Loading examples/tox21/tox21_IRV.py +1 −5 Original line number Diff line number Diff line Loading @@ -30,11 +30,7 @@ for transformer in transformers: test_dataset = transformer.transform(test_dataset) model = dc.models.TensorflowMultiTaskIRVClassifier( len(tox21_tasks), K=K, learning_rate=0.001, penalty=0.05, batch_size=32) len(tox21_tasks), K=K, learning_rate=0.001, penalty=0.05, batch_size=32) # Fit trained model model.fit(train_dataset, nb_epoch=10) Loading examples/tox21/tox21_logreg.py +4 −6 Original line number Diff line number Diff line Loading @@ -23,16 +23,14 @@ train_dataset, valid_dataset, test_dataset = tox21_datasets # Fit models metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean) def model_builder(model_dir_logreg): sklearn_model = LogisticRegression( penalty="l2", C=1./0.05, class_weight="balanced", n_jobs=-1) return dc.models.sklearn_models.SklearnModel( sklearn_model, model_dir_logreg) penalty="l2", C=1. / 0.05, class_weight="balanced", n_jobs=-1) return dc.models.sklearn_models.SklearnModel(sklearn_model, model_dir_logreg) model = dc.models.multitask.SingletaskToMultitask( tox21_tasks, model_builder) model = dc.models.multitask.SingletaskToMultitask(tox21_tasks, model_builder) # Fit trained model model.fit(train_dataset) Loading Loading
examples/tox21/tox21_IRV.py +1 −5 Original line number Diff line number Diff line Loading @@ -30,11 +30,7 @@ for transformer in transformers: test_dataset = transformer.transform(test_dataset) model = dc.models.TensorflowMultiTaskIRVClassifier( len(tox21_tasks), K=K, learning_rate=0.001, penalty=0.05, batch_size=32) len(tox21_tasks), K=K, learning_rate=0.001, penalty=0.05, batch_size=32) # Fit trained model model.fit(train_dataset, nb_epoch=10) Loading
examples/tox21/tox21_logreg.py +4 −6 Original line number Diff line number Diff line Loading @@ -23,16 +23,14 @@ train_dataset, valid_dataset, test_dataset = tox21_datasets # Fit models metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean) def model_builder(model_dir_logreg): sklearn_model = LogisticRegression( penalty="l2", C=1./0.05, class_weight="balanced", n_jobs=-1) return dc.models.sklearn_models.SklearnModel( sklearn_model, model_dir_logreg) penalty="l2", C=1. / 0.05, class_weight="balanced", n_jobs=-1) return dc.models.sklearn_models.SklearnModel(sklearn_model, model_dir_logreg) model = dc.models.multitask.SingletaskToMultitask( tox21_tasks, model_builder) model = dc.models.multitask.SingletaskToMultitask(tox21_tasks, model_builder) # Fit trained model model.fit(train_dataset) Loading
