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ca1226e 43176db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch
# 1. Load and Preprocess Data
df = pd.read_csv('mail_data.csv', names=['Category', 'Message'], header=None, skiprows=1)
df['label'] = df['Category'].map({'ham': 0, 'spam': 1})
train_texts, test_texts, train_labels, test_labels = train_test_split(
df['Message'].values.tolist(), df['label'].values.tolist(), test_size=0.2, random_state=42, stratify=df['label'].values
)
# 2. Tokenization
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=128)
test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=128)
class EmailDataset(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)
train_dataset = EmailDataset(train_encodings, train_labels)
test_dataset = EmailDataset(test_encodings, test_labels)
# 3. Model and Metrics
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
def compute_metrics(pred):
labels = pred.label_ids
preds = pred.predictions.argmax(-1)
precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary')
acc = accuracy_score(labels, preds)
return {
'accuracy': acc,
'f1': f1,
'precision': precision,
'recall': recall
}
# 4. Training Arguments
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500,
weight_decay=0.01,
logging_dir='./logs',
logging_steps=10,
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
)
# 5. Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=test_dataset,
compute_metrics=compute_metrics,
)
print("Starting training with HF Trainer...")
trainer.train()
# 6. Evaluation
print("Evaluating...")
eval_results = trainer.evaluate()
print(eval_results)
# Final predictions for detailed report
predictions = trainer.predict(test_dataset)
preds = predictions.predictions.argmax(-1)
labels = predictions.label_ids
from sklearn.metrics import classification_report
report = classification_report(labels, preds, target_names=['ham', 'spam'])
cm = confusion_matrix(labels, preds)
with open('results.txt', 'w') as f:
f.write(f"Final Evaluation Results:\n{eval_results}\n")
f.write(f"\nClassification Report:\n{report}\n")
f.write(f"\nConfusion Matrix:\n{cm}\n")
print("Training complete. Results saved to results.txt") |