Demo for survival analysis (regression).

Demo for survival analysis (regression). using Accelerated Failure Time (AFT) model.

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

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

# 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])

# Train gradient boosted trees using AFT loss and metric
params = {'verbosity': 0,
          'objective': 'survival:aft',
          'eval_metric': 'aft-nloglik',
          'tree_method': 'hist',
          'learning_rate': 0.05,
          'aft_loss_distribution': 'normal',
          'aft_loss_distribution_scale': 1.20,
          'max_depth': 6,
          'lambda': 0.01,
          'alpha': 0.02}
bst = xgb.train(params, dtrain, num_boost_round=10000,
                evals=[(dtrain, 'train'), (dvalid, 'valid')],

# 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)})
# Show only data points with right-censored labels
print(df[np.isinf(df['Label (upper bound)'])])

# Save trained model

Total running time of the script: ( 0 minutes 0.000 seconds)

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