KMeans¶
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class
pyspark.ml.clustering.
KMeans
(featuresCol='features', predictionCol='prediction', k=2, initMode='k-means||', initSteps=2, tol=0.0001, maxIter=20, seed=None, distanceMeasure='euclidean', weightCol=None)[source]¶ K-means clustering with a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al).
>>> from pyspark.ml.linalg import Vectors >>> data = [(Vectors.dense([0.0, 0.0]), 2.0), (Vectors.dense([1.0, 1.0]), 2.0), ... (Vectors.dense([9.0, 8.0]), 2.0), (Vectors.dense([8.0, 9.0]), 2.0)] >>> df = spark.createDataFrame(data, ["features", "weighCol"]) >>> kmeans = KMeans(k=2) >>> kmeans.setSeed(1) KMeans... >>> kmeans.setWeightCol("weighCol") KMeans... >>> kmeans.setMaxIter(10) KMeans... >>> kmeans.getMaxIter() 10 >>> kmeans.clear(kmeans.maxIter) >>> model = kmeans.fit(df) >>> model.getDistanceMeasure() 'euclidean' >>> model.setPredictionCol("newPrediction") KMeansModel... >>> model.predict(df.head().features) 0 >>> centers = model.clusterCenters() >>> len(centers) 2 >>> transformed = model.transform(df).select("features", "newPrediction") >>> rows = transformed.collect() >>> rows[0].newPrediction == rows[1].newPrediction True >>> rows[2].newPrediction == rows[3].newPrediction True >>> model.hasSummary True >>> summary = model.summary >>> summary.k 2 >>> summary.clusterSizes [2, 2] >>> summary.trainingCost 4.0 >>> kmeans_path = temp_path + "/kmeans" >>> kmeans.save(kmeans_path) >>> kmeans2 = KMeans.load(kmeans_path) >>> kmeans2.getK() 2 >>> model_path = temp_path + "/kmeans_model" >>> model.save(model_path) >>> model2 = KMeansModel.load(model_path) >>> model2.hasSummary False >>> model.clusterCenters()[0] == model2.clusterCenters()[0] array([ True, True], dtype=bool) >>> model.clusterCenters()[1] == model2.clusterCenters()[1] array([ True, True], dtype=bool)
New in version 1.5.0.
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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getDistanceMeasure
()¶ Gets the value of distanceMeasure or its default value.
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getFeaturesCol
()¶ Gets the value of featuresCol or its default value.
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getInitMode
()¶ Gets the value of initMode
New in version 1.5.0.
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getInitSteps
()¶ Gets the value of initSteps
New in version 1.5.0.
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getK
()¶ Gets the value of k
New in version 1.5.0.
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getMaxIter
()¶ Gets the value of maxIter 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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getParam
(paramName)¶ Gets a param by its name.
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getPredictionCol
()¶ Gets the value of predictionCol or its default value.
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getSeed
()¶ Gets the value of seed or its default value.
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getTol
()¶ Gets the value of tol or its default value.
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getWeightCol
()¶ Gets the value of weightCol or its default value.
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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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setDistanceMeasure
(value)[source]¶ Sets the value of
distanceMeasure
.New in version 2.4.0.
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setFeaturesCol
(value)[source]¶ Sets the value of
featuresCol
.New in version 1.5.0.
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setParams
(self, featuresCol='features', predictionCol='prediction', k=2, initMode='k-means||', initSteps=2, tol=0.0001, maxIter=20, seed=None, distanceMeasure='euclidean', weightCol=None)[source]¶ Sets params for KMeans.
New in version 1.5.0.
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setPredictionCol
(value)[source]¶ Sets the value of
predictionCol
.New in version 1.5.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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distanceMeasure
= Param(parent='undefined', name='distanceMeasure', doc="the distance measure. Supported options: 'euclidean' and 'cosine'.")¶
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featuresCol
= Param(parent='undefined', name='featuresCol', doc='features column name.')¶
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initMode
= Param(parent='undefined', name='initMode', doc='The initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++')¶
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initSteps
= Param(parent='undefined', name='initSteps', doc='The number of steps for k-means|| initialization mode. Must be > 0.')¶
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k
= Param(parent='undefined', name='k', doc='The number of clusters to create. Must be > 1.')¶
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maxIter
= Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')¶
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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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predictionCol
= Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶
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seed
= Param(parent='undefined', name='seed', doc='random seed.')¶
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tol
= Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')¶
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weightCol
= Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')¶
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