From c04ef57519e2c3571b484ca539abda76e0c7fa5b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gy=C3=B6rgy=20Kov=C3=A1cs?= Date: Mon, 21 Oct 2024 11:39:36 +0200 Subject: [PATCH] major updates --- mlscorecheck/auc/_acc_single.py | 4 +- mlscorecheck/auc/_auc_aggregated.py | 81 +- mlscorecheck/auc/_auc_single.py | 479 +- .../01-experiment-aggregated.ipynb | 159 +- .../01-experiment-single.ipynb | 29 +- .../02-processing-aggregated.ipynb | 406 +- notebooks/auc_experiments/02-processing.ipynb | 557 +- .../03-results-midpoints.ipynb | 4924 +++++++++++++++-- .../05-application-retinal-vessel.ipynb | 246 +- .../auc_experiments/06-illustration.ipynb | 296 + .../046-auc-average-curve-area.ipynb | 526 ++ notebooks/development/047-auc-ambiguity.ipynb | 120 + 12 files changed, 6683 insertions(+), 1144 deletions(-) create mode 100644 notebooks/auc_experiments/06-illustration.ipynb create mode 100644 notebooks/development/046-auc-average-curve-area.ipynb create mode 100644 notebooks/development/047-auc-ambiguity.ipynb diff --git a/mlscorecheck/auc/_acc_single.py b/mlscorecheck/auc/_acc_single.py index c50c347..482857a 100644 --- a/mlscorecheck/auc/_acc_single.py +++ b/mlscorecheck/auc/_acc_single.py @@ -332,7 +332,7 @@ def max_acc_lower_from(*, scores: dict, eps: float, p: int, n: int, lower: str = else: raise ValueError(f"unsupported lower bound {lower}") - return lower0 + return lower0, 1 def max_acc_upper_from(*, scores: dict, eps: float, p: int, n: int, upper: str = "min"): @@ -369,7 +369,7 @@ def max_acc_upper_from(*, scores: dict, eps: float, p: int, n: int, upper: str = else: raise ValueError(f"unsupported upper bound {upper}") - return upper0 + return upper0, 1 def max_acc_from( diff --git a/mlscorecheck/auc/_auc_aggregated.py b/mlscorecheck/auc/_auc_aggregated.py index 86db735..309fe5c 100644 --- a/mlscorecheck/auc/_auc_aggregated.py +++ b/mlscorecheck/auc/_auc_aggregated.py @@ -17,7 +17,18 @@ translate_scores, prepare_intervals, ) -from ._auc_single import auc_maxa, auc_armin +from ._auc_single import ( + auc_maxa, + auc_armin, + auc_min_grad, + auc_rmin_grad, + auc_max_grad, + auc_maxa_grad, + auc_min_profile, + auc_rmin_profile, + auc_max_profile, + auc_maxa_profile, +) __all__ = [ "auc_min_aggregated", @@ -821,6 +832,7 @@ def auc_lower_from_aggregated( ns: np.array = None, folding: dict = None, lower: str = "min", + correction: str = None ): """ This function applies the lower bound estimation schemes to estimate @@ -862,12 +874,26 @@ def auc_lower_from_aggregated( check_applicability_lower_aggregated(intervals, lower, ps, ns) + corr = 1.0 + if lower == "min": lower0 = auc_min_aggregated(intervals["fpr"][1], intervals["tpr"][0], k) - elif lower == "onmin": - lower0 = auc_onmin_aggregated(intervals["fpr"][1], intervals["tpr"][0], k) + if correction == 'gradient': + corr = auc_min_grad(intervals["fpr"][1], intervals["tpr"][0]) + elif correction == 'profile': + corr = auc_min_profile(intervals["fpr"][1], intervals["tpr"][0]) elif lower == "rmin": lower0 = auc_rmin_aggregated(intervals["fpr"][0], intervals["tpr"][1], k) + if correction == 'gradient': + corr = auc_rmin_grad(intervals["fpr"][1], intervals["tpr"][0]) + elif correction == 'profile': + corr = auc_rmin_profile(intervals["fpr"][1], intervals["tpr"][0]) + elif lower == "onmin": + lower0 = auc_onmin_aggregated(intervals["fpr"][1], intervals["tpr"][0], k) + if correction == 'gradient': + corr = auc_onmin_grad(intervals["fpr"][1], intervals["tpr"][0]) + elif correction == 'profile': + corr = auc_onmin_profile(intervals["fpr"][1], intervals["tpr"][0]) elif lower == "amin": lower0 = auc_amin_aggregated(intervals["acc"][0], ps, ns) elif lower == "armin": @@ -875,7 +901,7 @@ def auc_lower_from_aggregated( else: raise ValueError(f"unsupported lower bound {lower}") - return lower0 + return lower0, corr def auc_upper_from_aggregated( @@ -886,7 +912,8 @@ def auc_upper_from_aggregated( ps: np.array = None, ns: np.array = None, folding: dict = None, - upper: str = "min", + upper: str = "max", + correction: str = None ): """ This function applies the upper bound estimation schemes to estimate @@ -928,16 +955,26 @@ def auc_upper_from_aggregated( check_applicability_upper_aggregated(intervals, upper, ps, ns) + corr = 1.0 + if upper == "max": upper0 = auc_max_aggregated(intervals["fpr"][0], intervals["tpr"][1], k) + if correction == 'gradient': + corr = auc_max_grad(intervals["fpr"][0], intervals["tpr"][1]) + elif correction == 'profile': + corr = auc_max_profile(intervals["fpr"][0], intervals["tpr"][1]) elif upper == "amax": upper0 = auc_amax_aggregated(intervals["acc"][1], ps, ns) elif upper == "maxa": upper0 = auc_maxa_aggregated(intervals["acc"][1], ps, ns) + if correction == 'gradient': + corr = auc_maxa_grad(intervals["acc"][1], p, n) + elif correction == 'profile': + corr = auc_maxa_profile(intervals["acc"][1], p, n) else: raise ValueError(f"unsupported upper bound {upper}") - return upper0 + return upper0, corr def auc_from_aggregated( @@ -950,6 +987,7 @@ def auc_from_aggregated( folding: dict = None, lower: str = "min", upper: str = "max", + correction: str = None ) -> tuple: """ This function applies the estimation schemes to estimate AUC from scores @@ -977,12 +1015,29 @@ def auc_from_aggregated( infeasible, or not enough data is provided for the estimation method """ - lower0 = auc_lower_from_aggregated( - scores=scores, eps=eps, k=k, ps=ps, ns=ns, folding=folding, lower=lower - ) + try: + lower0, corr_lower = auc_lower_from_aggregated( + scores=scores, eps=eps, k=k, ps=ps, ns=ns, folding=folding, lower=lower, correction=correction + ) - upper0 = auc_upper_from_aggregated( - scores=scores, eps=eps, k=k, ps=ps, ns=ns, folding=folding, upper=upper - ) + upper0, corr_upper = auc_upper_from_aggregated( + scores=scores, eps=eps, k=k, ps=ps, ns=ns, folding=folding, upper=upper, correction=correction + ) + + if corr_lower == 1.0 and corr_upper == 1.0: + return (lower0, upper0) + + print(corr_lower, corr_upper) + + corr_lower = corr_lower + 0.01 + corr_upper = corr_upper + 0.01 + + corr_sum = corr_lower + corr_upper + corr_lower = corr_lower / corr_sum + corr_upper = corr_upper / corr_sum + + midpoint = lower0 * corr_upper + upper0 * corr_lower - return (lower0, upper0) + return (midpoint, midpoint) + except: + return np.nan, np.nan diff --git a/mlscorecheck/auc/_auc_single.py b/mlscorecheck/auc/_auc_single.py index 14ef6a2..7cf0641 100644 --- a/mlscorecheck/auc/_auc_single.py +++ b/mlscorecheck/auc/_auc_single.py @@ -4,6 +4,9 @@ import numpy as np +from scipy.stats import beta +from scipy.stats import norm as gaussian + from ._utils import translate_scores, prepare_intervals __all__ = [ @@ -33,11 +36,41 @@ "auc_min_grad", "auc_max_grad", "auc_rmin_grad", + "auc_onmin_profile", + "auc_maxa_profile", + "auc_min_profile", + "auc_max_profile", + "auc_rmin_profile", "check_lower_applicability", "check_upper_applicability", ] +def expected_value(a, b, start, end, n): + aucs = np.linspace(start, end, n) + aucs = (aucs[1:] + aucs[:-1])/2 + dx = (end - start)/n + norm = beta.cdf(end, a, b) - beta.cdf(start, a, b) + pdfs = beta.pdf(aucs, a, b) + pdfs = pdfs / norm + return np.sum(aucs * pdfs)*dx + + +def rline_intersect(sens, spec): + a = (1 - sens)/(1 - spec) + b = sens - a*spec + se0 = (a + b)/(1 + a) + sp0 = 1 - se0 + return se0, sp0 + +def rcirc_intersect(sens, spec): + a = (1 - sens)/(1 - spec) + b = sens - a*spec + se0 = (2*b + np.sqrt(4*b**2 - 4*(1 + a**2)*(b**2 - a**2)))/(2*(1 + a**2)) + sp0 = np.sqrt(1 - se0**2) + return se0, sp0 + + def augment_intervals(intervals: dict, p: int, n: int): """ Augment the intervals based on the relationship between tpr, fpr and acc @@ -125,6 +158,12 @@ def roc_max(fpr, tpr): return (np.array([0, 0, fpr, fpr, 1]), np.array([0, tpr, tpr, 1, 1])) +def roc_rmax(fpr, tpr): + d = max(tpr - fpr, 0) + return (np.array([0, 0, max(fpr - d, 0), fpr, fpr, max(1 - 2*d, 0), 1]), + np.array([0, min(2*d, 1), tpr, tpr, min(tpr + d, 1), 1, 1])) + + def roc_rmin(fpr, tpr): """ The regulated minimum ROC curve at fpr, tpr @@ -266,6 +305,21 @@ def auc_min_grad(fpr, tpr): return np.sqrt((1 - fpr)**2 + (-tpr)**2) +def auc_min_profile(fpr, tpr): + """ + The profile length of the minimum AUC + + Args: + fpr (float): upper bound on false positive rate + tpr (float): lower bound on true positive rate + + Returns: + float: the profile lenght + """ + + return 1 + tpr + + def auc_rmin(fpr, tpr): """ The area under the regulated minimum curve at fpr, tpr @@ -291,7 +345,7 @@ def auc_rmin(fpr, tpr): def auc_rmin_grad(fpr, tpr): """ - The gradient of the minimum AUC + The gradient of the regulated minimum AUC curve Args: fpr (float): upper bound on false positive rate @@ -304,6 +358,25 @@ def auc_rmin_grad(fpr, tpr): return np.sqrt((tpr-fpr)**2 + (fpr-tpr)**2) +def auc_rmin_profile(fpr, tpr): + """ + The profile length of the regulated minimum AUC curve + + Args: + fpr (float): upper bound on false positive rate + tpr (float): lower bound on true positive rate + + Returns: + float: the profile length + """ + + fprs, tprs = roc_rmin(fpr, tpr) + total = 0.0 + for idx in range(len(fprs) - 1): + total += np.sqrt((fprs[idx] - fprs[idx+1])**2 + (tprs[idx] - tprs[idx+1])**2) + return float(total) + + def auc_rmin_grid(fpr, tpr, p, n): """ The area under the regulated minimum curve at fpr, tpr, with grid @@ -358,7 +431,25 @@ def auc_max_grad(fpr, tpr): """ return np.sqrt(fpr**2 + (tpr - 1)**2) - #return max(fpr**2, (tpr - 1)**2) + + +def auc_max_profile(fpr, tpr): + """ + The profile length of the maximum AUC curve + + Args: + fpr (float): upper bound on false positive rate + tpr (float): lower bound on true positive rate + + Returns: + float: the profile lenght + """ + + return 1 + (1 - tpr) + + +def auc_rmax(fpr, tpr): + return integrate_roc_curve(*roc_rmax(fpr, tpr)) def auc_maxa(acc, p, n): @@ -396,12 +487,50 @@ def auc_maxa_grad(acc, p, n): float: the gradient magnitude """ - #d_sens = (1 - acc)*(p + n)/n - #d_spec = (1 - acc)*(p + n)/p + if acc < max(p, n) / (p + n): + raise ValueError("accuracy too small") - #return np.sqrt(d_sens**2 + d_spec**2) return - (2*acc - 2)*(n + p)**2/(2*n*p) +def auc_maxa_grad2(fpr, tpr, p, n): + """ + The gradient magnitude of the amax estimation + + Args: + acc (float): the accuracy + p (int): the number of positive samples + n (int): the number of negative samples + + Returns: + float: the gradient magnitude + """ + + acc = ((1 - fpr)*n + tpr*p)/(p + n) + + if acc < max(p, n) / (p + n): + raise ValueError("accuracy too small") + + dtpr = (fpr*n - p*tpr + p)/n + dfpr = tpr - 1 - fpr*n/p + + return np.sqrt(dtpr**2 + dfpr**2) + + +def auc_maxa_profile(acc, p, n): + """ + The profile length of the amax estimation + + Args: + acc (float): the accuracy + p (int): the number of positive samples + n (int): the number of negative samples + + Returns: + float: the profile length + """ + + fprs, tprs = roc_maxa(acc, p, n) + return float(np.sqrt((fprs[1] - fprs[2])**2 + (tprs[1] - tprs[2])**2)) def auc_amin(acc, p, n): """ @@ -492,8 +621,26 @@ def auc_onmin_grad(fpr, tpr): """ return np.sqrt(2*0.5**2) - #return 0.5 + + +def auc_onmin_profile(fpr, tpr): + """ + The profile length of the onmin estimation + + Args: + acc (float): the accuracy + p (int): the number of positive samples + n (int): the number of negative samples + Returns: + float: the profile length + """ + + fprs, tprs = roc_onmin(fpr, tpr) + segment_a = np.sqrt((fprs[0] - fprs[1])**2 + ([tprs[0] - tprs[1]])**2) + segment_b = np.sqrt((fprs[1] - fprs[2])**2 + ([tprs[1] - tprs[2]])**2) + + return float(segment_a + segment_b) def check_lower_applicability(intervals: dict, lower: str, p: int, n: int): """ @@ -533,7 +680,7 @@ def check_upper_applicability(intervals: dict, upper: str, p: int, n: int): ValueError: when the methods are not applicable with the specified scores """ - if upper in ["max"] and ("fpr" not in intervals or "tpr" not in intervals): + if upper in ["max","rmax"] and ("fpr" not in intervals or "tpr" not in intervals): raise ValueError("fpr, tpr or their complements must be specified") if upper in ["amax", "maxa"] and (p is None or n is None): raise ValueError("p and n must be specified") @@ -542,7 +689,13 @@ def check_upper_applicability(intervals: dict, upper: str, p: int, n: int): def auc_lower_from( - *, scores: dict, eps: float, p: int = None, n: int = None, lower: str = "min" + *, + scores: dict, + eps: float, + p: int = None, + n: int = None, + lower: str = "min", + correction: str = None ): """ This function applies the lower bound estimation schemes to estimate @@ -572,12 +725,26 @@ def auc_lower_from( check_lower_applicability(intervals, lower, p, n) + corr = 1.0 + if lower == "min": lower0 = auc_min(intervals["fpr"][1], intervals["tpr"][0]) + if correction == 'gradient': + corr = auc_min_grad(intervals["fpr"][1], intervals["tpr"][0]) + elif correction == 'profile': + corr = auc_min_profile(intervals["fpr"][1], intervals["tpr"][0]) elif lower == "rmin": lower0 = auc_rmin(intervals["fpr"][0], intervals["tpr"][1]) + if correction == 'gradient': + corr = auc_rmin_grad(intervals["fpr"][1], intervals["tpr"][0]) + elif correction == 'profile': + corr = auc_rmin_profile(intervals["fpr"][1], intervals["tpr"][0]) elif lower == "onmin": lower0 = auc_onmin(intervals["fpr"][0], intervals["tpr"][0]) + if correction == 'gradient': + corr = auc_onmin_grad(intervals["fpr"][1], intervals["tpr"][0]) + elif correction == 'profile': + corr = auc_onmin_profile(intervals["fpr"][1], intervals["tpr"][0]) elif lower == "grmin": lower0 = auc_rmin_grid(intervals["fpr"][0], intervals["tpr"][1], p, n) elif lower == "amin": @@ -587,11 +754,11 @@ def auc_lower_from( else: raise ValueError(f"unsupported lower bound {lower}") - return lower0 + return lower0, corr def auc_upper_from( - *, scores: dict, eps: float, p: int = None, n: int = None, upper: str = "max" + *, scores: dict, eps: float, p: int = None, n: int = None, upper: str = "max", correction: str = None ): """ This function applies the lower bound estimation schemes to estimate @@ -621,16 +788,28 @@ def auc_upper_from( check_upper_applicability(intervals, upper, p, n) + corr = 1.0 + if upper == "max": upper0 = auc_max(intervals["fpr"][0], intervals["tpr"][1]) + if correction == 'gradient': + corr = auc_max_grad(intervals["fpr"][0], intervals["tpr"][1]) + elif correction == 'profile': + corr = auc_max_profile(intervals["fpr"][0], intervals["tpr"][1]) + elif upper == "rmax": + upper0 = auc_rmax(intervals["fpr"][0], intervals["tpr"][1]) elif upper == "amax": upper0 = auc_amax(intervals["acc"][1], p, n) elif upper == "maxa": upper0 = auc_maxa(intervals["acc"][1], p, n) + if correction == 'gradient': + corr = auc_maxa_grad(intervals["acc"][1], p, n) + elif correction == 'profile': + corr = auc_maxa_profile(intervals["acc"][1], p, n) else: raise ValueError(f"unsupported upper bound {upper}") - return upper0 + return upper0, corr def auc_from( @@ -641,7 +820,7 @@ def auc_from( n: int = None, lower: str = "min", upper: str = "max", - gradient_correction: bool = False + correction: str = None ) -> tuple: """ This function applies the estimation schemes to estimate AUC from scores @@ -655,7 +834,7 @@ def auc_from( type of estimation for the lower bound upper (str): ('max'/'maxa'/'amax') - the type of estimation for the upper bound - gradient_correction (bool): whether to use gradient correction + correction (str): None/'gradient'/'profile' Returns: tuple(float, float): the interval for the AUC @@ -665,10 +844,274 @@ def auc_from( or the scores are inconsistent """ - lower0 = auc_lower_from(scores=scores, eps=eps, p=p, n=n, lower=lower) - lower_weight = 1.0 + try: + + lower0, grad_lower = auc_lower_from(scores=scores, eps=eps, p=p, n=n, lower=lower, correction='gradient') + lower0_min, grad_lower_min = auc_lower_from(scores=scores, eps=eps, p=p, n=n, lower='min', correction='gradient') + onmin, grad_onmin = auc_lower_from(scores=scores, eps=eps, p=p, n=n, lower='onmin', correction='gradient') + + upper0, grad_upper = auc_upper_from(scores=scores, eps=eps, p=p, n=n, upper=upper, correction='gradient') + + + vector = np.array([1.0 - scores['spec'], scores['sens']]) + direction = np.array([1.0, 1.0]) / np.sqrt(2.0) + inner = np.inner(vector, direction) + intersection = inner * direction + diff = vector - intersection + length = np.linalg.norm(diff) + length_sign = 1 if scores['sens'] > 1 - scores['spec'] else -1 + + dist_05 = np.sqrt((scores['sens'] - 0.5)**2 + (scores['spec'] - 0.5)**2) + dist_0 = (np.sqrt((scores['sens'] - 0)**2 + (scores['spec'] - 0)**2)) + dist_1 = np.sqrt((scores['sens'] - 1)**2 + (scores['spec'] - 1)**2) + + #dist_05 = (np.abs(scores['sens'] - 0.5) + np.abs(scores['spec'] - 0.5)) + #dist_1 = (np.abs(scores['sens'] - 1) + np.abs(scores['spec'] - 1)) + #dist_wall = min(1-scores['sens'], 1-scores['spec']) + 0.01 + + dist_05_norm = dist_05 / (np.sqrt(2)/2) + dist_random = length + dist_random_norm = dist_random / (np.sqrt(2)/2) + + #corr_lower = 1.0/(dist_random_norm + 0.0001) + #corr_upper = 1.0/(dist_1 + 0.0001) + + midpoint = lower0 * 0.5 + upper0 * 0.5 + + exponent = 1.0 + + corr_lower = dist_1 + 0.01 + #corr_upper = 0.5 + + corr_upper = dist_05 + 0.0 + 0.1 + #corr_lower = 1 - corr_upper + 0.1 + + #corr_upper = (grad_lower + 1)**exponent + #corr_lower = (grad_upper + 1)**exponent + + corr_upper = 0.01 + corr_lower = 0.01 + + #corr_upper = (1 + (midpoint - 0.75))**0 + #corr_lower = (1 - (midpoint - 0.75))**0 + + #corr_upper = dist_0 + #corr_lower = 1.0 - corr_upper + + #corr_lower = dist_1 + 0.1 + #corr_upper = dist_random + 0.1 + + #corr_upper = 1 - corr_lower + 0.1 + #corr_lower = 0.5 + 0.01 + #corr_lower = 1.0 - corr_upper + 0.0 + + exponent = 1.0 + + #corr_upper = (grad_lower)**exponent + #corr_lower = (grad_upper)**exponent + + # arbitrary + #corr_lower = (dist_1)**exponent + #corr_upper = (dist_random)**exponent + #corr_lower = 1.0 - corr_upper + #corr_upper = 1 - corr_lower + + #corr_lower = 1/(dist_random_norm + 0.01) + #corr_upper = 3 + #corr_upper = 1/(dist_1 + 0.01) + #corr_lower = 1 + + #corr_upper = 1/(dist_1 + 0.01) + #corr_lower = 1/((1 - dist_1) + 0.01) + + #dist_random_norm = + + #midpoint = (upper0 + ((1 - dist_random_norm)*lower0_min + (dist_random_norm)*lower0))/2 + + midpoint = (lower0 + upper0)/2 + + #corr_lower = 0.01 + #corr_upper = 0.01 + + beta0 = 20 + alpha_lower = lower0 * beta0 + alpha_upper = upper0 * beta0 + alpha_mid = midpoint * beta0 + + #midpoint = expected_value(alpha_mid, beta0 - alpha_mid, lower0, upper0, 10000) + + #lower0_new = expected_value(alpha_lower, beta0 - alpha_lower, lower0, upper0, 10000) + #upper0_new = expected_value(alpha_upper, beta0 - alpha_upper, lower0, upper0, 10000) + + #midpoint = np.mean([expected_value(tmp, beta0 - tmp, lower0, upper0, 1000) for tmp in np.linspace(alpha_lower, alpha_upper, 2)]) + + #print(lower0, upper0, corr_lower, corr_upper, beta0, alpha_lower, alpha_upper, lower0_new, upper0_new) + + #lower0 = lower0_new + #upper0 = upper0_new + + + """points = np.linspace(alpha_lower, alpha_upper, 50) + perc5 = [] + perc95 = [] + for point in points: + perc5.append(beta.ppf([0.01], point, beta0 - point)[0]) + perc95.append(beta.ppf([0.99], point, beta0 - point)[0]) + perc5 = np.array(perc5) + perc95 = np.array(perc95) + + idx = np.argmin(np.abs(lower0 - perc5)**2 + np.abs(upper0 - perc95)**2) + + midpoint = points[idx] / beta0""" + + + #corr_lower = (upper0 - onmin)**2 + #corr_upper = (onmin - lower0)**2 + + #corr_lower = dist_1 + if length_sign == -1: + dist_random = 0.0 + + dist01 = np.sqrt((scores['sens'] - 0)**2 + (scores['spec'] - 1)**2) + dist10 = np.sqrt((scores['sens'] - 1)**2 + (scores['spec'] - 0)**2) + + dist_corner = 1 - min(dist01, dist10) + + area = min(1 - (scores['sens'] + scores['spec'])/2, (scores['sens'] + scores['spec'])/2 - 0.5) + #area = min(dist_random, np.sqrt(2)/2 - dist_random) + + #midpoint = 0.5 + (dist_random/(np.sqrt(2)/2))*0.5 + dist_random**2*dist_corner + + #midpoint = 0.5 + (dist_random/(np.sqrt(2)/2))*0.5 + 1/min(p, n) + + + se0, sp0 = rline_intersect(scores['sens'], scores['spec']) + se1, sp1 = rcirc_intersect(scores['sens'], scores['spec']) + + dist_circ = np.sqrt((scores['sens'] - se1)**2 + (scores['spec'] - sp1)**2) + dist_rline = np.sqrt((scores['sens'] - se0)**2 + (scores['spec'] - sp0)**2) + + if (scores['sens'] < 0.001 and scores['spec'] > 0.999) or (scores['spec'] < 0.001 and scores['sens'] > 0.999): + return (0.5, 0.5) + + at = 0.75 + + if scores['sens']**2 + scores['spec']**2 < 1: + dist_rline = np.sqrt((scores['sens'] - se0)**2 + (scores['spec'] - sp0)**2) + dist_circ = np.sqrt((scores['sens'] - se1)**2 + (scores['spec'] - sp1)**2) + ratio = dist_rline / (dist_rline + dist_circ) + midpoint = (at - 0.5)*ratio + 0.5 + circ_sign = -1 + else: + dist_1 = np.sqrt((scores['sens'] - 1)**2 + (scores['spec'] - 1)**2) + dist_circ = np.sqrt((scores['sens'] - se1)**2 + (scores['spec'] - sp1)**2) + ratio = dist_circ / (dist_1 + dist_circ) + midpoint = (1 - at)*ratio + at + circ_sign = 1 + + + """dist_rline = np.sqrt((scores['sens'] - se0)**2 + (scores['spec'] - sp0)**2) + dist_1 = np.sqrt((scores['sens'] - 1)**2 + (scores['spec'] - 1)**2) + + ratio = dist_rline / (dist_rline + dist_1) + midpoint = 0.5 + ratio * 0.5""" + + #corr_lower = np.sqrt(2) - (dist_0) + #corr_upper = (dist_0) + + + #corr_lower = 1 + #corr_upper = (scores['sens']) + (scores['spec']) + + lower_extremity = 1 + upper_extremity = 1 + if lower == 'min' and upper == 'max': + #lower_extremity = 2/4 + #upper_extremity = (1 - scores['spec'] + 1 - scores['sens'])/2 + upper_extremity = auc_max_grad(1 - scores['spec'], scores['sens'])**0.5 + lower_extremity = auc_min_grad(1 - scores['spec'], scores['sens'])**0.5 + + if lower == 'rmin' and upper == 'max': + #lower_extremity = (scores['sens'] - (1 - scores['spec']))*2/4 + #upper_extremity = (1 - scores['spec'] + 1 - scores['sens'])/2 + upper_extremity = auc_max_grad(1 - scores['spec'], scores['sens'])**0.5 + lower_extremity = auc_rmin_grad(1 - scores['spec'], scores['sens'])**0.5 + + if lower == 'rmin' and upper == 'maxa': + + #lower_extremity = (scores['sens'] - (1 - scores['spec']))*2/2 + 0.25 + + #fprs, tprs = roc_maxa(scores['acc'], p, n) + + #upper_extremity = np.abs(fprs[2] - fprs[1] - (tprs[2] - tprs[1])) + 0.25 + upper_extremity = auc_maxa_grad2(1 - scores['spec'], scores['sens'], p, n)**0.5 + lower_extremity = auc_rmin_grad(1 - scores['spec'], scores['sens'])**0.5 + + + #lower_extremity = 1 - dist_1 + #upper_extremity = dist_1 + + + #upper_extremity = gaussian.pdf(lower0**0.5, 0, 0.65) + #lower_extremity = gaussian.pdf((1 - upper0)**0.5, 0, 0.65) + + p1 = p/(p + n) + p0 = n/(p + n) + + prob = np.sqrt(p1*p0) + + upper_extremity = prob**(scores['sens']) * prob**(scores['spec']) + lower_extremity = prob**(1 - scores['sens']) * prob**(1 - scores['spec']) + + corr_upper = lower_extremity + corr_lower = upper_extremity + + corr_upper = 1 + corr_lower = 1 + + #corr_lower = 2 + #corr_upper = auc_rmin_profile(1 - scores['spec'], scores['sens']) + #lower0 = 0.5 + #upper0 = 1.0 + + #corr_upper = ((1 - scores['spec']) + (1 - scores['spec'])*scores['sens'] + (1 - scores['sens'])*scores['spec'] + (1 - scores['sens'])) + #corr_upper = lower0 + corr_upper = ((lower0))**0.2 + corr_lower = ((1 - upper0))**0.2 + + corr_upper = 1 + corr_lower = 1 + + corr_sum = corr_lower + corr_upper + + + + corr_lower = corr_lower / corr_sum + corr_upper = corr_upper / corr_sum + + #print(corr_lower, corr_upper, alpha_lower, alpha_upper) + + #norm = np.sqrt(scores['sens']**2 + (scores['spec']**2))/np.sqrt(2) + #midpoint = (dist_random/(np.sqrt(2)) + 0.5) + + #midpoint = dist_circ / (np.sqrt(2) - 1) * 0.25 * circ_sign + 0.75 + tmp = (auc_min(1 - scores['spec'], scores['sens']) + auc_max(1 - scores['spec'], scores['sens']))/2.0 + #scaler = (tmp - 0.5)*2 + #midpoint = tmp*norm + (1 - tmp)*tmp + #midpoint = norm + #midpoint = min(tmp*1.035, 1.0) + #midpoint = ((tmp + norm)/2.0)**0.8 + #midpoint = dist_random/(np.sqrt(2)) + 0.5 + #midpoint = (((dist_rline)*1 + (dist_1)*0.5)/(dist_rline + dist_1)) + + midpoint = lower0 * corr_lower + upper0 * corr_upper + #midpoint = np.sqrt(scores['sens']**2 + (scores['spec']**2))/(np.sqrt(2)) + #midpoint = (lower0 + upper0)/2 + #tmp = max(dist_0 - np.sqrt(2)/2, 0) / (np.sqrt(2) - np.sqrt(2)/2) + #midpoint = 0.5 + tmp**1.2*0.5 - upper0 = auc_upper_from(scores=scores, eps=eps, p=p, n=n, upper=upper) - upper_weight = 1.0 + - return (lower0, upper0) + return (midpoint, midpoint) + except: + return np.nan, np.nan diff --git a/notebooks/auc_experiments/01-experiment-aggregated.ipynb b/notebooks/auc_experiments/01-experiment-aggregated.ipynb index 684b681..7655eb7 100644 --- a/notebooks/auc_experiments/01-experiment-aggregated.ipynb +++ b/notebooks/auc_experiments/01-experiment-aggregated.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 24, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -68,7 +68,7 @@ "28" ] }, - "execution_count": 27, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -163,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -213,6 +213,7 @@ "\n", " if threshold is None:\n", " threshold = random_state.choice(y_pred)\n", + " threshold = np.sum(y_train)/len(y_train)\n", "\n", " tp = np.sum((y_pred >= threshold) & (y_test == 1))\n", " tn = np.sum((y_pred < threshold) & (y_test == 0))\n", @@ -278,16 +279,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "203" + "202" ] }, - "execution_count": 33, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -298,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -308,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -353,16 +354,16 @@ " 0\n", " bupa\n", " 8\n", - " 0.562368\n", - " 0.855994\n", - " 0.350000\n", + " 0.695560\n", + " 0.663377\n", + " 0.720000\n", " 0.751001\n", - " 0.733417\n", - " 0.545687\n", - " 0.870000\n", - " 0.265560\n", - " 0.556736\n", - " 0.733417\n", + " 0.730510\n", + " 0.580409\n", + " 0.840000\n", + " 0.420530\n", + " 0.508864\n", + " 0.730510\n", " 145\n", " 200\n", " \n", @@ -387,14 +388,14 @@ " 2\n", " haberman\n", " 3\n", - " 0.637255\n", - " 0.703704\n", - " 0.613333\n", + " 0.653595\n", + " 0.617284\n", + " 0.666667\n", " 0.699095\n", " 0.738562\n", " 0.012346\n", " 1.000000\n", - " 0.226170\n", + " 0.264706\n", " 0.885074\n", " 0.738562\n", " 81\n", @@ -404,14 +405,14 @@ " 3\n", " dermatology-6\n", " 2\n", - " 0.055866\n", - " 1.000000\n", - " 0.000000\n", + " 0.966480\n", + " 0.950000\n", + " 0.967456\n", " 0.970710\n", " 0.980447\n", " 0.850000\n", " 0.988166\n", - " 0.000000\n", + " 0.055866\n", " 0.750000\n", " 0.980447\n", " 20\n", @@ -421,14 +422,14 @@ " 4\n", " monk-2\n", " 3\n", - " 0.909722\n", + " 0.974537\n", " 1.000000\n", - " 0.828947\n", + " 0.951754\n", " 1.000000\n", " 0.979167\n", " 1.000000\n", " 0.960526\n", - " 0.196400\n", + " 0.472222\n", " 0.595344\n", " 0.979167\n", " 204\n", @@ -440,21 +441,21 @@ ], "text/plain": [ " dataset k acc sens spec auc best_acc \\\n", - "0 bupa 8 0.562368 0.855994 0.350000 0.751001 0.733417 \n", + "0 bupa 8 0.695560 0.663377 0.720000 0.751001 0.730510 \n", "1 new_thyroid1 4 0.925577 1.000000 0.911111 0.994483 0.962788 \n", - "2 haberman 3 0.637255 0.703704 0.613333 0.699095 0.738562 \n", - "3 dermatology-6 2 0.055866 1.000000 0.000000 0.970710 0.980447 \n", - "4 monk-2 3 0.909722 1.000000 0.828947 1.000000 0.979167 \n", + "2 haberman 3 0.653595 0.617284 0.666667 0.699095 0.738562 \n", + "3 dermatology-6 2 0.966480 0.950000 0.967456 0.970710 0.980447 \n", + "4 monk-2 3 0.974537 1.000000 0.951754 1.000000 0.979167 \n", "\n", " best_sens best_spec threshold best_threshold best_acc_orig p n \n", - "0 0.545687 0.870000 0.265560 0.556736 0.733417 145 200 \n", + "0 0.580409 0.840000 0.420530 0.508864 0.730510 145 200 \n", "1 0.888889 0.977778 0.166667 0.333333 0.962788 35 180 \n", - "2 0.012346 1.000000 0.226170 0.885074 0.738562 81 225 \n", - "3 0.850000 0.988166 0.000000 0.750000 0.980447 20 338 \n", - "4 1.000000 0.960526 0.196400 0.595344 0.979167 204 228 " + "2 0.012346 1.000000 0.264706 0.885074 0.738562 81 225 \n", + "3 0.850000 0.988166 0.055866 0.750000 0.980447 20 338 \n", + "4 1.000000 0.960526 0.472222 0.595344 0.979167 204 228 " ] }, - "execution_count": 35, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -465,16 +466,16 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "data.to_csv('raw-aggregated.csv')" + "data.to_csv('raw-aggregated3.csv')" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -519,16 +520,16 @@ " 0\n", " bupa\n", " 8\n", - " 0.562368\n", - " 0.855994\n", - " 0.350000\n", + " 0.695560\n", + " 0.663377\n", + " 0.720000\n", " 0.751001\n", - " 0.733417\n", - " 0.545687\n", - " 0.870000\n", - " 0.265560\n", - " 0.556736\n", - " 0.733417\n", + " 0.730510\n", + " 0.580409\n", + " 0.840000\n", + " 0.420530\n", + " 0.508864\n", + " 0.730510\n", " 145\n", " 200\n", " \n", @@ -553,14 +554,14 @@ " 2\n", " haberman\n", " 3\n", - " 0.637255\n", - " 0.703704\n", - " 0.613333\n", + " 0.653595\n", + " 0.617284\n", + " 0.666667\n", " 0.699095\n", " 0.738562\n", " 0.012346\n", " 1.000000\n", - " 0.226170\n", + " 0.264706\n", " 0.885074\n", " 0.738562\n", " 81\n", @@ -570,14 +571,14 @@ " 3\n", " dermatology-6\n", " 2\n", - " 0.055866\n", - " 1.000000\n", - " 0.000000\n", + " 0.966480\n", + " 0.950000\n", + " 0.967456\n", " 0.970710\n", " 0.980447\n", " 0.850000\n", " 0.988166\n", - " 0.000000\n", + " 0.055866\n", " 0.750000\n", " 0.980447\n", " 20\n", @@ -587,14 +588,14 @@ " 4\n", " monk-2\n", " 3\n", - " 0.909722\n", + " 0.974537\n", " 1.000000\n", - " 0.828947\n", + " 0.951754\n", " 1.000000\n", " 0.979167\n", " 1.000000\n", " 0.960526\n", - " 0.196400\n", + " 0.472222\n", " 0.595344\n", " 0.979167\n", " 204\n", @@ -606,21 +607,21 @@ ], "text/plain": [ " dataset k acc sens spec auc best_acc \\\n", - "0 bupa 8 0.562368 0.855994 0.350000 0.751001 0.733417 \n", + "0 bupa 8 0.695560 0.663377 0.720000 0.751001 0.730510 \n", "1 new_thyroid1 4 0.925577 1.000000 0.911111 0.994483 0.962788 \n", - "2 haberman 3 0.637255 0.703704 0.613333 0.699095 0.738562 \n", - "3 dermatology-6 2 0.055866 1.000000 0.000000 0.970710 0.980447 \n", - "4 monk-2 3 0.909722 1.000000 0.828947 1.000000 0.979167 \n", + "2 haberman 3 0.653595 0.617284 0.666667 0.699095 0.738562 \n", + "3 dermatology-6 2 0.966480 0.950000 0.967456 0.970710 0.980447 \n", + "4 monk-2 3 0.974537 1.000000 0.951754 1.000000 0.979167 \n", "\n", " best_sens best_spec threshold best_threshold best_acc_orig p n \n", - "0 0.545687 0.870000 0.265560 0.556736 0.733417 145 200 \n", + "0 0.580409 0.840000 0.420530 0.508864 0.730510 145 200 \n", "1 0.888889 0.977778 0.166667 0.333333 0.962788 35 180 \n", - "2 0.012346 1.000000 0.226170 0.885074 0.738562 81 225 \n", - "3 0.850000 0.988166 0.000000 0.750000 0.980447 20 338 \n", - "4 1.000000 0.960526 0.196400 0.595344 0.979167 204 228 " + "2 0.012346 1.000000 0.264706 0.885074 0.738562 81 225 \n", + "3 0.850000 0.988166 0.055866 0.750000 0.980447 20 338 \n", + "4 1.000000 0.960526 0.472222 0.595344 0.979167 204 228 " ] }, - "execution_count": 37, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -638,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -656,12 +657,12 @@ "\u001b[0;31mKeyError\u001b[0m: 'auc_int'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[38], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhalf_width\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlower\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhalf_width\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlower\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mapply()\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_standard()\n", "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_generator()\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(v, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "Cell \u001b[0;32mIn[38], line 1\u001b[0m, in \u001b[0;36m\u001b[0;34m(row)\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhalf_width\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlower\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "Cell \u001b[0;32mIn[15], line 1\u001b[0m, in \u001b[0;36m\u001b[0;34m(row)\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhalf_width\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlower\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/series.py:1121\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m 1120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1121\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_value(key)\n\u001b[1;32m 1123\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m 1124\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m 1125\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n", "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/series.py:1237\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m 1236\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1237\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_loc(label)\n\u001b[1;32m 1239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m 1240\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n", "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", diff --git a/notebooks/auc_experiments/01-experiment-single.ipynb b/notebooks/auc_experiments/01-experiment-single.ipynb index abafee7..214f08a 100644 --- a/notebooks/auc_experiments/01-experiment-single.ipynb +++ b/notebooks/auc_experiments/01-experiment-single.ipynb @@ -13,6 +13,7 @@ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import roc_auc_score, roc_curve\n", + "from sklearn.preprocessing import StandardScaler\n", "\n", "import matplotlib.pyplot as plt\n", "\n", @@ -40,7 +41,7 @@ " 'random_state': 5}\n", " if mode == 2:\n", " classifier = SVC\n", - " params = {'probability': True, 'C': random_state.rand()*2 + 0.001}\n", + " params = {'probability': True, 'C': random_state.rand()/2 + 0.001}\n", " if mode == 3:\n", " classifier = KNeighborsClassifier\n", " params = {'n_neighbors': random_state.randint(2, 10)}\n", @@ -175,11 +176,14 @@ " loader = random_state.choice(datasets)\n", " dataset = loader()\n", "\n", - " X = dataset['data']\n", + " X = StandardScaler().fit_transform(dataset['data'])\n", " y = dataset['target']\n", " name = dataset['name']\n", "\n", - " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)\n", + " if random_state.randint(2) == 0:\n", + " y = 1 - y\n", + "\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5, stratify=y)\n", " classifier = generate_random_classifier(random_state)\n", "\n", " classifier_obj = classifier[0](**classifier[1])\n", @@ -189,11 +193,20 @@ "\n", " auc = roc_auc_score(y_test, y_pred)\n", "\n", + " fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n", + "\n", + " #print(fpr, tpr, thresholds)\n", + "\n", + " #plt.figure(figsize=(3.5, 3.5))\n", + " #plt.plot(fpr, tpr)\n", + "\n", " if auc < 0.5:\n", " dropped += 1\n", " continue\n", "\n", - " threshold = random_state.choice(roc_curve(y_test, y_pred)[2])\n", + " #threshold = random_state.choice(roc_curve(y_test, y_pred)[2])\n", + " #threshold = random_state.random_sample()\n", + " threshold = np.sum(y_train)/len(y_train)\n", "\n", " tp = np.sum((y_pred >= threshold) & (y_test == 1))\n", " tn = np.sum((y_pred < threshold) & (y_test == 0))\n", @@ -231,7 +244,7 @@ " best_sens = (tp) / (p)\n", " best_spec = (tn) / (n)\n", "\n", - " results.append((name, acc, sens, spec, auc, best_acc, best_sens, best_spec, threshold, th, p, n))" + " results.append((name, acc, sens, spec, auc, best_acc, best_sens, best_spec, threshold, th, p, n, len(fpr), str(fpr.tolist()), str(tpr.tolist()), str(thresholds.tolist()), str(classifier[0].__name__), str(classifier[1])))" ] }, { @@ -242,7 +255,7 @@ { "data": { "text/plain": [ - "271" + "51" ] }, "execution_count": 10, @@ -260,7 +273,7 @@ "metadata": {}, "outputs": [], "source": [ - "data = pd.DataFrame(results, columns=['dataset', 'acc', 'sens', 'spec', 'auc', 'best_acc', 'best_sens', 'best_spec', 'threshold', 'best_threshold', 'p', 'n'])" + "data = pd.DataFrame(results, columns=['dataset', 'acc', 'sens', 'spec', 'auc', 'best_acc', 'best_sens', 'best_spec', 'threshold', 'best_threshold', 'p', 'n', 'n_nodes','fprs', 'tprs', 'thresholds', 'classifier', 'classifier_params'])" ] }, { @@ -269,7 +282,7 @@ "metadata": {}, "outputs": [], "source": [ - "data.to_csv('raw-single.csv', index=False)" + "data.to_csv('raw-single3.csv', index=False)" ] } ], diff --git a/notebooks/auc_experiments/02-processing-aggregated.ipynb b/notebooks/auc_experiments/02-processing-aggregated.ipynb index 3c29cf3..9706cb7 100644 --- a/notebooks/auc_experiments/02-processing-aggregated.ipynb +++ b/notebooks/auc_experiments/02-processing-aggregated.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -16,16 +16,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "data = pd.read_csv('raw-aggregated.csv')" + "data = pd.read_csv('raw-aggregated3.csv')" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -72,16 +72,16 @@ " 0\n", " bupa\n", " 8\n", - " 0.562368\n", - " 0.855994\n", - " 0.350000\n", + " 0.695560\n", + " 0.663377\n", + " 0.720000\n", " 0.751001\n", - " 0.733417\n", - " 0.545687\n", - " 0.870000\n", - " 0.265560\n", - " 0.556736\n", - " 0.733417\n", + " 0.730510\n", + " 0.580409\n", + " 0.840000\n", + " 0.420530\n", + " 0.508864\n", + " 0.730510\n", " 145\n", " 200\n", " \n", @@ -108,14 +108,14 @@ " 2\n", " haberman\n", " 3\n", - " 0.637255\n", - " 0.703704\n", - " 0.613333\n", + " 0.653595\n", + " 0.617284\n", + " 0.666667\n", " 0.699095\n", " 0.738562\n", " 0.012346\n", " 1.000000\n", - " 0.226170\n", + " 0.264706\n", " 0.885074\n", " 0.738562\n", " 81\n", @@ -126,14 +126,14 @@ " 3\n", " dermatology-6\n", " 2\n", - " 0.055866\n", - " 1.000000\n", - " 0.000000\n", + " 0.966480\n", + " 0.950000\n", + " 0.967456\n", " 0.970710\n", " 0.980447\n", " 0.850000\n", " 0.988166\n", - " 0.000000\n", + " 0.055866\n", " 0.750000\n", " 0.980447\n", " 20\n", @@ -144,14 +144,14 @@ " 4\n", " monk-2\n", " 3\n", - " 0.909722\n", + " 0.974537\n", " 1.000000\n", - " 0.828947\n", + " 0.951754\n", " 1.000000\n", " 0.979167\n", " 1.000000\n", " 0.960526\n", - " 0.196400\n", + " 0.472222\n", " 0.595344\n", " 0.979167\n", " 204\n", @@ -163,18 +163,18 @@ ], "text/plain": [ " Unnamed: 0 dataset k acc sens spec auc \\\n", - "0 0 bupa 8 0.562368 0.855994 0.350000 0.751001 \n", + "0 0 bupa 8 0.695560 0.663377 0.720000 0.751001 \n", "1 1 new_thyroid1 4 0.925577 1.000000 0.911111 0.994483 \n", - "2 2 haberman 3 0.637255 0.703704 0.613333 0.699095 \n", - "3 3 dermatology-6 2 0.055866 1.000000 0.000000 0.970710 \n", - "4 4 monk-2 3 0.909722 1.000000 0.828947 1.000000 \n", + "2 2 haberman 3 0.653595 0.617284 0.666667 0.699095 \n", + "3 3 dermatology-6 2 0.966480 0.950000 0.967456 0.970710 \n", + "4 4 monk-2 3 0.974537 1.000000 0.951754 1.000000 \n", "\n", " best_acc best_sens best_spec threshold best_threshold best_acc_orig \\\n", - "0 0.733417 0.545687 0.870000 0.265560 0.556736 0.733417 \n", + "0 0.730510 0.580409 0.840000 0.420530 0.508864 0.730510 \n", "1 0.962788 0.888889 0.977778 0.166667 0.333333 0.962788 \n", - "2 0.738562 0.012346 1.000000 0.226170 0.885074 0.738562 \n", - "3 0.980447 0.850000 0.988166 0.000000 0.750000 0.980447 \n", - "4 0.979167 1.000000 0.960526 0.196400 0.595344 0.979167 \n", + "2 0.738562 0.012346 1.000000 0.264706 0.885074 0.738562 \n", + "3 0.980447 0.850000 0.988166 0.055866 0.750000 0.980447 \n", + "4 0.979167 1.000000 0.960526 0.472222 0.595344 0.979167 \n", "\n", " p n \n", "0 145 200 \n", @@ -184,7 +184,7 @@ "4 204 228 " ] }, - "execution_count": 3, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -205,34 +205,34 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def wrapper(func, **kwargs):\n", " #try:\n", - " return func(**kwargs)\n", + " return func(**kwargs)[0]\n", " #except:\n", " # return None" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "def wrapper_debug(func, **kwargs):\n", " try:\n", " #print(kwargs, flush=True)\n", - " return func(**kwargs)\n", + " return func(**kwargs)[0]\n", " except Exception as exc:\n", " return str(exc)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -280,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -329,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -339,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -368,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -397,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -407,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -436,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -465,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -518,9 +518,9 @@ " 4\n", " monk-2\n", " 3\n", - " 0.909722\n", + " 0.974537\n", " 1.000000\n", - " 0.828947\n", + " 0.951754\n", " 1.000000\n", " 0.979167\n", " 1.000000\n", @@ -529,22 +529,22 @@ " 1.0\n", " 0.999138\n", " 0.999138\n", - " 0.472175\n", - " 0.472222\n", - " 1.000000\n", - " 1.000000\n", - " 1.0\n", - " 1.000000\n", - " 1.000000\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", " \n", " \n", " 5\n", " 5\n", " page-blocks-1-3_vs_4\n", " 9\n", - " 0.059305\n", + " 0.940695\n", " 1.000000\n", - " 0.000000\n", + " 0.937007\n", " 1.000000\n", " 0.997863\n", " 1.000000\n", @@ -553,22 +553,22 @@ " 1.0\n", " 0.999963\n", " 0.999963\n", - " 0.059297\n", - " 0.059305\n", - " 1.000000\n", - " 1.000000\n", - " 1.0\n", - " 1.000000\n", - " 1.000000\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", " \n", " \n", " 26\n", " 26\n", " new_thyroid1\n", " 6\n", - " 0.361772\n", + " 0.953571\n", " 1.000000\n", - " 0.238889\n", + " 0.944444\n", " 0.999074\n", " 0.995370\n", " 0.972222\n", @@ -577,22 +577,22 @@ " 1.0\n", " 0.999925\n", " 0.999925\n", - " 0.162527\n", - " 0.162698\n", - " 0.999882\n", - " 0.999882\n", - " 1.0\n", - " 0.999882\n", - " 0.999882\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", " \n", " \n", " 30\n", " 30\n", " iris0\n", " 3\n", - " 0.333333\n", " 1.000000\n", - " 0.000000\n", + " 1.000000\n", + " 1.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", @@ -601,22 +601,22 @@ " 1.0\n", " 1.0\n", " 1.0\n", - " 0.333299\n", - " 0.333333\n", - " 1.000000\n", - " 1.000000\n", - " 1.0\n", - " 1.000000\n", - " 1.000000\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", " \n", " \n", " 31\n", " 31\n", " shuttle-c0-vs-c4\n", " 8\n", - " 0.067247\n", " 1.000000\n", - " 0.000000\n", + " 1.000000\n", + " 1.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", @@ -625,13 +625,13 @@ " 1.0\n", " 1.0\n", " 1.0\n", - " 0.067240\n", - " 0.067247\n", - " 1.000000\n", - " 1.000000\n", - " 0.998763\n", - " 1.000000\n", - " 1.000000\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", " \n", " \n", " ...\n", @@ -658,13 +658,13 @@ " ...\n", " \n", " \n", - " 9918\n", - " 9918\n", + " 9919\n", + " 9919\n", " new_thyroid1\n", " 10\n", - " 0.162338\n", + " 0.957576\n", " 1.000000\n", - " 0.000000\n", + " 0.950000\n", " 0.999074\n", " 0.972294\n", " 0.866667\n", @@ -673,22 +673,22 @@ " 1.0\n", " 0.99719\n", " 0.99719\n", - " 0.162151\n", - " 0.162338\n", - " 0.999882\n", - " 0.999882\n", - " 1.0\n", - " 0.999882\n", - " 0.999882\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", " \n", " \n", - " 9943\n", - " 9943\n", + " 9944\n", + " 9944\n", " new_thyroid1\n", " 7\n", - " 0.897542\n", + " 0.948848\n", " 1.000000\n", - " 0.877582\n", + " 0.938901\n", " 1.000000\n", " 0.995238\n", " 0.971429\n", @@ -697,22 +697,22 @@ " 1.0\n", " 0.99992\n", " 0.99992\n", - " 0.162810\n", - " 0.162826\n", - " 1.000000\n", - " 1.000000\n", - " 1.0\n", - " 1.000000\n", - " 1.000000\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", " \n", " \n", - " 9944\n", - " 9944\n", + " 9945\n", + " 9945\n", " vowel0\n", " 6\n", - " 0.091094\n", - " 1.000000\n", - " 0.000000\n", + " 0.959522\n", + " 0.988889\n", + " 0.956577\n", " 0.999109\n", " 0.995941\n", " 0.966667\n", @@ -721,22 +721,22 @@ " 1.0\n", " 0.999905\n", " 0.999905\n", - " 0.091003\n", - " 0.091094\n", - " 0.999928\n", - " 0.999928\n", - " 0.987187\n", - " 0.999928\n", - " 0.999928\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", " \n", " \n", - " 9972\n", - " 9972\n", + " 9973\n", + " 9973\n", " shuttle-c0-vs-c4\n", " 9\n", - " 0.067248\n", " 1.000000\n", - " 0.000000\n", + " 1.000000\n", + " 1.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", @@ -745,21 +745,21 @@ " 1.0\n", " 1.0\n", " 1.0\n", - " 0.067241\n", - " 0.067248\n", - " 1.000000\n", - " 1.000000\n", - " 0.998808\n", - " 1.000000\n", - " 1.000000\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", " \n", " \n", - " 9990\n", - " 9990\n", + " 9991\n", + " 9991\n", " iris0\n", " 7\n", - " 0.966296\n", - " 0.897959\n", + " 1.000000\n", + " 1.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", @@ -769,13 +769,13 @@ " 1.0\n", " 1.0\n", " 1.0\n", - " 0.333297\n", - " 0.333333\n", - " 1.000000\n", - " 1.000000\n", - " 1.0\n", - " 1.000000\n", - " 1.000000\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", " \n", " \n", "\n", @@ -784,17 +784,17 @@ ], "text/plain": [ " Unnamed: 0 dataset k acc sens spec \\\n", - "4 4 monk-2 3 0.909722 1.000000 0.828947 \n", - "5 5 page-blocks-1-3_vs_4 9 0.059305 1.000000 0.000000 \n", - "26 26 new_thyroid1 6 0.361772 1.000000 0.238889 \n", - "30 30 iris0 3 0.333333 1.000000 0.000000 \n", - "31 31 shuttle-c0-vs-c4 8 0.067247 1.000000 0.000000 \n", + "4 4 monk-2 3 0.974537 1.000000 0.951754 \n", + "5 5 page-blocks-1-3_vs_4 9 0.940695 1.000000 0.937007 \n", + "26 26 new_thyroid1 6 0.953571 1.000000 0.944444 \n", + "30 30 iris0 3 1.000000 1.000000 1.000000 \n", + "31 31 shuttle-c0-vs-c4 8 1.000000 1.000000 1.000000 \n", "... ... ... .. ... ... ... \n", - "9918 9918 new_thyroid1 10 0.162338 1.000000 0.000000 \n", - "9943 9943 new_thyroid1 7 0.897542 1.000000 0.877582 \n", - "9944 9944 vowel0 6 0.091094 1.000000 0.000000 \n", - "9972 9972 shuttle-c0-vs-c4 9 0.067248 1.000000 0.000000 \n", - "9990 9990 iris0 7 0.966296 0.897959 1.000000 \n", + "9919 9919 new_thyroid1 10 0.957576 1.000000 0.950000 \n", + "9944 9944 new_thyroid1 7 0.948848 1.000000 0.938901 \n", + "9945 9945 vowel0 6 0.959522 0.988889 0.956577 \n", + "9973 9973 shuttle-c0-vs-c4 9 1.000000 1.000000 1.000000 \n", + "9991 9991 iris0 7 1.000000 1.000000 1.000000 \n", "\n", " auc best_acc best_sens best_spec ... auc_amax_best auc_maxa \\\n", "4 1.000000 0.979167 1.000000 0.960526 ... 1.0 0.999138 \n", @@ -803,42 +803,68 @@ "30 1.000000 1.000000 1.000000 1.000000 ... 1.0 1.0 \n", "31 1.000000 1.000000 1.000000 1.000000 ... 1.0 1.0 \n", "... ... ... ... ... ... ... ... \n", - "9918 0.999074 0.972294 0.866667 0.994444 ... 1.0 0.99719 \n", - "9943 1.000000 0.995238 0.971429 1.000000 ... 1.0 0.99992 \n", - "9944 0.999109 0.995941 0.966667 0.998881 ... 1.0 0.999905 \n", - "9972 1.000000 1.000000 1.000000 1.000000 ... 1.0 1.0 \n", - "9990 1.000000 1.000000 1.000000 1.000000 ... 1.0 1.0 \n", + "9919 0.999074 0.972294 0.866667 0.994444 ... 1.0 0.99719 \n", + "9944 1.000000 0.995238 0.971429 1.000000 ... 1.0 0.99992 \n", + "9945 0.999109 0.995941 0.966667 0.998881 ... 1.0 0.999905 \n", + "9973 1.000000 1.000000 1.000000 1.000000 ... 1.0 1.0 \n", + "9991 1.000000 1.000000 1.000000 1.000000 ... 1.0 1.0 \n", + "\n", + " auc_maxa_best acc_min \\\n", + "4 0.999138 'float' object is not subscriptable \n", + "5 0.999963 'float' object is not subscriptable \n", + "26 0.999925 'float' object is not subscriptable \n", + "30 1.0 'float' object is not subscriptable \n", + "31 1.0 'float' object is not subscriptable \n", + "... ... ... \n", + "9919 0.99719 'float' object is not subscriptable \n", + "9944 0.99992 'float' object is not subscriptable \n", + "9945 0.999905 'float' object is not subscriptable \n", + "9973 1.0 'float' object is not subscriptable \n", + "9991 1.0 'float' object is not subscriptable \n", + "\n", + " acc_rmin acc_max \\\n", + "4 invalid index to scalar variable. 'float' object is not subscriptable \n", + "5 invalid index to scalar variable. 'float' object is not subscriptable \n", + "26 invalid index to scalar variable. 'float' object is not subscriptable \n", + "30 invalid index to scalar variable. 'float' object is not subscriptable \n", + "31 invalid index to scalar variable. 'float' object is not subscriptable \n", + "... ... ... \n", + "9919 invalid index to scalar variable. 'float' object is not subscriptable \n", + "9944 invalid index to scalar variable. 'float' object is not subscriptable \n", + "9945 invalid index to scalar variable. 'float' object is not subscriptable \n", + "9973 invalid index to scalar variable. 'float' object is not subscriptable \n", + "9991 invalid index to scalar variable. 'float' object is not subscriptable \n", "\n", - " auc_maxa_best acc_min acc_rmin acc_max acc_rmax max_acc_min \\\n", - "4 0.999138 0.472175 0.472222 1.000000 1.000000 1.0 \n", - "5 0.999963 0.059297 0.059305 1.000000 1.000000 1.0 \n", - "26 0.999925 0.162527 0.162698 0.999882 0.999882 1.0 \n", - "30 1.0 0.333299 0.333333 1.000000 1.000000 1.0 \n", - "31 1.0 0.067240 0.067247 1.000000 1.000000 0.998763 \n", - "... ... ... ... ... ... ... \n", - "9918 0.99719 0.162151 0.162338 0.999882 0.999882 1.0 \n", - "9943 0.99992 0.162810 0.162826 1.000000 1.000000 1.0 \n", - "9944 0.999905 0.091003 0.091094 0.999928 0.999928 0.987187 \n", - "9972 1.0 0.067241 0.067248 1.000000 1.000000 0.998808 \n", - "9990 1.0 0.333297 0.333333 1.000000 1.000000 1.0 \n", + " acc_rmax max_acc_min \\\n", + "4 invalid index to scalar variable. 'float' object is not subscriptable \n", + "5 invalid index to scalar variable. 'float' object is not subscriptable \n", + "26 invalid index to scalar variable. 'float' object is not subscriptable \n", + "30 invalid index to scalar variable. 'float' object is not subscriptable \n", + "31 invalid index to scalar variable. invalid index to scalar variable. \n", + "... ... ... \n", + "9919 invalid index to scalar variable. 'float' object is not subscriptable \n", + "9944 invalid index to scalar variable. 'float' object is not subscriptable \n", + "9945 invalid index to scalar variable. invalid index to scalar variable. \n", + "9973 invalid index to scalar variable. invalid index to scalar variable. \n", + "9991 invalid index to scalar variable. 'float' object is not subscriptable \n", "\n", - " max_acc_max max_acc_rmax \n", - "4 1.000000 1.000000 \n", - "5 1.000000 1.000000 \n", - "26 0.999882 0.999882 \n", - "30 1.000000 1.000000 \n", - "31 1.000000 1.000000 \n", - "... ... ... \n", - "9918 0.999882 0.999882 \n", - "9943 1.000000 1.000000 \n", - "9944 0.999928 0.999928 \n", - "9972 1.000000 1.000000 \n", - "9990 1.000000 1.000000 \n", + " max_acc_max max_acc_rmax \n", + "4 'float' object is not subscriptable invalid index to scalar variable. \n", + "5 'float' object is not subscriptable invalid index to scalar variable. \n", + "26 'float' object is not subscriptable invalid index to scalar variable. \n", + "30 'float' object is not subscriptable invalid index to scalar variable. \n", + "31 'float' object is not subscriptable invalid index to scalar variable. \n", + "... ... ... \n", + "9919 'float' object is not subscriptable invalid index to scalar variable. \n", + "9944 'float' object is not subscriptable invalid index to scalar variable. \n", + "9945 'float' object is not subscriptable invalid index to scalar variable. \n", + "9973 'float' object is not subscriptable invalid index to scalar variable. \n", + "9991 'float' object is not subscriptable invalid index to scalar variable. \n", "\n", "[1204 rows x 36 columns]" ] }, - "execution_count": 15, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -849,11 +875,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ - "data.to_csv('processed-aggregated.csv', index=False)" + "data.to_csv('processed-aggregated3.csv', index=False)" ] } ], diff --git a/notebooks/auc_experiments/02-processing.ipynb b/notebooks/auc_experiments/02-processing.ipynb index 9870d9a..d7a49ad 100644 --- a/notebooks/auc_experiments/02-processing.ipynb +++ b/notebooks/auc_experiments/02-processing.ipynb @@ -20,7 +20,7 @@ "metadata": {}, "outputs": [], "source": [ - "data = pd.read_csv('raw-single.csv')" + "data = pd.read_csv('raw-single3.csv')" ] }, { @@ -50,7 +50,6 @@ " \n", " \n", " dataset\n", - " classifier\n", " acc\n", " sens\n", " spec\n", @@ -62,209 +61,198 @@ " best_threshold\n", " p\n", " n\n", + " n_nodes\n", " \n", " \n", " \n", " \n", " 0\n", " bupa\n", - " {'max_depth': 9, 'random_state': 5}\n", + " 0.637681\n", + " 0.600000\n", + " 0.689655\n", + " 0.715086\n", + " 0.724638\n", + " 0.875000\n", + " 0.517241\n", " 0.579710\n", - " 0.000000\n", - " 1.000000\n", - " 0.605603\n", - " 0.608696\n", - " 0.586207\n", - " 0.625000\n", - " inf\n", - " 1.000000\n", - " 29\n", + " 0.571429\n", " 40\n", + " 29\n", + " 8\n", " \n", " \n", " 1\n", - " vehicle0\n", - " {'probability': True, 'C': 1.4971919355651315}\n", - " 0.847059\n", - " 0.868421\n", - " 0.840909\n", - " 0.934809\n", - " 0.882353\n", - " 0.842105\n", - " 0.893939\n", - " 0.271316\n", - " 0.339899\n", - " 38\n", - " 132\n", + " dermatology-6\n", + " 0.944444\n", + " 0.941176\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 0.944056\n", + " 0.906440\n", + " 68\n", + " 4\n", + " 12\n", " \n", " \n", " 2\n", - " yeast1\n", - " {'probability': True, 'C': 0.5420412117184014}\n", - " 0.646465\n", - " 0.802326\n", - " 0.582938\n", - " 0.788659\n", - " 0.797980\n", - " 0.488372\n", - " 0.924171\n", - " 0.208912\n", - " 0.373224\n", - " 86\n", - " 211\n", + " glass0\n", + " 0.674419\n", + " 0.655172\n", + " 0.714286\n", + " 0.806650\n", + " 0.767442\n", + " 0.896552\n", + " 0.500000\n", + " 0.672515\n", + " 0.375000\n", + " 29\n", + " 14\n", + " 9\n", " \n", " \n", " 3\n", " yeast1\n", - " {'max_depth': 2, 'random_state': 5}\n", - " 0.723906\n", - " 0.685393\n", - " 0.740385\n", - " 0.810960\n", - " 0.791246\n", - " 0.426966\n", - " 0.947115\n", - " 0.301002\n", - " 0.371073\n", - " 89\n", - " 208\n", + " 0.750842\n", + " 0.805687\n", + " 0.616279\n", + " 0.808332\n", + " 0.784512\n", + " 0.886256\n", + " 0.534884\n", + " 0.711036\n", + " 0.635724\n", + " 211\n", + " 86\n", + " 92\n", " \n", " \n", " 4\n", - " page-blocks-1-3_vs_4\n", - " {'max_depth': 7, 'random_state': 5}\n", - " 0.947368\n", + " iris0\n", + " 1.000000\n", + " 1.000000\n", " 1.000000\n", - " 0.945652\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", - " 0.080000\n", - " 0.850000\n", + " 0.333333\n", + " 1.000000\n", + " 10\n", + " 20\n", " 3\n", - " 92\n", " \n", " \n", " 5\n", - " CM1\n", - " {'max_depth': 7, 'random_state': 5}\n", - " 0.640000\n", - " 0.666667\n", - " 0.635294\n", - " 0.774902\n", - " 0.870000\n", - " 0.133333\n", - " 1.000000\n", - " 0.103770\n", - " 0.403494\n", - " 15\n", - " 85\n", + " ecoli1\n", + " 0.911765\n", + " 0.903846\n", + " 0.937500\n", + " 0.971755\n", + " 0.911765\n", + " 0.903846\n", + " 0.937500\n", + " 0.772388\n", + " 0.833333\n", + " 52\n", + " 16\n", + " 8\n", " \n", " \n", " 6\n", - " monk-2\n", - " {'probability': True, 'C': 0.6975343728942637}\n", - " 0.471264\n", - " 0.000000\n", - " 1.000000\n", - " 1.000000\n", - " 1.000000\n", - " 1.000000\n", - " 1.000000\n", - " inf\n", - " 0.800915\n", + " ionosphere\n", + " 0.845070\n", + " 0.600000\n", + " 0.978261\n", + " 0.937391\n", + " 0.929577\n", + " 0.920000\n", + " 0.934783\n", + " 0.360714\n", + " 0.125000\n", + " 25\n", " 46\n", - " 41\n", + " 9\n", " \n", " \n", " 7\n", - " page-blocks-1-3_vs_4\n", - " {'n_neighbors': 4}\n", - " 0.968421\n", - " 0.000000\n", - " 1.000000\n", - " 0.978261\n", - " 0.978947\n", - " 0.333333\n", - " 1.000000\n", - " inf\n", - " 1.000000\n", - " 3\n", - " 92\n", + " saheart\n", + " 0.666667\n", + " 0.672131\n", + " 0.656250\n", + " 0.727459\n", + " 0.752688\n", + " 0.852459\n", + " 0.562500\n", + " 0.653117\n", + " 0.550000\n", + " 61\n", + " 32\n", + " 8\n", " \n", " \n", " 8\n", " wdbc\n", - " {'probability': True, 'C': 0.5570006378295725}\n", - " 0.929825\n", - " 0.934783\n", - " 0.926471\n", - " 0.977302\n", - " 0.929825\n", - " 0.826087\n", - " 1.000000\n", - " 0.277218\n", - " 0.500000\n", - " 46\n", - " 68\n", + " 0.877193\n", + " 0.880952\n", + " 0.875000\n", + " 0.956349\n", + " 0.903509\n", + " 0.809524\n", + " 0.958333\n", + " 0.373626\n", + " 0.588016\n", + " 42\n", + " 72\n", + " 22\n", " \n", " \n", " 9\n", - " ecoli1\n", - " {'max_depth': 8, 'random_state': 5}\n", - " 0.823529\n", - " 0.076923\n", - " 1.000000\n", - " 0.981818\n", - " 0.955882\n", - " 0.923077\n", - " 0.963636\n", - " 0.913128\n", - " 0.494691\n", - " 13\n", - " 55\n", + " appendicitis\n", + " 0.727273\n", + " 0.722222\n", + " 0.750000\n", + " 0.819444\n", + " 0.909091\n", + " 0.944444\n", + " 0.750000\n", + " 0.797619\n", + " 0.500000\n", + " 18\n", + " 4\n", + " 4\n", " \n", " \n", "\n", "" ], "text/plain": [ - " dataset classifier \\\n", - "0 bupa {'max_depth': 9, 'random_state': 5} \n", - "1 vehicle0 {'probability': True, 'C': 1.4971919355651315} \n", - "2 yeast1 {'probability': True, 'C': 0.5420412117184014} \n", - "3 yeast1 {'max_depth': 2, 'random_state': 5} \n", - "4 page-blocks-1-3_vs_4 {'max_depth': 7, 'random_state': 5} \n", - "5 CM1 {'max_depth': 7, 'random_state': 5} \n", - "6 monk-2 {'probability': True, 'C': 0.6975343728942637} \n", - "7 page-blocks-1-3_vs_4 {'n_neighbors': 4} \n", - "8 wdbc {'probability': True, 'C': 0.5570006378295725} \n", - "9 ecoli1 {'max_depth': 8, 'random_state': 5} \n", + " dataset acc sens spec auc best_acc best_sens \\\n", + "0 bupa 0.637681 0.600000 0.689655 0.715086 0.724638 0.875000 \n", + "1 dermatology-6 0.944444 0.941176 1.000000 1.000000 1.000000 1.000000 \n", + "2 glass0 0.674419 0.655172 0.714286 0.806650 0.767442 0.896552 \n", + "3 yeast1 0.750842 0.805687 0.616279 0.808332 0.784512 0.886256 \n", + "4 iris0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "5 ecoli1 0.911765 0.903846 0.937500 0.971755 0.911765 0.903846 \n", + "6 ionosphere 0.845070 0.600000 0.978261 0.937391 0.929577 0.920000 \n", + "7 saheart 0.666667 0.672131 0.656250 0.727459 0.752688 0.852459 \n", + "8 wdbc 0.877193 0.880952 0.875000 0.956349 0.903509 0.809524 \n", + "9 appendicitis 0.727273 0.722222 0.750000 0.819444 0.909091 0.944444 \n", "\n", - " acc sens spec auc best_acc best_sens best_spec \\\n", - "0 0.579710 0.000000 1.000000 0.605603 0.608696 0.586207 0.625000 \n", - "1 0.847059 0.868421 0.840909 0.934809 0.882353 0.842105 0.893939 \n", - "2 0.646465 0.802326 0.582938 0.788659 0.797980 0.488372 0.924171 \n", - "3 0.723906 0.685393 0.740385 0.810960 0.791246 0.426966 0.947115 \n", - "4 0.947368 1.000000 0.945652 1.000000 1.000000 1.000000 1.000000 \n", - "5 0.640000 0.666667 0.635294 0.774902 0.870000 0.133333 1.000000 \n", - "6 0.471264 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n", - "7 0.968421 0.000000 1.000000 0.978261 0.978947 0.333333 1.000000 \n", - "8 0.929825 0.934783 0.926471 0.977302 0.929825 0.826087 1.000000 \n", - "9 0.823529 0.076923 1.000000 0.981818 0.955882 0.923077 0.963636 \n", - "\n", - " threshold best_threshold p n \n", - "0 inf 1.000000 29 40 \n", - "1 0.271316 0.339899 38 132 \n", - "2 0.208912 0.373224 86 211 \n", - "3 0.301002 0.371073 89 208 \n", - "4 0.080000 0.850000 3 92 \n", - "5 0.103770 0.403494 15 85 \n", - "6 inf 0.800915 46 41 \n", - "7 inf 1.000000 3 92 \n", - "8 0.277218 0.500000 46 68 \n", - "9 0.913128 0.494691 13 55 " + " best_spec threshold best_threshold p n n_nodes \n", + "0 0.517241 0.579710 0.571429 40 29 8 \n", + "1 1.000000 0.944056 0.906440 68 4 12 \n", + "2 0.500000 0.672515 0.375000 29 14 9 \n", + "3 0.534884 0.711036 0.635724 211 86 92 \n", + "4 1.000000 0.333333 1.000000 10 20 3 \n", + "5 0.937500 0.772388 0.833333 52 16 8 \n", + "6 0.934783 0.360714 0.125000 25 46 9 \n", + "7 0.562500 0.653117 0.550000 61 32 8 \n", + "8 0.958333 0.373626 0.588016 42 72 22 \n", + "9 0.750000 0.797619 0.500000 18 4 4 " ] }, "execution_count": 20, @@ -284,8 +272,8 @@ { "data": { "text/plain": [ - "Index(['dataset', 'classifier', 'acc', 'sens', 'spec', 'auc', 'best_acc',\n", - " 'best_sens', 'best_spec', 'threshold', 'best_threshold', 'p', 'n'],\n", + "Index(['dataset', 'acc', 'sens', 'spec', 'auc', 'best_acc', 'best_sens',\n", + " 'best_spec', 'threshold', 'best_threshold', 'p', 'n', 'n_nodes'],\n", " dtype='object')" ] }, @@ -305,7 +293,9 @@ "outputs": [], "source": [ "lower_bounds = ['min', 'rmin', 'grmin', 'amin', 'armin', 'onmin']\n", - "upper_bounds = ['max', 'amax', 'maxa']" + "#lower_bounds = ['min']\n", + "upper_bounds = ['max', 'amax', 'maxa']\n", + "#upper_bounds = ['max']" ] }, { @@ -316,7 +306,7 @@ "source": [ "def wrapper(func, **kwargs):\n", " try:\n", - " return func(**kwargs)\n", + " return func(**kwargs)[0]\n", " except Exception as exc:\n", " return str(exc)" ] @@ -523,14 +513,15 @@ { "data": { "text/plain": [ - "Index(['dataset', 'classifier', 'acc', 'sens', 'spec', 'auc', 'best_acc',\n", - " 'best_sens', 'best_spec', 'threshold', 'best_threshold', 'p', 'n',\n", + "Index(['dataset', 'acc', 'sens', 'spec', 'auc', 'best_acc', 'best_sens',\n", + " 'best_spec', 'threshold', 'best_threshold', 'p', 'n', 'n_nodes',\n", " 'auc_min', 'auc_min_best', 'auc_rmin', 'auc_rmin_best', 'auc_grmin',\n", " 'auc_grmin_best', 'auc_amin', 'auc_amin_best', 'auc_armin',\n", " 'auc_armin_best', 'auc_onmin', 'auc_onmin_best', 'auc_max',\n", " 'auc_max_best', 'auc_amax', 'auc_amax_best', 'auc_maxa',\n", " 'auc_maxa_best', 'acc_min', 'acc_rmin', 'acc_max', 'acc_rmax',\n", - " 'max_acc_min', 'max_acc_max', 'max_acc_rmax', 'max_acc_onmax'],\n", + " 'acc_onmax', 'max_acc_min', 'max_acc_max', 'max_acc_rmax',\n", + " 'max_acc_onmax'],\n", " dtype='object')" ] }, @@ -570,7 +561,6 @@ " \n", " \n", " dataset\n", - " classifier\n", " acc\n", " sens\n", " spec\n", @@ -579,13 +569,14 @@ " best_sens\n", " best_spec\n", " threshold\n", + " best_threshold\n", " ...\n", - " auc_maxa\n", " auc_maxa_best\n", " acc_min\n", " acc_rmin\n", " acc_max\n", " acc_rmax\n", + " acc_onmax\n", " max_acc_min\n", " max_acc_max\n", " max_acc_rmax\n", @@ -596,158 +587,172 @@ " \n", " 0\n", " bupa\n", - " {'max_depth': 9, 'random_state': 5}\n", + " 0.637681\n", + " 0.600000\n", + " 0.689655\n", + " 0.715086\n", + " 0.724638\n", + " 0.875000\n", + " 0.517241\n", " 0.579710\n", - " 0.000000\n", - " 1.000000\n", - " 0.605603\n", - " 0.608696\n", - " 0.586207\n", - " 0.625000\n", - " inf\n", + " 0.571429\n", " ...\n", - " 0.685936\n", - " 0.685936\n", - " 0.254487\n", - " 0.42029\n", - " 0.834281\n", - " 0.772955\n", - " 0.579710\n", - " 0.834281\n", - " 0.772955\n", - " 0.668562\n", + " 0.844510\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 0.627327\n", + " 0.880296\n", + " 0.855432\n", + " 0.760591\n", " \n", " \n", " 1\n", - " vehicle0\n", - " {'probability': True, 'C': 1.4971919355651315}\n", - " 0.847059\n", - " 0.868421\n", - " 0.840909\n", - " 0.934809\n", - " 0.882353\n", - " 0.842105\n", - " 0.893939\n", - " 0.271316\n", + " dermatology-6\n", + " 0.944444\n", + " 0.941176\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 0.944056\n", + " 0.906440\n", " ...\n", - " 0.960195\n", - " 0.960195\n", - " 0.208935\n", - " 0.223529\n", - " 0.985450\n", - " 0.984943\n", - " 0.849453\n", - " 0.985450\n", - " 0.984943\n", - " 0.970900\n", + " 1.000000\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 0.996761\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", " \n", " \n", " 2\n", - " yeast1\n", - " {'probability': True, 'C': 0.5420412117184014}\n", - " 0.646465\n", - " 0.802326\n", - " 0.582938\n", - " 0.788659\n", - " 0.797980\n", - " 0.488372\n", - " 0.924171\n", - " 0.208912\n", + " glass0\n", + " 0.674419\n", + " 0.655172\n", + " 0.714286\n", + " 0.806650\n", + " 0.767442\n", + " 0.896552\n", + " 0.500000\n", + " 0.672515\n", + " 0.375000\n", " ...\n", - " 0.900903\n", - " 0.900903\n", - " 0.228337\n", - " 0.289562\n", - " 0.938832\n", - " 0.930489\n", - " 0.710438\n", - " 0.938832\n", - " 0.930489\n", - " 0.877665\n", + " 0.876953\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 0.708530\n", + " 0.937081\n", + " 0.929434\n", + " 0.874163\n", " \n", " \n", " 3\n", " yeast1\n", - " {'max_depth': 2, 'random_state': 5}\n", - " 0.723906\n", - " 0.685393\n", - " 0.740385\n", - " 0.810960\n", - " 0.791246\n", - " 0.426966\n", - " 0.947115\n", - " 0.301002\n", + " 0.750842\n", + " 0.805687\n", + " 0.616279\n", + " 0.808332\n", + " 0.784512\n", + " 0.886256\n", + " 0.534884\n", + " 0.711036\n", + " 0.635724\n", " ...\n", - " 0.896275\n", - " 0.896275\n", - " 0.242985\n", - " 0.299663\n", - " 0.943382\n", - " 0.936695\n", - " 0.718242\n", - " 0.943382\n", - " 0.936695\n", - " 0.886764\n", + " 0.887242\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 0.719110\n", + " 0.944529\n", + " 0.937862\n", + " 0.889059\n", " \n", " \n", " 4\n", - " page-blocks-1-3_vs_4\n", - " {'max_depth': 7, 'random_state': 5}\n", - " 0.947368\n", + " iris0\n", " 1.000000\n", - " 0.945652\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", - " 0.080000\n", - " ...\n", " 1.000000\n", " 1.000000\n", - " 0.031576\n", - " 0.031579\n", + " 0.333333\n", " 1.000000\n", + " ...\n", " 1.000000\n", - " 0.997527\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " 'float' object is not subscriptable\n", + " invalid index to scalar variable.\n", + " 'float' object is not subscriptable\n", + " 0.993333\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", " \n", " \n", "\n", - "

5 rows × 39 columns

\n", + "

5 rows × 40 columns

\n", "" ], "text/plain": [ - " dataset classifier \\\n", - "0 bupa {'max_depth': 9, 'random_state': 5} \n", - "1 vehicle0 {'probability': True, 'C': 1.4971919355651315} \n", - "2 yeast1 {'probability': True, 'C': 0.5420412117184014} \n", - "3 yeast1 {'max_depth': 2, 'random_state': 5} \n", - "4 page-blocks-1-3_vs_4 {'max_depth': 7, 'random_state': 5} \n", + " dataset acc sens spec auc best_acc best_sens \\\n", + "0 bupa 0.637681 0.600000 0.689655 0.715086 0.724638 0.875000 \n", + "1 dermatology-6 0.944444 0.941176 1.000000 1.000000 1.000000 1.000000 \n", + "2 glass0 0.674419 0.655172 0.714286 0.806650 0.767442 0.896552 \n", + "3 yeast1 0.750842 0.805687 0.616279 0.808332 0.784512 0.886256 \n", + "4 iris0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " best_spec threshold best_threshold ... auc_maxa_best \\\n", + "0 0.517241 0.579710 0.571429 ... 0.844510 \n", + "1 1.000000 0.944056 0.906440 ... 1.000000 \n", + "2 0.500000 0.672515 0.375000 ... 0.876953 \n", + "3 0.534884 0.711036 0.635724 ... 0.887242 \n", + "4 1.000000 0.333333 1.000000 ... 1.000000 \n", + "\n", + " acc_min acc_rmin \\\n", + "0 'float' object is not subscriptable 'float' object is not subscriptable \n", + "1 'float' object is not subscriptable 'float' object is not subscriptable \n", + "2 'float' object is not subscriptable 'float' object is not subscriptable \n", + "3 'float' object is not subscriptable 'float' object is not subscriptable \n", + "4 'float' object is not subscriptable 'float' object is not subscriptable \n", "\n", - " acc sens spec auc best_acc best_sens best_spec \\\n", - "0 0.579710 0.000000 1.000000 0.605603 0.608696 0.586207 0.625000 \n", - "1 0.847059 0.868421 0.840909 0.934809 0.882353 0.842105 0.893939 \n", - "2 0.646465 0.802326 0.582938 0.788659 0.797980 0.488372 0.924171 \n", - "3 0.723906 0.685393 0.740385 0.810960 0.791246 0.426966 0.947115 \n", - "4 0.947368 1.000000 0.945652 1.000000 1.000000 1.000000 1.000000 \n", + " acc_max acc_rmax \\\n", + "0 'float' object is not subscriptable invalid index to scalar variable. \n", + "1 'float' object is not subscriptable invalid index to scalar variable. \n", + "2 'float' object is not subscriptable invalid index to scalar variable. \n", + "3 'float' object is not subscriptable invalid index to scalar variable. \n", + "4 'float' object is not subscriptable invalid index to scalar variable. \n", "\n", - " threshold ... auc_maxa auc_maxa_best acc_min acc_rmin acc_max \\\n", - "0 inf ... 0.685936 0.685936 0.254487 0.42029 0.834281 \n", - "1 0.271316 ... 0.960195 0.960195 0.208935 0.223529 0.985450 \n", - "2 0.208912 ... 0.900903 0.900903 0.228337 0.289562 0.938832 \n", - "3 0.301002 ... 0.896275 0.896275 0.242985 0.299663 0.943382 \n", - "4 0.080000 ... 1.000000 1.000000 0.031576 0.031579 1.000000 \n", + " acc_onmax max_acc_min max_acc_max max_acc_rmax \\\n", + "0 'float' object is not subscriptable 0.627327 0.880296 0.855432 \n", + "1 'float' object is not subscriptable 0.996761 1.000000 1.000000 \n", + "2 'float' object is not subscriptable 0.708530 0.937081 0.929434 \n", + "3 'float' object is not subscriptable 0.719110 0.944529 0.937862 \n", + "4 'float' object is not subscriptable 0.993333 1.000000 1.000000 \n", "\n", - " acc_rmax max_acc_min max_acc_max max_acc_rmax max_acc_onmax \n", - "0 0.772955 0.579710 0.834281 0.772955 0.668562 \n", - "1 0.984943 0.849453 0.985450 0.984943 0.970900 \n", - "2 0.930489 0.710438 0.938832 0.930489 0.877665 \n", - "3 0.936695 0.718242 0.943382 0.936695 0.886764 \n", - "4 1.000000 0.997527 1.000000 1.000000 1.000000 \n", + " max_acc_onmax \n", + "0 0.760591 \n", + "1 1.000000 \n", + "2 0.874163 \n", + "3 0.889059 \n", + "4 1.000000 \n", "\n", - "[5 rows x 39 columns]" + "[5 rows x 40 columns]" ] }, "execution_count": 33, @@ -765,7 +770,7 @@ "metadata": {}, "outputs": [], "source": [ - "data.to_csv('processed-single.csv', index=False)" + "data.to_csv('processed-single3.csv', index=False)" ] } ], diff --git a/notebooks/auc_experiments/03-results-midpoints.ipynb b/notebooks/auc_experiments/03-results-midpoints.ipynb index 937813a..28aea80 100644 --- a/notebooks/auc_experiments/03-results-midpoints.ipynb +++ b/notebooks/auc_experiments/03-results-midpoints.ipynb @@ -2,14 +2,14 @@ "cells": [ { "cell_type": "code", - "execution_count": 30, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from scipy.stats import wilcoxon\n", + "from scipy.stats import wilcoxon, beta\n", "from sklearn.metrics import r2_score, mean_absolute_error, mean_absolute_percentage_error\n", "from mlscorecheck.auc import (\n", " auc_onmin_grad,\n", @@ -17,46 +17,87 @@ " auc_max_grad,\n", " auc_maxa_grad,\n", " macc_min_grad,\n", - " acc_rmax_grad\n", - ")" + " acc_rmax_grad,\n", + " auc_from,\n", + " auc_from_aggregated\n", + ")\n", + "from sklearn.linear_model import LinearRegression" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 94, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/gykovacs/workspaces/mlscorecheck/mlscorecheck/auc/_acc_single.py:200: RuntimeWarning: divide by zero encountered in scalar divide\n", - " return n * p / ((n + p) * np.sqrt(-2 * auc * n * p + 2 * n * p))\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 31, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" + } + ], + "source": [ + "fpr = np.linspace(0.0, 1.0, 20)\n", + "tpr = np.linspace(0.0, 1.0, 20)\n", + "fpr = np.repeat(fpr, 20, 0)\n", + "tpr = np.hstack([tpr]*20)\n", + "dx = - tpr\n", + "dy = 1 - fpr\n", + "plt.quiver(fpr, tpr, dx, dy)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.009999666686665238)" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.atan2(1, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -66,50 +107,105 @@ } ], "source": [ - "fprs = np.linspace(0.65, 1.0, 100)[::-1]\n", - "tprs = np.linspace(0.6, 1.0, 100)\n", - "\n", - "p=10\n", - "n=200\n", - "\n", - "auc_max_grad_ = np.array([auc_max_grad(fpr, tpr) for fpr, tpr in zip(fprs, tprs)])\n", - "auc_rmin_grad_ = np.array([auc_rmin_grad(fpr, tpr) for fpr, tpr in zip(fprs, tprs)])\n", - "auc_maxa_grad_ = np.array([auc_maxa_grad((p*tpr + n*(1 - fpr))/(p + n), p, n) for fpr, tpr in zip(fprs, tprs)])/(n/p)\n", - "\n", - "acc_rmax_grad_ = np.array([acc_rmax_grad(auc_, p, n) for auc_ in tprs])\n", - "macc_min_grad_ = np.array([macc_min_grad(auc_, p, n) for auc_ in tprs])\n", - "\n", - "plt.plot(tprs, auc_max_grad_, label='max_grad')\n", - "plt.plot(tprs, auc_rmin_grad_, label='rmin_grad')\n", - "plt.plot(tprs, auc_maxa_grad_, label='maxa_grad')\n", - "plt.legend()\n", - "\n", - "plt.figure()\n", - "\n", - "plt.plot(tprs, acc_rmax_grad_, label='rmax_grad')\n", - "plt.plot(tprs, macc_min_grad_, label='macc_min')\n", - "plt.legend()" + "x = np.linspace(0, 1, 1000)\n", + "y = beta.pdf(x, 5, 10)\n", + "plt.plot(x, (y))" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(1.477953873055371)" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "beta.pdf(0.2, 10, 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "def expected_value(a, b, start, end, n):\n", + " aucs = np.linspace(start, end, n)\n", + " dx = (end - start)/n\n", + " norm = beta.cdf(end, a, b) - beta.cdf(start, a, b)\n", + " pdfs = beta.pdf(aucs, a, b)\n", + " pdfs = pdfs / norm\n", + " return np.sum(aucs * pdfs)*dx" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.9951574936863115)" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expected_value(1146, 1150 - 1146, 0.9091, 0.9965, 100000)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.15740085031641243)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expected_value(2, 10, 0.0, 0.4, 1000)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ "#label = 'aggregated-ns'\n", "#clabel = 'avg.'\n", "\n", - "#label = 'aggregated'\n", - "#clabel = 'avg.'\n", + "label = 'aggregated3'\n", + "clabel = 'avg.'\n", "\n", - "label = 'single'\n", + "label = 'single3'\n", "clabel = ''" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -118,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -127,212 +223,1387 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 287, "metadata": {}, "outputs": [], "source": [ - "data = pd.concat([\n", - " data[(data['auc'] >= 0.5) & (data['auc'] <= 0.55)].sample(200, random_state=5, replace=True),\n", - " data[(data['auc'] > 0.55) & (data['auc'] <= 0.6)].sample(200, random_state=5),\n", - " data[(data['auc'] > 0.6) & (data['auc'] <= 0.65)].sample(200, random_state=5),\n", - " data[(data['auc'] > 0.65) & (data['auc'] <= 0.7)].sample(200, random_state=5),\n", - " data[(data['auc'] > 0.7) & (data['auc'] <= 0.75)].sample(200, random_state=5),\n", - " data[(data['auc'] > 0.75) & (data['auc'] <= 0.8)].sample(200, random_state=5),\n", - " data[(data['auc'] > 0.8) & (data['auc'] <= 0.85)].sample(200, random_state=5),\n", - " data[(data['auc'] > 0.85) & (data['auc'] <= 0.9)].sample(200, random_state=5),\n", - " data[(data['auc'] > 0.9) & (data['auc'] <= 0.95)].sample(200, random_state=5),\n", - " data[(data['auc'] > 0.95) & (data['auc'] <= 1.0)].sample(200, random_state=5),\n", - " ])\n", - "\n", - "#data = data[data['auc'] > 0.75]" + "data2 = pd.read_csv(f'processed-{label}.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data2[(data2['sens'] > 0.0) & (data2['sens'] < 1.0) & (data2['spec'] > 0.0) & (data2['spec'] < 1.0)]\n", + "data2 = data2[(data2['best_sens'] > 0.0) & (data2['best_sens'] < 1.0) & (data2['best_spec'] > 0.0) & (data2['best_spec'] < 1.0)]" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 289, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Index(['dataset', 'classifier', 'acc', 'sens', 'spec', 'auc', 'best_acc',\n", - " 'best_sens', 'best_spec', 'threshold', 'best_threshold', 'p', 'n',\n", - " 'auc_min', 'auc_min_best', 'auc_rmin', 'auc_rmin_best', 'auc_grmin',\n", - " 'auc_grmin_best', 'auc_amin', 'auc_amin_best', 'auc_armin',\n", - " 'auc_armin_best', 'auc_onmin', 'auc_onmin_best', 'auc_max',\n", - " 'auc_max_best', 'auc_amax', 'auc_amax_best', 'auc_maxa',\n", - " 'auc_maxa_best', 'acc_min', 'acc_rmin', 'acc_max', 'acc_rmax',\n", - " 'max_acc_min', 'max_acc_max', 'max_acc_rmax', 'max_acc_onmax'],\n", - " dtype='object')" + "(array([ 21., 66., 233., 264., 522., 706., 607., 1055., 977.,\n", + " 812.]),\n", + " array([0.52931034, 0.57623886, 0.62316738, 0.67009589, 0.71702441,\n", + " 0.76395293, 0.81088144, 0.85780996, 0.90473847, 0.95166699,\n", + " 0.99859551]),\n", + " )" ] }, - "execution_count": 36, + "execution_count": 289, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "data.columns" + "plt.hist(data2['auc'])" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 104, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5263\n" + ] + } + ], "source": [ - "def convert(x):\n", - " try:\n", - " return float(x)\n", - " except:\n", - " return None" + "data = data[(data['sens'] > 0.0) & (data['sens'] < 1.0) & (data['spec'] > 0.0) & (data['spec'] < 1.0)]\n", + "data = data[(data['best_sens'] > 0.0) & (data['best_sens'] < 1.0) & (data['best_spec'] > 0.0) & (data['best_spec'] < 1.0)]\n", + "#data = data[(data['n']/data['p'] > 3) | (data['p']/data['n'] > 3)]\n", + "#data = data[(data['sens'] >= (1 - data['spec']))]\n", + "#data = data[data['p']*data['n'] < 1000]\n", + "#data = data[data['sens'] < 0.5]\n", + "#data = data[data['n'] > data['p']]\n", + "#data = data[data['dataset'].isin(['australian', 'yeast1', 'pima', 'crx'])]\n", + "#data = data[data['n_nodes'] < 5]\n", + "\n", + "print(len(data))\n", + "\n", + "data = data[data['n_nodes'] > 3]\n", + "\n", + "data = pd.concat([\n", + " data[(data['auc'] >= 0.5) & (data['auc'] <= 0.55)].sample(100, random_state=5, replace=True),\n", + " data[(data['auc'] > 0.55) & (data['auc'] <= 0.6)].sample(100, random_state=5, replace=True),\n", + " data[(data['auc'] > 0.6) & (data['auc'] <= 0.65)].sample(100, random_state=5, replace=True),\n", + " data[(data['auc'] > 0.65) & (data['auc'] <= 0.7)].sample(100, random_state=5, replace=True),\n", + " data[(data['auc'] > 0.7) & (data['auc'] <= 0.75)].sample(100, random_state=5, replace=True),\n", + " data[(data['auc'] > 0.75) & (data['auc'] <= 0.8)].sample(100, random_state=5, replace=True),\n", + " data[(data['auc'] > 0.8) & (data['auc'] <= 0.85)].sample(100, random_state=5, replace=True),\n", + " data[(data['auc'] > 0.85) & (data['auc'] <= 0.9)].sample(100, random_state=5, replace=True),\n", + " data[(data['auc'] > 0.9) & (data['auc'] <= 0.95)].sample(100, random_state=5, replace=True),\n", + " data[(data['auc'] > 0.95) & (data['auc'] <= 1.0)].sample(100, random_state=5, replace=True),\n", + " ])\n", + "\n", + "#data = data[data['auc'] > 0.75]\n", + "\n", + "#data = data[data['n'] / data['p'] > 2]\n", + "#data = data[np.sqrt(data['sens'] * data['spec']) > 0.5]" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ - "data['auc_min_max'] = (data['auc_min'].apply(convert) + data['auc_max'].apply(convert)) / 2.0\n", - "data['auc_rmin_max'] = (data['auc_rmin'].apply(convert) + data['auc_max'].apply(convert)) / 2.0\n", - "data['auc_onmin_max'] = (data['auc_onmin'].apply(convert) + data['auc_max'].apply(convert)) / 2.0\n", - "data['auc_rmin_maxa'] = (data['auc_rmin'].apply(convert) + data['auc_maxa'].apply(convert)) / 2.0\n", + "def auc_analytic(row):\n", + " frac = (row['sens']*row['p'] + (1 - row['spec'])*row['n']) / (row['p'] + row['n'])\n", "\n", - "data['auc_min_max_best'] = ((data['auc_min_best'].apply(convert)) + data['auc_max_best'].apply(convert)) / 2.0\n", - "data['auc_rmin_max_best'] = ((data['auc_rmin_best'].apply(convert)) + data['auc_max_best'].apply(convert)) / 2.0\n", + " exp_tpr = np.log(row['sens'])/np.log(frac)\n", + " exp_fpr = np.log(1 - row['spec'])/np.log(frac)\n", "\n", - "data['auc_min_maxa_best'] = ((data['auc_min_best'].apply(convert)) + data['auc_maxa_best'].apply(convert)) / 2.0\n", - "data['auc_rmin_maxa_best'] = ((data['auc_rmin_best'].apply(convert)) + data['auc_maxa_best'].apply(convert)) / 2.0\n", - "data['auc_onmin_maxa_best'] = ((data['auc_onmin_best'].apply(convert)) + data['auc_maxa_best'].apply(convert)) / 2.0\n", + " x = np.linspace(0, 1, 100)\n", + " tpr = x**exp_tpr\n", + " fpr = x**exp_fpr\n", "\n", - "data['max_acc_min_max'] = (data['max_acc_min'].apply(convert) + data['max_acc_max'].apply(convert)) / 2.0\n", - "data['max_acc_min_rmax'] = (data['max_acc_min'].apply(convert) + data['max_acc_rmax'].apply(convert)) / 2.0\n", - "data['max_acc_min_onmax'] = (data['max_acc_min'].apply(convert) + data['max_acc_onmax'].apply(convert)) / 2.0\n", - "\n" + " #print(exp_tpr, exp_fpr)\n", + "\n", + " return float(np.sum((fpr[1:] - fpr[:-1])*(tpr[:-1] + tpr[1:])/2))" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 106, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/gykovacs/workspaces/mlscorecheck/mlscorecheck/auc/_acc_single.py:200: RuntimeWarning: divide by zero encountered in scalar divide\n", - " return n * p / ((n + p) * np.sqrt(-2 * auc * n * p + 2 * n * p))\n", - "/home/gykovacs/workspaces/mlscorecheck/mlscorecheck/auc/_acc_single.py:129: RuntimeWarning: divide by zero encountered in scalar divide\n", - " return np.sqrt(2) * min(p, n) / 2 / (np.sqrt(auc - 0.5) * (p + n))\n" - ] + "data": { + "text/plain": [ + "array([0.001 , 0.0011514 , 0.00132571, 0.00152642, 0.00175751,\n", + " 0.00202359, 0.00232995, 0.0026827 , 0.00308884, 0.00355648,\n", + " 0.00409492, 0.00471487, 0.00542868, 0.00625055, 0.00719686,\n", + " 0.00828643, 0.00954095, 0.01098541, 0.01264855, 0.01456348,\n", + " 0.01676833, 0.01930698, 0.02222996, 0.02559548, 0.02947052,\n", + " 0.03393222, 0.0390694 , 0.04498433, 0.05179475, 0.05963623,\n", + " 0.06866488, 0.07906043, 0.09102982, 0.10481131, 0.12067926,\n", + " 0.13894955, 0.15998587, 0.184207 , 0.21209509, 0.24420531,\n", + " 0.28117687, 0.32374575, 0.37275937, 0.42919343, 0.49417134,\n", + " 0.5689866 , 0.65512856, 0.75431201, 0.86851137, 1. ])" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "exponent = 1\n", + "np.logspace(-3, 0, 50)" + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "metadata": {}, + "outputs": [], + "source": [ + "def auc_analytic_mod(row):\n", + " frac = (row['sens']*row['p'] + (1 - row['spec'])*row['n']) / (row['p'] + row['n'])\n", + " acc = row['best_acc']\n", + " sens_orig = row['sens']\n", + " spec_orig = row['spec']\n", + "\n", + " search_tpr = np.logspace(-3, 0, 100)\n", + " search_fpr = np.logspace(0, 2, 100)\n", + "\n", + " p = row['p']\n", + " n = row['n']\n", + "\n", + " x = np.linspace(0.00001, 1, 100)\n", + "\n", + " dist = np.inf\n", + "\n", + " for exp0 in search_tpr:\n", + " for exp1 in search_fpr:\n", + " sens = frac ** exp0\n", + " fpr = frac ** exp1 \n", + "\n", + " tprs = x**exp0\n", + " fprs = x**exp1\n", + "\n", + " auc = float(np.sum((fprs[1:] - fprs[:-1])*(tprs[:-1] + tprs[1:])/2))\n", + "\n", + " if auc < 0.5:\n", + " continue\n", + "\n", + " dist_tmp = max((np.abs(sens - sens_orig)/sens_orig)**2, (np.abs((1 - fpr) - spec_orig)/spec_orig)**2)\n", + " if dist_tmp < dist:\n", + " dist = dist_tmp\n", + " exp_tpr = exp0\n", + " exp_fpr = exp1\n", + "\n", + " if dist == np.inf:\n", + " exp_tpr = 1\n", + " exp_fpr = 1\n", + " \n", + " #print(exp_tpr, exp_fpr)\n", + " #print(exp_tpr, exp_fpr)\n", + "\n", + " tpr = x**exp_tpr\n", + " fpr = x**exp_fpr\n", + "\n", + " return float(np.sum((fpr[1:] - fpr[:-1])*(tpr[:-1] + tpr[1:])/2))" + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": {}, + "outputs": [], + "source": [ + "def auc_analytic_best(row):\n", + " frac = (row['best_sens']*row['p'] + (1 - row['best_spec'])*row['n']) / (row['p'] + row['n'])\n", + " acc = row['best_acc']\n", + "\n", + " exp_tpr = np.log(row['best_sens'])/np.log(frac)\n", + " exp_fpr = np.log(1 - row['best_spec'])/np.log(frac)\n", "\n", - "data['auc_rmin_best_grad'] = data.apply(lambda row: auc_rmin_grad(1 - row['best_spec'], row['best_sens']), axis=1) + 1\n", - "#data['auc_maxa_best_grad'] = data.apply(lambda row: auc_maxa_grad(row['best_acc'], row['p'], row['n']), axis=1) / (data['n']/data['p']) + 1\n", - "data['auc_maxa_best_grad'] = data.apply(lambda row: auc_maxa_grad(row['best_acc'], row['p'], row['n']), axis=1) + 1\n", - "data['auc_rmin_maxa_best'] = (data['auc_rmin_best'].apply(convert) * data['auc_maxa_best_grad']**exponent + data['auc_maxa_best'].apply(convert) * data['auc_rmin_best_grad']**exponent)/(data['auc_rmin_best_grad']**exponent + data['auc_maxa_best_grad']**exponent)\n", + " x = np.linspace(0, 1, 100)\n", + " tpr = x**exp_tpr\n", + " fpr = x**exp_fpr\n", "\n", - "data['auc_rmin_grad'] = data.apply(lambda row: auc_rmin_grad(1 - row['spec'], row['sens']), axis=1)\n", - "data['auc_max_grad'] = data.apply(lambda row: auc_max_grad(1 - row['spec'], row['sens']), axis=1)\n", - "data['auc_rmin_max'] = (data['auc_rmin_best'].apply(convert) * data['auc_max_grad'] + data['auc_max'].apply(convert) * data['auc_rmin_grad'])/(data['auc_rmin_grad'] + data['auc_max_grad'])\n", + " accs = (row['p'] * tpr + (1 - fpr) * row['n']) / (row['p'] + row['n'])\n", + " cap_mask = accs > acc\n", "\n", - "data['max_acc_min_grad'] = data.apply(lambda row: macc_min_grad(row['auc'], row['p'], row['n']), axis=1) + 1\n", - "data['max_acc_rmax_grad'] = data.apply(lambda row: acc_rmax_grad(row['auc'], row['p'], row['n']), axis=1) + 1\n", - "data['max_acc_min_rmax'] = (data['max_acc_min'].apply(convert) * data['max_acc_rmax_grad'] + data['max_acc_rmax'].apply(convert) * data['max_acc_min_grad'])/(data['max_acc_min_grad'] + data['max_acc_rmax_grad'])" + " tpr[cap_mask] = (acc * (row['p'] + row['n']) - (1 - fpr[cap_mask]) * row['n'])/row['p']\n", + "\n", + " #print(exp_tpr, exp_fpr)\n", + " \n", + " return float(np.sum((fpr[1:] - fpr[:-1])*(tpr[:-1] + tpr[1:])/2))" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 330, "metadata": {}, "outputs": [], "source": [ - "for col in data.columns[2:]:\n", - " data[col] = pd.to_numeric(data[col], errors='coerce')" + "def auc_analytic_best_mod(row):\n", + " frac = (row['best_sens']*row['p'] + (1 - row['best_spec'])*row['n']) / (row['p'] + row['n'])\n", + " acc = row['best_acc']\n", + "\n", + " search_tpr = np.logspace(-3, 1, 100)\n", + " search_fpr = np.logspace(-1, 2, 100)\n", + "\n", + " best_sens = row['best_sens']\n", + " best_spec = row['best_spec']\n", + " p = row['p']\n", + " n = row['n']\n", + "\n", + " x = np.linspace(0.00001, 1, 100)\n", + "\n", + " dist = np.inf\n", + "\n", + " for exp0 in search_tpr:\n", + " for exp1 in search_fpr:\n", + " sens = frac ** exp0\n", + " fpr = frac ** exp1 \n", + "\n", + " max_acc = (x**exp0 * p + (1 - x**exp1) * n) / (p + n)\n", + " #print(exp0, exp1, np.max(max_acc), acc)\n", + "\n", + " tprs = x**exp0\n", + " fprs = x**exp1\n", + "\n", + " auc = float(np.sum((fprs[1:] - fprs[:-1])*(tprs[:-1] + tprs[1:])/2))\n", + "\n", + " dist_tmp = ((np.abs(sens - best_sens)/best_sens) + (np.abs((1 - fpr) - best_spec)/best_spec) + (np.abs(exp0 - 1/exp1)/(exp0 + 1/exp1)/2)*(max(p, n)/min(p, n))**2)\n", + "\n", + " #print(exp0, exp1, dist_tmp, dist, auc, np.max(max_acc), acc)\n", + "\n", + " if auc < 0.5:\n", + " continue\n", + "\n", + " if np.max(max_acc) > acc:\n", + " continue\n", + "\n", + " if dist_tmp < dist:\n", + " dist = dist_tmp\n", + " exp_tpr = exp0\n", + " exp_fpr = exp1\n", + "\n", + " if dist == np.inf:\n", + " exp_tpr = 1\n", + " exp_fpr = 1\n", + " \n", + " print(exp_tpr, exp_fpr)\n", + "\n", + " tpr = x**exp_tpr\n", + " fpr = x**exp_fpr\n", + "\n", + " return float(np.sum((fpr[1:] - fpr[:-1])*(tpr[:-1] + tpr[1:])/2))" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 328, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[328], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_analytic\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(auc_analytic_mod, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_analytic_best\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(auc_analytic_best, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mapply()\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_standard()\n", + "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_generator()\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", + "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(v, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "Cell \u001b[0;32mIn[325], line 25\u001b[0m, in \u001b[0;36mauc_analytic_mod\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 22\u001b[0m tprs \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mexp0\n\u001b[1;32m 23\u001b[0m fprs \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mexp1\n\u001b[0;32m---> 25\u001b[0m auc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(np\u001b[38;5;241m.\u001b[39msum((fprs[\u001b[38;5;241m1\u001b[39m:] \u001b[38;5;241m-\u001b[39m fprs[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\u001b[38;5;241m*\u001b[39m(tprs[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m tprs[\u001b[38;5;241m1\u001b[39m:])\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m))\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m auc \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0.5\u001b[39m:\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:2250\u001b[0m, in \u001b[0;36m_sum_dispatcher\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 2180\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2181\u001b[0m \u001b[38;5;124;03m Clip (limit) the values in an array.\u001b[39;00m\n\u001b[1;32m 2182\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2245\u001b[0m \n\u001b[1;32m 2246\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 2247\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _wrapfunc(a, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclip\u001b[39m\u001b[38;5;124m'\u001b[39m, a_min, a_max, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 2250\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_sum_dispatcher\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 2251\u001b[0m initial\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 2252\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (a, out)\n\u001b[1;32m 2255\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_sum_dispatcher)\n\u001b[1;32m 2256\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msum\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue,\n\u001b[1;32m 2257\u001b[0m initial\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue):\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] } ], "source": [ - "plt.figure(figsize=(3.5, 3.5))\n", - "val_min = min(min(data['auc']), min(data['auc_min_max']), min(data['auc_rmin_max']))\n", - "plt.scatter(data['auc'], data['auc_min_max'], label='(min, max)', s=3)\n", - "plt.scatter(data['auc'], data['auc_rmin_max'], label='(rmin, max)', s=3)\n", - "plt.scatter(data['auc'], data['auc_onmin_max'], label='(onmin, max)', s=3)\n", - "plt.xlabel(f'{clabel} auc')\n", - "plt.ylabel(f'{clabel} auc midpoint estimation')\n", - "plt.plot([val_min, 1], [val_min, 1], label='x=y', c='black', linestyle='--')\n", - "plt.legend(markerscale=4)\n", - "plt.tight_layout()\n", - "plt.savefig(f'figures-midpoints/{label}-auc-midpoint.pdf')" + "data['auc_analytic'] = data.apply(auc_analytic_mod, axis=1)\n", + "data['auc_analytic_best'] = data.apply(auc_analytic_best, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7390722033525783 0.8111308307896873\n", + "0.7390722033525783 0.7564633275546289\n", + "0.7390722033525783 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axis=1)" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(-0.7530749351474522, 0.5844902956684934, 0.33683309247736226)" + "(0.6289333781300463, 0.630595424183228, np.float64(0.6700819672131149))" ] }, - "execution_count": 42, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tmp = data[['auc', 'auc_rmin_max']].dropna()\n", - "tmp1 = data[['auc', 'auc_onmin_max']].dropna()\n", - "(r2_score(data['auc'], data['auc_min_max']),\n", - "r2_score(tmp['auc'], tmp['auc_rmin_max']),\n", - "r2_score(tmp1['auc'], tmp1['auc_onmin_max']))" + "idx = 300\n", + "auc_analytic(data.iloc[idx]), auc_analytic_mod(data.iloc[idx]), data.iloc[idx]['auc']" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.1774165296058492), np.float64(0.09259201060877212))" + "(0.8723546354676024, 0.8598397741485314, np.float64(0.7826035781544256))" ] }, - "execution_count": 43, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "(mean_absolute_percentage_error(data['auc'], data['auc_min_max']),\n", - "mean_absolute_percentage_error(tmp['auc'], tmp['auc_rmin_max']))" + "idx = 500\n", + "auc_analytic_best(data.iloc[idx]), auc_analytic_best_mod(data.iloc[idx]), data.iloc[idx]['auc']" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -356,149 +1627,2430 @@ " \n", " \n", " \n", - " dataset\n", - " classifier\n", - " acc\n", - " sens\n", - " spec\n", " auc\n", - " best_acc\n", - " best_sens\n", - " best_spec\n", - " threshold\n", - " ...\n", - " auc_onmin_maxa_best\n", - " max_acc_min_max\n", - " max_acc_min_rmax\n", - " max_acc_min_onmax\n", - " auc_rmin_best_grad\n", - " auc_maxa_best_grad\n", - " auc_rmin_grad\n", - " auc_max_grad\n", - " max_acc_min_grad\n", - " max_acc_rmax_grad\n", + " auc_analytic\n", + " auc_analytic_best\n", + " auc_analytic_best_mod\n", " \n", " \n", " \n", " \n", - " 5971\n", - " appendicitis\n", - " {'max_depth': 9, 'random_state': 5}\n", - " 0.181818\n", - " 1.000000\n", - " 0.000000\n", - " 0.541667\n", - " 0.818182\n", - " 0.000000\n", - " 1.000000\n", - " 0.000000\n", + " count\n", + " 1000.000000\n", + " 1000.000000\n", + " 1000.000000\n", + " 1000.000000\n", + " \n", + " \n", + " mean\n", + " 0.751466\n", + " 0.772875\n", + " 0.803776\n", + " 0.781779\n", + " \n", + " \n", + " std\n", + " 0.139136\n", + " 0.145011\n", + " 0.129981\n", + " 0.141209\n", + " \n", + " \n", + " min\n", + " 0.529310\n", + " 0.511624\n", + " 0.579867\n", + " 0.511622\n", + " \n", + " \n", + " 25%\n", + " 0.639946\n", + " 0.641366\n", + " 0.663797\n", + " 0.625169\n", + " \n", + " \n", + " 50%\n", + " 0.748854\n", + " 0.786227\n", + " 0.812089\n", + " 0.790104\n", + " \n", + " \n", + " 75%\n", + " 0.869056\n", + " 0.903141\n", + " 0.923708\n", + " 0.910954\n", + " \n", + " \n", + " max\n", + " 0.997191\n", + " 0.994812\n", + " 0.996189\n", + " 0.994928\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " auc auc_analytic auc_analytic_best auc_analytic_best_mod\n", + "count 1000.000000 1000.000000 1000.000000 1000.000000\n", + "mean 0.751466 0.772875 0.803776 0.781779\n", + "std 0.139136 0.145011 0.129981 0.141209\n", + "min 0.529310 0.511624 0.579867 0.511622\n", + "25% 0.639946 0.641366 0.663797 0.625169\n", + "50% 0.748854 0.786227 0.812089 0.790104\n", + "75% 0.869056 0.903141 0.923708 0.910954\n", + "max 0.997191 0.994812 0.996189 0.994928" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[['auc', 'auc_analytic', 'auc_analytic_best', 'auc_analytic_best_mod']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8397428530475235" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r2_score(data['auc'], data['auc_analytic'])" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6129499688375019" + ] + }, + "execution_count": 266, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r2_score(data['auc'], data['auc_analytic_best'])" + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.3416642355972124, 1000, 600)" + ] + }, + "execution_count": 303, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#tmp = data[(data['p']/data['n'] < 5) & (data['n']/data['p'] < 5)]\n", + "tmp = data[data['auc'] >= 0.7]\n", + "r2_score(tmp['auc'], tmp['auc_analytic_best_mod']), len(data), len(tmp)" + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.32218641713093543" + ] + }, + "execution_count": 333, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r2_score(data['auc'], data['auc_analytic_best_mod'])" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 334, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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classifierclassifier_params
5316SVC{'probability': True, 'C': 0.10769179676107676}
1925RandomForestClassifier{'max_depth': 2, 'random_state': 5}
422SVC{'probability': True, 'C': 0.1834805252897047}
1326RandomForestClassifier{'max_depth': 3, 'random_state': 5}
1411KNeighborsClassifier{'n_neighbors': 2}
3873KNeighborsClassifier{'n_neighbors': 3}
1348RandomForestClassifier{'max_depth': 6, 'random_state': 5}
980RandomForestClassifier{'max_depth': 3, 'random_state': 5}
5188RandomForestClassifier{'max_depth': 4, 'random_state': 5}
5448SVC{'probability': True, 'C': 0.23897257364938707}
801RandomForestClassifier{'max_depth': 2, 'random_state': 5}
3064KNeighborsClassifier{'n_neighbors': 4}
6375RandomForestClassifier{'max_depth': 8, 'random_state': 5}
7029KNeighborsClassifier{'n_neighbors': 4}
8341RandomForestClassifier{'max_depth': 7, 'random_state': 5}
9181SVC{'probability': True, 'C': 0.5889132668809354}
7594SVC{'probability': True, 'C': 0.5572186052515157}
760SVC{'probability': True, 'C': 0.32256427783533037}
8472SVC{'probability': True, 'C': 0.1665840545418542}
1086KNeighborsClassifier{'n_neighbors': 9}
4267SVC{'probability': True, 'C': 0.023009529225837433}
8472SVC{'probability': True, 'C': 0.1665840545418542}
7447KNeighborsClassifier{'n_neighbors': 9}
\n", + "
" + ], + "text/plain": [ + " classifier classifier_params\n", + "5316 SVC {'probability': True, 'C': 0.10769179676107676}\n", + "1925 RandomForestClassifier {'max_depth': 2, 'random_state': 5}\n", + "422 SVC {'probability': True, 'C': 0.1834805252897047}\n", + "1326 RandomForestClassifier {'max_depth': 3, 'random_state': 5}\n", + "1411 KNeighborsClassifier {'n_neighbors': 2}\n", + "3873 KNeighborsClassifier {'n_neighbors': 3}\n", + "1348 RandomForestClassifier {'max_depth': 6, 'random_state': 5}\n", + "980 RandomForestClassifier {'max_depth': 3, 'random_state': 5}\n", + "5188 RandomForestClassifier {'max_depth': 4, 'random_state': 5}\n", + "5448 SVC {'probability': True, 'C': 0.23897257364938707}\n", + "801 RandomForestClassifier {'max_depth': 2, 'random_state': 5}\n", + "3064 KNeighborsClassifier {'n_neighbors': 4}\n", + "6375 RandomForestClassifier {'max_depth': 8, 'random_state': 5}\n", + "7029 KNeighborsClassifier {'n_neighbors': 4}\n", + "8341 RandomForestClassifier {'max_depth': 7, 'random_state': 5}\n", + "9181 SVC {'probability': True, 'C': 0.5889132668809354}\n", + "7594 SVC {'probability': True, 'C': 0.5572186052515157}\n", + "760 SVC {'probability': True, 'C': 0.32256427783533037}\n", + "8472 SVC {'probability': True, 'C': 0.1665840545418542}\n", + "1086 KNeighborsClassifier {'n_neighbors': 9}\n", + "4267 SVC {'probability': True, 'C': 0.023009529225837433}\n", + "8472 SVC {'probability': True, 'C': 0.1665840545418542}\n", + "7447 KNeighborsClassifier {'n_neighbors': 9}" + ] + }, + "execution_count": 336, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tmp = data[data['auc_analytic_best_mod'] - data['auc'] < -0.3]\n", + "tmp[['classifier', 'classifier_params']]" + ] + }, + { + "cell_type": "code", + "execution_count": 337, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 337, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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datasetaccsensspecaucbest_accbest_sensbest_specthresholdbest_threshold...auc_rmin_maxaauc_min_max_bestauc_rmin_max_bestauc_min_maxa_bestauc_rmin_maxa_bestmax_acc_min_maxmax_acc_min_rmaxauc_rmin_rmaxauc_onmin_maxauc_onmin_maxa_best
8951saheart0.6774190.6875000.6721310.7382170.7204300.3125000.9344260.3468830.683786...0.6958540.6234380.7427650.5594260.6787520.7830720.7748260.7311720.7887100.725216
5073yeast10.7643100.5465120.8530810.7273230.7710440.3720930.9336490.2889640.666667...0.7263090.6528410.7526040.6099870.7097500.8157550.8080810.7559570.8166150.762786
2540saheart0.6774190.7812500.6229510.7356560.7096770.5312500.8032790.3468830.483888...0.6975840.6672310.7319380.6200030.6847090.7824960.7740500.7496750.8098400.740331
2541saheart0.6236560.7500000.5573770.7192620.7204300.5000000.8360660.3468830.764706...0.6871350.6679990.7373180.6224340.6917530.7796750.7698690.7138050.7715510.747500
4362SPECTF0.7777780.4545450.8604650.7262160.8148150.3636360.9302330.2065730.850000...0.7220420.6469050.7494560.6162710.7188220.8702730.8648200.7235770.7907320.770670
\n", + "

5 rows × 61 columns

\n", + "
" + ], + "text/plain": [ + " dataset acc sens spec auc best_acc best_sens \\\n", + "8951 saheart 0.677419 0.687500 0.672131 0.738217 0.720430 0.312500 \n", + "5073 yeast1 0.764310 0.546512 0.853081 0.727323 0.771044 0.372093 \n", + "2540 saheart 0.677419 0.781250 0.622951 0.735656 0.709677 0.531250 \n", + "2541 saheart 0.623656 0.750000 0.557377 0.719262 0.720430 0.500000 \n", + "4362 SPECTF 0.777778 0.454545 0.860465 0.726216 0.814815 0.363636 \n", + "\n", + " best_spec threshold best_threshold ... auc_rmin_maxa \\\n", + "8951 0.934426 0.346883 0.683786 ... 0.695854 \n", + "5073 0.933649 0.288964 0.666667 ... 0.726309 \n", + "2540 0.803279 0.346883 0.483888 ... 0.697584 \n", + "2541 0.836066 0.346883 0.764706 ... 0.687135 \n", + "4362 0.930233 0.206573 0.850000 ... 0.722042 \n", + "\n", + " auc_min_max_best auc_rmin_max_best auc_min_maxa_best \\\n", + "8951 0.623438 0.742765 0.559426 \n", + "5073 0.652841 0.752604 0.609987 \n", + "2540 0.667231 0.731938 0.620003 \n", + "2541 0.667999 0.737318 0.622434 \n", + "4362 0.646905 0.749456 0.616271 \n", + "\n", + " auc_rmin_maxa_best max_acc_min_max max_acc_min_rmax auc_rmin_rmax \\\n", + "8951 0.678752 0.783072 0.774826 0.731172 \n", + "5073 0.709750 0.815755 0.808081 0.755957 \n", + "2540 0.684709 0.782496 0.774050 0.749675 \n", + "2541 0.691753 0.779675 0.769869 0.713805 \n", + "4362 0.718822 0.870273 0.864820 0.723577 \n", + "\n", + " auc_onmin_max auc_onmin_maxa_best \n", + "8951 0.788710 0.725216 \n", + "5073 0.816615 0.762786 \n", + "2540 0.809840 0.740331 \n", + "2541 0.771551 0.747500 \n", + "4362 0.790732 0.770670 \n", + "\n", + "[5 rows x 61 columns]" + ] + }, + "execution_count": 338, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data['auc_analytic_best_mod'] - data['auc'] < - 0.2].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 339, + "metadata": {}, + "outputs": [], + "source": [ + "row = data[data['auc_analytic_best_mod'] - data['auc'] < - 0.2].iloc[10]\n", + "#row = data.iloc[10]" + ] + }, + { + "cell_type": "code", + "execution_count": 340, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "best_acc 0.709677\n", + "best_sens 0.4375\n", + "best_spec 0.852459\n", + "auc 0.735656\n", + "auc_analytic 0.793871\n", + "auc_analytic_best 0.696046\n", + "auc_analytic_best_mod 0.505784\n", + "classifier RandomForestClassifier\n", + "classifier_params {'max_depth': 5, 'random_state': 5}\n", + "p 32\n", + "n 61\n", + "n_nodes 43\n", + "Name: 3546, dtype: object" + ] + }, + "execution_count": 340, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row[['best_acc', 'best_sens', 'best_spec', 'auc', 'auc_analytic', 'auc_analytic_best', 'auc_analytic_best_mod', 'classifier', 'classifier_params', 'p', 'n', 'n_nodes']]" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.838014379597704" + ] + }, + "execution_count": 322, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auc_analytic(row)" + ] + }, + { + "cell_type": "code", + "execution_count": 341, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5590810182512228 0.5722367659350217\n" + ] + }, + { + "data": { + "text/plain": [ + "0.5057839817112151" + ] + }, + "execution_count": 341, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auc_analytic_best_mod(row)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "#data = data[data['n_nodes'] > 10]" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "row = data.iloc[400]" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.7052777777777778)" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row['auc']" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.7026784647845745)" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frac = (row['sens']*row['p'] + (1 - row['spec'])*row['n']) / (row['p'] + row['n'])\n", + "\n", + "exp_tpr = np.log(row['sens'])/np.log(frac)\n", + "exp_fpr = np.log(1 - row['spec'])/np.log(frac)\n", + "\n", + "x = np.linspace(0, 1, 100)\n", + "tpr = x**exp_tpr\n", + "fpr = x**exp_fpr\n", + "\n", + "np.sum((fpr[1:] - fpr[:-1])*(tpr[:-1] + tpr[1:])/2)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "def r2_fun(row):\n", + "\n", + " tprs = np.array(eval(row['tprs']))\n", + " fprs = np.array(eval(row['fprs']))\n", + " ths = np.array(eval(row['thresholds'], {'inf': np.inf}))\n", + " counts = (tprs * row['p'] + fprs * row['n']) / (row['p'] + row['n'])\n", + "\n", + " mask = tprs[1:] > 0\n", + "\n", + " ln_tprs = np.log(tprs[1:][mask])\n", + " ln_counts = np.log(counts[1:][mask]).reshape(-1, 1)\n", + "\n", + " pred_tprs = LinearRegression(fit_intercept=False).fit(ln_counts, ln_tprs).predict(ln_counts)\n", + "\n", + " return r2_score(ln_tprs, pred_tprs)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "data['r2_tpr'] = data.apply(r2_fun, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "r2 = data['r2_tpr'].values\n", + "print(len(r2[r2 < -1]))\n", + "r2 = r2[r2 > -1]" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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gbT1cprsWf6ESR7Xax9i1cMIQ9esYZ3ZZgN95HEbGjh2rY8eOadq0aSouLtbAgQO1YsUK96DWoqKiBj0hjzzyiCwWix555BEdOnRI7du31+jRo/Xb3/7Wd98CAILchzuP6ud/3KDKGqe6J7bWotwh6timpdllAc3CYgTBsxKHw6G4uDiVlZUpNpYtsQGEljc+L9Kjf/9STpehYV3b6YUfZyiuBZvdIfg19f7N3jQAYBKXy9CT7+7UC6v2SJJ+OLij8m/up6gINrtDeCGMAIAJTtc69fCb/9bbm49Ikh7K6aH7r+rGHjMIS4QRAGhmJyprNPG1dVr31QlF2iyafXN//TCjo9llAaYhjABAM/rq60rduegL7SutVEx0hF78cYaGdUswuyzAVIQRAGgm6786oYmvrdPxyhqlxrfQotwh6pEUY3ZZgOkIIwDQDP615YgeXLpJ1XUu9UuN0yt3ZioxJtrssoCAQBgBAD8yDEMvf7xPs/61XYYh5fRO1HO3DVLLKP76BerxXwMA+Emd06UZ/9ymP3z2lSRpQvYlmjb6UtmszJgBvokwAgA+8uWhMv1t4yG5zq4luf2IQ5/tPS6LRfrN9b1192WdmboLNIIwAgA+8su/bNb2I44Gx+wRVj1760Bd2zfFpKqAwEcYAQAfOFZe7Q4i91zRVTarZLNYdF2/FPVOYRsL4EIIIwDgA6v3lEqS+qTEasp1vUyuBggubIAAAD7waeGZMHJZdxYwAzxFGAGAi2QYhj4t/FqSNKxrO5OrAYIPYQQALlLR8SodOnlKkTaLhnZua3Y5QNAhjADARfrk7COaQZ3asJgZ4AXCCABcpNVnH9FcxoZ3gFcIIwBwEVwuwz2TZng3xosA3iCMAMBF2HbEoRNVtWoVZVP/jvFmlwMEJcIIAFyE+im93+vSTpE2/koFvMF/OQBwET7dc3ZKL+NFAK8RRgDAS9V1Tq3dx+BV4GIRRgDASxuLTup0rUsJre3qkdTa7HKAoEUYAQAvrS48N4vGYrGYXA0QvAgjAOCl+sXOhnflEQ1wMQgjAOCF8tO1+vfBMknScDbHAy4KYQQAvPD53uNyugylt2up1PgWZpcDBDXCCAB44VP3qqv0igAXizACAF74tJAwAvgK20sCYWJfaaWWfFGkmjqX2aUEPZfL0K6SClksUnYX9qMBLhZhBAgDh06e0o/mr1FpRbXZpYSU/qlxatMqyuwygKBHGAFCXGV1nX7y6jqVVlSre2JrXXNpktklhQSbxaIfDOhgdhlASCCMACHM6TL0wJJN2n7EoYTWdi2+aygzPwAEHAawAiHsiRU79P72EkVFWPXS+AyCCICARBgBQtSf1x3Qix/tlSQ9eUt/De7UxuSKAKBxhBEgBH2292v95m9bJEn3X9VdNw1MNbkiADg/wggQYr76ulL3vL5etU5DN/RP0YNXdTe7JAC4IMIIEELKTtXqrsVf6GRVrQZ0jNPvfzRAViu7yQIIbIQRIETUOV36+RsbtOdYpVLiorVgfKaiI21mlwUA34kwAoSIx9/epo93l6pFpE0LxmcqMTba7JIAoEkII0AIeG3Nfr225itZLNKcWweqb2qc2SUBQJMRRoAg99GuY5rxz22SpF+N6qVRlyabXBEAeIYwAgSxwqPlmvTHDXK6DP1wcEfdc0UXs0sCAI8RRoAgdbyyRnctXqfy6joNTW+rWTf3lcXCzBkAwYcwAgShmjqX7nl9vYqOVymtbQvNvyND9ghmzgAIToQRIMgYhqHf/G2L1u47rhh7hBZOGKK2bGMPIIgRRoAg89JHe/Xm+oOyWqT/N26QuifFmF0SAFwUwggQRN7bVqLZK3ZIkqb9oI+u7JlockUAcPEII0CQ2Hq4TA8s2SjDkH78vU6aMCzd7JIAwCcII0AQOFp+WhNfXaeqGqdGdE/Q9NGXMnMGQMiIMLsAIBDsOVahZRsOqqrGaXYpjVqz52sdLjutru1bae64wYq08f8RAEIHYQRh7eCJKj37/m79dcNBuQyzq7mw+JaRemXCEMW1iDS7FADwKcIIwtJRx2nN/bBQf1pbpFrnmRTy/V6J6p0SmDNTbFarxgzsoPSEVmaXAgA+RxhBWDleWaP5/7dHr67er+o6lyTpsm4J+sU1PTSoUxuTqwOA8EQYQVhwnK7Vyx/v08JP9qmiuk6SlHFJGz18TU9ld21ncnUAEN4IIwhpVTV1enX1V3rxoz06WVUrSbq0Q6wevqanruzZnhkpABAAvBqSP2/ePKWnpys6OlpZWVlau3btBc8/efKkJk2apJSUFNntdvXo0UPLly/3qmCgKarrnFr86T5d/sQq/W7FDp2sqlW3xNZ6/vbB+ufPL9PIXokEEQAIEB73jCxdulR5eXmaP3++srKyNGfOHI0aNUo7d+5UYuK3V4OsqanR1VdfrcTERP3lL39RamqqvvrqK8XHx/uifqCBOqdLf1l/UM8V7NbhstOSpE5tW+rBnO66aWCqbFYCCAAEGothGB5NaMzKytKQIUM0d+5cSZLL5VJaWpruu+8+TZky5Vvnz58/X08++aR27NihyEjvpiQ6HA7FxcWprKxMsbGxXr0HQpvLZeifmw/rmfd2af/XVZKk5Nho3XdVN/13ZhrrcgCACZp6//aoZ6Smpkbr16/X1KlT3cesVqtycnK0Zs2aRq/5xz/+oezsbE2aNEl///vf1b59e40bN06TJ0+Wzdb4lufV1dWqrq5u8GWAxhiGoXe3lejpd3dpZ0m5JKldqyj97Mqu+vH3LlF0ZOO/YwCAwOFRGCktLZXT6VRSUlKD40lJSdqxY0ej1+zdu1cffPCBbr/9di1fvlyFhYW69957VVtbq+nTpzd6TX5+vmbMmOFJaQgzhmHoo92l+v27O7X5YJkkKSY6Qv9zeRflDu+sVnbGZgNAsPD739gul0uJiYl66aWXZLPZlJGRoUOHDunJJ588bxiZOnWq8vLy3D87HA6lpaX5u1QEibX7juuplTu1dv9xSVLLKJvuGt5ZE0d0UVxLVicFgGDjURhJSEiQzWZTSUlJg+MlJSVKTk5u9JqUlBRFRkY2eCTTu3dvFRcXq6amRlFRUd+6xm63y263e1IawsDmgyf11Lu79NGuY5KkqAir7vjeJfrZlV2V0JrfFwAIVh6N6ouKilJGRoYKCgrcx1wulwoKCpSdnd3oNcOHD1dhYaFcLpf72K5du5SSktJoEAH+087icv3PH9bpxrmf6qNdxxRhtWhcVif93y+v1KM/6EMQAYAg5/Fjmry8PE2YMEGZmZkaOnSo5syZo8rKSuXm5kqSxo8fr9TUVOXn50uSfvazn2nu3Ll64IEHdN9992n37t2aNWuW7r//ft9+E4Sc/aWVeub9XfrHvw/LMCSrRRozKFUPXtVDndq1NLs8AICPeBxGxo4dq2PHjmnatGkqLi7WwIEDtWLFCveg1qKiIlmt5zpc0tLStHLlSj300EPq37+/UlNT9cADD2jy5Mm++xYIKYdPntJzBbv15vqDcp7dSvf6fsl6KKeHuicF5kZ2AADvebzOiBlYZyQ8HCuv1rwPC/XG50WqcZ55rDeyZ3v94pqe6psaZ3J1AABP+WWdEcAfTlbV6MWP9mrxp/t1qtYpSfpel7b65aieyrikrcnVAQD8jTAC01RU1+mVj/fp5Y/3qvzsTroD0+L1y1E9NaxrO/aOAYAwQRhBsztd69Rra/brhVV7dOLsTrq9kmP08DU9dVVvNrADgHBDGEGzqalzaekXRfp/HxTqaPmZ5f67JLTSQ1f30A39UmRlEzsACEuEkSBReLRCf153QLVO13efHIAMQ3p/e4kOnjglSerYpoUeuKq7/mtQqiLYxA4AwhphJEjMfHub/u/syqPBLDHGrvu+301jh3RSVAQhBABAGAkKhmFo04GTkqRxWZ3UJkj3X+kQ30I3D+qoFlHspAsAOIcwEgQOHD+lslO1irJZNX10H9kjuJkDAEIH/eRBYPOhk5KkXikxBBEAQMghjASBLQfLJEn9WIUUABCCCCNBYPPZMNK/I2EEABB6CCMBzuUy9OWh+p6ReHOLAQDADwgjAW7/15Uqr66TPcKq7kmtzS4HAACfI4wEuC1ne0X6dIhVJIuDAQBCEHe3AOceL8LgVQBAiCKMBDj3TJqO8eYWAgCAnxBGApjTZejLw8ykAQCENsJIANtXWqGqGqdaRNrUtT2DVwEAoYkwEsDqx4tc2iFWNqvF5GoAAPAPwkgA2+weL8IjGgBA6CKMBLD6ab2MFwEAhDLCSICqc7q09TArrwIAQh9hJEAVHqvQ6VqXWkXZ1CWhldnlAADgN4SRAFU/XqRvapysDF4FAIQwwkiA2sJOvQCAMEEYCVCbD7HyKgAgPBBGAlBNnUvbjzgksScNACD0EUYC0K6SctXUuRQTHaFL2rU0uxwAAPyKMBKAvvzG+iIWC4NXAQChjTASgOrHi/TlEQ0AIAwQRgKQeyYNi50BAMIAYSTAVNc5taP47OBVpvUCAMIAYSTA7CwuV63TUHzLSHVs08LscgAA8DvCSIBx79SbyuBVAEB4IIwEGFZeBQCEG8JIgHGvvMrgVQBAmCCMBJDTtU7tKimXRM8IACB8EEYCyLYjDjldhhJaRyklLtrscgAAaBaEkQCyhcGrAIAwRBgJIFvYqRcAEIYIIwHk3MqrjBcBAIQPwkiAqKqp0+6jZwav9mPwKgAgjBBGAsS2ww65DCkxxq6kWAavAgDCB2EkQGxmsTMAQJgijASILSx2BgAIU4SRALH54ElJ9IwAAMJPhNkFhKuPdh3ThzuPSpIMQ9pbWilJ6stMGgBAmCGMmMAwDN33p40qO1Xb4Hha2xZqH2M3qSoAAMxBGDFB2aladxC598quslgkiyy6uk+SyZUBAND8CCMmOHTylCSpXaso/eraXiZXAwCAuRjAaoIjJ09LklLiWU8EAADCiAmOlJ3pGekQ18LkSgAAMB9hxASHzvaMdIgnjAAAQBgxQX3PSEocj2kAACCMmOAIPSMAALgRRkxQP5umAwNYAQDwLozMmzdP6enpio6OVlZWltauXduk65YsWSKLxaIxY8Z487EhwekyVOI4O5uGAawAAHgeRpYuXaq8vDxNnz5dGzZs0IABAzRq1CgdPXr0gtft379fDz/8sEaMGOF1saGgtKJadS5DNqtFiay2CgCA52Hk6aef1sSJE5Wbm6s+ffpo/vz5atmypRYuXHjea5xOp26//XbNmDFDXbp0uaiCg139I5qkGLsibDwlAwDAo7thTU2N1q9fr5ycnHNvYLUqJydHa9asOe91jz/+uBITE3X33Xc36XOqq6vlcDgavELFuQXPeEQDAIDkYRgpLS2V0+lUUlLDPVSSkpJUXFzc6DWffPKJXnnlFS1YsKDJn5Ofn6+4uDj3Ky0tzZMyA5p7wTPCCAAAkvw8m6a8vFx33HGHFixYoISEhCZfN3XqVJWVlblfBw4c8GOVzcs9k4Y1RgAAkOThRnkJCQmy2WwqKSlpcLykpETJycnfOn/Pnj3av3+/Ro8e7T7mcrnOfHBEhHbu3KmuXbt+6zq73S67PTQHd7of0xBGAACQ5GHPSFRUlDIyMlRQUOA+5nK5VFBQoOzs7G+d36tXL23ZskWbNm1yv2688UaNHDlSmzZtCqnHL03FYxoAABryqGdEkvLy8jRhwgRlZmZq6NChmjNnjiorK5WbmytJGj9+vFJTU5Wfn6/o6Gj17du3wfXx8fGS9K3j4YJ9aQAAaMjjMDJ27FgdO3ZM06ZNU3FxsQYOHKgVK1a4B7UWFRXJamXKamOq65wqraiWxGMaAADqWQzDMMwu4rs4HA7FxcWprKxMsbGxZpfjtaKvq3T5kx/KHmHVjpnXymKxmF0SAAB+09T7t8c9I/DcoZOn9PpnX6noeJWkM49oCCIAAJxBGGkGL6wq1OufFbl/Tm/X0sRqAAAILISRZlBcdmacyPd7Japvapz+a1CqyRUBABA4CCPN4GRVjSTpRxkddV2/FJOrAQAgsDDtpRkcPxtG4ltGmVwJAACBhzDSDE5W1UqS2rSKNLkSAAACD2HEz1wuw/2Ypi09IwAAfAthxM/KT9fJdXYlFx7TAADwbYQRP6sfL9IqyqaoCJobAID/xN3Rz04weBUAgAsijPhZ/XgRBq8CANA4woifnag8O5OGnhEAABpFGPGz+sc0hBEAABpHGPGzc2GExzQAADSGMOJnJ84ueMYAVgAAGkcY8TP3gmetCCMAADSGMOJn9QNY43lMAwBAowgjfsYAVgAALoww4meEEQAALoww4keGYXxjACuPaQAAaAxhxI9O1TpVU+eSxABWAADOhzDiJy6XoSdW7JQkRdmsahllM7kiAAACE2HET9buP67Fq/dLklLio2WxWMwtCACAAEUY8ZPSimr3n+eMHWheIQAABDjCiJ+Un66TJOX0TtSgTm1MrgYAgMBFGPGT8tNnZtG0tkeYXAkAAIGNMOInFWd7RmKimdILAMCFEEb8xOEOI/SMAABwIYQRP6moPhNGWhNGAAC4IMKIn9SPGeExDQAAF0YY8ZP6npEYBrACAHBBhBE/KWfMCAAATUIY8ZP62TRM7QUA4MIII37iYGovAABNQhjxk4rq+gGs9IwAAHAhhBE/qHW6dLrWJYkwAgDAdyGM+EH9eBGJMSMAAHwXwogf1M+kaRFpU4SNJgYA4EK4U/qB4zTjRQAAaCrCiB+wFDwAAE1HGPGDcqb1AgDQZIQRP6if1htLzwgAAN+Ju6UPfbjjqD7afUw7i8slMZMGAICm4G7pQ/f/aaPKq89N602KjTaxGgAAggNhxIfqg0ju8HS1bRmlsUPSTK4IAIDARxjxEcMw3H/++chuatfabmI1AAAEDwaw+sg3soisFot5hQAAEGQIIz7i+kYaIYwAANB0hBEfcX2jZ8RCqwIA0GTcNn2EnhEAALxDGPGRhmNGzKsDAIBgQxjxEXpGAADwDmHER74ZRsgiAAA0HWHER1xM7QUAwCuEER8xeEwDAIBXCCM+4mIAKwAAXvEqjMybN0/p6emKjo5WVlaW1q5de95zFyxYoBEjRqhNmzZq06aNcnJyLnh+sGo4ZoQ0AgBAU3kcRpYuXaq8vDxNnz5dGzZs0IABAzRq1CgdPXq00fNXrVql2267TR9++KHWrFmjtLQ0XXPNNTp06NBFFx9I6sMIvSIAAHjGYnxzsEMTZGVlaciQIZo7d64kyeVyKS0tTffdd5+mTJnyndc7nU61adNGc+fO1fjx45v0mQ6HQ3FxcSorK1NsbKwn5TabEsdpZc0qUITVosJZ15tdDgAApmvq/dujnpGamhqtX79eOTk5597AalVOTo7WrFnTpPeoqqpSbW2t2rZte95zqqur5XA4GrwC3bmeEbpGAADwhEdhpLS0VE6nU0lJSQ2OJyUlqbi4uEnvMXnyZHXo0KFBoPlP+fn5iouLc7/S0tI8KdMU9QNYySIAAHimWWfTzJ49W0uWLNHf/vY3RUdHn/e8qVOnqqyszP06cOBAM1bpHZeLnhEAALwR4cnJCQkJstlsKikpaXC8pKREycnJF7z2qaee0uzZs/X++++rf//+FzzXbrfLbrd7Uprp6kfeMIAVAADPeNQzEhUVpYyMDBUUFLiPuVwuFRQUKDs7+7zXPfHEE5o5c6ZWrFihzMxM76sNUCWO0/rdyh2S6BkBAMBTHvWMSFJeXp4mTJigzMxMDR06VHPmzFFlZaVyc3MlSePHj1dqaqry8/MlSb/73e80bdo0vfHGG0pPT3ePLWndurVat27tw69inoWf7tM7m49IkmKiPW5SAADCmsd3zrFjx+rYsWOaNm2aiouLNXDgQK1YscI9qLWoqEhW67kOlxdeeEE1NTW65ZZbGrzP9OnT9dhjj11c9QGi4nSd+8/P3TbIxEoAAAg+Hq8zYoZAX2dk6rLN+tPaA/rF1T1031XdzS4HAICA4Jd1RtA4l+vMP62MXgUAwGOEER9wsuAZAABeI4z4QP3qqzZaEwAAj3H79AEWPAMAwHuEER9wuRc8I4wAAOApwogPnBszYnIhAAAEIcKIDxjuMSOkEQAAPEUY8YH6qb0WHtMAAOAxwogPOOkZAQDAa4QRHzAYMwIAgNcIIz7gPDudhsc0AAB4jjDiA/VTe22EEQAAPEYY8YH6FVittCYAAB7j9ukDLvamAQDAa4QRH3CyHDwAAF4jjPiAe8wI02kAAPAYYcQHzm2UZ3IhAAAEIcKIDzBmBAAA7xFGfMDJrr0AAHiNMOIDbJQHAID3CCM+cG4FVpMLAQAgCBFGfIDZNAAAeI8w4gMGA1gBAPAaYcQHWPQMAADvEUZ84NzUXpMLAQAgCBFGfIAxIwAAeI8w4gP1PSMWHtMAAOAxwogP1I8ZoWcEAADPEUZ8wHCvwGpuHQAABCPCiA8wmwYAAO8RRnyAjfIAAPAeYcQH3GGE1gQAwGPcPn3APbWXnhEAADxGGPGBcxvlEUYAAPAUYcQH6h/TMLUXAADPEUZ8gKm9AAB4jzDiA0ztBQDAe4QRHzg3m4YwAgCApyLMLsBs24849Nf1B+Wsf9bihVqnSxKzaQAA8EbYh5FZy7fr492lF/0+NqtFLaJsPqgIAIDwEvZh5OuKGklSv9Q4Xd4jwev36d8xXnEtIn1VFgAAYSPsw0hVTZ0k6bEb+yjjkrYmVwMAQPgJ+wGsFdVOSVLLqLDPZQAAmCLsw0h9z0hrO2EEAAAzhHUYcbkMVdXU94ww+BQAADOEdRipqnW6/9yKnhEAAEwR1mGksvrMIxqb1SJ7RFg3BQAApgnrO3B9GGkZZWPHXQAATBLmYeTMY5pWzKQBAMA04R1Gzs6kaWVn8CoAAGYJ6zBS5Q4j9IwAAGCWsA4jFTymAQDAdGEdRqqqeUwDAIDZwjqMVLhn09AzAgCAWcI6jNSvvsqYEQAAzBPWYaR+nZFWLAUPAIBpvAoj8+bNU3p6uqKjo5WVlaW1a9de8Pw333xTvXr1UnR0tPr166fly5d7VayvVTKbBgAA03kcRpYuXaq8vDxNnz5dGzZs0IABAzRq1CgdPXq00fNXr16t2267TXfffbc2btyoMWPGaMyYMfryyy8vuviLVVU/m4YBrAAAmMbjMPL0009r4sSJys3NVZ8+fTR//ny1bNlSCxcubPT8Z599Vtdee61++ctfqnfv3po5c6YGDx6suXPnXnTxF4sBrAAAmM+jMFJTU6P169crJyfn3BtYrcrJydGaNWsavWbNmjUNzpekUaNGnfd8SaqurpbD4Wjw8of6AayteUwDAIBpPAojpaWlcjqdSkpKanA8KSlJxcXFjV5TXFzs0fmSlJ+fr7i4OPcrLS3NkzKbrOIbG+UBAABzBORsmqlTp6qsrMz9OnDggF8+578z03TPFV3VpX1rv7w/AAD4bh49n0hISJDNZlNJSUmD4yUlJUpOTm70muTkZI/OlyS73S673e5JaV4Zl9XJ758BAAAuzKOekaioKGVkZKigoMB9zOVyqaCgQNnZ2Y1ek52d3eB8SXrvvffOez4AAAgvHo/czMvL04QJE5SZmamhQ4dqzpw5qqysVG5uriRp/PjxSk1NVX5+viTpgQce0BVXXKHf//73uuGGG7RkyRKtW7dOL730km+/CQAACEoeh5GxY8fq2LFjmjZtmoqLizVw4ECtWLHCPUi1qKhIVuu5Dpdhw4bpjTfe0COPPKJf//rX6t69u9566y317dvXd98CAAAELYthGIbZRXwXh8OhuLg4lZWVKTY21uxyAABAEzT1/h2Qs2kAAED4IIwAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKbyeDl4M9QvEutwOEyuBAAANFX9ffu7FnsPijBSXl4uSUpLSzO5EgAA4Kny8nLFxcWd998Hxd40LpdLhw8fVkxMjCwWS7N9rsPhUFpamg4cOMCeOM2ENjcH7W4O2r350ebNyzAMlZeXq0OHDg020f1PQdEzYrVa1bFjR9M+PzY2ll/aZkabm4N2Nwft3vxo8+ZzoR6RegxgBQAApiKMAAAAUxFGLsBut2v69Omy2+1mlxI2aHNz0O7moN2bH20emIJiACsAAAhd9IwAAABTEUYAAICpCCMAAMBUhBEAAGCqsA8j8+bNU3p6uqKjo5WVlaW1a9ee99wFCxZoxIgRatOmjdq0aaOcnJwLno/GedLmy5YtU2ZmpuLj49WqVSsNHDhQf/jDH5qx2tDhSbt/05IlS2SxWDRmzBj/FhiiPGn3xYsXy2KxNHhFR0c3Y7WhwdPf9ZMnT2rSpElKSUmR3W5Xjx49tHz58maqFpIkI4wtWbLEiIqKMhYuXGhs3brVmDhxohEfH2+UlJQ0ev64ceOMefPmGRs3bjS2b99u3HnnnUZcXJxx8ODBZq48eHna5h9++KGxbNkyY9u2bUZhYaExZ84cw2azGStWrGjmyoObp+1eb9++fUZqaqoxYsQI46abbmqeYkOIp+2+aNEiIzY21jhy5Ij7VVxc3MxVBzdP27y6utrIzMw0rr/+euOTTz4x9u3bZ6xatcrYtGlTM1ce3sI6jAwdOtSYNGmS+2en02l06NDByM/Pb9L1dXV1RkxMjPHqq6/6q8SQc7FtbhiGMWjQIOORRx7xR3khy5t2r6urM4YNG2a8/PLLxoQJEwgjXvC03RctWmTExcU1U3WhydM2f+GFF4wuXboYNTU1zVUiGhG2j2lqamq0fv165eTkuI9ZrVbl5ORozZo1TXqPqqoq1dbWqm3btv4qM6RcbJsbhqGCggLt3LlTl19+uT9LDSnetvvjjz+uxMRE3X333c1RZsjxtt0rKip0ySWXKC0tTTfddJO2bt3aHOWGBG/a/B//+Ieys7M1adIkJSUlqW/fvpo1a5acTmdzlQ0FyUZ5/lBaWiqn06mkpKQGx5OSkrRjx44mvcfkyZPVoUOHBr/4OD9v27ysrEypqamqrq6WzWbT888/r6uvvtrf5YYMb9r9k08+0SuvvKJNmzY1Q4WhyZt279mzpxYuXKj+/furrKxMTz31lIYNG6atW7eaullosPCmzffu3asPPvhAt99+u5YvX67CwkLde++9qq2t1fTp05ujbCiMw8jFmj17tpYsWaJVq1YxwMzPYmJitGnTJlVUVKigoEB5eXnq0qWLrrzySrNLC0nl5eW64447tGDBAiUkJJhdTljJzs5Wdna2++dhw4apd+/eevHFFzVz5kwTKwtdLpdLiYmJeumll2Sz2ZSRkaFDhw7pySefJIw0o7ANIwkJCbLZbCopKWlwvKSkRMnJyRe89qmnntLs2bP1/vvvq3///v4sM6R42+ZWq1XdunWTJA0cOFDbt29Xfn4+YaSJPG33PXv2aP/+/Ro9erT7mMvlkiRFRERo586d6tq1q3+LDgEX83dMvcjISA0aNEiFhYX+KDHkeNPmKSkpioyMlM1mcx/r3bu3iouLVVNTo6ioKL/WjDPCdsxIVFSUMjIyVFBQ4D7mcrlUUFDQ4P9M/tMTTzyhmTNnasWKFcrMzGyOUkOGt23+n1wul6qrq/1RYkjytN179eqlLVu2aNOmTe7XjTfeqJEjR2rTpk1KS0trzvKDli9+351Op7Zs2aKUlBR/lRlSvGnz4cOHq7Cw0B24JWnXrl1KSUkhiDQns0fQmmnJkiWG3W43Fi9ebGzbts346U9/asTHx7un0t1xxx3GlClT3OfPnj3biIqKMv7yl780mHpXXl5u1lcIOp62+axZs4x3333X2LNnj7Ft2zbjqaeeMiIiIowFCxaY9RWCkqft/p+YTeMdT9t9xowZxsqVK409e/YY69evN2699VYjOjra2Lp1q1lfIeh42uZFRUVGTEyM8fOf/9zYuXOn8fbbbxuJiYnG//7v/5r1FcJS2D6mkaSxY8fq2LFjmjZtmoqLizVw4ECtWLHCPfipqKhIVuu5zqMXXnhBNTU1uuWWWxq8z/Tp0/XYY481Z+lBy9M2r6ys1L333quDBw+qRYsW6tWrl15//XWNHTvWrK8QlDxtd/iGp+1+4sQJTZw4UcXFxWrTpo0yMjK0evVq9enTx6yvEHQ8bfO0tDStXLlSDz30kPr376/U1FQ98MADmjx5sllfISxZDMMwzC4CAACEL/5XCAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABT/X/CqJju1jyq7gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "idx = 304\n", + "plt.plot(eval(data.iloc[idx]['thresholds'], {'inf': np.inf}), 1.0 - np.array(eval(data.iloc[idx]['tprs'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9018394570827077, np.float64(0.6589673913043479))" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row = data.iloc[304]\n", + "tprs = np.array(eval(row['tprs']))\n", + "fprs = np.array(eval(row['fprs']))\n", + "ths = np.array(eval(row['thresholds'], {'inf': np.inf}))\n", + "counts = (tprs * row['p'] + fprs * row['n']) / (row['p'] + row['n'])\n", + "\n", + "mask = tprs[1:] > 0\n", + "\n", + "ln_tprs = np.log(tprs[1:][mask])\n", + "ln_counts = np.log(counts[1:][mask]).reshape(-1, 1)\n", + "\n", + "pred_tprs = LinearRegression(fit_intercept=False).fit(ln_counts, ln_tprs).predict(ln_counts)\n", + "\n", + "r2_score(ln_tprs, pred_tprs), row['auc']" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0. , 0. , 0. , 0.0625, 0.0625, 0.25 , 0.25 , 0.3125,\n", + " 0.3125, 0.375 , 0.375 , 0.4375, 0.4375, 0.5625, 0.5625, 0.625 ,\n", + " 0.625 , 0.6875, 0.6875, 0.75 , 0.75 , 0.8125, 0.8125, 0.9375,\n", + " 0.9375, 0.9375, 0.9375, 1. ]),\n", + " array([0. , 0.02173913, 0.04347826, 0.04347826, 0.08695652,\n", + " 0.08695652, 0.10869565, 0.10869565, 0.17391304, 0.17391304,\n", + " 0.2173913 , 0.2173913 , 0.34782609, 0.34782609, 0.36956522,\n", + " 0.36956522, 0.41304348, 0.41304348, 0.47826087, 0.47826087,\n", + " 0.52173913, 0.52173913, 0.58695652, 0.58695652, 0.91304348,\n", + " 0.95652174, 1. , 1. ]),\n", + " array([ inf, 0.67343348, 0.67178404, 0.63701837, 0.56892521,\n", + " 0.49148953, 0.48780163, 0.48189521, 0.3929078 , 0.37407803,\n", + " 0.33230279, 0.28911099, 0.23811385, 0.22902276, 0.2250438 ,\n", + " 0.22247694, 0.22006412, 0.21838054, 0.21721934, 0.21594653,\n", + " 0.21325973, 0.2130882 , 0.21155681, 0.21112516, 0.1956945 ,\n", + " 0.19470329, 0.19322293, 0.19213642]),\n", + " array([0. , 0.01612903, 0.03225806, 0.0483871 , 0.08064516,\n", + " 0.12903226, 0.14516129, 0.16129032, 0.20967742, 0.22580645,\n", + " 0.25806452, 0.27419355, 0.37096774, 0.40322581, 0.41935484,\n", + " 0.43548387, 0.46774194, 0.48387097, 0.53225806, 0.5483871 ,\n", + " 0.58064516, 0.59677419, 0.64516129, 0.67741935, 0.91935484,\n", + " 0.9516129 , 0.98387097, 1. ]))" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tprs, fprs, ths, counts" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(counts, tprs)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5666014063441887" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row = data.iloc[10]\n", + "\n", + "tprs = 1.0 - np.array(eval(row['tprs'])[1:])\n", + "tprs_orig = tprs.copy()\n", + "#tprs = 1.0 - np.array(eval(row['fprs'])[1:-1])\n", + "ths = np.array(eval(row['thresholds'], {'inf': np.inf})[1:])\n", + "ths_orig = np.array(eval(row['thresholds'], {'inf': np.inf})[1:])\n", + "mask = tprs >= 0\n", + "\n", + "ths = np.hstack([ths, np.array([0.0])]).reshape(-1, 1)\n", + "ths_orig = np.hstack([ths_orig, np.array([0.0])])\n", + "\n", + "tprs = np.hstack([tprs, np.array([0.0])])\n", + "tprs_orig = np.hstack([tprs_orig, np.array([1.0])])\n", + "\n", + "tprs = tprs + 0.001\n", + "ths = ths + 0.001\n", + "ths_orig = ths_orig\n", + "tprs_orig = tprs_orig\n", + "\n", + "tprs = np.log(tprs)\n", + "ths = np.log(ths)\n", + "\n", + "tmp = (np.hstack([np.array([1]), ths_orig]))\n", + "sample_weight = tmp[:-1] - tmp[1:]\n", + "preds = LinearRegression(fit_intercept=False, positive=True).fit(ths, tprs, sample_weight=None).predict(ths)\n", + "r2_score(tprs, preds)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.92592593, 0.54545455, 0.52727273, 0.35849057, 0.15714286,\n", + " 0. ])" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ths_orig" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.exp(ths), tprs, label='orig')\n", + "plt.plot(np.exp(ths), np.exp(preds), label='pred')\n", + "plt.plot(np.exp(ths), tprs_orig, label='raw')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 2., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 3., 1., 0., 4., 0., 1., 0., 0., 1., 0., 0.,\n", + " 0., 0., 0., 0., 2., 0., 0., 1., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 1., 2., 1., 1., 0.,\n", + " 3., 0., 0., 0., 2., 5., 1., 0., 1., 0., 1.,\n", + " 4., 4., 1., 1., 0., 0., 6., 4., 1., 1., 3.,\n", + " 5., 2., 1., 9., 3., 2., 15., 9., 15., 4., 10.,\n", + " 14., 5., 5., 5., 7., 6., 11., 10., 22., 22., 20.,\n", + " 30., 25., 9., 31., 25., 33., 33., 64., 90., 113., 178.,\n", + " 109.]),\n", + " array([0.25255143, 0.26002531, 0.26749918, 0.27497306, 0.28244694,\n", + " 0.28992081, 0.29739469, 0.30486857, 0.31234245, 0.31981632,\n", + " 0.3272902 , 0.33476408, 0.34223795, 0.34971183, 0.35718571,\n", + " 0.36465958, 0.37213346, 0.37960734, 0.38708121, 0.39455509,\n", + " 0.40202897, 0.40950284, 0.41697672, 0.4244506 , 0.43192447,\n", + " 0.43939835, 0.44687223, 0.4543461 , 0.46181998, 0.46929386,\n", + " 0.47676773, 0.48424161, 0.49171549, 0.49918936, 0.50666324,\n", + " 0.51413712, 0.52161099, 0.52908487, 0.53655875, 0.54403263,\n", + " 0.5515065 , 0.55898038, 0.56645426, 0.57392813, 0.58140201,\n", + " 0.58887589, 0.59634976, 0.60382364, 0.61129752, 0.61877139,\n", + " 0.62624527, 0.63371915, 0.64119302, 0.6486669 , 0.65614078,\n", + " 0.66361465, 0.67108853, 0.67856241, 0.68603628, 0.69351016,\n", + " 0.70098404, 0.70845791, 0.71593179, 0.72340567, 0.73087954,\n", + " 0.73835342, 0.7458273 , 0.75330117, 0.76077505, 0.76824893,\n", + " 0.77572281, 0.78319668, 0.79067056, 0.79814444, 0.80561831,\n", + " 0.81309219, 0.82056607, 0.82803994, 0.83551382, 0.8429877 ,\n", + " 0.85046157, 0.85793545, 0.86540933, 0.8728832 , 0.88035708,\n", + " 0.88783096, 0.89530483, 0.90277871, 0.91025259, 0.91772646,\n", + " 0.92520034, 0.93267422, 0.94014809, 0.94762197, 0.95509585,\n", + " 0.96256972, 0.9700436 , 0.97751748, 0.98499136, 0.99246523,\n", + " 0.99993911]),\n", + " )" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3, 3))\n", + "plt.scatter(1 - data['spec'], data['sens'], s=1)\n", + "plt.plot([0, 1], [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3, 3))\n", + "plt.scatter(1 - data['best_spec'], data['best_sens'], s=1)\n", + "plt.plot([0, 1], [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['dataset', 'acc', 'sens', 'spec', 'auc', 'best_acc', 'best_sens',\n", + " 'best_spec', 'threshold', 'best_threshold', 'p', 'n', 'n_nodes', 'fprs',\n", + " 'tprs', 'thresholds', 'classifier', 'classifier_params', 'auc_min',\n", + " 'auc_min_best', 'auc_rmin', 'auc_rmin_best', 'auc_grmin',\n", + " 'auc_grmin_best', 'auc_amin', 'auc_amin_best', 'auc_armin',\n", + " 'auc_armin_best', 'auc_onmin', 'auc_onmin_best', 'auc_max',\n", + " 'auc_max_best', 'auc_amax', 'auc_amax_best', 'auc_maxa',\n", + " 'auc_maxa_best', 'acc_min', 'acc_rmin', 'acc_max', 'acc_rmax',\n", + " 'acc_onmax', 'max_acc_min', 'max_acc_max', 'max_acc_rmax',\n", + " 'max_acc_onmax', 'auc_analytic', 'auc_analytic_best',\n", + " 'auc_analytic_best_mod', 'r2_tpr'],\n", + " dtype='object')" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "def convert(x):\n", + " try:\n", + " return float(x)\n", + " except:\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "data['auc_min_max'] = (data['auc_min'].apply(convert) + data['auc_max'].apply(convert)) / 2.0\n", + "data['auc_rmin_max'] = (data['auc_rmin'].apply(convert) + data['auc_max'].apply(convert)) / 2.0\n", + "#data['auc_onmin_max'] = (data['auc_onmin'].apply(convert) + data['auc_max'].apply(convert)) / 2.0\n", + "data['auc_rmin_maxa'] = (data['auc_rmin'].apply(convert) + data['auc_maxa'].apply(convert)) / 2.0\n", + "\n", + "data['auc_min_max_best'] = ((data['auc_min_best'].apply(convert)) + data['auc_max_best'].apply(convert)) / 2.0\n", + "data['auc_rmin_max_best'] = ((data['auc_rmin_best'].apply(convert)) + data['auc_max_best'].apply(convert)) / 2.0\n", + "\n", + "data['auc_min_maxa_best'] = ((data['auc_min_best'].apply(convert)) + data['auc_maxa_best'].apply(convert)) / 2.0\n", + "data['auc_rmin_maxa_best'] = ((data['auc_rmin_best'].apply(convert)) + data['auc_maxa_best'].apply(convert)) / 2.0\n", + "#data['auc_onmin_maxa_best'] = ((data['auc_onmin_best'].apply(convert)) + data['auc_maxa_best'].apply(convert)) / 2.0\n", + "\n", + "data['max_acc_min_max'] = (data['max_acc_min'].apply(convert) + data['max_acc_max'].apply(convert)) / 2.0\n", + "data['max_acc_min_rmax'] = (data['max_acc_min'].apply(convert) + data['max_acc_rmax'].apply(convert)) / 2.0\n", + "#data['max_acc_min_onmax'] = (data['max_acc_min'].apply(convert) + data['max_acc_onmax'].apply(convert)) / 2.0\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " auc_min_best auc_max_best auc_min_max_best best_acc best_sens \\\n", + "5033 0.284366 0.885428 0.584897 0.623188 0.344828 \n", + "8670 0.163681 0.958708 0.561195 0.623188 0.172414 \n", + "6931 0.163681 0.958708 0.561195 0.623188 0.172414 \n", + "8670 0.163681 0.958708 0.561195 0.623188 0.172414 \n", + "8670 0.163681 0.958708 0.561195 0.623188 0.172414 \n", + "... ... ... ... ... ... \n", + "1123 0.962436 0.999673 0.981055 0.982456 0.986111 \n", + "5849 0.720983 0.990413 0.855698 0.911765 0.750000 \n", + "9333 0.916241 0.998604 0.957423 0.963504 0.977528 \n", + "2174 0.865199 0.995206 0.930202 0.926471 0.923077 \n", + "9113 0.948879 0.999344 0.974112 0.973684 0.972222 \n", + "\n", + " best_spec p n \n", + "5033 0.825000 29 40 \n", + "8670 0.950000 29 40 \n", + "6931 0.950000 29 40 \n", + "8670 0.950000 29 40 \n", + "8670 0.950000 29 40 \n", + "... ... .. .. \n", + "1123 0.976190 72 42 \n", + "5849 0.961538 16 52 \n", + "9333 0.937500 89 48 \n", + "2174 0.937500 52 16 \n", + "9113 0.976190 72 42 \n", + "\n", + "[1000 rows x 8 columns]" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[['auc_min_best', 'auc_max_best', 'auc_min_max_best', 'best_acc', 'best_sens', 'best_spec', 'p', 'n']]" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "data['auc_min_max'] = data.apply(lambda row: np.mean(auc_from(\n", + " scores = {'acc': row['acc'],\n", + " 'sens': row['sens'],\n", + " 'spec': row['spec']},\n", + " eps = 1e-4,\n", + " p=row['p'],\n", + " n=row['n'],\n", + " lower='min',\n", + " upper='max',\n", + " correction=None\n", + ")), axis=1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "data['auc_rmin_max'] = data.apply(lambda row: np.mean(auc_from(\n", + " scores = {'acc': row['acc'],\n", + " 'sens': row['sens'],\n", + " 'spec': row['spec']},\n", + " eps = 1e-4,\n", + " p=row['p'],\n", + " n=row['n'],\n", + " lower='rmin',\n", + " upper='max',\n", + " correction=None\n", + ")), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "data['auc_rmin_rmax'] = data.apply(lambda row: np.mean(auc_from(\n", + " scores = {'acc': row['acc'],\n", + " 'sens': row['sens'],\n", + " 'spec': row['spec']},\n", + " eps = 1e-4,\n", + " p=row['p'],\n", + " n=row['n'],\n", + " lower='rmin',\n", + " upper='rmax',\n", + " correction=None\n", + ")), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [], + "source": [ + "data['auc_onmin_max'] = data.apply(lambda row: np.mean(auc_from(\n", + " scores = {'acc': row['acc'],\n", + " 'sens': row['sens'],\n", + " 'spec': row['spec']},\n", + " eps = 1e-4,\n", + " p=row['p'],\n", + " n=row['n'],\n", + " lower='onmin',\n", + " upper='max',\n", + " correction=None\n", + ")), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [], + "source": [ + "data['auc_min_max_best'] = data.apply(lambda row: np.mean(auc_from(\n", + " scores = {'acc': row['best_acc'],\n", + " 'sens': row['best_sens'],\n", + " 'spec': row['best_spec']},\n", + " eps = 1e-4,\n", + " p=row['p'],\n", + " n=row['n'],\n", + " lower='min',\n", + " upper='max',\n", + " correction=None\n", + ")), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "data['auc_rmin_maxa_best'] = data.apply(lambda row: np.mean(auc_from(\n", + " scores = {'acc': row['best_acc'],\n", + " 'sens': row['best_sens'],\n", + " 'spec': row['best_spec']},\n", + " eps = 1e-4,\n", + " p=row['p'],\n", + " n=row['n'],\n", + " lower='rmin',\n", + " upper='maxa',\n", + " correction=None\n", + ")), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "data['auc_onmin_maxa_best'] = data.apply(lambda row: np.mean(auc_from(\n", + " scores = {'acc': row['best_acc'],\n", + " 'sens': row['best_sens'],\n", + " 'spec': row['best_spec']},\n", + " eps = 1e-4,\n", + " p=row['p'],\n", + " n=row['n'],\n", + " lower='onmin',\n", + " upper='maxa',\n", + " correction=None\n", + ")), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datasetaccsensspecaucbest_accbest_sensbest_specthresholdbest_threshold...auc_rmin_maxaauc_min_max_bestauc_rmin_max_bestauc_min_maxa_bestauc_rmin_maxa_bestmax_acc_min_maxmax_acc_min_rmaxauc_rmin_rmaxauc_onmin_maxauc_onmin_maxa_best
5033bupa0.6231880.3448280.8250.5461210.6231880.3448280.8250.420290.527273...0.6116150.5848970.6999410.4965710.6116150.6944960.6436030.6411180.7351710.646845
8670bupa0.5362320.3103450.7000.5293100.6231880.1724140.9500.420290.772727...0.6044150.5611950.7331130.4362280.6081460.6909630.6306770.5104340.6491870.634991
6931bupa0.5362320.3103450.7000.5293100.6231880.1724140.9500.420290.772727...0.6044150.5611950.7331130.4362280.6081460.6909630.6306770.5104340.6491870.634991
8670bupa0.5362320.3103450.7000.5293100.6231880.1724140.9500.420290.772727...0.6044150.5611950.7331130.4362280.6081460.6909630.6306770.5104340.6491870.634991
8670bupa0.5362320.3103450.7000.5293100.6231880.1724140.9500.420290.772727...0.6044150.5611950.7331130.4362280.6081460.6909630.6306770.5104340.6491870.634991
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5 rows × 61 columns

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" + ], + "text/plain": [ + " dataset acc sens spec auc best_acc best_sens \\\n", + "5033 bupa 0.623188 0.344828 0.825 0.546121 0.623188 0.344828 \n", + "8670 bupa 0.536232 0.310345 0.700 0.529310 0.623188 0.172414 \n", + "6931 bupa 0.536232 0.310345 0.700 0.529310 0.623188 0.172414 \n", + "8670 bupa 0.536232 0.310345 0.700 0.529310 0.623188 0.172414 \n", + "8670 bupa 0.536232 0.310345 0.700 0.529310 0.623188 0.172414 \n", + "\n", + " best_spec threshold best_threshold ... auc_rmin_maxa \\\n", + "5033 0.825 0.42029 0.527273 ... 0.611615 \n", + "8670 0.950 0.42029 0.772727 ... 0.604415 \n", + "6931 0.950 0.42029 0.772727 ... 0.604415 \n", + "8670 0.950 0.42029 0.772727 ... 0.604415 \n", + "8670 0.950 0.42029 0.772727 ... 0.604415 \n", + "\n", + " auc_min_max_best auc_rmin_max_best auc_min_maxa_best \\\n", + "5033 0.584897 0.699941 0.496571 \n", + "8670 0.561195 0.733113 0.436228 \n", + "6931 0.561195 0.733113 0.436228 \n", + "8670 0.561195 0.733113 0.436228 \n", + "8670 0.561195 0.733113 0.436228 \n", + "\n", + " auc_rmin_maxa_best max_acc_min_max max_acc_min_rmax auc_rmin_rmax \\\n", + "5033 0.611615 0.694496 0.643603 0.641118 \n", + "8670 0.608146 0.690963 0.630677 0.510434 \n", + "6931 0.608146 0.690963 0.630677 0.510434 \n", + "8670 0.608146 0.690963 0.630677 0.510434 \n", + "8670 0.608146 0.690963 0.630677 0.510434 \n", + "\n", + " auc_onmin_max auc_onmin_maxa_best \n", + "5033 0.735171 0.646845 \n", + "8670 0.649187 0.634991 \n", + "6931 0.649187 0.634991 \n", + "8670 0.649187 0.634991 \n", + "8670 0.649187 0.634991 \n", + "\n", + "[5 rows x 61 columns]" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "#for col in data.columns[2:]:\n", + "# data[col] = pd.to_numeric(data[col], errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "val_min = min(min(data['auc']), min(data['auc_min_max']), min(data['auc_rmin_max']))\n", + "plt.scatter(data['auc'], data['auc_min_max'], label='(min, max)', s=2)\n", + "#plt.scatter(data['auc'], data['auc_rmin_max'], label='(rmin, max)', s=2)\n", + "#plt.scatter(data['auc'], data['auc_rmin_rmax'], label='(rmin, rmax)', s=2)\n", + "plt.scatter(data['auc'], data['auc_analytic'], label='analytic', s=2)\n", + "#plt.scatter(data['auc'], data['auc_onmin_max'], label='(onmin, max)', s=2)\n", + "plt.xlabel(f'{clabel} auc')\n", + "plt.ylabel(f'{clabel} auc midpoint estimation')\n", + "plt.plot([val_min, 1], [val_min, 1], label='x=y', c='black', linestyle='--')\n", + "plt.legend(markerscale=4)\n", + "plt.tight_layout()\n", + "plt.savefig(f'figures-midpoints/{label}-auc-midpoint.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "cm = plt.get_cmap('seismic')\n", + "z = data['auc'] - data['auc_min_max']\n", + "val_min = min(min(data['auc']), min(data['auc_min_max']), min(data['auc_rmin_max']))\n", + "diff = np.max(np.abs((data['auc'] - data['auc_min_max']).values))\n", + "sc = plt.scatter(data['auc'], data['auc_min_max'], label='(min, max)', s=2, c=z, cmap=cm, vmin=-diff, vmax=diff)\n", + "plt.colorbar()\n", + "plt.xlabel(f'{clabel} auc')\n", + "plt.ylabel(f'{clabel} auc midpoint estimation')\n", + "plt.plot([val_min, 1], [val_min, 1], label='x=y', c='black', linestyle='--')\n", + "plt.legend(markerscale=4)\n", + "plt.tight_layout()\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "plt.savefig(f'figures-midpoints/{label}-auc-midpoint.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "z = data['auc'] - data['auc_rmin_max']\n", + "diff = np.max(np.abs((data['auc'] - data['auc_rmin_max']).values))\n", + "sc = plt.scatter(data['auc'], data['auc_rmin_max'], label='(rmin, max)', s=2, c=z, cmap=cm, vmin=-diff, vmax=diff)\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "z = data['n_nodes']\n", + "diff = np.max(np.abs((data['n_nodes']).values))\n", + "sc = plt.scatter(data['auc'], data['auc_min_max'], label='(min, max)', s=2, c=z, cmap=cm, vmin=0, vmax=diff)\n", + "plt.xlim(0.5, 1)\n", + "plt.ylim(0.5, 1)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "cm = plt.get_cmap('viridis')\n", + "z = data['auc'] - data['auc_min_max']\n", + "diff = np.max(np.abs((data['auc'] - data['auc_min_max']).values))\n", + "plt.scatter(1 - data['spec'], data['sens'], s=1, c=z, cmap=cm, vmin=-diff, vmax=diff)\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "cm = plt.get_cmap('viridis')\n", + "z = data['auc'] - data['auc_rmin_max']\n", + "diff = np.max(np.abs((data['auc'] - data['auc_rmin_max']).values))\n", + "plt.scatter(1 - data['spec'], data['sens'], s=1, c=z, cmap=cm, vmin=-diff, vmax=diff)\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Q7uMD/4EDoQOgBdDm7rsRqdHg4tataG21V9oRis+eBQAYNBp0WbQIaZ9+iuZDhsC7deta3RcAjH/8AV157IkQHg7PyZPtv1inA9LTgYgIoIoUbKLU9TwoQZRDcY4uirp3b9xQKMB0OhxasACPX7yIopwcNGvRolb3DZ86FcaSEihbtUJA//4I6N+/jiwGhDZtAF9foKgIsvKKhaJs3gycPs33xpkCzDdvBi5e5OI4fnyd2UQQjiI1ga27JLt1e3EEAFYe76jXaiEIApq1aIHrixcjd/t2RH/yCVQOeHtyb2+0nj27rk0FAMhatYJ3Whqg00EQm7/U6YDr14H33uPPPT0tCyKmrSC0JYRwEUw1ZKS0dwfcVhxL8vNxbNkyhPXqhUFbt+LCypW4Y948AEDp9etIeeklAEDSgw+iZ/luGTGMxcUoXLoUHu3awXvkyPowHQAg+PuLv1BUBKxcyRdPunXjK82mlWqApwA7exa47TbzKcOlS0BJCeRdujjVZoIQg+YcXYx9c+fi8EcfQZDL8VJmJiIfeMD8mlytNq/OKsLCkPrjjwCAqBEjKm0NLFy0CPlvvAEA8Dx/Hp5WotMg5OTw1GKCwBdPFi60zajt6wv07m1+akhKQmGPHoBej2a7d8OjihRmBOEsGqs4uoudlVC3agUA8GreHJ4VVns9fHxw59mzuG3VKqjj47Fj5EjsGDkSV3ftAgCsDwjAV4KAr2QyyMuH3IKPD2Tl3pyxpASX7rsPye3aobgar7O2GM+fR0mHDijt3x9Mq+UnW7UC2rUDAgK4ONYQB8ZycszJJIw3bjjNVoKoisa6Q8Zd7KxE75dfxjPHjmHSmTM4OmsWvvbzw5klS3Bq/Hhkb9sG79tuQ8gzz8DTxwcAoAbw14MPoiQvz1I1kDE0Gz0aISdPIuzCBcjLF3FKTp5E4a5d0F26hHxTZpsK3EhMxKVHH0VpSor5nGbRImR064birVvBdDrkvfMO8v/7X56RWwTDDz+AnT8P4x9/wHj4sOWFYcOAsWOBoKDKF+3bB2zbZt5KKL/nHnitWwevzz6D5xNPSPslEkQd0FjF0a12yFQVUb+qvEogAISX/+yyahWKrl1D29dew5aOHaEvFxOjpydkLVqg5Pp1CAoFnhIpqMXKynBl7FjoTp1Cs7w8eD34IAI/+8z8eumVKzgdFQUAaDFtGlotXgwAuCqTAYxB8POD35IluDl2LABANXw4PLt2RcDcuRCsgrCNV65A98QTEMLCoFi3DkJNoTkaDVAejA5PT74V0BHKS0hA5i4fU8LZ1GaHzDYAUsq2aQE8CNoh4zTyUlJQnJ2NsNhY0dWv488+CwDI2rEDzVq1Qt6VK3xVTaHAv69dq9SeGQwovXQJynbtIHh6Imr9eqTL5TAajdCuWAHvadOgKi/gpQgPh3fPnig+dQp+99/PF1EKCyH4+4Pl5sKjXTt4durEt90xhqItW4AtW6C6+254W82Nylq3hurAAfvftLe3ZaeLUmn3ZUYjkJTEF8Fv974I5ZryhBkvvsiLbhFELaA5Rxei8No1fNm5M9bfdReSv/4a/b/7Dn5dumDIzz8jsF8/RFkl9yvNzcWgHTvgFxcHZZs2+HcVRbBSH30UyR06IG3iRABcLJnJwwIgtxIjwdMTfnffDb/evaEICkJKmzZI69YNIevWIei339Di0CF4qtWIuHIFwbt2QfD2hiwwEIrqYhrtwcMDGDKEJ6G47z7xNvn5fEHHitxcHjeemQno/jrGX2cM+OcfSyPGuGfq+gMJwsUQHDjcAbf0HMu0WhjKh8PFOTlAeDguJCfj5ssv4+GvvkJgt27w6d0bp959FyeTkpDq54fxmZnwDAys8p7F5UJRfPw4AKBg8WIwAEaZDDKjEXmPPoqg48cheHri2gcfIOvjjwEApUOHoiw/HwDgvX49gtasQcnQoSj95Rd4NG+OZjk5aJWdDchk5tyPtaJZs6prT3/yCU8Wce+9wJtvmk+rfYzo1fwyPKAHOg0AMi/y/dbWu4hWrODFWO69F5g6tfZ2ugnvv38Sc+eewODBYfjxx5rL+BKVoSBwF0Lp5wcfLy+UFRfDLzwcKX/+CRiNKD1/HltiYxHcuzceOngQ5z78EABQoNej8PRpBNxzT5X3jNqwAblr16J5uedYUr5fW2AMHgD0SUkw5uVBd/w4Sj75xJz4wiMy0iyOXk89BQDQHznCf968CQCQeUvJWVILTCUdygXejFaLYM88AECx4AeUhy7ZcOoU/3nypPPsc0HefPMo9Hrgp5/SYTAYIJe7y1fXdWisw2q3FMeSnByguBieAAovX0ZAUBBCAgPhqVKh5Pp15B4+jIz4ePRetQqlo0ahRdeu1QojADSLiUEzq3rOAfPnQ+bvD2VsLAxHjkDRpw/kLVrg5rPPwuPaNfiq1ZCNHYuoxYtx6/334RERgWblSR28PvsMxdOnQ3bnnWA6HQRnZQWvyPTpfHvhEFsP6FqeN/zLvKGQ6ZF6yw9dokSunTIF+OUX7jk2Idq1U+PsWQ0UChkJo4PQDhkXIrBLFwxctw6Fly+jy4svYnWzZnyuTK1GhEoF/5ISlPzzD8K6dcMDyckO9eHZsSOC1qxB6cmTyFqxAl5qNbwBGAIDobt2DeoRIxCyZAkK1q1DXnkiXVX37lB27QrZ0KEou3UL+PFHCHPmwMu0DdDZdOrEjwqEhsuxP7UjjEbgjjukXdvYueOOQJw9m4+wMErk6yjkOboQ17dvx42vv0bnOXPg4eUFT7UaZfn58LvtNvT76CNoNm1CoIO1k416PXLffReqnj1R4ueHo4MHw1unQ+jx4/B69FGUnToFGYAi0yqzKRxGEMQzbDtQrEsSjAE3bvBA8MhI0f5UqibnENrNN9+kAhBw5UoRysrK4Onp2dAmuR0kji7EXw8/DKbX4+b+/RiZl4enrl9Hxv79CL/3XshkMjQrryPtCBn//je0mzcDALQPPQSdTgcdgBZyOZQdO/LwHJ0Onu3aoXjrVvjExUG2bBnkXbtCqVQCFy9CaNcO6qNHYTh5Ep7WBZX1euDgQaBr1xp3vtjNrVu8/B7AhVpKaA5jvDaMwcAXedw47rGwEDhwgG8s6tnT/v9JzZrJodXyqAQSRsdorOLoLnbaICsPq/Hw8+M/vb3RcsgQyKr4chuKi/Fry5bYERCAgvJhtu7aNVwaPx43Fi60acusst2EjRwJr9BQBAFQymQQdDpEXr4M/9deA9u5EznDh+PW00+jWcuWUE2axOuELlgApKZC3qEDFP/+t+1848yZwIcfAs89V3e/DE9PixIolcCJE7yfr76ybWc08vKs77wDmOI8DQae3aesDBAJhncnkpKA1FS+JmXaAGUPhYXjsWfPUOh0Y51mW2Onse6QcRc7bbj//Hl0+/BD3GdaYa2BpJdfRsm1a9Dn5eHYk08CAG4sXIicL79E2quvosRqC2DYDz9APWUKgtesQciECWjv44MWAAxlZSg+fRopERHIWrAApnzgZdev84WMzEweZf3zz1WXIrh5kwvR4cPAM88Apv3UVZGXB0ybxhdLbt0Sb6NW85Cc/v2BFi2Adet42z//5MHp5RQdOwMkJ/PEFhs28JOm8gkAF1k3pmVL/nYCA3luDikMHBhOXmMtaKxxjm4pjt7h4egQHw+FnUPTMKs9x2GPPgoAUA8cCHh4QNWxIxTh4ebXZSoVQpYuhV95IllDdrb5Nc2TT5qDpI0Ar0a4fj3wr39ZOrv33qprPM+axcUoO5vXg96zp3Ibvd4SiP3PP0BKCnD5siVMRwwfH6Dci0b79vynhwewfj0A7lWNf6s1DJDx2M3O5anNBIFfFxAgrbaMCxIRwf/fPPqo278Vt0PuwOEONImPUYtBg3BfXh4MhYXwiogAAAQ89BB65eVBplJBqCaEo8Xq1ch68knISksh3LoFHwBlAOReXijJyoLu2jV4PPQQr++cl1f9nF/79sDHH/PEEioVirvfBaHEqtrBqVPArl1AVBTP29irF59AMxgAe6seTpzId7pcvMjnInv0wN4Dd6JHP19cemQBZLoS+EVwbxhFRXxLYiPBjadM3Rqac3RzFH5+ZmEEgMxPP8UxtRrHQ0OhO30auS++iJLylGYmdLdu4eisWTC+8goCnngCxvIsOZ5yOV/IKCtDyc6dvLGPj0UYjUY+HH74YT7ctiYqCjhzBtk7j+GGvgWuX+fb+wBwLxHgGXeMRr5IMm8ezwguZaxo8h4BICICzz7Lh5zw8IDR24c//v574OuvgSqyDhGEvTTWOUe39ByNZWX47amnoElJwaBvvoFfu3aS75G9eDFgNMKQk4OcZ5+F4fBhaFetQkRhoTkh7vaQEJTq9bjx3//i/r//RsAjjyBr1CgYDAYovbygVKngGx9f+eZbtqDs+++Rn58P1ciR8CkP+2Hp6WCzZgF33IGSx1+FXJMLyGTQKvwQEAAgJobPF4aGclE9ehS4+24+7LWXzEygoAAYOZInxQ0IgNfZs5jZ9iSK8jtBdexv7mGZhu5SVi8IQoTGGgTuLiJuQ87x40j59lvkHDmCixVWZRljMB48CFZd4tfHHkNEejpkMhlUXbrAe+hQAIBnjx42mcINVnkYs++6C/pRo8pdMMAnOBgtXn0Vnm3aVL5/587Iz89HcXExcg8eNK+Asw8/BFu7Fuy11xB26U+0WvJ/aLX4/xBqLF89vnyZrzjn5gKzZwOrVwP/+5+0X86vv/Lh+e+/c28W4DkgMzLgfXQ/ZNoCLp6BgXxI3auXtPsTRAXqw3NcunQpoqKioFKpEBsbi0OHDlXZtqysDO+88w6io6OhUqnQvXt37NixQ3KfDomjFEMBYNGiRejQoQO8vLwQGRmJ6dOno6QWBaICu3VDxH33Qd2uHdr8+982rxmXLoXhrrug79QJTKMRv8GxYwhQKnG7nx8679sHv3feQVhKCoLL91ObGPjrr/D28EAY+CSyBoBgMMAzIAD+ly/bJHew4bbboJo3DxAEKAcMMKcXE+69l68WdOkCuYxBMBohGPTwyM3h11knpjAll5C69Goq89CmjWVloksX/rhjRz4xJ5MB99wDPPVUNVtmCMI+nC2OGzZsQHx8PBISEnDs2DF0794dQ4cORVZWlmj7t956C59++ikWL16MM2fO4IUXXsAjjzyC4xVzDtQEk8j69euZQqFgq1atYklJSWzSpEnM39+fZWZmirZfu3YtUyqVbO3atSw1NZXt3LmThYWFsenTp9vdZ35+PgPA8vPzGWOMld28yc7068fO9OnDdBX61b/xBtMBTCeTMeONG+I3XLeO3YqMZKcAdqZFC6bXaMwvGfV6VrR5M9OdPMkYY0x37hxL79KF3QgIYPkhISw9PJzlL1hgl90GrZYZjUamO3OGFW3axIxlZcxYVMSMBgNjBw4w1qkTY7ffztiVK5aLLl5k7No1xgoLGTt+nLHSUrt/T2ZKSxkzGu2z0cBYURFjxcX8sTVlZdyMkhLpJhDuRcXvmJRrkgCWJuFI4jlbWHp6OsvPzzcfJVV80GJiYtjUqVPNzw0GAwsPD2eJiYmi7cPCwtiSJUtszv3rX/9iY8aMsfu9McaYZHGUaujUqVPZoEGDbM7Fx8ezvn372t1nxT9czjffsEMAOwSw7DVrmNFoZIVz5rD8sWOZPiWF6T/6iBl+/bXae96YOZOdAtgpgJWmpZnPaz78kP8R5XKW/eijrOzqVbvtFMNw6xZLV6lYOsDy332XnywrY+zZZxlr04Yf771Xqz7swVhSyoxX0pgxLY0Ziy0fwuJixrRafuh0ttcUFDCWn88Pvd7pJhINSG3E8QzA0iUcZ8rFseKRkJBQqY/S0lIml8vZDz/8YHN+3LhxbMSIEaJ2BQYGss8//9zm3JgxY1jr1q3tfm+MMSZpQUan0+Ho0aOYNWuW+ZxMJsPgwYNxoIqM1n369MHXX3+NQ4cOISYmBikpKdi+fTvGjq16R0JpaSlKrXZsaCoMj/2GDIFPnz5gej38HngAhn/+QdGcOQAAedu2aFb+uDpazJwJQaGAqmtXKExxiVeuwGvFChgFARqDAcXffw+P6Gj4//e/Nd7Phps3gddeA9RqsBkzLGUJTHOYMhnPnPPXX/yxo+UOrNm7l++fi4sDBgyo9LIxKwtyHz5UN2ZmQmjNC5TJ5RbzKkY0mZKOO3t7OOHeOJrPMT093aZMglIku31OTg4MBgNCQkJszoeEhODs2bOi9x86dCgWLlyIe+65B9HR0di9ezc2bdpks4ZgD5LE0RFDR48ejZycHNx9991gjEGv1+OFF17AG2I5BctJTEzE3Llzqza6eXN02r/f/Nzo5QVZdDSMV6/C01oY9HogLIz/fO45wErk5H5+CKnYx+rV8Lh0Cb5yObTBwTDk5EA5aBBYdjaKR4yAvEsXKD//vEq7zGzZAuzezfsJCUHwrl3Q37gBL1OwuEwG9O0LmPoPDrZcq9VylZKaGPfXX7mS/forz67TvLlNNLTQrBmYQQ8IAphVbKOnJzdHECrHCSqVfCu5IFQWToKoLWq12ik1ZD7++GNMmjQJHTt2hCAIiI6OxjPPPINVq1ZJuo/TV6v37t2L9957D8uWLcOxY8ewadMmbNu2DfPmzavymlmzZiE/P998pKenV9uHzNcXgefOISg/HwprcVy+nHtrggCsWVOzscOHA0FBkMXEIOz8ebTMz4fX0KHQDhyI/L//xq2VK6H/6it+z9zcyiUFjh8Hnn6avx4SwrfzqdVQeHrCe9QoCNZb1Fq25F5e374WcczIAH78kR81bS2sSGgo/+nrC1y6BFy4YPs7CgwA/AMAP3/IW9hWNTQaLd6jzTUy258EIYYzF2SCgoIgl8uRWSFeODMzE6Gmz3wFWrRogc2bN0Or1eLKlSs4e/YsfHx80LZtW0nvS5Ln6Iihs2fPxtixYzGxPMN2165dodVq8dxzz+HNN98UTRahVCpFXezqEOTyyu7NM88ACQlcxEaMqPkmPXsC16/z+1mdlplWjAUBQosWwBdfcCHr148fJl58ke9O2b2bp4kBuEBGR4sYLFTeTaPRgG3bBmzfDgwYAOGHH2xfLy4GEhO5cL7xBvcOTUybxofWGzfybN4iQ2vBs/Kfu7DQsgU7IMB2i3VREe/Kw4O/RsNrQgxn7pBRKBTo2bMndu/ejZEjRwIAjEYjdu/ejWnTplV7rUqlQkREBMrKyvD999/j8ccfl9CzRM/R2lATJkPj4uJErykqKqokgKaMy8zJxZwYAHbtGvfyVqxw+D5ef/6J5pMno8Xnn0N+3308eQPABRLgc31nzljESqu1KMmoUbbDZhPZ2TwU6O23LYHY0dFcWPPygK1bK19z+jTfEpicDFhNK6CoiCea+P57/l6PHrWE9JSTnw9kZVWO+dbrxR9bP6943kRpKQ+nPHKE6nI1ZZwdyhMfH48VK1bgiy++QHJyMiZPngytVotnnnkGADBu3DibdZCDBw9i06ZNSElJwZ9//on7778fRqMRr7/+uqR+Je+QiY+Px/jx49GrVy/ExMRg0aJFlQyNiIhAYmIiAGD48OFYuHAh7rjjDsTGxuLixYuYPXs2hg8f7nBaerZoETB/PthTT4Hl5UFo1gzCBx+Y04MxrRZs/HgYNm+G3scHyn/+gax1a5t76NLSkPH661B1784XZ8TcooULgYMHITz3HBTLlvFzBw5wMWrfni+q/PADz3Ygl/P5vlWruHgWF1e+nzWXLlm8yytXAH9/fo/HHuPB32JlHTp35vOJRUV8r/Xp01ygAwK4KLZty7P+DBtWyYsuLuZDZ4OBd2VCreaCKZdXrvbq48OH1KZ5x4okJVkKGEZE8Oldounh7B0yo0aNQnZ2Nt5++21kZGSgR48e2LFjh3ntIy0tzcYBKykpwVtvvYWUlBT4+Phg2LBh+Oqrr+Bv/cG3B0lr2+UsXryYtWrViikUChYTE8P+/vtv82v9+/dn48ePNz8vKytjc+bMYdHR0UylUrHIyEg2ZcoUlpuba3d/FcMMjCoVMwLMIAhMDzA9wIw//WRub/zyS2YEmBFgJQDTb9tW6Z7Xpk9nJwF2EmAlFy9W7rS0lLEhQ/jx2GOW86NG8XOTJvHnq1czJgj8uO02xlq0YCwsrOY3VVTE2BdfMLZ2rWOxjHo9Y9u2WY5ff2Vs3z4eJiRCRgYPn8zIEL9dZiYPtxR7vayMX3vtmu3tr15lbPFixnbuZOz8ecZu3pT+NgjXoDahPGkAy5NwpJUP6qT01RAIjLn+gEij0cDPzw/5+flQq9VgAwYAv/8OFh0NlpMDqFSQHT0KoTyxBLtwAYiNBSspgWHCBHh89BGECnmsCnbswJWHH4aiQwe0O3zYnEDXhlGjuEc2YIClYt/ixXw1eswYYPx47o6tWwcEBfHwnaws7oZVt33RGlMNaUdWPbZv5z+VyhrrIOj1/PDwEE/plZZmedyqle1rWi2PTgL4zIF1ZdjSUuDqVe6RenryjTn/+Q/PoTF7Nn9uD2vX8pkCQQA++oh7qyYefPAstm/Px+23e+HUqW723ZCQRMXvmJRrrgKQsuasAdASkNRXQ+CW4ggATKOBoFaDlZUBMlmltGOsfKKsoihaY9BoYNy6FfJevSCzzmRjwmjk49GKdaJ1Ottvr4n0dD4UHz2aJ32oCb2ehxdducIXVLpJ/OIXF3MxNmV6rUBGBp+eDAvjOwerIzeXj/KbNePbrq0xGm3FsaKOazT8+sBA4Px5S4RSQABfu7IH65I/99wDWKXghCAcND9mzM7UbYQkaiOO1yFdHMPh+uLotkEaQvkvVfD0FM3HKHh4VCuMAGBISIB+zBiU9u5tUx4BABe6Bx/kK9DWcS4lJXwF4siRyvEvkZHc7bFHGAG+SnLpEhdJ0+SdFLy8gNatxYMQ9Xp4XjqLduw80tOMNS6YBARw8ysKI8DFsEULfog5uGo1N8PXFwgP5x6kn58lusgeWrSwPL7vvoqv8b+jxAAGop6oj8QTDYFbpiyTxM8/85XkSZMqF7UyrTKIVQ58803g3Dl+bNnCczMC/F6mhZRr16rO+m0PgYE8tVhqamVFAHhY0W+/cbevZ09p987KQnNDFpp7AJ6hQRAEEdVzAiEhfAYiIMDibdrD3LlVO+RZWT2h1+vhQSm+XRJnhvI0JO5ip2NkZPDM2B9+KJr6yzMxEZ4bNkB55AiEim6JKX5RJgN69LCcD7IKoJa6+lURQbD0s3Ahz95tzZo1wM6dfEJO4tYn+PvzyUWlEqHtfGpnpwS++caSfvLgwerbVkRMGE2QMLou5Dm6I76+3Du7eRNo2xZlJ07AmJkJZXn+RkGphEdVgaGTJvGQGLXaNm1Yq1b8nhkZfFhrD1eu8G++VayLXg/ItBrIXnqJD68BvjPm1Vct1507x4fx+fnS9u/p9Xxsa4o9FYnDyc3liymhoXw9qKyMa6nYsHn9er6wYk+lhg4d+Mak1q15xYca4nSJRgB5ju5Is2Y8OPr331HWpg1y7rgDt+6/H9rqElPk53NBAnjwnlg+xa+/5p6ePfusDx4EBg3iK96XLgHgoqTTASWZeWCARZUqxjbefz+faHvkETvebDllZXzpOT2dvw8RYczJ4aP1v/7iCzb5+VzrMzIqB3N/9RWfRywp4W1rolcvHv6Znc21nmj8kOforvj7A/7+MFiN8fRJSeJtDx/m32oAuOsu8dUJwNLGqjJhlVy7ZnHNsrKAqCh4blwPxZ7d0He7E8b7H4D80kXg+ef5hJ01990H3H67tHlN6+0sZWWi3q1pyhTgScFNMwViI3fr+ltZWTwGvSbuu098CpVonAiA+CaKqtq7foAMgKYgjuWonnoKPn/9BUN6OnxXrhRvZK50Bb4YUpU4TpzIV6vtKTEwYgSPdfH25oJ7+TJku34BCgrg8cceCJ9+CrQYbXvNuXP8/l5efJtKQQHQpw9XtS+/5K7nnXfypBWmubiCAl5/JiCAL/0ajVVmEY+K4m+1uJjfRhD45SpVZUezfXu+BqXVAs8+W/PbJZogHh7SNt4zVvWeVBeiyYgjAGj8/VGanw91VTP/PXrwfcmmkgJVERZmfw5GDw+eqcdE8+ZcvAoKeAiSWs3HrNYpyn7+mQuqjw/3HE17tvfts2yOTknhgmsKG8rI4J6iTldpX7UYFasjVLW21LUrP+qCvDzANKORkCCtbhjhwpA4uje3vv0WGeX7vcEY2q5bV7lRcDDwwAPONUStBqZO5SnFYmKAWbO4lzp9umXFo0sX4MAB6Lr0QG7nu6FuroAXAHTqBHbsGG/TvDkE6zGvWs0Xnlw4qPbECUv12RMngIEDG9Iaos5wRBzdgCYjjh5W83mKqubw8vL4kLl9e14rujZcv84n6azDgAA+fj18mD9OTuZ77wCexcEkjkOGAPfei4yrMsDIF1AiIwG0bo2Lj85EWdo1KJVAWJtImOUxJISLuwvnFevZE+jenX83qOhhI4LE0U05dAg4eBDypCSEAcCAAQivquzBs88CO3bwPcv33MNXix1h3z7AFCI0eDCfJzShUPAPk17Pl4Gffpq7UxXzTcpkEASrbdepqcD16/Bq3hXZzbi4G6/ZjqD1BgFFRfzWPj7Vxw1KwWjkK8+CwM10ZBt4cTEwbx4wbhyfKSAaEXK5tA+FWGZlF8QtxZEZDGDXrkGIjLSskhUV8W+gdQLYS5f43CBj0BcVwQuAzDrDQkWsMwWXJ7EQpTzZBXx8gGPHeBzjiBGWWMQtWyxtjxyxvVap5Eu533/P5xqHDauym4gI/ra8FXpgxyl+Lu9PKDzDcdm3K5RKD3MyCYA7vgUFfOoxJwdo167mz6xez3PjentXPc26Z4/F2fXx4XovlbZtgcxMYMECbh+VXWhEVBUgWxVuIo7uEnJkQ/G//gVt69YofeUVfiI3F/jkE57l4K+/LA2zsswufLPWraF46il4V1dHYsECnmHnwIGqVyH+/JPHs3TrxifOBgzgGXqWL7e0efddvtKtUIjHQv7nP3zhZ+nSytlnrZDJynMqesjMwxbBYEBwSTpaqzKh1/N1G+v2UkcsJ0/ytZyUlKoTCVkv1ji6KcjaTp3OsXsQRH3ilp6jsTxm0fD33/yEKTM3wOMKTcTFARMmAMeOwWPlSnhULEsgRnkq9io5dYoHBOblcTUxea7WrpCHB09EWxVWlRWh1doojtHIvTmbqg8yGU9Jdv06cOECmEwGgzoAgmAbhxgQwPU4L8+SqLYmrMMgq6rp1asXvx9Qc3afqvj5Z77p6Ikn7N9YRLgJjdRzdEtxVG3YAP0338DTlOcqOprvW8vLA8q3BpoxrVDXFePGcY80JITPSe7bx4fV1QyPK/HCCzzbd3R0peG7TlcukLfy4H3xNFcmlYqrYLt2QHQ0BABh5fmUrefBBYGLmEnI7KFTJ55Bx8ur+tAaR0XRxD338PBNohHSSMXRbfM5NlZ0Ou45ei1bAMFo5ML40ksNbRbRyKlNPsf8tm2hljCJrDEY4JeS4vLfZ7f0HBsznp7lCyym/65uECxLNHE8PKStsLlwuJk1brkg41KUlEhLXFgDgsBHKMLQoXwnjZSkE9Vw7BhfQ+rWjUcrVUleHo+9tCoQdugQX4wfMYLfRwobN/K1qcBAwDRFTDQyTLU3pBxuAIljbdBo+G6WVq14ctysrLq7d/fuvO52xSIs167x8q2XL0u6nXXqsLfeqqIRY/w9FBXxfdrlfPCBpcn8+ZK6xYsvWh6PHl11O8KNIXEkKnHjBj8Y44IltiWxJlJTeTxNTaVcTRw+zHOMHT8uqZvHHrM8rrIWl2lFB7BZ1enf39JkwABJ3drsxiyv3ks0NhqpOLqHlbVFo+E5Dtu1qzpexRE6dOB5Hb/9lqe6sQ4VMhh48azLl/m+abFcX4WFluDC8+e5twjw+cbSUj6PU3Gbi5cXoNWCKRTILXfuPDyq31Kdl8eH0x99xE3s0KGa9xQezsXeal5o2jQehgPY1nF5+20eXiqXA2fPAj//nIF581KxfHkH3HMPz2i0erUlcolqwDRS5HK3ETwpNL53JMbXX3OFaN/e1oWqC154gStHTg7f22wiJ8cS63jwoLg4WgufdcBiSQlfiBFLzz1kCJCVhTJ1EFCek7esDDY7ZSqSmmq5RUZGDeIIiE6Yiwnbxo38p8EA/PQTMG4cz5M5YMBxGI33Vnst0YhwI29QCk1jWG1dSMsZyOU87tH6/sHBPPaxY0cuaGIoFDyOsXVrG2E1smoKf8lkQGgo5Er+YWSs5vIynTpxZ9RotEMYJfDWW/ytBwbSfGKThobVbsxTT1mG1fWFIPB+a0KrNZdPQPfuuGXwA9N7IlAtQ7FODu8qBF0u50HbOh3Xy+o+byqVffuhS0oMUKnsD8kYM4YfJjZu7IK5cy9j+fI6VGDC9XEjwZNC0/AcfX35qrJSySfB+vcHNm9uaKs41jH4jIEx4GaeBy6kKZCT54HPPuMV/UyYpiONRq6/SiWPjQS4UOblObYBITCwEF5eBVCrCxx+K489FopTp+7C3XdTFtsmhWnO0d7DTbKOND65r4mPPuIKsnRpzfuo64PAQJ7DSxAAf39YcgoJ+PZbrp25uTwXRlwcdzQZ4wJpvQhjNFoqu+bmVo4Aqom8PD42Lyhwj61dhAsh1XN0/U15AJqK52jNCy/w+cGJE+v+3iUlPGh7wAC+fGsvgYE2G5ubN+eH9T9YPz/+s6rPlbW36Mimmv79PSGTydCnj6f0i4mmDc05NhKmTOGHM/jxR8v84UcfAZ9+WqvbPf00H/23bAl07szPeXtzr7FihI+HB9f8ggKb8th289tv3jU3IggxGqnn2PTE0ZkMHMi3k5SVAQ8/XOvbKRSWhOImqvscmjxOgqhXSByJGgkOrpz52wEMBqDk+i0o/9gFebs2EGJjzK+VllqKFaamAunplsDuqrJGFRXxIHC9nm/i6dOn6r5LSoCvvuILPWPGSJ87Nxp57PvNm3yxvmIJHaIRIjUI3E1SljW9OUdncOgQsGoV3+ViwrRP2SobuZmSEtuEtxXQ6QDPP36Fxz9HIXz/HVe3coqKuHjm5vJdh7m5fLt1SUnV5o0fb8kTOWFC9W/ljz94lYfvvuOJzqVy6RLf9KPT8QS3RBOgkc45kjjWBX//zVfAjx61nNNo+HaUjAzbGgFaLa9JcOlSZUXLyQFu3ICnXgtD62gwAMyqamJRERceg4F/vnx9+fmgoOqza1tvCoqJqbodwAt2KZV8a3WrVtW3FaNNG36tIAD9+km/nnBDGqk4uoeVrk7v3rx8wh13WM4pFJbdLdarJ9bbWawf6/Xm4ioe2gJ43N0T6Niav1ZSAnh5Qa8XkJfHdbhdO5703GCwxDlWxahRQN++fKhr2r5dFW3a8N2WMlnN9xXDw6P2VW0JwhUgcawL4uL4YY2Xl2U/tfV/Sl9fntzBRHExbyuT8cNohEHlBU0u4OXpC5VBaxZatZpvDzcYeKpHU+5He2jZ0jYvRnXQXmhCElK9QTeZcyRxdCZiH5jyYG8UFdkOq728gNBQAMCtbD5NWQYlZP5KG8czKMi5JhOEZEgciTqlYmWsCi+Z1nDcJKM80ZSRulpdU6YUF4HE0VFMq9G5uTz6urrSfWKYVlAEoVKOyYAAHsytUDg270cQ9YpUz5HE0c0xGPgiScUJuJISXmOlpITHNGZl8Ym/YcO4mpXPG0Im488jIqp2/6pYYpbLbUpZ23DPPbyOyyef8ADx/fv56nDv3tLe3pdfFiEry4CXXvKBQmGfe/rLL8CsWXzadO9eaf0RjRgSxyaEXg/8/juPRbzjDtv9eMnJQHY2FzzTH9lo5CnRPDz4Y7ncIohKJV89qQVGIw/4zs62FKmaMAGIjLQkEg8O5mkhy8qApCT+mvVumXPneOLxO+8EDh/WYfz4XABAs2YCJk+2lEQwGAyYNMkAf38BCxfauq0zZ/KfGg3fCPR//1ert0U0FkgcmxClpZYgbY3GVhxNGbubNQO6dgXOnOFunkJR+Y8uMmR2hJMneXC2IPBMPBoN11w/Px4ADlhKvmzdyjP4eHsDs2dznU5L4x4fALzyCtC6tQxeXnyhvG1b24/AqFEGfP89nzD39QXmzrUIpEJhjjaqMSSIaEJInXN0k3LDJI5imISvoKBy7q/27blYenvzD0WHDnxi0GCwxNaYorTrSBytWb8e+PJLYOVK3m1wMB+dV5zyrGr7KmNA69YeuHQpFFotQ7t29n8E/v6b5+y4+27gvvtq8SaIxoVUz5GCwN2U5GQeLxMZKf66yX0zYcol5kS6deNenK8vF0PrrN7WIZMA8NBDXM8jIy37olu1AhITudb37MnPhYWJb5resEGOCRMAf3/Bxms0sWxZXbwjolFRD+K4dOlSfPDBB8jIyED37t2xePFixFSz3WvRokVYvnw50tLSEBQUhMceewyJiYlQSXBWHNo+uHTpUkRFRUGlUiE2NhaHDh2qtn1eXh6mTp2KsLAwKJVKtG/fHtu3b3eka+eyfDlXkVdf5ZN8FSkr466XTsePgoJ6mT+RyYDoaNv6XVXh6cmTPVTMztOhAy9XU1NokFwux5o1Cixa5ImcHO6lnjzpsOlEU8DJ2wc3bNiA+Ph4JCQk4NixY+jevTuGDh2KrCrqxK9btw4zZ85EQkICkpOTsXLlSmzYsAFvvPGGtLclqbWVoZ988gliY2OxaNEiDB06FOfOnUOwyLdXp9NhyJAhCA4OxnfffYeIiAhcuXIF/lUtxzYkplTaAE9Waz2kPnyYZ2Ro0YKnwjHtahEEvjJR3eZmN2XNGuCvv4Bt24C1a91mNETUN072HBcuXIhJkybhmfLC55988gm2bduGVatWYaZpldCKv/76C3379sXo8qpvUVFRePLJJ3Hw4EFJ/Ur2HK0N7dy5Mz755BN4e3tj1apVou1XrVqFW7duYfPmzejbty+ioqLQv39/dHfFGf3p0/mQuXXrypNqFy7wn9nZfLxqEkYAxWcvIymJR/i4GkVFQH6+Y3PgppmF0FC3KftBNAQO1pDRaDQ2R6lIpiqdToejR49isNVckkwmw+DBg3HgwAFRc/r06YOjR4+aR7QpKSnYvn07hg0bJultSZJwk6GzTEufdhj6008/IS4uDlOnTsWPP/6IFi1aYPTo0ZgxYwbkVXzjSktLbX5RGuusNs6kZUtgyRLx1wYNMpdFxcWLfMm4qAgIDMR1dSdob/KEO35+lmw5zkCnAzIzq54StcZotOxQLCmxrGiL8fnnPD+vddTR44/zhBWmfdwEIYqDnmNkhQ9xQkIC5syZY3MuJycHBoMBIVbZqQAgJCQEZ6soRTJ69Gjk5OTg7rvvBmMMer0eL7zwgnOH1Y4YmpKSgj179mDMmDHYvn07Ll68iClTpqCsrAwJCQmi1yQmJmLu3LlSTHM+oaHAE0/wxwMG2LzUPI9nyvH3547lzZtAVFTdm6DX8/rQ0dG87MyMGdW3FwQ+B6nXVy6rYE2vXlxIly3jYUCmuHdBsD9ZBdGEcVAc09PTobZa3FTWUcaTvXv34r333sOyZcsQGxuLixcv4uWXX8a8efMwe/Zs+82sE2uqwWg0Ijg4GJ999hnkcjl69uyJa9eu4YMPPqhSHGfNmoX4+Hjzc41GU+m/jCvh789XgVNS+PPa7KtnjK/zWC+ImygsBN5/nwtWbi5w6xbPlNa/v22706eBtm15tJHJiy0p4fcV82qt7c3Kss8rJQgzDoqjWq22EUcxgoKCIJfLkZmZaXM+MzMToeWJWioye/ZsjB07FhPLi+h17doVWq0Wzz33HN58803I7ExlJWnO0RFDw8LC0L59e5shdKdOnZCRkQGdKaK4Akql0vyLs+cX6CqYkrxK3WZtzcSJvFKr2Oje39+cuAf+/nxVeuxY24DsceOAN94AnnySL64DfC70pZeA+HgeTF4RkyPs40PCSDiAE+tWKxQK9OzZE7t37zafMxqN2L17N+Iqpgksp6ioqJIAmvSHSahfI0kcHTG0b9++uHjxIoxW7sn58+cRFhYGRXVjPTckOJgvcNdGHPfv5z/FRAywDHn/+styLjfX8riggP805cUAeOgmwIX7n38q33PhQr5fu6o+CaJanBzKEx8fjxUrVuCLL75AcnIyJk+eDK1Wa169HjdunM06yPDhw7F8+XKsX78eqamp2LVrF2bPno3hw4dXuc4h+rYkWVlu6Pjx49GrVy/ExMRg0aJFlQyNiIhAYmIiAGDy5MlYsmQJXn75Zbz44ou4cOEC3nvvPbz00ktSu7ZlyhTg8mVg9WqeFaeR8PHHPHTmuefEXzflxB04kG++KSkBJk+2vD5sGLB9O49jj4jg5wYO5BUctFo+Z0kQdYqTQ3lGjRqF7OxsvP3228jIyECPHj2wY8cO89pHWlqajaf41ltvQRAEvPXWW7h27RpatGiB4cOH491335XUr8Ck+JnlLFmyxByt3qNHD/zvf/9DbGwsAGDAgAGIiorCmjVrzO0PHDiA6dOn48SJE4iIiMCECROqXa2uiEajgZ+fH/Lz8/kQe8sWGMaNAwPgce+90H32GTz9/SGUlvK4lYpD/PPn+TYRDw8etK1U8jGnTMZdfKORu1WOLskajXyjsiDweEdBQGEhn/OzN1O3PRgMfMGHMS5+FdOZvfIK30f91FPAv/5Vd/0SjZ9K3zEp13zzDdSmnAP2XFdUBL8nn5TUV0Pg0ILMtGnTMG3aNNHX9orksoqLi8PfpnQydYBWqcSBvDwAQKvsbKQ2bw7/uDjErFoFQSbjS8cdO/LGc+dywZTJeFoZQeCvm4TQ25uLm4eHJalERYxGSxoyMbXT6y07ZfR67NjtiZ9+4lUSXnyxzt42ysosiyc6na04ZmZaFoTWrSNxJIja4pZ7HgoKCmDatHezuBgAkH/4MFhZGYSK4QCmGEmTuFVEr7cki6gKg4G7a0YjV6SKHqYpyYQgAHK5uUKracONXm+bxcxRlEqeE4OxyhtygoJ4HwaD5f8CQdQLlHjCdQgePhxR5ckEw8eOxeUPP0SLYcMg8/TkAma9ufzee3luxttu4yrFGH9dp+Nq5e1dcwk/k3hWNfSWyWxiZB59FPj1V76anJMDXL/Ou4mOrp1ACgL3Hk0py6wFUi7ntaYfeQT47Tdg3z6ePWfBAr54o1BYcu+WTwfXOZ07H0NycgkGDlRjz57bndMJ4XpITVnmJtut3FIcL955J3RJSfC5/374du2Krlbzm5UYNowftcG0XdBOIiKA8eP5Y1NsfFER1+XaiKPBwOcUAX6fitnUtm2zbGGcOxfYtQvYs4eH/Zg+j6b8j84gOZlvx/ntt3ra0US4BuQ5ug6lZ84AALS//15/nTqgarm5PEQmNNRSQaE2yGQ8FrGwUDyY+667LI/L18fMiYTqA1NhsEYWoUXUBImj69D85ZeR99VXaGEV2yQKY3xMmZ3Ni68EBtaPgeXo9TzU5vLlynkXz57lCycAD/xu1arm+wkCnx1gTFxog4N5nGRBgSW92a5dwDvv5GPu3Ayo1Z5o3x4A2gIA/P3PIz+foVkzoLCwg6Nv04zR2AeHD2vQu7frrkASTqCRiqNDoTz1jSNhBgD4KvXatfxx165Av36S+y4pATIyuK46EnVw5gyfd7zrLluP6r//5XGHAA8anz5d+r3tRSY7Z+M9MsaFUBDOVTpHNE1qFcqzezfU1WU1qXhdYSH87r3X5UN56jAKzwXx9eUbkZVKvtnYAVJT+Xyho+nIOnfmTmvFoaZp2AtU3htd1zzzjOWDa5243OR91mUsJtEEcfIOmYbCPax0FJkMGDGioa0QZcCASsl9nMbKlRFYubLyeYOBvEWiDmikw2r3sLI+MBp5xUFTvenyBZjoaF7+tLbTlUlJPINOcDBPDUkQjYZGKo40oDJhioE0BXyXo1DwxOC1TWB75gzX34yM2qU0Ky3l+SLrcqb41i0dunc/ia++Eq/JQRDV0kiH1SSOJkyBgFblD+oS05Snv7/jc3xGI49n37/fEj9ZFwQFHcfJk8UYNy4VhYXiaeQIokqcmLKsIXEPCa8P5PKq91ZbodVaamtJoWdPS1lURzEVPgS4B1lXWHuhhYXVl1MgiErQsNp1KLl2DRdmzUJZfn699nvuHM+HeOhQ7YbGjiKX85ounTvzo65YvToKCoWAQYN8ERpKEdyERBrpsNo9rKzArtatUWIw4NKSJbjflN21Higs5D+NxprrstQlP//MS2nLZLwEQm2S6Yrx9NMhePrpxpMTk6hnyHN0HfTlGXTKyjPy1Bft2vEhdVhY/W6Re/VV/tNoBEaOrL9+CcIuGumco1uK450ffojgqCjEfPNNvfbr5wfccUflhA/1iYOx7AThPGhY7Tq0jo9Ha6vqhI2dM2eAmBiep3Hhwoa2hiAq0EiH1e5hJYFDhxraAoKoAhLHxsmxYzw5bMeOfB5xyJDKbc6f57tbwsJ4CrDbbqucZcdZnD8P/P03zwD+yCPAkSN8n3dcHLenJm7c4AW4AgKAZcsqZxAniFrTSMXRLecc65JNm/hCi3WW7YqcOMET/Fy4wOMcL12qP/tOnuQ/tVoudKdP8xI4pnKrNbFtG/8HsHs3eZ8EIYUmL44PPsjzLTLGF9HEMijdfjv3uKKieIKfqKj6s69zZ26bUsk9xQ4deKw6z8tYM0OGcE+3d+/aB6EThBhGyCQf7kDjzudIEIRd1CafY3a2tO+lRqNBixau/312DwlvpHz6KfDFF45fv3lzNsaMcXyTtV5vxD//5KKszHa7T48eSRg40M5xO9Hk0eulH+6Ae8yMNkImTOALQQCfP5w/X9r1R45o8MgjXBg3b86BVnu3ZBvGjduPb765guHDI/DTTwMBAErlMfP+7ZYt/8HVq90l35doWkgVPBJHolrOWSoU4J9/pF9/+LBl5ai42LGZkeRkfo+zZy330lkl5cnIqKaWN0GU01jFkYbVDcS2bTw8x9cX+P576ddPntwSLVsqIJMBX3xxm0M2rFt3N2bM6IKNG+8xn9u713Kv3FzH7ks0LQwGaUNqg5v8z6UFGYIgarUgc/ZsPnx97f9eFhRo0LGj63+faVjtCMuW8X+BajXw9NMNbQ1BNCiNdVhN4ugIpr+uWMQ4QTQxSBwJC3I5nzihlNkEYZ5zlNLeHSBxrALGgOxs/jM4uEJZmalTG8wugnA1GqvnSKvVVaDR8EqBmZl8X3VjZ/jwcxCEY7j33jqs3EU0CSgIvImhVFq8RZWqYW2pD7Zu1QIA9uwpamBLCHejsXqOJI5VoFIBnTrxx26SYYkgGoTGKo40rK4GZ2V0F5uQrlgOhzFLyVRDPcxgP/RQMwDAPfdQwkdCGo01CJx8onpm6lSeF9LLC/jxRy6KCxbwyoIGA/D220BBAbB8Oc8vef58Kr788gbkciAvLxY+Ps4pTrRlSwen3Jdo/JDnSNQJpkS5Jk/x6lUujIJgmeO8epUntNVqgQsXuPtoMAB//NEEVoYIt4MWZIg6YeRI7jF2KHfUbruNf1jkcr7XGuCZyXv25EkgWrVqhiNHgJYtlRg2LLDB7CaIqmisniPtrSYIolZ7qzduzIe3t/3fy6IiDf79b9f/PtOwmiAIQgQaVhMEUSsa67CaPMdGxuOP8xKzAQENbQnRVGisCzIkjo2MzZv5T60WOHy4QU0hmgj1IY5Lly5FVFQUVCoVYmNjcaiaOsMDBgyAIAiVjgcffFBSnw6JoxRDrVm/fj0EQcDIkSMd6ZawA+sEGV27Wh6fPm3AK6+U4OBBN4nAJdwGZ4vjhg0bEB8fj4SEBBw7dgzdu3fH0KFDkZWVJdp+06ZNuHHjhvk4ffo05HI5/v3vf0vqV7I4SjXUxOXLl/Haa6+hX79+UrskJFBaCkycCJw8absn/OmnS/Dxx2V4/PHiqi8mCAdw9g6ZhQsXYtKkSXjmmWfQuXNnfPLJJ/D29saqVatE2wcGBiI0NNR87Nq1C97e3s4XR6mGAnz725gxYzB37ly0bdu2xj5KS0uh0WhsDsJ+li0DOna0PdejB/9Td+9OMylE3eKo51jxO15aWlrp3jqdDkePHsXgwYPN52QyGQYPHowDBw7YZd/KlSvxxBNPoJkpkNhOJH1THDX0nXfeQXBwMCZMmGBXP4mJifDz8zMfkZGRUswkRPjsMxXOnWuGTZto7zRRtzgqjpGRkTbf88TExEr3zsnJgcFgQEhIiM35kJAQZGRk1GjboUOHcPr0aUycOFHy+5IUylOdoWfPiucB3LdvH1auXIkTJ07Y3c+sWbMQHx9vfq7RaEgga4lMJqB9e6HmhgQhEUdDedLT022CwJVKZR1bxr3Grl27IiYmRvK1To1zLCgowNixY7FixQoEBQXZfZ1SqXTKL4ogiLrH0TIJarW6xh0yQUFBkMvlyMzMtDmfmZmJ0NDQaq/VarVYv3493nnnHfuNs0KSOEo19NKlS7h8+TKGDx9uPmc0GnnHHh44d+4coqOjHbGbIAgXwZlB4AqFAj179sTu3bvNUS5GoxG7d+/GtGnTqr1248aNKC0txVNPPWV/h1ZImnO0NtSEydC4uLhK7Tt27IhTp07hxIkT5mPEiBEYOHAgTpw4QUNlgmgEODuUJz4+HitWrMAXX3yB5ORkTJ48GVqtFs888wwAYNy4cZg1a1al61auXImRI0eiefPmDr0vycPq+Ph4jB8/Hr169UJMTAwWLVpUydCIiAgkJiZCpVLh9ttvt7ne398fACqdJwjCPXH29sFRo0YhOzsbb7/9NjIyMtCjRw/s2LHDvPaRlpYGmczWzzt37hz27duHX375RVpnVkgWR0cMJQii8VIfe6unTZtW5TB67969lc516NABtU04RinLCIKoVcqy11/Ph1Jp//eytFSD9993/e9zo8rKk50N+PjwEgQmCgt5IgaFouHsIojGjClZs5T27kCjGf+uXw88/zwwdiyQm8vPHTkC3Hcfz77dFGpPE0RD0Fiz8jQaz9EUY240AqmpPGXXqVP8D5GTA1y/Dvj5NaiJBNEoaayeY6MRxylTgA8+AFq2BO68k5979FE+1A4JqbzXmCCIusHRIHBXp9GIY6tWwOLFtufUauD11xvGHoJoKuj1vIKmlPbuQKOZc3QmRUVFOHYsp9L5yZN/xrlz5xrAIoIgnE2j8RydRVpaHlq3/gEA4OEhoKzsaQCAICwEAHzySTLOnuVxVQTRFCHPsYnywQfJ5sd6vXhI6MCBv9aXOQThcjTW1WoSxxpYvNiyZ7xbN3/RNtevT60nawjC9Wis4tgohtX//MPDd3r0AKKi6v7+jD0jci5epCVBND1otdqFuXiR/8IvXXKOOBIEUTV6vW1hN3vauwONQhy7duWeY6dODW0JQTQ9SBxdmPbt+UEQRP1D4kgQBCECiSNBEIQIBoM0caQFGaLOEISZ5sd//PEg+vXr14DWEIQtUj1B8hwJp3DPPdvAGIkj4TqQOBIuQWAgxe0TrgWJI9FgMDYf7723B/36edKQmnA5pM4h0pwjUae88caghjaBIETR6wEplahIHAmCaBKQOBIEQYhA4kgQBCECiSNBEIQIBoM0cTQanWdLXULiSBBErZCaCdxdxJGC5giCIEQgz5EgiFrRWD1HEkeCIGoFiSNBEIQIBoM0wZOyeNOQkDgSBFErpOZzJHEkCKJJQOJIEAQhAokjQRCECCSOBEEQohjBmJQlaPdYriZxJAiilhjKDyntXR8SR4IgagmJI0EQhAgkjgRBECIYIW0ekeYcCYJoEpDnSBAEIYIR0gSPPEeCIJoE5DkSBEGI0DjFkZLdEgRRS4wOHNJYunQpoqKioFKpEBsbi0OHDlXbPi8vD1OnTkVYWBiUSiXat2+P7du3S+rTIXGUYuiKFSvQr18/BAQEICAgAIMHD67xjREE4U4YHDjsZ8OGDYiPj0dCQgKOHTuG7t27Y+jQocjKyhJtr9PpMGTIEFy+fBnfffcdzp07hxUrViAiIkLa22ISWb9+PVMoFGzVqlUsKSmJTZo0ifn7+7PMzEzR9qNHj2ZLly5lx48fZ8nJyezpp59mfn5+7OrVq3b3mZ+fzwCw/Px8qeYSBGEHjnzHTNcA/zAgRcLxj6S+YmJi2NSpU83PDQYDCw8PZ4mJiaLtly9fztq2bct0Op3d70UMgTFp28BjY2PRu3dvLFmyBABgNBoRGRmJF198ETNnzqzxeoPBgICAACxZsgTjxo0TbVNaWorS0lLzc41Gg8jISOTn50OtVksxlyAIO9BoNPDz85P0HTNdA/wDwFdCbwUAuiM9Pd2mL6VSCaVSadNSp9PB29sb3333HUaOHGk+P378eOTl5eHHH3+sdPdhw4YhMDAQ3t7e+PHHH9GiRQuMHj0aM2bMgFwut9tKScNqnU6Ho0ePYvDgwZYbyGQYPHgwDhw4YNc9ioqKUFZWhsDAwCrbJCYmws/Pz3xERkZKMZMgiHrFsWF1ZGSkzfc8MTGx0p1zcnJgMBgQEhJicz4kJAQZGRmi1qSkpOC7776DwWDA9u3bMXv2bHz44Yf4z3/+I+ldSVqtrs7Qs2fP2nWPGTNmIDw83EZgKzJr1izEx8ebn5s8R4IgXBHH4hzFPMc6scZoRHBwMD777DPI5XL07NkT165dwwcffICEhAS771OvoTzz58/H+vXrsXfvXqhUqirbibnXBEG4Ko5tH1Sr1TUO4YOCgiCXy5GZmWlzPjMzE6GhoaLXhIWFwdPT02YI3alTJ2RkZECn00GhUNhlpaRhtSOGmliwYAHmz5+PX375Bd26dZPSLUEQLo3zVqsVCgV69uyJ3bt3m88ZjUbs3r0bcXFxotf07dsXFy9ehNGq6tf58+cRFhZmtzACEsXREUMB4P3338e8efOwY8cO9OrVS0qXBEG4PM4N5YmPj8eKFSvwxRdfIDk5GZMnT4ZWq8UzzzwDABg3bhxmzZplbj958mTcunULL7/8Ms6fP49t27bhvffew9SpUyX1K3lYHR8fj/Hjx6NXr16IiYnBokWLKhkaERFhnlz973//i7fffhvr1q1DVFSUeRLVx8cHPj4+UrsnCMLlcO4OmVGjRiE7Oxtvv/02MjIy0KNHD+zYscO89pGWlgaZVeHsyMhI7Ny5E9OnT0e3bt0QERGBl19+GTNmzJDUr+RQHgBYsmQJPvjgA7Oh//vf/xAbGwsAGDBgAKKiorBmzRoAQFRUFK5cuVLpHgkJCZgzZ45d/TkSZkAQhP3ULpRnDwApjk4hgEEu/312SBzrGxJHgnAutRPH3QCaSehNC+Bel/8+U+IJgiBqSeNMPEHiSBBELSFxJAiCEIHEkSAIQgQSR4IgCBGowBZBEIQI5DkSBEGIQAW2CIIgRCDP0SUoLNRBoZBDobA/aSVBEM6kcc45ulWBrTlzjsPXdz2UyrU4fTq3oc0hCAKAsxNPNBRuJY6bN6ebH3/zTWoDWkIQhAUSxwZn6dJYeHvLERamQkJC94Y2hyCIRoxbzTn27RsCrXZMQ5tBEIQNtCBDEAQhAoXyEARBiNA4V6tJHAmCqCU0rCYIghCBxJEgCEIEEkeCIAgRSBwJgiBEoAUZgiAIESiUhyAIQgQaVhMEQYhA4kgQBCECiSNBEIQIJI4EQRAi0Go1QRCECLRaTRAEIQINqwmCIEQgcSQIghCB5hwJgiBEaJyeo1vVkCEIgqgvyHMkCKKWNE7PkcSRIIhaQuJIEAQhAsU5EgRBiECr1QRBECLQsJogCEIEEkeCIAgRSBwJgiBEIHEkCIIQgVarCYIgRKDVaoIgCBEMkLYT2T2G1Q7trV66dCmioqKgUqkQGxuLQ4cOVdt+48aN6NixI1QqFbp27Yrt27c7ZCxBEK6IwYFDGlI0Z82aNRAEweZQqVSS+5Qsjhs2bEB8fDwSEhJw7NgxdO/eHUOHDkVWVpZo+7/++gtPPvkkJkyYgOPHj2PkyJEYOXIkTp8+LdlYgiBcEeeKo1TNAQC1Wo0bN26YjytXrkh+VwJjjEm5IDY2Fr1798aSJUsAAEajEZGRkXjxxRcxc+bMSu1HjRoFrVaLrVu3ms/ddddd6NGjBz755BPRPkpLS1FaWmp+np+fj1atWiE9PR1qtVqKuQRB2IFGo0FkZCTy8vLg5+dn9zW87WBIm6HTA/i10vdZqVRCqVRWai1Vc9asWYNXXnkFeXl5EmwSgUmgtLSUyeVy9sMPP9icHzduHBsxYoToNZGRkeyjjz6yOff222+zbt26VdlPQkICA0AHHXTU83Hp0iW79aC4uJiFhoY61I+Pj0+lcwkJCXWiOatXr2ZyuZy1atWKtWzZko0YMYKdPn3a7vdlQtKCTE5ODgwGA0JCQmzOh4SE4OzZs6LXZGRkiLbPyMiosp9Zs2YhPj7e/DwvLw+tW7dGWlqa3f/VGgrTf2B38XLdyV6y1XmYRmeBgYF2X6NSqZCamgqdTie5P8YYBEGwOSfmNTqiOR06dMCqVavQrVs35OfnY8GCBejTpw+SkpLQsmVLu210ydXqqtxrPz8/t/igAXzOw11sBdzLXrLVechk0pYhVCqVQ4sdziQuLg5xcXHm53369EGnTp3w6aefYt68eXbfR9JvIigoCHK5HJmZmTbnMzMzERoaKnpNaGiopPYEQRAmHNGcinh6euKOO+7AxYsXJfUtSRwVCgV69uyJ3bt3m88ZjUbs3r3bRqmtiYuLs2kPALt27aqyPUEQhAlHNKciBoMBp06dQlhYmLTOpU5Srl+/nimVSrZmzRp25swZ9txzzzF/f3+WkZHBGGNs7NixbObMmeb2+/fvZx4eHmzBggUsOTmZJSQkME9PT3bq1Cm7+ywpKWEJCQmspKREqrn1jjvZyph72Uu2Og9Xtleq5sydO5ft3LmTXbp0iR09epQ98cQTTKVSsaSkJEn9ShZHxhhbvHgxa9WqFVMoFCwmJob9/fff5tf69+/Pxo8fb9P+22+/Ze3bt2cKhYJ16dKFbdu2zZFuCYJookjRnFdeecXcNiQkhA0bNowdO3ZMcp+S4xwJgiCaAlSalSAIQgQSR4IgCBFIHAmCIEQgcSQIghDBZcTRndKgSbF1xYoV6NevHwICAhAQEIDBgwfX+N4aylZr1q9fD0EQMHLkSOcaWAGp9ubl5WHq1KkICwuDUqlE+/bt6+2zINXWRYsWoUOHDvDy8kJkZCSmT5+OkpISp9v5xx9/YPjw4QgPD4cgCNi8eXON1+zduxd33nknlEol2rVrhzVr1jjdTpej9ovstWf9+vVMoVCwVatWsaSkJDZp0iTm7+/PMjMzRdvv37+fyeVy9v7777MzZ86wt956S3LsZH3ZOnr0aLZ06VJ2/PhxlpyczJ5++mnm5+fHrl696nK2mkhNTWURERGsX79+7OGHH3a6nSak2ltaWsp69erFhg0bxvbt28dSU1PZ3r172YkTJ1zO1rVr1zKlUsnWrl3LUlNT2c6dO1lYWBibPn26023dvn07e/PNN9mmTZsYgEpJHCqSkpLCvL29WXx8PDtz5gxbvHgxk8vlbMeOHU631ZVwCXGMiYlhU6dONT83GAwsPDycJSYmirZ//PHH2YMPPmhzLjY2lj3//PNOtZMx6bZWRK/XM19fX/bFF184y0Qzjtiq1+tZnz592Oeff87Gjx9fr+Io1d7ly5eztm3bMp1OV18mmpFq69SpU9mgQYNszsXHx7O+ffs61c6K2COOr7/+OuvSpYvNuVGjRrGhQ4c60TLXo8GH1TqdDkePHsXgwYPN52QyGQYPHowDBw6IXnPgwAGb9gAwdOjQKts3pK0VKSoqQllZmaTsJ47gqK3vvPMOgoODMWHCBKfaVxFH7P3pp58QFxeHqVOnIiQkBLfffjvee+89GAzOTcPviK19+vTB0aNHzUPvlJQUbN++HcOGDXOqrY7QUN8vV6PBs/LUVxq0usARWysyY8YMhIeHV/rw1TWO2Lpv3z6sXLkSJ06ccKptYjhib0pKCvbs2YMxY8Zg+/btuHjxIqZMmYKysjIkJCS4lK2jR49GTk4O7r77bjDGoNfr8cILL+CNN95wmp2OUtX3S6PRoLi4GF5eXg1kWf3S4J5jU2L+/PlYv349fvjhB5dL81RQUICxY8dixYoVCAoKamhz7MJoNCI4OBifffYZevbsiVGjRuHNN9+sMsN8Q7J371689957WLZsGY4dO4ZNmzZh27ZtklJoEfVLg3uO7pQGrTbpkxYsWID58+fj119/Rbdu3ZxpJgDptl66dAmXL1/G8OHDzeeMRl5C08PDA+fOnUN0dLTL2AsAYWFh8PT0hFwuN5/r1KkTMjIyoNPpoFAoXMbW2bNnY+zYsZg4cSIAoGvXrtBqtXjuuefw5ptvSs6j6Eyq+n6p1eom4zUCLuA5ulMaNEfTJ73//vuYN28eduzYgV69ejnVRhNSbe3YsSNOnTqFEydOmI8RI0Zg4MCBOHHiBCIjI13KXgDo27cvLl68aBZxADh//jzCwsKcJoyO2lpUVFRJAE2izlwsvQGlGSynoVeEGGuYNGj1Zev8+fOZQqFg3333Hbtx44b5KCgocDlbK1Lfq9VS7U1LS2O+vr5s2rRp7Ny5c2zr1q0sODiY/ec//3E5WxMSEpivry/75ptvWEpKCvvll19YdHQ0e/zxx51ua0FBATt+/Dg7fvw4A8AWLlzIjh8/zq5cucIYY2zmzJls7Nix5vamUJ7/+7//Y8nJyWzp0qUUytOQuFMaNCm2tm7dWrTAkFgxoYa2tSL1LY6MSbf3r7/+YrGxsUypVLK2bduyd999l+n1epeztaysjM2ZM4dFR0czlUrFIiMj2ZQpU1hubq7T7fztt99EP4Mm+8aPH8/69+9f6ZoePXowhULB2rZty1avXu10O10NSllGEAQhQoPPORIEQbgiJI4EQRAikDgSBEGIQOJIEAQhAokjQRCECCSOBEEQIpA4EgRBiEDiSBAEIQKJI0EQhAgkjgRBECKQOBIEQYjw/51DD63QXhiqAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "cm = plt.get_cmap('seismic')\n", + "z = data['auc']\n", + "diff = np.max(np.abs((data['auc']).values))\n", + "plt.scatter(1 - data['spec'], data['sens'], s=1, c=z, cmap=cm, vmin=0.5, vmax=diff)\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [], + "source": [ + "def rline_intersect(sens, spec):\n", + " a = (1 - sens)/(1 - spec)\n", + " b = sens - a*spec\n", + " se0 = (a + b)/(1 + a)\n", + " sp0 = 1 - se0\n", + " return se0, sp0\n", + "\n", + "def rcirc_intersect(sens, spec):\n", + " a = (1 - sens)/(1 - spec)\n", + " b = sens - a*spec\n", + " se0 = (2*b + np.sqrt(4*b**2 - 4*(1 + a**2)*(b**2 - a**2)))/(2*(1 + a**2))\n", + " sp0 = np.sqrt(1 - se0**2)\n", + " return se0, sp0" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.3333333333333333, 0.6666666666666667, np.float64(0.6), np.float64(0.8))" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "se = 0.8\n", + "sp = 0.9\n", + "se0, sp0 = rline_intersect(se, sp)\n", + "se1, sp1 = rcirc_intersect(se, sp)\n", + "\n", + "se0, sp0, se1, sp1" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5293103448275862\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "cm = plt.get_cmap('seismic')\n", + "z = data['auc']\n", + "diff = np.max(np.abs((data['auc']).values))\n", + "vmin = np.min(np.abs((data['auc']).values))\n", + "plt.scatter(1 - data['best_spec'], data['best_sens'], s=1, c=z, cmap=cm, vmin=vmin, vmax=diff)\n", + "#plt.scatter(1 - data['spec'], data['sens'], s=1, c=z, cmap=cm, vmin=0.5, vmax=diff, marker='x')\n", + "plt.colorbar()\n", + "plt.scatter([1 - sp, 1 - sp0, 1 - sp1], [se, se0, se1])\n", + "circ = np.linspace(-3.1415/2, 0, 1000)\n", + "x = np.sin(circ) + 1\n", + "y = np.cos(circ)\n", + "plt.plot(x, y)\n", + "print(vmin)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.83057860989232,\n", + " 0.7754129961832225,\n", + " 0.882240595119838,\n", + " 0.6382457024883663,\n", + " 0.8397428530475235)" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tmp = data[['auc', 'auc_rmin_max']].dropna()\n", + "tmp3 = data[['auc', 'auc_rmin_rmax']].dropna()\n", + "tmp1 = data[['auc', 'auc_onmin_max']].dropna()\n", + "tmp2 = data[['auc', 'auc_min_max', 'auc_analytic']].dropna()\n", + "(r2_score(tmp2['auc'], tmp2['auc_min_max']),\n", + "r2_score(tmp['auc'], tmp['auc_rmin_max']),\n", + "r2_score(tmp3['auc'], tmp3['auc_rmin_rmax']),\n", + "r2_score(tmp1['auc'], tmp1['auc_onmin_max']),\n", + "r2_score(tmp2['auc'], tmp2['auc_analytic']))" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.06352273429050345), np.float64(0.07829270416030586))" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(mean_absolute_percentage_error(tmp2['auc'], tmp2['auc_min_max']),\n", + "mean_absolute_percentage_error(tmp['auc'], tmp['auc_rmin_max']))" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 1000)" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data), len(tmp)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datasetaccsensspecaucbest_accbest_sensbest_specthresholdbest_threshold...auc_rmin_maxaauc_min_max_bestauc_rmin_max_bestauc_min_maxa_bestauc_rmin_maxa_bestmax_acc_min_maxmax_acc_min_rmaxauc_rmin_rmaxauc_onmin_maxauc_onmin_maxa_best
5033bupa0.6231880.3448280.8250000.5461210.6231880.3448280.8250000.4202900.527273...0.6945060.8674330.8381640.8257761.0000002.2222220.0000001.0000001.0000001.629837
7125hepatitis{'n_neighbors': 6}0.5161290.3333330.5909090.5404040.7419350.1111111.0000000.3333330.6116150.5848970.6999410.4965710.6116150.6944960.6436030.6411180.7351710.646845
8670bupa0.5362320.3103450.7000000.5293100.6231880.1724140.9500000.4202900.772727...0.6970070.7881380.7370270.7214371.1571352.2525250.1071370.7821761.0000002.0213000.6044150.5611950.7331130.4362280.6081460.6909630.6306770.5104340.6491870.634991
8716PC1{'n_neighbors': 4}0.8963960.2500000.9466020.5442960.9369370.1875000.9951460.5000006931bupa0.5362320.3103450.7000000.5293100.6231880.1724140.9500000.4202900.772727...0.7808420.9475460.9375060.9311281.2583001.9429610.2780370.7518981.0000001.2421410.6044150.5611950.7331130.4362280.6081460.6909630.6306770.5104340.6491870.634991
4332CM1{'max_depth': 7, 'random_state': 5}0.1000001.0000000.0000000.5166670.9000000.0000001.0000000.0000008670bupa0.5362320.3103450.7000000.5293100.6231880.1724140.9500000.4202900.772727...0.7222780.9258380.9071880.9016771.0000002.1111110.0000001.0000001.0000001.5477230.6044150.5611950.7331130.4362280.6081460.6909630.6306770.5104340.6491870.634991
637CM1{'n_neighbors': 9}0.4900000.5000000.4888890.5138890.9000000.0000001.0000000.1111118670bupa0.5362320.3103450.7000000.5293100.6231880.1724140.9500000.4202900.772727...0.7222780.9256990.9064330.9013991.0000002.1111110.0157130.7150071.0000001.6000000.6044150.5611950.7331130.4362280.6081460.6909630.6306770.5104340.6491870.634991
......
785shuttle-c0-vs-c4{'n_neighbors': 3}0.9289620.0000001.0000001.0000001.0000001.0000001.000000inf1123wdbc0.9649120.9583330.9761900.9857800.9824560.9861110.9761900.6263740.571429...0.9681000.9810550.9814390.9808910.9812750.9565820.9565640.9679340.9831380.990248
5849ecoli10.8970590.8750000.9038460.9627400.9117650.7500000.9615380.2276120.625021...0.9999750.998184NaN0.9981842.4142141.0000000.0000001.000000inf1.0710380.8909350.8556980.8718500.8496990.8658500.9376540.9375690.8957300.9387130.917092
4503monk-2{'n_neighbors': 5}0.6321840.2000001.0000000.9978720.9770110.9500001.0000001.000000...0.9869480.9829010.9943910.9824352.3435031.0925530.2828430.8000008.6400041.460751
1917monk-2{'n_neighbors': 6}1.0000001.0000001.0000001.0000001.0000001.0000001.0000000.5000009333wisconsin0.9635040.9775280.9375000.9866570.9635040.9775280.9375000.6501830.750000...0.9999750.996465NaN0.9964652.4142141.0000001.4142140.000000inf1.4942530.9579560.9574230.9587130.9566660.9579560.9585670.9585510.9587130.9780590.977302
1610dermatology-6{'max_depth': 3, 'random_state': 5}0.0694441.0000000.0000001.0000001.0000001.0000001.0000000.0000762174ecoli10.9264710.9230770.9375000.9693510.9264710.9230770.9375000.7723880.833333...0.9999750.998202NaN0.9982022.4142141.0000000.0000001.000000inf1.0694440.9277430.9302020.9328370.9251080.9277430.9438100.9437530.9328370.9627470.957653
4015vowel0{'max_depth': 6, 'random_state': 5}0.3282831.0000000.2651930.9961000.9949490.9411761.0000000.0013859113wdbc0.9736840.9722220.9761900.9970240.9736840.9722220.9761900.6263740.661103...0.9851910.9873080.9945650.9871452.3310251.0643480.3750400.7348074.1721721.0861950.9742280.9741120.9746380.9737010.9742280.9805520.9805510.9746380.9867750.986365
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2000 rows × 57 columns

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1000 rows × 61 columns

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" ], "text/plain": [ - " dataset classifier acc \\\n", - "5971 appendicitis {'max_depth': 9, 'random_state': 5} 0.181818 \n", - "7125 hepatitis {'n_neighbors': 6} 0.516129 \n", - "8716 PC1 {'n_neighbors': 4} 0.896396 \n", - "4332 CM1 {'max_depth': 7, 'random_state': 5} 0.100000 \n", - "637 CM1 {'n_neighbors': 9} 0.490000 \n", - "... ... ... ... \n", - "785 shuttle-c0-vs-c4 {'n_neighbors': 3} 0.928962 \n", - "4503 monk-2 {'n_neighbors': 5} 0.632184 \n", - "1917 monk-2 {'n_neighbors': 6} 1.000000 \n", - "1610 dermatology-6 {'max_depth': 3, 'random_state': 5} 0.069444 \n", - "4015 vowel0 {'max_depth': 6, 'random_state': 5} 0.328283 \n", + " dataset acc sens spec auc best_acc best_sens \\\n", + "5033 bupa 0.623188 0.344828 0.825000 0.546121 0.623188 0.344828 \n", + "8670 bupa 0.536232 0.310345 0.700000 0.529310 0.623188 0.172414 \n", + "6931 bupa 0.536232 0.310345 0.700000 0.529310 0.623188 0.172414 \n", + "8670 bupa 0.536232 0.310345 0.700000 0.529310 0.623188 0.172414 \n", + "8670 bupa 0.536232 0.310345 0.700000 0.529310 0.623188 0.172414 \n", + "... ... ... ... ... ... ... ... \n", + "1123 wdbc 0.964912 0.958333 0.976190 0.985780 0.982456 0.986111 \n", + "5849 ecoli1 0.897059 0.875000 0.903846 0.962740 0.911765 0.750000 \n", + "9333 wisconsin 0.963504 0.977528 0.937500 0.986657 0.963504 0.977528 \n", + "2174 ecoli1 0.926471 0.923077 0.937500 0.969351 0.926471 0.923077 \n", + "9113 wdbc 0.973684 0.972222 0.976190 0.997024 0.973684 0.972222 \n", "\n", - " sens spec auc best_acc best_sens best_spec threshold \\\n", - "5971 1.000000 0.000000 0.541667 0.818182 0.000000 1.000000 0.000000 \n", - "7125 0.333333 0.590909 0.540404 0.741935 0.111111 1.000000 0.333333 \n", - "8716 0.250000 0.946602 0.544296 0.936937 0.187500 0.995146 0.500000 \n", - "4332 1.000000 0.000000 0.516667 0.900000 0.000000 1.000000 0.000000 \n", - "637 0.500000 0.488889 0.513889 0.900000 0.000000 1.000000 0.111111 \n", - "... ... ... ... ... ... ... ... \n", - "785 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 inf \n", - "4503 0.200000 1.000000 0.997872 0.977011 0.950000 1.000000 1.000000 \n", - "1917 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.500000 \n", - "1610 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 0.000076 \n", - "4015 1.000000 0.265193 0.996100 0.994949 0.941176 1.000000 0.001385 \n", + " best_spec threshold best_threshold ... auc_rmin_maxa \\\n", + "5033 0.825000 0.420290 0.527273 ... 0.611615 \n", + "8670 0.950000 0.420290 0.772727 ... 0.604415 \n", + "6931 0.950000 0.420290 0.772727 ... 0.604415 \n", + "8670 0.950000 0.420290 0.772727 ... 0.604415 \n", + "8670 0.950000 0.420290 0.772727 ... 0.604415 \n", + "... ... ... ... ... ... \n", + "1123 0.976190 0.626374 0.571429 ... 0.968100 \n", + "5849 0.961538 0.227612 0.625021 ... 0.890935 \n", + "9333 0.937500 0.650183 0.750000 ... 0.957956 \n", + "2174 0.937500 0.772388 0.833333 ... 0.927743 \n", + "9113 0.976190 0.626374 0.661103 ... 0.974228 \n", "\n", - " ... auc_onmin_maxa_best max_acc_min_max max_acc_min_rmax \\\n", - "5971 ... 0.694506 0.867433 0.838164 \n", - "7125 ... 0.697007 0.788138 0.737027 \n", - "8716 ... 0.780842 0.947546 0.937506 \n", - "4332 ... 0.722278 0.925838 0.907188 \n", - "637 ... 0.722278 0.925699 0.906433 \n", - "... ... ... ... ... \n", - "785 ... 0.999975 0.998184 NaN \n", - "4503 ... 0.986948 0.982901 0.994391 \n", - "1917 ... 0.999975 0.996465 NaN \n", - "1610 ... 0.999975 0.998202 NaN \n", - "4015 ... 0.985191 0.987308 0.994565 \n", + " auc_min_max_best auc_rmin_max_best auc_min_maxa_best \\\n", + "5033 0.584897 0.699941 0.496571 \n", + "8670 0.561195 0.733113 0.436228 \n", + "6931 0.561195 0.733113 0.436228 \n", + "8670 0.561195 0.733113 0.436228 \n", + "8670 0.561195 0.733113 0.436228 \n", + "... ... ... ... \n", + "1123 0.981055 0.981439 0.980891 \n", + "5849 0.855698 0.871850 0.849699 \n", + "9333 0.957423 0.958713 0.956666 \n", + "2174 0.930202 0.932837 0.925108 \n", + "9113 0.974112 0.974638 0.973701 \n", "\n", - " max_acc_min_onmax auc_rmin_best_grad auc_maxa_best_grad \\\n", - "5971 0.825776 1.000000 2.222222 \n", - "7125 0.721437 1.157135 2.252525 \n", - "8716 0.931128 1.258300 1.942961 \n", - "4332 0.901677 1.000000 2.111111 \n", - "637 0.901399 1.000000 2.111111 \n", - "... ... ... ... \n", - "785 0.998184 2.414214 1.000000 \n", - "4503 0.982435 2.343503 1.092553 \n", - "1917 0.996465 2.414214 1.000000 \n", - "1610 0.998202 2.414214 1.000000 \n", - "4015 0.987145 2.331025 1.064348 \n", + " auc_rmin_maxa_best max_acc_min_max max_acc_min_rmax auc_rmin_rmax \\\n", + "5033 0.611615 0.694496 0.643603 0.641118 \n", + "8670 0.608146 0.690963 0.630677 0.510434 \n", + "6931 0.608146 0.690963 0.630677 0.510434 \n", + "8670 0.608146 0.690963 0.630677 0.510434 \n", + "8670 0.608146 0.690963 0.630677 0.510434 \n", + "... ... ... ... ... \n", + "1123 0.981275 0.956582 0.956564 0.967934 \n", + "5849 0.865850 0.937654 0.937569 0.895730 \n", + "9333 0.957956 0.958567 0.958551 0.958713 \n", + "2174 0.927743 0.943810 0.943753 0.932837 \n", + "9113 0.974228 0.980552 0.980551 0.974638 \n", "\n", - " auc_rmin_grad auc_max_grad max_acc_min_grad max_acc_rmax_grad \n", - "5971 0.000000 1.000000 1.000000 1.629837 \n", - "7125 0.107137 0.782176 1.000000 2.021300 \n", - "8716 0.278037 0.751898 1.000000 1.242141 \n", - "4332 0.000000 1.000000 1.000000 1.547723 \n", - "637 0.015713 0.715007 1.000000 1.600000 \n", - "... ... ... ... ... \n", - "785 0.000000 1.000000 inf 1.071038 \n", - "4503 0.282843 0.800000 8.640004 1.460751 \n", - "1917 1.414214 0.000000 inf 1.494253 \n", - "1610 0.000000 1.000000 inf 1.069444 \n", - "4015 0.375040 0.734807 4.172172 1.086195 \n", + " auc_onmin_max auc_onmin_maxa_best \n", + "5033 0.735171 0.646845 \n", + "8670 0.649187 0.634991 \n", + "6931 0.649187 0.634991 \n", + "8670 0.649187 0.634991 \n", + "8670 0.649187 0.634991 \n", + "... ... ... \n", + "1123 0.983138 0.990248 \n", + "5849 0.938713 0.917092 \n", + "9333 0.978059 0.977302 \n", + "2174 0.962747 0.957653 \n", + "9113 0.986775 0.986365 \n", "\n", - "[2000 rows x 57 columns]" + "[1000 rows x 61 columns]" ] }, - "execution_count": 44, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -729,7 +4281,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 171, "metadata": {}, "outputs": [ { @@ -738,7 +4290,7 @@ "\"tmp = data.dropna()\\nwilcoxon(np.abs(tmp['auc'] - tmp['auc_min_max']), \\n np.abs(tmp['auc'] - tmp['auc_rmin_max']))\"" ] }, - "execution_count": 45, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } @@ -751,27 +4303,27 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 172, "metadata": {}, "outputs": [], "source": [ "results.append({'target': ['auc', 'auc'],\n", " 'source': ['arbitrary sens, spec', 'arbitrary sens, spec'],\n", " 'estimation': ['(min, max)', '(rmin, max)'],\n", - " 'r2': [r2_score(data['auc'], data['auc_min_max']),\n", + " 'r2': [r2_score(tmp2['auc'], tmp2['auc_min_max']),\n", " r2_score(tmp['auc'], tmp['auc_rmin_max'])],\n", - " 'mape': [mean_absolute_percentage_error(data['auc'], data['auc_min_max']),\n", + " 'mape': [mean_absolute_percentage_error(tmp2['auc'], tmp2['auc_min_max']),\n", " mean_absolute_percentage_error(tmp['auc'], tmp['auc_rmin_max'])]})" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 173, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -789,11 +4341,16 @@ " min(data['auc_rmin_maxa_best']),\n", " min(data['auc_onmin_maxa_best'])\n", " )\n", + "cm = plt.get_cmap('seismic')\n", + "z = data['auc'] - data['auc_min_max_best']\n", + "diff = np.max(np.abs((data['auc'] - data['auc_min_max_best']).values))\n", "plt.scatter(data['auc'], data['auc_min_max_best'], label='(min, max)', s=2)\n", "#plt.scatter(data['auc'], data['auc_rmin_max_best'], label='(rmin, max)', s=2)\n", "#plt.scatter(data['auc'], data['auc_min_maxa_best'], label='(min, maxa)', s=2)\n", "plt.scatter(data['auc'], data['auc_rmin_maxa_best'], label='(rmin, maxa)', s=2)\n", - "plt.scatter(data['auc'], data['auc_onmin_maxa_best'], label='(onmin, maxa)', s=2)\n", + "#plt.scatter(data['auc'], data['auc_onmin_maxa_best'], label='(onmin, maxa)', s=2)\n", + "#plt.scatter(data['auc'], data['auc_analytic_best'], label='analytic', s=2)\n", + "plt.scatter(data['auc'], data['auc_analytic_best_mod'], label='analytic', s=2)\n", "plt.xlabel(f'{clabel} auc')\n", "plt.ylabel(f'{clabel} auc midpoint estimation')\n", "plt.plot([val_min, 1], [val_min, 1], label='x=y', c='black', linestyle='--')\n", @@ -804,19 +4361,162 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "cm = plt.get_cmap('seismic')\n", + "z = data['auc'] - data['auc_min_max_best']\n", + "diff = np.max(np.abs((data['auc'] - data['auc_min_max_best']).values))\n", + "plt.scatter(data['auc'], data['auc_min_max_best'], label='(rmin, max)', s=2, c=z, cmap=cm, vmin=-diff, vmax=diff)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "cm = plt.get_cmap('seismic')\n", + "z = data['auc'] - data['auc_rmin_maxa_best']\n", + "diff = np.max(np.abs((data['auc'] - data['auc_rmin_maxa_best']).values))\n", + "plt.scatter(data['auc'], data['auc_rmin_maxa_best'], label='(rmin, maxa)', s=2, c=z, cmap=cm, vmin=-diff, vmax=diff)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "cm = plt.get_cmap('seismic')\n", + "z = data['auc'] - data['auc_min_max_best']\n", + "diff = np.max(np.abs((data['auc'] - data['auc_min_max_best']).values))\n", + "plt.scatter(1 - data['best_spec'], data['best_sens'], s=1, c=z, cmap=cm, vmin=-diff, vmax=diff)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "cm = plt.get_cmap('seismic')\n", + "z = data['auc'] - data['auc_rmin_maxa_best']\n", + "diff = np.max(np.abs((data['auc'] - data['auc_rmin_maxa_best']).values))\n", + "plt.scatter(1 - data['best_spec'], data['best_sens'], s=1, c=z, cmap=cm, vmin=-diff, vmax=diff)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 178, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.5586380434671543,\n", - " 0.4816559984436065,\n", - " 0.7549865130874414,\n", - " 0.6313009781743928)" + "(0.771350390698377,\n", + " 0.5918088216317245,\n", + " 0.4739766037373889,\n", + " 0.8481531042942002,\n", + " 0.7703681664785309,\n", + " 0.6129499688375019,\n", + " 0.6506102117304811)" ] }, - "execution_count": 48, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } @@ -825,31 +4525,53 @@ "tmp = data[['auc', 'auc_min_max_best', 'auc_rmin_max_best', 'auc_min_maxa_best', 'auc_rmin_maxa_best', 'auc_onmin_maxa_best']].dropna()\n", "\n", "tmp0 = data[['auc', 'auc_rmin_max_best']].dropna()\n", - "tmp1 = data[['auc', 'auc_min_maxa_best']].dropna()\n", + "#tmp1 = data[['auc', 'auc_min_maxa_best']].dropna()\n", "tmp2 = data[['auc', 'auc_rmin_maxa_best']].dropna()\n", "tmp3 = data[['auc', 'auc_onmin_maxa_best']].dropna()\n", "(r2_score(tmp['auc'], tmp['auc_min_max_best']),\n", - "r2_score(tmp['auc'], tmp['auc_rmin_max_best']),\n", - "#r2_score(tmp['auc'], tmp['auc_min_maxa_best']),\n", - "r2_score(tmp['auc'], tmp['auc_rmin_maxa_best']),\n", - "r2_score(tmp['auc'], tmp['auc_onmin_maxa_best']))" + "r2_score(tmp0['auc'], tmp0['auc_rmin_max_best']),\n", + "r2_score(tmp['auc'], tmp['auc_min_maxa_best']),\n", + "r2_score(tmp2['auc'], tmp2['auc_rmin_maxa_best']),\n", + "r2_score(tmp3['auc'], tmp3['auc_onmin_maxa_best']),\n", + "r2_score(data['auc'], data['auc_analytic_best']),\n", + "r2_score(data['auc'], data['auc_analytic_best_mod']))" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 1000, 994, 1000, 1000)" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(tmp0), len(tmp1), len(tmp2), len(tmp3), len(data)" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 180, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.0937370540267925),\n", - " np.float64(0.12343139460112786),\n", - " np.float64(0.14020260317985894),\n", - " np.float64(0.07966783846282885))" + "(np.float64(0.06871215817705313),\n", + " np.float64(0.10569441369447687),\n", + " np.float64(0.11706903685227037),\n", + " np.float64(0.062044045919784))" ] }, - "execution_count": 49, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } @@ -863,16 +4585,16 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 181, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "WilcoxonResult(statistic=np.float64(394513.5), pvalue=np.float64(1.7145958148258168e-26))" + "WilcoxonResult(statistic=np.float64(203727.5), pvalue=np.float64(1.5223240841381665e-06))" ] }, - "execution_count": 50, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } @@ -885,9 +4607,31 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 182, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "'auc_min_maxa_best'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'auc_min_maxa_best'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[182], line 6\u001b[0m\n\u001b[1;32m 1\u001b[0m results\u001b[38;5;241m.\u001b[39mappend({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtarget\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msource\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msens, spec at max. acc\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msens, spec at max. acc\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msens, spec at max. acc\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msens, spec at max. acc\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mestimation\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(min, max)\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(rmin, max)\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(min, maxa)\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(rmin, maxa)\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr2\u001b[39m\u001b[38;5;124m'\u001b[39m: (r2_score(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m], data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_min_max_best\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 5\u001b[0m r2_score(tmp0[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m], tmp0[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_rmin_max_best\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[0;32m----> 6\u001b[0m r2_score(tmp1[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m], tmp1[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_min_maxa_best\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 7\u001b[0m r2_score(tmp2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m], tmp2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_rmin_maxa_best\u001b[39m\u001b[38;5;124m'\u001b[39m])),\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmape\u001b[39m\u001b[38;5;124m'\u001b[39m: (mean_absolute_percentage_error(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m], data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_min_max_best\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 9\u001b[0m mean_absolute_percentage_error(tmp0[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m], tmp0[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_rmin_max_best\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 10\u001b[0m mean_absolute_percentage_error(tmp1[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m], tmp1[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_min_maxa_best\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 11\u001b[0m mean_absolute_percentage_error(tmp2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m], tmp2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_rmin_maxa_best\u001b[39m\u001b[38;5;124m'\u001b[39m]))})\n", + "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 4101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m 4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 4104\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", + "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'auc_min_maxa_best'" + ] + } + ], "source": [ "results.append({'target': ['auc', 'auc', 'auc', 'auc'],\n", " 'source': ['sens, spec at max. acc', 'sens, spec at max. acc', 'sens, spec at max. acc', 'sens, spec at max. acc'],\n", @@ -904,12 +4648,204 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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best_accbest_sensbest_specpnmax_acc_min_maxmax_acc_min_rmax
56230.9455781.000.00000013980.9594560.949425
65320.9000000.001.00000010900.9260050.910025
81100.9000001.000.00000090100.9263940.911806
45130.9000000.001.00000010900.9260610.910298
3680.8064521.000.0000002560.8593650.836048
........................
31421.0000001.001.000000253410.9982160.998216
34171.0000001.001.0000006840.9983800.998380
14491.0000001.001.00000020100.9966670.996667
29440.9436620.880.97826125460.9210060.920826
7241.0000001.001.00000010200.9966670.996667
\n", + "

1000 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " best_acc best_sens best_spec p n max_acc_min_max \\\n", + "5623 0.945578 1.00 0.000000 139 8 0.959456 \n", + "6532 0.900000 0.00 1.000000 10 90 0.926005 \n", + "8110 0.900000 1.00 0.000000 90 10 0.926394 \n", + "4513 0.900000 0.00 1.000000 10 90 0.926061 \n", + "368 0.806452 1.00 0.000000 25 6 0.859365 \n", + "... ... ... ... ... ... ... \n", + "3142 1.000000 1.00 1.000000 25 341 0.998216 \n", + "3417 1.000000 1.00 1.000000 68 4 0.998380 \n", + "1449 1.000000 1.00 1.000000 20 10 0.996667 \n", + "2944 0.943662 0.88 0.978261 25 46 0.921006 \n", + "724 1.000000 1.00 1.000000 10 20 0.996667 \n", + "\n", + " max_acc_min_rmax \n", + "5623 0.949425 \n", + "6532 0.910025 \n", + "8110 0.911806 \n", + "4513 0.910298 \n", + "368 0.836048 \n", + "... ... \n", + "3142 0.998216 \n", + "3417 0.998380 \n", + "1449 0.996667 \n", + "2944 0.920826 \n", + "724 0.996667 \n", + "\n", + "[1000 rows x 7 columns]" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[['best_acc', 'best_sens', 'best_spec', 'p', 'n', 'max_acc_min_max', 'max_acc_min_rmax'\n", + " #, 'max_acc_min_onmax'\n", + " ]]" + ] + }, + { + "cell_type": "code", + "execution_count": 89, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -925,7 +4861,7 @@ " min(data['max_acc_min_rmax']))\n", "plt.scatter(data['best_acc'], data['max_acc_min_max'], label='(min, max)', s=3)\n", "plt.scatter(data['best_acc'], data['max_acc_min_rmax'], label='(min, rmax)', s=3)\n", - "plt.scatter(data['best_acc'], data['max_acc_min_onmax'], label='(min, onmax)', s=3)\n", + "#plt.scatter(data['best_acc'], data['max_acc_min_onmax'], label='(min, onmax)', s=3)\n", "plt.xlabel(f'{clabel} max. acc.')\n", "plt.ylabel(f'{clabel} max. acc. midpoint estimation')\n", "plt.plot([val_min, 1], [val_min, 1], label='x=y', c='black', linestyle='--')\n", @@ -936,16 +4872,16 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.8110504812547714, 0.8906824191553651)" + "(0.8084424256472821, 0.8777564442488769)" ] }, - "execution_count": 53, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -959,16 +4895,16 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.04864217372593979), np.float64(0.0354232068090639))" + "(np.float64(0.04899950493681104), np.float64(0.03854606442950721))" ] }, - "execution_count": 54, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -980,16 +4916,16 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "WilcoxonResult(statistic=np.float64(279533.0), pvalue=np.float64(1.1144610549059579e-67))" + "WilcoxonResult(statistic=np.float64(nan), pvalue=np.float64(nan))" ] }, - "execution_count": 55, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -1002,7 +4938,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -1017,7 +4953,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -1026,7 +4962,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ diff --git a/notebooks/auc_experiments/05-application-retinal-vessel.ipynb b/notebooks/auc_experiments/05-application-retinal-vessel.ipynb index 3978921..5772386 100644 --- a/notebooks/auc_experiments/05-application-retinal-vessel.ipynb +++ b/notebooks/auc_experiments/05-application-retinal-vessel.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -32,13 +32,13 @@ "import pandas as pd\n", "\n", "from mlscorecheck.auc import auc_from_aggregated, max_acc_from_aggregated, estimate_acc_interval, auc_from\n", - "from mlscorecheck.auc import auc_onmin_grad, auc_maxa_grad, auc_max_grad, auc_rmin_grad, auc_max\n", + "from mlscorecheck.auc import auc_onmin_grad, auc_maxa_grad, auc_max_grad, auc_rmin_grad, auc_max, auc_lower_from, auc_upper_from\n", "from mlscorecheck.experiments import load_drive" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ "uysal 0.7548 0.9682 0.9419 71.0 NaN" ] }, - "execution_count": 54, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -226,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -235,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -249,7 +249,7 @@ " 302108, 303816, 302589, 305695]))" ] }, - "execution_count": 57, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -356,7 +356,7 @@ "uysal 0.7548 0.9682 0.9419 71.0 NaN" ] }, - "execution_count": 58, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -367,16 +367,16 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "np.float64(0.928638415)" + "np.float64(0.8909279999999999)" ] }, - "execution_count": 79, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -399,28 +399,145 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9072292499999999, 0.9072292499999999)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auc_from(\n", + " scores={\n", + " 'sens': data.loc['liskowski']['sens'],\n", + " 'spec': data.loc['liskowski']['spec'],\n", + " 'acc': data.loc['liskowski']['acc'],\n", + " },\n", + " eps=1e-4,\n", + " #k=20,\n", + " #ps=ps,\n", + " #ns=ns,\n", + " p=ps[0],\n", + " n=ns[0],\n", + " lower='rmin',\n", + " upper='max',\n", + " correction=None\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((0.783730445, 1.0), (0.99483484, 1.0))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(auc_lower_from(\n", + " scores={\n", + " 'sens': data.loc['liskowski']['sens'],\n", + " 'spec': data.loc['liskowski']['spec'],\n", + " 'acc': data.loc['liskowski']['acc'],\n", + " },\n", + " eps=1e-4,\n", + " #k=20,\n", + " #ps=ps,\n", + " #ns=ns,\n", + " p=ps[0],\n", + " n=ns[0],\n", + " lower='rmin'\n", + "),\n", + "auc_upper_from(\n", + " scores={\n", + " 'sens': data.loc['liskowski']['sens'],\n", + " 'spec': data.loc['liskowski']['spec'],\n", + " 'acc': data.loc['liskowski']['acc'],\n", + " },\n", + " eps=1e-4,\n", + " #k=20,\n", + " #ps=ps,\n", + " #ns=ns,\n", + " p=ps[0],\n", + " n=ns[0],\n", + " upper='max'\n", + "))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.9072292499999999)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(auc_from(\n", + " scores={\n", + " 'sens': data.loc['liskowski']['sens'],\n", + " 'spec': data.loc['liskowski']['spec'],\n", + " 'acc': data.loc['liskowski']['acc'],\n", + " },\n", + " eps=1e-4,\n", + " #k=20,\n", + " #ps=ps,\n", + " #ns=ns,\n", + " p=ps[0],\n", + " n=ns[0],\n", + " lower='rmin',\n", + " upper='max',\n", + " correction=None\n", + "))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "10.20788043478261 1\n" + "0.7071067811865476 0.22489982214310444\n" ] }, { "data": { "text/plain": [ - "(np.float64(0.87655),\n", - " 0.99483484,\n", - " np.float64(0.08922293611346829),\n", - " np.float64(0.9107770638865317),\n", - " np.float64(0.9842811192774881),\n", - " np.float64(0.9356924200000001))" + "(np.float64(0.783730445),\n", + " 1.0,\n", + " np.float64(0.24130711235267618),\n", + " np.float64(0.7586928876473238),\n", + " np.float64(0.9478126181931517),\n", + " np.float64(0.8918652225))" ] }, - "execution_count": 89, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -436,18 +553,19 @@ " k=20,\n", " ps=ps,\n", " ns=ns,\n", - " lower='onmin',\n", + " lower='rmin',\n", " upper='max'\n", ")\n", "exponent = 1\n", + "#lweight = auc_onmin_grad(fpr=1 - data.loc['liskowski']['spec'], tpr=data.loc['liskowski']['sens'])**exponent\n", "lweight = auc_onmin_grad(fpr=1 - data.loc['liskowski']['spec'], tpr=data.loc['liskowski']['sens'])**exponent\n", "uweight = auc_max_grad(fpr=1 - data.loc['liskowski']['spec'], tpr=data.loc['liskowski']['sens'])**exponent\n", "#uweight = auc_maxa_grad(acc=data.loc['liskowski']['acc'], p=ps[0], n=ns[0])**exponent\n", "#uweight = 1 - np.sqrt(data.loc['liskowski']['sens'] * data.loc['liskowski']['spec'])\n", "#lweight = np.sqrt(data.loc['liskowski']['sens'] * data.loc['liskowski']['spec'])\n", "\n", - "uweight = 1\n", - "lweight = ns[0]/ps[0]\n", + "#uweight = 1\n", + "#lweight = ns[0]/ps[0]\n", "\n", "print(lweight, uweight)\n", "\n", @@ -459,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -491,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -500,7 +618,7 @@ "np.float64(0.935310450253265)" ] }, - "execution_count": 11, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -524,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -545,16 +663,16 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "np.float64(0.9366254109343337)" + "np.float64(0.935310450253265)" ] }, - "execution_count": 114, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -577,16 +695,16 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.78910408), 0.9932508218686673)" + "(np.float64(0.78910408), 0.9906209005065298)" ] }, - "execution_count": 115, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -609,16 +727,16 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "np.float64(0.9018002767275017)" + "np.float64(0.8992115244366155)" ] }, - "execution_count": 116, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -642,16 +760,16 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "np.float64(0.8743242525992344)" + "np.float64(0.8737035580494894)" ] }, - "execution_count": 117, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -674,16 +792,16 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "np.float64(0.8734012230347652)" + "np.float64(0.8725519741835043)" ] }, - "execution_count": 118, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -706,16 +824,16 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.783730445), 0.9885530434722373)" + "(np.float64(0.783730445), 0.9840925602966356)" ] }, - "execution_count": 119, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -739,16 +857,16 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "np.float64(0.9588783228414368)" + "np.float64(0.9653779392691764)" ] }, - "execution_count": 120, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -767,16 +885,16 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "np.float64(0.9539319465353054)" + "np.float64(0.961250475882503)" ] }, - "execution_count": 121, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -795,16 +913,16 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "np.float64(0.9413905581208072)" + "np.float64(0.9503445114559341)" ] }, - "execution_count": 122, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -815,16 +933,16 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.799615405, 0.9952401235227322)" + "(0.799615405, 0.9933853642342678)" ] }, - "execution_count": 123, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -848,16 +966,16 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "np.float64(0.9539319465353054)" + "np.float64(0.961250475882503)" ] }, - "execution_count": 124, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } diff --git a/notebooks/auc_experiments/06-illustration.ipynb b/notebooks/auc_experiments/06-illustration.ipynb new file mode 100644 index 0000000..792b5f4 --- /dev/null +++ b/notebooks/auc_experiments/06-illustration.ipynb @@ -0,0 +1,296 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import wilcoxon\n", + "from sklearn.metrics import r2_score, mean_absolute_error, mean_absolute_percentage_error\n", + "from mlscorecheck.auc import (\n", + " auc_onmin_grad,\n", + " auc_rmin_grad,\n", + " auc_max_grad,\n", + " auc_maxa_grad,\n", + " macc_min_grad,\n", + " acc_rmax_grad,\n", + " auc_rmin,\n", + " auc_maxa,\n", + " auc_max,\n", + " auc_onmin,\n", + " auc_max_profile,\n", + " auc_rmin_profile\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#fprs = 1 - np.linspace(0.5, 1.0, 100)\n", + "fprs = np.repeat(0.2, 100)\n", + "tprs = np.linspace(0.5, 1.0, 100)\n", + "\n", + "mask = tprs >= fprs\n", + "fprs = fprs[mask]\n", + "tprs = tprs[mask]\n", + "\n", + "p=100\n", + "n=100\n", + "\n", + "auc_rmin_ = np.array([auc_onmin(fpr, tpr) for fpr, tpr in zip(fprs, tprs)])\n", + "auc_max_ = np.array([auc_max(fpr, tpr) for fpr, tpr in zip(fprs, tprs)])\n", + "auc_rmin_grad_ = np.array([auc_rmin_grad(fpr, tpr) for fpr, tpr in zip(fprs, tprs)])\n", + "auc_max_grad_ = np.array([auc_max_grad(fpr, tpr) for fpr, tpr in zip(fprs, tprs)])\n", + "\n", + "auc_rmin_grad_ += 1\n", + "auc_max_grad_ += 1\n", + "\n", + "weight_upper = auc_rmin_grad_ / (auc_rmin_grad_ + auc_max_grad_)\n", + "weight_lower = auc_max_grad_ / (auc_rmin_grad_ + auc_max_grad_)\n", + "\n", + "plt.figure(figsize=(4, 4))\n", + "plt.plot(tprs, auc_rmin_, label=r'$auc_{rmin}$')\n", + "plt.plot(tprs, auc_max_, label=r'$auc_{max}$')\n", + "plt.plot(tprs, (auc_rmin_ + auc_max_) / 2.0, label=r'$(auc_{rmin} + auc_{max})/2$')\n", + "plt.plot(tprs, (weight_lower * auc_rmin_ + weight_upper * auc_max_), label=r'weighted')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fprs = 1 - np.linspace(0.5, 1.0, 100)\n", + "tprs = np.linspace(0.5, 1.0, 100)\n", + "\n", + "mask = tprs >= fprs\n", + "fprs = fprs[mask]\n", + "tprs = tprs[mask]\n", + "\n", + "p=100\n", + "n=100\n", + "\n", + "auc_rmin_ = np.array([auc_onmin(fpr, tpr) for fpr, tpr in zip(fprs, tprs)])\n", + "auc_maxa_ = np.array([auc_maxa((p*tpr + n*(1 - fpr))/(p + n), p, n) for fpr, tpr in zip(fprs, tprs)])\n", + "auc_rmin_grad_ = np.array([auc_rmin_grad(fpr, tpr) for fpr, tpr in zip(fprs, tprs)])\n", + "auc_maxa_grad_ = np.array([auc_maxa_grad((p*tpr + n*(1 - fpr))/(p + n), p, n) for fpr, tpr in zip(fprs, tprs)])\n", + "\n", + "weight_upper = auc_rmin_grad_ / (auc_rmin_grad_ + auc_maxa_grad_)\n", + "weight_lower = auc_maxa_grad_ / (auc_rmin_grad_ + auc_maxa_grad_)\n", + "\n", + "plt.figure(figsize=(4, 4))\n", + "plt.plot(tprs, auc_rmin_, label=r'$auc_{rmin}$')\n", + "plt.plot(tprs, auc_maxa_, label=r'$auc_{maxa}$')\n", + "plt.plot(tprs, (auc_rmin_ + auc_maxa_) / 2.0, label=r'$(auc_{rmin} + auc_{maxa})/2$')\n", + "plt.plot(tprs, (weight_lower * auc_rmin_ + weight_upper * auc_maxa_), label=r'weighted')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0.5 , 0.49494949, 0.48989899, 0.48484848, 0.47979798,\n", + " 0.47474747, 0.46969697, 0.46464646, 0.45959596, 0.45454545,\n", + " 0.44949495, 0.44444444, 0.43939394, 0.43434343, 0.42929293,\n", + " 0.42424242, 0.41919192, 0.41414141, 0.40909091, 0.4040404 ,\n", + " 0.3989899 , 0.39393939, 0.38888889, 0.38383838, 0.37878788,\n", + " 0.37373737, 0.36868687, 0.36363636, 0.35858586, 0.35353535,\n", + " 0.34848485, 0.34343434, 0.33838384, 0.33333333, 0.32828283,\n", + " 0.32323232, 0.31818182, 0.31313131, 0.30808081, 0.3030303 ,\n", + " 0.2979798 , 0.29292929, 0.28787879, 0.28282828, 0.27777778,\n", + " 0.27272727, 0.26767677, 0.26262626, 0.25757576, 0.25252525,\n", + " 0.24747475, 0.24242424, 0.23737374, 0.23232323, 0.22727273,\n", + " 0.22222222, 0.21717172, 0.21212121, 0.20707071, 0.2020202 ,\n", + " 0.1969697 , 0.19191919, 0.18686869, 0.18181818, 0.17676768,\n", + " 0.17171717, 0.16666667, 0.16161616, 0.15656566, 0.15151515,\n", + " 0.14646465, 0.14141414, 0.13636364, 0.13131313, 0.12626263,\n", + " 0.12121212, 0.11616162, 0.11111111, 0.10606061, 0.1010101 ,\n", + " 0.0959596 , 0.09090909, 0.08585859, 0.08080808, 0.07575758,\n", + " 0.07070707, 0.06565657, 0.06060606, 0.05555556, 0.05050505,\n", + " 0.04545455, 0.04040404, 0.03535354, 0.03030303, 0.02525253,\n", + " 0.02020202, 0.01515152, 0.01010101, 0.00505051, 0. ]),\n", + " array([0.5 , 0.50505051, 0.51010101, 0.51515152, 0.52020202,\n", + " 0.52525253, 0.53030303, 0.53535354, 0.54040404, 0.54545455,\n", + " 0.55050505, 0.55555556, 0.56060606, 0.56565657, 0.57070707,\n", + " 0.57575758, 0.58080808, 0.58585859, 0.59090909, 0.5959596 ,\n", + " 0.6010101 , 0.60606061, 0.61111111, 0.61616162, 0.62121212,\n", + " 0.62626263, 0.63131313, 0.63636364, 0.64141414, 0.64646465,\n", + " 0.65151515, 0.65656566, 0.66161616, 0.66666667, 0.67171717,\n", + " 0.67676768, 0.68181818, 0.68686869, 0.69191919, 0.6969697 ,\n", + " 0.7020202 , 0.70707071, 0.71212121, 0.71717172, 0.72222222,\n", + " 0.72727273, 0.73232323, 0.73737374, 0.74242424, 0.74747475,\n", + " 0.75252525, 0.75757576, 0.76262626, 0.76767677, 0.77272727,\n", + " 0.77777778, 0.78282828, 0.78787879, 0.79292929, 0.7979798 ,\n", + " 0.8030303 , 0.80808081, 0.81313131, 0.81818182, 0.82323232,\n", + " 0.82828283, 0.83333333, 0.83838384, 0.84343434, 0.84848485,\n", + " 0.85353535, 0.85858586, 0.86363636, 0.86868687, 0.87373737,\n", + " 0.87878788, 0.88383838, 0.88888889, 0.89393939, 0.8989899 ,\n", + " 0.9040404 , 0.90909091, 0.91414141, 0.91919192, 0.92424242,\n", + " 0.92929293, 0.93434343, 0.93939394, 0.94444444, 0.94949495,\n", + " 0.95454545, 0.95959596, 0.96464646, 0.96969697, 0.97474747,\n", + " 0.97979798, 0.98484848, 0.98989899, 0.99494949, 1. ]))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fprs, tprs" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/gykovacs/workspaces/mlscorecheck/mlscorecheck/auc/_acc_single.py:129: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return np.sqrt(2) * min(p, n) / 2 / (np.sqrt(auc - 0.5) * (p + n))\n", + "/home/gykovacs/workspaces/mlscorecheck/mlscorecheck/auc/_acc_single.py:200: RuntimeWarning: divide by zero encountered in scalar divide\n", + " return n * p / ((n + p) * np.sqrt(-2 * auc * n * p + 2 * n * p))\n" + ] + } + ], + "source": [ + "\n", + "auc_max_grad_ = np.array([auc_max_grad(fpr, tpr) for fpr, tpr in zip(fprs, tprs)])\n", + "auc_rmin_grad_ = np.array([auc_rmin_grad(fpr, tpr) for fpr, tpr in zip(fprs, tprs)])\n", + "auc_maxa_grad_ = np.array([auc_maxa_grad((p*tpr + n*(1 - fpr))/(p + n), p, n) for fpr, tpr in zip(fprs, tprs)])/(n/p)\n", + "\n", + "acc_rmax_grad_ = np.array([acc_rmax_grad(auc_, p, n) for auc_ in tprs])\n", + "macc_min_grad_ = np.array([macc_min_grad(auc_, p, n) for auc_ in tprs])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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3rsM1s2bNwtNPP40XXngBu3btwoYNG/Diiy8CAM4880yMHTsWl156Kb799lsUFxfj66+/xpIlS6T47RDJRxR3hcR2sGBXCFFY3XzzzZg8eTKuuOIKjBw5EjU1NZg2bVqHa6ZOnYrnnnsOr7zyCgYOHIgLL7wQu3btCrz/ySef4JRTTsFVV12FAQMG4N577+3weCgRdSKKu0IUghDkg+4nqLGxEQkJCWhoaIDVag3rZ+2rbsZZ/7cMJq0KWx7l6G6KDLvdjuLiYuTl5UGv10tdDkUA/8wp4uZPBEpWAn9ZAAycFJGP7Or3d0y3WBjbdYVEOD8RERGFT6ArJPpaLGI6WOh9wcIrAA43R3gTycnEiRNhNps73ebMmSN1eUTSiuJl02P6cVOjpm32vlanB3pN12bzIyLpvfnmm2htbe30Pc6ISXEvip8KielgoVYpoVUp4fR40eryIEnqgoioy3r06CF1CUTRi10h0tFrxN8inwyhSOO4nvjBP2uKuCjuCon5YMFJsijS/LM4trS0SFwJRYr/z/rwGTyJwsLrAdx28TgK57GI6a4QoOOTIUSRoFKpkJiYGFhrwmg0BqbBptgiCAJaWlpQVVWFxMTELq/KSnRCXO3GHjFYRJ4+MEmWW+JKKJ5kZmYCABeyihOJiYmBP3OisHO1aw3l4M3I49LpJAWFQoGsrCykp6fD5XJJXQ6FkUajYUsFRZazWdxrjOICh1Em5oMF1wshKalUKn7pEFFo+btCorAbBIiDwZtcL4SIiGJKFD8RAsRBsGBXCBERxZT2XSFRKOaDBbtCiIgoprArRFoGjTiMhF0hREQUE1y+FosoXDIdiINg0dYVwsdNiYgoBkTxOiFAHAQLdoUQEVFMYVeItPhUCBERxRR2hUiLT4UQEVFMYVeItNgVQkREMSWwZDq7QiTBrhAiIoopgQmy2BUiCS6bTkREMYVdIdIyaMXfIrtCiIgoJrArRFqcIIuIiGIKu0KkxQmyiIgoprArRFrtnwoRBEHiaoiIiE5QoCuELRaS8AcLrwA43F6JqyEiIjpBLrZYSMr/uCnAJ0OIiCgG+LtCtBy8KQmNSgmNSgGAT4YQEVEMYFeI9DhJFhERxQx2hUiPk2QREVFM8HoAt1085uOm0uF6IUREFBP8S6YDnCBLSm1dIZzLgoiIZMzfDQIAar10dRxDXAQL/yRZdrZYEBGRnLWfzlsZnV/h0VlViPm7Qjh4k4iIZM0Z3euEAPESLPhUCBERxYIoX4AMiJNgwa4QIiKKCa7onhwLiJNgwa4QIiKKCewKiQ5cOp2IiGKCq1ncM1hIy6AVf5vsCiEiIlnzz2PBrhBp+Wfe5DwWREQka7HeFfLEE09AoVDgjjvuCFE54cGnQoiIKCbEclfIunXr8Nprr2HIkCGhrCcsDHwqhIiIYkGsdoXYbDZcc801eOONN5CUlBTqmkLOyKdCiIgoFjhjtMVi+vTpuOCCC3DOOecc91qHw4HGxsYOW6SxK4SIiGKCDCbIUgf7AwsXLsSGDRuwbt26Ll0/d+5czJ49O+jCQoldIUREFBNirSuktLQUt99+O95//33o9V1bVW3mzJloaGgIbKWlpd0q9ESwK4SIiGKCDLpCgmqxWL9+PaqqqnDyyScHznk8HixfvhwvvfQSHA4HVCpVh5/R6XTQ6XShqbabOEEWERHFhFjrChk/fjz++OOPDueuv/569OvXD//4xz+OCBXRgl0hREQUE2TQFRJUsLBYLBg0aFCHcyaTCSkpKUecjyZtXSFuCIIAhUIhcUVERETdEOgKMUlbxzHExcyb/hYLrwA43F6JqyEiIuqmQFeIQdo6jiHop0IOt2zZshCUEV7+x00BsTtEr4nOLhsiIqJjkkFXSFy0WGhUSmhUYvcHB3ASEZFssSskenCSLCIikj0ZdIXET7DgkyFERCRnXi/gtovHWrZYSK5t6XQGCyIikiF/awUQ1fNYxE2waOsKcUtcCRERUTe0Dxbqrs1+LYX4CRbsCiEiIjlrP+umMnq/vqO3shDjeiFERCRrzuifzhuIo2DBp0KIiEjWZLBOCBBPwYJdIUREJGf+YBHFk2MBcRQs2BVCRESyxq6Q6MKl04mISNZc/lk3GSyigkEr/lbZFUJERLIkg3VCgDgKFm0TZHEeCyIikiF2hUQXPZ8KISIiOWNXSHQx8qkQIiKSM3aFRBc+FUJERLLmZItFVGFXCBERyRonyIou7AohIiJZY1dIdGFXCBERyRq7QqILu0KIiEjW2BUSXfzzWLArhIiIZIldIdGlrSvEDUEQJK6GiIgoSIGuEJO0dRxH3AQLf1eIVwAcbq/E1RAREQUp0BVikLaO44ibYOFvsQDYHUJERDLErpDoolEpoVEpAHAAJxERyRC7QqKPvzuklS0WREQkN/4WC3aFRA9/d0grWyyIiEhOvF7A7e8KYYtF1GhbOp3BgoiIZMQ/cBPgPBbRpG2SLLfElRAREQWhfbBQ66WrowviKlgkGTUAgPoWl8SVEBERBaGlVtwbkgBldH91R3d1IZZm0QEADjU5JK6EiIgoCLZKcW/OkLaOLoivYGH2BQsbgwUREcmIrUrcm9OlraML4itYsMWCiIjkiC0W0YnBgoiIZInBIjoxWBARkSyxKyQ6BYIFx1gQEZGcsMUiOvkHb9Y2O+HycIVTIiKSCbZYRKckoxYqpbgQWY3NKXE1REREXcQWi+ikVCqQatYC4DgLIiKSCY8LaKkRjxksok/bOAu7xJUQERF1QXM1AAFQqABDstTVHFf8BQsznwwhIiIZCXSDpEf9dN5APAYLPnJKRERyIqOBm0AcB4sqBgsiIpIDW4W4l8H4CiAegwW7QoiISE7ad4XIQNwFi3SruI49gwUREclCoCuELRZRibNvEhGRrMhoDgsgHoMFu0KIiEhOOHgzuvlbLFqcHjQ73BJXQ0REdBxssYhuJp0aRq0KAFstiIhIBjjGIvpxnAUREcmCwwY4beIxu0KiF8dZEBGRLDT7Wis0JkBnkbaWLorPYMHZN4mISA5kNnATYLCQuBIiIqJjkNnATSBegwW7QoiISA7YYiEPHLxJRESywBYLeWBXCBERyQKDhTwwWBARkSywK0Qe/MGi2uaA1ytIXA0REdFRsMVCHlJ9gzfdXgH1rS6JqyEiIjoKtljIg0alRLJJC4DdIUREFKW8XtlN5w3EabAA+MgpERFFOXs94PW1qpvSJC0lGPEbLAKPnNolroSIiKgT/vEVhmRArZW2liAwWLDFgoiIopEMB24CDBYMFkREFJ1kOHATCDJYzJs3D0OGDIHVaoXVasWoUaPw9ddfh6u2sPKPsahisCAiomjUVCHuY7nFomfPnnjiiSewfv16/Prrrxg3bhz+/Oc/Y8uWLeGqL2zYYkFERFEt0BUirxYLdTAXX3TRRR1eP/7445g3bx5Wr16NgQMHhrSwcGOwICKiqCbDR02BIINFex6PBx9//DGam5sxatSoo17ncDjgcLR9eTc2Nnb3I0OKC5EREVFUi5fBm3/88QfMZjN0Oh1uueUWLFq0CAMGDDjq9XPnzkVCQkJgy8nJOaGCQ8U/xqK+xQWH2yNxNURERIeJh8GbAFBUVIRNmzZhzZo1uPXWWzF16lRs3br1qNfPnDkTDQ0Nga20tPSECg6VBIMGGpUCAFBjc0pcDRER0WFk2mIRdFeIVqtFnz59AADDhw/HunXr8Pzzz+O1117r9HqdTgedTndiVYaBUqlAqlmH8gY7DjU5kJ1okLokIiIikdsJtNaKx5ZMaWsJ0gnPY+H1ejuMoZATDuAkIqKo1HxI3Cs1gD5R0lKCFVSLxcyZMzFx4kT06tULTU1N+OCDD7Bs2TIsXbo0XPWFVWC9EA7gJCKiaNL+UVOlvOayDCpYVFVV4dprr0V5eTkSEhIwZMgQLF26FOeee2646gurdCtbLIiIKArJdOAmEGSweOutt8JVhyS4wikREUUlmQ7cBOJ4rRCAYyyIiChKybjFgsECHGNBRERRhi0W8pRu1QMAyupbJa6EiIioncaD4p7BQl7yU00AgPIGO5odbomrISIi8qneKe5TC6WtoxviOlgkGrVINWsBAMXVzRJXQ0REBHFyrNpi8Ti1SNpauiGugwUA5KeZAQC7q2wSV0JERASgdi8geACtRXazbgIMFijwBYs9hxgsiIgoCrTvBlEopK2lG+I+WPRJZ7AgIqIoUr1D3KfJrxsEYLBAQZo4gJNdIUREFBWqd4l7GQ7cBBgsAl0h+6pb4PZ4Ja6GiIjiXqArpK+0dXRT3AeLHokG6DVKOD1eHKjjfBZERCQhQWjXYsGuEFlSKhXIT+WTIUREFAUaywCnDVCqgeQ8qavplrgPFgAHcBIRUZTwD9xMzgdUGmlr6SYGC7SNs2CLBRERSSrQDSLP8RUAgwUAoCBdfDKELRZERCQpGU/l7cdggfZdIc0QBEHiaoiIKG4d8nWFyHTgJsBgAQDITTFBoQAaWl2otjmlLoeIiOIVu0Jig16jQk6SEQC7Q4iISCL2BsBWIR6n9pG2lhPAYOHDJ0OIiEhS/tYKSxagT5C2lhPAYOHDqb2JiEhSMTBwE2CwCGhb5bRZ4kqIiCguBQZuynd8BcBgERDoCmGLBRERSUHmU3n7MVj4+FssDta3osXplrgaIiKKO+wKiS1JJi2STVoAwF52hxARUSS5nUDtXvGYXSGxo08anwwhIiIJ1BUDggfQmgFrttTVnBAGi3YCU3tznAUREUVSYOBmIaBQSFvLCWKwaIdPhhARkSQC4yvkPXATYLDooICTZBERkRQCT4TIe+AmwGDRgX+Mxd7qZni8XIyMiIgipDo25rAAGCw66JFogE6thNPtxYG6FqnLISKieCAIbS0WaewKiSlKpQL5fDKEiIgiqakccNoAhQpIypO6mhPGYHGYvhlisNh8sFHiSoiIKC6U/ybuU/oAaq20tYQAg8VhTu6VBAD4dX+dxJUQEVFcKFkt7nNOkbaOEGGwOMzw3mKw2Li/jgM4iYgo/ErXiPuc06StI0QYLA7TL9MCk1aFJocbOyubpC6HiIhimdsBHNwgHvdisIhJapUSJ/dmdwgREUVA+W+AxwEYU8QxFjGAwaIT/u6Q9ftqJa6EiIhiWmB8xWmyn8rbj8GiEyN6JwNgiwUREYWZf3xFr5HS1hFCDBadOKlXIpQK4EBdKyoa7FKXQ0REsUgQOrZYxAgGi06YdWr0z7ICAH7dz+4QIiIKg9q9QEs1oNIB2SdJXU3IMFgcxQj/AM597A4hIqIw8LdWZA8D1DppawkhBoujGJ4rjrNYz3EWREQUDiWrxH0Mja8AGCyOyt9isbW8Ec0Ot8TVEBFRzImxibH8GCyOIjvRgB6JBni8An4rrZe6HCIiiiUttUD1TvE4hy0WcWM4J8oiIqJw8LdWpPYFTCnS1hJiDBbHMCJXDBbrOFEWERGFUuAx09hqrQAYLI4psCBZST0XJCMiotAJTIwVW+MrAAaLY+qXaYVZp4bN4caOCi5IRkREIdB+4bEYG7gJMFgck0qpwLBeiQCA9Zwoi4iIQiGw8FgqkFIgdTUhx2BxHBzASUREIdV+fEWMLDzWHoPFcQQWJNtXB0HgOAsiIjpB/mARYxNj+TFYHMfJvROhUytxsL4VOyttUpdDRERy5rIDe5eJx7lnSFpKuDBYHIdRq8YZhakAgG+2VEhcDRERyVrxT4CrGbD2ENcIiUEMFl1w3oBMAMA3WyslroSIiGRt+5fivt8FMTm+AmCw6JLx/dOhVAB/HGxAWX2r1OUQEZEceT3Ajq/F434XSFtLGDFYdEGKWRcYxMnuECIi6pbStUDzIUCfAPQeI3U1YcNg0UXnDcwAwO4QIiLqJn83SN8/ASqNtLWEEYNFF507QAwWa4prUd/ilLgaIiKSFUEAtn8lHsdwNwjAYNFlvVNM6Jdpgccr4IftVVKXQ0REclK1DagrBlQ6oGC81NWEFYNFEM7ztVp8s4XdIUREFAR/a0XB2YDOLG0tYcZgEYTzBoqPnf608xDsLo/E1RARkWy0f8w0xjFYBGFgthXZCXq0ujz4ZVe11OUQEZEcNBwAyjcBUAB9J0pdTdgxWARBoVAEWi2+2crHTomIqAu2Lxb3vU4DzGnS1hIBDBZB8j92+t22Krg9XomrISKiqLf9C3Hf70Jp64iQoILF3Llzccopp8BisSA9PR2XXHIJduzYEa7aotKpuclIMGhQ2+zEei6lTkREx9JSC+xbIR73O1/aWiIkqGDx008/Yfr06Vi9ejW+/fZbuFwunHfeeWhubg5XfVFHrVJifP90AMAXv5dJXA0REUW1bZ8DggdIHwAk50tdTUSog7l4yZIlHV4vWLAA6enpWL9+PcaOHRvSwqLZpSf3xKcbDuKzjWWYObE/TLqgbiMREcWLX98W90OvkraOCDqhMRYNDQ0AgOTk5KNe43A40NjY2GGTu1H5KchNMcLmcOOL39hqQUREnSjbKD4NotICJ10jdTUR0+1g4fV6cccdd2DMmDEYNGjQUa+bO3cuEhISAltOTk53PzJqKJUKXHVqLwDAB2tLJK6GiIiikr+1ov/FgClF2loiqNvBYvr06di8eTMWLlx4zOtmzpyJhoaGwFZaWtrdj4wqlw3vCa1Kid8PNGDzwQapyyEiomhibwT++Ld4POJ6aWuJsG4FixkzZuDLL7/Ejz/+iJ49ex7zWp1OB6vV2mGLBSlmHSYMEue0eH8NWy2IiKidPz4GXM1Aat+YXiK9M0EFC0EQMGPGDCxatAg//PAD8vLywlWXLFzt6w75fNNB2BxuiashIqKoIAjAel83yPDrAIVC0nIiLahgMX36dLz33nv44IMPYLFYUFFRgYqKCrS2toarvqh2Wn4y8tNMaHZ68J9NB6Uuh4iIosHBDUDFH+JKpnH0NIhfUMFi3rx5aGhowFlnnYWsrKzA9uGHH4arvqimUCgCrRYfrCmBIAgSV0RERJJbP1/cD7wEMB79qclYFXRXSGfbddddF6byot+lJ/eEVq3ElrJG/H6AgziJiOJaaz2w+VPxeHh8Ddr041ohJyjJpMX5gUGc+yWuhoiIJPX7R4CrBUjrJy46FocYLELgmtN6AwC++K0cdc1OiashIiJJeL3Ar2+JxyP+GneDNv0YLEJgRO8kDMy2otXlwZu/7JW6HCIiksK2z4FD2wFdAjDkCqmrkQyDRQgoFArcPr4QALBgxT62WhARxRuvF/jpf8Xj024FDImSliMlBosQOXdABgZkWdHsZKsFEVHc2fY5ULVVbK047Vapq5EUg0WIKBQK3HEOWy2IiOIOWys6YLAIIbZaEBHFoUBrhRU47Rapq5Ecg0UIKRQK3M5WCyKi+HFEa0WStPVEAQaLEDuvXavFW78US10OERGF0/Yv2rVWxPfYCj8GixDr0Gqxkq0WREQxy+sFlrG14nAMFmHgb7WwOdx4dfkeqcshIqJw2LoIqNrC1orDMFiEgUKhwN3n9QUAzP+lGHsO2SSuiIiIQsphA5Y+KB6PmsHWinYYLMJkXL90nF2UBpdHwKzPt3DlUyKiWLL8SaCpDEjsDYy5TepqogqDRZgoFArMunggtGolft5Vja83V0hdEhERhULVdmDVy+Lx+U8BGoO09UQZBosw6p1iwq1nFgAAHv1iK5odbokrIiKiEyIIwOJ7AK8bKDof6DtB6oqiDoNFmN16VgFykg2oaLTjhR92SV0OERGdiM2fAPt+BtR64E9PSF1NVGKwCDO9RoXZFw8EALz1czF2VzVJXBEREXWLvRFY+oB4fMY9QFJvaeuJUgwWETCuXwbO6Z8Bt1fAQ59xICcRkSwtewKwVQDJ+RyweQwMFhHyyEUDoFMrsWpvDd5fUyJ1OUREFIz9q4A188TjiU8Bap209UQxBosIyUk24u8TigAAj321lV0iRERyYW8APr0JELzA0KuAwnOkriiqMVhE0F/H5OGMwlTYXV7c9q9NcLg9UpdERETH89XdQEMJkJQLTHxS6mqiHoNFBCmVCjz9l6FINmmxtbwR/7d0h9QlERHRsfz+EfDHx4BCBUx+A9Bbpa4o6jFYRFi6VY//vXQIAOCNn4vxy65qiSsiIqJO1e0TWysA4Mx/ADmnSlqOXDBYSODcARm4ZmQvAMBdH21CLVdAJSKKLh63OK7C0QjknAaccbfUFckGg4VEHrxgAPqkm1HV5MDdH22Cx8tHUImIosaPjwOla8SVSye/DqjUUlckGwwWEjFoVXj+ypOgUyvx445DeHLJdqlLIiIiAPj9Y+CXZ8TjC5/lRFhBYrCQ0MDsBPzfX4YCAF5bvhcf/1oqcUVERHHuwK/Af6aLx6ffCQy+TNp6ZIjBQmIXDc3GbeP6AAAeWLQZv+6rlbgiIqI41XAQWHg14HEAfScC4x6WuiJZYrCIAnec0xd/GpgJp8eLW95bjwN1LVKXREQUX5wtwMKrAFslkD4AuPQNQMmvyO7gXYsCSqUCz1wxFP2zrKi2OfHf7/wKG5dYJyKKDK8X+OxWoPw3wJgCXPUvQGeRuirZYrCIEkatGm9OHYFUsxbbK5pww4J1aHVyZk4iorASBGDx3cDWzwClBrj8n+IMm9RtDBZRpEeiAfOvOwVmnRprimtx6/vr4XR7pS6LiCg2CQLw7UPAr/MBKIBJrwK5Y6SuSvYYLKLMkJ6JmH/dKdBrlFi24xBuX7gRbg/DBRFRyP30JLDyRfH4ouf5BEiIMFhEoVPzkvH6lBHQqpT4enMF7v3kd3g5gRYRUeisehlYNkc8njAXGD5V2npiCINFlBrbNw0vXj0MKqUCn244iAc+28xwQUQUCmvfAJbeLx6f/QAwapq09cQYBosoNmFgJp7+y1AoFMC/1pbgjg83wcVuESKi7hEEYPn/AYvvEV+Pvg0Y+3dpa4pBnPw8yl0yrAeUSgXu+nATPv+tDE12F165ZjgMWpXUpRERyYcgAN88CKx6SXw99u9ia4VCIW1dMYgtFjJw8dBsvHHtCOg14roi185fg4ZWl9RlERHJg8cN/GdGW6iYMAcY9yBDRZgwWMjE2f3S8c8bRsKiV2Pdvjpc+fpqVDXapS6LiCi6uVqBj6cCm94DFErgz68Ao6ZLXVVMY7CQkVNyk7HwptOQatZiW3kjLn5pBf440CB1WURE0amxDHh7IrD9S0ClFSe/GnaN1FXFPAYLmRmYnYB/3zIaBWkmVDTa8ZfXVuLL38ukLouIKLocWA+8fjZQthEwJANTFgH9L5S6qrjAYCFDuakmLJo+Bmf2TYPd5cWMDzbimW938nFUIiIA+OPfwILzAVsFkNYfuPEHIPd0qauKGwwWMmXVazD/ulNw4xl5AIAXvt+FW99fz0GdRBS/PC7g24eBT24A3Hag75+AG74BkvOkriyuMFjImEqpwAMXDMCTlw2BRqXA0i2VuPDFn/Fbab3UpRERRVZ9KbDgAmDF8+LrMXcAV34A6K2SlhWPGCxiwOUjcvDvW0YjJ9mA0tpWXPbqSrz5814IArtGiCgObF8MvHo6ULoG0FmBv7wDnDsbUHK+HykwWMSIoTmJ+PJvZ+D8wZlweQQ89tU23Pjur6htdkpdGhFReLgdwJKZwMKrAHs9kH0ycPNyYOAlUlcW1xgsYkiCQYOXrz4Z/3PJIGjVSny3rQrnPfsTlm6pkLo0IqLQOrgBeG0ssPoV8fWoGcBfl3I8RRRQCBFuL29sbERCQgIaGhpgtbLvK1y2lDXgzg83YWelDQBwyUnZmHXxQCQatRJXRkR0AtwOcbnzX54FBA9gSgMufgko+pPUlcW8rn5/M1jEMIfbg+e/24VXf9oDrwCkWXSYO2kwzhmQIXVpRETBK9sEfDYNqNoivh50KTDxKcCUImlZ8YLBggI2ltThno9/w55DzQCACQMz8MhFA5GdaJC4MiKiLrA3Aj8+Dqx9HRC8gDEFuOAZjqWIMAYL6sDu8uDZ73birZ+L4fYKMGpVuH18If56eh40Kg61IaIoJAjAlk+BJfeLk10BwMDJwMQnAXOatLXFIQYL6tSOiiY8+NkfWLevDgDQN8OMWRcPxOiCVIkrIyJqp2o7sOQ+YO+P4uvkfOD8/wP6jJe2rjjGYEFH5fUK+PeGA5i7eBvqWsSZOs/pn4GZ5/dDQZpZ4uqIKK7ZDgHL5gLrF4iDM1U64Iy7gTG3Axq91NXFNQYLOq66Ziee/W4n3l9TAo9XgFqpwH+d1hu3jS9EsolPjxBRBLnswJp5wM/PAI5G8Vy/C4FzHwVSCqStjQAwWFAQdlfZMHfxNny/vQoAYNGpcePYfFw/JhcWvUbi6ogopnlcwKYPxEdIGw+I57KGAhPmcOGwKMNgQUFbsbsaj321DdvKxf9bSDJqcMuZBbh2VC4MWk6NS0Qh5PWIq5AumwvUFYvnLNnAOY8Agy8HlBxUHm0YLKhbvF4BX/1Rjme/24m9vsdTU8063HJmPq4e2QtGrVriColI1jxuYMsiYPlTQPUO8ZwxVRxHMeJ6QMPH4KMVgwWdELfHi882leG573biQF0rALEF4/oxeZg6KhcJRnaREFEQ3A6xy2PFc0DdPvGcPlEclHnqTYCOA8ejHYMFhYTT7cWnGw5g3k97sL+mBQBg0qrwX6f1xnVjcpGVwP+7IKJjsDcAG94FVr0MNJWL5wzJwGm3AiNvBvQJ0tZHXcZgQSHl9nixeHMFXvlxN7ZXNAEA1EoFzh+chRtOz8PQnERpCySi6FJbDKx5Ddj4T8AprlkESzYw+m/A8KmA1iRtfRQ0BgsKC0EQ8MP2Kry+fC/WFNcGzo/onYTrx+ThvIEZnMmTKF4JArDvZzFQbP8KgO/rJa0fcNo0YOiVgFonaYnUfQwWFHabDzZg/i/F+OL3Mrg84l+jdIsOV56Sg6tG9mI3CVG8aK0HflsI/Dq/bUAmABSMB0ZNE/cKhWTlUWgwWFDEVDba8d7q/fjX2lJU2xwAAJVSgfH90nHlqTkYW5gGNVsxiGKLIAAHfgU2vis+NuoSx2BBYwKGXA6MvAVI7ydtjRRSDBYUcU63F99srcB7q/dj9d62bpIMqw6XntwTl4/IQW4q+1WJZM12CPh9IbDxPeDQ9rbz6QOAEX8FhlwB6PlveywKW7BYvnw5nnrqKaxfvx7l5eVYtGgRLrnkkpAXRvK2q7IJC9eVYtHGg6htdgbOn5KbhEnDeuKCwVl8ZJVILlx2YOcS4PcPgV3fAF63eF5tEJcuP/laoNcodnfEuLAFi6+//horVqzA8OHDMXnyZAYLOian24vvt1Xiw19LsXznIXh9f9u0KiXO7peGScN64KyidOg1nNmTKKp4PcD+FcDvHwFb/9O2fgcA9BgODJsCDJrMx0XjSES6QhQKBYMFdVlFgx2f/3YQizaWBaYNBwCzTo1zB2TggsFZOKNvKnRqhgwiSXi9QOlqcWbMrf8BbJVt7yXkAIP/Io6fSO8vXY0kma5+f4d9fmaHwwGHw9GhMIpPmQl63DS2ADeNLcD2ikZ8trEMn286iLIGOxZtPIhFGw/CohdDxp8GZmJs3zS2ZBCFm8cNlKwCtn8phgn/JFaAODPmgIuBIVeKXR1cv4O6IOwtFrNmzcLs2bOPOM8WCwLEtUk2ltbjy9/LsPiPclQ2toVQg0aFs4rSMGFgJs4uSueYDKJQcbUCe38Ctn8B7PgaaKlpe09nFZcrHzgJyD8LUGslK5OiS9R0hXTWYpGTk8NgQUfwegX8ur8OSzZXYOmWChysbw28p1IqcEpuEs7pn4Hx/TOQx6dLiILTVAnsWioGib3L2h4PBQBDElB0PtD/IqBgHCexok5FTbDobmEU3wRBwOaDjVi6RQwZu6psHd7PTzXhzKI0nFWUjpF5yewyITqc1wMcXA/s+hbY/R1QtqHj+9YebWGi9xhAxZWL6diiZowFUXcoFAoM7pmAwT0TcM+EIpTUtOC7bZX4fnsl1uytxd7qZuytbsbbK/ZBr1FiVH4KzihMwxmFqeiTboaCj71RPGo4AOz5Edjzg7jZ6zu+nz0M6DsRKJoIZA7m46EUFkEHC5vNht27dwdeFxcXY9OmTUhOTkavXr1CWhyRX68UI/56eh7+enoeGu0urNhVjWU7DuGnnYdQ0WjHjzsO4ccdhwAAmVY9xvRJxRmFqRhdkIJ0q17i6onCpLUe2L9S7NrY+yNQvbPj+/oEIP9soPBcoM85gCVTiiopzgTdFbJs2TKcffbZR5yfOnUqFixYcNyfZ1cIhZIgCNhR2YSfdhzCL7ursaa4Fk63t8M1BWkmjCpIwaj8VJyWn4wUM/uPSaYcTUDJGmDfcqB4OVD+GyC0+/uuUALZJwMFZ4tBoscIdnFQyHBKb4pLdpcH6/bV4pdd1Vixpxpbyhpx+N/wPulmnJqXjJF5yRiZl4LMBLZoUJRqrQNKVgP7fhFbJsp/AwRPx2uSC4D8M8WWibwzxIGYRGHAYEEEoL7FiTXFtVi1pwar9tRgR2XTEdf0TDJgRO8kDM9NxojeSeibYYFKyb5nijBBAOr2AaVrxHklStYAh7YdeV1iLyB3LJA3VgwS1uyIl0rxicGCqBN1zU6s3VeLtcW1WFNcg61ljYFpxv0sOjWG5iTi5F6JGNY7CcNyEpFo5LP8FGLOFqBsI3BgLVC6DjiwDmiuOvK6lD7iUxu9xwC9RwOJOZGvlQgMFkRd0mR3YVNpPdbtq8P6/bXYWFKPFqfniOvyU00YmpOIoT0TMDQnEf2zrHzElbrO6xFXAj24vm2r3Hpkt4ZSA2SfBOSMBHqdBuScBpjTJCmZ6HAMFkTd4PZ4saOyCRtK6rGxpA4bS+pRXN18xHUalQJ9MywY0jMBg3skYkjPBPTNsECr5pTHcc/rAWr2iK0R/q3i944TUvlZsoCep4hbzqlA1lBAY4h8zURdwGBBFCJ1zU78dqAev5U2+Pb1qGm3FLyfRqVAYboFg3pYMTA7AYN6WFGUaYVZx1H5McvtEFsiyn8Xw0P5b0DFZsB1ZBiF1izOI9FjuG87WZykinNJkEwwWBCFiSAIOFjfis0HG/D7gQb84ds3tLo6vT43xYgB2Vb0z7Sif5YV/bIs6JFo4CRecmM7BFRuBiq3ABV/iFv1DsDrPvJatUFsfcge5ttOEsdKKNl9RvLFYEEUQYIg4EBdK7aUNWJLWQM2H2zA1vLGDouqtWfRqVGUaUG/LAuKMizom2FBUaaFg0SjgcMGHNoBVG0FqrYBVVvEMNF8qPPr9YniLJZZQ9s2hgiKQQwWRFGgxubAtvImbC1vwNayRmyvaMKeQza4PJ3/Z5du0aFvhgV90s0ozDCjMN2CwnQzkkwMHCHnsIkzVR7a7tt2iEGifv9RfkABJOcDGQOAzCFimMgczO4MihsMFkRRyun2Ym+1DTsqmrCtvAm7Kpuwo7IJB+paj/ozKSYtCtLNKEgzo0+6GQVpJhSkmdEj0QAl59w4OkEAbJVA9S4xRPi3QzuBxgNH/zlzBpDeH0gfAKT1AzIHiXstV9Wl+MVgQSQzNocbOyubsLvKhl2VTdhVZcOuSluH5eMPp1MrkZdqQn6aCfmpZuSlmpCbakJ+qim+WjkcNqB2D1CzW3wio2a3GCZqdgOOxqP/nClNDAxp/YC0InGf3h8wpUaudiKZYLAgihHNDjf2HmrGnkM27K4St73VNuyrboHT4z3qzyUYNMhNNSEvxYjeKSbkpZrQ23ecZNTIb/CoowmoLQZq9/q2PUCNb2+rPPrPKZRAYm8gtRBI7SsGiNQi8bUxOXL1E8kcgwVRjPN4BRyoawmEjuLq5sBW3mA/5s9a9GoxZCSbkJNsRK92W1aiHhqVBPNxeL2ArUKc1rr95g8TLdXH/nljqjhoMqUPkJIPpBSK4SE5H1Bz4TmiE8VgQRTHWpxu7K9pwf6aZhRXt2BfdTOKa5pRUtOCisZjhw6VUoGsBD1ykozISTb49kb0TDKgZ5IR6RZd98Z1CALQUiMOjqwvEbe6/WJ4qN8P1JcCns6fogkwpgLJeWJYSC4Q9yn54p6LbxGFFYMFEXXK7vKgtLYF+2paUFLbgtJaMYCU1LagtK71iGXnD6dVKZGVqEePRIO4Jfn2Vg16aRuR7j0Era0MaCgRw0JDadu+s9kn21OoxLUwknI7bsn5QFIeoOe/GURS6er3N6cEJIozeo0KhRkWFGZYjnjP6xVwyOYIBI7S2laU1rXgYG0LbPXlUDSWI0OoRlZ9DbIbapFVUoMsRQ2yFTXIRC3UimOHEgEKeM2ZUCb1hiKxl7hSZ1JvcQxEUm/A2hNQ8Z8lIjnjf8FE8c7jEgc/NpZD2VSGjMZyZDQexClN5UBjGdB4UNx7nIDm2L+US1ChXEhGGVJxUEjFAUHc+7cyIRVOuwaqWgXSLTpkJuiRlaBHptWArAQvMhKqkGnVI9OqR7pVx4XeiGSIwYIoVnk94myRTRVicGiq8G3lvn2Z770qAF3pEVUA5nTAmi1OCpWQAyT0ABJ6ii0NCT2hNqXBbPfCVNcKc0MrEupb0dxgR0t9K+wNdrga7KhotMPjFVDeYEd5gx0bj/GJiUaNL2TokWnVIcN3nG7RId0ivk4167j4G1EUYbAgkhtnsxgUbFUd9/4AYasEmiqB5ipAOHbXRIBSLa60acnyBYd2myVbDBDmTEB97LkxFACSTUCySYvBPRM6vcbjFVBtc6CsvhWVjWK4qGi0o8IXNCp9xw63F/UtLtS3uLC9oumYn5ts0iLdokOab0u36Du8TrPokGrWwapXy+8xWyKZYbAgkpogiHM0NB8SN1vVYcdVvvDg2zpbOfNoFEpxEihLphgMrL7wYMlst88GjCmAMjL/169SKpBh1SPDqj/qNYIgoLHVjYpGMWhUNtpR1eQIhI6qJgeqGu04ZHPA5RFQ2+xEbbPzuAFEq1YizaxDqkWHNLMWqWadb9Mi1dLu2KxDgkGGc30QRQEGC6JwcLaI8y40+7bA8SHxkUt/cPCfcx/7EdAjqA2AJUOcetqcDpjSfeEhw7dPF4OEKU2WgyEVCgUSjBokGDUoyjxykKmf1yugrsUpBo0mBw41OVDVZEdVowOHbOLr6ibxuMnuhtPtxcH61mPOZuqnViqQYtYixaTz7bVIMbcdJ5vaH2th1rE1hAhgsCA6Po8LaK0TA0FLDdBS69tXtzuu8QUI3/HxHqvsjNYsTiVtShcDgTlNPDant4UHs+89nYULXwFQKhW+L3sd+mcd+1q7yyMGDV/gOGRzoLrJiZpm8Vx1k1Pc2xxotLvh9gqobHQcdYXaw2lVSiSZNGLgMGmRZNIi2Si+TjZpfK+1SDSKQSTRqOHgVIpJDBYUPwRBHJ/QWusLCr59ay3Q4t/X+vb+AFELOBq693kqnRgUjCliGDClivvA63bnTGmA1hja3y91oNeokJMsTvZ1PA63B7XNzkDwqLGJXS3V7Y5rbA7U+LpgWpweOD3eoIIIABi1KiQZtUgyacS9UYskowaJvn2SSQwiiQbx/QSjhuNEKOoxWJD8eNyAvQGw1wOt9WI4sPv2R7yu6xgivK5ufqgCMCSKocCYAhiSAVNK22tjijgrZCBIpIotEPwCkCWdWoWsBAOyEgxdur7V6UFtixO1NjGI+Md81LU4UdvsQl271+LmgscroMXpQYuza10zfiqlAgkGDRINYldRklGLBINGPGfsuBe3tvf59AxFAoMFRZ4giGMK7A3i1lrfdmyvbwsM9vp279cDrb73j7VaZVeotGIwMCSJi1AF9snt9imHnUsClGy2ps4ZtCr00IozkHaF1yugye5uFzScqGt2oa7FifqWtn1tsxP1rS40+MJIq8sDj7dtsGrQdWpUgeBh9YUNq74thFgN6sA562GvjVoVW0qoSxgsKHget/jl7mgE7O329oZ2x/W+44bD3vMFCE/w/ygeQWsWv/D1iWJrgiGpbR841y446BPFvcbIlgSSlFLZNjg1F6Yu/5zd5UFDq8v3GK4YOupbnIFzDa0uXxDxHzvR0OJCo90NAGh1edDq8hx3vZjOqJQKWPVqWPRi4LDqNbDo/XvxnKXdOf+1Fr3at7HFJF4wWMQTrxdw2sRHG/2bs8kXDvznfMf2ho7n2geIYB53PBaFEtBZxQCgTwT0CW1b+3Ptg0LgXCKgOs40kEQxRq9RQa9RHfNR3c54vAKa7GLYaL81troPe+1Co10MIo2+c012F1weAR6vgLoWF+pautudCOjUSjGEtAsbZp14bPYHEZ3/WN32nk4Dk04Fi2+vlmL1XeoyBoto5/X4woCt3d7/hW9rCwqBwGATA8DhAcL/c6Gk1ovBQJ8gLg7V6XGi+Fqf0C5E+MIDxyAQRYRKqRAHgRqPPcFZZwRBgN3lFQNHIGy4OwSQRrvvnO+9JnvbNU12N1qcHgCAw+2Fw/fkzYkwaFRi+NCpYdKJAcTsCyLmwDlVu2Nx3+FarZohJUwYLEJJEABXq/jkgdPm23d23C4oBI47e6+5e48tHo9SLX7J6yy+rd2x3ip+4eutgC6h43mdtW2vsx53FkYikj+FQgGDVgWDNviWEj+3xwubw+0LHW3Bw+Zwo8khvrb53vNfZ3O0XWOzi9f5V971d+kcajqxgAKIk6aJwUMFk7YtgJi0qo5737FRp4ZJq4bRd71RKwYY/2uDRgWlMr7/hyk+g4XXI35hO1vEZn1ni++1rd1xc9sXu/+8s9l3/dE2G7q25kI3KNXiF77O4tu3P7aKr/3v63znAq8P29R6thQQUcSoVcput5i053B7YLO70ezwiIHDH0oc/nMu2Bz+a9ywOd1txw43mp1tP+sPKU63F7VuJ2pD1MMLiI8RG32hw+gLJoFjXyhp/75B6wstvmP/ef81Bq0KRo18WldiJ1j88Lg4/0CHsNB6WHDwBYVgZznsDo1JnJdA6/vC15raNp3Fd2zu5LW5XUgwA1rfe2odwwARxTWdWgWdWYUU84n/Wi6Pty1w+MJGi1MMIc0OD5qd4nstvuNmhxvNTg+a251rcYo/1+oUXwu+/68UHyP2nHiRh9GqlGLI8IcNrQpGjRp6X/DwnzdoVPjbuEIkGKUZhxY7wWLDO+LiS0FRiE8IaH0hQGNqO9aa271n6ngceH1YYPCHA40xYusuEBFR8DQhakXx849F8YcQMVyIIaXFF0KanR60Ot2B4NHiFENKiy+YtLY/3+7Y6wssTo8XzlYvGlqPP4D2pjPzATBYnJiRNwNuB6AxtLUWdDj2741twUBjYCsAERGdsPZjUVLNupD9uoIgwOH2iqHD1TGY+INIa7vz4rEHVr10T83FTrA4426pKyAiIgophUIReMw4Sepiuojt9URERBQyDBZEREQUMgwWREREFDIMFkRERBQyDBZEREQUMgwWREREFDIMFkRERBQyDBZEREQUMgwWREREFDIMFkRERBQyDBZEREQUMgwWREREFDIMFkRERBQyEV/dVBDEheUbGxsj/dFERETUTf7vbf/3+NFEPFg0NTUBAHJyciL90URERHSCmpqakJCQcNT3FcLxokeIeb1elJWVwWKxQKFQhOzXbWxsRE5ODkpLS2G1WkP261JHvM+Rw3sdGbzPkcH7HBnhvM+CIKCpqQnZ2dlQKo8+kiLiLRZKpRI9e/YM269vtVr5lzYCeJ8jh/c6MnifI4P3OTLCdZ+P1VLhx8GbREREFDIMFkRERBQyMRMsdDodHnnkEeh0OqlLiWm8z5HDex0ZvM+RwfscGdFwnyM+eJOIiIhiV8y0WBAREZH0GCyIiIgoZBgsiIiIKGQYLIiIiChkZBUsXn75ZeTm5kKv12PkyJFYu3btUa9dsGABFApFh02v10ewWvkK5j4DQH19PaZPn46srCzodDr07dsXixcvjlC18hXMfT7rrLOO+PusUChwwQUXRLBi+Qr27/Rzzz2HoqIiGAwG5OTk4M4774Tdbo9QtfIVzH12uVx49NFHUVBQAL1ej6FDh2LJkiURrFaeli9fjosuugjZ2dlQKBT47LPPjvszy5Ytw8knnwydToc+ffpgwYIF4S1SkImFCxcKWq1WmD9/vrBlyxbhxhtvFBITE4XKyspOr3/77bcFq9UqlJeXB7aKiooIVy0/wd5nh8MhjBgxQjj//POFX375RSguLhaWLVsmbNq0KcKVy0uw97mmpqbD3+XNmzcLKpVKePvttyNbuAwFe6/ff/99QafTCe+//75QXFwsLF26VMjKyhLuvPPOCFcuL8He53vvvVfIzs4WvvrqK2HPnj3CK6+8Iuj1emHDhg0RrlxeFi9eLDzwwAPCp59+KgAQFi1adMzr9+7dKxiNRuGuu+4Stm7dKrz44ouCSqUSlixZErYaZRMsTj31VGH69OmB1x6PR8jOzhbmzp3b6fVvv/22kJCQEKHqYkew93nevHlCfn6+4HQ6I1ViTAj2Ph/u2WefFSwWi2Cz2cJVYswI9l5Pnz5dGDduXIdzd911lzBmzJiw1il3wd7nrKws4aWXXupwbvLkycI111wT1jpjSVeCxb333isMHDiww7krrrhCmDBhQtjqkkVXiNPpxPr163HOOecEzimVSpxzzjlYtWrVUX/OZrOhd+/eyMnJwZ///Gds2bIlEuXKVnfu8+eff45Ro0Zh+vTpyMjIwKBBgzBnzhx4PJ5IlS073f373N5bb72FK6+8EiaTKVxlxoTu3OvRo0dj/fr1gWb8vXv3YvHixTj//PMjUrMcdec+OxyOI7qnDQYDfvnll7DWGm9WrVrV4c8FACZMmNDlf2u6QxbBorq6Gh6PBxkZGR3OZ2RkoKKiotOfKSoqwvz58/Gf//wH7733HrxeL0aPHo0DBw5EomRZ6s593rt3L/7973/D4/Fg8eLFeOihh/D000/jsccei0TJstSd+9ze2rVrsXnzZvz3f/93uEqMGd2511dffTUeffRRnH766dBoNCgoKMBZZ52F+++/PxIly1J37vOECRPwzDPPYNeuXfB6vfj222/x6aefory8PBIlx42KiopO/1waGxvR2toals+URbDojlGjRuHaa6/FSSedhDPPPBOffvop0tLS8Nprr0ldWkzxer1IT0/H66+/juHDh+OKK67AAw88gFdffVXq0mLWW2+9hcGDB+PUU0+VupSYtGzZMsyZMwevvPIKNmzYgE8//RRfffUV/ud//kfq0mLK888/j8LCQvTr1w9arRYzZszA9ddff8zluEkeIr5senekpqZCpVKhsrKyw/nKykpkZmZ26dfQaDQYNmwYdu/eHY4SY0J37nNWVhY0Gg1UKlXgXP/+/VFRUQGn0wmtVhvWmuXoRP4+Nzc3Y+HChXj00UfDWWLM6M69fuihhzBlypRAi9DgwYPR3NyMm266CQ888AC/+DrRnfuclpaGzz77DHa7HTU1NcjOzsZ9992H/Pz8SJQcNzIzMzv9c7FarTAYDGH5TFn8F6LVajF8+HB8//33gXNerxfff/89Ro0a1aVfw+Px4I8//kBWVla4ypS97tznMWPGYPfu3fB6vYFzO3fuRFZWFkPFUZzI3+ePP/4YDocD//Vf/xXuMmNCd+51S0vLEeHBH5wFLq3UqRP5O63X69GjRw+43W588skn+POf/xzucuPKqFGjOvy5AMC3337b5e/ObgnbsNAQW7hwoaDT6YQFCxYIW7duFW666SYhMTEx8AjplClThPvuuy9w/ezZs4WlS5cKe/bsEdavXy9ceeWVgl6vF7Zs2SLVb0EWgr3PJSUlgsViEWbMmCHs2LFD+PLLL4X09HThsccek+q3IAvB3me/008/XbjiiisiXa6sBXuvH3nkEcFisQj/+te/hL179wrffPONUFBQIFx++eVS/RZkIdj7vHr1auGTTz4R9uzZIyxfvlwYN26ckJeXJ9TV1Un0O5CHpqYmYePGjcLGjRsFAMIzzzwjbNy4Udi/f78gCIJw3333CVOmTAlc73/c9O9//7uwbds24eWXX+bjpu29+OKLQq9evQStViuceuqpwurVqwPvnXnmmcLUqVMDr++4447AtRkZGcL555/P56O7KJj7LAiCsHLlSmHkyJGCTqcT8vPzhccff1xwu90Rrlp+gr3P27dvFwAI33zzTYQrlb9g7rXL5RJmzZolFBQUCHq9XsjJyRGmTZvGL7wuCOY+L1u2TOjfv7+g0+mElJQUYcqUKcLBgwclqFpefvzxRwHAEZv/3k6dOlU488wzj/iZk046SdBqtUJ+fn7Y57/hsulEREQUMrIYY0FERETywGBBREREIcNgQURERCHDYEFEREQhw2BBREREIcNgQURERCHDYEFEREQhw2BBREREIcNgQURERCHDYEFEREQhw2BBREREIcNgQURERCHz/2ld1t1LjFoFAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(tprs, auc_max_grad_, label='max_grad')\n", + "plt.plot(tprs, auc_rmin_grad_, label='rmin_grad')\n", + "plt.plot(tprs, auc_maxa_grad_, label='maxa_grad')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(tprs, acc_rmax_grad_, label='rmax_grad')\n", + "plt.plot(tprs, macc_min_grad_, label='macc_min')\n", + "plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlscorecheck", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/development/046-auc-average-curve-area.ipynb b/notebooks/development/046-auc-average-curve-area.ipynb new file mode 100644 index 0000000..dbda482 --- /dev/null +++ b/notebooks/development/046-auc-average-curve-area.ipynb @@ -0,0 +1,526 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mlscorecheck.auc import integrate_roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [], + "source": [ + "def triangle_center(point0, point1, point2):\n", + " return (point0 + point1 + point2)/3.0\n", + "\n", + "def curve(point0, point1, point2, max_depth=3, depth=0, points=[], upper=False):\n", + " if depth == max_depth:\n", + " return\n", + " center = triangle_center(point0, point1, point2)\n", + " points.append(center)\n", + "\n", + " pointa = np.array([0, center[1]])\n", + " pointb = np.array([center[0], 1])\n", + "\n", + " if depth == 0 or not upper:\n", + " curve(point0, pointa, center, depth=depth+1, points=points, max_depth=max_depth, upper=False)\n", + " if depth == 0 or upper:\n", + " curve(center, pointb, point2, depth=depth+1, points=points, max_depth=max_depth, upper=True)\n", + "\n", + " return points" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [], + "source": [ + "points = curve(np.array([0, 0]), \n", + " np.array([0, 1]), \n", + " np.array([1, 1]), \n", + " max_depth=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [], + "source": [ + "points = np.vstack(points + [np.array([0, 0]), np.array([1, 1])])" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 280, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "plt.scatter(points[:, 0], points[:, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": {}, + "outputs": [], + "source": [ + "points_sorted = points[np.argsort(points[:, 0])]" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07407407407407407" + ] + }, + "execution_count": 282, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "integrate_roc_curve(points_sorted[:, 0], points_sorted[:, 1]) - 2/3" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = []\n", + "res = 50\n", + "for idx, value in enumerate(np.linspace(1/res, 1.0, res)):\n", + " y_pred.append(np.repeat(value, idx+1))\n", + "y_pred = np.hstack(y_pred).round(3)\n", + "#y_pred = np.hstack([y_pred, 1 - y_pred]).round(3)\n", + "count = len(y_pred)\n", + "y_pred = np.hstack([y_pred, np.linspace(1/res, 1.0, res)]).round(3)\n", + "y_true = np.hstack([np.repeat(1, count),np.repeat(0, res)])" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.02, 0.04, 0.04, ..., 0.96, 0.98, 1. ])" + ] + }, + "execution_count": 284, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.6633333333333333)" + ] + }, + "execution_count": 285, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import roc_auc_score, roc_curve\n", + "roc_auc_score(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7853981633974483" + ] + }, + "execution_count": 286, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.pi/4" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "662.5" + ] + }, + "execution_count": 287, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(y_true)/2" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": {}, + "outputs": [], + "source": [ + "fprs, tprs, ths = roc_curve(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ inf, 1. , 0.98, 0.96, 0.94, 0.92, 0.9 , 0.88, 0.86, 0.84, 0.82,\n", + " 0.8 , 0.78, 0.76, 0.74, 0.72, 0.7 , 0.68, 0.66, 0.64, 0.62, 0.6 ,\n", + " 0.58, 0.56, 0.54, 0.52, 0.5 , 0.48, 0.46, 0.44, 0.42, 0.4 , 0.38,\n", + " 0.36, 0.34, 0.32, 0.3 , 0.28, 0.26, 0.24, 0.22, 0.2 , 0.18, 0.16,\n", + " 0.14, 0.12, 0.1 , 0.08, 0.06, 0.04, 0.02])" + ] + }, + "execution_count": 289, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ths" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0. , 0.03921569, 0.07764706, 0.11529412, 0.15215686,\n", + " 0.18823529, 0.22352941, 0.25803922, 0.29176471, 0.32470588,\n", + " 0.35686275, 0.38823529, 0.41882353, 0.44862745, 0.47764706,\n", + " 0.50588235, 0.53333333, 0.56 , 0.58588235, 0.61098039,\n", + " 0.63529412, 0.65882353, 0.68156863, 0.70352941, 0.72470588,\n", + " 0.74509804, 0.76470588, 0.78352941, 0.80156863, 0.81882353,\n", + " 0.83529412, 0.85098039, 0.86588235, 0.88 , 0.89333333,\n", + " 0.90588235, 0.91764706, 0.92862745, 0.93882353, 0.94823529,\n", + " 0.95686275, 0.96470588, 0.97176471, 0.97803922, 0.98352941,\n", + " 0.98823529, 0.99215686, 0.99529412, 0.99764706, 0.99921569,\n", + " 1. ]),\n", + " array([0. , 0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 ,\n", + " 0.22, 0.24, 0.26, 0.28, 0.3 , 0.32, 0.34, 0.36, 0.38, 0.4 , 0.42,\n", + " 0.44, 0.46, 0.48, 0.5 , 0.52, 0.54, 0.56, 0.58, 0.6 , 0.62, 0.64,\n", + " 0.66, 0.68, 0.7 , 0.72, 0.74, 0.76, 0.78, 0.8 , 0.82, 0.84, 0.86,\n", + " 0.88, 0.9 , 0.92, 0.94, 0.96, 0.98, 1. ]))" + ] + }, + "execution_count": 290, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tprs, fprs" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00000000e+00, 7.84313725e-04, 2.35294118e-03, 4.70588235e-03,\n", + " 7.84313725e-03, 1.17647059e-02, 1.64705882e-02, 2.19607843e-02,\n", + " 2.82352941e-02, 3.52941176e-02, 4.31372549e-02, 5.17647059e-02,\n", + " 6.11764706e-02, 7.13725490e-02, 8.23529412e-02, 9.41176471e-02,\n", + " 1.06666667e-01, 1.20000000e-01, 1.34117647e-01, 1.49019608e-01,\n", + " 1.64705882e-01, 1.81176471e-01, 1.98431373e-01, 2.16470588e-01,\n", + " 2.35294118e-01, 2.54901961e-01, 2.75294118e-01, 2.96470588e-01,\n", + " 3.18431373e-01, 3.41176471e-01, 3.64705882e-01, 3.89019608e-01,\n", + " 4.14117647e-01, 4.40000000e-01, 4.66666667e-01, 4.94117647e-01,\n", + " 5.22352941e-01, 5.51372549e-01, 5.81176471e-01, 6.11764706e-01,\n", + " 6.43137255e-01, 6.75294118e-01, 7.08235294e-01, 7.41960784e-01,\n", + " 7.76470588e-01, 8.11764706e-01, 8.47843137e-01, 8.84705882e-01,\n", + " 9.22352941e-01, 9.60784314e-01])" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.cumsum(np.arange(res)) / ((res + 1)*res/2)" + ] + }, + { + "cell_type": "code", + "execution_count": 292, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.03921569, 0.07764706, 0.11529412, 0.15215686, 0.18823529,\n", + " 0.22352941, 0.25803922, 0.29176471, 0.32470588, 0.35686275,\n", + " 0.38823529, 0.41882353, 0.44862745, 0.47764706, 0.50588235,\n", + " 0.53333333, 0.56 , 0.58588235, 0.61098039, 0.63529412,\n", + " 0.65882353, 0.68156863, 0.70352941, 0.72470588, 0.74509804,\n", + " 0.76470588, 0.78352941, 0.80156863, 0.81882353, 0.83529412,\n", + " 0.85098039, 0.86588235, 0.88 , 0.89333333, 0.90588235,\n", + " 0.91764706, 0.92862745, 0.93882353, 0.94823529, 0.95686275,\n", + " 0.96470588, 0.97176471, 0.97803922, 0.98352941, 0.98823529,\n", + " 0.99215686, 0.99529412, 0.99764706, 0.99921569, 1. ])" + ] + }, + "execution_count": 292, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((((res + 1)*res/2) - np.cumsum(np.arange(res))) / (((res + 1)*res/2)))[::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1. , 0.96193787, 0.92762907, 0.89689273, 0.86955171,\n", + " 0.84543253, 0.8243654 , 0.80618424, 0.79072664, 0.77783391,\n", + " 0.76735102, 0.75912664, 0.75301315, 0.74886659, 0.74654671,\n", + " 0.74591696, 0.74684444, 0.7492 , 0.75285813, 0.75769704,\n", + " 0.76359862, 0.77044844, 0.77813579, 0.78655363, 0.79559862,\n", + " 0.80517109, 0.81517509, 0.82551834, 0.83611226, 0.84687197,\n", + " 0.85771626, 0.86856763, 0.87935225, 0.89 , 0.90044444,\n", + " 0.91062284, 0.92047612, 0.92994894, 0.93898962, 0.94755017,\n", + " 0.95558631, 0.96305744, 0.96992664, 0.97616071, 0.9817301 ,\n", + " 0.986609 , 0.99077524, 0.99421038, 0.99689965, 0.99883199,\n", + " 1. ])" + ] + }, + "execution_count": 293, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tprs**2 + (1 - fprs)**2" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9818181818181818" + ] + }, + "execution_count": 294, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "54/55" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ 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b+NgcWQmDkGySvqoGU9dlIEdbDrWnCp/PvAv+Xu5Sl0UOikFINqeqxoiZnx7AkUIdOrRVYsNMTqVF1sUgJJtSazRh9sZDyDhzCR4qF3z2eCR6+nJSVbIuBiHZDCEE5m89gl3HtVC6yJE8LQIDAr2kLoucAIOQbMabqdn44sB5yGXA+48Mxl3dO0pdEjkJBiHZhOQf8rDmv6cBAMsfHIjY/n4SV0TOhEFIktty8DzeSDkBAJg3JgQPDw2SuCJyNgxCktSPp0ow96tfAQBPjOiGp/7QXeKKyBkxCEkyx4p0eOafB1FrErg/LADxY/pCJpNJXRY5IQYhSeL85UpM/+QXlFfXIrp7R7z914GQyxmCJA0GIbU6XWUNHvvkFxSXVaOP2gNrpoRD5aKQuixyYgxCalVVNUY88Y8DyC0uh5+nGz6ZPpQzyZDkGITUaoQQmPvVr+anRtY/PhQB3nx+mKTHIKRW8+6uHGw/XAQXuQx/nxKOED9PqUsiAsAgpFbydeZ5/O27XADAsgdDMaynj8QVEd3AICSr+znvIuZtqRsr+Owfe+DhCA6YJtvCICSrOl1Sjqf+kYkao8C4UH+8NLqP1CUR3YRBSFZzucKAx9f/At3VGgzu4o13Hg7jWEGySQxCsgpDrQlP/zMT565Ns588NQJurhwrSLaJQUgtTgiBhO3HsP/MJbRTuWDdY0O51gjZNAYhtbjP0s9hY0Y+ZDLgb48MQm81Z5gm28YgpBb146kSvPbNcQBA/JgQ3BOilrgiottjEFKLySspx6wNB2E0CTw4JBBPjOCUWmQfmhWESUlJCA4OhpubG6KiopCRkXHL9leuXMGsWbPg7+8PlUqF3r17IyUlpVkFk23SXa3BzM8OQF9ViyFdvLHsz6GcUovshoulB2zevBlxcXFYs2YNoqKisGrVKsTGxiI7Oxu+vr43tTcYDLj33nvh6+uLr776CoGBgTh37hy8vb1bon6yAUaTwAubDiGvpAIBXm5YMyWcd4jJrsiEEMKSA6KiojB06FCsXr0aAGAymRAUFITZs2dj3rx5N7Vfs2YN3n77bZw8eRKurs2bZUSv18PLyws6nQ6ennw+1das2JmN1d/nQuUix9fPDOPKc2QzmpodFn01NhgMyMzMRExMzI0XkMsRExOD9PT0Bo/Zvn07oqOjMWvWLKjVagwYMADLli2D0Whs9H2qq6uh1+vrbWSbUo9ewOrv654hfvMvAxmCZJcsCsLS0lIYjUao1fXvBKrVamg0mgaPycvLw1dffQWj0YiUlBQsWrQI77zzDl5//fVG3ycxMRFeXl7mLSiIz6baomxNGeK+OAwAmHF3N0wYHChxRUTNY/W7xiaTCb6+vvjoo48QHh6OiRMnYsGCBVizZk2jx8THx0On05m3goICa5dJFtJV1uDJfxxApcGIYT06In5MiNQlETWbRTdLfHx8oFAooNVq6+3XarXw82t4HVp/f3+4urpCobhx8bxv377QaDQwGAxQKpU3HaNSqaBS8UkEW2U0CfzfpkM4d7ESgd7uWD1pCFwUHIlF9sui316lUonw8HCkpaWZ95lMJqSlpSE6OrrBY4YPH47c3FyYTCbzvpycHPj7+zcYgmT73tudg//mlMDNVY6PpoajQ1t+jmTfLP7feFxcHJKTk/Hpp5/ixIkTeOaZZ1BRUYHp06cDAKZOnYr4+Hhz+2eeeQaXLl3C888/j5ycHOzYsQPLli3DrFmzWq4X1Gq+O6k1T7C6/MGB6B/AmyNk/yweRzhx4kSUlJRg8eLF0Gg0GDRoEFJTU803UPLz8yGX38jXoKAg7Ny5E3PmzMHAgQMRGBiI559/Hq+88krL9YJaRf7FSrywKQsAMDW6K2+OkMOweByhFDiOUHpVNUY8+ME+HL+gx+Au3tj8ZDSULrwuSLbNKuMIyTkJIbBw21Ecv6BHx7ZKfDB5CEOQHAp/m+m2NmYU4KvM85DLgPcfGQx/Ly7BSY6FQUi3dLRQhyXbjwEAXo4N4epz5JAYhNQofVUNnt1wEAajCff2U+PpkZxWixwTg5AaJITA3C9/Rf6lujVHVjwUxmm1yGExCKlBn/x0FqnHNHBVyJA0aQi82jRv5iAie8AgpJtkFVxB4n9OAAAWjO2LsCBvaQsisjIGIdVzpdKAWRsOosYoMDbUD9OGBUtdEpHVMQjJTAiBl748jMIrV9G1Yxss/8tAXhckp8AgJLN1P53F7hPFUCrkSJo0BJ5uvC5IzoFBSACAI+d1WH7tuuDCP/XlTNPkVBiEhPLqWszeWHddcHQ/Nabc1VXqkohaFYPQyQkhsHDrEZy9WIkALze89RCvC5LzYRA6ua8PFmJbVhHkMuC9RwbDuw0nWSXnwyB0YqdLyrH4X0cBAHNiemNocAeJKyKSBoPQSVXXGjH780OoNBgR3b0jnh3VU+qSiCTDIHRSK3Zm4/gFPTq0VWLV/w6CQs7rguS8GIRO6MdTJUj+8QyAukXZ1Z5uEldEJC0GoZO5VGHAi9cWZX/0ri64t59a4oqIpMcgdCJCCLzy9a8oLqtGT992WDC2n9QlEdkEBqET2ZhRgF3HtXBVyPDe/w6Cu1IhdUlENoFB6CRyi8vx2jd1U+7PjQ3hesREv8EgdAKGWhNe2HwIVTUmjOjlgxl3d5O6JCKbwiB0An9LO4WjhXp4t3HFir+GQc6hMkT1MAgdXOa5S/hgTy4AIPHPoRwqQ9QABqEDq6iuRdwXh2ESwIODAzEm1F/qkohsEoPQgb2+4wTOXaxEoLc7ljzQX+pyiGwWg9BBpZ3QYmNGPmQyYMVfwzjbNNEtMAgd0MXyarzy9REAwIzh3RDdo6PEFRHZNgahgxFCYMHWoygtr0ZvdTu8FNtH6pKIbB6D0MH8K6vIvDD7uxMHwc2VT48Q3Q6D0IFo9VXmiVb/755efHqEqIkYhA5CCIF5X/8KfVUtBnb2wjN/7CF1SUR2g0HoIL48cB7fZ5dAqZDjnb+GwUXBj5aoqfjX4gAKr1zF0m+OAwDiRvdGL7WHxBUR2RcGoZ27/pW4rLoWQ7p444kR3aUuicjuMAjt3Ib9+fjxVCncXOVY8dcwrj1C1AwMQjt2/nIlElNOAKibY7B7p3YSV0Rkn5oVhElJSQgODoabmxuioqKQkZHRpOM2bdoEmUyGCRMmNOdt6TeEEIjfcgQVBiOGBrfHY8OCpS6JyG5ZHISbN29GXFwcEhIScPDgQYSFhSE2NhbFxcW3PO7s2bN46aWXMGLEiGYXSzd8caAAP54qhcpFjrce4hyDRHfC4iBcuXIlnnjiCUyfPh39+vXDmjVr0KZNG6xbt67RY4xGIyZPnoxXX30V3bvzYv6d0uiq8Po3dV+JXxrdB9182kpcEZF9sygIDQYDMjMzERMTc+MF5HLExMQgPT290eNee+01+Pr6YsaMGU16n+rqauj1+nob1RFCYP7WIyirrsWgIG88zmn3ie6YRUFYWloKo9EItbr+WrhqtRoajabBY/bu3YuPP/4YycnJTX6fxMREeHl5mbegoCBLynRo27IK8d3JYigVcrz90EDeJSZqAVa9a1xWVoYpU6YgOTkZPj4+TT4uPj4eOp3OvBUUFFixSvtRXFaFJdvrBk4/H9OLA6eJWoiLJY19fHygUCig1Wrr7ddqtfDz87up/enTp3H27FmMHz/evM9kMtW9sYsLsrOz0aPHzc/EqlQqqFQqS0pzCgn/Ogbd1RoMCPTEk3/gtVailmLRGaFSqUR4eDjS0tLM+0wmE9LS0hAdHX1T+5CQEBw5cgRZWVnm7f7778eoUaOQlZXFr7wWSD2qwX+OauAil+Gtv4TBlc8SE7UYi84IASAuLg7Tpk1DREQEIiMjsWrVKlRUVGD69OkAgKlTpyIwMBCJiYlwc3PDgAED6h3v7e0NADftp8bprtaYp9d6amR39AvwlLgiIsdicRBOnDgRJSUlWLx4MTQaDQYNGoTU1FTzDZT8/HzI5TxbaUnL/3MCxWXV6O7TFrPv6SV1OUQORyaEEFIXcTt6vR5eXl7Q6XTw9HSus6H00xfxSPLPAIDNT96FqO5cf4SoqZqaHTx1s2FVNUbM31q3CNOkqC4MQSIrYRDasPfSTuFMaQXUnirMGxMidTlEDotBaKOOFenw0Q95AIClDwzgusREVsQgtEFGU93MMkaTwNhQP4zuf/MYTSJqOQxCG/RZ+ln8el4HDzcXLBnfX+pyiBweg9DGXNBdxYqd2QCAV+4Lga+nm8QVETk+BqGNWbL9GCoMRgzp4o1JkV2kLofIKTAIbci3xzTYeUwLF7kMyx4M5WSrRK2EQWgjyqtrkbD9GADgiT90R4ifcw0cJ5ISg9BGrPw2Bxd0VQjq4I7/42N0RK2KQWgDjpzXYf2+MwCA1yeEwl2pkLgiIufCIJSY0VQ39b5JAPeHBWBk705Sl0TkdBiEEvt8/zkcKdTBQ+WChX/qK3U5RE6JQSihkrJqvHVtzOBLsX3g68Exg0RSYBBKaFnKCZRV1WJAoCcevaur1OUQOS0GoUTST1/E1kOFkMmANyaEcjU6IgkxCCVgqDVh0bWp9ydHdUFYkLe0BRE5OQahBNbuzUNucTl82inx8mjOM0gkNQZhKzt/uRJ/SzsFAJg/ti+82nCeQSKpMQhb2Wv/Po6qGhOiunXAnwcHSl0OEYFB2Kr2ZBfj2+NaKOQyLJ0wADIZb5AQ2QIGYSuprjViybVJFaYPC0ZvtYfEFRHRdQzCVrL2xzM4e7ESnTxUeD6GkyoQ2RIGYSsovHIV7393/QZJCDy4EBORTWEQtoI3dtTdIIkM7oAJg3iDhMjWMAitbO+pUqQc0UAhl+HVB/rzBgmRDWIQWpGh1oTF2+ueIJlyV1f09ees00S2iEFoRZ/8dAZ5JRXwaafEnHt7S10OETWCQWglxfoq8xMkc+8LgZc7b5AQ2SoGoZW8mZqNCoMRYUHeeGhIZ6nLIaJbYBBawaH8y/j64HkAwJLx/bgsJ5GNYxC2MJNJYMm/jwMAHhwSiMFd2ktcERHdDoOwhW05VIjDBVfQVqnAvPs4xRaRPWAQtqCyqhq8mXoSADD7f3rB15NrkBDZAwZhC1r9XS5KyqoR3LENpg8PlrocImoiBmELOVNagXU/1S3SvuhP/aBy4SLtRPaCQdhC3thxAjVGgZG9O+GeEF+pyyEiCzAIW8DeU6XYfaJuwtVFf+rH54mJ7EyzgjApKQnBwcFwc3NDVFQUMjIyGm2bnJyMESNGoH379mjfvj1iYmJu2d7e1BpNWPpN3XCZKXd1RU/fdhJXRESWsjgIN2/ejLi4OCQkJODgwYMICwtDbGwsiouLG2y/Z88ePPLII/j++++Rnp6OoKAgjB49GoWFhXdcvC3YfKAA2doyeLm74gVOuEpkl2RCCGHJAVFRURg6dChWr14NADCZTAgKCsLs2bMxb9682x5vNBrRvn17rF69GlOnTm3Se+r1enh5eUGn08HT03ZmcNFX1eCPb+/BpQoDlozvh8eGd5O6JCL6jaZmh0VnhAaDAZmZmYiJibnxAnI5YmJikJ6e3qTXqKysRE1NDTp06NBom+rqauj1+nqbLVr9XS4uVRjQo1NbTL6rq9TlEFEzWRSEpaWlMBqNUKvV9far1WpoNJomvcYrr7yCgICAemH6e4mJifDy8jJvQUFBlpTZKs6WVuCTa8NlFv6pH1wVvO9EZK9a9a93+fLl2LRpE7Zu3Qo3t8afuoiPj4dOpzNvBQUFrVhl0yxLqRsu88c+nTCqD4fLENkzF0sa+/j4QKFQQKvV1tuv1Wrh5+d3y2NXrFiB5cuXY/fu3Rg4cOAt26pUKqhUKktKa1X7Tpea1ydeOK6v1OUQ0R2y6IxQqVQiPDwcaWlp5n0mkwlpaWmIjo5u9Li33noLS5cuRWpqKiIiIppfrQ0wmQTe2HECAPBoVBf09OX6xET2zqIzQgCIi4vDtGnTEBERgcjISKxatQoVFRWYPn06AGDq1KkIDAxEYmIiAODNN9/E4sWL8fnnnyM4ONh8LbFdu3Zo187+xtxtOVSIY0V6eLi54PkYTr9P5AgsDsKJEyeipKQEixcvhkajwaBBg5Cammq+gZKfnw+5/MaJ5ocffgiDwYCHHnqo3uskJCRgyZIld1Z9K7tqMGLFzmwAwHOjeqJDW6XEFRFRS7B4HKEUbGUc4d/STmHlrhx0bu+O3XEj4ebKiRWIbJlVxhE6s2J9Fdb89zSAusWYGIJEjoNB2EQrd+Wg0mDEoCBvjB/oL3U5RNSCGIRNcFKjxxcH6sYyLvpTX84uQ+RgGIRNsCzlJEwCGBvqh/CujT8aSET2iUF4Gz/klOCHnBK4KmR4hYsxETkkBuEtGE0Cif+pW4xpyl3B6NqxrcQVEZE1MAhvYduhQpy4UDd4evY9PaUuh4ishEHYiKoaI975tm7w9KxRPdGeg6eJHBaDsBGf7juLIl0V/L3c8NiwYKnLISIrYhA24HKFAau/zwUAvDi6DwdPEzk4BmEDkr7PRVlVLUL8PPDnwYFSl0NEVsYg/J2CS5X4LP0cACB+bF8o5Bw8TeToGIS/s+LbbBiMJtzd0wd/6OUjdTlE1AoYhL9xtFCHf2UVAQDmjQnho3REToJB+BtvptYNnn5gUAAGBHpJXA0RtRYG4TX7ckvx46lSuCpkePHePlKXQ0StiEEIQAhhPhucFNkFXTq2kbgiImpNDEIA/zmqweHzOrRRKvDcPb2kLoeIWpnTB2Gt0WReh2TmiO7o5GG7y4gSkXU4fRB+mXkeeaUV6NBWiSdGdJO6HCKSgFMH4VWDEat25wCoW5XOw81V4oqISApOHYTr952FVl+NQG93TL6ri9TlEJFEnDYIdZU1+HBP3cQKcff2hsqFEysQOSunDcK//3Aa+qpa9FF7YAInViByak4ZhMVlVfjkp7MAgJdi+3BiBSIn55RB+MH3p3G1pm6N4pi+vlKXQ0QSc7ogPH+5Ehv2102zNTe2DydWICLnC8L3dp9CjVFgeM+OGNaT02wRkZMFYW5xOb4+eB4A8NJoTqxARHWcKgjf3ZUDkwDu7afG4C7tpS6HiGyE0wTh0UIddhy5AJkMeHF0b6nLISIb4jRBuOLaGsUPhAUgxM9T4mqIyJY4RRD+cvYS9mSXwEUuwwsxPBskovqcIgjfuXY2+NeIzgj2aStxNURkaxw+CPflluLnvEtQKuScdJWIGuTQQSiEwDu76qbZeiQyCIHe7hJXRES2yKGDcE9OCTLPXYbKRY5Zo3pKXQ4R2SiHDUIhBFZ+W3c2ODW6K3w93SSuiIhsVbOCMCkpCcHBwXBzc0NUVBQyMjJu2f7LL79ESEgI3NzcEBoaipSUlGYVa4lvj2txpLBuQaanR/aw+vsRkf2yOAg3b96MuLg4JCQk4ODBgwgLC0NsbCyKi4sbbL9v3z488sgjmDFjBg4dOoQJEyZgwoQJOHr06B0X3xiTSeDda9cGpw8PRsd2XJCJiBonE0IISw6IiorC0KFDsXr1agCAyWRCUFAQZs+ejXnz5t3UfuLEiaioqMA333xj3nfXXXdh0KBBWLNmTZPeU6/Xw8vLCzqdDp6etx8M/e/DRZi98RA8VC748ZVR8G6jbGLviMiRNDU7LDojNBgMyMzMRExMzI0XkMsRExOD9PT0Bo9JT0+v1x4AYmNjG20PANXV1dDr9fW2pjKahHlBppkjujMEiei2LArC0tJSGI1GqNXqevvVajU0Gk2Dx2g0GovaA0BiYiK8vLzMW1BQUJNrzL9UiYpqI7zbuOLxu4ObfBwROS+bvGscHx8PnU5n3goKCpp8bDefttjz8h/xj8ejuDwnETWJiyWNfXx8oFAooNVq6+3XarXw8/Nr8Bg/Pz+L2gOASqWCStX8GxxurgqEdvZq9vFE5FwsOiNUKpUIDw9HWlqaeZ/JZEJaWhqio6MbPCY6OrpeewDYtWtXo+2JiFqbRWeEABAXF4dp06YhIiICkZGRWLVqFSoqKjB9+nQAwNSpUxEYGIjExEQAwPPPP4+RI0finXfewbhx47Bp0yYcOHAAH330Ucv2hIiomSwOwokTJ6KkpASLFy+GRqPBoEGDkJqaar4hkp+fD7n8xonmsGHD8Pnnn2PhwoWYP38+evXqhW3btmHAgAEt1wsiojtg8ThCKVg6jpCICLDSOEIiIkfEICQip8cgJCKnxyAkIqdn8V1jKVy/n2PJM8dERNcz43b3hO0iCMvKygDAomeOiYiuKysrg5dX40+b2cXwGZPJhKKiInh4eEAmk922vV6vR1BQEAoKCux2uI0j9AFwjH6wD7bD0n4IIVBWVoaAgIB645t/zy7OCOVyOTp37mzxcZ6ennb9oQOO0QfAMfrBPtgOS/pxqzPB63izhIicHoOQiJyeQwahSqVCQkLCHU3lJTVH6APgGP1gH2yHtfphFzdLiIisySHPCImILMEgJCKnxyAkIqfHICQip2e3QZiUlITg4GC4ubkhKioKGRkZt2z/5ZdfIiQkBG5ubggNDUVKSkorVdo4S/qQnJyMESNGoH379mjfvj1iYmJu2+fWYulncd2mTZsgk8kwYcIE6xbYBJb24cqVK5g1axb8/f2hUqnQu3dvyX+nLO3DqlWr0KdPH7i7uyMoKAhz5sxBVVVVK1V7sx9++AHjx49HQEAAZDIZtm3bdttj9uzZgyFDhkClUqFnz55Yv359895c2KFNmzYJpVIp1q1bJ44dOyaeeOIJ4e3tLbRabYPtf/rpJ6FQKMRbb70ljh8/LhYuXChcXV3FkSNHWrnyGyztw6RJk0RSUpI4dOiQOHHihHjssceEl5eXOH/+fCtXXp+l/bjuzJkzIjAwUIwYMUI88MADrVNsIyztQ3V1tYiIiBBjx44Ve/fuFWfOnBF79uwRWVlZrVz5DZb2YcOGDUKlUokNGzaIM2fOiJ07dwp/f38xZ86cVq78hpSUFLFgwQKxZcsWAUBs3br1lu3z8vJEmzZtRFxcnDh+/Lh4//33hUKhEKmpqRa/t10GYWRkpJg1a5b5Z6PRKAICAkRiYmKD7R9++GExbty4evuioqLEU089ZdU6b8XSPvxebW2t8PDwEJ9++qm1SmyS5vSjtrZWDBs2TKxdu1ZMmzZN8iC0tA8ffvih6N69uzAYDK1V4m1Z2odZs2aJe+65p96+uLg4MXz4cKvW2VRNCcK5c+eK/v3719s3ceJEERsba/H72d1XY4PBgMzMTMTExJj3yeVyxMTEID09vcFj0tPT67UHgNjY2EbbW1tz+vB7lZWVqKmpQYcOHaxV5m01tx+vvfYafH19MWPGjNYo85aa04ft27cjOjoas2bNglqtxoABA7Bs2TIYjcbWKrue5vRh2LBhyMzMNH99zsvLQ0pKCsaOHdsqNbeElvy7totJF36rtLQURqPRvGredWq1GidPnmzwGI1G02B7jUZjtTpvpTl9+L1XXnkFAQEBN/0itKbm9GPv3r34+OOPkZWV1QoV3l5z+pCXl4fvvvsOkydPRkpKCnJzc/Hss8+ipqYGCQkJrVF2Pc3pw6RJk1BaWoq7774bQgjU1tbi6aefxvz581uj5BbR2N+1Xq/H1atX4e7u3uTXsrszQgKWL1+OTZs2YevWrXBzc5O6nCYrKyvDlClTkJycDB8fH6nLaTaTyQRfX1989NFHCA8Px8SJE7FgwQKsWbNG6tKabM+ePVi2bBk++OADHDx4EFu2bMGOHTuwdOlSqUuThN2dEfr4+EChUECr1dbbr9Vq4efn1+Axfn5+FrW3tub04boVK1Zg+fLl2L17NwYOHGjNMm/L0n6cPn0aZ8+exfjx4837TCYTAMDFxQXZ2dno0aOHdYv+neZ8Fv7+/nB1dYVCoTDv69u3LzQaDQwGA5RKpVVr/r3m9GHRokWYMmUKZs6cCQAIDQ1FRUUFnnzySSxYsOCWc/fZisb+rj09PS06GwTs8IxQqVQiPDwcaWlp5n0mkwlpaWmIjo5u8Jjo6Oh67QFg165djba3tub0AQDeeustLF26FKmpqYiIiGiNUm/J0n6EhITgyJEjyMrKMm/3338/Ro0ahaysLElmIG/OZzF8+HDk5uaaQxwAcnJy4O/v3+ohCDSvD5WVlTeF3fVgF3Yy/UCL/l1bfHvFBmzatEmoVCqxfv16cfz4cfHkk08Kb29vodFohBBCTJkyRcybN8/c/qeffhIuLi5ixYoV4sSJEyIhIcEmhs9Y0ofly5cLpVIpvvrqK3HhwgXzVlZWJlUXhBCW9+P3bOGusaV9yM/PFx4eHuK5554T2dnZ4ptvvhG+vr7i9ddfl6oLFvchISFBeHh4iI0bN4q8vDzx7bffih49eoiHH35Yqi6IsrIycejQIXHo0CEBQKxcuVIcOnRInDt3TgghxLx588SUKVPM7a8Pn3n55ZfFiRMnRFJSknMNnxFCiPfff1906dJFKJVKERkZKX7++Wfzv40cOVJMmzatXvsvvvhC9O7dWyiVStG/f3+xY8eOVq74Zpb0oWvXrgLATVtCQkLrF/47ln4Wv2ULQSiE5X3Yt2+fiIqKEiqVSnTv3l288cYbora2tpWrrs+SPtTU1IglS5aIHj16CDc3NxEUFCSeffZZcfny5dYv/Jrvv/++wd/x63VPmzZNjBw58qZjBg0aJJRKpejevbv45JNPmvXenIaLiJye3V0jJCJqaQxCInJ6DEIicnoMQiJyegxCInJ6DEIicnoMQiJyegxCInJ6DEIicnoMQiJyegxCInJ6DEIicnr/D1Nc8FM4i7JYAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3.5, 3.5))\n", + "plt.plot(fprs, tprs)" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "metadata": {}, + "outputs": [], + "source": [ + "ths = np.linspace(0, 1, 100)\n", + "tprs = (ths + 1)/2\n", + "fprs = (1 - ths)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 298, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 298, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(fprs, tprs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlscorecheck", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/development/047-auc-ambiguity.ipynb b/notebooks/development/047-auc-ambiguity.ipynb new file mode 100644 index 0000000..20ae66f --- /dev/null +++ b/notebooks/development/047-auc-ambiguity.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import roc_auc_score\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "X = np.random.random_sample((100, 3))\n", + "y = np.random.randint(0, 2, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "preds = KNeighborsClassifier().fit(X, y).predict_proba(X)[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "indices = np.arange(100)\n", + "np.random.shuffle(indices)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.7057291666666666)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roc_auc_score(y[indices], preds[indices])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.4, 0.6, 0.4, 0.2, 0.8, 0.4, 0.4, 0.6, 0.6, 0.8, 0.4, 0.2, 0.2,\n", + " 0.6, 0.6, 0.6, 0.6, 0.4, 0.2, 0.8, 0.6, 0.6, 0.4, 0.2, 0.4, 0.2,\n", + " 0.2, 0.4, 0.4, 0.8, 0.4, 0.4, 0.8, 0.6, 0.6, 0.4, 0.4, 0.4, 0.2,\n", + " 0.6, 0.4, 0.8, 0.4, 0.4, 1. , 1. , 0.8, 0.6, 0.8, 0.4, 0.6, 0.6,\n", + " 0.6, 0.6, 0.6, 0.8, 0.4, 0.2, 0.4, 1. , 0.4, 0.4, 0.2, 0.2, 0.4,\n", + " 0.4, 0.4, 0. , 0.4, 0.6, 1. , 0.6, 1. , 0.6, 0.4, 0.4, 0.4, 0.8,\n", + " 0.6, 0.2, 0.4, 0.8, 0.4, 0.4, 0.4, 0.4, 0.8, 0.6, 0.6, 0.4, 0.4,\n", + " 0.4, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.4])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlscorecheck", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}