Debugging (03351e0b) · Commits · 钟慕尧 / deepchem

deepchem/metrics/init.py

+12 −10

Original line number	Diff line number	Diff line
		@@ -164,15 +164,12 @@ def normalize_prediction_shape(y, mode="classification", n_classes=None):
		y_hot = to_one_hot(y, n_classes=n_classes)
		# Insert task dimension
		y_out = np.expand_dims(y_hot, 1)
		return y_out
		elif len(y.shape) == 2:
		# Insert a task dimension
		n_tasks = 1
		y_out = np.expand_dims(y, 1)
		return y_out
		elif len(y.shape) == 3:
		y_out = y
		return y_out
		else:
		raise ValueError("y must be an array of dimension 1, 2, or 3 for classification problems.")
		else:
		@@ -182,25 +179,30 @@ def normalize_prediction_shape(y, mode="classification", n_classes=None):
		y = np.reshape(y, (1,))
		y = to_one_hot(y, n_classes=n_classes)
		y_out = np.expand_dims(y, 1)
		return y_out
		elif mode == "regression":
		if isinstance(y, np.ndarray):
		if len(y.shape) == 1:
		# Insert a task dimension
		n_tasks = 1
		y_out = np.expand_dims(y, 1)
		return y_out
		elif len(y.shape) == 2:
		y_out = y
		return y_out
		elif len(y.shape) == 3:
		if y[-1] != 1:
		raise ValueError("y must be of shape `(N,)` or `(N, n_tasks)` or `(N, n_tasks, 1)` for regression problems.")
		if y.shape[-1] != 1:
		raise ValueError("y must a float sclar or a ndarray of shape `(N,)` or `(N, n_tasks)` or `(N, n_tasks, 1)` for regression problems.")
		y_out = np.squeeze(y, axis=-1)
		else:
		raise ValueError("y must be of shape `(N,)` or `(N, n_tasks)` or `(N, n_tasks, 1)` for regression problems.")
		raise ValueError("y must a float sclar or a ndarray of shape `(N,)` or `(N, n_tasks)` or `(N, n_tasks, 1)` for regression problems.")
		else:
		# In this clase, y is a scalar.
		try:
		y = float(y)
		except TypeError:
		#################
		print("y")
		print(y)
		#################
		raise ValueError("y must a float sclar or a ndarray of shape `(N,)` or `(N, n_tasks)` or `(N, n_tasks, 1)` for regression problems.")
		y = np.array(y)
		y_out = np.reshape(y, (1, 1))
		return y_out

deepchem/utils/evaluate.py

+7 −7

Original line number	Diff line number	Diff line
		@@ -156,13 +156,13 @@ class Evaluator(object):
		else:
		mode = metrics[0].mode
		y_pred = self.model.predict(self.dataset, self.output_transformers)
		########################################
		print("y.shape")
		print(y.shape)
		print("y_pred.shape")
		print(y_pred.shape)
		assert 0 == 1
		########################################
		#########################################
		#print("y.shape")
		#print(y.shape)
		#print("y_pred.shape")
		#print(y_pred.shape)
		#assert 0 == 1
		#########################################
		if mode == "classification":
		y_pred_print = np.argmax(y_pred, -1)
		else:

deepchem/utils/test/test_evaluate.py

+15 −15

Original line number	Diff line number	Diff line
		@@ -21,21 +21,21 @@ class TestEvaluator(unittest.TestCase):
		assert len(multitask_scores) == 1
		assert multitask_scores['mae_score'] > 0

		# def test_generator_evaluator_dc_metric_multitask(self):
		# """Test generator evaluator on a dataset."""
		# X = np.random.rand(10, 5)
		# y = np.random.rand(10, 3)
		# dataset = dc.data.NumpyDataset(X, y)
		# model = dc.models.MultitaskRegressor(1, 5)
		# generator = model.default_generator(dataset, pad_batches=False)
		# transformers = []
		# evaluator = GeneratorEvaluator(model, generator, transformers)
		# metric = dc.metrics.Metric(dc.metrics.mae_score)
		# multitask_scores = evaluator.compute_model_performance([metric])
		# assert isinstance(multitask_scores, dict)
		# assert len(multitask_scores) == 1
		# assert multitask_scores['mae_score'] > 0
		#
		def test_generator_evaluator_dc_metric_multitask(self):
		"""Test generator evaluator on a dataset."""
		X = np.random.rand(10, 5)
		y = np.random.rand(10, 3)
		dataset = dc.data.NumpyDataset(X, y)
		model = dc.models.MultitaskRegressor(1, 5)
		generator = model.default_generator(dataset, pad_batches=False)
		transformers = []
		evaluator = GeneratorEvaluator(model, generator, transformers)
		metric = dc.metrics.Metric(dc.metrics.mae_score)
		multitask_scores = evaluator.compute_model_performance([metric])
		assert isinstance(multitask_scores, dict)
		assert len(multitask_scores) == 1
		assert multitask_scores['mae_score'] > 0

		# def test_generator_evaluator_dc_metric_multitask_single_point(self):
		# """Test generator evaluator on a dataset."""
		# X = np.random.rand(1, 5)

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