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feat: Add ml.metrics.pairwise.paired_euclidean_distances #397

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Merged
merged 2 commits into from
Feb 29, 2024
Merged

feat: Add ml.metrics.pairwise.paired_euclidean_distances #397

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dadbf4b
feat: Add ml.metrics.pairwise.euclidean_distance
ashleyxuu Feb 27, 2024
280b9d7
Merge branch 'main' into ashleyxu-euclidean_distances
ashleyxuu Feb 28, 2024
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16 changes: 16 additions & 0 deletions 16 bigframes/ml/metrics/pairwise.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,3 +50,19 @@ def paired_manhattan_distance(
paired_manhattan_distance.__doc__ = inspect.getdoc(
vendored_metrics_pairwise.paired_manhattan_distance
)


def paired_euclidean_distances(
X: Union[bpd.DataFrame, bpd.Series], Y: Union[bpd.DataFrame, bpd.Series]
) -> bpd.DataFrame:
X, Y = utils.convert_to_dataframe(X, Y)
if len(X.columns) != 1 or len(Y.columns) != 1:
raise ValueError("Inputs X and Y can only contain 1 column.")

base_bqml = core.BaseBqml(session=X._session)
return base_bqml.distance(X, Y, type="EUCLIDEAN", name="euclidean_distance")


paired_euclidean_distances.__doc__ = inspect.getdoc(
vendored_metrics_pairwise.paired_euclidean_distances
)
16 changes: 16 additions & 0 deletions 16 tests/system/small/ml/test_metrics_pairwise.py
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Expand Up @@ -47,3 +47,19 @@ def test_paired_manhattan_distance():
pd.testing.assert_frame_equal(
result.to_pandas(), expected_pd_df, check_dtype=False, check_index_type=False
)


def test_paired_euclidean_distances():
x_col = [np.array([4.1, 0.5, 1.0])]
y_col = [np.array([3.0, 0.0, 2.5])]
X = bpd.read_pandas(pd.DataFrame({"X": x_col}))
Y = bpd.read_pandas(pd.DataFrame({"Y": y_col}))

result = metrics.pairwise.paired_euclidean_distances(X, Y)
expected_pd_df = pd.DataFrame(
{"X": x_col, "Y": y_col, "euclidean_distance": [1.926136]}
)

pd.testing.assert_frame_equal(
result.to_pandas(), expected_pd_df, check_dtype=False, check_index_type=False
)
15 changes: 15 additions & 0 deletions 15 third_party/bigframes_vendored/sklearn/metrics/pairwise.py
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Expand Up @@ -39,3 +39,18 @@ def paired_manhattan_distance(X, Y) -> bpd.DataFrame:
bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and manhattan_distance
"""
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)


def paired_euclidean_distances(X, Y) -> bpd.DataFrame:
"""Compute the paired euclidean distances between X and Y.

Args:
X (Series or single column DataFrame of array of numeric type):
Input data.
Y (Series or single column DataFrame of array of numeric type):
Input data. X and Y are mapped by indexes, must have the same index.

Returns:
bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and euclidean_distance
"""
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
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