def main():
args = sys.argv
if (len(args) < 2):
print("You should provide a filename to data.")
filename1 = 'car.c45-names.txt' #attributes
filename2 = 'car.data' # data examples
else:
filename1 = str(sys.argv[0])
filename2 = str(sys.argv[1]) # data examples
dataset = Data()
dataset.attr_file = filename1
dataset.data_file = filename2
dataset.read_attr_data()
dataset.read_examples_data()
# Proportion training set to testing set (1 means only training set)
PROPORTION = 1
train_dtset = copy.deepcopy(dataset)
test_dtset = copy.deepcopy(dataset)
train_dtset.examples, test_dtset.examples = [], []
total = len(dataset.examples)
# polluting train dataset
train_index_list = random.sample(xrange(total), int(total * PROPORTION))
train_dtset.examples = [
dataset.examples[index] for index in train_index_list
if (dataset.examples[index] not in train_dtset.examples)
]
# polluting test dataset
test_dtset.examples = [
ex for ex in dataset.examples if (ex not in train_dtset.examples)
]
print("Computing tree...")
root = compute_tree(train_dtset, None, None)
tree_filename = 'results/tree.txt'
with open(tree_filename, "w") as tree_file:
write_tree(root, 0, tree_file)
def main():
args = sys.argv
filename1 = 'car.c45-names.txt' #attributes
filename2 = 'car.data' # data examples
# repetition of tree creation
M = 10
# subset length of random elements from testing subset
N = 800
# repeatition of M time creation of tree
NM = 10
# Proportion training set to testing set
PROPORTION = 0.9
if ("-n" in args):
try:
N = int(args[args.index("-n") + 1])
except Exception as e:
print(e)
return
else:
print('Parametr for N is not defined.')
return
if ("-m" in args):
try:
M = int(args[args.index("-m") + 1])
except Exception as e:
print(e)
return
else:
print('Parametr for M is not defined.')
return
if ("-nm" in args):
try:
NM = int(args[args.index("-nm") + 1])
except Exception as e:
print(e)
return
else:
print('Parametr for NM is not defined.')
return
dataset = Data()
dataset.attr_file = filename1
dataset.data_file = filename2
dataset.read_attr_data()
dataset.read_examples_data()
print("Computing tree...")
# counts each appearance of attr in each tree
ranking_list_counting = Counter()
for attr in dataset.attr_names:
# ranking_list[attr] = 0
ranking_list_counting[attr] = 0
# counting ranking
ranking_list_final = []
num_attributes = []
# running method for ranking_list_counting, means that counts number of appearences in each tree
for i in range(NM):
ranking_list_counting = Counter(
{key: 0
for (key, val) in ranking_list_counting.items()})
for i in range(M):
# function that includes creating tree
processing_for_ranking(dataset, N, ranking_list_counting)
num_attributes.append(ranking_list_counting)
ranking_list_final.append(
final_rank(ranking_list_counting.most_common()))
title_rank = 'Ranking atrybutów drzewa wykonujacego sie ' + str(
M) + ' razy i rozmiar podzbioru losowego ' + str(N)
y_label = "Ranking atrytutów (1 - najczęsciej pojawiający się)"
filename = 'results/ranking_N' + str(N) + '_M' + str(M) + '.csv'
print(
"Ranking attributes for {0} tree repetations and {1} length of random subset."
.format(M, N))
diagram_printing_num(ranking_list_final, filename, title_rank, y_label)
title_count = 'Ilość atrybutów w drzewie` wykonujacego sie ' + str(
M) + ' razy i rozmiar podzbioru losowego ' + str(N)
y_label = "Ilość pojawianie się atr. (im wyższy - tym więcej)"
filename = 'results/attrs_number_N' + str(N) + '_M' + str(M) + '.csv'
print(
"Ranking attributes for {0} tree repetations and {1} length of random subset."
.format(M, N))
diagram_printing_num(num_attributes, filename, title_count, y_label)
