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Wendi Li
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examples/benchmarks/TFT/data_formatters/electricity.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 Electricity dataset.
Defines dataset specific column definitions and data transformations. Uses
entity specific z-score normalization.
"""
import data_formatters.base
import libs.utils as utils
import pandas as pd
import sklearn.preprocessing
GenericDataFormatter = data_formatters.base.GenericDataFormatter
DataTypes = data_formatters.base.DataTypes
InputTypes = data_formatters.base.InputTypes
class ElectricityFormatter(GenericDataFormatter):
"""Defines and formats data for the electricity dataset.
Note that per-entity z-score normalization is used here, and is implemented
across functions.
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),
("power_usage", DataTypes.REAL_VALUED, InputTypes.TARGET),
("hour", 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 __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
self._time_steps = self.get_fixed_params()["total_time_steps"]
def split_data(self, df, valid_boundary=1315, test_boundary=1339):
"""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["days_from_start"]
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])
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)
# Format real scalers
real_inputs = utils.extract_cols_from_data_type(
DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
)
# Initialise scaler caches
self._real_scalers = {}
self._target_scaler = {}
identifiers = []
for identifier, sliced in df.groupby(id_column):
if len(sliced) >= self._time_steps:
data = sliced[real_inputs].values
targets = sliced[[target_column]].values
self._real_scalers[identifier] = sklearn.preprocessing.StandardScaler().fit(data)
self._target_scaler[identifier] = sklearn.preprocessing.StandardScaler().fit(targets)
identifiers.append(identifier)
# 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
# Extract identifiers in case required
self.identifiers = identifiers
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.
"""
if self._real_scalers is None and self._cat_scalers is None:
raise ValueError("Scalers have not been set!")
# Extract relevant columns
column_definitions = self.get_column_definition()
id_col = utils.get_single_col_by_input_type(InputTypes.ID, column_definitions)
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}
)
# Transform real inputs per entity
df_list = []
for identifier, sliced in df.groupby(id_col):
# Filter out any trajectories that are too short
if len(sliced) >= self._time_steps:
sliced_copy = sliced.copy()
sliced_copy[real_inputs] = self._real_scalers[identifier].transform(sliced_copy[real_inputs].values)
df_list.append(sliced_copy)
output = pd.concat(df_list, axis=0)
# 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.
"""
if self._target_scaler is None:
raise ValueError("Scalers have not been set!")
column_names = predictions.columns
df_list = []
for identifier, sliced in predictions.groupby("identifier"):
sliced_copy = sliced.copy()
target_scaler = self._target_scaler[identifier]
for col in column_names:
if col not in {"forecast_time", "identifier"}:
sliced_copy[col] = target_scaler.inverse_transform(sliced_copy[col])
df_list.append(sliced_copy)
output = pd.concat(df_list, axis=0)
return output
# 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": 0.1,
"hidden_layer_size": 160,
"learning_rate": 0.001,
"minibatch_size": 64,
"max_gradient_norm": 0.01,
"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