training-data

name	training-data
description	Training data management including labeling strategies, data augmentation, handling imbalanced data, and data splitting best practices.

Training Data

Managing and improving training data quality.

Data Labeling Strategies

Manual Labeling

# Export for Label Studio
def export_for_labeling(data: pd.DataFrame, output_path: str):
    tasks = [
        {"data": {"text": row["text"]}, "id": idx}
        for idx, row in data.iterrows()
    ]
    with open(output_path, 'w') as f:
        json.dump(tasks, f)

Weak Supervision

from snorkel.labeling import labeling_function, LabelingFunction
from snorkel.labeling.model import LabelModel

@labeling_function()
def lf_keyword(x):
    keywords = ["urgent", "free", "winner"]
    return 1 if any(k in x.text.lower() for k in keywords) else -1

@labeling_function()
def lf_short_text(x):
    return 1 if len(x.text) < 20 else -1

# Combine weak labels
lfs = [lf_keyword, lf_short_text]
applier = PandasLFApplier(lfs)
L_train = applier.apply(df_train)

label_model = LabelModel(cardinality=2)
label_model.fit(L_train, n_epochs=100)
labels = label_model.predict(L_train)

Active Learning

from modAL.models import ActiveLearner
from sklearn.ensemble import RandomForestClassifier

learner = ActiveLearner(
    estimator=RandomForestClassifier(),
    query_strategy=uncertainty_sampling,
    X_training=X_initial, y_training=y_initial
)

for _ in range(n_queries):
    query_idx, query_instance = learner.query(X_pool)
    # Human labels the instance
    y_new = get_human_label(query_instance)
    learner.teach(X_pool[query_idx], y_new)
    X_pool = np.delete(X_pool, query_idx, axis=0)

Data Augmentation

# Text augmentation
import nlpaug.augmenter.word as naw

aug = naw.SynonymAug(aug_src='wordnet')
augmented = aug.augment("The quick brown fox jumps over the lazy dog")

# Image augmentation
import albumentations as A

transform = A.Compose([
    A.RandomCrop(width=256, height=256),
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(p=0.2),
    A.Normalize()
])

# Tabular augmentation (SMOTE)
from imblearn.over_sampling import SMOTE

smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X_train, y_train)

Handling Imbalanced Data

# Class weights
from sklearn.utils.class_weight import compute_class_weight

class_weights = compute_class_weight('balanced', classes=np.unique(y), y=y)

# Focal loss
def focal_loss(y_true, y_pred, gamma=2, alpha=0.25):
    bce = F.binary_cross_entropy(y_pred, y_true, reduction='none')
    p_t = y_true * y_pred + (1 - y_true) * (1 - y_pred)
    focal_weight = (1 - p_t) ** gamma
    return (alpha * focal_weight * bce).mean()

# Stratified sampling
from sklearn.model_selection import StratifiedKFold

skf = StratifiedKFold(n_splits=5, shuffle=True)
for train_idx, val_idx in skf.split(X, y):
    X_train, X_val = X[train_idx], X[val_idx]

Data Splitting

from sklearn.model_selection import train_test_split

# Random split (with stratification)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

# Temporal split (for time series)
def temporal_split(df, time_col, train_end, val_end):
    train = df[df[time_col] < train_end]
    val = df[(df[time_col] >= train_end) & (df[time_col] < val_end)]
    test = df[df[time_col] >= val_end]
    return train, val, test

# Group split (no data leakage)
from sklearn.model_selection import GroupShuffleSplit

gss = GroupShuffleSplit(n_splits=1, test_size=0.2)
for train_idx, test_idx in gss.split(X, y, groups=user_ids):
    X_train, X_test = X[train_idx], X[test_idx]

Commands

• /omgdata:label - Data labeling
• /omgdata:augment - Augmentation
• /omgdata:split - Data splitting

Best Practices

1. Start with a small, high-quality labeled set
2. Use weak supervision to scale labeling
3. Match augmentation to your domain
4. Prevent data leakage in splits
5. Monitor label quality over time