# 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 Union, Optional from sagemaker import image_uris from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase 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 def _list_check_subset(valid_super_list): """Provides a function to check validity of list subset. Args: valid_super_list: """ valid_superset = set(valid_super_list) def validate(value): if not isinstance(value, str): return False val_list = [s.strip() for s in value.split(",")] return set(val_list).issubset(valid_superset) return validate class Object2Vec(AmazonAlgorithmEstimatorBase): """A general-purpose neural embedding algorithm that is highly customizable. It can learn low-dimensional dense embeddings of high-dimensional objects. The embeddings are learned in a way that preserves the semantics of the relationship between pairs of objects in the original space in the embedding space. """ repo_name: str = "object2vec" repo_version: str = "1" MINI_BATCH_SIZE: int = 32 enc_dim: hp = hp("enc_dim", (ge(4), le(10000)), "An integer in [4, 10000]", int) mini_batch_size: hp = hp("mini_batch_size", (ge(1), le(10000)), "An integer in [1, 10000]", int) epochs: hp = hp("epochs", (ge(1), le(100)), "An integer in [1, 100]", int) early_stopping_patience: hp = hp( "early_stopping_patience", (ge(1), le(5)), "An integer in [1, 5]", int ) early_stopping_tolerance: hp = hp( "early_stopping_tolerance", (ge(1e-06), le(0.1)), "A float in [1e-06, 0.1]", float ) dropout: hp = hp("dropout", (ge(0.0), le(1.0)), "A float in [0.0, 1.0]", float) weight_decay: hp = hp( "weight_decay", (ge(0.0), le(10000.0)), "A float in [0.0, 10000.0]", float ) bucket_width: hp = hp("bucket_width", (ge(0), le(100)), "An integer in [0, 100]", int) num_classes: hp = hp("num_classes", (ge(2), le(30)), "An integer in [2, 30]", int) mlp_layers: hp = hp("mlp_layers", (ge(1), le(10)), "An integer in [1, 10]", int) mlp_dim: hp = hp("mlp_dim", (ge(2), le(10000)), "An integer in [2, 10000]", int) mlp_activation: hp = hp( "mlp_activation", isin("tanh", "relu", "linear"), 'One of "tanh", "relu", "linear"', str ) output_layer: hp = hp( "output_layer", isin("softmax", "mean_squared_error"), 'One of "softmax", "mean_squared_error"', str, ) optimizer: hp = hp( "optimizer", isin("adagrad", "adam", "rmsprop", "sgd", "adadelta"), 'One of "adagrad", "adam", "rmsprop", "sgd", "adadelta"', str, ) learning_rate: hp = hp("learning_rate", (ge(1e-06), le(1.0)), "A float in [1e-06, 1.0]", float) negative_sampling_rate: hp = hp( "negative_sampling_rate", (ge(0), le(100)), "An integer in [0, 100]", int ) comparator_list: hp = hp( "comparator_list", _list_check_subset(["hadamard", "concat", "abs_diff"]), 'Comma-separated of hadamard, concat, abs_diff. E.g. "hadamard,abs_diff"', str, ) tied_token_embedding_weight: hp = hp( "tied_token_embedding_weight", (), "Either True or False", bool ) token_embedding_storage_type: hp = hp( "token_embedding_storage_type", isin("dense", "row_sparse"), 'One of "dense", "row_sparse"', str, ) enc0_network: hp = hp( "enc0_network", isin("hcnn", "bilstm", "pooled_embedding"), 'One of "hcnn", "bilstm", "pooled_embedding"', str, ) enc1_network: hp = hp( "enc1_network", isin("hcnn", "bilstm", "pooled_embedding", "enc0"), 'One of "hcnn", "bilstm", "pooled_embedding", "enc0"', str, ) enc0_cnn_filter_width: hp = hp( "enc0_cnn_filter_width", (ge(1), le(9)), "An integer in [1, 9]", int ) enc1_cnn_filter_width: hp = hp( "enc1_cnn_filter_width", (ge(1), le(9)), "An integer in [1, 9]", int ) enc0_max_seq_len: hp = hp("enc0_max_seq_len", (ge(1), le(5000)), "An integer in [1, 5000]", int) enc1_max_seq_len: hp = hp("enc1_max_seq_len", (ge(1), le(5000)), "An integer in [1, 5000]", int) enc0_token_embedding_dim: hp = hp( "enc0_token_embedding_dim", (ge(2), le(1000)), "An integer in [2, 1000]", int ) enc1_token_embedding_dim: hp = hp( "enc1_token_embedding_dim", (ge(2), le(1000)), "An integer in [2, 1000]", int ) enc0_vocab_size: hp = hp( "enc0_vocab_size", (ge(2), le(3000000)), "An integer in [2, 3000000]", int ) enc1_vocab_size: hp = hp( "enc1_vocab_size", (ge(2), le(3000000)), "An integer in [2, 3000000]", int ) enc0_layers: hp = hp("enc0_layers", (ge(1), le(4)), "An integer in [1, 4]", int) enc1_layers: hp = hp("enc1_layers", (ge(1), le(4)), "An integer in [1, 4]", int) enc0_freeze_pretrained_embedding: hp = hp( "enc0_freeze_pretrained_embedding", (), "Either True or False", bool ) enc1_freeze_pretrained_embedding: hp = hp( "enc1_freeze_pretrained_embedding", (), "Either True or False", bool ) def __init__( self, role: Optional[Union[str, PipelineVariable]] = None, instance_count: Optional[Union[int, PipelineVariable]] = None, instance_type: Optional[Union[str, PipelineVariable]] = None, epochs: Optional[int] = None, enc0_max_seq_len: Optional[int] = None, enc0_vocab_size: Optional[int] = None, enc_dim: Optional[int] = None, mini_batch_size: Optional[int] = None, early_stopping_patience: Optional[int] = None, early_stopping_tolerance: Optional[float] = None, dropout: Optional[float] = None, weight_decay: Optional[float] = None, bucket_width: Optional[int] = None, num_classes: Optional[int] = None, mlp_layers: Optional[int] = None, mlp_dim: Optional[int] = None, mlp_activation: Optional[str] = None, output_layer: Optional[str] = None, optimizer: Optional[str] = None, learning_rate: Optional[float] = None, negative_sampling_rate: Optional[int] = None, comparator_list: Optional[str] = None, tied_token_embedding_weight: Optional[bool] = None, token_embedding_storage_type: Optional[str] = None, enc0_network: Optional[str] = None, enc1_network: Optional[str] = None, enc0_cnn_filter_width: Optional[int] = None, enc1_cnn_filter_width: Optional[int] = None, enc1_max_seq_len: Optional[int] = None, enc0_token_embedding_dim: Optional[int] = None, enc1_token_embedding_dim: Optional[int] = None, enc1_vocab_size: Optional[int] = None, enc0_layers: Optional[int] = None, enc1_layers: Optional[int] = None, enc0_freeze_pretrained_embedding: Optional[bool] = None, enc1_freeze_pretrained_embedding: Optional[bool] = None, **kwargs ): """Object2Vec is :class:`Estimator` used for anomaly detection. This Estimator may be fit via calls to :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`. 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. 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.Predictor` object that can be used for inference calls using the trained model hosted in the SageMaker Endpoint. Object2Vec Estimators can be configured by setting hyperparameters. The available hyperparameters for Object2Vec are documented below. For further information on the AWS Object2Vec algorithm, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/object2vec.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'. epochs (int): Total number of epochs for SGD training enc0_max_seq_len (int): Maximum sequence length enc0_vocab_size (int): Vocabulary size of tokens enc_dim (int): Optional. Dimension of the output of the embedding layer mini_batch_size (int): Optional. mini batch size for SGD training early_stopping_patience (int): Optional. The allowed number of consecutive epochs without improvement before early stopping is applied early_stopping_tolerance (float): Optional. The value used to determine whether the algorithm has made improvement between two consecutive epochs for early stopping dropout (float): Optional. Dropout probability on network layers weight_decay (float): Optional. Weight decay parameter during optimization bucket_width (int): Optional. The allowed difference between data sequence length when bucketing is enabled num_classes (int): Optional. Number of classes for classification training (ignored for regression problems) mlp_layers (int): Optional. Number of MLP layers in the network mlp_dim (int): Optional. Dimension of the output of MLP layer mlp_activation (str): Optional. Type of activation function for the MLP layer output_layer (str): Optional. Type of output layer optimizer (str): Optional. Type of optimizer for training learning_rate (float): Optional. Learning rate for SGD training negative_sampling_rate (int): Optional. Negative sampling rate comparator_list (str): Optional. Customization of comparator operator tied_token_embedding_weight (bool): Optional. Tying of token embedding layer weight token_embedding_storage_type (str): Optional. Type of token embedding storage enc0_network (str): Optional. Network model of encoder "enc0" enc1_network (str): Optional. Network model of encoder "enc1" enc0_cnn_filter_width (int): Optional. CNN filter width enc1_cnn_filter_width (int): Optional. CNN filter width enc1_max_seq_len (int): Optional. Maximum sequence length enc0_token_embedding_dim (int): Optional. Output dimension of token embedding layer enc1_token_embedding_dim (int): Optional. Output dimension of token embedding layer enc1_vocab_size (int): Optional. Vocabulary size of tokens enc0_layers (int): Optional. Number of layers in encoder enc1_layers (int): Optional. Number of layers in encoder enc0_freeze_pretrained_embedding (bool): Optional. Freeze pretrained embedding weights enc1_freeze_pretrained_embedding (bool): Optional. Freeze pretrained embedding weights **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(Object2Vec, self).__init__(role, instance_count, instance_type, **kwargs) self.enc_dim = enc_dim self.mini_batch_size = mini_batch_size self.epochs = epochs self.early_stopping_patience = early_stopping_patience self.early_stopping_tolerance = early_stopping_tolerance self.dropout = dropout self.weight_decay = weight_decay self.bucket_width = bucket_width self.num_classes = num_classes self.mlp_layers = mlp_layers self.mlp_dim = mlp_dim self.mlp_activation = mlp_activation self.output_layer = output_layer self.optimizer = optimizer self.learning_rate = learning_rate self.negative_sampling_rate = negative_sampling_rate self.comparator_list = comparator_list self.tied_token_embedding_weight = tied_token_embedding_weight self.token_embedding_storage_type = token_embedding_storage_type self.enc0_network = enc0_network self.enc1_network = enc1_network self.enc0_cnn_filter_width = enc0_cnn_filter_width self.enc1_cnn_filter_width = enc1_cnn_filter_width self.enc0_max_seq_len = enc0_max_seq_len self.enc1_max_seq_len = enc1_max_seq_len self.enc0_token_embedding_dim = enc0_token_embedding_dim self.enc1_token_embedding_dim = enc1_token_embedding_dim self.enc0_vocab_size = enc0_vocab_size self.enc1_vocab_size = enc1_vocab_size self.enc0_layers = enc0_layers self.enc1_layers = enc1_layers self.enc0_freeze_pretrained_embedding = enc0_freeze_pretrained_embedding self.enc1_freeze_pretrained_embedding = enc1_freeze_pretrained_embedding def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs): """Return a :class:`~sagemaker.amazon.Object2VecModel`. 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 Object2VecModel constructor. """ return Object2VecModel( self.model_data, self.role, sagemaker_session=self.sagemaker_session, vpc_config=self.get_vpc_config(vpc_config_override), **kwargs ) def _prepare_for_training(self, records, mini_batch_size=None, job_name=None): """Placeholder docstring""" if mini_batch_size is None: mini_batch_size = self.MINI_BATCH_SIZE super(Object2Vec, self)._prepare_for_training( records, mini_batch_size=mini_batch_size, job_name=job_name ) class Object2VecModel(Model): """Reference Object2Vec s3 model data. Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and returns a Predictor that calculates anomaly scores for datapoints. """ def __init__( self, model_data: Union[str, PipelineVariable], role: Optional[str] = None, sagemaker_session: Optional[Session] = None, **kwargs ): """Initialization for Object2VecModel 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( Object2Vec.repo_name, sagemaker_session.boto_region_name, version=Object2Vec.repo_version, ) pop_out_unused_kwarg("predictor_cls", kwargs, Predictor.__name__) pop_out_unused_kwarg("image_uri", kwargs, image_uri) super(Object2VecModel, self).__init__( image_uri, model_data, role, predictor_cls=Predictor, sagemaker_session=sagemaker_session, **kwargs )