mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-09 22:10:56 +08:00
334 lines
12 KiB
Python
334 lines
12 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 Favorita dataset.
|
|
|
|
Defines dataset specific column definitions and data transformations.
|
|
"""
|
|
|
|
import data_formatters.base
|
|
import libs.utils as utils
|
|
import pandas as pd
|
|
import sklearn.preprocessing
|
|
|
|
DataTypes = data_formatters.base.DataTypes
|
|
InputTypes = data_formatters.base.InputTypes
|
|
|
|
|
|
class FavoritaFormatter(data_formatters.base.GenericDataFormatter):
|
|
"""Defines and formats data for the Favorita dataset.
|
|
|
|
Attributes:
|
|
column_definition: Defines input and data type of column used in the
|
|
experiment.
|
|
identifiers: Entity identifiers used in experiments.
|
|
"""
|
|
|
|
_column_definition = [
|
|
("traj_id", DataTypes.REAL_VALUED, InputTypes.ID),
|
|
("date", DataTypes.DATE, InputTypes.TIME),
|
|
("log_sales", DataTypes.REAL_VALUED, InputTypes.TARGET),
|
|
("onpromotion", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
|
|
("transactions", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
|
|
("oil", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
|
|
("day_of_week", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
|
|
("day_of_month", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
|
|
("month", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
|
|
("national_hol", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
|
|
("regional_hol", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
|
|
("local_hol", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
|
|
("open", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
|
|
("item_nbr", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
|
|
("store_nbr", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
|
|
("city", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
|
|
("state", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
|
|
("type", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
|
|
("cluster", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
|
|
("family", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
|
|
("class", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
|
|
("perishable", 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=None, test_boundary=None):
|
|
"""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.")
|
|
|
|
if valid_boundary is None:
|
|
valid_boundary = pd.datetime(2015, 12, 1)
|
|
|
|
fixed_params = self.get_fixed_params()
|
|
time_steps = fixed_params["total_time_steps"]
|
|
lookback = fixed_params["num_encoder_steps"]
|
|
forecast_horizon = time_steps - lookback
|
|
|
|
df["date"] = pd.to_datetime(df["date"])
|
|
df_lists = {"train": [], "valid": [], "test": []}
|
|
for _, sliced in df.groupby("traj_id"):
|
|
index = sliced["date"]
|
|
train = sliced.loc[index < valid_boundary]
|
|
train_len = len(train)
|
|
valid_len = train_len + forecast_horizon
|
|
valid = sliced.iloc[train_len - lookback : valid_len, :]
|
|
test = sliced.iloc[valid_len - lookback : valid_len + forecast_horizon, :]
|
|
|
|
sliced_map = {"train": train, "valid": valid, "test": test}
|
|
|
|
for k in sliced_map:
|
|
item = sliced_map[k]
|
|
|
|
if len(item) >= time_steps:
|
|
df_lists[k].append(item)
|
|
|
|
dfs = {k: pd.concat(df_lists[k], axis=0) for k in df_lists}
|
|
|
|
train = dfs["train"]
|
|
self.set_scalers(train, set_real=True)
|
|
|
|
# Use all data for label encoding to handle labels not present in training.
|
|
self.set_scalers(df, set_real=False)
|
|
|
|
# Filter out identifiers not present in training (i.e. cold-started items).
|
|
def filter_ids(frame):
|
|
identifiers = set(self.identifiers)
|
|
index = frame["traj_id"]
|
|
return frame.loc[index.apply(lambda x: x in identifiers)]
|
|
|
|
valid = filter_ids(dfs["valid"])
|
|
test = filter_ids(dfs["test"])
|
|
|
|
return (self.transform_inputs(data) for data in [train, valid, test])
|
|
|
|
def set_scalers(self, df, set_real=True):
|
|
"""Calibrates scalers using the data supplied.
|
|
|
|
Label encoding is applied to the entire dataset (i.e. including test),
|
|
so that unseen labels can be handled at run-time.
|
|
|
|
Args:
|
|
df: Data to use to calibrate scalers.
|
|
set_real: Whether to fit set real-valued or categorical 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)
|
|
|
|
if set_real:
|
|
|
|
# Extract identifiers in case required
|
|
self.identifiers = list(df[id_column].unique())
|
|
|
|
# Format real scalers
|
|
self._real_scalers = {}
|
|
for col in ["oil", "transactions", "log_sales"]:
|
|
self._real_scalers[col] = (df[col].mean(), df[col].std())
|
|
|
|
self._target_scaler = (df[target_column].mean(), df[target_column].std())
|
|
|
|
else:
|
|
# Format categorical scalers
|
|
categorical_inputs = utils.extract_cols_from_data_type(
|
|
DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
|
|
)
|
|
|
|
categorical_scalers = {}
|
|
num_classes = []
|
|
if self.identifiers is None:
|
|
raise ValueError("Scale real-valued inputs first!")
|
|
id_set = set(self.identifiers)
|
|
valid_idx = df["traj_id"].apply(lambda x: x in id_set)
|
|
for col in categorical_inputs:
|
|
# Set all to str so that we don't have mixed integer/string columns
|
|
srs = df[col].apply(str).loc[valid_idx]
|
|
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()
|
|
|
|
categorical_inputs = utils.extract_cols_from_data_type(
|
|
DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
|
|
)
|
|
|
|
# Format real inputs
|
|
for col in ["log_sales", "oil", "transactions"]:
|
|
mean, std = self._real_scalers[col]
|
|
output[col] = (df[col] - mean) / std
|
|
|
|
if col == "log_sales":
|
|
output[col] = output[col].fillna(0.0) # mean imputation
|
|
|
|
# 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
|
|
mean, std = self._target_scaler
|
|
for col in column_names:
|
|
if col not in {"forecast_time", "identifier"}:
|
|
output[col] = (predictions[col] * std) + mean
|
|
|
|
return output
|
|
|
|
# Default params
|
|
def get_fixed_params(self):
|
|
"""Returns fixed model parameters for experiments."""
|
|
|
|
fixed_params = {
|
|
"total_time_steps": 120,
|
|
"num_encoder_steps": 90,
|
|
"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": 240,
|
|
"learning_rate": 0.001,
|
|
"minibatch_size": 128,
|
|
"max_gradient_norm": 100.0,
|
|
"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
|
|
|
|
def get_column_definition(self):
|
|
""" "Formats column definition in order expected by the TFT.
|
|
|
|
Modified for Favorita to match column order of original experiment.
|
|
|
|
Returns:
|
|
Favorita-specific column definition
|
|
"""
|
|
|
|
column_definition = self._column_definition
|
|
|
|
# Sanity checks first.
|
|
# Ensure only one ID and time column exist
|
|
def _check_single_column(input_type):
|
|
|
|
length = len([tup for tup in column_definition if tup[2] == input_type])
|
|
|
|
if length != 1:
|
|
raise ValueError("Illegal number of inputs ({}) of type {}".format(length, input_type))
|
|
|
|
_check_single_column(InputTypes.ID)
|
|
_check_single_column(InputTypes.TIME)
|
|
|
|
identifier = [tup for tup in column_definition if tup[2] == InputTypes.ID]
|
|
time = [tup for tup in column_definition if tup[2] == InputTypes.TIME]
|
|
real_inputs = [
|
|
tup
|
|
for tup in column_definition
|
|
if tup[1] == DataTypes.REAL_VALUED and tup[2] not in {InputTypes.ID, InputTypes.TIME}
|
|
]
|
|
|
|
col_definition_map = {tup[0]: tup for tup in column_definition}
|
|
col_order = [
|
|
"item_nbr",
|
|
"store_nbr",
|
|
"city",
|
|
"state",
|
|
"type",
|
|
"cluster",
|
|
"family",
|
|
"class",
|
|
"perishable",
|
|
"onpromotion",
|
|
"day_of_week",
|
|
"national_hol",
|
|
"regional_hol",
|
|
"local_hol",
|
|
]
|
|
categorical_inputs = [col_definition_map[k] for k in col_order if k in col_definition_map]
|
|
|
|
return identifier + time + real_inputs + categorical_inputs
|