Certainly! Here is the completed code for the remaining parts:
```python
# Import necessary libraries
from sklearn import svm
from sklearn.metrics import accuracy_score, precision_score, recall_score
from sklearn.feature_selection import SelectKBest, f_classif
import numpy as np

# Load the dataset
data = np.load('shape_texture_features.npz')
train_features = data['train_features']
train_labels = data['train_labels']
test_features = data['test_features']
test_labels = data['test_labels']

# Train a SVM classifier using shape features only
svm_shape = svm.SVC(kernel='linear')
svm_shape.fit(train_features[:, :4], train_labels)
pred_train_shape = svm_shape.predict(train_features[:, :4])
pred_test_shape = svm_shape.predict(test_features[:, :4])

# Train a SVM classifier using texture features only
svm_texture = svm.SVC(kernel='linear')
svm_texture.fit(train_features[:, 4:], train_labels)
pred_train_texture = svm_texture.predict(train_features[:, 4:])
pred_test_texture = svm_texture.predict(test_features[:, 4:])

# Train a SVM classifier using both shape and texture features
svm_all = svm.SVC(kernel='linear')
svm_all.fit(train_features, train_labels)
pred_train_all = svm_all.predict(train_features)
pred_test_all = svm_all.predict(test_features)

# Calculate accuracy, precision, and recall for each model
print('Accuracy (shape only):', accuracy_score(train_labels, pred_train_shape))
print('Precision (shape only):', precision_score(train_labels, pred_train_shape, average='weighted'))
print('Recall (shape only):', recall_score(train_labels, pred_train_shape, average='weighted'))
print('Accuracy (texture only):', accuracy_score(train_labels, pred_train_texture))
print('Precision (texture only):', precision_score(train_labels, pred_train_texture, average='weighted'))
print('Recall (texture only):', recall_score(train_labels, pred_train_texture, average='weighted'))
print('Accuracy (shape and texture):', accuracy_score(train_labels, pred_train_all))
print('Precision (shape and texture):', precision_score(train_labels, pred_train_all, average='weighted'))
print('Recall (shape and texture):', recall_score(train_labels, pred_train_all, average='weighted'))

# Perform feature selection and train a SVM classifier using the 10 most important features
selector = SelectKBest(f_classif, k=10)
selector.fit(train_features, train_labels)
train_features_reduced = selector.transform(train_features)
test_features_reduced = selector.transform(test_features)
svm_reduced = svm.SVC(kernel='linear')
svm_reduced.fit(train_features_reduced, train_labels)
pred_train_reduced = svm_reduced.predict(train_features_reduced)
pred_test_reduced = svm_reduced.predict(test_features_reduced)

# Calculate accuracy, precision, and recall for the reduced model
print('Accuracy (reduced):', accuracy_score(train_labels, pred_train_reduced))
print('Precision (reduced):', precision_score(train_labels, pred_train_reduced, average='weighted'))
print('Recall (reduced):', recall_score(train_labels, pred_train_reduced, average='weighted'))

# Calculate precision and recall scores per class for the full model
print('Precision per class (full model):', precision_score(test_labels, pred_test_all, average=None, labels=[0, 1]))
print('Recall per class (full model):', recall_score(test_labels, pred_test_all, average=None, labels=[0, 1]))

# Calculate precision and recall scores per class for the reduced model
print('Precision per class (reduced model):', precision_score(test_labels, pred_test_reduced, average=None, labels=[0, 1
```
물론이죠! 나머지 부분에 대한 완성된 코드는 다음과 같습니다:```python# 필요한 라이브러리 가져오기sklearn에서 svm 가져오기sklearn.metrics에서 정확도_점수, 정밀도_점수, 회상_점수 가져오기sklearn.feature_selection에서 SelectKBest, f_classif를 가져옵니다.numpy를 np로 가져옵니다.# 데이터 집합 로드data = np.load('shape_texture_features.npz')train_features = data['train_features']train_labels = data['train_labels']test_features = data['test_features']test_labels = data['test_labels']# 모양 특징만 사용하여 SVM 분류기 훈련하기svm_shape = svm.SVC(kernel='linear')svm_shape.fit(train_features[:, :4], train_labels)pred_train_shape = svm_shape.predict(train_features[:, :4])pred_test_shape = svm_shape.predict(test_features[:, :4])# 텍스처 특징만 사용하여 SVM 분류기 훈련하기svm_texture = svm.SVC(kernel='linear')svm_texture.fit(train_features[:, 4:], train_labels)pred_train_texture = svm_texture.predict(train_features[:, 4:])pred_test_texture = svm_texture.predict(test_features[:, 4:])# 모양과 텍스처 특징을 모두 사용하여 SVM 분류기 훈련하기svm_all = svm.SVC(커널='linear')svm_all.fit(train_features, train_labels)pred_train_all = svm_all.predict(train_features)pred_test_all = svm_all.predict(test_features)# 각 모델에 대한 정확도, 정밀도, 재현율 계산하기print('정확도(모양만):', accuracy_score(train_labels, pred_train_shape))print('정밀도(모양만):', precision_score(train_labels, pred_train_shape, average='weighted'))print('Recall(모양만):', recall_score(train_labels, pred_train_shape, average='weighted'))print('정확도(텍스처만):', accuracy_score(train_labels, pred_train_texture))print('정밀도(텍스처만):', precision_score(train_labels, pred_train_texture, average='weighted'))print('Recall (텍스처만):', recall_score(train_labels, pred_train_texture, average='weighted'))print('정확도(모양 및 텍스처):', accuracy_score(train_labels, pred_train_all))print('정밀도(모양 및 텍스처):', precision_score(train_labels, pred_train_all, average='weighted'))print('Recall(모양 및 텍스처):', recall_score(train_labels, pred_train_all, average='weighted'))# 특징 선택을 수행하고 가장 중요한 10개의 특징을 사용하여 SVM 분류기를 훈련합니다.selector = SelectKBest(f_classif, k=10)selector.fit(train_features, train_labels)train_features_reduced = selector.transform(train_features)test_features_reduced = selector.transform(test_features)svm_reduced = svm.SVC(커널='linear')svm_reduced.fit(train_features_reduced, train_labels)pred_train_reduced = svm_reduced.predict(train_features_reduced)pred_test_reduced = svm_reduced.predict(test_features_reduced)# 축소된 모델에 대한 정확도, 정밀도, 재현율을 계산합니다.print('정확도(감소):', accuracy_score(train_labels, pred_train_reduced))print('정밀도(감소):', precision_score(train_labels, pred_train_reduced, average='weighted'))print('Recall (reduced):', recall_score(train_labels, pred_train_reduced, average='weighted'))# 전체 모델에 대한 클래스별 정확도 및 회수 점수를 계산합니다.print('클래스당 정밀도(전체 모델):', precision_score(test_labels, pred_test_all, average=None, labels=[0, 1]))print('클래스별 정확도(전체 모델):', recall_score(test_labels, pred_test_all, average=None, labels=[0, 1]))# 축소된 모델에 대한 클래스별 정확도 및 재콜 점수를 계산합니다.print('클래스별 정밀도(축소된 모델):', precision_score(test_labels, pred_test_reduced, average=None, labels=[0, 1```