{Kgg*dZddlZddlZddlZddlZddlZddlZddlmZddl Z ddl m Z ddl mZmZddlmZddlmZmZdd lmZmZdd lmZdd lmZdd lmZdd lmZddl m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)dddZ*dddZ+GddeeZ,GddZ-GddZ.GddZ/GddZ0GddeZ1Gdd Z2Gd!d"Z3Gd#d$Z4Gd%d&Z5Gd'd(Z6Gd)d*Z7Gd+d,Z8d-Z9d.Z:d/Z;d0ZBase classes for all estimators and various utility functions.N) defaultdict) __version__)config_context get_config)InconsistentVersionWarning)_HTMLDocumentationLinkMixinestimator_html_repr)_MetadataRequester_routing_enabled)validate_parameter_constraints)_SetOutputMixin _DEFAULT_TAGS) _IS_32BIT) _check_feature_names_in_check_y_generate_get_feature_names_out_get_feature_names _is_fitted _num_features check_arraycheck_is_fitted check_X_yTsafec~t|dr%tj|s|jSt ||S)aConstruct a new unfitted estimator with the same parameters. Clone does a deep copy of the model in an estimator without actually copying attached data. It returns a new estimator with the same parameters that has not been fitted on any data. .. versionchanged:: 1.3 Delegates to `estimator.__sklearn_clone__` if the method exists. Parameters ---------- estimator : {list, tuple, set} of estimator instance or a single estimator instance The estimator or group of estimators to be cloned. safe : bool, default=True If safe is False, clone will fall back to a deep copy on objects that are not estimators. Ignored if `estimator.__sklearn_clone__` exists. Returns ------- estimator : object The deep copy of the input, an estimator if input is an estimator. Notes ----- If the estimator's `random_state` parameter is an integer (or if the estimator doesn't have a `random_state` parameter), an *exact clone* is returned: the clone and the original estimator will give the exact same results. Otherwise, *statistical clone* is returned: the clone might return different results from the original estimator. More details can be found in :ref:`randomness`. Examples -------- >>> from sklearn.base import clone >>> from sklearn.linear_model import LogisticRegression >>> X = [[-1, 0], [0, 1], [0, -1], [1, 0]] >>> y = [0, 0, 1, 1] >>> classifier = LogisticRegression().fit(X, y) >>> cloned_classifier = clone(classifier) >>> hasattr(classifier, "classes_") True >>> hasattr(cloned_classifier, "classes_") False >>> classifier is cloned_classifier False __sklearn_clone__r)hasattrinspectisclassr_clone_parametrized) estimatorrs P/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/sklearn/base.pycloner%(s7by-.wy7Q**,, yt 44c t|}|tur/|jDcic]\}}|t||c}}S|tt t tfvr!||Dcgc]}t||c}St|drt|trV|stj|St|tr tdtdt|dt|d|j}|jd}|jD]\}} t| d||<|d i|} tj|j | _| jd} |D]!}||} | |} | | ust%d |d |t|d r$tj|j&| _| Scc}}wcc}w#t"$rYwxYw) zLDefault implementation of clone. See :func:`sklearn.base.clone` for details.r get_paramszaCannot clone object. You should provide an instance of scikit-learn estimator instead of a class.zCannot clone object 'z' (type zb): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' method.FdeepzCannot clone object z?, as the constructor either does not set or modifies parameter _sklearn_output_config)typedictitemsr%listtupleset frozensetr isinstancecopydeepcopy TypeErrorrepr __class__r(_metadata_requestAttributeError RuntimeErrorr+)r#restimator_typekveklassnew_object_paramsnameparam new_object params_setparam1param2s r$r"r"^s)_N3`. Notes ----- All estimators should specify all the parameters that can be set at the class level in their ``__init__`` as explicit keyword arguments (no ``*args`` or ``**kwargs``). Examples -------- >>> import numpy as np >>> from sklearn.base import BaseEstimator >>> class MyEstimator(BaseEstimator): ... def __init__(self, *, param=1): ... self.param = param ... def fit(self, X, y=None): ... self.is_fitted_ = True ... return self ... def predict(self, X): ... return np.full(shape=X.shape[0], fill_value=self.param) >>> estimator = MyEstimator(param=2) >>> estimator.get_params() {'param': 2} >>> X = np.array([[1, 2], [2, 3], [3, 4]]) >>> y = np.array([1, 0, 1]) >>> estimator.fit(X, y).predict(X) array([2, 2, 2]) >>> estimator.set_params(param=3).fit(X, y).predict(X) array([3, 3, 3]) ct|jd|j}|tjurgStj|}|j j Dcgc],}|jdk7r|j|jk7r|.}}|D]-}|j|jk(std|d|dt|Dcgc]}|jc}Scc}wcc}w)z%Get parameter names for the estimatordeprecated_originalselfzpscikit-learn estimators should always specify their parameters in the signature of their __init__ (no varargs). z with constructor z! doesn't follow this convention.) getattr__init__objectr signature parametersvaluesrCkind VAR_KEYWORDVAR_POSITIONALr<sorted)clsinitinit_signatureprRs r$_get_param_nameszBaseEstimator._get_param_namess s||%:CLLI 6?? "I!**40$..557 7vvAFFamm$; 7  Avv)))" 36~ Gz2z!qvvz233 3s '1C/C4ct}|jD]it|}|rTt|drHt |t s8|j j}|jfd|D||<k|S)ae Get parameters for this estimator. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. r(c38K|]\}}dz|z|fyw)__Nr,).0r>valkeys r$ z+BaseEstimator.get_params..s#JzVQC$JNC0zs) r.r\rNrr4r-r(r/update)rMr*outvalue deep_itemsrbs @r$r(zBaseEstimator.get_paramssyf((*CD#&E|4Zt=T"--/557  JzJJCH +  r&c |s|S|jd}tt}|jD]`\}}|j d\}}}||vr%|j }t d|d|d|d|r ||||<Ot||||||<b|jD]\}} ||jdi| |S) a Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as :class:`~sklearn.pipeline.Pipeline`). The latter have parameters of the form ``__`` so that it's possible to update each component of a nested object. Parameters ---------- **params : dict Estimator parameters. Returns ------- self : estimator instance Estimator instance. Tr)r_zInvalid parameter z for estimator z. Valid parameters are: .r,) r(rr.r/ partitionr\ ValueErrorsetattr set_params) rMparams valid_params nested_paramsrbrfdelimsub_keylocal_valid_params sub_paramss r$rmzBaseEstimator.set_paramss$KD1 #D)  ,,.JC"%--"5 C,&%)%:%:%<" (tfE--?,B!E .3 c"7+c5)$) S!) -224OC (L  ( ( 6: 6 5 r&ct|SN)r"rMs r$rzBaseEstimator.__sklearn_clone__'s "4((r&c$ddlm}d}|ddd|}|j|}tdj |j }||kDr|dz}d|z}t j||j} t j||ddd j} d || | vr/|d z }t j||ddd j} d } | t| zt|| z kr|d| d z|| dz}|S) Nr)_EstimatorPrettyPrinterT)compactindentindent_at_namen_max_elements_to_showz ^(\s*\S){%d} z[^\n]*\nz...) utils._pprintrypformatlenjoinsplitrematchend) rM N_CHAR_MAXryN_MAX_ELEMENTS_TO_SHOWpprepr_ n_nonblanklimregexleft_lim right_limellipsiss r$__repr__zBaseEstimator.__repr__*s( ;!#%#9   4 /0  "/C#c)Exxu-113Hdd 488:IuXyj11$HHUE$B$K8<<> H#h-'#e*y*@@ix(505)3EE r&cft|ddr td t| }||jj }t|jjdrt|jtS|S#t $r|jj }YjwxYw)N __slots__zSYou cannot use `__slots__` in objects inheriting from `sklearn.base.BaseEstimator`.sklearn.)_sklearn_version) rNr7super __getstate____dict__r5r;r- __module__ startswithr.r/r)rMstater9s r$rzBaseEstimator.__getstate__^s 4d +0   )G(*E} **, : + +J 7 D DL )MM&&(E )s*B #B0/B0cjt|jjdrT|jdd}|tk7r9t j t|jjt| t|-|y#t$r|jj|YywxYw)Nrrzpre-0.18)estimator_namecurrent_sklearn_versionoriginal_sklearn_version)r-rrpoprwarningswarnrr9__name__r __setstate__r;rrd)rMrpickle_versionr9s r$rzBaseEstimator.__setstate__ts : + +J 7"YY'9:FN, .'+~~'>'>0;1? ( G  ' ( MM  ' (s;B $B21B2ctSrvrrws r$ _more_tagszBaseEstimator._more_tagssr&ci}ttj|jD]1}t |ds|j |}|j |3|S)Nr)reversedr getmror9rrrd)rMcollected_tags base_class more_tagss r$ _get_tagszBaseEstimator._get_tagssV"7>>$..#ABJz<0'11$7 %%i0 Cr&c x t|}|r||_yt|dsy||j k7r3td|d|jj d|j dy#t$rI}|s=t|dr1td|jj d|j d|Yd}~yd}~wwxYw) aSet the `n_features_in_` attribute, or check against it. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) The input samples. reset : bool If True, the `n_features_in_` attribute is set to `X.shape[1]`. If False and the attribute exists, then check that it is equal to `X.shape[1]`. If False and the attribute does *not* exist, then the check is skipped. .. note:: It is recommended to call reset=True in `fit` and in the first call to `partial_fit`. All other methods that validate `X` should set `reset=False`. n_features_in_z%X does not contain any features, but z is expecting z featuresNzX has z features, but z features as input.)rr7rrkr9rr)rMXreset n_featuresr@s r$_check_n_featureszBaseEstimator._check_n_featuress" &q)J ",D  t-.  ,, , ODNN4K4K3LM $ 3 344GI  -+ WT+;< ;~~../~**+96   s A'' B90?B44B9c|r.t|}|||_yt|dr t|dyt |dd}t|}||y|0|.t j d|jjdy|0|.t j d|jjdyt|t|k7stj||k7rod}t|}t|}t||z } t||z } d} | r|d z }|| | z }| r|d z }|| | z }| s| s|d z }t|y) aSet or check the `feature_names_in_` attribute. .. versionadded:: 1.0 Parameters ---------- X : {ndarray, dataframe} of shape (n_samples, n_features) The input samples. reset : bool Whether to reset the `feature_names_in_` attribute. If False, the input will be checked for consistency with feature names of data provided when reset was last True. .. note:: It is recommended to call `reset=True` in `fit` and in the first call to `partial_fit`. All other methods that validate `X` should set `reset=False`. Nfeature_names_in_zX has feature names, but z! was fitted without feature namesz)X does not have valid feature names, but z was fitted with feature nameszBThe feature names should match those that were passed during fit. c`d}d}t|D]\}}||k\r|dz }|S|d|dz }|S)Nrz- ... z- r) enumerate)namesoutput max_n_namesirCs r$ add_namesz5BaseEstimator._check_feature_names..add_namessT (/GAtK')+ 4&m+F 0  r&z"Feature names unseen at fit time: z1Feature names seen at fit time, yet now missing: z=Feature names must be in the same order as they were in fit. )rrrdelattrrNrrr9rrnpanyr2rWrk) rMrrfeature_names_infitted_feature_namesX_feature_namesmessagefitted_feature_names_setX_feature_names_setunexpected_names missing_namesrs r$_check_feature_namesz"BaseEstimator._check_feature_namess( 1!4 +)9&  2312 &t-@$G,Q/  'O,C   &+?+G MM+DNN,C,C+DE!!    "';'G MMNN++,,JL   # $O(< < O 3A V (++?'@ $"%o"6 %&9Q#QRM  @@9%566OO9]33 )9TW% %GA r&c |j|||6|jdr#td|jjdt |t xr|dk(}|duxst |t xr|dk(}|r |r tdd|i} i| |}|s|s|r|} n||r|s|} nu||f} np|s|rt|fd d i|} n]|r|s t|fi|} nL|r6|\} } d| vri| | } t|fd d i| }d| vri| | } t|fd d i| }nt||fi|\}}||f} |s%|jd d r|j||| S)ao Validate input data and set or check the `n_features_in_` attribute. Parameters ---------- X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features), default='no validation' The input samples. If `'no_validation'`, no validation is performed on `X`. This is useful for meta-estimator which can delegate input validation to their underlying estimator(s). In that case `y` must be passed and the only accepted `check_params` are `multi_output` and `y_numeric`. y : array-like of shape (n_samples,), default='no_validation' The targets. - If `None`, `check_array` is called on `X`. If the estimator's requires_y tag is True, then an error will be raised. - If `'no_validation'`, `check_array` is called on `X` and the estimator's requires_y tag is ignored. This is a default placeholder and is never meant to be explicitly set. In that case `X` must be passed. - Otherwise, only `y` with `_check_y` or both `X` and `y` are checked with either `check_array` or `check_X_y` depending on `validate_separately`. reset : bool, default=True Whether to reset the `n_features_in_` attribute. If False, the input will be checked for consistency with data provided when reset was last True. .. note:: It is recommended to call reset=True in `fit` and in the first call to `partial_fit`. All other methods that validate `X` should set `reset=False`. validate_separately : False or tuple of dicts, default=False Only used if y is not None. If False, call validate_X_y(). Else, it must be a tuple of kwargs to be used for calling check_array() on X and y respectively. `estimator=self` is automatically added to these dicts to generate more informative error message in case of invalid input data. cast_to_ndarray : bool, default=True Cast `X` and `y` to ndarray with checks in `check_params`. If `False`, `X` and `y` are unchanged and only `feature_names_in_` and `n_features_in_` are checked. **check_params : kwargs Parameters passed to :func:`sklearn.utils.check_array` or :func:`sklearn.utils.check_X_y`. Ignored if validate_separately is not False. `estimator=self` is automatically added to these params to generate more informative error message in case of invalid input data. Returns ------- out : {ndarray, sparse matrix} or tuple of these The validated input. A tuple is returned if both `X` and `y` are validated. )rN requires_yzThis z= estimator requires y to be passed, but the target y is None. no_validationz*Validation should be done on X, y or both.r# input_namery ensure_2dT) rrrkr9rr4strrrrgetr) rMrrrvalidate_separatelycast_to_ndarray check_paramsno_val_Xno_val_ydefault_check_paramsrecheck_X_paramscheck_y_paramss r$_validate_datazBaseEstimator._validate_datasN !!!5!1 9),7//01EE  a%>!*>9K 1c 2 KqO7K IJ J +T2?.?,? (dha@C@<@C h1- -C" 2E.n4%O(<%O%ONDcD^Dn4%O(<%O%ONDcD^D A661Q$CL,,[$?  " "1E " 2 r&c|t|j|jd|jjy)aYValidate types and values of constructor parameters The expected type and values must be defined in the `_parameter_constraints` class attribute, which is a dictionary `param_name: list of constraints`. See the docstring of `validate_parameter_constraints` for a description of the accepted constraints. Fr)) caller_nameN)r _parameter_constraintsr(r9rrws r$_validate_paramszBaseEstimator._validate_paramss0 '  ' ' OOO '// r&cPtddk7r td|jS)aHTML representation of estimator. This is redundant with the logic of `_repr_mimebundle_`. The latter should be favorted in the long term, `_repr_html_` is only implemented for consumers who do not interpret `_repr_mimbundle_`. displaydiagramzW_repr_html_ is only defined when the 'display' configuration option is set to 'diagram')rr;_repr_html_innerrws r$ _repr_html_zBaseEstimator._repr_html_s3 < "i /   $$$r&ct|S)zThis function is returned by the @property `_repr_html_` to make `hasattr(estimator, "_repr_html_") return `True` or `False` depending on `get_config()["display"]`. )r rws r$rzBaseEstimator._repr_html_inners #4((r&c \dt|i}tddk(rt||d<|S)z8Mime bundle used by jupyter kernels to display estimatorz text/plainrrz text/html)r8rr )rMkwargsrs r$_repr_mimebundle_zBaseEstimator._repr_mimebundle_s3T + < "i /"5d";F;  r&)T)i)rrTFT)rr __qualname____doc__ classmethodr\r(rmrrrrrrrrrrpropertyrrr __classcell__)r9s@r$rJrJs)V44<0*X)2h,(  ,\W&v  ! wr   % %)r&rJc"eZdZdZdZddZdZy)ClassifierMixinaMixin class for all classifiers in scikit-learn. This mixin defines the following functionality: - `_estimator_type` class attribute defaulting to `"classifier"`; - `score` method that default to :func:`~sklearn.metrics.accuracy_score`. - enforce that `fit` requires `y` to be passed through the `requires_y` tag. Read more in the :ref:`User Guide `. Examples -------- >>> import numpy as np >>> from sklearn.base import BaseEstimator, ClassifierMixin >>> # Mixin classes should always be on the left-hand side for a correct MRO >>> class MyEstimator(ClassifierMixin, BaseEstimator): ... def __init__(self, *, param=1): ... self.param = param ... def fit(self, X, y=None): ... self.is_fitted_ = True ... return self ... def predict(self, X): ... return np.full(shape=X.shape[0], fill_value=self.param) >>> estimator = MyEstimator(param=1) >>> X = np.array([[1, 2], [2, 3], [3, 4]]) >>> y = np.array([1, 0, 1]) >>> estimator.fit(X, y).predict(X) array([1, 1, 1]) >>> estimator.score(X, y) 0.66... classifierNcBddlm}|||j||S)a Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for `X`. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- score : float Mean accuracy of ``self.predict(X)`` w.r.t. `y`. r)accuracy_score sample_weight)metricsrpredict)rMrrrrs r$scorezClassifierMixin.scores0 ,aa NNr&c ddiSNrTr,rws r$rzClassifierMixin._more_tags d##r&rvrrrr_estimator_typerrr,r&r$rrs@#OO8$r&rc"eZdZdZdZddZdZy)RegressorMixinaMixin class for all regression estimators in scikit-learn. This mixin defines the following functionality: - `_estimator_type` class attribute defaulting to `"regressor"`; - `score` method that default to :func:`~sklearn.metrics.r2_score`. - enforce that `fit` requires `y` to be passed through the `requires_y` tag. Read more in the :ref:`User Guide `. Examples -------- >>> import numpy as np >>> from sklearn.base import BaseEstimator, RegressorMixin >>> # Mixin classes should always be on the left-hand side for a correct MRO >>> class MyEstimator(RegressorMixin, BaseEstimator): ... def __init__(self, *, param=1): ... self.param = param ... def fit(self, X, y=None): ... self.is_fitted_ = True ... return self ... def predict(self, X): ... return np.full(shape=X.shape[0], fill_value=self.param) >>> estimator = MyEstimator(param=0) >>> X = np.array([[1, 2], [2, 3], [3, 4]]) >>> y = np.array([-1, 0, 1]) >>> estimator.fit(X, y).predict(X) array([0, 0, 0]) >>> estimator.score(X, y) 0.0 regressorNcFddlm}|j|}||||S)aReturn the coefficient of determination of the prediction. The coefficient of determination :math:`R^2` is defined as :math:`(1 - \frac{u}{v})`, where :math:`u` is the residual sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v` is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of `y`, disregarding the input features, would get a :math:`R^2` score of 0.0. Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted`` is the number of samples used in the fitting for the estimator. y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for `X`. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- score : float :math:`R^2` of ``self.predict(X)`` w.r.t. `y`. Notes ----- The :math:`R^2` score used when calling ``score`` on a regressor uses ``multioutput='uniform_average'`` from version 0.23 to keep consistent with default value of :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). r)r2_scorer)rrr)rMrrrry_preds r$rzRegressorMixin.score%s$R &a6??r&c ddiSrr,rws r$rzRegressorMixin._more_tagsSrr&rvrr,r&r$rrs@"O,@\$r&rc"eZdZdZdZddZdZy) ClusterMixinaiMixin class for all cluster estimators in scikit-learn. - `_estimator_type` class attribute defaulting to `"clusterer"`; - `fit_predict` method returning the cluster labels associated to each sample. Examples -------- >>> import numpy as np >>> from sklearn.base import BaseEstimator, ClusterMixin >>> class MyClusterer(ClusterMixin, BaseEstimator): ... def fit(self, X, y=None): ... self.labels_ = np.ones(shape=(len(X),), dtype=np.int64) ... return self >>> X = [[1, 2], [2, 3], [3, 4]] >>> MyClusterer().fit_predict(X) array([1, 1, 1]) clustererNc @|j|fi||jS)a Perform clustering on `X` and returns cluster labels. Parameters ---------- X : array-like of shape (n_samples, n_features) Input data. y : Ignored Not used, present for API consistency by convention. **kwargs : dict Arguments to be passed to ``fit``. .. versionadded:: 1.4 Returns ------- labels : ndarray of shape (n_samples,), dtype=np.int64 Cluster labels. )fitlabels_)rMrrrs r$ fit_predictzClusterMixin.fit_predictls!0 f||r&c dgiS)Npreserves_dtyper,rws r$rzClusterMixin._more_tagss !2&&r&rv)rrrrrrrr,r&r$r r Ws$"O6'r&r c2eZdZdZedZdZdZdZy)BiclusterMixina?Mixin class for all bicluster estimators in scikit-learn. This mixin defines the following functionality: - `biclusters_` property that returns the row and column indicators; - `get_indices` method that returns the row and column indices of a bicluster; - `get_shape` method that returns the shape of a bicluster; - `get_submatrix` method that returns the submatrix corresponding to a bicluster. Examples -------- >>> import numpy as np >>> from sklearn.base import BaseEstimator, BiclusterMixin >>> class DummyBiClustering(BiclusterMixin, BaseEstimator): ... def fit(self, X, y=None): ... self.rows_ = np.ones(shape=(1, X.shape[0]), dtype=bool) ... self.columns_ = np.ones(shape=(1, X.shape[1]), dtype=bool) ... return self >>> X = np.array([[1, 1], [2, 1], [1, 0], ... [4, 7], [3, 5], [3, 6]]) >>> bicluster = DummyBiClustering().fit(X) >>> hasattr(bicluster, "biclusters_") True >>> bicluster.get_indices(0) (array([0, 1, 2, 3, 4, 5]), array([0, 1])) c2|j|jfS)z{Convenient way to get row and column indicators together. Returns the ``rows_`` and ``columns_`` members. )rows_columns_rws r$ biclusters_zBiclusterMixin.biclusters_s zz4==((r&c|j|}|j|}tj|dtj|dfS)aRow and column indices of the `i`'th bicluster. Only works if ``rows_`` and ``columns_`` attributes exist. Parameters ---------- i : int The index of the cluster. Returns ------- row_ind : ndarray, dtype=np.intp Indices of rows in the dataset that belong to the bicluster. col_ind : ndarray, dtype=np.intp Indices of columns in the dataset that belong to the bicluster. r)rrrnonzero)rMrrowscolumnss r$ get_indiceszBiclusterMixin.get_indicessF"zz!}--"zz$"BJJw$7$:::r&cH|j|}td|DS)a-Shape of the `i`'th bicluster. Parameters ---------- i : int The index of the cluster. Returns ------- n_rows : int Number of rows in the bicluster. n_cols : int Number of columns in the bicluster. c32K|]}t|ywrv)r)r`rs r$rcz+BiclusterMixin.get_shape..s-WSVWs)rr1)rMrindicess r$ get_shapezBiclusterMixin.get_shapes% ""1%-W---r&cddlm}||d}|j|\}}||ddtjf|fS)aReturn the submatrix corresponding to bicluster `i`. Parameters ---------- i : int The index of the cluster. data : array-like of shape (n_samples, n_features) The data. Returns ------- submatrix : ndarray of shape (n_rows, n_cols) The submatrix corresponding to bicluster `i`. Notes ----- Works with sparse matrices. Only works if ``rows_`` and ``columns_`` attributes exist. r)rcsr) accept_sparseN)utils.validationrrrnewaxis)rMrdatarrow_indcol_inds r$ get_submatrixzBiclusterMixin.get_submatrixsE( 24u5++A.GArzzM*G344r&N) rrrrrrrrr(r,r&r$rrs*6));*.&5r&rceZdZdZddZy)TransformerMixinaMixin class for all transformers in scikit-learn. This mixin defines the following functionality: - a `fit_transform` method that delegates to `fit` and `transform`; - a `set_output` method to output `X` as a specific container type. If :term:`get_feature_names_out` is defined, then :class:`BaseEstimator` will automatically wrap `transform` and `fit_transform` to follow the `set_output` API. See the :ref:`developer_api_set_output` for details. :class:`OneToOneFeatureMixin` and :class:`ClassNamePrefixFeaturesOutMixin` are helpful mixins for defining :term:`get_feature_names_out`. Examples -------- >>> import numpy as np >>> from sklearn.base import BaseEstimator, TransformerMixin >>> class MyTransformer(TransformerMixin, BaseEstimator): ... def __init__(self, *, param=1): ... self.param = param ... def fit(self, X, y=None): ... return self ... def transform(self, X): ... return np.full(shape=len(X), fill_value=self.param) >>> transformer = MyTransformer() >>> X = [[1, 2], [2, 3], [3, 4]] >>> transformer.fit_transform(X) array([1, 1, 1]) Nc jtrc|jjd|j}|r2t j d|j jdt|"|j|fi|j|S|j||fi|j|S)a Fit to data, then transform it. Fits transformer to `X` and `y` with optional parameters `fit_params` and returns a transformed version of `X`. Parameters ---------- X : array-like of shape (n_samples, n_features) Input samples. y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None Target values (None for unsupervised transformations). **fit_params : dict Additional fit parameters. Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array. transformmethodrn This object (ah) has a `transform` method which consumes metadata, but `fit_transform` does not forward metadata to `transform`. Please implement a custom `fit_transform` method to forward metadata to `transform` as well. Alternatively, you can explicitly do `set_transform_request`and set all values to `False` to disable metadata routed to `transform`, if that's an option.) r get_metadata_routingconsumeskeysrrr9r UserWarningr r,)rMrr fit_paramstransform_paramss r$ fit_transformzTransformerMixin.fit_transformsF  #88:CC":??+< D    '(?(?'@AXX   9488A,,66q9 9488Aq/J/99!< >> import numpy as np >>> from sklearn.base import OneToOneFeatureMixin >>> class MyEstimator(OneToOneFeatureMixin): ... def fit(self, X, y=None): ... self.n_features_in_ = X.shape[1] ... return self >>> X = np.array([[1, 2], [3, 4]]) >>> MyEstimator().fit(X).get_feature_names_out() array(['x0', 'x1'], dtype=object) Nc2t|dt||S)aGet output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Input features. - If `input_features` is `None`, then `feature_names_in_` is used as feature names in. If `feature_names_in_` is not defined, then the following input feature names are generated: `["x0", "x1", ..., "x(n_features_in_ - 1)"]`. - If `input_features` is an array-like, then `input_features` must match `feature_names_in_` if `feature_names_in_` is defined. Returns ------- feature_names_out : ndarray of str objects Same as input features. r)rrrMinput_featuress r$get_feature_names_outz*OneToOneFeatureMixin.get_feature_names_outcs( ./&t^<>> import numpy as np >>> from sklearn.base import ClassNamePrefixFeaturesOutMixin >>> class MyEstimator(ClassNamePrefixFeaturesOutMixin): ... def fit(self, X, y=None): ... self._n_features_out = X.shape[1] ... return self >>> X = np.array([[1, 2], [3, 4]]) >>> MyEstimator().fit(X).get_feature_names_out() array(['myestimator0', 'myestimator1'], dtype=object) NcJt|dt||j|S)aFGet output feature names for transformation. The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: `["class_name0", "class_name1", "class_name2"]`. Parameters ---------- input_features : array-like of str or None, default=None Only used to validate feature names with the names seen in `fit`. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. _n_features_out)r;)rrrAr:s r$r<z5ClassNamePrefixFeaturesOutMixin.get_feature_names_outs)" /0. $&&~  r&rvr=r,r&r$r?r?{s 0 r&r?ceZdZdZdZddZy) DensityMixinaMixin class for all density estimators in scikit-learn. This mixin defines the following functionality: - `_estimator_type` class attribute defaulting to `"DensityEstimator"`; - `score` method that default that do no-op. Examples -------- >>> from sklearn.base import DensityMixin >>> class MyEstimator(DensityMixin): ... def fit(self, X, y=None): ... self.is_fitted_ = True ... return self >>> estimator = MyEstimator() >>> hasattr(estimator, "score") True DensityEstimatorNcy)a=Return the score of the model on the data `X`. Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. y : Ignored Not used, present for API consistency by convention. Returns ------- score : float Nr,)rMrrs r$rzDensityMixin.scores r&rv)rrrrrrr,r&r$rCrCs&)O r&rCceZdZdZdZddZy) OutlierMixinaMixin class for all outlier detection estimators in scikit-learn. This mixin defines the following functionality: - `_estimator_type` class attribute defaulting to `outlier_detector`; - `fit_predict` method that default to `fit` and `predict`. Examples -------- >>> import numpy as np >>> from sklearn.base import BaseEstimator, OutlierMixin >>> class MyEstimator(OutlierMixin): ... def fit(self, X, y=None): ... self.is_fitted_ = True ... return self ... def predict(self, X): ... return np.ones(shape=len(X)) >>> estimator = MyEstimator() >>> X = np.array([[1, 2], [2, 3], [3, 4]]) >>> estimator.fit_predict(X) array([1., 1., 1.]) outlier_detectorNc trc|jjd|j}|r2t j d|j jdt|j|fi|j|S)a.Perform fit on X and returns labels for X. Returns -1 for outliers and 1 for inliers. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. y : Ignored Not used, present for API consistency by convention. **kwargs : dict Arguments to be passed to ``fit``. .. versionadded:: 1.4 Returns ------- y : ndarray of shape (n_samples,) 1 for inliers, -1 for outliers. rr-r/aY) has a `predict` method which consumes metadata, but `fit_predict` does not forward metadata to `predict`. Please implement a custom `fit_predict` method to forward metadata to `predict` as well.Alternatively, you can explicitly do `set_predict_request`and set all values to `False` to disable metadata routed to `predict`, if that's an option.) r r0r1r2rrr9rr3r r)rMrrrr5s r$rzOutlierMixin.fit_predicts>  #88:CC  D    '(?(?'@A::  txx$V$,,Q//r&rv)rrrrrrr,r&r$rGrGs.)O20r&rGceZdZdZdgZy)MetaEstimatorMixinaMixin class for all meta estimators in scikit-learn. This mixin defines the following functionality: - define `_required_parameters` that specify the mandatory `estimator` parameter. Examples -------- >>> from sklearn.base import MetaEstimatorMixin >>> from sklearn.datasets import load_iris >>> from sklearn.linear_model import LogisticRegression >>> class MyEstimator(MetaEstimatorMixin): ... def __init__(self, *, estimator=None): ... self.estimator = estimator ... def fit(self, X, y=None): ... if self.estimator is None: ... self.estimator_ = LogisticRegression() ... else: ... self.estimator_ = self.estimator ... return self >>> X, y = load_iris(return_X_y=True) >>> estimator = MyEstimator().fit(X, y) >>> estimator.estimator_ LogisticRegression() r#N)rrrr_required_parametersr,r&r$rKrK"s4(=r&rKceZdZdZdZy)MultiOutputMixinz2Mixin to mark estimators that support multioutput.c ddiS)N multioutputTr,rws r$rzMultiOutputMixin._more_tagsCs t$$r&Nrrrrrr,r&r$rNrN@s <%r&rNceZdZdZdZy)_UnstableArchMixinz=Mark estimators that are non-determinstic on 32bit or PowerPCc\dtxs#tjjdiS)Nnon_deterministic)ppcpowerpc)rplatformmachinerrws r$rz_UnstableArchMixin._more_tagsJs/ "A!,,-?@  r&NrQr,r&r$rSrSGs G r&rSc"t|dddk(S)aReturn True if the given estimator is (probably) a classifier. Parameters ---------- estimator : object Estimator object to test. Returns ------- out : bool True if estimator is a classifier and False otherwise. Examples -------- >>> from sklearn.base import is_classifier >>> from sklearn.svm import SVC, SVR >>> classifier = SVC() >>> regressor = SVR() >>> is_classifier(classifier) True >>> is_classifier(regressor) False rNrrNr#s r$ is_classifierr]Qs0 9/ 6, FFr&c"t|dddk(S)aReturn True if the given estimator is (probably) a regressor. Parameters ---------- estimator : estimator instance Estimator object to test. Returns ------- out : bool True if estimator is a regressor and False otherwise. Examples -------- >>> from sklearn.base import is_regressor >>> from sklearn.svm import SVC, SVR >>> classifier = SVC() >>> regressor = SVR() >>> is_regressor(classifier) False >>> is_regressor(regressor) True rNrr[r\s r$ is_regressorr_ls0 9/ 6+ EEr&c"t|dddk(S)aReturn True if the given estimator is (probably) an outlier detector. Parameters ---------- estimator : estimator instance Estimator object to test. Returns ------- out : bool True if estimator is an outlier detector and False otherwise. rNrHr[r\s r$is_outlier_detectorras 9/ 6:L LLr&cfd}|S)aCDecorator to run the fit methods of estimators within context managers. Parameters ---------- prefer_skip_nested_validation : bool If True, the validation of parameters of inner estimators or functions called during fit will be skipped. This is useful to avoid validating many times the parameters passed by the user from the public facing API. It's also useful to avoid validating parameters that we pass internally to inner functions that are guaranteed to be valid by the test suite. It should be set to True for most estimators, except for those that receive non-validated objects as parameters, such as meta-estimators that are given estimator objects. Returns ------- decorated_fit : method The decorated fit method. cFtjfd}|S)Nctd}jdk(xr t|}|s|s|jt xs|5|g|i|cdddS#1swYyxYw)Nskip_parameter_validation partial_fit)re)rrrrr)r#argsrglobal_skip_validationpartial_fit_and_fitted fit_methodprefer_skip_nested_validations r$wrapperz0_fit_context..decorator..wrappersy%/\2M%N "##}4NI9N #*2H**,1K5K ")=d=f= s  A%%A.) functoolswraps)rjrlrks` r$ decoratorz_fit_context..decorators%  $ > % >$r&r,)rkros` r$ _fit_contextrps0, r&)=rr5rmr rXrr collectionsrnumpyrrr_configrr exceptionsrutils._estimator_html_reprr r utils._metadata_requestsr r utils._param_validationr utils._set_outputr utils._tagsr utils.fixesrr#rrrrrrrrrr%r"rJrrr rr*r8r?rCrGrKrNrSr]r_rarpr,r&r$r{sD   #/2XJC.#   "35l,07td/1CdN@$@$FR$R$j1'1'hd5d5N[=[=|(=(=V- - `% % PL0L0^))<%%  G6F6 M .r&