File size: 30,219 Bytes
88b8fd6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 |
import pandas as pd
import numpy as np
import requests
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from transformers import (
AutoTokenizer, AutoModelForSequenceClassification,
TrainingArguments, Trainer, DataCollatorWithPadding
)
import torch
from datasets import Dataset
import logging
import os
logger = logging.getLogger(__name__)
class CBTBinaryClassifier:
"""Binary classifier to distinguish normal conversation from CBT-triggering statements."""
def __init__(self, model_name="distilbert-base-uncased"):
# Use a lightweight model that's good for your laptop
self.model_name = model_name
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = None
self.trainer = None
self.inference_pipeline = None
self.use_hf_api = False
self.api_url = None
self.api_token = None
self.headers = None
self.model_id = None
# Add padding token if it doesn't exist
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
def prepare_data(self, normal_csv_path, cbt_csv_path, text_column="text"):
"""Load and prepare training data from CSV files"""
logger.info(f"Loading normal conversations from {normal_csv_path}")
normal_df = pd.read_csv(normal_csv_path)
normal_df['label'] = 0 # Normal conversation = 0
normal_df['text'] = normal_df[text_column]
logger.info(f"Loading CBT conversations from {cbt_csv_path}")
cbt_df = pd.read_csv(cbt_csv_path)
cbt_df['label'] = 1 # CBT trigger = 1
cbt_df['text'] = cbt_df[text_column]
# Combine datasets
combined_df = pd.concat([
normal_df[['text', 'label']],
cbt_df[['text', 'label']]
], ignore_index=True)
# Shuffle the data
combined_df = combined_df.sample(frac=1, random_state=42).reset_index(drop=True)
logger.info(f"Total examples: {len(combined_df)}")
logger.info(f"Normal conversations: {len(normal_df)}")
logger.info(f"CBT triggers: {len(cbt_df)}")
return combined_df
def tokenize_data(self, df, max_length=128):
"""Tokenize the text data"""
def tokenize_function(examples):
return self.tokenizer(
examples['text'],
truncation=True,
padding='max_length',
max_length=max_length,
return_tensors=None
)
# Convert to HuggingFace Dataset
dataset = Dataset.from_pandas(df)
tokenized_dataset = dataset.map(
tokenize_function,
batched=True,
remove_columns=['text'])
return tokenized_dataset
def split_data(self, dataset, test_size=0.2, val_size=0.1):
"""Split data into train/validation/test sets"""
# First split: train + val vs test
train_val, test = dataset.train_test_split(
test_size=test_size,
seed=42
).values()
# Second split: train vs validation
val_ratio = val_size / (1 - test_size)
train, val = train_val.train_test_split(
test_size=val_ratio,
seed=42
).values()
logger.info(f"Train: {len(train)}, Val: {len(val)}, Test: {len(test)}")
return train, val, test
def train_model(self, train_dataset, val_dataset, output_dir="./cbt_classifier"):
"""Train the binary classifier with laptop-friendly settings"""
# Create output directory
os.makedirs(output_dir, exist_ok=True)
# Initialize model
self.model = AutoModelForSequenceClassification.from_pretrained(
self.model_name,
num_labels=2
)
# Create data collator for dynamic padding
data_collator = DataCollatorWithPadding(tokenizer=self.tokenizer)
# Laptop-friendly training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=2, # Reduced epochs
per_device_train_batch_size=8, # Smaller batch size
per_device_eval_batch_size=8,
gradient_accumulation_steps=2, # Simulate larger batch size
warmup_steps=100, # Reduced warmup
weight_decay=0.01,
logging_dir=f'{output_dir}/logs',
logging_steps=50,
eval_strategy="steps",
eval_steps=200,
save_strategy="steps",
save_steps=200,
load_best_model_at_end=True,
metric_for_best_model="eval_accuracy",
fp16=torch.cuda.is_available(), # Use mixed precision if GPU available
dataloader_num_workers=0, # Reduce CPU usage
remove_unused_columns=True,
)
# Metrics function
def compute_metrics(eval_pred):
predictions, labels = eval_pred
predictions = np.argmax(predictions, axis=1)
return {
'accuracy': accuracy_score(labels, predictions),
}
# Initialize trainer
self.trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset,
compute_metrics=compute_metrics,
data_collator=data_collator,
)
# Train the model
logger.info("Starting training...")
self.trainer.train()
# Save the model
self.trainer.save_model()
self.tokenizer.save_pretrained(output_dir)
logger.info(f"Model saved to {output_dir}")
def evaluate_model(self, test_dataset):
"""Evaluate the trained model"""
if self.trainer is None:
raise ValueError("Model not trained yet!")
# Get predictions
predictions = self.trainer.predict(test_dataset)
y_pred = np.argmax(predictions.predictions, axis=1)
y_true = predictions.label_ids
# Print results
print("\n=== Evaluation Results ===")
print(f"Accuracy: {accuracy_score(y_true, y_pred):.4f}")
print("\nClassification Report:")
print(classification_report(y_true, y_pred,
target_names=['Normal', 'CBT Trigger']))
print("\nConfusion Matrix:")
print(confusion_matrix(y_true, y_pred))
return y_true, y_pred
def load_model(self, model_path="./cbt_classifier"):
"""Load a pre-trained model for inference"""
from transformers import pipeline
self.inference_pipeline = pipeline(
"text-classification",
model=model_path,
tokenizer=model_path,
return_all_scores=True
)
logger.info(f"Model loaded from {model_path}")
def predict(self, text, threshold=0.7):
"""Predict if text is CBT-triggering"""
if self.inference_pipeline is None:
raise ValueError("Model not loaded! Call load_model() first.")
result = self.inference_pipeline(text)
# Extract confidence for CBT trigger class (LABEL_1)
cbt_confidence = next(
score['score'] for score in result[0]
if score['label'] == 'LABEL_1'
)
return {
'is_cbt_trigger': cbt_confidence > threshold,
'confidence': cbt_confidence,
'threshold': threshold
}
def batch_predict(self, texts, threshold=0.7):
"""Predict for multiple texts"""
if self.inference_pipeline is None:
raise ValueError("Model not loaded! Call load_model() first.")
results = []
for text in texts:
result = self.predict(text, threshold)
results.append(result)
return results |