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Add simple tsfresh unit tests #92

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merged 11 commits into from
Oct 18, 2023
Merged

Add simple tsfresh unit tests #92

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47a7892
fix: return 0 where mean is 0
TomBurdge Oct 17, 2023
d425b39
fix: add conditionals for x being a series
TomBurdge Oct 17, 2023
ea0905e
fix: rename second test_percent_reccuring_values
TomBurdge Oct 17, 2023
2a58e3c
clean with else statement
TomBurdge Oct 17, 2023
2d66b69
noqa on second percent_reoccuring
TomBurdge Oct 17, 2023
2a0c31d
Merge branch 'main' into add-simple-tsfresh-unit-tests
TomBurdge Oct 17, 2023
ecbdf6b
improve 0 float handling in variation_coefficient
TomBurdge Oct 17, 2023
7804909
feat: add tests for mean change
TomBurdge Oct 17, 2023
81df616
add negative float inf tests
TomBurdge Oct 17, 2023
f2de57d
Update functime/feature_extraction/tsfresh.py
TomBurdge Oct 18, 2023
1fa8100
Update tests/test_tsfresh.py
TomBurdge Oct 18, 2023
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Merge branch 'main' into add-simple-tsfresh-unit-tests
  • Loading branch information
@TomBurdge
TomBurdge committed Oct 17, 2023
commit 2a0c31dc7306b1c205298ecccfcf3507289ad695
111 changes: 111 additions & 0 deletions tests/test_tsfresh.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,9 @@
range_change,
range_count,
range_over_mean,
ratio_beyond_r_sigma,
ratio_n_unique_to_length,
root_mean_square,
sum_reocurring_points,
sum_reocurring_values,
symmetry_looking,
Expand Down Expand Up @@ -738,6 +741,114 @@ def test_longest_streak_above_mean(S, res):
)


@pytest.mark.parametrize(
"S, res, ratio",
[
([0, 1] * 10 + [10, 20, -30], [3.0 / 23.0], 1),
([0, 1] * 10 + [10, 20, -30], [2.0 / 23.0], 2),
([0, 1] * 10 + [10, 20, -30], [1.0 / 23.0], 3),
],
)
def test_ratio_beyond_r_sigma(S, res, ratio):
assert_frame_equal(
pl.DataFrame({"a": S}).select(ratio_beyond_r_sigma(pl.col("a"), ratio=ratio)),
pl.DataFrame(pl.Series("a", res)),
)
assert_frame_equal(
pl.LazyFrame({"a": S})
.select(ratio_beyond_r_sigma(pl.col("a"), ratio=ratio))
.collect(),
pl.DataFrame(pl.Series("a", res)),
)
assert ratio_beyond_r_sigma(pl.Series(S), ratio=ratio) == res[0]


@pytest.mark.parametrize("S, res, ratio", [([], [np.nan], 1)])
def test_ratio_beyond_r_sigma_nan_case(S, res, ratio):
assert_frame_equal(
pl.DataFrame({"a": S}).select(ratio_beyond_r_sigma(pl.col("a"), ratio=ratio)),
pl.DataFrame(pl.Series("a", res)),
)
assert_frame_equal(
pl.LazyFrame({"a": S})
.select(ratio_beyond_r_sigma(pl.col("a"), ratio=ratio))
.collect(),
pl.DataFrame(pl.Series("a", res)),
)
with pytest.raises(ZeroDivisionError):
ratio_beyond_r_sigma(pl.Series(S), ratio=ratio)


@pytest.mark.parametrize(
"S, res",
[
([1, 1, 2, 3, 4], [0.8]),
([1, 1.5, 2, 3], [1.0]),
([1], [1.0]),
([1.111, -2.45, 1.111, 2.45], [0.75]),
],
)
def test_ratio_n_unique_to_length(S, res):
assert_frame_equal(
pl.DataFrame({"a": S}).select(ratio_n_unique_to_length(pl.col("a"))),
pl.DataFrame(pl.Series("a", res)),
)
assert_frame_equal(
pl.LazyFrame({"a": S}).select(ratio_n_unique_to_length(pl.col("a"))).collect(),
pl.DataFrame(pl.Series("a", res)),
)
assert ratio_n_unique_to_length(pl.Series(S)) == res[0]


@pytest.mark.parametrize("S, res", [([], [np.nan])])
def test_ratio_n_unique_to_length_nan_case(S, res):
assert_frame_equal(
pl.DataFrame({"a": S}).select(ratio_n_unique_to_length(pl.col("a"))),
pl.DataFrame(pl.Series("a", res)),
)
assert_frame_equal(
pl.LazyFrame({"a": S}).select(ratio_n_unique_to_length(pl.col("a"))).collect(),
pl.DataFrame(pl.Series("a", res)),
)
with pytest.raises(ZeroDivisionError):
ratio_n_unique_to_length(pl.Series(S))


@pytest.mark.parametrize(
"S, res",
[
([1, 1, 1, 2, 2], [1.4832396974191326]),
([1.0, 1.0, 1.0, 2.0, 2.0], [1.4832396974191326]),
([0], [0.0]),
([1], [1.0]),
([-1], [1.0]),
],
)
def test_root_mean_square(S, res):
assert_frame_equal(
pl.DataFrame({"a": S}).select(root_mean_square(pl.col("a"))),
pl.DataFrame(pl.Series("a", res)),
)
assert_frame_equal(
pl.LazyFrame({"a": S}).select(root_mean_square(pl.col("a"))).collect(),
pl.DataFrame(pl.Series("a", res)),
)
assert root_mean_square(pl.Series(S)) == res[0]


@pytest.mark.parametrize("S, res", [([], [np.nan])])
def test_root_mean_square_nan_case(S, res):
assert_frame_equal(
pl.DataFrame({"a": S}).select(root_mean_square(pl.col("a"))),
pl.DataFrame(pl.Series("a", res)),
)
assert_frame_equal(
pl.LazyFrame({"a": S}).select(root_mean_square(pl.col("a"))).collect(),
pl.DataFrame(pl.Series("a", res)),
)
np.isnan(root_mean_square(pl.Series(S)))


@pytest.mark.parametrize(
"S, n_max, res",
[
Expand Down
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