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Delete favorita.py
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Wendi Li
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# coding=utf-8
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# Copyright 2020 The Google Research Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""Custom formatting functions for Favorita dataset.
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Defines dataset specific column definitions and data transformations.
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"""
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import data_formatters.base
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import libs.utils as utils
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import pandas as pd
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import sklearn.preprocessing
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DataTypes = data_formatters.base.DataTypes
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InputTypes = data_formatters.base.InputTypes
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class FavoritaFormatter(data_formatters.base.GenericDataFormatter):
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"""Defines and formats data for the Favorita dataset.
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Attributes:
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column_definition: Defines input and data type of column used in the
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experiment.
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identifiers: Entity identifiers used in experiments.
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"""
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_column_definition = [
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("traj_id", DataTypes.REAL_VALUED, InputTypes.ID),
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("date", DataTypes.DATE, InputTypes.TIME),
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("log_sales", DataTypes.REAL_VALUED, InputTypes.TARGET),
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("onpromotion", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
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("transactions", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("oil", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("day_of_week", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
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("day_of_month", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
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("month", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
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("national_hol", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
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("regional_hol", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
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("local_hol", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
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("open", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
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("item_nbr", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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("store_nbr", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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("city", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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("state", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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("type", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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("cluster", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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("family", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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("class", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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("perishable", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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]
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def __init__(self):
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"""Initialises formatter."""
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self.identifiers = None
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self._real_scalers = None
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self._cat_scalers = None
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self._target_scaler = None
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self._num_classes_per_cat_input = None
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def split_data(self, df, valid_boundary=None, test_boundary=None):
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"""Splits data frame into training-validation-test data frames.
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This also calibrates scaling object, and transforms data for each split.
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Args:
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df: Source data frame to split.
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valid_boundary: Starting year for validation data
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test_boundary: Starting year for test data
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Returns:
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Tuple of transformed (train, valid, test) data.
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"""
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print("Formatting train-valid-test splits.")
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if valid_boundary is None:
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valid_boundary = pd.datetime(2015, 12, 1)
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fixed_params = self.get_fixed_params()
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time_steps = fixed_params["total_time_steps"]
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lookback = fixed_params["num_encoder_steps"]
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forecast_horizon = time_steps - lookback
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df["date"] = pd.to_datetime(df["date"])
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df_lists = {"train": [], "valid": [], "test": []}
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for _, sliced in df.groupby("traj_id"):
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index = sliced["date"]
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train = sliced.loc[index < valid_boundary]
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train_len = len(train)
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valid_len = train_len + forecast_horizon
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valid = sliced.iloc[train_len - lookback : valid_len, :]
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test = sliced.iloc[valid_len - lookback : valid_len + forecast_horizon, :]
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sliced_map = {"train": train, "valid": valid, "test": test}
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for k in sliced_map:
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item = sliced_map[k]
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if len(item) >= time_steps:
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df_lists[k].append(item)
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dfs = {k: pd.concat(df_lists[k], axis=0) for k in df_lists}
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train = dfs["train"]
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self.set_scalers(train, set_real=True)
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# Use all data for label encoding to handle labels not present in training.
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self.set_scalers(df, set_real=False)
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# Filter out identifiers not present in training (i.e. cold-started items).
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def filter_ids(frame):
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identifiers = set(self.identifiers)
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index = frame["traj_id"]
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return frame.loc[index.apply(lambda x: x in identifiers)]
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valid = filter_ids(dfs["valid"])
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test = filter_ids(dfs["test"])
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return (self.transform_inputs(data) for data in [train, valid, test])
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def set_scalers(self, df, set_real=True):
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"""Calibrates scalers using the data supplied.
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Label encoding is applied to the entire dataset (i.e. including test),
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so that unseen labels can be handled at run-time.
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Args:
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df: Data to use to calibrate scalers.
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set_real: Whether to fit set real-valued or categorical scalers
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"""
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print("Setting scalers with training data...")
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column_definitions = self.get_column_definition()
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id_column = utils.get_single_col_by_input_type(InputTypes.ID, column_definitions)
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target_column = utils.get_single_col_by_input_type(InputTypes.TARGET, column_definitions)
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if set_real:
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# Extract identifiers in case required
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self.identifiers = list(df[id_column].unique())
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# Format real scalers
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self._real_scalers = {}
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for col in ["oil", "transactions", "log_sales"]:
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self._real_scalers[col] = (df[col].mean(), df[col].std())
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self._target_scaler = (df[target_column].mean(), df[target_column].std())
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else:
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# Format categorical scalers
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categorical_inputs = utils.extract_cols_from_data_type(
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DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
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)
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categorical_scalers = {}
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num_classes = []
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if self.identifiers is None:
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raise ValueError("Scale real-valued inputs first!")
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id_set = set(self.identifiers)
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valid_idx = df["traj_id"].apply(lambda x: x in id_set)
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for col in categorical_inputs:
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# Set all to str so that we don't have mixed integer/string columns
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srs = df[col].apply(str).loc[valid_idx]
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categorical_scalers[col] = sklearn.preprocessing.LabelEncoder().fit(srs.values)
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num_classes.append(srs.nunique())
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# Set categorical scaler outputs
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self._cat_scalers = categorical_scalers
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self._num_classes_per_cat_input = num_classes
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def transform_inputs(self, df):
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"""Performs feature transformations.
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This includes both feature engineering, preprocessing and normalisation.
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Args:
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df: Data frame to transform.
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Returns:
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Transformed data frame.
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"""
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output = df.copy()
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if self._real_scalers is None and self._cat_scalers is None:
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raise ValueError("Scalers have not been set!")
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column_definitions = self.get_column_definition()
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categorical_inputs = utils.extract_cols_from_data_type(
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DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
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)
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# Format real inputs
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for col in ["log_sales", "oil", "transactions"]:
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mean, std = self._real_scalers[col]
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output[col] = (df[col] - mean) / std
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if col == "log_sales":
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output[col] = output[col].fillna(0.0) # mean imputation
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# Format categorical inputs
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for col in categorical_inputs:
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string_df = df[col].apply(str)
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output[col] = self._cat_scalers[col].transform(string_df)
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return output
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def format_predictions(self, predictions):
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"""Reverts any normalisation to give predictions in original scale.
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Args:
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predictions: Dataframe of model predictions.
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Returns:
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Data frame of unnormalised predictions.
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"""
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output = predictions.copy()
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column_names = predictions.columns
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mean, std = self._target_scaler
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for col in column_names:
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if col not in {"forecast_time", "identifier"}:
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output[col] = (predictions[col] * std) + mean
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return output
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# Default params
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def get_fixed_params(self):
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"""Returns fixed model parameters for experiments."""
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fixed_params = {
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"total_time_steps": 120,
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"num_encoder_steps": 90,
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"num_epochs": 100,
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"early_stopping_patience": 5,
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"multiprocessing_workers": 5,
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}
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return fixed_params
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def get_default_model_params(self):
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"""Returns default optimised model parameters."""
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model_params = {
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"dropout_rate": 0.1,
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"hidden_layer_size": 240,
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"learning_rate": 0.001,
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"minibatch_size": 128,
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"max_gradient_norm": 100.0,
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"num_heads": 4,
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"stack_size": 1,
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}
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return model_params
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def get_num_samples_for_calibration(self):
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"""Gets the default number of training and validation samples.
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Use to sub-sample the data for network calibration and a value of -1 uses
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all available samples.
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Returns:
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Tuple of (training samples, validation samples)
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"""
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return 450000, 50000
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def get_column_definition(self):
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""" "Formats column definition in order expected by the TFT.
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Modified for Favorita to match column order of original experiment.
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Returns:
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Favorita-specific column definition
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"""
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column_definition = self._column_definition
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# Sanity checks first.
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# Ensure only one ID and time column exist
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def _check_single_column(input_type):
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length = len([tup for tup in column_definition if tup[2] == input_type])
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if length != 1:
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raise ValueError("Illegal number of inputs ({}) of type {}".format(length, input_type))
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_check_single_column(InputTypes.ID)
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_check_single_column(InputTypes.TIME)
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identifier = [tup for tup in column_definition if tup[2] == InputTypes.ID]
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time = [tup for tup in column_definition if tup[2] == InputTypes.TIME]
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real_inputs = [
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tup
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for tup in column_definition
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if tup[1] == DataTypes.REAL_VALUED and tup[2] not in {InputTypes.ID, InputTypes.TIME}
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]
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col_definition_map = {tup[0]: tup for tup in column_definition}
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col_order = [
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"item_nbr",
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"store_nbr",
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"city",
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"state",
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"type",
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"cluster",
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"family",
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"class",
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"perishable",
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"onpromotion",
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"day_of_week",
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"national_hol",
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"regional_hol",
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"local_hol",
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]
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categorical_inputs = [col_definition_map[k] for k in col_order if k in col_definition_map]
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return identifier + time + real_inputs + categorical_inputs
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