内容为对测试集中声音数据进行性别预测，测试文件夹中有951行数据
数据类别一共两类：female、male
推荐使用SVM分类，也可使用其他如决策树等方法分类
在jupyterlab中将识别结果写入文件 /home/ilab/submission
每行输出id号（第一列数据）+‘\t’+ female或者是male（不要改变测试集顺序）
例如：
6 female
7 male
8 male
9 female
代码：评分91.3
import pandas as pd
import numpy as np
from sklearn import svm
from sklearn import model_selection
import matplotlib.pyplot as plt
import matplotlib as mpl
train_data = pd.read_csv('/ilab/datasets/local/voice/train.csv')#导入数据集
test = pd.read_csv('/ilab/datasets/local/voice/test.csv')train = train_data.values.tolist() #转化成列表
test1 = test.values.tolist()
ss1 = {'male':0, 'female':1} #设置标签
ss2 = {}
a = -1
yy= []
for i in train: #一共二十列变量，逐个分析
a = a + 1
i[21] = ss1[i[21]]
yy.append(i[21])
i.pop(21)
train = np.mat(train)
train2 = train[:, 1:3]
test = np.mat(test)
test1 = test[:,1:3]
yy = np.array(yy)
print(train)
def classifier():
clf = svm.SVC(C=0.5, # 误差惩罚系数，默认1
kernel='linear', # 线性核
decision_function_shape='ovr') # 决策函数
return clf
clf = classifier()
print(yy)def train1(clf, x_train, y_train):
clf.fit(x_train, # 训练集特征向量
y_train.ravel()) # 训练集目标值
# 训练SVM 模型
train2 = train[:, 1:19]
test1 = test[:, 1:19]
print(test1)
train1(clf, train2, yy)
ha = clf.predict(test1)# 读取test.csv文件，输出submission
import pandas as pd
# 将id读取
test = pd.read_csv('/ilab/datasets/local/voice/test.csv')
test_sub = test.iloc[:, [0]]
# 训练出来性别
gender = []
for i in range(len(test_sub)):
if ha[i] == 1:
gender.append('female')
else:
gender.append('male')
print(gender)
df_gender = pd.DataFrame(gender)
# 将id列和预测出来的性别列拼接并输出submission文件
df=pd.concat([test_sub,df_gender],axis=1)
df.to_csv('/home/ilab/submission', sep='\t', header=None, index=False)版权声明：本文内容由互联网用户自发贡献，该文观点仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 举报，一经查实，本站将立刻删除。
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