LDA¶
The Amazon SageMaker LDA algorithm.
-
class
sagemaker.
LDA
(role, train_instance_type, num_topics, alpha0=None, max_restarts=None, max_iterations=None, tol=None, **kwargs)¶ Bases:
sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase
Latent Dirichlet Allocation (LDA) is
Estimator
used for unsupervised learning.Amazon SageMaker Latent Dirichlet Allocation is an unsupervised learning algorithm that attempts to describe a set of observations as a mixture of distinct categories. LDA is most commonly used to discover a user-specified number of topics shared by documents within a text corpus. Here each observation is a document, the features are the presence (or occurrence count) of each word, and the categories are the topics.
This Estimator may be fit via calls to
fit()
. It requires AmazonRecord
protobuf serialized data to be stored in S3. There is an utilityrecord_set()
that can be used to upload data to S3 and createsRecordSet
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 aLDAPredictor
object that can be used for inference calls using the trained model hosted in the SageMaker Endpoint.LDA Estimators can be configured by setting hyperparameters. The available hyperparameters for LDA are documented below.
For further information on the AWS LDA algorithm, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/lda.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_type (str) – Type of EC2 instance to use for training, for example, ‘ml.c4.xlarge’.
- num_topics (int) – The number of topics for LDA to find within the data.
- alpha0 (float) – Optional. Initial guess for the concentration parameter
- max_restarts (int) – Optional. The number of restarts to perform during the Alternating Least Squares (ALS) spectral decomposition phase of the algorithm.
- max_iterations (int) – Optional. The maximum number of iterations to perform during the ALS phase of the algorithm.
- tol (float) – Optional. Target error tolerance for the ALS phase of the algorithm.
- **kwargs – base class keyword argument values.
-
repo_name
= 'lda'¶
-
repo_version
= 1¶
-
create_model
()¶ Return a
LDAModel
referencing the latest s3 model data produced by this Estimator.
-
classmethod
attach
(training_job_name, sagemaker_session=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 bedeploy()
ed to create a SageMaker Endpoint and return aPredictor
.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 customizecreate_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:
-
fit
(records, mini_batch_size=None, wait=True, logs=True, job_name=None)¶ Fit this Estimator on serialized Record objects, stored in S3.
records
should be an instance ofRecordSet
. This defines a collection of S3 data files to train thisEstimator
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 aRecordSet
object fromndarray
arrays.Parameters: - records (
RecordSet
) – The records to train thisEstimator
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. - wait (bool) – Whether the call should wait until the job completes (default: True).
- logs (bool) – Whether to show the logs produced by the job. Only meaningful when wait is True (default: True).
- job_name (str) – Training job name. If not specified, the estimator generates a default job name, based on the training image name and current timestamp.
- records (
-
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 numpyndarray
matrix and label vector.For the 2D
ndarray
train
, each row is converted to aRecord
object. The vector is stored in the “values” entry of thefeatures
property of each Record. Iflabels
is not None, each corresponding label is assigned to the “values” entry of thelabels
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
-
training_job_analytics
¶ Return a
TrainingJobAnalytics
object for the current training job.
-
class
sagemaker.
LDAModel
(model_data, role, sagemaker_session=None)¶ Bases:
sagemaker.model.Model
Reference LDA s3 model data. Calling
deploy()
creates an Endpoint and return a Predictor that transforms vectors to a lower-dimensional representation.
-
class
sagemaker.
LDAPredictor
(endpoint, sagemaker_session=None)¶ Bases:
sagemaker.predictor.RealTimePredictor
Transforms input vectors to lower-dimesional representations.
The implementation of
predict()
in this RealTimePredictor requires a numpyndarray
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 ofRecord
objects, one for each row in the inputndarray
. The lower dimension vector result is stored in theprojection
key of theRecord.label
field.