Python 机器学习 简单实现

程序使用版本 ：
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:Python34TestCodesmnist.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:Python34TestCodesTestOne.py =================
reading training and testing data...
<gzip _io.BufferedReader name='C:\Python34\TestCodes\mnist.pkl.gz' 0x483470>
******************** 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%