SelectFromModel

class ibex.sklearn.feature_selection.SelectFromModel(estimator, threshold=None, prefit=False, norm_order=1)

Bases: sklearn.feature_selection.from_model.SelectFromModel, ibex._base.FrameMixin

Note

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

Meta-transformer for selecting features based on importance weights.

New in version 0.17.

estimator : object
The base estimator from which the transformer is built. This can be both a fitted (if prefit is set to True) or a non-fitted estimator. The estimator must have either a feature_importances_ or coef_ attribute after fitting.
threshold : string, float, optional default None
The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median” (resp. “mean”), then the threshold value is the median (resp. the mean) of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. If None and if the estimator has a parameter penalty set to l1, either explicitly or implicitly (e.g, Lasso), the threshold used is 1e-5. Otherwise, “mean” is used by default.
prefit : bool, default False
Whether a prefit model is expected to be passed into the constructor directly or not. If True, transform must be called directly and SelectFromModel cannot be used with cross_val_score, GridSearchCV and similar utilities that clone the estimator. Otherwise train the model using fit and then transform to do feature selection.
norm_order : non-zero int, inf, -inf, default 1
Order of the norm used to filter the vectors of coefficients below threshold in the case where the coef_ attribute of the estimator is of dimension 2.
estimator_ : an estimator
The base estimator from which the transformer is built. This is stored only when a non-fitted estimator is passed to the SelectFromModel, i.e when prefit is False.
threshold_ : float
The threshold value used for feature selection.
fit(X, y=None, **fit_params)[source]

Note

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

Fit the SelectFromModel meta-transformer.

X : array-like of shape (n_samples, n_features)
The training input samples.
y : array-like, shape (n_samples,)
The target values (integers that correspond to classes in classification, real numbers in regression).

**fit_params : Other estimator specific parameters

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.
partial_fit(X, y=None, **fit_params)[source]

Note

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

Fit the SelectFromModel meta-transformer only once.

X : array-like of shape (n_samples, n_features)
The training input samples.
y : array-like, shape (n_samples,)
The target values (integers that correspond to classes in classification, real numbers in regression).

**fit_params : Other estimator specific parameters

self : object
Returns self.
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.