Enable scoring of ML.NET models saved with new TransformerChain format

src/python/nimbusml.pyproj

@@ -67,9 +67,7 @@
     <Compile Include="nimbusml\ensemble\sub_model_selector\__init__.py" />
     <Compile Include="nimbusml\ensemble\sub_model_selector\diversity_measure\classifierdisagreement.py" />
     <Compile Include="nimbusml\ensemble\sub_model_selector\diversity_measure\regressordisagreement.py" />
-    <Compile Include="nimbusml\ensemble\sub_model_selector\diversity_measure\__init__.py">
-      <SubType>Code</SubType>
-    </Compile>
+    <Compile Include="nimbusml\ensemble\sub_model_selector\diversity_measure\__init__.py" />
     <Compile Include="nimbusml\ensemble\subset_selector\allinstanceselector.py" />
     <Compile Include="nimbusml\ensemble\subset_selector\bootstrapselector.py" />
     <Compile Include="nimbusml\ensemble\subset_selector\randompartitionselector.py" />

src/python/nimbusml/pipeline.py

@@ -19,6 +19,7 @@
 from scipy.sparse import csr_matrix
 from sklearn.utils.validation import check_X_y, check_array
 from sklearn.utils.multiclass import unique_labels
+from zipfile import ZipFile
 
 from .internal.core.base_pipeline_item import BasePipelineItem
 from .internal.entrypoints.data_customtextloader import \
@@ -40,6 +41,8 @@
 from .internal.entrypoints.models_summarizer import models_summarizer
 from .internal.entrypoints.transforms_datasetscorer import \
     transforms_datasetscorer
+from .internal.entrypoints.transforms_datasettransformscorer import \
+    transforms_datasettransformscorer
 from .internal.entrypoints.transforms_featurecombiner import \
     transforms_featurecombiner
 from .internal.entrypoints.transforms_featurecontributioncalculationtransformer import \
@@ -1816,22 +1819,44 @@ def _predict(self, X, y=None,
                 isinstance(X, DataFrame) and isinstance(y, (str, tuple))):
             y = y_temp
 
+        is_transformer_chain = False
+        with ZipFile(self.model) as model_zip:
+            is_transformer_chain = any('TransformerChain' in item
+                                   for item in model_zip.namelist())
+
         all_nodes = []
-        inputs = dict([('data', ''), ('predictor_model', self.model)])
-        if isinstance(X, FileDataStream):
-            importtext_node = data_customtextloader(
-                input_file="$file",
+        if is_transformer_chain:
+            inputs = dict([('data', ''), ('transform_model', self.model)])
+            if isinstance(X, FileDataStream):
+                importtext_node = data_customtextloader(
+                    input_file="$file",
+                    data="$data",
+                    custom_schema=schema.to_string(
+                        add_sep=True))
+                all_nodes = [importtext_node]
+                inputs = dict([('file', ''), ('transform_model', self.model)])
+
+            score_node = transforms_datasettransformscorer(
                 data="$data",
-                custom_schema=schema.to_string(
-                    add_sep=True))
-            all_nodes = [importtext_node]
-            inputs = dict([('file', ''), ('predictor_model', self.model)])
-
-        score_node = transforms_datasetscorer(
-            data="$data",
-            predictor_model="$predictor_model",
-            scored_data="$scoredVectorData")
-        all_nodes.extend([score_node])
+                transform_model="$transform_model",
+                scored_data="$scoredVectorData")
+            all_nodes.extend([score_node])
+        else:
+            inputs = dict([('data', ''), ('predictor_model', self.model)])
+            if isinstance(X, FileDataStream):
+                importtext_node = data_customtextloader(
+                    input_file="$file",
+                    data="$data",
+                    custom_schema=schema.to_string(
+                        add_sep=True))
+                all_nodes = [importtext_node]
+                inputs = dict([('file', ''), ('predictor_model', self.model)])
+
+            score_node = transforms_datasetscorer(
+                data="$data",
+                predictor_model="$predictor_model",
+                scored_data="$scoredVectorData")
+            all_nodes.extend([score_node])
 
         if (evaltype in ['binary', 'multiclass']) or \
            (hasattr(self, 'steps')
@@ -1889,6 +1914,10 @@ def _predict(self, X, y=None,
             self._run_time = time.time() - start_time
             raise e
 
+        if is_transformer_chain:
+            out_data['PredictedLabel'] = out_data['PredictedLabel']*1
+
+
         if y is not None:
             # We need to fix the schema for ranking metrics
             if evaltype == 'ranking':