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"""Matrix factorization with Sparse PCA."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: 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 Interval, StrOptions
from ..utils.extmath import svd_flip
from ..utils.validation import check_array, check_is_fitted, validate_data
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 = validate_data(self, 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 = validate_data(self, 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 __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.transformer_tags.preserves_dtype = ["float64", "float32"]
return tags
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)
np.float64(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=True)
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
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)
np.float64(0.9...)
"""
_parameter_constraints: dict = {
**_BaseSparsePCA._parameter_constraints,
"max_iter": [Interval(Integral, 0, None, closed="left")],
"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
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