-
Notifications
You must be signed in to change notification settings - Fork 1.1k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Add ntm algorithm with doc, unit tests, integ tests (#73)
- Loading branch information
Showing
15 changed files
with
603 additions
and
30 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,23 @@ | ||
NTM | ||
-------------------- | ||
|
||
The Amazon SageMaker NTM algorithm. | ||
|
||
.. autoclass:: sagemaker.NTM | ||
:members: | ||
:undoc-members: | ||
:show-inheritance: | ||
:inherited-members: | ||
:exclude-members: image, num_topics, encoder_layers, epochs, encoder_layers_activation, optimizer, tolerance, | ||
num_patience_epochs, batch_norm, rescale_gradient, clip_gradient, weight_decay, learning_rate | ||
|
||
|
||
.. autoclass:: sagemaker.NTMModel | ||
:members: | ||
:undoc-members: | ||
:show-inheritance: | ||
|
||
.. autoclass:: sagemaker.NTMPredictor | ||
:members: | ||
:undoc-members: | ||
:show-inheritance: |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,146 @@ | ||
# 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 ge, le, isin | ||
from sagemaker.predictor import RealTimePredictor | ||
from sagemaker.model import Model | ||
from sagemaker.session import Session | ||
|
||
|
||
class NTM(AmazonAlgorithmEstimatorBase): | ||
|
||
repo_name = 'ntm' | ||
repo_version = 1 | ||
|
||
num_topics = hp('num_topics', (ge(2), le(1000)), 'An integer in [2, 1000]', int) | ||
encoder_layers = hp(name='encoder_layers', validation_message='A comma separated list of ' | ||
'positive integers', data_type=list) | ||
epochs = hp('epochs', (ge(1), le(100)), 'An integer in [1, 100]', int) | ||
encoder_layers_activation = hp('encoder_layers_activation', isin('sigmoid', 'tanh', 'relu'), | ||
'One of "sigmoid", "tanh" or "relu"', str) | ||
optimizer = hp('optimizer', isin('adagrad', 'adam', 'rmsprop', 'sgd', 'adadelta'), | ||
'One of "adagrad", "adam", "rmsprop", "sgd" and "adadelta"', str) | ||
tolerance = hp('tolerance', (ge(1e-6), le(0.1)), 'A float in [1e-6, 0.1]', float) | ||
num_patience_epochs = hp('num_patience_epochs', (ge(1), le(10)), 'An integer in [1, 10]', int) | ||
batch_norm = hp(name='batch_norm', validation_message='Value must be a boolean', data_type=bool) | ||
rescale_gradient = hp('rescale_gradient', (ge(1e-3), le(1.0)), 'A float in [1e-3, 1.0]', float) | ||
clip_gradient = hp('clip_gradient', ge(1e-3), 'A float greater equal to 1e-3', float) | ||
weight_decay = hp('weight_decay', (ge(0.0), le(1.0)), 'A float in [0.0, 1.0]', float) | ||
learning_rate = hp('learning_rate', (ge(1e-6), le(1.0)), 'A float in [1e-6, 1.0]', float) | ||
|
||
def __init__(self, role, train_instance_count, train_instance_type, num_topics, | ||
encoder_layers=None, epochs=None, encoder_layers_activation=None, optimizer=None, tolerance=None, | ||
num_patience_epochs=None, batch_norm=None, rescale_gradient=None, clip_gradient=None, | ||
weight_decay=None, learning_rate=None, **kwargs): | ||
"""Neural Topic Model (NTM) is :class:`Estimator` used for unsupervised learning. | ||
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.ntm.NTMPredictor` object that can be used | ||
for inference calls using the trained model hosted in the SageMaker Endpoint. | ||
NTM Estimators can be configured by setting hyperparameters. The available hyperparameters for | ||
NTM are documented below. | ||
For further information on the AWS NTM algorithm, | ||
please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/ntm.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. | ||
train_instance_type (str): Type of EC2 instance to use for training, for example, 'ml.c4.xlarge'. | ||
num_topics (int): Required. The number of topics for NTM to find within the data. | ||
encoder_layers (list): Optional. Represents number of layers in the encoder and the output size of | ||
each layer. | ||
epochs (int): Optional. Maximum number of passes over the training data. | ||
encoder_layers_activation (str): Optional. Activation function to use in the encoder layers. | ||
optimizer (str): Optional. Optimizer to use for training. | ||
tolerance (float): Optional. Maximum relative change in the loss function within the last | ||
num_patience_epochs number of epochs below which early stopping is triggered. | ||
num_patience_epochs (int): Optional. Number of successive epochs over which early stopping criterion | ||
is evaluated. | ||
batch_norm (bool): Optional. Whether to use batch normalization during training. | ||
rescale_gradient (float): Optional. Rescale factor for gradient. | ||
clip_gradient (float): Optional. Maximum magnitude for each gradient component. | ||
weight_decay (float): Optional. Weight decay coefficient. Adds L2 regularization. | ||
learning_rate (float): Optional. Learning rate for the optimizer. | ||
**kwargs: base class keyword argument values. | ||
""" | ||
|
||
super(NTM, self).__init__(role, train_instance_count, train_instance_type, **kwargs) | ||
self.num_topics = num_topics | ||
self.encoder_layers = encoder_layers | ||
self.epochs = epochs | ||
self.encoder_layers_activation = encoder_layers_activation | ||
self.optimizer = optimizer | ||
self.tolerance = tolerance | ||
self.num_patience_epochs = num_patience_epochs | ||
self.batch_norm = batch_norm | ||
self.rescale_gradient = rescale_gradient | ||
self.clip_gradient = clip_gradient | ||
self.weight_decay = weight_decay | ||
self.learning_rate = learning_rate | ||
|
||
def create_model(self): | ||
"""Return a :class:`~sagemaker.amazon.NTMModel` referencing the latest | ||
s3 model data produced by this Estimator.""" | ||
|
||
return NTMModel(self.model_data, self.role, sagemaker_session=self.sagemaker_session) | ||
|
||
def fit(self, records, mini_batch_size=None, **kwargs): | ||
if mini_batch_size is not None and (mini_batch_size < 1 or mini_batch_size > 10000): | ||
raise ValueError("mini_batch_size must be in [1, 10000]") | ||
super(NTM, self).fit(records, mini_batch_size, **kwargs) | ||
|
||
|
||
class NTMPredictor(RealTimePredictor): | ||
"""Transforms input vectors to lower-dimesional representations. | ||
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. | ||
:meth:`predict()` returns a list of :class:`~sagemaker.amazon.record_pb2.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.""" | ||
|
||
def __init__(self, endpoint, sagemaker_session=None): | ||
super(NTMPredictor, self).__init__(endpoint, sagemaker_session, serializer=numpy_to_record_serializer(), | ||
deserializer=record_deserializer()) | ||
|
||
|
||
class NTMModel(Model): | ||
"""Reference NTM s3 model data. Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and return | ||
a Predictor that transforms vectors to a lower-dimensional representation.""" | ||
|
||
def __init__(self, model_data, role, sagemaker_session=None): | ||
sagemaker_session = sagemaker_session or Session() | ||
repo = '{}:{}'.format(NTM.repo_name, NTM.repo_version) | ||
image = '{}/{}'.format(registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo) | ||
super(NTMModel, self).__init__(model_data, image, role, predictor_cls=NTMPredictor, | ||
sagemaker_session=sagemaker_session) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Binary file not shown.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,23 @@ | ||
from six.moves.urllib.parse import urlparse | ||
|
||
from sagemaker.amazon.amazon_estimator import RecordSet | ||
from sagemaker.utils import sagemaker_timestamp | ||
|
||
|
||
def prepare_record_set_from_local_files(dir_path, destination, num_records, feature_dim, sagemaker_session): | ||
"""Build a :class:`~RecordSet` by pointing to local files. | ||
Args: | ||
dir_path (string): Path to local directory from where the files shall be uploaded. | ||
destination (string): S3 path to upload the file to. | ||
num_records (int): Number of records in all the files | ||
feature_dim (int): Number of features in the data set | ||
sagemaker_session (sagemaker.session.Session): Session object to manage interactions with Amazon SageMaker APIs. | ||
Returns: | ||
RecordSet: A RecordSet specified by S3Prefix to to be used in training. | ||
""" | ||
key_prefix = urlparse(destination).path | ||
key_prefix = key_prefix + '{}-{}'.format("testfiles", sagemaker_timestamp()) | ||
key_prefix = key_prefix.lstrip('/') | ||
uploaded_location = sagemaker_session.upload_data(path=dir_path, key_prefix=key_prefix) | ||
return RecordSet(uploaded_location, num_records, feature_dim, s3_data_type='S3Prefix') |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,57 @@ | ||
# 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. | ||
import boto3 | ||
import numpy as np | ||
import os | ||
|
||
import sagemaker | ||
from sagemaker import NTM, NTMModel | ||
from sagemaker.amazon.common import read_records | ||
from sagemaker.utils import name_from_base | ||
|
||
from tests.integ import DATA_DIR, REGION | ||
from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name | ||
from tests.integ.record_set import prepare_record_set_from_local_files | ||
|
||
|
||
def test_ntm(): | ||
|
||
with timeout(minutes=15): | ||
sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION)) | ||
data_path = os.path.join(DATA_DIR, 'ntm') | ||
data_filename = 'nips-train_1.pbr' | ||
|
||
with open(os.path.join(data_path, data_filename), 'rb') as f: | ||
all_records = read_records(f) | ||
|
||
# all records must be same | ||
feature_num = int(all_records[0].features['values'].float32_tensor.shape[0]) | ||
|
||
ntm = NTM(role='SageMakerRole', train_instance_count=1, train_instance_type='ml.c4.xlarge', num_topics=10, | ||
sagemaker_session=sagemaker_session, base_job_name='test-ntm') | ||
|
||
record_set = prepare_record_set_from_local_files(data_path, ntm.data_location, | ||
len(all_records), feature_num, sagemaker_session) | ||
ntm.fit(record_set, None) | ||
|
||
endpoint_name = name_from_base('ntm') | ||
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20): | ||
model = NTMModel(ntm.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session) | ||
predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name) | ||
|
||
predict_input = np.random.rand(1, feature_num) | ||
result = predictor.predict(predict_input) | ||
|
||
assert len(result) == 1 | ||
for record in result: | ||
assert record.label["topic_weights"] is not None |
Oops, something went wrong.