class dask_ml.decomposition.IncrementalPCA(n_components=None, whiten=False, copy=True, batch_size=None, svd_solver='auto', iterated_power=0, random_state=None)

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. Read more in the User Guide. .. versionadded:: 0.16

n_componentsint or None, (default=None)

Number of components to keep. If n_components `` is ``None, then n_components is set to min(n_samples, n_features).

whitenbool, optional

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 or None, (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.

svd_solverstring {‘auto’, ‘full’, ‘tsqr’, ‘randomized’}
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.

full :

run exact full SVD and select the components by postprocessing

randomized :

run randomized SVD by using da.linalg.svd_compressed.

iterated_power: integer
random_state: None or integer

Parameters used for randomized svd.

components_array, shape (n_components, n_features)

Components with maximum variance.

explained_variance_array, shape (n_components,)

Variance explained by each of the selected components.

explained_variance_ratio_array, 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_array, 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_array, shape (n_features,)

Per-feature empirical mean, aggregate over calls to partial_fit.

var_array, shape (n_features,)

Per-feature empirical variance, aggregate over calls to partial_fit.


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.


The estimated number of components. Relevant when n_components=None.


The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across partial_fit calls.


fit(X[, y])

Fit the model with X.

fit_transform(X[, y])

Fit the model with X and apply the dimensionality reduction on X.


Compute data covariance with the generative model.


Get output feature names for transformation.


Get metadata routing of this object.


Get parameters for this estimator.


Compute data precision matrix with the generative model.


Transform data back to its original space.

partial_fit(X[, y, check_input])

Incremental fit with X.

score(X[, y])

Return the average log-likelihood of all samples.


Return the log-likelihood of each sample.

set_output(*[, transform])

Set output container.


Set the parameters of this estimator.

set_partial_fit_request(*[, check_input])

Request metadata passed to the partial_fit method.


Apply dimensionality reduction on X.

__init__(n_components=None, whiten=False, copy=True, batch_size=None, svd_solver='auto', iterated_power=0, random_state=None)