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FIX reproducibility and parallelization of InstanceHardnessThreshold #598

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FIX reproducibility and parallelization of InstanceHardnessThreshold #598

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20 changes: 6 additions & 14 deletions imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@

from sklearn.base import ClassifierMixin, clone
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import StratifiedKFold,cross_val_predict
from sklearn.utils import safe_indexing

from ..base import BaseUnderSampler
Expand Down Expand Up @@ -126,6 +126,7 @@ def _validate_estimator(self):
isinstance(self.estimator, ClassifierMixin) and
hasattr(self.estimator, 'predict_proba')):
self.estimator_ = clone(self.estimator)
self.estimator_.set_params(n_jobs=1,random_state=self.random_state)
elif self.estimator is None:
self.estimator_ = RandomForestClassifier(
n_estimators=100, random_state=self.random_state,
Expand All @@ -143,19 +144,10 @@ def _fit_resample(self, X, y):
target_stats = Counter(y)
skf = StratifiedKFold(
n_splits=self.cv, shuffle=False,
random_state=self.random_state).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]
random_state=self.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)

Expand Down