move xgboost objective to attr

doc/xgboost_on_sqlflow_design.md

@@ -10,8 +10,9 @@ To explain the benefit of integrating XGBoost with SQLFlow, let us start with an
 
 ``` sql
 SELECT * FROM train_table
-TRAIN xgboost.multi.softmax
+TRAIN xgboost.gbtree
 WITH
+    objective=multi:softmax,
     train.num_round=2,
     max_depth=2,
     eta=1
@@ -29,10 +30,9 @@ USING my_xgb_model;
 
 The the above examples,
 - `my_xgb_model` names the trained model.
-- `xgboost.multi.softmax` is the model spec, where
-    - the prefix `xgboost.` tells the model is a XGBoost one, but not a Tensorflow model, and
-    - `multi.softmax` names an [XGBoost learning task](https://xgboost.readthedocs.io/en/latest/parameter.html#learning-task-parameters).
-- In the `WITH` clause, 
+- `xgboost.gbtree` is the model name, to use a different model provided by XGBoost, use `xgboost.gblinear` or `xgboost.dart`, see: [here](https://xgboost.readthedocs.io/en/latest/parameter.html#general-parameters) for details.
+- In the `WITH` clause,
+  - objective names an [XGBoost learning task](https://xgboost.readthedocs.io/en/latest/parameter.html#learning-task-parameters)
   - keys with the prefix `train.` identifies parameters of XGBoost API [`xgboost.train`](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.train), and
   - keys without any prefix identifies [XGBoost Parameters](https://xgboost.readthedocs.io/en/latest/parameter.html) except the `objective` parameter, which was specified by the identifier after the keyword `TRAIN`, as explained above.
 

sql/codegen_xgboost.go

@@ -76,12 +76,15 @@ func resolveParamsCfg(attrs map[string]*attribute) (map[string]interface{}, erro
 	return params, nil
 }
 
-func resolveObjective(pr *extendedSelect) (string, error) {
+func resolveModelName(pr *extendedSelect) (string, error) {
 	estimatorParts := strings.Split(pr.estimator, ".")
-	if len(estimatorParts) != 3 {
-		return "", fmt.Errorf("XGBoost Estimator should be xgboost.first_part.second_part, current: %s", pr.estimator)
+	if len(estimatorParts) != 2 {
+		return "", fmt.Errorf("XGBoost Estimator should be xgboost.modelname, current: %s", pr.estimator)
 	}
-	return strings.Join(estimatorParts[1:], ":"), nil
+	if strings.ToUpper(estimatorParts[1]) != "GBTREE" {
+		return "", fmt.Errorf("model name %s is not supported yet", estimatorParts[1])
+	}
+	return estimatorParts[1], nil
 }
 
 func newXGBFiller(pr *extendedSelect, ds *trainAndValDataset, db *DB) (*xgbFiller, error) {
@@ -109,18 +112,21 @@ func newXGBFiller(pr *extendedSelect, ds *trainAndValDataset, db *DB) (*xgbFille
 	}
 
 	if isTrain {
+		objective := getStringAttr(attrs, "objective", "gbtree")
 		// resolve the attribute keys without any prefix as the XGBoost Paremeters
 		params, err := resolveParamsCfg(attrs)
 		if err != nil {
 			return nil, err
 		}
+		params["objective"] = objective
 
-		// fill learning target
-		objective, err := resolveObjective(pr)
+		// get model name, could be gbtree, gblinear or dart.
+		// TODO(typhoonzero): only gbtree is supported here, use model name to generate
+		// differnet training code.
+		_, err = resolveModelName(pr)
 		if err != nil {
 			return nil, err
 		}
-		params["objective"] = objective
 
 		paramsJSON, err := json.Marshal(params)
 		if err != nil {

sql/codegen_xgboost_test.go

@@ -23,8 +23,9 @@ import (
 const testXGBoostTrainSelectIris = ` 
 SELECT *
 FROM iris.train
-TRAIN xgboost.multi.softprob
+TRAIN xgboost.gbtree
 WITH
+    objective="multi:softprob",
     train.num_boost_round = 30,
     eta = 3.1,
     num_class = 3