# GaussianMixture¶

class pyspark.ml.clustering.GaussianMixture(*, featuresCol='features', predictionCol='prediction', k=2, probabilityCol='probability', tol=0.01, maxIter=100, seed=None, aggregationDepth=2, weightCol=None)[source]

GaussianMixture clustering. This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs). A GMM represents a composite distribution of independent Gaussian distributions with associated “mixing” weights specifying each’s contribution to the composite.

Given a set of sample points, this class will maximize the log-likelihood for a mixture of k Gaussians, iterating until the log-likelihood changes by less than convergenceTol, or until it has reached the max number of iterations. While this process is generally guaranteed to converge, it is not guaranteed to find a global optimum.

New in version 2.0.0.

Notes

For high-dimensional data (with many features), this algorithm may perform poorly. This is due to high-dimensional data (a) making it difficult to cluster at all (based on statistical/theoretical arguments) and (b) numerical issues with Gaussian distributions.

Examples

>>> from pyspark.ml.linalg import Vectors

>>> data = [(Vectors.dense([-0.1, -0.05 ]),),
...         (Vectors.dense([-0.01, -0.1]),),
...         (Vectors.dense([0.9, 0.8]),),
...         (Vectors.dense([0.75, 0.935]),),
...         (Vectors.dense([-0.83, -0.68]),),
...         (Vectors.dense([-0.91, -0.76]),)]
>>> df = spark.createDataFrame(data, ["features"])
>>> gm = GaussianMixture(k=3, tol=0.0001, seed=10)
>>> gm.getMaxIter()
100
>>> gm.setMaxIter(30)
GaussianMixture...
>>> gm.getMaxIter()
30
>>> model = gm.fit(df)
>>> model.getAggregationDepth()
2
>>> model.getFeaturesCol()
'features'
>>> model.setPredictionCol("newPrediction")
GaussianMixtureModel...
2
DenseVector([0.0, 0.0, 1.0])
>>> model.hasSummary
True
>>> summary = model.summary
>>> summary.k
3
>>> summary.clusterSizes
[2, 2, 2]
>>> weights = model.weights
>>> len(weights)
3
>>> gaussians = model.gaussians
>>> len(gaussians)
3
>>> gaussians.mean
DenseVector([0.825, 0.8675])
>>> gaussians.cov
DenseMatrix(2, 2, [0.0056, -0.0051, -0.0051, 0.0046], 0)
>>> gaussians.mean
DenseVector([-0.87, -0.72])
>>> gaussians.cov
DenseMatrix(2, 2, [0.0016, 0.0016, 0.0016, 0.0016], 0)
>>> gaussians.mean
DenseVector([-0.055, -0.075])
>>> gaussians.cov
DenseMatrix(2, 2, [0.002, -0.0011, -0.0011, 0.0006], 0)
Row(mean=DenseVector([0.825, 0.8675]))
Row(cov=DenseMatrix(2, 2, [0.0056, -0.0051, -0.0051, 0.0046], False))
>>> transformed = model.transform(df).select("features", "newPrediction")
>>> rows = transformed.collect()
>>> rows.newPrediction == rows.newPrediction
True
>>> rows.newPrediction == rows.newPrediction
True
>>> gmm_path = temp_path + "/gmm"
>>> gm.save(gmm_path)
>>> gm2.getK()
3
>>> model_path = temp_path + "/gmm_model"
>>> model.save(model_path)
>>> model2.hasSummary
False
>>> model2.weights == model.weights
True
>>> model2.gaussians.mean == model.gaussians.mean
True
>>> model2.gaussians.cov == model.gaussians.cov
True
>>> model2.gaussians.mean == model.gaussians.mean
True
>>> model2.gaussians.cov == model.gaussians.cov
True
>>> model2.gaussians.mean == model.gaussians.mean
True
>>> model2.gaussians.cov == model.gaussians.cov
True
Row(mean=DenseVector([0.825, 0.8675]))
Row(cov=DenseMatrix(2, 2, [0.0056, -0.0051, -0.0051, 0.0046], False))
>>> model.transform(df).take(1) == model2.transform(df).take(1)
True
>>> gm2.setWeightCol("weight")
GaussianMixture...


Methods

 clear(param) Clears a param from the param map if it has been explicitly set. copy([extra]) Creates a copy of this instance with the same uid and some extra params. explainParam(param) Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string. Returns the documentation of all params with their optionally default values and user-supplied values. extractParamMap([extra]) 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. fit(dataset[, params]) Fits a model to the input dataset with optional parameters. fitMultiple(dataset, paramMaps) Fits a model to the input dataset for each param map in paramMaps. Gets the value of aggregationDepth or its default value. Gets the value of featuresCol or its default value. Gets the value of k Gets the value of maxIter or its default value. getOrDefault(param) Gets the value of a param in the user-supplied param map or its default value. getParam(paramName) Gets a param by its name. Gets the value of predictionCol or its default value. Gets the value of probabilityCol or its default value. Gets the value of seed or its default value. Gets the value of tol or its default value. Gets the value of weightCol or its default value. hasDefault(param) Checks whether a param has a default value. hasParam(paramName) Tests whether this instance contains a param with a given (string) name. isDefined(param) Checks whether a param is explicitly set by user or has a default value. isSet(param) Checks whether a param is explicitly set by user. load(path) Reads an ML instance from the input path, a shortcut of read().load(path). Returns an MLReader instance for this class. save(path) Save this ML instance to the given path, a shortcut of ‘write().save(path)’. set(param, value) Sets a parameter in the embedded param map. Sets the value of aggregationDepth. setFeaturesCol(value) Sets the value of featuresCol. setK(value) Sets the value of k. setMaxIter(value) Sets the value of maxIter. setParams(self, \*[, featuresCol, …]) Sets params for GaussianMixture. setPredictionCol(value) Sets the value of predictionCol. setProbabilityCol(value) Sets the value of probabilityCol. setSeed(value) Sets the value of seed. setTol(value) Sets the value of tol. setWeightCol(value) Sets the value of weightCol. Returns an MLWriter instance for this ML instance.

Attributes

 aggregationDepth featuresCol k maxIter params Returns all params ordered by name. predictionCol probabilityCol seed tol weightCol

Methods Documentation

clear(param)

Clears a param from the param map if it has been explicitly set.

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 parameters to copy to the new instance

Returns
JavaParams

Copy of this instance

explainParam(param)

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()

Returns the documentation of all params with their optionally default values and user-supplied values.

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 param values

Returns
dict

merged param map

fit(dataset, params=None)

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters
datasetpyspark.sql.DataFrame

input dataset.

paramsdict or list or tuple, optional

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
Transformer or a list of Transformer

fitted model(s)

fitMultiple(dataset, paramMaps)

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters
datasetpyspark.sql.DataFrame

input dataset.

paramMapscollections.abc.Sequence

A Sequence of param maps.

Returns
_FitMultipleIterator

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.

getAggregationDepth()

Gets the value of aggregationDepth or its default value.

getFeaturesCol()

Gets the value of featuresCol or its default value.

getK()

Gets the value of k

New in version 2.0.0.

getMaxIter()

Gets the value of maxIter or its default value.

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.

getParam(paramName)

Gets a param by its name.

getPredictionCol()

Gets the value of predictionCol or its default value.

getProbabilityCol()

Gets the value of probabilityCol or its default value.

getSeed()

Gets the value of seed or its default value.

getTol()

Gets the value of tol or its default value.

getWeightCol()

Gets the value of weightCol or its default value.

hasDefault(param)

Checks whether a param has a default value.

hasParam(paramName)

Tests whether this instance contains a param with a given (string) name.

isDefined(param)

Checks whether a param is explicitly set by user or has a default value.

isSet(param)

Checks whether a param is explicitly set by user.

classmethod load(path)

classmethod read()

Returns an MLReader instance for this class.

save(path)

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)

Sets a parameter in the embedded param map.

setAggregationDepth(value)[source]

Sets the value of aggregationDepth.

New in version 3.0.0.

setFeaturesCol(value)[source]

Sets the value of featuresCol.

New in version 2.0.0.

setK(value)[source]

Sets the value of k.

New in version 2.0.0.

setMaxIter(value)[source]

Sets the value of maxIter.

New in version 2.0.0.

setParams(self, \*, featuresCol="features", predictionCol="prediction", k=2, probabilityCol="probability", tol=0.01, maxIter=100, seed=None, aggregationDepth=2, weightCol=None)[source]

Sets params for GaussianMixture.

New in version 2.0.0.

setPredictionCol(value)[source]

Sets the value of predictionCol.

New in version 2.0.0.

setProbabilityCol(value)[source]

Sets the value of probabilityCol.

New in version 2.0.0.

setSeed(value)[source]

Sets the value of seed.

New in version 2.0.0.

setTol(value)[source]

Sets the value of tol.

New in version 2.0.0.

setWeightCol(value)[source]

Sets the value of weightCol.

New in version 3.0.0.

write()

Returns an MLWriter instance for this ML instance.

Attributes Documentation

aggregationDepth = Param(parent='undefined', name='aggregationDepth', doc='suggested depth for treeAggregate (>= 2).')
featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')
k = Param(parent='undefined', name='k', doc='Number of independent Gaussians in the mixture model. Must be > 1.')
maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')
params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')
probabilityCol = Param(parent='undefined', name='probabilityCol', doc='Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.')
seed = Param(parent='undefined', name='seed', doc='random seed.')
tol = Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')
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.')