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Wendi Li
2020-11-25 20:16:29 +08:00
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examples/benchmarks/TFT/data_formatters/traffic.py
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# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
# Lint as: python3
"""Custom formatting functions for Traffic dataset.
Defines dataset specific column definitions and data transformations. This also
performs z-score normalization across the entire dataset, hence re-uses most of
the same functions as volatility.
"""
import data_formatters.base
import data_formatters.volatility
VolatilityFormatter = data_formatters.volatility.VolatilityFormatter
DataTypes = data_formatters.base.DataTypes
InputTypes = data_formatters.base.InputTypes
class TrafficFormatter(VolatilityFormatter):
"""Defines and formats data for the traffic dataset.
This also performs z-score normalization across the entire dataset, hence
re-uses most of the same functions as volatility.
Attributes:
column_definition: Defines input and data type of column used in the
experiment.
identifiers: Entity identifiers used in experiments.
"""
_column_definition = [
("id", DataTypes.REAL_VALUED, InputTypes.ID),
("hours_from_start", DataTypes.REAL_VALUED, InputTypes.TIME),
("values", DataTypes.REAL_VALUED, InputTypes.TARGET),
("time_on_day", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
("day_of_week", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
("hours_from_start", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
("categorical_id", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
]
def split_data(self, df, valid_boundary=151, test_boundary=166):
"""Splits data frame into training-validation-test data frames.
This also calibrates scaling object, and transforms data for each split.
Args:
df: Source data frame to split.
valid_boundary: Starting year for validation data
test_boundary: Starting year for test data
Returns:
Tuple of transformed (train, valid, test) data.
"""
print("Formatting train-valid-test splits.")
index = df["sensor_day"]
train = df.loc[index < valid_boundary]
valid = df.loc[(index >= valid_boundary - 7) & (index < test_boundary)]
test = df.loc[index >= test_boundary - 7]
self.set_scalers(train)
return (self.transform_inputs(data) for data in [train, valid, test])
# Default params
def get_fixed_params(self):
"""Returns fixed model parameters for experiments."""
fixed_params = {
"total_time_steps": 8 * 24,
"num_encoder_steps": 7 * 24,
"num_epochs": 100,
"early_stopping_patience": 5,
"multiprocessing_workers": 5,
}
return fixed_params
def get_default_model_params(self):
"""Returns default optimised model parameters."""
model_params = {
"dropout_rate": 0.3,
"hidden_layer_size": 320,
"learning_rate": 0.001,
"minibatch_size": 128,
"max_gradient_norm": 100.0,
"num_heads": 4,
"stack_size": 1,
}
return model_params
def get_num_samples_for_calibration(self):
"""Gets the default number of training and validation samples.
Use to sub-sample the data for network calibration and a value of -1 uses
all available samples.
Returns:
Tuple of (training samples, validation samples)
"""
return 450000, 50000