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| import gzip |
| import json |
| from typing import List, Tuple |
|
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| import numpy as np |
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|
|
| class GzipModel: |
| """ |
| This class is a re-implementation of |
| “Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors |
| (Jiang et al., Findings 2023) |
| |
| :param list training_data: list [(text_sample,label)] |
| :param str model_path: Path for loading model (if you saved the model) |
| """ |
|
|
| def __init__(self, training_data: List[Tuple[str, str]] = None, model_path: str = None): |
| if model_path is not None: |
| self.load(model_path) |
| else: |
| self.training_data = np.array(training_data) |
| self.Cx2_list = self.train() |
|
|
| def train(self): |
| Cx2_list = [] |
| for i in range(len(self.training_data)): |
| Cx2_list.append( |
| len(gzip.compress(self.training_data[i][0].encode("utf-8"))) |
| ) |
| return Cx2_list |
|
|
| def predict(self, x1: str, k: int = 1) -> str: |
| """ |
| :param str x1: the text that we want to predict label for. |
| :param str k: k |
| :return: label |
| :rtype: str |
| |
| :Example: |
| :: |
| |
| from pythainlp.classify import GzipModel |
| |
| training_data = [ |
| ("รายละเอียดตามนี้เลยค่าา ^^", "Neutral"), |
| ("กลัวพวกมึงหาย อดกินบาบิก้อน", "Neutral"), |
| ("บริการแย่มากก เป็นหมอได้ไง😤", "Negative"), |
| ("ขับรถแย่มาก", "Negative"), |
| ("ดีนะครับ", "Positive"), |
| ("ลองแล้วรสนี้อร่อย... ชอบๆ", "Positive"), |
| ("ฉันรู้สึกโกรธ เวลามือถือแบตหมด", "Negative"), |
| ("เธอภูมิใจที่ได้ทำสิ่งดี ๆ และดีใจกับเด็ก ๆ", "Positive"), |
| ("นี่เป็นบทความหนึ่ง", "Neutral") |
| ] |
| model = GzipModel(training_data) |
| print(model.predict("ฉันดีใจ", k=1)) |
| # output: Positive |
| """ |
| Cx1 = len(gzip.compress(x1.encode("utf-8"))) |
| disance_from_x1 = [] |
| for i in range(len(self.Cx2_list)): |
| x2 = self.training_data[i][0] |
| Cx2 = self.Cx2_list[i] |
| x1x2 = "".join([x1, x2]) |
| Cx1x2 = len(gzip.compress(x1x2.encode("utf-8"))) |
| |
| ncd = (Cx1x2 - min(Cx1, Cx2)) / max(Cx1, Cx2) |
| disance_from_x1.append(ncd) |
|
|
| sorted_idx = np.argsort(np.array(disance_from_x1)) |
| top_k_class = self.training_data[sorted_idx[:k], 1] |
| _, counts = np.unique(top_k_class, return_counts=True) |
| predict_class = top_k_class[counts.argmax()] |
|
|
| return predict_class |
|
|
| def save(self, path: str): |
| """ |
| :param str path: path for save model |
| """ |
| with open(path, "w") as f: |
| json.dump({ |
| "training_data": self.training_data.tolist(), |
| "Cx2_list": self.Cx2_list |
| }, f, ensure_ascii=False) |
|
|
| def load(self, path: str): |
| with open(path, "r") as f: |
| data = json.load(f) |
| self.Cx2_list = data["Cx2_list"] |
| self.training_data = np.array(data["training_data"]) |
|
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