AttributeError: 'PCA' object has no attribute 'predict'
时间: 2023-06-21 10:15:44 浏览: 551
对不起,我之前的回答有误。PCA类确实没有predict方法,因为PCA只是将原始数据进行降维,而不是对数据进行分类。在机器学习中,通常使用PCA降维后的数据作为特征,再使用其他分类算法进行分类。下面是修改后的代码,使用SVM进行分类:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.manifold import LocallyLinearEmbedding
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, roc_auc_score
# 加载ORL数据集
data = fetch_olivetti_faces()
X = data["data"]
y = data["target"]
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 原始数据的维度
n_features = X.shape[1]
# PCA降维
pca = PCA()
pca.fit(X_train)
# 调参
params = {"n_components": [10, 20, 30, 40, 50]}
pca_grid = GridSearchCV(pca, params, scoring="accuracy")
pca_grid.fit(X_train, y_train)
pca_best = pca_grid.best_estimator_
pca_X_train = pca_best.transform(X_train)
pca_X_test = pca_best.transform(X_test)
print("PCA降维后的维度:", pca_X_train.shape[1])
# LDA降维
lda = LinearDiscriminantAnalysis()
lda.fit(X_train, y_train)
# 调参
params = {"n_components": [1, 2, 3, 4]}
lda_grid = GridSearchCV(lda, params, scoring="accuracy")
lda_grid.fit(X_train, y_train)
lda_best = lda_grid.best_estimator_
lda_X_train = lda_best.transform(X_train)
lda_X_test = lda_best.transform(X_test)
print("LDA降维后的维度:", lda_X_train.shape[1])
# LLE降维
lle = LocallyLinearEmbedding()
lle.fit(X_train)
# 调参
params = {"n_components": [10, 20, 30, 40, 50]}
lle_grid = GridSearchCV(lle, params, scoring="accuracy")
lle_grid.fit(X_train, y_train)
lle_best = lle_grid.best_estimator_
lle_X_train = lle_best.transform(X_train)
lle_X_test = lle_best.transform(X_test)
print("LLE降维后的维度:", lle_X_train.shape[1])
# 使用SVM分类器进行分类
svm = SVC(kernel="linear", probability=True)
# 原始数据集
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)
y
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