def NaiveBayesEvaluation(TransformedDataset):
nb = NaiveBayes()
nb.setLabelCol("LabelIndex")
nb.setPredictionCol("Label_Prediction")
training, test = TransformedDataset.randomSplit([0.8, 0.2], seed=11)
nvModel = nb.fit(training)
prediction = nvModel.transform(test)
# selected = prediction.select("body", "LabelIndex", "label", "Label_Prediction")
# for row in selected.collect():
# print(row)
from pyspark.mllib.evaluation import MulticlassMetrics
predictionAndLabels = prediction.select(
"Label_Prediction",
"LabelIndex").rdd.map(lambda r: (float(r[0]), float(r[1])))
# predictionAndLabels = test.rdd.map(lambda lp: (float(nvModel.predict(lp.features)), lp.label))
metrics = MulticlassMetrics(predictionAndLabels)
precision = metrics.precision()
recall = metrics.recall()
f1Score = metrics.fMeasure()
print("Summary Stats")
print("Precision = %s" % precision)
print("Recall = %s" % recall)
print("F1 Score = %s" % f1Score)
# Statistics by class
labels = prediction.rdd.map(lambda lp: lp.label).distinct().collect()
labelIndices = prediction.rdd.map(
lambda lp: lp.LabelIndex).distinct().collect()
labelIndicesPairs = prediction.rdd.map(
lambda lp: (lp.label, lp.LabelIndex)).distinct().collect()
print("Labels", labels)
print("Label Indices", labelIndices)
print("Label Indice Pairs", labelIndicesPairs)
for label, labelIndex in sorted(labelIndicesPairs):
print("\n Class %s precision = %s" %
(label, metrics.precision(labelIndex)))
print("Class %s recall = %s" % (label, metrics.recall(labelIndex)))
print(
"Class %s F1 Measure = %s" %
(label, metrics.fMeasure(labelIndex, beta=1.0)), "\n")
# Weighted stats
print("Weighted recall = %s" % metrics.weightedRecall)
print("Weighted precision = %s" % metrics.weightedPrecision)
print("Weighted F(1) Score = %s" % metrics.weightedFMeasure())
print("Weighted F(0.5) Score = %s" % metrics.weightedFMeasure(beta=0.5))
print("Weighted false positive rate = %s" %
metrics.weightedFalsePositiveRate)
def train2(self):
print("Training Model\n")
train_df = self.load_train()
test_df = self.load_test()
nb = NaiveBayes()
nb.setPredictionCol("predict_")
nb.setFeaturesCol("features")
nb.setLabelCol("label")
self.__model = nb.fit(train_df)
print("Complate\n")
self.saveModel()
self.testModel_df(test_df)
