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Utility Functions Reference

This section provides detailed documentation of the utility functions and tools in Tabular SSL.

Evaluation Utilities

Model Evaluation

from tabular_ssl.utils import evaluate_model

metrics = evaluate_model(
    model,
    test_data,
    metrics=['accuracy', 'f1', 'precision', 'recall']
)

Cross-Validation

from tabular_ssl.utils import cross_validate

cv_results = cross_validate(
    model,
    data,
    n_splits=5,
    metrics=['accuracy', 'f1']
)

Visualization Utilities

Training History

from tabular_ssl.utils import plot_training_history

fig = plot_training_history(history)
fig.show()

Performance Plots

from tabular_ssl.utils import plot_performance

fig = plot_performance(
    model,
    test_data,
    plot_types=['confusion_matrix', 'roc_curve']
)
fig.show()

Model Interpretation

Feature Importance

from tabular_ssl.utils import get_feature_importance

importance = get_feature_importance(model, test_data)

SHAP Values

from tabular_ssl.utils import get_shap_values

shap_values = get_shap_values(model, test_data)

Hyperparameter Tuning

from tabular_ssl.utils import grid_search

best_params = grid_search(
    model_class=TabularSSL,
    param_grid={
        'hidden_dim': [128, 256, 512],
        'num_layers': [2, 4, 6]
    },
    train_data=train_data,
    val_data=val_data
)

from tabular_ssl.utils import random_search

best_params = random_search(
    model_class=TabularSSL,
    param_distributions={
        'hidden_dim': [128, 256, 512],
        'num_layers': [2, 4, 6]
    },
    train_data=train_data,
    val_data=val_data,
    n_iter=10
)

Data Utilities

Data Validation

from tabular_ssl.utils import validate_data

validation_results = validate_data(data)

Feature Selection

from tabular_ssl.utils import select_features

selected_features = select_features(
    data,
    target_col='target',
    method='importance',
    threshold=0.01
)

Model Utilities

Model Saving

from tabular_ssl.utils import save_model

save_model(model, 'model.pt')

Model Loading

from tabular_ssl.utils import load_model

model = load_model('model.pt')

Training Utilities

Learning Rate Scheduling

from tabular_ssl.utils import get_lr_scheduler

scheduler = get_lr_scheduler(
    initial_lr=1e-3,
    scheduler_type='cosine',
    warmup_epochs=5
)

Early Stopping

from tabular_ssl.utils import EarlyStopping

early_stopping = EarlyStopping(
    patience=10,
    min_delta=0.001
)

Common Functions

Metrics

from tabular_ssl.utils import (
    accuracy_score,
    f1_score,
    precision_score,
    recall_score
)

# Compute metrics
acc = accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)

Data Processing

from tabular_ssl.utils import (
    normalize_data,
    encode_categorical,
    handle_missing
)

# Process data
normalized_data = normalize_data(data)
encoded_data = encode_categorical(data, categorical_cols)
cleaned_data = handle_missing(data, strategy='mean')

Best Practices

  1. Use appropriate evaluation metrics
  2. Implement proper cross-validation
  3. Visualize results for better understanding
  4. Document utility function usage
  5. Handle errors gracefully
  6. Use type hints for better code clarity
  7. Add proper docstrings
  8. Include examples in documentation