Demo for survival analysis (regression) with Optuna.

Demo for survival analysis (regression) using Accelerated Failure Time (AFT) model, using Optuna to tune hyperparameters

from sklearn.model_selection import ShuffleSplit
import pandas as pd
import numpy as np
import xgboost as xgb
import optuna

# The Veterans' Administration Lung Cancer Trial
# The Statistical Analysis of Failure Time Data by Kalbfleisch J. and Prentice R (1980)
df = pd.read_csv('../data/veterans_lung_cancer.csv')
print('Training data:')
print(df)

# Split features and labels
y_lower_bound = df['Survival_label_lower_bound']
y_upper_bound = df['Survival_label_upper_bound']
X = df.drop(['Survival_label_lower_bound', 'Survival_label_upper_bound'], axis=1)

# Split data into training and validation sets
rs = ShuffleSplit(n_splits=2, test_size=.7, random_state=0)
train_index, valid_index = next(rs.split(X))
dtrain = xgb.DMatrix(X.values[train_index, :])
dtrain.set_float_info('label_lower_bound', y_lower_bound[train_index])
dtrain.set_float_info('label_upper_bound', y_upper_bound[train_index])
dvalid = xgb.DMatrix(X.values[valid_index, :])
dvalid.set_float_info('label_lower_bound', y_lower_bound[valid_index])
dvalid.set_float_info('label_upper_bound', y_upper_bound[valid_index])

# Define hyperparameter search space
base_params = {'verbosity': 0,
              'objective': 'survival:aft',
              'eval_metric': 'aft-nloglik',
              'tree_method': 'hist'}  # Hyperparameters common to all trials
def objective(trial):
    params = {'learning_rate': trial.suggest_loguniform('learning_rate', 0.01, 1.0),
              'aft_loss_distribution': trial.suggest_categorical('aft_loss_distribution',
                                                                  ['normal', 'logistic', 'extreme']),
              'aft_loss_distribution_scale': trial.suggest_loguniform('aft_loss_distribution_scale', 0.1, 10.0),
              'max_depth': trial.suggest_int('max_depth', 3, 8),
              'lambda': trial.suggest_loguniform('lambda', 1e-8, 1.0),
              'alpha': trial.suggest_loguniform('alpha', 1e-8, 1.0)}  # Search space
    params.update(base_params)
    pruning_callback = optuna.integration.XGBoostPruningCallback(trial, 'valid-aft-nloglik')
    bst = xgb.train(params, dtrain, num_boost_round=10000,
                    evals=[(dtrain, 'train'), (dvalid, 'valid')],
                    early_stopping_rounds=50, verbose_eval=False, callbacks=[pruning_callback])
    if bst.best_iteration >= 25:
        return bst.best_score
    else:
        return np.inf  # Reject models with < 25 trees

# Run hyperparameter search
study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=200)
print('Completed hyperparameter tuning with best aft-nloglik = {}.'.format(study.best_trial.value))
params = {}
params.update(base_params)
params.update(study.best_trial.params)

# Re-run training with the best hyperparameter combination
print('Re-running the best trial... params = {}'.format(params))
bst = xgb.train(params, dtrain, num_boost_round=10000,
                evals=[(dtrain, 'train'), (dvalid, 'valid')],
                early_stopping_rounds=50)

# Run prediction on the validation set
df = pd.DataFrame({'Label (lower bound)': y_lower_bound[valid_index],
                   'Label (upper bound)': y_upper_bound[valid_index],
                   'Predicted label': bst.predict(dvalid)})
print(df)
# Show only data points with right-censored labels
print(df[np.isinf(df['Label (upper bound)'])])

# Save trained model
bst.save_model('aft_best_model.json')