criteo_preprocess.py

# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""TFX beam preprocessing pipeline for Criteo data.

Preprocessing util for criteo data. Transformations:
1. Fill missing features with zeros.
2. Set negative integer features to zeros.
3. Normalize integer features using log(x+1).
4. For categorical features (hex), convert to integer and take value modulus the
   max_vocab_size value.

Usage:
For raw Criteo data, this script should be run twice.
First run should set vocab_gen_mode to true.  This run is used to generate
  vocabulary files in the temp_dir location.
Second run should set vocab_gen_mode to false.  It is necessary to point to the
  same temp_dir used during the first run.
"""

import argparse
import datetime
import os
from absl import logging

import apache_beam as beam
import numpy as np
import tensorflow as tf
import tensorflow_transform as tft
import tensorflow_transform.beam as tft_beam
from tensorflow_transform.tf_metadata import dataset_metadata
from tensorflow_transform.tf_metadata import schema_utils
from tfx_bsl.public import tfxio


parser = argparse.ArgumentParser()
parser.add_argument(
    "--input_path",
    default=None,
    required=True,
    help="Input path. Be sure to set this to cover all data, to ensure "
    "that sparse vocabs are complete.")
parser.add_argument(
    "--output_path",
    default=None,
    required=True,
    help="Output path.")
parser.add_argument(
    "--temp_dir",
    default=None,
    required=True,
    help="Directory to store temporary metadata. Important because vocab "
         "dictionaries will be stored here. Co-located with data, ideally.")
parser.add_argument(
    "--csv_delimeter",
    default="\t",
    help="Delimeter string for input and output.")
parser.add_argument(
    "--vocab_gen_mode",
    action="store_true",
    default=False,
    help="If it is set, process full dataset and do not write CSV output. In "
         "this mode, See temp_dir for vocab files. input_path should cover all "
         "data, e.g. train, test, eval.")
parser.add_argument(
    "--runner",
    help="Runner for Apache Beam, needs to be one of {DirectRunner, "
    "DataflowRunner}.",
    default="DirectRunner")
parser.add_argument(
    "--project",
    default=None,
    help="ID of your project. Ignored by DirectRunner.")
parser.add_argument(
    "--region",
    default=None,
    help="Region. Ignored by DirectRunner.")
parser.add_argument(
    "--max_vocab_size",
    type=int,
    default=10_000_000,
    help="Max index range, categorical features convert to integer and take "
         "value modulus the max_vocab_size")


args = parser.parse_args()

NUM_NUMERIC_FEATURES = 13

NUMERIC_FEATURE_KEYS = [
    f"int-feature-{x + 1}" for x in range(NUM_NUMERIC_FEATURES)]
CATEGORICAL_FEATURE_KEYS = [
    "categorical-feature-%d" % x for x in range(NUM_NUMERIC_FEATURES + 1, 40)]
LABEL_KEY = "clicked"


# Data is first preprocessed in pure Apache Beam using numpy.
# This removes missing values and hexadecimal-encoded values.
# For the TF schema, we can thus specify the schema as FixedLenFeature
# for TensorFlow Transform.
FEATURE_SPEC = dict([(name, tf.io.FixedLenFeature([], dtype=tf.int64))
                     for name in CATEGORICAL_FEATURE_KEYS] +
                    [(name, tf.io.FixedLenFeature([], dtype=tf.float32))
                     for name in NUMERIC_FEATURE_KEYS] +
                    [(LABEL_KEY, tf.io.FixedLenFeature([], tf.float32))])
INPUT_METADATA = dataset_metadata.DatasetMetadata(
    schema_utils.schema_from_feature_spec(FEATURE_SPEC))


def apply_vocab_fn(inputs):
  """Preprocessing fn for sparse features.

  Applies vocab to bucketize sparse features. This function operates using
  previously-created vocab files.
  Pre-condition: Full vocab has been materialized.

  Args:
    inputs: Input features to transform.

  Returns:
    Output dict with transformed features.
  """
  outputs = {}

  outputs[LABEL_KEY] = inputs[LABEL_KEY]
  for key in NUMERIC_FEATURE_KEYS:
    outputs[key] = inputs[key]
  for idx, key in enumerate(CATEGORICAL_FEATURE_KEYS):
    vocab_fn = os.path.join(
        args.temp_dir, "tftransform_tmp", "feature_{}_vocab".format(idx))
    outputs[key] = tft.apply_vocabulary(inputs[key], vocab_fn)

  return outputs


def compute_vocab_fn(inputs):
  """Preprocessing fn for sparse features.

  This function computes unique IDs for the sparse features. We rely on implicit
  behavior which writes the vocab files to the vocab_filename specified in
  tft.compute_and_apply_vocabulary.

  Pre-condition: Sparse features have been converted to integer and mod'ed with
  args.max_vocab_size.

  Args:
    inputs: Input features to transform.

  Returns:
    Output dict with transformed features.
  """
  outputs = {}

  outputs[LABEL_KEY] = inputs[LABEL_KEY]
  for key in NUMERIC_FEATURE_KEYS:
    outputs[key] = inputs[key]
  for idx, key in enumerate(CATEGORICAL_FEATURE_KEYS):
    outputs[key] = tft.compute_and_apply_vocabulary(
        x=inputs[key],
        vocab_filename="feature_{}_vocab".format(idx))

  return outputs


class FillMissing(beam.DoFn):
  """Fills missing elements with zero string value."""

  def process(self, element):
    elem_list = element.split(args.csv_delimeter)
    out_list = []
    for val in elem_list:
      new_val = "0" if not val else val
      out_list.append(new_val)
    yield (args.csv_delimeter).join(out_list)


class NegsToZeroLog(beam.DoFn):
  """For int features, sets negative values to zero and takes log(x+1)."""

  def process(self, element):
    elem_list = element.split(args.csv_delimeter)
    out_list = []
    for i, val in enumerate(elem_list):
      if i > 0 and i <= NUM_NUMERIC_FEATURES:
        new_val = "0" if int(val) < 0 else val
        new_val = np.log(int(new_val) + 1)
        new_val = str(new_val)
      else:
        new_val = val
      out_list.append(new_val)
    yield (args.csv_delimeter).join(out_list)


class HexToIntModRange(beam.DoFn):
  """For categorical features, takes decimal value and mods with max value."""

  def process(self, element):
    elem_list = element.split(args.csv_delimeter)
    out_list = []
    for i, val in enumerate(elem_list):
      if i > NUM_NUMERIC_FEATURES:
        new_val = int(val, 16) % args.max_vocab_size
      else:
        new_val = val
      out_list.append(str(new_val))
    yield str.encode((args.csv_delimeter).join(out_list))


def transform_data(data_path, output_path):
  """Preprocesses Criteo data.

  Two processing modes are supported. Raw data will require two passes.
  If full vocab files already exist, only one pass is necessary.

  Args:
    data_path: File(s) to read.
    output_path: Path to which output CSVs are written, if necessary.
  """

  preprocessing_fn = compute_vocab_fn if args.vocab_gen_mode else apply_vocab_fn

  gcp_project = args.project
  region = args.region

  job_name = (f"criteo-preprocessing-"
              f"{datetime.datetime.now().strftime('%y%m%d-%H%M%S')}")

  # set up Beam pipeline.
  pipeline_options = None

  if args.runner == "DataflowRunner":
    options = {
        "staging_location": os.path.join(output_path, "tmp", "staging"),
        "temp_location": os.path.join(output_path, "tmp"),
        "job_name": job_name,
        "project": gcp_project,
        "save_main_session": True,
        "region": region,
        "setup_file": "./setup.py",
    }
    pipeline_options = beam.pipeline.PipelineOptions(flags=[], **options)
  elif args.runner == "DirectRunner":
    pipeline_options = beam.options.pipeline_options.DirectOptions(
        direct_num_workers=os.cpu_count(),
        direct_running_mode="multi_threading")

  with beam.Pipeline(args.runner, options=pipeline_options) as pipeline:
    with tft_beam.Context(temp_dir=args.temp_dir):
      processed_lines = (
          pipeline
          # Read in TSV data.
          | beam.io.ReadFromText(data_path, coder=beam.coders.StrUtf8Coder())
          # Fill in missing elements with the defaults (zeros).
          | "FillMissing" >> beam.ParDo(FillMissing())
          # For numerical features, set negatives to zero. Then take log(x+1).
          | "NegsToZeroLog" >> beam.ParDo(NegsToZeroLog())
          # For categorical features, mod the values with vocab size.
          | "HexToIntModRange" >> beam.ParDo(HexToIntModRange()))

      # CSV reader: List the cols in order, as dataset schema is not ordered.
      ordered_columns = [LABEL_KEY
                        ] + NUMERIC_FEATURE_KEYS + CATEGORICAL_FEATURE_KEYS

      csv_tfxio = tfxio.BeamRecordCsvTFXIO(
          physical_format="text",
          column_names=ordered_columns,
          delimiter=args.csv_delimeter,
          schema=INPUT_METADATA.schema)

      converted_data = (
          processed_lines
          | "DecodeData" >> csv_tfxio.BeamSource())

      raw_dataset = (converted_data, csv_tfxio.TensorAdapterConfig())

      # The TFXIO output format is chosen for improved performance.
      transformed_dataset, _ = (
          raw_dataset | tft_beam.AnalyzeAndTransformDataset(
              preprocessing_fn, output_record_batches=False))

      # Transformed metadata is not necessary for encoding.
      transformed_data, transformed_metadata = transformed_dataset

      if not args.vocab_gen_mode:
        # Write to CSV.
        transformed_csv_coder = tft.coders.CsvCoder(
            ordered_columns, transformed_metadata.schema,
            delimiter=args.csv_delimeter)
        _ = (
            transformed_data
            | "EncodeDataCsv" >> beam.Map(transformed_csv_coder.encode)
            | "WriteDataCsv" >> beam.io.WriteToText(output_path))


if __name__ == "__main__":
  logging.set_verbosity(logging.INFO)

  transform_data(data_path=args.input_path,
                 output_path=args.output_path)