166 lines
6.1 KiB
Python
166 lines
6.1 KiB
Python
import sys
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import os
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sys.path.append(os.path.expanduser("~/devel/gfun_multimodal"))
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from collections import defaultdict, Counter
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import numpy as np
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import re
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from tqdm import tqdm
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer
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from dataManager.glamiDataset import get_dataframe
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from dataManager.multilingualDataset import MultilingualDataset
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class SimpleGfunDataset:
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def __init__(
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self,
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dataset_name=None,
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datadir="~/datasets/rai/csv/",
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textual=True,
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visual=False,
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multilabel=False,
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set_tr_langs=None,
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set_te_langs=None,
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reduced=False
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):
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self.name = dataset_name
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self.datadir = os.path.expanduser(datadir)
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self.textual = textual
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self.visual = visual
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self.multilabel = multilabel
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self.reduced = reduced
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self.load_csv(set_tr_langs, set_te_langs)
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self.print_stats()
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def print_stats(self):
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print(f"Dataset statistics {'-' * 15}")
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tr = 0
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va = 0
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te = 0
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for lang in self.all_langs:
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n_tr = len(self.train_data[lang]) if lang in self.tr_langs else 0
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n_va = len(self.val_data[lang]) if lang in self.tr_langs else 0
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n_te = len(self.test_data[lang])
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tr += n_tr
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va += n_va
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te += n_te
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print(f"{lang} - tr: {n_tr} - va: {n_va} - te: {n_te}")
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print(f"Total {'-' * 15}")
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print(f"tr: {tr} - va: {va} - te: {te}")
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def load_csv(self, set_tr_langs, set_te_langs):
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_data_tr = pd.read_csv(os.path.join(self.datadir, "train.csv" if not self.reduced else "train.small.csv"))
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try:
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stratified = "class"
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train, val = train_test_split(_data_tr, test_size=0.2, random_state=42, stratify=_data_tr.label)
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except:
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stratified = "lang"
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train, val = train_test_split(_data_tr, test_size=0.2, random_state=42, stratify=_data_tr.lang)
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print(f"- dataset stratified by {stratified}")
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test = pd.read_csv(os.path.join(self.datadir, "test.small.csv" if not self.reduced else "test.small.csv"))
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self._set_langs (train, test, set_tr_langs, set_te_langs)
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self._set_labels(_data_tr)
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self.full_train = _data_tr
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self.full_test = self.test
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self.train_data = self._set_datalang(train)
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self.val_data = self._set_datalang(val)
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self.test_data = self._set_datalang(test)
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return
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def _set_labels(self, data):
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self.labels = sorted(list(data.label.unique()))
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def _set_langs(self, train, test, set_tr_langs=None, set_te_langs=None):
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self.tr_langs = set(train.lang.unique().tolist())
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self.te_langs = set(test.lang.unique().tolist())
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if set_tr_langs is not None:
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print(f"-- [SETTING TRAINING LANGS TO: {list(set_tr_langs)}]")
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self.tr_langs = self.tr_langs.intersection(set(set_tr_langs))
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if set_te_langs is not None:
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print(f"-- [SETTING TESTING LANGS TO: {list(set_tr_langs)}]")
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self.te_langs = self.te_langs.intersection(set(set_te_langs))
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self.all_langs = self.tr_langs.union(self.te_langs)
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return self.tr_langs, self.te_langs, self.all_langs
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def _set_datalang(self, data: pd.DataFrame):
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return {lang: data[data.lang == lang] for lang in self.all_langs}
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def training(self, merge_validation=False, mask_number=False, target_as_csr=False):
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apply_mask = lambda x: _mask_numbers(x) if _mask_numbers else x
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lXtr = {
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lang: {"text": apply_mask(self.train_data[lang].text.tolist())}
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for lang in self.tr_langs
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}
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if merge_validation:
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for lang in self.tr_langs:
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lXtr[lang]["text"] += apply_mask(self.val_data[lang].text.tolist())
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lYtr = {
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lang: self.train_data[lang].label.tolist() for lang in self.tr_langs
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}
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if merge_validation:
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for lang in self.tr_langs:
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lYtr[lang] += self.val_data[lang].label.tolist()
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for lang in self.tr_langs:
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lYtr[lang] = self.indices_to_one_hot(
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indices = lYtr[lang],
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n_labels = self.num_labels()
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)
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return lXtr, lYtr
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def test(self, mask_number=False, target_as_csr=False):
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apply_mask = lambda x: _mask_numbers(x) if _mask_numbers else x
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lXte = {
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lang: {"text": apply_mask(self.test_data[lang].text.tolist())}
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for lang in self.te_langs
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}
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lYte = {
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lang: self.indices_to_one_hot(
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indices=self.test_data[lang].label.tolist(),
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n_labels=self.num_labels())
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for lang in self.te_langs
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}
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return lXte, lYte
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def langs(self):
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return list(self.all_langs)
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def num_labels(self):
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return len(self.labels)
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def indices_to_one_hot(self, indices, n_labels):
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one_hot_matrix = np.zeros((len(indices), n_labels))
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one_hot_matrix[np.arange(len(indices)), indices] = 1
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return one_hot_matrix
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def _mask_numbers(data):
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mask_moredigit = re.compile(r"\s[\+-]?\d{5,}([\.,]\d*)*\b")
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mask_4digit = re.compile(r"\s[\+-]?\d{4}([\.,]\d*)*\b")
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mask_3digit = re.compile(r"\s[\+-]?\d{3}([\.,]\d*)*\b")
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mask_2digit = re.compile(r"\s[\+-]?\d{2}([\.,]\d*)*\b")
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mask_1digit = re.compile(r"\s[\+-]?\d{1}([\.,]\d*)*\b")
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masked = []
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for text in tqdm(data, desc="masking numbers", disable=True):
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text = " " + text
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text = mask_moredigit.sub(" MoreDigitMask", text)
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text = mask_4digit.sub(" FourDigitMask", text)
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text = mask_3digit.sub(" ThreeDigitMask", text)
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text = mask_2digit.sub(" TwoDigitMask", text)
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text = mask_1digit.sub(" OneDigitMask", text)
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masked.append(text.replace(".", "").replace(",", "").strip())
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return masked
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if __name__ == "__main__":
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data_rai = SimpleGfunDataset()
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lXtr, lYtr = data_rai.training(mask_number=False)
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lXte, lYte = data_rai.test(mask_number=False)
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exit() |