Adding all project files

venv/Lib/site-packages/sklearn/decomposition/_base.py

@@ -0,0 +1,164 @@
+"""Principal Component Analysis Base Classes"""
+
+# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
+#         Olivier Grisel <olivier.grisel@ensta.org>
+#         Mathieu Blondel <mathieu@mblondel.org>
+#         Denis A. Engemann <denis-alexander.engemann@inria.fr>
+#         Kyle Kastner <kastnerkyle@gmail.com>
+#
+# License: BSD 3 clause
+
+import numpy as np
+from scipy import linalg
+
+from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin
+from ..utils.validation import check_is_fitted
+from abc import ABCMeta, abstractmethod
+
+
+class _BasePCA(
+    ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator, metaclass=ABCMeta
+):
+    """Base class for PCA methods.
+
+    Warning: This class should not be used directly.
+    Use derived classes instead.
+    """
+
+    def get_covariance(self):
+        """Compute data covariance with the generative model.
+
+        ``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``
+        where S**2 contains the explained variances, and sigma2 contains the
+        noise variances.
+
+        Returns
+        -------
+        cov : array of shape=(n_features, n_features)
+            Estimated covariance of data.
+        """
+        components_ = self.components_
+        exp_var = self.explained_variance_
+        if self.whiten:
+            components_ = components_ * np.sqrt(exp_var[:, np.newaxis])
+        exp_var_diff = np.maximum(exp_var - self.noise_variance_, 0.0)
+        cov = np.dot(components_.T * exp_var_diff, components_)
+        cov.flat[:: len(cov) + 1] += self.noise_variance_  # modify diag inplace
+        return cov
+
+    def get_precision(self):
+        """Compute data precision matrix with the generative model.
+
+        Equals the inverse of the covariance but computed with
+        the matrix inversion lemma for efficiency.
+
+        Returns
+        -------
+        precision : array, shape=(n_features, n_features)
+            Estimated precision of data.
+        """
+        n_features = self.components_.shape[1]
+
+        # handle corner cases first
+        if self.n_components_ == 0:
+            return np.eye(n_features) / self.noise_variance_
+
+        if np.isclose(self.noise_variance_, 0.0, atol=0.0):
+            return linalg.inv(self.get_covariance())
+
+        # Get precision using matrix inversion lemma
+        components_ = self.components_
+        exp_var = self.explained_variance_
+        if self.whiten:
+            components_ = components_ * np.sqrt(exp_var[:, np.newaxis])
+        exp_var_diff = np.maximum(exp_var - self.noise_variance_, 0.0)
+        precision = np.dot(components_, components_.T) / self.noise_variance_
+        precision.flat[:: len(precision) + 1] += 1.0 / exp_var_diff
+        precision = np.dot(components_.T, np.dot(linalg.inv(precision), components_))
+        precision /= -(self.noise_variance_**2)
+        precision.flat[:: len(precision) + 1] += 1.0 / self.noise_variance_
+        return precision
+
+    @abstractmethod
+    def fit(self, X, y=None):
+        """Placeholder for fit. Subclasses should implement this method!
+
+        Fit the model with X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+
+    def transform(self, X):
+        """Apply dimensionality reduction to X.
+
+        X is projected on the first principal components previously extracted
+        from a training set.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            New data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        Returns
+        -------
+        X_new : array-like of shape (n_samples, n_components)
+            Projection of X in the first principal components, where `n_samples`
+            is the number of samples and `n_components` is the number of the components.
+        """
+        check_is_fitted(self)
+
+        X = self._validate_data(X, dtype=[np.float64, np.float32], reset=False)
+        if self.mean_ is not None:
+            X = X - self.mean_
+        X_transformed = np.dot(X, self.components_.T)
+        if self.whiten:
+            X_transformed /= np.sqrt(self.explained_variance_)
+        return X_transformed
+
+    def inverse_transform(self, X):
+        """Transform data back to its original space.
+
+        In other words, return an input `X_original` whose transform would be X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_components)
+            New data, where `n_samples` is the number of samples
+            and `n_components` is the number of components.
+
+        Returns
+        -------
+        X_original array-like of shape (n_samples, n_features)
+            Original data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        Notes
+        -----
+        If whitening is enabled, inverse_transform will compute the
+        exact inverse operation, which includes reversing whitening.
+        """
+        if self.whiten:
+            return (
+                np.dot(
+                    X,
+                    np.sqrt(self.explained_variance_[:, np.newaxis]) * self.components_,
+                )
+                + self.mean_
+            )
+        else:
+            return np.dot(X, self.components_) + self.mean_
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.components_.shape[0]