IDF¶
-
class
pyspark.ml.feature.
IDF
(minDocFreq=0, inputCol=None, outputCol=None)[source]¶ Compute the Inverse Document Frequency (IDF) given a collection of documents.
>>> from pyspark.ml.linalg import DenseVector >>> df = spark.createDataFrame([(DenseVector([1.0, 2.0]),), ... (DenseVector([0.0, 1.0]),), (DenseVector([3.0, 0.2]),)], ["tf"]) >>> idf = IDF(minDocFreq=3) >>> idf.setInputCol("tf") IDF... >>> idf.setOutputCol("idf") IDF... >>> model = idf.fit(df) >>> model.setOutputCol("idf") IDFModel... >>> model.getMinDocFreq() 3 >>> model.idf DenseVector([0.0, 0.0]) >>> model.docFreq [0, 3] >>> model.numDocs == df.count() True >>> model.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]) >>> idfPath = temp_path + "/idf" >>> idf.save(idfPath) >>> loadedIdf = IDF.load(idfPath) >>> loadedIdf.getMinDocFreq() == idf.getMinDocFreq() True >>> modelPath = temp_path + "/idf-model" >>> model.save(modelPath) >>> loadedModel = IDFModel.load(modelPath) >>> loadedModel.transform(df).head().idf == model.transform(df).head().idf True
New in version 1.4.0.
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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fit
(dataset, params=None)¶ Fits a model to 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. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.
- Returns
fitted model(s)
New in version 1.3.0.
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fitMultiple
(dataset, paramMaps)¶ Fits a model to the input dataset for each param map in paramMaps.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns
A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.
New in version 2.3.0.
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getInputCol
()¶ Gets the value of inputCol or its default value.
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getMinDocFreq
()¶ Gets the value of minDocFreq or its default value.
New in version 1.4.0.
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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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setMinDocFreq
(value)[source]¶ Sets the value of
minDocFreq
.New in version 1.4.0.
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setParams
(self, minDocFreq=0, inputCol=None, outputCol=None)[source]¶ Sets params for this IDF.
New in version 1.4.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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inputCol
= Param(parent='undefined', name='inputCol', doc='input column name.')¶
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minDocFreq
= Param(parent='undefined', name='minDocFreq', doc='minimum number of documents in which a term should appear for filtering')¶
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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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