HashingTF¶
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class
pyspark.ml.feature.
HashingTF
(numFeatures=262144, binary=False, inputCol=None, outputCol=None)[source]¶ 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 = spark.createDataFrame([(["a", "b", "c"],)], ["words"]) >>> hashingTF = HashingTF(inputCol="words", outputCol="features") >>> hashingTF.setNumFeatures(10) HashingTF... >>> hashingTF.transform(df).head().features SparseVector(10, {5: 1.0, 7: 1.0, 8: 1.0}) >>> hashingTF.setParams(outputCol="freqs").transform(df).head().freqs SparseVector(10, {5: 1.0, 7: 1.0, 8: 1.0}) >>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"} >>> hashingTF.transform(df, params).head().vector SparseVector(5, {0: 1.0, 2: 1.0, 3: 1.0}) >>> hashingTFPath = temp_path + "/hashing-tf" >>> hashingTF.save(hashingTFPath) >>> loadedHashingTF = HashingTF.load(hashingTFPath) >>> loadedHashingTF.getNumFeatures() == hashingTF.getNumFeatures() True >>> hashingTF.indexOf("b") 5
New in version 1.3.0.
Methods
Attributes
Methods Documentation
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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copy
(extra=None)¶ Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.
- Parameters
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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extractParamMap
(extra=None)¶ Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
- Parameters
extra – extra param values
- Returns
merged param map
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getInputCol
()¶ Gets the value of inputCol or its default value.
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getNumFeatures
()¶ Gets the value of numFeatures or its default value.
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getOrDefault
(param)¶ Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
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getOutputCol
()¶ Gets the value of outputCol or its default value.
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getParam
(paramName)¶ Gets a param by its name.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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setNumFeatures
(value)[source]¶ Sets the value of
numFeatures
.
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setParams
(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None)[source]¶ Sets params for this HashingTF.
New in version 1.3.0.
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transform
(dataset, params=None)¶ Transforms the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – an optional param map that overrides embedded params.
- Returns
transformed dataset
New in version 1.3.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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binary
= Param(parent='undefined', name='binary', doc='If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. Default False.')¶
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inputCol
= Param(parent='undefined', name='inputCol', doc='input column name.')¶
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numFeatures
= Param(parent='undefined', name='numFeatures', doc='Number of features. Should be greater than 0.')¶
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outputCol
= Param(parent='undefined', name='outputCol', doc='output column name.')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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