def find_opt_ratio(pblm):
"""
script to help find the correct value for the ratio threshold
>>> from ibeis.algo.verif.vsone import * # NOQA
>>> pblm = OneVsOneProblem.from_empty('PZ_PB_RF_TRAIN')
>>> pblm = OneVsOneProblem.from_empty('GZ_Master1')
"""
# Find best ratio threshold
pblm.load_samples()
infr = pblm.infr
edges = ut.emap(tuple, pblm.samples.aid_pairs.tolist())
task = pblm.samples['match_state']
pos_idx = task.class_names.tolist().index(POSTV)
config = {'ratio_thresh': 1.0, 'sv_on': False}
matches = infr._exec_pairwise_match(edges, config)
import plottool as pt
pt.qtensure()
thresholds = np.linspace(0, 1.0, 100)
pos_truth = task.y_bin.T[pos_idx]
ratio_fs = [m.local_measures['ratio'] for m in matches]
aucs = []
# Given the current correspondences: Find the optimal
# correspondence threshold.
for thresh in ut.ProgIter(thresholds, 'computing thresh'):
scores = np.array([fs[fs < thresh].sum() for fs in ratio_fs])
roc = sklearn.metrics.roc_auc_score(pos_truth, scores)
aucs.append(roc)
aucs = np.array(aucs)
opt_auc = aucs.max()
opt_thresh = thresholds[aucs.argmax()]
if True:
pt.plt.plot(thresholds, aucs, 'r-', label='')
pt.plt.plot(opt_thresh,
opt_auc,
'ro',
label='L opt=%r' % (opt_thresh, ))
pt.set_ylabel('auc')
pt.set_xlabel('ratio threshold')
pt.legend()
def shadowform_probability():
""" its hearthstone, but whatev
probability of
raza + no shadowform on turn 5 +
probability of
raza + shadowform on turn 5 +
probability of
kazakus turn 4, raza turn 5, + no shadowform
"""
from scipy.stats import hypergeom
def p_badstuff_shadowform(turn=5, hand_size=3):
deck_size = 30
num_shadowform = 2
def prob_nohave_card_never_mulled(copies=2, hand_size=3):
deck_size = 30
prb = hypergeom(deck_size, copies, hand_size)
# P(initial_miss)
p_none_premul = prb.cdf(0)
# GIVEN that we mul our first 3 what is prob we still are unlucky
# P(miss_turn0 | initial_miss)
prb = hypergeom(deck_size - hand_size, copies, hand_size)
p_none_in_mul = prb.cdf(0)
# TODO: add constraints about 2 drops
# P(miss_turn0) = P(miss_turn0 | initial_miss) * P(initial_miss)
p_none_at_start = p_none_in_mul * p_none_premul
return p_none_at_start
def prob_nohave_card_always_mulled(copies=2, hand_size=3):
# probability of getting the card initially
p_none_premul = hypergeom(deck_size, copies, hand_size).cdf(0)
# probability of getting the card if everything is thrown away
# (TODO: factor in the probability that you need to keep something)
# for now its fine because if we keep shadowform the end calculation is fine
p_nohave_postmul_given_nohave = hypergeom(deck_size - hand_size, copies, hand_size).cdf(0)
# not necessary, but it shows the theory
p_nohave_postmul_given_had = 1
p_nohave_turn0 = (
p_nohave_postmul_given_nohave * p_none_premul + (1 - p_none_premul) * p_nohave_postmul_given_had
)
return p_nohave_turn0
def prob_nohave_by_turn(p_none_turn0, turn, copies, hand_size):
# P(miss_turnN | miss_mul)
p_none_turnN_given_mulmis = hypergeom(deck_size - hand_size, copies, turn).cdf(0)
# P(miss_turnN) = P(miss_turnN | miss_mul) P(miss_mul)
p_none_turnN = p_none_turnN_given_mulmis * p_none_turn0
return p_none_turnN
p_no_shadowform_on_turn0 = prob_nohave_card_never_mulled(copies=num_shadowform, hand_size=hand_size)
no_shadowform_turnN = prob_nohave_by_turn(p_no_shadowform_on_turn0, turn, num_shadowform, hand_size)
# Assume you always mul raza
p_noraza_initial = prob_nohave_card_always_mulled(copies=1, hand_size=hand_size)
p_noraza_turnN = prob_nohave_by_turn(p_noraza_initial, turn, copies=1, hand_size=hand_size)
p_raza_turnN = 1 - p_noraza_turnN
# probability that you have raza and no shadowform by turn 5
p_raza_and_noshadowform_turnN = p_raza_turnN * no_shadowform_turnN
return p_raza_and_noshadowform_turnN
import plottool as pt # NOQA
turns = list(range(0, 26))
probs = [p_badstuff_shadowform(turn, hand_size=3) for turn in turns]
pt.plot(turns, probs, label="on play")
probs = [p_badstuff_shadowform(turn, hand_size=4) for turn in turns]
pt.plot(turns, probs, label="with coin")
pt.set_xlabel("turn")
pt.set_ylabel("probability")
pt.set_title("Probability of Having Raza without a Shadowform")
pt.legend()
pt.gca().set_ylim(0, 1)
