326 lines
12 KiB
Python
326 lines
12 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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):
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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.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.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"))
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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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class gFunDataset:
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def __init__(
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self,
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dataset_dir,
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is_textual,
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is_visual,
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is_multilabel,
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labels=None,
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nrows=None,
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data_langs=None,
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):
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self.dataset_dir = dataset_dir
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self.data_langs = data_langs
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self.is_textual = is_textual
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self.is_visual = is_visual
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self.is_multilabel = is_multilabel
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self.labels = labels
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self.nrows = nrows
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self.dataset = {}
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self._load_dataset()
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def get_label_binarizer(self, labels):
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if self.dataset_name in ["rcv1-2", "jrc", "cls", "rai"]:
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mlb = f"Labels are already binarized for {self.dataset_name} dataset"
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elif self.is_multilabel:
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mlb = MultiLabelBinarizer()
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mlb.fit([labels])
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else:
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mlb = LabelBinarizer()
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mlb.fit(labels)
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return mlb
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def _load_dataset(self):
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print(f"- Loading dataset from {self.dataset_dir}")
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self.dataset_name = "rai"
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self.dataset, self.labels, self.data_langs = self._load_multilingual(dataset_name=self.dataset_name,
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dataset_dir=self.dataset_dir,
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nrows=self.nrows)
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self.mlb = self.get_label_binarizer(self.labels)
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self.show_dimension()
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return
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def show_dimension(self):
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print(f"\n[Dataset: {self.dataset_name.upper()}]")
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for lang, data in self.dataset.items():
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print(
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f"-- Lang: {lang} - train docs: {len(data['train']['text'])} - test docs: {len(data['test']['text'])}"
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)
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if self.dataset_name in ["rcv1-2", "jrc", "cls", "rai"]:
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print(f"-- Labels: {self.labels}")
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else:
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print(f"-- Labels: {len(self.labels)}")
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def _load_multilingual(self, dataset_dir, nrows, dataset_name="rai"):
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if "csv" in dataset_dir:
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old_dataset = MultilingualDataset(dataset_name="rai").from_csv(
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path_tr=os.path.expanduser(os.path.join(dataset_dir, "train.small.csv")),
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path_te=os.path.expanduser(os.path.join(dataset_dir, "test.small.csv"))
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)
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if nrows is not None:
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if dataset_name == "cls":
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old_dataset.reduce_data(langs=["de", "en", "fr"], maxn=nrows)
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else:
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old_dataset.reduce_data(langs=["en", "it", "fr"], maxn=nrows)
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labels = old_dataset.num_labels()
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data_langs = old_dataset.langs()
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def _format_multilingual(data):
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text = data[0]
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image = None
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labels = data[1]
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return {"text": text, "image": image, "label": labels}
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dataset = {
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k: {"train": _format_multilingual(v[0]), "test": _format_multilingual(v[1])}
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for k, v in old_dataset.multiling_dataset.items()
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}
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return dataset, labels, data_langs
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def _load_glami(self, dataset_dir, nrows):
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train_split = get_dataframe("train", dataset_dir=dataset_dir).sample(n=nrows)
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test_split = get_dataframe("test", dataset_dir=dataset_dir).sample(
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n=int(nrows / 10)
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)
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gb_train = train_split.groupby("geo")
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gb_test = test_split.groupby("geo")
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if self.data_langs is None:
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data_langs = sorted(train_split.geo.unique().tolist())
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if self.labels is None:
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labels = train_split.category_name.unique().tolist()
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def _format_glami(data_df):
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text = (data_df.name + " " + data_df.description).tolist()
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image = data_df.image_file.tolist()
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labels = data_df.category_name.tolist()
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return {"text": text, "image": image, "label": labels}
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dataset = {
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lang: {
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"train": _format_glami(data_tr),
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"test": _format_glami(gb_test.get_group(lang)),
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}
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for lang, data_tr in gb_train
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if lang in data_langs
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}
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return dataset, labels, data_langs
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def binarize_labels(self, labels):
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if self.dataset_name in ["rcv1-2", "jrc", "cls", "rai"]:
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# labels are already binarized for rcv1-2 dataset
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return labels
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if hasattr(self, "mlb"):
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return self.mlb.transform(labels)
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else:
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raise AttributeError("Label binarizer not found")
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def training(self):
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lXtr = {}
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lYtr = {}
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for lang in self.data_langs:
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text = self.dataset[lang]["train"]["text"] if self.is_textual else None
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img = self.dataset[lang]["train"]["image"] if self.is_visual else None
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labels = self.dataset[lang]["train"]["label"]
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lXtr[lang] = {"text": text, "image": img}
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lYtr[lang] = self.binarize_labels(labels)
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return lXtr, lYtr
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def test(self):
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lXte = {}
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lYte = {}
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for lang in self.data_langs:
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text = self.dataset[lang]["test"]["text"] if self.is_textual else None
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img = self.dataset[lang]["test"]["image"] if self.is_visual else None
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labels = self.dataset[lang]["test"]["label"]
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lXte[lang] = {"text": text, "image": img}
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lYte[lang] = self.binarize_labels(labels)
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return lXte, lYte
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def langs(self):
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return self.data_langs
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def num_labels(self):
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if self.dataset_name not in ["rcv1-2", "jrc", "cls", "rai"]:
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return len(self.labels)
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else:
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return self.labels
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def save_as_pickle(self, path):
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import pickle
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filepath = os.path.join(path, f"{self.dataset_name}_{self.nrows}.pkl")
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with open(filepath, "wb") as f:
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print(f"- saving dataset in {filepath}")
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pickle.dump(self, f)
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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() |