Getting No loop matching the specified signature and casting error
Question:
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()
Traceback (most recent call last):
File “”, line 47, in
regressor_OLS = sm.OLS(y , X_opt).fit()
File
“E:Anacondalibsite-packagesstatsmodelsregressionlinear_model.py”,
line 190, in fit
self.pinv_wexog, singular_values = pinv_extended(self.wexog)
File “E:Anacondalibsite-packagesstatsmodelstoolstools.py”,
line 342, in pinv_extended
u, s, vt = np.linalg.svd(X, 0)
File “E:Anacondalibsite-packagesnumpylinalglinalg.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
code
#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
Couldn’t find anything helpful to solve this issue on stack-overflow or google .
Answers:
try specifiying the
dtype = ‘float’
When the matrix is created.
Example:
a=np.matrix([[1,2],[3,4]], dtype='float')
Hope this works!
As suggested previously, you need to ensure X_opt is a float type.
For example in your code, it would look like this:
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()
Downgrading from NumPy 1.18.4 to 1.15.2 worked for me:
pip install --upgrade numpy==1.15.2
Faced the similar problem. Solved the problem my mentioning dtype and flatten the array.
numpy version: 1.17.3
a = np.array(a, dtype=np.float)
a = a.flatten()
Was facing a similar problem, I used df.values[]
y = df.values[:, 4]
fixed the issue by using df.iloc[].values function.
y = dataset.iloc[:, 4].values
df.values[] function returns object datatype
array([192261.83, 191792.06, 191050.39, 182901.99, 166187.94, 156991.12,
156122.51, 155752.6, 152211.77, 149759.96, 146121.95, 144259.4,
141585.52, 134307.35, 132602.65, 129917.04, 126992.93, 125370.37,
124266.9, 122776.86, 118474.03, 111313.02, 110352.25, 108733.99,
108552.04, 107404.34, 105733.54, 105008.31, 103282.38, 101004.64,
99937.59, 97483.56, 97427.84, 96778.92, 96712.8, 96479.51,
90708.19, 89949.14, 81229.06, 81005.76, 78239.91, 77798.83,
71498.49, 69758.98, 65200.33, 64926.08, 49490.75, 42559.73,
35673.41, 14681.4], dtype=object)
but
df.iloc[:, 4].values returns floats array
which is what
regressor_OLS = sm.OLS(endog=y, exog=X_opt).fit()
OLS() fun accepts
OR
you can just change the datatype of y before inserting it into the fun OLS()
y = np.array(y, dtype = float)
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()
Traceback (most recent call last):
File “”, line 47, in
regressor_OLS = sm.OLS(y , X_opt).fit()File
“E:Anacondalibsite-packagesstatsmodelsregressionlinear_model.py”,
line 190, in fit
self.pinv_wexog, singular_values = pinv_extended(self.wexog)File “E:Anacondalibsite-packagesstatsmodelstoolstools.py”,
line 342, in pinv_extended
u, s, vt = np.linalg.svd(X, 0)File “E:Anacondalibsite-packagesnumpylinalglinalg.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
code
#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
Couldn’t find anything helpful to solve this issue on stack-overflow or google .
try specifiying the
dtype = ‘float’
When the matrix is created.
Example:
a=np.matrix([[1,2],[3,4]], dtype='float')
Hope this works!
As suggested previously, you need to ensure X_opt is a float type.
For example in your code, it would look like this:
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()
Downgrading from NumPy 1.18.4 to 1.15.2 worked for me:
pip install --upgrade numpy==1.15.2
Faced the similar problem. Solved the problem my mentioning dtype and flatten the array.
numpy version: 1.17.3
a = np.array(a, dtype=np.float)
a = a.flatten()
Was facing a similar problem, I used df.values[]
y = df.values[:, 4]
fixed the issue by using df.iloc[].values function.
y = dataset.iloc[:, 4].values
df.values[] function returns object datatype
array([192261.83, 191792.06, 191050.39, 182901.99, 166187.94, 156991.12,
156122.51, 155752.6, 152211.77, 149759.96, 146121.95, 144259.4,
141585.52, 134307.35, 132602.65, 129917.04, 126992.93, 125370.37,
124266.9, 122776.86, 118474.03, 111313.02, 110352.25, 108733.99,
108552.04, 107404.34, 105733.54, 105008.31, 103282.38, 101004.64,
99937.59, 97483.56, 97427.84, 96778.92, 96712.8, 96479.51,
90708.19, 89949.14, 81229.06, 81005.76, 78239.91, 77798.83,
71498.49, 69758.98, 65200.33, 64926.08, 49490.75, 42559.73,
35673.41, 14681.4], dtype=object)
but
df.iloc[:, 4].values returns floats array
which is what
regressor_OLS = sm.OLS(endog=y, exog=X_opt).fit()
OLS() fun accepts
OR
you can just change the datatype of y before inserting it into the fun OLS()
y = np.array(y, dtype = float)