GenericUnivariateSelect
¶
-
class
ibex.sklearn.feature_selection.
GenericUnivariateSelect
(score_func=<function f_classif>, mode='percentile', param=1e-05)¶ Bases:
sklearn.feature_selection.univariate_selection.GenericUnivariateSelect
,ibex._base.FrameMixin
Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Univariate feature selector with configurable strategy.
Read more in the User Guide.
- score_func : callable
- Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues). For modes ‘percentile’ or ‘kbest’ it can return a single array scores.
- mode : {‘percentile’, ‘k_best’, ‘fpr’, ‘fdr’, ‘fwe’}
- Feature selection mode.
- param : float or int depending on the feature selection mode
- Parameter of the corresponding mode.
- scores_ : array-like, shape=(n_features,)
- Scores of features.
- pvalues_ : array-like, shape=(n_features,)
- p-values of feature scores, None if score_func returned scores only.
f_classif: ANOVA F-value between label/feature for classification tasks. mutual_info_classif: Mutual information for a discrete target. chi2: Chi-squared stats of non-negative features for classification tasks. f_regression: F-value between label/feature for regression tasks. mutual_info_regression: Mutual information for a continuous target. SelectPercentile: Select features based on percentile of the highest scores. SelectKBest: Select features based on the k highest scores. SelectFpr: Select features based on a false positive rate test. SelectFdr: Select features based on an estimated false discovery rate. SelectFwe: Select features based on family-wise error rate.
-
fit
(X, y)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
inverse_transform
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
transform
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
- A parameter