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118 lines
4.0 KiB
Python
118 lines
4.0 KiB
Python
# coding=utf-8
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# Copyright 2020 The Google Research Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""Custom formatting functions for Traffic dataset.
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Defines dataset specific column definitions and data transformations. This also
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performs z-score normalization across the entire dataset, hence re-uses most of
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the same functions as volatility.
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"""
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import data_formatters.base
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import data_formatters.volatility
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VolatilityFormatter = data_formatters.volatility.VolatilityFormatter
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DataTypes = data_formatters.base.DataTypes
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InputTypes = data_formatters.base.InputTypes
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class TrafficFormatter(VolatilityFormatter):
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"""Defines and formats data for the traffic dataset.
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This also performs z-score normalization across the entire dataset, hence
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re-uses most of the same functions as volatility.
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Attributes:
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column_definition: Defines input and data type of column used in the
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experiment.
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identifiers: Entity identifiers used in experiments.
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"""
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_column_definition = [
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("id", DataTypes.REAL_VALUED, InputTypes.ID),
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("hours_from_start", DataTypes.REAL_VALUED, InputTypes.TIME),
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("values", DataTypes.REAL_VALUED, InputTypes.TARGET),
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("time_on_day", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
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("day_of_week", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
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("hours_from_start", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
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("categorical_id", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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]
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def split_data(self, df, valid_boundary=151, test_boundary=166):
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"""Splits data frame into training-validation-test data frames.
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This also calibrates scaling object, and transforms data for each split.
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Args:
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df: Source data frame to split.
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valid_boundary: Starting year for validation data
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test_boundary: Starting year for test data
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Returns:
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Tuple of transformed (train, valid, test) data.
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"""
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print("Formatting train-valid-test splits.")
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index = df["sensor_day"]
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train = df.loc[index < valid_boundary]
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valid = df.loc[(index >= valid_boundary - 7) & (index < test_boundary)]
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test = df.loc[index >= test_boundary - 7]
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self.set_scalers(train)
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return (self.transform_inputs(data) for data in [train, valid, test])
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# Default params
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def get_fixed_params(self):
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"""Returns fixed model parameters for experiments."""
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fixed_params = {
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"total_time_steps": 8 * 24,
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"num_encoder_steps": 7 * 24,
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"num_epochs": 100,
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"early_stopping_patience": 5,
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"multiprocessing_workers": 5,
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}
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return fixed_params
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def get_default_model_params(self):
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"""Returns default optimised model parameters."""
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model_params = {
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"dropout_rate": 0.3,
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"hidden_layer_size": 320,
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"learning_rate": 0.001,
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"minibatch_size": 128,
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"max_gradient_norm": 100.0,
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"num_heads": 4,
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"stack_size": 1,
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}
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return model_params
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def get_num_samples_for_calibration(self):
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"""Gets the default number of training and validation samples.
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Use to sub-sample the data for network calibration and a value of -1 uses
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all available samples.
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Returns:
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Tuple of (training samples, validation samples)
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"""
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return 450000, 50000
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