class_docstr: Vowpal Wabbit Class
docstr: Convert Pandas DataFrame to vw input format file
  
  Args:
      df (pandas.DataFrame): input DataFrame
      train (bool): flag for train mode (or test mode if False)
docstr_tok: ["Convert","Pandas","DataFrame","to","vw","input","format","file","Args",":","df","(","pandas.DataFrame",")",":","input","DataFrame","train","(","bool",")",":","flag","for","train","mode","(","or","test","mode","if","False",")"]
code: def to_vw_file(self, df, train=True):
      """Convert Pandas DataFrame to vw input format file
  
      Args:
          df (pandas.DataFrame): input DataFrame
          train (bool): flag for train mode (or test mode if False)
      """
  
      output = self.train_file if train else self.test_file
      with open(output, "w") as f:
          # extract columns and create a new dataframe
          tmp = df[
              [self.col_rating, self.col_user, self.col_item]
          ].reset_index()
  
          if train:
              # we need to reset the rating type to an integer to simplify the vw formatting
              tmp[self.col_rating] = tmp[self.col_rating].astype(
                  "int64"
              )
  
              # convert rating to binary value
              if self.logistic:
                  max_value = tmp[self.col_rating].max()
                  tmp[self.col_rating] = tmp[self.col_rating].apply(
                      lambda x: 2 * round(x / max_value) - 1
                  )
          else:
              tmp[self.col_rating] = ""
  
          # convert each row to VW input format (https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Input-format)
          # [label] [tag]|[user namespace] [user id feature] |[item namespace] [movie id feature]
          # label is the true rating, tag is a unique id for the example just used to link predictions to truth
          # user and item namespaces separate features to support interaction features through command line options
          for _, row in tmp.iterrows():
              f.write(
                  "{rating} {index}|user {userID} |item {itemID}\n".format(
                      rating=row[self.col_rating],
                      index=row["index"],
                      userID=row[self.col_user],
                      itemID=row[self.col_item],
                  )
              )
  
file_path: recommenders/recommenders/models/vowpal_wabbit/vw.py
imports: import os
  from subprocess import run
  from tempfile import TemporaryDirectory
  import pandas
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_TIMESTAMP_COL, DEFAULT_PREDICTION_COL
label_desc: Use image node_id 5 for calling the VW obj's underlying member method code with example usage: obj.to_vw_file(df, train) without return types