From 683a881f74dbd1e171f89be99976546c3bb6b5ac Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Fri, 4 Oct 2019 22:54:21 -0700
Subject: [PATCH 01/15] draft

---
 src/DotNetBridge/Entrypoints.cs               | 55 +++++++++++++
 .../entrypoints/transforms_datasetscorer.py   |  2 +-
 src/python/tests/test_copyright.py            | 81 +++++++++----------
 src/python/tools/entrypoint_compiler.py       |  5 ++
 4 files changed, 99 insertions(+), 44 deletions(-)

diff --git a/src/DotNetBridge/Entrypoints.cs b/src/DotNetBridge/Entrypoints.cs
index 039247fe..d2ca45b8 100644
--- a/src/DotNetBridge/Entrypoints.cs
+++ b/src/DotNetBridge/Entrypoints.cs
@@ -87,5 +87,60 @@ public static CommonOutputs.TransformOutput CreateVariableColumn(IHostEnvironmen
             var xf = VariableColumnTransform.Create(env, inputOptions, inputOptions.Data);
             return new CommonOutputs.TransformOutput { Model = new TransformModelImpl(env, xf, inputOptions.Data), OutputData = xf };
         }
+
+        public sealed class Input
+        {
+            [Argument(ArgumentType.Required, HelpText = "The dataset to be scored", SortOrder = 1)]
+            public IDataView Data;
+
+            [Argument(ArgumentType.Required, HelpText = "The predictor model to apply to data", SortOrder = 2)]
+            public PredictorModel PredictorModel;
+
+            [Argument(ArgumentType.AtMostOnce, HelpText = "Suffix to append to the score columns", SortOrder = 3)]
+            public string Suffix;
+        }
+
+        public sealed class Output
+        {
+            [TlcModule.Output(Desc = "The scored dataset", SortOrder = 1)]
+            public IDataView ScoredData;
+
+            [TlcModule.Output(Desc = "The scoring transform", SortOrder = 2)]
+            public TransformModel ScoringTransform;
+        }
+
+        [TlcModule.EntryPoint(Name = "Transforms.DatasetScorer1", Desc = "Score a dataset with a predictor model")]
+        public static Output Score(IHostEnvironment env, Input input)
+        {
+            Contracts.CheckValue(env, nameof(env));
+            var host = env.Register("ScoreModel");
+            host.CheckValue(input, nameof(input));
+            EntryPointUtils.CheckInputArgs(host, input);
+
+            var inputData = input.Data;
+            // input.PredictorModel.PrepareData(host, inputData, out RoleMappedData data, out IPredictor predictor);
+            IPredictor predictor = input.PredictorModel.Predictor;
+            var data = new RoleMappedData(input.Data, null, input.Data.Schema[0].Name);
+
+            IDataView scoredPipe;
+            using (var ch = host.Start("Creating scoring pipeline"))
+            {
+                ch.Trace("Creating pipeline");
+                var bindable = ScoreUtils.GetSchemaBindableMapper(host, predictor);
+                ch.AssertValue(bindable);
+
+                var mapper = bindable.Bind(host, data.Schema);
+                var scorer = ScoreUtils.GetScorerComponent(host, mapper, input.Suffix);
+                scoredPipe = scorer.CreateComponent(host, data.Data, mapper, input.PredictorModel.GetTrainingSchema(host));
+            }
+
+            return
+                new Output
+                {
+                    ScoredData = scoredPipe,
+                    ScoringTransform = new TransformModelImpl(host, scoredPipe, inputData)
+                };
+
+        }
     }
 }
diff --git a/src/python/nimbusml/internal/entrypoints/transforms_datasetscorer.py b/src/python/nimbusml/internal/entrypoints/transforms_datasetscorer.py
index 2aa14c74..983b3abd 100644
--- a/src/python/nimbusml/internal/entrypoints/transforms_datasetscorer.py
+++ b/src/python/nimbusml/internal/entrypoints/transforms_datasetscorer.py
@@ -27,7 +27,7 @@ def transforms_datasetscorer(
     :param scoring_transform: The scoring transform (outputs).
     """
 
-    entrypoint_name = 'Transforms.DatasetScorer'
+    entrypoint_name = 'Transforms.DatasetScorer1'
     inputs = {}
     outputs = {}
 
diff --git a/src/python/tests/test_copyright.py b/src/python/tests/test_copyright.py
index 87dc0830..03984b61 100644
--- a/src/python/tests/test_copyright.py
+++ b/src/python/tests/test_copyright.py
@@ -1,48 +1,43 @@
-# -------------------------------------------------------------------------
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License.
-# -------------------------------------------------------------------------
-import os
-import unittest
+import numpy as np
+import pandas as pd
+from nimbusml import Pipeline, FileDataStream
+from nimbusml.datasets import get_dataset
+from nimbusml.feature_extraction.categorical import OneHotVectorizer
+from nimbusml.linear_model import LogisticRegressionBinaryClassifier
+from nimbusml.preprocessing import DatasetTransformer
+from nimbusml.preprocessing.schema import PrefixColumnConcatenator
+from nimbusml.preprocessing.schema import ColumnDropper
 
+path = get_dataset('infert').as_filepath()
+data = FileDataStream.read_csv(path)
+ 
+# train featurizer
+featurization_pipeline = Pipeline([OneHotVectorizer(columns={'education': 'education'})])
+featurization_pipeline.fit(data)
+print(featurization_pipeline.get_schema())
 
-class TestCopyright(unittest.TestCase):
-    """
-    Tests that the copyright is present in everyfile.
-    """
+# need to remove extra columns from csr matrix 
+# and get csr_matrix featurized data
+csr_featurization_pipeline = Pipeline([DatasetTransformer(featurization_pipeline.model), ColumnDropper() << ['case', 'row_num']])
+sparse_featurized_data = csr_featurization_pipeline.fit_transform(data, as_csr=True)
+# Note: the relative order of all columns is still the same.
+print(csr_featurization_pipeline.get_schema())
 
-    def test_copyrigth_present(self):
-        root = os.path.join(os.path.abspath(os.path.dirname(__file__)),
-                            '..')
-        root = os.path.normpath(root)
-        allfiles = []
-        for r, dirs, files in os.walk(root):
-            for name in files:
-                if name.endswith('.py'):
-                    allfiles.append(os.path.join(r, name))
+concat_pipeline = Pipeline([DatasetTransformer(csr_featurization_pipeline.model), PrefixColumnConcatenator({'education': 'education.'})])
+concat_pipeline.fit(data)
+print(concat_pipeline.get_schema())
 
-        nothere = []
-        nb = 0
-        for name in allfiles:
-            if 'examples' in name or 'docs' in name or \
-                    '__init__.py' in name or 'entrypoints' in name:
-                continue
-            with open(name, "r") as f:
-                content = f.read()
-            if 'Copyright (c) Microsoft Corporation. All rights ' \
-               'reserved.' not in content:
-                nothere.append(name)
-            else:
-                nb += 1
+# train a featurizer + learner pipeline
+# Note! order of feature columns on input to learner should be the same as in csr_matrix above
+#feature_cols = ['parity', 'education', 'age', 'induced', 'spontaneous', 'stratum', 'pooled.stratum'] # 9 features
+feature_cols = csr_featurization_pipeline.get_schema()
+training_pipeline = Pipeline([DatasetTransformer(featurization_pipeline.model), LogisticRegressionBinaryClassifier(feature=feature_cols, label='case')])
+training_pipeline.fit(data, output_predictor_model=True)
+ 
+# load just a learner model
+predictor_pipeline = Pipeline()
+predictor_pipeline.load_model(training_pipeline.predictor_model)
+print(predictor_pipeline.get_schema())
 
-        if len(nothere) > 0:
-            nothere = ['  File "{0}", line 1'.format(_) for _ in nothere]
-            raise Exception(
-                "Copyright not found in\n{0}".format(
-                    "\n".join(nothere)))
-        if nb == 0:
-            raise Exception("No file found in '{0}'".format(root))
-
-
-if __name__ == '__main__':
-    unittest.main()
+# use just a learner model on csr_matrix featurized data
+predictor_pipeline.predict_proba(sparse_featurized_data.astype(np.float32))
\ No newline at end of file
diff --git a/src/python/tools/entrypoint_compiler.py b/src/python/tools/entrypoint_compiler.py
index b2765691..014264f6 100644
--- a/src/python/tools/entrypoint_compiler.py
+++ b/src/python/tools/entrypoint_compiler.py
@@ -2041,6 +2041,11 @@ def generate_code(pkg_path, generate_entrypoints, generate_api):
     script_args = arg_parser.parse_args()
     pkg_path = os.path.join(my_dir, r'..\nimbusml')
 
+
+    script_args.generate_api = False
+    script_args.generate_entrypoints = False
+    script_args.check_manual_changes = False
+
     if script_args.check_manual_changes:
         verbose = False
         if script_args.folder == 'temp':

From e1e304b34761614a8ef4704a28df1bc604fabfc5 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Fri, 4 Oct 2019 23:36:06 -0700
Subject: [PATCH 02/15] draft

---
 src/python/nimbusml.pyproj                    |  1 +
 .../nimbusml/tests/pipeline/test_csr_input.py | 59 +++++++++++++++++++
 2 files changed, 60 insertions(+)
 create mode 100644 src/python/nimbusml/tests/pipeline/test_csr_input.py

diff --git a/src/python/nimbusml.pyproj b/src/python/nimbusml.pyproj
index 345e7ccf..7f4e4d6d 100644
--- a/src/python/nimbusml.pyproj
+++ b/src/python/nimbusml.pyproj
@@ -682,6 +682,7 @@
     <Compile Include="nimbusml\tests\feature_extraction\text\test_sentiment.py" />
     <Compile Include="nimbusml\tests\idv\__init__.py" />
     <Compile Include="nimbusml\tests\linear_model\test_linearsvmbinaryclassifier.py" />
+    <Compile Include="nimbusml\tests\pipeline\test_csr_input.py" />
     <Compile Include="nimbusml\tests\pipeline\test_pipeline_get_schema.py" />
     <Compile Include="nimbusml\tests\pipeline\test_pipeline_split_models.py" />
     <Compile Include="nimbusml\tests\pipeline\test_pipeline_combining.py" />
diff --git a/src/python/nimbusml/tests/pipeline/test_csr_input.py b/src/python/nimbusml/tests/pipeline/test_csr_input.py
new file mode 100644
index 00000000..f54f1362
--- /dev/null
+++ b/src/python/nimbusml/tests/pipeline/test_csr_input.py
@@ -0,0 +1,59 @@
+# --------------------------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------------------------
+import os
+import unittest
+
+import numpy as np
+import pandas as pd
+from nimbusml import Pipeline, FileDataStream
+from nimbusml.datasets import get_dataset
+from nimbusml.feature_extraction.categorical import OneHotVectorizer
+from nimbusml.linear_model import LogisticRegressionBinaryClassifier
+from nimbusml.preprocessing import DatasetTransformer
+from nimbusml.preprocessing.schema import PrefixColumnConcatenator
+from nimbusml.preprocessing.schema import ColumnDropper
+
+class TestCsrInput(unittest.TestCase):
+
+    def test_predict_proba_on_csr(self):
+        path = get_dataset('infert').as_filepath()
+        data = FileDataStream.read_csv(path)
+        cols = list(data.head(1).columns.values) # ordered data column names.
+ 
+        # train featurizer
+        featurization_pipeline = Pipeline([OneHotVectorizer(columns={'education': 'education'})])
+        featurization_pipeline.fit(data)
+        # Note: the relative order of all columns is still the same as in raw data.
+        #print(featurization_pipeline.get_schema())
+
+        # need to remove extra columns before getting csr_matrix featurized data as it wont have column name information.
+        csr_featurization_pipeline = Pipeline([DatasetTransformer(featurization_pipeline.model), ColumnDropper() << ['case', 'row_num']])
+        sparse_featurized_data = csr_featurization_pipeline.fit_transform(data, as_csr=True)
+        # Note: the relative order of all columns is still the same.
+        #print(csr_featurization_pipeline.get_schema())
+
+        # train learner
+        # Note: order & number of feature columns for learner (parameter 'feature') should be the same as in csr_matrix above
+        cols.remove('row_num')
+        cols.remove('case')
+        feature_cols = cols
+        print(feature_cols)
+        #['education', 'age', 'parity', 'induced', 'spontaneous', 'stratum', 'pooled.stratum']
+        training_pipeline = Pipeline([DatasetTransformer(featurization_pipeline.model), LogisticRegressionBinaryClassifier(feature=feature_cols, label='case')])
+        training_pipeline.fit(data, output_predictor_model=True)
+ 
+        # load just a learner model
+        predictor_pipeline = Pipeline()
+        predictor_pipeline.load_model(training_pipeline.predictor_model)
+        # see the order of Feature.* columns that get passed to learner algo
+        #print(predictor_pipeline.get_schema())
+
+        # use just a learner model on csr_matrix featurized data
+        print(predictor_pipeline.predict_proba(sparse_featurized_data.astype(np.float32)))
+        # TBD assert
+
+if __name__ == '__main__':
+    unittest.main()
+

From 08d304972e19bdcfb7894330035e7670105e4582 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Fri, 4 Oct 2019 23:42:55 -0700
Subject: [PATCH 03/15] rollback

---
 src/python/tests/test_copyright.py | 81 ++++++++++++++++--------------
 1 file changed, 43 insertions(+), 38 deletions(-)

diff --git a/src/python/tests/test_copyright.py b/src/python/tests/test_copyright.py
index 03984b61..87dc0830 100644
--- a/src/python/tests/test_copyright.py
+++ b/src/python/tests/test_copyright.py
@@ -1,43 +1,48 @@
-import numpy as np
-import pandas as pd
-from nimbusml import Pipeline, FileDataStream
-from nimbusml.datasets import get_dataset
-from nimbusml.feature_extraction.categorical import OneHotVectorizer
-from nimbusml.linear_model import LogisticRegressionBinaryClassifier
-from nimbusml.preprocessing import DatasetTransformer
-from nimbusml.preprocessing.schema import PrefixColumnConcatenator
-from nimbusml.preprocessing.schema import ColumnDropper
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# -------------------------------------------------------------------------
+import os
+import unittest
 
-path = get_dataset('infert').as_filepath()
-data = FileDataStream.read_csv(path)
- 
-# train featurizer
-featurization_pipeline = Pipeline([OneHotVectorizer(columns={'education': 'education'})])
-featurization_pipeline.fit(data)
-print(featurization_pipeline.get_schema())
 
-# need to remove extra columns from csr matrix 
-# and get csr_matrix featurized data
-csr_featurization_pipeline = Pipeline([DatasetTransformer(featurization_pipeline.model), ColumnDropper() << ['case', 'row_num']])
-sparse_featurized_data = csr_featurization_pipeline.fit_transform(data, as_csr=True)
-# Note: the relative order of all columns is still the same.
-print(csr_featurization_pipeline.get_schema())
+class TestCopyright(unittest.TestCase):
+    """
+    Tests that the copyright is present in everyfile.
+    """
 
-concat_pipeline = Pipeline([DatasetTransformer(csr_featurization_pipeline.model), PrefixColumnConcatenator({'education': 'education.'})])
-concat_pipeline.fit(data)
-print(concat_pipeline.get_schema())
+    def test_copyrigth_present(self):
+        root = os.path.join(os.path.abspath(os.path.dirname(__file__)),
+                            '..')
+        root = os.path.normpath(root)
+        allfiles = []
+        for r, dirs, files in os.walk(root):
+            for name in files:
+                if name.endswith('.py'):
+                    allfiles.append(os.path.join(r, name))
 
-# train a featurizer + learner pipeline
-# Note! order of feature columns on input to learner should be the same as in csr_matrix above
-#feature_cols = ['parity', 'education', 'age', 'induced', 'spontaneous', 'stratum', 'pooled.stratum'] # 9 features
-feature_cols = csr_featurization_pipeline.get_schema()
-training_pipeline = Pipeline([DatasetTransformer(featurization_pipeline.model), LogisticRegressionBinaryClassifier(feature=feature_cols, label='case')])
-training_pipeline.fit(data, output_predictor_model=True)
- 
-# load just a learner model
-predictor_pipeline = Pipeline()
-predictor_pipeline.load_model(training_pipeline.predictor_model)
-print(predictor_pipeline.get_schema())
+        nothere = []
+        nb = 0
+        for name in allfiles:
+            if 'examples' in name or 'docs' in name or \
+                    '__init__.py' in name or 'entrypoints' in name:
+                continue
+            with open(name, "r") as f:
+                content = f.read()
+            if 'Copyright (c) Microsoft Corporation. All rights ' \
+               'reserved.' not in content:
+                nothere.append(name)
+            else:
+                nb += 1
 
-# use just a learner model on csr_matrix featurized data
-predictor_pipeline.predict_proba(sparse_featurized_data.astype(np.float32))
\ No newline at end of file
+        if len(nothere) > 0:
+            nothere = ['  File "{0}", line 1'.format(_) for _ in nothere]
+            raise Exception(
+                "Copyright not found in\n{0}".format(
+                    "\n".join(nothere)))
+        if nb == 0:
+            raise Exception("No file found in '{0}'".format(root))
+
+
+if __name__ == '__main__':
+    unittest.main()

From b90d1145ae9e8064019fb7955b3ccbdb301b891d Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Mon, 7 Oct 2019 14:25:49 -0700
Subject: [PATCH 04/15] new entrypoint

---
 src/DotNetBridge/Entrypoints.cs               |  3 +-
 src/python/nimbusml.pyproj                    |  1 +
 .../entrypoints/transforms_csrscorer.py       | 68 +++++++++++++++++++
 .../entrypoints/transforms_datasetscorer.py   |  2 +-
 src/python/nimbusml/pipeline.py               | 15 ++++
 5 files changed, 86 insertions(+), 3 deletions(-)
 create mode 100644 src/python/nimbusml/internal/entrypoints/transforms_csrscorer.py

diff --git a/src/DotNetBridge/Entrypoints.cs b/src/DotNetBridge/Entrypoints.cs
index d2ca45b8..7c53569f 100644
--- a/src/DotNetBridge/Entrypoints.cs
+++ b/src/DotNetBridge/Entrypoints.cs
@@ -109,7 +109,7 @@ public sealed class Output
             public TransformModel ScoringTransform;
         }
 
-        [TlcModule.EntryPoint(Name = "Transforms.DatasetScorer1", Desc = "Score a dataset with a predictor model")]
+        [TlcModule.EntryPoint(Name = "Transforms.CsrScorer", Desc = "Score a csr_matrix based dataset with a predictor model")]
         public static Output Score(IHostEnvironment env, Input input)
         {
             Contracts.CheckValue(env, nameof(env));
@@ -118,7 +118,6 @@ public static Output Score(IHostEnvironment env, Input input)
             EntryPointUtils.CheckInputArgs(host, input);
 
             var inputData = input.Data;
-            // input.PredictorModel.PrepareData(host, inputData, out RoleMappedData data, out IPredictor predictor);
             IPredictor predictor = input.PredictorModel.Predictor;
             var data = new RoleMappedData(input.Data, null, input.Data.Schema[0].Name);
 
diff --git a/src/python/nimbusml.pyproj b/src/python/nimbusml.pyproj
index 7f4e4d6d..bafc90ef 100644
--- a/src/python/nimbusml.pyproj
+++ b/src/python/nimbusml.pyproj
@@ -394,6 +394,7 @@
     <Compile Include="nimbusml\internal\entrypoints\transforms_categoricalhashonehotvectorizer.py" />
     <Compile Include="nimbusml\internal\entrypoints\transforms_categoricalonehotvectorizer.py" />
     <Compile Include="nimbusml\internal\entrypoints\transforms_charactertokenizer.py" />
+    <Compile Include="nimbusml\internal\entrypoints\transforms_csrscorer.py" />
     <Compile Include="nimbusml\internal\entrypoints\transforms_prefixcolumnconcatenator.py" />
     <Compile Include="nimbusml\internal\entrypoints\transforms_columnconcatenator.py" />
     <Compile Include="nimbusml\internal\entrypoints\transforms_columncopier.py" />
diff --git a/src/python/nimbusml/internal/entrypoints/transforms_csrscorer.py b/src/python/nimbusml/internal/entrypoints/transforms_csrscorer.py
new file mode 100644
index 00000000..50f21e7b
--- /dev/null
+++ b/src/python/nimbusml/internal/entrypoints/transforms_csrscorer.py
@@ -0,0 +1,68 @@
+"""
+Transforms.CsrScorer
+"""
+
+
+from ..utils.entrypoints import EntryPoint
+from ..utils.utils import try_set, unlist
+
+
+def transforms_csrscorer(
+        data,
+        predictor_model,
+        scored_data=None,
+        scoring_transform=None,
+        suffix=None,
+        **params):
+    """
+    **Description**
+        Score a dataset with a predictor model
+
+    :param data: The dataset to be scored (inputs).
+    :param predictor_model: The predictor model to apply to data
+        (inputs).
+    :param suffix: Suffix to append to the score columns (inputs).
+    :param scored_data: The scored dataset (outputs).
+    :param scoring_transform: The scoring transform (outputs).
+    """
+
+    entrypoint_name = 'Transforms.CsrScorer'
+    inputs = {}
+    outputs = {}
+
+    if data is not None:
+        inputs['Data'] = try_set(
+            obj=data,
+            none_acceptable=False,
+            is_of_type=str)
+    if predictor_model is not None:
+        inputs['PredictorModel'] = try_set(
+            obj=predictor_model,
+            none_acceptable=False,
+            is_of_type=str)
+    if suffix is not None:
+        inputs['Suffix'] = try_set(
+            obj=suffix,
+            none_acceptable=True,
+            is_of_type=str)
+    if scored_data is not None:
+        outputs['ScoredData'] = try_set(
+            obj=scored_data,
+            none_acceptable=False,
+            is_of_type=str)
+    if scoring_transform is not None:
+        outputs['ScoringTransform'] = try_set(
+            obj=scoring_transform, none_acceptable=False, is_of_type=str)
+
+    input_variables = {
+        x for x in unlist(inputs.values())
+        if isinstance(x, str) and x.startswith("$")}
+    output_variables = {
+        x for x in unlist(outputs.values())
+        if isinstance(x, str) and x.startswith("$")}
+
+    entrypoint = EntryPoint(
+        name=entrypoint_name, inputs=inputs, outputs=outputs,
+        input_variables=input_variables,
+        output_variables=output_variables)
+    return entrypoint
diff --git a/src/python/nimbusml/internal/entrypoints/transforms_datasetscorer.py b/src/python/nimbusml/internal/entrypoints/transforms_datasetscorer.py
index 983b3abd..2aa14c74 100644
--- a/src/python/nimbusml/internal/entrypoints/transforms_datasetscorer.py
+++ b/src/python/nimbusml/internal/entrypoints/transforms_datasetscorer.py
@@ -27,7 +27,7 @@ def transforms_datasetscorer(
     :param scoring_transform: The scoring transform (outputs).
     """
 
-    entrypoint_name = 'Transforms.DatasetScorer1'
+    entrypoint_name = 'Transforms.DatasetScorer'
     inputs = {}
     outputs = {}
 
diff --git a/src/python/nimbusml/pipeline.py b/src/python/nimbusml/pipeline.py
index b3be72f8..0d873c66 100644
--- a/src/python/nimbusml/pipeline.py
+++ b/src/python/nimbusml/pipeline.py
@@ -42,6 +42,8 @@
 from .internal.entrypoints.models_schema import models_schema
 from .internal.entrypoints.transforms_datasetscorer import \
     transforms_datasetscorer
+from .internal.entrypoints.transforms_csrscorer import \
+    transforms_csrscorer
 from .internal.entrypoints.transforms_datasettransformscorer import \
     transforms_datasettransformscorer
 from .internal.entrypoints.transforms_featurecombiner import \
@@ -1771,6 +1773,12 @@ def get_feature_contributions(self, X, top=10, bottom=10, verbose=0,
             data="$data",
             predictor_model="$predictor_model",
             scored_data="$scoredvectordata")
+        
+        if isinstance(X, csr_matrix):
+            score_node = transforms_csrscorer(
+                data="$data",
+                predictor_model="$predictor_model",
+                scored_data="$scoredvectordata")
 
         fcc_node = transforms_featurecontributioncalculationtransformer(
             data="$scoredvectordata",
@@ -1926,6 +1934,13 @@ def _predict(self, X, y=None,
                 data="$data",
                 predictor_model="$predictor_model",
                 scored_data="$scoredVectorData")
+
+            if isinstance(X, csr_matrix):
+                score_node = transforms_csrscorer(
+                    data="$data",
+                    predictor_model="$predictor_model",
+                    scored_data="$scoredVectorData")
+
             all_nodes.extend([score_node])
 
         if (evaltype in ['binary', 'multiclass']) or \

From 27e066c87576ca72b3ba8e7d0eecb77b04f9ade7 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Mon, 7 Oct 2019 14:39:10 -0700
Subject: [PATCH 05/15] add assert

---
 src/python/nimbusml/tests/pipeline/test_csr_input.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/python/nimbusml/tests/pipeline/test_csr_input.py b/src/python/nimbusml/tests/pipeline/test_csr_input.py
index f54f1362..51069087 100644
--- a/src/python/nimbusml/tests/pipeline/test_csr_input.py
+++ b/src/python/nimbusml/tests/pipeline/test_csr_input.py
@@ -14,6 +14,7 @@
 from nimbusml.preprocessing import DatasetTransformer
 from nimbusml.preprocessing.schema import PrefixColumnConcatenator
 from nimbusml.preprocessing.schema import ColumnDropper
+from numpy.testing import assert_equal
 
 class TestCsrInput(unittest.TestCase):
 
@@ -51,8 +52,11 @@ def test_predict_proba_on_csr(self):
         #print(predictor_pipeline.get_schema())
 
         # use just a learner model on csr_matrix featurized data
-        print(predictor_pipeline.predict_proba(sparse_featurized_data.astype(np.float32)))
-        # TBD assert
+        predictions = predictor_pipeline.predict_proba(sparse_featurized_data.astype(np.float32))
+        assert_equal(len(predictions), 248)
+        assert_equal(len(predictions[0]), 2)
+
+
 
 if __name__ == '__main__':
     unittest.main()

From 6c5d6f4559df70faef8ff69d187152db25f50dbd Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Mon, 7 Oct 2019 14:40:20 -0700
Subject: [PATCH 06/15] rollback

---
 src/python/tools/entrypoint_compiler.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/src/python/tools/entrypoint_compiler.py b/src/python/tools/entrypoint_compiler.py
index 1d79151a..ed829533 100644
--- a/src/python/tools/entrypoint_compiler.py
+++ b/src/python/tools/entrypoint_compiler.py
@@ -2041,11 +2041,6 @@ def generate_code(pkg_path, generate_entrypoints, generate_api):
     script_args = arg_parser.parse_args()
     pkg_path = os.path.join(my_dir, r'..\nimbusml')
 
-
-    script_args.generate_api = False
-    script_args.generate_entrypoints = False
-    script_args.check_manual_changes = False
-
     if script_args.check_manual_changes:
         verbose = False
         if script_args.folder == 'temp':

From f936e9f33afdbe1a3f7888ed5d650fb75ef1b7a4 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Mon, 7 Oct 2019 14:42:15 -0700
Subject: [PATCH 07/15] no print in test

---
 src/python/nimbusml/tests/pipeline/test_csr_input.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/python/nimbusml/tests/pipeline/test_csr_input.py b/src/python/nimbusml/tests/pipeline/test_csr_input.py
index 51069087..fd04a3c9 100644
--- a/src/python/nimbusml/tests/pipeline/test_csr_input.py
+++ b/src/python/nimbusml/tests/pipeline/test_csr_input.py
@@ -40,7 +40,7 @@ def test_predict_proba_on_csr(self):
         cols.remove('row_num')
         cols.remove('case')
         feature_cols = cols
-        print(feature_cols)
+        #print(feature_cols)
         #['education', 'age', 'parity', 'induced', 'spontaneous', 'stratum', 'pooled.stratum']
         training_pipeline = Pipeline([DatasetTransformer(featurization_pipeline.model), LogisticRegressionBinaryClassifier(feature=feature_cols, label='case')])
         training_pipeline.fit(data, output_predictor_model=True)

From 5ae56a65cee655733f7b930343deb0aac70091fd Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Mon, 7 Oct 2019 14:43:11 -0700
Subject: [PATCH 08/15] up version

---
 src/python/nimbusml/__init__.py | 2 +-
 src/python/setup.py             | 2 +-
 version.txt                     | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/python/nimbusml/__init__.py b/src/python/nimbusml/__init__.py
index 4e18a65b..0b508fcf 100644
--- a/src/python/nimbusml/__init__.py
+++ b/src/python/nimbusml/__init__.py
@@ -2,7 +2,7 @@
 Microsoft Machine Learning for Python
 """
 
-__version__ = '1.4.2'
+__version__ = '1.5.0'
 
 # CoreCLR version of MicrosoftML is built on Windows.
 # But file permissions are not preserved when it's copied to Linux.
diff --git a/src/python/setup.py b/src/python/setup.py
index 7b983db8..fc350275 100644
--- a/src/python/setup.py
+++ b/src/python/setup.py
@@ -45,7 +45,7 @@
     # Versions should comply with PEP440.  For a discussion on
     # single-sourcing the version across setup.py and the project code, see
     # https://packaging.python.org/en/latest/single_source_version.html
-    version='1.4.2',
+    version='1.5.0',
 
     description='NimbusML',
     long_description=long_description,
diff --git a/version.txt b/version.txt
index c9929e36..3e1ad720 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-1.4.2
\ No newline at end of file
+1.5.0
\ No newline at end of file

From dd72cc49922e7d9f9ee0a3aecaf00fe8334eb167 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Mon, 7 Oct 2019 15:37:56 -0700
Subject: [PATCH 09/15] only Single type is allowed for Feature vector

---
 src/python/nimbusml/pipeline.py                      | 2 ++
 src/python/nimbusml/tests/pipeline/test_csr_input.py | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/python/nimbusml/pipeline.py b/src/python/nimbusml/pipeline.py
index 0d873c66..468b8c14 100644
--- a/src/python/nimbusml/pipeline.py
+++ b/src/python/nimbusml/pipeline.py
@@ -1775,6 +1775,7 @@ def get_feature_contributions(self, X, top=10, bottom=10, verbose=0,
             scored_data="$scoredvectordata")
         
         if isinstance(X, csr_matrix):
+            X = X.astype(np.float32)
             score_node = transforms_csrscorer(
                 data="$data",
                 predictor_model="$predictor_model",
@@ -1936,6 +1937,7 @@ def _predict(self, X, y=None,
                 scored_data="$scoredVectorData")
 
             if isinstance(X, csr_matrix):
+                X = X.astype(np.float32)
                 score_node = transforms_csrscorer(
                     data="$data",
                     predictor_model="$predictor_model",
diff --git a/src/python/nimbusml/tests/pipeline/test_csr_input.py b/src/python/nimbusml/tests/pipeline/test_csr_input.py
index fd04a3c9..2a2f3bd2 100644
--- a/src/python/nimbusml/tests/pipeline/test_csr_input.py
+++ b/src/python/nimbusml/tests/pipeline/test_csr_input.py
@@ -52,7 +52,7 @@ def test_predict_proba_on_csr(self):
         #print(predictor_pipeline.get_schema())
 
         # use just a learner model on csr_matrix featurized data
-        predictions = predictor_pipeline.predict_proba(sparse_featurized_data.astype(np.float32))
+        predictions = predictor_pipeline.predict_proba(sparse_featurized_data)
         assert_equal(len(predictions), 248)
         assert_equal(len(predictions[0]), 2)
 

From c3ffe3c7a78a73a3da936501725eba4d6bf1b2e8 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Tue, 8 Oct 2019 15:12:35 -0700
Subject: [PATCH 10/15] fix comments, rename entrypoint

---
 src/DotNetBridge/Entrypoints.cs               | 25 +++++++++++++------
 src/python/nimbusml.pyproj                    |  2 +-
 ...corer.py => transforms_datasetscorerex.py} |  6 ++---
 src/python/nimbusml/pipeline.py               | 25 +++----------------
 4 files changed, 25 insertions(+), 33 deletions(-)
 rename src/python/nimbusml/internal/entrypoints/{transforms_csrscorer.py => transforms_datasetscorerex.py} (94%)

diff --git a/src/DotNetBridge/Entrypoints.cs b/src/DotNetBridge/Entrypoints.cs
index 7c53569f..c57d1950 100644
--- a/src/DotNetBridge/Entrypoints.cs
+++ b/src/DotNetBridge/Entrypoints.cs
@@ -65,7 +65,7 @@ public sealed class ModelSchemaOutput
             public IDataView Schema;
         }
 
-        [TlcModule.EntryPoint(Name = "Models.Schema", Desc = "Retrieve input and output model schemas")]
+        [TlcModule.EntryPoint(Name = "Models.Schema", Desc = "Retrieve output model schema")]
         public static ModelSchemaOutput GetSchema(IHostEnvironment env, TransformModelInput input)
         {
             Contracts.CheckValue(env, nameof(env));
@@ -88,7 +88,7 @@ public static CommonOutputs.TransformOutput CreateVariableColumn(IHostEnvironmen
             return new CommonOutputs.TransformOutput { Model = new TransformModelImpl(env, xf, inputOptions.Data), OutputData = xf };
         }
 
-        public sealed class Input
+        public sealed class ScoringTransformInput
         {
             [Argument(ArgumentType.Required, HelpText = "The dataset to be scored", SortOrder = 1)]
             public IDataView Data;
@@ -100,7 +100,7 @@ public sealed class Input
             public string Suffix;
         }
 
-        public sealed class Output
+        public sealed class ScoringTransformOutput
         {
             [TlcModule.Output(Desc = "The scored dataset", SortOrder = 1)]
             public IDataView ScoredData;
@@ -109,8 +109,8 @@ public sealed class Output
             public TransformModel ScoringTransform;
         }
 
-        [TlcModule.EntryPoint(Name = "Transforms.CsrScorer", Desc = "Score a csr_matrix based dataset with a predictor model")]
-        public static Output Score(IHostEnvironment env, Input input)
+        [TlcModule.EntryPoint(Name = "Transforms.DatasetScorerEx", Desc = "Score a dataset with a predictor model")]
+        public static ScoringTransformOutput Score(IHostEnvironment env, ScoringTransformInput input)
         {
             Contracts.CheckValue(env, nameof(env));
             var host = env.Register("ScoreModel");
@@ -118,8 +118,17 @@ public static Output Score(IHostEnvironment env, Input input)
             EntryPointUtils.CheckInputArgs(host, input);
 
             var inputData = input.Data;
-            IPredictor predictor = input.PredictorModel.Predictor;
-            var data = new RoleMappedData(input.Data, null, input.Data.Schema[0].Name);
+            RoleMappedData data;
+            IPredictor predictor;
+            try
+            {
+                input.PredictorModel.PrepareData(host, inputData, out data, out predictor);
+            }
+            catch (Exception)
+            {
+                predictor = input.PredictorModel.Predictor;
+                data = new RoleMappedData(input.Data, null, input.Data.Schema[0].Name);
+            }
 
             IDataView scoredPipe;
             using (var ch = host.Start("Creating scoring pipeline"))
@@ -134,7 +143,7 @@ public static Output Score(IHostEnvironment env, Input input)
             }
 
             return
-                new Output
+                new ScoringTransformOutput
                 {
                     ScoredData = scoredPipe,
                     ScoringTransform = new TransformModelImpl(host, scoredPipe, inputData)
diff --git a/src/python/nimbusml.pyproj b/src/python/nimbusml.pyproj
index bafc90ef..7da1cc62 100644
--- a/src/python/nimbusml.pyproj
+++ b/src/python/nimbusml.pyproj
@@ -394,7 +394,7 @@
     <Compile Include="nimbusml\internal\entrypoints\transforms_categoricalhashonehotvectorizer.py" />
     <Compile Include="nimbusml\internal\entrypoints\transforms_categoricalonehotvectorizer.py" />
     <Compile Include="nimbusml\internal\entrypoints\transforms_charactertokenizer.py" />
-    <Compile Include="nimbusml\internal\entrypoints\transforms_csrscorer.py" />
+    <Compile Include="nimbusml\internal\entrypoints\transforms_datasetscorerex.py" />
     <Compile Include="nimbusml\internal\entrypoints\transforms_prefixcolumnconcatenator.py" />
     <Compile Include="nimbusml\internal\entrypoints\transforms_columnconcatenator.py" />
     <Compile Include="nimbusml\internal\entrypoints\transforms_columncopier.py" />
diff --git a/src/python/nimbusml/internal/entrypoints/transforms_csrscorer.py b/src/python/nimbusml/internal/entrypoints/transforms_datasetscorerex.py
similarity index 94%
rename from src/python/nimbusml/internal/entrypoints/transforms_csrscorer.py
rename to src/python/nimbusml/internal/entrypoints/transforms_datasetscorerex.py
index 50f21e7b..7a5d8c71 100644
--- a/src/python/nimbusml/internal/entrypoints/transforms_csrscorer.py
+++ b/src/python/nimbusml/internal/entrypoints/transforms_datasetscorerex.py
@@ -1,5 +1,5 @@
 """
-Transforms.CsrScorer
+Transforms.DatasetScorerEx
 """
 
 
@@ -7,7 +7,7 @@
 from ..utils.utils import try_set, unlist
 
 
-def transforms_csrscorer(
+def transforms_datasetscorerex(
         data,
         predictor_model,
         scored_data=None,
@@ -26,7 +26,7 @@ def transforms_csrscorer(
     :param scoring_transform: The scoring transform (outputs).
     """
 
-    entrypoint_name = 'Transforms.CsrScorer'
+    entrypoint_name = 'Transforms.DatasetScorerEx'
     inputs = {}
     outputs = {}
 
diff --git a/src/python/nimbusml/pipeline.py b/src/python/nimbusml/pipeline.py
index 468b8c14..da57265a 100644
--- a/src/python/nimbusml/pipeline.py
+++ b/src/python/nimbusml/pipeline.py
@@ -40,10 +40,8 @@
     models_regressionevaluator
 from .internal.entrypoints.models_summarizer import models_summarizer
 from .internal.entrypoints.models_schema import models_schema
-from .internal.entrypoints.transforms_datasetscorer import \
-    transforms_datasetscorer
-from .internal.entrypoints.transforms_csrscorer import \
-    transforms_csrscorer
+from .internal.entrypoints.transforms_datasetscorerex import \
+    transforms_datasetscorerex
 from .internal.entrypoints.transforms_datasettransformscorer import \
     transforms_datasettransformscorer
 from .internal.entrypoints.transforms_featurecombiner import \
@@ -1769,17 +1767,10 @@ def get_feature_contributions(self, X, top=10, bottom=10, verbose=0,
             all_nodes = [importtext_node]
             inputs = dict([('file', ''), ('predictor_model', self.model)])
 
-        score_node = transforms_datasetscorer(
+        score_node = transforms_datasetscorerex(
             data="$data",
             predictor_model="$predictor_model",
             scored_data="$scoredvectordata")
-        
-        if isinstance(X, csr_matrix):
-            X = X.astype(np.float32)
-            score_node = transforms_csrscorer(
-                data="$data",
-                predictor_model="$predictor_model",
-                scored_data="$scoredvectordata")
 
         fcc_node = transforms_featurecontributioncalculationtransformer(
             data="$scoredvectordata",
@@ -1931,18 +1922,10 @@ def _predict(self, X, y=None,
                 all_nodes = [importtext_node]
                 inputs = dict([('file', ''), ('predictor_model', self.model)])
 
-            score_node = transforms_datasetscorer(
+            score_node = transforms_datasetscorerex(
                 data="$data",
                 predictor_model="$predictor_model",
                 scored_data="$scoredVectorData")
-
-            if isinstance(X, csr_matrix):
-                X = X.astype(np.float32)
-                score_node = transforms_csrscorer(
-                    data="$data",
-                    predictor_model="$predictor_model",
-                    scored_data="$scoredVectorData")
-
             all_nodes.extend([score_node])
 
         if (evaltype in ['binary', 'multiclass']) or \

From 840d97628965be6ebeaa773eebb700997d61c480 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Tue, 8 Oct 2019 15:33:39 -0700
Subject: [PATCH 11/15] convert to single

---
 src/DotNetBridge/Entrypoints.cs | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/DotNetBridge/Entrypoints.cs b/src/DotNetBridge/Entrypoints.cs
index c57d1950..70a1ceef 100644
--- a/src/DotNetBridge/Entrypoints.cs
+++ b/src/DotNetBridge/Entrypoints.cs
@@ -126,8 +126,11 @@ public static ScoringTransformOutput Score(IHostEnvironment env, ScoringTransfor
             }
             catch (Exception)
             {
+                var inputColumnName = inputData.Schema[0].Name;
                 predictor = input.PredictorModel.Predictor;
-                data = new RoleMappedData(input.Data, null, input.Data.Schema[0].Name);
+                var xf = new TypeConvertingTransformer(host, 
+                    new TypeConvertingEstimator.ColumnOptions(inputColumnName, DataKind.Single, inputColumnName)).Transform(inputData);
+                data = new RoleMappedData(xf, null, inputColumnName);
             }
 
             IDataView scoredPipe;

From c34b653f148ce1abe4a2482d7685075a6c9a0d75 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Tue, 8 Oct 2019 17:09:36 -0700
Subject: [PATCH 12/15] fix type

---
 src/DotNetBridge/Entrypoints.cs | 33 ++++++++++++++++++++++++++++-----
 1 file changed, 28 insertions(+), 5 deletions(-)

diff --git a/src/DotNetBridge/Entrypoints.cs b/src/DotNetBridge/Entrypoints.cs
index 70a1ceef..51795579 100644
--- a/src/DotNetBridge/Entrypoints.cs
+++ b/src/DotNetBridge/Entrypoints.cs
@@ -109,6 +109,24 @@ public sealed class ScoringTransformOutput
             public TransformModel ScoringTransform;
         }
 
+        private static bool AreSchemasCompatible(DataViewSchema schema1, DataViewSchema schema2)
+        {
+            if (schema1 == null)
+                return schema2 == null;
+            if (schema2 == null)
+                return schema1 == null;
+            if (schema1.Count != schema2.Count)
+                return false;
+
+            for (int i = 0; i < schema1.Count; i++)
+            {
+                if(schema1[i].Type != schema2[i].Type)
+                    return false;
+            }
+
+            return true;
+        }
+
         [TlcModule.EntryPoint(Name = "Transforms.DatasetScorerEx", Desc = "Score a dataset with a predictor model")]
         public static ScoringTransformOutput Score(IHostEnvironment env, ScoringTransformInput input)
         {
@@ -117,19 +135,24 @@ public static ScoringTransformOutput Score(IHostEnvironment env, ScoringTransfor
             host.CheckValue(input, nameof(input));
             EntryPointUtils.CheckInputArgs(host, input);
 
-            var inputData = input.Data;
             RoleMappedData data;
             IPredictor predictor;
-            try
+            var inputData = input.Data;
+            if (AreSchemasCompatible(inputData.Schema, input.PredictorModel.TransformModel.InputSchema))
             {
                 input.PredictorModel.PrepareData(host, inputData, out data, out predictor);
             }
-            catch (Exception)
+            else // use only trainer model.
             {
+                // feature vector provided only.
+                host.Assert(inputData.Schema.Count == 1);
                 var inputColumnName = inputData.Schema[0].Name;
+                var trainingSchema = input.PredictorModel.GetTrainingSchema(host);
+                // get feature vector item type.
+                var requiredType = ((DataViewSchema.Column)trainingSchema.Feature).Type.GetItemType().RawType;
                 predictor = input.PredictorModel.Predictor;
-                var xf = new TypeConvertingTransformer(host, 
-                    new TypeConvertingEstimator.ColumnOptions(inputColumnName, DataKind.Single, inputColumnName)).Transform(inputData);
+                var xf = new TypeConvertingTransformer(host,
+                    new TypeConvertingEstimator.ColumnOptions(inputColumnName, requiredType, inputColumnName)).Transform(inputData);
                 data = new RoleMappedData(xf, null, inputColumnName);
             }
 

From 38e8f7a98118a7d3a3f48970d6325d94b9f975fd Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Tue, 8 Oct 2019 23:15:11 -0700
Subject: [PATCH 13/15] add feature contribution test

---
 src/DotNetBridge/Entrypoints.cs                      | 8 +++++---
 src/python/nimbusml/tests/pipeline/test_csr_input.py | 4 +++-
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/DotNetBridge/Entrypoints.cs b/src/DotNetBridge/Entrypoints.cs
index 51795579..c08cf096 100644
--- a/src/DotNetBridge/Entrypoints.cs
+++ b/src/DotNetBridge/Entrypoints.cs
@@ -149,11 +149,13 @@ public static ScoringTransformOutput Score(IHostEnvironment env, ScoringTransfor
                 var inputColumnName = inputData.Schema[0].Name;
                 var trainingSchema = input.PredictorModel.GetTrainingSchema(host);
                 // get feature vector item type.
-                var requiredType = ((DataViewSchema.Column)trainingSchema.Feature).Type.GetItemType().RawType;
+                var trainingFeatureColumn = (DataViewSchema.Column)trainingSchema.Feature;
+                var requiredType = trainingFeatureColumn.Type.GetItemType().RawType;
+                var featuresColumnName = trainingFeatureColumn.Name;
                 predictor = input.PredictorModel.Predictor;
                 var xf = new TypeConvertingTransformer(host,
-                    new TypeConvertingEstimator.ColumnOptions(inputColumnName, requiredType, inputColumnName)).Transform(inputData);
-                data = new RoleMappedData(xf, null, inputColumnName);
+                    new TypeConvertingEstimator.ColumnOptions(featuresColumnName, requiredType, inputColumnName)).Transform(inputData);
+                data = new RoleMappedData(xf, null, featuresColumnName);
             }
 
             IDataView scoredPipe;
diff --git a/src/python/nimbusml/tests/pipeline/test_csr_input.py b/src/python/nimbusml/tests/pipeline/test_csr_input.py
index 2a2f3bd2..821107e0 100644
--- a/src/python/nimbusml/tests/pipeline/test_csr_input.py
+++ b/src/python/nimbusml/tests/pipeline/test_csr_input.py
@@ -56,7 +56,9 @@ def test_predict_proba_on_csr(self):
         assert_equal(len(predictions), 248)
         assert_equal(len(predictions[0]), 2)
 
-
+        # get feature contributions
+        fcc = predictor_pipeline.get_feature_contributions(sparse_featurized_data)
+        assert_equal(fcc.shape, (248,30))
 
 if __name__ == '__main__':
     unittest.main()

From 29a954498ce53a4ecc79501677e04fde32b6594b Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Tue, 8 Oct 2019 23:20:26 -0700
Subject: [PATCH 14/15] rename pipeline.get_schema() to
 pipeline.gat_output_columns()

---
 src/python/nimbusml/examples/Schema.py                      | 2 +-
 src/python/nimbusml/pipeline.py                             | 2 +-
 src/python/nimbusml/tests/pipeline/test_csr_input.py        | 6 +++---
 .../nimbusml/tests/pipeline/test_pipeline_get_schema.py     | 6 +++---
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/python/nimbusml/examples/Schema.py b/src/python/nimbusml/examples/Schema.py
index ddec3c0d..c0b8d493 100644
--- a/src/python/nimbusml/examples/Schema.py
+++ b/src/python/nimbusml/examples/Schema.py
@@ -27,7 +27,7 @@
 ])
 
 pipe.fit(data)
-schema = pipe.get_schema()
+schema = pipe.get_output_columns()
 
 print(schema[0:5])
 # ['Sentiment', 'SentimentText', 'features.Char.<?>|=|=', 'features.Char.=|=|r', 'features.Char.=|r|u']
diff --git a/src/python/nimbusml/pipeline.py b/src/python/nimbusml/pipeline.py
index 65240272..b60bc9fb 100644
--- a/src/python/nimbusml/pipeline.py
+++ b/src/python/nimbusml/pipeline.py
@@ -1812,7 +1812,7 @@ def get_feature_contributions(self, X, top=10, bottom=10, verbose=0,
 
         return out_data
 
-    def get_schema(self, verbose=0, **params):
+    def get_output_columns(self, verbose=0, **params):
         """
         Returns the output list of columns for the fitted model.
         :return: list .
diff --git a/src/python/nimbusml/tests/pipeline/test_csr_input.py b/src/python/nimbusml/tests/pipeline/test_csr_input.py
index 821107e0..176a7651 100644
--- a/src/python/nimbusml/tests/pipeline/test_csr_input.py
+++ b/src/python/nimbusml/tests/pipeline/test_csr_input.py
@@ -27,13 +27,13 @@ def test_predict_proba_on_csr(self):
         featurization_pipeline = Pipeline([OneHotVectorizer(columns={'education': 'education'})])
         featurization_pipeline.fit(data)
         # Note: the relative order of all columns is still the same as in raw data.
-        #print(featurization_pipeline.get_schema())
+        #print(featurization_pipeline.get_output_columns())
 
         # need to remove extra columns before getting csr_matrix featurized data as it wont have column name information.
         csr_featurization_pipeline = Pipeline([DatasetTransformer(featurization_pipeline.model), ColumnDropper() << ['case', 'row_num']])
         sparse_featurized_data = csr_featurization_pipeline.fit_transform(data, as_csr=True)
         # Note: the relative order of all columns is still the same.
-        #print(csr_featurization_pipeline.get_schema())
+        #print(csr_featurization_pipeline.get_output_columns())
 
         # train learner
         # Note: order & number of feature columns for learner (parameter 'feature') should be the same as in csr_matrix above
@@ -49,7 +49,7 @@ def test_predict_proba_on_csr(self):
         predictor_pipeline = Pipeline()
         predictor_pipeline.load_model(training_pipeline.predictor_model)
         # see the order of Feature.* columns that get passed to learner algo
-        #print(predictor_pipeline.get_schema())
+        #print(predictor_pipeline.get_output_columns())
 
         # use just a learner model on csr_matrix featurized data
         predictions = predictor_pipeline.predict_proba(sparse_featurized_data)
diff --git a/src/python/nimbusml/tests/pipeline/test_pipeline_get_schema.py b/src/python/nimbusml/tests/pipeline/test_pipeline_get_schema.py
index 5eccf4d7..63bb5310 100644
--- a/src/python/nimbusml/tests/pipeline/test_pipeline_get_schema.py
+++ b/src/python/nimbusml/tests/pipeline/test_pipeline_get_schema.py
@@ -28,7 +28,7 @@ def test_get_schema_returns_correct_value_for_single_valued_columns(self):
         pipeline.fit(df)
         df = pipeline.transform(df)
 
-        schema = pipeline.get_schema()
+        schema = pipeline.get_output_columns()
 
         self.assertTrue('c1' in schema)
         self.assertTrue('c2' in schema)
@@ -39,7 +39,7 @@ def test_get_schema_returns_correct_value_for_vector_valued_columns(self):
         pipeline = Pipeline([OneHotVectorizer() << 'c0'])
         pipeline.fit(train_df)
 
-        schema = pipeline.get_schema()
+        schema = pipeline.get_output_columns()
 
         self.assertTrue('c0.a' in schema)
         self.assertTrue('c0.b' in schema)
@@ -55,7 +55,7 @@ def test_get_schema_does_not_work_when_predictor_is_part_of_model(self):
         pipeline.fit(df)
 
         try:
-            schema = pipeline.get_schema()
+            schema = pipeline.get_output_columns()
         except Exception as e:
             pass
         else:

From 8836bafe63e784291c919f59c215c6185f7446d9 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Tue, 8 Oct 2019 23:48:58 -0700
Subject: [PATCH 15/15] fix build

---
 src/DotNetBridge/Entrypoints.cs | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/DotNetBridge/Entrypoints.cs b/src/DotNetBridge/Entrypoints.cs
index c08cf096..9be84e67 100644
--- a/src/DotNetBridge/Entrypoints.cs
+++ b/src/DotNetBridge/Entrypoints.cs
@@ -138,13 +138,13 @@ public static ScoringTransformOutput Score(IHostEnvironment env, ScoringTransfor
             RoleMappedData data;
             IPredictor predictor;
             var inputData = input.Data;
-            if (AreSchemasCompatible(inputData.Schema, input.PredictorModel.TransformModel.InputSchema))
+            try
             {
                 input.PredictorModel.PrepareData(host, inputData, out data, out predictor);
             }
-            else // use only trainer model.
+            catch (Exception)
             {
-                // feature vector provided only.
+                // this can happen in csr_matrix case, try to use only trainer model.
                 host.Assert(inputData.Schema.Count == 1);
                 var inputColumnName = inputData.Schema[0].Name;
                 var trainingSchema = input.PredictorModel.GetTrainingSchema(host);