GaussianMixtureModel

class pyspark.ml.clustering.GaussianMixtureModel(java_model: Optional[JavaObject] = None)

Model fitted by GaussianMixture.

New in version 2.0.0.

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.
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).
predict(value)	Predict label for the given features.
predictProbability(value)	Predict probability for the given features.
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.
setFeaturesCol(value)	Sets the value of featuresCol.
setPredictionCol(value)	Sets the value of predictionCol.
setProbabilityCol(value)	Sets the value of probabilityCol.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

aggregationDepth
featuresCol
gaussians	Array of MultivariateGaussian where gaussians[i] represents the Multivariate Gaussian (Normal) Distribution for Gaussian i
gaussiansDF	Retrieve Gaussian distributions as a DataFrame.
hasSummary	Indicates whether a training summary exists for this model instance.
k
maxIter
params	Returns all params ordered by name.
predictionCol
probabilityCol
seed
summary	Gets summary (cluster assignments, cluster sizes) of the model trained on the training set.
tol
weightCol
weights	Weight for each Gaussian distribution in the mixture.

Methods Documentation

clear(param: pyspark.ml.param.Param) → None

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

copy(extra: Optional[ParamMap] = None) → JP

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: Union[str, pyspark.ml.param.Param]) → str

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

explainParams() → str

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

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap

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

getAggregationDepth() → int

Gets the value of aggregationDepth or its default value.

getFeaturesCol() → str

Gets the value of featuresCol or its default value.

getK() → int

Gets the value of k

New in version 2.0.0.

getMaxIter() → int

Gets the value of maxIter or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]

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: str) → pyspark.ml.param.Param

Gets a param by its name.

getPredictionCol() → str

Gets the value of predictionCol or its default value.

getProbabilityCol() → str

Gets the value of probabilityCol or its default value.

getSeed() → int

Gets the value of seed or its default value.

getTol() → float

Gets the value of tol or its default value.

getWeightCol() → str

Gets the value of weightCol or its default value.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param has a default value.

hasParam(paramName: str) → bool

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

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

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

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL

Reads an ML instance from the input path, a shortcut of read().load(path).

predict(value: pyspark.ml.linalg.Vector) → int

Predict label for the given features.

New in version 3.0.0.

predictProbability(value: pyspark.ml.linalg.Vector) → pyspark.ml.linalg.Vector

Predict probability for the given features.

New in version 3.0.0.

classmethod read() → pyspark.ml.util.JavaMLReader[RL]

Returns an MLReader instance for this class.

save(path: str) → None

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

set(param: pyspark.ml.param.Param, value: Any) → None

Sets a parameter in the embedded param map.

setFeaturesCol(value: str) → pyspark.ml.clustering.GaussianMixtureModel

Sets the value of featuresCol.

New in version 3.0.0.

setPredictionCol(value: str) → pyspark.ml.clustering.GaussianMixtureModel

Sets the value of predictionCol.

New in version 3.0.0.

setProbabilityCol(value: str) → pyspark.ml.clustering.GaussianMixtureModel

Sets the value of probabilityCol.

New in version 3.0.0.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame

input dataset

paramsdict, optional

an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame

transformed dataset

write() → pyspark.ml.util.JavaMLWriter

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.')

gaussians

Array of MultivariateGaussian where gaussians[i] represents the Multivariate Gaussian (Normal) Distribution for Gaussian i

New in version 3.0.0.

gaussiansDF

Retrieve Gaussian distributions as a DataFrame. Each row represents a Gaussian Distribution. The DataFrame has two columns: mean (Vector) and cov (Matrix).

New in version 2.0.0.

hasSummary

Indicates whether a training summary exists for this model instance.

New in version 2.1.0.

k: pyspark.ml.param.Param[int] = 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.')

summary

Gets summary (cluster assignments, cluster sizes) of the model trained on the training set. An exception is thrown if no summary exists.

New in version 2.1.0.

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.')

weights

Weight for each Gaussian distribution in the mixture. This is a multinomial probability distribution over the k Gaussians, where weights[i] is the weight for Gaussian i, and weights sum to 1.

New in version 2.0.0.

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