:recycle: gdbt -> gbdt (72ab5773) · Commits · 钟慕尧 / deepchem

deepchem/models/init.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -26,7 +26,7 @@ from deepchem.models.chemnet_models import Smiles2Vec, ChemCeption

		# scikit-learn model
		from deepchem.models.sklearn_models import SklearnModel
		from deepchem.models.gdbt_models import GDBTModel
		from deepchem.models.gbdt_models import GBDTModel

		# PyTorch models
		try:
		@@ -40,7 +40,7 @@ except ModuleNotFoundError:
		# Compatibility imports for renamed XGBoost models. Remove below with DeepChem 3.0.
		#####################################################################################

		from deepchem.models.gdbt_models.gdbt_model import XGBoostModel
		from deepchem.models.gbdt_models.gbdt_model import XGBoostModel

		########################################################################################
		# Compatibility imports for renamed TensorGraph models. Remove below with DeepChem 3.0.

deepchem/models/gbdt_models/init.py

0 → 100644

+2 −0

Original line number	Diff line number	Diff line
		# flake8: noqa
		from deepchem.models.gbdt_models.gbdt_model import GBDTModel
		No newline at end of file

deepchem/models/gdbt_models/gdbt_model.py→deepchem/models/gbdt_models/gbdt_model.py

+5 −5

Original line number	Diff line number	Diff line
		"""
		Gradient boosting wrapper interface
		Gradient Boosting Decision Tree wrapper interface
		"""

		import os
		@@ -18,8 +18,8 @@ from deepchem.models.sklearn_models import SklearnModel
		logger = logging.getLogger(__name__)


		class GDBTModel(SklearnModel):
		"""Wrapper class that wraps GDBT models as DeepChem models.
		class GBDTModel(SklearnModel):
		"""Wrapper class that wraps GBDT models as DeepChem models.

		This class supports LightGBM/XGBoost models.
		"""
		@@ -145,10 +145,10 @@ class GDBTModel(SklearnModel):
		#########################################


		class XGBoostModel(GDBTModel):
		class XGBoostModel(GBDTModel):

		def __init__(self, args, *kwargs):
		warnings.warn(
		"XGBoostModel is deprecated and has been renamed to GDBTModel.",
		"XGBoostModel is deprecated and has been renamed to GBDTModel.",
		FutureWarning)
		super(XGBoostModel, self).__init__(args, *kwargs)

deepchem/models/gdbt_models/init.py

deleted100644 → 0

+0 −2

Original line number	Diff line number	Diff line
		# flake8: noqa
		from deepchem.models.gdbt_models.gdbt_model import GDBTModel
		No newline at end of file

deepchem/models/tests/test_gdbt_model.py→deepchem/models/tests/test_gbdt_model.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -29,7 +29,7 @@ def test_xgboost_regression():

		xgb_model = xgboost.XGBRegressor(
		n_estimators=50, random_state=123, verbose=False)
		model = dc.models.GDBTModel(xgb_model, **esr)
		model = dc.models.GBDTModel(xgb_model, **esr)

		# Fit trained model
		model.fit(train_dataset)
		@@ -62,7 +62,7 @@ def test_xgboost_multitask_regression():

		def model_builder(model_dir):
		xgb_model = xgboost.XGBRegressor(n_estimators=50, seed=123, verbose=False)
		return dc.models.GDBTModel(xgb_model, model_dir, **esr)
		return dc.models.GBDTModel(xgb_model, model_dir, **esr)

		model = dc.models.SingletaskToMultitask(tasks, model_builder)

		@@ -93,7 +93,7 @@ def test_xgboost_classification():
		classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score)
		esr = {'early_stopping_rounds': 50}
		xgb_model = xgboost.XGBClassifier(n_estimators=50, seed=123, verbose=False)
		model = dc.models.GDBTModel(xgb_model, **esr)
		model = dc.models.GBDTModel(xgb_model, **esr)

		# Fit trained model
		model.fit(train_dataset)
		@@ -123,7 +123,7 @@ def test_lightgbm_regression():

		lgbm_model = lightgbm.LGBMRegressor(
		n_estimators=50, random_state=123, silent=True)
		model = dc.models.GDBTModel(lgbm_model, **esr)
		model = dc.models.GBDTModel(lgbm_model, **esr)

		# Fit trained model
		model.fit(train_dataset)
		@@ -156,7 +156,7 @@ def test_lightgbm_multitask_regression():

		def model_builder(model_dir):
		lgbm_model = lightgbm.LGBMRegressor(n_estimators=50, seed=123, silent=True)
		return dc.models.GDBTModel(lgbm_model, model_dir, **esr)
		return dc.models.GBDTModel(lgbm_model, model_dir, **esr)

		model = dc.models.SingletaskToMultitask(tasks, model_builder)

		@@ -187,7 +187,7 @@ def test_lightgbm_classification():
		classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score)
		esr = {'early_stopping_rounds': 50}
		lgbm_model = lightgbm.LGBMClassifier(n_estimators=50, seed=123, silent=True)
		model = dc.models.GDBTModel(lgbm_model, **esr)
		model = dc.models.GBDTModel(lgbm_model, **esr)

		# Fit trained model
		model.fit(train_dataset)

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