mojolearn.IncrementalPCA#

class mojolearn.IncrementalPCA(n_components=None, *, whiten=False, copy=True, batch_size=None, numeric_mode=None)#

Incremental principal component analysis, fitted batch by batch.

Follows scikit-learn’s IncrementalPCA: the mean, variance and components are updated one batch at a time, so a dataset larger than memory can be fitted through partial_fit. Component signs follow scikit-learn’s svd_flip on the rows of the components. A scipy.sparse input is densified one batch at a time in fit and whole in partial_fit and transform.

Parameters:

  • n_components (int or None, default None) – Number of components to keep. None keeps n_features (or, in a later partial_fit, the number already fitted). Must be between 1 and n_features and at most the number of rows in each batch.

  • whiten (bool, default False) – Divide the projected data by the square root of each component’s explained variance.

  • copy (bool, default True) – Accepted for scikit-learn compatibility; the input is never modified.

  • batch_size (int or None, default None) – Rows per batch in fit. None uses 5 * n_features.

  • numeric_mode ({“identical”, “fast”} or None, default None) – Numeric mode for this estimator. None uses the package default ("identical" unless changed). In identical mode, mojolearn returns the same bits on Apple, NVIDIA and AMD GPUs. "fast" is available on Apple GPUs only.

Variables:

  • components_ (ndarray of shape (n_components, n_features)) – Principal axes, sorted by decreasing explained variance.

  • explained_variance_ (ndarray of shape (n_components,))

  • explained_variance_ratio_ (ndarray of shape (n_components,))

  • singular_values_ (ndarray of shape (n_components,))

  • mean_ (ndarray of shape (n_features,))

  • var_ (ndarray of shape (n_features,))

  • noise_variance_ (float)

  • n_components_ (int)

  • n_samples_seen_ (int)

  • n_features_in_ (int)

  • batch_size_ (int)

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).random((1000, 8), dtype=np.float32)
>>> model = mojolearn.IncrementalPCA(n_components=3, batch_size=200).fit(X)
>>> model.transform(X).shape
(1000, 3)

See also

PCA, KernelPCA, TruncatedSVD, SparsePCA

User guide: Decomposition and embeddings

Choosing an estimator

Methods

fit(X[, y])

Fit the model on X in batches of batch_size rows.

fit_transform(X[, y])

Fit to X, then return transform(X).

get_params([deep])

Return this estimator's constructor parameters.

inverse_transform(X)

Map projected data back to the original feature space.

partial_fit(X[, y])

Update the model with one more batch.

set_params(**params)

Set constructor parameters by keyword.

transform(X)

Project X onto the fitted components.
fit(X, y=None)#

Fit the model on X in batches of batch_size rows.

Parameters:

  • X (array-like or scipy.sparse matrix of shape (n_samples, n_features)) – Training data. Any earlier fit is discarded.

  • y (ignored)

Returns:

self

Raises:

ValueError – If X is empty or n_components is out of range.

partial_fit(X, y=None)#

Update the model with one more batch.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – The batch. n_features must match earlier batches and n_samples must be at least n_components.

  • y (ignored)

Returns:

self

Raises:

ValueError – If the number of features changed or n_components is out of range for this batch.

transform(X)#

Project X onto the fitted components.

Parameters:

X (array-like of shape (n_samples, n_features))

Returns:

ndarray of shape (n_samples, n_components), float32

Raises:

  • RuntimeError – If the model is not fitted.

  • ValueError – If X has a different number of features than the fitted data.

inverse_transform(X)#

Map projected data back to the original feature space.

Parameters:

X (array-like of shape (n_samples, n_components))

Returns:

ndarray of shape (n_samples, n_features), float32

fit_transform(X, y=None)#

Fit to X, then return transform(X).

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Training data.

  • y (ignored)

Returns:

ndarray of shape (n_samples, n_components), float32

get_params(deep=True)#

Return this estimator’s constructor parameters.

Parameters:

deep (bool, default True) – Accepted for scikit-learn compatibility; it has no effect.

Returns:

dict – Parameter name to current value.

set_params(**params)#

Set constructor parameters by keyword.

Raises:

ValueError – If a keyword is not a parameter of this estimator.

Returns:

self