import sys
sys.path.append('/usr/local/cellar/pil/1.1.7/lib/python2.7/site-packages/PIL/')
# 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 ''
#!/usr/bin/python
import sys
sys.path.append('/usr/local/cellar/pil/1.1.7/lib/python2.7/site-packages/PIL/')
# 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', 'fantasy', 'film-noir', 'horror', 'western'], 1)
tree = treepredict.buildtree(train_data,
gain_increment=0,
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, '):'
import sys
sys.path.append('/usr/local/cellar/pil/1.1.7/lib/python2.7/site-packages/PIL/')
# 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 ''
import sys
sys.path.append('/usr/local/cellar/pil/1.1.7/lib/python2.7/site-packages/PIL/')
# 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', 'fantasy','film-noir', 'horror', 'western'], 1)
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 ''
