def print_results(clf_scores, micro, macro, r2_all, mse_all, mrse_all):
metric_means = np.mean(clf_scores, axis=0)
metric_ci = hci(clf_scores, .95, axis=0)
print(clf_summary(metric_means, metric_ci))
print('Micro F1 - mean: %f +- %f' % (np.mean(micro), hci(micro, .95)))
print('Macro F1 - mean: %f +- %f' % (np.mean(macro), hci(macro, .95)))
print('R2 all - mean: %f +- %f' % (np.mean(r2_all), hci(r2_all, .95)))
print('MSE all - mean: %f +- %f' % (np.mean(mse_all), hci(mse_all, .95)))
print('MRSE all - mean: %f +- %f' %
(np.mean(mrse_all), hci(mrse_all, .95)))
def print_results(clf_scores, micro, macro, r2_all, mse_all, mrse_all):
metric_means = np.mean(clf_scores, axis=0)
metric_ci = hci(clf_scores, .95, axis=0)
print(clf_summary(metric_means, metric_ci))
print('Micro F1 - mean: %f +- %f' % (np.mean(micro), hci(micro, .95)))
print('Macro F1 - mean: %f +- %f' % (np.mean(macro), hci(macro, .95)))
print('R2 all - mean: %f +- %f' % (np.mean(r2_all), hci(r2_all, .95)))
print('MSE all - mean: %f +- %f' % (np.mean(mse_all), hci(mse_all, .95)))
print('MRSE all - mean: %f +- %f' % (np.mean(mrse_all),
hci(mrse_all, .95)))
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()
