from sklearn.model_selection import train_test_split ts = 0.3 # Percentage of images that we want to use for testing. X_train, X_test1, y_train, y_test1 = train_test_split(X, y, test_size=ts, random_state=42) X_test, X_cal, y_test, y_cal = train_test_spli
时间: 2023-07-20 11:12:10 浏览: 48
t(X_test1, y_test1, test_size=0.5, random_state=42)
这段代码使用`train_test_split`函数将数据集`X`和标签`y`分成训练集和测试集。`ts`变量表示测试集所占的百分比。在这个例子中,测试集占总数据集的30%。
首先,使用`train_test_split`函数将数据集`X`和标签`y`分成训练集和测试集。`test_size`参数设置为`ts`,即测试集所占的百分比。`random_state`参数用于设置随机种子,以便每次运行代码时都能得到相同的结果。
```python
X_train, X_test1, y_train, y_test1 = train_test_split(X, y, test_size=ts, random_state=42)
```
接下来,使用`train_test_split`函数将测试集分成测试集和验证集。`test_size`参数设置为0.5,即测试集的50%用于验证集。同样,`random_state`参数用于设置随机种子。
```python
X_test, X_cal, y_test, y_cal = train_test_split(X_test1, y_test1, test_size=0.5, random_state=42)
```
最终,我们得到了三个数据集:训练集`X_train`和`y_train`、测试集`X_test`和`y_test`、验证集`X_cal`和`y_cal`。这些数据集将用于训练、测试和评估机器学习模型。
相关问题
import pickle from sklearn.model_selection import train_test_split ts = 0.3 # Percentage of images that we want to use for testing. X_train, X_test1, y_train, y_test1 = train_test_split(X, y, test_size=ts, random_state=42) X_test, X_cal, y_test, y_cal
这段代码中存在错误,应该将X_test1和y_test1改为X_test和y_test,同时将X_cal和y_cal改为X_val和y_val,修改后的代码如下:
```python
import pickle
from sklearn.model_selection import train_test_split
ts = 0.3 # Percentage of images that we want to use for testing.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=ts, random_state=42)
X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=42)
data = {"X_train": X_train, "y_train": y_train, "X_val": X_val, "y_val": y_val, "X_test": X_test, "y_test": y_test}
with open("data.pkl", "wb") as f:
pickle.dump(data, f)
```
这段代码的作用是将数据集划分为训练集、验证集和测试集,并将处理后的数据保存到data.pkl文件中。其中,X_train和y_train是训练集的特征和标签,X_val和y_val是验证集的特征和标签,X_test和y_test是测试集的特征和标签。在划分数据集时,先将数据集分成训练集和测试集,再将测试集按照一定比例划分为测试集和验证集。保存数据时,使用pickle库将数据保存为二进制文件。
使用pytorch实现这串代码from sklearn.model_selection import train_test_split ts = 0.3 # Percentage of images that we want to use for testing. X_train, X_test1, y_train, y_test1 = train_test_split(X, y, test_size=ts, random_state=42) X_test, X_cal, y_test, y_cal = train_test_split(X_test1, y_test1, test_size=ts, random_state=4并生成pkl文件
你可以使用以下代码来实现:
```python
import pickle
from sklearn.model_selection import train_test_split
ts = 0.3 # Percentage of images that we want to use for testing.
X_train, X_test1, y_train, y_test1 = train_test_split(X, y, test_size=ts, random_state=42)
X_test, X_cal, y_test, y_cal = train_test_split(X_test1, y_test1, test_size=ts, random_state=4)
# Save the datasets as pickle files
with open('X_train.pkl', 'wb') as f:
pickle.dump(X_train, f)
with open('y_train.pkl', 'wb') as f:
pickle.dump(y_train, f)
with open('X_test.pkl', 'wb') as f:
pickle.dump(X_test, f)
with open('y_test.pkl', 'wb') as f:
pickle.dump(y_test, f)
with open('X_cal.pkl', 'wb') as f:
pickle.dump(X_cal, f)
with open('y_cal.pkl', 'wb') as f:
pickle.dump(y_cal, f)
```
首先,导入pickle模块和train_test_split函数。然后,设置测试集的比例`ts`。接着,使用train_test_split函数将数据集分成训练集和第一份测试集。然后,使用train_test_split函数将第一份测试集分成第二份测试集和验证集。最后,将训练集、测试集和验证集分别保存成pickle文件。注意,'wb'表示以二进制模式写入文件。