def demo_model_idependencies():
"""
Independences of the 3 annot 3 name model
CommandLine:
python -m ibeis.algo.hots.demobayes --exec-demo_model_idependencies --mode=1 --num-names=2 --show
python -m ibeis.algo.hots.demobayes --exec-demo_model_idependencies --mode=2
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.demobayes import * # NOQA
>>> result = demo_model_idependencies()
>>> print(result)
>>> ut.show_if_requested()
"""
num_names = ut.get_argval('--num-names', default=3)
model = test_model(num_annots=num_names,
num_names=num_names,
score_evidence=[],
name_evidence=[])[0]
# This model has the following independenceis
idens = model.get_independencies()
iden_strs = [
', '.join(sorted(iden.event1)) + ' _L ' +
','.join(sorted(iden.event2)) + ' | ' + ', '.join(sorted(iden.event3))
for iden in idens.independencies
]
print('general idependencies')
print(ut.align(ut.align('\n'.join(sorted(iden_strs)), '_'), '|'))
def demo_model_idependencies():
"""
Independences of the 3 annot 3 name model
CommandLine:
python -m ibeis.algo.hots.demobayes --exec-demo_model_idependencies --mode=1 --num-names=2 --show
python -m ibeis.algo.hots.demobayes --exec-demo_model_idependencies --mode=2
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.demobayes import * # NOQA
>>> result = demo_model_idependencies()
>>> print(result)
>>> ut.show_if_requested()
"""
num_names = ut.get_argval('--num-names', default=3)
model = test_model(num_annots=num_names, num_names=num_names, score_evidence=[], name_evidence=[])[0]
# This model has the following independenceis
idens = model.get_independencies()
iden_strs = [', '.join(sorted(iden.event1)) +
' _L ' +
','.join(sorted(iden.event2)) +
' | ' +
', '.join(sorted(iden.event3))
for iden in idens.independencies]
print('general idependencies')
print(ut.align(ut.align('\n'.join(sorted(iden_strs)), '_'), '|'))
def hack(ibs):
#ibs.get_imageset_text(imgsetid_list)
#imgsetid = ibs.get_imageset_imgsetids_from_text("NNP GZC Car '1PURPLE'")
def get_name_linked_imagesets_by_imgsetid(ibs, imgsetid):
import utool as ut
#gid_list = ibs.get_imageset_gids(imgsetid)
aid_list_ = ibs.get_imageset_aids(imgsetid)
aid_list = ut.filterfalse_items(aid_list_, ibs.is_aid_unknown(aid_list_))
#all(ibs.db.check_rowid_exists(const.ANNOTATION_TABLE, aid_list))
#aids_list2 = ibs.get_image_aids(gid_list)
#assert ut.flatten(aids_list2) == aids_list1
nid_list = list(set(ibs.get_annot_nids(aid_list, distinguish_unknowns=False)))
# remove unknown annots
name_imgsetids = ibs.get_name_imgsetids(nid_list)
name_imagesettexts = ibs.get_imageset_text(name_imgsetids)
return name_imagesettexts
imgsetid_list = ibs.get_valid_imgsetids()
linked_imagesettexts = [get_name_linked_imagesets_by_imgsetid(ibs, imgsetid) for imgsetid in imgsetid_list]
imagesettext_list = ibs.get_imageset_text(imgsetid_list)
print(ut.dict_str(dict(zip(imgsetid_list, linked_imagesettexts))))
print(ut.align(ut.dict_str(dict(zip(imagesettext_list, linked_imagesettexts))), ':'))
print(ut.align(ut.dict_str(dict(zip(imagesettext_list, imgsetid_list)), sorted_=True), ':'))
def join_tabular(parts, hline=False, align=True):
top, header, mid, bot = parts
if hline:
toprule = midrule = botrule = '\\hline'
else:
toprule = '\\toprule'
midrule = '\\midrule'
botrule = '\\bottomrule'
ut.flatten(ut.bzip(['a', 'b', 'c'], ['-']))
top_parts = [top, toprule, header]
if mid:
# join midblocks given as lists of lines instead of strings
midblocks = []
for m in mid:
if isinstance(m, str):
midblocks.append(m)
else:
midblocks.append('\n'.join(m))
mid_parts = ut.flatten(ut.bzip([midrule], midblocks))
else:
mid_parts = []
# middle_parts = ut.flatten(list(ut.bzip(body_parts, ['\\midrule'])))
bot_parts = [botrule, bot]
text = '\n'.join(top_parts + mid_parts + bot_parts)
if align:
text = ut.align(text, '&', pos=None)
# text = ut.align(text, r'\\', pos=None)
return text
def print_size_info(inva):
sizes = inva.get_size_info()
sizes = ut.sort_dict(sizes, 'vals', ut.identity)
total_nbytes = sum(sizes.values())
logger.info(
ut.align(ut.repr3(ut.map_dict_vals(ut.byte_str2, sizes), strvals=True), ':')
)
logger.info('total_nbytes = %r' % (ut.byte_str2(total_nbytes),))
def sympy_latex_repr(expr1):
expr1_repr = sympy.latex(expr1)
expr1_repr = expr1_repr.replace("\\\\", "\\\\\n")
expr1_repr = expr1_repr.replace(r"\left[\begin{smallmatrix}{}", "\\MAT{\n")
expr1_repr = expr1_repr.replace(r"\end{smallmatrix}\right]", "\n}")
expr1_repr = expr1_repr.replace(r"\left[\begin{matrix}", "\\BIGMAT{\n")
expr1_repr = expr1_repr.replace(r"\end{matrix}\right]", "\n}")
expr1_repr = expr1_repr.replace(r"\left (", "(")
expr1_repr = expr1_repr.replace(r"\right )", ")")
expr1_repr = expr1_repr.replace(r"\left(", "(")
expr1_repr = expr1_repr.replace(r"\right)", ")")
# hack of align
expr1_repr = ut.align(expr1_repr, "&", pos=None)
return expr1_repr
def sympy_latex_repr(expr1):
expr1_repr = sympy.latex(expr1)
expr1_repr = expr1_repr.replace('\\\\', '\\\\\n')
expr1_repr = expr1_repr.replace(r'\left[\begin{smallmatrix}{}', '\\MAT{\n')
expr1_repr = expr1_repr.replace(r'\end{smallmatrix}\right]', '\n}')
expr1_repr = expr1_repr.replace(r'\left[\begin{matrix}', '\\BIGMAT{\n')
expr1_repr = expr1_repr.replace(r'\end{matrix}\right]', '\n}')
expr1_repr = expr1_repr.replace(r'\left (', '(')
expr1_repr = expr1_repr.replace(r'\right )', ')')
expr1_repr = expr1_repr.replace(r'\left(', '(')
expr1_repr = expr1_repr.replace(r'\right)', ')')
# hack of align
expr1_repr = ut.align(expr1_repr, '&', pos=None)
return expr1_repr
def hack(ibs):
#ibs.get_imageset_text(imgsetid_list)
#imgsetid = ibs.get_imageset_imgsetids_from_text("NNP GZC Car '1PURPLE'")
def get_name_linked_imagesets_by_imgsetid(ibs, imgsetid):
import utool as ut
#gid_list = ibs.get_imageset_gids(imgsetid)
aid_list_ = ibs.get_imageset_aids(imgsetid)
aid_list = ut.filterfalse_items(aid_list_,
ibs.is_aid_unknown(aid_list_))
#all(ibs.db.check_rowid_exists(const.ANNOTATION_TABLE, aid_list))
#aids_list2 = ibs.get_image_aids(gid_list)
#assert ut.flatten(aids_list2) == aids_list1
nid_list = list(
set(ibs.get_annot_nids(aid_list, distinguish_unknowns=False)))
# remove unknown annots
name_imgsetids = ibs.get_name_imgsetids(nid_list)
name_imagesettexts = ibs.get_imageset_text(name_imgsetids)
return name_imagesettexts
imgsetid_list = ibs.get_valid_imgsetids()
linked_imagesettexts = [
get_name_linked_imagesets_by_imgsetid(ibs, imgsetid)
for imgsetid in imgsetid_list
]
imagesettext_list = ibs.get_imageset_text(imgsetid_list)
print(ut.dict_str(dict(zip(imgsetid_list, linked_imagesettexts))))
print(
ut.align(
ut.dict_str(dict(zip(imagesettext_list, linked_imagesettexts))),
':'))
print(
ut.align(
ut.dict_str(dict(zip(imagesettext_list, imgsetid_list)),
sorted_=True), ':'))
def test_model(num_annots, num_names, score_evidence=[], name_evidence=[],
other_evidence={}, noquery=False, verbose=None,
**kwargs):
if verbose is None:
verbose = ut.VERBOSE
method = kwargs.pop('method', None)
model = make_name_model(num_annots, num_names, verbose=verbose, **kwargs)
if verbose:
model.print_priors(ignore_ttypes=['match', 'score'])
model, evidence, soft_evidence = update_model_evidence(
model, name_evidence, score_evidence, other_evidence)
if verbose and len(soft_evidence) != 0:
model.print_priors(ignore_ttypes=['match', 'score'],
title='Soft Evidence', color='green')
#if verbose:
# ut.colorprint('\n --- Soft Evidence ---', 'white')
# for ttype, cpds in model.ttype2_cpds.items():
# if ttype != 'match':
# for fs_ in ut.ichunks(cpds, 4):
# ut.colorprint(ut.hz_str([f._cpdstr('psql') for f in fs_]),
# 'green')
if verbose:
ut.colorprint('\n --- Inference ---', 'red')
if (len(evidence) > 0 or len(soft_evidence) > 0) and not noquery:
evidence = model._ensure_internal_evidence(evidence)
query_vars = []
query_vars += ut.list_getattr(model.ttype2_cpds['name'], 'variable')
#query_vars += ut.list_getattr(model.ttype2_cpds['match'], 'variable')
query_vars = ut.setdiff(query_vars, evidence.keys())
#query_vars = ut.setdiff(query_vars, soft_evidence.keys())
query_results = cluster_query(model, query_vars, evidence,
soft_evidence, method)
else:
query_results = {}
factor_list = query_results['factor_list']
if verbose:
if verbose:
print('+--------')
semtypes = [model.var2_cpd[f.variables[0]].ttype
for f in factor_list]
for type_, factors in ut.group_items(factor_list, semtypes).items():
print('Result Factors (%r)' % (type_,))
factors = ut.sortedby(factors, [f.variables[0] for f in factors])
for fs_ in ut.ichunks(factors, 4):
ut.colorprint(ut.hz_str([f._str('phi', 'psql') for f in fs_]),
'yellow')
print('MAP assignments')
top_assignments = query_results.get('top_assignments', [])
tmp = []
for lbl, val in top_assignments:
tmp.append('%s : %.4f' % (ut.repr2(lbl), val))
print(ut.align('\n'.join(tmp), ' :'))
print('L_____\n')
showkw = dict(evidence=evidence,
soft_evidence=soft_evidence,
**query_results)
pgm_viz.show_model(model, **showkw)
return (model, evidence, query_results)
def temp_model(num_annots,
num_names,
score_evidence=[],
name_evidence=[],
other_evidence={},
noquery=False,
verbose=None,
**kwargs):
if verbose is None:
verbose = ut.VERBOSE
method = kwargs.pop('method', None)
model = make_name_model(num_annots, num_names, verbose=verbose, **kwargs)
if verbose:
model.print_priors(ignore_ttypes=[MATCH_TTYPE, SCORE_TTYPE])
model, evidence, soft_evidence = update_model_evidence(
model, name_evidence, score_evidence, other_evidence)
if verbose and len(soft_evidence) != 0:
model.print_priors(ignore_ttypes=[MATCH_TTYPE, SCORE_TTYPE],
title='Soft Evidence',
color='green')
# if verbose:
# ut.colorprint('\n --- Soft Evidence ---', 'white')
# for ttype, cpds in model.ttype2_cpds.items():
# if ttype != MATCH_TTYPE:
# for fs_ in ut.ichunks(cpds, 4):
# ut.colorprint(ut.hz_str([f._cpdstr('psql') for f in fs_]),
# 'green')
if verbose:
ut.colorprint('\n --- Inference ---', 'red')
if (len(evidence) > 0 or len(soft_evidence) > 0) and not noquery:
evidence = model._ensure_internal_evidence(evidence)
query_vars = []
query_vars += ut.list_getattr(model.ttype2_cpds[NAME_TTYPE],
'variable')
# query_vars += ut.list_getattr(model.ttype2_cpds[MATCH_TTYPE], 'variable')
query_vars = ut.setdiff(query_vars, evidence.keys())
# query_vars = ut.setdiff(query_vars, soft_evidence.keys())
query_results = cluster_query(model, query_vars, evidence,
soft_evidence, method)
else:
query_results = {}
factor_list = query_results['factor_list']
if verbose:
if verbose:
logger.info('+--------')
semtypes = [model.var2_cpd[f.variables[0]].ttype for f in factor_list]
for type_, factors in ut.group_items(factor_list, semtypes).items():
logger.info('Result Factors (%r)' % (type_, ))
factors = ut.sortedby(factors, [f.variables[0] for f in factors])
for fs_ in ut.ichunks(factors, 4):
ut.colorprint(ut.hz_str([f._str('phi', 'psql') for f in fs_]),
'yellow')
logger.info('MAP assignments')
top_assignments = query_results.get('top_assignments', [])
tmp = []
for lbl, val in top_assignments:
tmp.append('%s : %.4f' % (ut.repr2(lbl), val))
logger.info(ut.align('\n'.join(tmp), ' :'))
logger.info('L_____\n')
showkw = dict(evidence=evidence,
soft_evidence=soft_evidence,
**query_results)
from wbia.algo.hots import pgm_viz
pgm_viz.show_model(model, **showkw)
return (model, evidence, query_results)
def myquery():
r"""
BUG::
THERE IS A BUG SOMEWHERE: HOW IS THIS POSSIBLE?
if everything is weightd ) how di the true positive even get a score
while the true negative did not
qres_copy.filtkey_list = ['ratio', 'fg', 'homogerr', 'distinctiveness']
CORRECT STATS
{
'max' : [0.832, 0.968, 0.604, 0.000],
'min' : [0.376, 0.524, 0.000, 0.000],
'mean' : [0.561, 0.924, 0.217, 0.000],
'std' : [0.114, 0.072, 0.205, 0.000],
'nMin' : [1, 1, 1, 51],
'nMax' : [1, 1, 1, 1],
'shape': (52, 4),
}
INCORRECT STATS
{
'max' : [0.759, 0.963, 0.264, 0.000],
'min' : [0.379, 0.823, 0.000, 0.000],
'mean' : [0.506, 0.915, 0.056, 0.000],
'std' : [0.125, 0.039, 0.078, 0.000],
'nMin' : [1, 1, 1, 24],
'nMax' : [1, 1, 1, 1],
'shape': (26, 4),
# score_diff, tp_score, tn_score, p, K, dcvs_clip_max, fg_power, homogerr_power
0.494, 0.494, 0.000, 73.000, 2, 0.500, 0.100, 10.000
see how seperability changes as we very things
CommandLine:
python -m ibeis.algo.hots.devcases --test-myquery
python -m ibeis.algo.hots.devcases --test-myquery --show --index 0
python -m ibeis.algo.hots.devcases --test-myquery --show --index 1
python -m ibeis.algo.hots.devcases --test-myquery --show --index 2
References:
http://en.wikipedia.org/wiki/Pareto_distribution <- look into
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.all_imports import * # NOQA
>>> from ibeis.algo.hots.devcases import * # NOQA
>>> ut.dev_ipython_copypaster(myquery) if ut.inIPython() else myquery()
>>> pt.show_if_requested()
"""
from ibeis.algo.hots import special_query # NOQA
from ibeis.algo.hots import distinctiveness_normalizer # NOQA
from ibeis import viz # NOQA
import plottool as pt
index = ut.get_argval('--index', int, 0)
ibs, aid1, aid2, tn_aid = testdata_my_exmaples(index)
qaids = [aid1]
daids = [aid2] + [tn_aid]
qvuuid = ibs.get_annot_visual_uuids(aid1)
cfgdict_vsone = dict(
sv_on=True,
#sv_on=False,
#codename='vsone_unnorm_dist_ratio_extern_distinctiveness',
codename='vsone_unnorm_ratio_extern_distinctiveness',
sver_output_weighting=True,
)
use_cache = False
save_qcache = False
qres_list, qreq_ = ibs.query_chips(qaids,
daids,
cfgdict=cfgdict_vsone,
return_request=True,
use_cache=use_cache,
save_qcache=save_qcache,
verbose=True)
qreq_.load_distinctiveness_normalizer()
qres = qres_list[0]
top_aids = qres.get_top_aids() # NOQA
qres_orig = qres # NOQA
def test_config(qreq_, qres_orig, cfgdict):
""" function to grid search over """
qres_copy = copy.deepcopy(qres_orig)
qreq_vsone_ = qreq_
qres_vsone = qres_copy
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
newfsv_list, newscore_aids = special_query.get_extern_distinctiveness(
qreq_, qres_copy, **cfgdict)
special_query.apply_new_qres_filter_scores(qreq_vsone_, qres_vsone,
newfsv_list, newscore_aids,
filtkey)
tp_score = qres_copy.aid2_score[aid2]
tn_score = qres_copy.aid2_score[tn_aid]
return qres_copy, tp_score, tn_score
#[.01, .1, .2, .5, .6, .7, .8, .9, 1.0]),
#FiltKeys = hstypes.FiltKeys
# FIXME: Use other way of doing gridsearch
grid_basis = distinctiveness_normalizer.DCVS_DEFAULT.get_grid_basis()
gridsearch = ut.GridSearch(grid_basis, label='qvuuid=%r' % (qvuuid, ))
print('Begin Grid Search')
for cfgdict in ut.ProgressIter(gridsearch, lbl='GridSearch'):
qres_copy, tp_score, tn_score = test_config(qreq_, qres_orig, cfgdict)
gridsearch.append_result(tp_score, tn_score)
print('Finish Grid Search')
# Get best result
best_cfgdict = gridsearch.get_rank_cfgdict()
qres_copy, tp_score, tn_score = test_config(qreq_, qres_orig, best_cfgdict)
# Examine closely what you can do with scores
if False:
qres_copy = copy.deepcopy(qres_orig)
qreq_vsone_ = qreq_
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
newfsv_list, newscore_aids = special_query.get_extern_distinctiveness(
qreq_, qres_copy, **cfgdict)
ut.embed()
def make_cm_very_old_tuple(qres_copy):
assert ut.listfind(qres_copy.filtkey_list, filtkey) is None
weight_filters = hstypes.WEIGHT_FILTERS
weight_filtxs, nonweight_filtxs = special_query.index_partition(
qres_copy.filtkey_list, weight_filters)
aid2_fsv = {}
aid2_fs = {}
aid2_score = {}
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
pass
break
#scorex_vsone = ut.listfind(qres_copy.filtkey_list, filtkey)
#if scorex_vsone is None:
# TODO: add spatial verification as a filter score
# augment the vsone scores
# TODO: paramaterize
weighted_ave_score = True
if weighted_ave_score:
# weighted average scoring
new_fs_vsone = special_query.weighted_average_scoring(
new_fsv_vsone, weight_filtxs, nonweight_filtxs)
else:
# product scoring
new_fs_vsone = special_query.product_scoring(new_fsv_vsone)
new_score_vsone = new_fs_vsone.sum()
aid2_fsv[daid] = new_fsv_vsone
aid2_fs[daid] = new_fs_vsone
aid2_score[daid] = new_score_vsone
return aid2_fsv, aid2_fs, aid2_score
# Look at plot of query products
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
new_fs_vsone = special_query.product_scoring(new_fsv_vsone)
scores_list = np.array(new_fs_vsone)[:, None].T
pt.plot_sorted_scores(scores_list,
logscale=False,
figtitle=str(daid))
pt.iup()
special_query.apply_new_qres_filter_scores(qreq_vsone_, qres_copy,
newfsv_list, newscore_aids,
filtkey)
# PRINT INFO
import functools
#ut.rrrr()
get_stats_str = functools.partial(ut.get_stats_str,
axis=0,
newlines=True,
precision=3)
tp_stats_str = ut.align(get_stats_str(qres_copy.aid2_fsv[aid2]), ':')
tn_stats_str = ut.align(get_stats_str(qres_copy.aid2_fsv[tn_aid]), ':')
info_str_list = []
info_str_list.append('qres_copy.filtkey_list = %r' %
(qres_copy.filtkey_list, ))
info_str_list.append('CORRECT STATS')
info_str_list.append(tp_stats_str)
info_str_list.append('INCORRECT STATS')
info_str_list.append(tn_stats_str)
info_str = '\n'.join(info_str_list)
print(info_str)
# SHOW BEST RESULT
#qres_copy.ishow_top(ibs, fnum=pt.next_fnum())
#qres_orig.ishow_top(ibs, fnum=pt.next_fnum())
# Text Informatio
param_lbl = 'dcvs_power'
param_stats_str = gridsearch.get_dimension_stats_str(param_lbl)
print(param_stats_str)
csvtext = gridsearch.get_csv_results(10)
print(csvtext)
# Paramter visuzliation
fnum = pt.next_fnum()
# plot paramter influence
param_label_list = gridsearch.get_param_lbls()
pnum_ = pt.get_pnum_func(2, len(param_label_list))
for px, param_label in enumerate(param_label_list):
gridsearch.plot_dimension(param_label, fnum=fnum, pnum=pnum_(px))
# plot match figure
pnum2_ = pt.get_pnum_func(2, 2)
qres_copy.show_matches(ibs, aid2, fnum=fnum, pnum=pnum2_(2))
qres_copy.show_matches(ibs, tn_aid, fnum=fnum, pnum=pnum2_(3))
# Add figure labels
figtitle = 'Effect of parameters on vsone separation for a single case'
subtitle = 'qvuuid = %r' % (qvuuid)
figtitle += '\n' + subtitle
pt.set_figtitle(figtitle)
# Save Figure
#fig_fpath = pt.save_figure(usetitle=True)
#print(fig_fpath)
# Write CSV Results
#csv_fpath = fig_fpath + '.csv.txt'
#ut.write_to(csv_fpath, csvtext)
#qres_copy.ishow_top(ibs)
#from matplotlib import pyplot as plt
#plt.show()
#print(ut.list_str()))
# TODO: plot max variation dims
#import plottool as pt
#pt.plot(p_list, diff_list)
"""
def align2(str_):
return ut.align(str_, ':', ' :')
def argparse_dict(default_dict_, lbl=None, verbose=None,
only_specified=False, force_keys={}, type_hint=None,
alias_dict={}):
r"""
Gets values for a dict based on the command line
Args:
default_dict_ (?):
only_specified (bool): if True only returns keys that are specified on commandline. no defaults.
Returns:
dict_: dict_ - a dictionary
CommandLine:
python -m utool.util_arg --test-argparse_dict
python -m utool.util_arg --test-argparse_dict --foo=3
python -m utool.util_arg --test-argparse_dict --flag1
python -m utool.util_arg --test-argparse_dict --flag2
python -m utool.util_arg --test-argparse_dict --noflag2
python -m utool.util_arg --test-argparse_dict --thresh=43
python -m utool.util_arg --test-argparse_dict --bins=-10
python -m utool.util_arg --test-argparse_dict --bins=-10 --only-specified --helpx
Example:
>>> # DISABLE_DOCTEST
>>> from utool.util_arg import * # NOQA
>>> import utool as ut
>>> # build test data
>>> default_dict_ = {
... 'bins': 8,
... 'foo': None,
... 'flag1': False,
... 'flag2': True,
... 'max': 0.2,
... 'neg': -5,
... 'thresh': -5.333,
... }
>>> # execute function
>>> only_specified = ut.get_argflag('--only-specified')
>>> dict_ = argparse_dict(default_dict_, only_specified=only_specified)
>>> # verify results
>>> result = ut.dict_str(dict_, sorted_=True)
>>> print(result)
"""
if verbose is None:
verbose = VERBOSE_ARGPARSE
def make_argstrs(key, prefix_list):
for prefix in prefix_list:
yield prefix + key
yield prefix + key.replace('-', '_')
yield prefix + key.replace('_', '-')
def get_dictkey_cmdline_val(key, default, type_hint):
# see if the user gave a commandline value for this dict key
defaulttype_ = None if default is None else type(default)
if type_hint is None:
type_ = defaulttype_
elif isinstance(type_hint, dict):
type_ = type_hint.get(key, defaulttype_)
elif isinstance(type_hint, type):
type_ = type_hint
else:
raise NotImplementedError('Unknown type of type_hint=%r' % (type_hint,))
was_specified = False
if isinstance(default, bool):
val = default
if default is True:
falsekeys = list(set(make_argstrs(key, ['--no', '--no-'])))
notval, was_specified = get_argflag(falsekeys, return_specified=True)
val = not notval
if not was_specified:
truekeys = list(set(make_argstrs(key, ['--'])))
val_, was_specified = get_argflag(truekeys, return_specified=True)
if was_specified:
val = val_
elif default is False:
truekeys = list(set(make_argstrs(key, ['--'])))
val, was_specified = get_argflag(truekeys, return_specified=True)
else:
argtup = list(set(make_argstrs(key, ['--'])))
#if key == 'species':
# import utool as ut
# ut.embed()
val, was_specified = get_argval(argtup, type_=type_,
default=default,
return_specified=True)
return val, was_specified
dict_ = {}
num_specified = 0
for key, default in six.iteritems(default_dict_):
val, was_specified = get_dictkey_cmdline_val(key, default, type_hint)
if not was_specified:
alias_keys = meta_util_iter.ensure_iterable(alias_dict.get(key, []))
for alias_key in alias_keys:
val, was_specified = get_dictkey_cmdline_val(alias_key, default,
type_hint)
if was_specified:
break
if VERBOSE_ARGPARSE:
if was_specified:
num_specified += 1
print('[argparse_dict] Specified key=%r, val=%r' % (key, val))
#if key == 'foo':
# import utool as ut
# ut.embed()
if not only_specified or was_specified or key in force_keys:
dict_[key] = val
if VERBOSE_ARGPARSE:
print('[argparse_dict] num_specified = %r' % (num_specified,))
print('[argparse_dict] force_keys = %r' % (force_keys,))
#dict_ = {key: get_dictkey_cmdline_val(key, default) for key, default in
#six.iteritems(default_dict_)}
if verbose:
for key in dict_:
if dict_[key] != default_dict_[key]:
print('[argparse_dict] GOT ARGUMENT: cfgdict[%r] = %r' % (key, dict_[key]))
do_helpx = get_argflag('--helpx',
help_='Specifies that argparse_dict should print help and quit')
if get_argflag(('--help', '--help2')) or do_helpx:
import utool as ut
print('COMMAND LINE IS ACCEPTING THESE PARAMS WITH DEFAULTS:')
if lbl is not None:
print(lbl)
#print(ut.align(ut.dict_str(dict_, sorted_=True), ':'))
print(ut.align(ut.dict_str(default_dict_, sorted_=True), ':'))
if do_helpx:
sys.exit(1)
return dict_
def align2(str_):
return ut.align(str_, ':', ' :')
def argparse_dict(default_dict_,
lbl=None,
verbose=None,
only_specified=False,
force_keys={},
type_hint=None,
alias_dict={}):
r"""
Gets values for a dict based on the command line
Args:
default_dict_ (?):
only_specified (bool): if True only returns keys that are specified on commandline. no defaults.
Returns:
dict_: dict_ - a dictionary
CommandLine:
python -m utool.util_arg --test-argparse_dict
python -m utool.util_arg --test-argparse_dict --foo=3
python -m utool.util_arg --test-argparse_dict --flag1
python -m utool.util_arg --test-argparse_dict --flag2
python -m utool.util_arg --test-argparse_dict --noflag2
python -m utool.util_arg --test-argparse_dict --thresh=43
python -m utool.util_arg --test-argparse_dict --bins=-10
python -m utool.util_arg --test-argparse_dict --bins=-10 --only-specified --helpx
Example:
>>> # DISABLE_DOCTEST
>>> from utool.util_arg import * # NOQA
>>> import utool as ut
>>> # build test data
>>> default_dict_ = {
... 'bins': 8,
... 'foo': None,
... 'flag1': False,
... 'flag2': True,
... 'max': 0.2,
... 'neg': -5,
... 'thresh': -5.333,
... }
>>> # execute function
>>> only_specified = ut.get_argflag('--only-specified')
>>> dict_ = argparse_dict(default_dict_, only_specified=only_specified)
>>> # verify results
>>> result = ut.dict_str(dict_, sorted_=True)
>>> print(result)
"""
if verbose is None:
verbose = VERBOSE_ARGPARSE
def make_argstrs(key, prefix_list):
for prefix in prefix_list:
yield prefix + key
yield prefix + key.replace('-', '_')
yield prefix + key.replace('_', '-')
def get_dictkey_cmdline_val(key, default, type_hint):
# see if the user gave a commandline value for this dict key
defaulttype_ = None if default is None else type(default)
if type_hint is None:
type_ = defaulttype_
elif isinstance(type_hint, dict):
type_ = type_hint.get(key, defaulttype_)
elif isinstance(type_hint, type):
type_ = type_hint
else:
raise NotImplementedError('Unknown type of type_hint=%r' %
(type_hint, ))
was_specified = False
if isinstance(default, bool):
val = default
if default is True:
falsekeys = list(set(make_argstrs(key, ['--no', '--no-'])))
notval, was_specified = get_argflag(falsekeys,
return_specified=True)
val = not notval
if not was_specified:
truekeys = list(set(make_argstrs(key, ['--'])))
val_, was_specified = get_argflag(truekeys,
return_specified=True)
if was_specified:
val = val_
elif default is False:
truekeys = list(set(make_argstrs(key, ['--'])))
val, was_specified = get_argflag(truekeys,
return_specified=True)
else:
argtup = list(set(make_argstrs(key, ['--'])))
#if key == 'species':
# import utool as ut
# ut.embed()
val, was_specified = get_argval(argtup,
type_=type_,
default=default,
return_specified=True)
return val, was_specified
dict_ = {}
num_specified = 0
for key, default in six.iteritems(default_dict_):
val, was_specified = get_dictkey_cmdline_val(key, default, type_hint)
if not was_specified:
alias_keys = meta_util_iter.ensure_iterable(alias_dict.get(
key, []))
for alias_key in alias_keys:
val, was_specified = get_dictkey_cmdline_val(
alias_key, default, type_hint)
if was_specified:
break
if VERBOSE_ARGPARSE:
if was_specified:
num_specified += 1
print('[argparse_dict] Specified key=%r, val=%r' % (key, val))
#if key == 'foo':
# import utool as ut
# ut.embed()
if not only_specified or was_specified or key in force_keys:
dict_[key] = val
if VERBOSE_ARGPARSE:
print('[argparse_dict] num_specified = %r' % (num_specified, ))
print('[argparse_dict] force_keys = %r' % (force_keys, ))
#dict_ = {key: get_dictkey_cmdline_val(key, default) for key, default in
#six.iteritems(default_dict_)}
if verbose:
for key in dict_:
if dict_[key] != default_dict_[key]:
print('[argparse_dict] GOT ARGUMENT: cfgdict[%r] = %r' %
(key, dict_[key]))
do_helpx = get_argflag(
'--helpx',
help_='Specifies that argparse_dict should print help and quit')
if get_argflag(('--help', '--help2')) or do_helpx:
import utool as ut
print('COMMAND LINE IS ACCEPTING THESE PARAMS WITH DEFAULTS:')
if lbl is not None:
print(lbl)
#print(ut.align(ut.dict_str(dict_, sorted_=True), ':'))
print(ut.align(ut.dict_str(default_dict_, sorted_=True), ':'))
if do_helpx:
sys.exit(1)
return dict_
def myquery():
r"""
BUG::
THERE IS A BUG SOMEWHERE: HOW IS THIS POSSIBLE?
if everything is weightd ) how di the true positive even get a score
while the true negative did not
qres_copy.filtkey_list = ['ratio', 'fg', 'homogerr', 'distinctiveness']
CORRECT STATS
{
'max' : [0.832, 0.968, 0.604, 0.000],
'min' : [0.376, 0.524, 0.000, 0.000],
'mean' : [0.561, 0.924, 0.217, 0.000],
'std' : [0.114, 0.072, 0.205, 0.000],
'nMin' : [1, 1, 1, 51],
'nMax' : [1, 1, 1, 1],
'shape': (52, 4),
}
INCORRECT STATS
{
'max' : [0.759, 0.963, 0.264, 0.000],
'min' : [0.379, 0.823, 0.000, 0.000],
'mean' : [0.506, 0.915, 0.056, 0.000],
'std' : [0.125, 0.039, 0.078, 0.000],
'nMin' : [1, 1, 1, 24],
'nMax' : [1, 1, 1, 1],
'shape': (26, 4),
# score_diff, tp_score, tn_score, p, K, dcvs_clip_max, fg_power, homogerr_power
0.494, 0.494, 0.000, 73.000, 2, 0.500, 0.100, 10.000
see how seperability changes as we very things
CommandLine:
python -m ibeis.algo.hots.devcases --test-myquery
python -m ibeis.algo.hots.devcases --test-myquery --show --index 0
python -m ibeis.algo.hots.devcases --test-myquery --show --index 1
python -m ibeis.algo.hots.devcases --test-myquery --show --index 2
References:
http://en.wikipedia.org/wiki/Pareto_distribution <- look into
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.all_imports import * # NOQA
>>> from ibeis.algo.hots.devcases import * # NOQA
>>> ut.dev_ipython_copypaster(myquery) if ut.inIPython() else myquery()
>>> pt.show_if_requested()
"""
from ibeis.algo.hots import special_query # NOQA
from ibeis.algo.hots import distinctiveness_normalizer # NOQA
from ibeis import viz # NOQA
import plottool as pt
index = ut.get_argval('--index', int, 0)
ibs, aid1, aid2, tn_aid = testdata_my_exmaples(index)
qaids = [aid1]
daids = [aid2] + [tn_aid]
qvuuid = ibs.get_annot_visual_uuids(aid1)
cfgdict_vsone = dict(
sv_on=True,
#sv_on=False,
#codename='vsone_unnorm_dist_ratio_extern_distinctiveness',
codename='vsone_unnorm_ratio_extern_distinctiveness',
sver_output_weighting=True,
)
use_cache = False
save_qcache = False
qres_list, qreq_ = ibs.query_chips(qaids, daids, cfgdict=cfgdict_vsone,
return_request=True, use_cache=use_cache,
save_qcache=save_qcache, verbose=True)
qreq_.load_distinctiveness_normalizer()
qres = qres_list[0]
top_aids = qres.get_top_aids() # NOQA
qres_orig = qres # NOQA
def test_config(qreq_, qres_orig, cfgdict):
""" function to grid search over """
qres_copy = copy.deepcopy(qres_orig)
qreq_vsone_ = qreq_
qres_vsone = qres_copy
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
newfsv_list, newscore_aids = special_query.get_extern_distinctiveness(qreq_, qres_copy, **cfgdict)
special_query.apply_new_qres_filter_scores(qreq_vsone_, qres_vsone, newfsv_list, newscore_aids, filtkey)
tp_score = qres_copy.aid2_score[aid2]
tn_score = qres_copy.aid2_score[tn_aid]
return qres_copy, tp_score, tn_score
#[.01, .1, .2, .5, .6, .7, .8, .9, 1.0]),
#FiltKeys = hstypes.FiltKeys
# FIXME: Use other way of doing gridsearch
grid_basis = distinctiveness_normalizer.DCVS_DEFAULT.get_grid_basis()
gridsearch = ut.GridSearch(grid_basis, label='qvuuid=%r' % (qvuuid,))
print('Begin Grid Search')
for cfgdict in ut.ProgressIter(gridsearch, lbl='GridSearch'):
qres_copy, tp_score, tn_score = test_config(qreq_, qres_orig, cfgdict)
gridsearch.append_result(tp_score, tn_score)
print('Finish Grid Search')
# Get best result
best_cfgdict = gridsearch.get_rank_cfgdict()
qres_copy, tp_score, tn_score = test_config(qreq_, qres_orig, best_cfgdict)
# Examine closely what you can do with scores
if False:
qres_copy = copy.deepcopy(qres_orig)
qreq_vsone_ = qreq_
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
newfsv_list, newscore_aids = special_query.get_extern_distinctiveness(qreq_, qres_copy, **cfgdict)
ut.embed()
def make_cm_very_old_tuple(qres_copy):
assert ut.listfind(qres_copy.filtkey_list, filtkey) is None
weight_filters = hstypes.WEIGHT_FILTERS
weight_filtxs, nonweight_filtxs = special_query.index_partition(qres_copy.filtkey_list, weight_filters)
aid2_fsv = {}
aid2_fs = {}
aid2_score = {}
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
pass
break
#scorex_vsone = ut.listfind(qres_copy.filtkey_list, filtkey)
#if scorex_vsone is None:
# TODO: add spatial verification as a filter score
# augment the vsone scores
# TODO: paramaterize
weighted_ave_score = True
if weighted_ave_score:
# weighted average scoring
new_fs_vsone = special_query.weighted_average_scoring(new_fsv_vsone, weight_filtxs, nonweight_filtxs)
else:
# product scoring
new_fs_vsone = special_query.product_scoring(new_fsv_vsone)
new_score_vsone = new_fs_vsone.sum()
aid2_fsv[daid] = new_fsv_vsone
aid2_fs[daid] = new_fs_vsone
aid2_score[daid] = new_score_vsone
return aid2_fsv, aid2_fs, aid2_score
# Look at plot of query products
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
new_fs_vsone = special_query.product_scoring(new_fsv_vsone)
scores_list = np.array(new_fs_vsone)[:, None].T
pt.plot_sorted_scores(scores_list, logscale=False, figtitle=str(daid))
pt.iup()
special_query.apply_new_qres_filter_scores(qreq_vsone_, qres_copy, newfsv_list, newscore_aids, filtkey)
# PRINT INFO
import functools
#ut.rrrr()
get_stats_str = functools.partial(ut.get_stats_str, axis=0, newlines=True, precision=3)
tp_stats_str = ut.align(get_stats_str(qres_copy.aid2_fsv[aid2]), ':')
tn_stats_str = ut.align(get_stats_str(qres_copy.aid2_fsv[tn_aid]), ':')
info_str_list = []
info_str_list.append('qres_copy.filtkey_list = %r' % (qres_copy.filtkey_list,))
info_str_list.append('CORRECT STATS')
info_str_list.append(tp_stats_str)
info_str_list.append('INCORRECT STATS')
info_str_list.append(tn_stats_str)
info_str = '\n'.join(info_str_list)
print(info_str)
# SHOW BEST RESULT
#qres_copy.ishow_top(ibs, fnum=pt.next_fnum())
#qres_orig.ishow_top(ibs, fnum=pt.next_fnum())
# Text Informatio
param_lbl = 'dcvs_power'
param_stats_str = gridsearch.get_dimension_stats_str(param_lbl)
print(param_stats_str)
csvtext = gridsearch.get_csv_results(10)
print(csvtext)
# Paramter visuzliation
fnum = pt.next_fnum()
# plot paramter influence
param_label_list = gridsearch.get_param_lbls()
pnum_ = pt.get_pnum_func(2, len(param_label_list))
for px, param_label in enumerate(param_label_list):
gridsearch.plot_dimension(param_label, fnum=fnum, pnum=pnum_(px))
# plot match figure
pnum2_ = pt.get_pnum_func(2, 2)
qres_copy.show_matches(ibs, aid2, fnum=fnum, pnum=pnum2_(2))
qres_copy.show_matches(ibs, tn_aid, fnum=fnum, pnum=pnum2_(3))
# Add figure labels
figtitle = 'Effect of parameters on vsone separation for a single case'
subtitle = 'qvuuid = %r' % (qvuuid)
figtitle += '\n' + subtitle
pt.set_figtitle(figtitle)
# Save Figure
#fig_fpath = pt.save_figure(usetitle=True)
#print(fig_fpath)
# Write CSV Results
#csv_fpath = fig_fpath + '.csv.txt'
#ut.write_to(csv_fpath, csvtext)
#qres_copy.ishow_top(ibs)
#from matplotlib import pyplot as plt
#plt.show()
#print(ut.list_str()))
# TODO: plot max variation dims
#import plottool as pt
#pt.plot(p_list, diff_list)
"""