fix: SparkML StandardScaler conversion fails when withStd or withMean is set to true (#555)

Signed-off-by: Jason Wang <[email protected]>

Author: memoryz

onnxmltools/convert/sparkml/operator_converters/scaler.py

@@ -18,8 +18,8 @@ def convert_sparkml_scaler(scope, operator, container):
     attrs = {'name': scope.get_unique_operator_name(op_type)}
     if isinstance(op, StandardScalerModel):
         C = operator.inputs[0].type.shape[1]
-        attrs['offset'] = op.mean if op.getOrDefault("withMean") else [0.0] * C
-        attrs['scale'] = [1.0 / x for x in op.std] if op.getOrDefault("withStd") else [1.0] * C
+        attrs['offset'] = op.mean.toArray() if op.getOrDefault("withMean") else [0.0] * C
+        attrs['scale'] = [1.0 / x for x in op.std.toArray()] if op.getOrDefault("withStd") else [1.0] * C
     elif isinstance(op, MinMaxScalerModel):
         epsilon = 1.0e-8  # to avoid dividing by zero
         attrs['offset'] = [x for x in op.originalMin]

tests/sparkml/test_scaler.py

@@ -75,7 +75,7 @@ def test_standard_scaler(self):
             (1, Vectors.dense([2.0, 1.1, 1.0]),),
             (2, Vectors.dense([3.0, 10.1, 3.0]),)
         ], ["id", "features"])
-        scaler = StandardScaler(inputCol='features', outputCol='scaled_features')
+        scaler = StandardScaler(inputCol='features', outputCol='scaled_features', withStd=True, withMean=True)
         model = scaler.fit(data)
 
         # the input names must match the inputCol(s) above