Merge branch '1.1.X'

zillow · Dec 19, 2022 · b397a49 · b397a49
2 parents e707d8b + bc8308d
commit b397a49
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Showing 4 changed files with 34 additions and 29 deletions.
diff --git a/docs/user_guide.rst b/docs/user_guide.rst
@@ -104,9 +104,9 @@ The predictions of a standard random forest can also be recovered from a quantil
     >>> X, y = datasets.load_diabetes(return_X_y=True)
     >>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
     >>> rf = RandomForestRegressor(random_state=0)
-    >>> qrf = RandomForestQuantileRegressor(random_state=0)
+    >>> qrf = RandomForestQuantileRegressor(max_samples_leaf=None, random_state=0)
     >>> rf.fit(X_train, y_train), qrf.fit(X_train, y_train)
-    (RandomForestRegressor(random_state=0), RandomForestQuantileRegressor(random_state=0))
+    (RandomForestRegressor(random_state=0), RandomForestQuantileRegressor(max_samples_leaf=None, random_state=0))
     >>> y_pred_rf = rf.predict(X_test)
     >>> y_pred_qrf = qrf.predict(X_test, quantiles=None, aggregate_leaves_first=False)
     >>> np.allclose(y_pred_rf, y_pred_qrf)

diff --git a/quantile_forest/_quantile_forest.py b/quantile_forest/_quantile_forest.py
@@ -30,6 +30,7 @@ class calls the ``fit`` method of the ``ForestRegressor`` and creates a
 
 import joblib
 import numpy as np
+import sklearn
 
 from sklearn.ensemble._forest import ForestRegressor
 from sklearn.ensemble._forest import _generate_sample_indices
@@ -42,6 +43,8 @@ class calls the ``fit`` method of the ``ForestRegressor`` and creates a
 from ._quantile_forest_fast import QuantileForest
 from ._quantile_forest_fast import generate_unsampled_indices
 
+sklearn_version = tuple(map(int, (sklearn.__version__.split('.'))))
+
 
 def _generate_unsampled_indices(sample_indices, duplicates=None):
     """Private function used by forest._get_unsampled_indices function."""
@@ -980,10 +983,10 @@ def __init__(
         ccp_alpha=0.0,
         max_samples=None,
     ):
-        super(RandomForestQuantileRegressor, self).__init__(
-            base_estimator=DecisionTreeRegressor(),
-            n_estimators=n_estimators,
-            estimator_params=(
+        init_dict = {
+            'base_estimator' if sklearn_version < (1, 2) else 'estimator': DecisionTreeRegressor(),
+            'n_estimators': n_estimators,
+            'estimator_params': (
                 "criterion",
                 "max_depth",
                 "min_samples_split",
@@ -995,14 +998,15 @@ def __init__(
                 "random_state",
                 "ccp_alpha",
             ),
-            bootstrap=bootstrap,
-            oob_score=oob_score,
-            n_jobs=n_jobs,
-            random_state=random_state,
-            verbose=verbose,
-            warm_start=warm_start,
-            max_samples=max_samples,
-        )
+            'bootstrap': bootstrap,
+            'oob_score': oob_score,
+            'n_jobs': n_jobs,
+            'random_state': random_state,
+            'verbose': verbose,
+            'warm_start': warm_start,
+            'max_samples': max_samples,
+        }
+        super(RandomForestQuantileRegressor, self).__init__(**init_dict)
 
         self.criterion = criterion
         self.max_depth = max_depth
@@ -1253,10 +1257,10 @@ def __init__(
         ccp_alpha=0.0,
         max_samples=None,
     ):
-        super(ExtraTreesQuantileRegressor, self).__init__(
-            base_estimator=ExtraTreeRegressor(),
-            n_estimators=n_estimators,
-            estimator_params=(
+        init_dict = {
+            'base_estimator' if sklearn_version < (1, 2) else 'estimator': ExtraTreeRegressor(),
+            'n_estimators': n_estimators,
+            'estimator_params': (
                 "criterion",
                 "max_depth",
                 "min_samples_split",
@@ -1268,14 +1272,15 @@ def __init__(
                 "random_state",
                 "ccp_alpha",
             ),
-            bootstrap=bootstrap,
-            oob_score=oob_score,
-            n_jobs=n_jobs,
-            random_state=random_state,
-            verbose=verbose,
-            warm_start=warm_start,
-            max_samples=max_samples,
-        )
+            'bootstrap': bootstrap,
+            'oob_score': oob_score,
+            'n_jobs': n_jobs,
+            'random_state': random_state,
+            'verbose': verbose,
+            'warm_start': warm_start,
+            'max_samples': max_samples,
+        }
+        super(ExtraTreesQuantileRegressor, self).__init__(**init_dict)
 
         self.criterion = criterion
         self.max_depth = max_depth

diff --git a/quantile_forest/version.txt b/quantile_forest/version.txt
@@ -1 +1 @@
-1.1.0
+1.1.1
diff --git a/requirements.txt b/requirements.txt
@@ -1,4 +1,4 @@
 cython >= 3.0a4
-numpy
-scipy
+numpy >= 1.23
+scipy >= 1.4
 scikit-learn >= 1.0