# Copyright 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 typing import Optional, Union, List from sagemaker import image_uris from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase from sagemaker.amazon.common import RecordSerializer, RecordDeserializer 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.utils import pop_out_unused_kwarg from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT from sagemaker.workflow.entities import PipelineVariable class NTM(AmazonAlgorithmEstimatorBase): """An unsupervised learning algorithm used to organize a corpus of documents into topics. The resulting topics contain word groupings based on their statistical distribution. Documents that contain frequent occurrences of words such as "bike", "car", "train", "mileage", and "speed" are likely to share a topic on "transportation" for example. """ repo_name: str = "ntm" repo_version: str = "1" num_topics: hp = hp("num_topics", (ge(2), le(1000)), "An integer in [2, 1000]", int) encoder_layers: hp = hp( name="encoder_layers", validation_message="A comma separated list of " "positive integers", data_type=list, ) epochs: hp = hp("epochs", (ge(1), le(100)), "An integer in [1, 100]", int) encoder_layers_activation: hp = hp( "encoder_layers_activation", isin("sigmoid", "tanh", "relu"), 'One of "sigmoid", "tanh" or "relu"', str, ) optimizer: hp = hp( "optimizer", isin("adagrad", "adam", "rmsprop", "sgd", "adadelta"), 'One of "adagrad", "adam", "rmsprop", "sgd" and "adadelta"', str, ) tolerance: hp = hp("tolerance", (ge(1e-6), le(0.1)), "A float in [1e-6, 0.1]", float) num_patience_epochs: hp = hp( "num_patience_epochs", (ge(1), le(10)), "An integer in [1, 10]", int ) batch_norm: hp = hp( name="batch_norm", validation_message="Value must be a boolean", data_type=bool ) rescale_gradient: hp = hp( "rescale_gradient", (ge(1e-3), le(1.0)), "A float in [1e-3, 1.0]", float ) clip_gradient: hp = hp("clip_gradient", ge(1e-3), "A float greater equal to 1e-3", float) weight_decay: hp = hp("weight_decay", (ge(0.0), le(1.0)), "A float in [0.0, 1.0]", float) learning_rate: hp = hp("learning_rate", (ge(1e-6), le(1.0)), "A float in [1e-6, 1.0]", float) def __init__( self, role: Optional[Union[str, PipelineVariable]] = None, instance_count: Optional[Union[int, PipelineVariable]] = None, instance_type: Optional[Union[str, PipelineVariable]] = None, num_topics: Optional[int] = None, encoder_layers: Optional[List] = None, epochs: Optional[int] = None, encoder_layers_activation: Optional[str] = None, optimizer: Optional[str] = None, tolerance: Optional[float] = None, num_patience_epochs: Optional[int] = None, batch_norm: Optional[bool] = None, rescale_gradient: Optional[float] = None, clip_gradient: Optional[float] = None, weight_decay: Optional[float] = None, learning_rate: Optional[float] = 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. instance_count: (int or PipelineVariable): Number of Amazon EC2 instances to use for training. instance_type (str or PipelineVariable): 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, instance_count, 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`. It references 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 ): """Placeholder docstring""" 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 (assuming the default recordio-protobuf ``deserializer`` is used), 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, serializer=RecordSerializer(), deserializer=RecordDeserializer(), ): """Initialization for NTMPredictor class. 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. serializer (sagemaker.serializers.BaseSerializer): Optional. Default serializes input data to x-recordio-protobuf format. deserializer (sagemaker.deserializers.BaseDeserializer): Optional. Default parses responses from x-recordio-protobuf format. """ super(NTMPredictor, self).__init__( endpoint_name, sagemaker_session, serializer=serializer, deserializer=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: Union[str, PipelineVariable], role: Optional[str] = None, sagemaker_session: Optional[Session] = None, **kwargs ): """Initialization for NTMModel class. Args: model_data (str or PipelineVariable): The S3 location of a SageMaker model data ``.tar.gz`` file. 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 it needs to access an AWS resource. sagemaker_session (sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one using the default AWS configuration chain. **kwargs: Keyword arguments passed to the ``FrameworkModel`` initializer. """ sagemaker_session = sagemaker_session or Session() image_uri = image_uris.retrieve( NTM.repo_name, sagemaker_session.boto_region_name, version=NTM.repo_version, ) pop_out_unused_kwarg("predictor_cls", kwargs, NTMPredictor.__name__) pop_out_unused_kwarg("image_uri", kwargs, image_uri) super(NTMModel, self).__init__( image_uri, model_data, role, predictor_cls=NTMPredictor, sagemaker_session=sagemaker_session, **kwargs )