Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Fix code and tests for numpy >= 2.0 #1085

Merged
merged 2 commits into from
Jul 14, 2024
Merged

Fix code and tests for numpy >= 2.0 #1085

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0cb4068
Fix code and tests for numpy >= 2.0
nils-braun Jul 14, 2024
53f63f7
stylefix
nils-braun Jul 14, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions tests/units/feature_extraction/test_data.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -457,7 +457,7 @@ def test_f(chunk):
)

def test_with_wrong_input(self):
test_df = pd.DataFrame([{"id": 0, "kind": "a", "value": 3, "sort": np.NaN}])
test_df = pd.DataFrame([{"id": 0, "kind": "a", "value": 3, "sort": np.nan}])
self.assertRaises(ValueError, to_tsdata, test_df, "id", "kind", "value", "sort")

test_df = pd.DataFrame([{"id": 0, "kind": "a", "value": 3, "sort": 1}])
Expand Down Expand Up @@ -489,10 +489,10 @@ def test_with_wrong_input(self):
ValueError, to_tsdata, test_df, "id", "strange_kind", "value", "sort"
)

test_df = pd.DataFrame([{"id": np.NaN, "kind": "a", "value": 3, "sort": 1}])
test_df = pd.DataFrame([{"id": np.nan, "kind": "a", "value": 3, "sort": 1}])
self.assertRaises(ValueError, to_tsdata, test_df, "id", "kind", "value", "sort")

test_df = pd.DataFrame([{"id": 0, "kind": np.NaN, "value": 3, "sort": 1}])
test_df = pd.DataFrame([{"id": 0, "kind": np.nan, "value": 3, "sort": 1}])
self.assertRaises(ValueError, to_tsdata, test_df, "id", "kind", "value", "sort")

test_df = pd.DataFrame([{"id": 2}, {"id": 1}])
Expand All @@ -518,10 +518,10 @@ def test_with_wrong_input(self):
# If there are more than one column, the algorithm can not choose the correct column
self.assertRaises(ValueError, to_tsdata, test_dict, "id", None, None, None)

test_df = pd.DataFrame([{"id": 0, "value": np.NaN}])
test_df = pd.DataFrame([{"id": 0, "value": np.nan}])
self.assertRaises(ValueError, to_tsdata, test_df, "id", None, "value", None)

test_df = pd.DataFrame([{"id": 0, "value": np.NaN}])
test_df = pd.DataFrame([{"id": 0, "value": np.nan}])
self.assertRaises(ValueError, to_tsdata, test_df, None, None, "value", None)

test_df = pd.DataFrame([{"id": 0, "a_": 3, "b": 5, "sort": 1}])
Expand Down
62 changes: 29 additions & 33 deletions tests/units/feature_extraction/test_feature_calculations.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ def tearDown(self):
warnings.resetwarnings()

def assertIsNaN(self, result):
self.assertTrue(np.isnan(result), msg="{} is not np.NaN")
self.assertTrue(np.isnan(result), msg="{} is not np.nan")

def assertEqualOnAllArrayTypes(self, f, input_to_f, result, *args, **kwargs):
expected_result = f(input_to_f, *args, **kwargs)
Expand Down Expand Up @@ -575,7 +575,7 @@ def test_length(self):
self.assertEqualOnAllArrayTypes(length, [1, 2, 3, 4], 4)
self.assertEqualOnAllArrayTypes(length, [1, 2, 3], 3)
self.assertEqualOnAllArrayTypes(length, [1, 2], 2)
self.assertEqualOnAllArrayTypes(length, [1, 2, 3, np.NaN], 4)
self.assertEqualOnAllArrayTypes(length, [1, 2, 3, np.nan], 4)
self.assertEqualOnAllArrayTypes(length, [], 0)

def test_standard_deviation(self):
Expand Down Expand Up @@ -1180,26 +1180,26 @@ def test_binned_entropy(self):
self.assertAlmostEqualOnAllArrayTypes(
binned_entropy,
[10] * 10 + [1],
-(10 / 11 * np.math.log(10 / 11) + 1 / 11 * np.math.log(1 / 11)),
-(10 / 11 * math.log(10 / 11) + 1 / 11 * math.log(1 / 11)),
10,
)
self.assertAlmostEqualOnAllArrayTypes(
binned_entropy,
[10] * 10 + [1],
-(10 / 11 * np.math.log(10 / 11) + 1 / 11 * np.math.log(1 / 11)),
-(10 / 11 * math.log(10 / 11) + 1 / 11 * math.log(1 / 11)),
10,
)
self.assertAlmostEqualOnAllArrayTypes(
binned_entropy,
[10] * 10 + [1],
-(10 / 11 * np.math.log(10 / 11) + 1 / 11 * np.math.log(1 / 11)),
-(10 / 11 * math.log(10 / 11) + 1 / 11 * math.log(1 / 11)),
100,
)
self.assertAlmostEqualOnAllArrayTypes(
binned_entropy, list(range(10)), -np.math.log(1 / 10), 100
binned_entropy, list(range(10)), -math.log(1 / 10), 100
)
self.assertAlmostEqualOnAllArrayTypes(
binned_entropy, list(range(100)), -np.math.log(1 / 2), 2
binned_entropy, list(range(100)), -math.log(1 / 2), 2
)

def test_sample_entropy(self):
Expand Down Expand Up @@ -1512,24 +1512,22 @@ def test_value_count(self):
self.assertEqualPandasSeriesWrapper(value_count, [1] * 10, 10, value=1)
self.assertEqualPandasSeriesWrapper(value_count, list(range(10)), 1, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [1] * 10, 0, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [np.NaN, 0, 1] * 3, 3, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [np.nan, 0, 1] * 3, 3, value=0)
self.assertEqualPandasSeriesWrapper(
value_count, [np.NINF, 0, 1] * 3, 3, value=0
)
self.assertEqualPandasSeriesWrapper(
value_count, [np.PINF, 0, 1] * 3, 3, value=0
value_count, [-np.inf, 0, 1] * 3, 3, value=0
)
self.assertEqualPandasSeriesWrapper(value_count, [np.inf, 0, 1] * 3, 3, value=0)
self.assertEqualPandasSeriesWrapper(
value_count, [0.1, 0.2, 0.3] * 3, 3, value=0.2
)
self.assertEqualPandasSeriesWrapper(
value_count, [np.NaN, 0, 1] * 3, 3, value=np.NaN
value_count, [np.nan, 0, 1] * 3, 3, value=np.nan
)
self.assertEqualPandasSeriesWrapper(
value_count, [np.NINF, 0, 1] * 3, 3, value=np.NINF
value_count, [-np.inf, 0, 1] * 3, 3, value=-np.inf
)
self.assertEqualPandasSeriesWrapper(
value_count, [np.PINF, 0, 1] * 3, 3, value=np.PINF
value_count, [np.inf, 0, 1] * 3, 3, value=np.inf
)

def test_range_count(self):
Expand All @@ -1546,7 +1544,7 @@ def test_range_count(self):
range_count, list(range(0, -10, -1)), 9, min=-10, max=0
)
self.assertEqualPandasSeriesWrapper(
range_count, [np.NaN, np.PINF, np.NINF] + list(range(10)), 10, min=0, max=10
range_count, [np.nan, np.inf, -np.inf] + list(range(10)), 10, min=0, max=10
)

def test_approximate_entropy(self):
Expand Down Expand Up @@ -1685,7 +1683,7 @@ def test__aggregate_on_chunks(self):
)
self.assertListEqual(
_aggregate_on_chunks(
x=pd.Series([0, 1, 2, np.NaN, 5]), f_agg="median", chunk_len=2
x=pd.Series([0, 1, 2, np.nan, 5]), f_agg="median", chunk_len=2
),
[0.5, 2, 5],
)
Expand Down Expand Up @@ -1728,7 +1726,7 @@ def test_agg_linear_trend(self):
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"median"'], 1)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"median"'], 3)

x = pd.Series([np.NaN, np.NaN, np.NaN, -3, -3, -3])
x = pd.Series([np.nan, np.nan, np.nan, -3, -3, -3])
res = agg_linear_trend(x=x, param=param)

res = pd.Series(dict(res))
Expand All @@ -1742,7 +1740,7 @@ def test_agg_linear_trend(self):
self.assertIsNaN(res['attr_"intercept"__chunk_len_3__f_agg_"median"'])
self.assertIsNaN(res['attr_"slope"__chunk_len_3__f_agg_"median"'])

x = pd.Series([np.NaN, np.NaN, -3, -3, -3, -3])
x = pd.Series([np.nan, np.nan, -3, -3, -3, -3])
res = agg_linear_trend(x=x, param=param)

res = pd.Series(dict(res))
Expand Down Expand Up @@ -1946,19 +1944,19 @@ def test_count_above(self):
self.assertEqualPandasSeriesWrapper(
count_above, [0.1, 0.2, 0.3] * 3, 2 / 3, t=0.2
)
self.assertEqualPandasSeriesWrapper(count_above, [np.NaN, 0, 1] * 3, 2 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_above, [np.nan, 0, 1] * 3, 2 / 3, t=0)
self.assertEqualPandasSeriesWrapper(
count_above, [np.NINF, 0, 1] * 3, 2 / 3, t=0
count_above, [-np.inf, 0, 1] * 3, 2 / 3, t=0
)
self.assertEqualPandasSeriesWrapper(count_above, [np.PINF, 0, 1] * 3, 1, t=0)
self.assertEqualPandasSeriesWrapper(count_above, [np.inf, 0, 1] * 3, 1, t=0)
self.assertEqualPandasSeriesWrapper(
count_above, [np.NaN, 0, 1] * 3, 0, t=np.NaN
count_above, [np.nan, 0, 1] * 3, 0, t=np.nan
)
self.assertEqualPandasSeriesWrapper(
count_above, [np.NINF, 0, np.PINF] * 3, 1, t=np.NINF
count_above, [-np.inf, 0, np.inf] * 3, 1, t=-np.inf
)
self.assertEqualPandasSeriesWrapper(
count_above, [np.PINF, 0, 1] * 3, 1 / 3, t=np.PINF
count_above, [np.inf, 0, 1] * 3, 1 / 3, t=np.inf
)

def test_count_below(self):
Expand All @@ -1968,21 +1966,19 @@ def test_count_below(self):
self.assertEqualPandasSeriesWrapper(
count_below, [0.1, 0.2, 0.3] * 3, 2 / 3, t=0.2
)
self.assertEqualPandasSeriesWrapper(count_below, [np.NaN, 0, 1] * 3, 1 / 3, t=0)
self.assertEqualPandasSeriesWrapper(
count_below, [np.NINF, 0, 1] * 3, 2 / 3, t=0
)
self.assertEqualPandasSeriesWrapper(count_below, [np.nan, 0, 1] * 3, 1 / 3, t=0)
self.assertEqualPandasSeriesWrapper(
count_below, [np.PINF, 0, 1] * 3, 1 / 3, t=0
count_below, [-np.inf, 0, 1] * 3, 2 / 3, t=0
)
self.assertEqualPandasSeriesWrapper(count_below, [np.inf, 0, 1] * 3, 1 / 3, t=0)
self.assertEqualPandasSeriesWrapper(
count_below, [np.NaN, 0, 1] * 3, 0, t=np.NaN
count_below, [np.nan, 0, 1] * 3, 0, t=np.nan
)
self.assertEqualPandasSeriesWrapper(
count_below, [np.NINF, 0, np.PINF] * 3, 1 / 3, t=np.NINF
count_below, [-np.inf, 0, np.inf] * 3, 1 / 3, t=-np.inf
)
self.assertEqualPandasSeriesWrapper(
count_below, [np.PINF, 0, 1] * 3, 1, t=np.PINF
count_below, [np.inf, 0, 1] * 3, 1, t=np.inf
)

def test_benford_correlation(self):
Expand Down
2 changes: 1 addition & 1 deletion tests/units/feature_extraction/test_settings.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -92,7 +92,7 @@ def test_from_column_correct_for_selected_columns(self):

self.assertEqual(
kind_to_fc_parameters[tsn]["value_count"],
[{"value": np.PINF}, {"value": np.NINF}, {"value": np.NaN}],
[{"value": np.inf}, {"value": -np.inf}, {"value": np.nan}],
)

def test_from_column_correct_for_comprehensive_fc_parameters(self):
Expand Down
4 changes: 2 additions & 2 deletions tests/units/feature_selection/test_checks.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,12 +18,12 @@

@pytest.fixture()
def binary_series_with_nan():
return pd.Series([np.NaN, 1, 1])
return pd.Series([np.nan, 1, 1])


@pytest.fixture()
def real_series_with_nan():
return pd.Series([np.NaN, 1, 2])
return pd.Series([np.nan, 1, 2])


@pytest.fixture()
Expand Down
24 changes: 12 additions & 12 deletions tests/units/transformers/test_per_column_imputer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -32,8 +32,8 @@ def test_only_nans_and_infs(self):
X = pd.DataFrame(index=list(range(100)))

X["NaNs"] = np.nan * np.ones(100)
X["PINF"] = np.PINF * np.ones(100)
X["NINF"] = np.NINF * np.ones(100)
X["PINF"] = np.inf * np.ones(100)
X["NINF"] = -np.inf * np.ones(100)

with warnings.catch_warnings(record=True) as w:
imputer.fit(X)
Expand All @@ -53,8 +53,8 @@ def test_with_numpy_array(self):
X = pd.DataFrame(index=list(range(100)))

X["NaNs"] = np.nan * np.ones(100)
X["PINF"] = np.PINF * np.ones(100)
X["NINF"] = np.NINF * np.ones(100)
X["PINF"] = np.inf * np.ones(100)
X["NINF"] = -np.inf * np.ones(100)

X_numpy = X.values.copy()

Expand Down Expand Up @@ -87,7 +87,7 @@ def test_with_numpy_array(self):
def test_standard_replacement_behavior(self):
imputer = PerColumnImputer()

data = [np.NINF, np.PINF, np.nan, 100.0, -100.0, 1.0, 1.0]
data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]
truth = [-100.0, 100.0, 1.0, 100.0, -100.0, 1.0, 1.0]
X = pd.DataFrame({"a": data})
true_X = pd.DataFrame({"a": truth})
Expand All @@ -98,7 +98,7 @@ def test_standard_replacement_behavior(self):
pdt.assert_frame_equal(selected_X, true_X)

def test_partial_preset_col_to_NINF_given(self):
data = [np.NINF, np.PINF, np.nan, 100.0, -100.0, 1.0, 1.0]
data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]
truth = [-100.0, 100.0, 1.0, 100.0, -100.0, 1.0, 1.0]
X = pd.DataFrame({"a": data})
true_X = pd.DataFrame({"a": truth})
Expand All @@ -112,7 +112,7 @@ def test_partial_preset_col_to_NINF_given(self):
pdt.assert_frame_equal(selected_X, true_X)

def test_partial_preset_col_to_PINF_given(self):
data = [np.NINF, np.PINF, np.nan, 100.0, -100.0, 1.0, 1.0]
data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]
truth = [-100.0, 100.0, 1.0, 100.0, -100.0, 1.0, 1.0]
X = pd.DataFrame({"a": data})
true_X = pd.DataFrame({"a": truth})
Expand All @@ -126,7 +126,7 @@ def test_partial_preset_col_to_PINF_given(self):
pdt.assert_frame_equal(selected_X, true_X)

def test_partial_preset_col_to_NAN_given(self):
data = [np.NINF, np.PINF, np.nan, 100.0, -100.0, 1.0, 1.0]
data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]
truth = [-100.0, 100.0, 1.0, 100.0, -100.0, 1.0, 1.0]
X = pd.DataFrame({"a": data})
true_X = pd.DataFrame({"a": truth})
Expand All @@ -151,7 +151,7 @@ def test_different_shapes_fitted_and_transformed(self):
self.assertRaises(ValueError, imputer.transform, X)

def test_preset_has_higher_priority_than_fit(self):
data = [np.NINF, np.PINF, np.nan, 100.0, -100.0, 1.0, 1.0]
data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]
truth = [-100.0, 100.0, 0.0, 100.0, -100.0, 1.0, 1.0]

X = pd.DataFrame({"a": data})
Expand All @@ -166,8 +166,8 @@ def test_preset_has_higher_priority_than_fit(self):
pdt.assert_frame_equal(selected_X, true_X)

def test_only_parameters_of_last_fit_count(self):
data = [np.NINF, np.PINF, np.nan, 100.0, -100.0, 1.0, 1.0]
data_2 = [np.NINF, np.PINF, np.nan, 10.0, -10.0, 3.0, 3.0]
data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]
data_2 = [-np.inf, np.inf, np.nan, 10.0, -10.0, 3.0, 3.0]
truth_a = [-10.0, 10.0, 3.0, 10.0, -10.0, 3.0, 3.0]
truth_b = [-10.0, 10.0, 3.0, 10.0, -10.0, 3.0, 3.0]

Expand All @@ -185,7 +185,7 @@ def test_only_parameters_of_last_fit_count(self):
pdt.assert_frame_equal(selected_X, true_X)

def test_only_subset_of_columns_given(self):
data = [np.NINF, np.PINF, np.nan, 100.0, -100.0, 1.0, 1.0]
data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]
truth_a = [-100.0, 100.0, 0.0, 100.0, -100.0, 1.0, 1.0]
truth_b = [-100.0, 100.0, 1.0, 100.0, -100.0, 1.0, 1.0]
X = pd.DataFrame({"a": data, "b": data})
Expand Down
Loading
Loading