Fix label access on clustering model

go/executor/executor.go

@@ -25,10 +25,11 @@ import (
 "path"
 "path/filepath"
 "regexp"
-"sqlflow.org/sqlflow/go/codegen/experimental"
 "strings"
 "sync"
 
+"sqlflow.org/sqlflow/go/codegen/experimental"
+
 "sqlflow.org/sqlflow/go/verifier"
 
 "sqlflow.org/sqlflow/go/codegen/optimize"

go/ir/ir_generator.go

@@ -114,7 +114,8 @@ func GenerateTrainStmt(slct *parser.SQLFlowSelectStmt) (*TrainStmt, error) {
 }
 label := &NumericColumn{
 FieldDesc: &FieldDesc{
-Name: tc.Label,
+Name: tc.Label,
+Shape: []int{1},
 }}
 
 vslct, _ := parseValidationSelect(attrList)

go/sql/executor_ir_test.go

@@ -188,7 +188,6 @@ func TestExecutorTrainAndPredictDNN(t *testing.T) {
 }
 
 func TestExecutorTrainAndPredictClusteringLocalFS(t *testing.T) {
-t.Skip("fix random nan loss error then re-enable this test")
 a := assert.New(t)
 modelDir, e := ioutil.TempDir("/tmp", "sqlflow_models")
 a.Nil(e)

python/runtime/local/create_result_table.py

@@ -32,10 +32,11 @@ def create_predict_table(conn, select, result_table, train_label_desc,
  """
  name_and_types = db.selected_columns_and_types(conn, select)
  train_label_index = -1
- for i, (name, _) in enumerate(name_and_types):
- if name == train_label_desc.name:
- train_label_index = i
- break
+ if train_label_desc:
+ for i, (name, _) in enumerate(name_and_types):
+ if name == train_label_desc.name:
+ train_label_index = i
+ break
 
  if train_label_index >= 0:
  del name_and_types[train_label_index]
@@ -45,10 +46,12 @@ def create_predict_table(conn, select, result_table, train_label_desc,
  column_strs.append("%s %s" %
  (name, db.to_db_field_type(conn.driver, typ)))
 
- if train_label_desc.format == DataFormat.PLAIN:
+ if train_label_desc and train_label_desc.format == DataFormat.PLAIN:
  train_label_field_type = DataType.to_db_field_type(
  conn.driver, train_label_desc.dtype)
  else:
+ # if no train lable description is provided (clustering),
+ # we treat the column type as string
  train_label_field_type = DataType.to_db_field_type(
  conn.driver, DataType.STRING)
 

python/runtime/local/tensorflow_submitter/predict.py

@@ -46,8 +46,9 @@ def pred(datasource, select, result_table, pred_label_name, model):
 
  model_params = model.get_meta("attributes")
  train_fc_map = model.get_meta("features")
- train_label_desc = model.get_meta("label").get_field_desc()[0]
- train_label_name = train_label_desc.name
+ label_meta = model.get_meta("label")
+ train_label_desc = label_meta.get_field_desc()[0] if label_meta else None
+ train_label_name = train_label_desc.name if train_label_desc else None
  estimator_string = model.get_meta("class_name")
  save = "model_save"
 

python/runtime/step/tensorflow/train.py

@@ -80,7 +80,12 @@ def train_step(original_sql,
  feature_column_names = [fd.name for fd in field_descs]
  feature_metas = dict([(fd.name, fd.to_dict(dtype_to_string=True))
  for fd in field_descs])
- label_meta = fc_label_ir.get_field_desc()[0].to_dict(dtype_to_string=True)
+
+ # no label for clustering model
+ label_meta = None
+ if fc_label_ir:
+ label_meta = fc_label_ir.get_field_desc()[0].to_dict(
+ dtype_to_string=True)
 
  feature_column_names_map = dict()
  for target in fc_map_ir: