Fix issue1649

streamingpro-core/src/main/java/streaming/dsl/load/batch/ModelExplain.scala

@@ -20,10 +20,8 @@ package streaming.dsl.load.batch
 
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row, SparkSession}
-import tech.mlsql.common.utils.reflect.ClassPath
 import tech.mlsql.dsl.adaptor.MLMapping
 
-import scala.collection.JavaConversions._
 
 /**
   * Created by allwefantasy on 21/9/2018.

streamingpro-core/src/main/java/tech/mlsql/dsl/adaptor/LoadAdaptor.scala

@@ -136,10 +136,16 @@ class LoadProcessing(scriptSQLExecListener: ScriptSQLExecListener,
       // return the load table
       table
     }.getOrElse {
-      // calculate resource real absolute path
+      // path could be:
+      // 1) fileSystem path; code example: load  modelExplain.`/tmp/model` where alg="RandomForest" as output;
+      // 2) ET name; code example: load modelExample.`JsonExpandExt` AS output_1;  load modelParams.`JsonExpandExt` as output;
+      // For FileSystem path, pass the real path to ModelSelfExplain; for ET name pass original path
       val resourcePath = resourceRealPath(scriptSQLExecListener, option.get("owner"), path)
-
-      table = ModelSelfExplain(format, cleanStr(path), option, sparkSession).isMatch.thenDo.orElse(() => {
+      val fsPathOrETName = format match {
+        case "modelExplain" => resourceRealPath(scriptSQLExecListener, option.get("owner"), path)
+        case _ => cleanStr(path)
+      }
+      table = ModelSelfExplain(format, fsPathOrETName, option, sparkSession).isMatch.thenDo.orElse(() => {
         reader.format(format).load(resourcePath)
       }).get
     }

streamingpro-it/src/test/resources/sql/simple/23_random_forest_model_explain.mlsql

@@ -0,0 +1,38 @@
+--%comparator=tech.mlsql.it.IgnoreResultComparator
+
+set jsonStr='''
+{"features":[5.1,3.5,1.4,0.2],"label":0.0},
+{"features":[5.1,3.5,1.4,0.2],"label":1.0}
+{"features":[5.1,3.5,1.4,0.2],"label":0.0}
+{"features":[4.4,2.9,1.4,0.2],"label":0.0}
+{"features":[5.1,3.5,1.4,0.2],"label":1.0}
+{"features":[5.1,3.5,1.4,0.2],"label":0.0}
+{"features":[5.1,3.5,1.4,0.2],"label":0.0}
+{"features":[4.7,3.2,1.3,0.2],"label":1.0}
+{"features":[5.1,3.5,1.4,0.2],"label":0.0}
+{"features":[5.1,3.5,1.4,0.2],"label":0.0}
+''';
+load jsonStr.`jsonStr` as mock_data;
+
+
+select vec_dense(features) as features, label as label from mock_data as mock_data_1;
+
+
+train mock_data_1 as RandomForest.`/tmp/model` where
+keepVersion="true"
+and evaluateTable="mock_data_validate"
+
+and `fitParam.0.labelCol`="label"
+and `fitParam.0.featuresCol`="features"
+and `fitParam.0.maxDepth`="2"
+
+and `fitParam.1.featuresCol`="features"
+and `fitParam.1.labelCol`="label"
+and `fitParam.1.maxDepth`="10"
+;
+
+load modelExplain.`/tmp/model/_model_0/` where alg="RandomForest" as output_1;
+
+select `name` from output_1 where name="uid" as result;
+
+!assert result ''':name=="uid"'''  "RandomForest modelExplain should be successful";

streamingpro-it/src/test/resources/sql/simple/andie_huang_02_train_linear_regression_model.mlsql

@@ -48,4 +48,11 @@ select pred_func(features) as predict_label, label from data1 as output;
 
 select name,value from model_result where name="status" as result;
 -- make sure status of  all models are success.
-!assert result ''':value=="success"'''  "all model status should be success";
+!assert result ''':value=="success"'''  "all model status should be success";
+
+
+load modelExplain.`/tmp/linearregression` WHERE `alg`="LinearRegression" as lr_model_explain;
+
+select `name` from lr_model_explain where name="uid" as result;
+
+!assert result ''':name=="uid"'''  "LinearRegression modelExplain should be successful";