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chore(evaluation): remove flaky test #453

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1cde6e6
chore(evaluation): pathlibify
tristan-f-r Dec 8, 2025
9bb1fe6
chore: compare on df string instead
tristan-f-r Dec 8, 2025
e2cd2ab
test: try breaking it
tristan-f-r Dec 9, 2025
83c5b38
docs: as string
tristan-f-r Dec 9, 2025
05b089c
fix: pandas chosen equal
tristan-f-r Dec 9, 2025
93a0cf5
chore: inline assert call
tristan-f-r Dec 9, 2025
5aa4d36
chore: stop using inplace
tristan-f-r Dec 9, 2025
4ac1bf1
chore: drop rest of inplace
tristan-f-r Dec 9, 2025
9aa23b9
chore(ruff): disable inplace
tristan-f-r Dec 9, 2025
76d95a8
test: try expected pca coordinates
tristan-f-r Dec 9, 2025
386234d
chore: drop ml kde nondeterminism
tristan-f-r Dec 9, 2025
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3 changes: 2 additions & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,8 @@ select = [
"F823", # undefined-local
"F841", # unused-variable
"I", # isort
"W292", # missing-newline-at-end-of-file
"W292", # missing-newline-at-end-of-file,
"PD002", # pandas-use-of-inplace-argument
]

[tool.setuptools.packages.find]
Expand Down
16 changes: 8 additions & 8 deletions spras/evaluation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -231,7 +231,7 @@ def visualize_precision_and_recall_plot(pr_df: pd.DataFrame, output_file: str |
plt.close()

# save dataframe
pr_df.drop(columns=['Algorithm'], inplace=True)
pr_df = pr_df.drop(columns=['Algorithm'])
pr_df.to_csv(output_file, sep='\t', index=False)

@staticmethod
Expand All @@ -248,7 +248,7 @@ def precision_and_recall_per_pathway(pr_df: pd.DataFrame, output_file: str | Pat
"""
if not pr_df.empty:
pr_df['Algorithm'] = pr_df['Pathway'].apply(lambda p: Path(p).parent.name.split('-')[1])
pr_df.sort_values(by=['Recall', 'Pathway'], axis=0, ascending=True, inplace=True)
pr_df = pr_df.sort_values(by=['Recall', 'Pathway'], axis=0, ascending=True)

if aggregate_per_algorithm:
# Guaranteed to only have one algorithm in Algorithm column
Expand Down Expand Up @@ -281,7 +281,7 @@ def precision_and_recall_pca_chosen_pathway(pr_df: pd.DataFrame, output_file: st

if not pr_df.empty:
pr_df['Algorithm'] = pr_df['Pathway'].apply(lambda p: Path(p).parent.name.split('-')[1])
pr_df.sort_values(by=['Recall', 'Pathway'], axis=0, ascending=True, inplace=True)
pr_df = pr_df.sort_values(by=['Recall', 'Pathway'], axis=0, ascending=True)

if aggregate_per_algorithm:
title = "PCA-Chosen Pathway Per Algorithm Precision and Recall Plot"
Expand All @@ -305,7 +305,7 @@ def precision_and_recall_pca_chosen_pathway(pr_df: pd.DataFrame, output_file: st
plt.close()

@staticmethod
def pca_chosen_pathway(coordinates_files: Iterable[Union[str, PathLike]], pathway_summary_file: str, output_dir: str):
def pca_chosen_pathway(coordinates_files: Iterable[Union[str, PathLike]], pathway_summary_file: str | PathLike, output_dir: str | PathLike) -> list[str]:
"""
Identifies the pathway closest to a specified highest kernel density estimated (KDE) peak based on PCA
coordinates
Expand All @@ -323,7 +323,7 @@ def pca_chosen_pathway(coordinates_files: Iterable[Union[str, PathLike]], pathwa
"""
# TODO update to add in the pathways for the algorithms that do not provide a pca chosen pathway https://github.com/Reed-CompBio/spras/issues/341

rep_pathways = []
rep_pathways: list[str] = []

for coordinates_file in coordinates_files:
coord_df = pd.read_csv(coordinates_file, delimiter='\t', header=0)
Expand Down Expand Up @@ -356,7 +356,7 @@ def pca_chosen_pathway(coordinates_files: Iterable[Union[str, PathLike]], pathwa
return rep_pathways

@staticmethod
def edge_frequency_node_ensemble(node_table: pd.DataFrame, ensemble_files: Iterable[Union[str, PathLike]], dataset_file: str) -> dict:
def edge_frequency_node_ensemble(node_table: pd.DataFrame, ensemble_files: Iterable[Union[str, PathLike]], dataset_file: str | PathLike) -> dict:
"""
Generates a dictionary of node ensembles using edge frequency data from a list of ensemble files.
A list of ensemble files can contain an aggregated ensemble or algorithm-specific ensembles per dataset
Expand Down Expand Up @@ -387,11 +387,11 @@ def edge_frequency_node_ensemble(node_table: pd.DataFrame, ensemble_files: Itera

if interactome.empty:
raise ValueError(
f"Cannot compute PR curve or generate node ensemble. Input network for dataset \"{dataset_file.split('-')[0]}\" is empty."
f"Cannot compute PR curve or generate node ensemble. Input network for dataset \"{Path(dataset_file).name.split('-')[0]}\" is empty."
)
if node_table.empty:
raise ValueError(
f"Cannot compute PR curve or generate node ensemble. Gold standard associated with dataset \"{dataset_file.split('-')[0]}\" is empty."
f"Cannot compute PR curve or generate node ensemble. Gold standard associated with dataset \"{Path(dataset_file).name.split('-')[0]}\" is empty."
)

# set the initial default frequencies to 0 for all interactome and gold standard nodes
Expand Down
2 changes: 1 addition & 1 deletion spras/meo.py
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Expand Up @@ -215,7 +215,7 @@ def parse_output(raw_pathway_file, standardized_pathway_file, params):
# Would need to load the paths output file to rank edges correctly
df = add_rank_column(df)
df = reinsert_direction_col_directed(df)
df.drop(columns=['Type', 'Oriented', 'Weight'], inplace=True)
df = df.drop(columns=['Type', 'Oriented', 'Weight'])
df.columns = ['Node1', 'Node2', 'Rank', "Direction"]
df, has_duplicates = duplicate_edges(df)
if has_duplicates:
Expand Down
2 changes: 1 addition & 1 deletion spras/omicsintegrator1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -235,7 +235,7 @@ def parse_output(raw_pathway_file, standardized_pathway_file, params):
df.columns = ["Edge1", "InteractionType", "Edge2"]
df = add_rank_column(df)
df = reinsert_direction_col_mixed(df, "InteractionType", "pd", "pp")
df.drop(columns=['InteractionType'], inplace=True)
df = df.drop(columns=['InteractionType'])
df.columns = ['Node1', 'Node2', 'Rank', 'Direction']
df, has_duplicates = duplicate_edges(df)
if has_duplicates:
Expand Down
6 changes: 6 additions & 0 deletions test/evaluate/expected/expected-pca-coordinates.txt
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Original file line number Diff line number Diff line change
@@ -0,0 +1,6 @@
datapoint_labels PC1 PC2
data-test-params-123 0.52704628 0.70710678
data-test-params-456 -1.05409255 -0.0
data-test-params-789 0.52704628 -0.70710678
centroid 0.0 -0.0
kde_peak 0.11419336 -0.00785674
116 changes: 60 additions & 56 deletions test/evaluate/test_evaluate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,11 +9,11 @@
from spras.dataset import Dataset
from spras.evaluation import Evaluation

INPUT_DIR = 'test/evaluate/input/'
OUT_DIR = 'test/evaluate/output/'
EXPECT_DIR = 'test/evaluate/expected/'
GS_NODE_TABLE = pd.read_csv(INPUT_DIR + 'gs_node_table.csv', header=0)
SUMMARY_FILE = INPUT_DIR + 'example_summary.txt'
INPUT_DIR = Path('test', 'evaluate', 'input')
OUT_DIR = Path('test', 'evaluate', 'output')
EXPECT_DIR = Path('test', 'evaluate', 'expected')
GS_NODE_TABLE = pd.read_csv(INPUT_DIR / 'gs_node_table.csv', header=0)
SUMMARY_FILE = INPUT_DIR / 'example_summary.txt'


class TestEvaluate:
Expand All @@ -39,154 +39,158 @@ def setup_class(cls):
pickle.dump(dataset, f)

def test_node_precision_recall_per_pathway(self):
file_paths = [INPUT_DIR + 'data-test-params-123/pathway.txt', INPUT_DIR + 'data-test-params-456/pathway.txt', INPUT_DIR + 'data-test-params-789/pathway.txt', INPUT_DIR + 'data-test-params-empty/pathway.txt']
output_file = Path(OUT_DIR + 'pr-per-pathway.txt')
output_png = Path(OUT_DIR + 'pr-per-pathway.png')
file_paths = [INPUT_DIR / 'data-test-params-123/pathway.txt', INPUT_DIR / 'data-test-params-456/pathway.txt', INPUT_DIR / 'data-test-params-789/pathway.txt', INPUT_DIR / 'data-test-params-empty/pathway.txt']
output_file = Path(OUT_DIR, 'pr-per-pathway.txt')
output_png = Path(OUT_DIR, 'pr-per-pathway.png')
output_file.unlink(missing_ok=True)
output_png.unlink(missing_ok=True)

pr_df = Evaluation.node_precision_and_recall(file_paths, GS_NODE_TABLE)
Evaluation.precision_and_recall_per_pathway(pr_df, output_file, output_png, True)

output = pd.read_csv(output_file, sep='\t', header=0).round(8)
expected = pd.read_csv(EXPECT_DIR + 'expected-pr-per-pathway.txt', sep='\t', header=0).round(8)
expected = pd.read_csv(EXPECT_DIR / 'expected-pr-per-pathway.txt', sep='\t', header=0).round(8)

assert output.equals(expected)
assert output_png.exists()

def test_node_precision_recall_per_pathway_empty(self):

file_paths = [INPUT_DIR + 'data-test-params-empty/pathway.txt']
output_file = Path(OUT_DIR + 'pr-per-pathway-empty.txt')
output_png = Path(OUT_DIR + 'pr-per-pathway-empty.png')
file_paths = [INPUT_DIR / 'data-test-params-empty/pathway.txt']
output_file = Path(OUT_DIR, 'pr-per-pathway-empty.txt')
output_png = Path(OUT_DIR, 'pr-per-pathway-empty.png')
output_file.unlink(missing_ok=True)
output_png.unlink(missing_ok=True)

pr_df = Evaluation.node_precision_and_recall(file_paths, GS_NODE_TABLE)
Evaluation.precision_and_recall_per_pathway(pr_df, output_file, output_png, True)

output = pd.read_csv(output_file, sep='\t', header=0).round(8)
expected = pd.read_csv(EXPECT_DIR + 'expected-pr-per-pathway-empty.txt', sep='\t', header=0).round(8)
expected = pd.read_csv(EXPECT_DIR / 'expected-pr-per-pathway-empty.txt', sep='\t', header=0).round(8)

assert output.equals(expected)
assert output_png.exists()

def test_node_precision_recall_per_pathway_not_provided(self):
output_file = OUT_DIR + 'pr-per-pathway-not-provided.txt'
output_png = OUT_DIR + 'pr-per-pathway-not-provided.png'
output_file = OUT_DIR / 'pr-per-pathway-not-provided.txt'
output_png = OUT_DIR / 'pr-per-pathway-not-provided.png'
file_paths = []

pr_df = Evaluation.node_precision_and_recall(file_paths, GS_NODE_TABLE)
with pytest.raises(ValueError):
Evaluation.precision_and_recall_per_pathway(pr_df, output_file, output_png)

def test_node_precision_recall_pca_chosen_pathway_not_provided(self):
output_file = Path( OUT_DIR + 'pr-per-pathway-pca-chosen-not-provided.txt')
output_file = Path(OUT_DIR, 'pr-per-pathway-pca-chosen-not-provided.txt')
output_file.unlink(missing_ok=True)
output_png = Path(OUT_DIR + 'pr-per-pathway-pca-chosen-not-provided.png')
output_png = Path(OUT_DIR, 'pr-per-pathway-pca-chosen-not-provided.png')
output_png.unlink(missing_ok=True)
file_paths = []

pr_df = Evaluation.node_precision_and_recall(file_paths, GS_NODE_TABLE)
Evaluation.precision_and_recall_pca_chosen_pathway(pr_df, output_file, output_png)

output = pd.read_csv(output_file, sep='\t', header=0).round(8)
expected = pd.read_csv(EXPECT_DIR + 'expected-pr-pca-chosen-not-provided.txt', sep='\t', header=0).round(8)
expected = pd.read_csv(EXPECT_DIR / 'expected-pr-pca-chosen-not-provided.txt', sep='\t', header=0).round(8)

assert output.equals(expected)
assert output_png.exists()

def test_node_precision_recall_pca_chosen_pathway(self):
output_file = Path(OUT_DIR + 'pr-per-pathway-pca-chosen.txt')
output_file = Path(OUT_DIR / 'pr-per-pathway-pca-chosen.txt')
output_file.unlink(missing_ok=True)
output_png = Path(OUT_DIR + 'pr-per-pathway-pca-chosen.png')
output_png = Path(OUT_DIR / 'pr-per-pathway-pca-chosen.png')
output_png.unlink(missing_ok=True)
output_coordinates = Path(OUT_DIR + 'pca-coordinates.tsv')
output_coordinates = Path(OUT_DIR / 'pca-coordinates.tsv')
output_coordinates.unlink(missing_ok=True)

file_paths = [INPUT_DIR + 'data-test-params-123/pathway.txt', INPUT_DIR + 'data-test-params-456/pathway.txt',
INPUT_DIR + 'data-test-params-789/pathway.txt', INPUT_DIR + 'data-test-params-empty/pathway.txt']
file_paths = [INPUT_DIR / 'data-test-params-123' / 'pathway.txt', INPUT_DIR / 'data-test-params-456' / 'pathway.txt',
INPUT_DIR / 'data-test-params-789' / 'pathway.txt', INPUT_DIR / 'data-test-params-empty' / 'pathway.txt']

dataframe = ml.summarize_networks(file_paths)
ml.pca(dataframe, OUT_DIR + 'pca.png', OUT_DIR + 'pca-variance.txt', str(output_coordinates), kde=True, remove_empty_pathways=True)
ml.pca(dataframe, OUT_DIR / 'pca.png', OUT_DIR / 'pca-variance.txt', output_coordinates, kde=True, remove_empty_pathways=True)

pathway = Evaluation.pca_chosen_pathway([output_coordinates], SUMMARY_FILE, INPUT_DIR)
pathways = Evaluation.pca_chosen_pathway([output_coordinates], SUMMARY_FILE, INPUT_DIR)
assert len(pathways) == 1, f"There must only be one pathway, but got {len(pathways)} instead! ({pathways})"
pd.testing.assert_frame_equal(
pd.read_csv(output_coordinates, sep='\t', header=0),
pd.read_csv(EXPECT_DIR / 'expected-pca-coordinates.txt', sep='\t', header=0)
)

pr_df = Evaluation.node_precision_and_recall(pathway, GS_NODE_TABLE)
pr_df = Evaluation.node_precision_and_recall(pathways, GS_NODE_TABLE)
Evaluation.precision_and_recall_pca_chosen_pathway(pr_df, output_file, output_png, True)


chosen = pd.read_csv(output_file, sep='\t', header=0).round(8)
expected = pd.read_csv(EXPECT_DIR + 'expected-pr-per-pathway-pca-chosen.txt', sep='\t', header=0).round(8)
expected = pd.read_csv(EXPECT_DIR / 'expected-pr-per-pathway-pca-chosen.txt', sep='\t', header=0).round(8)

assert chosen.equals(expected)
pd.testing.assert_frame_equal(chosen, expected)
assert output_png.exists()

def test_node_ensemble(self):
out_path_file = Path(OUT_DIR + 'node-ensemble.csv')
out_path_file = Path(OUT_DIR, 'node-ensemble.csv')
out_path_file.unlink(missing_ok=True)
ensemble_network = [INPUT_DIR + 'ensemble-network.tsv']
input_network = OUT_DIR + 'data.pickle'
ensemble_network = [INPUT_DIR / 'ensemble-network.tsv']
input_network = OUT_DIR / 'data.pickle'
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(GS_NODE_TABLE, ensemble_network, input_network)
node_ensemble_dict['ensemble'].to_csv(out_path_file, sep='\t', index=False)
assert filecmp.cmp(out_path_file, EXPECT_DIR + 'expected-node-ensemble.csv', shallow=False)
assert filecmp.cmp(out_path_file, EXPECT_DIR / 'expected-node-ensemble.csv', shallow=False)

def test_empty_node_ensemble(self):
out_path_file = Path(OUT_DIR + 'empty-node-ensemble.csv')
out_path_file = Path(OUT_DIR, 'empty-node-ensemble.csv')
out_path_file.unlink(missing_ok=True)
empty_ensemble_network = [INPUT_DIR + 'empty-ensemble-network.tsv']
input_network = OUT_DIR + 'data.pickle'
empty_ensemble_network = [INPUT_DIR / 'empty-ensemble-network.tsv']
input_network = OUT_DIR / 'data.pickle'
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(GS_NODE_TABLE, empty_ensemble_network,
input_network)
node_ensemble_dict['empty'].to_csv(out_path_file, sep='\t', index=False)
assert filecmp.cmp(out_path_file, EXPECT_DIR + 'expected-empty-node-ensemble.csv', shallow=False)
assert filecmp.cmp(out_path_file, EXPECT_DIR / 'expected-empty-node-ensemble.csv', shallow=False)

def test_multiple_node_ensemble(self):
out_path_file = Path(OUT_DIR + 'node-ensemble.csv')
out_path_file = Path(OUT_DIR / 'node-ensemble.csv')
out_path_file.unlink(missing_ok=True)
out_path_empty_file = Path(OUT_DIR + 'empty-node-ensemble.csv')
out_path_empty_file = Path(OUT_DIR / 'empty-node-ensemble.csv')
out_path_empty_file.unlink(missing_ok=True)
ensemble_networks = [INPUT_DIR + 'ensemble-network.tsv', INPUT_DIR + 'empty-ensemble-network.tsv']
input_network = OUT_DIR + 'data.pickle'
ensemble_networks = [INPUT_DIR / 'ensemble-network.tsv', INPUT_DIR / 'empty-ensemble-network.tsv']
input_network = OUT_DIR / 'data.pickle'
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(GS_NODE_TABLE, ensemble_networks, input_network)
node_ensemble_dict['ensemble'].to_csv(out_path_file, sep='\t', index=False)
assert filecmp.cmp(out_path_file, EXPECT_DIR + 'expected-node-ensemble.csv', shallow=False)
assert filecmp.cmp(out_path_file, EXPECT_DIR / 'expected-node-ensemble.csv', shallow=False)
node_ensemble_dict['empty'].to_csv(out_path_empty_file, sep='\t', index=False)
assert filecmp.cmp(out_path_empty_file, EXPECT_DIR + 'expected-empty-node-ensemble.csv', shallow=False)
assert filecmp.cmp(out_path_empty_file, EXPECT_DIR / 'expected-empty-node-ensemble.csv', shallow=False)

def test_precision_recall_curve_ensemble_nodes(self):
out_path_png = Path(OUT_DIR + 'pr-curve-ensemble-nodes.png')
out_path_png = Path(OUT_DIR, 'pr-curve-ensemble-nodes.png')
out_path_png.unlink(missing_ok=True)
out_path_file = Path(OUT_DIR + 'pr-curve-ensemble-nodes.txt')
out_path_file = Path(OUT_DIR, 'pr-curve-ensemble-nodes.txt')
out_path_file.unlink(missing_ok=True)
ensemble_file = pd.read_csv(INPUT_DIR + 'node-ensemble.csv', sep='\t', header=0)
ensemble_file = pd.read_csv(INPUT_DIR / 'node-ensemble.csv', sep='\t', header=0)
node_ensembles_dict = {'ensemble': ensemble_file}
Evaluation.precision_recall_curve_node_ensemble(node_ensembles_dict, GS_NODE_TABLE, out_path_png,
out_path_file)
assert out_path_png.exists()
assert filecmp.cmp(out_path_file, EXPECT_DIR + 'expected-pr-curve-ensemble-nodes.txt', shallow=False)
assert filecmp.cmp(out_path_file, EXPECT_DIR / 'expected-pr-curve-ensemble-nodes.txt', shallow=False)

def test_precision_recall_curve_ensemble_nodes_empty(self):
out_path_png = Path(OUT_DIR + 'pr-curve-ensemble-nodes-empty.png')
out_path_png = Path(OUT_DIR, 'pr-curve-ensemble-nodes-empty.png')
out_path_png.unlink(missing_ok=True)
out_path_file = Path(OUT_DIR + 'pr-curve-ensemble-nodes-empty.txt')
out_path_file = Path(OUT_DIR, 'pr-curve-ensemble-nodes-empty.txt')
out_path_file.unlink(missing_ok=True)
empty_ensemble_file = pd.read_csv(INPUT_DIR + 'node-ensemble-empty.csv', sep='\t', header=0)
empty_ensemble_file = pd.read_csv(INPUT_DIR / 'node-ensemble-empty.csv', sep='\t', header=0)
node_ensembles_dict = {'ensemble': empty_ensemble_file}
Evaluation.precision_recall_curve_node_ensemble(node_ensembles_dict, GS_NODE_TABLE, out_path_png,
out_path_file)
assert out_path_png.exists()
assert filecmp.cmp(out_path_file, EXPECT_DIR + 'expected-pr-curve-ensemble-nodes-empty.txt', shallow=False)
assert filecmp.cmp(out_path_file, EXPECT_DIR / 'expected-pr-curve-ensemble-nodes-empty.txt', shallow=False)

def test_precision_recall_curve_multiple_ensemble_nodes(self):
out_path_png = Path(OUT_DIR + 'pr-curve-multiple-ensemble-nodes.png')
out_path_png = Path(OUT_DIR, 'pr-curve-multiple-ensemble-nodes.png')
out_path_png.unlink(missing_ok=True)
out_path_file = Path(OUT_DIR + 'pr-curve-multiple-ensemble-nodes.txt')
out_path_file = Path(OUT_DIR, 'pr-curve-multiple-ensemble-nodes.txt')
out_path_file.unlink(missing_ok=True)
ensemble_file = pd.read_csv(INPUT_DIR + 'node-ensemble.csv', sep='\t', header=0)
empty_ensemble_file = pd.read_csv(INPUT_DIR + 'node-ensemble-empty.csv', sep='\t', header=0)
ensemble_file = pd.read_csv(INPUT_DIR / 'node-ensemble.csv', sep='\t', header=0)
empty_ensemble_file = pd.read_csv(INPUT_DIR / 'node-ensemble-empty.csv', sep='\t', header=0)
node_ensembles_dict = {'ensemble1': ensemble_file, 'ensemble2': ensemble_file, 'ensemble3': empty_ensemble_file}
Evaluation.precision_recall_curve_node_ensemble(node_ensembles_dict, GS_NODE_TABLE, out_path_png,
out_path_file, True)
assert out_path_png.exists()
assert filecmp.cmp(out_path_file, EXPECT_DIR + 'expected-pr-curve-multiple-ensemble-nodes.txt', shallow=False)
assert filecmp.cmp(out_path_file, EXPECT_DIR / 'expected-pr-curve-multiple-ensemble-nodes.txt', shallow=False)
7 changes: 0 additions & 7 deletions test/ml/expected/expected-pca-coordinates-kde-negated.tsv
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