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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 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: str,
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: str,
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
)
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