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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: 2 additions & 2 deletions Snakefile
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Original file line number Diff line number Diff line change
Expand Up @@ -525,7 +525,7 @@ rule evaluation_ensemble_pr_curve:
run:
node_table = Evaluation.from_file(input.gold_standard_file).node_table
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(node_table, input.ensemble_file, input.dataset_file)
Evaluation.precision_recall_curve_node_ensemble(node_ensemble_dict, node_table, output.pr_curve_png, output.pr_curve_file)
Evaluation.precision_recall_curve_node_ensemble(node_ensemble_dict, node_table, input.dataset_file,output.pr_curve_png, output.pr_curve_file)

# Returns list of algorithm specific ensemble files per dataset
def collect_ensemble_per_algo_per_dataset(wildcards):
Expand All @@ -544,7 +544,7 @@ rule evaluation_per_algo_ensemble_pr_curve:
run:
node_table = Evaluation.from_file(input.gold_standard_file).node_table
node_ensembles_dict = Evaluation.edge_frequency_node_ensemble(node_table, input.ensemble_files, input.dataset_file)
Evaluation.precision_recall_curve_node_ensemble(node_ensembles_dict, node_table, output.pr_curve_png, output.pr_curve_file, include_aggregate_algo_eval)
Evaluation.precision_recall_curve_node_ensemble(node_ensembles_dict, node_table, input.dataset_file,output.pr_curve_png, output.pr_curve_file, include_aggregate_algo_eval)


# Remove the output directory
Expand Down
49 changes: 47 additions & 2 deletions spras/evaluation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -354,7 +354,7 @@ def edge_frequency_node_ensemble(node_table: pd.DataFrame, ensemble_files: list[
return node_ensembles_dict

@staticmethod
def precision_recall_curve_node_ensemble(node_ensembles: dict, node_table: pd.DataFrame, output_png: str | PathLike,
def precision_recall_curve_node_ensemble(node_ensembles: dict, node_table: pd.DataFrame, dataset_file: str, output_png: str | PathLike,
output_file: str | PathLike, aggregate_per_algorithm: bool = False):
"""
Plots precision-recall (PR) curves for a set of node ensembles evaluated against a gold standard.
Expand All @@ -380,13 +380,58 @@ def precision_recall_curve_node_ensemble(node_ensembles: dict, node_table: pd.Da

prc_dfs = []
metric_dfs = []
prc_input_nodes_baseline_df = None

baseline = None
precision_input_nodes = None
recall_input_nodes = None
thresholds_input_nodes = None


for label, node_ensemble in node_ensembles.items():
if not node_ensemble.empty:
y_true = [1 if node in gold_standard_nodes else 0 for node in node_ensemble['Node']]
y_scores = node_ensemble['Frequency'].tolist()

if precision_input_nodes is None and recall_input_nodes is None and thresholds_input_nodes is None:
pickle = Evaluation.from_file(dataset_file)
input_nodes_df = pickle.get_node_columns(["sources", "targets", "prize", "active"])
input_nodes = set(input_nodes_df['NODEID'])
input_nodes_gold_intersection = input_nodes & gold_standard_nodes # TODO should this be all inputs nodes or the intersection with the gold standard for this baseline?
input_nodes_ensemble_df = node_ensemble.copy()

input_nodes_ensemble_df.loc[
input_nodes_ensemble_df['Node'].isin(input_nodes_gold_intersection),
'Frequency'
] = 1.0

input_nodes_ensemble_df.loc[
~input_nodes_ensemble_df['Node'].isin(input_nodes_gold_intersection),
'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],
'Recall': recall_input_nodes[:-1],
}

prc_input_nodes_baseline_data = {'Ensemble_Source': ["input_nodes_baseline"] * len(thresholds_input_nodes), **prc_input_nodes_baseline_data}

prc_input_nodes_baseline_df = pd.DataFrame.from_dict(prc_input_nodes_baseline_data)



precision, recall, thresholds = precision_recall_curve(y_true, y_scores)
# avg precision summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold
avg_precision = average_precision_score(y_true, y_scores)
Expand Down Expand Up @@ -446,7 +491,7 @@ def precision_recall_curve_node_ensemble(node_ensembles: dict, node_table: pd.Da
combined_metrics_df['Baseline'] = baseline

# merge dfs and NaN out metric values except for first row of each Ensemble_Source
complete_df = combined_prc_df.merge(combined_metrics_df, on='Ensemble_Source', how='left')
complete_df = combined_prc_df.merge(combined_metrics_df, on='Ensemble_Source', how='left').merge(prc_input_nodes_baseline_df, on=['Ensemble_Source', 'Threshold', 'Precision', 'Recall'], how='outer')
not_last_rows = complete_df.duplicated(subset='Ensemble_Source', keep='first')
complete_df.loc[not_last_rows, ['Average_Precision', 'Baseline']] = None
complete_df.to_csv(output_file, index=False, sep='\t')
4 changes: 2 additions & 2 deletions test/evaluate/input/input-nodes.txt
View file
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Original file line number Diff line number Diff line change
@@ -1,3 +1,3 @@
NODEID prize active dummy sources targets
N
C 5.7 True True
N
C 5.7 True True
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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