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Pyspark Feature Importance Selector Estimator
import sys
import numpy as np
import pandas as pd
from pyspark import keyword_only
from import Estimator
from import Params, Param, TypeConverters
from import VectorSlicer
from import HasOutputCol
def ExtractFeatureImp(featureImp, dataset, featuresCol):
Takes in a feature importance from a random forest / GBT model and map it to the column names
Output as a pandas dataframe for easy reading
rf = RandomForestClassifier(featuresCol="features")
mod =
ExtractFeatureImp(mod.featureImportances, train, "features")
list_extract = []
for i in dataset.schema[featuresCol].metadata["ml_attr"]["attrs"]:
list_extract = list_extract + dataset.schema[featuresCol].metadata["ml_attr"]["attrs"][i]
varlist = pd.DataFrame(list_extract)
varlist['score'] = varlist['idx'].apply(lambda x: featureImp[x])
return(varlist.sort_values('score', ascending = False))
class FeatureImpSelector(Estimator, HasOutputCol):
Uses feature importance score to select features for training
Takes either the top n features or those above a certain threshold score
estimator should either be a DecisionTreeClassifier, RandomForestClassifier or GBTClassifier
featuresCol is inferred from the estimator
estimator = Param(Params._dummy(), "estimator", "estimator to be cross-validated")
selectorType = Param(Params._dummy(), "selectorType",
"The selector type of the FeatureImpSelector. " +
"Supported options: numTopFeatures (default), threshold",
numTopFeatures = \
Param(Params._dummy(), "numTopFeatures",
"Number of features that selector will select, ordered by descending feature imp score. " +
"If the number of features is < numTopFeatures, then this will select " +
"all features.", typeConverter=TypeConverters.toInt)
threshold = Param(Params._dummy(), "threshold", "The lowest feature imp score for features to be kept.",
def __init__(self, estimator = None, selectorType = "numTopFeatures",
numTopFeatures = 20, threshold = 0.01, outputCol = "features"):
super(FeatureImpSelector, self).__init__()
self._setDefault(selectorType="numTopFeatures", numTopFeatures=20, threshold=0.01)
kwargs = self._input_kwargs
def setParams(self, estimator = None, selectorType = "numTopFeatures",
numTopFeatures = 20, threshold = 0.01, outputCol = "features"):
setParams(self, estimator = None, selectorType = "numTopFeatures",
numTopFeatures = 20, threshold = 0.01, outputCol = "features")
Sets params for this ChiSqSelector.
kwargs = self._input_kwargs
return self._set(**kwargs)
def setEstimator(self, value):
Sets the value of :py:attr:`estimator`.
return self._set(estimator=value)
def getEstimator(self):
Gets the value of estimator or its default value.
return self.getOrDefault(self.estimator)
def setSelectorType(self, value):
Sets the value of :py:attr:`selectorType`.
return self._set(selectorType=value)
def getSelectorType(self):
Gets the value of selectorType or its default value.
return self.getOrDefault(self.selectorType)
def setNumTopFeatures(self, value):
Sets the value of :py:attr:`numTopFeatures`.
Only applicable when selectorType = "numTopFeatures".
return self._set(numTopFeatures=value)
def getNumTopFeatures(self):
Gets the value of numTopFeatures or its default value.
return self.getOrDefault(self.numTopFeatures)
def setThreshold(self, value):
Sets the value of :py:attr:`Threshold`.
Only applicable when selectorType = "threshold".
return self._set(threshold=value)
def getThreshold(self):
Gets the value of threshold or its default value.
return self.getOrDefault(self.threshold)
def _fit(self, dataset):
est = self.getOrDefault(self.estimator)
nfeatures = self.getOrDefault(self.numTopFeatures)
threshold = self.getOrDefault(self.threshold)
selectorType = self.getOrDefault(self.selectorType)
outputCol = self.getOrDefault(self.outputCol)
if ((est.__class__.__name__ != 'DecisionTreeClassifier') &
(est.__class__.__name__ != 'DecisionTreeRegressor') &
(est.__class__.__name__ != 'RandomForestClassifier') &
(est.__class__.__name__ != 'RandomForestRegressor') &
(est.__class__.__name__ != 'GBTClassifier') &
(est.__class__.__name__ != 'GBTRegressor')):
raise NameError("Estimator must be either DecisionTree, RandomForest or RandomForest Model")
# Fit classifier & extract feature importance
mod =
dataset2 = mod.transform(dataset)
varlist = ExtractFeatureImp(mod.featureImportances, dataset2, est.getFeaturesCol())
if (selectorType == "numTopFeatures"):
varidx = [x for x in varlist['idx'][0:nfeatures]]
elif (selectorType == "threshold"):
varidx = [x for x in varlist[varlist['score'] > threshold]['idx']]
raise NameError("Invalid selectorType")
# Extract relevant columns
return VectorSlicer(inputCol = est.getFeaturesCol(),
outputCol = outputCol,
indices = varidx)
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