ValueError: math domain error

[ukamath]

Running with discretized diabetes dataset now get this error
weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
from numpy.core.umath_tests import inner1d
Took 9.271s to generate 28063 rules
Screening rules using information gain
/Users/uday.kamath/BOA/BOAmodel.py:110: RuntimeWarning: divide by zero encountered in log
cond_entropy = -pp*(p1np.log(p1)+(1-p1)np.log(1-p1))-(1-pp)(p2np.log(p2)+(1-p2)np.log(1-p2))
/Users/uday.kamath/BOA/BOAmodel.py:110: RuntimeWarning: invalid value encountered in multiply
cond_entropy = -pp(p1np.log(p1)+(1-p1)np.log(1-p1))-(1-pp)(p2np.log(p2)+(1-p2)np.log(1-p2))
/Users/uday.kamath/BOA/BOAmodel.py:111: RuntimeWarning: divide by zero encountered in log
cond_entropy[p1(1-p1)==0] = -((1-pp)(p2np.log(p2)+(1-p2)np.log(1-p2)))[p1(1-p1)==0]
/Users/uday.kamath/BOA/BOAmodel.py:111: RuntimeWarning: invalid value encountered in multiply
cond_entropy[p1*(1-p1)==0] = -((1-pp)(p2np.log(p2)+(1-p2)np.log(1-p2)))[p1(1-p1)==0]
/Users/uday.kamath/BOA/BOAmodel.py:112: RuntimeWarning: divide by zero encountered in log
cond_entropy[p2*(1-p2)==0] = -(pp*(p1np.log(p1)+(1-p1)np.log(1-p1)))[p2(1-p2)==0]
/Users/uday.kamath/BOA/BOAmodel.py:112: RuntimeWarning: invalid value encountered in multiply
cond_entropy[p2(1-p2)==0] = -(pp*(p1*np.log(p1)+(1-p1)np.log(1-p1)))[p2(1-p2)==0]
Took 1.552s to generate 2000 rules
Computing sizes for pattern space ...
Took 0.000s to compute patternspace
No or wrong input for alpha_l and beta_l. The model will use default parameters!
alpha = 10, beta = 0.0
Traceback (most recent call last):
File "./diabetes.py", line 39, in
rules = model.SA_patternbased(Niteration,Nchain,print_message=True)
File "/Users/uday.kamath/BOA/BOAmodel.py", line 163, in SA_patternbased
cfmatrix,prob = self.compute_prob(rules_new)
File "/Users/uday.kamath/BOA/BOAmodel.py", line 269, in compute_prob
prior_ChsRules= sum([log_betabin(Kn_count[i],self.patternSpace[i],self.alpha_l[i],self.beta_l[i]) for i in range(1,len(Kn_count),1)])
File "/Users/uday.kamath/BOA/BOAmodel.py", line 269, in
prior_ChsRules= sum([log_betabin(Kn_count[i],self.patternSpace[i],self.alpha_l[i],self.beta_l[i]) for i in range(1,len(Kn_count),1)])
File "/Users/uday.kamath/BOA/BOAmodel.py", line 347, in log_betabin
return math.lgamma(k+alpha) + math.lgamma(n-k+beta) - math.lgamma(n+alpha+beta) + Const
ValueError: math domain error
diabetes-Y.txt
diabetes-X.txt

[ShiZihao-sudo]

Running with discretized diabetes dataset now get this error weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release. from numpy.core.umath_tests import inner1d Took 9.271s to generate 28063 rules Screening rules using information gain /Users/uday.kamath/BOA/BOAmodel.py:110: RuntimeWarning: divide by zero encountered in log cond_entropy = -pp*(p1_np.log(p1)+(1-p1)np.log(1-p1))-(1-pp)(p2_np.log(p2)+(1-p2)np.log(1-p2)) /Users/uday.kamath/BOA/BOAmodel.py:110: RuntimeWarning: invalid value encountered in multiply cond_entropy = -pp(p1_np.log(p1)+(1-p1)np.log(1-p1))-(1-pp)(p2_np.log(p2)+(1-p2)np.log(1-p2)) /Users/uday.kamath/BOA/BOAmodel.py:111: RuntimeWarning: divide by zero encountered in log cond_entropy[p1(1-p1)==0] = -((1-pp)(p2_np.log(p2)+(1-p2)np.log(1-p2)))[p1(1-p1)==0] /Users/uday.kamath/BOA/BOAmodel.py:111: RuntimeWarning: invalid value encountered in multiply cond_entropy[p1*(1-p1)==0] = -((1-pp)(p2_np.log(p2)+(1-p2)np.log(1-p2)))[p1(1-p1)==0] /Users/uday.kamath/BOA/BOAmodel.py:112: RuntimeWarning: divide by zero encountered in log cond_entropy[p2*(1-p2)==0] = -(pp*(p1_np.log(p1)+(1-p1)np.log(1-p1)))[p2(1-p2)==0] /Users/uday.kamath/BOA/BOAmodel.py:112: RuntimeWarning: invalid value encountered in multiply cond_entropy[p2_(1-p2)==0] = -(pp*(p1*np.log(p1)+(1-p1)np.log(1-p1)))[p2(1-p2)==0] Took 1.552s to generate 2000 rules Computing sizes for pattern space ... Took 0.000s to compute patternspace No or wrong input for alpha_l and beta_l. The model will use default parameters! alpha = 10, beta = 0.0 Traceback (most recent call last): File "./diabetes.py", line 39, in rules = model.SA_patternbased(Niteration,Nchain,print_message=True) File "/Users/uday.kamath/BOA/BOAmodel.py", line 163, in SA_patternbased cfmatrix,prob = self.compute_prob(rules_new) File "/Users/uday.kamath/BOA/BOAmodel.py", line 269, in compute_prob prior_ChsRules= sum([log_betabin(Kn_count[i],self.patternSpace[i],self.alpha_l[i],self.beta_l[i]) for i in range(1,len(Kn_count),1)]) File "/Users/uday.kamath/BOA/BOAmodel.py", line 269, in prior_ChsRules= sum([log_betabin(Kn_count[i],self.patternSpace[i],self.alpha_l[i],self.beta_l[i]) for i in range(1,len(Kn_count),1)]) File "/Users/uday.kamath/BOA/BOAmodel.py", line 347, in log_betabin return math.lgamma(k+alpha) + math.lgamma(n-k+beta) - math.lgamma(n+alpha+beta) + Const ValueError: math domain error diabetes-Y.txt diabetes-X.txt

Did you solve this problem? I'm also having this issue.