:ok_hand: fix test by review (09828fbd) · Commits · 钟慕尧 / deepchem

deepchem/models/tests/test_gbdt_model.py

+80 −6

Original line number	Diff line number	Diff line
		@@ -13,7 +13,7 @@ from sklearn.model_selection import train_test_split
		import deepchem as dc


		def test_signletask_regression():
		def test_signletask_regression_with_xgboost():
		np.random.seed(123)

		# prepare dataset
		@@ -40,6 +40,23 @@ def test_signletask_regression():
		scores = model.evaluate(test_dataset, [regression_metric])
		assert scores[regression_metric.name] < 55


		def test_signletask_regression_with_lightgbm():
		np.random.seed(123)

		# prepare dataset
		dataset = load_diabetes()
		X, y = dataset.data, dataset.target
		frac_train = .7
		X_train, X_test, y_train, y_test = \
		train_test_split(X, y, train_size=frac_train)
		train_dataset = dc.data.NumpyDataset(X_train, y_train)
		test_dataset = dc.data.NumpyDataset(X_test, y_test)

		# global setting
		regression_metric = dc.metrics.Metric(dc.metrics.mae_score)
		params = {'early_stopping_rounds': 25}

		# lightgbm test
		lgbm_model = lightgbm.LGBMRegressor(
		n_estimators=50, random_state=123, silent=True)
		@@ -52,7 +69,7 @@ def test_signletask_regression():
		assert scores[regression_metric.name] < 55


		def test_multitask_regression():
		def test_multitask_regression_with_xgboost():
		np.random.seed(123)

		# prepare dataset
		@@ -86,10 +103,31 @@ def test_multitask_regression():
		score = scores[regression_metric.name]
		assert score < 55


		def test_multitask_regression_with_lightgbm():
		np.random.seed(123)

		# prepare dataset
		n_tasks = 4
		tasks = range(n_tasks)
		dataset = load_diabetes()
		X, y = dataset.data, dataset.target
		y = np.reshape(y, (len(y), 1))
		y = np.hstack([y] * n_tasks)
		frac_train = .7
		X_train, X_test, y_train, y_test = \
		train_test_split(X, y, train_size=frac_train)
		train_dataset = dc.data.DiskDataset.from_numpy(X_train, y_train)
		test_dataset = dc.data.DiskDataset.from_numpy(X_test, y_test)

		# global setting
		regression_metric = dc.metrics.Metric(dc.metrics.mae_score)
		params = {'early_stopping_rounds': 25}

		# lightgbm test
		def lightgbm_builder(model_dir):
		xgb_model = lightgbm.LGBMRegressor(n_estimators=50, seed=123, silent=False)
		return dc.models.GBDTModel(xgb_model, model_dir, **params)
		lgbm_model = lightgbm.LGBMRegressor(n_estimators=50, seed=123, silent=False)
		return dc.models.GBDTModel(lgbm_model, model_dir, **params)

		model = dc.models.SingletaskToMultitask(tasks, lightgbm_builder)
		# fit trained model
		@@ -101,7 +139,7 @@ def test_multitask_regression():
		assert score < 55


		def test_classification():
		def test_classification_with_xgboost():
		"""Test that sklearn models can learn on simple classification datasets."""
		np.random.seed(123)

		@@ -128,6 +166,24 @@ def test_classification():
		scores = model.evaluate(test_dataset, [classification_metric])
		assert scores[classification_metric.name] > .9


		def test_classification_with_lightgbm():
		"""Test that sklearn models can learn on simple classification datasets."""
		np.random.seed(123)

		# prepare dataset
		dataset = load_digits(n_class=2)
		X, y = dataset.data, dataset.target
		frac_train = .7
		X_train, X_test, y_train, y_test = \
		train_test_split(X, y, train_size=frac_train)
		train_dataset = dc.data.NumpyDataset(X_train, y_train)
		test_dataset = dc.data.NumpyDataset(X_test, y_test)

		# global setting
		classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score)
		params = {'early_stopping_rounds': 25}

		# lightgbm test
		lgbm_model = lightgbm.LGBMClassifier(n_estimators=50, seed=123, silent=True)
		model = dc.models.GBDTModel(lgbm_model, **params)
		@@ -139,7 +195,7 @@ def test_classification():
		assert scores[classification_metric.name] > .9


		def test_reload():
		def test_reload_with_xgboost():
		np.random.seed(123)

		# prepare dataset
		@@ -174,6 +230,24 @@ def test_reload():
		scores = reloaded_model.evaluate(test_dataset, [regression_metric])
		assert scores[regression_metric.name] < 55


		def test_reload_with_lightgbm():
		np.random.seed(123)

		# prepare dataset
		dataset = load_diabetes()
		X, y = dataset.data, dataset.target
		frac_train = .7
		X_train, X_test, y_train, y_test = \
		train_test_split(X, y, train_size=frac_train)
		train_dataset = dc.data.NumpyDataset(X_train, y_train)
		test_dataset = dc.data.NumpyDataset(X_test, y_test)

		# global setting
		regression_metric = dc.metrics.Metric(dc.metrics.mae_score)
		model_dir = tempfile.mkdtemp()
		params = {'early_stopping_rounds': 25, 'model_dir': model_dir}

		# lightgbm test
		lgbm_model = lightgbm.LGBMRegressor(
		n_estimators=50, random_state=123, silent=True)

docs/requirements.rst

+12 −2

Original line number	Diff line number	Diff line
		@@ -34,6 +34,14 @@ DeepChem has a number of "soft" requirements.
		\| \| \| \|
		\| \| \| \|
		+--------------------------------+---------------+---------------------------------------------------+
		\| `HuggingFace Transformers`_ \| Not Testing \| :code:`dc.feat.smiles_tokenizer` \|
		\| \| \| \|
		\| \| \| \|
		+--------------------------------+---------------+---------------------------------------------------+
		\| `LightGBM`_ \| latest \| :code:`dc.models.gbdt_models` \|
		\| \| \| \|
		\| \| \| \|
		+--------------------------------+---------------+---------------------------------------------------+
		\| `OpenAI Gym`_ \| Not Testing \| :code:`dc.rl` \|
		\| \| \| \|
		\| \| \| \|
		@@ -102,7 +110,7 @@ DeepChem has a number of "soft" requirements.
		\| \| \| :code:`dc.models.callbacks` \|
		\| \| \| \|
		+--------------------------------+---------------+---------------------------------------------------+
		\| `HuggingFace Transformers`_ \| Not Testing \| :code:`dc.feat.smiles_tokenizer` \|
		\| `XGBoost`_ \| latest \| :code:`dc.models.gbdt_models` \|
		\| \| \| \|
		\| \| \| \|
		+--------------------------------+---------------+---------------------------------------------------+
		@@ -115,6 +123,8 @@ DeepChem has a number of "soft" requirements.
		.. _`TensorFlow`: https://www.tensorflow.org/
		.. _`BioPython`: https://biopython.org/wiki/Documentation
		.. _`Deep Graph Library`: https://www.dgl.ai/
		.. _`HuggingFace Transformers`: https://huggingface.co/transformers/
		.. _`LightGBM`: https://lightgbm.readthedocs.io/en/latest/index.html
		.. _`OpenAI Gym`: https://gym.openai.com/
		.. _`matminer`: https://hackingmaterials.lbl.gov/matminer/
		.. _`MDTraj`: http://mdtraj.org/
		@@ -132,4 +142,4 @@ DeepChem has a number of "soft" requirements.
		.. _`simdna`: https://github.com/kundajelab/simdna
		.. _`Tensorflow Probability`: https://www.tensorflow.org/probability
		.. _`Weights & Biases`: https://docs.wandb.com/
		.. _`HuggingFace Transformers`: https://huggingface.co/transformers/
		.. _`XGBoost`: https://xgboost.readthedocs.io/en/latest/

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