Table Of Contents
Table Of Contents

Source code for gluonts.model.simple_feedforward._estimator

# Copyright 2018, 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
# or in the "license" file accompanying this file. This file is distributed
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.

# Standard library imports
from typing import List, Optional

# Third-party imports
from mxnet.gluon import HybridBlock

# First-party imports
from gluonts.core.component import validated
from gluonts.distribution import DistributionOutput, StudentTOutput
from gluonts.model.estimator import GluonEstimator
from gluonts.model.predictor import Predictor, RepresentableBlockPredictor
from gluonts.trainer import Trainer
from gluonts.transform import (

# Relative imports
from ._network import (

[docs]class SimpleFeedForwardEstimator(GluonEstimator): """ SimpleFeedForwardEstimator shows how to build a simple MLP model predicting the next target time-steps given the previous ones. Given that we want to define a gluon model trainable by SGD, we inherit the parent class `GluonEstimator` that handles most of the logic for fitting a neural-network. We thus only have to define: 1. How the data is transformed before being fed to our model:: def create_transformation(self) -> Transformation 2. How the training happens:: def create_training_network(self) -> HybridBlock 3. how the predictions can be made for a batch given a trained network:: def create_predictor( self, transformation: Transformation, trained_net: HybridBlock, ) -> Predictor Parameters ---------- freq Time time granularity of the data prediction_length Length of the prediction horizon trainer Trainer object to be used (default: Trainer()) num_hidden_dimensions Number of hidden nodes in each layer (default: [40, 40]) context_length Number of time units that condition the predictions (default: None, in which case context_length = prediction_length) distr_output Distribution to fit (default: StudentTOutput()) batch_normalization Whether to use batch normalization (default: False) mean_scaling Scale the network input by the data mean and the network output by its inverse (default: True) num_parallel_samples Number of evaluation samples per time series to increase parallelism during inference. This is a model optimization that does not affect the accuracy (default: 100) """ # The validated() decorator makes sure that parameters are checked by # Pydantic and allows to serialize/print models. Note that all parameters # have defaults except for `freq` and `prediction_length`. which is # recommended in GluonTS to allow to compare models easily. @validated() def __init__( self, freq: str, prediction_length: int, trainer: Trainer = Trainer(), num_hidden_dimensions: List[int] = list([40, 40]), context_length: Optional[int] = None, distr_output: DistributionOutput = StudentTOutput(), batch_normalization: bool = False, mean_scaling: bool = True, num_parallel_samples: int = 100, ) -> None: """ Defines an estimator. All parameters should be serializable. """ super().__init__(trainer=trainer) assert ( prediction_length > 0 ), "The value of `prediction_length` should be > 0" assert ( context_length is None or context_length > 0 ), "The value of `context_length` should be > 0" assert all( [d > 0 for d in num_hidden_dimensions] ), "Elements of `num_hidden_dimensions` should be > 0" assert ( num_parallel_samples > 0 ), "The value of `num_parallel_samples` should be > 0" self.num_hidden_dimensions = num_hidden_dimensions self.prediction_length = prediction_length self.context_length = ( context_length if context_length is not None else prediction_length ) self.freq = freq self.distr_output = distr_output self.batch_normalization = batch_normalization self.mean_scaling = mean_scaling self.num_parallel_samples = num_parallel_samples # here we do only a simple operation to convert the input data to a form # that can be digested by our model by only splitting the target in two, a # conditioning part and a to-predict part, for each training example. # fFr a more complex transformation example, see the `gluonts.model.deepar` # transformation that includes time features, age feature, observed values # indicator, ...
[docs] def create_transformation(self) -> Transformation: return Chain( [ InstanceSplitter( target_field=FieldName.TARGET, is_pad_field=FieldName.IS_PAD, start_field=FieldName.START, forecast_start_field=FieldName.FORECAST_START, train_sampler=ExpectedNumInstanceSampler(num_instances=1), past_length=self.context_length, future_length=self.prediction_length, time_series_fields=[], # [FieldName.FEAT_DYNAMIC_REAL] ) ] )
# defines the network, we get to see one batch to initialize it. # the network should return at least one tensor that is used as a loss to minimize in the training loop. # several tensors can be returned for instance for analysis, see DeepARTrainingNetwork for an example.
[docs] def create_training_network(self) -> HybridBlock: return SimpleFeedForwardTrainingNetwork( num_hidden_dimensions=self.num_hidden_dimensions, prediction_length=self.prediction_length, context_length=self.context_length, distr_output=self.distr_output, batch_normalization=self.batch_normalization, mean_scaling=self.mean_scaling, )
# we now define how the prediction happens given that we are provided a # training network.
[docs] def create_predictor( self, transformation: Transformation, trained_network: HybridBlock ) -> Predictor: prediction_network = SimpleFeedForwardPredictionNetwork( num_hidden_dimensions=self.num_hidden_dimensions, prediction_length=self.prediction_length, context_length=self.context_length, distr_output=self.distr_output, batch_normalization=self.batch_normalization, mean_scaling=self.mean_scaling, params=trained_network.collect_params(), num_parallel_samples=self.num_parallel_samples, ) return RepresentableBlockPredictor( input_transform=transformation, prediction_net=prediction_network, batch_size=self.trainer.batch_size, freq=self.freq, prediction_length=self.prediction_length, ctx=self.trainer.ctx, )