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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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 afeature_importances_
orcoef_
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 withcross_val_score
,GridSearchCV
and similar utilities that clone the estimator. Otherwise train the model usingfit
and thentransform
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 thecoef_
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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- 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
-
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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- 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