Loading examples/nci/nci_rf.py +14 −6 Original line number Diff line number Diff line Loading @@ -27,24 +27,32 @@ nci_tasks, nci_dataset, transformers = load_nci() (train_dataset, valid_dataset, test_dataset) = nci_dataset classification_metric = Metric(metrics.roc_auc_score, np.mean, mode="classification") classification_metric = Metric( metrics.roc_auc_score, np.mean, mode="classification") def model_builder(model_dir): sklearn_model = RandomForestRegressor(n_estimators=500) return SklearnModel(sklearn_model, model_dir) model = SingletaskToMultitask(nci_tasks, model_builder) # Fit trained model model.fit(train_dataset) train_evaluator = Evaluator(model, train_dataset, transformers, verbosity=verbosity) train_scores = train_evaluator.compute_model_performance([classification_metric]) train_evaluator = Evaluator( model, train_dataset, transformers, verbosity=verbosity) train_scores = train_evaluator.compute_model_performance( [classification_metric]) print("Train scores") print(train_scores) valid_evaluator = Evaluator(model, valid_dataset, transformers, verbosity=verbosity) valid_scores = valid_evaluator.compute_model_performance([classification_metric]) valid_evaluator = Evaluator( model, valid_dataset, transformers, verbosity=verbosity) valid_scores = valid_evaluator.compute_model_performance( [classification_metric]) print("Validation scores") print(valid_scores) examples/pdbbind/pdbbind_rf.py +0 −1 Original line number Diff line number Diff line Loading @@ -40,4 +40,3 @@ print(train_scores) print("Validation scores") print(valid_scores) examples/toxcast/toxcast_rf.py +2 −0 Original line number Diff line number Diff line Loading @@ -15,11 +15,13 @@ toxcast_tasks, toxcast_datasets, transformers = load_toxcast() metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean) def model_builder(model_dir): sklearn_model = RandomForestClassifier( class_weight="balanced", n_estimators=500, n_jobs=-1) return dc.models.SklearnModel(sklearn_model, model_dir) model = dc.models.SingletaskToMultitask(toxcast_tasks, model_builder) # Fit trained model Loading Loading
examples/nci/nci_rf.py +14 −6 Original line number Diff line number Diff line Loading @@ -27,24 +27,32 @@ nci_tasks, nci_dataset, transformers = load_nci() (train_dataset, valid_dataset, test_dataset) = nci_dataset classification_metric = Metric(metrics.roc_auc_score, np.mean, mode="classification") classification_metric = Metric( metrics.roc_auc_score, np.mean, mode="classification") def model_builder(model_dir): sklearn_model = RandomForestRegressor(n_estimators=500) return SklearnModel(sklearn_model, model_dir) model = SingletaskToMultitask(nci_tasks, model_builder) # Fit trained model model.fit(train_dataset) train_evaluator = Evaluator(model, train_dataset, transformers, verbosity=verbosity) train_scores = train_evaluator.compute_model_performance([classification_metric]) train_evaluator = Evaluator( model, train_dataset, transformers, verbosity=verbosity) train_scores = train_evaluator.compute_model_performance( [classification_metric]) print("Train scores") print(train_scores) valid_evaluator = Evaluator(model, valid_dataset, transformers, verbosity=verbosity) valid_scores = valid_evaluator.compute_model_performance([classification_metric]) valid_evaluator = Evaluator( model, valid_dataset, transformers, verbosity=verbosity) valid_scores = valid_evaluator.compute_model_performance( [classification_metric]) print("Validation scores") print(valid_scores)
examples/pdbbind/pdbbind_rf.py +0 −1 Original line number Diff line number Diff line Loading @@ -40,4 +40,3 @@ print(train_scores) print("Validation scores") print(valid_scores)
examples/toxcast/toxcast_rf.py +2 −0 Original line number Diff line number Diff line Loading @@ -15,11 +15,13 @@ toxcast_tasks, toxcast_datasets, transformers = load_toxcast() metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean) def model_builder(model_dir): sklearn_model = RandomForestClassifier( class_weight="balanced", n_estimators=500, n_jobs=-1) return dc.models.SklearnModel(sklearn_model, model_dir) model = dc.models.SingletaskToMultitask(toxcast_tasks, model_builder) # Fit trained model Loading
