Sklearn Tsne绘制时的IndexError错误

Question

我尝试运行t-sne，但python显示了以下错误：

IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices

数据由link提供。你知道吗

代码如下：

import pandas as pd
import numpy as np
import sklearn 
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.manifold import TSNE

#Step 1 - Download the data
dataframe_all = pd.read_csv('https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv')
num_rows = dataframe_all.shape[0]

#Step 2 - Clearn the data
#count the number of missing elements (NaN) in  each column
counter_nan = dataframe_all.isnull().sum()
counter_without_nan = counter_nan[counter_nan==0]
#remove the columns with missing elements
dataframe_all = dataframe_all[counter_without_nan.keys()]
#remove the first 7 columns which contain no descriminative information
dataframe_all = dataframe_all.ix[:,7:]

#Step 3: Create feature vectors
x = dataframe_all.ix[:,:-1].values
standard_scalar = StandardScaler()
x_std = standard_scalar.fit_transform(x)

# t distributed stochastic neighbour embedding (t-SNE) visualization
tsne = TSNE(n_components=2, random_state = 0)
x_test_2d = tsne.fit_transform(x_std)

#scatter plot the sample points among 5 classes
markers=('s','d','o','^','v')
color_map = {0:'red', 1:'blue', 2:'lightgreen', 3:'purple', 4:'cyan'}
plt.figure()
for idx, cl in enumerate(np.unique(x_test_2d)):
    plt.scatter(x=x_test_2d[cl, 0],y =x_test_2d[cl, 1], c=color_map[idx], marker=markers[idx], label=cl)
plt.show()

我要做些什么才能让这一切顺利进行？你知道吗

Answer 1

错误是由于以下行引起的：

plt.scatter(x_test_2d[cl, 0], x_test_2d[cl, 1], c=color_map[idx], marker=markers[idx])

---

在这里，cl可以采用整数值，也可以不采用整数值（来自np.unique(x_test_2d)），这会产生错误，例如cl采用的最后一个值是99.46295，然后使用：x_test_2d[cl, 0]，它会转换成x_test_2d[99.46295, 0]

---

定义一个变量y，保存类标签，然后使用：

# variable holding the classes
y = dataframe_all.classe.values
y = np.array([ord(i) for i in y])

#scatter plot the sample points among 5 classes
plt.figure()
plt.scatter(x_test_2d[:, 0], x_test_2d[:, 1], c = y)
plt.show()

完整代码：

import pandas as pd
import numpy as np
import sklearn 
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.manifold import TSNE

#Step 1 - Download the data
dataframe_all = pd.read_csv('https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv')
num_rows = dataframe_all.shape[0]

#Step 2 - Clearn the data
#count the number of missing elements (NaN) in  each column
counter_nan = dataframe_all.isnull().sum()
counter_without_nan = counter_nan[counter_nan==0]
#remove the columns with missing elements
dataframe_all = dataframe_all[counter_without_nan.keys()]
#remove the first 7 columns which contain no descriminative information
dataframe_all = dataframe_all.ix[:,7:]

#Step 3: Create feature vectors
x = dataframe_all.ix[:,:-1].values
standard_scalar = StandardScaler()
x_std = standard_scalar.fit_transform(x)

# t distributed stochastic neighbour embedding (t-SNE) visualization
tsne = TSNE(n_components=2, random_state = 0)
x_test_2d = tsne.fit_transform(x_std)

# variable holding the classes
y = dataframe_all.classe.values # you need this for the colors
y = np.array([ord(i) for i in y]) # convert letters to numbers

#scatter plot the sample points among 5 classes
plt.figure()
plt.scatter(x_test_2d[:, 0], x_test_2d[:, 1], c = y)
plt.show()