diff options
Diffstat (limited to 'python')
| -rw-r--r-- | python/pyspark/ml/feature.py | 41 | ||||
| -rw-r--r-- | python/pyspark/ml/tests.py | 9 |
2 files changed, 14 insertions, 36 deletions
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index 0e578d48ca..610d167f3a 100644 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -517,8 +517,12 @@ class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures, Java """ .. note:: Experimental - Maps a sequence of terms to their term frequencies using the - hashing trick. + Maps a sequence of terms to their term frequencies using the hashing trick. + Currently we use Austin Appleby's MurmurHash 3 algorithm (MurmurHash3_x86_32) + to calculate the hash code value for the term object. + Since a simple modulo is used to transform the hash function to a column index, + it is advisable to use a power of two as the numFeatures parameter; + otherwise the features will not be mapped evenly to the columns. >>> df = sqlContext.createDataFrame([(["a", "b", "c"],)], ["words"]) >>> hashingTF = HashingTF(numFeatures=10, inputCol="words", outputCol="features") @@ -543,30 +547,22 @@ class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures, Java "rather than integer counts. Default False.", typeConverter=TypeConverters.toBoolean) - hashAlgorithm = Param(Params._dummy(), "hashAlgorithm", "The hash algorithm used when " + - "mapping term to integer. Supported options: murmur3(default) " + - "and native.", typeConverter=TypeConverters.toString) - @keyword_only - def __init__(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None, - hashAlgorithm="murmur3"): + def __init__(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None): """ - __init__(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None, \ - hashAlgorithm="murmur3") + __init__(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None) """ super(HashingTF, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.HashingTF", self.uid) - self._setDefault(numFeatures=1 << 18, binary=False, hashAlgorithm="murmur3") + self._setDefault(numFeatures=1 << 18, binary=False) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only @since("1.3.0") - def setParams(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None, - hashAlgorithm="murmur3"): + def setParams(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None): """ - setParams(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None, \ - hashAlgorithm="murmur3") + setParams(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None) Sets params for this HashingTF. """ kwargs = self.setParams._input_kwargs @@ -587,21 +583,6 @@ class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures, Java """ return self.getOrDefault(self.binary) - @since("2.0.0") - def setHashAlgorithm(self, value): - """ - Sets the value of :py:attr:`hashAlgorithm`. - """ - self._set(hashAlgorithm=value) - return self - - @since("2.0.0") - def getHashAlgorithm(self): - """ - Gets the value of hashAlgorithm or its default value. - """ - return self.getOrDefault(self.hashAlgorithm) - @inherit_doc class IDF(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py index d014da8d0e..ebef656632 100644 --- a/python/pyspark/ml/tests.py +++ b/python/pyspark/ml/tests.py @@ -911,15 +911,12 @@ class HashingTFTest(PySparkTestCase): sqlContext = SQLContext(self.sc) df = sqlContext.createDataFrame([(0, ["a", "a", "b", "c", "c", "c"])], ["id", "words"]) - n = 100 + n = 10 hashingTF = HashingTF() - hashingTF.setInputCol("words").setOutputCol("features").setNumFeatures(n)\ - .setBinary(True).setHashAlgorithm("native") + hashingTF.setInputCol("words").setOutputCol("features").setNumFeatures(n).setBinary(True) output = hashingTF.transform(df) features = output.select("features").first().features.toArray() - expected = Vectors.sparse(n, {(ord("a") % n): 1.0, - (ord("b") % n): 1.0, - (ord("c") % n): 1.0}).toArray() + expected = Vectors.dense([1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]).toArray() for i in range(0, n): self.assertAlmostEqual(features[i], expected[i], 14, "Error at " + str(i) + ": expected " + str(expected[i]) + ", got " + str(features[i])) |