sklearn框架使用例子，多种分类方法的集合，方便模型的融合

import numpy as np
import pandas as pd
from pandas import Series,DataFrame
import matplotlib.pyplot as plt
from sklearn.svm import NuSVC
import xgboost as xgb
from xgboost import plot_importance
#模型效果评估
from sklearn.metrics import r2_score
from datetime import datetime
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

#到多元回归之前是获取数据过程，下面分别采用多种模型对数据进行测试
df = pd.read_excel(r'C:\Users\27058\Desktop\task\program\1.xlsx')
# print(len(df))
heji = list() #所有训练数据
output_1 = list()
output_2 = list()
output_3 = list()
output_4 = list()
output_5 = list()
# 0：84
for i in range(0,72):
    # 每组训练数据
    input = list()
    input.append(df['镍片厚度（mm）'][i])
    input.append(df['结果状态'][i])     #是否合格
    input.append(df['焊点拉力（N）'][i])
    input.append(df['镍带是否撕裂'][i])
    heji.append(input)

    output_1.append(df['焊针压力(N)'][i])
    output_2.append(df['焊接电压1（V）'][i])
    output_3.append(df['焊接电压2（V）'][i])
    output_4.append(df['焊接时间1(ms)'][i])
    output_5.append(df['焊接时间2(ms)'][i])

x_train = heji[0:52]
y_train = output_2[0:52]
x_test = np.array(heji[52:])
y_true = output_2[52:]

standardScaler = StandardScaler()
x_train_fit = standardScaler.fit_transform(x_train)
x_test_fit = standardScaler.transform(x_test)
print("y_true:",y_true)
# logistic回归，用作二分类
from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression()
log_reg.fit(x_train_fit, y_train)
log_reg_pre = log_reg.predict(x_test_fit)
print('log:',log_reg_pre)

#sgd分类
from sklearn.linear_model import SGDClassifier
sgd_reg = SGDClassifier()
sgd_reg.fit(x_train_fit,y_train)
sgd_reg_pre = sgd_reg.predict(x_test_fit)
print('sgd:',sgd_reg_pre)

#朴素贝叶斯
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb_y_pred = gnb.fit(x_train_fit, y_train).predict(x_test_fit)
print('gnb:',gnb_y_pred)

# #KNN分类
# from sklearn import neighbors
# knn = neighbors.KNeighborsClassifier()
# knn.fit(x_train_fit, y_train)
# knn_pre = knn.predict(x_test_fit)
# print('knn:',knn_pre)

classifiers = [
    KNeighborsClassifier(3),
    # SVC(kernel="linear", C=0.025),
    SVC(gamma=2, C=1),
    GaussianProcessClassifier(1.0 * RBF(1.0), warm_start=True),
    DecisionTreeClassifier(max_depth=5),
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
    MLPClassifier(alpha=1),
    AdaBoostClassifier(),
    GaussianNB(),
    QuadraticDiscriminantAnalysis()]

names = ["knn",  "RBF_SVM", "g_c",
         "Deci", "Rand", "Net", "AdaBoost",
         "Bayes", "QDA"]
for name, clf in zip(names, classifiers):
    pre = clf.fit(x_train_fit, y_train).predict(x_test_fit)
    print(name,pre)