Description

Accelerated Failure Time Survival Regression. Based on the Weibull distribution of the survival time.

(https://en.wikipedia.org/wiki/Accelerated_failure_time_model)

Parameters

Name Description Type Required？ Default Value
quantileProbabilities Array of quantile probabilities. double[] [0.01,0.05,0.1,0.25,0.5,0.75,0.9,0.95,0.99]
reservedCols Names of the columns to be retained in the output table String[] null
predictionCol Column name of prediction. String ✓
vectorCol Name of a vector column String null
predictionDetailCol Column name of prediction result, it will include detailed info. String

Script Example

Code

  1. data = np.array([[1.218, 1.0, "1.560,-0.605"],\
  2. [2.949, 0.0, "0.346,2.158"],\
  3. [3.627, 0.0, "1.380,0.231"],\
  4. [0.273, 1.0, "0.520,1.151"],\
  5. [4.199, 0.0, "0.795,-0.226"]])
  6. df = pd.DataFrame({"label":data[:,0], "censor":data[:,1],"features":data[:,2]})
  7. data = dataframeToOperator(df, schemaStr="label double, censor double, features string",op_type="batch")
  8. dataStream = dataframeToOperator(df, schemaStr="label double, feature double",op_type="stream")
  9. trainOp = AftSurvivalRegTrainBatchOp()\
  10.                 .setVectorCol("features")\
  11.                     .setLabelCol("label")\
  12.                 .setCensorCol("censor")
  13. model = trainOp.linkFrom(data)
  14. predictOp = AftSurvivalRegPredictStreamOp(model).setPredictionCol("pred")
  15. res = predictOp.linkFrom(dataStream)
  16. res.print()
  17. StreamOperator.execute()

Results

Model
model_id model_info label_value
0 {“hasInterceptItem”:”true”,”vectorCol”:”\”features\””,”modelName”:”\”AFTSurvivalRegTrainBatchOp\””,”labelCol”:null,”linearModelType”:”\”AFT\””,”vectorSize”:”3”} NULL
1048576 {“featureColNames”:null,”featureColTypes”:null,”coefVector”:{“data”:[2.6373721387804276,-0.49591581739360013,0.19847648151323818,1.5469720551612485]},”coefVectors”:null} NULL
Prediction
label censor features pred
0.273 1.0 0.520,1.151 13.571097451777327
1.218 1.0 1.560,-0.605 5.718263596902868
3.627 0.0 1.380,0.231 7.380610641992667
4.199 0.0 0.795,-0.226 9.009354073821902
2.949 0.0 0.346,2.158 18.067188679653064