[Intermediate Representation] xgboost predict using IR

cmd/sqlflowserver/main_test.go

@@ -306,6 +306,7 @@ func TestEnd2EndMySQLIR(t *testing.T) {
 	t.Run("CaseSQLByPassLeftJoin", CaseSQLByPassLeftJoin)
 	t.Run("CaseTrainRegression", CaseTrainRegression)
 	t.Run("CaseTrainXGBoostRegressionIR", CaseTrainXGBoostRegression)
+	t.Run("CasePredictXGBoostRegressionIR", CasePredictXGBoostRegression)
 }
 
 func TestEnd2EndHive(t *testing.T) {

pkg/sql/codegen/tensorflow/codegen.go

@@ -238,20 +238,10 @@ func Pred(ir *codegen.PredictIR) (string, error) {
 	}
 	isKeras, estimatorStr := isKerasModel(ir.TrainIR.Estimator)
 
-	resultTableParts := strings.Split(ir.ResultTable, ".")
-	resultTable := ""
-	if len(resultTableParts) == 3 {
-		resultTable = strings.Join(resultTableParts[0:2], ".")
-	} else if len(resultTableParts) == 2 || len(resultTableParts) == 1 {
-		resultTable = ir.ResultTable
-	} else {
-		return "", fmt.Errorf("error result table format, should be db.table.class_col or db.table or table")
-	}
-
 	filler := predFiller{
 		DataSource:        ir.DataSource,
 		Select:            ir.Select,
-		ResultTable:       resultTable,
+		ResultTable:       ir.ResultTable,
 		Estimator:         estimatorStr,
 		IsKerasModel:      isKeras,
 		FieldMetas:        fieldMetas,

pkg/sql/codegen/xgboost/codegen.go

@@ -19,8 +19,9 @@ import (
 	"fmt"
 	"strings"
 
-	"sqlflow.org/sqlflow/pkg/sql/codegen"
 	"sqlflow.org/sqlflow/pkg/sql/codegen/attribute"
+
+	"sqlflow.org/sqlflow/pkg/sql/codegen"
 )
 
 func newFloat32(f float32) *float32 {
@@ -148,3 +149,31 @@ func Train(ir *codegen.TrainIR) (string, error) {
 
 	return program.String(), nil
 }
+
+// Pred generates a Python program for predict a xgboost model.
+func Pred(ir *codegen.PredictIR) (string, error) {
+	featureFieldMeta, labelFieldMeta, err := getFieldMeta(ir.TrainIR.Features["feature_columns"], ir.TrainIR.Label)
+	f, err := json.Marshal(featureFieldMeta)
+	if err != nil {
+		return "", err
+	}
+	l, err := json.Marshal(labelFieldMeta)
+	if err != nil {
+		return "", err
+	}
+
+	r := predFiller{
+		DataSource:      ir.DataSource,
+		PredSelect:      ir.Select,
+		FeatureMetaJSON: string(f),
+		LabelMetaJSON:   string(l),
+		ResultTable:     ir.ResultTable,
+	}
+
+	var program bytes.Buffer
+
+	if err := predTemplate.Execute(&program, r); err != nil {
+		return "", err
+	}
+	return program.String(), nil
+}

pkg/sql/codegen/xgboost/codegen_test.go

@@ -22,13 +22,25 @@ import (
 	"sqlflow.org/sqlflow/pkg/sql/codegen"
 )
 
-func TestTrain(t *testing.T) {
+func TestTrainAndPredict(t *testing.T) {
 	a := assert.New(t)
 	tir := mockTrainIR()
 	_, err := Train(tir)
 	a.NoError(err)
+
+	pir := mockPrdcIR(tir)
+	_, err = Pred(pir)
+	a.NoError(err)
 }
 
+func mockPrdcIR(trainIR *codegen.TrainIR) *codegen.PredictIR {
+	return &codegen.PredictIR{
+		DataSource:  trainIR.DataSource,
+		Select:      "select * from iris.test;",
+		ResultTable: "iris.predict",
+		TrainIR:     trainIR,
+	}
+}
 func mockTrainIR() *codegen.TrainIR {
 	cfg := &mysql.Config{
 		User:                 "root",

pkg/sql/codegen/xgboost/template_pred.go

@@ -0,0 +1,82 @@
+// Copyright 2019 The SQLFlow Authors. All rights reserved.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package xgboost
+
+import (
+	"text/template"
+)
+
+type predFiller struct {
+	DataSource      string
+	PredSelect      string
+	FeatureMetaJSON string
+	LabelMetaJSON   string
+	ResultTable     string
+}
+
+const predTemplateText = `
+import json
+import xgboost as xgb
+import numpy as np
+from sqlflow_submitter.db import connect_with_data_source, db_generator, buffered_db_writer
+
+feature_field_meta = json.loads('''{{.FeatureMetaJSON}}''')
+label_field_meta = json.loads('''{{.LabelMetaJSON}}''')
+
+feature_column_names = [k["name"] for k in feature_field_meta]
+label_name = label_field_meta["name"]
+
+feature_specs = {k['name']: k for k in feature_field_meta}
+
+conn = connect_with_data_source('''{{.DataSource}}''')
+
+def xgb_dataset(fn, dataset_sql):
+    gen = db_generator(conn.driver, conn, dataset_sql, feature_column_names, "", feature_specs)
+    with open(fn, 'w') as f:
+        for item in gen():
+            features, label = item
+            row_data = [str(label[0])] + ["%d:%f" % (i, v) for i, v in enumerate(features)]
+            f.write("\t".join(row_data) + "\n")
+    # TODO(yancey1989): genearte group and weight text file if necessary
+    return xgb.DMatrix(fn)
+
+dpred = xgb_dataset('predict.txt', """{{.PredSelect}}""")
+
+bst = xgb.Booster({'nthread': 4})  # init model
+bst.load_model("my_model")  # load data
+preds = bst.predict(dpred)
+
+# TODO(Yancey1989): using the train parameters to decide regressoin model or classifier model
+if len(preds.shape) == 2:
+    # classifier result
+    preds = np.argmax(np.array(preds), axis=1)
+
+feature_file_read = open("predict.txt", "r")
+
+result_column_names = feature_column_names
+result_column_names.append(label_name)
+line_no = 0
+with buffered_db_writer(conn.driver, conn, "{{.ResultTable}}", result_column_names, 100) as w:
+    while True:
+        line = feature_file_read.readline()
+        if not line:
+            break
+        row = [i.split(":")[1] for i in line.replace("\n", "").split("\t")[1:]]
+        row.append(str(preds[line_no]))
+        w.write(row)
+        line_no += 1
+print("Done predicting. Predict table : {{.ResultTable}}")
+`
+
+var predTemplate = template.Must(template.New("Pred").Parse(predTemplateText))

pkg/sql/executor.go

@@ -28,7 +28,7 @@ import (
 
 	pb "sqlflow.org/sqlflow/pkg/server/proto"
 	"sqlflow.org/sqlflow/pkg/sql/codegen/tensorflow"
-	xgb "sqlflow.org/sqlflow/pkg/sql/codegen/xgboost"
+	"sqlflow.org/sqlflow/pkg/sql/codegen/xgboost"
 )
 
 // Run executes a SQL query and returns a stream of rows or messages
@@ -413,12 +413,12 @@ func train(wr *PipeWriter, tr *extendedSelect, db *DB, cwd string, modelDir stri
 	var program bytes.Buffer
 	if strings.HasPrefix(strings.ToUpper(tr.estimator), `XGBOOST.`) {
 		// FIXME(weiguoz): Remove the condition after the codegen refactor
-		if os.Getenv("SQLFLOW_codegen") == "ir" {
+		if enableIR() {
 			ir, err := generateTrainIR(tr, db.String())
 			if err != nil {
 				return err
 			}
-			code, err := xgb.Train(ir)
+			code, err := xgboost.Train(ir)
 			if err != nil {
 				return err
 			}
@@ -430,7 +430,7 @@ func train(wr *PipeWriter, tr *extendedSelect, db *DB, cwd string, modelDir stri
 		}
 	} else {
 		// FIXME(typhoonzero): Remove the condition after the codegen refactor
-		if os.Getenv("SQLFLOW_codegen") == "ir" {
+		if enableIR() {
 			ir, err := generateTrainIR(tr, db.String())
 			if err != nil {
 				return err
@@ -497,6 +497,10 @@ func loadModelMeta(pr *extendedSelect, db *DB, cwd, modelDir, modelName string)
 	return pr, fts, nil
 }
 
+func enableIR() bool {
+	return os.Getenv("SQLFLOW_codegen") == "ir"
+}
+
 func pred(wr *PipeWriter, pr *extendedSelect, db *DB, cwd string, modelDir string, session *pb.Session) error {
 	pr, fts, e := loadModelMeta(pr, db, cwd, modelDir, pr.model)
 	if e != nil {
@@ -505,11 +509,27 @@ func pred(wr *PipeWriter, pr *extendedSelect, db *DB, cwd string, modelDir strin
 
 	var buf bytes.Buffer
 	if strings.HasPrefix(strings.ToUpper(pr.estimator), `XGBOOST.`) {
-		if e := genXGBoost(&buf, pr, nil, fts, db, session); e != nil {
-			return fmt.Errorf("genXGBoost %v", e)
+		if enableIR() {
+			ir, err := generatePredictIR(pr, db.String(), cwd, modelDir)
+			if err != nil {
+				return err
+			}
+			code, err := xgboost.Pred(ir)
+			if err != nil {
+				return err
+			}
+			err = createPredictionTable(pr, db, session)
+			if err != nil {
+				return err
+			}
+			buf.WriteString(code)
+		} else {
+			if e := genXGBoost(&buf, pr, nil, fts, db, session); e != nil {
+				return fmt.Errorf("genXGBoost %v", e)
+			}
 		}
 	} else {
-		if os.Getenv("SQLFLOW_codegen") == "ir" {
+		if enableIR() {
 			ir, err := generatePredictIR(pr, db.String(), cwd, modelDir)
 			if err != nil {
 				return err

pkg/sql/ir_generator.go

@@ -96,12 +96,16 @@ func generatePredictIR(slct *extendedSelect, connStr string, cwd string, modelDi
 	if err != nil {
 		return nil, err
 	}
-	fmt.Printf("select %s, result table: %s\n", slct.standardSelect.String(), slct.into)
+
+	resultTable, err := parseResultTable(slct.into)
+	if err != nil {
+		return nil, err
+	}
 
 	return &codegen.PredictIR{
 		DataSource:  connStr,
 		Select:      slct.standardSelect.String(),
-		ResultTable: slct.into,
+		ResultTable: resultTable,
 		Attributes:  attrMap,
 		TrainIR:     trainIR,
 	}, nil
@@ -558,3 +562,19 @@ func parseShape(e *expr) ([]int, error) {
 	}
 	return shape, nil
 }
+
+// parseResultTable parse out the table name from the INTO statment
+// as the following 3 cases:
+// db.table.class_col -> db.table # cut the column name
+// db.table -> db.table               # using the specified db
+// table -> table                     # using the default db
+func parseResultTable(intoStatement string) (string, error) {
+	resultTableParts := strings.Split(intoStatement, ".")
+	if len(resultTableParts) == 3 {
+		return strings.Join(resultTableParts[0:2], "."), nil
+	} else if len(resultTableParts) == 2 || len(resultTableParts) == 1 {
+		return intoStatement, nil
+	} else {
+		return "", fmt.Errorf("error result table format, should be db.table.class_col or db.table or table")
+	}
+}

pkg/sql/ir_generator_test.go

@@ -186,7 +186,7 @@ INTO sqlflow_models.mymodel;`, testDB, modelDir, nil)
 	a.NoError(err)
 
 	a.Equal(connStr, predIR.DataSource)
-	a.Equal("iris.predict.class", predIR.ResultTable)
+	a.Equal("iris.predict", predIR.ResultTable)
 	a.Equal("class", predIR.TrainIR.Label.GetFieldMeta()[0].Name)
 	a.Equal("DNNClassifier", predIR.TrainIR.Estimator)
 	nc, ok := predIR.TrainIR.Features["feature_columns"][0].(*codegen.NumericColumn)

pkg/sql/template_xgboost.go

@@ -144,7 +144,6 @@ feature_file_read = open("predict.txt", "r")
 
 result_column_names = feature_column_names
 result_column_names.append("{{.Y.FeatureName}}")
-
 line_no = 0
 with buffered_db_writer(driver, conn, "{{.TableName}}", result_column_names, 100, hdfs_namenode_addr="{{.HDFSNameNodeAddr}}", hive_location="{{.HiveLocation}}", hdfs_user="{{.HDFSUser}}", hdfs_pass="{{.HDFSPass}}") as w:
     while True: