forked from lineker/Random-Forests
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test.py
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test.py
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import treepredict
import treerandom
import big_treerandom
import kcrossvalidation
from exampleentry import *
import sys
import data_handling as dt
from prog_bar import ProgBar
import random
from math import sqrt
import argparse
def get_file(filename):
"""
Tries to extract a filename from the command line. If none is present, it
prompts the user for a filename and tries to open the file. If the file
exists, it returns it, otherwise it prints an error message and ends
execution.
"""
try:
fin = open(filename, "r")
except IOError:
print "Error: The file '%s' was not found on this system." % filename
sys.exit(0)
return fin
def classify_output(classifier, base, k = -1):
print "Classifying testx.txt --> result.csv"
fin = open("testx.txt", "r")
pb = ProgBar()
lines = dt.get_lines(fin,float," ", callback = pb.callback)
del pb
testdata = dt.transform_features(lines)
name = "result.csv"
if k > -1:
name = "result"+str(k)+".csv"
resultset = open(name,"w")
for example in testdata:
#print len(example)
result = base.classify(example,classifier)
resultset.write(str(result)+'\n')
fin.close()
resultset.close()
def get_training_validation_set(fin,finy,training_start,training_end,validation_start,validation_end):
labels = dt.get_lines(finy,int)
pb = ProgBar()
lines = dt.get_lines(fin,float," ", callback = pb.callback)
del pb
#selecting training set
training_features = dt.select_subset(lines,start=training_start,end=training_end)
training_labels = dt.select_subset(labels,start=training_start,end=training_end)
#normalizing features
training_features = dt.transform_features(training_features)
training_data = dt.add_labels_to_lines(training_features, labels)
#selecting validation set
validation_features = dt.select_subset(lines,start=validation_start,end=validation_end)
validation_labels = dt.select_subset(labels,start=validation_start,end=validation_end)
#handling features
validation_features = dt.transform_features(validation_features)
validation_data = [ exampleentry(validation_features[i],validation_labels[i]) for i in range(0,len(validation_features)) ]
#random.shuffle(training_data)
return (training_data,validation_data)
def train_simple_tree(training_data):
print "Training Simple Tree"
tree = treepredict.buildtree(training_data)
return tree
def train_randomized_forest(training_data):
print "Training Random Forest"
pb = ProgBar()
#m=100,kcandidates=10,nmin=15 -> 53%
forest = treerandom.build_randomized_forest(training_data,m=100,kcandidates=5,nmin=5, callback = pb.callback)
del pb
return forest
def train_big_randomized_forest(training_data):
print "Training Random Big Forest of Forest"
pb = ProgBar()
forest = big_treerandom.build_random_big_forest(training_data,m=100,kcandidates=5,nmin=5,number_of_forests=10, callback = pb.callback)
del pb
return forest
def accuracy(test_data, classifier, base):
print "Calculating accuracy"
corrects = 0
#classify a set of entries
for example in test_data:
#print example.features
result = base.classify(example.features,classifier)
if type(result) is dict:
print str(result.keys()[0]) + "-->" + str(example.label)
if(result.keys()[0] == example.label):
corrects = corrects + 1
else:
if(result == example.label):
corrects = corrects + 1
#calculate the % of accuracy
print "accuracy = " + str((corrects*100)/len(test_data)) + "%"
return float(corrects)/float(len(test_data))
def run_k_times(k=1):
for i in range(0,k):
fin = get_file("trainx.txt")
finy = get_file("trainy.csv")
kcrossvalidation.do_kcross_validation(fin,finy,10)
#kcrossvalidation.do_simpletree_kcross_validation(fin,finy,5)
#(training,validation) = get_training_validation_set(fin,finy,training_start=0,training_end=2500,validation_start=2000,validation_end=2500)
#tree = train_simple_tree(training)
#treepredict.prune(tree,1)
#print "accuracy : " + str(accuracy(validation,tree, treepredict))
#forest = train_randomized_forest(training)
#print "accuracy : " + str(accuracy(validation,forest, treerandom))
#big_forest = train_big_randomized_forest(training)
#print "accuracy : " + str(accuracy(validation,big_forest, big_treerandom))
#classify_output(forest, treerandom)
#classify_output(forest, big_treerandom)
fin.close()
finy.close()
def command_line():
args = parser.parse_args()
if(args.validation):
if(args.kfolds):
fin = get_file(args.trainx[0])
finy = get_file(args.trainy[0])
if(args.method and args.method[0]=='forest'):
kcrossvalidation.do_kcross_validation(fin,finy,int(args.kfolds[0]))
if(args.testx):
fin = get_file(args.trainx[0])
finy = get_file(args.trainy[0])
(training,validation) = get_training_validation_set(fin,finy,training_start=0,training_end=2500,validation_start=0,validation_end=1)
if(args.method and args.method[0]=='forest'):
forest = train_randomized_forest(training)
classify_output(forest, treerandom)
#print "accuracy : " + str(accuracy(validation,forest, treerandom))
print "done"
parser = argparse.ArgumentParser(description='Classify Genre of songs using Decision Tree & Random Forest Classifiers')
parser.add_argument('--trainx', '-x', metavar='X', type=str, nargs='+',help='training set without labels', required=True)
parser.add_argument('--trainy','-y', metavar='Y', type=str, nargs='+', help='training set labels', required=True)
parser.add_argument('--testx','-t', metavar='T', type=str, nargs='+', help='training set labels')
parser.add_argument('--validation','-v', metavar='V', type=str, nargs='+', help='activate k cross validation')
parser.add_argument('--kfolds','-k', metavar='K', type=str, nargs='+', help='choose number of folds for cross validation')
parser.add_argument('--method','-m', metavar='M', type=str, nargs='+', help='choose method random forest or decision tree',required=True)
parser.add_argument('--prune','-p', metavar='P', type=str, nargs='+', help='activate pruning for simple decision tree')
if __name__ == "__main__":
command_line()
#run_k_times(1)