def knn(self, predictData=None, trainData=None):
h = hp()
k = knn()
accuracy = []
precision = []
recall = []
f_score = []
mean, stdDev = h.normalizeData(trainData)
nn = int(input("Enter the number of closest neighbors to consider: "))
h.normalizeEvaluationSet(predictData, mean, stdDev)
for i in range(len(trainData)):
tmp = None
predictData = trainData[i]
tmp = [lt for j, lt in enumerate(trainData) if j != i]
td = h.convertToList(tmp)
k.classify(td, predictData, nn)
truePositives, trueNegatives, falsePositives, falseNegatives = h.findParams(
predictData)
accuracy.append(
h.findAccuracy(truePositives, trueNegatives, falsePositives,
falseNegatives))
tmpPrecision = h.findPrecision(truePositives, trueNegatives,
falsePositives, falseNegatives)
tmpRecall = h.findRecall(truePositives, trueNegatives,
falsePositives, falseNegatives)
precision.append(tmpPrecision)
recall.append(tmpRecall)
f_score.append(h.findFMeasure(tmpPrecision, tmpRecall))
return accuracy, precision, recall, f_score
def bayes_naive(self, predictData, trainData):
h = hp()
nb = bayes()
accuracy = []
precision = []
recall = []
f_score = []
for i in range(len(trainData)):
tmp = None
predictData = trainData[i]
tmp = [lt for j, lt in enumerate(trainData) if j != i]
td = h.convertToList(tmp)
classPriorProbabilities = nb.findClassPriorProbability(td)
classes = nb.segregateClasses(td)
occurences, means, stdDev = nb.findDescriptorPosteriorProbabilites(
classes, td)
nb.classify(predictData, classPriorProbabilities, occurences,
means, stdDev)
truePositives, trueNegatives, falsePositives, falseNegatives = h.findParams(
predictData)
accuracy.append(
h.findAccuracy(truePositives, trueNegatives, falsePositives,
falseNegatives))
tmpPrecision = h.findPrecision(truePositives, trueNegatives,
falsePositives, falseNegatives)
tmpRecall = h.findRecall(truePositives, trueNegatives,
falsePositives, falseNegatives)
precision.append(tmpPrecision)
recall.append(tmpRecall)
f_score.append(h.findFMeasure(tmpPrecision, tmpRecall))
return accuracy, precision, recall, f_score
def knn2(self, predictData, trainData, labels):
h = hp()
pd = list()
finalAnswer = defaultdict(list)
pca = PCA(n_components=30)
for point in predictData:
pd.append(point.point)
knn = KNeighborsClassifier(n_neighbors=9, metric='euclidean')
tmp = list()
tmpLabels = list()
for lt in trainData:
for point in lt:
tmp.append(point.point)
tmpLabels.append(labels[point.id])
# X_transformed = pca.fit_transform(tmp)
# newdata_transformed = pca.transform(pd)
std_scale = preprocessing.StandardScaler().fit(tmp)
X_transformed = std_scale.transform(tmp)
newdata_transformed = std_scale.transform(pd)
knn.fit(np.array(X_transformed), np.array(tmpLabels))
y_pred = knn.predict(np.array(newdata_transformed))
k = 0
for i in range(418, 796):
finalAnswer[i] = y_pred[k]
k+=1
return finalAnswer
def svm(self, predictData, trainData, labels):
h = hp()
matrix = defaultdict(list)
finalAnswer = defaultdict(list)
pca = PCA(n_components=25)
for i in range(len(trainData)):
tmp = list()
tmpLabels = list()
pd = list()
for j, lt in enumerate(trainData):
if j != i:
for point in lt:
tmp.append(point.point)
tmpLabels.append(labels[point.id])
for point in predictData:
pd.append(point.point)
clf = SVC()
X_transformed = pca.fit_transform(tmp)
newdata_transformed = pca.transform(pd)
# mean, stdDev = h.normalizeData(trainData)
# h.normalizeEvaluationSet(predictData, mean, stdDev)
clf.fit(np.array(X_transformed), np.array(tmpLabels))
y_pred = clf.predict(np.array(newdata_transformed))
k = 0
for i in range(418, 796):
finalAnswer[i].append(y_pred[k])
k+=1
for key in finalAnswer:
matrix[key] = 0 if finalAnswer[key].count(0) > finalAnswer[key].count(1) else 1
return matrix
def logisticRegression(self, predictData, trainData, labels):
h = hp()
# mean, stdDev = h.normalizeData(trainData)
# h.normalizeEvaluationSet(predictData, mean, stdDev)
finalAnswer = defaultdict(list)
pd = list()
matrix = defaultdict(list)
for point in predictData:
pd.append(point.point)
for i in range(len(trainData)):
tmp = list()
tmpLabels = list()
for j, lt in enumerate(trainData):
if j != i:
for point in lt:
tmp.append(point.point)
tmpLabels.append(labels[point.id])
pca = PCA(svd_solver='full')
pca_matrix = pca.fit_transform(tmp)
pca1_matrix = pca.transform(pd)
lr = LogisticRegression(solver='sag', max_iter=1500)
lr.fit(pca_matrix, np.array(tmpLabels))
y_pred = lr.predict(np.array(pca1_matrix))
k = 0
for d in range(418, 796):
finalAnswer[d].append(y_pred[k])
k+=1
for key in finalAnswer:
matrix[key] = 0 if finalAnswer[key].count(0) > finalAnswer[key].count(1) else 1
return matrix
def knn(self, predictData = None, trainData = None):
h = hp()
k = knn()
# mean, stdDev = h.normalizeData(trainData)
nn = int(input("Enter the number of closest neighbors to consider: "))
# h.normalizeEvaluationSet(predictData, mean, stdDev)
tmp = [lt for j, lt in enumerate(trainData)]
td = h.convertToList(tmp)
k.classify(td, predictData,nn)
def findDescriptorPosteriorProbabilites(self, classes, td):
occurences = defaultdict(int)
mean, stdDeviation = defaultdict(dict), defaultdict(dict)
for key in classes:
tmp = classes[key]
mean[key], stdDeviation[key] = hp().standardizeBayes(tmp)
for pt in tmp:
for index, i in enumerate(pt.categoricalData):
if (i, key) not in occurences:
count = self.countOccurence(i, index, tmp)
occurences[(i, key)] = count / len(tmp)
return occurences, mean, stdDeviation
def bayes_naive(self, predictData, trainData):
h = hp()
nb = bayes()
matrix = defaultdict(list)
pd = [pt for pt in predictData]
# for i in range(len(trainData)):
tmp = [lt for j, lt in enumerate(trainData)]
td = h.convertToList(tmp)
classPriorProbabilities = nb.findClassPriorProbability(td)
classes = nb.segregateClasses(td)
occurences, means, stdDev = nb.findDescriptorPosteriorProbabilites(classes, td)
nb.classify(predictData, classPriorProbabilities, occurences, means, stdDev)
return predictData
def knnDemo(self, predictData=None, trainData=None):
h = hp()
k = knn()
nn = int(input("Enter the number of closest neighbors to consider: "))
k.classify(trainData, predictData, nn)
truePositives, trueNegatives, falsePositives, falseNegatives = h.findParams(
predictData)
accuracy = h.findAccuracy(truePositives, trueNegatives, falsePositives,
falseNegatives)
precision = h.findPrecision(truePositives, trueNegatives,
falsePositives, falseNegatives)
recall = h.findRecall(truePositives, trueNegatives, falsePositives,
falseNegatives)
f_score = h.findFMeasure(precision, recall)
return accuracy, precision, recall, f_score
def bayes_naive_demo(self, predictData, trainData):
h = hp()
nb = bayes()
classPriorProbabilities = nb.findClassPriorProbability(trainData)
classes = nb.segregateClasses(trainData)
occurences, means, stdDev = nb.findDescriptorPosteriorProbabilites(
classes, trainData)
probabilities = nb.classify_demo(predictData, classPriorProbabilities,
occurences, means, stdDev)
maxProb = float('-inf')
classKey = -1
for key in probabilities:
print("P(X|H{})*P(H{}) = {}".format(key, key, probabilities[key]))
if probabilities[key] > maxProb:
maxProb = probabilities[key]
classKey = key
print("This test data record belongs to: Class {}".format(classKey))
def dt(data, labels, test_features=None):
from helpers import helpers as hp
from decision_tree import decisionTree
h = hp()
dt = decisionTree()
data = pd.concat([data, labels], axis=1)
data.dropna(inplace=True)
print(data.head())
trainAccuracy = []
testAccuracy = []
precision = []
recall = []
f_score = []
models = []
foldSize = int(data.shape[0] / 10)
for i in range(10):
print("Running iteration " + str(i+1) + " of k cross validation")
testData = data.loc[foldSize*i:foldSize*(i+1)-1]
# testData = pd.DataFrame(stats.zscore(testData.iloc[:,:-1], axis=1), columns=testData.columns)
trainData = data.loc[:foldSize*i-1].append(data.loc[foldSize*(i+1):])
trainData = trainData[(np.abs(stats.zscore(trainData.iloc[:,:-1])) < 3).all(axis=1)]
root = dt.decision(trainData, depth=10, minLeafRows=5)
testTarget = testData.iloc[:,-1].values.tolist()
# testPredicted = dt.predictData(testData.iloc[:, :-1], root)
testPredicted = dt.predictData(pd.DataFrame(stats.zscore(testData.iloc[:,:-1], axis=1), columns=testData.columns.values.tolist()[:-1]), root)
trainTarget = trainData.iloc[:,-1].values.tolist()
trainPredicted = dt.predictData(trainData.iloc[:, :-1], root)
models.append(root)
truePositives, trueNegatives, falsePositives, falseNegatives = h.findParameters(trainPredicted, trainTarget)
trainAccuracy.append(h.findAccuracy(truePositives, trueNegatives, falsePositives, falseNegatives))
truePositives, trueNegatives, falsePositives, falseNegatives = h.findParameters(testPredicted, testTarget)
testAccuracy.append(h.findAccuracy(truePositives, trueNegatives, falsePositives, falseNegatives))
tmpPrecision = h.findPrecision(truePositives, trueNegatives, falsePositives, falseNegatives)
tmpRecall = h.findRecall(truePositives, trueNegatives, falsePositives, falseNegatives)
precision.append(tmpPrecision)
recall.append(tmpRecall)
f_score.append(h.findFMeasure(tmpPrecision, tmpRecall))
h.calculateMetrics(testAccuracy, precision, recall, f_score)
return trainAccuracy, testAccuracy, precision, recall, models, dt
def rf(data, labels, test_features=None):
from random_forest import randomForest
from helpers import helpers as hp
from decision_tree import decisionTree
h = hp()
rf = randomForest()
dt = decisionTree()
data = pd.concat([data, labels], axis=1)
# print(data)
accuracy = []
precision = []
recall = []
f_score = []
models = []
fb_score = []
foldSize = int(data.shape[0] / 5)
for i in range(5):
print("Running iteration " + str(i+1) + " of k cross validation")
testData = data.loc[foldSize*i:foldSize*(i+1)-1]
trainData = data.loc[:foldSize*i-1].append(data.loc[foldSize*(i+1):])
forest = rf.forest(trainData)
target = testData.iloc[:,-1].values.tolist()
predicted = rf.predictForest(testData.iloc[:, :-1], forest)
models.append(forest)
calMetrics(target, predicted)
# truePositives, trueNegatives, falsePositives, falseNegatives = h.findParameters(predicted, target)
# print(truePositives, trueNegatives, falsePositives, falseNegatives)
# accuracy.append(h.findAccuracy(truePositives, trueNegatives, falsePositives, falseNegatives))
# tmpPrecision = h.findPrecision(truePositives, trueNegatives, falsePositives, falseNegatives)
# tmpRecall = h.findRecall(truePositives, trueNegatives, falsePositives, falseNegatives)
# precision.append(tmpPrecision)
# recall.append(tmpRecall)
# tm_fscore = h.findFMeasure(tmpPrecision, tmpRecall)
# print(tm_fscore)
# f_score.append(tm_fscore)
h.calculateMetrics(accuracy, precision, recall, f_score)
# print(accuracy, precision, recall, f_score)
# h.calculateMetrics(accuracy, precision, recall, f_score)
# ind = f_score.index(min(f_score))
# print(f_score[ind])
# pred = rf.predictForest(test_features, models[ind])
# print(pred)
predicted = pd.DataFrame()
for root in models:
pred = dt.predictData(test_features, root)
predicted = pd.concat([predicted, pd.DataFrame(pred)], axis=1)
print(predicted)
p = pd.DataFrame()
p = []
for idx, row in predicted.iterrows():
p.append(row.value_counts().index.tolist()[0])
print(p)
return p
def random_forest(self, kCrossValidation):
print("\nRunning Random Forest Classifier ....................\n")
from random_forest import randomForest
h = hp()
fileName = h.get_fileName()
filePath = "../Data/" + fileName + ".txt"
# filePath = "CSE-601/project3/Data/"+fileName+".txt"
data, labels = h.readData(filePath)
data = h.oneHotEncoding(data, labels)
rf = randomForest()
try:
numTrees = int(input("\nEnter number of trees: "))
numFeatures = int(input("Enter number of features to consider: "))
except:
print("\nExecution Failed - Wrong Input")
exit()
accuracy = []
precision = []
recall = []
f_score = []
models = []
foldSize = int(data.shape[0] / kCrossValidation)
for i in range(kCrossValidation):
print("Running iteration " + str(i + 1) +
" of k cross validation .....")
testData = data.loc[foldSize * i:foldSize * (i + 1) - 1]
trainData = data.loc[:foldSize * i - 1].append(data.loc[foldSize *
(i + 1):])
forest = rf.forest(trainData,
numTrees=numTrees,
numFeatures=numFeatures)
target = testData.iloc[:, -1].values.tolist()
predicted = rf.predictForest(testData.iloc[:, :-1], forest)
models.append(forest)
truePositives, trueNegatives, falsePositives, falseNegatives = h.findParameters(
predicted, target)
accuracy.append(
h.findAccuracy(truePositives, trueNegatives, falsePositives,
falseNegatives))
tmpPrecision = h.findPrecision(truePositives, trueNegatives,
falsePositives, falseNegatives)
tmpRecall = h.findRecall(truePositives, trueNegatives,
falsePositives, falseNegatives)
precision.append(tmpPrecision)
recall.append(tmpRecall)
f_score.append(h.findFMeasure(tmpPrecision, tmpRecall))
print("\nMetrics on train data with k-cross validation")
h.calculateMetrics(accuracy, precision, recall, f_score)
fileName = input(
"\nEnter test data file name without extension (if no test file, just press enter): "
)
if fileName != '':
filePath = "../Data/" + fileName + ".txt"
# filePath = "CSE-601/project3/Data/"+fileName+".txt"
testData, testLabels = h.readData(filePath)
testData = h.oneHotEncoding(testData, testLabels)
predLabels = []
for forest in models:
predLabels.append(rf.predictForest(testData, forest))
predLabels = pd.DataFrame(predLabels)
pred = []
for _, colData in predLabels.iteritems():
pred.append(colData.value_counts().index[0])
truePositives, trueNegatives, falsePositives, falseNegatives = h.findParameters(
pred, testData.iloc[:, -1].values.tolist())
accuracy = [
h.findAccuracy(truePositives, trueNegatives, falsePositives,
falseNegatives)
]
precision = h.findPrecision(truePositives, trueNegatives,
falsePositives, falseNegatives)
recall = h.findRecall(truePositives, trueNegatives, falsePositives,
falseNegatives)
f_score = [h.findFMeasure(precision, recall)]
print("\nMetrics on test data with bagging")
h.calculateMetrics(accuracy, [precision], [recall], f_score)
def decision_tree(self, kCrossValidation):
print("\nRunning Decision Tree Classifier ....................\n")
from decision_tree import decisionTree
h = hp()
fileName = h.get_fileName()
# filePath = "../Data/"+fileName+".txt"
filePath = "CSE-601/project3/Data/" + fileName + ".txt"
data, labels = h.readData(filePath)
data = h.oneHotEncoding(data, labels)
dt = decisionTree()
accuracy = []
precision = []
recall = []
f_score = []
models = []
if kCrossValidation <= 1:
root = dt.decision(data)
print(root)
target = data.iloc[:, -1]
predicted = dt.predictData(data.iloc[:, :-1], root)
truePositives, trueNegatives, falsePositives, falseNegatives = h.findParameters(
predicted, target)
accuracy.append(
h.findAccuracy(truePositives, trueNegatives, falsePositives,
falseNegatives))
tmpPrecision = h.findPrecision(truePositives, trueNegatives,
falsePositives, falseNegatives)
tmpRecall = h.findRecall(truePositives, trueNegatives,
falsePositives, falseNegatives)
precision.append(tmpPrecision)
recall.append(tmpRecall)
f_score.append(h.findFMeasure(tmpPrecision, tmpRecall))
else:
foldSize = int(data.shape[0] / kCrossValidation)
for i in range(kCrossValidation):
print("Running iteration " + str(i + 1) +
" of k cross validation .....")
testData = data.loc[foldSize * i:foldSize * (i + 1) - 1]
trainData = data.loc[:foldSize * i - 1].append(
data.loc[foldSize * (i + 1):])
root = dt.decision(trainData)
target = testData.iloc[:, -1].values.tolist()
predicted = dt.predictData(testData.iloc[:, :-1], root)
models.append(root)
truePositives, trueNegatives, falsePositives, falseNegatives = h.findParameters(
predicted, target)
accuracy.append(
h.findAccuracy(truePositives, trueNegatives,
falsePositives, falseNegatives))
tmpPrecision = h.findPrecision(truePositives, trueNegatives,
falsePositives, falseNegatives)
tmpRecall = h.findRecall(truePositives, trueNegatives,
falsePositives, falseNegatives)
precision.append(tmpPrecision)
recall.append(tmpRecall)
f_score.append(h.findFMeasure(tmpPrecision, tmpRecall))
print("\nMetrics on train data with k-cross validation")
h.calculateMetrics(accuracy, precision, recall, f_score)
fileName = input(
"\nEnter test data file name without extension (if no test file, just press enter): "
)
if fileName != '':
filePath = "../Data/" + fileName + ".txt"
# filePath = "CSE-601/project3/Data/"+fileName+".txt"
testData, testLabels = h.readData(filePath)
testData = h.oneHotEncoding(testData, testLabels)
predLabels = []
for _, row in testData.iloc[:, :-1].iterrows():
predictedRow = [dt.predictRow(row, root) for root in models]
predLabels.append(
max(set(predictedRow), key=predictedRow.count))
truePositives, trueNegatives, falsePositives, falseNegatives = h.findParameters(
predLabels, testData.iloc[:, -1].values.tolist())
accuracy = [
h.findAccuracy(truePositives, trueNegatives, falsePositives,
falseNegatives)
]
precision = h.findPrecision(truePositives, trueNegatives,
falsePositives, falseNegatives)
recall = h.findRecall(truePositives, trueNegatives, falsePositives,
falseNegatives)
f_score = [h.findFMeasure(precision, recall)]
print("\nMetrics on test data with bagging")
h.calculateMetrics(accuracy, [precision], [recall], f_score)
accuracy = [
h.findAccuracy(truePositives, trueNegatives, falsePositives,
falseNegatives)
]
precision = h.findPrecision(truePositives, trueNegatives,
falsePositives, falseNegatives)
recall = h.findRecall(truePositives, trueNegatives, falsePositives,
falseNegatives)
f_score = [h.findFMeasure(precision, recall)]
print("\nMetrics on test data with bagging")
h.calculateMetrics(accuracy, [precision], [recall], f_score)
if __name__ == "__main__":
m = main()
h = hp()
algorithm = int(
input(
"Enter 0 to run K-Nearest Neighbors in demo mode\nEnter 1 for K-Nearest Neigbour Algorithm\nEnter 2 for Decision Tree Algorithm\nEnter 3 for Naive Bayes Algorithm\nEnter 4 to run Naive Bayes Algorithm in demo mode\nEnter 5 for Random Forest Algorithm\n"
))
if algorithm == 0:
print("Enter train File name")
trainData = h.get_file_demo(h.get_fileName())
print("Enter test File name")
predictData = h.get_file_demo(h.get_fileName(), fileType='predictData')
accuracy, precision, recall, f_score = m.knnDemo(
predictData, trainData)
h.calculateMetricsDemo(accuracy, precision, recall, f_score)
elif algorithm == 1:
