# 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