Github user jkbradley commented on a diff in the pull request: https://github.com/apache/spark/pull/10085#discussion_r50175081 --- Diff: python/pyspark/ml/feature.py --- @@ -992,6 +993,88 @@ def getDegree(self): @inherit_doc +class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol): + """ + .. note:: Experimental + + `QuantileDiscretizer` takes a column with continuous features and outputs a column with binned + categorical features. The bin ranges are chosen by taking a sample of the data and dividing it + into roughly equal parts. The lower and upper bin bounds will be -Infinity and +Infinity, + covering all real values. This attempts to find numBuckets partitions based on a sample of data, + but it may find fewer depending on the data sample values. + + >>> df = sqlContext.createDataFrame([(0.1,), (0.4,), (1.2,), (1.5,)], ["values"]) + >>> qds = QuantileDiscretizer(numBuckets=2, + ... inputCol="values", outputCol="buckets") + >>> bucketizer = qds.fit(df) + >>> splits = bucketizer.getSplits() + >>> splits[0] + -inf + >>> int(splits[1]*10) --- End diff -- How about using round instead?
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