GaussianMixtureModel¶

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

Model fitted by 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.
`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

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.

predictProbability(value: pyspark.ml.linalg.Vector) → pyspark.ml.linalg.Vector ¶: Predict probability for the given features.

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.

setPredictionCol(value: str) → pyspark.ml.clustering.GaussianMixtureModel ¶: Sets the value of predictionCol.

setProbabilityCol(value: str) → pyspark.ml.clustering.GaussianMixtureModel ¶: Sets the value of probabilityCol.

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

Transforms the input dataset with optional parameters.

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

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

hasSummary¶: Indicates whether a training summary exists for this model instance.

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.

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.

GaussianMixture

GaussianMixtureSummary