I am trying to run my custom logistic regression model as below
from sklearn import linear_model
import scipy.stats as stat
class LogisticRegression_with_p_values:
def __init__(self,*args,**kwargs):#,**kwargs):
self.model = linear_model.LogisticRegression(*args,**kwargs)#,**args)
def fit(self,X,y):
self.model.fit(X,y)
#### Get p-values for the fitted model ####
denom = (2.0 * (1.0 + np.cosh(self.model.decision_function(X))))
denom = np.tile(denom,(X.shape[1],1)).T
F_ij = np.dot((X / denom).T,X) ## Fisher Information Matrix
Cramer_Rao = np.linalg.inv(F_ij) ## Inverse Information Matrix
sigma_estimates = np.sqrt(np.diagonal(Cramer_Rao))
z_scores = self.model.coef_[0] / sigma_estimates # z-score for eaach model coefficient
p_values = [stat.norm.sf(abs(x)) * 2 for x in z_scores] ### two tailed test for p-values
self.coef_ = self.model.coef_
self.intercept_ = self.mode
and when I call the fit method as below
reg = LogisticRegression_with_p_values()
reg.fit(inputs_train,loan_data_targets_train)
I get the error
---> 97 raise LinAlgError("Singular matrix")
98
99 def _raise_linalgerror_nonposdef(err, flag):
LinAlgError: Singular matrix
I have coded all numeric variables as dummy variables of 1 and 0
However when I run the normal scikit learn logistic regression there is no error
from sklearn.linear_model import LogisticRegression
reg = LogisticRegression()
reg.fit(inputs_train,loan_data_targets_train)
