# -*- coding: utf-8 -*-

"""

Created on Wed Aug 15 14:48:15 2018

@author: Administrator

"""

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.model_selection import cross_val_score

from sklearn.pipeline import Pipeline

from sklearn.model_selection import KFold

from sklearn.preprocessing import StandardScaler

from sklearn.model_selection import GridSearchCV

from sklearn.metrics import mean_squared_error

from sklearn.linear_model import LinearRegression

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

from sklearn.tree import DecisionTreeClassifier

from sklearn.svm import SVC

from sklearn.naive_bayes import GaussianNB

from sklearn.neighbors import KNeighborsClassifier

from sklearn.ensemble import AdaBoostClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.ensemble import GradientBoostingClassifier

from sklearn.ensemble import ExtraTreesClassifier

from sklearn.metrics import accuracy_score

from sklearn.metrics import confusion_matrix

from sklearn.metrics import classification_report

#导入数据

filename='sonar.csv'

dataset=pd.read_csv(filename)

#分析数据的维度

print(dataset.shape)

#(207, 61) 207行数据，60个特征，1个label

#查看数据的类型

pd.set_option('display.max_rows',500)

#print(dataset.dtypes)

#查看最开始的20条数据

pd.set_option('display.width',100)

#print(dataset.head(20))

#描述数据统计信息

pd.set_option('precision',3)

#print(dataset.describe())

#查看数据的分类分布

#print(dataset.groupby('60').size())

'''

数据可视化

'''

#直方图

#dataset.hist(sharex=False,sharey=False,xlabelsize=1,ylabelsize=1)

#plt.show()

#密度分布图

#dataset.plot(kind='density',subplots=True,layout=(8,8),sharex=False,legend=False,fontsize=1)

#plt.show()

#关系矩阵图

#fig=plt.figure()

#ax=fig.add_subplot(111)

#cax=ax.matshow(dataset.corr(),vmin=-1,vmax=1,interpolation='none')

#fig.colorbar(cax)

#plt.show()

'''

分离评估数据集

'''

array=dataset.values

X=array[:,0:60].astype(float)

y=array[:,60]

validation_size=0.2

seed=7

X_train,X_validation,y_train,y_validation=train_test_split(X,y,test_size=validation_size,random_state=seed)

'''

评估算法

'''

#评估算法的基准

num_folds=10

seed=7

scoring='accuracy'

'''

使用6种不同算法进行评估

LR LDA

CART SVM NB KNN

'''

models={}

models['LR']=LinearRegression()

models['LDA']=LinearDiscriminantAnalysis()

models['KNN']=KNeighborsClassifier()

models['CART']=DecisionTreeClassifier()

models['NB']=GaussianNB()

models['SVM']=SVC()

#比较各算法的准确度

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,cv=kfold,scoring=scoring)

results.append(cv_results)

print('%s准确度:%f ,方差:%f'%(models[key],cv_results.mean(),cv_results.std()))

#评估算法---箱线图

fig=plt.figure()

fig.suptitle('Algorithm Comparison')

ax=fig.add_subplot(111)

plt.boxplot(results)

ax.set_xticklabels(models.keys)

plt.show()

'''

正态化数据

'''

#使用pipeline

pipelines={}

pipelines['ScalerLR']=Pipeline([('Scaler',StandardScaler()),('LR',LinearRegression())])

pipelines['ScalerLDA']=Pipeline([('Scaler',StandardScaler()),('LDA',LinearDiscriminantAnalysis())])

pipelines['ScalerKNN']=Pipeline([('Scaler',StandardScaler()),('KNN',KNeighborsClassifier())])

pipelines['ScalerCART']=Pipeline([('Scaler',StandardScaler()),('CART',DecisionTreeClassifier())])

pipelines['ScalerNB']=Pipeline([('Scaler',StandardScaler()),('NB',GaussianNB())])

pipelines['ScalerSVM']=Pipeline([('Scaler',StandardScaler()),('SVM',SVC())])

results=[]

for key in pipelines:

kfold=KFold(random_state=seed,n_splits=num_folds)

cv_result=cross_val_score(pipelines[key],X_train,y_train,cv=kfold,scoring=scoring)

results.append(cv_result)

print('机器学习单一算法%s:%f(%f)'%(key,cv_result.mean(),cv_result.std()))

#评估算法---箱线图

fig=plt.figure()

fig.suptitle('Algorithm Comparison')

ax=fig.add_subplot(111)

plt.boxplot(results)

ax.set_xticklabels(pipelines.keys)

plt.show()

'''

主要对KNN进行参数调整

'''

#正态化数据

scaler=StandardScaler().fit(X_train)

rescaledX=scaler.transform(X_train)

#调整参数

param_grid={'n_neighbors':[1,3,5,7,9,11,13,15,17,19,21]}

#构建模型

model=KNeighborsClassifier()

#10折交叉验证

kfold=KFold(n_splits=num_folds,random_state=seed)

#网格搜索

grid=GridSearchCV(estimator=model,param_grid=param_grid,scoring=scoring,cv=kfold)

#获取结果

grid_result=grid.fit(X=rescaledX,y=y_train)

print('最优参数:%s,获取分数:%s'%(grid_result.best_params_,grid_result.best_score_))

#zip合并矩阵

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,param in cv_results:

print('%f (%f) with %r'%(mean,std,param))

'''

主要对SVM进行参数调整

'''

#正态化数据

scaler=StandardScaler().fit(X_train)

rescaledX=scaler.transform(X_train).astype(float)

#调整参数

param_grid={}

param_grid['C']={0.1,0.3,0.5,0.7,0.9,1.0,1.3,1.5,1.7,2.0}

param_grid['kernel']={'linear','poly','rbf','sigmoid','precomputed'}

#构建模型

model=SVC()

#10折交叉验证

kfold=KFold(n_splits=num_folds,random_state=seed)

#网格搜索

grid=GridSearchCV(estimator=model,param_grid=param_grid,scoring=scoring,cv=kfold)

#获取结果

grid_result=grid.fit(X=rescaledX,y=y_train)

print('最优参数:%s,获取分数:%s'%(grid_result.best_params_,grid_result.best_score_))

#zip合并矩阵

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,param in cv_results:

print('%f (%f) with %r'%(mean,std,param))

'''

集成算法

RF ET

AB GBM

'''

#使用pipeline

ensembles={}

ensembles['ScalerAB']=Pipeline([('Scaler',StandardScaler()),('AB',AdaBoostClassifier())])

ensembles['ScalerGBM']=Pipeline([('Scaler',StandardScaler()),('GBM',GradientBoostingClassifier())])

ensembles['ScalerRFR']=Pipeline([('Scaler',StandardScaler()),('RFR',RandomForestClassifier())])

ensembles['ScalerET']=Pipeline([('Scaler',StandardScaler()),('ET',ExtraTreesClassifier())])

results=[]

for key in pipelines:

kfold=KFold(random_state=seed,n_splits=num_folds)

cv_result=cross_val_score(ensembles[key],X_train,y_train,cv=kfold,scoring=scoring)

results.append(cv_result)

print('机器学习集成算法%s:%f(%f)'%(key,cv_result.mean(),cv_result.std()))

#评估算法---箱线图

fig=plt.figure()

fig.suptitle('Algorithm Comparison')

ax=fig.add_subplot(111)

plt.boxplot(results)

ax.set_xticklabels(ensembles.keys)

plt.show()

'''

GBM算法调参

'''

#正态化数据

scaler=StandardScaler().fit(X_train)

rescaledX=scaler.transform(X_train)

#调整参数

param_grid={'n_estimators':[10,50,100,200,300,400,500,600,700,800,900]}

#构建模型

model=GradientBoostingClassifier()

#10折交叉验证

kfold=KFold(n_splits=num_folds,random_state=seed)

#网格搜索

grid=GridSearchCV(estimator=model,param_grid=param_grid,scoring=scoring,cv=kfold)

#获取结果

grid_result=grid.fit(X=rescaledX,y=y_train)

print('最优参数:%s,获取分数:%s'%(grid_result.best_params_,grid_result.best_score_))

#zip合并矩阵

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,param in cv_results:

print('GBM%f (%f) with %r'%(mean,std,param))

'''

确定最终模型

ET

'''

#正态化数据

scaler=StandardScaler().fit(X_train)

rescaledX=scaler.transform(X_train)

model=SVC(C=1.0,kernel='rbf')

model.fit(X=rescaledX,y=y_train)

#评估算法模型

rescaledX_validation=scaler.transform(X_validation)

preditions=model.predict(X_validation)

print('准确度:',accuracy_score(preditions,y_validation))

print('混淆矩阵:',confusion_matrix(preditions,y_validation))

print('分类结果:',classification_report(preditions,y_validation))