Python 3.4
安装对应版本的依赖
numpy ， scipy ， matplotlib ， scikit_learn
参考
http://blog.csdn.net/zouxy09/article/details/48903179
http://www.jianshu.com/p/21b758541825
KNN 算法
一组（m）训练数据，一组（n）测试数据
测试数据每一组到训练数据集（m）的有效距离的升序排列，从序列中选区前K个值，对这K个值分组，求概率最高（K 个数据中值出现频率最高的）的即测试数据的值 。
迭代n组数据，观察结果是否符合预期。
#!usr/bin/env python
#-*- coding: utf-8 -*-
import sys
import os
import time
from sklearn import metrics
import numpy as np
import pickle
import importlib
importlib.reload(sys)
#sys.setdefaultencoding('utf8')
# Multinomial Naive Bayes Classifier
# 朴素贝叶斯
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
# K最近邻
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
# 逻辑回归
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
# 随机森林
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
# 决策树
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
# 梯度推进
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
# 支持向量机
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
# 支持向量机 交叉验证
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 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):
import gzip
f = gzip.open(data_file, "rb",'utf8')
train, val, test = pickle.load(f,encoding="bytes") # add ,encoding="bytes"
f.close()
train_x = train[0]
train_y = train[1]
test_x = test[0]
test_y = test[1]
return train_x, train_y, test_x, test_y
if __name__ == '__main__':
data_file = "C:\Python34\TestCodes\mnist.pkl.gz"
thresh = 0.5
model_save_file = None
model_save = {}
test_classifiers = ['NB 朴素贝叶斯', 'KNN K最近邻', 'LR 逻辑回归', 'RF 随机森林', 'DT 决策树', 'SVM 支持向量机', 'GBDT 梯度推进']
classifiers = {'NB':naive_bayes_classifier, # 朴素贝叶斯
'KNN':knn_classifier, # K最近邻
'LR':logistic_regression_classifier,# 逻辑回归
'RF':random_forest_classifier, # 随机森林
'DT':decision_tree_classifier, # 决策树
'SVM':svm_classifier, # 支持向量机
'SVMCV':svm_cross_validation, # 支持向量机 交叉验证
'GBDT':gradient_boosting_classifier # 梯度推进
}
print("reading training and testing data...")
train_x, train_y, test_x, test_y = read_data(data_file)
num_train, num_feat = train_x.shape
num_test, num_feat = test_x.shape
is_binary_class = (len(np.unique(train_y)) == 2)
print ('******************** Data Info *********************' )
print ('#training data: %d, #testing_data: %d, dimension: %d' % (num_train, num_test, num_feat) )
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
if is_binary_class:
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'))
================= RESTART: C:\Python34\TestCodes\TestOne.py =================
reading training and testing data...
******************** Data Info *********************
#training data: 50000, #testing_data: 10000, dimension: 784
******************* NB ********************
training took 1.250072s!
accuracy: 83.69%
******************* KNN ********************
training took 34.031946s!
accuracy: 96.64%
******************* LR ********************
training took 69.958001s!
accuracy: 91.99%
******************* RF ********************
training took 3.970227s!
accuracy: 93.94%
******************* DT ********************
training took 22.557290s!
accuracy: 87.02%
******************* SVM ********************
training took 3078.619087s!
accuracy: 94.35%
******************* GBDT ********************
training took 6595.662250s!
accuracy: 96.17%
