up version

build.sh

@@ -199,6 +199,7 @@ then
     cp  "${BuildOutputDir}/${__configuration}"/DotNetBridge.dll "${__currentScriptDir}/src/python/nimbusml/internal/libs/"
     cp  "${BuildOutputDir}/${__configuration}"/pybridge.so "${__currentScriptDir}/src/python/nimbusml/internal/libs/"
 
+	# ls "${BuildOutputDir}/${__configuration}/Platform/${PublishDir}"/publish/
     if [ ${PythonVersion} = 2.7 ]
     then
         cp  "${BuildOutputDir}/${__configuration}/Platform/${PublishDir}"/publish/*.dll "${__currentScriptDir}/src/python/nimbusml/internal/libs/"

build/libs_linux.txt

@@ -1,14 +1,16 @@
 Newtonsoft.Json.dll
 libCpuMathNative.so
-libFactorizationMachineNative.so
 libFastTreeNative.so
 libLdaNative.so
 libMklImports.so
+libMklProxyNative.so
 libSymSgdNative.so
 lib_lightgbm.so
 libtensorflow.so
-libtensorflow_framework.so
+libtensorflow_framework.so.1
+libonnxruntime.so
 System.Drawing.Common.dll
+TensorFlow.NET.dll
 Microsoft.DataPrep.dll
 Microsoft.DPrep.*
 Microsoft.ML.*

build/libs_mac.txt

@@ -1,14 +1,16 @@
 Newtonsoft.Json.dll
 libCpuMathNative.dylib
-libFactorizationMachineNative.dylib
 libFastTreeNative.dylib
 libLdaNative.dylib
 libMklImports.dylib
+libMklProxyNative.dylib
 libSymSgdNative.dylib
 lib_lightgbm.dylib
 libtensorflow.dylib
-libtensorflow_framework.dylib
+libonnxruntime.dylib
+libtensorflow_framework.1.dylib
 System.Drawing.Common.dll
+TensorFlow.NET.dll
 Microsoft.DataPrep.dll
 Microsoft.DPrep.*
 Microsoft.ML.*

build/libs_win.txt

@@ -1,14 +1,15 @@
 Google.Protobuf.dll
 Newtonsoft.Json.dll
 CpuMathNative.dll
-FactorizationMachineNative.dll
 FastTreeNative.dll
 LdaNative.dll
 lib_lightgbm.dll
 libiomp5md.dll
 MklImports.dll
+MklProxyNative.dll
 SymSgdNative.dll
 tensorflow.dll
+TensorFlow.NET.dll
 System.Drawing.Common.dll
 Microsoft.DataPrep.dll
 Microsoft.DPrep.*

src/DotNetBridge/DotNetBridge.csproj

@@ -31,17 +31,19 @@
       <PrivateAssets>all</PrivateAssets>
       <IncludeAssets>runtime; build; native; contentfiles; analyzers</IncludeAssets>
     </PackageReference>
-    <PackageReference Include="Microsoft.ML" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.CpuMath" Version="1.2.0" />    
-    <PackageReference Include="Microsoft.ML.EntryPoints" Version="0.14.0" />
-    <PackageReference Include="Microsoft.ML.Mkl.Components" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.Mkl.Redist" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.LightGBM" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.OnnxTransformer" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.TensorFlow" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.Ensemble" Version="0.14.0" />
-    <PackageReference Include="Microsoft.ML.TimeSeries" Version="1.2.0" />
+    <PackageReference Include="Microsoft.ML" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.CpuMath" Version="1.3.1" />    
+    <PackageReference Include="Microsoft.ML.EntryPoints" Version="0.15.1" />
+    <PackageReference Include="Microsoft.ML.Mkl.Components" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.LightGBM" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.OnnxTransformer" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.TensorFlow" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.Dnn" Version="0.15.1" />
+    <PackageReference Include="Microsoft.ML.Ensemble" Version="0.15.1" />
+    <PackageReference Include="Microsoft.ML.TimeSeries" Version="1.3.1" />
     <PackageReference Include="Microsoft.DataPrep" Version="0.0.1.5-preview" />
+    <PackageReference Include="TensorFlow.NET" Version="0.10.10" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="1.14.0" />
   </ItemGroup>
 </Project>

src/Platforms/build.csproj

@@ -11,17 +11,20 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Microsoft.ML" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.CpuMath" Version="1.2.0" />    
-    <PackageReference Include="Microsoft.ML.EntryPoints" Version="0.14.0" />
-    <PackageReference Include="Microsoft.ML.Mkl.Components" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.LightGBM" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.OnnxTransformer" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.TensorFlow" Version="1.2.0" />
-    <PackageReference Include="Microsoft.ML.Ensemble" Version="0.14.0" />
-    <PackageReference Include="Microsoft.ML.TimeSeries" Version="1.2.0" />
+    <PackageReference Include="Microsoft.ML" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.CpuMath" Version="1.3.1" />    
+    <PackageReference Include="Microsoft.ML.EntryPoints" Version="0.15.1" />
+    <PackageReference Include="Microsoft.ML.Mkl.Components" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.LightGBM" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.OnnxTransformer" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.TensorFlow" Version="1.3.1" />
+    <PackageReference Include="Microsoft.ML.Dnn" Version="0.15.1" />
+    <PackageReference Include="Microsoft.ML.Ensemble" Version="0.15.1" />
+    <PackageReference Include="Microsoft.ML.TimeSeries" Version="1.3.1" />
     <PackageReference Include="Microsoft.DataPrep" Version="0.0.1.5-preview" />
+    <PackageReference Include="TensorFlow.NET" Version="0.10.10" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="1.14.0" />
   </ItemGroup>
 
 </Project>

src/python/nimbusml/__init__.py

@@ -2,7 +2,7 @@
 Microsoft Machine Learning for Python
 """
 
-__version__ = '1.2.1'
+__version__ = '1.3.0'
 
 # CoreCLR version of MicrosoftML is built on Windows.
 # But file permissions are not preserved when it's copied to Linux.

src/python/nimbusml/ensemble/lightgbmbinaryclassifier.py

@@ -156,7 +156,7 @@ def __init__(
             unbalanced_sets=False,
             weight_of_positive_examples=1.0,
             sigmoid=0.5,
-            evaluation_metric='Default',
+            evaluation_metric='Logloss',
             maximum_bin_count_per_feature=255,
             verbose=False,
             silent=True,

src/python/nimbusml/ensemble/lightgbmclassifier.py

@@ -151,7 +151,7 @@ def __init__(
             unbalanced_sets=False,
             use_softmax=None,
             sigmoid=0.5,
-            evaluation_metric='Default',
+            evaluation_metric='Error',
             maximum_bin_count_per_feature=255,
             verbose=False,
             silent=True,

src/python/nimbusml/ensemble/lightgbmranker.py

@@ -150,7 +150,7 @@ def __init__(
             caching='Auto',
             custom_gains=[0, 3, 7, 15, 31, 63, 127, 255, 511, 1023, 2047, 4095],
             sigmoid=0.5,
-            evaluation_metric='Default',
+            evaluation_metric='NormalizedDiscountedCumulativeGain',
             maximum_bin_count_per_feature=255,
             verbose=False,
             silent=True,

src/python/nimbusml/ensemble/lightgbmregressor.py

@@ -141,7 +141,7 @@ def __init__(
             booster=None,
             normalize='Auto',
             caching='Auto',
-            evaluation_metric='Default',
+            evaluation_metric='RootMeanSquaredError',
             maximum_bin_count_per_feature=255,
             verbose=False,
             silent=True,

src/python/nimbusml/internal/core/ensemble/lightgbmbinaryclassifier.py

@@ -145,7 +145,7 @@ def __init__(
             unbalanced_sets=False,
             weight_of_positive_examples=1.0,
             sigmoid=0.5,
-            evaluation_metric='Default',
+            evaluation_metric='Logloss',
             maximum_bin_count_per_feature=255,
             verbose=False,
             silent=True,

src/python/nimbusml/internal/core/ensemble/lightgbmclassifier.py

@@ -143,7 +143,7 @@ def __init__(
             unbalanced_sets=False,
             use_softmax=None,
             sigmoid=0.5,
-            evaluation_metric='Default',
+            evaluation_metric='Error',
             maximum_bin_count_per_feature=255,
             verbose=False,
             silent=True,

src/python/nimbusml/internal/core/ensemble/lightgbmranker.py

@@ -140,7 +140,7 @@ def __init__(
             caching='Auto',
             custom_gains=[0, 3, 7, 15, 31, 63, 127, 255, 511, 1023, 2047, 4095],
             sigmoid=0.5,
-            evaluation_metric='Default',
+            evaluation_metric='NormalizedDiscountedCumulativeGain',
             maximum_bin_count_per_feature=255,
             verbose=False,
             silent=True,

src/python/nimbusml/internal/core/ensemble/lightgbmregressor.py

@@ -133,7 +133,7 @@ def __init__(
             booster=None,
             normalize='Auto',
             caching='Auto',
-            evaluation_metric='Default',
+            evaluation_metric='RootMeanSquaredError',
             maximum_bin_count_per_feature=255,
             verbose=False,
             silent=True,

src/python/nimbusml/internal/core/preprocessing/tensorflowscorer.py

@@ -52,41 +52,8 @@ class TensorFlowScorer(BasePipelineItem, DefaultSignature):
 
     :param output_columns: The name of the outputs.
 
-    :param label_column: Training labels.
-
-    :param tensor_flow_label: TensorFlow label node.
-
-    :param optimization_operation: The name of the optimization operation in
-        the TensorFlow graph.
-
-    :param loss_operation: The name of the operation in the TensorFlow graph to
-        compute training loss (Optional).
-
-    :param metric_operation: The name of the operation in the TensorFlow graph
-        to compute performance metric during training (Optional).
-
     :param batch_size: Number of samples to use for mini-batch training.
 
-    :param epoch: Number of training iterations.
-
-    :param learning_rate_operation: The name of the operation in the TensorFlow
-        graph which sets optimizer learning rate (Optional).
-
-    :param learning_rate: Determines the size of the step taken in the
-        direction of the gradient in each step of the learning process.  This
-        determines how fast or slow the learner converges on the optimal
-        solution. If the step size is too big, you might overshoot the optimal
-        solution.  If the step size is too small, training takes longer to
-        converge to the best solution.
-
-    :param save_location_operation: Name of the input in TensorFlow graph that
-        specifiy the location for saving/restoring models from disk.
-
-    :param save_operation: Name of the input in TensorFlow graph that specifiy
-        the location for saving/restoring models from disk.
-
-    :param re_train: Retrain TensorFlow model.
-
     :param add_batch_dimension_inputs: Add a batch dimension to the input e.g.
         input = [224, 224, 3] => [-1, 224, 224, 3].
 
@@ -105,18 +72,7 @@ def __init__(
             model_location,
             input_columns=None,
             output_columns=None,
-            label_column=None,
-            tensor_flow_label=None,
-            optimization_operation=None,
-            loss_operation=None,
-            metric_operation=None,
             batch_size=64,
-            epoch=5,
-            learning_rate_operation=None,
-            learning_rate=0.01,
-            save_location_operation='save/Const',
-            save_operation='save/control_dependency',
-            re_train=False,
             add_batch_dimension_inputs=False,
             **params):
         BasePipelineItem.__init__(
@@ -125,18 +81,7 @@ def __init__(
         self.model_location = model_location
         self.input_columns = input_columns
         self.output_columns = output_columns
-        self.label_column = label_column
-        self.tensor_flow_label = tensor_flow_label
-        self.optimization_operation = optimization_operation
-        self.loss_operation = loss_operation
-        self.metric_operation = metric_operation
         self.batch_size = batch_size
-        self.epoch = epoch
-        self.learning_rate_operation = learning_rate_operation
-        self.learning_rate = learning_rate
-        self.save_location_operation = save_location_operation
-        self.save_operation = save_operation
-        self.re_train = re_train
         self.add_batch_dimension_inputs = add_batch_dimension_inputs
 
     @property
@@ -149,18 +94,7 @@ def _get_node(self, **all_args):
             model_location=self.model_location,
             input_columns=self.input_columns,
             output_columns=self.output_columns,
-            label_column=self.label_column,
-            tensor_flow_label=self.tensor_flow_label,
-            optimization_operation=self.optimization_operation,
-            loss_operation=self.loss_operation,
-            metric_operation=self.metric_operation,
             batch_size=self.batch_size,
-            epoch=self.epoch,
-            learning_rate_operation=self.learning_rate_operation,
-            learning_rate=self.learning_rate,
-            save_location_operation=self.save_location_operation,
-            save_operation=self.save_operation,
-            re_train=self.re_train,
             add_batch_dimension_inputs=self.add_batch_dimension_inputs)
 
         all_args.update(algo_args)

src/python/nimbusml/internal/entrypoints/models_onnxconverter.py

@@ -10,14 +10,15 @@
 
 def models_onnxconverter(
         onnx,
-        model,
         data_file=None,
         json=None,
         name=None,
         domain=None,
         inputs_to_drop=None,
         outputs_to_drop=None,
+        model=None,
         onnx_version='Stable',
+        predictive_model=None,
         **params):
     """
     **Description**
@@ -40,6 +41,8 @@ def models_onnxconverter(
         "Stable" or "Experimental". If "Experimental" is used,
         produced model can contain components that is not officially
         supported in ONNX standard. (inputs).
+    :param predictive_model: Predictor model that needs to be
+        converted to ONNX format. (inputs).
     """
 
     entrypoint_name = 'Models.OnnxConverter'
@@ -85,7 +88,7 @@ def models_onnxconverter(
     if model is not None:
         inputs['Model'] = try_set(
             obj=model,
-            none_acceptable=False,
+            none_acceptable=True,
             is_of_type=str)
     if onnx_version is not None:
         inputs['OnnxVersion'] = try_set(
@@ -95,6 +98,9 @@ def models_onnxconverter(
             values=[
                 'Stable',
                 'Experimental'])
+    if predictive_model is not None:
+        inputs['PredictiveModel'] = try_set(
+            obj=predictive_model, none_acceptable=True, is_of_type=str)
 
     input_variables = {
         x for x in unlist(inputs.values())

src/python/nimbusml/internal/entrypoints/trainers_lightgbmbinaryclassifier.py

@@ -26,7 +26,7 @@ def trainers_lightgbmbinaryclassifier(
         unbalanced_sets=False,
         weight_of_positive_examples=1.0,
         sigmoid=0.5,
-        evaluation_metric='Default',
+        evaluation_metric='Logloss',
         maximum_bin_count_per_feature=255,
         verbose=False,
         silent=True,

src/python/nimbusml/internal/entrypoints/trainers_lightgbmclassifier.py

@@ -26,7 +26,7 @@ def trainers_lightgbmclassifier(
         unbalanced_sets=False,
         use_softmax=None,
         sigmoid=0.5,
-        evaluation_metric='Default',
+        evaluation_metric='Error',
         maximum_bin_count_per_feature=255,
         verbose=False,
         silent=True,

src/python/nimbusml/internal/entrypoints/trainers_lightgbmranker.py

@@ -25,7 +25,7 @@ def trainers_lightgbmranker(
         caching='Auto',
         custom_gains=[0, 3, 7, 15, 31, 63, 127, 255, 511, 1023, 2047, 4095],
         sigmoid=0.5,
-        evaluation_metric='Default',
+        evaluation_metric='NormalizedDiscountedCumulativeGain',
         maximum_bin_count_per_feature=255,
         verbose=False,
         silent=True,

src/python/nimbusml/internal/entrypoints/trainers_lightgbmregressor.py

@@ -23,7 +23,7 @@ def trainers_lightgbmregressor(
         row_group_column_name=None,
         normalize_features='Auto',
         caching='Auto',
-        evaluation_metric='Default',
+        evaluation_metric='RootMeanSquaredError',
         maximum_bin_count_per_feature=255,
         verbose=False,
         silent=True,