为什么我在 PCA 中的 n 分量只能是 1（索引暗示 1）

Question

from sklearn.decomposition import PCA
pca = PCA(n_components=1, random_state=42).fit_transform(X_train)
# 43 components according to the graph, but it only allows 1.
pca = pd.DataFrame(pca,columns=['pca'])
#pca
#X_train= pd.concat([X_train,pca],axis=1)

如果我将 n_components 更改为任何其他数字（例如 43），它将显示如下错误：

ValueError: Shape of passed values is (54708, 43), indices imply (54708, 1)

我在我的代码中得到这个来帮助我知道我应该使用的组件数量，即 43：

# find n components to explain variance.
# Code source: https://www.mikulskibartosz.name/pca-how-to-choose-the-number-of-components/
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data_rescaled = scaler.fit_transform(X_train)
pca = PCA().fit(data_rescaled)

% matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (24,12)

fig, ax = plt.subplots()
xi = np.arange(1, 117, step=1)
# The number 117 is to match y, otherwise it gives me error.
y = np.cumsum(pca.explained_variance_ratio_) 
# (explained_variance_ratio_)
# Percentage of variance explained by each of the selected components.
# np.cumsum
# Return the cumulative sum of the elements along a given axis.


plt.ylim(0.0,1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')

plt.xlabel('Number of Components')
plt.xticks(np.arange(0, 117, step=1)) #change from 0-based array index to 1-based human-readable label

plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')

plt.axhline(y=0.95, color='r', linestyle='-')
plt.text(0.5, 0.85, '95% cut-off threshold', color = 'red', fontsize=16)

ax.grid(axis='x')
plt.show()

# 43 number of components.

代码结果图：

Answer 1

试试这个：

pca = pd.DataFrame(pca,columns=[str(i) for i in range(pca.shape[1])])

为什么我在 PCA 中的 n 分量只能是 1（索引暗示 1）

Why my n-component in PCA can only be 1 (indices imply 1)

python

machine-learning

pca