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Fix typo for Mahalanobis in repo #25

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Fix typo for Mahalanobis in repo #25

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7ff1872
Update match.py with correct Mahalanobis
chiuhoward Jun 10, 2025
6c58ecb
Update test_match.py with Mahalanobis
chiuhoward Jun 10, 2025
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16 changes: 8 additions & 8 deletions afqinsight/match.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,10 +4,10 @@
import pandas as pd
from scipy.optimize import linear_sum_assignment

__all__ = ["mahalonobis_dist_match"]
__all__ = ["mahalanobis_dist_match"]


def mahalonobis_dist_match(
def mahalanobis_dist_match(
data=None, test=None, ctrl=None, status_col=None, feature_cols=None, threshold=0.2
):
"""
Expand Down Expand Up @@ -77,12 +77,12 @@ def mahalonobis_dist_match(
(
"There are NaNs in test or ctrl data. "
"Please replace these NaNs using interpolation or by removing "
"the subjects with NaNs before calling mahalonobis_dist_match. "
"the subjects with NaNs before calling mahalanobis_dist_match. "
)
)

# calculate Mahalonobis distance between test and control
nbrs = _mahalonobis_dist(test, ctrl)
# calculate Mahalanobis distance between test and control
nbrs = _mahalanobis_dist(test, ctrl)

# assign neighbors using Munkres algorithm
row_ind, col_ind = linear_sum_assignment(nbrs)
Expand Down Expand Up @@ -118,9 +118,9 @@ def mahalonobis_dist_match(
return data.iloc[all_idx]


def _mahalonobis_dist(arr1, arr2):
def _mahalanobis_dist(arr1, arr2):
"""
Calculate the Mahalonobis distance between two 2d arrays along the first axis.
Calculate the Mahalanobis distance between two 2d arrays along the first axis.

Parameters
----------
Expand All @@ -132,7 +132,7 @@ def _mahalonobis_dist(arr1, arr2):
Returns
-------
nbrs : array-like of shape (n_samples1, n_samples2)
Mahalonobis distance between each sample in the
Mahalanobis distance between each sample in the
two input arrays.
"""
v_inv = np.linalg.inv(np.cov(np.concatenate((arr1, arr2), axis=0).T, ddof=0))
Expand Down
26 changes: 13 additions & 13 deletions afqinsight/tests/test_match.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,8 +34,8 @@
data[f"feature_{ii}"] = [*test_features[:, ii], *ctrl_features[:, ii]]


def test_mahalonobis_dist():
nbrs = aim._mahalonobis_dist(test_features, ctrl_features)
def test_mahalanobis_dist():
nbrs = aim._mahalanobis_dist(test_features, ctrl_features)

v_inv = np.linalg.inv(
np.cov(np.concatenate((test_features, ctrl_features), axis=0).T, ddof=0)
Expand All @@ -48,8 +48,8 @@ def test_mahalonobis_dist():
assert_array_almost_equal(nbrs, nbrs_scipy)


def test_mahalonobis_dist_match_df():
matched_df = aim.mahalonobis_dist_match(
def test_mahalanobis_dist_match_df():
matched_df = aim.mahalanobis_dist_match(
data=data,
status_col="status",
feature_cols=["feature_0", "feature_1", "feature_2", "feature_3"],
Expand All @@ -64,16 +64,16 @@ def test_mahalonobis_dist_match_df():
)


def test_mahalonobis_dist_match_df_err():
def test_mahalanobis_dist_match_df_err():
with pytest.raises(ValueError): # no status column
aim.mahalonobis_dist_match(data=data)
aim.mahalanobis_dist_match(data=data)
with pytest.raises(ValueError): # status has more than two unique values
aim.mahalonobis_dist_match(data=data, status_col="feature_0")
aim.mahalanobis_dist_match(data=data, status_col="feature_0")


def test_mahalonobis_dist_match_df_feauture_none():
def test_mahalanobis_dist_match_df_feauture_none():
data_wo_extras = data.drop(columns=["eid", "age"])
matched_df = aim.mahalonobis_dist_match(
matched_df = aim.mahalanobis_dist_match(
data=data_wo_extras,
status_col="status",
threshold=1,
Expand All @@ -87,8 +87,8 @@ def test_mahalonobis_dist_match_df_feauture_none():
)


def test_mahalonobis_dist_match():
matched_df = aim.mahalonobis_dist_match(
def test_mahalanobis_dist_match():
matched_df = aim.mahalanobis_dist_match(
test=test_features, ctrl=ctrl_features, threshold=1
)

Expand All @@ -103,10 +103,10 @@ def test_mahalonobis_dist_match():
)


def test_mahalonobis_dist_match_err():
def test_mahalanobis_dist_match_err():
with pytest.raises(ValueError): # test if nan error is raised
test_features_with_nans = test_features.copy()
test_features_with_nans[0, 2] = np.nan
aim.mahalonobis_dist_match(
aim.mahalanobis_dist_match(
test=test_features_with_nans, ctrl=ctrl_features, threshold=1
)
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