mmlspark.isolationforest package¶
Submodules¶
mmlspark.isolationforest.IsolationForest module¶

class
mmlspark.isolationforest.IsolationForest.
IsolationForest
(*args, **kwargs)[source]¶ Bases:
mmlspark.core.schema.Utils.ComplexParamsMixin
,pyspark.ml.util.JavaMLReadable
,pyspark.ml.util.JavaMLWritable
,pyspark.ml.wrapper.JavaEstimator
 Parameters
bootstrap (bool) – If true, draw sample for each tree with replacement. If false, do not sample with replacement. (default: false)
contamination (double) – The fraction of outliers in the training data set. If this is set to 0.0, it speeds up the training and all predicted labels will be false. The model and outlier scores are otherwise unaffected by this parameter. (default: 0.0)
contaminationError (double) – The error allowed when calculating the threshold required to achieve the specified contamination fraction. The default is 0.0, which forces an exact calculation of the threshold. The exact calculation is slow and can fail for large datasets. If there are issues with the exact calculation, a good choice for this parameter is often 1% of the specified contamination value. (default: 0.0)
featuresCol (str) – The feature vector. (default: features)
maxFeatures (double) – The number of features used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count. (default: 1.0)
maxSamples (double) – The number of samples used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count. (default: 256.0)
numEstimators (int) – The number of trees in the ensemble. (default: 100)
predictionCol (str) – The predicted label. (default: predictedLabel)
randomSeed (long) – The seed used for the random number generator. (default: 1)
scoreCol (str) – The outlier score. (default: outlierScore)

getBootstrap
()[source]¶  Returns
If true, draw sample for each tree with replacement. If false, do not sample with replacement. (default: false)
 Return type

getContamination
()[source]¶  Returns
The fraction of outliers in the training data set. If this is set to 0.0, it speeds up the training and all predicted labels will be false. The model and outlier scores are otherwise unaffected by this parameter. (default: 0.0)
 Return type
double

getContaminationError
()[source]¶  Returns
The error allowed when calculating the threshold required to achieve the specified contamination fraction. The default is 0.0, which forces an exact calculation of the threshold. The exact calculation is slow and can fail for large datasets. If there are issues with the exact calculation, a good choice for this parameter is often 1% of the specified contamination value. (default: 0.0)
 Return type
double

getMaxFeatures
()[source]¶  Returns
The number of features used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count. (default: 1.0)
 Return type
double

getMaxSamples
()[source]¶  Returns
The number of samples used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count. (default: 256.0)
 Return type
double

getRandomSeed
()[source]¶  Returns
The seed used for the random number generator. (default: 1)
 Return type
long

setBootstrap
(value)[source]¶  Parameters
bootstrap – If true, draw sample for each tree with replacement. If false, do not sample with replacement. (default: false)

setContamination
(value)[source]¶  Parameters
contamination – The fraction of outliers in the training data set. If this is set to 0.0, it speeds up the training and all predicted labels will be false. The model and outlier scores are otherwise unaffected by this parameter. (default: 0.0)

setContaminationError
(value)[source]¶  Parameters
contaminationError – The error allowed when calculating the threshold required to achieve the specified contamination fraction. The default is 0.0, which forces an exact calculation of the threshold. The exact calculation is slow and can fail for large datasets. If there are issues with the exact calculation, a good choice for this parameter is often 1% of the specified contamination value. (default: 0.0)

setMaxFeatures
(value)[source]¶  Parameters
maxFeatures – The number of features used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count. (default: 1.0)

setMaxSamples
(value)[source]¶  Parameters
maxSamples – The number of samples used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count. (default: 256.0)

setNumEstimators
(value)[source]¶  Parameters
numEstimators – The number of trees in the ensemble. (default: 100)

setParams
(bootstrap=False, contamination=0.0, contaminationError=0.0, featuresCol='features', maxFeatures=1.0, maxSamples=256.0, numEstimators=100, predictionCol='predictedLabel', randomSeed=1, scoreCol='outlierScore')[source]¶ Set the (keyword only) parameters
 Parameters
bootstrap (bool) – If true, draw sample for each tree with replacement. If false, do not sample with replacement. (default: false)
contamination (double) – The fraction of outliers in the training data set. If this is set to 0.0, it speeds up the training and all predicted labels will be false. The model and outlier scores are otherwise unaffected by this parameter. (default: 0.0)
contaminationError (double) – The error allowed when calculating the threshold required to achieve the specified contamination fraction. The default is 0.0, which forces an exact calculation of the threshold. The exact calculation is slow and can fail for large datasets. If there are issues with the exact calculation, a good choice for this parameter is often 1% of the specified contamination value. (default: 0.0)
featuresCol (str) – The feature vector. (default: features)
maxFeatures (double) – The number of features used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count. (default: 1.0)
maxSamples (double) – The number of samples used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count. (default: 256.0)
numEstimators (int) – The number of trees in the ensemble. (default: 100)
predictionCol (str) – The predicted label. (default: predictedLabel)
randomSeed (long) – The seed used for the random number generator. (default: 1)
scoreCol (str) – The outlier score. (default: outlierScore)

setPredictionCol
(value)[source]¶  Parameters
predictionCol – The predicted label. (default: predictedLabel)

class
mmlspark.isolationforest.IsolationForest.
IsolationForestModel
(java_model=None)[source]¶ Bases:
mmlspark.core.schema.Utils.ComplexParamsMixin
,pyspark.ml.wrapper.JavaModel
,pyspark.ml.util.JavaMLWritable
,pyspark.ml.util.JavaMLReadable
Model fitted by
IsolationForest
.
Module contents¶
MicrosoftML is a library of Python classes to interface with the Microsoft scala APIs to utilize Apache Spark to create distibuted machine learning models.
MicrosoftML simplifies training and scoring classifiers and regressors, as well as facilitating the creation of models using the CNTK library, images, and text.