为什么在执行 sklearn 分解模块中存在的 KernalPCA 方法时出现此错误

Question

我在 kaggle (https://www.kaggle.com/ronitf/heart-disease-uci) 的心脏病数据集上使用 sklearn 库尝试了 Kernal PCA，因此，我创建了列表“P”中所有类型的内核的列表，并传递给 KernalPCA() 方法参数内核。

当我执行下面的代码时，我会在代码后面附加此错误消息。

情节的输出完全没问题，但我得到了错误

我很好奇为什么？有人可以帮忙吗？

from sklearn import decomposition
from sklearn.preprocessing import StandardScaler
from scipy import sparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import seaborn as sns
df = pd.read_csv('heart.csv')
target = df['target']
df.head()
Scaler = StandardScaler()
# X represents Standardized data of df
X = Scaler.fit_transform(df)
X.shape
n=2
p = ['linear','poly','rbf','sigmoid','cosine','precomputed']
for i in p:
    trans = decomposition.KernelPCA(n_components=n,kernel=i)
    Xli = trans.fit_transform(X)
    y = pd.DataFrame(Xli,columns=('PC1','PC2'))
    y['Target'] = target

Answer 1

它在您最后选择的内核上失败。这很好用：

np.random.seed(111)
X = np.random.uniform(0,1,(10,4))
target = np.random.normal(0,1,10)

p = ['linear','poly','rbf','sigmoid','cosine']
for i in p:
    trans = decomposition.KernelPCA(n_components=n,kernel=i)
    Xli = trans.fit_transform(X)
    y = pd.DataFrame(Xli,columns=('PC1','PC2'))
    y['Target'] = target

如果你指定kernel = 'precomputed'，那么你需要提供克矩阵，参见this answer，例如如果我们用线性核预先计算克矩阵：

def linear_kernel(X, Y):
    return X.dot(Y.T)

gram = linear_kernel(X, X)
trans = decomposition.KernelPCA(n_components=n,kernel="precomputed")
trans.fit_transform(gram)

array([[ 0.34115243,  0.08282281],
       [ 0.34927523, -0.51709   ],
       [-0.48173365, -0.05455087],
       [-0.34252946, -0.21207875],
       [ 0.66528647, -0.12052876],
       [ 0.04018184,  0.71760041],
       [-0.35535148, -0.2107046 ],
       [ 0.04163704,  0.16239367],
       [-0.48902704,  0.01668406],
       [ 0.23110862,  0.13545204]])

比较：

trans = decomposition.KernelPCA(n_components=n,kernel="linear")
trans.fit_transform(X)

array([[ 0.34115243,  0.08282281],
       [ 0.34927523, -0.51709   ],
       [-0.48173365, -0.05455087],
       [-0.34252946, -0.21207875],
       [ 0.66528647, -0.12052876],
       [ 0.04018184,  0.71760041],
       [-0.35535148, -0.2107046 ],
       [ 0.04163704,  0.16239367],
       [-0.48902704,  0.01668406],
       [ 0.23110862,  0.13545204]])