ntm.py

# Copyright 2017-2020 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.
"""Placeholder docstring"""
from __future__ import absolute_import

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 Predictor
from sagemaker.model import Model
from sagemaker.session import Session
from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT


class NTM(AmazonAlgorithmEstimatorBase):
    """Placeholder docstring"""

    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",
        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_count:
            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.

        .. tip::

            You can find additional parameters for initializing this class at
            :class:`~sagemaker.estimator.amazon_estimator.AmazonAlgorithmEstimatorBase` and
            :class:`~sagemaker.estimator.EstimatorBase`.
        """

        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, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
        """Return a :class:`~sagemaker.amazon.NTMModel` referencing the latest
        s3 model data produced by this Estimator.

        Args:
            vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
                the model. Default: use subnets and security groups from this Estimator.
                * 'Subnets' (list[str]): List of subnet ids.
                * 'SecurityGroupIds' (list[str]): List of security group ids.
            **kwargs: Additional kwargs passed to the NTMModel constructor.
        """
        return NTMModel(
            self.model_data,
            self.role,
            sagemaker_session=self.sagemaker_session,
            vpc_config=self.get_vpc_config(vpc_config_override),
            **kwargs
        )

    def _prepare_for_training(  # pylint: disable=signature-differs
        self, records, mini_batch_size, job_name=None
    ):
        """
        Args:
            records:
            mini_batch_size:
            job_name:
        """
        if mini_batch_size is not None and (mini_batch_size < 1 or mini_batch_size > 10000):
            raise ValueError("mini_batch_size must be in [1, 10000]")
        super(NTM, self)._prepare_for_training(
            records, mini_batch_size=mini_batch_size, job_name=job_name
        )


class NTMPredictor(Predictor):
    """Transforms input vectors to lower-dimesional representations.

    The implementation of
    :meth:`~sagemaker.predictor.Predictor.predict` in this
    `Predictor` 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_name, sagemaker_session=None):
        """
        Args:
            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
                requests are sent.
            sagemaker_session (sagemaker.session.Session): A SageMaker Session
                object, used for SageMaker interactions (default: None). If not
                specified, one is created using the default AWS configuration
                chain.
        """
        super(NTMPredictor, self).__init__(
            endpoint_name,
            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, **kwargs):
        """
        Args:
            model_data:
            role:
            sagemaker_session:
            **kwargs:
        """
        sagemaker_session = sagemaker_session or Session()
        repo = "{}:{}".format(NTM.repo_name, NTM.repo_version)
        image_uri = "{}/{}".format(
            registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
        )
        super(NTMModel, self).__init__(
            image_uri,
            model_data,
            role,
            predictor_cls=NTMPredictor,
            sagemaker_session=sagemaker_session,
            **kwargs
        )