提取特征
import os
import rpy2.robjects as robjects
from rpy2.robjects import pandas2ri, r
import pandas as pd
os.chdir('C:/Users/TStra/Desktop/signal')
data_list = []
#获取特征文件
def get_feature(fname):
pandas2ri.activate()
robjects.r.source('feature_extract.R') #利用rpy2读取R脚本
data_read = robjects.r.processFolder(fname) #得到数据文件
data_read = pandas2ri.ri2py(data_read) #转化为python可以使用的数据
return data_read
if __name__ == '__main__':
file_name_list = os.listdir('data') #存放.wav格式声音的文件夹
for file_name in file_name_list:
data = get_feature(file_name)
data_list.append(data)
result = pd.concat(data_list)
result['label'] = 'male'
result.to_csv("male.csv", index=False)
#result['label'] = 'female'
#result.to_csv("female.csv", index=False)
机器学习
import xgboost as xgb
import pandas as pd
import numpy as np
import sklearn
import pickle
import pprint
def xgb_score(preds, dtrain):
labels = dtrain.get_label()
return 'log_loss', sklearn.metrics.log_loss(labels, preds)
input_data = pd.read_csv('C:/Users/TStra/Desktop/signal/voice.csv') #pandas 读取csv文件
input_data = input_data.sample(frac=1) #利用pandas抽样 frac=1 即比例为1
gender = {'male' : 0, 'female' : 1} #性别判断
input_data['label'] = input_data['label'].map(gender) #map函数
cols = [c for c in input_data.columns if c not in ['label']]
print (cols)
train = input_data.iloc[0 :3300]
test = input_data.iloc[3300 : ]
test_label = test['label']
test_label = np.array(test_label).reshape([-1 , 1])
del(test['label'])
fold = 1
for i in range(fold):
params = {
'eta': 0.01, #use 0.002
'max_depth': 5,
'objective': 'binary:logistic',
'eval_metric': 'logloss',
'lambda':0.1,
'gamma':0.1,
'seed': i,
'silent': True
}
x1 = train[cols][0:3000]
x2 = train[cols][3000:]
y1 = train['label'][0:3000]
y2 = train['label'][3000 : ]
watchlist = [(xgb.DMatrix(x1, y1), 'train'), (xgb.DMatrix(x2, y2), 'valid')]
model = xgb.train(params, xgb.DMatrix(x1, y1), 1500, watchlist, feval=xgb_score, maximize=False, verbose_eval=50, early_stopping_rounds=50) #use 1500
if i != 0:
pred += model.predict(xgb.DMatrix(test[cols]), ntree_limit=model.best_ntree_limit)
else:
pred = model.predict(xgb.DMatrix(test[cols]), ntree_limit=model.best_ntree_limit)
pred /= fold
pre_label = np.zeros([pred.shape[0], 1])
for i in range(pred.shape[0]):
if pred[i] >= 0.5:
pre_label[i] = 1
else:
pre_label[i] = 0
acc = np.mean(np.equal(pre_label, test_label).astype(np.float))
print("the test acc is:", acc)
model_save = open('model.pkl', 'wb') #保存模型
pickle.dump(model, model_save)
model_save.close()
测试过程
import xgboost as xgb
import pandas as pd
import numpy as np
import sklearn
import pickle
import pprint
import rpy2.robjects as robjects
from rpy2.robjects import r, pandas2ri
import os
os.chdir('C:/Users/TStra/Desktop/signal')
#get feature file
def get_feature(fname):
pandas2ri.activate()
robjects.r.source('feature_extract.R')
data_read = robjects.r.processFolder(fname)
data_read = pandas2ri.ri2py(data_read)
return data_read
if __name__ == '__main__':
data_list = []
model_save = open('model.pkl', 'rb')
model = pickle.load(model_save)
model_save.close()
file_name_list = os.listdir('data') #读取声音文件
for file_name in file_name_list:
data = get_feature(file_name)
data_list.append(data)
test = pd.concat(data_list) #获取wav特征
pred = model.predict(xgb.DMatrix(test), ntree_limit=model.best_ntree_limit)
print (pred)
pre_label = np.zeros([pred.shape[0], 1])
for i in range(pred.shape[0]):
if pred[i] >= 0.5:
pre_label[i] = pred[i]
else:
pre_label[i] = pred[i]
num = 0
tlen = len(pre_label)
for i in pre_label:
num += i
print ('female is;'+str(num))
print ('male is:'+str(tlen-num))
print ((tlen-num)/tlen)
print (num/tlen)
