def _read_data(path, f_name, sep="|"): data = pd.read_csv(os.path.join(path, "data", f_name), sep=sep, low_memory=False) y_array = OrdinalEncoder().fit_transform( data[_target_column_name].values[:, np.newaxis] ) X_df = data.drop(columns=[_target_column_name]) return X_df, y_array.flatten()
plt.title('y (hold-out)') plt.subplot(122) plt.imshow(y_pred, cmap=plt.cm.get_cmap('magma')) plt.title('y (predicted)') plt.show() # In[ ]: # Plot histograms of test data and prediction plt.rcParams['figure.figsize'] = [9.6, 4.8] plt.rcParams['figure.dpi'] = 108 plt.subplot(121) plt.hist(y_test.flatten()) plt.title('y (hold-out)') plt.subplot(122) plt.hist(y_pred.flatten()) plt.title('y (predicted)') plt.show() # In[ ]: # Create predicted condition arrays y_pred_00 = (y_test.round() == 0) & (y_pred.round() == 0) y_pred_01 = (y_test.round() == 0) & (y_pred.round() == 1) y_pred_02 = (y_test.round() == 0) & (y_pred.round() == 2)