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import os
import shutil
from pathlib import Path
from typing import Dict, List, Optional
import pandas as pd
import torch
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
Trainer,
TrainingArguments,
)
from datasets import Dataset
import evaluate
# ============================================================================
# 2. MODÈLE DE CLASSIFICATION PAR CATÉGORIE
# ============================================================================
class CategoryClassificationModel:
"""
Modèle pour classifier automatiquement les annonces par catégorie
"""
def __init__(self, model_path: Optional[str] = None):
self.model_path = model_path
self.model = None
self.tokenizer = None
self.categories = []
def train(self, train_df: pd.DataFrame, test_df: pd.DataFrame,
categories: List[str], output_dir: str = "./organized_data/category_classification/model_classification_moderation"):
"""
Entraîne le modèle de classification
Args:
train_df: DataFrame avec colonnes ['title', 'description', 'category']
test_df: DataFrame de test
categories: Liste des catégories possibles
output_dir: Chemin de sauvegarde
"""
print("=" * 70)
print("ENTRAÎNEMENT DU MODÈLE DE CLASSIFICATION PAR CATÉGORIE")
print("=" * 70)
self.categories = categories
num_labels = len(categories)
# Mapper les catégories vers des entiers
category_to_id = {cat: i for i, cat in enumerate(categories)}
id_to_category = {i: cat for i, cat in enumerate(categories)}
# Préparation des données
train_df = train_df.copy()
test_df = test_df.copy()
train_df['text'] = (
train_df['title'].fillna('').astype(str) + " " +
train_df['description'].fillna('').astype(str)
).str.strip()
test_df['text'] = (
test_df['title'].fillna('').astype(str) + " " +
test_df['description'].fillna('').astype(str)
).str.strip()
train_df['category'] = train_df['category'].map(category_to_id)
test_df['category'] = test_df['category'].map(category_to_id)
train_dict = {
'text': train_df['text'].tolist(),
'category': train_df['category'].astype(int).tolist()
}
test_dict = {
'text': test_df['text'].tolist(),
'category': test_df['category'].astype(int).tolist()
}
train_dataset = Dataset.from_dict(train_dict)
test_dataset = Dataset.from_dict(test_dict)
# Modèle
model_name = "distilbert-base-multilingual-cased"
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForSequenceClassification.from_pretrained(
model_name,
num_labels=num_labels,
id2label=id_to_category,
label2id=category_to_id
)
# Tokenisation
def tokenize_function(examples):
result = self.tokenizer(
examples['text'],
padding='max_length',
truncation=True,
max_length=128
)
result['labels'] = examples['category']
return result
train_dataset = train_dataset.map(tokenize_function, batched=True, remove_columns=['text'])
test_dataset = test_dataset.map(tokenize_function, batched=True, remove_columns=['text'])
train_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
test_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
# Entraînement
base_dir = Path("./organized_data/text_moderation/model_text_moderation")
logs_dir = base_dir / "logs" / "model_text_moderation"
def clean_path(path):
"""Nettoie complètement un chemin (fichier ou dossier)"""
if os.path.exists(path):
try:
if os.path.isfile(path):
os.remove(path)
print(f"✓ Fichier supprimé: {path}")
elif os.path.isdir(path):
shutil.rmtree(path)
print(f"✓ Dossier supprimé: {path}")
except Exception as e:
print(f"⚠ Erreur lors du nettoyage de {path}: {e}")
return False
return True
clean_path(str(logs_dir))
logs_dir.mkdir(parents=True, exist_ok=True)
print(f"✓ Répertoire logs créé: {logs_dir}")
training_args = TrainingArguments(
output_dir=output_dir,
eval_strategy="epoch",
save_strategy="epoch",
logging_dir=str(logs_dir),
logging_steps=10,
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=10,
weight_decay=0.01,
load_best_model_at_end=True,
metric_for_best_model="f1",
report_to="none",
)
accuracy_metric = evaluate.load("accuracy")
f1_metric = evaluate.load("f1")
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = logits.argmax(axis=-1)
return {
"accuracy": accuracy_metric.compute(predictions=predictions, references=labels)["accuracy"],
"f1": f1_metric.compute(predictions=predictions, references=labels, average='weighted')["f1"],
}
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=test_dataset,
compute_metrics=compute_metrics
)
trainer.train()
trainer.save_model(output_dir)
self.tokenizer.save_pretrained(output_dir)
self.model_path = output_dir
# Sauvegarder les catégories
import json
with open(Path(output_dir) / "categories.json", "w") as f:
json.dump(categories, f)
print(f"\n✓ Modèle de classification sauvegardé dans: {output_dir}")
return trainer.evaluate()
def load(self, model_path: str):
"""Charge un modèle pré-entraîné"""
import json
self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
# Charger les catégories
with open(Path(model_path) / "categories.json", "r") as f:
self.categories = json.load(f)
self.model_path = model_path
print(f"✓ Modèle de classification chargé depuis: {model_path}")
def predict(self, title: str, description: str, top_k: int = 3) -> Dict:
"""
Prédit la catégorie d'une annonce
Returns:
{
'category': str,
'confidence': float,
'top_predictions': List[Tuple[str, float]]
}
"""
if self.model is None:
raise ValueError("Modèle non chargé.")
text = f"{title} {description}".strip()
inputs = self.tokenizer(
text,
return_tensors="pt",
padding=True,
truncation=True,
max_length=128
)
with torch.no_grad():
outputs = self.model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=-1)
# Top-K prédictions
top_probs, top_indices = torch.topk(probabilities[0], k=min(top_k, len(self.categories)))
top_predictions = [
(self.categories[idx.item()], prob.item())
for idx, prob in zip(top_indices, top_probs)
]
return {
'category': top_predictions[0][0],
'confidence': top_predictions[0][1],
'top_predictions': top_predictions
}
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