\!h*\SSKrSSKJr SSKJr SSKJrJr SSjr S Sjr S Sjr g) N)stats) get_scorer)KFoldtrain_test_splitc [U[5(d,[U[5(d[S[ U5-5e[ R RU5nUc1URS:XaSnOURS:XaSnO [S5e[U[5(a [U5n OUn /n [U5Hin URSSS 9n [X#X\S 9upnnURX5 URX5 U "XU5nU "XU5nU R!UU- 5 Mk [ R""U 5nU[ R$"U5-n[ R$"['U Vs/sH nUU- S -PM sn5US - - 5nUU- n[(R*R-[ R."U5US - 5S -n[U5[U54$s snf)a Implements the resampled paired t test procedure to compare the performance of two models (also called k-hold-out paired t test). Parameters ---------- estimator1 : scikit-learn classifier or regressor estimator2 : scikit-learn classifier or regressor X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values. num_rounds : int (default: 30) Number of resampling iterations (i.e., train/test splits) test_size : float or int (default: 0.3) If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to use as a test set. If int, represents the absolute number of test exsamples. scoring : str, callable, or None (default: None) If None (default), uses 'accuracy' for sklearn classifiers and 'r2' for sklearn regressors. If str, uses a sklearn scoring metric string identifier, for example {accuracy, f1, precision, recall, roc_auc} for classifiers, {'mean_absolute_error', 'mean_squared_error'/'neg_mean_squared_error', 'median_absolute_error', 'r2'} for regressors. If a callable object or function is provided, it has to be conform with sklearn's signature ``scorer(estimator, X, y)``; see https://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html for more information. random_seed : int or None (default: None) Random seed for creating the test/train splits. Returns ---------- t : float The t-statistic pvalue : float Two-tailed p-value. If the chosen significance level is larger than the p-value, we reject the null hypothesis and accept that there are significant differences in the two compared models. Examples ----------- For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/evaluate/paired_ttest_resampled/ z0train_size must be of type int or float. Got %s. classifieraccuracy regressorr2,Estimator must be a Classifier or Regressor.rlowhigh test_size random_state@) isinstanceintfloat ValueErrortypenprandom RandomState_estimator_typeAttributeErrorstrrrangerandintrfitappendmeansqrtsumrtsfabs) estimator1 estimator2Xy num_roundsrscoring random_seedrngscorer score_diffir#X_trainX_testy_trainy_test est1_score est2_scoreavg_diff numeratordiff denominatort_statpvalues iC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\mlxtend/evaluate/ttest.pypaired_ttest_resampledrDsN i % %jE.J.J ADO S   ))   ,C  % % 5 G  ' '; 6G !RS S'3G$J : ++!%+0+; I, (& w(w(J7 J7 *z12wwz"H277:..I'' ; dXo! # ;< QOK $F WWZZv Q 7# =F =%- '' r?r@rArBs rCpaired_ttest_kfold_cvrMs@  B 0 B' J  % % 5 G  ' '; 6G !RS S'3G$J#%88A; .!-.!-w(w(J7 J7 *z12$/wwz"H2772;&I'' ; dXo! # ;<QGK $F WWZZvQ /# 5F =%- '' TRX5 TRX5 T "TX5nT "TX5nXE- nU$)N)r$) X_1X_2y_1y_2r;r<r5r,r-r4s rCr5&paired_ttest_5x2cv..score_diff2sAs s J1 J1 , rr rg?rrrg?)rrrrr rr!rr"r#rr'rr)r*r+r)r,r-r.r/r1r2r3 variance_sum first_diffr5r6r#rPrQrRrS score_diff_1 score_diff_2 score_mean score_varr>r@rArBr4s`` @rCpaired_ttest_5x2cvr]seh ))   ,C  % % 5 G  ' '; 6G !RS S'3G$LJ1X++!%+0-acPWX#!#C5 !#C5 "\1S8 !J.14 z8QVW7WW  !  %JI'''L01K  $F WWZZv *S 0F =%- ''rU)g333333?NN) NFN)NN) numpyrscipyrsklearn.metricsrsklearn.model_selectionrrrDrMr]rUrCres<&; r(lSWg(T^(rU