support for cls dataset; update requirements

dataManager/clsDataset.py

@@ -0,0 +1,81 @@
+import sys
+import os
+
+sys.path.append(os.getcwd())
+
+import numpy as np
+import re
+from dataManager.multilingualDataset import MultilingualDataset
+
+CLS_PROCESSED_DATA_DIR = os.path.expanduser("~/datasets/cls-acl10-processed/")
+LANGS = ["de", "en", "fr", "jp"]
+DOMAINS = ["books", "dvd", "music"]
+
+regex = r":\d+"
+subst = ""
+
+
+def load_cls():
+    data = {}
+    for lang in LANGS:
+        data[lang] = {}
+        for domain in DOMAINS:
+            print(f"lang: {lang}, domain: {domain}")
+            train = (
+                open(
+                    os.path.join(
+                        CLS_PROCESSED_DATA_DIR, lang, domain, "train.processed"
+                    ),
+                    "r",
+                )
+                .read()
+                .splitlines()
+            )
+            test = (
+                open(
+                    os.path.join(
+                        CLS_PROCESSED_DATA_DIR, lang, domain, "test.processed"
+                    ),
+                    "r",
+                )
+                .read()
+                .splitlines()
+            )
+            print(f"train: {len(train)}, test: {len(test)}")
+            data[lang][domain] = {
+                "train": [process_data(t) for t in train],
+                "test": [process_data(t) for t in test],
+            }
+    return data
+
+
+def process_data(line):
+    # TODO: we are adding a space after each pucntuation mark (e.g., ". es ich das , langweilig lustig")
+    result = re.sub(regex, subst, line, 0, re.MULTILINE)
+    text, label = result.split("#label#:")
+    label = 0 if label == "negative" else 1
+    return text, label
+
+
+if __name__ == "__main__":
+    print(f"datapath: {CLS_PROCESSED_DATA_DIR}")
+    data = load_cls()
+    multilingualDataset = MultilingualDataset(dataset_name="cls")
+    for lang in LANGS:
+        # TODO: just using book domain atm
+        Xtr = [text[0] for text in data[lang]["books"]["train"]]
+        Ytr = np.expand_dims([text[1] for text in data[lang]["books"]["train"]], axis=1)
+        Xte = [text[0] for text in data[lang]["books"]["test"]]
+        Yte = np.expand_dims([text[1] for text in data[lang]["books"]["test"]], axis=1)
+        multilingualDataset.add(
+            lang=lang,
+            Xtr=Xtr,
+            Ytr=Ytr,
+            Xte=Xte,
+            Yte=Yte,
+            tr_ids=None,
+            te_ids=None,
+        )
+    multilingualDataset.save(
+        os.path.expanduser("~/datasets/cls-acl10-processed/cls-acl10-processed.pkl")
+    )

dataManager/gFunDataset.py

@@ -1,6 +1,6 @@
 from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer
 from dataManager.glamiDataset import get_dataframe
-from dataManager.multilingualDatset import MultilingualDataset
+from dataManager.multilingualDataset import MultilingualDataset
 
 
 class gFunDataset:
@@ -25,7 +25,7 @@ class gFunDataset:
         self.load_dataset()
 
     def get_label_binarizer(self, labels):
-        if self.dataset_name in ["rcv1-2", "jrc"]:
+        if self.dataset_name in ["rcv1-2", "jrc", "cls"]:
             mlb = "Labels are already binarized for rcv1-2 dataset"
         elif self.is_multilabel:
             mlb = MultiLabelBinarizer()
@@ -60,6 +60,14 @@ class gFunDataset:
             )
             self.mlb = self.get_label_binarizer(self.labels)
 
+        elif "cls" in self.dataset_dir.lower():
+            print(f"- Loading CLS dataset from {self.dataset_dir}")
+            self.dataset_name = "cls"
+            self.dataset, self.labels, self.data_langs = self._load_multilingual(
+                self.dataset_name, self.dataset_dir, self.nrows
+            )
+            self.mlb = self.get_label_binarizer(self.labels)
+
         self.show_dimension()
 
         return
@@ -70,7 +78,7 @@ class gFunDataset:
             print(
                 f"-- Lang: {lang} - train docs: {len(data['train']['text'])} - test docs: {len(data['test']['text'])}"
             )
-        if self.dataset_name in ["rcv1-2", "jrc"]:
+        if self.dataset_name in ["rcv1-2", "jrc", "cls"]:
             print(f"-- Labels: {self.labels}")
         else:
             print(f"-- Labels: {len(self.labels)}")
@@ -78,6 +86,9 @@ class gFunDataset:
     def _load_multilingual(self, dataset_name, dataset_dir, nrows):
         old_dataset = MultilingualDataset(dataset_name=dataset_name).load(dataset_dir)
         if nrows is not None:
+            if dataset_name == "cls":
+                old_dataset.reduce_data(langs=["de", "en", "fr"], maxn=nrows)
+            else:
                 old_dataset.reduce_data(langs=["en", "it", "fr"], maxn=nrows)
         labels = old_dataset.num_labels()
         data_langs = old_dataset.langs()
@@ -151,7 +162,7 @@ class gFunDataset:
         return dataset, labels, data_langs
 
     def binarize_labels(self, labels):
-        if self.dataset_name in ["rcv1-2", "jrc"]:
+        if self.dataset_name in ["rcv1-2", "jrc", "cls"]:
             # labels are already binarized for rcv1-2 dataset
             return labels
         if hasattr(self, "mlb"):

dataManager/multilingualDataset.py

@@ -9,32 +9,6 @@ import numpy as np
 from tqdm import tqdm
 
 
-class NewMultilingualDataset(ABC):
-    @abstractmethod
-    def get_training(self):
-        pass
-
-    @abstractmethod
-    def get_validation(self):
-        pass
-
-    @abstractmethod
-    def get_test(self):
-        pass
-
-    @abstractmethod
-    def mask_numbers(self):
-        pass
-
-    @abstractmethod
-    def save(self):
-        pass
-
-    @abstractmethod
-    def load(self):
-        pass
-
-
 # class RcvMultilingualDataset(MultilingualDataset):
 class RcvMultilingualDataset:
     def __init__(
@@ -242,7 +216,10 @@ class MultilingualDataset:
         new_data = []
         for split in multilingual_dataset:
             docs, labels, ids = split
+            if ids is not None:
                 new_data.append((docs[:maxn], labels[:maxn], ids[:maxn]))
+            else:
+                new_data.append((docs[:maxn], labels[:maxn], None))
         return new_data
 
 

main.py

@@ -4,7 +4,7 @@ from os.path import expanduser
 from time import time
 
 from dataManager.amazonDataset import AmazonDataset
-from dataManager.multilingualDatset import MultilingualDataset
+from dataManager.multilingualDataset import MultilingualDataset
 from dataManager.multiNewsDataset import MultiNewsDataset
 from dataManager.glamiDataset import GlamiDataset
 from dataManager.gFunDataset import gFunDataset
@@ -13,6 +13,7 @@ from gfun.generalizedFunnelling import GeneralizedFunnelling
 
 """ 
 TODO: 
+    - [!] add support for Binary Datasets (e.g. cls)
     - [!] logging
     - add documentations sphinx
     - [!] zero-shot setup
@@ -23,12 +24,13 @@ TODO:
 """
 
 
-def get_dataset(datasetname):
+def get_dataset(datasetname, args):
     assert datasetname in [
         "multinews",
         "amazon",
         "rcv1-2",
         "glami",
+        "cls",
     ], "dataset not supported"
 
     RCV_DATAPATH = expanduser(
@@ -37,6 +39,8 @@ def get_dataset(datasetname):
     JRC_DATAPATH = expanduser(
         "~/datasets/jrc/jrc_doclist_1958-2005vs2006_all_top300_noparallel_processed_run0.pickle"
     )
+    CLS_DATAPATH = expanduser("~/datasets/cls-acl10-processed/cls-acl10-processed.pkl")
+
     MULTINEWS_DATAPATH = expanduser("~/datasets/MultiNews/20110730/")
 
     GLAMI_DATAPATH = expanduser("~/datasets/GLAMI-1M-dataset")
@@ -73,13 +77,21 @@ def get_dataset(datasetname):
             is_multilabel=False,
             nrows=args.nrows,
         )
+    elif datasetname == "cls":
+        dataset = gFunDataset(
+            dataset_dir=CLS_DATAPATH,
+            is_textual=True,
+            is_visual=False,
+            is_multilabel=False,
+            nrows=args.nrows,
+        )
     else:
         raise NotImplementedError
     return dataset
 
 
 def main(args):
-    dataset = get_dataset(args.dataset)
+    dataset = get_dataset(args.dataset, args)
     if (
         isinstance(dataset, MultilingualDataset)
         or isinstance(dataset, MultiNewsDataset)

requirements.txt

@@ -1,13 +1,13 @@
 beautifulsoup4==4.11.2
 joblib==1.2.0
-matplotlib==3.6.3
+matplotlib==3.7.1
-numpy==1.24.1
+numpy==1.24.2
+pandas==1.5.3
 Pillow==9.4.0
 requests==2.28.2
 scikit_learn==1.2.1
-scipy==1.10.0
+scipy==1.10.1
 torch==1.13.1
 torchtext==0.14.1
-torchvision==0.14.1
+tqdm==4.65.0
-tqdm==4.64.1
+transformers==4.26.1
-transformers==4.26.0