MultilayerPerceptronClassifier

class pyspark.ml.classification.MultilayerPerceptronClassifier(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', maxIter: int = 100, tol: float = 1e-06, seed: Optional[int] = None, layers: Optional[List[int]] = None, blockSize: int = 128, stepSize: float = 0.03, solver: str = 'l-bfgs', initialWeights: Optional[pyspark.ml.linalg.Vector] = None, probabilityCol: str = 'probability', rawPredictionCol: str = 'rawPrediction')

Classifier trainer based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax. Number of inputs has to be equal to the size of feature vectors. Number of outputs has to be equal to the total number of labels.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([
...     (0.0, Vectors.dense([0.0, 0.0])),
...     (1.0, Vectors.dense([0.0, 1.0])),
...     (1.0, Vectors.dense([1.0, 0.0])),
...     (0.0, Vectors.dense([1.0, 1.0]))], ["label", "features"])
>>> mlp = MultilayerPerceptronClassifier(layers=[2, 2, 2], seed=123)
>>> mlp.setMaxIter(100)
MultilayerPerceptronClassifier...
>>> mlp.getMaxIter()
100
>>> mlp.getBlockSize()
128
>>> mlp.setBlockSize(1)
MultilayerPerceptronClassifier...
>>> mlp.getBlockSize()
1
>>> model = mlp.fit(df)
>>> model.setFeaturesCol("features")
MultilayerPerceptronClassificationModel...
>>> model.getMaxIter()
100
>>> model.getLayers()
[2, 2, 2]
>>> model.weights.size
12
>>> testDF = spark.createDataFrame([
...     (Vectors.dense([1.0, 0.0]),),
...     (Vectors.dense([0.0, 0.0]),)], ["features"])
>>> model.predict(testDF.head().features)
1.0
>>> model.predictRaw(testDF.head().features)
DenseVector([-16.208, 16.344])
>>> model.predictProbability(testDF.head().features)
DenseVector([0.0, 1.0])
>>> model.transform(testDF).select("features", "prediction").show()
+---------+----------+
| features|prediction|
+---------+----------+
|[1.0,0.0]|       1.0|
|[0.0,0.0]|       0.0|
+---------+----------+
...
>>> mlp_path = temp_path + "/mlp"
>>> mlp.save(mlp_path)
>>> mlp2 = MultilayerPerceptronClassifier.load(mlp_path)
>>> mlp2.getBlockSize()
1
>>> model_path = temp_path + "/mlp_model"
>>> model.save(model_path)
>>> model2 = MultilayerPerceptronClassificationModel.load(model_path)
>>> model.getLayers() == model2.getLayers()
True
>>> model.weights == model2.weights
True
>>> model.transform(testDF).take(1) == model2.transform(testDF).take(1)
True
>>> mlp2 = mlp2.setInitialWeights(list(range(0, 12)))
>>> model3 = mlp2.fit(df)
>>> model3.weights != model2.weights
True
>>> model3.getLayers() == model.getLayers()
True

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.
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).
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.
setBlockSize(value)	Sets the value of blockSize.
setFeaturesCol(value)	Sets the value of featuresCol.
setInitialWeights(value)	Sets the value of initialWeights.
setLabelCol(value)	Sets the value of labelCol.
setLayers(value)	Sets the value of layers.
setMaxIter(value)	Sets the value of maxIter.
setParams(*[, featuresCol, labelCol, …])	setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, maxIter=100, tol=1e-6, seed=None, layers=None, blockSize=128, stepSize=0.03, solver=”l-bfgs”, initialWeights=None, probabilityCol=”probability”, rawPredictionCol=”rawPrediction”): Sets params for MultilayerPerceptronClassifier.
setPredictionCol(value)	Sets the value of predictionCol.
setProbabilityCol(value)	Sets the value of probabilityCol.
setRawPredictionCol(value)	Sets the value of rawPredictionCol.
setSeed(value)	Sets the value of seed.
setSolver(value)	Sets the value of solver.
setStepSize(value)	Sets the value of stepSize.
setThresholds(value)	Sets the value of thresholds.
setTol(value)	Sets the value of tol.
write()	Returns an MLWriter instance for this ML instance.

Attributes

blockSize
featuresCol
initialWeights
labelCol
layers
maxIter
params	Returns all params ordered by name.
predictionCol
probabilityCol
rawPredictionCol
seed
solver
stepSize
thresholds
tol

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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]

Fits a model to the input dataset with optional parameters.

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: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]

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

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.

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).

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.

setBlockSize(value: int) → pyspark.ml.classification.MultilayerPerceptronClassifier

Sets the value of blockSize.

setFeaturesCol(value: str) → P

Sets the value of featuresCol.

setInitialWeights(value: pyspark.ml.linalg.Vector) → pyspark.ml.classification.MultilayerPerceptronClassifier

Sets the value of initialWeights.

setLabelCol(value: str) → P

Sets the value of labelCol.

setLayers(value: List[int]) → pyspark.ml.classification.MultilayerPerceptronClassifier

Sets the value of layers.

setMaxIter(value: int) → pyspark.ml.classification.MultilayerPerceptronClassifier

Sets the value of maxIter.

setParams(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', maxIter: int = 100, tol: float = 1e-06, seed: Optional[int] = None, layers: Optional[List[int]] = None, blockSize: int = 128, stepSize: float = 0.03, solver: str = 'l-bfgs', initialWeights: Optional[pyspark.ml.linalg.Vector] = None, probabilityCol: str = 'probability') → pyspark.ml.classification.MultilayerPerceptronClassifier

setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, maxIter=100, tol=1e-6, seed=None, layers=None, blockSize=128, stepSize=0.03, solver=”l-bfgs”, initialWeights=None, probabilityCol=”probability”, rawPredictionCol=”rawPrediction”): Sets params for MultilayerPerceptronClassifier.

setPredictionCol(value: str) → P

Sets the value of predictionCol.

setProbabilityCol(value: str) → P

Sets the value of probabilityCol.

setRawPredictionCol(value: str) → P

Sets the value of rawPredictionCol.

setSeed(value: int) → pyspark.ml.classification.MultilayerPerceptronClassifier

Sets the value of seed.

setSolver(value: str) → pyspark.ml.classification.MultilayerPerceptronClassifier

Sets the value of solver.

setStepSize(value: float) → pyspark.ml.classification.MultilayerPerceptronClassifier

Sets the value of stepSize.

setThresholds(value: List[float]) → P

Sets the value of thresholds.

setTol(value: float) → pyspark.ml.classification.MultilayerPerceptronClassifier

Sets the value of tol.

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.')

initialWeights = Param(parent='undefined', name='initialWeights', doc='The initial weights of the model.')

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

layers = 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).')

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 = 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).')

previous

NaiveBayesModel

next

MultilayerPerceptronClassificationModel