Stack-gan或者Attn-gan代码复现，能够跑起来就行
能够让代码跑起来，生成对应的图片，保存相应的模型
参照这个文章，内含完整代码：
https://blog.csdn.net/jp_zhou256/article/details/83051796
"""Stacking 思想的实现"""
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
#from sklearn.cross_validation import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
# from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
import warnings
from numpy import hstack, vstack, array, nan
warnings.filterwarnings('ignore')
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data # 训练特征
Y = iris.target # 测试特诊
# 4:1是为了方便做交叉验证
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=10)
# 归一化处理
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
# clf1=LogisticRegression()
# clf1.fit(X_train,y_train)
# y_predict=clf1.predict(X_test)
"""1.1.获得新特征"""
new_feature = [] # 装每个模型产生的新特征列
new_label_test = []
# 模型模块
clf1 = LogisticRegression()
# clf1.fit(XXXtrain,YYYtrain)
# y_predict=clf1.predict(XXXtest)
clf2 = KNeighborsClassifier()
clf3 = GaussianNB()
clf4 = RandomForestClassifier()
# 将训练集划分成5份
set_Train = []
set_Test = []
# 将X_train和y_train拆成5折以备后续做5折交叉验证使用
for k in range(5):
span = int(X_train.shape[0] / 5)
i = k * span
j = (k + 1) * span
# 将X_train和y_train均划分成为5分被后续交叉验证使用
set_Train.append(X_train[i:j])
set_Test.append(y_train[i:j])
model = [clf1, clf2, clf3, clf4]
# clf1.__class__.__bases__ #基本函数族
for index, clf in enumerate(model):
# print(index,'======',clf)
model_list = [] # 将每一轮交叉验证的预测label存入其中,再转为array做转置.
label_list = [] # 总的测试集对应的预测标签
# k折交叉验证
for k in range(5):
##选择做交叉验证的测试集
XXtest = []
XXtest.append(set_Train[k])
# 选择做交叉验证训练的训练集
XXtrain = []
YYtest = []
for kk in range(5):
if kk == k:
continue
else:
XXtrain.append(set_Train[kk])
YYtest.append(set_Test[kk])
# 模型的训练
XXXtrain = array(vstack((XXtrain[0], XXtrain[1], XXtrain[2], XXtrain[3])))
YYYtrain = array(hstack((YYtest[0], YYtest[1], YYtest[2], YYtest[3])))
XXXtest = array(vstack(XXtest)) # XXtest.shape=1*24*4,不是想要的96x4
clf.fit(XXXtrain, YYYtrain)
y_predict = clf.predict(XXXtest)
model_list.append(y_predict) # 将第k折验证中第k折为测试集的预测标签存储起来
test_y_pred = clf.predict(X_test)
label_list.append(test_y_pred)
# 拼接数据集时候是hstack还是vstack可以试试看
new_k_feature = array(hstack((model_list[0], model_list[1], model_list[2], model_list[3],
model_list[4]))) # hstack() takes 1 positional argument,所以参数使用一个tuple来封装
new_feature.append(new_k_feature)
new_k_test = array(vstack((label_list[0], label_list[1], label_list[2], label_list[3],
label_list[4]))).T # hstack() takes 1 positional argument,所以参数使用一个tuple来封装
new_label_test.append(array(list(map(int, list(map(round, new_k_test.mean(axis=1)))))))
# 将5个基模型训练好的预分类标签组合成为新的特征供后续使用(X_train')
newfeature_from_train = array(vstack((new_feature[0], new_feature[1], new_feature[2], new_feature[3]))).T # 拼接完成后做转置
# 将交叉验证获得的label拼接起来(X_test')
predict_from_test_average = array(
vstack((new_label_test[0], new_label_test[1], new_label_test[2], new_label_test[3]))).T
"""1.3.meta_classifier 模型"""
clf5 = GradientBoostingClassifier()
clf5.fit(newfeature_from_train, y_train)
predict1 = clf5.predict(predict_from_test_average)
print(predict1)
Score = accuracy_score(y_test, predict1)
print(Score)
需要修改两处导入模块，望采纳
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