File size: 5,464 Bytes
e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2025 PyThaiNLP Project
# SPDX-FileType: SOURCE
# SPDX-License-Identifier: Apache-2.0
import argparse
import json
import os
import yaml
from pythainlp import cli
from pythainlp.benchmarks import word_tokenization
from pythainlp.tools import safe_print
def _read_file(path):
with open(path, "r", encoding="utf-8") as f:
lines = map(lambda r: r.strip(), f.readlines())
return list(lines)
class App:
def __init__(self, argv):
parser = argparse.ArgumentParser(
prog="benchmark",
description=(
"Benchmark for various tasks;\n"
"currently, we have only for word tokenization."
),
usage=(
"thainlp benchmark [task] [task-options]\n\n"
"tasks:\n\n"
"word-tokenization benchmark word tokenization\n\n"
"--"
),
)
parser.add_argument("task", type=str, help="[word-tokenization]")
args = parser.parse_args(argv[2:3])
cli.exit_if_empty(args.task, parser)
task = str.lower(args.task)
task_argv = argv[3:]
if task == "word-tokenization":
WordTokenizationBenchmark(task, task_argv)
class WordTokenizationBenchmark:
def __init__(self, name, argv):
parser = argparse.ArgumentParser(**cli.make_usage("benchmark " + name))
parser.add_argument(
"--input-file",
action="store",
help="Path to input file to compare against the test file",
)
parser.add_argument(
"--test-file",
action="store",
help="Path to test file i.e. ground truth",
)
parser.add_argument(
"--save-details",
default=False,
action="store_true",
help=(
"Save comparison details to files (eval-XXX.json"
" and eval-details-XXX.json)"
),
)
args = parser.parse_args(argv)
actual = _read_file(args.input_file)
expected = _read_file(args.test_file)
assert len(actual) == len(
expected
), "Input and test files do not have the same number of samples"
safe_print(
"Benchmarking %s against %s with %d samples in total"
% (args.input_file, args.test_file, len(actual))
)
df_raw = word_tokenization.benchmark(expected, actual)
columns = [
"char_level:tp",
"char_level:fp",
"char_level:tn",
"char_level:fn",
"word_level:correctly_tokenised_words",
"word_level:total_words_in_sample",
"word_level:total_words_in_ref_sample",
]
statistics = {}
for c in columns:
statistics[c] = float(df_raw[c].sum())
statistics["char_level:precision"] = statistics["char_level:tp"] / (
statistics["char_level:tp"] + statistics["char_level:fp"]
)
statistics["char_level:recall"] = statistics["char_level:tp"] / (
statistics["char_level:tp"] + statistics["char_level:fn"]
)
statistics["word_level:precision"] = (
statistics["word_level:correctly_tokenised_words"]
/ statistics["word_level:total_words_in_sample"]
)
statistics["word_level:recall"] = (
statistics["word_level:correctly_tokenised_words"]
/ statistics["word_level:total_words_in_ref_sample"]
)
safe_print("============== Benchmark Result ==============")
for c in ["tp", "fn", "tn", "fp", "precision", "recall"]:
c = f"char_level:{c}"
v = statistics[c]
safe_print(f"{c:>40s} {v:.4f}")
for c in [
"total_words_in_sample",
"total_words_in_ref_sample",
"correctly_tokenised_words",
"precision",
"recall",
]:
c = f"word_level:{c}"
v = statistics[c]
safe_print(f"{c:>40s} {v:.4f}")
if args.save_details:
dir_name = os.path.dirname(args.input_file)
file_name = args.input_file.split("/")[-1].split(".")[0]
res_path = "%s/eval-%s.yml" % (dir_name, file_name)
safe_print("Evaluation result is saved to %s" % res_path)
with open(res_path, "w", encoding="utf-8") as outfile:
yaml.dump(statistics, outfile, default_flow_style=False)
res_path = "%s/eval-details-%s.json" % (dir_name, file_name)
safe_print("Details of comparisons is saved to %s" % res_path)
with open(res_path, "w", encoding="utf-8") as f:
samples = []
for i, r in enumerate(df_raw.to_dict("records")):
expected, actual = r["expected"], r["actual"]
del r["expected"]
del r["actual"]
samples.append(
{
"metrics": r,
"expected": expected,
"actual": actual,
"id": i,
}
)
details = {"metrics": statistics, "samples": samples}
json.dump(details, f, ensure_ascii=False)
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