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feat: add input nodes as evaluation baseline #372

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968bd09
precommit
ntalluri Aug 14, 2025
7202a99
updating the baseline
ntalluri Aug 19, 2025
1b3be75
make it all input nodes, not the intersection between gold standard a…
ntalluri Aug 19, 2025
8ca0e9e
update to be the baseline with gold standard intersection, added data…
ntalluri Aug 20, 2025
dda6264
debugging error in test cases
ntalluri Aug 20, 2025
d8a16fc
fix input-nodes file with tristan's update
ntalluri Aug 21, 2025
7f3c7f6
update test case
ntalluri Aug 21, 2025
8492f70
clean up code and outputs
ntalluri Aug 22, 2025
9b8e31c
update to use input nodes and interactome from the snakefile instead …
ntalluri Aug 22, 2025
04ddc1f
update test cases
ntalluri Aug 22, 2025
e98d1e2
removed unnecessary code
ntalluri Aug 22, 2025
5e10e95
Merge branch 'main' of github.com:ntalluri/spras into new-baseline
ntalluri Aug 25, 2025
2004c9a
update code to use helper function for input nodes
ntalluri Aug 25, 2025
82fbfce
update test case to use new helper function
ntalluri Aug 25, 2025
dcab781
update comment
ntalluri Aug 25, 2025
a57ef19
fix space formatting
ntalluri Aug 26, 2025
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4 changes: 4 additions & 0 deletions spras/evaluation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -410,12 +410,16 @@ def precision_recall_curve_node_ensemble(node_ensembles: dict, node_table: pd.Da
'Frequency'
] = 0.0

print(input_nodes_ensemble_df)

y_scores_input_nodes = input_nodes_ensemble_df['Frequency'].tolist()

precision_input_nodes, recall_input_nodes, thresholds_input_nodes = precision_recall_curve(y_true, y_scores_input_nodes)
plt.plot(recall_input_nodes, precision_input_nodes, color='black', marker='o', linestyle='--',
label=f'Input Nodes Baseline')

print(precision_input_nodes)
print(recall_input_nodes)
prc_input_nodes_baseline_data = {
'Threshold': thresholds_input_nodes,
'Precision': precision_input_nodes[:-1],
Expand Down
32 changes: 23 additions & 9 deletions test/evaluate/test_evaluate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,8 @@ def setup_class(cls):
'other_files': []
})

# TODO figure out why the input-nodes file is not being included in the data.pickle file
# it keeps coming up empty
with open(out_dataset, 'wb') as f:
pickle.dump(dataset, f)

Expand Down Expand Up @@ -126,18 +128,18 @@ def test_node_ensemble(self):
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'
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(GS_NODE_TABLE, ensemble_network, input_network)
input_data = OUT_DIR + 'data.pickle'
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(GS_NODE_TABLE, ensemble_network, input_data)
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)

def test_empty_node_ensemble(self):
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'
input_data = OUT_DIR + 'data.pickle'
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(GS_NODE_TABLE, empty_ensemble_network,
input_network)
input_data)
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)

Expand All @@ -147,8 +149,8 @@ def test_multiple_node_ensemble(self):
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'
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(GS_NODE_TABLE, ensemble_networks, input_network)
input_data = OUT_DIR + 'data.pickle'
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(GS_NODE_TABLE, ensemble_networks, input_data)
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)
node_ensemble_dict['empty'].to_csv(out_path_empty_file, sep='\t', index=False)
Expand All @@ -159,9 +161,19 @@ def test_precision_recall_curve_ensemble_nodes(self):
out_path_png.unlink(missing_ok=True)
out_path_file = Path(OUT_DIR + 'pr-curve-ensemble-nodes.txt')
out_path_file.unlink(missing_ok=True)
input_data = OUT_DIR + 'data.pickle'

pickle = Evaluation.from_file(input_data)
input_nodes_df = pickle.get_node_columns(["prize", "active"])

print(input_nodes_df)

ensemble_file = pd.read_csv(INPUT_DIR + 'node-ensemble.csv', sep='\t', header=0)

print(ensemble_file)

node_ensembles_dict = {'ensemble': ensemble_file}
Evaluation.precision_recall_curve_node_ensemble(node_ensembles_dict, GS_NODE_TABLE, out_path_png,
Evaluation.precision_recall_curve_node_ensemble(node_ensembles_dict, GS_NODE_TABLE, input_data, 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)
Expand All @@ -171,9 +183,10 @@ def test_precision_recall_curve_ensemble_nodes_empty(self):
out_path_png.unlink(missing_ok=True)
out_path_file = Path(OUT_DIR + 'pr-curve-ensemble-nodes-empty.txt')
out_path_file.unlink(missing_ok=True)
input_data = OUT_DIR + 'data.pickle'
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,
Evaluation.precision_recall_curve_node_ensemble(node_ensembles_dict, GS_NODE_TABLE, input_data, 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)
Expand All @@ -183,10 +196,11 @@ def test_precision_recall_curve_multiple_ensemble_nodes(self):
out_path_png.unlink(missing_ok=True)
out_path_file = Path(OUT_DIR + 'pr-curve-multiple-ensemble-nodes.txt')
out_path_file.unlink(missing_ok=True)
input_data = OUT_DIR + 'data.pickle'
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,
Evaluation.precision_recall_curve_node_ensemble(node_ensembles_dict, GS_NODE_TABLE, input_data, 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)
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