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...
>>> model.predict(df.head().features)
2
>>> model.predictProbability(df.head().features)
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[0].mean
DenseVector([0.825, 0.8675])
>>> gaussians[0].cov
DenseMatrix(2, 2, [0.0056, -0.0051, -0.0051, 0.0046], 0)
>>> gaussians[1].mean
DenseVector([-0.87, -0.72])
>>> gaussians[1].cov
DenseMatrix(2, 2, [0.0016, 0.0016, 0.0016, 0.0016], 0)
>>> gaussians[2].mean
DenseVector([-0.055, -0.075])
>>> gaussians[2].cov
DenseMatrix(2, 2, [0.002, -0.0011, -0.0011, 0.0006], 0)
>>> model.gaussiansDF.select("mean").head()
Row(mean=DenseVector([0.825, 0.8675]))
>>> model.gaussiansDF.select("cov").head()
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[4].newPrediction == rows[5].newPrediction
True
>>> rows[2].newPrediction == rows[3].newPrediction
True
>>> gmm_path = temp_path + "/gmm"
>>> gm.save(gmm_path)
>>> gm2 = GaussianMixture.load(gmm_path)
>>> gm2.getK()
3
>>> model_path = temp_path + "/gmm_model"
>>> model.save(model_path)
>>> model2 = GaussianMixtureModel.load(model_path)
>>> model2.hasSummary
False
>>> model2.weights == model.weights
True
>>> model2.gaussians[0].mean == model.gaussians[0].mean
True
>>> model2.gaussians[0].cov == model.gaussians[0].cov
True
>>> model2.gaussians[1].mean == model.gaussians[1].mean
True
>>> model2.gaussians[1].cov == model.gaussians[1].cov
True
>>> model2.gaussians[2].mean == model.gaussians[2].mean
True
>>> model2.gaussians[2].cov == model.gaussians[2].cov
True
>>> model2.gaussiansDF.select("mean").head()
Row(mean=DenseVector([0.825, 0.8675]))
>>> model2.gaussiansDF.select("cov").head()
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.
`explainParams`()	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.
`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
`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.
`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.
`load`(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
`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)	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`.
`write`()	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

extradict, optional: 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

extradict, optional: 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)#: Reads an ML instance from the input path, a shortcut of read().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.')#

uid#: A unique id for the object.