def test_get_pairwise_metric():
with pytest.raises(ValueError) as ve:
output = get_pairwise_metric(df, similarity_metric="pearson")
assert "check input features" in str(ve.value)
with pytest.raises(AssertionError) as ve:
output = get_pairwise_metric(feature_df,
similarity_metric="not supported")
assert "not supported not supported" in str(ve.value)
result_df = get_pairwise_metric(feature_df, similarity_metric="pearson")
assert np.diagonal(result_df).sum() == df.shape[0]
assert round(example_sample_corr, 3) == round(result_df.iloc[0, 1], 3)
example_file = "SQ00015054_normalized_feature_select.csv.gz"
example_file = pathlib.Path("{file}/../../example_data/compound/{eg}".format(
file=os.path.dirname(__file__), eg=example_file))
df = pd.read_csv(example_file)
meta_features = [x for x in df.columns if x.startswith("Metadata_")]
features = df.drop(meta_features, axis="columns").columns.tolist()
feature_df = df.loc[:, features]
meta_df = df.loc[:, meta_features]
sample_a = feature_df.iloc[0, ].values
sample_b = feature_df.iloc[1, ].values
example_sample_corr = np.corrcoef(sample_a, sample_b)[0, 1]
pairwise_metric_df = get_pairwise_metric(feature_df,
similarity_metric="pearson")
def test_get_pairwise_metric():
with pytest.raises(ValueError) as ve:
output = get_pairwise_metric(df, similarity_metric="pearson")
assert "check input features" in str(ve.value)
with pytest.raises(AssertionError) as ve:
output = get_pairwise_metric(feature_df,
similarity_metric="not supported")
assert "not supported not supported" in str(ve.value)
result_df = get_pairwise_metric(feature_df, similarity_metric="pearson")
assert np.diagonal(result_df).sum() == df.shape[0]