Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18970#discussion_r133790849
--- Diff: python/pyspark/ml/feature.py ---
@@ -697,6 +698,82 @@ def getScalingVec(self):
@inherit_doc
+class FeatureHasher(JavaTransformer, HasInputCols, HasOutputCol,
HasNumFeatures, JavaMLReadable,
+ JavaMLWritable):
+ """
+ .. note:: Experimental
+
+ Feature hashing projects a set of categorical or numerical features
into a feature vector of
+ specified dimension (typically substantially smaller than that of the
original feature
+ space). This is done using the hashing trick
(https://en.wikipedia.org/wiki/Feature_hashing)
+ to map features to indices in the feature vector.
+
+ The FeatureHasher transformer operates on multiple columns. Each
column may contain either
+ numeric or categorical features. Behavior and handling of column data
types is as follows:
+
+ * Numeric columns:
+ For numeric features, the hash value of the column name is used to
map the
+ feature value to its index in the feature vector. Numeric features
are never
+ treated as categorical, even when they are integers. You must
explicitly
+ convert numeric columns containing categorical features to strings
first.
+
+ * String columns:
+ For categorical features, the hash value of the string
"column_name=value"
+ is used to map to the vector index, with an indicator value of
`1.0`.
+ Thus, categorical features are "one-hot" encoded
+ (similarly to using :py:class:`OneHotEncoder` with
`dropLast=false`).
+
+ * Boolean columns:
+ Boolean values are treated in the same way as string columns. That
is,
+ boolean features are represented as "column_name=true" or
"column_name=false",
+ with an indicator value of `1.0`.
+
+ Null (missing) values are ignored (implicitly zero in the resulting
feature vector).
+
+ Since a simple modulo is used to transform the hash function to a
vector index,
+ it is advisable to use a power of two as the `numFeatures` parameter;
+ otherwise the features will not be mapped evenly to the vector indices.
+
+ >>> data = [(2.0, True, "1", "foo"), (3.0, False, "2", "bar")]
+ >>> cols = ["real", "bool", "stringNum", "string"]
+ >>> df = spark.createDataFrame(data, cols)
+ >>> hasher = FeatureHasher(inputCols=cols, outputCol="features")
+ >>> hasher.transform(df).head().features
+ SparseVector(262144, {51871: 1.0, 63643: 1.0, 174475: 2.0, 253195:
1.0})
+ >>> hasherPath = temp_path + "/hasher"
+ >>> hasher.save(hasherPath)
+ >>> loadedHasher = FeatureHasher.load(hasherPath)
+ >>> loadedHasher.getNumFeatures() == hasher.getNumFeatures()
+ True
+ >>> loadedHasher.transform(df).head().features ==
hasher.transform(df).head().features
+ True
+
+ .. versionadded:: 2.3.0
+ """
+
+ @keyword_only
+ def __init__(self, numFeatures=1 << 18, inputCols=None,
outputCol=None):
+ """
+ __init__(self, numFeatures=1 << 18, inputCols=None, outputCol=None)
+ """
+ super(FeatureHasher, self).__init__()
+ self._java_obj =
self._new_java_obj("org.apache.spark.ml.feature.FeatureHasher", self.uid)
+ self._setDefault(numFeatures=1 << 18)
+ kwargs = self._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ @since("2.3.0")
+ def setParams(self, numFeatures=1 << 18, inputCols=None,
outputCol=None):
+ """
+ setParams(self, numFeatures=1 << 18, inputCols=None,
outputCol=None)
+ Sets params for this FeatureHasher.
+ """
+ kwargs = self._input_kwargs
+ return self._set(**kwargs)
+
+
--- End diff --
Should there be a `getNumFeatures()` method to return the param?
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