Python pyspark AFTSurvivalRegression用法及代碼示例

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([
...     (1.0, Vectors.dense(1.0), 1.0),
...     (1e-40, Vectors.sparse(1, [], []), 0.0)], ["label", "features", "censor"])
>>> aftsr = AFTSurvivalRegression()
>>> aftsr.setMaxIter(10)
AFTSurvivalRegression...
>>> aftsr.getMaxIter()
10
>>> aftsr.clear(aftsr.maxIter)
>>> model = aftsr.fit(df)
>>> model.getMaxBlockSizeInMB()
0.0
>>> model.setFeaturesCol("features")
AFTSurvivalRegressionModel...
>>> model.predict(Vectors.dense(6.3))
1.0
>>> model.predictQuantiles(Vectors.dense(6.3))
DenseVector([0.0101, 0.0513, 0.1054, 0.2877, 0.6931, 1.3863, 2.3026, 2.9957, 4.6052])
>>> model.transform(df).show()
+-------+---------+------+----------+
|  label| features|censor|prediction|
+-------+---------+------+----------+
|    1.0|    [1.0]|   1.0|       1.0|
|1.0E-40|(1,[],[])|   0.0|       1.0|
+-------+---------+------+----------+
...
>>> aftsr_path = temp_path + "/aftsr"
>>> aftsr.save(aftsr_path)
>>> aftsr2 = AFTSurvivalRegression.load(aftsr_path)
>>> aftsr2.getMaxIter()
100
>>> model_path = temp_path + "/aftsr_model"
>>> model.save(model_path)
>>> model2 = AFTSurvivalRegressionModel.load(model_path)
>>> model.coefficients == model2.coefficients
True
>>> model.intercept == model2.intercept
True
>>> model.scale == model2.scale
True
>>> model.transform(df).take(1) == model2.transform(df).take(1)
True