diff options
Diffstat (limited to 'predPy/predictor.py')
| -rw-r--r-- | predPy/predictor.py | 170 |
1 files changed, 129 insertions, 41 deletions
diff --git a/predPy/predictor.py b/predPy/predictor.py index a24d541..8e8f26e 100644 --- a/predPy/predictor.py +++ b/predPy/predictor.py @@ -1,4 +1,4 @@ -#!/usr/bin/python2 +#!/usr/bin/python3 # Copyright (c) 2016 Huawei # All Rights Reserved. @@ -36,7 +36,8 @@ import csv import StringIO # import tempfile # from shutil import rmtree - +from pyspark.mllib.linalg import Vectors +from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.classification import SVMWithSGD # from pyspark.mllib.classification import SVMModel from pyspark.mllib.classification import LogisticRegressionWithSGD @@ -50,8 +51,6 @@ 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): @@ -61,12 +60,13 @@ def loadRecord(line): 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 + # if float(parameters[-1]) == 1 and float(parameters[3]) >= 360: + # parameters[-1] = 0 selectedParameters = ( parameters[12:17] + parameters[19:20] + parameters[23:26] + parameters[39:47] + parameters[54:61] + parameters[62:] ) + # selectedParameters = parameters return selectedParameters @@ -86,11 +86,12 @@ if __name__ == "__main__": 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) + # Split data aproximately into training (80%) and test (20%) + trainingData, testData = data.randomSplit([0.8, 0.2], seed=0) # Train a SVM model - model = SVMWithSGD.train(trainingData, iterations=2) + model = SVMWithSGD.train(trainingData, iterations=200, regParam=7e-2, + intercept=True) # Make prediction and test accuracy. # labelsAndPredictions = (testData @@ -99,78 +100,142 @@ if __name__ == "__main__": # .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) + tp = labelsAndPredictions.filter(lambda (v, p): v == p and p == 1).count() + tn = labelsAndPredictions.filter(lambda (v, p): v == p and p == 0).count() + fp = labelsAndPredictions.filter(lambda (v, p): v != p and p == 1).count() + fn = labelsAndPredictions.filter(lambda (v, p): v != p and p == 0).count() 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("true positive number: %d, false positive number: %d" % (tp, fp)) + print("false negative number: %d, true negative number: %d" % (fn, tn)) + recall = tp / float(tp + fn) + fprate = fp / float(fp + tn) + print("The test accuracy of SVM model is: %.4f" % accuracy) + print("The test recall of SVM model is: %.4f" % recall) + print("The test fprate of SVM model is: %.4f\n\n" % fprate) 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) + # Split data aproximately into training (80%) and test (20%) + trainingData, testData = data.randomSplit([0.8, 0.2], seed=0) # Train a logistic regression model - model = LogisticRegressionWithSGD.train(trainingData, iterations=3) + model = LogisticRegressionWithSGD.train(trainingData, iterations=200, + regParam=8e-2, intercept=True) # Make prediction and test accuracy. + print("The original threshold: %0.2f" % float(model.threshold)) + model.setThreshold(0.40) + print("The current threshold: %0.2f" % float(model.threshold)) predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda p: p.label).zip(predictions) + tp = labelsAndPredictions.filter(lambda (v, p): v == p and p == 1).count() + tn = labelsAndPredictions.filter(lambda (v, p): v == p and p == 0).count() + fp = labelsAndPredictions.filter(lambda (v, p): v != p and p == 1).count() + fn = labelsAndPredictions.filter(lambda (v, p): v != p and p == 0).count() accuracy = (labelsAndPredictions.filter(lambda (v, p): v == p). count() / float(testData.count())) + print("true positive number: %d, false positive number: %d" % (tp, fp)) + print("false negative number: %d, true negative number: %d" % (fn, tn)) + recall = tp / float(tp + fn) + fprate = fp / float(fp + tn) print("The test accuracy of Logistic Regression model with" - " SGD algorithm is: %.4f\n\n" % accuracy) + " SGD algorithm is: %.4f" % accuracy) + print("The test recall of Logistic Regression model with" + " SGD algorithm is: %.4f" % recall) + print("The test fprate of Logistic Regression model with" + " SGD algorithm is: %.4f\n\n" % fprate) 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) + # Split data aproximately into training (80%) and test (20%) + trainingData, testData = data.randomSplit([0.8, 0.2], seed=0) # Train a logistic regression model - model = LogisticRegressionWithLBFGS.train(trainingData) + model = LogisticRegressionWithLBFGS.train(trainingData, iterations=200, + regParam=7e-2, intercept=True) # Make prediction and test accuracy. + print("The original threshold: %0.2f" % float(model.threshold)) + model.setThreshold(0.45) + print("The current threshold: %0.2f" % float(model.threshold)) predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda p: p.label).zip(predictions) + tp = labelsAndPredictions.filter(lambda (v, p): v == p and p == 1).count() + tn = labelsAndPredictions.filter(lambda (v, p): v == p and p == 0).count() + fp = labelsAndPredictions.filter(lambda (v, p): v != p and p == 1).count() + fn = labelsAndPredictions.filter(lambda (v, p): v != p and p == 0).count() accuracy = (labelsAndPredictions.filter(lambda (v, p): v == p). count() / float(testData.count())) + print("true positive number: %d, false positive number: %d" % (tp, fp)) + print("false negative number: %d, true negative number: %d" % (fn, tn)) + recall = tp / float(tp + fn) + fprate = fp / float(fp + tn) print("The test accuracy of Logistic Regression model with" - " LBFGS algorithm is: %.4f\n\n" % accuracy) + " LBFGS algorithm is: %.4f" % accuracy) + print("The test recall of Logistic Regression model with" + " LBFGS algorithm is: %.4f" % recall) + print("The test fprate of Logistic Regression model with" + " LBFGS algorithm is: %.4f\n\n" % fprate) 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) + # Split data aproximately into training (80%) and test (20%) + trainingData, testData = data.randomSplit([0.8, 0.2], seed=0) # Train a multinomial naive Bayes model given an RDD of LabeledPoint. - model = NaiveBayes.train(trainingData, 0.8) + model = NaiveBayes.train(trainingData, 7e-1) # Make prediction and test accuracy. predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda p: p.label).zip(predictions) + tp = labelsAndPredictions.filter(lambda (v, p): v == p and p == 1).count() + tn = labelsAndPredictions.filter(lambda (v, p): v == p and p == 0).count() + fp = labelsAndPredictions.filter(lambda (v, p): v != p and p == 1).count() + fn = labelsAndPredictions.filter(lambda (v, p): v != p and p == 0).count() accuracy = (labelsAndPredictions.filter(lambda (v, p): v == p). count() / float(testData.count())) + print("true positive number: %d, false positive number: %d" % (tp, fp)) + print("false negative number: %d, true negative number: %d" % (fn, tn)) + recall = tp / float(tp + fn) + fprate = fp / float(fp + tn) print("The test accuracy of Multinomial Naive Bayes " - "is: %.4f\n\n" % accuracy) + "is: %.4f" % accuracy) + print("The test recall of Multinomial Naive Bayes " + "is: %.4f" % recall) + print("The test fprate of Multinomial Naive Bayes " + "is: %.4f\n\n" % fprate) print("===== Choose Decision Tree model =====") - # Split data aproximately into training (60%) and test (40%) - trainingData, testData = data.randomSplit([0.6, 0.4], seed=0) + # Split data aproximately into training (80%) and test (20%) + trainingData, testData = data.randomSplit([0.8, 0.2], 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, + impurity='entropy', maxDepth=4, maxBins=32) - print('Learned classification tree model:') - print(model.toDebugString()) + # 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) + tp = labelsAndPredictions.filter(lambda (v, p): v == p and p == 1).count() + tn = labelsAndPredictions.filter(lambda (v, p): v == p and p == 0).count() + fp = labelsAndPredictions.filter(lambda (v, p): v != p and p == 1).count() + fn = labelsAndPredictions.filter(lambda (v, p): v != p and p == 0).count() 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("true positive number: %d, false positive number: %d" % (tp, fp)) + print("false negative number: %d, true negative number: %d" % (fn, tn)) + recall = tp / float(tp + fn) + fprate = fp / float(fp + tn) + print("The test accuracy of decision tree model is: %.4f" % accuracy) + print("The test recall of decision tree model is: %.4f" % recall) + print("The test fprate of decision tree model is: %.4f\n\n" % fprate) print("===== Choose Random Forest model =====") - # Split data aproximately into training (60%) and test (40%) - trainingData, testData = data.randomSplit([0.6, 0.4], seed=0) + # Split data aproximately into training (80%) and test (20%) + trainingData, testData = data.randomSplit([0.8, 0.2], seed=0) # Train a Random Forest model. # Empty categoricalFeaturesInfo indicates all features are continuous. @@ -178,42 +243,65 @@ if __name__ == "__main__": # Setting featureSubsetStrategy="auto" lets the algorithm choose. model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={}, - numTrees=3, + numTrees=15, featureSubsetStrategy="auto", - impurity='gini', maxDepth=7, + impurity='gini', maxDepth=12, maxBins=32) - print('Learned classification tree model:') - print(model.toDebugString()) + # 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) + tp = labelsAndPredictions.filter(lambda (v, p): v == p and p == 1).count() + tn = labelsAndPredictions.filter(lambda (v, p): v == p and p == 0).count() + fp = labelsAndPredictions.filter(lambda (v, p): v != p and p == 1).count() + fn = labelsAndPredictions.filter(lambda (v, p): v != p and p == 0).count() 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("true positive number: %d, false positive number: %d" % (tp, fp)) + print("false negative number: %d, true negative number: %d" % (fn, tn)) + recall = tp / float(tp + fn) + fprate = fp / float(fp + tn) + print("The test accuracy of random forest model is: %.4f" % accuracy) + print("The test recall of random forest model is: %.4f" % recall) + print("The test fprate of random forest model is: %.4f\n\n" % fprate) 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) + # Split data aproximately into training (80%) and test (20%) + trainingData, testData = data.randomSplit([0.8, 0.2], seed=0) # Train a GradientBoostedTrees model. # Empty categoricalFeaturesInfo indicates all features are continuous. model = GradientBoostedTrees.trainClassifier(trainingData, categoricalFeaturesInfo={}, - numIterations=3, maxDepth=3, + numIterations=20, maxDepth=8, maxBins=32) - print('Learned classification tree model:') - print(model.toDebugString()) + # 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) + tp = labelsAndPredictions.filter(lambda (v, p): v == p and p == 1).count() + tn = labelsAndPredictions.filter(lambda (v, p): v == p and p == 0).count() + fp = labelsAndPredictions.filter(lambda (v, p): v != p and p == 1).count() + fn = labelsAndPredictions.filter(lambda (v, p): v != p and p == 0).count() accuracy = (labelsAndPredictions.filter(lambda (v, p): v == p). count() / float(testData.count())) + print("true positive number: %d, false positive number: %d" % (tp, fp)) + print("false negative number: %d, true negative number: %d" % (fn, tn)) + recall = tp / float(tp + fn) + fprate = fp / float(fp + tn) print("The test accuracy of Gradient Boosted Trees " "model is: %.4f" % accuracy) + print("The test recall of Gradient Boosted Trees " + "model is: %.4f" % recall) + print("The test fprate of Gradient Boosted Trees " + "model is: %.4f" % fprate) + - # Save and load model +# Save and load model # path = tempfile.mkdtemp(dir='.') # model.save(sc, path) # sameModel = SVMModel.load(sc, path) |