Description #

Character ratio calculator - Text feature extraction based on the ratio of alphabetic and numeric characters

Key features #

• Character ratio calculation: Accurately calculates the proportion of alphabetic and numeric characters in text
• Tokenization mode support: Allows selection between token-based or character-level ratio calculation
• Multilingual support: Supports character recognition for English, Chinese, Japanese, Korean, and many other languages

Application scenarios #

• Multilingual text quality assessment
• Data cleaning and preprocessing
• Text classification feature extraction
• Content safety detection
• Multilingual text analysis

Technical characteristics #

• Supports two calculation modes:
  • Character mode: The proportion of alphabetic and numeric characters to the total number of characters
  • Tokenization mode: The proportion of alphabetic characters to the total number of tokens
• Supports multilingual Unicode character recognition, including but not limited to:
  • English alphabet (a-z, A-Z)
  • Chinese characters
  • Japanese characters (Hiragana, Katakana, Kanji)
  • Korean characters (Hangul)
  • Numeric characters (0-9)

Operator parameters #

Input #

Output #

Ratio result column, with elements as floating-point numbers representing the proportion of alphabetic and numeric characters

Parameters #

If a parameter does not have a default value, it is required

Examples #

The following code demonstrates how to use daft to run the operator and calculate the proportion of alphabetic and numeric characters in text.

from __future__ import annotations

import os

import daft
from daft import col
from daft.las.functions.text.alphanumeric_ratio_calculator import AlphanumericRatioCalculator
from daft.las.functions.udf import las_udf

if __name__ == "__main__":

    if os.getenv("DAFT_RUNNER", "native") == "ray":
        import logging

        import ray

        def configure_logging():
            logging.basicConfig(
                level=logging.INFO,
                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
                datefmt="%Y-%m-%d %H:%M:%S.%s".format(),
            )
            logging.getLogger("tracing.span").setLevel(logging.WARNING)
            logging.getLogger("daft_io.stats").setLevel(logging.WARNING)
            logging.getLogger("DaftStatisticsManager").setLevel(logging.WARNING)
            logging.getLogger("DaftFlotillaScheduler").setLevel(logging.WARNING)
            logging.getLogger("DaftFlotillaDispatcher").setLevel(logging.WARNING)

        ray.init(dashboard_host="0.0.0.0", runtime_env={"worker_process_setup_hook": configure_logging})
        daft.set_runner_ray()
    daft.set_execution_config(actor_udf_ready_timeout=600)
    daft.set_execution_config(min_cpu_per_task=0)

    samples = {
        "text": [
            "HelloWorld123",
            "Hello, world!",
            "!!!@@@###$$$",
            "Test 123! Is it working?",
            "你好Hello123",
        ]
    }

    ds = daft.from_pydict(samples)
    ds = ds.with_column(
        "alphanumeric_ratio",
        las_udf(
            AlphanumericRatioCalculator,
            construct_args={"tokenization": False},
        )(col("text")),
    )

    ds.show()
    # ╭─────────────────────────┬─────────────────────╮
    # │ text                    ┆ alphanumeric_ratio  │
    # │ ---                     ┆ ---                 │
    # │ Utf8                    ┆ Float64             │
    # ╞═════════════════════════╪═════════════════════╡
    # │ HelloWorld123           ┆ 1.0                 │
    # │ Hello, world! ┆ 0.7692307692307693  │
    # │ !!!@@@###$$$            ┆ 0.0                 │
    # │ Test 123! Is it work…   ┆ 0.75                │
    # │ 你好Hello123             ┆ 1.0                 │
    # ╰─────────────────────────┴─────────────────────╯