Serializable, org.apache.spark.internal.Logging, Params, HasInputCols, HasNumFeatures, HasOutputCol, DefaultParamsWritable, Identifiable, MLWritable
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. By default, numeric features are not treated as categorical (even when they are integers). To treat them as categorical, specify the relevant columns in categoricalCols. -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 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).
The hash function used here is also the MurmurHash 3 used in HashingTF. Since a simple modulo on the hashed value is used to determine the 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.
val df = Seq(
(2.0, true, "1", "foo"),
(3.0, false, "2", "bar")
).toDF("real", "bool", "stringNum", "string")
val hasher = new FeatureHasher()
.setInputCols("real", "bool", "stringNum", "string")
.setOutputCol("features")
hasher.transform(df).show(false)
+----+-----+---------+------+------------------------------------------------------+
|real|bool |stringNum|string|features |
+----+-----+---------+------+------------------------------------------------------+
|2.0 |true |1 |foo |(262144,[51871,63643,174475,253195],[1.0,1.0,2.0,1.0])|
|3.0 |false|2 |bar |(262144,[6031,80619,140467,174475],[1.0,1.0,1.0,3.0]) |
+----+-----+---------+------+------------------------------------------------------+
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
Constructors
Numeric columns to treat as categorical features.
Creates a copy of this instance with the same UID and some extra params.
Param for input column names.
Param for Number of features.
Param for output column name.
Transforms the input dataset.
Check transform validity and derive the output schema from the input schema.
An immutable unique ID for the object and its derivatives.
Methods inherited from interface org.apache.spark.internal.LogginginitializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
Methods inherited from interface org.apache.spark.ml.util.MLWritablesave Methods inherited from interface org.apache.spark.ml.param.Paramsclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwnpublic FeatureHasher()
Param for Number of features. Should be greater than 0.
numFeatures in interface HasNumFeatures
Param for output column name.
outputCol in interface HasOutputCol
Param for input column names.
inputCols in interface HasInputCols
An immutable unique ID for the object and its derivatives.
uid in interface Identifiable
Numeric columns to treat as categorical features. By default only string and boolean columns are treated as categorical, so this param can be used to explicitly specify the numerical columns to treat as categorical. Note, the relevant columns should also be set in inputCols, categorical columns not set in inputCols will be listed in a warning.
()
Transforms the input dataset.
transform in class Transformer
dataset - (undocumented)
Params
Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().
copy in interface Params
copy in class Transformer
extra - (undocumented)
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
transformSchema in class PipelineStage
schema - (undocumented)
toString in interface Identifiable
toString in class Object
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