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-rw-r--r--predPy/predictor.py199
1 files changed, 144 insertions, 55 deletions
diff --git a/predPy/predictor.py b/predPy/predictor.py
index 159fcf4..a24d541 100644
--- a/predPy/predictor.py
+++ b/predPy/predictor.py
@@ -1,3 +1,5 @@
+#!/usr/bin/python2
+
# Copyright (c) 2016 Huawei
# All Rights Reserved.
#
@@ -17,7 +19,8 @@
# limitations under the License.
#
-"""
+"""Summary of models here.
+
SVM
Logistic Regression with SGD
Logistic Regression with LBFGS
@@ -26,40 +29,44 @@ Decision Tree
Random Forest
Gradient Boosted Trees
"""
-from __future__ import print_function
+from __future__ import print_function
from pyspark import SparkContext
-
import csv
import StringIO
+# import tempfile
+# from shutil import rmtree
-import tempfile
-from shutil import rmtree
-
-# from pyspark.mllib.classification import SVMWithSGD, SVMModel
+from pyspark.mllib.classification import SVMWithSGD
+# from pyspark.mllib.classification import 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.classification import LogisticRegressionWithLBFGS
+# from pyspark.mllib.classification import LogisticRegressionModel
+from pyspark.mllib.classification import NaiveBayes
+# from pyspark.mllib.classification import NaiveBayesModel
+from pyspark.mllib.tree import DecisionTree
+# from pyspark.mllib.tree import DecisionTreeModel
+from pyspark.mllib.tree import RandomForest
+# from pyspark.mllib.tree import RandomForestModel
+from pyspark.mllib.tree import GradientBoostedTrees
+# from pyspark.mllib.tree import 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)
+ inputLine = StringIO.StringIO(line)
+ reader = csv.reader(inputLine)
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:]
+ selectedParameters = (
+ parameters[12:17] + parameters[19:20] + parameters[23:26] +
+ parameters[39:47] + parameters[54:61] + parameters[62:]
+ )
return selectedParameters
@@ -75,65 +82,147 @@ if __name__ == "__main__":
sc = SparkContext(appName="HardDriveFailurePrediction")
# $example on$
- data = sc.textFile('hdd/harddrive1.csv').map(loadRecord)\
- .map(parseLine)
+ data = (sc.textFile('hdd/harddrive1.csv').map(loadRecord).
+ map(parseLine))
+ print("===== Choose SVM model =====")
# Split data aproximately into training (60%) and test (40%)
- [trainingData, testData] = data.randomSplit([0.6, 0.4], seed=0)
+ trainingData, testData = data.randomSplit([0.6, 0.4], seed=0)
# Train a SVM model
-# model = SVMWithSGD.train(trainingData, iterations=2)
+ model = SVMWithSGD.train(trainingData, iterations=2)
+
+ # 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("The test accuracy of SVM model is: %.4f\n\n" % accuracy)
+
+ print("===== Choose Logistic Regression model with SGD algorithm =====")
+ # Split data aproximately into training (60%) and test (40%)
+ trainingData, testData = data.randomSplit([0.6, 0.4], seed=0)
+
# Train a logistic regression model
model = LogisticRegressionWithSGD.train(trainingData, iterations=3)
-# model = LogisticRegressionWithLBFGS.train(trainingData)
+
+ # Make prediction and test accuracy.
+ 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("The test accuracy of Logistic Regression model with"
+ " SGD algorithm is: %.4f\n\n" % accuracy)
+
+ print("===== Choose Logistic Regression model with LBFGS algorithm =====")
+ # Split data aproximately into training (60%) and test (40%)
+ trainingData, testData = data.randomSplit([0.6, 0.4], seed=0)
+
+ # Train a logistic regression model
+ model = LogisticRegressionWithLBFGS.train(trainingData)
+
+ # Make prediction and test accuracy.
+ 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("The test accuracy of Logistic Regression model with"
+ " LBFGS algorithm is: %.4f\n\n" % accuracy)
+
+ print("===== Choose Multinomial Naive Bayes model =====")
+ # Split data aproximately into training (60%) and test (40%)
+ trainingData, testData = data.randomSplit([0.6, 0.4], seed=0)
+
# Train a multinomial naive Bayes model given an RDD of LabeledPoint.
-# model = NaiveBayes.train(trainingData, 0.8)
+ model = NaiveBayes.train(trainingData, 0.8)
+
+ # Make prediction and test accuracy.
+ 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("The test accuracy of Multinomial Naive Bayes "
+ "is: %.4f\n\n" % accuracy)
+
+ print("===== Choose Decision Tree model =====")
+ # Split data aproximately into training (60%) and test (40%)
+ trainingData, testData = data.randomSplit([0.6, 0.4], seed=0)
+
# 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.
+ model = DecisionTree.trainClassifier(trainingData, numClasses=2,
+ categoricalFeaturesInfo={},
+ impurity='entropy', maxDepth=5,
+ maxBins=32)
+ print('Learned classification tree model:')
+ print(model.toDebugString())
+
+ # Make prediction and test accuracy.
+ 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("The test accuracy of decision tree model is: %.4f\n\n" % accuracy)
+
+ print("===== Choose Random Forest model =====")
+ # Split data aproximately into training (60%) and test (40%)
+ trainingData, testData = data.randomSplit([0.6, 0.4], seed=0)
+
+ # Train a Random Forest 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)
+ model = RandomForest.trainClassifier(trainingData, numClasses=2,
+ categoricalFeaturesInfo={},
+ numTrees=3,
+ featureSubsetStrategy="auto",
+ impurity='gini', maxDepth=7,
+ maxBins=32)
+ print('Learned classification tree model:')
+ print(model.toDebugString())
+
+ # Make prediction and test accuracy.
+ 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("The test accuracy of random forest model is: %.4f\n\n" % accuracy)
+
+ print("===== Choose Gradient Boosted Trees model =====")
+ # Split data aproximately into training (60%) and test (40%)
+ trainingData, testData = data.randomSplit([0.6, 0.4], seed=0)
# Train a GradientBoostedTrees model.
# Empty categoricalFeaturesInfo indicates all features are continuous.
-# model = GradientBoostedTrees.trainClassifier(trainingData,
-# categoricalFeaturesInfo={},
-# numIterations=3, maxDepth=3,
-# maxBins=32)
+ model = GradientBoostedTrees.trainClassifier(trainingData,
+ categoricalFeaturesInfo={},
+ numIterations=3, maxDepth=3,
+ maxBins=32)
+ print('Learned classification tree model:')
+ print(model.toDebugString())
+
# 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())
+ accuracy = (labelsAndPredictions.filter(lambda (v, p): v == p).
+ count() / float(testData.count()))
+ print("The test accuracy of Gradient Boosted Trees "
+ "model is: %.4f" % accuracy)
# Save and load model
- path = tempfile.mkdtemp(dir='.')
- model.save(sc, path)
+# path = tempfile.mkdtemp(dir='.')
+# model.save(sc, path)
# sameModel = SVMModel.load(sc, path)
- sameModel = LogisticRegressionModel.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
+# try:
+# rmtree(path)
+# except OSError:
+# pass