[jira] [Created] (SPARK-29144) Binarizer handel sparse vector incorrectly with negative threshold

["zhengruifeng (Jira)" <ji...@apache.org>]

zhengruifeng created SPARK-29144:
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             Summary: Binarizer handel sparse vector incorrectly with negative threshold
                 Key: SPARK-29144
                 URL: https://issues.apache.org/jira/browse/SPARK-29144
             Project: Spark
          Issue Type: Bug
          Components: ML
    Affects Versions: 2.4.0, 2.3.0, 2.2.0, 2.1.0, 2.0.0
            Reporter: zhengruifeng


the process on sparse vector is wrong if thread<0:
{code:java}
scala> val data = Seq((0, Vectors.sparse(3, Array(1), Array(0.5))), (1, Vectors.dense(Array(0.0, 0.5, 0.0))))
data: Seq[(Int, org.apache.spark.ml.linalg.Vector)] = List((0,(3,[1],[0.5])), (1,[0.0,0.5,0.0]))

scala> val df = data.toDF("id", "feature")
df: org.apache.spark.sql.DataFrame = [id: int, feature: vector]

scala> val binarizer: Binarizer = new Binarizer().setInputCol("feature").setOutputCol("binarized_feature").setThreshold(-0.5)
binarizer: org.apache.spark.ml.feature.Binarizer = binarizer_1c07ac2ae3c8

scala> binarizer.transform(df).show()
+---+-------------+-----------------+
| id|      feature|binarized_feature|
+---+-------------+-----------------+
|  0|(3,[1],[0.5])|    [0.0,1.0,0.0]|
|  1|[0.0,0.5,0.0]|    [1.0,1.0,1.0]|
+---+-------------+-----------------+
{code}
expected outputs of the above two input vectors should be the same.

 

To deal with sparse vectors with threshold < 0, we have two options:

1, return 1 for non-active items, but this will convert sparse vectors to dense ones

2, throw an exception like what Scikit-Learn's [Binarizer|https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.htm] does:
{code:java}
import numpy as np
from scipy.sparse import csr_matrix
from sklearn.preprocessing import Binarizer

row = np.array([0, 0, 1, 2, 2, 2])
col = np.array([0, 2, 2, 0, 1, 2])
data = np.array([1, 2, 3, 4, 5, 6])
a = csr_matrix((data, (row, col)), shape=(3, 3))
binarizer = Binarizer(threshold=-1.0)
binarizer.transform(a)
Traceback (most recent call last):  File "<ipython-input-24-7e12ab26b3ed>", line 1, in <module>
    binarizer.transform(a)  File "/home/zrf/Applications/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py", line 1874, in transform
    return binarize(X, threshold=self.threshold, copy=copy)  File "/home/zrf/Applications/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py", line 1774, in binarize
    raise ValueError('Cannot binarize a sparse matrix with threshold 'ValueError: Cannot binarize a sparse matrix with threshold < 0 {code}
 



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