Loading deepchem/hyper/tests/test_hyperparam_opt.py +1 −1 Original line number Diff line number Diff line Loading @@ -19,7 +19,7 @@ import deepchem as dc from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor class TestHyperparamOptAPI(unittest.TestCase): class TestHyperparamOpt(unittest.TestCase): """ Test hyperparameter optimization API. """ Loading deepchem/utils/evaluate.py +1 −1 Original line number Diff line number Diff line Loading @@ -111,7 +111,7 @@ class Evaluator(object): all_task_scores[metric.name] = computed_metrics else: multitask_scores[metric.name] = metric.compute_metric( y, y_pred, w, per_task_metrics=True) y, y_pred, w, per_task_metrics=False) if stats_out is not None: log("Saving stats to %s" % stats_out, self.verbose) Loading Loading
deepchem/hyper/tests/test_hyperparam_opt.py +1 −1 Original line number Diff line number Diff line Loading @@ -19,7 +19,7 @@ import deepchem as dc from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor class TestHyperparamOptAPI(unittest.TestCase): class TestHyperparamOpt(unittest.TestCase): """ Test hyperparameter optimization API. """ Loading
deepchem/utils/evaluate.py +1 −1 Original line number Diff line number Diff line Loading @@ -111,7 +111,7 @@ class Evaluator(object): all_task_scores[metric.name] = computed_metrics else: multitask_scores[metric.name] = metric.compute_metric( y, y_pred, w, per_task_metrics=True) y, y_pred, w, per_task_metrics=False) if stats_out is not None: log("Saving stats to %s" % stats_out, self.verbose) Loading
