def cal_relative_mutual_strength(self, n_neighbors=3, mean=True):
r""" Relative strength for both axes of mutual information matrix.
Basically, is the mean of normalized maximum mutual information per code,
and per factor.
Return:
a scalar - higher is better
"""
matrix = self.create_mutualinfo_matrix(n_neighbors=n_neighbors)
return dict(rms=metrics.relative_strength(matrix))
def cal_relative_disentanglement_strength(self, method='spearman', mean=True):
r""" Relative strength for both axes of correlation matrix.
Basically, is the mean of normalized maximum correlation per code, and
per factor.
Arguments:
method : {'spearman', 'pearson', 'lasso', 'avg'}
spearman - rank or monotonic correlation
pearson - linear correlation
lasso - lasso regression
Return:
a scalar - higher is better
"""
corr_matrix = self.create_correlation_matrix(mean=mean, method=method)
return dict(rds=metrics.relative_strength(corr_matrix))