Add wrapper for FactorizationMachiones algorithm. #38
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| ========= | ||
| CHANGELOG | ||
| ========= | ||
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| 1.0.2 | ||
| ===== | ||
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| * feature: Estimators: add support for Amazon FactorizationMachines algorithm | ||
| * feature: Tests: add training failure tests for TF and MXNet | ||
| * feature: Documentation: show how to make predictions against existing endpoint | ||
| * feature: Estimators: implement write_spmatrix_to_sparse_tensor to support any scipy.sparse matrix | ||
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| 1.0.1 | ||
| ===== | ||
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| * api-change: Model: Remove support for 'supplemental_containers' when creating Model | ||
| * feature: Documentation: multiple updates | ||
| * feature: Tests: ignore tests data in tox.ini, increase timeout for endpoint creation, capture exceptions during endpoint deletion, tests for input-output functions | ||
| * feature: Logging: change to describe job every 30s when showing logs | ||
| * feature: Session: use custom user agent at all times | ||
| * feature: Setup: add travis file | ||
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| 1.0.0 | ||
| ===== | ||
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| * Initial commit | ||
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| # Copyright 2017 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. | ||
| from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry | ||
| from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer | ||
| from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa | ||
| from sagemaker.amazon.validation import gt, isin, isint, ge, isfloat | ||
| from sagemaker.predictor import RealTimePredictor | ||
| from sagemaker.model import Model | ||
| from sagemaker.session import Session | ||
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| class FactorizationMachines(AmazonAlgorithmEstimatorBase): | ||
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| repo = 'factorization-machines:1' | ||
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| num_factors = hp('num_factors', (gt(0), isint), 'An integer greater than zero') | ||
| predictor_type = hp('predictor_type', isin('binary_classifier', 'regressor'), | ||
| 'Value "binary_classifier" or "regressor"') | ||
| epochs = hp('epochs', (gt(0), isint), "An integer greater than 0") | ||
| clip_gradient = hp('clip_gradient', isfloat, "A float value") | ||
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There was a problem hiding this comment. This forces an explicit assignment, where an implicit assignment would be fine. If I have a FacotrizationMachines object fm, then: will fail, because 55 is not float - even though it can be represented as a float. Consider using the There was a problem hiding this comment. That's a good idea - will change |
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| eps = hp('eps', isfloat, "A float value") | ||
| rescale_grad = hp('rescale_grad', isfloat, "A float value") | ||
| bias_lr = hp('bias_lr', (ge(0), isfloat), "A non-negative float") | ||
| linear_lr = hp('linear_lr', (ge(0), isfloat), "A non-negative float") | ||
| factors_lr = hp('factors_lr', (ge(0), isfloat), "A non-negative float") | ||
| bias_wd = hp('bias_wd', (ge(0), isfloat), "A non-negative float") | ||
| linear_wd = hp('linear_wd', (ge(0), isfloat), "A non-negative float") | ||
| factors_wd = hp('factors_wd', (ge(0), isfloat), "A non-negative float") | ||
| bias_init_method = hp('bias_init_method', isin('normal', 'uniform', 'constant'), | ||
| 'Value "normal", "uniform" or "constant"') | ||
| bias_init_scale = hp('bias_init_scale', (ge(0), isfloat), "A non-negative float") | ||
| bias_init_sigma = hp('bias_init_sigma', (ge(0), isfloat), "A non-negative float") | ||
| bias_init_value = hp('bias_init_value', isfloat, "A float value") | ||
| linear_init_method = hp('linear_init_method', isin('normal', 'uniform', 'constant'), | ||
| 'Value "normal", "uniform" or "constant"') | ||
| linear_init_scale = hp('linear_init_scale', (ge(0), isfloat), "A non-negative float") | ||
| linear_init_sigma = hp('linear_init_sigma', (ge(0), isfloat), "A non-negative float") | ||
| linear_init_value = hp('linear_init_value', isfloat, "A float value") | ||
| factors_init_method = hp('factors_init_method', isin('normal', 'uniform', 'constant'), | ||
| 'Value "normal", "uniform" or "constant"') | ||
| factors_init_scale = hp('factors_init_scale', (ge(0), isfloat), "A non-negative float") | ||
| factors_init_sigma = hp('factors_init_sigma', (ge(0), isfloat), "A non-negative float") | ||
| factors_init_value = hp('factors_init_value', isfloat, "A float value") | ||
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| def __init__(self, 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): | ||
| """Factorization Machines is :class:`Estimator` for general-purpose supervised learning. | ||
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| 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. | ||
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| 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. | ||
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| 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 | ||
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| 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. | ||
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| FactorizationMachines Estimators can be configured by setting hyperparameters. The available hyperparameters for | ||
| FactorizationMachines are documented below. | ||
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| For further information on the AWS FactorizationMachines algorithm, | ||
| please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.html | ||
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| 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. | ||
| 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. | ||
| """ | ||
| super(FactorizationMachines, self).__init__(role, train_instance_count, train_instance_type, **kwargs) | ||
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| 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 | ||
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| def create_model(self): | ||
| """Return a :class:`~sagemaker.amazon.FactorizationMachinesModel` referencing the latest | ||
| s3 model data produced by this Estimator.""" | ||
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| return FactorizationMachinesModel(self.model_data, self.role, sagemaker_session=self.sagemaker_session) | ||
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| class FactorizationMachinesPredictor(RealTimePredictor): | ||
| """Performs binary-classification or regression prediction from input vectors. | ||
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| The implementation of :meth:`~sagemaker.predictor.RealTimePredictor.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. | ||
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| :meth:`predict()` returns a list of :class:`~sagemaker.amazon.record_pb2.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 | ||
| """ | ||
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| def __init__(self, endpoint, sagemaker_session=None): | ||
| super(FactorizationMachinesPredictor, self).__init__(endpoint, | ||
| sagemaker_session, | ||
| serializer=numpy_to_record_serializer(), | ||
| deserializer=record_deserializer()) | ||
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| class FactorizationMachinesModel(Model): | ||
| """Reference S3 model data created by FactorizationMachines estimator. Calling :meth:`~sagemaker.model.Model.deploy` | ||
| creates an Endpoint and returns :class:`FactorizationMachinesPredictor`.""" | ||
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| def __init__(self, model_data, role, sagemaker_session=None): | ||
| sagemaker_session = sagemaker_session or Session() | ||
| image = registry(sagemaker_session.boto_session.region_name) + "/" + FactorizationMachines.repo | ||
| super(FactorizationMachinesModel, self).__init__(model_data, | ||
| image, | ||
| role, | ||
| predictor_cls=FactorizationMachinesPredictor, | ||
| sagemaker_session=sagemaker_session) | ||
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Love this! Should have done this from the get-go.