Add ntm algorithm with doc, unit tests, integ tests

README.rst

@@ -39,7 +39,7 @@ You can install from source by cloning this repository and issuing a pip install
 
     git clone https://github.com/aws/sagemaker-python-sdk.git
     python setup.py sdist
-    pip install dist/sagemaker-1.0.3.tar.gz
+    pip install dist/sagemaker-1.0.4.tar.gz
 
 Supported Python versions
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1447,7 +1447,7 @@ Amazon SageMaker provides several built-in machine learning algorithms that you
 
 The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
 
-SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis, Linear Learner, Factorization Machines and LDA algorithms.
+SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis(PCA), Linear Learner, Factorization Machines, Latent Dirichlet Allocation(LDA) and Neural Topic Model(NTM) algorithms.
 
 Definition and usage
 ~~~~~~~~~~~~~~~~~~~~

doc/conf.py

@@ -18,7 +18,7 @@ def __getattr__(cls, name):
                 'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis']
 sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
 
-version = '1.0.3'
+version = '1.0.4'
 project = u'sagemaker'
 
 # Add any Sphinx extension module names here, as strings. They can be extensions

doc/index.rst

@@ -49,3 +49,4 @@ Amazon provides implementations of some common machine learning algortithms opti
     sagemaker.amazon.amazon_estimator
     factorization_machines
     lda
+    ntm

doc/ntm.rst

@@ -0,0 +1,23 @@
+NTM
+--------------------
+
+The Amazon SageMaker NTM algorithm.
+
+.. autoclass:: sagemaker.NTM
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:
+    :exclude-members: image, num_topics, encoder_layers, epochs, encoder_layers_activation, optimizer, tolerance,
+                 num_patience_epochs, batch_norm, rescale_gradient, clip_gradient, weight_decay, learning_rate
+
+
+.. autoclass:: sagemaker.NTMModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. autoclass:: sagemaker.NTMPredictor
+    :members:
+    :undoc-members:
+    :show-inheritance:

src/sagemaker/__init__.py

@@ -19,6 +19,7 @@
 from sagemaker.amazon.linear_learner import LinearLearner, LinearLearnerModel, LinearLearnerPredictor
 from sagemaker.amazon.factorization_machines import FactorizationMachines, FactorizationMachinesModel
 from sagemaker.amazon.factorization_machines import FactorizationMachinesPredictor
+from sagemaker.amazon.ntm import NTM, NTMModel, NTMPredictor
 
 from sagemaker.model import Model
 from sagemaker.predictor import RealTimePredictor
@@ -33,5 +34,5 @@
            LinearLearnerModel, LinearLearnerPredictor,
            LDA, LDAModel, LDAPredictor,
            FactorizationMachines, FactorizationMachinesModel, FactorizationMachinesPredictor,
-           Model, RealTimePredictor, Session,
+           Model, NTM, NTMModel, NTMPredictor, RealTimePredictor, Session,
            container_def, s3_input, production_variant, get_execution_role]

src/sagemaker/amazon/amazon_estimator.py

@@ -228,7 +228,7 @@ def upload_numpy_to_s3_shards(num_shards, s3, bucket, key_prefix, array, labels=
 
 def registry(region_name, algorithm=None):
     """Return docker registry for the given AWS region"""
-    if algorithm in [None, "pca", "kmeans", "linear-learner", "factorization-machines"]:
+    if algorithm in [None, "pca", "kmeans", "linear-learner", "factorization-machines", "ntm"]:
         account_id = {
             "us-east-1": "382416733822",
             "us-east-2": "404615174143",

src/sagemaker/amazon/ntm.py

@@ -0,0 +1,147 @@
+# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# 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.
+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 ge, le, isin
+from sagemaker.predictor import RealTimePredictor
+from sagemaker.model import Model
+from sagemaker.session import Session
+
+
+class NTM(AmazonAlgorithmEstimatorBase):
+
+    repo_name = 'ntm'
+    repo_version = 1
+
+    num_topics = hp('num_topics', (ge(2), le(1000)), 'An integer in [2, 1000]', int)
+    encoder_layers = hp(name='encoder_layers', validation_message='A comma separated list of '
+                                                                  'positive integers or "auto"', data_type=list)
+    epochs = hp('epochs', (ge(1), le(100)), 'An integer in [1, 100]', int)
+    encoder_layers_activation = hp('encoder_layers_activation', isin('sigmoid', 'tanh', 'relu'),
+                                   'One of "sigmoid", "tanh" or "relu"', str)
+    optimizer = hp('optimizer', isin('adagrad', 'adam', 'rmsprop', 'sgd', 'adadelta'),
+                   'One of "adagrad", "adam", "rmsprop", "sgd" and "adadelta"', str)
+    tolerance = hp('tolerance', (ge(1e-6), le(0.1)), 'A float in [1e-6, 0.1]', float)
+    num_patience_epochs = hp('num_patience_epochs', (ge(1), le(10)), 'An integer in [1, 10]', int)
+    batch_norm = hp(name='batch_norm', validation_message='Value must be a boolean', data_type=bool)
+    rescale_gradient = hp('rescale_gradient', (ge(1e-3), le(1.0)), 'A float in [1e-3, 1.0]', float)
+    clip_gradient = hp('clip_gradient', ge(1e-3), 'A float greater equal to 1e-3', float)
+    weight_decay = hp('weight_decay', (ge(0.0), le(1.0)), 'A float in [0.0, 1.0]', float)
+    learning_rate = hp('learning_rate', (ge(1e-6), le(1.0)), 'A float in [1e-6, 1.0]', float)
+
+    def __init__(self, role, train_instance_count, train_instance_type, num_topics,
+                 encoder_layers=None, epochs=None, encoder_layers_activation=None, optimizer=None, tolerance=None,
+                 num_patience_epochs=None, batch_norm=None, rescale_gradient=None, clip_gradient=None,
+                 weight_decay=None, learning_rate=None, **kwargs):
+        """Neural Topic Model (NTM) is :class:`Estimator` used for unsupervised learning.
+
+        This Estimator may be fit via calls to
+        :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`. It requires Amazon
+        :class:`~sagemaker.amazon.record_pb2.Record` protobuf serialized data to be stored in S3.
+        There is an utility :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.record_set` that
+        can be used to upload data to S3 and creates :class:`~sagemaker.amazon.amazon_estimator.RecordSet` to be passed
+        to the `fit` call.
+
+        To learn more about the Amazon protobuf Record class and how to prepare bulk data in this format, please
+        consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html
+
+        After this Estimator is fit, model data is stored in S3. The model may be deployed to an Amazon SageMaker
+        Endpoint by invoking :meth:`~sagemaker.amazon.estimator.EstimatorBase.deploy`. As well as deploying an Endpoint,
+        deploy returns a :class:`~sagemaker.amazon.ntm.NTMPredictor` object that can be used
+        for inference calls using the trained model hosted in the SageMaker Endpoint.
+
+        NTM Estimators can be configured by setting hyperparameters. The available hyperparameters for
+        NTM are documented below.
+
+        For further information on the AWS NTM algorithm,
+        please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/ntm.html
+
+        Args:
+            role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and
+                APIs that create Amazon SageMaker endpoints use this role to access
+                training data and model artifacts. After the endpoint is created,
+                the inference code might use the IAM role, if accessing AWS resource.
+            train_instance_type (str): Type of EC2 instance to use for training, for example, 'ml.c4.xlarge'.
+            num_topics (int): Required. The number of topics for NTM to find within the data.
+            encoder_layers (list): Optional. Represents number of layers in the encoder and the output size of
+                each layer.
+            epochs (int): Optional. Maximum number of passes over the training data.
+            encoder_layers_activation (str): Optional. Activation function to use in the encoder layers.
+            optimizer (str): Optional. Optimizer to use for training.
+            tolerance (float): Optional. Maximum relative change in the loss function within the last
+                num_patience_epochs number of epochs below which early stopping is triggered.
+            num_patience_epochs (int): Optional. Number of successive epochs over which early stopping criterion
+                is evaluated.
+            batch_norm (bool): Optional. Whether to use batch normalization during training.
+            rescale_gradient (float): Optional. Rescale factor for gradient.
+            clip_gradient (float): Optional. Maximum magnitude for each gradient component.
+            weight_decay (float): Optional. Weight decay coefficient. Adds L2 regularization.
+            learning_rate (float): Optional. Learning rate for the optimizer.
+            **kwargs: base class keyword argument values.
+        """
+
+        super(NTM, self).__init__(role, train_instance_count, train_instance_type, **kwargs)
+        self.num_topics = num_topics
+        self.encoder_layers = encoder_layers
+        self.epochs = epochs
+        self.encoder_layers_activation = encoder_layers_activation
+        self.optimizer = optimizer
+        self.tolerance = tolerance
+        self.num_patience_epochs = num_patience_epochs
+        self.batch_norm = batch_norm
+        self.rescale_gradient = rescale_gradient
+        self.clip_gradient = clip_gradient
+        self.weight_decay = weight_decay
+        self.learning_rate = learning_rate
+
+    def create_model(self):
+        """Return a :class:`~sagemaker.amazon.NTMModel` referencing the latest
+        s3 model data produced by this Estimator."""
+
+        return NTMModel(self.model_data, self.role, sagemaker_session=self.sagemaker_session)
+
+    def fit(self, records, mini_batch_size, **kwargs):
+        # mini_batch_size is required, prevent explicit calls with None
+        if mini_batch_size is None:
+            raise ValueError("mini_batch_size must be set")
+        super(NTM, self).fit(records, mini_batch_size, **kwargs)
+
+
+class NTMPredictor(RealTimePredictor):
+    """Transforms input vectors to lower-dimesional representations.
+
+    The implementation of :meth:`~sagemaker.predictor.RealTimePredictor.predict` in this
+    `RealTimePredictor` requires a numpy ``ndarray`` as input. The array should contain the
+    same number of columns as the feature-dimension of the data used to fit the model this
+    Predictor performs inference on.
+
+    :meth:`predict()` returns a list of :class:`~sagemaker.amazon.record_pb2.Record` objects, one
+    for each row in the input ``ndarray``. The lower dimension vector result is stored in the ``projection``
+    key of the ``Record.label`` field."""
+
+    def __init__(self, endpoint, sagemaker_session=None):
+        super(NTMPredictor, self).__init__(endpoint, sagemaker_session, serializer=numpy_to_record_serializer(),
+                                           deserializer=record_deserializer())
+
+
+class NTMModel(Model):
+    """Reference NTM s3 model data. Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and return
+    a Predictor that transforms vectors to a lower-dimensional representation."""
+
+    def __init__(self, model_data, role, sagemaker_session=None):
+        sagemaker_session = sagemaker_session or Session()
+        repo = '{}:{}'.format(NTM.repo_name, NTM.repo_version)
+        image = '{}/{}'.format(registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo)
+        super(NTMModel, self).__init__(model_data, image, role, predictor_cls=NTMPredictor,
+                                       sagemaker_session=sagemaker_session)

src/sagemaker/amazon/validation.py

@@ -30,6 +30,12 @@ def validate(value):
     return validate
 
 
+def le(maximum):
+    def validate(value):
+        return value <= maximum
+    return validate
+
+
 def isin(*expected):
     def validate(value):
         return value in expected

tests/integ/test_ntm.py

@@ -0,0 +1,77 @@
+# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# 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 boto3
+import numpy as np
+import os
+from six.moves.urllib.parse import urlparse
+
+import sagemaker
+from sagemaker import NTM, NTMModel
+from sagemaker.amazon.amazon_estimator import RecordSet
+from sagemaker.amazon.common import read_records
+from sagemaker.utils import name_from_base, sagemaker_timestamp
+
+from tests.integ import DATA_DIR, REGION
+from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
+
+
+def test_ntm():
+
+    with timeout(minutes=15):
+        sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
+        data_path = os.path.join(DATA_DIR, 'ntm')
+        data_filename = 'nips-train_1.pbr'
+
+        with open(os.path.join(data_path, data_filename), 'rb') as f:
+            all_records = read_records(f)
+
+        # all records must be same
+        feature_num = int(all_records[0].features['values'].float32_tensor.shape[0])
+
+        ntm = NTM(role='SageMakerRole', train_instance_count=1, train_instance_type='ml.c4.xlarge', num_topics=10,
+                  sagemaker_session=sagemaker_session, base_job_name='test-ntm')
+
+        record_set = _prepare_record_set_from_local_files(data_path, ntm.data_location,
+                                                          len(all_records), feature_num, sagemaker_session)
+        ntm.fit(record_set, 100)
+
+    endpoint_name = name_from_base('ntm')
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+        model = NTMModel(ntm.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
+        predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
+
+        predict_input = np.random.rand(1, feature_num)
+        result = predictor.predict(predict_input)
+
+        assert len(result) == 1
+        for record in result:
+            assert record.label["topic_mixture"] is not None
+
+
+def _prepare_record_set_from_local_files(dir_path, destination, num_records, feature_dim, sagemaker_session):
+    """Build a :class:`~RecordSet` by pointing to local files.
+
+    Args:
+        dir_path (string): Path to local directory from where the files shall be uploaded.
+        destination (string): S3 path to upload the file to.
+        num_records (int): Number of records in all the files
+        feature_dim (int): Number of features in the data set
+        sagemaker_session (sagemaker.session.Session): Session object to manage interactions with Amazon SageMaker APIs.
+    Returns:
+        RecordSet: A RecordSet specified by S3Prefix to to be used in training.
+    """
+    key_prefix = urlparse(destination).path
+    key_prefix = key_prefix + '{}-{}'.format("testfiles", sagemaker_timestamp())
+    key_prefix = key_prefix.lstrip('/')
+    uploaded_location = sagemaker_session.upload_data(path=dir_path, key_prefix=key_prefix)
+    return RecordSet(uploaded_location, num_records, feature_dim, s3_data_type='S3Prefix')