FactorizationMachines

The Amazon SageMaker Factorization Machines algorithm.

class sagemaker.FactorizationMachines(role, train_instance_count, train_instance_type, num_factors, predictor_type, epochs=None, clip_gradient=None, eps=None, rescale_grad=None, bias_lr=None, linear_lr=None, factors_lr=None, bias_wd=None, linear_wd=None, factors_wd=None, bias_init_method=None, bias_init_scale=None, bias_init_sigma=None, bias_init_value=None, linear_init_method=None, linear_init_scale=None, linear_init_sigma=None, linear_init_value=None, factors_init_method=None, factors_init_scale=None, factors_init_sigma=None, factors_init_value=None, **kwargs)

Bases: sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase

Factorization Machines is 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 fit(). It requires Amazon Record protobuf serialized data to be stored in S3. There is an utility record_set() that can be used to upload data to S3 and creates 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 deploy(). As well as deploying an Endpoint, deploy returns a 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

Parameters:
  • 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.
  • train_instance_count (int) – Number of Amazon EC2 instances to use for training.
  • train_instance_type (str) – 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 (string) – 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 (string) – 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 (string) – 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.
repo_name = 'factorization-machines'
repo_version = 1
classmethod attach(training_job_name, sagemaker_session=None, job_details=None)

Attach to an existing training job.

Create an Estimator bound to an existing training job, each subclass is responsible to implement _prepare_init_params_from_job_description() as this method delegates the actual conversion of a training job description to the arguments that the class constructor expects. After attaching, if the training job has a Complete status, it can be deploy() ed to create a SageMaker Endpoint and return a Predictor.

If the training job is in progress, attach will block and display log messages from the training job, until the training job completes.

Parameters:
  • training_job_name (str) – The name of the training job to attach to.
  • 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.

Examples

>>> my_estimator.fit(wait=False)
>>> training_job_name = my_estimator.latest_training_job.name
Later on:
>>> attached_estimator = Estimator.attach(training_job_name)
>>> attached_estimator.deploy()
Returns:Instance of the calling Estimator Class with the attached training job.
data_location
delete_endpoint()

Delete an Amazon SageMaker Endpoint.

Raises:ValueError – If the endpoint does not exist.
deploy(initial_instance_count, instance_type, endpoint_name=None, **kwargs)

Deploy the trained model to an Amazon SageMaker endpoint and return a sagemaker.RealTimePredictor object.

More information: http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html

Parameters:
  • initial_instance_count (int) – Minimum number of EC2 instances to deploy to an endpoint for prediction.
  • instance_type (str) – Type of EC2 instance to deploy to an endpoint for prediction, for example, ‘ml.c4.xlarge’.
  • endpoint_name (str) – Name to use for creating an Amazon SageMaker endpoint. If not specified, the name of the training job is used.
  • **kwargs – Passed to invocation of create_model(). Implementations may customize create_model() to accept **kwargs to customize model creation during deploy. For more, see the implementation docs.
Returns:

A predictor that provides a predict() method,

which can be used to send requests to the Amazon SageMaker endpoint and obtain inferences.
Return type:

sagemaker.predictor.RealTimePredictor
fit(records, mini_batch_size=None, **kwargs)

Fit this Estimator on serialized Record objects, stored in S3.

records should be an instance of RecordSet. This defines a collection of s3 data files to train this Estimator on.

Training data is expected to be encoded as dense or sparse vectors in the “values” feature on each Record. If the data is labeled, the label is expected to be encoded as a list of scalas in the “values” feature of the Record label.

More information on the Amazon Record format is available at: https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html

See record_set() to construct a RecordSet object from ndarray arrays.

Parameters:
  • records (RecordSet) – The records to train this Estimator on
  • mini_batch_size (int or None) – The size of each mini-batch to use when training. If None, a default value will be used.
hyperparameters()

Return the hyperparameters as a dictionary to use for training.

The fit() method, which trains the model, calls this method to find the hyperparameters.

Returns:The hyperparameters.
Return type:dict[str, str]
model_data

str – The model location in S3. Only set if Estimator has been fit().

record_set(train, labels=None, channel='train')

Build a RecordSet from a numpy ndarray matrix and label vector.

For the 2D ndarray train, each row is converted to a Record object. The vector is stored in the “values” entry of the features property of each Record. If labels is not None, each corresponding label is assigned to the “values” entry of the labels property of each Record.

The collection of Record objects are protobuf serialized and uploaded to new S3 locations. A manifest file is generated containing the list of objects created and also stored in S3.

The number of S3 objects created is controlled by the train_instance_count property on this Estimator. One S3 object is created per training instance.

Parameters:
  • train (numpy.ndarray) – A 2D numpy array of training data.
  • labels (numpy.ndarray) – A 1D numpy array of labels. Its length must be equal to the number of rows in train.
  • channel (str) – The SageMaker TrainingJob channel this RecordSet should be assigned to.
Returns:

A RecordSet referencing the encoded, uploading training and label data.
Return type:

RecordSet
train_image()

Return the Docker image to use for training.

The fit() method, which does the model training, calls this method to find the image to use for model training.

Returns:The URI of the Docker image.
Return type:str
create_model()

Return a FactorizationMachinesModel referencing the latest s3 model data produced by this Estimator.

class sagemaker.FactorizationMachinesModel(model_data, role, sagemaker_session=None)

Bases: sagemaker.model.Model

Reference S3 model data created by FactorizationMachines estimator. Calling deploy() creates an Endpoint and returns FactorizationMachinesPredictor.

class sagemaker.FactorizationMachinesPredictor(endpoint, sagemaker_session=None)

Bases: sagemaker.predictor.RealTimePredictor

Performs binary-classification or regression prediction from input vectors.

The implementation of predict() in this RealTimePredictor 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.

predict() returns a list of Record objects, 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