# 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.common import RecordSerializer, RecordDeserializer from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa from sagemaker.amazon.validation import gt, isin, ge 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 FactorizationMachines(AmazonAlgorithmEstimatorBase): """A supervised learning algorithm used in classification and regression. Factorization Machines combine the advantages of Support Vector Machines with factorization models. It is an extension of a linear model that is designed to capture interactions between features within high dimensional sparse datasets economically. """ repo_name: str = "factorization-machines" repo_version: str = "1" num_factors: hp = hp("num_factors", gt(0), "An integer greater than zero", int) predictor_type: hp = hp( "predictor_type", isin("binary_classifier", "regressor"), 'Value "binary_classifier" or "regressor"', str, ) epochs: hp = hp("epochs", gt(0), "An integer greater than 0", int) clip_gradient: hp = hp("clip_gradient", (), "A float value", float) eps: hp = hp("eps", (), "A float value", float) rescale_grad: hp = hp("rescale_grad", (), "A float value", float) bias_lr: hp = hp("bias_lr", ge(0), "A non-negative float", float) linear_lr: hp = hp("linear_lr", ge(0), "A non-negative float", float) factors_lr: hp = hp("factors_lr", ge(0), "A non-negative float", float) bias_wd: hp = hp("bias_wd", ge(0), "A non-negative float", float) linear_wd: hp = hp("linear_wd", ge(0), "A non-negative float", float) factors_wd: hp = hp("factors_wd", ge(0), "A non-negative float", float) bias_init_method: hp = hp( "bias_init_method", isin("normal", "uniform", "constant"), 'Value "normal", "uniform" or "constant"', str, ) bias_init_scale: hp = hp("bias_init_scale", ge(0), "A non-negative float", float) bias_init_sigma: hp = hp("bias_init_sigma", ge(0), "A non-negative float", float) bias_init_value: hp = hp("bias_init_value", (), "A float value", float) linear_init_method: hp = hp( "linear_init_method", isin("normal", "uniform", "constant"), 'Value "normal", "uniform" or "constant"', str, ) linear_init_scale: hp = hp("linear_init_scale", ge(0), "A non-negative float", float) linear_init_sigma: hp = hp("linear_init_sigma", ge(0), "A non-negative float", float) linear_init_value: hp = hp("linear_init_value", (), "A float value", float) factors_init_method: hp = hp( "factors_init_method", isin("normal", "uniform", "constant"), 'Value "normal", "uniform" or "constant"', str, ) factors_init_scale: hp = hp("factors_init_scale", ge(0), "A non-negative float", float) factors_init_sigma: hp = hp("factors_init_sigma", ge(0), "A non-negative float", float) factors_init_value: hp = hp("factors_init_value", (), "A float value", 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_factors: Optional[int] = None, predictor_type: Optional[str] = None, epochs: Optional[int] = None, clip_gradient: Optional[float] = None, eps: Optional[float] = None, rescale_grad: Optional[float] = None, bias_lr: Optional[float] = None, linear_lr: Optional[float] = None, factors_lr: Optional[float] = None, bias_wd: Optional[float] = None, linear_wd: Optional[float] = None, factors_wd: Optional[float] = None, bias_init_method: Optional[str] = None, bias_init_scale: Optional[float] = None, bias_init_sigma: Optional[float] = None, bias_init_value: Optional[float] = None, linear_init_method: Optional[str] = None, linear_init_scale: Optional[float] = None, linear_init_sigma: Optional[float] = None, linear_init_value: Optional[float] = None, factors_init_method: Optional[str] = None, factors_init_scale: Optional[float] = None, factors_init_sigma: Optional[float] = None, factors_init_value: Optional[float] = None, **kwargs ): """Factorization Machines is :class:`Estimator` for general-purpose supervised learning. Amazon SageMaker Factorization Machines is a general-purpose supervised learning algorithm that you can use for both classification and regression tasks. It is an extension of a linear model that is designed to parsimoniously capture interactions between features within high dimensional sparse datasets. 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.pca.FactorizationMachinesPredictor` object that can be used for inference calls using the trained model hosted in the SageMaker Endpoint. FactorizationMachines Estimators can be configured by setting hyperparameters. The available hyperparameters for FactorizationMachines are documented below. For further information on the AWS FactorizationMachines algorithm, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.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_factors (int): Dimensionality of factorization. predictor_type (str): Type of predictor 'binary_classifier' or 'regressor'. epochs (int): Number of training epochs to run. clip_gradient (float): Optimizer parameter. Clip the gradient by projecting onto the box [-clip_gradient, +clip_gradient] eps (float): Optimizer parameter. Small value to avoid division by 0. rescale_grad (float): Optimizer parameter. If set, multiplies the gradient with rescale_grad before updating. Often choose to be 1.0/batch_size. bias_lr (float): Non-negative learning rate for the bias term. linear_lr (float): Non-negative learning rate for linear terms. factors_lr (float): Noon-negative learning rate for factorization terms. bias_wd (float): Non-negative weight decay for the bias term. linear_wd (float): Non-negative weight decay for linear terms. factors_wd (float): Non-negative weight decay for factorization terms. bias_init_method (str): Initialization method for the bias term: 'normal', 'uniform' or 'constant'. bias_init_scale (float): Non-negative range for initialization of the bias term that takes effect when bias_init_method parameter is 'uniform' bias_init_sigma (float): Non-negative standard deviation for initialization of the bias term that takes effect when bias_init_method parameter is 'normal'. bias_init_value (float): Initial value of the bias term that takes effect when bias_init_method parameter is 'constant'. linear_init_method (str): Initialization method for linear term: 'normal', 'uniform' or 'constant'. linear_init_scale (float): Non-negative range for initialization of linear terms that takes effect when linear_init_method parameter is 'uniform'. linear_init_sigma (float): Non-negative standard deviation for initialization of linear terms that takes effect when linear_init_method parameter is 'normal'. linear_init_value (float): Initial value of linear terms that takes effect when linear_init_method parameter is 'constant'. factors_init_method (str): Initialization method for factorization term: 'normal', 'uniform' or 'constant'. factors_init_scale (float): Non-negative range for initialization of factorization terms that takes effect when factors_init_method parameter is 'uniform'. factors_init_sigma (float): Non-negative standard deviation for initialization of factorization terms that takes effect when factors_init_method parameter is 'normal'. factors_init_value (float): Initial value of factorization terms that takes effect when factors_init_method parameter is 'constant'. **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(FactorizationMachines, self).__init__(role, instance_count, instance_type, **kwargs) self.num_factors = num_factors self.predictor_type = predictor_type self.epochs = epochs self.clip_gradient = clip_gradient self.eps = eps self.rescale_grad = rescale_grad self.bias_lr = bias_lr self.linear_lr = linear_lr self.factors_lr = factors_lr self.bias_wd = bias_wd self.linear_wd = linear_wd self.factors_wd = factors_wd self.bias_init_method = bias_init_method self.bias_init_scale = bias_init_scale self.bias_init_sigma = bias_init_sigma self.bias_init_value = bias_init_value self.linear_init_method = linear_init_method self.linear_init_scale = linear_init_scale self.linear_init_sigma = linear_init_sigma self.linear_init_value = linear_init_value self.factors_init_method = factors_init_method self.factors_init_scale = factors_init_scale self.factors_init_sigma = factors_init_sigma self.factors_init_value = factors_init_value def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs): """Return a :class:`~sagemaker.amazon.FactorizationMachinesModel`. 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 FactorizationMachinesModel constructor. """ return FactorizationMachinesModel( self.model_data, self.role, sagemaker_session=self.sagemaker_session, vpc_config=self.get_vpc_config(vpc_config_override), **kwargs ) class FactorizationMachinesPredictor(Predictor): """Performs binary-classification or regression prediction from input vectors. 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 prediction is stored in the ``"score"`` key of the ``Record.label`` field. Please refer to the formats details described: https://docs.aws.amazon.com/sagemaker/latest/dg/fm-in-formats.html """ def __init__( self, endpoint_name, sagemaker_session=None, serializer=RecordSerializer(), deserializer=RecordDeserializer(), ): """Initialization for FactorizationMachinesPredictor 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(FactorizationMachinesPredictor, self).__init__( endpoint_name, sagemaker_session, serializer=serializer, deserializer=deserializer, ) class FactorizationMachinesModel(Model): """Reference S3 model data created by FactorizationMachines estimator. Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and returns :class:`FactorizationMachinesPredictor`. """ def __init__( self, model_data: Union[str, PipelineVariable], role: Optional[str] = None, sagemaker_session: Optional[Session] = None, **kwargs ): """Initialization for FactorizationMachinesModel 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( FactorizationMachines.repo_name, sagemaker_session.boto_region_name, version=FactorizationMachines.repo_version, ) pop_out_unused_kwarg("predictor_cls", kwargs, FactorizationMachinesPredictor.__name__) pop_out_unused_kwarg("image_uri", kwargs, image_uri) super(FactorizationMachinesModel, self).__init__( image_uri, model_data, role, predictor_cls=FactorizationMachinesPredictor, sagemaker_session=sagemaker_session, **kwargs )