numpy fixes

Author: bcebere

src/hyperimpute/plugins/prediction/classifiers/plugin_random_forest.py

@@ -49,7 +49,6 @@ def __init__(
         criterion: int = 0,
         max_features: int = 0,
         min_samples_split: int = 2,
-        bootstrap: bool = True,
         min_samples_leaf: int = 1,
         max_depth: Optional[int] = 3,
         random_state: int = 0,
@@ -66,7 +65,6 @@ def __init__(
             max_features=RandomForestPlugin.features[max_features],
             min_samples_split=min_samples_split,
             max_depth=max_depth,
-            bootstrap=bootstrap,
             min_samples_leaf=min_samples_leaf,
             random_state=random_state,
             n_jobs=max(1, int(multiprocessing.cpu_count() / 2)),

src/hyperimpute/plugins/prediction/regression/plugin_random_forest_regressor.py

@@ -49,7 +49,6 @@ def __init__(
         criterion: int = 0,
         max_features: int = 0,
         min_samples_split: int = 2,
-        bootstrap: bool = True,
         min_samples_leaf: int = 1,
         max_depth: Optional[int] = 3,
         hyperparam_search_iterations: Optional[int] = None,
@@ -67,7 +66,6 @@ def __init__(
             max_features=RandomForestRegressionPlugin.features[max_features],
             min_samples_split=min_samples_split,
             max_depth=max_depth,
-            bootstrap=bootstrap,
             min_samples_leaf=min_samples_leaf,
             random_state=random_state,
             n_jobs=max(1, int(multiprocessing.cpu_count() / 2)),

src/hyperimpute/version.py

@@ -1,2 +1,2 @@
-__version__ = "0.1.11"
+__version__ = "0.1.12"
 MAJOR_VERSION = "0.1"

tests/integrations/test_sklearn.py

@@ -29,8 +29,8 @@ def _eval_imputer_pipeline(test_imputer: str) -> None:
     n_missing_samples = int(np.floor(n_samples * missing_rate))
     missing_samples = np.hstack(
         (
-            np.zeros(n_samples - n_missing_samples, dtype=np.bool),
-            np.ones(n_missing_samples, dtype=np.bool),
+            np.zeros(n_samples - n_missing_samples, dtype=bool),
+            np.ones(n_missing_samples, dtype=bool),
         )
     )
     rng.shuffle(missing_samples)