SelectFpr

class ibex.sklearn.feature_selection.SelectFpr(score_func=<function f_classif>, alpha=0.05)

Bases: sklearn.feature_selection.univariate_selection.SelectFpr, ibex._base.FrameMixin

Note

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

Filter: Select the pvalues below alpha based on a FPR test.

FPR test stands for False Positive Rate test. It controls the total amount of false detections.

Read more in the User Guide.

score_func : callable
Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues). Default is f_classif (see below “See also”). The default function only works with classification tasks.
alpha : float, optional
The highest p-value for features to be kept.
scores_ : array-like, shape=(n_features,)
Scores of features.
pvalues_ : array-like, shape=(n_features,)
p-values of feature scores.

f_classif: ANOVA F-value between label/feature for classification tasks. chi2: Chi-squared stats of non-negative features for classification tasks. mutual_info_classif: f_regression: F-value between label/feature for regression tasks. mutual_info_regression: Mutual information between features and the target. SelectPercentile: Select features based on percentile of the highest scores. SelectKBest: Select features based on the k highest scores. SelectFdr: Select features based on an estimated false discovery rate. SelectFwe: Select features based on family-wise error rate. GenericUnivariateSelect: Univariate feature selector with configurable mode.

fit(X, y)

Note

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

Run score function on (X, y) and get the appropriate features.

X : array-like, shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples]
The target values (class labels in classification, real numbers in regression).
self : object
Returns self.
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.
inverse_transform(X)

Note

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

Reverse the transformation operation

X : array of shape [n_samples, n_selected_features]
The input samples.
X_r : array of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform.
transform(X)

Note

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

Reduce X to the selected features.

X : array of shape [n_samples, n_features]
The input samples.
X_r : array of shape [n_samples, n_selected_features]
The input samples with only the selected features.