sklearn version change

CHANGES.txt

@@ -177,4 +177,5 @@ v<1.0.8>, <03/08/2023> -- Improve clone compatibility (#471).
 v<1.0.8>, <03/08/2023> -- Add QMCD detector (#452).
 v<1.0.8>, <03/08/2023> -- Optimized ECDF and drop Statsmodels dependency (#467).
 v<1.0.9>, <03/19/2023> -- Hot fix for errors in ECOD and COPOD due to the issue of scipy.
-v<1.1.0>, <06/19/2023> -- Further integration of PyThresh.
+v<1.1.0>, <06/19/2023> -- Further integration of PyThresh.
+v<1.1.1>, <07/03/2023> -- Bump up sklearn requirement and some hot fixes.

pyod/models/iforest.py

@@ -16,7 +16,6 @@
 
 from .base import BaseDetector
 # noinspection PyProtectedMember
-from ..utils.utility import _get_sklearn_version
 from ..utils.utility import invert_order
 
 
@@ -207,28 +206,15 @@ def fit(self, X, y=None):
         # In sklearn 0.20+ new behaviour is added (arg behaviour={'new','old'})
         # to IsolationForest that shifts the location of the anomaly scores
         # noinspection PyProtectedMember
-        sklearn_version = _get_sklearn_version()
-        if sklearn_version == 21:
-            self.detector_ = IsolationForest(n_estimators=self.n_estimators,
-                                             max_samples=self.max_samples,
-                                             contamination=self.contamination,
-                                             max_features=self.max_features,
-                                             bootstrap=self.bootstrap,
-                                             n_jobs=self.n_jobs,
-                                             behaviour=self.behaviour,
-                                             random_state=self.random_state,
-                                             verbose=self.verbose)
-
-        # Do not pass behaviour argument when sklearn version is < 0.20 or >0.21
-        else:  # pragma: no cover
-            self.detector_ = IsolationForest(n_estimators=self.n_estimators,
-                                             max_samples=self.max_samples,
-                                             contamination=self.contamination,
-                                             max_features=self.max_features,
-                                             bootstrap=self.bootstrap,
-                                             n_jobs=self.n_jobs,
-                                             random_state=self.random_state,
-                                             verbose=self.verbose)
+
+        self.detector_ = IsolationForest(n_estimators=self.n_estimators,
+                                         max_samples=self.max_samples,
+                                         contamination=self.contamination,
+                                         max_features=self.max_features,
+                                         bootstrap=self.bootstrap,
+                                         n_jobs=self.n_jobs,
+                                         random_state=self.random_state,
+                                         verbose=self.verbose)
 
         self.detector_.fit(X=X, y=None, sample_weight=None)
 

pyod/models/sampling.py

@@ -7,12 +7,18 @@
 from __future__ import division, print_function
 
 import numpy as np
-from sklearn.neighbors import DistanceMetric
+
 from sklearn.utils import check_array, check_random_state
 from sklearn.utils.validation import check_is_fitted
 
 from .base import BaseDetector
+from ..utils.utility import _get_sklearn_version
 
+sklearn_version = _get_sklearn_version()
+if sklearn_version[:3] >= '1.3':
+    from sklearn.metrics import DistanceMetric
+else:
+    from sklearn.neighbors import DistanceMetric
 
 class Sampling(BaseDetector):
     """Sampling class for outlier detection.

pyod/utils/utility.py

@@ -443,8 +443,11 @@ def _get_sklearn_version():  # pragma: no cover
     # if int(sklearn_version.split(".")[1]) < 19 or int(
     #         sklearn_version.split(".")[1]) > 24:
     #     raise ValueError("Sklearn version error")
+    # print(sklearn_version)
+
+    return sklearn_version
 
-    return int(sklearn_version.split(".")[1])
+    # return int(sklearn_version.split(".")[1])
 
 
 # def _sklearn_version_21():  # pragma: no cover
@@ -544,6 +547,7 @@ def generate_indices(random_state, bootstrap, n_population, n_samples):
 
     return indices
 
+
 # todo: add a test for it in test_utility.py
 def get_optimal_n_bins(X, upper_bound=None, epsilon=1):
     """ Determine optimal number of bins for a histogram using the Birge 
@@ -579,6 +583,6 @@ def get_optimal_n_bins(X, upper_bound=None, epsilon=1):
 
         maximum_likelihood[i] = np.sum(
             histogram * np.log(b * histogram / n + epsilon) - (
-                        b - 1 + np.power(np.log(b), 2.5)))
+                    b - 1 + np.power(np.log(b), 2.5)))
 
     return np.argmax(maximum_likelihood) + 1

requirements.txt

@@ -3,5 +3,5 @@ matplotlib
 numpy>=1.19
 numba>=0.51
 scipy>=1.5.1
-scikit_learn>=0.20.0
+scikit_learn>=0.22.0
 six