用python写一段读取csv文件作为输入,基于回归特征消去(RFE)方法的高斯核SVM分类模型,以分类正确率作为评价标准对特征集维度优化的代码
时间: 2024-02-12 17:08:25 浏览: 22
好的,我来帮您写一下。
首先,您需要安装以下库:
```python
pip install numpy pandas sklearn
```
接下来,您可以使用以下代码读取您的 csv 文件:
```python
import pandas as pd
df = pd.read_csv('your_csv_file.csv')
```
然后,我们可以将数据集划分为特征集和目标变量。假设您的目标变量列名为 'target',您可以使用以下代码:
```python
X = df.drop('target', axis=1)
y = df['target']
```
接下来,我们将使用 RFE 方法对特征集进行优化。您可以使用以下代码:
```python
from sklearn.feature_selection import RFE
from sklearn.svm import SVC
estimator = SVC(kernel='linear')
selector = RFE(estimator, n_features_to_select=5, step=1)
selector.fit(X, y)
X_opt = selector.transform(X)
```
以上代码中,我们使用了 SVM 分类器,并设置了核函数为 'linear'。我们还指定了要选择的特征数量为 5 个。您可以根据需要调整这些参数。
最后,我们可以使用以下代码来训练和评估高斯核 SVM 分类模型:
```python
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.metrics.pairwise import rbf_kernel
X_train, X_test, y_train, y_test = train_test_split(X_opt, y, test_size=0.2, random_state=42)
clf = SVC(kernel=rbf_kernel)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")
```
以上代码中,我们使用了高斯核函数,并使用 train_test_split 函数将数据集划分为训练集和测试集。我们还计算了模型的分类准确率作为评价标准。
最终的代码如下所示:
```python
import pandas as pd
from sklearn.feature_selection import RFE
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics.pairwise import rbf_kernel
# Load data
df = pd.read_csv('your_csv_file.csv')
# Split data into features and target variable
X = df.drop('target', axis=1)
y = df['target']
# Feature selection using RFE
estimator = SVC(kernel='linear')
selector = RFE(estimator, n_features_to_select=5, step=1)
selector.fit(X, y)
X_opt = selector.transform(X)
# Train and evaluate Gaussian kernel SVM model
X_train, X_test, y_train, y_test = train_test_split(X_opt, y, test_size=0.2, random_state=42)
clf = SVC(kernel=rbf_kernel)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")
```
希望这可以帮助您!