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#
# 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: str,
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: str,
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
)
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