# -*- coding: utf-8 -*-
import re
import sys
from typing import List, Tuple
import numpy as np
import pandas as pd
SEPARATOR = "|"
# regex for removing to a space surrounded by separators, i.e. | |
SURROUNDING_SEPS_RX = re.compile(
"{sep}? ?{sep}$".format(sep=re.escape(SEPARATOR))
)
# regex for removing repeated separators, i.e. ||||
MULTIPLE_SEPS_RX = re.compile("{sep}+".format(sep=re.escape(SEPARATOR)))
# regex for removing tags, i.e. <NE>, </NE>
TAG_RX = re.compile(r"<\/?[A-Z]+>")
# regex for tailing separator, i.e. a|dog| -> a|dog
TAILING_SEP_RX = re.compile("{sep}$".format(sep=re.escape(SEPARATOR)))
def _f1(precision: float, recall: float) -> float:
"""
Compute f1.
:param float precision
:param float recall
:return: f1
:rtype: float
"""
if precision == recall == 0:
return 0
return 2 * precision * recall / (precision + recall)
def _flatten_result(my_dict: dict, sep: str = ":") -> dict:
"""
Flatten two-level dictionary.
Use keys in the first level as a prefix for keys in the two levels.
For example,
my_dict = { "a": { "b": 7 } }
flatten(my_dict)
{ "a:b": 7 }
:param dict my_dict: contains stats dictionary
:param str sep: separator between the two keys (default: ":")
:return: a one-level dictionary with key combined
:rtype: dict[str, float | str]
"""
items = []
for k1, kv2 in my_dict.items():
for k2, v in kv2.items():
new_key = f"{k1}{sep}{k2}"
items.append((new_key, v))
return dict(items)
[docs]def benchmark(ref_samples: List[str], samples: List[str]) -> pd.DataFrame:
"""
Performace benchmark of samples.
Please see :meth:`pythainlp.benchmarks.word_tokenization.compute_stats` for
metrics being computed.
:param list[str] ref_samples: ground truth samples
:param list[str] samples: samples that we want to evaluate
:return: dataframe with row x col = len(samples) x len(metrics)
:rtype: pandas.DataFrame
"""
results = []
for i, (r, s) in enumerate(zip(ref_samples, samples)):
try:
r, s = preprocessing(r), preprocessing(s)
if r and s:
stats = compute_stats(r, s)
stats = _flatten_result(stats)
stats["expected"] = r
stats["actual"] = s
results.append(stats)
except:
reason = """
[Error]
Reason: %s
Pair (i=%d)
--- label
%s
--- sample
%s
""" % (
sys.exc_info(),
i,
r,
s,
)
raise SystemExit(reason)
return pd.DataFrame(results)
[docs]def preprocessing(txt: str, remove_space: bool = True) -> str:
"""
Clean up text before performing evaluation.
:param str text: text to be preprocessed
:param bool remove_space: whether remove white space
:return: preprocessed text
:rtype: str
"""
txt = re.sub(SURROUNDING_SEPS_RX, "", txt)
if remove_space:
txt = re.sub(r"\s+", "", txt)
txt = re.sub(MULTIPLE_SEPS_RX, SEPARATOR, txt)
txt = re.sub(TAG_RX, "", txt)
txt = re.sub(TAILING_SEP_RX, "", txt).strip()
return txt
[docs]def compute_stats(ref_sample: str, raw_sample: str) -> dict:
"""
Compute statistics for tokenization quality
These statistics includes:
**Character-Level**:
True Positive, False Positive, True Negative, False Negative, Precision, Recall, and f1
**Word-Level**:
Precision, Recall, and f1
**Other**:
- Correct tokenization indicator: {0, 1} sequence indicating the correspoding
word is tokenized correctly.
:param str ref_sample: ground truth samples
:param str samples: samples that we want to evaluate
:return: metrics in character and word-level and correctly tokenized word indicators
:rtype: dict[str, float | str]
"""
ref_sample = _binary_representation(ref_sample)
sample = _binary_representation(raw_sample)
# Compute charater-level statistics
c_pos_pred, c_neg_pred = np.argwhere(sample == 1), np.argwhere(sample == 0)
c_pos_pred = c_pos_pred[c_pos_pred < ref_sample.shape[0]]
c_neg_pred = c_neg_pred[c_neg_pred < ref_sample.shape[0]]
c_tp = np.sum(ref_sample[c_pos_pred] == 1)
c_fp = np.sum(ref_sample[c_pos_pred] == 0)
c_tn = np.sum(ref_sample[c_neg_pred] == 0)
c_fn = np.sum(ref_sample[c_neg_pred] == 1)
c_precision = c_tp / (c_tp + c_fp)
c_recall = c_tp / (c_tp + c_fn)
c_f1 = _f1(c_precision, c_recall)
# Compute word-level statistics
# Find correctly tokenized words in the reference sample
word_boundaries = _find_word_boudaries(ref_sample)
# Find correctly tokenized words in the sample
ss_boundaries = _find_word_boudaries(sample)
tokenization_indicators = _find_words_correctly_tokenised(
word_boundaries, ss_boundaries
)
correctly_tokenised_words = np.sum(tokenization_indicators)
tokenization_indicators = list(
map(lambda x: str(x), tokenization_indicators)
)
return {
"char_level": {
"tp": c_tp,
"fp": c_fp,
"tn": c_tn,
"fn": c_fn,
},
"word_level": {
"correctly_tokenised_words": correctly_tokenised_words,
"total_words_in_sample": np.sum(sample),
"total_words_in_ref_sample": np.sum(ref_sample)
},
"global": {
"tokenisation_indicators": "".join(tokenization_indicators)
},
}
def _binary_representation(txt: str, verbose: bool = False):
"""
Transform text to {0, 1} sequence.
where (1) indicates that the corresponding character is the beginning of
a word. For example, ผม|ไม่|ชอบ|กิน|ผัก -> 10100...
:param str txt: input text that we want to transform
:param bool verbose: for debugging purposes
:return: {0, 1} sequence
:rtype: str
"""
chars = np.array(list(txt))
boundary = np.argwhere(chars == SEPARATOR).reshape(-1)
boundary = boundary - np.array(range(boundary.shape[0]))
bin_rept = np.zeros(len(txt) - boundary.shape[0])
bin_rept[list(boundary) + [0]] = 1
sample_wo_seps = list(txt.replace(SEPARATOR, ""))
# sanity check
assert len(sample_wo_seps) == len(bin_rept)
if verbose:
for c, m in zip(sample_wo_seps, bin_rept):
print("%s -- %d" % (c, m))
return bin_rept
def _find_word_boudaries(bin_reps) -> list:
"""
Find start and end location of each word.
:param str bin_reps: binary representation of a text
:return: list of tuples (start, end)
:rtype: list[tuple(int, int)]
"""
boundary = np.argwhere(bin_reps == 1).reshape(-1)
start_idx = boundary
end_idx = boundary[1:].tolist() + [bin_reps.shape[0]]
return list(zip(start_idx, end_idx))
def _find_words_correctly_tokenised(
ref_boundaries: List[Tuple[int, int]],
predicted_boundaries: List[Tuple[int, int]],
) -> Tuple[int]:
"""
Find whether each word is correctly tokenized.
:param list[tuple(int, int)] ref_boundaries: word boundaries of reference tokenization
:param list[tuple(int, int)] predicted_boundaries: word boundareies of predicted tokenization
:return: binary sequence where 1 indicates the corresponding word is tokenized correctly
:rtype: tuple[int]
"""
ref_b = dict(zip(ref_boundaries, [1] * len(ref_boundaries)))
labels = tuple(map(lambda x: ref_b.get(x, 0), predicted_boundaries))
return labels