print removed

Author: Julien Roussel

qolmat/benchmark/metrics.py

@@ -863,7 +863,6 @@ def kl_divergence_gaussian_exact(
     norm_M = (M**2).sum().sum()
     norm_y = (y**2).sum()
     term_diag_L = 2 * np.sum(np.log(np.diagonal(L2) / np.diagonal(L1)))
-    print(norm_M, "-", n_variables, "+", norm_y, "+", term_diag_L)
     div_kl = 0.5 * (norm_M - n_variables + norm_y + term_diag_L)
     return div_kl
 

qolmat/imputations/em_sampler.py

@@ -268,26 +268,16 @@ def _sample_ou(
             self.reset_learned_parameters()
         X_init = X.copy()
         gamma = self.get_gamma()
-        print("gamma:")
-        print(gamma)
         sqrt_gamma = np.real(spl.sqrtm(gamma))
         for i in range(self.n_iter_ou):
-            print(f"Iteration #{i}")
             noise = self.ampli * self.rng.normal(0, 1, size=(n_variables, n_samples))
             grad_X = self.gradient_X_loglik(X_copy)
-            print("grad")
-            print(self.dt * grad_X @ gamma)
-            print("noise")
-            print(np.sqrt(2 * self.dt) * noise @ sqrt_gamma)
             X_copy += self.dt * grad_X @ gamma + np.sqrt(2 * self.dt) * noise @ sqrt_gamma
             X_copy[~mask_na] = X_init[~mask_na]
             if estimate_params:
                 self.update_parameters(X_copy)
-        print("X_copy")
-        print(X_copy)
         if np.sum(np.abs(X_copy)) > 1e9:
             raise AssertionError
-        print()
 
         return X_copy
 
@@ -501,8 +491,6 @@ def get_gamma(self) -> NDArray:
         NDArray
             Gamma matrix
         """
-        print("get_gamma")
-        print(self.cov)
         # gamma = np.diag(np.diagonal(self.cov))
         gamma = self.cov
         # gamma = np.eye(len(self.cov))

tests/imputations/test_imputers.py

@@ -174,7 +174,6 @@ def test_ImputerShuffle_fit_transform1(df: pd.DataFrame) -> None:
 def test_ImputerShuffle_fit_transform2(df: pd.DataFrame) -> None:
     imputer = imputers.ImputerShuffle(random_state=42)
     result = imputer.fit_transform(df)
-    print(result)
     expected = pd.DataFrame({"col1": [0, 3, 2, 3, 0], "col2": [-1, 1.5, 0.5, 1.5, 1.5]})
     np.testing.assert_allclose(result, expected)
 

tests/imputations/test_imputers_pytorch.py

@@ -54,7 +54,6 @@ def test_ImputerRegressorPyTorch_fit_transform(df: pd.DataFrame) -> None:
             "col5": [93, 75, 2.132, 12, 2.345],
         }
     )
-    print(result["col5"])
     np.testing.assert_allclose(result, expected, atol=1e-3)
 
 

tests/utils/test_data.py

@@ -186,11 +186,9 @@ def test_utils_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFix
         assert df_result.columns.tolist() == expected_columns
     elif name_data == "Monach_weather":
         assert mock_download.call_count == 1
-        print(df_result)
         pd.testing.assert_frame_equal(df_result, df_monach_weather_preprocess)
     elif name_data == "Monach_electricity_australia":
         assert mock_download.call_count == 1
-        print(df_result)
         pd.testing.assert_frame_equal(df_result, df_monach_elec_preprocess)
     else:
         assert False