Added scaling_type to Integer and Continuous ranges

CHANGELOG.rst

@@ -2,13 +2,13 @@
 CHANGELOG
 =========
 
-
-1.18.5.dev
-==========
+1.18.5
+======
 
 * bug-fix: pass kms id as parameter for uploading code with Server side encryption
 * feature: ``PipelineModel``: Create a Transformer from a PipelineModel
 * bug-fix: ``AlgorithmEstimator``: Make SupportedHyperParameters optional
+* feature: ``Hyperparameter``: Support scaling hyperparameters
 * doc-fix: Remove duplicate content from main README.rst, /tensorflow/README.rst, and /sklearn/README.rst and add links to readthedocs content
 
 1.18.4

setup.py

@@ -33,7 +33,7 @@ def read(fname):
     required_packages.append('enum34>=1.1.6')
 
 setup(name="sagemaker",
-      version='1.18.4',
+      version='1.18.5',
       description="Open source library for training and deploying models on Amazon SageMaker.",
       packages=find_packages('src'),
       package_dir={'': 'src'},

src/sagemaker/parameter.py

@@ -25,15 +25,18 @@ class ParameterRange(object):
 
     __all_types__ = ('Continuous', 'Categorical', 'Integer')
 
-    def __init__(self, min_value, max_value):
+    def __init__(self, min_value, max_value, scaling_type='Auto'):
         """Initialize a parameter range.
 
         Args:
             min_value (float or int): The minimum value for the range.
             max_value (float or int): The maximum value for the range.
+            scaling_type (str): The scale used for searching the range during tuning (default: 'Auto').
+                Valid values: 'Auto', 'Linear', 'Logarithmic' and 'ReverseLogarithmic'.
         """
         self.min_value = min_value
         self.max_value = max_value
+        self.scaling_type = scaling_type
 
     def is_valid(self, value):
         """Determine if a value is valid within this ParameterRange.
@@ -62,7 +65,8 @@ def as_tuning_range(self, name):
         """
         return {'Name': name,
                 'MinValue': to_str(self.min_value),
-                'MaxValue': to_str(self.max_value)}
+                'MaxValue': to_str(self.max_value),
+                'ScalingType': self.scaling_type}
 
 
 class ContinuousParameter(ParameterRange):

src/sagemaker/tuner.py

@@ -512,17 +512,23 @@ def _validate_parameter_ranges(self):
                         parameter_range = self._hyperparameter_ranges[value.name]
 
                         if isinstance(parameter_range, ParameterRange):
-                            for _, parameter_range_value in parameter_range.__dict__.items():
-                                # Categorical ranges
-                                if isinstance(parameter_range_value, list):
-                                    for categorical_value in parameter_range_value:
-                                        value.validate(categorical_value)
-                                # Continuous, Integer ranges
-                                else:
-                                    value.validate(parameter_range_value)
+                            self._validate_parameter_range(value, parameter_range)
                     except KeyError:
                         pass
 
+    def _validate_parameter_range(self, value_hp, parameter_range):
+        for parameter_range_key, parameter_range_value in parameter_range.__dict__.items():
+            if parameter_range_key == 'scaling_type':
+                continue
+
+            # Categorical ranges
+            if isinstance(parameter_range_value, list):
+                for categorical_value in parameter_range_value:
+                    value_hp.validate(categorical_value)
+            # Continuous, Integer ranges
+            else:
+                value_hp.validate(parameter_range_value)
+
     def transfer_learning_tuner(self, additional_parents=None, estimator=None):
         """Creates a new ``HyperparameterTuner`` by copying the request fields from the provided parent to the new
         instance of ``HyperparameterTuner``. Followed by addition of warm start configuration with the type as

tests/unit/test_airflow.py

@@ -502,15 +502,17 @@ def test_framework_tuning_config(sagemaker_session):
                 'ContinuousParameterRanges': [{
                     'Name': 'learning_rate',
                     'MinValue': '0.01',
-                    'MaxValue': '0.2'}],
+                    'MaxValue': '0.2',
+                    'ScalingType': 'Auto'}],
                 'CategoricalParameterRanges': [{
                     'Name': 'optimizer',
                     'Values': ['"sgd"', '"Adam"']
                 }],
                 'IntegerParameterRanges': [{
                     'Name': 'num_epoch',
                     'MinValue': '10',
-                    'MaxValue': '50'
+                    'MaxValue': '50',
+                    'ScalingType': 'Auto'
                 }]
             }},
         'TrainingJobDefinition': {

tests/unit/test_tuner.py

@@ -71,6 +71,7 @@
                     'MaxValue': '100',
                     'Name': 'mini_batch_size',
                     'MinValue': '10',
+                    'ScalingType': 'Auto'
                 },
             ]
         },
@@ -631,10 +632,17 @@ def test_continuous_parameter():
 def test_continuous_parameter_ranges():
     cont_param = ContinuousParameter(0.1, 1e-2)
     ranges = cont_param.as_tuning_range('some')
-    assert len(ranges.keys()) == 3
+    assert len(ranges.keys()) == 4
     assert ranges['Name'] == 'some'
     assert ranges['MinValue'] == '0.1'
     assert ranges['MaxValue'] == '0.01'
+    assert ranges['ScalingType'] == 'Auto'
+
+
+def test_continuous_parameter_scaling_type():
+    cont_param = ContinuousParameter(0.1, 2, scaling_type='ReverseLogarithmic')
+    cont_range = cont_param.as_tuning_range('range')
+    assert cont_range['ScalingType'] == 'ReverseLogarithmic'
 
 
 def test_integer_parameter():
@@ -646,10 +654,17 @@ def test_integer_parameter():
 def test_integer_parameter_ranges():
     int_param = IntegerParameter(1, 2)
     ranges = int_param.as_tuning_range('some')
-    assert len(ranges.keys()) == 3
+    assert len(ranges.keys()) == 4
     assert ranges['Name'] == 'some'
     assert ranges['MinValue'] == '1'
     assert ranges['MaxValue'] == '2'
+    assert ranges['ScalingType'] == 'Auto'
+
+
+def test_integer_parameter_scaling_type():
+    int_param = IntegerParameter(2, 3, scaling_type='Linear')
+    int_range = int_param.as_tuning_range('range')
+    assert int_range['ScalingType'] == 'Linear'
 
 
 def test_categorical_parameter_list():