mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-09 14:00:55 +08:00
118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
# 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.,
|
|
'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
|