首页 > 代码库 > Sample a balance dataset from imbalance dataset and save it（从不平衡数据中抽取平衡数据，并保存）
Sample a balance dataset from imbalance dataset and save it（从不平衡数据中抽取平衡数据，并保存）
有时我们在实际分类数据挖掘中经常会遇到，类别样本很不均衡，直接使用这种不均衡数据会影响一些模型的分类效果，如logistic regression，SVM等，一种解决办法就是对数据进行均衡采样，这里就提供了一个建议代码实现，要求输入和输出数据格式为Label+Tab+Features, 如Libsvm format
-1 1:0.875 2:-1 3:-0.333333 4:-0.509434 5:-0.347032 6:-1 7:1 8:-0.236641 9:1 10:-0.935484 11:-1 12:-0.333333 13:-1 +1 1:0.166667 2:1 3:-0.333333 4:-0.433962 5:-0.383562 6:-1 7:-1 8:0.0687023 9:-1 10:-0.903226 11:-1 12:-1 13:1 +1 1:0.708333 2:1 3:1 4:-0.320755 5:-0.105023 6:-1 7:1 8:-0.419847 9:-1 10:-0.225806 12:1 13:-1 -1 1:0.583333 2:-1 3:0.333333 4:-0.603774 5:1 6:-1 7:1 8:0.358779 9:-1 10:-0.483871 12:-1 13:1
用法 Usage：
Usage: {0} [options] dataset subclass_size [output]
options:
-s method : method of selection (default 0)
0 -- over-sampling & under-sampling given subclass_size
1 -- over-sampling (subclass_size: any value)
2 -- under-sampling(subclass_size: any value)
Bash example：
python SampleDataset.py -s 0 heart_scale 20 heart_scale.txt
这里s参数表示抽样的方法，
-s 0：Over sampling &Under sampling ，即对类别多的进行降采样，对类别少的进行重采样
-s 1: Over sampling 对类别少的进行重采样，采样后的每类样本数与最多的那一类一致
-s 2：Under sampling 对类别多的进行降采样，采样后的每类样本数与最少的那一类一值
输入数据文件heart_scale
输出数据文件heart_scale.txt
下面是代码文件：SampleDataset.py：
#!/usr/bin/env python
from sklearn.datasets import load_svmlight_file
from sklearn.datasets import dump_svmlight_file
import numpy as np
from sklearn.utils import check_random_state
from scipy.sparse import hstack,vstack
import os, sys, math, random
from collections import defaultdict
if sys.version_info[0] >= 3:
xrange = range
def exit_with_help(argv):
print("""Usage: {0} [options] dataset subclass_size [output]
options:
-s method : method of selection (default 0)
0 -- over-sampling & under-sampling given subclass_size
1 -- over-sampling (subclass_size: any value)
2 -- under-sampling(subclass_size: any value)
output : balance set file (optional)
If output is omitted, the subset will be printed on the screen.""".format(argv[0]))
exit(1)
def process_options(argv):
argc = len(argv)
if argc < 3:
exit_with_help(argv)
# default method is over-sampling & under-sampling
method = 0
BalanceSet_file = sys.stdout
i = 1
while i < argc:
if argv[i][0] != "-":
break
if argv[i] == "-s":
i = i + 1
method = int(argv[i])
if method not in [0,1,2]:
print("Unknown selection method {0}".format(method))
exit_with_help(argv)
i = i + 1
dataset = argv[i]
BalanceSet_size = int(argv[i+1])
if i+2 < argc:
BalanceSet_file = open(argv[i+2],‘w‘)
return dataset, BalanceSet_size, method, BalanceSet_file
def stratified_selection(dataset, subset_size, method):
labels = [line.split(None,1)[0] for line in open(dataset)]
label_linenums = defaultdict(list)
for i, label in enumerate(labels):
label_linenums[label] += [i]
l = len(labels)
remaining = subset_size
ret = []
# classes with fewer data are sampled first;
label_list = sorted(label_linenums, key=lambda x: len(label_linenums[x]))
min_class = label_list[0]
maj_class = label_list[-1]
min_class_num = len(label_linenums[min_class])
maj_class_num = len(label_linenums[maj_class])
random_state = check_random_state(42)
for label in label_list:
linenums = label_linenums[label]
label_size = len(linenums)
if method == 0:
if label_size<subset_size:
ret += linenums
subnum = subset_size-label_size
else:
subnum = subset_size
ret += [linenums[i] for i in random_state.randint(low=0, high=label_size,size=subnum)]
elif method == 1:
if label == maj_class:
ret += linenums
continue
else:
ret += linenums
subnum = maj_class_num-label_size
ret += [linenums[i] for i in random_state.randint(low=0, high=label_size,size=subnum)]
elif method == 2:
if label == min_class:
ret += linenums
continue
else:
subnum = min_class_num
ret += [linenums[i] for i in random_state.randint(low=0, high=label_size,size=subnum)]
random.shuffle(ret)
return ret
def main(argv=sys.argv):
dataset, subset_size, method, subset_file = process_options(argv)
selected_lines = []
selected_lines = stratified_selection(dataset, subset_size,method)
#select instances based on selected_lines
dataset = open(dataset,‘r‘)
datalist = dataset.readlines()
for i in selected_lines:
subset_file.write(datalist[i])
subset_file.close()
dataset.close()
if __name__ == ‘__main__‘:
main(sys.argv)
Sample a balance dataset from imbalance dataset and save it（从不平衡数据中抽取平衡数据，并保存）
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