ca09dcecea454e6552a7f30e57aef1dc4f2f295e,pgmpy/estimators/StructureScore.py,BicScore,local_score,#BicScore#Any#Any#,246

Before Change

        sample_size = len(self.data)
        num_parents_states = float(len(state_counts.columns))

        score = 0
        for (
            parents_state
        ) in state_counts:  // iterate over df columns (only 1 if no parents)
            conditional_sample_size = sum(state_counts[parents_state])

            for state in var_states:
                if state_counts[parents_state][state] > 0:
                    score += state_counts[parents_state][state] * (
                        log(state_counts[parents_state][state])
                        - log(conditional_sample_size)
                    )

        score -= 0.5 * log(sample_size) * num_parents_states * (var_cardinality - 1)

        return score

After Change

        var_cardinality = len(var_states)
        state_counts = self.state_counts(variable, parents)
        sample_size = len(self.data)
        num_parents_states = float(state_counts.shape[1])

        counts = np.asarray(state_counts)
        log_likelihoods = np.zeros_like(counts, dtype=np.float_)

        // Compute the log-counts
        np.log(counts, out=log_likelihoods, where=counts > 0)

        // Compute the log-conditional sample size
        log_conditionals = np.sum(counts, axis=0, dtype=np.float_)
        np.log(log_conditionals, out=log_conditionals, where=log_conditionals > 0)

        // Compute the log-likelihoods
        log_likelihoods -= log_conditionals
        log_likelihoods *= counts

        score = np.sum(log_likelihoods)
        score -= 0.5 * log(sample_size) * num_parents_states * (var_cardinality - 1)

        return score

Instances