text: # 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 M5-Walmart 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 WalmartFormatter(data_formatters.base.GenericDataFormatter):
      """Defines and formats data for the M5 dataset.
  
    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),
          ("date", DataTypes.DATE, InputTypes.TIME),
          ("sales", DataTypes.REAL_VALUED, InputTypes.TARGET),
          ("item_id", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
          ("dept_id", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
          ("store_id", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
          ("cat_id", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
          ("state_id", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
          ("wm_yr_wk", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
          ("weekday", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
          # ('wday', DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
          ("day_of_month", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
          ("month", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
          ("year", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
          ("event_name_1", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
          ("event_type_1", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
          ("event_name_2", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
          ("event_type_2", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
          ("snap_CA", DataTypes.CATEGORICAL, InputTypes.OBSERVED_INPUT),
          ("snap_TX", DataTypes.CATEGORICAL, InputTypes.OBSERVED_INPUT),
          ("snap_WI", DataTypes.CATEGORICAL, InputTypes.OBSERVED_INPUT),
          ("sell_price", DataTypes.REAL_VALUED, InputTypes.OBSERVED_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, 10, 19)
  
          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("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["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 ["sell_price", "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["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 ["sales", "sell_price"]:
              mean, std = self._real_scalers[col]
              output[col] = (df[col] - mean) / std
  
              if col == "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": 12,  # change it back to 100   ### missing , ### restored  by philip
              "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,  # change it back to 240
              "learning_rate": 0.001,
              "minibatch_size": 256,  # change it back to 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 300000, 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_id",
              "store_id",
              "dept_id",
              "state_id",
              "cat_id",
              "weekday",
              "event_name_1",
              "event_type_1",
              "event_name_2",
              "event_type_2",
              "snap_CA",
              "snap_TX",
              "snap_WI",
          ]
          categorical_inputs = [
              col_definition_map[k] for k in col_order if k in col_definition_map
          ]
  
          return identifier + time + real_inputs + categorical_inputs
  