def main(features_fpath, tag_categ_fpath, tseries_fpath, num_days_to_use, assign_fpath, out_foldpath): X, feature_ids, _ = \ create_input_table(features_fpath, tseries_fpath, tag_categ_fpath, num_days_to_use) X = scale(X) y_clf = np.genfromtxt(assign_fpath) y_regr = scale(np.genfromtxt(tseries_fpath)[:, 1:].sum(axis=1)) run_experiment(X, y_clf, y_regr, feature_ids, out_foldpath)
def main(features_fpath, tag_categ_fpath, tseries_fpath, num_days_to_use, assign_fpath, out_foldpath): X, feature_ids, _ = \ create_input_table(features_fpath, tseries_fpath, tag_categ_fpath, num_days_to_use) X = scale(X) y_clf = np.genfromtxt(assign_fpath) y_regr = scale(np.genfromtxt(tseries_fpath)[:,1:].sum(axis=1)) run_experiment(X, y_clf, y_regr, feature_ids, out_foldpath)
def main(partial_features_fpath, tag_categ_fpath, tseries_fpath, num_days_to_use, assign_fpath, out_foldpath): X, feature_ids, feature_names = \ create_input_table(partial_features_fpath, tseries_fpath, tag_categ_fpath, num_pts = num_days_to_use) #Sort X by upload date up_date_col = feature_names['A_UPLOAD_DATE'] sort_by_date = X[:, up_date_col].argsort() X = X[sort_by_date].copy() y_clf = np.genfromtxt(assign_fpath)[sort_by_date] y_regr = np.genfromtxt(tseries_fpath)[:, 1:].sum(axis=1)[sort_by_date] run_experiment(X, y_clf, y_regr, feature_ids, out_foldpath)
def main(partial_features_fpath, tag_categ_fpath, tseries_fpath, num_days_to_use, assign_fpath, out_foldpath): X, feature_ids, feature_names = \ create_input_table(partial_features_fpath, tseries_fpath, tag_categ_fpath, num_pts = num_days_to_use) #Sort X by upload date up_date_col = feature_names['A_UPLOAD_DATE'] sort_by_date = X[:,up_date_col].argsort() X = X[sort_by_date].copy() y_clf = np.genfromtxt(assign_fpath)[sort_by_date] y_regr = np.genfromtxt(tseries_fpath)[:,1:].sum(axis=1)[sort_by_date] run_experiment(X, y_clf, y_regr, feature_ids, out_foldpath)
def main(features_fpath, tag_categ_fpath, tseries_fpath, assign_fpath): X, feature_ids, _ = \ create_input_table(features_fpath, None, tag_categ_fpath,-1) y_clf = np.genfromtxt(assign_fpath) y_rgr = np.genfromtxt(tseries_fpath)[:,1:].sum(axis=1) for feat_id in range(len(feature_ids)): print(feature_ids[feat_id], end=',') print('TREND', end=',') print('FINAL_VIEWS') M = np.column_stack((X, y_clf, y_rgr)) np.savetxt(sys.stdout, M, '%d', delimiter=',')
def main(features_fpath, tag_categ_fpath, tseries_fpath, assign_fpath): X, feature_ids, _ = \ create_input_table(features_fpath, None, tag_categ_fpath,-1) y_clf = np.genfromtxt(assign_fpath) y_rgr = np.genfromtxt(tseries_fpath)[:, 1:].sum(axis=1) for feat_id in range(len(feature_ids)): print(feature_ids[feat_id], end=',') print('TREND', end=',') print('FINAL_VIEWS') M = np.column_stack((X, y_clf, y_rgr)) np.savetxt(sys.stdout, M, '%d', delimiter=',')
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()