divik.feature_extraction module

Unsupervised feature extraction methods

class divik.feature_extraction.HistogramEqualization(n_bins=256, n_jobs=-1)[source]

Equalize histogram of the features to increase contrast

Based on https://github.com/scikit-image/scikit-image/blob/master/skimage/exposure/exposure.py#L187-L223

Parameters
n_binsint, default 256

Number of bins for histogram equalization.

n_jobsint, default -1

Number of CPU cores to use during equalization

Attributes
cdf_array

Values of cumulative distribution function for all the features

bins_array

Bin centers for all the features

Methods

fit_transform(X[, y])

Fit to data, then transform it.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

fit

transform
fit(X, y=None)[source]
fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and 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.

get_params(deep=True)

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.

set_params(**params)

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.

transform(X, y=None)[source]
class divik.feature_extraction.KneePCA(whiten=False, refit=False)[source]

Principal component analysis (PCA) with knee method

PCA with automated components selection based on knee method over cumulative explained variance. Remaining components are discarded.

Parameters
whitenbool, optional (default False)

When True (False by default) the pca_.components_ vectors are multiplied by the square root of n_samples and then divided by the singular values 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 sometime improve the predictive accuracy of the downstream estimators by making their data respect some hard-wired assumptions.

refitbool, optional (default False)

When True (False by default) the pca_ is re-fit with the smaller number of components. This could reduce memory footprint, but requires training fitting PCA.

Attributes
pca_PCA

Fit PCA estimator.

n_components_int

The number of selected components.

Methods

fit(X[, y])

Fit the model from data in X.

fit_transform(X[, y])

Fit to data, then transform it.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X)

Transform data back to its original space.

set_params(**params)

Set the parameters of this estimator.

transform(X[, y])

Apply dimensionality reduction to X.
fit(X, y=None)[source]

Fit the model from data in X.

Parameters
Xarray-like, 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.
Returns
selfobject

Returns the instance itself.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and 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.

get_params(deep=True)

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.

inverse_transform(X)[source]

Transform data back to its original space.

In other words, return an input X_original whose transform would be X.

Parameters
Xarray-like, 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, shape (n_samples, n_features)

Notes

If whitening is enabled, inverse_transform will compute the exact inverse operation, which includes reversing whitening.

set_params(**params)

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.

transform(X, y=None)[source]

Apply dimensionality reduction to X.

X is projected on the first principal components previously extracted from a training set.

Parameters
Xarray-like, 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_newarray-like, shape (n_samples, n_components)

Examples

>>> import numpy as np
>>> from divik.feature_extraction import KneePCA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> pca = KneePCA(refit=True)
>>> pca.fit(X)
KneePCA(refit=True)
>>> pca.transform(X)
class divik.feature_extraction.LocallyAdjustedRbfSpectralEmbedding(distance='euclidean', n_components=2, random_state=None, eigen_solver=None, n_neighbors=None, n_jobs=1)[source]

Spectral embedding for non-linear dimensionality reduction.

Forms an affinity matrix given by the specified function and applies spectral decomposition to the corresponding graph laplacian. The resulting transformation is given by the value of the eigenvectors for each data point.

Note : Laplacian Eigenmaps is the actual algorithm implemented here.

Parameters
distance{‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’,
‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’,
‘kulsinski’, ‘mahalanobis’, ‘atching’, ‘minkowski’, ‘rogerstanimoto’,
‘russellrao’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’}

Distance measure, defaults to euclidean. These are the distances supported by scipy package.

n_componentsinteger, default: 2

The dimension of the projected subspace.

random_stateint, RandomState instance or None, optional, default: None

A pseudo random number generator used for the initialization of the lobpcg eigenvectors. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when solver == amg.

eigen_solver{None, ‘arpack’, ‘lobpcg’, or ‘amg’}

The eigenvalue decomposition strategy to use. AMG requires pyamg to be installed. It can be faster on very large, sparse problems, but may also lead to instabilities.

n_neighborsint, defaultmax(n_samples/10 , 1)

Number of nearest neighbors for nearest_neighbors graph building.

n_jobsint, optional (default = 1)

The number of parallel jobs to run. If -1, then the number of jobs is set to the number of CPU cores.

References

Attributes
embedding_array, shape = (n_samples, n_components)

Spectral embedding of the training matrix.

Methods

fit(X[, y])

Fit the model from data in X.

fit_transform(X[, y])

Fit the model from data in X and transform X.

get_params([deep])

Get parameters for this estimator.

save(destination)

Save embedding to a directory

set_params(**params)

Set the parameters of this estimator.

transform
fit(X, y=None)[source]

Fit the model from data in X.

Parameters
Xarray-like, 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.
Returns
selfobject

Returns the instance itself.

fit_transform(X, y=None)[source]

Fit the model from data in X and transform X.

Parameters
Xarray-like, 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.
Returns
X_newarray-like, shape (n_samples, n_components)
get_params(deep=True)

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.

save(destination)[source]

Save embedding to a directory

Parameters
destinationstr

Directory to save the embedding.

set_params(**params)

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.

transform(X, y=None)[source]