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')
test_labels = test_data[:, 0]
test_data = test_data[:, 1:]
# create a map of the attributes so we can retain the original column numbers as the tree splits the data
attributes = list(range(len(train_data[0])))
# Do an initial run with the full training dataset
correct = []
p_max = 1.0
level_max = 9
tree = DecisionTree(train_data,
train_labels,
attributes,
p_threshold=p_max,
max_level=level_max)
y = tree.classify(test_data)
print("correct = {}".format(
sum(np.asarray(y == test_labels, dtype=int)) / len(y) * 100))
# Do 10 runs of 25-round bootstrap training varying the depth of the trees from 1 to 10 levels
n = 25
num_depths = 10
bias = np.zeros(num_depths)
variance = np.zeros(num_depths)
accuracy = np.zeros(num_depths)
depths = np.arange(1, num_depths + 1)
for depth in depths:
y = np.zeros((n, len(test_data)))
# We are assuming that N(x) = 0, so there's no noise. This means y_star = y_t
y_star = t = test_labels
for i in range(n):
train_data = np.array(read_file('data/spect_train.txt', sep=','))
train_labels = train_data[:,0]
train_data = train_data[:,1:]
test_data = np.array(read_file('data/spect_test.txt', sep=','))
test_labels = test_data[:,0]
test_data = test_data[:,1:]
# create a map of the attributes so we can retain the original column numbers as the tree splits the data
attributes = list(range(len(train_data[0])))
# Do an initial run with the full training dataset
correct=[]
p_max = 1.0
level_max = 9
tree = DecisionTree(train_data, train_labels, attributes, p_threshold=p_max, max_level=level_max)
y = tree.classify(test_data)
print("correct = {}".format(sum(np.asarray(y == test_labels, dtype=int))/len(y)*100))
# Do 10 runs of 25-round bootstrap training varying the depth of the trees from 1 to 10 levels
n = 25
num_depths = 10
bias = np.zeros(num_depths)
variance = np.zeros(num_depths)
accuracy = np.zeros(num_depths)
depths = np.arange(1,num_depths + 1)
for depth in depths:
y = np.zeros((n, len(test_data)))
# We are assuming that N(x) = 0, so there's no noise. This means y_star = y_t
y_star = t = test_labels
for i in range(n):
boot_data, boot_labels = bootstrap_replicate(train_data, train_labels)
from dtree import DecisionTree
from id3 import information_gain
values = [
"sunny hot high weak no", "sunny hot high strong no",
"overcast hot high weak yes", "rain mild high weak yes",
"rain cool normal weak yes", "rain cool normal strong no",
"overcast cool normal strong yes", "sunny mild high weak no",
"sunny cool normal weak yes", "rain mild normal weak yes",
"sunny mild normal strong yes", "overcast mild high strong yes",
"overcast hot normal weak yes", "rain mild high strong no"
]
keys = ["outlook", "temperature", "humidity", "wind", "target"]
data = [dict(zip(keys, x.split())) for x in values]
if __name__ == '__main__':
tree = DecisionTree(data,
keys[:-1],
information_gain,
target_name=keys[-1])
from pprint import PrettyPrinter
pp = PrettyPrinter()
pp.pprint(tree.tree)
print[(x[0]['target'], x[1]) for x in tree.classify(data)]
from dtree import DecisionTree
from id3 import information_gain
values = ["sunny hot high weak no",
"sunny hot high strong no",
"overcast hot high weak yes",
"rain mild high weak yes",
"rain cool normal weak yes",
"rain cool normal strong no",
"overcast cool normal strong yes",
"sunny mild high weak no",
"sunny cool normal weak yes",
"rain mild normal weak yes",
"sunny mild normal strong yes",
"overcast mild high strong yes",
"overcast hot normal weak yes",
"rain mild high strong no"]
keys = ["outlook", "temperature", "humidity", "wind", "target"]
data = [dict(zip(keys, x.split())) for x in values]
if __name__ == '__main__':
tree = DecisionTree(data, keys[:-1], information_gain, target_name=keys[-1])
from pprint import PrettyPrinter
pp = PrettyPrinter()
pp.pprint(tree.tree)
print [(x[0]['target'], x[1]) for x in tree.classify(data)]
