o tBh"<@sddlmZddlZddlZddlmZddlmZddlm Z m Z m Z ddl m Z dd lmZmZmZdd lmZdd lmZd d ZddZGddde ee ZdS))deepcopyN) SelectorMixin)_get_feature_importances) BaseEstimatorcloneMetaEstimatorMixin) _safe_tags)check_is_fitted check_scalar _num_features)NotFittedError) available_ifcCs|dur|jj}t|dr|jdksd|vrd}nd}t|trqd|vrU|d\}}t|}|}|dkr?t |}n|dkrIt |}nt d |||}|S|dkr`t |}|S|dkrkt |}|St d |t|}|S) zInterpret the threshold valueNpenaltyl1Lassogh㈵>mean*medianzUnknown reference: z6Expected threshold='mean' or threshold='median' got %s) __class____name__hasattrr isinstancestrsplitfloatstripnprr ValueError) estimator importances thresholdest_namescale referencer&|/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/feature_selection/_from_model.py_calculate_thresholds>          r(cs fddS)zCheck if we can delegate a method to the underlying estimator. First, we check the fitted estimator if available, otherwise we check the unfitted estimator. cs"t|dr t|jSt|jS)N estimator_)rr)r selfattrr&r'Gs z _estimator_has..r&r,r&r,r'_estimator_hasAs r/c@sveZdZdZddddddddZd d Zd d Zdd dZeddZ e e ddddZ eddZ ddZdS)SelectFromModelaMeta-transformer for selecting features based on importance weights. .. versionadded:: 0.17 Read more in the :ref:`User Guide `. Parameters ---------- 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 should have a ``feature_importances_`` or ``coef_`` attribute after fitting. Otherwise, the ``importance_getter`` parameter should be used. threshold : str or float, 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`, `estimator` must be a fitted estimator. If `False`, `estimator` is fitted and updated by calling `fit` and `partial_fit`, respectively. 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. max_features : int, callable, default=None The maximum number of features to select. - If an integer, then it specifies the maximum number of features to allow. - If a callable, then it specifies how to calculate the maximum number of features allowed by using the output of `max_feaures(X)`. - If `None`, then all features are kept. To only select based on ``max_features``, set ``threshold=-np.inf``. .. versionadded:: 0.20 .. versionchanged:: 1.1 `max_features` accepts a callable. importance_getter : str or callable, default='auto' If 'auto', uses the feature importance either through a ``coef_`` attribute or ``feature_importances_`` attribute of estimator. Also accepts a string that specifies an attribute name/path for extracting feature importance (implemented with `attrgetter`). For example, give `regressor_.coef_` in case of :class:`~sklearn.compose.TransformedTargetRegressor` or `named_steps.clf.feature_importances_` in case of :class:`~sklearn.pipeline.Pipeline` with its last step named `clf`. If `callable`, overrides the default feature importance getter. The callable is passed with the fitted estimator and it should return importance for each feature. .. versionadded:: 0.24 Attributes ---------- estimator_ : estimator The base estimator from which the transformer is built. This attribute exist only when `fit` has been called. - If `prefit=True`, it is a deep copy of `estimator`. - If `prefit=False`, it is a clone of `estimator` and fit on the data passed to `fit` or `partial_fit`. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 0.24 max_features_ : int Maximum number of features calculated during :term:`fit`. Only defined if the ``max_features`` is not `None`. - If `max_features` is an `int`, then `max_features_ = max_features`. - If `max_features` is a callable, then `max_features_ = max_features(X)`. .. versionadded:: 1.1 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 threshold_ : float The threshold value used for feature selection. See Also -------- RFE : Recursive feature elimination based on importance weights. RFECV : Recursive feature elimination with built-in cross-validated selection of the best number of features. SequentialFeatureSelector : Sequential cross-validation based feature selection. Does not rely on importance weights. Notes ----- Allows NaN/Inf in the input if the underlying estimator does as well. Examples -------- >>> from sklearn.feature_selection import SelectFromModel >>> from sklearn.linear_model import LogisticRegression >>> X = [[ 0.87, -1.34, 0.31 ], ... [-2.79, -0.02, -0.85 ], ... [-1.34, -0.48, -2.55 ], ... [ 1.92, 1.48, 0.65 ]] >>> y = [0, 1, 0, 1] >>> selector = SelectFromModel(estimator=LogisticRegression()).fit(X, y) >>> selector.estimator_.coef_ array([[-0.3252302 , 0.83462377, 0.49750423]]) >>> selector.threshold_ 0.55245... >>> selector.get_support() array([False, True, False]) >>> selector.transform(X) array([[-1.34], [-0.02], [-0.48], [ 1.48]]) Using a callable to create a selector that can use no more than half of the input features. >>> def half_callable(X): ... return round(len(X[0]) / 2) >>> half_selector = SelectFromModel(estimator=LogisticRegression(), ... max_features=half_callable) >>> _ = half_selector.fit(X, y) >>> half_selector.max_features_ 2 NFrauto)r"prefit norm_order max_featuresimportance_gettercCs(||_||_||_||_||_||_dSN)r r"r2r5r3r4)r+r r"r2r3r4r5r&r&r'__init__s   zSelectFromModel.__init__c Cst|d|j}t|d|j}|jr*zt|jWnty)}ztd|d}~wwt|r2td|durDt|tj sDt d|dt ||j d|j d}t|||j}|jdurrtj|td }tj| d d d|}d ||<ntj|td }d |||k<|S)Nr) max_features_EWhen `prefit=True`, `estimator` is expected to be a fitted estimator.z[When `prefit=True` and `max_features` is a callable, call `fit` before calling `transform`.z5`max_features` must be an integer. Got `max_features=z ` instead.normr gettertransform_funcr3)dtype mergesort)kindTF)getattrr r4r2r rcallablernumbersIntegralrrr5r3r(r"r zeros_likeboolargsort ones_like)r+r r4excscoresr"maskcandidate_indicesr&r&r'_get_support_masksJ     z!SelectFromModel._get_support_maskcCs|jdurCt|}t|jtjr!t|jdtjd|d|j|_dSt|jr:||}t|dtjd|d||_dStd|jddS)Nr4r)min_valmax_valzmax_features(X)zP'max_features' must be either an int or a callable that takes 'X' as input. Got z instead.) r4r rrCrDr r8rB TypeError)r+X n_featuresr4r&r&r'_check_max_features!s6     z#SelectFromModel._check_max_featuresc Ks|||jr(zt|jWnty }ztd|d}~wwt|j|_nt|j|_|jj||fi|t |jdrF|jj |_ |S|j |dd|S)aFit the SelectFromModel meta-transformer. Parameters ---------- X : array-like of shape (n_samples, n_features) The training input samples. y : array-like of shape (n_samples,), default=None The target values (integers that correspond to classes in classification, real numbers in regression). **fit_params : dict Other estimator specific parameters. Returns ------- self : object Fitted estimator. r9Nfeature_names_in_Treset) rSr2r r rrr)rfitrrT_check_feature_names)r+rQy fit_paramsrIr&r&r'rW>s(    zSelectFromModel.fitcCs&t|j|jd|jd}t|j||jS)z+Threshold value used for feature selection.r:r;)rr)r5r3r(r r")r+rJr&r&r' threshold_hszSelectFromModel.threshold_ partial_fitc Ks|||jr.t|ds,zt|jWnty%}ztd|d}~wwt|j|_|St|d }|rs      .