Python 获取没有匹配指定签名和转换错误的循环
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Getting No loop matching the specified signature and casting error
提问by Shane Ekanayake
I'm a beginner to python and machine learning . I get below error when i try to fit data into statsmodels.formula.api OLS.fit()
我是 python 和机器学习的初学者。当我尝试将数据放入 statsmodels.formula.api OLS.fit() 时出现以下错误
Traceback (most recent call last):
回溯（最近一次调用最后一次）：
File "", line 47, in regressor_OLS = sm.OLS(y , X_opt).fit()
File "E:\Anaconda\lib\site-packages\statsmodels\regression\linear_model.py", line 190, in fit self.pinv_wexog, singular_values = pinv_extended(self.wexog)
File "E:\Anaconda\lib\site-packages\statsmodels\tools\tools.py", line 342, in pinv_extended u, s, vt = np.linalg.svd(X, 0)
File "E:\Anaconda\lib\site-packages\numpy\linalg\linalg.py", line 1404, in svd u, s, vt = gufunc(a, signature=signature, extobj=extobj)
TypeError: No loop matching the specified signature and casting was found for ufunc svd_n_s
文件“”，第 47 行，在 regressor_OLS = sm.OLS(y , X_opt).fit()
文件“E:\Anaconda\lib\site-packages\statsmodels\regression\linear_model.py”，第 190 行，适合 self.pinv_wexog，singular_values = pinv_extended(self.wexog)
文件“E:\Anaconda\lib\site-packages\statsmodels\tools\tools.py”，第 342 行，在 pinv_extended u, s, vt = np.linalg.svd(X, 0)
文件“E:\Anaconda\lib\site-packages\numpy\linalg\linalg.py”，第 1404 行，在 svd u, s, vt = gufunc(a, signature=signature, extobj=extobj)
类型错误：未找到与 ufunc svd_n_s 匹配的指定签名和转换的循环
code
代码
#Importing Libraries
import numpy as np # linear algebra
import pandas as pd # data processing
import matplotlib.pyplot as plt #Visualization
#Importing the dataset
dataset = pd.read_csv('Video_Games_Sales_as_at_22_Dec_2016.csv')
#dataset.head(10)
#Encoding categorical data using panda get_dummies function . Easier and straight forward than OneHotEncoder in sklearn
#dataset = pd.get_dummies(data = dataset , columns=['Platform' , 'Genre' , 'Rating' ] , drop_first = True ) #drop_first use to fix dummy varible trap
dataset=dataset.replace('tbd',np.nan)
#Separating Independent & Dependant Varibles
#X = pd.concat([dataset.iloc[:,[11,13]], dataset.iloc[:,13: ]] , axis=1).values #Getting important variables
X = dataset.iloc[:,[10,12]].values
y = dataset.iloc[:,9].values #Dependant Varible (Global sales)
#Taking care of missing data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN' , strategy = 'mean' , axis = 0)
imputer = imputer.fit(X[:,0:2])
X[:,0:2] = imputer.transform(X[:,0:2])
#Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2 , random_state = 0)
#Fitting Mutiple Linear Regression to the Training Set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,y_train)
#Predicting the Test set Result
y_pred = regressor.predict(X_test)
#Building the optimal model using Backward Elimination (p=0.050)
import statsmodels.formula.api as sm
X = np.append(arr = np.ones((16719,1)).astype(float) , values = X , axis = 1)
X_opt = X[:, [0,1,2]]
regressor_OLS = sm.OLS(y , X_opt).fit()
regressor_OLS.summary()
Dataset
数据集
Couldn't find anything helpful to solve this issue on stack-overflow or google .
在 stack-overflow 或 google 上找不到任何有助于解决此问题的信息。
回答by Victor Sejas
try specifiying the
尝试指定
dtype = 'float'
dtype = '浮动'
When the matrix is created. Example:
创建矩阵时。例子：
a=np.matrix([[1,2],[3,4]], dtype='float')
Hope this works!
希望这有效！
回答by Muke888
As suggested previously, you need to ensure X_opt is a float type. For example in your code, it would look like this:
如前所述，您需要确保 X_opt 是浮点类型。例如，在您的代码中，它看起来像这样：
X_opt = X[:, [0,1,2]]
X_opt = X_opt.astype(float)
regressor_OLS = sm.OLS(endog=y, exog=X_opt).fit()
regressor_OLS.summary()
