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ValueError:在预处理数据时,输入包含NaN,无穷大或对于dtype('float64')而言太大的值

[英]ValueError: Input contains NaN, infinity or a value too large for dtype('float64') while preprocessing Data

我有两个CSV文件( 训练集测试集 )。 由于少数列中存在可见的NaN值( statushedge_valueindicator_codeportfolio_iddesk_idoffice_id )。

我通过将NaN值替换为与列对应的一些巨大值来启动该过程。 然后我做LabelEncoding删除文本数据并将它们转换为数字数据。 现在,当我尝试对分类数据执行OneHotEncoding时,我收到错误。 我尝试将输入逐个输入到OneHotEncoding构造函数中,但是每列都会得到相同的错误。

基本上,我的最终目标是预测返回值,但由于这个原因,我被困在数据预处理部分。 我该如何解决这个问题?

我正在使用Python3.6PandasSklearn进行数据处理。

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

test_data = pd.read_csv('test.csv')
train_data = pd.read_csv('train.csv')

# Replacing Nan values here
train_data['status']=train_data['status'].fillna(2.0)
train_data['hedge_value']=train_data['hedge_value'].fillna(2.0)
train_data['indicator_code']=train_data['indicator_code'].fillna(2.0)
train_data['portfolio_id']=train_data['portfolio_id'].fillna('PF99999999')
train_data['desk_id']=train_data['desk_id'].fillna('DSK99999999')
train_data['office_id']=train_data['office_id'].fillna('OFF99999999')

x_train = train_data.iloc[:, :-1].values
y_train = train_data.iloc[:, 17].values

# =============================================================================
# from sklearn.preprocessing import Imputer
# imputer = Imputer(missing_values="NaN", strategy="mean", axis=0)
# imputer.fit(x_train[:, 15:17])
# x_train[:, 15:17] = imputer.fit_transform(x_train[:, 15:17])
# 
# imputer.fit(x_train[:, 12:13])
# x_train[:, 12:13] = imputer.fit_transform(x_train[:, 12:13])
# =============================================================================


# Encoding categorical data, i.e. Text data, since calculation happens on numbers only, so having text like 
# Country name, Purchased status will give trouble
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
x_train[:, 0] = labelencoder_X.fit_transform(x_train[:, 0])
x_train[:, 1] = labelencoder_X.fit_transform(x_train[:, 1])
x_train[:, 2] = labelencoder_X.fit_transform(x_train[:, 2])
x_train[:, 3] = labelencoder_X.fit_transform(x_train[:, 3])
x_train[:, 6] = labelencoder_X.fit_transform(x_train[:, 6])
x_train[:, 8] = labelencoder_X.fit_transform(x_train[:, 8])
x_train[:, 14] = labelencoder_X.fit_transform(x_train[:, 14])


# =============================================================================
# import numpy as np
# x_train[:, 3] = x_train[:, 3].reshape(x_train[:, 3].size,1)
# x_train[:, 3] = x_train[:, 3].astype(np.float64, copy=False)
# np.isnan(x_train[:, 3]).any()
# =============================================================================


# =============================================================================
# from sklearn.preprocessing import StandardScaler
# sc_X = StandardScaler()
# x_train = sc_X.fit_transform(x_train)
# =============================================================================

onehotencoder = OneHotEncoder(categorical_features=[0,1,2,3,6,8,14])
x_train = onehotencoder.fit_transform(x_train).toarray() # Replace Country Names with One Hot Encoding.

错误

Traceback (most recent call last):

  File "<ipython-input-4-4992bf3d00b8>", line 58, in <module>
    x_train = onehotencoder.fit_transform(x_train).toarray() # Replace Country Names with One Hot Encoding.

  File "/Users/parthapratimneog/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py", line 2019, in fit_transform
    self.categorical_features, copy=True)

  File "/Users/parthapratimneog/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py", line 1809, in _transform_selected
    X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)

  File "/Users/parthapratimneog/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py", line 453, in check_array
    _assert_all_finite(array)

  File "/Users/parthapratimneog/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py", line 44, in _assert_all_finite
    " or a value too large for %r." % X.dtype)

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

我在发布问题后再次浏览数据集,然后找到另一个带NaN列。 当我可以使用Pandas函数获取具有NaN的列列表时,我无法相信我浪费了太多时间。 因此,使用以下代码,我发现我错过了三列。 当我刚刚使用这个功能时,我在视觉上搜索NaN 处理完这些新的NaN ,代码运行正常。

pd.isnull(train_data).sum() > 0

结果

portfolio_id      False
desk_id           False
office_id         False
pf_category       False
start_date        False
sold               True
country_code      False
euribor_rate      False
currency          False
libor_rate         True
bought             True
creation_date     False
indicator_code    False
sell_date         False
type              False
hedge_value       False
status            False
return            False
dtype: bool

该错误出现在您将其视为非分类功能的其他功能中。

'hedge_value''indicator_code'等那些列包含来自原始csv的TRUEFALSE和来自fillna()调用的2.0混合类型数据。 OneHotEncoder无法处理它们。

如OneHotEncoder fit()文档中所述:

 fit(X, y=None)

    Fit OneHotEncoder to X.
    Parameters: 

    X : array-like, shape [n_samples, n_feature]

        Input array of type int.

你可以看到它需要所有的X都是数字(int,但是浮点数)类型。

作为解决方法,您可以执行此操作来编码分类功能:

X_train_categorical = x_train[:, [0,1,2,3,6,8,14]]
onehotencoder = OneHotEncoder()
X_train_categorical = onehotencoder.fit_transform(X_train_categorical).toarray()

然后将其与您的非分类功能连接起来。

要在生产中使用它,最佳做法是使用Imputer,然后使用模型保存在pkl中

这是一个蠢货

df[df==np.inf]=np.nan
df.fillna(df.mean(), inplace=True)

最好使用

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