def add_parser_group(self, d, target_obj): gp_nm = d.pop('name') if 'title' not in d: d['title'] = gp_nm args = util.coerce_to_iter(d.pop('arguments', None)) gp_kwargs = util.filter_kwargs(d, argparse._ArgumentGroup.__init__) gp_obj = target_obj.add_argument_group(**gp_kwargs) self.parser_groups[gp_nm] = gp_obj for arg in args: self.add_parser_argument(arg, gp_obj, gp_nm)
def add_parser_group(self, d, target_obj): gp_nm = d.pop('name') _ = d.setdefault('title', gp_nm) args = util.coerce_to_iter(d.pop('arguments', None)) if args: # only add group if it has > 0 arguments gp_kwargs = util.filter_kwargs(d, argparse._ArgumentGroup.__init__) gp_obj = target_obj.add_argument_group(**gp_kwargs) self.parser_groups[gp_nm] = gp_obj for arg in args: self.add_parser_argument(arg, gp_obj, gp_nm)
def __init__(self, impl='KMEANS', **kwargs): # Finally, we create the model and let 'r rip! class_name = '_{}'.format(impl) model_class = getattr(sys.modules[__name__], class_name) kwargs, unused, _ = util.filter_kwargs(model_class, kwargs) if len(unused) > 0: logger.warning( 'Cluster model "{}" has unused arguments: {}'.format( impl, unused ) ) self._model = model_class(**kwargs)
def make_parser(self, d): args = util.coerce_to_iter(d.pop('arguments', None)) arg_groups = util.coerce_to_iter(d.pop('argument_groups', None)) d['formatter_class'] = CustomHelpFormatter p_kwargs = util.filter_kwargs(d, argparse.ArgumentParser.__init__) p = argparse.ArgumentParser(**p_kwargs) for arg in args: # add arguments not in any group self.add_parser_argument(arg, p, 'parser') for group in arg_groups: # add groups and arguments therein self.add_parser_group(group, p) return p
def __init__(self, impl='MalletLda', **kwargs): # Finally, we create the topic model and let 'r rip! class_name = '_{}'.format(impl) model_class = getattr(sys.modules[__name__], class_name) kwargs, unused, _ = util.filter_kwargs(model_class, kwargs) if len(unused) > 0: logger.warning('Topic model "{}" has unused arguments: {}'.format( impl, unused )) regen = kwargs.get('regen', None) if regen: logger.info('Forcing regeneration of topic model') self.model = model_class(**kwargs)
def evaluate(reference_sources, estimated_sources, **kwargs): """Compute all metrics for the given reference and estimated annotations. Examples -------- >>> # reference_sources[n] should be an ndarray of samples of the >>> # n'th reference source >>> # estimated_sources[n] should be the same for the n'th estimated >>> scores = mir_eval.separation.evaluate(reference_sources, ... estimated_sources) Parameters ---------- reference_sources : np.ndarray, shape=(nsrc, nsampl) matrix containing true sources estimated_sources : np.ndarray, shape=(nsrc, nsampl) matrix containing estimated sources kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all the metrics scores = collections.OrderedDict() sdr, sir, sar, perm = util.filter_kwargs(bss_eval_sources, reference_sources, estimated_sources, **kwargs) scores['Source to Distortion'] = sdr.tolist() scores['Source to Interference'] = sir.tolist() scores['Source to Artifact'] = sar.tolist() scores['Source permutation'] = perm return scores
def evaluate(reference_sources, estimated_sources, **kwargs): """Compute all metrics for the given reference and estimated annotations. Examples -------- >>> # reference_sources[n] should be an ndarray of samples of the >>> # n'th reference source >>> # estimated_sources[n] should be the same for the n'th estimated >>> scores = mir_eval.separation.evaluate(reference_sources, ... estimated_sources) Parameters ---------- reference_sources : np.ndarray, shape=(nsrc, nsampl) matrix containing true sources estimated_sources : np.ndarray, shape=(nsrc, nsampl) matrix containing estimated sources kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all the metrics scores = collections.OrderedDict() sdr, sir, sar, perm = util.filter_kwargs(bss_eval_sources, reference_sources, estimated_sources, **kwargs) scores['Source to Distortion'] = sdr.tolist() scores['Source to Interference'] = sir.tolist() scores['Source to Artifact'] = sar.tolist() scores['Source permutation'] = perm return scores
def evaluate(reference_sources, estimated_sources, **kwargs): """Compute all metrics for the given reference and estimated signals. NOTE: This will always compute :func:`mir_eval.separation.bss_eval_images` for any valid input and will additionally compute :func:`mir_eval.separation.bss_eval_sources` for valid input with fewer than 3 dimensions. Examples -------- >>> # reference_sources[n] should be an ndarray of samples of the >>> # n'th reference source >>> # estimated_sources[n] should be the same for the n'th estimated source >>> scores = mir_eval.separation.evaluate(reference_sources, ... estimated_sources) Parameters ---------- reference_sources : np.ndarray, shape=(nsrc, nsampl[, nchan]) matrix containing true sources estimated_sources : np.ndarray, shape=(nsrc, nsampl[, nchan]) matrix containing estimated sources kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all the metrics scores = collections.OrderedDict() sdr, isr, sir, sar, perm = util.filter_kwargs(bss_eval_images, reference_sources, estimated_sources, **kwargs) scores['Images - Source to Distortion'] = sdr.tolist() scores['Images - Image to Spatial'] = isr.tolist() scores['Images - Source to Interference'] = sir.tolist() scores['Images - Source to Artifact'] = sar.tolist() scores['Images - Source permutation'] = perm.tolist() sdr, isr, sir, sar, perm = util.filter_kwargs(bss_eval_images_framewise, reference_sources, estimated_sources, **kwargs) scores['Images Frames - Source to Distortion'] = sdr.tolist() scores['Images Frames - Image to Spatial'] = isr.tolist() scores['Images Frames - Source to Interference'] = sir.tolist() scores['Images Frames - Source to Artifact'] = sar.tolist() scores['Images Frames - Source permutation'] = perm.tolist() # Verify we can compute sources on this input if reference_sources.ndim < 3 and estimated_sources.ndim < 3: sdr, sir, sar, perm = util.filter_kwargs(bss_eval_sources_framewise, reference_sources, estimated_sources, **kwargs) scores['Sources Frames - Source to Distortion'] = sdr.tolist() scores['Sources Frames - Source to Interference'] = sir.tolist() scores['Sources Frames - Source to Artifact'] = sar.tolist() scores['Sources Frames - Source permutation'] = perm.tolist() sdr, sir, sar, perm = util.filter_kwargs(bss_eval_sources, reference_sources, estimated_sources, **kwargs) scores['Sources - Source to Distortion'] = sdr.tolist() scores['Sources - Source to Interference'] = sir.tolist() scores['Sources - Source to Artifact'] = sar.tolist() scores['Sources - Source permutation'] = perm.tolist() return scores