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