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Wendi Li
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examples/benchmarks/TFT/data_formatters/qlib_Alpha158.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 Alpha158 dataset.
Defines dataset specific column definitions and data transformations.
"""
import data_formatters.base
import libs.utils as utils
import sklearn.preprocessing
GenericDataFormatter = data_formatters.base.GenericDataFormatter
DataTypes = data_formatters.base.DataTypes
InputTypes = data_formatters.base.InputTypes
class Alpha158Formatter(GenericDataFormatter):
"""Defines and formats data for the Alpha158 dataset.
Attributes:
column_definition: Defines input and data type of column used in the
experiment.
identifiers: Entity identifiers used in experiments.
"""
_column_definition = [
("instrument", DataTypes.CATEGORICAL, InputTypes.ID),
("LABEL0", DataTypes.REAL_VALUED, InputTypes.TARGET),
("date", DataTypes.DATE, InputTypes.TIME),
("month", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
("day_of_week", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
# Selected 10 features
("RESI5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("WVMA5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("RSQR5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("KLEN", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("RSQR10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("CORR5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("CORD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("CORR10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("ROC60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("RESI10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("const", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
]
def __init__(self):
"""Initialises formatter."""
self.identifiers = None
self._real_scalers = None
self._cat_scalers = None
self._target_scaler = None
self._num_classes_per_cat_input = None
def split_data(self, df, valid_boundary=2016, test_boundary=2018):
"""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["year"]
train = df.loc[index < valid_boundary]
valid = df.loc[(index >= valid_boundary) & (index < test_boundary)]
test = df.loc[index >= test_boundary]
self.set_scalers(train)
return (self.transform_inputs(data) for data in [train, valid, test])
def set_scalers(self, df):
"""Calibrates scalers using the data supplied.
Args:
df: Data to use to calibrate scalers.
"""
print("Setting scalers with training data...")
column_definitions = self.get_column_definition()
id_column = utils.get_single_col_by_input_type(InputTypes.ID, column_definitions)
target_column = utils.get_single_col_by_input_type(InputTypes.TARGET, column_definitions)
# Extract identifiers in case required
self.identifiers = list(df[id_column].unique())
# Format real scalers
real_inputs = utils.extract_cols_from_data_type(
DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
)
data = df[real_inputs].values
self._real_scalers = sklearn.preprocessing.StandardScaler().fit(data)
self._target_scaler = sklearn.preprocessing.StandardScaler().fit(
df[[target_column]].values
) # used for predictions
# Format categorical scalers
categorical_inputs = utils.extract_cols_from_data_type(
DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
)
categorical_scalers = {}
num_classes = []
for col in categorical_inputs:
# Set all to str so that we don't have mixed integer/string columns
srs = df[col].apply(str)
categorical_scalers[col] = sklearn.preprocessing.LabelEncoder().fit(srs.values)
num_classes.append(srs.nunique())
# Set categorical scaler outputs
self._cat_scalers = categorical_scalers
self._num_classes_per_cat_input = num_classes
def transform_inputs(self, df):
"""Performs feature transformations.
This includes both feature engineering, preprocessing and normalisation.
Args:
df: Data frame to transform.
Returns:
Transformed data frame.
"""
output = df.copy()
if self._real_scalers is None and self._cat_scalers is None:
raise ValueError("Scalers have not been set!")
column_definitions = self.get_column_definition()
real_inputs = utils.extract_cols_from_data_type(
DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
)
categorical_inputs = utils.extract_cols_from_data_type(
DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
)
# Format real inputs
output[real_inputs] = self._real_scalers.transform(df[real_inputs].values)
# Format categorical inputs
for col in categorical_inputs:
string_df = df[col].apply(str)
output[col] = self._cat_scalers[col].transform(string_df)
return output
def format_predictions(self, predictions):
"""Reverts any normalisation to give predictions in original scale.
Args:
predictions: Dataframe of model predictions.
Returns:
Data frame of unnormalised predictions.
"""
output = predictions.copy()
column_names = predictions.columns
for col in column_names:
if col not in {"forecast_time", "identifier"}:
output[col] = self._target_scaler.inverse_transform(predictions[col])
return output
# Default params
def get_fixed_params(self):
"""Returns fixed model parameters for experiments."""
fixed_params = {
"total_time_steps": 6 + 6,
"num_encoder_steps": 6,
"num_epochs": 100,
"early_stopping_patience": 10,
"multiprocessing_workers": 5,
}
return fixed_params
def get_default_model_params(self):
"""Returns default optimised model parameters."""
model_params = {
"dropout_rate": 0.4,
"hidden_layer_size": 160,
"learning_rate": 0.0001,
"minibatch_size": 128,
"max_gradient_norm": 0.0135,
"num_heads": 1,
"stack_size": 1,
}
return model_params
# 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 Alpha158 dataset.
Defines dataset specific column definitions and data transformations.
"""
import data_formatters.base
import libs.utils as utils
import sklearn.preprocessing
GenericDataFormatter = data_formatters.base.GenericDataFormatter
DataTypes = data_formatters.base.DataTypes
InputTypes = data_formatters.base.InputTypes
class Alpha158Formatter(GenericDataFormatter):
"""Defines and formats data for the Alpha158 dataset.
Attributes:
column_definition: Defines input and data type of column used in the
experiment.
identifiers: Entity identifiers used in experiments.
"""
_column_definition = [
("instrument", DataTypes.CATEGORICAL, InputTypes.ID),
("LABEL0", DataTypes.REAL_VALUED, InputTypes.TARGET),
("date", DataTypes.DATE, InputTypes.TIME),
("month", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
("day_of_week", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
# Selected 10 features
("RESI5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("WVMA5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("RSQR5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("KLEN", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("RSQR10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("CORR5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("CORD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("CORR10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("ROC60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("RESI10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("VSTD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("RSQR60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("CORR60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("WVMA60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("STD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("RSQR20", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("CORD60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("CORD10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("CORR20", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("KLOW", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
("const", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
]
def __init__(self):
"""Initialises formatter."""
self.identifiers = None
self._real_scalers = None
self._cat_scalers = None
self._target_scaler = None
self._num_classes_per_cat_input = None
def split_data(self, df, valid_boundary=2016, test_boundary=2018):
"""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["year"]
train = df.loc[index < valid_boundary]
valid = df.loc[(index >= valid_boundary) & (index < test_boundary)]
test = df.loc[index >= test_boundary]
self.set_scalers(train)
return (self.transform_inputs(data) for data in [train, valid, test])
def set_scalers(self, df):
"""Calibrates scalers using the data supplied.
Args:
df: Data to use to calibrate scalers.
"""
print("Setting scalers with training data...")
column_definitions = self.get_column_definition()
id_column = utils.get_single_col_by_input_type(InputTypes.ID, column_definitions)
target_column = utils.get_single_col_by_input_type(InputTypes.TARGET, column_definitions)
# Extract identifiers in case required
self.identifiers = list(df[id_column].unique())
# Format real scalers
real_inputs = utils.extract_cols_from_data_type(
DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
)
data = df[real_inputs].values
self._real_scalers = sklearn.preprocessing.StandardScaler().fit(data)
self._target_scaler = sklearn.preprocessing.StandardScaler().fit(
df[[target_column]].values
) # used for predictions
# Format categorical scalers
categorical_inputs = utils.extract_cols_from_data_type(
DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
)
categorical_scalers = {}
num_classes = []
for col in categorical_inputs:
# Set all to str so that we don't have mixed integer/string columns
srs = df[col].apply(str)
categorical_scalers[col] = sklearn.preprocessing.LabelEncoder().fit(srs.values)
num_classes.append(srs.nunique())
# Set categorical scaler outputs
self._cat_scalers = categorical_scalers
self._num_classes_per_cat_input = num_classes
def transform_inputs(self, df):
"""Performs feature transformations.
This includes both feature engineering, preprocessing and normalisation.
Args:
df: Data frame to transform.
Returns:
Transformed data frame.
"""
output = df.copy()
if self._real_scalers is None and self._cat_scalers is None:
raise ValueError("Scalers have not been set!")
column_definitions = self.get_column_definition()
real_inputs = utils.extract_cols_from_data_type(
DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
)
categorical_inputs = utils.extract_cols_from_data_type(
DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
)
# Format real inputs
output[real_inputs] = self._real_scalers.transform(df[real_inputs].values)
# Format categorical inputs
for col in categorical_inputs:
string_df = df[col].apply(str)
output[col] = self._cat_scalers[col].transform(string_df)
return output
def format_predictions(self, predictions):
"""Reverts any normalisation to give predictions in original scale.
Args:
predictions: Dataframe of model predictions.
Returns:
Data frame of unnormalised predictions.
"""
output = predictions.copy()
column_names = predictions.columns
for col in column_names:
if col not in {"forecast_time", "identifier"}:
output[col] = self._target_scaler.inverse_transform(predictions[col])
return output
# Default params
def get_fixed_params(self):
"""Returns fixed model parameters for experiments."""
fixed_params = {
"total_time_steps": 6 + 6,
"num_encoder_steps": 6,
"num_epochs": 100,
"early_stopping_patience": 10,
"multiprocessing_workers": 5,
}
return fixed_params
def get_default_model_params(self):
"""Returns default optimised model parameters."""
model_params = {
"dropout_rate": 0.4,
"hidden_layer_size": 160,
"learning_rate": 0.0001,
"minibatch_size": 128,
"max_gradient_norm": 0.0135,
"num_heads": 1,
"stack_size": 1,
}
return model_params