118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
# scipy-yli: Helpful SciPy utilities and recipes
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# Copyright © 2022 Lee Yingtong Li (RunasSudo)
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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from .config import config
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class BayesFactor:
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"""A Bayes factor"""
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def __init__(self, factor, num_symbol, num_desc, denom_symbol, denom_desc):
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#: Value of the Bayes factor (*float*)
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self.factor = factor
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#: Symbol representing the hypothesis in the numerator (*str*)
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self.num_symbol = num_symbol
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#: Description of the hypothesis in the numerator (*str*)
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self.num_desc = num_desc
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#: Symbol representing the hypothesis in the denominator (*str*)
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self.denom_symbol = denom_symbol
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#: Description of the hypothesis in the denominator (*str*)
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self.denom_desc = denom_desc
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def __repr__(self):
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if config.repr_is_summary:
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return self.summary()
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return super().__repr__()
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def _repr_html_(self):
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return 'BF<sub>{0}{1}</sub> = {2:.2f}, {5}<br>H<sub>{0}</sub>: {3}<br>H<sub>{1}</sub>: {4}'.format(self.num_symbol, self.denom_symbol, self.factor, self.num_desc, self.denom_desc, self.interpret_lw(html=True))
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def summary(self):
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"""
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Return a stringified summary of the Bayes factor
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:rtype: str
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"""
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return 'BF{0}{1} = {2:.2f}, {5}\nH{0}: {3}\nH{1}: {4}'.format(self.num_symbol, self.denom_symbol, self.factor, self.num_desc, self.denom_desc, self.interpret_lw(html=False))
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def invert(self):
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"""
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Invert the Bayes factor
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:rtype: :class:`BayesFactor`
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"""
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return BayesFactor(1 / self.factor, self.denom_symbol, self.denom_desc, self.num_symbol, self.num_desc)
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def interpret_lw(self, html):
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"""
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Interpret the Bayes factor according to the Lee & Wagenmakers classification scheme
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:meta private:
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"""
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if self.factor == 1:
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return 'Evidence favours neither hypothesis to the other'
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if self.factor < 1:
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return self.invert().interpret_lw()
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if self.factor < 3:
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level = 'Anecdotal'
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elif self.factor < 10:
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level = 'Moderate'
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elif self.factor < 30:
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level = 'Strong'
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elif self.factor < 100:
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level = 'Very strong'
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else:
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level = 'Extreme'
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if html:
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return '{} evidence in favour of H<sub>{}</sub>'.format(level, self.num_symbol)
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else:
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return '{} evidence in favour of H{}'.format(level, self.num_symbol)
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def bayesfactor_afbf(params, cov, n, hypothesis):
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"""
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Compute an adjusted fractional Bayes factor for the hypothesis
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Uses R *BFpack* library. See R documentation for *BFpack.BF*.
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:return: Bayes factor for the hypothesis versus its complement
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:rtype: :class:`yli.bayes_factors.BayesFactor`
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"""
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import rpy2.robjects as ro
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import rpy2.robjects.packages
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import rpy2.robjects.pandas2ri
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# Import BFpack
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ro.packages.importr('BFpack')
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with ro.conversion.localconverter(ro.default_converter + ro.pandas2ri.converter):
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with ro.local_context() as lc:
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lc['params'] = params
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lc['cov'] = cov
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lc['n'] = n
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lc['hypothesis'] = hypothesis
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ro.r('bf_fit <- BF(params, Sigma=as.matrix(cov), n=n, hypothesis=hypothesis)')
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bf_matrix = ro.r('bf_fit$BFmatrix_confirmatory')
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return BayesFactor(bf_matrix[0][1], '1', hypothesis, 'C', 'Complement')
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