[SPARK-7794] [MLLIB] update RegexTokenizer default settings

The previous default is `{gaps: false, pattern: "\\p{L}+|[^\\p{L}\\s]+"}`. The default pattern is hard to understand. This PR changes the default to `{gaps: true, pattern: "\\s+"}`. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #6330 from mengxr/SPARK-7794 and squashes the following commits:

5ee7cde [Xiangrui Meng] update RegexTokenizer default settings

1 files changed, 19 insertions, 21 deletions

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 5511dceb70..b0479d9b07 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -446,23 +446,25 @@ class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol):
 @ignore_unicode_prefix
 class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
-    A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default)
-    or using it to split the text (set matching to false). Optional parameters also allow filtering
-    tokens using a minimal length.
+    A regex based tokenizer that extracts tokens either by using the
+    provided regex pattern (in Java dialect) to split the text
+    (default) or repeatedly matching the regex (if gaps is true).
+    Optional parameters also allow filtering tokens using a minimal
+    length.
     It returns an array of strings that can be empty.
 
-    >>> df = sqlContext.createDataFrame([("a b c",)], ["text"])
+    >>> df = sqlContext.createDataFrame([("a b  c",)], ["text"])
     >>> reTokenizer = RegexTokenizer(inputCol="text", outputCol="words")
     >>> reTokenizer.transform(df).head()
-    Row(text=u'a b c', words=[u'a', u'b', u'c'])
+    Row(text=u'a b  c', words=[u'a', u'b', u'c'])
     >>> # Change a parameter.
     >>> reTokenizer.setParams(outputCol="tokens").transform(df).head()
-    Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
+    Row(text=u'a b  c', tokens=[u'a', u'b', u'c'])
     >>> # Temporarily modify a parameter.
     >>> reTokenizer.transform(df, {reTokenizer.outputCol: "words"}).head()
-    Row(text=u'a b c', words=[u'a', u'b', u'c'])
+    Row(text=u'a b  c', words=[u'a', u'b', u'c'])
     >>> reTokenizer.transform(df).head()
-    Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
+    Row(text=u'a b  c', tokens=[u'a', u'b', u'c'])
     >>> # Must use keyword arguments to specify params.
     >>> reTokenizer.setParams("text")
     Traceback (most recent call last):
@@ -472,31 +474,27 @@ class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol):
 
     # a placeholder to make it appear in the generated doc
     minTokenLength = Param(Params._dummy(), "minTokenLength", "minimum token length (>= 0)")
-    gaps = Param(Params._dummy(), "gaps", "Set regex to match gaps or tokens")
-    pattern = Param(Params._dummy(), "pattern", "regex pattern used for tokenizing")
+    gaps = Param(Params._dummy(), "gaps", "whether regex splits on gaps (True) or matches tokens")
+    pattern = Param(Params._dummy(), "pattern", "regex pattern (Java dialect) used for tokenizing")
 
     @keyword_only
-    def __init__(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+",
-                 inputCol=None, outputCol=None):
+    def __init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None):
         """
-        __init__(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+", \
-                 inputCol=None, outputCol=None)
+        __init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None)
         """
         super(RegexTokenizer, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RegexTokenizer", self.uid)
         self.minTokenLength = Param(self, "minTokenLength", "minimum token length (>= 0)")
-        self.gaps = Param(self, "gaps", "Set regex to match gaps or tokens")
-        self.pattern = Param(self, "pattern", "regex pattern used for tokenizing")
-        self._setDefault(minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+")
+        self.gaps = Param(self, "gaps", "whether regex splits on gaps (True) or matches tokens")
+        self.pattern = Param(self, "pattern", "regex pattern (Java dialect) used for tokenizing")
+        self._setDefault(minTokenLength=1, gaps=True, pattern="\\s+")
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
-    def setParams(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+",
-                  inputCol=None, outputCol=None):
+    def setParams(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None):
         """
-        setParams(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+", \
-                  inputCol="input", outputCol="output")
+        setParams(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, outputCol=None)
         Sets params for this RegexTokenizer.
         """
         kwargs = self.setParams._input_kwargs