机器学习：wine 分类

标签：col   优化   numpy   square   sub   show   gis   models   its   

参考文献：《机器学习Python实战》魏贞原

博文目的：复习

工具：Geany

#导入类库

from pandas import read_csv                                    #读数据
from pandas.plotting import scatter_matrix            #画散点图
from pandas import set_option                                #设置打印数据精确度

import numpy as np

import matplotlib.pyplot as plt                                #画图

from sklearn.preprocessing import Normalizer            #数据预处理：归一化
from sklearn.preprocessing import StandardScaler     #数据预处理：正态化

from sklearn.preprocessing import MinMaxScaler      #数据预处理：调整数据尺度

from sklearn.model_selection import train_test_split        #分离数据集
from sklearn.model_selection import cross_val_score       #计算算法准确度
from sklearn.model_selection import KFold                        #交叉验证
from sklearn.model_selection import GridSearchCV        #机器学习算法的参数优化方法：网格优化法

from sklearn.linear_model import LinearRegression        #线性回归
from sklearn.linear_model import Lasso                           #套索回归
from sklearn.linear_model import ElasticNet                    #弹性网络回归
from sklearn.linear_model import LogisticRegression     #逻辑回归算法

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis            #线性判别分析
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis      #二次判别分析
from sklearn.tree import DecisionTreeRegressor        #决策树回归                                       
from sklearn.tree import DecisionTreeClassifier         #决策树分类

from sklearn.neighbors import KNeighborsRegressor    #KNN回归

from sklearn.neighbors import KNeighborsClassifier        #KNN分类

from sklearn.naive_bayes import GaussianNB    #贝叶斯分类器

from sklearn.svm import SVR    #支持向量机 回归
from sklearn.svm import SVC    #支持向量机 分类

from sklearn.pipeline import Pipeline    #pipeline能够将从数据转换到评估模型的整个机器学习流程进行自动化处理

from sklearn.ensemble import RandomForestRegressor        #随即森林回归
from sklearn.ensemble import RandomForestClassifier        #随即森林分类
from sklearn.ensemble import GradientBoostingRegressor    #随即梯度上升回归
from sklearn.ensemble import GradientBoostingClassifier    #随机梯度上分类
from sklearn.ensemble import ExtraTreesRegressor        #极端树回归
from sklearn.ensemble import ExtraTreesClassifier        #极端树分类
from sklearn.ensemble import AdaBoostRegressor        #AdaBoost回归
from sklearn.ensemble import AdaBoostClassifier        #AdaBoost分类

from sklearn.metrics import mean_squared_error        #
from sklearn.metrics import accuracy_score                #分类准确率

from sklearn.metrics import confusion_matrix        #混淆矩阵

from sklearn.metrics import classification_report    #分类报告

#导入数据
filename = 'wine.csv'
data = read_csv(filename, header=None, delimiter=',')
#数据理解
print(data.shape)
#print(data.dtypes)
#print(data.corr(method='pearson'))
#print(data.describe())
#print(data.groupby(0).size())

#数据可视化:直方图、散点图、密度图、关系矩阵图

#直方图

#data.hist()
#plt.show()

#密度图

#data.plot(kind='density', subplots=True, layout=(4,4), sharex=False, sharey=False)
#plt.show()

#散点图

#scatter_matrix(data)
#plt.show()

#关系矩阵图

#fig = plt.figure()
#ax = fig.add_subplot(111)
#cax = ax.matshow(data.corr(), vmin=-1, vmax=1)
#fig.colorbar(cax)
#plt.show()

#数据处理：调整数据尺度、归一化、正态化、二值化
array = data.values
X = array[:, 1:14].astype(float)
Y = array[:,0]

scaler = MinMaxScaler(feature_range=(0,1)).fit(X)
X_m = scaler.transform(X)

scaler = Normalizer().fit(X)
X_n = scaler.transform(X)

scaler = StandardScaler().fit(X)
X_s = scaler.transform(X)

#分离数据集
validation_size = 0.2
seed = 7

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=validation_size, random_state=seed)

X_m_train, X_m_test, Y_m_train, Y_m_test = train_test_split(X, Y, test_size=validation_size, random_state=seed)

X_n_train, X_n_test, Y_n_train, Y_n_test = train_test_split(X, Y, test_size=validation_size, random_state=seed)

X_s_train, X_s_test, Y_s_train, Y_s_test = train_test_split(X, Y, test_size=validation_size, random_state=seed)

#选择模型：(本例是一个分类问题)
#非线性：KNN, SVC, CART, GaussianNB,
#线性：KNN, SVR, LR, Lasso, ElasticNet,  LDA,
models = {}
models['KNN'] = KNeighborsClassifier()
models['SVM'] = SVC()
models['CART'] = DecisionTreeClassifier()
models['GN'] = GaussianNB()
#models['LR'] = LinearRegression()
#models['Lasso'] = Lasso()
#models['EN'] = ElasticNet()
models['LDA'] = LinearDiscriminantAnalysis()
models['QDA'] = QuadraticDiscriminantAnalysis()

#评估模型
scoring = 'accuracy'
num_folds = 10
seed = 7

results = []
for key in models:
    kfold = KFold(n_splits=num_folds, random_state=seed)
    cv_results =cross_val_score(models[key], X_train, Y_train, scoring=scoring, cv=kfold)
    results.append(cv_results)
    print('%s %f(%f)'%(key, cv_results.mean(), cv_results.std()))

results_m = []
for key in models:
    kfold = KFold(n_splits=num_folds, random_state=seed)
    cv_results_m =cross_val_score(models[key], X_m_train, Y_m_train, scoring=scoring, cv=kfold)
    results_m.append(cv_results_m)
    print('调整数据尺度：%s %f(%f)'%(key, cv_results_m.mean(), cv_results_m.std()))

results_n = []
for key in models:
    kfold = KFold(n_splits=num_folds, random_state=seed)
    cv_results_n =cross_val_score(models[key], X_n_train, Y_n_train, scoring=scoring, cv=kfold)
    results_n.append(cv_results_n)
    print('归一化数据：%s %f(%f)'%(key, cv_results_n.mean(), cv_results_n.std()))    

results_s = []
for key in models:
    kfold = KFold(n_splits=num_folds, random_state=seed)
    cv_results_s =cross_val_score(models[key], X_s_train, Y_s_train, scoring=scoring, cv=kfold)
    results_s.append(cv_results_s)
    print('正态化数据：%s %f(%f)'%(key, cv_results_s.mean(), cv_results_s.std()))
#箱线图

#算法优化：LDA
param_grid = {'solver':['svd', 'lsqr', 'eigen']}
model = LinearDiscriminantAnalysis()
kfold = KFold(n_splits=num_folds, random_state=seed)
grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)
grid_result = grid.fit(X=X_train, y=Y_train)
print('最优：%s 使用：%s'%(grid_result.best_score_, grid_result.best_params_))
cv_results = zip(grid_result.cv_results_['mean_test_score'], grid_result.cv_results_['std_test_score'], grid_result.cv_results_['params'])
for mean, std, params in cv_results:
    print('%f(%f) with %r'%(mean, std, params))


#算法集成
#bagging: 随机森林，极限树；
#boosting：ada， 随机梯度上升
ensembles = {}
ensembles['RF'] = RandomForestClassifier()
ensembles['ET'] = ExtraTreesClassifier()
ensembles['ADA'] = AdaBoostClassifier()
ensembles['GBM'] = GradientBoostingClassifier()

results = []
for key in ensembles:
    kfold = KFold(n_splits=num_folds, random_state=seed)
    cv_results =cross_val_score(ensembles[key], X_train, Y_train, scoring=scoring, cv=kfold)
    results.append(cv_results)
    print('%s %f(%f)'%(key, cv_results.mean(), cv_results.std()))

#集成算法调参gbm
param_grid = {'n_estimators':[10,50,100,200,300,400,500,600,700,800,900]}
model = GradientBoostingClassifier()
kfold = KFold(n_splits=num_folds, random_state=seed)
grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=kfold, scoring=scoring)
grid_result = grid.fit(X=X_train, y=Y_train)
print('最优：%s 使用：%s'%(grid_result.best_score_, grid_result.best_params_))
cv_results = zip(grid_result.cv_results_['mean_test_score'], grid_result.cv_results_['std_test_score'], grid_result.cv_results_['params'])
for mean, std, params in cv_results:
    print('%f(%f) with %r'%(mean, std, params))

#训练最终模型
model = LinearDiscriminantAnalysis(solver='svd')
model.fit(X=X_train, y=Y_train)

#评估最终模型
predictions = model.predict(X_test)
print(accuracy_score(Y_test, predictions))
print(confusion_matrix(Y_test, predictions))
print(classification_report(Y_test, predictions))

机器学习：wine 分类

标签：col   优化   numpy   square   sub   show   gis   models   its   

原文地址：http://blog.51cto.com/13542337/2057902