MultilayerPerceptronClassificationModel

class pyspark.ml.classification.MultilayerPerceptronClassificationModel(java_model: Optional[JavaObject] = None)

Model fitted by MultilayerPerceptronClassifier.

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.
evaluate(dataset)	Evaluates the model on a test dataset.
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.
getBlockSize()	Gets the value of blockSize or its default value.
getFeaturesCol()	Gets the value of featuresCol or its default value.
getInitialWeights()	Gets the value of initialWeights or its default value.
getLabelCol()	Gets the value of labelCol or its default value.
getLayers()	Gets the value of layers or its default value.
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.
getRawPredictionCol()	Gets the value of rawPredictionCol or its default value.
getSeed()	Gets the value of seed or its default value.
getSolver()	Gets the value of solver or its default value.
getStepSize()	Gets the value of stepSize or its default value.
getThresholds()	Gets the value of thresholds or its default value.
getTol()	Gets the value of tol 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 the probability of each class given the features.
predictRaw(value)	Raw prediction for each possible label.
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.
setRawPredictionCol(value)	Sets the value of rawPredictionCol.
setThresholds(value)	Sets the value of thresholds.
summary()	Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

blockSize
featuresCol
hasSummary	Indicates whether a training summary exists for this model instance.
initialWeights
labelCol
layers
maxIter
numClasses	Number of classes (values which the label can take).
numFeatures	Returns the number of features the model was trained on.
params	Returns all params ordered by name.
predictionCol
probabilityCol
rawPredictionCol
seed
solver
stepSize
thresholds
tol
weights	the weights of layers.

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

evaluate(dataset: pyspark.sql.dataframe.DataFrame) → pyspark.ml.classification.MultilayerPerceptronClassificationSummary

Evaluates the model on a test dataset.

Parameters

datasetpyspark.sql.DataFrame

Test dataset to evaluate model on.

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

getBlockSize() → int

Gets the value of blockSize or its default value.

getFeaturesCol() → str

Gets the value of featuresCol or its default value.

getInitialWeights() → pyspark.ml.linalg.Vector

Gets the value of initialWeights or its default value.

getLabelCol() → str

Gets the value of labelCol or its default value.

getLayers() → List[int]

Gets the value of layers or its default value.

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.

getRawPredictionCol() → str

Gets the value of rawPredictionCol or its default value.

getSeed() → int

Gets the value of seed or its default value.

getSolver() → str

Gets the value of solver or its default value.

getStepSize() → float

Gets the value of stepSize or its default value.

getThresholds() → List[float]

Gets the value of thresholds or its default value.

getTol() → float

Gets the value of tol 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: T) → float

Predict label for the given features.

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

Predict the probability of each class given the features.

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

Raw prediction for each possible label.

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) → P

Sets the value of featuresCol.

setPredictionCol(value: str) → P

Sets the value of predictionCol.

setProbabilityCol(value: str) → CM

Sets the value of probabilityCol.

setRawPredictionCol(value: str) → P

Sets the value of rawPredictionCol.

setThresholds(value: List[float]) → CM

Sets the value of thresholds.

summary() → pyspark.ml.classification.MultilayerPerceptronClassificationTrainingSummary

Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set. An exception is thrown if trainingSummary is None.

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

blockSize = Param(parent='undefined', name='blockSize', doc='block size for stacking input data in matrices. Data is stacked within partitions. If block size is more than remaining data in a partition then it is adjusted to the size of this data.')

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')

hasSummary

Indicates whether a training summary exists for this model instance.

initialWeights: pyspark.ml.param.Param[pyspark.ml.linalg.Vector] = Param(parent='undefined', name='initialWeights', doc='The initial weights of the model.')

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')

layers: pyspark.ml.param.Param[List[int]] = Param(parent='undefined', name='layers', doc='Sizes of layers from input layer to output layer E.g., Array(780, 100, 10) means 780 inputs, one hidden layer with 100 neurons and output layer of 10 neurons.')

maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')

numClasses

Number of classes (values which the label can take).

numFeatures

Returns the number of features the model was trained on. If unknown, returns -1

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.')

rawPredictionCol = Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.')

seed = Param(parent='undefined', name='seed', doc='random seed.')

solver: pyspark.ml.param.Param[str] = Param(parent='undefined', name='solver', doc='The solver algorithm for optimization. Supported options: l-bfgs, gd.')

stepSize = Param(parent='undefined', name='stepSize', doc='Step size to be used for each iteration of optimization (>= 0).')

thresholds = Param(parent='undefined', name='thresholds', doc="Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.")

tol = Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')

weights

the weights of layers.

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