def main():
parser = argparse.ArgumentParser(description="csv data file path")
parser.add_argument("--csv", type=str, help="The data file path")
parser.add_argument(
"--eval",
type=str,
default="gini",
help=
"The evaluation function, could be gini or entropy. Default using gini."
)
cli_args = parser.parse_args()
if cli_args.eval not in ['gini', 'entropy']:
print('The evaluation function should be gini or entropy')
exit(0)
data = pd.read_csv(cli_args.csv)
train = data.sample(frac=0.75, random_state=0)
test = pd.concat([train, data]).drop_duplicates(keep=False)
class_weights = {'setosa': 1, 'versicolor': 1, 'virginica': 1}
tree = DecisionTree()
tree.fit(train, class_weights, gini)
# print(tree._error_rate(tree.root))
print(tree._count_leaf(tree.root))
# tree.prune(test, 0.0)
print(tree.treeToString())
data = pd.DataFrame(
[[5.1, 3.5, np.nan, 1]],
columns=['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'])
print(tree.classify(data))
tree.savePDF('output.pdf')
tree.savePNG('output.png')
def main():
parser = argparse.ArgumentParser(description="csv data file path")
parser.add_argument("--csv", type=str, help="The data file path")
parser.add_argument(
"--eval",
type=str,
default="gini",
help=
"The evaluation function, could be gini or entropy. Default using gini."
)
cli_args = parser.parse_args()
if cli_args.eval not in ['gini', 'entropy']:
print('The evaluation function should be gini or entropy')
exit(0)
data = pd.read_csv(cli_args.csv)
tree = DecisionTree()
str_list = '''
0:[CRP(mg/L)<5.5] yes=1,no=2,missing=2
1:[白细胞总数(x10^9/L)<18.6850014] yes=3,no=4,missing=3
3:[血小板计数(x10^9/L)<171.5] yes=7,no=8,missing=7
7:[白细胞总数(x10^9/L)<11.8999996] yes=13,no=14,missing=13
13:[CRP(mg/L)<1.5] yes=19,no=20,missing=20
19:[中性粒细胞百分比(%)<52.5999985] yes=27,no=28,missing=28
27:leaf=-0.0121546965
28:leaf=0.0117647061
20:[出生时体重(g)<1840] yes=29,no=30,missing=29
29:leaf=0.0510822535
30:leaf=0.00118343194
14:[白细胞总数(x10^9/L)<14.71] yes=21,no=22,missing=21
21:leaf=-0.0139534893
22:leaf=0.00118343194
8:[临床表现异常数<1.5] yes=15,no=16,missing=15
15:[PCT(ng/ML)<0.375] yes=23,no=24,missing=23
23:[中性杆状核粒细胞百分比(%)<5] yes=31,no=32,missing=31
31:leaf=-0.146943495
32:leaf=0.00930232555
24:[中性粒细胞百分比(%)<41.0500031] yes=33,no=34,missing=34
33:leaf=0.0122905029
34:leaf=-0.00952380989
16:[出生时体重(g)<1340] yes=25,no=26,missing=25
25:leaf=-0.00346820801
26:[出生时体重(g)<1670] yes=35,no=36,missing=35
35:leaf=0.0171428584
36:leaf=-0.00116959063
4:[PCT(ng/ML)<0.13499999] yes=9,no=10,missing=10
9:leaf=-0.00952380989
10:[出生时体重(g)<2270] yes=17,no=18,missing=17
17:leaf=0.084153004
18:leaf=0.00118343194
2:[CRP(mg/L)<6.5] yes=5,no=6,missing=6
5:[白细胞总数(x10^9/L)<12.04] yes=11,no=12,missing=11
11:leaf=0.0200000014
12:leaf=-0.00952380989
6:leaf=0.117241383
'''
tree.buildFromString(str_list.split('\n'), data, {0: 1, 1: 2.5})
print(cal_metric(tree.classify(data), data.values[:, -1]))
tree.index_to_class = {0: '无感染', 1: '感染'}
tree.savePDF('parse_output.pdf')
tree.savePNG('parse_output.png')
