[Case Study]AFT Survival Analysis using Python

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1. from __future__ import print_function
   
2. 
   
3. from pyspark import SparkContext
   
4. from pyspark.sql import SQLContext
   
5. # $example on$
   
6. from pyspark.ml.regression import AFTSurvivalRegression
   
7. from pyspark.mllib.linalg import Vectors
   
8. # $example off$
   
9. 
   
10. if __name__ == "__main__":
    
11.     sc = SparkContext(appName="AFTSurvivalRegressionExample")
    
12.     sqlContext = SQLContext(sc)
    
13. 
    
14.     # $example on$
    
15.     training = sqlContext.createDataFrame([
    
16.         (1.218, 1.0, Vectors.dense(1.560, -0.605)),
    
17.         (2.949, 0.0, Vectors.dense(0.346, 2.158)),
    
18.         (3.627, 0.0, Vectors.dense(1.380, 0.231)),
    
19.         (0.273, 1.0, Vectors.dense(0.520, 1.151)),
    
20.         (4.199, 0.0, Vectors.dense(0.795, -0.226))], ["label", "censor", "features"])
    
21.     quantileProbabilities = [0.3, 0.6]
    
22.     aft = AFTSurvivalRegression(quantileProbabilities=quantileProbabilities,
    
23.                                 quantilesCol="quantiles")
    
24. 
    
25.     model = aft.fit(training)
    
26. 
    
27.     # Print the coefficients, intercept and scale parameter for AFT survival regression
    
28.     print("Coefficients: " + str(model.coefficients))
    
29.     print("Intercept: " + str(model.intercept))
    
30.     print("Scale: " + str(model.scale))
    
31.     model.transform(training).show(truncate=False)
    
32.     # $example off$
    
33. 
    
34.     sc.stop()

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