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Format TFT

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Wendi Li
2020-11-23 16:09:03 +08:00
committed by you-n-g
parent 93323ed6b3
commit c2c96a817f
15 changed files with 3821 additions and 3971 deletions
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examples/benchmarks/TFT/expt_settings/__init__.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.
# 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.

218
examples/benchmarks/TFT/expt_settings/configs.py
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@@ -1,111 +1,107 @@
# 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
"""Default configs for TFT experiments.
Contains the default output paths for data, serialised models and predictions
for the main experiments used in the publication.
"""
import os
import data_formatters.electricity
import data_formatters.favorita
import data_formatters.traffic
import data_formatters.volatility
import data_formatters.qlib_Alpha158
class ExperimentConfig(object):
"""Defines experiment configs and paths to outputs.
Attributes:
root_folder: Root folder to contain all experimental outputs.
experiment: Name of experiment to run.
data_folder: Folder to store data for experiment.
model_folder: Folder to store serialised models.
results_folder: Folder to store results.
data_csv_path: Path to primary data csv file used in experiment.
hyperparam_iterations: Default number of random search iterations for
experiment.
"""
default_experiments = ['volatility', 'electricity', 'traffic', 'favorita', 'Alpha158']
def __init__(self, experiment='volatility', root_folder=None):
"""Creates configs based on default experiment chosen.
Args:
experiment: Name of experiment.
root_folder: Root folder to save all outputs of training.
"""
if experiment not in self.default_experiments:
raise ValueError('Unrecognised experiment={}'.format(experiment))
# Defines all relevant paths
if root_folder is None:
root_folder = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..', 'outputs')
print('Using root folder {}'.format(root_folder))
self.root_folder = root_folder
self.experiment = experiment
self.data_folder = os.path.join(root_folder, 'data', experiment)
self.model_folder = os.path.join(root_folder, 'saved_models', experiment)
self.results_folder = os.path.join(root_folder, 'results', experiment)
# Creates folders if they don't exist
for relevant_directory in [
self.root_folder, self.data_folder, self.model_folder,
self.results_folder
]:
if not os.path.exists(relevant_directory):
os.makedirs(relevant_directory)
@property
def data_csv_path(self):
csv_map = {
'volatility': 'formatted_omi_vol.csv',
'electricity': 'hourly_electricity.csv',
'traffic': 'hourly_data.csv',
'favorita': 'favorita_consolidated.csv',
'Alpha158': 'Alpha158.csv',
}
return os.path.join(self.data_folder, csv_map[self.experiment])
@property
def hyperparam_iterations(self):
return 240 if self.experiment == 'volatility' else 60
def make_data_formatter(self):
"""Gets a data formatter object for experiment.
Returns:
Default DataFormatter per experiment.
"""
data_formatter_class = {
'volatility': data_formatters.volatility.VolatilityFormatter,
'electricity': data_formatters.electricity.ElectricityFormatter,
'traffic': data_formatters.traffic.TrafficFormatter,
'favorita': data_formatters.favorita.FavoritaFormatter,
'Alpha158': data_formatters.qlib_Alpha158.Alpha158Formatter,
}
return data_formatter_class[self.experiment]()
# 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
"""Default configs for TFT experiments.
Contains the default output paths for data, serialised models and predictions
for the main experiments used in the publication.
"""
import os
import data_formatters.electricity
import data_formatters.favorita
import data_formatters.traffic
import data_formatters.volatility
import data_formatters.qlib_Alpha158
class ExperimentConfig(object):
"""Defines experiment configs and paths to outputs.
Attributes:
root_folder: Root folder to contain all experimental outputs.
experiment: Name of experiment to run.
data_folder: Folder to store data for experiment.
model_folder: Folder to store serialised models.
results_folder: Folder to store results.
data_csv_path: Path to primary data csv file used in experiment.
hyperparam_iterations: Default number of random search iterations for
experiment.
"""
default_experiments = ["volatility", "electricity", "traffic", "favorita", "Alpha158"]
def __init__(self, experiment="volatility", root_folder=None):
"""Creates configs based on default experiment chosen.
Args:
experiment: Name of experiment.
root_folder: Root folder to save all outputs of training.
"""
if experiment not in self.default_experiments:
raise ValueError("Unrecognised experiment={}".format(experiment))
# Defines all relevant paths
if root_folder is None:
root_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "outputs")
print("Using root folder {}".format(root_folder))
self.root_folder = root_folder
self.experiment = experiment
self.data_folder = os.path.join(root_folder, "data", experiment)
self.model_folder = os.path.join(root_folder, "saved_models", experiment)
self.results_folder = os.path.join(root_folder, "results", experiment)
# Creates folders if they don't exist
for relevant_directory in [self.root_folder, self.data_folder, self.model_folder, self.results_folder]:
if not os.path.exists(relevant_directory):
os.makedirs(relevant_directory)
@property
def data_csv_path(self):
csv_map = {
"volatility": "formatted_omi_vol.csv",
"electricity": "hourly_electricity.csv",
"traffic": "hourly_data.csv",
"favorita": "favorita_consolidated.csv",
"Alpha158": "Alpha158.csv",
}
return os.path.join(self.data_folder, csv_map[self.experiment])
@property
def hyperparam_iterations(self):
return 240 if self.experiment == "volatility" else 60
def make_data_formatter(self):
"""Gets a data formatter object for experiment.
Returns:
Default DataFormatter per experiment.
"""
data_formatter_class = {
"volatility": data_formatters.volatility.VolatilityFormatter,
"electricity": data_formatters.electricity.ElectricityFormatter,
"traffic": data_formatters.traffic.TrafficFormatter,
"favorita": data_formatters.favorita.FavoritaFormatter,
"Alpha158": data_formatters.qlib_Alpha158.Alpha158Formatter,
}
return data_formatter_class[self.experiment]()