Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/11663#discussion_r56548734
--- Diff: python/pyspark/ml/param/_shared_params_code_gen.py ---
@@ -105,64 +104,71 @@ def get$Name(self):
if __name__ == "__main__":
print(header)
print("\n# DO NOT MODIFY THIS FILE! It was generated by
_shared_params_code_gen.py.\n")
- print("from pyspark.ml.param import Param, Params\n\n")
+ print("from pyspark.ml.param import *\n\n")
shared = [
- ("maxIter", "max number of iterations (>= 0).", None, int),
- ("regParam", "regularization parameter (>= 0).", None, float),
- ("featuresCol", "features column name.", "'features'", str),
- ("labelCol", "label column name.", "'label'", str),
- ("predictionCol", "prediction column name.", "'prediction'", str),
+ ("maxIter", "max number of iterations (>= 0).", None,
"TypeConverters.convertToInt"),
+ ("regParam", "regularization parameter (>= 0).", None,
"TypeConverters.convertToFloat"),
+ ("featuresCol", "features column name.", "'features'", None),
+ ("labelCol", "label column name.", "'label'", None),
+ ("predictionCol", "prediction column name.", "'prediction'", None),
("probabilityCol", "Column name for predicted class conditional
probabilities. " +
"Note: Not all models output well-calibrated probability
estimates! These probabilities " +
- "should be treated as confidences, not precise probabilities.",
"'probability'", str),
+ "should be treated as confidences, not precise probabilities.",
"'probability'", None),
("rawPredictionCol", "raw prediction (a.k.a. confidence) column
name.", "'rawPrediction'",
- str),
- ("inputCol", "input column name.", None, str),
- ("inputCols", "input column names.", None, None),
- ("outputCol", "output column name.", "self.uid + '__output'", str),
- ("numFeatures", "number of features.", None, int),
+ None),
+ ("inputCol", "input column name.", None, None),
+ ("inputCols", "input column names.", None,
"TypeConverters.convertToList"),
+ ("outputCol", "output column name.", "self.uid + '__output'",
None),
+ ("numFeatures", "number of features.", None,
"TypeConverters.convertToInt"),
("checkpointInterval", "set checkpoint interval (>= 1) or disable
checkpoint (-1). " +
- "E.g. 10 means that the cache will get checkpointed every 10
iterations.", None, int),
- ("seed", "random seed.", "hash(type(self).__name__)", int),
- ("tol", "the convergence tolerance for iterative algorithms.",
None, float),
- ("stepSize", "Step size to be used for each iteration of
optimization.", None, float),
+ "E.g. 10 means that the cache will get checkpointed every 10
iterations.", None,
+ "TypeConverters.convertToInt"),
+ ("seed", "random seed.", "hash(type(self).__name__)",
"TypeConverters.convertToInt"),
+ ("tol", "the convergence tolerance for iterative algorithms.",
None,
+ "TypeConverters.convertToFloat"),
+ ("stepSize", "Step size to be used for each iteration of
optimization.", None,
+ "TypeConverters.convertToFloat"),
("handleInvalid", "how to handle invalid entries. Options are skip
(which will filter " +
"out rows with bad values), or error (which will throw an
errror). More options may be " +
- "added later.", None, str),
+ "added later.", None, None),
("elasticNetParam", "the ElasticNet mixing parameter, in range [0,
1]. For alpha = 0, " +
- "the penalty is an L2 penalty. For alpha = 1, it is an L1
penalty.", "0.0", float),
- ("fitIntercept", "whether to fit an intercept term.", "True",
bool),
+ "the penalty is an L2 penalty. For alpha = 1, it is an L1
penalty.", "0.0",
+ "TypeConverters.convertToFloat"),
+ ("fitIntercept", "whether to fit an intercept term.", "True",
None),
--- End diff --
Added a `toBoolean` converter.
---
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