fix: artifacts saving and dataset loading

misc/__init__.py

@@ -1,8 +1,8 @@
 import csv
+import io
 import json
 import os
 import pickle
-from datetime import datetime
 from typing import Optional
 
 # Paths
@@ -16,6 +16,7 @@ GENDER_RESULT_DIR = os.path.join(ROOT_DIR, 'gender', 'results')
 NER_MODELS_DIR = os.path.join(MODELS_DIR, 'ner')
 NER_RESULT_DIR = os.path.join(ROOT_DIR, 'ner', 'results')
 
+
 def clean_spacing(filename: str) -> Optional[str]:
     try:
         with open(os.path.join(DATA_DIR, filename), 'r', encoding='utf8') as f:
@@ -42,14 +43,27 @@ def save_csv_dataset(data: list, path: str) -> None:
 def load_csv_dataset(path: str, limit: int = None) -> list:
     print(f">> Loading CSV dataset from {path}")
     data = []
-    with open(os.path.join(DATA_DIR, path), "r", encoding="utf-8") as f:
-        reader = csv.DictReader(f)
-        for row in reader:
-            data.append(row)
-            if limit and len(data) >= limit:
-                break
+    encodings = ['utf-8', 'utf-16', 'latin1']
 
-    return data
+    for enc in encodings:
+        try:
+            with open(os.path.join(DATA_DIR, path), "r", encoding=enc, errors="replace") as f:
+                raw_text = f.read().replace('\x00', '')
+
+            csv_buffer = io.StringIO(raw_text)
+            reader = csv.DictReader(csv_buffer)
+            print(f">> Detected fieldnames: {reader.fieldnames}")
+
+            for row in reader:
+                data.append(row)
+                if limit and len(data) >= limit:
+                    break
+            print(f">> Successfully loaded with encoding: {enc}")
+            return data
+        except Exception as e:
+            print(f">> Failed with encoding: {enc}, error: {e}")
+
+    raise UnicodeDecodeError("load_csv_dataset", path, 0, 0, "Unable to decode file with common encodings.")
 
 
 def save_json_dataset(data: list, path: str) -> None:
@@ -63,6 +77,7 @@ def save_pickle(obj, path):
     with open(path, "wb") as f:
         pickle.dump(obj, f)
 
+
 def load_pickle(path: str):
     with open(path, "rb") as f:
-        return pickle.load(f)
+        return pickle.load(f)

ners/gender/models/logreg.py

@@ -1,7 +1,6 @@
 import argparse
 import logging
 import os
-import pickle
 from dataclasses import dataclass
 from typing import Tuple, Optional
 
@@ -16,10 +15,11 @@ from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val
 from sklearn.pipeline import make_pipeline, Pipeline
 from sklearn.preprocessing import LabelEncoder
 
-from misc import GENDER_MODELS_DIR, load_csv_dataset
+from misc import GENDER_MODELS_DIR, load_csv_dataset, save_pickle
 
 logging.basicConfig(level=logging.INFO, format=">> %(message)s")
 
+
 @dataclass
 class Config:
     dataset_path: str
@@ -169,15 +169,10 @@ def save_artifacts(model, encoder, cfg: Config):
     :type cfg: Config
     :return: None
     """
-    model_path = os.path.join(GENDER_MODELS_DIR, "regression_model.pkl")
-    encoder_path = os.path.join(GENDER_MODELS_DIR, "regression_label_encoder.pkl")
+    save_pickle(model, os.path.join(GENDER_MODELS_DIR, "regression_model.pkl"))
+    save_pickle(encoder, os.path.join(GENDER_MODELS_DIR, "regression_label_encoder.pkl"))
 
-    with open(model_path, "wb") as f:
-        pickle.dump(model, f)
-    with open(encoder_path, "wb") as f:
-        pickle.dump(encoder, f)
-    logging.info(f"Saved model to: {model_path}")
-    logging.info(f"Saved label encoder to: {encoder_path}")
+    logging.info(f"Model and artifacts saved to {GENDER_MODELS_DIR}")
 
 
 def main():

ners/gender/models/lstm.py

@@ -1,7 +1,6 @@
 import argparse
 import logging
 import os
-import pickle
 from dataclasses import dataclass
 from typing import Tuple, Optional
 
@@ -18,7 +17,7 @@ from tensorflow.keras.models import Sequential
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.preprocessing.text import Tokenizer
 
-from misc import GENDER_MODELS_DIR, load_csv_dataset
+from misc import GENDER_MODELS_DIR, load_csv_dataset, save_pickle
 
 logging.basicConfig(level=logging.INFO, format=">> %(message)s")
 
@@ -214,15 +213,12 @@ def save_artifacts(model, tokenizer, encoder):
 
     :return: None
     """
-    model_path = os.path.join(GENDER_MODELS_DIR, "lstm_model.keras")
-    tokenizer_path = os.path.join(GENDER_MODELS_DIR, "lstm_tokenizer.pkl")
-    encoder_path = os.path.join(GENDER_MODELS_DIR, "lstm_label_encoder.pkl")
+    os.makedirs(GENDER_MODELS_DIR, exist_ok=True)
+    model.save(os.path.join(GENDER_MODELS_DIR, "lstm_model.keras"))
+
+    save_pickle(tokenizer, os.path.join(GENDER_MODELS_DIR, "lstm_tokenizer.pkl"))
+    save_pickle(encoder, os.path.join(GENDER_MODELS_DIR, "lstm_label_encoder.pkl"))
 
-    model.save(model_path)
-    with open(tokenizer_path, "wb") as f:
-        pickle.dump(tokenizer, f)
-    with open(encoder_path, "wb") as f:
-        pickle.dump(encoder, f)
     logging.info(f"Model and artifacts saved to {GENDER_MODELS_DIR}")
 
 

ners/gender/models/transformer.py

@@ -1,7 +1,6 @@
 import argparse
 import logging
 import os
-import pickle
 from dataclasses import dataclass
 from typing import Tuple, Optional
 
@@ -23,7 +22,7 @@ from tensorflow.keras.models import Model
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.preprocessing.text import Tokenizer
 
-from misc import GENDER_MODELS_DIR, load_csv_dataset
+from misc import GENDER_MODELS_DIR, load_csv_dataset, save_pickle
 
 logging.basicConfig(level=logging.INFO, format=">> %(message)s")
 
@@ -198,7 +197,7 @@ def evaluate_proba(y_true, y_proba, threshold, class_names):
     :return: None. Outputs performance metrics and confusion matrix to the logging
         system and the console.
     """
-    y_pred = 1 if y_proba[:, 1] >= threshold else 0
+    y_pred = (y_proba[:, 1] >= threshold).astype(int)
     acc = accuracy_score(y_true, y_pred)
     pr, rc, f1, _ = precision_recall_fscore_support(y_true, y_pred, average="binary")
     cm = confusion_matrix(y_true, y_pred)
@@ -257,16 +256,13 @@ def save_artifacts(model, tokenizer, encoder):
     :param encoder: The label encoder used for encoding target labels.
     :return: None
     """
-    model_path = os.path.join(GENDER_MODELS_DIR, "transformer.h5")
-    tokenizer_path = os.path.join(GENDER_MODELS_DIR, "transformer_tokenizer.pkl")
-    encoder_path = os.path.join(GENDER_MODELS_DIR, "transformer_label_encoder.pkl")
+    os.makedirs(GENDER_MODELS_DIR, exist_ok=True)
+    model.save(os.path.join(GENDER_MODELS_DIR, "transformer.keras"))
 
-    model.save(model_path)
-    with open(tokenizer_path, "wb") as f:
-        pickle.dump(tokenizer, f)
-    with open(encoder_path, "wb") as f:
-        pickle.dump(encoder, f)
-    logging.info("Model and artifacts saved.")
+    save_pickle(tokenizer, os.path.join(GENDER_MODELS_DIR, "transformer_tokenizer.pkl"))
+    save_pickle(encoder, os.path.join(GENDER_MODELS_DIR, "transformer_label_encoder.pkl"))
+
+    logging.info(f"Model and artifacts saved to {GENDER_MODELS_DIR}")
 
 
 def main():