minor update (204b9707) · Commits · 钟慕尧 / deepchem

deepchem/models/xgboost_models/init.py

+8 −6

Original line number	Diff line number	Diff line
		@@ -53,11 +53,12 @@ class XGBoostModel(SklearnModel):
		if isinstance(self.model_instance,xgb.XGBClassifier):
		xgb_metric = "auc"
		sklearn_metric = "roc_auc"
		stratify = y
		elif isinstance(self.model_instance,xgb.XGBRegressor):
		xgb_metric = "mae"
		sklearn_metric = "neg_mean_absolute_error"

		best_param = self._search_param(sklearn_metric)
		stratify = None
		best_param = self._search_param(sklearn_metric,X,y)
		# update model with best param
		self.model_instance = self.model_class(**best_param)

		@@ -66,11 +67,12 @@ class XGBoostModel(SklearnModel):
		X_train, X_test, y_train, y_test = train_test_split(X, y,
		test_size = 0.2,
		random_state=seed,
		stratify=y)
		stratify=stratify)

		self.model_instance.fit(X_train, y_train,
		early_stopping_rounds=self.early_stopping_rounds,
		eval_metric=xgb_metric,eval_set=[(X_test, y_test)],
		eval_metric=xgb_metric,
		eval_set=[(X_train,y_train),(X_test, y_test)],
		verbose=self.verbose)
		# Since test size is 20%, when retrain model to whole data, expect
		# n_estimator increased to 1/0.8 = 1.25 time.
		@@ -79,14 +81,14 @@ class XGBoostModel(SklearnModel):
		self.model_instance.fit(X_train, y_train, eval_metric=xgb_metric,
		verbose=self.verbose)

		def _search_param(self,metric):
		def _search_param(self,metric,X,y):
		'''
		Find best potential parameters set using few n_estimators
		'''
		# Make sure user specified params are in the grid.
		max_depth_grid = list(np.unique([self.model_instance.max_depth,5,7]))
		colsample_bytree_grid = list(np.unique(
		[self.model_instance.colsample_tree,0.66,0.9]))
		[self.model_instance.colsample_bytree,0.66,0.9]))
		reg_lambda_grid = list(np.unique(
		[self.model_instance.reg_lambda,1,5]))
		param_grid = {

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