def dualProblem(inputFiles):
errors = []
start_time = time.time()
for File in inputFiles:
data = tools.readData(File)
X = data[:, :-1]
Y = data[:-1]
kf = KFold(len(Y), n_folds=10)
trainError = 0
testError = 0
for train, test in kf:
G = gaussian_function(X[train], 0.05)
print "Done with G!"
alpha = computeAlpha(G, Y[train], 0.05)
theta = computeTheta(alpha, X[train])
Y_hat = np.dot(X[train], theta)
Y_hat_test = np.dot(X[test], theta)
trainError += tools.findError(Y_hat, Y[train])
testError += tools.findError(Y_hat_test, Y[test])
trainError = trainError / len(kf)
testError = testError / len(kf)
error = [trainError, testError]
errors.append(error)
time_taken = start_time - time.time()
print "Time Taken for all data sets: %s" % str(time_taken)
return np.asarray(errors)
def newtonRaphson(inputFiles):
pol = PolynomialFeatures(2)
errors = []
for File in inputFiles:
data = tools.readData(File)
X = data[:, :-1]
Y = data[:, -1]
kf = KFold(len(Y), n_folds=10)
trainError = 0
testError = 0
for train, test in kf:
Z = pol.fit_transform(X[train])
row, col = Z.shape
theta = np.empty(col, dtype='float')
meanDiff = 1.0
i = 1
#print "Theta iteration %s: \n%s" % ('0', str(theta))
while abs(meanDiff) > 1.0e-15:
theta_new = recalculateTheta(theta, Z, Y[train])
diff = np.subtract(theta_new, theta)
meanDiff = np.mean(diff)
#print "Theta iteration %s: \n%s" % (str(i), str(theta_new))
#print "Diff: %s" % str(meanDiff)
theta = theta_new
i += 1
Z_test = pol.fit_transform(X[test])
Y_hat_test = np.dot(Z_test, theta)
Y_hat = np.dot(Z, theta)
trainError += tools.findError(Y_hat, Y[train])
testError += tools.findError(Y_hat_test, Y[test])
trainError = trainError / len(kf)
testError = testError / len(kf)
iterative_error = [trainError, testError]
errors.append(iterative_error)
return np.asarray(errors)
def polyRegressionKFold(inputFiles, deg=2):
print "***************************"
print "Degree: %s" % deg
start_time = time.time()
errors = []
for File in inputFiles:
print "___________________________"
print "Data Set: %s" % File
data = tools.readData(File)
data = data[np.argsort(data[:,0])]
X = data[:, :-1]
Y = data[:, len(data[1,:]) - 1]
kf = KFold(len(data), n_folds = 10, shuffle = True)
TrainError = 0
TestError = 0
for train, test in kf:
pol = PolynomialFeatures(deg)
Z = pol.fit_transform(X[train])
Z_test = pol.fit_transform(X[test])
theta = regress(Z, Y[train])
Y_hat = np.dot(Z, theta)
Y_hat_test = np.dot(Z_test, theta)
TrainError += mean_squared_error(Y[train], Y_hat)
TestError += mean_squared_error(Y[test], Y_hat_test)
TestError /= len(kf)
TrainError /= len(kf)
errors.append([TestError, deg])
print "---------------------------"
print "Test Error: %s" % TestError
print "Train Error: %s" % TrainError
time_taken = start_time - time.time()
print "Time Taken for primal: %s" % str(time_taken)
return np.asarray(errors)
def dualProblem(inputFiles):
errors = []
start_time = time.time()
for File in inputFiles:
data = tools.readData(File)
X = data[:, :-1]
Y = data[:-1]
kf = KFold(len(Y), n_folds = 10)
trainError = 0
testError = 0
for train, test in kf:
G = gaussian_function(X[train], 0.05)
print "Done with G!"
alpha = computeAlpha(G, Y[train], 0.05)
theta = computeTheta(alpha, X[train])
Y_hat = np.dot(X[train], theta)
Y_hat_test = np.dot(X[test], theta)
trainError += tools.findError(Y_hat, Y[train])
testError += tools.findError(Y_hat_test, Y[test])
trainError = trainError/len(kf)
testError = testError/len(kf)
error = [trainError, testError]
errors.append(error)
time_taken = start_time - time.time()
print "Time Taken for all data sets: %s" % str(time_taken)
return np.asarray(errors)
def newtonRaphson(inputFiles):
pol = PolynomialFeatures(2)
errors = []
for File in inputFiles:
data = tools.readData(File)
X = data[:, :-1]
Y = data[:, -1]
kf = KFold(len(Y), n_folds = 10)
trainError = 0
testError = 0
for train, test in kf:
Z = pol.fit_transform(X[train])
row, col = Z.shape
theta = np.empty(col, dtype='float')
meanDiff = 1.0
i = 1
#print "Theta iteration %s: \n%s" % ('0', str(theta))
while abs(meanDiff) > 1.0e-15 :
theta_new = recalculateTheta(theta, Z, Y[train])
diff = np.subtract(theta_new, theta)
meanDiff = np.mean(diff)
#print "Theta iteration %s: \n%s" % (str(i), str(theta_new))
#print "Diff: %s" % str(meanDiff)
theta = theta_new
i += 1
Z_test = pol.fit_transform(X[test])
Y_hat_test = np.dot(Z_test, theta)
Y_hat = np.dot(Z, theta)
trainError += tools.findError(Y_hat, Y[train])
testError += tools.findError(Y_hat_test, Y[test])
trainError = trainError/len(kf)
testError = testError/len(kf)
iterative_error = [trainError, testError]
errors. append(iterative_error)
return np.asarray(errors)
def linearRegressionKFold(inputFiles, i=1):
print "\nSingle Variable, Degree: %s" % i
print "###########################"
for File in inputFiles:
print "==========================="
print "Data Set %s" % File
data = tools.readData(File)
X = data[:, 0]
Y = data[:, 1]
kf = KFold(len(data), n_folds=10, shuffle=True)
TrainError = 0
TestError = 0
for train, test in kf:
Z = tools.createZ(X[train], i)
theta = regress(Z, Y[train])
Y_hat = YHat(theta, X[train])
Y_hat_test = YHat(theta, X[test])
TrainError = TrainError + tools.findError(theta, Y[train])
TestError = TestError + tools.findError(theta, Y[test])
TestError = TestError / len(kf)
TrainError = TrainError / len(kf)
print "---------------------------"
print "Test Error: %s" % TestError
print "Train Error: %s" % TrainError
py_linearRegression(X, Y)
return TestError
def polyRegressionKFold(inputFiles, deg=2):
print "***************************"
print "Degree: %s" % deg
start_time = time.time()
errors = []
for File in inputFiles:
print "___________________________"
print "Data Set: %s" % File
data = tools.readData(File)
data = data[np.argsort(data[:, 0])]
X = data[:, :-1]
Y = data[:, len(data[1, :]) - 1]
kf = KFold(len(data), n_folds=10, shuffle=True)
TrainError = 0
TestError = 0
for train, test in kf:
pol = PolynomialFeatures(deg)
Z = pol.fit_transform(X[train])
Z_test = pol.fit_transform(X[test])
theta = regress(Z, Y[train])
Y_hat = np.dot(Z, theta)
Y_hat_test = np.dot(Z_test, theta)
TrainError += mean_squared_error(Y[train], Y_hat)
TestError += mean_squared_error(Y[test], Y_hat_test)
TestError /= len(kf)
TrainError /= len(kf)
errors.append([TestError, deg])
print "---------------------------"
print "Test Error: %s" % TestError
print "Train Error: %s" % TrainError
time_taken = start_time - time.time()
print "Time Taken for primal: %s" % str(time_taken)
return np.asarray(errors)
def linearRegression(inputFiles, i = 1, quarters = 4, dataReduction = False):
k = 1
regr = linear_model.LinearRegression(fit_intercept=False)
for File in inputFiles:
data = tools.readData(File)
data [np.argsort(data[:, 0])]
limit = quarters * (len(data)/4)
Z = tools.createZ(data[:, 0], i)
theta = regress(Z, data[:, 1])
Y_hat = YHat(theta, data[:, 0])
plt.subplot(2,2,k)
plt.scatter(data[:, 0], data[:, 1], color="green")
X = data[:, 0]
plt.plot(X, Y_hat, color="red", lw=3, label = "Original Method")
k = k + 1
if (dataReduction == False):
regr.fit(Z, data[:, 1])
#plt.plot(X, regr.predict(Z), color="blue", lw="1", label ="Python functions")
else:
Z = tools.createZ(data[0:limit, 0], i)
theta = regress(Z, data[0:limit, 1])
Y_hat_small = YHat(theta, data[:, 0])
plt.plot(X, Y_hat_small, color="blue", lw = 1, label = "Reduced Data Set")
plt.title("Reduced Data %sn/4" % quarters)
plt.suptitle("Single Variable Degree: %s" % i)
plt.show()
def plotData(inputFiles):
i = 1;
for File in inputFiles:
data = tools.readData(File)
plt.subplot(2, 2, i)
plt.scatter(data[:, 0], data[:, 1], color="black")
i = i+1
plt.show()
def polyRegression(inputFiles):
pol = PolynomialFeatures(2)
errors = []
for Files in inputFiles:
data = tools.readData(Files)
data = data[np.argsort(data[:, 0])]
X = data[:, :-1]
Y = data[:, -1]
kf = KFold(len(Y), n_folds=10)
trainError = 0
testError = 0
for train, test in kf:
Z = pol.fit_transform(X[train])
theta = regress(Z, Y[train])
Y_hat = np.dot(Z, theta)
Z_test = pol.fit_transform(X[test])
Y_hat_test = np.dot(Z_test, theta)
trainError += tools.findError(Y_hat, Y[train])
testError += tools.findError(Y_hat_test, Y[test])
testError = testError / len(kf)
trainError = trainError / len(kf)
explicit_error = [trainError, testError]
errors.append(explicit_error)
return np.asarray(errors)
def polyRegression(inputFiles):
pol = PolynomialFeatures(2)
errors = []
for Files in inputFiles:
data = tools.readData(Files)
data = data[np.argsort(data[:, 0])]
X = data[:, :-1]
Y = data[:, -1]
kf = KFold(len(Y), n_folds = 10)
trainError = 0
testError = 0
for train, test in kf:
Z = pol.fit_transform(X[train])
theta = regress(Z, Y[train])
Y_hat = np.dot(Z, theta)
Z_test = pol.fit_transform(X[test])
Y_hat_test = np.dot(Z_test, theta)
trainError += tools.findError(Y_hat, Y[train])
testError += tools.findError(Y_hat_test, Y[test])
testError = testError/len(kf)
trainError = trainError/len(kf)
explicit_error = [trainError, testError]
errors.append(explicit_error)
return np.asarray(errors)
return data, errors
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("-l", "--lfile", dest="learnFile",
help="Learning data (CSV file name)")
parser.add_option("-t", "--tfile", dest="testFile",
help="Testing data (CSV file name)")
parser.add_option("-o", "--ofile", dest="outFile", help="Output file name to store testing data classification")
(options, args) = parser.parse_args()
learnFile = options.learnFile # e. g. vertebral_learn.csv
testFile = options.testFile # e. g. vertebral_test.csv
outFile = options.outFile # e. g. res
learnData = readData(learnFile)
c45= C45(learnData)
tree = c45.constructTree()
tree = c45.pruneTree(tree)
printTree(tree)
testData = readData(testFile)
classifiedLearnData, learnErrors = classifyData(tree, learnData)
classifiedTestData, testErrors = classifyData(tree, testData)
writeData(classifiedTestData, outFile)
print "Learning data error: %d/%d (%f)" % (learnErrors, len(learnData), float(learnErrors)/len(learnData))
print "Testing data error: %d/%d (%f)" % (testErrors, len(testData), float(testErrors)/len(testData))
def drawTree(dataFile, treeFile, colors=colors, save_to=''):
'''Draws 2D plot and displays in it:
``data``: (att1, ..., attN, class)
``tree`` - displays as colored background
'''
if type(dataFile) == str:
data = readData(dataFile)
else:
data = dataFile
if type(treeFile) == str:
tree = loadTree(treeFile)
else:
tree = treeFile
attribute_count = len(data[0]) - 1
if attribute_count != 2:
print "Too many attributes (%d), could draw only 2D plots..." % attribute_count
return 1
# Divide data vectors by their class, x, y; find min, max values.
point_dict = dict()
min_x, max_x, min_y, max_y = None, None, None, None
for record in data:
vector, cls = record[:-1], record[-1]
if not point_dict.has_key(cls):
point_dict[cls] = [[], []]
x = vector[0]
y = vector[1]
point_dict[cls][0].append(x)
point_dict[cls][1].append(y)
if not min_x or x < min_x:
min_x = x
if not max_x or x > max_x:
max_x = x
if not min_y or y < min_y:
min_y = y
if not max_y or y > max_y:
max_y = y
# Draw tree:
point_cons = 400
min_x -= 2 * (min_x + max_x) / point_cons
max_x += 2 * (min_x + max_x) / point_cons
min_y -= 2 * (min_y + max_y) / point_cons
max_y += 2 * (min_y + max_y) / point_cons
## Draw tree:
tree_dict = dict()
for x in arange(min_x, max_x, (max_x - min_x) / point_cons):
for y in arange(min_y, max_y, (max_y - min_y) / point_cons):
point_cls = tree.getClass([x, y])
if not tree_dict.has_key(point_cls):
tree_dict[point_cls] = [[], []]
tree_dict[point_cls][0].append(x)
tree_dict[point_cls][1].append(y)
for cls in tree_dict:
plt.scatter(tree_dict[cls][0],
tree_dict[cls][1],
2,
c=colors[int(cls)][1],
marker='s',
linewidth=0)
# Draw data points:
for cls in point_dict:
plt.scatter(point_dict[cls][0],
point_dict[cls][1],
33,
c=colors[int(cls)][0],
marker='o')
plt.axis([min_x, max_x, min_y, max_y])
plt.title(', '.join([
colors[cls][2] + ': ' + str(count)
for (cls, count) in classDistribution(data).items()
]))
if save_to:
plt.savefig(save_to)
plt.cla()
else:
plt.show()
parser.add_option("-t",
"--tfile",
dest="testFile",
help="Testing data (CSV file name)")
parser.add_option(
"-o",
"--ofile",
dest="outFile",
help="Output file name to store testing data classification")
(options, args) = parser.parse_args()
learnFile = options.learnFile # e. g. vertebral_learn.csv
testFile = options.testFile # e. g. vertebral_test.csv
outFile = options.outFile # e. g. res
learnData = readData(learnFile)
c45 = C45(learnData)
tree = c45.constructTree()
tree = c45.pruneTree(tree)
printTree(tree)
testData = readData(testFile)
classifiedLearnData, learnErrors = classifyData(tree, learnData)
classifiedTestData, testErrors = classifyData(tree, testData)
writeData(classifiedTestData, outFile)
print "Learning data error: %d/%d (%f)" % (
learnErrors, len(learnData), float(learnErrors) / len(learnData))
print "Testing data error: %d/%d (%f)" % (
import tools
import time
while (True):
tools.readData()
time.sleep(600)
def drawTree(dataFile, treeFile, colors=colors, save_to=''):
'''Draws 2D plot and displays in it:
``data``: (att1, ..., attN, class)
``tree`` - displays as colored background
'''
if type(dataFile) == str:
data = readData(dataFile)
else:
data = dataFile
if type(treeFile) == str:
tree = loadTree(treeFile)
else:
tree = treeFile
attribute_count = len(data[0]) -1
if attribute_count != 2:
print "Too many attributes (%d), could draw only 2D plots..." % attribute_count
return 1
# Divide data vectors by their class, x, y; find min, max values.
point_dict = dict()
min_x, max_x, min_y, max_y = None, None, None, None
for record in data:
vector, cls = record[:-1], record[-1]
if not point_dict.has_key(cls):
point_dict[cls] = [[],[]]
x = vector[0]
y = vector[1]
point_dict[cls][0].append(x)
point_dict[cls][1].append(y)
if not min_x or x < min_x:
min_x = x
if not max_x or x > max_x:
max_x = x
if not min_y or y < min_y:
min_y = y
if not max_y or y > max_y:
max_y = y
# Draw tree:
point_cons = 400
min_x -= 2 * (min_x + max_x) / point_cons
max_x += 2 * (min_x + max_x) / point_cons
min_y -= 2 * (min_y + max_y) / point_cons
max_y += 2 * (min_y + max_y) / point_cons
## Draw tree:
tree_dict = dict()
for x in arange(min_x, max_x, (max_x - min_x) / point_cons):
for y in arange(min_y, max_y, (max_y - min_y) / point_cons):
point_cls = tree.getClass([x, y])
if not tree_dict.has_key(point_cls):
tree_dict[point_cls] = [[],[]]
tree_dict[point_cls][0].append(x)
tree_dict[point_cls][1].append(y)
for cls in tree_dict:
plt.scatter(tree_dict[cls][0], tree_dict[cls][1], 2,
c=colors[int(cls)][1], marker='s', linewidth=0)
# Draw data points:
for cls in point_dict:
plt.scatter(point_dict[cls][0], point_dict[cls][1], 33,
c=colors[int(cls)][0], marker='o')
plt.axis([min_x, max_x, min_y, max_y])
plt.title(', '.join([colors[cls][2] + ': ' + str(count) for (cls, count) in
classDistribution(data).items()]))
if save_to:
plt.savefig(save_to)
plt.cla()
else:
plt.show()
