File size: 2,444 Bytes
eb90a8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | """
Program for code classification by Bag or Tokens technique
using cluster analysis
Implements nearest cluster simple classifier of sorce code:
The programm processes all files in the given directory
Each file there contains all samples of one class of samples,
i.e. tokenised source code
Number of classes (lables) is defined automatically
"""
import sys
import os
import argparse
sys.path.extend(["../BagOfTokens"])
from DatasetLoader import BagOfTokensLoader
from NearestCluster import NearestClusterClassifier
def main(args):
"""
Main function of program for classifying source code
Parameters:
- args -- Parsed command line arguments
as object returned by ArgumentParser
"""
ds_loader = BagOfTokensLoader(args.samples, args.tokens)
all_samples, problem_samples = ds_loader.getPartitionedSampes()
val_train_split = args.valpart
val_samples = \
[_solutions[: int(float(len(_solutions)) * val_train_split)]
for _solutions in problem_samples]
train_samples = \
[_solutions[int(float(len(_solutions)) * val_train_split) :]
for _solutions in problem_samples]
classifier = NearestClusterClassifier(train_samples, val_samples)
#classifier.nClustersTrain(args.clusters)
classifier.scaledClustersTrain(args.clusters)
accuracy = classifier.validate()
print("Accuracy: ", accuracy)
#######################################################################
# Command line arguments of are described below
#######################################################################
if __name__ == '__main__':
print("\nCODE CLASSIFICATION WITH BAG OF TOKENS CLUTERING CLASSIFIER")
#Command-line arguments
parser = argparse.ArgumentParser(
description = "Bag of Tokens program source code classifier")
parser.add_argument('samples',
type=str, help='path to data directory')
parser.add_argument('--tokens', default=17, type=int,
help='number of tokens')
parser.add_argument('--valpart', default=0.2, type=float,
help='fraction of samples for validation')
parser.add_argument('--clusters', default=8, type=int,
help='number of clusters')
args = parser.parse_args()
print("Parameter settings used:")
for k,v in sorted(vars(args).items()):
print("{}: {}".format(k,v))
main(args)
|