FIX reproducibility and parallelization of InstanceHardnessThreshold (#599)

Author: Shihab-Shahriar

doc/whats_new/v0.6.rst

@@ -15,6 +15,14 @@ scikit-learn:
 - :class:`imblearn.under_sampling.ClusterCentroids`
 - :class:`imblearn.under_sampling.InstanceHardnessThreshold`
 
+Bug fixes
+.........
+
+- :class:`imblearn.under_sampling.InstanceHardnessThreshold` now take into
+ account the `random_state` and will give deterministic results. In addition,
+ `cross_val_predict` is used to take advantage of the parallelism.
+ :pr:`599` by :user:`Shihab Shahriar Khan <Shihab-Shahriar>`.
+
 Maintenance
 ...........
 

imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py

@@ -12,7 +12,10 @@
 
 from sklearn.base import ClassifierMixin, clone
 from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble._base import _set_random_states
 from sklearn.model_selection import StratifiedKFold
+from sklearn.model_selection import cross_val_predict
+from sklearn.utils import check_random_state
 from sklearn.utils import _safe_indexing
 
 from ..base import BaseUnderSampler
@@ -108,7 +111,7 @@ def __init__(
  self.cv = cv
  self.n_jobs = n_jobs
 
- def _validate_estimator(self):
+ def _validate_estimator(self, random_state):
  """Private function to create the classifier"""
 
  if (
@@ -117,6 +120,8 @@ def _validate_estimator(self):
  and hasattr(self.estimator, "predict_proba")
  ):
  self.estimator_ = clone(self.estimator)
+ _set_random_states(self.estimator_, random_state)
+
  elif self.estimator is None:
  self.estimator_ = RandomForestClassifier(
  n_estimators=100,
@@ -131,22 +136,18 @@ def _validate_estimator(self):
  )
 
  def _fit_resample(self, X, y):
- self._validate_estimator()
+ random_state = check_random_state(self.random_state)
+ self._validate_estimator(random_state)
 
  target_stats = Counter(y)
- skf = StratifiedKFold(n_splits=self.cv, shuffle=False).split(X, y)
- probabilities = np.zeros(y.shape[0], dtype=float)
-
- for train_index, test_index in skf:
- X_train = _safe_indexing(X, train_index)
- X_test = _safe_indexing(X, test_index)
- y_train = _safe_indexing(y, train_index)
- y_test = _safe_indexing(y, test_index)
-
- self.estimator_.fit(X_train, y_train)
-
- probs = self.estimator_.predict_proba(X_test)
- probabilities[test_index] = probs[range(len(y_test)), y_test]
+ skf = StratifiedKFold(
+ n_splits=self.cv, shuffle=True, random_state=random_state,
+ )
+ probabilities = cross_val_predict(
+ self.estimator_, X, y, cv=skf, n_jobs=self.n_jobs,
+ method='predict_proba'
+ )
+ probabilities = probabilities[range(len(y)), y]
 
  idx_under = np.empty((0,), dtype=int)
 

imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py

@@ -6,7 +6,10 @@
 import pytest
 import numpy as np
 
+
 from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.utils._testing import assert_array_equal
 
 from imblearn.under_sampling import InstanceHardnessThreshold
 
@@ -76,3 +79,16 @@ def test_iht_fit_resample_wrong_class_obj():
  iht = InstanceHardnessThreshold(estimator=est, random_state=RND_SEED)
  with pytest.raises(ValueError, match="Invalid parameter `estimator`"):
  iht.fit_resample(X, Y)
+
+
+def test_iht_reproducibility():
+ from sklearn.datasets import load_digits
+ X_digits, y_digits = load_digits(return_X_y=True)
+ idx_sampled = []
+ for seed in range(5):
+ est = RandomForestClassifier(n_estimators=10, random_state=seed)
+ iht = InstanceHardnessThreshold(estimator=est, random_state=RND_SEED)
+ iht.fit_resample(X_digits, y_digits)
+ idx_sampled.append(iht.sample_indices_.copy())
+ for idx_1, idx_2 in zip(idx_sampled, idx_sampled[1:]):
+ assert_array_equal(idx_1, idx_2)