fix lint (9a74887f) · Commits · 钟慕尧 / deepchem

deepchem/models/gbdt_models/gbdt_model.py

+5 −5

Original line number	Diff line number	Diff line
		@@ -6,7 +6,7 @@ import os
		import logging
		import tempfile
		import warnings
		from typing import Callable, Optional, Tuple, Union
		from typing import Callable, Optional, Union

		import numpy as np
		from sklearn.base import BaseEstimator
		@@ -28,7 +28,7 @@ class GBDTModel(SklearnModel):
		model: BaseEstimator,
		model_dir: Optional[str] = None,
		early_stopping_rounds: int = 50,
		eval_metric: Optional[Union[str, Callable[..., Tuple]]] = None,
		eval_metric: Optional[Union[str, Callable]] = None,
		**kwargs):
		"""
		Parameters
		@@ -57,11 +57,11 @@ class GBDTModel(SklearnModel):

		if eval_metric is None:
		if self.model_type == 'classification':
		self.eval_metric: Union[str, Callable[..., Tuple]] = 'auc'
		self.eval_metric: Optional[Union[str, Callable]] = 'auc'
		elif self.model_type == 'regression':
		self.eval_metric = 'mae'
		else:
		self.eval_metric = None
		self.eval_metric = eval_metric
		else:
		self.eval_metric = eval_metric

		@@ -72,7 +72,7 @@ class GBDTModel(SklearnModel):
		elif class_name.endswith('Regressor'):
		return 'regression'
		elif class_name == 'NoneType':
		return None
		return 'none'
		else:
		raise ValueError(
		'{} is not a supported model instance.'.format(class_name))

deepchem/models/tests/test_gbdt_model.py

+18 −8

Original line number	Diff line number	Diff line
		@@ -20,7 +20,8 @@ def test_signletask_regression():
		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)
		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)

		@@ -29,7 +30,8 @@ def test_signletask_regression():
		params = {'early_stopping_rounds': 25}

		# xgboost test
		xgb_model = xgboost.XGBRegressor(n_estimators=50, random_state=123, verbose=False)
		xgb_model = xgboost.XGBRegressor(
		n_estimators=50, random_state=123, verbose=False)
		model = dc.models.GBDTModel(xgb_model, **params)
		# fit trained model
		model.fit(train_dataset)
		@@ -39,7 +41,8 @@ def test_signletask_regression():
		assert scores[regression_metric.name] < 55

		# lightgbm test
		lgbm_model = lightgbm.LGBMRegressor(n_estimators=50, random_state=123, silent=True)
		lgbm_model = lightgbm.LGBMRegressor(
		n_estimators=50, random_state=123, silent=True)
		model = dc.models.GBDTModel(lgbm_model, **params)
		# fit trained model
		model.fit(train_dataset)
		@@ -60,7 +63,8 @@ def test_multitask_regression():
		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)
		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)

		@@ -72,6 +76,7 @@ def test_multitask_regression():
		def xgboost_builder(model_dir):
		xgb_model = xgboost.XGBRegressor(n_estimators=50, seed=123, verbose=False)
		return dc.models.GBDTModel(xgb_model, model_dir, **params)

		model = dc.models.SingletaskToMultitask(tasks, xgboost_builder)
		# fit trained model
		model.fit(train_dataset)
		@@ -85,6 +90,7 @@ def test_multitask_regression():
		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)

		model = dc.models.SingletaskToMultitask(tasks, lightgbm_builder)
		# fit trained model
		model.fit(train_dataset)
		@@ -103,7 +109,8 @@ def test_classification():
		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)
		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)

		@@ -139,7 +146,8 @@ def test_reload():
		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)
		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)

		@@ -149,7 +157,8 @@ def test_reload():
		params = {'early_stopping_rounds': 25, 'model_dir': model_dir}

		# xgboost test
		xgb_model = xgboost.XGBRegressor(n_estimators=50, random_state=123, verbose=False)
		xgb_model = xgboost.XGBRegressor(
		n_estimators=50, random_state=123, verbose=False)
		model = dc.models.GBDTModel(xgb_model, **params)
		# fit trained model
		model.fit(train_dataset)
		@@ -166,7 +175,8 @@ def test_reload():
		assert scores[regression_metric.name] < 55

		# lightgbm test
		lgbm_model = lightgbm.LGBMRegressor(n_estimators=50, random_state=123, silent=True)
		lgbm_model = lightgbm.LGBMRegressor(
		n_estimators=50, random_state=123, silent=True)
		model = dc.models.GBDTModel(lgbm_model, **params)
		# fit trained model
		model.fit(train_dataset)

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