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fb37e3c
ci: try out windows
tristan-f-r Jul 29, 2025
f25d482
Merge branch '4-core' into windows-ci
tristan-f-r Jul 29, 2025
cf40bf3
ci: typo
tristan-f-r Jul 29, 2025
ced0339
test: ignore cytoscape, attempt pca windows fix
tristan-f-r Jul 29, 2025
c9319ae
fix: use correct datatype
tristan-f-r Jul 29, 2025
2ea7737
ci: dbg
tristan-f-r Jul 29, 2025
0a2415c
fix: paths and more paths
tristan-f-r Jul 29, 2025
416baa6
Merge branch 'main' into windows-ci
tristan-f-r Sep 6, 2025
c271322
fix: type pca_chosen_pathway
tristan-f-r Sep 6, 2025
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18 changes: 18 additions & 0 deletions .github/workflows/test-spras.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,24 @@ jobs:
shell: bash --login {0}
run: conda list

test_windows:
name: Run weakened windows tests
runs-on: windows-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Install conda environment
uses: conda-incubator/setup-miniconda@v2
with:
activate-environment: spras
environment-file: environment.yml
auto-activate-base: false
miniconda-version: 'latest'
- name: Run tests
shell: bash --login {0}
# Verbose output and disable stdout and stderr capturing
run: pytest -vs --ignore=test/AllPairs --ignore=test/BowTieBuilder --ignore=test/DOMINO --ignore=test/analysis/test_cytoscape.py --ignore=test/MEO --ignore=test/MinCostFlow --ignore=test/OmicsIntegrator1 --ignore=test/OmicsIntegrator2 --ignore=test/PathLinker --ignore=test/ResponseNet --ignore=test/RWR --ignore=test/ST_RWR

# Runs the test code and Snakemake workflow in the conda environment
test:
name: Run tests
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2 changes: 1 addition & 1 deletion spras/evaluation.py
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Expand Up @@ -237,7 +237,7 @@ def precision_and_recall_pca_chosen_pathway(pr_df: pd.DataFrame, output_file: st
plt.close()

@staticmethod
def pca_chosen_pathway(coordinates_files: list[Union[str, PathLike]], pathway_summary_file: str, output_dir: str):
def pca_chosen_pathway(coordinates_files: list[Union[str, PathLike]], pathway_summary_file: Union[str, PathLike], output_dir: Union[str, PathLike]):
"""
Identifies the pathway closest to a specified highest kernel density estimated (KDE) peak based on PCA
coordinates
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4 changes: 2 additions & 2 deletions test/evaluate/expected/expected-pr-per-pathway-pca-chosen.txt
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Original file line number Diff line number Diff line change
@@ -1,2 +1,2 @@
Pathway Precision Recall
test/evaluate/input/data-test-params-123/pathway.txt 0.6666666666666666 0.5
Precision Recall
0.6666666666666666 0.5
110 changes: 55 additions & 55 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 @@ -39,76 +39,76 @@ 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 = OUT_DIR / 'pr-per-pathway-empty.txt'
output_png = 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 = 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 = 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 = 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 = OUT_DIR / 'pr-per-pathway-pca-chosen.png'
output_png.unlink(missing_ok=True)
output_coordinates = Path(OUT_DIR + 'pca-coordinates.tsv')
output_coordinates = 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, str(OUT_DIR / 'pca.png'), str(OUT_DIR / 'pca-variance.txt'), str(output_coordinates), kde=True, remove_empty_pathways=True)

pathway = Evaluation.pca_chosen_pathway([output_coordinates], SUMMARY_FILE, INPUT_DIR)

Expand All @@ -117,76 +117,76 @@ def test_node_precision_recall_pca_chosen_pathway(self):


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)
assert output_png.exists()

def test_node_ensemble(self):
out_path_file = Path(OUT_DIR + 'node-ensemble.csv')
out_path_file = 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'
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(GS_NODE_TABLE, ensemble_network, input_network)
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, str(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 = 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)
str(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 = 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 = 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'
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(GS_NODE_TABLE, ensemble_networks, input_network)
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, str(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 = 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 = 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 = 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 = 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 = 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 = 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)
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