Add script

.gitignore

@@ -6,3 +6,5 @@
 *.o
 .DS_Store
 .token
+
+**/.vscode/*

main.py

@@ -0,0 +1,105 @@
+import pandas as pd
+import os
+import sys
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
+import torch
+import csv
+
+IN_FILE_NAME = "in.tsv.xz"
+OUT_FILE_NAME = "out.tsv"
+TRAIN_PATH = "train"
+EXP_FILE_NAME = "expected.tsv"
+FILE_SEP = "\t"
+IN_HEADER_FILE_NAME = "in-header.tsv"
+OUT_HEADER_FILE_NAME = "out-header.tsv"
+PT_MODEL_NAME = "bert-base-cased"
+# PT_MODEL_NAME = "roberta-base"
+
+
+class CustomDataset(torch.utils.data.Dataset):
+    def __init__(self, encodings, labels):
+        self.encodings = encodings
+        self.labels = labels
+
+    def __getitem__(self, idx):
+        item = {key: torch.tensor(val[idx])
+                for key, val in self.encodings.items()}
+        item['labels'] = torch.tensor(self.labels[idx])
+        return item
+
+    def __len__(self):
+        return len(self.labels)
+
+
+def main(dirnames):
+    check_path(IN_HEADER_FILE_NAME)
+    in_cols = (pd.read_csv(IN_HEADER_FILE_NAME, sep=FILE_SEP)).columns
+    check_path(OUT_HEADER_FILE_NAME)
+    out_cols = (pd.read_csv(OUT_HEADER_FILE_NAME, sep=FILE_SEP)).columns
+
+    print("Reading train data...")
+    train_set_features = get_tsv_data(os.path.join(
+        TRAIN_PATH, IN_FILE_NAME), names=in_cols)
+    train_set_labels = get_tsv_data(os.path.join(
+        TRAIN_PATH, EXP_FILE_NAME), names=out_cols, compression=None)
+
+    print("Reading input data...")
+    in_sets = []
+    for d in dirnames:
+        print(f"\tReading dir: {d}...")
+        in_sets.append(get_tsv_data(
+            os.path.join(d, IN_FILE_NAME), names=in_cols))
+
+    tokenizer = AutoTokenizer.from_pretrained(PT_MODEL_NAME)
+    model = AutoModelForSequenceClassification.from_pretrained(
+        PT_MODEL_NAME, num_labels=2)
+    train_set_enc = tokenizer(
+        [t for t in train_set_features[in_cols].agg(' '.join, axis=1)], truncation=True, padding=True)
+    dataset = CustomDataset(
+        train_set_enc, [int(t) for t in train_set_labels[out_cols[0]]])
+
+    trainer = Trainer(
+        model=model,
+        args=TrainingArguments('./res'),
+        train_dataset=dataset,
+        num_train_epochs=5,
+        per_device_train_batch_size=16,
+        per_device_eval_batch_size=16,
+    )
+
+    trainer.train()
+
+    for i in range(len(in_sets)):
+        p = os.path.join(dirnames[i], IN_FILE_NAME)
+        with open(p) as f:
+            print(
+                f"\tPredicting for: {p}...")
+            X = [t for t in in_sets[i][in_cols].agg(' '.join, axis=1)]
+            out_file_path = os.path.join(dirnames[i], OUT_FILE_NAME)
+            f.write('\n'.join(trainer.predict(X)))
+            print(f"Saved predictions to file: {out_file_path}")
+
+
+def get_tsv_data(filename: str, names, compression="infer"):
+    check_path(filename)
+    return pd.read_csv(
+        filename,
+        sep=FILE_SEP,
+        compression=compression,
+        error_bad_lines=False,
+        quoting=csv.QUOTE_NONE,
+        header=None,
+        names=names,
+        dtype=str
+    )
+
+
+def check_path(filename: str):
+    if not os.path.exists(filename):
+        raise Exception(f"Path {filename} does not exist!")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        raise Exception("Name of working dir not specified!")
+    main(sys.argv[1:])