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| """ | |
| This file tests whether the model predictions for MHNfs match the predictions made on | |
| the JKU development server (varified model, server conda env with spec. packages ...) | |
| """ | |
| #--------------------------------------------------------------------------------------- | |
| # Dependencies | |
| import pytest | |
| import torch | |
| import pandas as pd | |
| from prediction_pipeline import ActivityPredictor | |
| #--------------------------------------------------------------------------------------- | |
| # Define tests | |
| class TestActivityPredictor: | |
| def test_mhnfs_prediction(self, model_input_query, model_input_support_actives, | |
| model_input_support_inactives, model_predictions): | |
| # Load model | |
| predictor = ActivityPredictor() | |
| # Define additional inputs to model - i.e. support set sizes | |
| support_actives_size = torch.tensor(model_input_support_actives.shape[1]) | |
| support_inactives_size = torch.tensor(model_input_support_inactives.shape[1]) | |
| # Make predictions | |
| predictions = predictor.model( | |
| model_input_query, | |
| model_input_support_actives, | |
| model_input_support_inactives, | |
| support_actives_size, | |
| support_inactives_size | |
| ).detach() | |
| # Compare predictions | |
| assert torch.allclose(predictions, model_predictions, atol=0.01, rtol=0.) | |
| def test_query_mol_return(self): | |
| # Support set | |
| support_actives_smiles = ["CCCCCCCCNC(C)C(O)c1ccc(SC(C)C)cc1"] | |
| support_inactives_smiles = ["CCN(CC)C(=S)SSC(=S)N(CC)CCCCC"] | |
| # Load activity predictor | |
| predictor = ActivityPredictor() | |
| # Check 1: Query mols given as a list | |
| query_smiles = ["CCCCCCCCNC(C)C(O)c1ccc(SC(C)C)cc1", | |
| "CCN(CC)C(=S)SSC(=S)N(CC)CC"] | |
| _ = predictor.predict(query_smiles, support_actives_smiles, | |
| support_inactives_smiles) | |
| query_output = predictor._return_query_mols_as_list() | |
| assert query_output == query_smiles | |
| # Check 2: Query mols given as a string | |
| query_smiles_str = ("CCCCCCCCNC(C)C(O)c1ccc(SC(C)C)cc1," | |
| "CCN(CC)C(=S)SSC(=S)N(CC)CC") | |
| _ = predictor.predict(query_smiles_str, support_actives_smiles, | |
| support_inactives_smiles) | |
| query_output = predictor._return_query_mols_as_list() | |
| assert query_output == query_smiles | |
| # Check 3: Query mols given as a pd.Series | |
| query_smiles_series = pd.DataFrame({"smiles": | |
| ["CCCCCCCCNC(C)C(O)c1ccc(SC(C)C)cc1", "CCN(CC)C(=S)SSC(=S)N(CC)CC"]}) | |
| _ = predictor.predict(query_smiles_series, support_actives_smiles, | |
| support_inactives_smiles) | |
| query_output = predictor._return_query_mols_as_list() | |
| assert query_output == query_smiles | |
| # Check 4: Query molecules storage is None | |
| predictor.query_molecules = None | |
| with pytest.raises(ValueError): | |
| predictor._return_query_mols_as_list() | |
| # Check 5: Other data types | |
| predictor.query_molecules = 123 # any other data type | |
| with pytest.raises(TypeError): | |
| predictor._return_query_mols_as_list() | |