def test_pyflann_searches():
"""
CommandLine:
python -m vtool.tests.test_pyflann --test-test_pyflann_searches
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.tests.test_pyflann import * # NOQA
>>> # build test data
>>> # execute function
>>> result = test_pyflann_searches()
>>> # verify results
>>> print(result)
"""
try:
num_neighbors = 3
pts = testdata_points(nPts=5743, nDims=2)
qpts = testdata_points(nPts=7, nDims=2)
import vtool as vt
# sample a radius
radius = vt.L2(pts[0:1], qpts[0:1])[0] * 2 + 1
flann = pyflann.FLANN()
print('NN_OnTheFly')
# build nn_index on the fly
indices1, dists1 = flann.nn(pts,
qpts,
num_neighbors,
algorithm='hierarchical')
print(utool.hz_str('indices1, dists1 = ', indices1, dists1))
_build_params = flann.build_index(pts, algorithm='kmeans')
del _build_params
print('NN_Index')
indices2, dists2 = flann.nn_index(qpts, num_neighbors=num_neighbors)
print(utool.hz_str('indices2, dists2 = ', indices2, dists2))
# this can only be called on one query point at a time
# because the output size is unknown
print('NN_Radius, radius=%r' % (radius, ))
indices3, dists3 = flann.nn_radius(pts[0], radius)
print('indices3 = %r ' % (indices3, ))
print('dists3 = %r ' % (dists3, ))
assert np.all(dists3 < radius)
except Exception as ex:
utool.printex(ex,
key_list=[
'query',
'query.shape',
'pts.shape',
],
pad_stdout=True)
#utool.embed()
raise
def check_expr_eq(expr1, expr2, verbose=True):
"""
Does not work in general. Problem is not decidable.
Thanks Richard.
Args:
expr1 (?):
expr2 (?):
CommandLine:
python -m vtool.symbolic --test-check_expr_eq
SeeALso:
vt.symbolic_randcheck
Example:
>>> # DISABLE_DOCTEST
>>> from vtool.symbolic import * # NOQA
>>> expr1 = sympy.Matrix([ [sx*x + 1.0*tx + w1*y], [sy*y + 1.0*ty + w2*x], [1.0]])
>>> expr2 = sympy.Matrix([ [sx*x + tx + w1*y], [sy*y + ty + w2*x], [1]])
>>> result = check_expr_eq(expr1, expr2)
>>> print(result)
"""
if isinstance(expr1, six.string_types):
expr1 = sympy.simplify(expr1)
if isinstance(expr2, six.string_types):
expr2 = sympy.simplify(expr2)
print(ut.hz_str('Checking if ', repr(expr1), ' == ', repr(expr2)))
random_point_check = expr1.equals(expr2)
if random_point_check is None:
failexpr = expr1.equals(expr2, failing_expression=True)
print('failexpr = %r' % (failexpr,))
random_point_check = False
print('... seems %r' % (random_point_check,))
#return random_point_check
expr3 = expr1 - expr2
if not random_point_check and True:
common_symbols = expr1.free_symbols.intersection(expr2.free_symbols)
if len(common_symbols):
y = sympy.symbols('y') # Hack, should be a new symbol
symbol = common_symbols.pop()
soln1 = sympy.solve(sympy.Eq(sympy.simplify(expr1), y), symbol)
soln2 = sympy.solve(sympy.Eq(sympy.simplify(expr2), y), symbol)
print('Solving expr1 for common symbol: ' + str(soln1))
print('Solving expr2 for common symbol: ' + str(soln2))
if soln1 == soln2:
print('This seems True')
else:
print('This seems False')
sympy.solve(sympy.Eq(sympy.simplify(expr2), y), 'd')
print(ut.hz_str('... checking 0 ', repr(expr3)))
# Does not always work.
print('(not gaurenteed to work) expr3.is_zero = %r' % (expr3.is_zero,))
return expr3.is_zero
def check_expr_eq(expr1, expr2, verbose=True):
"""
Does not work in general. Problem is not decidable.
Thanks Richard.
Args:
expr1 (?):
expr2 (?):
CommandLine:
python -m vtool.symbolic --test-check_expr_eq
SeeALso:
vt.symbolic_randcheck
Example:
>>> # DISABLE_DOCTEST
>>> from vtool.symbolic import * # NOQA
>>> expr1 = sympy.Matrix([ [sx*x + 1.0*tx + w1*y], [sy*y + 1.0*ty + w2*x], [1.0]])
>>> expr2 = sympy.Matrix([ [sx*x + tx + w1*y], [sy*y + ty + w2*x], [1]])
>>> result = check_expr_eq(expr1, expr2)
>>> print(result)
"""
if isinstance(expr1, six.string_types):
expr1 = sympy.simplify(expr1)
if isinstance(expr2, six.string_types):
expr2 = sympy.simplify(expr2)
print(ut.hz_str("Checking if ", repr(expr1), " == ", repr(expr2)))
random_point_check = expr1.equals(expr2)
if random_point_check is None:
failexpr = expr1.equals(expr2, failing_expression=True)
print("failexpr = %r" % (failexpr,))
random_point_check = False
print("... seems %r" % (random_point_check,))
# return random_point_check
expr3 = expr1 - expr2
if not random_point_check and True:
common_symbols = expr1.free_symbols.intersection(expr2.free_symbols)
if len(common_symbols):
y = sympy.symbols("y") # Hack, should be a new symbol
symbol = common_symbols.pop()
soln1 = sympy.solve(sympy.Eq(sympy.simplify(expr1), y), symbol)
soln2 = sympy.solve(sympy.Eq(sympy.simplify(expr2), y), symbol)
print("Solving expr1 for common symbol: " + str(soln1))
print("Solving expr2 for common symbol: " + str(soln2))
if soln1 == soln2:
print("This seems True")
else:
print("This seems False")
sympy.solve(sympy.Eq(sympy.simplify(expr2), y), "d")
print(ut.hz_str("... checking 0 ", repr(expr3)))
# Does not always work.
print("(not gaurenteed to work) expr3.is_zero = %r" % (expr3.is_zero,))
return expr3.is_zero
def test_pyflann_searches():
"""
CommandLine:
python -m vtool.tests.test_pyflann --test-test_pyflann_searches
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.tests.test_pyflann import * # NOQA
>>> # build test data
>>> # execute function
>>> result = test_pyflann_searches()
>>> # verify results
>>> print(result)
"""
try:
num_neighbors = 3
pts = testdata_points(nPts=5743, nDims=2)
qpts = testdata_points(nPts=7, nDims=2)
import vtool as vt
# sample a radius
radius = vt.L2(pts[0:1], qpts[0:1])[0] * 2 + 1
flann = pyflann.FLANN()
print('NN_OnTheFly')
# build nn_index on the fly
indices1, dists1 = flann.nn(pts, qpts, num_neighbors, algorithm='hierarchical')
print(utool.hz_str('indices1, dists1 = ', indices1, dists1))
_build_params = flann.build_index(pts, algorithm='kmeans')
del _build_params
print('NN_Index')
indices2, dists2 = flann.nn_index(qpts, num_neighbors=num_neighbors)
print(utool.hz_str('indices2, dists2 = ', indices2, dists2))
# this can only be called on one query point at a time
# because the output size is unknown
print('NN_Radius, radius=%r' % (radius,))
indices3, dists3 = flann.nn_radius(pts[0], radius)
print('indices3 = %r ' % (indices3,))
print('dists3 = %r ' % (dists3,))
assert np.all(dists3 < radius)
except Exception as ex:
utool.printex(ex, key_list=[
'query',
'query.shape',
'pts.shape',
], pad_stdout=True)
#utool.embed()
raise
def print_priors(model, ignore_ttypes=[], title='Priors', color='blue'):
ut.colorprint('\n --- %s ---' % (title, ), color=color)
for ttype, cpds in model.ttype2_cpds.items():
if ttype not in ignore_ttypes:
for fs_ in ut.ichunks(cpds, 4):
ut.colorprint(ut.hz_str([f._cpdstr('psql') for f in fs_]),
color)
def test_pyflann_tune():
"""
CommandLine:
python -m vtool.tests.test_pyflann --test-test_pyflann_tune
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.tests.test_pyflann import * # NOQA
>>> # build test data
>>> # execute function
>>> result = test_pyflann_tune()
>>> # verify results
>>> print(result)
"""
print('Create random qpts and database data')
pts = testdata_points(nPts=1009)
qpts = testdata_points(nPts=7)
num_neighbors = 3
#num_data = len(data)
# untuned query
flann = pyflann.FLANN()
index_untuned, dist_untuned = flann.nn(pts, qpts, num_neighbors)
# tuned query
flannkw = dict(algorithm='autotuned',
target_precision=.01,
build_weight=0.01,
memory_weight=0.0,
sample_fraction=0.001)
flann_tuned = pyflann.FLANN()
tuned_params = flann_tuned.build_index(pts, **flannkw)
index_tuned, dist_tuned = flann_tuned.nn_index(qpts,
num_neighbors=num_neighbors)
print(
utool.hz_str('index_tuned, dist_tuned = ', index_tuned,
dist_tuned))
print('')
print(
utool.hz_str('index_untuned, dist_untuned = ', index_untuned,
dist_untuned))
print(dist_untuned >= dist_tuned)
return tuned_params
def test_pyflann_tune():
"""
CommandLine:
python -m vtool.tests.test_pyflann --test-test_pyflann_tune
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.tests.test_pyflann import * # NOQA
>>> # build test data
>>> # execute function
>>> result = test_pyflann_tune()
>>> # verify results
>>> print(result)
"""
print('Create random qpts and database data')
pts = testdata_points(nPts=1009)
qpts = testdata_points(nPts=7)
num_neighbors = 3
#num_data = len(data)
# untuned query
flann = pyflann.FLANN()
index_untuned, dist_untuned = flann.nn(pts, qpts, num_neighbors)
# tuned query
flannkw = dict(
algorithm='autotuned',
target_precision=.01,
build_weight=0.01,
memory_weight=0.0,
sample_fraction=0.001
)
flann_tuned = pyflann.FLANN()
tuned_params = flann_tuned.build_index(pts, **flannkw)
index_tuned, dist_tuned = flann_tuned.nn_index(qpts, num_neighbors=num_neighbors)
print(utool.hz_str('index_tuned, dist_tuned = ', index_tuned, dist_tuned))
print('')
print(utool.hz_str('index_untuned, dist_untuned = ', index_untuned, dist_untuned))
print(dist_untuned >= dist_tuned)
return tuned_params
def test_pyflann_add_point():
"""
CommandLine:
python -m vtool.tests.test_pyflann --test-test_pyflann_add_point
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.tests.test_pyflann import * # NOQA
>>> # build test data
>>> # execute function
>>> result = test_pyflann_add_point()
>>> # verify results
>>> print(result)
"""
# Test parameters
num_neighbors = 3
pts = testdata_points(nPts=1009)
qpts = testdata_points(nPts=7)
newpts = testdata_points(nPts=1013)
# build index
print('Build Index')
flann = pyflann.FLANN()
_build_params = flann.build_index(pts)
print(_build_params)
print('NN_Index')
indices1, dists1 = flann.nn_index(qpts, num_neighbors=num_neighbors)
assert np.all(
indices1 < pts.shape[0]), 'indicies should be less than num pts'
print(utool.hz_str('indices1, dists1 = ', indices1, dists1))
print('Adding points')
flann.add_points(newpts, rebuild_threshold=2)
print('NN_Index')
indices2, dists2 = flann.nn_index(qpts, num_neighbors=num_neighbors)
print(utool.hz_str('indices2, dists2 = ', indices2, dists2))
assert np.any(
indices2 > pts.shape[0]), 'should be some indexes into new points'
assert np.all(indices2 < pts.shape[0] +
newpts.shape[0]), 'but not more than the points being added'
def print_factors(model, factor_list):
if hasattr(model, 'var2_cpd'):
semtypes = [model.var2_cpd[f.variables[0]].ttype for f in factor_list]
else:
semtypes = [0] * len(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')
def print_factors(model, factor_list):
if hasattr(model, 'var2_cpd'):
semtypes = [model.var2_cpd[f.variables[0]].ttype
for f in factor_list]
else:
semtypes = [0] * len(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')
def test_pyflann_add_point():
"""
CommandLine:
python -m vtool.tests.test_pyflann --test-test_pyflann_add_point
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.tests.test_pyflann import * # NOQA
>>> # build test data
>>> # execute function
>>> result = test_pyflann_add_point()
>>> # verify results
>>> print(result)
"""
# Test parameters
num_neighbors = 3
pts = testdata_points(nPts=1009)
qpts = testdata_points(nPts=7)
newpts = testdata_points(nPts=1013)
# build index
print('Build Index')
flann = pyflann.FLANN()
_build_params = flann.build_index(pts)
print(_build_params)
print('NN_Index')
indices1, dists1 = flann.nn_index(qpts, num_neighbors=num_neighbors)
assert np.all(indices1 < pts.shape[0]), 'indicies should be less than num pts'
print(utool.hz_str('indices1, dists1 = ', indices1, dists1))
print('Adding points')
flann.add_points(newpts, rebuild_threshold=2)
print('NN_Index')
indices2, dists2 = flann.nn_index(qpts, num_neighbors=num_neighbors)
print(utool.hz_str('indices2, dists2 = ', indices2, dists2))
assert np.any(indices2 > pts.shape[0]), 'should be some indexes into new points'
assert np.all(indices2 < pts.shape[0] + newpts.shape[0]), 'but not more than the points being added'
def evalprint(str_, globals_=None, locals_=None, simplify=False):
if globals_ is None:
globals_ = ut.get_parent_globals()
if locals_ is None:
locals_ = ut.get_parent_locals()
if isinstance(str_, six.string_types):
var = eval(str_, globals_, locals_)
else:
var = str_
str_ = ut.get_varname_from_stack(var, N=1)
if simplify is True:
var = sympy.simplify(var)
print(ut.hz_str(str_ + ' = ', repr(var)))
def evalprint(str_, globals_=None, locals_=None, simplify=False):
if globals_ is None:
globals_ = ut.get_parent_globals()
if locals_ is None:
locals_ = ut.get_parent_locals()
if isinstance(str_, six.string_types):
var = eval(str_, globals_, locals_)
else:
var = str_
str_ = ut.get_varname_from_stack(var, N=1)
if simplify is True:
var = sympy.simplify(var)
print(ut.hz_str(str_ + " = ", repr(var)))
def get_inspect_str(qres, ibs=None, name_scoring=False):
qres.assert_self()
#ut.embed()
top_lbls = [' top aids', ' scores', ' rawscores', ' ranks']
top_aids = np.array(qres.get_top_aids(num=6, name_scoring=name_scoring, ibs=ibs), dtype=np.int32)
top_scores = np.array(qres.get_aid_scores(top_aids), dtype=np.float64)
top_rawscores = np.array(qres.get_aid_scores(top_aids, rawscore=True), dtype=np.float64)
top_ranks = np.array(qres.get_aid_ranks(top_aids), dtype=np.int32)
top_list = [top_aids, top_scores, top_rawscores, top_ranks]
if ibs is not None:
top_lbls += [' isgt']
istrue = qres.get_aid_truth(ibs, top_aids)
top_list.append(np.array(istrue, dtype=np.int32))
if name_scoring:
top_lbls = ['top nid'] + top_lbls
top_list = [ibs.get_annot_name_rowids(top_aids)] + top_list
top_stack = np.vstack(top_list)
#top_stack = np.array(top_stack, dtype=object)
top_stack = np.array(top_stack, dtype=np.float32)
#np.int32)
top_str = np.array_str(top_stack, precision=3, suppress_small=True, max_line_width=200)
top_lbl = '\n'.join(top_lbls)
inspect_list = ['QueryResult',
qres.cfgstr,
]
if ibs is not None:
gt_ranks = qres.get_gt_ranks(ibs=ibs)
gt_scores = qres.get_gt_scores(ibs=ibs)
inspect_list.append('gt_ranks = %r' % gt_ranks)
inspect_list.append('gt_scores = %r' % gt_scores)
nFeatMatch_list = get_num_feats_in_matches(qres)
nFeatMatch_stats_str = ut.get_stats_str(nFeatMatch_list, newlines=True, exclude_keys=('nMin', 'nMax'))
inspect_list.extend([
'qaid=%r ' % qres.qaid,
ut.hz_str(top_lbl, ' ', top_str),
'num feat matches per annotation stats:',
#ut.indent(ut.dict_str(nFeatMatch_stats)),
ut.indent(nFeatMatch_stats_str),
])
inspect_str = '\n'.join(inspect_list)
#inspect_str = ut.indent(inspect_str, '[INSPECT] ')
return inspect_str
def get_inspect_str(qres, ibs=None, name_scoring=False):
qres.assert_self()
#ut.embed()
top_lbls = [' top aids', ' scores', ' rawscores', ' ranks']
top_aids = np.array(qres.get_top_aids(num=6, name_scoring=name_scoring, ibs=ibs), dtype=np.int32)
top_scores = np.array(qres.get_aid_scores(top_aids), dtype=np.float64)
top_rawscores = np.array(qres.get_aid_scores(top_aids, rawscore=True), dtype=np.float64)
top_ranks = np.array(qres.get_aid_ranks(top_aids), dtype=np.int32)
top_list = [top_aids, top_scores, top_rawscores, top_ranks]
if ibs is not None:
top_lbls += [' isgt']
istrue = qres.get_aid_truth(ibs, top_aids)
top_list.append(np.array(istrue, dtype=np.int32))
if name_scoring:
top_lbls = ['top nid'] + top_lbls
top_list = [ibs.get_annot_name_rowids(top_aids)] + top_list
top_stack = np.vstack(top_list)
#top_stack = np.array(top_stack, dtype=object)
top_stack = np.array(top_stack, dtype=np.float32)
#np.int32)
top_str = np.array_str(top_stack, precision=3, suppress_small=True, max_line_width=200)
top_lbl = '\n'.join(top_lbls)
inspect_list = ['QueryResult',
qres.cfgstr,
]
if ibs is not None:
gt_ranks = qres.get_gt_ranks(ibs=ibs)
gt_scores = qres.get_gt_scores(ibs=ibs)
inspect_list.append('gt_ranks = %r' % gt_ranks)
inspect_list.append('gt_scores = %r' % gt_scores)
nFeatMatch_list = get_num_feats_in_matches(qres)
nFeatMatch_stats_str = ut.get_stats_str(nFeatMatch_list, newlines=True, exclude_keys=('nMin', 'nMax'))
inspect_list.extend([
'qaid=%r ' % qres.qaid,
ut.hz_str(top_lbl, ' ', top_str),
'num feat matches per annotation stats:',
#ut.indent(ut.dict_str(nFeatMatch_stats)),
ut.indent(nFeatMatch_stats_str),
])
inspect_str = '\n'.join(inspect_list)
#inspect_str = ut.indent(inspect_str, '[INSPECT] ')
return inspect_str
def pandas_repr(df):
import utool as ut
args = [
df.values,
]
kwargs = [
('columns', df.columns.values.tolist()),
('index', df.index.values.tolist()),
]
header = 'pd.DataFrame('
footer = ')'
arg_parts = [
ut.hz_str(' ', ut.repr2(arg))
for arg in args if arg is not None
]
kwarg_parts = [
ut.hz_str(' {}={}'.format(key, ut.repr2(val)))
for key, val in kwargs if val is not None
]
body = ',\n'.join(arg_parts + kwarg_parts)
dfrepr = '\n'.join([header, body, footer])
print(dfrepr)
pass
def make_test_similarity(test_case):
# toy_params = {
# True: {'mu': 0.9, 'sigma': .1},
# False: {'mu': 0.1, 'sigma': .4}
# }
# tau = np.pi * 2
from wbia import constants as const
# view_to_ori = const.VIEWTEXT_TO_YAW_RADIANS
view_to_ori = ut.map_dict_keys(
lambda x: const.YAWALIAS[x], const.VIEWTEXT_TO_YAW_RADIANS
)
# view_to_ori = {
# 'F': -1 * tau / 4,
# 'L': 0 * tau / 4,
# 'B': 1 * tau / 4,
# 'R': 2 * tau / 4,
# }
import vtool as vt
nid_list = np.array(ut.dict_take_column(test_case, 'name'))
yaw_list = np.array(
ut.dict_take(view_to_ori, ut.dict_take_column(test_case, 'view'))
)
rng = np.random.RandomState(0)
pmat = []
for idx in range(len(test_case)):
nid = nid_list[idx]
yaw = yaw_list[idx]
p_same = nid == nid_list
p_comp = 1 - vt.ori_distance(yaw_list, yaw) / np.pi
# estimate noisy measurements
p_same_m = np.clip(p_same + rng.normal(0, 0.5, size=len(p_same)), 0, 0.9)
p_comp_m = np.clip(p_comp + rng.normal(0, 0.5, size=len(p_comp)), 0, 0.9)
#
p_same_and_comp = p_same_m * p_comp_m
pmat.append(p_same_and_comp)
#
P = np.array(pmat)
P[np.diag_indices(len(P))] = 0
P = P + P.T / 2
P = np.clip(P, 0.01, 0.99)
logger.info(ut.hz_str(' P = ', ut.repr2(P, precision=2, max_line_width=140)))
return P
def make_test_similarity(test_case):
#toy_params = {
# True: {'mu': 0.9, 'sigma': .1},
# False: {'mu': 0.1, 'sigma': .4}
#}
# tau = np.pi * 2
from ibeis import constants as const
# view_to_ori = const.VIEWTEXT_TO_YAW_RADIANS
view_to_ori = ut.map_dict_keys(lambda x: const.YAWALIAS[x], const.VIEWTEXT_TO_YAW_RADIANS)
# view_to_ori = {
# 'F': -1 * tau / 4,
# 'L': 0 * tau / 4,
# 'B': 1 * tau / 4,
# 'R': 2 * tau / 4,
# }
import vtool as vt
nid_list = np.array(ut.dict_take_column(test_case, 'name'))
yaw_list = np.array(ut.dict_take(view_to_ori, ut.dict_take_column(test_case, 'view')))
rng = np.random.RandomState(0)
pmat = []
for idx in range(len(test_case)):
nid = nid_list[idx]
yaw = yaw_list[idx]
p_same = nid == nid_list
p_comp = 1 - vt.ori_distance(yaw_list, yaw) / np.pi
# estimate noisy measurements
p_same_m = np.clip(p_same + rng.normal(0, .5, size=len(p_same)), 0, .9)
p_comp_m = np.clip(p_comp + rng.normal(0, .5, size=len(p_comp)), 0, .9)
#
p_same_and_comp = p_same_m * p_comp_m
pmat.append(p_same_and_comp)
#
P = np.array(pmat)
P[np.diag_indices(len(P))] = 0
P = P + P.T / 2
P = np.clip(P, .01, .99)
print(ut.hz_str(' P = ', ut.array_repr2(P, precision=2, max_line_width=140)))
return P
def make_graph(infr, show=False):
import networkx as nx
import itertools
cm_list = infr.cm_list
unique_nids, prob_names = infr.make_prob_names()
thresh = infr.choose_thresh()
# Simply cut any edge with a weight less than a threshold
qaid_list = [cm.qaid for cm in cm_list]
postcut = prob_names > thresh
qxs, nxs = np.where(postcut)
if False:
kw = dict(precision=2, max_line_width=140, suppress_small=True)
print(
ut.hz_str('prob_names = ', ut.array2string2((prob_names),
**kw)))
print(
ut.hz_str('postcut = ',
ut.array2string2((postcut).astype(np.int), **kw)))
matching_qaids = ut.take(qaid_list, qxs)
matched_nids = ut.take(unique_nids, nxs)
qreq_ = infr.qreq_
nodes = ut.unique(qreq_.qaids.tolist() + qreq_.daids.tolist())
if not hasattr(qreq_, 'dnids'):
qreq_.dnids = qreq_.ibs.get_annot_nids(qreq_.daids)
qreq_.qnids = qreq_.ibs.get_annot_nids(qreq_.qaids)
dnid2_daids = ut.group_items(qreq_.daids, qreq_.dnids)
grouped_aids = dnid2_daids.values()
matched_daids = ut.take(dnid2_daids, matched_nids)
name_cliques = [
list(itertools.combinations(aids, 2)) for aids in grouped_aids
]
aid_matches = [
list(ut.product([qaid], daids))
for qaid, daids in zip(matching_qaids, matched_daids)
]
graph = nx.Graph()
graph.add_nodes_from(nodes)
graph.add_edges_from(ut.flatten(name_cliques))
graph.add_edges_from(ut.flatten(aid_matches))
#matchless_quries = ut.take(qaid_list, ut.index_complement(qxs, len(qaid_list)))
name_nodes = [('nid', l) for l in qreq_.dnids]
db_aid_nid_edges = list(zip(qreq_.daids, name_nodes))
#query_aid_nid_edges = list(zip(matching_qaids, [('nid', l) for l in matched_nids]))
#G = nx.Graph()
#G.add_nodes_from(matchless_quries)
#G.add_edges_from(db_aid_nid_edges)
#G.add_edges_from(query_aid_nid_edges)
graph.add_edges_from(db_aid_nid_edges)
if infr.user_feedback is not None:
user_feedback = ut.map_dict_vals(np.array, infr.user_feedback)
p_bg = 0.0
part1 = user_feedback['p_match'] * (1 - user_feedback['p_notcomp'])
part2 = p_bg * user_feedback['p_notcomp']
p_same_list = part1 + part2
for aid1, aid2, p_same in zip(user_feedback['aid1'],
user_feedback['aid2'], p_same_list):
if p_same > .5:
if not graph.has_edge(aid1, aid2):
graph.add_edge(aid1, aid2)
else:
if graph.has_edge(aid1, aid2):
graph.remove_edge(aid1, aid2)
if show:
import plottool as pt
nx.set_node_attributes(graph, 'color',
{aid: pt.LIGHT_PINK
for aid in qreq_.daids})
nx.set_node_attributes(graph, 'color',
{aid: pt.TRUE_BLUE
for aid in qreq_.qaids})
nx.set_node_attributes(
graph, 'color', {
aid: pt.LIGHT_PURPLE
for aid in np.intersect1d(qreq_.qaids, qreq_.daids)
})
nx.set_node_attributes(
graph, 'label',
{node: 'n%r' % (node[1], )
for node in name_nodes})
nx.set_node_attributes(
graph, 'color', {node: pt.LIGHT_GREEN
for node in name_nodes})
if show:
import plottool as pt
pt.show_nx(graph, layoutkw={'prog': 'neato'}, verbose=False)
return graph
def draw_em_graph(P, Pn, PL, gam, num_labels):
"""
python -m ibeis.algo.hots.testem test_em --show --no-cnn
"""
num_labels = PL.shape[1]
name_nodes = ['N%d' % x for x in list(range(1, num_labels + 1))]
annot_nodes = ['X%d' % x for x in list(range(1, len(Pn) + 1))]
nodes = name_nodes + annot_nodes
PL2 = gam[:, num_labels:].T
PL2 += .01
PL2 = PL2 / PL2.sum(axis=1)[:, None]
# PL2 = PL2 / np.linalg.norm(PL2, axis=0)
zero_part = np.zeros((num_labels, len(Pn) + num_labels))
prob_part = np.hstack([PL2, Pn])
print(ut.hz_str(' PL2 = ', ut.repr2(PL2, precision=2)))
# Redo p with posteriors
if ut.get_argflag('--postem'):
P = np.vstack([zero_part, prob_part])
weight_matrix = P # NOQA
graph = ut.nx_from_matrix(P, nodes=nodes)
graph = graph.to_directed()
# delete graph
dup_edges = []
seen_ = set([])
for u, v in graph.edges():
if u < v:
u, v = v, u
if (u, v) not in seen_:
seen_.add((u, v))
else:
dup_edges.append((u, v))
graph.remove_edges_from(dup_edges)
import plottool_ibeis as pt
import networkx as nx
if len(name_nodes) == 3 and len(annot_nodes) == 4:
graph.nodes[annot_nodes[0]]['pos'] = (20., 200.)
graph.nodes[annot_nodes[1]]['pos'] = (220., 200.)
graph.nodes[annot_nodes[2]]['pos'] = (20., 100.)
graph.nodes[annot_nodes[3]]['pos'] = (220., 100.)
graph.nodes[name_nodes[0]]['pos'] = (10., 300.)
graph.nodes[name_nodes[1]]['pos'] = (120., 300.)
graph.nodes[name_nodes[2]]['pos'] = (230., 300.)
nx.set_node_attributes(graph, name='pin', values='true')
print('annot_nodes = %r' % (annot_nodes, ))
print('name_nodes = %r' % (name_nodes, ))
for u in annot_nodes:
for v in name_nodes:
if graph.has_edge(u, v):
print('1) u, v = %r' % ((u, v), ))
graph.edge[u][v]['taillabel'] = graph.edge[u][v]['label']
graph.edge[u][v]['color'] = pt.ORANGE
graph.edge[u][v]['labelcolor'] = pt.BLUE
del graph.edge[u][v]['label']
elif graph.has_edge(v, u):
print('2) u, v = %r' % ((u, v), ))
graph.edge[v][u]['headlabel'] = graph.edge[v][u]['label']
graph.edge[v][u]['color'] = pt.ORANGE
graph.edge[v][u]['labelcolor'] = pt.BLUE
del graph.edge[v][u]['label']
else:
print((u, v))
print('!!')
# import itertools
# name_const_edges = [(u, v, {'style': 'invis'}) for u, v in itertools.combinations(name_nodes, 2)]
# graph.add_edges_from(name_const_edges)
# nx.set_edge_attributes(graph, name='constraint', values={edge: False for edge in graph.edges() if edge[0] == 'b' or edge[1] == 'b'})
# nx.set_edge_attributes(graph, name='constraint', values={edge: False for edge in graph.edges() if edge[0] in annot_nodes and edge[1] in annot_nodes})
# nx.set_edge_attributes(graph, name='constraint', values={edge: True for edge in graph.edges() if edge[0] in name_nodes or edge[1] in name_nodes})
# nx.set_edge_attributes(graph, name='constraint', values={edge: True for edge in graph.edges() if (edge[0] in ['a', 'b'] and edge[1] in ['a', 'b']) and edge[0] in annot_nodes and edge[1] in annot_nodes})
# nx.set_edge_attributes(graph, name='constraint', values={edge: True for edge in graph.edges() if (edge[0] in ['c'] or edge[1] in ['c']) and edge[0] in annot_nodes and edge[1] in annot_nodes})
# nx.set_edge_attributes(graph, name='constraint', values={edge: True for edge in graph.edges() if (edge[0] in ['a'] or edge[1] in ['a']) and edge[0] in annot_nodes and edge[1] in annot_nodes})
# nx.set_edge_attributes(graph, name='constraint', values={edge: True for edge in graph.edges() if (edge[0] in ['b'] or edge[1] in ['b']) and edge[0] in annot_nodes and edge[1] in annot_nodes})
# graph.add_edges_from([('root', n) for n in nodes])
# {node: 'names' for node in name_nodes})
nx.set_node_attributes(graph,
name='color',
values={node: pt.RED
for node in name_nodes})
# nx.set_node_attributes(graph, name='width', values={node: 20 for node in nodes})
# nx.set_node_attributes(graph, name='height', values={node: 20 for node in nodes})
#nx.set_node_attributes(graph, name='group', values={node: 'names' for node in name_nodes})
#nx.set_node_attributes(graph, name='group', values={node: 'annots' for node in annot_nodes})
nx.set_node_attributes(graph,
name='groupid',
values={node: 'names'
for node in name_nodes})
nx.set_node_attributes(graph,
name='groupid',
values={node: 'annots'
for node in annot_nodes})
graph.graph['clusterrank'] = 'local'
# graph.graph['groupattrs'] = {
# 'names': {'rankdir': 'LR', 'rank': 'source'},
# 'annots': {'rankdir': 'TB', 'rank': 'source'},
# }
ut.nx_delete_edge_attr(graph, 'weight')
# pt.show_nx(graph, fontsize=10, layoutkw={'splines': 'spline', 'prog': 'dot', 'sep': 2.0}, verbose=1)
layoutkw = {
# 'rankdir': 'LR',
'splines': 'spline',
# 'splines': 'ortho',
# 'splines': 'curved',
# 'compound': 'True',
# 'prog': 'dot',
'prog': 'neato',
# 'packMode': 'clust',
# 'sep': 4,
# 'nodesep': 1,
# 'ranksep': 1,
}
#pt.show_nx(graph, fontsize=12, layoutkw=layoutkw, verbose=0, as_directed=False)
pt.show_nx(graph,
fontsize=6,
fontname='Ubuntu',
layoutkw=layoutkw,
verbose=0,
as_directed=False)
pt.interactions.zoom_factory()
def try_em2(prob_names, prob_annots=None):
"""
assert prob_names.shape == (nAnnots, nNames)
"""
learn_rate = 0.05
num_iters = 1
# Matrix if unary probabilites, The probability that each node takes on a
# given label, independent of its edges.
num_annots, num_names = prob_names.shape
# prevent zero probabilities
prob_names_ = prob_names + 1E-9
prob_names_ /= prob_names_.sum(axis=1)[:, None]
if prob_annots is None:
prob_annots_ = np.full((num_annots, num_annots), 1 / num_annots)
prob_annots_[np.diag_indices(num_annots)] *= 1.01
# perterb
rng = np.random.RandomState(0)
prob_annots_ += (rng.randn(*prob_annots_.shape)) / 100
prob_annots_ /= prob_annots_.sum(axis=1)[:, None]
prob_annots_ = (prob_annots_.T + prob_annots_) / 2
else:
prob_annots_ = prob_annots + 1E-9
prob_annots_ /= prob_annots_.sum(axis=1)[:, None]
# Stack everything into a single matrix
prob_part = np.hstack([prob_names_, prob_annots_])
zero_part = np.zeros((num_names, num_annots + num_names))
prior = np.vstack([zero_part, prob_part])
# Gamma will hold a probability distribution over the nodes
# The labeled nodes must match themselves.
# The unlabeld nodes are initialized with a uniform distribution.
gam = np.hstack(
[np.eye(num_names),
np.ones((num_names, num_annots)) / num_names])
verbose = 1
if verbose:
print('Initialize')
print('num_names = %r' % (num_names, ))
print(
ut.hz_str(
'prior = ',
ut.repr2(prob_part[:, :],
precision=2,
max_line_width=140,
suppress_small=True)))
print(
ut.hz_str(
'gamma = ',
ut.repr2(gam[:, :],
max_line_width=140,
precision=2,
suppress_small=True)))
#print(ut.hz_str(' gamma = ', ut.repr2(gam, max_line_width=140, precision=2)))
delta_i = np.zeros(num_names)
def dErr(i, gam, prior, delta_i=delta_i, num_names=num_names):
# exepcted liklihood is cross entropy error
delta_i[:] = 0
# Compute the gradient of the cross entropy error
# This is over both names and annotations
jdxs = [j for j in range(prior.shape[0]) if j != i]
prior_ij = prior[i, jdxs]
np.log(prior_ij / (1 - prior_ij))
gam[:, jdxs]
for j in range(prior.shape[0]):
if i != j:
delta_i += gam[:, j] * np.log(prior[i, j] / (1 - prior[i, j]))
# compute the projected gradient
delta_i_hat = delta_i - delta_i.sum() / num_names
return delta_i_hat
# Build node for each annot and each name
num_nodes = num_annots + num_names
# Maximies the expected liklihood of gamma
dGam = np.zeros(gam.shape)
# for count in range(num_iters):
for count in ut.ProgIter(range(num_iters), label='EM', bs=True):
# Compute error gradient
for i in range(num_names, num_nodes):
dGam[:, i] = dErr(i, gam, prior)
# Make a step in the gradient direction
# print(ut.hz_str(' dGam = ', ut.repr2(dGam, max_line_width=140, precision=2)))
gam = gam + learn_rate * dGam
# Normalize
gam = np.clip(gam, 0, 1)
for i in range(num_names, num_nodes):
gam[:, i] = gam[:, i] / np.sum(gam[:, i])
# print(ut.hz_str(' gamma = ', ut.repr2(gam, max_line_width=140, precision=2)))
if verbose:
print(
ut.hz_str(
' gamma = ',
ut.repr2(gam[:, num_names:],
max_line_width=140,
precision=2,
suppress_small=True)))
print('Finished')
return gam
def try_em():
"""
CommandLine:
python -m ibeis.algo.hots.testem test_em --show
python -m ibeis.algo.hots.testem test_em --show --no-cnn
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.testem import * # NOQA
>>> P, Pn, PL, gam, num_labels = test_em()
>>> ut.quit_if_noshow()
>>> import plottool_ibeis as pt
>>> pt.qt4ensure()
>>> draw_em_graph(P, Pn, PL, gam, num_labels)
>>> ut.show_if_requested()
"""
print('EM')
# Matrix if unary probabilites, The probability that each node takes on a
# given label, independent of its edges.
test_case = [
{
'name': 1,
'view': 'L'
},
{
'name': 1,
'view': 'L'
},
{
'name': 2,
'view': 'L'
},
{
'name': 2,
'view': 'R'
},
{
'name': 2,
'view': 'B'
},
{
'name': 3,
'view': 'L'
},
#{'name': 3, 'view': 'L'},
#{'name': 4, 'view': 'L'},
]
def make_test_similarity(test_case):
#toy_params = {
# True: {'mu': 0.9, 'sigma': .1},
# False: {'mu': 0.1, 'sigma': .4}
#}
# tau = np.pi * 2
from ibeis import constants as const
# view_to_ori = const.VIEWTEXT_TO_YAW_RADIANS
view_to_ori = ut.map_dict_keys(lambda x: const.YAWALIAS[x],
const.VIEWTEXT_TO_YAW_RADIANS)
# view_to_ori = {
# 'F': -1 * tau / 4,
# 'L': 0 * tau / 4,
# 'B': 1 * tau / 4,
# 'R': 2 * tau / 4,
# }
import vtool_ibeis as vt
nid_list = np.array(ut.dict_take_column(test_case, 'name'))
yaw_list = np.array(
ut.dict_take(view_to_ori, ut.dict_take_column(test_case, 'view')))
rng = np.random.RandomState(0)
pmat = []
for idx in range(len(test_case)):
nid = nid_list[idx]
yaw = yaw_list[idx]
p_same = nid == nid_list
p_comp = 1 - vt.ori_distance(yaw_list, yaw) / np.pi
# estimate noisy measurements
p_same_m = np.clip(p_same + rng.normal(0, .5, size=len(p_same)), 0,
.9)
p_comp_m = np.clip(p_comp + rng.normal(0, .5, size=len(p_comp)), 0,
.9)
#
p_same_and_comp = p_same_m * p_comp_m
pmat.append(p_same_and_comp)
#
P = np.array(pmat)
P[np.diag_indices(len(P))] = 0
P = P + P.T / 2
P = np.clip(P, .01, .99)
print(ut.hz_str(' P = ', ut.repr2(P, precision=2, max_line_width=140)))
return P
Pn = make_test_similarity(test_case)
if False:
Pn = np.array(
np.matrix(b"""
.0 .7 .3 .2 .4 .5;
.7 .0 .4 .4 .3 .5;
.3 .4 .0 .6 .1 .5;
.2 .4 .6 .0 .2 .3;
.4 .3 .1 .2 .0 .8;
.5 .5 .5 .3 .8 .0
"""))
PL = np.array(
np.matrix(b"""
.7 .5 .5;
.8 .4 .3;
.5 .7 .3;
.5 .8 .4;
.3 .2 .8;
.5 .5 .8
"""))
if True:
Pn = np.array(
np.matrix(b"""
1.0 0.7 0.4 0.2;
0.7 1.0 0.4 0.4;
0.4 0.4 1.0 0.6;
0.2 0.4 0.6 1.0
"""))
PL = np.array(
np.matrix(b"""
0.7 0.5 0.5;
0.8 0.4 0.3;
0.5 0.7 0.3;
0.5 0.8 0.4
"""))
num_nodes = Pn.shape[0]
for num_labels in range(1, 2):
#Pn = np.array(np.matrix(
# b"""
# .0 .7 .3 .2 .4 .5;
# .7 .0 .4 .4 .3 .5;
# .3 .4 .0 .6 .1 .5;
# .2 .4 .6 .0 .2 .3;
# .4 .3 .1 .2 .0 .8;
# .5 .5 .5 .3 .8 .0
# """))
# Uniform distribution over labels
if 0:
PL = np.ones((num_nodes, num_labels)) / num_labels
# Give nodes preferences
PL[np.diag_indices(num_labels)] *= 1.01
PL /= np.linalg.norm(PL, axis=0)
# PL[0, :] = .01 / (num_labels - 1)
# PL[0, 0] = .99
else:
PL /= np.linalg.norm(PL, axis=0)
# Number of nodes
num_nodes = Pn.shape[0]
# Number of classes
num_labels = PL.shape[1]
#num_labels = num_nodes
#if 0 or num_labels != 3:
# PL = np.ones((num_nodes, num_labels)) / num_labels
# # PL[0, :] = .01 / (num_labels - 1)
# # PL[0, 0] = .99
d = num_labels + num_nodes
# Stack everything into a single matrix
zero_part = np.zeros((num_labels, num_nodes + num_labels))
prob_part = np.hstack([PL, Pn])
#print(ut.hz_str(' prob_part = ', ut.repr2(prob_part[:, :], precision=2)))
P = np.vstack([zero_part, prob_part])
# Gamma will hold a probability distribution over the nodes
# The labeled nodes must match themselves.
# The unlabeld nodes are initialized with a uniform distribution.
gam = np.hstack([
np.eye(num_labels),
np.ones((num_labels, num_nodes)) / num_labels
])
print('Initialize')
print('num_labels = %r' % (num_labels, ))
# print(ut.hz_str(' gamma = ', ut.repr2(gam[:, num_labels:], max_line_width=140, precision=2)))
print(
ut.hz_str(' gamma = ',
ut.repr2(gam, max_line_width=140, precision=2)))
delta_i = np.zeros(num_labels)
def dErr(i, gam, P, delta_i=delta_i):
# exepcted liklihood is cross entropy error
delta_i[:] = 0
# Compute the gradient of the cross entropy error
# This is over both names and annotations
for j in range(d):
if i != j:
delta_i += gam[:, j] * np.log(P[i, j] / (1 - P[i, j]))
# compute the projected gradient
delta_i_hat = delta_i - delta_i.sum() / num_labels
return delta_i_hat
# Maximies the expected liklihood of gamma
learn_rate = 0.05
num_iters = 1000
dGam = np.zeros(gam.shape)
# for count in range(num_iters):
for count in ut.ProgIter(range(num_iters), label='EM', bs=True):
# Compute error gradient
for i in range(num_labels, d):
dGam[:, i] = dErr(i, gam, P)
# Make a step in the gradient direction
# print(ut.hz_str(' dGam = ', ut.repr2(dGam, max_line_width=140, precision=2)))
gam = gam + learn_rate * dGam
# Normalize
gam = np.clip(gam, 0, 1)
for i in range(num_labels, d):
gam[:, i] = gam[:, i] / np.sum(gam[:, i])
# print(ut.hz_str(' gamma = ', ut.repr2(gam, max_line_width=140, precision=2)))
# print(ut.hz_str(' gamma = ', ut.repr2(gam[:, num_labels:], max_line_width=140, precision=2)))
print('Finished')
return P, Pn, PL, gam, num_labels
def test_em2(prob_names, prob_annots=None):
"""
assert prob_names.shape == (nAnnots, nNames)
"""
learn_rate = 0.05
num_iters = 1
# Matrix if unary probabilites, The probability that each node takes on a
# given label, independent of its edges.
num_annots, num_names = prob_names.shape
# prevent zero probabilities
prob_names_ = prob_names + 1E-9
prob_names_ /= prob_names_.sum(axis=1)[:, None]
if prob_annots is None:
prob_annots_ = np.full((num_annots, num_annots), 1 / num_annots)
prob_annots_[np.diag_indices(num_annots)] *= 1.01
# perterb
rng = np.random.RandomState(0)
prob_annots_ += (rng.randn(*prob_annots_.shape)) / 100
prob_annots_ /= prob_annots_.sum(axis=1)[:, None]
prob_annots_ = (prob_annots_.T + prob_annots_) / 2
else:
prob_annots_ = prob_annots + 1E-9
prob_annots_ /= prob_annots_.sum(axis=1)[:, None]
# Stack everything into a single matrix
prob_part = np.hstack([prob_names_, prob_annots_])
zero_part = np.zeros((num_names, num_annots + num_names))
prior = np.vstack([zero_part, prob_part])
# Gamma will hold a probability distribution over the nodes
# The labeled nodes must match themselves.
# The unlabeld nodes are initialized with a uniform distribution.
gam = np.hstack([np.eye(num_names), np.ones((num_names, num_annots)) / num_names])
verbose = 1
if verbose:
print('Initialize')
print('num_names = %r' % (num_names,))
print(ut.hz_str('prior = ', ut.array2string2(prob_part[:, :], precision=2, max_line_width=140, suppress_small=True)))
print(ut.hz_str('gamma = ', ut.array2string2(gam[:, :], max_line_width=140, precision=2, suppress_small=True)))
#print(ut.hz_str(' gamma = ', ut.array_repr2(gam, max_line_width=140, precision=2)))
delta_i = np.zeros(num_names)
def dErr(i, gam, prior, delta_i=delta_i, num_names=num_names):
# exepcted liklihood is cross entropy error
delta_i[:] = 0
# Compute the gradient of the cross entropy error
# This is over both names and annotations
jdxs = [j for j in range(prior.shape[0]) if j != i]
prior_ij = prior[i, jdxs]
np.log(prior_ij / (1 - prior_ij))
gam[:, jdxs]
for j in range(prior.shape[0]):
if i != j:
delta_i += gam[:, j] * np.log(prior[i, j] / (1 - prior[i, j]))
# compute the projected gradient
delta_i_hat = delta_i - delta_i.sum() / num_names
return delta_i_hat
# Build node for each annot and each name
num_nodes = num_annots + num_names
# Maximies the expected liklihood of gamma
dGam = np.zeros(gam.shape)
# for count in range(num_iters):
for count in ut.ProgIter(range(num_iters), label='EM', bs=True):
# Compute error gradient
for i in range(num_names, num_nodes):
dGam[:, i] = dErr(i, gam, prior)
# Make a step in the gradient direction
# print(ut.hz_str(' dGam = ', ut.array_repr2(dGam, max_line_width=140, precision=2)))
gam = gam + learn_rate * dGam
# Normalize
gam = np.clip(gam, 0, 1)
for i in range(num_names, num_nodes):
gam[:, i] = gam[:, i] / np.sum(gam[:, i])
# print(ut.hz_str(' gamma = ', ut.array_repr2(gam, max_line_width=140, precision=2)))
if verbose:
print(ut.hz_str(' gamma = ', ut.array2string2(gam[:, num_names:], max_line_width=140, precision=2, suppress_small=True)))
print('Finished')
return gam
def draw_em_graph(P, Pn, PL, gam, num_labels):
"""
python -m ibeis.algo.hots.testem test_em --show --no-cnn
"""
num_labels = PL.shape[1]
name_nodes = ['N%d' % x for x in list(range(1, num_labels + 1))]
#annot_nodes = ut.chr_range(len(Pn), base='A')
annot_nodes = ['X%d' % x for x in list(range(1, len(Pn) + 1))]
# name_nodes = ut.chr_range(num_labels, base='A')
nodes = name_nodes + annot_nodes
PL2 = gam[:, num_labels:].T
PL2 += .01
PL2 = PL2 / PL2.sum(axis=1)[:, None]
# PL2 = PL2 / np.linalg.norm(PL2, axis=0)
zero_part = np.zeros((num_labels, len(Pn) + num_labels))
prob_part = np.hstack([PL2, Pn])
print(ut.hz_str(' PL2 = ', ut.array_repr2(PL2, precision=2)))
# Redo p with posteriors
if ut.get_argflag('--postem'):
P = np.vstack([zero_part, prob_part])
weight_matrix = P # NOQA
graph = ut.nx_from_matrix(P, nodes=nodes)
graph = graph.to_directed()
# delete graph
dup_edges = []
seen_ = set([])
for u, v in graph.edges():
if u < v:
u, v = v, u
if (u, v) not in seen_:
seen_.add((u, v))
else:
dup_edges.append((u, v))
graph.remove_edges_from(dup_edges)
import plottool as pt
import networkx as nx
if len(name_nodes) == 3 and len(annot_nodes) == 4:
graph.node[annot_nodes[0]]['pos'] = (20., 200.)
graph.node[annot_nodes[1]]['pos'] = (220., 200.)
graph.node[annot_nodes[2]]['pos'] = (20., 100.)
graph.node[annot_nodes[3]]['pos'] = (220., 100.)
graph.node[name_nodes[0]]['pos'] = (10., 300.)
graph.node[name_nodes[1]]['pos'] = (120., 300.)
graph.node[name_nodes[2]]['pos'] = (230., 300.)
nx.set_node_attributes(graph, 'pin', 'true')
print('annot_nodes = %r' % (annot_nodes,))
print('name_nodes = %r' % (name_nodes,))
for u in annot_nodes:
for v in name_nodes:
if graph.has_edge(u, v):
print('1) u, v = %r' % ((u, v),))
graph.edge[u][v]['taillabel'] = graph.edge[u][v]['label']
graph.edge[u][v]['color'] = pt.ORANGE
graph.edge[u][v]['labelcolor'] = pt.BLUE
del graph.edge[u][v]['label']
elif graph.has_edge(v, u):
print('2) u, v = %r' % ((u, v),))
graph.edge[v][u]['headlabel'] = graph.edge[v][u]['label']
graph.edge[v][u]['color'] = pt.ORANGE
graph.edge[v][u]['labelcolor'] = pt.BLUE
del graph.edge[v][u]['label']
else:
print((u, v))
print('!!')
# import itertools
# name_const_edges = [(u, v, {'style': 'invis'}) for u, v in itertools.combinations(name_nodes, 2)]
# graph.add_edges_from(name_const_edges)
# nx.set_edge_attributes(graph, 'constraint', {edge: False for edge in graph.edges() if edge[0] == 'b' or edge[1] == 'b'})
# nx.set_edge_attributes(graph, 'constraint', {edge: False for edge in graph.edges() if edge[0] in annot_nodes and edge[1] in annot_nodes})
# nx.set_edge_attributes(graph, 'constraint', {edge: True for edge in graph.edges() if edge[0] in name_nodes or edge[1] in name_nodes})
# nx.set_edge_attributes(graph, 'constraint', {edge: True for edge in graph.edges() if (edge[0] in ['a', 'b'] and edge[1] in ['a', 'b']) and edge[0] in annot_nodes and edge[1] in annot_nodes})
# nx.set_edge_attributes(graph, 'constraint', {edge: True for edge in graph.edges() if (edge[0] in ['c'] or edge[1] in ['c']) and edge[0] in annot_nodes and edge[1] in annot_nodes})
# nx.set_edge_attributes(graph, 'constraint', {edge: True for edge in graph.edges() if (edge[0] in ['a'] or edge[1] in ['a']) and edge[0] in annot_nodes and edge[1] in annot_nodes})
# nx.set_edge_attributes(graph, 'constraint', {edge: True for edge in graph.edges() if (edge[0] in ['b'] or edge[1] in ['b']) and edge[0] in annot_nodes and edge[1] in annot_nodes})
# graph.add_edges_from([('root', n) for n in nodes])
# {node: 'names' for node in name_nodes})
nx.set_node_attributes(graph, 'color', {node: pt.RED for node in name_nodes})
# nx.set_node_attributes(graph, 'width', {node: 20 for node in nodes})
# nx.set_node_attributes(graph, 'height', {node: 20 for node in nodes})
#nx.set_node_attributes(graph, 'group', {node: 'names' for node in name_nodes})
#nx.set_node_attributes(graph, 'group', {node: 'annots' for node in annot_nodes})
nx.set_node_attributes(graph, 'groupid', {node: 'names' for node in name_nodes})
nx.set_node_attributes(graph, 'groupid', {node: 'annots' for node in annot_nodes})
graph.graph['clusterrank'] = 'local'
# graph.graph['groupattrs'] = {
# 'names': {'rankdir': 'LR', 'rank': 'source'},
# 'annots': {'rankdir': 'TB', 'rank': 'source'},
# }
ut.nx_delete_edge_attr(graph, 'weight')
# pt.show_nx(graph, fontsize=10, layoutkw={'splines': 'spline', 'prog': 'dot', 'sep': 2.0}, verbose=1)
layoutkw = {
# 'rankdir': 'LR',
'splines': 'spline',
# 'splines': 'ortho',
# 'splines': 'curved',
# 'compound': 'True',
# 'prog': 'dot',
'prog': 'neato',
# 'packMode': 'clust',
# 'sep': 4,
# 'nodesep': 1,
# 'ranksep': 1,
}
#pt.show_nx(graph, fontsize=12, layoutkw=layoutkw, verbose=0, as_directed=False)
pt.show_nx(graph, fontsize=6, fontname='Ubuntu', layoutkw=layoutkw, verbose=0, as_directed=False)
pt.interactions.zoom_factory()
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 make_graph(infr, show=False):
import networkx as nx
import itertools
cm_list = infr.cm_list
unique_nids, prob_names = infr.make_prob_names()
thresh = infr.choose_thresh()
# Simply cut any edge with a weight less than a threshold
qaid_list = [cm.qaid for cm in cm_list]
postcut = prob_names > thresh
qxs, nxs = np.where(postcut)
if False:
kw = dict(precision=2, max_line_width=140, suppress_small=True)
print(ut.hz_str('prob_names = ', ut.array2string2((prob_names), **kw)))
print(ut.hz_str('postcut = ', ut.array2string2((postcut).astype(np.int), **kw)))
matching_qaids = ut.take(qaid_list, qxs)
matched_nids = ut.take(unique_nids, nxs)
qreq_ = infr.qreq_
nodes = ut.unique(qreq_.qaids.tolist() + qreq_.daids.tolist())
if not hasattr(qreq_, 'dnids'):
qreq_.dnids = qreq_.ibs.get_annot_nids(qreq_.daids)
qreq_.qnids = qreq_.ibs.get_annot_nids(qreq_.qaids)
dnid2_daids = ut.group_items(qreq_.daids, qreq_.dnids)
grouped_aids = dnid2_daids.values()
matched_daids = ut.take(dnid2_daids, matched_nids)
name_cliques = [list(itertools.combinations(aids, 2)) for aids in grouped_aids]
aid_matches = [list(ut.product([qaid], daids)) for qaid, daids in
zip(matching_qaids, matched_daids)]
graph = nx.Graph()
graph.add_nodes_from(nodes)
graph.add_edges_from(ut.flatten(name_cliques))
graph.add_edges_from(ut.flatten(aid_matches))
#matchless_quries = ut.take(qaid_list, ut.index_complement(qxs, len(qaid_list)))
name_nodes = [('nid', l) for l in qreq_.dnids]
db_aid_nid_edges = list(zip(qreq_.daids, name_nodes))
#query_aid_nid_edges = list(zip(matching_qaids, [('nid', l) for l in matched_nids]))
#G = nx.Graph()
#G.add_nodes_from(matchless_quries)
#G.add_edges_from(db_aid_nid_edges)
#G.add_edges_from(query_aid_nid_edges)
graph.add_edges_from(db_aid_nid_edges)
if infr.user_feedback is not None:
user_feedback = ut.map_dict_vals(np.array, infr.user_feedback)
p_bg = 0.0
part1 = user_feedback['p_match'] * (1 - user_feedback['p_notcomp'])
part2 = p_bg * user_feedback['p_notcomp']
p_same_list = part1 + part2
for aid1, aid2, p_same in zip(user_feedback['aid1'],
user_feedback['aid2'], p_same_list):
if p_same > .5:
if not graph.has_edge(aid1, aid2):
graph.add_edge(aid1, aid2)
else:
if graph.has_edge(aid1, aid2):
graph.remove_edge(aid1, aid2)
if show:
import plottool as pt
nx.set_node_attributes(graph, 'color', {aid: pt.LIGHT_PINK
for aid in qreq_.daids})
nx.set_node_attributes(graph, 'color', {aid: pt.TRUE_BLUE
for aid in qreq_.qaids})
nx.set_node_attributes(graph, 'color', {
aid: pt.LIGHT_PURPLE
for aid in np.intersect1d(qreq_.qaids, qreq_.daids)})
nx.set_node_attributes(graph, 'label', {node: 'n%r' % (node[1],)
for node in name_nodes})
nx.set_node_attributes(graph, 'color', {node: pt.LIGHT_GREEN
for node in name_nodes})
if show:
import plottool as pt
pt.show_nx(graph, layoutkw={'prog': 'neato'}, verbose=False)
return graph
def dummy_example_depcacahe():
r"""
CommandLine:
python -m dtool.example_depcache --exec-dummy_example_depcacahe
Example:
>>> # ENABLE_DOCTEST
>>> from dtool.example_depcache import * # NOQA
>>> depc = dummy_example_depcacahe()
>>> ut.show_if_requested()
"""
fname = None
# fname = 'dummy_default_depcache'
fname = ':memory:'
depc = testdata_depc(fname)
tablename = 'fgweight'
# print('[test] fgweight_path =\n%s' % (ut.repr3(depc.get_dependencies(tablename), nl=1),))
# print('[test] keypoint =\n%s' % (ut.repr3(depc.get_dependencies('keypoint'), nl=1),))
# print('[test] descriptor =\n%s' % (ut.repr3(depc.get_dependencies('descriptor'), nl=1),))
# print('[test] spam =\n%s' % (ut.repr3(depc.get_dependencies('spam'), nl=1),))
root_rowids = [5, 3]
desc_rowids = depc.get_rowids('descriptor', root_rowids) # NOQA
table = depc[tablename] # NOQA
#example_getter_methods(depc, 'vsmany', root_rowids)
# example_getter_methods(depc, 'chipmask', root_rowids)
# example_getter_methods(depc, 'keypoint', root_rowids)
# example_getter_methods(depc, 'chip', root_rowids)
test_getters(depc)
#import plottool as pt
# pt.ensure_pylab_qt4()
graph = depc.make_graph() # NOQA
#pt.show_nx(graph)
print('---------- 111 -----------')
# Try testing the algorithm
req = depc.new_request('vsmany', root_rowids, root_rowids, {})
print('req = %r' % (req,))
req.execute()
print('---------- 222 -----------')
cfgdict = {'sver_on': False}
req = depc.new_request('vsmany', root_rowids, root_rowids, cfgdict)
req.execute()
print('---------- 333 -----------')
cfgdict = {'sver_on': False, 'adapt_shape': False}
req = depc.new_request('vsmany', root_rowids, root_rowids, cfgdict)
req.execute()
print('---------- 444 -----------')
req = depc.new_request('vsmany', root_rowids, root_rowids, {})
req.execute()
#ut.InstanceList(
db = list(depc.fname_to_db.values())[0]
#db_list = ut.InstanceList(depc.fname_to_db.values())
#db_list.print_table_csv('config', exclude_columns='config_strid')
print('config table')
column_list, column_names = db.get_table_column_data(tablename,
['config_strid'])
print('\n'.join([ut.hz_str(*list(ut.interleave((r, [', '] * (len(r) - 1)))))
for r in list(zip(*[[ut.repr3(r, nl=2) for r in col] for col in column_list]))]))
return depc
def test_em():
"""
CommandLine:
python -m ibeis.algo.hots.testem test_em --show
python -m ibeis.algo.hots.testem test_em --show --no-cnn
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.testem import * # NOQA
>>> P, Pn, PL, gam, num_labels = test_em()
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> pt.qt4ensure()
>>> draw_em_graph(P, Pn, PL, gam, num_labels)
>>> ut.show_if_requested()
"""
print('EM')
# Matrix if unary probabilites, The probability that each node takes on a
# given label, independent of its edges.
test_case = [
{'name': 1, 'view': 'L'},
{'name': 1, 'view': 'L'},
{'name': 2, 'view': 'L'},
{'name': 2, 'view': 'R'},
{'name': 2, 'view': 'B'},
{'name': 3, 'view': 'L'},
#{'name': 3, 'view': 'L'},
#{'name': 4, 'view': 'L'},
]
def make_test_similarity(test_case):
#toy_params = {
# True: {'mu': 0.9, 'sigma': .1},
# False: {'mu': 0.1, 'sigma': .4}
#}
# tau = np.pi * 2
from ibeis import constants as const
# view_to_ori = const.VIEWTEXT_TO_YAW_RADIANS
view_to_ori = ut.map_dict_keys(lambda x: const.YAWALIAS[x], const.VIEWTEXT_TO_YAW_RADIANS)
# view_to_ori = {
# 'F': -1 * tau / 4,
# 'L': 0 * tau / 4,
# 'B': 1 * tau / 4,
# 'R': 2 * tau / 4,
# }
import vtool as vt
nid_list = np.array(ut.dict_take_column(test_case, 'name'))
yaw_list = np.array(ut.dict_take(view_to_ori, ut.dict_take_column(test_case, 'view')))
rng = np.random.RandomState(0)
pmat = []
for idx in range(len(test_case)):
nid = nid_list[idx]
yaw = yaw_list[idx]
p_same = nid == nid_list
p_comp = 1 - vt.ori_distance(yaw_list, yaw) / np.pi
# estimate noisy measurements
p_same_m = np.clip(p_same + rng.normal(0, .5, size=len(p_same)), 0, .9)
p_comp_m = np.clip(p_comp + rng.normal(0, .5, size=len(p_comp)), 0, .9)
#
p_same_and_comp = p_same_m * p_comp_m
pmat.append(p_same_and_comp)
#
P = np.array(pmat)
P[np.diag_indices(len(P))] = 0
P = P + P.T / 2
P = np.clip(P, .01, .99)
print(ut.hz_str(' P = ', ut.array_repr2(P, precision=2, max_line_width=140)))
return P
Pn = make_test_similarity(test_case)
if False:
Pn = np.array(np.matrix(
b"""
.0 .7 .3 .2 .4 .5;
.7 .0 .4 .4 .3 .5;
.3 .4 .0 .6 .1 .5;
.2 .4 .6 .0 .2 .3;
.4 .3 .1 .2 .0 .8;
.5 .5 .5 .3 .8 .0
"""))
PL = np.array(np.matrix(
b"""
.7 .5 .5;
.8 .4 .3;
.5 .7 .3;
.5 .8 .4;
.3 .2 .8;
.5 .5 .8
"""))
if True:
Pn = np.array(np.matrix(
b"""
1.0 0.7 0.4 0.2;
0.7 1.0 0.4 0.4;
0.4 0.4 1.0 0.6;
0.2 0.4 0.6 1.0
"""))
PL = np.array(np.matrix(
b"""
0.7 0.5 0.5;
0.8 0.4 0.3;
0.5 0.7 0.3;
0.5 0.8 0.4
"""))
num_nodes = Pn.shape[0]
for num_labels in range(1, 2):
#Pn = np.array(np.matrix(
# b"""
# .0 .7 .3 .2 .4 .5;
# .7 .0 .4 .4 .3 .5;
# .3 .4 .0 .6 .1 .5;
# .2 .4 .6 .0 .2 .3;
# .4 .3 .1 .2 .0 .8;
# .5 .5 .5 .3 .8 .0
# """))
# Uniform distribution over labels
if 0:
PL = np.ones((num_nodes, num_labels)) / num_labels
# Give nodes preferences
PL[np.diag_indices(num_labels)] *= 1.01
PL /= np.linalg.norm(PL, axis=0)
# PL[0, :] = .01 / (num_labels - 1)
# PL[0, 0] = .99
else:
PL /= np.linalg.norm(PL, axis=0)
# Number of nodes
num_nodes = Pn.shape[0]
# Number of classes
num_labels = PL.shape[1]
#num_labels = num_nodes
#if 0 or num_labels != 3:
# PL = np.ones((num_nodes, num_labels)) / num_labels
# # PL[0, :] = .01 / (num_labels - 1)
# # PL[0, 0] = .99
d = num_labels + num_nodes
# Stack everything into a single matrix
zero_part = np.zeros((num_labels, num_nodes + num_labels))
prob_part = np.hstack([PL, Pn])
#print(ut.hz_str(' prob_part = ', ut.array_repr2(prob_part[:, :], precision=2)))
P = np.vstack([zero_part, prob_part])
# Gamma will hold a probability distribution over the nodes
# The labeled nodes must match themselves.
# The unlabeld nodes are initialized with a uniform distribution.
gam = np.hstack([np.eye(num_labels), np.ones((num_labels, num_nodes)) / num_labels])
print('Initialize')
print('num_labels = %r' % (num_labels,))
# print(ut.hz_str(' gamma = ', ut.array_repr2(gam[:, num_labels:], max_line_width=140, precision=2)))
print(ut.hz_str(' gamma = ', ut.array_repr2(gam, max_line_width=140, precision=2)))
delta_i = np.zeros(num_labels)
def dErr(i, gam, P, delta_i=delta_i):
# exepcted liklihood is cross entropy error
delta_i[:] = 0
# Compute the gradient of the cross entropy error
# This is over both names and annotations
for j in range(d):
if i != j:
delta_i += gam[:, j] * np.log(P[i, j] / (1 - P[i, j]))
# compute the projected gradient
delta_i_hat = delta_i - delta_i.sum() / num_labels
return delta_i_hat
# Maximies the expected liklihood of gamma
learn_rate = 0.05
num_iters = 1000
dGam = np.zeros(gam.shape)
# for count in range(num_iters):
for count in ut.ProgIter(range(num_iters), label='EM', bs=True):
# Compute error gradient
for i in range(num_labels, d):
dGam[:, i] = dErr(i, gam, P)
# Make a step in the gradient direction
# print(ut.hz_str(' dGam = ', ut.array_repr2(dGam, max_line_width=140, precision=2)))
gam = gam + learn_rate * dGam
# Normalize
gam = np.clip(gam, 0, 1)
for i in range(num_labels, d):
gam[:, i] = gam[:, i] / np.sum(gam[:, i])
# print(ut.hz_str(' gamma = ', ut.array_repr2(gam, max_line_width=140, precision=2)))
# print(ut.hz_str(' gamma = ', ut.array_repr2(gam[:, num_labels:], max_line_width=140, precision=2)))
print('Finished')
return P, Pn, PL, gam, num_labels
def classification_report2(y_true, y_pred, target_names=None,
sample_weight=None, verbose=True):
"""
References:
https://csem.flinders.edu.au/research/techreps/SIE07001.pdf
https://www.mathworks.com/matlabcentral/fileexchange/5648-bm-cm-?requestedDomain=www.mathworks.com
Jurman, Riccadonna, Furlanello, (2012). A Comparison of MCC and CEN
Error Measures in MultiClass Prediction
Example:
>>> from ibeis.algo.verif.sklearn_utils import * # NOQA
>>> y_true = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3]
>>> y_pred = [1, 2, 1, 3, 1, 2, 2, 3, 2, 2, 3, 3, 2, 3, 3, 3, 1, 3]
>>> target_names = None
>>> sample_weight = None
>>> verbose = True
>>> report = classification_report2(y_true, y_pred, verbose=verbose)
Ignore:
>>> size = 100
>>> rng = np.random.RandomState(0)
>>> p_classes = np.array([.90, .05, .05][0:2])
>>> p_classes = p_classes / p_classes.sum()
>>> p_wrong = np.array([.03, .01, .02][0:2])
>>> y_true = testdata_ytrue(p_classes, p_wrong, size, rng)
>>> rs = []
>>> for x in range(17):
>>> p_wrong += .05
>>> y_pred = testdata_ypred(y_true, p_wrong, rng)
>>> report = classification_report2(y_true, y_pred, verbose='hack')
>>> rs.append(report)
>>> import plottool as pt
>>> pt.qtensure()
>>> df = pd.DataFrame(rs).drop(['raw'], axis=1)
>>> delta = df.subtract(df['target'], axis=0)
>>> sqrd_error = np.sqrt((delta ** 2).sum(axis=0))
>>> print('Error')
>>> print(sqrd_error.sort_values())
>>> ys = df.to_dict(orient='list')
>>> pt.multi_plot(ydata_list=ys)
"""
import sklearn.metrics
from sklearn.preprocessing import LabelEncoder
if target_names is None:
unique_labels = np.unique(np.hstack([y_true, y_pred]))
if len(unique_labels) == 1 and (unique_labels[0] == 0 or unique_labels[0] == 1):
target_names = np.array([False, True])
y_true_ = y_true
y_pred_ = y_pred
else:
lb = LabelEncoder()
lb.fit(unique_labels)
y_true_ = lb.transform(y_true)
y_pred_ = lb.transform(y_pred)
target_names = lb.classes_
else:
y_true_ = y_true
y_pred_ = y_pred
# Real data is on the rows,
# Pred data is on the cols.
cm = sklearn.metrics.confusion_matrix(
y_true_, y_pred_, sample_weight=sample_weight)
confusion = cm # NOQA
k = len(cm) # number of classes
N = cm.sum() # number of examples
real_total = cm.sum(axis=1)
pred_total = cm.sum(axis=0)
# the number of "positive" cases **per class**
n_pos = real_total # NOQA
# the number of times a class was predicted.
n_neg = N - n_pos # NOQA
# number of true positives per class
n_tps = np.diag(cm)
# number of true negatives per class
n_fps = (cm - np.diagflat(np.diag(cm))).sum(axis=0)
tprs = n_tps / real_total # true pos rate (recall)
tpas = n_tps / pred_total # true pos accuracy (precision)
unused = (real_total + pred_total) == 0
fprs = n_fps / n_neg # false pose rate
fprs[unused] = np.nan
# tnrs = 1 - fprs
rprob = real_total / N
pprob = pred_total / N
if len(cm) == 2:
[[A, B],
[C, D]] = cm
(A * D - B * C) / np.sqrt((A + C) * (B + D) * (A + B) * (C + D))
# c2 = vt.ConfusionMetrics().fit(scores, y)
# bookmaker is analogous to recall, but unbiased by class frequency
rprob_mat = np.tile(rprob, [k, 1]).T - (1 - np.eye(k))
bmcm = cm.T / rprob_mat
bms = np.sum(bmcm.T, axis=0) / N
# markedness is analogous to precision, but unbiased by class frequency
pprob_mat = np.tile(pprob, [k, 1]).T - (1 - np.eye(k))
mkcm = cm / pprob_mat
mks = np.sum(mkcm.T, axis=0) / N
mccs = np.sign(bms) * np.sqrt(np.abs(bms * mks))
perclass_data = ut.odict([
('precision', tpas),
('recall', tprs),
('fpr', fprs),
('markedness', mks),
('bookmaker', bms),
('mcc', mccs),
('support', real_total),
])
tpa = np.nansum(tpas * rprob)
tpr = np.nansum(tprs * rprob)
fpr = np.nansum(fprs * rprob)
mk = np.nansum(mks * rprob)
bm = np.nansum(bms * pprob)
# The simple mean seems to do the best
mccs_ = mccs[~np.isnan(mccs)]
if len(mccs_) == 0:
mcc_combo = np.nan
else:
mcc_combo = np.nanmean(mccs_)
combined_data = ut.odict([
('precision', tpa),
('recall', tpr),
('fpr', fpr),
('markedness', mk),
('bookmaker', bm),
# ('mcc', np.sign(bm) * np.sqrt(np.abs(bm * mk))),
('mcc', mcc_combo),
# np.sign(bm) * np.sqrt(np.abs(bm * mk))),
('support', real_total.sum())
])
# Not sure how to compute this. Should it agree with the sklearn impl?
if verbose == 'hack':
verbose = False
mcc_known = sklearn.metrics.matthews_corrcoef(
y_true, y_pred, sample_weight=sample_weight)
mcc_raw = np.sign(bm) * np.sqrt(np.abs(bm * mk))
import scipy as sp
def gmean(x, w=None):
if w is None:
return sp.stats.gmean(x)
return np.exp(np.nansum(w * np.log(x)) / np.nansum(w))
def hmean(x, w=None):
if w is None:
return sp.stats.hmean(x)
return 1 / (np.nansum(w * (1 / x)) / np.nansum(w))
def amean(x, w=None):
if w is None:
return np.mean(x)
return np.nansum(w * x) / np.nansum(w)
report = {
'target': mcc_known,
'raw': mcc_raw,
}
# print('%r <<<' % (mcc_known,))
means = {
'a': amean,
# 'h': hmean,
'g': gmean,
}
weights = {
'p': pprob,
'r': rprob,
'': None,
}
for mean_key, mean in means.items():
for w_key, w in weights.items():
# Hack of very wrong items
if mean_key == 'g':
if w_key in ['r', 'p', '']:
continue
if mean_key == 'g':
if w_key in ['r']:
continue
m = mean(mccs, w)
r_key = '{} {}'.format(mean_key, w_key)
report[r_key] = m
# print(r_key)
# print(np.abs(m - mcc_known))
# print(ut.repr4(report, precision=8))
return report
# print('mcc_known = %r' % (mcc_known,))
# print('mcc_combo1 = %r' % (mcc_combo1,))
# print('mcc_combo2 = %r' % (mcc_combo2,))
# print('mcc_combo3 = %r' % (mcc_combo3,))
# if target_names is None:
# target_names = list(range(k))
index = pd.Index(target_names, name='class')
perclass_df = pd.DataFrame(perclass_data, index=index)
# combined_df = pd.DataFrame(combined_data, index=['ave/sum'])
combined_df = pd.DataFrame(combined_data, index=['combined'])
metric_df = pd.concat([perclass_df, combined_df])
metric_df.index.name = 'class'
metric_df.columns.name = 'metric'
pred_id = ['%s' % m for m in target_names]
real_id = ['%s' % m for m in target_names]
confusion_df = pd.DataFrame(confusion, columns=pred_id, index=real_id)
confusion_df = confusion_df.append(pd.DataFrame(
[confusion.sum(axis=0)], columns=pred_id, index=['Σp']))
confusion_df['Σr'] = np.hstack([confusion.sum(axis=1), [0]])
confusion_df.index.name = 'real'
confusion_df.columns.name = 'pred'
if np.all(confusion_df - np.floor(confusion_df) < .000001):
confusion_df = confusion_df.astype(np.int)
confusion_df.iloc[(-1, -1)] = N
if np.all(confusion_df - np.floor(confusion_df) < .000001):
confusion_df = confusion_df.astype(np.int)
# np.nan
if verbose:
cfsm_str = confusion_df.to_string(float_format=lambda x: '%.1f' % (x,))
print('Confusion Matrix (real × pred) :')
print(ut.hz_str(' ', cfsm_str))
# ut.cprint('\nExtended Report', 'turquoise')
print('\nEvaluation Metric Report:')
float_precision = 2
float_format = '%.' + str(float_precision) + 'f'
ext_report = metric_df.to_string(float_format=float_format)
print(ut.hz_str(' ', ext_report))
report = {
'metrics': metric_df,
'confusion': confusion_df,
}
# FIXME: What is the difference between sklearn multiclass-MCC
# and BM * MK MCC?
def matthews_corrcoef(y_true, y_pred, sample_weight=None):
from sklearn.metrics.classification import (
_check_targets, LabelEncoder, confusion_matrix)
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
if y_type not in {"binary", "multiclass"}:
raise ValueError("%s is not supported" % y_type)
lb = LabelEncoder()
lb.fit(np.hstack([y_true, y_pred]))
y_true = lb.transform(y_true)
y_pred = lb.transform(y_pred)
C = confusion_matrix(y_true, y_pred, sample_weight=sample_weight)
t_sum = C.sum(axis=1)
p_sum = C.sum(axis=0)
n_correct = np.trace(C)
n_samples = p_sum.sum()
cov_ytyp = n_correct * n_samples - np.dot(t_sum, p_sum)
cov_ypyp = n_samples ** 2 - np.dot(p_sum, p_sum)
cov_ytyt = n_samples ** 2 - np.dot(t_sum, t_sum)
mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)
if np.isnan(mcc):
return 0.
else:
return mcc
try:
# mcc = sklearn.metrics.matthews_corrcoef(
# y_true, y_pred, sample_weight=sample_weight)
mcc = matthews_corrcoef(y_true, y_pred, sample_weight=sample_weight)
# These scales are chosen somewhat arbitrarily in the context of a
# computer vision application with relatively reasonable quality data
# https://stats.stackexchange.com/questions/118219/how-to-interpret
mcc_significance_scales = ut.odict([
(1.0, 'perfect'),
(0.9, 'very strong'),
(0.7, 'strong'),
(0.5, 'significant'),
(0.3, 'moderate'),
(0.2, 'weak'),
(0.0, 'negligible'),
])
for k, v in mcc_significance_scales.items():
if np.abs(mcc) >= k:
if verbose:
print('classifier correlation is %s' % (v,))
break
if verbose:
float_precision = 2
print(('MCC\' = %.' + str(float_precision) + 'f') % (mcc,))
report['mcc'] = mcc
except ValueError:
pass
return report
def print_priors(model, ignore_ttypes=[], title='Priors', color='darkblue'):
ut.colorprint('\n --- %s ---' % (title,), color=color)
for ttype, cpds in model.ttype2_cpds.items():
if ttype not in ignore_ttypes:
for fs_ in ut.ichunks(cpds, 4):
ut.colorprint(ut.hz_str([f._cpdstr('psql') for f in fs_]), color)
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 setcover_example():
"""
CommandLine:
python -m ibeis.scripts.specialdraw setcover_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = setcover_example()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import plottool as pt
from ibeis.viz import viz_graph
import networkx as nx
pt.ensure_pylab_qt4()
ibs = ibeis.opendb(defaultdb='testdb2')
if False:
# Select a good set
aids = ibs.get_name_aids(ibs.get_valid_nids())
# ibeis.testdata_aids('testdb2', a='default:mingt=2')
aids = [a for a in aids if len(a) > 1]
for a in aids:
print(ut.repr3(ibs.get_annot_stats_dict(a)))
print(aids[-2])
#aids = [78, 79, 80, 81, 88, 91]
aids = [78, 79, 81, 88, 91]
qreq_ = ibs.depc.new_request('vsone', aids, aids, cfgdict={})
cm_list = qreq_.execute()
from ibeis.algo.hots import graph_iden
infr = graph_iden.AnnotInference(cm_list)
unique_aids, prob_annots = infr.make_prob_annots()
import numpy as np
print(
ut.hz_str(
'prob_annots = ',
ut.array2string2(prob_annots,
precision=2,
max_line_width=140,
suppress_small=True)))
# ut.setcover_greedy(candidate_sets_dict)
max_weight = 3
prob_annots[np.diag_indices(len(prob_annots))] = np.inf
prob_annots = prob_annots
thresh_points = np.sort(prob_annots[np.isfinite(prob_annots)])
# probably not the best way to go about searching for these thresholds
# but when you have a hammer...
if False:
quant = sorted(np.diff(thresh_points))[(len(thresh_points) - 1) // 2]
candset = {
point: thresh_points[np.abs(thresh_points - point) < quant]
for point in thresh_points
}
check_thresholds = len(aids) * 2
thresh_points2 = np.array(
ut.setcover_greedy(candset, max_weight=check_thresholds).keys())
thresh_points = thresh_points2
# pt.plot(sorted(thresh_points), 'rx')
# pt.plot(sorted(thresh_points2), 'o')
# prob_annots = prob_annots.T
# thresh_start = np.mean(thresh_points)
current_idxs = []
current_covers = []
current_val = np.inf
for thresh in thresh_points:
covering_sets = [np.where(row >= thresh)[0] for row in (prob_annots)]
candidate_sets_dict = {
ax: others
for ax, others in enumerate(covering_sets)
}
soln_cover = ut.setcover_ilp(candidate_sets_dict,
max_weight=max_weight)
exemplar_idxs = list(soln_cover.keys())
soln_weight = len(exemplar_idxs)
val = max_weight - soln_weight
# print('val = %r' % (val,))
# print('soln_weight = %r' % (soln_weight,))
if val < current_val:
current_val = val
current_covers = covering_sets
current_idxs = exemplar_idxs
exemplars = ut.take(aids, current_idxs)
ensure_edges = [(aids[ax], aids[ax2])
for ax, other_xs in enumerate(current_covers)
for ax2 in other_xs]
graph = viz_graph.make_netx_graph_from_aid_groups(
ibs, [aids],
allow_directed=True,
ensure_edges=ensure_edges,
temp_nids=[1] * len(aids))
viz_graph.ensure_node_images(ibs, graph)
nx.set_node_attributes(graph, 'framewidth', False)
nx.set_node_attributes(graph, 'framewidth',
{aid: 4.0
for aid in exemplars})
nx.set_edge_attributes(graph, 'color', pt.ORANGE)
nx.set_node_attributes(graph, 'color', pt.LIGHT_BLUE)
nx.set_node_attributes(graph, 'shape', 'rect')
layoutkw = {
'sep': 1 / 10,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw)
pt.zoom_factory()
def dummy_example_depcacahe():
r"""
CommandLine:
python -m dtool.example_depcache --exec-dummy_example_depcacahe
Example:
>>> # ENABLE_DOCTEST
>>> from dtool.example_depcache import * # NOQA
>>> depc = dummy_example_depcacahe()
>>> ut.show_if_requested()
"""
fname = None
# fname = 'dummy_default_depcache'
fname = ':memory:'
depc = testdata_depc(fname)
tablename = 'fgweight'
# print('[test] fgweight_path =\n%s' % (ut.repr3(depc.get_dependencies(tablename), nl=1),))
# print('[test] keypoint =\n%s' % (ut.repr3(depc.get_dependencies('keypoint'), nl=1),))
# print('[test] descriptor =\n%s' % (ut.repr3(depc.get_dependencies('descriptor'), nl=1),))
# print('[test] spam =\n%s' % (ut.repr3(depc.get_dependencies('spam'), nl=1),))
root_rowids = [5, 3]
desc_rowids = depc.get_rowids('descriptor', root_rowids) # NOQA
table = depc[tablename] # NOQA
#example_getter_methods(depc, 'vsmany', root_rowids)
# example_getter_methods(depc, 'chipmask', root_rowids)
# example_getter_methods(depc, 'keypoint', root_rowids)
# example_getter_methods(depc, 'chip', root_rowids)
test_getters(depc)
#import plottool as pt
# pt.ensureqt()
graph = depc.make_graph() # NOQA
#pt.show_nx(graph)
print('---------- 111 -----------')
# Try testing the algorithm
req = depc.new_request('vsmany', root_rowids, root_rowids, {})
print('req = %r' % (req, ))
req.execute()
print('---------- 222 -----------')
cfgdict = {'sver_on': False}
req = depc.new_request('vsmany', root_rowids, [root_rowids], cfgdict)
req.execute()
print('---------- 333 -----------')
cfgdict = {'sver_on': False, 'adapt_shape': False}
req = depc.new_request('vsmany', root_rowids, root_rowids, cfgdict)
req.execute()
print('---------- 444 -----------')
req = depc.new_request('vsmany', root_rowids, root_rowids, {})
req.execute()
#ut.InstanceList(
db = list(depc.fname_to_db.values())[0]
#db_list = ut.InstanceList(depc.fname_to_db.values())
#db_list.print_table_csv('config', exclude_columns='config_strid')
print('config table')
tablename = 'config'
column_list, column_names = db.get_table_column_data(
tablename, ['config_strid'])
print('\n'.join([
ut.hz_str(*list(ut.interleave((r, [', '] * (len(r) - 1)))))
for r in list(
zip(*[[ut.repr3(r, nl=2) for r in col] for col in column_list]))
]))
return depc
def setcover_example():
"""
CommandLine:
python -m ibeis.scripts.specialdraw setcover_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = setcover_example()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import plottool as pt
from ibeis.viz import viz_graph
import networkx as nx
pt.ensure_pylab_qt4()
ibs = ibeis.opendb(defaultdb='testdb2')
if False:
# Select a good set
aids = ibs.get_name_aids(ibs.get_valid_nids())
# ibeis.testdata_aids('testdb2', a='default:mingt=2')
aids = [a for a in aids if len(a) > 1]
for a in aids:
print(ut.repr3(ibs.get_annot_stats_dict(a)))
print(aids[-2])
#aids = [78, 79, 80, 81, 88, 91]
aids = [78, 79, 81, 88, 91]
qreq_ = ibs.depc.new_request('vsone', aids, aids, cfgdict={})
cm_list = qreq_.execute()
from ibeis.algo.hots import graph_iden
infr = graph_iden.AnnotInference(cm_list)
unique_aids, prob_annots = infr.make_prob_annots()
import numpy as np
print(ut.hz_str('prob_annots = ', ut.array2string2(prob_annots, precision=2, max_line_width=140, suppress_small=True)))
# ut.setcover_greedy(candidate_sets_dict)
max_weight = 3
prob_annots[np.diag_indices(len(prob_annots))] = np.inf
prob_annots = prob_annots
thresh_points = np.sort(prob_annots[np.isfinite(prob_annots)])
# probably not the best way to go about searching for these thresholds
# but when you have a hammer...
if False:
quant = sorted(np.diff(thresh_points))[(len(thresh_points) - 1) // 2 ]
candset = {point: thresh_points[np.abs(thresh_points - point) < quant] for point in thresh_points}
check_thresholds = len(aids) * 2
thresh_points2 = np.array(ut.setcover_greedy(candset, max_weight=check_thresholds).keys())
thresh_points = thresh_points2
# pt.plot(sorted(thresh_points), 'rx')
# pt.plot(sorted(thresh_points2), 'o')
# prob_annots = prob_annots.T
# thresh_start = np.mean(thresh_points)
current_idxs = []
current_covers = []
current_val = np.inf
for thresh in thresh_points:
covering_sets = [np.where(row >= thresh)[0] for row in (prob_annots)]
candidate_sets_dict = {ax: others for ax, others in enumerate(covering_sets)}
soln_cover = ut.setcover_ilp(candidate_sets_dict, max_weight=max_weight)
exemplar_idxs = list(soln_cover.keys())
soln_weight = len(exemplar_idxs)
val = max_weight - soln_weight
# print('val = %r' % (val,))
# print('soln_weight = %r' % (soln_weight,))
if val < current_val:
current_val = val
current_covers = covering_sets
current_idxs = exemplar_idxs
exemplars = ut.take(aids, current_idxs)
ensure_edges = [(aids[ax], aids[ax2]) for ax, other_xs in enumerate(current_covers) for ax2 in other_xs]
graph = viz_graph.make_netx_graph_from_aid_groups(
ibs, [aids], allow_directed=True, ensure_edges=ensure_edges,
temp_nids=[1] * len(aids))
viz_graph.ensure_node_images(ibs, graph)
nx.set_node_attributes(graph, 'framewidth', False)
nx.set_node_attributes(graph, 'framewidth', {aid: 4.0 for aid in exemplars})
nx.set_edge_attributes(graph, 'color', pt.ORANGE)
nx.set_node_attributes(graph, 'color', pt.LIGHT_BLUE)
nx.set_node_attributes(graph, 'shape', 'rect')
layoutkw = {
'sep' : 1 / 10,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw)
pt.zoom_factory()
