10000 [BUG] fix `RotationForest` for a custom `base_estimator` by fkiraly · Pull Request #8270 · sktime/sktime · GitHub
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[BUG] fix RotationForest for a custom base_estimator #8270

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[BUG] fix RotationForest for a custom base_estimator #8270

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daa04ab
Update _rotation_forest.py
fkiraly May 20, 2025
0a2e235
Update test_all_classifiers.py
fkiraly May 20, 2025
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8 changes: 5 additions & 3 deletions sktime/classification/sklearn/_rotation_forest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,6 +137,11 @@ def __init__(

super().__init__()

if self.base_estimator is None:
self._base_estimator = DecisionTreeClassifier(criterion="entropy")
else:
self._base_estimator = self.base_estimator

def fit(self, X, y):
"""Fit a forest of trees on cases (X,y), where y is the target variable.

Expand Down Expand Up @@ -185,9 +190,6 @@ def fit(self, X, y):
start_time = time.time()
train_time = 0

if self.base_estimator is None:
self._base_estimator = DecisionTreeClassifier(criterion="entropy")

# remove useless attributes
self._useful_atts = ~np.all(X[1:] == X[:-1], axis=0)
X = X[:, self._useful_atts]
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9 changes: 8 additions & 1 deletion sktime/classification/sklearn/tests/test_all_classifiers.py
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Expand Up @@ -3,19 +3,26 @@
__author__ = ["MatthewMiddlehurst"]

import pytest
from sklearn.linear_model import LogisticRegression
from sklearn.utils.estimator_checks import parametrize_with_checks

from sktime.classification.sklearn import ContinuousIntervalTree, RotationForest
from sktime.tests.test_switch import run_test_for_class

ALL_SKLEARN_CLASSIFIERS = [RotationForest, ContinuousIntervalTree]

INSTANCES_TO_TEST = [
RotationForest(n_estimators=3),
RotationForest(n_estimators=3, base_estimator=LogisticRegression()),
ContinuousIntervalTree(),
]


@pytest.mark.skipif(
not run_test_for_class(ALL_SKLEARN_CLASSIFIERS),
reason="run test only if softdeps are present and incrementally (if requested)",
)
@parametrize_with_checks([RotationForest(n_estimators=3), ContinuousIntervalTree()])
@parametrize_with_checks(INSTANCES_TO_TEST)
def test_sklearn_compatible_estimator(estimator, check):
"""Test that sklearn estimators adhere to sklearn conventions."""
try:
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0