Change Linear SVM lambda_ parameter to regularization

src/python/nimbusml/internal/core/linear_model/linearsvmbinaryclassifier.py

@@ -69,7 +69,9 @@ class LinearSvmBinaryClassifier(
 
     :param caching: Whether trainer should cache input training data.
 
-    :param lambda_: Regularizer constant.
+    :param l2_regularization: L2 regularization weight. This also controls the
+        learning rate, with the learning rate being inversely proportional to
+        the regularization weight.
 
     :param perform_projection: Perform projection to unit-ball? Typically used
         with batch size > 1.
@@ -105,7 +107,7 @@ def __init__(
             self,
             normalize='Auto',
             caching='Auto',
-            lambda_=0.001,
+            l2_regularization=0.001,
             perform_projection=False,
             number_of_iterations=1,
             initial_weights_diameter=0.0,
@@ -119,7 +121,7 @@ def __init__(
 
         self.normalize = normalize
         self.caching = caching
-        self.lambda_ = lambda_
+        self.l2_regularization = l2_regularization
         self.perform_projection = perform_projection
         self.number_of_iterations = number_of_iterations
         self.initial_weights_diameter = initial_weights_diameter
@@ -146,7 +148,7 @@ def _get_node(self, **all_args):
                 all_args),
             normalize_features=self.normalize,
             caching=self.caching,
-            lambda_=self.lambda_,
+            lambda_=self.l2_regularization,
             perform_projection=self.perform_projection,
             number_of_iterations=self.number_of_iterations,
             initial_weights_diameter=self.initial_weights_diameter,
@@ -157,3 +159,4 @@ def _get_node(self, **all_args):
 
         all_args.update(algo_args)
         return self._entrypoint(**all_args)
+

src/python/nimbusml/linear_model/linearsvmbinaryclassifier.py

@@ -78,7 +78,9 @@ class LinearSvmBinaryClassifier(
 
     :param caching: Whether trainer should cache input training data.
 
-    :param lambda_: Regularizer constant.
+    :param l2_regularization: L2 regularization weight. This also controls the
+        learning rate, with the learning rate being inversely proportional to
+        the regularization weight.
 
     :param perform_projection: Perform projection to unit-ball? Typically used
         with batch size > 1.
@@ -114,7 +116,7 @@ def __init__(
             self,
             normalize='Auto',
             caching='Auto',
-            lambda_=0.001,
+            l2_regularization=0.001,
             perform_projection=False,
             number_of_iterations=1,
             initial_weights_diameter=0.0,
@@ -147,7 +149,7 @@ def __init__(
             self,
             normalize=normalize,
             caching=caching,
-            lambda_=lambda_,
+            l2_regularization=l2_regularization,
             perform_projection=perform_projection,
             number_of_iterations=number_of_iterations,
             initial_weights_diameter=initial_weights_diameter,

src/python/tools/manifest_diff.json

@@ -241,7 +241,13 @@
       "Module": "linear_model",
       "Type": "Classifier",
       "Predict_Proba" : true,
-      "Decision_Function" : true
+      "Decision_Function" : true,
+      "Inputs": [{
+        "Name": "Lambda",
+        "NewName": "l2_regularization",
+        "Desc": "L2 regularization weight. This also controls the learning rate, with the learning rate being inversely proportional to the regularization weight."
+      }
+      ]
     },
     {
       "Name": "Trainers.EnsembleClassification",