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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, get_linear_schedule_with_warmup
from torch.optim import AdamW
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import numpy as np
import time
import os
# 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, df['label'].values, test_size=0.2, random_state=42, stratify=df['label'].values
)
# 2. Dataset Class
class EmailDataset(Dataset):
def __init__(self, texts, labels, tokenizer, max_len=128):
self.texts = texts
self.labels = labels
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.texts)
def __getitem__(self, item):
text = str(self.texts[item])
label = self.labels[item]
encoding = self.tokenizer._encode_plus(
text,
add_special_tokens=True,
max_length=self.max_len,
return_token_type_ids=False,
padding='max_length',
truncation=True,
return_attention_mask=True,
return_tensors='pt',
)
return {
'text': text,
'input_ids': encoding['input_ids'].flatten(),
'attention_mask': encoding['attention_mask'].flatten(),
'labels': torch.tensor(label, dtype=torch.long)
}
# 3. Setup Training
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
PRE_TRAINED_MODEL_NAME = 'distilbert-base-uncased'
tokenizer = DistilBertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)
train_data_loader = DataLoader(EmailDataset(train_texts, train_labels, tokenizer), batch_size=16, shuffle=True)
test_data_loader = DataLoader(EmailDataset(test_texts, test_labels, tokenizer), batch_size=16, shuffle=False)
model = DistilBertForSequenceClassification.from_pretrained(PRE_TRAINED_MODEL_NAME, num_labels=2)
model = model.to(device)
EPOCHS = 3
optimizer = AdamW(model.parameters(), lr=2e-5)
total_steps = len(train_data_loader) * EPOCHS
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)
loss_fn = torch.nn.CrossEntropyLoss().to(device)
# 4. Training Loop
def train_epoch(model, data_loader, loss_fn, optimizer, device, scheduler, n_examples):
model = model.train()
losses = []
correct_predictions = 0
for d in data_loader:
input_ids = d["input_ids"].to(device)
attention_mask = d["attention_mask"].to(device)
labels = d["labels"].to(device)
outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
loss = outputs.loss
logits = outputs.logits
_, preds = torch.max(logits, dim=1)
correct_predictions += torch.sum(preds == labels)
losses.append(loss.item())
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
return correct_predictions.double() / n_examples, np.mean(losses)
def eval_model(model, data_loader, loss_fn, device, n_examples):
model = model.eval()
losses = []
correct_predictions = 0
with torch.no_grad():
for d in data_loader:
input_ids = d["input_ids"].to(device)
attention_mask = d["attention_mask"].to(device)
labels = d["labels"].to(device)
outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
loss = outputs.loss
logits = outputs.logits
_, preds = torch.max(logits, dim=1)
correct_predictions += torch.sum(preds == labels)
losses.append(loss.item())
return correct_predictions.double() / n_examples, np.mean(losses)
print("Starting training...")
for epoch in range(EPOCHS):
print(f'Epoch {epoch + 1}/{EPOCHS}')
train_acc, train_loss = train_epoch(model, train_data_loader, loss_fn, optimizer, device, scheduler, len(train_texts))
print(f'Train loss {train_loss} accuracy {train_acc}')
val_acc, val_loss = eval_model(model, test_data_loader, loss_fn, device, len(test_texts))
print(f'Val loss {val_loss} accuracy {val_acc}')
# 5. Final Evaluation
def get_predictions(model, data_loader):
model = model.eval()
messages = []
predictions = []
prediction_probs = []
real_values = []
with torch.no_grad():
for d in data_loader:
texts = d["text"]
input_ids = d["input_ids"].to(device)
attention_mask = d["attention_mask"].to(device)
labels = d["labels"].to(device)
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
logits = outputs.logits
_, preds = torch.max(logits, dim=1)
messages.extend(texts)
predictions.extend(preds)
prediction_probs.extend(logits)
real_values.extend(labels)
predictions = torch.stack(predictions).cpu()
real_values = torch.stack(real_values).cpu()
return messages, predictions, real_values
y_review_texts, y_pred, y_test = get_predictions(model, test_data_loader)
print("\nClassification Report:\n", classification_report(y_test, y_pred, target_names=['ham', 'spam']))
# Save results for report
with open('results.txt', 'w') as f:
f.write(f"Accuracy: {accuracy_score(y_test, y_pred)}\n")
f.write("\nClassification Report:\n")
f.write(classification_report(y_test, y_pred, target_names=['ham', 'spam']))
f.write("\nConfusion Matrix:\n")
f.write(str(confusion_matrix(y_test, y_pred)))
print("Training complete. Results saved to results.txt")
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