def assignment_score_slow(cm, normalize=True, rpad=False, cpad=False):
"""Calls Python/Numpy implementation of the Hungarian method
Testing version (uses SciPy's implementation)
"""
cost_matrix = -cm.to_array(rpad=rpad, cpad=cpad)
ris, cis = linear_sum_assignment(cost_matrix)
score = -cost_matrix[ris, cis].sum()
if normalize:
score = _div(score, cm.grand_total)
return score
def _kappa(a, c, d, b):
"""An alternative implementation of Cohen's kappa (for testing)
"""
n = a + b + c + d
p1 = a + b
p2 = a + c
q1 = c + d
q2 = b + d
if a == n or b == n or c == n or d == n:
# only one cell is non-zero
return np.nan
elif p1 == 0 or p2 == 0 or q1 == 0 or q2 == 0:
# one row or column is zero, another non-zero
return 0.0
else:
# no more than one cell is zero
po = a + d
pe = (p2 * p1 + q2 * q1) / float(n)
return _div(po - pe, n - pe)
def test_2x2_invariants():
"""Alternative implementations should coincide for 2x2 matrices
"""
for _ in xrange(100):
cm = ConfusionMatrix2.from_random_counts(low=0, high=10)
# object idempotency
assert_equal(
cm.to_ccw(),
ConfusionMatrix2.from_ccw(*cm.to_ccw()).to_ccw(),
msg="must be able to convert to tuple and create from tuple")
# pairwise H, C, V
h, c, v = cm.pairwise_hcv()[:3]
check_with_nans(v, geometric_mean(h, c), ensure_nans=False)
# informedness
actual_info = cm.informedness()
expected_info_1 = cm.TPR() + cm.TNR() - 1.0
expected_info_2 = cm.TPR() - cm.FPR()
check_with_nans(actual_info, expected_info_1, 4, ensure_nans=False)
check_with_nans(actual_info, expected_info_2, 4, ensure_nans=False)
# markedness
actual_mark = cm.markedness()
expected_mark_1 = cm.PPV() + cm.NPV() - 1.0
expected_mark_2 = cm.PPV() - cm.FOR()
check_with_nans(actual_mark, expected_mark_1, 4, ensure_nans=False)
check_with_nans(actual_mark, expected_mark_2, 4, ensure_nans=False)
# matthews corr coeff
# actual_mcc = cm.matthews_corr()
# expected_mcc = geometric_mean(actual_info, actual_mark)
# check_with_nans(actual_mcc, expected_mcc, 4, ensure_nans=False)
# kappas
actual_kappa = cm.kappa()
# kappa is the same as harmonic mean of kappa components
expected_kappa_1 = harmonic_mean(*cm.kappas()[:2])
check_with_nans(actual_kappa, expected_kappa_1, 4, ensure_nans=False)
# kappa is the same as accuracy adjusted for chance
expected_kappa_2 = harmonic_mean(*cm.adjust_to_null(cm.accuracy, model='m3'))
check_with_nans(actual_kappa, expected_kappa_2, 4, ensure_nans=False)
# kappa is the same as Dice coeff adjusted for chance
expected_kappa_3 = harmonic_mean(*cm.adjust_to_null(cm.dice_coeff, model='m3'))
check_with_nans(actual_kappa, expected_kappa_3, 4, ensure_nans=False)
# odds ratio and Yule's Q
actual_odds_ratio = cm.DOR()
actual_yule_q = cm.yule_q()
expected_yule_q = _div(actual_odds_ratio - 1.0, actual_odds_ratio + 1.0)
expected_odds_ratio = _div(cm.PLL(), cm.NLL())
check_with_nans(actual_odds_ratio, expected_odds_ratio, 4, ensure_nans=False)
check_with_nans(actual_yule_q, expected_yule_q, 4, ensure_nans=False)
# F-score and Dice
expected_f = harmonic_mean(cm.precision(), cm.recall())
actual_f = cm.fscore()
check_with_nans(expected_f, actual_f, 6)
check_with_nans(expected_f, cm.dice_coeff(), 6, ensure_nans=False)
# association coefficients (1)
dice = cm.dice_coeff()
expected_jaccard = _div(dice, 2.0 - dice)
actual_jaccard = cm.jaccard_coeff()
check_with_nans(actual_jaccard, expected_jaccard, 6, ensure_nans=False)
# association coefficients (2)
jaccard = cm.jaccard_coeff()
expected_ss2 = _div(jaccard, 2.0 - jaccard)
actual_ss2 = cm.sokal_sneath_coeff()
check_with_nans(actual_ss2, expected_ss2, 6, ensure_nans=False)
# adjusted ochiai
actual = cm.ochiai_coeff_adj()
expected = harmonic_mean(*cm.adjust_to_null(cm.ochiai_coeff, model='m3'))
check_with_nans(actual, expected, 6, ensure_nans=False)
