def crossValidateNaiveBayes():
f1Inputs, f1Labels, _ = read_libsvm_default('data/data_madelon/folds/fold1')
f2Inputs, f2Labels, _ = read_libsvm_default('data/data_madelon/folds/fold2')
f3Inputs, f3Labels, _ = read_libsvm_default('data/data_madelon/folds/fold3')
f4Inputs, f4Labels, _ = read_libsvm_default('data/data_madelon/folds/fold4')
f5Inputs, f5Labels, _ = read_libsvm_default('data/data_madelon/folds/fold5')
allFoldInputArrays = [f1Inputs.toarray(), f2Inputs.toarray(),
f3Inputs.toarray(), f4Inputs.toarray(), f5Inputs.toarray()]
allFoldLabelArrays = [f1Labels, f2Labels, f3Labels, f4Labels, f5Labels]
for array in allFoldInputArrays:
discreteizeData(array)
smoothingTerms = [2, 1.5, 1.0, 0.5]
bestSmoothingTerm = None
bestAccuracy = 0
counter = 1
everyAccuracy = []
for smoothing in smoothingTerms:
allAccuracies = []
for i in range(len(allFoldInputArrays)):
allTrainData = []
allTrainLabels = []
for j in range(len(allFoldInputArrays)):
if j != i:
allTrainData.extend(allFoldInputArrays[j])
allTrainLabels.extend(allFoldLabelArrays[j])
print("Hyperparameters: smoothing term: " + str(smoothing))
tempbayes = naiveBayes(numFeatures, smoothing)
tempbayes.train(allTrainData, allTrainLabels)
evaluation = tempbayes.evaluate(allFoldInputArrays[i], allFoldLabelArrays[i])
accuracy = evaluation
allAccuracies.append(accuracy)
everyAccuracy.append(accuracy)
if statistics.mean(allAccuracies) > bestAccuracy:
bestAccuracy = statistics.mean(allAccuracies)
bestSmoothingTerm = smoothing
avgAccuracy = statistics.mean(everyAccuracy)
print("Best smoothing term: " + str(bestSmoothingTerm))
print("Best accuracy: " + str(bestAccuracy))
print("Average accuracy: " + str(avgAccuracy))
def crossValidateRandomForest():
f1Inputs, f1Labels, _ = read_libsvm_default('data/data_semeion/folds/fold1')
f2Inputs, f2Labels, _ = read_libsvm_default('data/data_semeion/folds/fold2')
f3Inputs, f3Labels, _ = read_libsvm_default('data/data_semeion/folds/fold3')
f4Inputs, f4Labels, _ = read_libsvm_default('data/data_semeion/folds/fold4')
f5Inputs, f5Labels, _ = read_libsvm_default('data/data_semeion/folds/fold5')
allFoldInputArrays = [f1Inputs.toarray(), f2Inputs.toarray(),
f3Inputs.toarray(), f4Inputs.toarray(), f5Inputs.toarray()]
allFoldLabelArrays = [f1Labels, f2Labels, f3Labels, f4Labels, f5Labels]
forestSizes = [10, 50, 100]
bestForestSize = None
bestAccuracy = 0
counter = 1
everyAccuracy = []
for forestSize in forestSizes:
allAccuracies = []
for i in range(len(allFoldInputArrays)):
allTrainData = []
allTrainLabels = []
for j in range(len(allFoldInputArrays)):
if j != i:
allTrainData.extend(allFoldInputArrays[j])
allTrainLabels.extend(allFoldLabelArrays[j])
print("Hyperparameters: forest size: " + str(forestSize))
tempforest = randomForest(numFeatures, forestSize)
tempforest.train(allTrainData, allTrainLabels)
evaluation = tempforest.evaluate(allFoldInputArrays[i], allFoldLabelArrays[i])
accuracy = evaluation
allAccuracies.append(accuracy)
everyAccuracy.append(accuracy)
if statistics.mean(allAccuracies) > bestAccuracy:
bestAccuracy = statistics.mean(allAccuracies)
bestForestSize = forestSize
avgAccuracy = statistics.mean(everyAccuracy)
print("Best forest size: " + str(bestForestSize))
print("Best accuracy: " + str(bestAccuracy))
print("Average accuracy: " + str(avgAccuracy))
from libsvm import read_libsvm_default
from naive_bayes import *
import statistics
## Setup Data:
trainingInputs, trainingLabels, numFeatures = read_libsvm_default('data/data_madelon/madelon_data_train')
testInputs, testLabels, _ = read_libsvm_default('data/data_madelon/madelon_data_test', numFeatures)
trainingInputsArr = np.array(trainingInputs.toarray())
testInputsArr = testInputs.toarray()
## Discretize data:
def discreteizeData(nonDiscreteArr):
means = np.mean(nonDiscreteArr, axis=0)
for i in range(len(nonDiscreteArr)):
for j in range(len(nonDiscreteArr[i])):
if nonDiscreteArr[i][j] <= means[j]:
nonDiscreteArr[i][j] = 0
else:
nonDiscreteArr[i][j] = 1
return nonDiscreteArr
discreteizeData(trainingInputsArr)
def crossValidateNaiveBayes():
f1Inputs, f1Labels, _ = read_libsvm_default('data/data_madelon/folds/fold1')
f2Inputs, f2Labels, _ = read_libsvm_default('data/data_madelon/folds/fold2')
f3Inputs, f3Labels, _ = read_libsvm_default('data/data_madelon/folds/fold3')
f4Inputs, f4Labels, _ = read_libsvm_default('data/data_madelon/folds/fold4')
f5Inputs, f5Labels, _ = read_libsvm_default('data/data_madelon/folds/fold5')
from libsvm import read_libsvm_default
import csv
import numpy as np
from data import Data
from random_forest import randomForest
import statistics
np.random.seed(4)
## Setup Data:
trainingInputs, trainingLabels, numFeatures = read_libsvm_default(
'data/data-splits/data.train')
testInputs, testLabels, _ = read_libsvm_default('data/data-splits/data.test',
numFeatures)
trainingInputsArr = trainingInputs.toarray()
testInputsArr = testInputs.toarray()
## Discretize data:
def discreteizeData(nonDiscreteArr):
means = np.mean(nonDiscreteArr, axis=0)
for i in range(len(nonDiscreteArr)):
for j in range(len(nonDiscreteArr[i])):
if nonDiscreteArr[i][j] <= means[j]:
nonDiscreteArr[i][j] = 0
else:
nonDiscreteArr[i][j] = 1
return nonDiscreteArr
discreteizeData(trainingInputsArr)
from libsvm import read_libsvm_default
from naive_bayes import *
import statistics
## Setup Data:
trainingInputs, trainingLabels, numFeatures = read_libsvm_default('data/data_semeion/hand_data_train')
testInputs, testLabels, _ = read_libsvm_default('data/data_semeion/hand_data_test', numFeatures)
trainingInputsArr = trainingInputs.toarray()
testInputsArr = testInputs.toarray()
def crossValidateNaiveBayes():
f1Inputs, f1Labels, _ = read_libsvm_default('data/data_semeion/folds/fold1')
f2Inputs, f2Labels, _ = read_libsvm_default('data/data_semeion/folds/fold2')
f3Inputs, f3Labels, _ = read_libsvm_default('data/data_semeion/folds/fold3')
f4Inputs, f4Labels, _ = read_libsvm_default('data/data_semeion/folds/fold4')
f5Inputs, f5Labels, _ = read_libsvm_default('data/data_semeion/folds/fold5')
allFoldInputArrays = [f1Inputs.toarray(), f2Inputs.toarray(),
f3Inputs.toarray(), f4Inputs.toarray(), f5Inputs.toarray()]
allFoldLabelArrays = [f1Labels, f2Labels, f3Labels, f4Labels, f5Labels]
smoothingTerms = [2.0, 1.5, 1.0, 0.5]
bestSmoothingTerm = None
bestAccuracy = 0
counter = 1
everyAccuracy = []
for smoothing in smoothingTerms:
allAccuracies = []