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Fix tests that fail due to changes in sklearn 0.24.0 #1215

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Fix tests that fail due to changes in sklearn 0.24.0 #1215

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49 changes: 45 additions & 4 deletions tests/tpot_tests.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,7 @@
from shutil import rmtree
import platform

import sklearn
from sklearn.datasets import load_digits, load_boston, make_classification, make_regression
from sklearn import model_selection
from joblib import Memory
Expand Down Expand Up @@ -625,7 +626,21 @@ def test_score_2():
"""Assert that the TPOTClassifier score function outputs a known score for a fixed pipeline."""
tpot_obj = TPOTClassifier(random_state=34)
tpot_obj._fit_init(training_features.shape)
known_score = 0.977777777778 # Assumes use of the TPOT accuracy function
#Score changes between sklearn versions; dictionary for different versions
#Up to date as of sklearn 0.24.2
score_dict = {
'0.24.2': 0.9755555555555555,
'0.24.1': 0.9755555555555555,
'0.24.0': 0.9755555555555555, #introduced a change affecting KNeighborsClassifier: https://github.com/scikit-learn/scikit-learn/pull/17038
'0.23.2': 0.977777777778,
'0.23.1': 0.977777777778,
'0.23.0': 0.977777777778,
'0.22.2': 0.977777777778,
'0.22.1': 0.977777777778,
'0.22': 0.977777777778,

}
# Scores assume use of the TPOT accuracy function

# Create a pipeline with a known score
pipeline_string = (
Expand All @@ -642,14 +657,34 @@ def test_score_2():
# Get score from TPOT
score = tpot_obj.score(testing_features, testing_target)

assert np.allclose(known_score, score)
if sklearn.__version__ in score_dict.keys():
known_score = score_dict[sklearn.__version__]
assert np.allclose(known_score, score)
else:
#If the version isn't found, compare to all versions in the dict
closeness = [np.allclose(score, known_score) for known_score in score_dict.values()]
assert(closeness)


def test_score_3():
"""Assert that the TPOTRegressor score function outputs a known score for a fixed pipeline."""
tpot_obj = TPOTRegressor(scoring='neg_mean_squared_error', random_state=72)
tpot_obj._fit_init(training_features.shape)
known_score = -11.708199875921563
#Score changes between sklearn versions; dictionary for different versions
#Up to date as of sklearn 0.24.2
score_dict = {
'0.24.2': -11.708199875921563,
'0.24.1': -11.708199875921563,
'0.24.0': -11.708199875921563,
'0.23.2': -11.708199875921563,
'0.23.1': -11.708199875921563,
'0.23.0': -11.708199875921563,
'0.22.2': -11.708199875921563,
'0.22.1': -11.708199875921563,
'0.22': -11.708199875921563,

}
# Scores assume use of the TPOT accuracy function

# Reify pipeline with known score
pipeline_string = (
Expand All @@ -670,7 +705,13 @@ def test_score_3():
# Get score from TPOT
score = tpot_obj.score(testing_features_r, testing_target_r)

assert np.allclose(known_score, score, rtol=0.03)
if sklearn.__version__ in score_dict.keys():
known_score = score_dict[sklearn.__version__]
assert np.allclose(known_score, score)
else:
#If the version isn't found, compare to all versions in the dict
closeness = [np.allclose(score, known_score) for known_score in score_dict.values()]
assert(closeness)



Expand Down