def main(features_fpath, tseries_fpath, tags_fpath, classes_fpath, clf_name): X, params = create_input_table(features_fpath, tseries_fpath, tags_fpath) y = np.loadtxt(classes_fpath) clf = create_grid_search(clf_name) class_matrices, conf_matrices = run_classifier(clf, X, y) metric_means = np.mean(class_matrices, axis=0) metric_ci = hci(class_matrices, .95, axis=0) print(clf_summary(metric_means, metric_ci)) print() conf_means = np.mean(conf_matrices, axis=0) conf_ci = hci(conf_matrices, .95, axis=0) print("Average confusion matrix with .95 confidence interval") print(" \ttrue ") print("predic") for i in range(conf_means.shape[0]): print(i, end="\t \t") for j in range(conf_means.shape[1]): print('%.3f +- %.3f' % (conf_means[i, j], conf_ci[i, j]), end="\t") print()
def main(features_fpath, tseries_fpath, tags_fpath, classes_fpath, clf_name): X, params = create_input_table(features_fpath, tseries_fpath, tags_fpath) y = np.loadtxt(classes_fpath) clf = create_grid_search(clf_name) class_matrices, conf_matrices = run_classifier(clf, X, y) metric_means = np.mean(class_matrices, axis=0) metric_ci = hci(class_matrices, .95, axis=0) print(clf_summary(metric_means, metric_ci)) print() conf_means = np.mean(conf_matrices, axis=0) conf_ci = hci(conf_matrices, .95, axis=0) print("Average confusion matrix with .95 confidence interval") print(" \ttrue ") print("predic") for i in xrange(conf_means.shape[0]): print(i, end="\t \t") for j in xrange(conf_means.shape[1]): print('%.3f +- %.3f' % (conf_means[i, j], conf_ci[i, j]), end="\t") print()
def create_learners(learner_name='extra_trees'): clf = create_grid_search(learner_name, n_jobs=-1) rgr = create_grid_search(learner_name, regressor=True, n_jobs=-1) return clf, rgr
def create_learners(learner_name='rbf_svm'): clf = create_grid_search(learner_name, n_jobs=-1) rgr = create_grid_search(learner_name, regressor=True, n_jobs=-1) return clf, rgr