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# Copyright (c) 2016 Huawei
# All Rights Reserved.
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
SVM
Logistic Regression with SGD
Logistic Regression with LBFGS
Multinomial Naive Bayes
Decision Tree
Random Forest
Gradient Boosted Trees
"""
from __future__ import print_function
from pyspark import SparkContext
import csv
import StringIO
import tempfile
from shutil import rmtree
# from pyspark.mllib.classification import SVMWithSGD, SVMModel
from pyspark.mllib.classification import LogisticRegressionWithSGD
# from pyspark.mllib.classification import LogisticRegressionWithLBFGS
from pyspark.mllib.classification import LogisticRegressionModel
# from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
# from pyspark.mllib.tree import DecisionTree, DecisionTreeModel
# from pyspark.mllib.tree import RandomForest, RandomForestModel
# from pyspark.mllib.tree import GradientBoostedTrees, GradientBoostedTreesModel
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint
def loadRecord(line):
"""Load a CSV line and select 26 indicative parameters"""
input = StringIO.StringIO(line)
reader = csv.reader(input)
parameters = reader.next()
# Instances that were collected within seven days before the failures
# are used to train the failing model
if parameters[3] >= 168:
parameters[-1] = 0
selectedParameters = parameters[12:17] + parameters[19:20] \
+ parameters[23:26] + parameters[39:47] + parameters[54:61] \
+ parameters[62:]
return selectedParameters
def parseLine(line):
"""Parse a row """
label = float(line[-1])
features = Vectors.dense(map(float, line[:-1]))
return LabeledPoint(label, features)
if __name__ == "__main__":
sc = SparkContext(appName="HardDriveFailurePrediction")
# $example on$
data = sc.textFile('hdd/harddrive1.csv').map(loadRecord)\
.map(parseLine)
# Split data aproximately into training (60%) and test (40%)
[trainingData, testData] = data.randomSplit([0.6, 0.4], seed=0)
# Train a SVM model
# model = SVMWithSGD.train(trainingData, iterations=2)
# Train a logistic regression model
model = LogisticRegressionWithSGD.train(trainingData, iterations=3)
# model = LogisticRegressionWithLBFGS.train(trainingData)
# Train a multinomial naive Bayes model given an RDD of LabeledPoint.
# model = NaiveBayes.train(trainingData, 0.8)
# Train a decision tree model.
# Empty categoricalFeaturesInfo indicates all features are continuous.
# model = DecisionTree.trainClassifier(trainingData, numClasses=2,
# categoricalFeaturesInfo={},
# impurity='entropy', maxDepth=5,
# maxBins=32)
# Train a RandomForest model.
# Empty categoricalFeaturesInfo indicates all features are continuous.
# Note: Use larger numTrees in practice.
# Setting featureSubsetStrategy="auto" lets the algorithm choose.
# model = RandomForest.trainClassifier(trainingData, numClasses=2,
# categoricalFeaturesInfo={},
# numTrees=3,
# featureSubsetStrategy="auto",
# impurity='gini', maxDepth=7,
# maxBins=32)
# Train a GradientBoostedTrees model.
# Empty categoricalFeaturesInfo indicates all features are continuous.
# model = GradientBoostedTrees.trainClassifier(trainingData,
# categoricalFeaturesInfo={},
# numIterations=3, maxDepth=3,
# maxBins=32)
# Make prediction and test accuracy.
# labelsAndPredictions = testData\
# .map(lambda p: (p.label, model.predict(p.features)))
# accuracy = labelsAndPredictions\
# .filter(lambda (x, v): x == v).count() / float(testData.count())
predictions = model.predict(testData.map(lambda x: x.features))
labelsAndPredictions = testData.map(lambda p: p.label).zip(predictions)
accuracy = labelsAndPredictions.filter(lambda (v, p): v == p).\
count() / float(testData.count())
print('Test Accuracy = ' + str(accuracy))
# print('Learned classification tree model:')
# print(model.toDebugString())
# Save and load model
path = tempfile.mkdtemp(dir='.')
model.save(sc, path)
# sameModel = SVMModel.load(sc, path)
sameModel = LogisticRegressionModel.load(sc, path)
# sameModel = NaiveBayesModel.load(sc, path)
# sameModel = DecisionTreeModel.load(sc, path)
# sameModel = RandomForestModel.load(sc, path)
# sameModel = GradientBoostedTreesModel.load(sc, path)
try:
rmtree(path)
except OSError:
pass
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