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stat_significance_test.py
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import numpy as np
import os
from scipy import stats
import matplotlib.pyplot as plt
from tqdm import tqdm
from itertools import chain, combinations
#!/usr/bin/env python
# Copyright 2016-2020 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Adopted from https://github.com/yandexdataschool/roc_comparison."""
import numpy as np
import scipy.stats
# 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=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=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
def compute_midrank_weight(x, sample_weight):
"""Computes midranks.
Args:
x - a 1D numpy array
Returns:
array of midranks
"""
J = np.argsort(x)
Z = x[J]
cumulative_weight = np.cumsum(sample_weight[J])
N = len(x)
T = np.zeros(N, dtype=float)
i = 0
while i < N:
j = i
while j < N and Z[j] == Z[i]:
j += 1
T[i:j] = cumulative_weight[i:j].mean()
i = j
T2 = np.empty(N, dtype=float)
T2[J] = T
return T2
def fastDeLong(predictions_sorted_transposed, label_1_count):
"""Fast DeLong test computation.
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 Oerating 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]
tx = np.empty([k, m], dtype=float)
ty = np.empty([k, n], dtype=float)
tz = np.empty([k, m + n], dtype=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
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))
log10pval = np.log10(2) + scipy.stats.norm.logsf(z, loc=0, scale=1) / np.log(10)
return np.power(10, log10pval)
def compute_ground_truth_statistics(ground_truth, sample_weight=None):
assert np.array_equal(np.unique(ground_truth), [0, 1])
order = (-ground_truth).argsort()
label_1_count = int(ground_truth.sum())
if sample_weight is None:
ordered_sample_weight = None
else:
ordered_sample_weight = sample_weight[order]
return order, label_1_count, ordered_sample_weight
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
"""
sample_weight = None
order, label_1_count, ordered_sample_weight = compute_ground_truth_statistics(
ground_truth, sample_weight)
predictions_sorted_transposed = predictions[np.newaxis, order]
aucs, delongcov = fastDeLong(predictions_sorted_transposed, label_1_count, ordered_sample_weight)
assert len(aucs) == 1, "There is a bug in the code, please forward this to the developers"
return aucs[0], delongcov
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 calc_pvalue(aucs, delongcov)
def calc_delong(y_true, y_p, alpha=0.95):
# alpha = .95
y_true = y_true.reshape(-1)
y_p = y_p.reshape(-1)
auc, auc_cov = delong_roc_variance(
y_true,
y_p)
auc_std = np.sqrt(auc_cov)
lower_upper_q = np.abs(np.array([0, 1]) - (1 - alpha) / 2)
ci = stats.norm.ppf(
lower_upper_q,
loc=auc,
scale=auc_std)
ci[ci > 1] = 1
print('AUC:', auc)
print('AUC COV:', auc_cov)
print('95% AUC CI:', ci)
return auc, auc_cov, ci
testing_data_label_pairs = [
['data_simband_ecg_2400.npy', 'label_simband.npy', '/labs/hulab/Robust_learning_TESTDATA/simband_patient_idx_dict.pkl'],
['data_simband_ppg_2400.npy', 'label_simband.npy', '/labs/hulab/Robust_learning_TESTDATA/simband_patient_idx_dict.pkl'],
['data_ucla_ecg_2400.npy', 'label_ucla_ecg.npy', '/labs/hulab/Robust_learning_TESTDATA/UCLA_patient_idx_dict.pkl'],
['data_ucla_ppg_2400.npy', 'label_ucla_ppg.npy', '/labs/hulab/Robust_learning_TESTDATA/UCLA_patient_idx_dict.pkl'],
['data_staford_2400.npy', 'label_staford.npy', '/labs/hulab/Robust_learning_TESTDATA/stanford_patient_idx_dict.pkl'],
]
# # PPG FULL DATASET
# print('PPG FULL DATASET', flush=True)
# folders = ['simsiam', 'deepmi', 'deepbeat_baseline', 'ppg_only_baseline']
# model_names = ['simsiam', 'deepmi', 'deepbeat', 'ppgonly']
# dataset_lengths = ['2400', '2400', '800', '2400']
# combs = list(combinations(folders, 2))
# print(combs, flush=True)
# for pair in testing_data_label_pairs:
# dataset = pair[0].split('.')[0]
# print('---------------------------------------------------------------', flush=True)
# if 'ppg' in dataset or 'staford' in dataset:
# print(dataset, flush=True)
# for model_pair in combs:
# dataset_1 = dataset.replace('2400', dataset_lengths[folders.index(model_pair[0])])
# dataset_2 = dataset.replace('2400', dataset_lengths[folders.index(model_pair[1])])
# targets = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_targets.npy')
# pred_probs_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_pred_probs.npy')
# pred_probs_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_pred_probs.npy')
# auroc_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_aurocs.npy')
# auroc_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_aurocs.npy')
# auprc_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_auprcs.npy')
# auprc_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_auprcs.npy')
# # print(targets.shape, pred_probs_1.shape, pred_probs_2.shape, auroc_1.shape, auroc_2.shape, auprc_1.shape, auprc_2.shape)
# p_auroc = delong_roc_test(targets, pred_probs_1, pred_probs_2)[0][0]
# # p_auroc = stats.ttest_rel(auroc_1, auroc_2).pvalue
# p_auprc = stats.ttest_rel(auprc_1, auprc_2).pvalue
# print(f'{model_pair}: p_auroc={p_auroc} , p_auprc={p_auprc}', flush=True)
# # ECG DATASETS FULL
# print('ECG DATASETS FULL', flush=True)
# folders = ['simsiam', 'deepmi', 'ecg_only_baseline']
# model_names = ['simsiam', 'deepmi', 'ecgonly']
# dataset_lengths = ['2400', '2400', '2400']
# combs = list(combinations(folders, 2))
# print(combs, flush=True)
# for pair in testing_data_label_pairs:
# dataset = pair[0].split('.')[0]
# print('---------------------------------------------------------------', flush=True)
# if 'ecg' in dataset:
# print(dataset, flush=True)
# for model_pair in combs:
# print(model_pair, flush=True)
# dataset_1 = dataset.replace('2400', dataset_lengths[folders.index(model_pair[0])])
# dataset_2 = dataset.replace('2400', dataset_lengths[folders.index(model_pair[1])])
# targets = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_targets.npy')
# pred_probs_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_pred_probs.npy')
# pred_probs_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_pred_probs.npy')
# auroc_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_aurocs.npy')
# auroc_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_aurocs.npy')
# auprc_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_auprcs.npy')
# auprc_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_auprcs.npy')
# # print(targets.shape, pred_probs_1.shape, pred_probs_2.shape, auroc_1.shape, auroc_2.shape, auprc_1.shape, auprc_2.shape)
# p_auroc = delong_roc_test(targets, pred_probs_1, pred_probs_2)[0][0]
# # p_auroc = stats.ttest_rel(auroc_1, auroc_2).pvalue
# p_auprc = stats.ttest_rel(auprc_1, auprc_2).pvalue
# print(f'{model_pair}: p_auroc={p_auroc} , p_auprc={p_auprc}', flush=True)
# # PPG LIMITED
# print('PPG LIMITED', flush=True)
# folders = ['simsiam_limited_labeled', 'deepmi_limited_labels', 'ppg_only_baseline_limited_labeled']
# model_names = ['simsiamlimitedlabels', 'deepmilimitedlabels', 'ppgonlylimitedlabel']
# dataset_lengths = ['2400', '2400', '2400']
# combs = list(combinations(folders, 2))
# print(combs, flush=True)
# for pair in testing_data_label_pairs:
# dataset = pair[0].split('.')[0]
# print('---------------------------------------------------------------', flush=True)
# if 'ppg' in dataset or 'staford' in dataset:
# print(dataset, flush=True)
# for model_pair in combs:
# dataset_1 = dataset.replace('2400', dataset_lengths[folders.index(model_pair[0])])
# dataset_2 = dataset.replace('2400', dataset_lengths[folders.index(model_pair[1])])
# targets = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_targets.npy')
# pred_probs_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_pred_probs.npy')
# pred_probs_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_pred_probs.npy')
# auroc_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_aurocs.npy')
# auroc_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_aurocs.npy')
# auprc_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_auprcs.npy')
# auprc_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_auprcs.npy')
# # print(targets.shape, pred_probs_1.shape, pred_probs_2.shape, auroc_1.shape, auroc_2.shape, auprc_1.shape, auprc_2.shape)
# p_auroc = delong_roc_test(targets, pred_probs_1, pred_probs_2)[0][0]
# # p_auroc = stats.ttest_rel(auroc_1, auroc_2).pvalue
# p_auprc = stats.ttest_rel(auprc_1, auprc_2).pvalue
# print(f'{model_pair}: p_auroc={p_auroc} , p_auprc={p_auprc}', flush=True)
# ECG LIMITED
print('ECG LIMITED', flush=True)
folders = ['simsiam_limited_labeled', 'deepmi_limited_labels', 'ecg_only_baseline_limited_labeled']
model_names = ['simsiamlimitedlabels', 'deepmilimitedlabels', 'ecgonlylimitedlabel']
dataset_lengths = ['2400', '2400', '2400']
combs = list(combinations(folders, 2))
print(combs, flush=True)
for pair in testing_data_label_pairs:
dataset = pair[0].split('.')[0]
print('---------------------------------------------------------------', flush=True)
if 'ecg' in dataset:
print(dataset, flush=True)
for model_pair in combs:
dataset_1 = dataset.replace('2400', dataset_lengths[folders.index(model_pair[0])])
dataset_2 = dataset.replace('2400', dataset_lengths[folders.index(model_pair[1])])
targets = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_targets.npy')
pred_probs_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_pred_probs.npy')
pred_probs_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_pred_probs.npy')
auroc_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_aurocs.npy')
auroc_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_aurocs.npy')
auprc_1 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_1}_{model_names[folders.index(model_pair[0])]}_auprcs.npy')
auprc_2 = np.load(f'/labs/hulab/stark_stuff/ppg_ecg_project/data/{dataset_2}_{model_names[folders.index(model_pair[1])]}_auprcs.npy')
# print(targets.shape, pred_probs_1.shape, pred_probs_2.shape, auroc_1.shape, auroc_2.shape, auprc_1.shape, auprc_2.shape)
p_auroc = delong_roc_test(targets, pred_probs_1, pred_probs_2)[0][0]
# p_auroc = stats.ttest_rel(auroc_1, auroc_2).pvalue
p_auprc = stats.ttest_rel(auprc_1, auprc_2).pvalue
print(f'{model_pair}: p_auroc={p_auroc} , p_auprc={p_auprc}', flush=True)