script to run sentiment experiemnts

.gitignore

@@ -182,4 +182,5 @@ scripts/
 logger/*
 explore_data.ipynb
 run.sh
-wandb
+wandb
+local_datasets

dataManager/clsDataset.py

@@ -1,5 +1,6 @@
 import sys
 import os
+import xml.etree.ElementTree as ET
 
 sys.path.append(os.getcwd())
 
@@ -8,13 +9,87 @@ import re
 from dataManager.multilingualDataset import MultilingualDataset
 
 CLS_PROCESSED_DATA_DIR = os.path.expanduser("~/datasets/cls-acl10-processed/")
-LANGS = ["de", "en", "fr", "jp"]
+CLS_UNPROCESSED_DATA_DIR = os.path.expanduser("~/datasets/cls-acl10-unprocessed/")
+# LANGS = ["de", "en", "fr", "jp"]
+LANGS = ["de", "en", "fr"]
 DOMAINS = ["books", "dvd", "music"]
 
 regex = r":\d+"
 subst = ""
 
 
+def load_unprocessed_cls(reduce_target_space=False):
+    data = {}
+    data_tr = []
+    data_te = []
+    c_tr = 0
+    c_te = 0
+    for lang in LANGS:
+        data[lang] = {}
+        for domain in DOMAINS:
+            data[lang][domain] = {}
+            print(f"lang: {lang}, domain: {domain}")
+            for split in ["train", "test"]:
+                domain_data = []
+                fdir = os.path.join(
+                    CLS_UNPROCESSED_DATA_DIR, lang, domain, f"{split}.review"
+                )
+                tree = ET.parse(fdir)
+                root = tree.getroot()
+                for child in root:
+                    if reduce_target_space:
+                        rating = np.zeros(3, dtype=int)
+                        original_rating = int(float(child.find("rating").text))
+                        if original_rating < 3:
+                            new_rating = 1
+                        elif original_rating > 3:
+                            new_rating = 3
+                        else:
+                            new_rating = 2
+                        rating[new_rating - 1] = 1
+                        # rating = new_rating
+                    else:
+                        rating = np.zeros(5, dtype=int)
+                        rating[int(float(child.find("rating").text)) - 1] = 1
+                        # rating = new_rating
+                    # if split == "train":
+                    #     target_data = data_tr
+                    #     current_count = len(target_data)
+                    #     c_tr = +1
+                    # else:
+                    #     target_data = data_te
+                    #     current_count = len(target_data)
+                    #     c_te = +1
+                    domain_data.append(
+                    # target_data.append(
+                        {
+                            "asin": child.find("asin").text
+                            if child.find("asin") is not None
+                            else None,
+                            # "category": child.find("category").text
+                            # if child.find("category") is not None
+                            # else None,
+                            "category": domain,
+                            # "rating": child.find("rating").text
+                            # if child.find("rating") is not None
+                            # else None,
+                            "rating": rating,
+                            "title": child.find("title").text
+                            if child.find("title") is not None
+                            else None,
+                            "text": child.find("text").text
+                            if child.find("text") is not None
+                            else None,
+                            "summary": child.find("summary").text
+                            if child.find("summary") is not None
+                            else None,
+                            "lang": lang,
+                        }
+                    )
+                data[lang][domain].update({split: domain_data})
+    return data
+
+
 def load_cls():
     data = {}
     for lang in LANGS:
@@ -24,7 +99,7 @@ def load_cls():
             train = (
                 open(
                     os.path.join(
-                        CLS_PROCESSED_DATA_DIR, lang, domain, "train.processed"
+                        CLS_UNPROCESSED_DATA_DIR, lang, domain, "train.processed"
                     ),
                     "r",
                 )
@@ -34,7 +109,7 @@ def load_cls():
             test = (
                 open(
                     os.path.join(
-                        CLS_PROCESSED_DATA_DIR, lang, domain, "test.processed"
+                        CLS_UNPROCESSED_DATA_DIR, lang, domain, "test.processed"
                     ),
                     "r",
                 )
@@ -59,18 +134,33 @@ def process_data(line):
 
 
 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)
-        Ytr = np.vstack([text[1] for text in data[lang]["books"]["train"]])
+    print(f"datapath: {CLS_UNPROCESSED_DATA_DIR}")
+    # data = load_cls()
+    data = load_unprocessed_cls(reduce_target_space=True)
+    multilingualDataset = MultilingualDataset(dataset_name="webis-cls-unprocessed")
 
-        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)
-        Yte = np.vstack([text[1] for text in data[lang]["books"]["test"]])
+    for lang in LANGS:
+        # Xtr = [text["summary"] for text in data[lang]["books"]["train"]]
+        Xtr = [text["text"] for text in data[lang]["books"]["train"]]
+        Xtr += [text["text"] for text in data[lang]["dvd"]["train"]]
+        Xtr += [text["text"] for text in data[lang]["music"]["train"]]
+
+        Ytr =[text["rating"] for text in data[lang]["books"]["train"]]
+        Ytr += [text["rating"] for text in data[lang]["dvd"]["train"]]
+        Ytr += [text["rating"] for text in data[lang]["music"]["train"]]
+
+        Ytr = np.vstack(Ytr)
+
+        Xte = [text["text"] for text in data[lang]["books"]["test"]]
+        Xte += [text["text"] for text in data[lang]["dvd"]["test"]]
+        Xte += [text["text"] for text in data[lang]["music"]["test"]]
+
+
+        Yte = [text["rating"] for text in data[lang]["books"]["test"]]
+        Yte += [text["rating"] for text in data[lang]["dvd"]["test"]]
+        Yte += [text["rating"] for text in data[lang]["music"]["test"]]
+
+        Yte = np.vstack(Yte)
 
         multilingualDataset.add(
             lang=lang,
@@ -82,5 +172,7 @@ if __name__ == "__main__":
             te_ids=None,
         )
     multilingualDataset.save(
-        os.path.expanduser("~/datasets/cls-acl10-processed/cls-acl10-processed.pkl")
+        os.path.expanduser(
+            "~/datasets/cls-acl10-unprocessed/cls-acl10-unprocessed-all.pkl"
+        )
     )

dataManager/gFunDataset.py

@@ -62,14 +62,29 @@ 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}")
+        # WEBIS-CLS (processed)
+        elif (
+            "cls" in self.dataset_dir.lower()
+            and "unprocessed" not in self.dataset_dir.lower()
+        ):
+            print(f"- Loading WEBIS-CLS (processed) 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)
 
+        # WEBIS-CLS (unprocessed)
+        elif (
+            "cls" in self.dataset_dir.lower()
+            and "unprocessed" in self.dataset_dir.lower()
+        ):
+            print(f"- Loading WEBIS-CLS (unprocessed) 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

dataManager/utils.py

@@ -23,6 +23,7 @@ def get_dataset(dataset_name, args):
         "rcv1-2",
         "glami",
         "cls",
+        "webis",
     ], "dataset not supported"
 
     RCV_DATAPATH = expanduser(
@@ -37,6 +38,10 @@ def get_dataset(dataset_name, args):
 
     GLAMI_DATAPATH = expanduser("~/datasets/GLAMI-1M-dataset")
 
+    WEBIS_CLS = expanduser(
+        "~/datasets/cls-acl10-unprocessed/cls-acl10-unprocessed-all.pkl"
+    )
+
     if dataset_name == "multinews":
         # TODO: convert to gFunDataset
         raise NotImplementedError
@@ -91,6 +96,15 @@ def get_dataset(dataset_name, args):
             is_multilabel=False,
             nrows=args.nrows,
         )
+
+    elif dataset_name == "webis":
+        dataset = gFunDataset(
+            dataset_dir=WEBIS_CLS,
+            is_textual=True,
+            is_visual=False,
+            is_multilabel=False,
+            nrows=args.nrows,
+        )
     else:
         raise NotImplementedError
     return dataset

evaluation/evaluate.py

@@ -1,8 +1,9 @@
 from joblib import Parallel, delayed
 from collections import defaultdict
 
-from evaluation.metrics import *
-from sklearn.metrics import accuracy_score, top_k_accuracy_score, f1_score
+# from evaluation.metrics import *
+import numpy as np
+from sklearn.metrics import accuracy_score, top_k_accuracy_score, f1_score, precision_score, recall_score
 
 
 def evaluation_metrics(y, y_, clf_type):
@@ -13,13 +14,17 @@ def evaluation_metrics(y, y_, clf_type):
             # TODO: we need logits top_k_accuracy_score(y, y_, k=10),
             f1_score(y, y_, average="macro", zero_division=1),
             f1_score(y, y_, average="micro"),
+            precision_score(y, y_, zero_division=1, average="macro"),
+            recall_score(y, y_, zero_division=1, average="macro"),
         )
     elif clf_type == "multilabel":
         return (
-            macroF1(y, y_),
-            microF1(y, y_),
-            macroK(y, y_),
-            microK(y, y_),
+            f1_score(y, y_, average="macro", zero_division=1),
+            f1_score(y, y_, average="micro"),
+            0,
+            0,
+            # macroK(y, y_),
+            # microK(y, y_),
         )
     else:
         raise ValueError("clf_type must be either 'singlelabel' or 'multilabel'")
@@ -48,8 +53,10 @@ def log_eval(l_eval, phase="training", clf_type="multilabel", verbose=True):
 
     if clf_type == "multilabel":
         for lang in l_eval.keys():
-            macrof1, microf1, macrok, microk = l_eval[lang]
-            metrics.append([macrof1, microf1, macrok, microk])
+            # macrof1, microf1, macrok, microk = l_eval[lang]
+            # metrics.append([macrof1, microf1, macrok, microk])
+            macrof1, microf1, precision, recall = l_eval[lang]
+            metrics.append([macrof1, microf1, precision, recall])
             if phase != "validation":
                 print(f"Lang {lang}: macro-F1 = {macrof1:.3f} micro-F1 = {microf1:.3f}")
         averages = np.mean(np.array(metrics), axis=0)
@@ -69,12 +76,15 @@ def log_eval(l_eval, phase="training", clf_type="multilabel", verbose=True):
             # "acc10",  # "accuracy-at-10",
             "MF1",  # "macro-F1",
             "mF1",  # "micro-F1",
+            "precision",
+            "recall"
         ]
         for lang in l_eval.keys():
             # acc, top5, top10, macrof1, microf1 = l_eval[lang]
-            acc, macrof1, microf1 = l_eval[lang]
+            acc, macrof1, microf1, precision, recall= l_eval[lang]
             # metrics.append([acc, top5, top10, macrof1, microf1])
-            metrics.append([acc, macrof1, microf1])
+            # metrics.append([acc, macrof1, microf1])
+            metrics.append([acc, macrof1, microf1, precision, recall])
 
             for m, v in zip(_metrics, l_eval[lang]):
                 lang_metrics[m][lang] = v
@@ -82,7 +92,8 @@ def log_eval(l_eval, phase="training", clf_type="multilabel", verbose=True):
             if phase != "validation":
                 print(
                     # f"Lang {lang}: acc = {acc:.3f} acc-top5 = {top5:.3f} acc-top10 = {top10:.3f} macro-F1: {macrof1:.3f} micro-F1 = {microf1:.3f}"
-                    f"Lang {lang}: acc = {acc:.3f} macro-F1: {macrof1:.3f} micro-F1 = {microf1:.3f}"
+                    # f"Lang {lang}: acc = {acc:.3f} macro-F1: {macrof1:.3f} micro-F1 = {microf1:.3f}"
+                    f"Lang {lang}: acc = {acc:.3f} macro-F1: {macrof1:.3f} micro-F1 = {microf1:.3f} pr = {precision:.3f} re = {recall:.3f}"
                 )
         averages = np.mean(np.array(metrics), axis=0)
         if verbose:

gfun/generalizedFunnelling.py

@@ -124,6 +124,16 @@ class GeneralizedFunnelling:
                     epochs=self.epochs,
                     attn_stacking_type=attn_stacking,
                 )
+
+            self._model_id = get_unique_id(
+                self.dataset_name,
+                self.posteriors_vgf,
+                self.multilingual_vgf,
+                self.wce_vgf,
+                self.textual_trf_vgf,
+                self.visual_trf_vgf,
+                self.aggfunc,
+            )
             return self
 
         if self.posteriors_vgf:
@@ -317,7 +327,7 @@ class GeneralizedFunnelling:
 
         for vgf in self.first_tier_learners:
             vgf_config = vgf.get_config()
-            c.update(vgf_config)
+            c.update({vgf_config["name"]: vgf_config})
 
         gfun_config = {
             "id": self._model_id,
@@ -372,6 +382,7 @@ class GeneralizedFunnelling:
                 "rb",
             ) as vgf:
                 first_tier_learners.append(pickle.load(vgf))
+                print(f"- loaded trained VanillaFun VGF")
         if self.multilingual_vgf:
             with open(
                 os.path.join(
@@ -380,6 +391,7 @@ class GeneralizedFunnelling:
                 "rb",
             ) as vgf:
                 first_tier_learners.append(pickle.load(vgf))
+                print(f"- loaded trained Multilingual VGF")
         if self.wce_vgf:
             with open(
                 os.path.join(
@@ -388,20 +400,38 @@ class GeneralizedFunnelling:
                 "rb",
             ) as vgf:
                 first_tier_learners.append(pickle.load(vgf))
+                print(f"- loaded trained WCE VGF")
         if self.textual_trf_vgf:
             with open(
                 os.path.join(
-                    "models", "vgfs", "transformer", f"transformerGen_{model_id}.pkl"
+                    "models",
+                    "vgfs",
+                    "textual_transformer",
+                    f"textualTransformerGen_{model_id}.pkl",
                 ),
                 "rb",
             ) as vgf:
                 first_tier_learners.append(pickle.load(vgf))
+                print(f"- loaded trained Textual Transformer VGF")
+        if self.visual_trf_vgf:
+            with open(
+                os.path.join(
+                    "models",
+                    "vgfs",
+                    "visual_transformer",
+                    f"visualTransformerGen_{model_id}.pkl",
+                ),
+                "rb",
+                print(f"- loaded trained Visual Transformer VGF"),
+            ) as vgf:
+                first_tier_learners.append(pickle.load(vgf))
 
         if load_meta:
             with open(
                 os.path.join("models", "metaclassifier", f"meta_{model_id}.pkl"), "rb"
             ) as f:
                 metaclassifier = pickle.load(f)
+                print(f"- loaded trained metaclassifier")
         else:
             metaclassifier = None
         return first_tier_learners, metaclassifier, vectorizer

gfun/vgfs/textualTransformerGen.py

@@ -45,11 +45,12 @@ class MT5ForSequenceClassification(nn.Module):
 
     def save_pretrained(self, checkpoint_dir):
         torch.save(self.state_dict(), checkpoint_dir + ".pt")
-        return
+        return self
 
     def from_pretrained(self, checkpoint_dir):
         checkpoint_dir += ".pt"
-        return self.load_state_dict(torch.load(checkpoint_dir))
+        self.load_state_dict(torch.load(checkpoint_dir))
+        return self
 
 
 class TextualTransformerGen(ViewGen, TransformerGen):
@@ -113,6 +114,7 @@ class TextualTransformerGen(ViewGen, TransformerGen):
                 model_name, num_labels=num_labels, output_hidden_states=True
             )
         else:
+            model_name = "models/vgfs/trained_transformer/mbert-sentiment/checkpoint-8500"  # TODO hardcoded to pre-traiend mbert
             return AutoModelForSequenceClassification.from_pretrained(
                 model_name, num_labels=num_labels, output_hidden_states=True
             )
@@ -144,58 +146,60 @@ class TextualTransformerGen(ViewGen, TransformerGen):
             self.model_name, num_labels=self.num_labels
         )
 
-        tr_lX, tr_lY, val_lX, val_lY = self.get_train_val_data(
-            lX, lY, split=0.2, seed=42, modality="text"
-        )
+        self.model.to("cuda")
 
-        tra_dataloader = self.build_dataloader(
-            tr_lX,
-            tr_lY,
-            processor_fn=self._tokenize,
-            torchDataset=MultilingualDatasetTorch,
-            batch_size=self.batch_size,
-            split="train",
-            shuffle=True,
-        )
-
-        val_dataloader = self.build_dataloader(
-            val_lX,
-            val_lY,
-            processor_fn=self._tokenize,
-            torchDataset=MultilingualDatasetTorch,
-            batch_size=self.batch_size_eval,
-            split="val",
-            shuffle=False,
-        )
-
-        experiment_name = f"{self.model_name.replace('/', '-')}-{self.epochs}-{self.batch_size}-{self.dataset_name}"
-
-        trainer = Trainer(
-            model=self.model,
-            optimizer_name="adamW",
-            lr=self.lr,
-            device=self.device,
-            loss_fn=torch.nn.CrossEntropyLoss(),
-            print_steps=self.print_steps,
-            evaluate_step=self.evaluate_step,
-            patience=self.patience,
-            experiment_name=experiment_name,
-            checkpoint_path=os.path.join(
-                "models",
-                "vgfs",
-                "transformer",
-                self._format_model_name(self.model_name),
-            ),
-            vgf_name="textual_trf",
-            classification_type=self.clf_type,
-            n_jobs=self.n_jobs,
-            scheduler_name=self.scheduler,
-        )
-        trainer.train(
-            train_dataloader=tra_dataloader,
-            eval_dataloader=val_dataloader,
-            epochs=self.epochs,
-        )
+        # tr_lX, tr_lY, val_lX, val_lY = self.get_train_val_data(
+        #     lX, lY, split=0.2, seed=42, modality="text"
+        # )
+        # 
+        # tra_dataloader = self.build_dataloader(
+        #     tr_lX,
+        #     tr_lY,
+        #     processor_fn=self._tokenize,
+        #     torchDataset=MultilingualDatasetTorch,
+        #     batch_size=self.batch_size,
+        #     split="train",
+        #     shuffle=True,
+        # )
+        #
+        # val_dataloader = self.build_dataloader(
+        #     val_lX,
+        #     val_lY,
+        #     processor_fn=self._tokenize,
+        #     torchDataset=MultilingualDatasetTorch,
+        #     batch_size=self.batch_size_eval,
+        #     split="val",
+        #     shuffle=False,
+        # )
+        #
+        # experiment_name = f"{self.model_name.replace('/', '-')}-{self.epochs}-{self.batch_size}-{self.dataset_name}"
+        #
+        # trainer = Trainer(
+        #     model=self.model,
+        #     optimizer_name="adamW",
+        #     lr=self.lr,
+        #     device=self.device,
+        #     loss_fn=torch.nn.CrossEntropyLoss(),
+        #     print_steps=self.print_steps,
+        #     evaluate_step=self.evaluate_step,
+        #     patience=self.patience,
+        #     experiment_name=experiment_name,
+        #     checkpoint_path=os.path.join(
+        #         "models",
+        #         "vgfs",
+        #         "trained_transformer",
+        #         self._format_model_name(self.model_name),
+        #     ),
+        #     vgf_name="textual_trf",
+        #     classification_type=self.clf_type,
+        #     n_jobs=self.n_jobs,
+        #     scheduler_name=self.scheduler,
+        # )
+        # trainer.train(
+        #     train_dataloader=tra_dataloader,
+        #     eval_dataloader=val_dataloader,
+        #     epochs=self.epochs,
+        # )
 
         if self.probabilistic:
             self.feature2posterior_projector.fit(self.transform(lX), lY)
@@ -224,7 +228,6 @@ class TextualTransformerGen(ViewGen, TransformerGen):
         with torch.no_grad():
             for input_ids, lang in dataloader:
                 input_ids = input_ids.to(self.device)
-                # TODO: check this
                 if isinstance(self.model, MT5ForSequenceClassification):
                     batch_embeddings = self.model(input_ids).pooled.cpu().numpy()
                 else:
@@ -277,4 +280,4 @@ class TextualTransformerGen(ViewGen, TransformerGen):
 
     def get_config(self):
         c = super().get_config()
-        return {"textual_trf": c}
+        return {"name": "textual-trasnformer VGF", "textual_trf": c}

gfun/vgfs/vanillaFun.py

@@ -65,3 +65,6 @@ class VanillaFunGen(ViewGen):
         with open(_path, "wb") as f:
             pickle.dump(self, f)
         return self
+
+    def get_config(self):
+        return {"name": "Vanilla Funnelling VGF"}

gfun/vgfs/visualTransformerGen.py

@@ -186,4 +186,4 @@ class VisualTransformerGen(ViewGen, TransformerGen):
         return self
 
     def get_config(self):
-        return {"visual_trf": super().get_config()}
+        return {"name": "visual-transformer VGF", "visual_trf": super().get_config()}

hf_trainer.py

@@ -0,0 +1,191 @@
+import torch
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    TrainingArguments,
+)
+from gfun.vgfs.commons import Trainer
+from datasets import load_dataset, DatasetDict
+
+from transformers import Trainer
+
+import transformers
+import evaluate
+
+transformers.logging.set_verbosity_error()
+
+
+def init_callbacks(patience=-1, nosave=False):
+    callbacks = []
+    if patience != -1 and not nosave:
+        callbacks.append(transformers.EarlyStoppingCallback(early_stopping_patience=patience))
+    return callbacks
+
+
+def init_model(model_name):
+    if model_name == "mbert":
+        hf_name = "bert-base-multilingual-cased"
+    elif model_name == "xlm-roberta":
+        hf_name = "xlm-roberta-base"
+    else:
+        raise NotImplementedError
+    tokenizer = AutoTokenizer.from_pretrained(hf_name)
+    model = AutoModelForSequenceClassification.from_pretrained(hf_name, num_labels=3)
+    return tokenizer, model
+
+def main(args):
+    tokenizer, model = init_model(args.model)
+
+    data = load_dataset(
+        "json",
+        data_files={
+            "train": "local_datasets/webis-cls/all-domains/train.json",
+            "test": "local_datasets/webis-cls/all-domains/test.json",
+        },
+    )
+
+    def process_sample(sample):
+        inputs = sample["text"]
+        ratings = [r - 1 for r in sample["rating"]]
+        targets = torch.zeros((len(inputs), 3), dtype=float)
+        lang_mapper = {
+            lang: lang_id for lang_id, lang in enumerate(set(sample["lang"]))
+        }
+        lang_ids = [lang_mapper[l] for l in sample["lang"]]
+        for i, r in enumerate(ratings):
+            targets[i][r - 1] = 1
+
+        model_inputs = tokenizer(inputs, max_length=512, truncation=True)
+        model_inputs["labels"] = targets
+        model_inputs["lang_ids"] = torch.tensor(lang_ids)
+        return model_inputs
+
+    data = data.map(
+        process_sample,
+        batched=True,
+        num_proc=4,
+        load_from_cache_file=True,
+        remove_columns=["text", "category", "rating", "summary", "title"],
+    )
+    train_val_splits = data["train"].train_test_split(test_size=0.2, seed=42)
+    data.set_format("torch")
+    data = DatasetDict(
+        {
+            "train": train_val_splits["train"],
+            "validation": train_val_splits["test"],
+            "test": data["test"],
+        }
+    )
+
+    data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
+    callbacks = init_callbacks(args.patience, args.nosave)
+
+    f1_metric = evaluate.load("f1")
+    accuracy_metric = evaluate.load("accuracy")
+    precision_metric = evaluate.load("precision")
+    recall_metric = evaluate.load("recall")
+
+    training_args = TrainingArguments(
+        output_dir=f"{args.model}-sentiment",
+        do_train=True,
+        evaluation_strategy="steps",
+        per_device_train_batch_size=args.batch,
+        per_device_eval_batch_size=args.batch,
+        gradient_accumulation_steps=args.gradacc,
+        eval_accumulation_steps=10,
+        learning_rate=args.lr,
+        weight_decay=0.1,
+        max_grad_norm=5.0,
+        num_train_epochs=args.epochs,
+        lr_scheduler_type=args.scheduler,
+        warmup_steps=1000,
+        logging_strategy="steps",
+        logging_first_step=True,
+        logging_steps=args.steplog,
+        seed=42,
+        fp16=args.fp16,
+        load_best_model_at_end=False if args.nosave else True,
+        save_strategy="no" if args.nosave else "steps",
+        save_total_limit=3,
+        eval_steps=args.stepeval,
+        run_name=f"{args.model}-sentiment-run",
+        disable_tqdm=False,
+        log_level="warning",
+        report_to=["wandb"] if args.wandb else "none",
+        optim="adamw_torch",
+    )
+
+
+    def compute_metrics(eval_preds):
+        preds = eval_preds.predictions.argmax(-1)
+        targets = eval_preds.label_ids.argmax(-1)
+        setting = "macro"
+        f1_score_macro = f1_metric.compute(
+            predictions=preds, references=targets, average="macro"
+        )
+        f1_score_micro = f1_metric.compute(
+            predictions=preds, references=targets, average="micro"
+        )
+        accuracy_score = accuracy_metric.compute(predictions=preds, references=targets)
+        precision_score = precision_metric.compute(
+            predictions=preds, references=targets, average=setting, zero_division=1
+        )
+        recall_score = recall_metric.compute(
+            predictions=preds, references=targets, average=setting, zero_division=1
+        )
+        results = {
+            "macro_f1score": f1_score_macro["f1"],
+            "micro_f1score": f1_score_micro["f1"],
+            "accuracy": accuracy_score["accuracy"],
+            "precision": precision_score["precision"],
+            "recall": recall_score["recall"],
+        }
+        results = {k: round(v, 4) for k, v in results.items()}
+        return results
+
+    if args.wandb:
+        import wandb
+        wandb.init(entity="andreapdr", project=f"gfun-senti-hf", name="mbert-sent", config=vars(args))
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=data["train"],
+        eval_dataset=data["validation"],
+        compute_metrics=compute_metrics,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        callbacks=callbacks,
+    )
+
+    print("- Training:")
+    trainer.train()
+
+
+    print("- Testing:")
+    test_results = trainer.evaluate(eval_dataset=data["test"])
+    print(test_results)
+
+    exit()
+
+
+if __name__ == "__main__":
+    from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+    parser.add_argument("--model",              type=str,   metavar="", default="mbert")
+    parser.add_argument("--lr",                 type=float, metavar="", default=1e-5,       help="Set learning rate",)
+    parser.add_argument("--scheduler",          type=str,   metavar="", default="linear",   help="Accepted: [\"cosine\", \"cosine-reset\", \"cosine-warmup\", \"cosine-warmup-reset\", \"constant\"]")
+    parser.add_argument("--batch",              type=int,   metavar="", default=16,         help="Set batch size")
+    parser.add_argument("--gradacc",            type=int,   metavar="", default=1,          help="Gradient accumulation steps")
+    parser.add_argument("--epochs",             type=int,   metavar="", default=100,        help="Set epochs")
+    parser.add_argument("--stepeval",           type=int,   metavar="", default=50,         help="Run evaluation every n steps")
+    parser.add_argument("--steplog",            type=int,   metavar="", default=100,        help="Log training every n steps")
+    parser.add_argument("--patience",           type=int,   metavar="", default=10,         help="EarlyStopper patience")
+    parser.add_argument("--fp16",               action="store_true",                        help="Use fp16 precision")
+    parser.add_argument("--wandb",              action="store_true",                        help="Log to wandb")
+    parser.add_argument("--nosave",             action="store_true",                        help="Avoid saving model")
+    # parser.add_argument("--onlytest",           action="store_true",                        help="Simply test model on test set")
+    # parser.add_argument("--sanity",             action="store_true",                        help="Train and evaluate on the same reduced (1000) data")
+    args = parser.parse_args()
+    main(args)

main.py

@@ -1,8 +1,5 @@
-import os
 import wandb
 
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
 from argparse import ArgumentParser
 from time import time
 
@@ -12,16 +9,9 @@ from gfun.generalizedFunnelling import GeneralizedFunnelling
 
 """
 TODO:
-    - Transformers VGFs:
-        - scheduler with warmup and cosine
-        - freeze params method
     - General:
         [!] zero-shot setup
         - CLS dataset is loading only "books" domain data
-        - documents should be trimmed to the same length (for SVMs we are using way too long tokens)
-    - Attention Aggregator:
-        - experiment with weight init of Attention-aggregator
-        - FFNN posterior-probabilities' dependent
     - Docs:
         - add documentations sphinx
 """
@@ -150,7 +140,6 @@ def main(args):
             wandb.log(gfun_res)
 
     log_barplot_wandb(lang_metrics_gfun, title_affix="per language")
-    log_barplot_wandb(avg_metrics_gfun, title_affix="averages")
 
 
 if __name__ == "__main__":
@@ -178,7 +167,7 @@ if __name__ == "__main__":
     parser.add_argument("--features", action="store_false")
     parser.add_argument("--aggfunc", type=str, default="mean")
     # transformer parameters ---------------
-    parser.add_argument("--epochs", type=int, default=100)
+    parser.add_argument("--epochs", type=int, default=5)
     parser.add_argument("--textual_trf_name", type=str, default="mbert")
     parser.add_argument("--batch_size", type=int, default=32)
     parser.add_argument("--eval_batch_size", type=int, default=128)

run-senti.sh

@@ -0,0 +1,28 @@
+#!bin/bash
+
+config="-m"
+
+echo "[Running gFun config: $config]"
+
+epochs=100
+njobs=-1
+clf=singlelabel
+patience=5
+eval_every=5
+text_len=256
+text_lr=1e-4
+bsize=64
+txt_model=mbert
+
+python main.py $config \
+    -d webis \
+    --epochs $epochs \
+    --n_jobs $njobs \
+    --clf_type $clf \
+    --patience $patience \
+    --evaluate_step $eval_every \
+    --batch_size $bsize \
+    --max_length $text_len \
+    --textual_lr $text_lr \
+    --textual_trf_name $txt_model \
+    --load_trained webis_pmwt_mean_230621