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)
