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