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subject_match_class.py
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subject_match_class.py
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# matches patients and controls according age and gender
#
# cli:
# cwlVersion: v1.0-extended
# class: pythonclass
# baseCommand: result = subject_match_class.SubjectMatch(patients_df,controls_df,max_yeardiff)
#
# inputs:
# patients_df:
# type: pandas.DataFrame
# inputBinding:
# position: 0
# doc: "pandas dataframe with colums ['subjectID','age','gender'], age in years, gender='m','f','o','u'"
# controls_df:
# type: pandas.DataFrame
# inputBinding:
# position: 1
# doc: "pandas dataframe with colums ['subjectID','age','gender'], age in years, gender='m','f','o','u'"
# max_yeardiff:
# type: int?
# inputBinding:
# positions: 2
# doc: "acceptable devation from age in year in both directions, default = 2"
# outputs:
# results.matching:
# type: pandas.DataFrame
# doc: ""pandas dataframe with colums 'patientID', 'age_p', 'gender_p','controlID', 'age_c', 'gender_c']."
#
# s:author:
# - class: s:Person
# s:identifier: https://orcid.org/0000-0002-7238-5339
# s:email: mailto:dagmar.krefting@htw-berlin.de
# s:name: Dagmar Krefting
#
# s:dateCreated: "2019-07-19"
# s:license: https://spdx.org/licenses/Apache-2.0
#
# s:keywords: edam:topic_3063, edam:topic_2082
# doc: 3063: medical informatics, 2082: matrix
# s:programmingLanguage: python3
#
# $namespaces:
# s: https://schema.org/
# edam: http://edamontology.org/
#
# $schemas:
# - https://schema.org/docs/schema_org_rdfa.html
# - http://edamontology.org/EDAM_1.18.owl
#
#%% import libraries
import pandas as pd
import numpy as np
#%% class definition
class SubjectMatch:
def __init__(self, patients, controls, max_yeardiff=2):
self._patients = patients
self._controls = controls
self._max_yeardiff = max_yeardiff
self._matching = None
#%%
@property # a method that is presented to the user like an attribute
def matching(self):
"""return or calculate and return age and gender matching"""
# check if already calculated
if self._matching is not None:
return self._matching
# split into gender-matched subgroups
genders = ['m','f','o']
self._matching = pd.DataFrame(columns=['patientID', 'age_p', 'gender_p','controlID','age_c','gender_c'])
for gender in genders:
patients_gender = self._patients[self._patients['gender'] == gender]
controls_gender = self._controls[self._controls['gender'] == gender]
print(gender)
print(len(patients_gender))
print(len(controls_gender))
if (len(patients_gender) > 0) & (len(controls_gender) > 0):
# call function to calculate matching
self._matching_tmp = self._calc_matching(patients_gender,controls_gender,self._max_yeardiff)
self._matching = self._matching.append(self._matching_tmp)
return self._matching
@staticmethod
def _calc_matching(patients,controls,max_yeardiff,debug=True):
"""find the candidates and choose the one that fits best"""
final_matches = pd.DataFrame(columns=['patientID', 'age_p', 'gender_p','controlID','age_c','gender_c'])
patients_age = np.array(patients['age'])
controls_age = np.array(controls['age'])
# create matrix, this gives you one patient per row, with as many columns as the controls
patients_age_matrix = np.tile(patients_age,(len(controls_age),1)).transpose()
# create matrix with the age differences
age_diff = abs(patients_age_matrix - controls_age)
# mask with max_yeardiff
age_diff_mask = (age_diff <= max_yeardiff).astype(int)
if debug:
print('patient_age_matrix:')
print(patients_age_matrix)
print('age_diff:')
print(age_diff)
print('age_diff_mask:')
print(age_diff_mask)
print('sum(sum(age_diff_mask)): ', end="")
print(sum(sum(age_diff_mask)))
# loop until no matches exist any longer
while sum(sum(age_diff_mask)) > 0:
# sum along the columns (getting an array in the length of the controls
# find the index of the patients, where a patient has only one control that matches
patient_singlematch = np.where(sum(age_diff_mask.transpose()) == 1)
patient_singlematch = patient_singlematch[0]
# debug info
if debug:
print('sum(age_diff_mask.transpose()): ',end="")
print(sum(age_diff_mask.transpose()))
print('patient_singlematch: ', end="")
print(patient_singlematch)
print('len(patient_singlematch): ',end="")
print(len(patient_singlematch))
print('len(final_matches): ', end="")
print(len(final_matches))
# if this list is not empty, set patient
if len(patient_singlematch) > 0:
current_patient = patient_singlematch[0]
next_row = len(final_matches)
# set this patient to the final_matches dataframe, first entry to a new row, the following to the same row
final_matches.loc[next_row,'patientID'] = patients.iloc[current_patient]['subjectID']
final_matches.loc[len(final_matches)-1,'age_p'] = patients.iloc[current_patient]['age']
final_matches.loc[len(final_matches)-1,'gender_p'] = patients.iloc[current_patient]['gender']
# debug info
if debug:
print(patients.iloc[current_patient]['subjectID'])
# find the matching control
print(age_diff_mask[patient_singlematch[0], :])
# get the corresponding control
patient_singlematch_control_idx = np.where(age_diff_mask[patient_singlematch[0],:] == 1)
current_control = patient_singlematch_control_idx[0][0]
#set controls
final_matches.loc[len(final_matches)-1,'controlID'] = controls.iloc[current_control]['subjectID']
final_matches.loc[len(final_matches)-1,'age_c'] = controls.iloc[current_control]['age']
final_matches.loc[len(final_matches)-1,'gender_c'] = controls.iloc[current_control]['gender']
# debug info
if debug:
print('patient_singlematch_control_idx: ', end="")
print(patient_singlematch_control_idx[0])
print('controls.iloc[current_control][subjectID]')
print(controls.iloc[current_control]['subjectID'])
# remove this control from the age_diff_mask and set the age diff to 100 (to never be the minimum
age_diff_mask[:, current_control] = 0
age_diff[:, current_control] = 100
if debug:
print('age_diff_mask after deletion of control')
print(age_diff_mask)
print(age_diff)
# if only multiple matching controls exist
else:
# choose the next patient
patient_match = np.where(sum(age_diff_mask.transpose()) > 1)
patient_match = patient_match[0]
current_patient = patient_match[0]
# set this patient to the final_matches dataframe
final_matches.loc[len(final_matches),'patientID'] = patients.iloc[current_patient]['subjectID']
final_matches.loc[len(final_matches)-1,'age_p'] = patients.iloc[current_patient]['age']
final_matches.loc[len(final_matches)-1,'gender_p'] = patients.iloc[current_patient]['gender']
# debug info
if debug:
print('patient_match: ', end="")
print(patient_match[0])
print(min(age_diff[patient_match[0], :]))
# find the best matching control
selected_patient = age_diff[patient_match[0],:]
patient_match_control_idx = np.where(selected_patient == min(selected_patient))
current_control = patient_match_control_idx[0][0]
# set the selected control to final_matches
final_matches.loc[len(final_matches)-1,'controlID'] = controls.iloc[current_control]['subjectID']
final_matches.loc[len(final_matches)-1,'age_c'] = controls.iloc[current_control]['age']
final_matches.loc[len(final_matches)-1,'gender_c'] = controls.iloc[current_control]['gender']
# remove this control from the age_diff_mask
age_diff_mask[:, current_control] = 0
age_diff_mask[current_patient, :] = 0
age_diff[:, current_control] = 100
if debug:
print(selected_patient)
return final_matches