Jupiter Notebook：输入包含 NaN、无穷大或对于 dtype('float64') 来说太大的值

Posted 2023-03-12

【中文标题】Jupiter Notebook：输入包含 NaN、无穷大或对于 dtype(\'float64\') 来说太大的值

【英文标题】：Jupiter Notebook:Input contains NaN, infinity or a value too large for dtype('float64')Jupiter Notebook：输入包含 NaN、无穷大或对于 dtype('float64') 来说太大的值

【发布时间】：2022-01-06 16:53:12

【问题描述】：

我正在尝试使用我的特征选择来拟合我的数据，但每当我尝试时都会收到此错误

Input contains NaN, infinity or a value too large for dtype('float64').

不确定是否有解决方案，或者我是否可以给它一种不同的方法，但我知道如果我要使用特征选择，我必须在我的算法之前应用它，

#!/usr/bin/env python
# coding: utf-8

# In[1]:


from sklearn.model_selection import train_test_split


# In[2]:


import pandas as pd


# In[3]:


import matplotlib.pyplot as plt


# In[4]:


import numpy as np


# In[5]:


import seaborn as sns
import statsmodels.api as sm
import mglearn as mg
get_ipython().run_line_magic('matplotlib', 'inline')


# In[6]:


from sklearn.ensemble import AdaBoostRegressor
from sklearn.feature_selection import RFE
from sklearn. feature_selection import SelectFromModel


# In[7]:


first_file = pd.read_csv(r"/Users/feliperivas/Downloads/MachineLearningCVE/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv")


# In[8]:


print(first_file)


# In[9]:


second_file = pd.read_csv(r"/Users/feliperivas/Downloads/MachineLearningCVE/Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX.csv")
third_file = pd.read_csv(r"/Users/feliperivas/Downloads/MachineLearningCVE/Friday-WorkingHours-Morning.pcap_ISCX.csv")
fourth_file = pd.read_csv(r"/Users/feliperivas/Downloads/MachineLearningCVE/Monday-WorkingHours.pcap_ISCX.csv")
fifth_file = pd.read_csv(r"/Users/feliperivas/Downloads/MachineLearningCVE/Thursday-WorkingHours-Afternoon-Infilteration.pcap_ISCX.csv")
sixth_file = pd.read_csv(r"/Users/feliperivas/Downloads/MachineLearningCVE/Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv")
seventh_file = pd.read_csv(r"/Users/feliperivas/Downloads/MachineLearningCVE/Tuesday-WorkingHours.pcap_ISCX.csv")
eighth_file = pd.read_csv(r"/Users/feliperivas/Downloads/MachineLearningCVE/Wednesday-workingHours.pcap_ISCX.csv")


# In[10]:


print(second_file)


# In[11]:


print(third_file)


# In[12]:


print(fourth_file)


# In[13]:


print(fifth_file)


# In[14]:


print(sixth_file)


# In[15]:


print(seventh_file)


# In[16]:


print(eighth_file)


# In[17]:


first_file = first_file.loc[:,~first_file.columns.duplicated()]


# In[18]:


print(first_file)


# In[19]:


df_list = [first_file, second_file,third_file,fourth_file, fifth_file, sixth_file, seventh_file,eighth_file]


# In[20]:


merged_data = pd.concat(df_list)


# In[21]:


print(merged_data)


# print(merged_data.shape)

# In[22]:


print(merged_data.shape)


# In[23]:


print(first_file.shape)


# In[24]:


print(second_file.shape)


# In[25]:


print(third_file.shape)


# In[26]:


print(fourth_file.shape)


# In[27]:


print(fifth_file.shape)
        

# In[28]:


print(sixth_file.shape)


# In[29]:


print(seventh_file.shape)


# In[30]:


print(eighth_file.shape)


# In[31]:


# 2830540 number weretrying to get


# In[32]:


# df = merged_data.dropna()


# In[33]:


print(merged_data.shape)


# In[34]:


merged_data.dropna(inplace = True)


# In[35]:


print(merged_data.shape)


# In[36]:


df = merged_data 


# In[37]:


df.shape


# In[38]:


df.dropna(inplace = True)


# In[39]:


keys_num = len(df.keys())


# In[40]:


df.keys()


# In[41]:


df.head()


# In[42]:


df.keys()


# In[43]:


y= df[' Label']


# In[44]:


y


# In[45]:


X = df.drop(' Label',axis=1)


# In[46]:


X


# In[47]:


estimator = AdaBoostRegressor(n_estimators=100, random_state=0)


# In[48]:


selector = RFE(estimator, n_features_to_select=12, step=1)


# In[49]:


selector.fit(X, y)


# In[50]:


cid = AdaBoostRegressor( n_estimators=100,random_state=0)


# In[51]:


X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)


# In[52]:


cid.fit(X_train, y_train)


# In[ ]:


trainning_score = cid.score(X_train, y_train)


# In[ ]:


print("Training Score: 0".format(trainning_score))


# In[ ]:


testing_score = cid.score(X_test, y_test)


# In[ ]:


print("Test Score: 0".format(testing_score))


# In[ ]:


print(X_train.shape)


# In[ ]:


print(X_test.shape)


# In[ ]:


estimator = AdaBoostRegressor(n_estimators=100, random_state=0)


# In[ ]:


selector = RFE(estimator, n_features_to_select=5, step=1)


# In[ ]:


selector = selector.fit(X_train, y_train)


# In[ ]:


trainning_score = selector.score(X_train, y_train)


# In[ ]:


print("Training Score: 0".format(trainning_score))


# In[ ]:







   

这是错误！！！：

在[]: 选择器.fit(X, y)

----------------------------------- ---------------------------- ValueError Traceback（最近一次调用 最后）在 ----> 1 个选择器.fit(X, y)

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/feature_selection/_rfe.py 适合（自我，X，y） 182 目标值。 第183章 --> 184 返回 self._fit(X, y) 185 186 def _fit(self, X, y, step_score=None):

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/feature_selection/_rfe.py 在 _fit(self, X, y, step_score) 191 192 个标签 = self._get_tags() --> 193 X, y = self._validate_data( 第194章 195 确保_min_features=2，

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py _validate_data(self, X, y, reset, validate_separately, **check_params) 第431章 432 其他： --> 433 X, y = check_X_y(X, y, **check_params) 434 出 = X, y 435

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py 在 inner_f(*args, **kwargs) 61 extra_args = len(args) - len(all_args) 62 如果额外参数 63 返回 f(*args, **kwargs) 64 65 # extra_args > 0

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py 在 check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, 复制，force_all_finite，ensure_2d，allow_nd，multi_output， ensure_min_samples, ensure_min_features, y_numeric, estimator) 第812章 813 --> 814 X = check_array（X，accept_sparse=accept_sparse， 第815章 第816章

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py 在 inner_f(*args, **kwargs) 61 extra_args = len(args) - len(all_args) 62 如果额外参数 63 返回 f(*args, **kwargs) 64 65 # extra_args > 0

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py 在 check_array（数组，accept_sparse，accept_large_sparse，dtype， 订单、复制、force_all_finite、ensure_2d、allow_nd、 ensure_min_samples、ensure_min_features、估计器） 661 第662章 --> 663 _assert_all_finite（数组， 第664章 665

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py 在 _assert_all_finite(X, allow_nan, msg_dtype) 101 不是 allow_nan 而不是 np.isfinite(X).all())： 102 type_err = 'infinity' if allow_nan else 'NaN, infinity' --> 103 引发 ValueError( 104 msg_err.format 105 (type_err,ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

【问题讨论】：

请以 traceback 开头发布您的整个 erro5r 消息。

【参考方案1】：

这可能不是你想听到的答案，但它有一定的道理。

尝试进行几乎任何类型的训练或数据分析时，一个好的做法是首先清理数据。其中一个步骤可以包括删除或处理“Nan”、“infinity”或其他不合适的异常值。

有很多方法可以做到这一点，但对于你的情况，我建议先做所有这些：

删除具有 Nan 值的行。 删除具有无穷大值的行。 移动所有值，使其在 float64 数据大小范围内，或删除包含超出 float64 数据大小范围的数字的行。 删除范围过大的列。

这是我经常使用的一个函数，用于首先检查数据以进行清理。

def calc_summary_for(feature_name:str, data:DataFrame) -> DataFrame:
"""
Calculates Summary Features in list of 'summary_feature_names'.

Parameters
----------
feature_name : str
    Name of feature for summary to be calculated for. required
data : pandas.DataFrame object
    A DataFrame object containg column named feature_name. required

Returns : DataFrame object of summary calculated features. 
"""
summary_feature_names = ['Feature Name', 'Cardinality', 'Non-null Count', 'Null Count', 'Min', '25th', 'Mean', 
                         '50th', '75th', 'Max', 'Std. Dev','Outlier Count Low', 'Outlier Count High']

# Create DataFrame to return and list at loc to DataFram
frame2return = pd.DataFrame(columns=summary_feature_names)
list2add = []

# Find claculated features that have bult in functions
list2add.append(feature_name)
list2add.append(data.shape[0])
list2add.append(data[feature_name].notnull().sum())
list2add.append(data[feature_name].isnull().sum())
list2add.append(data[feature_name].min())
list2add.append(data[feature_name].quantile(q=0.25))
list2add.append(data[feature_name].mean())

# Save for Calcuating IQR
list2add.append(data[feature_name].quantile(q=0.5))
Q1 = data[feature_name].quantile(q=0.5)
list2add.append(data[feature_name].quantile(q=0.75))
Q3 = data[feature_name].quantile(q=0.75)

list2add.append(data[feature_name].max())
list2add.append(data[feature_name].std())
# Find IQR
IQR = Q3 - Q1

# Find Range for outliers
outerBoundLow = Q1 - (1.5 * IQR)
outerBoundHigh = Q3 + (1.5 * IQR)


# Find calculated features using IQR counting outliers
countLow = 0
countHigh = 0
countInBounds = 0
for i in data[feature_name]:
    if i < outerBoundLow:
        countLow +=1
    elif i > outerBoundHigh:
        countHigh +=1
    else :
        countInBounds +=1

list2add.append(countLow)
list2add.append(countHigh)

# Add list to Dataframe and return it
frame2return.loc[len(frame2return)] = list2add
return frame2return

【参考方案2】：

我能够通过用 nan 替换无限数据然后删除 nan 数据来解决问题

xy = X
xy[" Label"] = y_df
#xy = dataframe
# Replace infinite updated data with nan
X.replace([np.inf,-np. inf], np.nan, inplace=True)
# Drop rows with NaN
X. dropna (inplace=True)