0PhddlmZddlZddlmZmZddlmZddl m Z m Z ddl m Z mZdd lmZGd d eeZdS) )RealN) BaseEstimator _fit_context)Interval)mean_variance_axis min_max_axis)check_is_fitted validate_data) SelectorMixinceZdZUdZdeedddgiZeed<dd Z e d dd Z d Z fdZ xZS)VarianceThresholdatFeature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning. Read more in the :ref:`User Guide `. Parameters ---------- threshold : float, default=0 Features with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples. Attributes ---------- variances_ : array, shape (n_features,) Variances of individual features. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 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 See Also -------- SelectFromModel: Meta-transformer for selecting features based on importance weights. SelectPercentile : Select features according to a percentile of the highest scores. SequentialFeatureSelector : Transformer that performs Sequential Feature Selection. Notes ----- Allows NaN in the input. Raises ValueError if no feature in X meets the variance threshold. Examples -------- The following dataset has integer features, two of which are the same in every sample. These are removed with the default setting for threshold:: >>> from sklearn.feature_selection import VarianceThreshold >>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]] >>> selector = VarianceThreshold() >>> selector.fit_transform(X) array([[2, 0], [1, 4], [1, 1]]) thresholdrNleft)closed_parameter_constraintsc||_dSN)r)selfrs m/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/feature_selection/_variance_threshold.py__init__zVarianceThreshold.__init__Ns "T)prefer_skip_nested_validationct||dtjd}t|dr>t |d\}|_|jdkrt|d\}}||z }n!>!> At~""*11555 e %  i222DO~"" "qq 1 1 1 >Q  (DO]#CDDK i !<<z"VarianceThreshold.__sklearn_tags__s1ww''))$(!!% r)rr)__name__ __module__ __qualname____doc__rrrdict__annotations__rrr9r;r> __classcell__)rCs@rrrs88v hhtQV<<<=$D####\555000650d000 rr)numbersrnumpyr$baserrutils._param_validationrutils.sparsefuncsrr utils.validationr r _baser rrrrSs..............@@@@@@@@======== ~~~~~ }~~~~~r