SparsePCA

class ibex.sklearn.decomposition.SparsePCA(n_components=None, alpha=1, ridge_alpha=0.01, max_iter=1000, tol=1e-08, method='lars', n_jobs=1, U_init=None, V_init=None, verbose=False, random_state=None)

Bases: sklearn.decomposition.sparse_pca.SparsePCA, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute components_.

Example:

>>> import pandas as pd
>>> import numpy as np
>>> from ibex.sklearn import datasets
>>> from ibex.sklearn.decomposition import PCA as PdPCA

>>> iris = datasets.load_iris()
>>> features = iris['feature_names']
>>> iris = pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=features+['class'])

>>> iris[features]
sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2
...

>>> PdPCA(n_components=2).fit(iris[features], iris['class']).transform(iris[features])
    comp_0    comp_1
0   -2.684207 ...0.326607
1   -2.715391 ...0.169557
2   -2.889820 ...0.137346
3   -2.746437 ...0.311124
4   -2.728593 ...0.333925
...

Sparse Principal Components Analysis (SparsePCA)

Finds the set of sparse components that can optimally reconstruct the data. The amount of sparseness is controllable by the coefficient of the L1 penalty, given by the parameter alpha.

Read more in the User Guide.

n_components : int,
Number of sparse atoms to extract.
alpha : float,
Sparsity controlling parameter. Higher values lead to sparser components.
ridge_alpha : float,
Amount of ridge shrinkage to apply in order to improve conditioning when calling the transform method.
max_iter : int,
Maximum number of iterations to perform.
tol : float,
Tolerance for the stopping condition.
method : {‘lars’, ‘cd’}
lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse.
n_jobs : int,
Number of parallel jobs to run.
U_init : array of shape (n_samples, n_components),
Initial values for the loadings for warm restart scenarios.
V_init : array of shape (n_components, n_features),
Initial values for the components for warm restart scenarios.
verbose : int
Controls the verbosity; the higher, the more messages. Defaults to 0.
random_state : int, RandomState instance or None, optional (default=None)
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.
components_ : array, [n_components, n_features]
Sparse components extracted from the data.
error_ : array
Vector of errors at each iteration.
n_iter_ : int
Number of iterations run.

PCA MiniBatchSparsePCA DictionaryLearning

fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the model from data in X.

X : array-like, shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and n_features is the number of features.

y : Ignored.

self : object
Returns the instance itself.
fit_transform(X, y=None, **fit_params)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
transform(X, ridge_alpha='deprecated')[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Least Squares projection of the data onto the sparse components.

To avoid instability issues in case the system is under-determined, regularization can be applied (Ridge regression) via the ridge_alpha parameter.

Note that Sparse PCA components orthogonality is not enforced as in PCA hence one cannot use a simple linear projection.

X : array of shape (n_samples, n_features)
Test data to be transformed, must have the same number of features as the data used to train the model.
ridge_alpha : float, default: 0.01

Amount of ridge shrinkage to apply in order to improve conditioning.

Deprecated since version 0.19: This parameter will be removed in 0.21. Specify ridge_alpha in the SparsePCA constructor.

X_new array, shape (n_samples, n_components)
Transformed data.