PCA

The Amazon SageMaker PCA algorithm.

class sagemaker.PCA(role, train_instance_count, train_instance_type, num_components, algorithm_mode=None, subtract_mean=None, extra_components=None, **kwargs)

Bases: sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase

A Principal Components Analysis (PCA) AmazonAlgorithmEstimatorBase.

This Estimator may be fit via calls to fit_ndarray() or fit(). The former allows a PCA model to be fit on a 2-dimensional numpy array. The latter requires Amazon Record protobuf serialized data to be stored in S3.

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 PCAPredictor object that can be used to project input vectors to the learned lower-dimensional representation, using the trained PCA model hosted in the SageMaker Endpoint.

PCA Estimators can be configured by setting hyperparameters. The available hyperparameters for PCA are documented below. For further information on the AWS PCA algorithm, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/pca.html

This Estimator uses Amazon SageMaker PCA to perform training and host deployed models. To learn more about Amazon SageMaker PCA, please read: https://docs.aws.amazon.com/sagemaker/latest/dg/how-pca-works.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_components (int) – The number of principal components. Must be greater than zero.
  • algorithm_mode (str) – Mode for computing the principal components. One of ‘regular’, ‘stable’ or ‘randomized’.
  • subtract_mean (bool) – Whether the data should be unbiased both during train and at inference.
  • extra_components (int) – As the value grows larger, the solution becomes more accurate but the runtime and memory consumption increase linearly. If this value is unset, then a default value equal to the maximum of 10 and num_components will be used. Valid for randomized mode only.
  • **kwargs – base class keyword argument values.
repo = 'pca:1'
create_model()

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

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

Bases: sagemaker.model.Model

Reference PCA s3 model data. Calling deploy() creates an Endpoint and return a Predictor that transforms vectors to a lower-dimensional representation.

class sagemaker.PCAPredictor(endpoint, sagemaker_session=None)

Bases: sagemaker.predictor.RealTimePredictor

Transforms input vectors to lower-dimesional representations.

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 lower dimension vector result is stored in the projection key of the Record.label field.