test_images=xtest.reshape(-1,8,8)
时间: 2023-06-13 13:06:48 浏览: 57
这段代码的作用是将一维的测试数据 `xtest` 重新变成二维的图像数据 `test_images`,其中每个图像是 $8\times8$ 的矩阵。这样做的目的是为了方便对图像数据进行处理和可视化。`reshape` 函数中的第一个参数 `-1` 表示自动计算该维度的长度,以保证数据总长度不变。因此,这里将测试数据 `xtest` 的长度作为第一个维度,将其转换为一个三维数组。
相关问题
import scipy.io as sio from sklearn import svm import numpy as np import matplotlib.pyplot as plt data=sio.loadmat('AllData') labels=sio.loadmat('label') print(data) class1 = 0 class2 = 1 idx1 = np.where(labels['label']==class1)[0] idx2 = np.where(labels['label']==class2)[0] X1 = data['B007FFT0'] X2 = data['B014FFT0'] Y1 = labels['label'][idx1].reshape(-1, 1) Y2 = labels['label'][idx2].reshape(-1, 1) ## 随机选取训练数据和测试数据 np.random.shuffle(X1) np.random.shuffle(X2) # Xtrain = np.vstack((X1[:200,:], X2[:200,:])) # Xtest = np.vstack((X1[200:300,:], X2[200:300,:])) # Ytrain = np.vstack((Y1[:200,:], Y2[:200,:])) # Ytest = np.vstack((Y1[200:300,:], Y2[200:300,:])) # class1=data['B007FFT0'][0:1000, :] # class2=data['B014FFT0'][0:1000, :] train_data=np.vstack((X1[0:200, :],X2[0:200, :])) test_data=np.vstack((X1[200:300, :],X2[200:300, :])) train_labels=np.vstack((Y1[:200,:], Y2[:200,:])) test_labels=np.vstack((Y1[200:300,:], Y2[200:300,:])) ## 训练SVM模型 clf=svm.SVC(kernel='linear', C=1000) clf.fit(train_data,train_labels.reshape(-1)) ## 用测试数据测试模型准确率 train_accuracy = clf.score(train_data, train_labels) test_accuracy = clf.score(test_data, test_labels) # test_pred=clf.predict(test_data) # accuracy=np.mean(test_pred==test_labels) # print("分类准确率为:{:.2F}%".fromat(accuracy*100)) x_min,x_max=test_data[:,0].min()-1,test_data[:,0].max()+1 y_min,y_max=test_data[:,1].min()-1,test_data[:,1].max()+1 xx,yy=np.meshgrid(np.arange(x_min,x_max,0.02),np.arange(y_min,y_max,0.02)) # 生成一个由xx和yy组成的网格 # X, Y = np.meshgrid(xx, yy) # 将网格展平成一个二维数组xy xy = np.vstack([xx.ravel(), yy.ravel()]).T # Z = clf.decision_function(xy).reshape(xx.shape) # z=clf.predict(np.c_[xx.ravel(),yy.ravel()]) z=xy.reshape(xx.shape) plt.pcolormesh(xx.shape) plt.xlim(xx.min(),xx.max()) plt.ylim(yy.min(),yy.max()) plt.xtickes(()) plt.ytickes(()) # # 画出分界线 # axis.contour(X, Y, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--']) # axis.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,linewidth=1, facecolors='none') plt.scatter(test_data[:,0],test_data[:1],c=test_labels,cmap=plt.cm.Paired) plt.scatter(clf.support_vectors_[:,0],clf.support_vectors_[:,1],s=80,facecolors='none',linewidths=1.5,edgecolors='k') plt.show()处理一下代码出错问题
代码中出现了几个错误:
1. 在最后几行代码中,plt.pcolormesh(xx.shape)应该改为plt.pcolormesh(xx, yy, z),因为需要绘制的是网格xy上对应的值z。
2. 在plt.xtickes(())和plt.ytickes(())中,xtickes和ytickes应该改为xticks和yticks,因为正确的函数名是xticks和yticks。
3. 在plt.scatter(test_data[:,0],test_data[:1],c=test_labels,cmap=plt.cm.Paired)中,test_data[:,0]和test_data[:1]应该改为test_data[:,0]和test_data[:,1],因为需要绘制的是测试数据的两个特征值。
4. 在画出分界线的代码中,应该将注释的代码取消注释,即将axis.contour(X, Y, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--'])的注释去掉。
以下这段代码是关于CatBoost模型的超参数调整,但里面好像不是在五倍交叉验证下做的分析,请问应该怎么加上五倍交叉验证呢?import os import time import pandas as pd from catboost import CatBoostRegressor from hyperopt import fmin, hp, partial, Trials, tpe,rand from sklearn.metrics import r2_score, mean_squared_error from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold, cross_val_score as CVS, train_test_split as TTS 自定义hyperopt的参数空间 space = {"iterations": hp.choice("iterations", range(1, 30)), "depth": hp.randint("depth", 16), "l2_leaf_reg": hp.randint("l2_leaf_reg", 222), "border_count": hp.randint("border_count", 222), 'learning_rate': hp.uniform('learning_rate', 0.001, 0.9), } data = pd.read_csv(r"E:\exercise\synthesis\synthesis_dummy_2.csv") #验证随机森林填补缺失值方法是否有效 X = data.iloc[:,1:] y = data.iloc[:,0] Xtrain,Xtest,Ytrain,Ytest = TTS(X_wrapper,y,test_size=0.2,random_state=100) def epoch_time(start_time, end_time): elapsed_secs = end_time - start_time elapsed_mins = elapsed_secs / 60 return elapsed_mins, elapsed_secs 自动化调参并训练 def cat_factory(argsDict): estimator = CatBoostRegressor(loss_function='RMSE', random_seed=22, learning_rate=argsDict['learning_rate'], iterations=argsDict['iterations'], l2_leaf_reg=argsDict['l2_leaf_reg'], border_count=argsDict['border_count'], depth=argsDict['depth'], verbose=0) estimator.fit(Xtrain, Ytrain) val_pred = estimator.predict(Xtest) mse = mean_squared_error(Ytest, val_pred) return mse
要在这段代码中添加五倍交叉验证,你可以按照以下步骤进行修改:
1. 导入所需的包:
```python
from sklearn.model_selection import KFold, cross_val_score
```
2. 设置五倍交叉验证的参数:
```python
n_folds = 5
```
3. 修改数据划分部分,将数据划分为训练集和测试集:
```python
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X_wrapper, y, test_size=0.2, random_state=100)
```
4. 修改自动化调参并训练的函数,使用五倍交叉验证进行模型评估:
```python
def cat_factory(argsDict):
estimator = CatBoostRegressor(loss_function='RMSE', random_seed=22,
learning_rate=argsDict['learning_rate'],
iterations=argsDict['iterations'],
l2_leaf_reg=argsDict['l2_leaf_reg'],
border_count=argsDict['border_count'],
depth=argsDict['depth'], verbose=0)
# 设置五倍交叉验证
kf = KFold(n_splits=n_folds)
mse_scores = []
for train_index, val_index in kf.split(Xtrain):
X_train, X_val = Xtrain.iloc[train_index], Xtrain.iloc[val_index]
Y_train, Y_val = Ytrain.iloc[train_index], Ytrain.iloc[val_index]
estimator.fit(X_train, Y_train)
val_pred = estimator.predict(X_val)
mse = mean_squared_error(Y_val, val_pred)
mse_scores.append(mse)
mse_mean = np.mean(mse_scores)
return mse_mean
```
5. 修改自定义hyperopt的参数空间部分,将模型评估函数替换为修改后的函数:
```python
space = {"iterations": hp.choice("iterations", range(1, 30)),
"depth": hp.randint("depth", 16),
"l2_leaf_reg": hp.randint("l2_leaf_reg", 222),
"border_count": hp.randint("border_count", 222),
'learning_rate': hp.uniform('learning_rate', 0.001, 0.9),
}
trials = Trials()
best = fmin(fn=cat_factory, space=space, algo=tpe.suggest, max_evals=100, trials=trials)
```
这样,你就在代码中添加了五倍交叉验证来评估CatBoost模型的超参数调整效果。