U e@sddZddlZddlZddlZddlmZddlmZddlm Z ddl m Z ddl Z ddlmZddlmZdd lmZmZdd lmZmZmZdd lmZdd lmZdd lmZmZddl m!Z!ddl"m#Z#ddl$m%Z%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,ddddddgZ-dKddddddddde j.d ddZ/dd Z0dLd"d#Z1d$d%Z2dMddddddde j.d&d'dZ3ddddddde j.fd(d)Z4dNd+d,Z5dOddddddd-d.d/dZ6d0d1Z7d2d3Z8d4d5Z9ddd6dddddd7d8dZ:d9d:Z;d;d<Zd?ddddd@ddde j.dddA dBdZ>dCdDZ?dEdFZ@ddddd@de j.ddGdHdZAdIdJZBdS)Pzm The :mod:`sklearn.model_selection._validation` module includes classes and functions to validate the model. N)partial) format_exc)suppress)Counter)logger) is_classifierclone) indexablecheck_random_state_safe_indexing)_check_fit_params) _num_samples)delayedParallel) _safe_split) check_scoring)_check_multimetric_scoring_MultimetricScorer)FitFailedWarning)check_cv) LabelEncodercross_validatecross_val_scorecross_val_predictpermutation_test_scorelearning_curvevalidation_curvez2*n_jobsF) groupsscoringcvn_jobsverbose fit_params pre_dispatchreturn_train_scorereturn_estimator error_scorec  s@t|\}t|td}t|r2|n(|dksDt|trPt|n t|t|| d} | f dd| |D}t |t|rt |t |}i}|d|d<|d|d<r|d|d<t |d }rt |d }|D]0}|||d |<r d |}||||<q |S) avEvaluate metric(s) by cross-validation and also record fit/score times. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object implementing 'fit' The object to use to fit the data. X : array-like of shape (n_samples, n_features) The data to fit. Can be for example a list, or an array. y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None The target variable to try to predict in the case of supervised learning. groups : array-like of shape (n_samples,), default=None Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a "Group" :term:`cv` instance (e.g., :class:`GroupKFold`). scoring : str, callable, list, tuple, or dict, default=None Strategy to evaluate the performance of the cross-validated model on the test set. If `scoring` represents a single score, one can use: - a single string (see :ref:`scoring_parameter`); - a callable (see :ref:`scoring`) that returns a single value. If `scoring` represents multiple scores, one can use: - a list or tuple of unique strings; - a callable returning a dictionary where the keys are the metric names and the values are the metric scores; - a dictionary with metric names as keys and callables a values. See :ref:`multimetric_grid_search` for an example. cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - int, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For int/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold. n_jobs : int, default=None Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the cross-validation splits. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. verbose : int, default=0 The verbosity level. fit_params : dict, default=None Parameters to pass to the fit method of the estimator. pre_dispatch : int or str, default='2*n_jobs' Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs - An int, giving the exact number of total jobs that are spawned - A str, giving an expression as a function of n_jobs, as in '2*n_jobs' return_train_score : bool, default=False Whether to include train scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance. .. versionadded:: 0.19 .. versionchanged:: 0.21 Default value was changed from ``True`` to ``False`` return_estimator : bool, default=False Whether to return the estimators fitted on each split. .. versionadded:: 0.20 error_score : 'raise' or numeric, default=np.nan Value to assign to the score if an error occurs in estimator fitting. If set to 'raise', the error is raised. If a numeric value is given, FitFailedWarning is raised. .. versionadded:: 0.20 Returns ------- scores : dict of float arrays of shape (n_splits,) Array of scores of the estimator for each run of the cross validation. A dict of arrays containing the score/time arrays for each scorer is returned. The possible keys for this ``dict`` are: ``test_score`` The score array for test scores on each cv split. Suffix ``_score`` in ``test_score`` changes to a specific metric like ``test_r2`` or ``test_auc`` if there are multiple scoring metrics in the scoring parameter. ``train_score`` The score array for train scores on each cv split. Suffix ``_score`` in ``train_score`` changes to a specific metric like ``train_r2`` or ``train_auc`` if there are multiple scoring metrics in the scoring parameter. This is available only if ``return_train_score`` parameter is ``True``. ``fit_time`` The time for fitting the estimator on the train set for each cv split. ``score_time`` The time for scoring the estimator on the test set for each cv split. (Note time for scoring on the train set is not included even if ``return_train_score`` is set to ``True`` ``estimator`` The estimator objects for each cv split. This is available only if ``return_estimator`` parameter is set to ``True``. See Also -------- cross_val_score : Run cross-validation for single metric evaluation. cross_val_predict : Get predictions from each split of cross-validation for diagnostic purposes. sklearn.metrics.make_scorer : Make a scorer from a performance metric or loss function. Examples -------- >>> from sklearn import datasets, linear_model >>> from sklearn.model_selection import cross_validate >>> from sklearn.metrics import make_scorer >>> from sklearn.metrics import confusion_matrix >>> from sklearn.svm import LinearSVC >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data[:150] >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso() Single metric evaluation using ``cross_validate`` >>> cv_results = cross_validate(lasso, X, y, cv=3) >>> sorted(cv_results.keys()) ['fit_time', 'score_time', 'test_score'] >>> cv_results['test_score'] array([0.3315057 , 0.08022103, 0.03531816]) Multiple metric evaluation using ``cross_validate`` (please refer the ``scoring`` parameter doc for more information) >>> scores = cross_validate(lasso, X, y, cv=3, ... scoring=('r2', 'neg_mean_squared_error'), ... return_train_score=True) >>> print(scores['test_neg_mean_squared_error']) [-3635.5... -3573.3... -6114.7...] >>> print(scores['train_r2']) [0.28009951 0.3908844 0.22784907]  classifierNr"r#r%c3s<|]4\}}ttt||ddd VqdS)NT)r& return_timesr'r(r_fit_and_scorer .0traintest Xr( estimatorr$r'r&scorersr#yd/var/www/html/assets/scripts/venv/lib/python3.8/site-packages/sklearn/model_selection/_validation.py s z!cross_validate..fit_time score_timer5 test_scores train_scoresztest_%sztrain_%s)r rrcallable isinstancestrrrrsplit!_warn_or_raise_about_fit_failures_insert_error_scores_aggregate_score_dicts_normalize_score_results)r5r4r7rr r!r"r#r$r%r&r'r(parallelresultsretZtest_scores_dictZtrain_scores_dictnamekeyr8r3r9r1s<L          csd}g}t|D]0\}}|ddk r0||q|dkr|d}qt|trfdd|D}|D]0}|||d<d||krb|||d<qbdS)zInsert error in `results` by replacing them inplace with `error_score`. This only applies to multimetric scores because `_fit_and_score` will handle the single metric case. N fit_errorr=csi|] }|qSr8r8r0rJr(r8r9 Jsz(_insert_error_scores..r>) enumerateappendr@dictcopy)rHr(Zsuccessful_scoreZfailed_indicesiresultZformatted_errorr8rNr9rD;s     rDscorecCst|dtrt|S||iS)z:Creates a scoring dictionary based on the type of `scores`r)r@rRrE)scoresZscaler_score_keyr8r8r9rFQsrFc sdd|D}|rt|}t|}t|}ddfdd|D}||krld|d|}t|n(d|d |d |d |}t|tdS) NcSs g|]}|ddk r|dqS)rLNr8)r0rUr8r8r9 [s z5_warn_or_raise_about_fit_failures..zQ--------------------------------------------------------------------------------  c3s$|]\}}|d|VqdS)z' fits failed with the following error: Nr8)r0errorn delimiterr8r9r:csz4_warn_or_raise_about_fit_failures..z All the z fits failed. It is very likely that your model is misconfigured. You can try to debug the error by setting error_score='raise'. Below are more details about the failures: z fits failed out of a total of zO. The score on these train-test partitions for these parameters will be set to z. If these failures are not expected, you can try to debug them by setting error_score='raise'. Below are more details about the failures: )lenrjoinitems ValueErrorwarningswarnr) rHr(Z fit_errorsZnum_failed_fitsZnum_fitsZfit_errors_counterZfit_errors_summaryZall_fits_failed_messageZsome_fits_failed_messager8r\r9rCZs$ rC)rr r!r"r#r$r%r(c Cs6t||d} t||||d| i||||| | d } | dS)aEvaluate a score by cross-validation. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object implementing 'fit' The object to use to fit the data. X : array-like of shape (n_samples, n_features) The data to fit. Can be for example a list, or an array. y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None The target variable to try to predict in the case of supervised learning. groups : array-like of shape (n_samples,), default=None Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a "Group" :term:`cv` instance (e.g., :class:`GroupKFold`). scoring : str or callable, default=None A str (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)`` which should return only a single value. Similar to :func:`cross_validate` but only a single metric is permitted. If `None`, the estimator's default scorer (if available) is used. cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - `None`, to use the default 5-fold cross validation, - int, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable that generates (train, test) splits as arrays of indices. For `int`/`None` inputs, if the estimator is a classifier and `y` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.22 `cv` default value if `None` changed from 3-fold to 5-fold. n_jobs : int, default=None Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the cross-validation splits. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. verbose : int, default=0 The verbosity level. fit_params : dict, default=None Parameters to pass to the fit method of the estimator. pre_dispatch : int or str, default='2*n_jobs' Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - ``None``, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs - An int, giving the exact number of total jobs that are spawned - A str, giving an expression as a function of n_jobs, as in '2*n_jobs' error_score : 'raise' or numeric, default=np.nan Value to assign to the score if an error occurs in estimator fitting. If set to 'raise', the error is raised. If a numeric value is given, FitFailedWarning is raised. .. versionadded:: 0.20 Returns ------- scores : ndarray of float of shape=(len(list(cv)),) Array of scores of the estimator for each run of the cross validation. See Also -------- cross_validate : To run cross-validation on multiple metrics and also to return train scores, fit times and score times. cross_val_predict : Get predictions from each split of cross-validation for diagnostic purposes. sklearn.metrics.make_scorer : Make a scorer from a performance metric or loss function. Examples -------- >>> from sklearn import datasets, linear_model >>> from sklearn.model_selection import cross_val_score >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data[:150] >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso() >>> print(cross_val_score(lasso, X, y, cv=3)) [0.3315057 0.08022103 0.03531816] r rV) r5r4r7rr r!r"r#r$r%r(Z test_score)rr) r5r4r7rr r!r"r#r$r%r(scorerZ cv_resultsr8r8r9r}s   c'sttjsdkrtdd}|dkrx|dk rLd|ddd |d}|rx|d krx|d |ddd |d7}|dkrdkrd}n t}d fd d|D}|d krd|d|}t|dt|d|dk r|ni}t|||}dk r, ). candidate_progress : {list, tuple} of int, default=None A list or tuple of format (, ). return_n_test_samples : bool, default=False Whether to return the ``n_test_samples``. return_times : bool, default=False Whether to return the fit/score times. return_estimator : bool, default=False Whether to return the fitted estimator. Returns ------- result : dict with the following attributes train_scores : dict of scorer name -> float Score on training set (for all the scorers), returned only if `return_train_score` is `True`. test_scores : dict of scorer name -> float Score on testing set (for all the scorers). n_test_samples : int Number of test samples. fit_time : float Time spent for fitting in seconds. score_time : float Time spent for scoring in seconds. parameters : dict or None The parameters that have been evaluated. estimator : estimator object The fitted estimator. fit_error : str or None Traceback str if the fit failed, None if the fit succeeded. raisezerror_score must be the string 'raise' or a numeric value. (Hint: if using 'raise', please make sure that it has been spelled correctly.)rN rr/ z; z, c3s |]}|d|VqdS)=Nr8)r0k) parametersr8r9r:sz!_fit_and_score..z[CVz] START P.F)safecsi|] }|qSr8r8rMrNr8r9rOsz"_fit_and_score..rLz] END ;z: (ztrain=z.3fztest=)z, score=z(train=z, test=z total time=r=r>Zn_test_samplesr;r<rmr5)r@numbersNumberrasortedr_printr^r r`r Z set_paramstimerfit ExceptionrRrSr_scorerZshort_format_timer)'r5r4r7rer1r2r#rmr$r&Zreturn_parametersZreturn_n_test_samplesr,r'Zsplit_progressZcandidate_progressr(Z progress_msgZ params_msgZ sorted_keysZ start_msgZcloned_parametersrlv start_timeX_trainy_trainX_testy_testrUr;r<r=r> total_timeZend_msgZ result_msgZ scorer_nameZ scorer_scoresr8)r(rmr9r.sk                  r.rfc Cst|trt||dkd}z$|dkr0|||}n ||||}WnLtk rt|tr\n*|dkrhn|}td|dttYnXt|trdd|D}|r|D](\}}|||<td|d|tqd} t|trR|D]\\}} t | d r"t t | } W5QRXt| t jsFt | | t| |f| ||<qnLt |d rzt t | }W5QRXt|t jst | |t||f|S) zCompute the score(s) of an estimator on a given test set. Will return a dict of floats if `scorer` is a dict, otherwise a single float is returned. rf)r6Z raise_excNz[Scoring failed. The score on this train-test partition for these parameters will be set to z . Details: cSs"g|]\}}t|tr||fqSr8)r@rA)r0rJstr_er8r8r9rXs z_score..z>scoring must return a number, got %s (%s) instead. (scorer=%s)item)r@rRrrzrbrcr UserWarningr`hasattrrrarrtrutype) r5rrrer(rWZexception_messagesrJr error_msgrVr8r8r9r{sT            r{Zpredict)rr!r"r#r$r%methodcst|\}t|td}t||} tdd| D} t| ts`t ddkondk } | rt j dkrt } | nTj dkrtjtd }tjdD](t ddf|ddf<q|t||d }|fd d | D}tjt| td tt| | <t|d rftj||d jd}nf| rt|d trˆjd}g}t|D](tfdd|D}||q|}n t|}t|trfdd|DS|SdS)aqGenerate cross-validated estimates for each input data point. The data is split according to the cv parameter. Each sample belongs to exactly one test set, and its prediction is computed with an estimator fitted on the corresponding training set. Passing these predictions into an evaluation metric may not be a valid way to measure generalization performance. Results can differ from :func:`cross_validate` and :func:`cross_val_score` unless all tests sets have equal size and the metric decomposes over samples. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object implementing 'fit' and 'predict' The object to use to fit the data. X : array-like of shape (n_samples, n_features) The data to fit. Can be, for example a list, or an array at least 2d. y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None The target variable to try to predict in the case of supervised learning. groups : array-like of shape (n_samples,), default=None Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a "Group" :term:`cv` instance (e.g., :class:`GroupKFold`). cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - int, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable that generates (train, test) splits as arrays of indices. For int/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold. n_jobs : int, default=None Number of jobs to run in parallel. Training the estimator and predicting are parallelized over the cross-validation splits. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. verbose : int, default=0 The verbosity level. fit_params : dict, default=None Parameters to pass to the fit method of the estimator. pre_dispatch : int or str, default='2*n_jobs' Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs - An int, giving the exact number of total jobs that are spawned - A str, giving an expression as a function of n_jobs, as in '2*n_jobs' method : {'predict', 'predict_proba', 'predict_log_proba', 'decision_function'}, default='predict' The method to be invoked by `estimator`. Returns ------- predictions : ndarray This is the result of calling `method`. Shape: - When `method` is 'predict' and in special case where `method` is 'decision_function' and the target is binary: (n_samples,) - When `method` is one of {'predict_proba', 'predict_log_proba', 'decision_function'} (unless special case above): (n_samples, n_classes) - If `estimator` is :term:`multioutput`, an extra dimension 'n_outputs' is added to the end of each shape above. See Also -------- cross_val_score : Calculate score for each CV split. cross_validate : Calculate one or more scores and timings for each CV split. Notes ----- In the case that one or more classes are absent in a training portion, a default score needs to be assigned to all instances for that class if ``method`` produces columns per class, as in {'decision_function', 'predict_proba', 'predict_log_proba'}. For ``predict_proba`` this value is 0. In order to ensure finite output, we approximate negative infinity by the minimum finite float value for the dtype in other cases. Examples -------- >>> from sklearn import datasets, linear_model >>> from sklearn.model_selection import cross_val_predict >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data[:150] >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso() >>> y_pred = cross_val_predict(lasso, X, y, cv=3) r)cSsg|] \}}|qSr8r8)r0_r2r8r8r9rXsz%cross_val_predict..z+cross_val_predict only works for partitionsdecision_function predict_probapredict_log_probaNrrdtyper+c 3s0|](\}}ttt||VqdSN)r_fit_and_predictr r/)r4r5r$rr#r7r8r9r:sz$cross_val_predict..r)formatcsg|] }|qSr8r8r0p)i_labelr8r9rXscsg|] }|qSr8r8r)inv_test_indicesr8r9rXs)r rrlistrBnpZ concatenate_check_is_permutationrraasarrayndimrZ fit_transformZ zeros_likeintrangeshaperemptyr^arangespissparseZvstackrr@rQ)r5r4r7rr!r"r#r$r%rZsplitsZ test_indicesencodeleZy_encrG predictionsZn_labelsZ concat_predZ label_predsr8)r4r5r$rrrr#r7r9r5sN     &    cs|dk r |ni}t|||}t||\}} t|||\} } | dkrZj|f|nj|| f|t} | | dkodk } | rttrfddttDn0jdkrtt nj d}t j |S)aXFit estimator and predict values for a given dataset split. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object implementing 'fit' and 'predict' The object to use to fit the data. X : array-like of shape (n_samples, n_features) The data to fit. .. versionchanged:: 0.20 X is only required to be an object with finite length or shape now y : array-like of shape (n_samples,) or (n_samples, n_outputs) or None The target variable to try to predict in the case of supervised learning. train : array-like of shape (n_train_samples,) Indices of training samples. test : array-like of shape (n_test_samples,) Indices of test samples. verbose : int The verbosity level. fit_params : dict or None Parameters that will be passed to ``estimator.fit``. method : str Invokes the passed method name of the passed estimator. Returns ------- predictions : sequence Result of calling 'estimator.method' Nrc s:g|]2}tj||ttdd|fdqS)N) n_classesr)_enforce_prediction_orderclasses_r^set)r0rr5rrr7r8r9rX7sz$_fit_and_predict..r) r rrygetattrr@rrr^rrrrr)r5r4r7r1r2r#r$rr~rrrfuncrrr8rr9rs2)     rcCs|t|krd}tdt|||t|dkr|jdkrf|jdt|krftd|j|t|t|dkrtdt|||t |j j }||dd }tj t ||f|||j d }||d d |f<|}|S) a Ensure that prediction arrays have correct column order When doing cross-validation, if one or more classes are not present in the subset of data used for training, then the output prediction array might not have the same columns as other folds. Use the list of class names (assumed to be ints) to enforce the correct column order. Note that `classes` is the list of classes in this fold (a subset of the classes in the full training set) and `n_classes` is the number of classes in the full training set. zTTo fix this, use a cross-validation technique resulting in properly stratified foldszNumber of classes in training fold ({}) does not match total number of classes ({}). Results may not be appropriate for your use case. {}rrrzOutput shape {} of {} does not match number of classes ({}) in fold. Irregular decision_function outputs are not currently supported by cross_val_predictzOnly {} class/es in training fold, but {} in overall dataset. This is not supported for decision_function with imbalanced folds. {}r)rrrrN)r^rbrcrRuntimeWarningrrrarZfinforminfullr)classesrrrZrecommendationZ float_mindefault_valuesZpredictions_for_all_classesr8r8r9rIsR    rcCs8t||krdStj|td}d||<t|s4dSdS)a5Check whether indices is a reordering of the array np.arange(n_samples) Parameters ---------- indices : ndarray int array to test n_samples : int number of expected elements Returns ------- is_partition : bool True iff sorted(indices) is np.arange(n) FrT)r^rZzerosboolall)indicesZ n_sampleshitr8r8r9rs  rd)rr!n_permutationsr" random_stater#r r$c st\ttdt| dtttd} t||dfddt|D} t | } t | | kd|d} | | | fS) aEvaluate the significance of a cross-validated score with permutations. Permutes targets to generate 'randomized data' and compute the empirical p-value against the null hypothesis that features and targets are independent. The p-value represents the fraction of randomized data sets where the estimator performed as well or better than in the original data. A small p-value suggests that there is a real dependency between features and targets which has been used by the estimator to give good predictions. A large p-value may be due to lack of real dependency between features and targets or the estimator was not able to use the dependency to give good predictions. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object implementing 'fit' The object to use to fit the data. X : array-like of shape at least 2D The data to fit. y : array-like of shape (n_samples,) or (n_samples, n_outputs) or None The target variable to try to predict in the case of supervised learning. groups : array-like of shape (n_samples,), default=None Labels to constrain permutation within groups, i.e. ``y`` values are permuted among samples with the same group identifier. When not specified, ``y`` values are permuted among all samples. When a grouped cross-validator is used, the group labels are also passed on to the ``split`` method of the cross-validator. The cross-validator uses them for grouping the samples while splitting the dataset into train/test set. cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - `None`, to use the default 5-fold cross validation, - int, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For `int`/`None` inputs, if the estimator is a classifier and `y` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.22 `cv` default value if `None` changed from 3-fold to 5-fold. n_permutations : int, default=100 Number of times to permute ``y``. n_jobs : int, default=None Number of jobs to run in parallel. Training the estimator and computing the cross-validated score are parallelized over the permutations. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. random_state : int, RandomState instance or None, default=0 Pass an int for reproducible output for permutation of ``y`` values among samples. See :term:`Glossary `. verbose : int, default=0 The verbosity level. scoring : str or callable, default=None A single str (see :ref:`scoring_parameter`) or a callable (see :ref:`scoring`) to evaluate the predictions on the test set. If `None` the estimator's score method is used. fit_params : dict, default=None Parameters to pass to the fit method of the estimator. .. versionadded:: 0.24 Returns ------- score : float The true score without permuting targets. permutation_scores : array of shape (n_permutations,) The scores obtained for each permutations. pvalue : float The p-value, which approximates the probability that the score would be obtained by chance. This is calculated as: `(C + 1) / (n_permutations + 1)` Where C is the number of permutations whose score >= the true score. The best possible p-value is 1/(n_permutations + 1), the worst is 1.0. Notes ----- This function implements Test 1 in: Ojala and Garriga. `Permutation Tests for Studying Classifier Performance `_. The Journal of Machine Learning Research (2010) vol. 11 r)rdr$)r"r#c 3s4|],}ttttdVqdS)rN)r_permutation_test_scorer _shuffle)r0rr4r!r5r$rrrer7r8r9r:-s  z)permutation_test_score..?r) r rrrr rr rrrarraysum)r5r4r7rr!rr"rr#r r$rVZpermutation_scoresZpvaluer8rr9rs& "  cCs|dk r |ni}g}||||D]\\}} t||||\} } t|||| |\} } t|||}|j| | f||||| | q"t|S)z-Auxiliary function for permutation_test_scoreN)rBrr ryrQrZmean)r5r4r7rr!rer$Z avg_scorer1r2r~rrrr8r8r9r>s rcCsZ|dkr|t|}n8tt|}t|D]}||k}|||||<q0t||S)zAReturn a shuffled copy of y eventually shuffle among same groups.N) permutationr^rruniquer )r7rrrgroupZ this_maskr8r8r9rLsrg?rr) r train_sizesr!r exploit_incremental_learningr"r%r#shufflerr(r,r$c  s |rtdstdt |\ }t| td}t| |}t|dt|dd}t ||j d} dkrt dt t || d}| rt| fdd |D}|rtrt nd | f d d |D}t|d }ng}|D],\}}D]}||d ||fq*q| fd d |D}t|}|dd|j}|dd|j}||g}r|dd|j}|dd|j}|||g|d|df}r||d|df}|S)a~Learning curve. Determines cross-validated training and test scores for different training set sizes. A cross-validation generator splits the whole dataset k times in training and test data. Subsets of the training set with varying sizes will be used to train the estimator and a score for each training subset size and the test set will be computed. Afterwards, the scores will be averaged over all k runs for each training subset size. Read more in the :ref:`User Guide `. Parameters ---------- estimator : object type that implements the "fit" and "predict" methods An object of that type which is cloned for each validation. X : array-like of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) or (n_samples, n_outputs) Target relative to X for classification or regression; None for unsupervised learning. groups : array-like of shape (n_samples,), default=None Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a "Group" :term:`cv` instance (e.g., :class:`GroupKFold`). train_sizes : array-like of shape (n_ticks,), default=np.linspace(0.1, 1.0, 5) Relative or absolute numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum size of the training set (that is determined by the selected validation method), i.e. it has to be within (0, 1]. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class. cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - int, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For int/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold. scoring : str or callable, default=None A str (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)``. exploit_incremental_learning : bool, default=False If the estimator supports incremental learning, this will be used to speed up fitting for different training set sizes. n_jobs : int, default=None Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the different training and test sets. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. pre_dispatch : int or str, default='all' Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The str can be an expression like '2*n_jobs'. verbose : int, default=0 Controls the verbosity: the higher, the more messages. shuffle : bool, default=False Whether to shuffle training data before taking prefixes of it based on``train_sizes``. random_state : int, RandomState instance or None, default=None Used when ``shuffle`` is True. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. error_score : 'raise' or numeric, default=np.nan Value to assign to the score if an error occurs in estimator fitting. If set to 'raise', the error is raised. If a numeric value is given, FitFailedWarning is raised. .. versionadded:: 0.20 return_times : bool, default=False Whether to return the fit and score times. fit_params : dict, default=None Parameters to pass to the fit method of the estimator. .. versionadded:: 0.24 Returns ------- train_sizes_abs : array of shape (n_unique_ticks,) Numbers of training examples that has been used to generate the learning curve. Note that the number of ticks might be less than n_ticks because duplicate entries will be removed. train_scores : array of shape (n_ticks, n_cv_folds) Scores on training sets. test_scores : array of shape (n_ticks, n_cv_folds) Scores on test set. fit_times : array of shape (n_ticks, n_cv_folds) Times spent for fitting in seconds. Only present if ``return_times`` is True. score_times : array of shape (n_ticks, n_cv_folds) Times spent for scoring in seconds. Only present if ``return_times`` is True. Examples -------- >>> from sklearn.datasets import make_classification >>> from sklearn.tree import DecisionTreeClassifier >>> from sklearn.model_selection import learning_curve >>> X, y = make_classification(n_samples=100, n_features=10, random_state=42) >>> tree = DecisionTreeClassifier(max_depth=4, random_state=42) >>> train_size_abs, train_scores, test_scores = learning_curve( ... tree, X, y, train_sizes=[0.3, 0.6, 0.9] ... ) >>> for train_size, cv_train_scores, cv_test_scores in zip( ... train_size_abs, train_scores, test_scores ... ): ... print(f"{train_size} samples were used to train the model") ... print(f"The average train accuracy is {cv_train_scores.mean():.2f}") ... print(f"The average test accuracy is {cv_test_scores.mean():.2f}") 24 samples were used to train the model The average train accuracy is 1.00 The average test accuracy is 0.85 48 samples were used to train the model The average train accuracy is 1.00 The average test accuracy is 0.90 72 samples were used to train the model The average train accuracy is 1.00 The average test accuracy is 0.93 partial_fitzSAn estimator must support the partial_fit interface to exploit incremental learningr)rdrz%[learning_curve] Training set sizes: r"r%r#c3s |]\}}||fVqdSr)rr/)rngr8r9r:!sz!learning_curve..Nc3s:|]2\}}ttt ||d VqdS))r(r$N)r_incremental_fit_estimatorr r/) r4rr(r5r$r,retrain_sizes_absr#r7r8r9r:%s)rrrc3s:|]2\}}ttt||ddd VqdS)NT)rmr$r&r(r,r-r/)r4r(r5r$r,rer#r7r8r9r:=sr>r=r;r<rr)rrar rrrrBrr^_translate_train_sizesrrwrArr rrrZ transposerQrEreshapeTextend)r5r4r7rrr!r rr"r%r#rrr(r,r$Zcv_itern_max_training_samplesZn_unique_ticksrGoutZtrain_test_proportionsr1r2n_train_samplesrHr>r= fit_times score_timesrIr8) r4rr(r5r$r,rrerr#r7r9rXsV0    cCst|}|jd}t|}t|}t|jtjrz|dksH|dkrXtd||f||j t dd}t |d|}n"|dks||krtd|||ft |}||jdkrt d |jd|ft|S) a~Determine absolute sizes of training subsets and validate 'train_sizes'. Examples: _translate_train_sizes([0.5, 1.0], 10) -> [5, 10] _translate_train_sizes([5, 10], 10) -> [5, 10] Parameters ---------- train_sizes : array-like of shape (n_ticks,) Numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of 'n_max_training_samples', i.e. it has to be within (0, 1]. n_max_training_samples : int Maximum number of training samples (upper bound of 'train_sizes'). Returns ------- train_sizes_abs : array of shape (n_unique_ticks,) Numbers of training examples that will be used to generate the learning curve. Note that the number of ticks might be less than n_ticks because duplicate entries will be removed. rrqrztrain_sizes has been interpreted as fractions of the maximum number of training samples and must be within (0, 1], but is within [%f, %f].F)rrSrz|train_sizes has been interpreted as absolute numbers of training samples and must be within (0, %d], but is within [%d, %d].z|Removed duplicate entries from 'train_sizes'. Number of ticks will be less than the size of 'train_sizes': %d instead of %d.)rrrrmaxZ issubdtyperZfloatingraZastyperZcliprrbrcr)rrrZn_ticksZn_min_required_samplesZn_max_required_samplesr8r8r9r`sJ      rc Csdggggf\} } }}t|t||dd}| dkr:i} |dkrRt|jf| }nt|jfd|i| }|D]\}}|d|}t||||\}}t||||\}}t|||||\}}t}|dkr||n |||t|}||t}| t||||| | t||||| t|}||ql| rP| | ||fn| | f}t |j S)zETrain estimator on training subsets incrementally and compute scores.Nrr) ziprrBrrrrxrQr{rr) r5r4r7rr1r2rrer#r,r(r$r>r=rrZ partitionsZpartial_fit_funcrZ partial_trainZ train_subsetr~rZX_partial_trainZy_partial_trainrrZ start_fitr;Z start_scorer<rIr8r8r9rs8        r)rr!r r"r%r#r(r$c  st|\}t|td}t|dt|| d} | f dd||D}t}t|}|dd|j }|dd|j }||fS) aiValidation curve. Determine training and test scores for varying parameter values. Compute scores for an estimator with different values of a specified parameter. This is similar to grid search with one parameter. However, this will also compute training scores and is merely a utility for plotting the results. Read more in the :ref:`User Guide `. Parameters ---------- estimator : object type that implements the "fit" and "predict" methods An object of that type which is cloned for each validation. X : array-like of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) or (n_samples, n_outputs) or None Target relative to X for classification or regression; None for unsupervised learning. param_name : str Name of the parameter that will be varied. param_range : array-like of shape (n_values,) The values of the parameter that will be evaluated. groups : array-like of shape (n_samples,), default=None Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a "Group" :term:`cv` instance (e.g., :class:`GroupKFold`). cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - int, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For int/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold. scoring : str or callable, default=None A str (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)``. n_jobs : int, default=None Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the combinations of each parameter value and each cross-validation split. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. pre_dispatch : int or str, default='all' Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The str can be an expression like '2*n_jobs'. verbose : int, default=0 Controls the verbosity: the higher, the more messages. error_score : 'raise' or numeric, default=np.nan Value to assign to the score if an error occurs in estimator fitting. If set to 'raise', the error is raised. If a numeric value is given, FitFailedWarning is raised. .. versionadded:: 0.20 fit_params : dict, default=None Parameters to pass to the fit method of the estimator. .. versionadded:: 0.24 Returns ------- train_scores : array of shape (n_ticks, n_cv_folds) Scores on training sets. test_scores : array of shape (n_ticks, n_cv_folds) Scores on test set. Notes ----- See :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py` r)rdrc3sF|]>\}}D]0}ttt|||idd VqqdS)T)rmr$r&r(Nr-)r0r1r2r| r4r(r5r$ param_name param_rangerer#r7r8r9r:Ts z#validation_curve..r>rr=) r rrrrrBr^rErr)r5r4r7rrrr!r r"r%r#r(r$rGrHZn_paramsr>r=r8rr9rst  csfdddDS)aAggregate the list of dict to dict of np ndarray The aggregated output of _aggregate_score_dicts will be a list of dict of form [{'prec': 0.1, 'acc':1.0}, {'prec': 0.1, 'acc':1.0}, ...] Convert it to a dict of array {'prec': np.array([0.1 ...]), ...} Parameters ---------- scores : list of dict List of dicts of the scores for all scorers. This is a flat list, assumed originally to be of row major order. Example ------- >>> scores = [{'a': 1, 'b':10}, {'a': 2, 'b':2}, {'a': 3, 'b':3}, ... {'a': 10, 'b': 10}] # doctest: +SKIP >>> _aggregate_score_dicts(scores) # doctest: +SKIP {'a': array([1, 2, 3, 10]), 'b': array([10, 2, 3, 10])} csLi|]Dtdtjr6tfddDnfddDqS)rcsg|] }|qSr8r8r0rVrKr8r9rXsz5_aggregate_score_dicts...csg|] }|qSr8r8rrr8r9rXs)r@rtrurr)r0rWrr9rOs z*_aggregate_score_dicts..rr8rr8rr9rEos rE)N)rV)N)rf)N)C__doc__rbrtrx functoolsr tracebackr contextlibr collectionsrnumpyrZ scipy.sparsesparserZjoblibrbaserr utilsr r r Zutils.validationr rZutils.parallelrrZutils.metaestimatorsrZmetricsrZmetrics._scorerrr exceptionsr_splitrZ preprocessingr__all__nanrrDrFrCrr.r{rrrrrrrZlinspacerrrrrEr8r8r8r9s                  & ! _ G FOB    D=