| |
| |
| |
| |
| """ |
| Text generator using n-gram language model |
| |
| codes are from |
| https://towardsdatascience.com/understanding-word-n-grams-and-n-gram-probability-in-natural-language-processing-9d9eef0fa058 |
| """ |
|
|
| import random |
| from typing import List, Union |
|
|
| from pythainlp.corpus.oscar import ( |
| unigram_word_freqs as oscar_word_freqs_unigram, |
| ) |
| from pythainlp.corpus.tnc import bigram_word_freqs as tnc_word_freqs_bigram |
| from pythainlp.corpus.tnc import trigram_word_freqs as tnc_word_freqs_trigram |
| from pythainlp.corpus.tnc import unigram_word_freqs as tnc_word_freqs_unigram |
| from pythainlp.corpus.ttc import unigram_word_freqs as ttc_word_freqs_unigram |
|
|
|
|
| class Unigram: |
| """ |
| Text generator using Unigram |
| |
| :param str name: corpus name |
| * *tnc* - Thai National Corpus (default) |
| * *ttc* - Thai Textbook Corpus (TTC) |
| * *oscar* - OSCAR Corpus |
| """ |
|
|
| def __init__(self, name: str = "tnc"): |
| if name == "tnc": |
| self.counts = tnc_word_freqs_unigram() |
| elif name == "ttc": |
| self.counts = ttc_word_freqs_unigram() |
| elif name == "oscar": |
| self.counts = oscar_word_freqs_unigram() |
| self.word = list(self.counts.keys()) |
| self.n = 0 |
| for i in self.word: |
| self.n += self.counts[i] |
| self.prob = {i: self.counts[i] / self.n for i in self.word} |
| self._word_prob: dict = {} |
|
|
| def gen_sentence( |
| self, |
| start_seq: str = "", |
| N: int = 3, |
| prob: float = 0.001, |
| output_str: bool = True, |
| duplicate: bool = False, |
| ) -> Union[List[str], str]: |
| """ |
| :param str start_seq: word to begin sentence with |
| :param int N: number of words |
| :param bool output_str: output as string |
| :param bool duplicate: allow duplicate words in sentence |
| |
| :return: list of words or a word string |
| :rtype: List[str], str |
| |
| :Example: |
| :: |
| |
| from pythainlp.generate import Unigram |
| |
| gen = Unigram() |
| |
| gen.gen_sentence("แมว") |
| # output: 'แมวเวลานะนั้น' |
| """ |
| if not start_seq: |
| start_seq = random.choice(self.word) |
| rand_text = start_seq.lower() |
| self._word_prob = { |
| i: self.counts[i] / self.n |
| for i in self.word |
| if self.counts[i] / self.n >= prob |
| } |
| return self._next_word( |
| rand_text, N, output_str, prob=prob, duplicate=duplicate |
| ) |
|
|
| def _next_word( |
| self, |
| text: str, |
| N: int, |
| output_str: bool, |
| prob: float, |
| duplicate: bool = False, |
| ): |
| words = [] |
| words.append(text) |
| word_list = list(self._word_prob.keys()) |
| if N > len(word_list): |
| N = len(word_list) |
| for _ in range(N): |
| w = random.choice(word_list) |
| if duplicate is False: |
| while w in words: |
| w = random.choice(word_list) |
| words.append(w) |
|
|
| if output_str: |
| return "".join(words) |
| return words |
|
|
|
|
| class Bigram: |
| """ |
| Text generator using Bigram |
| |
| :param str name: corpus name |
| * *tnc* - Thai National Corpus (default) |
| """ |
|
|
| def __init__(self, name: str = "tnc"): |
| if name == "tnc": |
| self.uni = tnc_word_freqs_unigram() |
| self.bi = tnc_word_freqs_bigram() |
| self.uni_keys = list(self.uni.keys()) |
| self.bi_keys = list(self.bi.keys()) |
| self.words = [i[-1] for i in self.bi_keys] |
|
|
| def prob(self, t1: str, t2: str) -> float: |
| """ |
| probability of word |
| |
| :param int t1: text 1 |
| :param int t2: text 2 |
| |
| :return: probability value |
| :rtype: float |
| """ |
| try: |
| v = self.bi[(t1, t2)] / self.uni[t1] |
| except ZeroDivisionError: |
| v = 0.0 |
| return v |
|
|
| def gen_sentence( |
| self, |
| start_seq: str = "", |
| N: int = 4, |
| prob: float = 0.001, |
| output_str: bool = True, |
| duplicate: bool = False, |
| ) -> Union[List[str], str]: |
| """ |
| :param str start_seq: word to begin sentence with |
| :param int N: number of words |
| :param bool output_str: output as string |
| :param bool duplicate: allow duplicate words in sentence |
| |
| :return: list of words or a word string |
| :rtype: List[str], str |
| |
| :Example: |
| :: |
| |
| from pythainlp.generate import Bigram |
| |
| gen = Bigram() |
| |
| gen.gen_sentence("แมว") |
| # output: 'แมวไม่ได้รับเชื้อมัน' |
| """ |
| if not start_seq: |
| start_seq = random.choice(self.words) |
| late_word = start_seq |
| list_word = [] |
| list_word.append(start_seq) |
|
|
| for _ in range(N): |
| if duplicate: |
| temp = [j for j in self.bi_keys if j[0] == late_word] |
| else: |
| temp = [ |
| j |
| for j in self.bi_keys |
| if j[0] == late_word and j[1] not in list_word |
| ] |
| probs = [self.prob(late_word, next_word[-1]) for next_word in temp] |
| p2 = [j for j in probs if j >= prob] |
| if len(p2) == 0: |
| break |
| items = temp[probs.index(random.choice(p2))] |
| late_word = items[-1] |
| list_word.append(late_word) |
|
|
| if output_str: |
| return "".join(list_word) |
|
|
| return list_word |
|
|
|
|
| class Trigram: |
| """ |
| Text generator using Trigram |
| |
| :param str name: corpus name |
| * *tnc* - Thai National Corpus (default) |
| """ |
|
|
| def __init__(self, name: str = "tnc"): |
| if name == "tnc": |
| self.uni = tnc_word_freqs_unigram() |
| self.bi = tnc_word_freqs_bigram() |
| self.ti = tnc_word_freqs_trigram() |
| self.uni_keys = list(self.uni.keys()) |
| self.bi_keys = list(self.bi.keys()) |
| self.ti_keys = list(self.ti.keys()) |
| self.words = [i[-1] for i in self.bi_keys] |
|
|
| def prob(self, t1: str, t2: str, t3: str) -> float: |
| """ |
| probability of word |
| |
| :param int t1: text 1 |
| :param int t2: text 2 |
| :param int t3: text 3 |
| |
| :return: probability value |
| :rtype: float |
| """ |
| try: |
| v = self.ti[(t1, t2, t3)] / self.bi[(t1, t2)] |
| except ZeroDivisionError: |
| v = 0.0 |
|
|
| return v |
|
|
| def gen_sentence( |
| self, |
| start_seq: str = "", |
| N: int = 4, |
| prob: float = 0.001, |
| output_str: bool = True, |
| duplicate: bool = False, |
| ) -> Union[List[str], str]: |
| """ |
| :param str start_seq: word to begin sentence with |
| :param int N: number of words |
| :param bool output_str: output as string |
| :param bool duplicate: allow duplicate words in sentence |
| |
| :return: list of words or a word string |
| :rtype: List[str], str |
| |
| :Example: |
| :: |
| |
| from pythainlp.generate import Trigram |
| |
| gen = Trigram() |
| |
| gen.gen_sentence() |
| # output: 'ยังทำตัวเป็นเซิร์ฟเวอร์คือ' |
| """ |
| if not start_seq: |
| start_seq = random.choice(self.bi_keys) |
| late_word = start_seq |
| list_word = [] |
| list_word.append(start_seq) |
|
|
| for i in range(N): |
| if duplicate: |
| temp = [j for j in self.ti_keys if j[:2] == late_word] |
| else: |
| temp = [ |
| j |
| for j in self.ti_keys |
| if j[:2] == late_word and j[1:] not in list_word |
| ] |
| probs = [self.prob(word[0], word[1], word[2]) for word in temp] |
| p2 = [j for j in probs if j >= prob] |
| if len(p2) == 0: |
| break |
| items = temp[probs.index(random.choice(p2))] |
| late_word = items[1:] |
| list_word.append(late_word) |
|
|
| listdata = [] |
| for i in list_word: |
| for j in i: |
| if j not in listdata: |
| listdata.append(j) |
|
|
| if output_str: |
| return "".join(listdata) |
|
|
| return listdata |
|
|
