Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5991#discussion_r29917401
--- Diff: python/pyspark/ml/feature.py ---
@@ -113,6 +125,419 @@ def setParams(self, numFeatures=1 << 18,
inputCol=None, outputCol=None):
@inherit_doc
+class IDF(JavaEstimator, HasInputCol, HasOutputCol):
+ """
+ Compute the Inverse Document Frequency (IDF) given a collection of
documents.
+
+ >>> from pyspark.sql import Row
+ >>> from pyspark.mllib.linalg import DenseVector
+ >>> df = sc.parallelize([Row(tf=DenseVector([1.0, 2.0])),
+ ... Row(tf=DenseVector([0.0, 1.0])), Row(tf=DenseVector([3.0,
0.2]))]).toDF()
+ >>> idf = IDF(minDocFreq=3, inputCol="tf", outputCol="idf")
+ >>> idf.fit(df).transform(df).head().idf
+ DenseVector([0.0, 0.0])
+ >>>
idf.setParams(outputCol="freqs").fit(df).transform(df).collect()[1].freqs
+ DenseVector([0.0, 0.0])
+ >>> params = {idf.minDocFreq: 1, idf.outputCol: "vector"}
+ >>> idf.fit(df, params).transform(df).head().vector
+ DenseVector([0.2877, 0.0])
+ """
+
+ _java_class = "org.apache.spark.ml.feature.IDF"
+
+ # a placeholder to make it appear in the generated doc
+ minDocFreq = Param(Params._dummy(), "minDocFreq",
+ "minimum of documents in which a term should appear
for filtering")
+
+ @keyword_only
+ def __init__(self, minDocFreq=0, inputCol=None, outputCol=None):
+ """
+ __init__(self, minDocFreq=0, inputCol=None, outputCol=None)
+ """
+ super(IDF, self).__init__()
+ self.minDocFreq = Param(self, "minDocFreq",
+ "minimum of documents in which a term
should appear for filtering")
+ self._setDefault(minDocFreq=0)
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ def setParams(self, minDocFreq=0, inputCol=None, outputCol=None):
+ """
+ setParams(self, minDocFreq=0, inputCol=None, outputCol=None)
+ Sets params for this IDF.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+ def setMinDocFreq(self, value):
+ """
+ Sets the value of :py:attr:`minDocFreq`.
+ """
+ self.paramMap[self.minDocFreq] = value
+ return self
+
+ def getMinDocFreq(self):
+ """
+ Gets the value of minDocFreq or its default value.
+ """
+ return self.getOrDefault(self.minDocFreq)
+
+
+class IDFModel(JavaModel):
+ """
+ Model fitted by IDF.
+ """
+
+
+@inherit_doc
+class Normalizer(JavaTransformer, HasInputCol, HasOutputCol):
+ """
+ Normalize a vector to have unit norm using the given p-norm.
+
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> from pyspark.sql import Row
+ >>> svec = Vectors.sparse(4, {1: 4.0, 3: 3.0})
+ >>> df = sc.parallelize([Row(dense=Vectors.dense([3.0, -4.0]),
sparse=svec)]).toDF()
+ >>> Normalizer = Normalizer(p=2.0, inputCol="dense",
outputCol="features")
+ >>> Normalizer.transform(df).head().features
+ DenseVector([0.6, -0.8])
+ >>> Normalizer.setParams(inputCol="sparse",
outputCol="freqs").transform(df).head().freqs
+ SparseVector(4, {1: 0.8, 3: 0.6})
+ >>> params = {Normalizer.p: 1.0, Normalizer.inputCol: "dense",
Normalizer.outputCol: "vector"}
+ >>> Normalizer.transform(df, params).head().vector
+ DenseVector([0.4286, -0.5714])
+ """
+
+ # a placeholder to make it appear in the generated doc
+ p = Param(Params._dummy(), "p", "the p norm value.")
+
+ _java_class = "org.apache.spark.ml.feature.Normalizer"
+
+ @keyword_only
+ def __init__(self, p=2.0, inputCol=None, outputCol=None):
+ """
+ __init__(self, p=2.0, inputCol=None, outputCol=None)
+ """
+ super(Normalizer, self).__init__()
+ self.p = Param(self, "p", "the p norm value.")
+ self._setDefault(p=2.0)
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ def setParams(self, p=2.0, inputCol=None, outputCol=None):
+ """
+ setParams(self, p=2.0, inputCol=None, outputCol=None)
+ Sets params for this Normalizer.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+ def setP(self, value):
+ """
+ Sets the value of :py:attr:`p`.
+ """
+ self.paramMap[self.p] = value
+ return self
+
+ def getP(self):
+ """
+ Gets the value of p or its default value.
+ """
+ return self.getOrDefault(self.p)
+
+
+@inherit_doc
+class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol):
+ """
+ A one-hot encoder that maps a column of label indices to a column of
binary vectors, with
+ at most a single one-value. By default, the binary vector has an
element for each category, so
+ with 5 categories, an input value of 2.0 would map to an output vector
of
+ (0.0, 0.0, 1.0, 0.0, 0.0). If includeFirst is set to false, the first
category is omitted, so
+ the output vector for the previous example would be (0.0, 1.0, 0.0,
0.0) and an input value
+ of 0.0 would map to a vector of all zeros. Including the first
category makes the vector columns
+ linearly dependent because they sum up to one.
+
+ TODO: This method requires the use of StringIndexer first. Decouple
them.
+
+ >>> StringIndexer = StringIndexer(inputCol="label",
outputCol="indexed")
--- End diff --
`StringIndexer` -> `stringIndexer`
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