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asv_benchmarks/benchmarks/utils.py
47 строк
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Thomas J. Fan
MNT Applies black formatting to most of the code base (#18948)
17 июн 2021, 21:21
Не верифицирован
17 июн 2021, 21:21
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import numpy as np from sklearn.metrics import balanced_accuracy_score, r2_score def neg_mean_inertia(X, labels, centers): return -(np.asarray(X - centers[labels]) ** 2).sum(axis=1).mean() def make_gen_classif_scorers(caller): caller.train_scorer = balanced_accuracy_score caller.test_scorer = balanced_accuracy_score def make_gen_reg_scorers(caller): caller.test_scorer = r2_score caller.train_scorer = r2_score def neg_mean_data_error(X, U, V): return -np.sqrt(((X - U.dot(V)) ** 2).mean()) def make_dict_learning_scorers(caller): caller.train_scorer = lambda _, __: ( neg_mean_data_error( caller.X, caller.estimator.transform(caller.X), caller.estimator.components_ ) ) caller.test_scorer = lambda _, __: ( neg_mean_data_error( caller.X_val, caller.estimator.transform(caller.X_val), caller.estimator.components_, ) ) def explained_variance_ratio(Xt, X): return np.var(Xt, axis=0).sum() / np.var(X, axis=0).sum() def make_pca_scorers(caller): caller.train_scorer = lambda _, __: caller.estimator.explained_variance_ratio_.sum() caller.test_scorer = lambda _, __: ( explained_variance_ratio(caller.estimator.transform(caller.X_val), caller.X_val) )