original_docstring: 
      Computes the anomaly scores. Consumes original df and reduced df.
      :param original_df: original data frame
      :param reduced_df: predicted data frame
      :return: loss of each ts row (element) is a numpy array. Index of the loss same as original df
      
docstring: Computes the anomaly scores. Consumes original df and reduced df.
docstring_tokens: Computes the anomaly scores . Consumes original df and reduced df .
code: def anomaly_scores(original_df: pd.DataFrame, reduced_df: pd.DataFrame) -> np.ndarray:
      """
      Computes the anomaly scores. Consumes original df and reduced df.
      :param original_df: original data frame
      :param reduced_df: predicted data frame
      :return: loss of each ts row (element) is a numpy array. Index of the loss same as original df
      """
      loss = np.sum((np.array(original_df) -
                     np.array(reduced_df)) ** 2.0, axis=1)
      loss = pd.Series(data=loss, index=original_df.index)
      loss = (loss - np.min(loss)) / (np.max(loss) - np.min(loss))
      return loss
code_tokens: def anomaly_scores ( original_df : pd . DataFrame , reduced_df : pd . DataFrame ) -> np . ndarray : loss = np . sum ( ( np . array ( original_df ) - np . array ( reduced_df ) ) ** 2.0 , axis = 1 ) loss = pd . Series ( data = loss , index = original_df . index ) loss = ( loss - np . min ( loss ) ) / ( np . max ( loss ) - np . min ( loss ) ) return loss
short_docstring: Computes the anomaly scores.
short_docstring_tokens: Computes the anomaly scores .
comment: """
      Computes the anomaly scores. Consumes original df and reduced df.
      :param original_df: original data frame
      :param reduced_df: predicted data frame
      :return: loss of each ts row (element) is a numpy array. Index of the loss same as original df
      """