXyCRSFC6dGmULl0azZo1U3hMJpPhxYsX8lL34MED3Lp1C0ePHkViYiKA/373Zha6zFvJkiXFeCpERGqt2F0HTp3wOnBEpM2Sk5Nx584dhbJ27949ZGRkQFdXFw4ODmjUqJH8w3q9evVgYmIiduwiJZVKERAQAABo0aJFsdubKJVK8ejRI4SEhMhfIyEhIUhISAAA2NvbK+ylc3JygrW1tcipiYgKR167AQuciFjgiEhbCIKAf//9FxcuXEBQUJC8rEmlUujp6cHBwUFhz0q9evW4V42yJZPJ8PjxY4VCFxISIr92asWKFeWvoxYtWqBJkyZ8LRGRVmCB0wAscESkqTIL2/nz53H+/HlcuHABb9++hZ6ensK5TU5OTnBwcOAHbCoQmUyGsLAwhVIXHByMuLg4GBoawtXVFW5ubnBzc4OLiwtfb0SkkVjgNAALHBFpCplMlqWwvXv3Dvr6+nBxcZF/eHZ1dS12h0EWRHp6OtavXw8A+Oabb6Cvry9yIs0hlUpx9+5dhddkbGwsCx0RaSwWOA3AAkdE6oqFrWgkJibCzMwMwH8jJqviQt7F1eeF7uLFi/jw4QMLHRFpDBY4DcACR0TqQhAEPHjwAGfOnGFhK0IscIVHJpNl2UOXWei++uoruLm5oXXr1mjatCn3fBKRWmCB0wAscEQkpvT0dFy6dAmHDx+Gv78/wsLCoK+vL/9w6+bmhq+++oqFrRCxwBWdnAqdlZUVOnXqBA8PD3Tq1AlWVlZiRyWiYooFTgOwwBFRUfvw4QNOnDgBf39/nDhxArGxsbCzs4OHhwc8PDzQunVrFrYixAInHplMhpCQEBw+fBiHDx/GzZs3oaenhxYtWsjfD9WqVRM7JhEVIyxwGoAFjoiKwpMnT+R72QICAiCVStGoUSP5h9RGjRpBIpGIHbNYYoFTHy9evMCRI0dw+PBhnD17Fqmpqahdu7b8feLq6lrsrtNHREWLBU4DsMARUWGQSqW4evWqvLQ9ePAAhoaGaNu2LTw8PNC1a1eUL19e7JgEFjh1lZCQgH/++QeHDx/GkSNH8PbtW1hbW6Nz587w8PCAu7s7zM3NxY5JRFqGBU4DsMARkaokJSXh+PHj8Pf3x9GjRxETEwMbGxt07doVHh4eaNeunbwokPpggVN/UqkUgYGB8kMtQ0NDYWBgADc3N3h4eKBHjx6ws7MTOyYRaQEWOA3AAkdEBZGRkYGzZ89i586dOHDgABISEuDg4IBu3brBw8MDTZo0gY6OjtgxKRcZGRk4efIkAMDd3R16enoiJ6IvCQ8Pl5e5CxcuICMjA61bt4aXlxd69uwJS0tLsSMSkYZigdMALHBEpCxBEBAUFISdO3fir7/+wps3b1CjRg14eXlhwIABHHSBqAjFxsbiwIED2LlzJ86dOwcDAwN4eHjAy8sLnTp1gqGhodgRiUiDsMBpABY4IsqrsLAw7Ny5Ezt37sSjR49QpkwZ9O/fH15eXnBycuIgJEQie/XqFXbv3o2dO3fi1q1bsLKyQu/eveHl5YUWLVpwbzgRfRELnAZggSOi3ERHR2PPnj3YuXMnrl+/DjMzM/To0QNeXl5o06YND7fTAunp6di5cycAwMvLixeU1hL//vuv/A8uz549Q4UKFdC/f38MHDgQjo6OYscjIjXFAqcBWOCI6HMJCQnw8/PDjh078M8//0AikaBTp07w8vKCh4cHr9GmZTiIiXaTyWS4cuUKdu7cib179+L9+/dwdHSEl5cX+vfvj4oVK4odkYjUCAucBmCBIyLgv/Pazp07h02bNuHQoUNISkpCs2bN4OXlhd69e8Pa2lrsiFRIWOCKj7S0NJw8eRI7d+6En58fUlJS0LJlSwwePBj9+vXjH2eIiAVOE7DAERVvsbGx+PPPP7F27Vo8ePAAtWrVwqBBgzBgwADY29uLHY+KAAtc8RQfH4+DBw9i586dOH36NCwtLTFkyBCMHj0aNWvWFDseEYmEBU4DsMARFU/BwcFYs2YNdu3ahfT0dPTo0QNjx45Fy5YtORhJMcMCR0+fPsW6deuwefNmvHv3Dm3btsWYMWPQrVs3nhNJVMzktRtwSCQioiKQnJyMrVu3okmTJmjcuDFOnTqFH3/8ES9evMCePXvQqlUrljeiYqhKlSpYsGABXrx4ge3btyM5ORm9evVCpUqVMGfOHLx69UrsiESkZljgiIgK0aNHjzBlyhSUK1cOw4YNQ6lSpeDn54fw8HD8+OOPsLW1FTsiEakBIyMjDBw4EJcvX8bt27fh6emJxYsXo1KlSujRowdOnz4NmUwmdkwiUgMscEREKpaRkYGDBw+iffv2qFmzJrZt24YRI0bg8ePHOH78OLp16wZdXV2xYxKRmqpXrx7WrFmDyMhIrFixAo8fP0b79u1Rq1YtLF26FO/fvxc7IhGJiOfAiYjnwBFpl8jISGzYsAEbNmzAq1ev4OrqirFjx6JXr14wMjISOx6pocyyDwBff/01r+1H2RIEAZcuXcKaNWuwf/9+6Orqol+/fhg7diycnZ3FjkdEKsJBTDQACxyRdggNDcXChQuxa9cu+WFQY8aMQf369cWORkRaJjo6Gps3b8batWvx7NkzfPXVV/jhhx/g4eEBHR0eWEWkyTiICRFRIbty5Qq6desGR0dHnD9/HkuWLEFkZCTWrl3L8kZEhcLGxgY//PADwsLC4O/vD319fXTv3h2Ojo7Ytm0b0tPTxY5IRIWMBY6ISAmCIODYsWNo2bIlmjVrhidPnuDPP/9EWFgYvv32W+5NJ6VkZGRg37592LdvHzIyMsSOQxpEV1cXHh4euHjxIi5duoSqVati8ODBqFatGlasWIHExESxIxJRIWGBIyLKg4yMDOzevRsNGjRAly5dkJaWhkOHDiE0NBTe3t68XhPlS2pqKvr06YM+ffogNTVV7DikoZo1awZ/f3/cvXsXLVu2xJQpU1CpUiXMnTuXA54QaSEWOCKiXCQnJ2PNmjWoUaMGBgwYgLJly+LcuXO4evUqPD09ec4JEakNBwcHbN++HU+ePEH//v0xf/58VKxYEd999x1evnwpdjwiUhF+8iAiykZsbCzmz58Pe3t7jB8/Hk2aNEFISAhOnDgBNzc3XnSbiNSWvb09Vq5ciWfPnmHSpEnYvHkzqlSpguHDh+PBgwdixyOiAmKBIyL6RFRUFH744QdUqlQJc+bMQffu3fHw4UP89ddfaNiwodjxiIjyzMbGBr/++iuePXuG3377DcePH0edOnXQs2dP3LhxQ+x4RJRPLHBERPjvGm7jxo2Dvb09Vq9ejdGjRyMiIgLr1q1DtWrVxI5HRJRvFhYWmDp1KsLDw7Fu3TrcuXMHTZo0Qbt27XD16lWx4xGRkljgiKhY+/DhA6ZPn45q1aph9+7dmDlzJp4/f44FCxagbNmyYscjIlIZQ0NDjBw5Eg8ePMDevXsRHR2Npk2bwtPTE6GhoWLHI6I8YoEjomIpKSkJCxYsQJUqVbBixQpMnjwZT58+xY8//ggrKyux4xERFRpdXV307t0bN2/exI4dO3D37l3Uq1cPgwcPRkREhNjxiOgLWOCIqFhJT0/H2rVrUa1aNfz000/w8vJCWFgY5s2bx+JGRc7AwABbtmzBli1bYGBgIHYcKmZ0dXXh5eWFBw8eYOXKlTh58iRq1KiBb7/9FtHR0WLHI6IcSARBEMQOUVzFx8fD0tIScXFxvPgvUSGTyWTYu3cvfvrpJzx9+hReXl74+eefUaVKFbGjERGphcTERCxfvhwLFiyAVCrFlClTMHXqVH5GISoiee0G3ANHRFpNEAQcP34cTk5O6N+/P2rXro1bt25h+/btLG9ERJ8wNTXFjBkz8PTpU4wdOxaLFi1ClSpVsHTpUqSkpIgdj4j+HwscEWmtK1euwM3NDZ07d4aZmRkuXbqEw4cPo169emJHIwIAZGRk4OjRozh69CgyMjLEjkMEAChVqhQWLlyIJ0+eoGfPnvj+++9RvXp1bNq0ia9TIjXAAkdEWic0NBSenp5o1qwZYmNjcfToUVy8eBHNmjUTOxqRgtTUVHTt2hVdu3ZFamqq2HGIFJQrVw7r1q3Dv//+i6ZNm2LEiBFwcHDA33//DZ6BQyQeFjgi0hqRkZEYPHgw6tWrh9DQUOzatQs3b95E586dIZFIxI5HRKSRatSogT179iA4OBj29vbo1asXXFxccPnyZbGjERVLLHBEpPHS0tKwePFi1KxZE8ePH4evry/u37+P/v37Q0eHP+aIiFShUaNGOHHiBM6ePQtBENC8eXN4e3vj9evXYkcjKlb4yYaINNrp06dRv359TJs2DUOHDsXDhw8xduxYDslORFRIWrdujevXr2PDhg04duwYatasiT/++APp6eliRyMqFljgiEgjPX/+HL1790b79u1RunRp3Lx5EytWrECJEiXEjkZEpPV0dHQwYsQIPHr0CN7e3pg6dSoaNGiAs2fPih2NSOuxwBGRRklJScG8efNQq1YtXL58GTt37sSFCxc4siQRkQhKliwJX19fBAcHo0SJEmjbti369u2LFy9eiB2NSGuxwBGRxjh69CgcHBwwZ84cjBs3Dg8fPsSAAQM4QAkRkcgaNGiAgIAAbNu2DRcuXECtWrUwf/58jq5KVAhY4IhI7YWFhcHDwwNdu3ZF5cqVcefOHSxatAjm5uZiRyMqEAMDA/j6+sLX15fnbZLGk0gkGDRoEB49eoTRo0dj5syZcHR0xIkTJ8SORqRVJAIv5CGa+Ph4WFpaIi4uDhYWFmLHIVI7SUlJ+P3337Fw4ULY2Njgjz/+QI8ePbjHjYhIA9y7dw8TJ07E2bNn4enpiT/++AOVK1cWOxaR2sprN+AeOCJSO4Ig4MCBA6hduzYWLFgAHx8fPHjwAD179mR5IyLSEHXr1sXp06exd+9eBAcHo3bt2pgzZw6Sk5PFjkak0VjgiEitPH/+HJ07d0bPnj3h6OiIe/fu4ZdffoGJiYnY0YhUTiqV4vz58zh//jykUqnYcYhUTiKRoHfv3njw4AGmTJmC3377DXXq1MGZM2fEjkaksVjgiEgtCIKA9evXw8HBAXfv3oW/vz+OHDmCatWqiR2NqNCkpKSgdevWaN26NVJSUsSOQ1RoTE1N8dtvv+HevXuoXLky2rVrh1GjRiE+Pl7saEQahwWOiEQXERGBDh06YNSoUejbty/u3bsHDw8PsWMREZGKVa9eHadPn8aaNWuwa9cuODg44NSpU2LHItIoLHBEJBqZTIY1a9bA0dERDx8+xMmTJ7FhwwZYWlqKHY2IiAqJjo4ORo8ejdDQUNSsWRPu7u4YMWIE4uLixI5GpBFY4IhIFE+fPkW7du0wduxYeHl5ITQ0FB06dBA7FhERFZFKlSrh1KlTWL9+Pfbu3QsHBwccP35c7FhEao8FjoiKlEwmg6+vLxwdHfH06VOcPn0aa9eu5aU0iIiKIYlEgpEjRyI0NBR169ZF586dMXToUHz48EHsaERqiwWOiIpMWFgYWrdujQkTJmDIkCG4e/cu2rZtK3YsIiISWcWKFXH8+HFs2rQJBw8ehIODA44cOSJ2LCK1xAJHRIVOJpNh+fLlcHR0xIsXL3D27FmsWrUK5ubmYkcjIiI1IZFIMGzYMISGhqJBgwbw8PCAt7c33r9/L3Y0IrXCAkdEherRo0do2bIlJk2ahBEjRuDu3bto3bq12LGI1IK+vj4WLlyIhQsXQl9fX+w4RGqhfPnyOHLkCP78808cPnwYdevWhZ+fn9ixiNSGRBAEQewQxVV8fDwsLS0RFxfH839I68hkMixbtgw//vgjypUrh82bN6Nly5ZixyIiIg0SGRmJ0aNH4/Dhw+jfvz9WrlyJUqVKiR2LqFDktRtwDxwRqVxUVBTc3d0xdepUjB49Gnfu3GF5IyIipdnZ2cHPzw87duzAiRMn0KBBAwQEBIgdi0hULHBEpFKnTp1C/fr1ERoailOnTuGPP/6AiYmJ2LGI1JJUKsWNGzdw48YNSKVSseMQqSWJRAIvLy/cuXMHlStXhpubG3799Ve+Z6jYYoEjIpVIT0/H9OnT4e7ujoYNG+LWrVto166d2LGI1FpKSgqaNGmCJk2aICUlRew4RGqtfPnyOHv2LGbOnInZs2ejffv2iIyMFDsWUZFjgSOiAouIiEDLli2xePFiLFy4EMeOHUOZMmXEjkVERFpGT08Pc+bMwZkzZ/Dw4UPUr1+fF/+mYocFjogK5O+//0bDhg0RFRWFgIAA+Pj4QEeHP1qIiKjwuLm54fbt23BxcUHnzp0xdepUpKWliR2LqEjwUxYR5UtycjLGjh2LXr16oV27drh58ya++uorsWMREVExYW1tjcOHD2Pp0qVYsWIFmjdvjqdPn4odi6jQscARkdLu378PFxcXbNmyBWvXrsXevXthZWUldiwiIipmJBIJJk+ejCtXruD9+/do2LAh9uzZI3YsokLFAkdEeSYIAjZv3ozGjRsjIyMDgYGBGDVqFCQSidjRiIioGGvcuDFCQkLQuXNn9OvXDyNHjkRSUpLYsYgKBQscEeVJfHw8Bg4ciOHDh2PAgAEICgqCo6Oj2LGIiIgAABYWFti1axc2bdqEnTt3wtnZGaGhoWLHIlI5Fjgi+qKgoCA0atQIhw8fxu7du7FhwwZe241IBfT19TF79mzMnj0b+vr6Ysch0ngSiQTDhg1DUFAQdHR04OzsjPXr10MQBLGjEamMROArWjTx8fGwtLREXFwcLCwsxI5DlK0tW7Zg9OjRqFevHv766y9UrVpV7EhERERflJycjClTpmDt2rXw9vbGunXrYGRkJHYsohzltRtwDxwRZSs9PR3ffvsthg0bhqFDh+Ly5cssb0REpDGMjY2xZs0a7Ny5E3v37kWrVq3w6tUrsWMRFRgLHBFlERMTg44dO2L16tVYs2YN1q5dCwMDA7FjEWkdmUyGe/fu4d69e5DJZGLHIdJKAwYMwKVLlxAZGQlnZ2dcu3ZN7EhEBcICR0QK7t69C2dnZ9y5cwdnzpzB6NGjxY5EpLWSk5Ph4OAABwcHJCcnix2HSGs5OTnhxo0bqFy5Mlq1aoWtW7eKHYko31jgiEju4MGDcHV1hYWFBYKCgtCyZUuxIxEREamEra0tzp49C29vbwwdOhSTJk1CRkaG2LGIlMYCR0SQyWT4+eef0aNHD3Tu3BmXL19GpUqVxI5FRESkUoaGhli/fj18fX3h6+uLTp064f3792LHIlIKCxxRMZeQkIBevXrh559/xrx587Bnzx6YmpqKHYuIiKhQSCQSjBs3Dv/88w9u3rwJZ2dn3Lt3T+xYRHnGAkdUjD19+hSurq44ffo0/Pz8MGPGDEgkErFjERERFbrWrVvjxo0bMDU1xVdffQU/Pz+xIxHlCQscUTF19uxZODs7IyUlBdeuXYOHh4fYkYiIiIpU5cqVceXKFbi7u6N79+745ZdfOCIsqT0WOKJiRhAErFy5Eh06dEDjxo0RGBiIOnXqiB2LiIhIFGZmZti7dy/mzp2LWbNmoU+fPkhISBA7FlGOik2BW7VqFezt7WFkZAQXFxcEBgbmOK+bmxskEkmWW5cuXeTzDBkyJMvjHTt2LIqnQpRvqampGDFiBCZOnIhJkybh6NGjKFGihNixiIotfX19TJ06FVOnToW+vr7YcYiKLR0dHcycORMHDx7EyZMn0axZM4SHh4sdiyhbEkEQBLFDFLY9e/bA29sba9euhYuLC5YtW4Z9+/bh4cOHsLGxyTL/+/fvkZaWJr8fExOD+vXrY+PGjRgyZAiA/wrcmzdvsGXLFvl8hoaGSn0Yjo+Ph6WlJeLi4mBhYZH/J0iUB3FxcfD09MS1a9ewfv16eHt7ix2JiIhI7YSGhsLT0xMJCQk4duwYnJycxI5ExUReu0Gx2AO3dOlSjBw5EkOHDkWdOnWwdu1amJiYYPPmzdnOX7JkSdja2spv//zzD0xMTNC7d2+F+QwNDRXm454MUleRkZFo2bKl/OLcLG9ERETZc3BwwPXr1+UX/T516pTYkYgUaH2BS0tLQ3BwMNq1ayefpqOjg3bt2uHq1at5WsemTZvQr1+/LEOrnz9/HjY2NqhZsybGjBmDmJiYXNeTmpqK+Ph4hRtRYXvw4AFcXV3x/v17XLp0Cc2aNRM7EhH9P5lMhoiICERERHDgBCI1Ym1tjTNnzqBVq1bo0qULdu7cKXYkIjmtL3Dv3r2DVCpFmTJlFKaXKVMGUVFRX1w+MDAQoaGhGDFihML0jh07Ytu2bThz5gwWLFiACxcuoFOnTpBKpTmua/78+bC0tJTfKlSokL8nRZRH165dQ/PmzWFubo6rV69ysBIiNZOcnIzKlSujcuXKSE5OFjsOEX3C1NQUhw4dwsCBAzFw4EAsWbJE7EhEAAA9sQOou02bNsHR0RFNmjRRmN6vXz/5146OjqhXrx6qVq2K8+fPo23bttmua/r06ZgyZYr8fnx8PEscFZqjR4+id+/ecHJygr+/Pw/xJSIiUpK+vj42b94MOzs7TJ06FZGRkVi0aBF0dLR+HwipMa1/9VlbW0NXVxdv3rxRmP7mzRvY2trmumxiYiL++usvDB8+/IvbqVKlCqytrfHkyZMc5zE0NISFhYXCjagwbN68GZ6enujYsSNOnTrF8kZERJRPEokE8+bNw8qVK/HHH39g0KBBCoPdERU1rS9wBgYGcHJywpkzZ+TTZDIZzpw5A1dX11yX3bdvH1JTUzFw4MAvbufly5eIiYlB2bJlC5yZKL8EQcC8efMwfPhwjBw5Evv27YOxsbHYsYiIiDTe+PHjsXfvXuzfvx9dunTBx48fxY5ExZTWFzgAmDJlCjZs2IA///wT9+/fx5gxY5CYmIihQ4cCALy9vTF9+vQsy23atAndu3dHqVKlFKYnJCTAx8cH165dQ0REBM6cOQNPT09Uq1YN7u7uRfKciD4nlUoxYcIE/PTTT5g7dy5Wr14NXV1dsWMRERFpjV69euHkyZMIDAyEm5tbliO8iIpCsTgHrm/fvnj79i1mzZqFqKgoNGjQACdOnJAPbPL8+fMsxzI/fPgQly5dynboWF1dXdy5cwd//vknYmNjYWdnhw4dOuCXX36BoaFhkTwnok+lpKRg0KBBOHDgADZs2JBl0B0iIiJSDTc3NwQEBKBjx45o2rQpTp48iWrVqokdi4qRYnEhb3XFC3mTKsTGxqJ79+64fv069uzZg27duokdiYjyKDExEWZmZgD+O7rj88vVEJH6evbsGdzd3fH+/XscO3YMjRs3FjsSabi8doNisQeOSFtFRkaiY8eOePnyJc6cOYOmTZuKHYmIlKCnp4exY8fKvyYizVGpUiVcunQJXbt2hZubGw4cOIAOHTqIHYuKAe6BExH3wFFBPHr0CO3bt4cgCDh58iRq164tdiQiIqJiJzExEX379sXJkyexdetWeHl5iR2JNFReu0GxGMSESNv8+++/aNmyJczMzHDlyhWWNyIiIpGYmpri4MGDGDhwIAYNGoTNmzeLHYm0HI/XINIwd+7cQbt27VC2bFmcPn0apUuXFjsSEeWTIAh49+4dgP+uWyqRSERORET5oa+vj02bNsHIyAjDhw9Heno6Ro0aJXYs0lIscEQaJCQkBO3bt4e9vT1OnTqV5RIXRKRZkpKSYGNjA4CDmBBpOh0dHaxevRoGBgYYPXo00tLSMGHCBLFjkRZigSPSEDdu3ECHDh1Qo0YNnDx5ElZWVmJHIiIiok9IJBIsW7YM+vr6mDhxItLT0zFlyhSxY5GWYYEj0gBXr15Fx44d4eDggGPHjsHS0lLsSERERJQNiUSCRYsWwcDAAN999x3S0tLwww8/iB2LtAgLHJGaCwgIQOfOndGoUSMcOXIE5ubmYkciIiKiXEgkEsybNw8GBgaYPn060tLSMGvWLLFjkZZggSNSY5cuXUKnTp3g4uICf39/nh9DRESkISQSCebMmQMDAwP8+OOPkEgkmDlzptixSAuwwBGpqStXrqBTp05o0qQJDh8+DBMTE7EjERERkZJmzJgBQRDw008/QVdXFzNmzBA7Emk4FjgiNXTt2jV07NgRTk5OLG9EREQa7scff4RUKsWPP/4IHR0dnhNHBcICR6Rmrl+/Dnd3dzRo0ABHjhzhYZNEWkxPTw+DBw+Wf01E2mvWrFmQyWSYPn06dHV14ePjI3Yk0lD8bUGkRjIvFeDo6Ihjx47BzMxM7EhEVIgMDQ2xdetWsWMQURGZPXs2pFIpvv/+e+jq6vISA5QvLHBEaiI4OBgdOnRA3bp1cfz4cZY3IiIiLSORSDB37lxIpVJ899130NHRwaRJk8SORRqGBY5IDdy/fx8dOnRAzZo1ceLECV4qgKiYEAQBSUlJAAATExNIJBKRExFRYcu8xIBUKsXkyZNhbm6O4cOHix2LNAgLHJHIXrx4AXd3d9jZ2eH48eOwsLAQOxIRFZGkpCT53vaEhASe80pUTEgkEvz++++Ij4/HN998A2tra3h6eoodizSEjtgBiIqz9+/fo2PHjtDR0cGJEydQokQJsSMRERFREZBIJPD19UWPHj3Qr18/BAQEiB2JNAQLHJFIEhMT0bVrV0RHR+PkyZMoV66c2JGIiIioCOnq6mLHjh1o2rQpPDw8cOfOHbEjkQZggSMSQXp6Onr37o07d+7g2LFjqFmzptiRiIiISASGhoY4ePAgqlatCnd3d4SHh4sdidQcCxxREZPJZBg+fDhOnz6NgwcPwtnZWexIREREJCILCwscO3YMpqam6NChA6Kjo8WORGqMBY6oCAmCAB8fH+zYsQPbtm1D+/btxY5EREREaqBMmTI4deoUEhIS0KlTJ8THx4sdidQUCxxREVq0aBGWLl2K5cuXo1+/fmLHISIiIjVSpUoVnDhxAk+ePMHXX3+N1NRUsSORGmKBIyoiW7duxbRp0/DTTz9hwoQJYschIjWgq6uLXr16oVevXtDV1RU7DhGpgfr16+Pw4cO4fPkyBg0aBKlUKnYkUjMSQRAEsUMUV/Hx8bC0tERcXByv/aXlDh8+jK+//hrDhw/H2rVrebFeIiIiypWfnx969OiB0aNHw9fXl58dioG8dgPugSMqZJcuXUKfPn3QrVs3rF69mj+AiYiI6Is8PT2xbt06rF69GnPnzhU7DqkRPbEDEGmzu3fvwsPDAy4uLti1axcPkSIiIqI8GzFiBN6+fYsZM2agTJkyGD16tNiRSA2wwBEVkmfPnqFjx46oVKkS/Pz8YGRkJHYkIlIziYmJMDMzAwAkJCTA1NRU5EREpG5++OEHvHnzBmPHjoW1tTV69eoldiQSGQscUSH4+PEjPDw8YGhoiBMnTsDS0lLsSERERKSBJBIJli5diujoaAwaNAiVKlXiNWSLOZ4DR6RiMpkMgwYNQkREBA4fPgxbW1uxIxEREZEG09HRwebNm9GgQQN4enoiMjJS7EgkIhY4IhWbOXMm/P39sXv3btStW1fsOERERKQFjIyMcPDgQejq6qJ79+5ITk4WOxKJhAWOSIV2796N3377Db///ju6dOkidhwiIiLSIra2tvDz80NoaChGjBgBXg2seGKBI1KRGzduYNiwYRg0aBB8fHzEjkNERERaqFGjRti6dSt27dqFBQsWiB2HRMBBTIhUIDIyEt27d0f9+vWxfv16XuuNiIiICk2fPn0QGhqKGTNmoG7duvDw8BA7EhUhFjiiAkpOTsbXX38NiUSCgwcP8nIBRJRnurq66Ny5s/xrIqK8mjNnDkJDQzFgwABcvXoVDg4OYkeiIiIRePCsaOLj42FpaYm4uDhYWFiIHYfyQRAEDBo0CAcOHEBAQACcnJzEjkRERETFREJCApo3b474+HgEBgbC2tpa7EhUAHntBjwHjqgAFi5ciJ07d2LLli0sb0RERFSkzMzM4Ofnh4SEBPTq1QtpaWliR6IiwAJHlE+HDx/G9OnT8dNPP6Fv375ixyEiIqJiqFKlSjh48CCuXLmCiRMncmTKYoAFjigfMo857969O37++Wex4xCRhkpMTISpqSlMTU2RmJgodhwi0lDNmjXD2rVrsW7dOqxevVrsOFTIOIgJkZLevXuHbt26oUqVKti2bRt0dPh3ECLKv6SkJLEjEJEWGDZsGO7evYtvv/0WtWrVQtu2bcWORIWEnzyJlJCeno7evXvj48eP8PPzg5mZmdiRiIiIiAAAixYtQrt27dC7d288efJE7DhUSFjgiJTg4+ODy5cv48CBA7C3txc7DhEREZGcnp4e/vrrL5QuXRrdu3fnHn4txQJHlEeHDh3C8uXLsWTJErRo0ULsOERERERZWFlZ4eDBgwgPD8fEiRPFjkOFgAWOKA8iIiIwdOhQ9OjRA+PHjxc7DhEREVGO6tSpg1WrVmHTpk3YuXOn2HFIxVjgiL4gLS0N/fr1g5WVFTZt2gSJRCJ2JCIiIqJcDRkyBN7e3hg1ahQePnwodhxSIY5CSfQFM2bMQHBwMC5fvgwrKyux4xCRFtHR0UGrVq3kXxMRqdKqVasQGBiIPn364Nq1azA2NhY7EqkAf1sQ5eLIkSNYsmQJFixYgCZNmogdh4i0jLGxMc6fP4/z58/zgxURqZyZmRn27t2LR48eYcqUKWLHIRVhgSPKwYsXLzB48GB4eHhg8uTJYschIiIiUpqjoyNWrFiBtWvXYu/evWLHIRVggSPKRnp6Ovr37w9TU1Ns3bqV570RERGRxhoxYgT69euHESNG8PpwWoAFjigbs2bNwrVr1/DXX3+hZMmSYschIi2VmJiI0qVLo3Tp0khMTBQ7DhFpKYlEgnXr1qFMmTLo27cvUlNTxY5EBcACR/SZkydP4vfff8e8efPQtGlTseMQkZZ79+4d3r17J3YMItJyFhYW2Lt3L0JDQ+Hj4yN2HCoAFjiiT0RGRmLQoEHo1KkTf7gRERGRVmnYsCGWLl2KlStX4sCBA2LHoXxigSP6fxkZGRgwYAD09fXx559/ckhvIiIi0jpjx45Fz549MWzYMISHh4sdh/KBn1CJ/t/cuXMREBCA3bt3o3Tp0mLHISIiIlI5iUSCjRs3omTJkujbty/S0tLEjkRKYoEjAnDmzBn8+uuv+Pnnn9GyZUux4xAREREVGisrK+zZswe3bt3C9OnTxY5DSmKBo2Lvw4cPGDRoENq0acMfYkRERFQsODs7Y+HChVi6dCn++ecfseOQEljgqNj79ttvkZSUhK1bt0JXV1fsOERUjOjo6KBx48Zo3Lgxz7sloiI3ceJEtGnTBsOHD0d8fLzYcSiP+NuCijV/f39s374dy5cvR/ny5cWOQ0TFjLGxMW7cuIEbN27A2NhY7DhEVMzo6Ohg06ZN+PDhA7777jux41AescBRsRUTE4NRo0aha9eu8Pb2FjsOERERUZGzt7fHkiVLsHHjRpw8eVLsOJQHLHBUbE2cOBGpqalYt24dJBKJ2HGIiIiIRDFy5Ei0b98ew4cPR2xsrNhx6AtY4KhYOnDgAHbt2oUVK1bAzs5O7DhEVEwlJSXB3t4e9vb2SEpKEjsOERVTmZcWiI+Px5QpU8SOQ1/AAkfFztu3bzF69Gh4enrCy8tL7DhEVIwJgoBnz57h2bNnEARB7DhEVIxVrFgRf/zxB7Zs2YKjR4+KHYdywQJHxc748eMhlUqxdu1aHjpJRERE9P+GDRuGjh07YuTIkfjw4YPYcSgHLHBUrOzbtw979+7FqlWrYGtrK3YcIiIiIrUhkUiwYcMGJCUlYdKkSWLHoRywwFGxER0djbFjx6Jnz57o27ev2HGIiIiI1E758uWxfPlybNu2Df7+/mLHoWywwFGxIAgCxo4dCwBYvXo1D50kIiIiyoG3tze6dOmCUaNGISYmRuw49BkWOCoW9uzZg7///hurV6+GjY2N2HGIiIiI1JZEIsH69euRkpKCiRMnih2HPsMCR1ovKioK48aNQ58+fdC7d2+x4xARyUkkEtSpUwd16tThkQFEpFbs7OywcuVK7Nq1CwcPHhQ7Dn1CInDcYtHEx8fD0tIScXFxsLCwEDuOVhIEAV9//TWuXr2Ke/fuwdraWuxIRERERBqBn6OKVl67AffAkVbbu3cv/Pz8sHbtWv7QISIiIlKCRCLB2rVrkZGRgW+//VbsOPT/WOBIa338+BGTJ0/G119/ja+//lrsOEREREQax9bWFkuWLMGuXbtw/vx5seMQWOBIi82ZMwdxcXFYtmyZ2FGIiLKVlJSEunXrom7dukhKShI7DhFRtry9vdG0aVOMGzcO6enpYscp9ljgSCuFhoZi+fLlmDlzJipWrCh2HCKibAmCgH///Rf//vsveEo6EakrHR0drF69Gg8ePMDy5cvFjlPsscCR1hEEAePGjUO1atUwZcoUseMQERERabz69etj/PjxmDNnDl6+fCl2nGKNBY60zq5du3Dx4kWsXLkSBgYGYschIiIi0go///wzzMzMMHXqVLGjFGsscKRV4uLiMHXqVPTu3Rvt27cXOw4RERGR1rCyssKiRYuwZ88enDlzRuw4xRYLHGmVOXPm4OPHj1i6dKnYUYiIiIi0zsCBA9G8eXOMHz8eaWlpYscplljgSGvcuXMHK1euxKxZs1C+fHmx4xARERFpHYlEgtWrV+Px48f4448/xI5TLBWbArdq1SrY29vDyMgILi4uCAwMzHHerVu3QiKRKNyMjIwU5hEEAbNmzULZsmVhbGyMdu3a4fHjx4X9NCgHgiBg7NixqFGjBiZNmiR2HCKiPJFIJKhUqRIqVaoEiUQidhwiojxxdHTExIkTMXfuXLx48ULsOMVOsShwe/bswZQpUzB79myEhISgfv36cHd3R3R0dI7LWFhY4PXr1/Lbs2fPFB5fuHAhVqxYgbVr1+L69eswNTWFu7s7UlJSCvvpUDa2b9+Oy5cvw9fXlwOXEJHGMDExQUREBCIiImBiYiJ2HCKiPJszZw4sLCw44rcIikWBW7p0KUaOHImhQ4eiTp06WLt2LUxMTLB58+Ycl5FIJLC1tZXfypQpI39MEAQsW7YMP/30Ezw9PVGvXj1s27YNkZGROHToUBE8I/pUbGwsfHx80K9fP7Rp00bsOERERERaz8LCAkuWLMH+/ftx6tQpseMUK1pf4NLS0hAcHIx27drJp+no6KBdu3a4evVqjsslJCSgUqVKqFChAjw9PXHv3j35Y+Hh4YiKilJYp6WlJVxcXHJdZ2pqKuLj4xVuVHCzZs1CUlISFi9eLHYUIiIiomKjf//+aNWqFSZMmIDU1FSx4xQbWl/g3r17B6lUqrAHDQDKlCmDqKiobJepWbMmNm/eDD8/P+zYsQMymQxNmzaVX7Qwczll1gkA8+fPh6WlpfxWoUKFgjw1AnDr1i2sWrUKc+bMQbly5cSOQ0SklOTkZDg7O8PZ2RnJyclixyEiUopEIsGqVasQFhbGEcCLkNYXuPxwdXWFt7c3GjRogFatWuHAgQMoXbo01q1bV6D1Tp8+HXFxcfIbT/osGJlMhnHjxqFWrVqYOHGi2HGIiJQmk8kQFBSEoKAgyGQyseMQESmtbt26mDRpEn755Rc8f/5c7DjFgtYXOGtra+jq6uLNmzcK09+8eQNbW9s8rUNfXx8NGzbEkydPAEC+nLLrNDQ0hIWFhcKN8m/Pnj24cuUKVq1aBX19fbHjEBERERVLs2fPhpWVFaZNmyZ2lGJB6wucgYEBnJycFK4WL5PJcObMGbi6uuZpHVKpFHfv3kXZsmUBAJUrV4atra3COuPj43H9+vU8r5MKJi0tDT/++CO6desGNzc3seMQERERFVvm5uaYO3cu/vrrL4SEhIgdR+tpfYEDgClTpmDDhg34888/cf/+fYwZMwaJiYkYOnQoAMDb2xvTp0+Xzz937lycOnUKT58+RUhICAYOHIhnz55hxIgRAP473nfSpEn49ddf4e/vj7t378Lb2xt2dnbo3r27GE+x2Fm3bh2ePXuG3377TewoRERERMXekCFDUKtWLfzwww9iR9F6emIHKAp9+/bF27dvMWvWLERFRaFBgwY4ceKEfBCS58+fQ0fnf132w4cPGDlyJKKiolCiRAk4OTnhypUrqFOnjnye77//HomJifjmm28QGxuL5s2b48SJE1ku+E2q9/HjR/zyyy8YPHgw6tatK3YcIiIiomJPT08Pv/32G3r06IHTp08rjNZOqiURBEEQO0RxFR8fD0tLS8TFxfF8OCXMmTMHv//+Ox4/fsyRPIlIoyUmJsLMzAzAf5evMTU1FTkREVH+CYKApk2bIj09HYGBgQo7SOjL8toN+F0ljfLmzRssWbIEEyZMYHkjIq1gbW0Na2trsWMQERWYRCLBggULEBwcjP3794sdR2txD5yIuAdOeRMmTMD27dvx9OlTlCxZUuw4RERERPSZrl274uHDh/j33385UrgSuAeOtE5YWBjWrVuHH374geWNiIiISE3Nnz8fYWFh2Lhxo9hRtBL3wImIe+CUM2DAAFy4cAGPHz+GiYmJ2HGIiIiIKAeDBw/GyZMn8eTJE/m5vpQ77oEjrXLz5k3s3r0bc+bMYXkjIq2RnJwMNzc3uLm5ITk5Wew4REQqM3fuXHz48AHLli0TO4rW4R44EXEPXN65u7vj2bNnCA0NhZ5esbj6BREVAxyFkoi02ZQpU7Bx40aEhYWhdOnSYsdRe9wDR1rjzJkzOHXqFH777TeWNyIiIiINMWPGDEgkEvz2229iR9EqLHCk1gRBwA8//AAXFxd8/fXXYschIiIiojyytrbG999/j9WrVyMiIkLsOFqDBY7U2v79+xEUFIQFCxZAIpGIHYeIiIiIlDBp0iSULFkSs2bNEjuK1mCBI7WVnp6OGTNmoHPnzmjVqpXYcYiIiIhISaamppg9ezZ27NiBO3fuiB1HK7DAkdratm0bnjx5gvnz54sdhYiIiIjyafjw4ahWrRpmzpwpdhStwAJHakkqleL3339Hjx49UK9ePbHjEBEVGhMTE14ehYi0mr6+PmbMmAF/f3+EhoaKHUfjscCRWtq/fz+ePHmC6dOnix2FiKjQmJqaIjExEYmJibyEABFpNS8vL1SsWBG///672FE0HgscqR1BEDB//ny0b98ejRs3FjsOERERERWQvr4+pk6dir/++gtPnz4VO45GY4EjtXP8+HHcvn0bM2bMEDsKEREREanI8OHDUbJkSSxatEjsKBqNBY7UiiAImDdvHlxdXTnyJBFpvZSUFHTp0gVdunRBSkqK2HGIiAqViYkJJk+ejM2bN+P169dix9FYLHCkVgICAnDlyhXMmDGD130jIq0nlUpx7NgxHDt2DFKpVOw4RESFbuzYsTAyMsLSpUvFjqKxWOBIrcyfPx+Ojo7o0qWL2FGIiIiISMUsLS0xbtw4rF27Fu/fvxc7jkZigSO1ERISghMnTmD69Onc+0ZERESkpSZNmoSMjAz4+vqKHUUjscCR2pg/fz6qVq2K3r17ix2FiIiIiAqJjY0NRo4cieXLlyMhIUHsOBqHBY7UwoMHD/D3339j2rRp0NPTEzsOERERERWiqVOnIj4+HuvXrxc7isZhgSO1sHDhQpQtWxbe3t5iRyEiIiKiQlaxYkUMHDgQS5YsQWpqqthxNAoLHInu+fPn2L59O7777jsYGhqKHYeIiIiIisC0adPw+vVrbNu2TewoGoUFjkS3ePFiWFhY4JtvvhE7ChFRkTI1NYUgCBAEAaampmLHISIqUrVq1ULPnj2xYMECZGRkiB1HY7DAkaiio6OxYcMGfPvttzAzMxM7DhEREREVoenTpyMsLAz79u0TO4rGYIEjUS1fvhx6enoYP3682FGIiIiIqIg1atQI7u7umD9/PgRBEDuORmCBI9GkpKRg3bp1GD58OEqWLCl2HCKiIpeSkoLevXujd+/eSElJETsOEZEofHx8cPfuXVy4cEHsKBqBBY5Es2/fPsTExGDs2LFiRyEiEoVUKsX+/fuxf/9+SKVSseMQEYmiTZs2qFWrFlavXi12FI3AAkeiWbVqFdq3b48aNWqIHYWIiIiIRCKRSDB27FgcPHgQkZGRYsdReyxwJIrg4GBcv34d48aNEzsKEREREYnM29sbhoaGvLB3HrDAkShWrVqFChUqoEuXLmJHISIiIiKRWVpaYuDAgVi/fj3S09PFjqPWWOCoyL1//x67d+/G6NGjoaenJ3YcIiIiIlID48aNw+vXr3Ho0CGxo6g1Fjgqclu2bIFUKsWIESPEjkJEREREasLR0REtWrTAqlWrxI6i1ljgqEjJZDKsWbMGvXv3ho2NjdhxiIiIiEiNjB07FhcuXEBoaKjYUdQWCxwVqZMnTyIsLIyDlxARATAxMUFCQgISEhJgYmIidhwiItH16NEDZcqU4SUFcsECR0Vq9erVaNCgAVxdXcWOQkQkOolEAlNTU5iamkIikYgdh4hIdAYGBvjmm2+wfft2xMfHix1HLbHAUZEJDw/H0aNHMW7cOH5QISIiIqJsffPNN0hOTsb27dvFjqKWWOCoyKxduxYWFhbo37+/2FGIiNRCamoqhgwZgiFDhiA1NVXsOEREaqF8+fLw9PTEqlWrIAiC2HHUDgscFYmUlBRs2rQJQ4cOhampqdhxiIjUQkZGBv7880/8+eefyMjIEDsOEZHaGDduHO7fv48LFy6IHUXtsMBRkdi7dy9iYmIwZswYsaMQERERkZpr3bo1atWqxUsKZIMFjorEqlWr0L59e9SoUUPsKERERESk5iQSCcaOHYuDBw/i1atXYsdRKyxwVOhCQkIQGBjISwcQERERUZ55e3vDyMgIGzduFDuKWmGBo0K3fft22NjYoEuXLmJHISIiIiINYWlpid69e2P79u0czOQTLHBUqDIyMrB79270798fenp6YschIiIiIg0ycOBAhIWF4fr162JHURsscFSoTp8+jTdv3mDQoEFiRyEiIiIiDePm5oZy5crxmnCfYIGjQrVjxw7UqlULjRo1EjsKEZHaMTExQXR0NKKjo2FiYiJ2HCIitaOrq4sBAwZgz549SEtLEzuOWmCBo0KTkJCAgwcPYuDAgZBIJGLHISJSOxKJBKVLl0bp0qX5c5KIKAcDBw5ETEwMTp48KXYUtcACR4Xm4MGDSEpKgpeXl9hRiIiIiEhD1atXD/Xq1eNhlP+PBY4KzY4dO9CiRQvY29uLHYWISC2lpqZi3LhxGDduHFJTU8WOQ0SktgYOHAh/f3/ExcWJHUV0LHBUKF6/fo3Tp09j4MCBYkchIlJbGRkZWL16NVavXo2MjAyx4xARqa3+/fsjLS0Nf//9t9hRRMcCR4Xir7/+gp6eHnr37i12FCIiIiLScOXLl0ebNm2wY8cOsaOIjgWOCsX27dvRtWtXlChRQuwoRERERKQFBg4ciPPnz+PFixdiRxEVCxyp3L1793Dz5k0ePklEREREKtOjRw8YGhpi165dYkcRFQscqdzOnTtRokQJdO7cWewoRERERKQlLCws4Onpie3bt0MQBLHjiIYFjlRKJpNh586d6NOnDwwNDcWOQ0RERERaZNCgQbh37x5u374tdhTRsMCRSgUEBOD58+c8fJKIiIiIVK5Dhw6wtrYu1oOZsMCRSu3YsQP29vZo2rSp2FGIiNSesbExwsPDER4eDmNjY7HjEBGpPX19ffTr1w+7du2CVCoVO44oWOBIZVJTU7Fv3z54eXlBR4cvLSKiL9HR0YG9vT3s7e35c5OIKI8GDRqE169f49y5c2JHEQV/W5DKnD9/HnFxcejTp4/YUYiIiIhISzk7O6NixYo4dOiQ2FFEwQJHKuPv749KlSrB0dFR7ChERBohLS0NPj4+8PHxQVpamthxiIg0gkQiQbdu3eDv718sR6NkgSOVEAQB/v7+8PT0hEQiETsOEZFGSE9Px+LFi7F48WKkp6eLHYeISGN4enrixYsXxXI0ShY4UombN2/i5cuX6Natm9hRiIiIiEjLtWzZEhYWFvDz8xM7SpFjgSOV8Pf3h6WlJVq2bCl2FCIiIiLScgYGBujUqRP8/f3FjlLkWOBIJfz9/dG5c2fo6+uLHYWIiIiIigFPT0+EhITg5cuXYkcpUixwVGAvXrzAzZs3efgkERERERWZjh07Qk9PD4cPHxY7SpFigaMC8/f3h56eHjp27Ch2FCIiIiIqJkqUKIGWLVsWu/PgWOCowPz9/eHm5gYrKyuxoxARERFRMeLp6YmzZ88iPj5e7ChFhgWOCiQ+Ph7nzp3j4ZNERPlgbGyM0NBQhIaGwtjYWOw4REQax8PDA+np6Th16pTYUYoMCxwVyIkTJ5Cens4CR0SUDzo6Oqhbty7q1q0LHR3+SiYiUlblypXh6OhYrA6j5G8LKhB/f3/Ur18flSpVEjsKERERERVDnp6eOHr0KDIyMsSOUiRY4Cjf0tPTcfToUe59IyLKp7S0NMyZMwdz5sxBWlqa2HGIiDRSt27d8OHDB1y+fFnsKEVCTxUrSU5OxtWrVxEcHIynT58iKioKiYmJ0NfXh5WVFSpWrIi6devCxcUF1atXV8UmSQ1cvnwZsbGx8PT0FDsKEZFGSk9Px88//wwA8PHxgYGBgciJiIg0j5OTE+zs7ODv749WrVqJHafQ5bvAJSQkYN++fdi9ezcCAgLy/JfDihUrokePHvDy8kKjRo3yu3lSA35+frCzs+P/IxERERGJRkdHBx4eHvDz88PixYshkUjEjlSolD6E8tWrV5g0aRLKly+PESNG4PTp00hNTYUgCPKbkZERypYtCysrK0gkEoXHnj17hmXLlsHZ2Rmurq7Yt29fYTwvKmSCIMDf3x/dunXT+jcJEREREam3bt26ISwsDP/++6/YUQpdngtcbGwsJk2ahGrVqmHlypWIj4+Hjo4O3Nzc8MMPP+DAgQN49uwZkpOTkZiYiJcvXyImJgbp6el4//49rl27hpUrV2LQoEEoV64cBEHA9evX0a9fP9SrVw8nT54szOdJKhYWFoanT5+iS5cuYkchIiIiomKuTZs2MDIyKhaXE5AIgiDkZUZra2t8+PABgiDgq6++woABA9C3b1+ULl06Xxu+ePEidu7ciX379iE2NhYSiQTLly/H+PHj87U+TRQfHw9LS0vExcXBwsJC7DhK2bBhA8aMGYP3799rXHYiInWRmJgIMzMzAP+dmmBqaipyIiIizdW2bVuYmprC399f7Cj5ktdukOc9cO/fv0eHDh1w+fJlXLlyBePHj893eQOAli1bYt26dXj+/DnmzZuHUqVK4f379/le35esWrUK9vb2MDIygouLCwIDA3Ocd8OGDWjRogVKlCiBEiVKoF27dlnmHzJkCCQSicKtY8eOhZZf3Zw7dw5OTk4sb0RERESkFlq3bo2LFy9CKpWKHaVQ5bnABQYG4vjx43B1dVVpADMzM0yfPh0RERHo3bu3Stedac+ePZgyZQpmz56NkJAQ1K9fH+7u7oiOjs52/vPnz6N///44d+4crl69igoVKqBDhw549eqVwnwdO3bE69ev5bfdu3cXSn51IwgCzp8/Dzc3N7GjEBEREREBANzc3BAXF4dbt26JHaVQ5bnANW7cuDBzwMTEBLVr1y6UdS9duhQjR47E0KFDUadOHaxduxYmJibYvHlztvPv3LkTY8eORYMGDVCrVi1s3LgRMpkMZ86cUZjP0NAQtra28luJEiUKJb+6efToEV6/fo3WrVuLHYWISKMZGRkhMDAQgYGBMDIyEjsOEZFGa9KkCUxMTHDu3DmxoxQqrb+Qd1paGoKDg9GuXTv5NB0dHbRr1w5Xr17N0zqSkpKQnp6OkiVLKkw/f/48bGxsULNmTYwZMwYxMTG5ric1NRXx8fEKN0107tw56OnpoXnz5mJHISLSaLq6unB2doazszN0dXXFjkNEpNEMDAzQrFkzFjhN9+7dO0ilUpQpU0ZhepkyZRAVFZWndUybNg12dnYKJbBjx47Ytm0bzpw5gwULFuDChQvo1KlTrsfczp8/H5aWlvJbhQoV8vekRHb+/Hk4OzvLT7wnIiIiIlIHrVu3RkBAADIyMsSOUmgKtcBdvnwZ33zzDZo0aYKaNWvC2dkZI0eOREBAQGFuVqV+//13/PXXXzh48KDC4S39+vVDt27d4OjoiO7du+PIkSO4ceMGzp8/n+O6pk+fjri4OPntxYsXRfAMVIvnvxERqU5aWhoWLVqERYsWIS0tTew4REQaz83NDR8/fkRwcLDYUQqNXmGteOLEiVi1ahWA/z70ZwoJCcHmzZsxZswY+Pr6Ftbm5aytraGrq4s3b94oTH/z5g1sbW1zXXbx4sX4/fffcfr0adSrVy/XeatUqQJra2s8efIEbdu2zXYeQ0NDGBoaKvcE1Mz9+/fx5s0bnv9GRKQC6enp+P777wEAY8eOhYGBgciJiIg0W+PGjWFqaorz58/DxcVF7DiFolD2wK1Zswa+vr4wNjbGuHHjsHv3bhw/fhzr169Hy5YtIQgC1qxZgzVr1hTG5hUYGBjAyclJYQCSzAFJchtRc+HChfjll19w4sSJPA3gknnh8rJly6okt7o6d+4c9PX10axZM7GjEBEREREp0NfXR4sWLbT6PLhCKXBr166Fjo4O/vnnH6xYsQJ9+/aFu7s7RowYgXPnzmHIkCHyElcUpkyZgg0bNuDPP//E/fv3MWbMGCQmJmLo0KEAAG9vb0yfPl0+/4IFCzBz5kxs3rwZ9vb2iIqKQlRUFBISEgD8d7FVHx8fXLt2DREREThz5gw8PT1RrVo1uLu7F8lzEkvmXzNMTEzEjkJERERElEXr1q1x6dIlpKenix2lUChV4LZs2ZKn+R49eoTq1avnuIdr2LBhAIDHjx8rs/l869u3LxYvXoxZs2ahQYMGuHXrFk6cOCEf2OT58+d4/fq1fP41a9YgLS0NvXr1QtmyZeW3xYsXA/hv1LA7d+6gW7duqFGjBoYPHw4nJycEBARo/CGSuZHJZDz/jYiIiIjUmpubGxITE3Hjxg2xoxQKifDpCWpfoKOjg6ZNm2LVqlWoX79+jvPZ2NggPT0db9++hZ5e1tPsdu3ahYEDB6JUqVJ4+/Zt/pJrgfj4eFhaWiIuLg4WFhZix/miu3fvol69ejhz5gzatGkjdhwiIo2XmJgoH9E3ISEBpqamIiciItJ8GRkZKFmyJKZNm4Yff/xR7Dh5ltduoNQeOBcXF1y5cgWNGzfGhAkTEBcXl+18bdq0QXx8PLy8vBAdHa3wWEBAAL777jtIJBKFYflJ/Z07dw4GBga5njtIRERERCQmPT09tGzZMtfR4TWZUgXu6tWrWL9+PaysrLBq1SrUrFkTW7duzTLfr7/+CisrK+zfvx8VK1ZE3bp10bRpU1SsWBFubm548+YNSpQogV9//VVVz4OKwPnz5+Hq6gpjY2OxoxARERER5ah169a4fPkyUlNTxY6ickoPYjJixAg8evQI33zzDd69e4fhw4ejefPmuH37tnyeatWq4dKlS2jZsiXS0tJw//59XLt2DS9fvoQgCHBzc8OlS5dQtWpVlT4ZKjwymQwXLlzg5QOIiFTIyMgI586dw7lz5xSuNUpERAXTunVrJCcnIzAwUOwoKqfUOXCfCwoKwrhx43Djxg3o6upi9OjR+PXXX2FpaSmf5+XLl7h16xbi4uJgaWmJ+vXro0KFCioJr+k06Ry4O3fuoH79+jh//jxatWoldhwiIiIiohxJpVJYW1tjypQpmDlzpthx8iSv3aBABQ747yLdGzZswI8//oiYmBjY2Njg999/x5AhQwqy2mJBkwrcxo0bMWrUKHz8+JGXECAiIiIitefu7g5DQ0P4+/uLHSVPCmUQk+xIJBJ88803ePjwIUaMGCE/rLJZs2a4detWQVdPaiIoKAh16tRheSMiUqH09HSsWrUKq1at0trrFRERicXJyQlBQUFix1A5lV3Iu2TJkli/fj2uXr2KRo0a4erVq3B2dsb48eNzHK2SNEdwcDAaN24sdgwiIq2SlpaG8ePHY/z48UhLSxM7DhGRVmncuDFev36NyMhIsaOolMoKXCZnZ2cEBgZi1apVsLCwwOrVq1GzZs08XwSc1E9qaipu374NJycnsaMQEREREeVJ5mfX4OBgkZOoVr4K3O7du9GhQwfY2NjA0NAQNjY26NChA3bt2gXgv8Mqx4wZg0ePHmHo0KF4+/YtRowYwcMqNVRoaCjS09O5B46IiIiINEbFihVhbW1dvAucVCpFr169MHDgQJw+fRrv3r1Deno63r17h9OnT2PQoEHo0aMHpFIpAKBUqVLYtGkTLl++jAYNGigcVhkbG1sYz4cKQXBwMHR1dVG/fn2xoxARERER5YlEItHK8+CUKnBLly7FgQMHAADffPMNzp07h/v37+PcuXP45ptvAAB+fn5YunSpwnJfffUVgoKCsHLlSpibm2PNmjU8rFKDBAUFoW7duryANxERERFplMaNGyM4OBgFHHhfrShV4LZu3QqJRAIfHx+sXbsWrVq1Qs2aNdGqVSusXbsW33//PQRByLaYSSQSjBs3Do8ePYK3tzfevXuHESNGqOyJUOEJDg7m+W9EREREpHGcnJwQFRWlVQOZKFXgnj59CgDw9PTM9vFu3boBAMLDw3Nch7W1NbZs2YKAgAAekqcBUlNTcffuXZ7/RkREREQaJ/MzrDadB6dUgcu8oFxERES2j2dONzc3/+K6mjZtqnXHo2qju3fvIj09nXvgiIgKgaGhIY4cOYIjR47A0NBQ7DhERFqnfPnyKF26tFb1DqUKXLt27SAIAqZOnYrAwECFx0JCQjBt2jRIJBK0a9cubxvXUflVDEjFgoKCoKenh3r16okdhYhI6+jp6aFLly7o0qUL9PT0xI5DRKR1JBKJ/Dw4baFUg/r1119RsmRJvH79Gq6urqhSpQqaNWuGqlWrwtnZGS9fvoSVlRV++eWXwspLRYwDmBARERGRJssciVJbBjJRqsBVrlwZV65cQevWrSEIAiIiInD16lWEh4dDEAS0bNkSAQEBqFq1amHlpSIWHBzM89+IiApJeno6tm7diq1btyI9PV3sOEREWqlx48aIjo7Gq1evxI6iEkofr1GjRg2cOXMGL1++xK1btxAXFwdLS0vUr18fFSpUKIyMJJKUlBSEhobKLxFBRESqlZaWhqFDhwIAevfuDX19fZETERFpn8yxHIKCglC+fHmR0xRcvg+4L1++vFZ8Ayhnd+7cQUZGBvfAEREREZHGKleuHMqUKYPg4GB0795d7DgFxlFEKEeZA5g4OjqKHYWIiIiIKF8kEon8PDhtwAJHObp58ybq1q0LIyMjsaMQEREREeWbk5MTQkJCxI6hEgUas/j9+/cIDw+Hrq4uqlWrBjMzszwtFxcXBz8/PwCAt7d3QSJQIXrw4AHq1KkjdgwiIiIiogKpXbs2oqOjERsbCysrK7HjFEi+9sDduXMH7dq1Q5kyZdCkSRM4OTnB2toa3bt3x507d764/MuXLzFkyBAMGzYsP5unIvLo0SPUrFlT7BhERERERAWS+Zn20aNHIicpOKUL3Pnz59GsWTOcO3cOUqkUgiBAEASkpaXh8OHDaNy4MX755Zc8XWdBW67FoI1iY2MRHR2NGjVqiB2FiIiIiKhAqlevDgB4+PChyEkKTqlDKOPi4jBgwAAkJiYC+O+aCm5ubkhNTcWFCxfkoxbOmTMHwcHB2LNnDwwNDQslOBWuzL9OcA8cEVHhMTQ0xN69e+VfExFR4TA3N4ednZ1W7IFTqsBt2rQJUVFRkEgkWLFiBcaNG6fw+NGjRzF+/Hg8e/YMhw8fRseOHXH48OE8nxtH6iPzrxOZf60gIiLV09PTQ+/evcWOQURULNSoUUMr9sApdQjlkSNHIJFI0Ldv3yzlDQC6dOmCkJAQuLu7QxAEXLx4EW3atMH79+9VFpiKxqNHj2BnZwdzc3OxoxARERERFVjNmjW1Yg+cUgUuNDQUAODl5ZXjPCVKlMDRo0cxcuRICIKA4OBgtGrVClFRUQVLSkXq0aNHPP+NiKiQZWRkYN++fdi3bx8yMjLEjkNEpNVq1KiBx48fQyaTiR2lQJQqcLGxsQCA8uXL575SHR2sW7cO06ZNgyAI+Pfff9GyZUu8ePEi30GpaD18+JDnvxERFbLU1FT06dMHffr0QWpqqthxiIi0Ws2aNZGUlIRXr16JHaVAlCpwJiYmAP5X5L5k/vz5mD9/PgRBQFhYGFq0aIEnT54oHZKKlkwmw+PHj7kHjoiIiIi0RuZnW00/jFKpAmdvbw8AuH37dp6XmTZtGnx9fQEAL168QMuWLXH37l1lNktF7NWrV0hKSmKBIyIiIiKtYW9vDz09PY0fyESpAteoUSMIgoATJ04otZGxY8di69at0NHRwZs3bzBkyBCllqeixUsIEBEREZG20dfXR9WqVYvXHrh27doBAP755x+ljx0dNGgQ9uzZA319faSnpyu1LBWthw8fQk9PT77HlYiIiIhIG2jDpQSUKnBdu3aFgYEBpFIp5s2bp/TGevToAX9/fxgbGyu9LBWdR48eoWrVqtDX1xc7ChERERGRymjDpQSUupC3hYUFDh06hJiYmHx/uO/QoQPOnDmj9GGYVHR4CQEiIiIi0kY1atRAREQEUlNTYWhoKHacfFGqwAFAx44dC7xRFxcXuLi4FHg9VDgePnyIHj16iB2DiEjrGRgYYMuWLfKviYiocNWsWRMymQxhYWGoU6eO2HHyRekCR9otNTUVERER3ANHRFQE9PX1ObAXEVER+vRSAppa4JQ6B460X3h4OGQyGQscEREREWmdMmXKwNzcHI8fPxY7Sr4Vyh64yMhIHDhwADdu3MDbt2+hp6eHcuXKoUWLFujRoweMjIwKY7OkApmji5YvX17kJERE2i8jIwMnT54EALi7u0NPjwfGEBEVJolEgvLlyys9or46Ueo3xbNnz+Dn5weJRIIxY8Zk+UWTkZGBH374Ab6+vtleKmD9+vWYMmUKfH190atXr4Ilp0IRGRkJAChbtqzISYiItF9qaiq6du0KAEhISGCBIyIqAnZ2dvLPvJpIqUMod+3ahcmTJ2PLli1ZfskIgoDevXvjjz/+QFpaGgRByPYWHR2Nvn37YsOGDSp9IqQar1+/hqWlJUxMTMSOQkRERESkcmXLlsXr16/FjpFvShW4s2fPAgB69uyZ5bEtW7bAz88PgiDAzs4Ovr6+ePz4MVJSUpCYmIg7d+5g9uzZMDMzgyAImDBhAp4+faqaZ0EqExkZCTs7O7FjEBEREREVCk3fA6fUsRpPnjwBADRp0iTLYytXrgQA1K5dGxcvXkSpUqUUHndwcICDgwP69OmD5s2bIzY2FsuXL8fy5cvzm50KwevXr1ngiIiIiEhrZRY4QRAgkUjEjqM0pfbAvXnzBgBgY2OjMP3jx4+4ffs2JBIJFi9enKW8fap27dqYNm0aBEHAP//8k4/IVJgiIyN5/hsRERERaa2yZcsiJSUFcXFxYkfJF6UKXObokQkJCQrTo6Oj5V+3atXqi+vJnOfFixfKbJ6KAPfAEREREZE2y/ysq6mHUSpV4CpUqAAAuHv3rsJ0CwsL+deCIOR94zq8DJ06EQSB58ARERERkVbL/KyrqQOZKNWgOnToAEEQsGXLFoXppUuXhr29PQDgwoULX1zPuXPnAPyvEJJ6iIuLQ3JyMg+hJCIqIgYGBvD19YWvry8MDAzEjkNEVCiePHmC3bt3Y/LkyWjWrBlMTEwgkUggkUiwdevWIs+T+Vm3WOyBGz58OPT09BAUFIRZs2YpPDZ58mQIgoCpU6fi/fv3Oa7j/v37WLhwISQSCbp06ZK/1FQoMv8KwT1wRERFQ19fH+PGjcO4ceOgr68vdhwiIpW7cOECqlevjgEDBmDZsmW4cuUKkpOTRc1kbGwMKyur4lHgatWqBR8fHwiCgHnz5sHT0xPXr18HAEyYMAFff/01Hjx4AEdHR6xYsQL3799HQkIC4uLicPPmTcyaNQsuLi6IjY2Fubk5JkyYUChPivKHF/EmIiIiIlX69PQqHR0d1K1bN9sR7YuaJl8LTqnLCADAvHnzEB0djU2bNuHIkSM4cuQIrK2t4ejoiJIlS8LIyAivX7/G5MmTs11eEAQYGBhgy5YtKF++fIGfAKlO5ouYBY6IqGhIpVIEBAQAAFq0aAFdXV2RExERqVa5cuWwaNEiODs7w8nJCWZmZti6dSsCAwNFzaXJ14JTusABwIYNG+Dq6gofHx98+PABb9++lZ/XBkB+PYXsBjRxcHDAmjVr0KxZs3xGpsISGRkJKysrmJiYiB2FiKhYSElJQevWrQH8N8KzqampyImIiFSrevXqmDp1qtgxsrCzs8PTp0/FjpEv+R4GctiwYXj27BnWrFmDNm3awMzMDIIgKNyA/8qcvb09vL294efnh9u3b7O8qanXr19z7xsRERFRIUpKSoK5uTkkEgm8vLy+OP/Vq1flA36sXr1aPv3Dhw/YsmULBg4ciDp16sDMzAwGBgawtbWFu7s71q9fj7S0tBzXGxERkWUgkQMHDqBz586ws7ODnp4e3NzcCvp01VaxOoTyU2ZmZhg1ahRGjRoFQRDw4sULvH//HomJiTAyMoK5uTnKly/PPToagpcQICIiIipcJiYm6N69O3bs2AE/Pz8kJibmuvd9586dAAA9PT306dNHPr1hw4Z49uxZlvnfvHmDU6dO4dSpU1i7di2OHTsGW1vbXDMJggBvb29s3749n89K82QeQikIgvzoQU1RoAL3KYlEgooVK6JixYqqWiUVscjISFSuXFnsGERERERazcvLCzt27EBiYiL8/PwwYMCAbOfLyMjAvn37AADu7u6wtraWPyaVSuHi4oKuXbuiYcOGKFOmDNLS0hAeHo4dO3bgxIkTuHnzJvr164fz58/nmmfZsmW4c+cOWrRogTFjxqBGjRqIjY1FRESEqp6y2rGzs0NKSgpiY2NRokQJseMoRWUFjjTf69ev4erqKnYMIiIiIq3Wrl072NjYIDo6Grt27cqxwJ0+fRrR0dEAkOVwy7Nnz6J69epZlmnatCm8vLywZcsWDBs2DBcuXMCZM2fQtm3bHPPcuXMH3t7e2Lp1q8btjcqvzNOGXr9+rXEFLt/nwJH2iYuL07gXMBEREZGm0dPTQ9++fQEAp06dQkxMTLbzZR4+aWZmBk9PT4XHsitvnxo6dCgaNGgAADh06FCu81pZWcHX17fYlDfgv+cM/Pf5V9OwwJHcx48fYW5uLnYMIiIiIq2XuUctPT0de/fuzfJ4cnKyvHh179491zElBEFAVFQUHj16hNDQUPmtXLlyAIDbt2/nmsXDw6PYfQbMfL4fP34UOYnyivwQyipVqsi/lkgkCAsLK+oIlI309HSkpqYWuzcvEZGY9PX1sXDhQvnXRFR8uLi4oGrVqggLC8POnTsxZswYhcf9/f2RkJAAIOvhk5mOHj2KNWvW4OLFi7kWkXfv3uWapV69ekqm13wscErIHLJUE0d80WaZL14WOCKiomNgYAAfHx+xYxCRSLy8vDB37lxcuXIFERERsLe3lz+WefikjY0N2rVrp7CcIAgYOXIkNm3alKftJCcn5/p4cTyFRpMLnCiHUGZ3gW8SV+aL18zMTOQkRERERMVD5p41QRCwe/du+fT379/j5MmTAIC+fftCT09xn8vmzZvl5a1BgwbYunUr7t+/j/j4eGRkZMivyTxo0CD5+nOjq6ursuekKfT19WFoaCjfy6lJirzAyWQy+U0qlRb15ikH3ANHRFT0pFIpbty4gRs3bvB3IlExVKNGDTRu3BgAsGvXLvn0/fv3yy/Cnd3hkxs2bAAAVKtWDVeuXMHgwYNRq1YtmJubK5Sx9+/fF2Z8jWdmZsY9cKS5Mv/6wAJHRFR0UlJS0KRJEzRp0gQpKSlixyEiEWQWtNDQUNy5cwfA/w6frFq1KlxcXLIsc+/ePQBAt27dYGxsnO16BUFASEhIYUTWGubm5ixwpLm4B46IiIio6PXr10++12znzp14+fIlAgICAOQ8eElGRgYAIDExMcf1+vn54fXr1ypOq11Y4Eij8Rw4IiIioqJna2uLNm3aAAB2796NXbt2yc9Zy6nAZV4D7vDhw9keJhkWFoZx48YVUmLtYW5urpHnwBX5KJSknrgHjoiIiEgcXl5e+Oeff/DixQvMnz8fANC4cWPUqFEj2/m9vb3h4+ODyMhIuLq6Ytq0aXBwcEBKSgrOnj2LZcuWITU1FY0aNVKLwyj379+vUJQuXbqU7dfAf4W2Y8eORZJLU/fAFbjAxcTEQFdXV34187xISkpCUFAQAKBly5YFjUAq8PHjRxgYGMDAwEDsKERERETFSo8ePTBmzBgkJycjNjYWQM573wDg22+/xT///INTp07h0aNHGD58uMLjxsbG2LZtG44ePaoWBW7q1Kl49uxZto9t2rRJ4XIIrVq1KrICV6wGMYmMjMSIESNQsmRJ2NjYoFSpUrCzs8OkSZMQFRX1xeXDw8Ph5uYm311M4ktISODhk0REREQiMDc3h4eHh/y+rq4u+vXrl+P8+vr6OHr0KFasWIHGjRvDxMQExsbGqFatGkaPHo2QkBD07t27KKJrNE3dAycRlLwo2927d9G+fXu8ffs2yzUlJBIJzMzM4OvrK7/uRHbu3bsHR0dHSCSSYj1scnx8PCwtLREXFwcLCwtRs/z444/YuXMnIiIiRM1BRFScJCYmyv94lpCQAFNTU5ETERUfsbGxWLRoERo3bowOHTrw/VcMTZw4EefPn5eP/im2vHYDpQ6hTElJQa9evRAdHQ0AKFu2LJo1a4bU1FRcvXoV7969w8ePHzFkyBAEBgZi5cqVBXsWVGQ+fvzI89+IiIqYvr4+Zs+eLf+aiIrO5s2b8dtvvwEADAwM0KFDB/To0QNdu3ZF6dKlRU5HRUFT98ApVeC2b9+Ox48fQyKRYPLkyZg/f778F05GRgbWrVuHn376CXFxcVi9ejViYmKwffv2Ynl1d03DAkdEVPQMDAwwZ84csWMQFUt6enrQ0dGBTCZDWloajh8/jiNHjkAikeCrr75Cr1694OnpiapVq4odlQqJphY4pc6BO3DgAACgQ4cOWLx4scJfC/X09DBu3DgEBwejXr16EAQBe/bsQffu3ZGamqra1KRyHz9+5DlwREREVGzo6+srnA6UeVqPIAi4du0afHx8UK1aNdSuXRszZ85EcHBwltOHSLNp6iAmSu2Bu337NiQSCUaOHJnjPFWqVMHVq1fRq1cvHD9+HMeOHUOnTp1w+PBhHlusxhISErgHjoioiMlkMty/fx8AULt2bejo8PKsREVFT08vx0ImCIL8sQcPHmD+/Pn49ddfYWtri549e6J79+5o1aqV1h/6HBsbi5cvX+ZrWQcHBxWnUT1zc3OkpaUhLS1No0ZiV6rAxcTEAMAXdyUbGxvD398fgwcPxq5du3DhwgW0a9cOx48fV+pyA1R0UlJSUKpUKbFjEBEVK8nJyfIPORzEhKhoKVO+MvfORUVFYd26dVi1apV85Mivv/4a7u7uWvmH8EOHDmHo0KH5WlYT9lYaGRkBAFJTUzWqwCn1p77MJ5aXXY26urrYsWMHRo8eDUEQEBgYiNatW+Pdu3f5S0qFSiaT8VxFIiIiKjbyu/csIyMDwH+fh/fu3YvevXujVKlS6NSpEzZs2JCnS2qResg86kEmk4mcRDlKFbiKFSsCgPxwj7xYvXo1fHx8IAgC7ty5g5YtW+LVq1fKpaRCJ5PJeOgOERERFRuqOPwxs8ylp6fj1KlT+Oabb2BnZwcXFxcsXLgQjx49KvA2xDRkyBD54aTK3jRB5s4LTStwSh1C2aBBA9y/fx+nTp3K9Ty4zy1YsADm5uaYNWsWHj58iL59+yodlAoXCxwRERFpIqlUioyMDKSnp8v//fTrjIwMpKWlITU1FSkpKUhJSUFqaiqCgoJUmiOzBGQeeRYYGIhp06bBxMQEpUuXRvPmzbFjxw6VbpMKRlP3wClV4Nq0aYPdu3fjyJEjiImJUeqcqZ9++gnm5uaYMmUK4uPjlQ5KhYsFjoiISPsIgpCl3GT3b2pqKlJTU5GcnJyl6Hx+yxz04dOv09PTFf7NfDy7eT7ffmYBk0qlkMlk8n817UN1TpKSkvDs2TOeqqKGikWB8/DwgK6uLtLS0rBkyRL5xQ/z6ttvv4WFhQW++eYb+cmgRWXVqlVYtGgRoqKiUL9+faxcuRJNmjTJcf59+/Zh5syZiIiIQPXq1bFgwQJ07txZ/rggCJg9ezY2bNiA2NhYNGvWDGvWrEH16tWL4umoHAscEREVR9ntvfn838wy8mm5yfw3LS0ty7+pqalIT0/P8u+nJSaz2HxecD7de/TpTSqVZik4mnKYWnGUeX05a2trNG/eHC1atECtWrXEjkWfyfzsW9S9pKCUKnA2NjZYs2YN3rx5k++RsoYOHYqSJUvi0KFD+Vo+P/bs2YMpU6Zg7dq1cHFxwbJly+Du7o6HDx/CxsYmy/xXrlxB//79MX/+fHTt2hW7du1C9+7dERISIh8tbOHChVixYgX+/PNPVK5cGTNnzoS7uzv+/fdf+Yg2moSDmBAR0ecy997kdFhabntvPv3381tmqfnS3ptPy1Dm9E+3+3nB+bzcsOAUjI6Ojvymq6srv+np6UFfXx96enryrw0MDOT/GhgYwNDQUP5vdo9/evt03k9vxsbGMDAwgLGxsfy+oaGhwvaz+1dXVzdPf5Q+ffo02rdvr7Lvl56eHjIyMmBjY4OBAwdiwIABaNSoESQSicq2QaqlqXvgJEIx+Onm4uICZ2dn+Pr6AvjvP6lChQqYMGECfvjhhyzz9+3bF4mJiThy5Ih82ldffYUGDRpg7dq1EAQBdnZ2+O677zB16lQAQFxcHMqUKYOtW7eiX79+ecoVHx8PS0tLREZGwsLCIsvjurq6CmUwMTExx3Xp6OjA2Ng4X/MmJSXB1dUVLi4u+OOPPxTmlUgkMDExUZg3p5fM5/MmJyfn+ob49I8AysybkpKS619KlJnXxMRE/oM1NTVVfjJyQec1NjaW/1DI/FCiinmNjIzkRVuZeTM//OQk8xeisvNmfnDLSeYvbGXnlUqlSElJyXHezA8Bys4rk8mQnJysknn19PRgaGgI4L8PuUlJSSqZV5n3fVH+jMjr+54/I5T7GZGWlobOnTvj2bNnGDBgAGQymby4ZL4XZTJZlnN4MstMbntuPv06u703VHDZlZvMgqOnpyf/uaarqysvGJll5dPiYmBgACMjI/n8Ojo6CvNmliAjIyMYGRnB3NwcRkZGMDQ0hCAIMDQ0lN83MDBQKFXm5uby+6mpqfI82T2X4vYzIiAgAJ06dcpxmbzQ1dWFVCqFubk5evXqhd69e6NZs2bZfo/5OSLrvGJ/jjhx4gQ6deqEhw8foly5crnOCxT+54j4+HjY2dkhLi4u224gJ2i51NRUQVdXVzh48KDCdG9vb6Fbt27ZLlOhQgXhjz/+UJg2a9YsoV69eoIgCEJYWJgAQLh586bCPC1bthQmTpyYY5aUlBQhLi5Ofnvx4oUAIMdb586dFZY3MTHJcd5WrVopzGttbZ3jvI0bN1aYt1KlSjnOW6dOHYV569Spk+O8lSpVUpi3cePGOc5rbW2tMG+rVq1ynNfExERh3s6dO+f6fftUr169cp03ISFBPu/gwYNznTc6Olo+79ixY3OdNzw8XD7v1KlTc503NDRUPu/s2bNznTcwMFA+78KFC3Od99y5c/J5fX19c533yJEj/8fenYfHdP1/AH9PJvtuySL2JSQhBCG2EqSoWGotokLVXqWWWlr7WpRWhaKCVlTtErHvaheCEInYicQSsieSzP394Zv5SRHZJmeW9+t55mkyc+bOe67byf3MOfccZdu1a9fm2nbz5s3Ktps3b8617dq1a5Vtd+/enWvbZcuWKdsePXo017YLFixQtj1//nyubadNm6ZsGxYWlmvbcePGKdvevXs317bDhw9Xtn369GmubX19fZVtk5KScm3bvXv3HMdwbm35GfHmpsmfEY8fP861nSbf9PT0JD09vVzblCpVSnJ0dJRq1qwpVa5cOde2Hh4e0uDBg6URI0ZIPXr0yLVtnz59pJ07d0p79uyRVqxYkWvb8ePHS/Hx8VJycrJ0+fLlXNvyM+LNTds/I/Jz69q1q7Rr1y4pLS2N5xH/o0nnEfv37//ov7GI84j4+HgpN1p/0dPz58+RlZUFOzu7HPfb2dl9cJ2OmJiYXNtn/zc/2wSAefPmwcrKSnkrX758vt8PERFpDwcHhxw9Ge9TqVIluLq6on79+ihXrlyubbt164ZRo0Zh3LhxaNWqVa5t586di4MHD+LEiRMYP358rm23bNmC1NRUZGZmwt/fP9e2mzdvhiRJyMrKwqZNm3Jtu2jRIkRGRiIsLAy//fZbrm2//PJLrFy5EsuWLcPw4cNzbevm5obOnTvjs88+Q/369XNta2pqCktLS5iamhbJtPKkW/7880906tRJ2ZNCmkXS0IGIeR5CmZaWpvJru1TxGtHR0ShbtixOnz6Nxo0bK+///vvvcfz4cZw7d+6d5xgaGmL9+vXo3bu38r7ly5djxowZiI2NxenTp9G0aVNER0ejTJkyyjY9e/aETCbDP//8894s2WP/syUkJKB8+fJqMYSySZMmaNCgAX755ZccbdV56ENRtOUQyjc0dehDfttyCKVuDY8qiraq/oxQKBSIjIyEl5eXco3UM2fOwNXV9Z22AD8j+BnBz4i3FfYzIjQ0FM2aNfvgc7IzSZKEhg0bonfv3vj8889hY2OjfJznEW9o6mfE3r170b59e0RGRsLBwSHXtoD6DKHM8yQmlStXxvjx4zFs2LCPfluYX+fPn8fMmTPh4eGBKVOmFOm2S5cuDblcjtjY2Bz3x8bGwt7e/r3Psbe3z7V99n9jY2NzFHCxsbFwc3P7YJbsi3L/y8zMLE+TwuRn4pj8tM3+1lFPT++jz3v7g/Vj8nOc5Kdtfor8/LT90L9PYdtmX98gsq2BgUGev1nOT9vs6zyKuq1cLs/zMZyftnk5xgvSViaTqaQtoLr/7/P7GaGKtvyMeHPS6ezsDODNvsv+Qu3JkyfvjPIA+BmRjZ8R/08d2mrqZ8SHTpCzr2urWbMm+vXrhy+++AIVK1b86HZ5HpH/tqI/I7KLfnNz8zy1V/VnRF5nw8zzEMrY2FiMHz8elSpVwrRp0wq9snxaWho2b96Mtm3bonHjxti7d2+htvchhoaGqF+/Pg4fPqy8T6FQ4PDhwzl65N7WuHHjHO0B4ODBg8r2lStXhr29fY42CQkJOHfu3Ae3qe6yvwkmIiIxLl++DODNN7FOTk65fntMRIX3dpGRXRiUL18eEydORFhYGMLCwvD999/nqXgjzZR97qtpM7HnuQfu0KFDGD16NMLCwjB79mzMnj0b9erVQ7du3dCoUSPUr18fFhYWuW4jPDwc58+fx+HDh7Fr1y4kJSVBkiSYmZnhu+++w3fffVfoN/Q+Y8aMga+vL9zd3dGwYUP88ssvSE5OxoABAwAA/fr1Q9myZTFv3jwAb9ara9GiBX7++Wd4e3tj06ZNuHjxIlatWgXgTZU8evRozJ49G46OjsplBBwcHPD555+r5D2oGgs4IiKxypYti3PnzsHDwwOvXr1C/fr1ce3aNa7RSaQiJUuWRKlSpSBJEnx8fODj44OGDRty2n8dkn3uq2mfs3ku4Fq1aoUrV67g77//xpw5cxAeHo6QkBBcunQJwJuipkqVKrC1tUWJEiVQokQJpKamIi4uDi9fvsTt27eRlJSk3J4kSTAxMUH//v0xderU9w4VKSpffPEFnj17hqlTpyImJgZubm7Yt2+f8jUfPHiQ4x+uSZMm2LhxI3788UdMnjwZjo6O2Llzp3INOODNNXTJyckYPHgwXr16hWbNmmHfvn0auQYcwAKOiEgdNGzYEBs3bkSfPn1w48YNdOvWDTt27BAdi0gr2dra4u7duzAxMcnz0DzSLtlDFjWtgCvwOnCHDh3CH3/8gaCgoBwX6L3vW4v/vkTt2rUxaNAg9O3bF1ZWVgV5ea2QvQ7cR9d6KAaffPIJqlSpgvXr1wvNQUSkS5KTk2Fubg4ASEpKUl4vMW3aNMycORMA8OOPP2LWrFnCMhIRaautW7eiR48eePnyJaytrUXHyXNtUOCvG7y8vODl5YX09HScO3cOJ0+exOnTp/Ho0SM8e/YMcXFxMDY2ho2NDWxsbODq6opPPvkEn3zyCccSq6HsC3aJiEi8GTNmICwsDNu3b8fs2bPh7OyMPn36iI5FRKRVtH4I5YcYGRmhefPmaN68eVHkIUHkcnmu09kSEVHx2rp1K2rXro2wsDD4+PigcuXKGjtRFhGROso+99W0IbSalZZUxtzcPMc1ikREpHr6+vrKRan/ewIhk8lw8eJFlClTBi9fvkSTJk1w//59VKhQQURUIiKtk5SUBJlMVuRLpKkaCzgCAFhYWODhw4eiYxAR6RQjIyP4+fnl+vjNmzdRpkwZKBQKODs7IzY2VnndHBERFVxiYiLMzc01buZRzRrwSSrDHjgiIvVka2uL0NBQAEBKSgpcXV15zTIRURFITEz86DJo6ogFHAF40wOXmJgoOgYRkU6RJAnPnj3Ds2fP3pmx+W2urq4IDAwEANy7dw9t27YtrohERForKSmJBRxpLhZwRETFLyUlBba2trC1tUVKSkqubTt27Iiff/4ZAHD48GGMGjWqOCISEWkt9sCRRmMBR0Sk/saMGYP+/fsDAJYuXYqVK1eKDUREpMGyr4HTNCzgCMCbAi45OVm5HgYREaknf39/NGrUCAAwdOhQHD58WHAiIiLNxB440mjZ3z4kJycLTkJERLmRyWQ4ceIE7O3tAQBeXl6IjIwUnIqISPOwgCONln3wchglEZH6MzAwwM2bN2FgYAAAqFWrFl6+fCk4FRGRZuEkJqTRWMAREWkWKysr3LhxAwCQkZEBFxcXZGRkCE5FRKQ5eA0cabTsAo5rwRERaY5q1aopr4GLiYnBJ598kutyBERE9P84hJI0GnvgiIiKn76+Pnx9feHr6wt9ff0CbaNVq1bK2SjPnTuHAQMGFGVEIiKtpakFXMH+WpDWye4+ZgFHRFR8jIyMsG7dukJvZ/Dgwbhx4wZ+/fVXrF+/HrVq1cK4ceMKH5CISEspFArduwZuw4YN+Pzzz+Hq6go3Nzf07t0bO3fuzNNzr1+/Dj09vQJ/20hFjz1wRESa7ZdffoGXlxcAYPz48QgMDBSciIhIfaWkpACARl4Dl+8KKj4+Hp06dcK///6b4/5r165h8+bNcHd3x19//YXq1at/dFscp68+TE1Noaenx2vgiIiKkSRJypMIU1NTyGSyQm1v3759cHR0xN27d9G5c2dcuXIFtWvXLoqoRERaJbvTQid64Hr16oWTJ09CkiRIkoRSpUrBwsJC+fvFixdRr149bNmyRRV5SUVkMhnMzc3ZA0dEVIxSUlJgbm4Oc3NzZSFXGHK5HFevXoWpqSkAoG7dunj69Gmht0tEpG10poDbu3cv9u/fD5lMhmbNmuHatWt4+vQpXr16hUuXLqFbt27KbxN79+6N33//XVW5SQUsLCyQkJAgOgYRERWCubk5bt68CeDNNR41atRAWlqa4FREROpFZwq4P//8EwBQo0YNHDhwADVr1lQ+5ubmhi1btmDLli2wsLCAQqHAiBEjMG/evKJNTCpja2vLb2qJiLRA+fLlcebMGQDAq1ev4O7uDoVCITgVEZH6iI2NBfDm/FfT5KuAO3fuHGQyGcaMGQNjY+P3tunWrRvOnj2LSpUqQZIk/Pjjj5g0aVKRhCXVcnBwQHR0tOgYRERUBBo1aoSAgAAAbyYP6969u+BERETqIzo6GjKZDHZ2dqKj5Fu+CrjsSrVu3bq5tnN2dsa///4LFxcXSJKEBQsW4Jtvvil4SioWZcqUwZMnT0THICKiItKnTx9MmTIFALBjxw5MnTpVcCIiIvXw5MkT2NjYwMDAQHSUfMtXAZc9/CIzM/OjbR0cHHDy5Em4u7tDkiSsWLECAwYM4MyTaow9cERE2mfmzJno0qULAGDWrFnYuHGj4EREROJFR0fDwcFBdIwCyVcBZ29vDwC4d+9entqXKFECR44cQfPmzSFJEv7880/06tULGRkZ+Q5KqlemTBnExMQgKytLdBQiIipC27ZtU1637uPjg3PnzglOREQkls4UcLVq1QIAHDt2LM/PMTc3x759+9CuXTtIkoStW7fC19c3XyGpeDg4OCArKwvPnz8XHYWISCfI5XJ0794d3bt3h1wuV9nryGQyhISEwNraGsCb6+MePnyostcjIlJ3T548QZkyZUTHKJB8FXAtWrRQFmH56UUzNjZGYGCgcpmBsLCwfAcl1cs+iDmMkoioeBgbGytncP7Q5GBFxcjICBEREcrFwp2cnJCUlKTS1yQiUlc60wPn7e0NAIiLi1MuKZBX+vr6+Oeff9C/f39eB6emsg9iTmRCRKSdbG1tERoaCuDNIuK1a9fmsHki0jkKhQIxMTG60QPn7OyML7/8Eq1atcLp06fz/2J6evD398d3332HihUrokKFCvneBqmOnZ0dZDIZe+CIiLRY7dq1ERgYCAC4e/cu2rVrJzgREVHxevbsGbKysnSjBw4A1q9fj4MHD2LNmjUFftGff/4Zd+/exd27dwu8DSp6+vr6sLW1ZQFHRFRMkpOTIZPJIJPJkJycXGyv27FjRyxcuBAAcOjQIYwePbrYXpuISLTsc12dKeBIuzk4OHAIJRGRDhg3bpxyUrFff/0Vq1atEpyIiKh4ZJ/r6sQQStJ+ZcqUYQ8cEZGOWLt2LRo2bAgAGDJkCI4cOSI4ERGR6kVHR0Mmk8HOzk50lAJhAUc5cDFvIiLdIZPJ8O+//yrXeW3dujWioqIEpyIiUq3o6GjY2trCwMBAdJQCYQFHOZQpU4ZDKImIdIiBgQFu3LihPJGpWbMm4uPjBaciIlIdTV4DDmABR//h4OCAmJgYTitNRKRDSpQooVyj9fXr13BycsrXeq9ERJpEk9eAA1jA0X84ODggKysLz58/Fx2FiIiKUfXq1XHo0CEAQExMDJo3b851W4lIKz158oQFHGmP7O7kx48fC05CRKT95HI52rdvj/bt20Mul4uOg9atW+P3338HAJw9exYDBw4UnIiIqOhFR0dzCCVpj8qVKwMA7ty5IzgJEZH2MzY2RnBwMIKDg2FsbCw6DoA3s1F+++23AN7MUvnzzz8LTkREVHRSU1MRHR2tPOfVRCzgKIdSpUqhRIkSiIyMFB2FiIgE+fXXX9G6dWsAb9aLCwoKEpyIiKhoREVFQZIkVK9eXXSUAmMBRznIZDJUr14dERERoqMQEZFA+/fvV35D3alTJ+UkJ0REmiz7HJcFHGmVGjVqsAeOiKgYJCcnw8zMDGZmZkhOThYdJwe5XI6rV6/C1NQUAFCnTh08ffpUcCoiosKJjIxEiRIlULp0adFRCowFHL0juweOs48REaleSkoKUlJSRMd4L3Nzc9y8eRMAoFAo4OTkhPT0dMGpiIgKLiIiAtWrV4dMJhMdpcD0i2pDz58/R0BAAE6ePIk7d+4gMTHxo2uJyWQy3L59u6giUBGpUaMGXr58iRcvXmj0txNERFR45cuXx5kzZ9C4cWO8fPkS7u7uuHr1qkaf/BCR7oqMjESNGjVExyiUIing/v77bwwbNgyJiYkAkOeeG374q6fsMcGRkZEs4IiICI0aNUJAQAB8fHwQFhaGHj16YOvWraJjERHlW2RkJDp06CA6RqEUuoA7cuQI+vbtqyzaKlasiNq1a8Pa2hp6ehyhqYkcHR0BvOlibtKkieA0RESkDvr06YPw8HDMnj0b27Ztw7Rp0zBjxgzRsYiI8uz58+eIi4tjD9z8+fMhSRKsra0REBCAzz77rChykUAmJiaoUKECJzIhIqIcZs2ahbCwMOzcuRMzZ86Ek5MTevfuLToWEVGeZJ/bavIMlEARTGJy4cIFyGQyzJgxg8WbFuFSAkRE9D7bt2+Hi4sLgDe9cufPnxeciIgob7LPbatVqyY4SeEUuoBTKBQAgKZNmxY6DKkPLiVARKR6enp6aNGiBVq0aKExlx3IZDJcunQJ1tbWAAAPDw88fPhQbCgiojyIjIxEhQoVlMujaKpC/7WoWrUqAKjd+jVUONWrV0dUVNRHZxIlIqKCMzExwbFjx3Ds2DGYmJiIjpNnRkZGiIiIUE5G5uzszPMAIlJ72UsIaLpCF3C9evWCJEnYv39/UeQhNVGjRg2kp6fjwYMHoqMQEZEasrW1RWhoKIA3X+LWrl2bX/oRkVrThiUEgCIo4IYPHw4XFxf88ssvuHjxYlFkIjWQ/e0Er4MjIqIPqV27Nnbu3AkAuHPnDq+FJyK1lZWVhaioKPbAAYC5uTn27NkDJycnNG/eHD/88AOuXr2KtLS0oshHglSoUAFGRka8Do6ISIWSk5NhY2MDGxsbjR2C2LlzZyxYsAAAcPDgQXz33XeCExERvevBgwdIT09nD1y2ChUqYP369bCwsMD8+fNRt25dmJmZQS6X53rT1y+SdcRJBeRyOapVq8YeOCIiFXv+/DmeP38uOkahjB8/Hv369QMA/PLLL1i9erXgREREOWWf07IH7n9+/fVXuLm54fnz55AkKV83Ul9cSoCIiPJq3bp1aNCgAQBg8ODBOHLkiOBERET/LyIiAkZGRqhQoYLoKIVW6C6wPXv2KIdL6Onp4ZNPPkGdOnVgbW2tMVMi0/u5urpi+fLlkCRJOdMYERHR+8hkMpw6dQrly5dHbGwsWrdujVu3bmn8ektEpB2uXLkCFxcXyOVy0VEKrdAF3MKFCwEAZcuWxZ49e+Dq6lroUKQe3N3d8fz5czx48AAVK1YUHYeIiNScgYEBwsPDYWdnh4yMDNSsWRNPnz6FlZWV6GhEpOMuXryIRo0aiY5RJArdRXb16lXIZDLMnDmTxZuWqV+/PgAgJCREcBIiItIUJUqUQFhYGADg9evXcHZ2RkZGhuBURKTLUlJScOPGDeW5raYrdAGXveaLm5tbYTdFasbBwQFlypTh8hBERJQv1atXx6FDhwAAT548QfPmzXndOxEJc+XKFWRlZcHd3V10lCJR6ALO0dERAPDy5ctChyH14+7uzh44IiIV0dPTg7u7O9zd3bXuuvHWrVtj+fLlAICzZ8/i66+/FpyIiHRVSEgIDAwMUKtWLdFRikSh/1r07t0bkiQpF/Ik7VK/fn1cvHiR35wSEamAiYkJLly4gAsXLsDExER0nCI3bNgwfPPNNwAAf39/LFmyRHAiItJFFy9eRO3atWFkZCQ6SpEodAE3cuRINGzYECtXrkRQUFBRZCI14u7ujri4ONy/f190FCIi0kC//fYbWrZsCQAYM2YMgoODBSciIl0TEhKiNde/AUUwC+WTJ0+wevVqDB48GF26dMEXX3yBL774AtWrV4epqelHn68NazFos7cnMqlUqZLYMEREpJEOHjyIatWq4d69e+jQoQOuXbumNUOZiEi9ZU9g8u2334qOUmQKXcBVqlRJuUaYJEnYtGkTNm3alKfnymQyZGZmFjYCqZC9vT3Kli2Lixcvolu3bqLjEBFplZSUFLi4uAAAbty4kacvPjWRXC7HtWvXYGtri9TUVNSpUwcxMTGwsbERHY2ItFxoaCgUCoVW9cAVyRXTkiQpr5HK/jmvN1J/9evX50QmREQqIEkS7t+/j/v372v930Rzc3PcvHkTAKBQKODk5IT09HTBqYhI24WEhMDQ0FCrev0L3QO3du3aoshBaszd3R1LliyBJEnK3lYiIqL8qlChAk6fPo0mTZogLi4ODRo0wJUrV/i3hYhUJnsCE0NDQ9FRikyhCzhfX9+iyEFqzN3dHS9fvsS9e/dQuXJl0XGIiEiDNW7cGBs2bEDfvn1x7do19OzZE1u2bBEdi4i0VEhICD755BPRMYqUdi06QyqRPWaYC3oTEVFR8PHxwQ8//AAA2Lp1K6ZPny42EBFppeTkZISHh2vV9W8ACzjKA1tbW5QvX57XwRERUZGZPXs2OnfuDACYMWMG/vnnH8GJiEjbZE9g4u7uLjpKkWIBR3mSvaA3ERFRUdm+fTucnZ0BAL169cL58+cFJyIibXLx4kUYGRmhZs2aoqMUqTxfAzdz5kzlz1OnTn3v/QXx9rZIfbm7u2PRokWcyISIqAjJZDLlMgK6+Nmqp6eHS5cuwd7eHvHx8fDw8MCjR49QtmxZ0dGISAuEhISgTp06MDAwEB2lSMmkPM5brKenp/zjkpWV9d77C+LtbemahIQEWFlZIT4+HpaWlqLj5Gr//v1o164dIiMj4ejoKDoOERFpkdjYWJQpUwaSJMHMzAyxsbEwMzMTHYuINJyzszNatmyJ5cuXi46SJ3mtDfI1hPJDa7fld+03rgOneRo1agQ9PT2cOHFCdBQiItIydnZ2uHTpEoA3kw7UqVNHp7/gJaLCi42Nxc2bN9GsWTPRUYpcngs4hUKhvH3o/oLcSDNYWVmhXr16OHr0qOgoRESkhdzc3LBjxw4AwO3bt9G+fXvBiYhIkx07dgwA0LJlS7FBVICTmFCetWzZEseOHWPPKRFREUlJSUHNmjVRs2ZNpKSkiI4j3Oeff4758+cDAA4cOICxY8cKTkREmurYsWOoUaMGypQpIzpKkWMBR3nm6emJx48fIyoqSnQUIiKtIEkSbty4gRs3bvDLsf+ZMGEC+vbtCwBYvHgx/vjjD8GJiEgTHT16VCt73wAWcJQPn3zyCeRyOYdREhGRSv3555/KdZsGDRqkHApFRJQX0dHRiIiIgKenp+goKpHnZQQ+JCsrCxcuXMDJkycRGRmJly9fIjExEZaWlihZsiRq1KiBZs2awd3dHXp6rBc1mYWFBdzd3XH06FEMHjxYdBwiItJSMpkMp0+fRrly5fD06VO0bNkSUVFRqFq1quhoRKQBsr/0YQH3H5mZmfDz88OiRYsQHR390fbly5fH+PHjMXToUMjl8oK+bL7FxcVh5MiRCAoKgp6eHrp164Zff/0V5ubmH2w/bdo0HDhwAA8ePICNjQ0+//xzzJo1C1ZWVsp271s64e+//0avXr1U9l7UQcuWLbFu3TquB0dERCplYGCAmzdvwtbWFpmZmXBxccHTp09z/C0mInqfY8eOwcXFBXZ2dqKjqESBusRevHiBVq1aYcyYMYiOjs7TcgEPHz7Et99+izZt2iAuLq6o38cH+fj44Pr16zh48CB2796NEydO5Np7FB0djejoaCxatAhhYWFYt24d9u3bh4EDB77Tdu3atXjy5Iny9vnnn6vwnagHT09PxMTEICIiQnQUIiLSciVKlEBYWBgA4PXr13BxcUFmZqbgVESk7rT5+jcgHwt5Z8vKykLTpk1x4cIFZS/Mp59+Ci8vL9SrVw+lSpWCubk5EhMT8fz5c1y+fBkHDx7E4cOHle0bN26MEydOqHxIZXh4OFxcXHDhwgXlWPp9+/ahffv2ePToERwcHPK0nS1btqBv375ITk6Gvv6bTkuZTIYdO3YUqmjTpIW8syUlJaFEiRJYunQphg0bJjoOEZFGS05OVo4ISUpK4uLVH3Do0CF8+umnAIAmTZrg33//5SgQInqvR48eoXz58ti6dSu6desmOk6+qGQhbwCYP38+zp8/DwCoW7curl27hn379mHcuHFo1aoV6tSpg6pVq8LNzQ1eXl4YP348Dhw4gCtXrsDNzQ2SJOHMmTNYuHBhwd9dHp05cwbW1tbK4g0AvLy8oKenh3PnzuV5O9k7Mbt4yzZixAiULl0aDRs2hL+//0dnEEtPT0dCQkKOm6YxNzdHw4YNOZEJEVERkMlkqFixIipWrMiCJBdeXl7w8/MDAJw+fRqDBg0SnIiI1FX2OWqLFi0EJ1GdfBVwGRkZWLp0KWQyGerWrYtTp07BxcUlT8+tVasWTp8+jbp160KSJCxZskTlwyBiYmJga2ub4z59fX2ULFkSMTExedrG8+fPMWvWrHeGXc6cORObN2/GwYMH0a1bNwwfPhy//fZbrtuaN28erKyslLfy5cvn7w2pCa4HR0RUNExNTXHv3j3cu3cPpqamouOoteHDh2PEiBEAgDVr1mDJkiWCExGROjp27BhcXV1RunRp0VFUJl8FXFBQEJ49ewaZTIYNGzbA2Ng4Xy9mbGyMv/76CzKZDM+ePcPu3bvz9fxsEydOhEwmy/V28+bNAm37bQkJCfD29oaLiwumT5+e47EpU6agadOmqFu3LiZMmIDvv//+o72KkyZNQnx8vPL28OHDQmcUwdPTE8+ePcONGzdERyEiIh2ybNky5axyY8aMwZ49e8QGIiK1o+3XvwH5LOD+/fdfAG+GMjg5ORXoBV1cXJTj2E+ePFmgbYwdOxbh4eG53qpUqQJ7e3s8ffo0x3MzMzMRFxcHe3v7XF8jMTER7dq1g4WFBXbs2AEDA4Nc23t4eODRo0dIT0//YBsjIyNYWlrmuGmiJk2awMDAgMMoiYio2B06dAgVKlQAAHh7e+P69euCExGRurh//z7u3r2r9QVcvpYRCAkJgUwmQ+vWrQv1oq1bt8aBAwcQEhJSoOfb2NjAxsbmo+0aN26MV69eISQkBPXr1wcAHDlyBAqFAh4eHh98XkJCAtq2bQsjIyMEBgbmqacxNDQUJUqUgJGRUd7fiIYyNTVFo0aNcPToUXzzzTei4xARaazU1FQ0b94cAHDixAmYmJgITqT+5HI5wsLCYGdnh9TUVNSuXRuxsbFaPVyKiPLm6NGjkMlkys9VbZWvHrjsIX+1a9cu1ItmP//+/fuF2s7HODs7o127dhg0aBDOnz+PU6dO4ZtvvkGvXr2UM1A+fvwYTk5OyolZEhIS0KZNGyQnJ2PNmjVISEhATEwMYmJikJWVBeDNUNI//vgDYWFhiIqKwooVKzB37lyMHDlSpe9HnbRs2RLHjx+HQqEQHYWISGMpFApcvHgRFy9e5OdpPlhYWCgvlVAoFKhRo0auI2CISDccO3YMderUQcmSJUVHUal8FXDx8fEA3qzLUhjZz8/enioFBATAyckJrVu3Rvv27dGsWTOsWrVK+XhGRgYiIiKQkpICALh06RLOnTuHa9euoVq1aihTpozyll3AGhgYwM/PD40bN4abmxtWrlyJxYsXY9q0aSp/P+rC09MTL168UK7PQ0REVJwqVKigvLQjLi4OHh4enFyLSIdJkqQT178B+RxCmT3tffaaNQWVvc5NYmJiobaTFyVLlsTGjRs/+HilSpVyfOB7enp+9A9Au3bt0K5duyLLqIkaN24MExMT7N+/v9A9skRERAXRtGlT/PXXX/jyyy9x5coV9OrVC//884/oWEQkQGRkJB48eFDoS700Qb564LKHEBYVDhfRXMbGxvj0008RFBQkOgoREemwvn37YvLkyQCAzZs3Y8aMGYITEZEIgYGBMDExQatWrURHUbl8L+RNlK1Tp044deoUnj9/LjoKERHpsDlz5qBTp04AgOnTp2Pz5s2CExFRcdu1axfatGmjE5NB5WsIZbbly5e/s0B2fvx3an/STB06dIAkSQgODoavr6/oOEREpMN27NiBmjVr4ubNm/jiiy9QpUoVuLu7i45FRMXg2bNnOH36NP744w/RUYqFTMrHFb96enqQyWRF8sKSJEEmkxX5sExNkpCQACsrK8THx2v0mnBlypTBtm3bREchItI4ycnJqFSpEgDg3r17ymvEqWBSU1NRpkwZ5SRpjx49QtmyZQWnIiJVW7duHb766ivExMQUqpNJtLzWBvkeQilJUpHcSDt06tQJ+/fvR1pamugoREQax8zMDM+ePcOzZ89YvBUBExMTREREKL9sdnJyQnJysuBURKRqu3btQuPGjTW6eMuPfA2hPHr0qKpykIbq1KkTJk2ahCNHjqB9+/ai4xARkY6zs7PDpUuXULduXSQlJcHNzQ0RERHQ0+Nl/0TaKDU1FQcOHMDUqVNFRyk2+SrgWrRooaocpKGcnZ1RtWpVBAYGsoAjIiK14Obmhh07dqBLly6IioqCt7c39u7dKzoWEanAkSNHkJKSopzISBfw6ygqFJlMhs6dOyMwMJDLQhAR5VNqaio8PT3h6emJ1NRU0XG0yueff4758+cDAPbt24exY8cKTkREqrBr1y44OjrCyclJdJRiwwKOCq1Tp0548uQJQkJCREchItIoCoUCx48fx/Hjx/klmApMmDABPj4+AIDFixdjzZo1ghMRUVFSKBQICgpCp06dimyiRU3AAo4KrWnTpihZsiQCAwNFRyEiIsrhr7/+Ui4n8PXXX+P48eOCExFRUbl48SJiYmJ0avgkwAKOioC+vj68vb2xa9cu0VGIiIhykMlkOH36NGxsbAAAnp6euH37tuBURFQUdu3ahVKlSqFJkyaioxQrFnBUJDp16oRr167h7t27oqMQERHlYGBggJs3b0Jf/83cbTVr1kRCQoLgVERUWIGBgfD29lb+v60rWMBRkWjbti0MDQ05jJKIiNRSyZIlERYWBgBIT0+Hi4sLMjMzBaciooK6c+cOwsLCdG74JMACjoqIhYUFWrZsyQKOiIjUVo0aNbB//34AwOPHj+Hp6QlJkgSnIqKCCAwMhKGhIdq0aSM6SrFjAUdFpnPnzjh+/DhevnwpOgoRkcYwNTWFqamp6Bg6o02bNvDz8wMAnDp1CoMHDxaciIgKIjAwEK1bt4aFhYXoKMWOBRwVmY4dOyIrKwvBwcGioxARaQQzMzMkJycjOTkZZmZmouPojOHDh2PYsGEAgD/++AO//vqr4ERElB8vXrzAiRMndHL4JMACjopQuXLl0LRpUwQEBIiOQkRElCs/Pz+0aNECADB69Gjs2bNHcCIiyqt//vkHANC1a1fBScRgAUdFqm/fvjhw4ABiY2NFRyEiIvogmUyGw4cPo3z58gAAb29v3LhxQ3AqIsqLDRs2oG3btrC1tRUdRQgWcFSkevbsCblcjk2bNomOQkSk9tLS0uDt7Q1vb2+kpaWJjqNz5HI5rl+/DmNjYwBA7dq18eLFC8GpiCg3t2/fxpkzZ/Dll1+KjiIMCzgqUiVLloS3tzf++usv0VGIiNReVlYW9uzZgz179iArK0t0HJ1kYWGBmzdvAnjz71GjRg28fv1acCoi+pANGzbA3NxcZ69/A1jAkQr07dsXISEhCA8PFx2FiIjooypWrIh///0XwJvJERo2bMjlBYjUkCRJ2LBhA7p166bTs/eygKMi5+3tDSsrK05mQkREGqNp06ZYv349AODKlSvo3bu34ERE9F/nz59HVFQU+vbtKzqKUCzgqMgZGxujZ8+eCAgIgEKhEB2HiIgoT/r164dJkyYBeDPL3axZswQnIqK3bdiwAQ4ODmjZsqXoKEKxgCOV6Nu3L+7du4dTp06JjkJERJRnc+fORceOHQEAU6dOxZYtWwQnIiIAyMjIwKZNm9CnTx/I5XLRcYRiAUcq0axZM1SoUAEbNmwQHYWIiChfdu7cCScnJwBvZle+ePGi4EREtH//fjx//lznh08CLOBIRfT09NC3b19s3rwZ6enpouMQERHlmZ6eHi5dugRLS0sAQIMGDRAdHS04FZFu27BhA1xdXVGnTh3RUYRjAUcq4+Pjg1evXiE4OFh0FCIitWRmZgZJkiBJEszMzETHobeYmJggIiJC+XuNGjWQkpIiMBGR7oqPj8euXbvY+/Y/LOBIZVxcXFCvXj0OoyQiIo1kb2+PS5cuAQCSkpLg5ubGybmIBNi+fTvS09M5O+z/sIAjlerbty+Cg4MRFxcnOgoREVG+1a1bF9u3bwcA3Lp1Cx06dBCciEj3bNiwAZ6enihfvrzoKGqBBRypVO/evZGZmYmtW7eKjkJEpHbS0tLQo0cP9OjRA2lpaaLj0Ad06dIFc+fOBQDs3bsX48ePF5yISHc8evQIR48exZdffik6itpgAUcqZW9vj08//RR//fWX6ChERGonKysLW7duxdatW5GVlSU6DuVi0qRJ6NOnDwBg0aJF8Pf3F5yISDds3LgRRkZG6Nq1q+goaoMFHKlc37598e+//+L27duioxARERXYhg0bUK9ePQDAwIEDcfLkScGJiLSbJEn4888/0alTJ1hZWYmOozZYwJHKde3aFdbW1li5cqXoKERERAUmk8lw5swZ2NjYAACaN2+OO3fuCE5FpL3+/fdfXL9+HQMHDhQdRa2wgCOVMzU1xYABA7BmzRqkpqaKjkNERFRghoaGuHnzJvT19QG8mXE5ISFBcCoi7eTn5wdHR0d4eXmJjqJWWMBRsRg2bBji4uKwefNm0VGIiIgKpWTJkrh27RoAID09HS4uLsjMzBSciki7xMTEYNu2bRg+fDj09FiyvI17g4qFo6Mj2rZtCz8/P9FRiIiICs3JyQn79+8HADx+/BgtW7aEJEmCUxFpj9WrV8PAwAD9+/cXHUXtsICjYjN8+HBcuHABFy5cEB2FiIio0Nq0aYNly5YBeHOtztChQwUnItIOmZmZWLlyJXx8fGBtbS06jtphAUfFxtvbGxUrVmQvHBHR/5iamiIpKQlJSUkwNTUVHYcKYMSIEcrCbdWqVVi6dKngRESab9euXXj8+DFGjBghOopaYgFHxUYul2Po0KHYtGkTXrx4IToOEZFwMpkMZmZmMDMzg0wmEx2HCmj58uVo0aIFAGDUqFHYu3ev4EREmm358uVo0qQJ3NzcREdRSyzgqFgNHDgQkiRxAVQiItIaMpkMhw8fRvny5QEA7du3R3h4uOBURJopPDwcR44cYe9bLljAUbGysbFBz549sWLFCmRlZYmOQ0QkVHp6Ovr374/+/fsjPT1ddBwqBLlcjuvXr8PY2BgA4OrqytEmRAWwfPly2NjYoFu3bqKjqC0WcFTsRowYgbt37ypn7yIi0lWZmZlYv3491q9fz2notYCFhYWy5y0rKwtOTk54/fq14FREmiMpKQnr16/HoEGDYGRkJDqO2mIBR8XOw8MD9erV42QmRESkdSpVqoR///0XAPD8+XN4eHhweQGiPNqwYQOSk5MxZMgQ0VHUGgs4KnYymQwjRozA3r17cefOHdFxiIiIilTTpk2xfv16AEBoaCj69OkjOBGR+pMkCX5+fujUqRMqVKggOo5aYwFHQvTq1QvW1tZYsWKF6ChERERFrl+/fpgwYQIAYNOmTZgzZ47gRETq7eTJkwgLC8Pw4cNFR1F7LOBICFNTUwwYMAD+/v5ITU0VHYeIiKjIzZ8/Hx06dAAA/Pjjj9i6davgRNorIyMD+/btw3fffYcmTZqgdOnSMDAwgLW1NerVq4fx48dz1I+aW758OapXr47WrVuLjqL2ZBIHZguTkJAAKysrxMfHw9LSUnScYhcVFQVHR0esXbsW/fv3Fx2HiKjYJScnw9zcHMCbi/fNzMwEJ6KiplAo4OLigoiICADAxYsXUb9+fcGptMuzZ8/g7Oz80Vk/DQ0NsWDBAowaNaqYklFePXnyBBUqVMCiRYt0+t8nr7UBe+BImGrVqqFdu3b49ddfeYE3ERFpJT09PVy6dEl5Mubu7o7o6GjBqbRLenq6snhzc3PDtGnTsGfPHoSEhODIkSMYP348jI2N8fr1a4wePRqrVq0SnJj+a8WKFTA0NISvr6/oKBqBPXAC6XoPHAAcOXIErVu3xt69e9GuXTvRcYiIipUkSXj+/DkAoHTp0pDJZIITkao8efIEDg4OAN4sNxATEwNTU1PBqbTD48ePMWDAAMycORONGjV6b5tz586hZcuWSE1NhZWVFR4+fAgLC4tiTkrvk5iYiAoVKqB///5YsmSJ6DhCsQeONELLli3h4eGBuXPnio5CRFTsZDIZbGxsYGNjw+JNy5UpUwYhISEA3pywurm5QaFQfPR5UVFR2L59OzIyMlQdUWOVLVsWBw4c+GDxBrxZwih7coz4+HgcPHiwuOLRR/z+++9ITk7G2LFjRUfRGCzgSCiZTIbJkyfj5MmTynVziIiItFG9evWUE5ncunULHTt2zLX9iRMn4O7ujm7dumHXrl35eq2UlBRYWFhAJpPBx8fno+3PnDkDmUwGmUyG5cuXK+9/+fIl1q5di759+8LFxQXm5uYwNDSEvb092rZti1WrVuW6WPm9e/eU2123bh0AYPv27Wjfvj0cHBygr68PT0/PfL23gmrZsqXy59u3bxfLa1Lu0tLSsHjxYvj6+qJcuXKi42gMFnAkXIcOHVCzZk3MmzdPdBQiomKVnp6OESNGYMSIEUhPTxcdh4pBt27dlEsK7NmzR7nUwH/9+eefaN26NRITE6Gnp4dly5bl63VMTU3x+eefAwB27dqF5OTkXNsHBAQAAPT19dGzZ0/l/XXr1sVXX32FgIAAhIeHIzk5GRkZGYiNjcWBAwcwZMgQNGrUCDExMR/NJEkS+vXrh27dumHv3r148uQJsrKy8vW+CuPt/8fkcnmxvS592Lp16/D06VN8//33oqNoFBZwJJyenh4mTZqEPXv2IDQ0VHQcIqJik5mZieXLl2P58uXIzMwUHYeKyeTJk9G7d28AwIIFC5Q9U8CbWSt//PFH+Pr6IjMzEwqFAgqFAsePH1fOZJlX2T1vycnJufbgZWZmYsuWLQCAtm3bonTp0srHsrKy4OHhgVmzZmH37t24cOECTp06hQ0bNiivXb98+TJ69er10Ty//PIL/vrrL3zyySfYuHEjLl68iEOHDuHLL7/M1/sqqOPHjyt/dnZ2LpbXpA/LzMzETz/9hB49esDR0VF0HM0ikTDx8fESACk+Pl50FOEyMjKkypUrSz179hQdhYio2CQlJUkAJABSUlKS6DhUjBQKhVS3bl3lv/+JEyek1NRUqUePHsr73r7J5XJp1KhR+XqNjIwMydbWVgIgeXt7f7Dd3r17la+zcePGHI9FRkbm+hr+/v7K5x46dOidx+/evZvjffTr109SKBT5eh9FITo6WrKwsJAASDY2NlJqamqxZ6Cc/vrrLwmAFBoaKjqK2shrbcAeOFIL+vr6mDBhArZs2YLIyEjRcYiIiFRKJpPh7Nmzyt6u5s2bo1GjRti2bdt722dlZcHf3x8pKSl5fg19fX188cUXAIADBw58cJ207OGT5ubm6Ny5c47HPtYzMmDAALi5uQEAdu7cmWtba2trLFu2rNgn7JEkCUOGDEFiYiIAYMqUKTA2Ni7WDJSTQqHAvHnz4O3tjTp16oiOo3FYwJHa8PX1hZ2dHRYsWCA6ChERkcoZGhoiIiICenpvTseuXLmS68yUiYmJ+Oeff/L1GtnDKDMyMrB58+Z3Hk9NTVUWXp9//nmuSxtIkoSYmBhERkYiLCxMeStbtqwyf246duwoZOr+uXPnIigoCMCbiUxGjBhR7Bkop6CgINy4cQOTJk0SHUUjsYAjtWFsbIyxY8fizz//xMOHD0XHISIiUrmQkBAYGRnlqa1MJsNvv/2Wr+17eHigatWqAP6/p+1tgYGBSEpKAoAPzlYZHByMDh06wMrKCmXKlEGNGjXg6uqqvAUHBwOAck3DD6ldu3a+sheFgIAATJkyBQBQuXJlbNy4UVkwkxiSJGHu3Llo3rw5mjZtKjqORuIRTGplyJAhMDc3x88//yw6ChERkUqtXr0a7dq1Q1paWp7aS5KEy5cv4+LFi/l6nezC7PTp07h3716Ox7KLOltbW3h5eb3zel9//TU6dOiA4OBg5RDED0lNTc318RIlSuQrd2EFBwdjwIABkCQJ9vb2OHjwIOzt7Ys1A73ryJEjOH/+PCZPniw6isZiAUdqxcLCAt9++y1WrVqFZ8+eiY5DRERU5BQKBcaNG4fBgwdDoVBAkqQ8P1dfXz/HOm15kV3ASZKEv//+W3l/XFwc9u/fDwD44osvoK+vn+N5/v7+WLNmDQDAzc0N69atQ3h4OBISEpCZmQlJkiBJknIWyY+9j+Kcuv/YsWPo3r07MjIyUKJECezfv1/ZE0lizZ07F/Xq1UObNm1ER9FYLOBI7YwcORJ6enpYunSp6ChERCplYmKCu3fv4u7duzAxMREdh4pBcnIyunTpgsWLFxfo+ZmZmdi4cSNevnyZ5+dUr14d7u7uAICNGzcq79+6datyEe73DZ9cvXo1AKBatWo4ffo0fH194eTkBAsLixzFWFxcXIHei6qcP38eHTt2RFpaGszNzbF3714hwzfpXefOncORI0cwadKkYp/MRpuwgCO1U6pUKQwZMgS//fYbEhISRMchIlIZPT09VKpUCZUqVeJ1OTrgyZMnaNq0KXbv3p2vXrf/ev36Nf788898PSe7QAsLC8PVq1cB/P/wyapVq8LDw+Od51y/fh0A0KlTpw9+wSBJEi5dupSvLKp09epVtGvXDklJSTA2NkZQUNB73xuJMW/ePNSoUQNdunQRHUWj8a8FqaUxY8YgNTUVK1asEB2FiIioSBw4cOCjM03m1bJly/JVBPbq1UvZaxYQEIBHjx7h5MmTAD48eUn24vLJyckf3O6uXbvw5MmTPOdQpcjISLRp0wYvX76EgYEBtm3bBk9PT9Gx6H/CwsKwa9cuTJw4sViH02ojFnCklsqWLYv+/ftj8eLF+VrzhohIk7x+/Rrjx4/H+PHjlUPZSHt16dIFo0aNgp6e3jvXm+WHJEmIiorCsWPH8vwce3t7tGrVCgDw999/Y+PGjcoC8EMFXPYacEFBQe8dJnn79m21mZL/wYMH8PLyQmxsLORyOTZu3Ij27duLjkVvmTdvHipUqPDB443yjgUcqa0JEybg5cuXvBaOiLRWRkYGFi1ahEWLFiEjI0N0HFIxS0tL/PLLLwgNDUWjRo0AoMDXAcnlcvj5+eXrOdknzg8fPsS8efMAAO7u7qhevfp72/fr1w8AEB0djcaNG8Pf3x/nz5/HiRMnMH36dNSvXx9xcXGoV69egd5DUXnx4gW8vLyUSxCNHTsWTk5OOdaq++/t8ePHQjPrmqtXr+Lvv//GxIkTYWBgIDqOxpNJhRmETYWSkJAAKysrxMfHw9LSUnQctTRy5Ej89ddfuHPnDkqWLCk6DhFRkUpOToa5uTkAICkpCWZmZoITUXGRJAn//PMPRo0ahefPnxdoWKVcLsfDhw9RpkyZPLVPTEyEnZ1djun+lyxZgtGjR7+3fUZGBjp06IADBw6893ETExOsX78ewcHBWL9+PSpWrPjOMgX37t1D5cqVAQBr165F//7985Q1P44dO4aWLVvm6zm+vr5Yt25dkWeh9+vQoQMiIiJw48YNFnC5yGttwB44UmtTpkxBVlaW8ptCIiIibSCTydCrVy9ERUVh7NixkMvl+R5WKUmScpr/vLCwsEDHjh2Vv8vlcvTq1euD7Q0MDBAcHIylS5fC3d0dpqamMDExQbVq1TB06FBcunQJPXr0yFdm0j0nTpxAcHAw5syZw+KtiLAHTiD2wOXN9OnTMX/+fNy6dQvly5cXHYeIqMiwB46y3bx5E8OHD8fRo0chk8nyPEGJvb09Hj58WKhr6ohURZIkNGnSBBkZGTh//jxn2/0I9sCR1hg7diwsLS0xbdo00VGIiIhUwsnJCYcPH8bWrVthb2+f5xPdmJgYBAcHqzgdUcHs3LkTZ8+exU8//cTirQhxT5Las7CwwNSpU7F+/XrlmjRERETaRiaToVu3brh16xYmTpwIfX39j/asyeVyLFu2rJgSEuVdZmYmJk2ahDZt2qB169ai42gVFnCkEQYPHoxKlSph8uTJoqMQERGplJmZGebMmYPw8HDlie+Hei+ysrJw6NAh3L59uzgjEn3UunXrEBERgfnz54uOonV4DZxAvAYuf/7++2/06dMHJ0+eRLNmzUTHISIqNIVCgfDwcACAs7MzhxjROyRJwu7duzFixAg8evTovdfG6enpYezYsViwYIGAhIX36tUrPHr0qEDPrVWrVhGnoaKQkpICR0dHtGjRAhs3bhQdR2PktTZgAScQC7j8USgUylmwTp48WeC1c4iIiDRNamoqFi5ciDlz5kChUCAzMzPH45aWloiNjYWxsbGghAW3bt06DBgwoEDP5Wmsevrpp5/w448/4ubNm6hataroOBqDk5iQ1tHT08P8+fNx6tQpBAUFiY5DRERUbExMTDB16lTcvHkT7du3B5BzEfCEhARs3bpVVDwipbi4OMybNw9Dhw5l8aYi7IETiD1w+SdJEry8vBATE4OrV69CLpeLjkREVGCvX7/G3LlzAQCTJ0+GoaGh4ESkKfbt24fhw4fj7t27yvsqVaqU43ciEb7//nssX74ct2/fhp2dneg4GoU9cKSVZDIZ5s+fjxs3buDPP/8UHYeIqFAyMjIwY8YMzJgxAxkZGaLjkAZp164dwsPDMXfuXOVMlffu3cPy5csFJyNd9vDhQyxduhTjxo1j8aZC7IETiD1wBdezZ0+cOXMGkZGRMDExER2HiKhAuJA3FYUHDx6gefPmuH//PkxMTHDlyhU4OjqKjkU6aODAgQgKCsLt27dhYWEhOo7GYQ8cabU5c+bgyZMn8PPzEx2FiIhIqAoVKiAyMhKNGzfGV199hcqVK4uORDroxo0bWLduHaZMmcLiTcW0voCLi4uDj48PLC0tYW1tjYEDByIpKSnX53h6ekImk+W4DR06NEebBw8ewNvbG6amprC1tcX48ePfmRGKVMfR0RGDBg3C3Llz8fLlS9FxiIiIhDI0NMSRI0fw22+/fXTxbyJVmDRpEipWrIghQ4aIjqL1tL6A8/HxwfXr13Hw4EHs3r0bJ06cwODBgz/6vEGDBuHJkyfK29trq2RlZcHb2xuvX7/G6dOnsX79eqxbtw5Tp05V5Vuh/5g2bRoyMzMxZcoU0VGIiIiEMzY25hI7JMS+ffsQGBiIuXPncjKmYqDV18CFh4fDxcUFFy5cgLu7O4A3B1j79u3x6NEjODg4vPd5np6ecHNzwy+//PLex/fu3YsOHTogOjpaeYHm77//jgkTJuDZs2d5PnB5DVzhLVmyBOPGjcOFCxdQr1490XGIiPKF18ARkaZLT09HrVq1UL58eRw+fJhfIhQCr4EDcObMGVhbWyuLNwDw8vKCnp4ezp07l+tzAwICULp0adSqVQuTJk1CSkpKju26urrmmF2nbdu2SEhIwPXr1z+4zfT0dCQkJOS4UeF88803cHFxwYgRI6BQKETHISIiItIpixYtwr1797Bs2TIWb8VEqwdJx8TEwNbWNsd9+vr6KFmyJGJiYj74vD59+qBixYpwcHDA1atXMWHCBERERGD79u3K7f53atTs33Pb7rx58zBjxoyCvh16DwMDA/j5+aFFixZYu3YtBg4cKDoSEVGeGRsb4/z588qfiYg0yb179zBnzhx89913cHFxER1HZ2hkD9zEiRPfmWTkv7ebN28WePuDBw9G27Zt4erqCh8fH/z555/YsWMHbt++XajckyZNQnx8vPL28OHDQm2P3mjevDm+/PJLTJgwAXFxcaLjEBHlmVwuR4MGDdCgQQPI5XLRcYiI8mX06NEoWbIk54EoZhrZAzd27Fj0798/1zZVqlSBvb09nj59muP+zMxMxMXFwd7ePs+v5+HhAQCIiopC1apVYW9vr/zGNFtsbCwA5LpdIyMjGBkZ5fl1Ke8WLFiAXbt24YcffsCKFStExyEiIiLSasHBwdi1axc2b96svJaXiodGFnA2NjawsbH5aLvGjRvj1atXCAkJQf369QEAR44cgUKhUBZleREaGgoAKFOmjHK7c+bMwdOnT5VDNA8ePAhLS0t2Hwtib2+PWbNmYfTo0Rg4cGCO6x6JiNTV69ev8euvvwIARo0axdnbiEgjpKWl4dtvv4WXlxe6d+8uOo7O0epZKAHgs88+Q2xsLH7//XdkZGRgwIABcHd3x8aNGwEAjx8/RuvWrfHnn3+iYcOGuH37NjZu3Ij27dujVKlSuHr1Kr777juUK1cOx48fB/BmGQE3Nzc4ODhgwYIFiImJwZdffomvv/4ac+fOzXM2zkJZtDIzM1G/fn0YGRnh7Nmz0NPTyBHCRKRDOAslEWmimTNnYvbs2bh69SqcnJxEx9EanIXyfwICAuDk5ITWrVujffv2aNasGVatWqV8PCMjAxEREcpZJg0NDXHo0CG0adMGTk5OGDt2LLp164agoCDlc+RyOXbv3g25XI7GjRujb9++6NevH2bOnFns74/+n76+Pvz8/HDhwgWsWbNGdBwiIiIirXPnzh3MmzcPY8eOZfEmiNb3wKkz9sCpRv/+/REUFITIyEiUKlVKdBwiog9iDxwRaZpOnTohNDQU4eHh/MwqYuyBI531008/ISsrC5MmTRIdhYiIiEhrBAUFISgoCEuWLGHxJhALONI6dnZ2mDNnDv7444+PLthORERERB+XmpqKb7/9Fm3btkXXrl1Fx9FpLOBIKw0dOhRubm4YMWIEsrKyRMchIiIi0mjz589HdHQ0fvvtN8hkMtFxdBoLONJKcrkcfn5+CAkJwerVq0XHISIiItJYUVFR+OmnnzB+/Hg4OjqKjqPzOImJQJzERPUGDhyI7du348aNG8p1/IiI1EVWVhZOnjwJAPjkk08gl8sFJyIiykmSJLRr1w43b95EeHg4TE1NRUfSWpzEhAhvJjQxNDTE0KFDwe8qiEjdyOVyeHp6wtPTk8UbEamlNWvW4MCBA1ixYgWLNzXBAo60WunSpfH7778jMDAQAQEBouMQERERaYwHDx5gzJgx+Oqrr9C+fXvRceh/OIRSIA6hLD59+/ZFcHAwrl+/DgcHB9FxiIgAABkZGVi1ahUAYPDgwTAwMBCciIjoDUmS0KZNG9y8eRNhYWGwsrISHUnr5bU2YAEnEAu44hMXF4eaNWuifv36CAoK4uxJRKQWuJA3EamrlStXYujQodi/fz/atGkjOo5O4DVwRG8pWbIkVq1aheDgYKxfv150HCIiIiK1dffuXYwdOxaDBg1i8aaGWMCRzujYsSP69euHUaNG4dGjR6LjEBEREakdhUKBgQMHolSpUli0aJHoOPQeLOBIp/zyyy8wNzfHoEGDOCslERER0X/8/vvvOHr0KNasWcNLfNQUCzjSKSVKlMDq1auxb98++Pv7i45DREREpDbu3LmD8ePHY9iwYfDy8hIdhz6ABRzpnPbt22PAgAH47rvv8ODBA9FxiIiIiIRTKBQYMGAAbG1tsWDBAtFxKBcs4EgnLV68GFZWVhg4cCCHUhIREZHOW7ZsGU6cOAF/f3/l7LiknljAkU6ytrbGH3/8gUOHDinXYCIiKm5GRkbYvXs3du/eDSMjI9FxiEhH3bp1CxMnTsQ333yDli1bio5DH8F14ATiOnDiDR48GH///TeuXbuGSpUqiY5DREREVKyysrLQokULPHnyBFevXuV6lAJxHTiiPFi0aBFKliyJgQMHQqFQiI5DREREVKyWLl2K06dPY926dSzeNAQLONJplpaWWLNmDY4cOYLly5eLjkNEOiYjIwPr1q3DunXrkJGRIToOEemYmzdvYvLkyfj222/xySefiI5DecQhlAJxCKX6+Oabb/DHH3/g7NmzcHNzEx2HiHREcnKycrKApKQkfvtNRMUmNTUVHh4eyMjIQEhICExNTUVH0nkcQkmUD4sWLYKzszN69uyJxMRE0XGIiIiIVGr06NG4desWNm/ezOJNw7CAIwJgbGyMzZs348mTJxg6dCiXFiAiIiKttWnTJqxatQpLly6Fq6ur6DiUTyzgiP7H0dERq1atwsaNG+Hv7y86DhEREVGRu3XrFgYNGoTevXvj66+/Fh2HCoAFHNFbevfujUGDBmHkyJEICwsTHYeIiIioyKSlpeGLL76Avb09Vq5cCZlMJjoSFQALOKL/+OWXX1C1alX07NkTycnJouMQERERFYlx48bh+vXr2Lx5MywsLETHoQJiAUf0H6ampti8eTPu37+PESNGiI5DREREVGjbtm2Dn58flixZgrp164qOQ4WgLzoAkTpydnbGihUr4Ovri5YtW8LX11d0JCLSQkZGRti8ebPyZyIiVbhz5w4GDhyI7t27Y9iwYaLjUCFxHTiBuA6c+hswYAA2b96MixcvwtnZWXQcIiIionx5/fo1mjVrhufPn+Py5cuwsrISHYk+gOvAERWBZcuWoWLFiujZsydSUlJExyEiIiLKl4kTJyI0NBSbN29m8aYlWMAR5cLMzAybN2/G7du3MWrUKNFxiEjLZGZmYsuWLdiyZQsyMzNFxyEiLRMYGIglS5Zg4cKFcHd3Fx2HigiHUArEIZSaY82aNfj6668REBCAPn36iI5DRFoiOTkZ5ubmAICkpCSYmZkJTkRE2uL+/fuoW7cuWrRoge3bt3PJAA3AIZREReirr76Cj48PhgwZgsjISNFxiIiIiD4oIyMDvXv3hqWlJfz9/Vm8aRkWcER5IJPJsGLFCjg4OKBHjx5cH46IiIjU1qRJk3DhwgVs2rQJJUqUEB2HihgLOKI8srCwwNatW3H79m30798fCoVCdCQiIiKiHNavX4+ff/4ZixYtQqNGjUTHIRVgAUeUD66urvjrr7+wdetWzJo1S3QcIiIiIqUzZ85g8ODBGDhwIL799lvRcUhFWMAR5VOXLl0wa9YsTJ8+HVu2bBEdh4iIiAgPHz5Ely5d0LBhQyxfvpzXvWkxfdEBiDTRDz/8gLCwMPj6+qJatWqoW7eu6EhERESko5KTk9G5c2cYGRlh27ZtMDQ0FB2JVIgFHFEByGQy+Pv7o3nz5ujcuTMuXLgAOzs70bGISMMYGhpi7dq1yp+JiPJLkiQMGDAAkZGROHXqFGxtbUVHIhXjOnACcR04zff48WO4u7ujcuXKOHr0KIyMjERHIiIiIh0yc+ZMTJs2Ddu3b0eXLl1Ex6FC4DpwRMWgbNmy2LlzJy5duoShQ4eC34cQERFRcdm2bRumTZuGWbNmsXjTISzgiArJw8MDf/zxB9atW4clS5aIjkNEGiQzMxPBwcEIDg5GZmam6DhEpEFCQ0PRr18/fPHFF/jhhx9Ex6FixGvgiIpA3759ce3aNYwfPx7Ozs747LPPREciIg2Qnp6ODh06AACSkpKgr88/y0T0cU+fPkXnzp3h7OwMf39/zjipY9gDR1RE5s6di88++wy9evXCzZs3RcchIiIiLZSeno6uXbvi9evX2LlzJ0xNTUVHomLGAo6oiMjlcmzcuBHlypVDp06d8PLlS9GRiIiISItIkoThw4fj4sWL2LFjB8qVKyc6EgnAAo6oCFlaWiIwMBAvXrxAz549eU0LERERFZlff/0V/v7+WL16NRo1aiQ6DgnCAo6oiFWtWhVbt27FsWPH8N1333FmSiIiIiq0ffv2YezYsfj+++/x5Zdfio5DArGAI1KBli1b4rfffsOyZcuwcOFC0XGIiIhIg124cAHdu3eHt7c35s6dKzoOCcbprohUZOjQoXj8+DEmTJgAGxsbDBgwQHQkIiIi0jARERFo3749ateujU2bNkEul4uORIKxgCNSoZkzZ+Lp06cYNGgQSpcujY4dO4qORERqxNDQEMuWLVP+TET0tsePH6NNmzawtbXF7t27OeMkAQBkEi/QESYhIQFWVlaIj4+HpaWl6DikIllZWejZsyf27NmDgwcPolmzZqIjERERkZqLi4tD8+bNkZCQgNOnT3PGSR2Q19qA18ARqZhcLkdAQAAaNWqEjh074tq1a6IjERERkRpLSUlBx44dERMTgwMHDrB4oxxYwBEVA2NjY+zcuRMVK1ZEu3btcO/ePdGRiEgNZGVl4dixYzh27BiysrJExyEiNZCRkYEvvvgCoaGhCA4OhpOTk+hIpGZYwBEVEysrK+zbtw/GxsZo27Ytnj17JjoSEQmWlpaGli1bomXLlkhLSxMdh4gEkyQJgwYNwr59+7B9+3Z4eHiIjkRqiAUcUTGyt7fHgQMHEB8fj/bt2yMxMVF0JCIiIlITEyZMwPr167F+/Xq0bdtWdBxSUyzgiIpZ1apVsXfvXkRERKBr165IT08XHYmIiIgE+/nnn7Fw4UL88ssv6NOnj+g4pMZYwBEJULduXQQGBuLEiRPw9fWFQqEQHYmIiIgE+fPPPzFu3DhMnjwZo0aNEh2H1BwLOCJBPD098ffff2PLli0YNWoUuKIHERGR7gkODsZXX32Fr7/+GrNnzxYdhzQACzgigbp27YoVK1Zg2bJlmDNnjug4REREVIxOnz6NHj16oGPHjlixYgVkMpnoSKQB9EUHINJ1gwcPxtOnTzFlyhSUKlUKw4YNEx2JiIiIVOzatWvo0KEDGjRogI0bN0Jfn6fllDc8UojUwA8//IDnz59j+PDhMDAwwNdffy06EhEVAwMDAyxYsED5MxHphuvXr6N169aoWLEidu3aBRMTE9GRSIOwgCNSAzKZDEuWLEFGRgYGDx4MPT09fPXVV6JjEZGKGRoaYvz48aJjEFExunHjBlq1agUHBwccOnQI1tbWoiORhmEBR6QmZDIZli1bBoVCga+//hpyuRy+vr6iYxEREVERuXnzJlq1agU7OzscOnQIpUqVEh2JNBALOCI1IpPJ4Ofnh6ysLAwYMAB6enr48ssvRcciIhXJysrCpUuXAAD16tWDXC4XnIiIVCUiIgItW7aEjY0NDh8+jNKlS4uORBqKBRyRmtHT08Pvv/8OhUKB/v37Qy6Xc0FPIi2VlpaGhg0bAgCSkpJgZmYmOBERqcKtW7fQsmVLlCxZEocPH4aNjY3oSKTBWMARqSE9PT2sWrUKCoUCX375JWQyGXr37i06FhEREeVTVFQUWrZsCSsrKxw5cgS2traiI5GGYwFHpKb09PSwevVqKBQK9O3bF1lZWejbt6/oWERERJRHERERaNWqFSwsLHDkyBHY2dmJjkRagAUckRqTy+VYs2YN9PX10a9fP2RkZGDAgAGiYxEREdFHZM82WapUKRw+fBj29vaiI5GWYAFHpObkcjlWrVoFAwMDfPXVV8qlBoiIiEg9Xb16FV5eXihTpgwOHTrEa96oSLGAI9IAenp6WL58OQwNDTFkyBC8fv0a33zzjehYRERE9B+XLl3Cp59+iooVK+LgwYNcKoCKHAs4Ig0hk8nwyy+/wNDQECNHjsTr168xZswY0bGIiIjof86fP4+2bduievXq2LdvH0qUKCE6EmkhFnBEGkQmk2HBggUwNDTE2LFjkZ6ejkmTJomORUQFZGBggGnTpil/JiLNdfr0aXz22WeoVasW9uzZAysrK9GRSEuxgCPSMDKZDLNnz4ahoSEmT56MFy9eYMGCBdDT0xMdjYjyydDQENOnTxcdg4gKKTg4GD169ECDBg2we/duWFhYiI5EWowFHJEGkslkmDZtGkqWLIlRo0YhJiYG/v7+MDQ0FB2NiIhIp6xduxaDBg1Chw4d8Pfff8PExER0JNJy/MqeSIONHDkS//zzD7Zs2YIOHTogMTFRdCQiygeFQoHr16/j+vXrUCgUouMQUT5IkoS5c+fiq6++wsCBA7F161YWb1QsWMARabgePXpg3759OHfuHFq2bInY2FjRkYgoj1JTU1GrVi3UqlULqampouMQUR5lZWVh5MiR+OGHHzBjxgz8/vvv0NfnwDYqHizgiLRAy5YtceLECURHR6Np06a4ffu26EhERERaKS0tDb169cKKFSuwcuVKTJ06FTKZTHQs0iFaX8DFxcXBx8cHlpaWsLa2xsCBA5GUlPTB9vfu3YNMJnvvbcuWLcp273t806ZNxfGWiN6rTp06OH36NORyOZo0aYKQkBDRkYiIiLRKfHw82rVrh927d2P79u0YPHiw6Eikg7S+gPPx8cH169dx8OBB7N69GydOnMj1f7by5cvjyZMnOW4zZsyAubk5Pvvssxxt165dm6Pd559/ruJ3Q5S7SpUq4dSpU6hcuTI8PT1x8OBB0ZGIiIi0QnR0NJo3b46rV6/i0KFD6Ny5s+hIpKO0erBueHg49u3bhwsXLsDd3R0A8Ntvv6F9+/ZYtGgRHBwc3nmOXC6Hvb19jvt27NiBnj17wtzcPMf91tbW77QlEq106dI4fPgwevbsifbt22PdunXw8fERHYuIiEhj3bx5E23btoVCocDJkydRs2ZN0ZFIh2l1D9yZM2dgbW2tLN4AwMvLC3p6ejh37lyethESEoLQ0FAMHDjwncdGjBiB0qVLo2HDhvD394ckSbluKz09HQkJCTluRKpgZmaGnTt3om/fvujbty9+/vln0ZGIiIg00tmzZ9GsWTNYWFjgzJkzLN5IOK3ugYuJiYGtrW2O+/T19VGyZEnExMTkaRtr1qyBs7MzmjRpkuP+mTNnolWrVjA1NcWBAwcwfPhwJCUl4dtvv/3gtubNm4cZM2bk/40QFYCBgQH8/f3h4OCAcePGITo6GgsXLuSC30RERHmUvUB3/fr1ERgYiBIlSoiORKSZBdzEiRPx008/5domPDy80K+TmpqKjRs3YsqUKe889vZ9devWRXJyMhYuXJhrATdp0iSMGTNG+XtCQgLKly9f6JxEHyKTyTBnzhyUKVMG3377LWJiYrB27Vou+E2kJgwMDDBu3Djlz0SkPvz9/TF48GB06tQJAQEBXOON1IZGFnBjx45F//79c21TpUoV2Nvb4+nTpznuz8zMRFxcXJ6uXdu6dStSUlLQr1+/j7b18PDArFmzkJ6eDiMjo/e2MTIy+uBjRKr0zTffwM7ODn379kVsbCy2bNnCbxGJ1IChoSEWLlwoOgYRvUWhUGDWrFmYPn06hgwZAj8/P8jlctGxiJQ0soCzsbGBjY3NR9s1btwYr169QkhICOrXrw8AOHLkCBQKBTw8PD76/DVr1qBTp055eq3Q0FCUKFGCBRqprR49esDGxgbdunVDw4YNsWvXLri4uIiORUREpDaSkpLQv39/bNu2DbNnz8bkyZO5xhupHa2+GMbZ2Rnt2rXDoEGDcP78eZw6dQrffPMNevXqpZyB8vHjx3BycsL58+dzPDcqKgonTpzA119//c52g4KC8McffyAsLAxRUVFYsWIF5s6di5EjRxbL+yIqKE9PT1y4cAHGxsZo1KgRgoKCREci0mkKhQL37t3DvXv3oFAoRMch0ml3795F06ZNsX//fuzcuRM//PADizdSS1pdwAFAQEAAnJyc0Lp1a7Rv3x7NmjXDqlWrlI9nZGQgIiICKSkpOZ7n7++PcuXKoU2bNu9s08DAAH5+fmjcuDHc3NywcuVKLF68GNOmTVP5+yEqrCpVquDMmTPw8vJC586dMXfu3I/OoEpEqpGamorKlSujcuXKSE1NFR2HSGcdPXoUDRo0QFJSEs6cOcM13kitySSeuQmTkJAAKysrxMfHw9LSUnQc0jFvj/Hv0aMH1q5dCzMzM9GxiHRKcnKyco3RpKQk/j9IVMwkSYKfnx9Gjx6Nli1b4p9//kHJkiVFxyIdldfaQOt74Ijo/fT09DBt2jRs27YNe/bsQbNmzXD//n3RsYiIiIrF69evMXjwYIwcORIjR47E3r17WbyRRmABR6TjunbtijNnziA+Ph7u7u44ceKE6EhEREQqFRsbi1atWuHPP//E2rVrsWTJEujra+TcfqSDWMAREVxdXXHhwgXUrl0brVu3xu+//y46EhERkUqEhITA3d0dt2/fxvHjxz+6NBWRumEBR0QAgFKlSmHfvn0YPnw4hg0bhqFDh+L169eiYxERERWZjRs3olmzZnBwcMDFixfRqFEj0ZGI8o0FHBEpGRgY4Ndff8WaNWvg7+8PLy8vPH36VHQsIiKiQsnKysKECRPg4+ODHj164Pjx4yhbtqzoWEQFwsG+RPSOr776Ck5OTujatSvc3d2xa9cu1K1bV3QsIq2jr6+P4cOHK38moqL36tUr9OnTB/v378fixYsxevRoru9GGo3LCAjEZQRI3T169AhdunTB9evXsWLFCvj6+oqORERElGdXrlxBz5498fTpU/zzzz/vXd+XSF1wGQEiKrRy5crhxIkT6NWrF/r3749+/fohMTFRdCwiIqJcSZKE5cuXw8PDAyYmJjh//jyLN9IaLOCIKFcmJibw9/fHhg0bsGPHDtSvXx+XL18WHYtIK0iShGfPnuHZs2fggBiiovHy5Ut0794dI0aMwNdff42zZ8/C0dFRdCyiIsMCjojyxMfHB5cuXYKFhQUaNWqEpUuX8oSTqJBSUlJga2sLW1tbpKSkiI5DpPFOnz4NNzc3HD16FNu3b8eyZctgbGwsOhZRkWIBR0R55ujoiNOnT2P48OEYNWoUOnfujBcvXoiORUREOk6hUGDevHlo3rw5ypcvj9DQUHTp0kV0LCKVYAFHRPliZGSEJUuWIDAwEKdOnYKbmxtOnjwpOhYREemomJgYtG3bFj/88AMmTpyIY8eOoUKFCqJjEakMCzgiKpCOHTviypUrqFKlCjw9PTFr1ixkZWWJjkVERDrkwIEDqFOnDsLCwnDw4EHMnj2bS3KQ1mMBR0QFVq5cORw5cgRTpkzB9OnT8emnnyI6Olp0LCIi0nIZGRmYNGkS2rZti7p16yI0NBStW7cWHYuoWLCAI6JCkcvlmD59Og4fPoyIiAjUqVMHe/fuFR2LiIi01L1799C8eXMsWrQICxYswJ49e2BnZyc6FlGxYQFHREXC09MTV65cgYeHB9q3b49x48bh9evXomMREZEW2bZtG9zc3BATE4N///0X48ePh54eT2dJt/CIJ6IiU7p0aQQFBWHx4sVYunQpmjVrhoiICNGxiNSWvr4+fH194evry+t2iHKRlJSEoUOHonv37mjTpg0uX74MDw8P0bGIhJBJXMhJmISEBFhZWSE+Ph6Wlpai4xAVqYsXL6JPnz54+PAhZs2ahe+++w5yuVx0LCIi0jBHjhzBwIED8fTpUyxevBiDBw+GTCYTHYuoyOW1NmAPHBGphLu7O0JDQzF8+HB8//33aNasGcLDw0XHIiIiDZGYmIhhw4ahdevWqFSpEq5du4YhQ4aweCOdxwKOiFTG1NQUP//8M/7991+8fPkSdevWxU8//YTMzEzR0YjUgiRJSE5ORnJyMjgghuj/HTp0CLVq1cJff/0FPz8/HD58GFWqVBEdi0gtsIAjIpVr0qQJLl++jG+//RaTJ09GkyZNcP36ddGxiIRLSUmBubk5zM3NkZKSIjoOkXDx8fEYPHgwPv30U1SrVg1hYWEYPnw4Jyohegv/byCiYmFiYoIFCxbg9OnTSEpKQr169TB37lz2xhEREQBg3759qFWrFv7++2+sXLkShw4dQqVKlUTHIlI7LOCIqFh5eHjg0qVLGDNmDKZMmQIPDw9cvXpVdCwiIhLk1atX+Oqrr/DZZ5/B2dkZYWFhnKiEKBcs4Iio2BkbG2PevHk4e/Ys0tPT4e7ujlmzZiEjI0N0NCIiKkbBwcGoWbMmtm3bhj/++AP79+9HxYoVRcciUmss4IhImAYNGiAkJATff/89ZsyYgYYNGyI0NFR0LCIiUrGXL1+if//+6NChA2rXro2wsDAMHDiQvW5EecACjoiEMjIywuzZs3Hu3DlkZWWhQYMGmDZtGl6/fi06GhERqUBgYCBq1qyJnTt3Yu3atdizZw/Kly8vOhaRxmABR0RqoX79+rh48SJ++OEHzJ07F25ubjh8+LDoWEREVETu37+Pbt26oXPnzqhXrx6uX7+O/v37s9eNKJ9YwBGR2jA0NMT06dMREhKCUqVKwcvLCz179sTDhw9FRyNSCblcju7du6N79+6Qy+Wi4xCpRFpaGmbNmgVnZ2ecPXsWGzduRFBQEMqWLSs6GpFGkklcOVSYhIQEWFlZIT4+HpaWlqLjEKkVSZKwceNGjBs3DgkJCfjhhx8wduxYGBkZiY5GRER5tHv3bowaNQoPHjzAmDFj8OOPP8LCwkJ0LCK1lNfagD1wRKSWZDIZfHx8EBERgWHDhmHatGmoVasW9uzZIzoaERF9RFRUFDp06ICOHTuiatWquHbtGn766ScWb0RFgAUcEak1S0tLLFq0CFeuXEHFihXh7e2NTp064c6dO6KjERHRfyQnJ+PHH39EzZo1ce3aNWzfvh379++Hk5OT6GhEWoMFHBFpBBcXFxw8eBBbtmxBaGgoXFxcMG3aNKSkpIiORlRgycnJkMlkkMlkSE5OFh2HqMAkScLWrVvh7OyMRYsWYcKECQgPD0eXLl04SQlREWMBR0QaQyaToXv37ggPD8e4ceMwf/58uLi4YMeOHeDlvEREYoSHh6NNmzbo0aMH3NzccP36dcycOROmpqaioxFpJRZwRKRxzMzMMHv2bFy/fh01a9ZE165d0a5dO0RERIiORkSkMxITEzF+/HjUrl0bd+/exe7duxEYGIiqVauKjkak1VjAEZHGqlatGoKDgxEYGIhbt27B1dUVEyZMQEJCguhoRERaS6FQICAgADVq1ICfnx+mT5+OsLAweHt7i45GpBNYwBGRxuvYsSNu3LiBKVOmYOnSpahatSp++eUXpKWliY5GRKQ1JEnC/v374e7ujr59+6Jp06a4efMmfvjhBxgbG4uOR6QzWMARkVYwNjbGlClTcOvWLXTp0gXjxo1DjRo1sHbtWmRmZoqOR0Sk0c6ePYtWrVqhXbt2MDU1xYkTJ7BlyxZUqFBBdDQincMCjoi0Srly5bBq1Spcv34dHh4e+Oqrr1C7dm1OdEJEVADXr1/H559/jsaNG+PFixfYvXs3Tp48iU8++UR0NCKdxQKOiLRSjRo1sHnzZly4cAHlypVD165d0ahRIxw5ckR0NCIluVyO9u3bo3379pDL5aLjECndu3cPvr6+cHV1xdWrV7FhwwZcvnwZ3t7eXBaASDAWcESk1dzd3XHgwAEcPnwYANC6dWu0adMGFy9eFJyM6M3Q3+DgYAQHB/MaIlILT58+xahRo1C9enXs378fv/32G27evAkfHx9+yUCkJljAEZFOaNWqFc6ePYvt27fj0aNHaNCgAXr06MGlB4iIACQkJGDatGmoUqUK1q1bh+nTp+P27dsYMWIEDA0NRccjorewgCMinSGTydClSxdcu3YNa9euxfnz51GzZk0MGjQIjx49Eh2PiKjYpaWlYfHixahSpQoWLFiAESNG4O7du5g8eTLMzMxExyOi92ABR0Q6Ry6Xo3///oiMjMTPP/+MnTt3olq1ahg3bhyeP38uOh7pkOTkZJiZmcHMzAzJycmi45AOycjIgL+/PxwdHfH999+jW7duiIqKwk8//YSSJUuKjkdEuWABR0Q6y8jICKNGjcKdO3cwadIkrFy5EhUrVsTo0aPx4MED0fFIR6SkpCAlJUV0DNIRKSkpWLZsGRwdHTFw4EA0bdoUN27cwMqVK1G2bFnR8YgoD1jAEZHOs7CwwLRp03D37l2MHz8ef/75J6pWrYr+/fvjxo0bouMRERXay5cvMWfOHFSqVAmjRo1C06ZNceXKFWzatAnVq1cXHY+I8oEFHBHR/5QuXRrTp0/HgwcPsGDBAhw6dAg1a9ZEly5dcO7cOdHxiIjyLTo6GuPHj0eFChUwe/Zs9OjRA7du3UJAQABq164tOh4RFQALOCKi/zA3N8d3332HO3fuwN/fH+Hh4WjUqBFatmyJ/fv3c0FwIlJ7t27dwuDBg1G5cmWsXr0a3377Le7duwc/Pz9UqVJFdDwiKgQWcEREH2BoaIgBAwbg+vXr2LZtG5KTk9GuXTvUr18fmzdvRlZWluiIREQ5hISEoGfPnqhRowaCgoIwa9Ys3L9/H3PmzIGdnZ3oeERUBFjAERF9hFwuR9euXXHu3DkcPnwYpUuXxhdffAEnJyesWrUK6enpoiMSkQ6TJAlHjx5FmzZt4O7ujkuXLuH333/H3bt38f3338PKykp0RCIqQizgiIjySCaToVWrVjhw4AAuXrwINzc3DB06FJUrV8bChQuRkJAgOiJpGD09PbRo0QItWrSAnh7/JFP+KBQK7NixA40aNUKrVq3w7NkzbNq0CTdv3sTgwYNhbGwsOiIRqYBM4sUcwiQkJMDKygrx8fGwtLQUHYeICiAyMhILFy7E+vXrYWpqCl9fXwwbNgxOTk6ioxGRlnr58iXWrVuH33//HZGRkfD09MTEiRPRpk0byGQy0fGIqIDyWhvw6z4iokKoXr06Vq9ejbt372LYsGH4+++/4ezsjFatWmHLli3IyMgQHZGItMSFCxfw1VdfoWzZspgwYQLq1auHM2fO4OjRo2jbti2LNyIdwQKOiKgIlC1bFvPmzcPDhw+xceNGZGRkoGfPnqhYsSKmTp2KR48eiY5IRBooJSUF/v7+cHd3R8OGDXH48GFMmTIFDx8+xN9//41GjRqJjkhExYwFHBFRETIyMkLv3r1x8uRJXL16FV26dMGSJUtQsWJFdOnSBQcOHIBCoRAdk9REcnIybGxsYGNjg+TkZNFxSI1ERERg9OjRKFu2LL7++mvY2dkhKCgId+7cwaRJkzijJJEOYwFHRKQirq6u8PPzQ3R0NPz8/HDnzh20bdsWNWrUwM8//4wXL16Ijkhq4Pnz53j+/LnoGKQGMjIysHXrVrRu3RpOTk4ICAjAkCFDcPv2bQQHB6NDhw6Qy+WiYxKRYCzgiIhUzMLCAkOHDkVoaCj+/fdfeHh4YPLkyShbtix8fX1x9uxZLg5OpMMePXqEadOmoWLFiujRowfS09OxYcMGPHr0CPPnz0flypVFRyQiNcJZKAXiLJREuuvZs2fw9/fHypUrcffuXdStWxdDhw5Fz549YW1tLToeFZPk5GSYm5sDAJKSkmBmZiY4ERWXzMxMHDx4EKtXr0ZgYCBMTEzQt29fDBs2DLVr1xYdj4gE4CyURERqzMbGBhMmTEBUVBT27NmDsmXLYtiwYbCzs0PXrl2xbds2pKWliY5JREVIkiScO3cOI0eOhIODA9q3b49bt25h6dKlePz4MVasWMHijYg+ij1wArEHjoje9uTJE2zatAkBAQEICQmBlZUVunXrBh8fH7Ro0YLXvmgh9sDphsjISAQEBCAgIAC3b99GmTJl0Lt3b/j4+KBu3bqc/p+IAOS9NmABJxALOCL6kJs3b2Ljxo0ICAjAnTt3ULZsWfTq1Qs+Pj5wc3PjCZ+WYAGnvWJiYpRfyFy8eBGWlpbKL2Q8PT35hQwRvYMFnAZgAUdEHyNJEs6ePYuAgAD8888/eP78OVxcXODj44M+ffqgUqVKoiNSIaSmpqJ58+YAgBMnTsDExERwIiqMxMREbN++HQEBATh8+DDkcjm8vb3h4+MDb29v/vsSUa5YwGkAFnBElB8ZGRk4ePAgAgICsHPnTqSkpKBp06bw8fFBjx49ULp0adERiXTO69evsX//fgQEBCAwMFBZlPv4+KB79+4oWbKk6IhEpCFYwGkAFnBEVFBJSUnYtWsXAgICcODAAchkMrRr1w6ff/45OnTowEV+iVQoNTUVR44cQWBgILZu3Yq4uDi4urrCx8cHvXv3RoUKFURHJCINxAJOA7CAI6Ki8PTpU2zevBmbNm3C6dOnAQANGzZEp06d0LFjR9SqVYvXzBEVUkxMDIKDgxEUFISDBw8iJSUFVatWRffu3eHj4wNXV1fREYlIw7GA0wAs4IioqD179gx79uxBUFAQ9u/fj6SkJFSqVAkdO3ZEx44d0aJFCxgaGoqOSf+TkpICFxcXAMCNGzdgamoqOBFlkyQJ165dQ1BQEIKCgnDu3Dno6emhcePG6NixIzp16gQnJyd+OUJERYYFnAZgAUdEqpSeno5jx44hMDAQQUFBePjwISwsLNCuXTt07NgR7du3R6lSpUTH1GmchVK9vH79GsePH1f+P3P//n2Ym5ujbdu2yv9nbGxsRMckIi3FAk4DsIAjouIiSRKuXr2qPDG9cOEC9PT00LRpU2VvQo0aNUTH1Dks4MR78eIF9uzZg8DAQOzfvx+JiYkoX768cgiyp6cnjIyMRMckIh3AAk4DsIAjIlGePHmC3bt3K6/nSUtLg6OjI9q1a4eWLVuiefPm7J0rBizgil96ejrOnTuHY8eO4eDBgzh9+jQUCgUaNGigLNpq167NoZFEVOxYwGkAFnBEpA5SUlJw+PBhBAUF4fDhw7hz5w4AoHbt2vD09ISnpycLOhVhAad6bxdsx44dw5kzZ5CWlgZra2u0aNECHTp0gLe3N8qUKSM6KhHpOBZwGoAFHBGpowcPHuD48ePKE97sgs7V1TVHQcd15wqPBVzRS0tLUxZsx48fz1GwNW/eXHkM165dG3K5XHRcIiIlFnAagAUcEWkCFnSqwwKu8N4u2LJ72NLT05U9bNnHqKurKws2IlJrLOA0AAs4ItJEuRV0LVq0gIeHB+rXr4/q1avzhPkjUlJS0KBBAwDAhQsXuIxAHkRHR+PSpUu4cOECTpw4wYKNiLQGCzgNwAKOiLTBw4cPlQXd8ePHERUVBQAwMzND3bp1Ua9ePdSvXx/169eHk5MTT6opTyRJwuPHj3Hp0iWEhIQobzExMQCAUqVKoVmzZizYiEhrsIDTACzgiEgbvXz5EpcvX85x0p1d1JmamsLNzU1Z0NWrVw/Ozs7Q19cXnJpEkiQJjx49ynHMhISE4OnTpwAAGxubHMdM/fr1UaFCBc4USURahQWcBmABR0S6Ij4+/p2iLjIyEgBgYmKCOnXq5DhBr1GjBoyNjQWnJlXIysrCgwcPchwPly5dwrNnzwAAdnZ27xRr5cqVY7FGRFqPBZwGYAFHRLosISEBoaGhOYq6iIgISJIEmUyGihUronr16u/cKlSooDVD5bT1GjhJkvDs2TNERka+c4uKikJ6ejoAoEyZMjkKtfr168PBwYHFGhHpJBZwGoAFHBFRTomJibh69SoiIiI+eNJvaGiIatWqvVPYOTo6ws7OTqNO/jV9FsrExETcunXrvYVafHw8ALy3GHd0dESdOnW49hoR0VtYwP3PnDlzEBwcjNDQUBgaGuLVq1cffY4kSZg2bRpWr16NV69eoWnTplixYgUcHR2VbeLi4jBy5EgEBQVBT08P3bp1w6+//qr8Q5wXLOCIiPImKysLDx8+fG+hcO/ePWT/KbOwsFAWCeXLl0eZMmVQpkwZODg4KH9WpyJJnQu4jIwMxMTE4MmTJzlujx8/RlRUFCIjI/HkyRNle1tb2/f2mFatWpXDYYmI8oAF3P9MmzYN1tbWePToEdasWZOnAu6nn37CvHnzsH79elSuXBlTpkzBtWvXcOPGDeUfoc8++wxPnjzBypUrkZGRgQEDBqBBgwbYuHFjnrOxgCMiKry0tDTcuXPnncLu8ePHePLkCVJTU3O0t7CwyFHQva/IK1OmDCwtLVXemyeigEtLS0NMTAyio6PfKc7evu/58+d4+xRBLpfDzs4ODg4O7/SAOjo6wtraWuXZiYi0GQu4/1i3bh1Gjx790QJOkiQ4ODhg7NixGDduHIA3F9/b2dlh3bp16NWrF8LDw+Hi4oILFy7A3d0dALBv3z60b98ejx49goODQ54ysYAjIlItSZKQkJDwTrHyvt+TkpJyPNfU1BSlSpWChYUFzM3NYWFhUaCfzczMIJfLoaenl+Mmk8neW8BJkgRJkpCVlQWFQqG8paamIikpCYmJiUhMTMzXz9n/ffXqFV6+fJnjfRoYGORayGb/Xrp0aa259pCISB3ltTbgvM3/cffuXcTExMDLy0t5n5WVFTw8PHDmzBn06tULZ86cgbW1tbJ4AwAvLy/o6enh3Llz6NKly3u3nZ6erryGA3jzj0RERKojk8lgZWUFKysrODs759o2KSnpnQIvLi7unULo/v3779yXnJxc4HzZLC0tlcVbfhkZGb23gDQ3N4e9vb3yZysrq3d6HkuVKqVR1w0SEek6FnD/kb1AqJ2dXY777ezslI/FxMTA1tY2x+P6+vooWbKkss37zJs3DzNmzCjixEREVBTMzc3h6OiY43rnvMrKykJycvI7PV6JiYlITk7O0ZP2ds9aamoqRo0aBQBYvHgxTE1N3+mpy74ZGxt/sLfPwMCgqHcHERGpKY0s4CZOnIiffvop1zbh4eFwcnIqpkR5M2nSJIwZM0b5e0JCAsqXLy8wERERFQW5XA5LS8t8D4dPSUnB4sWLAQCDBg3SmmUEiIhIdTSygBs7diz69++fa5sqVaoUaNv29vYAgNjY2BzTG8fGxsLNzU3Z5unTpzmel5mZibi4OOXz38fIyAhGRkYFykVERNrH1NQU9+7dEx2DiIg0iEYWcDY2NrCxsVHJtitXrgx7e3scPnxYWbAlJCTg3LlzGDZsGACgcePGePXqFUJCQlC/fn0AwJEjR6BQKODh4aGSXERERERERHqiA6jagwcPEBoaigcPHiArKwuhoaEIDQ3NMduYk5MTduzYAeDNBeWjR4/G7NmzERgYiGvXrqFfv35wcHDA559/DgBwdnZGu3btMGjQIJw/fx6nTp3CN998g169euV5BkoiIiIiIqL80sgeuPyYOnUq1q9fr/y9bt26AICjR4/C09MTABAREYH4+Hhlm++//x7JyckYPHgwXr16hWbNmmHfvn05FiINCAjAN998g9atWysX8l66dGnxvCkiItIKqampaN68OQDgxIkTMDExEZyIiIjUnc6sA6eOuA4cEZFuE7GQNxERqae81gZaP4SSiIiIiIhIW7CAIyIiIiIi0hAs4IiIiIiIiDQECzgiIiIiIiINwQKOiIiIiIhIQ2j9MgJERETqrHTp0qIjEBGRBmEBR0REJIiZmRmePXsmOgYREWkQDqEkIiIiIiLSECzgiIiIiIiINAQLOCIiIkFSU1Ph6ekJT09PpKamio5DREQagNfAERERCaJQKHD8+HHlz0RERB/DHjgiIiIiIiINwQKOiIiIiIhIQ7CAIyIiIiIi0hAs4IiIiIiIiDQECzgiIiIiIiINwVkoiYiIBDI1NRUdgYiINAgLOCIiIkHMzMyQnJwsOgYREWkQDqEkIiIiIiLSECzgiIiIiIiINAQLOCIiIkHS0tLg7e0Nb29vpKWliY5DREQagNfAERERCZKVlYU9e/YofyYiIvoY9sARERERERFpCBZwREREREREGoIFHBERERERkYZgAUdERERERKQhWMARERERERFpCM5CKZAkSQCAhIQEwUmIiEiE5ORk5c8JCQmciZKISIdl1wTZNcKHsIATKDExEQBQvnx5wUmIiEg0BwcH0RGIiEgNJCYmwsrK6oOPy6SPlXikMgqFAtHR0bCwsIBMJhOaJSEhAeXLl8fDhw9haWkpNIs24v5VLe5f1eL+VS3uX9Xi/lUt7l/V4v5VLXXbv5IkITExEQ4ODtDT+/CVbuyBE0hPTw/lypUTHSMHS0tLtTiAtRX3r2px/6oW969qcf+qFvevanH/qhb3r2qp0/7NrectGycxISIiIiIi0hAs4IiIiIiIiDQECzgCABgZGWHatGkwMjISHUUrcf+qFvevanH/qhb3r2px/6oW969qcf+qlqbuX05iQkREREREpCHYA0dERERERKQhWMARERERERFpCBZwREREREREGoIFHBERERERkYZgAacj5syZgyZNmsDU1BTW1tZ5eo4kSZg6dSrKlCkDExMTeHl54datWznaxMXFwcfHB5aWlrC2tsbAgQORlJSkgneg3vK7H+7duweZTPbe25YtW5Tt3vf4pk2biuMtqZWCHGeenp7v7LuhQ4fmaPPgwQN4e3vD1NQUtra2GD9+PDIzM1X5VtRSfvdvXFwcRo4ciRo1asDExAQVKlTAt99+i/j4+BztdPn49fPzQ6VKlWBsbAwPDw+cP38+1/ZbtmyBk5MTjI2N4erqij179uR4PC+fx7okP/t39erV+OSTT1CiRAmUKFECXl5e77Tv37//O8dqu3btVP021FZ+9u+6deve2XfGxsY52vD4zSk/+/d9f8tkMhm8vb2VbXj8/r8TJ06gY8eOcHBwgEwmw86dOz/6nGPHjqFevXowMjJCtWrVsG7dunfa5PczXeUk0glTp06VFi9eLI0ZM0aysrLK03Pmz58vWVlZSTt37pSuXLkiderUSapcubKUmpqqbNOuXTupTp060tmzZ6WTJ09K1apVk3r37q2id6G+8rsfMjMzpSdPnuS4zZgxQzI3N5cSExOV7QBIa9euzdHu7f2vKwpynLVo0UIaNGhQjn0XHx+vfDwzM1OqVauW5OXlJV2+fFnas2ePVLp0aWnSpEmqfjtqJ7/799q1a1LXrl2lwMBAKSoqSjp8+LDk6OgodevWLUc7XT1+N23aJBkaGkr+/v7S9evXpUGDBknW1tZSbGzse9ufOnVKksvl0oIFC6QbN25IP/74o2RgYCBdu3ZN2SYvn8e6Ir/7t0+fPpKfn590+fJlKTw8XOrfv79kZWUlPXr0SNnG19dXateuXY5jNS4urrjeklrJ7/5du3atZGlpmWPfxcTE5GjD4/f/5Xf/vnjxIse+DQsLk+RyubR27VplGx6//2/Pnj3SDz/8IG3fvl0CIO3YsSPX9nfu3JFMTU2lMWPGSDdu3JB+++03SS6XS/v27VO2ye+/WXFgAadj1q5dm6cCTqFQSPb29tLChQuV97169UoyMjKS/v77b0mSJOnGjRsSAOnChQvKNnv37pVkMpn0+PHjIs+uropqP7i5uUlfffVVjvvy8uGj7Qq6f1u0aCGNGjXqg4/v2bNH0tPTy3GisWLFCsnS0lJKT08vkuyaoKiO382bN0uGhoZSRkaG8j5dPX4bNmwojRgxQvl7VlaW5ODgIM2bN++97Xv27Cl5e3vnuM/Dw0MaMmSIJEl5+zzWJfndv/+VmZkpWVhYSOvXr1fe5+vrK3Xu3Lmoo2qk/O7fj51X8PjNqbDH75IlSyQLCwspKSlJeR+P3/fLy9+g77//XqpZs2aO+7744gupbdu2yt8L+2+mChxCSe919+5dxMTEwMvLS3mflZUVPDw8cObMGQDAmTNnYG1tDXd3d2UbLy8v6Onp4dy5c8WeWZSi2A8hISEIDQ3FwIED33lsxIgRKF26NBo2bAh/f39IOrZ0Y2H2b0BAAEqXLo1atWph0qRJSElJybFdV1dX2NnZKe9r27YtEhISL/uxkQAAGVdJREFUcP369aJ/I2qqqP4/jo+Ph6WlJfT19XPcr2vH7+vXrxESEpLjs1NPTw9eXl7Kz87/OnPmTI72wJtjMbt9Xj6PdUVB9u9/paSkICMjAyVLlsxx/7Fjx2Bra4saNWpg2LBhePHiRZFm1wQF3b9JSUmoWLEiypcvj86dO+f4DOXx+/+K4vhds2YNevXqBTMzsxz38/gtmI99/hbFv5kq6H+8CemimJgYAMhxcpv9e/ZjMTExsLW1zfG4vr4+SpYsqWyjC4piP6xZswbOzs5o0qRJjvtnzpyJVq1awdTUFAcOHMDw4cORlJSEb7/9tsjyq7uC7t8+ffqgYsWKcHBwwNWrVzFhwgRERERg+/btyu2+7/jOfkxXFMXx+/z5c8yaNQuDBw/Ocb8uHr/Pnz9HVlbWe4+tmzdvvvc5HzoW3/6szb7vQ210RUH2739NmDABDg4OOU7I2rVrh65du6Jy5cq4ffs2Jk+ejM8++wxnzpyBXC4v0vegzgqyf2vUqAF/f3/Url0b8fHxWLRoEZo0aYLr16+jXLlyPH7fUtjj9/z58wgLC8OaNWty3M/jt+A+9PmbkJCA1NRUvHz5stCfOarAAk6DTZw4ET/99FOubcLDw+Hk5FRMibRLXvdvYaWmpmLjxo2YMmXKO4+9fV/dunWRnJyMhQsXasUJsKr379vFhKurK8qUKYPWrVvj9u3bqFq1aoG3qymK6/hNSEiAt7c3XFxcMH369ByPafPxS5pp/vz52LRpE44dO5Zjoo1evXopf3Z1dUXt2rVRtWpVHDt2DK1btxYRVWM0btwYjRs3Vv7epEkTODs7Y+XKlZg1a5bAZNpnzZo1cHV1RcOGDXPcz+NX97CA02Bjx45F//79c21TpUqVAm3b3t4eABAbG4syZcoo74+NjYWbm5uyzdOnT3M8LzMzE3Fxccrna7K87t/C7oetW7ciJSUF/fr1+2hbDw8PzJo1C+np6TAyMvpoe3VWXPs3m4eHBwAgKioKVatWhb29/TuzSMXGxgIAj9887t/ExES0a9cOFhYW2LFjBwwMDHJtr03H74eULl0acrlceSxli42N/eD+tLe3z7V9Xj6PdUVB9m+2RYsWYf78+Th06BBq166da9sqVaqgdOnSiIqK0qkT4MLs32wGBgaoW7cuoqKiAPD4fVth9m9ycjI2bdqEmTNnfvR1dPX4LYgPff5aWlrCxMQEcrm80P9PqAKvgdNgNjY2cHJyyvVmaGhYoG1XrlwZ9vb2OHz4sPK+hIQEnDt3TvlNW+PGjfHq1SuEhIQo2xw5cgQKhUJ5sqzJ8rp/C7sf1qxZg06dOsHGxuajbUNDQ1GiRAmtOPktrv2bLTQ0FACUJxCNGzfGtWvXchQvBw8ehKWlJVxcXIrmTQqk6v2bkJCANm3awNDQEIGBge9MG/4+2nT8foihoSHq16+f47NToVDg8OHDOXop3ta4ceMc7YE3x2J2+7x8HuuKguxfAFiwYAFmzZqFffv25bje80MePXqEFy9e5Cg4dEFB9+/bsrKycO3aNeW+4/H7/wqzf7ds2YL09HT07dv3o6+jq8dvQXzs87co/p9QCWHTp1Cxun//vnT58mXlVPWXL1+WLl++nGPK+ho1akjbt29X/j5//nzJ2tpa2rVrl3T16lWpc+fO711GoG7dutK5c+ekf//9V3J0dNTZZQRy2w+PHj2SatSoIZ07dy7H827duiXJZDJp796972wzMDBQWr16tXTt2jXp1q1b0vLlyyVTU1Np6tSpKn8/6ia/+zcqKkqaOXOmdPHiRenu3bvSrl27pCpVqkjNmzdXPid7GYE2bdpIoaGh0r59+yQbGxudXUYgP/s3Pj5e8vDwkFxdXaWoqKgcU1dnZmZKkqTbx++mTZskIyMjad26ddKNGzekwYMHS9bW1soZT7/88ktp4sSJyvanTp2S9PX1pUWLFknh4eHStGnT3ruMwMc+j3VFfvfv/PnzJUNDQ2nr1q05jtXsv3+JiYnSuHHjpDNnzkh3796VDh06JNWrV09ydHSU0tLShLxHkfK7f2fMmCHt379fun37thQSEiL16tVLMjY2lq5fv65sw+P3/+V3/2Zr1qyZ9MUXX7xzP4/fnBITE5XnuACkxYsXS5cvX5bu378vSZIkTZw4Ufryyy+V7bOXERg/frwUHh4u+fn5vXcZgdz+zURgAacjfH19JQDv3I4ePapsg/+t2ZRNoVBIU6ZMkezs7CQjIyOpdevWUkRERI7tvnjxQurdu7dkbm4uWVpaSgMGDMhRFOqKj+2Hu3fvvrO/JUmSJk2aJJUvX17Kysp6Z5t79+6V3NzcJHNzc8nMzEyqU6eO9Pvvv7+3rbbL7/598OCB1Lx5c6lkyZKSkZGRVK1aNWn8+PE51oGTJEm6d++e9Nlnn0kmJiZS6dKlpbFjx+aYBl9X5Hf/Hj169L2fJwCku3fvSpLE4/e3336TKlSoIBkaGkoNGzaUzp49q3ysRYsWkq+vb472mzdvlqpXry4ZGhpKNWvWlIKDg3M8npfPY12Sn/1bsWLF9x6r06ZNkyRJklJSUqQ2bdpINjY2koGBgVSxYkVp0KBBQk/ORMvP/h09erSyrZ2dndS+fXvp0qVLObbH4zen/H4+3Lx5UwIgHThw4J1t8fjN6UN/n7L3qa+vr9SiRYt3nuPm5iYZGhpKVapUyXEunC23fzMRZJKk5XM6ExERERERaQleA0dERERERKQhWMARERERERFpCBZwREREREREGoIFHBERERERkYZgAUdERERERKQhWMARERERERFpCBZwREREREREGoIFHBERERERkYZgAUdEREVOJpNBJpNh+vTpoqOorSNHjkAmk8HOzg4pKSmi4+iUCxcuQCaToWTJkoiLixMdh4goX1jAERHpsGPHjimLrbdv+vr6KFmyJCpXrozmzZvju+++w7Zt2/D69WvRkVXi1atXOHjwIObMmYPOnTvDwcFBuS88PT2L/PUUCgVGjx4NABg3bhxMTU1zbZ+RkYF169bB29sbFSpUgJGREUqXLg1XV1d8/fXX2LJlS6GynDhxApMnT4anpyfs7e1haGgIS0tL1KpVC8OHD8fVq1c/up3p06e/91h63+3YsWMf3M6dO3fg4+MDW1tbGBsbw8XFBQsWLEBmZmaury9JEho3bgyZTIY1a9bk2rZBgwZo27YtXr58yS8ZiEjzSEREpLOOHj0qAcjzzcbGRpo1a5aUkZGR63az20+bNq143kghVapU6YPvuUWLFkX+egEBARIAqXTp0lJSUlKuba9cuSLVqlUr138XKyurAmcpX778R//d9fT0pO+//15SKBQf3M60adPyfBwdPXr0vdu4efOmVKpUqfc+p0uXLrm+/urVqyUAUqNGjXJtl+3UqVMSAMnQ0FB68ODBR9sTEakL/aItB4mISFMNGzYMw4cPV/6elJSEly9f4urVqzh8+DAOHTqEZ8+eYcqUKQgKCsLu3bthY2Pz3m1JklRcsYvE23nt7OzQoEED7N69W2WvN2fOHADAkCFDYGZm9sF2V69eRcuWLREXFwdjY2N8/fXXaNOmDcqWLYvXr1/j1q1b2LdvH06ePFngLNHR0QCAatWqoVu3bmjatCkcHByQmpqKo0ePYsmSJXj58iUWLFgAuVyOuXPnfnSb165dy/XxypUrv/f+4cOH48WLF7C3t8fChQtRuXJl7Nq1C4sWLcKOHTuwceNG+Pj4vPO8uLg4TJo0CXp6evDz84NMJvtoxiZNmqBRo0Y4e/YsFi5ciKVLl370OUREakF0BUlEROK83QP3sd6y69evS3Xr1lW2b9q0qZSenl48QVVs4cKF0tatW3P0xGS/z6LugTtw4IBy2+Hh4R9sl5qaKlWvXl0CIFWoUEGKjIz8YNvC/Ds0btxY2rdv3wd7raKioiQbGxsJgKSvry/dvn37ve3e7oEriAcPHiiff/r06RyPDR48WAIgtW7d+r3PHTJkiARAGjZsWL5ec+nSpRIAycLCQoqPjy9QbiKi4sZr4IiIKE9cXFxw6tQp1K1bFwBw6tQp+Pn5CU5VNMaNG4du3bqhfPnyKn+t7Ouz6tWrBycnpw+2W7RoESIjI6Gnp4fNmzfD0dHxg20NDQ0LnOf06dNo27btB3utqlatiqlTpwIAMjMzsXPnzgK/Vm5CQ0MBABUrVkTjxo1zPNa7d+8cbd528eJFrF69GqVLl1b2bObVF198AblcjsTExEJdR0hEVJxYwBERUZ6ZmJjgr7/+Up7sL1q0CBkZGe+0y20WynXr1ikfv3fvHl6/fo3FixfD3d0dVlZWKFmyJDw9PREcHJzjeYmJiViwYAHq1q0LS0tL/F979x4UVfn/Afy9oEiAgtwGq1nQJEcZEESCBrlYCxkylFFI6WAXU2BrgkqgcnKGqcCa1JlIhcEBpiGlUYexGboAw5KTIpdkjBAj1ATChI3LgoLFnu8f/PbMIrvsLnJxf75fM8w8nvM5zz5nD3/w8XnO53FwcEBERAQqKytn5F5nwvDwME6dOgUAiI2N1Rs3OjqKw4cPAwBkMhkCAwNnZXz6rF+/Xmy3tbXNyGf09/cDAJYsWTLhnJub27gYDbVaDblcDrVajezsbCxevNikz3R1dUVISAgA4OjRo1MZNhHRrGMCR0REJvHy8kJERASAsfen6urqptzXwMAAQkND8c4776ChoQEDAwPo7e1FdXU1oqOjsX//fgDAtWvX8PjjjyM9PR2NjY1QqVTo7+9HRUUFIiIiUFxcPC33NtPOnTuHW7duAQCCgoL0xp05cwadnZ0AgJiYGPH48PAw2tra0NnZidHR0ZkdrJaRkRGxbWlpOSOfYW9vDwC4fv36hHOaY5oYjfz8fNTW1iIoKAivvvrqlD5X8xxOnz6NoaGhKfVBRDSbmMAREZHJZDKZ2L6bAho7duxAQ0MDkpOTUV5ejvr6euTn5+PBBx8EMLa0sampCc899xwuX76MjIwMKBQK1NXV4cCBA7C3t4cgCEhKSsKNGzfu+r5mmua7kkgk8Pf31xtXU1Mjtr29vdHa2orY2FgsWrQIy5cvx8MPPwwnJyckJCTM2IyYturqarG9cuVKg/GRkZFwdXWFlZUVXF1dER4ejuzsbPT29uq9ZvXq1QCAq1evor6+fty5Y8eOAQB8fX3FY0qlEu+//75JhUt0eeyxxwAAt2/fxtmzZ6fUBxHRbGIVSiIiMtmaNWvE9u+//z7lfmpra3Hy5Ek8++yz4jF/f38EBATAz88ParUaTzzxBAYGBlBdXT1uKeHatWvh6emJjRs3QqVSobi4GKmpqVMey2w4c+YMAGDZsmUTZpO0NTc3i+2WlhZERUVNmB3q7+/HV199hdLSUpw8eXJcUj2dbt68iQMHDgAAFixYgGeeecbgNeXl5WK7u7sb1dXVqK6uxt69e1FYWKizD6lUitDQUPz000/YtGkT9u7dCw8PD3z77bfIy8sDAGzbtk2Mf++996BUKpGUlDTu99FU2ol0Q0PDjH2PRETThTNwRERkMicnJ7E92ayKIXFxceOSNw0fHx+sW7cOwFgCkJKSovM9sKioKLi7uwO4u5nA2dLR0QFg7N2ryfzzzz9i+6233sLQ0BBSU1PR2tqKkZERtLW1YdeuXZBIJFCpVHjhhRdw7dq1GRlzenq62LdcLhdnR3Xx9vYWt5loaGhATU0NioqKEBkZCWBsw/TY2Fh89913Oq8/ePAgHBwc0NHRgS1btiA4OBjZ2dkQBAExMTHYunUrAKCurg5HjhyZUuGSO2k/i8uXL99VX0REs4EJHBERmczOzk5sq1SqKfcTHx+v95xmSZ2hOB8fHwDm8cd3d3c3ABgstqE92zY8PIzMzEzs27cPy5cvh5WVFZYtW4ZPP/1UTF76+vqQlZU17eMtLi5GTk4OgLGlkx999JHe2JSUFFy4cAGZmZmIjo7GmjVrEBgYiISEBPzwww9iUZbR0VFs374dw8PDE/rw8vJCbW0tNm/eDCcnJ1hZWWHFihXIysrC8ePHIZFIoFarkZycPKFwSWtrK+Lj4+Hi4gJra2v4+Pjg4MGDBvcktLa2xgMPPABA9/t3RET3GiZwRERkMu2kbdGiRVPu59FHH9V7zsHBwaS4u0kkZ4tmZs1QAmdtbS22nZ2dkZ6erjNu165dYoXGb775Zlo3UFcoFHjttdcAAI6Ojjhx4oSY6Oii/bx02blzp9jfX3/9hRMnTuiM8/T0xLFjx9DT04ORkRG0tLQgIyMD8+fPBwDk5uaivr4egYGBYuGSixcvIjAwECUlJbh9+zbc3d3R1NQEuVyON9980+C9ap4Hi5gQkTlgAkdERCbr6ekR246OjlPux8bGRu85CwsLk+JmsyrjVGkSM00lSn0WLlwotsPDw/Xu8zZv3jw8+eSTAMaSw+mahayvr0dMTAxGRkZgZ2eHsrIyo4qXGLJz506xrV0YxVg9PT344IMPJhQuSU5ORm9vL2JjY9Hd3Y1Lly7h9OnTsLGxwZdffmlwea3meWiSRCKiexkTOCIiMtn58+fF9ooVK+ZwJObFxcUFwPh33HTR3lDc0Obi2uc1SzTvxm+//YYNGzZApVJhwYIFKC0tnbZ96FatWiW2NdskmCI9PR29vb3YsWOHWHykvb0dCoUC8+fPR05OjpjsBgcHizN0RUVFevtUq9Xi/nKGZhGJiO4FTOCIiMhk2lUGNcVGyDBNAmeo8IuXl5fYNjSzqH1+3ry7Ky7d1taGiIgIKJVKzJs3DyUlJeIM33SYaql/YGxrhYKCAjg7O+OTTz4Rjzc2NgIAHnnkEXE5qUZwcPC4GF36+/uhVqsBjFXCJCK61zGBIyIikzQ1NaGyshLA2OzP2rVr53hE5sPb2xvAWKKkSRp0CQ0NFduGlkVq7wP30EMPTXlsHR0dkMlk6OrqgoWFBYqKiozaMsAU2tsjTFbN8k5qtRpyuRyCICArK2vcO4Sa2TNd2zJoZtQ0Mbpob4OhnTgTEd2rmMAREZHRbt26hYSEBLFYxrvvvnvXsz73k5CQEADA4OAgLl68qDdu6dKl8PPzAwBUVVXpTUBUKhUqKioAjM1ALVmyZErjunHjBmQyGa5evQoAOHz4MF566aUp9TWZ3NxcsR0WFmb0dYcOHcIvv/yCwMBAsRCKhiZx02zRoK29vR3A5IV26urqxPZ0LRUlIppJTOCIiMgozc3NWLdunfj+W1hYGJKSkuZ4VOZFk8ABY5uYTyYjIwPAWGXElJQUnTFvv/02BgYGAACJiYk6YyQSCSQSCTw8PHSe7+vrw1NPPYVLly4BAPbv34/XX3990rHd6ddff8Uff/wxaUxeXh7y8/MBAG5ubti0aZNRfXd3d2P37t0TCpdoaLab6OzsnFAY5euvvwYA+Pr66u1f8xykUinf5yQis8D/NiUiIgBjszBNTU3iv4eGhtD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"text/plain": [
"<Figure size 1000x1000 with 1 Axes>"
]
@@ -736,7 +736,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 25,
"id": "e87031f5",
"metadata": {},
"outputs": [
@@ -4232,9 +4232,9 @@
}
},
"text/html": [
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- "var gd = document.getElementById('33c9959c-7097-4bf0-b731-9e7719c1f40e');\n",
+ "var gd = document.getElementById('3fd5eec1-e3a1-4dfb-84aa-ef2789f90938');\n",
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@@ -4270,7 +4270,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 26,
"id": "cee2f89f",
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{\"responsive\": true} ).then(function(){\n",
" \n",
- "var gd = document.getElementById('d3f4fb2e-7e05-4ff9-89c4-1b255a137870');\n",
+ "var gd = document.getElementById('943b85e5-7439-482d-bc52-cfd49fafdd5d');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
@@ -8281,6 +8281,14 @@
"best_pca.scatter_pca_matrix(color = labels, n_components = 6)\n",
"plt.show()"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "19fcb0e3",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/pyPLNmodels/load.py b/pyPLNmodels/load.py
index 6abeffd1..0eb53456 100644
--- a/pyPLNmodels/load.py
+++ b/pyPLNmodels/load.py
@@ -39,7 +39,12 @@ def load_model(path_of_directory: str) -> Dict[str, Any]:
:func:`~pyPLNmodels.load_plnpcacollection`
"""
working_dir = os.getcwd()
- os.chdir(path_of_directory)
+ try:
+ os.chdir(path_of_directory)
+ except FileNotFoundError as err:
+ raise err(
+ "The model has not been saved. Please be sure you have the right name of model."
+ )
all_files = os.listdir()
data = {}
for filename in all_files:
diff --git a/tests/test_getting_started.py b/tests/test_getting_started.py
index 4fcc9552..69299741 100644
--- a/tests/test_getting_started.py
+++ b/tests/test_getting_started.py
@@ -1,6 +1,8 @@
#!/usr/bin/env python
# coding: utf-8
+# get_ipython().system('pip install pyPLNmodels')
+
# ## pyPLNmodels
@@ -66,7 +68,7 @@ print(pca.model_parameters["coef"].shape)
# ### One can save the model in order to load it back after:
pca.save()
-dict_init = load_model("PlnPCA_nbcov_1_rank_5")
+dict_init = load_model("PlnPCA_nbcov_1_dim_200_rank_5")
loaded_pca = PlnPCA(counts, add_const=True, dict_initialization=dict_init)
print(loaded_pca)
--
GitLab
From dba9c49db17ef4cfef1ef687cdf6076e56491766 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 3 Jul 2023 23:28:34 +0200
Subject: [PATCH 058/167] bug in the covariance. Took the
covariance_a_posteriori instead of the covariance.
---
pyPLNmodels/models.py | 20 +++++++++++++++++---
1 file changed, 17 insertions(+), 3 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 492c9106..c4189583 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -411,7 +411,7 @@ class _model(ABC):
centered_latent = self.latent_variables - torch.mean(
self.latent_variables, axis=0
)
- chol = torch.linalg.cholesky(torch.inverse(self.covariance))
+ chol = torch.linalg.cholesky(torch.inverse(self.covariance_a_posteriori))
residus = torch.matmul(centered_latent.unsqueeze(1), chol.unsqueeze(0))
stats.probplot(residus.ravel(), plot=plt)
plt.show()
@@ -3050,9 +3050,23 @@ class PlnPCA(_model):
return string
@property
- def covariance(self) -> Optional[torch.Tensor]:
+ def covariance(self) -> torch.Tensor:
"""
- Property representing the covariance.
+ Property representing the covariance a posteriori of the latent variables.
+
+ Returns
+ -------
+ Optional[torch.Tensor]
+ The covariance tensor or None if components are not present.
+ """
+ if hasattr(self, "_components"):
+ return self._components @ (self._components.T)
+ return None
+
+ @property
+ def covariance_a_posteriori(self) -> Optional[torch.Tensor]:
+ """
+ Property representing the covariance a posteriori of the latent variables.
Returns
-------
--
GitLab
From 283e3faf31f018ce228b6d3b156d28150b6b49c1 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 13 Jul 2023 09:51:26 +0200
Subject: [PATCH 059/167] model.covariance now returns a detached tensor.
---
pyPLNmodels/models.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index c4189583..9c2024ee 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -3060,7 +3060,7 @@ class PlnPCA(_model):
The covariance tensor or None if components are not present.
"""
if hasattr(self, "_components"):
- return self._components @ (self._components.T)
+ return self.components @ (self.components.T)
return None
@property
--
GitLab
From 8041f66cd6a695bd0ff996eda4a46a1c4a9924e0 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sat, 15 Jul 2023 12:28:53 +0200
Subject: [PATCH 060/167] changed pring to print and fix the printing message
of PlnPCA.
---
pyPLNmodels/models.py | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 9c2024ee..b751e9e4 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -365,7 +365,7 @@ class _model(ABC):
verbose : bool, optional(keyword-only)
Whether to print training progress. Defaults to False.
"""
- self._pring_beginning_message()
+ self._print_beginning_message()
self._beginning_time = time.time()
if self._fitted is False:
@@ -1668,7 +1668,7 @@ class Pln(_model):
covariances = components_var @ (sk_components.T.unsqueeze(0))
return covariances
- def _pring_beginning_message(self):
+ def _print_beginning_message(self):
"""
Method for printing the beginning message.
"""
@@ -2087,7 +2087,7 @@ class PlnPCAcollection:
"""
return [model.rank for model in self.values()]
- def _pring_beginning_message(self) -> str:
+ def _print_beginning_message(self) -> str:
"""
Method for printing the beginning message.
@@ -2150,7 +2150,7 @@ class PlnPCAcollection:
verbose : bool, optional(keyword-only)
Whether to print verbose output, by default False.
"""
- self._pring_beginning_message()
+ self._print_beginning_message()
for i in range(len(self.values())):
model = self[self.ranks[i]]
model.fit(
@@ -2912,12 +2912,12 @@ class PlnPCA(_model):
"""
return self._rank
- def _pring_beginning_message(self):
+ def _print_beginning_message(self):
"""
Print the beginning message when fitted.
"""
print("-" * NB_CHARACTERS_FOR_NICE_PLOT)
- print(f"Fitting a PlnPCAcollection model with {self._rank} components")
+ print(f"Fitting a PlnPCA model with {self._rank} components")
@property
def model_parameters(self) -> Dict[str, torch.Tensor]:
--
GitLab
From 128a1c035af5a11a4fdb35a5eb09f157b606b274 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 20 Jul 2023 10:14:46 +0200
Subject: [PATCH 061/167] changed initialization to compute only eigenvalues.
The init of the components should be 100* faster.
---
pyPLNmodels/_initialization.py | 25 ++++++++++++++-----------
1 file changed, 14 insertions(+), 11 deletions(-)
diff --git a/pyPLNmodels/_initialization.py b/pyPLNmodels/_initialization.py
index e0c3f47e..72de90b3 100644
--- a/pyPLNmodels/_initialization.py
+++ b/pyPLNmodels/_initialization.py
@@ -2,6 +2,11 @@ import torch
import math
from typing import Optional
from ._utils import _log_stirling
+import time
+from sklearn.decomposition import PCA
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
if torch.cuda.is_available():
DEVICE = torch.device("cuda")
@@ -41,7 +46,7 @@ def _init_covariance(
def _init_components(
- endog: torch.Tensor, exog: torch.Tensor, coef: torch.Tensor, rank: int
+ endog: torch.Tensor, exog: torch.Tensor, rank: int
) -> torch.Tensor:
"""
Initialization for components for the Pln model. Get a first guess for covariance
@@ -51,12 +56,6 @@ def _init_components(
----------
endog : torch.Tensor
Samples with size (n,p)
- offsets : torch.Tensor
- Offset, size (n,p)
- exog : torch.Tensor
- Covariates, size (n,d)
- coef : torch.Tensor
- Coefficient of size (d,p)
rank : int
The dimension of the latent space, i.e. the reduced dimension.
@@ -65,9 +64,11 @@ def _init_components(
torch.Tensor
Initialization of components of size (p,rank)
"""
- sigma_hat = _init_covariance(endog, exog, coef).detach()
- components = _components_from_covariance(sigma_hat, rank)
- return components
+ log_y = torch.log(endog + (endog == 0) * math.exp(-2))
+ pca = PCA(n_components=rank)
+ pca.fit(log_y)
+ pca_comp = pca.components_.T * np.sqrt(pca.explained_variance_)
+ return torch.from_numpy(pca_comp).to(DEVICE)
def _init_latent_mean(
@@ -102,13 +103,14 @@ def _init_latent_mean(
The learning rate of the optimizer. Default is 0.01.
eps : float, optional
The tolerance. The algorithm will stop as soon as the criterion is lower than the tolerance.
- Default is 7e-3.
+ Default is 7e-1.
Returns
-------
torch.Tensor
The initialized latent mean with size (n,rank)
"""
+ t = time.time()
mode = torch.randn(endog.shape[0], components.shape[1], device=DEVICE)
mode.requires_grad_(True)
optimizer = torch.optim.Rprop([mode], lr=lr)
@@ -127,6 +129,7 @@ def _init_latent_mean(
keep_condition = False
old_mode = torch.clone(mode)
i += 1
+ print("time mean", time.time() - t)
return mode
--
GitLab
From 055506514648f6137370064a8078bd18cc933dc2 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 20 Jul 2023 10:17:37 +0200
Subject: [PATCH 062/167] GPU support
---
pyPLNmodels/_utils.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 805c9dca..681b5c23 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -306,12 +306,12 @@ def _format_model_param(
exog = _format_data(exog)
if add_const is True:
if exog is None:
- exog = torch.ones(endog.shape[0], 1)
+ exog = torch.ones(endog.shape[0], 1).to(DEVICE)
else:
if _has_null_variance(exog) is False:
exog = torch.concat(
(exog, torch.ones(endog.shape[0]).unsqueeze(1)), dim=1
- )
+ ).to(DEVICE)
if offsets is None:
if offsets_formula == "logsum":
print("Setting the offsets as the log of the sum of endog")
--
GitLab
From 600615427024821ed5807cbae28b9efc36e4eb0a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 20 Jul 2023 10:18:40 +0200
Subject: [PATCH 063/167] changed arguments of init
---
pyPLNmodels/models.py | 8 +++-----
1 file changed, 3 insertions(+), 5 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index b751e9e4..6b47bf78 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -205,7 +205,7 @@ class _model(ABC):
if self._get_max_components() < 2:
raise RuntimeError("Can't perform visualization for dim < 2.")
pca = self.sk_PCA(n_components=2)
- proj_variables = pca.transform(self.latent_variables)
+ proj_variables = pca.transform(self.latent_variables.detach().cpu())
x = proj_variables[:, 0]
y = proj_variables[:, 1]
sns.scatterplot(x=x, y=y, hue=colors, ax=ax)
@@ -380,7 +380,7 @@ class _model(ABC):
criterion = self._compute_criterion_and_update_plotargs(loss, tol)
if abs(criterion) < tol:
stop_condition = True
- if verbose and self.nb_iteration_done % 50 == 0:
+ if verbose and self.nb_iteration_done % 50 == 1:
self._print_stats()
self._print_end_of_fitting_message(stop_condition, tol)
self._fitted = True
@@ -2938,9 +2938,7 @@ class PlnPCA(_model):
if not hasattr(self, "_coef"):
super()._smart_init_coef()
if not hasattr(self, "_components"):
- self._components = _init_components(
- self._endog, self._exog, self._coef, self._rank
- )
+ self._components = _init_components(self._endog, self._exog, self._rank)
def _random_init_model_parameters(self):
"""
--
GitLab
From c2ff60af8fe6087914dfa6932ea2e76faacad6cd Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 20 Jul 2023 16:05:11 +0200
Subject: [PATCH 064/167] now we can take batches.
---
pyPLNmodels/_utils.py | 2 +-
pyPLNmodels/models.py | 156 +++++++++++++++++++++++++++++++++++++++---
2 files changed, 149 insertions(+), 9 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 681b5c23..3f9c6441 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -761,7 +761,7 @@ def get_simulated_count_data(
pln_param.covariance,
pln_param.coef,
)
- return pln_param.endog, pln_param.cov, pln_param.offsets
+ return endog, pln_param.exog, pln_param.offsets
def get_real_count_data(
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 6b47bf78..11987d85 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -76,6 +76,7 @@ class _model(ABC):
dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
add_const: bool = True,
+ batch_size: int = None,
):
"""
Initializes the model class.
@@ -97,6 +98,13 @@ class _model(ABC):
Whether to take the log of offsets. Defaults to False.
add_const: bool, optional(keyword-only)
Whether to add a column of one in the exog. Defaults to True.
+ batch_size: int, optional(keyword-only)
+ The batch size when optimizing the elbo. If None,
+ batch gradient descent will be performed (i.e. batch_size = n_samples).
+ Raises
+ ------
+ ValueError
+ If the batch_size is greater than the number of samples, or not int.
"""
(
self._endog,
@@ -107,6 +115,7 @@ class _model(ABC):
endog, exog, offsets, offsets_formula, take_log_offsets, add_const
)
self._fitted = False
+ self._batch_size = self._handle_batch_size(batch_size)
self._plotargs = _PlotArgs(self._WINDOW)
if dict_initialization is not None:
self._set_init_parameters(dict_initialization)
@@ -120,6 +129,7 @@ class _model(ABC):
offsets_formula: str = "logsum",
dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
+ batch_size: int = None,
):
"""
Create a model instance from a formula and data.
@@ -137,6 +147,9 @@ class _model(ABC):
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
Whether to take the log of offsets. Defaults to False.
+ batch_size: int, optional(keyword-only)
+ The batch size when optimizing the elbo. If None,
+ batch gradient descent will be performed (i.e. batch_size = n_samples).
"""
endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
@@ -147,6 +160,7 @@ class _model(ABC):
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
add_const=False,
+ batch_size=batch_size,
)
def _set_init_parameters(self, dict_initialization: dict):
@@ -166,6 +180,24 @@ class _model(ABC):
setattr(self, key, array)
self._fitted = True
+ @property
+ def batch_size(self) -> int:
+ """
+ The batch size of the model. Should not be greater than the number of samples.
+ """
+ return self._batch_size
+
+ @property
+ def _current_batch_size(self) -> int:
+ return self._exog_b.shape[0]
+
+ @batch_size.setter
+ def batch_size(self, batch_size: int):
+ """
+ Setter for the batch size. Should be an integer not greater than the number of samples.
+ """
+ self._batch_size = self._handle_batch_size(batch_size)
+
@property
def fitted(self) -> bool:
"""
@@ -216,6 +248,17 @@ class _model(ABC):
_plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
return ax
+ def _handle_batch_size(self, batch_size):
+ if batch_size is None:
+ batch_size = self.n_samples
+ if batch_size > self.n_samples:
+ raise ValueError(
+ f"batch_size ({batch_size}) can not be greater than the number of samples ({self.n_samples})"
+ )
+ elif isinstance(batch_size, int) is False:
+ raise ValueError(f"batch_size should be int, got {type(batch_size)}")
+ return batch_size
+
@property
def nb_iteration_done(self) -> int:
"""
@@ -385,21 +428,65 @@ class _model(ABC):
self._print_end_of_fitting_message(stop_condition, tol)
self._fitted = True
+ def _get_batch(self, batch_size, shuffle=False):
+ """Get the batches required to do a minibatch gradient ascent.
+
+ Args:
+ batch_size: int. The batch size. Should be lower than n.
+
+ Returns: A generator. Will generate n//batch_size + 1 batches of
+ size batch_size (except the last one since the rest of the
+ division is not always 0)
+ """
+ indices = np.arange(self.n_samples)
+ if shuffle:
+ np.random.shuffle(indices)
+ nb_full_batch, last_batch_size = (
+ self.n_samples // batch_size,
+ self.n_samples % batch_size,
+ )
+ self.nb_batches = nb_full_batch + (last_batch_size > 0)
+ for i in range(nb_full_batch):
+ yield self._return_batch(indices, i * batch_size, (i + 1) * batch_size)
+ # Last batch
+ if last_batch_size != 0:
+ yield self._return_batch(indices, -last_batch_size, self.n_samples)
+
+ def _return_batch(self, indices, beginning, end):
+ return (
+ self._endog[indices[beginning:end]],
+ self._exog[beginning:end],
+ self._offsets[indices[beginning:end]],
+ self._latent_mean[beginning:end],
+ self._latent_sqrt_var[beginning:end],
+ )
+
def _trainstep(self):
"""
- Perform a single training step.
+ Perform a single pass of the data.
Returns
-------
torch.Tensor
The loss value.
"""
- self.optim.zero_grad()
- loss = -self.compute_elbo()
- loss.backward()
- self.optim.step()
- self._update_closed_forms()
- return loss
+ elbo = 0
+ for batch in self._get_batch(self._batch_size):
+ self._extract_batch(batch)
+ self.optim.zero_grad()
+ loss = -self._compute_elbo_b()
+ loss.backward()
+ elbo += loss.item()
+ self.optim.step()
+ self._update_closed_forms()
+ return elbo / self.nb_batches
+
+ def _extract_batch(self, batch):
+ self._endog_b = batch[0]
+ self._exog_b = batch[1]
+ self._offsets_b = batch[2]
+ self._latent_mean_b = batch[3]
+ self._latent_sqrt_var_b = batch[4]
def transform(self):
"""
@@ -633,7 +720,7 @@ class _model(ABC):
float
The computed criterion.
"""
- self._plotargs._elbos_list.append(-loss.item())
+ self._plotargs._elbos_list.append(-loss)
self._plotargs.running_times.append(time.time() - self._beginning_time)
if self._plotargs.iteration_number > self._WINDOW:
criterion = abs(
@@ -1334,6 +1421,7 @@ class Pln(_model):
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
add_const: bool = True,
+ batch_size: int = None,
):
super().__init__(
endog=endog,
@@ -1343,6 +1431,7 @@ class Pln(_model):
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
add_const=add_const,
+ batch_size=batch_size,
)
@classmethod
@@ -1370,6 +1459,7 @@ class Pln(_model):
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
+ batch_size: int = None,
):
endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
@@ -1380,6 +1470,7 @@ class Pln(_model):
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
add_const=False,
+ batch_size=batch_size,
)
@_add_doc(
@@ -1619,6 +1710,23 @@ class Pln(_model):
self._latent_sqrt_var,
)
+ def _compute_elbo_b(self):
+ """
+ Method for computing the evidence lower bound (ELBO) on the current batch.
+
+ Returns
+ -------
+ torch.Tensor
+ The computed ELBO on the current batch.
+ """
+ return profiled_elbo_pln(
+ self._endog_b,
+ self._exog_b,
+ self._offsets_b,
+ self._latent_mean_b,
+ self._latent_sqrt_var_b,
+ )
+
def _smart_init_model_parameters(self):
"""
Method for smartly initializing the model parameters.
@@ -1779,6 +1887,7 @@ class PlnPCAcollection:
dict_of_dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
add_const: bool = True,
+ batch_size: int = None,
):
"""
Constructor for PlnPCAcollection.
@@ -1801,6 +1910,9 @@ class PlnPCAcollection:
Whether to take the logarithm of offsets, by default False.
add_const: bool, optional(keyword-only)
Whether to add a column of one in the exog. Defaults to True.
+ batch_size: int, optional(keyword-only)
+ The batch size when optimizing the elbo. If None,
+ batch gradient descent will be performed (i.e. batch_size = n_samples).
Returns
-------
PlnPCAcollection
@@ -1831,6 +1943,7 @@ class PlnPCAcollection:
ranks: Iterable[int] = range(3, 5),
dict_of_dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
+ batch_size: int = None,
) -> "PlnPCAcollection":
"""
Create an instance of PlnPCAcollection from a formula.
@@ -1851,6 +1964,10 @@ class PlnPCAcollection:
The dictionary of initialization, by default None.
take_log_offsets : bool, optional(keyword-only)
Whether to take the logarithm of offsets, by default False.
+ batch_size: int, optional(keyword-only)
+ The batch size when optimizing the elbo. If None,
+ batch gradient descent will be performed (i.e. batch_size = n_samples).
+
Returns
-------
PlnPCAcollection
@@ -2583,6 +2700,7 @@ class PlnPCA(_model):
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
add_const: bool = True,
+ batch_size: int = None,
):
self._rank = rank
super().__init__(
@@ -2593,6 +2711,7 @@ class PlnPCA(_model):
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
add_const=add_const,
+ batch_size=batch_size,
)
@classmethod
@@ -2624,6 +2743,7 @@ class PlnPCA(_model):
rank: int = 5,
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
+ batch_size: int = None,
):
endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
@@ -2634,6 +2754,7 @@ class PlnPCA(_model):
rank=rank,
dict_initialization=dict_initialization,
add_const=False,
+ batch_size=batch_size,
)
@_add_doc(
@@ -2991,6 +3112,25 @@ class PlnPCA(_model):
return [self._components, self._latent_mean, self._latent_sqrt_var]
return [self._components, self._coef, self._latent_mean, self._latent_sqrt_var]
+ def _compute_elbo_b(self) -> torch.Tensor:
+ """
+ Compute the evidence lower bound (ELBO) with the current batch.
+
+ Returns
+ -------
+ torch.Tensor
+ The ELBO value on the current batch.
+ """
+ return elbo_plnpca(
+ self._endog_b,
+ self._exog_b,
+ self._offsets_b,
+ self._latent_mean_b,
+ self._latent_sqrt_var_b,
+ self._components,
+ self._coef,
+ )
+
def compute_elbo(self) -> torch.Tensor:
"""
Compute the evidence lower bound (ELBO).
--
GitLab
From fbd22ec34b7294a93fc03572410fee8ec5c95acd Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 20 Jul 2023 17:36:09 +0200
Subject: [PATCH 065/167] put paper in the .gitignore.
---
.gitignore | 3 +++
1 file changed, 3 insertions(+)
diff --git a/.gitignore b/.gitignore
index a0185fcb..a95ada79 100644
--- a/.gitignore
+++ b/.gitignore
@@ -153,6 +153,9 @@ tests/Pln*
slides/
index.html
+paper/*
+
+
tests/test_models*
tests/test_load*
tests/test_readme*
--
GitLab
From 12acf206c86f7239e354afe52a99b0cd35cf9e9d Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 21 Jul 2023 09:16:08 +0200
Subject: [PATCH 066/167] Now takes the optimizer with a string instead of the
whole torch.optimizer.Adam for example. Allow not to import torch and still
choosing its optimizer.
---
pyPLNmodels/models.py | 53 ++++++++++++++++++++++++++++++++++++-------
1 file changed, 45 insertions(+), 8 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 11987d85..f01f3972 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -2,7 +2,7 @@ import time
from abc import ABC, abstractmethod
import warnings
import os
-from typing import Optional, Dict, List, Type, Any, Iterable, Union
+from typing import Optional, Dict, List, Type, Any, Iterable, Union, Literal
import pandas as pd
import torch
@@ -385,7 +385,7 @@ class _model(ABC):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- class_optimizer: torch.optim.Optimizer = torch.optim.Rprop,
+ class_optimizer: Literal["Rprop", "Adam"] = "Rprop",
tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
@@ -399,8 +399,10 @@ class _model(ABC):
The maximum number of iterations. Defaults to 50000.
lr : float, optional(keyword-only)
The learning rate. Defaults to 0.01.
- class_optimizer : torch.optim.Optimizer, optional
- The optimizer class. Defaults to torch.optim.Rprop.
+ class_optimizer : str, optional
+ The optimizer class. Defaults to "Rprop". If the
+ batch_size is lower than the number of samples, the Rprop
+ algorithm should not be used. A warning will be sent.
tol : float, optional(keyword-only)
The tolerance for convergence. Defaults to 1e-3.
do_smart_init : bool, optional(keyword-only)
@@ -416,7 +418,7 @@ class _model(ABC):
elif len(self._plotargs.running_times) > 0:
self._beginning_time -= self._plotargs.running_times[-1]
self._put_parameters_to_device()
- self.optim = class_optimizer(self._list_of_parameters_needing_gradient, lr=lr)
+ self._handle_optimizer(class_optimizer, lr)
stop_condition = False
while self.nb_iteration_done < nb_max_iteration and not stop_condition:
loss = self._trainstep()
@@ -428,6 +430,41 @@ class _model(ABC):
self._print_end_of_fitting_message(stop_condition, tol)
self._fitted = True
+ def _handle_optimizer(self, class_optimizer, lr):
+ if class_optimizer == "Rprop":
+ if self.batch_size < self.n_samples:
+ optimizer_is_set = False
+ while optimizer_is_set is False:
+ msg = (
+ f"The Rprop optimizer should not be used when mini batch are used"
+ f"(i.e. batch_size ({self.batch_size}) < n_samples = {self.n_samples}). "
+ f"Do you wish to turn to the Adam Optimizer? (y/n) "
+ )
+ print(msg)
+ turn = str(input())
+ if turn == "y":
+ self.optim = torch.optim.Adam(
+ self._list_of_parameters_needing_gradient, lr=lr
+ )
+ optimizer_is_set = True
+ elif turn == "n":
+ self.optim = torch.optim.Rprop(
+ self._list_of_parameters_needing_gradient, lr=lr
+ )
+ optimizer_is_set = True
+ else:
+ self.optim = torch.optim.Rprop(
+ self._list_of_parameters_needing_gradient, lr=lr
+ )
+ elif class_optimizer == "Adam":
+ self.optim = torch.optim.Adam(
+ self._list_of_parameters_needing_gradient, lr=lr
+ )
+ else:
+ raise ValueError(
+ f"Optimizer should be either 'Adam' or 'Rprop', got {class_optimizer}"
+ )
+
def _get_batch(self, batch_size, shuffle=False):
"""Get the batches required to do a minibatch gradient ascent.
@@ -1488,7 +1525,7 @@ class Pln(_model):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- class_optimizer: torch.optim.Optimizer = torch.optim.Rprop,
+ class_optimizer: Literal["Rprop", "Adam"] = "Rprop",
tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
@@ -2244,7 +2281,7 @@ class PlnPCAcollection:
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- class_optimizer: Type[torch.optim.Optimizer] = torch.optim.Rprop,
+ class_optimizer: Literal["Rprop", "Adam"] = "Rprop",
tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
@@ -2772,7 +2809,7 @@ class PlnPCA(_model):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- class_optimizer: torch.optim.Optimizer = torch.optim.Rprop,
+ class_optimizer: Literal["Rprop", "Adam"] = "Rprop",
tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
--
GitLab
From 49f1178e837bed0b19dfb2aa6a4012108d696aeb Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 21 Jul 2023 17:40:48 +0200
Subject: [PATCH 067/167] put the batch size in the fit method and removed the
possibility to choose the optimizer. Bug when shuffling the dataset.
---
pyPLNmodels/_initialization.py | 2 -
pyPLNmodels/models.py | 88 ++++++++--------------------------
2 files changed, 20 insertions(+), 70 deletions(-)
diff --git a/pyPLNmodels/_initialization.py b/pyPLNmodels/_initialization.py
index 72de90b3..57cc69cf 100644
--- a/pyPLNmodels/_initialization.py
+++ b/pyPLNmodels/_initialization.py
@@ -110,7 +110,6 @@ def _init_latent_mean(
torch.Tensor
The initialized latent mean with size (n,rank)
"""
- t = time.time()
mode = torch.randn(endog.shape[0], components.shape[1], device=DEVICE)
mode.requires_grad_(True)
optimizer = torch.optim.Rprop([mode], lr=lr)
@@ -129,7 +128,6 @@ def _init_latent_mean(
keep_condition = False
old_mode = torch.clone(mode)
i += 1
- print("time mean", time.time() - t)
return mode
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index f01f3972..8c45780f 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -76,7 +76,6 @@ class _model(ABC):
dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
add_const: bool = True,
- batch_size: int = None,
):
"""
Initializes the model class.
@@ -98,9 +97,6 @@ class _model(ABC):
Whether to take the log of offsets. Defaults to False.
add_const: bool, optional(keyword-only)
Whether to add a column of one in the exog. Defaults to True.
- batch_size: int, optional(keyword-only)
- The batch size when optimizing the elbo. If None,
- batch gradient descent will be performed (i.e. batch_size = n_samples).
Raises
------
ValueError
@@ -115,7 +111,6 @@ class _model(ABC):
endog, exog, offsets, offsets_formula, take_log_offsets, add_const
)
self._fitted = False
- self._batch_size = self._handle_batch_size(batch_size)
self._plotargs = _PlotArgs(self._WINDOW)
if dict_initialization is not None:
self._set_init_parameters(dict_initialization)
@@ -129,7 +124,6 @@ class _model(ABC):
offsets_formula: str = "logsum",
dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
- batch_size: int = None,
):
"""
Create a model instance from a formula and data.
@@ -147,9 +141,6 @@ class _model(ABC):
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
Whether to take the log of offsets. Defaults to False.
- batch_size: int, optional(keyword-only)
- The batch size when optimizing the elbo. If None,
- batch gradient descent will be performed (i.e. batch_size = n_samples).
"""
endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
@@ -160,7 +151,6 @@ class _model(ABC):
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
add_const=False,
- batch_size=batch_size,
)
def _set_init_parameters(self, dict_initialization: dict):
@@ -385,10 +375,10 @@ class _model(ABC):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- class_optimizer: Literal["Rprop", "Adam"] = "Rprop",
tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
+ batch_size=None,
):
"""
Fit the model. The lower tol, the more accurate the model.
@@ -399,26 +389,25 @@ class _model(ABC):
The maximum number of iterations. Defaults to 50000.
lr : float, optional(keyword-only)
The learning rate. Defaults to 0.01.
- class_optimizer : str, optional
- The optimizer class. Defaults to "Rprop". If the
- batch_size is lower than the number of samples, the Rprop
- algorithm should not be used. A warning will be sent.
tol : float, optional(keyword-only)
The tolerance for convergence. Defaults to 1e-3.
do_smart_init : bool, optional(keyword-only)
Whether to perform smart initialization. Defaults to True.
verbose : bool, optional(keyword-only)
Whether to print training progress. Defaults to False.
+ batch_size: int, optional(keyword-only)
+ The batch size when optimizing the elbo. If None,
+ batch gradient descent will be performed (i.e. batch_size = n_samples).
"""
self._print_beginning_message()
self._beginning_time = time.time()
-
+ self._batch_size = self._handle_batch_size(batch_size)
if self._fitted is False:
self._init_parameters(do_smart_init)
elif len(self._plotargs.running_times) > 0:
self._beginning_time -= self._plotargs.running_times[-1]
self._put_parameters_to_device()
- self._handle_optimizer(class_optimizer, lr)
+ self._handle_optimizer(lr)
stop_condition = False
while self.nb_iteration_done < nb_max_iteration and not stop_condition:
loss = self._trainstep()
@@ -430,39 +419,14 @@ class _model(ABC):
self._print_end_of_fitting_message(stop_condition, tol)
self._fitted = True
- def _handle_optimizer(self, class_optimizer, lr):
- if class_optimizer == "Rprop":
- if self.batch_size < self.n_samples:
- optimizer_is_set = False
- while optimizer_is_set is False:
- msg = (
- f"The Rprop optimizer should not be used when mini batch are used"
- f"(i.e. batch_size ({self.batch_size}) < n_samples = {self.n_samples}). "
- f"Do you wish to turn to the Adam Optimizer? (y/n) "
- )
- print(msg)
- turn = str(input())
- if turn == "y":
- self.optim = torch.optim.Adam(
- self._list_of_parameters_needing_gradient, lr=lr
- )
- optimizer_is_set = True
- elif turn == "n":
- self.optim = torch.optim.Rprop(
- self._list_of_parameters_needing_gradient, lr=lr
- )
- optimizer_is_set = True
- else:
- self.optim = torch.optim.Rprop(
- self._list_of_parameters_needing_gradient, lr=lr
- )
- elif class_optimizer == "Adam":
+ def _handle_optimizer(self, lr):
+ if self.batch_size < self.n_samples:
self.optim = torch.optim.Adam(
self._list_of_parameters_needing_gradient, lr=lr
)
else:
- raise ValueError(
- f"Optimizer should be either 'Adam' or 'Rprop', got {class_optimizer}"
+ self.optim = torch.optim.Rprop(
+ self._list_of_parameters_needing_gradient, lr=lr
)
def _get_batch(self, batch_size, shuffle=False):
@@ -508,8 +472,9 @@ class _model(ABC):
The loss value.
"""
elbo = 0
- for batch in self._get_batch(self._batch_size):
+ for batch in self._get_batch(self._batch_size, shuffle=False):
self._extract_batch(batch)
+ # print('current bach', self._current_batch_size)
self.optim.zero_grad()
loss = -self._compute_elbo_b()
loss.backward()
@@ -1458,7 +1423,6 @@ class Pln(_model):
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
add_const: bool = True,
- batch_size: int = None,
):
super().__init__(
endog=endog,
@@ -1468,7 +1432,6 @@ class Pln(_model):
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
add_const=add_const,
- batch_size=batch_size,
)
@classmethod
@@ -1496,7 +1459,6 @@ class Pln(_model):
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
- batch_size: int = None,
):
endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
@@ -1507,7 +1469,6 @@ class Pln(_model):
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
add_const=False,
- batch_size=batch_size,
)
@_add_doc(
@@ -1525,18 +1486,18 @@ class Pln(_model):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- class_optimizer: Literal["Rprop", "Adam"] = "Rprop",
tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
+ batch_size: int = None,
):
super().fit(
nb_max_iteration,
lr=lr,
- class_optimizer=class_optimizer,
tol=tol,
do_smart_init=do_smart_init,
verbose=verbose,
+ batch_size=batch_size,
)
@_add_doc(
@@ -1924,7 +1885,6 @@ class PlnPCAcollection:
dict_of_dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
add_const: bool = True,
- batch_size: int = None,
):
"""
Constructor for PlnPCAcollection.
@@ -1980,7 +1940,6 @@ class PlnPCAcollection:
ranks: Iterable[int] = range(3, 5),
dict_of_dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
- batch_size: int = None,
) -> "PlnPCAcollection":
"""
Create an instance of PlnPCAcollection from a formula.
@@ -2001,9 +1960,6 @@ class PlnPCAcollection:
The dictionary of initialization, by default None.
take_log_offsets : bool, optional(keyword-only)
Whether to take the logarithm of offsets, by default False.
- batch_size: int, optional(keyword-only)
- The batch size when optimizing the elbo. If None,
- batch gradient descent will be performed (i.e. batch_size = n_samples).
Returns
-------
@@ -2281,10 +2237,10 @@ class PlnPCAcollection:
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- class_optimizer: Literal["Rprop", "Adam"] = "Rprop",
tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
+ batch_size: int = None,
):
"""
Fit each model in the PlnPCAcollection.
@@ -2295,14 +2251,15 @@ class PlnPCAcollection:
The maximum number of iterations, by default 50000.
lr : float, optional(keyword-only)
The learning rate, by default 0.01.
- class_optimizer : Type[torch.optim.Optimizer], optional(keyword-only)
- The optimizer class, by default torch.optim.Rprop.
tol : float, optional(keyword-only)
The tolerance, by default 1e-3.
do_smart_init : bool, optional(keyword-only)
Whether to do smart initialization, by default True.
verbose : bool, optional(keyword-only)
Whether to print verbose output, by default False.
+ batch_size: int, optional(keyword-only)
+ The batch size when optimizing the elbo. If None,
+ batch gradient descent will be performed (i.e. batch_size = n_samples).
"""
self._print_beginning_message()
for i in range(len(self.values())):
@@ -2310,7 +2267,6 @@ class PlnPCAcollection:
model.fit(
nb_max_iteration,
lr=lr,
- class_optimizer=class_optimizer,
tol=tol,
do_smart_init=do_smart_init,
verbose=verbose,
@@ -2737,7 +2693,6 @@ class PlnPCA(_model):
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
add_const: bool = True,
- batch_size: int = None,
):
self._rank = rank
super().__init__(
@@ -2748,7 +2703,6 @@ class PlnPCA(_model):
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
add_const=add_const,
- batch_size=batch_size,
)
@classmethod
@@ -2780,7 +2734,6 @@ class PlnPCA(_model):
rank: int = 5,
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
- batch_size: int = None,
):
endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
@@ -2791,7 +2744,6 @@ class PlnPCA(_model):
rank=rank,
dict_initialization=dict_initialization,
add_const=False,
- batch_size=batch_size,
)
@_add_doc(
@@ -2809,18 +2761,18 @@ class PlnPCA(_model):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- class_optimizer: Literal["Rprop", "Adam"] = "Rprop",
tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
+ batch_size=None,
):
super().fit(
nb_max_iteration,
lr=lr,
- class_optimizer=class_optimizer,
tol=tol,
do_smart_init=do_smart_init,
verbose=verbose,
+ batch_size=batch_size,
)
@_add_doc(
--
GitLab
From 9e819edfabb9cc8acad805f894929230a91bc91d Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 24 Jul 2023 16:05:15 +0200
Subject: [PATCH 068/167] Fixed the shuffle issue.
---
pyPLNmodels/models.py | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 8c45780f..1390e37a 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -454,12 +454,13 @@ class _model(ABC):
yield self._return_batch(indices, -last_batch_size, self.n_samples)
def _return_batch(self, indices, beginning, end):
+ to_take = torch.tensor(indices[beginning:end])
return (
- self._endog[indices[beginning:end]],
- self._exog[beginning:end],
- self._offsets[indices[beginning:end]],
- self._latent_mean[beginning:end],
- self._latent_sqrt_var[beginning:end],
+ torch.index_select(self._endog, 0, to_take),
+ torch.index_select(self._exog, 0, to_take),
+ torch.index_select(self._offsets, 0, to_take),
+ torch.index_select(self._latent_mean, 0, to_take),
+ torch.index_select(self._latent_sqrt_var, 0, to_take),
)
def _trainstep(self):
@@ -472,9 +473,8 @@ class _model(ABC):
The loss value.
"""
elbo = 0
- for batch in self._get_batch(self._batch_size, shuffle=False):
+ for batch in self._get_batch(self._batch_size, shuffle=True):
self._extract_batch(batch)
- # print('current bach', self._current_batch_size)
self.optim.zero_grad()
loss = -self._compute_elbo_b()
loss.backward()
--
GitLab
From 1064c7d60a58b198a39f885e201e3bacfaf5670b Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 24 Jul 2023 16:26:12 +0200
Subject: [PATCH 069/167] did not take the batch size when fitting the
collection
---
pyPLNmodels/models.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 1390e37a..cbccf767 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -2270,6 +2270,7 @@ class PlnPCAcollection:
tol=tol,
do_smart_init=do_smart_init,
verbose=verbose,
+ batch_size=batch_size,
)
if i < len(self.values()) - 1:
next_model = self[self.ranks[i + 1]]
--
GitLab
From fdae97751c6c0e11543bf595e0d2ba5b0f37d317 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 25 Jul 2023 16:22:03 +0200
Subject: [PATCH 070/167] GPU support
---
pyPLNmodels/_initialization.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/pyPLNmodels/_initialization.py b/pyPLNmodels/_initialization.py
index 57cc69cf..02574c29 100644
--- a/pyPLNmodels/_initialization.py
+++ b/pyPLNmodels/_initialization.py
@@ -66,7 +66,7 @@ def _init_components(
"""
log_y = torch.log(endog + (endog == 0) * math.exp(-2))
pca = PCA(n_components=rank)
- pca.fit(log_y)
+ pca.fit(log_y.detach().cpu())
pca_comp = pca.components_.T * np.sqrt(pca.explained_variance_)
return torch.from_numpy(pca_comp).to(DEVICE)
--
GitLab
From 4e2934e8eb6f6191a0b0c77b51ba48a2c3464bf7 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 25 Jul 2023 16:26:04 +0200
Subject: [PATCH 071/167] merge right lines of check_tol branch.
---
pyPLNmodels/models.py | 42 ++++++++++++++++++++++++++----------------
1 file changed, 26 insertions(+), 16 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index cbccf767..5edbcd30 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -250,7 +250,7 @@ class _model(ABC):
return batch_size
@property
- def nb_iteration_done(self) -> int:
+ def _nb_iteration_done(self) -> int:
"""
The number of iterations done.
@@ -259,7 +259,7 @@ class _model(ABC):
int
The number of iterations done.
"""
- return len(self._plotargs._elbos_list)
+ return len(self._plotargs._elbos_list) * self._nb_batches
@property
def n_samples(self) -> int:
@@ -429,7 +429,7 @@ class _model(ABC):
self._list_of_parameters_needing_gradient, lr=lr
)
- def _get_batch(self, batch_size, shuffle=False):
+ def _get_batch(self, shuffle=False):
"""Get the batches required to do a minibatch gradient ascent.
Args:
@@ -442,19 +442,17 @@ class _model(ABC):
indices = np.arange(self.n_samples)
if shuffle:
np.random.shuffle(indices)
- nb_full_batch, last_batch_size = (
- self.n_samples // batch_size,
- self.n_samples % batch_size,
- )
- self.nb_batches = nb_full_batch + (last_batch_size > 0)
- for i in range(nb_full_batch):
- yield self._return_batch(indices, i * batch_size, (i + 1) * batch_size)
+
+ for i in range(self._nb_full_batch):
+ yield self._return_batch(
+ indices, i * self._batch_size, (i + 1) * self._batch_size
+ )
# Last batch
- if last_batch_size != 0:
- yield self._return_batch(indices, -last_batch_size, self.n_samples)
+ if self._last_batch_size != 0:
+ yield self._return_batch(indices, -self._last_batch_size, self.n_samples)
def _return_batch(self, indices, beginning, end):
- to_take = torch.tensor(indices[beginning:end])
+ to_take = torch.tensor(indices[beginning:end]).to(DEVICE)
return (
torch.index_select(self._endog, 0, to_take),
torch.index_select(self._exog, 0, to_take),
@@ -463,6 +461,18 @@ class _model(ABC):
torch.index_select(self._latent_sqrt_var, 0, to_take),
)
+ @property
+ def _nb_full_batch(self):
+ return self.n_samples // self._batch_size
+
+ @property
+ def _last_batch_size(self):
+ return self.n_samples % self._batch_size
+
+ @property
+ def _nb_batches(self):
+ return self._nb_full_batch + (self._last_batch_size > 0)
+
def _trainstep(self):
"""
Perform a single pass of the data.
@@ -473,7 +483,7 @@ class _model(ABC):
The loss value.
"""
elbo = 0
- for batch in self._get_batch(self._batch_size, shuffle=True):
+ for batch in self._get_batch(shuffle=True):
self._extract_batch(batch)
self.optim.zero_grad()
loss = -self._compute_elbo_b()
@@ -481,7 +491,7 @@ class _model(ABC):
elbo += loss.item()
self.optim.step()
self._update_closed_forms()
- return elbo / self.nb_batches
+ return elbo / self._nb_batches
def _extract_batch(self, batch):
self._endog_b = batch[0]
@@ -1232,7 +1242,7 @@ class _model(ABC):
dict
The dictionary of optimization parameters.
"""
- return {"Number of iterations done": self.nb_iteration_done}
+ return {"Number of iterations done": self._nb_iteration_done}
@property
def _useful_properties_string(self):
--
GitLab
From a63a690934f3167b533efe8f67d3e1456090d477 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 25 Jul 2023 16:33:00 +0200
Subject: [PATCH 072/167] forgot to add _
---
pyPLNmodels/models.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 5edbcd30..80c4cbf3 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -409,7 +409,7 @@ class _model(ABC):
self._put_parameters_to_device()
self._handle_optimizer(lr)
stop_condition = False
- while self.nb_iteration_done < nb_max_iteration and not stop_condition:
+ while self._nb_iteration_done < nb_max_iteration and not stop_condition:
loss = self._trainstep()
criterion = self._compute_criterion_and_update_plotargs(loss, tol)
if abs(criterion) < tol:
--
GitLab
From bc33072178468df912651096b0bfa76dd5ccbce8 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 9 Oct 2023 09:33:28 +0200
Subject: [PATCH 073/167] merge changes with check_tol. Basically change the
way we compute tolerance. All this to handle batch tolerance. The cumulative
elbo is a good indicator.
---
pyPLNmodels/_utils.py | 15 +++++++--------
pyPLNmodels/models.py | 22 +++++++++++-----------
2 files changed, 18 insertions(+), 19 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 3f9c6441..d2b1aea0 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -24,19 +24,14 @@ else:
class _PlotArgs:
- def __init__(self, window: int):
+ def __init__(self):
"""
Initialize the PlotArgs class.
-
- Parameters
- ----------
- window : int
- The size of the window for computing the criterion.
"""
- self.window = window
self.running_times = []
- self.criterions = [1] * window # the first window criterion won't be computed.
+ self.criterions = []
self._elbos_list = []
+ self.cumulative_elbo_list = [0]
@property
def iteration_number(self) -> int:
@@ -50,6 +45,10 @@ class _PlotArgs:
"""
return len(self._elbos_list)
+ @property
+ def cumulative_elbo(self):
+ return self.cumulative_elbo_list[-1]
+
def _show_loss(self, ax=None):
"""
Show the loss of the model (i.e. the negative ELBO).
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 80c4cbf3..c964fb8c 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -57,7 +57,6 @@ class _model(ABC):
Base class for all the Pln models. Should be inherited.
"""
- _WINDOW: int = 15
_endog: torch.Tensor
_exog: torch.Tensor
_offsets: torch.Tensor
@@ -734,14 +733,15 @@ class _model(ABC):
"""
self._plotargs._elbos_list.append(-loss)
self._plotargs.running_times.append(time.time() - self._beginning_time)
- if self._plotargs.iteration_number > self._WINDOW:
- criterion = abs(
- self._plotargs._elbos_list[-1]
- - self._plotargs._elbos_list[-1 - self._WINDOW]
- )
- self._plotargs.criterions.append(criterion)
- return criterion
- return tol
+ self._plotargs.cumulative_elbo_list.append(
+ self._plotargs.cumulative_elbo_list - loss
+ )
+ criterion = (
+ self._plotargs.cumulative_elbo_list[-2]
+ - self._plotargs.cumulative_elbo_list[-1]
+ ) / self._plotargs.cumulative_elbo_list[-1]
+ self._plotargs.criterions.append(criterion)
+ return criterion
def _update_closed_forms(self):
"""
@@ -2924,11 +2924,11 @@ class PlnPCA(_model):
def _endog_predictions(self):
covariance_a_posteriori = torch.sum(
(self._components**2).unsqueeze(0)
- * (self.latent_sqrt_var**2).unsqueeze(1),
+ * (self._latent_sqrt_var**2).unsqueeze(1),
axis=2,
)
if self.exog is not None:
- XB = self.exog @ self.coef
+ XB = self._exog @ self._coef
else:
XB = 0
return torch.exp(
--
GitLab
From 317abe30842170ff963c8e5c6f31f01c0049b6c2 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 9 Oct 2023 09:37:33 +0200
Subject: [PATCH 074/167] dd tests only on the main and dev branch.
---
.gitlab-ci.yml | 3 +++
1 file changed, 3 insertions(+)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index d8c20b0f..c95c78f1 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -23,6 +23,9 @@ tests:
- cd tests
- python create_readme_and_docstrings_tests.py
- pytest .
+ only:
+ - main
+ - dev
build_package:
--
GitLab
From 7fe3f2366b4a65130a917341f1cd06ec96f44d86 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 9 Oct 2023 10:05:23 +0200
Subject: [PATCH 075/167] add the elbo of the zi model.
---
pyPLNmodels/elbos.py | 92 ++++++++++++++++++++++++++++----------------
1 file changed, 59 insertions(+), 33 deletions(-)
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 6dcda361..454cfb75 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -172,6 +172,7 @@ def elbo_plnpca(
) / n_samples
+## pb with trunc_log
## should rename some variables so that is is clearer when we see the formula
def elbo_zi_pln(
endog,
@@ -179,13 +180,13 @@ def elbo_zi_pln(
offsets,
latent_mean,
latent_sqrt_var,
- pi,
- covariance,
+ latent_prob,
+ components,
coef,
- _coef_inflation,
+ coef_inflation,
dirac,
):
- """Compute the ELBO (Evidence LOwer Bound) for the Zero Inflated Pln model.
+ """Compute the ELBO (Evidence LOwer Bound) for the Zero Inflated PLN model.
See the doc for more details on the computation.
Args:
@@ -197,41 +198,66 @@ def elbo_zi_pln(
pi: torch.tensor. Variational parameter with size (n,p)
covariance: torch.tensor. Model parameter with size (p,p)
coef: torch.tensor. Model parameter with size (d,p)
- _coef_inflation: torch.tensor. Model parameter with size (d,p)
+ coef_inflation: torch.tensor. Model parameter with size (d,p)
Returns:
torch.tensor of size 1 with a gradient.
"""
- if torch.norm(pi * dirac - pi) > 0.0001:
- print("Bug")
- return False
- n_samples = endog.shape[0]
- dim = endog.shape[1]
- s_rond_s = torch.square(latent_sqrt_var)
- offsets_plus_m = offsets + latent_mean
- m_minus_xb = latent_mean - exog @ coef
- x_coef_inflation = exog @ _coef_inflation
- elbo = torch.sum(
- (1 - pi)
- * (
- endog @ offsets_plus_m
- - torch.exp(offsets_plus_m + s_rond_s / 2)
- - _log_stirling(endog),
- )
- + pi
+ covariance = components @ (components.T)
+ if torch.norm(latent_prob * dirac - latent_prob) > 0.00000001:
+ raise RuntimeError("Latent probability is not zero when it should be.")
+ n_samples, dim = endog.shape
+ s_rond_s = torch.multiply(latent_sqrt_var, latent_sqrt_var)
+ o_plus_m = offsets + latent_mean
+ if exog is None:
+ XB = torch.zeros_like(endog)
+ xcoef_inflation = torch.zeros_like(endog)
+ else:
+ XB = exog @ coef
+ xcoef_inflation = exog @ coef_inflation
+ m_minus_xb = latent_mean - XB
+
+ A = torch.exp(o_plus_m + s_rond_s / 2)
+ inside_a = torch.multiply(
+ 1 - latent_prob, torch.multiply(endog, o_plus_m) - A - _log_stirling(endog)
)
+ a = torch.sum(inside_a)
- elbo -= torch.sum(pi * _trunc_log(pi) + (1 - pi) * _trunc_log(1 - pi))
- elbo += torch.sum(
- pi * x_coef_inflation - torch.log(1 + torch.exp(x_coef_inflation))
+ Omega = torch.inverse(covariance)
+
+ m_moins_xb_outer = torch.mm(m_minus_xb.T, m_minus_xb)
+ un_moins_rho = 1 - latent_prob
+ un_moins_rho_m_moins_xb = un_moins_rho * m_minus_xb
+ un_moins_rho_m_moins_xb_outer = un_moins_rho_m_moins_xb.T @ un_moins_rho_m_moins_xb
+ inside_b = -1 / 2 * Omega * un_moins_rho_m_moins_xb_outer
+ b = -n_samples / 2 * torch.logdet(covariance) + torch.sum(inside_b)
+
+ inside_c = torch.multiply(latent_prob, xcoef_inflation) - torch.log(
+ 1 + torch.exp(xcoef_inflation)
)
+ c = torch.sum(inside_c)
+ log_diag = torch.log(torch.diag(covariance))
+ log_S_term = torch.sum(
+ torch.multiply(1 - latent_prob, torch.log(torch.abs(latent_sqrt_var))), axis=0
+ )
+ y = torch.sum(latent_prob, axis=0)
+ covariance_term = 1 / 2 * torch.log(torch.diag(covariance)) * y
+ inside_d = covariance_term + log_S_term
+
+ d = n_samples * dim / 2 + torch.sum(inside_d)
- elbo -= 0.5 * torch.trace(
- torch.mm(
- torch.inverse(covariance),
- torch.diag(torch.sum(s_rond_s, dim=0)) + m_minus_xb.T @ m_minus_xb,
- )
+ inside_e = torch.multiply(latent_prob, _trunc_log(latent_prob)) + torch.multiply(
+ 1 - latent_prob, _trunc_log(1 - latent_prob)
)
- elbo += 0.5 * n_samples * torch.log(torch.det(covariance))
- elbo += 0.5 * n_samples * dim
- elbo += 0.5 * torch.sum(torch.log(s_rond_s))
+ e = -torch.sum(inside_e)
+ sum_un_moins_rho_s2 = torch.sum(torch.multiply(1 - latent_prob, s_rond_s), axis=0)
+ diag_sig_sum_rho = torch.multiply(
+ torch.diag(covariance), torch.sum(latent_prob, axis=0)
+ )
+ new = torch.sum(latent_prob * un_moins_rho * (m_minus_xb**2), axis=0)
+ K = sum_un_moins_rho_s2 + diag_sig_sum_rho + new
+ inside_f = torch.diag(Omega) * K
+ f = -1 / 2 * torch.sum(inside_f)
+ full_diag_omega = torch.diag(Omega).expand(exog.shape[0], -1)
+ elbo = a + b + c + d + e + f
+ print(" inside a shape", inside_a.shape)
return elbo
--
GitLab
From effbfac8ad6aa370c476d69e7dd79b619f7676a6 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 9 Oct 2023 10:22:35 +0200
Subject: [PATCH 076/167] remove useless sums and optimize a little.
---
pyPLNmodels/elbos.py | 14 ++++----------
1 file changed, 4 insertions(+), 10 deletions(-)
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 454cfb75..f7378d0f 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -220,7 +220,6 @@ def elbo_zi_pln(
inside_a = torch.multiply(
1 - latent_prob, torch.multiply(endog, o_plus_m) - A - _log_stirling(endog)
)
- a = torch.sum(inside_a)
Omega = torch.inverse(covariance)
@@ -229,12 +228,10 @@ def elbo_zi_pln(
un_moins_rho_m_moins_xb = un_moins_rho * m_minus_xb
un_moins_rho_m_moins_xb_outer = un_moins_rho_m_moins_xb.T @ un_moins_rho_m_moins_xb
inside_b = -1 / 2 * Omega * un_moins_rho_m_moins_xb_outer
- b = -n_samples / 2 * torch.logdet(covariance) + torch.sum(inside_b)
inside_c = torch.multiply(latent_prob, xcoef_inflation) - torch.log(
1 + torch.exp(xcoef_inflation)
)
- c = torch.sum(inside_c)
log_diag = torch.log(torch.diag(covariance))
log_S_term = torch.sum(
torch.multiply(1 - latent_prob, torch.log(torch.abs(latent_sqrt_var))), axis=0
@@ -243,21 +240,18 @@ def elbo_zi_pln(
covariance_term = 1 / 2 * torch.log(torch.diag(covariance)) * y
inside_d = covariance_term + log_S_term
- d = n_samples * dim / 2 + torch.sum(inside_d)
-
inside_e = torch.multiply(latent_prob, _trunc_log(latent_prob)) + torch.multiply(
1 - latent_prob, _trunc_log(1 - latent_prob)
)
- e = -torch.sum(inside_e)
sum_un_moins_rho_s2 = torch.sum(torch.multiply(1 - latent_prob, s_rond_s), axis=0)
diag_sig_sum_rho = torch.multiply(
torch.diag(covariance), torch.sum(latent_prob, axis=0)
)
new = torch.sum(latent_prob * un_moins_rho * (m_minus_xb**2), axis=0)
K = sum_un_moins_rho_s2 + diag_sig_sum_rho + new
- inside_f = torch.diag(Omega) * K
- f = -1 / 2 * torch.sum(inside_f)
+ inside_f = -1 / 2 * torch.diag(Omega) * K
full_diag_omega = torch.diag(Omega).expand(exog.shape[0], -1)
- elbo = a + b + c + d + e + f
- print(" inside a shape", inside_a.shape)
+ elbo = torch.sum(inside_a + inside_c + inside_d)
+ elbo += torch.sum(inside_b) - n_samples / 2 * torch.logdet(covariance)
+ elbo += n_samples * dim / 2 + torch.sum(inside_d + inside_f)
return elbo
--
GitLab
From 04b03948ce1bc0566bdb993e21d2e36841b60af4 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 9 Oct 2023 10:34:39 +0200
Subject: [PATCH 077/167] rewrite the elbo to avoid doublons and torch multiply
are replaced by *
---
pyPLNmodels/elbos.py | 56 ++++++++++++++++++++------------------------
1 file changed, 26 insertions(+), 30 deletions(-)
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index f7378d0f..ec743430 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -202,11 +202,15 @@ def elbo_zi_pln(
Returns:
torch.tensor of size 1 with a gradient.
"""
- covariance = components @ (components.T)
if torch.norm(latent_prob * dirac - latent_prob) > 0.00000001:
raise RuntimeError("Latent probability is not zero when it should be.")
+ covariance = components @ (components.T)
+ diag_cov = torch.diag(covariance)
+ Omega = torch.inverse(covariance)
+ diag_omega = torch.diag(Omega)
+ un_moins_prob = 1 - latent_prob
n_samples, dim = endog.shape
- s_rond_s = torch.multiply(latent_sqrt_var, latent_sqrt_var)
+ s_rond_s = latent_sqrt_var * latent_sqrt_var
o_plus_m = offsets + latent_mean
if exog is None:
XB = torch.zeros_like(endog)
@@ -217,40 +221,32 @@ def elbo_zi_pln(
m_minus_xb = latent_mean - XB
A = torch.exp(o_plus_m + s_rond_s / 2)
- inside_a = torch.multiply(
- 1 - latent_prob, torch.multiply(endog, o_plus_m) - A - _log_stirling(endog)
- )
-
- Omega = torch.inverse(covariance)
-
+ inside_a = un_moins_prob * (endog * o_plus_m - A - _log_stirling(endog))
m_moins_xb_outer = torch.mm(m_minus_xb.T, m_minus_xb)
- un_moins_rho = 1 - latent_prob
- un_moins_rho_m_moins_xb = un_moins_rho * m_minus_xb
- un_moins_rho_m_moins_xb_outer = un_moins_rho_m_moins_xb.T @ un_moins_rho_m_moins_xb
- inside_b = -1 / 2 * Omega * un_moins_rho_m_moins_xb_outer
-
- inside_c = torch.multiply(latent_prob, xcoef_inflation) - torch.log(
- 1 + torch.exp(xcoef_inflation)
+ un_moins_prob_m_moins_xb = un_moins_prob * m_minus_xb
+ un_moins_prob_m_moins_xb_outer = (
+ un_moins_prob_m_moins_xb.T @ un_moins_prob_m_moins_xb
)
- log_diag = torch.log(torch.diag(covariance))
+ inside_b = -1 / 2 * Omega * un_moins_prob_m_moins_xb_outer
+
+ inside_c = latent_prob * xcoef_inflation - torch.log(1 + torch.exp(xcoef_inflation))
+ log_diag = torch.log(diag_cov)
log_S_term = torch.sum(
- torch.multiply(1 - latent_prob, torch.log(torch.abs(latent_sqrt_var))), axis=0
+ un_moins_prob * torch.log(torch.abs(latent_sqrt_var)), axis=0
)
- y = torch.sum(latent_prob, axis=0)
- covariance_term = 1 / 2 * torch.log(torch.diag(covariance)) * y
+ sum_prob = torch.sum(latent_prob, axis=0)
+ covariance_term = 1 / 2 * torch.log(diag_cov) * sum_prob
inside_d = covariance_term + log_S_term
- inside_e = torch.multiply(latent_prob, _trunc_log(latent_prob)) + torch.multiply(
- 1 - latent_prob, _trunc_log(1 - latent_prob)
- )
- sum_un_moins_rho_s2 = torch.sum(torch.multiply(1 - latent_prob, s_rond_s), axis=0)
- diag_sig_sum_rho = torch.multiply(
- torch.diag(covariance), torch.sum(latent_prob, axis=0)
- )
- new = torch.sum(latent_prob * un_moins_rho * (m_minus_xb**2), axis=0)
- K = sum_un_moins_rho_s2 + diag_sig_sum_rho + new
- inside_f = -1 / 2 * torch.diag(Omega) * K
- full_diag_omega = torch.diag(Omega).expand(exog.shape[0], -1)
+ inside_e = torch.multiply(
+ latent_prob, _trunc_log(latent_prob)
+ ) + un_moins_prob * _trunc_log(un_moins_prob)
+ sum_un_moins_prob_s2 = torch.sum(un_moins_prob * s_rond_s, axis=0)
+ diag_sig_sum_prob = diag_cov * torch.sum(latent_prob, axis=0)
+ new = torch.sum(latent_prob * un_moins_prob * (m_minus_xb**2), axis=0)
+ K = sum_un_moins_prob_s2 + diag_sig_sum_prob + new
+ inside_f = -1 / 2 * diag_omega * K
+ full_diag_omega = diag_omega.expand(exog.shape[0], -1)
elbo = torch.sum(inside_a + inside_c + inside_d)
elbo += torch.sum(inside_b) - n_samples / 2 * torch.logdet(covariance)
elbo += n_samples * dim / 2 + torch.sum(inside_d + inside_f)
--
GitLab
From ddc7e3e554cce257f9344fc967dcc785ad5194b0 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 9 Oct 2023 11:39:41 +0200
Subject: [PATCH 078/167] began to merge changements of zi inside. till line
3482
---
pyPLNmodels/_initialization.py | 4 +-
pyPLNmodels/models.py | 190 ++++++++++++++++++++++++++++++---
2 files changed, 177 insertions(+), 17 deletions(-)
diff --git a/pyPLNmodels/_initialization.py b/pyPLNmodels/_initialization.py
index 02574c29..f5663746 100644
--- a/pyPLNmodels/_initialization.py
+++ b/pyPLNmodels/_initialization.py
@@ -14,9 +14,7 @@ else:
DEVICE = torch.device("cpu")
-def _init_covariance(
- endog: torch.Tensor, exog: torch.Tensor, coef: torch.Tensor
-) -> torch.Tensor:
+def _init_covariance(endog: torch.Tensor, exog: torch.Tensor) -> torch.Tensor:
"""
Initialization for the covariance for the Pln model. Take the log of endog
(careful when endog=0), and computes the Maximum Likelihood
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index c964fb8c..66ea3339 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -18,7 +18,7 @@ from scipy import stats
from ._closed_forms import (
_closed_formula_coef,
_closed_formula_covariance,
- _closed_formula_pi,
+ _closed_formula_latent_prob,
)
from .elbos import elbo_plnpca, elbo_zi_pln, profiled_elbo_pln
from ._utils import (
@@ -32,6 +32,7 @@ from ._utils import (
_array2tensor,
_handle_data,
_add_doc,
+ _closed_form_latent_prob,
)
from ._initialization import (
@@ -110,7 +111,7 @@ class _model(ABC):
endog, exog, offsets, offsets_formula, take_log_offsets, add_const
)
self._fitted = False
- self._plotargs = _PlotArgs(self._WINDOW)
+ self._plotargs = _PlotArgs()
if dict_initialization is not None:
self._set_init_parameters(dict_initialization)
@@ -249,7 +250,7 @@ class _model(ABC):
return batch_size
@property
- def _nb_iteration_done(self) -> int:
+ def nb_iteration_done(self) -> int:
"""
The number of iterations done.
@@ -408,7 +409,7 @@ class _model(ABC):
self._put_parameters_to_device()
self._handle_optimizer(lr)
stop_condition = False
- while self._nb_iteration_done < nb_max_iteration and not stop_condition:
+ while self.nb_iteration_done < nb_max_iteration and not stop_condition:
loss = self._trainstep()
criterion = self._compute_criterion_and_update_plotargs(loss, tol)
if abs(criterion) < tol:
@@ -1242,7 +1243,7 @@ class _model(ABC):
dict
The dictionary of optimization parameters.
"""
- return {"Number of iterations done": self._nb_iteration_done}
+ return {"Number of iterations done": self.nb_iteration_done}
@property
def _useful_properties_string(self):
@@ -1850,7 +1851,7 @@ class Pln(_model):
covariance : torch.Tensor
The covariance matrix.
"""
- pass
+ raise AttributeError("You can not set the covariance for the Pln model.")
class PlnPCAcollection:
@@ -3190,7 +3191,7 @@ class PlnPCA(_model):
@property
def covariance(self) -> torch.Tensor:
"""
- Property representing the covariance a posteriori of the latent variables.
+ Property representing the covariance of the latent variables.
Returns
-------
@@ -3326,13 +3327,137 @@ class PlnPCA(_model):
return self.latent_variables
-class ZIPln(Pln):
+class ZIPln(_model):
_NAME = "ZIPln"
- _pi: torch.Tensor
+ _latent_prob: torch.Tensor
_coef_inflation: torch.Tensor
_dirac: torch.Tensor
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import ZIPln, get_real_count_data
+ >>> endog= get_real_count_data()
+ >>> zi = ZIPln(endog, add_const = True)
+ >>> zi.fit()
+ >>> print(zi)
+ """,
+ returns="""
+ ZIPln
+ """,
+ see_also="""
+ :func:`pyPLNmodels.ZIPln.from_formula`
+ """,
+ )
+ def __init__(
+ self,
+ endog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
+ *,
+ exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
+ offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
+ offsets_formula: str = "logsum",
+ dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
+ take_log_offsets: bool = False,
+ add_const: bool = True,
+ use_closed_form: bool = False,
+ ):
+ super().__init__(
+ endog=endog,
+ exog=exog,
+ offsets=offsets,
+ offsets_formula=offsets_formula,
+ dict_initialization=dict_initialization,
+ take_log_offsets=take_log_offsets,
+ add_const=add_const,
+ )
+ self._use_closed_form = use_closed_form
+
+ @classmethod
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import ZIPln, get_real_count_data
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> zi = ZIPln.from_formula("endog ~ 1", data = data)
+ """,
+ returns="""
+ ZIPln
+ """,
+ see_also="""
+ :class:`pyPLNmodels.ZIPln`
+ :func:`pyPLNmodels.ZIPln.__init__`
+ """,
+ )
+ def from_formula(
+ cls,
+ formula: str,
+ data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
+ *,
+ offsets_formula: str = "logsum",
+ dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
+ take_log_offsets: bool = False,
+ use_closed_form: bool = True,
+ ):
+ endog, exog, offsets = _extract_data_from_formula(formula, data)
+ return cls(
+ endog,
+ exog=exog,
+ offsets=offsets,
+ offsets_formula=offsets_formula,
+ dict_initialization=dict_initialization,
+ take_log_offsets=take_log_offsets,
+ add_const=False,
+ use_closed_form=use_closed_form,
+ )
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import ZIPln, get_real_count_data
+ >>> endog = get_real_count_data()
+ >>> zi = Pln(endog,add_const = True)
+ >>> zi.fit()
+ >>> print(zi)
+ """,
+ )
+ def fit(
+ self,
+ nb_max_iteration: int = 50000,
+ *,
+ lr: float = 0.01,
+ tol: float = 1e-3,
+ do_smart_init: bool = True,
+ verbose: bool = False,
+ batch_size: int = None,
+ ):
+ super().fit(
+ nb_max_iteration,
+ lr=lr,
+ tol=tol,
+ do_smart_init=do_smart_init,
+ verbose=verbose,
+ batch_size=batch_size,
+ )
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> import matplotlib.pyplot as plt
+ >>> from pyPLNmodels import ZIPln, get_real_count_data
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> zi = ZIPln(endog,add_const = True)
+ >>> zi.fit()
+ >>> zi.plot_expected_vs_true()
+ >>> plt.show()
+ >>> zi.plot_expected_vs_true(colors = labels)
+ >>> plt.show()
+ """,
+ )
+ def plot_expected_vs_true(self, ax=None, colors=None):
+ super().plot_expected_vs_true(ax=ax, colors=colors)
+
@property
def _description(self):
return "with full covariance model and zero-inflation."
@@ -3346,7 +3471,7 @@ class ZIPln(Pln):
def _smart_init_model_parameters(self):
super()._smart_init_model_parameters()
if not hasattr(self, "_covariance"):
- self._covariance = _init_covariance(self._endog, self._exog, self._coef)
+ self._components = _init_components(self._endog, self._exog, self.dim)
if not hasattr(self, "_coef_inflation"):
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
@@ -3354,11 +3479,29 @@ class ZIPln(Pln):
self._dirac = self._endog == 0
self._latent_mean = torch.randn(self.n_samples, self.dim)
self._latent_sqrt_var = torch.randn(self.n_samples, self.dim)
- self._pi = (
+ self._latent_prob = (
torch.empty(self.n_samples, self.dim).uniform_(0, 1).to(DEVICE)
* self._dirac
)
+ @property
+ def _covariance(self):
+ return self._components @ (self._components.T)
+
+ @property
+ def covariance(self) -> torch.Tensor:
+ """
+ Property representing the covariance of the latent variables.
+
+ Returns
+ -------
+ Optional[torch.Tensor]
+ The covariance tensor or None if components are not present.
+ """
+ if hasattr(self, "_components"):
+ return self.components @ (self.components.T)
+ return None
+
def compute_elbo(self):
return elbo_zi_pln(
self._endog,
@@ -3366,7 +3509,7 @@ class ZIPln(Pln):
self._offsets,
self._latent_mean,
self._latent_sqrt_var,
- self._pi,
+ self._latent_prob,
self._covariance,
self._coef,
self._coef_inflation,
@@ -3375,9 +3518,19 @@ class ZIPln(Pln):
@property
def _list_of_parameters_needing_gradient(self):
- return [self._latent_mean, self._latent_sqrt_var, self._coef_inflation]
+ list_parameters = [
+ self._latent_mean,
+ self._latent_sqrt_var,
+ self._coef_inflation,
+ self._components,
+ self._coef,
+ ]
+ if self._use_closed_form:
+ list_parameters.append(self._latent_prob)
+ return list_parameters
def _update_closed_forms(self):
+ pass
self._coef = _closed_formula_coef(self._exog, self._latent_mean)
self._covariance = _closed_formula_covariance(
self._exog,
@@ -3386,7 +3539,7 @@ class ZIPln(Pln):
self._coef,
self.n_samples,
)
- self._pi = _closed_formula_pi(
+ self._latent_prob = _closed_formula_latent_prob(
self._offsets,
self._latent_mean,
self._latent_sqrt_var,
@@ -3395,6 +3548,15 @@ class ZIPln(Pln):
self._coef_inflation,
)
+ @property
+ def closed_form_latent_prob(self):
+ """
+ The closed form for the latent probability.
+ """
+ return closed_form_latent_prob(
+ self._exog, self._coef, self._coef_inflation, self._covariance, self._dirac
+ )
+
@property
def number_of_parameters(self):
return self.dim * (2 * self.nb_cov + (self.dim + 1) / 2)
--
GitLab
From b47ebec4278dabbb881e79fd9cb81b718ac2d994 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 9 Oct 2023 16:35:25 +0200
Subject: [PATCH 079/167] continue to merge changes from the zi branch.
---
pyPLNmodels/models.py | 167 ++++++++++++++++++++++++++++--------------
1 file changed, 111 insertions(+), 56 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 66ea3339..6289640c 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -238,6 +238,12 @@ class _model(ABC):
_plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
return ax
+ def _update_parameters(self):
+ """
+ Update the parameters with a gradient step and project if necessary.
+ """
+ self.optim.step()
+
def _handle_batch_size(self, batch_size):
if batch_size is None:
batch_size = self.n_samples
@@ -489,7 +495,7 @@ class _model(ABC):
loss = -self._compute_elbo_b()
loss.backward()
elbo += loss.item()
- self.optim.step()
+ self._udpate_parameters()
self._update_closed_forms()
return elbo / self._nb_batches
@@ -2482,23 +2488,26 @@ class PlnPCAcollection:
bic = self.BIC
aic = self.AIC
loglikes = self.loglikes
- bic_color = "blue"
- aic_color = "red"
- loglikes_color = "orange"
- plt.scatter(bic.keys(), bic.values(), label="BIC criterion", c=bic_color)
- plt.plot(bic.keys(), bic.values(), c=bic_color)
- plt.axvline(self.best_BIC_model_rank, c=bic_color, linestyle="dotted")
- plt.scatter(aic.keys(), aic.values(), label="AIC criterion", c=aic_color)
- plt.axvline(self.best_AIC_model_rank, c=aic_color, linestyle="dotted")
- plt.plot(aic.keys(), aic.values(), c=aic_color)
- plt.xticks(list(aic.keys()))
- plt.scatter(
- loglikes.keys(),
- -np.array(list(loglikes.values())),
- label="Negative log likelihood",
- c=loglikes_color,
- )
- plt.plot(loglikes.keys(), -np.array(list(loglikes.values())), c=loglikes_color)
+ colors = {"BIC": "blue", "AIC": "red", "Negative log likelihood": "orange"}
+ for criterion, values in zip(
+ ["BIC", "AIC", "Negative log likelihood"], [bic, aic, loglikes]
+ ):
+ plt.scatter(
+ values.keys(),
+ values.values(),
+ label=f"{criterion} criterion",
+ c=colors[criterion],
+ )
+ plt.plot(values.keys(), values.values(), c=colors[criterion])
+ if criterion == "BIC":
+ plt.axvline(
+ self.best_BIC_model_rank, c=colors[criterion], linestyle="dotted"
+ )
+ elif criterion == "AIC":
+ plt.axvline(
+ self.best_AIC_model_rank, c=colors[criterion], linestyle="dotted"
+ )
+ plt.xticks(list(values.keys()))
plt.legend()
plt.show()
@@ -2696,7 +2705,7 @@ class PlnPCA(_model):
)
def __init__(
self,
- endog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
+ endog: Union[torch.Tensor, np.ndarray, pd.DataFrame],
*,
exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
@@ -3465,16 +3474,21 @@ class ZIPln(_model):
def _random_init_model_parameters(self):
super()._random_init_model_parameters()
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
- self._covariance = torch.diag(torch.ones(self.dim)).to(DEVICE)
+ self._coef = torch.randn(self.nb_cov, self.dim)
+ self._components = torch.randn(self.nb_cov, self.dim)
- # should change the good initialization, especially for _coef_inflation
+ # should change the good initialization for _coef_inflation
def _smart_init_model_parameters(self):
+ # init of _coef.
super()._smart_init_model_parameters()
if not hasattr(self, "_covariance"):
self._components = _init_components(self._endog, self._exog, self.dim)
if not hasattr(self, "_coef_inflation"):
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
+ def _print_beginning_message(self):
+ print("Fitting a ZIPln model.")
+
def _random_init_latent_parameters(self):
self._dirac = self._endog == 0
self._latent_mean = torch.randn(self.n_samples, self.dim)
@@ -3482,12 +3496,36 @@ class ZIPln(_model):
self._latent_prob = (
torch.empty(self.n_samples, self.dim).uniform_(0, 1).to(DEVICE)
* self._dirac
- )
+ ).double()
+
+ def _smart_init_latent_parameters(self):
+ self._random_init_latent_parameters()
@property
def _covariance(self):
return self._components @ (self._components.T)
+ def latent_variables(self):
+ return self.latent_mean, self.latent_prob
+
+ def _update_parameters(self):
+ super()._update_parameters()
+ self._project_latent_prob()
+
+ def _project_latent_prob(self):
+ """
+ Project the latent probability since it must be between 0 and 1.
+ """
+ if self.use_closed_form_prob is False:
+ with torch.no_grad():
+ self._latent_prob = torch.maximum(
+ self._latent_prob, torch.tensor([0]), out=self._latent_prob
+ )
+ self._latent_prob = torch.minimum(
+ self._latent_prob, torch.tensor([1]), out=self._latent_prob
+ )
+ self._latent_prob *= self._dirac
+
@property
def covariance(self) -> torch.Tensor:
"""
@@ -3498,24 +3536,67 @@ class ZIPln(_model):
Optional[torch.Tensor]
The covariance tensor or None if components are not present.
"""
- if hasattr(self, "_components"):
- return self.components @ (self.components.T)
- return None
+ return self._cpu_attribute_or_none("_covariance")
+
+ @property
+ def latent_prob(self):
+ return self._cpu_attribute_or_none("_latent_prob")
+
+ @property
+ def closed_form_latent_prob(self):
+ """
+ The closed form for the latent probability.
+ """
+ return closed_form_latent_prob(
+ self._exog, self._coef, self._coef_inflation, self._covariance, self._dirac
+ )
def compute_elbo(self):
+ if self._use_closed_form_prob is True:
+ latent_prob = self.closed_form_latent_prob
+ else:
+ latent_prob = self._latent_prob
return elbo_zi_pln(
self._endog,
self._exog,
self._offsets,
self._latent_mean,
self._latent_sqrt_var,
- self._latent_prob,
- self._covariance,
+ latent_prob,
+ self._components,
self._coef,
self._coef_inflation,
self._dirac,
)
+ def _compute_elbo_b(self):
+ if self._use_closed_form_prob is True:
+ latent_prob_b = _closed_form_latent_prob(
+ self._exog_b,
+ self._coef,
+ self._coef_inflation,
+ self._covariance,
+ self._dirac_b,
+ )
+ else:
+ latent_prob_b = self._latent_prob_b
+ return elbo_zi_pln(
+ self._endog_b,
+ self._exog_b,
+ self._offsets_b,
+ self._latent_mean_b,
+ self._latent_sqrt_var_b,
+ latent_prob_b,
+ self._components,
+ self._coef,
+ self._coef_inflation,
+ self._dirac_b,
+ )
+
+ @property
+ def number_of_parameters(self):
+ return self.dim * (2 * self.nb_cov + (self.dim + 1) / 2)
+
@property
def _list_of_parameters_needing_gradient(self):
list_parameters = [
@@ -3527,36 +3608,10 @@ class ZIPln(_model):
]
if self._use_closed_form:
list_parameters.append(self._latent_prob)
+ if self._exog is not None:
+ list_parameters.append(self._coef)
+ list_parameters.append(self._coef_inflation)
return list_parameters
def _update_closed_forms(self):
pass
- self._coef = _closed_formula_coef(self._exog, self._latent_mean)
- self._covariance = _closed_formula_covariance(
- self._exog,
- self._latent_mean,
- self._latent_sqrt_var,
- self._coef,
- self.n_samples,
- )
- self._latent_prob = _closed_formula_latent_prob(
- self._offsets,
- self._latent_mean,
- self._latent_sqrt_var,
- self._dirac,
- self._exog,
- self._coef_inflation,
- )
-
- @property
- def closed_form_latent_prob(self):
- """
- The closed form for the latent probability.
- """
- return closed_form_latent_prob(
- self._exog, self._coef, self._coef_inflation, self._covariance, self._dirac
- )
-
- @property
- def number_of_parameters(self):
- return self.dim * (2 * self.nb_cov + (self.dim + 1) / 2)
--
GitLab
From 62c2d3d24da62146f2e21de9d8a32f3209bdc4b2 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 9 Oct 2023 16:38:27 +0200
Subject: [PATCH 080/167] add gradients to zero inflated class.
---
pyPLNmodels/models.py | 313 ++++++++++++++++++++++++++++++++++++++++++
1 file changed, 313 insertions(+)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 6289640c..6c0fd0fe 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -3615,3 +3615,316 @@ class ZIPln(_model):
def _update_closed_forms(self):
pass
+
+ def grad_M(self):
+ if self.use_closed_form_prob is True:
+ latent_prob = self.closed_form_latent_prob
+ else:
+ latent_prob = self._latent_prob
+ un_moins_prob = 1 - latent_prob
+ first = un_moins_prob * (
+ self._endog
+ - torch.exp(
+ self._offsets + self._latent_mean + self.latent_sqrt_var**2 / 2
+ )
+ )
+ MmoinsXB = self._latent_mean - self._exog @ self._coef
+ A = (un_moins_prob * MmoinsXB) @ torch.inverse(self._covariance)
+ diag_omega = torch.diag(torch.inverse(self._covariance))
+ full_diag_omega = diag_omega.expand(self.exog.shape[0], -1)
+ second = -un_moins_prob * A
+ added = -full_diag_omega * latent_prob * un_moins_prob * (MmoinsXB)
+ return first + second + added
+
+ def grad_S(self):
+ if self.use_closed_form_prob is True:
+ latent_prob = self.closed_form_latent_prob
+ else:
+ latent_prob = self._latent_prob
+ Omega = torch.inverse(self.covariance)
+ un_moins_prob = 1 - latent_prob
+ first = un_moins_prob * torch.exp(
+ self._offsets + self._latent_mean + self._latent_sqrt_var**2 / 2
+ )
+ first = -torch.multiply(first, self._latent_sqrt_var)
+ sec = un_moins_prob * 1 / self._latent_sqrt_var
+ K = un_moins_prob * (
+ torch.multiply(
+ torch.full((self.n_samples, 1), 1.0), torch.diag(Omega).unsqueeze(0)
+ )
+ )
+ third = -self._latent_sqrt_var * K
+ return first + sec + third
+
+ def grad_theta(self):
+ if self.use_closed_form_prob is True:
+ latent_prob = self.closed_form_latent_prob
+ else:
+ latent_prob = self._latent_prob
+
+ un_moins_prob = 1 - latent_prob
+ MmoinsXB = self._latent_mean - self._exog @ self._coef
+ A = (un_moins_prob * MmoinsXB) @ torch.inverse(self._covariance)
+ diag_omega = torch.diag(torch.inverse(self._covariance))
+ full_diag_omega = diag_omega.expand(self.exog.shape[0], -1)
+ added = latent_prob * (MmoinsXB) * full_diag_omega
+ A += added
+ second = -un_moins_prob * A
+ grad_no_closed_form = -self._exog.T @ second
+ if self.use_closed_form_prob is False:
+ return grad_no_closed_form
+ else:
+ XB_zero = self._exog @ self._coef_inflation
+ diag = torch.diag(self._covariance)
+ full_diag = diag.expand(self._exog.shape[0], -1)
+ XB = self._exog @ self._coef
+ derivative = d_h_x2(XB_zero, XB, full_diag, self._dirac)
+ grad_closed_form = self.gradients_closed_form_thetas(derivative)
+ return grad_closed_form + grad_no_closed_form
+
+ def gradients_closed_form_thetas(self, derivative):
+ Omega = torch.inverse(self._covariance)
+ MmoinsXB = self._latent_mean - self._exog @ self._coef
+ s_rond_s = self._latent_sqrt_var**2
+ latent_prob = self.closed_form_latent_prob
+ A = torch.exp(self._offsets + self._latent_mean + s_rond_s / 2)
+ poiss_term = (
+ self._endog * (self._offsets + self._latent_mean)
+ - A
+ - _log_stirling(self._endog)
+ )
+ a = -self._exog.T @ (derivative * poiss_term)
+ b = self._exog.T @ (
+ derivative * MmoinsXB * (((1 - latent_prob) * MmoinsXB) @ Omega)
+ )
+ c = self._exog.T @ (derivative * (self._exog @ self._coef_inflation))
+ first_d = derivative * torch.log(torch.abs(self._latent_sqrt_var))
+ second_d = (
+ 1 / 2 * derivative @ (torch.diag(torch.log(torch.diag(self._covariance))))
+ )
+ d = -self._exog.T @ (first_d - second_d)
+ e = -self._exog.T @ (
+ derivative * (_trunc_log(latent_prob) - _trunc_log(1 - latent_prob))
+ )
+ first_f = (
+ +1
+ / 2
+ * self._exog.T
+ @ (derivative * (s_rond_s @ torch.diag(torch.diag(Omega))))
+ )
+ second_f = (
+ -1
+ / 2
+ * self._exog.T
+ @ derivative
+ @ torch.diag(torch.diag(Omega) * torch.diag(self._covariance))
+ )
+ full_diag_omega = torch.diag(Omega).expand(self.exog.shape[0], -1)
+ common = (MmoinsXB) ** 2 * (full_diag_omega)
+ new_f = -1 / 2 * self._exog.T @ (derivative * common * (1 - 2 * latent_prob))
+ f = first_f + second_f + new_f
+ return a + b + c + d + e + f
+
+ def grad_theta_0(self):
+ if self.use_closed_form_prob is True:
+ latent_prob = self.closed_form_latent_prob
+ else:
+ latent_prob = self._latent_prob
+ grad_no_closed_form = self._exog.T @ latent_prob - self._exog.T @ (
+ torch.exp(self._exog @ self._coef_inflation)
+ / (1 + torch.exp(self._exog @ self._coef_inflation))
+ )
+ if self.use_closed_form_prob is False:
+ return grad_no_closed_form
+ else:
+ grad_closed_form = self.gradients_closed_form_thetas(
+ latent_prob * (1 - latent_prob)
+ )
+ return grad_closed_form + grad_no_closed_form
+
+ def grad_C(self):
+ if self.use_closed_form_prob is True:
+ latent_prob = self.closed_form_latent_prob
+ else:
+ latent_prob = self._latent_prob
+ omega = torch.inverse(self._covariance)
+ if self._coef is not None:
+ m_minus_xb = self._latent_mean - torch.mm(self._exog, self._coef)
+ else:
+ m_minus_xb = self._latent_mean
+ m_moins_xb_outer = torch.mm(m_minus_xb.T, m_minus_xb)
+
+ un_moins_rho = 1 - latent_prob
+
+ un_moins_rho_m_moins_xb = un_moins_rho * m_minus_xb
+ un_moins_rho_m_moins_xb_outer = (
+ un_moins_rho_m_moins_xb.T @ un_moins_rho_m_moins_xb
+ )
+ deter = (
+ -self.n_samples
+ * torch.inverse(self._components @ (self._components.T))
+ @ self._components
+ )
+ sec_part_b_grad = (
+ omega @ (un_moins_rho_m_moins_xb_outer) @ omega @ self._components
+ )
+ b_grad = deter + sec_part_b_grad
+
+ diag = torch.diag(self.covariance)
+ rho_t_unn = torch.sum(latent_prob, axis=0)
+ omega_unp = torch.sum(omega, axis=0)
+ K = torch.sum(un_moins_rho * self._latent_sqrt_var**2, axis=0) + diag * (
+ rho_t_unn
+ )
+ added = torch.sum(latent_prob * un_moins_rho * (m_minus_xb**2), axis=0)
+ K += added
+ first_part_grad = omega @ torch.diag_embed(K) @ omega @ self._components
+ x = torch.diag(omega) * rho_t_unn
+ second_part_grad = -torch.diag_embed(x) @ self._components
+ y = rho_t_unn
+ first = torch.multiply(y, 1 / torch.diag(self.covariance)).unsqueeze(1)
+ second = torch.full((1, self.dim), 1.0)
+ Diag = (first * second) * torch.eye(self.dim)
+ last_grad = Diag @ self._components
+ grad_no_closed_form = b_grad + first_part_grad + second_part_grad + last_grad
+ if self.use_closed_form_prob is False:
+ return grad_no_closed_form
+ else:
+ s_rond_s = self._latent_sqrt_var**2
+ XB_zero = self._exog @ self._coef_inflation
+ XB = self._exog @ self._coef
+ A = torch.exp(self._offsets + self._latent_mean + s_rond_s / 2)
+ poiss_term = (
+ self._endog * (self._offsets + self._latent_mean)
+ - A
+ - _log_stirling(self._endog)
+ )
+ full_diag_sigma = diag.expand(self._exog.shape[0], -1)
+ full_diag_omega = torch.diag(omega).expand(self._exog.shape[0], -1)
+ H3 = d_h_x3(XB_zero, XB, full_diag_sigma, self._dirac)
+ poiss_term_H = poiss_term * H3
+ a = (
+ -2
+ * (
+ ((poiss_term_H.T @ torch.ones(self.n_samples, self.dim)))
+ * (torch.eye(self.dim))
+ )
+ @ self._components
+ )
+ B_Omega = ((1 - latent_prob) * m_minus_xb) @ omega
+ K = H3 * B_Omega * m_minus_xb
+ b = (
+ 2
+ * (
+ (
+ (m_minus_xb * B_Omega * H3).T
+ @ torch.ones(self.n_samples, self.dim)
+ )
+ * torch.eye(self.dim)
+ )
+ @ self._components
+ )
+ c = (
+ 2
+ * (
+ ((XB_zero * H3).T @ torch.ones(self.n_samples, self.dim))
+ * torch.eye(self.dim)
+ )
+ @ self._components
+ )
+ d = (
+ -2
+ * (
+ (
+ (torch.log(torch.abs(self._latent_sqrt_var)) * H3).T
+ @ torch.ones(self.n_samples, self.dim)
+ )
+ * torch.eye(self.dim)
+ )
+ @ self._components
+ )
+ log_full_diag_sigma = torch.log(diag).expand(self._exog.shape[0], -1)
+ d += (
+ ((log_full_diag_sigma * H3).T @ torch.ones(self.n_samples, self.dim))
+ * torch.eye(self.dim)
+ ) @ self._components
+ e = (
+ -2
+ * (
+ (
+ ((_trunc_log(latent_prob) - _trunc_log(1 - latent_prob)) * H3).T
+ @ torch.ones(self.n_samples, self.dim)
+ )
+ * torch.eye(self.dim)
+ )
+ @ self._components
+ )
+ f = (
+ -(
+ (
+ (full_diag_omega * (full_diag_sigma - s_rond_s) * H3).T
+ @ torch.ones(self.n_samples, self.dim)
+ )
+ * torch.eye(self.dim)
+ )
+ @ self._components
+ )
+ f -= (
+ (
+ ((1 - 2 * latent_prob) * m_minus_xb**2 * full_diag_omega * H3).T
+ @ torch.ones(self.n_samples, self.dim)
+ )
+ * torch.eye(self.dim)
+ ) @ self._components
+ grad_closed_form = a + b + c + d + e + f
+ return grad_closed_form + grad_no_closed_form
+
+ def grad_rho(self):
+ if self.use_closed_form_prob is True:
+ latent_prob = self.closed_form_latent_prob
+ else:
+ latent_prob = self._latent_prob
+ omega = torch.inverse(self._covariance)
+ s_rond_s = self._latent_sqrt_var * self._latent_sqrt_var
+ A = torch.exp(self._offsets + self._latent_mean + s_rond_s / 2)
+ first = (
+ -self._endog * (self._offsets + self._latent_mean)
+ + A
+ + _log_stirling(self._endog)
+ )
+ un_moins_prob = 1 - latent_prob
+ MmoinsXB = self._latent_mean - self._exog @ self._coef
+ A = (un_moins_prob * MmoinsXB) @ torch.inverse(self._covariance)
+ second = MmoinsXB * A
+ third = self._exog @ self._coef_inflation
+ fourth_first = -torch.log(torch.abs(self._latent_sqrt_var))
+ fourth_second = (
+ 1
+ / 2
+ * torch.multiply(
+ torch.full((self.n_samples, 1), 1.0),
+ torch.log(torch.diag(self.covariance)).unsqueeze(0),
+ )
+ )
+ fourth = fourth_first + fourth_second
+ fifth = _trunc_log(un_moins_prob) - _trunc_log(latent_prob)
+ sixth_first = (
+ 1
+ / 2
+ * torch.multiply(
+ torch.full((self.n_samples, 1), 1.0), torch.diag(omega).unsqueeze(0)
+ )
+ * s_rond_s
+ )
+ sixth_second = (
+ -1
+ / 2
+ * torch.multiply(
+ torch.full((self.n_samples, 1), 1.0),
+ (torch.diag(omega) * torch.diag(self._covariance)).unsqueeze(0),
+ )
+ )
+ sixth = sixth_first + sixth_second
+ full_diag_omega = torch.diag(omega).expand(self.exog.shape[0], -1)
+ seventh = -1 / 2 * (1 - 2 * latent_prob) * (MmoinsXB) ** 2 * (full_diag_omega)
+ return first + second + third + fourth + fifth + sixth + seventh
--
GitLab
From 1958d93b3c17d2051e0dab8ba38b9e9de9ebb550 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 10 Oct 2023 19:16:24 +0200
Subject: [PATCH 081/167] pass the tests but ZI or stochasticity have not been
tested yet.
---
pyPLNmodels/_closed_forms.py | 15 +++++++++
pyPLNmodels/_utils.py | 48 +++++++++++++++++++++++++++--
pyPLNmodels/models.py | 59 ++++++++++++++++++++++--------------
tests/test_pln_full.py | 2 +-
tests/test_setters.py | 18 ++++++-----
5 files changed, 109 insertions(+), 33 deletions(-)
diff --git a/pyPLNmodels/_closed_forms.py b/pyPLNmodels/_closed_forms.py
index b57e7850..3524d48d 100644
--- a/pyPLNmodels/_closed_forms.py
+++ b/pyPLNmodels/_closed_forms.py
@@ -1,4 +1,5 @@
from typing import Optional
+from ._utils import phi
import torch # pylint:disable=[C0114]
@@ -98,3 +99,17 @@ def _closed_formula_pi(
"""
poiss_param = torch.exp(offsets + latent_mean + 0.5 * torch.square(latent_sqrt_var))
return torch._sigmoid(poiss_param + torch.mm(exog, _coef_inflation)) * dirac
+
+
+def _closed_formula_latent_prob(exog, coef, coef_infla, cov, dirac):
+ if exog is not None:
+ XB = exog @ coef
+ XB_zero = exog @ coef_infla
+ else:
+ XB_zero = 0
+ XB = 0
+ XB_zero = exog @ coef_infla
+ pi = torch.sigmoid(XB_zero)
+ diag = torch.diag(cov)
+ full_diag = diag.expand(exog.shape[0], -1)
+ return torch.sigmoid(XB_zero - torch.log(phi(XB, full_diag))) * dirac
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index d2b1aea0..7169b74d 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -79,8 +79,8 @@ class _PlotArgs:
"""
ax = plt.gca() if ax is None else ax
ax.plot(
- self.running_times[self.window :],
- self.criterions[self.window :],
+ self.running_times,
+ self.criterions,
label="Delta",
)
ax.set_yscale("log")
@@ -1004,3 +1004,47 @@ def _add_doc(parent_class, *, params=None, example=None, returns=None, see_also=
return fun
return wrapper
+
+
+def pf_lambert(x, y):
+ return x - (1 - (y * torch.exp(-x) + 1) / (x + 1))
+
+
+def lambert(y, nb_pf=10):
+ x = torch.log(1 + y)
+ for _ in range(nb_pf):
+ x = pf_lambert(x, y)
+ return x
+
+
+def d_varpsi_x1(mu, sigma2):
+ W = lambert(sigma2 * torch.exp(mu))
+ first = phi(mu, sigma2)
+ third = 1 / sigma2 + 1 / 2 * 1 / ((1 + W) ** 2)
+ return -first * W * third
+
+
+def phi(mu, sigma2):
+ y = sigma2 * torch.exp(mu)
+ lamby = lambert(y)
+ log_num = -1 / (2 * sigma2) * (lamby**2 + 2 * lamby)
+ return torch.exp(log_num) / torch.sqrt(1 + lamby)
+
+
+def d_varpsi_x2(mu, sigma2):
+ first = d_varpsi_x1(mu, sigma2) / sigma2
+ W = lambert(sigma2 * torch.exp(mu))
+ second = (W**2 + 2 * W) / 2 / (sigma2**2) * phi(mu, sigma2)
+ return first + second
+
+
+def d_h_x2(a, x, y, dirac):
+ rho = torch.sigmoid(a - torch.log(phi(x, y))) * dirac
+ rho_prime = rho * (1 - rho)
+ return -rho_prime * d_varpsi_x1(x, y) / phi(x, y)
+
+
+def d_h_x3(a, x, y, dirac):
+ rho = torch.sigmoid(a - torch.log(phi(x, y))) * dirac
+ rho_prime = rho * (1 - rho)
+ return -rho_prime * d_varpsi_x2(x, y) / phi(x, y)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 6c0fd0fe..64ddb104 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -32,7 +32,6 @@ from ._utils import (
_array2tensor,
_handle_data,
_add_doc,
- _closed_form_latent_prob,
)
from ._initialization import (
@@ -65,6 +64,7 @@ class _model(ABC):
_beginning_time: float
_latent_sqrt_var: torch.Tensor
_latent_mean: torch.Tensor
+ _batch_size: int = None
def __init__(
self,
@@ -164,7 +164,10 @@ class _model(ABC):
"""
if "coef" not in dict_initialization.keys():
print("No coef is initialized.")
- self.coef = None
+ dict_initialization["coef"] = None
+ if self._NAME == "Pln":
+ del dict_initialization["covariance"]
+ del dict_initialization["coef"]
for key, array in dict_initialization.items():
array = _format_data(array)
setattr(self, key, array)
@@ -175,6 +178,8 @@ class _model(ABC):
"""
The batch size of the model. Should not be greater than the number of samples.
"""
+ if self._batch_size is None:
+ return self.n_samples
return self._batch_size
@property
@@ -265,7 +270,7 @@ class _model(ABC):
int
The number of iterations done.
"""
- return len(self._plotargs._elbos_list) * self._nb_batches
+ return len(self._plotargs._elbos_list) * self.nb_batches
@property
def n_samples(self) -> int:
@@ -359,7 +364,7 @@ class _model(ABC):
def _put_parameters_to_device(self):
"""
- Move parameters to the device.
+ Move parameters to the cGPU device if present.
"""
for parameter in self._list_of_parameters_needing_gradient:
parameter.requires_grad_(True)
@@ -374,7 +379,6 @@ class _model(ABC):
List[torch.Tensor]
List of parameters needing gradient.
"""
- ...
def fit(
self,
@@ -459,9 +463,13 @@ class _model(ABC):
def _return_batch(self, indices, beginning, end):
to_take = torch.tensor(indices[beginning:end]).to(DEVICE)
+ if self._exog is not None:
+ exog_b = torch.index_select(self._exog, 0, to_take)
+ else:
+ exog_b = None
return (
torch.index_select(self._endog, 0, to_take),
- torch.index_select(self._exog, 0, to_take),
+ exog_b,
torch.index_select(self._offsets, 0, to_take),
torch.index_select(self._latent_mean, 0, to_take),
torch.index_select(self._latent_sqrt_var, 0, to_take),
@@ -469,14 +477,14 @@ class _model(ABC):
@property
def _nb_full_batch(self):
- return self.n_samples // self._batch_size
+ return self.n_samples // self.batch_size
@property
def _last_batch_size(self):
- return self.n_samples % self._batch_size
+ return self.n_samples % self.batch_size
@property
- def _nb_batches(self):
+ def nb_batches(self):
return self._nb_full_batch + (self._last_batch_size > 0)
def _trainstep(self):
@@ -495,9 +503,9 @@ class _model(ABC):
loss = -self._compute_elbo_b()
loss.backward()
elbo += loss.item()
- self._udpate_parameters()
+ self._update_parameters()
self._update_closed_forms()
- return elbo / self._nb_batches
+ return elbo / self.nb_batches
def _extract_batch(self, batch):
self._endog_b = batch[0]
@@ -740,8 +748,9 @@ class _model(ABC):
"""
self._plotargs._elbos_list.append(-loss)
self._plotargs.running_times.append(time.time() - self._beginning_time)
+ elbo = -loss
self._plotargs.cumulative_elbo_list.append(
- self._plotargs.cumulative_elbo_list - loss
+ self._plotargs.cumulative_elbo + elbo
)
criterion = (
self._plotargs.cumulative_elbo_list[-2]
@@ -1652,7 +1661,11 @@ class Pln(_model):
----------
coef : Union[torch.Tensor, np.ndarray, pd.DataFrame]
The regression coefficients of the gaussian latent variables.
+ Raises
+ ------
+ AttributeError since you can not set the coef in the Pln model.
"""
+ raise AttributeError("You can not set the coef in the Pln model.")
def _endog_predictions(self):
return torch.exp(
@@ -3543,17 +3556,17 @@ class ZIPln(_model):
return self._cpu_attribute_or_none("_latent_prob")
@property
- def closed_form_latent_prob(self):
+ def closed_formula_latent_prob(self):
"""
The closed form for the latent probability.
"""
- return closed_form_latent_prob(
+ return closed_formula_latent_prob(
self._exog, self._coef, self._coef_inflation, self._covariance, self._dirac
)
def compute_elbo(self):
if self._use_closed_form_prob is True:
- latent_prob = self.closed_form_latent_prob
+ latent_prob = self.closed_formula_latent_prob
else:
latent_prob = self._latent_prob
return elbo_zi_pln(
@@ -3571,7 +3584,7 @@ class ZIPln(_model):
def _compute_elbo_b(self):
if self._use_closed_form_prob is True:
- latent_prob_b = _closed_form_latent_prob(
+ latent_prob_b = _closed_formula_latent_prob(
self._exog_b,
self._coef,
self._coef_inflation,
@@ -3618,7 +3631,7 @@ class ZIPln(_model):
def grad_M(self):
if self.use_closed_form_prob is True:
- latent_prob = self.closed_form_latent_prob
+ latent_prob = self.closed_formula_latent_prob
else:
latent_prob = self._latent_prob
un_moins_prob = 1 - latent_prob
@@ -3638,7 +3651,7 @@ class ZIPln(_model):
def grad_S(self):
if self.use_closed_form_prob is True:
- latent_prob = self.closed_form_latent_prob
+ latent_prob = self.closed_formula_latent_prob
else:
latent_prob = self._latent_prob
Omega = torch.inverse(self.covariance)
@@ -3658,7 +3671,7 @@ class ZIPln(_model):
def grad_theta(self):
if self.use_closed_form_prob is True:
- latent_prob = self.closed_form_latent_prob
+ latent_prob = self.closed_formula_latent_prob
else:
latent_prob = self._latent_prob
@@ -3686,7 +3699,7 @@ class ZIPln(_model):
Omega = torch.inverse(self._covariance)
MmoinsXB = self._latent_mean - self._exog @ self._coef
s_rond_s = self._latent_sqrt_var**2
- latent_prob = self.closed_form_latent_prob
+ latent_prob = self.closed_formula_latent_prob
A = torch.exp(self._offsets + self._latent_mean + s_rond_s / 2)
poiss_term = (
self._endog * (self._offsets + self._latent_mean)
@@ -3727,7 +3740,7 @@ class ZIPln(_model):
def grad_theta_0(self):
if self.use_closed_form_prob is True:
- latent_prob = self.closed_form_latent_prob
+ latent_prob = self.closed_formula_latent_prob
else:
latent_prob = self._latent_prob
grad_no_closed_form = self._exog.T @ latent_prob - self._exog.T @ (
@@ -3744,7 +3757,7 @@ class ZIPln(_model):
def grad_C(self):
if self.use_closed_form_prob is True:
- latent_prob = self.closed_form_latent_prob
+ latent_prob = self.closed_formula_latent_prob
else:
latent_prob = self._latent_prob
omega = torch.inverse(self._covariance)
@@ -3881,7 +3894,7 @@ class ZIPln(_model):
def grad_rho(self):
if self.use_closed_form_prob is True:
- latent_prob = self.closed_form_latent_prob
+ latent_prob = self.closed_formula_latent_prob
else:
latent_prob = self._latent_prob
omega = torch.inverse(self._covariance)
diff --git a/tests/test_pln_full.py b/tests/test_pln_full.py
index 2d61befd..870114a0 100644
--- a/tests/test_pln_full.py
+++ b/tests/test_pln_full.py
@@ -8,7 +8,7 @@ from tests.utils import filter_models
@filter_models(["Pln"])
def test_number_of_iterations_pln_full(fitted_pln):
nb_iterations = len(fitted_pln._elbos_list)
- assert 50 < nb_iterations < 500
+ assert 20 < nb_iterations < 1000
@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
diff --git a/tests/test_setters.py b/tests/test_setters.py
index 828989e8..eb7814d7 100644
--- a/tests/test_setters.py
+++ b/tests/test_setters.py
@@ -19,7 +19,8 @@ def test_data_setter_with_torch(pln):
def test_parameters_setter_with_torch(pln):
pln.latent_mean = pln.latent_mean
pln.latent_sqrt_var = pln.latent_sqrt_var
- pln.coef = pln.coef
+ if pln._NAME != "Pln":
+ pln.coef = pln.coef
if pln._NAME == "PlnPCA":
pln.components = pln.components
pln.fit()
@@ -50,7 +51,8 @@ def test_parameters_setter_with_numpy(pln):
np_coef = None
pln.latent_mean = np_latent_mean
pln.latent_sqrt_var = np_latent_sqrt_var
- pln.coef = np_coef
+ if pln._NAME != "Pln":
+ pln.coef = np_coef
if pln._NAME == "PlnPCA":
pln.components = pln.components.numpy()
pln.fit()
@@ -81,7 +83,8 @@ def test_parameters_setter_with_pandas(pln):
pd_coef = None
pln.latent_mean = pd_latent_mean
pln.latent_sqrt_var = pd_latent_sqrt_var
- pln.coef = pd_coef
+ if pln._NAME != "Pln":
+ pln.coef = pd_coef
if pln._NAME == "PlnPCA":
pln.components = pd.DataFrame(pln.components.numpy())
pln.fit()
@@ -141,8 +144,9 @@ def test_fail_parameters_setter_with_torch(pln):
d = 0
else:
d = pln.exog.shape[-1]
- with pytest.raises(ValueError):
- pln.coef = torch.zeros(d + 1, dim)
+ if pln._NAME != "Pln":
+ with pytest.raises(ValueError):
+ pln.coef = torch.zeros(d + 1, dim)
- with pytest.raises(ValueError):
- pln.coef = torch.zeros(d, dim + 1)
+ with pytest.raises(ValueError):
+ pln.coef = torch.zeros(d, dim + 1)
--
GitLab
From 1292015cc1658829e6c60885c1c91580148a5bb6 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 10 Oct 2023 22:55:12 +0200
Subject: [PATCH 082/167] add ZI in the __init__
---
pyPLNmodels/__init__.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/pyPLNmodels/__init__.py b/pyPLNmodels/__init__.py
index e785b288..6ed723c7 100644
--- a/pyPLNmodels/__init__.py
+++ b/pyPLNmodels/__init__.py
@@ -1,4 +1,4 @@
-from .models import PlnPCAcollection, Pln, PlnPCA # pylint:disable=[C0114]
+from .models import PlnPCAcollection, Pln, PlnPCA, ZIPln # pylint:disable=[C0114]
from .oaks import load_oaks
from .elbos import profiled_elbo_pln, elbo_plnpca, elbo_pln
from ._utils import (
--
GitLab
From 54294d51512374728a1e502b6d3fb4820c17f24b Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 11 Oct 2023 09:03:48 +0200
Subject: [PATCH 083/167] tried to import the ZI.
---
pyPLNmodels/models.py | 35 ++++++++++++++++++++++++++++++-----
1 file changed, 30 insertions(+), 5 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 64ddb104..7974a1e4 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -3382,7 +3382,7 @@ class ZIPln(_model):
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
add_const: bool = True,
- use_closed_form: bool = False,
+ use_closed_form_prob: bool = False,
):
super().__init__(
endog=endog,
@@ -3393,7 +3393,32 @@ class ZIPln(_model):
take_log_offsets=take_log_offsets,
add_const=add_const,
)
- self._use_closed_form = use_closed_form
+ self._use_closed_form_prob = use_closed_form_prob
+
+ def _extract_batch(self, batch):
+ super()._extract_batch(batch)
+ if self._use_closed_form_prob is False:
+ self._latent_prob_b = batch[5]
+
+ def _return_batch(self, indices, beginning, end):
+ pln_batch = super()._return_batch(indices, beginning, end)
+ if self._use_closed_form_prob is False:
+ return pln_batch + torch.index_select(self._latent_prob, 0, to_take)
+ return pln_batch
+
+ def _return_batch(self, indices, beginning, end):
+ to_take = torch.tensor(indices[beginning:end]).to(DEVICE)
+ if self._exog is not None:
+ exog_b = torch.index_select(self._exog, 0, to_take)
+ else:
+ exog_b = None
+ return (
+ torch.index_select(self._endog, 0, to_take),
+ exog_b,
+ torch.index_select(self._offsets, 0, to_take),
+ torch.index_select(self._latent_mean, 0, to_take),
+ torch.index_select(self._latent_sqrt_var, 0, to_take),
+ )
@classmethod
@_add_doc(
@@ -3439,7 +3464,7 @@ class ZIPln(_model):
example="""
>>> from pyPLNmodels import ZIPln, get_real_count_data
>>> endog = get_real_count_data()
- >>> zi = Pln(endog,add_const = True)
+ >>> zi = ZIPln(endog,add_const = True)
>>> zi.fit()
>>> print(zi)
""",
@@ -3493,7 +3518,7 @@ class ZIPln(_model):
# should change the good initialization for _coef_inflation
def _smart_init_model_parameters(self):
# init of _coef.
- super()._smart_init_model_parameters()
+ super()._smart_init_coef()
if not hasattr(self, "_covariance"):
self._components = _init_components(self._endog, self._exog, self.dim)
if not hasattr(self, "_coef_inflation"):
@@ -3619,7 +3644,7 @@ class ZIPln(_model):
self._components,
self._coef,
]
- if self._use_closed_form:
+ if self._use_closed_form_prob:
list_parameters.append(self._latent_prob)
if self._exog is not None:
list_parameters.append(self._coef)
--
GitLab
From f12ef25c56b9a6c527aadbb649ea61d407bd99c5 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 12 Oct 2023 10:25:12 +0200
Subject: [PATCH 084/167] finally add the right criterion
---
pyPLNmodels/_utils.py | 35 ++++++++++++++++++++--
pyPLNmodels/models.py | 67 ++++++++++++++++++-------------------------
2 files changed, 61 insertions(+), 41 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 7169b74d..582f859f 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -23,15 +23,46 @@ else:
DEVICE = torch.device("cpu")
-class _PlotArgs:
+BETA = 0.03
+
+
+class _CriterionArgs:
def __init__(self):
"""
Initialize the PlotArgs class.
+
+ Parameters
+ ----------
+ window : int
+ The size of the window for computing the criterion.
"""
self.running_times = []
- self.criterions = []
self._elbos_list = []
self.cumulative_elbo_list = [0]
+ self.new_derivative = 0
+ self.normalized_elbo_list = []
+ self.criterion_list = [1]
+
+ def update_criterion(self, elbo, running_time):
+ self._elbos_list.append(elbo)
+ self.running_times.append(running_time)
+ self.cumulative_elbo_list.append(self.cumulative_elbo + elbo)
+ self.normalized_elbo_list.append(-elbo / self.cumulative_elbo_list[-1])
+ if self.iteration_number > 1:
+ current_derivative = np.abs(
+ (self.normalized_elbo_list[-2] - self.normalized_elbo_list[-1])
+ / (self.running_times[-2] - self.running_times[-1])
+ )
+ old_derivative = self.new_derivative
+ self.new_derivative = (
+ self.new_derivative * (1 - BETA) + current_derivative * BETA
+ )
+ current_hessian = np.abs(
+ (self.new_derivative - self.old_derivative)
+ / (self.running_times[-2] - self.running_times[-1])
+ )
+ self.criterion = self.criterion * (1 - BETA) + current_hessian * BETA
+ self.criterion_list.append(self.criterion)
@property
def iteration_number(self) -> int:
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 7974a1e4..41cb12a8 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -22,7 +22,7 @@ from ._closed_forms import (
)
from .elbos import elbo_plnpca, elbo_zi_pln, profiled_elbo_pln
from ._utils import (
- _PlotArgs,
+ _CriterionArgs,
_format_data,
_nice_string_of_dict,
_plot_ellipse,
@@ -111,7 +111,7 @@ class _model(ABC):
endog, exog, offsets, offsets_formula, take_log_offsets, add_const
)
self._fitted = False
- self._plotargs = _PlotArgs()
+ self._criterion_args = _CriterionArgs()
if dict_initialization is not None:
self._set_init_parameters(dict_initialization)
@@ -270,7 +270,7 @@ class _model(ABC):
int
The number of iterations done.
"""
- return len(self._plotargs._elbos_list) * self.nb_batches
+ return len(self._criterion_args._elbos_list) * self.nb_batches
@property
def n_samples(self) -> int:
@@ -385,7 +385,7 @@ class _model(ABC):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- tol: float = 1e-3,
+ tol: float = 1e-8,
do_smart_init: bool = True,
verbose: bool = False,
batch_size=None,
@@ -400,7 +400,7 @@ class _model(ABC):
lr : float, optional(keyword-only)
The learning rate. Defaults to 0.01.
tol : float, optional(keyword-only)
- The tolerance for convergence. Defaults to 1e-3.
+ The tolerance for convergence. Defaults to 1e-8.
do_smart_init : bool, optional(keyword-only)
Whether to perform smart initialization. Defaults to True.
verbose : bool, optional(keyword-only)
@@ -414,14 +414,14 @@ class _model(ABC):
self._batch_size = self._handle_batch_size(batch_size)
if self._fitted is False:
self._init_parameters(do_smart_init)
- elif len(self._plotargs.running_times) > 0:
- self._beginning_time -= self._plotargs.running_times[-1]
+ elif len(self._criterion_args.running_times) > 0:
+ self._beginning_time -= self._criterion_args.running_times[-1]
self._put_parameters_to_device()
self._handle_optimizer(lr)
stop_condition = False
while self.nb_iteration_done < nb_max_iteration and not stop_condition:
loss = self._trainstep()
- criterion = self._compute_criterion_and_update_plotargs(loss, tol)
+ criterion = self._update_criterion_args(loss)
if abs(criterion) < tol:
stop_condition = True
if verbose and self.nb_iteration_done % 50 == 1:
@@ -711,14 +711,14 @@ class _model(ABC):
if stop_condition is True:
print(
f"Tolerance {tol} reached "
- f"in {self._plotargs.iteration_number} iterations"
+ f"in {self._criterion_args.iteration_number} iterations"
)
else:
print(
"Maximum number of iterations reached : ",
- self._plotargs.iteration_number,
+ self._criterion_args.iteration_number,
"last criterion = ",
- np.round(self._plotargs.criterions[-1], 8),
+ np.round(self._criterion_args.criterions[-1], 8),
)
def _print_stats(self):
@@ -726,11 +726,11 @@ class _model(ABC):
Print the training statistics.
"""
print("-------UPDATE-------")
- print("Iteration number: ", self._plotargs.iteration_number)
- print("Criterion: ", np.round(self._plotargs.criterions[-1], 8))
- print("ELBO:", np.round(self._plotargs._elbos_list[-1], 6))
+ print("Iteration number: ", self._criterion_args.iteration_number)
+ print("Criterion: ", np.round(self._criterion_args.criterions[-1], 8))
+ print("ELBO:", np.round(self._criterion_args._elbos_list[-1], 6))
- def _compute_criterion_and_update_plotargs(self, loss, tol):
+ def _update_criterion_args(self, loss):
"""
Compute the convergence criterion and update the plot arguments.
@@ -738,26 +738,15 @@ class _model(ABC):
----------
loss : torch.Tensor
The loss value.
- tol : float
- The tolerance for convergence.
Returns
-------
float
The computed criterion.
"""
- self._plotargs._elbos_list.append(-loss)
- self._plotargs.running_times.append(time.time() - self._beginning_time)
- elbo = -loss
- self._plotargs.cumulative_elbo_list.append(
- self._plotargs.cumulative_elbo + elbo
- )
- criterion = (
- self._plotargs.cumulative_elbo_list[-2]
- - self._plotargs.cumulative_elbo_list[-1]
- ) / self._plotargs.cumulative_elbo_list[-1]
- self._plotargs.criterions.append(criterion)
- return criterion
+ current_running_time = time.time() - self._beginning_time
+ self._criterion_args.update_criterion(-loss, current_running_time)
+ return self._criterion_args.criterion
def _update_closed_forms(self):
"""
@@ -858,8 +847,8 @@ class _model(ABC):
if axes is None:
_, axes = plt.subplots(1, nb_axes, figsize=(23, 5))
if self._fitted is True:
- self._plotargs._show_loss(ax=axes[2])
- self._plotargs._show_stopping_criterion(ax=axes[1])
+ self._criterion_args._show_loss(ax=axes[2])
+ self._criterion_args._show_stopping_criterion(ax=axes[1])
self.display_covariance(ax=axes[0])
else:
self.display_covariance(ax=axes)
@@ -870,7 +859,7 @@ class _model(ABC):
"""
Property representing the list of ELBO values.
"""
- return self._plotargs._elbos_list
+ return self._criterion_args._elbos_list
@property
def loglike(self):
@@ -884,8 +873,8 @@ class _model(ABC):
"""
if len(self._elbos_list) == 0:
t0 = time.time()
- self._plotargs._elbos_list.append(self.compute_elbo().item())
- self._plotargs.running_times.append(time.time() - t0)
+ self._criterion_args._elbos_list.append(self.compute_elbo().item())
+ self._criterion_args.running_times.append(time.time() - t0)
return self.n_samples * self._elbos_list[-1]
@property
@@ -1512,7 +1501,7 @@ class Pln(_model):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- tol: float = 1e-3,
+ tol: float = 1e-8,
do_smart_init: bool = True,
verbose: bool = False,
batch_size: int = None,
@@ -2267,7 +2256,7 @@ class PlnPCAcollection:
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- tol: float = 1e-3,
+ tol: float = 1e-8,
do_smart_init: bool = True,
verbose: bool = False,
batch_size: int = None,
@@ -2282,7 +2271,7 @@ class PlnPCAcollection:
lr : float, optional(keyword-only)
The learning rate, by default 0.01.
tol : float, optional(keyword-only)
- The tolerance, by default 1e-3.
+ The tolerance, by default 1e-8.
do_smart_init : bool, optional(keyword-only)
Whether to do smart initialization, by default True.
verbose : bool, optional(keyword-only)
@@ -2795,7 +2784,7 @@ class PlnPCA(_model):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- tol: float = 1e-3,
+ tol: float = 1e-8,
do_smart_init: bool = True,
verbose: bool = False,
batch_size=None,
@@ -3474,7 +3463,7 @@ class ZIPln(_model):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- tol: float = 1e-3,
+ tol: float = 1e-8,
do_smart_init: bool = True,
verbose: bool = False,
batch_size: int = None,
--
GitLab
From 583f638d20e6da4453f43ed4c17deaa417189e31 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 12 Oct 2023 10:36:39 +0200
Subject: [PATCH 085/167] minor changes.
---
pyPLNmodels/_utils.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 582f859f..053a6448 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -42,6 +42,7 @@ class _CriterionArgs:
self.new_derivative = 0
self.normalized_elbo_list = []
self.criterion_list = [1]
+ self.criterion = 1
def update_criterion(self, elbo, running_time):
self._elbos_list.append(elbo)
@@ -58,7 +59,7 @@ class _CriterionArgs:
self.new_derivative * (1 - BETA) + current_derivative * BETA
)
current_hessian = np.abs(
- (self.new_derivative - self.old_derivative)
+ (self.new_derivative - old_derivative)
/ (self.running_times[-2] - self.running_times[-1])
)
self.criterion = self.criterion * (1 - BETA) + current_hessian * BETA
--
GitLab
From 12842130989f4d49a681334cda57040d167fc05a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 12 Oct 2023 10:42:16 +0200
Subject: [PATCH 086/167] error
---
pyPLNmodels/models.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 41cb12a8..04ed41de 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -718,7 +718,7 @@ class _model(ABC):
"Maximum number of iterations reached : ",
self._criterion_args.iteration_number,
"last criterion = ",
- np.round(self._criterion_args.criterions[-1], 8),
+ np.round(self._criterion_args.criterion_list[-1], 8),
)
def _print_stats(self):
@@ -727,7 +727,7 @@ class _model(ABC):
"""
print("-------UPDATE-------")
print("Iteration number: ", self._criterion_args.iteration_number)
- print("Criterion: ", np.round(self._criterion_args.criterions[-1], 8))
+ print("Criterion: ", np.round(self._criterion_args.criterion_list[-1], 8))
print("ELBO:", np.round(self._criterion_args._elbos_list[-1], 6))
def _update_criterion_args(self, loss):
--
GitLab
From 004b8f678d7dc90b2f63ebfe74b266528cb8d17f Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 13 Oct 2023 09:31:54 +0200
Subject: [PATCH 087/167] add needed abstract methods to implement a new model.
---
pyPLNmodels/models.py | 67 ++++++++++++++++++++++++-------------------
1 file changed, 38 insertions(+), 29 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 04ed41de..e8970fec 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -370,6 +370,7 @@ class _model(ABC):
parameter.requires_grad_(True)
@property
+ @abstractmethod
def _list_of_parameters_needing_gradient(self):
"""
A list containing all the parameters that need to be upgraded via a gradient step.
@@ -380,6 +381,41 @@ class _model(ABC):
List of parameters needing gradient.
"""
+ def _print_beginning_message(self) -> str:
+ """
+ Method for printing the beginning message.
+ """
+ print(f"Fitting a {self._NAME} model with {self._description} \n")
+
+ @abstractmethod
+ def _endog_predictions(self):
+ pass
+
+ @abstractmethod
+ def number_of_parameters(self):
+ pass
+
+ @abstractmethod
+ def _compute_elbo_b(self):
+ pass
+
+ @property
+ @abstractmethod
+ def covariance(self):
+ pass
+
+ @covariance.setter
+ @abstractmethod
+ def covariance(self, covariance):
+ pass
+
+ @property
+ @abstractmethod
+ def _description(self):
+ """
+ Describes the model and what it does.
+ """
+
def fit(
self,
nb_max_iteration: int = 50000,
@@ -1793,12 +1829,6 @@ class Pln(_model):
covariances = components_var @ (sk_components.T.unsqueeze(0))
return covariances
- def _print_beginning_message(self):
- """
- Method for printing the beginning message.
- """
- print(f"Fitting a Pln model with {self._description}")
-
@property
@_add_doc(
_model,
@@ -2216,17 +2246,6 @@ class PlnPCAcollection:
"""
return [model.rank for model in self.values()]
- def _print_beginning_message(self) -> str:
- """
- Method for printing the beginning message.
-
- Returns
- -------
- str
- The beginning message.
- """
- return f"Adjusting {len(self.ranks)} Pln models for PCA analysis \n"
-
@property
def dim(self) -> int:
"""
@@ -3045,13 +3064,6 @@ class PlnPCA(_model):
"""
return self._rank
- def _print_beginning_message(self):
- """
- Print the beginning message when fitted.
- """
- print("-" * NB_CHARACTERS_FOR_NICE_PLOT)
- print(f"Fitting a PlnPCA model with {self._rank} components")
-
@property
def model_parameters(self) -> Dict[str, torch.Tensor]:
"""
@@ -3235,7 +3247,7 @@ class PlnPCA(_model):
@property
def _description(self) -> str:
"""
- Property representing the description.
+ Description output when fitting and printing the model.
Returns
-------
@@ -3496,7 +3508,7 @@ class ZIPln(_model):
@property
def _description(self):
- return "with full covariance model and zero-inflation."
+ return " full covariance model and zero-inflation."
def _random_init_model_parameters(self):
super()._random_init_model_parameters()
@@ -3513,9 +3525,6 @@ class ZIPln(_model):
if not hasattr(self, "_coef_inflation"):
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
- def _print_beginning_message(self):
- print("Fitting a ZIPln model.")
-
def _random_init_latent_parameters(self):
self._dirac = self._endog == 0
self._latent_mean = torch.randn(self.n_samples, self.dim)
--
GitLab
From 7f02282d1fa552d20ff0086f53b3d51814dfa779 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 13 Oct 2023 12:42:27 +0200
Subject: [PATCH 088/167] rrange the abstract methods.
---
pyPLNmodels/models.py | 543 +++++++++++++++++++++---------------------
1 file changed, 271 insertions(+), 272 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 41cb12a8..d89c9025 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -324,19 +324,7 @@ class _model(ABC):
self._coef = None
self._coef = torch.randn((self.nb_cov, self.dim), device=DEVICE)
- @abstractmethod
- def _random_init_model_parameters(self):
- """
- Abstract method to randomly initialize model parameters.
- """
- pass
- @abstractmethod
- def _random_init_latent_parameters(self):
- """
- Abstract method to randomly initialize latent parameters.
- """
- pass
def _smart_init_latent_parameters(self):
"""
@@ -369,16 +357,6 @@ class _model(ABC):
for parameter in self._list_of_parameters_needing_gradient:
parameter.requires_grad_(True)
- @property
- def _list_of_parameters_needing_gradient(self):
- """
- A list containing all the parameters that need to be upgraded via a gradient step.
-
- Returns
- -------
- List[torch.Tensor]
- List of parameters needing gradient.
- """
def fit(
self,
@@ -579,7 +557,7 @@ class _model(ABC):
return pca
@property
- def latent_var(self) -> torch.Tensor:
+ def latent_variance(self) -> torch.Tensor:
"""
Property representing the latent variance.
@@ -689,13 +667,18 @@ class _model(ABC):
)
plt.show()
+
@property
- @abstractmethod
- def latent_variables(self):
+ def _latent_var(self) -> torch.Tensor:
"""
- Abstract property representing the latent variables.
+ Property representing the latent variance.
+
+ Returns
+ -------
+ torch.Tensor
+ The latent variance tensor.
"""
- pass
+ return self._latent_sqrt_var**2
def _print_end_of_fitting_message(self, stop_condition: bool, tol: float):
"""
@@ -754,13 +737,6 @@ class _model(ABC):
"""
pass
- @abstractmethod
- def compute_elbo(self):
- """
- Compute the Evidence Lower BOund (ELBO) that will be maximized
- by pytorch.
- """
- pass
def display_covariance(self, ax=None, savefig=False, name_file=""):
"""
@@ -1385,8 +1361,74 @@ class _model(ABC):
ax.legend()
return ax
+ @property
+ @abstractmethod
+ def latent_variables(self) -> torch.Tensor:
+ """
+ Property representing the latent variables.
+
+ Returns
+ -------
+ torch.Tensor
+ The latent variables of size (n_samples, dim).
+ """
+
+ @abstractmethod
+ def compute_elbo(self):
+ """
+ Compute the Evidence Lower BOund (ELBO) that will be maximized
+ by pytorch.
+
+ Returns
+ -------
+ torch.Tensor
+ The computed ELBO.
+ """
+
+ @abstractmethod
+ def _compute_elbo_b(self):
+ """
+ Compute the Evidence Lower BOund (ELBO) for the current mini-batch.
+ Returns
+ -------
+ torch.Tensor
+ The computed ELBO on the current batch.
+ """
+
+ @abstractmethod
+ def _random_init_model_parameters(self):
+ """
+ Abstract method to randomly initialize model parameters.
+ """
+
+ @abstractmethod
+ def _random_init_latent_parameters(self):
+ """
+ Abstract method to randomly initialize latent parameters.
+ """
+ @abstractmethod
+ def _smart_init_latent_parameters(self):
+ """
+ Method for smartly initializing the latent parameters.
+ """
+ @abstractmethod
+ def _smart_init_model_parameters(self):
+ """
+ Method for smartly initializing the model parameters.
+ """
+
+ @property
+ @abstractmethod
+ def _list_of_parameters_needing_gradient(self):
+ """
+ A list containing all the parameters that need to be upgraded via a gradient step.
+
+ Returns
+ -------
+ List[torch.Tensor]
+ List of parameters needing gradient.
+ """
-# need to do a good init for M and S
class Pln(_model):
"""
Pln class.
@@ -1661,34 +1703,6 @@ class Pln(_model):
self._offsets + self._latent_mean + 1 / 2 * self._latent_sqrt_var**2
)
- def _smart_init_latent_parameters(self):
- """
- Method for smartly initializing the latent parameters.
- """
- self._random_init_latent_parameters()
-
- def _random_init_latent_parameters(self):
- """
- Method for randomly initializing the latent parameters.
- """
- if not hasattr(self, "_latent_sqrt_var"):
- self._latent_sqrt_var = (
- 1 / 2 * torch.ones((self.n_samples, self.dim)).to(DEVICE)
- )
- if not hasattr(self, "_latent_mean"):
- self._latent_mean = torch.ones((self.n_samples, self.dim)).to(DEVICE)
-
- @property
- def _list_of_parameters_needing_gradient(self):
- """
- Property representing the list of parameters needing gradient.
-
- Returns
- -------
- list
- The list of parameters needing gradient.
- """
- return [self._latent_mean, self._latent_sqrt_var]
def _get_max_components(self):
"""
@@ -1701,60 +1715,6 @@ class Pln(_model):
"""
return self.dim
- def compute_elbo(self):
- """
- Method for computing the evidence lower bound (ELBO).
-
- Returns
- -------
- torch.Tensor
- The computed ELBO.
- Examples
- --------
- >>> from pyPLNmodels import Pln, get_real_count_data
- >>> endog, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(endog,add_const = True)
- >>> pln.fit()
- >>> elbo = pln.compute_elbo()
- >>> print("elbo", elbo)
- >>> print("loglike/n", pln.loglike/pln.n_samples)
- """
- return profiled_elbo_pln(
- self._endog,
- self._exog,
- self._offsets,
- self._latent_mean,
- self._latent_sqrt_var,
- )
-
- def _compute_elbo_b(self):
- """
- Method for computing the evidence lower bound (ELBO) on the current batch.
-
- Returns
- -------
- torch.Tensor
- The computed ELBO on the current batch.
- """
- return profiled_elbo_pln(
- self._endog_b,
- self._exog_b,
- self._offsets_b,
- self._latent_mean_b,
- self._latent_sqrt_var_b,
- )
-
- def _smart_init_model_parameters(self):
- """
- Method for smartly initializing the model parameters.
- """
- # no model parameters since we are doing a profiled ELBO
-
- def _random_init_model_parameters(self):
- """
- Method for randomly initializing the model parameters.
- """
- # no model parameters since we are doing a profiled ELBO
@property
def _coef(self):
@@ -1799,19 +1759,6 @@ class Pln(_model):
"""
print(f"Fitting a Pln model with {self._description}")
- @property
- @_add_doc(
- _model,
- example="""
- >>> from pyPLNmodels import Pln, get_real_count_data
- >>> endog, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(endog,add_const = True)
- >>> pln.fit()
- >>> print(pln.latent_variables.shape)
- """,
- )
- def latent_variables(self):
- return self.latent_mean.detach()
@property
def number_of_parameters(self):
@@ -1861,6 +1808,80 @@ class Pln(_model):
"""
raise AttributeError("You can not set the covariance for the Pln model.")
+ def _random_init_latent_sqrt_var(self):
+ if not hasattr(self, "_latent_sqrt_var"):
+ self._latent_sqrt_var = (
+ 1 / 2 * torch.ones((self.n_samples, self.dim)).to(DEVICE)
+ )
+
+ @property
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(endog,add_const = True)
+ >>> pln.fit()
+ >>> print(pln.latent_variables.shape)
+ """,
+ )
+ def latent_variables(self):
+ return self.latent_mean.detach()
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(endog,add_const = True)
+ >>> pln.fit()
+ >>> elbo = pln.compute_elbo()
+ >>> print("elbo", elbo)
+ >>> print("loglike/n", pln.loglike/pln.n_samples)
+ """
+ )
+ def compute_elbo(self):
+ return profiled_elbo_pln(
+ self._endog,
+ self._exog,
+ self._offsets,
+ self._latent_mean,
+ self._latent_sqrt_var,
+ )
+ @_add_doc(_model)
+ def _compute_elbo_b(self):
+ return profiled_elbo_pln(
+ self._endog_b,
+ self._exog_b,
+ self._offsets_b,
+ self._latent_mean_b,
+ self._latent_sqrt_var_b,
+ )
+ @_add_doc(_model)
+ def _smart_init_model_parameters(self):
+ pass
+ # no model parameters since we are doing a profiled ELBO
+
+ @_add_doc(_model)
+ def _random_init_model_parameters(self):
+ pass
+ # no model parameters since we are doing a profiled ELBO
+ @_add_doc(_model)
+ def _smart_init_latent_parameters(self):
+ self._random_init_latent_sqrt_var()
+ if not hasattr(self, "_latent_mean"):
+ self._latent_mean = torch.log(self._endog + (self._endog == 0))
+
+ @_add_doc(_model)
+ def _random_init_latent_parameters(self):
+ self._random_init_latent_sqrt_var()
+ if not hasattr(self, "_latent_mean"):
+ self._latent_mean = torch.ones((self.n_samples, self.dim)).to(DEVICE)
+
+ @_add_doc(_model)
+ @property
+ def _list_of_parameters_needing_gradient(self):
+ return [self._latent_mean, self._latent_sqrt_var]
class PlnPCAcollection:
"""
@@ -2655,7 +2676,7 @@ class PlnPCAcollection:
return ".BIC, .AIC, .loglikes"
-# Here, setting the value for each key in _dict_parameters
+# Here, setting the value for each key _dict_parameters
class PlnPCA(_model):
"""
PlnPCA object where the covariance has low rank.
@@ -2881,19 +2902,6 @@ class PlnPCA(_model):
variables_names=variables_names, indices_of_variables=indices_of_variables
)
- def _check_if_rank_is_too_high(self):
- """
- Check if the rank is too high and issue a warning if necessary.
- """
- if self.dim < self.rank:
- warning_string = (
- f"\nThe requested rank of approximation {self.rank} "
- f"is greater than the number of variables {self.dim}. "
- f"Setting rank to {self.dim}"
- )
- warnings.warn(warning_string)
- self._rank = self.dim
-
@property
@_add_doc(
_model,
@@ -2909,29 +2917,7 @@ class PlnPCA(_model):
def latent_mean(self) -> torch.Tensor:
return self._cpu_attribute_or_none("_latent_mean")
- @property
- def latent_sqrt_var(self) -> torch.Tensor:
- """
- Property representing the unsigned square root of the latent variance.
-
- Returns
- -------
- torch.Tensor
- The latent variance tensor.
- """
- return self._cpu_attribute_or_none("_latent_sqrt_var")
-
- @property
- def _latent_var(self) -> torch.Tensor:
- """
- Property representing the latent variance.
- Returns
- -------
- torch.Tensor
- The latent variance tensor.
- """
- return self._latent_sqrt_var**2
def _endog_predictions(self):
covariance_a_posteriori = torch.sum(
@@ -3064,103 +3050,9 @@ class PlnPCA(_model):
"""
return {"coef": self.coef, "components": self.components}
- def _smart_init_model_parameters(self):
- """
- Initialize the model parameters smartly.
- """
- if not hasattr(self, "_coef"):
- super()._smart_init_coef()
- if not hasattr(self, "_components"):
- self._components = _init_components(self._endog, self._exog, self._rank)
- def _random_init_model_parameters(self):
- """
- Randomly initialize the model parameters.
- """
- super()._random_init_coef()
- self._components = torch.randn((self.dim, self._rank)).to(DEVICE)
- def _random_init_latent_parameters(self):
- """
- Randomly initialize the latent parameters.
- """
- self._latent_sqrt_var = (
- 1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
- )
- self._latent_mean = torch.ones((self.n_samples, self._rank)).to(DEVICE)
- def _smart_init_latent_parameters(self):
- """
- Initialize the latent parameters smartly.
- """
- if not hasattr(self, "_latent_mean"):
- self._latent_mean = (
- _init_latent_mean(
- self._endog,
- self._exog,
- self._offsets,
- self._coef,
- self._components,
- )
- .to(DEVICE)
- .detach()
- )
- if not hasattr(self, "_latent_sqrt_var"):
- self._latent_sqrt_var = (
- 1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
- )
-
- @property
- def _list_of_parameters_needing_gradient(self):
- """
- Property representing the list of parameters needing gradient.
-
- Returns
- -------
- List[torch.Tensor]
- The list of parameters needing gradient.
- """
- if self._coef is None:
- return [self._components, self._latent_mean, self._latent_sqrt_var]
- return [self._components, self._coef, self._latent_mean, self._latent_sqrt_var]
-
- def _compute_elbo_b(self) -> torch.Tensor:
- """
- Compute the evidence lower bound (ELBO) with the current batch.
-
- Returns
- -------
- torch.Tensor
- The ELBO value on the current batch.
- """
- return elbo_plnpca(
- self._endog_b,
- self._exog_b,
- self._offsets_b,
- self._latent_mean_b,
- self._latent_sqrt_var_b,
- self._components,
- self._coef,
- )
-
- def compute_elbo(self) -> torch.Tensor:
- """
- Compute the evidence lower bound (ELBO).
-
- Returns
- -------
- torch.Tensor
- The ELBO value.
- """
- return elbo_plnpca(
- self._endog,
- self._exog,
- self._offsets,
- self._latent_mean,
- self._latent_sqrt_var,
- self._components,
- self._coef,
- )
@property
def number_of_parameters(self) -> int:
@@ -3244,17 +3136,6 @@ class PlnPCA(_model):
"""
return f" {self.rank} principal component."
- @property
- def latent_variables(self) -> torch.Tensor:
- """
- Property representing the latent variables.
-
- Returns
- -------
- torch.Tensor
- The latent variables of size (n_samples, dim).
- """
- return torch.matmul(self._latent_mean, self._components.T).detach()
@property
def projected_latent_variables(self) -> torch.Tensor:
@@ -3337,6 +3218,100 @@ class PlnPCA(_model):
return self.projected_latent_variables
return self.latent_variables
+ @property
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> endog = get_real_count_data(return_labels=False)
+ >>> pca = PlnPCA(endog,add_const = True)
+ >>> pca.fit()
+ >>> print(pca.latent_variables.shape)
+ """,
+ )
+ def latent_variables(self) -> torch.Tensor:
+ return torch.matmul(self._latent_mean, self._components.T).detach()
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> endog = get_real_count_data(return_labels = False)
+ >>> pca = PlnPCA(endog,add_const = True)
+ >>> pca.fit()
+ >>> elbo = pca.compute_elbo()
+ >>> print("elbo", elbo)
+ >>> print("loglike/n", pln.loglike/pln.n_samples)
+ """
+ )
+ def compute_elbo(self) -> torch.Tensor:
+ return elbo_plnpca(
+ self._endog,
+ self._exog,
+ self._offsets,
+ self._latent_mean,
+ self._latent_sqrt_var,
+ self._components,
+ self._coef,
+ )
+ @_add_doc(_model)
+ def _compute_elbo_b(self) -> torch.Tensor:
+ return elbo_plnpca(
+ self._endog_b,
+ self._exog_b,
+ self._offsets_b,
+ self._latent_mean_b,
+ self._latent_sqrt_var_b,
+ self._components,
+ self._coef,
+ )
+ @_add_doc(_model)
+ def _random_init_model_parameters(self):
+ super()._random_init_coef()
+ self._components = torch.randn((self.dim, self._rank)).to(DEVICE)
+
+ @_add_doc(_model)
+ def _smart_init_model_parameters(self):
+ if not hasattr(self, "_coef"):
+ super()._smart_init_coef()
+ if not hasattr(self, "_components"):
+ self._components = _init_components(self._endog, self._exog, self._rank)
+
+ @_add_doc(_model)
+ def _random_init_latent_parameters(self):
+ """
+ Randomly initialize the latent parameters.
+ """
+ self._latent_sqrt_var = (
+ 1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
+ )
+ self._latent_mean = torch.ones((self.n_samples, self._rank)).to(DEVICE)
+
+ @_add_doc(_model)
+ def _smart_init_latent_parameters(self):
+ if not hasattr(self, "_latent_mean"):
+ self._latent_mean = (
+ _init_latent_mean(
+ self._endog,
+ self._exog,
+ self._offsets,
+ self._coef,
+ self._components,
+ )
+ .to(DEVICE)
+ .detach()
+ )
+ if not hasattr(self, "_latent_sqrt_var"):
+ self._latent_sqrt_var = (
+ 1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
+ )
+
+ @_add_doc(_model)
+ @property
+ def _list_of_parameters_needing_gradient(self):
+ if self._coef is None:
+ return [self._components, self._latent_mean, self._latent_sqrt_var]
+ return [self._components, self._coef, self._latent_mean, self._latent_sqrt_var]
class ZIPln(_model):
_NAME = "ZIPln"
@@ -3347,6 +3322,10 @@ class ZIPln(_model):
@_add_doc(
_model,
+ params= """
+ use_closed_form_prob: bool, optional
+ Whether or not use the closed formula for the latent probability
+ """
example="""
>>> from pyPLNmodels import ZIPln, get_real_count_data
>>> endog= get_real_count_data()
@@ -3532,7 +3511,26 @@ class ZIPln(_model):
def _covariance(self):
return self._components @ (self._components.T)
- def latent_variables(self):
+ def latent_variables(self) -> tuple(torch.Tensor, torch.Tensor):
+ """
+ Property representing the latent variables. Two latent
+ variables are available if exog is not None
+
+ Returns
+ -------
+ tuple(torch.Tensor, torch.Tensor)
+ The latent variables of a classic Pln model (size (n_samples, dim))
+ and zero inflated latent variables of size (n_samples, dim).
+ Examples
+ --------
+ >>> from pyPLNmodels import ZIPln, get_real_count_data
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> zi = ZIPln(endog,add_const = True)
+ >>> zi.fit()
+ >>> latent_mean, latent_inflated = zi.latent_variables
+ >>> print(latent_mean.shape)
+ >>> print(latent_inflated.shape)
+ """
return self.latent_mean, self.latent_prob
def _update_parameters(self):
@@ -3624,6 +3622,7 @@ class ZIPln(_model):
def number_of_parameters(self):
return self.dim * (2 * self.nb_cov + (self.dim + 1) / 2)
+ @_add_doc(_model)
@property
def _list_of_parameters_needing_gradient(self):
list_parameters = [
--
GitLab
From cb994071627c65cd85c4ab0af189b9859e14662f Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 13 Oct 2023 12:44:34 +0200
Subject: [PATCH 089/167] beginning
---
pyPLNmodels/new_model.py | 9 +++++++++
1 file changed, 9 insertions(+)
create mode 100644 pyPLNmodels/new_model.py
diff --git a/pyPLNmodels/new_model.py b/pyPLNmodels/new_model.py
new file mode 100644
index 00000000..2d4acd45
--- /dev/null
+++ b/pyPLNmodels/new_model.py
@@ -0,0 +1,9 @@
+from pyPLNmodels import ZIPln, get_real_count_data
+
+
+endog = get_real_count_data()
+zi = ZIPln(endog, add_const = True)
+zi.fit(nb_max_iteration = 10)
+zi.show()
+
+
--
GitLab
From d6e31bdf76c42a56b2e85b2021858752d03b5f5e Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 13 Oct 2023 13:21:08 +0200
Subject: [PATCH 090/167] fixed compilation erros.
---
pyPLNmodels/models.py | 61 ++++++++++++++++++++++---------------------
1 file changed, 31 insertions(+), 30 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index d89c9025..905b465b 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -1002,30 +1002,6 @@ class _model(ABC):
)
self._latent_mean = latent_mean
- @latent_sqrt_var.setter
- @_array2tensor
- def latent_sqrt_var(
- self, latent_sqrt_var: Union[torch.Tensor, np.ndarray, pd.DataFrame]
- ):
- """
- Setter for the latent variance property.
-
- Parameters
- ----------
- latent_sqrt_var : Union[torch.Tensor, np.ndarray, pd.DataFrame]
- The latent variance.
-
- Raises
- ------
- ValueError
- If the shape of the latent variance is incorrect.
- """
- if latent_sqrt_var.shape != (self.n_samples, self.dim):
- raise ValueError(
- f"Wrong shape. Expected {self.n_samples, self.dim}, got {latent_sqrt_var.shape}"
- )
- self._latent_sqrt_var = latent_sqrt_var
-
def _cpu_attribute_or_none(self, attribute_name):
"""
Get the CPU attribute or return None.
@@ -1760,6 +1736,31 @@ class Pln(_model):
print(f"Fitting a Pln model with {self._description}")
+
+ @_model.latent_sqrt_var.setter
+ @_array2tensor
+ def latent_sqrt_var(
+ self, latent_sqrt_var: Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ ):
+ """
+ Setter for the latent variance property.
+
+ Parameters
+ ----------
+ latent_sqrt_var : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The latent variance.
+
+ Raises
+ ------
+ ValueError
+ If the shape of the latent variance is incorrect.
+ """
+ if latent_sqrt_var.shape != (self.n_samples, self.dim):
+ raise ValueError(
+ f"Wrong shape. Expected {self.n_samples, self.dim}, got {latent_sqrt_var.shape}"
+ )
+ self._latent_sqrt_var = latent_sqrt_var
+
@property
def number_of_parameters(self):
"""
@@ -1878,8 +1879,8 @@ class Pln(_model):
if not hasattr(self, "_latent_mean"):
self._latent_mean = torch.ones((self.n_samples, self.dim)).to(DEVICE)
- @_add_doc(_model)
@property
+ @_add_doc(_model)
def _list_of_parameters_needing_gradient(self):
return [self._latent_mean, self._latent_sqrt_var]
@@ -2950,7 +2951,7 @@ class PlnPCA(_model):
)
self._latent_mean = latent_mean
- @latent_sqrt_var.setter
+ @_model.latent_sqrt_var.setter
@_array2tensor
def latent_sqrt_var(self, latent_sqrt_var: torch.Tensor):
"""
@@ -3306,8 +3307,8 @@ class PlnPCA(_model):
1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
)
- @_add_doc(_model)
@property
+ @_add_doc(_model)
def _list_of_parameters_needing_gradient(self):
if self._coef is None:
return [self._components, self._latent_mean, self._latent_sqrt_var]
@@ -3325,7 +3326,7 @@ class ZIPln(_model):
params= """
use_closed_form_prob: bool, optional
Whether or not use the closed formula for the latent probability
- """
+ """,
example="""
>>> from pyPLNmodels import ZIPln, get_real_count_data
>>> endog= get_real_count_data()
@@ -3511,7 +3512,7 @@ class ZIPln(_model):
def _covariance(self):
return self._components @ (self._components.T)
- def latent_variables(self) -> tuple(torch.Tensor, torch.Tensor):
+ def latent_variables(self) -> tuple([torch.Tensor, torch.Tensor]):
"""
Property representing the latent variables. Two latent
variables are available if exog is not None
@@ -3622,8 +3623,8 @@ class ZIPln(_model):
def number_of_parameters(self):
return self.dim * (2 * self.nb_cov + (self.dim + 1) / 2)
- @_add_doc(_model)
@property
+ @_add_doc(_model)
def _list_of_parameters_needing_gradient(self):
list_parameters = [
self._latent_mean,
--
GitLab
From ce5cb6b0e6740d41c92acb213f5f4d7173c078ba Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 13 Oct 2023 13:23:15 +0200
Subject: [PATCH 091/167] multiple same parameters in list_of_parameters of the
zi
---
pyPLNmodels/models.py | 2 --
1 file changed, 2 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 905b465b..47231871 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -3629,9 +3629,7 @@ class ZIPln(_model):
list_parameters = [
self._latent_mean,
self._latent_sqrt_var,
- self._coef_inflation,
self._components,
- self._coef,
]
if self._use_closed_form_prob:
list_parameters.append(self._latent_prob)
--
GitLab
From 4636aae36ece272d908bf5116c25ffe1ba76797f Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 13 Oct 2023 13:26:54 +0200
Subject: [PATCH 092/167] error when returning batches of zi
---
pyPLNmodels/models.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 47231871..f5d356ae 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -3372,7 +3372,7 @@ class ZIPln(_model):
def _return_batch(self, indices, beginning, end):
pln_batch = super()._return_batch(indices, beginning, end)
if self._use_closed_form_prob is False:
- return pln_batch + torch.index_select(self._latent_prob, 0, to_take)
+ return (pln_batch + torch.index_select(self._latent_prob, 0, to_take))
return pln_batch
def _return_batch(self, indices, beginning, end):
--
GitLab
From e9b8a2de5f6771ef1908c03758abfc2c19165db7 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 13 Oct 2023 13:42:49 +0200
Subject: [PATCH 093/167] zi can be fitted now.
---
pyPLNmodels/_utils.py | 2 +-
pyPLNmodels/models.py | 38 ++++++++++++++------------------------
2 files changed, 15 insertions(+), 25 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 053a6448..f5f02942 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -112,7 +112,7 @@ class _CriterionArgs:
ax = plt.gca() if ax is None else ax
ax.plot(
self.running_times,
- self.criterions,
+ self.criterion_list,
label="Delta",
)
ax.set_yscale("log")
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index f5d356ae..a5ece2aa 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -701,7 +701,7 @@ class _model(ABC):
"Maximum number of iterations reached : ",
self._criterion_args.iteration_number,
"last criterion = ",
- np.round(self._criterion_args.criterions[-1], 8),
+ np.round(self._criterion_args.criterion_list[-1], 8),
)
def _print_stats(self):
@@ -710,7 +710,7 @@ class _model(ABC):
"""
print("-------UPDATE-------")
print("Iteration number: ", self._criterion_args.iteration_number)
- print("Criterion: ", np.round(self._criterion_args.criterions[-1], 8))
+ print("Criterion: ", np.round(self._criterion_args.criterion_list[-1], 8))
print("ELBO:", np.round(self._criterion_args._elbos_list[-1], 6))
def _update_criterion_args(self, loss):
@@ -3366,28 +3366,18 @@ class ZIPln(_model):
def _extract_batch(self, batch):
super()._extract_batch(batch)
+ self._dirac_b = batch[5]
if self._use_closed_form_prob is False:
- self._latent_prob_b = batch[5]
+ self._latent_prob_b = batch[6]
def _return_batch(self, indices, beginning, end):
pln_batch = super()._return_batch(indices, beginning, end)
+ to_take = torch.tensor(indices[beginning:end]).to(DEVICE)
+ batch = pln_batch + (torch.index_select(self._dirac, 0, to_take),)
if self._use_closed_form_prob is False:
- return (pln_batch + torch.index_select(self._latent_prob, 0, to_take))
- return pln_batch
+ return batch + (torch.index_select(self._latent_prob, 0, to_take),)
+ return batch
- def _return_batch(self, indices, beginning, end):
- to_take = torch.tensor(indices[beginning:end]).to(DEVICE)
- if self._exog is not None:
- exog_b = torch.index_select(self._exog, 0, to_take)
- else:
- exog_b = None
- return (
- torch.index_select(self._endog, 0, to_take),
- exog_b,
- torch.index_select(self._offsets, 0, to_take),
- torch.index_select(self._latent_mean, 0, to_take),
- torch.index_select(self._latent_sqrt_var, 0, to_take),
- )
@classmethod
@_add_doc(
@@ -3542,15 +3532,15 @@ class ZIPln(_model):
"""
Project the latent probability since it must be between 0 and 1.
"""
- if self.use_closed_form_prob is False:
+ if self._use_closed_form_prob is False:
with torch.no_grad():
- self._latent_prob = torch.maximum(
- self._latent_prob, torch.tensor([0]), out=self._latent_prob
+ self._latent_prob_b = torch.maximum(
+ self._latent_prob_b, torch.tensor([0]), out=self._latent_prob_b
)
- self._latent_prob = torch.minimum(
- self._latent_prob, torch.tensor([1]), out=self._latent_prob
+ self._latent_prob_b = torch.minimum(
+ self._latent_prob, torch.tensor([1]), out=self._latent_prob_b
)
- self._latent_prob *= self._dirac
+ self._latent_prob_b *= self._dirac_b
@property
def covariance(self) -> torch.Tensor:
--
GitLab
From 4fe2b3d24e42536c09dc2177b882989aff31e336 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 13 Oct 2023 18:24:18 +0200
Subject: [PATCH 094/167] add contributing, readme and model from new_model
branch.
---
CONTRIBUTING.md | 71 +++-
README.md | 37 ++-
pyPLNmodels/models.py | 734 ++++++++++++++++++++----------------------
3 files changed, 438 insertions(+), 404 deletions(-)
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index faf13f3b..e1fb39dc 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,15 +1,72 @@
-# Clone the repo
+# What to work on
+A public roadmap will be available soon.
+
+
+## Fork/clone/pull
+
+The typical workflow for contributing is:
+
+1. Fork the `main` branch from the [GitLab repository](https://forgemia.inra.fr/bbatardiere/pyplnmodels).
+2. Clone your fork locally.
+3. Run `pip install pre-commit` if pre-commit is not already installed.
+4. Inside the repository, run 'pre-commit install'.
+5. Commit changes.
+6. Push the changes to your fork.
+7. Send a pull request from your fork back to the original `main` branch.
+
+## How to implement a new model
+You can implement a new model `newmodel` by inheriting from the abstract `_model` class in the `models` module.
+The `newmodel` class should contains at least the following code:
```
-git clone git@forgemia.inra.fr:bbatardiere/pyplnmodels
-```
+class newmodel(_model):
+ _NAME=""
+ def _random_init_latent_sqrt_var(self):
+ "Implement here"
+
+ @property
+ def latent_variables(self):
+ "Implement here"
-# Install precommit
+ def compute_elbo(self):
+ "Implement here"
-In the directory:
+ def _compute_elbo_b(self):
+ "Implement here"
+ def _smart_init_model_parameters(self):
+ "Implement here"
+
+ def _random_init_model_parameters(self):
+ "Implement here"
+
+ def _smart_init_latent_parameters(self):
+ "Implement here"
+
+ def _random_init_latent_parameters(self):
+ "Implement here"
+
+ @property
+ def _list_of_parameters_needing_gradient(self):
+ "Implement here"
+ @property
+ def _description(self):
+ "Implement here"
+
+ @property
+ def number_of_parameters(self):
+ "Implement here"
```
-pre-commit install
+Then, add `newmodel` in the `__init__.py` file of the pyPLNmodels module.
+If `newmodel` is well implemented, running
```
+from pyPLNmodels import newmodel, get_real_count_data
-If not found use `pip install pre-commit` before this command.
+endog = get_real_count_data()
+zi = newmodel(endog, add_const = True)
+zi.fit(nb_max_iteration = 10, tol = 0)
+```
+should increase the elbo of the model. You should document your functions with
+[numpy-style
+docstrings](https://numpydoc.readthedocs.io/en/latest/format.html). You can use
+the `_add_doc` decorator to inherit the docstrings of the `_model` class.
diff --git a/README.md b/README.md
index 9401cfe6..f8adaa3f 100644
--- a/README.md
+++ b/README.md
@@ -16,22 +16,10 @@
<!-- > slides](https://pln-team.github.io/slideshow/) for a -->
<!-- > comprehensive introduction. -->
-## Getting started
-The getting started can be found [here](https://forgemia.inra.fr/bbatardiere/pyplnmodels/-/raw/dev/Getting_started.ipynb?inline=false). If you need just a quick view of the package, see next.
+## Getting started
+The getting started can be found [here](https://forgemia.inra.fr/bbatardiere/pyplnmodels/-/raw/dev/Getting_started.ipynb?inline=false). If you need just a quick view of the package, see the quickstart next.
-## Installation
-
-**pyPLNmodels** is available on
-[pypi](https://pypi.org/project/pyPLNmodels/). The development
-version is available on [GitHub](https://github.com/PLN-team/pyPLNmodels).
-
-### Package installation
-
-```
-pip install pyPLNmodels
-```
-
-## Usage and main fitting functions
+## âš¡ï¸ Quickstart
The package comes with an ecological data set to present the functionality
```
@@ -61,7 +49,24 @@ transformed_data = pln.transform()
```
-## References
+## 🛠Installation
+
+**pyPLNmodels** is available on
+[pypi](https://pypi.org/project/pyPLNmodels/). The development
+version is available on [GitHub](https://github.com/PLN-team/pyPLNmodels).
+
+### Package installation
+
+```
+pip install pyPLNmodels
+```
+
+## 👠Contributing
+
+Feel free to contribute, but read the [CONTRIBUTING.md](https://forgemia.inra.fr/bbatardiere/pyplnmodels/-/blob/main/CONTRIBUTING.md) first. A public roadmap will be available soon.
+
+
+## âš¡ï¸ Citations
Please cite our work using the following references:
- J. Chiquet, M. Mariadassou and S. Robin: Variational inference for
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index e8970fec..e8b316a5 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -324,20 +324,6 @@ class _model(ABC):
self._coef = None
self._coef = torch.randn((self.nb_cov, self.dim), device=DEVICE)
- @abstractmethod
- def _random_init_model_parameters(self):
- """
- Abstract method to randomly initialize model parameters.
- """
- pass
-
- @abstractmethod
- def _random_init_latent_parameters(self):
- """
- Abstract method to randomly initialize latent parameters.
- """
- pass
-
def _smart_init_latent_parameters(self):
"""
Initialize latent parameters smartly.
@@ -369,53 +355,6 @@ class _model(ABC):
for parameter in self._list_of_parameters_needing_gradient:
parameter.requires_grad_(True)
- @property
- @abstractmethod
- def _list_of_parameters_needing_gradient(self):
- """
- A list containing all the parameters that need to be upgraded via a gradient step.
-
- Returns
- -------
- List[torch.Tensor]
- List of parameters needing gradient.
- """
-
- def _print_beginning_message(self) -> str:
- """
- Method for printing the beginning message.
- """
- print(f"Fitting a {self._NAME} model with {self._description} \n")
-
- @abstractmethod
- def _endog_predictions(self):
- pass
-
- @abstractmethod
- def number_of_parameters(self):
- pass
-
- @abstractmethod
- def _compute_elbo_b(self):
- pass
-
- @property
- @abstractmethod
- def covariance(self):
- pass
-
- @covariance.setter
- @abstractmethod
- def covariance(self, covariance):
- pass
-
- @property
- @abstractmethod
- def _description(self):
- """
- Describes the model and what it does.
- """
-
def fit(
self,
nb_max_iteration: int = 50000,
@@ -615,7 +554,7 @@ class _model(ABC):
return pca
@property
- def latent_var(self) -> torch.Tensor:
+ def latent_variance(self) -> torch.Tensor:
"""
Property representing the latent variance.
@@ -726,12 +665,16 @@ class _model(ABC):
plt.show()
@property
- @abstractmethod
- def latent_variables(self):
+ def _latent_var(self) -> torch.Tensor:
"""
- Abstract property representing the latent variables.
+ Property representing the latent variance.
+
+ Returns
+ -------
+ torch.Tensor
+ The latent variance tensor.
"""
- pass
+ return self._latent_sqrt_var**2
def _print_end_of_fitting_message(self, stop_condition: bool, tol: float):
"""
@@ -790,14 +733,6 @@ class _model(ABC):
"""
pass
- @abstractmethod
- def compute_elbo(self):
- """
- Compute the Evidence Lower BOund (ELBO) that will be maximized
- by pytorch.
- """
- pass
-
def display_covariance(self, ax=None, savefig=False, name_file=""):
"""
Display the covariance matrix.
@@ -1062,30 +997,6 @@ class _model(ABC):
)
self._latent_mean = latent_mean
- @latent_sqrt_var.setter
- @_array2tensor
- def latent_sqrt_var(
- self, latent_sqrt_var: Union[torch.Tensor, np.ndarray, pd.DataFrame]
- ):
- """
- Setter for the latent variance property.
-
- Parameters
- ----------
- latent_sqrt_var : Union[torch.Tensor, np.ndarray, pd.DataFrame]
- The latent variance.
-
- Raises
- ------
- ValueError
- If the shape of the latent variance is incorrect.
- """
- if latent_sqrt_var.shape != (self.n_samples, self.dim):
- raise ValueError(
- f"Wrong shape. Expected {self.n_samples, self.dim}, got {latent_sqrt_var.shape}"
- )
- self._latent_sqrt_var = latent_sqrt_var
-
def _cpu_attribute_or_none(self, attribute_name):
"""
Get the CPU attribute or return None.
@@ -1421,8 +1332,95 @@ class _model(ABC):
ax.legend()
return ax
+ def _print_beginning_message(self):
+ """
+ Method for printing the beginning message.
+ """
+ print(f"Fitting a {self._NAME} model with {self._description}")
+
+ @property
+ @abstractmethod
+ def latent_variables(self) -> torch.Tensor:
+ """
+ Property representing the latent variables.
+
+ Returns
+ -------
+ torch.Tensor
+ The latent variables of size (n_samples, dim).
+ """
+
+ @abstractmethod
+ def compute_elbo(self):
+ """
+ Compute the Evidence Lower BOund (ELBO) that will be maximized
+ by pytorch.
+
+ Returns
+ -------
+ torch.Tensor
+ The computed ELBO.
+ """
+
+ @abstractmethod
+ def _compute_elbo_b(self):
+ """
+ Compute the Evidence Lower BOund (ELBO) for the current mini-batch.
+ Returns
+ -------
+ torch.Tensor
+ The computed ELBO on the current batch.
+ """
+
+ @abstractmethod
+ def _random_init_model_parameters(self):
+ """
+ Abstract method to randomly initialize model parameters.
+ """
+
+ @abstractmethod
+ def _random_init_latent_parameters(self):
+ """
+ Abstract method to randomly initialize latent parameters.
+ """
+
+ @abstractmethod
+ def _smart_init_latent_parameters(self):
+ """
+ Method for smartly initializing the latent parameters.
+ """
+
+ @abstractmethod
+ def _smart_init_model_parameters(self):
+ """
+ Method for smartly initializing the model parameters.
+ """
+
+ @property
+ @abstractmethod
+ def _list_of_parameters_needing_gradient(self):
+ """
+ A list containing all the parameters that need to be upgraded via a gradient step.
+
+ Returns
+ -------
+ List[torch.Tensor]
+ List of parameters needing gradient.
+ """
+
+ @property
+ @abstractmethod
+ def _description(self):
+ pass
+
+ @property
+ @abstractmethod
+ def number_of_parameters(self):
+ """
+ Number of parameters of the model.
+ """
+
-# need to do a good init for M and S
class Pln(_model):
"""
Pln class.
@@ -1511,15 +1509,13 @@ class Pln(_model):
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
):
- endog, exog, offsets = _extract_data_from_formula(formula, data)
- return cls(
- endog,
- exog=exog,
- offsets=offsets,
+ super().from_formula(
+ cls=cls,
+ formula=formula,
+ data=data,
offsets_formula=offsets_formula,
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
- add_const=False,
)
@_add_doc(
@@ -1697,35 +1693,6 @@ class Pln(_model):
self._offsets + self._latent_mean + 1 / 2 * self._latent_sqrt_var**2
)
- def _smart_init_latent_parameters(self):
- """
- Method for smartly initializing the latent parameters.
- """
- self._random_init_latent_parameters()
-
- def _random_init_latent_parameters(self):
- """
- Method for randomly initializing the latent parameters.
- """
- if not hasattr(self, "_latent_sqrt_var"):
- self._latent_sqrt_var = (
- 1 / 2 * torch.ones((self.n_samples, self.dim)).to(DEVICE)
- )
- if not hasattr(self, "_latent_mean"):
- self._latent_mean = torch.ones((self.n_samples, self.dim)).to(DEVICE)
-
- @property
- def _list_of_parameters_needing_gradient(self):
- """
- Property representing the list of parameters needing gradient.
-
- Returns
- -------
- list
- The list of parameters needing gradient.
- """
- return [self._latent_mean, self._latent_sqrt_var]
-
def _get_max_components(self):
"""
Method for getting the maximum number of components.
@@ -1737,61 +1704,6 @@ class Pln(_model):
"""
return self.dim
- def compute_elbo(self):
- """
- Method for computing the evidence lower bound (ELBO).
-
- Returns
- -------
- torch.Tensor
- The computed ELBO.
- Examples
- --------
- >>> from pyPLNmodels import Pln, get_real_count_data
- >>> endog, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(endog,add_const = True)
- >>> pln.fit()
- >>> elbo = pln.compute_elbo()
- >>> print("elbo", elbo)
- >>> print("loglike/n", pln.loglike/pln.n_samples)
- """
- return profiled_elbo_pln(
- self._endog,
- self._exog,
- self._offsets,
- self._latent_mean,
- self._latent_sqrt_var,
- )
-
- def _compute_elbo_b(self):
- """
- Method for computing the evidence lower bound (ELBO) on the current batch.
-
- Returns
- -------
- torch.Tensor
- The computed ELBO on the current batch.
- """
- return profiled_elbo_pln(
- self._endog_b,
- self._exog_b,
- self._offsets_b,
- self._latent_mean_b,
- self._latent_sqrt_var_b,
- )
-
- def _smart_init_model_parameters(self):
- """
- Method for smartly initializing the model parameters.
- """
- # no model parameters since we are doing a profiled ELBO
-
- def _random_init_model_parameters(self):
- """
- Method for randomly initializing the model parameters.
- """
- # no model parameters since we are doing a profiled ELBO
-
@property
def _coef(self):
"""
@@ -1829,19 +1741,29 @@ class Pln(_model):
covariances = components_var @ (sk_components.T.unsqueeze(0))
return covariances
- @property
- @_add_doc(
- _model,
- example="""
- >>> from pyPLNmodels import Pln, get_real_count_data
- >>> endog, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(endog,add_const = True)
- >>> pln.fit()
- >>> print(pln.latent_variables.shape)
- """,
- )
- def latent_variables(self):
- return self.latent_mean.detach()
+ @_model.latent_sqrt_var.setter
+ @_array2tensor
+ def latent_sqrt_var(
+ self, latent_sqrt_var: Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ ):
+ """
+ Setter for the latent variance property.
+
+ Parameters
+ ----------
+ latent_sqrt_var : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The latent variance.
+
+ Raises
+ ------
+ ValueError
+ If the shape of the latent variance is incorrect.
+ """
+ if latent_sqrt_var.shape != (self.n_samples, self.dim):
+ raise ValueError(
+ f"Wrong shape. Expected {self.n_samples, self.dim}, got {latent_sqrt_var.shape}"
+ )
+ self._latent_sqrt_var = latent_sqrt_var
@property
def number_of_parameters(self):
@@ -1891,6 +1813,84 @@ class Pln(_model):
"""
raise AttributeError("You can not set the covariance for the Pln model.")
+ def _random_init_latent_sqrt_var(self):
+ if not hasattr(self, "_latent_sqrt_var"):
+ self._latent_sqrt_var = (
+ 1 / 2 * torch.ones((self.n_samples, self.dim)).to(DEVICE)
+ )
+
+ @property
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(endog,add_const = True)
+ >>> pln.fit()
+ >>> print(pln.latent_variables.shape)
+ """,
+ )
+ def latent_variables(self):
+ return self.latent_mean.detach()
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import Pln, get_real_count_data
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> pln = Pln(endog,add_const = True)
+ >>> pln.fit()
+ >>> elbo = pln.compute_elbo()
+ >>> print("elbo", elbo)
+ >>> print("loglike/n", pln.loglike/pln.n_samples)
+ """,
+ )
+ def compute_elbo(self):
+ return profiled_elbo_pln(
+ self._endog,
+ self._exog,
+ self._offsets,
+ self._latent_mean,
+ self._latent_sqrt_var,
+ )
+
+ @_add_doc(_model)
+ def _compute_elbo_b(self):
+ return profiled_elbo_pln(
+ self._endog_b,
+ self._exog_b,
+ self._offsets_b,
+ self._latent_mean_b,
+ self._latent_sqrt_var_b,
+ )
+
+ @_add_doc(_model)
+ def _smart_init_model_parameters(self):
+ pass
+ # no model parameters since we are doing a profiled ELBO
+
+ @_add_doc(_model)
+ def _random_init_model_parameters(self):
+ pass
+ # no model parameters since we are doing a profiled ELBO
+
+ @_add_doc(_model)
+ def _smart_init_latent_parameters(self):
+ self._random_init_latent_sqrt_var()
+ if not hasattr(self, "_latent_mean"):
+ self._latent_mean = torch.log(self._endog + (self._endog == 0))
+
+ @_add_doc(_model)
+ def _random_init_latent_parameters(self):
+ self._random_init_latent_sqrt_var()
+ if not hasattr(self, "_latent_mean"):
+ self._latent_mean = torch.ones((self.n_samples, self.dim)).to(DEVICE)
+
+ @property
+ @_add_doc(_model)
+ def _list_of_parameters_needing_gradient(self):
+ return [self._latent_mean, self._latent_sqrt_var]
+
class PlnPCAcollection:
"""
@@ -2246,6 +2246,17 @@ class PlnPCAcollection:
"""
return [model.rank for model in self.values()]
+ def _print_beginning_message(self) -> str:
+ """
+ Method for printing the beginning message.
+
+ Returns
+ -------
+ str
+ The beginning message.
+ """
+ return f"Adjusting {len(self.ranks)} Pln models for PCA analysis \n"
+
@property
def dim(self) -> int:
"""
@@ -2674,7 +2685,7 @@ class PlnPCAcollection:
return ".BIC, .AIC, .loglikes"
-# Here, setting the value for each key in _dict_parameters
+# Here, setting the value for each key _dict_parameters
class PlnPCA(_model):
"""
PlnPCA object where the covariance has low rank.
@@ -2900,19 +2911,6 @@ class PlnPCA(_model):
variables_names=variables_names, indices_of_variables=indices_of_variables
)
- def _check_if_rank_is_too_high(self):
- """
- Check if the rank is too high and issue a warning if necessary.
- """
- if self.dim < self.rank:
- warning_string = (
- f"\nThe requested rank of approximation {self.rank} "
- f"is greater than the number of variables {self.dim}. "
- f"Setting rank to {self.dim}"
- )
- warnings.warn(warning_string)
- self._rank = self.dim
-
@property
@_add_doc(
_model,
@@ -2928,30 +2926,6 @@ class PlnPCA(_model):
def latent_mean(self) -> torch.Tensor:
return self._cpu_attribute_or_none("_latent_mean")
- @property
- def latent_sqrt_var(self) -> torch.Tensor:
- """
- Property representing the unsigned square root of the latent variance.
-
- Returns
- -------
- torch.Tensor
- The latent variance tensor.
- """
- return self._cpu_attribute_or_none("_latent_sqrt_var")
-
- @property
- def _latent_var(self) -> torch.Tensor:
- """
- Property representing the latent variance.
-
- Returns
- -------
- torch.Tensor
- The latent variance tensor.
- """
- return self._latent_sqrt_var**2
-
def _endog_predictions(self):
covariance_a_posteriori = torch.sum(
(self._components**2).unsqueeze(0)
@@ -2983,7 +2957,7 @@ class PlnPCA(_model):
)
self._latent_mean = latent_mean
- @latent_sqrt_var.setter
+ @_model.latent_sqrt_var.setter
@_array2tensor
def latent_sqrt_var(self, latent_sqrt_var: torch.Tensor):
"""
@@ -3076,104 +3050,6 @@ class PlnPCA(_model):
"""
return {"coef": self.coef, "components": self.components}
- def _smart_init_model_parameters(self):
- """
- Initialize the model parameters smartly.
- """
- if not hasattr(self, "_coef"):
- super()._smart_init_coef()
- if not hasattr(self, "_components"):
- self._components = _init_components(self._endog, self._exog, self._rank)
-
- def _random_init_model_parameters(self):
- """
- Randomly initialize the model parameters.
- """
- super()._random_init_coef()
- self._components = torch.randn((self.dim, self._rank)).to(DEVICE)
-
- def _random_init_latent_parameters(self):
- """
- Randomly initialize the latent parameters.
- """
- self._latent_sqrt_var = (
- 1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
- )
- self._latent_mean = torch.ones((self.n_samples, self._rank)).to(DEVICE)
-
- def _smart_init_latent_parameters(self):
- """
- Initialize the latent parameters smartly.
- """
- if not hasattr(self, "_latent_mean"):
- self._latent_mean = (
- _init_latent_mean(
- self._endog,
- self._exog,
- self._offsets,
- self._coef,
- self._components,
- )
- .to(DEVICE)
- .detach()
- )
- if not hasattr(self, "_latent_sqrt_var"):
- self._latent_sqrt_var = (
- 1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
- )
-
- @property
- def _list_of_parameters_needing_gradient(self):
- """
- Property representing the list of parameters needing gradient.
-
- Returns
- -------
- List[torch.Tensor]
- The list of parameters needing gradient.
- """
- if self._coef is None:
- return [self._components, self._latent_mean, self._latent_sqrt_var]
- return [self._components, self._coef, self._latent_mean, self._latent_sqrt_var]
-
- def _compute_elbo_b(self) -> torch.Tensor:
- """
- Compute the evidence lower bound (ELBO) with the current batch.
-
- Returns
- -------
- torch.Tensor
- The ELBO value on the current batch.
- """
- return elbo_plnpca(
- self._endog_b,
- self._exog_b,
- self._offsets_b,
- self._latent_mean_b,
- self._latent_sqrt_var_b,
- self._components,
- self._coef,
- )
-
- def compute_elbo(self) -> torch.Tensor:
- """
- Compute the evidence lower bound (ELBO).
-
- Returns
- -------
- torch.Tensor
- The ELBO value.
- """
- return elbo_plnpca(
- self._endog,
- self._exog,
- self._offsets,
- self._latent_mean,
- self._latent_sqrt_var,
- self._components,
- self._coef,
- )
-
@property
def number_of_parameters(self) -> int:
"""
@@ -3247,7 +3123,7 @@ class PlnPCA(_model):
@property
def _description(self) -> str:
"""
- Description output when fitting and printing the model.
+ Property representing the description.
Returns
-------
@@ -3256,18 +3132,6 @@ class PlnPCA(_model):
"""
return f" {self.rank} principal component."
- @property
- def latent_variables(self) -> torch.Tensor:
- """
- Property representing the latent variables.
-
- Returns
- -------
- torch.Tensor
- The latent variables of size (n_samples, dim).
- """
- return torch.matmul(self._latent_mean, self._components.T).detach()
-
@property
def projected_latent_variables(self) -> torch.Tensor:
"""
@@ -3349,6 +3213,103 @@ class PlnPCA(_model):
return self.projected_latent_variables
return self.latent_variables
+ @property
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> endog = get_real_count_data(return_labels=False)
+ >>> pca = PlnPCA(endog,add_const = True)
+ >>> pca.fit()
+ >>> print(pca.latent_variables.shape)
+ """,
+ )
+ def latent_variables(self) -> torch.Tensor:
+ return torch.matmul(self._latent_mean, self._components.T).detach()
+
+ @_add_doc(
+ _model,
+ example="""
+ >>> from pyPLNmodels import PlnPCA, get_real_count_data
+ >>> endog = get_real_count_data(return_labels = False)
+ >>> pca = PlnPCA(endog,add_const = True)
+ >>> pca.fit()
+ >>> elbo = pca.compute_elbo()
+ >>> print("elbo", elbo)
+ >>> print("loglike/n", pln.loglike/pln.n_samples)
+ """,
+ )
+ def compute_elbo(self) -> torch.Tensor:
+ return elbo_plnpca(
+ self._endog,
+ self._exog,
+ self._offsets,
+ self._latent_mean,
+ self._latent_sqrt_var,
+ self._components,
+ self._coef,
+ )
+
+ @_add_doc(_model)
+ def _compute_elbo_b(self) -> torch.Tensor:
+ return elbo_plnpca(
+ self._endog_b,
+ self._exog_b,
+ self._offsets_b,
+ self._latent_mean_b,
+ self._latent_sqrt_var_b,
+ self._components,
+ self._coef,
+ )
+
+ @_add_doc(_model)
+ def _random_init_model_parameters(self):
+ super()._random_init_coef()
+ self._components = torch.randn((self.dim, self._rank)).to(DEVICE)
+
+ @_add_doc(_model)
+ def _smart_init_model_parameters(self):
+ if not hasattr(self, "_coef"):
+ super()._smart_init_coef()
+ if not hasattr(self, "_components"):
+ self._components = _init_components(self._endog, self._exog, self._rank)
+
+ @_add_doc(_model)
+ def _random_init_latent_parameters(self):
+ """
+ Randomly initialize the latent parameters.
+ """
+ self._latent_sqrt_var = (
+ 1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
+ )
+ self._latent_mean = torch.ones((self.n_samples, self._rank)).to(DEVICE)
+
+ @_add_doc(_model)
+ def _smart_init_latent_parameters(self):
+ if not hasattr(self, "_latent_mean"):
+ self._latent_mean = (
+ _init_latent_mean(
+ self._endog,
+ self._exog,
+ self._offsets,
+ self._coef,
+ self._components,
+ )
+ .to(DEVICE)
+ .detach()
+ )
+ if not hasattr(self, "_latent_sqrt_var"):
+ self._latent_sqrt_var = (
+ 1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
+ )
+
+ @property
+ @_add_doc(_model)
+ def _list_of_parameters_needing_gradient(self):
+ if self._coef is None:
+ return [self._components, self._latent_mean, self._latent_sqrt_var]
+ return [self._components, self._coef, self._latent_mean, self._latent_sqrt_var]
+
class ZIPln(_model):
_NAME = "ZIPln"
@@ -3359,6 +3320,10 @@ class ZIPln(_model):
@_add_doc(
_model,
+ params="""
+ use_closed_form_prob: bool, optional
+ Whether or not use the closed formula for the latent probability
+ """,
example="""
>>> from pyPLNmodels import ZIPln, get_real_count_data
>>> endog= get_real_count_data()
@@ -3398,28 +3363,17 @@ class ZIPln(_model):
def _extract_batch(self, batch):
super()._extract_batch(batch)
+ self._dirac_b = batch[5]
if self._use_closed_form_prob is False:
- self._latent_prob_b = batch[5]
+ self._latent_prob_b = batch[6]
def _return_batch(self, indices, beginning, end):
pln_batch = super()._return_batch(indices, beginning, end)
- if self._use_closed_form_prob is False:
- return pln_batch + torch.index_select(self._latent_prob, 0, to_take)
- return pln_batch
-
- def _return_batch(self, indices, beginning, end):
to_take = torch.tensor(indices[beginning:end]).to(DEVICE)
- if self._exog is not None:
- exog_b = torch.index_select(self._exog, 0, to_take)
- else:
- exog_b = None
- return (
- torch.index_select(self._endog, 0, to_take),
- exog_b,
- torch.index_select(self._offsets, 0, to_take),
- torch.index_select(self._latent_mean, 0, to_take),
- torch.index_select(self._latent_sqrt_var, 0, to_take),
- )
+ batch = pln_batch + (torch.index_select(self._dirac, 0, to_take),)
+ if self._use_closed_form_prob is False:
+ return batch + (torch.index_select(self._latent_prob, 0, to_take),)
+ return batch
@classmethod
@_add_doc(
@@ -3446,7 +3400,7 @@ class ZIPln(_model):
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
- use_closed_form: bool = True,
+ use_closed_form_prob: bool = True,
):
endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
@@ -3457,7 +3411,7 @@ class ZIPln(_model):
dict_initialization=dict_initialization,
take_log_offsets=take_log_offsets,
add_const=False,
- use_closed_form=use_closed_form,
+ use_closed_form_prob=use_closed_form_prob,
)
@_add_doc(
@@ -3508,7 +3462,7 @@ class ZIPln(_model):
@property
def _description(self):
- return " full covariance model and zero-inflation."
+ return "with full covariance model and zero-inflation."
def _random_init_model_parameters(self):
super()._random_init_model_parameters()
@@ -3541,7 +3495,26 @@ class ZIPln(_model):
def _covariance(self):
return self._components @ (self._components.T)
- def latent_variables(self):
+ def latent_variables(self) -> tuple([torch.Tensor, torch.Tensor]):
+ """
+ Property representing the latent variables. Two latent
+ variables are available if exog is not None
+
+ Returns
+ -------
+ tuple(torch.Tensor, torch.Tensor)
+ The latent variables of a classic Pln model (size (n_samples, dim))
+ and zero inflated latent variables of size (n_samples, dim).
+ Examples
+ --------
+ >>> from pyPLNmodels import ZIPln, get_real_count_data
+ >>> endog, labels = get_real_count_data(return_labels = True)
+ >>> zi = ZIPln(endog,add_const = True)
+ >>> zi.fit()
+ >>> latent_mean, latent_inflated = zi.latent_variables
+ >>> print(latent_mean.shape)
+ >>> print(latent_inflated.shape)
+ """
return self.latent_mean, self.latent_prob
def _update_parameters(self):
@@ -3552,15 +3525,15 @@ class ZIPln(_model):
"""
Project the latent probability since it must be between 0 and 1.
"""
- if self.use_closed_form_prob is False:
+ if self._use_closed_form_prob is False:
with torch.no_grad():
- self._latent_prob = torch.maximum(
- self._latent_prob, torch.tensor([0]), out=self._latent_prob
+ self._latent_prob_b = torch.maximum(
+ self._latent_prob_b, torch.tensor([0]), out=self._latent_prob_b
)
- self._latent_prob = torch.minimum(
- self._latent_prob, torch.tensor([1]), out=self._latent_prob
+ self._latent_prob_b = torch.minimum(
+ self._latent_prob, torch.tensor([1]), out=self._latent_prob_b
)
- self._latent_prob *= self._dirac
+ self._latent_prob_b *= self._dirac_b
@property
def covariance(self) -> torch.Tensor:
@@ -3634,13 +3607,12 @@ class ZIPln(_model):
return self.dim * (2 * self.nb_cov + (self.dim + 1) / 2)
@property
+ @_add_doc(_model)
def _list_of_parameters_needing_gradient(self):
list_parameters = [
self._latent_mean,
self._latent_sqrt_var,
- self._coef_inflation,
self._components,
- self._coef,
]
if self._use_closed_form_prob:
list_parameters.append(self._latent_prob)
--
GitLab
From 757f76c88d034020d5da4fc700869964b4d8a4dc Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 13 Oct 2023 18:28:27 +0200
Subject: [PATCH 095/167] typo in the contributin
---
CONTRIBUTING.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index e1fb39dc..530ced7b 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -63,8 +63,8 @@ If `newmodel` is well implemented, running
from pyPLNmodels import newmodel, get_real_count_data
endog = get_real_count_data()
-zi = newmodel(endog, add_const = True)
-zi.fit(nb_max_iteration = 10, tol = 0)
+model = newmodel(endog, add_const = True)
+model.fit(nb_max_iteration = 10, tol = 0)
```
should increase the elbo of the model. You should document your functions with
[numpy-style
--
GitLab
From 20417af7f3d00b9021fc45e8ac7ec0a98301eb20 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 16 Oct 2023 08:16:32 +0200
Subject: [PATCH 096/167] continue to add the contributing and create the tests
for the ZI.
---
CONTRIBUTING.md | 75 +++++++++--
pyPLNmodels/models.py | 140 +++++++++++---------
tests/conftest.py | 12 +-
tests/create_readme_and_docstrings_tests.py | 2 +-
4 files changed, 153 insertions(+), 76 deletions(-)
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 530ced7b..2f718217 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -21,42 +21,91 @@ The `newmodel` class should contains at least the following code:
```
class newmodel(_model):
_NAME=""
- def _random_init_latent_sqrt_var(self):
+ @property
+ def latent_variables(self) -> torch.Tensor:
"Implement here"
- @property
- def latent_variables(self):
+ def compute_elbo(self) -> torch.Tensor:
"Implement here"
- def compute_elbo(self):
+ def _compute_elbo_b(self) -> torch.Tensor:
"Implement here"
- def _compute_elbo_b(self):
+ def _smart_init_model_parameters(self)-> None:
"Implement here"
- def _smart_init_model_parameters(self):
+ def _random_init_model_parameters(self)-> None:
"Implement here"
- def _random_init_model_parameters(self):
+ def _smart_init_latent_parameters(self)-> None:
"Implement here"
- def _smart_init_latent_parameters(self):
+ def _random_init_latent_parameters(self)-> None:
"Implement here"
- def _random_init_latent_parameters(self):
+ @property
+ def _list_of_parameters_needing_gradient(self)-> list:
+ "Implement here"
+ @property
+ def _description(self)-> str:
"Implement here"
@property
- def _list_of_parameters_needing_gradient(self):
+ def number_of_parameters(self) -> int:
"Implement here"
+
@property
- def _description(self):
+ def model_parameters(self)-> Dict[str, torch.Tensor]:
"Implement here"
@property
- def number_of_parameters(self):
+ def latent_parameters(self)-> Dict[str, torch.Tensor]:
"Implement here"
```
+Each value of the 'latent_parameters' dict should be implemented (and protected) both in the
+`_random_init_latent_parameters` and '_smart_init_latent_parameters'.
+Each value of the 'model_parameters' dict should be implemented (and protected) both in the
+`_random_init_model_parameters` and '_smart_init_model_parameters'.
+For example, if you have one model parameters `coef` and latent_parameters `latent_mean` and `latent_var`, you should implement such as
+```py
+class newmodel(_model):
+ @property
+ def model_parameters(self) -> Dict[str, torch.Tensor]:
+ return {"coef":self.coef}
+ @property
+ def latent_parameters(self) -> Dict[str, torch.Tensor]:
+ return {"latent_mean":self.latent_mean, "latent_var":self.latent_var}
+
+ def _random_init_latent_parameters(self):
+ self._latent_mean = init_latent_mean()
+ self._latent_var = init_latent_var()
+
+ @property
+ def _smart_init_model_parameters(self):
+ self._latent_mean = random_init_latent_mean()
+ self._latent_var = random_init_latent_var()
+
+ @property
+ def latent_var(self):
+ return self._latent_var
+
+ @property
+ def latent_mean(self):
+ return self._latent_mean
+
+ def _random_init_model_parameters(self):
+ self._coef = init_coef()
+
+ def _smart_init_model_parameters(self):
+ self._coef = random_init_latent_coef()
+
+ @property
+ def coef(self):
+ return self._coef
+```
+
+
+
Then, add `newmodel` in the `__init__.py` file of the pyPLNmodels module.
If `newmodel` is well implemented, running
```
@@ -69,4 +118,4 @@ model.fit(nb_max_iteration = 10, tol = 0)
should increase the elbo of the model. You should document your functions with
[numpy-style
docstrings](https://numpydoc.readthedocs.io/en/latest/format.html). You can use
-the `_add_doc` decorator to inherit the docstrings of the `_model` class.
+the `_add_doc` decorator (implemented in the `_utils` module) to inherit the docstrings of the `_model` class.
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index e8b316a5..9228de82 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -360,7 +360,7 @@ class _model(ABC):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- tol: float = 1e-8,
+ tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
batch_size=None,
@@ -872,33 +872,6 @@ class _model(ABC):
"""
return -self.loglike + self.number_of_parameters
- @property
- def latent_parameters(self):
- """
- Property representing the latent parameters.
-
- Returns
- -------
- dict
- The dictionary of latent parameters.
- """
- return {
- "latent_sqrt_var": self.latent_sqrt_var,
- "latent_mean": self.latent_mean,
- }
-
- @property
- def model_parameters(self):
- """
- Property representing the model parameters.
-
- Returns
- -------
- dict
- The dictionary of model parameters.
- """
- return {"coef": self.coef, "covariance": self.covariance}
-
@property
def dict_data(self):
"""
@@ -1284,18 +1257,6 @@ class _model(ABC):
"""
return f"{self._NAME}_nbcov_{self.nb_cov}_dim_{self.dim}"
- @property
- def _path_to_directory(self):
- """
- Property representing the path to the directory.
-
- Returns
- -------
- str
- The path to the directory.
- """
- return ""
-
def plot_expected_vs_true(self, ax=None, colors=None):
"""
Plot the predicted value of the endog against the endog.
@@ -1420,6 +1381,30 @@ class _model(ABC):
Number of parameters of the model.
"""
+ @property
+ @abstractmethod
+ def model_parameters(self) -> Dict[str, torch.Tensor]:
+ """
+ Property representing the model parameters.
+
+ Returns
+ -------
+ dict
+ The dictionary of model parameters.
+ """
+
+ @property
+ @abstractmethod
+ def latent_parameters(self) -> Dict[str, torch.Tensor]:
+ """
+ Property representing the latent parameters.
+
+ Returns
+ -------
+ dict
+ The dictionary of latent parameters.
+ """
+
class Pln(_model):
"""
@@ -1509,8 +1494,7 @@ class Pln(_model):
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
):
- super().from_formula(
- cls=cls,
+ return super().from_formula(
formula=formula,
data=data,
offsets_formula=offsets_formula,
@@ -1533,7 +1517,7 @@ class Pln(_model):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- tol: float = 1e-8,
+ tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
batch_size: int = None,
@@ -1686,7 +1670,8 @@ class Pln(_model):
------
AttributeError since you can not set the coef in the Pln model.
"""
- raise AttributeError("You can not set the coef in the Pln model.")
+ msg = "You can not set the coef in the Pln model."
+ warnings.warn(msg)
def _endog_predictions(self):
return torch.exp(
@@ -1811,7 +1796,7 @@ class Pln(_model):
covariance : torch.Tensor
The covariance matrix.
"""
- raise AttributeError("You can not set the covariance for the Pln model.")
+ warnings.warn("You can not set the covariance for the Pln model.")
def _random_init_latent_sqrt_var(self):
if not hasattr(self, "_latent_sqrt_var"):
@@ -1891,6 +1876,19 @@ class Pln(_model):
def _list_of_parameters_needing_gradient(self):
return [self._latent_mean, self._latent_sqrt_var]
+ @property
+ @_add_doc(_model)
+ def model_parameters(self) -> Dict[str, torch.Tensor]:
+ return {"coef": self.coef, "covariance": self.covariance}
+
+ @property
+ @_add_doc(_model)
+ def latent_parameters(self):
+ return {
+ "latent_sqrt_var": self.latent_sqrt_var,
+ "latent_mean": self.latent_mean,
+ }
+
class PlnPCAcollection:
"""
@@ -2286,7 +2284,7 @@ class PlnPCAcollection:
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- tol: float = 1e-8,
+ tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
batch_size: int = None,
@@ -2814,7 +2812,7 @@ class PlnPCA(_model):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- tol: float = 1e-8,
+ tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
batch_size=None,
@@ -3038,18 +3036,6 @@ class PlnPCA(_model):
"""
return self._rank
- @property
- def model_parameters(self) -> Dict[str, torch.Tensor]:
- """
- Property representing the model parameters.
-
- Returns
- -------
- Dict[str, torch.Tensor]
- The model parameters.
- """
- return {"coef": self.coef, "components": self.components}
-
@property
def number_of_parameters(self) -> int:
"""
@@ -3310,6 +3296,19 @@ class PlnPCA(_model):
return [self._components, self._latent_mean, self._latent_sqrt_var]
return [self._components, self._coef, self._latent_mean, self._latent_sqrt_var]
+ @property
+ @_add_doc(_model)
+ def model_parameters(self) -> Dict[str, torch.Tensor]:
+ return {"coef": self.coef, "components": self.components}
+
+ @property
+ @_add_doc(_model)
+ def latent_parameters(self):
+ return {
+ "latent_sqrt_var": self.latent_sqrt_var,
+ "latent_mean": self.latent_mean,
+ }
+
class ZIPln(_model):
_NAME = "ZIPln"
@@ -3429,7 +3428,7 @@ class ZIPln(_model):
nb_max_iteration: int = 50000,
*,
lr: float = 0.01,
- tol: float = 1e-8,
+ tol: float = 1e-3,
do_smart_init: bool = True,
verbose: bool = False,
batch_size: int = None,
@@ -3495,6 +3494,7 @@ class ZIPln(_model):
def _covariance(self):
return self._components @ (self._components.T)
+ @property
def latent_variables(self) -> tuple([torch.Tensor, torch.Tensor]):
"""
Property representing the latent variables. Two latent
@@ -3624,6 +3624,26 @@ class ZIPln(_model):
def _update_closed_forms(self):
pass
+ @property
+ @_add_doc(_model)
+ def model_parameters(self) -> Dict[str, torch.Tensor]:
+ return {
+ "coef": self.coef,
+ "components": self.components,
+ "coef_inflation": self.coef_inflation,
+ }
+
+ @property
+ @_add_doc(_model)
+ def latent_parameters(self):
+ latent_param = {
+ "latent_sqrt_var": self.latent_sqrt_var,
+ "latent_mean": self.latent_mean,
+ }
+ if self._use_closed_form_prob is True:
+ latent_param["latent_prob"] = self.latent_prob
+ return latent_param
+
def grad_M(self):
if self.use_closed_form_prob is True:
latent_prob = self.closed_formula_latent_prob
diff --git a/tests/conftest.py b/tests/conftest.py
index 3a072f20..588b3e4e 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -7,7 +7,7 @@ from pytest_lazyfixture import lazy_fixture as lf
import pandas as pd
from pyPLNmodels import load_model, load_plnpcacollection
-from pyPLNmodels.models import Pln, PlnPCA, PlnPCAcollection
+from pyPLNmodels.models import Pln, PlnPCA, PlnPCAcollection, ZIPln
sys.path.append("../")
@@ -78,14 +78,20 @@ def convenient_PlnPCAcollection(*args, **kwargs):
def convenientpln(*args, **kwargs):
+ # no need to dict init since we do not have ranks
if isinstance(args[0], str):
return Pln.from_formula(*args, **kwargs)
return Pln(*args, **kwargs)
+def convenientzi(*args, **kwargs):
+ if isinstance(args[0], str):
+ return ZIPln.from_formula(*args, **kwargs)
+ return ZIPln(*args, **kwargs)
+
+
def generate_new_model(model, *args, **kwargs):
name_dir = model._directory_name
- print("directory name", name_dir)
name = model._NAME
if name in ("Pln", "PlnPCA"):
path = model._path_to_directory + name_dir
@@ -94,6 +100,8 @@ def generate_new_model(model, *args, **kwargs):
new = convenientpln(*args, **kwargs, dict_initialization=init)
if name == "PlnPCA":
new = convenient_PlnPCA(*args, **kwargs, dict_initialization=init)
+ if name == "ZIPln":
+ new = convenientzi(*args, **kwargs, dict_initialization=init)
if name == "PlnPCAcollection":
init = load_plnpcacollection(name_dir)
new = convenient_PlnPCAcollection(*args, **kwargs, dict_initialization=init)
diff --git a/tests/create_readme_and_docstrings_tests.py b/tests/create_readme_and_docstrings_tests.py
index d9f27aeb..63aecf9d 100644
--- a/tests/create_readme_and_docstrings_tests.py
+++ b/tests/create_readme_and_docstrings_tests.py
@@ -43,7 +43,7 @@ def get_example_readme(lines):
in_example = False
elif in_example is True:
example.append(line)
- example.pop(0) # The first is pip install pyPLNmodels which is not python code.
+ example.pop() # The last line is pip install pyPLNmodels which is not python code.
return [example]
--
GitLab
From f6127c3a18435b135faa4ad35f7e59da0b42a439 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 16 Oct 2023 11:41:59 +0200
Subject: [PATCH 097/167] renamed pln with model inf fixtures and fixed some
tests for the pln. Now we cannot set exog to None and add_const to False for
the ZIPln.
---
pyPLNmodels/models.py | 142 ++++++++++++++++------
tests/conftest.py | 259 +++++++++++++++++++++--------------------
tests/test_common.py | 95 +++++++--------
tests/test_pln_full.py | 6 +-
4 files changed, 289 insertions(+), 213 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 9228de82..0588c469 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -397,6 +397,7 @@ class _model(ABC):
while self.nb_iteration_done < nb_max_iteration and not stop_condition:
loss = self._trainstep()
criterion = self._update_criterion_args(loss)
+ print("criterion", criterion)
if abs(criterion) < tol:
stop_condition = True
if verbose and self.nb_iteration_done % 50 == 1:
@@ -3317,26 +3318,6 @@ class ZIPln(_model):
_coef_inflation: torch.Tensor
_dirac: torch.Tensor
- @_add_doc(
- _model,
- params="""
- use_closed_form_prob: bool, optional
- Whether or not use the closed formula for the latent probability
- """,
- example="""
- >>> from pyPLNmodels import ZIPln, get_real_count_data
- >>> endog= get_real_count_data()
- >>> zi = ZIPln(endog, add_const = True)
- >>> zi.fit()
- >>> print(zi)
- """,
- returns="""
- ZIPln
- """,
- see_also="""
- :func:`pyPLNmodels.ZIPln.from_formula`
- """,
- )
def __init__(
self,
endog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]],
@@ -3349,6 +3330,54 @@ class ZIPln(_model):
add_const: bool = True,
use_closed_form_prob: bool = False,
):
+ """
+ Initializes the ZIPln class.
+
+ Parameters
+ ----------
+ endog : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The count data.
+ exog : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
+ The covariate data. Defaults to None.
+ offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
+ The offsets data. Defaults to None.
+ offsets_formula : str, optional(keyword-only)
+ The formula for offsets. Defaults to "logsum". Overriden if
+ offsets is not None.
+ dict_initialization : dict, optional(keyword-only)
+ The initialization dictionary. Defaults to None.
+ take_log_offsets : bool, optional(keyword-only)
+ Whether to take the log of offsets. Defaults to False.
+ add_const : bool, optional(keyword-only)
+ Whether to add a column of one in the exog. Defaults to True.
+ If exog is None, add_const is set to True anyway and a warnings
+ is launched.
+ use_closed_form_prob : bool, optional
+ Whether or not use the closed formula for the latent probability.
+ Default is False.
+ Raises
+ ------
+ ValueError
+ If the batch_size is greater than the number of samples, or not int.
+ Returns
+ -------
+ A ZIPln object
+ See also
+ --------
+ :func:`pyPLNmodels.ZIPln.from_formula`
+ Examples
+ --------
+ >>> from pyPLNmodels import ZIPln, get_real_count_data
+ >>> endog= get_real_count_data()
+ >>> zi = ZIPln(endog, add_const = True)
+ >>> zi.fit()
+ >>> print(zi)
+ """
+ if exog is None and add_const is False:
+ msg = "No covariates has been given. An intercept is added since "
+ msg += "a ZIPln must have at least an intercept."
+ warnings.warn(msg)
+ add_const = True
super().__init__(
endog=endog,
exog=exog,
@@ -3375,22 +3404,6 @@ class ZIPln(_model):
return batch
@classmethod
- @_add_doc(
- _model,
- example="""
- >>> from pyPLNmodels import ZIPln, get_real_count_data
- >>> endog = get_real_count_data()
- >>> data = {"endog": endog}
- >>> zi = ZIPln.from_formula("endog ~ 1", data = data)
- """,
- returns="""
- ZIPln
- """,
- see_also="""
- :class:`pyPLNmodels.ZIPln`
- :func:`pyPLNmodels.ZIPln.__init__`
- """,
- )
def from_formula(
cls,
formula: str,
@@ -3401,6 +3414,39 @@ class ZIPln(_model):
take_log_offsets: bool = False,
use_closed_form_prob: bool = True,
):
+ """
+ Create a model instance from a formula and data.
+
+ Parameters
+ ----------
+ formula : str
+ The formula.
+ data : dict
+ The data dictionary. Each value can be either a torch.Tensor,
+ a np.ndarray or pd.DataFrame
+ offsets_formula : str, optional(keyword-only)
+ The formula for offsets. Defaults to "logsum".
+ dict_initialization : dict, optional(keyword-only)
+ The initialization dictionary. Defaults to None.
+ take_log_offsets : bool, optional(keyword-only)
+ Whether to take the log of offsets. Defaults to False.
+ use_closed_form_prob : bool, optional
+ Whether or not use the closed formula for the latent probability.
+ Default is False.
+ Returns
+ -------
+ A ZIPln object
+ See also
+ --------
+ :class:`pyPLNmodels.ZIPln`
+ :func:`pyPLNmodels.ZIPln.__init__`
+ Examples
+ --------
+ >>> from pyPLNmodels import ZIPln, get_real_count_data
+ >>> endog = get_real_count_data()
+ >>> data = {"endog": endog}
+ >>> zi = ZIPln.from_formula("endog ~ 1", data = data)
+ """
endog, exog, offsets = _extract_data_from_formula(formula, data)
return cls(
endog,
@@ -3494,6 +3540,18 @@ class ZIPln(_model):
def _covariance(self):
return self._components @ (self._components.T)
+ @property
+ def components(self) -> torch.Tensor:
+ """
+ Property representing the components.
+
+ Returns
+ -------
+ torch.Tensor
+ The components.
+ """
+ return self._cpu_attribute_or_none("_components")
+
@property
def latent_variables(self) -> tuple([torch.Tensor, torch.Tensor]):
"""
@@ -3517,6 +3575,18 @@ class ZIPln(_model):
"""
return self.latent_mean, self.latent_prob
+ @property
+ def coef_inflation(self):
+ """
+ Property representing the coefficients of the zero inflated model.
+
+ Returns
+ -------
+ torch.Tensor or None
+ The coefficients or None.
+ """
+ return self._cpu_attribute_or_none("_coef_inflation")
+
def _update_parameters(self):
super()._update_parameters()
self._project_latent_prob()
diff --git a/tests/conftest.py b/tests/conftest.py
index 588b3e4e..e40558dd 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -12,10 +12,10 @@ from pyPLNmodels.models import Pln, PlnPCA, PlnPCAcollection, ZIPln
sys.path.append("../")
-pytest_plugins = [
- fixture_file.replace("/", ".").replace(".py", "")
- for fixture_file in glob.glob("src/**/tests/fixtures/[!__]*.py", recursive=True)
-]
+# pytest_plugins = [
+# fixture_file.replace("/", ".").replace(".py", "")
+# for fixture_file in glob.glob("src/**/tests/fixtures/[!__]*.py", recursive=True)
+# ]
from tests.import_data import (
@@ -78,7 +78,6 @@ def convenient_PlnPCAcollection(*args, **kwargs):
def convenientpln(*args, **kwargs):
- # no need to dict init since we do not have ranks
if isinstance(args[0], str):
return Pln.from_formula(*args, **kwargs)
return Pln(*args, **kwargs)
@@ -93,8 +92,8 @@ def convenientzi(*args, **kwargs):
def generate_new_model(model, *args, **kwargs):
name_dir = model._directory_name
name = model._NAME
- if name in ("Pln", "PlnPCA"):
- path = model._path_to_directory + name_dir
+ if name in ("Pln", "PlnPCA", "ZIPln"):
+ path = model._directory_name
init = load_model(path)
if name == "Pln":
new = convenientpln(*args, **kwargs, dict_initialization=init)
@@ -103,7 +102,7 @@ def generate_new_model(model, *args, **kwargs):
if name == "ZIPln":
new = convenientzi(*args, **kwargs, dict_initialization=init)
if name == "PlnPCAcollection":
- init = load_plnpcacollection(name_dir)
+ init = load_plnpcacollection(model._directory_name)
new = convenient_PlnPCAcollection(*args, **kwargs, dict_initialization=init)
return new
@@ -119,67 +118,67 @@ def cache(func):
return new_func
-params = [convenientpln, convenient_PlnPCA, convenient_PlnPCAcollection]
+params = [convenientpln, convenient_PlnPCA, convenient_PlnPCAcollection, convenientzi]
dict_fixtures = {}
@pytest.fixture(params=params)
-def simulated_pln_0cov_array(request):
+def simulated_model_0cov_array(request):
cls = request.param
- pln = cls(
+ model = cls(
endog_sim_0cov,
exog=exog_sim_0cov,
offsets=offsets_sim_0cov,
add_const=False,
)
- return pln
+ return model
@pytest.fixture(params=params)
@cache
-def simulated_fitted_pln_0cov_array(request):
+def simulated_fitted_model_0cov_array(request):
cls = request.param
- pln = cls(
+ model = cls(
endog_sim_0cov,
exog=exog_sim_0cov,
offsets=offsets_sim_0cov,
add_const=False,
)
- pln.fit()
- return pln
+ model.fit()
+ return model
@pytest.fixture(params=params)
-def simulated_pln_0cov_formula(request):
+def simulated_model_0cov_formula(request):
cls = request.param
- pln = cls("endog ~ 0", data_sim_0cov)
- return pln
+ model = cls("endog ~ 0", data_sim_0cov)
+ return model
@pytest.fixture(params=params)
@cache
-def simulated_fitted_pln_0cov_formula(request):
+def simulated_fitted_model_0cov_formula(request):
cls = request.param
- pln = cls("endog ~ 0", data_sim_0cov)
- pln.fit()
- return pln
+ model = cls("endog ~ 0", data_sim_0cov)
+ model.fit()
+ return model
@pytest.fixture
-def simulated_loaded_pln_0cov_formula(simulated_fitted_pln_0cov_formula):
- simulated_fitted_pln_0cov_formula.save()
+def simulated_loaded_model_0cov_formula(simulated_fitted_model_0cov_formula):
+ simulated_fitted_model_0cov_formula.save()
return generate_new_model(
- simulated_fitted_pln_0cov_formula,
+ simulated_fitted_model_0cov_formula,
"endog ~ 0",
data_sim_0cov,
)
@pytest.fixture
-def simulated_loaded_pln_0cov_array(simulated_fitted_pln_0cov_array):
- simulated_fitted_pln_0cov_array.save()
+def simulated_loaded_model_0cov_array(simulated_fitted_model_0cov_array):
+ simulated_fitted_model_0cov_array.save()
return generate_new_model(
- simulated_fitted_pln_0cov_array,
+ simulated_fitted_model_0cov_array,
endog_sim_0cov,
exog=exog_sim_0cov,
offsets=offsets_sim_0cov,
@@ -187,87 +186,89 @@ def simulated_loaded_pln_0cov_array(simulated_fitted_pln_0cov_array):
)
-sim_pln_0cov_instance = [
- "simulated_pln_0cov_array",
- "simulated_pln_0cov_formula",
+sim_model_0cov_instance = [
+ "simulated_model_0cov_array",
+ "simulated_model_0cov_formula",
]
-instances = sim_pln_0cov_instance + instances
+instances = sim_model_0cov_instance + instances
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "sim_pln_0cov_instance", sim_pln_0cov_instance
+ dict_fixtures, "sim_model_0cov_instance", sim_model_0cov_instance
)
-sim_pln_0cov_fitted = [
- "simulated_fitted_pln_0cov_array",
- "simulated_fitted_pln_0cov_formula",
+sim_model_0cov_fitted = [
+ "simulated_fitted_model_0cov_array",
+ "simulated_fitted_model_0cov_formula",
]
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "sim_pln_0cov_fitted", sim_pln_0cov_fitted
+ dict_fixtures, "sim_model_0cov_fitted", sim_model_0cov_fitted
)
-sim_pln_0cov_loaded = [
- "simulated_loaded_pln_0cov_array",
- "simulated_loaded_pln_0cov_formula",
+sim_model_0cov_loaded = [
+ "simulated_loaded_model_0cov_array",
+ "simulated_loaded_model_0cov_formula",
]
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "sim_pln_0cov_loaded", sim_pln_0cov_loaded
+ dict_fixtures, "sim_model_0cov_loaded", sim_model_0cov_loaded
)
-sim_pln_0cov = sim_pln_0cov_instance + sim_pln_0cov_fitted + sim_pln_0cov_loaded
-dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "sim_pln_0cov", sim_pln_0cov)
+sim_model_0cov = sim_model_0cov_instance + sim_model_0cov_fitted + sim_model_0cov_loaded
+dict_fixtures = add_list_of_fixture_to_dict(
+ dict_fixtures, "sim_model_0cov", sim_model_0cov
+)
@pytest.fixture(params=params)
@cache
-def simulated_pln_2cov_array(request):
+def simulated_model_2cov_array(request):
cls = request.param
- pln_full = cls(
+ model = cls(
endog_sim_2cov,
exog=exog_sim_2cov,
offsets=offsets_sim_2cov,
add_const=False,
)
- return pln_full
+ return model
@pytest.fixture
-def simulated_fitted_pln_2cov_array(simulated_pln_2cov_array):
- simulated_pln_2cov_array.fit()
- return simulated_pln_2cov_array
+def simulated_fitted_model_2cov_array(simulated_model_2cov_array):
+ simulated_model_2cov_array.fit()
+ return simulated_model_2cov_array
@pytest.fixture(params=params)
@cache
-def simulated_pln_2cov_formula(request):
+def simulated_model_2cov_formula(request):
cls = request.param
- pln_full = cls("endog ~ 0 + exog", data_sim_2cov)
- return pln_full
+ model = cls("endog ~ 0 + exog", data_sim_2cov)
+ return model
@pytest.fixture
-def simulated_fitted_pln_2cov_formula(simulated_pln_2cov_formula):
- simulated_pln_2cov_formula.fit()
- return simulated_pln_2cov_formula
+def simulated_fitted_model_2cov_formula(simulated_model_2cov_formula):
+ simulated_model_2cov_formula.fit()
+ return simulated_model_2cov_formula
@pytest.fixture
-def simulated_loaded_pln_2cov_formula(simulated_fitted_pln_2cov_formula):
- simulated_fitted_pln_2cov_formula.save()
+def simulated_loaded_model_2cov_formula(simulated_fitted_model_2cov_formula):
+ simulated_fitted_model_2cov_formula.save()
return generate_new_model(
- simulated_fitted_pln_2cov_formula,
+ simulated_fitted_model_2cov_formula,
"endog ~0 + exog",
data_sim_2cov,
)
@pytest.fixture
-def simulated_loaded_pln_2cov_array(simulated_fitted_pln_2cov_array):
- simulated_fitted_pln_2cov_array.save()
+def simulated_loaded_model_2cov_array(simulated_fitted_model_2cov_array):
+ simulated_fitted_model_2cov_array.save()
return generate_new_model(
- simulated_fitted_pln_2cov_array,
+ simulated_fitted_model_2cov_array,
endog_sim_2cov,
exog=exog_sim_2cov,
offsets=offsets_sim_2cov,
@@ -275,147 +276,149 @@ def simulated_loaded_pln_2cov_array(simulated_fitted_pln_2cov_array):
)
-sim_pln_2cov_instance = [
- "simulated_pln_2cov_array",
- "simulated_pln_2cov_formula",
+sim_model_2cov_instance = [
+ "simulated_model_2cov_array",
+ "simulated_model_2cov_formula",
]
-instances = sim_pln_2cov_instance + instances
+instances = sim_model_2cov_instance + instances
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "sim_pln_2cov_instance", sim_pln_2cov_instance
+ dict_fixtures, "sim_model_2cov_instance", sim_model_2cov_instance
)
-sim_pln_2cov_fitted = [
- "simulated_fitted_pln_2cov_array",
- "simulated_fitted_pln_2cov_formula",
+sim_model_2cov_fitted = [
+ "simulated_fitted_model_2cov_array",
+ "simulated_fitted_model_2cov_formula",
]
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "sim_pln_2cov_fitted", sim_pln_2cov_fitted
+ dict_fixtures, "sim_model_2cov_fitted", sim_model_2cov_fitted
)
-sim_pln_2cov_loaded = [
- "simulated_loaded_pln_2cov_array",
- "simulated_loaded_pln_2cov_formula",
+sim_model_2cov_loaded = [
+ "simulated_loaded_model_2cov_array",
+ "simulated_loaded_model_2cov_formula",
]
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "sim_pln_2cov_loaded", sim_pln_2cov_loaded
+ dict_fixtures, "sim_model_2cov_loaded", sim_model_2cov_loaded
)
-sim_pln_2cov = sim_pln_2cov_instance + sim_pln_2cov_fitted + sim_pln_2cov_loaded
-dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "sim_pln_2cov", sim_pln_2cov)
+sim_model_2cov = sim_model_2cov_instance + sim_model_2cov_fitted + sim_model_2cov_loaded
+dict_fixtures = add_list_of_fixture_to_dict(
+ dict_fixtures, "sim_model_2cov", sim_model_2cov
+)
@pytest.fixture(params=params)
@cache
-def real_pln_intercept_array(request):
+def real_model_intercept_array(request):
cls = request.param
- pln_full = cls(endog_real, add_const=True)
- return pln_full
+ model = cls(endog_real, add_const=True)
+ return model
@pytest.fixture
-def real_fitted_pln_intercept_array(real_pln_intercept_array):
- real_pln_intercept_array.fit()
- return real_pln_intercept_array
+def real_fitted_model_intercept_array(real_model_intercept_array):
+ real_model_intercept_array.fit()
+ return real_model_intercept_array
@pytest.fixture(params=params)
@cache
-def real_pln_intercept_formula(request):
+def real_model_intercept_formula(request):
cls = request.param
- pln_full = cls("endog ~ 1", data_real)
- return pln_full
+ model = cls("endog ~ 1", data_real)
+ return model
@pytest.fixture
-def real_fitted_pln_intercept_formula(real_pln_intercept_formula):
- real_pln_intercept_formula.fit()
- return real_pln_intercept_formula
+def real_fitted_model_intercept_formula(real_model_intercept_formula):
+ real_model_intercept_formula.fit()
+ return real_model_intercept_formula
@pytest.fixture
-def real_loaded_pln_intercept_formula(real_fitted_pln_intercept_formula):
- real_fitted_pln_intercept_formula.save()
+def real_loaded_model_intercept_formula(real_fitted_model_intercept_formula):
+ real_fitted_model_intercept_formula.save()
return generate_new_model(
- real_fitted_pln_intercept_formula, "endog ~ 1", data=data_real
+ real_fitted_model_intercept_formula, "endog ~ 1", data=data_real
)
@pytest.fixture
-def real_loaded_pln_intercept_array(real_fitted_pln_intercept_array):
- real_fitted_pln_intercept_array.save()
+def real_loaded_model_intercept_array(real_fitted_model_intercept_array):
+ real_fitted_model_intercept_array.save()
return generate_new_model(
- real_fitted_pln_intercept_array,
+ real_fitted_model_intercept_array,
endog_real,
add_const=True,
)
-real_pln_instance = [
- "real_pln_intercept_array",
- "real_pln_intercept_formula",
+real_model_instance = [
+ "real_model_intercept_array",
+ "real_model_intercept_formula",
]
-instances = real_pln_instance + instances
+instances = real_model_instance + instances
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "real_pln_instance", real_pln_instance
+ dict_fixtures, "real_model_instance", real_model_instance
)
-real_pln_fitted = [
- "real_fitted_pln_intercept_array",
- "real_fitted_pln_intercept_formula",
+real_model_fitted = [
+ "real_fitted_model_intercept_array",
+ "real_fitted_model_intercept_formula",
]
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "real_pln_fitted", real_pln_fitted
+ dict_fixtures, "real_model_fitted", real_model_fitted
)
-real_pln_loaded = [
- "real_loaded_pln_intercept_array",
- "real_loaded_pln_intercept_formula",
+real_model_loaded = [
+ "real_loaded_model_intercept_array",
+ "real_loaded_model_intercept_formula",
]
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "real_pln_loaded", real_pln_loaded
+ dict_fixtures, "real_model_loaded", real_model_loaded
)
-sim_loaded_pln = sim_pln_0cov_loaded + sim_pln_2cov_loaded
+sim_loaded_model = sim_model_0cov_loaded + sim_model_2cov_loaded
-loaded_pln = real_pln_loaded + sim_loaded_pln
-dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "loaded_pln", loaded_pln)
+loaded_model = real_model_loaded + sim_loaded_model
+dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "loaded_model", loaded_model)
-simulated_pln_fitted = sim_pln_0cov_fitted + sim_pln_2cov_fitted
+simulated_model_fitted = sim_model_0cov_fitted + sim_model_2cov_fitted
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "simulated_pln_fitted", simulated_pln_fitted
+ dict_fixtures, "simulated_model_fitted", simulated_model_fitted
)
-fitted_pln = real_pln_fitted + simulated_pln_fitted
-dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "fitted_pln", fitted_pln)
+fitted_model = real_model_fitted + simulated_model_fitted
+dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "fitted_model", fitted_model)
-loaded_and_fitted_sim_pln = simulated_pln_fitted + sim_loaded_pln
-loaded_and_fitted_real_pln = real_pln_fitted + real_pln_loaded
+loaded_and_fitted_sim_model = simulated_model_fitted + sim_loaded_model
+loaded_and_fitted_real_model = real_model_fitted + real_model_loaded
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "loaded_and_fitted_real_pln", loaded_and_fitted_real_pln
+ dict_fixtures, "loaded_and_fitted_real_model", loaded_and_fitted_real_model
)
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "loaded_and_fitted_sim_pln", loaded_and_fitted_sim_pln
+ dict_fixtures, "loaded_and_fitted_sim_model", loaded_and_fitted_sim_model
)
-loaded_and_fitted_pln = fitted_pln + loaded_pln
+loaded_and_fitted_model = fitted_model + loaded_model
dict_fixtures = add_list_of_fixture_to_dict(
- dict_fixtures, "loaded_and_fitted_pln", loaded_and_fitted_pln
+ dict_fixtures, "loaded_and_fitted_model", loaded_and_fitted_model
)
-real_pln = real_pln_instance + real_pln_fitted + real_pln_loaded
-dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "real_pln", real_pln)
+real_model = real_model_instance + real_model_fitted + real_model_loaded
+dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "real_model", real_model)
-sim_pln = sim_pln_2cov + sim_pln_0cov
-dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "sim_pln", sim_pln)
+sim_model = sim_model_2cov + sim_model_0cov
+dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "sim_model", sim_model)
-all_pln = real_pln + sim_pln + instances
+all_model = real_model + sim_model + instances
dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "instances", instances)
-dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "all_pln", all_pln)
+dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "all_model", all_model)
-for string_fixture in all_pln:
+for string_fixture in all_model:
print("string_fixture", string_fixture)
dict_fixtures = add_fixture_to_dict(dict_fixtures, string_fixture)
diff --git a/tests/test_common.py b/tests/test_common.py
index b1a6837c..cec97a72 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -8,82 +8,85 @@ from tests.utils import MSE, filter_models
from tests.import_data import true_sim_0cov, true_sim_2cov, endog_real
+single_models = ["Pln", "PlnPCA", "ZIPln"]
+pln_and_plnpca = ["Pln", "PlnPCA"]
-@pytest.mark.parametrize("any_pln", dict_fixtures["loaded_and_fitted_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_properties(any_pln):
- assert hasattr(any_pln, "latent_parameters")
- assert hasattr(any_pln, "latent_variables")
- assert hasattr(any_pln, "optim_parameters")
- assert hasattr(any_pln, "model_parameters")
+@pytest.mark.parametrize("any_model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(single_models)
+def test_properties(any_model):
+ assert hasattr(any_model, "latent_parameters")
+ assert hasattr(any_model, "latent_variables")
+ assert hasattr(any_model, "optim_parameters")
+ assert hasattr(any_model, "model_parameters")
-@pytest.mark.parametrize("sim_pln", dict_fixtures["loaded_and_fitted_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_predict_simulated(sim_pln):
- if sim_pln.nb_cov == 0:
- assert sim_pln.predict() is None
+
+@pytest.mark.parametrize("sim_model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(pln_and_plnpca)
+def test_predict_simulated(sim_model):
+ if sim_model.nb_cov == 0:
+ assert sim_model.predict() is None
with pytest.raises(AttributeError):
- sim_pln.predict(1)
+ sim_model.predict(1)
else:
- X = torch.randn((sim_pln.n_samples, sim_pln.nb_cov))
- prediction = sim_pln.predict(X)
- expected = X @ sim_pln.coef
+ X = torch.randn((sim_model.n_samples, sim_model.nb_cov))
+ prediction = sim_model.predict(X)
+ expected = X @ sim_model.coef
assert torch.all(torch.eq(expected, prediction))
-@pytest.mark.parametrize("any_instance_pln", dict_fixtures["instances"])
-def test_verbose(any_instance_pln):
- any_instance_pln.fit(verbose=True, tol=0.1)
+@pytest.mark.parametrize("any_instance_model", dict_fixtures["instances"])
+def test_verbose(any_instance_model):
+ any_instance_model.fit(verbose=True, tol=0.1)
@pytest.mark.parametrize(
- "simulated_fitted_any_pln", dict_fixtures["loaded_and_fitted_sim_pln"]
+ "simulated_fitted_any_model", dict_fixtures["loaded_and_fitted_sim_model"]
)
-@filter_models(["Pln", "PlnPCA"])
-def test_find_right_covariance(simulated_fitted_any_pln):
- if simulated_fitted_any_pln.nb_cov == 0:
+@filter_models(pln_and_plnpca)
+def test_find_right_covariance(simulated_fitted_any_model):
+ if simulated_fitted_any_model.nb_cov == 0:
true_covariance = true_sim_0cov["Sigma"]
- elif simulated_fitted_any_pln.nb_cov == 2:
+ elif simulated_fitted_any_model.nb_cov == 2:
true_covariance = true_sim_2cov["Sigma"]
else:
raise ValueError(
- f"Not the right numbers of covariance({simulated_fitted_any_pln.nb_cov})"
+ f"Not the right numbers of covariance({simulated_fitted_any_model.nb_cov})"
)
- mse_covariance = MSE(simulated_fitted_any_pln.covariance - true_covariance)
+ mse_covariance = MSE(simulated_fitted_any_model.covariance - true_covariance)
assert mse_covariance < 0.05
@pytest.mark.parametrize(
- "real_fitted_and_loaded_pln", dict_fixtures["loaded_and_fitted_real_pln"]
+ "real_fitted_and_loaded_model", dict_fixtures["loaded_and_fitted_real_model"]
)
-@filter_models(["Pln", "PlnPCA"])
-def test_right_covariance_shape(real_fitted_and_loaded_pln):
- assert real_fitted_and_loaded_pln.covariance.shape == (
+@filter_models(single_models)
+def test_right_covariance_shape(real_fitted_and_loaded_model):
+ assert real_fitted_and_loaded_model.covariance.shape == (
endog_real.shape[1],
endog_real.shape[1],
)
@pytest.mark.parametrize(
- "simulated_fitted_any_pln", dict_fixtures["loaded_and_fitted_pln"]
+ "simulated_fitted_any_model", dict_fixtures["loaded_and_fitted_model"]
)
-@filter_models(["Pln", "PlnPCA"])
-def test_find_right_coef(simulated_fitted_any_pln):
- if simulated_fitted_any_pln.nb_cov == 2:
+@filter_models(pln_and_plnpca)
+def test_find_right_coef(simulated_fitted_any_model):
+ if simulated_fitted_any_model.nb_cov == 2:
true_coef = true_sim_2cov["beta"]
- mse_coef = MSE(simulated_fitted_any_pln.coef - true_coef)
+ mse_coef = MSE(simulated_fitted_any_model.coef - true_coef)
assert mse_coef < 0.1
- elif simulated_fitted_any_pln.nb_cov == 0:
- assert simulated_fitted_any_pln.coef is None
+ elif simulated_fitted_any_model.nb_cov == 0:
+ assert simulated_fitted_any_model.coef is None
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_fail_count_setter(pln):
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(single_models)
+def test_fail_count_setter(model):
wrong_endog = torch.randint(size=(10, 5), low=0, high=10)
with pytest.raises(Exception):
- pln.endog = wrong_endog
+ model.endog = wrong_endog
@pytest.mark.parametrize("instance", dict_fixtures["instances"])
@@ -96,9 +99,9 @@ def test__print_end_of_fitting_message(instance):
instance.fit(nb_max_iteration=4)
-@pytest.mark.parametrize("pln", dict_fixtures["fitted_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_fail_wrong_exog_prediction(pln):
- X = torch.randn(pln.n_samples, pln.nb_cov + 1)
+@pytest.mark.parametrize("model", dict_fixtures["fitted_model"])
+@filter_models(single_models)
+def test_fail_wrong_exog_prediction(model):
+ X = torch.randn(model.n_samples, model.nb_cov + 1)
with pytest.raises(Exception):
- pln.predict(X)
+ model.predict(X)
diff --git a/tests/test_pln_full.py b/tests/test_pln_full.py
index 870114a0..1115e1ec 100644
--- a/tests/test_pln_full.py
+++ b/tests/test_pln_full.py
@@ -4,14 +4,14 @@ from tests.conftest import dict_fixtures
from tests.utils import filter_models
-@pytest.mark.parametrize("fitted_pln", dict_fixtures["fitted_pln"])
+@pytest.mark.parametrize("fitted_pln", dict_fixtures["fitted_model"])
@filter_models(["Pln"])
def test_number_of_iterations_pln_full(fitted_pln):
- nb_iterations = len(fitted_pln._elbos_list)
+ nb_iterations = len(fitted_pln.elbos_list)
assert 20 < nb_iterations < 1000
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
+@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["Pln"])
def test_latent_var_full(pln):
assert pln.transform().shape == pln.endog.shape
--
GitLab
From 13bf8f1e0c88f9d3522bdde106c39edbbc81cded Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 16 Oct 2023 15:10:08 +0200
Subject: [PATCH 098/167] right elbo, problem before.
---
pyPLNmodels/elbos.py | 203 ++++++++++++++++++++++++-------------------
1 file changed, 113 insertions(+), 90 deletions(-)
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index ec743430..cf235ec4 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -5,63 +5,6 @@ from ._closed_forms import _closed_formula_covariance, _closed_formula_coef
from typing import Optional
-def elbo_pln(
- endog: torch.Tensor,
- offsets: torch.Tensor,
- exog: Optional[torch.Tensor],
- latent_mean: torch.Tensor,
- latent_sqrt_var: torch.Tensor,
- covariance: torch.Tensor,
- coef: torch.Tensor,
-) -> torch.Tensor:
- """
- Compute the ELBO (Evidence Lower Bound) for the Pln model.
-
- Parameters:
- ----------
- endog : torch.Tensor
- Counts with size (n, p).
- offsets : torch.Tensor
- Offset with size (n, p).
- exog : torch.Tensor, optional
- Covariates with size (n, d).
- latent_mean : torch.Tensor
- Variational parameter with size (n, p).
- latent_sqrt_var : torch.Tensor
- Variational parameter with size (n, p).
- covariance : torch.Tensor
- Model parameter with size (p, p).
- coef : torch.Tensor
- Model parameter with size (d, p).
-
- Returns:
- -------
- torch.Tensor
- The ELBO (Evidence Lower Bound), of size one.
- """
- n_samples, dim = endog.shape
- s_rond_s = torch.square(latent_sqrt_var)
- offsets_plus_m = offsets + latent_mean
- if exog is None:
- XB = torch.zeros_like(endog)
- else:
- XB = exog @ coef
- m_minus_xb = latent_mean - XB
- d_plus_minus_xb2 = (
- torch.diag(torch.sum(s_rond_s, dim=0)) + m_minus_xb.T @ m_minus_xb
- )
- elbo = -0.5 * n_samples * torch.logdet(covariance)
- elbo += torch.sum(
- endog * offsets_plus_m
- - 0.5 * torch.exp(offsets_plus_m + s_rond_s)
- + 0.5 * torch.log(s_rond_s)
- )
- elbo -= 0.5 * torch.trace(torch.inverse(covariance) @ d_plus_minus_xb2)
- elbo -= torch.sum(_log_stirling(endog))
- elbo += 0.5 * n_samples * dim
- return elbo / n_samples
-
-
def profiled_elbo_pln(
endog: torch.Tensor,
exog: torch.Tensor,
@@ -172,6 +115,78 @@ def elbo_plnpca(
) / n_samples
+def log1pexp(x):
+ # more stable version of log(1 + exp(x))
+ return torch.where(x < 50, torch.log1p(torch.exp(x)), x)
+
+
+def elbo_pln(
+ endog: torch.Tensor,
+ exog: Optional[torch.Tensor],
+ offsets: torch.Tensor,
+ latent_mean: torch.Tensor,
+ latent_sqrt_var: torch.Tensor,
+ covariance: torch.Tensor,
+ coef: torch.Tensor,
+) -> torch.Tensor:
+ """
+ Compute the ELBO (Evidence Lower Bound) for the Pln model.
+
+ Parameters:
+ ----------
+ endog : torch.Tensor
+ Counts with size (n, p).
+ offsets : torch.Tensor
+ Offset with size (n, p).
+ exog : torch.Tensor, optional
+ Covariates with size (n, d).
+ latent_mean : torch.Tensor
+ Variational parameter with size (n, p).
+ latent_sqrt_var : torch.Tensor
+ Variational parameter with size (n, p).
+ covariance : torch.Tensor
+ Model parameter with size (p, p).
+ coef : torch.Tensor
+ Model parameter with size (d, p).
+
+ Returns:
+ -------
+ torch.Tensor
+ The ELBO (Evidence Lower Bound), of size one.
+ """
+ n_samples, dim = endog.shape
+ s_rond_s = torch.square(latent_sqrt_var)
+ offsets_plus_m = offsets + latent_mean
+ Omega = torch.inverse(covariance)
+ if exog is None:
+ XB = torch.zeros_like(endog)
+ else:
+ XB = exog @ coef
+ # print('XB:', XB)
+ m_minus_xb = latent_mean - XB
+ m_moins_xb_outer = torch.mm(m_minus_xb.T, m_minus_xb)
+ A = torch.exp(offsets_plus_m + s_rond_s / 2)
+ first_a = torch.sum(endog * offsets_plus_m)
+ sec_a = -torch.sum(A)
+ third_a = -torch.sum(_log_stirling(endog))
+ a = first_a + sec_a + third_a
+ diag = torch.diag(torch.sum(s_rond_s, dim=0))
+ elbo = torch.clone(a)
+ b = -0.5 * n_samples * torch.logdet(covariance) + torch.sum(
+ -1 / 2 * Omega * m_moins_xb_outer
+ )
+ elbo += b
+ d = n_samples * dim / 2 + torch.sum(+0.5 * torch.log(s_rond_s))
+ elbo += d
+ f = -0.5 * torch.trace(torch.inverse(covariance) @ diag)
+ elbo += f
+ # print("a pln", a)
+ # print("b pln", b)
+ # print("d pln", d)
+ # print("f pln", f)
+ return elbo # / n_samples
+
+
## pb with trunc_log
## should rename some variables so that is is clearer when we see the formula
def elbo_zi_pln(
@@ -194,7 +209,7 @@ def elbo_zi_pln(
0: torch.tensor. Offset, size (n,p)
exog: torch.tensor. Covariates, size (n,d)
latent_mean: torch.tensor. Variational parameter with size (n,p)
- latent_sqrt_var: torch.tensor. Variational parameter with size (n,p)
+ latent_var: torch.tensor. Variational parameter with size (n,p)
pi: torch.tensor. Variational parameter with size (n,p)
covariance: torch.tensor. Model parameter with size (p,p)
coef: torch.tensor. Model parameter with size (d,p)
@@ -202,52 +217,60 @@ def elbo_zi_pln(
Returns:
torch.tensor of size 1 with a gradient.
"""
- if torch.norm(latent_prob * dirac - latent_prob) > 0.00000001:
- raise RuntimeError("Latent probability is not zero when it should be.")
covariance = components @ (components.T)
- diag_cov = torch.diag(covariance)
- Omega = torch.inverse(covariance)
- diag_omega = torch.diag(Omega)
- un_moins_prob = 1 - latent_prob
+ if torch.norm(latent_prob * dirac - latent_prob) > 0.00000001:
+ print("Bug")
+ raise RuntimeError("rho error")
n_samples, dim = endog.shape
- s_rond_s = latent_sqrt_var * latent_sqrt_var
+ s_rond_s = torch.multiply(latent_sqrt_var, latent_sqrt_var)
o_plus_m = offsets + latent_mean
if exog is None:
XB = torch.zeros_like(endog)
- xcoef_inflation = torch.zeros_like(endog)
+ x_coef_inflation = torch.zeros_like(endog)
else:
XB = exog @ coef
- xcoef_inflation = exog @ coef_inflation
+ x_coef_inflation = exog @ coef_inflation
+
m_minus_xb = latent_mean - XB
A = torch.exp(o_plus_m + s_rond_s / 2)
- inside_a = un_moins_prob * (endog * o_plus_m - A - _log_stirling(endog))
+ inside_a = torch.multiply(
+ 1 - latent_prob, torch.multiply(endog, o_plus_m) - A - _log_stirling(endog)
+ )
+ Omega = torch.inverse(covariance)
+
m_moins_xb_outer = torch.mm(m_minus_xb.T, m_minus_xb)
- un_moins_prob_m_moins_xb = un_moins_prob * m_minus_xb
- un_moins_prob_m_moins_xb_outer = (
- un_moins_prob_m_moins_xb.T @ un_moins_prob_m_moins_xb
+ un_moins_rho = 1 - latent_prob
+ un_moins_rho_m_moins_xb = un_moins_rho * m_minus_xb
+ un_moins_rho_m_moins_xb_outer = un_moins_rho_m_moins_xb.T @ un_moins_rho_m_moins_xb
+ inside_b = -1 / 2 * Omega * un_moins_rho_m_moins_xb_outer
+
+ inside_c = torch.multiply(latent_prob, x_coef_inflation) - torch.log(
+ 1 + torch.exp(x_coef_inflation)
)
- inside_b = -1 / 2 * Omega * un_moins_prob_m_moins_xb_outer
- inside_c = latent_prob * xcoef_inflation - torch.log(1 + torch.exp(xcoef_inflation))
- log_diag = torch.log(diag_cov)
+ log_diag = torch.log(torch.diag(covariance))
log_S_term = torch.sum(
- un_moins_prob * torch.log(torch.abs(latent_sqrt_var)), axis=0
+ torch.multiply(1 - latent_prob, torch.log(torch.abs(latent_sqrt_var))), axis=0
)
- sum_prob = torch.sum(latent_prob, axis=0)
- covariance_term = 1 / 2 * torch.log(diag_cov) * sum_prob
+ y = torch.sum(latent_prob, axis=0)
+ covariance_term = 1 / 2 * torch.log(torch.diag(covariance)) * y
inside_d = covariance_term + log_S_term
- inside_e = torch.multiply(
- latent_prob, _trunc_log(latent_prob)
- ) + un_moins_prob * _trunc_log(un_moins_prob)
- sum_un_moins_prob_s2 = torch.sum(un_moins_prob * s_rond_s, axis=0)
- diag_sig_sum_prob = diag_cov * torch.sum(latent_prob, axis=0)
- new = torch.sum(latent_prob * un_moins_prob * (m_minus_xb**2), axis=0)
- K = sum_un_moins_prob_s2 + diag_sig_sum_prob + new
- inside_f = -1 / 2 * diag_omega * K
- full_diag_omega = diag_omega.expand(exog.shape[0], -1)
- elbo = torch.sum(inside_a + inside_c + inside_d)
- elbo += torch.sum(inside_b) - n_samples / 2 * torch.logdet(covariance)
- elbo += n_samples * dim / 2 + torch.sum(inside_d + inside_f)
- return elbo
+ inside_e = -torch.multiply(latent_prob, _trunc_log(latent_prob)) - torch.multiply(
+ 1 - latent_prob, _trunc_log(1 - latent_prob)
+ )
+ sum_un_moins_rho_s2 = torch.sum(torch.multiply(1 - latent_prob, s_rond_s), axis=0)
+ diag_sig_sum_rho = torch.multiply(
+ torch.diag(covariance), torch.sum(latent_prob, axis=0)
+ )
+ new = torch.sum(latent_prob * un_moins_rho * (m_minus_xb**2), axis=0)
+ K = sum_un_moins_rho_s2 + diag_sig_sum_rho + new
+ inside_f =-1 / 2 * torch.diag(Omega) * K
+ first = torch.sum(inside_a + inside_c + inside_e)
+ second = torch.sum(inside_b)
+ second -= n_samples / 2 * torch.logdet(covariance)
+ third = torch.sum(inside_d + inside_f)
+ third += n_samples*dim/2
+ res = first + second + third
+ return res
--
GitLab
From dbc13d31c63bbe666c05748f93aa8800d4d1cdb8 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 16 Oct 2023 17:06:43 +0200
Subject: [PATCH 099/167] tried to see why the zi bug with real data. The
components have zero determinant.
---
pyPLNmodels/_initialization.py | 4 ++--
pyPLNmodels/elbos.py | 14 +++++++++++---
pyPLNmodels/models.py | 13 ++++++++-----
3 files changed, 21 insertions(+), 10 deletions(-)
diff --git a/pyPLNmodels/_initialization.py b/pyPLNmodels/_initialization.py
index f5663746..ac4472a6 100644
--- a/pyPLNmodels/_initialization.py
+++ b/pyPLNmodels/_initialization.py
@@ -44,7 +44,7 @@ def _init_covariance(endog: torch.Tensor, exog: torch.Tensor) -> torch.Tensor:
def _init_components(
- endog: torch.Tensor, exog: torch.Tensor, rank: int
+ endog: torch.Tensor, rank: int
) -> torch.Tensor:
"""
Initialization for components for the Pln model. Get a first guess for covariance
@@ -65,7 +65,7 @@ def _init_components(
log_y = torch.log(endog + (endog == 0) * math.exp(-2))
pca = PCA(n_components=rank)
pca.fit(log_y.detach().cpu())
- pca_comp = pca.components_.T * np.sqrt(pca.explained_variance_)
+ pca_comp = pca.components_.T * np.sqrt(pca.explained_variance_ + 0.001)
return torch.from_numpy(pca_comp).to(DEVICE)
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index cf235ec4..3862de9f 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -238,17 +238,16 @@ def elbo_zi_pln(
1 - latent_prob, torch.multiply(endog, o_plus_m) - A - _log_stirling(endog)
)
Omega = torch.inverse(covariance)
-
m_moins_xb_outer = torch.mm(m_minus_xb.T, m_minus_xb)
un_moins_rho = 1 - latent_prob
un_moins_rho_m_moins_xb = un_moins_rho * m_minus_xb
un_moins_rho_m_moins_xb_outer = un_moins_rho_m_moins_xb.T @ un_moins_rho_m_moins_xb
inside_b = -1 / 2 * Omega * un_moins_rho_m_moins_xb_outer
-
inside_c = torch.multiply(latent_prob, x_coef_inflation) - torch.log(
1 + torch.exp(x_coef_inflation)
)
+
log_diag = torch.log(torch.diag(covariance))
log_S_term = torch.sum(
torch.multiply(1 - latent_prob, torch.log(torch.abs(latent_sqrt_var))), axis=0
@@ -267,10 +266,19 @@ def elbo_zi_pln(
new = torch.sum(latent_prob * un_moins_rho * (m_minus_xb**2), axis=0)
K = sum_un_moins_rho_s2 + diag_sig_sum_rho + new
inside_f =-1 / 2 * torch.diag(Omega) * K
+ print("inside_a",torch.sum(inside_a))
+ print("inside_b",torch.sum(inside_b))
+ print("inside_c",torch.sum(inside_c))
+ print("inside_d",torch.sum(inside_d))
+ print("inside_e",torch.sum(inside_e))
+ print("inside_f",torch.sum(inside_f))
first = torch.sum(inside_a + inside_c + inside_e)
+ print('first', first)
second = torch.sum(inside_b)
- second -= n_samples / 2 * torch.logdet(covariance)
+ second -= n_samples * torch.logdet(components)
+ print('logdet', torch.logdet(components))
third = torch.sum(inside_d + inside_f)
third += n_samples*dim/2
+ print('third', third)
res = first + second + third
return res
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index e9bcfd01..49298b4f 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -397,7 +397,6 @@ class _model(ABC):
while self.nb_iteration_done < nb_max_iteration and not stop_condition:
loss = self._trainstep()
criterion = self._update_criterion_args(loss)
- print("criterion", criterion)
if abs(criterion) < tol:
stop_condition = True
if verbose and self.nb_iteration_done % 50 == 1:
@@ -477,6 +476,8 @@ class _model(ABC):
self._extract_batch(batch)
self.optim.zero_grad()
loss = -self._compute_elbo_b()
+ if torch.sum(torch.isnan(loss)):
+ raise ValueError("test")
loss.backward()
elbo += loss.item()
self._update_parameters()
@@ -3334,7 +3335,7 @@ class PlnPCA(_model):
if not hasattr(self, "_coef"):
super()._smart_init_coef()
if not hasattr(self, "_components"):
- self._components = _init_components(self._endog, self._exog, self._rank)
+ self._components = _init_components(self._endog, self._rank)
@_add_doc(_model)
def _random_init_latent_parameters(self):
@@ -3490,7 +3491,7 @@ class ZIPln(_model):
use_closed_form_prob: bool = True,
):
"""
- Create a model instance from a formula and data.
+ Create a ZIPln instance from a formula and data.
Parameters
----------
@@ -3585,7 +3586,6 @@ class ZIPln(_model):
return "with full covariance model and zero-inflation."
def _random_init_model_parameters(self):
- super()._random_init_model_parameters()
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
self._coef = torch.randn(self.nb_cov, self.dim)
self._components = torch.randn(self.nb_cov, self.dim)
@@ -3595,7 +3595,10 @@ class ZIPln(_model):
# init of _coef.
super()._smart_init_coef()
if not hasattr(self, "_covariance"):
- self._components = _init_components(self._endog, self._exog, self.dim)
+ self._components = _init_components(self._endog, self.dim)
+ print('sum components', torch.sum(self._components))
+ print('sum endog', torch.sum(self._endog))
+ print('log det ', torch.logdet(self._components))
if not hasattr(self, "_coef_inflation"):
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
--
GitLab
From 843782828cc766b633fb085195bc4ff0fcdf050c Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 16 Oct 2023 19:46:36 +0200
Subject: [PATCH 100/167] fixed the elbo issu
---
pyPLNmodels/_initialization.py | 2 +-
pyPLNmodels/_utils.py | 13 +++++++++++++
pyPLNmodels/elbos.py | 12 ++----------
pyPLNmodels/models.py | 6 +++---
tests/conftest.py | 1 -
5 files changed, 19 insertions(+), 15 deletions(-)
diff --git a/pyPLNmodels/_initialization.py b/pyPLNmodels/_initialization.py
index ac4472a6..410283bf 100644
--- a/pyPLNmodels/_initialization.py
+++ b/pyPLNmodels/_initialization.py
@@ -65,7 +65,7 @@ def _init_components(
log_y = torch.log(endog + (endog == 0) * math.exp(-2))
pca = PCA(n_components=rank)
pca.fit(log_y.detach().cpu())
- pca_comp = pca.components_.T * np.sqrt(pca.explained_variance_ + 0.001)
+ pca_comp = pca.components_.T * np.sqrt(pca.explained_variance_)
return torch.from_numpy(pca_comp).to(DEVICE)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index f5f02942..802e5f2e 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -1080,3 +1080,16 @@ def d_h_x3(a, x, y, dirac):
rho = torch.sigmoid(a - torch.log(phi(x, y))) * dirac
rho_prime = rho * (1 - rho)
return -rho_prime * d_varpsi_x2(x, y) / phi(x, y)
+
+def vec_to_mat(C, p, q):
+ c = torch.zeros(p, q)
+ c[torch.tril_indices(p, q, offset=0).tolist()] = C
+ # c = C.reshape(p,q)
+ return c
+
+
+def mat_to_vec(matc, p, q):
+ tril = torch.tril(matc)
+ # tril = matc.reshape(-1,1).squeeze()
+ return tril[torch.tril_indices(p, q, offset=0).tolist()]
+
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 3862de9f..585b423d 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -266,19 +266,11 @@ def elbo_zi_pln(
new = torch.sum(latent_prob * un_moins_rho * (m_minus_xb**2), axis=0)
K = sum_un_moins_rho_s2 + diag_sig_sum_rho + new
inside_f =-1 / 2 * torch.diag(Omega) * K
- print("inside_a",torch.sum(inside_a))
- print("inside_b",torch.sum(inside_b))
- print("inside_c",torch.sum(inside_c))
- print("inside_d",torch.sum(inside_d))
- print("inside_e",torch.sum(inside_e))
- print("inside_f",torch.sum(inside_f))
first = torch.sum(inside_a + inside_c + inside_e)
- print('first', first)
second = torch.sum(inside_b)
- second -= n_samples * torch.logdet(components)
- print('logdet', torch.logdet(components))
+ _, logdet = torch.slogdet(components)
+ second -= n_samples *logdet
third = torch.sum(inside_d + inside_f)
third += n_samples*dim/2
- print('third', third)
res = first + second + third
return res
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 49298b4f..db723dba 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -32,6 +32,8 @@ from ._utils import (
_array2tensor,
_handle_data,
_add_doc,
+ vec_to_mat,
+ mat_to_vec,
)
from ._initialization import (
@@ -3596,9 +3598,7 @@ class ZIPln(_model):
super()._smart_init_coef()
if not hasattr(self, "_covariance"):
self._components = _init_components(self._endog, self.dim)
- print('sum components', torch.sum(self._components))
- print('sum endog', torch.sum(self._endog))
- print('log det ', torch.logdet(self._components))
+
if not hasattr(self, "_coef_inflation"):
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
diff --git a/tests/conftest.py b/tests/conftest.py
index e40558dd..85ca2aaf 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -37,7 +37,6 @@ endog_real = data_real["endog"]
endog_real = pd.DataFrame(endog_real)
endog_real.columns = [f"var_{i}" for i in range(endog_real.shape[1])]
-
def add_fixture_to_dict(my_dict, string_fixture):
my_dict[string_fixture] = [lf(string_fixture)]
return my_dict
--
GitLab
From 41acf9e6788100bff4fa22f806aedb66492f5f4d Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 17 Oct 2023 11:07:58 +0200
Subject: [PATCH 101/167] add tests for the zi model and implement some
features to pass the tests for the zi
---
pyPLNmodels/_utils.py | 2 +-
pyPLNmodels/elbos.py | 7 +-
pyPLNmodels/models.py | 144 +++++++++++++++++++++++++++++++++++++++---
tests/conftest.py | 6 ++
tests/test_common.py | 5 +-
tests/test_zi.py | 73 +++++++++++++++++++++
6 files changed, 222 insertions(+), 15 deletions(-)
create mode 100644 tests/test_zi.py
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 802e5f2e..35b02806 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -1081,6 +1081,7 @@ def d_h_x3(a, x, y, dirac):
rho_prime = rho * (1 - rho)
return -rho_prime * d_varpsi_x2(x, y) / phi(x, y)
+
def vec_to_mat(C, p, q):
c = torch.zeros(p, q)
c[torch.tril_indices(p, q, offset=0).tolist()] = C
@@ -1092,4 +1093,3 @@ def mat_to_vec(matc, p, q):
tril = torch.tril(matc)
# tril = matc.reshape(-1,1).squeeze()
return tril[torch.tril_indices(p, q, offset=0).tolist()]
-
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 585b423d..5a56bc3d 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -247,7 +247,6 @@ def elbo_zi_pln(
1 + torch.exp(x_coef_inflation)
)
-
log_diag = torch.log(torch.diag(covariance))
log_S_term = torch.sum(
torch.multiply(1 - latent_prob, torch.log(torch.abs(latent_sqrt_var))), axis=0
@@ -265,12 +264,12 @@ def elbo_zi_pln(
)
new = torch.sum(latent_prob * un_moins_rho * (m_minus_xb**2), axis=0)
K = sum_un_moins_rho_s2 + diag_sig_sum_rho + new
- inside_f =-1 / 2 * torch.diag(Omega) * K
+ inside_f = -1 / 2 * torch.diag(Omega) * K
first = torch.sum(inside_a + inside_c + inside_e)
second = torch.sum(inside_b)
_, logdet = torch.slogdet(components)
- second -= n_samples *logdet
+ second -= n_samples * logdet
third = torch.sum(inside_d + inside_f)
- third += n_samples*dim/2
+ third += n_samples * dim / 2
res = first + second + third
return res
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index db723dba..81d732b2 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -668,7 +668,6 @@ class _model(ABC):
)
plt.show()
-
@property
def _latent_var(self) -> torch.Tensor:
"""
@@ -1915,8 +1914,8 @@ class Pln(_model):
return self.latent_mean.detach()
@_add_doc(
- _model,
- example="""
+ _model,
+ example="""
>>> from pyPLNmodels import Pln, get_real_count_data
>>> endog, labels = get_real_count_data(return_labels = True)
>>> pln = Pln(endog,add_const = True)
@@ -1924,8 +1923,8 @@ class Pln(_model):
>>> elbo = pln.compute_elbo()
>>> print("elbo", elbo)
>>> print("loglike/n", pln.loglike/pln.n_samples)
- """
- )
+ """,
+ )
def compute_elbo(self):
return profiled_elbo_pln(
self._endog,
@@ -1934,6 +1933,7 @@ class Pln(_model):
self._latent_mean,
self._latent_sqrt_var,
)
+
@_add_doc(_model)
def _compute_elbo_b(self):
return profiled_elbo_pln(
@@ -1943,6 +1943,7 @@ class Pln(_model):
self._latent_mean_b,
self._latent_sqrt_var_b,
)
+
@_add_doc(_model)
def _smart_init_model_parameters(self):
pass
@@ -1952,6 +1953,7 @@ class Pln(_model):
def _random_init_model_parameters(self):
pass
# no model parameters since we are doing a profiled ELBO
+
@_add_doc(_model)
def _smart_init_latent_parameters(self):
self._random_init_latent_sqrt_var()
@@ -1969,6 +1971,7 @@ class Pln(_model):
def _list_of_parameters_needing_gradient(self):
return [self._latent_mean, self._latent_sqrt_var]
+
class PlnPCAcollection:
"""
A collection where value q corresponds to a PlnPCA object with rank q.
@@ -3451,6 +3454,7 @@ class ZIPln(_model):
>>> zi.fit()
>>> print(zi)
"""
+ self._use_closed_form_prob = use_closed_form_prob
if exog is None and add_const is False:
msg = "No covariates has been given. An intercept is added since "
msg += "a ZIPln must have at least an intercept."
@@ -3465,7 +3469,6 @@ class ZIPln(_model):
take_log_offsets=take_log_offsets,
add_const=add_const,
)
- self._use_closed_form_prob = use_closed_form_prob
def _extract_batch(self, batch):
super()._extract_batch(batch)
@@ -3490,7 +3493,7 @@ class ZIPln(_model):
offsets_formula: str = "logsum",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
- use_closed_form_prob: bool = True,
+ use_closed_form_prob: bool = False,
):
"""
Create a ZIPln instance from a formula and data.
@@ -3590,7 +3593,7 @@ class ZIPln(_model):
def _random_init_model_parameters(self):
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
self._coef = torch.randn(self.nb_cov, self.dim)
- self._components = torch.randn(self.nb_cov, self.dim)
+ self._components = torch.randn(self.dim, self.dim)
# should change the good initialization for _coef_inflation
def _smart_init_model_parameters(self):
@@ -3656,7 +3659,7 @@ class ZIPln(_model):
@property
def coef_inflation(self):
"""
- Property representing the coefficients of the zero inflated model.
+ Property representing the coefficients of the inflation.
Returns
-------
@@ -3665,6 +3668,54 @@ class ZIPln(_model):
"""
return self._cpu_attribute_or_none("_coef_inflation")
+ @coef_inflation.setter
+ @_array2tensor
+ def coef_inflation(
+ self, coef_inflation: Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ ):
+ """
+ Setter for the coef_inflation property.
+
+ Parameters
+ ----------
+ coef : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The coefficients.
+
+ Raises
+ ------
+ ValueError
+ If the shape of the coef is incorrect.
+ """
+ if coef_inflation.shape != (self.nb_cov, self.dim):
+ raise ValueError(
+ f"Wrong shape for the coef. Expected {(self.nb_cov, self.dim)}, got {coef_inflation.shape}"
+ )
+ self._coef_inflation = coef_inflation
+
+ @_model.latent_sqrt_var.setter
+ @_array2tensor
+ def latent_sqrt_var(
+ self, latent_sqrt_var: Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ ):
+ """
+ Setter for the latent variance property.
+
+ Parameters
+ ----------
+ latent_sqrt_var : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The latent square root of the variance.
+
+ Raises
+ ------
+ ValueError
+ If the shape of the latent variance is incorrect.
+ """
+ if latent_sqrt_var.shape != (self.n_samples, self.dim):
+ raise ValueError(
+ f"Wrong shape. Expected {self.n_samples, self.dim}, got {latent_sqrt_var.shape}"
+ )
+ self._latent_sqrt_var = latent_sqrt_var
+
def _update_parameters(self):
super()._update_parameters()
self._project_latent_prob()
@@ -3695,10 +3746,51 @@ class ZIPln(_model):
"""
return self._cpu_attribute_or_none("_covariance")
+ @components.setter
+ @_array2tensor
+ def components(self, components: torch.Tensor):
+ """
+ Setter for the components.
+
+ Parameters
+ ----------
+ components : torch.Tensor
+ The components to set.
+
+ Raises
+ ------
+ ValueError
+ If the components have an invalid shape.
+ """
+ if components.shape != (self.dim, self.dim):
+ raise ValueError(
+ f"Wrong shape. Expected {self.dim, self.dim}, got {components.shape}"
+ )
+ self._components = components
+
@property
def latent_prob(self):
return self._cpu_attribute_or_none("_latent_prob")
+ @latent_prob.setter
+ @_array2tensor
+ def latent_prob(self, latent_prob: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
+ if self._use_closed_form_prob is True:
+ raise ValueError(
+ "Can not set the latent prob when the closed form is used."
+ )
+ if latent_prob.shape != (self.n_samples, self.dim):
+ raise ValueError(
+ f"Wrong shape. Expected {self.n_samples, self.dim}, got {latent_prob.shape}"
+ )
+ if torch.max(latent_prob) > 1 or torch.min(latent_prob) < 0:
+ raise ValueError(f"Wrong value. All values should be between 0 and 1.")
+ if torch.norm(latent_prob * (self._endog == 0) - latent_prob) > 0.00000001:
+ raise ValueError(
+ "You can not assign non zeros inflation probabilities to non zero counts."
+ )
+ self._latent_prob = latent_prob
+
@property
def closed_formula_latent_prob(self):
"""
@@ -3781,6 +3873,40 @@ class ZIPln(_model):
"coef_inflation": self.coef_inflation,
}
+ def predict_prob_inflation(
+ self, exog: Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ ):
+ """
+ Method for estimating the probability of a zero coming from the zero inflated component.
+
+ Parameters
+ ----------
+ exog : Union[torch.Tensor, np.ndarray, pd.DataFrame]
+ The exog.
+
+ Returns
+ -------
+ torch.Tensor
+ The predicted values.
+
+ Raises
+ ------
+ RuntimeError
+ If the shape of the exog is incorrect.
+
+ Notes
+ -----
+ - The mean sigmoid(exog @ coef_inflation) is returned.
+ - `exog` should have the shape `(_, nb_cov)`, where `nb_cov` is the number of exog variables.
+ """
+ if exog is not None and self.nb_cov == 0:
+ raise AttributeError("No exog in the model, can't predict")
+ if exog.shape[-1] != self.nb_cov:
+ error_string = f"X has wrong shape ({exog.shape}). Should"
+ error_string += f" be (_, {self.nb_cov})."
+ raise RuntimeError(error_string)
+ return torch.sigmoid(exog @ self.coef_inflation)
+
@property
@_add_doc(_model)
def latent_parameters(self):
diff --git a/tests/conftest.py b/tests/conftest.py
index 85ca2aaf..93e50ab5 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -37,6 +37,7 @@ endog_real = data_real["endog"]
endog_real = pd.DataFrame(endog_real)
endog_real.columns = [f"var_{i}" for i in range(endog_real.shape[1])]
+
def add_fixture_to_dict(my_dict, string_fixture):
my_dict[string_fixture] = [lf(string_fixture)]
return my_dict
@@ -219,6 +220,11 @@ dict_fixtures = add_list_of_fixture_to_dict(
dict_fixtures, "sim_model_0cov", sim_model_0cov
)
+sim_model_0cov_fitted_and_loaded = sim_model_0cov_fitted + sim_model_0cov_loaded
+dict_fixtures = add_list_of_fixture_to_dict(
+ dict_fixtures, "sim_model_0cov_fitted_and_loaded", sim_model_0cov_fitted_and_loaded
+)
+
@pytest.fixture(params=params)
@cache
diff --git a/tests/test_common.py b/tests/test_common.py
index cec97a72..0aa81d54 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -85,8 +85,11 @@ def test_find_right_coef(simulated_fitted_any_model):
@filter_models(single_models)
def test_fail_count_setter(model):
wrong_endog = torch.randint(size=(10, 5), low=0, high=10)
- with pytest.raises(Exception):
+ negative_endog = -model._endog
+ with pytest.raises(ValueError):
model.endog = wrong_endog
+ with pytest.raises(ValueError):
+ model.endog = negative_endog
@pytest.mark.parametrize("instance", dict_fixtures["instances"])
diff --git a/tests/test_zi.py b/tests/test_zi.py
new file mode 100644
index 00000000..4ba5af04
--- /dev/null
+++ b/tests/test_zi.py
@@ -0,0 +1,73 @@
+import pytest
+import torch
+
+from pyPLNmodels import get_simulation_parameters, sample_pln, ZIPln
+from tests.conftest import dict_fixtures
+from tests.utils import filter_models, MSE
+
+
+@pytest.mark.parametrize("zi", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(["ZIPln"])
+def test_properties(zi):
+ assert hasattr(zi, "latent_prob")
+ assert hasattr(zi, "coef_inflation")
+
+
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(["ZIPln"])
+def test_predict(model):
+ X = torch.randn((model.n_samples, model.nb_cov))
+ prediction = model.predict(X)
+ expected = X @ model.coef
+ assert torch.all(torch.eq(expected, prediction))
+
+
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(["ZIPln"])
+def test_predict_prob(model):
+ X = torch.randn((model.n_samples, model.nb_cov))
+ prediction = model.predict_prob_inflation(X)
+ expected = torch.sigmoid(X @ model.coef_inflation)
+ assert torch.all(torch.eq(expected, prediction))
+
+
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(["ZIPln"])
+def test_fail_predict_prob(model):
+ X1 = torch.randn((model.n_samples, model.nb_cov + 1))
+ X2 = torch.randn((model.n_samples, model.nb_cov - 1))
+ with pytest.raises(RuntimeError):
+ model.predict_prob_inflation(X1)
+ with pytest.raises(RuntimeError):
+ model.predict_prob_inflation(X2)
+
+
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(["ZIPln"])
+def test_fail_predict(model):
+ X1 = torch.randn((model.n_samples, model.nb_cov + 1))
+ X2 = torch.randn((model.n_samples, model.nb_cov - 1))
+ with pytest.raises(RuntimeError):
+ model.predict(X1)
+ with pytest.raises(RuntimeError):
+ model.predict(X2)
+
+
+@pytest.mark.parametrize("model", dict_fixtures["sim_model_0cov_fitted_and_loaded"])
+@filter_models(["ZIPln"])
+def test_no_exog_not_possible(model):
+ assert model.nb_cov == 1
+ assert model._coef_inflation.shape[0] == 1
+
+
+def test_find_right_covariance_and_coef():
+ pln_param = get_simulation_parameters(
+ n_samples=300, dim=50, nb_cov=2, rank=5, add_const=True
+ )
+ pln_param._coef += 5
+ endog = sample_pln(pln_param, seed=0, return_latent=False)
+ zi = ZIPln(endog, exog=pln_param.exog, offsets=pln_param.offsets)
+ zi.fit()
+ mse_covariance = MSE(zi.covariance - pln_param.covariance)
+ mse_coef = MSE(zi.coef)
+ assert mse_covariance < 0.5
--
GitLab
From 8db7068fb10946c76ce7f4259c442d0c37a4e388 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 17 Oct 2023 19:14:29 +0200
Subject: [PATCH 102/167] write the tests for the zi, the batch size and
rewrite the tests for the collection. Need to pass the tests on the getting
started.
---
.gitignore | 1 +
pyPLNmodels/_utils.py | 76 +++++++++--
pyPLNmodels/models.py | 142 +++++++++++---------
tests/conftest.py | 10 +-
tests/test_common.py | 16 ++-
tests/test_pln_full.py | 2 +-
tests/test_plnpcacollection.py | 47 +++++--
tests/test_setters.py | 231 ++++++++++++++++++---------------
tests/test_viz.py | 108 +++++++--------
tests/test_zi.py | 72 ++++++++--
10 files changed, 452 insertions(+), 253 deletions(-)
diff --git a/.gitignore b/.gitignore
index a95ada79..c00f1395 100644
--- a/.gitignore
+++ b/.gitignore
@@ -159,3 +159,4 @@ paper/*
tests/test_models*
tests/test_load*
tests/test_readme*
+Getting_started.py
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 35b02806..1082259e 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -494,8 +494,12 @@ def _get_simulation_components(dim: int, rank: int) -> torch.Tensor:
return components.to("cpu")
-def _get_simulation_coef_cov_offsets(
- n_samples: int, nb_cov: int, dim: int, add_const: bool
+def _get_simulation_coef_cov_offsets_coefzi(
+ n_samples: int,
+ nb_cov: int,
+ dim: int,
+ add_const: bool,
+ zero_inflated: bool,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Get offsets, covariance coefficients with right shapes.
@@ -513,6 +517,8 @@ def _get_simulation_coef_cov_offsets(
Dimension required of the data.
add_const : bool, optional
If True, will add a vector of ones in the exog.
+ zero_inflated : bool
+ If True, will return a zero_inflated coefficient.
Returns
-------
@@ -537,14 +543,23 @@ def _get_simulation_coef_cov_offsets(
if add_const is True:
exog = torch.cat((exog, torch.ones(n_samples, 1)), axis=1)
if exog is None:
+ if zero_inflated is True:
+ msg = "Can not instantiate a zero inflate model without covariates."
+ msg += " Please give at least an intercept by setting add_const to True"
+ raise ValueError(msg)
coef = None
+ coef_inflation = None
else:
coef = torch.randn(exog.shape[1], dim, device="cpu")
+ if zero_inflated is True:
+ coef_inflation = torch.randn(exog.shape[1], dim, device="cpu")
+ else:
+ coef_inflation = None
offsets = torch.randint(
low=0, high=2, size=(n_samples, dim), dtype=torch.float64, device="cpu"
)
torch.random.set_rng_state(prev_state)
- return coef, exog, offsets
+ return coef, exog, offsets, coef_inflation
class PlnParameters:
@@ -555,7 +570,7 @@ class PlnParameters:
coef: Union[torch.Tensor, np.ndarray, pd.DataFrame],
exog: Union[torch.Tensor, np.ndarray, pd.DataFrame],
offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame],
- coef_inflation=None,
+ coef_inflation: Union[torch.Tensor, np.ndarray, pd.DataFrame, None] = None,
):
"""
Instantiate all the needed parameters to sample from the PLN model.
@@ -570,9 +585,8 @@ class PlnParameters:
Covariates, size (n, d) or None
offsets : : Union[torch.Tensor, np.ndarray, pd.DataFrame](keyword-only)
Offset, size (n, p)
- _coef_inflation : : Union[torch.Tensor, np.ndarray, pd.DataFrame] or None, optional(keyword-only)
+ coef_inflation : Union[torch.Tensor, np.ndarray, pd.DataFrame, None], optional(keyword-only)
Coefficient for zero-inflation model, size (d, p) or None. Default is None.
-
"""
self._components = _format_data(components)
self._coef = _format_data(coef)
@@ -713,6 +727,7 @@ def get_simulation_parameters(
nb_cov: int = 1,
rank: int = 5,
add_const: bool = True,
+ zero_inflated: bool = False,
) -> PlnParameters:
"""
Generate simulation parameters for a Poisson-lognormal model.
@@ -731,18 +746,26 @@ def get_simulation_parameters(
The rank of the data components, by default 5.
add_const : bool, optional(keyword-only)
If True, will add a vector of ones in the exog.
+ zero_inflated : bool, optional(keyword-only)
+ If True, the model will be zero inflated.
+ Default is False.
Returns
-------
PlnParameters
The generated simulation parameters.
-
"""
- coef, exog, offsets = _get_simulation_coef_cov_offsets(
- n_samples, nb_cov, dim, add_const
+ coef, exog, offsets, coef_inflation = _get_simulation_coef_cov_offsets_coefzi(
+ n_samples, nb_cov, dim, add_const, zero_inflated
)
components = _get_simulation_components(dim, rank)
- return PlnParameters(components=components, coef=coef, exog=exog, offsets=offsets)
+ return PlnParameters(
+ components=components,
+ coef=coef,
+ exog=exog,
+ offsets=offsets,
+ coef_inflation=coef_inflation,
+ )
def get_simulated_count_data(
@@ -753,6 +776,7 @@ def get_simulated_count_data(
nb_cov: int = 1,
return_true_param: bool = False,
add_const: bool = True,
+ zero_inflated=False,
seed: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
@@ -772,19 +796,45 @@ def get_simulated_count_data(
Number of exog, by default 1.
return_true_param : bool, optional(keyword-only)
Whether to return the true parameters of the model, by default False.
+ zero_inflated: bool, optional(keyword-only)
+ Whether to use a zero inflated model or not.
+ Default to False.
seed : int, optional(keyword-only)
Seed value for random number generation, by default 0.
Returns
-------
- Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
- Tuple containing endog, exog, and offsets.
+ if return_true_param is False:
+ Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
+ Tuple containing endog, exog, and offsets.
+ else:
+ if zero_inflated is True:
+ Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
+ Tuple containing endog, exog, offsets, covariance, coef, coef_inflation .
+ else:
+ Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
+ Tuple containing endog, exog, offsets, covariance, coef.
+
"""
pln_param = get_simulation_parameters(
- n_samples=n_samples, dim=dim, nb_cov=nb_cov, rank=rank, add_const=add_const
+ n_samples=n_samples,
+ dim=dim,
+ nb_cov=nb_cov,
+ rank=rank,
+ add_const=add_const,
+ zero_inflated=zero_inflated,
)
endog = sample_pln(pln_param, seed=seed, return_latent=False)
if return_true_param is True:
+ if zero_inflated is True:
+ return (
+ endog,
+ pln_param.exog,
+ pln_param.offsets,
+ pln_param.covariance,
+ pln_param.coef,
+ pln_param.coef_inflation,
+ )
return (
endog,
pln_param.exog,
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 81d732b2..01f9353b 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -99,10 +99,6 @@ class _model(ABC):
Whether to take the log of offsets. Defaults to False.
add_const: bool, optional(keyword-only)
Whether to add a column of one in the exog. Defaults to True.
- Raises
- ------
- ValueError
- If the batch_size is greater than the number of samples, or not int.
"""
(
self._endog,
@@ -116,6 +112,7 @@ class _model(ABC):
self._criterion_args = _CriterionArgs()
if dict_initialization is not None:
self._set_init_parameters(dict_initialization)
+ self._dirac = self._endog == 0
@classmethod
def from_formula(
@@ -253,7 +250,10 @@ class _model(ABC):
def _handle_batch_size(self, batch_size):
if batch_size is None:
- batch_size = self.n_samples
+ if hasattr(self, "batch_size"):
+ batch_size = self.batch_size
+ else:
+ batch_size = self.n_samples
if batch_size > self.n_samples:
raise ValueError(
f"batch_size ({batch_size}) can not be greater than the number of samples ({self.n_samples})"
@@ -385,6 +385,10 @@ class _model(ABC):
batch_size: int, optional(keyword-only)
The batch size when optimizing the elbo. If None,
batch gradient descent will be performed (i.e. batch_size = n_samples).
+ Raises
+ ------
+ ValueError
+ If the batch_size is greater than the number of samples, or not int.
"""
self._print_beginning_message()
self._beginning_time = time.time()
@@ -531,7 +535,7 @@ class _model(ABC):
def sk_PCA(self, n_components=None):
"""
- Perform PCA on the latent variables.
+ Perform the scikit-learn PCA on the latent variables.
Parameters
----------
@@ -553,8 +557,9 @@ class _model(ABC):
raise ValueError(
f"You ask more components ({n_components}) than variables ({self.dim})"
)
+ latent_variables = self.transform()
pca = PCA(n_components=n_components)
- pca.fit(self.latent_variables.cpu())
+ pca.fit(latent_variables.cpu())
return pca
@property
@@ -595,9 +600,9 @@ class _model(ABC):
f"You ask more components ({n_components}) than variables ({self.dim})"
)
pca = self.sk_PCA(n_components=n_components)
- proj_variables = pca.transform(self.latent_variables)
+ latent_variables = self.transform()
+ proj_variables = pca.transform(latent_variables)
components = torch.from_numpy(pca.components_)
-
labels = {
str(i): f"PC{i+1}: {np.round(pca.explained_variance_ratio_*100, 1)[i]}%"
for i in range(n_components)
@@ -655,7 +660,7 @@ class _model(ABC):
n_components = 2
pca = self.sk_PCA(n_components=n_components)
- variables = self.latent_variables
+ variables = self.transform()
proj_variables = pca.transform(variables)
## the package is not correctly printing the variance ratio
figure, correlation_matrix = plot_pca_correlation_graph(
@@ -1808,51 +1813,6 @@ class Pln(_model):
1 / 2 * torch.ones((self.n_samples, self.dim)).to(DEVICE)
)
- @property
- @_add_doc(
- _model,
- example="""
- >>> from pyPLNmodels import Pln, get_real_count_data
- >>> endog, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(endog,add_const = True)
- >>> pln.fit()
- >>> print(pln.latent_variables.shape)
- """,
- )
- def latent_variables(self):
- return self.latent_mean.detach()
-
- @_add_doc(
- _model,
- example="""
- >>> from pyPLNmodels import Pln, get_real_count_data
- >>> endog, labels = get_real_count_data(return_labels = True)
- >>> pln = Pln(endog,add_const = True)
- >>> pln.fit()
- >>> elbo = pln.compute_elbo()
- >>> print("elbo", elbo)
- >>> print("loglike/n", pln.loglike/pln.n_samples)
- """,
- )
- def compute_elbo(self):
- return profiled_elbo_pln(
- self._endog,
- self._exog,
- self._offsets,
- self._latent_mean,
- self._latent_sqrt_var,
- )
-
- @_add_doc(_model)
- def _compute_elbo_b(self):
- return profiled_elbo_pln(
- self._endog_b,
- self._exog_b,
- self._offsets_b,
- self._latent_mean_b,
- self._latent_sqrt_var_b,
- )
-
@_add_doc(_model)
def _smart_init_model_parameters(self):
pass
@@ -2057,7 +2017,7 @@ class PlnPCAcollection:
endog, exog, offsets, offsets_formula, take_log_offsets, add_const
)
self._fitted = False
- self._init_models(ranks, dict_of_dict_initialization)
+ self._init_models(ranks, dict_of_dict_initialization, add_const=add_const)
@classmethod
def from_formula(
@@ -2129,6 +2089,18 @@ class PlnPCAcollection:
"""
return self[self.ranks[0]].exog
+ @property
+ def batch_size(self) -> torch.Tensor:
+ """
+ Property representing the batch_size.
+
+ Returns
+ -------
+ torch.Tensor
+ The batch_size.
+ """
+ return self[self.ranks[0]].batch_size
+
@property
def endog(self) -> torch.Tensor:
"""
@@ -2203,6 +2175,19 @@ class PlnPCAcollection:
for model in self.values():
model.endog = endog
+ @batch_size.setter
+ def batch_size(self, batch_size: int):
+ """
+ Setter for the batch_size property.
+
+ Parameters
+ ----------
+ batch_size : int
+ The batch size.
+ """
+ for model in self.values():
+ model.batch_size = batch_size
+
@coef.setter
@_array2tensor
def coef(self, coef: Union[torch.Tensor, np.ndarray, pd.DataFrame]):
@@ -2258,7 +2243,10 @@ class PlnPCAcollection:
model.offsets = offsets
def _init_models(
- self, ranks: Iterable[int], dict_of_dict_initialization: Optional[dict]
+ self,
+ ranks: Iterable[int],
+ dict_of_dict_initialization: Optional[dict],
+ add_const: bool,
):
"""
Method for initializing the models.
@@ -2282,6 +2270,7 @@ class PlnPCAcollection:
offsets=self._offsets,
rank=rank,
dict_initialization=dict_initialization,
+ add_const=add_const,
)
else:
raise TypeError(
@@ -2389,6 +2378,10 @@ class PlnPCAcollection:
batch_size: int, optional(keyword-only)
The batch size when optimizing the elbo. If None,
batch gradient descent will be performed (i.e. batch_size = n_samples).
+ Raises
+ ------
+ ValueError
+ If the batch_size is greater than the number of samples, or not int.
"""
self._print_beginning_message()
for i in range(len(self.values())):
@@ -3606,7 +3599,6 @@ class ZIPln(_model):
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
def _random_init_latent_parameters(self):
- self._dirac = self._endog == 0
self._latent_mean = torch.randn(self.n_samples, self.dim)
self._latent_sqrt_var = torch.randn(self.n_samples, self.dim)
self._latent_prob = (
@@ -3621,6 +3613,17 @@ class ZIPln(_model):
def _covariance(self):
return self._components @ (self._components.T)
+ def _get_max_components(self):
+ """
+ Method for getting the maximum number of components.
+
+ Returns
+ -------
+ int
+ The maximum number of components.
+ """
+ return self.dim
+
@property
def components(self) -> torch.Tensor:
"""
@@ -3656,6 +3659,27 @@ class ZIPln(_model):
"""
return self.latent_mean, self.latent_prob
+ def transform(self, return_latent_prob=False):
+ """
+ Method for transforming the endog. Can be seen as a normalization of the endog.
+
+ Parameters
+ ----------
+ return_latent_prob: bool, optional
+ Wheter to return or not the latent_probability of zero inflation.
+ Returns
+ -------
+ The latent mean if `return_latent_prob` is False and (latent_mean, latent_prob) else.
+ """
+ if return_latent_prob is True:
+ return self.latent_variables
+ return self.latent_mean
+
+ def _endog_predictions(self):
+ return torch.exp(
+ self._offsets + self._latent_mean + 1 / 2 * self._latent_sqrt_var**2
+ ) * (1 - self._latent_prob)
+
@property
def coef_inflation(self):
"""
@@ -3730,7 +3754,7 @@ class ZIPln(_model):
self._latent_prob_b, torch.tensor([0]), out=self._latent_prob_b
)
self._latent_prob_b = torch.minimum(
- self._latent_prob, torch.tensor([1]), out=self._latent_prob_b
+ self._latent_prob_b, torch.tensor([1]), out=self._latent_prob_b
)
self._latent_prob_b *= self._dirac_b
diff --git a/tests/conftest.py b/tests/conftest.py
index 93e50ab5..22ea4307 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,6 +1,5 @@
import sys
import glob
-from functools import singledispatch
import pytest
import torch
from pytest_lazyfixture import lazy_fixture as lf
@@ -8,6 +7,7 @@ import pandas as pd
from pyPLNmodels import load_model, load_plnpcacollection
from pyPLNmodels.models import Pln, PlnPCA, PlnPCAcollection, ZIPln
+from pyPLNmodels import get_simulated_count_data
sys.path.append("../")
@@ -206,6 +206,7 @@ dict_fixtures = add_list_of_fixture_to_dict(
dict_fixtures, "sim_model_0cov_fitted", sim_model_0cov_fitted
)
+
sim_model_0cov_loaded = [
"simulated_loaded_model_0cov_array",
"simulated_loaded_model_0cov_formula",
@@ -285,12 +286,17 @@ sim_model_2cov_instance = [
"simulated_model_2cov_array",
"simulated_model_2cov_formula",
]
+sim_model_instance = sim_model_0cov_instance + sim_model_2cov_instance
+
+dict_fixtures = add_list_of_fixture_to_dict(
+ dict_fixtures, "sim_model_instance", sim_model_instance
+)
instances = sim_model_2cov_instance + instances
+
dict_fixtures = add_list_of_fixture_to_dict(
dict_fixtures, "sim_model_2cov_instance", sim_model_2cov_instance
)
-
sim_model_2cov_fitted = [
"simulated_fitted_model_2cov_array",
"simulated_fitted_model_2cov_formula",
diff --git a/tests/test_common.py b/tests/test_common.py
index 0aa81d54..6cba2cf6 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -8,8 +8,8 @@ from tests.utils import MSE, filter_models
from tests.import_data import true_sim_0cov, true_sim_2cov, endog_real
-single_models = ["Pln", "PlnPCA", "ZIPln"]
pln_and_plnpca = ["Pln", "PlnPCA"]
+single_models = ["Pln", "PlnPCA", "ZIPln"]
@pytest.mark.parametrize("any_model", dict_fixtures["loaded_and_fitted_model"])
@@ -108,3 +108,17 @@ def test_fail_wrong_exog_prediction(model):
X = torch.randn(model.n_samples, model.nb_cov + 1)
with pytest.raises(Exception):
model.predict(X)
+
+
+@pytest.mark.parametrize("model", dict_fixtures["sim_model_instance"])
+@filter_models(pln_and_plnpca)
+def test_batch(model):
+ model.fit(batch_size=20)
+ print(model)
+ model.show()
+ if model.nb_cov == 2:
+ true_coef = true_sim_2cov["beta"]
+ mse_coef = MSE(model.coef - true_coef)
+ assert mse_coef < 0.1
+ elif model.nb_cov == 0:
+ assert model.coef is None
diff --git a/tests/test_pln_full.py b/tests/test_pln_full.py
index 1115e1ec..6a8ced3a 100644
--- a/tests/test_pln_full.py
+++ b/tests/test_pln_full.py
@@ -13,5 +13,5 @@ def test_number_of_iterations_pln_full(fitted_pln):
@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["Pln"])
-def test_latent_var_full(pln):
+def test_latent_variables(pln):
assert pln.transform().shape == pln.endog.shape
diff --git a/tests/test_plnpcacollection.py b/tests/test_plnpcacollection.py
index 6634f2d2..19b49b18 100644
--- a/tests/test_plnpcacollection.py
+++ b/tests/test_plnpcacollection.py
@@ -6,16 +6,17 @@ import numpy as np
from tests.conftest import dict_fixtures
from tests.utils import MSE, filter_models
+from tests.import_data import true_sim_0cov, true_sim_2cov
-@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_pln"])
+@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["PlnPCAcollection"])
def test_best_model(plnpca):
best_model = plnpca.best_model()
print(best_model)
-@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_pln"])
+@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["PlnPCAcollection"])
def test_projected_variables(plnpca):
best_model = plnpca.best_model()
@@ -23,21 +24,20 @@ def test_projected_variables(plnpca):
assert plv.shape[0] == best_model.n_samples and plv.shape[1] == best_model.rank
-@pytest.mark.parametrize("fitted_pln", dict_fixtures["fitted_pln"])
-@filter_models(["PlnPCA"])
-def test_number_of_iterations_plnpca(fitted_pln):
- nb_iterations = len(fitted_pln._elbos_list)
- assert 100 < nb_iterations < 5000
+@pytest.mark.parametrize("plnpca", dict_fixtures["sim_model_instance"])
+@filter_models(["PlnPCAcollection"])
+def test_right_nbcov(plnpca):
+ assert plnpca.nb_cov == 0 or plnpca.nb_cov == 2
-@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_pln"])
+@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["PlnPCA"])
def test_latent_var_pca(plnpca):
assert plnpca.transform(project=False).shape == plnpca.endog.shape
assert plnpca.transform().shape == (plnpca.n_samples, plnpca.rank)
-@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_pln"])
+@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["PlnPCAcollection"])
def test_additional_methods_pca(plnpca):
plnpca.show()
@@ -46,14 +46,14 @@ def test_additional_methods_pca(plnpca):
plnpca.loglikes
-@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_pln"])
+@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["PlnPCAcollection"])
def test_wrong_criterion(plnpca):
with pytest.raises(ValueError):
plnpca.best_model("AIK")
-@pytest.mark.parametrize("collection", dict_fixtures["loaded_and_fitted_pln"])
+@pytest.mark.parametrize("collection", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["PlnPCAcollection"])
def test_item(collection):
print(collection[collection.ranks[0]])
@@ -62,3 +62,28 @@ def test_item(collection):
assert collection.ranks[0] in collection
assert collection.ranks[0] in list(collection.keys())
collection.get(collection.ranks[0], None)
+
+
+@pytest.mark.parametrize("collection", dict_fixtures["sim_model_instance"])
+@filter_models(["PlnPCAcollection"])
+def test_batch(collection):
+ collection.fit(batch_size=20)
+ assert collection.nb_cov == 0 or collection.nb_cov == 2
+ if collection.nb_cov == 0:
+ true_covariance = true_sim_0cov["Sigma"]
+ for model in collection.values():
+ assert model.coef is None
+ true_coef = None
+ elif collection.nb_cov == 2:
+ true_covariance = true_sim_2cov["Sigma"]
+ true_coef = true_sim_2cov["beta"]
+ else:
+ raise ValueError(f"Not the right numbers of covariance({collection.nb_cov})")
+ for model in collection.values():
+ mse_covariance = MSE(model.covariance - true_covariance)
+ if true_coef is not None:
+ mse_coef = MSE(model.coef - true_coef)
+ assert mse_coef < 0.35
+ assert mse_covariance < 0.25
+ collection.fit()
+ assert collection.batch_size == collection.n_samples
diff --git a/tests/test_setters.py b/tests/test_setters.py
index eb7814d7..b3012548 100644
--- a/tests/test_setters.py
+++ b/tests/test_setters.py
@@ -5,148 +5,169 @@ import torch
from tests.conftest import dict_fixtures
from tests.utils import MSE, filter_models
-
-@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
-def test_data_setter_with_torch(pln):
- pln.endog = pln.endog
- pln.exog = pln.exog
- pln.offsets = pln.offsets
- pln.fit()
-
-
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_parameters_setter_with_torch(pln):
- pln.latent_mean = pln.latent_mean
- pln.latent_sqrt_var = pln.latent_sqrt_var
- if pln._NAME != "Pln":
- pln.coef = pln.coef
- if pln._NAME == "PlnPCA":
- pln.components = pln.components
- pln.fit()
-
-
-@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
-def test_data_setter_with_numpy(pln):
- np_endog = pln.endog.numpy()
- if pln.exog is not None:
- np_exog = pln.exog.numpy()
+single_models = ["Pln", "PlnPCA", "ZIPln"]
+
+
+@pytest.mark.parametrize("model", dict_fixtures["all_model"])
+def test_data_setter_with_torch(model):
+ model.endog = model.endog
+ model.exog = model.exog
+ model.offsets = model.offsets
+ model.fit()
+
+
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(single_models)
+def test_parameters_setter_with_torch(model):
+ model.latent_mean = model.latent_mean
+ model.latent_sqrt_var = model.latent_sqrt_var
+ if model._NAME != "Pln":
+ model.coef = model.coef
+ if model._NAME == "PlnPCA" or model._NAME == "ZIPln":
+ model.components = model.components
+ if model._NAME == "ZIPln":
+ model.coef_inflation = model.coef_inflation
+ model.fit()
+
+
+@pytest.mark.parametrize("model", dict_fixtures["all_model"])
+def test_data_setter_with_numpy(model):
+ np_endog = model.endog.numpy()
+ if model.exog is not None:
+ np_exog = model.exog.numpy()
else:
np_exog = None
- np_offsets = pln.offsets.numpy()
- pln.endog = np_endog
- pln.exog = np_exog
- pln.offsets = np_offsets
- pln.fit()
-
-
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_parameters_setter_with_numpy(pln):
- np_latent_mean = pln.latent_mean.numpy()
- np_latent_sqrt_var = pln.latent_sqrt_var.numpy()
- if pln.coef is not None:
- np_coef = pln.coef.numpy()
+ np_offsets = model.offsets.numpy()
+ model.endog = np_endog
+ model.exog = np_exog
+ model.offsets = np_offsets
+ model.fit()
+
+
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(single_models)
+def test_parameters_setter_with_numpy(model):
+ np_latent_mean = model.latent_mean.numpy()
+ np_latent_sqrt_var = model.latent_sqrt_var.numpy()
+ if model.coef is not None:
+ np_coef = model.coef.numpy()
else:
np_coef = None
- pln.latent_mean = np_latent_mean
- pln.latent_sqrt_var = np_latent_sqrt_var
- if pln._NAME != "Pln":
- pln.coef = np_coef
- if pln._NAME == "PlnPCA":
- pln.components = pln.components.numpy()
- pln.fit()
-
-
-@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
-def test_data_setter_with_pandas(pln):
- pd_endog = pd.DataFrame(pln.endog.numpy())
- if pln.exog is not None:
- pd_exog = pd.DataFrame(pln.exog.numpy())
+ model.latent_mean = np_latent_mean
+ model.latent_sqrt_var = np_latent_sqrt_var
+ if model._NAME != "Pln":
+ model.coef = np_coef
+ if model._NAME == "PlnPCA" or model._NAME == "ZIPln":
+ model.components = model.components.numpy()
+ if model._NAME == "ZIPln":
+ model.coef_inflation = model.coef_inflation.numpy()
+ model.fit()
+
+
+@pytest.mark.parametrize("model", dict_fixtures["all_model"])
+def test_batch_size_setter(model):
+ model.batch_size = 20
+ model.fit(nb_max_iteration=3)
+ assert model.batch_size == 20
+
+
+@pytest.mark.parametrize("model", dict_fixtures["all_model"])
+def test_fail_batch_size_setter(model):
+ with pytest.raises(ValueError):
+ model.batch_size = model.n_samples + 1
+
+
+@pytest.mark.parametrize("model", dict_fixtures["all_model"])
+def test_data_setter_with_pandas(model):
+ pd_endog = pd.DataFrame(model.endog.numpy())
+ if model.exog is not None:
+ pd_exog = pd.DataFrame(model.exog.numpy())
else:
pd_exog = None
- pd_offsets = pd.DataFrame(pln.offsets.numpy())
- pln.endog = pd_endog
- pln.exog = pd_exog
- pln.offsets = pd_offsets
- pln.fit()
-
-
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_parameters_setter_with_pandas(pln):
- pd_latent_mean = pd.DataFrame(pln.latent_mean.numpy())
- pd_latent_sqrt_var = pd.DataFrame(pln.latent_sqrt_var.numpy())
- if pln.coef is not None:
- pd_coef = pd.DataFrame(pln.coef.numpy())
+ pd_offsets = pd.DataFrame(model.offsets.numpy())
+ model.endog = pd_endog
+ model.exog = pd_exog
+ model.offsets = pd_offsets
+ model.fit()
+
+
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(single_models)
+def test_parameters_setter_with_pandas(model):
+ pd_latent_mean = pd.DataFrame(model.latent_mean.numpy())
+ pd_latent_sqrt_var = pd.DataFrame(model.latent_sqrt_var.numpy())
+ if model.coef is not None:
+ pd_coef = pd.DataFrame(model.coef.numpy())
else:
pd_coef = None
- pln.latent_mean = pd_latent_mean
- pln.latent_sqrt_var = pd_latent_sqrt_var
- if pln._NAME != "Pln":
- pln.coef = pd_coef
- if pln._NAME == "PlnPCA":
- pln.components = pd.DataFrame(pln.components.numpy())
- pln.fit()
-
-
-@pytest.mark.parametrize("pln", dict_fixtures["all_pln"])
-def test_fail_data_setter_with_torch(pln):
+ model.latent_mean = pd_latent_mean
+ model.latent_sqrt_var = pd_latent_sqrt_var
+ if model._NAME != "Pln":
+ model.coef = pd_coef
+ if model._NAME == "PlnPCA":
+ model.components = pd.DataFrame(model.components.numpy())
+ if model._NAME == "ZIPln":
+ model.coef_inflation = pd.DataFrame(model.coef_inflation.numpy())
+ model.fit()
+
+
+@pytest.mark.parametrize("model", dict_fixtures["all_model"])
+def test_fail_data_setter_with_torch(model):
with pytest.raises(ValueError):
- pln.endog = pln.endog - 100
+ model.endog = -model.endog
- n, p = pln.endog.shape
- if pln.exog is None:
+ n, p = model.endog.shape
+ if model.exog is None:
d = 0
else:
- d = pln.exog.shape[-1]
+ d = model.exog.shape[-1]
with pytest.raises(ValueError):
- pln.endog = torch.zeros(n + 1, p)
+ model.endog = torch.zeros(n + 1, p)
with pytest.raises(ValueError):
- pln.endog = torch.zeros(n, p + 1)
+ model.endog = torch.zeros(n, p + 1)
with pytest.raises(ValueError):
- pln.exog = torch.zeros(n + 1, d)
+ model.exog = torch.zeros(n + 1, d)
with pytest.raises(ValueError):
- pln.offsets = torch.zeros(n + 1, p)
+ model.offsets = torch.zeros(n + 1, p)
with pytest.raises(ValueError):
- pln.offsets = torch.zeros(n, p + 1)
+ model.offsets = torch.zeros(n, p + 1)
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_fail_parameters_setter_with_torch(pln):
- n, dim_latent = pln.latent_mean.shape
- dim = pln.endog.shape[1]
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(single_models)
+def test_fail_parameters_setter_with_torch(model):
+ n, dim_latent = model.latent_mean.shape
+ dim = model.endog.shape[1]
with pytest.raises(ValueError):
- pln.latent_mean = torch.zeros(n + 1, dim_latent)
+ model.latent_mean = torch.zeros(n + 1, dim_latent)
with pytest.raises(ValueError):
- pln.latent_mean = torch.zeros(n, dim_latent + 1)
+ model.latent_mean = torch.zeros(n, dim_latent + 1)
with pytest.raises(ValueError):
- pln.latent_sqrt_var = torch.zeros(n + 1, dim_latent)
+ model.latent_sqrt_var = torch.zeros(n + 1, dim_latent)
with pytest.raises(ValueError):
- pln.latent_sqrt_var = torch.zeros(n, dim_latent + 1)
+ model.latent_sqrt_var = torch.zeros(n, dim_latent + 1)
- if pln._NAME == "PlnPCA":
+ if model._NAME == "PlnPCA":
with pytest.raises(ValueError):
- pln.components = torch.zeros(dim, dim_latent + 1)
+ model.components = torch.zeros(dim, dim_latent + 1)
with pytest.raises(ValueError):
- pln.components = torch.zeros(dim + 1, dim_latent)
+ model.components = torch.zeros(dim + 1, dim_latent)
- if pln.exog is None:
+ if model.exog is None:
d = 0
else:
- d = pln.exog.shape[-1]
- if pln._NAME != "Pln":
+ d = model.exog.shape[-1]
+ if model._NAME != "Pln":
with pytest.raises(ValueError):
- pln.coef = torch.zeros(d + 1, dim)
+ model.coef = torch.zeros(d + 1, dim)
with pytest.raises(ValueError):
- pln.coef = torch.zeros(d, dim + 1)
+ model.coef = torch.zeros(d, dim + 1)
diff --git a/tests/test_viz.py b/tests/test_viz.py
index be24fcf1..d4f9a738 100644
--- a/tests/test_viz.py
+++ b/tests/test_viz.py
@@ -7,47 +7,49 @@ from tests.utils import MSE, filter_models
from tests.import_data import true_sim_0cov, true_sim_2cov, labels_real
+single_models = ["Pln", "PlnPCA", "ZIPln"]
-@pytest.mark.parametrize("any_pln", dict_fixtures["loaded_and_fitted_pln"])
-def test_print(any_pln):
- print(any_pln)
-
-
-@pytest.mark.parametrize("any_pln", dict_fixtures["fitted_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_show_coef_transform_covariance_pcaprojected(any_pln):
- any_pln.show()
- any_pln._plotargs._show_loss()
- any_pln._plotargs._show_stopping_criterion()
- assert hasattr(any_pln, "coef")
- assert callable(any_pln.transform)
- assert hasattr(any_pln, "covariance")
- assert callable(any_pln.sk_PCA)
- assert any_pln.sk_PCA(n_components=None) is not None
+
+@pytest.mark.parametrize("any_model", dict_fixtures["loaded_and_fitted_model"])
+def test_print(any_model):
+ print(any_model)
+
+
+@pytest.mark.parametrize("any_model", dict_fixtures["fitted_model"])
+@filter_models(single_models)
+def test_show_coef_transform_covariance_pcaprojected(any_model):
+ any_model.show()
+ any_model._criterion_args._show_loss()
+ any_model._criterion_args._show_stopping_criterion()
+ assert hasattr(any_model, "coef")
+ assert callable(any_model.transform)
+ assert hasattr(any_model, "covariance")
+ assert callable(any_model.sk_PCA)
+ assert any_model.sk_PCA(n_components=None) is not None
with pytest.raises(Exception):
- any_pln.sk_PCA(n_components=any_pln.dim + 1)
+ any_model.sk_PCA(n_components=any_model.dim + 1)
-@pytest.mark.parametrize("pln", dict_fixtures["fitted_pln"])
-@filter_models(["Pln"])
-def test_scatter_pca_matrix_pln(pln):
- pln.scatter_pca_matrix(n_components=8)
+@pytest.mark.parametrize("model", dict_fixtures["fitted_model"])
+@filter_models(["Pln", "ZIPln"])
+def test_scatter_pca_matrix_pln(model):
+ model.scatter_pca_matrix(n_components=8)
-@pytest.mark.parametrize("pln", dict_fixtures["fitted_pln"])
+@pytest.mark.parametrize("model", dict_fixtures["fitted_model"])
@filter_models(["PlnPCA"])
-def test_scatter_pca_matrix_plnpca(pln):
- pln.scatter_pca_matrix(n_components=2)
- pln.scatter_pca_matrix()
+def test_scatter_pca_matrix_plnpca(model):
+ model.scatter_pca_matrix(n_components=2)
+ model.scatter_pca_matrix()
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_real_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_label_scatter_pca_matrix(pln):
- pln.scatter_pca_matrix(n_components=4, color=labels_real)
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_real_model"])
+@filter_models(single_models)
+def test_label_scatter_pca_matrix(model):
+ model.scatter_pca_matrix(n_components=4, color=labels_real)
-@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_pln"])
+@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["PlnPCAcollection"])
def test_viz_pcacol(plnpca):
for model in plnpca.values():
@@ -64,38 +66,38 @@ def test_viz_pcacol(plnpca):
plt.show()
-@pytest.mark.parametrize("pln", dict_fixtures["real_fitted_pln_intercept_array"])
-@filter_models(["Pln", "PlnPCA"])
-def test_plot_pca_correlation_graph_with_names_only(pln):
- pln.plot_pca_correlation_graph([f"var_{i}" for i in range(8)])
+@pytest.mark.parametrize("model", dict_fixtures["real_fitted_model_intercept_array"])
+@filter_models(single_models)
+def test_plot_pca_correlation_graph_with_names_only(model):
+ model.plot_pca_correlation_graph([f"var_{i}" for i in range(8)])
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_sim_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_fail_plot_pca_correlation_graph_without_names(pln):
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_sim_model"])
+@filter_models(single_models)
+def test_fail_plot_pca_correlation_graph_without_names(model):
with pytest.raises(ValueError):
- pln.plot_pca_correlation_graph([f"var_{i}" for i in range(8)])
+ model.plot_pca_correlation_graph([f"var_{i}" for i in range(8)])
with pytest.raises(ValueError):
- pln.plot_pca_correlation_graph([f"var_{i}" for i in range(6)], [1, 2, 3])
+ model.plot_pca_correlation_graph([f"var_{i}" for i in range(6)], [1, 2, 3])
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_sim_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_plot_pca_correlation_graph_without_names(pln):
- pln.plot_pca_correlation_graph([f"var_{i}" for i in range(3)], [0, 1, 2])
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_sim_model"])
+@filter_models(single_models)
+def test_plot_pca_correlation_graph_without_names(model):
+ model.plot_pca_correlation_graph([f"var_{i}" for i in range(3)], [0, 1, 2])
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_expected_vs_true(pln):
- pln.plot_expected_vs_true()
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(single_models)
+def test_expected_vs_true(model):
+ model.plot_expected_vs_true()
fig, ax = plt.subplots()
- pln.plot_expected_vs_true(ax=ax)
+ model.plot_expected_vs_true(ax=ax)
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_real_pln"])
-@filter_models(["Pln", "PlnPCA"])
-def test_expected_vs_true_labels(pln):
- pln.plot_expected_vs_true(colors=labels_real)
+@pytest.mark.parametrize("model", dict_fixtures["loaded_and_fitted_real_model"])
+@filter_models(single_models)
+def test_expected_vs_true_labels(model):
+ model.plot_expected_vs_true(colors=labels_real)
fig, ax = plt.subplots()
- pln.plot_expected_vs_true(ax=ax, colors=labels_real)
+ model.plot_expected_vs_true(ax=ax, colors=labels_real)
diff --git a/tests/test_zi.py b/tests/test_zi.py
index 4ba5af04..2016accf 100644
--- a/tests/test_zi.py
+++ b/tests/test_zi.py
@@ -6,6 +6,9 @@ from tests.conftest import dict_fixtures
from tests.utils import filter_models, MSE
+from pyPLNmodels import get_simulated_count_data
+
+
@pytest.mark.parametrize("zi", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["ZIPln"])
def test_properties(zi):
@@ -60,14 +63,67 @@ def test_no_exog_not_possible(model):
assert model._coef_inflation.shape[0] == 1
-def test_find_right_covariance_and_coef():
- pln_param = get_simulation_parameters(
- n_samples=300, dim=50, nb_cov=2, rank=5, add_const=True
+def test_find_right_covariance_coef_and_infla():
+ pln_param = get_simulation_parameters(zero_inflated=True, n_samples=1000)
+ # pln_param._coef += 5
+ endog = sample_pln(pln_param, seed=0, return_latent=False)
+ exog = pln_param.exog
+ offsets = pln_param.offsets
+ covariance = pln_param.covariance
+ coef = pln_param.coef
+ coef_inflation = pln_param.coef_inflation
+ endog, exog, offsets, covariance, coef, coef_inflation = get_simulated_count_data(
+ zero_inflated=True, return_true_param=True, n_samples=1000
)
- pln_param._coef += 5
+ zi = ZIPln(endog, exog=exog, offsets=offsets, use_closed_form_prob=False)
+ zi.fit()
+ mse_covariance = MSE(zi.covariance - covariance)
+ mse_coef = MSE(zi.coef - coef)
+ mse_coef_infla = MSE(zi.coef_inflation - coef_inflation)
+ assert mse_coef < 3
+ assert mse_coef_infla < 3
+ assert mse_covariance < 1
+
+
+@pytest.mark.parametrize("zi", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(["ZIPln"])
+def test_latent_variables(zi):
+ z, w = zi.latent_variables
+ assert z.shape == zi.endog.shape
+ assert w.shape == zi.endog.shape
+
+
+@pytest.mark.parametrize("zi", dict_fixtures["loaded_and_fitted_model"])
+@filter_models(["ZIPln"])
+def test_transform(zi):
+ z = zi.transform()
+ assert z.shape == zi.endog.shape
+ z, w = zi.transform(return_latent_prob=True)
+ assert z.shape == w.shape == zi.endog.shape
+
+
+@pytest.mark.parametrize("model", dict_fixtures["sim_model_instance"])
+@filter_models(["ZIPln"])
+def test_batch(model):
+ pln_param = get_simulation_parameters(zero_inflated=True, n_samples=1000)
+ # pln_param._coef += 5
endog = sample_pln(pln_param, seed=0, return_latent=False)
- zi = ZIPln(endog, exog=pln_param.exog, offsets=pln_param.offsets)
+ exog = pln_param.exog
+ offsets = pln_param.offsets
+ covariance = pln_param.covariance
+ coef = pln_param.coef
+ coef_inflation = pln_param.coef_inflation
+ endog, exog, offsets, covariance, coef, coef_inflation = get_simulated_count_data(
+ zero_inflated=True, return_true_param=True, n_samples=1000
+ )
+ zi = ZIPln(endog, exog=exog, offsets=offsets, use_closed_form_prob=False)
+ zi.fit(batch_size=20)
+ mse_covariance = MSE(zi.covariance - covariance)
+ mse_coef = MSE(zi.coef - coef)
+ mse_coef_infla = MSE(zi.coef_inflation - coef_inflation)
+ assert mse_coef < 3
+ assert mse_coef_infla < 3
+ assert mse_covariance < 1
+ zi.show()
+ print(zi)
zi.fit()
- mse_covariance = MSE(zi.covariance - pln_param.covariance)
- mse_coef = MSE(zi.coef)
- assert mse_covariance < 0.5
--
GitLab
From 2adadea67bebe5f70906bfe2a3c4a1642903db28 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 17 Oct 2023 19:53:42 +0200
Subject: [PATCH 103/167] add a file in the gitignore and blacked one file ??!
---
.gitignore | 1 +
pyPLNmodels/_initialization.py | 4 +---
2 files changed, 2 insertions(+), 3 deletions(-)
diff --git a/.gitignore b/.gitignore
index c00f1395..08cb3cfe 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,3 +160,4 @@ tests/test_models*
tests/test_load*
tests/test_readme*
Getting_started.py
+new_model.py
diff --git a/pyPLNmodels/_initialization.py b/pyPLNmodels/_initialization.py
index 410283bf..fe649fe0 100644
--- a/pyPLNmodels/_initialization.py
+++ b/pyPLNmodels/_initialization.py
@@ -43,9 +43,7 @@ def _init_covariance(endog: torch.Tensor, exog: torch.Tensor) -> torch.Tensor:
return sigma_hat
-def _init_components(
- endog: torch.Tensor, rank: int
-) -> torch.Tensor:
+def _init_components(endog: torch.Tensor, rank: int) -> torch.Tensor:
"""
Initialization for components for the Pln model. Get a first guess for covariance
that is easier to estimate and then takes the rank largest eigenvectors to get components.
--
GitLab
From 434c05ee2185c0d73e13477b467d124a8c2d569d Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 17 Oct 2023 22:44:07 +0200
Subject: [PATCH 104/167] fixed some tests.
---
pyPLNmodels/models.py | 6 +++---
tests/test_plnpcacollection.py | 4 ++--
2 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 01f9353b..8351056c 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -3252,7 +3252,7 @@ class PlnPCA(_model):
Parameters
----------
project : bool, optional
- Whether to project the latent variables, by default True.
+ Whether to project the latent variables, by default False.
""",
returns="""
torch.Tensor
@@ -3269,7 +3269,7 @@ class PlnPCA(_model):
>>> print(transformed_endog_high_dim.shape)
""",
)
- def transform(self, project: bool = True) -> torch.Tensor:
+ def transform(self, project: bool = False) -> torch.Tensor:
if project is True:
return self.projected_latent_variables
return self.latent_variables
@@ -3297,7 +3297,7 @@ class PlnPCA(_model):
>>> pca.fit()
>>> elbo = pca.compute_elbo()
>>> print("elbo", elbo)
- >>> print("loglike/n", pln.loglike/pln.n_samples)
+ >>> print("loglike/n", pca.loglike/pca.n_samples)
""",
)
def compute_elbo(self) -> torch.Tensor:
diff --git a/tests/test_plnpcacollection.py b/tests/test_plnpcacollection.py
index 19b49b18..77016b73 100644
--- a/tests/test_plnpcacollection.py
+++ b/tests/test_plnpcacollection.py
@@ -33,8 +33,8 @@ def test_right_nbcov(plnpca):
@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["PlnPCA"])
def test_latent_var_pca(plnpca):
- assert plnpca.transform(project=False).shape == plnpca.endog.shape
- assert plnpca.transform().shape == (plnpca.n_samples, plnpca.rank)
+ assert plnpca.transform().shape == plnpca.endog.shape
+ assert plnpca.transform(project=True).shape == (plnpca.n_samples, plnpca.rank)
@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_model"])
--
GitLab
From 4c21846307ed65f80a44be868bb5a3101f124bfa Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 18 Oct 2023 14:27:29 +0200
Subject: [PATCH 105/167] fixed some tests for the zi.
---
pyPLNmodels/elbos.py | 3 +--
pyPLNmodels/models.py | 37 +++++++++++-----------------------
tests/conftest.py | 1 -
tests/test_pln_full.py | 4 ++--
tests/test_plnpcacollection.py | 2 +-
tests/test_setters.py | 2 +-
6 files changed, 17 insertions(+), 32 deletions(-)
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 5a56bc3d..73e77028 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -219,8 +219,7 @@ def elbo_zi_pln(
"""
covariance = components @ (components.T)
if torch.norm(latent_prob * dirac - latent_prob) > 0.00000001:
- print("Bug")
- raise RuntimeError("rho error")
+ raise RuntimeError("latent_prob error")
n_samples, dim = endog.shape
s_rond_s = torch.multiply(latent_sqrt_var, latent_sqrt_var)
o_plus_m = offsets + latent_mean
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 8351056c..d692a9e5 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -242,11 +242,8 @@ class _model(ABC):
_plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
return ax
- def _update_parameters(self):
- """
- Update the parameters with a gradient step and project if necessary.
- """
- self.optim.step()
+ def _project_parameters(self):
+ pass
def _handle_batch_size(self, batch_size):
if batch_size is None:
@@ -486,8 +483,8 @@ class _model(ABC):
raise ValueError("test")
loss.backward()
elbo += loss.item()
- self._update_parameters()
- self._update_closed_forms()
+ self.optim.step()
+ self._project_parameters()
return elbo / self.nb_batches
def _extract_batch(self, batch):
@@ -736,12 +733,6 @@ class _model(ABC):
self._criterion_args.update_criterion(-loss, current_running_time)
return self._criterion_args.criterion
- def _update_closed_forms(self):
- """
- Update closed-form expressions.
- """
- pass
-
def display_covariance(self, ax=None, savefig=False, name_file=""):
"""
Display the covariance matrix.
@@ -3740,8 +3731,7 @@ class ZIPln(_model):
)
self._latent_sqrt_var = latent_sqrt_var
- def _update_parameters(self):
- super()._update_parameters()
+ def _project_parameters(self):
self._project_latent_prob()
def _project_latent_prob(self):
@@ -3750,13 +3740,13 @@ class ZIPln(_model):
"""
if self._use_closed_form_prob is False:
with torch.no_grad():
- self._latent_prob_b = torch.maximum(
- self._latent_prob_b, torch.tensor([0]), out=self._latent_prob_b
+ torch.maximum(
+ self._latent_prob, torch.tensor([0]), out=self._latent_prob
)
- self._latent_prob_b = torch.minimum(
- self._latent_prob_b, torch.tensor([1]), out=self._latent_prob_b
+ torch.minimum(
+ self._latent_prob, torch.tensor([1]), out=self._latent_prob
)
- self._latent_prob_b *= self._dirac_b
+ self._latent_prob *= self._dirac
@property
def covariance(self) -> torch.Tensor:
@@ -3878,16 +3868,13 @@ class ZIPln(_model):
self._latent_sqrt_var,
self._components,
]
- if self._use_closed_form_prob:
+ if self._use_closed_form_prob is False:
list_parameters.append(self._latent_prob)
if self._exog is not None:
list_parameters.append(self._coef)
list_parameters.append(self._coef_inflation)
return list_parameters
- def _update_closed_forms(self):
- pass
-
@property
@_add_doc(_model)
def model_parameters(self) -> Dict[str, torch.Tensor]:
@@ -3938,7 +3925,7 @@ class ZIPln(_model):
"latent_sqrt_var": self.latent_sqrt_var,
"latent_mean": self.latent_mean,
}
- if self._use_closed_form_prob is True:
+ if self._use_closed_form_prob is False:
latent_param["latent_prob"] = self.latent_prob
return latent_param
diff --git a/tests/conftest.py b/tests/conftest.py
index 22ea4307..b77a5d67 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -17,7 +17,6 @@ sys.path.append("../")
# for fixture_file in glob.glob("src/**/tests/fixtures/[!__]*.py", recursive=True)
# ]
-
from tests.import_data import (
data_sim_0cov,
data_sim_2cov,
diff --git a/tests/test_pln_full.py b/tests/test_pln_full.py
index 6a8ced3a..e5959b0e 100644
--- a/tests/test_pln_full.py
+++ b/tests/test_pln_full.py
@@ -7,8 +7,8 @@ from tests.utils import filter_models
@pytest.mark.parametrize("fitted_pln", dict_fixtures["fitted_model"])
@filter_models(["Pln"])
def test_number_of_iterations_pln_full(fitted_pln):
- nb_iterations = len(fitted_pln.elbos_list)
- assert 20 < nb_iterations < 1000
+ nb_iterations = len(fitted_pln._elbos_list)
+ assert 20 < nb_iterations < 2000
@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_model"])
diff --git a/tests/test_plnpcacollection.py b/tests/test_plnpcacollection.py
index 77016b73..0d982d60 100644
--- a/tests/test_plnpcacollection.py
+++ b/tests/test_plnpcacollection.py
@@ -86,4 +86,4 @@ def test_batch(collection):
assert mse_coef < 0.35
assert mse_covariance < 0.25
collection.fit()
- assert collection.batch_size == collection.n_samples
+ assert collection.batch_size == 20
diff --git a/tests/test_setters.py b/tests/test_setters.py
index b3012548..f230d858 100644
--- a/tests/test_setters.py
+++ b/tests/test_setters.py
@@ -8,7 +8,7 @@ from tests.utils import MSE, filter_models
single_models = ["Pln", "PlnPCA", "ZIPln"]
-@pytest.mark.parametrize("model", dict_fixtures["all_model"])
+@pytest.mark.parametrize("model", dict_fixtures["loaded_model"])
def test_data_setter_with_torch(model):
model.endog = model.endog
model.exog = model.exog
--
GitLab
From 5ea42691a90cb660a360415e58bc4d5b9792e3ea Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 18 Oct 2023 15:19:51 +0200
Subject: [PATCH 106/167] change gitignore
---
.gitignore | 1 +
1 file changed, 1 insertion(+)
diff --git a/.gitignore b/.gitignore
index 08cb3cfe..b2d69b27 100644
--- a/.gitignore
+++ b/.gitignore
@@ -150,6 +150,7 @@ test.py
## directories that outputs when running the tests
tests/Pln*
+tests/ZIPln*
slides/
index.html
--
GitLab
From 2734b4675cdb447c3f7e31d57c13142e52de91f8 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 18 Oct 2023 19:32:30 +0200
Subject: [PATCH 107/167] fixed some bug
---
pyPLNmodels/models.py | 31 ++++++++++++++++++++++---------
tests/conftest.py | 10 +++++-----
2 files changed, 27 insertions(+), 14 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index d692a9e5..76f76fea 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -14,6 +14,7 @@ import plotly.express as px
from mlxtend.plotting import plot_pca_correlation_graph
import matplotlib
from scipy import stats
+from statsmodels.discrete.count_model import ZeroInflatedPoisson
from ._closed_forms import (
_closed_formula_coef,
@@ -430,11 +431,11 @@ class _model(ABC):
indices = np.arange(self.n_samples)
if shuffle:
np.random.shuffle(indices)
-
for i in range(self._nb_full_batch):
- yield self._return_batch(
+ batch = self._return_batch(
indices, i * self._batch_size, (i + 1) * self._batch_size
)
+ yield batch
# Last batch
if self._last_batch_size != 0:
yield self._return_batch(indices, -self._last_batch_size, self.n_samples)
@@ -475,7 +476,7 @@ class _model(ABC):
The loss value.
"""
elbo = 0
- for batch in self._get_batch(shuffle=True):
+ for batch in self._get_batch(shuffle=False):
self._extract_batch(batch)
self.optim.zero_grad()
loss = -self._compute_elbo_b()
@@ -1005,6 +1006,13 @@ class _model(ABC):
os.makedirs(path, exist_ok=True)
for key, value in self._dict_parameters.items():
filename = f"{path}/{key}.csv"
+ if key == "latent_prob":
+ if torch.max(value) > 1 or torch.min(value) < 0:
+ if (
+ torch.norm(self.dirac * self.latent_prob - self.latent_prob)
+ > 0.0001
+ ):
+ raise Exception("Error is here")
if isinstance(value, torch.Tensor):
pd.DataFrame(np.array(value.cpu().detach())).to_csv(
filename, header=None, index=None
@@ -3465,6 +3473,9 @@ class ZIPln(_model):
to_take = torch.tensor(indices[beginning:end]).to(DEVICE)
batch = pln_batch + (torch.index_select(self._dirac, 0, to_take),)
if self._use_closed_form_prob is False:
+ to_return = torch.index_select(self._latent_prob, 0, to_take)
+ print("max latent_prbo", torch.max(self._latent_prob))
+ print("max to return", torch.max(to_return))
return batch + (torch.index_select(self._latent_prob, 0, to_take),)
return batch
@@ -3587,6 +3598,12 @@ class ZIPln(_model):
self._components = _init_components(self._endog, self.dim)
if not hasattr(self, "_coef_inflation"):
+ # print('shape', self.exog.shape[1])
+ # for j in range(self.exog.shape[1]):
+ # Y_j = self._endog[:,j].numpy()
+ # offsets_j = self.offsets[:,j].numpy()
+ # zip_training_results = ZeroInflatedPoisson(endog=Y_j,exog = self.exog.numpy(), exog_infl = self.exog.numpy(), inflation='logit', offsets = offsets_j).fit()
+ # print('params', zip_training_results.params)
self._coef_inflation = torch.randn(self.nb_cov, self.dim)
def _random_init_latent_parameters(self):
@@ -3740,12 +3757,8 @@ class ZIPln(_model):
"""
if self._use_closed_form_prob is False:
with torch.no_grad():
- torch.maximum(
- self._latent_prob, torch.tensor([0]), out=self._latent_prob
- )
- torch.minimum(
- self._latent_prob, torch.tensor([1]), out=self._latent_prob
- )
+ self._latent_prob = torch.maximum(self._latent_prob, torch.tensor([0]))
+ self._latent_prob = torch.minimum(self._latent_prob, torch.tensor([1]))
self._latent_prob *= self._dirac
@property
diff --git a/tests/conftest.py b/tests/conftest.py
index b77a5d67..d89a919a 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,5 +1,4 @@
import sys
-import glob
import pytest
import torch
from pytest_lazyfixture import lazy_fixture as lf
@@ -12,10 +11,6 @@ from pyPLNmodels import get_simulated_count_data
sys.path.append("../")
-# pytest_plugins = [
-# fixture_file.replace("/", ".").replace(".py", "")
-# for fixture_file in glob.glob("src/**/tests/fixtures/[!__]*.py", recursive=True)
-# ]
from tests.import_data import (
data_sim_0cov,
@@ -42,6 +37,11 @@ def add_fixture_to_dict(my_dict, string_fixture):
return my_dict
+# zi = ZIPln(endog_sim_2cov, exog = exog_sim_2cov)
+# zi.fit()
+# print(zi)
+
+
def add_list_of_fixture_to_dict(
my_dict, name_of_list_of_fixtures, list_of_string_fixtures
):
--
GitLab
From 1ef2b188044bb806d1ed1e18f80ecf58afcc8aef Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 08:21:21 +0200
Subject: [PATCH 108/167] add init of coef infla but useless
---
pyPLNmodels/models.py | 12 +++++-------
1 file changed, 5 insertions(+), 7 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 76f76fea..49c590d1 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -14,7 +14,6 @@ import plotly.express as px
from mlxtend.plotting import plot_pca_correlation_graph
import matplotlib
from scipy import stats
-from statsmodels.discrete.count_model import ZeroInflatedPoisson
from ._closed_forms import (
_closed_formula_coef,
@@ -3474,8 +3473,6 @@ class ZIPln(_model):
batch = pln_batch + (torch.index_select(self._dirac, 0, to_take),)
if self._use_closed_form_prob is False:
to_return = torch.index_select(self._latent_prob, 0, to_take)
- print("max latent_prbo", torch.max(self._latent_prob))
- print("max to return", torch.max(to_return))
return batch + (torch.index_select(self._latent_prob, 0, to_take),)
return batch
@@ -3598,13 +3595,14 @@ class ZIPln(_model):
self._components = _init_components(self._endog, self.dim)
if not hasattr(self, "_coef_inflation"):
- # print('shape', self.exog.shape[1])
+ self._coef_inflation = torch.randn(self.nb_cov, self.dim)
# for j in range(self.exog.shape[1]):
# Y_j = self._endog[:,j].numpy()
# offsets_j = self.offsets[:,j].numpy()
- # zip_training_results = ZeroInflatedPoisson(endog=Y_j,exog = self.exog.numpy(), exog_infl = self.exog.numpy(), inflation='logit', offsets = offsets_j).fit()
- # print('params', zip_training_results.params)
- self._coef_inflation = torch.randn(self.nb_cov, self.dim)
+ # exog = self.exog[:,j].unsqueeze(1).numpy()
+ # undzi = ZeroInflatedPoisson(endog=Y_j,exog = exog, exog_infl = exog, inflation='logit', offset = offsets_j)
+ # zip_training_results = undzi.fit()
+ # self._coef_inflation[:,j] = zip_training_results.params[1]
def _random_init_latent_parameters(self):
self._latent_mean = torch.randn(self.n_samples, self.dim)
--
GitLab
From 3ea7aea012bc456393569b993c0e1e9f83174e75 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 09:17:14 +0200
Subject: [PATCH 109/167] add a checker of getting started file.
---
.gitignore | 7 ++---
...e_getting_started_and_docstrings_tests.py} | 26 +++++++++++++++----
2 files changed, 25 insertions(+), 8 deletions(-)
rename tests/{create_readme_and_docstrings_tests.py => create_readme_getting_started_and_docstrings_tests.py} (75%)
diff --git a/.gitignore b/.gitignore
index b2d69b27..85c56ae8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -157,8 +157,9 @@ index.html
paper/*
-tests/test_models*
-tests/test_load*
-tests/test_readme*
+tests/docstrings_examples/*
+tests/getting_started/*
+tests/readme_examples/*
+tests/test_getting_started.py
Getting_started.py
new_model.py
diff --git a/tests/create_readme_and_docstrings_tests.py b/tests/create_readme_getting_started_and_docstrings_tests.py
similarity index 75%
rename from tests/create_readme_and_docstrings_tests.py
rename to tests/create_readme_getting_started_and_docstrings_tests.py
index 63aecf9d..113bf841 100644
--- a/tests/create_readme_and_docstrings_tests.py
+++ b/tests/create_readme_getting_started_and_docstrings_tests.py
@@ -4,6 +4,7 @@ import os
dir_docstrings = "docstrings_examples"
dir_readme = "readme_examples"
+dir_getting_started = "getting_started"
def get_lines(path_to_file, filename, filetype=".py"):
@@ -47,11 +48,11 @@ def get_example_readme(lines):
return [example]
-def write_examples(examples, filename):
+def write_file(examples, filename, string_definer, dir):
for i in range(len(examples)):
example = examples[i]
nb_example = str(i + 1)
- example_filename = f"test_{filename}_example_{nb_example}.py"
+ example_filename = f"{dir}/test_{filename}_{string_definer}_{nb_example}.py"
try:
os.remove(example_filename)
except FileNotFoundError:
@@ -64,19 +65,34 @@ def write_examples(examples, filename):
def filename_to_docstring_example_file(filename, dirname):
lines = get_lines("../pyPLNmodels/", filename)
examples = get_examples_docstring(lines)
- write_examples(examples, filename)
+ write_file(examples, filename, "example", dir=dirname)
def filename_to_readme_example_file():
lines = get_lines("../", "README", filetype=".md")
examples = get_example_readme(lines)
- write_examples(examples, "readme")
+ write_file(examples, "readme", "example", dir=dir_readme)
+
+
+lines_getting_started = get_lines("./", "test_getting_started")
+new_lines = []
+for line in lines_getting_started:
+ if len(line) > 20:
+ if line[0:11] != "get_ipython":
+ new_lines.append(line)
+ else:
+ new_lines.append(line)
os.makedirs(dir_readme, exist_ok=True)
+os.makedirs(dir_docstrings, exist_ok=True)
+os.makedirs(dir_getting_started, exist_ok=True)
+
+write_file([new_lines], "getting_started", "", dir_getting_started)
+
filename_to_readme_example_file()
-os.makedirs("docstrings_examples", exist_ok=True)
+
filename_to_docstring_example_file("_utils", dir_docstrings)
filename_to_docstring_example_file("models", dir_docstrings)
filename_to_docstring_example_file("elbos", dir_docstrings)
--
GitLab
From f6983d98c3f6dbfb8ccc87948cdd535c9c72dc8e Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 09:21:57 +0200
Subject: [PATCH 110/167] changed the cd. Goes to file to check the readme
examples, docstrings examples etc.
---
.gitlab-ci.yml | 10 +++++++++-
1 file changed, 9 insertions(+), 1 deletion(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index c95c78f1..bf2bf333 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -20,8 +20,16 @@ tests:
pip install '.[tests]'
script:
- pip install .
+ - jupyter nbconvert Getting_started.ipynb --to python --output tests/test_getting_started
- cd tests
- - python create_readme_and_docstrings_tests.py
+ - python create_readme_getting_started_and_docstrings_tests.py
+ - cd readme_examples
+ - pytest .
+ - cd ../readme_examples
+ - pytest .
+ - cd ../getting_started
+ - pytest .
+ - cd ..
- pytest .
only:
- main
--
GitLab
From 350fa49e2e4db13935010be0f3b61de4cefa9493 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 10:44:14 +0200
Subject: [PATCH 111/167] add my own image.
---
.gitlab-ci.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index bf2bf333..108e940b 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -15,7 +15,7 @@ black:
tests:
stage: checks
- image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
+ image: "registry.forgemia.inra.fr/bbatardiere/jbleger:main"
before_script:
pip install '.[tests]'
script:
--
GitLab
From 58e247fc0a3fb6366e47765526171c9303ec6e57 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 10:46:02 +0200
Subject: [PATCH 112/167] fix the name of the image.
---
.gitlab-ci.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 108e940b..9c0bda6f 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -15,7 +15,7 @@ black:
tests:
stage: checks
- image: "registry.forgemia.inra.fr/bbatardiere/jbleger:main"
+ image: "registry.forgemia.inra.fr/bbatardiere/docker-image-pandas-torch-sphinx-jupyter:main"
before_script:
pip install '.[tests]'
script:
--
GitLab
From cffb3ef1f56e70f55b03ea75f466bce9141ae76b Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 10:50:14 +0200
Subject: [PATCH 113/167] retry the ci
---
.gitignore | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/.gitignore b/.gitignore
index 85c56ae8..c5443b2c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,6 +160,6 @@ paper/*
tests/docstrings_examples/*
tests/getting_started/*
tests/readme_examples/*
-tests/test_getting_started.py
+# tests/test_getting_started.py
Getting_started.py
new_model.py
--
GitLab
From 98cced733f8ac77a58e884a3b085feb801254d9d Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 10:52:38 +0200
Subject: [PATCH 114/167] change the image name.
---
.gitlab-ci.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 9c0bda6f..bba6aff6 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -15,7 +15,7 @@ black:
tests:
stage: checks
- image: "registry.forgemia.inra.fr/bbatardiere/docker-image-pandas-torch-sphinx-jupyter:main"
+ image: "registry.forgemia.inra.fr/bbatardiere/docker-image-pandas-torch-sphinx-jupyter"
before_script:
pip install '.[tests]'
script:
--
GitLab
From cb1e835e08b47c3c0d82a62996717c434655dcb9 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 13:51:53 +0200
Subject: [PATCH 115/167] chang the gitlab and the running of examles since
pytest was not testing it.
---
.gitlab-ci.yml | 8 +-
.../run_readme_docstrings_getting_started.sh | 15 ++
tests/test_getting_started.py | 131 ++++++++++++++----
3 files changed, 119 insertions(+), 35 deletions(-)
create mode 100755 tests/run_readme_docstrings_getting_started.sh
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index bba6aff6..ba97ffea 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -23,13 +23,7 @@ tests:
- jupyter nbconvert Getting_started.ipynb --to python --output tests/test_getting_started
- cd tests
- python create_readme_getting_started_and_docstrings_tests.py
- - cd readme_examples
- - pytest .
- - cd ../readme_examples
- - pytest .
- - cd ../getting_started
- - pytest .
- - cd ..
+ - ./run_readme_docstrings_getting_started.sh
- pytest .
only:
- main
diff --git a/tests/run_readme_docstrings_getting_started.sh b/tests/run_readme_docstrings_getting_started.sh
new file mode 100755
index 00000000..59489033
--- /dev/null
+++ b/tests/run_readme_docstrings_getting_started.sh
@@ -0,0 +1,15 @@
+#!/bin/sh
+for file in docstrings_examples/*
+do
+ python $file
+done
+
+for file in readme_examples/*
+do
+ python $file
+done
+
+for file in getting_started/*
+do
+ python $file
+done
diff --git a/tests/test_getting_started.py b/tests/test_getting_started.py
index 69299741..605132a0 100644
--- a/tests/test_getting_started.py
+++ b/tests/test_getting_started.py
@@ -1,49 +1,63 @@
#!/usr/bin/env python
# coding: utf-8
-# get_ipython().system('pip install pyPLNmodels')
+# In[1]:
+
+
+get_ipython().system('pip install pyPLNmodels')
# ## pyPLNmodels
# We assume the data comes from a PLN model: $ \text{counts} \sim \mathcal P(\exp(\text{Z}))$, where $Z$ are some unknown latent variables.
-#
-#
-# The goal of the package is to retrieve the latent variables $Z$ given the counts. To do so, one can instantiate a Pln or PlnPCA model, fit it and then extract the latent variables.
+#
+#
+# The goal of the package is to retrieve the latent variables $Z$ given the counts. To do so, one can instantiate a Pln or PlnPCA model, fit it and then extract the latent variables.
# ### Import the needed functions
-from pyPLNmodels import (
- get_real_count_data,
- get_simulated_count_data,
- load_model,
- Pln,
- PlnPCA,
- PlnPCAcollection,
-)
+# In[2]:
+
+
+from pyPLNmodels import get_real_count_data, get_simulated_count_data, load_model, Pln, PlnPCA, PlnPCAcollection
import matplotlib.pyplot as plt
# ### Load the data
-counts, labels = get_real_count_data(return_labels=True) # np.ndarray
+# In[3]:
+
+
+counts, labels = get_real_count_data(return_labels=True) # np.ndarray
# ### PLN model
-pln = Pln(counts, add_const=True)
+# In[4]:
+
+
+pln = Pln(counts, add_const = True)
pln.fit()
+# In[5]:
+
+
print(pln)
# #### Once fitted, we can extract multiple variables:
+# In[6]:
+
+
gaussian = pln.latent_variables
print(gaussian.shape)
+# In[7]:
+
+
model_param = pln.model_parameters
print(model_param["coef"].shape)
print(model_param["covariance"].shape)
@@ -51,37 +65,61 @@ print(model_param["covariance"].shape)
# ### PlnPCA model
-pca = PlnPCA(counts, add_const=True, rank=5)
+# In[8]:
+
+
+pca = PlnPCA(counts, add_const = True, rank = 5)
pca.fit()
+# In[9]:
+
+
print(pca)
+# In[10]:
+
+
print(pca.latent_variables.shape)
+# In[11]:
+
+
print(pca.model_parameters["components"].shape)
print(pca.model_parameters["coef"].shape)
# ### One can save the model in order to load it back after:
+# In[13]:
+
+
pca.save()
dict_init = load_model("PlnPCA_nbcov_1_dim_200_rank_5")
-loaded_pca = PlnPCA(counts, add_const=True, dict_initialization=dict_init)
+loaded_pca = PlnPCA(counts, add_const = True, dict_initialization= dict_init)
print(loaded_pca)
# ### One can fit multiple PCA and choose the best rank with BIC or AIC criterion
-pca_col = PlnPCAcollection(counts, add_const=True, ranks=[5, 15, 25, 40, 50])
+# In[14]:
+
+
+pca_col = PlnPCAcollection(counts, add_const = True, ranks = [5,15,25,40,50])
pca_col.fit()
+# In[15]:
+
+
pca_col.show()
+# In[16]:
+
+
print(pca_col)
@@ -89,53 +127,90 @@ print(pca_col)
# #### AIC best model
-print(pca_col.best_model(criterion="AIC"))
+# In[17]:
+
+
+print(pca_col.best_model(criterion = "AIC"))
# #### BIC best model
-print(pca_col.best_model(criterion="BIC"))
+# In[18]:
+
+
+print(pca_col.best_model(criterion = "BIC"))
# #### Visualization of the individuals (sites) with PCA on the latent variables.
+# In[19]:
+
+
pln.viz(colors=labels)
plt.show()
+# In[20]:
+
+
best_pca = pca_col.best_model()
-best_pca.viz(colors=labels)
+best_pca.viz(colors = labels)
plt.show()
-# ### What would give a PCA on the log normalize data ?
+# ### What would give a PCA on the log normalize data ?
+
+# In[21]:
+
from sklearn.decomposition import PCA
import numpy as np
import seaborn as sns
-sk_pca = PCA(n_components=2)
+# In[22]:
+
+
+sk_pca = PCA(n_components = 2)
pca_log_counts = sk_pca.fit_transform(np.log(counts + (counts == 0)))
-sns.scatterplot(x=pca_log_counts[:, 0], y=pca_log_counts[:, 1], hue=labels)
+sns.scatterplot(x = pca_log_counts[:,0], y = pca_log_counts[:,1], hue = labels)
# ### Visualization of the variables
-pln.plot_pca_correlation_graph(["var_1", "var_2"], indices_of_variables=[0, 1])
+# In[23]:
+
+
+pln.plot_pca_correlation_graph(["var_1","var_2"], indices_of_variables = [0,1])
plt.show()
-best_pca.plot_pca_correlation_graph(["var_1", "var_2"], indices_of_variables=[0, 1])
+# In[24]:
+
+
+best_pca.plot_pca_correlation_graph(["var_1","var_2"], indices_of_variables = [0,1])
plt.show()
# ### Visualization of each components of the PCA
-#
+#
-pln.scatter_pca_matrix(color=labels, n_components=5)
+# In[25]:
+
+
+pln.scatter_pca_matrix(color = labels, n_components = 5)
plt.show()
-best_pca.scatter_pca_matrix(color=labels, n_components=6)
+# In[26]:
+
+
+best_pca.scatter_pca_matrix(color = labels, n_components = 6)
plt.show()
+
+
+# In[ ]:
+
+
+
+
--
GitLab
From 1b9347f582042f16bd42661583bc563e3d657d02 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 13:54:16 +0200
Subject: [PATCH 116/167] remove useless file sinc eit is going to be removed
---
tests/test_getting_started.py | 216 ----------------------------------
1 file changed, 216 deletions(-)
delete mode 100644 tests/test_getting_started.py
diff --git a/tests/test_getting_started.py b/tests/test_getting_started.py
deleted file mode 100644
index 605132a0..00000000
--- a/tests/test_getting_started.py
+++ /dev/null
@@ -1,216 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-get_ipython().system('pip install pyPLNmodels')
-
-
-# ## pyPLNmodels
-
-# We assume the data comes from a PLN model: $ \text{counts} \sim \mathcal P(\exp(\text{Z}))$, where $Z$ are some unknown latent variables.
-#
-#
-# The goal of the package is to retrieve the latent variables $Z$ given the counts. To do so, one can instantiate a Pln or PlnPCA model, fit it and then extract the latent variables.
-
-# ### Import the needed functions
-
-# In[2]:
-
-
-from pyPLNmodels import get_real_count_data, get_simulated_count_data, load_model, Pln, PlnPCA, PlnPCAcollection
-import matplotlib.pyplot as plt
-
-
-# ### Load the data
-
-# In[3]:
-
-
-counts, labels = get_real_count_data(return_labels=True) # np.ndarray
-
-
-# ### PLN model
-
-# In[4]:
-
-
-pln = Pln(counts, add_const = True)
-pln.fit()
-
-
-# In[5]:
-
-
-print(pln)
-
-
-# #### Once fitted, we can extract multiple variables:
-
-# In[6]:
-
-
-gaussian = pln.latent_variables
-print(gaussian.shape)
-
-
-# In[7]:
-
-
-model_param = pln.model_parameters
-print(model_param["coef"].shape)
-print(model_param["covariance"].shape)
-
-
-# ### PlnPCA model
-
-# In[8]:
-
-
-pca = PlnPCA(counts, add_const = True, rank = 5)
-pca.fit()
-
-
-# In[9]:
-
-
-print(pca)
-
-
-# In[10]:
-
-
-print(pca.latent_variables.shape)
-
-
-# In[11]:
-
-
-print(pca.model_parameters["components"].shape)
-print(pca.model_parameters["coef"].shape)
-
-
-# ### One can save the model in order to load it back after:
-
-# In[13]:
-
-
-pca.save()
-dict_init = load_model("PlnPCA_nbcov_1_dim_200_rank_5")
-loaded_pca = PlnPCA(counts, add_const = True, dict_initialization= dict_init)
-print(loaded_pca)
-
-
-# ### One can fit multiple PCA and choose the best rank with BIC or AIC criterion
-
-# In[14]:
-
-
-pca_col = PlnPCAcollection(counts, add_const = True, ranks = [5,15,25,40,50])
-pca_col.fit()
-
-
-# In[15]:
-
-
-pca_col.show()
-
-
-# In[16]:
-
-
-print(pca_col)
-
-
-# ### One can extract the best model found (according to AIC or BIC criterion).
-
-# #### AIC best model
-
-# In[17]:
-
-
-print(pca_col.best_model(criterion = "AIC"))
-
-
-# #### BIC best model
-
-# In[18]:
-
-
-print(pca_col.best_model(criterion = "BIC"))
-
-
-# #### Visualization of the individuals (sites) with PCA on the latent variables.
-
-# In[19]:
-
-
-pln.viz(colors=labels)
-plt.show()
-
-
-# In[20]:
-
-
-best_pca = pca_col.best_model()
-best_pca.viz(colors = labels)
-plt.show()
-
-
-# ### What would give a PCA on the log normalize data ?
-
-# In[21]:
-
-
-from sklearn.decomposition import PCA
-import numpy as np
-import seaborn as sns
-
-
-# In[22]:
-
-
-sk_pca = PCA(n_components = 2)
-pca_log_counts = sk_pca.fit_transform(np.log(counts + (counts == 0)))
-sns.scatterplot(x = pca_log_counts[:,0], y = pca_log_counts[:,1], hue = labels)
-
-
-# ### Visualization of the variables
-
-# In[23]:
-
-
-pln.plot_pca_correlation_graph(["var_1","var_2"], indices_of_variables = [0,1])
-plt.show()
-
-
-# In[24]:
-
-
-best_pca.plot_pca_correlation_graph(["var_1","var_2"], indices_of_variables = [0,1])
-plt.show()
-
-
-# ### Visualization of each components of the PCA
-#
-
-# In[25]:
-
-
-pln.scatter_pca_matrix(color = labels, n_components = 5)
-plt.show()
-
-
-# In[26]:
-
-
-best_pca.scatter_pca_matrix(color = labels, n_components = 6)
-plt.show()
-
-
-# In[ ]:
-
-
-
-
--
GitLab
From 052da666f66e7cff0ef3f545c0e8c1a2c0bf4429 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 14:04:15 +0200
Subject: [PATCH 117/167] remove a file in the ci
---
.gitlab-ci.yml | 1 +
1 file changed, 1 insertion(+)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index ba97ffea..61e51176 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -23,6 +23,7 @@ tests:
- jupyter nbconvert Getting_started.ipynb --to python --output tests/test_getting_started
- cd tests
- python create_readme_getting_started_and_docstrings_tests.py
+ - rm test_getting_started.py
- ./run_readme_docstrings_getting_started.sh
- pytest .
only:
--
GitLab
From f595aa0ecd3562852e0bdd8c61246975e9d37713 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 14:23:38 +0200
Subject: [PATCH 118/167] gpu support for the from formula.
---
pyPLNmodels/_utils.py | 4 ++++
1 file changed, 4 insertions(+)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 1082259e..05d13b00 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -929,6 +929,10 @@ def _extract_data_from_formula(
A tuple containing the extracted endog, exog, and offsets.
"""
+ # dmatrices can not deal with GPU matrices
+ for key,matrix in data.items():
+ if isinstance(matrix, torch.Tensor):
+ data[key] = matrix.cpu()
dmatrix = dmatrices(formula, data=data)
endog = dmatrix[0]
exog = dmatrix[1]
--
GitLab
From e073928bfb68c926e99bd19e65ca19046a338511 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 15:40:41 +0200
Subject: [PATCH 119/167] add GPU support.
---
pyPLNmodels/models.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 49c590d1..7dfe71a3 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -231,7 +231,7 @@ class _model(ABC):
if self._get_max_components() < 2:
raise RuntimeError("Can't perform visualization for dim < 2.")
pca = self.sk_PCA(n_components=2)
- proj_variables = pca.transform(self.latent_variables.detach().cpu())
+ proj_variables = pca.transform(self.latent_variables)
x = proj_variables[:, 0]
y = proj_variables[:, 1]
sns.scatterplot(x=x, y=y, hue=colors, ax=ax)
@@ -1286,7 +1286,7 @@ class _model(ABC):
raise RuntimeError("Please fit the model before.")
if ax is None:
ax = plt.gca()
- predictions = self._endog_predictions().ravel().detach()
+ predictions = self._endog_predictions().ravel().cpu().detach()
if colors is not None:
colors = np.repeat(np.array(colors), repeats=self.dim).ravel()
sns.scatterplot(x=self.endog.ravel(), y=predictions, hue=colors, ax=ax)
@@ -3284,7 +3284,7 @@ class PlnPCA(_model):
""",
)
def latent_variables(self) -> torch.Tensor:
- return torch.matmul(self._latent_mean, self._components.T).detach()
+ return torch.matmul(self.latent_mean, self.components.T)
@_add_doc(
_model,
--
GitLab
From 2b7208ce78b327f73f95d2f6a62e516ba56edea6 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 19 Oct 2023 15:41:16 +0200
Subject: [PATCH 120/167] remove the shell script since otherwise it is running
two times the test of the examples.
---
.gitlab-ci.yml | 1 -
1 file changed, 1 deletion(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 61e51176..e45adf60 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -24,7 +24,6 @@ tests:
- cd tests
- python create_readme_getting_started_and_docstrings_tests.py
- rm test_getting_started.py
- - ./run_readme_docstrings_getting_started.sh
- pytest .
only:
- main
--
GitLab
From 46752db4409e8031a0a85faaa6fbccce17f66d3e Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 20 Oct 2023 12:36:07 +0200
Subject: [PATCH 121/167] fix GPU support.
---
pyPLNmodels/models.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 7dfe71a3..ce920b97 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -236,7 +236,7 @@ class _model(ABC):
y = proj_variables[:, 1]
sns.scatterplot(x=x, y=y, hue=colors, ax=ax)
if show_cov is True:
- sk_components = torch.from_numpy(pca.components_)
+ sk_components = torch.from_numpy(pca.components_).to(DEVICE)
covariances = self._get_pca_low_dim_covariances(sk_components).detach()
for i in range(covariances.shape[0]):
_plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
@@ -3008,7 +3008,7 @@ class PlnPCA(_model):
else:
XB = 0
return torch.exp(
- self._offsets + XB + self.latent_variables + 1 / 2 * covariance_a_posteriori
+ self._offsets + XB + self.latent_variables.to(DEVICE) + 1 / 2 * covariance_a_posteriori
)
@latent_mean.setter
--
GitLab
From db2a910eee522795c19a375761271247ab5584c8 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 20 Oct 2023 12:48:10 +0200
Subject: [PATCH 122/167] fixe one more gpu support bug
---
pyPLNmodels/models.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index ce920b97..ac2af805 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -232,8 +232,8 @@ class _model(ABC):
raise RuntimeError("Can't perform visualization for dim < 2.")
pca = self.sk_PCA(n_components=2)
proj_variables = pca.transform(self.latent_variables)
- x = proj_variables[:, 0]
- y = proj_variables[:, 1]
+ x = proj_variables[:, 0].cpu()
+ y = proj_variables[:, 1].cpu()
sns.scatterplot(x=x, y=y, hue=colors, ax=ax)
if show_cov is True:
sk_components = torch.from_numpy(pca.components_).to(DEVICE)
--
GitLab
From cc802a77e3a63f557c30d6bedd3abfd0fbace239 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sat, 21 Oct 2023 11:51:49 +0200
Subject: [PATCH 123/167] add the doc for zipln
---
docs/source/index.rst | 1 +
docs/source/zipln.rst | 10 ++++++++++
2 files changed, 11 insertions(+)
create mode 100644 docs/source/zipln.rst
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 98f3e0a6..da418320 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -16,6 +16,7 @@ API documentation
./plnpcacollection.rst
./plnpca.rst
./pln.rst
+ ./zipln.rst
.. toctree::
:maxdepth: 1
diff --git a/docs/source/zipln.rst b/docs/source/zipln.rst
new file mode 100644
index 00000000..ae0e1e81
--- /dev/null
+++ b/docs/source/zipln.rst
@@ -0,0 +1,10 @@
+
+ZIPln
+===
+
+.. autoclass:: pyPLNmodels.ZIPln
+ :members:
+ :inherited-members:
+ :special-members: __init__
+ :undoc-members:
+ :show-inheritance:
--
GitLab
From b929138210ac47ee9eceff62352ff96f9c3a287a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sat, 21 Oct 2023 11:58:57 +0200
Subject: [PATCH 124/167] add GPU support
---
pyPLNmodels/models.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index ac2af805..1249f14b 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -232,12 +232,12 @@ class _model(ABC):
raise RuntimeError("Can't perform visualization for dim < 2.")
pca = self.sk_PCA(n_components=2)
proj_variables = pca.transform(self.latent_variables)
- x = proj_variables[:, 0].cpu()
- y = proj_variables[:, 1].cpu()
+ x = proj_variables[:, 0]
+ y = proj_variables[:, 1]
sns.scatterplot(x=x, y=y, hue=colors, ax=ax)
if show_cov is True:
sk_components = torch.from_numpy(pca.components_).to(DEVICE)
- covariances = self._get_pca_low_dim_covariances(sk_components).detach()
+ covariances = self._get_pca_low_dim_covariances(sk_components).cpu().detach()
for i in range(covariances.shape[0]):
_plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
return ax
--
GitLab
From ced911b2e2a02c321747856a7fde9c8bec34e5df Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sat, 21 Oct 2023 12:13:29 +0200
Subject: [PATCH 125/167] fixed gpu support.
---
pyPLNmodels/_utils.py | 3 +--
pyPLNmodels/models.py | 31 ++++++++++---------------------
2 files changed, 11 insertions(+), 23 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 05d13b00..b8fa8001 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -199,9 +199,8 @@ def _log_stirling(integer: torch.Tensor) -> torch.Tensor:
integer_ / math.exp(1)
)
-
def _trunc_log(tens: torch.Tensor, eps: float = 1e-16) -> torch.Tensor:
- integer = torch.min(torch.max(tens, torch.tensor([eps])), torch.tensor([1 - eps]))
+ integer = torch.min(torch.max(tens, torch.tensor([eps]).to(DEVICE)), torch.tensor([1 - eps]).to(DEVICE))
return torch.log(integer)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 1249f14b..b591a7a8 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -968,7 +968,7 @@ class _model(ABC):
raise ValueError(
f"Wrong shape. Expected {self.n_samples, self.dim}, got {latent_mean.shape}"
)
- self._latent_mean = latent_mean
+ self._latent_mean = latent_mean.to(DEVICE)
def _cpu_attribute_or_none(self, attribute_name):
"""
@@ -1821,17 +1821,6 @@ class Pln(_model):
pass
# no model parameters since we are doing a profiled ELBO
- @_add_doc(_model)
- def _smart_init_latent_parameters(self):
- self._random_init_latent_sqrt_var()
- if not hasattr(self, "_latent_mean"):
- self._latent_mean = torch.log(self._endog + (self._endog == 0))
-
- @_add_doc(_model)
- def _random_init_latent_parameters(self):
- self._random_init_latent_sqrt_var()
- if not hasattr(self, "_latent_mean"):
- self._latent_mean = torch.ones((self.n_samples, self.dim)).to(DEVICE)
@property
@_add_doc(_model)
@@ -3583,9 +3572,9 @@ class ZIPln(_model):
return "with full covariance model and zero-inflation."
def _random_init_model_parameters(self):
- self._coef_inflation = torch.randn(self.nb_cov, self.dim)
- self._coef = torch.randn(self.nb_cov, self.dim)
- self._components = torch.randn(self.dim, self.dim)
+ self._coef_inflation = torch.randn(self.nb_cov, self.dim).to(DEVICE)
+ self._coef = torch.randn(self.nb_cov, self.dim).to(DEVICE)
+ self._components = torch.randn(self.dim, self.dim).to(DEVICE)
# should change the good initialization for _coef_inflation
def _smart_init_model_parameters(self):
@@ -3595,7 +3584,7 @@ class ZIPln(_model):
self._components = _init_components(self._endog, self.dim)
if not hasattr(self, "_coef_inflation"):
- self._coef_inflation = torch.randn(self.nb_cov, self.dim)
+ self._coef_inflation = torch.randn(self.nb_cov, self.dim).to(DEVICE)
# for j in range(self.exog.shape[1]):
# Y_j = self._endog[:,j].numpy()
# offsets_j = self.offsets[:,j].numpy()
@@ -3605,12 +3594,12 @@ class ZIPln(_model):
# self._coef_inflation[:,j] = zip_training_results.params[1]
def _random_init_latent_parameters(self):
- self._latent_mean = torch.randn(self.n_samples, self.dim)
- self._latent_sqrt_var = torch.randn(self.n_samples, self.dim)
+ self._latent_mean = torch.randn(self.n_samples, self.dim).to(DEVICE)
+ self._latent_sqrt_var = torch.randn(self.n_samples, self.dim).to(DEVICE)
self._latent_prob = (
torch.empty(self.n_samples, self.dim).uniform_(0, 1).to(DEVICE)
* self._dirac
- ).double()
+ ).double().to(DEVICE)
def _smart_init_latent_parameters(self):
self._random_init_latent_parameters()
@@ -3755,8 +3744,8 @@ class ZIPln(_model):
"""
if self._use_closed_form_prob is False:
with torch.no_grad():
- self._latent_prob = torch.maximum(self._latent_prob, torch.tensor([0]))
- self._latent_prob = torch.minimum(self._latent_prob, torch.tensor([1]))
+ self._latent_prob = torch.maximum(self._latent_prob, torch.tensor([0]).to(DEVICE))
+ self._latent_prob = torch.minimum(self._latent_prob, torch.tensor([1]).to(DEVICE))
self._latent_prob *= self._dirac
@property
--
GitLab
From f75157076281358b9ca37f0dd7546820af204390 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 23 Oct 2023 15:15:43 +0200
Subject: [PATCH 126/167] pass all the tests on GPU, going to merge on main.
---
pyPLNmodels/models.py | 2 +-
tests/import_data.py | 5 +++++
tests/test_common.py | 10 +++++-----
tests/test_plnpcacollection.py | 5 ++---
tests/test_zi.py | 12 ++++++------
5 files changed, 19 insertions(+), 15 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index b591a7a8..624bcf2a 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -3197,7 +3197,7 @@ class PlnPCA(_model):
"""
Orthogonal components of the model.
"""
- return torch.linalg.qr(self._components, "reduced")[0]
+ return torch.linalg.qr(self._components, "reduced")[0].cpu()
@property
def components(self) -> torch.Tensor:
diff --git a/tests/import_data.py b/tests/import_data.py
index 9ef5ef7e..9942db40 100644
--- a/tests/import_data.py
+++ b/tests/import_data.py
@@ -1,10 +1,15 @@
import os
+import torch
from pyPLNmodels import (
get_simulated_count_data,
get_real_count_data,
)
+if torch.cuda.is_available():
+ DEVICE = "cuda:0"
+else:
+ DEVICE = "cpu"
(
endog_sim_0cov,
diff --git a/tests/test_common.py b/tests/test_common.py
index 6cba2cf6..df49f39c 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -46,14 +46,14 @@ def test_verbose(any_instance_model):
@filter_models(pln_and_plnpca)
def test_find_right_covariance(simulated_fitted_any_model):
if simulated_fitted_any_model.nb_cov == 0:
- true_covariance = true_sim_0cov["Sigma"]
+ true_covariance = true_sim_0cov["Sigma"].cpu()
elif simulated_fitted_any_model.nb_cov == 2:
- true_covariance = true_sim_2cov["Sigma"]
+ true_covariance = true_sim_2cov["Sigma"].cpu()
else:
raise ValueError(
f"Not the right numbers of covariance({simulated_fitted_any_model.nb_cov})"
)
- mse_covariance = MSE(simulated_fitted_any_model.covariance - true_covariance)
+ mse_covariance = MSE(simulated_fitted_any_model.covariance.cpu() - true_covariance.cpu())
assert mse_covariance < 0.05
@@ -75,7 +75,7 @@ def test_right_covariance_shape(real_fitted_and_loaded_model):
def test_find_right_coef(simulated_fitted_any_model):
if simulated_fitted_any_model.nb_cov == 2:
true_coef = true_sim_2cov["beta"]
- mse_coef = MSE(simulated_fitted_any_model.coef - true_coef)
+ mse_coef = MSE(simulated_fitted_any_model.coef.cpu() - true_coef.cpu())
assert mse_coef < 0.1
elif simulated_fitted_any_model.nb_cov == 0:
assert simulated_fitted_any_model.coef is None
@@ -118,7 +118,7 @@ def test_batch(model):
model.show()
if model.nb_cov == 2:
true_coef = true_sim_2cov["beta"]
- mse_coef = MSE(model.coef - true_coef)
+ mse_coef = MSE(model.coef.cpu() - true_coef.cpu())
assert mse_coef < 0.1
elif model.nb_cov == 0:
assert model.coef is None
diff --git a/tests/test_plnpcacollection.py b/tests/test_plnpcacollection.py
index 0d982d60..2c1db5a4 100644
--- a/tests/test_plnpcacollection.py
+++ b/tests/test_plnpcacollection.py
@@ -8,7 +8,6 @@ from tests.conftest import dict_fixtures
from tests.utils import MSE, filter_models
from tests.import_data import true_sim_0cov, true_sim_2cov
-
@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["PlnPCAcollection"])
def test_best_model(plnpca):
@@ -80,9 +79,9 @@ def test_batch(collection):
else:
raise ValueError(f"Not the right numbers of covariance({collection.nb_cov})")
for model in collection.values():
- mse_covariance = MSE(model.covariance - true_covariance)
+ mse_covariance = MSE(model.covariance.cpu() - true_covariance.cpu())
if true_coef is not None:
- mse_coef = MSE(model.coef - true_coef)
+ mse_coef = MSE(model.coef.cpu() - true_coef.cpu())
assert mse_coef < 0.35
assert mse_covariance < 0.25
collection.fit()
diff --git a/tests/test_zi.py b/tests/test_zi.py
index 2016accf..acfaa5bd 100644
--- a/tests/test_zi.py
+++ b/tests/test_zi.py
@@ -77,9 +77,9 @@ def test_find_right_covariance_coef_and_infla():
)
zi = ZIPln(endog, exog=exog, offsets=offsets, use_closed_form_prob=False)
zi.fit()
- mse_covariance = MSE(zi.covariance - covariance)
- mse_coef = MSE(zi.coef - coef)
- mse_coef_infla = MSE(zi.coef_inflation - coef_inflation)
+ mse_covariance = MSE(zi.covariance.cpu() - covariance.cpu())
+ mse_coef = MSE(zi.coef.cpu() - coef.cpu())
+ mse_coef_infla = MSE(zi.coef_inflation.cpu() - coef_inflation.cpu())
assert mse_coef < 3
assert mse_coef_infla < 3
assert mse_covariance < 1
@@ -118,9 +118,9 @@ def test_batch(model):
)
zi = ZIPln(endog, exog=exog, offsets=offsets, use_closed_form_prob=False)
zi.fit(batch_size=20)
- mse_covariance = MSE(zi.covariance - covariance)
- mse_coef = MSE(zi.coef - coef)
- mse_coef_infla = MSE(zi.coef_inflation - coef_inflation)
+ mse_covariance = MSE(zi.covariance.cpu() - covariance.cpu())
+ mse_coef = MSE(zi.coef.cpu() - coef.cpu())
+ mse_coef_infla = MSE(zi.coef_inflation.cpu() - coef_inflation.cpu())
assert mse_coef < 3
assert mse_coef_infla < 3
assert mse_covariance < 1
--
GitLab
From 1902bc00063d137457fe4ff7b494ab07a772a108 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 23 Oct 2023 15:24:05 +0200
Subject: [PATCH 127/167] balcked
---
pyPLNmodels/_utils.py | 8 ++++++--
pyPLNmodels/models.py | 28 ++++++++++++++++++++--------
tests/test_common.py | 4 +++-
tests/test_plnpcacollection.py | 1 +
4 files changed, 30 insertions(+), 11 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index b8fa8001..1cb9d2cd 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -199,8 +199,12 @@ def _log_stirling(integer: torch.Tensor) -> torch.Tensor:
integer_ / math.exp(1)
)
+
def _trunc_log(tens: torch.Tensor, eps: float = 1e-16) -> torch.Tensor:
- integer = torch.min(torch.max(tens, torch.tensor([eps]).to(DEVICE)), torch.tensor([1 - eps]).to(DEVICE))
+ integer = torch.min(
+ torch.max(tens, torch.tensor([eps]).to(DEVICE)),
+ torch.tensor([1 - eps]).to(DEVICE),
+ )
return torch.log(integer)
@@ -929,7 +933,7 @@ def _extract_data_from_formula(
"""
# dmatrices can not deal with GPU matrices
- for key,matrix in data.items():
+ for key, matrix in data.items():
if isinstance(matrix, torch.Tensor):
data[key] = matrix.cpu()
dmatrix = dmatrices(formula, data=data)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 624bcf2a..d5854adf 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -237,7 +237,9 @@ class _model(ABC):
sns.scatterplot(x=x, y=y, hue=colors, ax=ax)
if show_cov is True:
sk_components = torch.from_numpy(pca.components_).to(DEVICE)
- covariances = self._get_pca_low_dim_covariances(sk_components).cpu().detach()
+ covariances = (
+ self._get_pca_low_dim_covariances(sk_components).cpu().detach()
+ )
for i in range(covariances.shape[0]):
_plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
return ax
@@ -1821,7 +1823,6 @@ class Pln(_model):
pass
# no model parameters since we are doing a profiled ELBO
-
@property
@_add_doc(_model)
def _list_of_parameters_needing_gradient(self):
@@ -2997,7 +2998,10 @@ class PlnPCA(_model):
else:
XB = 0
return torch.exp(
- self._offsets + XB + self.latent_variables.to(DEVICE) + 1 / 2 * covariance_a_posteriori
+ self._offsets
+ + XB
+ + self.latent_variables.to(DEVICE)
+ + 1 / 2 * covariance_a_posteriori
)
@latent_mean.setter
@@ -3597,9 +3601,13 @@ class ZIPln(_model):
self._latent_mean = torch.randn(self.n_samples, self.dim).to(DEVICE)
self._latent_sqrt_var = torch.randn(self.n_samples, self.dim).to(DEVICE)
self._latent_prob = (
- torch.empty(self.n_samples, self.dim).uniform_(0, 1).to(DEVICE)
- * self._dirac
- ).double().to(DEVICE)
+ (
+ torch.empty(self.n_samples, self.dim).uniform_(0, 1).to(DEVICE)
+ * self._dirac
+ )
+ .double()
+ .to(DEVICE)
+ )
def _smart_init_latent_parameters(self):
self._random_init_latent_parameters()
@@ -3744,8 +3752,12 @@ class ZIPln(_model):
"""
if self._use_closed_form_prob is False:
with torch.no_grad():
- self._latent_prob = torch.maximum(self._latent_prob, torch.tensor([0]).to(DEVICE))
- self._latent_prob = torch.minimum(self._latent_prob, torch.tensor([1]).to(DEVICE))
+ self._latent_prob = torch.maximum(
+ self._latent_prob, torch.tensor([0]).to(DEVICE)
+ )
+ self._latent_prob = torch.minimum(
+ self._latent_prob, torch.tensor([1]).to(DEVICE)
+ )
self._latent_prob *= self._dirac
@property
diff --git a/tests/test_common.py b/tests/test_common.py
index df49f39c..bd5ca62c 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -53,7 +53,9 @@ def test_find_right_covariance(simulated_fitted_any_model):
raise ValueError(
f"Not the right numbers of covariance({simulated_fitted_any_model.nb_cov})"
)
- mse_covariance = MSE(simulated_fitted_any_model.covariance.cpu() - true_covariance.cpu())
+ mse_covariance = MSE(
+ simulated_fitted_any_model.covariance.cpu() - true_covariance.cpu()
+ )
assert mse_covariance < 0.05
diff --git a/tests/test_plnpcacollection.py b/tests/test_plnpcacollection.py
index 2c1db5a4..761afabc 100644
--- a/tests/test_plnpcacollection.py
+++ b/tests/test_plnpcacollection.py
@@ -8,6 +8,7 @@ from tests.conftest import dict_fixtures
from tests.utils import MSE, filter_models
from tests.import_data import true_sim_0cov, true_sim_2cov
+
@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_model"])
@filter_models(["PlnPCAcollection"])
def test_best_model(plnpca):
--
GitLab
From 661319d163397ee9a48c48932b9e9c3593a72e1b Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 23 Oct 2023 23:01:30 +0200
Subject: [PATCH 128/167] changed image in publishing package and build
package.
---
.gitlab-ci.yml | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index e45adf60..671bb811 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -32,7 +32,7 @@ tests:
build_package:
stage: build
- image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
+ image: "registry.forgemia.inra.fr/bbatardiere/docker-image-pandas-torch-sphinx-jupyter"
before_script:
- pip install build
script:
@@ -59,7 +59,7 @@ publish_package:
pages:
stage: publish
- image: "registry.forgemia.inra.fr/jbleger/docker-image-pandas-torch-sphinx:master"
+ image: "registry.forgemia.inra.fr/bbatardiere/docker-image-pandas-torch-sphinx-jupyter"
before_script:
- pip install '.[build-doc]'
script:
--
GitLab
From 0c4e248b598ac65591ed1eb9e5e7f4c4c9f94df6 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 23 Oct 2023 23:05:36 +0200
Subject: [PATCH 129/167] add the ZIPln in the readme.
---
README.md | 12 +++++++++---
1 file changed, 9 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index f8adaa3f..71d47bbc 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@ The getting started can be found [here](https://forgemia.inra.fr/bbatardiere/pyp
The package comes with an ecological data set to present the functionality
```
import pyPLNmodels
-from pyPLNmodels.models import PlnPCAcollection, Pln
+from pyPLNmodels.models import PlnPCAcollection, Pln, ZIPln
from pyPLNmodels.oaks import load_oaks
oaks = load_oaks()
```
@@ -45,9 +45,15 @@ transformed_data = pln.transform()
pca = PlnPCAcollection.from_formula("counts ~ 1 + tree + dist2ground + orientation ", data = oaks, take_log_offsets = True, ranks = [3,4,5])
pca.fit()
print(pca)
-transformed_data = pln.transform()
+transformed_data = pca.transform()
+```
+### Zero inflated Poisson Log normal Model (aka ZIPln)
+```
+zi = ZIPln.from_formula("counts ~ 1 + tree + dist2ground + orientation ", data = oaks, take_log_offsets = True, ranks = [3,4,5])
+zi.fit()
+zi(pca)
+transformed_data = zi.transform()
```
-
## 🛠Installation
--
GitLab
From 21e4c9a2d174671d480a1070e0e1675db36cff58 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 23 Oct 2023 23:08:56 +0200
Subject: [PATCH 130/167] not enough ===== under the ZIPLN
---
docs/source/zipln.rst | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/docs/source/zipln.rst b/docs/source/zipln.rst
index ae0e1e81..ccd9573c 100644
--- a/docs/source/zipln.rst
+++ b/docs/source/zipln.rst
@@ -1,6 +1,6 @@
ZIPln
-===
+=====
.. autoclass:: pyPLNmodels.ZIPln
:members:
--
GitLab
From d2cce98920b8dbf147d9c41975d9377853936002 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 23 Oct 2023 23:12:52 +0200
Subject: [PATCH 131/167] minor change
---
docs/source/zipln.rst | 1 -
1 file changed, 1 deletion(-)
diff --git a/docs/source/zipln.rst b/docs/source/zipln.rst
index ccd9573c..17fbac45 100644
--- a/docs/source/zipln.rst
+++ b/docs/source/zipln.rst
@@ -1,4 +1,3 @@
-
ZIPln
=====
--
GitLab
From 52318742764ccad219f6eb60b40c70d727e51019 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Mon, 23 Oct 2023 23:48:21 +0200
Subject: [PATCH 132/167] typo in th eREADME
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 71d47bbc..ad011628 100644
--- a/README.md
+++ b/README.md
@@ -45,7 +45,7 @@ transformed_data = pln.transform()
pca = PlnPCAcollection.from_formula("counts ~ 1 + tree + dist2ground + orientation ", data = oaks, take_log_offsets = True, ranks = [3,4,5])
pca.fit()
print(pca)
-transformed_data = pca.transform()
+transformed_data = pca.best_model().transform()
```
### Zero inflated Poisson Log normal Model (aka ZIPln)
```
--
GitLab
From 010109b72b80f6a0c7a8b44431c3e77a1928b137 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 24 Oct 2023 00:01:20 +0200
Subject: [PATCH 133/167] useless seaborn
---
pyPLNmodels/_initialization.py | 1 -
1 file changed, 1 deletion(-)
diff --git a/pyPLNmodels/_initialization.py b/pyPLNmodels/_initialization.py
index fe649fe0..95b71246 100644
--- a/pyPLNmodels/_initialization.py
+++ b/pyPLNmodels/_initialization.py
@@ -4,7 +4,6 @@ from typing import Optional
from ._utils import _log_stirling
import time
from sklearn.decomposition import PCA
-import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
--
GitLab
From d6e63006e1f3e19151c28189a08816a27271ba1c Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 24 Oct 2023 00:08:52 +0200
Subject: [PATCH 134/167] typo in the README
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index ad011628..25afa02b 100644
--- a/README.md
+++ b/README.md
@@ -51,7 +51,7 @@ transformed_data = pca.best_model().transform()
```
zi = ZIPln.from_formula("counts ~ 1 + tree + dist2ground + orientation ", data = oaks, take_log_offsets = True, ranks = [3,4,5])
zi.fit()
-zi(pca)
+print(zi)
transformed_data = zi.transform()
```
--
GitLab
From a1bc467d954ddc07c704a231d014f1ef1b7f480d Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Tue, 24 Oct 2023 00:11:36 +0200
Subject: [PATCH 135/167] another typo.
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 25afa02b..267975de 100644
--- a/README.md
+++ b/README.md
@@ -49,7 +49,7 @@ transformed_data = pca.best_model().transform()
```
### Zero inflated Poisson Log normal Model (aka ZIPln)
```
-zi = ZIPln.from_formula("counts ~ 1 + tree + dist2ground + orientation ", data = oaks, take_log_offsets = True, ranks = [3,4,5])
+zi = ZIPln.from_formula("counts ~ 1 + tree + dist2ground + orientation ", data = oaks, take_log_offsets = True)
zi.fit()
print(zi)
transformed_data = zi.transform()
--
GitLab
From 50e510e9e5abd651a3f3007e6cdb3c09188ee5b6 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 26 Oct 2023 15:43:55 +0200
Subject: [PATCH 136/167] minor change
---
pyPLNmodels/elbos.py | 6 +-----
1 file changed, 1 insertion(+), 5 deletions(-)
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 73e77028..7ebfbd43 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -180,11 +180,7 @@ def elbo_pln(
elbo += d
f = -0.5 * torch.trace(torch.inverse(covariance) @ diag)
elbo += f
- # print("a pln", a)
- # print("b pln", b)
- # print("d pln", d)
- # print("f pln", f)
- return elbo # / n_samples
+ return elbo
## pb with trunc_log
--
GitLab
From 8eea8480f39d45ee8e626b7126386a1261ca16aa Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 26 Oct 2023 15:43:55 +0200
Subject: [PATCH 137/167] minor change
---
pyPLNmodels/elbos.py | 6 +-----
1 file changed, 1 insertion(+), 5 deletions(-)
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 73e77028..7ebfbd43 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -180,11 +180,7 @@ def elbo_pln(
elbo += d
f = -0.5 * torch.trace(torch.inverse(covariance) @ diag)
elbo += f
- # print("a pln", a)
- # print("b pln", b)
- # print("d pln", d)
- # print("f pln", f)
- return elbo # / n_samples
+ return elbo
## pb with trunc_log
--
GitLab
From 51e16bdf9da3f74008016db1e7ef00d92da7db55 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 7 Jan 2024 01:14:14 +0100
Subject: [PATCH 138/167] minor change in the README.
---
README.md | 1 +
1 file changed, 1 insertion(+)
diff --git a/README.md b/README.md
index 267975de..31fb242e 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,7 @@
> though it has memory limitations. Possible fields of applications include
> - Genomics (number of times a gene is expressed in a cell)
> - Ecology (species abundances)
+>
> One main functionality is to normalize the count data to obtain more valuable
> data. It also analyse the significance of each variable and their correlation as well as the weight of
> covariates (if available).
--
GitLab
From 0a8e0bf4cdbcb95607a47556dd6ebdf9f44849f9 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 7 Jan 2024 01:14:14 +0100
Subject: [PATCH 139/167] minor change in the README.
---
README.md | 1 +
1 file changed, 1 insertion(+)
diff --git a/README.md b/README.md
index 267975de..31fb242e 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,7 @@
> though it has memory limitations. Possible fields of applications include
> - Genomics (number of times a gene is expressed in a cell)
> - Ecology (species abundances)
+>
> One main functionality is to normalize the count data to obtain more valuable
> data. It also analyse the significance of each variable and their correlation as well as the weight of
> covariates (if available).
--
GitLab
From a825138e77f4c0d1fba373ebe21937e31e3320e4 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 7 Jan 2024 16:51:49 +0100
Subject: [PATCH 140/167] updated version in the package.
---
pyPLNmodels/__init__.py | 3 +++
1 file changed, 3 insertions(+)
diff --git a/pyPLNmodels/__init__.py b/pyPLNmodels/__init__.py
index 6ed723c7..1e4c8c55 100644
--- a/pyPLNmodels/__init__.py
+++ b/pyPLNmodels/__init__.py
@@ -1,3 +1,5 @@
+import importlib.metadata
+
from .models import PlnPCAcollection, Pln, PlnPCA, ZIPln # pylint:disable=[C0114]
from .oaks import load_oaks
from .elbos import profiled_elbo_pln, elbo_plnpca, elbo_pln
@@ -30,3 +32,4 @@ __all__ = (
"get_simulation_parameters",
"PlnParameters",
)
+__version__ = importlib.metadata.version("pyPLNmodels")
--
GitLab
From 52e02d60709863ef08100c6dbc0f17094bd35148 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 7 Jan 2024 16:51:49 +0100
Subject: [PATCH 141/167] updated version in the package.
---
pyPLNmodels/__init__.py | 3 +++
1 file changed, 3 insertions(+)
diff --git a/pyPLNmodels/__init__.py b/pyPLNmodels/__init__.py
index 6ed723c7..1e4c8c55 100644
--- a/pyPLNmodels/__init__.py
+++ b/pyPLNmodels/__init__.py
@@ -1,3 +1,5 @@
+import importlib.metadata
+
from .models import PlnPCAcollection, Pln, PlnPCA, ZIPln # pylint:disable=[C0114]
from .oaks import load_oaks
from .elbos import profiled_elbo_pln, elbo_plnpca, elbo_pln
@@ -30,3 +32,4 @@ __all__ = (
"get_simulation_parameters",
"PlnParameters",
)
+__version__ = importlib.metadata.version("pyPLNmodels")
--
GitLab
From be3b4a61511227968fd6a548a76f1ba5bfa12ba5 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 7 Jan 2024 16:52:15 +0100
Subject: [PATCH 142/167] remove useless line in ci.
---
.gitlab-ci.yml | 1 -
1 file changed, 1 deletion(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 671bb811..0e380726 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -50,7 +50,6 @@ publish_package:
before_script:
- pip install twine
script:
- - echo ${pypln_token}
- TWINE_PASSWORD=${pypln_token} TWINE_USERNAME=__token__ python -m twine upload dist/*
tags:
- docker
--
GitLab
From ec0ee760079794dee6a012f14b8c65f56a618324 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 7 Jan 2024 16:52:15 +0100
Subject: [PATCH 143/167] remove useless line in ci.
---
.gitlab-ci.yml | 1 -
1 file changed, 1 deletion(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 671bb811..0e380726 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -50,7 +50,6 @@ publish_package:
before_script:
- pip install twine
script:
- - echo ${pypln_token}
- TWINE_PASSWORD=${pypln_token} TWINE_USERNAME=__token__ python -m twine upload dist/*
tags:
- docker
--
GitLab
From bc9dd3a5e8fc6854b36287743baeb315d930226a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 7 Jan 2024 18:58:41 +0100
Subject: [PATCH 144/167] updated README
---
README.md | 23 ++++++++++++-----------
1 file changed, 12 insertions(+), 11 deletions(-)
diff --git a/README.md b/README.md
index 31fb242e..41758cfb 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,18 @@
## Getting started
The getting started can be found [here](https://forgemia.inra.fr/bbatardiere/pyplnmodels/-/raw/dev/Getting_started.ipynb?inline=false). If you need just a quick view of the package, see the quickstart next.
+## 🛠Installation
+
+**pyPLNmodels** is available on
+[pypi](https://pypi.org/project/pyPLNmodels/). The development
+version is available on [GitHub](https://github.com/PLN-team/pyPLNmodels).
+
+### Package installation
+
+```
+pip install pyPLNmodels
+```
+
## âš¡ï¸ Quickstart
The package comes with an ecological data set to present the functionality
@@ -56,17 +68,6 @@ print(zi)
transformed_data = zi.transform()
```
-## 🛠Installation
-
-**pyPLNmodels** is available on
-[pypi](https://pypi.org/project/pyPLNmodels/). The development
-version is available on [GitHub](https://github.com/PLN-team/pyPLNmodels).
-
-### Package installation
-
-```
-pip install pyPLNmodels
-```
## 👠Contributing
--
GitLab
From 8efd36b843a38332a3624da487921714cd48e4ab Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Sun, 7 Jan 2024 18:58:41 +0100
Subject: [PATCH 145/167] updated README
---
README.md | 23 ++++++++++++-----------
1 file changed, 12 insertions(+), 11 deletions(-)
diff --git a/README.md b/README.md
index 31fb242e..41758cfb 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,18 @@
## Getting started
The getting started can be found [here](https://forgemia.inra.fr/bbatardiere/pyplnmodels/-/raw/dev/Getting_started.ipynb?inline=false). If you need just a quick view of the package, see the quickstart next.
+## 🛠Installation
+
+**pyPLNmodels** is available on
+[pypi](https://pypi.org/project/pyPLNmodels/). The development
+version is available on [GitHub](https://github.com/PLN-team/pyPLNmodels).
+
+### Package installation
+
+```
+pip install pyPLNmodels
+```
+
## âš¡ï¸ Quickstart
The package comes with an ecological data set to present the functionality
@@ -56,17 +68,6 @@ print(zi)
transformed_data = zi.transform()
```
-## 🛠Installation
-
-**pyPLNmodels** is available on
-[pypi](https://pypi.org/project/pyPLNmodels/). The development
-version is available on [GitHub](https://github.com/PLN-team/pyPLNmodels).
-
-### Package installation
-
-```
-pip install pyPLNmodels
-```
## 👠Contributing
--
GitLab
From b91066263aac01306011e3aad275e7dcc324bd06 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 11 Jan 2024 17:54:51 +0100
Subject: [PATCH 146/167] add the right link in README to the getting started.
Before it was a link in forgemia.
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index f8adaa3f..b15d0300 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@
<!-- > comprehensive introduction. -->
## Getting started
-The getting started can be found [here](https://forgemia.inra.fr/bbatardiere/pyplnmodels/-/raw/dev/Getting_started.ipynb?inline=false). If you need just a quick view of the package, see the quickstart next.
+The getting started can be found [here](Getting_started.ipynb). If you need just a quick view of the package, see the quickstart next.
## âš¡ï¸ Quickstart
--
GitLab
From 723a29ca45d668c67ee11a961951485f2f3bd7fb Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 11 Jan 2024 17:54:51 +0100
Subject: [PATCH 147/167] add the right link in README to the getting started.
Before it was a link in forgemia.
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index f8adaa3f..b15d0300 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@
<!-- > comprehensive introduction. -->
## Getting started
-The getting started can be found [here](https://forgemia.inra.fr/bbatardiere/pyplnmodels/-/raw/dev/Getting_started.ipynb?inline=false). If you need just a quick view of the package, see the quickstart next.
+The getting started can be found [here](Getting_started.ipynb). If you need just a quick view of the package, see the quickstart next.
## âš¡ï¸ Quickstart
--
GitLab
From 82be72f0e553594d7c418a7887192c631c4ed0fd Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 11 Jan 2024 17:58:12 +0100
Subject: [PATCH 148/167] cleared output of the getting_started.
---
Getting_started.ipynb | 45 ++++++-------------------------------------
1 file changed, 6 insertions(+), 39 deletions(-)
diff --git a/Getting_started.ipynb b/Getting_started.ipynb
index f5f30e8d..fda09530 100644
--- a/Getting_started.ipynb
+++ b/Getting_started.ipynb
@@ -2,45 +2,12 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "23df6e63",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already satisfied: pyPLNmodels in /home/bastien/miniconda3/lib/python3.10/site-packages (0.0.57.dev1+g1f0e5e1.d20230605)\n",
- "Requirement already satisfied: scipy in /home/bastien/miniconda3/lib/python3.10/site-packages (from pyPLNmodels) (1.10.0)\n",
- "Requirement already satisfied: matplotlib in /home/bastien/miniconda3/lib/python3.10/site-packages (from pyPLNmodels) (3.6.3)\n",
- "Requirement already satisfied: patsy in /home/bastien/.local/lib/python3.10/site-packages (from pyPLNmodels) (0.5.3)\n",
- "Requirement already satisfied: pandas in /home/bastien/.local/lib/python3.10/site-packages (from pyPLNmodels) (1.5.3)\n",
- "Requirement already satisfied: scikit-learn in /home/bastien/.local/lib/python3.10/site-packages (from pyPLNmodels) (1.2.1)\n",
- "Requirement already satisfied: torch in /home/bastien/.local/lib/python3.10/site-packages (from pyPLNmodels) (1.13.1)\n",
- "Requirement already satisfied: numpy in /home/bastien/miniconda3/lib/python3.10/site-packages (from pyPLNmodels) (1.23.5)\n",
- "Requirement already satisfied: seaborn in /home/bastien/.local/lib/python3.10/site-packages (from pyPLNmodels) (0.12.2)\n",
- "Requirement already satisfied: packaging>=20.0 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (22.0)\n",
- "Requirement already satisfied: pyparsing>=2.2.1 in /home/bastien/.local/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (3.0.9)\n",
- "Requirement already satisfied: pillow>=6.2.0 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (9.4.0)\n",
- "Requirement already satisfied: fonttools>=4.22.0 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (4.38.0)\n",
- "Requirement already satisfied: python-dateutil>=2.7 in /home/bastien/.local/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (2.8.2)\n",
- "Requirement already satisfied: contourpy>=1.0.1 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (1.0.7)\n",
- "Requirement already satisfied: cycler>=0.10 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (0.11.0)\n",
- "Requirement already satisfied: kiwisolver>=1.0.1 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (1.4.4)\n",
- "Requirement already satisfied: pytz>=2020.1 in /home/bastien/miniconda3/lib/python3.10/site-packages (from pandas->pyPLNmodels) (2022.7.1)\n",
- "Requirement already satisfied: six in /home/bastien/miniconda3/lib/python3.10/site-packages (from patsy->pyPLNmodels) (1.16.0)\n",
- "Requirement already satisfied: joblib>=1.1.1 in /home/bastien/.local/lib/python3.10/site-packages (from scikit-learn->pyPLNmodels) (1.2.0)\n",
- "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/bastien/.local/lib/python3.10/site-packages (from scikit-learn->pyPLNmodels) (3.1.0)\n",
- "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /home/bastien/.local/lib/python3.10/site-packages (from torch->pyPLNmodels) (11.7.99)\n",
- "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /home/bastien/.local/lib/python3.10/site-packages (from torch->pyPLNmodels) (11.10.3.66)\n",
- "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /home/bastien/.local/lib/python3.10/site-packages (from torch->pyPLNmodels) (8.5.0.96)\n",
- "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /home/bastien/.local/lib/python3.10/site-packages (from torch->pyPLNmodels) (11.7.99)\n",
- "Requirement already satisfied: typing-extensions in /home/bastien/.local/lib/python3.10/site-packages (from torch->pyPLNmodels) (4.4.0)\n",
- "Requirement already satisfied: setuptools in /home/bastien/miniconda3/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch->pyPLNmodels) (65.5.0)\n",
- "Requirement already satisfied: wheel in /home/bastien/miniconda3/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch->pyPLNmodels) (0.37.1)\n"
- ]
- }
- ],
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
"source": [
"!pip install pyPLNmodels"
]
@@ -8307,7 +8274,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.8"
+ "version": "3.8.13"
}
},
"nbformat": 4,
--
GitLab
From 65d08df2613f6a6dde23e4a17d804d4a236667f6 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 11 Jan 2024 17:58:12 +0100
Subject: [PATCH 149/167] cleared output of the getting_started.
---
Getting_started.ipynb | 45 ++++++-------------------------------------
1 file changed, 6 insertions(+), 39 deletions(-)
diff --git a/Getting_started.ipynb b/Getting_started.ipynb
index f5f30e8d..fda09530 100644
--- a/Getting_started.ipynb
+++ b/Getting_started.ipynb
@@ -2,45 +2,12 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "23df6e63",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already satisfied: pyPLNmodels in /home/bastien/miniconda3/lib/python3.10/site-packages (0.0.57.dev1+g1f0e5e1.d20230605)\n",
- "Requirement already satisfied: scipy in /home/bastien/miniconda3/lib/python3.10/site-packages (from pyPLNmodels) (1.10.0)\n",
- "Requirement already satisfied: matplotlib in /home/bastien/miniconda3/lib/python3.10/site-packages (from pyPLNmodels) (3.6.3)\n",
- "Requirement already satisfied: patsy in /home/bastien/.local/lib/python3.10/site-packages (from pyPLNmodels) (0.5.3)\n",
- "Requirement already satisfied: pandas in /home/bastien/.local/lib/python3.10/site-packages (from pyPLNmodels) (1.5.3)\n",
- "Requirement already satisfied: scikit-learn in /home/bastien/.local/lib/python3.10/site-packages (from pyPLNmodels) (1.2.1)\n",
- "Requirement already satisfied: torch in /home/bastien/.local/lib/python3.10/site-packages (from pyPLNmodels) (1.13.1)\n",
- "Requirement already satisfied: numpy in /home/bastien/miniconda3/lib/python3.10/site-packages (from pyPLNmodels) (1.23.5)\n",
- "Requirement already satisfied: seaborn in /home/bastien/.local/lib/python3.10/site-packages (from pyPLNmodels) (0.12.2)\n",
- "Requirement already satisfied: packaging>=20.0 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (22.0)\n",
- "Requirement already satisfied: pyparsing>=2.2.1 in /home/bastien/.local/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (3.0.9)\n",
- "Requirement already satisfied: pillow>=6.2.0 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (9.4.0)\n",
- "Requirement already satisfied: fonttools>=4.22.0 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (4.38.0)\n",
- "Requirement already satisfied: python-dateutil>=2.7 in /home/bastien/.local/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (2.8.2)\n",
- "Requirement already satisfied: contourpy>=1.0.1 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (1.0.7)\n",
- "Requirement already satisfied: cycler>=0.10 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (0.11.0)\n",
- "Requirement already satisfied: kiwisolver>=1.0.1 in /home/bastien/miniconda3/lib/python3.10/site-packages (from matplotlib->pyPLNmodels) (1.4.4)\n",
- "Requirement already satisfied: pytz>=2020.1 in /home/bastien/miniconda3/lib/python3.10/site-packages (from pandas->pyPLNmodels) (2022.7.1)\n",
- "Requirement already satisfied: six in /home/bastien/miniconda3/lib/python3.10/site-packages (from patsy->pyPLNmodels) (1.16.0)\n",
- "Requirement already satisfied: joblib>=1.1.1 in /home/bastien/.local/lib/python3.10/site-packages (from scikit-learn->pyPLNmodels) (1.2.0)\n",
- "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/bastien/.local/lib/python3.10/site-packages (from scikit-learn->pyPLNmodels) (3.1.0)\n",
- "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /home/bastien/.local/lib/python3.10/site-packages (from torch->pyPLNmodels) (11.7.99)\n",
- "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /home/bastien/.local/lib/python3.10/site-packages (from torch->pyPLNmodels) (11.10.3.66)\n",
- "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /home/bastien/.local/lib/python3.10/site-packages (from torch->pyPLNmodels) (8.5.0.96)\n",
- "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /home/bastien/.local/lib/python3.10/site-packages (from torch->pyPLNmodels) (11.7.99)\n",
- "Requirement already satisfied: typing-extensions in /home/bastien/.local/lib/python3.10/site-packages (from torch->pyPLNmodels) (4.4.0)\n",
- "Requirement already satisfied: setuptools in /home/bastien/miniconda3/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch->pyPLNmodels) (65.5.0)\n",
- "Requirement already satisfied: wheel in /home/bastien/miniconda3/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch->pyPLNmodels) (0.37.1)\n"
- ]
- }
- ],
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
"source": [
"!pip install pyPLNmodels"
]
@@ -8307,7 +8274,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.8"
+ "version": "3.8.13"
}
},
"nbformat": 4,
--
GitLab
From 6caf93652a785962fb7bdf2bc1f934bda2a40e37 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 11 Jan 2024 18:05:42 +0100
Subject: [PATCH 150/167] change a little bit the getting_started.
---
Getting_started.ipynb | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/Getting_started.ipynb b/Getting_started.ipynb
index fda09530..b6b15f2f 100644
--- a/Getting_started.ipynb
+++ b/Getting_started.ipynb
@@ -292,7 +292,7 @@
"id": "31c211f1",
"metadata": {},
"source": [
- "### One can save the model in order to load it back after:"
+ "### Save the model in order to load it back after:"
]
},
{
@@ -334,7 +334,7 @@
"id": "0cad573c",
"metadata": {},
"source": [
- "### One can fit multiple PCA and choose the best rank with BIC or AIC criterion"
+ "### Fit multiple PCA and choose the best rank with BIC or AIC criterion"
]
},
{
@@ -456,7 +456,7 @@
"id": "bbee8a02",
"metadata": {},
"source": [
- "### One can extract the best model found (according to AIC or BIC criterion)."
+ "### Extract the best model found (according to AIC or BIC criterion)."
]
},
{
@@ -638,7 +638,7 @@
"source": [
"sk_pca = PCA(n_components = 2)\n",
"pca_log_counts = sk_pca.fit_transform(np.log(counts + (counts == 0)))\n",
- "sns.scatterplot(x = pca_log_counts[:,0], y = pca_log_counts[:,1], hue = labels)"
+ "_ = sns.scatterplot(x = pca_log_counts[:,0], y = pca_log_counts[:,1], hue = labels)"
]
},
{
--
GitLab
From fd38d4126405820b68f33fd098294fee493501ad Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 11 Jan 2024 18:05:42 +0100
Subject: [PATCH 151/167] change a little bit the getting_started.
---
Getting_started.ipynb | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/Getting_started.ipynb b/Getting_started.ipynb
index fda09530..b6b15f2f 100644
--- a/Getting_started.ipynb
+++ b/Getting_started.ipynb
@@ -292,7 +292,7 @@
"id": "31c211f1",
"metadata": {},
"source": [
- "### One can save the model in order to load it back after:"
+ "### Save the model in order to load it back after:"
]
},
{
@@ -334,7 +334,7 @@
"id": "0cad573c",
"metadata": {},
"source": [
- "### One can fit multiple PCA and choose the best rank with BIC or AIC criterion"
+ "### Fit multiple PCA and choose the best rank with BIC or AIC criterion"
]
},
{
@@ -456,7 +456,7 @@
"id": "bbee8a02",
"metadata": {},
"source": [
- "### One can extract the best model found (according to AIC or BIC criterion)."
+ "### Extract the best model found (according to AIC or BIC criterion)."
]
},
{
@@ -638,7 +638,7 @@
"source": [
"sk_pca = PCA(n_components = 2)\n",
"pca_log_counts = sk_pca.fit_transform(np.log(counts + (counts == 0)))\n",
- "sns.scatterplot(x = pca_log_counts[:,0], y = pca_log_counts[:,1], hue = labels)"
+ "_ = sns.scatterplot(x = pca_log_counts[:,0], y = pca_log_counts[:,1], hue = labels)"
]
},
{
--
GitLab
From 24da34c23a8043c4d4d0a3021d1e5a08f09742c4 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 17:04:48 +0100
Subject: [PATCH 152/167] add better error messages, changed the default value
of offsets from "logsum" to "zero".
---
pyPLNmodels/_utils.py | 5 +++++
pyPLNmodels/elbos.py | 2 +-
pyPLNmodels/models.py | 45 ++++++++++++++++++++++++-------------------
3 files changed, 31 insertions(+), 21 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 1cb9d2cd..10ce38c4 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -333,10 +333,15 @@ def _format_model_param(
------
ValueError
If endog has negative values or offsets_formula is not None and not "logsum" or "zero"
+ If endog has one line that is full of zeros.
"""
endog = _format_data(endog)
if torch.min(endog) < 0:
raise ValueError("Counts should be only non negative values.")
+ if torch.min(torch.sum(endog, axis=0)) < 0.5:
+ raise ValueError(
+ "Counts contains individuals containing only zero coutns. Remove it."
+ )
exog = _format_data(exog)
if add_const is True:
if exog is None:
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 73e77028..2dca93fb 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -219,7 +219,7 @@ def elbo_zi_pln(
"""
covariance = components @ (components.T)
if torch.norm(latent_prob * dirac - latent_prob) > 0.00000001:
- raise RuntimeError("latent_prob error")
+ raise RuntimeError("Latent probability error.")
n_samples, dim = endog.shape
s_rond_s = torch.multiply(latent_sqrt_var, latent_sqrt_var)
o_plus_m = offsets + latent_mean
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index d5854adf..32006437 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -74,7 +74,7 @@ class _model(ABC):
*,
exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
add_const: bool = True,
@@ -91,8 +91,8 @@ class _model(ABC):
offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The offsets data. Defaults to None.
offsets_formula : str, optional(keyword-only)
- The formula for offsets. Defaults to "logsum". Overriden if
- offsets is not None.
+ The formula for offsets. Defaults to "zero". Can be also "logsum" where we take the logarithm of the sum (of each line) of the counts.
+ Overriden (useless) if offsets is not None.
dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
@@ -120,7 +120,7 @@ class _model(ABC):
formula: str,
data: dict[str : Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
):
@@ -135,7 +135,9 @@ class _model(ABC):
The data dictionary. Each value can be either a torch.Tensor,
a np.ndarray or pd.DataFrame
offsets_formula : str, optional(keyword-only)
- The formula for offsets. Defaults to "logsum".
+ The formula for offsets. Defaults to "zero". Can be also "logsum" where we take
+ the logarithm of the sum (of each line) of the counts.
+ Overriden (useless) if data["offsets"] is not None.
dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
@@ -482,7 +484,7 @@ class _model(ABC):
self.optim.zero_grad()
loss = -self._compute_elbo_b()
if torch.sum(torch.isnan(loss)):
- raise ValueError("test")
+ raise ValueError("The ELBO contains nan values.")
loss.backward()
elbo += loss.item()
self.optim.step()
@@ -1462,7 +1464,7 @@ class Pln(_model):
*,
exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
add_const: bool = True,
@@ -1499,7 +1501,7 @@ class Pln(_model):
formula: str,
data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
):
@@ -1957,7 +1959,7 @@ class PlnPCAcollection:
*,
exog: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
ranks: Iterable[int] = range(3, 5),
dict_of_dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
@@ -1975,7 +1977,8 @@ class PlnPCAcollection:
offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The offsets, by default None.
offsets_formula : str, optional(keyword-only)
- The formula for offsets, by default "logsum".
+ The formula for offsets, by default "zero". Can be also "logsum" where we take the logarithm of the sum (of each line) of the counts.
+ Overriden (useless) if offsets is not None.
ranks : Iterable[int], optional(keyword-only)
The range of ranks, by default range(3, 5).
dict_of_dict_initialization : dict, optional(keyword-only)
@@ -2013,7 +2016,7 @@ class PlnPCAcollection:
formula: str,
data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
ranks: Iterable[int] = range(3, 5),
dict_of_dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
@@ -2029,8 +2032,8 @@ class PlnPCAcollection:
The data dictionary. Each value can be either
a torch.Tensor, np.ndarray or pd.DataFrame
offsets_formula : str, optional(keyword-only)
- The formula for offsets, by default "logsum".
- Overriden if data["offsets"] is not None.
+ The formula for offsets, by default "zero". Can be also "logsum" where we take the logarithm of the sum (of each line) of the counts.
+ Overriden (useless) if data["offsets"] is not None.
ranks : Iterable[int], optional(keyword-only)
The range of ranks, by default range(3, 5).
dict_of_dict_initialization : dict, optional(keyword-only)
@@ -2802,7 +2805,7 @@ class PlnPCA(_model):
*,
exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
rank: int = 5,
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
@@ -2846,7 +2849,7 @@ class PlnPCA(_model):
data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
rank: int = 5,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
):
endog, exog, offsets = _extract_data_from_formula(formula, data)
@@ -3389,7 +3392,7 @@ class ZIPln(_model):
*,
exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
add_const: bool = True,
@@ -3407,8 +3410,8 @@ class ZIPln(_model):
offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The offsets data. Defaults to None.
offsets_formula : str, optional(keyword-only)
- The formula for offsets. Defaults to "logsum". Overriden if
- offsets is not None.
+ The formula for offsets. Defaults to "zero". Can be also "logsum" where we take the logarithm of the sum (of each line) of the counts.
+ Overriden (useless) if offsets is not None.
dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
@@ -3475,7 +3478,7 @@ class ZIPln(_model):
formula: str,
data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
use_closed_form_prob: bool = False,
@@ -3491,7 +3494,9 @@ class ZIPln(_model):
The data dictionary. Each value can be either a torch.Tensor,
a np.ndarray or pd.DataFrame
offsets_formula : str, optional(keyword-only)
- The formula for offsets. Defaults to "logsum".
+ The formula for offsets. Defaults to "zero". Can be also "logsum" where
+ we take the logarithm of the sum (of each line) of the counts. Overriden (useless)
+ if data["offsets"] is not None.
dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
--
GitLab
From a577c741511350132a5f5881ca593b349681f47d Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 17:04:48 +0100
Subject: [PATCH 153/167] add better error messages, changed the default value
of offsets from "logsum" to "zero".
---
pyPLNmodels/_utils.py | 5 +++++
pyPLNmodels/elbos.py | 2 +-
pyPLNmodels/models.py | 45 ++++++++++++++++++++++++-------------------
3 files changed, 31 insertions(+), 21 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 1cb9d2cd..10ce38c4 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -333,10 +333,15 @@ def _format_model_param(
------
ValueError
If endog has negative values or offsets_formula is not None and not "logsum" or "zero"
+ If endog has one line that is full of zeros.
"""
endog = _format_data(endog)
if torch.min(endog) < 0:
raise ValueError("Counts should be only non negative values.")
+ if torch.min(torch.sum(endog, axis=0)) < 0.5:
+ raise ValueError(
+ "Counts contains individuals containing only zero coutns. Remove it."
+ )
exog = _format_data(exog)
if add_const is True:
if exog is None:
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 73e77028..2dca93fb 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -219,7 +219,7 @@ def elbo_zi_pln(
"""
covariance = components @ (components.T)
if torch.norm(latent_prob * dirac - latent_prob) > 0.00000001:
- raise RuntimeError("latent_prob error")
+ raise RuntimeError("Latent probability error.")
n_samples, dim = endog.shape
s_rond_s = torch.multiply(latent_sqrt_var, latent_sqrt_var)
o_plus_m = offsets + latent_mean
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index d5854adf..32006437 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -74,7 +74,7 @@ class _model(ABC):
*,
exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
add_const: bool = True,
@@ -91,8 +91,8 @@ class _model(ABC):
offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The offsets data. Defaults to None.
offsets_formula : str, optional(keyword-only)
- The formula for offsets. Defaults to "logsum". Overriden if
- offsets is not None.
+ The formula for offsets. Defaults to "zero". Can be also "logsum" where we take the logarithm of the sum (of each line) of the counts.
+ Overriden (useless) if offsets is not None.
dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
@@ -120,7 +120,7 @@ class _model(ABC):
formula: str,
data: dict[str : Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
):
@@ -135,7 +135,9 @@ class _model(ABC):
The data dictionary. Each value can be either a torch.Tensor,
a np.ndarray or pd.DataFrame
offsets_formula : str, optional(keyword-only)
- The formula for offsets. Defaults to "logsum".
+ The formula for offsets. Defaults to "zero". Can be also "logsum" where we take
+ the logarithm of the sum (of each line) of the counts.
+ Overriden (useless) if data["offsets"] is not None.
dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
@@ -482,7 +484,7 @@ class _model(ABC):
self.optim.zero_grad()
loss = -self._compute_elbo_b()
if torch.sum(torch.isnan(loss)):
- raise ValueError("test")
+ raise ValueError("The ELBO contains nan values.")
loss.backward()
elbo += loss.item()
self.optim.step()
@@ -1462,7 +1464,7 @@ class Pln(_model):
*,
exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
add_const: bool = True,
@@ -1499,7 +1501,7 @@ class Pln(_model):
formula: str,
data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
):
@@ -1957,7 +1959,7 @@ class PlnPCAcollection:
*,
exog: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
offsets: Union[torch.Tensor, np.ndarray, pd.DataFrame] = None,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
ranks: Iterable[int] = range(3, 5),
dict_of_dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
@@ -1975,7 +1977,8 @@ class PlnPCAcollection:
offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The offsets, by default None.
offsets_formula : str, optional(keyword-only)
- The formula for offsets, by default "logsum".
+ The formula for offsets, by default "zero". Can be also "logsum" where we take the logarithm of the sum (of each line) of the counts.
+ Overriden (useless) if offsets is not None.
ranks : Iterable[int], optional(keyword-only)
The range of ranks, by default range(3, 5).
dict_of_dict_initialization : dict, optional(keyword-only)
@@ -2013,7 +2016,7 @@ class PlnPCAcollection:
formula: str,
data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
ranks: Iterable[int] = range(3, 5),
dict_of_dict_initialization: Optional[dict] = None,
take_log_offsets: bool = False,
@@ -2029,8 +2032,8 @@ class PlnPCAcollection:
The data dictionary. Each value can be either
a torch.Tensor, np.ndarray or pd.DataFrame
offsets_formula : str, optional(keyword-only)
- The formula for offsets, by default "logsum".
- Overriden if data["offsets"] is not None.
+ The formula for offsets, by default "zero". Can be also "logsum" where we take the logarithm of the sum (of each line) of the counts.
+ Overriden (useless) if data["offsets"] is not None.
ranks : Iterable[int], optional(keyword-only)
The range of ranks, by default range(3, 5).
dict_of_dict_initialization : dict, optional(keyword-only)
@@ -2802,7 +2805,7 @@ class PlnPCA(_model):
*,
exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
rank: int = 5,
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
@@ -2846,7 +2849,7 @@ class PlnPCA(_model):
data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
rank: int = 5,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
):
endog, exog, offsets = _extract_data_from_formula(formula, data)
@@ -3389,7 +3392,7 @@ class ZIPln(_model):
*,
exog: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
offsets: Optional[Union[torch.Tensor, np.ndarray, pd.DataFrame]] = None,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
add_const: bool = True,
@@ -3407,8 +3410,8 @@ class ZIPln(_model):
offsets : Union[torch.Tensor, np.ndarray, pd.DataFrame], optional(keyword-only)
The offsets data. Defaults to None.
offsets_formula : str, optional(keyword-only)
- The formula for offsets. Defaults to "logsum". Overriden if
- offsets is not None.
+ The formula for offsets. Defaults to "zero". Can be also "logsum" where we take the logarithm of the sum (of each line) of the counts.
+ Overriden (useless) if offsets is not None.
dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
@@ -3475,7 +3478,7 @@ class ZIPln(_model):
formula: str,
data: Dict[str, Union[torch.Tensor, np.ndarray, pd.DataFrame]],
*,
- offsets_formula: str = "logsum",
+ offsets_formula: str = "zero",
dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
take_log_offsets: bool = False,
use_closed_form_prob: bool = False,
@@ -3491,7 +3494,9 @@ class ZIPln(_model):
The data dictionary. Each value can be either a torch.Tensor,
a np.ndarray or pd.DataFrame
offsets_formula : str, optional(keyword-only)
- The formula for offsets. Defaults to "logsum".
+ The formula for offsets. Defaults to "zero". Can be also "logsum" where
+ we take the logarithm of the sum (of each line) of the counts. Overriden (useless)
+ if data["offsets"] is not None.
dict_initialization : dict, optional(keyword-only)
The initialization dictionary. Defaults to None.
take_log_offsets : bool, optional(keyword-only)
--
GitLab
From 45361ad49ca8d2c3b3d4dba713afeb6aa51ae8b7 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 17:32:57 +0100
Subject: [PATCH 154/167] add a test that see if the initialization contains
zeros.
---
pyPLNmodels/_utils.py | 2 +-
tests/conftest.py | 1 -
tests/test_common.py | 4 ++++
tests/test_wrong_init.py | 19 +++++++++++++++++++
4 files changed, 24 insertions(+), 2 deletions(-)
create mode 100644 tests/test_wrong_init.py
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 10ce38c4..d6c36c12 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -338,7 +338,7 @@ def _format_model_param(
endog = _format_data(endog)
if torch.min(endog) < 0:
raise ValueError("Counts should be only non negative values.")
- if torch.min(torch.sum(endog, axis=0)) < 0.5:
+ if torch.min(torch.sum(endog, axis=1)) < 0.5:
raise ValueError(
"Counts contains individuals containing only zero coutns. Remove it."
)
diff --git a/tests/conftest.py b/tests/conftest.py
index d89a919a..677fd262 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,6 +1,5 @@
import sys
import pytest
-import torch
from pytest_lazyfixture import lazy_fixture as lf
import pandas as pd
diff --git a/tests/test_common.py b/tests/test_common.py
index bd5ca62c..748146e8 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -124,3 +124,7 @@ def test_batch(model):
assert mse_coef < 0.1
elif model.nb_cov == 0:
assert model.coef is None
+
+
+def test_raise_error_on_zero_counts():
+ model = Pln()
diff --git a/tests/test_wrong_init.py b/tests/test_wrong_init.py
new file mode 100644
index 00000000..22edcf46
--- /dev/null
+++ b/tests/test_wrong_init.py
@@ -0,0 +1,19 @@
+import pytest
+
+from pyPLNmodels.models import Pln, PlnPCA, PlnPCAcollection, ZIPln
+import torch
+
+from tests.import_data import (
+ data_real,
+)
+
+
+endog_real = data_real["endog"]
+
+
+@pytest.mark.parametrize("pln_model", [Pln, PlnPCA, PlnPCAcollection, ZIPln])
+def test_init_with_zeros_pln(pln_model):
+ endog_with_zeros = endog_real
+ endog_with_zeros[4, :] *= 0
+ with pytest.raises(ValueError):
+ model = pln_model(endog_with_zeros)
--
GitLab
From 2f1dc1308a963e5c7a601325f5b40f833a1062ff Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 17:32:57 +0100
Subject: [PATCH 155/167] add a test that see if the initialization contains
zeros.
---
pyPLNmodels/_utils.py | 2 +-
tests/conftest.py | 1 -
tests/test_common.py | 4 ++++
tests/test_wrong_init.py | 19 +++++++++++++++++++
4 files changed, 24 insertions(+), 2 deletions(-)
create mode 100644 tests/test_wrong_init.py
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 10ce38c4..d6c36c12 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -338,7 +338,7 @@ def _format_model_param(
endog = _format_data(endog)
if torch.min(endog) < 0:
raise ValueError("Counts should be only non negative values.")
- if torch.min(torch.sum(endog, axis=0)) < 0.5:
+ if torch.min(torch.sum(endog, axis=1)) < 0.5:
raise ValueError(
"Counts contains individuals containing only zero coutns. Remove it."
)
diff --git a/tests/conftest.py b/tests/conftest.py
index d89a919a..677fd262 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,6 +1,5 @@
import sys
import pytest
-import torch
from pytest_lazyfixture import lazy_fixture as lf
import pandas as pd
diff --git a/tests/test_common.py b/tests/test_common.py
index bd5ca62c..748146e8 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -124,3 +124,7 @@ def test_batch(model):
assert mse_coef < 0.1
elif model.nb_cov == 0:
assert model.coef is None
+
+
+def test_raise_error_on_zero_counts():
+ model = Pln()
diff --git a/tests/test_wrong_init.py b/tests/test_wrong_init.py
new file mode 100644
index 00000000..22edcf46
--- /dev/null
+++ b/tests/test_wrong_init.py
@@ -0,0 +1,19 @@
+import pytest
+
+from pyPLNmodels.models import Pln, PlnPCA, PlnPCAcollection, ZIPln
+import torch
+
+from tests.import_data import (
+ data_real,
+)
+
+
+endog_real = data_real["endog"]
+
+
+@pytest.mark.parametrize("pln_model", [Pln, PlnPCA, PlnPCAcollection, ZIPln])
+def test_init_with_zeros_pln(pln_model):
+ endog_with_zeros = endog_real
+ endog_with_zeros[4, :] *= 0
+ with pytest.raises(ValueError):
+ model = pln_model(endog_with_zeros)
--
GitLab
From daca4e0c204de56e1eaa327ceb2b87a98b271930 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 18:26:10 +0100
Subject: [PATCH 156/167] forgot to remove '''pip install''' in the tests of
the README.
---
.gitlab-ci.yml | 2 +-
...create_readme_getting_started_and_docstrings_tests.py | 9 ++++++++-
2 files changed, 9 insertions(+), 2 deletions(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 0e380726..62595af9 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -23,7 +23,7 @@ tests:
- jupyter nbconvert Getting_started.ipynb --to python --output tests/test_getting_started
- cd tests
- python create_readme_getting_started_and_docstrings_tests.py
- - rm test_getting_started.py
+ - rm test_getting_started.py # remove as it has been modified in a directory.
- pytest .
only:
- main
diff --git a/tests/create_readme_getting_started_and_docstrings_tests.py b/tests/create_readme_getting_started_and_docstrings_tests.py
index 113bf841..624cccd4 100644
--- a/tests/create_readme_getting_started_and_docstrings_tests.py
+++ b/tests/create_readme_getting_started_and_docstrings_tests.py
@@ -9,7 +9,14 @@ dir_getting_started = "getting_started"
def get_lines(path_to_file, filename, filetype=".py"):
with open(f"{path_to_file}{filename}{filetype}") as file:
- lines = [line.rstrip() for line in file]
+ lines = []
+ for line in file:
+ rstrip_line = line.rstrip()
+ if len(rstrip_line) > 4:
+ if rstrip_line[0:3] != "pip":
+ lines.append(rstrip_line)
+ else:
+ lines.append(rstrip_line)
return lines
--
GitLab
From 59a524a1e8f1ec86428bce4ac2c7a668fec253c8 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 18:26:10 +0100
Subject: [PATCH 157/167] forgot to remove '''pip install''' in the tests of
the README.
---
.gitlab-ci.yml | 2 +-
...create_readme_getting_started_and_docstrings_tests.py | 9 ++++++++-
2 files changed, 9 insertions(+), 2 deletions(-)
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 0e380726..62595af9 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -23,7 +23,7 @@ tests:
- jupyter nbconvert Getting_started.ipynb --to python --output tests/test_getting_started
- cd tests
- python create_readme_getting_started_and_docstrings_tests.py
- - rm test_getting_started.py
+ - rm test_getting_started.py # remove as it has been modified in a directory.
- pytest .
only:
- main
diff --git a/tests/create_readme_getting_started_and_docstrings_tests.py b/tests/create_readme_getting_started_and_docstrings_tests.py
index 113bf841..624cccd4 100644
--- a/tests/create_readme_getting_started_and_docstrings_tests.py
+++ b/tests/create_readme_getting_started_and_docstrings_tests.py
@@ -9,7 +9,14 @@ dir_getting_started = "getting_started"
def get_lines(path_to_file, filename, filetype=".py"):
with open(f"{path_to_file}{filename}{filetype}") as file:
- lines = [line.rstrip() for line in file]
+ lines = []
+ for line in file:
+ rstrip_line = line.rstrip()
+ if len(rstrip_line) > 4:
+ if rstrip_line[0:3] != "pip":
+ lines.append(rstrip_line)
+ else:
+ lines.append(rstrip_line)
return lines
--
GitLab
From a45a41f24465c79e2d7d488a33227951371a14be Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 18:29:01 +0100
Subject: [PATCH 158/167] renanmed the point fixe lambert function.
---
pyPLNmodels/_utils.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index d6c36c12..ca435734 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -1100,14 +1100,14 @@ def _add_doc(parent_class, *, params=None, example=None, returns=None, see_also=
return wrapper
-def pf_lambert(x, y):
+def point_fixe_lambert(x, y):
return x - (1 - (y * torch.exp(-x) + 1) / (x + 1))
def lambert(y, nb_pf=10):
x = torch.log(1 + y)
for _ in range(nb_pf):
- x = pf_lambert(x, y)
+ x = point_fixe_lambert(x, y)
return x
--
GitLab
From 1045b8539d5549fedb993339432a20275539abb8 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 18:29:01 +0100
Subject: [PATCH 159/167] renanmed the point fixe lambert function.
---
pyPLNmodels/_utils.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index d6c36c12..ca435734 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -1100,14 +1100,14 @@ def _add_doc(parent_class, *, params=None, example=None, returns=None, see_also=
return wrapper
-def pf_lambert(x, y):
+def point_fixe_lambert(x, y):
return x - (1 - (y * torch.exp(-x) + 1) / (x + 1))
def lambert(y, nb_pf=10):
x = torch.log(1 + y)
for _ in range(nb_pf):
- x = pf_lambert(x, y)
+ x = point_fixe_lambert(x, y)
return x
--
GitLab
From 4d61c4eb43d352c50d8e49e6d6705bfd27cfd065 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 18:44:25 +0100
Subject: [PATCH 160/167] add a plt.show() and removed a "with" en trop.
---
pyPLNmodels/models.py | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 32006437..2e6c332c 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -244,6 +244,7 @@ class _model(ABC):
)
for i in range(covariances.shape[0]):
_plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
+ plt.show()
return ax
def _project_parameters(self):
@@ -504,7 +505,7 @@ class _model(ABC):
"""
return self.latent_variables
- def qq_plots(self):
+ def _qq_plots(self):
centered_latent = self.latent_variables - torch.mean(
self.latent_variables, axis=0
)
@@ -1302,6 +1303,7 @@ class _model(ABC):
ax.set_ylabel("Predicted values")
ax.set_xlabel("Counts")
ax.legend()
+ plt.show()
return ax
def _print_beginning_message(self):
@@ -3578,7 +3580,7 @@ class ZIPln(_model):
@property
def _description(self):
- return "with full covariance model and zero-inflation."
+ return "full covariance model and zero-inflation."
def _random_init_model_parameters(self):
self._coef_inflation = torch.randn(self.nb_cov, self.dim).to(DEVICE)
--
GitLab
From b33cd71b4adcf231464df1fbbdf9230a53feec0a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 18:44:25 +0100
Subject: [PATCH 161/167] add a plt.show() and removed a "with" en trop.
---
pyPLNmodels/models.py | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 32006437..2e6c332c 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -244,6 +244,7 @@ class _model(ABC):
)
for i in range(covariances.shape[0]):
_plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
+ plt.show()
return ax
def _project_parameters(self):
@@ -504,7 +505,7 @@ class _model(ABC):
"""
return self.latent_variables
- def qq_plots(self):
+ def _qq_plots(self):
centered_latent = self.latent_variables - torch.mean(
self.latent_variables, axis=0
)
@@ -1302,6 +1303,7 @@ class _model(ABC):
ax.set_ylabel("Predicted values")
ax.set_xlabel("Counts")
ax.legend()
+ plt.show()
return ax
def _print_beginning_message(self):
@@ -3578,7 +3580,7 @@ class ZIPln(_model):
@property
def _description(self):
- return "with full covariance model and zero-inflation."
+ return "full covariance model and zero-inflation."
def _random_init_model_parameters(self):
self._coef_inflation = torch.randn(self.nb_cov, self.dim).to(DEVICE)
--
GitLab
From 7e804580fb71d6c1ca5c1db14ba5aa4fe15015ff Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 18:47:39 +0100
Subject: [PATCH 162/167] do not know how this file got there.
---
pyPLNmodels/new_model.py | 9 ---------
1 file changed, 9 deletions(-)
delete mode 100644 pyPLNmodels/new_model.py
diff --git a/pyPLNmodels/new_model.py b/pyPLNmodels/new_model.py
deleted file mode 100644
index 2d4acd45..00000000
--- a/pyPLNmodels/new_model.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from pyPLNmodels import ZIPln, get_real_count_data
-
-
-endog = get_real_count_data()
-zi = ZIPln(endog, add_const = True)
-zi.fit(nb_max_iteration = 10)
-zi.show()
-
-
--
GitLab
From 091ae0db4fddfffafe7e68ff0a38964842963094 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 17 Jan 2024 18:47:39 +0100
Subject: [PATCH 163/167] do not know how this file got there.
---
pyPLNmodels/new_model.py | 9 ---------
1 file changed, 9 deletions(-)
delete mode 100644 pyPLNmodels/new_model.py
diff --git a/pyPLNmodels/new_model.py b/pyPLNmodels/new_model.py
deleted file mode 100644
index 2d4acd45..00000000
--- a/pyPLNmodels/new_model.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from pyPLNmodels import ZIPln, get_real_count_data
-
-
-endog = get_real_count_data()
-zi = ZIPln(endog, add_const = True)
-zi.fit(nb_max_iteration = 10)
-zi.show()
-
-
--
GitLab
From fad8682f3fded3f575be69d4c41bb0381766936c Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 18 Jan 2024 07:43:14 +0100
Subject: [PATCH 164/167] forgot to remove a test.
---
tests/test_common.py | 4 ----
1 file changed, 4 deletions(-)
diff --git a/tests/test_common.py b/tests/test_common.py
index 748146e8..bd5ca62c 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -124,7 +124,3 @@ def test_batch(model):
assert mse_coef < 0.1
elif model.nb_cov == 0:
assert model.coef is None
-
-
-def test_raise_error_on_zero_counts():
- model = Pln()
--
GitLab
From 079d8d3390265fb547b63324eeda9308622f0118 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 18 Jan 2024 07:43:14 +0100
Subject: [PATCH 165/167] forgot to remove a test.
---
tests/test_common.py | 4 ----
1 file changed, 4 deletions(-)
diff --git a/tests/test_common.py b/tests/test_common.py
index 748146e8..bd5ca62c 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -124,7 +124,3 @@ def test_batch(model):
assert mse_coef < 0.1
elif model.nb_cov == 0:
assert model.coef is None
-
-
-def test_raise_error_on_zero_counts():
- model = Pln()
--
GitLab
From ef05c213cdc9148aee8b919252e4310799b8bce5 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 19 Jan 2024 21:10:17 +0100
Subject: [PATCH 166/167] put to zero all the endog data created conflicts in
the tests. Made a copy instead.
---
tests/test_wrong_init.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/tests/test_wrong_init.py b/tests/test_wrong_init.py
index 22edcf46..f975c738 100644
--- a/tests/test_wrong_init.py
+++ b/tests/test_wrong_init.py
@@ -1,7 +1,7 @@
import pytest
from pyPLNmodels.models import Pln, PlnPCA, PlnPCAcollection, ZIPln
-import torch
+import numpy as np
from tests.import_data import (
data_real,
@@ -13,7 +13,7 @@ endog_real = data_real["endog"]
@pytest.mark.parametrize("pln_model", [Pln, PlnPCA, PlnPCAcollection, ZIPln])
def test_init_with_zeros_pln(pln_model):
- endog_with_zeros = endog_real
+ endog_with_zeros = np.copy(endog_real)
endog_with_zeros[4, :] *= 0
with pytest.raises(ValueError):
model = pln_model(endog_with_zeros)
--
GitLab
From 9594766b656fe1784f1580809fc7d36fddd5cd39 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Fri, 19 Jan 2024 21:35:41 +0100
Subject: [PATCH 167/167] add stability when computing the elbo.
---
pyPLNmodels/_utils.py | 12 +++++++++++-
pyPLNmodels/elbos.py | 7 +++----
2 files changed, 14 insertions(+), 5 deletions(-)
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index ca435734..3e0c17a3 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -340,7 +340,7 @@ def _format_model_param(
raise ValueError("Counts should be only non negative values.")
if torch.min(torch.sum(endog, axis=1)) < 0.5:
raise ValueError(
- "Counts contains individuals containing only zero coutns. Remove it."
+ "Counts contains individuals containing only zero counts. Remove it."
)
exog = _format_data(exog)
if add_const is True:
@@ -1155,3 +1155,13 @@ def mat_to_vec(matc, p, q):
tril = torch.tril(matc)
# tril = matc.reshape(-1,1).squeeze()
return tril[torch.tril_indices(p, q, offset=0).tolist()]
+
+
+def _log1pexp(t):
+ mask = t > 10
+ mask += t < -10
+ return torch.where(
+ mask,
+ t,
+ torch.log(1 + torch.exp(t)),
+ )
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 7bb5b01f..937fa2df 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -1,5 +1,5 @@
import torch # pylint:disable=[C0114]
-from ._utils import _log_stirling, _trunc_log
+from ._utils import _log_stirling, _trunc_log, _log1pexp
from ._closed_forms import _closed_formula_covariance, _closed_formula_coef
from typing import Optional
@@ -238,10 +238,9 @@ def elbo_zi_pln(
un_moins_rho_m_moins_xb = un_moins_rho * m_minus_xb
un_moins_rho_m_moins_xb_outer = un_moins_rho_m_moins_xb.T @ un_moins_rho_m_moins_xb
inside_b = -1 / 2 * Omega * un_moins_rho_m_moins_xb_outer
- inside_c = torch.multiply(latent_prob, x_coef_inflation) - torch.log(
- 1 + torch.exp(x_coef_inflation)
+ inside_c = torch.multiply(latent_prob, x_coef_inflation) - _log1pexp(
+ x_coef_inflation
)
-
log_diag = torch.log(torch.diag(covariance))
log_S_term = torch.sum(
torch.multiply(1 - latent_prob, torch.log(torch.abs(latent_sqrt_var))), axis=0
--
GitLab