Build fix for rolling ML.NET 1.5.0-preview* and update to Pandas 1.0

src/DotNetBridge/RmlEnvironment.cs

@@ -52,14 +52,9 @@ protected override IHost RegisterCore(HostEnvironmentBase<RmlEnvironment> source
         }
 
         public RmlEnvironment(Bridge.CheckCancelled checkDelegate, int? seed = null, bool verbose = false)
-            : this(RandomUtils.Create(seed), verbose)
+            : base(seed, verbose)
         {
             CheckCancelled = checkDelegate;
-        }
-
-        public RmlEnvironment(Random rand, bool verbose = false)
-            : base(rand, verbose)
-        {
             CultureInfo.CurrentUICulture = CultureInfo.InvariantCulture;
             EnsureDispatcher<ChannelMessage>();
         }

src/python/nimbusml.pyproj

@@ -401,6 +401,7 @@
     <Compile Include="nimbusml\internal\entrypoints\trainers_lightgbmranker.py" />
     <Compile Include="nimbusml\internal\entrypoints\trainers_lightgbmregressor.py" />
     <Compile Include="nimbusml\internal\entrypoints\trainers_linearsvmbinaryclassifier.py" />
+    <Compile Include="nimbusml\internal\entrypoints\trainers_localdeepsvmbinaryclassifier.py" />
     <Compile Include="nimbusml\internal\entrypoints\trainers_logisticregressionbinaryclassifier.py" />
     <Compile Include="nimbusml\internal\entrypoints\trainers_logisticregressionclassifier.py" />
     <Compile Include="nimbusml\internal\entrypoints\trainers_naivebayesclassifier.py" />

src/python/nimbusml/ensemble/lightgbmbinaryclassifier.py

@@ -110,6 +110,9 @@ class LightGbmBinaryClassifier(
     :param handle_missing_value: Enable special handling of missing value or
         not.
 
+    :param use_zero_as_missing_value: Enable usage of zero (0) as missing
+        value.
+
     :param minimum_example_count_per_group: Minimum number of instances per
         categorical group.
 
@@ -165,6 +168,7 @@ def __init__(
             batch_size=1048576,
             use_categorical_split=None,
             handle_missing_value=True,
+            use_zero_as_missing_value=False,
             minimum_example_count_per_group=100,
             maximum_categorical_split_point_count=32,
             categorical_smoothing=10.0,
@@ -219,6 +223,7 @@ def __init__(
             batch_size=batch_size,
             use_categorical_split=use_categorical_split,
             handle_missing_value=handle_missing_value,
+            use_zero_as_missing_value=use_zero_as_missing_value,
             minimum_example_count_per_group=minimum_example_count_per_group,
             maximum_categorical_split_point_count=maximum_categorical_split_point_count,
             categorical_smoothing=categorical_smoothing,

src/python/nimbusml/ensemble/lightgbmclassifier.py

@@ -105,6 +105,9 @@ class LightGbmClassifier(core, BasePredictor, ClassifierMixin):
     :param handle_missing_value: Enable special handling of missing value or
         not.
 
+    :param use_zero_as_missing_value: Enable usage of zero (0) as missing
+        value.
+
     :param minimum_example_count_per_group: Minimum number of instances per
         categorical group.
 
@@ -160,6 +163,7 @@ def __init__(
             batch_size=1048576,
             use_categorical_split=None,
             handle_missing_value=True,
+            use_zero_as_missing_value=False,
             minimum_example_count_per_group=100,
             maximum_categorical_split_point_count=32,
             categorical_smoothing=10.0,
@@ -214,6 +218,7 @@ def __init__(
             batch_size=batch_size,
             use_categorical_split=use_categorical_split,
             handle_missing_value=handle_missing_value,
+            use_zero_as_missing_value=use_zero_as_missing_value,
             minimum_example_count_per_group=minimum_example_count_per_group,
             maximum_categorical_split_point_count=maximum_categorical_split_point_count,
             categorical_smoothing=categorical_smoothing,

src/python/nimbusml/ensemble/lightgbmranker.py

@@ -105,6 +105,9 @@ class LightGbmRanker(core, BasePredictor, ClassifierMixin):
     :param handle_missing_value: Enable special handling of missing value or
         not.
 
+    :param use_zero_as_missing_value: Enable usage of zero (0) as missing
+        value.
+
     :param minimum_example_count_per_group: Minimum number of instances per
         categorical group.
 
@@ -159,6 +162,7 @@ def __init__(
             batch_size=1048576,
             use_categorical_split=None,
             handle_missing_value=True,
+            use_zero_as_missing_value=False,
             minimum_example_count_per_group=100,
             maximum_categorical_split_point_count=32,
             categorical_smoothing=10.0,
@@ -212,6 +216,7 @@ def __init__(
             batch_size=batch_size,
             use_categorical_split=use_categorical_split,
             handle_missing_value=handle_missing_value,
+            use_zero_as_missing_value=use_zero_as_missing_value,
             minimum_example_count_per_group=minimum_example_count_per_group,
             maximum_categorical_split_point_count=maximum_categorical_split_point_count,
             categorical_smoothing=categorical_smoothing,

src/python/nimbusml/ensemble/lightgbmregressor.py

@@ -98,6 +98,9 @@ class LightGbmRegressor(core, BasePredictor, RegressorMixin):
     :param handle_missing_value: Enable special handling of missing value or
         not.
 
+    :param use_zero_as_missing_value: Enable usage of zero (0) as missing
+        value.
+
     :param minimum_example_count_per_group: Minimum number of instances per
         categorical group.
 
@@ -150,6 +153,7 @@ def __init__(
             batch_size=1048576,
             use_categorical_split=None,
             handle_missing_value=True,
+            use_zero_as_missing_value=False,
             minimum_example_count_per_group=100,
             maximum_categorical_split_point_count=32,
             categorical_smoothing=10.0,
@@ -201,6 +205,7 @@ def __init__(
             batch_size=batch_size,
             use_categorical_split=use_categorical_split,
             handle_missing_value=handle_missing_value,
+            use_zero_as_missing_value=use_zero_as_missing_value,
             minimum_example_count_per_group=minimum_example_count_per_group,
             maximum_categorical_split_point_count=maximum_categorical_split_point_count,
             categorical_smoothing=categorical_smoothing,

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

@@ -99,6 +99,9 @@ class LightGbmBinaryClassifier(
     :param handle_missing_value: Enable special handling of missing value or
         not.
 
+    :param use_zero_as_missing_value: Enable usage of zero (0) as missing
+        value.
+
     :param minimum_example_count_per_group: Minimum number of instances per
         categorical group.
 
@@ -154,6 +157,7 @@ def __init__(
             batch_size=1048576,
             use_categorical_split=None,
             handle_missing_value=True,
+            use_zero_as_missing_value=False,
             minimum_example_count_per_group=100,
             maximum_categorical_split_point_count=32,
             categorical_smoothing=10.0,
@@ -183,6 +187,7 @@ def __init__(
         self.batch_size = batch_size
         self.use_categorical_split = use_categorical_split
         self.handle_missing_value = handle_missing_value
+        self.use_zero_as_missing_value = use_zero_as_missing_value
         self.minimum_example_count_per_group = minimum_example_count_per_group
         self.maximum_categorical_split_point_count = maximum_categorical_split_point_count
         self.categorical_smoothing = categorical_smoothing
@@ -220,6 +225,7 @@ def _get_node(self, **all_args):
             batch_size=self.batch_size,
             use_categorical_split=self.use_categorical_split,
             handle_missing_value=self.handle_missing_value,
+            use_zero_as_missing_value=self.use_zero_as_missing_value,
             minimum_example_count_per_group=self.minimum_example_count_per_group,
             maximum_categorical_split_point_count=self.maximum_categorical_split_point_count,
             categorical_smoothing=self.categorical_smoothing,

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

@@ -97,6 +97,9 @@ class LightGbmClassifier(
     :param handle_missing_value: Enable special handling of missing value or
         not.
 
+    :param use_zero_as_missing_value: Enable usage of zero (0) as missing
+        value.
+
     :param minimum_example_count_per_group: Minimum number of instances per
         categorical group.
 
@@ -152,6 +155,7 @@ def __init__(
             batch_size=1048576,
             use_categorical_split=None,
             handle_missing_value=True,
+            use_zero_as_missing_value=False,
             minimum_example_count_per_group=100,
             maximum_categorical_split_point_count=32,
             categorical_smoothing=10.0,
@@ -181,6 +185,7 @@ def __init__(
         self.batch_size = batch_size
         self.use_categorical_split = use_categorical_split
         self.handle_missing_value = handle_missing_value
+        self.use_zero_as_missing_value = use_zero_as_missing_value
         self.minimum_example_count_per_group = minimum_example_count_per_group
         self.maximum_categorical_split_point_count = maximum_categorical_split_point_count
         self.categorical_smoothing = categorical_smoothing
@@ -218,6 +223,7 @@ def _get_node(self, **all_args):
             batch_size=self.batch_size,
             use_categorical_split=self.use_categorical_split,
             handle_missing_value=self.handle_missing_value,
+            use_zero_as_missing_value=self.use_zero_as_missing_value,
             minimum_example_count_per_group=self.minimum_example_count_per_group,
             maximum_categorical_split_point_count=self.maximum_categorical_split_point_count,
             categorical_smoothing=self.categorical_smoothing,

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

@@ -95,6 +95,9 @@ class LightGbmRanker(BasePipelineItem, DefaultSignatureWithRoles):
     :param handle_missing_value: Enable special handling of missing value or
         not.
 
+    :param use_zero_as_missing_value: Enable usage of zero (0) as missing
+        value.
+
     :param minimum_example_count_per_group: Minimum number of instances per
         categorical group.
 
@@ -149,6 +152,7 @@ def __init__(
             batch_size=1048576,
             use_categorical_split=None,
             handle_missing_value=True,
+            use_zero_as_missing_value=False,
             minimum_example_count_per_group=100,
             maximum_categorical_split_point_count=32,
             categorical_smoothing=10.0,
@@ -176,6 +180,7 @@ def __init__(
         self.batch_size = batch_size
         self.use_categorical_split = use_categorical_split
         self.handle_missing_value = handle_missing_value
+        self.use_zero_as_missing_value = use_zero_as_missing_value
         self.minimum_example_count_per_group = minimum_example_count_per_group
         self.maximum_categorical_split_point_count = maximum_categorical_split_point_count
         self.categorical_smoothing = categorical_smoothing
@@ -212,6 +217,7 @@ def _get_node(self, **all_args):
             batch_size=self.batch_size,
             use_categorical_split=self.use_categorical_split,
             handle_missing_value=self.handle_missing_value,
+            use_zero_as_missing_value=self.use_zero_as_missing_value,
             minimum_example_count_per_group=self.minimum_example_count_per_group,
             maximum_categorical_split_point_count=self.maximum_categorical_split_point_count,
             categorical_smoothing=self.categorical_smoothing,

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

@@ -90,6 +90,9 @@ class LightGbmRegressor(
     :param handle_missing_value: Enable special handling of missing value or
         not.
 
+    :param use_zero_as_missing_value: Enable usage of zero (0) as missing
+        value.
+
     :param minimum_example_count_per_group: Minimum number of instances per
         categorical group.
 
@@ -142,6 +145,7 @@ def __init__(
             batch_size=1048576,
             use_categorical_split=None,
             handle_missing_value=True,
+            use_zero_as_missing_value=False,
             minimum_example_count_per_group=100,
             maximum_categorical_split_point_count=32,
             categorical_smoothing=10.0,
@@ -168,6 +172,7 @@ def __init__(
         self.batch_size = batch_size
         self.use_categorical_split = use_categorical_split
         self.handle_missing_value = handle_missing_value
+        self.use_zero_as_missing_value = use_zero_as_missing_value
         self.minimum_example_count_per_group = minimum_example_count_per_group
         self.maximum_categorical_split_point_count = maximum_categorical_split_point_count
         self.categorical_smoothing = categorical_smoothing
@@ -202,6 +207,7 @@ def _get_node(self, **all_args):
             batch_size=self.batch_size,
             use_categorical_split=self.use_categorical_split,
             handle_missing_value=self.handle_missing_value,
+            use_zero_as_missing_value=self.use_zero_as_missing_value,
             minimum_example_count_per_group=self.minimum_example_count_per_group,
             maximum_categorical_split_point_count=self.maximum_categorical_split_point_count,
             categorical_smoothing=self.categorical_smoothing,

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

@@ -35,6 +35,7 @@ def trainers_lightgbmbinaryclassifier(
         batch_size=1048576,
         use_categorical_split=None,
         handle_missing_value=True,
+        use_zero_as_missing_value=False,
         minimum_example_count_per_group=100,
         maximum_categorical_split_point_count=32,
         categorical_smoothing=10.0,
@@ -88,6 +89,8 @@ def trainers_lightgbmbinaryclassifier(
         (inputs).
     :param handle_missing_value: Enable special handling of missing
         value or not. (inputs).
+    :param use_zero_as_missing_value: Enable usage of zero (0) as
+        missing value. (inputs).
     :param minimum_example_count_per_group: Minimum number of
         instances per categorical group. (inputs).
     :param maximum_categorical_split_point_count: Max number of
@@ -243,6 +246,11 @@ def trainers_lightgbmbinaryclassifier(
             obj=handle_missing_value,
             none_acceptable=True,
             is_of_type=bool)
+    if use_zero_as_missing_value is not None:
+        inputs['UseZeroAsMissingValue'] = try_set(
+            obj=use_zero_as_missing_value,
+            none_acceptable=True,
+            is_of_type=bool)
     if minimum_example_count_per_group is not None:
         inputs['MinimumExampleCountPerGroup'] = try_set(
             obj=minimum_example_count_per_group,

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

@@ -35,6 +35,7 @@ def trainers_lightgbmclassifier(
         batch_size=1048576,
         use_categorical_split=None,
         handle_missing_value=True,
+        use_zero_as_missing_value=False,
         minimum_example_count_per_group=100,
         maximum_categorical_split_point_count=32,
         categorical_smoothing=10.0,
@@ -86,6 +87,8 @@ def trainers_lightgbmclassifier(
         (inputs).
     :param handle_missing_value: Enable special handling of missing
         value or not. (inputs).
+    :param use_zero_as_missing_value: Enable usage of zero (0) as
+        missing value. (inputs).
     :param minimum_example_count_per_group: Minimum number of
         instances per categorical group. (inputs).
     :param maximum_categorical_split_point_count: Max number of
@@ -240,6 +243,11 @@ def trainers_lightgbmclassifier(
             obj=handle_missing_value,
             none_acceptable=True,
             is_of_type=bool)
+    if use_zero_as_missing_value is not None:
+        inputs['UseZeroAsMissingValue'] = try_set(
+            obj=use_zero_as_missing_value,
+            none_acceptable=True,
+            is_of_type=bool)
     if minimum_example_count_per_group is not None:
         inputs['MinimumExampleCountPerGroup'] = try_set(
             obj=minimum_example_count_per_group,

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

@@ -34,6 +34,7 @@ def trainers_lightgbmranker(
         batch_size=1048576,
         use_categorical_split=None,
         handle_missing_value=True,
+        use_zero_as_missing_value=False,
         minimum_example_count_per_group=100,
         maximum_categorical_split_point_count=32,
         categorical_smoothing=10.0,
@@ -83,6 +84,8 @@ def trainers_lightgbmranker(
         (inputs).
     :param handle_missing_value: Enable special handling of missing
         value or not. (inputs).
+    :param use_zero_as_missing_value: Enable usage of zero (0) as
+        missing value. (inputs).
     :param minimum_example_count_per_group: Minimum number of
         instances per categorical group. (inputs).
     :param maximum_categorical_split_point_count: Max number of
@@ -232,6 +235,11 @@ def trainers_lightgbmranker(
             obj=handle_missing_value,
             none_acceptable=True,
             is_of_type=bool)
+    if use_zero_as_missing_value is not None:
+        inputs['UseZeroAsMissingValue'] = try_set(
+            obj=use_zero_as_missing_value,
+            none_acceptable=True,
+            is_of_type=bool)
     if minimum_example_count_per_group is not None:
         inputs['MinimumExampleCountPerGroup'] = try_set(
             obj=minimum_example_count_per_group,

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

@@ -32,6 +32,7 @@ def trainers_lightgbmregressor(
         batch_size=1048576,
         use_categorical_split=None,
         handle_missing_value=True,
+        use_zero_as_missing_value=False,
         minimum_example_count_per_group=100,
         maximum_categorical_split_point_count=32,
         categorical_smoothing=10.0,
@@ -78,6 +79,8 @@ def trainers_lightgbmregressor(
         (inputs).
     :param handle_missing_value: Enable special handling of missing
         value or not. (inputs).
+    :param use_zero_as_missing_value: Enable usage of zero (0) as
+        missing value. (inputs).
     :param minimum_example_count_per_group: Minimum number of
         instances per categorical group. (inputs).
     :param maximum_categorical_split_point_count: Max number of
@@ -218,6 +221,11 @@ def trainers_lightgbmregressor(
             obj=handle_missing_value,
             none_acceptable=True,
             is_of_type=bool)
+    if use_zero_as_missing_value is not None:
+        inputs['UseZeroAsMissingValue'] = try_set(
+            obj=use_zero_as_missing_value,
+            none_acceptable=True,
+            is_of_type=bool)
     if minimum_example_count_per_group is not None:
         inputs['MinimumExampleCountPerGroup'] = try_set(
             obj=minimum_example_count_per_group,