op_label_encoder support for keys_strings & values_floats (2) (replaces #335)

_unittests/ut_onnxrt/test_onnxrt_python_runtime_ml_text.py

@@ -58,6 +58,19 @@ def test_onnxrt_label_encoder_floats(self):
         self.assertEqualArray(
             res['out'], numpy.array([0.3, 0.4, 0.5, 0.4], dtype=numpy.float32))
 
+    def test_onnxrt_label_encoder_string_floats(self):
+
+        op = OnnxLabelEncoder(
+            'text', op_version=get_opset_number_from_onnx(),
+            keys_strings=['AA', 'BB', 'CC'],
+            values_floats=[0.1, 0.2, 0.3],
+            output_names=['out'])
+
+        onx = op.to_onnx(inputs=[('text', StringTensorType())])
+        oinf = OnnxInference(onx)
+        res = oinf.run({'text': numpy.array(['AA', 'DD']).reshape((-1, 1))})
+        self.assertEqualArray(res['out'], numpy.array([0.1, 0]))
+
     def test_onnxrt_label_encoder_raise(self):
 
         self.assertRaise(
@@ -68,15 +81,6 @@ def test_onnxrt_label_encoder_raise(self):
                 output_names=['out']),
             TypeError)
 
-        op = OnnxLabelEncoder(
-            'text', op_version=get_opset_number_from_onnx(),
-            keys_strings=['AA', 'BB', 'CC'],
-            values_floats=[0.1, 0.2, 0.3],
-            output_names=['out'])
-
-        onx = op.to_onnx(inputs=[('text', StringTensorType())])
-        self.assertRaise(lambda: OnnxInference(onx), RuntimeError)
-
         op = OnnxLabelEncoder(
             'text', op_version=get_opset_number_from_onnx(),
             keys_strings=['AA', 'BB', 'CC'],

mlprodict/onnxrt/ops_cpu/op_label_encoder.py

@@ -45,6 +45,11 @@ def __init__(self, onnx_node, desc=None, **options):
                 self.keys_int64s, self.values_floats)}
             self.default_ = self.default_int64
             self.dtype_ = numpy.float32
+        elif len(self.keys_strings) > 0 and len(self.values_floats) > 0:
+            self.classes_ = {k.decode('utf-8'): v for k, v in zip(
+                self.keys_strings, self.values_floats)}
+            self.default_ = self.default_float
+            self.dtype_ = numpy.float32
         elif len(self.keys_strings) > 0 and len(self.values_int64s) > 0:
             self.classes_ = {k.decode('utf-8'): v for k, v in zip(
                 self.keys_strings, self.values_int64s)}