diff --git a/mlscorecheck/auc/12-exponential-p-norm-illustration.ipynb b/mlscorecheck/auc/12-exponential-p-norm-illustration.ipynb
new file mode 100644
index 0000000..709d82c
--- /dev/null
+++ b/mlscorecheck/auc/12-exponential-p-norm-illustration.ipynb
@@ -0,0 +1,18 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/mlscorecheck/auc/_acc_single.py b/mlscorecheck/auc/_acc_single.py
index 482857a..7c8b916 100644
--- a/mlscorecheck/auc/_acc_single.py
+++ b/mlscorecheck/auc/_acc_single.py
@@ -179,7 +179,10 @@ def macc_min(auc, p, n):
         float: the accuracy
     """
     if auc >= 1 - min(p, n) / (2 * max(p, n)):
-        return 1 - (np.sqrt(2 * p * n - 2 * auc * p * n)) / (p + n)
+        tmp = 2 * p * n - 2 * auc * p * n
+        if np.abs(tmp) < 1e-8:
+            tmp = 0
+        return 1 - (np.sqrt(tmp)) / (p + n)
 
     return max(p, n) / (p + n)
 
diff --git a/mlscorecheck/auc/_auc_single.py b/mlscorecheck/auc/_auc_single.py
index 07ac59b..60d402e 100644
--- a/mlscorecheck/auc/_auc_single.py
+++ b/mlscorecheck/auc/_auc_single.py
@@ -7,7 +7,7 @@
 from scipy.stats import beta
 from scipy.stats import norm as gaussian
 
-from ._utils import translate_scores, prepare_intervals
+from ._utils import translate_scores, prepare_intervals, integrate_roc_curve, integrate_roc_curves
 
 __all__ = [
     "augment_intervals",
@@ -23,11 +23,13 @@
     "roc_rmin_grid_correction",
     "roc_maxa",
     "roc_onmin",
+    "roc_maxa2",
     "auc_min",
     "auc_max",
     "auc_rmin",
     "auc_rmin_grid",
     "auc_maxa",
+    "auc_maxa2",
     "auc_amin",
     "auc_armin",
     "auc_amax",
@@ -114,38 +116,6 @@ def augment_intervals(intervals: dict, p: int, n: int):
     return intervals
 
 
-def integrate_roc_curve(fprs, tprs):
-    """
-    Integrates a ROC curve
-
-    Args:
-        fprs (np.array): the fpr values
-        tprs (np.array): the tpr values
-
-    Returns:
-        float: the integral
-    """
-    diffs = np.diff(fprs)
-    avgs = (tprs[:-1] + tprs[1:]) / 2
-    return float(np.sum(diffs * avgs))
-
-
-def integrate_roc_curves(fprs, tprs):
-    """
-    Integrates ROC curves
-
-    Args:
-        fprs (np.array): the fpr values
-        tprs (np.array): the tpr values
-
-    Returns:
-        float: the integral
-    """
-    diffs = np.diff(fprs, axis=1)
-    avgs = (tprs[:, :-1] + tprs[:, 1:]) / 2
-    return (np.sum(diffs * avgs, axis=1)).astype(float)
-
-
 def roc_min(fpr, tpr):
     """
     The minimum ROC curve at fpr, tpr
@@ -157,7 +127,12 @@ def roc_min(fpr, tpr):
     Returns:
         np.array, np.array: the fpr and tpr values
     """
-    return (np.array([0, fpr, fpr, 1, 1]), np.array([0, 0, tpr, tpr, 1]))
+    if fpr != 0 and tpr != 1:
+        return (np.array([0, fpr, fpr, 1, 1]), np.array([0, 0, tpr, tpr, 1]))
+    elif fpr == 0:
+        return (np.array([0, fpr, 1, 1]), np.array([0, tpr, tpr, 1]))
+    elif tpr == 1:
+        return (np.array([0, fpr, fpr, 1]), np.array([0, 0, tpr, tpr]))
 
 
 def roc_max(fpr, tpr):
@@ -172,7 +147,12 @@ def roc_max(fpr, tpr):
         np.array, np.array: the fpr and tpr values
     """
 
-    return (np.array([0, 0, fpr, fpr, 1]), np.array([0, tpr, tpr, 1, 1]))
+    if fpr != 1 and tpr != 0:
+        return (np.array([0, 0, fpr, fpr, 1]), np.array([0, tpr, tpr, 1, 1]))
+    elif fpr == 1:
+        return (np.array([0, 0, fpr, fpr]), np.array([0, tpr, tpr, 1]))
+    elif tpr == 0:
+        return (np.array([0, fpr, fpr, 1]), np.array([tpr, tpr, 1, 1]))
 
 
 def roc_rmax(fpr, tpr):
@@ -197,7 +177,8 @@ def roc_rmin(fpr, tpr):
     """
 
     if tpr < fpr:
-        raise ValueError("the regulated minimum curve does not exist when tpr < fpr")
+        #raise ValueError("the regulated minimum curve does not exist when tpr < fpr")
+        return (None, None)
 
     return (np.array([0, fpr, fpr, tpr, 1]), np.array([0, fpr, tpr, tpr, 1]))
 
@@ -267,15 +248,55 @@ def roc_maxa(acc, p, n):
     """
 
     if acc < max(p, n) / (p + n):
-        raise ValueError(
-            "the maximum accuracy curve does not exist when acc < max(p,n)/(p + n)"
-        )
+        #raise ValueError(
+        #    "the maximum accuracy curve does not exist when acc < max(p,n)/(p + n)"
+        #)
+        return (None, None)
 
     tpr_a = (acc * (p + n) - n) / p
     fpr_b = 1 - (acc * (p + n) - p) / n
 
-    return (np.array([0, 0, fpr_b, 1]), np.array([0, tpr_a, 1, 1]))
+    if fpr_b != 1 and tpr_a != 0:
+        return (np.array([0, 0, fpr_b, 1]), np.array([0, tpr_a, 1, 1]))
+    elif fpr_b == 1:
+        return (np.array([0, 0, 0.5, fpr_b]), np.array([0, tpr_a, tpr_a + (1 - tpr_a)/2, 1]))
+    elif tpr_a == 0:
+        return (np.array([0, fpr_b/2, fpr_b, 1]), np.array([0, 0.5, 1, 1]))
+    
 
+def roc_maxa2(acc, p, n):
+    """
+    The maximuma accuracy ROC curve with acc accuracy
+
+    Args:
+        acc (float): the accuracy
+        p (int): the number of positive samples
+        n: (int): the number of negative samples
+
+    Returns:
+        np.array, np.array: the fpr and tpr values
+
+    Raises:
+        ValueError: when acc < max(p, n) / (p + n)
+    """
+
+    if acc < max(p, n) / (p + n):
+        #raise ValueError(
+        #    "the maximum accuracy curve does not exist when acc < max(p,n)/(p + n)"
+        #)
+        return (None, None)
+
+    tpr_a = (acc * (p + n) - n) / p
+    fpr_b = 1 - (acc * (p + n) - p) / n
+
+    if fpr_b != 1 and tpr_a != 0:
+        fprs, tprs = (np.array([0, 0, fpr_b, 1]), np.array([0, tpr_a, 1, 1]))
+    elif fpr_b == 1:
+        fprs, tprs = (np.array([0, 0, 0.5, fpr_b]), np.array([0, tpr_a, tpr_a + (1 - tpr_a)/2, 1]))
+    elif tpr_a == 0:
+        fprs, tprs = (np.array([0, fpr_b/2, fpr_b, 1]), np.array([0, 0.5, 1, 1]))
+
+    return tprs, fprs
 
 def roc_onmin(fpr, tpr):
     """
@@ -490,6 +511,27 @@ def auc_maxa(acc, p, n):
 
     return float(1 - ((1 - acc) * (p + n)) ** 2 / (2 * n * p))
 
+def auc_maxa2(acc, p, n):
+    """
+    The area under the maximum accuracy curve at acc
+
+    Args:
+        acc (float): upper bound on accuracy
+        p (int): the number of positive test samples
+        n (int): the number of negative test samples
+
+    Returns:
+        float: the area
+
+    Raises:
+        ValueError: when acc < max(p, n) / (p + n)
+    """
+
+    if acc < max(p, n) / (p + n):
+        raise ValueError("accuracy too small")
+
+    return 1.0 - float(1 - ((1 - acc) * (p + n)) ** 2 / (2 * n * p))
+
 
 def auc_maxa_grad(acc, p, n):
     """
@@ -864,270 +906,17 @@ def auc_from(
     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
-
-        
 
         return (midpoint, midpoint)
     except:
diff --git a/mlscorecheck/auc/_utils.py b/mlscorecheck/auc/_utils.py
index 54a291f..78b816e 100644
--- a/mlscorecheck/auc/_utils.py
+++ b/mlscorecheck/auc/_utils.py
@@ -4,9 +4,13 @@
 
 import numpy as np
 
+from scipy.optimize import minimize_scalar
+
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import r2_score
 
+from scipy.optimize import root_scalar
+
 from ..aggregated import determine_fold_configurations
 
 __all__ = [
@@ -23,15 +27,56 @@
     "average_roc_curves_to_1",
     "average_n_roc_curves_",
     "average_n_roc_curves",
-    "exponential_fitting",
-    "exponential_fitting2",
     "generate_roc_curve",
     "generate_roc_curve_slope",
     "generate_1_roc_curve",
-    "sample_triangle"
+    "sample_triangle",
+    "p_norm_fit",
+    "p_norm_fit_joint",
+    "p_norm_fit_best",
+    "auc_estimator",
+    "p_norm_fit_auc",
+    "max_acc_estimator",
+    "integrate_roc_curve",
+    "integrate_roc_curves",
+    "sample1",
+    "sample2"
 ]
 
 
+def integrate_roc_curve(fprs, tprs):
+    """
+    Integrates a ROC curve
+
+    Args:
+        fprs (np.array): the fpr values
+        tprs (np.array): the tpr values
+
+    Returns:
+        float: the integral
+    """
+    diffs = np.diff(fprs)
+    avgs = (tprs[:-1] + tprs[1:]) / 2
+    return float(np.sum(diffs * avgs))
+
+
+def integrate_roc_curves(fprs, tprs):
+    """
+    Integrates ROC curves
+
+    Args:
+        fprs (np.array): the fpr values
+        tprs (np.array): the tpr values
+
+    Returns:
+        float: the integral
+    """
+    diffs = np.diff(fprs, axis=1)
+    avgs = (tprs[:, :-1] + tprs[:, 1:]) / 2
+    return (np.sum(diffs * avgs, axis=1)).astype(float)
+
+
+
 def generate_roc_curve(
         tpr: float,
         fpr: float,
@@ -765,117 +810,288 @@ def average_n_roc_curves(curves, random_state=None):
     return fprs1, tprs1
 
 
-def exponential_fitting(row, label, frac_label):
+def p_norm_fit(x, y, bracket=(-5, 3), mode='implicit'):
+    p = np.logspace(bracket[0], bracket[1], 2000)
+    if mode == 'implicit':
+        return p[np.argmin(np.mean(np.abs(1 - x**p[:, None] - y**p[:, None])**1, axis=1))]
+    elif mode == 'explicit':
+        return p[np.argmin(np.mean(np.abs(y - (1 - x**p[:, None])**(1/p[:, None]))**1, axis=1))]
+
+
+def p_norm_fit_joint(x, y0, y1, bracket=(-5, 0, 3), p=None, n=None, max_acc=None):
+    p0 = np.logspace(bracket[0], bracket[1], 500)
+    p1 = np.logspace(bracket[1], bracket[2], 500)
+    #p0 = np.logspace(-1, bracket[1], 4)
+    #p1 = np.logspace(1, bracket[2], 5)
+
+    err0 = np.mean(np.abs(1 - x**p0[:, None] - y0**p0[:, None])**1, axis=1)
+    err1 = np.mean(np.abs(1 - x**(p1[:, None]) - y1**(p1[:, None]))**1, axis=1)
+
+    err = err0[:, None] + err1
+
+    if max_acc is not None:
+        #z = np.linspace(0, 1, 6)
+        z = np.linspace(0, 1, 100)
+        tmp = (((1 - (1 - z**p0[:, None])**(1/p0[:, None])) * n)[:, None] + ((1 - z**p1[:, None])**(1/p1[:, None]) * p)) / (p + n)
+        #print(tmp)
+        max_accs = np.max(tmp, axis=2)
+        mask = max_accs > max_acc
+        #print(max_accs)
+        #print(np.sum(mask), np.prod(mask.shape))
+        err[mask] = np.inf
+
+    min0, min1 = np.unravel_index(np.argmin(err), err.shape)
+
+    return p0[min0], p1[min1]
+
+
+def p_norm_fit_best(x, y, bracket=(-5, 3), mode='implicit', p=None, n=None, max_acc=None):
+    exp = np.logspace(bracket[0], bracket[1], 2000)
+    err = np.mean(np.abs(1 - x**exp[:, None] - y**exp[:, None])**1, axis=1)
+    if max_acc is not None:
+        z = np.linspace(0, 1, min(100, n if n is not None else 100))
+        fprs = (z)[:, None]
+        tprs = ((1 - z[:, None]**exp)**(1/exp))
+        tmp = (fprs * n + tprs * p) / (p + n)
+        max_accs = np.max(tmp, axis=0)
+        mask = max_accs > max_acc
+        err[mask] = np.inf
+    
+    return exp[np.argmin(err)]
 
-    values = row[label].copy()
-    counts = row[frac_label].copy()
 
-    mask = values > 1e-6
-    values_nz = values[mask]
-    counts_nz = counts[mask]
+def auc_estimator(fpr, tpr, p, n, mode='separate', return_details=False, integral=200, best=False, rasterize=False):
+    if fpr < 1e-6 and tpr > 1 - 1e-6 and not best:
+        return 1.0, -1, -1
+    if fpr < 1e-6 and tpr < 1e-6 and not best:
+        return 0.5, -1, -1
+    if fpr > 1 - 1e-6 and tpr > 1 - 1e-6 and not best:
+        return 0.5, -1, -1
+    
+    fpr = min(max(fpr, 1/n), 1 - 1/n)
+    tpr = min(max(tpr, 1/p), 1 - 1/p)
 
-    ln_values = np.log(values_nz)
-    ln_counts = np.log(counts_nz).reshape(-1, 1)
+    fprs = np.array([0.0, fpr, 1.0])
+    tprs = np.array([0.0, tpr, 1.0])
+    fracs = 1.0 - (p*tprs + n*fprs)/(p + n)
 
-    linreg_a = LinearRegression(fit_intercept=False, positive=True)
-    pred_values = linreg_a\
-        .fit(ln_counts, ln_values)\
-        .predict(ln_counts)
+    if mode == 'separate':
+        p_fpr = p_norm_fit(fracs, fprs, bracket=(-5, 0))
+        p_tpr = p_norm_fit(fracs, tprs, bracket=(0, 2))
 
-    if len(values) > 3:
-        r2_a = r2_score(ln_values, pred_values)
-    else:
-        r2_a = 1.0
+        #print(p_fpr, p_tpr)
 
-    values = (1 - values)
-    counts = (1 - counts)
+        fracs = 1.0 - np.linspace(0, 1, integral)
+        x = (1.0 - fracs**p_fpr)**(1/p_fpr)
+        y = (1.0 - fracs**p_tpr)**(1/p_tpr)
+    elif mode == 'joint':
+        if best:
+            max_acc = (n*(1 - fpr) + p*tpr) / (p + n)
+        else:
+            max_acc = None
+        p_fpr, p_tpr = p_norm_fit_joint(fracs, 
+                                        fprs, 
+                                        tprs, 
+                                        bracket=(-5, 0, 3),
+                                        p=p,
+                                        n=n,
+                                        max_acc=max_acc)
+
+        if not rasterize:
+            fracs = 1.0 - np.linspace(0, 1, integral)
+            x = (1.0 - fracs**p_fpr)**(1/p_fpr)
+            y = (1.0 - fracs**p_tpr)**(1/p_tpr)
+        else:
+            fracs = 1.0 - np.linspace(0, 1, p + n)
+            x = np.round((1.0 - fracs**p_fpr)**(1/p_fpr)*n)/n
+            y = np.round((1.0 - fracs**p_tpr)**(1/p_tpr)*p)/p
+    elif mode == 'roc':
+        p_both = p_norm_fit(1 - fprs, tprs, bracket=(0, 2))
+        p_fpr, p_tpr = p_both, p_both
+        #print(p_both)
+        if not rasterize:
+            x = np.linspace(0, 1, integral)
+            y = (1.0 - (1 - x)**p_both)**(1/p_both)
+        else:
+            x = np.linspace(0, 1, n)
+            y = np.round((1.0 - x**p_both)**(1/p_both)*p)/p
+    
+    elif mode == 'roc2':
+        if best:
+            max_acc = (n*(1 - fpr) + p*tpr) / (p + n)
+        else:
+            max_acc = None
+
+        p_both = p_norm_fit_best(1.0 - fprs, 
+                                    tprs, 
+                                    bracket=(0, 2),
+                                    p=p,
+                                    n=n,
+                                    max_acc=max_acc)
 
-    mask = values > 1e-6
-    values_nz = values[mask]
-    counts_nz = counts[mask]
+        p_fpr, p_tpr = p_both, p_both
 
-    ln_values = np.log(values_nz)
-    ln_counts = np.log(counts_nz).reshape(-1, 1)
+        if not rasterize:
+            x = np.linspace(0, 1, integral)
+            y = (1.0 - (1 - x)**p_both)**(1/p_both)
+        else:
+            x = np.linspace(0, 1, n)
+            y = np.round((1.0 - x**p_both)**(1/p_both)*p)/p
+
+    if best and mode != 'joint' and mode != 'roc2':
+        best_acc = ((1 - fpr)*n + tpr*p) / (p + n)
+        accs = ((1 - x)*n + y * p) / (p + n)
+        mask = accs > best_acc
+        x_change = x[mask]
+        y_change = y[mask]
 
-    linreg_b = LinearRegression(fit_intercept=False, positive=True)
-    pred_values = linreg_b\
-        .fit(ln_counts, ln_values)\
-        .predict(ln_counts)
+        y_change = (best_acc * (p + n) - (1 - x_change)*n)/p
+        y[mask] = y_change
 
-    if len(values) > 3:
-        r2_b = r2_score(ln_values, pred_values)
+    if not return_details:
+        return integrate_roc_curve(x, y), float(p_fpr), float(p_tpr)
     else:
-        r2_b = 1.0
+        return (integrate_roc_curve(x, y), x, y)
 
-    #print(r2_a, linreg_a.coef_[0], 0)
-    #print(r2_b, linreg_b.coef_[0], 1)
+def auc_error(auc, p):
+    x = np.linspace(0, 1, 4000)
+    tprs = (1 - (1 - x)**(1/p))**p
+    auc0 = integrate_roc_curve(x, tprs[::-1])
+    return (auc0 - auc)
 
-    if r2_a > r2_b:
-        return (r2_a, linreg_a.coef_[0], 0)
-    return (r2_b, linreg_b.coef_[0], 1)
+def p_norm_fit_auc(auc, bracket=(1e-20, 1)):
 
-    if label == 'fprs':
-        return (r2_b, linreg_b.coef_[0], 1)
-    return (r2_a, linreg_a.coef_[0], 0)
+    if auc_error(auc, bracket[0]) < 0 and auc_error(auc, bracket[1]) < 0:
+        if auc_error(auc, bracket[0]) > auc_error(auc, bracket[1]):
+            return bracket[0]
+        else:
+            return bracket[1]
+    
+    if auc_error(auc, bracket[0]) > 0 and auc_error(auc, bracket[1]) > 0:
+        if auc_error(auc, bracket[0]) > auc_error(auc, bracket[1]):
+            return bracket[1]
+        else:
+            return bracket[0]
 
-def exponential_fitting2(row, label, frac_label):
-    values = row[label].copy()
-    counts = row[frac_label].copy()
+    res = root_scalar(
+        lambda p: auc_error(auc, p), 
+        bracket=bracket
+    )
+    
+    return float(res['root'])
+
+def max_acc_estimator(auc, p, n):
+    if auc >= 1 - 1e-4:
+        return 1.0
+    #print(auc)
+    exp = p_norm_fit_auc(auc)
+    x = np.linspace(0, 1, 100)
+    tprs = (1 - (1 - x)**(1/exp))**exp
+    return np.max(((1 - x)*n + tprs*p)/(p + n))
+
+def sample0_min_max(fpr1, tpr1, fpr2, tpr2):
+    active = np.repeat(True, len(fpr1))
+    fpr_result = np.repeat(-1.0, len(fpr1))
+    tpr_result = np.repeat(-1.0, len(fpr1))
+    n_active = len(fpr1)
+
+    fpr_result[active] = (fpr2[active] - fpr1[active]) * np.random.random_sample(n_active) + fpr1[active]
+    tpr_result[active] = (tpr2[active] - tpr1[active]) * np.random.random_sample(n_active) + tpr1[active]
+    #tpr_result[active] = (tpr2[active] - tpr1[active]) * 0.9 + tpr1[active]
+
+    return fpr_result, tpr_result
+
+def sample0_rmin_max(fpr1, tpr1, fpr2, tpr2):
+    active = np.repeat(True, len(fpr1))
+    fpr_result = np.repeat(-1.0, len(fpr1))
+    tpr_result = np.repeat(-1.0, len(fpr1))
+    n_active = len(fpr1)
+
+    while n_active > 0:
+        
+        fpr_result[active] = (fpr2[active] - fpr1[active]) * np.random.random_sample(n_active) + fpr1[active]
+        tpr_result[active] = (tpr2[active] - tpr1[active]) * np.random.random_sample(n_active) + tpr1[active]
 
-    if label == 'tprs':
-        r2, coef, _ = exponential_fitting2_(values, counts)
-        return r2, 1/coef, -3
-    else:
-        r2, coef, _ = exponential_fitting2_(values, 1 - counts)
-        return r2, coef, -3
+        lower_bounds = np.max(np.vstack([tpr1, fpr_result]).T, axis=1)
 
-    r2a, coefa, _ = exponential_fitting2_(values, counts)
-    r2b, coefb, _ = exponential_fitting2_(1 - values, 1 - counts)
+        active = active & (tpr_result < lower_bounds)
 
-    print(r2a, coefa, r2b, coefb)
+        n_active = np.sum(active)
 
-    if r2a > r2b:
-        return r2a, coefa, -2
-    else:
-        return r2b, 1.0/coefb, -3
+    return fpr_result, tpr_result
 
-def exponential_fitting2_(values, counts):
+def sample0_rmin_maxa(fpr1, tpr1, fpr2, tpr2, max_acc, p, n):
+    active = np.repeat(True, len(fpr1))
+    fpr_result = np.repeat(-1.0, len(fpr1))
+    tpr_result = np.repeat(-1.0, len(fpr1))
+    n_active = len(fpr1)
 
-    """if len(values) <= 3:
-        return (1.0, 1.0, -1)"""
+    while n_active > 0:
+        
+        fpr_result[active] = (fpr2[active] - fpr1[active]) * np.random.random_sample(n_active) + fpr1[active]
+        tpr_result[active] = (tpr2[active] - tpr1[active]) * np.random.random_sample(n_active) + tpr1[active]
+        #tpr_result[active] = (tpr2[active] - tpr1[active]) * 0.5 + tpr1[active]
 
+        maxa_bounds = (max_acc * (p + n) - (1 - fpr_result) * n) / p
 
-    mask = (values > 1e-6) & (counts > 1e-6)
-    values_nz = values[mask]
-    counts_nz = counts[mask]
+        upper_bounds = np.min(np.vstack([tpr2, maxa_bounds]).T, axis=1)
+        lower_bounds = np.max(np.vstack([tpr1, fpr_result]).T, axis=1)
 
-    ln_values = np.log(values_nz)
-    ln_counts = np.log(counts_nz)
+        active = active & ((tpr_result < lower_bounds) | (tpr_result > upper_bounds))
 
-    """values2 = (1 - values)
-    counts2 = (1 - counts)
+        n_active = np.sum(active)
 
-    mask2 = (values2 > 1e-6) & (values2 < 1)
-    values2_nz = values2[mask2]
-    counts2_nz = counts2[mask2]"""
+    return fpr_result, tpr_result
 
-    """ln_values2 = 1/np.log(values2_nz)
-    ln_counts2 = 1/np.log(counts2_nz)"""
+def sample1(fpr0, tpr0, n_samples, n_nodes, p=None, n=None, max_acc=None, mode='min-max'):
+    fpr0s = np.repeat(fpr0, n_samples)
+    tpr0s = np.repeat(tpr0, n_samples)
+    zeros = np.repeat(0.0, n_samples)
+    ones = np.repeat(1.0, n_samples)
 
-    ln_x = np.hstack([ln_counts]).reshape(-1, 1)
-    ln_y = np.hstack([ln_values])
-    
-    if len(ln_x) <= 1:
-        return (1.0, 1.0, -1)
+    curves_fpr = np.zeros((n_samples, n_nodes))
+    curves_tpr = np.zeros((n_samples, n_nodes))
+
+    curves_fpr[:, 0] = zeros
+    curves_tpr[:, 0] = zeros
+    curves_fpr[:, 1] = ones
+    curves_tpr[:, 1] = ones
+
+    curves_fpr[:, 2] = fpr0s
+    curves_tpr[:, 2] = tpr0s
+
+    pool = [(0, 2), (2, 1)]
+
+    for idx in range(n_nodes - 3):
+        left, right = pool[0]
+        pool = pool[1:]
+        if mode == 'min-max':
+            fprs_new, tprs_new = sample0_min_max(curves_fpr[:, left], curves_tpr[:, left], curves_fpr[:, right], curves_tpr[:, right])
+        elif mode == 'rmin-max':
+            fprs_new, tprs_new = sample0_rmin_max(curves_fpr[:, left], curves_tpr[:, left], curves_fpr[:, right], curves_tpr[:, right])
+        elif mode == 'rmin-maxa':
+            fprs_new, tprs_new = sample0_rmin_maxa(curves_fpr[:, left], curves_tpr[:, left], curves_fpr[:, right], curves_tpr[:, right], max_acc, p, n)
+        curves_fpr[:, idx+3] = fprs_new
+        curves_tpr[:, idx+3] = tprs_new
+        pool = pool + [(left, idx+3), (idx+3, right)]
     
-    linreg_a = LinearRegression(fit_intercept=False, positive=True)
-    pred_values = linreg_a\
-        .fit(ln_x, ln_y)\
-        .predict(ln_x)
+    sorting = np.argsort(curves_fpr, axis=1)
+    curves_fpr = curves_fpr[np.arange(n_samples)[:, None], sorting]
+    curves_tpr = curves_tpr[np.arange(n_samples)[:, None], sorting]
 
-    if len(values) >= 3:
-        r2_a = r2_score(ln_y, pred_values)
-    else:
-        r2_a = 1.0
+    if n is not None:
+        curves_fpr = np.round(curves_fpr * n) / n
+
+    if p is not None:
+        curves_tpr = np.round(curves_tpr * p) / p
     
-    return (r2_a, linreg_a.coef_[0], -1)
+    return curves_fpr, curves_tpr
+
+def sample2(fpr0, tpr0, n_samples, n_nodes, p=None, n=None, max_acc=None, mode='min-max', raw=False):
+    fprs, tprs = sample1(fpr0, tpr0, n_samples, n_nodes, p, n, max_acc, mode)
+    aucs = integrate_roc_curves(fprs, tprs)
+    n_nodes = n_nodes - np.sum((fprs[:, :-1] == fprs[:, 1:]) & (tprs[:, :-1] == tprs[:, 1:]), axis=1)
+    if not raw:
+        return np.mean(aucs)
+    else:
+        return aucs, n_nodes
\ No newline at end of file
diff --git a/notebooks/auc_experiments/00-integrals.ipynb b/notebooks/auc_experiments/00-integrals.ipynb
index 1a8351f..e4a71bb 100644
--- a/notebooks/auc_experiments/00-integrals.ipynb
+++ b/notebooks/auc_experiments/00-integrals.ipynb
@@ -2,12 +2,779 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import sympy as sp"
+    "import sympy as sp\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sample0(sample_fpr, sample_tpr, curve_fpr, curve_tpr):\n",
+    "    if len(sample_fpr) == 0:\n",
+    "        return\n",
+    "    idx = np.random.choice(range(len(sample_fpr)))\n",
+    "    fpr_new = sample_fpr[idx]\n",
+    "    tpr_new = sample_tpr[idx]\n",
+    "    curve_fpr.append(fpr_new)\n",
+    "    curve_tpr.append(tpr_new)\n",
+    "\n",
+    "    mask = (sample_fpr < fpr_new) & (sample_tpr < tpr_new)\n",
+    "    sample0(sample_fpr[mask], sample_tpr[mask], curve_fpr, curve_tpr)\n",
+    "    mask = (sample_fpr > fpr_new) & (sample_tpr > tpr_new)\n",
+    "    sample0(sample_fpr[mask], sample_tpr[mask], curve_fpr, curve_tpr)\n",
+    "\n",
+    "def sample1(fprs, tprs, n_samples):\n",
+    "    curves_fpr = []\n",
+    "    curves_tpr = []\n",
+    "\n",
+    "    for _ in range(n_samples):\n",
+    "        curve_fpr = []\n",
+    "        curve_tpr = []\n",
+    "        sample0(fprs, tprs, curve_fpr, curve_tpr)\n",
+    "\n",
+    "        sorting = np.argsort(curve_fpr)\n",
+    "        curve_fpr = np.array(curve_fpr)[sorting]\n",
+    "        curve_tpr = np.array(curve_tpr)[sorting]\n",
+    "\n",
+    "        curves_fpr.append((curve_fpr))\n",
+    "        curves_tpr.append((curve_tpr))\n",
+    "    \n",
+    "    return curves_fpr, curves_tpr\n",
+    "\n",
+    "def sample2(fpr0, tpr0, p, n, n_samples):\n",
+    "    fprs = np.tile(np.linspace(0, 1, n), (p, 1)).reshape(p*n)\n",
+    "    tprs = np.tile(np.linspace(0, 1, p), (n, 1)).T.reshape(p*n)\n",
+    "    mask = tprs >= fprs\n",
+    "    fprs = fprs[mask]\n",
+    "    tprs = tprs[mask]\n",
+    "\n",
+    "    mask_left = (fprs <= fpr0) & (tprs <= tpr0)\n",
+    "    mask_right = (fprs >= fpr0) & (tprs >= tpr0)\n",
+    "    curves_left_fpr, curves_left_tpr = sample1(fprs[mask_left], tprs[mask_left], n_samples)\n",
+    "    curves_right_fpr, curves_right_tpr = sample1(fprs[mask_right], tprs[mask_right], n_samples)\n",
+    "\n",
+    "    fprs_all = [np.hstack([[0], fprs_left, [fpr0], fprs_right, [1]]) for fprs_left, fprs_right in zip(curves_left_fpr, curves_right_fpr)]\n",
+    "    tprs_all = [np.hstack([[0], tprs_left, [tpr0], tprs_right, [1]]) for tprs_left, tprs_right in zip(curves_left_tpr, curves_right_tpr)]\n",
+    "\n",
+    "    return fprs_all, tprs_all\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlscorecheck.auc import integrate_roc_curves\n",
+    "\n",
+    "def sample0_min_max(fpr1, tpr1, fpr2, tpr2):\n",
+    "    active = np.repeat(True, len(fpr1))\n",
+    "    fpr_result = np.repeat(-1.0, len(fpr1))\n",
+    "    tpr_result = np.repeat(-1.0, len(fpr1))\n",
+    "    n_active = len(fpr1)\n",
+    "\n",
+    "    fpr_result[active] = (fpr2[active] - fpr1[active]) * np.random.random_sample(n_active) + fpr1[active]\n",
+    "    tpr_result[active] = (tpr2[active] - tpr1[active]) * np.random.random_sample(n_active) + tpr1[active]\n",
+    "\n",
+    "    return fpr_result, tpr_result\n",
+    "\n",
+    "def sample0_rmin_max(fpr1, tpr1, fpr2, tpr2):\n",
+    "    active = np.repeat(True, len(fpr1))\n",
+    "    fpr_result = np.repeat(-1.0, len(fpr1))\n",
+    "    tpr_result = np.repeat(-1.0, len(fpr1))\n",
+    "    n_active = len(fpr1)\n",
+    "\n",
+    "    while n_active > 0:\n",
+    "        \n",
+    "        fpr_result[active] = (fpr2[active] - fpr1[active]) * np.random.random_sample(n_active) + fpr1[active]\n",
+    "        tpr_result[active] = (tpr2[active] - tpr1[active]) * np.random.random_sample(n_active) + tpr1[active]\n",
+    "\n",
+    "        lower_bounds = np.max(np.vstack([tpr1, fpr_result]).T, axis=1)\n",
+    "\n",
+    "        active = active & (tpr_result < lower_bounds)\n",
+    "\n",
+    "        n_active = np.sum(active)\n",
+    "\n",
+    "    return fpr_result, tpr_result\n",
+    "\n",
+    "def sample0_rmin_maxa(fpr1, tpr1, fpr2, tpr2, max_acc, p, n):\n",
+    "    active = np.repeat(True, len(fpr1))\n",
+    "    fpr_result = np.repeat(-1.0, len(fpr1))\n",
+    "    tpr_result = np.repeat(-1.0, len(fpr1))\n",
+    "    n_active = len(fpr1)\n",
+    "\n",
+    "    while n_active > 0:\n",
+    "        \n",
+    "        fpr_result[active] = (fpr2[active] - fpr1[active]) * np.random.random_sample(n_active) + fpr1[active]\n",
+    "        tpr_result[active] = (tpr2[active] - tpr1[active]) * np.random.random_sample(n_active) + tpr1[active]\n",
+    "\n",
+    "        maxa_bounds = (max_acc * (p + n) - (1 - fpr_result) * n) / p\n",
+    "\n",
+    "        upper_bounds = np.min(np.vstack([tpr2, maxa_bounds]).T, axis=1)\n",
+    "        lower_bounds = np.max(np.vstack([tpr1, fpr_result]).T, axis=1)\n",
+    "\n",
+    "        active = active & ((tpr_result < lower_bounds) | (tpr_result > upper_bounds))\n",
+    "\n",
+    "        n_active = np.sum(active)\n",
+    "\n",
+    "    return fpr_result, tpr_result\n",
+    "\n",
+    "def sample1(fpr0, tpr0, n_samples, n_nodes, p=None, n=None, max_acc=None, mode='min-max'):\n",
+    "    fpr0s = np.repeat(fpr0, n_samples)\n",
+    "    tpr0s = np.repeat(tpr0, n_samples)\n",
+    "    zeros = np.repeat(0.0, n_samples)\n",
+    "    ones = np.repeat(1.0, n_samples)\n",
+    "\n",
+    "    curves_fpr = np.zeros((n_samples, n_nodes))\n",
+    "    curves_tpr = np.zeros((n_samples, n_nodes))\n",
+    "\n",
+    "    curves_fpr[:, 0] = zeros\n",
+    "    curves_tpr[:, 0] = zeros\n",
+    "    curves_fpr[:, 1] = ones\n",
+    "    curves_tpr[:, 1] = ones\n",
+    "\n",
+    "    curves_fpr[:, 2] = fpr0s\n",
+    "    curves_tpr[:, 2] = tpr0s\n",
+    "\n",
+    "    pool = [(0, 2), (2, 1)]\n",
+    "\n",
+    "    for idx in range(n_nodes - 3):\n",
+    "        left, right = pool[0]\n",
+    "        pool = pool[1:]\n",
+    "        if mode == 'min-max':\n",
+    "            fprs_new, tprs_new = sample0_min_max(curves_fpr[:, left], curves_tpr[:, left], curves_fpr[:, right], curves_tpr[:, right])\n",
+    "        elif mode == 'rmin-max':\n",
+    "            fprs_new, tprs_new = sample0_rmin_max(curves_fpr[:, left], curves_tpr[:, left], curves_fpr[:, right], curves_tpr[:, right])\n",
+    "        elif mode == 'rmin-maxa':\n",
+    "            fprs_new, tprs_new = sample0_rmin_maxa(curves_fpr[:, left], curves_tpr[:, left], curves_fpr[:, right], curves_tpr[:, right], max_acc, p, n)\n",
+    "        curves_fpr[:, idx+3] = fprs_new\n",
+    "        curves_tpr[:, idx+3] = tprs_new\n",
+    "        pool = pool + [(left, idx+3), (idx+3, right)]\n",
+    "    \n",
+    "    sorting = np.argsort(curves_fpr, axis=1)\n",
+    "    curves_fpr = curves_fpr[np.arange(n_samples)[:, None], sorting]\n",
+    "    curves_tpr = curves_tpr[np.arange(n_samples)[:, None], sorting]\n",
+    "\n",
+    "    if n is not None:\n",
+    "        curves_tpr = np.round(curves_tpr * n) / n\n",
+    "\n",
+    "    if p is not None:\n",
+    "        curves_tpr = np.round(curves_tpr * p) / p\n",
+    "    \n",
+    "    return curves_fpr, curves_tpr\n",
+    "\n",
+    "def sample2(fpr0, tpr0, n_samples, n_nodes, p=None, n=None, max_acc=None):\n",
+    "    aucs = integrate_roc_curves(*sample1(fpr0, tpr0, n_samples, n_nodes, p, n, max_acc))\n",
+    "    return np.mean(aucs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlscorecheck.auc import auc_rmin, auc_maxa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7692307692307693, 0.6230769230769231)"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(p, n)/(p + n), max_acc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.7422240840403453), 0.7424166666666667)"
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fpr = 0.2\n",
+    "tpr = 0.7\n",
+    "p=30_000\n",
+    "n=100_000\n",
+    "max_acc = ((1 - fpr) *  n + tpr * p) / (p + n)\n",
+    "sample2(fpr, tpr, 20000, 100, p, n, max_acc), (auc_rmin(fpr, tpr) + auc_maxa(max_acc, p, n))/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for +: 'NoneType' and 'NoneType'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[231], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fprs, tprs \u001b[38;5;241m=\u001b[39m sample1(fpr, tpr, \u001b[38;5;241m1000\u001b[39m, \u001b[38;5;241m100\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[209], line 77\u001b[0m, in \u001b[0;36msample1\u001b[0;34m(fpr0, tpr0, n_samples, n_nodes, p, n, max_acc)\u001b[0m\n\u001b[1;32m     75\u001b[0m left, right \u001b[38;5;241m=\u001b[39m pool[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     76\u001b[0m pool \u001b[38;5;241m=\u001b[39m pool[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m---> 77\u001b[0m fprs_new, tprs_new \u001b[38;5;241m=\u001b[39m sample0_rmin_maxa(curves_fpr[:, left], curves_tpr[:, left], curves_fpr[:, right], curves_tpr[:, right], max_acc, p, n)\n\u001b[1;32m     78\u001b[0m curves_fpr[:, idx\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m3\u001b[39m] \u001b[38;5;241m=\u001b[39m fprs_new\n\u001b[1;32m     79\u001b[0m curves_tpr[:, idx\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m3\u001b[39m] \u001b[38;5;241m=\u001b[39m tprs_new\n",
+      "Cell \u001b[0;32mIn[209], line 44\u001b[0m, in \u001b[0;36msample0_rmin_maxa\u001b[0;34m(fpr1, tpr1, fpr2, tpr2, max_acc, p, n)\u001b[0m\n\u001b[1;32m     41\u001b[0m fpr_result[active] \u001b[38;5;241m=\u001b[39m (fpr2[active] \u001b[38;5;241m-\u001b[39m fpr1[active]) \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandom_sample(n_active) \u001b[38;5;241m+\u001b[39m fpr1[active]\n\u001b[1;32m     42\u001b[0m tpr_result[active] \u001b[38;5;241m=\u001b[39m (tpr2[active] \u001b[38;5;241m-\u001b[39m tpr1[active]) \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandom_sample(n_active) \u001b[38;5;241m+\u001b[39m tpr1[active]\n\u001b[0;32m---> 44\u001b[0m maxa_bounds \u001b[38;5;241m=\u001b[39m (max_acc \u001b[38;5;241m*\u001b[39m (p \u001b[38;5;241m+\u001b[39m n) \u001b[38;5;241m-\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m fpr_result) \u001b[38;5;241m*\u001b[39m n) \u001b[38;5;241m/\u001b[39m p\n\u001b[1;32m     46\u001b[0m upper_bounds \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmin(np\u001b[38;5;241m.\u001b[39mvstack([tpr2, maxa_bounds])\u001b[38;5;241m.\u001b[39mT, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     47\u001b[0m lower_bounds \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmax(np\u001b[38;5;241m.\u001b[39mvstack([tpr1, fpr_result])\u001b[38;5;241m.\u001b[39mT, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'NoneType' and 'NoneType'"
+     ]
+    }
+   ],
+   "source": [
+    "fprs, tprs = sample1(fpr, tpr, 1000, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for idx in range(20):\n",
+    "    plt.plot(fprs[idx], tprs[idx])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.2623352497568346)"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aucs = [integrate_roc_curve(fpr, tpr) for fpr, tpr in zip(fprs, tprs)]\n",
+    "np.mean(aucs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fprs, tprs = sample2(0.024, 0.776, 300, 3000, 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from mlscorecheck.auc import integrate_roc_curve, auc_min, auc_max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aucs = [integrate_roc_curve(fpr, tpr) for fpr, tpr in zip(fprs, tprs)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'for fpr, tpr in zip(fprs, tprs):\\n    plt.plot(fpr, tpr)'"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"for fpr, tpr in zip(fprs, tprs):\n",
+    "    plt.plot(fpr, tpr)\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 29.,  87., 113., 133., 134., 139., 142., 108.,  81.,  34.]),\n",
+       " array([0.79662978, 0.81553803, 0.83444629, 0.85335454, 0.8722628 ,\n",
+       "        0.89117105, 0.91007931, 0.92898756, 0.94789582, 0.96680407,\n",
+       "        0.98571233]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(aucs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.891373880488591)"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(aucs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.876"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(auc_min(0.024, 0.776) + auc_max(0.024, 0.776))/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7eff4ada2db0>]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(curve_fpr, curve_tpr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0, 1, 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7eff60bcf500>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.plot(x, 1 - np.exp(-0.5*x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ress = []\n",
+    "\n",
+    "for _ in range(1):\n",
+    "    fprs = np.cumsum(np.random.random_sample((1000, 10000)), axis=1)\n",
+    "    fprs = (fprs.T / (fprs[:, -1])).T\n",
+    "    lower_bounds = fprs\n",
+    "    upper_bounds = np.ones(fprs.shape)\n",
+    "    tprs = np.cumsum(np.random.random_sample((1000, 10000)), axis=1)\n",
+    "    tprs = (tprs.T / (tprs[:, -1])).T\n",
+    "\n",
+    "    ress.append(np.sum((tprs[:, 1:] + tprs[:, :-1])/2*(fprs[:, 1:] - fprs[:, :-1]), axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7eff73e61340>]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.plot(fprs[100], tprs[100])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.6665404626043141)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(np.hstack(ress))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7853981633974483"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.pi/4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.7486049399116135)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(1.3602613016103413)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1/np.mean(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tpr = sp.Symbol('tpr', nonnegative=True)\n",
+    "fpr = sp.Symbol('fpr', nonnegative=True)\n",
+    "fracs = sp.Symbol('fracs', nonnegative=True)\n",
+    "y = sp.Symbol('y', nonnegative=True)\n",
+    "x = sp.Symbol('x', nonnegative=True)\n",
+    "p = sp.Symbol('p', nonnegative=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2*(y**p*log(y) + (1 - x)**p*log(1 - x))*(y**p + (1 - x)**p - 1.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# implicit\n",
+    "print(sp.simplify(sp.diff((1.0 - (1 - x)**p - y**p)**2, p)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2*(y**p*log(y) + (1 - x)**p*log(1 - x))**2 + 2*(y**p*log(y)**2 + (1 - x)**p*log(1 - x)**2)*(y**p + (1 - x)**p - 1.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sp.simplify(sp.diff(sp.diff((1.0 - (1 - x)**p - y**p)**2, p), p)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2*(y - (1 - x**p)**(1/p))*(p*x**p*log(x) - (x**p - 1)*log(1 - x**p))/(p**2*(1 - x**p)**((p - 1)/p))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# explicit\n",
+    "print(sp.simplify(sp.diff(((1 - x**p)**(1/p) - y)**2, p)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2*(p*(1 - x**p)**((2*p + 1)/p)*(y - (1 - x**p)**(1/p))*(p**2*x**(2*p)*log(x)**2 - p**2*x**p*(x**p - 1)*log(x)**2 + 2*p*x**p*(x**p - 1)*log(x) - 2*(x**p - 1)**2*log(1 - x**p)) + (1 - x**p)**(2 + 1/p)*(-y + 2*(1 - x**p)**(1/p))*(p*x**p*log(x) - (x**p - 1)*log(1 - x**p))**2)/(p**4*(x**p - 1)**4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sp.simplify(sp.diff(sp.diff(((1 - x**p)**(1/p) - y)**2, p), p)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2*(1 - (1 - fracs)**p)**(1/p)*(-y + (1 - (1 - fracs)**p)**(1/p))*(-(1 - fracs)**p*log(1 - fracs)/(p*(1 - (1 - fracs)**p)) - log(1 - (1 - fracs)**p)/p**2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((sp.diff(((1 - (1 - fracs)**p)**(1/p) - y)**2, p)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2*(1 - (1 - fracs)**p)**(2/p)*(-(1 - fracs)**p*log(1 - fracs)/(p*(1 - (1 - fracs)**p)) - log(1 - (1 - fracs)**p)/p**2)**2 + 2*(1 - (1 - fracs)**p)**(1/p)*(-y + (1 - (1 - fracs)**p)**(1/p))*(-(1 - fracs)**p*log(1 - fracs)/(p*(1 - (1 - fracs)**p)) - log(1 - (1 - fracs)**p)/p**2)**2 + 2*(1 - (1 - fracs)**p)**(1/p)*(-y + (1 - (1 - fracs)**p)**(1/p))*(-(1 - fracs)**(2*p)*log(1 - fracs)**2/(p*(1 - (1 - fracs)**p)**2) - (1 - fracs)**p*log(1 - fracs)**2/(p*(1 - (1 - fracs)**p)) + 2*(1 - fracs)**p*log(1 - fracs)/(p**2*(1 - (1 - fracs)**p)) + 2*log(1 - (1 - fracs)**p)/p**3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sp.diff((sp.diff(((1 - (1 - fracs)**p)**(1/p) - y)**2, p)), p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(-2*x**p*log(x) - 2*y**p*log(y))*(-x**p - y**p + 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sp.diff((1 - x**p - y**p)**2, p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(-2*x**p*log(x) - 2*y**p*log(y))*(-x**p*log(x) - y**p*log(y)) + (-2*x**p*log(x)**2 - 2*y**p*log(y)**2)*(-x**p - y**p + 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sp.diff(sp.diff((1 - x**p - y**p)**2, p), p))"
    ]
   },
   {
diff --git a/notebooks/auc_experiments/01-experiment-aggregated.ipynb b/notebooks/auc_experiments/01-experiment-aggregated.ipynb
index 404f8c9..4027298 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": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,27 +22,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "N_SAMPLES = 20_000\n",
-    "N_SAMPLES = 2_400\n",
-    "output_file = 'raw-aggregated-20k-b.csv'"
+    "#N_SAMPLES = 2_400\n",
+    "output_file = 'raw-aggregated-20k.csv'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3"
+       "31"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -53,18 +53,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<function common_datasets.binary_classification._binary_classification_part1.load_hypothyroid()>,\n",
+       "[<function common_datasets.binary_classification._binary_classification_part0.load_abalone9_18()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_appendicitis()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_australian()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_bupa()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_cm1()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_crx()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_dermatology_6()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part0.load_ecoli1()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part0.load_glass0()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_haberman()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_hepatitis()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_hypothyroid()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_ionosphere()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_iris0()>,\n",
        " <function common_datasets.binary_classification._binary_classification_part1.load_kc1()>,\n",
-       " <function common_datasets.binary_classification._binary_classification_part1.load_segment0()>]"
+       " <function common_datasets.binary_classification._binary_classification_part1.load_mammographic()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_monk_2()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_new_thyroid1()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_page_blocks_1_3_vs_4()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_pc1()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_pima()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_saheart()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_satimage()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_segment0()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_shuttle_c0_vs_c4()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_spectf()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_vehicle0()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_vowel0()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_wdbc()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part1.load_wisconsin()>,\n",
+       " <function common_datasets.binary_classification._binary_classification_part0.load_yeast1()>]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -75,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -119,9 +147,37 @@
       "\\toprule\n",
       " & name & size & attr. & p & n & imb. ratio \\\\\n",
       "\\midrule\n",
-      "1 & hypothyroid \\cite{krnn} & 3163 & 25 & 151 & 3012 & 19.95 \\\\\n",
-      "2 & KC1 \\cite{krnn} & 2109 & 21 & 326 & 1783 & 5.47 \\\\\n",
-      "3 & segment0 \\cite{keel} & 2308 & 19 & 329 & 1979 & 6.02 \\\\\n",
+      "1 & abalone9 18 \\cite{keel} & 731 & 9 & 42 & 689 & 16.40 \\\\\n",
+      "2 & appendicitis \\cite{keel} & 106 & 7 & 21 & 85 & 4.05 \\\\\n",
+      "3 & australian \\cite{keel} & 690 & 16 & 307 & 383 & 1.25 \\\\\n",
+      "4 & bupa \\cite{keel} & 345 & 6 & 145 & 200 & 1.38 \\\\\n",
+      "5 & CM1 \\cite{krnn} & 498 & 21 & 49 & 449 & 9.16 \\\\\n",
+      "6 & crx \\cite{keel} & 653 & 37 & 296 & 357 & 1.21 \\\\\n",
+      "7 & dermatology-6 \\cite{keel} & 358 & 34 & 20 & 338 & 16.90 \\\\\n",
+      "8 & ecoli1 \\cite{keel} & 336 & 7 & 77 & 259 & 3.36 \\\\\n",
+      "9 & glass0 \\cite{keel} & 214 & 9 & 70 & 144 & 2.06 \\\\\n",
+      "10 & haberman \\cite{keel} & 306 & 3 & 81 & 225 & 2.78 \\\\\n",
+      "11 & hepatitis \\cite{krnn} & 155 & 19 & 32 & 123 & 3.84 \\\\\n",
+      "12 & hypothyroid \\cite{krnn} & 3163 & 25 & 151 & 3012 & 19.95 \\\\\n",
+      "13 & ionosphere \\cite{keel} & 351 & 33 & 126 & 225 & 1.79 \\\\\n",
+      "14 & iris0 \\cite{keel} & 150 & 4 & 50 & 100 & 2.00 \\\\\n",
+      "15 & KC1 \\cite{krnn} & 2109 & 21 & 326 & 1783 & 5.47 \\\\\n",
+      "16 & mammographic \\cite{keel} & 830 & 5 & 403 & 427 & 1.06 \\\\\n",
+      "17 & monk-2 \\cite{keel} & 432 & 6 & 204 & 228 & 1.12 \\\\\n",
+      "18 & new thyroid1 \\cite{keel} & 215 & 5 & 35 & 180 & 5.14 \\\\\n",
+      "19 & page-blocks-1-3 vs 4 \\cite{keel} & 472 & 10 & 28 & 444 & 15.86 \\\\\n",
+      "20 & PC1 \\cite{krnn} & 1109 & 21 & 77 & 1032 & 13.40 \\\\\n",
+      "21 & pima \\cite{keel} & 768 & 8 & 268 & 500 & 1.87 \\\\\n",
+      "22 & saheart \\cite{keel} & 462 & 9 & 160 & 302 & 1.89 \\\\\n",
+      "23 & SATIMAGE \\cite{krnn} & 6435 & 36 & 626 & 5809 & 9.28 \\\\\n",
+      "24 & segment0 \\cite{keel} & 2308 & 19 & 329 & 1979 & 6.02 \\\\\n",
+      "25 & shuttle-c0-vs-c4 \\cite{keel} & 1829 & 9 & 123 & 1706 & 13.87 \\\\\n",
+      "26 & SPECTF \\cite{krnn} & 267 & 44 & 55 & 212 & 3.85 \\\\\n",
+      "27 & vehicle0 \\cite{keel} & 846 & 18 & 199 & 647 & 3.25 \\\\\n",
+      "28 & vowel0 \\cite{keel} & 988 & 13 & 90 & 898 & 9.98 \\\\\n",
+      "29 & wdbc \\cite{keel} & 569 & 30 & 212 & 357 & 1.68 \\\\\n",
+      "30 & wisconsin \\cite{keel} & 683 & 9 & 239 & 444 & 1.86 \\\\\n",
+      "31 & yeast1 \\cite{keel} & 1484 & 8 & 429 & 1055 & 2.46 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n"
@@ -135,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -287,14 +343,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "   RandomForestClassifier                  KC1: 100%|██████████| 2400/2400 [1:13:58<00:00,  1.85s/it]\n"
+      "   DecisionTreeClassifier                 bupa:   0%|          | 0/20000 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "            XGBClassifier           australian: 100%|██████████| 20000/20000 [4:25:21<00:00,  1.26it/s]   \n"
      ]
     }
    ],
@@ -380,16 +443,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0"
+       "4"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -400,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -459,123 +522,123 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>329</td>\n",
-       "      <td>1979</td>\n",
-       "      <td>8</td>\n",
-       "      <td>segment0</td>\n",
-       "      <td>RandomForestClassifier</td>\n",
-       "      <td>{'max_depth': 302, 'random_state': 5}</td>\n",
-       "      <td>0.998698</td>\n",
-       "      <td>0.990854</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>200</td>\n",
+       "      <td>145</td>\n",
+       "      <td>9</td>\n",
+       "      <td>bupa</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>{'max_depth': 76, 'random_state': 5}</td>\n",
+       "      <td>0.634578</td>\n",
+       "      <td>0.705753</td>\n",
+       "      <td>0.537582</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.998556</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>5</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>10</td>\n",
        "      <td>3</td>\n",
-       "      <td>3.750000</td>\n",
        "      <td>3.000000</td>\n",
-       "      <td>[0.0, 0.0, 0.0030364372469635637, 0.0030364372...</td>\n",
-       "      <td>[0.0, 0.975609756097561, 0.975609756097561, 1....</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>[0.0, 0.3891389826132473, 0.4026173392062139, ...</td>\n",
+       "      <td>[0.0, 0.5909090909090909, 0.6086956521739131, ...</td>\n",
        "      <td>[0.0, 0.0, 1.0]</td>\n",
        "      <td>[0.0, 1.0, 1.0]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>3012</td>\n",
-       "      <td>151</td>\n",
-       "      <td>6</td>\n",
-       "      <td>hypothyroid</td>\n",
-       "      <td>KNeighborsClassifier</td>\n",
-       "      <td>{'n_neighbors': 81}</td>\n",
-       "      <td>0.980398</td>\n",
-       "      <td>0.994024</td>\n",
-       "      <td>0.708718</td>\n",
-       "      <td>0.980651</td>\n",
+       "      <td>144</td>\n",
+       "      <td>70</td>\n",
+       "      <td>3</td>\n",
+       "      <td>glass0</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>{'max_depth': 2, 'random_state': 5}</td>\n",
+       "      <td>0.831834</td>\n",
+       "      <td>0.812500</td>\n",
+       "      <td>0.871377</td>\n",
+       "      <td>0.862110</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.950974</td>\n",
-       "      <td>0.983162</td>\n",
-       "      <td>41</td>\n",
-       "      <td>92</td>\n",
-       "      <td>15.166667</td>\n",
-       "      <td>31.000000</td>\n",
-       "      <td>[0.0, 0.006474543707973068, 0.0075438596491227...</td>\n",
-       "      <td>[0.0, 0.6713147410358565, 0.6912350597609562, ...</td>\n",
-       "      <td>[0.0, 0.0, 0.0001165117745428601, 0.0002770955...</td>\n",
-       "      <td>[0.0, 0.6804780876494024, 0.6872509960159362, ...</td>\n",
+       "      <td>0.942800</td>\n",
+       "      <td>0.929068</td>\n",
+       "      <td>35</td>\n",
+       "      <td>49</td>\n",
+       "      <td>21.666667</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>[0.0, 0.0, 0.01449275362318847, 0.014492753623...</td>\n",
+       "      <td>[0.0, 0.3125, 0.3125, 0.5, 0.5, 0.5625, 0.5625...</td>\n",
+       "      <td>[0.0, 0.0, 0.007092198581560294, 0.00709219858...</td>\n",
+       "      <td>[0.0, 0.45833333333333326, 0.45833333333333326...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1979</td>\n",
-       "      <td>329</td>\n",
-       "      <td>9</td>\n",
-       "      <td>segment0</td>\n",
-       "      <td>XGBClassifier</td>\n",
-       "      <td>{'random_state': 5, 'max_depth': 4}</td>\n",
-       "      <td>0.997837</td>\n",
-       "      <td>0.998990</td>\n",
-       "      <td>0.990908</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>77</td>\n",
+       "      <td>259</td>\n",
        "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.888889</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>[0.0, 0.0, 0.02702702702702703, 0.027027027027...</td>\n",
-       "      <td>[0.0, 0.9474747474747475, 0.9474747474747475, ...</td>\n",
-       "      <td>[0.0, 0.0, 1.0]</td>\n",
-       "      <td>[0.0, 1.0, 1.0]</td>\n",
+       "      <td>ecoli1</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>{'n_neighbors': 70}</td>\n",
+       "      <td>0.877976</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>0.949807</td>\n",
+       "      <td>0.875992</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.848134</td>\n",
+       "      <td>0.939335</td>\n",
+       "      <td>19</td>\n",
+       "      <td>75</td>\n",
+       "      <td>9.571429</td>\n",
+       "      <td>27.571429</td>\n",
+       "      <td>[0.0, 0.0, 0.0038610038610038533, 0.0193050193...</td>\n",
+       "      <td>[0.0, 0.18181818181818188, 0.2727272727272727,...</td>\n",
+       "      <td>[0.0, 0.0, 0.0003217503217503026, 0.0008273579...</td>\n",
+       "      <td>[0.0, 0.10606060606060608, 0.13636363636363635...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>151</td>\n",
-       "      <td>3012</td>\n",
+       "      <td>259</td>\n",
+       "      <td>77</td>\n",
        "      <td>2</td>\n",
-       "      <td>hypothyroid</td>\n",
-       "      <td>DecisionTreeClassifier</td>\n",
-       "      <td>{'max_depth': 474, 'random_state': 5}</td>\n",
-       "      <td>0.987038</td>\n",
-       "      <td>0.861228</td>\n",
-       "      <td>0.993360</td>\n",
-       "      <td>0.999684</td>\n",
+       "      <td>ecoli1</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>{'probability': True, 'C': 0.19005782496750956...</td>\n",
+       "      <td>0.889881</td>\n",
+       "      <td>0.896064</td>\n",
+       "      <td>0.870108</td>\n",
+       "      <td>0.910714</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.999668</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.500000</td>\n",
-       "      <td>3.500000</td>\n",
-       "      <td>[0.0, 0.006640106241699867, 0.0079681274900398...</td>\n",
-       "      <td>[0.0, 0.8612280701754386, 0.8613512055539919, ...</td>\n",
-       "      <td>[0.0, 0.0, 0.0006640106241699867, 1.0]</td>\n",
-       "      <td>[0.0, 0.993421052631579, 1.0, 1.0]</td>\n",
+       "      <td>0.896086</td>\n",
+       "      <td>0.960603</td>\n",
+       "      <td>53</td>\n",
+       "      <td>43</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>[0.0, 0.0, 0.02564102564102564, 0.025641025641...</td>\n",
+       "      <td>[0.0, 0.4860763267740012, 0.4860763267740012, ...</td>\n",
+       "      <td>[0.0, 0.0, 0.02564102564102564, 0.025641025641...</td>\n",
+       "      <td>[0.0, 0.657304710793083, 0.657304710793083, 0....</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1783</td>\n",
-       "      <td>326</td>\n",
-       "      <td>7</td>\n",
-       "      <td>KC1</td>\n",
-       "      <td>XGBClassifier</td>\n",
-       "      <td>{'random_state': 5, 'max_depth': 4}</td>\n",
-       "      <td>0.863426</td>\n",
-       "      <td>0.965795</td>\n",
-       "      <td>0.303225</td>\n",
-       "      <td>0.978900</td>\n",
+       "      <td>204</td>\n",
+       "      <td>228</td>\n",
+       "      <td>5</td>\n",
+       "      <td>monk-2</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>{'max_depth': 1, 'random_state': 5}</td>\n",
+       "      <td>0.768511</td>\n",
+       "      <td>0.533780</td>\n",
+       "      <td>0.978164</td>\n",
+       "      <td>0.800922</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.949898</td>\n",
-       "      <td>0.983133</td>\n",
-       "      <td>174</td>\n",
-       "      <td>181</td>\n",
-       "      <td>63.571429</td>\n",
-       "      <td>60.571429</td>\n",
-       "      <td>[0.0, 0.0, 0.02127659574468085, 0.021276595744...</td>\n",
-       "      <td>[0.0, 0.27932684884977615, 0.2837936246705082,...</td>\n",
-       "      <td>[0.0, 0.0, 0.0035714285714285713, 0.0035714285...</td>\n",
-       "      <td>[0.0, 0.3798920814392207, 0.40171949684907193,...</td>\n",
+       "      <td>0.966072</td>\n",
+       "      <td>0.791285</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>[0.0, 0.0, 0.022222222222222223, 0.08695652173...</td>\n",
+       "      <td>[0.0, 0.2801219512195122, 0.442840108401084, 0...</td>\n",
+       "      <td>[0.0, 0.0, 0.04395604395604396, 0.060109289617...</td>\n",
+       "      <td>[0.0, 0.22017806374382762, 0.544220174558346, ...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -583,66 +646,66 @@
        "</div>"
       ],
       "text/plain": [
-       "      p     n  k      dataset              classifier  \\\n",
-       "0   329  1979  8     segment0  RandomForestClassifier   \n",
-       "1  3012   151  6  hypothyroid    KNeighborsClassifier   \n",
-       "2  1979   329  9     segment0           XGBClassifier   \n",
-       "3   151  3012  2  hypothyroid  DecisionTreeClassifier   \n",
-       "4  1783   326  7          KC1           XGBClassifier   \n",
+       "     p    n  k dataset              classifier  \\\n",
+       "0  200  145  9    bupa  DecisionTreeClassifier   \n",
+       "1  144   70  3  glass0  RandomForestClassifier   \n",
+       "2   77  259  7  ecoli1    KNeighborsClassifier   \n",
+       "3  259   77  2  ecoli1                     SVC   \n",
+       "4  204  228  5  monk-2  DecisionTreeClassifier   \n",
        "\n",
-       "                       classifier_params  best_acc  best_sens  best_spec  \\\n",
-       "0  {'max_depth': 302, 'random_state': 5}  0.998698   0.990854   1.000000   \n",
-       "1                    {'n_neighbors': 81}  0.980398   0.994024   0.708718   \n",
-       "2    {'random_state': 5, 'max_depth': 4}  0.997837   0.998990   0.990908   \n",
-       "3  {'max_depth': 474, 'random_state': 5}  0.987038   0.861228   0.993360   \n",
-       "4    {'random_state': 5, 'max_depth': 4}  0.863426   0.965795   0.303225   \n",
+       "                                   classifier_params  best_acc  best_sens  \\\n",
+       "0               {'max_depth': 76, 'random_state': 5}  0.634578   0.705753   \n",
+       "1                {'max_depth': 2, 'random_state': 5}  0.831834   0.812500   \n",
+       "2                                {'n_neighbors': 70}  0.877976   0.636364   \n",
+       "3  {'probability': True, 'C': 0.19005782496750956...  0.889881   0.896064   \n",
+       "4                {'max_depth': 1, 'random_state': 5}  0.768511   0.533780   \n",
        "\n",
-       "   best_acc_train  ...  spec_train  auc_train  n_nodes  n_nodes_train  \\\n",
-       "0        1.000000  ...    0.998556   1.000000        5              3   \n",
-       "1        0.980651  ...    0.950974   0.983162       41             92   \n",
-       "2        1.000000  ...    1.000000   1.000000        7              3   \n",
-       "3        0.999684  ...    0.999668   0.999998        4              4   \n",
-       "4        0.978900  ...    0.949898   0.983133      174            181   \n",
+       "   best_spec  best_acc_train  ...  spec_train  auc_train  n_nodes  \\\n",
+       "0   0.537582        1.000000  ...    1.000000   1.000000       10   \n",
+       "1   0.871377        0.862110  ...    0.942800   0.929068       35   \n",
+       "2   0.949807        0.875992  ...    0.848134   0.939335       19   \n",
+       "3   0.870108        0.910714  ...    0.896086   0.960603       53   \n",
+       "4   0.978164        0.800922  ...    0.966072   0.791285        5   \n",
        "\n",
-       "   avg_n_nodes  avg_n_nodes_train  \\\n",
-       "0     3.750000           3.000000   \n",
-       "1    15.166667          31.000000   \n",
-       "2     3.888889           3.000000   \n",
-       "3     3.500000           3.500000   \n",
-       "4    63.571429          60.571429   \n",
+       "   n_nodes_train  avg_n_nodes  avg_n_nodes_train  \\\n",
+       "0              3     3.000000           3.000000   \n",
+       "1             49    21.666667          29.000000   \n",
+       "2             75     9.571429          27.571429   \n",
+       "3             43    28.000000          23.000000   \n",
+       "4              6     3.000000           3.000000   \n",
        "\n",
        "                                                fprs  \\\n",
-       "0  [0.0, 0.0, 0.0030364372469635637, 0.0030364372...   \n",
-       "1  [0.0, 0.006474543707973068, 0.0075438596491227...   \n",
-       "2  [0.0, 0.0, 0.02702702702702703, 0.027027027027...   \n",
-       "3  [0.0, 0.006640106241699867, 0.0079681274900398...   \n",
-       "4  [0.0, 0.0, 0.02127659574468085, 0.021276595744...   \n",
+       "0  [0.0, 0.3891389826132473, 0.4026173392062139, ...   \n",
+       "1  [0.0, 0.0, 0.01449275362318847, 0.014492753623...   \n",
+       "2  [0.0, 0.0, 0.0038610038610038533, 0.0193050193...   \n",
+       "3  [0.0, 0.0, 0.02564102564102564, 0.025641025641...   \n",
+       "4  [0.0, 0.0, 0.022222222222222223, 0.08695652173...   \n",
        "\n",
        "                                                tprs  \\\n",
-       "0  [0.0, 0.975609756097561, 0.975609756097561, 1....   \n",
-       "1  [0.0, 0.6713147410358565, 0.6912350597609562, ...   \n",
-       "2  [0.0, 0.9474747474747475, 0.9474747474747475, ...   \n",
-       "3  [0.0, 0.8612280701754386, 0.8613512055539919, ...   \n",
-       "4  [0.0, 0.27932684884977615, 0.2837936246705082,...   \n",
+       "0  [0.0, 0.5909090909090909, 0.6086956521739131, ...   \n",
+       "1  [0.0, 0.3125, 0.3125, 0.5, 0.5, 0.5625, 0.5625...   \n",
+       "2  [0.0, 0.18181818181818188, 0.2727272727272727,...   \n",
+       "3  [0.0, 0.4860763267740012, 0.4860763267740012, ...   \n",
+       "4  [0.0, 0.2801219512195122, 0.442840108401084, 0...   \n",
        "\n",
        "                                          fprs_train  \\\n",
        "0                                    [0.0, 0.0, 1.0]   \n",
-       "1  [0.0, 0.0, 0.0001165117745428601, 0.0002770955...   \n",
-       "2                                    [0.0, 0.0, 1.0]   \n",
-       "3             [0.0, 0.0, 0.0006640106241699867, 1.0]   \n",
-       "4  [0.0, 0.0, 0.0035714285714285713, 0.0035714285...   \n",
+       "1  [0.0, 0.0, 0.007092198581560294, 0.00709219858...   \n",
+       "2  [0.0, 0.0, 0.0003217503217503026, 0.0008273579...   \n",
+       "3  [0.0, 0.0, 0.02564102564102564, 0.025641025641...   \n",
+       "4  [0.0, 0.0, 0.04395604395604396, 0.060109289617...   \n",
        "\n",
        "                                          tprs_train  \n",
        "0                                    [0.0, 1.0, 1.0]  \n",
-       "1  [0.0, 0.6804780876494024, 0.6872509960159362, ...  \n",
-       "2                                    [0.0, 1.0, 1.0]  \n",
-       "3                 [0.0, 0.993421052631579, 1.0, 1.0]  \n",
-       "4  [0.0, 0.3798920814392207, 0.40171949684907193,...  \n",
+       "1  [0.0, 0.45833333333333326, 0.45833333333333326...  \n",
+       "2  [0.0, 0.10606060606060608, 0.13636363636363635...  \n",
+       "3  [0.0, 0.657304710793083, 0.657304710793083, 0....  \n",
+       "4  [0.0, 0.22017806374382762, 0.544220174558346, ...  \n",
        "\n",
        "[5 rows x 28 columns]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -653,44 +716,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "p                                                                  329\n",
-       "n                                                                 1979\n",
-       "k                                                                    8\n",
-       "dataset                                                       segment0\n",
-       "classifier                                      RandomForestClassifier\n",
-       "classifier_params                {'max_depth': 302, 'random_state': 5}\n",
-       "best_acc                                                      0.998698\n",
-       "best_sens                                                     0.990854\n",
-       "best_spec                                                          1.0\n",
+       "p                                                                  200\n",
+       "n                                                                  145\n",
+       "k                                                                    9\n",
+       "dataset                                                           bupa\n",
+       "classifier                                      DecisionTreeClassifier\n",
+       "classifier_params                 {'max_depth': 76, 'random_state': 5}\n",
+       "best_acc                                                      0.634578\n",
+       "best_sens                                                     0.705753\n",
+       "best_spec                                                     0.537582\n",
        "best_acc_train                                                     1.0\n",
        "best_sens_train                                                    1.0\n",
        "best_spec_train                                                    1.0\n",
-       "acc                                                           0.991768\n",
-       "sens                                                          0.996951\n",
-       "spec                                                          0.990909\n",
-       "auc                                                           0.999926\n",
-       "acc_train                                                     0.998762\n",
+       "acc                                                           0.634578\n",
+       "sens                                                          0.705753\n",
+       "spec                                                          0.537582\n",
+       "auc                                                           0.621667\n",
+       "acc_train                                                          1.0\n",
        "sens_train                                                         1.0\n",
-       "spec_train                                                    0.998556\n",
+       "spec_train                                                         1.0\n",
        "auc_train                                                          1.0\n",
-       "n_nodes                                                              5\n",
+       "n_nodes                                                             10\n",
        "n_nodes_train                                                        3\n",
-       "avg_n_nodes                                                       3.75\n",
+       "avg_n_nodes                                                        3.0\n",
        "avg_n_nodes_train                                                  3.0\n",
-       "fprs                 [0.0, 0.0, 0.0030364372469635637, 0.0030364372...\n",
-       "tprs                 [0.0, 0.975609756097561, 0.975609756097561, 1....\n",
+       "fprs                 [0.0, 0.3891389826132473, 0.4026173392062139, ...\n",
+       "tprs                 [0.0, 0.5909090909090909, 0.6086956521739131, ...\n",
        "fprs_train                                             [0.0, 0.0, 1.0]\n",
        "tprs_train                                             [0.0, 1.0, 1.0]\n",
        "Name: 0, dtype: object"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -701,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/notebooks/auc_experiments/01-experiment-single.ipynb b/notebooks/auc_experiments/01-experiment-single.ipynb
index 7e16c61..9f6067a 100644
--- a/notebooks/auc_experiments/01-experiment-single.ipynb
+++ b/notebooks/auc_experiments/01-experiment-single.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,27 +25,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#N_SAMPLES = 20_000\n",
-    "N_SAMPLES = 2400\n",
-    "output_file = 'raw-single-20k-b.csv'"
+    "N_SAMPLES = 50_000\n",
+    "#N_SAMPLES = 2400\n",
+    "output_file = 'raw-single-50k-rs5-syn.csv'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3"
+       "31"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -91,9 +91,37 @@
       "\\toprule\n",
       " & name & size & attr. & p & n & imb. ratio \\\\\n",
       "\\midrule\n",
-      "1 & hypothyroid \\cite{krnn} & 3163 & 25 & 151 & 3012 & 19.95 \\\\\n",
-      "2 & KC1 \\cite{krnn} & 2109 & 21 & 326 & 1783 & 5.47 \\\\\n",
-      "3 & segment0 \\cite{keel} & 2308 & 19 & 329 & 1979 & 6.02 \\\\\n",
+      "1 & abalone9 18 \\cite{keel} & 731 & 9 & 42 & 689 & 16.40 \\\\\n",
+      "2 & appendicitis \\cite{keel} & 106 & 7 & 21 & 85 & 4.05 \\\\\n",
+      "3 & australian \\cite{keel} & 690 & 16 & 307 & 383 & 1.25 \\\\\n",
+      "4 & bupa \\cite{keel} & 345 & 6 & 145 & 200 & 1.38 \\\\\n",
+      "5 & CM1 \\cite{krnn} & 498 & 21 & 49 & 449 & 9.16 \\\\\n",
+      "6 & crx \\cite{keel} & 653 & 37 & 296 & 357 & 1.21 \\\\\n",
+      "7 & dermatology-6 \\cite{keel} & 358 & 34 & 20 & 338 & 16.90 \\\\\n",
+      "8 & ecoli1 \\cite{keel} & 336 & 7 & 77 & 259 & 3.36 \\\\\n",
+      "9 & glass0 \\cite{keel} & 214 & 9 & 70 & 144 & 2.06 \\\\\n",
+      "10 & haberman \\cite{keel} & 306 & 3 & 81 & 225 & 2.78 \\\\\n",
+      "11 & hepatitis \\cite{krnn} & 155 & 19 & 32 & 123 & 3.84 \\\\\n",
+      "12 & hypothyroid \\cite{krnn} & 3163 & 25 & 151 & 3012 & 19.95 \\\\\n",
+      "13 & ionosphere \\cite{keel} & 351 & 33 & 126 & 225 & 1.79 \\\\\n",
+      "14 & iris0 \\cite{keel} & 150 & 4 & 50 & 100 & 2.00 \\\\\n",
+      "15 & KC1 \\cite{krnn} & 2109 & 21 & 326 & 1783 & 5.47 \\\\\n",
+      "16 & mammographic \\cite{keel} & 830 & 5 & 403 & 427 & 1.06 \\\\\n",
+      "17 & monk-2 \\cite{keel} & 432 & 6 & 204 & 228 & 1.12 \\\\\n",
+      "18 & new thyroid1 \\cite{keel} & 215 & 5 & 35 & 180 & 5.14 \\\\\n",
+      "19 & page-blocks-1-3 vs 4 \\cite{keel} & 472 & 10 & 28 & 444 & 15.86 \\\\\n",
+      "20 & PC1 \\cite{krnn} & 1109 & 21 & 77 & 1032 & 13.40 \\\\\n",
+      "21 & pima \\cite{keel} & 768 & 8 & 268 & 500 & 1.87 \\\\\n",
+      "22 & saheart \\cite{keel} & 462 & 9 & 160 & 302 & 1.89 \\\\\n",
+      "23 & SATIMAGE \\cite{krnn} & 6435 & 36 & 626 & 5809 & 9.28 \\\\\n",
+      "24 & segment0 \\cite{keel} & 2308 & 19 & 329 & 1979 & 6.02 \\\\\n",
+      "25 & shuttle-c0-vs-c4 \\cite{keel} & 1829 & 9 & 123 & 1706 & 13.87 \\\\\n",
+      "26 & SPECTF \\cite{krnn} & 267 & 44 & 55 & 212 & 3.85 \\\\\n",
+      "27 & vehicle0 \\cite{keel} & 846 & 18 & 199 & 647 & 3.25 \\\\\n",
+      "28 & vowel0 \\cite{keel} & 988 & 13 & 90 & 898 & 9.98 \\\\\n",
+      "29 & wdbc \\cite{keel} & 569 & 30 & 212 & 357 & 1.68 \\\\\n",
+      "30 & wisconsin \\cite{keel} & 683 & 9 & 239 & 444 & 1.86 \\\\\n",
+      "31 & yeast1 \\cite{keel} & 1484 & 8 & 429 & 1055 & 2.46 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n"
@@ -107,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,14 +186,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sklearn.datasets\n",
+    "def synthetic_dataset(random_state):\n",
+    "    n_features = random_state.randint(2, 20)\n",
+    "    n_informative = random_state.randint(1, n_features+1)\n",
+    "    if n_informative < n_features:\n",
+    "        n_redundant = random_state.randint(1, n_features - n_informative + 1)\n",
+    "    else:\n",
+    "        n_redundant = 0\n",
+    "\n",
+    "    n_clusters_per_class = random_state.randint(1, 2**(n_informative)/2 + 1)\n",
+    "    weights = random_state.random_sample() * 0.8 + 0.1\n",
+    "\n",
+    "    X, y = sklearn.datasets.make_classification(\n",
+    "        n_samples=random_state.randint(100, 2000), \n",
+    "        n_features=n_features,\n",
+    "        n_informative=n_informative, \n",
+    "        n_redundant=n_redundant, \n",
+    "        n_repeated=0, \n",
+    "        n_classes=2, \n",
+    "        n_clusters_per_class=n_clusters_per_class, \n",
+    "        weights=(weights, 1 - weights), \n",
+    "        flip_y=0.01, \n",
+    "        class_sep=1.0, \n",
+    "        hypercube=True, \n",
+    "        shift=0.0, \n",
+    "        scale=1.0, \n",
+    "        shuffle=True, \n",
+    "        random_state=random_state\n",
+    "        )\n",
+    "    \n",
+    "    return {'data': X, 'target': y, 'name': 'synthetic'}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "   RandomForestClassifier             segment0: 100%|██████████| 2400/2400 [12:42<00:00,  3.15it/s]\n"
+      "   DecisionTreeClassifier            synthetic: 100%|██████████| 50000/50000 [1:40:34<00:00,  8.29it/s]  \n"
      ]
     }
    ],
@@ -179,7 +246,8 @@
     "        record = {}\n",
     "\n",
     "        loader = random_state.choice(datasets)\n",
-    "        dataset = loader()\n",
+    "        #dataset = loader()\n",
+    "        dataset = synthetic_dataset(random_state)\n",
     "\n",
     "        X = dataset['data']\n",
     "        y = dataset['target']\n",
@@ -254,16 +322,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0"
+       "6946"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -274,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,45 +351,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dataset                                                            KC1\n",
-       "classifier                                        KNeighborsClassifier\n",
-       "classifier_params                                 {'n_neighbors': 484}\n",
-       "p                                                                   65\n",
-       "n                                                                  357\n",
-       "p_train                                                            261\n",
-       "n_train                                                           1426\n",
-       "auc                                                           0.775738\n",
-       "auc_train                                                     0.799978\n",
-       "fprs                 [0.0, 0.0, 0.011204481792717087, 0.01680672268...\n",
-       "tprs                 [0.0, 0.046153846153846156, 0.0461538461538461...\n",
-       "thresholds           [inf, 0.3574380165289256, 0.34710743801652894,...\n",
-       "n_nodes                                                             80\n",
-       "fprs_train           [0.0, 0.0, 0.0021037868162692847, 0.0035063113...\n",
-       "tprs_train           [0.0, 0.022988505747126436, 0.0268199233716475...\n",
-       "thresholds_train     [inf, 0.359504132231405, 0.3574380165289256, 0...\n",
-       "n_nodes_train                                                      136\n",
-       "acc                                                           0.718009\n",
-       "sens                                                          0.615385\n",
-       "spec                                                          0.736695\n",
-       "best_acc                                                      0.853081\n",
-       "best_sens                                                     0.046154\n",
-       "best_spec                                                          1.0\n",
-       "acc_train                                                      0.73029\n",
-       "sens_train                                                    0.685824\n",
-       "spec_train                                                    0.738429\n",
-       "best_acc_train                                                0.857143\n",
-       "best_sens_train                                               0.206897\n",
-       "best_spec_train                                               0.976157\n",
+       "dataset                                        synthetic\n",
+       "classifier                        DecisionTreeClassifier\n",
+       "classifier_params    {'max_depth': 1, 'random_state': 5}\n",
+       "p                                                     55\n",
+       "n                                                     14\n",
+       "p_train                                              217\n",
+       "n_train                                               57\n",
+       "auc                                                  1.0\n",
+       "auc_train                                            1.0\n",
+       "fprs                                     [0.0, 0.0, 1.0]\n",
+       "tprs                                     [0.0, 1.0, 1.0]\n",
+       "thresholds                               [inf, 1.0, 0.0]\n",
+       "n_nodes                                                3\n",
+       "fprs_train                               [0.0, 0.0, 1.0]\n",
+       "tprs_train                               [0.0, 1.0, 1.0]\n",
+       "thresholds_train                         [inf, 1.0, 0.0]\n",
+       "n_nodes_train                                          3\n",
+       "acc                                                  1.0\n",
+       "sens                                                 1.0\n",
+       "spec                                                 1.0\n",
+       "best_acc                                             1.0\n",
+       "best_sens                                            1.0\n",
+       "best_spec                                            1.0\n",
+       "acc_train                                            1.0\n",
+       "sens_train                                           1.0\n",
+       "spec_train                                           1.0\n",
+       "best_acc_train                                       1.0\n",
+       "best_sens_train                                      1.0\n",
+       "best_spec_train                                      1.0\n",
        "Name: 1, dtype: object"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -332,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -382,123 +450,123 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>segment0</td>\n",
-       "      <td>SVC</td>\n",
-       "      <td>{'probability': True, 'C': 0.16616016339367984...</td>\n",
-       "      <td>66</td>\n",
-       "      <td>396</td>\n",
-       "      <td>263</td>\n",
-       "      <td>1583</td>\n",
-       "      <td>0.999847</td>\n",
-       "      <td>0.999536</td>\n",
-       "      <td>[0.0, 0.0, 0.010101010101010102, 0.01010101010...</td>\n",
+       "      <td>synthetic</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>{'max_depth': 6, 'random_state': 5}</td>\n",
+       "      <td>152</td>\n",
+       "      <td>153</td>\n",
+       "      <td>610</td>\n",
+       "      <td>609</td>\n",
+       "      <td>0.513867</td>\n",
+       "      <td>0.773231</td>\n",
+       "      <td>[0.0, 0.0457516339869281, 0.0718954248366013, ...</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.994949</td>\n",
-       "      <td>0.997835</td>\n",
-       "      <td>0.984848</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.991874</td>\n",
-       "      <td>0.984791</td>\n",
-       "      <td>0.993051</td>\n",
-       "      <td>0.995125</td>\n",
-       "      <td>0.969582</td>\n",
-       "      <td>0.999368</td>\n",
+       "      <td>0.411765</td>\n",
+       "      <td>0.521311</td>\n",
+       "      <td>0.197368</td>\n",
+       "      <td>0.843137</td>\n",
+       "      <td>0.694011</td>\n",
+       "      <td>0.777049</td>\n",
+       "      <td>0.610837</td>\n",
+       "      <td>0.694011</td>\n",
+       "      <td>0.781967</td>\n",
+       "      <td>0.605911</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>KC1</td>\n",
-       "      <td>KNeighborsClassifier</td>\n",
-       "      <td>{'n_neighbors': 484}</td>\n",
-       "      <td>65</td>\n",
-       "      <td>357</td>\n",
-       "      <td>261</td>\n",
-       "      <td>1426</td>\n",
-       "      <td>0.775738</td>\n",
-       "      <td>0.799978</td>\n",
-       "      <td>[0.0, 0.0, 0.011204481792717087, 0.01680672268...</td>\n",
+       "      <td>synthetic</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>{'max_depth': 1, 'random_state': 5}</td>\n",
+       "      <td>55</td>\n",
+       "      <td>14</td>\n",
+       "      <td>217</td>\n",
+       "      <td>57</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>[0.0, 0.0, 1.0]</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.736695</td>\n",
-       "      <td>0.853081</td>\n",
-       "      <td>0.046154</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.730290</td>\n",
-       "      <td>0.685824</td>\n",
-       "      <td>0.738429</td>\n",
-       "      <td>0.857143</td>\n",
-       "      <td>0.206897</td>\n",
-       "      <td>0.976157</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>segment0</td>\n",
-       "      <td>RandomForestClassifier</td>\n",
-       "      <td>{'max_depth': 1, 'random_state': 5}</td>\n",
-       "      <td>396</td>\n",
-       "      <td>66</td>\n",
-       "      <td>1583</td>\n",
-       "      <td>263</td>\n",
-       "      <td>0.982266</td>\n",
-       "      <td>0.987365</td>\n",
-       "      <td>[0.0, 0.0, 0.015151515151515152, 0.01515151515...</td>\n",
+       "      <td>synthetic</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>{'n_neighbors': 7}</td>\n",
+       "      <td>14</td>\n",
+       "      <td>11</td>\n",
+       "      <td>54</td>\n",
+       "      <td>44</td>\n",
+       "      <td>0.766234</td>\n",
+       "      <td>0.768308</td>\n",
+       "      <td>[0.0, 0.09090909090909091, 0.09090909090909091...</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.980519</td>\n",
-       "      <td>0.989899</td>\n",
-       "      <td>0.924242</td>\n",
-       "      <td>0.578007</td>\n",
-       "      <td>0.507896</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.985915</td>\n",
-       "      <td>0.993051</td>\n",
-       "      <td>0.942966</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>0.785714</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>0.683673</td>\n",
+       "      <td>0.944444</td>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.722222</td>\n",
+       "      <td>0.704545</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>hypothyroid</td>\n",
+       "      <td>synthetic</td>\n",
        "      <td>XGBClassifier</td>\n",
-       "      <td>{'random_state': 5, 'max_depth': 5}</td>\n",
-       "      <td>30</td>\n",
-       "      <td>603</td>\n",
-       "      <td>121</td>\n",
-       "      <td>2409</td>\n",
-       "      <td>0.995412</td>\n",
-       "      <td>0.999995</td>\n",
-       "      <td>[0.0, 0.0, 0.001658374792703151, 0.00165837479...</td>\n",
+       "      <td>{'random_state': 5, 'max_depth': 2}</td>\n",
+       "      <td>182</td>\n",
+       "      <td>46</td>\n",
+       "      <td>727</td>\n",
+       "      <td>184</td>\n",
+       "      <td>0.688605</td>\n",
+       "      <td>0.954279</td>\n",
+       "      <td>[0.0, 0.0, 0.021739130434782608, 0.02173913043...</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.991708</td>\n",
-       "      <td>0.995261</td>\n",
-       "      <td>0.966667</td>\n",
-       "      <td>0.996683</td>\n",
-       "      <td>0.992885</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.992528</td>\n",
-       "      <td>0.999605</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999585</td>\n",
+       "      <td>0.586957</td>\n",
+       "      <td>0.811404</td>\n",
+       "      <td>0.978022</td>\n",
+       "      <td>0.152174</td>\n",
+       "      <td>0.834248</td>\n",
+       "      <td>0.811554</td>\n",
+       "      <td>0.923913</td>\n",
+       "      <td>0.919868</td>\n",
+       "      <td>0.977992</td>\n",
+       "      <td>0.690217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>KC1</td>\n",
-       "      <td>KNeighborsClassifier</td>\n",
-       "      <td>{'n_neighbors': 8}</td>\n",
-       "      <td>357</td>\n",
-       "      <td>65</td>\n",
-       "      <td>1426</td>\n",
-       "      <td>261</td>\n",
-       "      <td>0.752510</td>\n",
-       "      <td>0.880647</td>\n",
-       "      <td>[0.0, 0.16923076923076924, 0.4461538461538462,...</td>\n",
+       "      <td>synthetic</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>{'max_depth': 7, 'random_state': 5}</td>\n",
+       "      <td>316</td>\n",
+       "      <td>84</td>\n",
+       "      <td>1263</td>\n",
+       "      <td>335</td>\n",
+       "      <td>0.809637</td>\n",
+       "      <td>0.971730</td>\n",
+       "      <td>[0.0, 0.0, 0.011904761904761904, 0.01190476190...</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.553846</td>\n",
-       "      <td>0.857820</td>\n",
-       "      <td>0.997199</td>\n",
-       "      <td>0.092308</td>\n",
-       "      <td>0.815056</td>\n",
-       "      <td>0.820477</td>\n",
-       "      <td>0.785441</td>\n",
-       "      <td>0.866034</td>\n",
-       "      <td>0.950912</td>\n",
-       "      <td>0.402299</td>\n",
+       "      <td>0.690476</td>\n",
+       "      <td>0.852500</td>\n",
+       "      <td>0.965190</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.889237</td>\n",
+       "      <td>0.876485</td>\n",
+       "      <td>0.937313</td>\n",
+       "      <td>0.932416</td>\n",
+       "      <td>0.973872</td>\n",
+       "      <td>0.776119</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -506,52 +574,45 @@
        "</div>"
       ],
       "text/plain": [
-       "       dataset              classifier  \\\n",
-       "0     segment0                     SVC   \n",
-       "1          KC1    KNeighborsClassifier   \n",
-       "2     segment0  RandomForestClassifier   \n",
-       "3  hypothyroid           XGBClassifier   \n",
-       "4          KC1    KNeighborsClassifier   \n",
-       "\n",
-       "                                   classifier_params    p    n  p_train  \\\n",
-       "0  {'probability': True, 'C': 0.16616016339367984...   66  396      263   \n",
-       "1                               {'n_neighbors': 484}   65  357      261   \n",
-       "2                {'max_depth': 1, 'random_state': 5}  396   66     1583   \n",
-       "3                {'random_state': 5, 'max_depth': 5}   30  603      121   \n",
-       "4                                 {'n_neighbors': 8}  357   65     1426   \n",
+       "     dataset              classifier                    classifier_params  \\\n",
+       "0  synthetic  DecisionTreeClassifier  {'max_depth': 6, 'random_state': 5}   \n",
+       "1  synthetic  DecisionTreeClassifier  {'max_depth': 1, 'random_state': 5}   \n",
+       "2  synthetic    KNeighborsClassifier                   {'n_neighbors': 7}   \n",
+       "3  synthetic           XGBClassifier  {'random_state': 5, 'max_depth': 2}   \n",
+       "4  synthetic  RandomForestClassifier  {'max_depth': 7, 'random_state': 5}   \n",
        "\n",
-       "   n_train       auc  auc_train  \\\n",
-       "0     1583  0.999847   0.999536   \n",
-       "1     1426  0.775738   0.799978   \n",
-       "2      263  0.982266   0.987365   \n",
-       "3     2409  0.995412   0.999995   \n",
-       "4      261  0.752510   0.880647   \n",
+       "     p    n  p_train  n_train       auc  auc_train  \\\n",
+       "0  152  153      610      609  0.513867   0.773231   \n",
+       "1   55   14      217       57  1.000000   1.000000   \n",
+       "2   14   11       54       44  0.766234   0.768308   \n",
+       "3  182   46      727      184  0.688605   0.954279   \n",
+       "4  316   84     1263      335  0.809637   0.971730   \n",
        "\n",
        "                                                fprs  ...      spec  best_acc  \\\n",
-       "0  [0.0, 0.0, 0.010101010101010102, 0.01010101010...  ...  0.994949  0.997835   \n",
-       "1  [0.0, 0.0, 0.011204481792717087, 0.01680672268...  ...  0.736695  0.853081   \n",
-       "2  [0.0, 0.0, 0.015151515151515152, 0.01515151515...  ...  1.000000  0.980519   \n",
-       "3  [0.0, 0.0, 0.001658374792703151, 0.00165837479...  ...  0.991708  0.995261   \n",
-       "4  [0.0, 0.16923076923076924, 0.4461538461538462,...  ...  0.553846  0.857820   \n",
+       "0  [0.0, 0.0457516339869281, 0.0718954248366013, ...  ...  0.411765  0.521311   \n",
+       "1                                    [0.0, 0.0, 1.0]  ...  1.000000  1.000000   \n",
+       "2  [0.0, 0.09090909090909091, 0.09090909090909091...  ...  0.636364  0.720000   \n",
+       "3  [0.0, 0.0, 0.021739130434782608, 0.02173913043...  ...  0.586957  0.811404   \n",
+       "4  [0.0, 0.0, 0.011904761904761904, 0.01190476190...  ...  0.690476  0.852500   \n",
        "\n",
        "   best_sens best_spec acc_train sens_train  spec_train  best_acc_train  \\\n",
-       "0   0.984848  1.000000  0.991874   0.984791    0.993051        0.995125   \n",
-       "1   0.046154  1.000000  0.730290   0.685824    0.738429        0.857143   \n",
-       "2   0.989899  0.924242  0.578007   0.507896    1.000000        0.985915   \n",
-       "3   0.966667  0.996683  0.992885   1.000000    0.992528        0.999605   \n",
-       "4   0.997199  0.092308  0.815056   0.820477    0.785441        0.866034   \n",
+       "0   0.197368  0.843137  0.694011   0.777049    0.610837        0.694011   \n",
+       "1   1.000000  1.000000  1.000000   1.000000    1.000000        1.000000   \n",
+       "2   0.785714  0.636364  0.683673   0.944444    0.363636        0.714286   \n",
+       "3   0.978022  0.152174  0.834248   0.811554    0.923913        0.919868   \n",
+       "4   0.965190  0.428571  0.889237   0.876485    0.937313        0.932416   \n",
        "\n",
        "   best_sens_train  best_spec_train  \n",
-       "0         0.969582         0.999368  \n",
-       "1         0.206897         0.976157  \n",
-       "2         0.993051         0.942966  \n",
-       "3         1.000000         0.999585  \n",
-       "4         0.950912         0.402299  \n",
+       "0         0.781967         0.605911  \n",
+       "1         1.000000         1.000000  \n",
+       "2         0.722222         0.704545  \n",
+       "3         0.977992         0.690217  \n",
+       "4         0.973872         0.776119  \n",
        "\n",
        "[5 rows x 29 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -562,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/notebooks/auc_experiments/02-exponential-fit.ipynb b/notebooks/auc_experiments/02-exponential-fit.ipynb
index c0ba594..97c6b2c 100644
--- a/notebooks/auc_experiments/02-exponential-fit.ipynb
+++ b/notebooks/auc_experiments/02-exponential-fit.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,116 +12,176 @@
     "\n",
     "from sklearn.metrics import r2_score\n",
     "\n",
-    "from mlscorecheck.auc import exponential_fitting, exponential_fitting2"
+    "from mlscorecheck.auc import p_norm_fit"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "input_label = 'aggregated'\n",
+    "input_label = 'single'\n",
     "\n",
-    "input_file = f'raw-{input_label}-20k.csv'\n",
-    "input_file2 = f'raw-{input_label}-20k-b.csv'\n",
-    "output_file = f'fit-{input_label}-20k.csv'"
+    "input_file = f'raw-{input_label}-50k-syn.csv'\n",
+    "output_file = f'fit-{input_label}-50k-syn.csv'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def fit_curve(row, values, fracs):\n",
-    "    if values == 'fprs':\n",
-    "        values = row[values]\n",
-    "        fracs = row[fracs]\n",
-    "    else:\n",
-    "        values = row[values]\n",
-    "        fracs = row[fracs]\n",
-    "    p = np.logspace(-3, 3, 3000)\n",
-    "    err = np.abs(values[:, None]**p - 1 + (1 - fracs)[:, None]**p)\n",
-    "    err = np.mean(err, axis=0)\n",
-    "    exp = p[np.argmin(err)]\n",
-    "\n",
-    "    pred = (1 - (1 - fracs)**exp)**(1/exp)\n",
-    "\n",
-    "    r2 = r2_score(values, pred)\n",
-    "\n",
-    "    return (r2, exp, 0)"
+    "data = pd.read_csv(input_file)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#data = pd.concat([pd.read_csv(input_file), pd.read_csv(input_file2)])\n",
-    "data = pd.read_csv(input_file)"
+    "data['fprs'] = data['fprs'].apply(lambda x: np.array(eval(x)))\n",
+    "data['tprs'] = data['tprs'].apply(lambda x: np.array(eval(x)))\n",
+    "data['fracs'] = 1.0 - (data['fprs'] * data['n'] + data['tprs'] * data['p']) / (data['n'] + data['p'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data['fprs'] = data['fprs'].apply(lambda x: np.array(eval(x)))\n",
-    "data['tprs'] = data['tprs'].apply(lambda x: np.array(eval(x)))\n",
-    "\n",
-    "data['fracs'] = (data['fprs'] * data['n'] + data['tprs'] * data['p']) / (data['n'] + data['p'])"
+    "data['exp_fpr'] = data.apply(lambda row: p_norm_fit(row['fracs'], row['fprs'], mode='explicit'), axis=1)\n",
+    "data['exp_tpr'] = data.apply(lambda row: p_norm_fit(row['fracs'], row['tprs'], mode='explicit'), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['r2_fpr'] = data.apply(lambda row: r2_score(row['fprs'], (1 - row['fracs']**row['exp_fpr'])**(1/row['exp_fpr'])), axis=1)\n",
+    "data['r2_tpr'] = data.apply(lambda row: r2_score(row['tprs'], (1 - row['fracs']**row['exp_tpr'])**(1/row['exp_tpr'])), axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data['r2_fpr'] = data.apply(lambda row: fit_curve(row, 'fprs', 'fracs'), axis=1)\n",
-    "data['exp_fpr'] = data['r2_fpr'].apply(lambda x: x[1])\n",
-    "data['fit_mode_fpr'] = data['r2_fpr'].apply(lambda x: x[2])\n",
-    "data['r2_fpr'] = data['r2_fpr'].apply(lambda x: x[0])\n",
+    "def fit_roc(row):\n",
+    "    fprs = row['fprs']\n",
+    "    tprs = row['tprs']\n",
+    "    p_both = p_norm_fit(1 - fprs, tprs, bracket=(-5, 2))\n",
+    "    \n",
+    "    tprs_pred = (1.0 - (1 - fprs)**p_both)**(1/p_both)\n",
+    "    fprs_pred = 1.0 - (1.0 - tprs**p_both)**(1/p_both)\n",
     "\n",
-    "data['r2_tpr'] = data.apply(lambda row: fit_curve(row, 'tprs', 'fracs'), axis=1)\n",
-    "data['exp_tpr'] = data['r2_tpr'].apply(lambda x: x[1])\n",
-    "data['fit_mode_tpr'] = data['r2_tpr'].apply(lambda x: x[2])\n",
-    "data['r2_tpr'] = data['r2_tpr'].apply(lambda x: x[0])"
+    "    return max(r2_score(tprs, tprs_pred), r2_score(fprs, fprs_pred))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['r2_roc'] = data.apply(fit_roc, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#tmp = data[data['n_nodes'] > 100]\n",
+    "tmp = data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['p', 'n', 'k', 'dataset', 'classifier', 'classifier_params', 'best_acc',\n",
-       "       'best_sens', 'best_spec', 'best_acc_train', 'best_sens_train',\n",
-       "       'best_spec_train', 'acc', 'sens', 'spec', 'auc', 'acc_train',\n",
-       "       'sens_train', 'spec_train', 'auc_train', 'n_nodes', 'n_nodes_train',\n",
-       "       'avg_n_nodes', 'avg_n_nodes_train', 'fprs', 'tprs', 'fprs_train',\n",
-       "       'tprs_train', 'fracs', 'r2_fpr', 'exp_fpr', 'fit_mode_fpr', 'r2_tpr',\n",
-       "       'exp_tpr', 'fit_mode_tpr'],\n",
-       "      dtype='object')"
+       "(array([0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
+       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 2.0000e-05,\n",
+       "        0.0000e+00, 8.0000e-05, 4.6000e-04, 1.5200e-03, 5.8800e-03,\n",
+       "        1.7440e-02, 4.3860e-02, 1.0134e-01, 8.2940e-01]),\n",
+       " array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
+       "        0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
+       "        0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
+       "        0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ]),\n",
+       " <BarContainer object of 19 artists>)"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(tmp['r2_fpr'], alpha=0.5, bins=np.linspace(0, 1, 20), weights=np.repeat(1/len(tmp), len(tmp)))\n",
+    "plt.hist(tmp['r2_tpr'], alpha=0.5, bins=np.linspace(0, 1, 20), weights=np.repeat(1/len(tmp), len(tmp)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  222.,   209.,   199.,   272.,   345.,   476.,   361.,   368.,\n",
+       "          428.,   549.,   705.,   930.,  1170.,  1433.,  1646.,  1988.,\n",
+       "         2704.,  4611., 28673.]),\n",
+       " array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
+       "        0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
+       "        0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
+       "        0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ]),\n",
+       " <BarContainer object of 19 artists>)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "data.columns"
+    "plt.hist(data['r2_roc'], alpha=0.5, bins=np.linspace(0, 1, 20))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/notebooks/auc_experiments/03-estimates.ipynb b/notebooks/auc_experiments/03-estimates.ipynb
new file mode 100644
index 0000000..3f10be9
--- /dev/null
+++ b/notebooks/auc_experiments/03-estimates.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from mlscorecheck.auc import (\n",
+    "    auc_min, \n",
+    "    auc_max, \n",
+    "    auc_rmin, \n",
+    "    auc_maxa, \n",
+    "    auc_rmin,\n",
+    "    auc_estimator,\n",
+    "    max_acc_estimator,\n",
+    "    macc_min,\n",
+    "    acc_max)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_label = 'aggregated'\n",
+    "\n",
+    "input_file = f'fit-{input_label}-50k.csv'\n",
+    "output_file = f'processed-{input_label}-50k-clipped.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "exp_tpr    1.685052\n",
+       "exp_fpr    0.634456\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(input_file)\n",
+    "data[['exp_tpr', 'exp_fpr']].median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def protect(function, args):\n",
+    "    try:\n",
+    "        return function(*args)\n",
+    "    except:\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_min'] = data.apply(lambda row: auc_min(1 - row['spec'], row['sens']), axis=1)\n",
+    "data['auc_min_best'] = data.apply(lambda row: auc_min(1 - row['best_spec'], row['best_sens']), axis=1)\n",
+    "data['auc_max'] = data.apply(lambda row: auc_max(1 - row['spec'], row['sens']), axis=1)\n",
+    "data['auc_max_best'] = data.apply(lambda row: auc_max(1 - row['best_spec'], row['best_sens']), axis=1)\n",
+    "data['auc_maxa_best'] = data.apply(lambda row: protect(auc_maxa, (row['best_acc'], row['p'], row['n'])), axis=1)\n",
+    "data['auc_rmin'] = data.apply(lambda row: protect(auc_rmin, (1 - row['spec'], row['sens'])), axis=1)\n",
+    "data['auc_rmin_best'] = data.apply(lambda row: protect(auc_rmin, (1 - row['best_spec'], row['best_sens'])), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def estimate(label0, label1, mode='normal', weighted=False):\n",
+    "    if mode != 'best':\n",
+    "        mode = 'normal'\n",
+    "    if weighted:\n",
+    "        weighted_label = 'w'\n",
+    "    else:\n",
+    "        weighted_label = 'nw'\n",
+    "    if mode != 'best':\n",
+    "        if not weighted:\n",
+    "            weights0 = np.repeat(1.0, len(data))\n",
+    "            weights1 = np.repeat(1.0, len(data))\n",
+    "        else:\n",
+    "            weights0 = data[f'err_{label1}']\n",
+    "            weights1 = data[f'err_{label0}']\n",
+    "\n",
+    "        weights0, weights1 = weights0 / (weights0 + weights1), weights1 / (weights0 + weights1)\n",
+    "        data[f'auc_{label0}_{label1}_{mode}_{weighted_label}'] = weights0*data[f'auc_{label0}'] + weights1*data[f'auc_{label1}']\n",
+    "    else:\n",
+    "        if not weighted:\n",
+    "            weights0 = np.repeat(1.0, len(data))\n",
+    "            weights1 = np.repeat(1.0, len(data))\n",
+    "        else:\n",
+    "            weights0 = data[f'err_{label1}_best']\n",
+    "            weights1 = data[f'err_{label0}_best']\n",
+    "\n",
+    "        weights0, weights1 = weights0 / (weights0 + weights1), weights1 / (weights0 + weights1)\n",
+    "        data[f'auc_{label0}_{label1}_{mode}_{weighted_label}'] = weights0*data[f'auc_{label0}_best'] + weights1*data[f'auc_{label1}_best']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimate('min', 'max', 'normal', False)\n",
+    "estimate('min', 'max', 'best', False)\n",
+    "estimate('rmin', 'max', 'normal', False)\n",
+    "estimate('rmin', 'max', 'best', False)\n",
+    "estimate('min', 'maxa', 'best', False)\n",
+    "estimate('rmin', 'maxa', 'best', False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((0.9741123400395921,\n",
+       "  np.float64(0.7243191458012499),\n",
+       "  np.float64(4.26889190155123)),\n",
+       " (0.9658991740311904,\n",
+       "  np.float64(0.7939617845382275),\n",
+       "  np.float64(4.278214201767616)),\n",
+       " (0.9718685824057304,\n",
+       "  np.float64(7.201595336442369),\n",
+       "  np.float64(7.201595336442369)))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(auc_estimator(0.024, 0.776, 30000, 300000, mode='separate', best=True),\n",
+    "auc_estimator(0.024, 0.776, 30000, 300000, mode='joint', best=True),\n",
+    "auc_estimator(0.024, 0.776, 30000, 300000, mode='roc', best=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[['auc_integral_joint_best', 'aijb_p_fpr', 'aijb_p_tpr']] = \\\n",
+    "    data.apply(lambda row: auc_estimator(1.0 - row['best_spec'], row['best_sens'], row['p'], row['n'], mode='joint', best=True), \n",
+    "               axis=1, result_type=\"expand\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[['auc_integral_roc', 'air_p_fpr', 'air_p_tpr']] = \\\n",
+    "    data.apply(lambda row: auc_estimator(1.0 - row['spec'], row['sens'], row['p'], row['n'], mode='roc', best=False), \n",
+    "               axis=1, result_type=\"expand\")\n",
+    "data[['auc_integral_roc_best', 'airb_p_fpr', 'airb_p_tpr']] = \\\n",
+    "    data.apply(lambda row: auc_estimator(1.0 - row['best_spec'], row['best_sens'], row['p'], row['n'], mode='roc', best=True), \n",
+    "               axis=1, result_type=\"expand\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data['auc_integral_roc_rast'] = data.apply(lambda row: auc_estimator(1.0 - row['spec'], row['sens'], row['p'], row['n'], mode='roc', best=False, rasterize=True), axis=1)\n",
+    "#data['auc_integral_roc_best_rast'] = data.apply(lambda row: auc_estimator(1.0 - row['best_spec'], row['best_sens'], row['p'], row['n'], mode='roc', best=True, rasterize=True), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['max_acc_integral'] = data.apply(lambda row: max_acc_estimator(row['auc'], row['p'], row['n']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['max_acc_min'] = data.apply(lambda row: macc_min(row['auc'], row['p'], row['n']), axis=1)\n",
+    "data['max_acc_max'] = data.apply(lambda row: acc_max(row['auc'], row['p'], row['n']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.to_csv(output_file, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8631458670203263"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(data['auc'], data['auc_integral_joint_best'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data_tmp = data[data['p_train'] + data['n_train'] > 1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#r2_score(data_tmp['auc'], data_tmp['auc_integral_joint_best']), r2_score(data_tmp['auc'], data_tmp['auc_integral_joint_best_rast'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fe30ea7d7c0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data['auc_integral_joint_best'], data['auc_integral_roc_best'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8551631085580392, 0.8631458670203263)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(data['auc'], data['auc_integral_roc_best']), r2_score(data['auc'], data['auc_integral_joint_best'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'auc_integral_sep_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_integral_sep_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[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(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_integral_sep_best\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(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_integral_joint_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_integral_sep_best'"
+     ]
+    }
+   ],
+   "source": [
+    "plt.scatter(data['auc'], data['auc_integral_sep_best'])\n",
+    "plt.scatter(data['auc'], data['auc_integral_joint_best'])"
+   ]
+  },
+  {
+   "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/auc_experiments/04-midpoints.ipynb b/notebooks/auc_experiments/04-midpoints.ipynb
new file mode 100644
index 0000000..1730510
--- /dev/null
+++ b/notebooks/auc_experiments/04-midpoints.ipynb
@@ -0,0 +1,2339 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from mlscorecheck.auc import integrate_roc_curve, auc_min, auc_max, roc_min, roc_max, auc_rmin, auc_maxa, roc_maxa, roc_rmin, auc_estimator, p_norm_fit, p_norm_fit_best\n",
+    "\n",
+    "from sklearn.metrics import r2_score as r2_score_orig\n",
+    "from sklearn.metrics import mean_absolute_percentage_error as mean_absolute_percentage_error_orig\n",
+    "\n",
+    "from scipy.stats import beta, binom\n",
+    "\n",
+    "from scipy.stats import skew, skewtest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.5474811673454448)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_norm_fit(np.array([0.0, 0.024, 1.0]), np.array([0.0, 0.776, 1.0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def p_norm_fit_best(x, y, bracket=(-5, 3), mode='implicit', p=None, n=None, max_acc=None):\n",
+    "    exp = np.logspace(bracket[0], bracket[1], 2000)\n",
+    "    err = np.mean(np.abs(1 - x**exp[:, None] - y**exp[:, None])**1, axis=1)\n",
+    "    if max_acc is not None:\n",
+    "        z = np.linspace(0, 1, 100)\n",
+    "        fprs = (z)[:, None]\n",
+    "        tprs = ((1 - z[:, None]**exp)**(1/exp))\n",
+    "        tmp = (fprs * n + tprs * p) / (p + n)\n",
+    "        max_accs = np.max(tmp, axis=0)\n",
+    "        mask = max_accs > max_acc\n",
+    "        err[mask] = np.inf\n",
+    "    \n",
+    "    return exp[np.argmin(err)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100, 2000) (2000,)\n",
+      "[[0.09090909 0.09090909 0.09090909 ... 0.09090909 0.09090909 0.09090909]\n",
+      " [0.00918274 0.00918274 0.00918274 ... 0.10009183 0.10009183 0.10009183]\n",
+      " [0.01836547 0.01836547 0.01836547 ... 0.10927456 0.10927456 0.10927456]\n",
+      " ...\n",
+      " [0.89072544 0.89072544 0.89072544 ... 0.98163453 0.98163453 0.98163453]\n",
+      " [0.89990817 0.89990817 0.89990817 ... 0.99081726 0.99081726 0.99081726]\n",
+      " [0.90909091 0.90909091 0.90909091 ... 0.90909091 0.90909091 0.90909091]]\n",
+      "[0.90909091 0.90909091 0.90909091 ... 0.99081726 0.99081726 0.99081726]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(7.226524255927731)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_norm_fit_best(1.0 - np.array([0.0, 0.024, 1.0]), np.array([0.0, 0.776, 1.0]), p=30_000, n=300_000, max_acc=0.98)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def r2_score(y_true, y_pred):\n",
+    "    data = np.vstack([y_true, y_pred]).T\n",
+    "    data = data[~np.any(np.isnan(data), axis=1)]\n",
+    "    return r2_score_orig(data[:, 0], data[:, 1])\n",
+    "\n",
+    "def mean_absolute_percentage_error(y_true, y_pred):\n",
+    "    data = np.vstack([y_true, y_pred]).T\n",
+    "    data = data[~np.any(np.isnan(data), axis=1)]\n",
+    "    return mean_absolute_percentage_error_orig(data[:, 0], data[:, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_label = 'single'\n",
+    "\n",
+    "#input_file = f'processed-{input_label}-50k-rs6-clipped.csv'\n",
+    "input_file = f'processed-{input_label}-100k.csv'\n",
+    "\n",
+    "#equalize = 'n_nodes_bins'\n",
+    "equalize = 'auc'\n",
+    "noedge = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_n_neighbors(params):\n",
+    "    params = eval(params)\n",
+    "    if 'n_neighbors' in params:\n",
+    "        return params['n_neighbors']\n",
+    "    return 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'processed-single-100k.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(input_file)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[1;32m   1881\u001b[0m     f,\n\u001b[1;32m   1882\u001b[0m     mode,\n\u001b[1;32m   1883\u001b[0m     encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1884\u001b[0m     compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1885\u001b[0m     memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m   1886\u001b[0m     is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[1;32m   1887\u001b[0m     errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1888\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1889\u001b[0m )\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[1;32m    874\u001b[0m             handle,\n\u001b[1;32m    875\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[1;32m    876\u001b[0m             encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[1;32m    877\u001b[0m             errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[1;32m    878\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    879\u001b[0m         )\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'processed-single-100k.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(input_file)\n",
+    "#data = pd.concat([data, pd.read_csv('processed-single-50k-rs6-clipped.csv')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data[data['sens'] > 1 - data['spec']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data[~data['dataset'].isin(['KC1'])]\n",
+    "data['n_neighbors'] = data['classifier_params'].apply(extract_n_neighbors)\n",
+    "#data['n_nodes_per_size'] = data['n_nodes']/(data['p'] + data['n'])\n",
+    "data['n_nodes_per_size'] = data['n_nodes']/(data.apply(lambda row: 2*min(row['p'], row['n']) + 2, axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([37682.,  5835.,  2188.,  1213.,   649.,   301.,   144.,   107.,\n",
+       "          123.,    90.]),\n",
+       " array([  3. ,  55.5, 108. , 160.5, 213. , 265.5, 318. , 370.5, 423. ,\n",
+       "        475.5, 528. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 470,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data['n_nodes'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f46ba1fa510>"
+      ]
+     },
+     "execution_count": 471,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plt.scatter(data['n_nodes']/(data['p'] + data['n']), data['auc'], s=1)\n",
+    "plt.scatter(data['n_nodes']/data.apply(lambda row: 2*min(row['p'], row['n']) + 2, axis=1), data['auc'], s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bins = np.percentile(data['n_nodes'], np.linspace(20, 100, 8))\n",
+    "bins = (bins[:-1] + bins[1:])/2\n",
+    "while bins[0] == bins[1]:\n",
+    "    bins = bins[1:]\n",
+    "#bins = np.hstack([bins, [np.percentile(data['n_nodes'], 95)]])\n",
+    "#bins = np.array([6, 15, 25, 45, 90, 135])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['p', 'n', 'k', 'dataset', 'classifier', 'classifier_params', 'best_acc',\n",
+       "       'best_sens', 'best_spec', 'best_acc_train', 'best_sens_train',\n",
+       "       'best_spec_train', 'acc', 'sens', 'spec', 'auc', 'acc_train',\n",
+       "       'sens_train', 'spec_train', 'auc_train', 'n_nodes', 'n_nodes_train',\n",
+       "       'avg_n_nodes', 'avg_n_nodes_train', 'fprs', 'tprs', 'fprs_train',\n",
+       "       'tprs_train', 'fracs', 'exp_fpr', 'exp_tpr', 'r2_fpr', 'r2_tpr',\n",
+       "       'r2_roc', 'auc_min', 'auc_min_best', 'auc_max', 'auc_max_best',\n",
+       "       'auc_maxa_best', 'auc_rmin', 'auc_rmin_best', 'auc_min_max_normal_nw',\n",
+       "       'auc_min_max_best_nw', 'auc_rmin_max_normal_nw', 'auc_rmin_max_best_nw',\n",
+       "       'auc_min_maxa_best_nw', 'auc_rmin_maxa_best_nw',\n",
+       "       'auc_integral_joint_best', 'aijb_p_fpr', 'aijb_p_tpr',\n",
+       "       'auc_integral_roc', 'air_p_fpr', 'air_p_tpr', 'auc_integral_roc_best',\n",
+       "       'airb_p_fpr', 'airb_p_tpr', 'max_acc_integral', 'max_acc_min',\n",
+       "       'max_acc_max', 'n_neighbors', 'n_nodes_per_size'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 473,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fprs, tprs, air_p_fpr, air_p_tpr, spec, sens = data.iloc[0][['fprs', 'tprs', 'air_p_fpr', 'air_p_tpr', 'spec', 'sens']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f46a2a0ae40>"
+      ]
+     },
+     "execution_count": 479,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 1, 100)\n",
+    "plt.plot(np.array(eval(fprs)), np.array(eval(tprs)))\n",
+    "plt.plot(x, (1 - (1 - x)**air_p_fpr)**(1/air_p_fpr))\n",
+    "plt.scatter([1 - spec], [sens])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def downsample_roc_0(fpr0, tpr0, p, n_nodes, n_samples):\n",
+    "    fprs = np.zeros((n_samples, n_nodes + 3), dtype=float)\n",
+    "    tprs = np.zeros((n_samples, n_nodes + 3), dtype=float)\n",
+    "\n",
+    "    fprs[:, 1] = fpr0\n",
+    "    fprs[:, 2] = 1.0\n",
+    "    tprs[:, 1] = tpr0\n",
+    "    tprs[:, 2] = 1.0\n",
+    "\n",
+    "    random_samples = np.random.random_sample((n_samples, n_nodes))\n",
+    "    fprs[:, 3:] = random_samples\n",
+    "    tprs[:, 3:] = (1 - (1 - random_samples)**p)**(1/p)\n",
+    "\n",
+    "    sorting = np.argsort(fprs)\n",
+    "    fprs = fprs[np.arange(fprs.shape[0])[:, None], sorting]\n",
+    "    tprs = tprs[np.arange(fprs.shape[0])[:, None], sorting]\n",
+    "\n",
+    "    return fprs, tprs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'np' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fprs, tprs \u001b[38;5;241m=\u001b[39m downsample_roc_0(\u001b[38;5;241m0.024\u001b[39m, \u001b[38;5;241m0.776\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m10\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[2], line 2\u001b[0m, in \u001b[0;36mdownsample_roc_0\u001b[0;34m(fpr0, tpr0, p, n_nodes, n_samples)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdownsample_roc_0\u001b[39m(fpr0, tpr0, p, n_nodes, n_samples):\n\u001b[0;32m----> 2\u001b[0m     fprs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros((n_samples, n_nodes \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m3\u001b[39m), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfloat\u001b[39m)\n\u001b[1;32m      3\u001b[0m     tprs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros((n_samples, n_nodes \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m3\u001b[39m), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfloat\u001b[39m)\n\u001b[1;32m      5\u001b[0m     fprs[:, \u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m fpr0\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "fprs, tprs = downsample_roc_0(0.024, 0.776, 3, 4, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for idx in range(len(fprs)):\n",
+    "    plt.plot(fprs[idx], tprs[idx])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def downsample_roc_1(fpr0, tpr0, p, n_nodes):\n",
+    "    fprs = np.linspace(0, 1, 100)\n",
+    "    tprs = (1 - (1 - x)**p)**(1/p)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 407,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['n_nodes_bin'] = data.apply(lambda row: bins[np.argmin(np.abs(bins - row['n_nodes']))], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 408,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_final'] = (data['auc_min_max_normal_nw'] + data['auc_integral_roc'])/2\n",
+    "data['auc_final_best'] = (data['auc_min_max_best_nw'] + data['auc_integral_joint_best'])/2\n",
+    "\n",
+    "data['max_acc_min_max'] = (data['max_acc_min'] + data['max_acc_max'])/2\n",
+    "\n",
+    "data['max_acc_final'] = (data['max_acc_integral'] + data['max_acc_min_max'])/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 409,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "auc\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(equalize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 410,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if equalize == 'auc':\n",
+    "    n_samples = 1000\n",
+    "\n",
+    "    tmp = []\n",
+    "    lower_bounds = np.linspace(0.5, 1.0, 11)\n",
+    "    for lower, upper in zip(lower_bounds[:-1], lower_bounds[1:]):\n",
+    "        if upper == 1.0:\n",
+    "            tmp2 = data[(data['auc'] >= lower) & (data['auc'] <= upper)]\n",
+    "        else:\n",
+    "            tmp2 = data[(data['auc'] >= lower) & (data['auc'] < upper)]\n",
+    "        if len(tmp2) == 0:\n",
+    "            continue\n",
+    "        if len(tmp2) > n_samples:\n",
+    "            tmp.append(tmp2.sample(n_samples, random_state=5, replace=False))\n",
+    "        else:\n",
+    "            tmp.append(tmp2.sample(n_samples, random_state=5, replace=True))\n",
+    "    data_eq = pd.concat(tmp)\n",
+    "else:\n",
+    "    data_eq = data\n",
+    "\n",
+    "if equalize == 'n_nodes_bins':\n",
+    "    n_samples = 1000\n",
+    "\n",
+    "    tmp = []\n",
+    "    bins = data['n_nodes_bin'].drop_duplicates().values\n",
+    "    for bin in bins:\n",
+    "        tmp2 = data[data['n_nodes_bin'] == bin]\n",
+    "        if len(tmp2) > n_samples:\n",
+    "            tmp.append(tmp2.sample(n_samples, random_state=5, replace=False))\n",
+    "        else:\n",
+    "            tmp.append(tmp2.sample(n_samples, random_state=5, replace=True))\n",
+    "    data_eq = pd.concat(tmp)\n",
+    "else:\n",
+    "    data_eq = data\n",
+    "\n",
+    "\n",
+    "\n",
+    "if noedge:\n",
+    "    data_eq = data_eq[(~data_eq['best_sens'].isin([0])) & (~data_eq['best_spec'].isin([0]))]\n",
+    "    data_eq = data_eq[(~data_eq['sens'].isin([0])) & (~data_eq['spec'].isin([0]))]\n",
+    "\n",
+    "    data = data[(~data['best_sens'].isin([0])) & (~data['best_spec'].isin([0]))]\n",
+    "    data = data[(~data['sens'].isin([0])) & (~data['spec'].isin([0]))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 440,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f46a2763890>"
+      ]
+     },
+     "execution_count": 440,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.plot([0.5, 1.0], [0.5, 1.0], color='black')\n",
+    "plt.scatter(data_eq['auc'], data_eq['auc_min_max_normal_nw'], s=1)\n",
+    "plt.scatter(data_eq['auc'], data_eq['auc_rmin_max_normal_nw'], s=1)\n",
+    "plt.scatter(data_eq['auc'], data_eq['auc_integral_roc'], s=1)\n",
+    "plt.scatter(data_eq['auc'], data_eq['auc_final'], s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 412,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.01993053069422745)"
+      ]
+     },
+     "execution_count": 412,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "skew(data_eq['auc'] - data_eq['auc_final'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 413,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SkewtestResult(statistic=np.float64(1.7911877436895294), pvalue=np.float64(0.07326317234103441))"
+      ]
+     },
+     "execution_count": 413,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "skewtest(data_eq['auc'] - data_eq['auc_final'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 414,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.9189290692080273,\n",
+       " 0.8598527080892462,\n",
+       " 0.9427438828518209,\n",
+       " 0.9656562587236314)"
+      ]
+     },
+     "execution_count": 414,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(r2_score(data_eq['auc'], data_eq['auc_min_max_normal_nw']),\n",
+    "r2_score(data_eq['auc'], data_eq['auc_rmin_max_normal_nw']),\n",
+    "r2_score(data_eq['auc'], data_eq['auc_integral_roc']),\n",
+    "r2_score(data_eq['auc'], data_eq['auc_final']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 415,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_r2(pdf, target):\n",
+    "    \"\"\"if np.std(pdf[target]) < 1e-6:\n",
+    "        return None\n",
+    "    else:\"\"\"\n",
+    "    #r2 = r2_score(pdf['auc'], pdf[target])\n",
+    "    r2 = mean_absolute_percentage_error(pdf['auc'], pdf[target])\n",
+    "    if r2 < 0:\n",
+    "        return 0.0\n",
+    "    else:\n",
+    "        return r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 416,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1181145/251532501.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  tmp = data_eq.groupby(['n_nodes_bin', 'classifier']).apply(lambda pdf:\n"
+     ]
+    }
+   ],
+   "source": [
+    "tmp = data_eq.groupby(['n_nodes_bin', 'classifier']).apply(lambda pdf: \n",
+    "        pd.Series({'r2_min_max': calculate_r2(pdf, 'auc_min_max_normal_nw'),\n",
+    "                   'r2_rmin_max': calculate_r2(pdf, 'auc_rmin_max_normal_nw'),\n",
+    "                   'r2_integral': calculate_r2(pdf, 'auc_integral_roc'),\n",
+    "                   'r2_final': calculate_r2(pdf, 'auc_final')})).reset_index(drop=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 417,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_nodes_bin</th>\n",
+       "      <th>classifier</th>\n",
+       "      <th>r2_min_max</th>\n",
+       "      <th>r2_rmin_max</th>\n",
+       "      <th>r2_integral</th>\n",
+       "      <th>r2_final</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>0.002695</td>\n",
+       "      <td>0.089291</td>\n",
+       "      <td>0.076753</td>\n",
+       "      <td>0.038332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>0.031263</td>\n",
+       "      <td>0.050142</td>\n",
+       "      <td>0.032373</td>\n",
+       "      <td>0.021503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.013051</td>\n",
+       "      <td>0.012444</td>\n",
+       "      <td>0.015447</td>\n",
+       "      <td>0.011970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.015363</td>\n",
+       "      <td>0.015709</td>\n",
+       "      <td>0.011175</td>\n",
+       "      <td>0.012388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.012524</td>\n",
+       "      <td>0.011665</td>\n",
+       "      <td>0.010402</td>\n",
+       "      <td>0.010297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>0.038488</td>\n",
+       "      <td>0.090330</td>\n",
+       "      <td>0.068438</td>\n",
+       "      <td>0.038121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>0.052396</td>\n",
+       "      <td>0.075121</td>\n",
+       "      <td>0.030762</td>\n",
+       "      <td>0.029069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.036183</td>\n",
+       "      <td>0.032246</td>\n",
+       "      <td>0.022583</td>\n",
+       "      <td>0.021374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.029312</td>\n",
+       "      <td>0.028648</td>\n",
+       "      <td>0.023413</td>\n",
+       "      <td>0.021046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.034256</td>\n",
+       "      <td>0.030411</td>\n",
+       "      <td>0.023021</td>\n",
+       "      <td>0.022569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>0.040096</td>\n",
+       "      <td>0.107490</td>\n",
+       "      <td>0.058939</td>\n",
+       "      <td>0.031900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>0.059479</td>\n",
+       "      <td>0.082560</td>\n",
+       "      <td>0.024599</td>\n",
+       "      <td>0.030806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.052422</td>\n",
+       "      <td>0.047875</td>\n",
+       "      <td>0.028796</td>\n",
+       "      <td>0.029226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.044810</td>\n",
+       "      <td>0.044408</td>\n",
+       "      <td>0.029570</td>\n",
+       "      <td>0.026728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.047513</td>\n",
+       "      <td>0.043174</td>\n",
+       "      <td>0.028596</td>\n",
+       "      <td>0.027172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>0.041760</td>\n",
+       "      <td>0.121647</td>\n",
+       "      <td>0.052664</td>\n",
+       "      <td>0.031680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>0.070057</td>\n",
+       "      <td>0.062728</td>\n",
+       "      <td>0.017077</td>\n",
+       "      <td>0.031161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.060702</td>\n",
+       "      <td>0.063133</td>\n",
+       "      <td>0.030667</td>\n",
+       "      <td>0.032975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.054494</td>\n",
+       "      <td>0.052420</td>\n",
+       "      <td>0.033288</td>\n",
+       "      <td>0.029853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.059920</td>\n",
+       "      <td>0.057957</td>\n",
+       "      <td>0.028500</td>\n",
+       "      <td>0.031366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>0.050913</td>\n",
+       "      <td>0.094152</td>\n",
+       "      <td>0.028651</td>\n",
+       "      <td>0.019706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>0.077242</td>\n",
+       "      <td>0.062903</td>\n",
+       "      <td>0.013333</td>\n",
+       "      <td>0.033107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.061365</td>\n",
+       "      <td>0.094281</td>\n",
+       "      <td>0.029977</td>\n",
+       "      <td>0.035071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.060940</td>\n",
+       "      <td>0.067525</td>\n",
+       "      <td>0.033604</td>\n",
+       "      <td>0.031414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.068097</td>\n",
+       "      <td>0.073884</td>\n",
+       "      <td>0.029179</td>\n",
+       "      <td>0.035050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>56.0</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.070703</td>\n",
+       "      <td>0.079137</td>\n",
+       "      <td>0.027601</td>\n",
+       "      <td>0.037349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>56.0</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.066854</td>\n",
+       "      <td>0.081922</td>\n",
+       "      <td>0.030393</td>\n",
+       "      <td>0.033372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>56.0</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.071429</td>\n",
+       "      <td>0.097365</td>\n",
+       "      <td>0.029960</td>\n",
+       "      <td>0.039035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>143.5</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.047234</td>\n",
+       "      <td>0.133076</td>\n",
+       "      <td>0.029639</td>\n",
+       "      <td>0.031341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>143.5</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.055752</td>\n",
+       "      <td>0.109314</td>\n",
+       "      <td>0.028503</td>\n",
+       "      <td>0.031594</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>143.5</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.051395</td>\n",
+       "      <td>0.129525</td>\n",
+       "      <td>0.027781</td>\n",
+       "      <td>0.031889</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    n_nodes_bin              classifier  r2_min_max  r2_rmin_max  r2_integral  \\\n",
+       "0           5.0  DecisionTreeClassifier    0.002695     0.089291     0.076753   \n",
+       "1           5.0    KNeighborsClassifier    0.031263     0.050142     0.032373   \n",
+       "2           5.0  RandomForestClassifier    0.013051     0.012444     0.015447   \n",
+       "3           5.0                     SVC    0.015363     0.015709     0.011175   \n",
+       "4           5.0           XGBClassifier    0.012524     0.011665     0.010402   \n",
+       "5           8.5  DecisionTreeClassifier    0.038488     0.090330     0.068438   \n",
+       "6           8.5    KNeighborsClassifier    0.052396     0.075121     0.030762   \n",
+       "7           8.5  RandomForestClassifier    0.036183     0.032246     0.022583   \n",
+       "8           8.5                     SVC    0.029312     0.028648     0.023413   \n",
+       "9           8.5           XGBClassifier    0.034256     0.030411     0.023021   \n",
+       "10         12.5  DecisionTreeClassifier    0.040096     0.107490     0.058939   \n",
+       "11         12.5    KNeighborsClassifier    0.059479     0.082560     0.024599   \n",
+       "12         12.5  RandomForestClassifier    0.052422     0.047875     0.028796   \n",
+       "13         12.5                     SVC    0.044810     0.044408     0.029570   \n",
+       "14         12.5           XGBClassifier    0.047513     0.043174     0.028596   \n",
+       "15         20.0  DecisionTreeClassifier    0.041760     0.121647     0.052664   \n",
+       "16         20.0    KNeighborsClassifier    0.070057     0.062728     0.017077   \n",
+       "17         20.0  RandomForestClassifier    0.060702     0.063133     0.030667   \n",
+       "18         20.0                     SVC    0.054494     0.052420     0.033288   \n",
+       "19         20.0           XGBClassifier    0.059920     0.057957     0.028500   \n",
+       "20         33.0  DecisionTreeClassifier    0.050913     0.094152     0.028651   \n",
+       "21         33.0    KNeighborsClassifier    0.077242     0.062903     0.013333   \n",
+       "22         33.0  RandomForestClassifier    0.061365     0.094281     0.029977   \n",
+       "23         33.0                     SVC    0.060940     0.067525     0.033604   \n",
+       "24         33.0           XGBClassifier    0.068097     0.073884     0.029179   \n",
+       "25         56.0  RandomForestClassifier    0.070703     0.079137     0.027601   \n",
+       "26         56.0                     SVC    0.066854     0.081922     0.030393   \n",
+       "27         56.0           XGBClassifier    0.071429     0.097365     0.029960   \n",
+       "28        143.5  RandomForestClassifier    0.047234     0.133076     0.029639   \n",
+       "29        143.5                     SVC    0.055752     0.109314     0.028503   \n",
+       "30        143.5           XGBClassifier    0.051395     0.129525     0.027781   \n",
+       "\n",
+       "    r2_final  \n",
+       "0   0.038332  \n",
+       "1   0.021503  \n",
+       "2   0.011970  \n",
+       "3   0.012388  \n",
+       "4   0.010297  \n",
+       "5   0.038121  \n",
+       "6   0.029069  \n",
+       "7   0.021374  \n",
+       "8   0.021046  \n",
+       "9   0.022569  \n",
+       "10  0.031900  \n",
+       "11  0.030806  \n",
+       "12  0.029226  \n",
+       "13  0.026728  \n",
+       "14  0.027172  \n",
+       "15  0.031680  \n",
+       "16  0.031161  \n",
+       "17  0.032975  \n",
+       "18  0.029853  \n",
+       "19  0.031366  \n",
+       "20  0.019706  \n",
+       "21  0.033107  \n",
+       "22  0.035071  \n",
+       "23  0.031414  \n",
+       "24  0.035050  \n",
+       "25  0.037349  \n",
+       "26  0.033372  \n",
+       "27  0.039035  \n",
+       "28  0.031341  \n",
+       "29  0.031594  \n",
+       "30  0.031889  "
+      ]
+     },
+     "execution_count": 417,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tmp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 418,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', 'black', 'yellow']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 419,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f46a2ee81a0>"
+      ]
+     },
+     "execution_count": 419,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7, 3.5))\n",
+    "for idx, classifier in enumerate(tmp['classifier'].drop_duplicates()):\n",
+    "    tmp2 = tmp[tmp['classifier'] == classifier]\n",
+    "    plt.plot(tmp2['n_nodes_bin'], tmp2['r2_min_max'], ls='solid', c=colors[idx], label=classifier)\n",
+    "    plt.plot(tmp2['n_nodes_bin'], tmp2['r2_integral'], ls='--', c=colors[idx])\n",
+    "    #plt.plot(tmp2['n_nodes_bin'], tmp2['r2_final'], ls=':', c=colors[idx])\n",
+    "plt.xscale('log')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 420,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1181145/2811395525.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  tmp = data.groupby('n_nodes_bin').apply(lambda pdf:\n"
+     ]
+    }
+   ],
+   "source": [
+    "tmp = data.groupby('n_nodes_bin').apply(lambda pdf: \n",
+    "        pd.Series({'r2_min_max': r2_score(pdf['auc'], pdf['auc_min_max_normal_nw']),\n",
+    "                   'r2_rmin_max': r2_score(pdf['auc'], pdf['auc_rmin_max_normal_nw']),\n",
+    "                   'r2_integral': r2_score(pdf['auc'], pdf['auc_integral_roc']),\n",
+    "                   'r2_final': r2_score(pdf['auc'], pdf['auc_final']),\n",
+    "                   'mape_min_max': mean_absolute_percentage_error(pdf['auc'], pdf['auc_min_max_normal_nw']),\n",
+    "                   'mape_rmin_max': mean_absolute_percentage_error(pdf['auc'], pdf['auc_rmin_max_normal_nw']),\n",
+    "                   'mape_integral': mean_absolute_percentage_error(pdf['auc'], pdf['auc_integral_roc']),\n",
+    "                   'mape_final': mean_absolute_percentage_error(pdf['auc'], pdf['auc_final']),\n",
+    "                   'count': len(pdf)})).reset_index(drop=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 421,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_nodes_bin</th>\n",
+       "      <th>r2_min_max</th>\n",
+       "      <th>r2_rmin_max</th>\n",
+       "      <th>r2_integral</th>\n",
+       "      <th>r2_final</th>\n",
+       "      <th>mape_min_max</th>\n",
+       "      <th>mape_rmin_max</th>\n",
+       "      <th>mape_integral</th>\n",
+       "      <th>mape_final</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.983124</td>\n",
+       "      <td>0.866744</td>\n",
+       "      <td>0.895048</td>\n",
+       "      <td>0.968630</td>\n",
+       "      <td>0.010925</td>\n",
+       "      <td>0.064655</td>\n",
+       "      <td>0.053649</td>\n",
+       "      <td>0.029046</td>\n",
+       "      <td>15175.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>0.908968</td>\n",
+       "      <td>0.880510</td>\n",
+       "      <td>0.949526</td>\n",
+       "      <td>0.962489</td>\n",
+       "      <td>0.042766</td>\n",
+       "      <td>0.056757</td>\n",
+       "      <td>0.030506</td>\n",
+       "      <td>0.026587</td>\n",
+       "      <td>5807.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>0.883207</td>\n",
+       "      <td>0.865636</td>\n",
+       "      <td>0.958698</td>\n",
+       "      <td>0.960137</td>\n",
+       "      <td>0.052867</td>\n",
+       "      <td>0.064904</td>\n",
+       "      <td>0.028540</td>\n",
+       "      <td>0.029306</td>\n",
+       "      <td>6046.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.808629</td>\n",
+       "      <td>0.829874</td>\n",
+       "      <td>0.943988</td>\n",
+       "      <td>0.940373</td>\n",
+       "      <td>0.060373</td>\n",
+       "      <td>0.060535</td>\n",
+       "      <td>0.028596</td>\n",
+       "      <td>0.031405</td>\n",
+       "      <td>5307.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>0.864580</td>\n",
+       "      <td>0.835164</td>\n",
+       "      <td>0.964921</td>\n",
+       "      <td>0.959698</td>\n",
+       "      <td>0.063274</td>\n",
+       "      <td>0.080991</td>\n",
+       "      <td>0.030586</td>\n",
+       "      <td>0.034035</td>\n",
+       "      <td>5963.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0.826253</td>\n",
+       "      <td>0.793090</td>\n",
+       "      <td>0.967432</td>\n",
+       "      <td>0.952596</td>\n",
+       "      <td>0.069518</td>\n",
+       "      <td>0.086527</td>\n",
+       "      <td>0.029466</td>\n",
+       "      <td>0.036451</td>\n",
+       "      <td>7042.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>143.5</td>\n",
+       "      <td>0.807400</td>\n",
+       "      <td>0.371337</td>\n",
+       "      <td>0.953359</td>\n",
+       "      <td>0.936292</td>\n",
+       "      <td>0.052315</td>\n",
+       "      <td>0.122414</td>\n",
+       "      <td>0.028393</td>\n",
+       "      <td>0.031677</td>\n",
+       "      <td>3116.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   n_nodes_bin  r2_min_max  r2_rmin_max  r2_integral  r2_final  mape_min_max  \\\n",
+       "0          5.0    0.983124     0.866744     0.895048  0.968630      0.010925   \n",
+       "1          8.5    0.908968     0.880510     0.949526  0.962489      0.042766   \n",
+       "2         12.5    0.883207     0.865636     0.958698  0.960137      0.052867   \n",
+       "3         20.0    0.808629     0.829874     0.943988  0.940373      0.060373   \n",
+       "4         33.0    0.864580     0.835164     0.964921  0.959698      0.063274   \n",
+       "5         56.0    0.826253     0.793090     0.967432  0.952596      0.069518   \n",
+       "6        143.5    0.807400     0.371337     0.953359  0.936292      0.052315   \n",
+       "\n",
+       "   mape_rmin_max  mape_integral  mape_final    count  \n",
+       "0       0.064655       0.053649    0.029046  15175.0  \n",
+       "1       0.056757       0.030506    0.026587   5807.0  \n",
+       "2       0.064904       0.028540    0.029306   6046.0  \n",
+       "3       0.060535       0.028596    0.031405   5307.0  \n",
+       "4       0.080991       0.030586    0.034035   5963.0  \n",
+       "5       0.086527       0.029466    0.036451   7042.0  \n",
+       "6       0.122414       0.028393    0.031677   3116.0  "
+      ]
+     },
+     "execution_count": 421,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tmp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 422,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax1 = plt.subplot()\n",
+    "ax2 = ax1.twinx()\n",
+    "ax1.plot(tmp['n_nodes_bin'], tmp['r2_min_max'], ls='solid')\n",
+    "ax1.plot(tmp['n_nodes_bin'], tmp['r2_rmin_max'], ls='solid')\n",
+    "ax1.plot(tmp['n_nodes_bin'], tmp['r2_integral'], ls='solid')\n",
+    "ax1.plot(tmp['n_nodes_bin'], tmp['r2_final'], ls='solid')\n",
+    "ax2.plot(tmp['n_nodes_bin'], tmp['mape_min_max'], ls='dashed')\n",
+    "ax2.plot(tmp['n_nodes_bin'], tmp['mape_rmin_max'], ls='dashed')\n",
+    "ax2.plot(tmp['n_nodes_bin'], tmp['mape_integral'], ls='dashed')\n",
+    "ax2.plot(tmp['n_nodes_bin'], tmp['mape_final'], ls='dashed')\n",
+    "plt.xscale('log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 423,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f469d74cec0>"
+      ]
+     },
+     "execution_count": 423,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.scatter(data_eq['auc'], data_eq['auc_min_max_best_nw'], s=1)\n",
+    "plt.scatter(data_eq['auc'], data_eq['auc_integral_roc_best'], s=1)\n",
+    "plt.scatter(data_eq['auc'], data_eq['auc_integral_joint_best'], s=1)\n",
+    "plt.scatter(data_eq['auc'], data_eq['auc_final_best'], s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 424,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8504617629108224,\n",
+       " 0.7172120441455948,\n",
+       " 0.6583759699431599,\n",
+       " 0.8810263069652237,\n",
+       " 0.894514433575184,\n",
+       " 0.8827413459484725,\n",
+       " 0.9480175533584715)"
+      ]
+     },
+     "execution_count": 424,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(r2_score(data_eq['auc'], data_eq['auc_min_max_best_nw']),\n",
+    "r2_score(data_eq['auc'], data_eq['auc_rmin_max_best_nw']),\n",
+    "r2_score(data_eq['auc'], data_eq['auc_min_maxa_best_nw']),\n",
+    "r2_score(data_eq['auc'], data_eq['auc_rmin_maxa_best_nw']),\n",
+    "r2_score(data_eq['auc'], data_eq['auc_integral_joint_best']),\n",
+    "r2_score(data_eq['auc'], data_eq['auc_integral_roc_best']),\n",
+    "r2_score(data_eq['auc'], data_eq['auc_final_best']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 425,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1181145/3926694275.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  tmp = data_eq.groupby(['n_nodes_bin', 'classifier']).apply(lambda pdf:\n"
+     ]
+    }
+   ],
+   "source": [
+    "tmp = data_eq.groupby(['n_nodes_bin', 'classifier']).apply(lambda pdf: \n",
+    "        pd.Series({'r2_min_max': calculate_r2(pdf, 'auc_min_max_best_nw'),\n",
+    "                   'r2_rmin_max': calculate_r2(pdf, 'auc_rmin_max_best_nw'),\n",
+    "                   'r2_integral': calculate_r2(pdf, 'auc_integral_joint_best'),\n",
+    "                   'r2_final': calculate_r2(pdf, 'auc_final_best'),\n",
+    "                   'count': len(pdf)})).reset_index(drop=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 426,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_nodes_bin</th>\n",
+       "      <th>classifier</th>\n",
+       "      <th>r2_min_max</th>\n",
+       "      <th>r2_rmin_max</th>\n",
+       "      <th>r2_integral</th>\n",
+       "      <th>r2_final</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>0.028356</td>\n",
+       "      <td>0.116507</td>\n",
+       "      <td>0.074803</td>\n",
+       "      <td>0.042507</td>\n",
+       "      <td>8754.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>0.048375</td>\n",
+       "      <td>0.071101</td>\n",
+       "      <td>0.045650</td>\n",
+       "      <td>0.029710</td>\n",
+       "      <td>3102.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.007564</td>\n",
+       "      <td>0.007460</td>\n",
+       "      <td>0.016750</td>\n",
+       "      <td>0.010666</td>\n",
+       "      <td>1285.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.007989</td>\n",
+       "      <td>0.008729</td>\n",
+       "      <td>0.012347</td>\n",
+       "      <td>0.009260</td>\n",
+       "      <td>1046.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.009681</td>\n",
+       "      <td>0.008418</td>\n",
+       "      <td>0.013568</td>\n",
+       "      <td>0.010612</td>\n",
+       "      <td>988.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>0.060055</td>\n",
+       "      <td>0.128306</td>\n",
+       "      <td>0.082412</td>\n",
+       "      <td>0.046637</td>\n",
+       "      <td>482.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>0.080026</td>\n",
+       "      <td>0.112198</td>\n",
+       "      <td>0.051425</td>\n",
+       "      <td>0.041769</td>\n",
+       "      <td>2782.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.027964</td>\n",
+       "      <td>0.026237</td>\n",
+       "      <td>0.027370</td>\n",
+       "      <td>0.017591</td>\n",
+       "      <td>724.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.025830</td>\n",
+       "      <td>0.026325</td>\n",
+       "      <td>0.026122</td>\n",
+       "      <td>0.019607</td>\n",
+       "      <td>907.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.030050</td>\n",
+       "      <td>0.027061</td>\n",
+       "      <td>0.026800</td>\n",
+       "      <td>0.020880</td>\n",
+       "      <td>912.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>0.065053</td>\n",
+       "      <td>0.150966</td>\n",
+       "      <td>0.078278</td>\n",
+       "      <td>0.042718</td>\n",
+       "      <td>305.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>0.083314</td>\n",
+       "      <td>0.120134</td>\n",
+       "      <td>0.040301</td>\n",
+       "      <td>0.040982</td>\n",
+       "      <td>2684.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.052556</td>\n",
+       "      <td>0.052135</td>\n",
+       "      <td>0.043914</td>\n",
+       "      <td>0.030777</td>\n",
+       "      <td>1037.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.042064</td>\n",
+       "      <td>0.047518</td>\n",
+       "      <td>0.041742</td>\n",
+       "      <td>0.027313</td>\n",
+       "      <td>954.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.044048</td>\n",
+       "      <td>0.042721</td>\n",
+       "      <td>0.036570</td>\n",
+       "      <td>0.025122</td>\n",
+       "      <td>1066.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>0.060364</td>\n",
+       "      <td>0.179768</td>\n",
+       "      <td>0.066489</td>\n",
+       "      <td>0.039327</td>\n",
+       "      <td>130.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>0.084246</td>\n",
+       "      <td>0.076470</td>\n",
+       "      <td>0.021691</td>\n",
+       "      <td>0.037201</td>\n",
+       "      <td>1057.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.070302</td>\n",
+       "      <td>0.079082</td>\n",
+       "      <td>0.051975</td>\n",
+       "      <td>0.037700</td>\n",
+       "      <td>1468.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.059208</td>\n",
+       "      <td>0.060615</td>\n",
+       "      <td>0.050695</td>\n",
+       "      <td>0.033296</td>\n",
+       "      <td>1308.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.064605</td>\n",
+       "      <td>0.064972</td>\n",
+       "      <td>0.044907</td>\n",
+       "      <td>0.033947</td>\n",
+       "      <td>1344.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>0.046554</td>\n",
+       "      <td>0.142907</td>\n",
+       "      <td>0.040287</td>\n",
+       "      <td>0.021165</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>KNeighborsClassifier</td>\n",
+       "      <td>0.078872</td>\n",
+       "      <td>0.060468</td>\n",
+       "      <td>0.015313</td>\n",
+       "      <td>0.033507</td>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.083888</td>\n",
+       "      <td>0.139870</td>\n",
+       "      <td>0.063155</td>\n",
+       "      <td>0.045812</td>\n",
+       "      <td>2605.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.073971</td>\n",
+       "      <td>0.085585</td>\n",
+       "      <td>0.058794</td>\n",
+       "      <td>0.036947</td>\n",
+       "      <td>1620.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.078968</td>\n",
+       "      <td>0.092086</td>\n",
+       "      <td>0.054436</td>\n",
+       "      <td>0.041375</td>\n",
+       "      <td>1677.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>56.0</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.090832</td>\n",
+       "      <td>0.120160</td>\n",
+       "      <td>0.061090</td>\n",
+       "      <td>0.045142</td>\n",
+       "      <td>1958.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>56.0</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.090504</td>\n",
+       "      <td>0.119342</td>\n",
+       "      <td>0.069432</td>\n",
+       "      <td>0.043380</td>\n",
+       "      <td>2631.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>56.0</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.093343</td>\n",
+       "      <td>0.139155</td>\n",
+       "      <td>0.070193</td>\n",
+       "      <td>0.050171</td>\n",
+       "      <td>2453.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>143.5</td>\n",
+       "      <td>RandomForestClassifier</td>\n",
+       "      <td>0.070240</td>\n",
+       "      <td>0.217889</td>\n",
+       "      <td>0.072812</td>\n",
+       "      <td>0.045336</td>\n",
+       "      <td>562.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>143.5</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>0.073356</td>\n",
+       "      <td>0.179373</td>\n",
+       "      <td>0.070349</td>\n",
+       "      <td>0.040361</td>\n",
+       "      <td>1195.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>143.5</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>0.071144</td>\n",
+       "      <td>0.204234</td>\n",
+       "      <td>0.073483</td>\n",
+       "      <td>0.047877</td>\n",
+       "      <td>1359.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    n_nodes_bin              classifier  r2_min_max  r2_rmin_max  r2_integral  \\\n",
+       "0           5.0  DecisionTreeClassifier    0.028356     0.116507     0.074803   \n",
+       "1           5.0    KNeighborsClassifier    0.048375     0.071101     0.045650   \n",
+       "2           5.0  RandomForestClassifier    0.007564     0.007460     0.016750   \n",
+       "3           5.0                     SVC    0.007989     0.008729     0.012347   \n",
+       "4           5.0           XGBClassifier    0.009681     0.008418     0.013568   \n",
+       "5           8.5  DecisionTreeClassifier    0.060055     0.128306     0.082412   \n",
+       "6           8.5    KNeighborsClassifier    0.080026     0.112198     0.051425   \n",
+       "7           8.5  RandomForestClassifier    0.027964     0.026237     0.027370   \n",
+       "8           8.5                     SVC    0.025830     0.026325     0.026122   \n",
+       "9           8.5           XGBClassifier    0.030050     0.027061     0.026800   \n",
+       "10         12.5  DecisionTreeClassifier    0.065053     0.150966     0.078278   \n",
+       "11         12.5    KNeighborsClassifier    0.083314     0.120134     0.040301   \n",
+       "12         12.5  RandomForestClassifier    0.052556     0.052135     0.043914   \n",
+       "13         12.5                     SVC    0.042064     0.047518     0.041742   \n",
+       "14         12.5           XGBClassifier    0.044048     0.042721     0.036570   \n",
+       "15         20.0  DecisionTreeClassifier    0.060364     0.179768     0.066489   \n",
+       "16         20.0    KNeighborsClassifier    0.084246     0.076470     0.021691   \n",
+       "17         20.0  RandomForestClassifier    0.070302     0.079082     0.051975   \n",
+       "18         20.0                     SVC    0.059208     0.060615     0.050695   \n",
+       "19         20.0           XGBClassifier    0.064605     0.064972     0.044907   \n",
+       "20         33.0  DecisionTreeClassifier    0.046554     0.142907     0.040287   \n",
+       "21         33.0    KNeighborsClassifier    0.078872     0.060468     0.015313   \n",
+       "22         33.0  RandomForestClassifier    0.083888     0.139870     0.063155   \n",
+       "23         33.0                     SVC    0.073971     0.085585     0.058794   \n",
+       "24         33.0           XGBClassifier    0.078968     0.092086     0.054436   \n",
+       "25         56.0  RandomForestClassifier    0.090832     0.120160     0.061090   \n",
+       "26         56.0                     SVC    0.090504     0.119342     0.069432   \n",
+       "27         56.0           XGBClassifier    0.093343     0.139155     0.070193   \n",
+       "28        143.5  RandomForestClassifier    0.070240     0.217889     0.072812   \n",
+       "29        143.5                     SVC    0.073356     0.179373     0.070349   \n",
+       "30        143.5           XGBClassifier    0.071144     0.204234     0.073483   \n",
+       "\n",
+       "    r2_final   count  \n",
+       "0   0.042507  8754.0  \n",
+       "1   0.029710  3102.0  \n",
+       "2   0.010666  1285.0  \n",
+       "3   0.009260  1046.0  \n",
+       "4   0.010612   988.0  \n",
+       "5   0.046637   482.0  \n",
+       "6   0.041769  2782.0  \n",
+       "7   0.017591   724.0  \n",
+       "8   0.019607   907.0  \n",
+       "9   0.020880   912.0  \n",
+       "10  0.042718   305.0  \n",
+       "11  0.040982  2684.0  \n",
+       "12  0.030777  1037.0  \n",
+       "13  0.027313   954.0  \n",
+       "14  0.025122  1066.0  \n",
+       "15  0.039327   130.0  \n",
+       "16  0.037201  1057.0  \n",
+       "17  0.037700  1468.0  \n",
+       "18  0.033296  1308.0  \n",
+       "19  0.033947  1344.0  \n",
+       "20  0.021165     7.0  \n",
+       "21  0.033507    54.0  \n",
+       "22  0.045812  2605.0  \n",
+       "23  0.036947  1620.0  \n",
+       "24  0.041375  1677.0  \n",
+       "25  0.045142  1958.0  \n",
+       "26  0.043380  2631.0  \n",
+       "27  0.050171  2453.0  \n",
+       "28  0.045336   562.0  \n",
+       "29  0.040361  1195.0  \n",
+       "30  0.047877  1359.0  "
+      ]
+     },
+     "execution_count": 426,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tmp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 427,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f469d7efb90>"
+      ]
+     },
+     "execution_count": 427,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7, 3.5))\n",
+    "for idx, classifier in enumerate(tmp['classifier'].drop_duplicates()):\n",
+    "    tmp2 = tmp[tmp['classifier'] == classifier]\n",
+    "    plt.plot(tmp2['n_nodes_bin'], tmp2['r2_min_max'], ls='solid', c=colors[idx], label=classifier)\n",
+    "    plt.plot(tmp2['n_nodes_bin'], tmp2['r2_integral'], ls='--', c=colors[idx])\n",
+    "    #plt.plot(tmp2['n_nodes_bin'], tmp2['r2_final'], ls=':', c=colors[idx])\n",
+    "plt.xscale('log')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 428,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1181145/3403290292.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  tmp = data.groupby('n_nodes_bin').apply(lambda pdf:\n"
+     ]
+    }
+   ],
+   "source": [
+    "tmp = data.groupby('n_nodes_bin').apply(lambda pdf: \n",
+    "        pd.Series({'r2_min_max': r2_score(pdf['auc'], pdf['auc_min_max_best_nw']),\n",
+    "                   'r2_rmin_max': r2_score(pdf['auc'], pdf['auc_rmin_max_best_nw']),\n",
+    "                   'r2_rmin_maxa': r2_score(pdf['auc'], pdf['auc_rmin_maxa_best_nw']),\n",
+    "                   'r2_integral': r2_score(pdf['auc'], pdf['auc_integral_joint_best']),\n",
+    "                   'r2_final': r2_score(pdf['auc'], pdf['auc_final_best']),\n",
+    "                   'mape_min_max': mean_absolute_percentage_error(pdf['auc'], pdf['auc_min_max_best_nw']),\n",
+    "                   'mape_rmin_max': mean_absolute_percentage_error(pdf['auc'], pdf['auc_rmin_max_best_nw']),\n",
+    "                   'mape_rmin_maxa': mean_absolute_percentage_error(pdf['auc'], pdf['auc_rmin_maxa_best_nw']),\n",
+    "                   'mape_integral': mean_absolute_percentage_error(pdf['auc'], pdf['auc_integral_joint_best']),\n",
+    "                   'mape_final': mean_absolute_percentage_error(pdf['auc'], pdf['auc_final_best']),\n",
+    "                   'count': len(pdf)})).reset_index(drop=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 429,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_nodes_bin</th>\n",
+       "      <th>r2_min_max</th>\n",
+       "      <th>r2_rmin_max</th>\n",
+       "      <th>r2_rmin_maxa</th>\n",
+       "      <th>r2_integral</th>\n",
+       "      <th>r2_final</th>\n",
+       "      <th>mape_min_max</th>\n",
+       "      <th>mape_rmin_max</th>\n",
+       "      <th>mape_rmin_maxa</th>\n",
+       "      <th>mape_integral</th>\n",
+       "      <th>mape_final</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.933705</td>\n",
+       "      <td>0.754216</td>\n",
+       "      <td>0.920948</td>\n",
+       "      <td>0.891109</td>\n",
+       "      <td>0.957110</td>\n",
+       "      <td>0.028068</td>\n",
+       "      <td>0.083525</td>\n",
+       "      <td>0.038521</td>\n",
+       "      <td>0.055636</td>\n",
+       "      <td>0.032827</td>\n",
+       "      <td>15175.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.5</td>\n",
+       "      <td>0.827797</td>\n",
+       "      <td>0.760817</td>\n",
+       "      <td>0.882573</td>\n",
+       "      <td>0.902509</td>\n",
+       "      <td>0.939244</td>\n",
+       "      <td>0.055563</td>\n",
+       "      <td>0.076034</td>\n",
+       "      <td>0.050635</td>\n",
+       "      <td>0.043178</td>\n",
+       "      <td>0.032416</td>\n",
+       "      <td>5807.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12.5</td>\n",
+       "      <td>0.812117</td>\n",
+       "      <td>0.743678</td>\n",
+       "      <td>0.874762</td>\n",
+       "      <td>0.909565</td>\n",
+       "      <td>0.939796</td>\n",
+       "      <td>0.063685</td>\n",
+       "      <td>0.084919</td>\n",
+       "      <td>0.056333</td>\n",
+       "      <td>0.042406</td>\n",
+       "      <td>0.034366</td>\n",
+       "      <td>6046.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.718360</td>\n",
+       "      <td>0.728826</td>\n",
+       "      <td>0.817384</td>\n",
+       "      <td>0.868794</td>\n",
+       "      <td>0.915244</td>\n",
+       "      <td>0.068659</td>\n",
+       "      <td>0.072903</td>\n",
+       "      <td>0.059141</td>\n",
+       "      <td>0.044194</td>\n",
+       "      <td>0.035604</td>\n",
+       "      <td>5307.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>0.771748</td>\n",
+       "      <td>0.667991</td>\n",
+       "      <td>0.829394</td>\n",
+       "      <td>0.879162</td>\n",
+       "      <td>0.937257</td>\n",
+       "      <td>0.079721</td>\n",
+       "      <td>0.110968</td>\n",
+       "      <td>0.075495</td>\n",
+       "      <td>0.059058</td>\n",
+       "      <td>0.042015</td>\n",
+       "      <td>5963.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0.690602</td>\n",
+       "      <td>0.535303</td>\n",
+       "      <td>0.769241</td>\n",
+       "      <td>0.832735</td>\n",
+       "      <td>0.921509</td>\n",
+       "      <td>0.091584</td>\n",
+       "      <td>0.126471</td>\n",
+       "      <td>0.084951</td>\n",
+       "      <td>0.067378</td>\n",
+       "      <td>0.046235</td>\n",
+       "      <td>7042.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>143.5</td>\n",
+       "      <td>0.655305</td>\n",
+       "      <td>-0.671998</td>\n",
+       "      <td>0.639070</td>\n",
+       "      <td>0.681566</td>\n",
+       "      <td>0.879894</td>\n",
+       "      <td>0.071829</td>\n",
+       "      <td>0.197162</td>\n",
+       "      <td>0.078926</td>\n",
+       "      <td>0.072160</td>\n",
+       "      <td>0.044536</td>\n",
+       "      <td>3116.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   n_nodes_bin  r2_min_max  r2_rmin_max  r2_rmin_maxa  r2_integral  r2_final  \\\n",
+       "0          5.0    0.933705     0.754216      0.920948     0.891109  0.957110   \n",
+       "1          8.5    0.827797     0.760817      0.882573     0.902509  0.939244   \n",
+       "2         12.5    0.812117     0.743678      0.874762     0.909565  0.939796   \n",
+       "3         20.0    0.718360     0.728826      0.817384     0.868794  0.915244   \n",
+       "4         33.0    0.771748     0.667991      0.829394     0.879162  0.937257   \n",
+       "5         56.0    0.690602     0.535303      0.769241     0.832735  0.921509   \n",
+       "6        143.5    0.655305    -0.671998      0.639070     0.681566  0.879894   \n",
+       "\n",
+       "   mape_min_max  mape_rmin_max  mape_rmin_maxa  mape_integral  mape_final  \\\n",
+       "0      0.028068       0.083525        0.038521       0.055636    0.032827   \n",
+       "1      0.055563       0.076034        0.050635       0.043178    0.032416   \n",
+       "2      0.063685       0.084919        0.056333       0.042406    0.034366   \n",
+       "3      0.068659       0.072903        0.059141       0.044194    0.035604   \n",
+       "4      0.079721       0.110968        0.075495       0.059058    0.042015   \n",
+       "5      0.091584       0.126471        0.084951       0.067378    0.046235   \n",
+       "6      0.071829       0.197162        0.078926       0.072160    0.044536   \n",
+       "\n",
+       "     count  \n",
+       "0  15175.0  \n",
+       "1   5807.0  \n",
+       "2   6046.0  \n",
+       "3   5307.0  \n",
+       "4   5963.0  \n",
+       "5   7042.0  \n",
+       "6   3116.0  "
+      ]
+     },
+     "execution_count": 429,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tmp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 430,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax1 = plt.subplot()\n",
+    "ax2 = plt.twinx()\n",
+    "ax1.plot(tmp['n_nodes_bin'], tmp['r2_min_max'])\n",
+    "ax1.plot(tmp['n_nodes_bin'], tmp['r2_rmin_max'])\n",
+    "ax1.plot(tmp['n_nodes_bin'], tmp['r2_rmin_maxa'])\n",
+    "ax1.plot(tmp['n_nodes_bin'], tmp['r2_integral'])\n",
+    "ax1.plot(tmp['n_nodes_bin'], tmp['r2_final'])\n",
+    "ax2.plot(tmp['n_nodes_bin'], tmp['mape_min_max'], ls='dashed')\n",
+    "ax2.plot(tmp['n_nodes_bin'], tmp['mape_rmin_max'], ls='dashed')\n",
+    "ax2.plot(tmp['n_nodes_bin'], tmp['mape_rmin_maxa'], ls='dashed')\n",
+    "ax2.plot(tmp['n_nodes_bin'], tmp['mape_integral'], ls='dashed')\n",
+    "ax2.plot(tmp['n_nodes_bin'], tmp['mape_final'], ls='dashed')\n",
+    "plt.xscale('log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 431,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f46aa0abb30>"
+      ]
+     },
+     "execution_count": 431,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.xlim((0.5, 1.0))\n",
+    "plt.ylim((0.5, 1.0))\n",
+    "plt.scatter(data_eq['best_acc'], data_eq['max_acc_min_max'], s=1)\n",
+    "plt.scatter(data_eq['best_acc'], data_eq['max_acc_integral'], s=1)\n",
+    "plt.scatter(data_eq['best_acc'], data_eq['max_acc_final'], s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 432,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.15719019183066738)"
+      ]
+     },
+     "execution_count": 432,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "skew(data_eq['best_acc'] - data_eq['max_acc_final'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 433,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8706086103529596, 0.9281397161747473, 0.9491926932370038)"
+      ]
+     },
+     "execution_count": 433,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(r2_score(data_eq['best_acc'], data_eq['max_acc_min_max']),\n",
+    "r2_score(data_eq['best_acc'], data_eq['max_acc_integral']),\n",
+    "r2_score(data_eq['best_acc'], data_eq['max_acc_final']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 434,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1181145/3505634563.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  tmp = data.groupby('n_nodes_bin').apply(lambda pdf:\n"
+     ]
+    }
+   ],
+   "source": [
+    "tmp = data.groupby('n_nodes_bin').apply(lambda pdf: \n",
+    "        pd.Series({'r2_min_max': r2_score(pdf['best_acc'], pdf['max_acc_min_max']),\n",
+    "                   'r2_integral': r2_score(pdf['best_acc'], pdf['max_acc_integral']),\n",
+    "                   'r2_final': r2_score(pdf['best_acc'], pdf['max_acc_final'])}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 435,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>r2_min_max</th>\n",
+       "      <th>r2_integral</th>\n",
+       "      <th>r2_final</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>n_nodes_bin</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5.0</th>\n",
+       "      <td>0.878525</td>\n",
+       "      <td>0.901044</td>\n",
+       "      <td>0.929299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.5</th>\n",
+       "      <td>0.880119</td>\n",
+       "      <td>0.910262</td>\n",
+       "      <td>0.935010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12.5</th>\n",
+       "      <td>0.877440</td>\n",
+       "      <td>0.926299</td>\n",
+       "      <td>0.947846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20.0</th>\n",
+       "      <td>0.888803</td>\n",
+       "      <td>0.889917</td>\n",
+       "      <td>0.945796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33.0</th>\n",
+       "      <td>0.873595</td>\n",
+       "      <td>0.929197</td>\n",
+       "      <td>0.964856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56.0</th>\n",
+       "      <td>0.760082</td>\n",
+       "      <td>0.940115</td>\n",
+       "      <td>0.957091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143.5</th>\n",
+       "      <td>0.230125</td>\n",
+       "      <td>0.940436</td>\n",
+       "      <td>0.868617</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             r2_min_max  r2_integral  r2_final\n",
+       "n_nodes_bin                                   \n",
+       "5.0            0.878525     0.901044  0.929299\n",
+       "8.5            0.880119     0.910262  0.935010\n",
+       "12.5           0.877440     0.926299  0.947846\n",
+       "20.0           0.888803     0.889917  0.945796\n",
+       "33.0           0.873595     0.929197  0.964856\n",
+       "56.0           0.760082     0.940115  0.957091\n",
+       "143.5          0.230125     0.940436  0.868617"
+      ]
+     },
+     "execution_count": 435,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tmp"
+   ]
+  }
+ ],
+ "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/auc_experiments/10-exponential-fit-illustration.ipynb b/notebooks/auc_experiments/10-exponential-fit-illustration.ipynb
index 6425367..5c0423c 100644
--- a/notebooks/auc_experiments/10-exponential-fit-illustration.ipynb
+++ b/notebooks/auc_experiments/10-exponential-fit-illustration.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 245,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,6 +44,182 @@
     "data['fracs'] = data['fracs'].apply(lambda x: np.array(eval(x)))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.optimize import newton"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "row = data.iloc[0]\n",
+    "fprs = row['fprs']\n",
+    "tprs = row['tprs']\n",
+    "fracs = row['fracs']\n",
+    "\n",
+    "mask = (fracs > 0) & (fracs < 1)\n",
+    "fprs = fprs[mask]\n",
+    "tprs = tprs[mask]\n",
+    "fracs = fracs[mask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(1.156168165168247)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_range = np.logspace(-3, 3, 3000)\n",
+    "p_range[np.argmin(np.mean(((1 - fracs[:, None]**p_range)**(1/p_range) - fprs[:, None])**2, axis=0))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_p(p):\n",
+    "    return np.mean(((1.0 - (fracs)**p)**(1/p) - fprs)**2)\n",
+    "\n",
+    "log = np.log\n",
+    "\n",
+    "def fit_p_diff(p):\n",
+    "    y = tprs\n",
+    "    return np.mean(2*(1 - (1 - fracs)**p)**(1/p)*(-y + (1 - (1 - fracs)**p)**(1/p))*(-(1 - fracs)**p*log(1 - fracs)/(p*(1 - (1 - fracs)**p)) - log(1 - (1 - fracs)**p)/p**2))\n",
+    "\n",
+    "def fit_p_diff2(p):\n",
+    "    y = tprs\n",
+    "    return np.mean(2*(1 - (1 - fracs)**p)**(2/p)*(-(1 - fracs)**p*log(1 - fracs)/(p*(1 - (1 - fracs)**p)) - log(1 - (1 - fracs)**p)/p**2)**2 + 2*(1 - (1 - fracs)**p)**(1/p)*(-y + (1 - (1 - fracs)**p)**(1/p))*(-(1 - fracs)**p*log(1 - fracs)/(p*(1 - (1 - fracs)**p)) - log(1 - (1 - fracs)**p)/p**2)**2 + 2*(1 - (1 - fracs)**p)**(1/p)*(-y + (1 - (1 - fracs)**p)**(1/p))*(-(1 - fracs)**(2*p)*log(1 - fracs)**2/(p*(1 - (1 - fracs)**p)**2) - (1 - fracs)**p*log(1 - fracs)**2/(p*(1 - (1 - fracs)**p)) + 2*(1 - fracs)**p*log(1 - fracs)/(p**2*(1 - (1 - fracs)**p)) + 2*log(1 - (1 - fracs)**p)/p**3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.025659542898885793), np.float64(0.20759594504246442))"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fit_p(1.1561), fit_p_diff(1.1561)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "715 μs ± 134 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "newton(fit_p_diff, 1.0, fit_p_diff2, maxiter=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(1.1456769764942591)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "newton(fit_p_diff, 1.0, fit_p_diff2, maxiter=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ff63745ecf0>]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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tit9WOoNq28CGmvn3nhrYjqsfqB1OXRnx9vZWRESE1qxZU7bN4XBozZo1io6OrvCYEydOnFY4PDw8JJWu7AgAMIdhGHp33QHFzP5G8dvS5Gm1aOLg9vr03kEUEdQqp6eDj42N1ZgxY9S/f39FRkZqzpw5ys/P19ixYyVJo0ePVsuWLTVz5kxJ0tChQzV79mz17dtXUVFRSk5O1qOPPqqhQ4eWlRIAQPWxO4xzDjBNycrXlBWb9dPe0tV1e7UK0Ky/91K3Fv5mREY953QZGTZsmDIzMzVt2jSlpaWpT58+io+PLxvUeuDAgXJXQqZOnSqLxaKpU6fq8OHDat68uYYOHaonn3yy+n4KAICk0pV1p6/cXm4dmdAAH8UN7abLe4SqxO7QG9+naPYXu1VY4pCvl4f+dVknjb2wLU/EwDQWwwXuleTk5CggIEDZ2dny96e1A0BF4rem6s53kk5bWfdUxZh6dVd9vPGINh3KliQN6hiop/6vJ6vrosZU9v2bVXsBwA3YHYamr9x+WhGRVLZtxic7JEl+Pp569Kpu+kf/VixqhzqBMgIAbmBdytFyt2bOpF/rxnp5ZIRCAljQDnUHq/YCgBvIyK3c4qOjo9tQRFDnUEYAwA0E+VWuYAT7+9ZwEsB5lBEAcAORbZuetoDdH1lU+lRNZNumtRcKqCTKCAC4gcT9x+Q4w7ORp4aoxg3txuO7qJMYwAoALiy/sETPfrZLbyXsk2FIAb5eslqkYyeKy/YJ+cM8I0BdRBkBABf1Q3KWHvrPZh06dlKSNKx/mB6+qqsa2TzPOQMrUJdQRgDAxeQUFGvm6h1lq+u2bOyrWdf31KCOzcv2iW7P2jJwHZQRAHAhX+/K0MMrtpTNKTJqYBs9dEUXNbLx5xyui99eAHABOQXFmrFyu95PPCRJatOsgZ6+vher68ItUEYAoI77ZnemJv9ns1KzC2SxSLde2Fb3X9ZZvt6sfA73QBkBABPYHcY5B5nmFZboyVXby8aGtGnWQM/9o7cGhDNXCNwLZQQAaln81lRNX7m93FoyoX96/PaH5Cw9+MFmHT5e+qTMLReE68HLO6uBN3+24X74rQaAWhS/NVV3vpN02uq6adkFuvOdJP17WG8l7j+uxT/tlyS1auKrZ2/ozdMxcGuUEQCoJXaHoekrt59WRCSVbfvX+5tl/30q1ZsHttaUK7qqIU/KwM3xGw4AtWRdytFyt2YqYncYCmzkrTnD+uqijoG1lAwwF2vTAEAtycg9exE55YEhnSkiqFcoIwBQS4L8fCq1X+umDWs4CVC3UEYAoJZEtm2q0IAzFxKLSp+qiWzLo7uoXygjAFBLShwO9WoVUOHnTs0wEje0G4vaod5hACsA1IIth7L1r/c3and6niTJx8uqgmJH2edD/jTPCFCfUEYAoAYV2x2a93WyXvoquexJmSeu66m/dQs+5wysQH1BGQGAGpKckafY9zZq86FsSdJVvUI149oeatrQW5KYyAz4HWUEAKqZw2Fo0Y/79HT8ThWWOBTg66UZ1/XQNb1bmB0NqJMoIwBQjQ4fP6n739ukhL2/SZIu7tRcz1zfSyFneYoGqO8oIwBQDQzD0H+SDmv6x9uUW1giXy8PPXJVV42Mai2LhbEgwNlQRgDgHIpKHFqcsE/7j55Qm6YNNCo6XN6e/5sZISuvUA+v2KLPt6dLkvq1bqzZN/ZReCCTlwGVQRkBgLOYuXq7FnyXIscfVrd7cvUOTRjUVlOu7KbPt6Xp4Q+3KCuvSF4eFt0X00m3X9xOnh5M4wRUFmUEAM5g5urtevXblNO2Owzp1W9TtHZXpnb9Pm9I52A/zR7WW91bVDypGYAzo4wAQAWKShxa8N3pReSPdqXnyWKRbru4nWL/1kk2T49aSge4F8oIAFRgccK+crdmzmT0wDaackXXmg8EuDFuagJABfYfPVGp/SrRVwCcA2UEACrQpmmDat0PwJlRRgCgAqOiw3Wu2UGsltL9AJwfyggA/IlhGPog8dA5F66bMKhtuflGAFQNA1gB4A+y8go1+T+b9eWODElSi8Y+Sj1eUG5siNWisnlGAJw/ygiAesXuMLQu5agycgsU5OejyLZNy66AfLUzXQ9+sFlZeUXy9rDqwcs769YL26rEYZx1BlYA54cyAqDeiN+aqukrtys1u6BsW2iAjyZf0VnrUo5pyc8HJJVOYDbnpj7qGuovSfK2WjRuUDtTMgP1AWUEQL0QvzVVd76TdNqjuKnZBbp32aayj8dd1FYPDOksHy8mMANqC2UEgNuzOwxNX7n9rHOCWC3SorGRurhT81rLBaAUNz0BuL11KUfL3ZqpiMOQvFjcDjAF/+UBcHsZuWcvIs7uB6B6UUYAuL0gP59q3Q9A9aKMAHB7RSV2nW3+MotKn6qJbNu01jIB+B8GsAJwWwXFdj0Tv0sLf0g54z6nOkrc0G7nnHEVQM2gjABwSztSc3Tfso3alZ4rSRo1sI0GhDfRzE93lhvMGhLgo7ih3XR5j1CzogL1HmUEgFtxOAwt/CFFz8TvUpHdocBG3nr2ht4a3CVIknRVrxZnnIEVgDkoIwDcRkZuge5/f7O+3Z0pSYrpGqxZ1/dUYCNb2T4eVoui2zczKyKAClBGALiFr3am64H3N+u3/CL5eFn16NXdNCKytSwWrnoAdR1lBIBLKyi2a9anO7Xox32SpK6h/nrxpj7qGOxnbjAAlUYZAeCydqXl6t5lG7QzrXSQ6q0XttWDl7OuDOBqKCMA6jy7wyg36HRAeBMtXXdAT67aocKS0kGqz/2jty7tHGR2VABVQBkBUKfFb03V9JXbyz2Oa/O0qrDEIUm6tHNzPXtDbzX3s53pSwCo4ygjAOqs+K2puvOdpNNW2z1VRG7s30pPX9+LQaqAi2M6eAB1kt1haPrK7acVkT/67tcsOc62AwCXUKUyMm/ePIWHh8vHx0dRUVFat27dWfc/fvy4Jk6cqNDQUNlsNnXq1EmrV6+uUmAA9cO6lKPlbs1UJDW7QOtSjtZSIgA1xenbNMuXL1dsbKzmz5+vqKgozZkzR0OGDNGuXbsUFHT64LGioiL97W9/U1BQkD744AO1bNlS+/fvV+PGjasjPwA3lZ5zslL7ZeSevbAAqPucLiOzZ8/WhAkTNHbsWEnS/PnztWrVKi1cuFCTJ08+bf+FCxfq6NGj+vHHH+Xl5SVJCg8PP7/UANzKn5+W6RTcSEt/PlipY4P8fGo4HYCa5lQZKSoqUmJioqZMmVK2zWq1KiYmRgkJCRUe8/HHHys6OloTJ07Uf//7XzVv3lwjRozQQw89JA+PiucCKCwsVGFhYdnHOTk5zsQE4EIqelrGatE5x4JYVLrIXWTbpjUbEECNc2rMSFZWlux2u4KDg8ttDw4OVlpaWoXH7N27Vx988IHsdrtWr16tRx99VM8//7yeeOKJM36fmTNnKiAgoOwVFhbmTEwALuLU0zJ/Hhtyqoj8vW8LWVRaPP7o1MdxQ7uxyB3gBmr8aRqHw6GgoCC99tprioiI0LBhw/TII49o/vz5ZzxmypQpys7OLnsdPFi5y7UAXEdlnpZJ2HtU80b0U0hA+VsxIQE+euXmfrq8R2jNhgRQK5y6TRMYGCgPDw+lp6eX256enq6QkJAKjwkNDZWXl1e5WzJdu3ZVWlqaioqK5O3tfdoxNptNNhsTGAHurLJPyzRp6K3vH/pLuTElkW2bckUEcCNOXRnx9vZWRESE1qxZU7bN4XBozZo1io6OrvCYCy+8UMnJyXI4HGXbdu/erdDQ0AqLCID64dCxE5XaLyO3QB5Wi6LbN9O1fVoqun0zigjgZpy+TRMbG6sFCxborbfe0o4dO3TnnXcqPz+/7Oma0aNHlxvgeuedd+ro0aO69957tXv3bq1atUpPPfWUJk6cWH0/BQCXkpyRqxe/Sq7UvjwtA7g/px/tHTZsmDIzMzVt2jSlpaWpT58+io+PLxvUeuDAAVmt/+s4YWFh+uyzzzRp0iT16tVLLVu21L333quHHnqo+n4KAC7BMAy9t/6gHvt4u04W28/61AxPywD1h8UwjDo/mXJOTo4CAgKUnZ0tf39/s+MAqIKcgmI98uFWrdx0RJI0qGOgru4Vqsn/2SJJ5QaynroJwyBVwLVV9v2bhfIA1LhNB4/rnnc36MDRE/K0WvSvyzrr9ovbyWq1KMDX67R5RkICfBQ3tBtFBKgnKCMAaozDYej17/fqmfhdKnEYatXEVy8O76t+rZuU7XN5j1D9rVsIT8sA9RhlBECNyMorVOx7m/Tt7kxJ0lU9Q/XU33sqwNfrtH1PPS0DoH6ijACodt//mqVJ721UZm6hbJ5WPXZNd900IEwWC1c7AJyOMgKg2hTbHfr3F7v1yjd7ZBhSp+BGmjuinzoF+5kdDUAdRhkBUC0OHj2he5dtUNKB45KkEVGt9ehV3eTrXfGCmABwCmUEwHmL35qqBz/YrJyCEvn5eOrp63vpyp48CQOgcigjAKqssMSup1bt0FsJ+yVJ/Vo31gs39VVY0wYmJwPgSigjAKpk/2/5unvpBm05nC1JuuOS9vrXZZ3k5VHji4EDcDOUEQBOW70lVQ99sFm5hSVq0sBLs2/so8FdgsyOBcBFUUYAVFpBsV1PrtqhxT+V3pbp36aJXhrRV6EBviYnA+DKKCMAKmVfVr4mLk3StiM5kqS7Lm2v2L91kie3ZQCcJ8oIgHNauemIpqzYorzCEjVt6K3ZN/bWpZ25LQOgelBGAJxRQbFdT6zarnd+OiBJigxvqheH91VIgI/JyQC4E8oIgAqlZOVr4pIkbU/NkcUiTby0g+6L6chtGQDVjjIC4DT/3XhYD6/Yovwiu5o19Na/h/XRxZ2amx0LgJuijAAoU1Bs1/SV2/XuutLbMlFtS2/LBPtzWwZAzaGMAJAk7cnM08QlSdqZliuLRbpncAf986/clgFQ8ygjAPTRhsN6+MMtOlFkV2Ajb80Z1lcXdQw0OxaAeoIyAtRjBcV2PfbxNi375aAkKbpdM71wUx8FcVsGQC2ijAD1VEpWvu58J7Hstsw//9JR//xrR3lYLWZHA1DPUEaAemj1llQ9+MFm5RWWKLCRt164qa8u7MBtGQDmoIwA9UhRiUMzP92hN3/YJ0mKbNtUc4f35bYMAFNRRoB64vDxk7p7aZI2HDguSbrjkva6/zLWlgFgPsoIUA+s3ZWhScs36tiJYvn7eGr2jX0U0y3Y7FgAIIkyArg1u8PQC1/u1ktfJ8swpJ4tA/TyyH4Ka9rA7GgAUIYyAriprLxC3btsg35I/k2SdPPA1pp6VTf5eHmYnAwAyqOMAG7ol31HdffSJKXnFMrXy0Ozru+pa/u0NDsWAFSIMgK4EcMwtOC7vXo6fpfsDkMdghrplZH91DHYz+xoAHBGlBHATWSfLNYD72/S59vTJUnX9WmhJ/+vpxra+M8cQN3GXynADWw9nK27liTpwNET8vawatrQbhoZ1VoWC7OpAqj7KCOACzMMQ++uO6jHVm5TUYlDrZr46pWREerZKsDsaABQaZQRwEWdKCrR1A+3asWGw5KkmK5Bev4ffRTQwMvkZADgHMoI4IKSM/J015JE7U7Pk4fVogeGdNZtg9rJyiJ3AFwQZQRwMau3pOqB9zcpv8iu5n42zR3eV1HtmpkdCwCqjDICuIgSu0NPx+/Ugu9SJEkD2zXVi8P7KsiPRe4AuDbKCOACMnMLdffSJP2cclSSdPvF7fTAkM4scgfALVBGgDoucf9R3bWkdDbVRjZPPXtDL13RM9TsWABQbSgjQB1lGIbe+nGfnli1QyUOQx2DGmn+qAi1b97I7GgAUK0oI0AddKKoRFNWbNF/Nx6RJF3dK1RPX9+L2VQBuCX+sgF1zN7MPN35TpJ2pefK02rRlCu76tYLw5lNFYDboowAdchn29J0/3ublFtYouZ+Ns0b0U+RbZuaHQsAahRlBKgDSuwOPf/Fbr2ydo8kaUB4E80b0U9B/jy2C8D9UUYAk2XlFeqf727Qj3t+kySNu6itJl/RRV48tgugnqCMACbacOCY7lqSpNTsAjXw9tDT1/fS0N4tzI4FALWKMgKYwDAMLfn5gKav3KZiu6F2zRvq1Zsj1DHYz+xoAFDrKCNALSsotuvhD7doRVLparuXdw/Rs//oJT8fVtsFUD9RRoBadPDoCd3xTqK2HcmR1SI9dHkX3XZxOx7bBVCvUUaAWvL9r1m6590kHTtRrGYNvfXSiL66oH2g2bEAwHSUEaCGGYah177dq6fjd8phSL1aBWj+zRFq0djX7GgAUCdQRoAadKKoRA98sFmrNqdKkv4R0UozrushHy8Pk5MBQN1BGQFqyP7f8nX74kTtTCud1j1uaDfdPLAN40MA4E8oI0ANWLsrQ/98d4NyCkoU2MimV27upwHhTOsOABWhjADVyDAMvbx2j577fJcMQ+rburFeGRmhkACmdQeAM6GMANUkr7BE97+3SfHb0iRJI6JaK25oN9k8GR8CAGdDGQGqwZ7MPN2+OFHJGXny9rBq+rXdNTyytdmxAMAlVGklrnnz5ik8PFw+Pj6KiorSunXrKnXcsmXLZLFYdN1111Xl2wJ10pfb03Xd3B+UnJGnYH+blt0+kCICAE5wuowsX75csbGxiouLU1JSknr37q0hQ4YoIyPjrMft27dP999/vwYNGlTlsEBd4nAYmvPlbo1/e71yC0s0ILyJVt5zkfq1bmJ2NABwKU6XkdmzZ2vChAkaO3asunXrpvnz56tBgwZauHDhGY+x2+0aOXKkpk+frnbt2p1XYKAuyCko1m2L12vOl79KksZEt9GS8QMV5MdAVQBwllNlpKioSImJiYqJifnfF7BaFRMTo4SEhDMe9/jjjysoKEjjxo2r1PcpLCxUTk5OuRdQV/yanqvr5v6gL3dkyNvTqmdv6KXp1/aQt2eV7noCQL3n1ADWrKws2e12BQcHl9seHBysnTt3VnjM999/rzfeeEMbN26s9PeZOXOmpk+f7kw0oFZ8vi1Nk5ZvVH6RXS0CfDR/VIR6tWpsdiwAcGk1+r9yubm5GjVqlBYsWKDAwMovCDZlyhRlZ2eXvQ4ePFiDKYFzczgMvbjmV922OFH5RXYNbNdUK++5iCICANXAqSsjgYGB8vDwUHp6ernt6enpCgkJOW3/PXv2aN++fRo6dGjZNofDUfqNPT21a9cutW/f/rTjbDabbDabM9GAGpNfWKL739+kT7eWzh8yJrqNpl7dTV4e3JYBgOrgVBnx9vZWRESE1qxZU/Z4rsPh0Jo1a3T33Xeftn+XLl20ZcuWctumTp2q3NxcvfDCCwoLC6t6cqAWHDx6QhPeXq+dabny8rBoxrU9dBOP7QJAtXJ60rPY2FiNGTNG/fv3V2RkpObMmaP8/HyNHTtWkjR69Gi1bNlSM2fOlI+Pj3r06FHu+MaNG0vSaduBuubHPVmauCRJx04UK7CRTa+O6qeINqwvAwDVzekyMmzYMGVmZmratGlKS0tTnz59FB8fXzao9cCBA7JauXwN12UYht5O2K/HP9kuu8NQr1YBenVUhEIDfM2OBgBuyWIYhmF2iHPJyclRQECAsrOz5e/vb3YcuLHCErumfbRNy9eXDpr+v74tNfPvPeXjxfoyAOCsyr5/szYN8LuM3ALdsThRSQeOy2qRJl/RRRMGtZPFYjE7GgC4NcoIIGnTweO6fXGi0nIK5OfjqZeG99WlnYPMjgUA9QJlBPXehxsO6aH/bFFRiUPtmzfUgtH91a55I7NjAUC9QRlBvWV3GHo6fqde+3avJOmvXYI056Y+8vPxMjkZANQvlBHUS9kninX3u0n67tcsSdLdgzso9m+dZLUyPgQAahtlBPXOr+m5mvD2eu377YR8vTz07D966epeLcyOBQD1FmUE9cqaHem6d9lG5RWWqGVjX702OkLdWwSYHQsA6jXKCOoFwzD02rd7NSt+pwxDimrbVC+P7KdmjVgDCQDMRhmB2ysssevhFVv1n6RDkqQRUa01/ZruLHQHAHUEZQRuLSuvULcvTlTi/mOyWqRpV3fTmAvCmcgMAOoQygjc1o7UHI1/a70OHz8pPx9PzRvRTxd3am52LADAn1BG4JY+35am+5Zv1Ikiu9oGNtTrY/qrPROZAUCdRBmBWzEMQ698s0fPfrZLhiFd2KGZ5o3op8YNvM2OBgA4A8oI3EZBsV0Pr9iiFRsOS5JGDWyjaUO7MVAVAOo4ygjcQkZugW5fnKgNB47Lw2rRY0O7aVR0uNmxAACVQBmBy9t2JFsT3lqvI9kFCvD10ssj++nCDoFmxwIAVBJlBC4tfmuqJi3fpJPFdrVr3lBvjBmgtoENzY4FAHACZQQuyTAMzfs6Wc99vluSNKhjoOaO6KcAX1bcBQBXQxmByykotuvBDzbr401HJEm3XBCuqVd1lScDVQHAJVFG4FIycws14e312njwuDytFj1+bQ+NiGptdiwAwHmgjMBl7ErL1a2LftHh4ycV4OulV27upwvaM1AVAFwdZQQu4ZvdmZq4JEl5hSVqG9hQb4zpr3bMqAoAboEygjpvccI+PbZyu+wOQ1Ftm+rVURHMqAoAboQygjrL7jD0xKrtevOHfZKkGyJa6an/6ylvTwaqAoA7oYygTsorLNE/392gr3ZmSJIevLyz7rykvSwWi8nJAADVjTKCOufI8ZO6ddEv2pmWK5unVf8e1kdX9gw1OxYAoIZQRlCnbD50XOPeWq/M3EIFNrLp9TH91SessdmxAAA1iDKCOiN+a6ruW75RBcUOdQnx0xu3DFDLxr5mxwIA1DDKCExnGIZe/XavZn26U5J0aefmeml4X/n5MLU7ANQHlBGYqqjEoakfbdF76w9JYmp3AKiPKCMwTfaJYt3xTqIS9v4mq0WKG9pdYy4INzsWAKCWUUZgin1Z+bp10S/am5WvRjZPvTS8rwZ3CTI7FgDABJQR1LrE/Uc1/q31OnaiWC0b++qNW/qrS4i/2bEAACahjKBWrd5S+sRMUYlDvVoF6PUx/RXk52N2LACAiSgjqBWGYej171L01Kc7ZBhSTNdgvTi8jxp48ysIAPUd7wSocXaHoekrt+nthP2SSp+YefTqbvKwMrU7AIAyghp2oqh0jZkvd2TIYpEeubKrxl3UljVmAABlKCOoMRm5BRr/1nptPpQtm6dVc4b10RWsMQMA+BPKCGpEckauxiz8RYePn1TTht5aMLq/Ito0MTsWAKAOooyg2iXs+U23L16vnIIShTdroEVjIxUe2NDsWACAOooygmr10YbDeuCDTSq2G4po00QLRvdX04beZscCANRhlBFUC8MwNO/rZD33+W5J0lU9Q/X8jb3l4+VhcjIAQF1HGcF5K7Y79OhHW7Xsl4OSpNsvbqeHLu8iK4/uAgAqgTKC85JXWKK7liTp292Zslqk6dd016jocLNjAQBcCGUEVZaRW6Cxb/6ibUdy5Ovlobkj+uqvXYPNjgUAcDGUEVTJnsw8jVm4ToeOnVRgI28tvGWAerVqbHYsAIALoozAaUkHjmncol907ESxwps10Fu3RqpNMx7dBQBUDWUETvlye7rufjdJBcUO9W4VoDduGaDARjazYwEAXBhlBJX27roDeuTDLXIY0uDOzTVvZD9W3QUAnDfeSXBOhmFozpe/6oU1v0qSbuzfSk/9X095elhNTgYAcAeUEZxVid2hqX+YQ+Sff+mgSX/rxKq7AIBqQxnBGZ0oKtE9Szdozc4MWS3SjOt6aGRUG7NjAQDcDGUEFfotr1Dj3lqvjQePy+Zp1UvD++qy7iFmxwIAuCHKCE5z4LcTGvPmOqVk5atxAy+9Maa/Ito0NTsWAMBNUUZQztbD2brlzV+UlVeolo199datkeoQ1MjsWAAAN0YZQZnvfs3UHYsTlV9kV9dQfy0aO0DB/j5mxwIAuLkqPZs5b948hYeHy8fHR1FRUVq3bt0Z912wYIEGDRqkJk2aqEmTJoqJiTnr/jDHx5uO6NZFvyi/yK4LOzTTe7cPpIgAAGqF02Vk+fLlio2NVVxcnJKSktS7d28NGTJEGRkZFe6/du1aDR8+XF9//bUSEhIUFhamyy67TIcPHz7v8Kgebyfs073LNqjYbujqXqF685ZI+fl4mR0LAFBPWAzDMJw5ICoqSgMGDNDcuXMlSQ6HQ2FhYbrnnns0efLkcx5vt9vVpEkTzZ07V6NHj67U98zJyVFAQICys7Pl7+/vTFycxZ8nMxsd3UaPDe0uq5U5RAAA56+y799OjRkpKipSYmKipkyZUrbNarUqJiZGCQkJlfoaJ06cUHFxsZo2PfPTGYWFhSosLCz7OCcnx5mYqASHw9BjK7fp7YT9kqT7Yjrq3r92ZDIzAECtc+o2TVZWlux2u4KDg8ttDw4OVlpaWqW+xkMPPaQWLVooJibmjPvMnDlTAQEBZa+wsDBnYuIcikoc+ueyDXo7Yb8sFunxa7vrvhhmVQUAmKNWFxeZNWuWli1bpg8//FA+PmceHDllyhRlZ2eXvQ4ePFiLKd1bfmGJxr31iz7ZnCovD4teuKmvRkeHmx0LAFCPOXWbJjAwUB4eHkpPTy+3PT09XSEhZ5+d87nnntOsWbP05ZdfqlevXmfd12azyWZjWfrqdiy/SGMX/aKNB4/L18tD80dF6JJOzc2OBQCo55y6MuLt7a2IiAitWbOmbJvD4dCaNWsUHR19xuOeeeYZzZgxQ/Hx8erfv3/V06LKUrNP6h+vJmjjweNq3MBLSyZEUUQAAHWC05OexcbGasyYMerfv78iIyM1Z84c5efna+zYsZKk0aNHq2XLlpo5c6Yk6emnn9a0adO0dOlShYeHl40tadSokRo1YmbP2rAnM0+j31inw8dPKsTfR4vHRapjsJ/ZsQAAkFSFMjJs2DBlZmZq2rRpSktLU58+fRQfH182qPXAgQOyWv93weWVV15RUVGRbrjhhnJfJy4uTo899tj5pcc5bT50XLe8+YuO5hepXWBDvT0uUq2aNDA7FgAAZZyeZ8QMzDNSNT8kZ+m2t9crv8iuni0DtGjsADVrxFgcAEDtqJF5RuA6PtuWpnuWblCR3aEL2jfTa6P7q5GNf24AQN3Du5Mb+nDDId3//mbZHYaGdA/Wi8P7yubpYXYsAAAqRBlxM+/8tF+P/nerDEP6e7+Weub6XvL0qNXpZAAAcAplxI3M/2aPZn26UxLrzAAAXAdlxA0YhqHnP9+tuV8nS5LuurS9HhjSmendAQAugTLi4hwOQ49/sl2LftwnSXrw8s6669IO5oYCAMAJlBEXZncYmvyfzXo/8ZAkaca13TWKdWYAAC6GMuKiikocmrR8o1ZtSZXVIj17Q29dH9HK7FgAADiNMuKCThbZdeeSRK3dlSkvD4teGt5Xl/cINTsWAABVQhlxMbkFxRr31nqtSzkqHy+rXhvVXxez4B0AwIVRRlxI9slijVm4ThsPHpefzVMLxw7QgPCmZscCAOC8UEZcxLH8Io1a+LO2Hs5R4wZeWnxrlHq2CjA7FgAA540y4gKy8gp18+s/a2darpo19NaSCVHqEsKCgQAA90AZqeMycgo04vWflZyRp+Z+Ni0dH6WOwX5mxwIAoNpQRuqw1OyTGrHgZ6Vk5SvE30dLJ0SpXfNGZscCAKBaUUbqqEPHTmjEgp914OgJtWzsq3cnDFTrZg3MjgUAQLWjjNRB+3/L14gFP+vw8ZNq3bSBlk6IUqsmFBEAgHuijNQxezLzNHLBz0rLKVC7wIZaMiFKoQG+ZscCAKDGUEbqkF/TczV8wc/KyitUx6BGWjI+SkH+PmbHAgCgRlFG6ojSIvKTsvKK1CXET++Mj1JgI5vZsQAAqHGUkTogOSPv9ysiReoa6q+l46PUpKG32bEAAKgVVrMD1Hd7MvN+vyJSqC4hflpCEQEA1DOUERPtzczT8Nd+UmZuaRFZOmGgmlJEAAD1DGXEJClZ+Rq+4Cdl5Baqc3DpFRGKCACgPqKMmCAlK183vZag9JxCdQpupCUTotSMwaoAgHqKMlLL9mXla/hrPyk9p/Tx3aUTBvLUDACgXqOM1KL9v5XemknLKVAHiggAAJIoI7Xm4NETGv7aT0rNLlD75g21dEKUmvtRRAAAoIzUgtTskxq+4Ccd+b2IvHvbQAX5MbMqAAASZaTGZeYWauSCn3Xo2EmFN2ugdydQRAAA+CPKSA06fqJIo974WXuz8tWysa+WTBjIWjMAAPwJZaSG5BYUa8zCddqZlqvmfjYtGR+llo1ZfRcAgD+jjNSAk0V2jVu0XpsOZatJAy8tGR+l8MCGZscCAKBOooxUs8ISu25bvF7r9h2Vn81Ti8dFqVOwn9mxAACosygj1ajY7tDdSzfou1+z1MDbQ4tuHaAeLQPMjgUAQJ1GGakmdoehf723SV9sT5e3p1Wvj+6viDZNzY4FAECdRxmpBoZh6LGPt+njTUfkabVo/s39dEGHQLNjAQDgEigj1eDFNcla/NN+WSzSv4f10V+6BJsdCQAAl0EZOU+Lf9qvf3+5W5L0+DXdNbR3C5MTAQDgWigj52HV5lRN++9WSdI//9pRo6LDzQ0EAIALooxU0Q/JWZq0fKMMQxoZ1VqTYjqaHQkAAJdEGamCLYeyddvb61Vkd+jKniF6/NoeslgsZscCAMAlUUactP+3fN3y5jrlF9l1Qftm+vewPvKwUkQAAKgqyogTjuUX6ZY3f9Fv+UXq3sJfr46KkM3Tw+xYAAC4NMpIJRUUl07znvL7Crxv3jJAfj5eZscCAMDlUUYqweEwdP/7m/TLvmPy8/HUm2MHKMjfx+xYAAC4BcpIJTzz2S59sjlVXh4WvXpzBAvfAQBQjSgj5/DuugOa/80eSdKsv/dimncAAKoZZeQsft77mx79qHRSs/tiOur6iFYmJwIAwP1QRs7g0LETumtJkkochq7uFap7/8qkZgAA1ATKSAVOFtl129uJZY/wPntDbyY1AwCghlBG/sQwDD3wwSZtT81Rs4beem10f/l6M5cIAAA1hTLyJy+v3aNPNqfK02rRKzdHqGVjX7MjAQDg1igjf/Ddr5l67vNdkqTp13ZXZNumJicCAMD9UUZ+l5lbqEnLN8kwpOGRYRoZ1cbsSAAA1AuUEZXOsBr73kZl5RWqc7Cf4oZ2NzsSAAD1RpXKyLx58xQeHi4fHx9FRUVp3bp1Z93//fffV5cuXeTj46OePXtq9erVVQpbU177bq+++zVLPl5WzR3RVz5eDFgFAKC2OF1Gli9frtjYWMXFxSkpKUm9e/fWkCFDlJGRUeH+P/74o4YPH65x48Zpw4YNuu6663Tddddp69at5x2+Omw9nK3nPvt9nMg13dWRqd4BAKhVFsMwDGcOiIqK0oABAzR37lxJksPhUFhYmO655x5Nnjz5tP2HDRum/Px8ffLJJ2XbBg4cqD59+mj+/PmV+p45OTkKCAhQdna2/P39nYl7Vg6HoRvm/6ikA8d1RY8QvTyyH/OJAABQTSr7/u3UlZGioiIlJiYqJibmf1/AalVMTIwSEhIqPCYhIaHc/pI0ZMiQM+4vSYWFhcrJySn3qgkfbjispAPH1cDbQ9OGdqOIAABgAqfKSFZWlux2u4KDg8ttDw4OVlpaWoXHpKWlObW/JM2cOVMBAQFlr7CwMGdiVkpBsV2z4ndKku75S0eFBjCfCAAAZqiTT9NMmTJF2dnZZa+DBw9W+/fw8fLQvBH9dEWPEN16UXi1f30AAFA5ns7sHBgYKA8PD6Wnp5fbnp6erpCQkAqPCQkJcWp/SbLZbLLZbM5Eq5LItk2Z2AwAAJM5dWXE29tbERERWrNmTdk2h8OhNWvWKDo6usJjoqOjy+0vSV988cUZ9wcAAPWLU1dGJCk2NlZjxoxR//79FRkZqTlz5ig/P19jx46VJI0ePVotW7bUzJkzJUn33nuvLrnkEj3//PO66qqrtGzZMq1fv16vvfZa9f4kAADAJTldRoYNG6bMzExNmzZNaWlp6tOnj+Lj48sGqR44cEBW6/8uuFxwwQVaunSppk6dqocfflgdO3bURx99pB49elTfTwEAAFyW0/OMmKGm5hkBAAA1p0bmGQEAAKhulBEAAGAqyggAADAVZQQAAJiKMgIAAExFGQEAAKaijAAAAFNRRgAAgKkoIwAAwFROTwdvhlOTxObk5JicBAAAVNap9+1zTfbuEmUkNzdXkhQWFmZyEgAA4Kzc3FwFBASc8fMusTaNw+HQkSNH5OfnJ4vFUm1fNycnR2FhYTp48CBr3tQgznPt4VzXDs5z7eA8146aPM+GYSg3N1ctWrQot4jun7nElRGr1apWrVrV2Nf39/fnF70WcJ5rD+e6dnCeawfnuXbU1Hk+2xWRUxjACgAATEUZAQAApqrXZcRmsykuLk42m83sKG6N81x7ONe1g/NcOzjPtaMunGeXGMAKAADcV72+MgIAAMxHGQEAAKaijAAAAFNRRgAAgKncvozMmzdP4eHh8vHxUVRUlNatW3fW/d9//3116dJFPj4+6tmzp1avXl1LSV2bM+d5wYIFGjRokJo0aaImTZooJibmnP8u+B9nf6dPWbZsmSwWi6677rqaDegmnD3Px48f18SJExUaGiqbzaZOnTrx96MSnD3Pc+bMUefOneXr66uwsDBNmjRJBQUFtZTWNX377bcaOnSoWrRoIYvFoo8++uicx6xdu1b9+vWTzWZThw4dtGjRopoNabixZcuWGd7e3sbChQuNbdu2GRMmTDAaN25spKenV7j/Dz/8YHh4eBjPPPOMsX37dmPq1KmGl5eXsWXLllpO7lqcPc8jRoww5s2bZ2zYsMHYsWOHccsttxgBAQHGoUOHajm563H2XJ+SkpJitGzZ0hg0aJBx7bXX1k5YF+bseS4sLDT69+9vXHnllcb3339vpKSkGGvXrjU2btxYy8ldi7PnecmSJYbNZjOWLFlipKSkGJ999pkRGhpqTJo0qZaTu5bVq1cbjzzyiLFixQpDkvHhhx+edf+9e/caDRo0MGJjY43t27cbL730kuHh4WHEx8fXWEa3LiORkZHGxIkTyz622+1GixYtjJkzZ1a4/4033mhcddVV5bZFRUUZt99+e43mdHXOnuc/KykpMfz8/Iy33nqrpiK6jaqc65KSEuOCCy4wXn/9dWPMmDGUkUpw9jy/8sorRrt27YyioqLaiugWnD3PEydONP7yl7+U2xYbG2tceOGFNZrTnVSmjDz44ING9+7dy20bNmyYMWTIkBrL5ba3aYqKipSYmKiYmJiybVarVTExMUpISKjwmISEhHL7S9KQIUPOuD+qdp7/7MSJEyouLlbTpk1rKqZbqOq5fvzxxxUUFKRx48bVRkyXV5Xz/PHHHys6OloTJ05UcHCwevTooaeeekp2u722YrucqpznCy64QImJiWW3cvbu3avVq1fryiuvrJXM9YUZ74UusVBeVWRlZclutys4OLjc9uDgYO3cubPCY9LS0ircPy0trcZyurqqnOc/e+ihh9SiRYvTfvlRXlXO9ffff6833nhDGzdurIWE7qEq53nv3r366quvNHLkSK1evVrJycm66667VFxcrLi4uNqI7XKqcp5HjBihrKwsXXTRRTIMQyUlJbrjjjv08MMP10bkeuNM74U5OTk6efKkfH19q/17uu2VEbiGWbNmadmyZfrwww/l4+Njdhy3kpubq1GjRmnBggUKDAw0O45bczgcCgoK0muvvaaIiAgNGzZMjzzyiObPn292NLeydu1aPfXUU3r55ZeVlJSkFStWaNWqVZoxY4bZ0XCe3PbKSGBgoDw8PJSenl5ue3p6ukJCQio8JiQkxKn9UbXzfMpzzz2nWbNm6csvv1SvXr1qMqZbcPZc79mzR/v27dPQoUPLtjkcDkmSp6endu3apfbt29dsaBdUld/p0NBQeXl5ycPDo2xb165dlZaWpqKiInl7e9doZldUlfP86KOPatSoURo/frwkqWfPnsrPz9dtt92mRx55RFYr/39dHc70Xujv718jV0UkN74y4u3trYiICK1Zs6Zsm8Ph0Jo1axQdHV3hMdHR0eX2l6QvvvjijPujaudZkp555hnNmDFD8fHx6t+/f21EdXnOnusuXbpoy5Yt2rhxY9nrmmuu0eDBg7Vx40aFhYXVZnyXUZXf6QsvvFDJycllZU+Sdu/erdDQUIrIGVTlPJ84ceK0wnGqABoss1ZtTHkvrLGhsXXAsmXLDJvNZixatMjYvn27cdtttxmNGzc20tLSDMMwjFGjRhmTJ08u2/+HH34wPD09jeeee87YsWOHERcXx6O9leDseZ41a5bh7e1tfPDBB0ZqamrZKzc316wfwWU4e67/jKdpKsfZ83zgwAHDz8/PuPvuu41du3YZn3zyiREUFGQ88cQTZv0ILsHZ8xwXF2f4+fkZ7777rrF3717j888/N9q3b2/ceOONZv0ILiE3N9fYsGGDsWHDBkOSMXv2bGPDhg3G/v37DcMwjMmTJxujRo0q2//Uo70PPPCAsWPHDmPevHk82nu+XnrpJaN169aGt7e3ERkZafz0009ln7vkkkuMMWPGlNv/vffeMzp16mR4e3sb3bt3N1atWlXLiV2TM+e5TZs2hqTTXnFxcbUf3AU5+zv9R5SRynP2PP/4449GVFSUYbPZjHbt2hlPPvmkUVJSUsupXY8z57m4uNh47LHHjPbt2xs+Pj5GWFiYcddddxnHjh2r/eAu5Ouvv67wb+6pcztmzBjjkksuOe2YPn36GN7e3ka7du2MN998s0YzWgyDa1sAAMA8bjtmBAAAuAbKCAAAMBVlBAAAmIoyAgAATEUZAQAApqKMAAAAU1FGAACAqSgjAADAVJQRAABgKsoIAAAwFWUEAACYijICAABM9f9Wlu2fIw0GCwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(fracs, tprs)\n",
+    "x = np.linspace(0, 1, 300)\n",
+    "p = 1.14\n",
+    "plt.plot(x, (1 - (1 - x)**p)**(1/p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "604 μs ± 114 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "newton(fit_p_diff, 1.0, fit_p_diff2, maxiter=50)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 248,
diff --git a/notebooks/auc_experiments/11-midpoint-illustration.ipynb b/notebooks/auc_experiments/11-midpoint-illustration.ipynb
index 7efe9ff..09a63f1 100644
--- a/notebooks/auc_experiments/11-midpoint-illustration.ipynb
+++ b/notebooks/auc_experiments/11-midpoint-illustration.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,7 +10,7 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from mlscorecheck.auc import integrate_roc_curve, exponential_fitting2\n",
+    "from mlscorecheck.auc import integrate_roc_curve, auc_min, auc_max, score_curve_p_norm, exponential_fit, roc_min, roc_max, auc_rmin, auc_maxa, roc_maxa\n",
     "\n",
     "from sklearn.metrics import r2_score\n",
     "\n",
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -43,7 +43,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +78,515 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def protect(function, args):\n",
+    "    try:\n",
+    "        return function(*args)\n",
+    "    except:\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_min'] = data.apply(lambda row: auc_min(1 - row['spec'], row['sens']), axis=1)\n",
+    "data['auc_min_best'] = data.apply(lambda row: auc_min(1 - row['best_spec'], row['best_sens']), axis=1)\n",
+    "data['auc_max'] = data.apply(lambda row: auc_max(1 - row['spec'], row['sens']), axis=1)\n",
+    "data['auc_max_best'] = data.apply(lambda row: auc_max(1 - row['best_spec'], row['best_sens']), axis=1)\n",
+    "data['auc_maxa_best'] = data.apply(lambda row: protect(auc_maxa, (row['best_acc'], row['p'], row['n'])), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score_curve(fprs, tprs, p, n):\n",
+    "    fracs = 1.0 - (p*tprs + n*fprs)/(p + n)\n",
+    "    p_fpr = min(1.0, exponential_fit(fracs, fprs))\n",
+    "    r2_fpr = score_curve_p_norm(fracs, fprs, p_fpr)\n",
+    "\n",
+    "    p_tpr = max(1.0, exponential_fit(fracs, tprs))\n",
+    "    r2_tpr = score_curve_p_norm(fracs, tprs, p_tpr)\n",
+    "\n",
+    "    return (r2_fpr + r2_tpr)\n",
+    "\n",
+    "def auc_est(row):\n",
+    "    fprs = np.array([0.0, 1.0 - row['spec'], 1.0])\n",
+    "    tprs = np.array([0.0, row['sens'], 1.0])\n",
+    "    fracs = 1.0 - (row['p']*tprs + row['n']*fprs)/(row['p'] + row['n'])\n",
+    "\n",
+    "    p_fpr = exponential_fit(fracs, fprs)\n",
+    "    p_tpr = exponential_fit(fracs, tprs)\n",
+    "\n",
+    "    fracs = np.linspace(0, 1, 100)\n",
+    "    x = (1.0 - fracs**p_fpr)**(1/p_fpr)\n",
+    "    y = (1.0 - fracs**p_tpr)**(1/p_tpr)\n",
+    "\n",
+    "    return integrate_roc_curve(x[::-1], y[::-1])\n",
+    "\n",
+    "def auc_est_best(row):\n",
+    "    fprs = np.array([0.0, 1.0 - row['best_spec'], 1.0])\n",
+    "    tprs = np.array([0.0, row['best_sens'], 1.0])\n",
+    "    fracs = 1.0 - (row['p']*tprs + row['n']*fprs)/(row['p'] + row['n'])\n",
+    "\n",
+    "    p_fpr = exponential_fit(fracs, fprs)\n",
+    "    p_tpr = exponential_fit(fracs, tprs)\n",
+    "\n",
+    "    fracs = np.linspace(0, 1, 100)\n",
+    "    x = (1.0 - fracs**p_fpr)**(1/p_fpr)\n",
+    "    y = (1.0 - fracs**p_tpr)**(1/p_tpr)\n",
+    "\n",
+    "    return integrate_roc_curve(x[::-1], y[::-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['r2_min'] = data.apply(lambda row: score_curve(*roc_min(1.0 - row['spec'], row['sens']), row['p'], row['n']), axis=1)\n",
+    "data['r2_max'] = data.apply(lambda row: score_curve(*roc_max(1.0 - row['spec'], row['sens']), row['p'], row['n']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['r2_min_best'] = data.apply(lambda row: score_curve(*roc_min(1.0 - row['best_spec'], row['best_sens']), row['p'], row['n']), axis=1)\n",
+    "data['r2_max_best'] = data.apply(lambda row: score_curve(*roc_max(1.0 - row['best_spec'], row['best_sens']), row['p'], row['n']), axis=1)\n",
+    "data['r2_maxa_best'] = data.apply(lambda row: protect(score_curve, (*protect(roc_maxa, (row['best_acc'], row['p'], row['n'])), row['p'], row['n'])), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dataset                                        abalone9_18\n",
+       "classifier                          DecisionTreeClassifier\n",
+       "classifier_params    {'max_depth': 116, 'random_state': 5}\n",
+       "p                                                        8\n",
+       "n                                                      139\n",
+       "p_train                                                 34\n",
+       "n_train                                                550\n",
+       "auc                                               0.526529\n",
+       "auc_train                                              1.0\n",
+       "fprs                       [0.0, 0.07194244604316546, 1.0]\n",
+       "tprs                                     [0.0, 0.125, 1.0]\n",
+       "thresholds                                 [inf, 1.0, 0.0]\n",
+       "n_nodes                                                  3\n",
+       "fprs_train                                 [0.0, 0.0, 1.0]\n",
+       "tprs_train                                 [0.0, 1.0, 1.0]\n",
+       "thresholds_train                           [inf, 1.0, 0.0]\n",
+       "n_nodes_train                                            3\n",
+       "acc                                               0.884354\n",
+       "sens                                                 0.125\n",
+       "spec                                              0.928058\n",
+       "best_acc                                          0.945578\n",
+       "best_sens                                              0.0\n",
+       "best_spec                                              1.0\n",
+       "acc_train                                              1.0\n",
+       "sens_train                                             1.0\n",
+       "spec_train                                             1.0\n",
+       "best_acc_train                                         1.0\n",
+       "best_sens_train                                        1.0\n",
+       "best_spec_train                                        1.0\n",
+       "fracs                      [0.0, 0.07482993197278912, 1.0]\n",
+       "r2_fpr                                                 1.0\n",
+       "exp_fpr                                           0.988549\n",
+       "fit_mode_fpr                                             0\n",
+       "r2_tpr                                                 1.0\n",
+       "exp_tpr                                           1.172257\n",
+       "fit_mode_tpr                                             0\n",
+       "auc_min                                           0.116007\n",
+       "auc_min_best                                           0.0\n",
+       "auc_max                                            0.93705\n",
+       "auc_max_best                                           1.0\n",
+       "auc_maxa_best                                     0.971223\n",
+       "r2_min                                                 0.2\n",
+       "r2_max                                            0.281664\n",
+       "r2_min_best                                            0.2\n",
+       "r2_max_best                                            0.0\n",
+       "r2_maxa_best                                          0.25\n",
+       "Name: 9333, dtype: object"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['r2_sum'] = data['r2_min'] + data['r2_max']\n",
+    "data['r2_min'] = data['r2_min'] / data['r2_sum']\n",
+    "data['r2_max'] = data['r2_max'] / data['r2_sum']\n",
+    "\n",
+    "data['r2_sum_best'] = data['r2_min_best'] + data['r2_max_best']\n",
+    "data['r2_min_best'] = data['r2_min_best'] / data['r2_sum_best']\n",
+    "data['r2_max_best'] = data['r2_max_best'] / data['r2_sum_best']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_min_max'] = (data['auc_min'] + data['auc_max'])/2.0\n",
+    "data['auc_min_max_best'] = (data['auc_min_best'] + data['auc_max_best'])/2.0\n",
+    "#data['auc_min_maxa_best'] = (data['auc_min_best'] + data['auc_maxa_best'])/2.0\n",
+    "data['auc_min_maxa_best'] = (data['auc_min_best'] * data['r2_max_best'] + data['auc_max_best'] * data['r2_min_best'])\n",
+    "data['auc_min_max_weighted'] = (data['r2_min'] * data['auc_max'] + data['r2_max'] * data['auc_min'])\n",
+    "data['auc_min_maxa_best_weighted'] = (data['r2_min_best'] * data['auc_maxa_best'] + data['r2_maxa_best'] * data['auc_min_best'])\n",
+    "data['auc_integral'] = data.apply(auc_est, axis=1)\n",
+    "data['auc_integral_best'] = data.apply(auc_est_best, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "r2_min                  0.650614\n",
+       "r2_max                  0.349386\n",
+       "r2_sum                       1.0\n",
+       "auc_min                 0.416379\n",
+       "auc_max                 0.882759\n",
+       "auc                     0.759914\n",
+       "auc_min_max             0.649569\n",
+       "auc_min_max_weighted    0.719812\n",
+       "n_nodes                       25\n",
+       "Name: 13215, dtype: object"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idx = 1500\n",
+    "row = data.iloc[idx]\n",
+    "row[['r2_min', 'r2_max', 'r2_sum', 'auc_min', 'auc_max', 'auc', 'auc_min_max', 'auc_min_max_weighted', 'n_nodes']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "auc                           0.759914\n",
+       "auc_min_best                  0.524138\n",
+       "auc_maxa_best                 0.903017\n",
+       "r2_min_best                   0.899821\n",
+       "r2_maxa_best                  0.232364\n",
+       "auc_min_maxa_best_weighted    0.934345\n",
+       "Name: 13215, dtype: object"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row[['auc', 'auc_min_best', 'auc_maxa_best', 'r2_min_best', 'r2_maxa_best', 'auc_min_maxa_best_weighted']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8690299382903445, 0.9216840567591656)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fprs, tprs = roc_min(1.0 - row['spec'], row['sens'])\n",
+    "fracs = 1.0 - (row['p']*tprs + row['n']*fprs)/(row['p'] + row['n'])\n",
+    "p = exponential_fit(fracs, fprs)\n",
+    "p_tpr = exponential_fit(fracs, tprs)\n",
+    "x = np.linspace(0, 1, 100)\n",
+    "plt.scatter(fracs, fprs)\n",
+    "plt.scatter(fracs, tprs)\n",
+    "plt.scatter(x, ((1.0 - x**p)**(1.0/p)))\n",
+    "plt.scatter(x, ((1.0 - x**p_tpr)**(1.0/p_tpr)))\n",
+    "(r2_score(fprs, ((1.0 - fracs**p)**(1.0/p))),\n",
+    "r2_score(tprs, ((1.0 - fracs**p_tpr)**(1.0/p_tpr))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('[0.0, 0.0, 0.034482758620689655, 0.06896551724137931, 0.13793103448275862, 0.13793103448275862, 0.20689655172413793, 0.20689655172413793, 0.2413793103448276, 0.2413793103448276, 0.27586206896551724, 0.27586206896551724, 0.3103448275862069, 0.3103448275862069, 0.3448275862068966, 0.3448275862068966, 0.41379310344827586, 0.41379310344827586, 0.4482758620689655, 0.4482758620689655, 0.6206896551724138, 0.6206896551724138, 0.7931034482758621, 0.8275862068965517, 1.0]',\n",
+       " '[0.0, 0.225, 0.225, 0.25, 0.25, 0.3, 0.3, 0.45, 0.45, 0.55, 0.575, 0.65, 0.675, 0.7, 0.7, 0.8, 0.8, 0.875, 0.9, 0.95, 0.95, 0.975, 0.975, 1.0, 1.0]',\n",
+       " np.float64(0.7241379310344828),\n",
+       " np.float64(0.575))"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row['fprs'], row['tprs'], row['spec'], row['sens']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fprs = np.array([0.0, 1.0 - row['spec'], 1.0])\n",
+    "tprs = np.array([0.0, row['sens'], 1.0])\n",
+    "fracs = 1.0 - (row['p']*tprs + row['n']*fprs)/(row['p'] + row['n'])\n",
+    "\n",
+    "p_fpr = exponential_fit(fracs, fprs)\n",
+    "p_tpr = exponential_fit(fracs, tprs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7731380223583438, 1.206160775482911)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.plot(eval(row['fprs']), eval(row['tprs']))\n",
+    "plt.plot(*roc_min(1.0 - row['spec'], row['sens']))\n",
+    "plt.plot(*roc_max(1.0 - row['spec'], row['sens']))\n",
+    "plt.plot(((1.0 - x**p_fpr)**(1.0/p_fpr)), ((1.0 - x**p_tpr)**(1.0/p_tpr)))\n",
+    "p_fpr, p_tpr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp = data[data['n_nodes'] > 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f0071aa1e50>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "#plt.xlim((0.5, 1))\n",
+    "#plt.ylim((0.5, 1))\n",
+    "plt.scatter(tmp['auc'], tmp['auc_min_max'], s=1)\n",
+    "plt.scatter(tmp['auc'], tmp['auc_min_max_weighted'], s=1)\n",
+    "plt.scatter(tmp['auc'], tmp['auc_integral'], s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6464459659423477, 0.8033864568145052, 0.6298287243049956)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(tmp['auc'], tmp['auc_min_max']), r2_score(tmp['auc'], tmp['auc_min_max_weighted']), r2_score(tmp['auc'], tmp['auc_integral'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f006c9a7a10>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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8b8T7pHY+k0b1pTg/i0mj+jJhZC/WPHAVE0b2AqBwzN18N2sxb1a+D6MfIqizqPpqAgJYi4/rvrWFjNHMIIMTwMmMRSbD8Rjl+BilgIAoicxeO5uqjVXJS4nGLqD6J1uwj5UTPcp+8RIXNpONAkuBeh6TTla0NAiG5IxCggSigCQJXNiYzZxD9SzYa8bubcJvtLHEeCN7Bt4JthIKxz+oGkPKp4KtRH5MgsVrd1P25DsAUSM6YiLGhw/y4aCZuOnK5wPvgbvXpX3f0kWm9zyDDM5iuLa7ov3cF0+OiVOm6jOP7+EesXiEmqix6C0ExEBSEmAttMcAjOszjk2HNlHvq8cX9mE1WWkKNqmF6p/c9gmu7S4eX/u4fIBiViIZdIvn+6z0LqZT6KBM3QayUZy6JXrSCLEH5VNhxER1c9mT77CvoZXi/CzWPHBVzLa7c99lkmEpC0PXUjjm7qjRjeBU2I6Mp5lBBqcZHVlya7ts4r1DxQtdsHFBzHjx3q+iby4gYNabk7Yqura7KFtSRvmS8qTzUsqXzHqzOpaS3FEe7f3sjLP2i+qPayR1zXkufnBOHq68XNRCpIgXuXjtbubNvh9x2X3ykvydx9V4JhCzPFegbjMsVZf5ajwU4NWJMKsTvHF3u/e4o8gYzQwyOMWIN5IdXXKnCgEo2wWEmPHis+dK6+PMkTMpKy5LIMVQ5qRkxZ2bnapx1J4LZLo3m8mGhKTWaaodQK9OZNPRz6L95hFYRBFJEnFLAZw2K668XCp6FuKy5gFym+QtwX+gQwRBDxK4xHoqNs/Htd3FBP0q1pgnM0G/Sl2Wf7hT7lv/7NyJNFkKWWK8kVv1q2h+pCvio/lIW14FKYy47c207nFHkFmeZ3DWoy1dmbMV2qWzYiSV5XL8srqjdG7x96MjxytLd4X/Uit+tmDjAgLhAGa9mbLiMpWF3aK3MG3ENEA2rs3BZrwBr3w9vW6G2vlM792fFZ7PGeAPUGc04NXpYlQhHd5GnD3O4bIDRt63HsJtNKj3Y/Ha3bhXPSt7jaPl81Rsno9bL8j71O2XPVBbCWX+KvY1tKIXICwRs2Tf+0hfegoyQYcISJLAkYCBHk8ezahRZvDNwtCXhybEzk6HdG1baG8O2riiVjCsvX3TUZVU7gfAQyMfih0zEhd0DRyNs3FbjCqkYqAVpnWlz1xrzJXXlG3q3MISWGRZCpvJRrYxWx5z+WPgqWNo7xKZoEOSsIoiXr3sMQpAZXMzsw438enAafzvHDsLPnwJU+d3mXLJnamlerX319uoxjoXh8ewcPUOhvcqYMPuejWjDvDKwkf57oFnsRDkX+KlXKL7L3mBwyddwverJXfxRE8wC1A0DH62Wv6AVM+EYCsMvhFuWpT62FcnwpZXI08EyMqXa2JHPxQtb7AUgDkXSkph+3J5XEMWhFpA0MGgG+Dz5RBqBZ0BdEb5UxEOghgEYzb0Gwd166LBbCW4XVIa3b77fdjyj+gYRjNc9ZA8tXcel+fVqTfsj8SdBD2YrfJcNQFy+Zoi4wCIISi6CA5vh2CLvE25V0nvR+RYUy6cNxp21EDQj5oEVSL2enPiuZXryukC7k+h8EI4tgvCfvn1cFCej/Z9SRHoj9kOUPO4fN7IPals8bPCYqBSnw9ze+PskoPboMP5wRzsS34qXyPI90v72dDe9/j7/9nrMOj66NxenRjddmxHdKxhP0qcW9iPs0cneQ6KPIT2+F6X4Ti0H2e+Vf3S2+v2Ayth+WPRsSLvtWPYdTgh7Qx8Ze9Klu9cDsien9aoKJ9l574a3HoB9053dL+Igco15iIgUFZcliCzqxjTTpZODH15qMx56d2Po76eDXlw0KKnrLgs6vF7G3Gtn49O0MttkoKcQ+8REmkWBBr1Ah+bzWRxlJ11T/MH/yuYupqZcvGUBIO58w8/oOTASup6jMX+87/Fvh75vEyAhGSPguCwH7Nq2TrG69YCcgH8/dYVwOdp3dd08dXyNB/Iw2qOfKMf9cjBYk9ErlPQwyPHUg8yqxNISfRCbCXRMRQI+uT7ptqebB8lM6jMUbvduz9xHFuJ/Bg/l/h9tNnGVNcUj0c9idvij23v2uLPrb33bUH7vijHpBor/h7EP4/M0ZWXi7OgAEd9PfbGpsRzaj8bynUlu//auSn3I/4+KJ+PuLmocyh7WP5ya4+3FsVeZ7LPQPx1au9HGojxxCLenlqiE/E05xxcrXqk4/qMY+7lc1XPUYlHBsIBTHoTUy6ewoaDG1ix8y0q/SIrzLKuuICAFR1+MYhPJ6dArCYra25ZI89hw9M0+714dRE2dgRmNIvY64/isuhw2qxM9Hi5ubGJip5FuI3yD7zqVSuecY8+OJu/wOHxckNjC4ZHU4jvxv3wLl67m4WrdzBpVF8Wrt7Bu603YhBEwpJAsy4XJB22WXWZ7LnqWZRPlb07BPkXvi3EvC5AVoHsWSr1YCA/t5XI+yrjGrIjh+jk7YbIdp1R/t+YLf8P8v+Dro+tL1PG124fdH3sGFmReZRPjc5LuUaQv2zKXBOuKTKOzij/XzQsMve4e5X0fkSOtRTIz7MKotdkzI7+n+zcynUVDZPnVzRM3s+ovSdx70uq2jvt9vKp8jjJ7klkjvagkeohU7HnnR+9RuU6tZ8N7X2Pv/+CPnZu2m3asZLNzZgdnYPiDWmPj7/OZJ8B7XWlqEVMhni5W4CK7lamF5VQ0d2Ky5qHa9zDOBu3Udm7Ep0gf8Vr99WqzEcWvQVvwKv2jHsDXuasm8OafWsQkVhjEDGJoppt9xBWDSZAwNdAxcuytIU72IggiVhFuaVyRo8rsfvBlWPBabPi8HixNzYhARM9XmzhMFZRinrVimfc8oWqBVTXY2zqG6Apel+8djcPvbGFfQ2t/G7ldiaN6ku1cCkhSYcPI1apEfwNad/bdPHV8jQzMc0MviI4VXHX+Pin8lxAUHvBATwBDzaTjckXT8a52anWV9pMNhqDjQmqkUo2PEeSVM/RarIiINAcaCQkhTFIkCOJSIBXr0eQJKzomHzsGPZzxsqhjid7g6+eipJi3AY9hcEQ1Xv3A/C3vFz+X0GB3HUUKaKP8TRbd8a+lgwaT7Ospg/7Glq5Vb+KuwxL+avxRgrH3M35e1z03/o02boQraI542lmkMFXAServTK+XCm+/Eh5rpQIeQNe/JG4stL/XX1TNSFR7v9uDjarnJkWvYVxfcapxwbCARzD72HKZQ+rpUyegAdRAASBLqLEO3sOMrneg06SkASB7HBQjqnWyZ03wYDMmP4TT6PclplzHkGdBREBZ0Fnue/8YG20DnPERJi6Bfstb8Z0JKVEZH9GTGTSqL7kZxm527iUYuGIWqtZuu8lbDRhzOsK93Ys7JEOMkYzgwxOAdpqr0xH66fi1QqmvzedOevmtFmDqTy/6hy57EZCwqw3q95j1cYqAEJSSH0c3n04n97+KesnrGfToU1q548v7FMTRtU3Vas1mSadCaso8dOGBky2Qm7+1b5or7pkk8MSJTIDUYsoxyzt3kaq99RhP7AT48MH0T3awM98olx+dPiAusQ+EUwY2YuPH6mgbuDP2U8XXhSulwvg22nBPFFkjGYGGZwCtEXPlo7Wj7vZzYpdK5Lq6SRD7b5aALXdMs8kF47H944DzFs/T/2/k6UTILOqKwJp2mvINmbjC/vIMVux66LxV6VXfdyRBpDC+D6vhrm95SUxZiTBAAhydcWcQng0H7uhC9X7DsrJO0EvlxBFitWTUcKp0DAeJcO9Xw7nMl8Vb1nGAVBW04fFly6LrdA4icgYzQwyOM1I5YUqHqYiU1HZu5LCnMIEPZ1kUMgyrCYrGw5uoDHQiEVvYcrFUwA5e67AF/apXq5KDgzU3lKbmgC48wh5jp+8QIVzAK75feDViYR9jdRLOfhDIrTWYxR9ZAkh9FIQkOTSrWCL+v/O7hV4yMVvyAWIoX1Ldi9c213R5M/yaUkNp7JMb/aHmLNsK/saWnnojS1tG+ITQMZoZpDBaUYqLzRepmLu5XNjNH/a6lVX2iPListYsWsFEhKBUCsbtroY+vJQdnt3Y9Fb1P0Vqjglw17ZuxKI9qCPWDyCsiVlgBwTVXTM5xia5Cx3ngU+ex0bTegEAbNBp1ZauIovoKJnEa683IR5Gt3raZQsmIOeBNq3ZPfCudkpe7dKuVZkSR/voTb6gjS0BmkNygkuCZizbCtDH13ZkbcmLWSMZgYZHCfaik0me63NWOb6RTgO7Ze1vNvQyEkFrWZ4Ze9KdBHuyhWezxElkc+OfqZmzxWyjQUbFzC8+3Byjbms2bdGzfhrS5EWfPhbee49+jC7cwGiICBIEo5Gn1pCZbMYsYS8EGxler6FxxXDmh/JVlsKAIFWzCwMXcsfQtdyzNAdB4NZuOMnTLn+SELBeow3PmIijJsXE6fUeqgLV+8gLIFeiL0nvqBI+BTUBmWMZgYZHCfaMmbJXmvT+NXOx36oTtbybkMjJx7xS3rHEAdzsy7gkwNNDA/r5fpi5OW71WRl8sWT1cJ2ASGGqGN27TMMyb1efj1SiSiE5CSRs3UnUqSXPE9vwT51J/S6DAJNkS4wAZBYYTGohB2Ohgh7u68ekLAQYJnpav5lupqrws/wQe4+vKFDLPjo+YS4ZoI3rsmaQyzzkfL/rOsGk2WUrzfLqOOaoUUJhvRkIGM0zxC+DjIJZ3K8tMdPkkRQ9105pc0EQ3uIN2bx5L/xhq5NwmJtxlcz5/bqPZMqT9bOB189zlwzYiQRJCGRY8yBXWvw+xoQgJ55PTncclgeSNTTevgK1n7cj/LicgRBwCJJTC6StXwcxaPlwvRwmCmNEZ7N6pnQWh+NWQKVzS3oJIlxzS3YG5tj5iogcat+FQ2tssxudut3sBq6ETh6RdK4JpAyCaRlcNf+P2P8QIrzs5gxfiBVtwzjk0fbKJQ/TmSK288QOkrScLpxsud3yq430vNd0acPbimQOL6mRdM17mGcm520BFvwBDwUhkSq6/bKy8cHdiWOnapXPgXKlpThDXix6C2Y9KYE4uB02Jpc21041zwmt4jqCqgoKeoYQ9L6RWrv/vQLLmG5dztIEhadkat6V7Diy2UysQbRonYiz6zup3nq3A38rOUlREFAJ0l8su+ozD1w3mj4okZ2KPuOlusyPXtRRc10RpBEtfeedx4Hf1OUD8H9KUhhmiyFjOW5GKINbStkQl95fOttB9+TDAnx1winWibhRHGy53fKrvez10EK4zhyMPn4Gg9O8cokJHnf5oC8T6olnKZlLx0oGWx/2B/DTalAKSGK19XRwrnZKReAF8jlPY4hDmwmG83BZlzbXaoMhVJ/GbOMXb8Ilk/DZQxS0TWXWp9M1lEYCvPv/a289b8PVCYiAL2gV89r0ZtZ88BVlO57SfUWK5tbZC/SVy/fZ1+9TO5Sty7SNx81uIghkML4t7+Ne9kTBP2tMokNEjQfgXHzaLIUsjB0bYJxjNf9gWii5xXLTbjpyrri24/rPTkVyHiaGXy1oWUXaovliiStje15LR30apTxh3UbRu2+2hgyDJDrI/1hP1f3ubptTzNuOa710hUuS4veQoGlQP2RqNpYheRrYMqxepz5VtwGg0zhJokMa/Lyrj6LhuZ+5Ods54pwK5tyrdSLQXxhHwICM0fOBMC54WludR/gR96j6LQ/JoNvirJF7aiB1gaIrwEV9HikLGw0qeTtyrGuQ+t4ypzF0cOV5Pi/TY7ZkOBt/m7ldgAuv6Aryz7dryZ3YngzzwJPM2M0M/jK4Wzg00wHWmMHpBWeSHZt2m1ATIhBJ+jIM+bhCXgAsIgSfp2AOUIcHKgv5Y+fXUe9IYwgwX3eMLcd2ycT+vaw4Q14VdYilQHJ0I3l//scGxEGKQ29oOu5wVRZZP6jKfUNEZYpQSW02XmkmRL3SnSIUaMr6Kko7o7baCA7aOEfdcdYGLqW1XnXJGj+QNRQ6gUYf2FRAm+miogBXVd8O/d+OTxmn2v/r5ZP93nQB1v58qnvZ5bnGXy90NEk0amWzT1Z0IYk0g1PJL22XWtkOrtda+TleK+bmXz4oKxVLol4Ah6MOiNWkxW/TocE+MM+nv/PLFatuZ/SVi9IEpIAi/Oz1LbHKRdPUQk+tMmrKZfciW38Y3JYY/BNHDu0n5kzpjJ5ySac+VY8ej1evQ5nfn50jlKYY5//B6N7PQZBlBPoEIl1hvmJx0tncnB4vPQUjjDJsDRB8yc/y0h+lpHxFxap2fCqW4aptG8JxeqRpXrxZ39QmY4umlXNRbOq+XSf/CMSFE++T5gxmhmcccQbingj2h5pxelGe/NTtmk9xrbaKtvLui/YLxMKL9hfI++/fj5VuUZMkqTGJ0NiiDW3rKF/3rdBEjCJEoeMOupsW3gvO09dKw/z+eUi8QjBRmOwEW/Aq5IZV9ftlwk4lBKfunV0Ch3k54alLPt0P0M6/QjEbCy6PBw9x0QM6424OnXD3sPAwtwieWkOCIKeZuSC+rFeiaD7N1xY4sBNV/YN+nlCXPPjRyr4+JEKqm4ZFhPjTNU1RPlUPOSSQysT9KsAaGgNqtl5AKMuVcD6+JExmhmcccQbingjGv+8LQOk4FSWOLU3v1Tb0hkv2bUJBkvMo+Lt+TQ6PGZRhPWL+N8hmbsyO2AjLyzRbLTg0+gzbMq1ypUEA0fHyF44hjiSJ1nKp3LM0J0N4gV8mnUnD6+dzbVfXMaxrTOoPXYHF3mf4qLPbuaFHDMHjXrWFxzhU+lcQujY2b2CJ/zfZ6/Uhd+FbmbSqL6U2qdR+OgXlNqnpX2/k3YNRZbmZoOOAqGZB/Le4r6x/WJyesX5WWx6pCLt86SLr5bcxcnAH0fFyiK0hbaCzvESDdr9FCkJfUTOQiv98EVNpNhXA51RzkqOfigqhYEkbzfnyrIPyrifvS4zg3v3xyY/1i+SyzyCflk+QykLyekSlc1QZD6U11JJcmilIU4R6YEWiiem1OQ5evTBGZZw5A0AiNHYSRfxhihtpJFoiJ9PsvmlO2fXdhctwRYsegstwRZc210J8538rfvVsVzbXTQLglrWVF5czqYvluGor8e1fj6hHDmH32BpokeOXK6kzdc4ht8D/ew4/3qZbDARor3tWtkMjZxJp9BBrtMflNXKgEmGpfzFPwbrlpf5t+EVABZ4zud92yEcHi+dhQDn+RZz95F3uTfrDZ5qvZbNPW5k9eodfLjzWOoYZQootZgxiBh4S1YB5JSQWz6VCSPkfZSEUnxr5snCNy8R9KhN87+n7X1TyTPEvwax+6WSoUhHUiKVFEaycZNJSaRzrvYkOeJfP11IJguR4vztJYOOO1nU1nt+CqBVh9QKmkHbCSCrzkxO0CeT9na6EGrnU97FgkeUuTTH9RnH8O7Dmb12tlqLOc4vMbzXaJxH1zOs/gCbzGZ+4vHyvVYdlrGPyhNSfjwjpVwQkfZQWdib2dmjkgkNP+PN0M/pFDoIQJOlkNzR0zi2ci6/bx3Pa7oK1udMJdfn5pihO79vHc8kw1L+ELqWxeExMSqSHYbiICgaX238sJ81dZrPPvssvXv3xmKxUFpayocffphy32AwyGOPPUbfvn2xWCwMHTqUFStS16mdcsTLIrSFtnj54mUQtPspUhJ6Y6L0g6UgcSxFckIrhaFsV2QflHEFvXyueLkGRT5Bkc9QpBVirlOIfS2VHEP866cLyWQhUqC95W86S/g253Carl0JTQzoNCBBj1y9xjWP4dzwdEyNaSDsw60XmO2uwXXsU1zjHsYrynWnFsHApi+Wwa41KsGwRRSZu78O595VuIONrMjJZqLHyw8am+S+8dr50eV5RDNckTFx2qyqFAVI9Dn6LmseuIpOY6ern7lcQe4SusbwB/4SHoPZoGdh6FqaLIU8FTGYSgIoP8uY1AtMiyYO5Hm21uMPi7iXPcE617y29z/J6LCn+corr3Dbbbfxhz/8gdLSUp5++mn+/ve/s337drp165aw//Tp01m8eDEvvPAC/fv3Z+XKldx77728//77DBuWhuEiU3J0pnA2l/acirm1V+7TkfN0ZKxk0rnq/pHuILIKcHYrUiV561sO44sQC1skiYBOr8Yn88JhPDqZWEPpFxckiZnHGsCQxRyrOdLxA9OOejEQ4v917YGjezk3ffAyOkREdOgG3wB163Dp/TizDareDwi4uvWU1TaH3xMj7LYzazAlB1ZSzaXc5bsbvQCDimxMPDSH8bq1LBNH8tucaUm9TKXsqF0vNBI68HjqsdGEm64UPvpF0l3PijrN0tJSRowYwTPPPAOAKIqUlJTwy1/+kgceeCBh/6KiImbMmMHdd9+tbrvxxhvJyspi8eLFaZ0zYzTPDM72Vs+OoiNa5VpKtuO5B22OZcyj+qBXjZtql+gzSmfESvLGyQ1rWzX9YZ+qsiwBOkliRqHcKz7bXaMaTAWCJGEVDJT5Q6raZGFYAjGM22igMCxx397eDAxvY4nxRqblLI+VtlbkmvVmKnrk4zYasBq6seaCH0bnu+xXgERYEng49GMmGZayMHQtdxlkSQo3Xdkz8E5K970UEzfWFrffN7ZfynintuXy/D0uztn6PL4el9CnYW1U9lmzXD/jy/NAIMCGDRsYM2ZMdACdjjFjxvDBBx8kPcbv92OxWGK2ZWVlUVtbm/I8fr8fr9cb85fBiSOdjHJ75S9fRSjXpLQgplrSd5hkI2587X11DHFgNVnV5E7MWA3emCy1sl1NyGiz2Dtq5KXyDrncKBCWl+AhMYRZb0FAYGDnQRSGRWYcrcf+8T+xj12ANcKdKUgS45qaVU0fD2FWmAUq/SKFggmHtxmHx0thKIyjvp7LTF9wc9YLFI65WzaSIBvMQJOsaz9uHoydzfc9IllBC+cf6CIvkYtvl42VMQsAPyZ1SX5OwXKuL+nEH/N6UDj+QQZ9uQg8dTTVRJfVC1fvoKE1SI7Z0GaCSFt+pGTi+zSslZOrrfWnpb2yQ0bzyJEjhMNhunfvHrO9e/fuHDhwIOkxY8eO5amnnuJ///sfoijy9ttv89prr+F2u1Oe54knnsBms6l/JSUlHZlmBimQThlMe+UvX0Uk9JynMIDJrjede5Dsvtr72VVhsoQ+8RGxcdOEc0Tiqq6BoynvlkvZOcW4smSZaCVGGZbC+MI+JCR2NOwACTZYzPL+f76EslYfhaEwM4/WM/fwUWYcrccaDiNIEiKwSSf36ztzZeNafbgFu66A3NHTojWS7k/l+XjqZIPkq6epZh5lNX3YXPQ3jEfm8pvGjyjkMOdsfR6Adeffy3668KR4KwtD17JX6sKifCutRh9Vtm4wYqK6fWHoWvV+pSIjVqDEO4f3KkjcT9D8cxpi0ae8TnPBggWcf/759O/fH5PJxC9+8Qt+/OMfo9OlPvWDDz6Ix+NR/+rq6lLum0F6cG130RxsTtCBicfXxbvUQu10uXjKKfkRSOZVAqoqpPKoIo4bcvp707nwpaFc81wFTU/2l/eZugXn0fV4dAJevR5nrgnWL5L5MA3dkJouAkm2Fr6wD7dBx4qcbHl/0c87xtiom72xmTXuemZ6/bKHGdDj7NI9muAJ+2XNnvCYaDKm8EL5YFsJfqMND7k87fsu+xpa2bC7ninXH+GHfbryQl4P9gy8E4A7tw3lMl8Vf6eC/Cwj2UY95x/rjRjI5waPALM6cVOXPVFvNoJkpB1aKB7mht31sfutXyTHJrIKYPzvT0uJXIeMZpcuXdDr9Rw8eDBm+8GDB+nRo0fSY7p27cobb7xBc3Mzu3fv5vPPPyc3N5dzzz035XnMZjNWqzXmL4MTg0I2m23MbtNofF28S+2S+VRcU/z4OcacBFYjxStUHuOPUyDLU4jszt7PDV0EXOsjy/YGL7ZwGIMockAnMH3TfOz97ASOXoFo2knQe6FqOHWCjspzx6tMSz5BwG3Q4yzsJWfBkSC7M/ZLH6DafRS7twlH93JZUdLjldmMah7HvepZ/tU6AfuKizX1vVAl/YBGSfZKFU/PudnJUZr5e69elPbpBPMHc5Mkx299QZFbgv+gU+ggz4ubyT86i0caN4MUpteBlW0ayGRI6YlGuEMx5Z4WgwkdNJomk4nhw4dTU1OjbhNFkZqaGi699NI2j7VYLBQXFxMKhfjHP/7Bddddd3wzzuC48HX0INvCifSnpxP7XbBxAe5mNws2LgCS39/JF0+mMKeQyRdPbnNesg65Dp2kj5GJsI+YSq1Hh6jTIQkCK8zy19XU+V10pgaMOXuo6DFJjod2H8XcDcuY2eNKdAggCOgQcAy/B9fIH1FR0hOX3i9zbUaW2va1f6baWholCxbkwvUCoRkTcmZeAigp5VehF+gpHOE+wyusMU9mgn5V7DVH4rD3WP5FcX4WQ4ptqqzFZ+fKxmwbvZEk+FxI7TClQkpP9DSXiMFxLM/vvfdeXnjhBV566SW2bdvGpEmTaG5u5sc//jEAt912Gw8++KC6/7p163jttdf48ssv+c9//kNlZSWiKHL//fefvKvIoF2cKg/yTDDQp3POE/mRaMvgKudWEjKKZ5duPDTZvOZePpeZI2eQa8nDapLLeAB1GV/ZZ5zsSfa5GoApl9xJYU4hD5X/gt9XTpLP8fE/wVOH/YOXmNHjStmQjpyJ3duIc18NboMOZ7ZBjv9lFQCCnGDa8g/QRRoDQ35yzysDSwEBDIQlgbd134a6degQCaFDEAQ1SRWoL6X5iweo3Xg+sz2V7JO68AvjUCiZg1t6RyYOCYm8vfUg+xpayZe8CALYxAYumlWdWI/ZjlRvUiihDjghFv6OoMNG8+abb+Z3v/sdDz/8MBdddBEff/wxK1asUJNDe/bsiUny+Hw+Zs6cycCBA7n++uspLi6mtraWfC1DSkfw6kS5M+bVNF3x+YPlLqD5g4/vfKnQ3ht8PB+Ak3HsiYzd3nn/OEq+l38cpW5SDcyGp2OPjR8r/nkb72N7RlEp9HZueDrlMSfyI6EatrwBMXOe/t50Hl/7OO5mN2a9OcGLTOca2lKi9AQ8NAWbEsYY3n04M0pnsGbfGlkhctcalVjDtXIKFf9vMC6Tkg2RsG+tiZ5j5Uwc9fUUhkI4WkK4zAIVXbL4QWE3hvYuYXrXTiAGceXlUtE9H9ehdfDALlyVG7k86zUOVTwL5VNx05VHgncwL2hXSYGVOOPST/bj9F1Jmb+K9db9eEOHMHV+l7uNSynkMD+WXgdQE0DPha6loTWYSMCRgmA4raL300hOfFyJoF/84hfs3r0bv9/PunXrKC0tVV9bvXo1L774ovr8iiuuYOvWrfh8Po4cOcLLL79MUVHR8c9Yae/67PX09ldaCz0nOZnU3pt0Im/iqfwAtDV2G6+5truo0B2UZVk1sa5UZTQJY9XOxyXWU7F5vmxI2ngfEzy9OAPraPBSGAxFhbuSHXMCUA3b1pqYa9AyrpcVlyU1foqhW/Dhb3E3u5mzdnaC4UwwqBElSl1EJTKeCKRqYxVz1s3BE/DIjER735bnVT1T9iL1giypq3SDaZeqoRbsjU2ykQ2bceZacBsMfGY2IQoCK3Ky5XMpXT/5Vli/iAmrr2CN8BOZPWjERJ7o93e+pfucB6RFfBg+nzu3DWXSqL4xwmVGvYC+cTRmOhM4egX/j+vZK3XhT8L15GcZyTLqyTbqyTLqk3cFpVhqp2Q5SuPYU4GvHsuR0kqobSFsC0pvuPJ4stDem3Qib+Kp/ACk2xoaB+dmZzTTqmnNTFVGkzBW+VScBQXyF3yzs833MWEJG2dg7d1Kqd53EHu30qRqjB1FSqq3gaPV0p+KVysY0GmAesymvf+JET9TjlcMnRDyyXyXSDg3O2P2iY+HKkqUM5rFmGtQ7oM/7EeUZLYMq8nKMJ9P1hW36HAUj5a9SI8XcjrD9F2xCRG9MfpYPhVHS4jCYIhB/oAsaWHrD4JertUMSziG34Nv5aMJdY8bdtczXrcWgyAyXrcWkOOMs64brBrOYFhiVOF1hHb9msP7h/OX8GhuznqB3pW/5ONHKphpW0Gn0EFm2lbw8SMVifHJuKoCBe2VI7V17KnAN4+wI4PjwsloW0xHWCwp4iUtNKQa8cJjx4P47h3luc1kI9uYrUpMKAbNudmJ49B+7IcS56C+njcAdtRQlWeRkykhH16dQGFOoUq6YTPZqL2lNiWzkmJgvQHZo1YZ1l8ejlsKoJNgRosEplyceHA0+bD3vCpSEI9MZlHzOPjqZdKNfBuO5iD2ostj2Kx2Zg3GcuAjuVPHPo2GR4vJpwkRAV2kjGfx2t10q76b0eIaanRlHKp4NkaqYuYbUXKTLKOO1qCIUS8gihI9bBYOeHz85pz13Ox79YTYs9oUYUuCM94RdFbiRON/HYmRKud6deJpCzqnur50EzAdStS0cS+PK0YYN96mQ5sQJZFNhzal3CfmufJ/r8tkNqdel8nPS0pVLzZV/LEjiPdsHUMcWPQWPAEP7mY3gXAAAYH6CKWf6llbCiDQxDC9VSXbsPezy4azcRsbBo+nUQriFf0Ikih7ckMciVn1OC/Jtd1F+ZJyZq+drRpMnaBTtYYcpdNlViQBnKYwTsEjrwJyLXJiJ5Idp3a+rCJJZPlt0OPMNuDas5KK7lY5fumpw3LgIy71LeDeL4cD8Ay3sFfqwm+ISBM/Nxjn9pu5L/cShguvYOt/BRM+GM861zzKnnyHD3cei7mfZoOe4vwsQmGJsAT7GnyEJfj1nhEd9wbjPh8LV+9gVOObjF4xGtYvSp/k4yTiK2U0H17zcOLGE43/dSRGqpzrs9dPnyJeiutLN4bXoVjfyY6lxo2XNKMdH+vUHhN3vGv9fCrywvKXPfLlSxZ/7Gi7aLIfBF/Yp/5v0puQkPCFfdH7OGKi3F7YWs+m+s9jfgyUe77iy2UqZ+Vkn47qIVPbZXFXjvcEPBp5XRjQaUCMZG9l70oKjXk4Anoc2edhDYu06PW4bDKLVlgS+Eg8X/1cOzyROLDHi9OahzvYyOM5Ahf2LmFm3wExy9/elb9kifFG7jIspWHZwzhNYY7STDivhobWoNz9o5GZWPrJfkBOzOdnGblvbD/WPHAVRflyXWeWUYdegN+csx7m9oYne6f/4xb3GZg0qq+aYGqqmZdevPMk4ytlNFftWZW48UTjfx2JkXaAuuykIcX1pVtS06HSm5MdS40bL6mxiI91tkG558y3xtQxpjpXR9tFtVAYhxQM6jyIKRdPwWqyJnZTlU/F1a2EeoMRAUGldXMMcVAYliVwC8MSM0bOZMPg8QzdVsX096a3fc8iSSGbzqyWMwFsO7KFda556rw3fbGM6vMnYr9rC/Zb3iTHWoxHJ+DsXsheqQsPh35MD6+sNS4B329sonrvfiqbBG5sEBEiRfGSIPBReCdrRu9Ul7sTRvZikmEpnUIHkSSJGxtEsoMWRniLeN8yGV+PS3DTNaYNEuDx7w3m4whTusxYJP/wtAZFZl03WF6aa9ox00LcZ2DCyF78xXCj2oaZVrzzJOMrFdOcsnwKT1/99JmeztcGZwv1W7rz0Erkbjq0CccQB7Ubz2fZp/sZf2ERVbcMSxgvUF+aNAbm2u7it2ufo/HAtxl7zg3qsUo8U4EQKuD+QX9OGT/T7q/EHIGEOOWFL12IhISAwKe3f5o4kLJ/oAla63HTld+N/BXV7hdACGCRJH52LITtu49H6eKM2ThzzTiKI0vwfTWMtozkxW034guKPNlrPeM9f2OZoZFF+VYmNnj5tN7OX8JjKOv0Iv/tuhW/IHB1cwtzA9kxpMuzH72PO6TXWRi6lr+Ex5CfZeRj673gqWM/XRgdfobWoKjuf2GxjaW/LAdilSUV5GcZuVW/ip+G/oIkSTiNE5g287cp3+u20JG45llBDXcmkEkEnRqcbdRv6RpP7bx3bJiiyr3ueGJ8wniza5+h9fAVdOPKBI7Gvg8uIyyBuWAtvfqui2FIH9ZtGG/974OUxyrjaxM1NpONyRdPTsqj+fjaxwEwCAY23SYv46e/N53lO5fLg0kS45pbmNso4vYZeCZ4DavzrmHSqL7835abaDH6ZGmKSME6tfOpsEq4DToKwxLVHsBTh4dcGiWLTO0287fMm30/b/d4E7fRgE6S8B+8gbAkcU6X17jTo0jwIuuaa3Tjz//18hglx9nfG8wE/So8yx5GlCR+F7qZv4SjbGcKB6ZC8aYVN8uPkI00tAbJzzImaJ6fSmQSQemgo4mhZPunU7jeVmymvcLuk4XjvdZIIsuRNyB26X46iuq1SbS4pI9zzWMxhfKulVOSxia1IYfxFxZhLlhLQf95Cfs5NzuRDPV07fY65u734Xou9trGX1iEXoC8Hv9JYHZiz1oE/TEuzatOufRzbnha1g3XyYXuZcVlzFk3R2VbV86lDQFkG7PV/1fsfCs6mFIzOfohaiprVIM5YWQvRl0gG2wRiQUf/paK3a8wvWtnmgUJW1iUPc0Ic7+VJnoKR/glSwC5LdLh8crlT4JAbo/3MHVZzSGjIsEbCQFE1CkVhOIN5sheMGIiekseBUIzdxuXxuyv3COF4k1BllHHx49UcN/YfhTnZ6nxzmQG80wkdY4HXz+jGRc4nv7edIa+PDR1LClZ8iOdwnVthrK9MTuYYGkvkaG+vj4uidIe4hJZMZ0jacxTey872j6pJnH2rEye6KmdL3euhCW1UN65r6bd2GTVLcPo1Xcdfo6yYOOCRKnfsISAyGFdEKcpHHNtVbcMY8cT47l/5F3YTDaag81Mf286Fa9W8FboKKIg8ElOM6aCdeq4Sma7bEkZw5rk5MqURh/VN1Wr1QE6SVKFzpQaUpvJhtVkVTPgrF9EZVOTLMEb+eueWwQjJmIqWEfOeU+yOfAcFa9WsGafvOTXSRJCyCcnmUJH8er1ZIsi9q2y5MV3uuTy97xcACxGPQC5o6dhDxmZUd9MYSjML4/s4aJjPRBCBTjKH5GZgTQxQ8VwDSm2oRfg2qFFMQYud/Q0sJVQNP7XXDu0KGGf4b0K0Avycr04P4sZ4wcC7bMYQZpF7GcBvn5GMy5wvGLXCkRJjOnmaGv/lNvij8kqiOr6tDdmJGFQ0d2alpFRyHIVHsZ4qEmMfGtsEoV2DG5cIksp2lb3bee6tfeyvTkmzFlJ4nTpnpDocQ0cTUV3K2QVyBnmSKG8o3h00iRWfBJH8TwFhESp3yFTmeLTqXRo8demhAQkJLwBLyt2rcDd7MYg6ECSGKDLiTmfktn2BrxsyrVS3ajH3q00xnOfUTgau65AvuZmN5sObWLyxZPJMeZEz7t+PpvMZh46Wi9/CQV57mVLytR7u3zncpUH1KozkydBmbGznGQSzXJRe6NP/YE5YBD4o81GK2Z8wTDzZt/P4vAY1vW5m5s89VTX7eP2xqP8rmUT9w/6MwAVu1/BNe5htRbzkX9uYVTjmzx/9A7+Omwr7/33cGyf+IiJ8j2snU/VeRvZ8cR4NR4MchF8WIKjzYEYI5mOF3kmkjrHg699TPO4C6pPMjoSP1QkDWISCxokUylUYmgdOU9HY5rae1m7r7bNObY15/h4ZUfn0ZbWTtXGKiQkplw8JaZEJ9m+WnVHpZBdSTJpC9qHdRumXvfw7sMTzpFMwVI7l/LiclbsfEuWlzDm4Rh+D3PWzlafD+v57WhsE7DoLQTEgNoFNK7PODYd2iTfo7BE9Z46miyFjOU5njp3A6X7XsI1cDQLjmwhcPQKahudmIMeWiQzQcFIHs3otDq+45+KkdkoDEtUD5lKWU0f9jW0UmueTM+INMUzwWuYZFiqxkiBpNe7zjWPkq1/YKN4AcOE//In4Xp6V/6SCSN7qcY4LEVp5TpSoH4iyCSCvsKJoI5kqk8kq302nud0jpWquyfeICvbrSYrOcaclEZV6fCJ3xeiP1bsWoNzX40spztWbo2s+OtluIONFBrzwJQrawBJEjNawNmtKFYTqJ89JilkEAxkG7NpDDQiIWEz2QBkQ90cxr53G1uF8xjX+hh6QY7NxmiJP9kbfPWqfpB8rPK/IC/Ja+fjGjhannd9PXZdAYsvXcbC1TtUQ7yu+HZKtv6BIo7QZCnkjVErY15XvXaNyFlI0mEQRPZKXbg56wXWPHCVmk2foF/FA3lvsTB0Lc82XXFCMr5aTaHLL+jKe/89DCTqC2WM5lfYaGaQGifbAGs9wQ0HN/DWzrcw681MGzEtqTIk0Ob5471SbSuk1iuMUZJ8bjBOUxiHt5ENfUbylnc7ZkliWuFo6F2WcG7FswWZbk5CUo10c+sxvKJfFmT73+dq7eVDwZ/ITOudXuRAp630aRyCs/w7sHImhFoisxfAkg+desvyFYOul5M+HtlbXRi6lkmGpXKsMlmnTqQUal3x7fxw00DVW1SNXcTr9BmsHAuZ+Jh+jDR8wSK+R+GYu1VPc+HqHazkLnJ9btVLPhFPU1vWpBcgHLFi8YY4YzQzRvNriZNd+qQdD1DrKFONH3/+VEZcCU8M6DSArUe3qnWXSufOuD7joiGg9Ytg+TSQwpT1KsGrE9Q5aJf7SpjDordg0psQECgrLlPrUO397FEDHIjET7e8CoDPYKU+ZOa2EhMHDHq6BUX+eSRMrk++XhEduvG/SzCGylL62WC0BjNVGZDi0Xlag0jIBmrWdYNVY7hrxf/xE16nbuDPKbVPa/uNSdFjfzzQeprndMpm8z4PFqOOGeMHnnJP8+uXCMrgK4eTzSqvHc8xxKFmrlONr+w/rNswmdYtwkIUn7VXsuPbjm1DQkIn6Li6z9XoBJ36OkSScbtfwTXyR7i6leBVpHQlAccQR0xCTen6MevNrLllDbW31DL38rkxVQ32EVPlhNOIqXIt5finwFKAJdRIIYf5aXOQ7sEwP2po4f3AeXjIpUUy00g2yfDefw+jVBQJgKc1yL6GVra9+TT7H+3LOleiSqTiWZkMupjXnL4rucxXxZ3bhiYkehKSPyeRiWjCyF58/EgFHz9SwdHmABLQKcd8Wmo/M0Yzg3ZxKtnZT0VXkrZd097PTu0ttWqyqi1iYCXZIiDEGHEt/ZxVZ8YUDmMLi8xoaGFu1gUqZVwnSydAk91v3IazW1FEmFygosck7P3s6v4DOg1IKomRgHhjo/S9I4Ggx37pA7xxROSOpmOMEdfw+cB7aBDysNGktitOXrKJvg8uY/KSTUzkDXoKR7jLsBRbllE1iJMMSyniiKosCXJG26iLtnO2BkXVw5s0qi/5WUa1eH1fQyu/W7ldNZSnq4TodGfdM0Yzg3ZxMgl+FahkvSm8ulNhqLWkvm0VzU++eHKMp6f2ex/aBCEfPp28JLcfO4Rr/Xw+O/oZAJ8d/SyqF2/Mw3Fov1qG9NClM/l95SQAjvlkVqBtx7YBtEnesXjtbubNvl9WqdQ2HZSUAgJERNtyR08DQY8OkdJ9L7FFN4CwJKD3HYNXJ3L/5zfxA90qln26n0V8j71SF/5qvJHLL+iq1lW+KFzPfrqoypIge3SiJoJ3q34Vy8RJMlFxxNu7b2w/QO788YfCqvHUGrNTWbieTg3oyUQmpplBu+ioN5jO/vGclfH7auOMKkdlkvHiz5WsPz0++aMkXVJlztu6nqoPf4tH9GMRRQpEaDZl4RWjEr1qCZamLMc17uGEOc5ZN0dNHinXp50zyB1H33cf5SZvIwVCc0yJjzo+gKBn3YAHee+/h9WkTlPNPDW2qSAk6fjYOor+wW0sDF1L4Zi7VW8wWSZb8RY755j4bL8HnSDwb+Mv6SkciZmLkpQpzs+i2R9ifOAt7jYupWj8r1XvWLvP8WbMjweZRFDGaJ5SnKylcjqJnfbOlazkJ2a8SFKhortVLu2JKzHSCbqEjLaCmJ5vUieI2pp3fH1nva8eX9iHQTAgIlKZdz5zd30O5VOp2P1KzPwXr93Ngg9fwtT5XcpLLlFjnEgSCAJWQzcafUEkQz2FwRCuvR6yjAbMep1MLDxionz97zwOrQ2AhJuuXOpbEDVKca8rJUdKSZBSQpQqkRNfW6n0lbtXPctE3kiaHVeWx6NXjKaQw1HDun6RTOMWMdSnyyOETCIoAw3SbrXswPL2ZC3D00nstMcpqX09FQ8nnjpZLyiOQNhqsmLSmRKp3CKIIUGOHJNS8mK7K2lPfllxWXQp3+tmpnlbKTTmEZJCiJLIcu92tdMmfv4LV+/g8P7hUDdDTS4pBlOQJLyBJgLN55AVtHBjg8gLhgmYs62xbbsjJsrSFoNvBEGPr8clMUvheSu30yRZoOgiEPRsE85jr9SFZeJIlVZt4eod3CG9ThFHGPRlLN/AwtU7VCIUxRhOGNmLaTN/yzWGP/Bs0xVqrDJ+ebzYcANNlsKYOs5cn5tpOctPusHMkBBnkDbaM3DHYwBPVha7LYN4PMa8duP57NgwhdqN5wPwqxULGWnN4uVOxXJmWZtp7mcnx5iDL+wj25iddA4KM7uAwLg+47D3syfcr5jnmh55ZXtt3Uc0f/EA63YepeLTp6C1nuo9+7HoLep5Ztc+w+K1u+X70etm7MtlEg9trM8xxIFBMAMCZlHCKJpA14LR+gl5pot4ObSQwjF3x7S4apM61K0DKUyf1i2q4Vq4ege3BP9Brs9NaP8nIIXpoW/k2/4qpgR/Qbm/irq+tzBpVF/+EFGIjOHGXL+IldzF3bnvquVFWrSVeFm4egfPNl3BWJ6LJq6Oh6c1TfIYJbygTUCdamSM5lcU7Rm44zGAqYzdyUzKtGfMk51r2af7CUvyI8Db+5bQbPAxz2rAZc1LGKO9a7f3s7N+wnpmjpzJpkOboskb5RgNEXBzsFkVWNPKawSOXsG+hlbW7Pt/sqa4zQoCTBsxDavJCmI2rYdlb8y13UXF5vlMN7VQsXk+poJ1MZ5ZSPSDAD5Bh/fQeLV954i0ljW+G5hQM1KeePlUqHmcWZ+PV5M6yQzSpFF9WWK8kf10YVlY9iwX8T0e/15UBG3D7nomjOxF/2vu4easF2TDHIESD51kWJrUM2wr8aIY1KfO3UDTk/3V/vcOlxqlSXKjnA84bWQfmZhmBu3iZBaftxfLTHauyUs2xRAN/2rFQqoPLARBOu45uba7mL12ttp5o+2fV4rJm/V6VQwtPpa6rvh27v1yOHdm/4LF2RLD/H42FfTAMfwe7P3sMXFLQefHE/Co9GyFxjyqD3pj4p1I0E03Et/+W+hfPJ+PwjupbG5huM+P02blVo+fH/hCmIMeAPZKXfht/1djyDLioS0AV9oLk22LP+bzN5/m53H95krccnivgtiWzVSIJKq07ZQdQgcL4VMRE2cSQRmjeUrRHrnF6WB47yiLe0fnFJ/IAbltcebImYCcsW7xe/HoBGyihGSwIBgsah2lwppu1xWoSY5kCanFa3fz289+hGSoV7P0w7oNY9Pe/+A4sEcmEk6SWQdiMuMVPYtwGw10D4b5x14PNppQ+8fTMCbKD86gIhtHmwNMGtVXJQnOzzLy8SMVqsF56twNnLP1eZ4JXsNfw2OwRfR+JozslcDGnpAFjzdypyP5k4ZhzRjNjNE8pThVTO4nanRPBaGH1SR/jhRSjEJjHvi8uPUCFlEkIAhUNrewKSsLt14X05KpsAJpv6jT35se0+M+b+XntOb8C0EQeKjsV9FyqDWP0SkYYKvZhFlnZFrpg4BsrB0NXrnrB+TMd9CPK9fCC3nZ9PIO4afFwyn971MQapUTPM1HVIORzNNSpHWN+WsxdV5N4OgoRM9IdIJAUJTIirQdKlnyDyxTKOQwbrpytfAcDa1BtW0SUPfTC/DXYVujpB0jJiZlPuqo3G6HkeSc8chkz88wTmVnzJlA/PUcbyIofpx44ud0uTdT3V8lDjpn3ZyE19p6T5K9plzjlIunqIJpVpMVR4OXYa0t6CJZbDHCpN4pLKnyvMO6DUMn6OjUbbDcJqkZ950978QoVpo6v4tgaMVqzo0tktcLfGY2IQkCPimEc81jLPjwt7iDjSywiLLnpGTGczpjbzjG214B59S/UWqfhitbT0XPQlyN/5NJN2pkGd05y7aprZBKIbwS3zN1Xo3O1ICp82rCEgRFiVv1q6jR/4IvllepZBfPi9fRZCmkcPyD3De2n0qEsXD1DiaM7MWs6wZTnJ/FrOsGywZTG3NMEls95R1BJ1sIME1kjGYHcCo6Y84k4q/nuLTNk4wTT/ysEFpoJWnbHCdO7mKY3irHAyUx4d639Z4kM9aqLvlmJws2LsAT8JBjzME+YiqbsrIRBQGT2YYO2XBuNekRJZE1+9bE9J7HG3F/OFrg7hjioMTWFUB9VLYX5hQyqMtgBAQsosSw1hYaI3LBgqBrlwzb2aW7TObcuQvYSlgYupZ9Da34gmFAboXM9bmhdr7a5ig1XAmhAvpnfU9NBCktkw7hDXXsFwNXxWS98yxGsow6mv0hFq/dzYc7j3HA0yrrnMfPLUlf+SlvbzyJvewdQcZodgBKDWBLsOWs8zaPxws+WSVG8eNU9q5EJ+io7F0JwJSLp6jeXVrjxMldbKr/nBlHI1IYcXNt6xqSGWulG8fd7EbweWRG90NyVt5R9rA8z06XMKNZpNCYhzlSQuQNeBnWbRiFOYXy9SHIRnzD0wAqcce4PuMA1NbKrUe3qp73hoMbqL6pmr999298evunrB8sG2pJENBJEpMb/bEGQGsUIiU4ju7l8vVeOgOmbqFwzN0U52dxzdAi8rOMvChcr9ZIThjZi/vG9qMgPIrpg/7MPyb8ilnXyRn0haFrcdOVNT1+hAAYdQL5WUa1zvORf26hoTVIICTS0BrkkX9u4c1PNFUMaRis+Cx7TKnUVxiZmGYHcbYpOCo4W+d1XIgE+F0DR+Ns3IYjbwDsqMGZb1Wz0+lAjYVqjm8WBLwBWWhsxtF67E2tIIWTtyhGEjVKy6OWNDiGrm3EVFzr5+PMtzKs57ejHT6gFq2DrPHzyYEmOG+0XF9ZPhWXNQ/nmlk46huwh4zyslxzH5RkiupBJonfxWfEARZ8+BKS7R087m8TbBhJfiSpo2TAFdLeyy/ompANV5I+ArLWUGvEizXqBERJipFL7kjcss8Dy9TOpJ1Pjm9zX+21nUhcNBPTPAtwsmnMThZORjwymbeaTudR2ZIyypeUnzTv22XNo6yHjapjG+Tkz9gFMtt5sLFDxB5quGFrDU5TGHewUWUwmlE4GrIKqOjTB1e31BpRdm8jM5rCqmepiLdtKOxPi17PgjyLbDAj47+1862owQTVYCJJVDa3yF09EWE7audHdIzulbPxVz0Ue12b5/N4XoC3e7zJ70zdE3SmlG6YVWvup0vxPVzZ+W7+b8tNPP6uk5ast/FzlHO6vMYC4zP8S5zEtjefZl9DKxt215NjNjA+8Bb3f34ToxrfjIk7KstqW5aR1mBYZX/PMRuimkARz9e96tm045YWoy7mMR2cjWJrXylP80/OsfzVJp6W0pevG1JloJMR9mq91fY8WOV1SLOHe/0i1StzDL8HSCzjSTZmOvOP3w9QPc0Nu2tYYdZR2edqlSg4peSF1tONyEGQJQulKeVIOgTEyLK/MCziaArg7NKVejGILxKjtIgiIQTCOoGr9Z2Y+8UnYMhieu8LWBE6SqUvxNzhSRjT1y+iYvN83HpBre3sEZIQbEUx16p4hH3Pu59DRp26rxjIJ3B0FL26vs7PGur5vrcJQYB6KYdmstg36Of87xw7o966ip7CEfZKXVh99TtM0K+Ss/YSMPohFofHxNRmamUw+m99GhtN+I02rtK/GJXAKCmVvWjlUVMOdDxeY8bTPEG83PrlaUvEnOpM+enOxKdKmMQT9sZ7q+l0HllN1pR93gmojXplirqjWy/IHTWR2sRh3YbJiRK9Re0LV1iAnJudMf3hLcGWGILheOVIhddyU6ciRCRW7FqRUC0Qr2KpdKM491bLcysowD5iKo4GL5IkYhMlKv0itnAYazgsL63DZqp/+D7TRkxTSYkLwiKbGuDT2zcz9/BRQIKczqwQG+TsvMUgG6kk90iRM6609acwLPLTZr9MM2fMY8L+vcybfT/DexVwd+67/KTRR2EoTKUvRHbQQuDoKIINIxlVV8kN3hYEQSbqEASBnsIRSve9xISRvdg36OfspwsvCtdHr1sjTa3EJKtuGcaaB65SM+bnbH1epYsz63Uxr6letMabVnA8FG7JjjnT+uiGM3LW48RtWefy1xxRjlE93gXCEVF6QQ+SKJMX3LQoljbLEGGvDrUAglzf5v4UrEXg3S8/qhRbsmwrSDgjRcXOD+Zg/9vPZW2VXpdB9UwItsrn6nUZvDUdxGB0knqj/EsthpLvEzmHs6QIt0GPc8PTck9y+VTY/T5s+QcYsqBbP9i/KXoNOZ3l/z118hgWG5hy5eeWAvA1ABIMvkm+ByB7TCtnQqgVR79v44xwPLJ+kfzr/+pE7J+9jr3wQlj+GJSUYq/bD70a4dWJ8Nnr2Addj73XzfLrm1dGvYfd78tfjAvKZC8tb4B8Hd7G1MmBVyfKJBtiLs7OXaKGrvYRHB6vfF3IhBoSEgWWghiFzYNf7kcUZLlipV9c0exRvE/HEAdVG6toDjZTXlxOy+5jNDfupzy/Pwc4gCiJVG2sUgmKQc6yxzC7l0/F9Z9HaBEErKLEsPPGU7H7FY7k6AkiYEJPl0MVLOUNPhIv4Ar9p6BvgvWLsEeu/bm1T/F9Tz3rSn5CKeDKy8WZV4TD62UAhXwmNTPAHwApySqgfCr22vnYI7WgTU/2J9d3CPGDl7Fn2aC1nr3SP1iyA6aFnbjELBCyGH7lowypL5Xjm1nwKhW0BsNMMixlYehajHqB+7KXk1s+lcVrdzNn82Bag3JlQfHqHUwYPVX1NNcV3869T74T6+FFpHv3FN8eQ0OnfY2cLvL3q/DCaB2pBiejdlO7ZD+djEkKvlLLc89v+mN9cFusUdRC0MMjx+BR2wmf05WXi9NmxeHxYm9skseOMbBxz5OhjX1cebk4Cwpw+MB+KFKg690vJyVOBJF7oBRSO+rrE+evJBNmdYo9n6CPJkW8+3HlZsnLaL9enmPc60hhKkqKcBsMFIZFqvfslfXgtckMLbTnU94rSOjs0CZw7FtrcPXog7P5C7lV0WLGUT4rVp5X2S+SONLK7+Ldj1svUBgK0WzKwSv6sYbDTMnqi9PojxFLi1GrTKImqUKCxs+fZIJ+FT83LGWVVeSvNgsTWgRum7wV1i/Cs+xhREnCaZzAtJm/pcI5ALfRQGEwBALyPQuGqBa7U9HJpC67b+v5QoJRqX7su3wn/B85PGopoAkLT/u+y0+QGYrU9yDuGn61YiFv71tC6+ErsDc2McmwlK36AVTk7WJe8ziebbpC3feVi2OL1Y+b/7KdgvOTwavZEcP7jV+e490nP5ZPlT06BYIeEGRvEOQ3TIEhO+ptIkDRMHl/W0n0UR1HhyJ0am9sovpIK/a88+X9Bl0vn9eYHT1X+VTQaeYB8rx0xtT7RM5hbxWpHjJV7gBR6t0GXS8fZ8iW56m9BltJdK6CTjZOynNLgTpv5R6oy958W+xctLV1g66Xr61omLx90PUxc3HmW3EbDMzJ0cnJkrjXEfQ4ss+Tl+9NAXnMuJ9g13YX5UvKKVtShuuCsuj7EJmnoqczffh4tWBcm8BxifU4W77A4fEy9/BRqm2XxTAaKftpS5QEBLU0bJipM4XBEI4GL1MafRSGQpS3+pjj34m72Y034MVmsjGs2zDKl5Qz7OVhXPjShXSyniNf1/B71KJ2qyiBJNE/KFKcn8W9WcvoKRxhsS0Lt9HAczk58uXVzsdGEwVCM5MMSwFwNPnkeXi88j2L/I/7UznEYehG/f5yfrdyu+pFKSGcA/lfyktsdKw7925yH/ictyzjeDYolw05elaoIRTt0nVz0+tIhnrMXVYzybCUnsIRxohrwFPHJMNSsiIJmTtM7zBi629iltPHXWPZTsH5yajdPN1M7fH4anqa33Ck01Z4MloP2yK1SNh35ZQE7W+A8iXlao+3RW+hwFIQMyc16SMJKgGH4m09de4GftX8Eh6dTJuWq8vlrpH3JsrwxnmaMcTFYYnqPREvedw82P0+FZ73cRsMajlQvGolyP3on97+qVzXuXY2IhI6QASZcOOH76tJradyzDSHDVQU3UFpn85yS+RBN3ZPfTRkFN8nHQl/MOh6Fvd8WG1RdFj+zUTeYM/AO3lI97bMbi9m87ddR1gYupbVedeohMDx5UP3je0X01f+6x80sOCj5zlUdxn2xiaZTX3wqJgETdmT7/BK609lNnblHp3mYvFkOFktmBlP8+51Z3oGZwXikzrJkkod4bRMlZSy97OrPdqCWniSYk6N22TPtjH6o+ba7lK1vEHumolPRjmGOBBCBQS9F4IoM6DP3WbnEP/m3i+H4zfKtF8+QeAozTHHahM9rnEPqwaTXWtoadyPVWfGURyhdVOMwU2LcJTPUtslBUmiOdiccD1mQY/rucGqwUSSGBCUfzxaBJ18r0ZMpKogn2YC2LKM/L5ykjynYCNOWw4gyQYqGbHETYvk8ESvy2hdU0Hnvo9iLljLPZZ/UchhSrc9oeoLHT5UQbm/ir+ExyR4aG9vPUBDa5CG1qAqtat9/6ibQbBhJH8Tx/BO5TvyeTUF8yu5i636AXJB/Ck2mB1J4JyNpUYKvlpG8yuE482Op3NcfEa7o+2dbRLuxiEttcQkcwJYsHGB2o1j0Vu4us/VCfvY+9m5f9Cf6dzyE6ymXLlcR9eCpXs1lMxR9xMkE1ZDt5SZ/Zis+b4aPDoBQj7VoGqNgb2fnRmFoykMS1j1FrwBb9TLlCSsosS0piBOU1gtK0IQqDNbaAo24Ql41Huldh2FfDB/sNzyKegYVtA/ukxtixuydj5/t+loNfro0vN9VSANKYx9aw3VN1Uz9pwb0Atw7dAiQI4LKst4X1DkVv0qas2TGR94S9Umv29sPxav3U2zP0SWUUeexZj03Lk+NxV5u8h94PNT7mF2xBCeboXJjuCrtTw/CzqC0sXxdugcz3EdWYq7truo2liFhER5cTmbDm1KKkJ2MqBdmqvX0wadl2u7iwUbFyAgqz16A14EBMx6M2a9mckXT04rHMGuNTj3raJFEPDE82Fq7oE/2CxXYOiNBBARJZFBQYm/DZWrA1x7VlLVuQt+vRFzRPXRE/DEdgYp5VDH9rNJF6ZFr8ejk3V+qJshLy/1q1JTmL06EdeelTi7dMdROj1aJ1rzuBymvuqhmGMumlVNQ2uQLKOOTjlmhvcq4MHt36eQw+yVunBFoIrxFxaxYXc9zf6QylSk1foB1HP4wyJV0g9i6NvaWxq39Xpbr8Xzop4OZJbnXyEcb4fOyWRcj4fSd62QVCg635sObUo4/mTUkU6+eDI2ky22nKcNr8vez86aW9ZQe0stUy6eohpPf9gf490lm1+M1vnYBVR7BCYfi+1XV5JSs9fOxhPw4JNC+HQCATGodvEcyy+SjVTdOuzeRrLDIVk6o7WBMksPtac+Phm1KdeK22hAMlhimN0Xrt6RtE9bWao2fbEGu7eRavdRVQ5D1TVvrU+4T/6QXH0QilATfatPJwrHP0iTpZAlxhuZdd1gNuyuV7kv87OMmAw6sox6lXhDfR989RwLmni26Qrcq56VM9+vTmT0itEJXUJatOUxtvXae/89TFhCjcHGIF7eIk25izOBjNE8RegIY5DWABwv01A6cG52qj3UqYrZtfueaCOBvZ+d2ltqWXPLmuj1pEnnpY2n6gW9Ss8G0QSVu9nNgo0Lkg9QUoq9qZVqaykge/BVG6vwBDxISAiShAW5DrPS1j/RuEfm6SiWl/HDWltY4fkcURJVYTb1fVs5RRZ4M+ZRntsbvPu5qcue6PIyiUFQDVPoWvl+SKSmWtMcbzboAQiJUoxRzn3gc6bN/C0TRvZS2Y0U3CBW87bubsYH3ooas8j4ewbeKc/TsFQtSi/kML8wvplyadzW0vm4l9XxP6Zpyl2cCWSW52cBThfZRkeX8fH7nkoG97bOF69TrsjlAth0ZmqP+BKXvpF6QVe3Embn6JEQsejyMBt0SL4GyltaWJOdrSaaTHoTUy6ekpyxPm+AXM4UaWucUShXCKjvWzBE9d79NFkKGdvdgFcnG+NJA5bhXvUsvwq9gA4RbCUsvnSZKnHrpis1lTXy8l27HIfY5XzkWposhZT75WL0yy/oyttbD+ILhhlSHGVlB9nbU5bm+VlG3pLuopDDhNCxYeCvY6R6VUTIQd4PnMdlpi/k2OpJjnG2uexPwvzeEbmLVMgwt39NjebZxGzeFsqWlKnGq63yow4hhVyEFvESFcqyXUBgZrMYbQ7QyE8ovc/KuJIkkOW9ifWTH47p7dYiocBd05seCAfwh1q5urmFuYFsmLolpoFAkuDZgq60GiR8iNjQI7mfUst5RHT83vBT/hIew/jAW/zC+CaF4x+MdIG9Kp/QkA0z3YkF4pFrUgrSrd3Wg+0dWg9fQbBhpDpfrcCYAFG5Cv0qWD4tkc0pDskKz9Mt/Ulnv9Mpm6LgrIlpPvvss/Tu3RuLxUJpaSkffvhhm/s//fTT9OvXj6ysLEpKSpg6dSo+n++4Jvx1xIkuyVMtpU92f7tSdtRe+VE6UOe2fn5S/fJkIQslk69k4WeOnAl9R1NxTomsGAnRZV3dOpi6Bcfwe7AaupHlvYkp37pdPb/DJ3up2muL1z/X9qb7wj6kCJu7a+BoWTjt9S5M9N/EDY0tLMq3Um8IYxLDFAZDTG4OqaqQTZZCfm/4qdqBszrvGmoqa2QP6rPXozcl1CobzJLSpCGMyy/oSnF+FpLtHbVoXS/AhcU28rOMNPtDDO9VgF6QV/s5ZoNswEZMZN2AB3HTlXXFtyeU/ijPh/cqSFhax8coU5UNpZMZ/7qQeHfYaL7yyivce++9PPLII2zcuJGhQ4cyduxYDh06lHT/v/71rzzwwAM88sgjbNu2jUWLFvHKK6/w61//+oQn/02H8gXvZOkUE/NTcLwyEanOU1Zcllb5UTrjL9i4AHezm9k5gtxt1Hd00teVmKXWS5l7+Vz1R0ZbH+ra7pKp07rJRrTi1Qo2HNxAjlnPtLH9ox5Q7Xzsh+qYfPgANp0Zi96C1WRlw8ENKkGx0t+uGGubSW7NFQWBBcc+Uo3Er/eM4JHgHXzfI5MWTykaTXWjzLE5Qb+KaTnLyR09TSULvm9sv9huFqULDGTOAY3BV5elkR+C0n0vseaBq6govgUhVMDYnj9kxxPjWfrLcnLMBhpag2p2WjGiinG798vhXOpbwL1fDmfh6h2ManyT0StGq7IYCmVcfKdNfIxSKXVS+DtT7ZcMZyutYkfR4eV5aWkpI0aM4JlnngFAFEVKSkr45S9/yQMPPJCw/y9+8Qu2bdtGTU2Nuu1Xv/oV69ato7a2Nq1zft2X58cLZfmoE3SIkpi0tEYh0E219Ex2jFL2o5T4dCTmGr+UTrXcVjqNQO6wUciBrSYra877CeVb5suKkCYbtbfUxsxBqcuMoX8b4sC54elovzjgDjaqFG5a6rgFH/4WIdgCgEevV++fsuwHsJlsCSVOIxaPkGOpookrLU427K5XadNu7fwkb+jqcEg2uddfW5/ZxpI4BnGhBdXT1NC1xWfglSUxRIXPivOzYmKaWsVJZV8lrqrEWdPtvlFKnpRxk86/o3HIkxS/TIYzvjwPBAJs2LCBMWPGRAfQ6RgzZgwffPBB0mMuu+wyNmzYoC7hv/zyS5YvX864ceNSnsfv9+P1emP+vio42UvitsZTfrkre1cm/QW397Or0hPxXmiqX/2qjVV4A96YEp9k+7Yngqb0dKfKzCsZbFs4jKPBqy75A+EAFZvnU9bSQmFYUr3aZIXsCzYuiImRORq8ap+5o8GLLRzGJIox83BuduIV/Xj0erw6HVadWb1/ekGvztET8KhSFgpMehMAoqhXvTKFNu0NoQ63wYATTzTrm6pSIFU5jVKaVLcOPHUcWzmXvf/6DbTW4/YZWBweE3O8lgBYK3wW7+3FxxsnjOwlx1Mjc+tIL/d9Y/upHnMCjjfjfTzHncGSpA4ZzSNHjhAOh+nevXvM9u7du3PgwIGkx/zwhz/kscceo7y8HKPRSN++fRk1alSby/MnnngCm82m/pWUlKTc92zDyY7btDWesnzULlfjoYiBbTq0Ka3SJsXTUmJ82vNo90219FeMs4REY7Ax6TUpHJxmnVEmH+k7Wo1XmvQm3HqBTVnZMqFJPDlHRBgtGQ+mfcRUdWlsHzGVbHT4dLL3qPBwKucWEJAEAcFgUQv7c4wy4YZFlCgMhhjW5KVsSRkj/nwpI6oeY2TBBKyGbpiCA6BEc93rF+HwRAy2t1Hu6MnpEuM9xcQCtUYi2Ze/pBQEPf/x92Vh6Fr2Sl14JnhNNF4YOf7W0D+4w1TD330/ZZ1rXozxu/yCrugFOQ6aLC550fISLvI+FTXEaaJNA3u86pDHc9wZLEk65XWaq1ev5je/+Q3PPfccGzdu5LXXXmPZsmU8/ngS8tUIHnzwQTwej/pXV9cG/dpZhpMdtznR8VK1GqaCIoI2c+TMBIManyRRlrXa8Wr3RUMuydQjQTaAOcYcfFIIj06ORypGsby4HAGBelMWLmtewvnLlpRRtbEKxxAHZcVlsV60pojcZc2j3pSFgEAgHIiJU665ZQ0DOw8EoDnYLL+25jEmdxpOYU4h0yJxydosucXSJzbRkvU2az/ux5pba+jaxY03dEj+wVg5Rc5MgxyaNGTJWer9H8tf6prH1brM+5vnMXrFaHZmDVaTMuqXf/m0qOGsWwdSmG+bd/A3cQxLc2/mbuNSrvYtl41u+VTcdOXZ4LU4hH9SxBHO2fp8zL3asLteLSRv9odU0TSQkzZKv/pJ7e0+XnXI4znuDMn3QgdjmoFAgOzsbF599VW+973vqdtvv/12Ghoa+Oc//5lwzLe//W1GjhzJvHnz1G2LFy/mZz/7GU1NTeh07dvttuISP/jXD/js6GcM6jyIv333b+leyjcSJ1LykSw+mmw8pXVSy2gEJOyXLHYKbctnaF+zmWxkG7NTxmWVuWr3dQxxYPc2Qu18hnbSIUbKlnqExRi5DQVqG6gERtHAPU1+brv0V7iseSqRR2EoTHXdPsrOKcar10frRluOQrBFphIMtgISIgI6JNx05VLfArm0Z/ROWPYrQMJDLm9Wvp9Qt9lUM49cn5u9UhduznohhuXoVv0qrm16Bad0HeeNmxLTCqkQeEgQU4IExAixHQ+L0PG2Up5unPGYpslkYvjw4TFJHVEUqamp4dJLL016TEtLS4Jh1Ovl2NHJKBFVpFKVx28C2opzpiU0dhylTfHdRKnGU5bZ00ZMi2a4k3i42pZJIMaDjZHP0CxfHUMcauxTQkopqazMVeHVnHzx5Og8I55dpV9Eh8DVfpFqayl2XYGacVfGmnzxZAjLXKxBfYjF2RLUPI59+WPM8LRG4qeeyHwi8zKYZcNbMVv2hAxmFJJRnc4Q24Uzqq/sXVnyARAlKdp2qWmjVJbofwhdq3qLyjL5jy2jKPdX8WJgdIzXOGFkL3LMBpXeVALVs5wwshcfP1LBx49UnBT29I689nVAh5fn9957Ly+88AIvvfQS27ZtY9KkSTQ3N/PjH/8YgNtuu40HH3xQ3f+aa65h4cKF/O1vf2Pnzp28/fbbPPTQQ1xzzTWq8TwRDOo8KObxm4C2ltmnqhZOWeYrRBWpoDWkru0uKv56GcOOyTRtWuOmVbGct34e7ma3KkOhGFOtkVOUG2eOnElhTiEjCyaw4PUu+ANGPAEPCz6KLk+VuV7d52o1TqkisqybO2wqnxzwMnd/Heyogalb5PKlZjePr53D+U8+xKNLt4DeJ7tpksQwvx98DTIxco5Jlu/tVUm9lMPPjrWQH9Qzpd4T7R+fuiXS4SMb1Pqwick9/kxpn06sMU+WPUqA0Q/hN9rIEfzU+m6QuTbLp9JkKWS2p5LmQAidANdoWI7i6yQFSEgAKWVA10Y00bVL9GRYvHY3F82q5qJZ1e3St3W4lfIs7iXvKI6rI+iZZ55h3rx5HDhwgIsuuoiqqipKS+Ue31GjRtG7d29efPFFAEKhEHPmzOHPf/4z+/bto2vXrlxzzTXMmTOH/Pz8tM73TSo5OlGC4envTWfFrhVU9q5UVRdPx5ySQdtm2KzXy+2FJitTLp4Ss3yO6fAZOVNdQrsGjsZ5dD2OBq/McK+Rwjh8pJCA/kvE1l7osnbTRRJpMHmotPVn7vWvxp4/VanUk73BV4+rUzcWdO1GIBzAF/YDEmIgHwCdqUHdvTAUorpuPxUR/SiFMFlRdQyhwxBpl1RLjiIxt73/+g0LQ9fyN3EMO/Lvlr1IrTSIRsJFRODbltfUsqFa82R6CkdoshQy1PP7GMaiDi+Fk5U1RWKJSkcQcEJyFEnRjgxGPM5mEuJMG2USnIl2LwUn2od+KvrYk42ZtnHf8DTDmry8ZRYisTWBPFOeSvtmNVkpKy7jrZ1vISHJdZkHGsBTR8U5JbK+j2CieudOGHQ9FdRFJC10SIiY6YzZ/TBNPSYjCnJv+Cf1yOJo1rzYOWrrATUidhXn9sGtZPrD2YhhE4GjozDqBQqK1lBecomcYd/9GfbGJl7Jy2VhlxLuGnkvC17vwqjGN5lkWEqwcAR9WrckrdGMoUXbfb2s+GgpgAd2AbDONY8RW2ejA1oxM8D3J/KzjHhag/xQv4q7jUv5i+FGnm26Ar0As64bnJYxiTc+skibGxGd2g+vGDElDgrHH+tMiQ7WYp4MLSE4C2Ka3xScyXavk5ktP5VzSuce2fvZqf7h+2zqVKSJrckepbJ8zjZmM7z7cPJMeerrKsNQVh+Z2u3wAZDCuPaspDnYjM1k4+o+cm3l/T2GssY8mUpDZ3SSRKUvFLOcr76pmkB9qUzDVjMvWqby2euABGG/3GoZiaNWFN1B/tFZXN3rBgrCo5jU9/9FS7p6VSIBNzc2sWTnEez97Ewa1ZfVedew+up36PPzv7H40mXMW7mdYONhQJA9OqDqlmHseGK8zCM5+iHZWI1+SL1X9345nIeCP8FNVz4dOE2thXz8e4NZnXcN71S+o3YVpWswIRpf/N3K7ZQ9+Q5P+77LXqkLq3RlCdnnE4l1tsvK3sEMeYaE+ATxTfI0zyRSZbRT7ZvOPUpGeqwck6rLRx0vRnVUoOLcvrilQKwXrV32lU9N2j2jeC13577LtJzl8rL/YC2Owwewt4owdjaLw2NUXaLSfS8x21OJ03dlTOeLa7uL59Y+xa1H6rmwxJGULShGcwfaXI7Gd+loPcJUXl9bx7R1DmWpH0PkoT3mBLtyTpZn2BGks4TPeJqnCaeS0/JU4GR1ITk3OxO6geLPoyRvgLTukaJNnmPMUT02kJf8w7oNw6Yz09y4H3atSRyvfGpEaRSw9cRROj2xM2mghrCjdr4cKzTnRunF5g/mqXM3UJyfReGYu2HqFhYc+wi3FGBBpwIItcgZ6tU7OMS/+bX3RVxiPT9BQ6QRGce5bi5HaebFbnk8pHs76f2eNKovj+aP4DslPXF16tZmHWG8fre2aDy+llLx5LRqlelkqZVx7xvbL5HIQwtNwq0jWj7a607lGR7PeOngTGXpM0azHXTUIJ3sNsp00N5SOd05JZT7JDlPW0Y12fnaWtpvOrSJ7KAPr07Aua8mcYzdr+Aa+SOwyUYxmWcbQ9gxMDnj0e4jL5Fz3pOYCmRhPqVsySeZOGbozrzmcQzvVUBW13c5ZNThLCjA32MEH1im8PyAT9RxHIcPUBgMIYR8Ke/3BP0q9ti2cMCgY3ZOYZsdN+1loLUZb8VAgJykGd6rIKFovS201WYJxBSLH48xaqtT6FQZtzO1hM8sz9tBRxMrp4tQWIv2lsrtLoPTHHvDwQ28tfMtzHoz00ZMk8uK4qR7p783neU7lwPIWjrdR2Hf9M8YvZuYMbe6WOH5PCbrrZ0zyG2NZoMFj+iX72uvm9WlpDbZoxhjJdk0hU7Y/7uGij595GW9MY/qg15cA0cze+8mWg9fgegZSViCu3PfpVen5TjzrTg6j8C+9s9R/kklsRMpWHfl5fJclxKaPeOY8q3bYw3F/MG4xHqet+Wz58gNdOPKhCw3xC2rUy2NNduV8IFyzKlcDp9Q5jrJtZzJYvdM9vwMGM2OxjfPtnioNqY45eIpUT3w4/gRAFRS3hxjjmyo1jwmZ7jDEtU/2cLQl4eqpURAVHccksb3Ygx6hCF9WEF/an3uGPlfqyiRk1ck39flj8lLSUM2hP0yvdpNixI6gZRzu7qVyMbwwB65nEnD7KOwFK3kLnJ9bnmO/iY5u40A438fNWTrF6lkvh5yaZQsLDHeyLSZv41eUMRorCu+nXu/HJ6UgQiIMXhKRrvJUiirQkaQajucXV03MehgadGpRiameQbQ0fjm6YyHtrfsjhdS05JdxC+/U42l3T8ZUYaioeMolpfECqvSoM6D5OOKR8ulNVkFMfE95XzDug2LGsy91bj1Ais8n+MNeLHoLfLOkkR5fv/ofS2fiqtbCRU98nHlZqlEvnZvIzOawtgkAWs4jEPMhawC7N4mquv2ywZT0Mcw+1TdMkxe/oaulbW/y6eq9JYYs6KkGsDi8Bjm6R00WQox6AR6CkeYyBvyvusXyXWf7zwO5VMptU9Tl6vuVc/yrkkuZp80qm/CslLJaD/t+27MvVc6gRaGrk14bzvCTHRacQZ7wk8XMp7mVxhtcWJqOS21krMdHSt+TGWZrhTQD+8+/Lg867I/X4JX9GPVmVnzo4/UZe2CAhsBgxmzMYfJF09WPeP4ayj/8yV4RD+2cJha66XQ67KopIMCQY+raxFOUxhHSwh72Jx0CexZ9jCiJLFefzEVebv4SDyfHt5PKdD7yREbVa9JuyT+XmgFtwT/EfU0NZn+JkshC0PX8kuWIAgCrcEQ+TSn9BgfemMLEiRwVKbyJs+Gvu8Tkfk9nch4mt8UpNlyltRrXL9IXjI3u5Ei5LspDWZcX3dbHqiWSf2tnW8hSiLv7FjGnIgqpHPNYx2SXxVCvphHyqdi1xWQY8nHJ4XIbm3AvnkljkP7ZSLhOMYkKXKcV6fDNWSs7BFKYdmTLBomPxZeyAtmCbfRwHO2ztHlonZutfOx0cTbVoHfFe3AJdbTw/sp5f4qnvB/P8ZrUqQkevTcxBt9NnB959u5/IKuND3Zn0elznLGPL8TYV8jdwX/hCXkxRz0IEngpqssVhaHhat3IAF6gQSOSkVZUsmeK1jw4Us0dH6EBR++pG7TZte1rOxt4USy2u0ldzqa/DlVGfZTgYzRPBuRJldg0lBA7Xw6BQMgSZTklagJkqRL+Li+7mRhBW1yRVmaK5ybfgFEJHSShKO+PkF+1bV+fsrwweQieVk/uSiS6R4xEde4h2kRdFjFyHifvY79UB0zmmUpCcf+nTC3N6xfxJSi0egkCUkQZGOqLAvHzcN1xV1UnN8fV9MX/LShgW5BkWNHK2Pm5l72BL9asVCWx8jLxWmTdcuft+XjlK5DL4B38G0wdQuLw2Moe/IdKrfP4L+mW/H6FuENHaKgqJbSfS+R63PzH+sRDhh0OK052GgiSwgCMpGH0zghqgkUB2WpnqpgPRkXpi9nFTpTA6bO7ybsB8iibRxu9/NzIlnt9jLXHc1sf5VIPjJG82zEicSFyqey1WwCQWDr0a1tlyNpztNeTFNhClL0cqw6M1cHZKmKGYWjIatANkDbXeq4znxragLlsQuo/skW7GOjuuVqTafZKtO0DboebCUyufBBL/Zjh1TmH3qXkWfOj2qVR4xuxe5XZK842IjTmscNjS2M3FmBrvEyVUPohbwePBO8hrf3LZH3K+yFI6CnMzl4W27mvHFTmHXdYDbsrmedax5XrbiKUY1vUiF9gF6Q+KnHQ7egKJ83QqzRp3EIVkM3OYZrK0E3WBZU+53hZxSOuTtheT370fvY/2hfzt/jajM2GW98Fq7ege/wFQihAqZccmfCfveN7RfDyq4iifd/IiU77cVUOxpzPZs7gOKRiWl+jaCSWbQcJiSFsOgtTBsxLa2YoxrTDEtUD0mzK0RD/lDhXSdn0TvYn55s/grvpWv9fDnrPfweOYmj4Zis2P1KQgxWuQabyUa2JOJo8NKrSzSL/fLen8rZf0M3qJvByIu2s7np9aTzU+KXH1imUMhh9kpd+FToRyWydPHuHlfT5+dt87fGlAWN3qmW4pTV9FG7htx0pfDRL9q9Lws+ep7A0Su4tOs1bNhd36FY4eK1u2M0gc6GrDZwSrWBFGRimhm0CcWrzDHmqJyW6WbzHUMcchZcu8xuD8ry/rPXcdTXUxgK4cgboL6ccO52Yp0x+9fOx2kKy56g4qmac+Gqh3BZ89QedDUGu34RjkP7KTTmUVpwK817ZhO4eFlMFlvxmqdccidrRu9k1scL+M7nFxGoL4X1i3A9N5iKv8oe6aRRfbk79106GQP4DFaWGG/kWOVCdLYSdCATc9B2LC7Ge4rcq9Cy+5hse48XhevZTxf2DLwz4bh4ODc78YYO0ZL1dlLFyPawcPUOngleg5uuZ1dW+wxKVpwIMp7mGYTiWQ3rNiymJzvVfqlY0JPtp2VIT+ccQEKhervz1NKMffa6WgzuGvdwzHyrNlYhhXxMOXxA9iA7dcOZa8LRFMB+aUTBNL5YPW8Avu1v81xODmXFP+H3255V6//Ke+TjCXhUpcp4T6rMXxVb+J3Mo4nQwoUlgRp9ORXS+1QUd1cp36pvqk6sOVy/KMbbZcRE1Ztsi3lo8drduFc9y9TgCxgEkf10oejRHQn7KL3ml1/Qlff+exiQk0ObGpZTvW8Jgucq7r/sx4kF8Zr7l8xjO52Z7A6d6yvqaWaM5hlEexK88ftpC8w7Wpze3jm0+2qL1+397FT89bKIHC6IgE6SmFEYa1i1XwDt0lmZL0BhMET13v1UlBTjNujl542R3vKIcaooKZLni8AvjvjZdWwcq/OukZe3EYNVVtQFr+hX+TcVerZfGN9kz8A7uXPbUEBDdJGs4HpOoSxHAYTRoUeUDXaPc+RwQD+7TAa85R9yvWbF7KSSvIvX7lYL1/UCjL+wKGH5rBjWW/WrmGRYyovC9cx89Hcx917LZSmAygoVXwyvZNNjivGV+yfoYdy8U2aA0sGZIO5oC5nl+dcM7Unwxu+nLTBvj/otvni8rXPE7xuv8qhI41Y2NaOTJEQhsVdcS/0VP18lceTwypyVjuYghWERR0tI9jI0CSlFJE1E4u82Hb8wvhmVhYhIQExp9KETdKrKpELPVlNZw71fDqehNRhLShFRdySnSzQ8oDcDEJYEtgvngqDHnnuenHCKzJO6dYAkG9cUkrxKT7degLAEyz7dn5AFVpbpjYNv4+asF+hd+cuE90Dba3676R1qzZNlIxtXDK9mmUPXRueiEJtI4bSXuqeqxOerlNA5XmQ8zbMBp2CZ0pEeeK03OqN0BhAXAtAsw13735OX1sVjYj3NdNAGa7jyWkV3q+rVzjhyTKZu6z8OvqiRWyYNZjWuqQ0BtJkoUTxNQY8rN4vnbflU+rsz9fAmHssbwgf5x/hpQz32plaQwjRZCrky+4fk2JZzl+co1zX6eTd8IQfyv2Rxl/xoYipOonfh6h10zjHx2X6PTDZ8y7C0b412Wfu91WOTtk9qE0IJPe9niOT3bEfG0zwevDoRZnWSH89WxAfE2ysO176eYt+OeKQtwRYETQF5QgJH8SIBe/1Rqq0jsXe6UD2vtlwp1f/anmzPlhXgqWP6hnkMfXko09+bHmUSOrBHLmPy+LA3NsnUbZ+9LveCB1vAlIvr2Kc41zyGI2+AKtyWkCjRr5JrOp/sLRtpWwkMup7nbfkcMupYYT6IAZE1tqMcMAg4861gLQIEsn1uLNmvcZRmnEV9uEr/IgPD21icLUUTU3HvmVJic7Q5QFiSJXTTgVJ+NOotuaxp4eodchG8rSShGF65zoJcV1RfSMEJkvy26Xl+jfR9Tga+/p7mrE7RTpFHjp2aCZ4o4r2E9kgPtK/DCREkxJTpKDK3qTLtj+aDIghr65kQh4yPuTYHm/EGvFhFiTWHmqC1HjddsUgtFAjNDO1dIktUCDo+GTA52gYZT5ox+EbZ01TKjTbPj5KEDJmKa/18nsox0ywaqCi8g99XTsI/5xzMQVkpUntvfrViIW/vW8J19fBI42Yesw5hbcExWcI34mkCLMnL45ke/ZhyyZ0E6ktxr3qWopx/8JLNgiPrXOx7NieQHUPUYxzeq4CSHUuYZFgqG78k+0wa1ZddK/6PB6RFGASRvVIXVl/9TsoEimu7C+eaWTjqG7CHjKq+UELyJU2vU3vcwtU71Lhw4fgHY487y0g4OoJvvKd5yey3O37QoOtlgzno+pM/oXaQNrdmvJfQXnG79nWFvEIpLO8g4ovX2yxNMmZFHzXnHdZtGFZDN+r3lzMk93qVgEPwyUZLkETZwNhKeK/7BH4fupljhu5cou8DkoDYeCG9/9GDeXpH9LoUSYjxv2dxz4dxmEfynS6yl6mShGT1gWW/wn6oDmvYB7oW3t63RO6aCcrGL4wgjxfxlr618yCN/5vOzxv3YRBE7g8eonrIVMYF86gWLiOoswACt/Qay5pba7B7G5nwwXimje3HdX4j1Xv2ctP2WmitpwkLZTV9YryzCfpVrDFPpmHfE6zosYzlxkaaauYx4KEV9H5gGQMeWqESCc98Ywt3SK9jEERCko6luTcDMOCht+jzwDImL9kU4wHa+9mpPtQse+AaVyehmyYJofDkJZtiHhWDqRw3vFcBkwxLKeQw+5f9JtbjjHze1hXfrh4b75kqSpYDHnqLi2ZVx5ynPRxPfPVMtl1+pTzNkntc7Jn//TM9nbRxurg1T9V5EkqY4jwY7Xmbv3ggNkYWIeBw5ufjaA5gbwnC6Icoq+mj7geoGWMFu54cnzCPsiffIavLLzho1Ecz91trNN4ouGydmG0tofXwFXTjSibb3qOheQl/7pzP5Mt+pdLJ7ZW6UO6v4lb9qhivat7s+2MIONa55nHO1ucpMPixhLw0WQp5svFqfm5Yylb9ACrydjGveRzPNl1BfpaRHLNB9vRWXwG+esb0LOagUU+3oMiVe6/G6btSvZ4so47WoExfp2TUFaXKHrYs9Z7oBdTnbZVPteVpKvdbSVQpj9rE0qRRffndyu2MD7ylzmV13jUJsU5tHFR575R5abP/yty1qplt4Xjiq+ke8433NC3Gr9R0T4nIWbrnSYc2rj0vWG3BVMg44jxi7XlHXrQd6/lzGXmRXGuoEHBUX3ivzC7kk4vmtbG0p87dwAeWKTgs7wAyEXCy2NmkUX35kccXm7n31EUo3GQeN3tA4v5Bf6YbVzJpVF+qPJdTZeuGR9cS05u+psePEIDXdBUx/eCTDEvpKRxhkmEpAOdsfZ5CDuMPyYqNC0PXsjg8hisCVRyqeBamblGFzkA2II/8cwvNgRAAExpaEQMyEfFfwqPVa8ky6jEb9OrzZaZKyv1V/CU8hvEXFjFpVF+yjDoEUJ+nzEZHvOcJ+lWxBe+a90k5fvyFRTGPioHVHveX8BjK/VUsM13drp55/LyU7H+WUUd+ljHmPNCBJoA0cSaz9F8pT/Nrmz0/BWjP+0zHO5VjaI/J8T5dQUw8K94LbXO8VDG2SKzMTVdqKmuY8MH41LGz9YuibZWdR0TZ4PuOTszEI39JF3z4EqbO7zLlkjtjww7x81m/CKpnQrBVjp/etEj1NPcMvJNS+7TkRdua5NYPNg5EgphazD/5ryQsyZ5lICSqGfUYOd90Muza+WprRSHt+sz4+ad1fScRZypb/433NL9pOBG9ofa83HS8U3s/O9VDpspJB39TjAcYTwRyXF51SSkhdHwYPp+Fq3ewrvh23HRlXfHtiRnbEROx37WF6h++L5c6RWo2qVsXNbBKtnz9IiaM7MW0sf3JMesTz/vO4+Cpw189i3mz70dcdl+k0F2CL2pwPTeYh0Kv859bfqMqTipemalgXfQeRQxY6ZfPssYi11X+JeKN9q78parJYzboYzLqG3bXJ82wp/TGtJn6uHh2uvWZ8XFPxZM+Z+vzMdd3qjqGvk71mxmjGYczIYyWCicimKawtCejhUtFpJH0fCMmgilXXV4riDeS2jKlhHmlooqrW4cBkVH6T7kz+xdMaXRxueUW7tw2FPeyJ2RDsXxaYqnL+kWyEdeywStKlL56Wd881fWsXwStDQD4gmFuCf4DHSKy2ypA2J/Y857qPYkwHHl8QYqQl/Z3mN7hE9uvmKBfpfJhAjgs/2Yld8H6RTEGRGsoU9KjaQzl4vAYyvxVrNt5TL7mCBOUNtmVrDQo3mjtGXgnbrqm1ft+MnDWMs0fB77Zy/P1i6I62efJy7yKzuaoCNcP3293iBPSBGqnNCRdwbR4VvN45vZCYx6OBq/KGJRKJ2j6e9MThNPSmWeqeanjxxWuFxrzqN6zP1qsDlR0zsJtNCCECsBzFRbrMqZ63NzS2BhdvmuK1V1iPU6bFUfOedhveTOGgd1pnMC0mb9Nfv80he6vdJvM5r0e7s1axiqrhDPbIHcpGcxRdqV2+vvLnnyHUY1vqgkUJTaq6BApLZYKW1J86EHbu56sBTMeyv7vWyZTxJHYAvgTLA06W9jWTyYyy/PjwPT3pkcLqOOh8U747HW5uPrIQQqDIRwN3sT9k6A9b7BNpChqd62cQsWrsuxBW2VAjiEOtadce35lTgpzu6PBG/WeNjytsgFpGYKYP5hNe/+DhIQv7Ev0NiOJhWTerbJt+nvTY3V/lPFHTMQ1cDTNfi82nRlHg5fpeTqGFndmus0CEjhaQhQa85hZ/gvyuv+HVqOPJ7t0UrkvH/nnluiytXwqVQU23EYDVa071HO8Wfk+381aLOubr1+EffljVPe6OVFLPUJWXOW5nMXhMVxj+APOLl1xGw08lZNP4OJlchhAqRjQeG/xhf9KC+dv+7/K38QxLAxdq7IJLVy9Q81Y7xl4Z9IysqfO3cBn5jv4r+lWvvu/h2K8scVrdzNv9v00PdlfPb/iMf4/rk/UD1LaRUtK2/jQpcZXiQj4TOJrbzRX7FqBKIms2LUi8cXyqfISz6IhvD1nLNWNeuwj0qPQOqEMeXw9ZsSIOvfVpGWI7f3szCidkXB+lQLt4imy0WhsUo2So8GL/VCd3GOtGBOlG6fBm1T3XGsok/1IVG2swt3s5q2db+FudlO7rxYCTVATlcBw7qvBqxPIDvqwj5jKipxsREFghcUAvnrsjU1U/+9z7JtXMuWSOxGQ+8//2CmHv4THEJaIfplHTESKZM2VR5CXgFOuP8LLe3+Ka30K2rEkmeVJo/riGH4PQqiAo4crY42G9oetjeXvt/p0YtZ1g9UeeO34s64bLMdGk3TslO57iRwhgF6QGCOuiXlt4eod3BL8B7k+N+5lT7B47W51mdu78pfcnPWC/AOhoG6dHN+sW5f8A9MOvk5xx1OJr/3yfPp701URsLmXzz1FM0wP7S7lI8tY18DROBu3pUXn1i6S0ZvFL7WTZZNT1GMm000vW1KmqkcWWAqiYYFgiNeOSIzlOa4956/U+NbG6KOv2LWCyrzzmbv9I7XeUuncKv/TYDw6gbywxP7/zlWp10wF62SaOr2VTfWfJ9DYKYxMVvTkhEMJr7eFZMtT18opvLC3hl7ewTzt/zjKLBRZ/sZnhVNm7dvSNl85E0LRzH18V9GtoX/wbDCxdjJmvvpV0VBTXJfScaED1HNnMzLUcF/xkqOOFqG3pzaZljGN+7KmdWycoY1nVI8nqogxEgrjeq4ZR5Of3cfkAvA2S000So4UDWNd/ng+r1vIX/It3O71s7/5RlUyQjGKBSE9yw/71BZFxYDcZf4pL+WZaNEJePT6WCZ5jQEcdenchNKhZAZNadnsFhSZsvscLtb9lzU9fkRw2I9jdNMVQztv9v2s6LGMQ0YdQqiA+wf9OTU9XRyUa2j2h2hoDao/FEDSWGOMwTZPTiueqTXIbcZPj7NV92yLi2ZimqcAbcY8TxISNL7TXMprl/7aJbKyHK7aWJV4UJJSHe2y0LnhaXl5veHp1CeOCxvEM6prl70LV+/g8P7hUDdDfd1+qI7qnTuxj5iqFoC3ueSLkA9X9CzC5dvH8K2/4fbGoyyvc3Or5wjTcparnS6OA3soDIaYVH9EZgKKZMqVeNx3PT6q9+5ncr2s4zMkN9o++8LeGg4YBHbmbY4uwdcvkjP0njrZU4vcu8Vrd+Ne9oTMSB8McaengYt1/6XcX8X03SPU8ymGx73qWZqe7M8vWcKdngYKgyHura/DverZpPc0GbTiaEpHzcLVO1JmnmOW09oMeyQjv841LyGcoJwjGYVdys9ABzSrvglx0W+8pzn05aGIkiiTRtz2yUkdW8HJaHOMaVlUiDBMVtbcEhsHa8+jcT03WNYBD+ix33Uc5AtxXlnSFj4t8UaqkEAcRr00kqM00yMk8nbdXkLoqOsxlj4NawkGWmkRDZgNOiwhLxICLZKRAEY+EC5inG0Pr1hu4td7RvCbc9Zzs/dFPK1Bfhu0xyxpHfN/wM68zfQ4NpDOJb+mZMcS7gv9EUEhITFmyfWaWQWMyPoJQvab/NTjZUzOMCwHPuLD8PkM1/2XhaFrGXDNPTFkF4rmD1kFuIwhnNY8HB4v44J5vDFqZcw9Uljaxbz3yev+H3UZr72XkNy7TAfx+kZYCuS61kjJUlqe5nHim+BpfuON5umIeZ5QWVKSMSC15EW7BkrbWTP8npixUv6/a40sg5HVB/uBncnHbisGlsyQx81zRNVjtGS9zaVNxTjZwrpiWRBNZSgHPORisxjB1wBI7JW6oBOgCFmg7FLfgpjY4sLVO3jq3A2U7nspxmA8de4Gztn6vMq2BMgJQQk5tmopoKykL97QIayGbqy5tQaAQ4+dTzfxEId03ej28P/US1fkLBRGo1FbX4j+AFx4b0y/vdKnParxTdb2qeaQUaf+mKYyOB01RAnXHpAZpr6KLEUniozR/IrHNE82jtcYJ5XPMOaBz6sqSqrbcwrBu1/eHgpRXbc/+ZevLQ83mSGP7N9kKWQszyV4Poq3NEG/iodMf8EgBdiUdyXFjZsp5DAiOn5v+CmXX9CV0n0vqUY2wbAoNZ0FBTjKHpbvU+TcHnKxGPWY9To5eQJRsuVD6xJqNeNJPVJB+QHIbv0O6yc/nLSFcfjW3/BaXjYv5Bfw0/KH1ZrPZK2GJ9yCeBq0eM5WZGKax4HJSzbR90GZZuurgvbmrMSsFnz0PO5mN7Nrn4mh6GqPMkuJlQ7JvZ7DRwoR0DGsySvH78JSgqyGQsX2HV8he6UuvGK5SZ3jjYt/L8daB45OHfcaMZHFly6LpVGLxMkWhq7lEP9mdcsUplx/RDV4k0b1RS/A4vAYjklW9EgUN27mmeA17KcLvzf8lMIxd6ulPIrqJBBb21g+FWdBAW69EC2TinTy/NFwK38f/R9c1/+Wit2v4LLmyQa/bp0cl/3f51HpC6BwzN2JZT5JMOVbt5N/dBZTviW3g074YDxrRu9Ur61030sYELE3NvP2kSZ6fSK/Z8N7FSTGf9cvYiV3cXfuuzHb23ufY17vIEFx2viGkhN/7T3Nvg8uUwuMdzyRSDt2NkHxHHfvKMVfPzLlnBXPo2vRBvw5q1Q6NC1FV7qUXA2dH0FnapC7dA5621zW7/3Xb1QKM5ATFTnnPYnO2BATr1XIIF6+YBQ1pj04hjhixM+0JLeL1+7mt5/9CMlQL4/R62bVK/rV0QBv71vCrVkXMP3gWnZmDcZy4CMW8T2cvitjKdk0ImZqfDFZ9l8pk9Lcp5zznkzsYIqPyx4vUoUm3nk80s4pJYQW2j2e9r3PdL3iE0HTk/2TynKcTch4mseB8RcWqS1qpwPxHTNtddDE94QrheN5Pf7T5pyVrOmUb92u0qE9de4GmD+Yp87dQHF+FiMv2p54Do1n4NrugpI5mMLnYjV0k+ObKbp+XNtdVGyez/tWH7MML3Ljua9Q0H8e5oK13OE3yh1Ue/8nj//qRL61dTaFHKamZY1aCD9pVF/ZYHJYzby7trt4ee9Pufr8S7EaunFgz2U0LHtYzWRvbnodyVBPjWkPTN1Cn9YtFHKYeyz/iqFkixcxW2K8kWOG7sz2VHLRrGoC9aVU3yQ/Kt6XQmXW7A8xJPd6lURZ9czGzUvwnBWi3YtmVadPfpss86z08yOBoGfPwDtTVxhEvOJ5zeNiztleIXo81d2pwMLQtYldSR3AmSQSPhF87T3N48IJxIDiM+Xxz13bXcxZNwdREhOkcuM9onRjlq7tLqrefwxJEpni02G/a4tacB6TYdd4LVqJCrWOMb5nPbI8V+arxDQrzilRY5/Vn32kSkS46Up3jqJDRAL+npdLVacCJIuNKRdPYcS/X6PkwErqeozlUKfh/Nr7opoIUUiMN5l/KidnLAW4rv9tahJk4NjKufy+dTyv6cYyY/wAgJiMtlK+E0+UqxgbrfRunsVIQ2sw1nvVr4r5HFw0q5qG1iC36ldxt3EpReN/rSaXOpwtTvcztn4R7mVP8EzwGvKzjEzLWZ68QP6dxyHoB6MsPAec2jjm+kU01cxjYehatYa2ozgddHEZT/N0Ib4nvB1oPbP4tsr4587NTrXEKV4qN76vOd2+dudmJx6dgFevlwXCACHSXugNeClfUi57jXFSuTpBp0rmas+n9qxHun+U+Tp6VoCtRI5xKtc06Hpcebl8p2cx92UPY5WuDJc1j7G9e0NWAWFBjzfgZe6aJzEfWI8BEcuBjzhn6/Pc6WmgW1DEMcShen7PCj+kyVIIox/C3s/ObT1fYMHrXWJic4vDY3Ave4JOoYOcU7Ac3TlzWPDhSzE1glpJXC1R7t2577KSu3CvepZwxF1QHuO916aaeXKyKlILquA+wysUcQRqHk9Zl9iuF9VGnDHm2Nr5FHJYljI2LE3+uVQ4FEIt8mPt/FMXx9ScM9fnjtbQHge+qm2bGU8zGTRegFYqNpW315E6TNV7zBvAht01rDDrqOxzddJyp3TLjFzbXcxbPw9/2M/VkbG0Hi2QELNM5gFXbaxCQqK8uJza3TUIIR9l+f3ZFPamdf1KB8zLe3+qZuSbfQ149XrywiJv7z5ElhBkd49KPpT6U3bgzzHdNcm8tWTeiJZZ6IaSTrQafVgN3ZjU9/+17/VFvG0JeFv3bf51/uO899/DANw3th/n73Gp5Lyez99ldLiWgGAia/A1+Le/TUsghJkQ2YIfD7k81O9f0ay/xjO9aHmJ6rl+/EhFm5+JeCjXfHfuu0wzuFRBOSB1K2a8p5mOsTyRrPpXJCOf8TRPMVSPUcmijpiYlreXDmmHMjbIzEXsqGGFSSalWLFrRUpOTMXzjJ+H1ru197NTYClAQmLToU3qsTNKZ0QJOBq8spdS8ziu5wbT0noMq8mKI28AzB+Mc8PTeAIecow5bDq0Ca/ox6MT2FS/TSb9WP5Yyiypcv0zy3/BhJG9ovejwcuUerkH/WfHWsgRAuiQ6NO6hSrP5ZT7q6jyXJ7grWk9LW3sUfHatMxCUss1WA3dmHLJnelxNkaW9gJQIb1P1S3DyDEbaGgNsnD1Dkr3vUQhhynd9xKXmb5AL0hk4YfPXscc9FAgNOPHwF6pC78N2lXJYCDKAZrmCkVBvFeqemCGpXLdqClXNkzJqhBAfm36Lpjplh/jjFj8+OpnJxWpSTvzU895Kj3ZsxgZo6lBMgOZjkFM0AlPY2xnvhVRkJfQoiRStbGqTfLjZMt8Vb/n1YmJdG+A3dvIlHoP2ZIoy0LYSkAApymMR/TTFGyCHTUqw5FaYjTEgTUsYguHcTQFVKZz3nk8cWJJKNjs3kaq6/Zj71aKXVfA5XvH87/671Ev5eA32qB8alK9GSWZ5V71rGpEJ4zsFWPUIEpoW3XLMNZPflhWjdTce6Uc6tr/q01M3IyYCINvilEojTm/htw4d/Q0uZsmS2bB8pBLvZTD70M389v+r7I675qY7h2lHGpe8zguv6ArxflZ3De2X8rPhIL4Hw3l+hT9c20S6XjaFOOPUT87+da02iO/Ca2RHUFmea7ByejcSXds5bnCZKS0Rqbbahmj36PodceXx8wfTEVeGLfREFNO41o/nzk5crpGu2yPSWpokyA1j6udMjywK3YiGp2fPQPvpPTLZ9WOHWU+SgKl3aXqk73BV4/PYGVkWPZqFaPzu5Xb8YfCmA16nh/widrlk8zTUcrMtBAAW5aR+8b2U1sZ41sWle6jJkuhSiis1SxvqzNHee1Yc4DWYDgioKZTryFlh49+VYcSKsfTphh/TEc/52dba2RHcNZ0BD377LPMmzePAwcOMHToUP7v//6Pb33rW0n3HTVqFO+++27C9nHjxrFs2bK0zvd17whybXexYOMCBAQmXzw5fYOtxJVKSpOKi8W3TGrHVb84QTP2/66BQddT9sWtybOZbcjGPnXuBkq2/oH14QsYr1+LATmGGkLHhoG/TiraFd92qM55bm85kZFVwEW+P6qSssHCEVgOfMQzwWv4S3hMShZ0BZOXbGLpJ/uT3rJkmXRAjSFOMixVpXp7CkcSWePbgfIDIRCVJm+zwycNdqL2jNZX2aidapwVRvOVV17htttu4w9/+AOlpaU8/fTT/P3vf2f79u1069YtYf9jx44RCATU50ePHmXo0KE4nU7uuOOOtM75dTeaqWQrTgtmdZK9VEHP4rGbEsk3ah6PJiI0BB0KfVlxfhZX+5bzgLQIgyBr7XjISSDL0CJZAToQY5wvWl7Cv8RJ9BSOEEKHAZH9dGGcsLBdTxNkQ6KUFOVnGdXtbXma2lKlCfpV3GVcmpTHsi0o4062vce3D/6Z/8f19K78ZQwNXYxnqfXoIWlypb3SnJjXR+/8SiRoThfOCqNZWlrKiBEjeOaZZwAQRZGSkhJ++ctf8sADD7R7/NNPP83DDz+M2+0mJycn6T5+vx+/368+93q9lJSUfDWM5nFkFbWZ7o4yIcVn2Bd89DwDDnWVCXPjuCaTeiKvTpSlPgZdDzfFJXq0PJcR46Z8Qa3d1iPZ3kHwXMXqlr/SKXQQCQHBks8rtjuijEO+V5PK6yb1NDVYvHY3u1b8Hz/hdfw9RmA7spGnWsfjHXxberK3kGAY2ztnsmM65MGlktpVfhRenQhbXo25nzFI0T3U3vJdYU0CqDVPlpneO+ghf11xxo1mIBAgOzubV199le9973vq9ttvv52Ghgb++c9/tjvGkCFDuPTSS/njH/+Ycp9HH32UWbNmJWz/ShjNNogr2oolnUzyjW5BkZq9exMMXbtFxPGlVhuexnHkMPbWoFryonyB3+izAW/oEGIgnzsOXioXXfubwFePh1waJQt5gg8bTW1/gTXnXBwek9KwpWqHTWAvT/GDFe/dLr50WVLjqP2/w/FF7XuvGE7tXBSvHmD8U20zRcURm7TXzqktU5oQeo1ngtd0yEP+uuKMlxwdOXKEcDhM9+7dY7Z3796dAwcOtHv8hx9+yJYtW3A42ibhffDBB/F4POpfXV1dR6Z5WpBSPrcNwtb2yobay8Br99cSG1tNVlqCLfL/hm70afz/7X15eBRV9vbb3emkMwRCMCxJCIGAoOyIELaBDEFQEFzGD0QDjAOKGERhhEEQwyaoYUCBGBGi+JvMsAwigmEAiUaHVSaiCAgO+5ImsiWQkO70cr8/qm+lqvpWdVWns1Lv82iHWu5W3afOPct7OnEB4p4xUO8wn1q58xVRjXAeOQv4sKQ1P6/hCrFFtyoPYxEENL/y4EQ0CGqC35U+xBFYTD0KJM3B/zWKwR+bh2NfAxtCgoywojFWOobLe14FiQTCmjjFOWm4f84OtJrJEZd0iA4HAP6TQuTZ9bSVn71I5DHPOnAeJXYn1hqeQLElCgdjxiH1i6P8fcI2luw8icuFpbzmJiVPUSy5K3z2rJCcDk9wXvuOT7E1XdY9PgiA6Xgo2UfUoBTkPJwj8uwr3VfbUhhrAjRpmvn5+YiJicG+ffvQu3dv/viMGTPw7bff4uBB5YJOEydOxP79+3HkyBFNg6yJNk1/7JBSbVJNULzwHmHpXQDMv6UpkULNle/PRbDrgnjbDUDkiNn4+Lte91MijgvtJ3LsQgx0zuwPEnST03a7TBNpj8fix2Pame5iGjiBdii8NsM5AunFAwBw2mWz8FBeW+4eF4HsI/kY1jkaPVs1KueOPJOOIhtHPgyAJwdh8VnSFMsRXbj8fuo4MhsNcLgJGno87W9sOcqP4fTiYXxg/WTzNlxoP5FJRyfcLks954GAUMOlgpu1i1Ayy1RFCmNNQLVrmpGRkTCZTCgoKBAdLygoQLNmzRTvLSkpwfr16zF+fN0wTsuVz1WCVJtUEwMq1E5FdG2ev7s16YY7jjtcoLqgHcWY05ik8iqcQg1m4BxOiA6cw9R8J/7SBb1t72PiL11kx/tQzGgYnBHo2nwy0GM8knvFYXq97QizWdHi+CrvUgsC7Sq5Vxymv/EuwmaeQNSgFISaTTCAIy4RxnVmH8mHiwDZR/L5mMaEy58CtpswWeojO/gRpJi3IgpX4cx+DVPCvxOl61HauWdNu/H6yf+H2NPr+PE73YSPr7TuTseekCl41rSbJ09ZGp+Heea1fAA8K5g+I/c0CksdothS1ZqdCro1aboonZu0D6X4ytqawlgT4JcjqGfPnlixYgUAzhHUokULTJ48WdERtHbtWrz44ou4fPky7rnnHk2DrImaJhC4uE4ljcBXH76Kr/mqaClMn3zlgVcU56E63lIKj62OEgXfUy8Yx/KL0CE6HNdLynzaD6XrM2XdYV7T5J1CjIqazuzXEAQ33DDAGNpQlF44Zd1hzDjxFJobruFGUFM8UMxlxYSajZg9rD2Se8WJqM9oyQqeSd5g4piQGNk3wphSqmkKQ5EWPN7Ry4PPz19iw2Q5pqSEzeUxonaUOtz889FDkWqAIwjgQo7GjRuHVatWoWfPnnjvvfewceNGnDhxAk2bNsXYsWMRExODxYsXi+77/e9/j5iYGKxfv17zIGuq0AwUNG+VBPGZLIZxIWj1xihzfex6Zp/4/n5TMfj8BlhLuHISSmYCv7acMvWEhNUWXcQ7jlEK1vqo8cBThnQaOyotvZt4extSPGFF/3AN4mMr+X4E46db/JSwb9lMQ4KxzihJ42JWOz7JRyRQoQmArzIp3Vqz5sSKJ5WL+6Tj9yffva6i2rfnADBq1CgsWbIEb775Jrp27Yoff/wRO3bs4J1DFy5cgNVqFd1z8uRJ7Nmzp3q25hVhl1a611e7GvrVvFWizpNjnzMZxoWYUHiL47ssvOV9/55lmNBpAsKDw72291LQLeezpt1I3j/M57xoNUdp5UpqS0wJ+xaH6k31YiQH4LV2rPUROo3kcqcTRk5H0LAl5amQAlMEzV8fasjAP1wcqbLFbBL3IzAd0DHwTi+W59vDZ8oH+R/7nD/92pB2aBhqhgHlVSal88rIPY304gEYgg/49qXpppSlSbj+ND/fYjbxttiKIqCOojrG8F730yhVhmzw8BVrp7ZdFf0KGY9GHs9hs9ewMn6Ex499DhAXX2vHayvGCmNRis1krcfXC2BzuLACozktyGZVFQIjZWoXBn7/seB9TrCERnCEFHSOgjnR1MwOZzK94hSpVjYeW5CJx/lzzLreHmcTZX4XOrLKt9NuhAQZFbVo4fj7F2Tx7WQdOI+kHUl8ltLZ0I48b2irF9cz25BurVWHO/nJ5K4VAW1P628wgKgRmmatg4aazQDEXJpK9/pqV0WoyMI9KzlnzeUceZ5Ej0YpOk81oKcyeYbxDOcIttGfFcZy8SAXL3hREO0g0QaoplGckwaU3oTFeat826hiPan2U98SxLdHBUL/giwEeYiK4bADRRfh/HkzUHQRrqObAeKCE0asdAxHi+OrEGazYrTjM2TknubHBQDTh7SD3elGYakDS3aeRN+3v+ZDhrb+xDmbrLvT+brmsVd2IgpX0fzYh7wWlWzajR8bTMOfQ75GYamDu35ZR+7FItGOrLvTsaH0eQy+8hGicBUxxz7k57XSMRyXSSRmX3sIfzg3Bm1sWUgufEG0JlPWHUbqF0fRPS6CF4rW3enYVToKz+zoguRL870cS14an8z3yh/HjpI2GVBHkdbfYA1H7dI0P01Gg7F/V7z26S+fxrHrx9Dhng5Y/6hv+6lXCd8q4gns+/bX+A3fwNJkF8LNLky5bcPIHio1TSEkThZVRn8VQdTCYOnnnf8AIQTphmfwxtwl8m1IIWizr305r7ksjc8rtzWGRuDSHROukwboYDiHY6Ql7jHcwlrDE/i3ZSiWxuehw5lMvGd7FJsMnJ2Opm/SvG0rGuMRwwe8k6qo1EErmePnhn/hHTfbSS90JieR4eRsmA1DzfixwTS+KmY/+3JsJ5M4gmGDyYsEhTqGSoz1cdMVggznCD4ecsnOk7zNEuDslv/sdlyU7skK0KdtcjAAcwu9vidSu2egnDt3Q9iRrmn+8qXPS45dPyb69IUd53bATdzYcW4Hd6AyeQIF2tykxNZogj+gQUgYitx2rGkS7d2nUKOUG5NHG5ULf6HwWZ1Qog1QTeNi69HoZvsI3eyreaEl7FeRi5G2GZsgqqiYMHI68trPghWNcbBVCt69bxPuMdxCkMGN2OASjApdjZYPv8yFEo2cjrCZJ/Bvy1BeKKWEfYttzhex63ZLFFuiEDXsdd5mCHC2SYBjNeI146FpuPFwBkaFrsZm42CvMYYlTUe9kCCkO0bAisac6SI8FgdjxvHrRtuq98g85D7yNS8wk3vF4Smyiw9PCjUbMe+xjuhwJlPE/M6qVxWWJIh3NYd6LaFU45MG31cEVRV2VNcC6XVNU6ppViYYtp0Khy0paXwCwo00x0ikFw9geoaVXhBUG6EeXyHxhGqNnDFvYbv/7Haco5QTEINIIdKw9g8Dii7iEonEo8YMvqYPdTRNsHyD8dgiG4QvDNL/X4uRijZFtWFW+XNbIxrXkI9IRM/lTCSqK0JqWEu/w76qEdWp0daIkKPqAJ34xDXf4cPxv6/u4XCgJQYIgCT2D515T0W2/pLCYl5tCc+f31dODgF4c0QKnFzCPGw5nki/SCs842JlEgnp2/ZZpiAa15ilYJn9H8rEjZ3vYGnpMASZDJhg+ALrzH9E1KAUcSylCied0GxAGYIOxozjA/jtThcf+/jakHayee50jt81Tcbyov7+EX6oQHXEXla0z+qMF73rhWbLqRtxdun/q+7hcGAwAKmBSLO8dVubEBVqbABvz1vc7l/IO39TLCxu5ZeTQ4RGcMztHq+0lFBCmmYoBf3S9+p6Ej8Xf66oFbNqYcvV+aGa5nMh3+BP5HOmRianpdDjVOC6YYRx2BJxdIFCDXdh6qZUg6V1yAExeTHVZA0A9nr65Z+9p820kqHlGr0CTZucIKmJAem12fZ519s0h3RQTtX0B09/+TQ6fdoJT3/5tM9rRSQd/aYyUxFZpBpCUg9ReqMau6DQq+2xv21sn4TBTRtgdf1mWOkYXp6W6BxRLhApOUR0Ny6k51QOz5spJZSgtq3ucRHo+/bXOLgxTeQ5psLiq8vr+LHL2an2lbWBkxixr6wNf4xlO6PH5j3WES0ffhmjQldzMZAqYjSFxy+2fxEwmGCE2zu6QO5FJEnd5G3BnvW90H4iH1NJANQLCUJyrzg+/ZIA5bZP+uw9JUGmGNaXj1fh+cqlONbE0hJ6yqUYtUrTrIyMoE6fduL//nncz4rXskg6lEg4APBVGWlJCVrdsluTbjh86T+YUHjL22suBMMeSPtoENQEuDjbK62OeT+Nh5TRvjae3IiFe1ai9OoAfGv7Z/l2OWk6Twd35cFWOHB9E8quD4DjZq9yL7ZA+6CaJq/5AT5JdlmxsUIN2qfWpcHswdLktGh9WQfO463sX2BzuDC8iyCN01OqQ1QSRGFcqvq8NN93PO1dUFGyIrjrNc3KQId7Oog+lcAi6ZASY1BSjE5hT+Bmfj80CGpSXglyzzKeCOPwb4c56rUmHk+qXMaENMbtUCZfRO2VBydyBcba/IC9IVM4O5sHVMv9ICwaThhxzRwNlFwHsv/CxSB6kHXgPNIWzsDqPfNBgm4itPG3+BhP4BKJ5DRXAR3c3x6eBFycjav53QGAqX1w3mADjHCjMHsO8rMX8R5k2WqNVFBmL8bBmHGwojG+d92LGSeeQuLtbXwMJq/VHsqEbWEsiubGcFox1RwBNuWdACxNTk67Y1W3TO4Vh0b1gkEA5J2/WX5xmyROi2+TxI9RmkIqnINc5UzRcWpOEWQWecGzdsU5ado91Gp2Ojq8cNcLzfWPrsfP435W5WmnZXGFzERSpiIqFA/82A6DC25i0//OY2STBK/gXtF9Sl986VZzzzIudbLgVrldUfrlP5SJNXvnw1pixWbDJQTBjUa3jgPOOwAInEc383yTNB3x+cKbiHIRvNFvMr9dvth6NNJKhjK5OV8b0o4d4tRjPBDaEABASPk2NsM5Aisdw8VbWniEdslQ5CMSKx3DMe1Md+Q8nIPuxl/R3HCNK2ML8GE2NODe4ryFcBSjxfFV5X3vWcZR29luygqR7nERMAC4UVLmXTKXsf1kmSGY10sTBiTPRK5M8ZR1h3H/nH/zvKEidHgCbhixy9BHXhh6Xqo0uSH1i6PqBWcFgs7rWhiRFtz12/PKqkApIovw5ShiOREEW15F5xGjjg+WdcRG902sadgAzxXdwehbhXAYLTAbjXA776CUBGORMxm7mkYg+J5vvcpjUPgdWO0p0UAD0oUVJYHyCpNC8o6GoWY+dCi5VxwzLIhemxL2LV7GOtidbpxo/2p5WJEgoiHNOZK5lkIuTTWODb/DhmTISuj8hI4wWjlTykrPegZyENZEqgqHTW1xDunb80qAaHu9aTxXkmCTjH3H13kBEs6me9h1DN5vcmk7Hm2yf9vG2G95hau/7QGtH8SPscd43va3cecrGPzzMmw0Ozh7JRV4/aYCBs5jYQoKBsJjYX5kEfCGFbcQhnqGMtwbsQX28M9wy/kbLsTe4DzdErsWTYUssTvZTOXw1jiyDpxH35xW2JK4E2/MXYIfUwcjuVccgiMOwt18IUpC/iNiS7c73TAZgBd+l4tNthdgPvwJ0hbOQIczmYga9jovMLvHRcDudMEA4GLr0bC8cRHhcy+L4zB7jOdY5meeQ9SgFFEtdaFjqWGoGQ1DzZym6INMYpKnKuWzzs9E5gGfBBSSHYJ0O0611WGdoxFqNvK8oVJ0j4uAycB98mD0n9wrDvMe68gk9KgM3M3OIV3TFGpx618s9zCn3vC+2FeNFyFYjgFpO9J+FJw+IoZ4z3WDW8TCajJwTOydxMb8wf/XHVZShihDMHaNLRfCJfObo577Nh6KjcGVIBPfbtnNBKYWKdQolsbnecVb0mDrULMRjeqFiKpU8hrIoUwM/nkZrCYDDM4IzOjAJSgItcc9IVPQ3HANl0gkAHC1fAwmpJkmIL14gEgjk2qlihCsKSselcWZKY0JtWYv5qIUgh9BvZAgdhxoJZBSyGqPSn1pGEdNDG8KNHRNsxIgYiinYTodnmBfLDzuy3ieNIf74ibN4f5NtYNN44GgEAAGcXuHMoGyYq8QJmr7FJXU8NiiJsQkcXbRvm96CfDkwlJEOZxI9mxFKT4wPoNLJBJ9ixojykXwTFAPvP95JJbsPInE29uQtCMJOJQpqj1Dtc0OZzJ5xnIhnjXtxlfGyUi8vQ0Aw0G0Zxkm3Cy3mQp/oP3bNuYEQrMxsKIx1pDHkOEcASeMAHHhL87VSAn7FsM6R/MaIgDlsBxGmBb6TWU7gZwjeKeXMEUx68B5dJ23C123x2Jxu38hO/gRFJU6vEO7KAT9CO2Vqu1+DO0xI/c0n6suWs+KEMkIQAlIrLvTfY9PB4+7XtPUBJb9UC2oBsAggqDnN7pvYk1EBCb0fbNcQPoZFiJni6PaBdWWaCD3BMs3mIlM3gYrzJQBIEvAK6RFk606yZiDnE2MUr5NMaxHiOMWAOK1Vj7p1ARhVsXEwtPK0WtpiBYlA9lX1gZ9gk9hyZ2hWFvGeb9DzUaUOjjyYuEaeKWTMuZJbdNqSZZFYxbMlclQH0CwEhECghoUyqRrmtWNPcu4LbfQfqgWVAPwEEF4aQL9pmJNRASsJoO45pDWAHgP+rdtDJPBgP5tG4supbY1Sj5xof1ExDQMxauWLzmB6Ql+Xxqfh58sLyC3bDRynOPw0e8yOE82K+Yw6EmeOIMpTBjB5nI2MVpTKMRRxHnhGWsltA8yw4Vo4kFpIcJsVkxwZGHgjoG49wKXZPDdr1dxubCUp50bTPYhzGbFq8YNPOmGzZM6SW2fS+PzsN/yCnbH/V2ehNnzrF7GOuy3vIINkWuwzzIFj9i2c9omyxZ6KBN4uyUct6+iCGE4GDOOP5V3/iZcRBLapBXSPgU7HrPrDooQhmPxARZsdTyUSdc0taCS36BMT74WCjZh3RpGrRnFchXSvPbt08vttwJQYmBKeSZNwWSSAGu1l2kICpe1y3nm74QRt0koIgwlyEck+tiW81v8x1078YJxC+zNeqBV6VGU3C5EPfdtXCKRePe+TWLtTtCebEQEHXdZMRf65NlVXCKRGBW6mqeyY9pCgfLrBCU9KmxzlGqwkh2PtM+AoI5rmrrQZKDWGcgPZZYLOUkuNP3iCkNtFOvcCNuCgaMrc5QCIHDCiFTHnzg2dg9LeVbvbJzbsQJ/xue42P5FTDvTHYm3t2FS0Fbkuduip+l/XsztFVlX1aEuAp7RbT/l48WgrThiaIdu+JUvvSsNPfIyaQj5TE/loMjmQK6rs2hOSn2fDe2IkCuH8DGewEPtmyLhbLo3wcuhTNh2zoXN6cZKjEbLh1/Wtja+TEZyoVCxCSg+tdeLEb+uQd+eVxGYWz65MBOF8BNW7jnrmN+gfQPA0DRsbBKLwU0bcG0LQpMofye/3Qza6r19OpTJZdNk/6Xcsz/sb8BsK/cZHou89rOQW384LrSfyG+bk3vFYTy2IBrX0OL4KkxKbI3J5m1obriGYaYDnHCVCfAWYsq6w2j9uifAW2FN5bb1XsHWHpNAwsjpuG/4qxgVuhr9LWd4R5ZX6BGAqEEp5TnwgJg531Me+N1605HzcA5TA6b9Z7kGoa99OZ4o+DP62Jbj35ahnGZeehMIkZh2eozHCoxGMbEgJMioXXhRk1HpTfZ2WGoaEXC0hs08gelvvFtnBWZlQdc0GVBTWpWHQogHq7yu6FjcKC7esn0S1lw/xKVbtk7Cmtu/qAu2l/Qt7e/GwrZo5CzAjaCmaPTGr6KaREMP7xRrGULWJhg4Qam0tRJoOP8N6oZmt45gb7MxGDVpLh+Ybmv2IFoVHuC1IBGjkOSHKmI1b/JXn2EzUro5VRqoBvYjId2brdmDaFV6VHG7yaoaKQqNklDJCVEhh0xFnJN3AXRNszLwUSIwN5z79ICZF9xvKootUUgrGSoOIWHkhlMtSZpiCXAhRA2CG+CO4w42HuI0mTWXc7g89GAX97cgl10Rnr4pu3insCdE/S0tHYabpB4MjmJsyJiLNXnvcW1fP4Qh+ADpxQPKtb7YhPJ2LQ25T6UAboGGc+/t79HPvhzLi/oDAKad6Y7etve5GjkhYbwWJFxXqWZIWc07RId7pW6yEHv8Q0ThKmKPc3V6SuxOkdYIQDaJQMQ/KpyjxIEhnEdW72z0zWklGz4krRrplWqqwLxEnXIiFne16DGeiwP+6zldYFYR7lpNk9e6zh/DyNvF3MG5ReJzkrRFX7yTAFQFF/MaoYf9SE7TzCvIU2aV92hGu263RHvXL17hRVPWHcaME0/xQeP7moZiTbALE8pMKHuAC/ReGp/HsabbCkHtlnntZ3HbSU/tHGGVS6qFL43PQ8LxtwAQ2IIaoJcrE3anCyFBJrzeZF95tcZWjfj123jjCNZczsGEmCS8f/gxRZ5MXzbLhXNfw5/I58hwjkB28COigHqqITbFNRhptaDw5t5anvRZKaQ+SmuUS5+BKqcHi0TaYy+Fyw6YQrzsnYpE0zqrkU/ommYAwadPhnsvpCi10sOTiK8XqEsdUxFczGughnuAW/kYebsYu57Zh5E9pmLk8RzsihuFke1GetcvoqAakmdsg9x70dxwDX9xrgYOZfL2QQD4yMUFb3/kGoGRPaZi120TRvaYymt9CZc/5TRGgaNn2pnuXkQQVCOlwmPame68rdMyZC7qhQSh1MFVhuxfkIUoXEWHM5mcdtY7G+gxntOiTQasuZzDXMsp6w4jv7AUoWaTz/S8lg+/jAFly/ma5cK2WhxfhShchR3BnG3WHMoOgZE+K0bqIxWY3eMi+D5EWrKW8BrhtRJ7KRx3uE9hO6y2KxLOU8dDgaoKd63Q5AVXvTbcD6vjU97nOk3gvJ0AQOTpvERQUZiNZiHBehiDY5pi44Wd3AnJl/rhlg/DaDDi4ZYPixug1xEA4bEwdnxSRMSbfSQfLgJkH8nHVvMj6Gdfjq3mR9iD6TeVy0IKjUBe+1loGGrmcpcBYOpRPoebCiSRsJMQGVPHCnUUSQXuhJgkRLkIJsQkIdm0m6OzuzSf3yJnH8kHAVDmdPl0TghzrfeETMHepLP8PRfaT0Q+IvE3jEXWkMPA4IUi4UiF3sGzjFRZAWgaY+LtbXj95P/j+xAVNxMKXupMo9R0wiywZR05rZJeS++L6gzAABjNXtlgzBdwRcrh0vjVkuvcOCs5P72u4q7dnquGmi2Nn9uevp92xS240AAm7B33o/p2WKV9Af7eKaceQPaRfCxqcQjDitZjX1kbDDAd4TNshFtuoDyjBgCf3cMyL6gJGRJu34Wld/nYUDp2ezFgu8mRFcONYksUZsWt054BI2MOUdrmS0tlyDlg6HU0L572IVvcTFoCBVDOAlMYf6XBzzIttRX69ryqIHQQCDVHuVAYuW2PEhvOoUwY3E4AgCE4jDumpKWyxnTxYHm/gnuXj+6G04uHYZRtE5fx4t7DZdiAAAaTSAOk2+3UL45iyc6TWOkYjiKEAXe8tRG5kCFhrnXqF0dF2TbjsQWFpY7ye+haGQCEx2K3sS+f+03HvXx0N99MQvR8rDdXKaDMwkPPiciWJRA6ly53eFHUx2tD2vGOHtFWXVoCxVcWGAD0mwq7ORxFRTc5QuXKhmBn4Ze2qkMXmkzICUG543JbJoE91CuOcM8yTLnBkVhMeWCKb1JXVt+xCZwWI/R8S8a1sUksBsdGY2P9MAAGYGgaogal8CQclJTXRQC7043c+sNhstTnbGyS2D85QUSFKTULmAzgt+g0TZO/h67VwDnI6p2NGWQK1pDH8KzzM7HQ8GV/o+cvHmS+aKgpBYDXutJzotpEEmTknkZhqQP1QoI4RicZmjfRi0RATZflGlRuz1WoW5/lGoSrZWZvQmU/4fN7pHvbKwxdaLIgJwTljgu0PNGXVmAP9dLS+k3FSGMEdnWaipHtRvLn38o+Xh7kDShrVFK2cCl6jMeaJtGwBpmwJiKCj71M7hWHeiFBKCx1YNtP+fwwn/XYGa827IoihMFuDhf1J2fTFXJDNgw1o77FjP+1GMkHl1NnStaB86K1su5Ox5fuSXjFsJ4PjgfK2dwVw44k4VZyQkIpoF5JsPpy+glZoLjYTIKWM7Nx76ztstyjcuOjTEsX2k9k9qHEkiS9piYUZqvrrO61S2gujlNFAFwRUBJd6vEVQYWTR/SlFdDDef0IJW3R8zaHm3fiABBrVIIMHwB8PR0hyYMUvFNLQh8nrKwIcNrhq5YvgaKLsFz5L7rYPsJA01pV2ggVPsvb/IB/k5cwrOzfSP3iKE+NRh0n0h8yJfgNNhlRhDBEBNmBQ5nIyD2N9OIB6GdfLh8b6Vk/mg5JhbJW4QewBY0vpx+9J+/8TeydORCXC20AAIeb8HZfvl8fGU659Ycj95GvxYTKMuPyNfaaQA5cEwR3ZaJ2CU24lItMBQAVfeBy3mXWj1D4I6fnh3fhgrx5Fm+hdksF6NcLUPz2fdj2Uz56297nwn9kIOQLlfY377GOvMd7WOdoZDhH4EZQU2TicTQMNfMlfVVrDHuWIQpXMSloKy/4hfndzCJs4bGo98g8hIdHwOK8BexZxq8h4IM3U7LerDo8anLdpSWMvebLEHpS4dQ5JhwAYDYa+P7omIpz0mRNDcm94tA9LoJ/ycjNTc38aXtKwt4vrk+NqAmCuzJRu7znMxuiQfcn5cuZBgBVSdZBvbOqmcglnmcnMWKu80+41XGsPKOQwCOvFJwvrVnji0dTaXwbLE9h1oUe6BAdjuslZerWkhE5oPVZSK+nXm6AY3v38uBL5iPncZemOSqNixUQP8HyDcZji4jxXnj9G1s4DzarRlCg0fftr3lClQznCOTWH16ja/xUFLr3/PXzgReYklQ7VbGYAQJTo1IiBqE/9DZJIACCDG68ZN7K80OyavcItRwljUpol5SmAzIJPljwaNbLi/rDRYALN+6oXwzPvVmuQV7atz8CUwqWB19asVLW0SVgdweUdyPCc7S9TYbBsjsCYRusGkGBxqTE1rxZZFLQ1jqrDVYmapfQVANJLrlPo7SgtrSIaYdxr4jJhpZD8JTClULaFuvc92dvYO/MgXwIy6TE1qo89zdO/AcGAE5iRLpjBF+jR/gDoHbE92yPihiJJiW25rfNLBve8tHdeM2D/vDpNlptiAol7H2K7GKWraXrx3outATDL9veY66dHITCij6bx107JPXgCRfUHjOOWfZWLje+xT2/AwD0NJ5A/tzWeP3OEuyzTBEVwKPP9J56wfyzpO29NqQdGoaa8dstG1+ql7ZPrcpGA0eOLFwP1hr5OubrO7tk50msIY8hH5G43OFFneHID9Su7bkaFXtuuODvIt+5zJvGc4KzwxNonfdkOdPO4mFe97KYbAB2OQMRa49ny5V14Dze//5T3An9CmXXE+Es7IVoT8EySuoLwCtIvUN0OH5ftBWTgrYiLGk63thyFC96tlc0jVC6tZMLwBZuw6VlG3zlWqvdLtPt7I2gphjoWgm7042QIO79TMd02+ZgloKgzEyXSCT62Zdj4ePcGH2VWqYCwe50weZwgwCiYm397Mv5a4WEyaLCZUlnmaaMTbYXEIWrcBIjggxu/lMYHP7G7Kl4MWgrPnSOwMK3lnmRMdMCcvRZNQsP5b8/QgjXg/Xd9XUMEDMsCQvd0XPSfuoy9O25GkR3E33SLVKvrifFPJZ0GxzXh6sI+VQmz7RDt0nS7dqkxNZoHJ0HxL6FXl1P8k4Ur7YBr7Y27nwFHxwfCnuDjTAGFyIicgfGBX+NDaXP477j7zGD1Hde2AxL/Nv4pWQn0osHYAg+AHqMx62OY/F7+3JsNg5B55hwmAzAohaHRNv6Vff/hJ8sL+Bg0ASmA2PeYxwPp1ArERbaks6dChglTzUF3c5m4nFPTrqLFxj0x8ssGAYgE497bYUBMR8Aq28aRlXqEZgGAGsNT6DYEoW9zcbAZOCcNVKnybzHOjK1fKEpIxOPIx+R+LFBIvIRiV2G3qJwqKwD5/kt75SQL/lx09hVKqhCzSa+VC9tv3NMOAzgHEhSliaWuUB4TBr2JDSpAGIBSc81DDUj1GziyzLr0I66p2nKwIvb0s/0NZ8cmZ5jXvd93BFWkwEAYCQEs2/ZMeKWDRbnLTiMFpjrN/ZyTDz4aSLsuA6TuxHqFaSKmIa8SrtKS15QAQDIzlGqtSjxOko1VFnWH3gXPnsr+xfYHC4M78KlR6pxpEjLZQg1zfc/j5QtyqZY0sMXDmVyCQkEPNuQGtalvm9/jRklaRhmPICvTX0x+M0vtZf98CNdV2lsSmusmv2+DkDXNCsAL25LP4kP5Dgypce87otJQgOXG+EuF2Zfv4mRpQ5YzCYAgDkklBn/OaPXS4iqF4VZfVJEDhFpaVcaDE5g4OyzXy/AwZhxKEQ9FEqKdQkh1WSOxY+HFY2ZhbaEGmqyaTd24iWkhH3LdCQIbYPJveLQqF4wCMoLhKlx8PRs1Uh0jTB0Ss5hk9wrDj+mDsaPqYP9s9X1GM8VzROwDakN++lh+hVBBjf6BJ8SzZHaiOVCzei/rdmLfTvbJPZupbEprXFdDwmqbNw1mmag4W9okohxXMA1qSaIXE57oZrDT5YXEA6udnpffOxlv/I1ZpYGcnBjGmKPf4iP8UR5/Zp3WnIplqERXDpeANdKSxhWpYSHVZQfU8V9LFt54u1tXO0lWnuIxch+F/JhVhS6plkJUPJyK0FLELxQs6Bs4N/9elWzwKQ2xe9+vSo6RzWHHU2f5zKE4lP4nHKhV52OecnOk5wXeWOayA7K0kBij3+IaFzjCH/pXAXpodL5CSGNElAj2FQFtnvs0dbd6QHNPJFmg8l5omlgOB/OlbMAxTlpHKu/xzGnZo5CW3lu/eHi2kOs2j+SsCzp89NRNbjrhaaQe1IL1Gxx6I9MmEaoOfbRA+GWHBALFH4rWNSfjwfMO38TBECjeiG8sJIKpBbHV4nGwNrSCZmA+LkK0kNxKBNJO5KQeHubl/DyZ21pWBQA7/IVHlBhNR5bArrNlL4I5f5NnTv7yjxcrC47wmxWTHWshnV3ule7UuHLc4l6QqHkyqvIsRHRcUifnyJ8sUbpUI27XmhKvdxqIfqiy3wh6ZcbKPdg0vu0xj4KbYrCuE7hD1IoyGmspDCWkKJ/28aIaRgqqiopB8oENKJLNB7PHYJd8x/lhBbVkD2pk5PN27yEl3Rt5TRSFukEZRhiaafUO7+0dJjP7buWVEGWBsj6N00A6BN8irMhm0LgBheGNCloq/d4pbsSNQzqCmxEdBzM56eVvlCHZug2zUBAxhNf0TRANW3I2sM8tcsdRgsM7jJcbDYEyYUvlNvSBDGJquxjnjnyMYoAx3Yf18dvWx610aWVDEV68QDeMw9Acc7M6AE1/QUaKm2aXs+wMm2TclEhd6k9tDJkhy40AwGtX0gNuc++fvhZB857s61TRw0AN7jthBNGrH/4Rz4kZ0/IFITZrPw90h8264denJOGfWVt8JCby0iCwcTFuKqEV5uecdrN4WhfnOEtBBXWNevAeTTZlYJB7r1cuQ9Geq2mlxaLDV/YZ3UIHaU+5c7dpcJRDjXGEZSeno6WLVvCYrEgISEB33//veL1hYWFSElJQVRUFEJCQtC2bVts377drwHXSKigjBOBsVXKOsAuQ+vLdprcK47TMIXbNMFr0GEIgRNGTtMU8GguuTMUl0gkNli42ki+7HnoMR5hM09g8JtfwtDxKU5gdnhC5QKVj1Vou7M5XNxwCREHmSusk7CtwWQfVxcpEMxXwkJnWgioKxNKfcqd0/pd1KEZmoXmhg0bMG3aNKSmpuKHH35Aly5dMGTIEPz222/M68vKyvDQQw/h3Llz2LRpE06ePInVq1cjJiamwoP3AqOGuSykNbGFkBbIEkApt9cXDm5Mg3VuG5wN7cgZ+cuKgUOZ/HaTZceTChpmf9KSHAagFCG4SeohjYzFsqDnEX7tB+ya/yi2OV9ESti3+LQsCf3syzHrQg8A5cJ5aXwesKwjpoR/B5MB6B4X4d3nU5l8FpVqMGxtKzAal0gkVmC0vDOEYXPNOnAeu+Y/CjfhhC5XnMwbogJownG83RJ4pyUObhQQdiiUpqBxsL5qsQcckvmLnkNFCqzpqBA0b88TEhLQo0cPrFy5EgDgdrsRGxuLl19+GTNnzvS6/sMPP0RaWhpOnDgBs9msqg+73Q673c7/+9atW4iNjWWq2KKc5HXPl5/w1DCXg3tuBIxwc4W95t4Un1QoPiWXj87K5ZZulaxz2yAKV2FFY0SFW3jbU1/7clwuLOXK5ggpzBig/cnStXnGTounldid+NI9Cc0N18rtkQYTNjSZglkXengXMfPcb0Vj9La9L/K2U6cODXnq37Yx/7fPDByGrU0txZp0Ta3Zi9GEXIPJQJjPiIKZfy94tsI5KmXUKGU/seDLLCCNt5XLGpK2o8ZGW5XUhrUB1b49LysrQ15eHgYNKo9FMxqNGDRoEPbv38+8Z+vWrejduzdSUlLQtGlTdOzYEYsWLYLL5ZLtZ/HixQgPD+f/i42Nlb1WVKOc5p2bzD5DK3Yb+8JJjNht7Ot9UlogS6AlSb3SkxJb8zV2luw8Kdao6BZq+3TgUCYutJ8IKxpzXk+BpkC1vPBQs5jCjAGfIUuedsOSpvMMOx96vM3Z7l5wwwgQF0bZNuH0k1ew/MoY8Vp57hfW9pmU2Jrn2dz2Uz4KSx0oLHUg+0j53yxaOiHrDmUWUlM+A1CIg/V468sMwXDDAJh/J6ttCQugiebnCeX5rmkyr03L9W/dna6Y/cSCrxheaegSi3WK1Y6aMLe6zppeE6BJ08zPz0dMTAz27duH3r1788dnzJiBb7/9FgcPeteque+++3Du3Dk8++yzeOmll3Dq1Cm89NJLmDJlClJTU5n9+K1pthvppdFMWXeYWRaWvpEBgsuFNnSOCcfWl/uxJy7Q3m7bnF4lboUaDXWwFFuiuLAijxdbWDZXzjPOyvgBZDzJGgz+on5Mu8vvo0JdhfZHj98osaPU4YYBwPAu0UxNU4l1R9bRpDRmybyLc9KQ4RyBqEEpfmlTtG0hA5CcprkTL4kcZmrG6K+mKdU4/dEa/R1TXUW1e8/9EZpt27aFzWbD2bNnYTJxudZLly5FWloarFarqn41TVwiTFgUbUK0nJnN/33u7fLzLEGTVjIUhaUOcXiP5Frr7nSMdnyGdeY/Yvob73qF1cjSkgl+tCyhU9GwGdaPRpTS6WEUl25pfXrVffQFgCmg/CGbUCPsfMKzvV/pGI7s4Ed8pmqy1ohCtv65nxCuCTULBErI3U0kHUJUhtAM0nJxZGQkTCYTCgoKRMcLCgrQrFkz5j1RUVEwm828wASA+++/H1euXEFZWRmCg4NV999l7k4M79FGbIOTosd4keY1rHM0r2my0DkmHEcuF/E1XiiE25zkmVybUQfOY0vuaYyIj0aUILWNElMAQBZSMCr34fItlGc8/TemIfn4K7gQPxEHN+Yh8diHeNo4Auvdg7y2W8IfDB2L1qwXqeCh85mzhatv/tqQdsg40x2Xbe8j5kwo9nrue4rswp9CPkdG2QhkHWjH35f6BadlCUlDAIg1V0GJW6kzSypIJyW2xpKdJ0UUZVIbYkbuaVE79Hio2SS7rfYJQTD+/UNe9SmQpjHWqLKwND6PE9DxEzEtF8w18BfS75QO/+GXI6hnz55YsWIFAM4R1KJFC0yePJnpCJo1axb++c9/4syZMzAaORPq+++/j3feeQf5+erS6+jbIvbVjTBbfifWGCspLk24hdp/dRuC7/kWrzw4kWkCUAXBPdYiG6JwFZdIJN69b5PyS8BPSDULoWYLwEubATihtM35Ik8CPCp0NSYltsaJbe/hxaCtnPY8pB2vqeXWH469IVPEa3EoEzd2voOlpcNwq+NYxbnJadR0XHLbVdWaJuO7oaQ5AvImE03OKj8gjbfN6p2tqu1ate2uhhjSancEAcC0adOwevVqfPrpp/jll18wadIklJSU4LnnngMAjB07Fq+//jp//aRJk3Djxg288sor+PXXX5GdnY1FixYhJSVF82CZ6Y6VFD9HnRR552/iTuhXuOX8jXM2Af6FewjuudB+Ip/PTenStEBNmJPUaZDcKw7DOkeLSDyEjhiqxWXicRRborDO/Ef+mpn1/83XlPFKm5SuxZ5laOQswItBW33mnAvHKPxbuPasHHtReRAF0Bz14pw0/hglTHnmcHuRo4r+LSRaFvbJEkhqKO7UIiP3NFY6hsOKxnxpEjVt1yrHT11J5SR+YMWKFaRFixYkODiY9OzZkxw4cIA/N2DAADJu3DjR9fv27SMJCQkkJCSExMfHk7feeos4nU7V/RUVFREApKioyPvk92sIWdqB+wwkPO0e2PAuefD9eaRP1kCy4cQG8vf950ifxTnk7/vP+bxXaUwHNrxL8lNbkwMb3tU8tD6Lc0jcX78kfRbnBOw+xXl9v4bcXtyOvLtgOjdepbl9v4ZcX3AvmT3rVfLyP3/QND5hG2RpB3ImY5R4jei6/uvPqp75uwumk4tvxpN3F0wXzTN+5pf8OtA1eXfBdFKYGk1uvBlFZs96Vfn5VgJ8rb/cfFV9H2sKKuu3qgBF2eEn9DRKIYTbB4lnWdPWUGn7LinDK7pG5faFjkVUW4iRSncwZhymneku2mpad6fztYa0bJHk8sY1804KrpXdWgpZ6ImrfI3kjvtYJyVPMgCxlxzwYq6v9i2wn1UGdNSQ7XmdRs4Cnh9Ruu1kMRbJQmn7ToWxAdw1sQnecZ2S9EpWPZy9SWeR8IsM27ennRbHV3ltNafX284Jh5wFXhk6Stt+rxhBFVstmgFl3zWPOS+6FU794qiYG9KzfmebDub4QSnzvCRrZ4PlKUUuVLktrpRZfu/MgTgWP55juidheM/2qGgNzu1YwVfIVDKJaGVV8gWlGNcNGXNxKbU1NmTMDUhfOtRDF5pCeLgqYSvkPgU5vFRovDaknW9bU4/x5dqqNMie/vAHzkFW72zkH80tt7sxhK1SkDeIC04Yy4UKDayPTQDCY/Fd02QYANwosZf/kGkfBogEGcueR0E11J14Ccmm3arTClscX4UoXOVyzEMjOO3asx6UHxTgguZF3JCe9bNc+S9WOoaX1wun6aJPZQJTj2LWhR5+caGyMO1Md3S1rUZX+0dYY/uDaA3+jM95m66S7VDRvqjEZ6lALZh4exu3NlJylyt/R3PDNfS98nftk/UHOh8nD11oCjFwDrf1A/HSoDQb/VUQKmTknka6g8vWefv2Ixzrt4RsQTYLpN9UWNEYqY4/lQsV2ufFg8DUo1he1B8EQKnDXf5Dpv0PnOOlSctViMzIPY3Rjs84DXXPMizZeRLpxQO4srgKW3OaAXWi/auytXdGdIlmc0Mq8HRS+MuFysKkRFqp0ehFmnKx/YvIRyTWGp5Q3GEoZuz4Qb4xKbE1FxOMq17n9jYbg0skEnubjdE2UX9RV5w4AYBu05RCYn/z256lwuY3Zd1hbPspnycl0hp4LBzb92dvoMHR/8O00Gw0GvJXfuxqqzP6Cq0R2kK7bo/VHtTtB31eRTN/ahT8oXnznKsR6xCAcKHqsA1Xe0ZQdaE6+TQrM5NCSxExX5DLfPL5RfXjJVFVX35/S9RW13grC3Ulm6c65qE7gqoBakgS5MByDGQdOI+u83ah67xd6B4Xod5O6gM0BjM4yCTqz2ccn2TbpSbuL5DxiUpQWnst8Yn0WlpQTuSoqQW2uop8B2sS6so87npNU0jo0bNVo4BqJKzcZPq2BbyzcrRsn1nakzR32bo7HeOxBZl43GtrJxe2RLf0dqcLIUEm+W19AEKOKgKm9ijTD80CysTjWGP7g1jT0cN56jR0TbMSIKyYWBXZFdThQJ0Naj2u0utY9wnf5NR508hZgOn1tnsJPnr/tDPdy4XFso5INu1GvZAglDrcKCx1cNUVtRTqYmluAXQi0DCmey9s9NZ2ZfpJuPwponAVrwVt8C42R6MJYhNkiadlIZir167ClwYbaA1X2J6/bdcCrbsm4K4XmkIPbCC2D8IfD4vPMblXHH5MHcxrnqwSFzwEQkA6tu5xEV7M6gCwd+ZAfH/2BvILS7GGPCYbFqQUdykS7AzeThpydCOoKVfrW7jdZQmuALKM0zCmFsdX8WPxyWbuOW4xcLR+CWcFZXZpNMHFg1xdJYGH3ycEc/V6iSm9KGjhu0B6o4X9+fuS0j3kqlC7hGbe/wX8Tbh8dDecXjwMy0d3C4itzro7HRtKn8e5HSt8brszcpVL1QqFgHRseedvwkW4T6nNjnrk/+5I4jJbFKojAvAKoBYKdlap4Yzc00gvHoAeJcuQXjxArCWzBFeP8cjqnY2+Oa28A79ltBu5QHERkTMkGrdcfRwaN+vgzCLCGkqicQuJp9WAQSTNv4QUSlU4/j0LIC5uGP2mKgbFqw6YF/bn70tK4b5AB+7XZtQum+ai+9DAnl+j7U/Fb9+HMJsV+YhEH9vySitNoMRZGWo24o/uXaLwIyEU+TpV2B9pCJKcvZQFWc+pjE1RradV9RoKUzCHpqm3rfpjj2XcI5zPf2xPwggCNwwwzi1UnGtN8ZzXlHFoxV1v0/xv1DPitLqahkOZCIMNCI3AxfYvijQPodecvq0rotmyUgGpOWD2sPZYGPkVGjkLmFstOXYhAKItmpx2QdMx5eylLMiZPg7GjGM+U7nrpWNSu4Z8P/e/rs0Z5ceWlcWuJJzPf4L7w0mM+E9wf69zUgQ6esNf1BXPdyBQqzTNnqlbUWAzKr7tRqzYw5MKy5avEIClsfntPZdoTaziXEA5xZ20mJav8hdSfkk61xn37MVLQVu5rZ5rEJbsPImnyC68avkSYUnTkeUa5DXH7nERolIV9LjQm943p5Vs0TgWL6UwmL5/28bIO38TS+Pz0OFMJh+c/f3ZG6LyI1o1GBZPqJpn5us+mmhgMRsxe1h7pkdeuI5KXJtpC2dgtOMzfOgcgfuGexMd05haAFj4eMcKmYMo1LDl+11ipBbjrtc0J/y+lc+33ZHLRaJPXxDaxCrsPZch+aBfxIahZr4IG6uYlpyHXK4AF53jiOINIodEYakDa2x/wBB8wKdrCueYeHsb5p0Yhm/cf8I85zIk7UiCdXe62JveYzwmJZYXVJOuCeWl5FM4UW6jpUXXLheWosXxVQizWTHa8Rkyck+LohUA7RqM9Hq1z8zXfdlH8r1TTgGvtFdfEQwA98IAONMpa1yLWhzCnpApeNa0O2CRGqzvMQBNa1WruDmrEbVKaI7q0cJrKzZixR60nJmNESv2AABftkJavkIOiltVrZA4IqTEuj+mDsaCxzsipmEohnWO9upL2j/9N71Weg+d49awUSKHhDCkiTXHyeZtiDCUIMJQgmGmA4jCVUwK2oqYhqHoHhfBb+mSe8Vh3mMdmWsi9N4Lx0/7pmO90H6iiNRYmi+uxURxcGMaknYkYWl8Hn+92mcm7Ud6n5SgmQW55yO9PuHyp2huuCabNz/KtknxvD9gfY+lSRP0OK1tL3W+6VtwdahV23OWii1XGK264E8hsirHoUzg6wWcKtQmiQu38Tgt1G6Xq8MxIKobP/dUlfTpF3w5j6qh7IMId1FA/12/PWdBq2ZZ2VC7hQskNBv8e4wH/noOmHkOWc3fRF/7co5hCeq1jUBrJWrmIA03qrGQC31Se76yEcC42bsRtV5obn25H869PUyV0ydQ0ELWK/23tCZNILybS3ae5CtN0rZo21PWHVbsQyjUtWjFdLsLQLF9tZkyal4uCSOnI2ruKXFBtABkv6h+DrU9Y4aOH6heoV3LUeuFZnVAi/YotaUF1PEkgdDxQNve+pO3w0kIaeql2vHQEKo5W8TExdLQKtpm6hcega7AHelTc5UIrawD52HN5tjrrdmLMWLFHnkmd6nAU8rmkUNtz5ip7eOvIdCFph+oCPtOQB1PHrw2pB3T+WPyMNGziIUphEJdy3iop5xI2hd60KnWKvLAy2wNVTmEGIxMKx3DcYlEYqVjOI5cLoKLANt+yvfWHKUCQ5LN0zDULKrBzkRt39bW9vHXENR6R1BNgxbi36oYS2U5oeTmyToesHHIcH/S+NV76gXjWH4RgoNMKHW4xE4qH86X2prxokMZOglxDRSaXgLhUCas2Yux0jEcufWHV+gH6C8hsL9CqqZ4+tVUkZQGyfu6XyhghUH99PyGjLnoe+Xv2NtsDBzdnpNdh5qyRjrUQfee10Cw2G181bbxu22ZaxJvb0PSjiRR0TJ/bKVe91WT40Nu/MLj0iB5IVhbfWGigNB8QNG/IAvNDdfQvyBLcf1qVQB4bXdc1VDoQpMBLV5tLztgbAJgMCGq44AKayL31AsWfcr1Ly2+5a+tVCkPnQm5H6VG5iKf42AcH9Y5Gsmm3ThUbyqwaTzXn/RT0L8wUUBq/wXE4UyByAWvEaxAuuOnUqBvzxmokH0rgIHDcnV/vOCx1x2MGYdpZ7oHbuvoKwhbbq4VZC5SDSFzEXF5f1Zj8HaNsJFWdxB9DYC+PfeFTeOBeY24zwpgUmJrpIR9i514iam1KILyMgpqfPNQs12i13yUiF9DkvG+eSUWtTikfJ8nWHrame7ato5KmuLbLbmsIaUfnA/SX+nxgKfp0X46PMH+VPASq41j9XW/3H01IiWxuoPo6yjqlqY5r1G5ppF6o2KdSrQYKxoj5+EcdRqcRg1MhLdbcuzhFAYT0CBalfYq66SQ0zik46HX2YvLx6BGW2O1v2k8cOxzToA9VQGbWiVpS1QTNBmAp427ORPHMGXaOBZrlSwPaU3T8gI9npo2PxnomqYvdHgCbhixnfQS8Vb6BYEWY0VjrHQM96nBUe1DyILObFMpTs4TWwmTmROYHZ5QHV/HcoBkHTiP/OxFXvyOrPFQHki7y80xmIeqYzGn993Y+Q6vfbmObgaIi/uE/zY+FjelvxCOQWjjlNqE5SBlrRJqktJxstakIuOtKITjC0S7gXwutQ11Smh2PTYS8bYsvGRL8fKOSjFl3WHZ7JGsA+dx/9ZotCp4ByOsz+ERwwfIDn7E51aL/qgm/tJFlM/N93nqAbT+7R1MOfWA7Bf3YKsUWNEYH1heQGvb3zHFMRlZrkHM9liQtpuRexrpjhG4RCKx5M5Q0TlpuxlO7ro0x0h0tX+ErraPVPVJ71taOowXKl+6esFJjPjS1QtAeaonjeFUC9p2hnOEz7n6bEsg9OgLZvnobpyGqeKlJGWtEmV6ScbJWhOtCKSnXji+QLSr9FzqOuqU0CwsdfJ/yxYr80ApZCUj9zRKHW4QcJyVinV8BKA/KgDML6WaypeUp/Ld6339qpIpvXZSYmv80zUI/ezL8WlZkiKZSNSgFIwKXY1NhsHMsBw50PtudRzLC5XMJrPRxp6FzCazZe9TI/Ro21GDUnzO1Rdk7YwqbX9KWUvScfZv2xgmgwGdmof7bdsMpF1UOL5AtKv0XOo66pRNs+u8nSgsdaJhaBB+TB2i2Kaw3jkrOPqt7OOwOdzoFBOO6yVlmjzScrZFNTXW6b00u4V1rVKAta/AbiHzu1IQeUWzmtQwrFfUw1yhQHOVNjnNdmLPPUk7krgt/11Av1aToWcEMSZ+N2Zo1IhwFh/wN5upyqAyNExrQTh6T+LtbaqcS9WBu+k3ozuCGKhVGRoBQo0IZ/EBNQQcgSiZ7DdUOtdk11rh/kmJrZFbfzhyHs6pcQITuDt/M4GErmnWMNS1+dQp1JIwG1+4m75juqbJgFptJVDhG6x2tLTt61opKXC1p+L5iwDnPdeItagjaYnVquHXAdR6oakWgdqSsNqpiHdbCn9JgWscAixgasRa6HyUOnAXCc1AES2w2tFiY/R1rb+kwDUOARYwNWIt9LREHagDNs1AozZ4pnXo0KEOuk2zClAjNBodOmoqdI5OXdOslagjXlwdtRC1rGa6rmmqRV1/G2pxslTlWtT1dfcHFVmTylrPirSrO8PqqNCsxtCQKgmN0fLFrcq1qMUhOZX23CqyJpW1nhVpV3eG1VGhWS9S/KkFSm9h1jnJMU2hMf6+8bV8cVkCVgsZssJcvY7HJsgTMPvqRyvZc4BRaSFNal5wcutaWVqdri1WCH4JzfT0dLRs2RIWiwUJCQn4/vvvZa9du3YtDAaD6D+LxeL3gFXBekT8qQVKb2HWOckxTY6kqtDMWAJWTb8q5up1/OJBIDiMIzBWOyd677HPq1VLrTQHoJoXnNy6VpZWp2uLFYJmoblhwwZMmzYNqamp+OGHH9ClSxcMGTIEv/32m+w9DRo0gNVq5f87f76Sszo6PFFO4KsVSm9h1jnJMU3ZFtX1xlfTr4q5Mo9rnZO0ZEU1aT+1IQ9eR82AZu95QkICevTogZUrVwIA3G43YmNj8fLLL2PmzJle169duxavvvoqCgsLVfdht9tht9v5fxcVFaFFixa4929tYQo1iScAACAADNz/3aEgxlItUxKDoJw9XXrKbYbB6KhQe4QABlb7xAgY3ArtGEGIievf0yZtS7ZNRv9ep4kBBgNRvpYYAAPh5290NUSk+V785v4viDtINCavfokRSU3/DADIKfgUMHDrZ3Q15C91mwqZfRtdDUEMZSAGuuau8jUiZhhIEIixFEZXQ5gRBrvpEkJczdEv6mF8feWfAIBg0gh202UYXeFwm4rQxPggOkV2wTfWTfhD1FNY9NAEAMCsr9bgG+smbl6uH7lxEiMMxILGQR1wzfE/RJrvZX7+IeopAODbFP696KEJoravOo8BABoHdcBV5zF+bgZixsBmz3iNRzhGOfi6Vktbvvqh6yoca03GrVu3EBsbi8LCQoSHhwemUaIBdrudmEwm8vnnn4uOjx07lowYMYJ5zyeffEJMJhNp0aIFad68ORkxYgQ5evSoYj+pqakE3M9I/0//T/9P/6/C/50+fVqLqFNEEDTg2rVrcLlcaNq0qeh406ZNceLECeY97dq1w8cff4zOnTujqKgIS5YsQZ8+fXDs2DE0b96cec/rr7+OadOm8f8uLCxEXFwcLly4ELi3RQ0AfQtevHixTsWf6vOqfairc6O71EaNGgWsTU1C0x/07t0bvXv35v/dp08f3H///Vi1ahUWLFjAvCckJAQhISFex8PDw+vUA6Vo0KCBPq9ahLo6L6Duzs1oDFygkKaWIiMjYTKZUFBQIDpeUFCAZs2aqWrDbDajW7duOHXqlJaudejQoaNGQJPQDA4ORvfu3ZGTk8Mfc7vdyMnJEWmTSnC5XPj5558RFRWlbaQ6dOjQUQOgeXs+bdo0jBs3Dg8++CB69uyJ9957DyUlJXjuuecAAGPHjkVMTAwWL14MAJg/fz569eqFNm3aoLCwEGlpaTh//jwmTFDveQsJCUFqaipzy16boc+rdqGuzguou3OrjHn5RdixcuVKpKWl4cqVK+jatSuWL1+OhIQEAEBiYiJatmyJtWvXAgCmTp2KzZs348qVK4iIiED37t2xcOFCdOvWTaEHHTp06KiZqBUsRzp06NBRU1A3c8916NCho5KgC00dOnTo0ABdaOrQoUOHBuhCU4cOHTo0oMYIzRpPN+cntMwL4FJGU1JSEBUVhZCQELRt2xbbt2+votGqh5Z5JSYmej0vg8GAYcOGVeGI1UHr83rvvffQrl07hIaGIjY2FlOnToXNZqui0WqDlrk5HA7Mnz8frVu3hsViQZcuXbBjx44qHK06fPfddxg+fDiio6NhMBiwZcsWn/fk5ubigQceQEhICNq0acNH+qhGwLLYK4D169eT4OBg8vHHH5Njx46R559/njRs2JAUFBQwr//kk09IgwYNiNVq5f+7cuVKFY/aN7TOy263kwcffJAMHTqU7Nmzh5w9e5bk5uaSH3/8sYpHrgyt87p+/broWR09epSYTCbyySefVO3AfUDrvP7xj3+QkJAQ8o9//IOcPXuW7Ny5k0RFRZGpU6dW8ch9Q+vcZsyYQaKjo0l2djY5ffo0+eCDD4jFYiE//PBDFY9cGdu3byezZ88mmzdvJgC8yISkOHPmDPnd735Hpk2bRo4fP05WrFhBTCYT2bFjh+o+a4TQ7NmzJ0lJSeH/7XK5SHR0NFm8eDHz+k8++YSEh4dX0ej8h9Z5ZWRkkPj4eFJWVlZVQ/QLWuclxbJly0j9+vVJcXFxZQ3RL2idV0pKChk4cKDo2LRp00jfvn0rdZz+QOvcoqKiyMqVK0XHnnzySfLss89W6jgrAjVCc8aMGaRDhw6iY6NGjSJDhgxR3U+1b8/LysqQl5eHQYMG8ceMRiMGDRqE/fv3y95XXFyMuLg4xMbG4rHHHsOxY8eqYriq4c+8tm7dit69eyMlJQVNmzZFx44dsWjRIrhcrqoatk/4+7yEyMzMxNNPP4169epV1jA1w5959enTB3l5efw298yZM9i+fTuGDh1aJWNWC3/mZrfbvUxeoaGh2LNnT6WOtbKxf/9+0ToAwJAhQ1R/d4EaYNNUopu7cuUK8x5KN/fFF18gKysLbrcbffr0waVLl6piyKrgz7zOnDmDTZs2weVyYfv27ZgzZw7+9re/YeHChVUxZFXwZ15CfP/99zh69KimNNqqgD/zeuaZZzB//nz069cPZrMZrVu3RmJiImbNmlUVQ1YNf+Y2ZMgQLF26FP/73//gdrvx1VdfYfPmzbBarVUx5ErDlStXmOtw69YtlJaqIy+vdqHpD3r37o2xY8eia9euGDBgADZv3ozGjRtj1apV1T20CsHtdqNJkyb46KOP0L17d4waNQqzZ8/Ghx9+WN1DCxgyMzPRqVMn9OzZs7qHUmHk5uZi0aJF+OCDD/DDDz9g8+bNyM7OlqU8rE14//33ce+99+K+++5DcHAwJk+ejOeeey6gFGu1FZXOp+kLdZVuzp95RUVFwWw2w2QqL+lx//3348qVKygrK0NwcHCljlkNKvK8SkpKsH79esyfP78yh+gX/JnXnDlzMGbMGF5r7tSpE0pKSvDCCy9g9uzZNUbA+DO3xo0bY8uWLbDZbLh+/Tqio6Mxc+ZMxMfHV8WQKw3NmjVjrkODBg0QGhqqqo1qf6p1lW7On3n17dsXp06dgttdXivo119/RVRUVI0QmEDFnte//vUv2O12JCcnV/YwNcOfed25c8dLMNIXHqlBlA4VeWYWiwUxMTFwOp347LPP8Nhjj1X2cCsVvXv3Fq0DAHz11VeqZQ2AmhNyFBISQtauXUuOHz9OXnjhBdKwYUM+jGjMmDFk5syZ/PXz5s0jO3fuJKdPnyZ5eXnk6aefJhaLhRw7dqy6psCE1nlduHCB1K9fn0yePJmcPHmSfPnll6RJkyZk4cKF1TUFJrTOi6Jfv35k1KhRVT1c1dA6r9TUVFK/fn2ybt06cubMGbJr1y7SunVrMnLkyOqagiy0zu3AgQPks88+I6dPnybfffcdGThwIGnVqhW5efNmNc2Ajdu3b5PDhw+Tw4cPEwBk6dKl5PDhw+T8+fOEEEJmzpxJxowZw19PQ46mT59OfvnlF5Kenl47Q44IIWTFihWkRYsWJDg4mPTs2ZMcOHCAPzdgwAAybtw4/t+vvvoqf23Tpk3J0KFDa1z8GIWWeRFCyL59+0hCQgIJCQkh8fHx5K233iJOp7OKR+0bWud14sQJAoDs2rWrikeqDVrm5XA4yNy5c0nr1q2JxWIhsbGx5KWXXqpxgoVCy9xyc3PJ/fffT0JCQsg999xDxowZQy5fvlwNo1bGN998wyykRucybtw4MmDAAK97unbtSoKDg0l8fLzmeGGdGk6HDh06NKDabZo6dOjQUZugC00dOnTo0ABdaOrQoUOHBuhCU4cOHTo0QBeaOnTo0KEButDUoUOHDg3QhaYOHTp0aIAuNHXo0KFDA3ShqUOHDh0aoAtNHTp06NAAXWjq0KFDhwb8fyqEO1AX3B3FAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.xlim((0.5, 1))\n",
+    "plt.ylim((0.5, 1))\n",
+    "plt.scatter(tmp['auc'], tmp['auc_min_max_best'], s=1)\n",
+    "#plt.scatter(tmp['auc'], tmp['auc_min_maxa_best'], s=1)\n",
+    "plt.scatter(tmp['auc'], tmp['auc_min_maxa_best_weighted'], s=1)\n",
+    "plt.scatter(tmp['auc'], tmp['auc_integral_best'], s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp2 = tmp[['auc', 'auc_min_max_best', 'auc_min_maxa_best', 'auc_min_maxa_best_weighted', 'auc_integral_best']].dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.25937737096156743, -0.1315558311072753, -0.19677445407509464)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(tmp2['auc'], tmp2['auc_min_max_best']), r2_score(tmp2['auc'], tmp2['auc_min_maxa_best']), r2_score(tmp2['auc'], tmp2['auc_min_maxa_best_weighted'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -155,7 +663,7 @@
        "\"def auc_est6(row):\\n    return row['exp_fpr'] / (row['exp_fpr'] + row['exp_tpr'])\""
       ]
      },
-     "execution_count": 31,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -167,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,22 +693,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fa5a274f830>"
+       "<matplotlib.collections.PathCollection at 0x7fe2f54b50d0>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -215,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,7 +732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,7 +791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -292,19 +800,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.7377783842277135,\n",
-       " 0.7837138138020591,\n",
-       " 0.8740085352559344,\n",
-       " -0.6813436513880635)"
+       "(0.8003842662327819,\n",
+       " 0.839017739487883,\n",
+       " 0.958715476443063,\n",
+       " -0.21635288511704487)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -315,22 +823,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fa5b93ab620>"
+       "<matplotlib.legend.Legend at 0x7fe2f546e060>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 350x350 with 1 Axes>"
       ]
@@ -349,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -358,7 +866,7 @@
        "np.float64(0.9763938791759392)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,7 +877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -387,7 +895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -396,7 +904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -426,54 +934,54 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1403</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.571429</td>\n",
+       "      <th>18364</th>\n",
+       "      <td>0.957977</td>\n",
+       "      <td>0.960859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15255</th>\n",
-       "      <td>0.875000</td>\n",
-       "      <td>0.881356</td>\n",
+       "      <th>6235</th>\n",
+       "      <td>0.918968</td>\n",
+       "      <td>0.934199</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19914</th>\n",
-       "      <td>0.915385</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <th>9606</th>\n",
+       "      <td>0.977767</td>\n",
+       "      <td>0.943750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6327</th>\n",
-       "      <td>0.825000</td>\n",
-       "      <td>0.689655</td>\n",
+       "      <th>11594</th>\n",
+       "      <td>0.897569</td>\n",
+       "      <td>0.631944</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20832</th>\n",
-       "      <td>0.738462</td>\n",
-       "      <td>0.722689</td>\n",
+       "      <th>8302</th>\n",
+       "      <td>0.779804</td>\n",
+       "      <td>0.708000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6482</th>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.655738</td>\n",
+       "      <th>10399</th>\n",
+       "      <td>0.631250</td>\n",
+       "      <td>0.705082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10021</th>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.840000</td>\n",
+       "      <th>18476</th>\n",
+       "      <td>0.724973</td>\n",
+       "      <td>0.668750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13905</th>\n",
-       "      <td>0.921348</td>\n",
-       "      <td>0.333333</td>\n",
+       "      <th>8043</th>\n",
+       "      <td>0.878641</td>\n",
+       "      <td>0.954955</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1731</th>\n",
-       "      <td>0.437500</td>\n",
-       "      <td>0.804348</td>\n",
+       "      <th>7577</th>\n",
+       "      <td>0.750167</td>\n",
+       "      <td>0.690236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21855</th>\n",
-       "      <td>0.941176</td>\n",
-       "      <td>0.461538</td>\n",
+       "      <th>7129</th>\n",
+       "      <td>0.619307</td>\n",
+       "      <td>0.713138</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -481,19 +989,19 @@
       ],
       "text/plain": [
        "           sens      spec\n",
-       "1403   1.000000  0.571429\n",
-       "15255  0.875000  0.881356\n",
-       "19914  0.915385  1.000000\n",
-       "6327   0.825000  0.689655\n",
-       "20832  0.738462  0.722689\n",
-       "6482   0.687500  0.655738\n",
-       "10021  0.333333  0.840000\n",
-       "13905  0.921348  0.333333\n",
-       "1731   0.437500  0.804348\n",
-       "21855  0.941176  0.461538"
+       "18364  0.957977  0.960859\n",
+       "6235   0.918968  0.934199\n",
+       "9606   0.977767  0.943750\n",
+       "11594  0.897569  0.631944\n",
+       "8302   0.779804  0.708000\n",
+       "10399  0.631250  0.705082\n",
+       "18476  0.724973  0.668750\n",
+       "8043   0.878641  0.954955\n",
+       "7577   0.750167  0.690236\n",
+       "7129   0.619307  0.713138"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -504,16 +1012,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "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[37], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tmp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maucs\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m tmp\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: auc_est5_stats(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msens\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspec\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m]), 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[37], line 1\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(row)\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tmp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maucs\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m tmp\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: auc_est5_stats(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msens\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspec\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[34], line 9\u001b[0m, in \u001b[0;36mauc_est5_stats\u001b[0;34m(sens, spec, p, n)\u001b[0m\n\u001b[1;32m      6\u001b[0m     p_samp \u001b[38;5;241m=\u001b[39m binom\u001b[38;5;241m.\u001b[39mrvs(p, sens_samp)\n\u001b[1;32m      7\u001b[0m     n_samp \u001b[38;5;241m=\u001b[39m binom\u001b[38;5;241m.\u001b[39mrvs(n, spec_samp)\n\u001b[0;32m----> 9\u001b[0m     aucs\u001b[38;5;241m.\u001b[39mappend(auc_est5(p_samp\u001b[38;5;241m/\u001b[39mp, n_samp\u001b[38;5;241m/\u001b[39mn, p, n))\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m aucs\n",
+      "Cell \u001b[0;32mIn[19], line 8\u001b[0m, in \u001b[0;36mauc_est5\u001b[0;34m(sens, spec, p, n)\u001b[0m\n\u001b[1;32m      5\u001b[0m exp_tpr \u001b[38;5;241m=\u001b[39m fit_curve({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtprs\u001b[39m\u001b[38;5;124m'\u001b[39m: np\u001b[38;5;241m.\u001b[39marray([sens]), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfracs\u001b[39m\u001b[38;5;124m'\u001b[39m: np\u001b[38;5;241m.\u001b[39marray([frac])}, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtprs\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfracs\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m      6\u001b[0m exp_fpr \u001b[38;5;241m=\u001b[39m fit_curve({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfprs\u001b[39m\u001b[38;5;124m'\u001b[39m: np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m spec]), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfracs\u001b[39m\u001b[38;5;124m'\u001b[39m: np\u001b[38;5;241m.\u001b[39marray([frac])}, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfprs\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfracs\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m----> 8\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1000\u001b[39m)\n\u001b[1;32m      9\u001b[0m fprs \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m x)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mexp_fpr)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39mexp_fpr)\n\u001b[1;32m     10\u001b[0m tprs \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m x)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mexp_tpr)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39mexp_tpr)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/numpy/_core/function_base.py:146\u001b[0m, in \u001b[0;36mlinspace\u001b[0;34m(start, stop, num, endpoint, retstep, dtype, axis, device)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;66;03m# Use `dtype=type(dt)` to enforce a floating point evaluation:\u001b[39;00m\n\u001b[1;32m    145\u001b[0m delta \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msubtract(stop, start, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m(dt))\n\u001b[0;32m--> 146\u001b[0m y \u001b[38;5;241m=\u001b[39m _nx\u001b[38;5;241m.\u001b[39marange(\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;241m0\u001b[39m, num, dtype\u001b[38;5;241m=\u001b[39mdt, device\u001b[38;5;241m=\u001b[39mdevice\n\u001b[1;32m    148\u001b[0m )\u001b[38;5;241m.\u001b[39mreshape((\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,) \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m,) \u001b[38;5;241m*\u001b[39m ndim(delta))\n\u001b[1;32m    150\u001b[0m \u001b[38;5;66;03m# In-place multiplication y *= delta/div is faster, but prevents\u001b[39;00m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;66;03m# the multiplicant from overriding what class is produced, and thus\u001b[39;00m\n\u001b[1;32m    152\u001b[0m \u001b[38;5;66;03m# prevents, e.g. use of Quantities, see gh-7142. Hence, we multiply\u001b[39;00m\n\u001b[1;32m    153\u001b[0m \u001b[38;5;66;03m# in place only for standard scalar types.\u001b[39;00m\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m div \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "tmp['aucs'] = tmp.apply(lambda row: auc_est5_stats(row['sens'], row['spec'], row['p'], row['n']), axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -550,7 +1078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,7 +1087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -609,7 +1137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1184,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -633,7 +1161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1107,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -694,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1108,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -720,7 +1248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1109,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -759,7 +1287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1110,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -786,7 +1314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1111,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -810,7 +1338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -837,7 +1365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -867,14 +1395,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -883,7 +1411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -919,7 +1447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1087,7 +1615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1117,7 +1645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1137,7 +1665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1160,7 +1688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1172,7 +1700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,7 +1716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1201,7 +1729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1438,7 +1966,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1447,7 +1975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1474,7 +2002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1492,7 +2020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1524,7 +2052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1544,7 +2072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1564,7 +2092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1584,7 +2112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1604,7 +2132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1630,7 +2158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1666,7 +2194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1681,7 +2209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1710,7 +2238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1730,7 +2258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1751,7 +2279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1763,7 +2291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1775,7 +2303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1795,7 +2323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1816,7 +2344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1849,7 +2377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1869,7 +2397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1889,7 +2417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1935,7 +2463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1956,7 +2484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2012,7 +2540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2035,7 +2563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2062,7 +2590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2071,7 +2599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2081,7 +2609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2091,7 +2619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2153,7 +2681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2367,7 +2895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2376,7 +2904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2385,7 +2913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2405,7 +2933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
diff --git a/notebooks/auc_experiments/config.py b/notebooks/auc_experiments/config.py
index a3e1c33..b216215 100644
--- a/notebooks/auc_experiments/config.py
+++ b/notebooks/auc_experiments/config.py
@@ -10,7 +10,7 @@
 
 datasets = binclas.get_filtered_data_loaders(
     n_col_bounds=(0, 50), 
-    n_bounds=(2000, 10000), 
+    n_bounds=(100, 10000), 
     n_minority_bounds=(20, 1000), 
     n_from_phenotypes=1, 
     imbalance_ratio_bounds=(0.2, 20.0)
@@ -51,7 +51,7 @@ def generate_random_classifier(random_state, p=None, n=None):
         params = {'probability': True, 'C': random_state.rand()/2 + 0.001, 'tol': 1e-4}
     if mode == 3:
         classifier = KNeighborsClassifier
-        params = {'n_neighbors': random_state.randint(2, int(n_class/2))}
+        params = {'n_neighbors': random_state.randint(2, int(np.sqrt(n_class)))}
     if mode == 4:
         classifier = XGBClassifier
         params = {'random_state': 5, 'max_depth': random_state.randint(2, max(3, int(np.log(n_class))))}
diff --git a/notebooks/auc_experiments/xx-03-add-estimates.ipynb b/notebooks/auc_experiments/xx-03-add-estimates.ipynb
new file mode 100644
index 0000000..830fa5e
--- /dev/null
+++ b/notebooks/auc_experiments/xx-03-add-estimates.ipynb
@@ -0,0 +1,3341 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'exponential_fit' from 'mlscorecheck.auc' (/home/gykovacs/workspaces/mlscorecheck/mlscorecheck/auc/__init__.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmlscorecheck\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m      6\u001b[0m     integrate_roc_curve, \n\u001b[1;32m      7\u001b[0m     auc_min, \n\u001b[1;32m      8\u001b[0m     auc_max, \n\u001b[1;32m      9\u001b[0m     exponential_fit, \n\u001b[1;32m     10\u001b[0m     roc_min, \n\u001b[1;32m     11\u001b[0m     roc_max, \n\u001b[1;32m     12\u001b[0m     auc_rmin, \n\u001b[1;32m     13\u001b[0m     auc_maxa, \n\u001b[1;32m     14\u001b[0m     roc_maxa, \n\u001b[1;32m     15\u001b[0m     auc_rmin, \n\u001b[1;32m     16\u001b[0m     roc_rmin, \n\u001b[1;32m     17\u001b[0m     roc_maxa2, \n\u001b[1;32m     18\u001b[0m     auc_maxa2, \n\u001b[1;32m     19\u001b[0m     p_norm_fit,\n\u001b[1;32m     20\u001b[0m     p_norm_fit_joint,\n\u001b[1;32m     21\u001b[0m     auc_estimator,\n\u001b[1;32m     22\u001b[0m     max_acc_estimator)\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m r2_score\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m beta, binom\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'exponential_fit' from 'mlscorecheck.auc' (/home/gykovacs/workspaces/mlscorecheck/mlscorecheck/auc/__init__.py)"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from mlscorecheck.auc import (\n",
+    "    integrate_roc_curve, \n",
+    "    auc_min, \n",
+    "    auc_max, \n",
+    "    p_norm_fit_auc, \n",
+    "    roc_min, \n",
+    "    roc_max, \n",
+    "    auc_rmin, \n",
+    "    auc_maxa, \n",
+    "    roc_maxa, \n",
+    "    auc_rmin, \n",
+    "    roc_rmin, \n",
+    "    roc_maxa2, \n",
+    "    auc_maxa2, \n",
+    "    p_norm_fit,\n",
+    "    p_norm_fit_joint,\n",
+    "    auc_estimator,\n",
+    "    max_acc_estimator)\n",
+    "\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "from scipy.stats import beta, binom\n",
+    "from scipy.optimize import minimize_scalar, root_scalar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_label = 'single'\n",
+    "\n",
+    "input_file = f'fit-{input_label}-50k.csv'\n",
+    "output_file = f'processed-{input_label}-50k.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "exp_tpr    1.780842\n",
+       "exp_fpr    0.605886\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 1018,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(input_file)\n",
+    "data[['exp_tpr', 'exp_fpr']].median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def protect(function, args):\n",
+    "    try:\n",
+    "        return function(*args)\n",
+    "    except:\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_min'] = data.apply(lambda row: auc_min(1 - row['spec'], row['sens']), axis=1)\n",
+    "data['auc_min_best'] = data.apply(lambda row: auc_min(1 - row['best_spec'], row['best_sens']), axis=1)\n",
+    "data['auc_max'] = data.apply(lambda row: auc_max(1 - row['spec'], row['sens']), axis=1)\n",
+    "data['auc_max_best'] = data.apply(lambda row: auc_max(1 - row['best_spec'], row['best_sens']), axis=1)\n",
+    "data['auc_maxa_best'] = data.apply(lambda row: protect(auc_maxa, (row['best_acc'], row['p'], row['n'])), axis=1)\n",
+    "data['auc_maxa2_best'] = data.apply(lambda row: protect(auc_maxa2, (row['best_acc'], row['p'], row['n'])), axis=1)\n",
+    "data['auc_rmin_best'] = data.apply(lambda row: protect(auc_rmin, (1 - row['best_spec'], row['best_sens'])), axis=1)\n",
+    "data['auc_rmin'] = data.apply(lambda row: protect(auc_rmin, (1 - row['spec'], row['sens'])), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score_curve(fprs, tprs, p, n):\n",
+    "    fracs = 1.0 - (p*tprs + n*fprs)/(p + n)\n",
+    "    p_fpr = min(1.0, exponential_fit(fracs, fprs))\n",
+    "    #p_fpr = exponential_fit(fracs, fprs)\n",
+    "    r2_fpr = score_curve_p_norm(fracs, fprs, p_fpr)\n",
+    "\n",
+    "    p_tpr = max(1.0, exponential_fit(fracs, tprs))\n",
+    "    #p_tpr = exponential_fit(fracs, tprs)\n",
+    "    r2_tpr = score_curve_p_norm(fracs, tprs, p_tpr)\n",
+    "\n",
+    "    return (r2_fpr + r2_tpr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['err_min'] = data.apply(lambda row: score_curve(*roc_min(1.0 - row['spec'], row['sens']), row['p'], row['n']), axis=1)\n",
+    "data['err_max'] = data.apply(lambda row: score_curve(*roc_max(1.0 - row['spec'], row['sens']), row['p'], row['n']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['err_min_best'] = data.apply(lambda row: score_curve(*roc_min(1.0 - row['best_spec'], row['best_sens']), row['p'], row['n']), axis=1)\n",
+    "data['err_max_best'] = data.apply(lambda row: score_curve(*roc_max(1.0 - row['best_spec'], row['best_sens']), row['p'], row['n']), axis=1)\n",
+    "data['err_maxa_best'] = data.apply(lambda row: protect(score_curve, (*protect(roc_maxa, (row['best_acc'], row['p'], row['n'])), row['p'], row['n'])), axis=1)\n",
+    "data['err_maxa2_best'] = data.apply(lambda row: protect(score_curve, (*protect(roc_maxa2, (row['best_acc'], row['p'], row['n'])), row['p'], row['n'])), axis=1)\n",
+    "data['err_rmin_best'] = data.apply(lambda row: protect(score_curve, (*protect(roc_rmin, (1.0 - row['best_spec'], row['best_sens'])), row['p'], row['n'])), axis=1)\n",
+    "data['err_rmin'] = data.apply(lambda row: protect(score_curve, (*protect(roc_rmin, (1.0 - row['spec'], row['sens'])), row['p'], row['n'])), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.optimize import minimize_scalar, root_scalar\n",
+    "from mlscorecheck.auc import p_norm_fit_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, x0, y0 = auc_estimator(0.024, 0.776, 30000, 300000, mode='separate', return_details=True, best=False)\n",
+    "_, x1, y1 = auc_estimator(0.024, 0.776, 30000, 300000, mode='joint', return_details=True, best=False)\n",
+    "_, x2, y2 = auc_estimator(0.024, 0.776, 30000, 300000, mode='roc', return_details=True, best=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f50204f59d0>"
+      ]
+     },
+     "execution_count": 1034,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x0, y0)\n",
+    "plt.plot(x1, y1)\n",
+    "plt.plot(x2, y2)\n",
+    "plt.scatter([0.024], [0.776])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.9741123400395921, 0.9892126094609782, 0.9719590818924887)"
+      ]
+     },
+     "execution_count": 1035,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(auc_estimator(0.024, 0.776, 30000, 300000, mode='separate', best=True),\n",
+    "auc_estimator(0.024, 0.776, 30000, 300000, mode='joint', best=True),\n",
+    "auc_estimator(0.024, 0.776, 30000, 300000, mode='roc', best=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.00020032072730817121)"
+      ]
+     },
+     "execution_count": 1036,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = 7.2\n",
+    "x = np.array([0.0, 1.0 - 0.976, 1.0])\n",
+    "y = np.array([0.0, 0.776, 1.0])\n",
+    "np.mean(np.abs((1 - x)**p + y**p - 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_integral'] = data.apply(lambda row: auc_estimator(1.0 - row['spec'], row['sens'], row['p'], row['n'], mode='separate', best=False), axis=1)\n",
+    "data['auc_integral_best'] = data.apply(lambda row: auc_estimator(1.0 - row['best_spec'], row['best_sens'], row['p'], row['n'], mode='separate', best=True), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_integral3'] = data.apply(lambda row: auc_estimator(1.0 - row['spec'], row['sens'], row['p'], row['n'], mode='joint', best=False), axis=1)\n",
+    "data['auc_integral3_best'] = data.apply(lambda row: auc_estimator(1.0 - row['best_spec'], row['best_sens'], row['p'], row['n'], mode='joint', best=True), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_integral4'] = data.apply(lambda row: auc_estimator(1.0 - row['spec'], row['sens'], row['p'], row['n'], mode='roc', best=False), axis=1)\n",
+    "data['auc_integral4_best'] = data.apply(lambda row: auc_estimator(1.0 - row['best_spec'], row['best_sens'], row['p'], row['n'], mode='roc', best=True), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.713047352900976), np.float64(0.7681159420289855))"
+      ]
+     },
+     "execution_count": 1042,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row = data.iloc[0]\n",
+    "max_acc_estimator(row['auc'], row['p'], row['n']), row['best_acc']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['max_acc_integral'] = data.apply(lambda row: max_acc_estimator(row['auc'], row['p'], row['n']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlscorecheck.auc import macc_min, acc_max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gykovacs/workspaces/mlscorecheck/mlscorecheck/auc/_acc_single.py:182: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  return 1 - (np.sqrt(2 * p * n - 2 * auc * p * n)) / (p + n)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data['max_acc_min'] = data.apply(lambda row: macc_min(row['auc'], row['p'], row['n']), axis=1)\n",
+    "data['max_acc_max'] = data.apply(lambda row: acc_max(row['auc'], row['p'], row['n']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp = data[['best_acc', 'max_acc_min', 'max_acc_max', 'max_acc_integral']].dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8727635757234633"
+      ]
+     },
+     "execution_count": 1047,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(tmp['best_acc'], (tmp['max_acc_min'] + tmp['max_acc_max'])/2.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8926588402930946"
+      ]
+     },
+     "execution_count": 1048,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(data['best_acc'], data['max_acc_integral'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.936464671621682"
+      ]
+     },
+     "execution_count": 1049,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(tmp['best_acc'], ((tmp['max_acc_min'] + tmp['max_acc_max'])/2.0 + tmp['max_acc_integral'])/2.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f50125f2e10>"
+      ]
+     },
+     "execution_count": 1050,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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8ztKlS5Gfn69/Skoyl1dKEO3lcH2LaSvilgmD8PHCqfDlJ3PD05GnuScjKIqpjcMcKw6pG7eKRNbrncQz3eIdXs5TAkeF1/ota53/UlcPJNSq7IGc5DUtdcq0HMj42mane8+fe+45XHTRRbjkkkuQlZWFu+++G7Nnz4YkOT960aJFiEQi+qeqqqqzh0kQHYbEzFsR2pqm1mM8q2+la81xrfAGB5QyaIA4tdEqSCLcBM7r+qTX+4vubbjOGAMZlmXMOnUaa/J6KfnlueYZa6an5Rqe4jTPOecc+Hw+1NbWmvbX1tZiwIABwmv69euHdevWoaWlBSdOnEBxcTEWLlyICy64wPE5wWAQwWDnFxMliM5AC9yWXXRGW9MMYAqy+lai9cQUoaWZM/gFSKHDAJfAJBnL+hboRStMOMVLpsKtdqbxZ7fUyXRSK9N5LudKlpK6VjuvTgm/er53PiI+H57Pz0XpuVOB3e8BRZcCjcfPSMiRJ9HMysrCuHHjUFFRgZ/97GcAAFmWUVFRgbvvvtv12lAohIEDByIWi+Hdd99FaWmm0usJIrNITBFMq6VpDGTvm5uFw/XNiNVPQKx+gvO9QofVDB8ZnANxJsFUZd1jnKQN0XVOhTis1xiD21OJr+i7xZk1s7EJKL4MH0S+VuIzAXBIerpn1BdQOk12crfJVHieni9YsACvvvoq3njjDezZswdz5sxBY2MjZs+eDQC47bbbsGjRIv38Tz/9FGvXrsV3332H//7v/8aMGTMgyzLuu+++jnsLgjgDGNvhGnGyNI2B7HsaN5rqXFrRpuNGGIPjWmCn4bYu6iTaqdZSHZ5x0O/Hsi8+xIB4Ai2ShBX5YTDIuoAGA7lteYMOx7Nozpo1C08//TQeeughjBkzBp9//jk2bNigO4e+//57k5OnpaUFDz74IIYPH44bbrgBAwcOxObNm1FQUNBhL0EQZwJrNg+gCGnukKfQ65KFNtHTPOZzpgxJrmUWvo9elyxEzuAXTOdmqXUwXfGyztjWe6QQujZfb93NGHarAexlkQYUxeIoizQgDj/m1jegiGVh7mVz3Z+VIRjnHfEn37k0NDQgPz8fkUgE4XD4TA+HIABAzxO/dGA+3v+XyQCASU/9FZEB8/RZ565/2im89qKnFivCGajX48xPf/0UsrU1THhfJhTidKHbtNrtYamm7tbjgPh8wXkjoq14q1rxl6zJ64UV+WEMqB+Jf7vnj+m+rY3O0A7KPSeINqLliRvzxasjzeCxAmX2GivA9S9sxuCF5bj+BXNccqx+Ahr3LUSQy2qOtQyO5Bpmh828nW7UEQ9IJ/A9nTRMlUHxOOBXwopW5IdRHfBjW34N1uxdg6vfuRpr9q5p74g7BBJNgkjB3NU7MGRROeauTp36K3Ogcd9CnP76KTTuW2gT1je3HMSYRzbpa5ZRplQeijIJvS5J+gI6bP7Xlht5CRlyylm3nmN/iPl5jGFDbg4QV9IrtSn6Jdk/w4qdK1DdWI0VO1ek/w6dCIkmQaTg/S+O6Fk7TmhOIQCmZmY+VRu07cuV+1DfHNPXNA21N5TSbg602S5MN+XRSDpCa5x6O6Rzmo4br1Pz403TczW/XKP01GlsOnIM797yP1E2qkxpojaqLPW4MgCJJkF4QOQtB4CnN+7F4XqlH0+wf7nSzKx/ORKqlmjbOVOGoCA7AJ7IFWUY6ti0rK2q6fYQ0fd0cRLbVPdXr7Pmve/U8suvXQ6AKQHtxf2wZu8alDacwqaqIyhtONW2sXYwJJoE4QGjt9zaHndggdJ+lkmtpq3Gz998Bkt33YIhQ3bCp61dqsdSzojPpL/WqubW3HKr1WnEYd1Tdnrhg38DkFzTXLFzxRkpyuEGiSZBeEALGQJgSoG8Z/pQzJkyxBR3GeLJmpUMwNct6yAF6vF18zrANDGHabZqoqPFMp31SjfhExUhTqfwh/F6zsV9z5mkZPuAo6zhVHJKXjIeYD5l2wUg0SQID3y8cCpumTAIANB6Ygrk1gK0npiCrftP4sF1u5DVtxJgDBLnKDsRN0mjr2Gqfr5uY+oCZBcRzg3HMxHIbnpwGhitTqO16XL9zgNV2HmgCjsOHrIfnPk0MOIGAAylp1uw6dtvlCn5txUATyjbLgD1CCKINmJMgXy/XnESJZoHgQUiiDaMxtLjN5vObzh6BXD0CgBAcMC61HU1tP4+QNsDNTtabJ1SJ1NlBHGupEm6sePfgV9WAlWfYo1chxX52Sj77FmUGs31LgBZmgTRgfjzvgRjHP68L9O7wNjqobXAvD+d1MR07u9W37It97NamJZybqJnSpxjXEtUqVaU10t87yNqSNfk+VjRu0BZ0ywIA1MXKzUzpy5u39g7CLI0CaIDCPgYYgkOvRc540oaZaIXWOgQ5JaBaDrwL/r5IgkcwE7iqGbHdJSF6CXY3Am3KkZWERWFFzEGGclqRSvywyg9ddr+nOKxyvbyO1AWzsOKnSuUNc2hpWekF5ATZGkSRAfgt5Q0YgxggXqw0CEwBj01MoldZI4GJIjWNs2XeYihFH1vb7C7k9NHGIsJ0/Q9ypieUy54CNBniP5tW+021DbVYlvttjPSB8gNsjSJsxprX/F0mLt6B8q/PIJrLy0GAISKV8Mf/hLxhksx6alsjBvUG9sO1mFSn9dR0+crDDg5HB+f/CflYsMypJJSzsw7rbhNw0XWW6rrU9Geab7VyaN9FzmBjMfUZwY5x6ZDR8z31Ldc8ZyrZd827P8AMjg27P8Ay06WJ0OOuoDFSaJJnNU8vXEv6ptjeHrjXkfRfHPLQTy9cS8A4J7pQ/XMH23b6xJ1nTL8JQ4fadaD2Bsu2QMwCUf770FA3qJ6zo135gBnekEOIaKCF6L97aG9lT+E4UECgRQ8b2ZjEz7IzUGQc8yti9jvYRzLiBv0H2dEZWzIAma0cqXQ8OZnz0jBYREkmsRZTX1zzLQ1olmhjdG4fvyRP++2ncdlPyDFwDlD7pCn0Hpiiuo1T7p1s/pthORvtogjM61xXjLwWVTn1aDo1ABYJ+smnKoPace8ko5QWj3hqTzj1lAjwdgZgB3BIDhj6B1PiNcxNa5dbrIil42dj2WaUF5+R5ewMDVoTZPoURgLB2v1MI3EEtw2kWZqr3HGZD2Y/frRxUl9VI1JxfttFBGzoFTn1UBmDIdFXSLdHC1GOmJNMtU5buXkrBalKLdcFc7h0VZTbUwTkqXDpFUUL78j4w3T0oVEk+hRGAsHa0WB75k+1FRYw02WtOD0j745qu/jHIg3Xqx+c7bowi3hpKiILDY3L3V7aIvQOk3JrdaoU745Yzjp86H01GmURRqwIj9sDjWS7ZZ/d4FEk+hRGKuna210b5kwCHkhxfLJCwUQcGkj2bhvIWL1E1DfHDft9/X6Sq20bhQQZtLEk4E4wNR9grVL21NTZNekTTrC61otBPbpuFHkRWFNnOvWpZ5Hnm8oAiwFnEUX6HIecyO0pkn0KG6ZMEjoELpn+lC8XLkP4wb1xodf1SDm1krSAmMAJLvlZMuClKLqAQlgCdtNhE0UOsrSdJuWiyxcp/ukGpd6zohoq76GqVmaZZEG4OGkM2jnw/nOYzYW6ehiU3SyNIkeg7aWKWqIplmd2w7WoTkme7qvYiyJ/yqZMgCZel9muH+6Fl5bEQmi1Vq04mQ9ah/D9WFZtr3DzMYmvW0FoNbGPHQEpXkXpT/uyfOVLKAu4jE3QpYm0WM4XN+MpzfuxamWGBIceok3cxxn20SKwQdAtonN8GgUXwWz4OccMSYQVtEaZkekTxqvF8WCivLH3RCIe5hzNEiSLfxoQ26OUhvTiD9HyStPly7mMTdCliZxVjOwIAQAKMj26/UutYLA4wb11osHP71xL+au3oHD9S2u9xO13bXGeBs5FAgAjCEuSQCXkhrlVkqtPdallwpFxq3xWqd7GCxNJsuYVxfBiGgrwDlChvXJGaLCHJfM7NLrlF4gS5M46zBmAbXmfIzcvh9CPnUV0PpD/PDifvjzF0fAAXz0zTE0RhWHTmM0jvIvlWB2YwZQoul80xSbqSFHsfoJSccNB3wsDs5gs9xajUYc95mn5lY6Yv3S6jwSxVEaEcVjOmX2GO7BAWwLBU3TcFd2vQPsqwCa69Jbp3znDiVDaMQNepZQV4EsTeKsw2g9ZvX930ovnvy/4nB9M7YdrNMn4PXNMd3hE5O5njbpDyczgLL6bYRRHfvHZIypG6B/BwAwgOvrlUZvOEerUaSkWNuMyI7woBvvJVpHdfOKqz/vPFCVtCjVabgnYtH01yl3vwfwhFqUuGtBokmcdRizgOb94P9H2N8fseNTAAB9c7McrztwvBEAILcUg3Nla7X9jvsZ+uTsSnssRruSMUCyNk9Lx2vdHgs01RqpKD7UYelg1OAStBim6MJpuBuBYPoB6yNuAJjPlFrZVSDRJLoVWgvcMY9sEjY4s1I6tBSoegAtdUqrBGOPciOBgi3YF1qEQMEWSIGTisAFToLLQZN4yIyhspdoVYsJNS9ppDoseqYinWvczrF6y624CbLIg67epygeNzl71uT1stfKHHkjEOqtXeytHuaNK4ElJ7vc1Bwg0SS6GVoL3PrmmO79dmPN3jVAyRM2540VY78fzpR1Ts7iYIF6m3hoOeiGHQAE+qOu/ekHRaE8BiHTEzi9es/dQpVS3cvoRU8FV3v7KJ4s0yFhAPu3FcBVagHha5/pst5wr5AjiOhWVEeSueJagzM3ntiyFDLiyCp8X29NoREoUCoTJZoHgfkbFKMskQtJigKIgXE/AEG6H+NK9o+mM7roOAui/t1FBIVH3KoeOQidn3PE053SWwXTODW3hCQVxRMYG41iRzBoyyU3BbAbX6gLhw61FRJNolthTNRJpz6mzOMAA5jAa61ZlywQAVPXGqXQYcQbRsMf/gKQ4lBKu9nFSdGSFMLkJEiOqMLrtMaYDpyjXyKBar/fOFDX89087n7OIasjqw74cdzvw/YDVbbblJ46ba9iNOSq9MfdjaDpOdFtMa5pzl29A0MWlWPu6h2mc9wmnVo3yXjDpUmd4AH4cr9RZtJSzEEwGeSWgWJLMhXpnOMyhU/n2mq/39nBZM33FgmmZlnG4lh0sh5fHKjS/xxNdre1UpGVqk+V7VkSn6lBokl0W54o36P/XP7lESQ49FhLJwIFW/QA9Vj9BDTuW4iWIzcj3jAanDPETw0Hk9Q1TUdnNocUrDYJk9QZnm8viFIjuaC/uDUd0miJGgRV4skq61efWwym3sf2nmpfH6EjSAstMuaRnwWQaBLdluZYQs8hH5CvZP5oWyeMDh8j/txvlNjM3G9M5inTAtZh2WfJH5eZ5Lzu6GmKbsHoPHKzOC0xldrPcZHzCUA4YSgYogqnZLheVq/RHDzaPfI4TwpkbhCo/tJ0nu4IKh6bXMvswnnkbYFEk+jWaEHsWvpjqjRIbUreemKKaT83bHOgWJq61qSo6KMqq/Mxw/VpzbRF02m3cbgh8Iz7OUeuzG2CLBuerVmUWhHhGY1NKIrF8S91EbNABsOm88oiDUoVdmOeeRcuKNwWyBFEdBvSicu0YkyB1FpVNO5baDuv9dh03ZMe6LUdLTIDtzpjnOIWhQ8WH/Ose+kW1HC41uZJ5xwJKCL3WN/eyWdISfvJWGnd6ODRgwWgWJZluRcChz6ynYeKx9ITSEOZuO4EiSZxxkm3Y6TW/MyKqP9PEi0UiOvTcl/Ofj23vOXIzQCAWP0ExOonIHfIU2jw+exrfdo23YpA1mutoTxupBJrA2FZTlYaEtwnLtjHgeQ02jKuAGDuGGm8VN3qApnvU6bhR3YoVYziTeYTz1Joek6ccYwtKERc/8JmDF5YnkIcnUjamtq03JhbbiRQsAXMH4GperDpVimm6aLz2usIMk6vBXN7W2m2VNlBKtWBZEhSiHMsPlGHolgcC0/UpRiPQTJyz1Gm4Q9HgAerlWl5fom3zJ9uCFmaxBlnzpQhuqVp5c0tBx1TH9MhaWdCn5YbLU0twL31xBRk9a0Ek9IL7zFpIWPOsU1GAbZaoG4iLDpP4PG2WZoWAR0RbcXuYJb9mGHsrXCIs7TycAQwVluvNv+jczYGsosg0SQyhtM0XNSCQjv3ZGOr5+cYhdCItqZpJNhvI5i/GXn9/4LBsSj+zn1pTS/tmifICLLut1qgqcKUtDVJ0bXaj5bzrRwyeL6tgeyS6vwZFrX/Ga/J66Vn+JjE1DgN74LFNDIBiSaRMYzTcKNIzl29A+VfHsG1lxbjivP7qGIZ9dx2QsMYVmR0BGm1MFmgHowB/vAX4Fzpzx2TYvh7UBLrnjVQPNWU28s5KdzpOaplKVq3lGAxcK3xlur6ZTiRwClJUhxbXGlRkStzNEkMEUnCSZ/P9lyjh1wXTWtPny5YTCMT0JomkTGMnSCNGAPTNWFtq2ACQKJ5EDhnSDQPwkXRhJJaGJchtxYg0ZwUa8XHkkY4j0DY2rRUmU68puWcBp/P0dEjA5hXFxGvZ6oCOa8ugnl1EQQ5B+McMxub8PH3h7Hp0BFMam6BxDnGRqO2ezv2K9fILzF/P8uyftwg0SQyxtb9J1ETacbW/Sf1fXNX79DbT1x7aTHGDeqt9yBvK8ZA9WENfRGWZRz3SWCBevjzdib1J91oHsHU2K6jHeQytli1ftmhCZtKaYust5wwrXmqU/vSU6extE8BWlRLc31uDu7v1xcAsCMYhMwYdgSD+v20wHVA8aLb1jmdUifPsqwfN0g0iYzx/heKRfn+F0dM+zS2HazDBzurdRF1wpgKKYJLzfp2a+/jaPD5lK65aiaPrj2CbB/xDdOxNNOwIi3TcSZ6tsValFOpeqwJg+KGwCJDmqR2bdw4bWfJiutjo1GbpSks8abxcAS4Zpk4u+csy/pxg9Y0iTPCpKf+inGDepv2Ha5vdjjbjHHN0lruDTA7ksvqG/B8n3zXWEYA6aUotsX77XIvnzHwXJ0+c0kyjWVGYxPWp2grsSE3R+js0Rw8mgc9LMs4LUl6xXWTpTnyRmBfhb3EmxQAuAz4gsqaZn6Jkt1jpYd4zgGyNIkORoupvP6Fza7nHa5vNlmZ6RIo2AJIUfB4ts0T7sS8OnHIkkkn05mnp7uQmea94oYsHDCGoDYoQ0C7rRWuhTV5vZBlrFzEGCQ1zElz8LxVXYudB6qQK8uQAexQQ5BM65a73gGa65I9yk+dVgTymmVKBXXNYx6xl4XrabRJNF988UUMHjwYoVAI48ePx9atW13P/93vfoehQ4ciOzsbJSUlmD9/Plpa3HOEie6JFlPZnthKN7L6VkLyN4PLQaGVaYQx4Om+BXi8b29zmI4gAki8I8XNvWIt3mH5WQ8tMuw3VQ2yIHGOFflhtEgSwrKMfFlGOJHA9MZG5CcSaJQY1uT30c/XSsZptTZNAmlFsyhV61FYxaiH4lk03377bSxYsABLlizB9u3bMXr0aEyfPh1Hjx4Vnv+//tf/wsKFC7FkyRLs2bMHK1euxNtvv43f/OY37R480fXQ+oxr246m9cQUyLLi1MkZ/ILwHKOetTBzDnmb3DVpVdlI83yXaXvcqOaq1fhc73zx+VD+8pY1xVEUi4MBiPh8yJU5njp2AjkyR4PPhxX5ubrghWUZ4Ep/H/VB5gwfEapXfEVBvrLW2bvA9TzyngtYvnw57rzzTsyePRvDhw/HK6+8gpycHKxatUp4/t/+9jdMmjQJv/jFLzB48GBcffXVuPnmm1Nap0R3hVm2Cm0ptiEiq385GJPBmFJlXSNUvBq9LlmI3ItTpfB5FEDH23gURtE6ZxoFP9xEPs4YSptj2HToCM6NxQHOUe33YVG/vsmpd10dnuudr6dN7jxQhU2HqtWGZ1xZr9SfaYjX1Kbhqle8rDGGogRH2cBp4sGQ91xMa2srtm3bhmnTkn9wkiRh2rRp+OSTT4TXXHnlldi2bZsukt999x3Wr1+PmTNnOj4nGo2ioaHB9CG6B1os5rhBvfVal29uOYjF68zOg4EF2bh+dLHn+zMploysiRXo+/3hLxQh9aXKT++g0CA3RILq5nBSp+1F8bhtyj6pucV+rkqIc6C5HgCwJ5ilr4Wuz80xTb1t/4z5c4CgYJrNDTU2NQtU9YqXTlyITf/fLpROf05sVfYg77kn0Tx+/DgSiQQKCwtN+wsLC1FTUyO85he/+AUeffRRTJ48GYFAAEOGDMGUKVNcp+dLly5Ffn6+/ikpKXE8l+g80nXqGHnk/d26k0fL/nm5cp/Nvvt44VRccX4f4T2cMIYYMQYwfwS5Q55CTt9PwXk38GlavO96DKYqdnpfHw3GsCMUhBDOce/JemiW84zGJkfrd25dBEWxOOZqDrFEi7u45ZcAM59WflZrYa4J5+Hqd65WunuKrMqzrGamG53+f1plZSWefPJJvPTSS9i+fTvWrl2L8vJyPPbYY47XLFq0CJFIRP9UVZHH7kzQFqdOzND5TMv+mTNlCAKS2cKb9NRfHUu9OZHVt9JSKEMp9yb1ew+ixmliOmh63lYMpeKEHSMtwe1lDY3mY4bjpadO6xbhsmMndEu1KG4uCGdz+HDZWdx8AaH4rdi5AtWN1Vixc0WPsipFeBLNc845Bz6fD7W1tab9tbW1GDBggPCaxYsX49Zbb0VZWRlGjRqFG264AU8++SSWLl0KWRb/jx4MBhEOh00fovvx8cKpejEO2WIFHa5vRmNU+cudKlhdo/XElHRqXKSgE6bn6TqKvOawAyiNRHQHjlbGzSSMS5Kl3OrUEKM6Uy65wzOeHZnM7vHnJMu6zVgmPL1sVBmKcotQNqqsR1mVIjyJZlZWFsaNG4eKigp9nyzLqKiowMSJE4XXNDU1QZLMj/Gpv1Tu1StJZBSxS8c7b245KLTvNKvUqW+P7XxDiJEpeoebDDhX2vR/XKqbOjl+rOFFlv/f8xMJ276QJpAq9/fri9NqYL4EIKo+r8bnU6zMZ0cCUEKCWlRLtMU4npE/B6SAOWRICijTa1ld/01EUwph6dBSbLpxE0qHlrr/WfQAPE/PFyxYgFdffRVvvPEG9uzZgzlz5qCxsRGzZ88GANx2221YtGiRfv51112Hl19+GW+99Rb279+PDz/8EIsXL8Z1112niyfRNeGWrVc0R9DLlftM/cqtOPXt0VA844uQe9Ej+j7TTJWlZbRpp3qnLf+4GweoXm/qDMmUCkPWgZvKwKkpj8PU3PJh0VY9fEqpWCTrXu4V+WE9VClkfM6NK4FgL3N6pCaW2p9G0aX28fegECKveE6jnDVrFo4dO4aHHnoINTU1GDNmDDZs2KA7h77//nuTZfnggw+CMYYHH3wQhw8fRr9+/XDdddfhiSee6Li3ILokmiNozpQheHCdIPVORWs1AUAvGGxE84zD19y26kImnOpeuiCaVhtiKV0rtxu+29YwBdfGLUI7Qw5ihy9hyvARYUx/NAWrv3MHwIGyhlNYEc4TVy1qPG7fZ3T29NBpuBOMd4M5ckNDA/Lz8xGJRGh9M4MMXliu/3zgqWs9X8MAPPazkbhlwiDTfo2CbD/qm21dbGz0umShSV8EadbOYmo92AbNTLn+6DIwP+fmdEnbeAT57IbtzgNVpoLAT/QpgCxJkGQZXxyqBYb9VEmBdIL5zKFEgBKjGewFlIwHqj5VHDpWYfxspSKYomPdiM7Qjm4Qp0F0Vd7cclCfgmtIAm2xBrYzKCKcG1QcEdr0O1S82uYUsjqHhA5nT6Nu41Tbdpv07jO0NeZtem+ojTlCLbixLRRErd+HbaGgXrlIZgxIxJTpt1O5NsBeXf3a5cDCA8r65aArna/r4c4eN0g0iTYjaohmXLvk6jnWhmmjBuabqhwZG51ZnULBwnLHAucd4gdPJWgCB47ycIdBWc75KpiVHKdHL/tb1bWAFMCG3BzI6vqmVjtzhLFFhWy11tUnSgF7dfXy/6lM2YEelcXTkZBoEm2mb26WaStCi9MEkqFFB2L/icP1zdh2UAmXiTdcCs4Z4g2X6k4hnshFr0sWAUxxWohnx1z/r6h4eVqkilUSHRe46kMO4hrkPGnbtqnIh4wZjU2QOMeMxiZ8laVYldoWAJB/rvUi/VoASptd47Hd7yk/iuItyQGUEhJNIi2s03DAHvwuyi839gLSrEg5XKEHvgcKtsCXfRDRmp+i5cjNiNVPQOO+hZBCR6C3onAkKUJtchBZvNmOx9K4vndCFt5jSKvHtsN6jqh67xE3YNnxOnxxoArLjtcpXnNLERI0KCX2TGFFzJecmv+yUpmWB3IAMPeGaGR9poREk9ARrVFqHK5vxpI/OXvA567eIfSQayFHgDm0SGt7Eey3EVJWPYID1iFQsEW3Rnks39i5wRUGi/HXFt+mkwc8jQDQAIA+gphLMJZsn5vG83VrVfPOa9NrzWLkySB3cK4c/2ylUiAYlqrr4WLzmuXldwAPVAMP1yen7CKB7OHZPulAotkDcRLHJ8r34HB9M54o3yO8LsHFFicA/FktKJwz+AX0umShXrbt6Y179em7ZkW21k3QG6lpMsMYECxchyxVRLWOkcboHivGOFJzemW6QZsCr7X23XiOxdvt50qVdUkVLx/nijh6NXe50uhMe36r9Xo5Zpsm56vn5nOuHN/8rFIgOLu3UiYuIaOsKS62Fp8dqVRfVwPihQJJDqCUkGj2QEQOHABojiVMWxGi6wAgFFD+V5JCh21l27Tpu9Uznhfyo/XY9KRGMYAx1anBGTh3F6H2h2xys2Ba61kazzNs42qTMlmUgePhuWFZxrJjJ3TnzrC4GgCvCjMAm/CZEg6YlBS+IVehNBHEplELUDpxIZDdG4ieVkRXW6fUyr1pWy8CSWudOiSaPRCt46O1R086iFrwAsAD1w4HAPBYgals23l9kv1tNCsyq99GAEB9cxyBgs/MN9KzfLhpTdPNEWTeleac3u3G1tjJdM+1np/imR9/fxhr8nrhK9VKPSTJSnC7sZhHyXjTpfPUikXz6iKAP5QUvqpPsUauw9U7n8WacB6Q1QtoqVNEt+IxVSjVe1rb76YDrXXqkGj2QD765hgSXNl6RSvC4YZRQ4wVkpjUatoCScuUc4AnsnVvear7qnsM/7WclE4wutN3p31u93NZ+/RbRVy1MgFlHVJPjUTSC6+nQlZ9arqXqWKRcYi55yhrmj6GFZ8uM0+9tSFlFygdJUWN0VJBa506JJo9kPrmmGnrhEh2nNY0tSLDrnnkLGHaWgPXJX963SiTuAibm+g5pDmarnMSXeN+46Kri0jvOHjIdo/TapbQ2NY4GOcIyTLmyWFoJYf10sMWSxMjbzTcx7C/+stktfZjNeap99TFiuBNTVXV3gVa69Qh0SQc6XX+iyanDqCsaVqrsANJ+coqfB8sUI+swvdNx60CmTvkKQT7bUzLMLQ9xO1AqpRH7RzjQ41TejdBNZ7rkZlqrKWGVhRxR5YfnDH0TsgorT1oF2GLpYmv1yd/9hkKFI+4wblRWkcIHq1p6pBo9hDcwom0YzZCVTanDuCeiKj172FM1p0+gYItCA5YZ9IfKasekJrT1h/OUyRAGm4kGUUwHdysRadsoFT3s7Ds2Al8caDKUiiYmdvoxprsa7LW6bDWShcArjJYjjeuTK5VtmXNMhW0pqnjucoR0T15euNe1DfHhNXSNW+6kWGLP8Do8SOw6/huyC0D036OUWO0dEjH2av7rNZ8qijsSL3Yz7mpgtAXB6owanBJeqmO7a1X42bZaiFFUALPn++dj0bGwACMNaZBGthpncpffgdQviD5XQoooUah3nbLsS1rlekyeX6ygEcPh0Szh9DQEjNtjTRG7fuaY+m2jzBjjKuUWwvA66eiNSEjWPg+oFqh+rke7ivUNlWwrCXX0u7NrV4vcQ7b2xqDzFOtjzo4kvycY1xLFIDi8IkYSrttyM3BsmMn9ID0J/oqkQylpxphsqnfuUOxHCNVyS2geMatPF6kWKL+HODB6tTv74XL76D1TBWanvcQtEIaomLATuXZdp/YLZyeW/v9ABB2lmzctxBZTZOQG/1/bIIJQKiajv4Xzu2nW50yKnpB3nTgSnFfU2y85bjrtF31hIes02rGEJckZSxQ6l3mJxLwyzKYmkcO5kNZpEERbcaUc6155Lvf09Mk9a2Iz1Ymp+7GKbzxOK1Jdggkmj0ETedEpduc5GVE3xEAYJue5wbtE5Tnbx4rvMc904fiNzfVOz7DGkPuaNQxwUBFAegAxkaj9ps53Zgx7MsKmNZLbWe6rWsyhgZJ0ltNWDOLtKK/padOY/P3h9EvkQAHsCOYBfAESk+dxgMn6pLrmpolqTHiBuWj5ZJrZeCs5eCMa43+HNigNckOg0Szh+BmaeZn2+sxXj+6GG/9v28hXP0cmg78i+mYFrCerWYBaVsrvS58Cjvq1ysdDJ0sSOPPDOByQJwJlKoakYEdwaB7GJBlv2tGTyoHEVfKtIUtvX0A5d2snuxqvx9g5na9jl7v/BLFwXPjSmDJSWWbpzYwzLM0MtTiKK9dLp6aU5xlh0GiSeCe6UMxsCDbtO/5m8di0upJaCiaZ+rNAwA71YD1B64djoEF2Xo2kBEGgAXq8eHh1UoHQzcLUoMDjCXgmOkDpDXtLos0IJBWpY80pvBpiPRb1bXKazAGpnnGOcdwgbMnLCdTKFMSOZT6HI1UYUUUZ9lhkGgSuGXCIHy8cKptf0OrMrWU/M3wsaS++X3MdJ0wQ4gB4Aw/GXiz0sFQpE8J6zSSg0myybhrU8k3AAnrxemGE6UT5ykgql4T5By1qjW5J5gF5JeYSrblykoJuVy3TnMaAfUfsnfuAB7po2yt+eNExiHRJFIjKxWOtL/m8YTy05q9a3D1O1djzd41wsvCwTw8M2OOftyoUeHq59By9GqLbjFIssWhksa6ZMiyf0V+WCmm0d5wIiucm//CGCzGLPVZWQZPvAwAkSpTyTZTXKaIUG/1BwZc/bjy4653AZ5QtlpBYVNhYSKTkGj2EArUdcsCwfqlhqOzSIJp+q5J0YqdK1DdWK2sWQpoijVhzd41+nHTsmTJE8jqv9Fk1HEOvXKQ0NpzsBathX7LIg1gXizGVBap+pyieFx32iw+UYedB6rw8fdKZMHk5hZInGNyc4u+vqkJqlEoHdcvAaVqkRZKxKTkVFqzOAPZwJHPlZ+1LZFxSDR7CNq65T3Thzqe4+YsEk3fx/YfC4lJGNtfbPXEeRwrdq5Q1jQNMAY0xI8iJ+AzLV8y1UNuMxBdPN+iQr+lp04j6KHakNCaFaRT1vr9KD11Go0Sw2N9e2PSecmogs05Sh+fzdkhnFLHdUq9h0komctfOW5Y5zR2kLz6ccWJc/XjsBSHM0NhRRmBRLOH4Lr+qFKQ7TdtU/Hx4Y8hcxkfH/5Y32f9H8oqmOAcvVvyUJRbhHuvmA8Ign0Ys+wWFdRI4ehJ2RjYqeSbXnKJ287P4hz39+uLBknSQ430ywxbvWoRY/p0W1/X7GVdxzU+2+GvY7pOHAoryggkmj0IxxxzFS3IXRTsfvU7V9uKbkQTUdMWAB44ftImONbp++rqWtx27qt47r1zXEabvufbuqa5Jq+XLUvI+TF2cTTFWxqOt0gSNuTm6MeMHvC5ofNRFItjbl3EPD1Xp9umVhRGiscYevf8Q+rxamue+tqnAQoryggkmj2IpzfuNeWYizJ7nKhurNbb6mq0JFr07f0f3Y9elyzC7/oUmM4RTc9vLirEsq+vw4mcVRC61dOpd6kicY6pTea8+ed757epqIa+Xy3VtvhEHRafrEdRLI6ZjU0oisUxo7EJflUsS2LJf1xK//6JPgWfVxdBUTyhFApWcXQANR5XqxVx4NsK5+B1javUMm/GYh0aFFaUESj3vAcRjSt/2bMDPux5bIana4tyi7C/erzj8Q0HNoAxjtOSwYnDgVG9bkDp0FI8tuUxZR9jqAudAmNKv3NdM63TZbfvhvNlxvBBrnnKG03XytTubdxyjsUn6kyOGqvTZr1aDMS0lionnVHP985HxOfD873z9WtLT53Wf16T10v3pJeWjFe84gCQiCbvIzvUOnXLAf9sZbKoBglnp0GWZg+iRe390+LSA8iJ2qZa+HL2Ox4f1meYomMGweIAtnw+1B6SxBmg9jk3i2EaWT8CQbSe3WIQQGHoksszGIBtoSBGDy7B/f36Cs9JTr+5ckXALNourhoAhqn6oBFqvUzDmVoKpCgVMhW0ppkRSDR7EKn+MoeKV6PXJYsQKl4NAAjLgFbIUuYy/OEvTI3RjJxsOWk3EAFMGLMXv/3st7aRhAP9kJ3lM5xs9f6kepk0z3WKkhc5l9QRfJCbk7RgBWuHp1VH0GlJHXOsyRTAbgw/Uh9mur4s0oCieFxZtjBWZvcF3YtupILWNDMCiWYPJFCwRRiU7g9/AcY4/OEvAACtkG1iptXItFI2qgw8ng0Gs2ruPP2eyVEEJEOOEtk7zGE/HlIlwZjesdFvEVBT/3AnXLzvPnW/j3NhCbYZahX2GY1JYTM6enYEg5AZU3LgAeDaZ0zXl546jU3HmpVMKa0yO/OJ1ym9QGuaGYFEsweS1bdSGJTO5YBp26KHwCTFx6n/T+nQUrWtrNmCG+sL6z8bDgAA/JLfLJROAe0O4pZQz8+xHL+3eJr5RKepuXpvvfe4eky7b8IwHqMlqVVhX3bshH7c6OixOX0uv8OewaOJsWYdzvwtiV03gUTzLGLu6h0Ysqgcc1fvcD2v9cQUFOUW2bzavLW/YoC19ld2GIMPAYArNTJj9ROE9032LE+K1Ob6r8EhsPo4EJfjqa1Kl2k4V0UvOQ1WeP7wfzpn+ag/a9ao1nvceFy0jOEYMiTAFMyuecF/Wal0gkzVkuLhSPJDdElINLsZbr1+/vzFESS4snUjVj8Bm27cpEwPDUihI2rRYYfrU82auRqMYVhHbJH8YLJgKsygiKmTo0Y/j9nXH9VybFqq5MfZIfMwDHIXUsOHrN54rf7lKUkCwPRYz5ChSpHSx0chVc64o6gas3w+Wwk0nVBeXlvLJOdNt4NEs5uh9fN5uXKf7Zhfjbv0p4i/dFrT5LJf0STZORLN6CgyMmn1JMDXbFsrbJUBLjlMvdNx5tjEVrnPP5xu1NceGx3uLXGOe0/W4zNr3x3OoUVBBjkHAtm4V43HvPdkPTYdqsbOA1XYdChZl9I1Zxwuolp0qbL9bCWw/l6leRq4UpEdIOdNN4TiNLsZc6YMwcuV+zBnypA23yOr30ZUNzbjue3PmaxNJsUUTZLUGEGBM0VxFH1pu2dDa4N6qmUNUvIe3uSKOqbf9inQs37ilum3luYoc24XOTUOE4AeKwlfEKWn6h0FMR2McZgmGo8r283Pqvnk6lhH3KBstXVMzdKkdc0uD1ma3Yyt+0+iJtKMrftP2o7F1EobsRS1GplPyaDR6mU6nieYOnMtvtJCyBdSThMJrcP9rV5vEQHA1C9cu3fU0lbCMEBDbJXyg7HRmp9zfQq96dARlJ5uBi68Cmv69NMdPd5h4rRGsKQFqVmUoQIA3NzPnKbo3QoSzW5G+ZfKumX5l+7rlh2CVpDCJIIcvmz7empcjqtViuzebp+DODpOc9T1xKJYHJwrTceMx8A5rmlsco7BlMz79UZrnMMPmNceeQLYux7P9QqgOuDHc73znUblTCDbEpqkPr94TNJy1MKBtPOMRYRpit6tINHsZowozjdt02XN3jWOgelOXHP+TNs+xgAWqLftj3OD59wQeynJ3BS6Y0Tc+VthbLQVtX6fvfAGY3aPN5JWMRPEX5ZFGvRxxQH72mOsyRwo4JT37cTVj1sqFKnPF9W8FHnPKb6yW0Gi2c34/mSTaZsuK3aucAxM1yqwW1mWfXH6mTcaxupAjKEwGFbEV3AfmTGMiLbaPNUAsF7NygEs03MADZJkm0b71XP9gnhP41pjnDGhQ2duXUSvUoRrlnl758vvAELaP2Jmq9wGWZXdHhLNbkZ9c8y0TZex/ceCc4ZE8yDbMa0Cu5Gb/nITsP6etg9UFc+ycb/GuMJx9uNq2NCgeBwSgGM+n3m6rV4vAehlbULGlB7hydCgBLQ/jRiAmeGhkLSgde0SoyWaivL/6fVtgSFXKVk9I3/ufh6tX3Z7SDS7GOkGqHvl48MfK57v3G9sx/qE+pi+MwbsPrEb4LJYZLwYnwc+xhNbHretdRbJwD80tWCDalEK618yBpkxNPh85t1c6SdeozYwqwn49RCiAIBlB762ZeyYCgMLMMdZerSuAWBfhbI+uq/CXvPSWFGdLM1uD4lmF+N9NUD9/RQB6l5pjDUCALgUtR3bfWK3+CImOWTsqCJm8F7PPH+mcCb//JEKyOC2KXa1j2FFuBeGGafnRm845+I2vJzjQUvpNnAOrp7HOVcFyTxuUcC6+pJAIAdjo1FInGNs1P7nYyK/RLEoi8cm+4wD5mooLfXKz9rWaF3S+mW3h0Qzw7hl9HQmCbXnDGNp9NvWCA9UvNS2AHM19tIwnV72Q8E6IGPg/hCKAnmm4hYAEE4k0CRJ2JcVABhDo6F1BBhDUTyB7QeqzGKqogmmUadMMZuX34E1+b0tIUTaWqf1HwHlLrYiG0KYInjBMHBkBxA9nRS/C9Xp+YVXWUYGsi7PMkg0M4xWPf3pjXsz+tygL6hm+9g9wwGBt1iCBESqMK5FYHlxu6UphHPMu+I+bLroDiXV0WC15kJCRGJ67ctmo2iq0+81eb30HuJGNCHUnEgjonY//IpwyBRa5JoGGWtK3VoXSK5XamFDxjCjb9Xp+beC6TlZl2cVbRLNF198EYMHD0YoFML48eOxdetWx3OnTJkCxpjtc+2117Z50N2Ztjpy2ktLokXxr0j252b7s03fOQcemPAAAEOMIwyGpaRYqyNiSk73iJgMfLbSHAyvpSoe+BjY/KxtlbBs0kMoyi3Sc75jxgdAsSZN/csNVq0mhG9VH8XOA1V4qynLJqBl9WYRTJUGmeo4ACUg/Z07oFurxoB2Y8zSwgNKwY2FB5LHqVPkWYNn0Xz77bexYMECLFmyBNu3b8fo0aMxffp0HD16VHj+2rVrUV1drX927doFn8+H//E//ke7B090DPMum4ei3CLTPi29Uo9xBGyZNm/x/opo8f7AXx8zCIcyvY4xhhWHK4DJ85V+OQaLtPStX2ETSvScbxGa9WdCtUJNA2o4gpOq9/2kzwd8tjKFCKaqPAJg5I32SkORKjVnXH1un8HJY1PV3j1TF4sFkrzmZw2eRXP58uW48847MXv2bAwfPhyvvPIKcnJysGrVKuH5ffr0wYABA/TPhx9+iJycHFfRjEajaGhoMH26Cxf9Zj0GLyzHRb9Zf6aHkjalQ0ux6cZNsNWCy+6N0lOnsfhEnSpeqrNFu/DIjuTWYkrq092BVwGX3yHIAU8Au9/TxU2zFE3jUo/ZxqvfSx1n0aVmR85fH0vxxml4x7WCGloQeqi38rOWMw4k3x8wT8FFAknrmmcNnkSztbUV27Ztw7RpySKvkiRh2rRp+OSTT9K6x8qVK3HTTTchNzfX8ZylS5ciPz9f/5SUONQe7IKkm/99xnAdlsWB0ays2ZWeOo1Np3zQREpop114lemrbulNf05wMsOacB4mDT4Pk88biDV5vXRLUUTYUNpNGAJV/YXZkRMzrsOmYVXahudLiuP8Xcmp9vxdwI0r3dvoAmKBNIoqTdW7NZ5E8/jx40gkEigsLDTtLywsRE1NTcrrt27dil27dqGsrMz1vEWLFiESieifqqoq1/OJ1ISz1FxrFw1hco6yfCgLmnrN32UvSmzEWIDCik0cOFacNxwNSCDi8yWrDQkEcU1eL+Qa/gEyx1lqyway2ZHD1XVbn8d0SEBJoRxxg2JpvuPguIk2mLdeoal6tyaj3vOVK1di1KhRuOKKK1zPCwaDCIfDpk9PwU2XgGSvclHP8uyAZNoamXfZPHDOTMacLqQavibluE+QovnsSPeBO007P1sJVNiny2VHjyAsy8hPJJRWtg4OGC3oXFKdS/Y4SwXTGmZCFc1EDMg/133cVnwBpaUuTyRb61oZcYPZGrWSShRpqt6t8SSa55xzDnw+H2pra037a2trMWDAANdrGxsb8dZbb+GOOyjswg3LBNmG2/T/J8MHwMeUrZXSoaUIs8HgHAiz87Hz9p34+OaP3QdjDEWKVLnnoV9+h3h6vflZpZ+3leY65Mocc+siKD11Wtgud01eLzRJDOFEAj41tbLOmB2kTY+t/XeMRA45HxMRawYktf6StrVy40pgyUllq2GccpeMV0S1xKFPPIUgdWs8iWZWVhbGjRuHiooKfZ8sy6ioqMDEiRNdr/3jH/+IaDSKW265pW0jPUtwsxSBZFUzp+LrAwtCpq2RD3ZWI8GVrYhT/AAYU7ZCrHGXskNYVBr523ojsuFXKa1pLazID6Pa79PDhzbk5thEd0V+GBGfD7ky13PLW4znBNXAda3Qr4ap/04aa8umaTxP3jfoobam0bqs+lSxVN2WLIhui+fp+YIFC/Dqq6/ijTfewJ49ezBnzhw0NjZi9uzZAIDbbrsNixYtsl23cuVK/OxnP0PfvnaLoiex5PoRGFiQjSXXjxAe1wxIJz9SYzRh2hpJ6YTS5/48PUeEJj7M8r9JGi129VzuE58Brfapd1mrD0UsSw8fmqFlHnGux24a1ymNPXx0rP12NLxOe2dYspmM4UMaqZw3xik3Tb/PajyL5qxZs/D000/joYcewpgxY/D5559jw4YNunPo+++/R3W12dLZu3cvNm/eTFPzNEhlibbvWoMlmY4jQvvLz3zu5+m3TwqaLnj1DUKLtfSfd2HTiai+lrns2AlFEC0B7psOHUHpDx819fDR0frtfFthvnnKkCMAftXZFeptnyaL1iRTrVMap9w0/T6raZMj6O6778bBgwcRjUbx6aefYvz45L/0lZWVeP31103nDx06FJxz/OQnP2nXYM8G3BqjAUBu0G/aWvnhxf3gY8rWSiorlmkhQ4yJLSFrJfRNDypC4TRNd0EXvP7idb2bfn+xkrttQJt6t1gt2fIF2BYKotbvw7aQOtUP9YZuOovTyRUCOUmBNBJXnV2aB1xbFy0em6yq7qW6OoUR9Rgo9zzDzJkyBAMLsh0bo90zfSgGFmTjnulDhcc/+uYYElzZWrllwiB8vHAqbplgr5kJQG9ty4H0LKGYt0LHwmn7txV2Rw1j2B3MsrSIgHgKrqKVkNuQqwpgtEFpJwEAvQc7jMenrKfGBe+hLT1o3SK1ddHG422rrk5hRD0GEs0OJpUjJ5WwpTremWj9wUPWor/p8NlKsYOIQW/7oLei0HLELRbg1KZmSJxjalOz7TYzGpsgcZ6sljTihmQ7CWtbCU2MeQK4arHFMYRkOTcgKZZGS1ILaJ+/K+Vr69A6Zo+BRLODefSnIzGwIBuP/jRFXGMbuWr8PoQvWoarxtun9/d/dD9G/9to3P/R/W26d4sa1mObHlsRieP6e/VYSlPtTIMVyA2tKN6qrgXiZnE0ZfVo4U5SAGA+jGuJojCeSFZdGnRl6tAgKGXibEK2+VnFedSRIkfrmD0GEs0OprMtxZ2n3wP312Hn6fdsxz7Y/wFkLuOD/R+kvlF71t9EosoTSkUitdq6HnJ06u9wDvux7teKDvPkOqocB8LFlsrqUIRPP8dpzZUnzzUSqVLCgYwiR9NrIk1INLsZZaPKUJRbhLJR9lRU3dHjkE/kZ4pF5pdlRSAEmTptxpKHbRI5pxxtSx3ParV2ZrXfYDlKfiBSZa93mXuO5WYuuVST5ysOIbBkxXWrhUnTayJNSDS7Gdtqt6G2qRbbarfZjsmQTVsrWptdvR+PKFOnrVjuZRI5pxxt1+BxljzHuiYJANVfmr+HCsxbI5ffoRqdHDi6l6bRRLsg0exmbDiwATKXseHABtsxSf11Sun+Wr16x13vZV6fNOWCh4sBWLzjzKd0cDQwU3X2zGxsAvzZQHZvZU204Yh9ej7iBtV6hLLValv2GazUwmQ+ZauhrZ/G7U4mADQ9J9KGRLObMazPMNPWiKul6eTdNmLwbgNQRUe0fim4j5MzJr8EaFBqYpq84zwBfPUn06nLjp1IdpGMNylxnEd2ADxhtlzzS5S8by090xc0e9JFueFa4Q6nAh6p8sUJQoVEs5txsuWkaZs26VhQmndbm77fuBIIZLtfo1/r0PFx8ny9GpCteVmqoHnDcZPlqq07GnPEU3nStcIdTgU8KF+cSBMSzW7G2P5jITEJY/u7VPYR4ebg0LJZRKQ7hU84CODmZxXxlQL2NrnFY5NTabdKRVa09Uhj5o5WdMOxhmaK+lHkCCLShESzm7HpwCbIXMamA5s67qbaep6RP0zxfBuh93vyfEWU5ZjZ0rx2eTKwPFwMjL3VfDMt5131vOshTHm9gGWD7eFS8Rbz1ooxTZIg2gGJZhdjzd41uPqdq7Fm7xrhcd0DzsXFeB1xm55rVpYRvf9NMubSHaa3pQhbM4rW3wsAZktz/b2KUO96Vw9/MgljVHUgXXgVkF9idgQ11ynvYxRCrj5T21pzwY1pkk5/PuQIItKARLOL8dz251DdWI3ntot667QDW1yjgYN/0501IjTBcmXkz5W2FIyZ2lNg87PKWiHzmS1NdZ++Bhk7bRbGRDTZ/dEapxnIUYT+l5VKuuMvK80ZRNpzjSKYavpN03MiTUg0uxinY6dN2w7Dmp9tRGvvIGLkz8WtdK3cuNK+Zgkk0xVn/tZ8XN2HLNWZk4iZhdEXNHV/NDmCcvra4yyvWaacf41aG9MqgqnSHCkNkkiTFOYDkWlkdXqpbTsMVy+4SyjSjStR+nABSk+dxqjB7l1BN2eHIDOGzdmGqvJauuJnK82W5l1qMYyDf1OsSZ5A6anTyV5B1y5PCtiud8wPSsca1OpaEkQHQ5Zmhkm1ZtlpCFpOJI8JPM5a6uNnK6GJaqqmb61qqFKrMTddE7i/PmZPhQSAfRW6lWta0zS2urUiEkNakyQyBIlmhlmxcwWqG6uxYueKzD7YUrvSRECQzniV2urBIELDo60A58oWsHmkg2rQu7JlyYwdwG7MaoIYV6fy/hw83zsf1QE/nu+dn3x2pAommba23tCg4HQiQ5BoZpg2x1l2JmrNSc3eDABJa85Q7OKkzwcwdQuYHTEAJjW3QOIck5pblMybWFNSdK9abHb0aIKorV1Of9weSTl5vpJKacwnd8pqouB0IkOQaGaYDfvV3PH99tzxjMGYInYPR5TvqhPEpwqSzyhMl98BPFANPFwvnl4bMK1ZChwxpuu141ct1h0w8+oiKIrFMa/OMK6sXmYr2Snjh7zfRIYgR1CGSVWJqDMJAIgBCDhYa1mco0XdijD26SkVHC+LNGBFflgRRYEjxuToAZQulVp5uoN/sx8HFBHc/CxwukbJOnLK+CHHD5EhyNLsKYy8ETFVDGMOrpzJ6vR6crM4q8bWp8eCKSwoFZufVYLUW9RA9d32osr4bKVybPJ8gKlimUYbc4LoTEg0O5ix/zYWo94YhbH/1oXWLAEl/1v3aouVRxgyZGCY6ggapjmC3EinT3h2b8VLbyjqYR6QwSOeynVPEBmCRLODaXOaYwYIqWXfRN0eAYeQIQM2R5AFU8iQIATIFlJ0/wFg4QHl5xtX2rtAGtcp/WrIlN8ldIogMgCJ5llEqnYXQa6kOQYdRNMcMmQnlSPI5B0XOGZshYStNJ0wb41ZOlPVrpJTF4uvJYgMQaLZg4iqFmTUwZKcq3qv52reawup1ixNoipISzQdF1VR0srQicrRafnxB//m/IIEkQFINLsYqaxFt5YW15x/DSQm4Zrzr2nTs1OJoml63YbrTcf1KkoGjO0rrKiplkKHEUFkEBLNLgZXnTTcwVnjFrK04+gOyFzGjqMCQUIylMgppCiVKKacXqfCmM0jqms5dKaS1TN0pv3YiBuUYyKHEUFkEBLNDKO30WVtC5H1q78yv+BXV91YbdpasQWPW0gliqnWNHVRdEp1NBYhUbOITLhl9Qy6UqmvOehK8b0JIkOQaGaYHHXqmSOagqYBU4tbMEEpN0kt/itZiwCreFqTbMP1tkLAXnHLH6eCHEQXgUQzw8y7bB6Kcosw77J5wuMzz58JiUmYeb5giopkz/K4wJnzQN0pFMXieKDulPjh1kK9FjwFp6ciVZymiL3rFUtz73r7MUqTJLoIlEbZwYSzwmhobUA4SzzFLR1aitKhoiREhWU/XIZlP1zmePya82diw4ENmDF4hv3ezTGUNhwRO1KAZHfHVF0gOwKjZah50JmkWKFO03c37zmlSRJdBBLNDubjmz/u1PsvO3ocy/ZXATmCXjdambV41H4s02g540bL0B9SBNEvzjhS0n3UsnJWjCmVJJ7EGYSm590Nt9AbbZ3TqXVFJhG1j9AKITsVRB75c2VNc+TP7cdoTZPoIpBodjeKLjVvjaTyXqcixZpnu7nwKkUUL7xKfPzGlcCSk8rWCq1pEl0Emp53N9xa0bbXe93eNc/8EsUatLYD1vhWbW3xbYX3e9OaJtFFIEuzu9Gd2zpQpSLiLIBEs7vx1Z8Ua+2rP53pkdiJVJm3VtpTdKMtIUwE0QmQaHY3Mhk25BWtg6W2tdKe3uLkCCK6CCSaXY2uaFHZWuk6zK+Dvcxbp/s4vZvbcXIEEV0EEs2uRsVjikWl9c7pCuitdDUcek6kErZU1qLb8fZYqQTRgZBodjUSUfO2K6CJYXtJ5cQia5LoBpBodjXiLeZtV0Cz8rROkE4dIVNZkql6k5M1SXQDSDS7Gu2NtexMCkeat1bIkiR6AG0SzRdffBGDBw9GKBTC+PHjsXXrVtfz6+vrcdddd6GoqAjBYBAXX3wx1q8XVLLpCZxJR097M36qvzRvraQKXk9lSXZFJxhBWPAsmm+//TYWLFiAJUuWYPv27Rg9ejSmT5+Oo0ePCs9vbW3FT37yExw4cADvvPMO9u7di1dffRUDBw5s9+C7JammsP4c87YjSRWulCoN0y2FE2h/8PpfVSfYX7uQE4wgLHgWzeXLl+POO+/E7NmzMXz4cLzyyivIycnBqlWrhOevWrUKJ0+exLp16zBp0iQMHjwYP/rRjzB69GjHZ0SjUTQ0NJg+Zw2ppqjxJvM2k2htMBzaYbimcAKpg9dTWZLcsiWILogn0WxtbcW2bdswbdq05A0kCdOmTcMnn3wivOb999/HxIkTcdddd6GwsBAjR47Ek08+iUTCuRLP0qVLkZ+fr39KSjrAc0ukDj4vHmPeWmnvmmQqK/sqVXSvoja9RNfFk2geP34ciUQChYWFpv2FhYWoqakRXvPdd9/hnXfeQSKRwPr167F48WI888wzePzxxx2fs2jRIkQiEf1TVeWQltcdWX+PIhzr78n8s6MN5q2VVJZkqjXJVKKYSnTJe050Azrdey7LMvr3748//OEPGDduHGbNmoUHHngAr7zyiuM1wWAQ4XDY9Dlr6EzvOPOZt1ZSrUmm8n6nml6TKBI9AE+l4c455xz4fD7U1taa9tfW1mLAgAHCa4qKihAIBODzJf8iDxs2DDU1NWhtbUVWVlYbhk0ICRcrll64WHz82F7z1so+1fu9z8H7XfEY0FKnbEXCR+XbiB6AJ0szKysL48aNQ0VF8i+VLMuoqKjAxIkThddMmjQJ3377LWRDh8RvvvkGRUVFJJgdTaoqQ249eIDUjhgq7UYQ3qfnCxYswKuvvoo33ngDe/bswZw5c9DY2IjZs2cDAG677TYsWrRIP3/OnDk4efIk5s2bh2+++Qbl5eV48skncdddd3XcW3QlHi8CHs5Xtt2NVJXVh6jHhzgcJ4gegOfK7bNmzcKxY8fw0EMPoaamBmPGjMGGDRt059D3338PSUpqcUlJCTZu3Ij58+fj0ksvxcCBAzFv3jzcf//9HfcWXYkzGTKUivZWVk+VBknNz4geQJvaXdx99924++67hccqKytt+yZOnIgtW7a05VGEF0K9lTVHp5CikvFAwxFnR0+q6beow6QRUdteIySqxFkA5Z5nnHYuDLqlQrbUm7dWdr2rWIq73hUfTxWcnsr73d7ScATRDaDGahknHW+LQ+9vwL1NbyBbcfIEssXXpjreXu93qutTWaoE0Q0gS7PLkUJU3eI8U+nx0JmKI2foTPHx9lRWTweK0yTOAkg0zyZSOaFSOXLaU1mdIHoIJJqZpniseWslVX645vkWecBTXZtqzbG9xwmiB8A4dypp03VoaGhAfn4+IpHI2ZVSSRBEp9IZ2kGWJkEQhAdINAmCIDxAokkQBOEBEk2CIAgPkGgSBEF4gESTIAjCAySaBEEQHiDRJAiC8ACJJkEQhAdINAmCIDxAokkQBOEBEk2CIAgPkGgSBEF4gESTIAjCAySaBEEQHiDRJAiC8ACJJkEQhAdINAmCIDxAokkQBOEBEk2CIAgPkGgSBEF4gESTIAjCAySaBEEQHiDRJAiC8ACJJkEQhAdINAmCIDxAokkQBOEBEk2CIAgPkGgSBEF4gESTIAjCAySaBEEQHiDRJAiC8ACJJkEQhAdINAmCIDxAokkQBOEBEk2CIAgPkGgSBEF4oE2i+eKLL2Lw4MEIhUIYP348tm7d6nju66+/DsaY6RMKhdo8YIIgiDOJZ9F8++23sWDBAixZsgTbt2/H6NGjMX36dBw9etTxmnA4jOrqav1z8ODBdg2aIAjiTOH3esHy5ctx5513Yvbs2QCAV155BeXl5Vi1ahUWLlwovIYxhgEDBqT9jGg0img0qn+PRCIAgIaGBq/DJQiiB6NpBue8427KPRCNRrnP5+Pvvfeeaf9tt93Gr7/+euE1r732Gvf5fPy8887j5557Lr/++uv5rl27XJ+zZMkSDoA+9KEPfTrks2/fPi9S54onS/P48eNIJBIoLCw07S8sLMTXX38tvGbo0KFYtWoVLr30UkQiETz99NO48sorsXv3bpx77rnCaxYtWoQFCxbo3+vr6zFo0CB8//33yM/P9zLkLk1DQwNKSkpQVVWFcDh8pofTYdB7dT/O1neLRCI477zz0KdPnw67p+fpuVcmTpyIiRMn6t+vvPJKDBs2DL///e/x2GOPCa8JBoMIBoO2/fn5+WfVL1QjHA7Te3Ujztb3As7ed5OkjgsU8nSnc845Bz6fD7W1tab9tbW1aa9ZBgIBjB07Ft9++62XRxMEQXQJPIlmVlYWxo0bh4qKCn2fLMuoqKgwWZNuJBIJ7Ny5E0VFRd5GShAE0QXwPD1fsGABbr/9dvzgBz/AFVdcgd/97ndobGzUvem33XYbBg4ciKVLlwIAHn30UUyYMAEXXngh6uvr8dvf/hYHDx5EWVlZ2s8MBoNYsmSJcMrenaH36l6cre8FnL3v1hnvxTj37ov/13/9V/z2t79FTU0NxowZg+effx7jx48HAEyZMgWDBw/G66+/DgCYP38+1q5di5qaGvTu3Rvjxo3D448/jrFjx3bYSxAEQWSKNokmQRBET4VyzwmCIDxAokkQBOEBEk2CIAgPkGgSBEF4oMuI5tlabs7LewFKyuhdd92FoqIiBINBXHzxxVi/fn2GRps+Xt5rypQptt8XYwzXXnttBkecHl5/X7/73e8wdOhQZGdno6SkBPPnz0dLS0uGRusNL+8Wi8Xw6KOPYsiQIQiFQhg9ejQ2bNiQwdGmx0cffYTrrrsOxcXFYIxh3bp1Ka+prKzEZZddhmAwiAsvvFCP9EmbDstibwdvvfUWz8rK4qtWreK7d+/md955Jy8oKOC1tbXC81977TUeDod5dXW1/qmpqcnwqFPj9b2i0Sj/wQ9+wGfOnMk3b97M9+/fzysrK/nnn3+e4ZG74/W9Tpw4Yfpd7dq1i/t8Pv7aa69lduAp8Ppe//Ef/8GDwSD/j//4D75//36+ceNGXlRUxOfPn5/hkafG67vdd999vLi4mJeXl/N9+/bxl156iYdCIb59+/YMj9yd9evX8wceeICvXbuWA7AVE7Ly3Xff8ZycHL5gwQL+1Vdf8RdeeIH7fD6+YcOGtJ/ZJUTziiuu4HfddZf+PZFI8OLiYr506VLh+a+99hrPz8/P0Ojajtf3evnll/kFF1zAW1tbMzXENuH1vaw8++yzPC8vj58+fbqzhtgmvL7XXXfdxadOnWrat2DBAj5p0qROHWdb8PpuRUVF/F//9V9N+/7hH/6B/+M//mOnjrM9pCOa9913Hx8xYoRp36xZs/j06dPTfs4Zn563trZi27ZtmDZtmr5PkiRMmzYNn3zyieN1p0+fxqBBg1BSUoKf/vSn2L17dyaGmzZtea/3338fEydOxF133YXCwkKMHDkSTz75JBKJRKaGnZK2/r6MrFy5EjfddBNyc3M7a5ieact7XXnlldi2bZs+zf3uu++wfv16zJw5MyNjTpe2vFs0GrUteWVnZ2Pz5s2dOtbO5pNPPjH9OQDA9OnT0/5/F+gCa5pu5eZqamqE12jl5v70pz/hzTffhCzLuPLKK3Ho0KFMDDkt2vJe3333Hd555x0kEgmsX78eixcvxjPPPIPHH388E0NOi7a8l5GtW7di165dntJoM0Fb3usXv/gFHn30UUyePBmBQABDhgzBlClT8Jvf/CYTQ06btrzb9OnTsXz5cvz973+HLMv48MMPsXbtWlRXV2diyJ1GTU2N8M+hoaEBzc3Nad3jjItmW5g4cSJuu+02jBkzBj/60Y+wdu1a9OvXD7///e/P9NDahSzL6N+/P/7whz9g3LhxmDVrFh544AG88sorZ3poHcbKlSsxatQoXHHFFWd6KO2msrISTz75JF566SVs374da9euRXl5uWPJw+7Ec889h4suugiXXHIJsrKycPfdd2P27NkdWmKtu9Lp9TRTcbaWm2vLexUVFSEQCMDn8+n7hg0bhpqaGrS2tiIrK6tTx5wO7fl9NTY24q233sKjjz7amUNsE215r8WLF+PWW2/VreZRo0ahsbERv/zlL/HAAw90GYFpy7v169cP69atQ0tLC06cOIHi4mIsXLgQF1xwQSaG3GkMGDBA+OcQDoeRnZ2d1j3O+G/1bC0315b3mjRpEr799lvIsqzv++abb1BUVNQlBBNo3+/rj3/8I6LRKG655ZbOHqZn2vJeTU1NNmHU/sHjXaikQ3t+Z6FQCAMHDkQ8Hse7776Ln/70p5093E5l4sSJpj8HAPjwww/T1hoAXSfkKBgM8tdff51/9dVX/Je//CUvKCjQw4huvfVWvnDhQv38Rx55hG/cuJHv27ePb9u2jd900008FArx3bt3n6lXEOL1vb7//nuel5fH7777br53717+l7/8hffv358//vjjZ+oVhHh9L43JkyfzWbNmZXq4aeP1vZYsWcLz8vL46tWr+Xfffcc3bdrEhwwZwktLS8/UKzji9d22bNnC3333Xb5v3z7+0Ucf8alTp/Lzzz+f19XVnaE3EHPq1Cm+Y8cOvmPHDg6AL1++nO/YsYMfPHiQc875woUL+a233qqfr4Uc3XvvvXzPnj38xRdf7J4hR5xz/sILL/DzzjuPZ2Vl8SuuuIJv2bJFP/ajH/2I33777fr3X//61/q5hYWFfObMmV0ufkzDy3txzvnf/vY3Pn78eB4MBvkFF1zAn3jiCR6PxzM86tR4fa+vv/6aA+CbNm3K8Ei94eW9YrEYf/jhh/mQIUN4KBTiJSUl/J//+Z+7nLBoeHm3yspKPmzYMB4MBnnfvn35rbfeyg8fPnwGRu3Of/3XfwkbqWnvcvvtt/Mf/ehHtmvGjBnDs7Ky+AUXXOA5XphKwxEEQXjgjK9pEgRBdCdINAmCIDxAokkQBOEBEk2CIAgPkGgSBEF4gESTIAjCAySaBEEQHiDRJAiC8ACJJkEQhAdINAmCIDxAokkQBOGB/wtXdp6Sk6yLOgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.xlim((0.5, 1.0))\n",
+    "plt.ylim((0.5, 1.0))\n",
+    "plt.scatter(tmp['best_acc'], (tmp['max_acc_min'] + tmp['max_acc_max'])/2.0, s=1)\n",
+    "plt.scatter(data['best_acc'], data['max_acc_integral'], s=1)\n",
+    "plt.scatter(tmp['best_acc'], ((tmp['max_acc_min'] + tmp['max_acc_max'])/2.0 + tmp['max_acc_integral'])/2.0, s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.to_csv(output_file, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dataset                                                            bupa\n",
+       "classifier                                       RandomForestClassifier\n",
+       "classifier_params                  {'max_depth': 55, 'random_state': 5}\n",
+       "p                                                                    40\n",
+       "n                                                                    29\n",
+       "p_train                                                             160\n",
+       "n_train                                                             116\n",
+       "auc                                                            0.781466\n",
+       "auc_train                                                           1.0\n",
+       "fprs                  [0.0, 0.0, 0.06896551724137931, 0.068965517241...\n",
+       "tprs                  [0.0, 0.05, 0.05, 0.125, 0.125, 0.45, 0.45, 0....\n",
+       "thresholds            [inf, 0.95, 0.9, 0.84, 0.83, 0.76, 0.74, 0.73,...\n",
+       "n_nodes                                                              24\n",
+       "fprs_train                                              [0.0, 0.0, 1.0]\n",
+       "tprs_train                                              [0.0, 1.0, 1.0]\n",
+       "thresholds_train                                      [inf, 0.71, 0.02]\n",
+       "n_nodes_train                                                         3\n",
+       "acc                                                            0.768116\n",
+       "sens                                                              0.875\n",
+       "spec                                                            0.62069\n",
+       "best_acc                                                       0.768116\n",
+       "best_sens                                                          0.95\n",
+       "best_spec                                                      0.517241\n",
+       "acc_train                                                           1.0\n",
+       "sens_train                                                          1.0\n",
+       "spec_train                                                          1.0\n",
+       "best_acc_train                                                      1.0\n",
+       "best_sens_train                                                     1.0\n",
+       "best_spec_train                                                     1.0\n",
+       "fracs                 [1.0, 0.9710144927536232, 0.9420289855072463, ...\n",
+       "exp_fpr                                                        0.708639\n",
+       "exp_tpr                                                        1.289898\n",
+       "r2_fpr                                                         0.023557\n",
+       "r2_tpr                                                         0.047447\n",
+       "r2_roc                                                         0.849462\n",
+       "auc_min                                                        0.543103\n",
+       "auc_min_best                                                   0.491379\n",
+       "auc_max                                                        0.952586\n",
+       "auc_max_best                                                   0.975862\n",
+       "auc_maxa_best                                                  0.889655\n",
+       "auc_maxa2_best                                                 0.110345\n",
+       "auc_rmin_best                                                  0.609157\n",
+       "auc_rmin                                                       0.622854\n",
+       "err_min                                                        0.204787\n",
+       "err_max                                                        0.085033\n",
+       "err_min_best                                                   0.204814\n",
+       "err_max_best                                                   0.089652\n",
+       "err_maxa_best                                                  0.060889\n",
+       "err_maxa2_best                                                 0.262069\n",
+       "err_rmin_best                                                  0.113617\n",
+       "err_rmin                                                       0.124626\n",
+       "auc_integral                                                   0.850695\n",
+       "auc_integral_best                                              0.868354\n",
+       "auc_integral3                                                  0.845896\n",
+       "auc_integral3_best                                             0.838766\n",
+       "auc_integral4                                                  0.853308\n",
+       "auc_integral4_best                                             0.869493\n",
+       "max_acc_integral                                               0.713047\n",
+       "max_acc_min                                                    0.673672\n",
+       "max_acc_max                                                    0.908152\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 1052,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'r2_min'",
+     "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: 'r2_min'",
+      "\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[1053], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr2_sum\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr2_min\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr2_max\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr2_min\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr2_min\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr2_sum\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr2_max\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr2_max\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr2_sum\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: 'r2_min'"
+     ]
+    }
+   ],
+   "source": [
+    "data['r2_sum'] = data['r2_min'] + data['r2_max']\n",
+    "data['r2_min'] = data['r2_min'] / data['r2_sum']\n",
+    "data['r2_max'] = data['r2_max'] / data['r2_sum']\n",
+    "\n",
+    "data['r2_sum_best'] = data['r2_min_best'] + data['r2_max_best']\n",
+    "data['r2_min_best'] = data['r2_min_best'] / data['r2_sum_best']\n",
+    "data['r2_max_best'] = data['r2_max_best'] / data['r2_sum_best']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_min_max'] = (data['auc_min'] + data['auc_max'])/2.0\n",
+    "data['auc_min_max_best'] = (data['auc_min_best'] + data['auc_max_best'])/2.0\n",
+    "#data['auc_min_maxa_best'] = (data['auc_min_best'] + data['auc_maxa_best'])/2.0\n",
+    "data['auc_min_maxa_best'] = (data['auc_min_best'] * data['r2_max_best'] + data['auc_max_best'] * data['r2_min_best'])\n",
+    "data['auc_min_max_weighted'] = (data['r2_min'] * data['auc_max'] + data['r2_max'] * data['auc_min'])\n",
+    "data['auc_min_maxa_best_weighted'] = (data['r2_min_best'] * data['auc_maxa_best'] + data['r2_maxa_best'] * data['auc_min_best'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "r2_min                  0.650614\n",
+       "r2_max                  0.349386\n",
+       "r2_sum                       1.0\n",
+       "auc_min                 0.416379\n",
+       "auc_max                 0.882759\n",
+       "auc                     0.759914\n",
+       "auc_min_max             0.649569\n",
+       "auc_min_max_weighted    0.719812\n",
+       "n_nodes                       25\n",
+       "Name: 13215, dtype: object"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idx = 1500\n",
+    "row = data.iloc[idx]\n",
+    "row[['r2_min', 'r2_max', 'r2_sum', 'auc_min', 'auc_max', 'auc', 'auc_min_max', 'auc_min_max_weighted', 'n_nodes']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "auc                           0.759914\n",
+       "auc_min_best                  0.524138\n",
+       "auc_maxa_best                 0.903017\n",
+       "r2_min_best                   0.899821\n",
+       "r2_maxa_best                  0.232364\n",
+       "auc_min_maxa_best_weighted    0.934345\n",
+       "Name: 13215, dtype: object"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row[['auc', 'auc_min_best', 'auc_maxa_best', 'r2_min_best', 'r2_maxa_best', 'auc_min_maxa_best_weighted']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8690299382903445, 0.9216840567591656)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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7tm655Radc845uvzyy3X77befMU1TWVmp9PT07kd2draZZSJIgbb/NldV9VlD4kPqBgBwqrB302zcuFFz5szR4sWLtXXrVr3xxht699139eyzz/Z5TllZmVpaWrofe/fuDfcycQpfDcklK1bogunT/T7vbLNIpOA23JOkw8cPq+y9Mt237j66bgAgDpiqGeno6FBqaqrWrFmj8ePHdz9fVFSkI0eO6K233jrtnFtvvVWjRo3Sc8891/3cq6++qmnTpunYsWOy288eD1EzYq1A6kjsGRnKLCtTP6dTqTeN7HMmSTBTWyX2uQGAaBaWmpGkpCSNHDmyRzGq1+tVfX298vPzez2nvb39tIDD0fXBFAPz1qDA6ki8hw9rf0nJGdt/peDSNtI3qZuK9yu0af8m0jYAEINMp2mKi4u1dOlSrVixQtu2bdMDDzygtrY2TZkyRZI0efJklZWVdR8/btw4/fKXv9SqVau0a9curV+/XjNnztS4ceO6gxJEv3CPkV939zotK1ymylsqlZGcwbA0AEgg/cyeMGHCBB08eFCzZs2Sy+XS8OHDVVtb213UumfPnh53Qp5++mnZbDY9/fTT2rdvny666CKNGzdOP/3pT0P3WyAi0saO1YAxY/TlK6+quarK/xMNQ7LZ5J5TqQFjxvSasvF13EhSSr8UFW8slk0206kbX8cNaRsAiB3sTQPTAppF0mVQaakGTrqnzxoSnw27N6iqsUrudrfp9dlkkzPVqdq7a9nfBgAs5O/nN8EIAtJaV6d9j87o/MbkW6hfZqacT5YpbezYMx4X7LC0aTdM06isURoxaARBCQBYgGAEYddaVyf3nEqd6GPGTJ+6imCH/GLBWQMSn2C6bpjcCgDWIBhBRBgej9o/3KKv3W41V1bKc+SIf3dKzjJCvjeBpm5o/wUAa4SltRc4lc3hUP+8XJ1/1zhlPlPR9aQfnTBdI+QPvrBQbR809jpG/lSBDkuj/RcAoht3RhBSgaZu/K0j8SFtAwDRjzsjsIRvjPygXnZwPhN/xsifLJhhaWy4BwDRhTsjCItA23/t6ekasuDn6p+b61ctia/jZtP+TVry8RJTa8xIzlBJTomc/Z103ABAGFDACstFov3Xx+P1qPD1QjW3Nwe0xw2pGwAIPdI0sFw4R8ifymF3qDS3MzVkdpS8ROoGAKxEMIKw8tWQXLJihS6YPt3/E7vupLjnVPrVaSMFV0dCxw0AWIc0DSIm0DqSC6ZPV//8fKXeNNLvOpIP3R/q8Y2Pq6WjJaC1krYBgOCRpkHUsTkccj7ZtaOzP7NIunxRU6M9RUXaMabAr7SNw+5QXlaeKkZXyNb1ZRZpGwCIHIIRRFQk60iCTdtI0tzGuaRsACDMSNPAEobHo7bGzdo3Y4a8LSZSKQGMkWfDPQCwBq29iAmBtv+arSPxYXIrAEQONSOICYGmbczWkfgwuRUAog93RhAVfLv/tjU06IuaGv9P7CqEHfKLBX4PSJOC67hJT0rXvNvmKSczh7QNAJwBaRrEpEiNkfchbQMA4UOaBjEp0PZfb0uL9k65j7QNAMQg7owgKrXW1ck9p1InXC5zJwaRtmHDPQAILdI0iHnB1JGYbf/1YcM9AAgd0jSIeTaHQ/3zcnXRIw+rX2am/2kbw9AJl0sHX1iotg8a/d7bRmLDPQCwAsEIol6kxsj7hGLDvdkNs/XOX9/RZtdmJrgCwFmQpkHMsKKOJNgN9yRSNwASF2kaxJ20sWN1Rf0GZb/8suzp6f6faBiSYejAzFk61tDgd9omFBvuSaRuAOBsCEYQU2wOh87LH6WsZ2d33vGI8vZfiU33AOBsCEYQkyK5+6/UGZCsu3udlhUuU+UtlcpIzjB1p8SQIVe7Syu3ryQgAYBTUDOCmGZF+6/E5FYA8Ac1I0gIVrT/SkxuBYBQ4s4I4kZrXZ32PTqj8xuTb+t+mZlyPllmqttG+mZyq7vNrerN1Tpy/Ijfd0qY3Aog3jGBFQkp0u2/JyN1AwA9kaZBQgqq/VeSe06l6ZSND6kbAAgMwQjiTsDtv111JF++8mpQAcm6u9epJKfE1Hm+ya0V71do0/5NdNwASCgEI4hbgbb/NldVmZ5FcjKH3aGJwybKmeo0PSitpaNFU9dPVeHrhdwlAZAwCEYQ13xpm0tWrNAF06f7fV4gs0hOxoZ7AOA/ghHEvYDaf30j5Msr1PL2byPe/svUVgCJhG4aJJRYa/+VpGk3TNOorFG0/wKIObT2An2g/RcAIoPWXqAPvjqSQaWl5k6k/RcAwoJgBAnJ5nBo4KR7zI2Ql0La/vvi2BeVnuT/LBTafwHEK4IRJCybwyHnk2Vd35jreAlF+29eVp4qRlfI1vXlL9p/AcQbghEktEBnkUjBt/9KpG0AQKKAFZAkGR6P2j/coq/dbjVXVspz5Ijf3Tb2jAxllpWpn9Op1JtGyuYw3/Hi67jZtH+Tlny8xNS5bLgHIFrRTQMEyIr2Xx+P16PC1wvV3N5suttGouMGQHShmwYIkJWpGya3AkhEBCNAL2K1/ZeOGwCxiDQNcAaGx6MdYwp0wu02nbK5YPp09c/PD6qO5EP3h3p84+Nq6Wgxfb5E2gaAtUjTACEQTPvvFzU12lNUFHALcDDtvz6kbQDEAoIR4CyCqSGRgq8jYcM9APGONA3gp2DafyXJnp6uIQt+rv65uUG1/7LhHoBYQWsvEEZWtv9KbLgHIDZQMwKEEZNbASB0uDMCBMGXumlraNAXNTX+n2izqZ/TqSvqNwSUsvEJpuOGya0Awo00DRBBgbYAB9v+6xNM2kYidQMgPEjTABEUaAtwsO2/PsGkbSRSNwCsxZ0RIIRa6+rknlOpEy6XuRO7Apghv1gQVGFrMBvuSVJ6Urrm3TZPOZk5pG0ABC2sd0YWLVqkoUOHKiUlRXl5eWpsbDzj8UeOHNFDDz2krKwsJScn68orr9TatWsDeWkgqvnGyGe//LLs6en+n2gYkmHowMxZOtbQEPAoeYfdoZzMHD04/EE5U52mB6W1dLRo6vqpKny9kLskACLGdDCyevVqFRcXq7y8XFu3btWNN96owsJCNTc393p8R0eHvvOd7+jzzz/XmjVr9Nlnn2np0qUaMmRI0IsHopHN4dB5+aOU9ezszjseJtI23pYW7Z1yX9BpGzbcAxBLTKdp8vLylJOTo4ULF0qSvF6vsrOz9cgjj6i0l03Fampq9Nxzz2n79u0655xzAlokaRrEKqvTNht2b1BVY5Xc7W7T59pkkzPVqdq7a0nZAAhIWLppOjo6lJqaqjVr1mj8+PHdzxcVFenIkSN66623Tjvnzjvv1MCBA5Wamqq33npLF110kSZOnKgnnnhCjj66B44fP67jx4/3+GWys7MJRhCToqH9l8mtAKwQlpqRQ4cOyePxyHnKoCen0ylXH//y27lzp9asWSOPx6O1a9dq5syZ+tnPfqaf/OQnfb5OZWWl0tPTux/Z2dlmlglEFZvDof55ubrokYfVLzPT/7SNYeiEy6WDLyxU2weNQdeRfP/y72tW/qzONZlI3Sz58xLdt+4+6kgAhE3YW3u9Xq8GDRqkJUuWaOTIkZowYYKeeuop1ZzhX4hlZWVqaWnpfuzduzfcywTCzur2X4nJrQCik6lg5MILL5TD4ZDb3TP/7Ha7lZmZ2es5WVlZuvLKK3ukZK6++mq5XC51dHT0ek5ycrLS0tJ6PIB4YPUYeakzIFl39zq9OPZFpSf53/FjdH1VvF+hTfs3sQswgJAxFYwkJSVp5MiRqq+v737O6/Wqvr5e+fn5vZ5z8803a8eOHfJ6vd3P/eUvf1FWVpaSkpICXDYSmtcj7fov6eM1nf8bYx+KVrf/Sp2pm7ysPFWMrpCt68tfMdP+G+PvEyCRmO6mWb16tYqKivTv//7vys3N1YIFC/Taa69p+/btcjqdmjx5soYMGaLKykpJ0t69e3XttdeqqKhIjzzyiP7nf/5H9913n/7lX/5FTz31lF+vSTcNun36tlT7hNS6/5vn0gZL350rXXOXdesKkNW7/0qBd9z4Apj5t8+PvjHycfY+AWJVWPemWbhwoZ577jm5XC4NHz5czz//vPLy8iRJt99+u4YOHarly5d3H9/Q0KDHHntMTU1NGjJkiO6///4zdtME+ssgzn36tvTaZOm0TpCuf9X/4D9i8oPG6vZfKfDJrVHZ/hun7xMgFrFRHuKL1yMtuK7nv3R7sHX+y3fGx1K0fCiaYHX7r4/H61Hh64Vqbm+OzfbfOH+fALGGjfIQX3a/f4YPGEkypNZ9ncfFIKvbf30CndwaNe2/cf4+AeIVwQhiwzE/6xn8PS5K0f4bpAR5nwDxhmAEseE8P1th/T0uitH+G4QEep8A8YRgBLHh0tGduf4+Uwc2KW1I53FxgPbfACXY+wSIFwQjiA12R2dbpqTTP2i6vv9uVVwVJUbD7r9SjKVtEvB9AsQDumkQW3qdHzGk8wMmjts1Y7n9V5IykjNUklMiZ39nZDpuEvR9AkQbWnsRv7yezm6IY+7O3P+loxPiX7qx3v7r40x1qjS3NPyD0hL0fQJEE4IRIE4ZHo92jCnQCbfb1NTWC6ZPV//8fKXeNDLooGTD7g0q3ljcuR6TAUlUT24FEFLMGQHiVKy3//qCl7mNc9lsD4AkghEgJsVy+6/UGZC42l1auX0lAQkA0jRALDM8HrU1bta+GTPkbWkxda49I0OZZWXq53QGnboJJm0TsRoSABFHzQiQQILZ/VcKzQ7Acbn7L4CgEIwACSbg9l8pZC3AvvZfd5tb1ZurdeT4Eb/vlES8/RdA2BGMAAko4PbfLvb0dA1Z8HP1z821tOOG1A0QH+imARJQwLv/dknYya0ALEUwAsShQNt/fULdcVOSU2LqPN+Ge7MbZuudv76jza7NdN0AcYw0DRDHgq0jCdXk1mCntkqkboBYRJoGQPfuv5esWKGs6mo5MjL8v1NiGDrhcungCwvV9kFj0DsAl+aWSpKp3X9PRuoGiF/cGQESSDAtwFa2//rYZJMz1anau2vptgFiAHdGAJzG6smtvhqSZYXLVHlLpTKSM0zdKWFyKxCfuDMCJKCgJrfS/gvAT9wZAdAnm8Oh8/JHKevZ2Z01JCY6bmj/BRBq3BkBElzAHTchmtoqMbkViFdMYAXgt4Ant4aw/deH1A0QP0jTAPBbwJNbQ9j+60PqBkg8BCMAugU6ufWLmhrtKSoKSR2JFNzkVkma2ziXbhsghhCMAOjB6vZfH4fdoYnDJsqZ6qT9F4hzBCMATuOb3Jr98suyp6f7f6JhSIahAzNn6VhDQ9Bpm2Amt1Zvrlbh64WkbIAYQDACoFe0/wKIFLppAJwV7b8AAkFrL4CQCrj9V5I9I0OZZWXq53Qq9aaRTG4FEgTBCICwMDwe7RhToBNut+nN9qTQbLgnBb7pnq/2ZP7t8wlIgDBjzgiAsAi0/dcnVB03wbT/GjJU8X6FNu3fRMcNEAUIRgCYFkz7r+9uintOZUi6bQJp/5Wklo4WTV0/lY4bIAoQjAAISMDtv1L35NYvX3nV0vZfiY4bIBoQjAAIWDDtv5LUXFVlefsvU1sB61HACiAkYr39V5Km3TBNo7JG0f4LhAjdNAAiztf++7XbrebKSnmOHPG744b2XyD+EIwAsFRrXZ32PTqj8xuT/5mh/ReID7T2ArBUNGy452v/fXHsi0pP8r/IlvZfILIIRgCEja/jZlBpqbkTQ9z+m5eVp4rRFbJ1ffmL9l8gMghGAISVzeHQwEn3qF9mprlum67234MvLFTbB41BByVsuAdEL2pGAEREMDUkUujqSHwdN5v2b9KSj5f4fZ5NNjlTnaq9u5ZOG8BP1IwAiCpBTW1V6OpIHHaHcjJz9ODwB01NbjVkyNXu0uI/LdZm12bqSIAQ4s4IgIgKpv1Xkuzp6Rqy4Ofqn5tL+y8Q5bgzAiAq2RwO9c/L1fl3jVPmMxVdT/pfS+JtadHeKfdZPrmVOhIgdLgzAsBS0TK59UP3h3p84+Nq6WgxdW5GcoZKckrk7O9kcitwCoaeAYgZvtRNW0ODvqipMXVuKCe3BpO2kUjdAKciGAEQcwyPRzvGFOiE221Zx02gU1slJrcCp6JmBEDMsTkccj5Z1vWNuR2ApdB03Pimti4rXKZp108zdS47AAOBIRgBEFWCagEO0eTWQNt/pW9agFduX0lAAviJYARA1PGNkc9++WXZ0/3fU0ZS9+TWL195NSSj5EtzO0fZmwlIJKl6czVj5AE/EYwAiEo2h0Pn5Y9S1rOzO1M2JtM2zVVVtP8CMYICVgBRL1raf7c2b5W7za3qzdU6cvyI3x03tP8iUdFNAyCuBDO5NZTtvxKTWwF/EYwAiFvBbLoXqg33Am0Bpv0XiSSsrb2LFi3S0KFDlZKSory8PDU2Nvp13qpVq2Sz2TR+/PhAXhYAJAXXcROqDfd8LcAlOSWmzqP9Fzid6WBk9erVKi4uVnl5ubZu3aobb7xRhYWFam5uPuN5n3/+uf71X/9Vt956a8CLBQAfX8fNoNJScycahmQYOjBzlo41NATdAjxx2ETaf4EgmQ5G5s+fr6lTp2rKlCm65pprVFNTo9TUVC1btqzPczwej370ox/pmWee0WWXXRbUggHAx+ZwaOCke9QvM9N0t02oNtyj/RcInqlgpKOjQ1u2bFFBwTd5TrvdroKCAjU0NPR53uzZszVo0CDdf//9fr3O8ePH1dra2uMBAL2JlqmttP8CgTMVjBw6dEgej0fOU/K0TqdTrj5a7t577z299NJLWrp0qd+vU1lZqfT09O5Hdna2mWUCSDDRMLX15DHylbdUKiM5w687JUbX1+yG2Xrnr+9os2szqRsknH7h/OFHjx7VpEmTtHTpUl144YV+n1dWVqbi4uLu71tbWwlIAJxR2tixGjBmTGDtv11TWw++sFD98/MDbv/1jZGXpJR+KSreWCybbH61/x4+flhl73Xe4aH9F4nGVGtvR0eHUlNTtWbNmh4dMUVFRTpy5IjeeuutHsc3NTXp29/+thwn/Z/a6/VK6kzvfPbZZ7r88svP+rq09gIwi/ZfwHphae1NSkrSyJEjVV9f3/2c1+tVfX298vPzTzt+2LBh+vjjj9XU1NT9uOuuu3THHXeoqamJux0Awob2XyB2mO6mKS4u1tKlS7VixQpt27ZNDzzwgNra2jRlyhRJ0uTJk1VW1nmrMSUlRdddd12Px/nnn68BAwbouuuuU1JSUmh/GwA4ScAb7tH+C0SU6WBkwoQJmjdvnmbNmqXhw4erqalJtbW13UWte/bs0YEDB0K+UAAIRDAb7tH+C0QG4+ABJAyrN9yjhgSJhr1pAKAXvg332hoa9EVNjalzQ7HhHrv/IpEQjADAGRgej3aMKdAJt9t0t40Umo4bdv9FvAvrRnkAEOuY3ApED4IRAAkrmia30v6LREYwAiChBdz+K/WY3Nr2QWPAQQntv0h0BCMAEl4w7b+S9EVNjfYUFQXVAkz7LxIZwQgAdAkqbaPg60ioIUGiopsGAE7ha/81veFeF3t6uoYs+Ln65+bS/ouERmsvAISA1Rvu0f6LWEZrLwCEgNUb7pG6QSLgzggA+CEaJreu3L5S1ZurTZ1nk03OVKdq764lZYOII00DAGFg5eRWj9ejwtcL1dzebDplM+2GaRqVNYo6EkQUaRoACAMrJ7cG0/675M9LdN+6+2gBRlQiGAEAk4Ke3GoYOjBzlo41NJgelBZMDYlEHQmiE2kaAAiQ4fGorXGz9s2YIW9LS0A/I9C0TTDtv5KUnpSuebfNU05mDmkbhA01IwAQIcG0//pSPUN+sSDgFmDafxGtqBkBgAixesM92n8R67gzAgAhEuzk1gumT1f//Pyg2n+3Nm/Vpv2btOTjJX6fR/svwoU0DQBYyMrJrYG2ANP+i1AjTQMAFrJycmugLcC0/8IqBCMAECZpY8fqivoNyn75ZdnT0/0/Mcj2X4k6EsQWghEACCObw6Hz8kcp69nZnZ0zJgaleVtatHfKfdoxpiCguyQFlxZo3d3r9OLYF5We5H8wZHR9VbxfoU37N8njDaywFvAXwQgARIBVaRuH3aG8rDxVjK6QrevLXy0dLZq6fippG4QdBawAEEFWbri3YfcGVTVWyd3uNnWeL4CZf/t85pHAFLppACCKWbXhHu2/iCS6aQAgilm14Z7D7lBOZo4eHP6gnKlOv9M2hgy52l1a/KfF2uzaTB0JQopgBAAsYuWGe7T/IpoQjACAhQJu/+0STMcN7b+IFgQjAGCxYNp/fQJN29D+i2hAMAIAUcKqDfdo/4XV6KYBEJc8XkONu75U89GvNGhAinK/NVAOu/k7DlawcsM92n/PLJbfV1agtRdAwqr95ICe+e2nOtDyVfdzWekpKh93jb57XZaFKzPPig33aP/tXTy9ryKF1l4ACan2kwN64NWtPT4wJMnV8pUeeHWraj85YNHKAmPF5Fbaf08Xb++raEMwAiBueLyGnvntp+rt/oHvuWd++6k83qi/IdxD0BvulVeo5e3fqu2DRlP1JLT/dorX91U0IRgBEDcad3152r9cT2ZIOtDylRp3fRm5RYVIUBvuHT6s/SUl2lNUZLoFmPbf+H5fRQuCEQBxo/lo3x8YgRwXjYLquFFgqZtEb/9NhPeV1QhGAMSNQQNSQnpctPKlbS5ZsUIXTJ9u7uQAJ7cmcvtvoryvrEQwAiBu5H5roLLSU/r8mLSps/sh91sDI7mssLA5HOqfl6uLHnlY/TIzTQ9KC3RyayKmbRLpfWUVghEAccNht6l83DWSdNoHh+/78nHXxNVcCCs23POlbZYVLtO066f5fZ7RVe45t3FuTKVsEvF9FWkEIwDiynevy9Iv7xmhzPSet8wz01P0y3tGxOU8iJBsuGey4ybR2n8T8X0VSQw9AxCXEnFSZrCTW33MDkvbsHuDijcWd66h1wbYvjlTnSrNLY2Zqa2J+L4KBhNYASCBBTO51ZfuGfKLBaYCEsbI41RMYAWABBaS1I2Jjptg2n+l2KsjQWgRjABAnAp4cmsXsx03gbb/xmodCUKHNA0AJIBYSdtIsVdHgr5RMwIA6KG1rk7uOZU64XKZP9lmUz+nU1fUb5DN4d+OvMHs/itRRxIPCEYAAKcJtuPmgunT1T8/X6k3jTQVlBS+Xqjm9mZT3TbpSemad9s85WTmyGH377UQXQhGAABnFEzqhvZf+INuGgDAGQXTcWN2cmsijpGH/7gzAgAJzvB41Na4WftmzJC3pcXUufb0dA1Z8HP1z831K20TTB2JM9Wp2rtrSdnEEO6MAAD8YnM4dF7+KGU9O7uzc8bEHjeBtP8m0hh5+IdgBAAgKbJpG4fdodLcUknyOyCRpCV/XqL71t2nwtcLSdvEEdI0AIAefB03bQ0N+qKmxtS59owMZZaVqZ/T6VfHDWPk4xvdNACAoBgej3aMKdAJtzusG+55vB596P5Qj298XC0d5mpWaP+NbtSMAACCYnM45HyyrOsb8zvT+pu6CXSMvCS1dLRo6vqppG1iHMEIAKBPkdxwj/bfxBVQMLJo0SINHTpUKSkpysvLU2NjY5/HLl26VLfeeqsyMjKUkZGhgoKCMx4PAIgukdxwz7f777LCZZp2/TS/X4Pdf2Ob6WBk9erVKi4uVnl5ubZu3aobb7xRhYWFam5u7vX4jRs36oc//KF+//vfq6GhQdnZ2Ro7dqz27dsX9OIBAJERTPuvj5m0De2/icV0AWteXp5ycnK0cOFCSZLX61V2drYeeeQRlZaWnvV8j8ejjIwMLVy4UJMnT/brNSlgBYDoEdSGezLXccMY+dgWlgLWjo4ObdmyRQUF3/zF2u12FRQUqKGhwa+f0d7erq+//loDBw7s85jjx4+rtbW1xwMAEB18aZtLVqxQVnW1HBkZ5galHT6s/SUl2lNUdNbUDXUkicFUMHLo0CF5PB45TylkcjqdcvkZIT/xxBMaPHhwj4DmVJWVlUpPT+9+ZGdnm1kmACDMbA6H+ufl6vy7xinzmYquJ8OTuvHVkbw49kWlJ/lfs0IdSeyIaDdNVVWVVq1apTfffFMpKSl9HldWVqaWlpbux969eyO4SgCAGSHpuCmvUMvbv1XbB429dt0E2v7rqyNZuX0lAUkUMxWMXHjhhXI4HHK7e07Kc7vdyszMPOO58+bNU1VVlerq6nTDDTec8djk5GSlpaX1eAAAolfQHTd+pm4CTdtUb65mFkkUMxWMJCUlaeTIkaqvr+9+zuv1qr6+Xvn5+X2eV11drWeffVa1tbW66aabAl8tACBqhaLjRjp76ibQ9l9qSKKX6TRNcXGxli5dqhUrVmjbtm164IEH1NbWpilTpkiSJk+erLKysu7j586dq5kzZ2rZsmUaOnSoXC6XXC6Xjh07FrrfAgAQNYJK20h+DUsLpP3X6Pqa3TBb7/z1Hdp/o0hAe9MsXLhQzz33nFwul4YPH67nn39eeXl5kqTbb79dQ4cO1fLlyyVJQ4cO1e7du0/7GeXl5aqoqPDr9WjtBYDY49tw72u3W82VlfIcORKWPW5o/41ebJQHAIgarXV12vfojM5vzH7sdKV7hvxiwRkDEnb/jT5slAcAiBrh7rjx1ZGU5JSY+9G0/0YF7owAACIm3Kkbj9ejwtcL1dzebDplU5JToonDJsph73siLMzhzggAIOqEe1iaw+5QaW7n1iT+ziLxof3XOgQjAABLBJ26keSeU9lryoYR8rGFNA0AwFKGx6O2xs3aN2OGvC0tps8fVFqqgZPuOW3DPY/Xo63NW+Vuc6t6c7WOHD/iV+rGJpucqU7V3l1LyiZIpGkAADEh2GFpzVVVvU5t9c0i+f7l39es/Fmdr+XnPBJXu0uL/7SYWSQRwp0RAEDUaK2rk3tOpU74uflqtzC2/zKLJHDMGQEAxKRgOm7sGRnKLCtTP6dTqTeN7DV1s3L7SlVvrvZ7PcwiCRzBCAAg5gUzLC3U7b8ZyRkqySmRs79TIwaNoJ7EDwQjAIC4EI7UTTAj5CVSN/6igBUAEBfSxo7VFfUbNKi01NyJZ9hwL5j2X4kW4FDjzggAICYYHo92jCnQCbc7ZFNbfe2/m/Zv0pKPl5j6ebQAnx13RgAAccXmcMj5ZFnXN6GZ2upr/31w+INypjpNTW31tQCv3L6S9t8gEYwAAGJGuDbcY4y8tUjTAABiTrg23At0Fgntv72jmwYAkBCCaf/treOGMfKhQ80IACAhhCR1c1LHDWPkI487IwCAuBDshntS7x03jJEPHHdGAAAJJdgN96TeO24KLi3QurvXqSSnxNTPYhaJ/whGAABxJRwdNw67QxOHTTTV/uurM5nbOJeUzVmQpgEAxKVwdNwEOka+JKdEE4dNTLjCVrppAADoEsqOG2pI/EcwAgDASQLecK+LPSNDmWVl6ud0KnnEcH30xZ9MjZFPxFkkBCMAAJwiFB030jepm/4FY1T4eqGa25uZRdILumkAADhFKDpupG+6bto21JsaI88skt5xZwQAkJCCTdtIkj09XUMW/FybnEdVtaWaOpJTkKYBAOAsQtlxc1HZE/pt9kFVb672+7x4ryMhTQMAwFnYHA71z8vV+XeNU+YzFV1PBjYs7cCMYo3be5HpWSSGDFW8X6FN+zclbNqGYAQAAIVmWJq7YraeOTpG1+z2yu71//SWjhZNXT9Vha8XJuTEVtI0AACcJFSpm8Npdr1UIDVe5f+/++MtbUOaBgCAAIQqdZNx1NDjb3j11N/G+H1Ooo6QJxgBAKAPwaZubJK+/fIm3XIg3e+0TSK2/5KmAQDgLEIxLO3QAGnFdxz64Cpzd1liuf2XNA0AACESimFpFxyVit/wKPczE5Wtkprbm1W8sTiuC1sJRgAA8FMwaRtb16O4vr++sz1Z1+z2yuY9e3IiEepISNMAAGBSqDpuDg2Qln/H7nfHTUlOiSYOmxgz+9owgRUAgAhoravTvkdndH5j8iPVd/TP/tH/gCSWakioGQEAIAKCTd1I0r/UnaNrP/cvbROPNSTcGQEAIARC1XHjT9rGJpucqU7V3l0b1Skb7owAABBBoei4GXhUevwNr+5+z6Ob/9vbZ5FrvM0i4c4IAAAh1lpXJ/ecSp1wuUyfa0g9ttnz525JtNaRUMAKAICFQtVx41VncHKmItdo3dPG38/vfhFcEwAACcO3x40k2VOSOztubDbTAYldnQHJtLVetSVLn15ik2HvmQIyZMgmm+Y2ztUd2XdEdR1Jb6gZAQAgzILa40adH9ZpX0nl/+nVosW9T3GN5ToS0jQAAERIKDpu/EnbSNFRR0I3DQAAUSYUHTd2dRa5Tv2/Xt38iafPjptYmkfCnREAACwQTMfNqfrquLF6HgndNAAARDlfx82JgwfVsXu3Di1c2PUH5j6az5a6sWpPG4IRAABiTDB3S7ySjp4rLS+w6fAAm7Zl9+y6saKGhGAEAIAYFIoiV+n01I0Vs0goYAUAIAaFoshV+ma0/OQNnUWu8na2A89tnBt1bb8EIwAARKFQzCaxSfr+ZkMVKzvnk+R85onKWSSkaQAAiGLhHCsf7joS0jQAAMQB31j58+8ap8xnKrqeDM18koPH3FExi4Q7IwAAxJBQzydZ8R2Hdn87KyyzSOimAQAg1Lweaff70jG3dJ5TunS0ZMEwsVCnbt7NsWngyNG655+fV/K5qSFbZ1jTNIsWLdLQoUOVkpKivLw8NTY2nvH4X//61xo2bJhSUlJ0/fXXa+3atYG8LAAA1vn0bWnBddKK70uv39/5vwuu63w+wkKZuvEVuY6u+aO23DpSr//03hCu1P91mLJ69WoVFxervLxcW7du1Y033qjCwkI1Nzf3evz777+vH/7wh7r//vv10Ucfafz48Ro/frw++eSToBcPAEBEfPq29NpkqXV/z+dbD3Q+b0FA4hNs183J0o9JV7/yQcQDEtNpmry8POXk5Ghh18har9er7OxsPfLIIyotLT3t+AkTJqitrU3vvPNO93OjRo3S8OHDVVNT49drkqYBAFjG6+m8A3JqINLNJqUNlmZ8bEnKxseXujn6u9/p8IoVnXdKAkzdHBkg3fSHLUGnbMKSpuno6NCWLVtUUPBNC5DdbldBQYEaGhp6PaehoaHH8ZJUWFjY5/GSdPz4cbW2tvZ4AABgid3vnyEQkSRDat3XeZyFfKmbzLJSDXn+F0HNJxl4VNrw8jOhXeBZXtNvhw4dksfjkfOUX9DpdMrVR1Wvy+UydbwkVVZWKj09vfuRnZ1tZpkAAITOMXdoj4uAtLFjdUX9Bl2yYoWyqqvlyMiQ2Xsk7a49YVlbb6JyzkhZWZlaWlq6H3v37rV6SQCARHWen3cY/D0uQk4tcrWZHC2fmnlJGFfXk6lg5MILL5TD4ZDb3TP6c7vdyszM7PWczMxMU8dLUnJystLS0no8AACwxKWjO2tC1NcHuU1KG9J5XJQyU+TqlfTlAKlgSnn4F9bFVDCSlJSkkSNHqr6+vvs5r9er+vp65efn93pOfn5+j+Mlaf369X0eDwBAVLE7pO/O7frm1ICk6/vvVllavOqPk1M3/3vL5TLUGXiczDd3xDU+L6TzRs7GdJqmuLhYS5cu1YoVK7Rt2zY98MADamtr05QpUyRJkydPVllZWffxjz76qGpra/Wzn/1M27dvV0VFhT788EM9/PDDofstAAAIp2vukn7wH1JaVs/n0wZ3Pn/NXdasyyRf6uY7L76jbZPy1HJezz8/MkDaNilPdz+1PKLr6mf2hAkTJujgwYOaNWuWXC6Xhg8frtra2u4i1T179shu/ybGGT16tFauXKmnn35aTz75pP7u7/5Ov/nNb3TdddeF7rcAACDcrrlLGva9qJjAGgp3P7Vcx4vbteHlZ9Tu2qPUzEtUMKU8ondEfBgHDwAAwoJdewEAQEwgGAEAAJYiGAEAAJYiGAEAAJYiGAEAAJYiGAEAAJYiGAEAAJYiGAEAAJYiGAEAAJYyPQ7eCr4hsa2trRavBAAA+Mv3uX22Ye8xEYwcPXpUkpSdnW3xSgAAgFlHjx5Venp6n38eE3vTeL1e7d+/XwMGDJDNdur2zYFrbW1Vdna29u7dy543YcR1jhyudWRwnSOD6xwZ4bzOhmHo6NGjGjx4cI9NdE8VE3dG7Ha7Lr744rD9/LS0NN7oEcB1jhyudWRwnSOD6xwZ4brOZ7oj4kMBKwAAsBTBCAAAsFRCByPJyckqLy9XcnKy1UuJa1znyOFaRwbXOTK4zpERDdc5JgpYAQBA/EroOyMAAMB6BCMAAMBSBCMAAMBSBCMAAMBScR+MLFq0SEOHDlVKSory8vLU2Nh4xuN//etfa9iwYUpJSdH111+vtWvXRmilsc3MdV66dKluvfVWZWRkKCMjQwUFBWf9e8E3zL6nfVatWiWbzabx48eHd4Fxwux1PnLkiB566CFlZWUpOTlZV155Jf/98IPZ67xgwQJdddVVOvfcc5Wdna3HHntMX331VYRWG5v+8Ic/aNy4cRo8eLBsNpt+85vfnPWcjRs3asSIEUpOTtYVV1yh5cuXh3eRRhxbtWqVkZSUZCxbtsz47//+b2Pq1KnG+eefb7jd7l6P/+Mf/2g4HA6jurra+PTTT42nn37aOOecc4yPP/44wiuPLWav88SJE41FixYZH330kbFt2zbj3nvvNdLT043//d//jfDKY4/Za+2za9cuY8iQIcatt95q/MM//ENkFhvDzF7n48ePGzfddJNx5513Gu+9956xa9cuY+PGjUZTU1OEVx5bzF7nX/3qV0ZycrLxq1/9yti1a5exbt06Iysry3jssccivPLYsnbtWuOpp54y3njjDUOS8eabb57x+J07dxqpqalGcXGx8emnnxovvPCC4XA4jNra2rCtMa6DkdzcXOOhhx7q/t7j8RiDBw82Kisrez3+Bz/4gfG9732vx3N5eXnGP//zP4d1nbHO7HU+1YkTJ4wBAwYYK1asCNcS40Yg1/rEiRPG6NGjjRdffNEoKioiGPGD2ev8y1/+0rjsssuMjo6OSC0xLpi9zg899JDx93//9z2eKy4uNm6++eawrjOe+BOMlJSUGNdee22P5yZMmGAUFhaGbV1xm6bp6OjQli1bVFBQ0P2c3W5XQUGBGhoaej2noaGhx/GSVFhY2OfxCOw6n6q9vV1ff/21Bg4cGK5lxoVAr/Xs2bM1aNAg3X///ZFYZswL5Dq//fbbys/P10MPPSSn06nrrrtOc+bMkcfjidSyY04g13n06NHasmVLdypn586dWrt2re68886IrDlRWPFZGBMb5QXi0KFD8ng8cjqdPZ53Op3avn17r+e4XK5ej3e5XGFbZ6wL5Dqf6oknntDgwYNPe/Ojp0Cu9XvvvaeXXnpJTU1NEVhhfAjkOu/cuVO/+93v9KMf/Uhr167Vjh079OCDD+rrr79WeXl5JJYdcwK5zhMnTtShQ4d0yy23yDAMnThxQtOnT9eTTz4ZiSUnjL4+C1tbW/W3v/1N5557bshfM27vjCA2VFVVadWqVXrzzTeVkpJi9XLiytGjRzVp0iQtXbpUF154odXLiWter1eDBg3SkiVLNHLkSE2YMEFPPfWUampqrF5aXNm4caPmzJmjxYsXa+vWrXrjjTf07rvv6tlnn7V6aQhS3N4ZufDCC+VwOOR2u3s873a7lZmZ2es5mZmZpo5HYNfZZ968eaqqqtKGDRt0ww03hHOZccHstf7rX/+qzz//XOPGjet+zuv1SpL69eunzz77TJdffnl4Fx2DAnlPZ2Vl6ZxzzpHD4eh+7uqrr5bL5VJHR4eSkpLCuuZYFMh1njlzpiZNmqQf//jHkqTrr79ebW1tmjZtmp566inZ7fz7OhT6+ixMS0sLy10RKY7vjCQlJWnkyJGqr6/vfs7r9aq+vl75+fm9npOfn9/jeElav359n8cjsOssSdXV1Xr22WdVW1urm266KRJLjXlmr/WwYcP08ccfq6mpqftx11136Y477lBTU5Oys7MjufyYEch7+uabb9aOHTu6gz1J+stf/qKsrCwCkT4Ecp3b29tPCzh8AaDBNmshY8lnYdhKY6PAqlWrjOTkZGP58uXGp59+akybNs04//zzDZfLZRiGYUyaNMkoLS3tPv6Pf/yj0a9fP2PevHnGtm3bjPLyclp7/WD2OldVVRlJSUnGmjVrjAMHDnQ/jh49atWvEDPMXutT0U3jH7PXec+ePcaAAQOMhx9+2Pjss8+Md955xxg0aJDxk5/8xKpfISaYvc7l5eXGgAEDjP/8z/80du7cadTV1RmXX3658YMf/MCqXyEmHD161Pjoo4+Mjz76yJBkzJ8/3/joo4+M3bt3G4ZhGKWlpcakSZO6j/e19v7bv/2bsW3bNmPRokW09gbrhRdeMC655BIjKSnJyM3NNTZt2tT9Z7fddptRVFTU4/jXXnvNuPLKK42kpCTj2muvNd59990Irzg2mbnOl156qSHptEd5eXnkFx6DzL6nT0Yw4j+z1/n999838vLyjOTkZOOyyy4zfvrTnxonTpyI8Kpjj5nr/PXXXxsVFRXG5ZdfbqSkpBjZ2dnGgw8+aBw+fDjyC48hv//973v9b67v2hYVFRm33XbbaecMHz7cSEpKMi677DLj5ZdfDusabYbBvS0AAGCduK0ZAQAAsYFgBAAAWIpgBAAAWIpgBAAAWIpgBAAAWIpgBAAAWIpgBAAAWIpgBAAAWIpgBAAAWIpgBAAAWIpgBAAAWIpgBAAAWOr/AXT4WsvZDrL2AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fprs, tprs = roc_min(1.0 - row['spec'], row['sens'])\n",
+    "fracs = 1.0 - (row['p']*tprs + row['n']*fprs)/(row['p'] + row['n'])\n",
+    "p = exponential_fit(fracs, fprs)\n",
+    "p_tpr = exponential_fit(fracs, tprs)\n",
+    "x = np.linspace(0, 1, 100)\n",
+    "plt.scatter(fracs, fprs)\n",
+    "plt.scatter(fracs, tprs)\n",
+    "plt.scatter(x, ((1.0 - x**p)**(1.0/p)))\n",
+    "plt.scatter(x, ((1.0 - x**p_tpr)**(1.0/p_tpr)))\n",
+    "(r2_score(fprs, ((1.0 - fracs**p)**(1.0/p))),\n",
+    "r2_score(tprs, ((1.0 - fracs**p_tpr)**(1.0/p_tpr))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('[0.0, 0.0, 0.034482758620689655, 0.06896551724137931, 0.13793103448275862, 0.13793103448275862, 0.20689655172413793, 0.20689655172413793, 0.2413793103448276, 0.2413793103448276, 0.27586206896551724, 0.27586206896551724, 0.3103448275862069, 0.3103448275862069, 0.3448275862068966, 0.3448275862068966, 0.41379310344827586, 0.41379310344827586, 0.4482758620689655, 0.4482758620689655, 0.6206896551724138, 0.6206896551724138, 0.7931034482758621, 0.8275862068965517, 1.0]',\n",
+       " '[0.0, 0.225, 0.225, 0.25, 0.25, 0.3, 0.3, 0.45, 0.45, 0.55, 0.575, 0.65, 0.675, 0.7, 0.7, 0.8, 0.8, 0.875, 0.9, 0.95, 0.95, 0.975, 0.975, 1.0, 1.0]',\n",
+       " np.float64(0.7241379310344828),\n",
+       " np.float64(0.575))"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row['fprs'], row['tprs'], row['spec'], row['sens']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fprs = np.array([0.0, 1.0 - row['spec'], 1.0])\n",
+    "tprs = np.array([0.0, row['sens'], 1.0])\n",
+    "fracs = 1.0 - (row['p']*tprs + row['n']*fprs)/(row['p'] + row['n'])\n",
+    "\n",
+    "p_fpr = exponential_fit(fracs, fprs)\n",
+    "p_tpr = exponential_fit(fracs, tprs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7731380223583438, 1.206160775482911)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.plot(eval(row['fprs']), eval(row['tprs']))\n",
+    "plt.plot(*roc_min(1.0 - row['spec'], row['sens']))\n",
+    "plt.plot(*roc_max(1.0 - row['spec'], row['sens']))\n",
+    "plt.plot(((1.0 - x**p_fpr)**(1.0/p_fpr)), ((1.0 - x**p_tpr)**(1.0/p_tpr)))\n",
+    "p_fpr, p_tpr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp = data[data['n_nodes'] > 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f0071aa1e50>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "#plt.xlim((0.5, 1))\n",
+    "#plt.ylim((0.5, 1))\n",
+    "plt.scatter(tmp['auc'], tmp['auc_min_max'], s=1)\n",
+    "plt.scatter(tmp['auc'], tmp['auc_min_max_weighted'], s=1)\n",
+    "plt.scatter(tmp['auc'], tmp['auc_integral'], s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6464459659423477, 0.8033864568145052, 0.6298287243049956)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(tmp['auc'], tmp['auc_min_max']), r2_score(tmp['auc'], tmp['auc_min_max_weighted']), r2_score(tmp['auc'], tmp['auc_integral'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f006c9a7a10>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.xlim((0.5, 1))\n",
+    "plt.ylim((0.5, 1))\n",
+    "plt.scatter(tmp['auc'], tmp['auc_min_max_best'], s=1)\n",
+    "#plt.scatter(tmp['auc'], tmp['auc_min_maxa_best'], s=1)\n",
+    "plt.scatter(tmp['auc'], tmp['auc_min_maxa_best_weighted'], s=1)\n",
+    "plt.scatter(tmp['auc'], tmp['auc_integral_best'], s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp2 = tmp[['auc', 'auc_min_max_best', 'auc_min_maxa_best', 'auc_min_maxa_best_weighted', 'auc_integral_best']].dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.25937737096156743, -0.1315558311072753, -0.19677445407509464)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(tmp2['auc'], tmp2['auc_min_max_best']), r2_score(tmp2['auc'], tmp2['auc_min_maxa_best']), r2_score(tmp2['auc'], tmp2['auc_min_maxa_best_weighted'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_curve(row, values, fracs):\n",
+    "    if values == 'fprs':\n",
+    "        values = row[values]\n",
+    "        fracs = row[fracs]\n",
+    "        p = np.logspace(-2, 0, 3000)\n",
+    "    else:\n",
+    "        values = row[values]\n",
+    "        fracs = row[fracs]\n",
+    "        p = np.logspace(0, 2, 3000)\n",
+    "    \n",
+    "    err = np.abs(values[:, None]**p - 1 + (1 - fracs)[:, None]**p)\n",
+    "    err = np.mean(err, axis=0)\n",
+    "    exp = p[np.argmin(err)]\n",
+    "\n",
+    "    pred = (1 - (1 - fracs)**exp)**(1/exp)\n",
+    "\n",
+    "    if len(values) < 2:\n",
+    "        return (1, exp, 0)\n",
+    "\n",
+    "    r2 = r2_score(values, pred)\n",
+    "\n",
+    "    return (r2, exp, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def auc_est6(sens, spec):\n",
+    "    p = np.logspace(-3, 3, 3000)\n",
+    "    \n",
+    "    err = np.abs(sens**p - 1 + spec**p)\n",
+    "    exp = p[np.argmin(err)]\n",
+    "\n",
+    "    x = np.linspace(0, 1, 1000)\n",
+    "    y = (1 - (1 - x)**exp)**(1/exp)\n",
+    "    \n",
+    "    return np.sum((y[1:] + y[:-1])/2*(x[1:] - x[:-1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def auc_est5(sens, spec, p, n):\n",
+    "    spec = max(min(spec, 0.999), 0.001)\n",
+    "    sens = max(min(sens, 0.999), 0.001)\n",
+    "    frac = (sens*p + (1 - spec)*n)/(p + n)\n",
+    "    exp_tpr = fit_curve({'tprs': np.array([sens]), 'fracs': np.array([frac])}, 'tprs', 'fracs')[1]\n",
+    "    exp_fpr = fit_curve({'fprs': np.array([1 - spec]), 'fracs': np.array([frac])}, 'fprs', 'fracs')[1]\n",
+    "\n",
+    "    x = np.linspace(0, 1, 1000)\n",
+    "    fprs = (1 - (1 - x)**exp_fpr)**(1/exp_fpr)\n",
+    "    tprs = (1 - (1 - x)**exp_tpr)**(1/exp_tpr)\n",
+    "\n",
+    "    return np.sum((tprs[1:] + tprs[:-1])/2*(fprs[1:] - fprs[:-1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"def auc_est6(row):\\n    return row['exp_fpr'] / (row['exp_fpr'] + row['exp_tpr'])\""
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"def auc_est6(row):\n",
+    "    return row['exp_fpr'] / (row['exp_fpr'] + row['exp_tpr'])\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data = data[(data['sens'] > 0) & (data['sens'] < 1) & (data['spec'] > 0) & (data['spec'] < 1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['min_class'] = data.apply(lambda row: min(row['p'], row['n']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fe2f54b50d0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data['min_class'], data['n_nodes'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data = data[data.apply(lambda row: min(row['p'], row['n']), axis=1) > 40]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_est5'] = data.apply(lambda row: auc_est5(row['sens'], row['spec'], row['p'], row['n']), axis=1)\n",
+    "\n",
+    "data['auc_min_max'] = (data['sens'] + data['spec'])/2.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_est6'] = data.apply(lambda row: auc_est6(row['sens'], row['spec']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def auc_composite(row):\n",
+    "    auc_upper = row['auc_est5']\n",
+    "    auc_lower = row['auc_min_max']\n",
+    "    weight = row['n_nodes']\n",
+    "    weight_upper = weight**2\n",
+    "    weight_lower = max(25 - weight, 0)**2\n",
+    "    weight_sum = weight_lower + weight_upper\n",
+    "    weight_upper = weight_upper / weight_sum\n",
+    "    weight_lower = weight_lower / weight_sum\n",
+    "\n",
+    "    return auc_upper * weight_upper + auc_lower * weight_lower\n",
+    "\n",
+    "data['auc_comp'] = data.apply(auc_composite, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data = data[data['classifier'] != 'DecisionTreeClassifier']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data = data[data['n_nodes'] > 20]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data = data[(data['sens'] > 0) & (data['sens'] < 1) & (data['spec'] > 0) & (data['spec'] < 1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8003842662327819,\n",
+       " 0.839017739487883,\n",
+       " 0.958715476443063,\n",
+       " -0.21635288511704487)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(data['auc'], data['auc_est5']), r2_score(data['auc'], data['auc_min_max']), r2_score(data['auc'], data['auc_comp']), r2_score(data['auc'], data['auc_est6'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fe2f546e060>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "for classifier in data['classifier'].drop_duplicates():\n",
+    "    tmp = data[data['classifier'] == classifier]\n",
+    "    plt.scatter(tmp['auc'], tmp['auc_comp'], s=1, label=classifier)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.9763938791759392)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auc_est5(0.776, 0.978, 30000, 300000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def auc_est5_stats(sens, spec, p, n):\n",
+    "    aucs = []\n",
+    "    for _ in range(100000):\n",
+    "        sens_samp = beta.rvs(b=p-sens*p+1, a=sens*p)\n",
+    "        spec_samp = beta.rvs(b=n-spec*n+1, a=spec*n)\n",
+    "        p_samp = binom.rvs(p, sens_samp)\n",
+    "        n_samp = binom.rvs(n, spec_samp)\n",
+    "\n",
+    "        aucs.append(auc_est5(p_samp/p, n_samp/n, p, n))\n",
+    "    return aucs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp = data.sample(10, random_state=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sens</th>\n",
+       "      <th>spec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>18364</th>\n",
+       "      <td>0.957977</td>\n",
+       "      <td>0.960859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6235</th>\n",
+       "      <td>0.918968</td>\n",
+       "      <td>0.934199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9606</th>\n",
+       "      <td>0.977767</td>\n",
+       "      <td>0.943750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11594</th>\n",
+       "      <td>0.897569</td>\n",
+       "      <td>0.631944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8302</th>\n",
+       "      <td>0.779804</td>\n",
+       "      <td>0.708000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10399</th>\n",
+       "      <td>0.631250</td>\n",
+       "      <td>0.705082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18476</th>\n",
+       "      <td>0.724973</td>\n",
+       "      <td>0.668750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8043</th>\n",
+       "      <td>0.878641</td>\n",
+       "      <td>0.954955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7577</th>\n",
+       "      <td>0.750167</td>\n",
+       "      <td>0.690236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7129</th>\n",
+       "      <td>0.619307</td>\n",
+       "      <td>0.713138</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           sens      spec\n",
+       "18364  0.957977  0.960859\n",
+       "6235   0.918968  0.934199\n",
+       "9606   0.977767  0.943750\n",
+       "11594  0.897569  0.631944\n",
+       "8302   0.779804  0.708000\n",
+       "10399  0.631250  0.705082\n",
+       "18476  0.724973  0.668750\n",
+       "8043   0.878641  0.954955\n",
+       "7577   0.750167  0.690236\n",
+       "7129   0.619307  0.713138"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tmp[['sens', 'spec']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "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[37], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tmp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maucs\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m tmp\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: auc_est5_stats(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msens\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspec\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m]), 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[37], line 1\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(row)\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tmp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maucs\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m tmp\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: auc_est5_stats(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msens\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspec\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[34], line 9\u001b[0m, in \u001b[0;36mauc_est5_stats\u001b[0;34m(sens, spec, p, n)\u001b[0m\n\u001b[1;32m      6\u001b[0m     p_samp \u001b[38;5;241m=\u001b[39m binom\u001b[38;5;241m.\u001b[39mrvs(p, sens_samp)\n\u001b[1;32m      7\u001b[0m     n_samp \u001b[38;5;241m=\u001b[39m binom\u001b[38;5;241m.\u001b[39mrvs(n, spec_samp)\n\u001b[0;32m----> 9\u001b[0m     aucs\u001b[38;5;241m.\u001b[39mappend(auc_est5(p_samp\u001b[38;5;241m/\u001b[39mp, n_samp\u001b[38;5;241m/\u001b[39mn, p, n))\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m aucs\n",
+      "Cell \u001b[0;32mIn[19], line 8\u001b[0m, in \u001b[0;36mauc_est5\u001b[0;34m(sens, spec, p, n)\u001b[0m\n\u001b[1;32m      5\u001b[0m exp_tpr \u001b[38;5;241m=\u001b[39m fit_curve({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtprs\u001b[39m\u001b[38;5;124m'\u001b[39m: np\u001b[38;5;241m.\u001b[39marray([sens]), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfracs\u001b[39m\u001b[38;5;124m'\u001b[39m: np\u001b[38;5;241m.\u001b[39marray([frac])}, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtprs\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfracs\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m      6\u001b[0m exp_fpr \u001b[38;5;241m=\u001b[39m fit_curve({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfprs\u001b[39m\u001b[38;5;124m'\u001b[39m: np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m spec]), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfracs\u001b[39m\u001b[38;5;124m'\u001b[39m: np\u001b[38;5;241m.\u001b[39marray([frac])}, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfprs\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfracs\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m----> 8\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1000\u001b[39m)\n\u001b[1;32m      9\u001b[0m fprs \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m x)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mexp_fpr)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39mexp_fpr)\n\u001b[1;32m     10\u001b[0m tprs \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m x)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mexp_tpr)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39mexp_tpr)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/numpy/_core/function_base.py:146\u001b[0m, in \u001b[0;36mlinspace\u001b[0;34m(start, stop, num, endpoint, retstep, dtype, axis, device)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;66;03m# Use `dtype=type(dt)` to enforce a floating point evaluation:\u001b[39;00m\n\u001b[1;32m    145\u001b[0m delta \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msubtract(stop, start, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m(dt))\n\u001b[0;32m--> 146\u001b[0m y \u001b[38;5;241m=\u001b[39m _nx\u001b[38;5;241m.\u001b[39marange(\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;241m0\u001b[39m, num, dtype\u001b[38;5;241m=\u001b[39mdt, device\u001b[38;5;241m=\u001b[39mdevice\n\u001b[1;32m    148\u001b[0m )\u001b[38;5;241m.\u001b[39mreshape((\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,) \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m,) \u001b[38;5;241m*\u001b[39m ndim(delta))\n\u001b[1;32m    150\u001b[0m \u001b[38;5;66;03m# In-place multiplication y *= delta/div is faster, but prevents\u001b[39;00m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;66;03m# the multiplicant from overriding what class is produced, and thus\u001b[39;00m\n\u001b[1;32m    152\u001b[0m \u001b[38;5;66;03m# prevents, e.g. use of Quantities, see gh-7142. Hence, we multiply\u001b[39;00m\n\u001b[1;32m    153\u001b[0m \u001b[38;5;66;03m# in place only for standard scalar types.\u001b[39;00m\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m div \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "tmp['aucs'] = tmp.apply(lambda row: auc_est5_stats(row['sens'], row['spec'], row['p'], row['n']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((array([419.,  95.,  82.,  82.,  89.,  82.,  59.,  42.,  38.,  12.]),\n",
+       "  array([0.5       , 0.53959259, 0.57918518, 0.61877777, 0.65837037,\n",
+       "         0.69796296, 0.73755555, 0.77714814, 0.81674073, 0.85633332,\n",
+       "         0.89592592]),\n",
+       "  <BarContainer object of 10 artists>),\n",
+       " np.float64(0.7333333333333333),\n",
+       " np.float64(0.6546049467138704))"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "row = tmp.iloc[6]\n",
+    "plt.hist(row['aucs']), row['auc'], row['auc_est5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aucs = auc_est5_stats(0.7763, 0.9768, 20_000, 200_000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((array([5.9000e+01, 6.8600e+02, 4.4330e+03, 1.4509e+04, 3.0116e+04,\n",
+       "         2.9231e+04, 1.6381e+04, 4.0490e+03, 5.1300e+02, 2.3000e+01]),\n",
+       "  array([0.97267136, 0.9732576 , 0.97384383, 0.97443006, 0.97501629,\n",
+       "         0.97560253, 0.97618876, 0.97677499, 0.97736122, 0.97794746,\n",
+       "         0.97853369]),\n",
+       "  <BarContainer object of 10 artists>),\n",
+       " np.float64(0.9726713642682261))"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(aucs), min(aucs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "single positional indexer is out-of-bounds",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1184], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m row \u001b[38;5;241m=\u001b[39m data[(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0.8\u001b[39m) \u001b[38;5;241m&\u001b[39m (data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_est5\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.99\u001b[39m)]\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m2\u001b[39m]\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/indexing.py:1191\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1189\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m   1190\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_deprecated_callable_usage(key, maybe_callable)\n\u001b[0;32m-> 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(maybe_callable, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/indexing.py:1752\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index by location index with a non-integer key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1751\u001b[0m \u001b[38;5;66;03m# validate the location\u001b[39;00m\n\u001b[0;32m-> 1752\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_integer(key, axis)\n\u001b[1;32m   1754\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_ixs(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/indexing.py:1685\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1683\u001b[0m len_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis))\n\u001b[1;32m   1684\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis:\n\u001b[0;32m-> 1685\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle positional indexer is out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
+     ]
+    }
+   ],
+   "source": [
+    "row = data[(data['auc'] < 0.8) & (data['auc_est5'] > 0.99)].iloc[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dataset                                                   appendicitis\n",
+       "classifier                                                         SVC\n",
+       "classifier_params    {'probability': True, 'C': 0.10549567054834408...\n",
+       "p                                                                    4\n",
+       "n                                                                   18\n",
+       "p_train                                                             17\n",
+       "n_train                                                             67\n",
+       "auc                                                           0.763889\n",
+       "auc_train                                                     0.852502\n",
+       "fprs                 [0.0, 0.0, 0.9444444444444444, 0.9444444444444...\n",
+       "tprs                                       [0.0, 0.75, 0.75, 1.0, 1.0]\n",
+       "thresholds           [inf, 0.3377494314457134, 0.08287531095442542,...\n",
+       "n_nodes                                                              5\n",
+       "fprs_train           [0.0, 0.0, 0.014925373134328358, 0.01492537313...\n",
+       "tprs_train           [0.0, 0.4117647058823529, 0.4117647058823529, ...\n",
+       "thresholds_train     [inf, 0.9008302699372828, 0.8633769506146745, ...\n",
+       "n_nodes_train                                                       21\n",
+       "acc                                                           0.954545\n",
+       "sens                                                              0.75\n",
+       "spec                                                               1.0\n",
+       "best_acc                                                      0.954545\n",
+       "best_sens                                                         0.75\n",
+       "best_spec                                                          1.0\n",
+       "acc_train                                                     0.880952\n",
+       "sens_train                                                    0.705882\n",
+       "spec_train                                                    0.925373\n",
+       "best_acc_train                                                0.892857\n",
+       "best_sens_train                                               0.647059\n",
+       "best_spec_train                                               0.955224\n",
+       "fracs                [0.0, 0.13636363636363635, 0.9090909090909091,...\n",
+       "r2_fpr                                                        0.985208\n",
+       "exp_fpr                                                       0.952781\n",
+       "fit_mode_fpr                                                         0\n",
+       "r2_tpr                                                        0.907485\n",
+       "exp_tpr                                                       3.320272\n",
+       "fit_mode_tpr                                                         0\n",
+       "min_class                                                            4\n",
+       "auc_est5                                                       0.99595\n",
+       "auc_min_max                                                      0.875\n",
+       "auc_est6                                                      0.999419\n",
+       "auc_comp                                                      0.882115\n",
+       "Name: 13866, dtype: object"
+      ]
+     },
+     "execution_count": 1107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def auc_est5(sens, spec, p, n):\n",
+    "    frac = (sens*p + (1 - spec)*n)/(p + n)\n",
+    "    exp_tpr = fit_curve({'tprs': np.array([sens]), 'fracs': np.array([frac])}, 'tprs', 'fracs')[1]\n",
+    "    exp_fpr = fit_curve({'fprs': np.array([1 - spec]), 'fracs': np.array([frac])}, 'fprs', 'fracs')[1]\n",
+    "\n",
+    "    print(exp_tpr, exp_fpr)\n",
+    "\n",
+    "    x = np.linspace(0, 1, 10000)\n",
+    "    fprs = (1 - (1 - x)**exp_fpr)**(1/exp_fpr)\n",
+    "    tprs = (1 - (1 - x)**exp_tpr)**(1/exp_tpr)\n",
+    "\n",
+    "    plt.plot(fprs, tprs)\n",
+    "    plt.plot(x, fprs)\n",
+    "    plt.plot(x, tprs)\n",
+    "\n",
+    "    print(fprs, tprs)\n",
+    "\n",
+    "    return np.sum((tprs[1:] + tprs[:-1])/2*(fprs[1:] - fprs[:-1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.3177241960960204 0.01\n",
+      "[0.00000000e+000 0.00000000e+000 0.00000000e+000 ... 1.55884352e-109\n",
+      " 2.76992055e-106 1.00000000e+000] [0.         0.08940039 0.11016867 ... 1.         1.         1.        ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.9999999999999919)"
+      ]
+     },
+     "execution_count": 1109,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "auc_est5(row['sens'], row['spec'], row['p'], row['n'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def auc_est4(row):\n",
+    "\n",
+    "    p = row['p']\n",
+    "    n = row['n']\n",
+    "\n",
+    "    th = p/(p + n)\n",
+    "\n",
+    "    tpr = row['sens']\n",
+    "    fpr = 1 - row['spec']\n",
+    "\n",
+    "    alpha = max(0, np.log(1 - tpr)/ np.log(th))\n",
+    "    beta = max(0, np.log(fpr)/np.log(1 - th))\n",
+    "\n",
+    "    print(alpha, beta)\n",
+    "\n",
+    "    tprs = 1 - np.linspace(0, 1, 2000)**alpha\n",
+    "    fprs = (1 - np.linspace(0, 1, 2000))**beta\n",
+    "\n",
+    "    return np.sum((tprs[1:] + tprs[:-1])/2 * (fprs[:-1] - fprs[1:]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "\"['auc_est4'] not in index\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1111], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tmp2[[\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_est4\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msens\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspec\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn_nodes\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:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4106\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m   4107\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 4108\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[39m_get_indexer_strict(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m   4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m   4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/indexes/base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   6198\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 6200\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_if_missing(keyarr, indexer, axis_name)\n\u001b[1;32m   6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m   6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[1;32m   6204\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/indexes/base.py:6252\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   6249\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   6251\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m-> 6252\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mKeyError\u001b[0m: \"['auc_est4'] not in index\""
+     ]
+    }
+   ],
+   "source": [
+    "tmp2[['auc', 'auc_est4', 'sens', 'spec', 'p', 'n', 'n_nodes']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 6.383263099338103\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.8645583979077229)"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auc_est4(data.iloc[19999])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fb5d6f737d0>]"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(fprs, tprs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp = data[(data['sens'] > 0) & (data['sens'] < 1) & (data['spec'] > 0) & (data['spec'] < 1)].sample(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5038167938931297 0.45038167938931295 2.2887054167495595 2.460921578846517 59 72 6.092286952696924e-08 0.8388804247851294 0.8813559322033898 0.22925087386907328 0.19444444444444442\n",
+      "0.5481927710843374 0.4879518072289157 6.133338360122559 3.0557743442915037 81 85 2.7150456327618144e-09 0.987733793378303 0.8641975308641975 0.12933602689011645 0.24705882352941178\n",
+      "0.3376623376623376 0.35064935064935066 2.6736636812950803 10.098364971651748 54 100 3.4541369764440333e-09 0.9393063055524823 0.6111111111111112 0.012774600321030468 0.18999999999999995\n",
+      "0.3064516129032258 0.25806451612903225 2.777223955699531 8.754584902464511 16 46 1.0321265397905677e-08 0.9767599144030501 0.5 0.07330091324934027 0.23913043478260865\n",
+      "0.572289156626506 0.4879518072289157 3.0663495109125387 1.9546669424687464 81 85 3.3571593394121635e-08 0.8892216851815073 0.8518518518518519 0.2702710344401577 0.3058823529411765\n",
+      "0.5915492957746479 0.647887323943662 4.09876636139165 1.8136966095989817 46 25 3.597732800564302e-08 0.8311968985781684 0.8913043478260869 0.15059760444055864 0.040000000000000036\n",
+      "0.34210526315789475 0.3684210526315789 2.427148083780218 10.01728863426509 42 72 5.289007343201035e-09 0.9113959167955067 0.9047619047619048 0.01001904016169285 0.01388888888888884\n",
+      "0.22580645161290322 0.1935483870967742 4.9280280177065245 14.95525072647426 6 25 4.013247015266508e-08 0.9996943116046819 0.6666666666666666 0.040073315450613343 0.12\n",
+      "0.4946236559139785 0.34408602150537637 2.68600784527522 3.1998254006604614 32 61 2.3905395818424324e-07 0.9430512747795403 0.625 0.2593833022798824 0.42622950819672134\n",
+      "0.6086956521739131 0.5797101449275363 3.811933242385137 1.6389044649628666 40 29 3.431038664292174e-08 0.8748658751817077 0.65 0.2415642284591385 0.5517241379310345\n",
+      "0.4032258064516129 0.25806451612903225 5.14254137374281 5.459895066210372 16 46 4.596825703417906e-08 0.9990563989650301 0.4375 0.19598044492590114 0.3913043478260869\n",
+      "0.40860215053763443 0.34408602150537637 8.565114701721441 5.497750747420089 32 61 8.306458021412055e-08 0.9998924773486616 0.46875 0.09841693442355527 0.3770491803278688\n",
+      "0.4578313253012048 0.5120481927710844 3.354527896502286 15.660250318291546 85 81 4.401567998968403e-11 0.8941050875169688 0.7294117647058823 1.3179675938782458e-05 0.1728395061728395\n",
+      "0.7306397306397306 0.7104377104377104 37.6611843606967 2.1482410977051067 211 86 6.842413524665858e-09 0.9999974396841873 0.8436018957345972 0.06977374719573581 0.4534883720930233\n",
+      "0.5602409638554217 0.4879518072289157 3.027751863300086 2.0729037861846837 81 85 3.018256755638049e-09 0.8861107660706464 0.8395061728395061 0.24970620526172968 0.2941176470588235\n",
+      "0.4175084175084175 0.2895622895622896 7.748583288553034 5.013892794376777 86 211 4.8027951060625185e-08 0.9999325063257345 0.6976744186046512 0.1801222283966131 0.3033175355450237\n",
+      "0.56 0.9 9.06231825389081 1.1940410421117038 90 10 1.8619966224431295e-07 0.6151149359828789 0.6111111111111112 0.06396743815071226 0.09999999999999998\n",
+      "0.09523809523809522 0.05442176870748299 4.567356551740081 56.1613267970889 8 139 5.468867858571258e-10 0.9999983180118168 0.75 0.04316556385261226 0.05755395683453235\n",
+      "0.4090909090909091 0.35064935064935066 3.9550254942480763 5.366392334953542 54 100 2.1000399086368304e-08 0.984152481549515 0.6851851851851852 0.09855762762264739 0.26\n",
+      "0.4032258064516129 0.25806451612903225 5.14254137374281 5.459895066210372 16 46 4.596825703417906e-08 0.9990563989650301 0.625 0.19598044492590114 0.32608695652173914\n"
+     ]
+    }
+   ],
+   "source": [
+    "tmp['auc_est3'] = tmp.apply(lambda row: auc_est3(row['spec'], row['sens'], row['p'], row['n']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>auc</th>\n",
+       "      <th>auc_est3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>18549</th>\n",
+       "      <td>0.924788</td>\n",
+       "      <td>0.892745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11156</th>\n",
+       "      <td>0.867901</td>\n",
+       "      <td>0.989294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10920</th>\n",
+       "      <td>0.710556</td>\n",
+       "      <td>0.994631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19394</th>\n",
+       "      <td>0.730978</td>\n",
+       "      <td>0.993539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2191</th>\n",
+       "      <td>0.837981</td>\n",
+       "      <td>0.899333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16046</th>\n",
+       "      <td>0.981739</td>\n",
+       "      <td>0.922677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5161</th>\n",
+       "      <td>0.993386</td>\n",
+       "      <td>0.992180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7794</th>\n",
+       "      <td>0.820000</td>\n",
+       "      <td>0.999928</td>\n",
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+       "    <tr>\n",
+       "      <th>10307</th>\n",
+       "      <td>0.711066</td>\n",
+       "      <td>0.945325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8183</th>\n",
+       "      <td>0.620690</td>\n",
+       "      <td>0.898618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5979</th>\n",
+       "      <td>0.539402</td>\n",
+       "      <td>0.997301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2834</th>\n",
+       "      <td>0.631660</td>\n",
+       "      <td>0.999617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8292</th>\n",
+       "      <td>0.855192</td>\n",
+       "      <td>0.999393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13895</th>\n",
+       "      <td>0.800727</td>\n",
+       "      <td>0.999131</td>\n",
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+       "    <tr>\n",
+       "      <th>7756</th>\n",
+       "      <td>0.861147</td>\n",
+       "      <td>0.906701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8827</th>\n",
+       "      <td>0.801444</td>\n",
+       "      <td>0.999137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4880</th>\n",
+       "      <td>0.806667</td>\n",
+       "      <td>0.931047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2355</th>\n",
+       "      <td>0.930306</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2582</th>\n",
+       "      <td>0.806111</td>\n",
+       "      <td>0.993234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15332</th>\n",
+       "      <td>0.680707</td>\n",
+       "      <td>0.997301</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            auc  auc_est3\n",
+       "18549  0.924788  0.892745\n",
+       "11156  0.867901  0.989294\n",
+       "10920  0.710556  0.994631\n",
+       "19394  0.730978  0.993539\n",
+       "2191   0.837981  0.899333\n",
+       "16046  0.981739  0.922677\n",
+       "5161   0.993386  0.992180\n",
+       "7794   0.820000  0.999928\n",
+       "10307  0.711066  0.945325\n",
+       "8183   0.620690  0.898618\n",
+       "5979   0.539402  0.997301\n",
+       "2834   0.631660  0.999617\n",
+       "8292   0.855192  0.999393\n",
+       "13895  0.800727  0.999131\n",
+       "7756   0.861147  0.906701\n",
+       "8827   0.801444  0.999137\n",
+       "4880   0.806667  0.931047\n",
+       "2355   0.930306  1.000000\n",
+       "2582   0.806111  0.993234\n",
+       "15332  0.680707  0.997301"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tmp[['auc', 'auc_est3']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fb5d6d3be90>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UFMTsdQAAgOQTVXhZsGCBHA7HgLc9e/YMuZhHH31Ura2t+sc//qF///vfmjdvnm6//XZ99tln/a6zcOFChUKhyO3AgQNDfn4AAJD8hkUzeP78+ZoxY8aAYwoLC+X1etXS0tJr+YkTJxQMBuX1evtcb+/evXrhhRfU0NCgiy++WJJ06aWX6p///KdefPFFvfzyy32u53K55HK5onkZAADAYFGFl9zcXOXm5p52XFlZmVpbW7Vt2zaNHz9ekrRhwwaFw2FNnDixz3WOHTsmSXI6ex8MysjIUDgcjqZMAACQwmyZ8zJ27FhVVFRo1qxZ8vv92rx5s2pqajR16tTIlUaHDh1SUVGR/H6/JKmoqEgXXnih7r33Xvn9fu3du1e///3vtX79et188812lAkAAAxkW5+XVatWqaioSFOmTFFlZaUmT56sV155JXJ/V1eXGhsbI0dczjrrLK1bt065ubmqqqrSz372M/3pT3/S66+/rsrKSrvKBAAAhrGlz0si0ecFAADzJLzPCwAAgF0ILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMMqwRBcAACbpDlvyNwXV0t6hvEy3SkfnKMPpSHRZQFohvADAINU1NGvxml1qDnVElvk8btVWFatinC+BlQHphdNGADAIdQ3NmrNye6/gIkmBUIfmrNyuuobmBFUGpB/CCwCcRnfY0uI1u2T1cV/PssVrdqk73NcIALFGeAGA0/A3BU854vJ9lqTmUIf8TcH4FQWkMcILAJxGS3v/wWUo4wCcGcILAJxGXqY7puMAnBnCCwCcRunoHPk8bvV3QbRDJ686Kh2dE8+ygLRFeAGA08hwOlRbVSxJpwSYnr9rq4rp9wLECeEFAAahYpxPy6aVyOvpfWrI63Fr2bQS+rwAcUSTOgAYpIpxPl1X7O2zwy6dd4H4IbwAQBQynA6VjRnZaxmdd4H44rQRAJwBOu8C8Ud4AYAhovMukBiEFwAYIjrvAonBnBcAGKJ077zLJGUkCuEFAIYonTvvMkkZicRpIwAYonTtvMskZSQa4QUAhigdO+8ySRnJgPACAGcg3TrvMkkZyYA5LwBwhgbqvJtq0n2SMpID4QUAYqCvzrupKJ0nKSN5cNoIADBo6TpJGcmF8AIAGLR0nKSM5EN4AQBEJd0mKSP5MOcFABC1dJqkjORDeAEADEm6TFJG8uG0EQAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYxbbwEgwGVV1draysLGVnZ2vmzJk6cuTIgOvs3btXv/zlL5Wbm6usrCzdfvvtOnz4sF0lAgAAA9kWXqqrq7Vz506tX79ea9eu1caNGzV79ux+xx89elTXX3+9HA6HNmzYoM2bN+v48eOqqqpSOBy2q0wAAGAYh2VZVqwfdPfu3SouLtbWrVs1YcIESVJdXZ0qKyt18OBB5efnn7LOBx98oBtvvFHffvutsrKyJEmhUEg/+tGP9MEHH6i8vHxQz93W1iaPx6NQKBR5HAAAkNyi+f625chLfX29srOzI8FFksrLy+V0OrVly5Y+1+ns7JTD4ZDL5Yosc7vdcjqd2rRpkx1lAgAAA9kSXgKBgPLy8notGzZsmHJychQIBPpc54orrtCIESP0yCOP6NixYzp69KgefPBBdXd3q7m5ud/n6uzsVFtbW68bAABIXVGFlwULFsjhcAx427Nnz5AKyc3N1RtvvKE1a9bo3HPPlcfjUWtrq0pKSuR09l/mkiVL5PF4IreCgoIhPT8AADDDsGgGz58/XzNmzBhwTGFhobxer1paWnotP3HihILBoLxeb7/rXn/99dq7d6++/vprDRs2TNnZ2fJ6vSosLOx3nYULF2revHmRv9va2ggwAACksKjCS25urnJzc087rqysTK2trdq2bZvGjx8vSdqwYYPC4bAmTpx42vXPO++8yDotLS266aab+h3rcrl6zZMBAACpzZY5L2PHjlVFRYVmzZolv9+vzZs3q6amRlOnTo1caXTo0CEVFRXJ7/dH1nvttdf0ySefaO/evVq5cqVuu+02zZ07VxdddJEdZQIAAANFdeQlGqtWrVJNTY2mTJkip9OpW2+9VUuXLo3c39XVpcbGRh07diyyrLGxUQsXLlQwGNQFF1ygRYsWae7cuXaVCAAADGRLn5dEos8LAADmSXifFwAAALsQXgAAgFEILwAAwCiEFwAAYBTbrjYCEH/dYUv+pqBa2juUl+lW6egcZTgdiS4LAGKK8AKkiLqGZi1es0vNoY7IMp/HrdqqYlWM8yWwMgCILU4bASmgrqFZc1Zu7xVcJCkQ6tCcldtV19D/j5sCgGkIL4DhusOWFq/Zpb4aNvUsW7xml7rDKdXSCUAaI7wAhvM3BU854vJ9lqTmUIf8TcH4FQUANiK8AIZrae8/uAxlHAAkO8ILYLi8THdMxwFAsiO8AIYrHZ0jn8et/i6IdujkVUelo3PiWRYA2IbwAhguw+lQbVWxJJ0SYHr+rq0qpt+LzbrDlur3fqP/u+OQ6vd+wwRpwEb0eQFSQMU4n5ZNKzmlz4uXPi9xQY8dIL4clmWl1H8PovlJbSDV0GE3/np67Pzwg7TnXV82rYQAAwxCNN/fHHkBUkiG06GyMSMTXUbaOF2PHYdO9ti5rthLiARiiDkvADBE9NiB6Uydq8WRFwAYInrswGQmz9XiyAsADBE9dmAq038PjfACAENEjx2YKBV+D43wAgBDRI8dmCgV5moRXgDgDPT02PF6ep8a8nrcXCaNpJQKc7WYsAsAZ6hinE/XFXvpsQMjpMJcLcILAMQAPXZgip65WoFQR5/zXhw6eeQwmedqcdoIAIA0kgpztQgvAACkmaHO1UqWpnacNgIAIA1FO1crmZra8cOMAABgQPH4AdJovr85bQQAAPqVjE3tCC8AAKBfydjUjjkvQArpDlv0GgEQU8nY1I7wAqSIZJpMByB1JGNTO04bASnA9F+IBZC8kvEHSAkvgOGScTIdgNSRjE3tCC+A4ZJxMh2A1JJsP0DKnBfAcMk4mQ5A6kmmHyAlvACGS8bJdABSU7L8ACmnjQDDJeNkOgCwE+EFMFwyTqYDADsRXoAUkGyT6QDATsx5AVJEMk2mAwA7EV6AFJIsk+kAwE6cNgIAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKPwwIwAA/egOW/xSexIivAAA0Ie6hmYtXrNLzaGOyDKfx63aqmJVjPMlsDJw2ggAgB+oa2jWnJXbewUXSQqEOjRn5XbVNTQnqDJIHHkBAKCX7rClxWt2yerjvp5lv3nnM33XFZY3i1NJiUB4GSTOeyIaqbK/pMrrSGa8x8nH3xQ85YjLDwWPdmnuX3dI4lRSIhBeBoHznvFn8gd6quwvqfI67BCr/ZP3ODm1tA8cXH6o51TSsmklbLc4cViW1deRMWO1tbXJ4/EoFAopKyvrjB+v57znD9+kno8pdtbYM/kDPVX2l1R5HXaI1f7Je5y86vd+o//zv59EtY5Dktfj1qZHrjXmP1rJJprvbybsDmAw5z0Xr9ml7nBK5b+EMnmSXKrsL6nyOuwQq/2T9zi5lY7Okc/jVjQRxJLUHOqQvyloV1n4HsLLAE533pOdNbZM/0BPlf0lVV5HrMVy/+Q9Tm4ZTodqq4olKaoAI0V/yglDQ3gZwGB3QnbW2DD9Az1V9pdUeR2xFsv9k/c4+VWM82nZtBJ5Pe6o1svLjG48hoYJuwMY7E7Izhobpn+gp8r+kiqvI9ZiuX/yHpuhYpxP1xV75W8KKhD6Tk++v1vfHj3e59G3njkvpaNz4l1mWuLIywBOd97ToZMT9dhZY8P0D/RU2V9S5XXEWiz3T95jc2Q4HSobM1K/LDlfz/xynKRTTyX1/F1bVcxk3TixLbw8/fTTmjRpks455xxlZ2cPah3LsvTYY4/J5/Pp7LPPVnl5ub744gu7Sjytgc57srPGnukf6Kmyv6TK64i1WO6fvMdm6u9Uktfj5uqwOLMtvBw/fly33Xab5syZM+h1fve732np0qV6+eWXtWXLFo0YMUI33HCDOjoSd5qAnTV+UuEDPVX2l1R5HbEU6/2T99hMFeN82vTItfrLrCv0x6mX6S+zrtCmR65le8WZ7X1eVqxYoQceeECtra0DjrMsS/n5+Zo/f74efPBBSVIoFNKoUaO0YsUKTZ06dVDPF+s+Lz1MbppmGpP7vPRIlf0lVV5HLMV6/+Q9Bk6K5vs7aSbsNjU1KRAIqLy8PLLM4/Fo4sSJqq+v7ze8dHZ2qrOzM/J3W1ubLfX1nPeE/b4/Sc7UD/RU2V9S5XXEUqz3T95jIHpJE14CgYAkadSoUb2Wjxo1KnJfX5YsWaLFixfbWhvijw90JDP2TyCxoprzsmDBAjkcjgFve/bssavWPi1cuFChUChyO3DgQFyfHwAAxFdUR17mz5+vGTNmDDimsLBwSIV4vV5J0uHDh+Xz/f/zxocPH9Zll13W73oul0sul2tIzwkAAMwTVXjJzc1Vbm6uLYWMHj1aXq9XH374YSSstLW1acuWLVFdsQQAAFKbbZdK79+/Xzt27ND+/fvV3d2tHTt2aMeOHTpy5EhkTFFRkd555x1JksPh0AMPPKCnnnpK7733nj777DPdeeedys/P180332xXmQAAwDC2Tdh97LHH9Prrr0f+vvzyyyVJH330ka655hpJUmNjo0KhUGTMww8/rKNHj2r27NlqbW3V5MmTVVdXJ7c7OTuqAgCA+LO9z0u82dXnBQAA2Cea729+2wgAABiF8AIAAIxCeAEAAEZJmg67sdIzhceunwkAAACx1/O9PZipuCkXXtrb2yVJBQUFCa4EAABEq729XR6PZ8AxKXe1UTgc1ldffaXMzEw5HOb8kF8yamtrU0FBgQ4cOMCVWwnGtkgubI/kwbZIHme6LSzLUnt7u/Lz8+V0DjyrJeWOvDidTp1//vmJLiOlZGVl8aGQJNgWyYXtkTzYFsnjTLbF6Y649GDCLgAAMArhBQAAGIXwgn65XC7V1tbyq91JgG2RXNgeyYNtkTziuS1SbsIuAABIbRx5AQAARiG8AAAAoxBeAACAUQgvAADAKISXNPfiiy/qggsukNvt1sSJE+X3+wcc39raqvvvv18+n08ul0s//elPtW7dujhVm9qi2RbXXHONHA7HKbef//zncaw4tUX7b+MPf/iDLrroIp199tkqKCjQ3Llz1dHREadqU1s026Krq0tPPPGExowZI7fbrUsvvVR1dXVxrDZ1bdy4UVVVVcrPz5fD4dC777572nU+/vhjlZSUyOVy6cILL9SKFStiU4yFtLV69Wpr+PDh1quvvmrt3LnTmjVrlpWdnW0dPny4z/GdnZ3WhAkTrMrKSmvTpk1WU1OT9fHHH1s7duyIc+WpJ9pt8c0331jNzc2RW0NDg5WRkWG99tpr8S08RUW7PVatWmW5XC5r1apVVlNTk/X3v//d8vl81ty5c+NceeqJdls8/PDDVn5+vvX+++9be/futV566SXL7XZb27dvj3PlqWfdunXWokWLrLffftuSZL3zzjsDjt+3b591zjnnWPPmzbN27dplPf/881ZGRoZVV1d3xrUQXtJYaWmpdf/990f+7u7utvLz860lS5b0OX7ZsmVWYWGhdfz48XiVmDai3RY/9Nxzz1mZmZnWkSNH7CoxrUS7Pe6//37r2muv7bVs3rx51pVXXmlrnekg2m3h8/msF154odeyW265xaqurra1znQzmPDy8MMPWxdffHGvZXfccYd1ww03nPHzc9ooTR0/flzbtm1TeXl5ZJnT6VR5ebnq6+v7XOe9995TWVmZ7r//fo0aNUrjxo3TM888o+7u7niVnZKGsi1+aPny5Zo6dapGjBhhV5lpYyjbY9KkSdq2bVvkdMa+ffu0bt06VVZWxqXmVDWUbdHZ2Sm3291r2dlnn61NmzbZWitOVV9f32vbSdINN9ww6M+1gaTcDzNicL7++mt1d3dr1KhRvZaPGjVKe/bs6XOdffv2acOGDaqurta6dev05Zdf6r777lNXV5dqa2vjUXZKGsq2+D6/36+GhgYtX77crhLTylC2x69+9St9/fXXmjx5sizL0okTJ/TrX/9av/nNb+JRcsoayra44YYb9Oyzz+qqq67SmDFj9OGHH+rtt9/mP1kJEAgE+tx2bW1t+u6773T22WcP+bE58oJBC4fDysvL0yuvvKLx48frjjvu0KJFi/Tyyy8nurS0tnz5cl1yySUqLS1NdClp6+OPP9Yzzzyjl156Sdu3b9fbb7+t999/X08++WSiS0s7f/zjH/WTn/xERUVFGj58uGpqanT33XfL6eTrLpVw5CVNnXfeecrIyNDhw4d7LT98+LC8Xm+f6/h8Pp111lnKyMiILBs7dqwCgYCOHz+u4cOH21pzqhrKtuhx9OhRrV69Wk888YSdJaaVoWyPRx99VNOnT9c999wjSbrkkkt09OhRzZ49W4sWLeKLc4iGsi1yc3P17rvvqqOjQ998843y8/O1YMECFRYWxqNkfI/X6+1z22VlZZ3RUReJIy9pa/jw4Ro/frw+/PDDyLJwOKwPP/xQZWVlfa5z5ZVX6ssvv1Q4HI4s+/zzz+Xz+QguZ2Ao26LHG2+8oc7OTk2bNs3uMtPGULbHsWPHTgkoPSHf4ufjhuxM/m243W79+Mc/1okTJ/TWW2/pF7/4hd3l4gfKysp6bTtJWr9+/Wm33aCc8ZRfGGv16tWWy+WyVqxYYe3atcuaPXu2lZ2dbQUCAcuyLGv69OnWggULIuP3799vZWZmWjU1NVZjY6O1du1aKy8vz3rqqacS9RJSRrTbosfkyZOtO+64I97lprxot0dtba2VmZlp/eUvf7H27dtnffDBB9aYMWOs22+/PVEvIWVEuy0++eQT66233rL27t1rbdy40br22mut0aNHW99++22CXkHqaG9vtz799FPr008/tSRZzz77rPXpp59a//3vfy3LsqwFCxZY06dPj4zvuVT6oYcesnbv3m29+OKLXCqN2Hj++eet//mf/7GGDx9ulZaWWp988knkvquvvtq66667eo3/17/+ZU2cONFyuVxWYWGh9fTTT1snTpyIc9WpKdptsWfPHkuS9cEHH8S50vQQzfbo6uqyHn/8cWvMmDGW2+22CgoKrPvuu48vzBiJZlt8/PHH1tixYy2Xy2WNHDnSmj59unXo0KEEVJ16PvroI0vSKbee9/+uu+6yrr766lPWueyyy6zhw4dbhYWFMetF5bAsjmkCAABzMOcFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKP8P/4izXxY5U3IAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(tmp['auc'], tmp['auc_est3'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.45497661734405814"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(tmp['auc'], tmp['auc_est2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if equalized:\n",
+    "    n_samples = 300\n",
+    "\n",
+    "    tmp = []\n",
+    "    lower_bounds = np.linspace(0.5, 1.0, 11)\n",
+    "    for lower, upper in zip(lower_bounds[:-1], lower_bounds[1:]):\n",
+    "        if upper == 1.0:\n",
+    "            tmp2 = data[(data['auc'] >= lower) & (data['auc'] <= upper)]\n",
+    "        else:\n",
+    "            tmp2 = data[(data['auc'] >= lower) & (data['auc'] < upper)]\n",
+    "        if len(tmp2) > n_samples:\n",
+    "            tmp.append(tmp2.sample(n_samples, random_state=5, replace=False))\n",
+    "        else:\n",
+    "            tmp.append(tmp2.sample(n_samples, random_state=5, replace=True))\n",
+    "    data = pd.concat(tmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['tprs'] = data['tprs'].apply(lambda x: np.array(eval(x)))\n",
+    "data['fprs'] = data['fprs'].apply(lambda x: np.array(eval(x)))\n",
+    "data['fracs'] = data['fracs'].apply(lambda x: np.array(eval(x)))\n",
+    "data['frac'] = (data['sens']*data['p'] + (1 - data['spec'])*data['n'])/(data['p'] + data['n'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_p3(sens, frac):\n",
+    "    tpr = sens\n",
+    "\n",
+    "    p = np.logspace(-2, 2, 10000)\n",
+    "    etpr = np.abs(frac**p - tpr) / tpr + np.abs((1 - frac)**(1/p) - (1 - tpr))/(1 - tpr)\n",
+    "\n",
+    "    idx = np.argmin(etpr)\n",
+    "    return p[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def auc_est(sens, spec, frac):\n",
+    "    p0 = fit_p3(sens, frac)\n",
+    "    p1 = 1.0/fit_p3(spec, 1-frac)\n",
+    "\n",
+    "    return p1/(p0 + p1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dataset</th>\n",
+       "      <th>classifier</th>\n",
+       "      <th>classifier_params</th>\n",
+       "      <th>p</th>\n",
+       "      <th>n</th>\n",
+       "      <th>p_train</th>\n",
+       "      <th>n_train</th>\n",
+       "      <th>auc</th>\n",
+       "      <th>auc_train</th>\n",
+       "      <th>fprs</th>\n",
+       "      <th>...</th>\n",
+       "      <th>best_sens_train</th>\n",
+       "      <th>best_spec_train</th>\n",
+       "      <th>fracs</th>\n",
+       "      <th>r2_fpr</th>\n",
+       "      <th>exp_fpr</th>\n",
+       "      <th>fit_mode_fpr</th>\n",
+       "      <th>r2_tpr</th>\n",
+       "      <th>exp_tpr</th>\n",
+       "      <th>fit_mode_tpr</th>\n",
+       "      <th>frac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9333</th>\n",
+       "      <td>abalone9_18</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>{'max_depth': 116, 'random_state': 5}</td>\n",
+       "      <td>8</td>\n",
+       "      <td>139</td>\n",
+       "      <td>34</td>\n",
+       "      <td>550</td>\n",
+       "      <td>0.526529</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>[0.0, 0.07194244604316546, 1.0]</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>[0.0, 0.07482993197278912, 1.0]</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.015179</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.582468</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>0.074830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18298</th>\n",
+       "      <td>appendicitis</td>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>{'random_state': 5, 'max_depth': 3}</td>\n",
+       "      <td>18</td>\n",
+       "      <td>4</td>\n",
+       "      <td>67</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.527778</td>\n",
+       "      <td>0.997366</td>\n",
+       "      <td>[0.0, 0.25, 0.25, 0.5, 0.5, 0.75, 0.75, 1.0]</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.985075</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>[0.0, 0.045454545454545456, 0.3181818181818182...</td>\n",
+       "      <td>0.410385</td>\n",
+       "      <td>0.570687</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0.988386</td>\n",
+       "      <td>0.937009</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>0.772727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11554</th>\n",
+       "      <td>saheart</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>{'max_depth': 73, 'random_state': 5}</td>\n",
+       "      <td>61</td>\n",
+       "      <td>32</td>\n",
+       "      <td>241</td>\n",
+       "      <td>128</td>\n",
+       "      <td>0.539959</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>[0.0, 0.625, 1.0]</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>[0.0, 0.6774193548387096, 1.0]</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.206794</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.926997</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>0.677419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13791</th>\n",
+       "      <td>saheart</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>{'max_depth': 18, 'random_state': 5}</td>\n",
+       "      <td>32</td>\n",
+       "      <td>61</td>\n",
+       "      <td>128</td>\n",
+       "      <td>241</td>\n",
+       "      <td>0.524334</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>[0.0, 0.29508196721311475, 1.0]</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>[0.0, 0.3118279569892473, 1.0]</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.047368</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.887238</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>0.311828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7159</th>\n",
+       "      <td>CM1</td>\n",
+       "      <td>SVC</td>\n",
+       "      <td>{'probability': True, 'C': 0.3716572355638991,...</td>\n",
+       "      <td>90</td>\n",
+       "      <td>10</td>\n",
+       "      <td>359</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.513333</td>\n",
+       "      <td>0.881830</td>\n",
+       "      <td>[0.0, 0.0, 0.3, 0.3, 0.4, 0.4, 0.5, 0.5, 0.7, ...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.963788</td>\n",
+       "      <td>0.794872</td>\n",
+       "      <td>[0.0, 0.08, 0.11, 0.13, 0.14, 0.54, 0.55, 0.77...</td>\n",
+       "      <td>0.623334</td>\n",
+       "      <td>0.599823</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0.996598</td>\n",
+       "      <td>0.992561</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>0.900000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            dataset              classifier  \\\n",
+       "9333    abalone9_18  DecisionTreeClassifier   \n",
+       "18298  appendicitis           XGBClassifier   \n",
+       "11554       saheart  DecisionTreeClassifier   \n",
+       "13791       saheart  DecisionTreeClassifier   \n",
+       "7159            CM1                     SVC   \n",
+       "\n",
+       "                                       classifier_params   p    n  p_train  \\\n",
+       "9333               {'max_depth': 116, 'random_state': 5}   8  139       34   \n",
+       "18298                {'random_state': 5, 'max_depth': 3}  18    4       67   \n",
+       "11554               {'max_depth': 73, 'random_state': 5}  61   32      241   \n",
+       "13791               {'max_depth': 18, 'random_state': 5}  32   61      128   \n",
+       "7159   {'probability': True, 'C': 0.3716572355638991,...  90   10      359   \n",
+       "\n",
+       "       n_train       auc  auc_train  \\\n",
+       "9333       550  0.526529   1.000000   \n",
+       "18298       17  0.527778   0.997366   \n",
+       "11554      128  0.539959   1.000000   \n",
+       "13791      241  0.524334   1.000000   \n",
+       "7159        39  0.513333   0.881830   \n",
+       "\n",
+       "                                                    fprs  ... best_sens_train  \\\n",
+       "9333                     [0.0, 0.07194244604316546, 1.0]  ...        1.000000   \n",
+       "18298       [0.0, 0.25, 0.25, 0.5, 0.5, 0.75, 0.75, 1.0]  ...        0.985075   \n",
+       "11554                                  [0.0, 0.625, 1.0]  ...        1.000000   \n",
+       "13791                    [0.0, 0.29508196721311475, 1.0]  ...        1.000000   \n",
+       "7159   [0.0, 0.0, 0.3, 0.3, 0.4, 0.4, 0.5, 0.5, 0.7, ...  ...        0.963788   \n",
+       "\n",
+       "      best_spec_train                                              fracs  \\\n",
+       "9333         1.000000                    [0.0, 0.07482993197278912, 1.0]   \n",
+       "18298        1.000000  [0.0, 0.045454545454545456, 0.3181818181818182...   \n",
+       "11554        1.000000                     [0.0, 0.6774193548387096, 1.0]   \n",
+       "13791        1.000000                     [0.0, 0.3118279569892473, 1.0]   \n",
+       "7159         0.794872  [0.0, 0.08, 0.11, 0.13, 0.14, 0.54, 0.55, 0.77...   \n",
+       "\n",
+       "         r2_fpr   exp_fpr fit_mode_fpr    r2_tpr   exp_tpr  fit_mode_tpr  \\\n",
+       "9333   1.000000  1.015179           -1  1.000000  0.582468            -3   \n",
+       "18298  0.410385  0.570687           -1  0.988386  0.937009            -3   \n",
+       "11554  1.000000  1.206794           -1  1.000000  0.926997            -3   \n",
+       "13791  1.000000  1.047368           -1  1.000000  0.887238            -3   \n",
+       "7159   0.623334  0.599823           -1  0.996598  0.992561            -3   \n",
+       "\n",
+       "           frac  \n",
+       "9333   0.074830  \n",
+       "18298  0.772727  \n",
+       "11554  0.677419  \n",
+       "13791  0.311828  \n",
+       "7159   0.900000  \n",
+       "\n",
+       "[5 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_est2'] = data['exp_fpr'] / (data['exp_fpr'] + data['exp_tpr'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['dataset', 'classifier', 'classifier_params', 'p', 'n', 'p_train',\n",
+       "       'n_train', 'auc', 'auc_train', 'fprs', 'tprs', 'thresholds', 'n_nodes',\n",
+       "       'fprs_train', 'tprs_train', 'thresholds_train', 'n_nodes_train', 'acc',\n",
+       "       'sens', 'spec', 'best_acc', 'best_sens', 'best_spec', 'acc_train',\n",
+       "       'sens_train', 'spec_train', 'best_acc_train', 'best_sens_train',\n",
+       "       'best_spec_train', 'fracs', 'r2_fpr', 'exp_fpr', 'fit_mode_fpr',\n",
+       "       'r2_tpr', 'exp_tpr', 'fit_mode_tpr', 'frac', 'auc_est2'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3311169/2336068737.py:5: RuntimeWarning: divide by zero encountered in divide\n",
+      "  etpr = np.abs(frac**p - tpr) / tpr + np.abs((1 - frac)**(1/p) - (1 - tpr))/(1 - tpr)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data['auc_est'] = data.apply(lambda row: auc_est(row['sens'], row['spec'], row['frac']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8131877917941088"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmp = data[(data['sens'] > 0) & (data['sens'] < 1) &(data['spec'] > 0) & (data['spec'] < 1)].dropna()\n",
+    "plt.scatter(tmp['auc'], tmp['auc_est'])\n",
+    "r2_score(tmp['auc'], tmp['auc_est'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.9668847398440344)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auc_est(0.776, 0.9768, 0.07)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.05572814761090012)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fit_p3(0.776, 0.08)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(1.4907929892623273)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fit_p3(1 - 0.9768, 0.08)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.964401294498382"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.49/(1.49 + 0.055)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_p1(spec, sens, frac, mode_tpr=0, mode_fpr=0):\n",
+    "    tpr = sens\n",
+    "    fpr = 1 - spec\n",
+    "\n",
+    "    p = np.logspace(-2, 0, 10000)\n",
+    "    if mode_tpr == 0:\n",
+    "        etpr = np.abs(frac**p - tpr) / tpr\n",
+    "    else:\n",
+    "        etpr = np.abs((1 - frac)**(1/p) - (1 - tpr))/(1 - tpr)\n",
+    "\n",
+    "    if mode_fpr == 0:\n",
+    "        efpr = np.abs(frac**(1/p) - fpr) / fpr\n",
+    "    else:\n",
+    "        efpr = np.abs((1 - frac)**(p) - (1 - fpr))/(1 - fpr)\n",
+    "\n",
+    "    idx = np.argmin(etpr + efpr)\n",
+    "    print(etpr[idx], efpr[idx])\n",
+    "    return p[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7634027463416212 1.7787864410589995e-06\n",
+      "0.00011148300482790944 1.0\n",
+      "3.9188450620303236e-05 0.015217108071320144\n",
+      "0.00011148300482790944 0.01900499243364162\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.6710929527817348),\n",
+       " np.float64(0.05572814761090012),\n",
+       " np.float64(0.10039224589211208),\n",
+       " np.float64(0.05572814761090012))"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(fit_p1(0.9768, 0.776, 0.08, 0, 0),\n",
+    " fit_p1(0.9768, 0.776, 0.08, 1, 0),\n",
+    " fit_p1(0.9768, 0.776, 0.08, 0, 1),\n",
+    " fit_p1(0.9768, 0.776, 0.08, 1, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_p2(spec, sens, frac):\n",
+    "    tpr = sens\n",
+    "    fpr = 1 - spec\n",
+    "\n",
+    "    p = np.logspace(-2, 2, 10000)\n",
+    "    idx = np.argmin(np.abs((1 - frac)**p - (1 - tpr)) / tpr + np.abs((1 - frac)**(p) - (1 - fpr))/(1 - spec))\n",
+    "    return p[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7634027463416212 1.7787864410589995e-06\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.6710929527817348), np.float64(0.2814135044524948))"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p1 = fit_p1(0.9768, 0.776, 0.08)\n",
+    "p2 = fit_p2(0.9768, 0.776, 0.08)\n",
+    "p1, p2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.31051874389471884), np.float64(0.07338216552404798))"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p1/(p1 + (1/p1)), (p2)/(p2 + (1/p2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'p' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mfloat\u001b[39m(p\u001b[38;5;241m/\u001b[39m(p \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39mp)))\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'p' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "float(p/(p + (1/p)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_p(spec, sens):\n",
+    "    x = np.linspace(1, 5, 10000)\n",
+    "    idx = np.argmin(np.abs(spec**x + (1 - sens)**x - 1))\n",
+    "    return x[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def norm_area(spec, sens):\n",
+    "    p = fit_p(spec, sens)\n",
+    "    x = np.linspace(0, 1, 1000)\n",
+    "    return integrate_roc_curve(x**p, (1 - (1 - x)**p)**(1/p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9109848333984671"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm_area(0.9768, 0.7763)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(2.2033203320332033)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = fit_p(0.982, 0.77)\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8b34279700>]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#p = 3\n",
+    "x = np.linspace(0, 1, 1000)\n",
+    "plt.figure(figsize=(3.5, 3.5))\n",
+    "plt.plot(x**p, (1 - (1 - x)**p)**(1/p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(2.1493149314931497)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fit_p(0.98, 0.77)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9253901055010616"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm_area(0.98, 0.77)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "def exponential_fitting2(row, label, frac_label):\n",
+    "\n",
+    "    values = row[label].copy()\n",
+    "    counts = row[frac_label].copy()\n",
+    "    \n",
+    "    mask = values > 1e-6\n",
+    "    values_nz = values[mask]\n",
+    "    counts_nz = counts[mask]\n",
+    "\n",
+    "    ln_values = np.log(values_nz)\n",
+    "    ln_counts = np.log(counts_nz)\n",
+    "\n",
+    "    values2 = (1 - values)\n",
+    "    counts2 = (1 - counts)\n",
+    "\n",
+    "    mask2 = (values2 > 1e-6) & (values2 < 1)\n",
+    "    values2_nz = values2[mask2]\n",
+    "    counts2_nz = counts2[mask2]\n",
+    "\n",
+    "    ln_values2 = 1/np.log(values2_nz)\n",
+    "    ln_counts2 = 1/np.log(counts2_nz)\n",
+    "\n",
+    "    ln_y = np.hstack([ln_values, ln_values2])\n",
+    "    ln_x = np.hstack([ln_counts, ln_counts2]).reshape(-1, 1)\n",
+    "\n",
+    "    print(ln_x, ln_y)\n",
+    "\n",
+    "    linreg_a = LinearRegression(fit_intercept=False, positive=True)\n",
+    "    pred_values = linreg_a\\\n",
+    "        .fit(ln_x, ln_y)\\\n",
+    "        .predict(ln_x)\n",
+    "\n",
+    "    r2_a = r2_score(ln_y, pred_values)\n",
+    "\n",
+    "    return (r2_a, linreg_a.coef_[0], 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"row = data.iloc[0]\\nexponential_fitting(row, 'tprs', 'fracs')\""
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"row = data.iloc[0]\n",
+    "exponential_fitting(row, 'tprs', 'fracs')\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dataset                                                           bupa\n",
+       "classifier                                      RandomForestClassifier\n",
+       "classifier_params                 {'max_depth': 22, 'random_state': 5}\n",
+       "p                                                                   40\n",
+       "n                                                                   29\n",
+       "p_train                                                            160\n",
+       "n_train                                                            116\n",
+       "auc                                                           0.781466\n",
+       "auc_train                                                          1.0\n",
+       "fprs                 [0.0, 0.0, 0.06896551724137931, 0.068965517241...\n",
+       "tprs                 [0.0, 0.05, 0.05, 0.125, 0.125, 0.45, 0.45, 0....\n",
+       "thresholds           [inf, 0.95, 0.9, 0.84, 0.83, 0.76, 0.74, 0.73,...\n",
+       "n_nodes                                                             24\n",
+       "fprs_train                                             [0.0, 0.0, 1.0]\n",
+       "tprs_train                                             [0.0, 1.0, 1.0]\n",
+       "thresholds_train                                     [inf, 0.71, 0.02]\n",
+       "n_nodes_train                                                        3\n",
+       "acc                                                           0.768116\n",
+       "sens                                                             0.875\n",
+       "spec                                                           0.62069\n",
+       "best_acc                                                      0.768116\n",
+       "best_sens                                                        0.925\n",
+       "best_spec                                                     0.551724\n",
+       "acc_train                                                          1.0\n",
+       "sens_train                                                         1.0\n",
+       "spec_train                                                         1.0\n",
+       "best_acc_train                                                     1.0\n",
+       "best_sens_train                                                    1.0\n",
+       "best_spec_train                                                    1.0\n",
+       "fracs                [0.0, 0.028985507246376812, 0.0579710144927536...\n",
+       "r2_fpr                                                        0.974198\n",
+       "exp_fpr                                                       0.503067\n",
+       "fit_mode_fpr                                                         1\n",
+       "r2_tpr                                                        0.949818\n",
+       "exp_tpr                                                       0.866674\n",
+       "fit_mode_tpr                                                         0\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if equalized:\n",
+    "    n_samples = 300\n",
+    "\n",
+    "    tmp = []\n",
+    "    lower_bounds = np.linspace(0.5, 1.0, 11)\n",
+    "    for lower, upper in zip(lower_bounds[:-1], lower_bounds[1:]):\n",
+    "        if upper == 1.0:\n",
+    "            tmp2 = data[(data['auc'] >= lower) & (data['auc'] <= upper)]\n",
+    "        else:\n",
+    "            tmp2 = data[(data['auc'] >= lower) & (data['auc'] < upper)]\n",
+    "        if len(tmp2) > n_samples:\n",
+    "            tmp.append(tmp2.sample(n_samples, random_state=5, replace=False))\n",
+    "        else:\n",
+    "            tmp.append(tmp2.sample(n_samples, random_state=5, replace=True))\n",
+    "    data = pd.concat(tmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['p', 'n', 'k', 'dataset', 'classifier', 'classifier_params', 'best_acc',\n",
+       "       'best_sens', 'best_spec', 'best_acc_train', 'best_sens_train',\n",
+       "       'best_spec_train', 'acc', 'sens', 'spec', 'auc', 'acc_train',\n",
+       "       'sens_train', 'spec_train', 'auc_train', 'n_nodes', 'n_nodes_train',\n",
+       "       'avg_n_nodes', 'avg_n_nodes_train', 'fprs', 'tprs', 'fprs_train',\n",
+       "       'tprs_train', 'fracs', 'r2_fpr', 'exp_fpr', 'fit_mode_fpr', 'r2_tpr',\n",
+       "       'exp_tpr', 'fit_mode_tpr'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['auc_analytic'] = data['exp_fpr'] / (data['exp_fpr'] + data['exp_tpr'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['frac'] = (data['sens'] * data['p'] + (1 - data['spec']) * data['n']) / (data['p'] + data['n'])\n",
+    "data['frac'] = data['frac'].apply(lambda x: np.round(x, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['flag'] = (data['p'] / data['n'] < 1.3) & (data['p'] / data['n'] > 0.7)\n",
+    "data['n_nodes'] = data['n_nodes'].apply(lambda x: np.round(x/10, 0)*10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>p</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fit_mode_tpr</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>871</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                p\n",
+       "fit_mode_tpr     \n",
+       "0             575\n",
+       "1             871"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[(data['fit_mode_tpr'] != -1) & (data['p'] < data['n'])].groupby(['fit_mode_tpr']).agg({'p': 'count'}).iloc[:30]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>p</th>\n",
+       "      <th>n</th>\n",
+       "      <th>n_nodes</th>\n",
+       "      <th>r2_fpr</th>\n",
+       "      <th>exp_fpr</th>\n",
+       "      <th>fit_mode_fpr</th>\n",
+       "      <th>r2_tpr</th>\n",
+       "      <th>exp_tpr</th>\n",
+       "      <th>fit_mode_tpr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16789</th>\n",
+       "      <td>81</td>\n",
+       "      <td>225</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.999835</td>\n",
+       "      <td>1.059050</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.998477</td>\n",
+       "      <td>0.856185</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4864</th>\n",
+       "      <td>49</td>\n",
+       "      <td>449</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.999549</td>\n",
+       "      <td>1.070177</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.997862</td>\n",
+       "      <td>0.688733</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18884</th>\n",
+       "      <td>225</td>\n",
+       "      <td>81</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.996687</td>\n",
+       "      <td>1.195727</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.999438</td>\n",
+       "      <td>0.933275</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2702</th>\n",
+       "      <td>225</td>\n",
+       "      <td>81</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.999691</td>\n",
+       "      <td>0.844438</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999951</td>\n",
+       "      <td>1.068382</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16895</th>\n",
+       "      <td>49</td>\n",
+       "      <td>449</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.999822</td>\n",
+       "      <td>0.946262</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.996732</td>\n",
+       "      <td>0.807258</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15871</th>\n",
+       "      <td>444</td>\n",
+       "      <td>239</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.853574</td>\n",
+       "      <td>7.886440</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.855360</td>\n",
+       "      <td>2.960262</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7498</th>\n",
+       "      <td>28</td>\n",
+       "      <td>444</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.955269</td>\n",
+       "      <td>0.557980</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.910346</td>\n",
+       "      <td>11.178554</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5666</th>\n",
+       "      <td>444</td>\n",
+       "      <td>239</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.762624</td>\n",
+       "      <td>7.471571</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.905277</td>\n",
+       "      <td>3.025893</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11136</th>\n",
+       "      <td>180</td>\n",
+       "      <td>35</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2908</th>\n",
+       "      <td>1706</td>\n",
+       "      <td>123</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3000 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          p    n  n_nodes    r2_fpr   exp_fpr  fit_mode_fpr    r2_tpr  \\\n",
+       "16789    81  225        8  0.999835  1.059050             0  0.998477   \n",
+       "4864     49  449        4  0.999549  1.070177             0  0.997862   \n",
+       "18884   225   81        6  0.996687  1.195727             0  0.999438   \n",
+       "2702    225   81        8  0.999691  0.844438             1  0.999951   \n",
+       "16895    49  449        7  0.999822  0.946262             1  0.996732   \n",
+       "...     ...  ...      ...       ...       ...           ...       ...   \n",
+       "15871   444  239       17  0.853574  7.886440             0  0.855360   \n",
+       "7498     28  444        9  0.955269  0.557980             1  0.910346   \n",
+       "5666    444  239       17  0.762624  7.471571             0  0.905277   \n",
+       "11136   180   35        3  1.000000 -1.000000            -1  1.000000   \n",
+       "2908   1706  123        3  1.000000 -1.000000            -1  1.000000   \n",
+       "\n",
+       "         exp_tpr  fit_mode_tpr  \n",
+       "16789   0.856185             0  \n",
+       "4864    0.688733             0  \n",
+       "18884   0.933275             0  \n",
+       "2702    1.068382             1  \n",
+       "16895   0.807258             0  \n",
+       "...          ...           ...  \n",
+       "15871   2.960262             1  \n",
+       "7498   11.178554             1  \n",
+       "5666    3.025893             1  \n",
+       "11136  -1.000000            -1  \n",
+       "2908   -1.000000            -1  \n",
+       "\n",
+       "[3000 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[['p', 'n', 'n_nodes', 'r2_fpr', 'exp_fpr', 'fit_mode_fpr', 'r2_tpr', 'exp_tpr', 'fit_mode_tpr']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp = data[(data['fit_mode_fpr'] == 0) & (data['fit_mode_tpr'] == 0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp2 = tmp[tmp['auc_analytic'] >= 0.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8928208790464232"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(tmp2['auc'], tmp2['auc_analytic'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f8b398970b0>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(tmp2['auc'], tmp2['auc_analytic'])"
+   ]
+  },
+  {
+   "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
+}