INNER CODE UNIT · Python
mae
SalvatoreRa/tutorial · machine learning/scripts/MNAR.py:166
mae = mean_absolute_error(np.array(df.iloc[:, :-1])[train_index], X_train_imp)
df_mask = np.array(np.full((X_train_imp.shape[0],
X_train_imp.shape[1],), True, dtype=bool))
RMSR = metrics.root_mean_squared_error(pd.DataFrame(np.array(df.iloc[:, :-1])[train_index]),
pd.DataFrame(X_train_imp), pd.DataFrame(df_mask))
RMSR= np.sum(RMSR)
print(RMSR)
KL = metrics.kl_divergence(pd.DataFrame(np.array(df.iloc[:, :-1])[train_index]),
pd.DataFrame(X_train_imp), pd.DataFrame(df_mask))
KL= np.sum(KL)
print(KL)
E = metrics.sum_energy_distances(pd.DataFrame(np.array(df.iloc[:, :-1])[train_index]),
pd.DataFrame(X_train_imp), pd.DataFrame(df_mask))
E= np.sum(E)
print(E)
FR = metrics.frechet_distance(pd.DataFrame(np.array(df.iloc[:, :-1])[train_index]),
pd.DataFrame(X_train_imp), pd.DataFrame(df_mask))
FR= np.sum(FR)