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import pandas as pd
import time
import random
import numpy as np
from datetime import timedelta
from datetime import datetime
import MAUC
import argparse
parser = argparse.ArgumentParser(usage='python3 evalOneSubmission.py',
description=r'''
TADPOLE Evaluation Script:
The program computes the following matrics:
Clinical diagnosis prediction:
1. Multiclass area under the receiver operating curve (mAUC)
2. Balanced classification accuracy (BCA)
Continuous feature predictions:
3. Mean Absolute Error (MAE)
4. Coverage Probability Accuracy (CPA)
5. Weighted Error Score (WES)
Author: Razvan V. Marinescu, razvan.marinescu.14@ucl.ac.uk
''')
def calcBCA(estimLabels, trueLabels, nrClasses):
# Balanced Classification Accuracy
bcaAll = []
for c0 in range(nrClasses):
# c0 can be either CTL, MCI or AD
# one example when c0=CTL
# TP - label was estimated as CTL, and the true label was also CTL
# FP - label was estimated as CTL, but the true label was not CTL (was either MCI or AD).
TP = np.sum((estimLabels == c0) & (trueLabels == c0))
TN = np.sum((estimLabels != c0) & (trueLabels != c0))
FP = np.sum((estimLabels == c0) & (trueLabels != c0))
FN = np.sum((estimLabels != c0) & (trueLabels == c0))
# sometimes the sensitivity of specificity can be NaN, if the user doesn't forecast one of the classes.
# In this case we assume a default value for sensitivity/specificity
if (TP+FN) == 0:
sensitivity = 0.5
else:
sensitivity = (1. * TP)/(TP+FN)
if (TN+FP) == 0:
specificity = 0.5
else:
specificity = (1. * TN)/(TN+FP)
bcaCurr = 0.5*(sensitivity+specificity)
bcaAll += [bcaCurr]
# print('bcaCurr %f TP %f TN %f FP %f FN %f' % (bcaCurr, TP, TN, FP, FN))
return np.mean(bcaAll)
def parseData(d4Df, forecastDf, diagLabels):
trueDiag = d4Df['Diagnosis']
trueADAS = d4Df['ADAS13']
trueVents = d4Df['Ventricles']
nrSubj = d4Df.shape[0]
zipTrueLabelAndProbs = []
hardEstimClass = -1 * np.ones(nrSubj, int)
adasEstim = -1 * np.ones(nrSubj, float)
adasEstimLo = -1 * np.ones(nrSubj, float) # lower margin
adasEstimUp = -1 * np.ones(nrSubj, float) # upper margin
ventriclesEstim = -1 * np.ones(nrSubj, float)
ventriclesEstimLo = -1 * np.ones(nrSubj, float) # lower margin
ventriclesEstimUp = -1 * np.ones(nrSubj, float) # upper margin
# print('subDf.keys()', forecastDf['Forecast Date'])
invalidResultReturn = (None,None,None,None,None,None,None,None,None,None,None)
invalidFlag = False
# for each subject in D4 match the closest user forecasts
for s in range(nrSubj):
currSubjMask = d4Df['RID'].iloc[s] == forecastDf['RID']
currSubjData = forecastDf[currSubjMask]
# if subject is missing
if currSubjData.shape[0] == 0:
print('WARNING: Subject RID %s missing from user forecasts' % d4Df['RID'].iloc[s])
invalidFlag = True
continue
# if not all forecast months are present
if currSubjData.shape[0] < 5*12: # check if at least 5 years worth of forecasts exist
print('WARNING: Missing forecast months for subject with RID %s' % d4Df['RID'].iloc[s])
invalidFlag = True
continue
currSubjData = currSubjData.reset_index(drop=True)
timeDiffsScanCog = [d4Df['CognitiveAssessmentDate'].iloc[s] - d for d in currSubjData['Forecast Date']]
# print('Forecast Date 2',currSubjData['Forecast Date'])
indexMin = np.argsort(np.abs(timeDiffsScanCog))[0]
# print('timeDiffsScanMri', indexMin, timeDiffsScanMri)
pCN = currSubjData['CN relative probability'].iloc[indexMin]
pMCI = currSubjData['MCI relative probability'].iloc[indexMin]
pAD = currSubjData['AD relative probability'].iloc[indexMin]
# normalise the relative probabilities by their sum
pSum = (pCN + pMCI + pAD)/3
pCN /= pSum
pMCI /= pSum
pAD /= pSum
hardEstimClass[s] = np.argmax([pCN, pMCI, pAD])
adasEstim[s] = currSubjData['ADAS13'].iloc[indexMin]
adasEstimLo[s] = currSubjData['ADAS13 50% CI lower'].iloc[indexMin]
adasEstimUp[s] = currSubjData['ADAS13 50% CI upper'].iloc[indexMin]
# for the mri scan find the forecast closest to the scan date,
# which might be different from the cognitive assessment date
timeDiffsScanMri = [d4Df['ScanDate'].iloc[s] - d for d in currSubjData['Forecast Date']]
indexMinMri = np.argsort(np.abs(timeDiffsScanMri))[0]
ventriclesEstim[s] = currSubjData['Ventricles_ICV'].iloc[indexMinMri]
ventriclesEstimLo[s] = currSubjData['Ventricles_ICV 50% CI lower'].iloc[indexMinMri]
ventriclesEstimUp[s] = currSubjData['Ventricles_ICV 50% CI upper'].iloc[indexMinMri]
# print('%d probs' % d4Df['RID'].iloc[s], pCN, pMCI, pAD)
if not np.isnan(trueDiag.iloc[s]):
zipTrueLabelAndProbs += [(trueDiag.iloc[s], [pCN, pMCI, pAD])]
if invalidFlag:
# if at least one subject was missing or if
raise ValueError('Submission was incomplete. Please resubmit')
# If there are NaNs in D4, filter out them along with the corresponding user forecasts
# This can happen if rollover subjects don't come for visit in ADNI3.
notNanMaskDiag = np.logical_not(np.isnan(trueDiag))
trueDiagFilt = trueDiag[notNanMaskDiag]
hardEstimClassFilt = hardEstimClass[notNanMaskDiag]
notNanMaskADAS = np.logical_not(np.isnan(trueADAS))
trueADASFilt = trueADAS[notNanMaskADAS]
adasEstim = adasEstim[notNanMaskADAS]
adasEstimLo = adasEstimLo[notNanMaskADAS]
adasEstimUp = adasEstimUp[notNanMaskADAS]
notNanMaskVents = np.logical_not(np.isnan(trueVents))
trueVentsFilt = trueVents[notNanMaskVents]
ventriclesEstim = ventriclesEstim[notNanMaskVents]
ventriclesEstimLo = ventriclesEstimLo[notNanMaskVents]
ventriclesEstimUp = ventriclesEstimUp[notNanMaskVents]
assert trueDiagFilt.shape[0] == hardEstimClassFilt.shape[0]
assert trueADASFilt.shape[0] == adasEstim.shape[0] == adasEstimLo.shape[0] == adasEstimUp.shape[0]
assert trueVentsFilt.shape[0] == ventriclesEstim.shape[0] == \
ventriclesEstimLo.shape[0] == ventriclesEstimUp.shape[0]
return zipTrueLabelAndProbs, hardEstimClassFilt, adasEstim, adasEstimLo, adasEstimUp, \
ventriclesEstim, ventriclesEstimLo, ventriclesEstimUp, trueDiagFilt, trueADASFilt, trueVentsFilt
def evalOneSub(d4Df, forecastDf):
"""
Evaluates one submission.
Parameters
----------
d4Df - Pandas data frame containing the D4 dataset
subDf - Pandas data frame containing user forecasts for D2 subjects.
Returns
-------
mAUC - multiclass Area Under Curve
bca - balanced classification accuracy
adasMAE - ADAS13 Mean Aboslute Error
ventsMAE - Ventricles Mean Aboslute Error
adasCovProb - ADAS13 Coverage Probability for 50% confidence interval
ventsCovProb - Ventricles Coverage Probability for 50% confidence interval
"""
forecastDf['Forecast Date'] = [datetime.strptime(x, '%Y-%m') for x in forecastDf['Forecast Date']] # considers every month estimate to be the actual first day 2017-01
if isinstance(d4Df['Diagnosis'].iloc[0], str):
d4Df['CognitiveAssessmentDate'] = [datetime.strptime(x, '%Y-%m-%d') for x in d4Df['CognitiveAssessmentDate']]
d4Df['ScanDate'] = [datetime.strptime(x, '%Y-%m-%d') for x in d4Df['ScanDate']]
mapping = {'CN' : 0, 'MCI' : 1, 'AD' : 2}
d4Df.replace({'Diagnosis':mapping}, inplace=True)
diagLabels = ['CN', 'MCI', 'AD']
zipTrueLabelAndProbs, hardEstimClass, adasEstim, adasEstimLo, adasEstimUp, \
ventriclesEstim, ventriclesEstimLo, ventriclesEstimUp, trueDiagFilt, trueADASFilt, trueVentsFilt = \
parseData(d4Df, forecastDf, diagLabels)
zipTrueLabelAndProbs = list(zipTrueLabelAndProbs)
########## compute metrics for the clinical status #############
##### Multiclass AUC (mAUC) #####
nrClasses = len(diagLabels)
mAUC = MAUC.MAUC(zipTrueLabelAndProbs, num_classes=nrClasses)
### Balanced Classification Accuracy (BCA) ###
# print('hardEstimClass', np.unique(hardEstimClass), hardEstimClass)
trueDiagFilt = trueDiagFilt.astype(int)
# print('trueDiagFilt', np.unique(trueDiagFilt), trueDiagFilt)
bca = calcBCA(hardEstimClass, trueDiagFilt, nrClasses=nrClasses)
####### compute metrics for Ventricles and ADAS13 ##########
#### Mean Absolute Error (MAE) #####
adasMAE = np.mean(np.abs(adasEstim - trueADASFilt))
ventsMAE = np.mean(np.abs(ventriclesEstim - trueVentsFilt))
##### Weighted Error Score (WES) ####
adasCoeffs = 1.0/(adasEstimUp - adasEstimLo)
adasWES = np.sum(adasCoeffs * np.abs(adasEstim - trueADASFilt))/np.sum(adasCoeffs)
ventsCoeffs = 1.0/(ventriclesEstimUp - ventriclesEstimLo)
ventsWES = np.sum(ventsCoeffs * np.abs(ventriclesEstim - trueVentsFilt))/np.sum(ventsCoeffs)
#### Coverage Probability Accuracy (CPA) ####
adasCovProb = np.sum((adasEstimLo < trueADASFilt) &
(adasEstimUp > trueADASFilt))/trueADASFilt.shape[0]
adasCPA = np.abs(adasCovProb - 0.5)
ventsCovProb = np.sum((ventriclesEstimLo < trueVentsFilt) &
(ventriclesEstimUp > trueVentsFilt))/trueVentsFilt.shape[0]
ventsCPA = np.abs(ventsCovProb - 0.5)
return mAUC, bca, adasMAE, ventsMAE, adasWES, ventsWES, adasCPA, ventsCPA
if __name__ == "__main__":
parser.add_argument('--d4File', dest='d4File', help='CSV file containing the D4 dataset. '\
'Needs to be in the same format of D4_dummy.csv')
parser.add_argument('--forecastFile', dest='forecastFile', help='CSV file containing the user '
'forecasts for subjects in D2. Needs to be in the same format as '
'TADPOLE_Submission_TeamName1.xlsx or TADPOLE_Submission_Leaderboard_TeamName1.csv')
parser.add_argument('--leaderboard', action='store_true', help='pass this flag if the submission is a leaderboard submission. It ensures the filename is in the right format')
args = parser.parse_args()
d4File = args.d4File
forecastFile = args.forecastFile
if args.leaderboard:
if ('TADPOLE_Submission_Leaderboard_' not in forecastFile) or (not forecastFile.endswith('.csv')):
raise ValueError('Leaderboard submission filename is not in the correct format: TADPOLE_Submission_Leaderboard_TeamName.csv')
else:
if ('TADPOLE_Submission_' not in forecastFile) or (not forecastFile.endswith('.csv')):
raise ValueError('Submission filename is not in the correct format: TADPOLE_Submission_TeamName.csv.')
d4Df = pd.read_csv(d4File)
subDf = pd.read_csv(forecastFile)
# don't catch the exception here, as this main function is used to test if the submission if correct
mAUC, bca, adasMAE, ventsMAE, adasWES, ventsWES, adasCPA, ventsCPA = \
evalOneSub(d4Df, subDf)
print('########### Metrics for clinical status ##################')
print('mAUC', mAUC)
print('bca', bca)
print('\n########### Mean Absolute Error (MAE) ##################')
print('adasMAE', adasMAE, 'ventsMAE', ventsMAE)
print('\n########### Weighted Error Score (WES) ##################')
print('adasWES', adasWES, 'ventsWES', ventsWES)
print('\n########### Coverage Probability Accuracy ##################')
print('adasCPA', adasCPA, 'ventsCPA', ventsCPA)
print('\n\n########### File is ready for submission to TADPOLE ###########')