list index out of range

125404629

发生异常: IndexError
list index out of range
  File "D:\0000可见光2\程序\InceptionTime\Test3.py", line 38, in __init__
    self.dataset_name = output_directory.split('/')[-2]
  File "D:\0000可见光2\程序\InceptionTime\Test3.py", line 133, in <module>
    model = Classifier_NNE(output_directory='output_directory', input_shape=X_train.shape[1:], nb_classes=8)
IndexError: list index out of range

1. import keras
   
2. import numpy as np
   
3. from utils import calculate_metrics
   
4. from utils import create_directory
   
5. from utils import check_if_file_exits
   
6. import gc
   
7. from constants import UNIVARIATE_ARCHIVE_NAMES  as ARCHIVE_NAMES
   
8. import time
   
9. 
   
10. 
    
11. class Classifier_NNE:
    
12. 
    
13.     def create_classifier(self, model_name, input_shape, nb_classes, output_directory, verbose=True,
    
14.                           build=True):
    
15.         if self.check_if_match('inception*', model_name):
    
16.             import inception
    
17.             return inception.Classifier_INCEPTION(output_directory, input_shape, nb_classes, verbose,
    
18.                                                   build=build)
    
19. 
    
20.     def check_if_match(self, rex, name2):
    
21.         import re
    
22.         pattern = re.compile(rex)
    
23.         return pattern.match(name2)
    
24. 
    
25.     def __init__(self, output_directory, input_shape, nb_classes, verbose=False, nb_iterations=5,
    
26.                  clf_name='inception'):
    
27.         self.classifiers = [clf_name]
    
28.         out_add = ''
    
29.         for cc in self.classifiers:
    
30.             out_add = out_add + cc + '-'
    
31.         self.archive_name = ARCHIVE_NAMES[0]
    
32.         self.iterations_to_take = [i for i in range(nb_iterations)]
    
33.         for cc in self.iterations_to_take:
    
34.             out_add = out_add + str(cc) + '-'
    
35.         self.output_directory = output_directory.replace('nne',
    
36.                                                          'nne' + '/' + out_add)
    
37.         create_directory(self.output_directory)
    
38.         self.dataset_name = output_directory.split('/')[-2]
    
39.         self.verbose = verbose
    
40.         self.models_dir = output_directory.replace('nne', 'classifier')
    
41. 
    
42.     def fit(self, x_train, y_train, x_test, y_test, y_true):
    
43.         # no training since models are pre-trained
    
44.         start_time = time.time()
    
45. 
    
46.         y_pred = np.zeros(shape=y_test.shape)
    
47. 
    
48.         ll = 0
    
49. 
    
50.         # loop through all classifiers
    
51.         for model_name in self.classifiers:
    
52.             # loop through different initialization of classifiers
    
53.             for itr in self.iterations_to_take:
    
54.                 if itr == 0:
    
55.                     itr_str = ''
    
56.                 else:
    
57.                     itr_str = '_itr_' + str(itr)
    
58. 
    
59.                 curr_archive_name = self.archive_name + itr_str
    
60. 
    
61.                 curr_dir = self.models_dir.replace('classifier', model_name).replace(
    
62.                     self.archive_name, curr_archive_name)
    
63. 
    
64.                 model = self.create_classifier(model_name, None, None,
    
65.                                                curr_dir, build=False)
    
66. 
    
67.                 predictions_file_name = curr_dir + 'y_pred.npy'
    
68.                 # check if predictions already made
    
69.                 if check_if_file_exits(predictions_file_name):
    
70.                     # then load only the predictions from the file
    
71.                     curr_y_pred = np.load(predictions_file_name)
    
72.                 else:
    
73.                     # then compute the predictions
    
74.                     curr_y_pred = model.predict(x_test, y_true, x_train, y_train, y_test,
    
75.                                                 return_df_metrics=False)
    
76.                     keras.backend.clear_session()
    
77. 
    
78.                     np.save(predictions_file_name, curr_y_pred)
    
79. 
    
80.                 y_pred = y_pred + curr_y_pred
    
81. 
    
82.                 ll += 1
    
83. 
    
84.         # average predictions
    
85.         y_pred = y_pred / ll
    
86. 
    
87.         # save predictions
    
88.         np.save(self.output_directory + 'y_pred.npy', y_pred)
    
89. 
    
90.         # convert the predicted from binary to integer
    
91.         y_pred = np.argmax(y_pred, axis=1)
    
92. 
    
93.         duration = time.time() - start_time
    
94. 
    
95.         df_metrics = calculate_metrics(y_true, y_pred, duration)
    
96. 
    
97.         df_metrics.to_csv(self.output_directory + 'df_metrics.csv', index=False)
    
98. 
    
99.         gc.collect()
    
100. 
     
101. import numpy as np
     
102. import pandas as pd
     
103. from sklearn.model_selection import train_test_split
     
104. import sklearn
     
105. 
     
106. df1 = pd.read_csv("train.csv")
     
107. df1 = np.array(df1)
     
108. X = np.expand_dims(df1[:, 1:891].astype(float), axis=2)  # 对数据进行增维并转化为32为
     
109. #X = np.expand_dims(df1[:, 1:891].astype(float), axis=1)
     
110. Y = df1[:, 0]
     
111. X_train, X_val, y_train, y_val = train_test_split(X, Y, test_size=0.2, random_state=42)
     
112. 
     
113. #y_train, y_val = transform_labels(y_train, y_val)
     
114. # save orignal y because later we will use binary
     
115. y_true = y_val.astype(np.int64)
     
116. y_true_train = y_train.astype(np.int64)
     
117. # transform the labels from integers to one hot vectors
     
118. enc = sklearn.preprocessing.OneHotEncoder()
     
119. enc.fit(np.concatenate((y_train, y_val), axis=0).reshape(-1, 1))
     
120. y_train = enc.transform(y_train.reshape(-1, 1)).toarray()
     
121. y_val = enc.transform(y_val.reshape(-1, 1)).toarray()
     
122. 
     
123. '''y_train = to_categorical(y_train)  # one-hot encoding
     
124. y_val= to_categorical(y_val)
     
125. y_true = y_val'''
     
126. df2 = pd.read_csv("test.csv")
     
127. df2 = np.array(df1)
     
128. X_test = np.expand_dims(df2[:, 1:891].astype(float), axis=2)
     
129. y_test=df2[:, 0]
     
130. y_true1 = y_test.astype(np.int64)
     
131. y_test = enc.transform(y_test.reshape(-1, 1)).toarray()
     
132. # 创建模型
     
133. model = Classifier_NNE(output_directory='output_directory', input_shape=X_train.shape[1:], nb_classes=8)
     
134. 
     
135. # 训练模型
     
136. model.fit(X_train, y_train, X_val, y_val,y_true)

复制代码

陶远航

The error you're encountering, IndexError: list index out of range, is due to trying to access an index in a list that doesn't exist. In your case, the error is occurring at this line:

1. self.dataset_name = output_directory.split('/')[-2]
   

复制代码

Here, you're trying to split the output_directory string by the '/' character and access the second last element. If the output_directory string doesn't contain enough '/' characters, you'll get an IndexError.

To fix this, you need to ensure that output_directory contains at least two '/' characters. If you're not sure whether output_directory will always contain enough '/' characters, you can add a check before this line to prevent the error:

1. split_directory = output_directory.split('/')
   
2. if len(split_directory) >= 2:
   
3.     self.dataset_name = split_directory[-2]
   
4. else:
   
5.     # Handle the case where output_directory doesn't contain enough '/' characters
   
6.     # For example, you could set self.dataset_name to a default value
   
7.     self.dataset_name = "default"
   

复制代码

This code will prevent the IndexError by checking that split_directory contains enough elements before trying to access the second last element. If split_directory doesn't contain enough elements, self.dataset_name is set to a default value. You should replace \"default\" with a value that makes sense in your context.
请注意，本回答来自GPT-4
如果问题已经解决，请设置最佳答案

isdkz

这个错误通常表示您正在尝试访问列表中不存在的索引。在这种情况下，错误发生在第133行，因为您的output_directory参数是一个字符串，而不是列表。您需要检查代码中是否有任何错误，确保您正在使用正确的参数类型。

125404629

陶远航 发表于 2023-10-13 21:50
The error you're encountering, IndexError: list index out of range, is due to trying to access an in ...

谢谢哥 都没翻译 热乎的