IncrementalPCA (original) (raw)
class sklearn.decomposition.IncrementalPCA(n_components=None, *, whiten=False, copy=True, batch_size=None)[source]#
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 isO(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, seeIncremental PCA.
Read more in the User Guide.
Added in version 0.16.
Parameters:
n_componentsint, default=None
Number of components to keep. If n_components is None, then n_components is set to min(n_samples, n_features).
whitenbool, 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.
copybool, default=True
If False, X will be overwritten. copy=False can be used to save memory but is unsafe for general use.
batch_sizeint, default=None
The number of samples to use for each batch. Only used when callingfit. If batch_size is None, then batch_sizeis 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_componentsvariables 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 topartial_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 orhttp://www.miketipping.com/papers/met-mppca.pdf.
**n_components_**int
The estimated number of components. Relevant whenn_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 fit.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
See also
Principal component analysis (PCA).
Kernel Principal component analysis (KPCA).
Sparse Principal Components Analysis (SparsePCA).
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: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.
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)
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 andn_features is the number of features.
yIgnored
Not used, present for API consistency by convention.
Returns:
selfobject
Returns the instance itself.
fit_transform(X, y=None, **fit_params)[source]#
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_paramsand returns a transformed version of X.
Parameters:
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
**fit_paramsdict
Additional fit parameters.
Returns:
X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
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:
covarray of shape=(n_features, n_features)
Estimated covariance of data.
get_feature_names_out(input_features=None)[source]#
Get output feature names for transformation.
The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: ["class_name0", "class_name1", "class_name2"].
Parameters:
input_featuresarray-like of str or None, default=None
Only used to validate feature names with the names seen in fit.
Returns:
feature_names_outndarray of str objects
Transformed feature names.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
get_params(deep=True)[source]#
Get parameters for this estimator.
Parameters:
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
paramsdict
Parameter names mapped to their values.
Compute data precision matrix with the generative model.
Equals the inverse of the covariance but computed with the matrix inversion lemma for efficiency.
Returns:
precisionarray, shape=(n_features, n_features)
Estimated precision of data.
Transform data back to its original space.
In other words, return an input X_original whose transform would be X.
Parameters:
Xarray-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.
partial_fit(X, y=None, check_input=True)[source]#
Incremental fit with X. All of X is processed as a single batch.
Parameters:
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples andn_features is the number of features.
yIgnored
Not used, present for API consistency by convention.
check_inputbool, default=True
Run check_array on X.
Returns:
selfobject
Returns the instance itself.
set_output(*, transform=None)[source]#
Set output container.
See Introducing the set_output APIfor an example on how to use the API.
Parameters:
transform{“default”, “pandas”, “polars”}, default=None
Configure output of transform and fit_transform.
"default": Default output format of a transformer"pandas": DataFrame output"polars": Polars outputNone: Transform configuration is unchanged
Added in version 1.4: "polars" option was added.
Returns:
selfestimator instance
Estimator instance.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Parameters:
**paramsdict
Estimator parameters.
Returns:
selfestimator instance
Estimator instance.
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_newndarray 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)