分类： Python/Ruby
2021-04-19 17:17:44
machine_learning.py
这部分代码对应文章的机器学习部分。
# -*- coding: utf-8 -*-
import os
import warnings
import numpy as np
from sklearn import preprocessing
import pickle
# 用于机器学习的第三方库导入
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from read_data import read_data_from_path
from read_data import plot_cluster
from read_data import plot_surface
warnings.filterwarnings("ignore") #不显示警告
def select_knn(X,Y):
""""筛选kNN算法的最合适参数k"""
grid = {'n_neighbors':[3,5,7,9,11,13,15,17,19,21,23,25,27]}
grid_search = GridSearchCV(KNeighborsClassifier(),\
param_grid=grid,
cv=5,
scoring='accuracy')
grid_search.fit(X,Y)
print(grid_search.best_params_)
return grid_search.best_params_
def select_svc(X,Y):
grid = {'C':[0.1,0.25,0.5,0.75,1,1.25,1.5,1.75],\
'kernel':['linear','rbf','poly']}
grid_search = GridSearchCV(SVC(),param_grid=grid,cv=5,
scoring='accuracy')
grid_search.fit(X,Y)
print(grid_search.best_params_)
return grid_search.best_params_
def select_dtc(X,Y):
grid = {'max_depth':[19,24,29,34,39,44,49,54,59,64,69,74,79],\
'ccp_alpha':[0,0.00025,0.0005,0.001,0.00125,0.0015,0.002,0.005,0.01,0.05,0.1]}
grid_search = GridSearchCV(DecisionTreeClassifier(),\
param_grid=grid, cv=5, \
scoring='accuracy')
grid_search.fit(X,Y)
print(grid_search.best_params_)
return grid_search.best_params_
def select_rf(X,Y):
grid = {'n_estimators':[15,25,35,45,50,65,75,85,95]}
grid_search = GridSearchCV(RandomForestClassifier(max_samples=0.67,\
max_features=0.33, max_depth=5), \
param_grid=grid, cv=5,\
scoring='accuracy')
grid_search.fit(X,Y)
print(grid_search.best_params_)
return grid_search.best_params_
def select_ada(X,Y):
grid = {'n_estimators':[15,25,35,45,50,65,75,85,95]}
grid_search = GridSearchCV(AdaBoostClassifier( \
base_estimator=LogisticRegression()),\
param_grid=grid,
cv=5,
scoring='r2')
grid_search.fit(X,Y)
print(grid_search.best_params_)
return grid_search.best_params_
def select_model(X,Y):
knn_param = select_knn(X,Y)
svc_param = select_svc(X,Y)
dtc_param = select_dtc(X,Y)
rf_param = select_rf(X,Y)
ada_param = select_ada(X,Y)
return knn_param, svc_param, dtc_param, rf_param, ada_param
def cv_score(X, Y, \
knn_param={'n_neighbors':25}, \
svc_param={'C': 0.1, 'kernel': 'rbf'},\
dtc_param={'ccp_alpha':0.01, 'max_depth':19}, \
rf_param={'n_estimators':75},\
ada_param={'n_estimators':15}):
"""根据上述最优参数，构建模型"""
lg = LogisticRegression()
knn = KNeighborsClassifier(n_neighbors=knn_param['n_neighbors'])
svc = SVC货币代码(C=svc_param['C'], kernel=svc_param['kernel'])
dtc = DecisionTreeClassifier(max_depth=dtc_param['max_depth'],
ccp_alpha=dtc_param['ccp_alpha'])
rf = RandomForestClassifier(n_estimators=rf_param['n_estimators'],\
max_samples=0.67,\
max_features=0.33, max_depth=5)
ada = AdaBoostClassifier(base_estimator=lg,\
n_estimators=ada_param['n_estimators'])
NB = MultinomialNB(alpha=1)
"""用5折交叉验证，计算所有模型的 r2,并计算其均值"""
S_lg_i = cross_val_score(lg, X, Y, \
scoring='accuracy',cv=5)
S_knn_i = cross_val_score(knn, X, Y, \
scoring='accuracy',cv=5)
S_svc_i = cross_val_score(svc, X, Y, \
scoring='accuracy',cv=5)
S_dtc_i = cross_val_score(dtc, X, Y, \
scoring='accuracy',cv=5)
S_rf_i = cross_val_score(rf, X, Y, \
scoring='accuracy',cv=5)
S_ada_i = cross_val_score(ada, X, Y, \
scoring='accuracy',cv=5)
S_NB_i = cross_val_score(NB, X, Y,\
scoring='accuracy',cv=5)
print(f'lg ： {np.mean(S_lg_i)}')
print(f'knn ： {np.mean(S_knn_i)}')
print(f'svc : {np.mean(S_svc_i)}')
print(f'dtc :{np.mean(S_dtc_i)}')
print(f'rf : {np.mean(S_rf_i)}')
print(f'ada : {np.mean(S_ada_i)}')
print(f'NB : {np.mean(S_NB_i)}')
return S_lg_i, S_knn_i, S_svc_i, S_dtc_i, S_rf_i, S_ada_i, S_NB_i
if __name__ == '__main__':
data_after_clu = pickle.load(open(r'.\model_and_data\data_after_clu.pkl','rb'))
ener_div = pickle.load(open(r'.\model_and_data\ener_div.pkl','rb'))
# print(data_after_clu)
# print(ener_div)
# knn_param, svc_param, dtc_param, rf_param, ada_param = select_model(data_after_clu,
# ener_div)
S_lg_i, S_knn_i, S_svc_i, S_dtc_i, \
S_rf_i, S_ada_i, S_NB_i= cv_score(data_after_clu,ener_div)
