Add files using upload-large-folder tool

.gitattributes

@@ -213,3 +213,4 @@ llmeval-env/lib/python3.10/site-packages/scipy/special/_ufuncs.cpython-310-x86_6
 llmeval-env/lib/python3.10/site-packages/scipy/special/cython_special.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 llmeval-env/lib/python3.10/site-packages/scipy/linalg/_flapack.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 llmeval-env/lib/python3.10/site-packages/scipy/io/_fast_matrix_market/_fmm_core.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+env-llmeval/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so filter=lfs diff=lfs merge=lfs -text

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_292/api-v1-jd-292.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e6a38d79d8f9e53a2ce11b68b4153062d4e96ec0b368d02b2e64f1b33c51693
+size 551

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_292/api-v1-jdf-292.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:255c16f33ed2967fe100cd8011a7e69f789603724b1ec2ecf91dfeb72067c190
+size 306

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_292/api-v1-jdf-40981.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:255c16f33ed2967fe100cd8011a7e69f789603724b1ec2ecf91dfeb72067c190
+size 306

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_292/api-v1-jdl-dn-australian-l-2-dv-1-s-dact.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ef6025425fdfc5f736555ea385252af5bcbf62383615db82489366d4f96a0a7
+size 327

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_292/api-v1-jdl-dn-australian-l-2-dv-1.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9da09e9a6031d060ec416f639a6bf34989e6c88ce641d10621eb906ba1d8c293
+size 99

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_292/api-v1-jdl-dn-australian-l-2-s-act-.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:35890d08165c804526b48aad462d7ccc09e808bd7975ba604bd612b9608797ac
+size 319

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_292/data-v1-dl-49822.arff.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b7ee24adabd4aaed6419b43fe9d3f86d55fcf4bee0f1698ae21d86c2701314e3
+size 2532

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_3/api-v1-jd-3.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:066a216679b197cc51946e17ee9a2e28215425991b0ceb7f10988c14f7f3f869
+size 2473

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_3/api-v1-jdf-3.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ec4f2d6bc4df3882b08bba01571e0792a56f79e0a922d984897773acd284b426
+size 535

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_3/api-v1-jdq-3.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09ef19cfad25c5de487ddbaef3c4d068ca3063777730a288dfd6f5096a0c6f46
+size 1407

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_3/data-v1-dl-3.arff.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c63fdf8861761f1ca70509f7d2d169a7cc053988c7b7c09c09a6db6124e208be
+size 19485

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_40589/api-v1-jd-40589.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:59d1aa6b02d2358c16fa9e4fbeff523a3bd10ebd38c7c371911fa8335e7bdcbf
+size 598

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_40589/api-v1-jdq-40589.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0f7973193eb35d19e99d1d8bca3c7f3a8b8d0410508af34ad571aee8ec5ab05
+size 913

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_40966/api-v1-jd-40966.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36c63c3ac8c9db59910acbf4c772cd53040ccd0eac0b0452611dd7ad8da50474
+size 1660

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_40966/api-v1-jdf-40966.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8adac8e2f8cbcbfa9677acdd4927a961430465d2c99401832160be455cfaced8
+size 3690

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_40966/api-v1-jdl-dn-miceprotein-l-2-dv-4.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f0c203b4627175cebbf527d81917a499911af915f6f2f46ee7248428a948d603
+size 325

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_40966/api-v1-jdl-dn-miceprotein-l-2-s-act-.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:301396b4a42c814b1a15038ddfcbcf5c8590501231747d0dc2a500b84b2fd0df
+size 328

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_40966/api-v1-jdq-40966.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3dee83987fffa8ec20e23b3cabc00d42beb7a469af6bd803909998c1687fa634
+size 934

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/tests/data/openml/id_40966/data-v1-dl-17928620.arff.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c5fd93ffec7deb63a940fd698534dd7ebb7db349fc183930041cbf17e60e2cc
+size 6471

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/__init__.py

@@ -0,0 +1,52 @@
+"""
+The :mod:`sklearn.decomposition` module includes matrix decomposition
+algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
+this module can be regarded as dimensionality reduction techniques.
+"""
+
+
+from ..utils.extmath import randomized_svd
+from ._dict_learning import (
+    DictionaryLearning,
+    MiniBatchDictionaryLearning,
+    SparseCoder,
+    dict_learning,
+    dict_learning_online,
+    sparse_encode,
+)
+from ._factor_analysis import FactorAnalysis
+from ._fastica import FastICA, fastica
+from ._incremental_pca import IncrementalPCA
+from ._kernel_pca import KernelPCA
+from ._lda import LatentDirichletAllocation
+from ._nmf import (
+    NMF,
+    MiniBatchNMF,
+    non_negative_factorization,
+)
+from ._pca import PCA
+from ._sparse_pca import MiniBatchSparsePCA, SparsePCA
+from ._truncated_svd import TruncatedSVD
+
+__all__ = [
+    "DictionaryLearning",
+    "FastICA",
+    "IncrementalPCA",
+    "KernelPCA",
+    "MiniBatchDictionaryLearning",
+    "MiniBatchNMF",
+    "MiniBatchSparsePCA",
+    "NMF",
+    "PCA",
+    "SparseCoder",
+    "SparsePCA",
+    "dict_learning",
+    "dict_learning_online",
+    "fastica",
+    "non_negative_factorization",
+    "randomized_svd",
+    "sparse_encode",
+    "FactorAnalysis",
+    "TruncatedSVD",
+    "LatentDirichletAllocation",
+]

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_base.py

@@ -0,0 +1,193 @@
+"""Principal Component Analysis Base Classes"""
+
+# Author: Alexandre Gramfort <[email protected]>
+#         Olivier Grisel <[email protected]>
+#         Mathieu Blondel <[email protected]>
+#         Denis A. Engemann <[email protected]>
+#         Kyle Kastner <[email protected]>
+#
+# License: BSD 3 clause
+
+from abc import ABCMeta, abstractmethod
+
+import numpy as np
+from scipy import linalg
+from scipy.sparse import issparse
+
+from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin
+from ..utils._array_api import _add_to_diagonal, device, get_namespace
+from ..utils.sparsefuncs import _implicit_column_offset
+from ..utils.validation import check_is_fitted
+
+
+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.
+        """
+        xp, _ = get_namespace(self.components_)
+
+        components_ = self.components_
+        exp_var = self.explained_variance_
+        if self.whiten:
+            components_ = components_ * xp.sqrt(exp_var[:, np.newaxis])
+        exp_var_diff = exp_var - self.noise_variance_
+        exp_var_diff = xp.where(
+            exp_var > self.noise_variance_,
+            exp_var_diff,
+            xp.asarray(0.0, device=device(exp_var)),
+        )
+        cov = (components_.T * exp_var_diff) @ components_
+        _add_to_diagonal(cov, self.noise_variance_, xp)
+        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.
+        """
+        xp, is_array_api_compliant = get_namespace(self.components_)
+
+        n_features = self.components_.shape[1]
+
+        # handle corner cases first
+        if self.n_components_ == 0:
+            return xp.eye(n_features) / self.noise_variance_
+
+        if is_array_api_compliant:
+            linalg_inv = xp.linalg.inv
+        else:
+            linalg_inv = linalg.inv
+
+        if self.noise_variance_ == 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_ * xp.sqrt(exp_var[:, np.newaxis])
+        exp_var_diff = exp_var - self.noise_variance_
+        exp_var_diff = xp.where(
+            exp_var > self.noise_variance_,
+            exp_var_diff,
+            xp.asarray(0.0, device=device(exp_var)),
+        )
+        precision = components_ @ components_.T / self.noise_variance_
+        _add_to_diagonal(precision, 1.0 / exp_var_diff, xp)
+        precision = components_.T @ linalg_inv(precision) @ components_
+        precision /= -(self.noise_variance_**2)
+        _add_to_diagonal(precision, 1.0 / self.noise_variance_, xp)
+        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, sparse matrix} 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.
+        """
+        xp, _ = get_namespace(X)
+
+        check_is_fitted(self)
+
+        X = self._validate_data(
+            X, accept_sparse=("csr", "csc"), dtype=[xp.float64, xp.float32], reset=False
+        )
+        if self.mean_ is not None:
+            if issparse(X):
+                X = _implicit_column_offset(X, self.mean_)
+            else:
+                X = X - self.mean_
+        X_transformed = X @ self.components_.T
+        if self.whiten:
+            X_transformed /= xp.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.
+        """
+        xp, _ = get_namespace(X)
+
+        if self.whiten:
+            scaled_components = (
+                xp.sqrt(self.explained_variance_[:, np.newaxis]) * self.components_
+            )
+            return X @ scaled_components + self.mean_
+        else:
+            return X @ self.components_ + self.mean_
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.components_.shape[0]

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_dict_learning.py

@@ -0,0 +1,2301 @@
+""" Dictionary learning.
+"""
+# Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
+# License: BSD 3 clause
+
+import itertools
+import sys
+import time
+from numbers import Integral, Real
+from warnings import warn
+
+import numpy as np
+from joblib import effective_n_jobs
+from scipy import linalg
+
+from ..base import (
+    BaseEstimator,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from ..linear_model import Lars, Lasso, LassoLars, orthogonal_mp_gram
+from ..utils import check_array, check_random_state, gen_batches, gen_even_slices
+from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
+from ..utils.extmath import randomized_svd, row_norms, svd_flip
+from ..utils.parallel import Parallel, delayed
+from ..utils.validation import check_is_fitted
+
+
+def _check_positive_coding(method, positive):
+    if positive and method in ["omp", "lars"]:
+        raise ValueError(
+            "Positive constraint not supported for '{}' coding method.".format(method)
+        )
+
+
+def _sparse_encode_precomputed(
+    X,
+    dictionary,
+    *,
+    gram=None,
+    cov=None,
+    algorithm="lasso_lars",
+    regularization=None,
+    copy_cov=True,
+    init=None,
+    max_iter=1000,
+    verbose=0,
+    positive=False,
+):
+    """Generic sparse coding with precomputed Gram and/or covariance matrices.
+
+    Each row of the result is the solution to a Lasso problem.
+
+    Parameters
+    ----------
+    X : ndarray of shape (n_samples, n_features)
+        Data matrix.
+
+    dictionary : ndarray of shape (n_components, n_features)
+        The dictionary matrix against which to solve the sparse coding of
+        the data. Some of the algorithms assume normalized rows.
+
+    gram : ndarray of shape (n_components, n_components), default=None
+        Precomputed Gram matrix, `dictionary * dictionary'`
+        gram can be `None` if method is 'threshold'.
+
+    cov : ndarray of shape (n_components, n_samples), default=None
+        Precomputed covariance, `dictionary * X'`.
+
+    algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'}, \
+            default='lasso_lars'
+        The algorithm used:
+
+        * `'lars'`: uses the least angle regression method
+          (`linear_model.lars_path`);
+        * `'lasso_lars'`: uses Lars to compute the Lasso solution;
+        * `'lasso_cd'`: uses the coordinate descent method to compute the
+          Lasso solution (`linear_model.Lasso`). lasso_lars will be faster if
+          the estimated components are sparse;
+        * `'omp'`: uses orthogonal matching pursuit to estimate the sparse
+          solution;
+        * `'threshold'`: squashes to zero all coefficients less than
+          regularization from the projection `dictionary * data'`.
+
+    regularization : int or float, default=None
+        The regularization parameter. It corresponds to alpha when
+        algorithm is `'lasso_lars'`, `'lasso_cd'` or `'threshold'`.
+        Otherwise it corresponds to `n_nonzero_coefs`.
+
+    init : ndarray of shape (n_samples, n_components), default=None
+        Initialization value of the sparse code. Only used if
+        `algorithm='lasso_cd'`.
+
+    max_iter : int, default=1000
+        Maximum number of iterations to perform if `algorithm='lasso_cd'` or
+        `'lasso_lars'`.
+
+    copy_cov : bool, default=True
+        Whether to copy the precomputed covariance matrix; if `False`, it may
+        be overwritten.
+
+    verbose : int, default=0
+        Controls the verbosity; the higher, the more messages.
+
+    positive: bool, default=False
+        Whether to enforce a positivity constraint on the sparse code.
+
+        .. versionadded:: 0.20
+
+    Returns
+    -------
+    code : ndarray of shape (n_components, n_features)
+        The sparse codes.
+    """
+    n_samples, n_features = X.shape
+    n_components = dictionary.shape[0]
+
+    if algorithm == "lasso_lars":
+        alpha = float(regularization) / n_features  # account for scaling
+        try:
+            err_mgt = np.seterr(all="ignore")
+
+            # Not passing in verbose=max(0, verbose-1) because Lars.fit already
+            # corrects the verbosity level.
+            lasso_lars = LassoLars(
+                alpha=alpha,
+                fit_intercept=False,
+                verbose=verbose,
+                precompute=gram,
+                fit_path=False,
+                positive=positive,
+                max_iter=max_iter,
+            )
+            lasso_lars.fit(dictionary.T, X.T, Xy=cov)
+            new_code = lasso_lars.coef_
+        finally:
+            np.seterr(**err_mgt)
+
+    elif algorithm == "lasso_cd":
+        alpha = float(regularization) / n_features  # account for scaling
+
+        # TODO: Make verbosity argument for Lasso?
+        # sklearn.linear_model.coordinate_descent.enet_path has a verbosity
+        # argument that we could pass in from Lasso.
+        clf = Lasso(
+            alpha=alpha,
+            fit_intercept=False,
+            precompute=gram,
+            max_iter=max_iter,
+            warm_start=True,
+            positive=positive,
+        )
+
+        if init is not None:
+            # In some workflows using coordinate descent algorithms:
+            #  - users might provide NumPy arrays with read-only buffers
+            #  - `joblib` might memmap arrays making their buffer read-only
+            # TODO: move this handling (which is currently too broad)
+            # closer to the actual private function which need buffers to be writable.
+            if not init.flags["WRITEABLE"]:
+                init = np.array(init)
+            clf.coef_ = init
+
+        clf.fit(dictionary.T, X.T, check_input=False)
+        new_code = clf.coef_
+
+    elif algorithm == "lars":
+        try:
+            err_mgt = np.seterr(all="ignore")
+
+            # Not passing in verbose=max(0, verbose-1) because Lars.fit already
+            # corrects the verbosity level.
+            lars = Lars(
+                fit_intercept=False,
+                verbose=verbose,
+                precompute=gram,
+                n_nonzero_coefs=int(regularization),
+                fit_path=False,
+            )
+            lars.fit(dictionary.T, X.T, Xy=cov)
+            new_code = lars.coef_
+        finally:
+            np.seterr(**err_mgt)
+
+    elif algorithm == "threshold":
+        new_code = (np.sign(cov) * np.maximum(np.abs(cov) - regularization, 0)).T
+        if positive:
+            np.clip(new_code, 0, None, out=new_code)
+
+    elif algorithm == "omp":
+        new_code = orthogonal_mp_gram(
+            Gram=gram,
+            Xy=cov,
+            n_nonzero_coefs=int(regularization),
+            tol=None,
+            norms_squared=row_norms(X, squared=True),
+            copy_Xy=copy_cov,
+        ).T
+
+    return new_code.reshape(n_samples, n_components)
+
+
+@validate_params(
+    {
+        "X": ["array-like"],
+        "dictionary": ["array-like"],
+        "gram": ["array-like", None],
+        "cov": ["array-like", None],
+        "algorithm": [
+            StrOptions({"lasso_lars", "lasso_cd", "lars", "omp", "threshold"})
+        ],
+        "n_nonzero_coefs": [Interval(Integral, 1, None, closed="left"), None],
+        "alpha": [Interval(Real, 0, None, closed="left"), None],
+        "copy_cov": ["boolean"],
+        "init": ["array-like", None],
+        "max_iter": [Interval(Integral, 0, None, closed="left")],
+        "n_jobs": [Integral, None],
+        "check_input": ["boolean"],
+        "verbose": ["verbose"],
+        "positive": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+# XXX : could be moved to the linear_model module
+def sparse_encode(
+    X,
+    dictionary,
+    *,
+    gram=None,
+    cov=None,
+    algorithm="lasso_lars",
+    n_nonzero_coefs=None,
+    alpha=None,
+    copy_cov=True,
+    init=None,
+    max_iter=1000,
+    n_jobs=None,
+    check_input=True,
+    verbose=0,
+    positive=False,
+):
+    """Sparse coding.
+
+    Each row of the result is the solution to a sparse coding problem.
+    The goal is to find a sparse array `code` such that::
+
+        X ~= code * dictionary
+
+    Read more in the :ref:`User Guide <SparseCoder>`.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Data matrix.
+
+    dictionary : array-like of shape (n_components, n_features)
+        The dictionary matrix against which to solve the sparse coding of
+        the data. Some of the algorithms assume normalized rows for meaningful
+        output.
+
+    gram : array-like of shape (n_components, n_components), default=None
+        Precomputed Gram matrix, `dictionary * dictionary'`.
+
+    cov : array-like of shape (n_components, n_samples), default=None
+        Precomputed covariance, `dictionary' * X`.
+
+    algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'}, \
+            default='lasso_lars'
+        The algorithm used:
+
+        * `'lars'`: uses the least angle regression method
+          (`linear_model.lars_path`);
+        * `'lasso_lars'`: uses Lars to compute the Lasso solution;
+        * `'lasso_cd'`: uses the coordinate descent method to compute the
+          Lasso solution (`linear_model.Lasso`). lasso_lars will be faster if
+          the estimated components are sparse;
+        * `'omp'`: uses orthogonal matching pursuit to estimate the sparse
+          solution;
+        * `'threshold'`: squashes to zero all coefficients less than
+          regularization from the projection `dictionary * data'`.
+
+    n_nonzero_coefs : int, default=None
+        Number of nonzero coefficients to target in each column of the
+        solution. This is only used by `algorithm='lars'` and `algorithm='omp'`
+        and is overridden by `alpha` in the `omp` case. If `None`, then
+        `n_nonzero_coefs=int(n_features / 10)`.
+
+    alpha : float, default=None
+        If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the
+        penalty applied to the L1 norm.
+        If `algorithm='threshold'`, `alpha` is the absolute value of the
+        threshold below which coefficients will be squashed to zero.
+        If `algorithm='omp'`, `alpha` is the tolerance parameter: the value of
+        the reconstruction error targeted. In this case, it overrides
+        `n_nonzero_coefs`.
+        If `None`, default to 1.
+
+    copy_cov : bool, default=True
+        Whether to copy the precomputed covariance matrix; if `False`, it may
+        be overwritten.
+
+    init : ndarray of shape (n_samples, n_components), default=None
+        Initialization value of the sparse codes. Only used if
+        `algorithm='lasso_cd'`.
+
+    max_iter : int, default=1000
+        Maximum number of iterations to perform if `algorithm='lasso_cd'` or
+        `'lasso_lars'`.
+
+    n_jobs : int, default=None
+        Number of parallel jobs to run.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    check_input : bool, default=True
+        If `False`, the input arrays X and dictionary will not be checked.
+
+    verbose : int, default=0
+        Controls the verbosity; the higher, the more messages.
+
+    positive : bool, default=False
+        Whether to enforce positivity when finding the encoding.
+
+        .. versionadded:: 0.20
+
+    Returns
+    -------
+    code : ndarray of shape (n_samples, n_components)
+        The sparse codes.
+
+    See Also
+    --------
+    sklearn.linear_model.lars_path : Compute Least Angle Regression or Lasso
+        path using LARS algorithm.
+    sklearn.linear_model.orthogonal_mp : Solves Orthogonal Matching Pursuit problems.
+    sklearn.linear_model.Lasso : Train Linear Model with L1 prior as regularizer.
+    SparseCoder : Find a sparse representation of data from a fixed precomputed
+        dictionary.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.decomposition import sparse_encode
+    >>> X = np.array([[-1, -1, -1], [0, 0, 3]])
+    >>> dictionary = np.array(
+    ...     [[0, 1, 0],
+    ...      [-1, -1, 2],
+    ...      [1, 1, 1],
+    ...      [0, 1, 1],
+    ...      [0, 2, 1]],
+    ...    dtype=np.float64
+    ... )
+    >>> sparse_encode(X, dictionary, alpha=1e-10)
+    array([[ 0.,  0., -1.,  0.,  0.],
+           [ 0.,  1.,  1.,  0.,  0.]])
+    """
+    if check_input:
+        if algorithm == "lasso_cd":
+            dictionary = check_array(
+                dictionary, order="C", dtype=[np.float64, np.float32]
+            )
+            X = check_array(X, order="C", dtype=[np.float64, np.float32])
+        else:
+            dictionary = check_array(dictionary)
+            X = check_array(X)
+
+    if dictionary.shape[1] != X.shape[1]:
+        raise ValueError(
+            "Dictionary and X have different numbers of features:"
+            "dictionary.shape: {} X.shape{}".format(dictionary.shape, X.shape)
+        )
+
+    _check_positive_coding(algorithm, positive)
+
+    return _sparse_encode(
+        X,
+        dictionary,
+        gram=gram,
+        cov=cov,
+        algorithm=algorithm,
+        n_nonzero_coefs=n_nonzero_coefs,
+        alpha=alpha,
+        copy_cov=copy_cov,
+        init=init,
+        max_iter=max_iter,
+        n_jobs=n_jobs,
+        verbose=verbose,
+        positive=positive,
+    )
+
+
+def _sparse_encode(
+    X,
+    dictionary,
+    *,
+    gram=None,
+    cov=None,
+    algorithm="lasso_lars",
+    n_nonzero_coefs=None,
+    alpha=None,
+    copy_cov=True,
+    init=None,
+    max_iter=1000,
+    n_jobs=None,
+    verbose=0,
+    positive=False,
+):
+    """Sparse coding without input/parameter validation."""
+
+    n_samples, n_features = X.shape
+    n_components = dictionary.shape[0]
+
+    if algorithm in ("lars", "omp"):
+        regularization = n_nonzero_coefs
+        if regularization is None:
+            regularization = min(max(n_features / 10, 1), n_components)
+    else:
+        regularization = alpha
+        if regularization is None:
+            regularization = 1.0
+
+    if gram is None and algorithm != "threshold":
+        gram = np.dot(dictionary, dictionary.T)
+
+    if cov is None and algorithm != "lasso_cd":
+        copy_cov = False
+        cov = np.dot(dictionary, X.T)
+
+    if effective_n_jobs(n_jobs) == 1 or algorithm == "threshold":
+        code = _sparse_encode_precomputed(
+            X,
+            dictionary,
+            gram=gram,
+            cov=cov,
+            algorithm=algorithm,
+            regularization=regularization,
+            copy_cov=copy_cov,
+            init=init,
+            max_iter=max_iter,
+            verbose=verbose,
+            positive=positive,
+        )
+        return code
+
+    # Enter parallel code block
+    n_samples = X.shape[0]
+    n_components = dictionary.shape[0]
+    code = np.empty((n_samples, n_components))
+    slices = list(gen_even_slices(n_samples, effective_n_jobs(n_jobs)))
+
+    code_views = Parallel(n_jobs=n_jobs, verbose=verbose)(
+        delayed(_sparse_encode_precomputed)(
+            X[this_slice],
+            dictionary,
+            gram=gram,
+            cov=cov[:, this_slice] if cov is not None else None,
+            algorithm=algorithm,
+            regularization=regularization,
+            copy_cov=copy_cov,
+            init=init[this_slice] if init is not None else None,
+            max_iter=max_iter,
+            verbose=verbose,
+            positive=positive,
+        )
+        for this_slice in slices
+    )
+    for this_slice, this_view in zip(slices, code_views):
+        code[this_slice] = this_view
+    return code
+
+
+def _update_dict(
+    dictionary,
+    Y,
+    code,
+    A=None,
+    B=None,
+    verbose=False,
+    random_state=None,
+    positive=False,
+):
+    """Update the dense dictionary factor in place.
+
+    Parameters
+    ----------
+    dictionary : ndarray of shape (n_components, n_features)
+        Value of the dictionary at the previous iteration.
+
+    Y : ndarray of shape (n_samples, n_features)
+        Data matrix.
+
+    code : ndarray of shape (n_samples, n_components)
+        Sparse coding of the data against which to optimize the dictionary.
+
+    A : ndarray of shape (n_components, n_components), default=None
+        Together with `B`, sufficient stats of the online model to update the
+        dictionary.
+
+    B : ndarray of shape (n_features, n_components), default=None
+        Together with `A`, sufficient stats of the online model to update the
+        dictionary.
+
+    verbose: bool, default=False
+        Degree of output the procedure will print.
+
+    random_state : int, RandomState instance or None, default=None
+        Used for randomly initializing the dictionary. Pass an int for
+        reproducible results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    positive : bool, default=False
+        Whether to enforce positivity when finding the dictionary.
+
+        .. versionadded:: 0.20
+    """
+    n_samples, n_components = code.shape
+    random_state = check_random_state(random_state)
+
+    if A is None:
+        A = code.T @ code
+    if B is None:
+        B = Y.T @ code
+
+    n_unused = 0
+
+    for k in range(n_components):
+        if A[k, k] > 1e-6:
+            # 1e-6 is arbitrary but consistent with the spams implementation
+            dictionary[k] += (B[:, k] - A[k] @ dictionary) / A[k, k]
+        else:
+            # kth atom is almost never used -> sample a new one from the data
+            newd = Y[random_state.choice(n_samples)]
+
+            # add small noise to avoid making the sparse coding ill conditioned
+            noise_level = 0.01 * (newd.std() or 1)  # avoid 0 std
+            noise = random_state.normal(0, noise_level, size=len(newd))
+
+            dictionary[k] = newd + noise
+            code[:, k] = 0
+            n_unused += 1
+
+        if positive:
+            np.clip(dictionary[k], 0, None, out=dictionary[k])
+
+        # Projection on the constraint set ||V_k|| <= 1
+        dictionary[k] /= max(linalg.norm(dictionary[k]), 1)
+
+    if verbose and n_unused > 0:
+        print(f"{n_unused} unused atoms resampled.")
+
+
+def _dict_learning(
+    X,
+    n_components,
+    *,
+    alpha,
+    max_iter,
+    tol,
+    method,
+    n_jobs,
+    dict_init,
+    code_init,
+    callback,
+    verbose,
+    random_state,
+    return_n_iter,
+    positive_dict,
+    positive_code,
+    method_max_iter,
+):
+    """Main dictionary learning algorithm"""
+    t0 = time.time()
+    # Init the code and the dictionary with SVD of Y
+    if code_init is not None and dict_init is not None:
+        code = np.array(code_init, order="F")
+        # Don't copy V, it will happen below
+        dictionary = dict_init
+    else:
+        code, S, dictionary = linalg.svd(X, full_matrices=False)
+        # flip the initial code's sign to enforce deterministic output
+        code, dictionary = svd_flip(code, dictionary)
+        dictionary = S[:, np.newaxis] * dictionary
+    r = len(dictionary)
+    if n_components <= r:  # True even if n_components=None
+        code = code[:, :n_components]
+        dictionary = dictionary[:n_components, :]
+    else:
+        code = np.c_[code, np.zeros((len(code), n_components - r))]
+        dictionary = np.r_[
+            dictionary, np.zeros((n_components - r, dictionary.shape[1]))
+        ]
+
+    # Fortran-order dict better suited for the sparse coding which is the
+    # bottleneck of this algorithm.
+    dictionary = np.asfortranarray(dictionary)
+
+    errors = []
+    current_cost = np.nan
+
+    if verbose == 1:
+        print("[dict_learning]", end=" ")
+
+    # If max_iter is 0, number of iterations returned should be zero
+    ii = -1
+
+    for ii in range(max_iter):
+        dt = time.time() - t0
+        if verbose == 1:
+            sys.stdout.write(".")
+            sys.stdout.flush()
+        elif verbose:
+            print(
+                "Iteration % 3i (elapsed time: % 3is, % 4.1fmn, current cost % 7.3f)"
+                % (ii, dt, dt / 60, current_cost)
+            )
+
+        # Update code
+        code = sparse_encode(
+            X,
+            dictionary,
+            algorithm=method,
+            alpha=alpha,
+            init=code,
+            n_jobs=n_jobs,
+            positive=positive_code,
+            max_iter=method_max_iter,
+            verbose=verbose,
+        )
+
+        # Update dictionary in place
+        _update_dict(
+            dictionary,
+            X,
+            code,
+            verbose=verbose,
+            random_state=random_state,
+            positive=positive_dict,
+        )
+
+        # Cost function
+        current_cost = 0.5 * np.sum((X - code @ dictionary) ** 2) + alpha * np.sum(
+            np.abs(code)
+        )
+        errors.append(current_cost)
+
+        if ii > 0:
+            dE = errors[-2] - errors[-1]
+            # assert(dE >= -tol * errors[-1])
+            if dE < tol * errors[-1]:
+                if verbose == 1:
+                    # A line return
+                    print("")
+                elif verbose:
+                    print("--- Convergence reached after %d iterations" % ii)
+                break
+        if ii % 5 == 0 and callback is not None:
+            callback(locals())
+
+    if return_n_iter:
+        return code, dictionary, errors, ii + 1
+    else:
+        return code, dictionary, errors
+
+
+def dict_learning_online(
+    X,
+    n_components=2,
+    *,
+    alpha=1,
+    max_iter=100,
+    return_code=True,
+    dict_init=None,
+    callback=None,
+    batch_size=256,
+    verbose=False,
+    shuffle=True,
+    n_jobs=None,
+    method="lars",
+    random_state=None,
+    positive_dict=False,
+    positive_code=False,
+    method_max_iter=1000,
+    tol=1e-3,
+    max_no_improvement=10,
+):
+    """Solve a dictionary learning matrix factorization problem online.
+
+    Finds the best dictionary and the corresponding sparse code for
+    approximating the data matrix X by solving::
+
+        (U^*, V^*) = argmin 0.5 || X - U V ||_Fro^2 + alpha * || U ||_1,1
+                     (U,V)
+                     with || V_k ||_2 = 1 for all  0 <= k < n_components
+
+    where V is the dictionary and U is the sparse code. ||.||_Fro stands for
+    the Frobenius norm and ||.||_1,1 stands for the entry-wise matrix norm
+    which is the sum of the absolute values of all the entries in the matrix.
+    This is accomplished by repeatedly iterating over mini-batches by slicing
+    the input data.
+
+    Read more in the :ref:`User Guide <DictionaryLearning>`.
+
+    Parameters
+    ----------
+    X : ndarray of shape (n_samples, n_features)
+        Data matrix.
+
+    n_components : int or None, default=2
+        Number of dictionary atoms to extract. If None, then ``n_components``
+        is set to ``n_features``.
+
+    alpha : float, default=1
+        Sparsity controlling parameter.
+
+    max_iter : int, default=100
+        Maximum number of iterations over the complete dataset before
+        stopping independently of any early stopping criterion heuristics.
+
+        .. versionadded:: 1.1
+
+        .. deprecated:: 1.4
+           `max_iter=None` is deprecated in 1.4 and will be removed in 1.6.
+           Use the default value (i.e. `100`) instead.
+
+    return_code : bool, default=True
+        Whether to also return the code U or just the dictionary `V`.
+
+    dict_init : ndarray of shape (n_components, n_features), default=None
+        Initial values for the dictionary for warm restart scenarios.
+        If `None`, the initial values for the dictionary are created
+        with an SVD decomposition of the data via
+        :func:`~sklearn.utils.extmath.randomized_svd`.
+
+    callback : callable, default=None
+        A callable that gets invoked at the end of each iteration.
+
+    batch_size : int, default=256
+        The number of samples to take in each batch.
+
+        .. versionchanged:: 1.3
+           The default value of `batch_size` changed from 3 to 256 in version 1.3.
+
+    verbose : bool, default=False
+        To control the verbosity of the procedure.
+
+    shuffle : bool, default=True
+        Whether to shuffle the data before splitting it in batches.
+
+    n_jobs : int, default=None
+        Number of parallel jobs to run.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    method : {'lars', 'cd'}, default='lars'
+        * `'lars'`: uses the least angle regression method to solve the lasso
+          problem (`linear_model.lars_path`);
+        * `'cd'`: uses the coordinate descent method to compute the
+          Lasso solution (`linear_model.Lasso`). Lars will be faster if
+          the estimated components are sparse.
+
+    random_state : int, RandomState instance or None, default=None
+        Used for initializing the dictionary when ``dict_init`` is not
+        specified, randomly shuffling the data when ``shuffle`` is set to
+        ``True``, and updating the dictionary. Pass an int for reproducible
+        results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    positive_dict : bool, default=False
+        Whether to enforce positivity when finding the dictionary.
+
+        .. versionadded:: 0.20
+
+    positive_code : bool, default=False
+        Whether to enforce positivity when finding the code.
+
+        .. versionadded:: 0.20
+
+    method_max_iter : int, default=1000
+        Maximum number of iterations to perform when solving the lasso problem.
+
+        .. versionadded:: 0.22
+
+    tol : float, default=1e-3
+        Control early stopping based on the norm of the differences in the
+        dictionary between 2 steps.
+
+        To disable early stopping based on changes in the dictionary, set
+        `tol` to 0.0.
+
+        .. versionadded:: 1.1
+
+    max_no_improvement : int, default=10
+        Control early stopping based on the consecutive number of mini batches
+        that does not yield an improvement on the smoothed cost function.
+
+        To disable convergence detection based on cost function, set
+        `max_no_improvement` to None.
+
+        .. versionadded:: 1.1
+
+    Returns
+    -------
+    code : ndarray of shape (n_samples, n_components),
+        The sparse code (only returned if `return_code=True`).
+
+    dictionary : ndarray of shape (n_components, n_features),
+        The solutions to the dictionary learning problem.
+
+    n_iter : int
+        Number of iterations run. Returned only if `return_n_iter` is
+        set to `True`.
+
+    See Also
+    --------
+    dict_learning : Solve a dictionary learning matrix factorization problem.
+    DictionaryLearning : Find a dictionary that sparsely encodes data.
+    MiniBatchDictionaryLearning : A faster, less accurate, version of the dictionary
+        learning algorithm.
+    SparsePCA : Sparse Principal Components Analysis.
+    MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.datasets import make_sparse_coded_signal
+    >>> from sklearn.decomposition import dict_learning_online
+    >>> X, _, _ = make_sparse_coded_signal(
+    ...     n_samples=30, n_components=15, n_features=20, n_nonzero_coefs=10,
+    ...     random_state=42,
+    ... )
+    >>> U, V = dict_learning_online(
+    ...     X, n_components=15, alpha=0.2, max_iter=20, batch_size=3, random_state=42
+    ... )
+
+    We can check the level of sparsity of `U`:
+
+    >>> np.mean(U == 0)
+    0.53...
+
+    We can compare the average squared euclidean norm of the reconstruction
+    error of the sparse coded signal relative to the squared euclidean norm of
+    the original signal:
+
+    >>> X_hat = U @ V
+    >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1))
+    0.05...
+    """
+    # TODO(1.6): remove in 1.6
+    if max_iter is None:
+        warn(
+            (
+                "`max_iter=None` is deprecated in version 1.4 and will be removed in "
+                "version 1.6. Use the default value (i.e. `100`) instead."
+            ),
+            FutureWarning,
+        )
+        max_iter = 100
+
+    transform_algorithm = "lasso_" + method
+
+    est = MiniBatchDictionaryLearning(
+        n_components=n_components,
+        alpha=alpha,
+        max_iter=max_iter,
+        n_jobs=n_jobs,
+        fit_algorithm=method,
+        batch_size=batch_size,
+        shuffle=shuffle,
+        dict_init=dict_init,
+        random_state=random_state,
+        transform_algorithm=transform_algorithm,
+        transform_alpha=alpha,
+        positive_code=positive_code,
+        positive_dict=positive_dict,
+        transform_max_iter=method_max_iter,
+        verbose=verbose,
+        callback=callback,
+        tol=tol,
+        max_no_improvement=max_no_improvement,
+    ).fit(X)
+
+    if not return_code:
+        return est.components_
+    else:
+        code = est.transform(X)
+        return code, est.components_
+
+
+@validate_params(
+    {
+        "X": ["array-like"],
+        "method": [StrOptions({"lars", "cd"})],
+        "return_n_iter": ["boolean"],
+        "method_max_iter": [Interval(Integral, 0, None, closed="left")],
+    },
+    prefer_skip_nested_validation=False,
+)
+def dict_learning(
+    X,
+    n_components,
+    *,
+    alpha,
+    max_iter=100,
+    tol=1e-8,
+    method="lars",
+    n_jobs=None,
+    dict_init=None,
+    code_init=None,
+    callback=None,
+    verbose=False,
+    random_state=None,
+    return_n_iter=False,
+    positive_dict=False,
+    positive_code=False,
+    method_max_iter=1000,
+):
+    """Solve a dictionary learning matrix factorization problem.
+
+    Finds the best dictionary and the corresponding sparse code for
+    approximating the data matrix X by solving::
+
+        (U^*, V^*) = argmin 0.5 || X - U V ||_Fro^2 + alpha * || U ||_1,1
+                     (U,V)
+                    with || V_k ||_2 = 1 for all  0 <= k < n_components
+
+    where V is the dictionary and U is the sparse code. ||.||_Fro stands for
+    the Frobenius norm and ||.||_1,1 stands for the entry-wise matrix norm
+    which is the sum of the absolute values of all the entries in the matrix.
+
+    Read more in the :ref:`User Guide <DictionaryLearning>`.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Data matrix.
+
+    n_components : int
+        Number of dictionary atoms to extract.
+
+    alpha : int or float
+        Sparsity controlling parameter.
+
+    max_iter : int, default=100
+        Maximum number of iterations to perform.
+
+    tol : float, default=1e-8
+        Tolerance for the stopping condition.
+
+    method : {'lars', 'cd'}, default='lars'
+        The method used:
+
+        * `'lars'`: uses the least angle regression method to solve the lasso
+           problem (`linear_model.lars_path`);
+        * `'cd'`: uses the coordinate descent method to compute the
+          Lasso solution (`linear_model.Lasso`). Lars will be faster if
+          the estimated components are sparse.
+
+    n_jobs : int, default=None
+        Number of parallel jobs to run.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    dict_init : ndarray of shape (n_components, n_features), default=None
+        Initial value for the dictionary for warm restart scenarios. Only used
+        if `code_init` and `dict_init` are not None.
+
+    code_init : ndarray of shape (n_samples, n_components), default=None
+        Initial value for the sparse code for warm restart scenarios. Only used
+        if `code_init` and `dict_init` are not None.
+
+    callback : callable, default=None
+        Callable that gets invoked every five iterations.
+
+    verbose : bool, default=False
+        To control the verbosity of the procedure.
+
+    random_state : int, RandomState instance or None, default=None
+        Used for randomly initializing the dictionary. Pass an int for
+        reproducible results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    return_n_iter : bool, default=False
+        Whether or not to return the number of iterations.
+
+    positive_dict : bool, default=False
+        Whether to enforce positivity when finding the dictionary.
+
+        .. versionadded:: 0.20
+
+    positive_code : bool, default=False
+        Whether to enforce positivity when finding the code.
+
+        .. versionadded:: 0.20
+
+    method_max_iter : int, default=1000
+        Maximum number of iterations to perform.
+
+        .. versionadded:: 0.22
+
+    Returns
+    -------
+    code : ndarray of shape (n_samples, n_components)
+        The sparse code factor in the matrix factorization.
+
+    dictionary : ndarray of shape (n_components, n_features),
+        The dictionary factor in the matrix factorization.
+
+    errors : array
+        Vector of errors at each iteration.
+
+    n_iter : int
+        Number of iterations run. Returned only if `return_n_iter` is
+        set to True.
+
+    See Also
+    --------
+    dict_learning_online : Solve a dictionary learning matrix factorization
+        problem online.
+    DictionaryLearning : Find a dictionary that sparsely encodes data.
+    MiniBatchDictionaryLearning : A faster, less accurate version
+        of the dictionary learning algorithm.
+    SparsePCA : Sparse Principal Components Analysis.
+    MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.datasets import make_sparse_coded_signal
+    >>> from sklearn.decomposition import dict_learning
+    >>> X, _, _ = make_sparse_coded_signal(
+    ...     n_samples=30, n_components=15, n_features=20, n_nonzero_coefs=10,
+    ...     random_state=42,
+    ... )
+    >>> U, V, errors = dict_learning(X, n_components=15, alpha=0.1, random_state=42)
+
+    We can check the level of sparsity of `U`:
+
+    >>> np.mean(U == 0)
+    0.6...
+
+    We can compare the average squared euclidean norm of the reconstruction
+    error of the sparse coded signal relative to the squared euclidean norm of
+    the original signal:
+
+    >>> X_hat = U @ V
+    >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1))
+    0.01...
+    """
+    estimator = DictionaryLearning(
+        n_components=n_components,
+        alpha=alpha,
+        max_iter=max_iter,
+        tol=tol,
+        fit_algorithm=method,
+        n_jobs=n_jobs,
+        dict_init=dict_init,
+        callback=callback,
+        code_init=code_init,
+        verbose=verbose,
+        random_state=random_state,
+        positive_code=positive_code,
+        positive_dict=positive_dict,
+        transform_max_iter=method_max_iter,
+    ).set_output(transform="default")
+    code = estimator.fit_transform(X)
+    if return_n_iter:
+        return (
+            code,
+            estimator.components_,
+            estimator.error_,
+            estimator.n_iter_,
+        )
+    return code, estimator.components_, estimator.error_
+
+
+class _BaseSparseCoding(ClassNamePrefixFeaturesOutMixin, TransformerMixin):
+    """Base class from SparseCoder and DictionaryLearning algorithms."""
+
+    def __init__(
+        self,
+        transform_algorithm,
+        transform_n_nonzero_coefs,
+        transform_alpha,
+        split_sign,
+        n_jobs,
+        positive_code,
+        transform_max_iter,
+    ):
+        self.transform_algorithm = transform_algorithm
+        self.transform_n_nonzero_coefs = transform_n_nonzero_coefs
+        self.transform_alpha = transform_alpha
+        self.transform_max_iter = transform_max_iter
+        self.split_sign = split_sign
+        self.n_jobs = n_jobs
+        self.positive_code = positive_code
+
+    def _transform(self, X, dictionary):
+        """Private method allowing to accommodate both DictionaryLearning and
+        SparseCoder."""
+        X = self._validate_data(X, reset=False)
+
+        if hasattr(self, "alpha") and self.transform_alpha is None:
+            transform_alpha = self.alpha
+        else:
+            transform_alpha = self.transform_alpha
+
+        code = sparse_encode(
+            X,
+            dictionary,
+            algorithm=self.transform_algorithm,
+            n_nonzero_coefs=self.transform_n_nonzero_coefs,
+            alpha=transform_alpha,
+            max_iter=self.transform_max_iter,
+            n_jobs=self.n_jobs,
+            positive=self.positive_code,
+        )
+
+        if self.split_sign:
+            # feature vector is split into a positive and negative side
+            n_samples, n_features = code.shape
+            split_code = np.empty((n_samples, 2 * n_features))
+            split_code[:, :n_features] = np.maximum(code, 0)
+            split_code[:, n_features:] = -np.minimum(code, 0)
+            code = split_code
+
+        return code
+
+    def transform(self, X):
+        """Encode the data as a sparse combination of the dictionary atoms.
+
+        Coding method is determined by the object parameter
+        `transform_algorithm`.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test data to be transformed, must have the same number of
+            features as the data used to train the model.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Transformed data.
+        """
+        check_is_fitted(self)
+        return self._transform(X, self.components_)
+
+
+class SparseCoder(_BaseSparseCoding, BaseEstimator):
+    """Sparse coding.
+
+    Finds a sparse representation of data against a fixed, precomputed
+    dictionary.
+
+    Each row of the result is the solution to a sparse coding problem.
+    The goal is to find a sparse array `code` such that::
+
+        X ~= code * dictionary
+
+    Read more in the :ref:`User Guide <SparseCoder>`.
+
+    Parameters
+    ----------
+    dictionary : ndarray of shape (n_components, n_features)
+        The dictionary atoms used for sparse coding. Lines are assumed to be
+        normalized to unit norm.
+
+    transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', \
+            'threshold'}, default='omp'
+        Algorithm used to transform the data:
+
+        - `'lars'`: uses the least angle regression method
+          (`linear_model.lars_path`);
+        - `'lasso_lars'`: uses Lars to compute the Lasso solution;
+        - `'lasso_cd'`: uses the coordinate descent method to compute the
+          Lasso solution (linear_model.Lasso). `'lasso_lars'` will be faster if
+          the estimated components are sparse;
+        - `'omp'`: uses orthogonal matching pursuit to estimate the sparse
+          solution;
+        - `'threshold'`: squashes to zero all coefficients less than alpha from
+          the projection ``dictionary * X'``.
+
+    transform_n_nonzero_coefs : int, default=None
+        Number of nonzero coefficients to target in each column of the
+        solution. This is only used by `algorithm='lars'` and `algorithm='omp'`
+        and is overridden by `alpha` in the `omp` case. If `None`, then
+        `transform_n_nonzero_coefs=int(n_features / 10)`.
+
+    transform_alpha : float, default=None
+        If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the
+        penalty applied to the L1 norm.
+        If `algorithm='threshold'`, `alpha` is the absolute value of the
+        threshold below which coefficients will be squashed to zero.
+        If `algorithm='omp'`, `alpha` is the tolerance parameter: the value of
+        the reconstruction error targeted. In this case, it overrides
+        `n_nonzero_coefs`.
+        If `None`, default to 1.
+
+    split_sign : bool, default=False
+        Whether to split the sparse feature vector into the concatenation of
+        its negative part and its positive part. This can improve the
+        performance of downstream classifiers.
+
+    n_jobs : int, default=None
+        Number of parallel jobs to run.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    positive_code : bool, default=False
+        Whether to enforce positivity when finding the code.
+
+        .. versionadded:: 0.20
+
+    transform_max_iter : int, default=1000
+        Maximum number of iterations to perform if `algorithm='lasso_cd'` or
+        `lasso_lars`.
+
+        .. versionadded:: 0.22
+
+    Attributes
+    ----------
+    n_components_ : int
+        Number of atoms.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    DictionaryLearning : Find a dictionary that sparsely encodes data.
+    MiniBatchDictionaryLearning : A faster, less accurate, version of the
+        dictionary learning algorithm.
+    MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis.
+    SparsePCA : Sparse Principal Components Analysis.
+    sparse_encode : Sparse coding where each row of the result is the solution
+        to a sparse coding problem.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.decomposition import SparseCoder
+    >>> X = np.array([[-1, -1, -1], [0, 0, 3]])
+    >>> dictionary = np.array(
+    ...     [[0, 1, 0],
+    ...      [-1, -1, 2],
+    ...      [1, 1, 1],
+    ...      [0, 1, 1],
+    ...      [0, 2, 1]],
+    ...    dtype=np.float64
+    ... )
+    >>> coder = SparseCoder(
+    ...     dictionary=dictionary, transform_algorithm='lasso_lars',
+    ...     transform_alpha=1e-10,
+    ... )
+    >>> coder.transform(X)
+    array([[ 0.,  0., -1.,  0.,  0.],
+           [ 0.,  1.,  1.,  0.,  0.]])
+    """
+
+    _required_parameters = ["dictionary"]
+
+    def __init__(
+        self,
+        dictionary,
+        *,
+        transform_algorithm="omp",
+        transform_n_nonzero_coefs=None,
+        transform_alpha=None,
+        split_sign=False,
+        n_jobs=None,
+        positive_code=False,
+        transform_max_iter=1000,
+    ):
+        super().__init__(
+            transform_algorithm,
+            transform_n_nonzero_coefs,
+            transform_alpha,
+            split_sign,
+            n_jobs,
+            positive_code,
+            transform_max_iter,
+        )
+        self.dictionary = dictionary
+
+    def fit(self, X, y=None):
+        """Do nothing and return the estimator unchanged.
+
+        This method is just there to implement the usual API and hence
+        work in pipelines.
+
+        Parameters
+        ----------
+        X : Ignored
+            Not used, present for API consistency by convention.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        return self
+
+    def transform(self, X, y=None):
+        """Encode the data as a sparse combination of the dictionary atoms.
+
+        Coding method is determined by the object parameter
+        `transform_algorithm`.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Transformed data.
+        """
+        return super()._transform(X, self.dictionary)
+
+    def _more_tags(self):
+        return {
+            "requires_fit": False,
+            "preserves_dtype": [np.float64, np.float32],
+        }
+
+    @property
+    def n_components_(self):
+        """Number of atoms."""
+        return self.dictionary.shape[0]
+
+    @property
+    def n_features_in_(self):
+        """Number of features seen during `fit`."""
+        return self.dictionary.shape[1]
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.n_components_
+
+
+class DictionaryLearning(_BaseSparseCoding, BaseEstimator):
+    """Dictionary learning.
+
+    Finds a dictionary (a set of atoms) that performs well at sparsely
+    encoding the fitted data.
+
+    Solves the optimization problem::
+
+        (U^*,V^*) = argmin 0.5 || X - U V ||_Fro^2 + alpha * || U ||_1,1
+                    (U,V)
+                    with || V_k ||_2 <= 1 for all  0 <= k < n_components
+
+    ||.||_Fro stands for the Frobenius norm and ||.||_1,1 stands for
+    the entry-wise matrix norm which is the sum of the absolute values
+    of all the entries in the matrix.
+
+    Read more in the :ref:`User Guide <DictionaryLearning>`.
+
+    Parameters
+    ----------
+    n_components : int, default=None
+        Number of dictionary elements to extract. If None, then ``n_components``
+        is set to ``n_features``.
+
+    alpha : float, default=1.0
+        Sparsity controlling parameter.
+
+    max_iter : int, default=1000
+        Maximum number of iterations to perform.
+
+    tol : float, default=1e-8
+        Tolerance for numerical error.
+
+    fit_algorithm : {'lars', 'cd'}, default='lars'
+        * `'lars'`: uses the least angle regression method to solve the lasso
+          problem (:func:`~sklearn.linear_model.lars_path`);
+        * `'cd'`: uses the coordinate descent method to compute the
+          Lasso solution (:class:`~sklearn.linear_model.Lasso`). Lars will be
+          faster if the estimated components are sparse.
+
+        .. versionadded:: 0.17
+           *cd* coordinate descent method to improve speed.
+
+    transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', \
+            'threshold'}, default='omp'
+        Algorithm used to transform the data:
+
+        - `'lars'`: uses the least angle regression method
+          (:func:`~sklearn.linear_model.lars_path`);
+        - `'lasso_lars'`: uses Lars to compute the Lasso solution.
+        - `'lasso_cd'`: uses the coordinate descent method to compute the
+          Lasso solution (:class:`~sklearn.linear_model.Lasso`). `'lasso_lars'`
+          will be faster if the estimated components are sparse.
+        - `'omp'`: uses orthogonal matching pursuit to estimate the sparse
+          solution.
+        - `'threshold'`: squashes to zero all coefficients less than alpha from
+          the projection ``dictionary * X'``.
+
+        .. versionadded:: 0.17
+           *lasso_cd* coordinate descent method to improve speed.
+
+    transform_n_nonzero_coefs : int, default=None
+        Number of nonzero coefficients to target in each column of the
+        solution. This is only used by `algorithm='lars'` and
+        `algorithm='omp'`. If `None`, then
+        `transform_n_nonzero_coefs=int(n_features / 10)`.
+
+    transform_alpha : float, default=None
+        If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the
+        penalty applied to the L1 norm.
+        If `algorithm='threshold'`, `alpha` is the absolute value of the
+        threshold below which coefficients will be squashed to zero.
+        If `None`, defaults to `alpha`.
+
+        .. versionchanged:: 1.2
+            When None, default value changed from 1.0 to `alpha`.
+
+    n_jobs : int or None, default=None
+        Number of parallel jobs to run.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    code_init : ndarray of shape (n_samples, n_components), default=None
+        Initial value for the code, for warm restart. Only used if `code_init`
+        and `dict_init` are not None.
+
+    dict_init : ndarray of shape (n_components, n_features), default=None
+        Initial values for the dictionary, for warm restart. Only used if
+        `code_init` and `dict_init` are not None.
+
+    callback : callable, default=None
+        Callable that gets invoked every five iterations.
+
+        .. versionadded:: 1.3
+
+    verbose : bool, default=False
+        To control the verbosity of the procedure.
+
+    split_sign : bool, default=False
+        Whether to split the sparse feature vector into the concatenation of
+        its negative part and its positive part. This can improve the
+        performance of downstream classifiers.
+
+    random_state : int, RandomState instance or None, default=None
+        Used for initializing the dictionary when ``dict_init`` is not
+        specified, randomly shuffling the data when ``shuffle`` is set to
+        ``True``, and updating the dictionary. Pass an int for reproducible
+        results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    positive_code : bool, default=False
+        Whether to enforce positivity when finding the code.
+
+        .. versionadded:: 0.20
+
+    positive_dict : bool, default=False
+        Whether to enforce positivity when finding the dictionary.
+
+        .. versionadded:: 0.20
+
+    transform_max_iter : int, default=1000
+        Maximum number of iterations to perform if `algorithm='lasso_cd'` or
+        `'lasso_lars'`.
+
+        .. versionadded:: 0.22
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        dictionary atoms extracted from the data
+
+    error_ : array
+        vector of errors at each iteration
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : int
+        Number of iterations run.
+
+    See Also
+    --------
+    MiniBatchDictionaryLearning: A faster, less accurate, version of the
+        dictionary learning algorithm.
+    MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis.
+    SparseCoder : Find a sparse representation of data from a fixed,
+        precomputed dictionary.
+    SparsePCA : Sparse Principal Components Analysis.
+
+    References
+    ----------
+
+    J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009: Online dictionary learning
+    for sparse coding (https://www.di.ens.fr/sierra/pdfs/icml09.pdf)
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.datasets import make_sparse_coded_signal
+    >>> from sklearn.decomposition import DictionaryLearning
+    >>> X, dictionary, code = make_sparse_coded_signal(
+    ...     n_samples=30, n_components=15, n_features=20, n_nonzero_coefs=10,
+    ...     random_state=42,
+    ... )
+    >>> dict_learner = DictionaryLearning(
+    ...     n_components=15, transform_algorithm='lasso_lars', transform_alpha=0.1,
+    ...     random_state=42,
+    ... )
+    >>> X_transformed = dict_learner.fit(X).transform(X)
+
+    We can check the level of sparsity of `X_transformed`:
+
+    >>> np.mean(X_transformed == 0)
+    0.52...
+
+    We can compare the average squared euclidean norm of the reconstruction
+    error of the sparse coded signal relative to the squared euclidean norm of
+    the original signal:
+
+    >>> X_hat = X_transformed @ dict_learner.components_
+    >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1))
+    0.05...
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [Interval(Integral, 1, None, closed="left"), None],
+        "alpha": [Interval(Real, 0, None, closed="left")],
+        "max_iter": [Interval(Integral, 0, None, closed="left")],
+        "tol": [Interval(Real, 0, None, closed="left")],
+        "fit_algorithm": [StrOptions({"lars", "cd"})],
+        "transform_algorithm": [
+            StrOptions({"lasso_lars", "lasso_cd", "lars", "omp", "threshold"})
+        ],
+        "transform_n_nonzero_coefs": [Interval(Integral, 1, None, closed="left"), None],
+        "transform_alpha": [Interval(Real, 0, None, closed="left"), None],
+        "n_jobs": [Integral, None],
+        "code_init": [np.ndarray, None],
+        "dict_init": [np.ndarray, None],
+        "callback": [callable, None],
+        "verbose": ["verbose"],
+        "split_sign": ["boolean"],
+        "random_state": ["random_state"],
+        "positive_code": ["boolean"],
+        "positive_dict": ["boolean"],
+        "transform_max_iter": [Interval(Integral, 0, None, closed="left")],
+    }
+
+    def __init__(
+        self,
+        n_components=None,
+        *,
+        alpha=1,
+        max_iter=1000,
+        tol=1e-8,
+        fit_algorithm="lars",
+        transform_algorithm="omp",
+        transform_n_nonzero_coefs=None,
+        transform_alpha=None,
+        n_jobs=None,
+        code_init=None,
+        dict_init=None,
+        callback=None,
+        verbose=False,
+        split_sign=False,
+        random_state=None,
+        positive_code=False,
+        positive_dict=False,
+        transform_max_iter=1000,
+    ):
+        super().__init__(
+            transform_algorithm,
+            transform_n_nonzero_coefs,
+            transform_alpha,
+            split_sign,
+            n_jobs,
+            positive_code,
+            transform_max_iter,
+        )
+        self.n_components = n_components
+        self.alpha = alpha
+        self.max_iter = max_iter
+        self.tol = tol
+        self.fit_algorithm = fit_algorithm
+        self.code_init = code_init
+        self.dict_init = dict_init
+        self.callback = callback
+        self.verbose = verbose
+        self.random_state = random_state
+        self.positive_dict = positive_dict
+
+    def fit(self, X, y=None):
+        """Fit the model from data in X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        self.fit_transform(X)
+        return self
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit_transform(self, X, y=None):
+        """Fit the model from data in X and return the transformed data.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        V : ndarray of shape (n_samples, n_components)
+            Transformed data.
+        """
+        _check_positive_coding(method=self.fit_algorithm, positive=self.positive_code)
+
+        method = "lasso_" + self.fit_algorithm
+
+        random_state = check_random_state(self.random_state)
+        X = self._validate_data(X)
+
+        if self.n_components is None:
+            n_components = X.shape[1]
+        else:
+            n_components = self.n_components
+
+        V, U, E, self.n_iter_ = _dict_learning(
+            X,
+            n_components,
+            alpha=self.alpha,
+            tol=self.tol,
+            max_iter=self.max_iter,
+            method=method,
+            method_max_iter=self.transform_max_iter,
+            n_jobs=self.n_jobs,
+            code_init=self.code_init,
+            dict_init=self.dict_init,
+            callback=self.callback,
+            verbose=self.verbose,
+            random_state=random_state,
+            return_n_iter=True,
+            positive_dict=self.positive_dict,
+            positive_code=self.positive_code,
+        )
+        self.components_ = U
+        self.error_ = E
+
+        return V
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.components_.shape[0]
+
+    def _more_tags(self):
+        return {
+            "preserves_dtype": [np.float64, np.float32],
+        }
+
+
+class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):
+    """Mini-batch dictionary learning.
+
+    Finds a dictionary (a set of atoms) that performs well at sparsely
+    encoding the fitted data.
+
+    Solves the optimization problem::
+
+       (U^*,V^*) = argmin 0.5 || X - U V ||_Fro^2 + alpha * || U ||_1,1
+                    (U,V)
+                    with || V_k ||_2 <= 1 for all  0 <= k < n_components
+
+    ||.||_Fro stands for the Frobenius norm and ||.||_1,1 stands for
+    the entry-wise matrix norm which is the sum of the absolute values
+    of all the entries in the matrix.
+
+    Read more in the :ref:`User Guide <DictionaryLearning>`.
+
+    Parameters
+    ----------
+    n_components : int, default=None
+        Number of dictionary elements to extract.
+
+    alpha : float, default=1
+        Sparsity controlling parameter.
+
+    max_iter : int, default=1_000
+        Maximum number of iterations over the complete dataset before
+        stopping independently of any early stopping criterion heuristics.
+
+        .. versionadded:: 1.1
+
+        .. deprecated:: 1.4
+           `max_iter=None` is deprecated in 1.4 and will be removed in 1.6.
+           Use the default value (i.e. `1_000`) instead.
+
+    fit_algorithm : {'lars', 'cd'}, default='lars'
+        The algorithm used:
+
+        - `'lars'`: uses the least angle regression method to solve the lasso
+          problem (`linear_model.lars_path`)
+        - `'cd'`: uses the coordinate descent method to compute the
+          Lasso solution (`linear_model.Lasso`). Lars will be faster if
+          the estimated components are sparse.
+
+    n_jobs : int, default=None
+        Number of parallel jobs to run.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    batch_size : int, default=256
+        Number of samples in each mini-batch.
+
+        .. versionchanged:: 1.3
+           The default value of `batch_size` changed from 3 to 256 in version 1.3.
+
+    shuffle : bool, default=True
+        Whether to shuffle the samples before forming batches.
+
+    dict_init : ndarray of shape (n_components, n_features), default=None
+        Initial value of the dictionary for warm restart scenarios.
+
+    transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', \
+            'threshold'}, default='omp'
+        Algorithm used to transform the data:
+
+        - `'lars'`: uses the least angle regression method
+          (`linear_model.lars_path`);
+        - `'lasso_lars'`: uses Lars to compute the Lasso solution.
+        - `'lasso_cd'`: uses the coordinate descent method to compute the
+          Lasso solution (`linear_model.Lasso`). `'lasso_lars'` will be faster
+          if the estimated components are sparse.
+        - `'omp'`: uses orthogonal matching pursuit to estimate the sparse
+          solution.
+        - `'threshold'`: squashes to zero all coefficients less than alpha from
+          the projection ``dictionary * X'``.
+
+    transform_n_nonzero_coefs : int, default=None
+        Number of nonzero coefficients to target in each column of the
+        solution. This is only used by `algorithm='lars'` and
+        `algorithm='omp'`. If `None`, then
+        `transform_n_nonzero_coefs=int(n_features / 10)`.
+
+    transform_alpha : float, default=None
+        If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the
+        penalty applied to the L1 norm.
+        If `algorithm='threshold'`, `alpha` is the absolute value of the
+        threshold below which coefficients will be squashed to zero.
+        If `None`, defaults to `alpha`.
+
+        .. versionchanged:: 1.2
+            When None, default value changed from 1.0 to `alpha`.
+
+    verbose : bool or int, default=False
+        To control the verbosity of the procedure.
+
+    split_sign : bool, default=False
+        Whether to split the sparse feature vector into the concatenation of
+        its negative part and its positive part. This can improve the
+        performance of downstream classifiers.
+
+    random_state : int, RandomState instance or None, default=None
+        Used for initializing the dictionary when ``dict_init`` is not
+        specified, randomly shuffling the data when ``shuffle`` is set to
+        ``True``, and updating the dictionary. Pass an int for reproducible
+        results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    positive_code : bool, default=False
+        Whether to enforce positivity when finding the code.
+
+        .. versionadded:: 0.20
+
+    positive_dict : bool, default=False
+        Whether to enforce positivity when finding the dictionary.
+
+        .. versionadded:: 0.20
+
+    transform_max_iter : int, default=1000
+        Maximum number of iterations to perform if `algorithm='lasso_cd'` or
+        `'lasso_lars'`.
+
+        .. versionadded:: 0.22
+
+    callback : callable, default=None
+        A callable that gets invoked at the end of each iteration.
+
+        .. versionadded:: 1.1
+
+    tol : float, default=1e-3
+        Control early stopping based on the norm of the differences in the
+        dictionary between 2 steps.
+
+        To disable early stopping based on changes in the dictionary, set
+        `tol` to 0.0.
+
+        .. versionadded:: 1.1
+
+    max_no_improvement : int, default=10
+        Control early stopping based on the consecutive number of mini batches
+        that does not yield an improvement on the smoothed cost function.
+
+        To disable convergence detection based on cost function, set
+        `max_no_improvement` to None.
+
+        .. versionadded:: 1.1
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        Components extracted from the data.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : int
+        Number of iterations over the full dataset.
+
+    n_steps_ : int
+        Number of mini-batches processed.
+
+        .. versionadded:: 1.1
+
+    See Also
+    --------
+    DictionaryLearning : Find a dictionary that sparsely encodes data.
+    MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis.
+    SparseCoder : Find a sparse representation of data from a fixed,
+        precomputed dictionary.
+    SparsePCA : Sparse Principal Components Analysis.
+
+    References
+    ----------
+
+    J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009: Online dictionary learning
+    for sparse coding (https://www.di.ens.fr/sierra/pdfs/icml09.pdf)
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.datasets import make_sparse_coded_signal
+    >>> from sklearn.decomposition import MiniBatchDictionaryLearning
+    >>> X, dictionary, code = make_sparse_coded_signal(
+    ...     n_samples=30, n_components=15, n_features=20, n_nonzero_coefs=10,
+    ...     random_state=42)
+    >>> dict_learner = MiniBatchDictionaryLearning(
+    ...     n_components=15, batch_size=3, transform_algorithm='lasso_lars',
+    ...     transform_alpha=0.1, max_iter=20, random_state=42)
+    >>> X_transformed = dict_learner.fit_transform(X)
+
+    We can check the level of sparsity of `X_transformed`:
+
+    >>> np.mean(X_transformed == 0) > 0.5
+    True
+
+    We can compare the average squared euclidean norm of the reconstruction
+    error of the sparse coded signal relative to the squared euclidean norm of
+    the original signal:
+
+    >>> X_hat = X_transformed @ dict_learner.components_
+    >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1))
+    0.052...
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [Interval(Integral, 1, None, closed="left"), None],
+        "alpha": [Interval(Real, 0, None, closed="left")],
+        "max_iter": [Interval(Integral, 0, None, closed="left"), Hidden(None)],
+        "fit_algorithm": [StrOptions({"cd", "lars"})],
+        "n_jobs": [None, Integral],
+        "batch_size": [Interval(Integral, 1, None, closed="left")],
+        "shuffle": ["boolean"],
+        "dict_init": [None, np.ndarray],
+        "transform_algorithm": [
+            StrOptions({"lasso_lars", "lasso_cd", "lars", "omp", "threshold"})
+        ],
+        "transform_n_nonzero_coefs": [Interval(Integral, 1, None, closed="left"), None],
+        "transform_alpha": [Interval(Real, 0, None, closed="left"), None],
+        "verbose": ["verbose"],
+        "split_sign": ["boolean"],
+        "random_state": ["random_state"],
+        "positive_code": ["boolean"],
+        "positive_dict": ["boolean"],
+        "transform_max_iter": [Interval(Integral, 0, None, closed="left")],
+        "callback": [None, callable],
+        "tol": [Interval(Real, 0, None, closed="left")],
+        "max_no_improvement": [Interval(Integral, 0, None, closed="left"), None],
+    }
+
+    def __init__(
+        self,
+        n_components=None,
+        *,
+        alpha=1,
+        max_iter=1_000,
+        fit_algorithm="lars",
+        n_jobs=None,
+        batch_size=256,
+        shuffle=True,
+        dict_init=None,
+        transform_algorithm="omp",
+        transform_n_nonzero_coefs=None,
+        transform_alpha=None,
+        verbose=False,
+        split_sign=False,
+        random_state=None,
+        positive_code=False,
+        positive_dict=False,
+        transform_max_iter=1000,
+        callback=None,
+        tol=1e-3,
+        max_no_improvement=10,
+    ):
+        super().__init__(
+            transform_algorithm,
+            transform_n_nonzero_coefs,
+            transform_alpha,
+            split_sign,
+            n_jobs,
+            positive_code,
+            transform_max_iter,
+        )
+        self.n_components = n_components
+        self.alpha = alpha
+        self.max_iter = max_iter
+        self.fit_algorithm = fit_algorithm
+        self.dict_init = dict_init
+        self.verbose = verbose
+        self.shuffle = shuffle
+        self.batch_size = batch_size
+        self.split_sign = split_sign
+        self.random_state = random_state
+        self.positive_dict = positive_dict
+        self.callback = callback
+        self.max_no_improvement = max_no_improvement
+        self.tol = tol
+
+    def _check_params(self, X):
+        # n_components
+        self._n_components = self.n_components
+        if self._n_components is None:
+            self._n_components = X.shape[1]
+
+        # fit_algorithm
+        _check_positive_coding(self.fit_algorithm, self.positive_code)
+        self._fit_algorithm = "lasso_" + self.fit_algorithm
+
+        # batch_size
+        self._batch_size = min(self.batch_size, X.shape[0])
+
+    def _initialize_dict(self, X, random_state):
+        """Initialization of the dictionary."""
+        if self.dict_init is not None:
+            dictionary = self.dict_init
+        else:
+            # Init V with SVD of X
+            _, S, dictionary = randomized_svd(
+                X, self._n_components, random_state=random_state
+            )
+            dictionary = S[:, np.newaxis] * dictionary
+
+        if self._n_components <= len(dictionary):
+            dictionary = dictionary[: self._n_components, :]
+        else:
+            dictionary = np.concatenate(
+                (
+                    dictionary,
+                    np.zeros(
+                        (self._n_components - len(dictionary), dictionary.shape[1]),
+                        dtype=dictionary.dtype,
+                    ),
+                )
+            )
+
+        dictionary = check_array(dictionary, order="F", dtype=X.dtype, copy=False)
+        dictionary = np.require(dictionary, requirements="W")
+
+        return dictionary
+
+    def _update_inner_stats(self, X, code, batch_size, step):
+        """Update the inner stats inplace."""
+        if step < batch_size - 1:
+            theta = (step + 1) * batch_size
+        else:
+            theta = batch_size**2 + step + 1 - batch_size
+        beta = (theta + 1 - batch_size) / (theta + 1)
+
+        self._A *= beta
+        self._A += code.T @ code / batch_size
+        self._B *= beta
+        self._B += X.T @ code / batch_size
+
+    def _minibatch_step(self, X, dictionary, random_state, step):
+        """Perform the update on the dictionary for one minibatch."""
+        batch_size = X.shape[0]
+
+        # Compute code for this batch
+        code = _sparse_encode(
+            X,
+            dictionary,
+            algorithm=self._fit_algorithm,
+            alpha=self.alpha,
+            n_jobs=self.n_jobs,
+            positive=self.positive_code,
+            max_iter=self.transform_max_iter,
+            verbose=self.verbose,
+        )
+
+        batch_cost = (
+            0.5 * ((X - code @ dictionary) ** 2).sum()
+            + self.alpha * np.sum(np.abs(code))
+        ) / batch_size
+
+        # Update inner stats
+        self._update_inner_stats(X, code, batch_size, step)
+
+        # Update dictionary
+        _update_dict(
+            dictionary,
+            X,
+            code,
+            self._A,
+            self._B,
+            verbose=self.verbose,
+            random_state=random_state,
+            positive=self.positive_dict,
+        )
+
+        return batch_cost
+
+    def _check_convergence(
+        self, X, batch_cost, new_dict, old_dict, n_samples, step, n_steps
+    ):
+        """Helper function to encapsulate the early stopping logic.
+
+        Early stopping is based on two factors:
+        - A small change of the dictionary between two minibatch updates. This is
+          controlled by the tol parameter.
+        - No more improvement on a smoothed estimate of the objective function for a
+          a certain number of consecutive minibatch updates. This is controlled by
+          the max_no_improvement parameter.
+        """
+        batch_size = X.shape[0]
+
+        # counts steps starting from 1 for user friendly verbose mode.
+        step = step + 1
+
+        # Ignore 100 first steps or 1 epoch to avoid initializing the ewa_cost with a
+        # too bad value
+        if step <= min(100, n_samples / batch_size):
+            if self.verbose:
+                print(f"Minibatch step {step}/{n_steps}: mean batch cost: {batch_cost}")
+            return False
+
+        # Compute an Exponentially Weighted Average of the cost function to
+        # monitor the convergence while discarding minibatch-local stochastic
+        # variability: https://en.wikipedia.org/wiki/Moving_average
+        if self._ewa_cost is None:
+            self._ewa_cost = batch_cost
+        else:
+            alpha = batch_size / (n_samples + 1)
+            alpha = min(alpha, 1)
+            self._ewa_cost = self._ewa_cost * (1 - alpha) + batch_cost * alpha
+
+        if self.verbose:
+            print(
+                f"Minibatch step {step}/{n_steps}: mean batch cost: "
+                f"{batch_cost}, ewa cost: {self._ewa_cost}"
+            )
+
+        # Early stopping based on change of dictionary
+        dict_diff = linalg.norm(new_dict - old_dict) / self._n_components
+        if self.tol > 0 and dict_diff <= self.tol:
+            if self.verbose:
+                print(f"Converged (small dictionary change) at step {step}/{n_steps}")
+            return True
+
+        # Early stopping heuristic due to lack of improvement on smoothed
+        # cost function
+        if self._ewa_cost_min is None or self._ewa_cost < self._ewa_cost_min:
+            self._no_improvement = 0
+            self._ewa_cost_min = self._ewa_cost
+        else:
+            self._no_improvement += 1
+
+        if (
+            self.max_no_improvement is not None
+            and self._no_improvement >= self.max_no_improvement
+        ):
+            if self.verbose:
+                print(
+                    "Converged (lack of improvement in objective function) "
+                    f"at step {step}/{n_steps}"
+                )
+            return True
+
+        return False
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the model from data in X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        X = self._validate_data(
+            X, dtype=[np.float64, np.float32], order="C", copy=False
+        )
+
+        self._check_params(X)
+        self._random_state = check_random_state(self.random_state)
+
+        dictionary = self._initialize_dict(X, self._random_state)
+        old_dict = dictionary.copy()
+
+        if self.shuffle:
+            X_train = X.copy()
+            self._random_state.shuffle(X_train)
+        else:
+            X_train = X
+
+        n_samples, n_features = X_train.shape
+
+        if self.verbose:
+            print("[dict_learning]")
+
+        # Inner stats
+        self._A = np.zeros(
+            (self._n_components, self._n_components), dtype=X_train.dtype
+        )
+        self._B = np.zeros((n_features, self._n_components), dtype=X_train.dtype)
+
+        # TODO(1.6): remove in 1.6
+        if self.max_iter is None:
+            warn(
+                (
+                    "`max_iter=None` is deprecated in version 1.4 and will be removed"
+                    " in version 1.6. Use the default value (i.e. `1_000`) instead."
+                ),
+                FutureWarning,
+            )
+            max_iter = 1_000
+        else:
+            max_iter = self.max_iter
+
+        # Attributes to monitor the convergence
+        self._ewa_cost = None
+        self._ewa_cost_min = None
+        self._no_improvement = 0
+
+        batches = gen_batches(n_samples, self._batch_size)
+        batches = itertools.cycle(batches)
+        n_steps_per_iter = int(np.ceil(n_samples / self._batch_size))
+        n_steps = max_iter * n_steps_per_iter
+
+        i = -1  # to allow max_iter = 0
+
+        for i, batch in zip(range(n_steps), batches):
+            X_batch = X_train[batch]
+
+            batch_cost = self._minibatch_step(
+                X_batch, dictionary, self._random_state, i
+            )
+
+            if self._check_convergence(
+                X_batch, batch_cost, dictionary, old_dict, n_samples, i, n_steps
+            ):
+                break
+
+            # XXX callback param added for backward compat in #18975 but a common
+            # unified callback API should be preferred
+            if self.callback is not None:
+                self.callback(locals())
+
+            old_dict[:] = dictionary
+
+        self.n_steps_ = i + 1
+        self.n_iter_ = np.ceil(self.n_steps_ / n_steps_per_iter)
+        self.components_ = dictionary
+
+        return self
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def partial_fit(self, X, y=None):
+        """Update the model using the data in X as a mini-batch.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Return the instance itself.
+        """
+        has_components = hasattr(self, "components_")
+
+        X = self._validate_data(
+            X, dtype=[np.float64, np.float32], order="C", reset=not has_components
+        )
+
+        if not has_components:
+            # This instance has not been fitted yet (fit or partial_fit)
+            self._check_params(X)
+            self._random_state = check_random_state(self.random_state)
+
+            dictionary = self._initialize_dict(X, self._random_state)
+
+            self.n_steps_ = 0
+
+            self._A = np.zeros((self._n_components, self._n_components), dtype=X.dtype)
+            self._B = np.zeros((X.shape[1], self._n_components), dtype=X.dtype)
+        else:
+            dictionary = self.components_
+
+        self._minibatch_step(X, dictionary, self._random_state, self.n_steps_)
+
+        self.components_ = dictionary
+        self.n_steps_ += 1
+
+        return self
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.components_.shape[0]
+
+    def _more_tags(self):
+        return {
+            "preserves_dtype": [np.float64, np.float32],
+        }

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_factor_analysis.py

@@ -0,0 +1,458 @@
+"""Factor Analysis.
+
+A latent linear variable model.
+
+FactorAnalysis is similar to probabilistic PCA implemented by PCA.score
+While PCA assumes Gaussian noise with the same variance for each
+feature, the FactorAnalysis model assumes different variances for
+each of them.
+
+This implementation is based on David Barber's Book,
+Bayesian Reasoning and Machine Learning,
+http://www.cs.ucl.ac.uk/staff/d.barber/brml,
+Algorithm 21.1
+"""
+
+# Author: Christian Osendorfer <[email protected]>
+#         Alexandre Gramfort <[email protected]>
+#         Denis A. Engemann <[email protected]>
+
+# License: BSD3
+
+import warnings
+from math import log, sqrt
+from numbers import Integral, Real
+
+import numpy as np
+from scipy import linalg
+
+from ..base import (
+    BaseEstimator,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from ..exceptions import ConvergenceWarning
+from ..utils import check_random_state
+from ..utils._param_validation import Interval, StrOptions
+from ..utils.extmath import fast_logdet, randomized_svd, squared_norm
+from ..utils.validation import check_is_fitted
+
+
+class FactorAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
+    """Factor Analysis (FA).
+
+    A simple linear generative model with Gaussian latent variables.
+
+    The observations are assumed to be caused by a linear transformation of
+    lower dimensional latent factors and added Gaussian noise.
+    Without loss of generality the factors are distributed according to a
+    Gaussian with zero mean and unit covariance. The noise is also zero mean
+    and has an arbitrary diagonal covariance matrix.
+
+    If we would restrict the model further, by assuming that the Gaussian
+    noise is even isotropic (all diagonal entries are the same) we would obtain
+    :class:`PCA`.
+
+    FactorAnalysis performs a maximum likelihood estimate of the so-called
+    `loading` matrix, the transformation of the latent variables to the
+    observed ones, using SVD based approach.
+
+    Read more in the :ref:`User Guide <FA>`.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    n_components : int, default=None
+        Dimensionality of latent space, the number of components
+        of ``X`` that are obtained after ``transform``.
+        If None, n_components is set to the number of features.
+
+    tol : float, default=1e-2
+        Stopping tolerance for log-likelihood increase.
+
+    copy : bool, default=True
+        Whether to make a copy of X. If ``False``, the input X gets overwritten
+        during fitting.
+
+    max_iter : int, default=1000
+        Maximum number of iterations.
+
+    noise_variance_init : array-like of shape (n_features,), default=None
+        The initial guess of the noise variance for each feature.
+        If None, it defaults to np.ones(n_features).
+
+    svd_method : {'lapack', 'randomized'}, default='randomized'
+        Which SVD method to use. If 'lapack' use standard SVD from
+        scipy.linalg, if 'randomized' use fast ``randomized_svd`` function.
+        Defaults to 'randomized'. For most applications 'randomized' will
+        be sufficiently precise while providing significant speed gains.
+        Accuracy can also be improved by setting higher values for
+        `iterated_power`. If this is not sufficient, for maximum precision
+        you should choose 'lapack'.
+
+    iterated_power : int, default=3
+        Number of iterations for the power method. 3 by default. Only used
+        if ``svd_method`` equals 'randomized'.
+
+    rotation : {'varimax', 'quartimax'}, default=None
+        If not None, apply the indicated rotation. Currently, varimax and
+        quartimax are implemented. See
+        `"The varimax criterion for analytic rotation in factor analysis"
+        <https://link.springer.com/article/10.1007%2FBF02289233>`_
+        H. F. Kaiser, 1958.
+
+        .. versionadded:: 0.24
+
+    random_state : int or RandomState instance, default=0
+        Only used when ``svd_method`` equals 'randomized'. Pass an int for
+        reproducible results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        Components with maximum variance.
+
+    loglike_ : list of shape (n_iterations,)
+        The log likelihood at each iteration.
+
+    noise_variance_ : ndarray of shape (n_features,)
+        The estimated noise variance for each feature.
+
+    n_iter_ : int
+        Number of iterations run.
+
+    mean_ : ndarray of shape (n_features,)
+        Per-feature empirical mean, estimated from the training set.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    PCA: Principal component analysis is also a latent linear variable model
+        which however assumes equal noise variance for each feature.
+        This extra assumption makes probabilistic PCA faster as it can be
+        computed in closed form.
+    FastICA: Independent component analysis, a latent variable model with
+        non-Gaussian latent variables.
+
+    References
+    ----------
+    - David Barber, Bayesian Reasoning and Machine Learning,
+      Algorithm 21.1.
+
+    - Christopher M. Bishop: Pattern Recognition and Machine Learning,
+      Chapter 12.2.4.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.decomposition import FactorAnalysis
+    >>> X, _ = load_digits(return_X_y=True)
+    >>> transformer = FactorAnalysis(n_components=7, random_state=0)
+    >>> X_transformed = transformer.fit_transform(X)
+    >>> X_transformed.shape
+    (1797, 7)
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [Interval(Integral, 0, None, closed="left"), None],
+        "tol": [Interval(Real, 0.0, None, closed="left")],
+        "copy": ["boolean"],
+        "max_iter": [Interval(Integral, 1, None, closed="left")],
+        "noise_variance_init": ["array-like", None],
+        "svd_method": [StrOptions({"randomized", "lapack"})],
+        "iterated_power": [Interval(Integral, 0, None, closed="left")],
+        "rotation": [StrOptions({"varimax", "quartimax"}), None],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(
+        self,
+        n_components=None,
+        *,
+        tol=1e-2,
+        copy=True,
+        max_iter=1000,
+        noise_variance_init=None,
+        svd_method="randomized",
+        iterated_power=3,
+        rotation=None,
+        random_state=0,
+    ):
+        self.n_components = n_components
+        self.copy = copy
+        self.tol = tol
+        self.max_iter = max_iter
+        self.svd_method = svd_method
+
+        self.noise_variance_init = noise_variance_init
+        self.iterated_power = iterated_power
+        self.random_state = random_state
+        self.rotation = rotation
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the FactorAnalysis model to X using SVD based approach.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : Ignored
+            Ignored parameter.
+
+        Returns
+        -------
+        self : object
+            FactorAnalysis class instance.
+        """
+        X = self._validate_data(X, copy=self.copy, dtype=np.float64)
+
+        n_samples, n_features = X.shape
+        n_components = self.n_components
+        if n_components is None:
+            n_components = n_features
+
+        self.mean_ = np.mean(X, axis=0)
+        X -= self.mean_
+
+        # some constant terms
+        nsqrt = sqrt(n_samples)
+        llconst = n_features * log(2.0 * np.pi) + n_components
+        var = np.var(X, axis=0)
+
+        if self.noise_variance_init is None:
+            psi = np.ones(n_features, dtype=X.dtype)
+        else:
+            if len(self.noise_variance_init) != n_features:
+                raise ValueError(
+                    "noise_variance_init dimension does not "
+                    "with number of features : %d != %d"
+                    % (len(self.noise_variance_init), n_features)
+                )
+            psi = np.array(self.noise_variance_init)
+
+        loglike = []
+        old_ll = -np.inf
+        SMALL = 1e-12
+
+        # we'll modify svd outputs to return unexplained variance
+        # to allow for unified computation of loglikelihood
+        if self.svd_method == "lapack":
+
+            def my_svd(X):
+                _, s, Vt = linalg.svd(X, full_matrices=False, check_finite=False)
+                return (
+                    s[:n_components],
+                    Vt[:n_components],
+                    squared_norm(s[n_components:]),
+                )
+
+        else:  # svd_method == "randomized"
+            random_state = check_random_state(self.random_state)
+
+            def my_svd(X):
+                _, s, Vt = randomized_svd(
+                    X,
+                    n_components,
+                    random_state=random_state,
+                    n_iter=self.iterated_power,
+                )
+                return s, Vt, squared_norm(X) - squared_norm(s)
+
+        for i in range(self.max_iter):
+            # SMALL helps numerics
+            sqrt_psi = np.sqrt(psi) + SMALL
+            s, Vt, unexp_var = my_svd(X / (sqrt_psi * nsqrt))
+            s **= 2
+            # Use 'maximum' here to avoid sqrt problems.
+            W = np.sqrt(np.maximum(s - 1.0, 0.0))[:, np.newaxis] * Vt
+            del Vt
+            W *= sqrt_psi
+
+            # loglikelihood
+            ll = llconst + np.sum(np.log(s))
+            ll += unexp_var + np.sum(np.log(psi))
+            ll *= -n_samples / 2.0
+            loglike.append(ll)
+            if (ll - old_ll) < self.tol:
+                break
+            old_ll = ll
+
+            psi = np.maximum(var - np.sum(W**2, axis=0), SMALL)
+        else:
+            warnings.warn(
+                "FactorAnalysis did not converge."
+                + " You might want"
+                + " to increase the number of iterations.",
+                ConvergenceWarning,
+            )
+
+        self.components_ = W
+        if self.rotation is not None:
+            self.components_ = self._rotate(W)
+        self.noise_variance_ = psi
+        self.loglike_ = loglike
+        self.n_iter_ = i + 1
+        return self
+
+    def transform(self, X):
+        """Apply dimensionality reduction to X using the model.
+
+        Compute the expected mean of the latent variables.
+        See Barber, 21.2.33 (or Bishop, 12.66).
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            The latent variables of X.
+        """
+        check_is_fitted(self)
+
+        X = self._validate_data(X, reset=False)
+        Ih = np.eye(len(self.components_))
+
+        X_transformed = X - self.mean_
+
+        Wpsi = self.components_ / self.noise_variance_
+        cov_z = linalg.inv(Ih + np.dot(Wpsi, self.components_.T))
+        tmp = np.dot(X_transformed, Wpsi.T)
+        X_transformed = np.dot(tmp, cov_z)
+
+        return X_transformed
+
+    def get_covariance(self):
+        """Compute data covariance with the FactorAnalysis model.
+
+        ``cov = components_.T * components_ + diag(noise_variance)``
+
+        Returns
+        -------
+        cov : ndarray of shape (n_features, n_features)
+            Estimated covariance of data.
+        """
+        check_is_fitted(self)
+
+        cov = np.dot(self.components_.T, self.components_)
+        cov.flat[:: len(cov) + 1] += self.noise_variance_  # modify diag inplace
+        return cov
+
+    def get_precision(self):
+        """Compute data precision matrix with the FactorAnalysis model.
+
+        Returns
+        -------
+        precision : ndarray of shape (n_features, n_features)
+            Estimated precision of data.
+        """
+        check_is_fitted(self)
+
+        n_features = self.components_.shape[1]
+
+        # handle corner cases first
+        if self.n_components == 0:
+            return np.diag(1.0 / self.noise_variance_)
+        if self.n_components == n_features:
+            return linalg.inv(self.get_covariance())
+
+        # Get precision using matrix inversion lemma
+        components_ = self.components_
+        precision = np.dot(components_ / self.noise_variance_, components_.T)
+        precision.flat[:: len(precision) + 1] += 1.0
+        precision = np.dot(components_.T, np.dot(linalg.inv(precision), components_))
+        precision /= self.noise_variance_[:, np.newaxis]
+        precision /= -self.noise_variance_[np.newaxis, :]
+        precision.flat[:: len(precision) + 1] += 1.0 / self.noise_variance_
+        return precision
+
+    def score_samples(self, X):
+        """Compute the log-likelihood of each sample.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The data.
+
+        Returns
+        -------
+        ll : ndarray of shape (n_samples,)
+            Log-likelihood of each sample under the current model.
+        """
+        check_is_fitted(self)
+        X = self._validate_data(X, reset=False)
+        Xr = X - self.mean_
+        precision = self.get_precision()
+        n_features = X.shape[1]
+        log_like = -0.5 * (Xr * (np.dot(Xr, precision))).sum(axis=1)
+        log_like -= 0.5 * (n_features * log(2.0 * np.pi) - fast_logdet(precision))
+        return log_like
+
+    def score(self, X, y=None):
+        """Compute the average log-likelihood of the samples.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The data.
+
+        y : Ignored
+            Ignored parameter.
+
+        Returns
+        -------
+        ll : float
+            Average log-likelihood of the samples under the current model.
+        """
+        return np.mean(self.score_samples(X))
+
+    def _rotate(self, components, n_components=None, tol=1e-6):
+        "Rotate the factor analysis solution."
+        # note that tol is not exposed
+        return _ortho_rotation(components.T, method=self.rotation, tol=tol)[
+            : self.n_components
+        ]
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.components_.shape[0]
+
+
+def _ortho_rotation(components, method="varimax", tol=1e-6, max_iter=100):
+    """Return rotated components."""
+    nrow, ncol = components.shape
+    rotation_matrix = np.eye(ncol)
+    var = 0
+
+    for _ in range(max_iter):
+        comp_rot = np.dot(components, rotation_matrix)
+        if method == "varimax":
+            tmp = comp_rot * np.transpose((comp_rot**2).sum(axis=0) / nrow)
+        elif method == "quartimax":
+            tmp = 0
+        u, s, v = np.linalg.svd(np.dot(components.T, comp_rot**3 - tmp))
+        rotation_matrix = np.dot(u, v)
+        var_new = np.sum(s)
+        if var != 0 and var_new < var * (1 + tol):
+            break
+        var = var_new
+
+    return np.dot(components, rotation_matrix).T

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_fastica.py

@@ -0,0 +1,795 @@
+"""
+Python implementation of the fast ICA algorithms.
+
+Reference: Tables 8.3 and 8.4 page 196 in the book:
+Independent Component Analysis, by  Hyvarinen et al.
+"""
+
+# Authors: Pierre Lafaye de Micheaux, Stefan van der Walt, Gael Varoquaux,
+#          Bertrand Thirion, Alexandre Gramfort, Denis A. Engemann
+# License: BSD 3 clause
+
+import warnings
+from numbers import Integral, Real
+
+import numpy as np
+from scipy import linalg
+
+from ..base import (
+    BaseEstimator,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from ..exceptions import ConvergenceWarning
+from ..utils import as_float_array, check_array, check_random_state
+from ..utils._param_validation import Interval, Options, StrOptions, validate_params
+from ..utils.validation import check_is_fitted
+
+__all__ = ["fastica", "FastICA"]
+
+
+def _gs_decorrelation(w, W, j):
+    """
+    Orthonormalize w wrt the first j rows of W.
+
+    Parameters
+    ----------
+    w : ndarray of shape (n,)
+        Array to be orthogonalized
+
+    W : ndarray of shape (p, n)
+        Null space definition
+
+    j : int < p
+        The no of (from the first) rows of Null space W wrt which w is
+        orthogonalized.
+
+    Notes
+    -----
+    Assumes that W is orthogonal
+    w changed in place
+    """
+    w -= np.linalg.multi_dot([w, W[:j].T, W[:j]])
+    return w
+
+
+def _sym_decorrelation(W):
+    """Symmetric decorrelation
+    i.e. W <- (W * W.T) ^{-1/2} * W
+    """
+    s, u = linalg.eigh(np.dot(W, W.T))
+    # Avoid sqrt of negative values because of rounding errors. Note that
+    # np.sqrt(tiny) is larger than tiny and therefore this clipping also
+    # prevents division by zero in the next step.
+    s = np.clip(s, a_min=np.finfo(W.dtype).tiny, a_max=None)
+
+    # u (resp. s) contains the eigenvectors (resp. square roots of
+    # the eigenvalues) of W * W.T
+    return np.linalg.multi_dot([u * (1.0 / np.sqrt(s)), u.T, W])
+
+
+def _ica_def(X, tol, g, fun_args, max_iter, w_init):
+    """Deflationary FastICA using fun approx to neg-entropy function
+
+    Used internally by FastICA.
+    """
+
+    n_components = w_init.shape[0]
+    W = np.zeros((n_components, n_components), dtype=X.dtype)
+    n_iter = []
+
+    # j is the index of the extracted component
+    for j in range(n_components):
+        w = w_init[j, :].copy()
+        w /= np.sqrt((w**2).sum())
+
+        for i in range(max_iter):
+            gwtx, g_wtx = g(np.dot(w.T, X), fun_args)
+
+            w1 = (X * gwtx).mean(axis=1) - g_wtx.mean() * w
+
+            _gs_decorrelation(w1, W, j)
+
+            w1 /= np.sqrt((w1**2).sum())
+
+            lim = np.abs(np.abs((w1 * w).sum()) - 1)
+            w = w1
+            if lim < tol:
+                break
+
+        n_iter.append(i + 1)
+        W[j, :] = w
+
+    return W, max(n_iter)
+
+
+def _ica_par(X, tol, g, fun_args, max_iter, w_init):
+    """Parallel FastICA.
+
+    Used internally by FastICA --main loop
+
+    """
+    W = _sym_decorrelation(w_init)
+    del w_init
+    p_ = float(X.shape[1])
+    for ii in range(max_iter):
+        gwtx, g_wtx = g(np.dot(W, X), fun_args)
+        W1 = _sym_decorrelation(np.dot(gwtx, X.T) / p_ - g_wtx[:, np.newaxis] * W)
+        del gwtx, g_wtx
+        # builtin max, abs are faster than numpy counter parts.
+        # np.einsum allows having the lowest memory footprint.
+        # It is faster than np.diag(np.dot(W1, W.T)).
+        lim = max(abs(abs(np.einsum("ij,ij->i", W1, W)) - 1))
+        W = W1
+        if lim < tol:
+            break
+    else:
+        warnings.warn(
+            (
+                "FastICA did not converge. Consider increasing "
+                "tolerance or the maximum number of iterations."
+            ),
+            ConvergenceWarning,
+        )
+
+    return W, ii + 1
+
+
+# Some standard non-linear functions.
+# XXX: these should be optimized, as they can be a bottleneck.
+def _logcosh(x, fun_args=None):
+    alpha = fun_args.get("alpha", 1.0)  # comment it out?
+
+    x *= alpha
+    gx = np.tanh(x, x)  # apply the tanh inplace
+    g_x = np.empty(x.shape[0], dtype=x.dtype)
+    # XXX compute in chunks to avoid extra allocation
+    for i, gx_i in enumerate(gx):  # please don't vectorize.
+        g_x[i] = (alpha * (1 - gx_i**2)).mean()
+    return gx, g_x
+
+
+def _exp(x, fun_args):
+    exp = np.exp(-(x**2) / 2)
+    gx = x * exp
+    g_x = (1 - x**2) * exp
+    return gx, g_x.mean(axis=-1)
+
+
+def _cube(x, fun_args):
+    return x**3, (3 * x**2).mean(axis=-1)
+
+
+@validate_params(
+    {
+        "X": ["array-like"],
+        "return_X_mean": ["boolean"],
+        "compute_sources": ["boolean"],
+        "return_n_iter": ["boolean"],
+    },
+    prefer_skip_nested_validation=False,
+)
+def fastica(
+    X,
+    n_components=None,
+    *,
+    algorithm="parallel",
+    whiten="unit-variance",
+    fun="logcosh",
+    fun_args=None,
+    max_iter=200,
+    tol=1e-04,
+    w_init=None,
+    whiten_solver="svd",
+    random_state=None,
+    return_X_mean=False,
+    compute_sources=True,
+    return_n_iter=False,
+):
+    """Perform Fast Independent Component Analysis.
+
+    The implementation is based on [1]_.
+
+    Read more in the :ref:`User Guide <ICA>`.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Training vector, where `n_samples` is the number of samples and
+        `n_features` is the number of features.
+
+    n_components : int, default=None
+        Number of components to use. If None is passed, all are used.
+
+    algorithm : {'parallel', 'deflation'}, default='parallel'
+        Specify which algorithm to use for FastICA.
+
+    whiten : str or bool, default='unit-variance'
+        Specify the whitening strategy to use.
+
+        - If 'arbitrary-variance', a whitening with variance
+          arbitrary is used.
+        - If 'unit-variance', the whitening matrix is rescaled to ensure that
+          each recovered source has unit variance.
+        - If False, the data is already considered to be whitened, and no
+          whitening is performed.
+
+        .. versionchanged:: 1.3
+            The default value of `whiten` changed to 'unit-variance' in 1.3.
+
+    fun : {'logcosh', 'exp', 'cube'} or callable, default='logcosh'
+        The functional form of the G function used in the
+        approximation to neg-entropy. Could be either 'logcosh', 'exp',
+        or 'cube'.
+        You can also provide your own function. It should return a tuple
+        containing the value of the function, and of its derivative, in the
+        point. The derivative should be averaged along its last dimension.
+        Example::
+
+            def my_g(x):
+                return x ** 3, (3 * x ** 2).mean(axis=-1)
+
+    fun_args : dict, default=None
+        Arguments to send to the functional form.
+        If empty or None and if fun='logcosh', fun_args will take value
+        {'alpha' : 1.0}.
+
+    max_iter : int, default=200
+        Maximum number of iterations to perform.
+
+    tol : float, default=1e-4
+        A positive scalar giving the tolerance at which the
+        un-mixing matrix is considered to have converged.
+
+    w_init : ndarray of shape (n_components, n_components), default=None
+        Initial un-mixing array. If `w_init=None`, then an array of values
+        drawn from a normal distribution is used.
+
+    whiten_solver : {"eigh", "svd"}, default="svd"
+        The solver to use for whitening.
+
+        - "svd" is more stable numerically if the problem is degenerate, and
+          often faster when `n_samples <= n_features`.
+
+        - "eigh" is generally more memory efficient when
+          `n_samples >= n_features`, and can be faster when
+          `n_samples >= 50 * n_features`.
+
+        .. versionadded:: 1.2
+
+    random_state : int, RandomState instance or None, default=None
+        Used to initialize ``w_init`` when not specified, with a
+        normal distribution. Pass an int, for reproducible results
+        across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    return_X_mean : bool, default=False
+        If True, X_mean is returned too.
+
+    compute_sources : bool, default=True
+        If False, sources are not computed, but only the rotation matrix.
+        This can save memory when working with big data. Defaults to True.
+
+    return_n_iter : bool, default=False
+        Whether or not to return the number of iterations.
+
+    Returns
+    -------
+    K : ndarray of shape (n_components, n_features) or None
+        If whiten is 'True', K is the pre-whitening matrix that projects data
+        onto the first n_components principal components. If whiten is 'False',
+        K is 'None'.
+
+    W : ndarray of shape (n_components, n_components)
+        The square matrix that unmixes the data after whitening.
+        The mixing matrix is the pseudo-inverse of matrix ``W K``
+        if K is not None, else it is the inverse of W.
+
+    S : ndarray of shape (n_samples, n_components) or None
+        Estimated source matrix.
+
+    X_mean : ndarray of shape (n_features,)
+        The mean over features. Returned only if return_X_mean is True.
+
+    n_iter : int
+        If the algorithm is "deflation", n_iter is the
+        maximum number of iterations run across all components. Else
+        they are just the number of iterations taken to converge. This is
+        returned only when return_n_iter is set to `True`.
+
+    Notes
+    -----
+    The data matrix X is considered to be a linear combination of
+    non-Gaussian (independent) components i.e. X = AS where columns of S
+    contain the independent components and A is a linear mixing
+    matrix. In short ICA attempts to `un-mix' the data by estimating an
+    un-mixing matrix W where ``S = W K X.``
+    While FastICA was proposed to estimate as many sources
+    as features, it is possible to estimate less by setting
+    n_components < n_features. It this case K is not a square matrix
+    and the estimated A is the pseudo-inverse of ``W K``.
+
+    This implementation was originally made for data of shape
+    [n_features, n_samples]. Now the input is transposed
+    before the algorithm is applied. This makes it slightly
+    faster for Fortran-ordered input.
+
+    References
+    ----------
+    .. [1] A. Hyvarinen and E. Oja, "Fast Independent Component Analysis",
+           Algorithms and Applications, Neural Networks, 13(4-5), 2000,
+           pp. 411-430.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.decomposition import fastica
+    >>> X, _ = load_digits(return_X_y=True)
+    >>> K, W, S = fastica(X, n_components=7, random_state=0, whiten='unit-variance')
+    >>> K.shape
+    (7, 64)
+    >>> W.shape
+    (7, 7)
+    >>> S.shape
+    (1797, 7)
+    """
+    est = FastICA(
+        n_components=n_components,
+        algorithm=algorithm,
+        whiten=whiten,
+        fun=fun,
+        fun_args=fun_args,
+        max_iter=max_iter,
+        tol=tol,
+        w_init=w_init,
+        whiten_solver=whiten_solver,
+        random_state=random_state,
+    )
+    est._validate_params()
+    S = est._fit_transform(X, compute_sources=compute_sources)
+
+    if est.whiten in ["unit-variance", "arbitrary-variance"]:
+        K = est.whitening_
+        X_mean = est.mean_
+    else:
+        K = None
+        X_mean = None
+
+    returned_values = [K, est._unmixing, S]
+    if return_X_mean:
+        returned_values.append(X_mean)
+    if return_n_iter:
+        returned_values.append(est.n_iter_)
+
+    return returned_values
+
+
+class FastICA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
+    """FastICA: a fast algorithm for Independent Component Analysis.
+
+    The implementation is based on [1]_.
+
+    Read more in the :ref:`User Guide <ICA>`.
+
+    Parameters
+    ----------
+    n_components : int, default=None
+        Number of components to use. If None is passed, all are used.
+
+    algorithm : {'parallel', 'deflation'}, default='parallel'
+        Specify which algorithm to use for FastICA.
+
+    whiten : str or bool, default='unit-variance'
+        Specify the whitening strategy to use.
+
+        - If 'arbitrary-variance', a whitening with variance
+          arbitrary is used.
+        - If 'unit-variance', the whitening matrix is rescaled to ensure that
+          each recovered source has unit variance.
+        - If False, the data is already considered to be whitened, and no
+          whitening is performed.
+
+        .. versionchanged:: 1.3
+            The default value of `whiten` changed to 'unit-variance' in 1.3.
+
+    fun : {'logcosh', 'exp', 'cube'} or callable, default='logcosh'
+        The functional form of the G function used in the
+        approximation to neg-entropy. Could be either 'logcosh', 'exp',
+        or 'cube'.
+        You can also provide your own function. It should return a tuple
+        containing the value of the function, and of its derivative, in the
+        point. The derivative should be averaged along its last dimension.
+        Example::
+
+            def my_g(x):
+                return x ** 3, (3 * x ** 2).mean(axis=-1)
+
+    fun_args : dict, default=None
+        Arguments to send to the functional form.
+        If empty or None and if fun='logcosh', fun_args will take value
+        {'alpha' : 1.0}.
+
+    max_iter : int, default=200
+        Maximum number of iterations during fit.
+
+    tol : float, default=1e-4
+        A positive scalar giving the tolerance at which the
+        un-mixing matrix is considered to have converged.
+
+    w_init : array-like of shape (n_components, n_components), default=None
+        Initial un-mixing array. If `w_init=None`, then an array of values
+        drawn from a normal distribution is used.
+
+    whiten_solver : {"eigh", "svd"}, default="svd"
+        The solver to use for whitening.
+
+        - "svd" is more stable numerically if the problem is degenerate, and
+          often faster when `n_samples <= n_features`.
+
+        - "eigh" is generally more memory efficient when
+          `n_samples >= n_features`, and can be faster when
+          `n_samples >= 50 * n_features`.
+
+        .. versionadded:: 1.2
+
+    random_state : int, RandomState instance or None, default=None
+        Used to initialize ``w_init`` when not specified, with a
+        normal distribution. Pass an int, for reproducible results
+        across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        The linear operator to apply to the data to get the independent
+        sources. This is equal to the unmixing matrix when ``whiten`` is
+        False, and equal to ``np.dot(unmixing_matrix, self.whitening_)`` when
+        ``whiten`` is True.
+
+    mixing_ : ndarray of shape (n_features, n_components)
+        The pseudo-inverse of ``components_``. It is the linear operator
+        that maps independent sources to the data.
+
+    mean_ : ndarray of shape(n_features,)
+        The mean over features. Only set if `self.whiten` is True.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : int
+        If the algorithm is "deflation", n_iter is the
+        maximum number of iterations run across all components. Else
+        they are just the number of iterations taken to converge.
+
+    whitening_ : ndarray of shape (n_components, n_features)
+        Only set if whiten is 'True'. This is the pre-whitening matrix
+        that projects data onto the first `n_components` principal components.
+
+    See Also
+    --------
+    PCA : Principal component analysis (PCA).
+    IncrementalPCA : Incremental principal components analysis (IPCA).
+    KernelPCA : Kernel Principal component analysis (KPCA).
+    MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis.
+    SparsePCA : Sparse Principal Components Analysis (SparsePCA).
+
+    References
+    ----------
+    .. [1] A. Hyvarinen and E. Oja, Independent Component Analysis:
+           Algorithms and Applications, Neural Networks, 13(4-5), 2000,
+           pp. 411-430.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.decomposition import FastICA
+    >>> X, _ = load_digits(return_X_y=True)
+    >>> transformer = FastICA(n_components=7,
+    ...         random_state=0,
+    ...         whiten='unit-variance')
+    >>> X_transformed = transformer.fit_transform(X)
+    >>> X_transformed.shape
+    (1797, 7)
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [Interval(Integral, 1, None, closed="left"), None],
+        "algorithm": [StrOptions({"parallel", "deflation"})],
+        "whiten": [
+            StrOptions({"arbitrary-variance", "unit-variance"}),
+            Options(bool, {False}),
+        ],
+        "fun": [StrOptions({"logcosh", "exp", "cube"}), callable],
+        "fun_args": [dict, None],
+        "max_iter": [Interval(Integral, 1, None, closed="left")],
+        "tol": [Interval(Real, 0.0, None, closed="left")],
+        "w_init": ["array-like", None],
+        "whiten_solver": [StrOptions({"eigh", "svd"})],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(
+        self,
+        n_components=None,
+        *,
+        algorithm="parallel",
+        whiten="unit-variance",
+        fun="logcosh",
+        fun_args=None,
+        max_iter=200,
+        tol=1e-4,
+        w_init=None,
+        whiten_solver="svd",
+        random_state=None,
+    ):
+        super().__init__()
+        self.n_components = n_components
+        self.algorithm = algorithm
+        self.whiten = whiten
+        self.fun = fun
+        self.fun_args = fun_args
+        self.max_iter = max_iter
+        self.tol = tol
+        self.w_init = w_init
+        self.whiten_solver = whiten_solver
+        self.random_state = random_state
+
+    def _fit_transform(self, X, compute_sources=False):
+        """Fit the model.
+
+        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.
+
+        compute_sources : bool, default=False
+            If False, sources are not computes but only the rotation matrix.
+            This can save memory when working with big data. Defaults to False.
+
+        Returns
+        -------
+        S : ndarray of shape (n_samples, n_components) or None
+            Sources matrix. `None` if `compute_sources` is `False`.
+        """
+        XT = self._validate_data(
+            X, copy=self.whiten, dtype=[np.float64, np.float32], ensure_min_samples=2
+        ).T
+        fun_args = {} if self.fun_args is None else self.fun_args
+        random_state = check_random_state(self.random_state)
+
+        alpha = fun_args.get("alpha", 1.0)
+        if not 1 <= alpha <= 2:
+            raise ValueError("alpha must be in [1,2]")
+
+        if self.fun == "logcosh":
+            g = _logcosh
+        elif self.fun == "exp":
+            g = _exp
+        elif self.fun == "cube":
+            g = _cube
+        elif callable(self.fun):
+
+            def g(x, fun_args):
+                return self.fun(x, **fun_args)
+
+        n_features, n_samples = XT.shape
+        n_components = self.n_components
+        if not self.whiten and n_components is not None:
+            n_components = None
+            warnings.warn("Ignoring n_components with whiten=False.")
+
+        if n_components is None:
+            n_components = min(n_samples, n_features)
+        if n_components > min(n_samples, n_features):
+            n_components = min(n_samples, n_features)
+            warnings.warn(
+                "n_components is too large: it will be set to %s" % n_components
+            )
+
+        if self.whiten:
+            # Centering the features of X
+            X_mean = XT.mean(axis=-1)
+            XT -= X_mean[:, np.newaxis]
+
+            # Whitening and preprocessing by PCA
+            if self.whiten_solver == "eigh":
+                # Faster when num_samples >> n_features
+                d, u = linalg.eigh(XT.dot(X))
+                sort_indices = np.argsort(d)[::-1]
+                eps = np.finfo(d.dtype).eps
+                degenerate_idx = d < eps
+                if np.any(degenerate_idx):
+                    warnings.warn(
+                        "There are some small singular values, using "
+                        "whiten_solver = 'svd' might lead to more "
+                        "accurate results."
+                    )
+                d[degenerate_idx] = eps  # For numerical issues
+                np.sqrt(d, out=d)
+                d, u = d[sort_indices], u[:, sort_indices]
+            elif self.whiten_solver == "svd":
+                u, d = linalg.svd(XT, full_matrices=False, check_finite=False)[:2]
+
+            # Give consistent eigenvectors for both svd solvers
+            u *= np.sign(u[0])
+
+            K = (u / d).T[:n_components]  # see (6.33) p.140
+            del u, d
+            X1 = np.dot(K, XT)
+            # see (13.6) p.267 Here X1 is white and data
+            # in X has been projected onto a subspace by PCA
+            X1 *= np.sqrt(n_samples)
+        else:
+            # X must be casted to floats to avoid typing issues with numpy
+            # 2.0 and the line below
+            X1 = as_float_array(XT, copy=False)  # copy has been taken care of
+
+        w_init = self.w_init
+        if w_init is None:
+            w_init = np.asarray(
+                random_state.normal(size=(n_components, n_components)), dtype=X1.dtype
+            )
+
+        else:
+            w_init = np.asarray(w_init)
+            if w_init.shape != (n_components, n_components):
+                raise ValueError(
+                    "w_init has invalid shape -- should be %(shape)s"
+                    % {"shape": (n_components, n_components)}
+                )
+
+        kwargs = {
+            "tol": self.tol,
+            "g": g,
+            "fun_args": fun_args,
+            "max_iter": self.max_iter,
+            "w_init": w_init,
+        }
+
+        if self.algorithm == "parallel":
+            W, n_iter = _ica_par(X1, **kwargs)
+        elif self.algorithm == "deflation":
+            W, n_iter = _ica_def(X1, **kwargs)
+        del X1
+
+        self.n_iter_ = n_iter
+
+        if compute_sources:
+            if self.whiten:
+                S = np.linalg.multi_dot([W, K, XT]).T
+            else:
+                S = np.dot(W, XT).T
+        else:
+            S = None
+
+        if self.whiten:
+            if self.whiten == "unit-variance":
+                if not compute_sources:
+                    S = np.linalg.multi_dot([W, K, XT]).T
+                S_std = np.std(S, axis=0, keepdims=True)
+                S /= S_std
+                W /= S_std.T
+
+            self.components_ = np.dot(W, K)
+            self.mean_ = X_mean
+            self.whitening_ = K
+        else:
+            self.components_ = W
+
+        self.mixing_ = linalg.pinv(self.components_, check_finite=False)
+        self._unmixing = W
+
+        return S
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit_transform(self, X, y=None):
+        """Fit the model and recover the sources from 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.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Estimated sources obtained by transforming the data with the
+            estimated unmixing matrix.
+        """
+        return self._fit_transform(X, compute_sources=True)
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the model to 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.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        self._fit_transform(X, compute_sources=False)
+        return self
+
+    def transform(self, X, copy=True):
+        """Recover the sources from X (apply the unmixing matrix).
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Data to transform, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        copy : bool, default=True
+            If False, data passed to fit can be overwritten. Defaults to True.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Estimated sources obtained by transforming the data with the
+            estimated unmixing matrix.
+        """
+        check_is_fitted(self)
+
+        X = self._validate_data(
+            X, copy=(copy and self.whiten), dtype=[np.float64, np.float32], reset=False
+        )
+        if self.whiten:
+            X -= self.mean_
+
+        return np.dot(X, self.components_.T)
+
+    def inverse_transform(self, X, copy=True):
+        """Transform the sources back to the mixed data (apply mixing matrix).
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_components)
+            Sources, where `n_samples` is the number of samples
+            and `n_components` is the number of components.
+        copy : bool, default=True
+            If False, data passed to fit are overwritten. Defaults to True.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_features)
+            Reconstructed data obtained with the mixing matrix.
+        """
+        check_is_fitted(self)
+
+        X = check_array(X, copy=(copy and self.whiten), dtype=[np.float64, np.float32])
+        X = np.dot(X, self.mixing_.T)
+        if self.whiten:
+            X += self.mean_
+
+        return X
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.components_.shape[0]
+
+    def _more_tags(self):
+        return {"preserves_dtype": [np.float32, np.float64]}

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_incremental_pca.py

@@ -0,0 +1,409 @@
+"""Incremental Principal Components Analysis."""
+
+# Author: Kyle Kastner <[email protected]>
+#         Giorgio Patrini
+# License: BSD 3 clause
+
+from numbers import Integral
+
+import numpy as np
+from scipy import linalg, sparse
+
+from ..base import _fit_context
+from ..utils import gen_batches
+from ..utils._param_validation import Interval
+from ..utils.extmath import _incremental_mean_and_var, svd_flip
+from ._base import _BasePCA
+
+
+class IncrementalPCA(_BasePCA):
+    """Incremental principal components analysis (IPCA).
+
+    Linear dimensionality reduction using Singular Value Decomposition of
+    the data, keeping only the most significant singular vectors to
+    project the data to a lower dimensional space. The input data is centered
+    but not scaled for each feature before applying the SVD.
+
+    Depending on the size of the input data, this algorithm can be much more
+    memory efficient than a PCA, and allows sparse input.
+
+    This algorithm has constant memory complexity, on the order
+    of ``batch_size * n_features``, enabling use of np.memmap files without
+    loading the entire file into memory. For sparse matrices, the input
+    is converted to dense in batches (in order to be able to subtract the
+    mean) which avoids storing the entire dense matrix at any one time.
+
+    The computational overhead of each SVD is
+    ``O(batch_size * n_features ** 2)``, but only 2 * batch_size samples
+    remain in memory at a time. There will be ``n_samples / batch_size`` SVD
+    computations to get the principal components, versus 1 large SVD of
+    complexity ``O(n_samples * n_features ** 2)`` for PCA.
+
+    For a usage example, see
+    :ref:`sphx_glr_auto_examples_decomposition_plot_incremental_pca.py`.
+
+    Read more in the :ref:`User Guide <IncrementalPCA>`.
+
+    .. versionadded:: 0.16
+
+    Parameters
+    ----------
+    n_components : int, default=None
+        Number of components to keep. If ``n_components`` is ``None``,
+        then ``n_components`` is set to ``min(n_samples, n_features)``.
+
+    whiten : bool, default=False
+        When True (False by default) the ``components_`` vectors are divided
+        by ``n_samples`` times ``components_`` to ensure uncorrelated outputs
+        with unit component-wise variances.
+
+        Whitening will remove some information from the transformed signal
+        (the relative variance scales of the components) but can sometimes
+        improve the predictive accuracy of the downstream estimators by
+        making data respect some hard-wired assumptions.
+
+    copy : bool, default=True
+        If False, X will be overwritten. ``copy=False`` can be used to
+        save memory but is unsafe for general use.
+
+    batch_size : int, default=None
+        The number of samples to use for each batch. Only used when calling
+        ``fit``. If ``batch_size`` is ``None``, then ``batch_size``
+        is inferred from the data and set to ``5 * n_features``, to provide a
+        balance between approximation accuracy and memory consumption.
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        Principal axes in feature space, representing the directions of
+        maximum variance in the data. Equivalently, the right singular
+        vectors of the centered input data, parallel to its eigenvectors.
+        The components are sorted by decreasing ``explained_variance_``.
+
+    explained_variance_ : ndarray of shape (n_components,)
+        Variance explained by each of the selected components.
+
+    explained_variance_ratio_ : ndarray of shape (n_components,)
+        Percentage of variance explained by each of the selected components.
+        If all components are stored, the sum of explained variances is equal
+        to 1.0.
+
+    singular_values_ : ndarray of shape (n_components,)
+        The singular values corresponding to each of the selected components.
+        The singular values are equal to the 2-norms of the ``n_components``
+        variables in the lower-dimensional space.
+
+    mean_ : ndarray of shape (n_features,)
+        Per-feature empirical mean, aggregate over calls to ``partial_fit``.
+
+    var_ : ndarray of shape (n_features,)
+        Per-feature empirical variance, aggregate over calls to
+        ``partial_fit``.
+
+    noise_variance_ : float
+        The estimated noise covariance following the Probabilistic PCA model
+        from Tipping and Bishop 1999. See "Pattern Recognition and
+        Machine Learning" by C. Bishop, 12.2.1 p. 574 or
+        http://www.miketipping.com/papers/met-mppca.pdf.
+
+    n_components_ : int
+        The estimated number of components. Relevant when
+        ``n_components=None``.
+
+    n_samples_seen_ : int
+        The number of samples processed by the estimator. Will be reset on
+        new calls to fit, but increments across ``partial_fit`` calls.
+
+    batch_size_ : int
+        Inferred batch size from ``batch_size``.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    PCA : Principal component analysis (PCA).
+    KernelPCA : Kernel Principal component analysis (KPCA).
+    SparsePCA : Sparse Principal Components Analysis (SparsePCA).
+    TruncatedSVD : Dimensionality reduction using truncated SVD.
+
+    Notes
+    -----
+    Implements the incremental PCA model from:
+    *D. Ross, J. Lim, R. Lin, M. Yang, Incremental Learning for Robust Visual
+    Tracking, International Journal of Computer Vision, Volume 77, Issue 1-3,
+    pp. 125-141, May 2008.*
+    See https://www.cs.toronto.edu/~dross/ivt/RossLimLinYang_ijcv.pdf
+
+    This model is an extension of the Sequential Karhunen-Loeve Transform from:
+    :doi:`A. Levy and M. Lindenbaum, Sequential Karhunen-Loeve Basis Extraction and
+    its Application to Images, IEEE Transactions on Image Processing, Volume 9,
+    Number 8, pp. 1371-1374, August 2000. <10.1109/83.855432>`
+
+    We have specifically abstained from an optimization used by authors of both
+    papers, a QR decomposition used in specific situations to reduce the
+    algorithmic complexity of the SVD. The source for this technique is
+    *Matrix Computations, Third Edition, G. Holub and C. Van Loan, Chapter 5,
+    section 5.4.4, pp 252-253.*. This technique has been omitted because it is
+    advantageous only when decomposing a matrix with ``n_samples`` (rows)
+    >= 5/3 * ``n_features`` (columns), and hurts the readability of the
+    implemented algorithm. This would be a good opportunity for future
+    optimization, if it is deemed necessary.
+
+    References
+    ----------
+    D. Ross, J. Lim, R. Lin, M. Yang. Incremental Learning for Robust Visual
+    Tracking, International Journal of Computer Vision, Volume 77,
+    Issue 1-3, pp. 125-141, May 2008.
+
+    G. Golub and C. Van Loan. Matrix Computations, Third Edition, Chapter 5,
+    Section 5.4.4, pp. 252-253.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.decomposition import IncrementalPCA
+    >>> from scipy import sparse
+    >>> X, _ = load_digits(return_X_y=True)
+    >>> transformer = IncrementalPCA(n_components=7, batch_size=200)
+    >>> # either partially fit on smaller batches of data
+    >>> transformer.partial_fit(X[:100, :])
+    IncrementalPCA(batch_size=200, n_components=7)
+    >>> # or let the fit function itself divide the data into batches
+    >>> X_sparse = sparse.csr_matrix(X)
+    >>> X_transformed = transformer.fit_transform(X_sparse)
+    >>> X_transformed.shape
+    (1797, 7)
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [Interval(Integral, 1, None, closed="left"), None],
+        "whiten": ["boolean"],
+        "copy": ["boolean"],
+        "batch_size": [Interval(Integral, 1, None, closed="left"), None],
+    }
+
+    def __init__(self, n_components=None, *, whiten=False, copy=True, batch_size=None):
+        self.n_components = n_components
+        self.whiten = whiten
+        self.copy = copy
+        self.batch_size = batch_size
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the model with X, using minibatches of size batch_size.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        self.components_ = None
+        self.n_samples_seen_ = 0
+        self.mean_ = 0.0
+        self.var_ = 0.0
+        self.singular_values_ = None
+        self.explained_variance_ = None
+        self.explained_variance_ratio_ = None
+        self.noise_variance_ = None
+
+        X = self._validate_data(
+            X,
+            accept_sparse=["csr", "csc", "lil"],
+            copy=self.copy,
+            dtype=[np.float64, np.float32],
+        )
+        n_samples, n_features = X.shape
+
+        if self.batch_size is None:
+            self.batch_size_ = 5 * n_features
+        else:
+            self.batch_size_ = self.batch_size
+
+        for batch in gen_batches(
+            n_samples, self.batch_size_, min_batch_size=self.n_components or 0
+        ):
+            X_batch = X[batch]
+            if sparse.issparse(X_batch):
+                X_batch = X_batch.toarray()
+            self.partial_fit(X_batch, check_input=False)
+
+        return self
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def partial_fit(self, X, y=None, check_input=True):
+        """Incremental fit with X. All of X is processed as a single batch.
+
+        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.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        check_input : bool, default=True
+            Run check_array on X.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        first_pass = not hasattr(self, "components_")
+
+        if check_input:
+            if sparse.issparse(X):
+                raise TypeError(
+                    "IncrementalPCA.partial_fit does not support "
+                    "sparse input. Either convert data to dense "
+                    "or use IncrementalPCA.fit to do so in batches."
+                )
+            X = self._validate_data(
+                X, copy=self.copy, dtype=[np.float64, np.float32], reset=first_pass
+            )
+        n_samples, n_features = X.shape
+        if first_pass:
+            self.components_ = None
+
+        if self.n_components is None:
+            if self.components_ is None:
+                self.n_components_ = min(n_samples, n_features)
+            else:
+                self.n_components_ = self.components_.shape[0]
+        elif not self.n_components <= n_features:
+            raise ValueError(
+                "n_components=%r invalid for n_features=%d, need "
+                "more rows than columns for IncrementalPCA "
+                "processing" % (self.n_components, n_features)
+            )
+        elif not self.n_components <= n_samples:
+            raise ValueError(
+                "n_components=%r must be less or equal to "
+                "the batch number of samples "
+                "%d." % (self.n_components, n_samples)
+            )
+        else:
+            self.n_components_ = self.n_components
+
+        if (self.components_ is not None) and (
+            self.components_.shape[0] != self.n_components_
+        ):
+            raise ValueError(
+                "Number of input features has changed from %i "
+                "to %i between calls to partial_fit! Try "
+                "setting n_components to a fixed value."
+                % (self.components_.shape[0], self.n_components_)
+            )
+
+        # This is the first partial_fit
+        if not hasattr(self, "n_samples_seen_"):
+            self.n_samples_seen_ = 0
+            self.mean_ = 0.0
+            self.var_ = 0.0
+
+        # Update stats - they are 0 if this is the first step
+        col_mean, col_var, n_total_samples = _incremental_mean_and_var(
+            X,
+            last_mean=self.mean_,
+            last_variance=self.var_,
+            last_sample_count=np.repeat(self.n_samples_seen_, X.shape[1]),
+        )
+        n_total_samples = n_total_samples[0]
+
+        # Whitening
+        if self.n_samples_seen_ == 0:
+            # If it is the first step, simply whiten X
+            X -= col_mean
+        else:
+            col_batch_mean = np.mean(X, axis=0)
+            X -= col_batch_mean
+            # Build matrix of combined previous basis and new data
+            mean_correction = np.sqrt(
+                (self.n_samples_seen_ / n_total_samples) * n_samples
+            ) * (self.mean_ - col_batch_mean)
+            X = np.vstack(
+                (
+                    self.singular_values_.reshape((-1, 1)) * self.components_,
+                    X,
+                    mean_correction,
+                )
+            )
+
+        U, S, Vt = linalg.svd(X, full_matrices=False, check_finite=False)
+        U, Vt = svd_flip(U, Vt, u_based_decision=False)
+        explained_variance = S**2 / (n_total_samples - 1)
+        explained_variance_ratio = S**2 / np.sum(col_var * n_total_samples)
+
+        self.n_samples_seen_ = n_total_samples
+        self.components_ = Vt[: self.n_components_]
+        self.singular_values_ = S[: self.n_components_]
+        self.mean_ = col_mean
+        self.var_ = col_var
+        self.explained_variance_ = explained_variance[: self.n_components_]
+        self.explained_variance_ratio_ = explained_variance_ratio[: self.n_components_]
+        # we already checked `self.n_components <= n_samples` above
+        if self.n_components_ not in (n_samples, n_features):
+            self.noise_variance_ = explained_variance[self.n_components_ :].mean()
+        else:
+            self.noise_variance_ = 0.0
+        return self
+
+    def transform(self, X):
+        """Apply dimensionality reduction to X.
+
+        X is projected on the first principal components previously extracted
+        from a training set, using minibatches of size batch_size if X is
+        sparse.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} 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 : ndarray of shape (n_samples, n_components)
+            Projection of X in the first principal components.
+
+        Examples
+        --------
+
+        >>> import numpy as np
+        >>> from sklearn.decomposition import IncrementalPCA
+        >>> X = np.array([[-1, -1], [-2, -1], [-3, -2],
+        ...               [1, 1], [2, 1], [3, 2]])
+        >>> ipca = IncrementalPCA(n_components=2, batch_size=3)
+        >>> ipca.fit(X)
+        IncrementalPCA(batch_size=3, n_components=2)
+        >>> ipca.transform(X) # doctest: +SKIP
+        """
+        if sparse.issparse(X):
+            n_samples = X.shape[0]
+            output = []
+            for batch in gen_batches(
+                n_samples, self.batch_size_, min_batch_size=self.n_components or 0
+            ):
+                output.append(super().transform(X[batch].toarray()))
+            return np.vstack(output)
+        else:
+            return super().transform(X)

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_kernel_pca.py

@@ -0,0 +1,572 @@
+"""Kernel Principal Components Analysis."""
+
+# Author: Mathieu Blondel <[email protected]>
+#         Sylvain Marie <[email protected]>
+# License: BSD 3 clause
+
+from numbers import Integral, Real
+
+import numpy as np
+from scipy import linalg
+from scipy.linalg import eigh
+from scipy.sparse.linalg import eigsh
+
+from ..base import (
+    BaseEstimator,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from ..exceptions import NotFittedError
+from ..metrics.pairwise import pairwise_kernels
+from ..preprocessing import KernelCenterer
+from ..utils._arpack import _init_arpack_v0
+from ..utils._param_validation import Interval, StrOptions
+from ..utils.extmath import _randomized_eigsh, svd_flip
+from ..utils.validation import (
+    _check_psd_eigenvalues,
+    check_is_fitted,
+)
+
+
+class KernelPCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
+    """Kernel Principal component analysis (KPCA) [1]_.
+
+    Non-linear dimensionality reduction through the use of kernels (see
+    :ref:`metrics`).
+
+    It uses the :func:`scipy.linalg.eigh` LAPACK implementation of the full SVD
+    or the :func:`scipy.sparse.linalg.eigsh` ARPACK implementation of the
+    truncated SVD, depending on the shape of the input data and the number of
+    components to extract. It can also use a randomized truncated SVD by the
+    method proposed in [3]_, see `eigen_solver`.
+
+    For a usage example, see
+    :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py`.
+
+    Read more in the :ref:`User Guide <kernel_PCA>`.
+
+    Parameters
+    ----------
+    n_components : int, default=None
+        Number of components. If None, all non-zero components are kept.
+
+    kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed'} \
+            or callable, default='linear'
+        Kernel used for PCA.
+
+    gamma : float, default=None
+        Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other
+        kernels. If ``gamma`` is ``None``, then it is set to ``1/n_features``.
+
+    degree : float, default=3
+        Degree for poly kernels. Ignored by other kernels.
+
+    coef0 : float, default=1
+        Independent term in poly and sigmoid kernels.
+        Ignored by other kernels.
+
+    kernel_params : dict, default=None
+        Parameters (keyword arguments) and
+        values for kernel passed as callable object.
+        Ignored by other kernels.
+
+    alpha : float, default=1.0
+        Hyperparameter of the ridge regression that learns the
+        inverse transform (when fit_inverse_transform=True).
+
+    fit_inverse_transform : bool, default=False
+        Learn the inverse transform for non-precomputed kernels
+        (i.e. learn to find the pre-image of a point). This method is based
+        on [2]_.
+
+    eigen_solver : {'auto', 'dense', 'arpack', 'randomized'}, \
+            default='auto'
+        Select eigensolver to use. If `n_components` is much
+        less than the number of training samples, randomized (or arpack to a
+        smaller extent) may be more efficient than the dense eigensolver.
+        Randomized SVD is performed according to the method of Halko et al
+        [3]_.
+
+        auto :
+            the solver is selected by a default policy based on n_samples
+            (the number of training samples) and `n_components`:
+            if the number of components to extract is less than 10 (strict) and
+            the number of samples is more than 200 (strict), the 'arpack'
+            method is enabled. Otherwise the exact full eigenvalue
+            decomposition is computed and optionally truncated afterwards
+            ('dense' method).
+        dense :
+            run exact full eigenvalue decomposition calling the standard
+            LAPACK solver via `scipy.linalg.eigh`, and select the components
+            by postprocessing
+        arpack :
+            run SVD truncated to n_components calling ARPACK solver using
+            `scipy.sparse.linalg.eigsh`. It requires strictly
+            0 < n_components < n_samples
+        randomized :
+            run randomized SVD by the method of Halko et al. [3]_. The current
+            implementation selects eigenvalues based on their module; therefore
+            using this method can lead to unexpected results if the kernel is
+            not positive semi-definite. See also [4]_.
+
+        .. versionchanged:: 1.0
+           `'randomized'` was added.
+
+    tol : float, default=0
+        Convergence tolerance for arpack.
+        If 0, optimal value will be chosen by arpack.
+
+    max_iter : int, default=None
+        Maximum number of iterations for arpack.
+        If None, optimal value will be chosen by arpack.
+
+    iterated_power : int >= 0, or 'auto', default='auto'
+        Number of iterations for the power method computed by
+        svd_solver == 'randomized'. When 'auto', it is set to 7 when
+        `n_components < 0.1 * min(X.shape)`, other it is set to 4.
+
+        .. versionadded:: 1.0
+
+    remove_zero_eig : bool, default=False
+        If True, then all components with zero eigenvalues are removed, so
+        that the number of components in the output may be < n_components
+        (and sometimes even zero due to numerical instability).
+        When n_components is None, this parameter is ignored and components
+        with zero eigenvalues are removed regardless.
+
+    random_state : int, RandomState instance or None, default=None
+        Used when ``eigen_solver`` == 'arpack' or 'randomized'. Pass an int
+        for reproducible results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+        .. versionadded:: 0.18
+
+    copy_X : bool, default=True
+        If True, input X is copied and stored by the model in the `X_fit_`
+        attribute. If no further changes will be done to X, setting
+        `copy_X=False` saves memory by storing a reference.
+
+        .. versionadded:: 0.18
+
+    n_jobs : int, default=None
+        The number of parallel jobs to run.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+        .. versionadded:: 0.18
+
+    Attributes
+    ----------
+    eigenvalues_ : ndarray of shape (n_components,)
+        Eigenvalues of the centered kernel matrix in decreasing order.
+        If `n_components` and `remove_zero_eig` are not set,
+        then all values are stored.
+
+    eigenvectors_ : ndarray of shape (n_samples, n_components)
+        Eigenvectors of the centered kernel matrix. If `n_components` and
+        `remove_zero_eig` are not set, then all components are stored.
+
+    dual_coef_ : ndarray of shape (n_samples, n_features)
+        Inverse transform matrix. Only available when
+        ``fit_inverse_transform`` is True.
+
+    X_transformed_fit_ : ndarray of shape (n_samples, n_components)
+        Projection of the fitted data on the kernel principal components.
+        Only available when ``fit_inverse_transform`` is True.
+
+    X_fit_ : ndarray of shape (n_samples, n_features)
+        The data used to fit the model. If `copy_X=False`, then `X_fit_` is
+        a reference. This attribute is used for the calls to transform.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    gamma_ : float
+        Kernel coefficient for rbf, poly and sigmoid kernels. When `gamma`
+        is explicitly provided, this is just the same as `gamma`. When `gamma`
+        is `None`, this is the actual value of kernel coefficient.
+
+        .. versionadded:: 1.3
+
+    See Also
+    --------
+    FastICA : A fast algorithm for Independent Component Analysis.
+    IncrementalPCA : Incremental Principal Component Analysis.
+    NMF : Non-Negative Matrix Factorization.
+    PCA : Principal Component Analysis.
+    SparsePCA : Sparse Principal Component Analysis.
+    TruncatedSVD : Dimensionality reduction using truncated SVD.
+
+    References
+    ----------
+    .. [1] `Schölkopf, Bernhard, Alexander Smola, and Klaus-Robert Müller.
+       "Kernel principal component analysis."
+       International conference on artificial neural networks.
+       Springer, Berlin, Heidelberg, 1997.
+       <https://people.eecs.berkeley.edu/~wainwrig/stat241b/scholkopf_kernel.pdf>`_
+
+    .. [2] `Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf.
+       "Learning to find pre-images."
+       Advances in neural information processing systems 16 (2004): 449-456.
+       <https://papers.nips.cc/paper/2003/file/ac1ad983e08ad3304a97e147f522747e-Paper.pdf>`_
+
+    .. [3] :arxiv:`Halko, Nathan, Per-Gunnar Martinsson, and Joel A. Tropp.
+       "Finding structure with randomness: Probabilistic algorithms for
+       constructing approximate matrix decompositions."
+       SIAM review 53.2 (2011): 217-288. <0909.4061>`
+
+    .. [4] `Martinsson, Per-Gunnar, Vladimir Rokhlin, and Mark Tygert.
+       "A randomized algorithm for the decomposition of matrices."
+       Applied and Computational Harmonic Analysis 30.1 (2011): 47-68.
+       <https://www.sciencedirect.com/science/article/pii/S1063520310000242>`_
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.decomposition import KernelPCA
+    >>> X, _ = load_digits(return_X_y=True)
+    >>> transformer = KernelPCA(n_components=7, kernel='linear')
+    >>> X_transformed = transformer.fit_transform(X)
+    >>> X_transformed.shape
+    (1797, 7)
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [
+            Interval(Integral, 1, None, closed="left"),
+            None,
+        ],
+        "kernel": [
+            StrOptions({"linear", "poly", "rbf", "sigmoid", "cosine", "precomputed"}),
+            callable,
+        ],
+        "gamma": [
+            Interval(Real, 0, None, closed="left"),
+            None,
+        ],
+        "degree": [Interval(Real, 0, None, closed="left")],
+        "coef0": [Interval(Real, None, None, closed="neither")],
+        "kernel_params": [dict, None],
+        "alpha": [Interval(Real, 0, None, closed="left")],
+        "fit_inverse_transform": ["boolean"],
+        "eigen_solver": [StrOptions({"auto", "dense", "arpack", "randomized"})],
+        "tol": [Interval(Real, 0, None, closed="left")],
+        "max_iter": [
+            Interval(Integral, 1, None, closed="left"),
+            None,
+        ],
+        "iterated_power": [
+            Interval(Integral, 0, None, closed="left"),
+            StrOptions({"auto"}),
+        ],
+        "remove_zero_eig": ["boolean"],
+        "random_state": ["random_state"],
+        "copy_X": ["boolean"],
+        "n_jobs": [None, Integral],
+    }
+
+    def __init__(
+        self,
+        n_components=None,
+        *,
+        kernel="linear",
+        gamma=None,
+        degree=3,
+        coef0=1,
+        kernel_params=None,
+        alpha=1.0,
+        fit_inverse_transform=False,
+        eigen_solver="auto",
+        tol=0,
+        max_iter=None,
+        iterated_power="auto",
+        remove_zero_eig=False,
+        random_state=None,
+        copy_X=True,
+        n_jobs=None,
+    ):
+        self.n_components = n_components
+        self.kernel = kernel
+        self.kernel_params = kernel_params
+        self.gamma = gamma
+        self.degree = degree
+        self.coef0 = coef0
+        self.alpha = alpha
+        self.fit_inverse_transform = fit_inverse_transform
+        self.eigen_solver = eigen_solver
+        self.tol = tol
+        self.max_iter = max_iter
+        self.iterated_power = iterated_power
+        self.remove_zero_eig = remove_zero_eig
+        self.random_state = random_state
+        self.n_jobs = n_jobs
+        self.copy_X = copy_X
+
+    def _get_kernel(self, X, Y=None):
+        if callable(self.kernel):
+            params = self.kernel_params or {}
+        else:
+            params = {"gamma": self.gamma_, "degree": self.degree, "coef0": self.coef0}
+        return pairwise_kernels(
+            X, Y, metric=self.kernel, filter_params=True, n_jobs=self.n_jobs, **params
+        )
+
+    def _fit_transform(self, K):
+        """Fit's using kernel K"""
+        # center kernel
+        K = self._centerer.fit_transform(K)
+
+        # adjust n_components according to user inputs
+        if self.n_components is None:
+            n_components = K.shape[0]  # use all dimensions
+        else:
+            n_components = min(K.shape[0], self.n_components)
+
+        # compute eigenvectors
+        if self.eigen_solver == "auto":
+            if K.shape[0] > 200 and n_components < 10:
+                eigen_solver = "arpack"
+            else:
+                eigen_solver = "dense"
+        else:
+            eigen_solver = self.eigen_solver
+
+        if eigen_solver == "dense":
+            # Note: subset_by_index specifies the indices of smallest/largest to return
+            self.eigenvalues_, self.eigenvectors_ = eigh(
+                K, subset_by_index=(K.shape[0] - n_components, K.shape[0] - 1)
+            )
+        elif eigen_solver == "arpack":
+            v0 = _init_arpack_v0(K.shape[0], self.random_state)
+            self.eigenvalues_, self.eigenvectors_ = eigsh(
+                K, n_components, which="LA", tol=self.tol, maxiter=self.max_iter, v0=v0
+            )
+        elif eigen_solver == "randomized":
+            self.eigenvalues_, self.eigenvectors_ = _randomized_eigsh(
+                K,
+                n_components=n_components,
+                n_iter=self.iterated_power,
+                random_state=self.random_state,
+                selection="module",
+            )
+
+        # make sure that the eigenvalues are ok and fix numerical issues
+        self.eigenvalues_ = _check_psd_eigenvalues(
+            self.eigenvalues_, enable_warnings=False
+        )
+
+        # flip eigenvectors' sign to enforce deterministic output
+        self.eigenvectors_, _ = svd_flip(
+            self.eigenvectors_, np.zeros_like(self.eigenvectors_).T
+        )
+
+        # sort eigenvectors in descending order
+        indices = self.eigenvalues_.argsort()[::-1]
+        self.eigenvalues_ = self.eigenvalues_[indices]
+        self.eigenvectors_ = self.eigenvectors_[:, indices]
+
+        # remove eigenvectors with a zero eigenvalue (null space) if required
+        if self.remove_zero_eig or self.n_components is None:
+            self.eigenvectors_ = self.eigenvectors_[:, self.eigenvalues_ > 0]
+            self.eigenvalues_ = self.eigenvalues_[self.eigenvalues_ > 0]
+
+        # Maintenance note on Eigenvectors normalization
+        # ----------------------------------------------
+        # there is a link between
+        # the eigenvectors of K=Phi(X)'Phi(X) and the ones of Phi(X)Phi(X)'
+        # if v is an eigenvector of K
+        #     then Phi(X)v  is an eigenvector of Phi(X)Phi(X)'
+        # if u is an eigenvector of Phi(X)Phi(X)'
+        #     then Phi(X)'u is an eigenvector of Phi(X)'Phi(X)
+        #
+        # At this stage our self.eigenvectors_ (the v) have norm 1, we need to scale
+        # them so that eigenvectors in kernel feature space (the u) have norm=1
+        # instead
+        #
+        # We COULD scale them here:
+        #       self.eigenvectors_ = self.eigenvectors_ / np.sqrt(self.eigenvalues_)
+        #
+        # But choose to perform that LATER when needed, in `fit()` and in
+        # `transform()`.
+
+        return K
+
+    def _fit_inverse_transform(self, X_transformed, X):
+        if hasattr(X, "tocsr"):
+            raise NotImplementedError(
+                "Inverse transform not implemented for sparse matrices!"
+            )
+
+        n_samples = X_transformed.shape[0]
+        K = self._get_kernel(X_transformed)
+        K.flat[:: n_samples + 1] += self.alpha
+        self.dual_coef_ = linalg.solve(K, X, assume_a="pos", overwrite_a=True)
+        self.X_transformed_fit_ = X_transformed
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the model from data in X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        if self.fit_inverse_transform and self.kernel == "precomputed":
+            raise ValueError("Cannot fit_inverse_transform with a precomputed kernel.")
+        X = self._validate_data(X, accept_sparse="csr", copy=self.copy_X)
+        self.gamma_ = 1 / X.shape[1] if self.gamma is None else self.gamma
+        self._centerer = KernelCenterer().set_output(transform="default")
+        K = self._get_kernel(X)
+        self._fit_transform(K)
+
+        if self.fit_inverse_transform:
+            # no need to use the kernel to transform X, use shortcut expression
+            X_transformed = self.eigenvectors_ * np.sqrt(self.eigenvalues_)
+
+            self._fit_inverse_transform(X_transformed, X)
+
+        self.X_fit_ = X
+        return self
+
+    def fit_transform(self, X, y=None, **params):
+        """Fit the model from data in X and transform X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        **params : kwargs
+            Parameters (keyword arguments) and values passed to
+            the fit_transform instance.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Returns the instance itself.
+        """
+        self.fit(X, **params)
+
+        # no need to use the kernel to transform X, use shortcut expression
+        X_transformed = self.eigenvectors_ * np.sqrt(self.eigenvalues_)
+
+        if self.fit_inverse_transform:
+            self._fit_inverse_transform(X_transformed, X)
+
+        return X_transformed
+
+    def transform(self, X):
+        """Transform X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Returns the instance itself.
+        """
+        check_is_fitted(self)
+        X = self._validate_data(X, accept_sparse="csr", reset=False)
+
+        # Compute centered gram matrix between X and training data X_fit_
+        K = self._centerer.transform(self._get_kernel(X, self.X_fit_))
+
+        # scale eigenvectors (properly account for null-space for dot product)
+        non_zeros = np.flatnonzero(self.eigenvalues_)
+        scaled_alphas = np.zeros_like(self.eigenvectors_)
+        scaled_alphas[:, non_zeros] = self.eigenvectors_[:, non_zeros] / np.sqrt(
+            self.eigenvalues_[non_zeros]
+        )
+
+        # Project with a scalar product between K and the scaled eigenvectors
+        return np.dot(K, scaled_alphas)
+
+    def inverse_transform(self, X):
+        """Transform X back to original space.
+
+        ``inverse_transform`` approximates the inverse transformation using
+        a learned pre-image. The pre-image is learned by kernel ridge
+        regression of the original data on their low-dimensional representation
+        vectors.
+
+        .. note:
+            :meth:`~sklearn.decomposition.fit` internally uses a centered
+            kernel. As the centered kernel no longer contains the information
+            of the mean of kernel features, such information is not taken into
+            account in reconstruction.
+
+        .. note::
+            When users want to compute inverse transformation for 'linear'
+            kernel, it is recommended that they use
+            :class:`~sklearn.decomposition.PCA` instead. Unlike
+            :class:`~sklearn.decomposition.PCA`,
+            :class:`~sklearn.decomposition.KernelPCA`'s ``inverse_transform``
+            does not reconstruct the mean of data when 'linear' kernel is used
+            due to the use of centered kernel.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_components)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_features)
+            Returns the instance itself.
+
+        References
+        ----------
+        `Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf.
+        "Learning to find pre-images."
+        Advances in neural information processing systems 16 (2004): 449-456.
+        <https://papers.nips.cc/paper/2003/file/ac1ad983e08ad3304a97e147f522747e-Paper.pdf>`_
+        """
+        if not self.fit_inverse_transform:
+            raise NotFittedError(
+                "The fit_inverse_transform parameter was not"
+                " set to True when instantiating and hence "
+                "the inverse transform is not available."
+            )
+
+        K = self._get_kernel(X, self.X_transformed_fit_)
+        return np.dot(K, self.dual_coef_)
+
+    def _more_tags(self):
+        return {
+            "preserves_dtype": [np.float64, np.float32],
+            "pairwise": self.kernel == "precomputed",
+        }
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.eigenvalues_.shape[0]

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_lda.py

@@ -0,0 +1,929 @@
+"""
+
+=============================================================
+Online Latent Dirichlet Allocation with variational inference
+=============================================================
+
+This implementation is modified from Matthew D. Hoffman's onlineldavb code
+Link: https://github.com/blei-lab/onlineldavb
+"""
+
+# Author: Chyi-Kwei Yau
+# Author: Matthew D. Hoffman (original onlineldavb implementation)
+from numbers import Integral, Real
+
+import numpy as np
+import scipy.sparse as sp
+from joblib import effective_n_jobs
+from scipy.special import gammaln, logsumexp
+
+from ..base import (
+    BaseEstimator,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from ..utils import check_random_state, gen_batches, gen_even_slices
+from ..utils._param_validation import Interval, StrOptions
+from ..utils.parallel import Parallel, delayed
+from ..utils.validation import check_is_fitted, check_non_negative
+from ._online_lda_fast import (
+    _dirichlet_expectation_1d as cy_dirichlet_expectation_1d,
+)
+from ._online_lda_fast import (
+    _dirichlet_expectation_2d,
+)
+from ._online_lda_fast import (
+    mean_change as cy_mean_change,
+)
+
+EPS = np.finfo(float).eps
+
+
+def _update_doc_distribution(
+    X,
+    exp_topic_word_distr,
+    doc_topic_prior,
+    max_doc_update_iter,
+    mean_change_tol,
+    cal_sstats,
+    random_state,
+):
+    """E-step: update document-topic distribution.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Document word matrix.
+
+    exp_topic_word_distr : ndarray of shape (n_topics, n_features)
+        Exponential value of expectation of log topic word distribution.
+        In the literature, this is `exp(E[log(beta)])`.
+
+    doc_topic_prior : float
+        Prior of document topic distribution `theta`.
+
+    max_doc_update_iter : int
+        Max number of iterations for updating document topic distribution in
+        the E-step.
+
+    mean_change_tol : float
+        Stopping tolerance for updating document topic distribution in E-step.
+
+    cal_sstats : bool
+        Parameter that indicate to calculate sufficient statistics or not.
+        Set `cal_sstats` to `True` when we need to run M-step.
+
+    random_state : RandomState instance or None
+        Parameter that indicate how to initialize document topic distribution.
+        Set `random_state` to None will initialize document topic distribution
+        to a constant number.
+
+    Returns
+    -------
+    (doc_topic_distr, suff_stats) :
+        `doc_topic_distr` is unnormalized topic distribution for each document.
+        In the literature, this is `gamma`. we can calculate `E[log(theta)]`
+        from it.
+        `suff_stats` is expected sufficient statistics for the M-step.
+            When `cal_sstats == False`, this will be None.
+
+    """
+    is_sparse_x = sp.issparse(X)
+    n_samples, n_features = X.shape
+    n_topics = exp_topic_word_distr.shape[0]
+
+    if random_state:
+        doc_topic_distr = random_state.gamma(100.0, 0.01, (n_samples, n_topics)).astype(
+            X.dtype, copy=False
+        )
+    else:
+        doc_topic_distr = np.ones((n_samples, n_topics), dtype=X.dtype)
+
+    # In the literature, this is `exp(E[log(theta)])`
+    exp_doc_topic = np.exp(_dirichlet_expectation_2d(doc_topic_distr))
+
+    # diff on `component_` (only calculate it when `cal_diff` is True)
+    suff_stats = (
+        np.zeros(exp_topic_word_distr.shape, dtype=X.dtype) if cal_sstats else None
+    )
+
+    if is_sparse_x:
+        X_data = X.data
+        X_indices = X.indices
+        X_indptr = X.indptr
+
+    # These cython functions are called in a nested loop on usually very small arrays
+    # (length=n_topics). In that case, finding the appropriate signature of the
+    # fused-typed function can be more costly than its execution, hence the dispatch
+    # is done outside of the loop.
+    ctype = "float" if X.dtype == np.float32 else "double"
+    mean_change = cy_mean_change[ctype]
+    dirichlet_expectation_1d = cy_dirichlet_expectation_1d[ctype]
+    eps = np.finfo(X.dtype).eps
+
+    for idx_d in range(n_samples):
+        if is_sparse_x:
+            ids = X_indices[X_indptr[idx_d] : X_indptr[idx_d + 1]]
+            cnts = X_data[X_indptr[idx_d] : X_indptr[idx_d + 1]]
+        else:
+            ids = np.nonzero(X[idx_d, :])[0]
+            cnts = X[idx_d, ids]
+
+        doc_topic_d = doc_topic_distr[idx_d, :]
+        # The next one is a copy, since the inner loop overwrites it.
+        exp_doc_topic_d = exp_doc_topic[idx_d, :].copy()
+        exp_topic_word_d = exp_topic_word_distr[:, ids]
+
+        # Iterate between `doc_topic_d` and `norm_phi` until convergence
+        for _ in range(0, max_doc_update_iter):
+            last_d = doc_topic_d
+
+            # The optimal phi_{dwk} is proportional to
+            # exp(E[log(theta_{dk})]) * exp(E[log(beta_{dw})]).
+            norm_phi = np.dot(exp_doc_topic_d, exp_topic_word_d) + eps
+
+            doc_topic_d = exp_doc_topic_d * np.dot(cnts / norm_phi, exp_topic_word_d.T)
+            # Note: adds doc_topic_prior to doc_topic_d, in-place.
+            dirichlet_expectation_1d(doc_topic_d, doc_topic_prior, exp_doc_topic_d)
+
+            if mean_change(last_d, doc_topic_d) < mean_change_tol:
+                break
+        doc_topic_distr[idx_d, :] = doc_topic_d
+
+        # Contribution of document d to the expected sufficient
+        # statistics for the M step.
+        if cal_sstats:
+            norm_phi = np.dot(exp_doc_topic_d, exp_topic_word_d) + eps
+            suff_stats[:, ids] += np.outer(exp_doc_topic_d, cnts / norm_phi)
+
+    return (doc_topic_distr, suff_stats)
+
+
+class LatentDirichletAllocation(
+    ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator
+):
+    """Latent Dirichlet Allocation with online variational Bayes algorithm.
+
+    The implementation is based on [1]_ and [2]_.
+
+    .. versionadded:: 0.17
+
+    Read more in the :ref:`User Guide <LatentDirichletAllocation>`.
+
+    Parameters
+    ----------
+    n_components : int, default=10
+        Number of topics.
+
+        .. versionchanged:: 0.19
+            ``n_topics`` was renamed to ``n_components``
+
+    doc_topic_prior : float, default=None
+        Prior of document topic distribution `theta`. If the value is None,
+        defaults to `1 / n_components`.
+        In [1]_, this is called `alpha`.
+
+    topic_word_prior : float, default=None
+        Prior of topic word distribution `beta`. If the value is None, defaults
+        to `1 / n_components`.
+        In [1]_, this is called `eta`.
+
+    learning_method : {'batch', 'online'}, default='batch'
+        Method used to update `_component`. Only used in :meth:`fit` method.
+        In general, if the data size is large, the online update will be much
+        faster than the batch update.
+
+        Valid options::
+
+            'batch': Batch variational Bayes method. Use all training data in
+                each EM update.
+                Old `components_` will be overwritten in each iteration.
+            'online': Online variational Bayes method. In each EM update, use
+                mini-batch of training data to update the ``components_``
+                variable incrementally. The learning rate is controlled by the
+                ``learning_decay`` and the ``learning_offset`` parameters.
+
+        .. versionchanged:: 0.20
+            The default learning method is now ``"batch"``.
+
+    learning_decay : float, default=0.7
+        It is a parameter that control learning rate in the online learning
+        method. The value should be set between (0.5, 1.0] to guarantee
+        asymptotic convergence. When the value is 0.0 and batch_size is
+        ``n_samples``, the update method is same as batch learning. In the
+        literature, this is called kappa.
+
+    learning_offset : float, default=10.0
+        A (positive) parameter that downweights early iterations in online
+        learning.  It should be greater than 1.0. In the literature, this is
+        called tau_0.
+
+    max_iter : int, default=10
+        The maximum number of passes over the training data (aka epochs).
+        It only impacts the behavior in the :meth:`fit` method, and not the
+        :meth:`partial_fit` method.
+
+    batch_size : int, default=128
+        Number of documents to use in each EM iteration. Only used in online
+        learning.
+
+    evaluate_every : int, default=-1
+        How often to evaluate perplexity. Only used in `fit` method.
+        set it to 0 or negative number to not evaluate perplexity in
+        training at all. Evaluating perplexity can help you check convergence
+        in training process, but it will also increase total training time.
+        Evaluating perplexity in every iteration might increase training time
+        up to two-fold.
+
+    total_samples : int, default=1e6
+        Total number of documents. Only used in the :meth:`partial_fit` method.
+
+    perp_tol : float, default=1e-1
+        Perplexity tolerance in batch learning. Only used when
+        ``evaluate_every`` is greater than 0.
+
+    mean_change_tol : float, default=1e-3
+        Stopping tolerance for updating document topic distribution in E-step.
+
+    max_doc_update_iter : int, default=100
+        Max number of iterations for updating document topic distribution in
+        the E-step.
+
+    n_jobs : int, default=None
+        The number of jobs to use in the E-step.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    verbose : int, default=0
+        Verbosity level.
+
+    random_state : int, RandomState instance or None, default=None
+        Pass an int for reproducible results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        Variational parameters for topic word distribution. Since the complete
+        conditional for topic word distribution is a Dirichlet,
+        ``components_[i, j]`` can be viewed as pseudocount that represents the
+        number of times word `j` was assigned to topic `i`.
+        It can also be viewed as distribution over the words for each topic
+        after normalization:
+        ``model.components_ / model.components_.sum(axis=1)[:, np.newaxis]``.
+
+    exp_dirichlet_component_ : ndarray of shape (n_components, n_features)
+        Exponential value of expectation of log topic word distribution.
+        In the literature, this is `exp(E[log(beta)])`.
+
+    n_batch_iter_ : int
+        Number of iterations of the EM step.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : int
+        Number of passes over the dataset.
+
+    bound_ : float
+        Final perplexity score on training set.
+
+    doc_topic_prior_ : float
+        Prior of document topic distribution `theta`. If the value is None,
+        it is `1 / n_components`.
+
+    random_state_ : RandomState instance
+        RandomState instance that is generated either from a seed, the random
+        number generator or by `np.random`.
+
+    topic_word_prior_ : float
+        Prior of topic word distribution `beta`. If the value is None, it is
+        `1 / n_components`.
+
+    See Also
+    --------
+    sklearn.discriminant_analysis.LinearDiscriminantAnalysis:
+        A classifier with a linear decision boundary, generated by fitting
+        class conditional densities to the data and using Bayes' rule.
+
+    References
+    ----------
+    .. [1] "Online Learning for Latent Dirichlet Allocation", Matthew D.
+           Hoffman, David M. Blei, Francis Bach, 2010
+           https://github.com/blei-lab/onlineldavb
+
+    .. [2] "Stochastic Variational Inference", Matthew D. Hoffman,
+           David M. Blei, Chong Wang, John Paisley, 2013
+
+    Examples
+    --------
+    >>> from sklearn.decomposition import LatentDirichletAllocation
+    >>> from sklearn.datasets import make_multilabel_classification
+    >>> # This produces a feature matrix of token counts, similar to what
+    >>> # CountVectorizer would produce on text.
+    >>> X, _ = make_multilabel_classification(random_state=0)
+    >>> lda = LatentDirichletAllocation(n_components=5,
+    ...     random_state=0)
+    >>> lda.fit(X)
+    LatentDirichletAllocation(...)
+    >>> # get topics for some given samples:
+    >>> lda.transform(X[-2:])
+    array([[0.00360392, 0.25499205, 0.0036211 , 0.64236448, 0.09541846],
+           [0.15297572, 0.00362644, 0.44412786, 0.39568399, 0.003586  ]])
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [Interval(Integral, 0, None, closed="neither")],
+        "doc_topic_prior": [None, Interval(Real, 0, 1, closed="both")],
+        "topic_word_prior": [None, Interval(Real, 0, 1, closed="both")],
+        "learning_method": [StrOptions({"batch", "online"})],
+        "learning_decay": [Interval(Real, 0, 1, closed="both")],
+        "learning_offset": [Interval(Real, 1.0, None, closed="left")],
+        "max_iter": [Interval(Integral, 0, None, closed="left")],
+        "batch_size": [Interval(Integral, 0, None, closed="neither")],
+        "evaluate_every": [Interval(Integral, None, None, closed="neither")],
+        "total_samples": [Interval(Real, 0, None, closed="neither")],
+        "perp_tol": [Interval(Real, 0, None, closed="left")],
+        "mean_change_tol": [Interval(Real, 0, None, closed="left")],
+        "max_doc_update_iter": [Interval(Integral, 0, None, closed="left")],
+        "n_jobs": [None, Integral],
+        "verbose": ["verbose"],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(
+        self,
+        n_components=10,
+        *,
+        doc_topic_prior=None,
+        topic_word_prior=None,
+        learning_method="batch",
+        learning_decay=0.7,
+        learning_offset=10.0,
+        max_iter=10,
+        batch_size=128,
+        evaluate_every=-1,
+        total_samples=1e6,
+        perp_tol=1e-1,
+        mean_change_tol=1e-3,
+        max_doc_update_iter=100,
+        n_jobs=None,
+        verbose=0,
+        random_state=None,
+    ):
+        self.n_components = n_components
+        self.doc_topic_prior = doc_topic_prior
+        self.topic_word_prior = topic_word_prior
+        self.learning_method = learning_method
+        self.learning_decay = learning_decay
+        self.learning_offset = learning_offset
+        self.max_iter = max_iter
+        self.batch_size = batch_size
+        self.evaluate_every = evaluate_every
+        self.total_samples = total_samples
+        self.perp_tol = perp_tol
+        self.mean_change_tol = mean_change_tol
+        self.max_doc_update_iter = max_doc_update_iter
+        self.n_jobs = n_jobs
+        self.verbose = verbose
+        self.random_state = random_state
+
+    def _init_latent_vars(self, n_features, dtype=np.float64):
+        """Initialize latent variables."""
+
+        self.random_state_ = check_random_state(self.random_state)
+        self.n_batch_iter_ = 1
+        self.n_iter_ = 0
+
+        if self.doc_topic_prior is None:
+            self.doc_topic_prior_ = 1.0 / self.n_components
+        else:
+            self.doc_topic_prior_ = self.doc_topic_prior
+
+        if self.topic_word_prior is None:
+            self.topic_word_prior_ = 1.0 / self.n_components
+        else:
+            self.topic_word_prior_ = self.topic_word_prior
+
+        init_gamma = 100.0
+        init_var = 1.0 / init_gamma
+        # In the literature, this is called `lambda`
+        self.components_ = self.random_state_.gamma(
+            init_gamma, init_var, (self.n_components, n_features)
+        ).astype(dtype, copy=False)
+
+        # In the literature, this is `exp(E[log(beta)])`
+        self.exp_dirichlet_component_ = np.exp(
+            _dirichlet_expectation_2d(self.components_)
+        )
+
+    def _e_step(self, X, cal_sstats, random_init, parallel=None):
+        """E-step in EM update.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Document word matrix.
+
+        cal_sstats : bool
+            Parameter that indicate whether to calculate sufficient statistics
+            or not. Set ``cal_sstats`` to True when we need to run M-step.
+
+        random_init : bool
+            Parameter that indicate whether to initialize document topic
+            distribution randomly in the E-step. Set it to True in training
+            steps.
+
+        parallel : joblib.Parallel, default=None
+            Pre-initialized instance of joblib.Parallel.
+
+        Returns
+        -------
+        (doc_topic_distr, suff_stats) :
+            `doc_topic_distr` is unnormalized topic distribution for each
+            document. In the literature, this is called `gamma`.
+            `suff_stats` is expected sufficient statistics for the M-step.
+            When `cal_sstats == False`, it will be None.
+
+        """
+
+        # Run e-step in parallel
+        random_state = self.random_state_ if random_init else None
+
+        # TODO: make Parallel._effective_n_jobs public instead?
+        n_jobs = effective_n_jobs(self.n_jobs)
+        if parallel is None:
+            parallel = Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1))
+        results = parallel(
+            delayed(_update_doc_distribution)(
+                X[idx_slice, :],
+                self.exp_dirichlet_component_,
+                self.doc_topic_prior_,
+                self.max_doc_update_iter,
+                self.mean_change_tol,
+                cal_sstats,
+                random_state,
+            )
+            for idx_slice in gen_even_slices(X.shape[0], n_jobs)
+        )
+
+        # merge result
+        doc_topics, sstats_list = zip(*results)
+        doc_topic_distr = np.vstack(doc_topics)
+
+        if cal_sstats:
+            # This step finishes computing the sufficient statistics for the
+            # M-step.
+            suff_stats = np.zeros(self.components_.shape, dtype=self.components_.dtype)
+            for sstats in sstats_list:
+                suff_stats += sstats
+            suff_stats *= self.exp_dirichlet_component_
+        else:
+            suff_stats = None
+
+        return (doc_topic_distr, suff_stats)
+
+    def _em_step(self, X, total_samples, batch_update, parallel=None):
+        """EM update for 1 iteration.
+
+        update `_component` by batch VB or online VB.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Document word matrix.
+
+        total_samples : int
+            Total number of documents. It is only used when
+            batch_update is `False`.
+
+        batch_update : bool
+            Parameter that controls updating method.
+            `True` for batch learning, `False` for online learning.
+
+        parallel : joblib.Parallel, default=None
+            Pre-initialized instance of joblib.Parallel
+
+        Returns
+        -------
+        doc_topic_distr : ndarray of shape (n_samples, n_components)
+            Unnormalized document topic distribution.
+        """
+
+        # E-step
+        _, suff_stats = self._e_step(
+            X, cal_sstats=True, random_init=True, parallel=parallel
+        )
+
+        # M-step
+        if batch_update:
+            self.components_ = self.topic_word_prior_ + suff_stats
+        else:
+            # online update
+            # In the literature, the weight is `rho`
+            weight = np.power(
+                self.learning_offset + self.n_batch_iter_, -self.learning_decay
+            )
+            doc_ratio = float(total_samples) / X.shape[0]
+            self.components_ *= 1 - weight
+            self.components_ += weight * (
+                self.topic_word_prior_ + doc_ratio * suff_stats
+            )
+
+        # update `component_` related variables
+        self.exp_dirichlet_component_ = np.exp(
+            _dirichlet_expectation_2d(self.components_)
+        )
+        self.n_batch_iter_ += 1
+        return
+
+    def _more_tags(self):
+        return {
+            "preserves_dtype": [np.float64, np.float32],
+            "requires_positive_X": True,
+        }
+
+    def _check_non_neg_array(self, X, reset_n_features, whom):
+        """check X format
+
+        check X format and make sure no negative value in X.
+
+        Parameters
+        ----------
+        X :  array-like or sparse matrix
+
+        """
+        dtype = [np.float64, np.float32] if reset_n_features else self.components_.dtype
+
+        X = self._validate_data(
+            X,
+            reset=reset_n_features,
+            accept_sparse="csr",
+            dtype=dtype,
+        )
+        check_non_negative(X, whom)
+
+        return X
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def partial_fit(self, X, y=None):
+        """Online VB with Mini-Batch update.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Document word matrix.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self
+            Partially fitted estimator.
+        """
+        first_time = not hasattr(self, "components_")
+
+        X = self._check_non_neg_array(
+            X, reset_n_features=first_time, whom="LatentDirichletAllocation.partial_fit"
+        )
+        n_samples, n_features = X.shape
+        batch_size = self.batch_size
+
+        # initialize parameters or check
+        if first_time:
+            self._init_latent_vars(n_features, dtype=X.dtype)
+
+        if n_features != self.components_.shape[1]:
+            raise ValueError(
+                "The provided data has %d dimensions while "
+                "the model was trained with feature size %d."
+                % (n_features, self.components_.shape[1])
+            )
+
+        n_jobs = effective_n_jobs(self.n_jobs)
+        with Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) as parallel:
+            for idx_slice in gen_batches(n_samples, batch_size):
+                self._em_step(
+                    X[idx_slice, :],
+                    total_samples=self.total_samples,
+                    batch_update=False,
+                    parallel=parallel,
+                )
+
+        return self
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Learn model for the data X with variational Bayes method.
+
+        When `learning_method` is 'online', use mini-batch update.
+        Otherwise, use batch update.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Document word matrix.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self
+            Fitted estimator.
+        """
+        X = self._check_non_neg_array(
+            X, reset_n_features=True, whom="LatentDirichletAllocation.fit"
+        )
+        n_samples, n_features = X.shape
+        max_iter = self.max_iter
+        evaluate_every = self.evaluate_every
+        learning_method = self.learning_method
+
+        batch_size = self.batch_size
+
+        # initialize parameters
+        self._init_latent_vars(n_features, dtype=X.dtype)
+        # change to perplexity later
+        last_bound = None
+        n_jobs = effective_n_jobs(self.n_jobs)
+        with Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) as parallel:
+            for i in range(max_iter):
+                if learning_method == "online":
+                    for idx_slice in gen_batches(n_samples, batch_size):
+                        self._em_step(
+                            X[idx_slice, :],
+                            total_samples=n_samples,
+                            batch_update=False,
+                            parallel=parallel,
+                        )
+                else:
+                    # batch update
+                    self._em_step(
+                        X, total_samples=n_samples, batch_update=True, parallel=parallel
+                    )
+
+                # check perplexity
+                if evaluate_every > 0 and (i + 1) % evaluate_every == 0:
+                    doc_topics_distr, _ = self._e_step(
+                        X, cal_sstats=False, random_init=False, parallel=parallel
+                    )
+                    bound = self._perplexity_precomp_distr(
+                        X, doc_topics_distr, sub_sampling=False
+                    )
+                    if self.verbose:
+                        print(
+                            "iteration: %d of max_iter: %d, perplexity: %.4f"
+                            % (i + 1, max_iter, bound)
+                        )
+
+                    if last_bound and abs(last_bound - bound) < self.perp_tol:
+                        break
+                    last_bound = bound
+
+                elif self.verbose:
+                    print("iteration: %d of max_iter: %d" % (i + 1, max_iter))
+                self.n_iter_ += 1
+
+        # calculate final perplexity value on train set
+        doc_topics_distr, _ = self._e_step(
+            X, cal_sstats=False, random_init=False, parallel=parallel
+        )
+        self.bound_ = self._perplexity_precomp_distr(
+            X, doc_topics_distr, sub_sampling=False
+        )
+
+        return self
+
+    def _unnormalized_transform(self, X):
+        """Transform data X according to fitted model.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Document word matrix.
+
+        Returns
+        -------
+        doc_topic_distr : ndarray of shape (n_samples, n_components)
+            Document topic distribution for X.
+        """
+        doc_topic_distr, _ = self._e_step(X, cal_sstats=False, random_init=False)
+
+        return doc_topic_distr
+
+    def transform(self, X):
+        """Transform data X according to the fitted model.
+
+           .. versionchanged:: 0.18
+              *doc_topic_distr* is now normalized
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Document word matrix.
+
+        Returns
+        -------
+        doc_topic_distr : ndarray of shape (n_samples, n_components)
+            Document topic distribution for X.
+        """
+        check_is_fitted(self)
+        X = self._check_non_neg_array(
+            X, reset_n_features=False, whom="LatentDirichletAllocation.transform"
+        )
+        doc_topic_distr = self._unnormalized_transform(X)
+        doc_topic_distr /= doc_topic_distr.sum(axis=1)[:, np.newaxis]
+        return doc_topic_distr
+
+    def _approx_bound(self, X, doc_topic_distr, sub_sampling):
+        """Estimate the variational bound.
+
+        Estimate the variational bound over "all documents" using only the
+        documents passed in as X. Since log-likelihood of each word cannot
+        be computed directly, we use this bound to estimate it.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Document word matrix.
+
+        doc_topic_distr : ndarray of shape (n_samples, n_components)
+            Document topic distribution. In the literature, this is called
+            gamma.
+
+        sub_sampling : bool, default=False
+            Compensate for subsampling of documents.
+            It is used in calculate bound in online learning.
+
+        Returns
+        -------
+        score : float
+
+        """
+
+        def _loglikelihood(prior, distr, dirichlet_distr, size):
+            # calculate log-likelihood
+            score = np.sum((prior - distr) * dirichlet_distr)
+            score += np.sum(gammaln(distr) - gammaln(prior))
+            score += np.sum(gammaln(prior * size) - gammaln(np.sum(distr, 1)))
+            return score
+
+        is_sparse_x = sp.issparse(X)
+        n_samples, n_components = doc_topic_distr.shape
+        n_features = self.components_.shape[1]
+        score = 0
+
+        dirichlet_doc_topic = _dirichlet_expectation_2d(doc_topic_distr)
+        dirichlet_component_ = _dirichlet_expectation_2d(self.components_)
+        doc_topic_prior = self.doc_topic_prior_
+        topic_word_prior = self.topic_word_prior_
+
+        if is_sparse_x:
+            X_data = X.data
+            X_indices = X.indices
+            X_indptr = X.indptr
+
+        # E[log p(docs | theta, beta)]
+        for idx_d in range(0, n_samples):
+            if is_sparse_x:
+                ids = X_indices[X_indptr[idx_d] : X_indptr[idx_d + 1]]
+                cnts = X_data[X_indptr[idx_d] : X_indptr[idx_d + 1]]
+            else:
+                ids = np.nonzero(X[idx_d, :])[0]
+                cnts = X[idx_d, ids]
+            temp = (
+                dirichlet_doc_topic[idx_d, :, np.newaxis] + dirichlet_component_[:, ids]
+            )
+            norm_phi = logsumexp(temp, axis=0)
+            score += np.dot(cnts, norm_phi)
+
+        # compute E[log p(theta | alpha) - log q(theta | gamma)]
+        score += _loglikelihood(
+            doc_topic_prior, doc_topic_distr, dirichlet_doc_topic, self.n_components
+        )
+
+        # Compensate for the subsampling of the population of documents
+        if sub_sampling:
+            doc_ratio = float(self.total_samples) / n_samples
+            score *= doc_ratio
+
+        # E[log p(beta | eta) - log q (beta | lambda)]
+        score += _loglikelihood(
+            topic_word_prior, self.components_, dirichlet_component_, n_features
+        )
+
+        return score
+
+    def score(self, X, y=None):
+        """Calculate approximate log-likelihood as score.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Document word matrix.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        score : float
+            Use approximate bound as score.
+        """
+        check_is_fitted(self)
+        X = self._check_non_neg_array(
+            X, reset_n_features=False, whom="LatentDirichletAllocation.score"
+        )
+
+        doc_topic_distr = self._unnormalized_transform(X)
+        score = self._approx_bound(X, doc_topic_distr, sub_sampling=False)
+        return score
+
+    def _perplexity_precomp_distr(self, X, doc_topic_distr=None, sub_sampling=False):
+        """Calculate approximate perplexity for data X with ability to accept
+        precomputed doc_topic_distr
+
+        Perplexity is defined as exp(-1. * log-likelihood per word)
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Document word matrix.
+
+        doc_topic_distr : ndarray of shape (n_samples, n_components), \
+                default=None
+            Document topic distribution.
+            If it is None, it will be generated by applying transform on X.
+
+        Returns
+        -------
+        score : float
+            Perplexity score.
+        """
+        if doc_topic_distr is None:
+            doc_topic_distr = self._unnormalized_transform(X)
+        else:
+            n_samples, n_components = doc_topic_distr.shape
+            if n_samples != X.shape[0]:
+                raise ValueError(
+                    "Number of samples in X and doc_topic_distr do not match."
+                )
+
+            if n_components != self.n_components:
+                raise ValueError("Number of topics does not match.")
+
+        current_samples = X.shape[0]
+        bound = self._approx_bound(X, doc_topic_distr, sub_sampling)
+
+        if sub_sampling:
+            word_cnt = X.sum() * (float(self.total_samples) / current_samples)
+        else:
+            word_cnt = X.sum()
+        perword_bound = bound / word_cnt
+
+        return np.exp(-1.0 * perword_bound)
+
+    def perplexity(self, X, sub_sampling=False):
+        """Calculate approximate perplexity for data X.
+
+        Perplexity is defined as exp(-1. * log-likelihood per word)
+
+        .. versionchanged:: 0.19
+           *doc_topic_distr* argument has been deprecated and is ignored
+           because user no longer has access to unnormalized distribution
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Document word matrix.
+
+        sub_sampling : bool
+            Do sub-sampling or not.
+
+        Returns
+        -------
+        score : float
+            Perplexity score.
+        """
+        check_is_fitted(self)
+        X = self._check_non_neg_array(
+            X, reset_n_features=True, whom="LatentDirichletAllocation.perplexity"
+        )
+        return self._perplexity_precomp_distr(X, sub_sampling=sub_sampling)
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.components_.shape[0]

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_nmf.py

@@ -0,0 +1,2443 @@
+""" Non-negative matrix factorization.
+"""
+# Author: Vlad Niculae
+#         Lars Buitinck
+#         Mathieu Blondel <[email protected]>
+#         Tom Dupre la Tour
+# License: BSD 3 clause
+
+import itertools
+import time
+import warnings
+from abc import ABC
+from math import sqrt
+from numbers import Integral, Real
+
+import numpy as np
+import scipy.sparse as sp
+from scipy import linalg
+
+from .._config import config_context
+from ..base import (
+    BaseEstimator,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from ..exceptions import ConvergenceWarning
+from ..utils import check_array, check_random_state, gen_batches, metadata_routing
+from ..utils._param_validation import (
+    Hidden,
+    Interval,
+    StrOptions,
+    validate_params,
+)
+from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm
+from ..utils.validation import (
+    check_is_fitted,
+    check_non_negative,
+)
+from ._cdnmf_fast import _update_cdnmf_fast
+
+EPSILON = np.finfo(np.float32).eps
+
+
+def norm(x):
+    """Dot product-based Euclidean norm implementation.
+
+    See: http://fa.bianp.net/blog/2011/computing-the-vector-norm/
+
+    Parameters
+    ----------
+    x : array-like
+        Vector for which to compute the norm.
+    """
+    return sqrt(squared_norm(x))
+
+
+def trace_dot(X, Y):
+    """Trace of np.dot(X, Y.T).
+
+    Parameters
+    ----------
+    X : array-like
+        First matrix.
+    Y : array-like
+        Second matrix.
+    """
+    return np.dot(X.ravel(), Y.ravel())
+
+
+def _check_init(A, shape, whom):
+    A = check_array(A)
+    if shape[0] != "auto" and A.shape[0] != shape[0]:
+        raise ValueError(
+            f"Array with wrong first dimension passed to {whom}. Expected {shape[0]}, "
+            f"but got {A.shape[0]}."
+        )
+    if shape[1] != "auto" and A.shape[1] != shape[1]:
+        raise ValueError(
+            f"Array with wrong second dimension passed to {whom}. Expected {shape[1]}, "
+            f"but got {A.shape[1]}."
+        )
+    check_non_negative(A, whom)
+    if np.max(A) == 0:
+        raise ValueError(f"Array passed to {whom} is full of zeros.")
+
+
+def _beta_divergence(X, W, H, beta, square_root=False):
+    """Compute the beta-divergence of X and dot(W, H).
+
+    Parameters
+    ----------
+    X : float or array-like of shape (n_samples, n_features)
+
+    W : float or array-like of shape (n_samples, n_components)
+
+    H : float or array-like of shape (n_components, n_features)
+
+    beta : float or {'frobenius', 'kullback-leibler', 'itakura-saito'}
+        Parameter of the beta-divergence.
+        If beta == 2, this is half the Frobenius *squared* norm.
+        If beta == 1, this is the generalized Kullback-Leibler divergence.
+        If beta == 0, this is the Itakura-Saito divergence.
+        Else, this is the general beta-divergence.
+
+    square_root : bool, default=False
+        If True, return np.sqrt(2 * res)
+        For beta == 2, it corresponds to the Frobenius norm.
+
+    Returns
+    -------
+        res : float
+            Beta divergence of X and np.dot(X, H).
+    """
+    beta = _beta_loss_to_float(beta)
+
+    # The method can be called with scalars
+    if not sp.issparse(X):
+        X = np.atleast_2d(X)
+    W = np.atleast_2d(W)
+    H = np.atleast_2d(H)
+
+    # Frobenius norm
+    if beta == 2:
+        # Avoid the creation of the dense np.dot(W, H) if X is sparse.
+        if sp.issparse(X):
+            norm_X = np.dot(X.data, X.data)
+            norm_WH = trace_dot(np.linalg.multi_dot([W.T, W, H]), H)
+            cross_prod = trace_dot((X @ H.T), W)
+            res = (norm_X + norm_WH - 2.0 * cross_prod) / 2.0
+        else:
+            res = squared_norm(X - np.dot(W, H)) / 2.0
+
+        if square_root:
+            return np.sqrt(res * 2)
+        else:
+            return res
+
+    if sp.issparse(X):
+        # compute np.dot(W, H) only where X is nonzero
+        WH_data = _special_sparse_dot(W, H, X).data
+        X_data = X.data
+    else:
+        WH = np.dot(W, H)
+        WH_data = WH.ravel()
+        X_data = X.ravel()
+
+    # do not affect the zeros: here 0 ** (-1) = 0 and not infinity
+    indices = X_data > EPSILON
+    WH_data = WH_data[indices]
+    X_data = X_data[indices]
+
+    # used to avoid division by zero
+    WH_data[WH_data < EPSILON] = EPSILON
+
+    # generalized Kullback-Leibler divergence
+    if beta == 1:
+        # fast and memory efficient computation of np.sum(np.dot(W, H))
+        sum_WH = np.dot(np.sum(W, axis=0), np.sum(H, axis=1))
+        # computes np.sum(X * log(X / WH)) only where X is nonzero
+        div = X_data / WH_data
+        res = np.dot(X_data, np.log(div))
+        # add full np.sum(np.dot(W, H)) - np.sum(X)
+        res += sum_WH - X_data.sum()
+
+    # Itakura-Saito divergence
+    elif beta == 0:
+        div = X_data / WH_data
+        res = np.sum(div) - np.prod(X.shape) - np.sum(np.log(div))
+
+    # beta-divergence, beta not in (0, 1, 2)
+    else:
+        if sp.issparse(X):
+            # slow loop, but memory efficient computation of :
+            # np.sum(np.dot(W, H) ** beta)
+            sum_WH_beta = 0
+            for i in range(X.shape[1]):
+                sum_WH_beta += np.sum(np.dot(W, H[:, i]) ** beta)
+
+        else:
+            sum_WH_beta = np.sum(WH**beta)
+
+        sum_X_WH = np.dot(X_data, WH_data ** (beta - 1))
+        res = (X_data**beta).sum() - beta * sum_X_WH
+        res += sum_WH_beta * (beta - 1)
+        res /= beta * (beta - 1)
+
+    if square_root:
+        res = max(res, 0)  # avoid negative number due to rounding errors
+        return np.sqrt(2 * res)
+    else:
+        return res
+
+
+def _special_sparse_dot(W, H, X):
+    """Computes np.dot(W, H), only where X is non zero."""
+    if sp.issparse(X):
+        ii, jj = X.nonzero()
+        n_vals = ii.shape[0]
+        dot_vals = np.empty(n_vals)
+        n_components = W.shape[1]
+
+        batch_size = max(n_components, n_vals // n_components)
+        for start in range(0, n_vals, batch_size):
+            batch = slice(start, start + batch_size)
+            dot_vals[batch] = np.multiply(W[ii[batch], :], H.T[jj[batch], :]).sum(
+                axis=1
+            )
+
+        WH = sp.coo_matrix((dot_vals, (ii, jj)), shape=X.shape)
+        return WH.tocsr()
+    else:
+        return np.dot(W, H)
+
+
+def _beta_loss_to_float(beta_loss):
+    """Convert string beta_loss to float."""
+    beta_loss_map = {"frobenius": 2, "kullback-leibler": 1, "itakura-saito": 0}
+    if isinstance(beta_loss, str):
+        beta_loss = beta_loss_map[beta_loss]
+    return beta_loss
+
+
+def _initialize_nmf(X, n_components, init=None, eps=1e-6, random_state=None):
+    """Algorithms for NMF initialization.
+
+    Computes an initial guess for the non-negative
+    rank k matrix approximation for X: X = WH.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        The data matrix to be decomposed.
+
+    n_components : int
+        The number of components desired in the approximation.
+
+    init :  {'random', 'nndsvd', 'nndsvda', 'nndsvdar'}, default=None
+        Method used to initialize the procedure.
+        Valid options:
+
+        - None: 'nndsvda' if n_components <= min(n_samples, n_features),
+            otherwise 'random'.
+
+        - 'random': non-negative random matrices, scaled with:
+            sqrt(X.mean() / n_components)
+
+        - 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
+            initialization (better for sparseness)
+
+        - 'nndsvda': NNDSVD with zeros filled with the average of X
+            (better when sparsity is not desired)
+
+        - 'nndsvdar': NNDSVD with zeros filled with small random values
+            (generally faster, less accurate alternative to NNDSVDa
+            for when sparsity is not desired)
+
+        - 'custom': use custom matrices W and H
+
+        .. versionchanged:: 1.1
+            When `init=None` and n_components is less than n_samples and n_features
+            defaults to `nndsvda` instead of `nndsvd`.
+
+    eps : float, default=1e-6
+        Truncate all values less then this in output to zero.
+
+    random_state : int, RandomState instance or None, default=None
+        Used when ``init`` == 'nndsvdar' or 'random'. Pass an int for
+        reproducible results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    Returns
+    -------
+    W : array-like of shape (n_samples, n_components)
+        Initial guesses for solving X ~= WH.
+
+    H : array-like of shape (n_components, n_features)
+        Initial guesses for solving X ~= WH.
+
+    References
+    ----------
+    C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for
+    nonnegative matrix factorization - Pattern Recognition, 2008
+    http://tinyurl.com/nndsvd
+    """
+    check_non_negative(X, "NMF initialization")
+    n_samples, n_features = X.shape
+
+    if (
+        init is not None
+        and init != "random"
+        and n_components > min(n_samples, n_features)
+    ):
+        raise ValueError(
+            "init = '{}' can only be used when "
+            "n_components <= min(n_samples, n_features)".format(init)
+        )
+
+    if init is None:
+        if n_components <= min(n_samples, n_features):
+            init = "nndsvda"
+        else:
+            init = "random"
+
+    # Random initialization
+    if init == "random":
+        avg = np.sqrt(X.mean() / n_components)
+        rng = check_random_state(random_state)
+        H = avg * rng.standard_normal(size=(n_components, n_features)).astype(
+            X.dtype, copy=False
+        )
+        W = avg * rng.standard_normal(size=(n_samples, n_components)).astype(
+            X.dtype, copy=False
+        )
+        np.abs(H, out=H)
+        np.abs(W, out=W)
+        return W, H
+
+    # NNDSVD initialization
+    U, S, V = randomized_svd(X, n_components, random_state=random_state)
+    W = np.zeros_like(U)
+    H = np.zeros_like(V)
+
+    # The leading singular triplet is non-negative
+    # so it can be used as is for initialization.
+    W[:, 0] = np.sqrt(S[0]) * np.abs(U[:, 0])
+    H[0, :] = np.sqrt(S[0]) * np.abs(V[0, :])
+
+    for j in range(1, n_components):
+        x, y = U[:, j], V[j, :]
+
+        # extract positive and negative parts of column vectors
+        x_p, y_p = np.maximum(x, 0), np.maximum(y, 0)
+        x_n, y_n = np.abs(np.minimum(x, 0)), np.abs(np.minimum(y, 0))
+
+        # and their norms
+        x_p_nrm, y_p_nrm = norm(x_p), norm(y_p)
+        x_n_nrm, y_n_nrm = norm(x_n), norm(y_n)
+
+        m_p, m_n = x_p_nrm * y_p_nrm, x_n_nrm * y_n_nrm
+
+        # choose update
+        if m_p > m_n:
+            u = x_p / x_p_nrm
+            v = y_p / y_p_nrm
+            sigma = m_p
+        else:
+            u = x_n / x_n_nrm
+            v = y_n / y_n_nrm
+            sigma = m_n
+
+        lbd = np.sqrt(S[j] * sigma)
+        W[:, j] = lbd * u
+        H[j, :] = lbd * v
+
+    W[W < eps] = 0
+    H[H < eps] = 0
+
+    if init == "nndsvd":
+        pass
+    elif init == "nndsvda":
+        avg = X.mean()
+        W[W == 0] = avg
+        H[H == 0] = avg
+    elif init == "nndsvdar":
+        rng = check_random_state(random_state)
+        avg = X.mean()
+        W[W == 0] = abs(avg * rng.standard_normal(size=len(W[W == 0])) / 100)
+        H[H == 0] = abs(avg * rng.standard_normal(size=len(H[H == 0])) / 100)
+    else:
+        raise ValueError(
+            "Invalid init parameter: got %r instead of one of %r"
+            % (init, (None, "random", "nndsvd", "nndsvda", "nndsvdar"))
+        )
+
+    return W, H
+
+
+def _update_coordinate_descent(X, W, Ht, l1_reg, l2_reg, shuffle, random_state):
+    """Helper function for _fit_coordinate_descent.
+
+    Update W to minimize the objective function, iterating once over all
+    coordinates. By symmetry, to update H, one can call
+    _update_coordinate_descent(X.T, Ht, W, ...).
+
+    """
+    n_components = Ht.shape[1]
+
+    HHt = np.dot(Ht.T, Ht)
+    XHt = safe_sparse_dot(X, Ht)
+
+    # L2 regularization corresponds to increase of the diagonal of HHt
+    if l2_reg != 0.0:
+        # adds l2_reg only on the diagonal
+        HHt.flat[:: n_components + 1] += l2_reg
+    # L1 regularization corresponds to decrease of each element of XHt
+    if l1_reg != 0.0:
+        XHt -= l1_reg
+
+    if shuffle:
+        permutation = random_state.permutation(n_components)
+    else:
+        permutation = np.arange(n_components)
+    # The following seems to be required on 64-bit Windows w/ Python 3.5.
+    permutation = np.asarray(permutation, dtype=np.intp)
+    return _update_cdnmf_fast(W, HHt, XHt, permutation)
+
+
+def _fit_coordinate_descent(
+    X,
+    W,
+    H,
+    tol=1e-4,
+    max_iter=200,
+    l1_reg_W=0,
+    l1_reg_H=0,
+    l2_reg_W=0,
+    l2_reg_H=0,
+    update_H=True,
+    verbose=0,
+    shuffle=False,
+    random_state=None,
+):
+    """Compute Non-negative Matrix Factorization (NMF) with Coordinate Descent
+
+    The objective function is minimized with an alternating minimization of W
+    and H. Each minimization is done with a cyclic (up to a permutation of the
+    features) Coordinate Descent.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Constant matrix.
+
+    W : array-like of shape (n_samples, n_components)
+        Initial guess for the solution.
+
+    H : array-like of shape (n_components, n_features)
+        Initial guess for the solution.
+
+    tol : float, default=1e-4
+        Tolerance of the stopping condition.
+
+    max_iter : int, default=200
+        Maximum number of iterations before timing out.
+
+    l1_reg_W : float, default=0.
+        L1 regularization parameter for W.
+
+    l1_reg_H : float, default=0.
+        L1 regularization parameter for H.
+
+    l2_reg_W : float, default=0.
+        L2 regularization parameter for W.
+
+    l2_reg_H : float, default=0.
+        L2 regularization parameter for H.
+
+    update_H : bool, default=True
+        Set to True, both W and H will be estimated from initial guesses.
+        Set to False, only W will be estimated.
+
+    verbose : int, default=0
+        The verbosity level.
+
+    shuffle : bool, default=False
+        If true, randomize the order of coordinates in the CD solver.
+
+    random_state : int, RandomState instance or None, default=None
+        Used to randomize the coordinates in the CD solver, when
+        ``shuffle`` is set to ``True``. Pass an int for reproducible
+        results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    Returns
+    -------
+    W : ndarray of shape (n_samples, n_components)
+        Solution to the non-negative least squares problem.
+
+    H : ndarray of shape (n_components, n_features)
+        Solution to the non-negative least squares problem.
+
+    n_iter : int
+        The number of iterations done by the algorithm.
+
+    References
+    ----------
+    .. [1] :doi:`"Fast local algorithms for large scale nonnegative matrix and tensor
+       factorizations" <10.1587/transfun.E92.A.708>`
+       Cichocki, Andrzej, and P. H. A. N. Anh-Huy. IEICE transactions on fundamentals
+       of electronics, communications and computer sciences 92.3: 708-721, 2009.
+    """
+    # so W and Ht are both in C order in memory
+    Ht = check_array(H.T, order="C")
+    X = check_array(X, accept_sparse="csr")
+
+    rng = check_random_state(random_state)
+
+    for n_iter in range(1, max_iter + 1):
+        violation = 0.0
+
+        # Update W
+        violation += _update_coordinate_descent(
+            X, W, Ht, l1_reg_W, l2_reg_W, shuffle, rng
+        )
+        # Update H
+        if update_H:
+            violation += _update_coordinate_descent(
+                X.T, Ht, W, l1_reg_H, l2_reg_H, shuffle, rng
+            )
+
+        if n_iter == 1:
+            violation_init = violation
+
+        if violation_init == 0:
+            break
+
+        if verbose:
+            print("violation:", violation / violation_init)
+
+        if violation / violation_init <= tol:
+            if verbose:
+                print("Converged at iteration", n_iter + 1)
+            break
+
+    return W, Ht.T, n_iter
+
+
+def _multiplicative_update_w(
+    X,
+    W,
+    H,
+    beta_loss,
+    l1_reg_W,
+    l2_reg_W,
+    gamma,
+    H_sum=None,
+    HHt=None,
+    XHt=None,
+    update_H=True,
+):
+    """Update W in Multiplicative Update NMF."""
+    if beta_loss == 2:
+        # Numerator
+        if XHt is None:
+            XHt = safe_sparse_dot(X, H.T)
+        if update_H:
+            # avoid a copy of XHt, which will be re-computed (update_H=True)
+            numerator = XHt
+        else:
+            # preserve the XHt, which is not re-computed (update_H=False)
+            numerator = XHt.copy()
+
+        # Denominator
+        if HHt is None:
+            HHt = np.dot(H, H.T)
+        denominator = np.dot(W, HHt)
+
+    else:
+        # Numerator
+        # if X is sparse, compute WH only where X is non zero
+        WH_safe_X = _special_sparse_dot(W, H, X)
+        if sp.issparse(X):
+            WH_safe_X_data = WH_safe_X.data
+            X_data = X.data
+        else:
+            WH_safe_X_data = WH_safe_X
+            X_data = X
+            # copy used in the Denominator
+            WH = WH_safe_X.copy()
+            if beta_loss - 1.0 < 0:
+                WH[WH < EPSILON] = EPSILON
+
+        # to avoid taking a negative power of zero
+        if beta_loss - 2.0 < 0:
+            WH_safe_X_data[WH_safe_X_data < EPSILON] = EPSILON
+
+        if beta_loss == 1:
+            np.divide(X_data, WH_safe_X_data, out=WH_safe_X_data)
+        elif beta_loss == 0:
+            # speeds up computation time
+            # refer to /numpy/numpy/issues/9363
+            WH_safe_X_data **= -1
+            WH_safe_X_data **= 2
+            # element-wise multiplication
+            WH_safe_X_data *= X_data
+        else:
+            WH_safe_X_data **= beta_loss - 2
+            # element-wise multiplication
+            WH_safe_X_data *= X_data
+
+        # here numerator = dot(X * (dot(W, H) ** (beta_loss - 2)), H.T)
+        numerator = safe_sparse_dot(WH_safe_X, H.T)
+
+        # Denominator
+        if beta_loss == 1:
+            if H_sum is None:
+                H_sum = np.sum(H, axis=1)  # shape(n_components, )
+            denominator = H_sum[np.newaxis, :]
+
+        else:
+            # computation of WHHt = dot(dot(W, H) ** beta_loss - 1, H.T)
+            if sp.issparse(X):
+                # memory efficient computation
+                # (compute row by row, avoiding the dense matrix WH)
+                WHHt = np.empty(W.shape)
+                for i in range(X.shape[0]):
+                    WHi = np.dot(W[i, :], H)
+                    if beta_loss - 1 < 0:
+                        WHi[WHi < EPSILON] = EPSILON
+                    WHi **= beta_loss - 1
+                    WHHt[i, :] = np.dot(WHi, H.T)
+            else:
+                WH **= beta_loss - 1
+                WHHt = np.dot(WH, H.T)
+            denominator = WHHt
+
+    # Add L1 and L2 regularization
+    if l1_reg_W > 0:
+        denominator += l1_reg_W
+    if l2_reg_W > 0:
+        denominator = denominator + l2_reg_W * W
+    denominator[denominator == 0] = EPSILON
+
+    numerator /= denominator
+    delta_W = numerator
+
+    # gamma is in ]0, 1]
+    if gamma != 1:
+        delta_W **= gamma
+
+    W *= delta_W
+
+    return W, H_sum, HHt, XHt
+
+
+def _multiplicative_update_h(
+    X, W, H, beta_loss, l1_reg_H, l2_reg_H, gamma, A=None, B=None, rho=None
+):
+    """update H in Multiplicative Update NMF."""
+    if beta_loss == 2:
+        numerator = safe_sparse_dot(W.T, X)
+        denominator = np.linalg.multi_dot([W.T, W, H])
+
+    else:
+        # Numerator
+        WH_safe_X = _special_sparse_dot(W, H, X)
+        if sp.issparse(X):
+            WH_safe_X_data = WH_safe_X.data
+            X_data = X.data
+        else:
+            WH_safe_X_data = WH_safe_X
+            X_data = X
+            # copy used in the Denominator
+            WH = WH_safe_X.copy()
+            if beta_loss - 1.0 < 0:
+                WH[WH < EPSILON] = EPSILON
+
+        # to avoid division by zero
+        if beta_loss - 2.0 < 0:
+            WH_safe_X_data[WH_safe_X_data < EPSILON] = EPSILON
+
+        if beta_loss == 1:
+            np.divide(X_data, WH_safe_X_data, out=WH_safe_X_data)
+        elif beta_loss == 0:
+            # speeds up computation time
+            # refer to /numpy/numpy/issues/9363
+            WH_safe_X_data **= -1
+            WH_safe_X_data **= 2
+            # element-wise multiplication
+            WH_safe_X_data *= X_data
+        else:
+            WH_safe_X_data **= beta_loss - 2
+            # element-wise multiplication
+            WH_safe_X_data *= X_data
+
+        # here numerator = dot(W.T, (dot(W, H) ** (beta_loss - 2)) * X)
+        numerator = safe_sparse_dot(W.T, WH_safe_X)
+
+        # Denominator
+        if beta_loss == 1:
+            W_sum = np.sum(W, axis=0)  # shape(n_components, )
+            W_sum[W_sum == 0] = 1.0
+            denominator = W_sum[:, np.newaxis]
+
+        # beta_loss not in (1, 2)
+        else:
+            # computation of WtWH = dot(W.T, dot(W, H) ** beta_loss - 1)
+            if sp.issparse(X):
+                # memory efficient computation
+                # (compute column by column, avoiding the dense matrix WH)
+                WtWH = np.empty(H.shape)
+                for i in range(X.shape[1]):
+                    WHi = np.dot(W, H[:, i])
+                    if beta_loss - 1 < 0:
+                        WHi[WHi < EPSILON] = EPSILON
+                    WHi **= beta_loss - 1
+                    WtWH[:, i] = np.dot(W.T, WHi)
+            else:
+                WH **= beta_loss - 1
+                WtWH = np.dot(W.T, WH)
+            denominator = WtWH
+
+    # Add L1 and L2 regularization
+    if l1_reg_H > 0:
+        denominator += l1_reg_H
+    if l2_reg_H > 0:
+        denominator = denominator + l2_reg_H * H
+    denominator[denominator == 0] = EPSILON
+
+    if A is not None and B is not None:
+        # Updates for the online nmf
+        if gamma != 1:
+            H **= 1 / gamma
+        numerator *= H
+        A *= rho
+        B *= rho
+        A += numerator
+        B += denominator
+        H = A / B
+
+        if gamma != 1:
+            H **= gamma
+    else:
+        delta_H = numerator
+        delta_H /= denominator
+        if gamma != 1:
+            delta_H **= gamma
+        H *= delta_H
+
+    return H
+
+
+def _fit_multiplicative_update(
+    X,
+    W,
+    H,
+    beta_loss="frobenius",
+    max_iter=200,
+    tol=1e-4,
+    l1_reg_W=0,
+    l1_reg_H=0,
+    l2_reg_W=0,
+    l2_reg_H=0,
+    update_H=True,
+    verbose=0,
+):
+    """Compute Non-negative Matrix Factorization with Multiplicative Update.
+
+    The objective function is _beta_divergence(X, WH) and is minimized with an
+    alternating minimization of W and H. Each minimization is done with a
+    Multiplicative Update.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Constant input matrix.
+
+    W : array-like of shape (n_samples, n_components)
+        Initial guess for the solution.
+
+    H : array-like of shape (n_components, n_features)
+        Initial guess for the solution.
+
+    beta_loss : float or {'frobenius', 'kullback-leibler', \
+            'itakura-saito'}, default='frobenius'
+        String must be in {'frobenius', 'kullback-leibler', 'itakura-saito'}.
+        Beta divergence to be minimized, measuring the distance between X
+        and the dot product WH. Note that values different from 'frobenius'
+        (or 2) and 'kullback-leibler' (or 1) lead to significantly slower
+        fits. Note that for beta_loss <= 0 (or 'itakura-saito'), the input
+        matrix X cannot contain zeros.
+
+    max_iter : int, default=200
+        Number of iterations.
+
+    tol : float, default=1e-4
+        Tolerance of the stopping condition.
+
+    l1_reg_W : float, default=0.
+        L1 regularization parameter for W.
+
+    l1_reg_H : float, default=0.
+        L1 regularization parameter for H.
+
+    l2_reg_W : float, default=0.
+        L2 regularization parameter for W.
+
+    l2_reg_H : float, default=0.
+        L2 regularization parameter for H.
+
+    update_H : bool, default=True
+        Set to True, both W and H will be estimated from initial guesses.
+        Set to False, only W will be estimated.
+
+    verbose : int, default=0
+        The verbosity level.
+
+    Returns
+    -------
+    W : ndarray of shape (n_samples, n_components)
+        Solution to the non-negative least squares problem.
+
+    H : ndarray of shape (n_components, n_features)
+        Solution to the non-negative least squares problem.
+
+    n_iter : int
+        The number of iterations done by the algorithm.
+
+    References
+    ----------
+    Lee, D. D., & Seung, H., S. (2001). Algorithms for Non-negative Matrix
+    Factorization. Adv. Neural Inform. Process. Syst.. 13.
+    Fevotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix
+    factorization with the beta-divergence. Neural Computation, 23(9).
+    """
+    start_time = time.time()
+
+    beta_loss = _beta_loss_to_float(beta_loss)
+
+    # gamma for Maximization-Minimization (MM) algorithm [Fevotte 2011]
+    if beta_loss < 1:
+        gamma = 1.0 / (2.0 - beta_loss)
+    elif beta_loss > 2:
+        gamma = 1.0 / (beta_loss - 1.0)
+    else:
+        gamma = 1.0
+
+    # used for the convergence criterion
+    error_at_init = _beta_divergence(X, W, H, beta_loss, square_root=True)
+    previous_error = error_at_init
+
+    H_sum, HHt, XHt = None, None, None
+    for n_iter in range(1, max_iter + 1):
+        # update W
+        # H_sum, HHt and XHt are saved and reused if not update_H
+        W, H_sum, HHt, XHt = _multiplicative_update_w(
+            X,
+            W,
+            H,
+            beta_loss=beta_loss,
+            l1_reg_W=l1_reg_W,
+            l2_reg_W=l2_reg_W,
+            gamma=gamma,
+            H_sum=H_sum,
+            HHt=HHt,
+            XHt=XHt,
+            update_H=update_H,
+        )
+
+        # necessary for stability with beta_loss < 1
+        if beta_loss < 1:
+            W[W < np.finfo(np.float64).eps] = 0.0
+
+        # update H (only at fit or fit_transform)
+        if update_H:
+            H = _multiplicative_update_h(
+                X,
+                W,
+                H,
+                beta_loss=beta_loss,
+                l1_reg_H=l1_reg_H,
+                l2_reg_H=l2_reg_H,
+                gamma=gamma,
+            )
+
+            # These values will be recomputed since H changed
+            H_sum, HHt, XHt = None, None, None
+
+            # necessary for stability with beta_loss < 1
+            if beta_loss <= 1:
+                H[H < np.finfo(np.float64).eps] = 0.0
+
+        # test convergence criterion every 10 iterations
+        if tol > 0 and n_iter % 10 == 0:
+            error = _beta_divergence(X, W, H, beta_loss, square_root=True)
+
+            if verbose:
+                iter_time = time.time()
+                print(
+                    "Epoch %02d reached after %.3f seconds, error: %f"
+                    % (n_iter, iter_time - start_time, error)
+                )
+
+            if (previous_error - error) / error_at_init < tol:
+                break
+            previous_error = error
+
+    # do not print if we have already printed in the convergence test
+    if verbose and (tol == 0 or n_iter % 10 != 0):
+        end_time = time.time()
+        print(
+            "Epoch %02d reached after %.3f seconds." % (n_iter, end_time - start_time)
+        )
+
+    return W, H, n_iter
+
+
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "W": ["array-like", None],
+        "H": ["array-like", None],
+        "update_H": ["boolean"],
+    },
+    prefer_skip_nested_validation=False,
+)
+def non_negative_factorization(
+    X,
+    W=None,
+    H=None,
+    n_components="warn",
+    *,
+    init=None,
+    update_H=True,
+    solver="cd",
+    beta_loss="frobenius",
+    tol=1e-4,
+    max_iter=200,
+    alpha_W=0.0,
+    alpha_H="same",
+    l1_ratio=0.0,
+    random_state=None,
+    verbose=0,
+    shuffle=False,
+):
+    """Compute Non-negative Matrix Factorization (NMF).
+
+    Find two non-negative matrices (W, H) whose product approximates the non-
+    negative matrix X. This factorization can be used for example for
+    dimensionality reduction, source separation or topic extraction.
+
+    The objective function is:
+
+        .. math::
+
+            L(W, H) &= 0.5 * ||X - WH||_{loss}^2
+
+            &+ alpha\\_W * l1\\_ratio * n\\_features * ||vec(W)||_1
+
+            &+ alpha\\_H * l1\\_ratio * n\\_samples * ||vec(H)||_1
+
+            &+ 0.5 * alpha\\_W * (1 - l1\\_ratio) * n\\_features * ||W||_{Fro}^2
+
+            &+ 0.5 * alpha\\_H * (1 - l1\\_ratio) * n\\_samples * ||H||_{Fro}^2
+
+    Where:
+
+    :math:`||A||_{Fro}^2 = \\sum_{i,j} A_{ij}^2` (Frobenius norm)
+
+    :math:`||vec(A)||_1 = \\sum_{i,j} abs(A_{ij})` (Elementwise L1 norm)
+
+    The generic norm :math:`||X - WH||_{loss}^2` may represent
+    the Frobenius norm or another supported beta-divergence loss.
+    The choice between options is controlled by the `beta_loss` parameter.
+
+    The regularization terms are scaled by `n_features` for `W` and by `n_samples` for
+    `H` to keep their impact balanced with respect to one another and to the data fit
+    term as independent as possible of the size `n_samples` of the training set.
+
+    The objective function is minimized with an alternating minimization of W
+    and H. If H is given and update_H=False, it solves for W only.
+
+    Note that the transformed data is named W and the components matrix is named H. In
+    the NMF literature, the naming convention is usually the opposite since the data
+    matrix X is transposed.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Constant matrix.
+
+    W : array-like of shape (n_samples, n_components), default=None
+        If `init='custom'`, it is used as initial guess for the solution.
+        If `update_H=False`, it is initialised as an array of zeros, unless
+        `solver='mu'`, then it is filled with values calculated by
+        `np.sqrt(X.mean() / self._n_components)`.
+        If `None`, uses the initialisation method specified in `init`.
+
+    H : array-like of shape (n_components, n_features), default=None
+        If `init='custom'`, it is used as initial guess for the solution.
+        If `update_H=False`, it is used as a constant, to solve for W only.
+        If `None`, uses the initialisation method specified in `init`.
+
+    n_components : int or {'auto'} or None, default=None
+        Number of components, if n_components is not set all features
+        are kept.
+        If `n_components='auto'`, the number of components is automatically inferred
+        from `W` or `H` shapes.
+
+        .. versionchanged:: 1.4
+            Added `'auto'` value.
+
+    init : {'random', 'nndsvd', 'nndsvda', 'nndsvdar', 'custom'}, default=None
+        Method used to initialize the procedure.
+
+        Valid options:
+
+        - None: 'nndsvda' if n_components < n_features, otherwise 'random'.
+        - 'random': non-negative random matrices, scaled with:
+          `sqrt(X.mean() / n_components)`
+        - 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
+          initialization (better for sparseness)
+        - 'nndsvda': NNDSVD with zeros filled with the average of X
+          (better when sparsity is not desired)
+        - 'nndsvdar': NNDSVD with zeros filled with small random values
+          (generally faster, less accurate alternative to NNDSVDa
+          for when sparsity is not desired)
+        - 'custom': If `update_H=True`, use custom matrices W and H which must both
+          be provided. If `update_H=False`, then only custom matrix H is used.
+
+        .. versionchanged:: 0.23
+            The default value of `init` changed from 'random' to None in 0.23.
+
+        .. versionchanged:: 1.1
+            When `init=None` and n_components is less than n_samples and n_features
+            defaults to `nndsvda` instead of `nndsvd`.
+
+    update_H : bool, default=True
+        Set to True, both W and H will be estimated from initial guesses.
+        Set to False, only W will be estimated.
+
+    solver : {'cd', 'mu'}, default='cd'
+        Numerical solver to use:
+
+        - 'cd' is a Coordinate Descent solver that uses Fast Hierarchical
+          Alternating Least Squares (Fast HALS).
+        - 'mu' is a Multiplicative Update solver.
+
+        .. versionadded:: 0.17
+           Coordinate Descent solver.
+
+        .. versionadded:: 0.19
+           Multiplicative Update solver.
+
+    beta_loss : float or {'frobenius', 'kullback-leibler', \
+            'itakura-saito'}, default='frobenius'
+        Beta divergence to be minimized, measuring the distance between X
+        and the dot product WH. Note that values different from 'frobenius'
+        (or 2) and 'kullback-leibler' (or 1) lead to significantly slower
+        fits. Note that for beta_loss <= 0 (or 'itakura-saito'), the input
+        matrix X cannot contain zeros. Used only in 'mu' solver.
+
+        .. versionadded:: 0.19
+
+    tol : float, default=1e-4
+        Tolerance of the stopping condition.
+
+    max_iter : int, default=200
+        Maximum number of iterations before timing out.
+
+    alpha_W : float, default=0.0
+        Constant that multiplies the regularization terms of `W`. Set it to zero
+        (default) to have no regularization on `W`.
+
+        .. versionadded:: 1.0
+
+    alpha_H : float or "same", default="same"
+        Constant that multiplies the regularization terms of `H`. Set it to zero to
+        have no regularization on `H`. If "same" (default), it takes the same value as
+        `alpha_W`.
+
+        .. versionadded:: 1.0
+
+    l1_ratio : float, default=0.0
+        The regularization mixing parameter, with 0 <= l1_ratio <= 1.
+        For l1_ratio = 0 the penalty is an elementwise L2 penalty
+        (aka Frobenius Norm).
+        For l1_ratio = 1 it is an elementwise L1 penalty.
+        For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
+
+    random_state : int, RandomState instance or None, default=None
+        Used for NMF initialisation (when ``init`` == 'nndsvdar' or
+        'random'), and in Coordinate Descent. Pass an int for reproducible
+        results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    verbose : int, default=0
+        The verbosity level.
+
+    shuffle : bool, default=False
+        If true, randomize the order of coordinates in the CD solver.
+
+    Returns
+    -------
+    W : ndarray of shape (n_samples, n_components)
+        Solution to the non-negative least squares problem.
+
+    H : ndarray of shape (n_components, n_features)
+        Solution to the non-negative least squares problem.
+
+    n_iter : int
+        Actual number of iterations.
+
+    References
+    ----------
+    .. [1] :doi:`"Fast local algorithms for large scale nonnegative matrix and tensor
+       factorizations" <10.1587/transfun.E92.A.708>`
+       Cichocki, Andrzej, and P. H. A. N. Anh-Huy. IEICE transactions on fundamentals
+       of electronics, communications and computer sciences 92.3: 708-721, 2009.
+
+    .. [2] :doi:`"Algorithms for nonnegative matrix factorization with the
+       beta-divergence" <10.1162/NECO_a_00168>`
+       Fevotte, C., & Idier, J. (2011). Neural Computation, 23(9).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
+    >>> from sklearn.decomposition import non_negative_factorization
+    >>> W, H, n_iter = non_negative_factorization(
+    ...     X, n_components=2, init='random', random_state=0)
+    """
+    est = NMF(
+        n_components=n_components,
+        init=init,
+        solver=solver,
+        beta_loss=beta_loss,
+        tol=tol,
+        max_iter=max_iter,
+        random_state=random_state,
+        alpha_W=alpha_W,
+        alpha_H=alpha_H,
+        l1_ratio=l1_ratio,
+        verbose=verbose,
+        shuffle=shuffle,
+    )
+    est._validate_params()
+
+    X = check_array(X, accept_sparse=("csr", "csc"), dtype=[np.float64, np.float32])
+
+    with config_context(assume_finite=True):
+        W, H, n_iter = est._fit_transform(X, W=W, H=H, update_H=update_H)
+
+    return W, H, n_iter
+
+
+class _BaseNMF(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator, ABC):
+    """Base class for NMF and MiniBatchNMF."""
+
+    # This prevents ``set_split_inverse_transform`` to be generated for the
+    # non-standard ``W`` arg on ``inverse_transform``.
+    # TODO: remove when W is removed in v1.5 for inverse_transform
+    __metadata_request__inverse_transform = {"W": metadata_routing.UNUSED}
+
+    _parameter_constraints: dict = {
+        "n_components": [
+            Interval(Integral, 1, None, closed="left"),
+            None,
+            StrOptions({"auto"}),
+            Hidden(StrOptions({"warn"})),
+        ],
+        "init": [
+            StrOptions({"random", "nndsvd", "nndsvda", "nndsvdar", "custom"}),
+            None,
+        ],
+        "beta_loss": [
+            StrOptions({"frobenius", "kullback-leibler", "itakura-saito"}),
+            Real,
+        ],
+        "tol": [Interval(Real, 0, None, closed="left")],
+        "max_iter": [Interval(Integral, 1, None, closed="left")],
+        "random_state": ["random_state"],
+        "alpha_W": [Interval(Real, 0, None, closed="left")],
+        "alpha_H": [Interval(Real, 0, None, closed="left"), StrOptions({"same"})],
+        "l1_ratio": [Interval(Real, 0, 1, closed="both")],
+        "verbose": ["verbose"],
+    }
+
+    def __init__(
+        self,
+        n_components="warn",
+        *,
+        init=None,
+        beta_loss="frobenius",
+        tol=1e-4,
+        max_iter=200,
+        random_state=None,
+        alpha_W=0.0,
+        alpha_H="same",
+        l1_ratio=0.0,
+        verbose=0,
+    ):
+        self.n_components = n_components
+        self.init = init
+        self.beta_loss = beta_loss
+        self.tol = tol
+        self.max_iter = max_iter
+        self.random_state = random_state
+        self.alpha_W = alpha_W
+        self.alpha_H = alpha_H
+        self.l1_ratio = l1_ratio
+        self.verbose = verbose
+
+    def _check_params(self, X):
+        # n_components
+        self._n_components = self.n_components
+        if self.n_components == "warn":
+            warnings.warn(
+                (
+                    "The default value of `n_components` will change from `None` to"
+                    " `'auto'` in 1.6. Set the value of `n_components` to `None`"
+                    " explicitly to suppress the warning."
+                ),
+                FutureWarning,
+            )
+            self._n_components = None  # Keeping the old default value
+        if self._n_components is None:
+            self._n_components = X.shape[1]
+
+        # beta_loss
+        self._beta_loss = _beta_loss_to_float(self.beta_loss)
+
+    def _check_w_h(self, X, W, H, update_H):
+        """Check W and H, or initialize them."""
+        n_samples, n_features = X.shape
+
+        if self.init == "custom" and update_H:
+            _check_init(H, (self._n_components, n_features), "NMF (input H)")
+            _check_init(W, (n_samples, self._n_components), "NMF (input W)")
+            if self._n_components == "auto":
+                self._n_components = H.shape[0]
+
+            if H.dtype != X.dtype or W.dtype != X.dtype:
+                raise TypeError(
+                    "H and W should have the same dtype as X. Got "
+                    "H.dtype = {} and W.dtype = {}.".format(H.dtype, W.dtype)
+                )
+
+        elif not update_H:
+            if W is not None:
+                warnings.warn(
+                    "When update_H=False, the provided initial W is not used.",
+                    RuntimeWarning,
+                )
+
+            _check_init(H, (self._n_components, n_features), "NMF (input H)")
+            if self._n_components == "auto":
+                self._n_components = H.shape[0]
+
+            if H.dtype != X.dtype:
+                raise TypeError(
+                    "H should have the same dtype as X. Got H.dtype = {}.".format(
+                        H.dtype
+                    )
+                )
+
+            # 'mu' solver should not be initialized by zeros
+            if self.solver == "mu":
+                avg = np.sqrt(X.mean() / self._n_components)
+                W = np.full((n_samples, self._n_components), avg, dtype=X.dtype)
+            else:
+                W = np.zeros((n_samples, self._n_components), dtype=X.dtype)
+
+        else:
+            if W is not None or H is not None:
+                warnings.warn(
+                    (
+                        "When init!='custom', provided W or H are ignored. Set "
+                        " init='custom' to use them as initialization."
+                    ),
+                    RuntimeWarning,
+                )
+
+            if self._n_components == "auto":
+                self._n_components = X.shape[1]
+
+            W, H = _initialize_nmf(
+                X, self._n_components, init=self.init, random_state=self.random_state
+            )
+
+        return W, H
+
+    def _compute_regularization(self, X):
+        """Compute scaled regularization terms."""
+        n_samples, n_features = X.shape
+        alpha_W = self.alpha_W
+        alpha_H = self.alpha_W if self.alpha_H == "same" else self.alpha_H
+
+        l1_reg_W = n_features * alpha_W * self.l1_ratio
+        l1_reg_H = n_samples * alpha_H * self.l1_ratio
+        l2_reg_W = n_features * alpha_W * (1.0 - self.l1_ratio)
+        l2_reg_H = n_samples * alpha_H * (1.0 - self.l1_ratio)
+
+        return l1_reg_W, l1_reg_H, l2_reg_W, l2_reg_H
+
+    def fit(self, X, y=None, **params):
+        """Learn a NMF model for the data X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        **params : kwargs
+            Parameters (keyword arguments) and values passed to
+            the fit_transform instance.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        # param validation is done in fit_transform
+
+        self.fit_transform(X, **params)
+        return self
+
+    def inverse_transform(self, Xt=None, W=None):
+        """Transform data back to its original space.
+
+        .. versionadded:: 0.18
+
+        Parameters
+        ----------
+        Xt : {ndarray, sparse matrix} of shape (n_samples, n_components)
+            Transformed data matrix.
+
+        W : deprecated
+            Use `Xt` instead.
+
+            .. deprecated:: 1.3
+
+        Returns
+        -------
+        X : ndarray of shape (n_samples, n_features)
+            Returns a data matrix of the original shape.
+        """
+        if Xt is None and W is None:
+            raise TypeError("Missing required positional argument: Xt")
+
+        if W is not None and Xt is not None:
+            raise ValueError("Please provide only `Xt`, and not `W`.")
+
+        if W is not None:
+            warnings.warn(
+                (
+                    "Input argument `W` was renamed to `Xt` in v1.3 and will be removed"
+                    " in v1.5."
+                ),
+                FutureWarning,
+            )
+            Xt = W
+
+        check_is_fitted(self)
+        return Xt @ self.components_
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.components_.shape[0]
+
+    def _more_tags(self):
+        return {
+            "requires_positive_X": True,
+            "preserves_dtype": [np.float64, np.float32],
+        }
+
+
+class NMF(_BaseNMF):
+    """Non-Negative Matrix Factorization (NMF).
+
+    Find two non-negative matrices, i.e. matrices with all non-negative elements, (W, H)
+    whose product approximates the non-negative matrix X. This factorization can be used
+    for example for dimensionality reduction, source separation or topic extraction.
+
+    The objective function is:
+
+        .. math::
+
+            L(W, H) &= 0.5 * ||X - WH||_{loss}^2
+
+            &+ alpha\\_W * l1\\_ratio * n\\_features * ||vec(W)||_1
+
+            &+ alpha\\_H * l1\\_ratio * n\\_samples * ||vec(H)||_1
+
+            &+ 0.5 * alpha\\_W * (1 - l1\\_ratio) * n\\_features * ||W||_{Fro}^2
+
+            &+ 0.5 * alpha\\_H * (1 - l1\\_ratio) * n\\_samples * ||H||_{Fro}^2
+
+    Where:
+
+    :math:`||A||_{Fro}^2 = \\sum_{i,j} A_{ij}^2` (Frobenius norm)
+
+    :math:`||vec(A)||_1 = \\sum_{i,j} abs(A_{ij})` (Elementwise L1 norm)
+
+    The generic norm :math:`||X - WH||_{loss}` may represent
+    the Frobenius norm or another supported beta-divergence loss.
+    The choice between options is controlled by the `beta_loss` parameter.
+
+    The regularization terms are scaled by `n_features` for `W` and by `n_samples` for
+    `H` to keep their impact balanced with respect to one another and to the data fit
+    term as independent as possible of the size `n_samples` of the training set.
+
+    The objective function is minimized with an alternating minimization of W
+    and H.
+
+    Note that the transformed data is named W and the components matrix is named H. In
+    the NMF literature, the naming convention is usually the opposite since the data
+    matrix X is transposed.
+
+    Read more in the :ref:`User Guide <NMF>`.
+
+    Parameters
+    ----------
+    n_components : int or {'auto'} or None, default=None
+        Number of components, if n_components is not set all features
+        are kept.
+        If `n_components='auto'`, the number of components is automatically inferred
+        from W or H shapes.
+
+        .. versionchanged:: 1.4
+            Added `'auto'` value.
+
+    init : {'random', 'nndsvd', 'nndsvda', 'nndsvdar', 'custom'}, default=None
+        Method used to initialize the procedure.
+        Valid options:
+
+        - `None`: 'nndsvda' if n_components <= min(n_samples, n_features),
+          otherwise random.
+
+        - `'random'`: non-negative random matrices, scaled with:
+          `sqrt(X.mean() / n_components)`
+
+        - `'nndsvd'`: Nonnegative Double Singular Value Decomposition (NNDSVD)
+          initialization (better for sparseness)
+
+        - `'nndsvda'`: NNDSVD with zeros filled with the average of X
+          (better when sparsity is not desired)
+
+        - `'nndsvdar'` NNDSVD with zeros filled with small random values
+          (generally faster, less accurate alternative to NNDSVDa
+          for when sparsity is not desired)
+
+        - `'custom'`: Use custom matrices `W` and `H` which must both be provided.
+
+        .. versionchanged:: 1.1
+            When `init=None` and n_components is less than n_samples and n_features
+            defaults to `nndsvda` instead of `nndsvd`.
+
+    solver : {'cd', 'mu'}, default='cd'
+        Numerical solver to use:
+
+        - 'cd' is a Coordinate Descent solver.
+        - 'mu' is a Multiplicative Update solver.
+
+        .. versionadded:: 0.17
+           Coordinate Descent solver.
+
+        .. versionadded:: 0.19
+           Multiplicative Update solver.
+
+    beta_loss : float or {'frobenius', 'kullback-leibler', \
+            'itakura-saito'}, default='frobenius'
+        Beta divergence to be minimized, measuring the distance between X
+        and the dot product WH. Note that values different from 'frobenius'
+        (or 2) and 'kullback-leibler' (or 1) lead to significantly slower
+        fits. Note that for beta_loss <= 0 (or 'itakura-saito'), the input
+        matrix X cannot contain zeros. Used only in 'mu' solver.
+
+        .. versionadded:: 0.19
+
+    tol : float, default=1e-4
+        Tolerance of the stopping condition.
+
+    max_iter : int, default=200
+        Maximum number of iterations before timing out.
+
+    random_state : int, RandomState instance or None, default=None
+        Used for initialisation (when ``init`` == 'nndsvdar' or
+        'random'), and in Coordinate Descent. Pass an int for reproducible
+        results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    alpha_W : float, default=0.0
+        Constant that multiplies the regularization terms of `W`. Set it to zero
+        (default) to have no regularization on `W`.
+
+        .. versionadded:: 1.0
+
+    alpha_H : float or "same", default="same"
+        Constant that multiplies the regularization terms of `H`. Set it to zero to
+        have no regularization on `H`. If "same" (default), it takes the same value as
+        `alpha_W`.
+
+        .. versionadded:: 1.0
+
+    l1_ratio : float, default=0.0
+        The regularization mixing parameter, with 0 <= l1_ratio <= 1.
+        For l1_ratio = 0 the penalty is an elementwise L2 penalty
+        (aka Frobenius Norm).
+        For l1_ratio = 1 it is an elementwise L1 penalty.
+        For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
+
+        .. versionadded:: 0.17
+           Regularization parameter *l1_ratio* used in the Coordinate Descent
+           solver.
+
+    verbose : int, default=0
+        Whether to be verbose.
+
+    shuffle : bool, default=False
+        If true, randomize the order of coordinates in the CD solver.
+
+        .. versionadded:: 0.17
+           *shuffle* parameter used in the Coordinate Descent solver.
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        Factorization matrix, sometimes called 'dictionary'.
+
+    n_components_ : int
+        The number of components. It is same as the `n_components` parameter
+        if it was given. Otherwise, it will be same as the number of
+        features.
+
+    reconstruction_err_ : float
+        Frobenius norm of the matrix difference, or beta-divergence, between
+        the training data ``X`` and the reconstructed data ``WH`` from
+        the fitted model.
+
+    n_iter_ : int
+        Actual number of iterations.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    DictionaryLearning : Find a dictionary that sparsely encodes data.
+    MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis.
+    PCA : Principal component analysis.
+    SparseCoder : Find a sparse representation of data from a fixed,
+        precomputed dictionary.
+    SparsePCA : Sparse Principal Components Analysis.
+    TruncatedSVD : Dimensionality reduction using truncated SVD.
+
+    References
+    ----------
+    .. [1] :doi:`"Fast local algorithms for large scale nonnegative matrix and tensor
+       factorizations" <10.1587/transfun.E92.A.708>`
+       Cichocki, Andrzej, and P. H. A. N. Anh-Huy. IEICE transactions on fundamentals
+       of electronics, communications and computer sciences 92.3: 708-721, 2009.
+
+    .. [2] :doi:`"Algorithms for nonnegative matrix factorization with the
+       beta-divergence" <10.1162/NECO_a_00168>`
+       Fevotte, C., & Idier, J. (2011). Neural Computation, 23(9).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> X = np.array([[1, 1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
+    >>> from sklearn.decomposition import NMF
+    >>> model = NMF(n_components=2, init='random', random_state=0)
+    >>> W = model.fit_transform(X)
+    >>> H = model.components_
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseNMF._parameter_constraints,
+        "solver": [StrOptions({"mu", "cd"})],
+        "shuffle": ["boolean"],
+    }
+
+    def __init__(
+        self,
+        n_components="warn",
+        *,
+        init=None,
+        solver="cd",
+        beta_loss="frobenius",
+        tol=1e-4,
+        max_iter=200,
+        random_state=None,
+        alpha_W=0.0,
+        alpha_H="same",
+        l1_ratio=0.0,
+        verbose=0,
+        shuffle=False,
+    ):
+        super().__init__(
+            n_components=n_components,
+            init=init,
+            beta_loss=beta_loss,
+            tol=tol,
+            max_iter=max_iter,
+            random_state=random_state,
+            alpha_W=alpha_W,
+            alpha_H=alpha_H,
+            l1_ratio=l1_ratio,
+            verbose=verbose,
+        )
+
+        self.solver = solver
+        self.shuffle = shuffle
+
+    def _check_params(self, X):
+        super()._check_params(X)
+
+        # solver
+        if self.solver != "mu" and self.beta_loss not in (2, "frobenius"):
+            # 'mu' is the only solver that handles other beta losses than 'frobenius'
+            raise ValueError(
+                f"Invalid beta_loss parameter: solver {self.solver!r} does not handle "
+                f"beta_loss = {self.beta_loss!r}"
+            )
+        if self.solver == "mu" and self.init == "nndsvd":
+            warnings.warn(
+                (
+                    "The multiplicative update ('mu') solver cannot update "
+                    "zeros present in the initialization, and so leads to "
+                    "poorer results when used jointly with init='nndsvd'. "
+                    "You may try init='nndsvda' or init='nndsvdar' instead."
+                ),
+                UserWarning,
+            )
+
+        return self
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit_transform(self, X, y=None, W=None, H=None):
+        """Learn a NMF model for the data X and returns the transformed data.
+
+        This is more efficient than calling fit followed by transform.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        W : array-like of shape (n_samples, n_components), default=None
+            If `init='custom'`, it is used as initial guess for the solution.
+            If `None`, uses the initialisation method specified in `init`.
+
+        H : array-like of shape (n_components, n_features), default=None
+            If `init='custom'`, it is used as initial guess for the solution.
+            If `None`, uses the initialisation method specified in `init`.
+
+        Returns
+        -------
+        W : ndarray of shape (n_samples, n_components)
+            Transformed data.
+        """
+        X = self._validate_data(
+            X, accept_sparse=("csr", "csc"), dtype=[np.float64, np.float32]
+        )
+
+        with config_context(assume_finite=True):
+            W, H, n_iter = self._fit_transform(X, W=W, H=H)
+
+        self.reconstruction_err_ = _beta_divergence(
+            X, W, H, self._beta_loss, square_root=True
+        )
+
+        self.n_components_ = H.shape[0]
+        self.components_ = H
+        self.n_iter_ = n_iter
+
+        return W
+
+    def _fit_transform(self, X, y=None, W=None, H=None, update_H=True):
+        """Learn a NMF model for the data X and returns the transformed data.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data matrix to be decomposed
+
+        y : Ignored
+
+        W : array-like of shape (n_samples, n_components), default=None
+            If `init='custom'`, it is used as initial guess for the solution.
+            If `update_H=False`, it is initialised as an array of zeros, unless
+            `solver='mu'`, then it is filled with values calculated by
+            `np.sqrt(X.mean() / self._n_components)`.
+            If `None`, uses the initialisation method specified in `init`.
+
+        H : array-like of shape (n_components, n_features), default=None
+            If `init='custom'`, it is used as initial guess for the solution.
+            If `update_H=False`, it is used as a constant, to solve for W only.
+            If `None`, uses the initialisation method specified in `init`.
+
+        update_H : bool, default=True
+            If True, both W and H will be estimated from initial guesses,
+            this corresponds to a call to the 'fit_transform' method.
+            If False, only W will be estimated, this corresponds to a call
+            to the 'transform' method.
+
+        Returns
+        -------
+        W : ndarray of shape (n_samples, n_components)
+            Transformed data.
+
+        H : ndarray of shape (n_components, n_features)
+            Factorization matrix, sometimes called 'dictionary'.
+
+        n_iter_ : int
+            Actual number of iterations.
+        """
+        check_non_negative(X, "NMF (input X)")
+
+        # check parameters
+        self._check_params(X)
+
+        if X.min() == 0 and self._beta_loss <= 0:
+            raise ValueError(
+                "When beta_loss <= 0 and X contains zeros, "
+                "the solver may diverge. Please add small values "
+                "to X, or use a positive beta_loss."
+            )
+
+        # initialize or check W and H
+        W, H = self._check_w_h(X, W, H, update_H)
+
+        # scale the regularization terms
+        l1_reg_W, l1_reg_H, l2_reg_W, l2_reg_H = self._compute_regularization(X)
+
+        if self.solver == "cd":
+            W, H, n_iter = _fit_coordinate_descent(
+                X,
+                W,
+                H,
+                self.tol,
+                self.max_iter,
+                l1_reg_W,
+                l1_reg_H,
+                l2_reg_W,
+                l2_reg_H,
+                update_H=update_H,
+                verbose=self.verbose,
+                shuffle=self.shuffle,
+                random_state=self.random_state,
+            )
+        elif self.solver == "mu":
+            W, H, n_iter, *_ = _fit_multiplicative_update(
+                X,
+                W,
+                H,
+                self._beta_loss,
+                self.max_iter,
+                self.tol,
+                l1_reg_W,
+                l1_reg_H,
+                l2_reg_W,
+                l2_reg_H,
+                update_H,
+                self.verbose,
+            )
+        else:
+            raise ValueError("Invalid solver parameter '%s'." % self.solver)
+
+        if n_iter == self.max_iter and self.tol > 0:
+            warnings.warn(
+                "Maximum number of iterations %d reached. Increase "
+                "it to improve convergence."
+                % self.max_iter,
+                ConvergenceWarning,
+            )
+
+        return W, H, n_iter
+
+    def transform(self, X):
+        """Transform the data X according to the fitted NMF model.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        Returns
+        -------
+        W : ndarray of shape (n_samples, n_components)
+            Transformed data.
+        """
+        check_is_fitted(self)
+        X = self._validate_data(
+            X, accept_sparse=("csr", "csc"), dtype=[np.float64, np.float32], reset=False
+        )
+
+        with config_context(assume_finite=True):
+            W, *_ = self._fit_transform(X, H=self.components_, update_H=False)
+
+        return W
+
+
+class MiniBatchNMF(_BaseNMF):
+    """Mini-Batch Non-Negative Matrix Factorization (NMF).
+
+    .. versionadded:: 1.1
+
+    Find two non-negative matrices, i.e. matrices with all non-negative elements,
+    (`W`, `H`) whose product approximates the non-negative matrix `X`. This
+    factorization can be used for example for dimensionality reduction, source
+    separation or topic extraction.
+
+    The objective function is:
+
+        .. math::
+
+            L(W, H) &= 0.5 * ||X - WH||_{loss}^2
+
+            &+ alpha\\_W * l1\\_ratio * n\\_features * ||vec(W)||_1
+
+            &+ alpha\\_H * l1\\_ratio * n\\_samples * ||vec(H)||_1
+
+            &+ 0.5 * alpha\\_W * (1 - l1\\_ratio) * n\\_features * ||W||_{Fro}^2
+
+            &+ 0.5 * alpha\\_H * (1 - l1\\_ratio) * n\\_samples * ||H||_{Fro}^2
+
+    Where:
+
+    :math:`||A||_{Fro}^2 = \\sum_{i,j} A_{ij}^2` (Frobenius norm)
+
+    :math:`||vec(A)||_1 = \\sum_{i,j} abs(A_{ij})` (Elementwise L1 norm)
+
+    The generic norm :math:`||X - WH||_{loss}^2` may represent
+    the Frobenius norm or another supported beta-divergence loss.
+    The choice between options is controlled by the `beta_loss` parameter.
+
+    The objective function is minimized with an alternating minimization of `W`
+    and `H`.
+
+    Note that the transformed data is named `W` and the components matrix is
+    named `H`. In the NMF literature, the naming convention is usually the opposite
+    since the data matrix `X` is transposed.
+
+    Read more in the :ref:`User Guide <MiniBatchNMF>`.
+
+    Parameters
+    ----------
+    n_components : int or {'auto'} or None, default=None
+        Number of components, if `n_components` is not set all features
+        are kept.
+        If `n_components='auto'`, the number of components is automatically inferred
+        from W or H shapes.
+
+        .. versionchanged:: 1.4
+            Added `'auto'` value.
+
+    init : {'random', 'nndsvd', 'nndsvda', 'nndsvdar', 'custom'}, default=None
+        Method used to initialize the procedure.
+        Valid options:
+
+        - `None`: 'nndsvda' if `n_components <= min(n_samples, n_features)`,
+          otherwise random.
+
+        - `'random'`: non-negative random matrices, scaled with:
+          `sqrt(X.mean() / n_components)`
+
+        - `'nndsvd'`: Nonnegative Double Singular Value Decomposition (NNDSVD)
+          initialization (better for sparseness).
+
+        - `'nndsvda'`: NNDSVD with zeros filled with the average of X
+          (better when sparsity is not desired).
+
+        - `'nndsvdar'` NNDSVD with zeros filled with small random values
+          (generally faster, less accurate alternative to NNDSVDa
+          for when sparsity is not desired).
+
+        - `'custom'`: Use custom matrices `W` and `H` which must both be provided.
+
+    batch_size : int, default=1024
+        Number of samples in each mini-batch. Large batch sizes
+        give better long-term convergence at the cost of a slower start.
+
+    beta_loss : float or {'frobenius', 'kullback-leibler', \
+            'itakura-saito'}, default='frobenius'
+        Beta divergence to be minimized, measuring the distance between `X`
+        and the dot product `WH`. Note that values different from 'frobenius'
+        (or 2) and 'kullback-leibler' (or 1) lead to significantly slower
+        fits. Note that for `beta_loss <= 0` (or 'itakura-saito'), the input
+        matrix `X` cannot contain zeros.
+
+    tol : float, default=1e-4
+        Control early stopping based on the norm of the differences in `H`
+        between 2 steps. To disable early stopping based on changes in `H`, set
+        `tol` to 0.0.
+
+    max_no_improvement : int, default=10
+        Control early stopping based on the consecutive number of mini batches
+        that does not yield an improvement on the smoothed cost function.
+        To disable convergence detection based on cost function, set
+        `max_no_improvement` to None.
+
+    max_iter : int, default=200
+        Maximum number of iterations over the complete dataset before
+        timing out.
+
+    alpha_W : float, default=0.0
+        Constant that multiplies the regularization terms of `W`. Set it to zero
+        (default) to have no regularization on `W`.
+
+    alpha_H : float or "same", default="same"
+        Constant that multiplies the regularization terms of `H`. Set it to zero to
+        have no regularization on `H`. If "same" (default), it takes the same value as
+        `alpha_W`.
+
+    l1_ratio : float, default=0.0
+        The regularization mixing parameter, with 0 <= l1_ratio <= 1.
+        For l1_ratio = 0 the penalty is an elementwise L2 penalty
+        (aka Frobenius Norm).
+        For l1_ratio = 1 it is an elementwise L1 penalty.
+        For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
+
+    forget_factor : float, default=0.7
+        Amount of rescaling of past information. Its value could be 1 with
+        finite datasets. Choosing values < 1 is recommended with online
+        learning as more recent batches will weight more than past batches.
+
+    fresh_restarts : bool, default=False
+        Whether to completely solve for W at each step. Doing fresh restarts will likely
+        lead to a better solution for a same number of iterations but it is much slower.
+
+    fresh_restarts_max_iter : int, default=30
+        Maximum number of iterations when solving for W at each step. Only used when
+        doing fresh restarts. These iterations may be stopped early based on a small
+        change of W controlled by `tol`.
+
+    transform_max_iter : int, default=None
+        Maximum number of iterations when solving for W at transform time.
+        If None, it defaults to `max_iter`.
+
+    random_state : int, RandomState instance or None, default=None
+        Used for initialisation (when ``init`` == 'nndsvdar' or
+        'random'), and in Coordinate Descent. Pass an int for reproducible
+        results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    verbose : bool, default=False
+        Whether to be verbose.
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        Factorization matrix, sometimes called 'dictionary'.
+
+    n_components_ : int
+        The number of components. It is same as the `n_components` parameter
+        if it was given. Otherwise, it will be same as the number of
+        features.
+
+    reconstruction_err_ : float
+        Frobenius norm of the matrix difference, or beta-divergence, between
+        the training data `X` and the reconstructed data `WH` from
+        the fitted model.
+
+    n_iter_ : int
+        Actual number of started iterations over the whole dataset.
+
+    n_steps_ : int
+        Number of mini-batches processed.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+    See Also
+    --------
+    NMF : Non-negative matrix factorization.
+    MiniBatchDictionaryLearning : Finds a dictionary that can best be used to represent
+        data using a sparse code.
+
+    References
+    ----------
+    .. [1] :doi:`"Fast local algorithms for large scale nonnegative matrix and tensor
+       factorizations" <10.1587/transfun.E92.A.708>`
+       Cichocki, Andrzej, and P. H. A. N. Anh-Huy. IEICE transactions on fundamentals
+       of electronics, communications and computer sciences 92.3: 708-721, 2009.
+
+    .. [2] :doi:`"Algorithms for nonnegative matrix factorization with the
+       beta-divergence" <10.1162/NECO_a_00168>`
+       Fevotte, C., & Idier, J. (2011). Neural Computation, 23(9).
+
+    .. [3] :doi:`"Online algorithms for nonnegative matrix factorization with the
+       Itakura-Saito divergence" <10.1109/ASPAA.2011.6082314>`
+       Lefevre, A., Bach, F., Fevotte, C. (2011). WASPA.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> X = np.array([[1, 1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
+    >>> from sklearn.decomposition import MiniBatchNMF
+    >>> model = MiniBatchNMF(n_components=2, init='random', random_state=0)
+    >>> W = model.fit_transform(X)
+    >>> H = model.components_
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseNMF._parameter_constraints,
+        "max_no_improvement": [Interval(Integral, 1, None, closed="left"), None],
+        "batch_size": [Interval(Integral, 1, None, closed="left")],
+        "forget_factor": [Interval(Real, 0, 1, closed="both")],
+        "fresh_restarts": ["boolean"],
+        "fresh_restarts_max_iter": [Interval(Integral, 1, None, closed="left")],
+        "transform_max_iter": [Interval(Integral, 1, None, closed="left"), None],
+    }
+
+    def __init__(
+        self,
+        n_components="warn",
+        *,
+        init=None,
+        batch_size=1024,
+        beta_loss="frobenius",
+        tol=1e-4,
+        max_no_improvement=10,
+        max_iter=200,
+        alpha_W=0.0,
+        alpha_H="same",
+        l1_ratio=0.0,
+        forget_factor=0.7,
+        fresh_restarts=False,
+        fresh_restarts_max_iter=30,
+        transform_max_iter=None,
+        random_state=None,
+        verbose=0,
+    ):
+        super().__init__(
+            n_components=n_components,
+            init=init,
+            beta_loss=beta_loss,
+            tol=tol,
+            max_iter=max_iter,
+            random_state=random_state,
+            alpha_W=alpha_W,
+            alpha_H=alpha_H,
+            l1_ratio=l1_ratio,
+            verbose=verbose,
+        )
+
+        self.max_no_improvement = max_no_improvement
+        self.batch_size = batch_size
+        self.forget_factor = forget_factor
+        self.fresh_restarts = fresh_restarts
+        self.fresh_restarts_max_iter = fresh_restarts_max_iter
+        self.transform_max_iter = transform_max_iter
+
+    def _check_params(self, X):
+        super()._check_params(X)
+
+        # batch_size
+        self._batch_size = min(self.batch_size, X.shape[0])
+
+        # forget_factor
+        self._rho = self.forget_factor ** (self._batch_size / X.shape[0])
+
+        # gamma for Maximization-Minimization (MM) algorithm [Fevotte 2011]
+        if self._beta_loss < 1:
+            self._gamma = 1.0 / (2.0 - self._beta_loss)
+        elif self._beta_loss > 2:
+            self._gamma = 1.0 / (self._beta_loss - 1.0)
+        else:
+            self._gamma = 1.0
+
+        # transform_max_iter
+        self._transform_max_iter = (
+            self.max_iter
+            if self.transform_max_iter is None
+            else self.transform_max_iter
+        )
+
+        return self
+
+    def _solve_W(self, X, H, max_iter):
+        """Minimize the objective function w.r.t W.
+
+        Update W with H being fixed, until convergence. This is the heart
+        of `transform` but it's also used during `fit` when doing fresh restarts.
+        """
+        avg = np.sqrt(X.mean() / self._n_components)
+        W = np.full((X.shape[0], self._n_components), avg, dtype=X.dtype)
+        W_buffer = W.copy()
+
+        # Get scaled regularization terms. Done for each minibatch to take into account
+        # variable sizes of minibatches.
+        l1_reg_W, _, l2_reg_W, _ = self._compute_regularization(X)
+
+        for _ in range(max_iter):
+            W, *_ = _multiplicative_update_w(
+                X, W, H, self._beta_loss, l1_reg_W, l2_reg_W, self._gamma
+            )
+
+            W_diff = linalg.norm(W - W_buffer) / linalg.norm(W)
+            if self.tol > 0 and W_diff <= self.tol:
+                break
+
+            W_buffer[:] = W
+
+        return W
+
+    def _minibatch_step(self, X, W, H, update_H):
+        """Perform the update of W and H for one minibatch."""
+        batch_size = X.shape[0]
+
+        # get scaled regularization terms. Done for each minibatch to take into account
+        # variable sizes of minibatches.
+        l1_reg_W, l1_reg_H, l2_reg_W, l2_reg_H = self._compute_regularization(X)
+
+        # update W
+        if self.fresh_restarts or W is None:
+            W = self._solve_W(X, H, self.fresh_restarts_max_iter)
+        else:
+            W, *_ = _multiplicative_update_w(
+                X, W, H, self._beta_loss, l1_reg_W, l2_reg_W, self._gamma
+            )
+
+        # necessary for stability with beta_loss < 1
+        if self._beta_loss < 1:
+            W[W < np.finfo(np.float64).eps] = 0.0
+
+        batch_cost = (
+            _beta_divergence(X, W, H, self._beta_loss)
+            + l1_reg_W * W.sum()
+            + l1_reg_H * H.sum()
+            + l2_reg_W * (W**2).sum()
+            + l2_reg_H * (H**2).sum()
+        ) / batch_size
+
+        # update H (only at fit or fit_transform)
+        if update_H:
+            H[:] = _multiplicative_update_h(
+                X,
+                W,
+                H,
+                beta_loss=self._beta_loss,
+                l1_reg_H=l1_reg_H,
+                l2_reg_H=l2_reg_H,
+                gamma=self._gamma,
+                A=self._components_numerator,
+                B=self._components_denominator,
+                rho=self._rho,
+            )
+
+            # necessary for stability with beta_loss < 1
+            if self._beta_loss <= 1:
+                H[H < np.finfo(np.float64).eps] = 0.0
+
+        return batch_cost
+
+    def _minibatch_convergence(
+        self, X, batch_cost, H, H_buffer, n_samples, step, n_steps
+    ):
+        """Helper function to encapsulate the early stopping logic"""
+        batch_size = X.shape[0]
+
+        # counts steps starting from 1 for user friendly verbose mode.
+        step = step + 1
+
+        # Ignore first iteration because H is not updated yet.
+        if step == 1:
+            if self.verbose:
+                print(f"Minibatch step {step}/{n_steps}: mean batch cost: {batch_cost}")
+            return False
+
+        # Compute an Exponentially Weighted Average of the cost function to
+        # monitor the convergence while discarding minibatch-local stochastic
+        # variability: https://en.wikipedia.org/wiki/Moving_average
+        if self._ewa_cost is None:
+            self._ewa_cost = batch_cost
+        else:
+            alpha = batch_size / (n_samples + 1)
+            alpha = min(alpha, 1)
+            self._ewa_cost = self._ewa_cost * (1 - alpha) + batch_cost * alpha
+
+        # Log progress to be able to monitor convergence
+        if self.verbose:
+            print(
+                f"Minibatch step {step}/{n_steps}: mean batch cost: "
+                f"{batch_cost}, ewa cost: {self._ewa_cost}"
+            )
+
+        # Early stopping based on change of H
+        H_diff = linalg.norm(H - H_buffer) / linalg.norm(H)
+        if self.tol > 0 and H_diff <= self.tol:
+            if self.verbose:
+                print(f"Converged (small H change) at step {step}/{n_steps}")
+            return True
+
+        # Early stopping heuristic due to lack of improvement on smoothed
+        # cost function
+        if self._ewa_cost_min is None or self._ewa_cost < self._ewa_cost_min:
+            self._no_improvement = 0
+            self._ewa_cost_min = self._ewa_cost
+        else:
+            self._no_improvement += 1
+
+        if (
+            self.max_no_improvement is not None
+            and self._no_improvement >= self.max_no_improvement
+        ):
+            if self.verbose:
+                print(
+                    "Converged (lack of improvement in objective function) "
+                    f"at step {step}/{n_steps}"
+                )
+            return True
+
+        return False
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit_transform(self, X, y=None, W=None, H=None):
+        """Learn a NMF model for the data X and returns the transformed data.
+
+        This is more efficient than calling fit followed by transform.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data matrix to be decomposed.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        W : array-like of shape (n_samples, n_components), default=None
+            If `init='custom'`, it is used as initial guess for the solution.
+            If `None`, uses the initialisation method specified in `init`.
+
+        H : array-like of shape (n_components, n_features), default=None
+            If `init='custom'`, it is used as initial guess for the solution.
+            If `None`, uses the initialisation method specified in `init`.
+
+        Returns
+        -------
+        W : ndarray of shape (n_samples, n_components)
+            Transformed data.
+        """
+        X = self._validate_data(
+            X, accept_sparse=("csr", "csc"), dtype=[np.float64, np.float32]
+        )
+
+        with config_context(assume_finite=True):
+            W, H, n_iter, n_steps = self._fit_transform(X, W=W, H=H)
+
+        self.reconstruction_err_ = _beta_divergence(
+            X, W, H, self._beta_loss, square_root=True
+        )
+
+        self.n_components_ = H.shape[0]
+        self.components_ = H
+        self.n_iter_ = n_iter
+        self.n_steps_ = n_steps
+
+        return W
+
+    def _fit_transform(self, X, W=None, H=None, update_H=True):
+        """Learn a NMF model for the data X and returns the transformed data.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            Data matrix to be decomposed.
+
+        W : array-like of shape (n_samples, n_components), default=None
+            If `init='custom'`, it is used as initial guess for the solution.
+            If `update_H=False`, it is initialised as an array of zeros, unless
+            `solver='mu'`, then it is filled with values calculated by
+            `np.sqrt(X.mean() / self._n_components)`.
+            If `None`, uses the initialisation method specified in `init`.
+
+        H : array-like of shape (n_components, n_features), default=None
+            If `init='custom'`, it is used as initial guess for the solution.
+            If `update_H=False`, it is used as a constant, to solve for W only.
+            If `None`, uses the initialisation method specified in `init`.
+
+        update_H : bool, default=True
+            If True, both W and H will be estimated from initial guesses,
+            this corresponds to a call to the `fit_transform` method.
+            If False, only W will be estimated, this corresponds to a call
+            to the `transform` method.
+
+        Returns
+        -------
+        W : ndarray of shape (n_samples, n_components)
+            Transformed data.
+
+        H : ndarray of shape (n_components, n_features)
+            Factorization matrix, sometimes called 'dictionary'.
+
+        n_iter : int
+            Actual number of started iterations over the whole dataset.
+
+        n_steps : int
+            Number of mini-batches processed.
+        """
+        check_non_negative(X, "MiniBatchNMF (input X)")
+        self._check_params(X)
+
+        if X.min() == 0 and self._beta_loss <= 0:
+            raise ValueError(
+                "When beta_loss <= 0 and X contains zeros, "
+                "the solver may diverge. Please add small values "
+                "to X, or use a positive beta_loss."
+            )
+
+        n_samples = X.shape[0]
+
+        # initialize or check W and H
+        W, H = self._check_w_h(X, W, H, update_H)
+        H_buffer = H.copy()
+
+        # Initialize auxiliary matrices
+        self._components_numerator = H.copy()
+        self._components_denominator = np.ones(H.shape, dtype=H.dtype)
+
+        # Attributes to monitor the convergence
+        self._ewa_cost = None
+        self._ewa_cost_min = None
+        self._no_improvement = 0
+
+        batches = gen_batches(n_samples, self._batch_size)
+        batches = itertools.cycle(batches)
+        n_steps_per_iter = int(np.ceil(n_samples / self._batch_size))
+        n_steps = self.max_iter * n_steps_per_iter
+
+        for i, batch in zip(range(n_steps), batches):
+            batch_cost = self._minibatch_step(X[batch], W[batch], H, update_H)
+
+            if update_H and self._minibatch_convergence(
+                X[batch], batch_cost, H, H_buffer, n_samples, i, n_steps
+            ):
+                break
+
+            H_buffer[:] = H
+
+        if self.fresh_restarts:
+            W = self._solve_W(X, H, self._transform_max_iter)
+
+        n_steps = i + 1
+        n_iter = int(np.ceil(n_steps / n_steps_per_iter))
+
+        if n_iter == self.max_iter and self.tol > 0:
+            warnings.warn(
+                (
+                    f"Maximum number of iterations {self.max_iter} reached. "
+                    "Increase it to improve convergence."
+                ),
+                ConvergenceWarning,
+            )
+
+        return W, H, n_iter, n_steps
+
+    def transform(self, X):
+        """Transform the data X according to the fitted MiniBatchNMF model.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data matrix to be transformed by the model.
+
+        Returns
+        -------
+        W : ndarray of shape (n_samples, n_components)
+            Transformed data.
+        """
+        check_is_fitted(self)
+        X = self._validate_data(
+            X, accept_sparse=("csr", "csc"), dtype=[np.float64, np.float32], reset=False
+        )
+
+        W = self._solve_W(X, self.components_, self._transform_max_iter)
+
+        return W
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def partial_fit(self, X, y=None, W=None, H=None):
+        """Update the model using the data in `X` as a mini-batch.
+
+        This method is expected to be called several times consecutively
+        on different chunks of a dataset so as to implement out-of-core
+        or online learning.
+
+        This is especially useful when the whole dataset is too big to fit in
+        memory at once (see :ref:`scaling_strategies`).
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data matrix to be decomposed.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        W : array-like of shape (n_samples, n_components), default=None
+            If `init='custom'`, it is used as initial guess for the solution.
+            Only used for the first call to `partial_fit`.
+
+        H : array-like of shape (n_components, n_features), default=None
+            If `init='custom'`, it is used as initial guess for the solution.
+            Only used for the first call to `partial_fit`.
+
+        Returns
+        -------
+        self
+            Returns the instance itself.
+        """
+        has_components = hasattr(self, "components_")
+
+        X = self._validate_data(
+            X,
+            accept_sparse=("csr", "csc"),
+            dtype=[np.float64, np.float32],
+            reset=not has_components,
+        )
+
+        if not has_components:
+            # This instance has not been fitted yet (fit or partial_fit)
+            self._check_params(X)
+            _, H = self._check_w_h(X, W=W, H=H, update_H=True)
+
+            self._components_numerator = H.copy()
+            self._components_denominator = np.ones(H.shape, dtype=H.dtype)
+            self.n_steps_ = 0
+        else:
+            H = self.components_
+
+        self._minibatch_step(X, None, H, update_H=True)
+
+        self.n_components_ = H.shape[0]
+        self.components_ = H
+        self.n_steps_ += 1
+
+        return self

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_pca.py

@@ -0,0 +1,747 @@
+""" Principal Component Analysis.
+"""
+
+# Author: Alexandre Gramfort <[email protected]>
+#         Olivier Grisel <[email protected]>
+#         Mathieu Blondel <[email protected]>
+#         Denis A. Engemann <[email protected]>
+#         Michael Eickenberg <[email protected]>
+#         Giorgio Patrini <[email protected]>
+#
+# License: BSD 3 clause
+
+from math import log, sqrt
+from numbers import Integral, Real
+
+import numpy as np
+from scipy import linalg
+from scipy.sparse import issparse
+from scipy.sparse.linalg import svds
+from scipy.special import gammaln
+
+from ..base import _fit_context
+from ..utils import check_random_state
+from ..utils._arpack import _init_arpack_v0
+from ..utils._array_api import _convert_to_numpy, get_namespace
+from ..utils._param_validation import Interval, RealNotInt, StrOptions
+from ..utils.extmath import fast_logdet, randomized_svd, stable_cumsum, svd_flip
+from ..utils.sparsefuncs import _implicit_column_offset, mean_variance_axis
+from ..utils.validation import check_is_fitted
+from ._base import _BasePCA
+
+
+def _assess_dimension(spectrum, rank, n_samples):
+    """Compute the log-likelihood of a rank ``rank`` dataset.
+
+    The dataset is assumed to be embedded in gaussian noise of shape(n,
+    dimf) having spectrum ``spectrum``. This implements the method of
+    T. P. Minka.
+
+    Parameters
+    ----------
+    spectrum : ndarray of shape (n_features,)
+        Data spectrum.
+    rank : int
+        Tested rank value. It should be strictly lower than n_features,
+        otherwise the method isn't specified (division by zero in equation
+        (31) from the paper).
+    n_samples : int
+        Number of samples.
+
+    Returns
+    -------
+    ll : float
+        The log-likelihood.
+
+    References
+    ----------
+    This implements the method of `Thomas P. Minka:
+    Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604
+    <https://proceedings.neurips.cc/paper/2000/file/7503cfacd12053d309b6bed5c89de212-Paper.pdf>`_
+    """
+    xp, _ = get_namespace(spectrum)
+
+    n_features = spectrum.shape[0]
+    if not 1 <= rank < n_features:
+        raise ValueError("the tested rank should be in [1, n_features - 1]")
+
+    eps = 1e-15
+
+    if spectrum[rank - 1] < eps:
+        # When the tested rank is associated with a small eigenvalue, there's
+        # no point in computing the log-likelihood: it's going to be very
+        # small and won't be the max anyway. Also, it can lead to numerical
+        # issues below when computing pa, in particular in log((spectrum[i] -
+        # spectrum[j]) because this will take the log of something very small.
+        return -xp.inf
+
+    pu = -rank * log(2.0)
+    for i in range(1, rank + 1):
+        pu += (
+            gammaln((n_features - i + 1) / 2.0)
+            - log(xp.pi) * (n_features - i + 1) / 2.0
+        )
+
+    pl = xp.sum(xp.log(spectrum[:rank]))
+    pl = -pl * n_samples / 2.0
+
+    v = max(eps, xp.sum(spectrum[rank:]) / (n_features - rank))
+    pv = -log(v) * n_samples * (n_features - rank) / 2.0
+
+    m = n_features * rank - rank * (rank + 1.0) / 2.0
+    pp = log(2.0 * xp.pi) * (m + rank) / 2.0
+
+    pa = 0.0
+    spectrum_ = xp.asarray(spectrum, copy=True)
+    spectrum_[rank:n_features] = v
+    for i in range(rank):
+        for j in range(i + 1, spectrum.shape[0]):
+            pa += log(
+                (spectrum[i] - spectrum[j]) * (1.0 / spectrum_[j] - 1.0 / spectrum_[i])
+            ) + log(n_samples)
+
+    ll = pu + pl + pv + pp - pa / 2.0 - rank * log(n_samples) / 2.0
+
+    return ll
+
+
+def _infer_dimension(spectrum, n_samples):
+    """Infers the dimension of a dataset with a given spectrum.
+
+    The returned value will be in [1, n_features - 1].
+    """
+    xp, _ = get_namespace(spectrum)
+
+    ll = xp.empty_like(spectrum)
+    ll[0] = -xp.inf  # we don't want to return n_components = 0
+    for rank in range(1, spectrum.shape[0]):
+        ll[rank] = _assess_dimension(spectrum, rank, n_samples)
+    return xp.argmax(ll)
+
+
+class PCA(_BasePCA):
+    """Principal component analysis (PCA).
+
+    Linear dimensionality reduction using Singular Value Decomposition of the
+    data to project it to a lower dimensional space. The input data is centered
+    but not scaled for each feature before applying the SVD.
+
+    It uses the LAPACK implementation of the full SVD or a randomized truncated
+    SVD by the method of Halko et al. 2009, depending on the shape of the input
+    data and the number of components to extract.
+
+    It can also use the scipy.sparse.linalg ARPACK implementation of the
+    truncated SVD.
+
+    Notice that this class does not support sparse input. See
+    :class:`TruncatedSVD` for an alternative with sparse data.
+
+    For a usage example, see
+    :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py`
+
+    Read more in the :ref:`User Guide <PCA>`.
+
+    Parameters
+    ----------
+    n_components : int, float or 'mle', default=None
+        Number of components to keep.
+        if n_components is not set all components are kept::
+
+            n_components == min(n_samples, n_features)
+
+        If ``n_components == 'mle'`` and ``svd_solver == 'full'``, Minka's
+        MLE is used to guess the dimension. Use of ``n_components == 'mle'``
+        will interpret ``svd_solver == 'auto'`` as ``svd_solver == 'full'``.
+
+        If ``0 < n_components < 1`` and ``svd_solver == 'full'``, select the
+        number of components such that the amount of variance that needs to be
+        explained is greater than the percentage specified by n_components.
+
+        If ``svd_solver == 'arpack'``, the number of components must be
+        strictly less than the minimum of n_features and n_samples.
+
+        Hence, the None case results in::
+
+            n_components == min(n_samples, n_features) - 1
+
+    copy : bool, default=True
+        If False, data passed to fit are overwritten and running
+        fit(X).transform(X) will not yield the expected results,
+        use fit_transform(X) instead.
+
+    whiten : bool, default=False
+        When True (False by default) the `components_` vectors are multiplied
+        by the square root of n_samples and then divided by the singular values
+        to ensure uncorrelated outputs with unit component-wise variances.
+
+        Whitening will remove some information from the transformed signal
+        (the relative variance scales of the components) but can sometime
+        improve the predictive accuracy of the downstream estimators by
+        making their data respect some hard-wired assumptions.
+
+    svd_solver : {'auto', 'full', 'arpack', 'randomized'}, default='auto'
+        If auto :
+            The solver is selected by a default policy based on `X.shape` and
+            `n_components`: if the input data is larger than 500x500 and the
+            number of components to extract is lower than 80% of the smallest
+            dimension of the data, then the more efficient 'randomized'
+            method is enabled. Otherwise the exact full SVD is computed and
+            optionally truncated afterwards.
+        If full :
+            run exact full SVD calling the standard LAPACK solver via
+            `scipy.linalg.svd` and select the components by postprocessing
+        If arpack :
+            run SVD truncated to n_components calling ARPACK solver via
+            `scipy.sparse.linalg.svds`. It requires strictly
+            0 < n_components < min(X.shape)
+        If randomized :
+            run randomized SVD by the method of Halko et al.
+
+        .. versionadded:: 0.18.0
+
+    tol : float, default=0.0
+        Tolerance for singular values computed by svd_solver == 'arpack'.
+        Must be of range [0.0, infinity).
+
+        .. versionadded:: 0.18.0
+
+    iterated_power : int or 'auto', default='auto'
+        Number of iterations for the power method computed by
+        svd_solver == 'randomized'.
+        Must be of range [0, infinity).
+
+        .. versionadded:: 0.18.0
+
+    n_oversamples : int, default=10
+        This parameter is only relevant when `svd_solver="randomized"`.
+        It corresponds to the additional number of random vectors to sample the
+        range of `X` so as to ensure proper conditioning. See
+        :func:`~sklearn.utils.extmath.randomized_svd` for more details.
+
+        .. versionadded:: 1.1
+
+    power_iteration_normalizer : {'auto', 'QR', 'LU', 'none'}, default='auto'
+        Power iteration normalizer for randomized SVD solver.
+        Not used by ARPACK. See :func:`~sklearn.utils.extmath.randomized_svd`
+        for more details.
+
+        .. versionadded:: 1.1
+
+    random_state : int, RandomState instance or None, default=None
+        Used when the 'arpack' or 'randomized' solvers are used. Pass an int
+        for reproducible results across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+        .. versionadded:: 0.18.0
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        Principal axes in feature space, representing the directions of
+        maximum variance in the data. Equivalently, the right singular
+        vectors of the centered input data, parallel to its eigenvectors.
+        The components are sorted by decreasing ``explained_variance_``.
+
+    explained_variance_ : ndarray of shape (n_components,)
+        The amount of variance explained by each of the selected components.
+        The variance estimation uses `n_samples - 1` degrees of freedom.
+
+        Equal to n_components largest eigenvalues
+        of the covariance matrix of X.
+
+        .. versionadded:: 0.18
+
+    explained_variance_ratio_ : ndarray of shape (n_components,)
+        Percentage of variance explained by each of the selected components.
+
+        If ``n_components`` is not set then all components are stored and the
+        sum of the ratios is equal to 1.0.
+
+    singular_values_ : ndarray of shape (n_components,)
+        The singular values corresponding to each of the selected components.
+        The singular values are equal to the 2-norms of the ``n_components``
+        variables in the lower-dimensional space.
+
+        .. versionadded:: 0.19
+
+    mean_ : ndarray of shape (n_features,)
+        Per-feature empirical mean, estimated from the training set.
+
+        Equal to `X.mean(axis=0)`.
+
+    n_components_ : int
+        The estimated number of components. When n_components is set
+        to 'mle' or a number between 0 and 1 (with svd_solver == 'full') this
+        number is estimated from input data. Otherwise it equals the parameter
+        n_components, or the lesser value of n_features and n_samples
+        if n_components is None.
+
+    n_samples_ : int
+        Number of samples in the training data.
+
+    noise_variance_ : float
+        The estimated noise covariance following the Probabilistic PCA model
+        from Tipping and Bishop 1999. See "Pattern Recognition and
+        Machine Learning" by C. Bishop, 12.2.1 p. 574 or
+        http://www.miketipping.com/papers/met-mppca.pdf. It is required to
+        compute the estimated data covariance and score samples.
+
+        Equal to the average of (min(n_features, n_samples) - n_components)
+        smallest eigenvalues of the covariance matrix of X.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    KernelPCA : Kernel Principal Component Analysis.
+    SparsePCA : Sparse Principal Component Analysis.
+    TruncatedSVD : Dimensionality reduction using truncated SVD.
+    IncrementalPCA : Incremental Principal Component Analysis.
+
+    References
+    ----------
+    For n_components == 'mle', this class uses the method from:
+    `Minka, T. P.. "Automatic choice of dimensionality for PCA".
+    In NIPS, pp. 598-604 <https://tminka.github.io/papers/pca/minka-pca.pdf>`_
+
+    Implements the probabilistic PCA model from:
+    `Tipping, M. E., and Bishop, C. M. (1999). "Probabilistic principal
+    component analysis". Journal of the Royal Statistical Society:
+    Series B (Statistical Methodology), 61(3), 611-622.
+    <http://www.miketipping.com/papers/met-mppca.pdf>`_
+    via the score and score_samples methods.
+
+    For svd_solver == 'arpack', refer to `scipy.sparse.linalg.svds`.
+
+    For svd_solver == 'randomized', see:
+    :doi:`Halko, N., Martinsson, P. G., and Tropp, J. A. (2011).
+    "Finding structure with randomness: Probabilistic algorithms for
+    constructing approximate matrix decompositions".
+    SIAM review, 53(2), 217-288.
+    <10.1137/090771806>`
+    and also
+    :doi:`Martinsson, P. G., Rokhlin, V., and Tygert, M. (2011).
+    "A randomized algorithm for the decomposition of matrices".
+    Applied and Computational Harmonic Analysis, 30(1), 47-68.
+    <10.1016/j.acha.2010.02.003>`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.decomposition import PCA
+    >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
+    >>> pca = PCA(n_components=2)
+    >>> pca.fit(X)
+    PCA(n_components=2)
+    >>> print(pca.explained_variance_ratio_)
+    [0.9924... 0.0075...]
+    >>> print(pca.singular_values_)
+    [6.30061... 0.54980...]
+
+    >>> pca = PCA(n_components=2, svd_solver='full')
+    >>> pca.fit(X)
+    PCA(n_components=2, svd_solver='full')
+    >>> print(pca.explained_variance_ratio_)
+    [0.9924... 0.00755...]
+    >>> print(pca.singular_values_)
+    [6.30061... 0.54980...]
+
+    >>> pca = PCA(n_components=1, svd_solver='arpack')
+    >>> pca.fit(X)
+    PCA(n_components=1, svd_solver='arpack')
+    >>> print(pca.explained_variance_ratio_)
+    [0.99244...]
+    >>> print(pca.singular_values_)
+    [6.30061...]
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [
+            Interval(Integral, 0, None, closed="left"),
+            Interval(RealNotInt, 0, 1, closed="neither"),
+            StrOptions({"mle"}),
+            None,
+        ],
+        "copy": ["boolean"],
+        "whiten": ["boolean"],
+        "svd_solver": [StrOptions({"auto", "full", "arpack", "randomized"})],
+        "tol": [Interval(Real, 0, None, closed="left")],
+        "iterated_power": [
+            StrOptions({"auto"}),
+            Interval(Integral, 0, None, closed="left"),
+        ],
+        "n_oversamples": [Interval(Integral, 1, None, closed="left")],
+        "power_iteration_normalizer": [StrOptions({"auto", "QR", "LU", "none"})],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(
+        self,
+        n_components=None,
+        *,
+        copy=True,
+        whiten=False,
+        svd_solver="auto",
+        tol=0.0,
+        iterated_power="auto",
+        n_oversamples=10,
+        power_iteration_normalizer="auto",
+        random_state=None,
+    ):
+        self.n_components = n_components
+        self.copy = copy
+        self.whiten = whiten
+        self.svd_solver = svd_solver
+        self.tol = tol
+        self.iterated_power = iterated_power
+        self.n_oversamples = n_oversamples
+        self.power_iteration_normalizer = power_iteration_normalizer
+        self.random_state = random_state
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the model with X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Ignored.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        self._fit(X)
+        return self
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit_transform(self, X, y=None):
+        """Fit the model with X and apply the dimensionality reduction on X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Ignored.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Transformed values.
+
+        Notes
+        -----
+        This method returns a Fortran-ordered array. To convert it to a
+        C-ordered array, use 'np.ascontiguousarray'.
+        """
+        U, S, Vt = self._fit(X)
+        U = U[:, : self.n_components_]
+
+        if self.whiten:
+            # X_new = X * V / S * sqrt(n_samples) = U * sqrt(n_samples)
+            U *= sqrt(X.shape[0] - 1)
+        else:
+            # X_new = X * V = U * S * Vt * V = U * S
+            U *= S[: self.n_components_]
+
+        return U
+
+    def _fit(self, X):
+        """Dispatch to the right submethod depending on the chosen solver."""
+        xp, is_array_api_compliant = get_namespace(X)
+
+        # Raise an error for sparse input and unsupported svd_solver
+        if issparse(X) and self.svd_solver != "arpack":
+            raise TypeError(
+                'PCA only support sparse inputs with the "arpack" solver, while '
+                f'"{self.svd_solver}" was passed. See TruncatedSVD for a possible'
+                " alternative."
+            )
+        # Raise an error for non-Numpy input and arpack solver.
+        if self.svd_solver == "arpack" and is_array_api_compliant:
+            raise ValueError(
+                "PCA with svd_solver='arpack' is not supported for Array API inputs."
+            )
+
+        X = self._validate_data(
+            X,
+            dtype=[xp.float64, xp.float32],
+            accept_sparse=("csr", "csc"),
+            ensure_2d=True,
+            copy=self.copy,
+        )
+
+        # Handle n_components==None
+        if self.n_components is None:
+            if self.svd_solver != "arpack":
+                n_components = min(X.shape)
+            else:
+                n_components = min(X.shape) - 1
+        else:
+            n_components = self.n_components
+
+        # Handle svd_solver
+        self._fit_svd_solver = self.svd_solver
+        if self._fit_svd_solver == "auto":
+            # Small problem or n_components == 'mle', just call full PCA
+            if max(X.shape) <= 500 or n_components == "mle":
+                self._fit_svd_solver = "full"
+            elif 1 <= n_components < 0.8 * min(X.shape):
+                self._fit_svd_solver = "randomized"
+            # This is also the case of n_components in (0,1)
+            else:
+                self._fit_svd_solver = "full"
+
+        # Call different fits for either full or truncated SVD
+        if self._fit_svd_solver == "full":
+            return self._fit_full(X, n_components)
+        elif self._fit_svd_solver in ["arpack", "randomized"]:
+            return self._fit_truncated(X, n_components, self._fit_svd_solver)
+
+    def _fit_full(self, X, n_components):
+        """Fit the model by computing full SVD on X."""
+        xp, is_array_api_compliant = get_namespace(X)
+
+        n_samples, n_features = X.shape
+
+        if n_components == "mle":
+            if n_samples < n_features:
+                raise ValueError(
+                    "n_components='mle' is only supported if n_samples >= n_features"
+                )
+        elif not 0 <= n_components <= min(n_samples, n_features):
+            raise ValueError(
+                "n_components=%r must be between 0 and "
+                "min(n_samples, n_features)=%r with "
+                "svd_solver='full'" % (n_components, min(n_samples, n_features))
+            )
+
+        # Center data
+        self.mean_ = xp.mean(X, axis=0)
+        X -= self.mean_
+
+        if not is_array_api_compliant:
+            # Use scipy.linalg with NumPy/SciPy inputs for the sake of not
+            # introducing unanticipated behavior changes. In the long run we
+            # could instead decide to always use xp.linalg.svd for all inputs,
+            # but that would make this code rely on numpy's SVD instead of
+            # scipy's. It's not 100% clear whether they use the same LAPACK
+            # solver by default though (assuming both are built against the
+            # same BLAS).
+            U, S, Vt = linalg.svd(X, full_matrices=False)
+        else:
+            U, S, Vt = xp.linalg.svd(X, full_matrices=False)
+        # flip eigenvectors' sign to enforce deterministic output
+        U, Vt = svd_flip(U, Vt)
+
+        components_ = Vt
+
+        # Get variance explained by singular values
+        explained_variance_ = (S**2) / (n_samples - 1)
+        total_var = xp.sum(explained_variance_)
+        explained_variance_ratio_ = explained_variance_ / total_var
+        singular_values_ = xp.asarray(S, copy=True)  # Store the singular values.
+
+        # Postprocess the number of components required
+        if n_components == "mle":
+            n_components = _infer_dimension(explained_variance_, n_samples)
+        elif 0 < n_components < 1.0:
+            # number of components for which the cumulated explained
+            # variance percentage is superior to the desired threshold
+            # side='right' ensures that number of features selected
+            # their variance is always greater than n_components float
+            # passed. More discussion in issue: #15669
+            if is_array_api_compliant:
+                # Convert to numpy as xp.cumsum and xp.searchsorted are not
+                # part of the Array API standard yet:
+                #
+                # https://github.com/data-apis/array-api/issues/597
+                # https://github.com/data-apis/array-api/issues/688
+                #
+                # Furthermore, it's not always safe to call them for namespaces
+                # that already implement them: for instance as
+                # cupy.searchsorted does not accept a float as second argument.
+                explained_variance_ratio_np = _convert_to_numpy(
+                    explained_variance_ratio_, xp=xp
+                )
+            else:
+                explained_variance_ratio_np = explained_variance_ratio_
+            ratio_cumsum = stable_cumsum(explained_variance_ratio_np)
+            n_components = np.searchsorted(ratio_cumsum, n_components, side="right") + 1
+
+        # Compute noise covariance using Probabilistic PCA model
+        # The sigma2 maximum likelihood (cf. eq. 12.46)
+        if n_components < min(n_features, n_samples):
+            self.noise_variance_ = xp.mean(explained_variance_[n_components:])
+        else:
+            self.noise_variance_ = 0.0
+
+        self.n_samples_ = n_samples
+        self.components_ = components_[:n_components, :]
+        self.n_components_ = n_components
+        self.explained_variance_ = explained_variance_[:n_components]
+        self.explained_variance_ratio_ = explained_variance_ratio_[:n_components]
+        self.singular_values_ = singular_values_[:n_components]
+
+        return U, S, Vt
+
+    def _fit_truncated(self, X, n_components, svd_solver):
+        """Fit the model by computing truncated SVD (by ARPACK or randomized)
+        on X.
+        """
+        xp, _ = get_namespace(X)
+
+        n_samples, n_features = X.shape
+
+        if isinstance(n_components, str):
+            raise ValueError(
+                "n_components=%r cannot be a string with svd_solver='%s'"
+                % (n_components, svd_solver)
+            )
+        elif not 1 <= n_components <= min(n_samples, n_features):
+            raise ValueError(
+                "n_components=%r must be between 1 and "
+                "min(n_samples, n_features)=%r with "
+                "svd_solver='%s'"
+                % (n_components, min(n_samples, n_features), svd_solver)
+            )
+        elif svd_solver == "arpack" and n_components == min(n_samples, n_features):
+            raise ValueError(
+                "n_components=%r must be strictly less than "
+                "min(n_samples, n_features)=%r with "
+                "svd_solver='%s'"
+                % (n_components, min(n_samples, n_features), svd_solver)
+            )
+
+        random_state = check_random_state(self.random_state)
+
+        # Center data
+        total_var = None
+        if issparse(X):
+            self.mean_, var = mean_variance_axis(X, axis=0)
+            total_var = var.sum() * n_samples / (n_samples - 1)  # ddof=1
+            X = _implicit_column_offset(X, self.mean_)
+        else:
+            self.mean_ = xp.mean(X, axis=0)
+            X -= self.mean_
+
+        if svd_solver == "arpack":
+            v0 = _init_arpack_v0(min(X.shape), random_state)
+            U, S, Vt = svds(X, k=n_components, tol=self.tol, v0=v0)
+            # svds doesn't abide by scipy.linalg.svd/randomized_svd
+            # conventions, so reverse its outputs.
+            S = S[::-1]
+            # flip eigenvectors' sign to enforce deterministic output
+            U, Vt = svd_flip(U[:, ::-1], Vt[::-1])
+
+        elif svd_solver == "randomized":
+            # sign flipping is done inside
+            U, S, Vt = randomized_svd(
+                X,
+                n_components=n_components,
+                n_oversamples=self.n_oversamples,
+                n_iter=self.iterated_power,
+                power_iteration_normalizer=self.power_iteration_normalizer,
+                flip_sign=True,
+                random_state=random_state,
+            )
+
+        self.n_samples_ = n_samples
+        self.components_ = Vt
+        self.n_components_ = n_components
+
+        # Get variance explained by singular values
+        self.explained_variance_ = (S**2) / (n_samples - 1)
+
+        # Workaround in-place variance calculation since at the time numpy
+        # did not have a way to calculate variance in-place.
+        #
+        # TODO: update this code to either:
+        # * Use the array-api variance calculation, unless memory usage suffers
+        # * Update sklearn.utils.extmath._incremental_mean_and_var to support array-api
+        # See: https://github.com/scikit-learn/scikit-learn/pull/18689#discussion_r1335540991
+        if total_var is None:
+            N = X.shape[0] - 1
+            X **= 2
+            total_var = xp.sum(X) / N
+
+        self.explained_variance_ratio_ = self.explained_variance_ / total_var
+        self.singular_values_ = xp.asarray(S, copy=True)  # Store the singular values.
+
+        if self.n_components_ < min(n_features, n_samples):
+            self.noise_variance_ = total_var - xp.sum(self.explained_variance_)
+            self.noise_variance_ /= min(n_features, n_samples) - n_components
+        else:
+            self.noise_variance_ = 0.0
+
+        return U, S, Vt
+
+    def score_samples(self, X):
+        """Return the log-likelihood of each sample.
+
+        See. "Pattern Recognition and Machine Learning"
+        by C. Bishop, 12.2.1 p. 574
+        or http://www.miketipping.com/papers/met-mppca.pdf
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The data.
+
+        Returns
+        -------
+        ll : ndarray of shape (n_samples,)
+            Log-likelihood of each sample under the current model.
+        """
+        check_is_fitted(self)
+        xp, _ = get_namespace(X)
+        X = self._validate_data(X, dtype=[xp.float64, xp.float32], reset=False)
+        Xr = X - self.mean_
+        n_features = X.shape[1]
+        precision = self.get_precision()
+        log_like = -0.5 * xp.sum(Xr * (Xr @ precision), axis=1)
+        log_like -= 0.5 * (n_features * log(2.0 * np.pi) - fast_logdet(precision))
+        return log_like
+
+    def score(self, X, y=None):
+        """Return the average log-likelihood of all samples.
+
+        See. "Pattern Recognition and Machine Learning"
+        by C. Bishop, 12.2.1 p. 574
+        or http://www.miketipping.com/papers/met-mppca.pdf
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The data.
+
+        y : Ignored
+            Ignored.
+
+        Returns
+        -------
+        ll : float
+            Average log-likelihood of the samples under the current model.
+        """
+        xp, _ = get_namespace(X)
+        return float(xp.mean(self.score_samples(X)))
+
+    def _more_tags(self):
+        return {"preserves_dtype": [np.float64, np.float32], "array_api_support": True}

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_sparse_pca.py

@@ -0,0 +1,551 @@
+"""Matrix factorization with Sparse PCA."""
+# Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
+# License: BSD 3 clause
+
+from numbers import Integral, Real
+
+import numpy as np
+
+from ..base import (
+    BaseEstimator,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from ..linear_model import ridge_regression
+from ..utils import check_random_state
+from ..utils._param_validation import Hidden, Interval, StrOptions
+from ..utils.extmath import svd_flip
+from ..utils.validation import check_array, check_is_fitted
+from ._dict_learning import MiniBatchDictionaryLearning, dict_learning
+
+
+class _BaseSparsePCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
+    """Base class for SparsePCA and MiniBatchSparsePCA"""
+
+    _parameter_constraints: dict = {
+        "n_components": [None, Interval(Integral, 1, None, closed="left")],
+        "alpha": [Interval(Real, 0.0, None, closed="left")],
+        "ridge_alpha": [Interval(Real, 0.0, None, closed="left")],
+        "max_iter": [Interval(Integral, 0, None, closed="left")],
+        "tol": [Interval(Real, 0.0, None, closed="left")],
+        "method": [StrOptions({"lars", "cd"})],
+        "n_jobs": [Integral, None],
+        "verbose": ["verbose"],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(
+        self,
+        n_components=None,
+        *,
+        alpha=1,
+        ridge_alpha=0.01,
+        max_iter=1000,
+        tol=1e-8,
+        method="lars",
+        n_jobs=None,
+        verbose=False,
+        random_state=None,
+    ):
+        self.n_components = n_components
+        self.alpha = alpha
+        self.ridge_alpha = ridge_alpha
+        self.max_iter = max_iter
+        self.tol = tol
+        self.method = method
+        self.n_jobs = n_jobs
+        self.verbose = verbose
+        self.random_state = random_state
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the model from data in X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        random_state = check_random_state(self.random_state)
+        X = self._validate_data(X)
+
+        self.mean_ = X.mean(axis=0)
+        X = X - self.mean_
+
+        if self.n_components is None:
+            n_components = X.shape[1]
+        else:
+            n_components = self.n_components
+
+        return self._fit(X, n_components, random_state)
+
+    def transform(self, X):
+        """Least Squares projection of the data onto the sparse components.
+
+        To avoid instability issues in case the system is under-determined,
+        regularization can be applied (Ridge regression) via the
+        `ridge_alpha` parameter.
+
+        Note that Sparse PCA components orthogonality is not enforced as in PCA
+        hence one cannot use a simple linear projection.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test data to be transformed, must have the same number of
+            features as the data used to train the model.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Transformed data.
+        """
+        check_is_fitted(self)
+
+        X = self._validate_data(X, reset=False)
+        X = X - self.mean_
+
+        U = ridge_regression(
+            self.components_.T, X.T, self.ridge_alpha, solver="cholesky"
+        )
+
+        return U
+
+    def inverse_transform(self, X):
+        """Transform data from the latent space to the original space.
+
+        This inversion is an approximation due to the loss of information
+        induced by the forward decomposition.
+
+        .. versionadded:: 1.2
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_components)
+            Data in the latent space.
+
+        Returns
+        -------
+        X_original : ndarray of shape (n_samples, n_features)
+            Reconstructed data in the original space.
+        """
+        check_is_fitted(self)
+        X = check_array(X)
+
+        return (X @ self.components_) + self.mean_
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.components_.shape[0]
+
+    def _more_tags(self):
+        return {
+            "preserves_dtype": [np.float64, np.float32],
+        }
+
+
+class SparsePCA(_BaseSparsePCA):
+    """Sparse Principal Components Analysis (SparsePCA).
+
+    Finds the set of sparse components that can optimally reconstruct
+    the data.  The amount of sparseness is controllable by the coefficient
+    of the L1 penalty, given by the parameter alpha.
+
+    Read more in the :ref:`User Guide <SparsePCA>`.
+
+    Parameters
+    ----------
+    n_components : int, default=None
+        Number of sparse atoms to extract. If None, then ``n_components``
+        is set to ``n_features``.
+
+    alpha : float, default=1
+        Sparsity controlling parameter. Higher values lead to sparser
+        components.
+
+    ridge_alpha : float, default=0.01
+        Amount of ridge shrinkage to apply in order to improve
+        conditioning when calling the transform method.
+
+    max_iter : int, default=1000
+        Maximum number of iterations to perform.
+
+    tol : float, default=1e-8
+        Tolerance for the stopping condition.
+
+    method : {'lars', 'cd'}, default='lars'
+        Method to be used for optimization.
+        lars: uses the least angle regression method to solve the lasso problem
+        (linear_model.lars_path)
+        cd: uses the coordinate descent method to compute the
+        Lasso solution (linear_model.Lasso). Lars will be faster if
+        the estimated components are sparse.
+
+    n_jobs : int, default=None
+        Number of parallel jobs to run.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    U_init : ndarray of shape (n_samples, n_components), default=None
+        Initial values for the loadings for warm restart scenarios. Only used
+        if `U_init` and `V_init` are not None.
+
+    V_init : ndarray of shape (n_components, n_features), default=None
+        Initial values for the components for warm restart scenarios. Only used
+        if `U_init` and `V_init` are not None.
+
+    verbose : int or bool, default=False
+        Controls the verbosity; the higher, the more messages. Defaults to 0.
+
+    random_state : int, RandomState instance or None, default=None
+        Used during dictionary learning. Pass an int for reproducible results
+        across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        Sparse components extracted from the data.
+
+    error_ : ndarray
+        Vector of errors at each iteration.
+
+    n_components_ : int
+        Estimated number of components.
+
+        .. versionadded:: 0.23
+
+    n_iter_ : int
+        Number of iterations run.
+
+    mean_ : ndarray of shape (n_features,)
+        Per-feature empirical mean, estimated from the training set.
+        Equal to ``X.mean(axis=0)``.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    PCA : Principal Component Analysis implementation.
+    MiniBatchSparsePCA : Mini batch variant of `SparsePCA` that is faster but less
+        accurate.
+    DictionaryLearning : Generic dictionary learning problem using a sparse code.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.datasets import make_friedman1
+    >>> from sklearn.decomposition import SparsePCA
+    >>> X, _ = make_friedman1(n_samples=200, n_features=30, random_state=0)
+    >>> transformer = SparsePCA(n_components=5, random_state=0)
+    >>> transformer.fit(X)
+    SparsePCA(...)
+    >>> X_transformed = transformer.transform(X)
+    >>> X_transformed.shape
+    (200, 5)
+    >>> # most values in the components_ are zero (sparsity)
+    >>> np.mean(transformer.components_ == 0)
+    0.9666...
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseSparsePCA._parameter_constraints,
+        "U_init": [None, np.ndarray],
+        "V_init": [None, np.ndarray],
+    }
+
+    def __init__(
+        self,
+        n_components=None,
+        *,
+        alpha=1,
+        ridge_alpha=0.01,
+        max_iter=1000,
+        tol=1e-8,
+        method="lars",
+        n_jobs=None,
+        U_init=None,
+        V_init=None,
+        verbose=False,
+        random_state=None,
+    ):
+        super().__init__(
+            n_components=n_components,
+            alpha=alpha,
+            ridge_alpha=ridge_alpha,
+            max_iter=max_iter,
+            tol=tol,
+            method=method,
+            n_jobs=n_jobs,
+            verbose=verbose,
+            random_state=random_state,
+        )
+        self.U_init = U_init
+        self.V_init = V_init
+
+    def _fit(self, X, n_components, random_state):
+        """Specialized `fit` for SparsePCA."""
+
+        code_init = self.V_init.T if self.V_init is not None else None
+        dict_init = self.U_init.T if self.U_init is not None else None
+        code, dictionary, E, self.n_iter_ = dict_learning(
+            X.T,
+            n_components,
+            alpha=self.alpha,
+            tol=self.tol,
+            max_iter=self.max_iter,
+            method=self.method,
+            n_jobs=self.n_jobs,
+            verbose=self.verbose,
+            random_state=random_state,
+            code_init=code_init,
+            dict_init=dict_init,
+            return_n_iter=True,
+        )
+        # flip eigenvectors' sign to enforce deterministic output
+        code, dictionary = svd_flip(code, dictionary, u_based_decision=False)
+        self.components_ = code.T
+        components_norm = np.linalg.norm(self.components_, axis=1)[:, np.newaxis]
+        components_norm[components_norm == 0] = 1
+        self.components_ /= components_norm
+        self.n_components_ = len(self.components_)
+
+        self.error_ = E
+        return self
+
+
+class MiniBatchSparsePCA(_BaseSparsePCA):
+    """Mini-batch Sparse Principal Components Analysis.
+
+    Finds the set of sparse components that can optimally reconstruct
+    the data.  The amount of sparseness is controllable by the coefficient
+    of the L1 penalty, given by the parameter alpha.
+
+    For an example comparing sparse PCA to PCA, see
+    :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
+
+    Read more in the :ref:`User Guide <SparsePCA>`.
+
+    Parameters
+    ----------
+    n_components : int, default=None
+        Number of sparse atoms to extract. If None, then ``n_components``
+        is set to ``n_features``.
+
+    alpha : int, default=1
+        Sparsity controlling parameter. Higher values lead to sparser
+        components.
+
+    ridge_alpha : float, default=0.01
+        Amount of ridge shrinkage to apply in order to improve
+        conditioning when calling the transform method.
+
+    max_iter : int, default=1_000
+        Maximum number of iterations over the complete dataset before
+        stopping independently of any early stopping criterion heuristics.
+
+        .. versionadded:: 1.2
+
+        .. deprecated:: 1.4
+           `max_iter=None` is deprecated in 1.4 and will be removed in 1.6.
+           Use the default value (i.e. `100`) instead.
+
+    callback : callable, default=None
+        Callable that gets invoked every five iterations.
+
+    batch_size : int, default=3
+        The number of features to take in each mini batch.
+
+    verbose : int or bool, default=False
+        Controls the verbosity; the higher, the more messages. Defaults to 0.
+
+    shuffle : bool, default=True
+        Whether to shuffle the data before splitting it in batches.
+
+    n_jobs : int, default=None
+        Number of parallel jobs to run.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    method : {'lars', 'cd'}, default='lars'
+        Method to be used for optimization.
+        lars: uses the least angle regression method to solve the lasso problem
+        (linear_model.lars_path)
+        cd: uses the coordinate descent method to compute the
+        Lasso solution (linear_model.Lasso). Lars will be faster if
+        the estimated components are sparse.
+
+    random_state : int, RandomState instance or None, default=None
+        Used for random shuffling when ``shuffle`` is set to ``True``,
+        during online dictionary learning. Pass an int for reproducible results
+        across multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    tol : float, default=1e-3
+        Control early stopping based on the norm of the differences in the
+        dictionary between 2 steps.
+
+        To disable early stopping based on changes in the dictionary, set
+        `tol` to 0.0.
+
+        .. versionadded:: 1.1
+
+    max_no_improvement : int or None, default=10
+        Control early stopping based on the consecutive number of mini batches
+        that does not yield an improvement on the smoothed cost function.
+
+        To disable convergence detection based on cost function, set
+        `max_no_improvement` to `None`.
+
+        .. versionadded:: 1.1
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        Sparse components extracted from the data.
+
+    n_components_ : int
+        Estimated number of components.
+
+        .. versionadded:: 0.23
+
+    n_iter_ : int
+        Number of iterations run.
+
+    mean_ : ndarray of shape (n_features,)
+        Per-feature empirical mean, estimated from the training set.
+        Equal to ``X.mean(axis=0)``.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    DictionaryLearning : Find a dictionary that sparsely encodes data.
+    IncrementalPCA : Incremental principal components analysis.
+    PCA : Principal component analysis.
+    SparsePCA : Sparse Principal Components Analysis.
+    TruncatedSVD : Dimensionality reduction using truncated SVD.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.datasets import make_friedman1
+    >>> from sklearn.decomposition import MiniBatchSparsePCA
+    >>> X, _ = make_friedman1(n_samples=200, n_features=30, random_state=0)
+    >>> transformer = MiniBatchSparsePCA(n_components=5, batch_size=50,
+    ...                                  max_iter=10, random_state=0)
+    >>> transformer.fit(X)
+    MiniBatchSparsePCA(...)
+    >>> X_transformed = transformer.transform(X)
+    >>> X_transformed.shape
+    (200, 5)
+    >>> # most values in the components_ are zero (sparsity)
+    >>> np.mean(transformer.components_ == 0)
+    0.9...
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseSparsePCA._parameter_constraints,
+        "max_iter": [Interval(Integral, 0, None, closed="left"), Hidden(None)],
+        "callback": [None, callable],
+        "batch_size": [Interval(Integral, 1, None, closed="left")],
+        "shuffle": ["boolean"],
+        "max_no_improvement": [Interval(Integral, 0, None, closed="left"), None],
+    }
+
+    def __init__(
+        self,
+        n_components=None,
+        *,
+        alpha=1,
+        ridge_alpha=0.01,
+        max_iter=1_000,
+        callback=None,
+        batch_size=3,
+        verbose=False,
+        shuffle=True,
+        n_jobs=None,
+        method="lars",
+        random_state=None,
+        tol=1e-3,
+        max_no_improvement=10,
+    ):
+        super().__init__(
+            n_components=n_components,
+            alpha=alpha,
+            ridge_alpha=ridge_alpha,
+            max_iter=max_iter,
+            tol=tol,
+            method=method,
+            n_jobs=n_jobs,
+            verbose=verbose,
+            random_state=random_state,
+        )
+        self.callback = callback
+        self.batch_size = batch_size
+        self.shuffle = shuffle
+        self.max_no_improvement = max_no_improvement
+
+    def _fit(self, X, n_components, random_state):
+        """Specialized `fit` for MiniBatchSparsePCA."""
+
+        transform_algorithm = "lasso_" + self.method
+        est = MiniBatchDictionaryLearning(
+            n_components=n_components,
+            alpha=self.alpha,
+            max_iter=self.max_iter,
+            dict_init=None,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            n_jobs=self.n_jobs,
+            fit_algorithm=self.method,
+            random_state=random_state,
+            transform_algorithm=transform_algorithm,
+            transform_alpha=self.alpha,
+            verbose=self.verbose,
+            callback=self.callback,
+            tol=self.tol,
+            max_no_improvement=self.max_no_improvement,
+        )
+        est.set_output(transform="default")
+        est.fit(X.T)
+
+        self.components_, self.n_iter_ = est.transform(X.T).T, est.n_iter_
+
+        components_norm = np.linalg.norm(self.components_, axis=1)[:, np.newaxis]
+        components_norm[components_norm == 0] = 1
+        self.components_ /= components_norm
+        self.n_components_ = len(self.components_)
+
+        return self

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/_truncated_svd.py

@@ -0,0 +1,319 @@
+"""Truncated SVD for sparse matrices, aka latent semantic analysis (LSA).
+"""
+
+# Author: Lars Buitinck
+#         Olivier Grisel <[email protected]>
+#         Michael Becker <[email protected]>
+# License: 3-clause BSD.
+
+from numbers import Integral, Real
+
+import numpy as np
+import scipy.sparse as sp
+from scipy.sparse.linalg import svds
+
+from ..base import (
+    BaseEstimator,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from ..utils import check_array, check_random_state
+from ..utils._arpack import _init_arpack_v0
+from ..utils._param_validation import Interval, StrOptions
+from ..utils.extmath import randomized_svd, safe_sparse_dot, svd_flip
+from ..utils.sparsefuncs import mean_variance_axis
+from ..utils.validation import check_is_fitted
+
+__all__ = ["TruncatedSVD"]
+
+
+class TruncatedSVD(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
+    """Dimensionality reduction using truncated SVD (aka LSA).
+
+    This transformer performs linear dimensionality reduction by means of
+    truncated singular value decomposition (SVD). Contrary to PCA, this
+    estimator does not center the data before computing the singular value
+    decomposition. This means it can work with sparse matrices
+    efficiently.
+
+    In particular, truncated SVD works on term count/tf-idf matrices as
+    returned by the vectorizers in :mod:`sklearn.feature_extraction.text`. In
+    that context, it is known as latent semantic analysis (LSA).
+
+    This estimator supports two algorithms: a fast randomized SVD solver, and
+    a "naive" algorithm that uses ARPACK as an eigensolver on `X * X.T` or
+    `X.T * X`, whichever is more efficient.
+
+    Read more in the :ref:`User Guide <LSA>`.
+
+    Parameters
+    ----------
+    n_components : int, default=2
+        Desired dimensionality of output data.
+        If algorithm='arpack', must be strictly less than the number of features.
+        If algorithm='randomized', must be less than or equal to the number of features.
+        The default value is useful for visualisation. For LSA, a value of
+        100 is recommended.
+
+    algorithm : {'arpack', 'randomized'}, default='randomized'
+        SVD solver to use. Either "arpack" for the ARPACK wrapper in SciPy
+        (scipy.sparse.linalg.svds), or "randomized" for the randomized
+        algorithm due to Halko (2009).
+
+    n_iter : int, default=5
+        Number of iterations for randomized SVD solver. Not used by ARPACK. The
+        default is larger than the default in
+        :func:`~sklearn.utils.extmath.randomized_svd` to handle sparse
+        matrices that may have large slowly decaying spectrum.
+
+    n_oversamples : int, default=10
+        Number of oversamples for randomized SVD solver. Not used by ARPACK.
+        See :func:`~sklearn.utils.extmath.randomized_svd` for a complete
+        description.
+
+        .. versionadded:: 1.1
+
+    power_iteration_normalizer : {'auto', 'QR', 'LU', 'none'}, default='auto'
+        Power iteration normalizer for randomized SVD solver.
+        Not used by ARPACK. See :func:`~sklearn.utils.extmath.randomized_svd`
+        for more details.
+
+        .. versionadded:: 1.1
+
+    random_state : int, RandomState instance or None, default=None
+        Used during randomized svd. Pass an int for reproducible results across
+        multiple function calls.
+        See :term:`Glossary <random_state>`.
+
+    tol : float, default=0.0
+        Tolerance for ARPACK. 0 means machine precision. Ignored by randomized
+        SVD solver.
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        The right singular vectors of the input data.
+
+    explained_variance_ : ndarray of shape (n_components,)
+        The variance of the training samples transformed by a projection to
+        each component.
+
+    explained_variance_ratio_ : ndarray of shape (n_components,)
+        Percentage of variance explained by each of the selected components.
+
+    singular_values_ : ndarray of shape (n_components,)
+        The singular values corresponding to each of the selected components.
+        The singular values are equal to the 2-norms of the ``n_components``
+        variables in the lower-dimensional space.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    DictionaryLearning : Find a dictionary that sparsely encodes data.
+    FactorAnalysis : A simple linear generative model with
+        Gaussian latent variables.
+    IncrementalPCA : Incremental principal components analysis.
+    KernelPCA : Kernel Principal component analysis.
+    NMF : Non-Negative Matrix Factorization.
+    PCA : Principal component analysis.
+
+    Notes
+    -----
+    SVD suffers from a problem called "sign indeterminacy", which means the
+    sign of the ``components_`` and the output from transform depend on the
+    algorithm and random state. To work around this, fit instances of this
+    class to data once, then keep the instance around to do transformations.
+
+    References
+    ----------
+    :arxiv:`Halko, et al. (2009). "Finding structure with randomness:
+    Stochastic algorithms for constructing approximate matrix decompositions"
+    <0909.4061>`
+
+    Examples
+    --------
+    >>> from sklearn.decomposition import TruncatedSVD
+    >>> from scipy.sparse import csr_matrix
+    >>> import numpy as np
+    >>> np.random.seed(0)
+    >>> X_dense = np.random.rand(100, 100)
+    >>> X_dense[:, 2 * np.arange(50)] = 0
+    >>> X = csr_matrix(X_dense)
+    >>> svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
+    >>> svd.fit(X)
+    TruncatedSVD(n_components=5, n_iter=7, random_state=42)
+    >>> print(svd.explained_variance_ratio_)
+    [0.0157... 0.0512... 0.0499... 0.0479... 0.0453...]
+    >>> print(svd.explained_variance_ratio_.sum())
+    0.2102...
+    >>> print(svd.singular_values_)
+    [35.2410...  4.5981...   4.5420...  4.4486...  4.3288...]
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [Interval(Integral, 1, None, closed="left")],
+        "algorithm": [StrOptions({"arpack", "randomized"})],
+        "n_iter": [Interval(Integral, 0, None, closed="left")],
+        "n_oversamples": [Interval(Integral, 1, None, closed="left")],
+        "power_iteration_normalizer": [StrOptions({"auto", "OR", "LU", "none"})],
+        "random_state": ["random_state"],
+        "tol": [Interval(Real, 0, None, closed="left")],
+    }
+
+    def __init__(
+        self,
+        n_components=2,
+        *,
+        algorithm="randomized",
+        n_iter=5,
+        n_oversamples=10,
+        power_iteration_normalizer="auto",
+        random_state=None,
+        tol=0.0,
+    ):
+        self.algorithm = algorithm
+        self.n_components = n_components
+        self.n_iter = n_iter
+        self.n_oversamples = n_oversamples
+        self.power_iteration_normalizer = power_iteration_normalizer
+        self.random_state = random_state
+        self.tol = tol
+
+    def fit(self, X, y=None):
+        """Fit model on training data X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training data.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the transformer object.
+        """
+        self.fit_transform(X)
+        return self
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit_transform(self, X, y=None):
+        """Fit model to X and perform dimensionality reduction on X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training data.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Reduced version of X. This will always be a dense array.
+        """
+        X = self._validate_data(X, accept_sparse=["csr", "csc"], ensure_min_features=2)
+        random_state = check_random_state(self.random_state)
+
+        if self.algorithm == "arpack":
+            v0 = _init_arpack_v0(min(X.shape), random_state)
+            U, Sigma, VT = svds(X, k=self.n_components, tol=self.tol, v0=v0)
+            # svds doesn't abide by scipy.linalg.svd/randomized_svd
+            # conventions, so reverse its outputs.
+            Sigma = Sigma[::-1]
+            U, VT = svd_flip(U[:, ::-1], VT[::-1])
+
+        elif self.algorithm == "randomized":
+            if self.n_components > X.shape[1]:
+                raise ValueError(
+                    f"n_components({self.n_components}) must be <="
+                    f" n_features({X.shape[1]})."
+                )
+            U, Sigma, VT = randomized_svd(
+                X,
+                self.n_components,
+                n_iter=self.n_iter,
+                n_oversamples=self.n_oversamples,
+                power_iteration_normalizer=self.power_iteration_normalizer,
+                random_state=random_state,
+            )
+
+        self.components_ = VT
+
+        # As a result of the SVD approximation error on X ~ U @ Sigma @ V.T,
+        # X @ V is not the same as U @ Sigma
+        if self.algorithm == "randomized" or (
+            self.algorithm == "arpack" and self.tol > 0
+        ):
+            X_transformed = safe_sparse_dot(X, self.components_.T)
+        else:
+            X_transformed = U * Sigma
+
+        # Calculate explained variance & explained variance ratio
+        self.explained_variance_ = exp_var = np.var(X_transformed, axis=0)
+        if sp.issparse(X):
+            _, full_var = mean_variance_axis(X, axis=0)
+            full_var = full_var.sum()
+        else:
+            full_var = np.var(X, axis=0).sum()
+        self.explained_variance_ratio_ = exp_var / full_var
+        self.singular_values_ = Sigma  # Store the singular values.
+
+        return X_transformed
+
+    def transform(self, X):
+        """Perform dimensionality reduction on X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            New data.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Reduced version of X. This will always be a dense array.
+        """
+        check_is_fitted(self)
+        X = self._validate_data(X, accept_sparse=["csr", "csc"], reset=False)
+        return safe_sparse_dot(X, self.components_.T)
+
+    def inverse_transform(self, X):
+        """Transform X back to its original space.
+
+        Returns an array X_original whose transform would be X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_components)
+            New data.
+
+        Returns
+        -------
+        X_original : ndarray of shape (n_samples, n_features)
+            Note that this is always a dense array.
+        """
+        X = check_array(X)
+        return np.dot(X, self.components_)
+
+    def _more_tags(self):
+        return {"preserves_dtype": [np.float64, np.float32]}
+
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.components_.shape[0]

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/tests/test_fastica.py

@@ -0,0 +1,451 @@
+"""
+Test the fastica algorithm.
+"""
+import itertools
+import os
+import warnings
+
+import numpy as np
+import pytest
+from scipy import stats
+
+from sklearn.decomposition import PCA, FastICA, fastica
+from sklearn.decomposition._fastica import _gs_decorrelation
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.utils._testing import assert_allclose
+
+
+def center_and_norm(x, axis=-1):
+    """Centers and norms x **in place**
+
+    Parameters
+    -----------
+    x: ndarray
+        Array with an axis of observations (statistical units) measured on
+        random variables.
+    axis: int, optional
+        Axis along which the mean and variance are calculated.
+    """
+    x = np.rollaxis(x, axis)
+    x -= x.mean(axis=0)
+    x /= x.std(axis=0)
+
+
+def test_gs():
+    # Test gram schmidt orthonormalization
+    # generate a random orthogonal  matrix
+    rng = np.random.RandomState(0)
+    W, _, _ = np.linalg.svd(rng.randn(10, 10))
+    w = rng.randn(10)
+    _gs_decorrelation(w, W, 10)
+    assert (w**2).sum() < 1.0e-10
+    w = rng.randn(10)
+    u = _gs_decorrelation(w, W, 5)
+    tmp = np.dot(u, W.T)
+    assert (tmp[:5] ** 2).sum() < 1.0e-10
+
+
+def test_fastica_attributes_dtypes(global_dtype):
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10)).astype(global_dtype, copy=False)
+    fica = FastICA(
+        n_components=5, max_iter=1000, whiten="unit-variance", random_state=0
+    ).fit(X)
+    assert fica.components_.dtype == global_dtype
+    assert fica.mixing_.dtype == global_dtype
+    assert fica.mean_.dtype == global_dtype
+    assert fica.whitening_.dtype == global_dtype
+
+
+def test_fastica_return_dtypes(global_dtype):
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10)).astype(global_dtype, copy=False)
+    k_, mixing_, s_ = fastica(
+        X, max_iter=1000, whiten="unit-variance", random_state=rng
+    )
+    assert k_.dtype == global_dtype
+    assert mixing_.dtype == global_dtype
+    assert s_.dtype == global_dtype
+
+
+@pytest.mark.parametrize("add_noise", [True, False])
+def test_fastica_simple(add_noise, global_random_seed, global_dtype):
+    if (
+        global_random_seed == 20
+        and global_dtype == np.float32
+        and not add_noise
+        and os.getenv("DISTRIB") == "ubuntu"
+    ):
+        pytest.xfail(
+            "FastICA instability with Ubuntu Atlas build with float32 "
+            "global_dtype. For more details, see "
+            "https://github.com/scikit-learn/scikit-learn/issues/24131#issuecomment-1208091119"  # noqa
+        )
+
+    # Test the FastICA algorithm on very simple data.
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 1000
+    # Generate two sources:
+    s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
+    s2 = stats.t.rvs(1, size=n_samples, random_state=global_random_seed)
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+    s = s.astype(global_dtype)
+    s1, s2 = s
+
+    # Mixing angle
+    phi = 0.6
+    mixing = np.array([[np.cos(phi), np.sin(phi)], [np.sin(phi), -np.cos(phi)]])
+    mixing = mixing.astype(global_dtype)
+    m = np.dot(mixing, s)
+
+    if add_noise:
+        m += 0.1 * rng.randn(2, 1000)
+
+    center_and_norm(m)
+
+    # function as fun arg
+    def g_test(x):
+        return x**3, (3 * x**2).mean(axis=-1)
+
+    algos = ["parallel", "deflation"]
+    nls = ["logcosh", "exp", "cube", g_test]
+    whitening = ["arbitrary-variance", "unit-variance", False]
+    for algo, nl, whiten in itertools.product(algos, nls, whitening):
+        if whiten:
+            k_, mixing_, s_ = fastica(
+                m.T, fun=nl, whiten=whiten, algorithm=algo, random_state=rng
+            )
+            with pytest.raises(ValueError):
+                fastica(m.T, fun=np.tanh, whiten=whiten, algorithm=algo)
+        else:
+            pca = PCA(n_components=2, whiten=True, random_state=rng)
+            X = pca.fit_transform(m.T)
+            k_, mixing_, s_ = fastica(
+                X, fun=nl, algorithm=algo, whiten=False, random_state=rng
+            )
+            with pytest.raises(ValueError):
+                fastica(X, fun=np.tanh, algorithm=algo)
+        s_ = s_.T
+        # Check that the mixing model described in the docstring holds:
+        if whiten:
+            # XXX: exact reconstruction to standard relative tolerance is not
+            # possible. This is probably expected when add_noise is True but we
+            # also need a non-trivial atol in float32 when add_noise is False.
+            #
+            # Note that the 2 sources are non-Gaussian in this test.
+            atol = 1e-5 if global_dtype == np.float32 else 0
+            assert_allclose(np.dot(np.dot(mixing_, k_), m), s_, atol=atol)
+
+        center_and_norm(s_)
+        s1_, s2_ = s_
+        # Check to see if the sources have been estimated
+        # in the wrong order
+        if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
+            s2_, s1_ = s_
+        s1_ *= np.sign(np.dot(s1_, s1))
+        s2_ *= np.sign(np.dot(s2_, s2))
+
+        # Check that we have estimated the original sources
+        if not add_noise:
+            assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-2)
+            assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-2)
+        else:
+            assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-1)
+            assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-1)
+
+    # Test FastICA class
+    _, _, sources_fun = fastica(
+        m.T, fun=nl, algorithm=algo, random_state=global_random_seed
+    )
+    ica = FastICA(fun=nl, algorithm=algo, random_state=global_random_seed)
+    sources = ica.fit_transform(m.T)
+    assert ica.components_.shape == (2, 2)
+    assert sources.shape == (1000, 2)
+
+    assert_allclose(sources_fun, sources)
+    # Set atol to account for the different magnitudes of the elements in sources
+    # (from 1e-4 to 1e1).
+    atol = np.max(np.abs(sources)) * (1e-5 if global_dtype == np.float32 else 1e-7)
+    assert_allclose(sources, ica.transform(m.T), atol=atol)
+
+    assert ica.mixing_.shape == (2, 2)
+
+    ica = FastICA(fun=np.tanh, algorithm=algo)
+    with pytest.raises(ValueError):
+        ica.fit(m.T)
+
+
+def test_fastica_nowhiten():
+    m = [[0, 1], [1, 0]]
+
+    # test for issue #697
+    ica = FastICA(n_components=1, whiten=False, random_state=0)
+    warn_msg = "Ignoring n_components with whiten=False."
+    with pytest.warns(UserWarning, match=warn_msg):
+        ica.fit(m)
+    assert hasattr(ica, "mixing_")
+
+
+def test_fastica_convergence_fail():
+    # Test the FastICA algorithm on very simple data
+    # (see test_non_square_fastica).
+    # Ensure a ConvergenceWarning raised if the tolerance is sufficiently low.
+    rng = np.random.RandomState(0)
+
+    n_samples = 1000
+    # Generate two sources:
+    t = np.linspace(0, 100, n_samples)
+    s1 = np.sin(t)
+    s2 = np.ceil(np.sin(np.pi * t))
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+
+    # Mixing matrix
+    mixing = rng.randn(6, 2)
+    m = np.dot(mixing, s)
+
+    # Do fastICA with tolerance 0. to ensure failing convergence
+    warn_msg = (
+        "FastICA did not converge. Consider increasing tolerance "
+        "or the maximum number of iterations."
+    )
+    with pytest.warns(ConvergenceWarning, match=warn_msg):
+        ica = FastICA(
+            algorithm="parallel", n_components=2, random_state=rng, max_iter=2, tol=0.0
+        )
+        ica.fit(m.T)
+
+
+@pytest.mark.parametrize("add_noise", [True, False])
+def test_non_square_fastica(add_noise):
+    # Test the FastICA algorithm on very simple data.
+    rng = np.random.RandomState(0)
+
+    n_samples = 1000
+    # Generate two sources:
+    t = np.linspace(0, 100, n_samples)
+    s1 = np.sin(t)
+    s2 = np.ceil(np.sin(np.pi * t))
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+    s1, s2 = s
+
+    # Mixing matrix
+    mixing = rng.randn(6, 2)
+    m = np.dot(mixing, s)
+
+    if add_noise:
+        m += 0.1 * rng.randn(6, n_samples)
+
+    center_and_norm(m)
+
+    k_, mixing_, s_ = fastica(
+        m.T, n_components=2, whiten="unit-variance", random_state=rng
+    )
+    s_ = s_.T
+
+    # Check that the mixing model described in the docstring holds:
+    assert_allclose(s_, np.dot(np.dot(mixing_, k_), m))
+
+    center_and_norm(s_)
+    s1_, s2_ = s_
+    # Check to see if the sources have been estimated
+    # in the wrong order
+    if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
+        s2_, s1_ = s_
+    s1_ *= np.sign(np.dot(s1_, s1))
+    s2_ *= np.sign(np.dot(s2_, s2))
+
+    # Check that we have estimated the original sources
+    if not add_noise:
+        assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-3)
+        assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-3)
+
+
+def test_fit_transform(global_random_seed, global_dtype):
+    """Test unit variance of transformed data using FastICA algorithm.
+
+    Check that `fit_transform` gives the same result as applying
+    `fit` and then `transform`.
+
+    Bug #13056
+    """
+    # multivariate uniform data in [0, 1]
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.random_sample((100, 10)).astype(global_dtype)
+    max_iter = 300
+    for whiten, n_components in [["unit-variance", 5], [False, None]]:
+        n_components_ = n_components if n_components is not None else X.shape[1]
+
+        ica = FastICA(
+            n_components=n_components, max_iter=max_iter, whiten=whiten, random_state=0
+        )
+        with warnings.catch_warnings():
+            # make sure that numerical errors do not cause sqrt of negative
+            # values
+            warnings.simplefilter("error", RuntimeWarning)
+            # XXX: for some seeds, the model does not converge.
+            # However this is not what we test here.
+            warnings.simplefilter("ignore", ConvergenceWarning)
+            Xt = ica.fit_transform(X)
+        assert ica.components_.shape == (n_components_, 10)
+        assert Xt.shape == (X.shape[0], n_components_)
+
+        ica2 = FastICA(
+            n_components=n_components, max_iter=max_iter, whiten=whiten, random_state=0
+        )
+        with warnings.catch_warnings():
+            # make sure that numerical errors do not cause sqrt of negative
+            # values
+            warnings.simplefilter("error", RuntimeWarning)
+            warnings.simplefilter("ignore", ConvergenceWarning)
+            ica2.fit(X)
+        assert ica2.components_.shape == (n_components_, 10)
+        Xt2 = ica2.transform(X)
+
+        # XXX: we have to set atol for this test to pass for all seeds when
+        # fitting with float32 data. Is this revealing a bug?
+        if global_dtype:
+            atol = np.abs(Xt2).mean() / 1e6
+        else:
+            atol = 0.0  # the default rtol is enough for float64 data
+        assert_allclose(Xt, Xt2, atol=atol)
+
+
+@pytest.mark.filterwarnings("ignore:Ignoring n_components with whiten=False.")
+@pytest.mark.parametrize(
+    "whiten, n_components, expected_mixing_shape",
+    [
+        ("arbitrary-variance", 5, (10, 5)),
+        ("arbitrary-variance", 10, (10, 10)),
+        ("unit-variance", 5, (10, 5)),
+        ("unit-variance", 10, (10, 10)),
+        (False, 5, (10, 10)),
+        (False, 10, (10, 10)),
+    ],
+)
+def test_inverse_transform(
+    whiten, n_components, expected_mixing_shape, global_random_seed, global_dtype
+):
+    # Test FastICA.inverse_transform
+    n_samples = 100
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.random_sample((n_samples, 10)).astype(global_dtype)
+
+    ica = FastICA(n_components=n_components, random_state=rng, whiten=whiten)
+    with warnings.catch_warnings():
+        # For some dataset (depending on the value of global_dtype) the model
+        # can fail to converge but this should not impact the definition of
+        # a valid inverse transform.
+        warnings.simplefilter("ignore", ConvergenceWarning)
+        Xt = ica.fit_transform(X)
+    assert ica.mixing_.shape == expected_mixing_shape
+    X2 = ica.inverse_transform(Xt)
+    assert X.shape == X2.shape
+
+    # reversibility test in non-reduction case
+    if n_components == X.shape[1]:
+        # XXX: we have to set atol for this test to pass for all seeds when
+        # fitting with float32 data. Is this revealing a bug?
+        if global_dtype:
+            # XXX: dividing by a smaller number makes
+            # tests fail for some seeds.
+            atol = np.abs(X2).mean() / 1e5
+        else:
+            atol = 0.0  # the default rtol is enough for float64 data
+        assert_allclose(X, X2, atol=atol)
+
+
+def test_fastica_errors():
+    n_features = 3
+    n_samples = 10
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((n_samples, n_features))
+    w_init = rng.randn(n_features + 1, n_features + 1)
+    with pytest.raises(ValueError, match=r"alpha must be in \[1,2\]"):
+        fastica(X, fun_args={"alpha": 0})
+    with pytest.raises(
+        ValueError, match="w_init has invalid shape.+" r"should be \(3L?, 3L?\)"
+    ):
+        fastica(X, w_init=w_init)
+
+
+def test_fastica_whiten_unit_variance():
+    """Test unit variance of transformed data using FastICA algorithm.
+
+    Bug #13056
+    """
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10))
+    n_components = X.shape[1]
+    ica = FastICA(n_components=n_components, whiten="unit-variance", random_state=0)
+    Xt = ica.fit_transform(X)
+
+    assert np.var(Xt) == pytest.approx(1.0)
+
+
+@pytest.mark.parametrize("whiten", ["arbitrary-variance", "unit-variance", False])
+@pytest.mark.parametrize("return_X_mean", [True, False])
+@pytest.mark.parametrize("return_n_iter", [True, False])
+def test_fastica_output_shape(whiten, return_X_mean, return_n_iter):
+    n_features = 3
+    n_samples = 10
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((n_samples, n_features))
+
+    expected_len = 3 + return_X_mean + return_n_iter
+
+    out = fastica(
+        X, whiten=whiten, return_n_iter=return_n_iter, return_X_mean=return_X_mean
+    )
+
+    assert len(out) == expected_len
+    if not whiten:
+        assert out[0] is None
+
+
+@pytest.mark.parametrize("add_noise", [True, False])
+def test_fastica_simple_different_solvers(add_noise, global_random_seed):
+    """Test FastICA is consistent between whiten_solvers."""
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 1000
+    # Generate two sources:
+    s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
+    s2 = stats.t.rvs(1, size=n_samples, random_state=rng)
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+    s1, s2 = s
+
+    # Mixing angle
+    phi = rng.rand() * 2 * np.pi
+    mixing = np.array([[np.cos(phi), np.sin(phi)], [np.sin(phi), -np.cos(phi)]])
+    m = np.dot(mixing, s)
+
+    if add_noise:
+        m += 0.1 * rng.randn(2, 1000)
+
+    center_and_norm(m)
+
+    outs = {}
+    for solver in ("svd", "eigh"):
+        ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver=solver)
+        sources = ica.fit_transform(m.T)
+        outs[solver] = sources
+        assert ica.components_.shape == (2, 2)
+        assert sources.shape == (1000, 2)
+
+    # compared numbers are not all on the same magnitude. Using a small atol to
+    # make the test less brittle
+    assert_allclose(outs["eigh"], outs["svd"], atol=1e-12)
+
+
+def test_fastica_eigh_low_rank_warning(global_random_seed):
+    """Test FastICA eigh solver raises warning for low-rank data."""
+    rng = np.random.RandomState(global_random_seed)
+    A = rng.randn(10, 2)
+    X = A @ A.T
+    ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver="eigh")
+    msg = "There are some small singular values"
+    with pytest.warns(UserWarning, match=msg):
+        ica.fit(X)

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/tests/test_nmf.py

@@ -0,0 +1,1062 @@
+import re
+import sys
+import warnings
+from io import StringIO
+
+import numpy as np
+import pytest
+from scipy import linalg
+
+from sklearn.base import clone
+from sklearn.decomposition import NMF, MiniBatchNMF, non_negative_factorization
+from sklearn.decomposition import _nmf as nmf  # For testing internals
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+    ignore_warnings,
+)
+from sklearn.utils.extmath import squared_norm
+from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
+
+
+@pytest.mark.parametrize(
+    ["Estimator", "solver"],
+    [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
+)
+def test_convergence_warning(Estimator, solver):
+    convergence_warning = (
+        "Maximum number of iterations 1 reached. Increase it to improve convergence."
+    )
+    A = np.ones((2, 2))
+    with pytest.warns(ConvergenceWarning, match=convergence_warning):
+        Estimator(max_iter=1, n_components="auto", **solver).fit(A)
+
+
+def test_initialize_nn_output():
+    # Test that initialization does not return negative values
+    rng = np.random.mtrand.RandomState(42)
+    data = np.abs(rng.randn(10, 10))
+    for init in ("random", "nndsvd", "nndsvda", "nndsvdar"):
+        W, H = nmf._initialize_nmf(data, 10, init=init, random_state=0)
+        assert not ((W < 0).any() or (H < 0).any())
+
+
+# TODO(1.6): remove the warning filter for `n_components`
+@pytest.mark.filterwarnings(
+    r"ignore:The multiplicative update \('mu'\) solver cannot update zeros present in"
+    r" the initialization",
+    "ignore:The default value of `n_components` will change",
+)
+def test_parameter_checking():
+    # Here we only check for invalid parameter values that are not already
+    # automatically tested in the common tests.
+
+    A = np.ones((2, 2))
+
+    msg = "Invalid beta_loss parameter: solver 'cd' does not handle beta_loss = 1.0"
+    with pytest.raises(ValueError, match=msg):
+        NMF(solver="cd", beta_loss=1.0).fit(A)
+    msg = "Negative values in data passed to"
+    with pytest.raises(ValueError, match=msg):
+        NMF().fit(-A)
+    clf = NMF(2, tol=0.1).fit(A)
+    with pytest.raises(ValueError, match=msg):
+        clf.transform(-A)
+    with pytest.raises(ValueError, match=msg):
+        nmf._initialize_nmf(-A, 2, "nndsvd")
+
+    for init in ["nndsvd", "nndsvda", "nndsvdar"]:
+        msg = re.escape(
+            "init = '{}' can only be used when "
+            "n_components <= min(n_samples, n_features)".format(init)
+        )
+        with pytest.raises(ValueError, match=msg):
+            NMF(3, init=init).fit(A)
+        with pytest.raises(ValueError, match=msg):
+            MiniBatchNMF(3, init=init).fit(A)
+        with pytest.raises(ValueError, match=msg):
+            nmf._initialize_nmf(A, 3, init)
+
+
+def test_initialize_close():
+    # Test NNDSVD error
+    # Test that _initialize_nmf error is less than the standard deviation of
+    # the entries in the matrix.
+    rng = np.random.mtrand.RandomState(42)
+    A = np.abs(rng.randn(10, 10))
+    W, H = nmf._initialize_nmf(A, 10, init="nndsvd")
+    error = linalg.norm(np.dot(W, H) - A)
+    sdev = linalg.norm(A - A.mean())
+    assert error <= sdev
+
+
+def test_initialize_variants():
+    # Test NNDSVD variants correctness
+    # Test that the variants 'nndsvda' and 'nndsvdar' differ from basic
+    # 'nndsvd' only where the basic version has zeros.
+    rng = np.random.mtrand.RandomState(42)
+    data = np.abs(rng.randn(10, 10))
+    W0, H0 = nmf._initialize_nmf(data, 10, init="nndsvd")
+    Wa, Ha = nmf._initialize_nmf(data, 10, init="nndsvda")
+    War, Har = nmf._initialize_nmf(data, 10, init="nndsvdar", random_state=0)
+
+    for ref, evl in ((W0, Wa), (W0, War), (H0, Ha), (H0, Har)):
+        assert_almost_equal(evl[ref != 0], ref[ref != 0])
+
+
+# ignore UserWarning raised when both solver='mu' and init='nndsvd'
+@ignore_warnings(category=UserWarning)
+@pytest.mark.parametrize(
+    ["Estimator", "solver"],
+    [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
+)
+@pytest.mark.parametrize("init", (None, "nndsvd", "nndsvda", "nndsvdar", "random"))
+@pytest.mark.parametrize("alpha_W", (0.0, 1.0))
+@pytest.mark.parametrize("alpha_H", (0.0, 1.0, "same"))
+def test_nmf_fit_nn_output(Estimator, solver, init, alpha_W, alpha_H):
+    # Test that the decomposition does not contain negative values
+    A = np.c_[5.0 - np.arange(1, 6), 5.0 + np.arange(1, 6)]
+    model = Estimator(
+        n_components=2,
+        init=init,
+        alpha_W=alpha_W,
+        alpha_H=alpha_H,
+        random_state=0,
+        **solver,
+    )
+    transf = model.fit_transform(A)
+    assert not ((model.components_ < 0).any() or (transf < 0).any())
+
+
+@pytest.mark.parametrize(
+    ["Estimator", "solver"],
+    [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
+)
+def test_nmf_fit_close(Estimator, solver):
+    rng = np.random.mtrand.RandomState(42)
+    # Test that the fit is not too far away
+    pnmf = Estimator(
+        5,
+        init="nndsvdar",
+        random_state=0,
+        max_iter=600,
+        **solver,
+    )
+    X = np.abs(rng.randn(6, 5))
+    assert pnmf.fit(X).reconstruction_err_ < 0.1
+
+
+def test_nmf_true_reconstruction():
+    # Test that the fit is not too far away from an exact solution
+    # (by construction)
+    n_samples = 15
+    n_features = 10
+    n_components = 5
+    beta_loss = 1
+    batch_size = 3
+    max_iter = 1000
+
+    rng = np.random.mtrand.RandomState(42)
+    W_true = np.zeros([n_samples, n_components])
+    W_array = np.abs(rng.randn(n_samples))
+    for j in range(n_components):
+        W_true[j % n_samples, j] = W_array[j % n_samples]
+    H_true = np.zeros([n_components, n_features])
+    H_array = np.abs(rng.randn(n_components))
+    for j in range(n_features):
+        H_true[j % n_components, j] = H_array[j % n_components]
+    X = np.dot(W_true, H_true)
+
+    model = NMF(
+        n_components=n_components,
+        solver="mu",
+        beta_loss=beta_loss,
+        max_iter=max_iter,
+        random_state=0,
+    )
+    transf = model.fit_transform(X)
+    X_calc = np.dot(transf, model.components_)
+
+    assert model.reconstruction_err_ < 0.1
+    assert_allclose(X, X_calc)
+
+    mbmodel = MiniBatchNMF(
+        n_components=n_components,
+        beta_loss=beta_loss,
+        batch_size=batch_size,
+        random_state=0,
+        max_iter=max_iter,
+    )
+    transf = mbmodel.fit_transform(X)
+    X_calc = np.dot(transf, mbmodel.components_)
+
+    assert mbmodel.reconstruction_err_ < 0.1
+    assert_allclose(X, X_calc, atol=1)
+
+
+@pytest.mark.parametrize("solver", ["cd", "mu"])
+def test_nmf_transform(solver):
+    # Test that fit_transform is equivalent to fit.transform for NMF
+    # Test that NMF.transform returns close values
+    rng = np.random.mtrand.RandomState(42)
+    A = np.abs(rng.randn(6, 5))
+    m = NMF(
+        solver=solver,
+        n_components=3,
+        init="random",
+        random_state=0,
+        tol=1e-6,
+    )
+    ft = m.fit_transform(A)
+    t = m.transform(A)
+    assert_allclose(ft, t, atol=1e-1)
+
+
+def test_minibatch_nmf_transform():
+    # Test that fit_transform is equivalent to fit.transform for MiniBatchNMF
+    # Only guaranteed with fresh restarts
+    rng = np.random.mtrand.RandomState(42)
+    A = np.abs(rng.randn(6, 5))
+    m = MiniBatchNMF(
+        n_components=3,
+        random_state=0,
+        tol=1e-3,
+        fresh_restarts=True,
+    )
+    ft = m.fit_transform(A)
+    t = m.transform(A)
+    assert_allclose(ft, t)
+
+
+@pytest.mark.parametrize(
+    ["Estimator", "solver"],
+    [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
+)
+def test_nmf_transform_custom_init(Estimator, solver):
+    # Smoke test that checks if NMF.transform works with custom initialization
+    random_state = np.random.RandomState(0)
+    A = np.abs(random_state.randn(6, 5))
+    n_components = 4
+    avg = np.sqrt(A.mean() / n_components)
+    H_init = np.abs(avg * random_state.randn(n_components, 5))
+    W_init = np.abs(avg * random_state.randn(6, n_components))
+
+    m = Estimator(
+        n_components=n_components, init="custom", random_state=0, tol=1e-3, **solver
+    )
+    m.fit_transform(A, W=W_init, H=H_init)
+    m.transform(A)
+
+
+@pytest.mark.parametrize("solver", ("cd", "mu"))
+def test_nmf_inverse_transform(solver):
+    # Test that NMF.inverse_transform returns close values
+    random_state = np.random.RandomState(0)
+    A = np.abs(random_state.randn(6, 4))
+    m = NMF(
+        solver=solver,
+        n_components=4,
+        init="random",
+        random_state=0,
+        max_iter=1000,
+    )
+    ft = m.fit_transform(A)
+    A_new = m.inverse_transform(ft)
+    assert_array_almost_equal(A, A_new, decimal=2)
+
+
+# TODO(1.6): remove the warning filter
+@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
+def test_mbnmf_inverse_transform():
+    # Test that MiniBatchNMF.transform followed by MiniBatchNMF.inverse_transform
+    # is close to the identity
+    rng = np.random.RandomState(0)
+    A = np.abs(rng.randn(6, 4))
+    nmf = MiniBatchNMF(
+        random_state=rng,
+        max_iter=500,
+        init="nndsvdar",
+        fresh_restarts=True,
+    )
+    ft = nmf.fit_transform(A)
+    A_new = nmf.inverse_transform(ft)
+    assert_allclose(A, A_new, rtol=1e-3, atol=1e-2)
+
+
+@pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])
+def test_n_components_greater_n_features(Estimator):
+    # Smoke test for the case of more components than features.
+    rng = np.random.mtrand.RandomState(42)
+    A = np.abs(rng.randn(30, 10))
+    Estimator(n_components=15, random_state=0, tol=1e-2).fit(A)
+
+
+@pytest.mark.parametrize(
+    ["Estimator", "solver"],
+    [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
+)
+@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
+@pytest.mark.parametrize("alpha_W", (0.0, 1.0))
+@pytest.mark.parametrize("alpha_H", (0.0, 1.0, "same"))
+def test_nmf_sparse_input(Estimator, solver, sparse_container, alpha_W, alpha_H):
+    # Test that sparse matrices are accepted as input
+    rng = np.random.mtrand.RandomState(42)
+    A = np.abs(rng.randn(10, 10))
+    A[:, 2 * np.arange(5)] = 0
+    A_sparse = sparse_container(A)
+
+    est1 = Estimator(
+        n_components=5,
+        init="random",
+        alpha_W=alpha_W,
+        alpha_H=alpha_H,
+        random_state=0,
+        tol=0,
+        max_iter=100,
+        **solver,
+    )
+    est2 = clone(est1)
+
+    W1 = est1.fit_transform(A)
+    W2 = est2.fit_transform(A_sparse)
+    H1 = est1.components_
+    H2 = est2.components_
+
+    assert_allclose(W1, W2)
+    assert_allclose(H1, H2)
+
+
+@pytest.mark.parametrize(
+    ["Estimator", "solver"],
+    [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
+)
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_nmf_sparse_transform(Estimator, solver, csc_container):
+    # Test that transform works on sparse data.  Issue #2124
+    rng = np.random.mtrand.RandomState(42)
+    A = np.abs(rng.randn(3, 2))
+    A[1, 1] = 0
+    A = csc_container(A)
+
+    model = Estimator(random_state=0, n_components=2, max_iter=400, **solver)
+    A_fit_tr = model.fit_transform(A)
+    A_tr = model.transform(A)
+    assert_allclose(A_fit_tr, A_tr, atol=1e-1)
+
+
+# TODO(1.6): remove the warning filter
+@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
+@pytest.mark.parametrize("init", ["random", "nndsvd"])
+@pytest.mark.parametrize("solver", ("cd", "mu"))
+@pytest.mark.parametrize("alpha_W", (0.0, 1.0))
+@pytest.mark.parametrize("alpha_H", (0.0, 1.0, "same"))
+def test_non_negative_factorization_consistency(init, solver, alpha_W, alpha_H):
+    # Test that the function is called in the same way, either directly
+    # or through the NMF class
+    max_iter = 500
+    rng = np.random.mtrand.RandomState(42)
+    A = np.abs(rng.randn(10, 10))
+    A[:, 2 * np.arange(5)] = 0
+
+    W_nmf, H, _ = non_negative_factorization(
+        A,
+        init=init,
+        solver=solver,
+        max_iter=max_iter,
+        alpha_W=alpha_W,
+        alpha_H=alpha_H,
+        random_state=1,
+        tol=1e-2,
+    )
+    W_nmf_2, H, _ = non_negative_factorization(
+        A,
+        H=H,
+        update_H=False,
+        init=init,
+        solver=solver,
+        max_iter=max_iter,
+        alpha_W=alpha_W,
+        alpha_H=alpha_H,
+        random_state=1,
+        tol=1e-2,
+    )
+
+    model_class = NMF(
+        init=init,
+        solver=solver,
+        max_iter=max_iter,
+        alpha_W=alpha_W,
+        alpha_H=alpha_H,
+        random_state=1,
+        tol=1e-2,
+    )
+    W_cls = model_class.fit_transform(A)
+    W_cls_2 = model_class.transform(A)
+
+    assert_allclose(W_nmf, W_cls)
+    assert_allclose(W_nmf_2, W_cls_2)
+
+
+def test_non_negative_factorization_checking():
+    # Note that the validity of parameter types and range of possible values
+    # for scalar numerical or str parameters is already checked in the common
+    # tests. Here we only check for problems that cannot be captured by simple
+    # declarative constraints on the valid parameter values.
+
+    A = np.ones((2, 2))
+    # Test parameters checking in public function
+    nnmf = non_negative_factorization
+    msg = re.escape("Negative values in data passed to NMF (input H)")
+    with pytest.raises(ValueError, match=msg):
+        nnmf(A, A, -A, 2, init="custom")
+    msg = re.escape("Negative values in data passed to NMF (input W)")
+    with pytest.raises(ValueError, match=msg):
+        nnmf(A, -A, A, 2, init="custom")
+    msg = re.escape("Array passed to NMF (input H) is full of zeros")
+    with pytest.raises(ValueError, match=msg):
+        nnmf(A, A, 0 * A, 2, init="custom")
+
+
+def _beta_divergence_dense(X, W, H, beta):
+    """Compute the beta-divergence of X and W.H for dense array only.
+
+    Used as a reference for testing nmf._beta_divergence.
+    """
+    WH = np.dot(W, H)
+
+    if beta == 2:
+        return squared_norm(X - WH) / 2
+
+    WH_Xnonzero = WH[X != 0]
+    X_nonzero = X[X != 0]
+    np.maximum(WH_Xnonzero, 1e-9, out=WH_Xnonzero)
+
+    if beta == 1:
+        res = np.sum(X_nonzero * np.log(X_nonzero / WH_Xnonzero))
+        res += WH.sum() - X.sum()
+
+    elif beta == 0:
+        div = X_nonzero / WH_Xnonzero
+        res = np.sum(div) - X.size - np.sum(np.log(div))
+    else:
+        res = (X_nonzero**beta).sum()
+        res += (beta - 1) * (WH**beta).sum()
+        res -= beta * (X_nonzero * (WH_Xnonzero ** (beta - 1))).sum()
+        res /= beta * (beta - 1)
+
+    return res
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_beta_divergence(csr_container):
+    # Compare _beta_divergence with the reference _beta_divergence_dense
+    n_samples = 20
+    n_features = 10
+    n_components = 5
+    beta_losses = [0.0, 0.5, 1.0, 1.5, 2.0, 3.0]
+
+    # initialization
+    rng = np.random.mtrand.RandomState(42)
+    X = rng.randn(n_samples, n_features)
+    np.clip(X, 0, None, out=X)
+    X_csr = csr_container(X)
+    W, H = nmf._initialize_nmf(X, n_components, init="random", random_state=42)
+
+    for beta in beta_losses:
+        ref = _beta_divergence_dense(X, W, H, beta)
+        loss = nmf._beta_divergence(X, W, H, beta)
+        loss_csr = nmf._beta_divergence(X_csr, W, H, beta)
+
+        assert_almost_equal(ref, loss, decimal=7)
+        assert_almost_equal(ref, loss_csr, decimal=7)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_special_sparse_dot(csr_container):
+    # Test the function that computes np.dot(W, H), only where X is non zero.
+    n_samples = 10
+    n_features = 5
+    n_components = 3
+    rng = np.random.mtrand.RandomState(42)
+    X = rng.randn(n_samples, n_features)
+    np.clip(X, 0, None, out=X)
+    X_csr = csr_container(X)
+
+    W = np.abs(rng.randn(n_samples, n_components))
+    H = np.abs(rng.randn(n_components, n_features))
+
+    WH_safe = nmf._special_sparse_dot(W, H, X_csr)
+    WH = nmf._special_sparse_dot(W, H, X)
+
+    # test that both results have same values, in X_csr nonzero elements
+    ii, jj = X_csr.nonzero()
+    WH_safe_data = np.asarray(WH_safe[ii, jj]).ravel()
+    assert_array_almost_equal(WH_safe_data, WH[ii, jj], decimal=10)
+
+    # test that WH_safe and X_csr have the same sparse structure
+    assert_array_equal(WH_safe.indices, X_csr.indices)
+    assert_array_equal(WH_safe.indptr, X_csr.indptr)
+    assert_array_equal(WH_safe.shape, X_csr.shape)
+
+
+@ignore_warnings(category=ConvergenceWarning)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_nmf_multiplicative_update_sparse(csr_container):
+    # Compare sparse and dense input in multiplicative update NMF
+    # Also test continuity of the results with respect to beta_loss parameter
+    n_samples = 20
+    n_features = 10
+    n_components = 5
+    alpha = 0.1
+    l1_ratio = 0.5
+    n_iter = 20
+
+    # initialization
+    rng = np.random.mtrand.RandomState(1337)
+    X = rng.randn(n_samples, n_features)
+    X = np.abs(X)
+    X_csr = csr_container(X)
+    W0, H0 = nmf._initialize_nmf(X, n_components, init="random", random_state=42)
+
+    for beta_loss in (-1.2, 0, 0.2, 1.0, 2.0, 2.5):
+        # Reference with dense array X
+        W, H = W0.copy(), H0.copy()
+        W1, H1, _ = non_negative_factorization(
+            X,
+            W,
+            H,
+            n_components,
+            init="custom",
+            update_H=True,
+            solver="mu",
+            beta_loss=beta_loss,
+            max_iter=n_iter,
+            alpha_W=alpha,
+            l1_ratio=l1_ratio,
+            random_state=42,
+        )
+
+        # Compare with sparse X
+        W, H = W0.copy(), H0.copy()
+        W2, H2, _ = non_negative_factorization(
+            X_csr,
+            W,
+            H,
+            n_components,
+            init="custom",
+            update_H=True,
+            solver="mu",
+            beta_loss=beta_loss,
+            max_iter=n_iter,
+            alpha_W=alpha,
+            l1_ratio=l1_ratio,
+            random_state=42,
+        )
+
+        assert_allclose(W1, W2, atol=1e-7)
+        assert_allclose(H1, H2, atol=1e-7)
+
+        # Compare with almost same beta_loss, since some values have a specific
+        # behavior, but the results should be continuous w.r.t beta_loss
+        beta_loss -= 1.0e-5
+        W, H = W0.copy(), H0.copy()
+        W3, H3, _ = non_negative_factorization(
+            X_csr,
+            W,
+            H,
+            n_components,
+            init="custom",
+            update_H=True,
+            solver="mu",
+            beta_loss=beta_loss,
+            max_iter=n_iter,
+            alpha_W=alpha,
+            l1_ratio=l1_ratio,
+            random_state=42,
+        )
+
+        assert_allclose(W1, W3, atol=1e-4)
+        assert_allclose(H1, H3, atol=1e-4)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_nmf_negative_beta_loss(csr_container):
+    # Test that an error is raised if beta_loss < 0 and X contains zeros.
+    # Test that the output has not NaN values when the input contains zeros.
+    n_samples = 6
+    n_features = 5
+    n_components = 3
+
+    rng = np.random.mtrand.RandomState(42)
+    X = rng.randn(n_samples, n_features)
+    np.clip(X, 0, None, out=X)
+    X_csr = csr_container(X)
+
+    def _assert_nmf_no_nan(X, beta_loss):
+        W, H, _ = non_negative_factorization(
+            X,
+            init="random",
+            n_components=n_components,
+            solver="mu",
+            beta_loss=beta_loss,
+            random_state=0,
+            max_iter=1000,
+        )
+        assert not np.any(np.isnan(W))
+        assert not np.any(np.isnan(H))
+
+    msg = "When beta_loss <= 0 and X contains zeros, the solver may diverge."
+    for beta_loss in (-0.6, 0.0):
+        with pytest.raises(ValueError, match=msg):
+            _assert_nmf_no_nan(X, beta_loss)
+        _assert_nmf_no_nan(X + 1e-9, beta_loss)
+
+    for beta_loss in (0.2, 1.0, 1.2, 2.0, 2.5):
+        _assert_nmf_no_nan(X, beta_loss)
+        _assert_nmf_no_nan(X_csr, beta_loss)
+
+
+# TODO(1.6): remove the warning filter
+@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
+@pytest.mark.parametrize("beta_loss", [-0.5, 0.0])
+def test_minibatch_nmf_negative_beta_loss(beta_loss):
+    """Check that an error is raised if beta_loss < 0 and X contains zeros."""
+    rng = np.random.RandomState(0)
+    X = rng.normal(size=(6, 5))
+    X[X < 0] = 0
+
+    nmf = MiniBatchNMF(beta_loss=beta_loss, random_state=0)
+
+    msg = "When beta_loss <= 0 and X contains zeros, the solver may diverge."
+    with pytest.raises(ValueError, match=msg):
+        nmf.fit(X)
+
+
+@pytest.mark.parametrize(
+    ["Estimator", "solver"],
+    [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
+)
+def test_nmf_regularization(Estimator, solver):
+    # Test the effect of L1 and L2 regularizations
+    n_samples = 6
+    n_features = 5
+    n_components = 3
+    rng = np.random.mtrand.RandomState(42)
+    X = np.abs(rng.randn(n_samples, n_features))
+
+    # L1 regularization should increase the number of zeros
+    l1_ratio = 1.0
+    regul = Estimator(
+        n_components=n_components,
+        alpha_W=0.5,
+        l1_ratio=l1_ratio,
+        random_state=42,
+        **solver,
+    )
+    model = Estimator(
+        n_components=n_components,
+        alpha_W=0.0,
+        l1_ratio=l1_ratio,
+        random_state=42,
+        **solver,
+    )
+
+    W_regul = regul.fit_transform(X)
+    W_model = model.fit_transform(X)
+
+    H_regul = regul.components_
+    H_model = model.components_
+
+    eps = np.finfo(np.float64).eps
+    W_regul_n_zeros = W_regul[W_regul <= eps].size
+    W_model_n_zeros = W_model[W_model <= eps].size
+    H_regul_n_zeros = H_regul[H_regul <= eps].size
+    H_model_n_zeros = H_model[H_model <= eps].size
+
+    assert W_regul_n_zeros > W_model_n_zeros
+    assert H_regul_n_zeros > H_model_n_zeros
+
+    # L2 regularization should decrease the sum of the squared norm
+    # of the matrices W and H
+    l1_ratio = 0.0
+    regul = Estimator(
+        n_components=n_components,
+        alpha_W=0.5,
+        l1_ratio=l1_ratio,
+        random_state=42,
+        **solver,
+    )
+    model = Estimator(
+        n_components=n_components,
+        alpha_W=0.0,
+        l1_ratio=l1_ratio,
+        random_state=42,
+        **solver,
+    )
+
+    W_regul = regul.fit_transform(X)
+    W_model = model.fit_transform(X)
+
+    H_regul = regul.components_
+    H_model = model.components_
+
+    assert (linalg.norm(W_model)) ** 2.0 + (linalg.norm(H_model)) ** 2.0 > (
+        linalg.norm(W_regul)
+    ) ** 2.0 + (linalg.norm(H_regul)) ** 2.0
+
+
+@ignore_warnings(category=ConvergenceWarning)
+@pytest.mark.parametrize("solver", ("cd", "mu"))
+def test_nmf_decreasing(solver):
+    # test that the objective function is decreasing at each iteration
+    n_samples = 20
+    n_features = 15
+    n_components = 10
+    alpha = 0.1
+    l1_ratio = 0.5
+    tol = 0.0
+
+    # initialization
+    rng = np.random.mtrand.RandomState(42)
+    X = rng.randn(n_samples, n_features)
+    np.abs(X, X)
+    W0, H0 = nmf._initialize_nmf(X, n_components, init="random", random_state=42)
+
+    for beta_loss in (-1.2, 0, 0.2, 1.0, 2.0, 2.5):
+        if solver != "mu" and beta_loss != 2:
+            # not implemented
+            continue
+        W, H = W0.copy(), H0.copy()
+        previous_loss = None
+        for _ in range(30):
+            # one more iteration starting from the previous results
+            W, H, _ = non_negative_factorization(
+                X,
+                W,
+                H,
+                beta_loss=beta_loss,
+                init="custom",
+                n_components=n_components,
+                max_iter=1,
+                alpha_W=alpha,
+                solver=solver,
+                tol=tol,
+                l1_ratio=l1_ratio,
+                verbose=0,
+                random_state=0,
+                update_H=True,
+            )
+
+            loss = (
+                nmf._beta_divergence(X, W, H, beta_loss)
+                + alpha * l1_ratio * n_features * W.sum()
+                + alpha * l1_ratio * n_samples * H.sum()
+                + alpha * (1 - l1_ratio) * n_features * (W**2).sum()
+                + alpha * (1 - l1_ratio) * n_samples * (H**2).sum()
+            )
+            if previous_loss is not None:
+                assert previous_loss > loss
+            previous_loss = loss
+
+
+def test_nmf_underflow():
+    # Regression test for an underflow issue in _beta_divergence
+    rng = np.random.RandomState(0)
+    n_samples, n_features, n_components = 10, 2, 2
+    X = np.abs(rng.randn(n_samples, n_features)) * 10
+    W = np.abs(rng.randn(n_samples, n_components)) * 10
+    H = np.abs(rng.randn(n_components, n_features))
+
+    X[0, 0] = 0
+    ref = nmf._beta_divergence(X, W, H, beta=1.0)
+    X[0, 0] = 1e-323
+    res = nmf._beta_divergence(X, W, H, beta=1.0)
+    assert_almost_equal(res, ref)
+
+
+# TODO(1.6): remove the warning filter
+@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
+@pytest.mark.parametrize(
+    "dtype_in, dtype_out",
+    [
+        (np.float32, np.float32),
+        (np.float64, np.float64),
+        (np.int32, np.float64),
+        (np.int64, np.float64),
+    ],
+)
+@pytest.mark.parametrize(
+    ["Estimator", "solver"],
+    [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
+)
+def test_nmf_dtype_match(Estimator, solver, dtype_in, dtype_out):
+    # Check that NMF preserves dtype (float32 and float64)
+    X = np.random.RandomState(0).randn(20, 15).astype(dtype_in, copy=False)
+    np.abs(X, out=X)
+
+    nmf = Estimator(
+        alpha_W=1.0,
+        alpha_H=1.0,
+        tol=1e-2,
+        random_state=0,
+        **solver,
+    )
+
+    assert nmf.fit(X).transform(X).dtype == dtype_out
+    assert nmf.fit_transform(X).dtype == dtype_out
+    assert nmf.components_.dtype == dtype_out
+
+
+# TODO(1.6): remove the warning filter
+@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
+@pytest.mark.parametrize(
+    ["Estimator", "solver"],
+    [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
+)
+def test_nmf_float32_float64_consistency(Estimator, solver):
+    # Check that the result of NMF is the same between float32 and float64
+    X = np.random.RandomState(0).randn(50, 7)
+    np.abs(X, out=X)
+    nmf32 = Estimator(random_state=0, tol=1e-3, **solver)
+    W32 = nmf32.fit_transform(X.astype(np.float32))
+    nmf64 = Estimator(random_state=0, tol=1e-3, **solver)
+    W64 = nmf64.fit_transform(X)
+
+    assert_allclose(W32, W64, atol=1e-5)
+
+
+# TODO(1.6): remove the warning filter
+@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
+@pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])
+def test_nmf_custom_init_dtype_error(Estimator):
+    # Check that an error is raise if custom H and/or W don't have the same
+    # dtype as X.
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((20, 15))
+    H = rng.random_sample((15, 15)).astype(np.float32)
+    W = rng.random_sample((20, 15))
+
+    with pytest.raises(TypeError, match="should have the same dtype as X"):
+        Estimator(init="custom").fit(X, H=H, W=W)
+
+    with pytest.raises(TypeError, match="should have the same dtype as X"):
+        non_negative_factorization(X, H=H, update_H=False)
+
+
+@pytest.mark.parametrize("beta_loss", [-0.5, 0, 0.5, 1, 1.5, 2, 2.5])
+def test_nmf_minibatchnmf_equivalence(beta_loss):
+    # Test that MiniBatchNMF is equivalent to NMF when batch_size = n_samples and
+    # forget_factor 0.0 (stopping criterion put aside)
+    rng = np.random.mtrand.RandomState(42)
+    X = np.abs(rng.randn(48, 5))
+
+    nmf = NMF(
+        n_components=5,
+        beta_loss=beta_loss,
+        solver="mu",
+        random_state=0,
+        tol=0,
+    )
+    mbnmf = MiniBatchNMF(
+        n_components=5,
+        beta_loss=beta_loss,
+        random_state=0,
+        tol=0,
+        max_no_improvement=None,
+        batch_size=X.shape[0],
+        forget_factor=0.0,
+    )
+    W = nmf.fit_transform(X)
+    mbW = mbnmf.fit_transform(X)
+    assert_allclose(W, mbW)
+
+
+def test_minibatch_nmf_partial_fit():
+    # Check fit / partial_fit equivalence. Applicable only with fresh restarts.
+    rng = np.random.mtrand.RandomState(42)
+    X = np.abs(rng.randn(100, 5))
+
+    n_components = 5
+    batch_size = 10
+    max_iter = 2
+
+    mbnmf1 = MiniBatchNMF(
+        n_components=n_components,
+        init="custom",
+        random_state=0,
+        max_iter=max_iter,
+        batch_size=batch_size,
+        tol=0,
+        max_no_improvement=None,
+        fresh_restarts=False,
+    )
+    mbnmf2 = MiniBatchNMF(n_components=n_components, init="custom", random_state=0)
+
+    # Force the same init of H (W is recomputed anyway) to be able to compare results.
+    W, H = nmf._initialize_nmf(
+        X, n_components=n_components, init="random", random_state=0
+    )
+
+    mbnmf1.fit(X, W=W, H=H)
+    for i in range(max_iter):
+        for j in range(batch_size):
+            mbnmf2.partial_fit(X[j : j + batch_size], W=W[:batch_size], H=H)
+
+    assert mbnmf1.n_steps_ == mbnmf2.n_steps_
+    assert_allclose(mbnmf1.components_, mbnmf2.components_)
+
+
+def test_feature_names_out():
+    """Check feature names out for NMF."""
+    random_state = np.random.RandomState(0)
+    X = np.abs(random_state.randn(10, 4))
+    nmf = NMF(n_components=3).fit(X)
+
+    names = nmf.get_feature_names_out()
+    assert_array_equal([f"nmf{i}" for i in range(3)], names)
+
+
+# TODO(1.6): remove the warning filter
+@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
+def test_minibatch_nmf_verbose():
+    # Check verbose mode of MiniBatchNMF for better coverage.
+    A = np.random.RandomState(0).random_sample((100, 10))
+    nmf = MiniBatchNMF(tol=1e-2, random_state=0, verbose=1)
+    old_stdout = sys.stdout
+    sys.stdout = StringIO()
+    try:
+        nmf.fit(A)
+    finally:
+        sys.stdout = old_stdout
+
+
+# TODO(1.5): remove this test
+def test_NMF_inverse_transform_W_deprecation():
+    rng = np.random.mtrand.RandomState(42)
+    A = np.abs(rng.randn(6, 5))
+    est = NMF(
+        n_components=3,
+        init="random",
+        random_state=0,
+        tol=1e-6,
+    )
+    Xt = est.fit_transform(A)
+
+    with pytest.raises(TypeError, match="Missing required positional argument"):
+        est.inverse_transform()
+
+    with pytest.raises(ValueError, match="Please provide only"):
+        est.inverse_transform(Xt=Xt, W=Xt)
+
+    with warnings.catch_warnings(record=True):
+        warnings.simplefilter("error")
+        est.inverse_transform(Xt)
+
+    with pytest.warns(FutureWarning, match="Input argument `W` was renamed to `Xt`"):
+        est.inverse_transform(W=Xt)
+
+
+@pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])
+def test_nmf_n_components_auto(Estimator):
+    # Check that n_components is correctly inferred
+    # from the provided custom initialization.
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((6, 5))
+    W = rng.random_sample((6, 2))
+    H = rng.random_sample((2, 5))
+    est = Estimator(
+        n_components="auto",
+        init="custom",
+        random_state=0,
+        tol=1e-6,
+    )
+    est.fit_transform(X, W=W, H=H)
+    assert est._n_components == H.shape[0]
+
+
+def test_nmf_non_negative_factorization_n_components_auto():
+    # Check that n_components is correctly inferred from the provided
+    # custom initialization.
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((6, 5))
+    W_init = rng.random_sample((6, 2))
+    H_init = rng.random_sample((2, 5))
+    W, H, _ = non_negative_factorization(
+        X, W=W_init, H=H_init, init="custom", n_components="auto"
+    )
+    assert H.shape == H_init.shape
+    assert W.shape == W_init.shape
+
+
+# TODO(1.6): remove
+def test_nmf_n_components_default_value_warning():
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((6, 5))
+    H = rng.random_sample((2, 5))
+    with pytest.warns(
+        FutureWarning, match="The default value of `n_components` will change from"
+    ):
+        non_negative_factorization(X, H=H)
+
+
+def test_nmf_n_components_auto_no_h_update():
+    # Tests that non_negative_factorization does not fail when setting
+    # n_components="auto" also tests that the inferred n_component
+    # value is the right one.
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((6, 5))
+    H_true = rng.random_sample((2, 5))
+    W, H, _ = non_negative_factorization(
+        X, H=H_true, n_components="auto", update_H=False
+    )  # should not fail
+    assert_allclose(H, H_true)
+    assert W.shape == (X.shape[0], H_true.shape[0])
+
+
+def test_nmf_w_h_not_used_warning():
+    # Check that warnings are raised if user provided W and H are not used
+    # and initialization overrides value of W or H
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((6, 5))
+    W_init = rng.random_sample((6, 2))
+    H_init = rng.random_sample((2, 5))
+    with pytest.warns(
+        RuntimeWarning,
+        match="When init!='custom', provided W or H are ignored",
+    ):
+        non_negative_factorization(X, H=H_init, update_H=True, n_components="auto")
+
+    with pytest.warns(
+        RuntimeWarning,
+        match="When init!='custom', provided W or H are ignored",
+    ):
+        non_negative_factorization(
+            X, W=W_init, H=H_init, update_H=True, n_components="auto"
+        )
+
+    with pytest.warns(
+        RuntimeWarning, match="When update_H=False, the provided initial W is not used."
+    ):
+        # When update_H is False, W is ignored regardless of init
+        # TODO: use the provided W when init="custom".
+        non_negative_factorization(
+            X, W=W_init, H=H_init, update_H=False, n_components="auto"
+        )
+
+
+def test_nmf_custom_init_shape_error():
+    # Check that an informative error is raised when custom initialization does not
+    # have the right shape
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((6, 5))
+    H = rng.random_sample((2, 5))
+    nmf = NMF(n_components=2, init="custom", random_state=0)
+
+    with pytest.raises(ValueError, match="Array with wrong first dimension passed"):
+        nmf.fit(X, H=H, W=rng.random_sample((5, 2)))
+
+    with pytest.raises(ValueError, match="Array with wrong second dimension passed"):
+        nmf.fit(X, H=H, W=rng.random_sample((6, 3)))

env-llmeval/lib/python3.10/site-packages/sklearn/decomposition/tests/test_pca.py

@@ -0,0 +1,987 @@
+import re
+import warnings
+
+import numpy as np
+import pytest
+import scipy as sp
+from numpy.testing import assert_array_equal
+
+from sklearn import config_context, datasets
+from sklearn.base import clone
+from sklearn.datasets import load_iris, make_classification
+from sklearn.decomposition import PCA
+from sklearn.decomposition._pca import _assess_dimension, _infer_dimension
+from sklearn.utils._array_api import (
+    _atol_for_type,
+    _convert_to_numpy,
+    yield_namespace_device_dtype_combinations,
+)
+from sklearn.utils._array_api import device as array_device
+from sklearn.utils._testing import _array_api_for_tests, assert_allclose
+from sklearn.utils.estimator_checks import (
+    _get_check_estimator_ids,
+    check_array_api_input_and_values,
+)
+from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
+
+iris = datasets.load_iris()
+PCA_SOLVERS = ["full", "arpack", "randomized", "auto"]
+
+# `SPARSE_M` and `SPARSE_N` could be larger, but be aware:
+# * SciPy's generation of random sparse matrix can be costly
+# * A (SPARSE_M, SPARSE_N) dense array is allocated to compare against
+SPARSE_M, SPARSE_N = 1000, 300  # arbitrary
+SPARSE_MAX_COMPONENTS = min(SPARSE_M, SPARSE_N)
+
+
+def _check_fitted_pca_close(pca1, pca2, rtol):
+    assert_allclose(pca1.components_, pca2.components_, rtol=rtol)
+    assert_allclose(pca1.explained_variance_, pca2.explained_variance_, rtol=rtol)
+    assert_allclose(pca1.singular_values_, pca2.singular_values_, rtol=rtol)
+    assert_allclose(pca1.mean_, pca2.mean_, rtol=rtol)
+    assert_allclose(pca1.n_components_, pca2.n_components_, rtol=rtol)
+    assert_allclose(pca1.n_samples_, pca2.n_samples_, rtol=rtol)
+    assert_allclose(pca1.noise_variance_, pca2.noise_variance_, rtol=rtol)
+    assert_allclose(pca1.n_features_in_, pca2.n_features_in_, rtol=rtol)
+
+
+@pytest.mark.parametrize("svd_solver", PCA_SOLVERS)
+@pytest.mark.parametrize("n_components", range(1, iris.data.shape[1]))
+def test_pca(svd_solver, n_components):
+    X = iris.data
+    pca = PCA(n_components=n_components, svd_solver=svd_solver)
+
+    # check the shape of fit.transform
+    X_r = pca.fit(X).transform(X)
+    assert X_r.shape[1] == n_components
+
+    # check the equivalence of fit.transform and fit_transform
+    X_r2 = pca.fit_transform(X)
+    assert_allclose(X_r, X_r2)
+    X_r = pca.transform(X)
+    assert_allclose(X_r, X_r2)
+
+    # Test get_covariance and get_precision
+    cov = pca.get_covariance()
+    precision = pca.get_precision()
+    assert_allclose(np.dot(cov, precision), np.eye(X.shape[1]), atol=1e-12)
+
+
+@pytest.mark.parametrize("density", [0.01, 0.1, 0.30])
+@pytest.mark.parametrize("n_components", [1, 2, 10])
+@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + CSC_CONTAINERS)
+@pytest.mark.parametrize("svd_solver", ["arpack"])
+@pytest.mark.parametrize("scale", [1, 10, 100])
+def test_pca_sparse(
+    global_random_seed, svd_solver, sparse_container, n_components, density, scale
+):
+    # Make sure any tolerance changes pass with SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all"
+    rtol = 5e-07
+    transform_rtol = 3e-05
+
+    random_state = np.random.default_rng(global_random_seed)
+    X = sparse_container(
+        sp.sparse.random(
+            SPARSE_M,
+            SPARSE_N,
+            random_state=random_state,
+            density=density,
+        )
+    )
+    # Scale the data + vary the column means
+    scale_vector = random_state.random(X.shape[1]) * scale
+    X = X.multiply(scale_vector)
+
+    pca = PCA(
+        n_components=n_components,
+        svd_solver=svd_solver,
+        random_state=global_random_seed,
+    )
+    pca.fit(X)
+
+    Xd = X.toarray()
+    pcad = PCA(
+        n_components=n_components,
+        svd_solver=svd_solver,
+        random_state=global_random_seed,
+    )
+    pcad.fit(Xd)
+
+    # Fitted attributes equality
+    _check_fitted_pca_close(pca, pcad, rtol=rtol)
+
+    # Test transform
+    X2 = sparse_container(
+        sp.sparse.random(
+            SPARSE_M,
+            SPARSE_N,
+            random_state=random_state,
+            density=density,
+        )
+    )
+    X2d = X2.toarray()
+
+    assert_allclose(pca.transform(X2), pca.transform(X2d), rtol=transform_rtol)
+    assert_allclose(pca.transform(X2), pcad.transform(X2d), rtol=transform_rtol)
+
+
+@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + CSC_CONTAINERS)
+def test_pca_sparse_fit_transform(global_random_seed, sparse_container):
+    random_state = np.random.default_rng(global_random_seed)
+    X = sparse_container(
+        sp.sparse.random(
+            SPARSE_M,
+            SPARSE_N,
+            random_state=random_state,
+            density=0.01,
+        )
+    )
+    X2 = sparse_container(
+        sp.sparse.random(
+            SPARSE_M,
+            SPARSE_N,
+            random_state=random_state,
+            density=0.01,
+        )
+    )
+
+    pca_fit = PCA(n_components=10, svd_solver="arpack", random_state=global_random_seed)
+    pca_fit_transform = PCA(
+        n_components=10, svd_solver="arpack", random_state=global_random_seed
+    )
+
+    pca_fit.fit(X)
+    transformed_X = pca_fit_transform.fit_transform(X)
+
+    _check_fitted_pca_close(pca_fit, pca_fit_transform, rtol=1e-10)
+    assert_allclose(transformed_X, pca_fit_transform.transform(X), rtol=2e-9)
+    assert_allclose(transformed_X, pca_fit.transform(X), rtol=2e-9)
+    assert_allclose(pca_fit.transform(X2), pca_fit_transform.transform(X2), rtol=2e-9)
+
+
+@pytest.mark.parametrize("svd_solver", ["randomized", "full", "auto"])
+@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + CSC_CONTAINERS)
+def test_sparse_pca_solver_error(global_random_seed, svd_solver, sparse_container):
+    random_state = np.random.RandomState(global_random_seed)
+    X = sparse_container(
+        sp.sparse.random(
+            SPARSE_M,
+            SPARSE_N,
+            random_state=random_state,
+        )
+    )
+    pca = PCA(n_components=30, svd_solver=svd_solver)
+    error_msg_pattern = (
+        f'PCA only support sparse inputs with the "arpack" solver, while "{svd_solver}"'
+        " was passed"
+    )
+    with pytest.raises(TypeError, match=error_msg_pattern):
+        pca.fit(X)
+
+
+def test_no_empty_slice_warning():
+    # test if we avoid numpy warnings for computing over empty arrays
+    n_components = 10
+    n_features = n_components + 2  # anything > n_comps triggered it in 0.16
+    X = np.random.uniform(-1, 1, size=(n_components, n_features))
+    pca = PCA(n_components=n_components)
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", RuntimeWarning)
+        pca.fit(X)
+
+
+@pytest.mark.parametrize("copy", [True, False])
+@pytest.mark.parametrize("solver", PCA_SOLVERS)
+def test_whitening(solver, copy):
+    # Check that PCA output has unit-variance
+    rng = np.random.RandomState(0)
+    n_samples = 100
+    n_features = 80
+    n_components = 30
+    rank = 50
+
+    # some low rank data with correlated features
+    X = np.dot(
+        rng.randn(n_samples, rank),
+        np.dot(np.diag(np.linspace(10.0, 1.0, rank)), rng.randn(rank, n_features)),
+    )
+    # the component-wise variance of the first 50 features is 3 times the
+    # mean component-wise variance of the remaining 30 features
+    X[:, :50] *= 3
+
+    assert X.shape == (n_samples, n_features)
+
+    # the component-wise variance is thus highly varying:
+    assert X.std(axis=0).std() > 43.8
+
+    # whiten the data while projecting to the lower dim subspace
+    X_ = X.copy()  # make sure we keep an original across iterations.
+    pca = PCA(
+        n_components=n_components,
+        whiten=True,
+        copy=copy,
+        svd_solver=solver,
+        random_state=0,
+        iterated_power=7,
+    )
+    # test fit_transform
+    X_whitened = pca.fit_transform(X_.copy())
+    assert X_whitened.shape == (n_samples, n_components)
+    X_whitened2 = pca.transform(X_)
+    assert_allclose(X_whitened, X_whitened2, rtol=5e-4)
+
+    assert_allclose(X_whitened.std(ddof=1, axis=0), np.ones(n_components))
+    assert_allclose(X_whitened.mean(axis=0), np.zeros(n_components), atol=1e-12)
+
+    X_ = X.copy()
+    pca = PCA(
+        n_components=n_components, whiten=False, copy=copy, svd_solver=solver
+    ).fit(X_.copy())
+    X_unwhitened = pca.transform(X_)
+    assert X_unwhitened.shape == (n_samples, n_components)
+
+    # in that case the output components still have varying variances
+    assert X_unwhitened.std(axis=0).std() == pytest.approx(74.1, rel=1e-1)
+    # we always center, so no test for non-centering.
+
+
+@pytest.mark.parametrize("svd_solver", ["arpack", "randomized"])
+def test_pca_explained_variance_equivalence_solver(svd_solver):
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 100, 80
+    X = rng.randn(n_samples, n_features)
+
+    pca_full = PCA(n_components=2, svd_solver="full")
+    pca_other = PCA(n_components=2, svd_solver=svd_solver, random_state=0)
+
+    pca_full.fit(X)
+    pca_other.fit(X)
+
+    assert_allclose(
+        pca_full.explained_variance_, pca_other.explained_variance_, rtol=5e-2
+    )
+    assert_allclose(
+        pca_full.explained_variance_ratio_,
+        pca_other.explained_variance_ratio_,
+        rtol=5e-2,
+    )
+
+
+@pytest.mark.parametrize(
+    "X",
+    [
+        np.random.RandomState(0).randn(100, 80),
+        datasets.make_classification(100, 80, n_informative=78, random_state=0)[0],
+    ],
+    ids=["random-data", "correlated-data"],
+)
+@pytest.mark.parametrize("svd_solver", PCA_SOLVERS)
+def test_pca_explained_variance_empirical(X, svd_solver):
+    pca = PCA(n_components=2, svd_solver=svd_solver, random_state=0)
+    X_pca = pca.fit_transform(X)
+    assert_allclose(pca.explained_variance_, np.var(X_pca, ddof=1, axis=0))
+
+    expected_result = np.linalg.eig(np.cov(X, rowvar=False))[0]
+    expected_result = sorted(expected_result, reverse=True)[:2]
+    assert_allclose(pca.explained_variance_, expected_result, rtol=5e-3)
+
+
+@pytest.mark.parametrize("svd_solver", ["arpack", "randomized"])
+def test_pca_singular_values_consistency(svd_solver):
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 100, 80
+    X = rng.randn(n_samples, n_features)
+
+    pca_full = PCA(n_components=2, svd_solver="full", random_state=rng)
+    pca_other = PCA(n_components=2, svd_solver=svd_solver, random_state=rng)
+
+    pca_full.fit(X)
+    pca_other.fit(X)
+
+    assert_allclose(pca_full.singular_values_, pca_other.singular_values_, rtol=5e-3)
+
+
+@pytest.mark.parametrize("svd_solver", PCA_SOLVERS)
+def test_pca_singular_values(svd_solver):
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 100, 80
+    X = rng.randn(n_samples, n_features)
+
+    pca = PCA(n_components=2, svd_solver=svd_solver, random_state=rng)
+    X_trans = pca.fit_transform(X)
+
+    # compare to the Frobenius norm
+    assert_allclose(
+        np.sum(pca.singular_values_**2), np.linalg.norm(X_trans, "fro") ** 2
+    )
+    # Compare to the 2-norms of the score vectors
+    assert_allclose(pca.singular_values_, np.sqrt(np.sum(X_trans**2, axis=0)))
+
+    # set the singular values and see what er get back
+    n_samples, n_features = 100, 110
+    X = rng.randn(n_samples, n_features)
+
+    pca = PCA(n_components=3, svd_solver=svd_solver, random_state=rng)
+    X_trans = pca.fit_transform(X)
+    X_trans /= np.sqrt(np.sum(X_trans**2, axis=0))
+    X_trans[:, 0] *= 3.142
+    X_trans[:, 1] *= 2.718
+    X_hat = np.dot(X_trans, pca.components_)
+    pca.fit(X_hat)
+    assert_allclose(pca.singular_values_, [3.142, 2.718, 1.0])
+
+
+@pytest.mark.parametrize("svd_solver", PCA_SOLVERS)
+def test_pca_check_projection(svd_solver):
+    # Test that the projection of data is correct
+    rng = np.random.RandomState(0)
+    n, p = 100, 3
+    X = rng.randn(n, p) * 0.1
+    X[:10] += np.array([3, 4, 5])
+    Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])
+
+    Yt = PCA(n_components=2, svd_solver=svd_solver).fit(X).transform(Xt)
+    Yt /= np.sqrt((Yt**2).sum())
+
+    assert_allclose(np.abs(Yt[0][0]), 1.0, rtol=5e-3)
+
+
+@pytest.mark.parametrize("svd_solver", PCA_SOLVERS)
+def test_pca_check_projection_list(svd_solver):
+    # Test that the projection of data is correct
+    X = [[1.0, 0.0], [0.0, 1.0]]
+    pca = PCA(n_components=1, svd_solver=svd_solver, random_state=0)
+    X_trans = pca.fit_transform(X)
+    assert X_trans.shape, (2, 1)
+    assert_allclose(X_trans.mean(), 0.00, atol=1e-12)
+    assert_allclose(X_trans.std(), 0.71, rtol=5e-3)
+
+
+@pytest.mark.parametrize("svd_solver", ["full", "arpack", "randomized"])
+@pytest.mark.parametrize("whiten", [False, True])
+def test_pca_inverse(svd_solver, whiten):
+    # Test that the projection of data can be inverted
+    rng = np.random.RandomState(0)
+    n, p = 50, 3
+    X = rng.randn(n, p)  # spherical data
+    X[:, 1] *= 0.00001  # make middle component relatively small
+    X += [5, 4, 3]  # make a large mean
+
+    # same check that we can find the original data from the transformed
+    # signal (since the data is almost of rank n_components)
+    pca = PCA(n_components=2, svd_solver=svd_solver, whiten=whiten).fit(X)
+    Y = pca.transform(X)
+    Y_inverse = pca.inverse_transform(Y)
+    assert_allclose(X, Y_inverse, rtol=5e-6)
+
+
+@pytest.mark.parametrize(
+    "data", [np.array([[0, 1, 0], [1, 0, 0]]), np.array([[0, 1, 0], [1, 0, 0]]).T]
+)
+@pytest.mark.parametrize(
+    "svd_solver, n_components, err_msg",
+    [
+        ("arpack", 0, r"must be between 1 and min\(n_samples, n_features\)"),
+        ("randomized", 0, r"must be between 1 and min\(n_samples, n_features\)"),
+        ("arpack", 2, r"must be strictly less than min"),
+        (
+            "auto",
+            3,
+            (
+                r"n_components=3 must be between 0 and min\(n_samples, "
+                r"n_features\)=2 with svd_solver='full'"
+            ),
+        ),
+    ],
+)
+def test_pca_validation(svd_solver, data, n_components, err_msg):
+    # Ensures that solver-specific extreme inputs for the n_components
+    # parameter raise errors
+    smallest_d = 2  # The smallest dimension
+    pca_fitted = PCA(n_components, svd_solver=svd_solver)
+
+    with pytest.raises(ValueError, match=err_msg):
+        pca_fitted.fit(data)
+
+    # Additional case for arpack
+    if svd_solver == "arpack":
+        n_components = smallest_d
+
+        err_msg = (
+            "n_components={}L? must be strictly less than "
+            r"min\(n_samples, n_features\)={}L? with "
+            "svd_solver='arpack'".format(n_components, smallest_d)
+        )
+        with pytest.raises(ValueError, match=err_msg):
+            PCA(n_components, svd_solver=svd_solver).fit(data)
+
+
+@pytest.mark.parametrize(
+    "solver, n_components_",
+    [
+        ("full", min(iris.data.shape)),
+        ("arpack", min(iris.data.shape) - 1),
+        ("randomized", min(iris.data.shape)),
+    ],
+)
+@pytest.mark.parametrize("data", [iris.data, iris.data.T])
+def test_n_components_none(data, solver, n_components_):
+    pca = PCA(svd_solver=solver)
+    pca.fit(data)
+    assert pca.n_components_ == n_components_
+
+
+@pytest.mark.parametrize("svd_solver", ["auto", "full"])
+def test_n_components_mle(svd_solver):
+    # Ensure that n_components == 'mle' doesn't raise error for auto/full
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 600, 10
+    X = rng.randn(n_samples, n_features)
+    pca = PCA(n_components="mle", svd_solver=svd_solver)
+    pca.fit(X)
+    assert pca.n_components_ == 1
+
+
+@pytest.mark.parametrize("svd_solver", ["arpack", "randomized"])
+def test_n_components_mle_error(svd_solver):
+    # Ensure that n_components == 'mle' will raise an error for unsupported
+    # solvers
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 600, 10
+    X = rng.randn(n_samples, n_features)
+    pca = PCA(n_components="mle", svd_solver=svd_solver)
+    err_msg = "n_components='mle' cannot be a string with svd_solver='{}'".format(
+        svd_solver
+    )
+    with pytest.raises(ValueError, match=err_msg):
+        pca.fit(X)
+
+
+def test_pca_dim():
+    # Check automated dimensionality setting
+    rng = np.random.RandomState(0)
+    n, p = 100, 5
+    X = rng.randn(n, p) * 0.1
+    X[:10] += np.array([3, 4, 5, 1, 2])
+    pca = PCA(n_components="mle", svd_solver="full").fit(X)
+    assert pca.n_components == "mle"
+    assert pca.n_components_ == 1
+
+
+def test_infer_dim_1():
+    # TODO: explain what this is testing
+    # Or at least use explicit variable names...
+    n, p = 1000, 5
+    rng = np.random.RandomState(0)
+    X = (
+        rng.randn(n, p) * 0.1
+        + rng.randn(n, 1) * np.array([3, 4, 5, 1, 2])
+        + np.array([1, 0, 7, 4, 6])
+    )
+    pca = PCA(n_components=p, svd_solver="full")
+    pca.fit(X)
+    spect = pca.explained_variance_
+    ll = np.array([_assess_dimension(spect, k, n) for k in range(1, p)])
+    assert ll[1] > ll.max() - 0.01 * n
+
+
+def test_infer_dim_2():
+    # TODO: explain what this is testing
+    # Or at least use explicit variable names...
+    n, p = 1000, 5
+    rng = np.random.RandomState(0)
+    X = rng.randn(n, p) * 0.1
+    X[:10] += np.array([3, 4, 5, 1, 2])
+    X[10:20] += np.array([6, 0, 7, 2, -1])
+    pca = PCA(n_components=p, svd_solver="full")
+    pca.fit(X)
+    spect = pca.explained_variance_
+    assert _infer_dimension(spect, n) > 1
+
+
+def test_infer_dim_3():
+    n, p = 100, 5
+    rng = np.random.RandomState(0)
+    X = rng.randn(n, p) * 0.1
+    X[:10] += np.array([3, 4, 5, 1, 2])
+    X[10:20] += np.array([6, 0, 7, 2, -1])
+    X[30:40] += 2 * np.array([-1, 1, -1, 1, -1])
+    pca = PCA(n_components=p, svd_solver="full")
+    pca.fit(X)
+    spect = pca.explained_variance_
+    assert _infer_dimension(spect, n) > 2
+
+
+@pytest.mark.parametrize(
+    "X, n_components, n_components_validated",
+    [
+        (iris.data, 0.95, 2),  # row > col
+        (iris.data, 0.01, 1),  # row > col
+        (np.random.RandomState(0).rand(5, 20), 0.5, 2),
+    ],  # row < col
+)
+def test_infer_dim_by_explained_variance(X, n_components, n_components_validated):
+    pca = PCA(n_components=n_components, svd_solver="full")
+    pca.fit(X)
+    assert pca.n_components == pytest.approx(n_components)
+    assert pca.n_components_ == n_components_validated
+
+
+@pytest.mark.parametrize("svd_solver", PCA_SOLVERS)
+def test_pca_score(svd_solver):
+    # Test that probabilistic PCA scoring yields a reasonable score
+    n, p = 1000, 3
+    rng = np.random.RandomState(0)
+    X = rng.randn(n, p) * 0.1 + np.array([3, 4, 5])
+    pca = PCA(n_components=2, svd_solver=svd_solver)
+    pca.fit(X)
+
+    ll1 = pca.score(X)
+    h = -0.5 * np.log(2 * np.pi * np.exp(1) * 0.1**2) * p
+    assert_allclose(ll1 / h, 1, rtol=5e-2)
+
+    ll2 = pca.score(rng.randn(n, p) * 0.2 + np.array([3, 4, 5]))
+    assert ll1 > ll2
+
+    pca = PCA(n_components=2, whiten=True, svd_solver=svd_solver)
+    pca.fit(X)
+    ll2 = pca.score(X)
+    assert ll1 > ll2
+
+
+def test_pca_score3():
+    # Check that probabilistic PCA selects the right model
+    n, p = 200, 3
+    rng = np.random.RandomState(0)
+    Xl = rng.randn(n, p) + rng.randn(n, 1) * np.array([3, 4, 5]) + np.array([1, 0, 7])
+    Xt = rng.randn(n, p) + rng.randn(n, 1) * np.array([3, 4, 5]) + np.array([1, 0, 7])
+    ll = np.zeros(p)
+    for k in range(p):
+        pca = PCA(n_components=k, svd_solver="full")
+        pca.fit(Xl)
+        ll[k] = pca.score(Xt)
+
+    assert ll.argmax() == 1
+
+
+@pytest.mark.parametrize("svd_solver", PCA_SOLVERS)
+def test_pca_sanity_noise_variance(svd_solver):
+    # Sanity check for the noise_variance_. For more details see
+    # https://github.com/scikit-learn/scikit-learn/issues/7568
+    # https://github.com/scikit-learn/scikit-learn/issues/8541
+    # https://github.com/scikit-learn/scikit-learn/issues/8544
+    X, _ = datasets.load_digits(return_X_y=True)
+    pca = PCA(n_components=30, svd_solver=svd_solver, random_state=0)
+    pca.fit(X)
+    assert np.all((pca.explained_variance_ - pca.noise_variance_) >= 0)
+
+
+@pytest.mark.parametrize("svd_solver", ["arpack", "randomized"])
+def test_pca_score_consistency_solvers(svd_solver):
+    # Check the consistency of score between solvers
+    X, _ = datasets.load_digits(return_X_y=True)
+    pca_full = PCA(n_components=30, svd_solver="full", random_state=0)
+    pca_other = PCA(n_components=30, svd_solver=svd_solver, random_state=0)
+    pca_full.fit(X)
+    pca_other.fit(X)
+    assert_allclose(pca_full.score(X), pca_other.score(X), rtol=5e-6)
+
+
+# arpack raises ValueError for n_components == min(n_samples,  n_features)
+@pytest.mark.parametrize("svd_solver", ["full", "randomized"])
+def test_pca_zero_noise_variance_edge_cases(svd_solver):
+    # ensure that noise_variance_ is 0 in edge cases
+    # when n_components == min(n_samples, n_features)
+    n, p = 100, 3
+    rng = np.random.RandomState(0)
+    X = rng.randn(n, p) * 0.1 + np.array([3, 4, 5])
+
+    pca = PCA(n_components=p, svd_solver=svd_solver)
+    pca.fit(X)
+    assert pca.noise_variance_ == 0
+    # Non-regression test for gh-12489
+    # ensure no divide-by-zero error for n_components == n_features < n_samples
+    pca.score(X)
+
+    pca.fit(X.T)
+    assert pca.noise_variance_ == 0
+    # Non-regression test for gh-12489
+    # ensure no divide-by-zero error for n_components == n_samples < n_features
+    pca.score(X.T)
+
+
+@pytest.mark.parametrize(
+    "data, n_components, expected_solver",
+    [  # case: n_components in (0,1) => 'full'
+        (np.random.RandomState(0).uniform(size=(1000, 50)), 0.5, "full"),
+        # case: max(X.shape) <= 500 => 'full'
+        (np.random.RandomState(0).uniform(size=(10, 50)), 5, "full"),
+        # case: n_components >= .8 * min(X.shape) => 'full'
+        (np.random.RandomState(0).uniform(size=(1000, 50)), 50, "full"),
+        # n_components >= 1 and n_components < .8*min(X.shape) => 'randomized'
+        (np.random.RandomState(0).uniform(size=(1000, 50)), 10, "randomized"),
+    ],
+)
+def test_pca_svd_solver_auto(data, n_components, expected_solver):
+    pca_auto = PCA(n_components=n_components, random_state=0)
+    pca_test = PCA(
+        n_components=n_components, svd_solver=expected_solver, random_state=0
+    )
+    pca_auto.fit(data)
+    pca_test.fit(data)
+    assert_allclose(pca_auto.components_, pca_test.components_)
+
+
+@pytest.mark.parametrize("svd_solver", PCA_SOLVERS)
+def test_pca_deterministic_output(svd_solver):
+    rng = np.random.RandomState(0)
+    X = rng.rand(10, 10)
+
+    transformed_X = np.zeros((20, 2))
+    for i in range(20):
+        pca = PCA(n_components=2, svd_solver=svd_solver, random_state=rng)
+        transformed_X[i, :] = pca.fit_transform(X)[0]
+    assert_allclose(transformed_X, np.tile(transformed_X[0, :], 20).reshape(20, 2))
+
+
+@pytest.mark.parametrize("svd_solver", PCA_SOLVERS)
+def test_pca_dtype_preservation(svd_solver):
+    check_pca_float_dtype_preservation(svd_solver)
+    check_pca_int_dtype_upcast_to_double(svd_solver)
+
+
+def check_pca_float_dtype_preservation(svd_solver):
+    # Ensure that PCA does not upscale the dtype when input is float32
+    X_64 = np.random.RandomState(0).rand(1000, 4).astype(np.float64, copy=False)
+    X_32 = X_64.astype(np.float32)
+
+    pca_64 = PCA(n_components=3, svd_solver=svd_solver, random_state=0).fit(X_64)
+    pca_32 = PCA(n_components=3, svd_solver=svd_solver, random_state=0).fit(X_32)
+
+    assert pca_64.components_.dtype == np.float64
+    assert pca_32.components_.dtype == np.float32
+    assert pca_64.transform(X_64).dtype == np.float64
+    assert pca_32.transform(X_32).dtype == np.float32
+
+    # the rtol is set such that the test passes on all platforms tested on
+    # conda-forge: PR#15775
+    # see: https://github.com/conda-forge/scikit-learn-feedstock/pull/113
+    assert_allclose(pca_64.components_, pca_32.components_, rtol=2e-4)
+
+
+def check_pca_int_dtype_upcast_to_double(svd_solver):
+    # Ensure that all int types will be upcast to float64
+    X_i64 = np.random.RandomState(0).randint(0, 1000, (1000, 4))
+    X_i64 = X_i64.astype(np.int64, copy=False)
+    X_i32 = X_i64.astype(np.int32, copy=False)
+
+    pca_64 = PCA(n_components=3, svd_solver=svd_solver, random_state=0).fit(X_i64)
+    pca_32 = PCA(n_components=3, svd_solver=svd_solver, random_state=0).fit(X_i32)
+
+    assert pca_64.components_.dtype == np.float64
+    assert pca_32.components_.dtype == np.float64
+    assert pca_64.transform(X_i64).dtype == np.float64
+    assert pca_32.transform(X_i32).dtype == np.float64
+
+    assert_allclose(pca_64.components_, pca_32.components_, rtol=1e-4)
+
+
+def test_pca_n_components_mostly_explained_variance_ratio():
+    # when n_components is the second highest cumulative sum of the
+    # explained_variance_ratio_, then n_components_ should equal the
+    # number of features in the dataset #15669
+    X, y = load_iris(return_X_y=True)
+    pca1 = PCA().fit(X, y)
+
+    n_components = pca1.explained_variance_ratio_.cumsum()[-2]
+    pca2 = PCA(n_components=n_components).fit(X, y)
+    assert pca2.n_components_ == X.shape[1]
+
+
+def test_assess_dimension_bad_rank():
+    # Test error when tested rank not in [1, n_features - 1]
+    spectrum = np.array([1, 1e-30, 1e-30, 1e-30])
+    n_samples = 10
+    for rank in (0, 5):
+        with pytest.raises(ValueError, match=r"should be in \[1, n_features - 1\]"):
+            _assess_dimension(spectrum, rank, n_samples)
+
+
+def test_small_eigenvalues_mle():
+    # Test rank associated with tiny eigenvalues are given a log-likelihood of
+    # -inf. The inferred rank will be 1
+    spectrum = np.array([1, 1e-30, 1e-30, 1e-30])
+
+    assert _assess_dimension(spectrum, rank=1, n_samples=10) > -np.inf
+
+    for rank in (2, 3):
+        assert _assess_dimension(spectrum, rank, 10) == -np.inf
+
+    assert _infer_dimension(spectrum, 10) == 1
+
+
+def test_mle_redundant_data():
+    # Test 'mle' with pathological X: only one relevant feature should give a
+    # rank of 1
+    X, _ = datasets.make_classification(
+        n_features=20,
+        n_informative=1,
+        n_repeated=18,
+        n_redundant=1,
+        n_clusters_per_class=1,
+        random_state=42,
+    )
+    pca = PCA(n_components="mle").fit(X)
+    assert pca.n_components_ == 1
+
+
+def test_fit_mle_too_few_samples():
+    # Tests that an error is raised when the number of samples is smaller
+    # than the number of features during an mle fit
+    X, _ = datasets.make_classification(n_samples=20, n_features=21, random_state=42)
+
+    pca = PCA(n_components="mle", svd_solver="full")
+    with pytest.raises(
+        ValueError,
+        match="n_components='mle' is only supported if n_samples >= n_features",
+    ):
+        pca.fit(X)
+
+
+def test_mle_simple_case():
+    # non-regression test for issue
+    # https://github.com/scikit-learn/scikit-learn/issues/16730
+    n_samples, n_dim = 1000, 10
+    X = np.random.RandomState(0).randn(n_samples, n_dim)
+    X[:, -1] = np.mean(X[:, :-1], axis=-1)  # true X dim is ndim - 1
+    pca_skl = PCA("mle", svd_solver="full")
+    pca_skl.fit(X)
+    assert pca_skl.n_components_ == n_dim - 1
+
+
+def test_assess_dimesion_rank_one():
+    # Make sure assess_dimension works properly on a matrix of rank 1
+    n_samples, n_features = 9, 6
+    X = np.ones((n_samples, n_features))  # rank 1 matrix
+    _, s, _ = np.linalg.svd(X, full_matrices=True)
+    # except for rank 1, all eigenvalues are 0 resp. close to 0 (FP)
+    assert_allclose(s[1:], np.zeros(n_features - 1), atol=1e-12)
+
+    assert np.isfinite(_assess_dimension(s, rank=1, n_samples=n_samples))
+    for rank in range(2, n_features):
+        assert _assess_dimension(s, rank, n_samples) == -np.inf
+
+
+def test_pca_randomized_svd_n_oversamples():
+    """Check that exposing and setting `n_oversamples` will provide accurate results
+    even when `X` as a large number of features.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/20589
+    """
+    rng = np.random.RandomState(0)
+    n_features = 100
+    X = rng.randn(1_000, n_features)
+
+    # The default value of `n_oversamples` will lead to inaccurate results
+    # We force it to the number of features.
+    pca_randomized = PCA(
+        n_components=1,
+        svd_solver="randomized",
+        n_oversamples=n_features,
+        random_state=0,
+    ).fit(X)
+    pca_full = PCA(n_components=1, svd_solver="full").fit(X)
+    pca_arpack = PCA(n_components=1, svd_solver="arpack", random_state=0).fit(X)
+
+    assert_allclose(np.abs(pca_full.components_), np.abs(pca_arpack.components_))
+    assert_allclose(np.abs(pca_randomized.components_), np.abs(pca_arpack.components_))
+
+
+def test_feature_names_out():
+    """Check feature names out for PCA."""
+    pca = PCA(n_components=2).fit(iris.data)
+
+    names = pca.get_feature_names_out()
+    assert_array_equal([f"pca{i}" for i in range(2)], names)
+
+
+@pytest.mark.parametrize("copy", [True, False])
+def test_variance_correctness(copy):
+    """Check the accuracy of PCA's internal variance calculation"""
+    rng = np.random.RandomState(0)
+    X = rng.randn(1000, 200)
+    pca = PCA().fit(X)
+    pca_var = pca.explained_variance_ / pca.explained_variance_ratio_
+    true_var = np.var(X, ddof=1, axis=0).sum()
+    np.testing.assert_allclose(pca_var, true_var)
+
+
+def check_array_api_get_precision(name, estimator, array_namespace, device, dtype_name):
+    xp = _array_api_for_tests(array_namespace, device)
+    iris_np = iris.data.astype(dtype_name)
+    iris_xp = xp.asarray(iris_np, device=device)
+
+    estimator.fit(iris_np)
+    precision_np = estimator.get_precision()
+    covariance_np = estimator.get_covariance()
+
+    with config_context(array_api_dispatch=True):
+        estimator_xp = clone(estimator).fit(iris_xp)
+        precision_xp = estimator_xp.get_precision()
+        assert precision_xp.shape == (4, 4)
+        assert precision_xp.dtype == iris_xp.dtype
+
+        assert_allclose(
+            _convert_to_numpy(precision_xp, xp=xp),
+            precision_np,
+            atol=_atol_for_type(dtype_name),
+        )
+        covariance_xp = estimator_xp.get_covariance()
+        assert covariance_xp.shape == (4, 4)
+        assert covariance_xp.dtype == iris_xp.dtype
+
+        assert_allclose(
+            _convert_to_numpy(covariance_xp, xp=xp),
+            covariance_np,
+            atol=_atol_for_type(dtype_name),
+        )
+
+
+@pytest.mark.parametrize(
+    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
+)
+@pytest.mark.parametrize(
+    "check",
+    [check_array_api_input_and_values, check_array_api_get_precision],
+    ids=_get_check_estimator_ids,
+)
+@pytest.mark.parametrize(
+    "estimator",
+    [
+        PCA(n_components=2, svd_solver="full"),
+        PCA(n_components=0.1, svd_solver="full", whiten=True),
+        PCA(
+            n_components=2,
+            svd_solver="randomized",
+            power_iteration_normalizer="QR",
+            random_state=0,  # how to use global_random_seed here?
+        ),
+    ],
+    ids=_get_check_estimator_ids,
+)
+def test_pca_array_api_compliance(
+    estimator, check, array_namespace, device, dtype_name
+):
+    name = estimator.__class__.__name__
+    check(name, estimator, array_namespace, device=device, dtype_name=dtype_name)
+
+
+@pytest.mark.parametrize(
+    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
+)
+@pytest.mark.parametrize(
+    "check",
+    [check_array_api_get_precision],
+    ids=_get_check_estimator_ids,
+)
+@pytest.mark.parametrize(
+    "estimator",
+    [
+        # PCA with mle cannot use check_array_api_input_and_values because of
+        # rounding errors in the noisy (low variance) components. Even checking
+        # the shape of the `components_` is problematic because the number of
+        # components depends on trimming threshold of the mle algorithm which
+        # can depend on device-specific rounding errors.
+        PCA(n_components="mle", svd_solver="full"),
+    ],
+    ids=_get_check_estimator_ids,
+)
+def test_pca_mle_array_api_compliance(
+    estimator, check, array_namespace, device, dtype_name
+):
+    name = estimator.__class__.__name__
+    check(name, estimator, array_namespace, device=device, dtype_name=dtype_name)
+
+    # Simpler variant of the generic check_array_api_input checker tailored for
+    # the specific case of PCA with mle-trimmed components.
+    xp = _array_api_for_tests(array_namespace, device)
+
+    X, y = make_classification(random_state=42)
+    X = X.astype(dtype_name, copy=False)
+    atol = _atol_for_type(X.dtype)
+
+    est = clone(estimator)
+
+    X_xp = xp.asarray(X, device=device)
+    y_xp = xp.asarray(y, device=device)
+
+    est.fit(X, y)
+
+    components_np = est.components_
+    explained_variance_np = est.explained_variance_
+
+    est_xp = clone(est)
+    with config_context(array_api_dispatch=True):
+        est_xp.fit(X_xp, y_xp)
+        components_xp = est_xp.components_
+        assert array_device(components_xp) == array_device(X_xp)
+        components_xp_np = _convert_to_numpy(components_xp, xp=xp)
+
+        explained_variance_xp = est_xp.explained_variance_
+        assert array_device(explained_variance_xp) == array_device(X_xp)
+        explained_variance_xp_np = _convert_to_numpy(explained_variance_xp, xp=xp)
+
+    assert components_xp_np.dtype == components_np.dtype
+    assert components_xp_np.shape[1] == components_np.shape[1]
+    assert explained_variance_xp_np.dtype == explained_variance_np.dtype
+
+    # Check that the explained variance values match for the
+    # common components:
+    min_components = min(components_xp_np.shape[0], components_np.shape[0])
+    assert_allclose(
+        explained_variance_xp_np[:min_components],
+        explained_variance_np[:min_components],
+        atol=atol,
+    )
+
+    # If the number of components differ, check that the explained variance of
+    # the trimmed components is very small.
+    if components_xp_np.shape[0] != components_np.shape[0]:
+        reference_variance = explained_variance_np[-1]
+        extra_variance_np = explained_variance_np[min_components:]
+        extra_variance_xp_np = explained_variance_xp_np[min_components:]
+        assert all(np.abs(extra_variance_np - reference_variance) < atol)
+        assert all(np.abs(extra_variance_xp_np - reference_variance) < atol)
+
+
+def test_array_api_error_and_warnings_on_unsupported_params():
+    pytest.importorskip("array_api_compat")
+    xp = pytest.importorskip("numpy.array_api")
+    iris_xp = xp.asarray(iris.data)
+
+    pca = PCA(n_components=2, svd_solver="arpack", random_state=0)
+    expected_msg = re.escape(
+        "PCA with svd_solver='arpack' is not supported for Array API inputs."
+    )
+    with pytest.raises(ValueError, match=expected_msg):
+        with config_context(array_api_dispatch=True):
+            pca.fit(iris_xp)
+
+    pca.set_params(svd_solver="randomized", power_iteration_normalizer="LU")
+    expected_msg = re.escape(
+        "Array API does not support LU factorization. Set"
+        " `power_iteration_normalizer='QR'` instead."
+    )
+    with pytest.raises(ValueError, match=expected_msg):
+        with config_context(array_api_dispatch=True):
+            pca.fit(iris_xp)
+
+    pca.set_params(svd_solver="randomized", power_iteration_normalizer="auto")
+    expected_msg = re.escape(
+        "Array API does not support LU factorization, falling back to QR instead. Set"
+        " `power_iteration_normalizer='QR'` explicitly to silence this warning."
+    )
+    with pytest.warns(UserWarning, match=expected_msg):
+        with config_context(array_api_dispatch=True):
+            pca.fit(iris_xp)

env-llmeval/lib/python3.10/site-packages/sklearn/feature_extraction/__init__.py

@@ -0,0 +1,19 @@
+"""
+The :mod:`sklearn.feature_extraction` module deals with feature extraction
+from raw data. It currently includes methods to extract features from text and
+images.
+"""
+
+from . import text
+from ._dict_vectorizer import DictVectorizer
+from ._hash import FeatureHasher
+from .image import grid_to_graph, img_to_graph
+
+__all__ = [
+    "DictVectorizer",
+    "image",
+    "img_to_graph",
+    "grid_to_graph",
+    "text",
+    "FeatureHasher",
+]

env-llmeval/lib/python3.10/site-packages/sklearn/feature_extraction/_dict_vectorizer.py

@@ -0,0 +1,452 @@
+# Authors: Lars Buitinck
+#          Dan Blanchard <[email protected]>
+# License: BSD 3 clause
+
+from array import array
+from collections.abc import Iterable, Mapping
+from numbers import Number
+from operator import itemgetter
+
+import numpy as np
+import scipy.sparse as sp
+
+from ..base import BaseEstimator, TransformerMixin, _fit_context
+from ..utils import check_array
+from ..utils.validation import check_is_fitted
+
+
+class DictVectorizer(TransformerMixin, BaseEstimator):
+    """Transforms lists of feature-value mappings to vectors.
+
+    This transformer turns lists of mappings (dict-like objects) of feature
+    names to feature values into Numpy arrays or scipy.sparse matrices for use
+    with scikit-learn estimators.
+
+    When feature values are strings, this transformer will do a binary one-hot
+    (aka one-of-K) coding: one boolean-valued feature is constructed for each
+    of the possible string values that the feature can take on. For instance,
+    a feature "f" that can take on the values "ham" and "spam" will become two
+    features in the output, one signifying "f=ham", the other "f=spam".
+
+    If a feature value is a sequence or set of strings, this transformer
+    will iterate over the values and will count the occurrences of each string
+    value.
+
+    However, note that this transformer will only do a binary one-hot encoding
+    when feature values are of type string. If categorical features are
+    represented as numeric values such as int or iterables of strings, the
+    DictVectorizer can be followed by
+    :class:`~sklearn.preprocessing.OneHotEncoder` to complete
+    binary one-hot encoding.
+
+    Features that do not occur in a sample (mapping) will have a zero value
+    in the resulting array/matrix.
+
+    For an efficiency comparison of the different feature extractors, see
+    :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`.
+
+    Read more in the :ref:`User Guide <dict_feature_extraction>`.
+
+    Parameters
+    ----------
+    dtype : dtype, default=np.float64
+        The type of feature values. Passed to Numpy array/scipy.sparse matrix
+        constructors as the dtype argument.
+    separator : str, default="="
+        Separator string used when constructing new features for one-hot
+        coding.
+    sparse : bool, default=True
+        Whether transform should produce scipy.sparse matrices.
+    sort : bool, default=True
+        Whether ``feature_names_`` and ``vocabulary_`` should be
+        sorted when fitting.
+
+    Attributes
+    ----------
+    vocabulary_ : dict
+        A dictionary mapping feature names to feature indices.
+
+    feature_names_ : list
+        A list of length n_features containing the feature names (e.g., "f=ham"
+        and "f=spam").
+
+    See Also
+    --------
+    FeatureHasher : Performs vectorization using only a hash function.
+    sklearn.preprocessing.OrdinalEncoder : Handles nominal/categorical
+        features encoded as columns of arbitrary data types.
+
+    Examples
+    --------
+    >>> from sklearn.feature_extraction import DictVectorizer
+    >>> v = DictVectorizer(sparse=False)
+    >>> D = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}]
+    >>> X = v.fit_transform(D)
+    >>> X
+    array([[2., 0., 1.],
+           [0., 1., 3.]])
+    >>> v.inverse_transform(X) == [{'bar': 2.0, 'foo': 1.0},
+    ...                            {'baz': 1.0, 'foo': 3.0}]
+    True
+    >>> v.transform({'foo': 4, 'unseen_feature': 3})
+    array([[0., 0., 4.]])
+    """
+
+    _parameter_constraints: dict = {
+        "dtype": "no_validation",  # validation delegated to numpy,
+        "separator": [str],
+        "sparse": ["boolean"],
+        "sort": ["boolean"],
+    }
+
+    def __init__(self, *, dtype=np.float64, separator="=", sparse=True, sort=True):
+        self.dtype = dtype
+        self.separator = separator
+        self.sparse = sparse
+        self.sort = sort
+
+    def _add_iterable_element(
+        self,
+        f,
+        v,
+        feature_names,
+        vocab,
+        *,
+        fitting=True,
+        transforming=False,
+        indices=None,
+        values=None,
+    ):
+        """Add feature names for iterable of strings"""
+        for vv in v:
+            if isinstance(vv, str):
+                feature_name = "%s%s%s" % (f, self.separator, vv)
+                vv = 1
+            else:
+                raise TypeError(
+                    f"Unsupported type {type(vv)} in iterable "
+                    "value. Only iterables of string are "
+                    "supported."
+                )
+            if fitting and feature_name not in vocab:
+                vocab[feature_name] = len(feature_names)
+                feature_names.append(feature_name)
+
+            if transforming and feature_name in vocab:
+                indices.append(vocab[feature_name])
+                values.append(self.dtype(vv))
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Learn a list of feature name -> indices mappings.
+
+        Parameters
+        ----------
+        X : Mapping or iterable over Mappings
+            Dict(s) or Mapping(s) from feature names (arbitrary Python
+            objects) to feature values (strings or convertible to dtype).
+
+            .. versionchanged:: 0.24
+               Accepts multiple string values for one categorical feature.
+
+        y : (ignored)
+            Ignored parameter.
+
+        Returns
+        -------
+        self : object
+            DictVectorizer class instance.
+        """
+        feature_names = []
+        vocab = {}
+
+        for x in X:
+            for f, v in x.items():
+                if isinstance(v, str):
+                    feature_name = "%s%s%s" % (f, self.separator, v)
+                elif isinstance(v, Number) or (v is None):
+                    feature_name = f
+                elif isinstance(v, Mapping):
+                    raise TypeError(
+                        f"Unsupported value type {type(v)} "
+                        f"for {f}: {v}.\n"
+                        "Mapping objects are not supported."
+                    )
+                elif isinstance(v, Iterable):
+                    feature_name = None
+                    self._add_iterable_element(f, v, feature_names, vocab)
+
+                if feature_name is not None:
+                    if feature_name not in vocab:
+                        vocab[feature_name] = len(feature_names)
+                        feature_names.append(feature_name)
+
+        if self.sort:
+            feature_names.sort()
+            vocab = {f: i for i, f in enumerate(feature_names)}
+
+        self.feature_names_ = feature_names
+        self.vocabulary_ = vocab
+
+        return self
+
+    def _transform(self, X, fitting):
+        # Sanity check: Python's array has no way of explicitly requesting the
+        # signed 32-bit integers that scipy.sparse needs, so we use the next
+        # best thing: typecode "i" (int). However, if that gives larger or
+        # smaller integers than 32-bit ones, np.frombuffer screws up.
+        assert array("i").itemsize == 4, (
+            "sizeof(int) != 4 on your platform; please report this at"
+            " https://github.com/scikit-learn/scikit-learn/issues and"
+            " include the output from platform.platform() in your bug report"
+        )
+
+        dtype = self.dtype
+        if fitting:
+            feature_names = []
+            vocab = {}
+        else:
+            feature_names = self.feature_names_
+            vocab = self.vocabulary_
+
+        transforming = True
+
+        # Process everything as sparse regardless of setting
+        X = [X] if isinstance(X, Mapping) else X
+
+        indices = array("i")
+        indptr = [0]
+        # XXX we could change values to an array.array as well, but it
+        # would require (heuristic) conversion of dtype to typecode...
+        values = []
+
+        # collect all the possible feature names and build sparse matrix at
+        # same time
+        for x in X:
+            for f, v in x.items():
+                if isinstance(v, str):
+                    feature_name = "%s%s%s" % (f, self.separator, v)
+                    v = 1
+                elif isinstance(v, Number) or (v is None):
+                    feature_name = f
+                elif not isinstance(v, Mapping) and isinstance(v, Iterable):
+                    feature_name = None
+                    self._add_iterable_element(
+                        f,
+                        v,
+                        feature_names,
+                        vocab,
+                        fitting=fitting,
+                        transforming=transforming,
+                        indices=indices,
+                        values=values,
+                    )
+                else:
+                    raise TypeError(
+                        f"Unsupported value Type {type(v)} "
+                        f"for {f}: {v}.\n"
+                        f"{type(v)} objects are not supported."
+                    )
+
+                if feature_name is not None:
+                    if fitting and feature_name not in vocab:
+                        vocab[feature_name] = len(feature_names)
+                        feature_names.append(feature_name)
+
+                    if feature_name in vocab:
+                        indices.append(vocab[feature_name])
+                        values.append(self.dtype(v))
+
+            indptr.append(len(indices))
+
+        if len(indptr) == 1:
+            raise ValueError("Sample sequence X is empty.")
+
+        indices = np.frombuffer(indices, dtype=np.intc)
+        shape = (len(indptr) - 1, len(vocab))
+
+        result_matrix = sp.csr_matrix(
+            (values, indices, indptr), shape=shape, dtype=dtype
+        )
+
+        # Sort everything if asked
+        if fitting and self.sort:
+            feature_names.sort()
+            map_index = np.empty(len(feature_names), dtype=np.int32)
+            for new_val, f in enumerate(feature_names):
+                map_index[new_val] = vocab[f]
+                vocab[f] = new_val
+            result_matrix = result_matrix[:, map_index]
+
+        if self.sparse:
+            result_matrix.sort_indices()
+        else:
+            result_matrix = result_matrix.toarray()
+
+        if fitting:
+            self.feature_names_ = feature_names
+            self.vocabulary_ = vocab
+
+        return result_matrix
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit_transform(self, X, y=None):
+        """Learn a list of feature name -> indices mappings and transform X.
+
+        Like fit(X) followed by transform(X), but does not require
+        materializing X in memory.
+
+        Parameters
+        ----------
+        X : Mapping or iterable over Mappings
+            Dict(s) or Mapping(s) from feature names (arbitrary Python
+            objects) to feature values (strings or convertible to dtype).
+
+            .. versionchanged:: 0.24
+               Accepts multiple string values for one categorical feature.
+
+        y : (ignored)
+            Ignored parameter.
+
+        Returns
+        -------
+        Xa : {array, sparse matrix}
+            Feature vectors; always 2-d.
+        """
+        return self._transform(X, fitting=True)
+
+    def inverse_transform(self, X, dict_type=dict):
+        """Transform array or sparse matrix X back to feature mappings.
+
+        X must have been produced by this DictVectorizer's transform or
+        fit_transform method; it may only have passed through transformers
+        that preserve the number of features and their order.
+
+        In the case of one-hot/one-of-K coding, the constructed feature
+        names and values are returned rather than the original ones.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Sample matrix.
+        dict_type : type, default=dict
+            Constructor for feature mappings. Must conform to the
+            collections.Mapping API.
+
+        Returns
+        -------
+        D : list of dict_type objects of shape (n_samples,)
+            Feature mappings for the samples in X.
+        """
+        check_is_fitted(self, "feature_names_")
+
+        # COO matrix is not subscriptable
+        X = check_array(X, accept_sparse=["csr", "csc"])
+        n_samples = X.shape[0]
+
+        names = self.feature_names_
+        dicts = [dict_type() for _ in range(n_samples)]
+
+        if sp.issparse(X):
+            for i, j in zip(*X.nonzero()):
+                dicts[i][names[j]] = X[i, j]
+        else:
+            for i, d in enumerate(dicts):
+                for j, v in enumerate(X[i, :]):
+                    if v != 0:
+                        d[names[j]] = X[i, j]
+
+        return dicts
+
+    def transform(self, X):
+        """Transform feature->value dicts to array or sparse matrix.
+
+        Named features not encountered during fit or fit_transform will be
+        silently ignored.
+
+        Parameters
+        ----------
+        X : Mapping or iterable over Mappings of shape (n_samples,)
+            Dict(s) or Mapping(s) from feature names (arbitrary Python
+            objects) to feature values (strings or convertible to dtype).
+
+        Returns
+        -------
+        Xa : {array, sparse matrix}
+            Feature vectors; always 2-d.
+        """
+        check_is_fitted(self, ["feature_names_", "vocabulary_"])
+        return self._transform(X, fitting=False)
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        check_is_fitted(self, "feature_names_")
+        if any(not isinstance(name, str) for name in self.feature_names_):
+            feature_names = [str(name) for name in self.feature_names_]
+        else:
+            feature_names = self.feature_names_
+        return np.asarray(feature_names, dtype=object)
+
+    def restrict(self, support, indices=False):
+        """Restrict the features to those in support using feature selection.
+
+        This function modifies the estimator in-place.
+
+        Parameters
+        ----------
+        support : array-like
+            Boolean mask or list of indices (as returned by the get_support
+            member of feature selectors).
+        indices : bool, default=False
+            Whether support is a list of indices.
+
+        Returns
+        -------
+        self : object
+            DictVectorizer class instance.
+
+        Examples
+        --------
+        >>> from sklearn.feature_extraction import DictVectorizer
+        >>> from sklearn.feature_selection import SelectKBest, chi2
+        >>> v = DictVectorizer()
+        >>> D = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}]
+        >>> X = v.fit_transform(D)
+        >>> support = SelectKBest(chi2, k=2).fit(X, [0, 1])
+        >>> v.get_feature_names_out()
+        array(['bar', 'baz', 'foo'], ...)
+        >>> v.restrict(support.get_support())
+        DictVectorizer()
+        >>> v.get_feature_names_out()
+        array(['bar', 'foo'], ...)
+        """
+        check_is_fitted(self, "feature_names_")
+
+        if not indices:
+            support = np.where(support)[0]
+
+        names = self.feature_names_
+        new_vocab = {}
+        for i in support:
+            new_vocab[names[i]] = len(new_vocab)
+
+        self.vocabulary_ = new_vocab
+        self.feature_names_ = [
+            f for f, i in sorted(new_vocab.items(), key=itemgetter(1))
+        ]
+
+        return self
+
+    def _more_tags(self):
+        return {"X_types": ["dict"]}

env-llmeval/lib/python3.10/site-packages/sklearn/feature_extraction/_hash.py

@@ -0,0 +1,197 @@
+# Author: Lars Buitinck
+# License: BSD 3 clause
+
+from itertools import chain
+from numbers import Integral
+
+import numpy as np
+import scipy.sparse as sp
+
+from ..base import BaseEstimator, TransformerMixin, _fit_context
+from ..utils._param_validation import Interval, StrOptions
+from ._hashing_fast import transform as _hashing_transform
+
+
+def _iteritems(d):
+    """Like d.iteritems, but accepts any collections.Mapping."""
+    return d.iteritems() if hasattr(d, "iteritems") else d.items()
+
+
+class FeatureHasher(TransformerMixin, BaseEstimator):
+    """Implements feature hashing, aka the hashing trick.
+
+    This class turns sequences of symbolic feature names (strings) into
+    scipy.sparse matrices, using a hash function to compute the matrix column
+    corresponding to a name. The hash function employed is the signed 32-bit
+    version of Murmurhash3.
+
+    Feature names of type byte string are used as-is. Unicode strings are
+    converted to UTF-8 first, but no Unicode normalization is done.
+    Feature values must be (finite) numbers.
+
+    This class is a low-memory alternative to DictVectorizer and
+    CountVectorizer, intended for large-scale (online) learning and situations
+    where memory is tight, e.g. when running prediction code on embedded
+    devices.
+
+    For an efficiency comparison of the different feature extractors, see
+    :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`.
+
+    Read more in the :ref:`User Guide <feature_hashing>`.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    n_features : int, default=2**20
+        The number of features (columns) in the output matrices. Small numbers
+        of features are likely to cause hash collisions, but large numbers
+        will cause larger coefficient dimensions in linear learners.
+    input_type : str, default='dict'
+        Choose a string from {'dict', 'pair', 'string'}.
+        Either "dict" (the default) to accept dictionaries over
+        (feature_name, value); "pair" to accept pairs of (feature_name, value);
+        or "string" to accept single strings.
+        feature_name should be a string, while value should be a number.
+        In the case of "string", a value of 1 is implied.
+        The feature_name is hashed to find the appropriate column for the
+        feature. The value's sign might be flipped in the output (but see
+        non_negative, below).
+    dtype : numpy dtype, default=np.float64
+        The type of feature values. Passed to scipy.sparse matrix constructors
+        as the dtype argument. Do not set this to bool, np.boolean or any
+        unsigned integer type.
+    alternate_sign : bool, default=True
+        When True, an alternating sign is added to the features as to
+        approximately conserve the inner product in the hashed space even for
+        small n_features. This approach is similar to sparse random projection.
+
+        .. versionchanged:: 0.19
+            ``alternate_sign`` replaces the now deprecated ``non_negative``
+            parameter.
+
+    See Also
+    --------
+    DictVectorizer : Vectorizes string-valued features using a hash table.
+    sklearn.preprocessing.OneHotEncoder : Handles nominal/categorical features.
+
+    Notes
+    -----
+    This estimator is :term:`stateless` and does not need to be fitted.
+    However, we recommend to call :meth:`fit_transform` instead of
+    :meth:`transform`, as parameter validation is only performed in
+    :meth:`fit`.
+
+    Examples
+    --------
+    >>> from sklearn.feature_extraction import FeatureHasher
+    >>> h = FeatureHasher(n_features=10)
+    >>> D = [{'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5}]
+    >>> f = h.transform(D)
+    >>> f.toarray()
+    array([[ 0.,  0., -4., -1.,  0.,  0.,  0.,  0.,  0.,  2.],
+           [ 0.,  0.,  0., -2., -5.,  0.,  0.,  0.,  0.,  0.]])
+
+    With `input_type="string"`, the input must be an iterable over iterables of
+    strings:
+
+    >>> h = FeatureHasher(n_features=8, input_type="string")
+    >>> raw_X = [["dog", "cat", "snake"], ["snake", "dog"], ["cat", "bird"]]
+    >>> f = h.transform(raw_X)
+    >>> f.toarray()
+    array([[ 0.,  0.,  0., -1.,  0., -1.,  0.,  1.],
+           [ 0.,  0.,  0., -1.,  0., -1.,  0.,  0.],
+           [ 0., -1.,  0.,  0.,  0.,  0.,  0.,  1.]])
+    """
+
+    _parameter_constraints: dict = {
+        "n_features": [Interval(Integral, 1, np.iinfo(np.int32).max, closed="both")],
+        "input_type": [StrOptions({"dict", "pair", "string"})],
+        "dtype": "no_validation",  # delegate to numpy
+        "alternate_sign": ["boolean"],
+    }
+
+    def __init__(
+        self,
+        n_features=(2**20),
+        *,
+        input_type="dict",
+        dtype=np.float64,
+        alternate_sign=True,
+    ):
+        self.dtype = dtype
+        self.input_type = input_type
+        self.n_features = n_features
+        self.alternate_sign = alternate_sign
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X=None, y=None):
+        """Only validates estimator's parameters.
+
+        This method allows to: (i) validate the estimator's parameters and
+        (ii) be consistent with the scikit-learn transformer API.
+
+        Parameters
+        ----------
+        X : Ignored
+            Not used, present here for API consistency by convention.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            FeatureHasher class instance.
+        """
+        return self
+
+    def transform(self, raw_X):
+        """Transform a sequence of instances to a scipy.sparse matrix.
+
+        Parameters
+        ----------
+        raw_X : iterable over iterable over raw features, length = n_samples
+            Samples. Each sample must be iterable an (e.g., a list or tuple)
+            containing/generating feature names (and optionally values, see
+            the input_type constructor argument) which will be hashed.
+            raw_X need not support the len function, so it can be the result
+            of a generator; n_samples is determined on the fly.
+
+        Returns
+        -------
+        X : sparse matrix of shape (n_samples, n_features)
+            Feature matrix, for use with estimators or further transformers.
+        """
+        raw_X = iter(raw_X)
+        if self.input_type == "dict":
+            raw_X = (_iteritems(d) for d in raw_X)
+        elif self.input_type == "string":
+            first_raw_X = next(raw_X)
+            if isinstance(first_raw_X, str):
+                raise ValueError(
+                    "Samples can not be a single string. The input must be an iterable"
+                    " over iterables of strings."
+                )
+            raw_X_ = chain([first_raw_X], raw_X)
+            raw_X = (((f, 1) for f in x) for x in raw_X_)
+
+        indices, indptr, values = _hashing_transform(
+            raw_X, self.n_features, self.dtype, self.alternate_sign, seed=0
+        )
+        n_samples = indptr.shape[0] - 1
+
+        if n_samples == 0:
+            raise ValueError("Cannot vectorize empty sequence.")
+
+        X = sp.csr_matrix(
+            (values, indices, indptr),
+            dtype=self.dtype,
+            shape=(n_samples, self.n_features),
+        )
+        X.sum_duplicates()  # also sorts the indices
+
+        return X
+
+    def _more_tags(self):
+        return {"X_types": [self.input_type]}

env-llmeval/lib/python3.10/site-packages/sklearn/feature_extraction/_stop_words.py

@@ -0,0 +1,325 @@
+# This list of English stop words is taken from the "Glasgow Information
+# Retrieval Group". The original list can be found at
+# http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words
+ENGLISH_STOP_WORDS = frozenset(
+    [
+        "a",
+        "about",
+        "above",
+        "across",
+        "after",
+        "afterwards",
+        "again",
+        "against",
+        "all",
+        "almost",
+        "alone",
+        "along",
+        "already",
+        "also",
+        "although",
+        "always",
+        "am",
+        "among",
+        "amongst",
+        "amoungst",
+        "amount",
+        "an",
+        "and",
+        "another",
+        "any",
+        "anyhow",
+        "anyone",
+        "anything",
+        "anyway",
+        "anywhere",
+        "are",
+        "around",
+        "as",
+        "at",
+        "back",
+        "be",
+        "became",
+        "because",
+        "become",
+        "becomes",
+        "becoming",
+        "been",
+        "before",
+        "beforehand",
+        "behind",
+        "being",
+        "below",
+        "beside",
+        "besides",
+        "between",
+        "beyond",
+        "bill",
+        "both",
+        "bottom",
+        "but",
+        "by",
+        "call",
+        "can",
+        "cannot",
+        "cant",
+        "co",
+        "con",
+        "could",
+        "couldnt",
+        "cry",
+        "de",
+        "describe",
+        "detail",
+        "do",
+        "done",
+        "down",
+        "due",
+        "during",
+        "each",
+        "eg",
+        "eight",
+        "either",
+        "eleven",
+        "else",
+        "elsewhere",
+        "empty",
+        "enough",
+        "etc",
+        "even",
+        "ever",
+        "every",
+        "everyone",
+        "everything",
+        "everywhere",
+        "except",
+        "few",
+        "fifteen",
+        "fifty",
+        "fill",
+        "find",
+        "fire",
+        "first",
+        "five",
+        "for",
+        "former",
+        "formerly",
+        "forty",
+        "found",
+        "four",
+        "from",
+        "front",
+        "full",
+        "further",
+        "get",
+        "give",
+        "go",
+        "had",
+        "has",
+        "hasnt",
+        "have",
+        "he",
+        "hence",
+        "her",
+        "here",
+        "hereafter",
+        "hereby",
+        "herein",
+        "hereupon",
+        "hers",
+        "herself",
+        "him",
+        "himself",
+        "his",
+        "how",
+        "however",
+        "hundred",
+        "i",
+        "ie",
+        "if",
+        "in",
+        "inc",
+        "indeed",
+        "interest",
+        "into",
+        "is",
+        "it",
+        "its",
+        "itself",
+        "keep",
+        "last",
+        "latter",
+        "latterly",
+        "least",
+        "less",
+        "ltd",
+        "made",
+        "many",
+        "may",
+        "me",
+        "meanwhile",
+        "might",
+        "mill",
+        "mine",
+        "more",
+        "moreover",
+        "most",
+        "mostly",
+        "move",
+        "much",
+        "must",
+        "my",
+        "myself",
+        "name",
+        "namely",
+        "neither",
+        "never",
+        "nevertheless",
+        "next",
+        "nine",
+        "no",
+        "nobody",
+        "none",
+        "noone",
+        "nor",
+        "not",
+        "nothing",
+        "now",
+        "nowhere",
+        "of",
+        "off",
+        "often",
+        "on",
+        "once",
+        "one",
+        "only",
+        "onto",
+        "or",
+        "other",
+        "others",
+        "otherwise",
+        "our",
+        "ours",
+        "ourselves",
+        "out",
+        "over",
+        "own",
+        "part",
+        "per",
+        "perhaps",
+        "please",
+        "put",
+        "rather",
+        "re",
+        "same",
+        "see",
+        "seem",
+        "seemed",
+        "seeming",
+        "seems",
+        "serious",
+        "several",
+        "she",
+        "should",
+        "show",
+        "side",
+        "since",
+        "sincere",
+        "six",
+        "sixty",
+        "so",
+        "some",
+        "somehow",
+        "someone",
+        "something",
+        "sometime",
+        "sometimes",
+        "somewhere",
+        "still",
+        "such",
+        "system",
+        "take",
+        "ten",
+        "than",
+        "that",
+        "the",
+        "their",
+        "them",
+        "themselves",
+        "then",
+        "thence",
+        "there",
+        "thereafter",
+        "thereby",
+        "therefore",
+        "therein",
+        "thereupon",
+        "these",
+        "they",
+        "thick",
+        "thin",
+        "third",
+        "this",
+        "those",
+        "though",
+        "three",
+        "through",
+        "throughout",
+        "thru",
+        "thus",
+        "to",
+        "together",
+        "too",
+        "top",
+        "toward",
+        "towards",
+        "twelve",
+        "twenty",
+        "two",
+        "un",
+        "under",
+        "until",
+        "up",
+        "upon",
+        "us",
+        "very",
+        "via",
+        "was",
+        "we",
+        "well",
+        "were",
+        "what",
+        "whatever",
+        "when",
+        "whence",
+        "whenever",
+        "where",
+        "whereafter",
+        "whereas",
+        "whereby",
+        "wherein",
+        "whereupon",
+        "wherever",
+        "whether",
+        "which",
+        "while",
+        "whither",
+        "who",
+        "whoever",
+        "whole",
+        "whom",
+        "whose",
+        "why",
+        "will",
+        "with",
+        "within",
+        "without",
+        "would",
+        "yet",
+        "you",
+        "your",
+        "yours",
+        "yourself",
+        "yourselves",
+    ]
+)

env-llmeval/lib/python3.10/site-packages/sklearn/feature_extraction/image.py

@@ -0,0 +1,671 @@
+"""
+The :mod:`sklearn.feature_extraction.image` submodule gathers utilities to
+extract features from images.
+"""
+
+# Authors: Emmanuelle Gouillart <[email protected]>
+#          Gael Varoquaux <[email protected]>
+#          Olivier Grisel
+#          Vlad Niculae
+# License: BSD 3 clause
+
+from itertools import product
+from numbers import Integral, Number, Real
+
+import numpy as np
+from numpy.lib.stride_tricks import as_strided
+from scipy import sparse
+
+from ..base import BaseEstimator, TransformerMixin, _fit_context
+from ..utils import check_array, check_random_state
+from ..utils._param_validation import Hidden, Interval, RealNotInt, validate_params
+
+__all__ = [
+    "PatchExtractor",
+    "extract_patches_2d",
+    "grid_to_graph",
+    "img_to_graph",
+    "reconstruct_from_patches_2d",
+]
+
+###############################################################################
+# From an image to a graph
+
+
+def _make_edges_3d(n_x, n_y, n_z=1):
+    """Returns a list of edges for a 3D image.
+
+    Parameters
+    ----------
+    n_x : int
+        The size of the grid in the x direction.
+    n_y : int
+        The size of the grid in the y direction.
+    n_z : integer, default=1
+        The size of the grid in the z direction, defaults to 1
+    """
+    vertices = np.arange(n_x * n_y * n_z).reshape((n_x, n_y, n_z))
+    edges_deep = np.vstack((vertices[:, :, :-1].ravel(), vertices[:, :, 1:].ravel()))
+    edges_right = np.vstack((vertices[:, :-1].ravel(), vertices[:, 1:].ravel()))
+    edges_down = np.vstack((vertices[:-1].ravel(), vertices[1:].ravel()))
+    edges = np.hstack((edges_deep, edges_right, edges_down))
+    return edges
+
+
+def _compute_gradient_3d(edges, img):
+    _, n_y, n_z = img.shape
+    gradient = np.abs(
+        img[
+            edges[0] // (n_y * n_z),
+            (edges[0] % (n_y * n_z)) // n_z,
+            (edges[0] % (n_y * n_z)) % n_z,
+        ]
+        - img[
+            edges[1] // (n_y * n_z),
+            (edges[1] % (n_y * n_z)) // n_z,
+            (edges[1] % (n_y * n_z)) % n_z,
+        ]
+    )
+    return gradient
+
+
+# XXX: Why mask the image after computing the weights?
+
+
+def _mask_edges_weights(mask, edges, weights=None):
+    """Apply a mask to edges (weighted or not)"""
+    inds = np.arange(mask.size)
+    inds = inds[mask.ravel()]
+    ind_mask = np.logical_and(np.isin(edges[0], inds), np.isin(edges[1], inds))
+    edges = edges[:, ind_mask]
+    if weights is not None:
+        weights = weights[ind_mask]
+    if len(edges.ravel()):
+        maxval = edges.max()
+    else:
+        maxval = 0
+    order = np.searchsorted(np.flatnonzero(mask), np.arange(maxval + 1))
+    edges = order[edges]
+    if weights is None:
+        return edges
+    else:
+        return edges, weights
+
+
+def _to_graph(
+    n_x, n_y, n_z, mask=None, img=None, return_as=sparse.coo_matrix, dtype=None
+):
+    """Auxiliary function for img_to_graph and grid_to_graph"""
+    edges = _make_edges_3d(n_x, n_y, n_z)
+
+    if dtype is None:  # To not overwrite input dtype
+        if img is None:
+            dtype = int
+        else:
+            dtype = img.dtype
+
+    if img is not None:
+        img = np.atleast_3d(img)
+        weights = _compute_gradient_3d(edges, img)
+        if mask is not None:
+            edges, weights = _mask_edges_weights(mask, edges, weights)
+            diag = img.squeeze()[mask]
+        else:
+            diag = img.ravel()
+        n_voxels = diag.size
+    else:
+        if mask is not None:
+            mask = mask.astype(dtype=bool, copy=False)
+            edges = _mask_edges_weights(mask, edges)
+            n_voxels = np.sum(mask)
+        else:
+            n_voxels = n_x * n_y * n_z
+        weights = np.ones(edges.shape[1], dtype=dtype)
+        diag = np.ones(n_voxels, dtype=dtype)
+
+    diag_idx = np.arange(n_voxels)
+    i_idx = np.hstack((edges[0], edges[1]))
+    j_idx = np.hstack((edges[1], edges[0]))
+    graph = sparse.coo_matrix(
+        (
+            np.hstack((weights, weights, diag)),
+            (np.hstack((i_idx, diag_idx)), np.hstack((j_idx, diag_idx))),
+        ),
+        (n_voxels, n_voxels),
+        dtype=dtype,
+    )
+    if return_as is np.ndarray:
+        return graph.toarray()
+    return return_as(graph)
+
+
+@validate_params(
+    {
+        "img": ["array-like"],
+        "mask": [None, np.ndarray],
+        "return_as": [type],
+        "dtype": "no_validation",  # validation delegated to numpy
+    },
+    prefer_skip_nested_validation=True,
+)
+def img_to_graph(img, *, mask=None, return_as=sparse.coo_matrix, dtype=None):
+    """Graph of the pixel-to-pixel gradient connections.
+
+    Edges are weighted with the gradient values.
+
+    Read more in the :ref:`User Guide <image_feature_extraction>`.
+
+    Parameters
+    ----------
+    img : array-like of shape (height, width) or (height, width, channel)
+        2D or 3D image.
+    mask : ndarray of shape (height, width) or \
+            (height, width, channel), dtype=bool, default=None
+        An optional mask of the image, to consider only part of the
+        pixels.
+    return_as : np.ndarray or a sparse matrix class, \
+            default=sparse.coo_matrix
+        The class to use to build the returned adjacency matrix.
+    dtype : dtype, default=None
+        The data of the returned sparse matrix. By default it is the
+        dtype of img.
+
+    Returns
+    -------
+    graph : ndarray or a sparse matrix class
+        The computed adjacency matrix.
+
+    Notes
+    -----
+    For scikit-learn versions 0.14.1 and prior, return_as=np.ndarray was
+    handled by returning a dense np.matrix instance.  Going forward, np.ndarray
+    returns an np.ndarray, as expected.
+
+    For compatibility, user code relying on this method should wrap its
+    calls in ``np.asarray`` to avoid type issues.
+    """
+    img = np.atleast_3d(img)
+    n_x, n_y, n_z = img.shape
+    return _to_graph(n_x, n_y, n_z, mask, img, return_as, dtype)
+
+
+@validate_params(
+    {
+        "n_x": [Interval(Integral, left=1, right=None, closed="left")],
+        "n_y": [Interval(Integral, left=1, right=None, closed="left")],
+        "n_z": [Interval(Integral, left=1, right=None, closed="left")],
+        "mask": [None, np.ndarray],
+        "return_as": [type],
+        "dtype": "no_validation",  # validation delegated to numpy
+    },
+    prefer_skip_nested_validation=True,
+)
+def grid_to_graph(
+    n_x, n_y, n_z=1, *, mask=None, return_as=sparse.coo_matrix, dtype=int
+):
+    """Graph of the pixel-to-pixel connections.
+
+    Edges exist if 2 voxels are connected.
+
+    Parameters
+    ----------
+    n_x : int
+        Dimension in x axis.
+    n_y : int
+        Dimension in y axis.
+    n_z : int, default=1
+        Dimension in z axis.
+    mask : ndarray of shape (n_x, n_y, n_z), dtype=bool, default=None
+        An optional mask of the image, to consider only part of the
+        pixels.
+    return_as : np.ndarray or a sparse matrix class, \
+            default=sparse.coo_matrix
+        The class to use to build the returned adjacency matrix.
+    dtype : dtype, default=int
+        The data of the returned sparse matrix. By default it is int.
+
+    Returns
+    -------
+    graph : np.ndarray or a sparse matrix class
+        The computed adjacency matrix.
+
+    Notes
+    -----
+    For scikit-learn versions 0.14.1 and prior, return_as=np.ndarray was
+    handled by returning a dense np.matrix instance.  Going forward, np.ndarray
+    returns an np.ndarray, as expected.
+
+    For compatibility, user code relying on this method should wrap its
+    calls in ``np.asarray`` to avoid type issues.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.feature_extraction.image import grid_to_graph
+    >>> shape_img = (4, 4, 1)
+    >>> mask = np.zeros(shape=shape_img, dtype=bool)
+    >>> mask[[1, 2], [1, 2], :] = True
+    >>> graph = grid_to_graph(*shape_img, mask=mask)
+    >>> print(graph)
+      (0, 0)    1
+      (1, 1)    1
+    """
+    return _to_graph(n_x, n_y, n_z, mask=mask, return_as=return_as, dtype=dtype)
+
+
+###############################################################################
+# From an image to a set of small image patches
+
+
+def _compute_n_patches(i_h, i_w, p_h, p_w, max_patches=None):
+    """Compute the number of patches that will be extracted in an image.
+
+    Read more in the :ref:`User Guide <image_feature_extraction>`.
+
+    Parameters
+    ----------
+    i_h : int
+        The image height
+    i_w : int
+        The image with
+    p_h : int
+        The height of a patch
+    p_w : int
+        The width of a patch
+    max_patches : int or float, default=None
+        The maximum number of patches to extract. If `max_patches` is a float
+        between 0 and 1, it is taken to be a proportion of the total number
+        of patches. If `max_patches` is None, all possible patches are extracted.
+    """
+    n_h = i_h - p_h + 1
+    n_w = i_w - p_w + 1
+    all_patches = n_h * n_w
+
+    if max_patches:
+        if isinstance(max_patches, (Integral)) and max_patches < all_patches:
+            return max_patches
+        elif isinstance(max_patches, (Integral)) and max_patches >= all_patches:
+            return all_patches
+        elif isinstance(max_patches, (Real)) and 0 < max_patches < 1:
+            return int(max_patches * all_patches)
+        else:
+            raise ValueError("Invalid value for max_patches: %r" % max_patches)
+    else:
+        return all_patches
+
+
+def _extract_patches(arr, patch_shape=8, extraction_step=1):
+    """Extracts patches of any n-dimensional array in place using strides.
+
+    Given an n-dimensional array it will return a 2n-dimensional array with
+    the first n dimensions indexing patch position and the last n indexing
+    the patch content. This operation is immediate (O(1)). A reshape
+    performed on the first n dimensions will cause numpy to copy data, leading
+    to a list of extracted patches.
+
+    Read more in the :ref:`User Guide <image_feature_extraction>`.
+
+    Parameters
+    ----------
+    arr : ndarray
+        n-dimensional array of which patches are to be extracted
+
+    patch_shape : int or tuple of length arr.ndim.default=8
+        Indicates the shape of the patches to be extracted. If an
+        integer is given, the shape will be a hypercube of
+        sidelength given by its value.
+
+    extraction_step : int or tuple of length arr.ndim, default=1
+        Indicates step size at which extraction shall be performed.
+        If integer is given, then the step is uniform in all dimensions.
+
+
+    Returns
+    -------
+    patches : strided ndarray
+        2n-dimensional array indexing patches on first n dimensions and
+        containing patches on the last n dimensions. These dimensions
+        are fake, but this way no data is copied. A simple reshape invokes
+        a copying operation to obtain a list of patches:
+        result.reshape([-1] + list(patch_shape))
+    """
+
+    arr_ndim = arr.ndim
+
+    if isinstance(patch_shape, Number):
+        patch_shape = tuple([patch_shape] * arr_ndim)
+    if isinstance(extraction_step, Number):
+        extraction_step = tuple([extraction_step] * arr_ndim)
+
+    patch_strides = arr.strides
+
+    slices = tuple(slice(None, None, st) for st in extraction_step)
+    indexing_strides = arr[slices].strides
+
+    patch_indices_shape = (
+        (np.array(arr.shape) - np.array(patch_shape)) // np.array(extraction_step)
+    ) + 1
+
+    shape = tuple(list(patch_indices_shape) + list(patch_shape))
+    strides = tuple(list(indexing_strides) + list(patch_strides))
+
+    patches = as_strided(arr, shape=shape, strides=strides)
+    return patches
+
+
+@validate_params(
+    {
+        "image": [np.ndarray],
+        "patch_size": [tuple, list],
+        "max_patches": [
+            Interval(RealNotInt, 0, 1, closed="neither"),
+            Interval(Integral, 1, None, closed="left"),
+            None,
+        ],
+        "random_state": ["random_state"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def extract_patches_2d(image, patch_size, *, max_patches=None, random_state=None):
+    """Reshape a 2D image into a collection of patches.
+
+    The resulting patches are allocated in a dedicated array.
+
+    Read more in the :ref:`User Guide <image_feature_extraction>`.
+
+    Parameters
+    ----------
+    image : ndarray of shape (image_height, image_width) or \
+        (image_height, image_width, n_channels)
+        The original image data. For color images, the last dimension specifies
+        the channel: a RGB image would have `n_channels=3`.
+
+    patch_size : tuple of int (patch_height, patch_width)
+        The dimensions of one patch.
+
+    max_patches : int or float, default=None
+        The maximum number of patches to extract. If `max_patches` is a float
+        between 0 and 1, it is taken to be a proportion of the total number
+        of patches. If `max_patches` is None it corresponds to the total number
+        of patches that can be extracted.
+
+    random_state : int, RandomState instance, default=None
+        Determines the random number generator used for random sampling when
+        `max_patches` is not None. Use an int to make the randomness
+        deterministic.
+        See :term:`Glossary <random_state>`.
+
+    Returns
+    -------
+    patches : array of shape (n_patches, patch_height, patch_width) or \
+        (n_patches, patch_height, patch_width, n_channels)
+        The collection of patches extracted from the image, where `n_patches`
+        is either `max_patches` or the total number of patches that can be
+        extracted.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_sample_image
+    >>> from sklearn.feature_extraction import image
+    >>> # Use the array data from the first image in this dataset:
+    >>> one_image = load_sample_image("china.jpg")
+    >>> print('Image shape: {}'.format(one_image.shape))
+    Image shape: (427, 640, 3)
+    >>> patches = image.extract_patches_2d(one_image, (2, 2))
+    >>> print('Patches shape: {}'.format(patches.shape))
+    Patches shape: (272214, 2, 2, 3)
+    >>> # Here are just two of these patches:
+    >>> print(patches[1])
+    [[[174 201 231]
+      [174 201 231]]
+     [[173 200 230]
+      [173 200 230]]]
+    >>> print(patches[800])
+    [[[187 214 243]
+      [188 215 244]]
+     [[187 214 243]
+      [188 215 244]]]
+    """
+    i_h, i_w = image.shape[:2]
+    p_h, p_w = patch_size
+
+    if p_h > i_h:
+        raise ValueError(
+            "Height of the patch should be less than the height of the image."
+        )
+
+    if p_w > i_w:
+        raise ValueError(
+            "Width of the patch should be less than the width of the image."
+        )
+
+    image = check_array(image, allow_nd=True)
+    image = image.reshape((i_h, i_w, -1))
+    n_colors = image.shape[-1]
+
+    extracted_patches = _extract_patches(
+        image, patch_shape=(p_h, p_w, n_colors), extraction_step=1
+    )
+
+    n_patches = _compute_n_patches(i_h, i_w, p_h, p_w, max_patches)
+    if max_patches:
+        rng = check_random_state(random_state)
+        i_s = rng.randint(i_h - p_h + 1, size=n_patches)
+        j_s = rng.randint(i_w - p_w + 1, size=n_patches)
+        patches = extracted_patches[i_s, j_s, 0]
+    else:
+        patches = extracted_patches
+
+    patches = patches.reshape(-1, p_h, p_w, n_colors)
+    # remove the color dimension if useless
+    if patches.shape[-1] == 1:
+        return patches.reshape((n_patches, p_h, p_w))
+    else:
+        return patches
+
+
+@validate_params(
+    {"patches": [np.ndarray], "image_size": [tuple, Hidden(list)]},
+    prefer_skip_nested_validation=True,
+)
+def reconstruct_from_patches_2d(patches, image_size):
+    """Reconstruct the image from all of its patches.
+
+    Patches are assumed to overlap and the image is constructed by filling in
+    the patches from left to right, top to bottom, averaging the overlapping
+    regions.
+
+    Read more in the :ref:`User Guide <image_feature_extraction>`.
+
+    Parameters
+    ----------
+    patches : ndarray of shape (n_patches, patch_height, patch_width) or \
+        (n_patches, patch_height, patch_width, n_channels)
+        The complete set of patches. If the patches contain colour information,
+        channels are indexed along the last dimension: RGB patches would
+        have `n_channels=3`.
+
+    image_size : tuple of int (image_height, image_width) or \
+        (image_height, image_width, n_channels)
+        The size of the image that will be reconstructed.
+
+    Returns
+    -------
+    image : ndarray of shape image_size
+        The reconstructed image.
+    """
+    i_h, i_w = image_size[:2]
+    p_h, p_w = patches.shape[1:3]
+    img = np.zeros(image_size)
+    # compute the dimensions of the patches array
+    n_h = i_h - p_h + 1
+    n_w = i_w - p_w + 1
+    for p, (i, j) in zip(patches, product(range(n_h), range(n_w))):
+        img[i : i + p_h, j : j + p_w] += p
+
+    for i in range(i_h):
+        for j in range(i_w):
+            # divide by the amount of overlap
+            # XXX: is this the most efficient way? memory-wise yes, cpu wise?
+            img[i, j] /= float(min(i + 1, p_h, i_h - i) * min(j + 1, p_w, i_w - j))
+    return img
+
+
+class PatchExtractor(TransformerMixin, BaseEstimator):
+    """Extracts patches from a collection of images.
+
+    Read more in the :ref:`User Guide <image_feature_extraction>`.
+
+    .. versionadded:: 0.9
+
+    Parameters
+    ----------
+    patch_size : tuple of int (patch_height, patch_width), default=None
+        The dimensions of one patch. If set to None, the patch size will be
+        automatically set to `(img_height // 10, img_width // 10)`, where
+        `img_height` and `img_width` are the dimensions of the input images.
+
+    max_patches : int or float, default=None
+        The maximum number of patches per image to extract. If `max_patches` is
+        a float in (0, 1), it is taken to mean a proportion of the total number
+        of patches. If set to None, extract all possible patches.
+
+    random_state : int, RandomState instance, default=None
+        Determines the random number generator used for random sampling when
+        `max_patches is not None`. Use an int to make the randomness
+        deterministic.
+        See :term:`Glossary <random_state>`.
+
+    See Also
+    --------
+    reconstruct_from_patches_2d : Reconstruct image from all of its patches.
+
+    Notes
+    -----
+    This estimator is stateless and does not need to be fitted. However, we
+    recommend to call :meth:`fit_transform` instead of :meth:`transform`, as
+    parameter validation is only performed in :meth:`fit`.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_sample_images
+    >>> from sklearn.feature_extraction import image
+    >>> # Use the array data from the second image in this dataset:
+    >>> X = load_sample_images().images[1]
+    >>> X = X[None, ...]
+    >>> print(f"Image shape: {X.shape}")
+    Image shape: (1, 427, 640, 3)
+    >>> pe = image.PatchExtractor(patch_size=(10, 10))
+    >>> pe_trans = pe.transform(X)
+    >>> print(f"Patches shape: {pe_trans.shape}")
+    Patches shape: (263758, 10, 10, 3)
+    >>> X_reconstructed = image.reconstruct_from_patches_2d(pe_trans, X.shape[1:])
+    >>> print(f"Reconstructed shape: {X_reconstructed.shape}")
+    Reconstructed shape: (427, 640, 3)
+    """
+
+    _parameter_constraints: dict = {
+        "patch_size": [tuple, None],
+        "max_patches": [
+            None,
+            Interval(RealNotInt, 0, 1, closed="neither"),
+            Interval(Integral, 1, None, closed="left"),
+        ],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(self, *, patch_size=None, max_patches=None, random_state=None):
+        self.patch_size = patch_size
+        self.max_patches = max_patches
+        self.random_state = random_state
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Only validate the parameters of the estimator.
+
+        This method allows to: (i) validate the parameters of the estimator  and
+        (ii) be consistent with the scikit-learn transformer API.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, image_height, image_width) or \
+                (n_samples, image_height, image_width, n_channels)
+            Array of images from which to extract patches. For color images,
+            the last dimension specifies the channel: a RGB image would have
+            `n_channels=3`.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        return self
+
+    def transform(self, X):
+        """Transform the image samples in `X` into a matrix of patch data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, image_height, image_width) or \
+                (n_samples, image_height, image_width, n_channels)
+            Array of images from which to extract patches. For color images,
+            the last dimension specifies the channel: a RGB image would have
+            `n_channels=3`.
+
+        Returns
+        -------
+        patches : array of shape (n_patches, patch_height, patch_width) or \
+                (n_patches, patch_height, patch_width, n_channels)
+            The collection of patches extracted from the images, where
+            `n_patches` is either `n_samples * max_patches` or the total
+            number of patches that can be extracted.
+        """
+        X = self._validate_data(
+            X=X,
+            ensure_2d=False,
+            allow_nd=True,
+            ensure_min_samples=1,
+            ensure_min_features=1,
+            reset=False,
+        )
+        random_state = check_random_state(self.random_state)
+        n_imgs, img_height, img_width = X.shape[:3]
+        if self.patch_size is None:
+            patch_size = img_height // 10, img_width // 10
+        else:
+            if len(self.patch_size) != 2:
+                raise ValueError(
+                    "patch_size must be a tuple of two integers. Got"
+                    f" {self.patch_size} instead."
+                )
+            patch_size = self.patch_size
+
+        n_imgs, img_height, img_width = X.shape[:3]
+        X = np.reshape(X, (n_imgs, img_height, img_width, -1))
+        n_channels = X.shape[-1]
+
+        # compute the dimensions of the patches array
+        patch_height, patch_width = patch_size
+        n_patches = _compute_n_patches(
+            img_height, img_width, patch_height, patch_width, self.max_patches
+        )
+        patches_shape = (n_imgs * n_patches,) + patch_size
+        if n_channels > 1:
+            patches_shape += (n_channels,)
+
+        # extract the patches
+        patches = np.empty(patches_shape)
+        for ii, image in enumerate(X):
+            patches[ii * n_patches : (ii + 1) * n_patches] = extract_patches_2d(
+                image,
+                patch_size,
+                max_patches=self.max_patches,
+                random_state=random_state,
+            )
+        return patches
+
+    def _more_tags(self):
+        return {"X_types": ["3darray"], "stateless": True}