\!h6SSKJr SSKrSSKrSrSrSSjrg)) combinationsNcXU4H'n[UR5S:wdM[S5e URSURS:wa [S5eURSURS:wa [S5eX:HR[5nX:HR[5nXE-nXE- n[ R "S[S9n[ R"US:H5US '[ R"US:H5US '[ R"US :H5US '[ R"US:H5US 'U$)a_ Compute a 2x2 contigency table for McNemar's test. Parameters ----------- y_target : array-like, shape=[n_samples] True class labels as 1D NumPy array. y_model1 : array-like, shape=[n_samples] Predicted class labels from model as 1D NumPy array. y_model2 : array-like, shape=[n_samples] Predicted class labels from model 2 as 1D NumPy array. Returns ---------- tb : array-like, shape=[2, 2] 2x2 contingency table with the following contents: a: tb[0, 0]: # of samples that both models predicted correctly b: tb[0, 1]: # of samples that model 1 got right and model 2 got wrong c: tb[1, 0]: # of samples that model 2 got right and model 1 got wrong d: tb[1, 1]: # of samples that both models predicted incorrectly Examples ----------- For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/evaluate/mcnemar_table/ /One or more input arrays are not 1-dimensional.rz=y_target and y_model1 contain a different number of elements.z=y_target and y_model2 contain a different number of elements.r)dtyperrrrrrrrr)lenshape ValueErrorastypeintnpzerossum) y_targety_model1y_model2ary m1_vs_true m2_vs_true plus_true minus_truetbs kC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\mlxtend/evaluate/mcnemar.py mcnemar_tabler!s-8H- syy>Q NO O.~~aHNN1-- N  ~~aHNN1-- N  &..s3J&..s3J'I(J & $Bvvi1n%BtHvvjAo&BtHvvjB&'BtHvvi1n%BtH Ic.[5n[U5nU/U-HEn[UR5S:wa [ S5eUR URS5 MG [U5S:a [ S5e[U5nUS:a [ S5e0n[ [U5S5Hn[R"S5nXUS:HR[5nXUS:HR[5n X-n X- n [R"U S:H5US'[R"U S:H5US '[R"U S :H5US '[R"U S:H5US 'S US<SUS<3n XuU 'M U$)a Compute multiple 2x2 contigency tables for McNemar's test or Cochran's Q test. Parameters ----------- y_target : array-like, shape=[n_samples] True class labels as 1D NumPy array. y_model_predictions : array-like, shape=[n_samples] Predicted class labels for a model. Returns ---------- tables : dict Dictionary of NumPy arrays with shape=[2, 2]. Each dictionary key names the two models to be compared based on the order the models were passed as `*y_model_predictions`. The number of dictionary entries is equal to the number of pairwise combinations between the m models, i.e., "m choose 2." For example the following target array (containing the true labels) and 3 models - y_true = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) - y_mod0 = np.array([0, 1, 0, 0, 0, 1, 1, 0, 0, 0]) - y_mod1 = np.array([0, 0, 1, 1, 0, 1, 1, 0, 0, 0]) - y_mod2 = np.array([0, 1, 1, 1, 0, 1, 0, 0, 0, 0]) would result in the following dictionary: {'model_0 vs model_1': array([[ 4., 1.], [ 2., 3.]]), 'model_0 vs model_2': array([[ 3., 0.], [ 3., 4.]]), 'model_1 vs model_2': array([[ 3., 0.], [ 2., 5.]])} Each array is structured in the following way: - tb[0, 0]: # of samples that both models predicted correctly - tb[0, 1]: # of samples that model a got right and model b got wrong - tb[1, 0]: # of samples that model b got right and model a got wrong - tb[1, 1]: # of samples that both models predicted incorrectly Examples ----------- For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/evaluate/mcnemar_tables/ rrrz;Each prediction array must have the same number of samples.rz+Provide at least 2 model prediction arrays.rr r r r rmodel_z vs model_) setlistrrraddrrangerrrrr) ry_model_predictions model_lensr num_modelstablescombrmodel1_vs_truemodel2_vs_truerrname_strs r mcnemar_tablesr1HsnJ23z// syy>Q NO Osyy|$0  : L  ()JA~FGG FU:.2 XXf "$q'&BBJJ3O"$q'&BBJJ3O"3 #4 66)q.)466*/*466**+466)q.)4-1!Wd1g>x3 Mr"cURS:Xd [S5eUSnUSnX4-nU(dU(a'[USUS- 5S- S-[U5- nOUSUS- S-[U5- n[R R RRUS5nXg4$Sn[[R RR[X45X4-S 5S -S5nXg4$) a McNemar test for paired nominal data Parameters ----------- ary : array-like, shape=[2, 2] 2 x 2 contigency table (as returned by evaluate.mcnemar_table), where a: ary[0, 0]: # of samples that both models predicted correctly b: ary[0, 1]: # of samples that model 1 got right and model 2 got wrong c: ary[1, 0]: # of samples that model 2 got right and model 1 got wrong d: aryCell [1, 1]: # of samples that both models predicted incorrectly corrected : array-like, shape=[n_samples] (default: True) Uses Edward's continuity correction for chi-squared if `True` exact : bool, (default: False) If `True`, uses an exact binomial test comparing b to a binomial distribution with n = b + c and p = 0.5. It is highly recommended to use `exact=True` for sample sizes < 25 since chi-squared is not well-approximated by the chi-squared distribution! Returns ----------- chi2, p : float or None, float Returns the chi-squared value and the p-value; if `exact=True` (default: `False`), `chi2` is `None` Examples ----------- For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/evaluate/mcnemar/ rz Input array must be a 2x2 array.r r g?rrNg?g@) rrabsfloatscipystats distributionschi2sfminbinomcdf)r correctedexactbcnr8ps r mcnemarrCsH 99 ;<< D A D A A  D CI-.4:U1XEDID )a/%(:D KK % % * * - -dA 6 7N  !!%%c!irHs"#7tYx=r"