def rfdef(self):
print("RF Start")
self.trainname.setText("RandomForest")
file = self.trainfile.text()
print(file)
start = time.time()
s = RandomForest()
a = s.accuracy(file)
end = time.time()
t = (end - start)
self.traintime.setText(str(round(t, 2)) + " (sec)")
self.label_4.setText("Accuracy")
self.trainstatus.setText(str(round(a, 3)))
AccuracyStore.store('rf', a)
if __name__ == '__main__':
seed(1)
dataSet = read_csv('sonar-all-data.csv') # 读取数据
feature_to_float(dataSet) # 转换特征值
n_folds = 5 # 分割交叉验证集
max_depth = 15 # 树的深度
min_size = 1 # 停止的分枝样本最小数目
ratio = 1.0 # 随机采样的比例
n_features = 15 # 选取特征
n_trees = 10 # 决策树数量
folds = spilt_dataSet(dataSet, n_folds)
scores = []
alg = RandomForest()
for fold in folds:
train_set = folds[:] # 拷贝
train_set.remove(fold)
train_set = sum(train_set, []) # 将多个fold列表组合成一个train_set列表
test_set = []
for row in fold:
row_copy = list(row)
row_copy[-1] = None
test_set.append(row_copy)
actual = [row[-1] for row in fold]
predict_values = alg.RF(train_set, test_set, ratio, n_features, max_depth, min_size, n_trees)
accur = alg.accuracy(predict_values, actual)
scores.append(accur)
print ('准确率:%s' % scores)
print ('平均准确率:', "{:2%}".format((sum(scores) / float(len(scores)))))
