def Dealing_with_Missing_Data(): print '>>Dealing with Missing Data' reload(treepredict) tree=treepredict.buildtree(treepredict.my_data) print '------------------' print treepredict.mdclassify(['google',None,'yes',None],tree) print treepredict.mdclassify(['google','France',None,None],tree)
def train_random_trees(train_data, origin_attribute_list, label_list,
sample_copy_count, attribute_count_per_tree):
trees = [] #用随机选取的多个训练集
for sample_copies_index in range(0, sample_copy_count):
sample_copy = generate_random_sample(train_data)
#每棵决策树使用的属性集（随机）
random_attributes_lists = choose_attributes_lists(
len(origin_attribute_list), attribute_count_per_tree)
#用不同属性集训练的决策树
for attributes_lists_per_tree in random_attributes_lists: #根据随机选定的属性集训练每棵决策树
#根据当前决策树使用的属性集，重新生成训练集（只剩下用到的属性）
reduced_data = organize_sample_with_selected_attributes(
sample_copy, attributes_lists_per_tree)
#将属性序号(attributes_lists_per_tree)转换成属性取值信息(real_attribute_list)
real_attribute_list = []
for index in attributes_lists_per_tree:
real_attribute_list.append(origin_attribute_list[index])
tree = treepredict.buildtree(reduced_data, real_attribute_list,
label_list)
tree_with_attribute_index = {
'tree': tree,
'attributes_index': attributes_lists_per_tree
}
trees.append(tree_with_attribute_index)
return trees
def testing_gain_increments(increments=[]):
classresults = {}
for increment in increments:
tree = treepredict.buildtree(train_data,
gain_increment=increment,
gain_threshold=0,
instance_minimum=1)
trainConfMat, crTrain = treepredict.testTree(train_data, tree)
print 'Training set confusion matrix (Classification rate:', crTrain, '):'
for row in trainConfMat:
print '\t'.join(map(lambda x: str(x), row))
print ''
testConfMat, crTest = treepredict.testTree(test_data, tree)
print 'Test set confusion matrix (Classification rate:', crTest, '):'
for row in testConfMat:
print '\t'.join(map(lambda x: str(x), row))
print ''
classresults[increment] = [crTest]
return classresults
def Modeling_Home_Prices():
print '>>Modeling Home Prices'
import zillow
if os.path.exists('housedata.txt'):
f=open('housedata.txt','r')
lines=f.readlines()
housedata=[]
for line in lines:
fields=line.split('\t')
l1=[fields[0],fields[1],fields[2],fields[3],fields[4],fields[5],fields[6]]
housedata.append(l1)
f.close();
else:
housedata=zillow.getpricelist( )
f=open('housedata.txt','w')
for l in housedata:
if l is None:
continue
print l
for k in l:
f.write('%s\t' % (k))
f.write('\n')
f.close
reload(treepredict)
housetree=treepredict.buildtree(housedata,scoref=treepredict.variance)
treepredict.drawtree(housetree,'housetree.jpg')
def main():
from treepredict import buildtree, entropy, drawtree
# house_data = getpricelist()
# print house_data
print 'build tree'
t = buildtree(house_data, scoref=entropy)
print 'draw tree'
drawtree(t, 'house_price_tree.jpeg')
def Pruning_the_Tree(): print '>>Pruning the Tree' reload(treepredict) tree=treepredict.buildtree(treepredict.my_data) print '------------------' treepredict.prune(tree,0.1) treepredict.printtree(tree) treepredict.prune(tree,1.0) treepredict.printtree(tree)
def main(rows):
# fruits with their colors and size
tree = treepredict.buildtree(rows)
# print(treepredict.classify([2, 'red'], tree))
# print(treepredict.classify([5, 'red'], tree))
# print(treepredict.classify([1, 'green'], tree))
# 决策树
treepredict.printtree(tree)
treepredict.drawtree(tree, jpeg='treeview.jpg')
def testing_gain_increments(increments=[]):
classresults={}
for increment in increments:
tree=treepredict.buildtree(train_data,gain_increment=increment,gain_threshold=0,instance_minimum=1)
trainConfMat, crTrain = treepredict.testTree(train_data, tree)
print 'Training set confusion matrix (Classification rate:', crTrain,'):'
for row in trainConfMat:
print '\t'.join(map(lambda x:str(x), row))
print ''
testConfMat, crTest = treepredict.testTree(test_data, tree)
print 'Test set confusion matrix (Classification rate:', crTest,'):'
for row in testConfMat:
print '\t'.join(map(lambda x:str(x), row))
print ''
classresults[increment]=[crTest]
return classresults
#print doc.toxml()
gender = doc.getElementsByTagName('gender')[0].firstChild.data
age = doc.getElementsByTagName('age')[0].firstChild.data
loc = doc.getElementsByTagName('location')[0].firstChild.data
region = None
for r, s in stateregions.iteritems():
if loc[0:2] in s: region = r
if region:
result.append((gender, int(age), region, rating))
except:
pass
return result
if __name__ == '__main__':
d = getrandomratings(50)
# hu, all results are always of the same gender?
pdata = getpeopledata(d)
print pdata
import drawtree
import treepredict
tree = treepredict.buildtree(pdata, treepredict.variance)
treepredict.prune(tree, 0.5)
drawtree.drawtree(tree, 'hottree.png')
print 'Wrote hottree.png'
def train_simple_tree(training_data):
print "Training Simple Tree"
tree = treepredict.buildtree(training_data)
return tree
def decision_tree1(self, evt):
import treepredict
reload(treepredict)
full_price = 1130
flights = self.fl_lines[('PEK','PVG')].set_of_flights
data = []
for deptid in flights.keys():
flight = flights[deptid]
ftype = flight['ftype']
deptdate = flight['date']
deptdate = deptdate.split('/')
# 获得周几
weekday = datetime.datetime(int(deptdate[0]),int(deptdate[1]),int(deptdate[2])).weekday()
weekday = int(weekday)
#print weekday
time = flight['time']
# 若起飞时间非常早或者非常晚，取为1，否则为0
if int(time[0:2]) < 9 or int(time[0:2]) > 20:
time = 1
else:
time = 0
# 处理价格
dd = flight['date']
# str-->date
dd = dd.split('/')
deptdate = datetime.date(int(dd[0]), int(dd[1]), int(dd[2]))
price = flight['price']
points = []
for ftdate in price.keys():
ff = ftdate.split('/')
fetchdate = datetime.date(int(ff[0]), int(ff[1]), int(ff[2]))
days = (deptdate-fetchdate).days
points.append((days, price[ftdate]))
points = self.pre(points)
p = []
for i in points.keys():
if i >= 6:
p.append(points[i])
#print p
if len(p) <= 1:
continue
result_price = p[0]
p.pop(0)
avg_price = sum(p)/len(p)
result_price = int(float(result_price)/float(full_price)*10)
avg_price = int(float(avg_price)/float(full_price)*10)
data.append((weekday, time, avg_price, result_price))
## fout = open('task.txt', 'w')
## lines = ['%s %s %s %s\n' %v for v in data]
## fout.writelines(lines)
## fout.close()
flighttree = treepredict.buildtree(data, scoref = treepredict.giniimpurity)
treepredict.drawtree(flighttree,'flighttree_entropy.jpg')
def testBasics(self):
t = treepredict.buildtree(treepredict.testdata())
self.assertEquals(treepredict.classify(['(direct)', 'USA', 'yes', 5], t),
{'Basic': 4})
try:
zipcode = doc.getElementsByTagName('zipcode')[0].firstChild.data
use = doc.getElementsByTagName('useCode')[0].firstChild.data
year = doc.getElementsByTagName('yearBuilt')[0].firstChild.data
bath = doc.getElementsByTagName('bathrooms')[0].firstChild.data
bed = doc.getElementsByTagName('bedrooms')[0].firstChild.data
#rooms = doc.getElementsByTagName('totalRooms')[0].firstChild.data
price = doc.getElementsByTagName('amount')[0].firstChild.data
except Exception, e:
#print e
return None
#return zipcode, use, int(year), float(bath), int(bed), int(rooms), price
return zipcode, use, int(year), float(bath), int(bed), price
def getpricelist():
return filter(None, [getaddressdata(line.strip(), 'Cambridge,MA')
for line in open('addresslist.txt')])
if __name__ == '__main__':
import drawtree
import treepredict
housedata = getpricelist()
print housedata
tree = treepredict.buildtree(housedata, scorefun=treepredict.variance)
drawtree.drawtree(tree, 'zillow.png')
print "Wrote zillow.png"
import treepredict as tr tree = tr.buildtree(tr.my_data) tr.printtree(tree) print tr.mdclassify(['google',None,'yes',None],tree) print tr.mdclassify(['google','France',None,None],tree)
def train_simple_tree(training_data):
print "Training Simple Tree"
tree = treepredict.buildtree(training_data)
return tree
import treepredict # fruits with their colors and size fruits = [ [4, 'red', 'apple'], [4, 'green', 'apple'], [1, 'red', 'cherry'], [1, 'green', 'grape'], [5, 'red', 'apple'] ] tree = treepredict.buildtree(fruits) treepredict.classify([2, 'red'], tree) treepredict.classify([5, 'red'], tree) treepredict.classify([1, 'green'], tree) treepredict.printtree(tree) #treepredict.drawtree(tree, jpeg='treeview.jpg')
orig_date = datetime.date(2014, 2, 1)
for i in range(60):
timedelta = datetime.timedelta(i)
cur_date = orig_date + timedelta
str_date = cur_date.strftime('%y/%m/%d')
str_date = "20" + str_date
new_flights = fl_lines.get_flights_by_dpdate(str_date)
train_flights.update(new_flights)
#train_flights = fl_lines.get_flights_by_dpdate('2014/05/25')
#print train_flights
print '训练数据读入完成,用时', time.clock()
train_data = get_data(train_flights)
#writeintxt('train_data.txt',train_data)
flighttree = treepredict.buildtree(train_data, scoref=treepredict.giniimpurity)
#treepredict.drawtree(flighttree, 'test.jpg')
print '树训练完成，用时%s，数据%s条' % (time.clock(), len(train_data))
test_flights = {}
orig_date = datetime.date(2014, 5, 2)
for i in range(10):
timedelta = datetime.timedelta(i)
cur_date = orig_date + timedelta
str_date = cur_date.strftime('%y/%m/%d')
str_date = "20" + str_date
new_flights = fl_lines.get_flights_by_dpdate(str_date)
test_flights.update(new_flights)
test_data = get_data(test_flights)
#writeintxt('test_data.txt', test_data)
def make_tree(self, data):
'''
Make a decision tree with the supplied data
'''
return treepredict.buildtree(data)
def Classifying_New_Observations(): reload(treepredict) tree=treepredict.buildtree(treepredict.my_data) print '>>Classifying New Observations' print treepredict.classify(['(direct)','USA','yes',5],tree)
def Recursive_Tree_Building(): print '>>Recursive Tree Building' reload(treepredict) tree=treepredict.buildtree(treepredict.my_data) treepredict.printtree(tree)
increments=[0] for i in xrange(1,10): x=10**(-i) increments.append(x) print 'Increments to be tested and passed to gain_increments',increments accuracyTest=testing_gain_increments(increments) #print accuracyTest values=accuracyTest.keys() values.sort(cmp=lambda a,b:cmp(accuracyTest[a],accuracyTest[b])) print 'Increment value with best classification rate was ',values[-1] # Let's see what it looks like... #print "\nFinal tree...\n" treepredict.printtree(treepredict.buildtree(train_data,gain_increment=values[-1],gain_threshold=0,instance_minimum=1)) # Produce a png of the tree treepredict.drawtree(tree,jpeg="sample_tree.jpg") #print "\npng of tree generated using PIL (Python Imaging Library) modules.\n" # Let's classify an incoming record of '(direct), USA, yes, 5' ... #incoming = ['(direct)','USA','yes',5] #print "Prediction of new record: ",treepredict.classify(incoming,tree) # Finally, what does pruning do with say a mingain = 0.9 ? #print "\nPruned tree...\n" #treepredict.prune(tree,0.9) #treepredict.printtree(tree) # For group homework, modify "buildtree" function so that it stops
code = doc.getElementsByTagName('code')[0].firstChild.data
if code != '0':
print 'Code Error!'
return None
try:
zipcode = doc.getElementsByTagName('zipcode')[0].firstChild.data
use = doc.getElementsByTagName('useCode')[0].firstChild.data
year = doc.getElementsByTagName('yearBuilt')[0].firstChild.data
bath = doc.getElementsByTagName('bathrooms')[0].firstChild.data
bed = doc.getElementsByTagName('bedrooms')[0].firstChild.data
rooms = doc.getElementsByTagName('totalRooms')[0].firstChild.data
price = doc.getElementsByTagName('amount')[0].firstChild.data
except:
print 'Error!'
return None
return (str(zipcode), str(use), int(year), float(bath), int(bed), int(rooms), int(price))
def getPriceList(filename):
pricelist = []
for line in open(filename):
data = getAddressData(line.strip(), 'Cambridge,MA')
if data != None: pricelist.append(data)
return pricelist
if __name__ == '__main__':
housedata = getPriceList('addresslist.txt')
housetree = treepredict.buildtree(housedata, scoreFunction=treepredict.variance)
treepredict.drawTree(housetree, 'housetree.jpg')
if best_gain > 0:
trueBranch = buildtree(best_sets[0])
falseBranch = buildtree(best_sets[1])
return decisionnode(col_num=best_criteria[0],
test=best_criteria[1],
tb=trueBranch,
fb=falseBranch)
else:
return decisionnode(results=uniquecounts(data))
set0, set1 = dividedata(data, 2, lambda answer: answer == 2017)
print(entropy(set0), entropy(set1), sep='\n')
tree = treepredict.buildtree(treepredict.data)
print(tree.col_num)
print(tree.test)
print(tree.results)
print("")
print(tree.tb.col_num)
print(tree.tb.test)
print(tree.tb.results)
print("")
print(tree.tb.tb.col_num)
print(tree.tb.tb.test)
print(tree.tb.tb.results)
print("")
print(tree.tb.fb.col_num)
print(tree.tb.fb.test)
# Script to demonstrate the CART-like DT classifier from
# Chapter 7 of "Programming Collective Intelligence" by
# T. Segaran, O'Reilly, (c) 2007
#
import treepredict
import fileinput
import Image
import ImageDraw
# If the last parameter is set to 0, then all attributes other than 'age' and 'war' would be used.
train_data, test_data = fileinput.loadDataset(5, ['age','gender','occupation','fantasy','film-noir', 'drama', 'western'], 1)
tree=treepredict.buildtree(train_data,gain_increment=0,gain_threshold=0,instance_minimum=100)
# Let's see what it looks like...
print "\nFinal tree...\n"
treepredict.printtree(tree)
trainConfMat, crTrain = treepredict.testTree(train_data, tree)
print 'Training set confusion matrix (Classification rate:', crTrain,'):'
for row in trainConfMat:
print '\t'.join(map(lambda x:str(x), row))
print ''
testConfMat, crTest = treepredict.testTree(test_data, tree)
print 'Test set confusion matrix (Classification rate:', crTest,'):'
def testBasics(self):
t = treepredict.buildtree(treepredict.testdata())
self.assertEquals(
treepredict.classify(['(direct)', 'USA', 'yes', 5], t),
{'Basic': 4})
if code != '0':
return None
# extract the info about this property
try:
zipcode = doc.getElementsByTagName('zipcode')[0].firstChild.data
use = doc.getElementsByTagName('useCode')[0].firstChild.data
year = doc.getElementsByTagName('yearBuilt')[0].firstChild.data
bath = doc.getElementsByTagName('bathrooms')[0].firstChild.data
bed = doc.getElementsByTagName('bedrooms')[0].firstChild.data
rooms = doc.getElementsByTagName('totalRooms')[0].firstChild.data
price = doc.getElementsByTagName('amount')[0].firstChild.data
except:
return None
return zipcode, use, int(year), float(bath), int(bed), int(rooms), price
def getpricelist():
l1 = []
for line in file('addresslist.txt'):
data = getaddressdata(line.strip(), 'Cambridge+MA')
l1.append(data)
return l1
if __name__ == "__main__":
housedata = getpricelist()
housedata = [data for data in housedata if data != None]
housetree = treepredict.buildtree(housedata, treepredict.variance)
treepredict.drawtree(housetree, 'housetree.jpg')
sort=False)
dataset = ads_df_final[['Adjective', 'Adverb', 'Noun', 'Verb', 'Sentiment']]
############# Splitting the Dataset into Testing and Training Sets ##############
final_acc = 0.0
for i in range(no_of_trials):
splitRatio = 0.7
trainingSet, testSet = splitDataset(dataset, splitRatio)
#print(trainingSet)
# print(type(trainingSet))
print('Split {0} rows into train = {1} and test = {2} rows'.format(
len(dataset), len(trainingSet), len(testSet)))
############# Model Building ##############
b = dt.buildtree(trainingSet)
dt.drawtree(b, jpeg='treeview.jpg')
#print("original_testset=",testSet)
############# Preparing Testing DataSet ##############
testlabels = []
for i in range(len(testSet)):
label = testSet[i].pop(-1)
testlabels.append(label)
#print("testSet=",testSet)
#print("testlabels=",testlabels)
############# Classification of Test Records ##############
number = 0
for i in range(len(testSet)):
#print("\ntest_data",testSet[i])
gender = doc2.getElementsByTagName('gender')[0].firstChild.data
age = doc2.getElementsByTagName('age')[0].firstChild.data
loc = doc2.getElementsByTagName('location')[0].firstChild.data[0:2]
# 将州转换成地区
for r, s in stateregions.items():
if loc in s: region = r
if region != None:
result.append((gender, int(age), region, rating))
except:
pass
return result
l1 = getrandomratings(500)
print len(l1)
pdata = getpeopledata(l1)
print pdata[0]
import treepredict
hottree = treepredict.buildtree(pdata, scoref=treepredict.variance)
treepredict.prune(hottree, 0.5)
treepredict.drawtree(hottree, 'hottree.jpg')
south = treepredict.mdclassify((None, None, 'south'), hottree)
midat = treepredict.mdclassify((None, None, 'Mid Atlantic'), hottree)
print south[10] / sum(south.values())
print midat[10] / sum(midat.values())
import treepredict
# fruits with their colors and size
fruits = [[4, 'red', 'apple'], [4, 'green', 'apple'], [1, 'red', 'cherry'],
[1, 'green', 'grape'], [5, 'red', 'apple']]
tree = treepredict.buildtree(fruits)
treepredict.classify([2, 'red'], tree)
treepredict.classify([5, 'red'], tree)
treepredict.classify([1, 'green'], tree)
treepredict.printtree(tree)
#treepredict.drawtree(tree, jpeg='treeview.jpg')
orig_date = datetime.date(2014, 2, 1)
for i in range(60):
timedelta = datetime.timedelta(i)
cur_date = orig_date + timedelta
str_date = cur_date.strftime('%y/%m/%d')
str_date = "20"+str_date
new_flights = fl_lines.get_flights_by_dpdate(str_date)
train_flights.update(new_flights)
#train_flights = fl_lines.get_flights_by_dpdate('2014/05/25')
#print train_flights
print '训练数据读入完成,用时', time.clock()
train_data = get_data(train_flights)
#writeintxt('train_data.txt',train_data)
flighttree = treepredict.buildtree(train_data, scoref=treepredict.giniimpurity)
#treepredict.drawtree(flighttree, 'test.jpg')
print '树训练完成，用时%s，数据%s条' %(time.clock(), len(train_data))
test_flights = {}
orig_date = datetime.date(2014, 5, 2)
for i in range(10):
timedelta = datetime.timedelta(i)
cur_date = orig_date + timedelta
str_date = cur_date.strftime('%y/%m/%d')
str_date = "20"+str_date
new_flights = fl_lines.get_flights_by_dpdate(str_date)
test_flights.update(new_flights)
test_data = get_data(test_flights)
#writeintxt('test_data.txt', test_data)
for i in xrange(1, 10):
x = 10**(-i)
increments.append(x)
print 'Increments to be tested and passed to gain_increments', increments
accuracyTest = testing_gain_increments(increments)
#print accuracyTest
values = accuracyTest.keys()
values.sort(cmp=lambda a, b: cmp(accuracyTest[a], accuracyTest[b]))
print 'Increment value with best classification rate was ', values[-1]
# Let's see what it looks like...
#print "\nFinal tree...\n"
treepredict.printtree(
treepredict.buildtree(train_data,
gain_increment=values[-1],
gain_threshold=0,
instance_minimum=1))
# Produce a png of the tree
treepredict.drawtree(tree, jpeg="sample_tree.jpg")
#print "\npng of tree generated using PIL (Python Imaging Library) modules.\n"
# Let's classify an incoming record of '(direct), USA, yes, 5' ...
#incoming = ['(direct)','USA','yes',5]
#print "Prediction of new record: ",treepredict.classify(incoming,tree)
# Finally, what does pruning do with say a mingain = 0.9 ?
#print "\nPruned tree...\n"
#treepredict.prune(tree,0.9)
#treepredict.printtree(tree)
def do_simpletree_kcross_validation(fin,finy,kfolds):
print "Starting k=" + str(kfolds)+" validation for Simple tree"
#there is 2500 tracks
labels = dt.get_lines(finy,int)
pb = ProgBar()
lines = dt.get_lines(fin,float," ", callback = pb.callback)
del pb
#normalize features
lines = dt.transform_features(lines)
data = dt.add_labels_to_lines(lines, labels)
block_size = len(lines)/kfolds
print "chunk size = " + str(block_size)
example_chunks = list(dt.chunks(data, block_size))
#labels_chunks = list(dt.chunks(labels, block_size))
print "number of chunks = " +str(len(example_chunks))
#holds avg accuracy for one forest
accuracy_results = []
for i in range(0,len(example_chunks)):
#we leave set in index i out of train
print "prepare validation set"
validationdata = example_chunks[i]
#extract validation chunk
print "leaving out block " + str(i) + " for validation"
leaveout = i
validationdata = [ exampleentry(validationdata[i][0:len(validationdata[i])-1],validationdata[i][-1]) for i in range(0,len(validationdata)) ]
trainingdata = []
print("merging blocks "),
for j in range(0,len(example_chunks)):
if(j != leaveout):
#print "j="+str(j) + " i="+ str(leaveout)
print(str(j) + ","),
trainingdata = trainingdata + example_chunks[j]
print "\nprepare training set"
print "training on " + str(len(trainingdata))
print "each track has " + str(len(trainingdata[0])) + " features"
tree = treepredict.buildtree(trainingdata)
print "testing on " + str(len(validationdata))
corrects = 0
#classify a set of entries
for example in validationdata:
#print example.features
result = treepredict.classify(example.features,tree)
#print 'expected : ' + str(example.label) + ' result : '+ str(result)
if(result == example.label):
corrects = corrects + 1
#calculate the % of accuracy
accuracy_percentage = (corrects*100)/len(validationdata)
print "accuracy = " + str(accuracy_percentage) + "%"
accuracy_results.append(accuracy_percentage)
avgcc = dt.average(accuracy_results)
print "average accuracy ="+ str(avgcc) + "%"
def do_simpletree_kcross_validation(fin, finy, kfolds):
print "Starting k=" + str(kfolds) + " validation for Simple tree"
#there is 2500 tracks
labels = dt.get_lines(finy, int)
pb = ProgBar()
lines = dt.get_lines(fin, float, " ", callback=pb.callback)
del pb
#normalize features
lines = dt.transform_features(lines)
data = dt.add_labels_to_lines(lines, labels)
block_size = len(lines) / kfolds
print "chunk size = " + str(block_size)
example_chunks = list(dt.chunks(data, block_size))
#labels_chunks = list(dt.chunks(labels, block_size))
print "number of chunks = " + str(len(example_chunks))
#holds avg accuracy for one forest
accuracy_results = []
for i in range(0, len(example_chunks)):
#we leave set in index i out of train
print "prepare validation set"
validationdata = example_chunks[i]
#extract validation chunk
print "leaving out block " + str(i) + " for validation"
leaveout = i
validationdata = [
exampleentry(validationdata[i][0:len(validationdata[i]) - 1],
validationdata[i][-1])
for i in range(0, len(validationdata))
]
trainingdata = []
print("merging blocks "),
for j in range(0, len(example_chunks)):
if (j != leaveout):
#print "j="+str(j) + " i="+ str(leaveout)
print(str(j) + ","),
trainingdata = trainingdata + example_chunks[j]
print "\nprepare training set"
print "training on " + str(len(trainingdata))
print "each track has " + str(len(trainingdata[0])) + " features"
tree = treepredict.buildtree(trainingdata)
print "testing on " + str(len(validationdata))
corrects = 0
#classify a set of entries
for example in validationdata:
#print example.features
result = treepredict.classify(example.features, tree)
#print 'expected : ' + str(example.label) + ' result : '+ str(result)
if (result == example.label):
corrects = corrects + 1
#calculate the % of accuracy
accuracy_percentage = (corrects * 100) / len(validationdata)
print "accuracy = " + str(accuracy_percentage) + "%"
accuracy_results.append(accuracy_percentage)
avgcc = dt.average(accuracy_results)
print "average accuracy =" + str(avgcc) + "%"
import treepredict
# main function
# print('<----DivideSet---->')
# for item in treepredict.divideset(treepredict.my_data, 2, 'yes'):
# print(item)
#
print('\n<----Build and Display the Tree---->')
tree = treepredict.buildtree(treepredict.my_data)
treepredict.printtree(tree)
#
# print('\n<----Graphical Display---->')
# path = 'output/treeview.jpg'
# treepredict.drawtree(tree, jpeg=path)
# print("picture has been saved in " + path)
#
# print('\n<----Classify and prune---->')
# test = ['(direct)', 'USA', 'yes', 5]
# print(test)
# print(treepredict.classify(test, tree), '\n')
#
# print('Before pune:')
# treepredict.printtree(tree)
# treepredict.prune(tree, 1.0)
# print('\nAfter pune:')
# treepredict.printtree(tree)
# print('<----Zillow API---->')
# import zillow
# # housedata = zillow.getpricelist()
# # print('house data saved!')
print "\nParent node...\n" gini = treepredict.giniimpurity(treepredict.my_data) entr = treepredict.entropy(treepredict.my_data) print "Gini: %8f Entropy: %8f" % (gini, entr) # Let's now split on the Read FAQ field and assess impurity node1, node2 = treepredict.divideset(treepredict.my_data, 2, "yes") print "\nRead FAQ = Yes leaf node...\n" gini = treepredict.giniimpurity(node1) entr = treepredict.entropy(node1) print "Gini: %8f Entropy: %8f" % (gini, entr) # Build the DT recursively using the buildtree function; assumes # last column/field is the classification attribute. tree = treepredict.buildtree(treepredict.my_data) # Let's see what it looks like... print "\nFinal tree...\n" treepredict.printtree(tree) # Produce a png of the tree treepredict.drawtree(tree, jpeg="sample_tree.jpg") print "\npng of tree generated using PIL (Python Imaging Library) modules.\n" # Let's classify an incoming record of '(direct), USA, yes, 5' ... incoming = ["(direct)", "USA", "yes", 5] print "Prediction of new record: ", treepredict.classify(incoming, tree) # Let's see how the missing data classification via # the "mdclassify" function performs on our sample data.
["t", "China", "no", 17, "None"],
]
my_data2 = [
["a", "USA", "yes", "18", "None"],
["b", "France", "yes", "23", "Premium"],
["c", "USA", "yes", "24", "Basic"],
["d", "France", "yes", "23", "Basic"],
]
train_flowers = data.read_filedata("..//data//train_data.txt", "ALL", ",", [0, 1, 2, 3])
test_flowers = data.read_filedata("..//data//test_data.txt", "ALL", ",", [0, 1, 2, 3])
tree = DecisionTree(train_flowers)
treepredict.buildtree(tree)
tree.printTree()
right = 0
wrong = 0
for flower in test_flowers:
result = treepredict.predic(tree, flower)
if flower[-1] in result:
if right == 49:
pass
right += 1
else:
wrong += 1
print "正确预测：" + str(right) + "个"
print "错误预测：" + str(wrong) + "个"
import treepredict as tp
if __name__ == '__main__':
print("ESERCIZIO SU IRIS DATASET\n")
train_data = []
test_data = []
mydata = tp.aprifile("iris.txt")
train_data, test_data = tp.createdataset2(mydata, 60, [])
print("TRAIN DATA : \n")
print(train_data, "\n")
print("TEST DATA: \n")
print(train_data)
iris_tree = tp.buildtree(train_data)
tp.drawtree(iris_tree, "iris_tree.jpeg")
tp.fperformance(mydata)
import advancedclassify as ad
import treepredict as tr
agesonly = ad.loadmatch('agesonly.csv', allnum=True)
matchmaker = ad.loadmatch('matchmaker.csv')
# ad.plotagematches(agesonly)
age = []
for line in file('agesonly.csv'):
l = []
for w in line.split(','):
l.append(int(w))
age.append(l)
tree = tr.buildtree(age)
tr.printtree(tree)
tr.drawtree(tree)
print tr.classify(tree, [65, 63])
avgs = ad.lineartrain(agesonly)
print avgs
print ad.dpclassify([30, 25], avgs.values())
print ad.dpclassify([25, 40], avgs.values())
print ad.dpclassify([48, 20], avgs.values())
print tr.classify(tree, [30, 25])
print tr.classify(tree, [25, 40])
print tr.classify(tree, [48, 20])
def tree_view():
from treepredict import buildtree, drawtree
my_data = [map(float, line.split(',')) for line in open('data/agesonly.csv')]
tree = buildtree(my_data)
drawtree(tree, 'treeview.png')
import treepredict
import preprocessor
import postprocessor
import arff
import copy
label_count = 6
train_data_file = '.\\scene\\scene-train-tiny.arff'
test_data_file = '.\\scene\\scene-test-tiny.arff'
method = input('1 单标签；2 多个二类分类')
if method == '1':
#读取训练集，建树(多标签转换成单标签)
(attributes_list, label_value_list,train_data) = preprocessor.read_data(train_data_file, label_count, arff.DENSE)
train_data = preprocessor.translate_label_multiclass(train_data, label_count)
tree = treepredict.buildtree(train_data, attributes_list, label_value_list)
treepredict.printtree(tree)
#读取测试集，验证效果
(test_attributes_list, test_label_value_list, test_data) = preprocessor.read_data(test_data_file, label_count, arff.DENSE)
test_data_copy = copy.deepcopy(test_data)
predicted_labels_list = []
for row in test_data:
result = treepredict.classify(row, tree, test_attributes_list)
post_result = treepredict.post_classify(result)
decoded_result = preprocessor.label_decoding(post_result)
predicted_labels_list.append(decoded_result)
hamming_loss = postprocessor.hamming_loss(test_data_copy, predicted_labels_list)
print('hamming loss of merging labels:', hamming_loss)
else :
#当做多个二类分类问题处理
# Script to demonstrate the CART-like DT classifier from
# Chapter 7 of "Programming Collective Intelligence" by
# T. Segaran, O'Reilly, (c) 2007
#
import treepredict
import fileinput
import Image
import ImageDraw
# If the last parameter is set to 0, then all attributes other than 'age' and 'war' would be used.
train_data, test_data = fileinput.loadDataset(2, ['age', 'gender','occupation','unknown genre','film-noir', 'horror', 'western'], 1)
tree=treepredict.buildtree(train_data,gain_increment=0,gain_threshold=0,instance_minimum=0)
# Let's see what it looks like...
print "\nFinal tree...\n"
treepredict.printtree(tree)
trainConfMat, crTrain = treepredict.testTree(train_data, tree)
print 'Training set confusion matrix (Classification rate:', crTrain,'):'
for row in trainConfMat:
print '\t'.join(map(lambda x:str(x), row))
print ''
testConfMat, crTest = treepredict.testTree(test_data, tree)
print 'Test set confusion matrix (Classification rate:', crTest,'):'
# 状态码为0代表操作成功， 否则代表有错误发生
if code != '0': return None
# 提取有关该房产的信息
try:
zipcode = doc.getElementsByTagName('zipcode')[0].firstChild.data
use = doc.getElementsByTagName('useCode')[0].firstChild.data
year = doc.getElementsByTagName('yearBuilt')[0].firstChild.data
bath = doc.getElementsByTagName('bathrooms')[0].firstChild.data
bed = doc.getElementsByTagName('bedrooms')[0].firstChild.data
rooms = doc.getElementsByTagName('totalRooms')[0].firstChild.data
price = doc.getElementsByTagName('amount')[0].firstChild.data
except:
return None
return (zipcode, use, int(year), float(bath), int(bed), int(rooms), price)
'''读取addresslist.txt文件并构造一个数据列表'''
def getpricelist():
l1 = []
for line in file('../data/addresslist.txt'):
data = getaddressdata(line.strip(), 'Cambridge, MA')
l1.append(data)
return l1
import treepredict
housedata = getpricelist()
housetree = treepredict.buildtree(housedata, scoref=treepredict.variance)
treepredict.drawtree(housetree, 'housetree.jpg')
import treepredict as tp
if __name__ == '__main__':
print("ESERCIZIO SU MUSHROOMS DATASET\n")
train_data = []
test_data = []
print("ALL DATASET:\n")
mydata = tp.aprifile("mushrooms_final.txt")
train_data, test_data = tp.createdataset2(mydata, 3250, [])
print("TRAIN DATA : \n")
print(train_data, "\n")
print("TEST DATA: \n")
print(train_data)
mushrooms_tree = tp.buildtree(train_data)
tp.drawtree(mushrooms_tree, "mushrooms_tree.jpeg")
tp.fperformance(mydata)