Loading deepchem/metrics/__init__.py +8 −0 Original line number Diff line number Diff line Loading @@ -579,6 +579,14 @@ class Metric(object): metric_value = self.compute_singletask_metric(y_task, y_pred_task, w_task) computed_metrics.append(metric_value) ################## print("y_true.shape") print(y_true.shape) print("y_pred.shape") print(y_pred.shape) print("computed_metrics") print(computed_metrics) ################## logger.info("computed_metrics: %s" % str(computed_metrics)) if n_tasks == 1: computed_metrics = computed_metrics[0] Loading deepchem/utils/evaluate.py +2 −0 Original line number Diff line number Diff line Loading @@ -139,6 +139,8 @@ def threshold_predictions(y, threshold=0.5): threshold: float, optional (Default 0.5) The threshold probability for the positive class. TODO: This needs to be generalized to multiclass probabilities Returns ------- y_out: np.ndarray Loading deepchem/utils/test/test_evaluate.py +11 −1 Original line number Diff line number Diff line Loading @@ -15,6 +15,13 @@ class TestEvaluator(unittest.TestCase): self.dataset = dc.data.NumpyDataset(X, y) self.model = dc.models.MultitaskRegressor(1, 5) def threshold_predictions(self): """Check prediction thresholding works correctly.""" # TODO: Finish this test y = np.random.rand(10, 5) y_sums = np.sum(y, axis=1) y = y / y_sums def test_evaluator_dc_metric(self): """Test an evaluator on a dataset.""" evaluator = Evaluator(self.model, self.dataset, []) Loading Loading @@ -101,8 +108,11 @@ class TestEvaluator(unittest.TestCase): dataset = dc.data.NumpyDataset(X, y) model = dc.models.MultitaskClassifier(1, 5, n_classes=5) evaluator = Evaluator(model, dataset, []) # TODO: Fix this case with correct thresholding #multitask_scores = evaluator.compute_model_performance( # sklearn.metrics.accuracy_score, n_classes=5) multitask_scores = evaluator.compute_model_performance( sklearn.metrics.accuracy_score, n_classes=5) sklearn.metrics.roc_auc_score, n_classes=5) assert len(multitask_scores) == 1 assert multitask_scores["metric-1"] >= 0 Loading Loading
deepchem/metrics/__init__.py +8 −0 Original line number Diff line number Diff line Loading @@ -579,6 +579,14 @@ class Metric(object): metric_value = self.compute_singletask_metric(y_task, y_pred_task, w_task) computed_metrics.append(metric_value) ################## print("y_true.shape") print(y_true.shape) print("y_pred.shape") print(y_pred.shape) print("computed_metrics") print(computed_metrics) ################## logger.info("computed_metrics: %s" % str(computed_metrics)) if n_tasks == 1: computed_metrics = computed_metrics[0] Loading
deepchem/utils/evaluate.py +2 −0 Original line number Diff line number Diff line Loading @@ -139,6 +139,8 @@ def threshold_predictions(y, threshold=0.5): threshold: float, optional (Default 0.5) The threshold probability for the positive class. TODO: This needs to be generalized to multiclass probabilities Returns ------- y_out: np.ndarray Loading
deepchem/utils/test/test_evaluate.py +11 −1 Original line number Diff line number Diff line Loading @@ -15,6 +15,13 @@ class TestEvaluator(unittest.TestCase): self.dataset = dc.data.NumpyDataset(X, y) self.model = dc.models.MultitaskRegressor(1, 5) def threshold_predictions(self): """Check prediction thresholding works correctly.""" # TODO: Finish this test y = np.random.rand(10, 5) y_sums = np.sum(y, axis=1) y = y / y_sums def test_evaluator_dc_metric(self): """Test an evaluator on a dataset.""" evaluator = Evaluator(self.model, self.dataset, []) Loading Loading @@ -101,8 +108,11 @@ class TestEvaluator(unittest.TestCase): dataset = dc.data.NumpyDataset(X, y) model = dc.models.MultitaskClassifier(1, 5, n_classes=5) evaluator = Evaluator(model, dataset, []) # TODO: Fix this case with correct thresholding #multitask_scores = evaluator.compute_model_performance( # sklearn.metrics.accuracy_score, n_classes=5) multitask_scores = evaluator.compute_model_performance( sklearn.metrics.accuracy_score, n_classes=5) sklearn.metrics.roc_auc_score, n_classes=5) assert len(multitask_scores) == 1 assert multitask_scores["metric-1"] >= 0 Loading
