-
Notifications
You must be signed in to change notification settings - Fork 2
/
TestCal500.py
120 lines (100 loc) · 4.67 KB
/
TestCal500.py
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
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
# -*- coding=utf-8 -*-
import csv
import numpy as np
from sklearn.utils import shuffle
from learner.cascade import Cascade
from learner.measure import *
# 随机排列实例数，将实例划分为训练集和测试集
def shuffle_index(num_samples):
# a = range(0, 502),502是实例数
a = range(0, num_samples)
# 利用shuffle函数将序列a中的元素重新随机排列
a = shuffle(a)
# 去实例数的一半，上取整
length = int((num_samples + 1) / 2)
# 上半做训练集
train_index = a[:length]
# 下半做测试集
test_index = a[length:]
return [train_index, test_index]
# 加载数据和标签
def load_csv():
"""
从CSV文件中读取数据信息
:param csv_file_name: CSV文件名
:return: Data：二维数组
"""
# CAL500数据集是music数据集
# shape是(502, 242)，即502个实例；242 = features + labels
# 前68列是features（个人认为应该是一个音频的特征表示，类比于一张图片的像素点表示）；
# 后174列是labels，一个music实例有174个标签；
data_csv = r'D:\Pycharm2020.1.3\WorkSpace\MLDF\dataset\CAL500_data.csv'
label_csv = r'D:\Pycharm2020.1.3\WorkSpace\MLDF\dataset\CAL500_label.csv'
with open(data_csv, encoding='utf-8') as f:
data = np.loadtxt(f, str, delimiter=",")
with open(label_csv, encoding='utf-8') as f:
label = np.loadtxt(f, str, delimiter=",")
# 将数据label强制转换为指定的类型，astype函数是在副本上进行，并非修改原数组。
# 从文件中load出来的数据类型是“class 'numpy.int16'”类型，需要进行类型转化
label = label.astype("int")
# 取数据集的行数，即是实例数
num_samples = len(data)
# 用shuffle_index函数将502这个整数随机划分成两个长为251的list，list中的元素是502以内的整数
# data是<class 'numpy.ndarray'>的二维矩阵，将上一步的list传入，会将data中按list中的元素按行取出
# 这两步就是将（502，68）的data二维矩阵划分成了两个（251，68）的二维矩阵，分别代表训练集和测试集
# 针对label这个（502, 174）的二维矩阵也是这么操作，而且采集时用的是同一组list，保证实例和标签对应
train_index, test_index = shuffle_index(num_samples)
"""
划分结果如下：
train_data <class 'numpy.ndarray'> (251, 68)
train_label <class 'numpy.ndarray'> (251, 174)
test_data <class 'numpy.ndarray'> (251, 68)
test_label <class 'numpy.ndarray'> (251, 174)
"""
train_data = data[train_index]
train_label = label[train_index]
test_data = data[test_index]
test_label = label[test_index]
print("加载CAL500数据集完成！！!")
# 返回值是训练数据、测试数据、标签数
return [train_data, train_label, test_data, test_label]
if __name__ == '__main__':
dataset = "CAL500"
# 初始化数据集、测试数据集、标签集
train_data, train_label, test_data, test_label = load_csv()
# 构造森林，将另个森林级联，最大层数设为10，5折交叉验证
model = Cascade(dataset, max_layer=20, num_forests=4, n_fold=5, step=3)
# 训练森林，传入训练集、训练标签、指标名称、每个森林中的树的数量设为40
model.train(train_data, train_label, "hamming loss", n_estimators=40)
test_prob = model.predict(test_data, "hamming loss")
value = do_metric(test_prob, test_label, 0.5)
meatures = ["hamming loss", "one-error", "coverage", "ranking loss", "average precision", "F1", "macro-auc"]
res = zip(meatures, value)
for item in res:
print(item)
"""
Cascade(dataset, max_layer=20, num_forests=4, n_fold=5, step=3),4个RandomForestClassifier
('hamming loss', 0.14051838622521395)
('one-error', 0.11155378486055777)
('coverage', 0.9820488162293355)
('ranking loss', 0.3610636364104504)
('average precision', 0.39686267384338425)
('F1', 0.020788149789399652)
('macro-auc', 0.5687069202548994)
Cascade(dataset, max_layer=20, num_forests=4, n_fold=5, step=3),4个ExtraTreesClassifier
('hamming loss', 0.14065576773366295)
('one-error', 0.11553784860557768)
('coverage', 0.9834913220680497)
('ranking loss', 0.3408500255409789)
('average precision', 0.4049751258539804)
('F1', 0.020489512868118297)
('macro-auc', 0.5613066773548133)
Cascade(dataset, max_layer=20, num_forests=4, n_fold=5, step=3),2个RandomForestClassifier+2个ExtraTreesClassifier
('hamming loss', 0.1384118697623299)
('one-error', 0.12350597609561753)
('coverage', 0.9866281998443011)
('ranking loss', 0.3587622949060082)
('average precision', 0.3934053298378544)
('F1', 0.01936401533044234)
('macro-auc', 0.5688667452665612)
"""