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import os
import random
import shutil
from datasets import Dataset
import joblib
from loguru import logger
import mlflow
import numpy as np
from numpy import ndarray
from peft import LoraConfig, TaskType, get_peft_model
from sentence_transformers import SentenceTransformer
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from sklearn.multioutput import MultiOutputClassifier
import torch
import torch.nn as nn
from transformers import get_linear_schedule_with_warmup
from xgboost import XGBClassifier
from turing.modeling.baseModel import BaseModel
from turing.modeling.models.MiniLMClassifierWrapper import MiniLMClassifierWrapper
def drop_tokens(text, drop_prob=0.1):
"""
Randomly drops tokens from the input text based on the specified drop probability.
"""
tokens = text.split()
if len(tokens) <= 3:
return text
return " ".join(
t for t in tokens if random.random() > drop_prob
)
def drop_tokens_batch(texts, drop_prob=0.1, apply_prob=0.3):
"""
Apply token dropping augmentation to a batch of texts.
"""
augmented = []
for text in texts:
if random.random() < apply_prob:
augmented.append(drop_tokens(text, drop_prob))
elif random.random() < 0.15:
x = " ".join(text.split())
augmented.append(x)
else:
augmented.append(text)
return augmented
def finetune_miniLM(X_train, y_train, device,model_save_path="sentence-transformers/minilm.pt"):
"""
Train MiniLM model with temporary classification head using java dataset only.
Args:
X_train: Input training data.
y_train: True labels for training data.
"""
encoder = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2').to(device)
peft_config = LoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
lora_alpha=16,
bias="none",
lora_dropout=0.1,
)
encoder[0].auto_model = get_peft_model(
encoder[0].auto_model,
peft_config
)
encoder[0].auto_model.print_trainable_parameters()
y_train = np.array(y_train,dtype=np.float32)
dataset = Dataset.from_dict({"text": X_train, "labels": y_train})
split_set = dataset.train_test_split(test_size= 0.2, seed=42)
train_set = split_set['train']
eval_set = split_set['test']
epoch = 10
batch_size = 32
total_steps = len(train_set) // batch_size * epoch
warm_up_steps = int(0.1 * total_steps)
y_train = np.array(y_train,dtype=np.float32)
classifier = nn.Sequential(
nn.Linear(encoder.get_sentence_embedding_dimension(), 128),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(128, len(y_train[0]))
).to(device)
logger.info(f"Training set size: {len(train_set)}, Evaluation set size: {len(eval_set)}")
criterion = nn.BCEWithLogitsLoss()
optimizer = torch.optim.AdamW(list(classifier.parameters()) + list(encoder.parameters()), lr=1e-4, weight_decay=0.01)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps= warm_up_steps,
num_training_steps=total_steps
)
logger.info("Starting training of MiniLM model with classification head...")
low_loss = float('inf')
patience_counter = 0
for epoch in range(epoch):
encoder.train()
classifier.train()
losses = []
for i in range(0, len(train_set), batch_size):
batch = train_set[i:i+batch_size]
labels = torch.tensor(batch['labels']).to(device)
texts = drop_tokens_batch(batch['text'])
features = encoder.tokenize(texts)
features = {k: v.to(device) for k, v in features.items()}
embeddings = encoder(features)['sentence_embedding']
embeddings = torch.tensor(embeddings).to(device)
logits = classifier(embeddings)
loss = criterion(logits, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
if i % 100 == 0:
logger.info("Done {} out of {} batches".format(i, len(train_set)))
encoder.eval()
classifier.eval()
with torch.no_grad():
for i in range(0, len(eval_set), batch_size):
batch = eval_set[i:i+batch_size]
labels = torch.tensor(batch['labels']).to(device)
embeddings = encoder.encode(batch['text'])
embeddings = torch.tensor(embeddings).to(device)
logits = classifier(embeddings)
loss = criterion(logits, labels)
losses.append(loss.item())
avg_loss = sum(losses) / len(losses)
logger.info(f"Epoch {epoch+1} completed, Loss: {avg_loss:.4f}")
if(avg_loss < low_loss):
low_loss = avg_loss
patience_counter = 0
encoder.save(model_save_path)
logger.info(f"encoder saved at {model_save_path}.")
else:
patience_counter += 1
if(patience_counter >= 2):
logger.info("Early stopping triggered.")
break
logger.info("MiniLM model trained with classification head.")
return {
"total_steps": total_steps,
"warm_up_steps": warm_up_steps,
"batch_size": batch_size,
"epochs": epoch,
"model_save_path": model_save_path
}
class MiniLMModel(BaseModel):
"""
MiniLM model implementation for efficient text embeddings.
"""
def __init__(self, language, path=None):
"""
Initialize the MiniLM model with configuration parameters.
Args:
language (str): Language for the model.
path (str, optional): Path to load a pre-trained model. Defaults to None.
If None, a new model is initialized.
"""
self.number_of_estimators = 300
self.learning_rate = 0.1
self.max_depth = 4
self.tree_method = 'hist'
self.objective = 'binary:logistic'
self.eval_metric = 'logloss'
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.params = {
"number_of_estimators": self.number_of_estimators,
"learning_rate": self.learning_rate,
"max_depth": self.max_depth,
"tree_method": self.tree_method,
"objective": self.objective,
"eval_metric": self.eval_metric
}
super().__init__(language, path)
def setup_model(self):
"""
Initialize the MiniLM SentenceTransformer model.
"""
self.encoder = None
self.model_path = "sentence-transformers/minilm.pt"
xgb_classifier = XGBClassifier(n_estimators=self.number_of_estimators,
eval_metric=self.eval_metric,
objective=self.objective,
learning_rate=self.learning_rate,
max_depth=self.max_depth,
tree_method=self.tree_method)
self.classifier = MultiOutputClassifier(xgb_classifier)
logger.info("MiniLM model initialized.")
def train(self, X_train, y_train):
"""
Train the MiniLM model with a classification head.
Args:
X_train: Input training data.
y_train: True labels for training data.
"""
if self.encoder is None and self.language == "java":
if os.path.exists(self.model_path):
logger.info(f"Loading existing MiniLM model from {self.model_path} for fine-tuning...")
self.encoder = SentenceTransformer(self.model_path).to(self.device)
else:
logger.info(f"Fine-tuning MiniLM encoder using {self.language} training data...")
parameters = finetune_miniLM(X_train, y_train,device=self.device, model_save_path=self.model_path)
self.params.update(parameters)
self.encoder = SentenceTransformer(parameters["model_save_path"]).to(self.device)
if self.encoder is None:
self.encoder = SentenceTransformer(self.model_path).to(self.device)
y_train = np.array(y_train,dtype=np.float32)
train_embeddings = self.encoder.encode(X_train)
logger.info("Starting training of MiniLM model with Xgboost...")
self.classifier.fit(train_embeddings, y_train)
return {
"n_estimators": self.number_of_estimators,
"learning_rate": self.learning_rate,
"max_depth": self.max_depth,
"tree_method": self.tree_method,
"objective": self.objective,
"eval_metric": self.eval_metric
}
def evaluate(self, X_test, y_test) -> dict[str,any]:
"""
Evaluate the MiniLM model on test data.
Args:
X_test: Input test data.
y_test: True labels for test data.
"""
y_test = np.array(y_test,dtype=np.float32)
test_embeddings = self.encoder.encode(X_test)
predictions = self.classifier.predict(test_embeddings)
accuracy = accuracy_score(y_test, predictions)
f1_micro = f1_score(y_test, predictions, average='micro')
f1_macro = f1_score(y_test, predictions, average='macro')
f1_weighted = f1_score(y_test, predictions, average='weighted')
recall = recall_score(y_test, predictions, average='weighted')
precision = precision_score(y_test, predictions, average='weighted')
metrics = {
"accuracy": accuracy,
"f1_micro_score": f1_micro,
"f1_macro_score": f1_macro,
"f1_weighted_score": f1_weighted,
"recall": recall,
"precision": precision
}
return metrics
def predict(self, X) -> ndarray:
"""
Make predictions using the trained MiniLM model.
Args:
X: Input data for prediction.
Returns:
Predictions made by the model.
"""
if self.encoder is None or self.classifier is None:
raise ValueError("Model is not trained. Call train() or load() before prediction.")
encodedText = self.encoder.encode(X)
predictions = self.classifier.predict(encodedText)
logger.info(f"Predictions: {predictions}.")
return predictions
def save(self, path, model_name):
"""
Save model and log to MLflow.
Args:
path (str): Path to save the model.
model_name (str): Name to use when saving the model (without extension).
"""
if self.encoder is None and self.classifier is None:
raise ValueError("Model is not trained. Cannot save uninitialized model.")
complete_path = os.path.join(path, model_name)
encoder_path = complete_path+f"_encoder_{self.language}"
classifier_path = complete_path+f"_xgb_classifier_{self.language}.joblib"
if os.path.exists(complete_path) and os.path.isdir(complete_path):
shutil.rmtree(complete_path)
self.encoder.save(encoder_path)
joblib.dump(self.classifier, classifier_path)
try:
# Log to MLflow
logger.info("Logging artifacts to MLflow...")
mlflow.pyfunc.log_model(
artifact_path=f"{model_name}_{self.language}",
python_model=MiniLMClassifierWrapper(),
artifacts={
"encoder_path": encoder_path,
"classifier_path": classifier_path
},
code_paths=["turing/modeling/models/MiniLMClassifierWrapper.py"]
)
except Exception as e:
logger.error(f"Failed to log model artifacts to MLflow: {e}")
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