prints removed

Author: Julien Roussel

examples/benchmark.md

@@ -19,18 +19,6 @@ In Qolmat, a few data imputation methods are implemented as well as a way to eva
 
 First, import some useful librairies
 
-```python
-X= np.array([[0], [1], [2]])
-```
-
-```python
-np.cov(X)
-```
-
-```python
-
-```
-
 ```python
 import warnings
 # warnings.filterwarnings('error')
@@ -146,7 +134,7 @@ imputer_tsmle = imputers.ImputerEM(groups=("station",), model="VAR1", method="ml
 
 
 imputer_knn = imputers.ImputerKNN(groups=("station",), n_neighbors=10)
-imputer_mice = imputers.ImputerMICE(groups=("station",), estimator=LinearRegression(), sample_posterior=False, max_iter=100, missing_values=np.nan)
+imputer_mice = imputers.ImputerMICE(groups=("station",), estimator=LinearRegression(), sample_posterior=False, max_iter=100)
 imputer_regressor = imputers.ImputerRegressor(groups=("station",), estimator=LinearRegression())
 ```
 
@@ -388,7 +376,7 @@ dict_imputers["MLP"] = imputer_mlp = imputers_pytorch.ImputerRegressorPyTorch(es
 ```
 
 We can re-run the imputation model benchmark as before.
-```python jupyter={"outputs_hidden": true} tags=[]
+```python tags=[]
 generator_holes = missing_patterns.EmpiricalHoleGenerator(n_splits=2, groups=["station"], subset=cols_to_impute, ratio_masked=ratio_masked)
 
 comparison = comparator.Comparator(
@@ -402,7 +390,7 @@ comparison = comparator.Comparator(
 results = comparison.compare(df_data)
 results
 ```
-```python jupyter={"outputs_hidden": true, "source_hidden": true} tags=[]
+```python jupyter={"source_hidden": true} tags=[]
 df_plot = df_data
 dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.items()}
 station = df_plot.index.get_level_values("station")[0]

qolmat/imputations/imputers.py

@@ -1422,10 +1422,6 @@ def __init__(
         self.handler_nan = handler_nan
 
     def _fit_estimator(self, X, y) -> Self:
-        print("Xy")
-        print(X)
-        print(y)
-
         return self.estimator.fit(X, y)
 
     def _predict_estimator(self, X) -> pd.Series:

qolmat/imputations/imputers_pytorch.py

@@ -138,7 +138,5 @@ def _predict_estimator(self, X: pd.DataFrame) -> pd.Series:
             input_data = torch.Tensor(X.values)
             output_data = self.estimator(input_data)
             pred = output_data.detach().numpy().flatten()
-            print("pred")
-            print(pred)
             return pd.Series(pred, index=X.index, dtype=float)
         assert AssertionError("Estimator is not provided.")

tests/imputations/test_imputers.py

@@ -250,7 +250,6 @@ def test_ImputerMICE_fit_transform(df: pd.DataFrame) -> None:
 def test_ImputerRegressor_fit_transform(df: pd.DataFrame) -> None:
     imputer = imputers.ImputerRegressor(estimator=ExtraTreesRegressor())
     result = imputer.fit_transform(df)
-    print(result)
     expected = pd.DataFrame(
         {
             "col1": [0, 2, 2, 3, 2],
@@ -270,9 +269,7 @@ def test_ImputerRPCA_fit_transform(df: pd.DataFrame) -> None:
             "col2": [0, 1, 2, 2, 2] + [i for i in range(5, 20)],
         }
     )
-    print(result)
     result = np.around(result)
-    print(result)
     np.testing.assert_allclose(result, expected, atol=1e-2)