61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
"""
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Demo for survival analysis (regression).
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========================================
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Demo for survival analysis (regression). using Accelerated Failure Time (AFT) model.
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"""
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import os
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from sklearn.model_selection import ShuffleSplit
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import pandas as pd
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import numpy as np
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import xgboost as xgb
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# The Veterans' Administration Lung Cancer Trial
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# The Statistical Analysis of Failure Time Data by Kalbfleisch J. and Prentice R (1980)
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CURRENT_DIR = os.path.dirname(__file__)
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df = pd.read_csv(os.path.join(CURRENT_DIR, '../data/veterans_lung_cancer.csv'))
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print('Training data:')
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print(df)
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# Split features and labels
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y_lower_bound = df['Survival_label_lower_bound']
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y_upper_bound = df['Survival_label_upper_bound']
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X = df.drop(['Survival_label_lower_bound', 'Survival_label_upper_bound'], axis=1)
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# Split data into training and validation sets
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rs = ShuffleSplit(n_splits=2, test_size=.7, random_state=0)
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train_index, valid_index = next(rs.split(X))
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dtrain = xgb.DMatrix(X.values[train_index, :])
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dtrain.set_float_info('label_lower_bound', y_lower_bound[train_index])
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dtrain.set_float_info('label_upper_bound', y_upper_bound[train_index])
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dvalid = xgb.DMatrix(X.values[valid_index, :])
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dvalid.set_float_info('label_lower_bound', y_lower_bound[valid_index])
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dvalid.set_float_info('label_upper_bound', y_upper_bound[valid_index])
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# Train gradient boosted trees using AFT loss and metric
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params = {'verbosity': 0,
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'objective': 'survival:aft',
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'eval_metric': 'aft-nloglik',
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'tree_method': 'hist',
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'learning_rate': 0.05,
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'aft_loss_distribution': 'normal',
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'aft_loss_distribution_scale': 1.20,
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'max_depth': 6,
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'lambda': 0.01,
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'alpha': 0.02}
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bst = xgb.train(params, dtrain, num_boost_round=10000,
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evals=[(dtrain, 'train'), (dvalid, 'valid')],
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early_stopping_rounds=50)
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# Run prediction on the validation set
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df = pd.DataFrame({'Label (lower bound)': y_lower_bound[valid_index],
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'Label (upper bound)': y_upper_bound[valid_index],
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'Predicted label': bst.predict(dvalid)})
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print(df)
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# Show only data points with right-censored labels
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print(df[np.isinf(df['Label (upper bound)'])])
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# Save trained model
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bst.save_model('aft_model.json')
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