Add example for OnnxRunner.

src/python/nimbusml.pyproj

@@ -92,6 +92,7 @@
     <Compile Include="nimbusml\examples\BootStrapSample.py" />
     <Compile Include="nimbusml\examples\CharTokenizer.py" />
     <Compile Include="nimbusml\examples\ColumnConcatenator.py" />
+    <Compile Include="nimbusml\examples\examples_from_dataframe\OnnxRunner_df.py" />
     <Compile Include="nimbusml\examples\examples_from_dataframe\RobustScaler_df.py" />
     <Compile Include="nimbusml\examples\examples_from_dataframe\TimeSeriesImputer_df.py" />
     <Compile Include="nimbusml\examples\examples_from_dataframe\VotingRegressor.py" />

src/python/nimbusml/examples/examples_from_dataframe/OnnxRunner_df.py

@@ -0,0 +1,49 @@
+import os
+import tempfile
+import numpy as np
+import pandas as pd
+from nimbusml import Pipeline
+from nimbusml.preprocessing import OnnxRunner
+from nimbusml.preprocessing.normalization import MinMaxScaler
+
+
+def get_tmp_file(suffix=None):
+    fd, file_name = tempfile.mkstemp(suffix=suffix)
+    fl = os.fdopen(fd, 'w')
+    fl.close()
+    return file_name
+
+# Generate the train and test data
+np.random.seed(0)
+x = np.arange(100, step=0.1)
+y = x * 10 + (np.random.standard_normal(len(x)) * 10)
+train_data = {'c1': x, 'c2': y}
+train_df = pd.DataFrame(train_data).astype({'c1': np.float32, 'c2': np.float32})
+
+test_data = {'c1': [2.5, 30.5], 'c2': [1, 1]}
+test_df = pd.DataFrame(test_data).astype({'c1': np.float32, 'c2': np.float32})
+
+# Fit a MinMaxScaler Pipeline
+r1 = Pipeline([MinMaxScaler()])
+r1.fit(train_df)
+
+# Export the pipeline to ONNX
+onnx_path = get_tmp_file('.onnx')
+r1.export_to_onnx(onnx_path, 'com.microsoft.ml', onnx_version='Stable')
+
+# Perform the transform using the standard ML.Net backend
+result_standard = r1.transform(test_df)
+print(result_standard)
+#          c1        c2
+# 0  0.025025  0.000998
+# 1  0.305305  0.000998
+
+# Perform the transform using the ONNX backend.
+# Note, the extra columns and column name differences
+# is a known issue with the ML.Net backend.
+onnxrunner = OnnxRunner(model_file=onnx_path)
+result_onnx = onnxrunner.fit_transform(test_df)
+print(result_onnx)
+#      c1   c2     c12.0     c22.0
+# 0   2.5  1.0  0.025025  0.000998
+# 1  30.5  1.0  0.305305  0.000998