hldZddlZddlZddlmZddlZddlZddlZddlZ ddl m Z ddl m Z ddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlm Z dddZ!GddZ"GddZ#GddZ$GddZ%Gdd Z&Gd!d"eZ'Gd#d$Z(Gd%d&Z)Gd'd(Z*Gd)d*Z+Gd+d,Z,Gd-d.Z-Gd/d0Z.d1Z/d2Z0d3Z1dS)4z Base classes for all estimators.N) defaultdict) __version__) get_config) _IS_32BIT)_SetOutputMixin _DEFAULT_TAGS) check_X_y check_array)_check_y) _num_features_check_feature_names_in)_generate_get_feature_names_out)check_is_fitted)_get_feature_namesestimator_html_repr)validate_parameter_constraintsTsafec4t|}|ttttfvr|fd|DSt |drt |trjstj|St |trtdtdt|dt|d|j }| d}| D]\}}t|d ||<|d i|}| d}|D]+}||} ||} | | urtd |d |,t |d rtj|j|_|S)arConstruct 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. 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. 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`. c2g|]}t|S)r)clone).0ers H/var/www/html/Sam_Eipo/venv/lib/python3.11/site-packages/sklearn/base.py zclone..Cs&FFFquQT222FFF 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.FdeeprzCannot clone object z?, as the constructor either does not set or modifies parameter _sklearn_output_config)typelisttupleset frozensethasattr isinstancecopydeepcopy TypeErrorrepr __class__r"itemsr RuntimeErrorr%) estimatorrestimator_typeklassnew_object_paramsnameparam new_object params_setparam1param2s ` rrr"s<)__N$sI666~FFFFIFFFGGG Y - -It1L1L =++ +)T** C  i/39ooootIP  E!,,%,88(..00;; e"'E":":":$++*++J&&E&22J""4(D!   ,BK))TTS  y233 ,0M  ,- - ) r!ceZdZdZedZddZdZddZfdZ fd Z d Z d Z d Z d Z ddZdZedZdZdZxZS) BaseEstimatorzBase class for all estimators in scikit-learn. 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``). cNt|jd|j}|tjurgStj|}d|jD}|D](}|j|jkrtd|d|d)td|DS)z%Get parameter names for the estimatordeprecated_originalcHg|]}|jdk |j|jk| Sself)r9kind VAR_KEYWORDrps rr z2BaseEstimator._get_param_names..s=   vAFam$;$; $;$;$;r!zpscikit-learn estimators should always specify their parameters in the signature of their __init__ (no varargs). z with constructor z! doesn't follow this convention.cg|] }|j Sr&)r9rHs rr z2BaseEstimator._get_param_names..s222!qv222r!) getattr__init__objectinspect signature parametersvaluesrFVAR_POSITIONALr4sorted)clsinitinit_signaturerPrIs r_get_param_nameszBaseEstimator._get_param_nameszs s|%:CLII 6? " "I!*400  #.5577     Av)))"l 36##~~~ G*22z222333r!TcXt}|D]t|}|rlt|dr\t |t sG|}|fd|D||<|S)ae Get parameters for this estimator. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. r"c32K|]\}}dz|z|fVdS)__Nr&)rkvalkeys r z+BaseEstimator.get_params..s4JJVQC$JNC0JJJJJJr!) dictrWrKr,r-r'r"r3update)rEr$outvalue deep_itemsr]s @rr"zBaseEstimator.get_paramssff((**  CD#&&E K|44 KZt=T=T K"--//5577  JJJJzJJJJJJCHH r!c |s|S|d}tt}|D]s\}}|d\}}}||vr-|}t d|d|d|d|r ||||<]t||||||<t|D]}\}} |dkr_||d krS|j d r9tj d |j j d |j j d tdd}||jdi| ~|S)a Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as :class:`~sklearn.pipeline.Pipeline`). The latter have parameters of the form ``__`` so that it's possible to update each component of a nested object. Parameters ---------- **params : dict Estimator parameters. Returns ------- self : estimator instance Estimator instance. Tr#rZzInvalid parameter z for estimator z. Valid parameters are: .base_estimator deprecatedsklearn.zParameter 'base_estimator' of z, is deprecated in favor of 'estimator'. See z's docstring for more details.) stacklevelr5r&)r"rr_r3 partitionrW ValueErrorsetattr __module__ startswithwarningswarnr2__name__ FutureWarning set_params) rEparams valid_params nested_paramsr]rbdelimsub_keylocal_valid_params sub_paramss rrtzBaseEstimator.set_paramss$ KD11 #D))  ,,.. * *JC"%--"5"5 C,&&%)%:%:%<%<" EEEtEE-?EEE  *.3 c"7++c5)))$) S!!,2244 7 7OC''' %55O..z::6 PT^5LPP/PPP" " (L  ( 6 6: 6 6 6 6 r!cddlm}d}|ddd|}||}td|}||kr|dz}d|z}t j||} t j||ddd } d || | vr5|d z }t j||ddd } d } | t| zt|| z kr|d| d z|| dz}|S) Nr)_EstimatorPrettyPrinterT)compactindentindent_at_namen_max_elements_to_showriz ^(\s*\S){%d} z[^\n]*\nz...) utils._pprintr~pformatlenjoinsplitrematchend) rE N_CHAR_MAXr~N_MAX_ELEMENTS_TO_SHOWpprepr_ n_nonblanklimregexleft_lim right_limellipsiss r__repr__zBaseEstimator.__repr__ss ;:::::!#% $#9     4  //00  " "/C#c)Exu--1133Hddd 4488::IuXyj0111$HUE$$B$K88<<>> H#h--'#e**y*@@@ixi(505)3EE r!ct|ddrtd t}||j}n)#t $r|j}YnwxYwt|j dr(t| tS|S)N __slots__zSYou cannot use `__slots__` in objects inheriting from `sklearn.base.BaseEstimator`.rh)_sklearn_version) rKr0super __getstate____dict__r.AttributeErrorr'rnror_r3r)rEstater2s rrzBaseEstimator.__getstate__$s 4d + + 0   )GG((**E} **,, ) ) )M&&((EEE ) :: + +J 7 7  DDD DLs;A#BBct|jdr_|dd}|tkr>t jd|jj |tt t |dS#t$r|j|YdSwxYw)Nrhrzpre-0.18aTrying to unpickle estimator {0} from version {1} when using version {2}. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to: https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations)r'rnropoprrprqformatr2rr UserWarningr __setstate__rrr`)rErpickle_versionr2s rrzBaseEstimator.__setstate__:s :: + +J 7 7 "YY'9:FFN,, < =CF/==     ( GG  ' ' ' ' ' ( ( ( M  ' ' ' ' ' ' (s !B,,$CCctSNr rDs r _more_tagszBaseEstimator._more_tagsNsr!ci}ttj|jD]<}t |dr*||}||=|S)Nr)reversedrNgetmror2r,rr`)rEcollected_tags base_class more_tagss r _get_tagszBaseEstimator._get_tagsQsl"7>$.#A#ABB 1 1Jz<00 1'11$77 %%i000r!c h t|}nP#t$rC}|s6t|dr&td|jjd|jd|Yd}~dSd}~wwxYw|r ||_dSt|dsdS||jkr(td|d|jjd|jddS) 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.)rr0r,rlr2rrr)rEXreset n_featuresrs r_check_n_featureszBaseEstimator._check_n_features\s9" &q))JJ    WT+;<<  6~.66*666  FFFFF   ",D  Ft-..  F , , ,IIIDN4KII $ 3III  - ,s A8AAc|r;t|}|||_n t|drt|ddSt |dd}t|}||dS|&|$t jd|jjddS|&|$t jd|jjddSt|t|kstj ||krd}t|}t|}t||z } t||z } d} | r|d z }|| | z }| r|d z }|| | z }| s| s|d z }t|dS) 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. cdd}d}t|D]\}}||kr|dz }n |d|dz }|S)Nrz- ... z- r) enumerate)namesoutput max_n_namesir9s r add_namesz5BaseEstimator._check_feature_names..add_namessZ (//,,GAtK'')+m4mmm+FF 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. )rrr,delattrrKrprqr2rrrnpanyr*rSrl) rErrfeature_names_infitted_feature_namesX_feature_namesmessagefitted_feature_names_setX_feature_names_setunexpected_names missing_namesrs r_check_feature_namesz"BaseEstimator._check_feature_namess1(  1!44 +)9&&233 31222 F&t-@$GG,Q//  'O,C F  &+?+G M!DN,C!!!    F  "';'G MLN+LLL    F # $ $O(<(< < < O 3A A <V (++?'@'@ $"%o"6"6 %&9Q#QRRM     7@@99%5666 4OO99]333  )9 TW%% %G = !*>9K 1c 2 2 KqO7K +T2?.?,?   IJJ J h A>>#>>>ACC  h ++l++ACC" 7 2E.n44%O(<%O%ONDDcD^DDn44%O(<%O%ONDDcD^DD A66661Q$C 3L,,[$?? 3  " "1E " 2 2 2 r!cpt|j|d|jjdS)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"r2rrrDs r_validate_paramszBaseEstimator._validate_paramsPsD '  ' OOO ' '/      r!c^tddkrtd|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_innerrDs r _repr_html_zBaseEstimator._repr_html_^s: << "i / /   $$r!c t|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"]`. rrDs rrzBaseEstimator._repr_html_innerns #4(((r!c |dt|i}tddkrt||d<|S)z8Mime bundle used by jupyter kernels to display estimatorz text/plainrrz text/html)r1rr)rEkwargsrs r_repr_mimebundle_zBaseEstimator._repr_mimebundle_us=T + << "i / /"5d";";F;  r!)T)r|)rrTF)rrrn __qualname____doc__ classmethodrWr"rtrrrrrrrrrpropertyrrr __classcell__)r2s@rr@r@ps_44[4<0===~2222h,((((((   ,,,\W&W&W&v  ! kkkkZ     % %X %)))r!r@c$eZdZdZdZddZdZdS)ClassifierMixinz0Mixin class for all classifiers in scikit-learn. classifierNcPddlm}|||||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)rErrrrs rscorezClassifierMixin.scores70 ,+++++~aa NNNNr!c ddiSNrTr&rDs rrzClassifierMixin._more_tags d##r!rrrrnrr_estimator_typerrr&r!rrr}sF::"OOOOO8$$$$$r!rc$eZdZdZdZddZdZdS)RegressorMixinz:Mixin class for all regression estimators in scikit-learn. regressorNcTddlm}||}||||S)aReturn the coefficient of determination of the prediction. The coefficient of determination :math:`R^2` is defined as :math:`(1 - \frac{u}{v})`, where :math:`u` is the residual sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v` is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of `y`, disregarding the input features, would get a :math:`R^2` score of 0.0. Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted`` is the number of samples used in the fitting for the estimator. y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for `X`. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- score : float :math:`R^2` of ``self.predict(X)`` w.r.t. `y`. Notes ----- The :math:`R^2` score used when calling ``score`` on a regressor uses ``multioutput='uniform_average'`` from version 0.23 to keep consistent with default value of :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). r)r2_scorer)rrr)rErrrry_preds rrzRegressorMixin.scores=R &%%%%%ax6????r!c ddiSrr&rDs rrzRegressorMixin._more_tagsrr!rrr&r!rrrsGDD!O,@,@,@,@\$$$$$r!rc$eZdZdZdZddZdZdS) ClusterMixinz7Mixin class for all cluster estimators in scikit-learn. clustererNc:|||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. Returns ------- labels : ndarray of shape (n_samples,), dtype=np.int64 Cluster labels. )fitlabels_rErrs r fit_predictzClusterMixin.fit_predicts&  |r!c dgiS)Npreserves_dtyper&rDs rrzClusterMixin._more_tagss !2&&r!r)rrrnrrrr rr&r!rrrsBAA!O,'''''r!rc:eZdZdZedZdZdZdZdS)BiclusterMixinz9Mixin class for all bicluster estimators in scikit-learn.c|j|jfS)z{Convenient way to get row and column indicators together. Returns the ``rows_`` and ``columns_`` members. )rows_columns_rDs r biclusters_zBiclusterMixin.biclusters_s z4=((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)rErrowscolumnss r get_indiceszBiclusterMixin.get_indicessD"z!}-"z$"BJw$7$7$:::r!c^||}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. c34K|]}t|VdSr)r)rrs rr^z+BiclusterMixin.get_shape..)s(--SVV------r!)rr))rErindicess r get_shapezBiclusterMixin.get_shapes4 ""1%%--W------r!cddlm}||d}||\}}||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. rr csr) accept_sparseN)utils.validationr rrnewaxis)rErdatar row_indcol_inds r get_submatrixzBiclusterMixin.get_submatrix+sa( 211111{4u555++A..GAAArzM*G344r!N) rrrnrrrrrrr'r&r!rrrs`CC ))X);;;*...&55555r!rceZdZdZddZdS)TransformerMixinaMixin class for all transformers in scikit-learn. If :term:`get_feature_names_out` is defined, then `BaseEstimator` will automatically wrap `transform` and `fit_transform` to follow the `set_output` API. See the :ref:`developer_api_set_output` for details. :class:`base.OneToOneFeatureMixin` and :class:`base.ClassNamePrefixFeaturesOutMixin` are helpful mixins for defining :term:`get_feature_names_out`. Nc |!|j|fi||S|j||fi||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. )r  transform)rErr fit_paramss r fit_transformzTransformerMixin.fit_transformRs]4 948A,,,,66q99 948Aq//J//99!<< r>s3KK(O&&&&&&r!r>ceZdZdgZdS)MetaEstimatorMixinr5N)rrrnr_required_parametersr&r!rrBrBs'=>>r!rBceZdZdZdZdS)MultiOutputMixinz2Mixin to mark estimators that support multioutput.c ddiS)N multioutputTr&rDs rrzMultiOutputMixin._more_tagss t$$r!Nrrrnrrrr&r!rrErEs)<<%%%%%r!rEceZdZdZdZdS)_UnstableArchMixinz=Mark estimators that are non-determinstic on 32bit or PowerPCc`dtp%tjdiS)Nnon_deterministic)ppcpowerpc)rplatformmachinerorDs rrz_UnstableArchMixin._more_tagss/ NX-//::;MNN  r!NrHr&r!rrJrJs)GG     r!rJc,t|dddkS)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. rNrrKr5s r is_classifierrTs 9/ 6 6, FFr!c,t|dddkS)a Return 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. rNrrRrSs r is_regressorrVs 9/ 6 6+ EEr!c,t|dddkS)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. rNr?rRrSs ris_outlier_detectorrXs 9/ 6 6:L LLr!)2rr.rp collectionsrrOrNrnumpyrrr_configrutilsrutils._set_outputr utils._tagsr r"r r rrrrrrutils._estimator_html_reprrutils._param_validationrrr@rrrrr)r/r6r:r>rBrErJrTrVrXr&r!rras&&  ###### ......('''''))))))&&&&&&++++++555555======------000000;;;;;;CCCCCC"KKKKK\JJJJJJJJZ"$"$"$"$"$"$"$"$J4$4$4$4$4$4$4$4$n''''''''>K5K5K5K5K5K5K5K5\+=+=+=+=+=+=+=+=\========