Add data_type to hyperparameters

src/sagemaker/amazon/amazon_estimator.py

@@ -28,8 +28,8 @@ class AmazonAlgorithmEstimatorBase(EstimatorBase):
     """Base class for Amazon first-party Estimator implementations. This class isn't intended
     to be instantiated directly."""
 
-    feature_dim = hp('feature_dim', (validation.isint, validation.gt(0)))
-    mini_batch_size = hp('mini_batch_size', (validation.isint, validation.gt(0)))
+    feature_dim = hp('feature_dim', validation.gt(0), data_type=int)
+    mini_batch_size = hp('mini_batch_size', validation.gt(0), data_type=int)
 
     def __init__(self, role, train_instance_count, train_instance_type, data_location=None, **kwargs):
         """Initialize an AmazonAlgorithmEstimatorBase.

src/sagemaker/amazon/factorization_machines.py

@@ -13,7 +13,7 @@
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
-from sagemaker.amazon.validation import gt, isin, isint, ge, isnumber
+from sagemaker.amazon.validation import gt, isin, ge
 from sagemaker.predictor import RealTimePredictor
 from sagemaker.model import Model
 from sagemaker.session import Session
@@ -23,34 +23,34 @@ class FactorizationMachines(AmazonAlgorithmEstimatorBase):
 
     repo = 'factorization-machines:1'
 
-    num_factors = hp('num_factors', (gt(0), isint), 'An integer greater than zero')
+    num_factors = hp('num_factors', gt(0), 'An integer greater than zero', int)
     predictor_type = hp('predictor_type', isin('binary_classifier', 'regressor'),
-                        'Value "binary_classifier" or "regressor"')
-    epochs = hp('epochs', (gt(0), isint), "An integer greater than 0")
-    clip_gradient = hp('clip_gradient', isnumber, "A float value")
-    eps = hp('eps', isnumber, "A float value")
-    rescale_grad = hp('rescale_grad', isnumber, "A float value")
-    bias_lr = hp('bias_lr', (ge(0), isnumber), "A non-negative float")
-    linear_lr = hp('linear_lr', (ge(0), isnumber), "A non-negative float")
-    factors_lr = hp('factors_lr', (ge(0), isnumber), "A non-negative float")
-    bias_wd = hp('bias_wd', (ge(0), isnumber), "A non-negative float")
-    linear_wd = hp('linear_wd', (ge(0), isnumber), "A non-negative float")
-    factors_wd = hp('factors_wd', (ge(0), isnumber), "A non-negative float")
+                        'Value "binary_classifier" or "regressor"', str)
+    epochs = hp('epochs', gt(0), "An integer greater than 0", int)
+    clip_gradient = hp('clip_gradient', (), "A float value", float)
+    eps = hp('eps', (), "A float value", float)
+    rescale_grad = hp('rescale_grad', (), "A float value", float)
+    bias_lr = hp('bias_lr', ge(0), "A non-negative float", float)
+    linear_lr = hp('linear_lr', ge(0), "A non-negative float", float)
+    factors_lr = hp('factors_lr', ge(0), "A non-negative float", float)
+    bias_wd = hp('bias_wd', ge(0), "A non-negative float", float)
+    linear_wd = hp('linear_wd', ge(0), "A non-negative float", float)
+    factors_wd = hp('factors_wd', ge(0), "A non-negative float", float)
     bias_init_method = hp('bias_init_method', isin('normal', 'uniform', 'constant'),
-                          'Value "normal", "uniform" or "constant"')
-    bias_init_scale = hp('bias_init_scale', (ge(0), isnumber), "A non-negative float")
-    bias_init_sigma = hp('bias_init_sigma', (ge(0), isnumber), "A non-negative float")
-    bias_init_value = hp('bias_init_value', isnumber, "A float value")
+                          'Value "normal", "uniform" or "constant"', str)
+    bias_init_scale = hp('bias_init_scale', ge(0), "A non-negative float", float)
+    bias_init_sigma = hp('bias_init_sigma', ge(0), "A non-negative float", float)
+    bias_init_value = hp('bias_init_value', (), "A float value", float)
     linear_init_method = hp('linear_init_method', isin('normal', 'uniform', 'constant'),
-                            'Value "normal", "uniform" or "constant"')
-    linear_init_scale = hp('linear_init_scale', (ge(0), isnumber), "A non-negative float")
-    linear_init_sigma = hp('linear_init_sigma', (ge(0), isnumber), "A non-negative float")
-    linear_init_value = hp('linear_init_value', isnumber, "A float value")
+                            'Value "normal", "uniform" or "constant"', str)
+    linear_init_scale = hp('linear_init_scale', ge(0), "A non-negative float", float)
+    linear_init_sigma = hp('linear_init_sigma', ge(0), "A non-negative float", float)
+    linear_init_value = hp('linear_init_value', (), "A float value", float)
     factors_init_method = hp('factors_init_method', isin('normal', 'uniform', 'constant'),
-                             'Value "normal", "uniform" or "constant"')
-    factors_init_scale = hp('factors_init_scale', (ge(0), isnumber), "A non-negative float")
-    factors_init_sigma = hp('factors_init_sigma', (ge(0), isnumber), "A non-negative float")
-    factors_init_value = hp('factors_init_value', isnumber, "A float value")
+                             'Value "normal", "uniform" or "constant"', str)
+    factors_init_scale = hp('factors_init_scale', ge(0), "A non-negative float", float)
+    factors_init_sigma = hp('factors_init_sigma', ge(0), "A non-negative float", float)
+    factors_init_value = hp('factors_init_value', (), "A float value", float)
 
     def __init__(self, role, train_instance_count, train_instance_type,
                  num_factors, predictor_type,

src/sagemaker/amazon/hyperparameter.py

@@ -16,7 +16,7 @@ class Hyperparameter(object):
     """An algorithm hyperparameter with optional validation. Implemented as a python
     descriptor object."""
 
-    def __init__(self, name, validate=lambda _: True, validation_message=""):
+    def __init__(self, name, validate=lambda _: True, validation_message="", data_type=str):
         """Args:
             name (str): The name of this hyperparameter
             validate (callable[object]->[bool]): A validation function or list of validation functions.
@@ -27,6 +27,7 @@ def __init__(self, name, validate=lambda _: True, validation_message=""):
         self.validation = validate
         self.validation_message = validation_message
         self.name = name
+        self.data_type = data_type
         try:
             iter(self.validation)
         except TypeError:
@@ -35,9 +36,10 @@ def __init__(self, name, validate=lambda _: True, validation_message=""):
     def validate(self, value):
         if value is None:  # We allow assignment from None, but Nones are not sent to training.
             return
+
         for valid in self.validation:
             if not valid(value):
-                error_message = "Invalid hyperparameter value {}".format(value)
+                error_message = "Invalid hyperparameter value {} for {}".format(value, self.name)
                 if self.validation_message:
                     error_message = error_message + ". Expecting: " + self.validation_message
                 raise ValueError(error_message)
@@ -50,6 +52,7 @@ def __get__(self, obj, objtype):
 
     def __set__(self, obj, value):
         """Validate the supplied value and set this hyperparameter to value"""
+        value = None if value is None else self.data_type(value)
         self.validate(value)
         if '_hyperparameters' not in dir(obj):
             obj._hyperparameters = dict()

src/sagemaker/amazon/kmeans.py

@@ -13,7 +13,7 @@
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
-from sagemaker.amazon.validation import gt, isin, isint, ge
+from sagemaker.amazon.validation import gt, isin, ge
 from sagemaker.predictor import RealTimePredictor
 from sagemaker.model import Model
 from sagemaker.session import Session
@@ -23,15 +23,15 @@ class KMeans(AmazonAlgorithmEstimatorBase):
 
     repo = 'kmeans:1'
 
-    k = hp('k', (gt(1), isint), 'An integer greater-than 1')
-    init_method = hp('init_method', isin('random', 'kmeans++'), 'One of "random", "kmeans++"')
-    max_iterations = hp('local_lloyd_max_iterations', (gt(0), isint), 'An integer greater-than 0')
-    tol = hp('local_lloyd_tol', (gt(0), isint), 'An integer greater-than 0')
-    num_trials = hp('local_lloyd_num_trials', (gt(0), isint), 'An integer greater-than 0')
-    local_init_method = hp('local_lloyd_init_method', isin('random', 'kmeans++'), 'One of "random", "kmeans++"')
-    half_life_time_size = hp('half_life_time_size', (ge(0), isint), 'An integer greater-than-or-equal-to 0')
-    epochs = hp('epochs', (gt(0), isint), 'An integer greater-than 0')
-    center_factor = hp('extra_center_factor', (gt(0), isint), 'An integer greater-than 0')
+    k = hp('k', gt(1), 'An integer greater-than 1', int)
+    init_method = hp('init_method', isin('random', 'kmeans++'), 'One of "random", "kmeans++"', str)
+    max_iterations = hp('local_lloyd_max_iterations', gt(0), 'An integer greater-than 0', int)
+    tol = hp('local_lloyd_tol', gt(0), 'An integer greater-than 0', int)
+    num_trials = hp('local_lloyd_num_trials', gt(0), 'An integer greater-than 0', int)
+    local_init_method = hp('local_lloyd_init_method', isin('random', 'kmeans++'), 'One of "random", "kmeans++"', str)
+    half_life_time_size = hp('half_life_time_size', ge(0), 'An integer greater-than-or-equal-to 0', int)
+    epochs = hp('epochs', gt(0), 'An integer greater-than 0', int)
+    center_factor = hp('extra_center_factor', gt(0), 'An integer greater-than 0', int)
 
     def __init__(self, role, train_instance_count, train_instance_type, k, init_method=None,
                  max_iterations=None, tol=None, num_trials=None, local_init_method=None,

src/sagemaker/amazon/linear_learner.py

@@ -13,7 +13,7 @@
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
-from sagemaker.amazon.validation import isin, gt, lt, isint, isbool, isnumber
+from sagemaker.amazon.validation import isin, gt, lt
 from sagemaker.predictor import RealTimePredictor
 from sagemaker.model import Model
 from sagemaker.session import Session
@@ -27,40 +27,41 @@ class LinearLearner(AmazonAlgorithmEstimatorBase):
 
     binary_classifier_model_selection_criteria = hp('binary_classifier_model_selection_criteria',
                                                     isin('accuracy', 'f1', 'precision_at_target_recall',
-                                                         'recall_at_target_precision', 'cross_entropy_loss'))
-    target_recall = hp('target_recall', (gt(0), lt(1)), "A float in (0,1)")
-    target_precision = hp('target_precision', (gt(0), lt(1)), "A float in (0,1)")
-    positive_example_weight_mult = hp('positive_example_weight_mult', gt(0), "A float greater than 0")
-    epochs = hp('epochs', (gt(0), isint), "An integer greater-than 0")
+                                                         'recall_at_target_precision', 'cross_entropy_loss'),
+                                                    data_type=str)
+    target_recall = hp('target_recall', (gt(0), lt(1)), "A float in (0,1)", float)
+    target_precision = hp('target_precision', (gt(0), lt(1)), "A float in (0,1)", float)
+    positive_example_weight_mult = hp('positive_example_weight_mult', gt(0), "A float greater than 0", float)
+    epochs = hp('epochs', gt(0), "An integer greater-than 0", int)
     predictor_type = hp('predictor_type', isin('binary_classifier', 'regressor'),
-                        'One of "binary_classifier" or "regressor"')
-    use_bias = hp('use_bias', isbool, "Either True or False")
-    num_models = hp('num_models', (gt(0), isint), "An integer greater-than 0")
-    num_calibration_samples = hp('num_calibration_samples', (gt(0), isint), "An integer greater-than 0")
-    init_method = hp('init_method', isin('uniform', 'normal'), 'One of "uniform" or "normal"')
-    init_scale = hp('init_scale', (gt(-1), lt(1)), 'A float in (-1, 1)')
-    init_sigma = hp('init_sigma', (gt(0), lt(1)), 'A float in (0, 1)')
-    init_bias = hp('init_bias', isnumber, 'A number')
-    optimizer = hp('optimizer', isin('sgd', 'adam', 'auto'), 'One of "sgd", "adam" or "auto')
+                        'One of "binary_classifier" or "regressor"', str)
+    use_bias = hp('use_bias', (), "Either True or False", bool)
+    num_models = hp('num_models', gt(0), "An integer greater-than 0", int)
+    num_calibration_samples = hp('num_calibration_samples', gt(0), "An integer greater-than 0", int)
+    init_method = hp('init_method', isin('uniform', 'normal'), 'One of "uniform" or "normal"', str)
+    init_scale = hp('init_scale', (gt(-1), lt(1)), 'A float in (-1, 1)', float)
+    init_sigma = hp('init_sigma', (gt(0), lt(1)), 'A float in (0, 1)', float)
+    init_bias = hp('init_bias', (), 'A number', float)
+    optimizer = hp('optimizer', isin('sgd', 'adam', 'auto'), 'One of "sgd", "adam" or "auto', str)
     loss = hp('loss', isin('logistic', 'squared_loss', 'absolute_loss', 'auto'),
-              '"logistic", "squared_loss", "absolute_loss" or"auto"')
-    wd = hp('wd', (gt(0), lt(1)), 'A float in (0,1)')
-    l1 = hp('l1', (gt(0), lt(1)), 'A float in (0,1)')
-    momentum = hp('momentum', (gt(0), lt(1)), 'A float in (0,1)')
-    learning_rate = hp('learning_rate', (gt(0), lt(1)), 'A float in (0,1)')
-    beta_1 = hp('beta_1', (gt(0), lt(1)), 'A float in (0,1)')
-    beta_2 = hp('beta_1', (gt(0), lt(1)), 'A float in (0,1)')
-    bias_lr_mult = hp('bias_lr_mult', gt(0), 'A float greater-than 0')
-    bias_wd_mult = hp('bias_wd_mult', gt(0), 'A float greater-than 0')
-    use_lr_scheduler = hp('use_lr_scheduler', isbool, 'A boolean')
-    lr_scheduler_step = hp('lr_scheduler_step', (gt(0), isint), 'An integer greater-than 0')
-    lr_scheduler_factor = hp('lr_scheduler_factor', (gt(0), lt(1)), 'A float in (0,1)')
-    lr_scheduler_minimum_lr = hp('lr_scheduler_minimum_lr', gt(0), 'A float greater-than 0')
-    normalize_data = hp('normalize_data', isbool, 'A boolean')
-    normalize_label = hp('normalize_label', isbool, 'A boolean')
-    unbias_data = hp('unbias_data', isbool, 'A boolean')
-    unbias_label = hp('unbias_label', isbool, 'A boolean')
-    num_point_for_scalar = hp('num_point_for_scalar', (isint, gt(0)), 'An integer greater-than 0')
+              '"logistic", "squared_loss", "absolute_loss" or"auto"', str)
+    wd = hp('wd', (gt(0), lt(1)), 'A float in (0,1)', float)
+    l1 = hp('l1', (gt(0), lt(1)), 'A float in (0,1)', float)
+    momentum = hp('momentum', (gt(0), lt(1)), 'A float in (0,1)', float)
+    learning_rate = hp('learning_rate', (gt(0), lt(1)), 'A float in (0,1)', float)
+    beta_1 = hp('beta_1', (gt(0), lt(1)), 'A float in (0,1)', float)
+    beta_2 = hp('beta_1', (gt(0), lt(1)), 'A float in (0,1)', float)
+    bias_lr_mult = hp('bias_lr_mult', gt(0), 'A float greater-than 0', float)
+    bias_wd_mult = hp('bias_wd_mult', gt(0), 'A float greater-than 0', float)
+    use_lr_scheduler = hp('use_lr_scheduler', (), 'A boolean', bool)
+    lr_scheduler_step = hp('lr_scheduler_step', gt(0), 'An integer greater-than 0', int)
+    lr_scheduler_factor = hp('lr_scheduler_factor', (gt(0), lt(1)), 'A float in (0,1)', float)
+    lr_scheduler_minimum_lr = hp('lr_scheduler_minimum_lr', gt(0), 'A float greater-than 0', float)
+    normalize_data = hp('normalize_data', (), 'A boolean', bool)
+    normalize_label = hp('normalize_label', (), 'A boolean', bool)
+    unbias_data = hp('unbias_data', (), 'A boolean', bool)
+    unbias_label = hp('unbias_label', (), 'A boolean', bool)
+    num_point_for_scalar = hp('num_point_for_scalar', gt(0), 'An integer greater-than 0', int)
 
     def __init__(self, role, train_instance_count, train_instance_type, predictor_type='binary_classifier',
                  binary_classifier_model_selection_criteria=None, target_recall=None, target_precision=None,

src/sagemaker/amazon/pca.py

@@ -24,14 +24,13 @@ class PCA(AmazonAlgorithmEstimatorBase):
 
     DEFAULT_MINI_BATCH_SIZE = 500
 
-    num_components = hp(name='num_components', validate=lambda x: x > 0 and isinstance(x, int),
-                        validation_message='Value must be an integer greater than zero')
+    num_components = hp(name='num_components', validate=lambda x: x > 0,
+                        validation_message='Value must be an integer greater than zero', data_type=int)
     algorithm_mode = hp(name='algorithm_mode', validate=lambda x: x in ['regular', 'stable', 'randomized'],
-                        validation_message='Value must be one of "regular", "stable", "randomized"')
-    subtract_mean = hp(name='subtract_mean', validate=lambda x: isinstance(x, bool),
-                       validation_message='Value must be a boolean')
-    extra_components = hp(name='extra_components', validate=lambda x: x >= 0 and isinstance(x, int),
-                          validation_message="Value must be an integer greater than or equal to 0")
+                        validation_message='Value must be one of "regular", "stable", "randomized"', data_type=str)
+    subtract_mean = hp(name='subtract_mean', validation_message='Value must be a boolean', data_type=bool)
+    extra_components = hp(name='extra_components', validate=lambda x: x >= 0,
+                          validation_message="Value must be an integer greater than or equal to 0", data_type=int)
 
     def __init__(self, role, train_instance_count, train_instance_type, num_components,
                  algorithm_mode=None, subtract_mean=None, extra_components=None, **kwargs):

src/sagemaker/amazon/validation.py

@@ -10,7 +10,6 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-import numbers
 
 
 def gt(minimum):
@@ -41,8 +40,3 @@ def istype(expected):
     def validate(value):
         return isinstance(value, expected)
     return validate
-
-
-isint = istype(int)
-isbool = istype(bool)
-isnumber = istype(numbers.Number)  # noqa

tests/unit/test_hyperparameter.py

@@ -16,9 +16,9 @@
 
 class Test(object):
 
-    blank = Hyperparameter(name="some-name")
+    blank = Hyperparameter(name="some-name", data_type=int)
     elizabeth = Hyperparameter(name='elizabeth')
-    validated = Hyperparameter(name="validated", validate=lambda value: value > 55)
+    validated = Hyperparameter(name="validated", validate=lambda value: value > 55, data_type=int)
 
 
 def test_blank_access():
@@ -55,3 +55,20 @@ def test_validated():
     x.validated = 66
     with pytest.raises(ValueError):
         x.validated = 23
+
+
+def test_data_type():
+    x = Test()
+    x.validated = 66
+    assert type(x.validated) == Test.__dict__["validated"].data_type
+
+
+def test_from_string():
+    x = Test()
+    value = 65
+
+    x.validated = value
+    from_api = str(value)
+
+    x.validated = from_api
+    assert x.validated == value