added tests

tests/test_eval_graph.py

@@ -60,6 +60,107 @@ def test_get_recovered_offtargets(network):
     assert result['perc_offtargets'][0] == 2 / 3 * 100
 
 
+
+@patch('networkcommons.data.omics.panacea_experiments')
+@patch('networkcommons.data.omics.panacea_gold_standard')
+@patch('networkcommons.data.network.get_omnipath')
+@patch('networkcommons.utils.network_from_df')
+@patch('networkcommons.data.omics.panacea_tables')
+@patch('networkcommons.methods.run_shortest_paths')
+@patch('networkcommons.methods.run_sign_consistency')
+@patch('networkcommons.methods.run_all_paths')
+@patch('networkcommons.methods.add_pagerank_scores')
+@patch('networkcommons.methods.run_corneto_carnival')
+@patch('networkcommons.eval._metrics.get_metric_from_networks')
+@patch('networkcommons.eval._metrics._log')
+def test_get_offtarget_panacea_evaluation(
+    mock_log,
+    mock_get_metric_from_networks,
+    mock_run_corneto_carnival,
+    mock_add_pagerank_scores,
+    mock_run_all_paths,
+    mock_run_sign_consistency,
+    mock_run_shortest_paths,
+    mock_panacea_tables,
+    mock_network_from_df,
+    mock_get_omnipath,
+    mock_panacea_gold_standard,
+    mock_panacea_experiments
+):
+    # Mocks
+    mock_panacea_experiments.return_value = pd.DataFrame({
+        'group': ['CellA_DrugA', 'CellA_DrugB'],
+        'tf_scores': [True, True]
+    })
+
+    mock_panacea_gold_standard.return_value = pd.DataFrame({
+        'cmpd': ['DrugA', 'DrugA', 'DrugA'],
+        'target': ['TargetA', 'TargetB', 'TargetC'],
+        'rank': [1, 2, 3]
+    })
+
+    mock_network_df = pd.DataFrame({
+        'source': ['TargetA', 'Node1', 'TargetC', 'TargetD'],
+        'target': ['Node1', 'TargetC', 'TargetC', 'Node2'],
+        'interaction': [1, 1, 1, 1]
+    })
+    mock_network_df = mock_network_df.astype({'source': str, 'target': str})
+    mock_graph = nx.from_pandas_edgelist(mock_network_df, source='source', target='target', create_using=nx.DiGraph)
+    mock_network_from_df.return_value = mock_graph
+
+    mock_panacea_tables.side_effect = lambda **kwargs: pd.DataFrame({
+        'items': ['Node1', 'Node2'],
+        'act': [0.5, -0.3]
+    })
+
+    mock_run_shortest_paths.return_value = (MagicMock(), [])
+    mock_run_sign_consistency.return_value = (MagicMock(), [])
+    mock_run_all_paths.return_value = (MagicMock(), [])
+    mock_add_pagerank_scores.return_value = MagicMock()
+    mock_run_corneto_carnival.return_value = MagicMock()
+
+    mock_get_metric_from_networks.return_value = pd.DataFrame({
+        'perc_offtargets': [10, 25],
+        'network': ['shortest_path', 'all_paths']
+    })
+
+    # Test
+    _metrics.get_offtarget_panacea_evaluation(cell='CellY') # No info
+    mock_log.assert_called_with("EVAL: no cell-drug combinations found with TF activity scores. Exiting...")
+
+    result_df = _metrics.get_offtarget_panacea_evaluation(cell='CellA') # Only cell
+
+    # Assertins
+    assert isinstance(result_df, pd.DataFrame)
+    assert 'perc_offtargets' in result_df.columns
+    assert len(result_df) > 0
+
+    result_df = _metrics.get_offtarget_panacea_evaluation(cell='CellA', drug='DrugA') # both
+
+    assert isinstance(result_df, pd.DataFrame)
+    assert 'perc_offtargets' in result_df.columns
+    assert len(result_df) > 0
+
+    result_df = _metrics.get_offtarget_panacea_evaluation() # None
+
+    assert isinstance(result_df, pd.DataFrame)
+    assert 'perc_offtargets' in result_df.columns
+    assert len(result_df) > 0
+
+    mock_panacea_experiments.assert_called()
+    mock_panacea_gold_standard.assert_called()
+    mock_get_omnipath.assert_called()
+    mock_network_from_df.assert_called()
+    mock_panacea_tables.assert_called()
+    mock_run_shortest_paths.assert_called()
+    mock_run_sign_consistency.assert_called()
+    mock_run_all_paths.assert_called()
+    mock_add_pagerank_scores.assert_called()
+    mock_run_corneto_carnival.assert_called()
+    mock_get_metric_from_networks.assert_called()
+    mock_log.assert_called_with("EVAL: finished offtarget recovery evaluation using PANACEA TF activity scores.")
+
+
 def test_all_nodes_in_ec50_dict():
     network = nx.Graph([(1, 2), (2, 3)])
     ec50_dict = {1: 5.0, 2: 10.0, 3: 15.0}

tests/test_methods_causal.py

@@ -4,6 +4,7 @@
 from unittest.mock import patch, MagicMock
 import io
 import sys
+import pytest
 
 @patch('networkcommons.methods._causal._log')  # Mock the _log function
 def test_run_corneto_carnival(mock_log):
@@ -92,4 +93,15 @@ def test_run_corneto_carnival(mock_log):
     assert any("CVXPY" in line for line in lines_in_output)
     assert any("Solver terminated with message: Optimization terminated successfully. (HiGHS Status 7: Optimal)" in line for line in lines_in_output)
     # Check that stdout has printed lines when verbose=True
-    assert len(captured_verbose_output) > 0
+    assert len(captured_verbose_output) > 0
+
+    # TODO: why does corneto raise a TypeError? Thought it was the empty network, but it's not
+    # with pytest.raises(TypeError):
+    #     _causal.run_corneto_carnival(
+    #         nx.DiGraph(),  # Empty network
+    #         source_dict,
+    #         target_dict,
+    #         betaWeight=0.1,
+    #         solver='scipy',
+    #         verbose=True
+    #     )

tests/test_methods_graph.py

@@ -273,10 +273,19 @@ def test_run_all_paths_exceptions():
     assert list(subnetwork.edges) == []
 
 
-def test_add_pagerank_scores(net2):
+@patch('networkcommons.methods._graph._log')
+def test_add_pagerank_scores(mock_log, net2):
 
     network, source_dict, target_dict = net2
 
+    network_with_pagerank = _graph.add_pagerank_scores(
+        nx.DiGraph(),  # empty
+        source_dict,
+        target_dict,
+        personalize_for='source',
+    )
+    mock_log.assert_any_call('PPR: WARNING: Empty network, no scores added.')
+
     network_with_pagerank = _graph.add_pagerank_scores(
         network,
         source_dict,

tests/test_omics.py

@@ -334,25 +334,52 @@ def test_decryptm_experiment_no_dataset(mock_decryptm_datasets):
 
 # FILE: omics/_panacea.py
 
-@patch('networkcommons.data.omics._panacea._common._baseurl', return_value='http://example.com')
-@patch('pandas.read_pickle')
+@patch('urllib.request.urlopen')
+@patch('pandas.read_csv')
 @patch('os.path.exists', return_value=False)
-@patch('os.makedirs')
 @patch('pandas.DataFrame.to_pickle')
-@patch('urllib.request.urlopen')
-def test_panacea_experiments(mock_urlopen, mock_to_pickle, mock_makedirs, mock_path_exists, mock_read_pickle, mock_baseurl):
-    # Mock the HTTP response for the metadata file
+def test_panacea_experiments(mock_to_pickle, mock_path_exists, mock_read_csv, mock_urlopen):
+    # Mock the metadata file (panacea__metadata.tsv)
+    mock_metadata = pd.DataFrame({
+        'group': ['ASPC_AEE788', 'ASPC_AFATINIB', 'DU145_CRIZOTINIB', 'ASPC_CEDIRANIB'],
+        'sample_ID': ['ID1', 'ID2', 'ID3', 'ID4']
+    })
+    mock_read_csv.return_value = mock_metadata
+
+    # Mock the HTML content returned by the remote server, 
+    # with some lines containing the "__TF_scores.tsv" pattern
+    mock_html_content = """
+        <html>
+        <body>
+            <a href="ASPC_AEE788__TF_scores.tsv">ASPC_AEE788__TF_scores.tsv</a><br>
+            <a href="ASPC_AFATINIB__TF_scores.tsv">ASPC_AFATINIB__TF_scores.tsv</a><br>
+            <a href="DU145_CRIZOTINIB__TF_scores.tsv">DU145_CRIZOTINIB__TF_scores.tsv</a><br>
+        </body>
+        </html>
+    """
+
+    # Mock the HTTP response for the TF scores directory
     mock_response = MagicMock()
-    mock_response.read.return_value = b"group\tsample_ID\nA_B\tID1\nC_D\tID2"
-    mock_response.__enter__.return_value = mock_response
+    mock_response.read.return_value = mock_html_content.encode('utf-8')
     mock_urlopen.return_value = mock_response
 
+    # Call the function under test
     result_df = omics.panacea_experiments(update=True)
 
+    # Check that the result is a DataFrame
     assert isinstance(result_df, pd.DataFrame)
+
+    # Check that the expected columns are in the DataFrame
+    assert 'group' in result_df.columns
     assert 'cell' in result_df.columns
     assert 'drug' in result_df.columns
+    assert 'tf_scores' in result_df.columns
+
+    # Check that the 'tf_scores' column has the correct True/False values
+    expected_tf_scores = [True, True, True, False]  # CEDIRANIB should not have TF scores
+    assert result_df['tf_scores'].tolist() == expected_tf_scores
 
+    # Verify that the function attempts to save the result as a pickle file
     mock_to_pickle.assert_called_once()
 
 
@@ -583,6 +610,7 @@ def test_panacea_gold_standard_update(mock_urllib, mock_pandas_topickle, mock_ma
     mock_log.assert_any_call('DATA: found in cache, loading...')
 
 
+
 # FILE: omics/_scperturb.py
 import pytest
 from unittest.mock import patch, MagicMock