python sklearn常用分类算法模型的调用
作者:海涛anywn 发布时间:2021-06-18 11:42:25
标签:python,sklearn,分类算法模型
本文实例为大家分享了python sklearn分类算法模型调用的具体代码,供大家参考,具体内容如下
实现对'NB', 'KNN', 'LR', 'RF', 'DT', 'SVM','SVMCV', 'GBDT'模型的简单调用。
# coding=gbk
import time
from sklearn import metrics
import pickle as pickle
import pandas as pd
# Multinomial Naive Bayes Classifier
def naive_bayes_classifier(train_x, train_y):
from sklearn.naive_bayes import MultinomialNB
model = MultinomialNB(alpha=0.01)
model.fit(train_x, train_y)
return model
# KNN Classifier
def knn_classifier(train_x, train_y):
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier()
model.fit(train_x, train_y)
return model
# Logistic Regression Classifier
def logistic_regression_classifier(train_x, train_y):
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(penalty='l2')
model.fit(train_x, train_y)
return model
# Random Forest Classifier
def random_forest_classifier(train_x, train_y):
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=8)
model.fit(train_x, train_y)
return model
# Decision Tree Classifier
def decision_tree_classifier(train_x, train_y):
from sklearn import tree
model = tree.DecisionTreeClassifier()
model.fit(train_x, train_y)
return model
# GBDT(Gradient Boosting Decision Tree) Classifier
def gradient_boosting_classifier(train_x, train_y):
from sklearn.ensemble import GradientBoostingClassifier
model = GradientBoostingClassifier(n_estimators=200)
model.fit(train_x, train_y)
return model
# SVM Classifier
def svm_classifier(train_x, train_y):
from sklearn.svm import SVC
model = SVC(kernel='rbf', probability=True)
model.fit(train_x, train_y)
return model
# SVM Classifier using cross validation
def svm_cross_validation(train_x, train_y):
from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVC
model = SVC(kernel='rbf', probability=True)
param_grid = {'C': [1e-3, 1e-2, 1e-1, 1, 10, 100, 1000], 'gamma': [0.001, 0.0001]}
grid_search = GridSearchCV(model, param_grid, n_jobs = 1, verbose=1)
grid_search.fit(train_x, train_y)
best_parameters = grid_search.best_estimator_.get_params()
for para, val in list(best_parameters.items()):
print(para, val)
model = SVC(kernel='rbf', C=best_parameters['C'], gamma=best_parameters['gamma'], probability=True)
model.fit(train_x, train_y)
return model
def read_data(data_file):
data = pd.read_csv(data_file)
train = data[:int(len(data)*0.9)]
test = data[int(len(data)*0.9):]
train_y = train.label
train_x = train.drop('label', axis=1)
test_y = test.label
test_x = test.drop('label', axis=1)
return train_x, train_y, test_x, test_y
if __name__ == '__main__':
data_file = "H:\\Research\\data\\trainCG.csv"
thresh = 0.5
model_save_file = None
model_save = {}
test_classifiers = ['NB', 'KNN', 'LR', 'RF', 'DT', 'SVM','SVMCV', 'GBDT']
classifiers = {'NB':naive_bayes_classifier,
'KNN':knn_classifier,
'LR':logistic_regression_classifier,
'RF':random_forest_classifier,
'DT':decision_tree_classifier,
'SVM':svm_classifier,
'SVMCV':svm_cross_validation,
'GBDT':gradient_boosting_classifier
}
print('reading training and testing data...')
train_x, train_y, test_x, test_y = read_data(data_file)
for classifier in test_classifiers:
print('******************* %s ********************' % classifier)
start_time = time.time()
model = classifiers[classifier](train_x, train_y)
print('training took %fs!' % (time.time() - start_time))
predict = model.predict(test_x)
if model_save_file != None:
model_save[classifier] = model
precision = metrics.precision_score(test_y, predict)
recall = metrics.recall_score(test_y, predict)
print('precision: %.2f%%, recall: %.2f%%' % (100 * precision, 100 * recall))
accuracy = metrics.accuracy_score(test_y, predict)
print('accuracy: %.2f%%' % (100 * accuracy))
if model_save_file != None:
pickle.dump(model_save, open(model_save_file, 'wb'))
来源:https://blog.csdn.net/lihaitao000/article/details/66972039
0
投稿
猜你喜欢
- 使用xmlhttp中的getResponseHeader 从响应信息中获取指定的http头strValue = oXML
- 一、事件捕捉(Event Capture)的实现问题首先在说这件事前,先感谢一下Realazy。 W3C DOM Level2的事
- 本文实例为大家分享了opencv+python实现均值滤波的具体代码,供大家参考,具体内容如下原理均值滤波其实就是对目标像素及周边像素取平均
- 往列表头部和尾部添加元素往头部添加元素list.insert(index,new_element)@@@index为新元素的插入位置,当in
- 语言是信息传播的主要障碍。多语言网站,顾名思义就是能够以多种语言(而不是单种语言)为用户提供信息服务,让使用不同语言的用户都能够从同个网站获
- 如果你忘记了你的MYSQL的root口令的话,你可以通过下面的过程恢复。1. 向mysqld server 发
- 网站标准(或称“WEB标准”)对于每一个开发网站和做网页的人来说,都是不可忽视的,这不仅是一个潮流,而是一个标准,一个更加符合规范的做法,而
- 工厂方法(Factory Method)模式又称为虚拟构造器(Virtual Constructor)模式或者多态工厂(Polymorphi
- 前言本文主要给大家介绍了关于golang分页算法的相关内容,分享出来供大家参考学习,下面话不多说了,来一起看看详细的介绍吧示例代码如下://
- 本文实例为大家分享了js实现放大镜效果的具体代码,供大家参考,具体内容如下该放大区域用背景图片放大<!DOCTYPE html>
- 你的SQL Server最近是否运行不正常?不,我指的不是我们肯定会遇到的通常的数据库和操作系统问题。我的意思是,你是否经历过服务器的反应迟
- 前两天在做一个站内版的企搜引擎,发现某些站点可以链接站点内容。。奇怪之下看了看,原来是按照数据库ID的自动编号规律进行链接的~~闲暇之余弄了
- Access 连接字符串 strConnect = “Provider=Microsoft.Jet.OLEDB.4.0;
- 前言最近学完Python,写了几个爬虫练练手,网上的教程有很多,但是有的已经不能爬了,主要是网站经常改,可是爬虫还是有通用的思路的,即下载数
- 如何制作一个倒计时的程序? 见下:<%CountdownDate = #1/1
- 我们已经在Python运算中看到Python最基本的数学运算功能。此外,math包补充了更多的函数。当然,如果想要更加高级的数学功能,可以考
- 俗话说,“工欲善其事,必先利其器”。对于前端开发工程师来说,基于Firefox丰富的Web开发辅助插件无疑就是最好的利器。下面就与大家分享2
- 引言经过函数学习之后我们会发现函数不被调用是不会直接执行的,我们在之前的函数调用之后发现运行的结果都是函数体内print()打印出来的结果,
- 1.执行cmd命令,不显示执行过程中弹出的黑框def run_cmd( cmd_str='', echo_print=1):
- 1.python安装包下载路径:https://www.python.org/downloads/2我下载安装包路径:https://www