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Merge pull request #2 from francescopisu/dev
feat: comparison between median AUCs + tests
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from src.modelsight.curves.roc import average_roc_curves | ||
from src.modelsight.curves.compare import ( | ||
roc_single_comparison, roc_comparisons, | ||
add_annotations | ||
) | ||
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__all__ = [ | ||
"average_roc_curves" | ||
"average_roc_curves", | ||
"roc_single_comparison", | ||
"roc_comparisons", | ||
] |
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import pandas as pd | ||
import numpy as np | ||
import scipy.stats | ||
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# AUC comparison adapted from | ||
# https://github.com/Netflix/vmaf/ | ||
def compute_midrank(x): | ||
"""Computes midranks. | ||
Args: | ||
x - a 1D numpy array | ||
Returns: | ||
array of midranks | ||
""" | ||
J = np.argsort(x) | ||
Z = x[J] | ||
N = len(x) | ||
T = np.zeros(N, dtype=np.float) | ||
i = 0 | ||
while i < N: | ||
j = i | ||
while j < N and Z[j] == Z[i]: | ||
j += 1 | ||
T[i:j] = 0.5*(i + j - 1) | ||
i = j | ||
T2 = np.empty(N, dtype=np.float) | ||
# Note(kazeevn) +1 is due to Python using 0-based indexing | ||
# instead of 1-based in the AUC formula in the paper | ||
T2[J] = T + 1 | ||
return T2 | ||
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def fastDeLong(predictions_sorted_transposed, label_1_count): | ||
""" | ||
The fast version of DeLong's method for computing the covariance of | ||
unadjusted AUC. | ||
Args: | ||
predictions_sorted_transposed: a 2D numpy.array[n_classifiers, n_examples] | ||
sorted such as the examples with label "1" are first | ||
Returns: | ||
(AUC value, DeLong covariance) | ||
Reference: | ||
@article{sun2014fast, | ||
title={Fast Implementation of DeLong's Algorithm for | ||
Comparing the Areas Under Correlated Receiver Operating Characteristic Curves}, | ||
author={Xu Sun and Weichao Xu}, | ||
journal={IEEE Signal Processing Letters}, | ||
volume={21}, | ||
number={11}, | ||
pages={1389--1393}, | ||
year={2014}, | ||
publisher={IEEE} | ||
} | ||
""" | ||
# Short variables are named as they are in the paper | ||
m = label_1_count | ||
n = predictions_sorted_transposed.shape[1] - m | ||
positive_examples = predictions_sorted_transposed[:, :m] | ||
negative_examples = predictions_sorted_transposed[:, m:] | ||
k = predictions_sorted_transposed.shape[0] | ||
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tx = np.empty([k, m], dtype=np.float) | ||
ty = np.empty([k, n], dtype=np.float) | ||
tz = np.empty([k, m + n], dtype=np.float) | ||
for r in range(k): | ||
tx[r, :] = compute_midrank(positive_examples[r, :]) | ||
ty[r, :] = compute_midrank(negative_examples[r, :]) | ||
tz[r, :] = compute_midrank(predictions_sorted_transposed[r, :]) | ||
aucs = tz[:, :m].sum(axis=1) / m / n - float(m + 1.0) / 2.0 / n | ||
v01 = (tz[:, :m] - tx[:, :]) / n | ||
v10 = 1.0 - (tz[:, m:] - ty[:, :]) / m | ||
sx = np.cov(v01) | ||
sy = np.cov(v10) | ||
delongcov = sx / m + sy / n | ||
return aucs, delongcov | ||
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def calc_pvalue(aucs, sigma): | ||
"""Computes log(10) of p-values. | ||
Args: | ||
aucs: 1D array of AUCs | ||
sigma: AUC DeLong covariances | ||
Returns: | ||
log10(pvalue) | ||
""" | ||
l = np.array([[1, -1]]) | ||
z = np.abs(np.diff(aucs)) / np.sqrt(np.dot(np.dot(l, sigma), l.T)) | ||
return np.log10(2) + scipy.stats.norm.logsf(z, loc=0, scale=1) / np.log(10) | ||
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def compute_ground_truth_statistics(ground_truth): | ||
assert np.array_equal(np.unique(ground_truth), [0, 1]) | ||
order = (-ground_truth).argsort() | ||
label_1_count = int(ground_truth.sum()) | ||
return order, label_1_count | ||
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def delong_roc_variance(ground_truth, predictions): | ||
""" | ||
Computes ROC AUC variance for a single set of predictions | ||
Args: | ||
ground_truth: np.array of 0 and 1 | ||
predictions: np.array of floats of the probability of being class 1 | ||
""" | ||
order, label_1_count = compute_ground_truth_statistics(ground_truth) | ||
predictions_sorted_transposed = predictions[np.newaxis, order] | ||
aucs, delongcov = fastDeLong(predictions_sorted_transposed, label_1_count) | ||
assert len(aucs) == 1, "There is a bug in the code, please forward this to the developers" | ||
return aucs[0], delongcov | ||
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def delong_roc_test(ground_truth, predictions_one, predictions_two): | ||
""" | ||
Computes log(p-value) for hypothesis that two ROC AUCs are different | ||
Args: | ||
ground_truth: np.array of 0 and 1 | ||
predictions_one: predictions of the first model, | ||
np.array of floats of the probability of being class 1 | ||
predictions_two: predictions of the second model, | ||
np.array of floats of the probability of being class 1 | ||
""" | ||
order, label_1_count = compute_ground_truth_statistics(ground_truth) | ||
predictions_sorted_transposed = np.vstack((predictions_one, predictions_two))[:, order] | ||
aucs, delongcov = fastDeLong(predictions_sorted_transposed, label_1_count) | ||
return 10**calc_pvalue(aucs, delongcov).item() |
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from typing import Callable, Dict, Tuple, List | ||
import matplotlib | ||
from matplotlib import patches | ||
import matplotlib.pyplot as plt | ||
from scipy.stats import ttest_ind | ||
from sklearn.metrics import average_precision_score | ||
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from src.modelsight.curves._delong import delong_roc_test | ||
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def annot_stat_vertical(text, x, y1, y2, ww, | ||
col='k', | ||
fontsize=13, | ||
voffset = 0, | ||
n_elems = None, | ||
ax=None, | ||
**kwargs): | ||
""" | ||
ww: float | ||
whisker width | ||
""" | ||
ax = plt.gca() if ax is None else ax | ||
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# we want the text to be centered on the whisker | ||
text_x_pos = x + ww | ||
#+ 0.01 | ||
text_y_pos = (y1+y2)/2 | ||
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# draw whisker from y1 to y2 with width `ww` | ||
ax.plot([x, x + ww, x + ww, x], [y1, y1, y2, y2], lw=1, c=col) | ||
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if len(text) == 1: | ||
#text_y_pos = (y1+y2)/2 | ||
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# draw text at (text_x_pos, text_y_pos) # + 0.15 | ||
ax.text( | ||
text_x_pos, (text_y_pos - voffset) + 0.17, text, | ||
ha='center', va='center', color=col, | ||
size=fontsize, zorder=10 | ||
) | ||
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# Rectangle's props | ||
rect_h_base = kwargs.get("rect_h_base", 0.1) | ||
rect_w = 0.05 - (0.375 * 0.05) # on a scale from 0 to 1 | ||
rect_h = rect_h_base * n_elems # transform to scale from 0 to n_elems-1 | ||
rect_x_offset = -0.002 | ||
rect_y_offset = 0.01 # move rectangle to the bottom. (0,0) is top left in the inserted barplot | ||
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# draw white rectangle and put it beneath the text | ||
# specifying a zorder inferior to that of the text | ||
rect = patches.Rectangle( | ||
( | ||
text_x_pos - (rect_w/2) + rect_x_offset, | ||
text_y_pos - (rect_h/2) + rect_y_offset | ||
), | ||
width = rect_w, height = rect_h, | ||
linewidth=1, | ||
edgecolor='w', | ||
facecolor='w', | ||
zorder=9 | ||
) | ||
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ax.add_patch(rect) | ||
else: | ||
fontsize_nonsignif = kwargs.pop("fontsize_nonsignif", fontsize) | ||
ax.text( | ||
text_x_pos, text_y_pos, text, | ||
ha='center', va='center', color=col, | ||
size=fontsize_nonsignif, zorder=10, | ||
bbox=dict( | ||
boxstyle='square,pad=0', | ||
facecolor="white", | ||
edgecolor="white" | ||
) | ||
) | ||
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from matplotlib import patches | ||
def annot_stat_horizontal(text, x1, x2, y, wh, col='k', fontsize=13, | ||
voffset = 0, | ||
n_elems = None, | ||
ax=None, | ||
**kwargs): | ||
""" | ||
ww: float | ||
whisker width | ||
""" | ||
ax = plt.gca() if ax is None else ax | ||
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# we want the text to be centered on the whisker | ||
text_y_pos = y + wh | ||
#+ 0.01 | ||
text_x_pos = (x1+x2)/2 | ||
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# draw whisker from y1 to y2 with width `ww` | ||
ax.plot([x1, x1, x2, x2], [y, y + wh, y + wh, y], lw=1, c=col, | ||
clip_on=False) | ||
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if len(text) == 1: | ||
#text_y_pos = (y1+y2)/2 | ||
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# draw text at (text_x_pos, text_y_pos) # + 0.15 | ||
ax.text( | ||
text_x_pos, text_y_pos + voffset, text, | ||
ha='center', va='center', color=col, | ||
size=fontsize, zorder=10 | ||
) | ||
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# Rectangle's props | ||
rect_w = 0.09 # transform to scale from 0 to n_elems-1 | ||
rect_h = 0.05 - (0.375 * 0.05) # on a scale from 0 to 1 | ||
rect_x_offset = 0.005 | ||
rect_y_offset = -0.001 # move rectangle to the bottom. (0,0) is top left in the inserted barplot | ||
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# draw white rectangle and put it beneath the text | ||
# specifying a zorder inferior to that of the text | ||
rect = patches.Rectangle( | ||
( | ||
text_x_pos - (rect_w/2) + rect_x_offset, | ||
text_y_pos - (rect_h/2) + rect_y_offset | ||
), | ||
width = rect_w, height = rect_h, | ||
linewidth=1, | ||
edgecolor='w', | ||
facecolor='w', | ||
zorder=9, | ||
clip_on=False | ||
) | ||
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ax.add_patch(rect) | ||
else: | ||
fontsize_nonsignif = kwargs.pop("fontsize_nonsignif", fontsize) | ||
ax.text( | ||
text_x_pos, text_y_pos, text, | ||
ha='center', va='center', color=col, | ||
size=fontsize_nonsignif, zorder=10, | ||
bbox=dict( | ||
boxstyle='square,pad=0', | ||
facecolor="white", | ||
edgecolor="white" | ||
) | ||
) | ||
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from typing import Tuple, List, Dict | ||
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def add_annotations(comparisons: Dict[str, Tuple[str, str, float]], | ||
alpha: float, | ||
bars: matplotlib.container.BarContainer, | ||
direction: str, | ||
order: List[Tuple[str, str]], | ||
symbol: str, | ||
symbol_fontsize: int = 22, | ||
voffset: float = 0, | ||
ext_voffset: float = 0, | ||
ext_hoffset: float = 0, | ||
P_val_rounding: int = 2, | ||
ax: plt.Axes = None, | ||
**kwargs): | ||
if not ax: | ||
raise ValueError("I need an Axes to draw comparisons on.") | ||
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comparisons_list = [] | ||
if order: | ||
for fst_algo, snd_algo in order: | ||
cmp_key = f"{fst_algo}_{snd_algo}" | ||
cmp = comparisons.get(cmp_key, None) | ||
if not cmp: | ||
raise ValueError(f"The comparison {cmp_key} does not exist in the order list.") | ||
comparisons_list.append(cmp) | ||
else: | ||
comparisons_list = list(comparisons.values()) | ||
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if direction == "horizontal": | ||
width = bars[0].get_width() | ||
entity_labels = ax.get_xticklabels() | ||
entity_idx = {label.get_text(): (i + 0.03) for i, label in enumerate(entity_labels)} | ||
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whisker_y_offset = kwargs.pop("whisker_y_offset", 0) | ||
y_lim_upper = ax.get_ylim()[1] + 0.05 + whisker_y_offset | ||
v_offset = 0.07 | ||
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for i, (fst_model, snd_model, P) in enumerate(comparisons_list): | ||
P_str = symbol if P <= alpha else f"{P:.{P_val_rounding}f}" | ||
annot_stat_horizontal(text=P_str, | ||
x1=entity_idx[fst_model] + width/2, | ||
x2=entity_idx[snd_model] + width/2, | ||
y=(y_lim_upper - 0.17) + (i * v_offset), # overall distance from top of bars and upper limit of y + inter-distance between whiskers | ||
wh=0.02, | ||
col="black", | ||
fontsize=symbol_fontsize, | ||
voffset = -0.02, | ||
ext_offset = ext_hoffset, | ||
n_elems = len(entity_labels), | ||
ax=ax, | ||
**kwargs) | ||
elif direction == "vertical": | ||
height = bars[0].get_height() | ||
entity_labels = ax.get_yticklabels() | ||
entity_idx = {label.get_text(): (i + 0.03) for i, label in enumerate(entity_labels)} | ||
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space_between_whiskers = kwargs.pop("space_between_whiskers", 0) | ||
x_lim_upper = ax.get_xlim()[1] + 0 | ||
h_offset = 0.07 + space_between_whiskers | ||
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for i, (fst_model, snd_model, P) in enumerate(comparisons_list): | ||
P_str = symbol if P <= alpha else f"{P:.{P_val_rounding}f}" | ||
annot_stat_vertical(text=P_str, | ||
x=x_lim_upper + (i * h_offset), | ||
y1=entity_idx[fst_model], | ||
y2=entity_idx[snd_model], | ||
ww=0.02, | ||
col="black", | ||
fontsize=symbol_fontsize if P_str == "*" else 16, | ||
voffset=voffset, | ||
ext_offset = ext_voffset, | ||
n_elems = len(entity_labels), | ||
ax=ax, | ||
**kwargs) | ||
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return ax | ||
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def roc_single_comparison(cv_preds, fst_algo, snd_algo): | ||
ground_truths = cv_preds[fst_algo].gts_val_conc | ||
fst_algo_probas = cv_preds[fst_algo].probas_val_conc | ||
snd_algo_probas = cv_preds[snd_algo].probas_val_conc | ||
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print("A"*100) | ||
print(fst_algo_probas.shape, snd_algo_probas.shape) | ||
P = delong_roc_test(ground_truths, fst_algo_probas, snd_algo_probas) | ||
cmp_key = f"{fst_algo}_{snd_algo}" | ||
return {cmp_key: (fst_algo, snd_algo, P)} | ||
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def roc_comparisons(cv_preds, target_algo): | ||
comparisons = dict() | ||
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for algo_name in cv_preds.keys(): | ||
if algo_name != target_algo: | ||
cmp = roc_single_comparison(cv_preds, target_algo, algo_name) | ||
comparisons = dict(cmp, **comparisons) | ||
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return comparisons |
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