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temporarily disable test_tsfeatures
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Summary:
This test is failing in LLVM17 for firstmin_ac.

After further inspection and running that tsfeatur step by step, it looks like LLVM17 returns the correct value, while the current code and test is incorrect.

After D61452958 lands, we should be safe to update the expected value of firstmin_ac to 8 and enable the test, which this diff already does.

Reviewed By: meyering

Differential Revision: D64551795

fbshipit-source-id: 56955458747423b41e28a41f3d6f20aecc1861f6
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@islijepcevic @facebook-github-bot
islijepcevic authored and facebook-github-bot committed Oct 18, 2024
1 parent 16df5c3 commit eca0d4b
Showing 1 changed file with 78 additions and 78 deletions.
156 changes: 78 additions & 78 deletions kats/tests/tsfeatures/test_tsfeatures.py
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Original file line number Diff line number Diff line change
Expand Up @@ -202,84 +202,84 @@ def test_tsfeatures_basic(self, test_name: str, ts_name: str) -> None:
for feature_vector in feature_list:
self.assertDictAlmostEqual(expected, feature_vector)

def test_tsfeatures(self) -> None:
feature_vector = _univariate_features(TsFeatures().transform(self.TSData))

feature_vector_round = {
key: round(feature_vector[key], 6) for key in feature_vector
}

# test there is no nan in feature vector
self.assertEqual(
np.isnan(np.asarray(list(feature_vector.values()))).any(),
False,
)

# test there are 40 features in the feature vector now
self.assertEqual(
len(np.asarray(list(feature_vector.values()))) == 40,
True,
)

# test feature vector value
rounded_truth = {
"length": 144,
"mean": 280.298611,
"var": 14291.973331,
"entropy": 0.428737,
"lumpiness": 3041164.562906,
"stability": 12303.627267,
"flat_spots": 2,
"hurst": -0.080233,
"std1st_der": 27.206288,
"crossing_points": 7,
"binarize_mean": 0.444444,
"unitroot_kpss": 0.128475,
"heterogeneity": 126.064506,
"histogram_mode": 155.8,
"linearity": 0.853638,
"trend_strength": 0.9681,
"seasonality_strength": 0.440863,
"spikiness": 33.502886,
"peak": 6,
"trough": 3,
"level_shift_idx": 118,
"level_shift_size": 15.6,
"y_acf1": 0.948047,
"y_acf5": 3.392072,
"diff1y_acf1": 0.302855,
"diff1y_acf5": 0.259459,
"diff2y_acf1": -0.191006,
"diff2y_acf5": 0.134207,
"y_pacf5": 1.003288,
"diff1y_pacf5": 0.219412,
"diff2y_pacf5": 0.26101,
"seas_acf1": 0.662904,
"seas_pacf1": 0.15617,
"firstmin_ac": 2,
"firstzero_ac": 52,
"holt_alpha": 1.0,
"holt_beta": 0.0,
"hw_alpha": 0.842106,
"hw_beta": 0.052631,
"hw_gamma": 0.157901,
}
if statsmodels.version >= "0.12":
rounded_truth["trend_strength"] = 0.93833
rounded_truth["seasonality_strength"] = 0.329934
rounded_truth["spikiness"] = 111.697325
feature_vector_round["holt_alpha"] = np.round(
feature_vector_round["holt_alpha"], 1
)
feature_vector_round["holt_beta"] = np.round(
feature_vector_round["holt_beta"], 1
)
rounded_truth["holt_alpha"] = 1.0
rounded_truth["holt_beta"] = 0.0
rounded_truth["hw_alpha"] = 1.0
rounded_truth["hw_beta"] = 0.0
rounded_truth["hw_gamma"] = 0.0
self.assertEqual(feature_vector_round, rounded_truth)
# def test_tsfeatures(self) -> None:
# feature_vector = _univariate_features(TsFeatures().transform(self.TSData))

# feature_vector_round = {
# key: round(feature_vector[key], 6) for key in feature_vector
# }

# # test there is no nan in feature vector
# self.assertEqual(
# np.isnan(np.asarray(list(feature_vector.values()))).any(),
# False,
# )

# # test there are 40 features in the feature vector now
# self.assertEqual(
# len(np.asarray(list(feature_vector.values()))) == 40,
# True,
# )

# # test feature vector value
# rounded_truth = {
# "length": 144,
# "mean": 280.298611,
# "var": 14291.973331,
# "entropy": 0.428737,
# "lumpiness": 3041164.562906,
# "stability": 12303.627267,
# "flat_spots": 2,
# "hurst": -0.080233,
# "std1st_der": 27.206288,
# "crossing_points": 7,
# "binarize_mean": 0.444444,
# "unitroot_kpss": 0.128475,
# "heterogeneity": 126.064506,
# "histogram_mode": 155.8,
# "linearity": 0.853638,
# "trend_strength": 0.9681,
# "seasonality_strength": 0.440863,
# "spikiness": 33.502886,
# "peak": 6,
# "trough": 3,
# "level_shift_idx": 118,
# "level_shift_size": 15.6,
# "y_acf1": 0.948047,
# "y_acf5": 3.392072,
# "diff1y_acf1": 0.302855,
# "diff1y_acf5": 0.259459,
# "diff2y_acf1": -0.191006,
# "diff2y_acf5": 0.134207,
# "y_pacf5": 1.003288,
# "diff1y_pacf5": 0.219412,
# "diff2y_pacf5": 0.26101,
# "seas_acf1": 0.662904,
# "seas_pacf1": 0.15617,
# "firstmin_ac": 8,
# "firstzero_ac": 52,
# "holt_alpha": 1.0,
# "holt_beta": 0.0,
# "hw_alpha": 0.842106,
# "hw_beta": 0.052631,
# "hw_gamma": 0.157901,
# }
# if statsmodels.version >= "0.12":
# rounded_truth["trend_strength"] = 0.93833
# rounded_truth["seasonality_strength"] = 0.329934
# rounded_truth["spikiness"] = 111.697325
# feature_vector_round["holt_alpha"] = np.round(
# feature_vector_round["holt_alpha"], 1
# )
# feature_vector_round["holt_beta"] = np.round(
# feature_vector_round["holt_beta"], 1
# )
# rounded_truth["holt_alpha"] = 1.0
# rounded_truth["holt_beta"] = 0.0
# rounded_truth["hw_alpha"] = 1.0
# rounded_truth["hw_beta"] = 0.0
# rounded_truth["hw_gamma"] = 0.0
# self.assertEqual(feature_vector_round, rounded_truth)

# pyre-fixme[56]: Pyre was not able to infer the type of the decorator `parameter...
@parameterized.expand(
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