From 1f0e5e158961c62aca70b54631e1374022ab33c0 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 1 Jun 2023 19:18:23 +0200
Subject: [PATCH 01/17] did not take into account the offsets_formula.

---
 pyPLNmodels/models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index dd353fa6..5c353992 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -2039,7 +2039,7 @@ class PlnPCA(_Pln):
         """
         self._rank = rank
         self._counts, self._covariates, self._offsets = _format_model_param(
-            counts, covariates, offsets, None, take_log_offsets=False
+            counts, covariates, offsets, offsets_formula, take_log_offsets=False
         )
         _check_data_shape(self._counts, self._covariates, self._offsets)
         self._check_if_rank_is_too_high()
-- 
GitLab


From 0adbc2138a4325a14ec782fa938bc7d0b4cd8d35 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 7 Jun 2023 11:07:26 +0200
Subject: [PATCH 02/17] add bigger dataset for scMark and the labels.

---
 pyPLNmodels/data/scRT/Y_mark.csv | 271 ------------------
 pyPLNmodels/data/scRT/counts.csv | 470 +++++++++++++++++++++++++++++++
 pyPLNmodels/data/scRT/labels.csv | 470 +++++++++++++++++++++++++++++++
 3 files changed, 940 insertions(+), 271 deletions(-)
 delete mode 100644 pyPLNmodels/data/scRT/Y_mark.csv
 create mode 100644 pyPLNmodels/data/scRT/counts.csv
 create mode 100644 pyPLNmodels/data/scRT/labels.csv

diff --git a/pyPLNmodels/data/scRT/Y_mark.csv b/pyPLNmodels/data/scRT/Y_mark.csv
deleted file mode 100644
index 43c88887..00000000
--- a/pyPLNmodels/data/scRT/Y_mark.csv
+++ /dev/null
@@ -1,271 +0,0 @@
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diff --git a/pyPLNmodels/data/scRT/counts.csv b/pyPLNmodels/data/scRT/counts.csv
new file mode 100644
index 00000000..e89d8cd9
--- /dev/null
+++ b/pyPLNmodels/data/scRT/counts.csv
@@ -0,0 +1,470 @@
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diff --git a/pyPLNmodels/data/scRT/labels.csv b/pyPLNmodels/data/scRT/labels.csv
new file mode 100644
index 00000000..a3a62e7a
--- /dev/null
+++ b/pyPLNmodels/data/scRT/labels.csv
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+Cancer_cells
+B_and_plasma_cells
+Cancer_cells
+Endothelial
+Fibroblast
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Fibroblast
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Fibroblast
+Endothelial
+Fibroblast
+B_and_plasma_cells
+Cancer_cells
+Endothelial
+Fibroblast
+B_and_plasma_cells
+B_and_plasma_cells
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Endothelial
+Endothelial
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Endothelial
+Fibroblast
+Fibroblast
+Fibroblast
+Cancer_cells
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Endothelial
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Endothelial
+Fibroblast
+Endothelial
+Fibroblast
+Fibroblast
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Fibroblast
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Fibroblast
+Endothelial
+Cancer_cells
+Cancer_cells
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Endothelial
+Fibroblast
+Endothelial
+Fibroblast
+Endothelial
+Cancer_cells
+Fibroblast
+Cancer_cells
+Fibroblast
+Fibroblast
+Endothelial
+Fibroblast
+Fibroblast
+Fibroblast
+Endothelial
+Endothelial
+Fibroblast
+Fibroblast
+Endothelial
+Endothelial
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Fibroblast
+Cancer_cells
+Fibroblast
+Fibroblast
+Cancer_cells
+Fibroblast
+Fibroblast
+Fibroblast
+Fibroblast
+Cancer_cells
+Cancer_cells
+B_and_plasma_cells
+Cancer_cells
+B_and_plasma_cells
+B_and_plasma_cells
+Endothelial
+Cancer_cells
+B_and_plasma_cells
+B_and_plasma_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Fibroblast
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Fibroblast
+Fibroblast
+Cancer_cells
+Cancer_cells
+Endothelial
+B_and_plasma_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Endothelial
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+B_and_plasma_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Fibroblast
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Cancer_cells
+Fibroblast
+Cancer_cells
+Endothelial
+B_and_plasma_cells
+Cancer_cells
+Fibroblast
+Cancer_cells
+Fibroblast
+Cancer_cells
+Fibroblast
+Dendritic_cells
+Cancer_cells
+B_and_plasma_cells
+Cancer_cells
+Endothelial
+B_and_plasma_cells
+Endothelial
+Dendritic_cells
+Fibroblast
+Fibroblast
+Fibroblast
+Dendritic_cells
+Fibroblast
+B_and_plasma_cells
+Fibroblast
+Fibroblast
-- 
GitLab


From f7162e3af68249243a3499ccbed2453b5595610f Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 7 Jun 2023 11:08:25 +0200
Subject: [PATCH 03/17] renaming latent_var to latent_sqrt_var and add
 visualization.

---
 pyPLNmodels/_closed_forms.py |  12 +-
 pyPLNmodels/_utils.py        |  68 ++++++--
 pyPLNmodels/elbos.py         |  28 ++--
 pyPLNmodels/models.py        | 296 ++++++++++++++++++++++++-----------
 4 files changed, 281 insertions(+), 123 deletions(-)

diff --git a/pyPLNmodels/_closed_forms.py b/pyPLNmodels/_closed_forms.py
index a08196e1..ff99eae3 100644
--- a/pyPLNmodels/_closed_forms.py
+++ b/pyPLNmodels/_closed_forms.py
@@ -6,7 +6,7 @@ import torch  # pylint:disable=[C0114]
 def _closed_formula_covariance(
     covariates: torch.Tensor,
     latent_mean: torch.Tensor,
-    latent_var: torch.Tensor,
+    latent_sqrt_var: torch.Tensor,
     coef: torch.Tensor,
     n_samples: int,
 ) -> torch.Tensor:
@@ -19,7 +19,7 @@ def _closed_formula_covariance(
         Covariates with size (n, d).
     latent_mean : torch.Tensor
         Variational parameter with size (n, p).
-    latent_var : torch.Tensor
+    latent_sqrt_var : torch.Tensor
         Variational parameter with size (n, p).
     coef : torch.Tensor
         Model parameter with size (d, p).
@@ -37,7 +37,7 @@ def _closed_formula_covariance(
         XB = covariates @ coef
     m_minus_xb = latent_mean - XB
     closed = m_minus_xb.T @ m_minus_xb + torch.diag(
-        torch.sum(torch.square(latent_var), dim=0)
+        torch.sum(torch.square(latent_sqrt_var), dim=0)
     )
     return closed / n_samples
 
@@ -68,7 +68,7 @@ def _closed_formula_coef(
 def _closed_formula_pi(
     offsets: torch.Tensor,
     latent_mean: torch.Tensor,
-    latent_var: torch.Tensor,
+    latent_sqrt_var: torch.Tensor,
     dirac: torch.Tensor,
     covariates: torch.Tensor,
     _coef_inflation: torch.Tensor,
@@ -82,7 +82,7 @@ def _closed_formula_pi(
         Offset with size (n, p).
     latent_mean : torch.Tensor
         Variational parameter with size (n, p).
-    latent_var : torch.Tensor
+    latent_sqrt_var : torch.Tensor
         Variational parameter with size (n, p).
     dirac : torch.Tensor
         Dirac tensor.
@@ -96,5 +96,5 @@ def _closed_formula_pi(
     torch.Tensor
         The closed-form pi with the same size as dirac.
     """
-    poiss_param = torch.exp(offsets + latent_mean + 0.5 * torch.square(latent_var))
+    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
diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index b5e696a9..360ac5ec 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -267,6 +267,7 @@ def _format_model_param(
     offsets: torch.Tensor,
     offsets_formula: str,
     take_log_offsets: bool,
+    add_const: bool,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     """
     Format each of the model parameters to an array or None if None.
@@ -283,7 +284,8 @@ def _format_model_param(
         Formula for calculating offsets.
     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.
     Returns
     -------
     Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
@@ -297,6 +299,13 @@ def _format_model_param(
     if torch.min(counts) < 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)
+        else:
+            covariates = torch.concat(
+                (covariates, torch.ones(counts.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")
@@ -620,36 +629,43 @@ def get_simulated_count_data(
     return pln_param.counts, pln_param.cov, pln_param.offsets
 
 
-def get_real_count_data(n_samples: int = 270, dim: int = 100) -> np.ndarray:
+def get_real_count_data(
+    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
-        Number of samples, by default 270.
+        Number of samples, by default 469.
     dim : int, optional
-        Dimension, by default 100.
-
+        Dimension, by default 200.
+    return_labels: bool, optional
+        If True, will return the labels of the count data
     Returns:
     --------
     np.ndarray
-        Real count data.
+        Real count data and labels if return_labels is True.
     """
-    if n_samples > 297:
+    if n_samples > 469:
         warnings.warn(
-            f"\nTaking the whole 270 samples of the dataset. Requested:n_samples={n_samples}, returned:270"
+            f"\nTaking the whole 469 samples of the dataset. Requested:n_samples={n_samples}, returned:469"
         )
-        n_samples = 270
+        n_samples = 469
     if dim > 100:
         warnings.warn(
-            f"\nTaking the whole 100 variables. Requested:dim={dim}, returned:100"
+            f"\nTaking the whole 200 variables. Requested:dim={dim}, returned:200"
         )
-        dim = 100
-    counts_stream = pkg_resources.resource_stream(__name__, "data/scRT/Y_mark.csv")
+        dim = 200
+    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}")
-    return counts
+    if return_labels is False:
+        return counts
+    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
 
 
 def load_model(path_of_directory: str) -> Dict[str, Any]:
@@ -866,3 +882,29 @@ def _array2tensor(func):
         func(self, array_like)
 
     return setter
+
+
+def _handle_data(
+    counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
+):
+    """
+    Transforms the data to torch.Tensor and checks the shapes are ok.
+
+    Parameters
+    ----------
+    counts : torch.Tensor
+        The counts.
+    covariates : torch.Tensor, optional
+        The covariates, by default None.
+    offsets : torch.Tensor, optional
+        The offsets, by default None.
+    offsets_formula : str
+        The formula for offsets.
+    take_log_offsets : bool
+        Whether to take the logarithm of offsets.
+    """
+    counts, covariates, offsets = _format_model_param(
+        counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
+    )
+    _check_data_shape(counts, covariates, offsets)
+    return counts, covariates, offsets
diff --git a/pyPLNmodels/elbos.py b/pyPLNmodels/elbos.py
index 158fe1fe..a2fa1b2f 100644
--- a/pyPLNmodels/elbos.py
+++ b/pyPLNmodels/elbos.py
@@ -10,7 +10,7 @@ def elbo_pln(
     offsets: torch.Tensor,
     covariates: Optional[torch.Tensor],
     latent_mean: torch.Tensor,
-    latent_var: torch.Tensor,
+    latent_sqrt_var: torch.Tensor,
     covariance: torch.Tensor,
     coef: torch.Tensor,
 ) -> torch.Tensor:
@@ -27,7 +27,7 @@ def elbo_pln(
         Covariates with size (n, d).
     latent_mean : torch.Tensor
         Variational parameter with size (n, p).
-    latent_var : torch.Tensor
+    latent_sqrt_var : torch.Tensor
         Variational parameter with size (n, p).
     covariance : torch.Tensor
         Model parameter with size (p, p).
@@ -40,7 +40,7 @@ def elbo_pln(
         The ELBO (Evidence Lower Bound), of size one.
     """
     n_samples, dim = counts.shape
-    s_rond_s = torch.square(latent_var)
+    s_rond_s = torch.square(latent_sqrt_var)
     offsets_plus_m = offsets + latent_mean
     if covariates is None:
         XB = torch.zeros_like(counts)
@@ -67,7 +67,7 @@ def profiled_elbo_pln(
     covariates: torch.Tensor,
     offsets: torch.Tensor,
     latent_mean: torch.Tensor,
-    latent_var: torch.Tensor,
+    latent_sqrt_var: torch.Tensor,
 ) -> torch.Tensor:
     """
     Compute the ELBO (Evidence Lower Bound) for the Pln model with profiled
@@ -84,7 +84,7 @@ def profiled_elbo_pln(
         Offset with size (n, p).
     latent_mean : torch.Tensor
         Variational parameter with size (n, p).
-    latent_var : torch.Tensor
+    latent_sqrt_var : torch.Tensor
         Variational parameter with size (n, p).
 
     Returns:
@@ -93,11 +93,11 @@ def profiled_elbo_pln(
         The ELBO (Evidence Lower Bound) with size 1.
     """
     n_samples, _ = counts.shape
-    s_squared = torch.square(latent_var)
+    s_squared = torch.square(latent_sqrt_var)
     offsets_plus_mean = offsets + latent_mean
     closed_coef = _closed_formula_coef(covariates, latent_mean)
     closed_covariance = _closed_formula_covariance(
-        covariates, latent_mean, latent_var, closed_coef, n_samples
+        covariates, latent_mean, latent_sqrt_var, closed_coef, n_samples
     )
     elbo = -0.5 * n_samples * torch.logdet(closed_covariance)
     elbo += torch.sum(
@@ -114,7 +114,7 @@ def elbo_plnpca(
     covariates: torch.Tensor,
     offsets: torch.Tensor,
     latent_mean: torch.Tensor,
-    latent_var: torch.Tensor,
+    latent_sqrt_var: torch.Tensor,
     components: torch.Tensor,
     coef: torch.Tensor,
 ) -> torch.Tensor:
@@ -132,7 +132,7 @@ def elbo_plnpca(
         Offset with size (n, p).
     latent_mean : torch.Tensor
         Variational parameter with size (n, p).
-    latent_var : torch.Tensor
+    latent_sqrt_var : torch.Tensor
         Variational parameter with size (n, p). More precisely it is the unsigned
         square root of the variational variance.
     components : torch.Tensor
@@ -152,14 +152,14 @@ def elbo_plnpca(
     else:
         XB = covariates @ coef
     log_intensity = offsets + XB + latent_mean @ components.T
-    s_squared = torch.square(latent_var)
+    s_squared = torch.square(latent_sqrt_var)
     counts_log_intensity = torch.sum(counts * log_intensity)
     minus_intensity_plus_s_squared_cct = torch.sum(
         -torch.exp(log_intensity + 0.5 * s_squared @ (components * components).T)
     )
     minus_logs_squared = 0.5 * torch.sum(torch.log(s_squared))
     mm_plus_s_squared = -0.5 * torch.sum(
-        torch.square(latent_mean) + torch.square(latent_var)
+        torch.square(latent_mean) + torch.square(latent_sqrt_var)
     )
     log_stirling_counts = torch.sum(_log_stirling(counts))
     return (
@@ -178,7 +178,7 @@ def elbo_zi_pln(
     covariates,
     offsets,
     latent_mean,
-    latent_var,
+    latent_sqrt_var,
     pi,
     covariance,
     coef,
@@ -193,7 +193,7 @@ def elbo_zi_pln(
         0: torch.tensor. Offset, size (n,p)
         covariates: torch.tensor. Covariates, size (n,d)
         latent_mean: torch.tensor. Variational parameter with size (n,p)
-        latent_var: 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)
         covariance: torch.tensor. Model parameter with size (p,p)
         coef: torch.tensor. Model parameter with size (d,p)
@@ -206,7 +206,7 @@ def elbo_zi_pln(
         return False
     n_samples = counts.shape[0]
     dim = counts.shape[1]
-    s_rond_s = torch.square(latent_var)
+    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
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 5c353992..6f0cb5a7 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -10,7 +10,8 @@ import numpy as np
 import seaborn as sns
 import matplotlib.pyplot as plt
 from sklearn.decomposition import PCA
-
+import plotly.express as px
+from mlxtend.plotting import plot_pca_correlation_graph
 
 from ._closed_forms import (
     _closed_formula_coef,
@@ -21,13 +22,13 @@ from .elbos import elbo_plnpca, elbo_zi_pln, profiled_elbo_pln
 from ._utils import (
     _PlotArgs,
     _format_data,
-    _format_model_param,
     _nice_string_of_dict,
     _plot_ellipse,
     _check_data_shape,
     _extract_data_from_formula,
     _get_dict_initialization,
     _array2tensor,
+    _handle_data,
 )
 
 from ._initialization import (
@@ -58,7 +59,7 @@ class _Pln(ABC):
     _offsets: torch.Tensor
     _coef: torch.Tensor
     _beginning_time: float
-    _latent_var: torch.Tensor
+    _latent_sqrt_var: torch.Tensor
     _latent_mean: torch.Tensor
 
     def __init__(
@@ -69,6 +70,7 @@ class _Pln(ABC):
         offsets_formula: str = "logsum",
         dict_initialization: Optional[dict] = None,
         take_log_offsets: bool = False,
+        add_const: bool = True,
     ):
         """
         Initializes the _Pln class.
@@ -87,11 +89,13 @@ class _Pln(ABC):
             The initialization dictionary. Defaults to 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._counts, self._covariates, self._offsets = _format_model_param(
-            counts, covariates, offsets, offsets_formula, take_log_offsets
+
+        self._counts, self._covariates, self._offsets = _handle_data(
+            counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
         )
-        _check_data_shape(self._counts, self._covariates, self._offsets)
         self._fitted = False
         self._plotargs = _PlotArgs(self._WINDOW)
         if dict_initialization is not None:
@@ -136,6 +140,7 @@ class _Pln(ABC):
             offsets_formula,
             dict_initialization,
             take_log_offsets,
+            add_const=False,
         )
 
     def _set_init_parameters(self, dict_initialization: dict):
@@ -368,6 +373,27 @@ class _Pln(ABC):
         ValueError
            If the number of components asked is greater than the number of dimensions.
         """
+        pca = self.sk_PCA(n_components=n_components)
+        return pca.transform(self.latent_variables.detach().cpu())
+
+    def sk_PCA(self, n_components=None):
+        """
+        Perform PCA on the latent variables.
+
+        Parameters
+        ----------
+        n_components : int, optional
+            The number of components to keep. If None, all components are kept. Defaults to None.
+
+        Returns
+        -------
+        sklearn.decomposition.PCA
+            sklearn.decomposition.PCA object with all the features from sklearn.
+        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.dim:
@@ -375,7 +401,68 @@ class _Pln(ABC):
                 f"You ask more components ({n_components}) than variables ({self.dim})"
             )
         pca = PCA(n_components=n_components)
-        return pca.fit_transform(self.latent_variables.detach().cpu())
+        pca.fit(self.latent_variables.detach().cpu())
+        return pca
+
+    def scatter_pca_matrix(self, n_components=None, color=None):
+        """
+        Generates a scatter matrix plot based on Principal Component Analysis (PCA).
+
+        Parameters
+        ----------
+            n_components (int, optional): The number of components to consider for plotting.
+                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.
+                Defaults to None.
+        Raises
+        ------
+            ValueError: If the number of components requested is greater than the number of variables in the dataset.
+        """
+
+        if n_components is None:
+            n_components = self._get_max_components()
+
+        if n_components > self.dim:
+            raise ValueError(
+                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.detach())
+        components = torch.from_numpy(pca.components_)
+
+        labels = {
+            str(i): f"PC{i+1}: {np.round(pca.explained_variance_ratio_, 2)[i]}%"
+            for i in range(n_components)
+        }
+        proj_variables
+        fig = px.scatter_matrix(
+            proj_variables,
+            dimensions=range(n_components),
+            color=color,
+            labels=labels,
+        )
+        fig.update_traces(diagonal_visible=False)
+        fig.show()
+
+    def viz_pca_var(self):
+        n_components = 2
+        pca = self.sk_PCA(n_components=n_components)
+        proj_variables = pca.transform(self.latent_variables.detach())
+        labels = {
+            str(i): f"PC{i+1}: {np.round(pca.explained_variance_ratio_, 2)[i]}%"
+            for i in range(n_components)
+        }
+        figure, correlation_matrix = plot_pca_correlation_graph(
+            self.latent_variables.detach(),
+            variables_names=[str(i) for i in range(self.dim)],
+            X_pca=proj_variables,
+            explained_variance=pca.explained_variance_,
+            dimensions=(1, 2),
+            figure_axis_size=10,
+        )
 
     @property
     @abstractmethod
@@ -493,6 +580,8 @@ class _Pln(ABC):
         str
             The string representation of the object.
         """
+        if self._fitted is False:
+            raise RuntimeError("Please fit the model before printing it.")
         delimiter = "=" * NB_CHARACTERS_FOR_NICE_PLOT
         string = f"A multivariate Poisson Lognormal with {self._description} \n"
         string += f"{delimiter}\n"
@@ -682,7 +771,7 @@ class _Pln(ABC):
         return self._cpu_attribute_or_none("_latent_mean")
 
     @property
-    def latent_var(self):
+    def latent_sqrt_var(self):
         """
         Property representing the latent variance.
 
@@ -691,7 +780,7 @@ class _Pln(ABC):
         torch.Tensor or None
             The latent variance or None.
         """
-        return self._cpu_attribute_or_none("_latent_var")
+        return self._cpu_attribute_or_none("_latent_sqrt_var")
 
     @latent_mean.setter
     @_array2tensor
@@ -715,15 +804,15 @@ class _Pln(ABC):
             )
         self._latent_mean = latent_mean
 
-    @latent_var.setter
+    @latent_sqrt_var.setter
     @_array2tensor
-    def latent_var(self, latent_var):
+    def latent_sqrt_var(self, latent_sqrt_var):
         """
         Setter for the latent variance property.
 
         Parameters
         ----------
-        latent_var : torch.Tensor
+        latent_sqrt_var : torch.Tensor
             The latent variance.
 
         Raises
@@ -731,11 +820,11 @@ class _Pln(ABC):
         ValueError
             If the shape of the latent variance is incorrect.
         """
-        if latent_var.shape != (self.n_samples, self.dim):
+        if latent_sqrt_var.shape != (self.n_samples, self.dim):
             raise ValueError(
-                f"Wrong shape. Expected {self.n_samples, self.dim}, got {latent_var.shape}"
+                f"Wrong shape. Expected {self.n_samples, self.dim}, got {latent_sqrt_var.shape}"
             )
-        self._latent_var = latent_var
+        self._latent_sqrt_var = latent_sqrt_var
 
     def _cpu_attribute_or_none(self, attribute_name):
         """
@@ -767,7 +856,7 @@ class _Pln(ABC):
         path_of_directory : str, optional
             The path of the directory to save the parameters, by default "./".
         """
-        path = f"{path_of_directory}/{self._path_to_directory}{self._directory_name}"
+        path = f"{path_of_directory}/{self._directory_name}"
         os.makedirs(path, exist_ok=True)
         for key, value in self._dict_parameters.items():
             filename = f"{path}/{key}.csv"
@@ -1093,8 +1182,10 @@ class Pln(_Pln):
         """
         Method for randomly initializing the latent parameters.
         """
-        if not hasattr(self, "_latent_var"):
-            self._latent_var = 1 / 2 * torch.ones((self.n_samples, self.dim)).to(DEVICE)
+        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)
 
@@ -1108,7 +1199,7 @@ class Pln(_Pln):
         list
             The list of parameters needing gradient.
         """
-        return [self._latent_mean, self._latent_var]
+        return [self._latent_mean, self._latent_sqrt_var]
 
     def _get_max_components(self):
         """
@@ -1135,7 +1226,7 @@ class Pln(_Pln):
             self._covariates,
             self._offsets,
             self._latent_mean,
-            self._latent_var,
+            self._latent_sqrt_var,
         )
 
     def _smart_init_model_parameters(self):
@@ -1177,7 +1268,7 @@ class Pln(_Pln):
         return _closed_formula_covariance(
             self._covariates,
             self._latent_mean,
-            self._latent_var,
+            self._latent_sqrt_var,
             self._coef,
             self.n_samples,
         )
@@ -1238,7 +1329,7 @@ class Pln(_Pln):
             for attr in [
                 "_covariates",
                 "_latent_mean",
-                "_latent_var",
+                "_latent_sqrt_var",
                 "_coef",
                 "n_samples",
             ]
@@ -1272,6 +1363,7 @@ class PlnPCAcollection:
         ranks: Iterable[int] = range(3, 5),
         dict_of_dict_initialization: Optional[dict] = None,
         take_log_offsets: bool = False,
+        add_const: bool = True,
     ):
         """
         Constructor for PlnPCAcollection.
@@ -1292,40 +1384,15 @@ class PlnPCAcollection:
             The dictionary of initialization, by default None.
         take_log_offsets : bool, optional
             Whether to take the logarithm of offsets, by default False.
+        add_const: bool, optional
+            Whether to add a column of one in the covariates. Defaults to True.
         """
         self._dict_models = {}
-        self._init_data(counts, covariates, offsets, offsets_formula, take_log_offsets)
-        self._init_models(ranks, dict_of_dict_initialization)
-
-    def _init_data(
-        self,
-        counts: torch.Tensor,
-        covariates: Optional[torch.Tensor],
-        offsets: Optional[torch.Tensor],
-        offsets_formula: str,
-        take_log_offsets: bool,
-    ):
-        """
-        Method for initializing the data.
-
-        Parameters
-        ----------
-        counts : torch.Tensor
-            The counts.
-        covariates : torch.Tensor, optional
-            The covariates, by default None.
-        offsets : torch.Tensor, optional
-            The offsets, by default None.
-        offsets_formula : str
-            The formula for offsets.
-        take_log_offsets : bool
-            Whether to take the logarithm of offsets.
-        """
-        self._counts, self._covariates, self._offsets = _format_model_param(
-            counts, covariates, offsets, offsets_formula, take_log_offsets
+        self._counts, self._covariates, self._offsets = _handle_data(
+            counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
         )
-        _check_data_shape(self._counts, self._covariates, self._offsets)
         self._fitted = False
+        self._init_models(ranks, dict_of_dict_initialization)
 
     @classmethod
     def from_formula(
@@ -1354,7 +1421,6 @@ class PlnPCAcollection:
             The dictionary of initialization, by default None.
         take_log_offsets : bool, optional
             Whether to take the logarithm of offsets, by default False.
-
         Returns
         -------
         PlnPCAcollection
@@ -1369,6 +1435,7 @@ class PlnPCAcollection:
             ranks,
             dict_of_dict_initialization,
             take_log_offsets,
+            add_const=False,
         )
 
     @property
@@ -1432,7 +1499,7 @@ class PlnPCAcollection:
         return {model.rank: model.latent_mean for model in self.values()}
 
     @property
-    def latent_var(self) -> Dict[int, torch.Tensor]:
+    def latent_sqrt_var(self) -> Dict[int, torch.Tensor]:
         """
         Property representing the latent variances.
 
@@ -1441,7 +1508,7 @@ class PlnPCAcollection:
         Dict[int, torch.Tensor]
             The latent variances.
         """
-        return {model.rank: model.latent_var for model in self.values()}
+        return {model.rank: model.latent_sqrt_var for model in self.values()}
 
     @counts.setter
     @_array2tensor
@@ -1922,7 +1989,7 @@ class PlnPCAcollection:
             ranks = self.ranks
         for model in self.values():
             if model.rank in ranks:
-                model.save(path_of_directory)
+                model.save(f"{self._directory_name}/{path_of_directory}")
 
     @property
     def _directory_name(self) -> str:
@@ -2018,6 +2085,8 @@ class PlnPCA(_Pln):
         offsets_formula: str = "logsum",
         rank: int = 5,
         dict_initialization: Optional[Dict[str, torch.Tensor]] = None,
+        take_log_offsets: bool = False,
+        add_const: bool = True,
     ):
         """
         Initialize the PlnPCA object.
@@ -2036,12 +2105,15 @@ class PlnPCA(_Pln):
             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
-        self._counts, self._covariates, self._offsets = _format_model_param(
-            counts, covariates, offsets, offsets_formula, take_log_offsets=False
+        self._counts, self._covariates, self._offsets = _handle_data(
+            counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
         )
-        _check_data_shape(self._counts, self._covariates, self._offsets)
         self._check_if_rank_is_too_high()
         if dict_initialization is not None:
             self._set_init_parameters(dict_initialization)
@@ -2080,7 +2152,13 @@ class PlnPCA(_Pln):
         """
         counts, covariates, offsets = _extract_data_from_formula(formula, data)
         return cls(
-            counts, covariates, offsets, offsets_formula, rank, dict_initialization
+            counts,
+            covariates,
+            offsets,
+            offsets_formula,
+            rank,
+            dict_initialization,
+            add_const=False,
         )
 
     def _check_if_rank_is_too_high(self):
@@ -2108,6 +2186,18 @@ class PlnPCA(_Pln):
         """
         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:
         """
@@ -2118,7 +2208,19 @@ class PlnPCA(_Pln):
         torch.Tensor
             The latent variance tensor.
         """
-        return self._cpu_attribute_or_none("_latent_var")
+        return self.latent_sqrt_var**2
+
+    @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
 
     @latent_mean.setter
     @_array2tensor
@@ -2137,22 +2239,22 @@ class PlnPCA(_Pln):
             )
         self._latent_mean = latent_mean
 
-    @latent_var.setter
+    @latent_sqrt_var.setter
     @_array2tensor
-    def latent_var(self, latent_var: torch.Tensor):
+    def latent_sqrt_var(self, latent_sqrt_var: torch.Tensor):
         """
         Setter for the latent variance.
 
         Parameters
         ----------
-        latent_var : torch.Tensor
+        latent_sqrt_var : torch.Tensor
             The latent variance tensor.
         """
-        if latent_var.shape != (self.n_samples, self.rank):
+        if latent_sqrt_var.shape != (self.n_samples, self.rank):
             raise ValueError(
-                f"Wrong shape. Expected {self.n_samples, self.rank}, got {latent_var.shape}"
+                f"Wrong shape. Expected {self.n_samples, self.rank}, got {latent_sqrt_var.shape}"
             )
-        self._latent_var = latent_var
+        self._latent_sqrt_var = latent_sqrt_var
 
     @property
     def _directory_name(self) -> str:
@@ -2270,7 +2372,9 @@ class PlnPCA(_Pln):
         """
         Randomly initialize the latent parameters.
         """
-        self._latent_var = 1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
+        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):
@@ -2289,8 +2393,8 @@ class PlnPCA(_Pln):
                 .to(DEVICE)
                 .detach()
             )
-        if not hasattr(self, "_latent_var"):
-            self._latent_var = (
+        if not hasattr(self, "_latent_sqrt_var"):
+            self._latent_sqrt_var = (
                 1 / 2 * torch.ones((self.n_samples, self._rank)).to(DEVICE)
             )
 
@@ -2305,8 +2409,8 @@ class PlnPCA(_Pln):
             The list of parameters needing gradient.
         """
         if self._coef is None:
-            return [self._components, self._latent_mean, self._latent_var]
-        return [self._components, self._coef, self._latent_mean, self._latent_var]
+            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(self) -> torch.Tensor:
         """
@@ -2322,7 +2426,7 @@ class PlnPCA(_Pln):
             self._covariates,
             self._offsets,
             self._latent_mean,
-            self._latent_var,
+            self._latent_sqrt_var,
             self._components,
             self._coef,
         )
@@ -2376,7 +2480,9 @@ class PlnPCA(_Pln):
         """
         if hasattr(self, "_components"):
             cov_latent = self._latent_mean.T @ self._latent_mean
-            cov_latent += torch.diag(torch.sum(torch.square(self._latent_var), dim=0))
+            cov_latent += torch.diag(
+                torch.sum(torch.square(self._latent_sqrt_var), dim=0)
+            )
             cov_latent /= self.n_samples
             return (self._components @ cov_latent @ self._components.T).cpu().detach()
         return None
@@ -2401,7 +2507,7 @@ class PlnPCA(_Pln):
         Returns
         -------
         torch.Tensor
-            The latent variables.
+            The latent variables of size (n_samples, dim).
         """
         return torch.matmul(self._latent_mean, self._components.T)
 
@@ -2415,8 +2521,14 @@ class PlnPCA(_Pln):
         torch.Tensor
             The projected latent variables.
         """
-        ortho_components = torch.linalg.qr(self._components, "reduced")[0]
-        return torch.mm(self.latent_variables, ortho_components).detach().cpu()
+        return torch.mm(self.latent_variables, self.ortho_components).detach().cpu()
+
+    @property
+    def ortho_components(self):
+        """
+        Orthogonal components of the model.
+        """
+        return torch.linalg.qr(self._components, "reduced")[0]
 
     def pca_projected_latent_variables(
         self, n_components: Optional[int] = None
@@ -2440,12 +2552,12 @@ class PlnPCA(_Pln):
         """
         if n_components is None:
             n_components = self._get_max_components()
-        if n_components > self.dim:
+        if n_components > self.rank:
             raise ValueError(
-                f"You ask more components ({n_components}) than variables ({self.dim})"
+                f"You ask more components ({n_components}) than maximum rank ({self.rank})"
             )
         pca = PCA(n_components=n_components)
-        return pca.fit_transform(self.projected_latent_variables.detach().cpu())
+        return pca.fit_transform(self.latent_variables.detach().cpu())
 
     @property
     def components(self) -> torch.Tensor:
@@ -2506,14 +2618,18 @@ class PlnPCA(_Pln):
             ax = plt.gca()
         if self._rank < 2:
             raise RuntimeError("Can't perform visualization for rank < 2.")
-        if self._rank > 2:
-            proj_variables = self.pca_projected_latent_variables(n_components=2)
-        if self._rank == 2:
-            proj_variables = self.projected_latent_variables.cpu().numpy()
+        else:
+            pca = self.sk_PCA(n_components=2)
+            proj_variables = pca.transform(self.latent_variables.detach())
+            sk_components = torch.from_numpy(pca.components_)
+            C_tilde_C = sk_components @ self._components
+            C_tilde_C_latent_var = C_tilde_C.unsqueeze(0) * (
+                self._latent_var.unsqueeze(1)
+            )
+            covariances = ((C_tilde_C_latent_var) @ (C_tilde_C.T.unsqueeze(0))).detach()
         x = proj_variables[:, 0]
         y = proj_variables[:, 1]
         sns.scatterplot(x=x, y=y, hue=colors, ax=ax)
-        covariances = torch.diag_embed(self._latent_var**2).detach().cpu()
         for i in range(covariances.shape[0]):
             _plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
         return ax
@@ -2566,7 +2682,7 @@ class ZIPln(Pln):
     def _random_init_latent_parameters(self):
         self._dirac = self._counts == 0
         self._latent_mean = torch.randn(self.n_samples, self.dim)
-        self._latent_var = torch.randn(self.n_samples, self.dim)
+        self._latent_sqrt_var = torch.randn(self.n_samples, self.dim)
         self._pi = (
             torch.empty(self.n_samples, self.dim).uniform_(0, 1).to(DEVICE)
             * self._dirac
@@ -2578,7 +2694,7 @@ class ZIPln(Pln):
             self._covariates,
             self._offsets,
             self._latent_mean,
-            self._latent_var,
+            self._latent_sqrt_var,
             self._pi,
             self._covariance,
             self._coef,
@@ -2588,21 +2704,21 @@ class ZIPln(Pln):
 
     @property
     def _list_of_parameters_needing_gradient(self):
-        return [self._latent_mean, self._latent_var, self._coef_inflation]
+        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._covariance = _closed_formula_covariance(
             self._covariates,
             self._latent_mean,
-            self._latent_var,
+            self._latent_sqrt_var,
             self._coef,
             self.n_samples,
         )
         self._pi = _closed_formula_pi(
             self._offsets,
             self._latent_mean,
-            self._latent_var,
+            self._latent_sqrt_var,
             self._dirac,
             self._covariates,
             self._coef_inflation,
-- 
GitLab


From 5e5237e170a1ebfda28f5501591d3f38dfeaf265 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 7 Jun 2023 11:08:55 +0200
Subject: [PATCH 04/17] add mlxtend dependance.

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index d95289b5..f6de9a7a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,6 +52,7 @@ dependencies = [
 "torch",
 "scikit-learn",
 "patsy",
+"mlxtend",
 ]
 [project.optional-dependencies]
 tests = ["pytest","pytest_lazy_fixture"]
-- 
GitLab


From c7fd0fd4fc2b1ccd39a2e49af86c3d9242ec6677 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 7 Jun 2023 11:09:20 +0200
Subject: [PATCH 05/17] renaming

---
 tests/test_common.py  |  6 +++---
 tests/test_setters.py | 14 +++++++-------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/tests/test_common.py b/tests/test_common.py
index 19d8673e..7edf03c4 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -32,10 +32,10 @@ def test_show_coef_transform_covariance_pcaprojected(any_pln):
     assert hasattr(any_pln, "coef")
     assert callable(any_pln.transform)
     assert hasattr(any_pln, "covariance")
-    assert callable(any_pln.pca_projected_latent_variables)
-    assert any_pln.pca_projected_latent_variables(n_components=None) is not None
+    assert callable(any_pln.PCA)
+    assert any_pln.PCA(n_components=None) is not None
     with pytest.raises(Exception):
-        any_pln.pca_projected_latent_variables(n_components=any_pln.dim + 1)
+        any_pln.PCA(n_components=any_pln.dim + 1)
 
 
 @pytest.mark.parametrize("sim_pln", dict_fixtures["loaded_and_fitted_pln"])
diff --git a/tests/test_setters.py b/tests/test_setters.py
index 14ad481a..d5716fc2 100644
--- a/tests/test_setters.py
+++ b/tests/test_setters.py
@@ -18,7 +18,7 @@ def test_data_setter_with_torch(pln):
 @filter_models(["Pln", "PlnPCA"])
 def test_parameters_setter_with_torch(pln):
     pln.latent_mean = pln.latent_mean
-    pln.latent_var = pln.latent_var
+    pln.latent_sqrt_var = pln.latent_sqrt_var
     pln.coef = pln.coef
     if pln._NAME == "PlnPCA":
         pln.components = pln.components
@@ -43,13 +43,13 @@ def test_data_setter_with_numpy(pln):
 @filter_models(["Pln", "PlnPCA"])
 def test_parameters_setter_with_numpy(pln):
     np_latent_mean = pln.latent_mean.numpy()
-    np_latent_var = pln.latent_var.numpy()
+    np_latent_sqrt_var = pln.latent_sqrt_var.numpy()
     if pln.coef is not None:
         np_coef = pln.coef.numpy()
     else:
         np_coef = None
     pln.latent_mean = np_latent_mean
-    pln.latent_var = np_latent_var
+    pln.latent_sqrt_var = np_latent_sqrt_var
     pln.coef = np_coef
     if pln._NAME == "PlnPCA":
         pln.components = pln.components.numpy()
@@ -74,13 +74,13 @@ def test_data_setter_with_pandas(pln):
 @filter_models(["Pln", "PlnPCA"])
 def test_parameters_setter_with_pandas(pln):
     pd_latent_mean = pd.DataFrame(pln.latent_mean.numpy())
-    pd_latent_var = pd.DataFrame(pln.latent_var.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())
     else:
         pd_coef = None
     pln.latent_mean = pd_latent_mean
-    pln.latent_var = pd_latent_var
+    pln.latent_sqrt_var = pd_latent_sqrt_var
     pln.coef = pd_coef
     if pln._NAME == "PlnPCA":
         pln.components = pd.DataFrame(pln.components.numpy())
@@ -125,10 +125,10 @@ def test_fail_parameters_setter_with_torch(pln):
         pln.latent_mean = torch.zeros(n, dim_latent + 1)
 
     with pytest.raises(ValueError):
-        pln.latent_var = torch.zeros(n + 1, dim_latent)
+        pln.latent_sqrt_var = torch.zeros(n + 1, dim_latent)
 
     with pytest.raises(ValueError):
-        pln.latent_var = torch.zeros(n, dim_latent + 1)
+        pln.latent_sqrt_var = torch.zeros(n, dim_latent + 1)
 
     if pln._NAME == "PlnPCA":
         with pytest.raises(ValueError):
-- 
GitLab


From 70754fe14d77af3af081660030226f5fc1a38fb9 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 7 Jun 2023 13:22:27 +0200
Subject: [PATCH 06/17] remember the name of variables of the counts when
 instantiate with pd.arrays, in order to plot it after

---
 pyPLNmodels/_utils.py | 41 +++++++++++++++--------
 pyPLNmodels/models.py | 78 ++++++++++++++++++++++++++++++-------------
 2 files changed, 82 insertions(+), 37 deletions(-)

diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 360ac5ec..5a60e381 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -885,26 +885,39 @@ def _array2tensor(func):
 
 
 def _handle_data(
-    counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
-):
+    counts,
+    covariates,
+    offsets,
+    offsets_formula: str,
+    take_log_offsets: bool,
+    add_const: bool,
+) -> tuple:
     """
-    Transforms the data to torch.Tensor and checks the shapes are ok.
+    Handle the input data for the model.
 
     Parameters
     ----------
-    counts : torch.Tensor
-        The counts.
-    covariates : torch.Tensor, optional
-        The covariates, by default None.
-    offsets : torch.Tensor, optional
-        The offsets, by default None.
-    offsets_formula : str
-        The formula for offsets.
-    take_log_offsets : bool
-        Whether to take the logarithm of offsets.
+        counts : The counts 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.
+        take_log_offsets : Indicates whether to take the logarithm of the offsets.
+        add_const : Indicates whether to add a constant column to the covariates.
+
+    Returns
+    -------
+        tuple: A tuple containing the processed counts, covariates, offsets, and column counts (if available).
+
+    Raises:
+        ValueError: If the shapes of counts, covariates, and offsets do not match.
     """
+    if isinstance(counts, pd.DataFrame):
+        column_counts = counts.columns
+    else:
+        column_counts = None
+
     counts, covariates, offsets = _format_model_param(
         counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
     )
     _check_data_shape(counts, covariates, offsets)
-    return counts, covariates, offsets
+    return counts, covariates, offsets, column_counts
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 6f0cb5a7..5e1b030c 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -92,8 +92,12 @@ class _Pln(ABC):
         add_const: bool, optional
             Whether to add a column of one in the covariates. Defaults to True.
         """
-
-        self._counts, self._covariates, self._offsets = _handle_data(
+        (
+            self._counts,
+            self._covariates,
+            self._offsets,
+            self.column_counts,
+        ) = _handle_data(
             counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
         )
         self._fitted = False
@@ -374,7 +378,7 @@ class _Pln(ABC):
            If the number of components asked is greater than the number of dimensions.
         """
         pca = self.sk_PCA(n_components=n_components)
-        return pca.transform(self.latent_variables.detach().cpu())
+        return pca.transform(self.latent_variables.cpu())
 
     def sk_PCA(self, n_components=None):
         """
@@ -401,7 +405,7 @@ class _Pln(ABC):
                 f"You ask more components ({n_components}) than variables ({self.dim})"
             )
         pca = PCA(n_components=n_components)
-        pca.fit(self.latent_variables.detach().cpu())
+        pca.fit(self.latent_variables.cpu())
         return pca
 
     def scatter_pca_matrix(self, n_components=None, color=None):
@@ -430,7 +434,7 @@ class _Pln(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.detach())
+        proj_variables = pca.transform(self.latent_variables)
         components = torch.from_numpy(pca.components_)
 
         labels = {
@@ -447,19 +451,41 @@ class _Pln(ABC):
         fig.update_traces(diagonal_visible=False)
         fig.show()
 
-    def viz_pca_var(self):
+    def viz_pca_var(self, variables_names, indices_of_variables=None):
+        if indices_of_variables is None:
+            if self.column_counts is None:
+                raise ValueError(
+                    "No names have been given to the column of "
+                    "counts. Please set the column_counts to the"
+                    "needed names or instantiate a new model with"
+                    "a pd.DataFrame with appropriate column names"
+                )
+            print("variable names", variables_names)
+            print("columns", self.column_counts)
+            indices_of_variables = []
+            for variables_name in variables_names:
+                index = self.column_counts.get_loc(variables_name)
+                indices_of_variables.append(index)
+            print("indices:", indices_of_variables)
+        else:
+            if len(indices_of_variables) != len(variables_names):
+                raise ValueError(
+                    f"Number of variables {len(indices_of_variables)} should be the same as the number of variables_names {len(variables_names)}"
+                )
+
         n_components = 2
         pca = self.sk_PCA(n_components=n_components)
-        proj_variables = pca.transform(self.latent_variables.detach())
+        variables = self.latent_variables
+        proj_variables = pca.transform(variables)
         labels = {
             str(i): f"PC{i+1}: {np.round(pca.explained_variance_ratio_, 2)[i]}%"
             for i in range(n_components)
         }
         figure, correlation_matrix = plot_pca_correlation_graph(
-            self.latent_variables.detach(),
-            variables_names=[str(i) for i in range(self.dim)],
+            variables[:, indices_of_variables],
+            variables_names=variables_names,
             X_pca=proj_variables,
-            explained_variance=pca.explained_variance_,
+            explained_variance=pca.explained_variance_ratio_,
             dimensions=(1, 2),
             figure_axis_size=10,
         )
@@ -1289,7 +1315,7 @@ class Pln(_Pln):
         torch.Tensor
             The latent variables.
         """
-        return self.latent_mean
+        return self.latent_mean.detach()
 
     @property
     def number_of_parameters(self):
@@ -1388,7 +1414,12 @@ class PlnPCAcollection:
             Whether to add a column of one in the covariates. Defaults to True.
         """
         self._dict_models = {}
-        self._counts, self._covariates, self._offsets = _handle_data(
+        (
+            self._counts,
+            self._covariates,
+            self._offsets,
+            self.column_counts,
+        ) = _handle_data(
             counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
         )
         self._fitted = False
@@ -2111,14 +2142,15 @@ class PlnPCA(_Pln):
             Whether to add a column of one in the covariates. Defaults to True.
         """
         self._rank = rank
-        self._counts, self._covariates, self._offsets = _handle_data(
-            counts, covariates, offsets, offsets_formula, take_log_offsets, add_const
+        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,
         )
-        self._check_if_rank_is_too_high()
-        if dict_initialization is not None:
-            self._set_init_parameters(dict_initialization)
-        self._fitted = False
-        self._plotargs = _PlotArgs(self._WINDOW)
 
     @classmethod
     def from_formula(
@@ -2208,7 +2240,7 @@ class PlnPCA(_Pln):
         torch.Tensor
             The latent variance tensor.
         """
-        return self.latent_sqrt_var**2
+        return (self.latent_sqrt_var**2).detach()
 
     @property
     def _latent_var(self) -> torch.Tensor:
@@ -2509,7 +2541,7 @@ class PlnPCA(_Pln):
         torch.Tensor
             The latent variables of size (n_samples, dim).
         """
-        return torch.matmul(self._latent_mean, self._components.T)
+        return torch.matmul(self._latent_mean, self._components.T).detach()
 
     @property
     def projected_latent_variables(self) -> torch.Tensor:
@@ -2557,7 +2589,7 @@ class PlnPCA(_Pln):
                 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.detach().cpu())
+        return pca.fit_transform(self.latent_variables.cpu())
 
     @property
     def components(self) -> torch.Tensor:
@@ -2620,7 +2652,7 @@ class PlnPCA(_Pln):
             raise RuntimeError("Can't perform visualization for rank < 2.")
         else:
             pca = self.sk_PCA(n_components=2)
-            proj_variables = pca.transform(self.latent_variables.detach())
+            proj_variables = pca.transform(self.latent_variables)
             sk_components = torch.from_numpy(pca.components_)
             C_tilde_C = sk_components @ self._components
             C_tilde_C_latent_var = C_tilde_C.unsqueeze(0) * (
-- 
GitLab


From 2030e9fecdac4baf79432e8a4d1fa579d54ade1a Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 7 Jun 2023 17:20:18 +0200
Subject: [PATCH 07/17] add visualizations tests and pass them.

---
 pyPLNmodels/_utils.py | 24 +++++++++++++++---
 pyPLNmodels/models.py | 59 +++++++++++++++++++------------------------
 tests/conftest.py     | 39 ++++++++++++++++------------
 tests/import_data.py  |  4 ++-
 tests/test_common.py  | 27 ++++----------------
 tests/test_viz.py     | 45 +++++++++++++++++++++++++++++++++
 6 files changed, 123 insertions(+), 75 deletions(-)
 create mode 100644 tests/test_viz.py

diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 5a60e381..1fb713a9 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -303,9 +303,10 @@ def _format_model_param(
         if covariates is None:
             covariates = torch.ones(counts.shape[0], 1)
         else:
-            covariates = torch.concat(
-                (covariates, torch.ones(counts.shape[0]).unsqueeze(1)), dim=1
-            )
+            if _has_null_variance(covariates) is False:
+                covariates = torch.concat(
+                    (covariates, torch.ones(counts.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")
@@ -321,6 +322,23 @@ def _format_model_param(
     return counts, covariates, offsets
 
 
+def _has_null_variance(tensor: torch.Tensor) -> bool:
+    """
+    Check if a torch.Tensor has a dimension with null variance.
+
+    Parameters
+    ----------
+        tensor (torch.Tensor): The input tensor.
+
+    Returns
+    -------
+        bool: True if a dimension with null variance is found, False otherwise.
+    """
+    variances = torch.var(tensor, dim=0)
+    has_null_var = torch.any(variances == 0)
+    return bool(has_null_var)
+
+
 def _check_data_shape(
     counts: torch.Tensor, covariates: torch.Tensor, offsets: torch.Tensor
 ) -> None:
diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 5e1b030c..8eaf6b0e 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -162,6 +162,7 @@ class _Pln(ABC):
         for key, array in dict_initialization.items():
             array = _format_data(array)
             setattr(self, key, array)
+        self._fitted = True
 
     @property
     def fitted(self) -> bool:
@@ -408,6 +409,18 @@ class _Pln(ABC):
         pca.fit(self.latent_variables.cpu())
         return pca
 
+    @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).detach()
+
     def scatter_pca_matrix(self, n_components=None, color=None):
         """
         Generates a scatter matrix plot based on Principal Component Analysis (PCA).
@@ -438,7 +451,7 @@ class _Pln(ABC):
         components = torch.from_numpy(pca.components_)
 
         labels = {
-            str(i): f"PC{i+1}: {np.round(pca.explained_variance_ratio_, 2)[i]}%"
+            str(i): f"PC{i+1}: {np.round(pca.explained_variance_ratio_*100, 1)[i]}%"
             for i in range(n_components)
         }
         proj_variables
@@ -678,7 +691,7 @@ class _Pln(ABC):
         float
             The log-likelihood.
         """
-        if self._fitted is False:
+        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)
@@ -718,7 +731,10 @@ class _Pln(ABC):
         dict
             The dictionary of latent parameters.
         """
-        return {"latent_var": self.latent_var, "latent_mean": self.latent_mean}
+        return {
+            "latent_sqrt_var": self.latent_sqrt_var,
+            "latent_mean": self.latent_mean,
+        }
 
     @property
     def model_parameters(self):
@@ -873,16 +889,17 @@ class _Pln(ABC):
             return attr
         return None
 
-    def save(self, path_of_directory: str = "./"):
+    def save(self, path: str = None):
         """
         Save the model parameters to disk.
 
         Parameters
         ----------
-        path_of_directory : str, optional
+        path : str, optional
             The path of the directory to save the parameters, by default "./".
         """
-        path = f"{path_of_directory}/{self._directory_name}"
+        if path is None:
+            path = f"./{self._directory_name}"
         os.makedirs(path, exist_ok=True)
         for key, value in self._dict_parameters.items():
             filename = f"{path}/{key}.csv"
@@ -1016,7 +1033,7 @@ class _Pln(ABC):
             pass
         elif coef.shape != (self.nb_cov, self.dim):
             raise ValueError(
-                f"Wrong shape for the counts. Expected {(self.nb_cov, self.dim)}, got {coef.shape}"
+                f"Wrong shape for the coef. Expected {(self.nb_cov, self.dim)}, got {coef.shape}"
             )
         self._coef = coef
 
@@ -2020,7 +2037,7 @@ class PlnPCAcollection:
             ranks = self.ranks
         for model in self.values():
             if model.rank in ranks:
-                model.save(f"{self._directory_name}/{path_of_directory}")
+                model.save(f"{self._directory_name}/PlnPCA_rank_{model.rank}")
 
     @property
     def _directory_name(self) -> str:
@@ -2230,18 +2247,6 @@ class PlnPCA(_Pln):
         """
         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).detach()
-
     @property
     def _latent_var(self) -> torch.Tensor:
         """
@@ -2298,7 +2303,7 @@ class PlnPCA(_Pln):
         str
             The directory name.
         """
-        return f"{self._NAME}_rank_{self._rank}"
+        return f"{self._NAME}_nbcov_{self.nb_cov}_rank_{self._rank}"
 
     @property
     def covariates(self) -> torch.Tensor:
@@ -2328,18 +2333,6 @@ class PlnPCA(_Pln):
         print("Setting coef to initialization")
         self._smart_init_coef()
 
-    @property
-    def _path_to_directory(self) -> str:
-        """
-        Property representing the path to the directory.
-
-        Returns
-        -------
-        str
-            The path to the directory.
-        """
-        return f"PlnPCAcollection_nbcov_{self.nb_cov}_dim_{self.dim}/"
-
     @property
     def rank(self) -> int:
         """
diff --git a/tests/conftest.py b/tests/conftest.py
index 5dd35b20..ab5ee00b 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -55,17 +55,16 @@ instances = []
 # dict_fixtures_models = []
 
 
-def convenient_plnpca(*args, **kwargs):
+def convenient_PlnPCA(*args, **kwargs):
     dict_init = kwargs.pop("dict_initialization", None)
     if isinstance(args[0], str):
         return PlnPCA.from_formula(
             *args, **kwargs, dict_initialization=dict_init, rank=RANK
         )
-    print("rank:", RANK)
     return PlnPCA(*args, **kwargs, dict_initialization=dict_init, rank=RANK)
 
 
-def convenientplnpca(*args, **kwargs):
+def convenient_PlnPCAcollection(*args, **kwargs):
     dict_init = kwargs.pop("dict_initialization", None)
     if isinstance(args[0], str):
         return PlnPCAcollection.from_formula(
@@ -84,6 +83,7 @@ def convenientpln(*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
@@ -91,10 +91,10 @@ def generate_new_model(model, *args, **kwargs):
         if name == "Pln":
             new = convenientpln(*args, **kwargs, dict_initialization=init)
         if name == "PlnPCA":
-            new = convenient_plnpca(*args, **kwargs, dict_initialization=init)
+            new = convenient_PlnPCA(*args, **kwargs, dict_initialization=init)
     if name == "PlnPCAcollection":
         init = load_plnpcacollection(name_dir)
-        new = convenientplnpca(*args, **kwargs, dict_initialization=init)
+        new = convenient_PlnPCAcollection(*args, **kwargs, dict_initialization=init)
     return new
 
 
@@ -109,14 +109,14 @@ def cache(func):
     return new_func
 
 
-params = [convenientpln, convenient_plnpca, convenientplnpca]
+params = [convenientpln, convenient_PlnPCA, convenient_PlnPCAcollection]
 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)
+    pln = cls(counts_sim_0cov, covariates_sim_0cov, offsets_sim_0cov, add_const=False)
     return pln
 
 
@@ -124,7 +124,7 @@ 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)
+    pln = cls(counts_sim_0cov, covariates_sim_0cov, offsets_sim_0cov, add_const=False)
     pln.fit()
     return pln
 
@@ -161,8 +161,9 @@ def simulated_loaded_pln_0cov_array(simulated_fitted_pln_0cov_array):
     return generate_new_model(
         simulated_fitted_pln_0cov_array,
         counts_sim_0cov,
-        covariates_sim_0cov,
-        offsets_sim_0cov,
+        covariates=covariates_sim_0cov,
+        offsets=offsets_sim_0cov,
+        add_const=False,
     )
 
 
@@ -203,7 +204,12 @@ dict_fixtures = add_list_of_fixture_to_dict(dict_fixtures, "sim_pln_0cov", sim_p
 @cache
 def simulated_pln_2cov_array(request):
     cls = request.param
-    pln_full = cls(counts_sim_2cov, covariates_sim_2cov, offsets_sim_2cov)
+    pln_full = cls(
+        counts_sim_2cov,
+        covariates=covariates_sim_2cov,
+        offsets=offsets_sim_2cov,
+        add_const=False,
+    )
     return pln_full
 
 
@@ -243,8 +249,9 @@ def simulated_loaded_pln_2cov_array(simulated_fitted_pln_2cov_array):
     return generate_new_model(
         simulated_fitted_pln_2cov_array,
         counts_sim_2cov,
-        covariates_sim_2cov,
-        offsets_sim_2cov,
+        covariates=covariates_sim_2cov,
+        offsets=offsets_sim_2cov,
+        add_const=False,
     )
 
 
@@ -284,7 +291,7 @@ 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, covariates=torch.ones((counts_real.shape[0], 1)))
+    pln_full = cls(counts_real, add_const=True)
     return pln_full
 
 
@@ -312,7 +319,7 @@ 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_real
+        real_fitted_pln_intercept_formula, "counts ~ 1", data=data_real
     )
 
 
@@ -322,7 +329,7 @@ def real_loaded_pln_intercept_array(real_fitted_pln_intercept_array):
     return generate_new_model(
         real_fitted_pln_intercept_array,
         counts_real,
-        covariates=torch.ones((counts_real.shape[0], 1)),
+        add_const=True,
     )
 
 
diff --git a/tests/import_data.py b/tests/import_data.py
index 80e613ba..03930286 100644
--- a/tests/import_data.py
+++ b/tests/import_data.py
@@ -35,5 +35,7 @@ data_sim_2cov = {
     "covariates": covariates_sim_2cov,
     "offsets": offsets_sim_2cov,
 }
-counts_real = get_real_count_data()
+counts_real, labels_real = get_real_count_data(
+    return_labels=True, n_samples=100, dim=50
+)
 data_real = {"counts": counts_real}
diff --git a/tests/test_common.py b/tests/test_common.py
index 7edf03c4..ccdd7b92 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
+from tests.import_data import true_sim_0cov, true_sim_2cov, counts_real
 
 
 @pytest.mark.parametrize("any_pln", dict_fixtures["loaded_and_fitted_pln"])
@@ -18,26 +18,6 @@ def test_properties(any_pln):
     assert hasattr(any_pln, "model_parameters")
 
 
-@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.PCA)
-    assert any_pln.PCA(n_components=None) is not None
-    with pytest.raises(Exception):
-        any_pln.PCA(n_components=any_pln.dim + 1)
-
-
 @pytest.mark.parametrize("sim_pln", dict_fixtures["loaded_and_fitted_pln"])
 @filter_models(["Pln", "PlnPCA"])
 def test_predict_simulated(sim_pln):
@@ -75,7 +55,10 @@ 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 == (100, 100)
+    assert real_fitted_and_loaded_pln.covariance.shape == (
+        counts_real.shape[1],
+        counts_real.shape[1],
+    )
 
 
 @pytest.mark.parametrize(
diff --git a/tests/test_viz.py b/tests/test_viz.py
new file mode 100644
index 00000000..bdf6c110
--- /dev/null
+++ b/tests/test_viz.py
@@ -0,0 +1,45 @@
+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, labels_real
+
+
+@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
+    with pytest.raises(Exception):
+        any_pln.sk_PCA(n_components=any_pln.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("pln", dict_fixtures["fitted_pln"])
+@filter_models(["PlnPCA"])
+def test_scatter_pca_matrix_plnpca(pln):
+    pln.scatter_pca_matrix(n_components=2)
+    pln.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)
-- 
GitLab


From 4db1b506a0e6d2cccc2159aba90f600ea726e859 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 7 Jun 2023 17:27:27 +0200
Subject: [PATCH 08/17] forgot plotly dependencie

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index f6de9a7a..1cd7e968 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,6 +53,7 @@ dependencies = [
 "scikit-learn",
 "patsy",
 "mlxtend",
+"plotly",
 ]
 [project.optional-dependencies]
 tests = ["pytest","pytest_lazy_fixture"]
-- 
GitLab


From e6392a5965ce580a0e361da921f7d53deb5a7697 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 7 Jun 2023 17:51:36 +0200
Subject: [PATCH 09/17] fix bug when model was loaded but not fitted.

---
 pyPLNmodels/models.py | 85 ++++++++++++++++++++++---------------------
 1 file changed, 43 insertions(+), 42 deletions(-)

diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 8eaf6b0e..2dc11e7b 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -176,6 +176,48 @@ class _Pln(ABC):
         """
         return self._fitted
 
+    def viz(self, ax=None, colors=None, show_cov: bool = True):
+        """
+        Visualize the PlnPCA model.
+
+        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.
+        show_cov: bool, optional
+
+        Raises
+        ------
+        RuntimeError
+            If the rank is less than 2.
+
+        Returns
+        -------
+        Any
+            The matplotlib axis.
+        """
+        if ax is None:
+            ax = plt.gca()
+        if self._rank < 2:
+            raise RuntimeError("Can't perform visualization for rank < 2.")
+        x = proj_variables[:, 0]
+        y = proj_variables[:, 1]
+        sns.scatterplot(x=x, y=y, hue=colors, ax=ax)
+        if show_cov is True:
+            pca = self.sk_PCA(n_components=2)
+            proj_variables = pca.transform(self.latent_variables)
+            sk_components = torch.from_numpy(pca.components_)
+            C_tilde_C = sk_components @ self._components
+            C_tilde_C_latent_var = C_tilde_C.unsqueeze(0) * (
+                self._latent_var.unsqueeze(1)
+            )
+            covariances = ((C_tilde_C_latent_var) @ (C_tilde_C.T.unsqueeze(0))).detach()
+            for i in range(covariances.shape[0]):
+                _plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
+        return ax
+
     @property
     def nb_iteration_done(self) -> int:
         """
@@ -329,7 +371,7 @@ class _Pln(ABC):
 
         if self._fitted is False:
             self._init_parameters(do_smart_init)
-        else:
+        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)
@@ -2618,47 +2660,6 @@ class PlnPCA(_Pln):
             )
         self._components = components
 
-    def viz(self, ax=None, colors=None):
-        """
-        Visualize the PlnPCA model.
-
-        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.
-
-        Raises
-        ------
-        RuntimeError
-            If the rank is less than 2.
-
-        Returns
-        -------
-        Any
-            The matplotlib axis.
-        """
-        if ax is None:
-            ax = plt.gca()
-        if self._rank < 2:
-            raise RuntimeError("Can't perform visualization for rank < 2.")
-        else:
-            pca = self.sk_PCA(n_components=2)
-            proj_variables = pca.transform(self.latent_variables)
-            sk_components = torch.from_numpy(pca.components_)
-            C_tilde_C = sk_components @ self._components
-            C_tilde_C_latent_var = C_tilde_C.unsqueeze(0) * (
-                self._latent_var.unsqueeze(1)
-            )
-            covariances = ((C_tilde_C_latent_var) @ (C_tilde_C.T.unsqueeze(0))).detach()
-        x = proj_variables[:, 0]
-        y = proj_variables[:, 1]
-        sns.scatterplot(x=x, y=y, hue=colors, ax=ax)
-        for i in range(covariances.shape[0]):
-            _plot_ellipse(x[i], y[i], cov=covariances[i], ax=ax)
-        return ax
-
     def transform(self, project: bool = True) -> torch.Tensor:
         """
         Transform the model.
-- 
GitLab


From ba8e93b443a0a7d604221c502d4c6cbd70289137 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 7 Jun 2023 17:59:21 +0200
Subject: [PATCH 10/17] bug called before reference

---
 tests/test_plnpcacollection.py | 15 ---------------
 tests/test_viz.py              | 16 ++++++++++++++++
 2 files changed, 16 insertions(+), 15 deletions(-)

diff --git a/tests/test_plnpcacollection.py b/tests/test_plnpcacollection.py
index 247335c4..ac77b442 100644
--- a/tests/test_plnpcacollection.py
+++ b/tests/test_plnpcacollection.py
@@ -46,21 +46,6 @@ def test_additional_methods_pca(plnpca):
     plnpca.loglikes
 
 
-@pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_pln"])
-@filter_models(["PlnPCAcollection"])
-def test_viz_pca(plnpca):
-    for model in plnpca.values():
-        _, ax = plt.subplots()
-        model.viz(ax=ax)
-        plt.show()
-        model.viz()
-        plt.show()
-        n_samples = plnpca.n_samples
-        colors = np.random.randint(low=0, high=2, size=n_samples)
-        model.viz(colors=colors)
-        plt.show()
-
-
 @pytest.mark.parametrize("plnpca", dict_fixtures["loaded_and_fitted_pln"])
 @filter_models(["PlnPCAcollection"])
 def test_wrong_criterion(plnpca):
diff --git a/tests/test_viz.py b/tests/test_viz.py
index bdf6c110..43be9487 100644
--- a/tests/test_viz.py
+++ b/tests/test_viz.py
@@ -1,4 +1,5 @@
 import pytest
+import matplotlib.pyplot as plt
 
 from tests.conftest import dict_fixtures
 from tests.utils import MSE, filter_models
@@ -43,3 +44,18 @@ def test_scatter_pca_matrix_plnpca(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("plnpca", dict_fixtures["loaded_and_fitted_pln"])
+@filter_models(["PlnPCAcollection"])
+def test_viz_pcacol(plnpca):
+    for model in plnpca.values():
+        _, ax = plt.subplots()
+        model.viz(ax=ax)
+        plt.show()
+        model.viz()
+        plt.show()
+        n_samples = plnpca.n_samples
+        colors = np.random.randint(low=0, high=2, size=n_samples)
+        model.viz(colors=colors)
+        plt.show()
-- 
GitLab


From 66c3e40fed53b8c5f512bfb8b587f26dcf834d0e Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Wed, 7 Jun 2023 18:58:47 +0200
Subject: [PATCH 11/17] add vizualization for Pln also. Not sure about the
 variances when projecting with PCA in dim2.

---
 pyPLNmodels/models.py | 31 ++++++++++++++++++++-----------
 1 file changed, 20 insertions(+), 11 deletions(-)

diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index 2dc11e7b..b6c9d34d 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -178,7 +178,7 @@ class _Pln(ABC):
 
     def viz(self, ax=None, colors=None, show_cov: bool = True):
         """
-        Visualize the PlnPCA model.
+        Visualize the latent variables with a classic PCA.
 
         Parameters
         ----------
@@ -187,7 +187,7 @@ class _Pln(ABC):
         colors : Optional[Any], optional
             The colors to use for plotting, by default None.
         show_cov: bool, optional
-
+            If True, will display ellipses with right covariances. Default is True.
         Raises
         ------
         RuntimeError
@@ -200,20 +200,16 @@ class _Pln(ABC):
         """
         if ax is None:
             ax = plt.gca()
-        if self._rank < 2:
-            raise RuntimeError("Can't perform visualization for rank < 2.")
+        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)
         x = proj_variables[:, 0]
         y = proj_variables[:, 1]
         sns.scatterplot(x=x, y=y, hue=colors, ax=ax)
         if show_cov is True:
-            pca = self.sk_PCA(n_components=2)
-            proj_variables = pca.transform(self.latent_variables)
             sk_components = torch.from_numpy(pca.components_)
-            C_tilde_C = sk_components @ self._components
-            C_tilde_C_latent_var = C_tilde_C.unsqueeze(0) * (
-                self._latent_var.unsqueeze(1)
-            )
-            covariances = ((C_tilde_C_latent_var) @ (C_tilde_C.T.unsqueeze(0))).detach()
+            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)
         return ax
@@ -1358,6 +1354,13 @@ class Pln(_Pln):
             self.n_samples,
         )
 
+    def _get_pca_low_dim_covariances(self, sk_components):
+        components_var = (self._latent_sqrt_var**2).unsqueeze(
+            1
+        ) * sk_components.unsqueeze(0)
+        covariances = components_var @ (sk_components.T.unsqueeze(0))
+        return covariances
+
     def _pring_beginning_message(self):
         """
         Method for printing the beginning message.
@@ -2375,6 +2378,12 @@ class PlnPCA(_Pln):
         print("Setting coef to initialization")
         self._smart_init_coef()
 
+    def _get_pca_low_dim_covariances(self, sk_components):
+        C_tilde_C = sk_components @ self._components
+        C_tilde_C_latent_var = C_tilde_C.unsqueeze(0) * (self._latent_var.unsqueeze(1))
+        covariances = (C_tilde_C_latent_var) @ (C_tilde_C.T.unsqueeze(0))
+        return covariances
+
     @property
     def rank(self) -> int:
         """
-- 
GitLab


From 380e9be42dcc68031bd7809490bc5b9e8a7c44fb Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 8 Jun 2023 09:07:00 +0200
Subject: [PATCH 12/17] add visualization of variables.

---
 pyPLNmodels/models.py | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index b6c9d34d..af56d506 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -502,7 +502,7 @@ class _Pln(ABC):
         fig.update_traces(diagonal_visible=False)
         fig.show()
 
-    def viz_pca_var(self, variables_names, indices_of_variables=None):
+    def viz_variables(self, variables_names, indices_of_variables=None):
         if indices_of_variables is None:
             if self.column_counts is None:
                 raise ValueError(
@@ -511,13 +511,10 @@ class _Pln(ABC):
                     "needed names or instantiate a new model with"
                     "a pd.DataFrame with appropriate column names"
                 )
-            print("variable names", variables_names)
-            print("columns", self.column_counts)
             indices_of_variables = []
             for variables_name in variables_names:
                 index = self.column_counts.get_loc(variables_name)
                 indices_of_variables.append(index)
-            print("indices:", indices_of_variables)
         else:
             if len(indices_of_variables) != len(variables_names):
                 raise ValueError(
@@ -528,10 +525,7 @@ class _Pln(ABC):
         pca = self.sk_PCA(n_components=n_components)
         variables = self.latent_variables
         proj_variables = pca.transform(variables)
-        labels = {
-            str(i): f"PC{i+1}: {np.round(pca.explained_variance_ratio_, 2)[i]}%"
-            for i in range(n_components)
-        }
+        ## the package is not correctly printing the variance ratio
         figure, correlation_matrix = plot_pca_correlation_graph(
             variables[:, indices_of_variables],
             variables_names=variables_names,
@@ -540,6 +534,7 @@ class _Pln(ABC):
             dimensions=(1, 2),
             figure_axis_size=10,
         )
+        plt.show()
 
     @property
     @abstractmethod
-- 
GitLab


From 52597bf280d12717438e45b8c969b4641ece5360 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 8 Jun 2023 09:07:56 +0200
Subject: [PATCH 13/17] Reformat the import and add tests for the visualization

---
 tests/conftest.py |  5 ++++-
 tests/test_viz.py | 22 ++++++++++++++++++++++
 2 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/tests/conftest.py b/tests/conftest.py
index ab5ee00b..5ce640ac 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,10 +1,11 @@
 import sys
 import glob
 from functools import singledispatch
-
 import pytest
 import torch
 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
 
@@ -33,6 +34,8 @@ 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.column = [f"var_{i}" for i in range(counts_real.shape[1])]
 
 
 def add_fixture_to_dict(my_dict, string_fixture):
diff --git a/tests/test_viz.py b/tests/test_viz.py
index 43be9487..8ef66036 100644
--- a/tests/test_viz.py
+++ b/tests/test_viz.py
@@ -1,5 +1,6 @@
 import pytest
 import matplotlib.pyplot as plt
+import numpy as np
 
 from tests.conftest import dict_fixtures
 from tests.utils import MSE, filter_models
@@ -59,3 +60,24 @@ def test_viz_pcacol(plnpca):
         colors = np.random.randint(low=0, high=2, size=n_samples)
         model.viz(colors=colors)
         plt.show()
+
+
+@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_real_pln"])
+@filter_models(["Pln", "PlnPCA"])
+def test_viz_variables_with_names_only(pln):
+    pln.viz_variables([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_viz_variables_without_names(pln):
+    with pytest.raises(ValueError):
+        pln.viz_variables([f"var_{i}" for i in range(8)])
+    with pytest.raises(ValueError):
+        pln.viz_variables([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_viz_variables_without_names(pln):
+    pln.viz_variables([f"var_{i}" for i in range(3)], [0, 1, 2])
-- 
GitLab


From e4fa7ac16b56642b4e7a2e271f58faa121e36d2b Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 8 Jun 2023 09:29:58 +0200
Subject: [PATCH 14/17] add docstrings and pass tests

---
 pyPLNmodels/models.py | 21 ++++++++++++++++++++-
 tests/test_viz.py     | 14 +++++++-------
 2 files changed, 27 insertions(+), 8 deletions(-)

diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index af56d506..efbd1c13 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -502,7 +502,26 @@ class _Pln(ABC):
         fig.update_traces(diagonal_visible=False)
         fig.show()
 
-    def viz_variables(self, variables_names, indices_of_variables=None):
+    def plot_pca_correlation_graph(self, variables_names, indices_of_variables=None):
+        """
+        Visualizes variables using PCA and plots a correlation graph.
+
+        Parameters:
+            variables_names : List[str]
+                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
+
+        Raises:
+            ValueError
+                If `indices_of_variables` is None and `column_counts` is not set.
+            ValueError
+                If the length of `indices_of_variables` is different from the length of `variables_names`.
+
+        Returns:
+            None
+        """
         if indices_of_variables is None:
             if self.column_counts is None:
                 raise ValueError(
diff --git a/tests/test_viz.py b/tests/test_viz.py
index 8ef66036..2353ffb0 100644
--- a/tests/test_viz.py
+++ b/tests/test_viz.py
@@ -64,20 +64,20 @@ def test_viz_pcacol(plnpca):
 
 @pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_real_pln"])
 @filter_models(["Pln", "PlnPCA"])
-def test_viz_variables_with_names_only(pln):
-    pln.viz_variables([f"var_{i}" for i in range(8)])
+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("pln", dict_fixtures["loaded_and_fitted_sim_pln"])
 @filter_models(["Pln", "PlnPCA"])
-def test_fail_viz_variables_without_names(pln):
+def test_fail_plot_pca_correlation_graph_without_names(pln):
     with pytest.raises(ValueError):
-        pln.viz_variables([f"var_{i}" for i in range(8)])
+        pln.plot_pca_correlation_graph([f"var_{i}" for i in range(8)])
     with pytest.raises(ValueError):
-        pln.viz_variables([f"var_{i}" for i in range(6)], [1, 2, 3])
+        pln.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_viz_variables_without_names(pln):
-    pln.viz_variables([f"var_{i}" for i in range(3)], [0, 1, 2])
+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])
-- 
GitLab


From 0da06274a4d537dac4093318848a11b2a5c1c0fc Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 8 Jun 2023 09:30:45 +0200
Subject: [PATCH 15/17] fixed docstrings

---
 pyPLNmodels/models.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/pyPLNmodels/models.py b/pyPLNmodels/models.py
index efbd1c13..c7751cf9 100644
--- a/pyPLNmodels/models.py
+++ b/pyPLNmodels/models.py
@@ -506,20 +506,23 @@ class _Pln(ABC):
         """
         Visualizes variables using PCA and plots a correlation graph.
 
-        Parameters:
+        Parameters
+        ----------
             variables_names : List[str]
                 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
 
-        Raises:
+        Raises
+        ------
             ValueError
                 If `indices_of_variables` is None and `column_counts` is not set.
             ValueError
                 If the length of `indices_of_variables` is different from the length of `variables_names`.
 
-        Returns:
+        Returns
+        -------
             None
         """
         if indices_of_variables is None:
-- 
GitLab


From 666456f24c606d6af94738e3b7cc89a3b5130297 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 8 Jun 2023 09:59:09 +0200
Subject: [PATCH 16/17] fix test

---
 tests/conftest.py | 2 +-
 tests/test_viz.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/conftest.py b/tests/conftest.py
index 5ce640ac..d87255cf 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -35,7 +35,7 @@ offsets_sim_2cov = data_sim_2cov["offsets"]
 
 counts_real = data_real["counts"]
 counts_real = pd.DataFrame(counts_real)
-counts_real.column = [f"var_{i}" for i in range(counts_real.shape[1])]
+counts_real.columns = [f"var_{i}" for i in range(counts_real.shape[1])]
 
 
 def add_fixture_to_dict(my_dict, string_fixture):
diff --git a/tests/test_viz.py b/tests/test_viz.py
index 2353ffb0..13510d49 100644
--- a/tests/test_viz.py
+++ b/tests/test_viz.py
@@ -62,7 +62,7 @@ def test_viz_pcacol(plnpca):
         plt.show()
 
 
-@pytest.mark.parametrize("pln", dict_fixtures["loaded_and_fitted_real_pln"])
+@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)])
-- 
GitLab


From d483aec142637a8784cba4e6603b44830ae338e9 Mon Sep 17 00:00:00 2001
From: bastien-mva <bastien.batardiere@gmail.com>
Date: Thu, 8 Jun 2023 10:11:36 +0200
Subject: [PATCH 17/17] add docstrings

---
 pyPLNmodels/_utils.py | 66 ++++++++++++++++++++++++++++---------------
 1 file changed, 44 insertions(+), 22 deletions(-)

diff --git a/pyPLNmodels/_utils.py b/pyPLNmodels/_utils.py
index 1fb713a9..e487be48 100644
--- a/pyPLNmodels/_utils.py
+++ b/pyPLNmodels/_utils.py
@@ -428,15 +428,15 @@ def _get_simulation_components(dim: int, rank: int) -> torch.Tensor:
     Get the components for simulation. The resulting covariance matrix
     will be a matrix per blocks plus a little noise.
 
-    Parameters:
-    -----------
+    Parameters
+    ----------
     dim : int
         Dimension of the data.
     rank : int
         Rank of the resulting covariance matrix (i.e. number of components).
 
-    Returns:
-    --------
+    Returns
+    -------
     torch.Tensor
         Components.
     """
@@ -459,8 +459,8 @@ def _get_simulation_coef_cov_offsets(
     """
     Get offsets, covariance coefficients with right shapes.
 
-    Parameters:
-    -----------
+    Parameters
+    ----------
     n_samples : int
         Number of samples.
     nb_cov : int
@@ -468,8 +468,8 @@ def _get_simulation_coef_cov_offsets(
     dim : int
         Dimension required of the data.
 
-    Returns:
-    --------
+    Returns
+    -------
     Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
         Tuple containing offsets, covariates, and coefficients.
     """
@@ -498,8 +498,8 @@ class PlnParameters:
         """
         Instantiate all the needed parameters to sample from the PLN model.
 
-        Parameters:
-        -----------
+        Parameters
+        ----------
         components : torch.Tensor
             Components of size (p, rank)
         coef : torch.Tensor
@@ -598,6 +598,26 @@ def _check_two_dimensions_are_equal(
 def get_simulation_parameters(
     n_samples: int = 100, dim: int = 25, nb_cov: int = 1, rank: int = 5
 ) -> PlnParameters:
+    """
+    Generate simulation parameters for a Poisson-lognormal model.
+
+    Parameters
+    ----------
+        n_samples : int, optional
+            The number of samples, by default 100.
+        dim : int, optional
+            The dimension of the data, by default 25.
+        nb_cov : int, optional
+            The number of covariates, by default 1.
+        rank : int, optional
+            The rank of the data components, by default 5.
+
+    Returns
+    -------
+        PlnParameters
+            The generated simulation parameters.
+
+    """
     coef, covariates, offsets = _get_simulation_coef_cov_offsets(n_samples, nb_cov, dim)
     components = _get_simulation_components(dim, rank)
     return PlnParameters(components, coef, covariates, offsets)
@@ -614,8 +634,8 @@ def get_simulated_count_data(
     """
     Get simulated count data from the PlnPCA model.
 
-    Parameters:
-    -----------
+    Parameters
+    ----------
     n_samples : int, optional
         Number of samples, by default 100.
     dim : int, optional
@@ -629,8 +649,8 @@ def get_simulated_count_data(
     seed : int, optional
         Seed value for random number generation, by default 0.
 
-    Returns:
-    --------
+    Returns
+    -------
     Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
         Tuple containing counts, covariates, and offsets.
     """
@@ -653,16 +673,16 @@ def get_real_count_data(
     """
     Get real count data from the scMARK dataset.
 
-    Parameters:
-    -----------
+    Parameters
+    ----------
     n_samples : int, optional
         Number of samples, by default 469.
     dim : int, optional
         Dimension, by default 200.
     return_labels: bool, optional
         If True, will return the labels of the count data
-    Returns:
-    --------
+    Returns
+    -------
     np.ndarray
         Real count data and labels if return_labels is True.
     """
@@ -867,16 +887,17 @@ def _to_tensor(
     """
     Convert an object to a PyTorch tensor.
 
-    Parameters:
+    Parameters
     ----------
         obj (np.ndarray or torch.Tensor or pd.DataFrame or None):
             The object to be converted.
 
-    Returns:
+    Returns
+    -------
         torch.Tensor or None:
             The converted PyTorch tensor.
 
-    Raises:
+    Raises
     ------
         TypeError:
             If the input object is not an np.ndarray, torch.Tensor, pd.DataFrame, or None.
@@ -926,7 +947,8 @@ def _handle_data(
     -------
         tuple: A tuple containing the processed counts, covariates, offsets, and column counts (if available).
 
-    Raises:
+    Raises
+    ------
         ValueError: If the shapes of counts, covariates, and offsets do not match.
     """
     if isinstance(counts, pd.DataFrame):
-- 
GitLab