♻️ gdbt -> gbdt

deepchem/models/__init__.py

@@ -26,7 +26,7 @@
 
 # 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 @@
 # 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

@@ -0,0 +1,2 @@
+# flake8: noqa
+from deepchem.models.gbdt_models.gbdt_model import GBDTModel

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

@@ -1,5 +1,5 @@
 """
-Gradient boosting wrapper interface
+Gradient Boosting Decision Tree wrapper interface
 """
 
 import os
@@ -18,8 +18,8 @@
 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 @@ def fit_with_eval(self, train_dataset: Dataset, valid_dataset: Dataset):
 #########################################
 
 
-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/tests/test_gdbt_model.py → deepchem/models/tests/test_gbdt_model.py

@@ -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)

docs/models.rst

@@ -12,7 +12,7 @@ Model Cheatsheet
 If you're just getting started with DeepChem, you're probably interested in the
 basics. The place to get started is this "model cheatsheet" that lists various
 types of custom DeepChem models. Note that some wrappers like :code:`SklearnModel`
-and :code:`GDBTModel` which wrap external machine learning libraries are excluded,
+and :code:`GBDTModel` which wrap external machine learning libraries are excluded,
 but this table is otherwise complete.
 
 As a note about how to read this table, each row describes what's needed to
@@ -151,10 +151,10 @@ Gradient Boosting Models
 
 Gradient Boosting Models (LightGBM and XGBoost) can be wrapped so they can interact with DeepChem.
 
-GDBTModel
+GBDTModel
 ------------
 
-.. autoclass:: deepchem.models.GDBTModel
+.. autoclass:: deepchem.models.GBDTModel
   :members: