Python IndexError：索引1超出LDA的范围

2019年7月27日 261次阅读

我有一个如下所示的数据集：

    Out  Revolver   Ratio     Num ...
0   1    0.766127   0.802982  0   ...
1   0    0.957151   0.121876  1 
2   0    0.658180   0.085113  0 
3   0    0.233810   0.036050  3 
4   1    0.907239   0.024926  5 
...

Out只能取值0和1.
然后,我尝试使用下面的代码生成PCA和LCA图,类似于此处：http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html

features = Train.columns[1:]
Xf = newTrain[features]
yf = newTrain.Out
pca = PCA(n_components=2)
X_r = pca.fit(Xf).transform(Xf)
lda = LinearDiscriminantAnalysis(n_components=2)
X_r2 = lda.fit(Xf, yf).transform(Xf)

plt.figure()
for c, i, name in zip("rgb", [0, 1], names):
    plt.scatter(X_r[yf == i, 0], X_r[yf == i, 1], c=c, label=name)
plt.legend()
plt.title('PCA plt')

plt.figure()
for c, i, name in zip("rgb", [0, 1], names):
    plt.scatter(X_r2[yf == i, 0], X_r2[yf == i, 1], c=c, label=name)
plt.legend()
plt.title('LDA plt')

我可以让PCA情节发挥作用.然而,它没有意义,因为它只显示2个点.一个在(-4000,30)左右,另一个在(2400,23.7).我没有在该链接的图中看到一堆数据点

LDA图不起作用并给出错误

IndexError: index 1 is out of bounds for axis 1 with size 1

我也尝试了下面的代码生成LDA图但得到了同样的错误

for c, i, name in zip("rgb", [0, 1], names):
    plt.scatter(x=X_LDA_sklearn[:, 0][yf==i], y=X_LDA_sklearn[:, 1][yf==i], c=c, label=name)
plt.legend()

谁知道这有什么问题？

编辑：这是我的进口

import pandas as pd
from pandas import Series,DataFrame
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import csv

from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn import metrics
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.lda import LDA

至于错误发生的地方：

我明白了

FutureWarning: in the future, boolean array-likes will be handled as a boolean array index
plt.scatter(X_r[yf == i,0], X_r[yf == i, 1], c=c, label=name)

在PCA图的for循环内的线上

至于线路上的LDA

plt.scatter(X_r2[yf == i, 0], X_r2[yf == i, 1], c=c, label=name)

我明白了

FutureWarning: in the future, boolean array-likes will be handled as a boolean array index

和

IndexError: index 1 is out of bounds for axis 1 with size 1

最佳答案 您看到此错误的原因是X_r2只包含一列(至少给出了您提供的数据).但是,在命令y = X_LDA_sklearn [：,1] [yf == i]中,您尝试访问第二列,因此会抛出您观察到的错误.

我为您提供的示例数据添加了第三个类(两个类的维数降低不合理)并且还将数据帧转换为数组.它现在很好地运行并生成以下图(由于少量数据而不是信息量)：

《Python IndexError：索引1超出LDA的范围》
《Python IndexError：索引1超出LDA的范围》

这是更新的代码：

import pandas as pd
from pandas import Series,DataFrame
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import csv

from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn import metrics
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

trainDF = pd.DataFrame({'Out': [1, 0, 0, 0, 1, 3, 3],
                        'Revolver': [0.766, 0.957, 0.658, 0.233, 0.907, 0.1, 0.15],
                        'Ratio': [0.803, 0.121, 0.085, 0.036, 0.024, 0.6, 0.8],
                        'Num': [0, 1, 0, 3, 5, 4, 4]})
#drop NA values
trainDF = trainDF.dropna()

trainDF['Num'].loc[(trainDF['Num']==8) | (trainDF['Num']==17)] = trainDF['Num'].median()

# convert dataframe to numpy array
y = trainDF['Out'].as_matrix()

# convert dataframe to numpy array
X = trainDF.drop('Out', 1).as_matrix()

target_names = ['out', 'in']

pca = PCA(n_components=2)
X_r = pca.fit(X).transform(X)

lda = LinearDiscriminantAnalysis(n_components=2)
X_r2 = lda.fit(X, y).transform(X)

# Percentage of variance explained for each components
print('explained variance ratio (first two components): %s'
      % str(pca.explained_variance_ratio_))

plt.figure()
for c, i, target_name in zip("rgb", [0, 1], target_names):
    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=target_name)
plt.legend()
plt.title('PCA of Out')

plt.figure()
for c, i, target_name in zip("rgb", [0, 1], target_names):
    plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], c=c, label=target_name)
plt.legend()
plt.title('LDA of Out')

plt.show()

因此,当您遇到这些“索引超出界限”错误时,请始终先检查阵列的尺寸.

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