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"""
Synthetic Data Generator for Drift Testing
Generates synthetic drifted datasets to test drift detection.
"""
import random
import string
from typing import List, Tuple
from loguru import logger
import numpy as np
class SyntheticDataGenerator:
"""
Generates synthetic code comment data with controlled drift characteristics.
"""
def __init__(self, seed: int = 42):
"""
Initialize synthetic data generator.
"""
self.seed = seed
np.random.seed(seed)
random.seed(seed)
def generate_short_comments(
self,
reference_texts: List[str],
ratio: float = 0.5,
n_samples: int = 100,
) -> List[str]:
"""
Generate shorter comments (text length drift).
"""
short_comments = []
for _ in range(n_samples):
ref_text = np.random.choice(reference_texts)
words = ref_text.split()
truncated_len = max(1, int(len(words) * ratio))
short_text = " ".join(words[:truncated_len])
short_comments.append(short_text)
logger.debug(f"Generated {len(short_comments)} short comments")
return short_comments
def generate_long_comments(
self,
reference_texts: List[str],
ratio: float = 1.5,
n_samples: int = 100,
) -> List[str]:
"""
Generate longer comments (text length drift upward).
"""
long_comments = []
for _ in range(n_samples):
ref_text = np.random.choice(reference_texts)
words = ref_text.split()
target_len = max(1, int(len(words) * ratio))
extended_words = words.copy()
while len(extended_words) < target_len:
extended_words.append(np.random.choice(words))
long_text = " ".join(extended_words[:target_len])
long_comments.append(long_text)
logger.debug(f"Generated {len(long_comments)} long comments")
return long_comments
def generate_corrupted_vocabulary(
self,
reference_texts: List[str],
corruption_rate: float = 0.5,
n_samples: int = 100,
) -> List[str]:
"""
Generate texts with corrupted vocabulary (typos, character swaps).
Args:
reference_texts: Reference training texts
corruption_rate: Fraction of words to corrupt (0.0-1.0)
n_samples: Number of samples to generate
Returns:
List of corrupted texts
"""
corrupted_texts = []
for _ in range(n_samples):
ref_text = np.random.choice(reference_texts)
words = ref_text.split()
# Corrupt some words
for i in range(len(words)):
if random.random() < corruption_rate:
word = words[i]
if len(word) > 2:
# Random character swap or substitution
if random.random() < 0.5:
# Character swap
idx = random.randint(0, len(word) - 2)
word = word[:idx] + word[idx + 1] + word[idx] + word[idx + 2 :]
else:
# Character substitution
idx = random.randint(0, len(word) - 1)
word = (
word[:idx]
+ random.choice(string.ascii_lowercase)
+ word[idx + 1 :]
)
words[i] = word
corrupted_text = " ".join(words)
corrupted_texts.append(corrupted_text)
logger.debug(f"Generated {len(corrupted_texts)} corrupted texts (rate={corruption_rate})")
return corrupted_texts
def generate_label_shift(
self,
reference_texts: List[str],
reference_labels: np.ndarray,
shift_type: str = "class_imbalance",
n_samples: int = 100,
) -> Tuple[List[str], np.ndarray]:
"""
Generate batch with label distribution shift (class imbalance).
Args:
reference_texts: Reference training texts
reference_labels: Reference training labels (binary matrix)
shift_type: 'class_imbalance' - favor majority class
n_samples: Number of samples to generate
Returns:
Tuple of (texts, shifted_labels)
"""
texts = []
shifted_labels = []
if reference_labels.ndim == 2:
# Multi-label: get the first label per sample
label_indices = np.argmax(reference_labels, axis=1)
else:
label_indices = reference_labels
# Get class distribution
unique_labels, counts = np.unique(label_indices, return_counts=True)
majority_class = unique_labels[np.argmax(counts)]
minority_classes = unique_labels[unique_labels != majority_class]
# Create imbalanced distribution: 80% majority, 20% minority
n_majority = int(n_samples * 0.8)
n_minority = n_samples - n_majority
# Sample indices with bias toward majority class
majority_indices = np.where(label_indices == majority_class)[0]
minority_indices = np.where(np.isin(label_indices, minority_classes))[0]
selected_indices = []
selected_indices.extend(np.random.choice(majority_indices, size=n_majority, replace=True))
if len(minority_indices) > 0:
selected_indices.extend(
np.random.choice(minority_indices, size=n_minority, replace=True)
)
np.random.shuffle(selected_indices)
selected_indices = selected_indices[:n_samples]
# Get texts and labels
texts = [reference_texts[i] for i in selected_indices]
shifted_labels = reference_labels[selected_indices]
logger.debug(f"Generated {len(texts)} samples with class imbalance")
return texts, shifted_labels
def generate_synthetic_batch(
self,
reference_texts: List[str],
reference_labels: np.ndarray,
drift_type: str = "none",
batch_size: int = 50,
) -> Tuple[List[str], np.ndarray]:
"""
Generate a synthetic batch with specified drift.
Args:
reference_texts: Reference training texts
reference_labels: Reference training labels
drift_type: Type of drift to introduce:
- 'none': No drift (baseline)
- 'text_length_short': Shortened texts
- 'text_length_long': Elongated texts
- 'corrupted_vocab': Typos and character swaps
- 'class_imbalance': Biased label distribution
batch_size: Number of samples to generate
Returns:
Tuple of (texts, labels)
"""
if drift_type == "none":
indices = np.random.choice(len(reference_texts), size=batch_size, replace=True)
texts = [reference_texts[i] for i in indices]
labels = reference_labels[indices]
elif drift_type == "text_length_short":
texts = self.generate_short_comments(reference_texts, ratio=0.5, n_samples=batch_size)
indices = np.random.choice(len(reference_labels), size=batch_size)
labels = reference_labels[indices]
elif drift_type == "text_length_long":
texts = self.generate_long_comments(reference_texts, ratio=1.5, n_samples=batch_size)
indices = np.random.choice(len(reference_labels), size=batch_size)
labels = reference_labels[indices]
elif drift_type == "corrupted_vocab":
texts = self.generate_corrupted_vocabulary(
reference_texts, corruption_rate=0.2, n_samples=batch_size
)
indices = np.random.choice(len(reference_labels), size=batch_size)
labels = reference_labels[indices]
elif drift_type == "class_imbalance":
texts, labels = self.generate_label_shift(
reference_texts,
reference_labels,
shift_type="class_imbalance",
n_samples=batch_size,
)
else:
raise ValueError(f"Unknown drift type: {drift_type}")
logger.info(f"Generated synthetic batch: {drift_type}, size={batch_size}")
return texts, labels
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