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# assumes working directory is the root of the project
# source the environment variables (paths)
set -a
source config/.env
# pick PREVENT-AD imaging sessions to use
./scripts/choose_pad_imaging_sessions.py $FPATH_PAD_MANIFEST $FPATH_PAD_MCI $DPATH_FL_DATA
# wrangle tabular phenotypic data for some of the datasets
./scripts/get_pheno-adni.py $FPATH_ADNI_PHENO $FPATH_ADNI_PHENO_CLEAN
./scripts/get_pheno-pad.py $FPATH_PAD_MANIFEST $FPATH_PAD_DEMOGRAPHICS $FPATH_PAD_AGE $FPATH_PAD_MCI $FPATH_PAD_PHENO_CLEAN
./scripts/get_pheno-qpn.py $FPATH_QPN_DEMOGRAPHICS $FPATH_QPN_MRI $FPATH_QPN_DIAGNOSIS $FPATH_QPN_MOCA $FPATH_QPN_PHENO_CLEAN
# annotate clean phenotypic TSV files with Neurobagel annotation tool
# with ENIGMA-PD configuration: https://beta-annotate.neurobagel.org/
# install and run cog_decline_harmonize pipeline in Nipoppy datasets
# this creates a harmonized.tsv file inside <NIPOPPY_ROOT>/tabular
# update global_config.json to have CUSTOM.FL_PD.SINGLE_SESSION or CUSTOM.FL_PD.MAPPING_FILE
# create Fed-BioMed node if needed
fedbiomed component create -p $DPATH_FEDBIOMED/node-<DATASET> -c NODE -n <DATASET>
# add dataset to node (only needs to be done once)
# datatype: 6 (custom)
# tags: <DATASET>,<TARGET1>,<TARGET2>,...,federated
# eg: ppmi,federated,nb:Age,nb:Diagnosis,fl:cognitive_decline_status
# adni,federated,nb:Age,nb:Diagnosis,fl:cognitive_decline_status
# path to dataset is Nipoppy root path
# NOTE: <DATASET> is lowercase dataset name (e.g., adni, ppmi, qpn, pad, calgary)
fedbiomed node -p $DPATH_FEDBIOMED/node-<DATASET> dataset add
fedbiomed node -p $DPATH_FEDBIOMED/node-<DATASET> start
# build TSV for mega-analysis node
./scripts/get_mega.py --output $DPATH_FL_DATA_LATEST_MEGA --dataset adni $DPATH_FL_DATA_LATEST/adni --dataset calgary $DPATH_FL_DATA_LATEST/calgary --dataset pad $DPATH_FL_DATA_LATEST/pad --dataset ppmi $DPATH_FL_DATA_LATEST/ppmi --dataset qpn $DPATH_FL_DATA_LATEST/qpn --target 'fl:cognitive_decline_status'
./scripts/get_mega.py --output $DPATH_FL_DATA_LATEST_MEGA --dataset adni $DPATH_FL_DATA_LATEST/adni --dataset calgary $DPATH_FL_DATA_LATEST/calgary --dataset pad $DPATH_FL_DATA_LATEST/pad --dataset ppmi $DPATH_FL_DATA_LATEST/ppmi --dataset qpn $DPATH_FL_DATA_LATEST/qpn --target 'nb:Age'
./scripts/get_mega.py --output $DPATH_FL_DATA_LATEST_MEGA --dataset adni $DPATH_FL_DATA_LATEST/adni --dataset calgary $DPATH_FL_DATA_LATEST/calgary --dataset ppmi $DPATH_FL_DATA_LATEST/ppmi --dataset qpn $DPATH_FL_DATA_LATEST/qpn --target 'nb:Diagnosis'
# create mega node if needed
fedbiomed component create -p $DPATH_FEDBIOMED/node-mega -c NODE -n mega
# add TSV data to node (needs to be done multiple times)
# datatype: 6 (custom)
# tags: mega_<DATASET1>_<DATASET2>_<...>,<TARGET>
# eg: mega_adni_calgary_pad_ppmi_qpn,fl:cognitive_decline_status
# mega_adni_calgary_pad_ppmi_qpn,nb:Age
# mega_adni_calgary_ppmi_qpn,nb:Diagnosis
# path to dataset is TSV file path
fedbiomed node -p $DPATH_FEDBIOMED/node-mega dataset add
# start node if needed
fedbiomed node -p $DPATH_FEDBIOMED/node-mega start
# get statistics TSV files
./scripts/get_statistics_custom_dataset.py --dataset adni $DPATH_FL_DATA_LATEST/adni --dataset calgary $DPATH_FL_DATA_LATEST/calgary --dataset pad $DPATH_FL_DATA_LATEST/pad --dataset ppmi $DPATH_FL_DATA_LATEST/ppmi --dataset qpn $DPATH_FL_DATA_LATEST/qpn --mega $DPATH_FL_DATA_LATEST_MEGA --federated adni --federated calgary --federated pad --federated ppmi --federated qpn --target 'fl:cognitive_decline_status'
./scripts/get_statistics_custom_dataset.py --dataset adni $DPATH_FL_DATA_LATEST/adni --dataset calgary $DPATH_FL_DATA_LATEST/calgary --dataset pad $DPATH_FL_DATA_LATEST/pad --dataset ppmi $DPATH_FL_DATA_LATEST/ppmi --dataset qpn $DPATH_FL_DATA_LATEST/qpn --mega $DPATH_FL_DATA_LATEST_MEGA --federated adni --federated calgary --federated pad --federated ppmi --federated qpn --target 'nb:Age'
./scripts/get_statistics_custom_dataset.py --dataset adni $DPATH_FL_DATA_LATEST/adni --dataset calgary $DPATH_FL_DATA_LATEST/calgary --dataset ppmi $DPATH_FL_DATA_LATEST/ppmi --dataset qpn $DPATH_FL_DATA_LATEST/qpn --mega $DPATH_FL_DATA_LATEST_MEGA --federated adni --federated calgary --federated ppmi --federated qpn --target 'nb:Diagnosis'
# cogtips (to be run on other node)
./scripts/get_statistics_custom_dataset.py --dataset cogtips $DPATH_FL_DATA_LATEST/vumc2 --output $DPATH_FL_STATS --n-splits 10 --random-state $RANDOM_SEED --target 'nb:Age'
./scripts/get_statistics_custom_dataset.py --dataset cogtips $DPATH_FL_DATA_LATEST/vumc2 --output $DPATH_FL_STATS --n-splits 10 --random-state $RANDOM_SEED --target 'nb:Diagnosis'
# add cogtips to federated case
./scripts/get_statistics_custom_dataset.py --federated adni --federated calgary --federated cogtips --federated ppmi --federated qpn --target 'nb:Diagnosis'
# run_fedbiomed_custom_dataset.py
# use --split-range START END and --split-range-null START END to control which splits to run
# e.g., --split-range 0 5 to run splits 0 to 4 (inclusive)
./scripts/run_fedbiomed_custom_dataset.py --n-null 10 $DPATH_FL_DATA $DPATH_FL_RESULTS $DPATH_FEDBIOMED $DPATH_FL_STATS --target 'fl:cognitive_decline_status'
./scripts/run_fedbiomed_custom_dataset.py --n-null 10 $DPATH_FL_DATA $DPATH_FL_RESULTS $DPATH_FEDBIOMED $DPATH_FL_STATS --target 'nb:Age'
./scripts/run_fedbiomed_custom_dataset.py --n-null 10 $DPATH_FL_DATA $DPATH_FL_RESULTS $DPATH_FEDBIOMED $DPATH_FL_STATS --target 'nb:Diagnosis' --tag-mega 'mega_adni_calgary_ppmi_qpn' --train-dataset adni --train-dataset calgary --train-dataset ppmi --train-dataset qpn --test-dataset adni --test-dataset calgary --test-dataset ppmi --test-dataset qpn
# troubleshooting diagnosis
./scripts/run_fedbiomed_custom_dataset.py --n-null 0 --split-range 0 1 $DPATH_FL_DATA $DPATH_FL_RESULTS $DPATH_FEDBIOMED $DPATH_FL_STATS --target 'nb:Diagnosis' --tag-mega 'mega_adni_calgary_ppmi_qpn' --train-dataset adni --train-dataset calgary --train-dataset ppmi --train-dataset qpn --test-dataset adni --test-dataset calgary --test-dataset ppmi --test-dataset qpn --n-updates 100
./scripts/run_fedbiomed_custom_dataset.py --n-null 0 --split-range 0 1 --setup mega $DPATH_FL_DATA $DPATH_FL_RESULTS $DPATH_FEDBIOMED $DPATH_FL_STATS --target 'nb:Diagnosis' --tag-mega 'mega_adni_calgary_ppmi_qpn' --train-dataset adni --train-dataset calgary --train-dataset ppmi --train-dataset qpn --test-dataset adni --test-dataset calgary --test-dataset ppmi --test-dataset qpn
# # ===== OLD =====
# # create single large TSV for dach dataset
# ./scripts/get_data-adni.py $FPATH_ADNI_PHENO $FPATH_ADNI_ASEG $FPATH_ADNI_APARC
# ./scripts/get_data-calgary.py $FPATH_CALGARY_PHENO $FPATH_CALGARY_ASEG $FPATH_CALGARY_APARC
# ./scripts/get_data-pad.py $FPATH_PAD_DEMOGRAPHICS $FPATH_PAD_AGE $FPATH_PAD_MCI $FPATH_PAD_ASEG $FPATH_PAD_APARC $DPATH_FL_DATA
# ./scripts/get_data-ppmi.py $FPATH_PPMI_PHENO $FPATH_PPMI_ASEG $FPATH_PPMI_APARC --fs7 --fpath-aseg-fs6 $FPATH_PPMI_ASEG_FS6 --fpath-aparc-fs6 $FPATH_PPMI_APARC_FS6
# ./scripts/get_data-qpn.py $FPATH_QPN_DEMOGRAPHICS $FPATH_QPN_AGE $FPATH_QPN_DIAGNOSIS $FPATH_QPN_MOCA $FPATH_QPN_ASEG $FPATH_QPN_APARC $DPATH_FL_DATA
# # extract columns
# ./scripts/subset_data.py --dropna COG_DECLINE --decline --age --sex --no-diag --cases --controls --aparc --no-aseg $DPATH_FL_DATA_LATEST {adni,calgary,pad,ppmi,qpn}
# ./scripts/subset_data.py --dropna AGE --no-decline --age --sex --no-diag --no-cases --controls --aparc --aseg $DPATH_FL_DATA_LATEST {adni,calgary,pad,ppmi,qpn}
# ./scripts/subset_data.py --dropna DIAGNOSIS --no-decline --age --sex --diag --cases --controls --aparc --aseg $DPATH_FL_DATA_LATEST {adni,calgary,ppmi,qpn}
# # # also get the entire control subset
# # ./scripts/subset_data.py --dropna AGE --dropna SEX --decline --age --sex --diag --no-cases --controls --aparc --aseg $DPATH_FL_DATA_LATEST {adni,ppmi,qpn}
# # # simulated (MMSE)
# # ./scripts/get_data-simulated.py $DPATH_FL_DATA
# # split into training and testing sets
# # no normative model (also no z-scoring)
# ./scripts/split_train_test.py --n-splits 10 --stratify-col COG_DECLINE --shuffle --no-standardize --random-state $RANDOM_SEED --tag-adaptation decline-age-sex-diag-hc-aparc-aseg --no-norm $DPATH_FL_DATA_LATEST $DPATH_NORMATIVE_MODELLING_DATA {adni,calgary,pad,ppmi,qpn}-decline-age-sex-case-hc-aparc
# ./scripts/split_train_test.py --n-splits 10 --stratify-col AGE --shuffle --no-standardize --random-state $RANDOM_SEED --tag-adaptation decline-age-sex-diag-hc-aparc-aseg --no-norm $DPATH_FL_DATA_LATEST $DPATH_NORMATIVE_MODELLING_DATA {adni,calgary,pad,ppmi,qpn}-age-sex-hc-aparc-aseg
# # ./scripts/split_train_test.py --n-splits 10 --stratify-col AGE --min-age 55 --shuffle --no-standardize --random-state $RANDOM_SEED --tag-adaptation decline-age-sex-diag-hc-aparc-aseg --no-norm $DPATH_FL_DATA_LATEST $DPATH_NORMATIVE_MODELLING_DATA {adni,ppmi,qpn,pad}-age-sex-hc-aseg
# ./scripts/split_train_test.py --n-splits 10 --stratify-col DIAGNOSIS --shuffle --no-standardize --random-state $RANDOM_SEED --tag-adaptation decline-age-sex-diag-hc-aparc-aseg --no-norm $DPATH_FL_DATA_LATEST $DPATH_NORMATIVE_MODELLING_DATA {adni,calgary,ppmi,qpn}-age-sex-diag-case-hc-aparc-aseg
# # # no normative model but with z-scoring
# # ./scripts/split_train_test.py --n-splits 10 --stratify-col COG_DECLINE --shuffle --standardize --random-state $RANDOM_SEED --tag-adaptation decline-age-sex-diag-hc-aparc-aseg --no-norm $DPATH_FL_DATA_LATEST $DPATH_NORMATIVE_MODELLING_DATA {adni,calgary,pad,ppmi,qpn}-decline-age-sex-case-hc-aparc
# # ./scripts/split_train_test.py --n-splits 10 --stratify-col AGE --shuffle --standardize --random-state $RANDOM_SEED --tag-adaptation decline-age-sex-diag-hc-aparc-aseg --no-norm $DPATH_FL_DATA_LATEST $DPATH_NORMATIVE_MODELLING_DATA {adni,calgary,pad,ppmi,qpn}-age-sex-hc-aparc-aseg
# # # with normative model
# # ./scripts/split_train_test.py --n-splits 10 --stratify-col COG_DECLINE --shuffle --no-standardize --random-state $RANDOM_SEED --tag-adaptation decline-age-sex-diag-hc-aparc-aseg --norm $DPATH_FL_DATA_LATEST $DPATH_NORMATIVE_MODELLING_DATA {adni,ppmi,qpn}-decline-age-sex-case-aparc
# # ./scripts/split_train_test.py --n-splits 10 --stratify-col AGE --shuffle --no-standardize --random-state $RANDOM_SEED --tag-adaptation decline-age-sex-diag-hc-aparc-aseg --norm $DPATH_FL_DATA_LATEST $DPATH_NORMATIVE_MODELLING_DATA {adni,ppmi,qpn}-age-sex-hc-aseg
# # ./scripts/split_train_test.py --n-splits 10 --stratify-col AGE --min-age 55 --shuffle --no-standardize --random-state $RANDOM_SEED --tag-adaptation decline-age-sex-diag-hc-aparc-aseg --norm $DPATH_FL_DATA_LATEST $DPATH_NORMATIVE_MODELLING_DATA {adni,ppmi,qpn}-age-sex-hc-aseg
# # ./scripts/split_train_test.py --n-splits 10 --stratify-col DIAGNOSIS --shuffle --no-standardize --random-state $RANDOM_SEED --tag-adaptation decline-age-sex-diag-hc-aparc-aseg --norm $DPATH_FL_DATA_LATEST $DPATH_NORMATIVE_MODELLING_DATA {adni,ppmi,qpn}-age-sex-diag-case-hc-aparc-aseg
# # # simulated data
# # ./scripts/split_train_test.py --n-splits 10 --shuffle --standardize --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST {site1,site2,site3}-simulated
# # combine for mega-analysis case
# # no normative model (also no z-scoring)
# parallel ./scripts/get_data-mega.py --tag decline-age-sex-case-hc-aparc --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST adni calgary pad ppmi qpn ::: {0..9}
# parallel ./scripts/get_data-mega.py --tag age-sex-hc-aparc-aseg --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST adni calgary pad ppmi qpn ::: {0..9}
# # parallel ./scripts/get_data-mega.py --tag age-sex-hc-aseg-55 --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST adni ppmi qpn ::: {0..9}
# parallel ./scripts/get_data-mega.py --tag age-sex-diag-case-hc-aparc-aseg --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST adni calgary ppmi qpn ::: {0..9}
# # # no normative model but with z-scoring
# # parallel ./scripts/get_data-mega.py --tag decline-age-sex-case-hc-aparc-standardized --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST adni calgary pad ppmi qpn ::: {0..9}
# # parallel ./scripts/get_data-mega.py --tag age-sex-hc-aseg-standardized --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST adni calgary pad ppmi qpn ::: {0..9}
# # # with normative model
# # parallel ./scripts/get_data-mega.py --tag decline-age-sex-case-aparc-norm --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST adni ppmi qpn ::: {0..9}
# # parallel ./scripts/get_data-mega.py --tag age-sex-hc-aseg-norm --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST adni ppmi qpn ::: {0..9}
# # parallel ./scripts/get_data-mega.py --tag age-sex-hc-aseg-55-norm --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST adni ppmi qpn ::: {0..9}
# # parallel ./scripts/get_data-mega.py --tag age-sex-diag-case-hc-aparc-aseg-norm --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST adni ppmi qpn ::: {0..9}
# # # simulated data
# # parallel ./scripts/get_data-mega.py --tag simulated-standardized --suffix '-{}train' --random-state $RANDOM_SEED $DPATH_FL_DATA_LATEST site1 site2 site3 ::: {0..9}
# parallel ./scripts/get_statistics.py --tag decline-age-sex-case-hc-aparc --suffix '-{}train' $DPATH_FL_DATA_LATEST adni calgary pad ppmi qpn mega_adni_calgary_pad_ppmi_qpn ::: {0..9}
# parallel ./scripts/get_statistics.py --tag age-sex-hc-aparc-aseg --suffix '-{}train' $DPATH_FL_DATA_LATEST adni calgary pad ppmi qpn mega_adni_calgary_pad_ppmi_qpn ::: {0..9}
# parallel ./scripts/get_statistics.py --tag age-sex-diag-case-hc-aparc-aseg --suffix '-{}train' $DPATH_FL_DATA_LATEST adni calgary ppmi qpn mega_adni_calgary_ppmi_qpn ::: {0..9}
# # create the nodes
# fedbiomed component create -p ./fedbiomed/node-mega -c NODE
# fedbiomed component create -p ./fedbiomed/node-adni -c NODE
# fedbiomed component create -p ./fedbiomed/node-ppmi -c NODE
# fedbiomed component create -p ./fedbiomed/node-qpn -c NODE
# fedbiomed component create -p ./fedbiomed/node-pad -c NODE
# fedbiomed component create -p ./fedbiomed/node-calgary -c NODE
# # rename nodes to NODE-{site} in etc/config.ini (and optionally database filenames)
# # add data to nodes
# ./scripts/add_data_to_nodes.py $DPATH_FL_DATA_LATEST $DPATH_FEDBIOMED
# # start the nodes (in different terminal windows)
# # in tmux: tmux new -s <dataset>; conda activate fl-pd
# fedbiomed node -p ./fedbiomed/node-mega start
# fedbiomed node -p ./fedbiomed/node-adni start
# fedbiomed node -p ./fedbiomed/node-ppmi start
# fedbiomed node -p ./fedbiomed/node-qpn start
# fedbiomed node -p ./fedbiomed/node-pad start
# fedbiomed node -p ./fedbiomed/node-calgary start
# # run Fed-BioMed
# ./scripts/run_fedbiomed.py --tag decline-age-sex-case-hc-aparc --standardize --framework sklearn --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS $DPATH_FEDBIOMED
# ./scripts/run_fedbiomed.py --tag age-sex-hc-aparc-aseg --standardize --framework sklearn --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS $DPATH_FEDBIOMED
# ./scripts/run_fedbiomed.py --tag age-sex-diag-case-hc-aparc-aseg --standardize --framework sklearn --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS $DPATH_FEDBIOMED --dataset adni --dataset calgary --dataset ppmi --dataset qpn
# # ./scripts/run_fedbiomed.py --tag decline-age-sex-case-hc-aparc-standardized --sgdc-loss log_loss --framework sklearn --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS $DPATH_FEDBIOMED $FPATH_FEDBIOMED_CONFIG
# # ./scripts/run_fedbiomed.py --tag age-sex-hc-aseg-standardized --framework sklearn --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS $DPATH_FEDBIOMED $FPATH_FEDBIOMED_CONFIG
# # ./scripts/run_fedbiomed.py --tag simulated-standardized --dataset site1 --dataset site2 --dataset site3 --framework sklearn --n-splits 1 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS $DPATH_RESEARCHER $FPATH_FEDBIOMED_CONFIG --sloppy --null
# # run non-Fed-BioMed implementation
# # no normative model (also no z-scoring)
# ./scripts/run_without_fedbiomed.py --tag decline-age-sex-case-hc-aparc --no-scaler --standardize --n-rounds 3 --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS
# ./scripts/run_without_fedbiomed.py --tag age-sex-hc-aparc-aseg --no-scaler --standardize --n-rounds 3 --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS
# # ./scripts/run_without_fedbiomed.py --tag age-sex-hc-aseg-55 --n-rounds 3 --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS
# ./scripts/run_without_fedbiomed.py --tag age-sex-diag-case-hc-aparc-aseg --n-rounds 3 --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS --dataset adni --dataset calgary --dataset ppmi --dataset qpn
# # # no normative model but with z-scoring
# # ./scripts/run_without_fedbiomed.py --tag decline-age-sex-case-hc-aparc-standardized --n-rounds 3 --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS
# # ./scripts/run_without_fedbiomed.py --tag age-sex-hc-aseg-standardized --n-rounds 3 --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS
# # # with normative model
# # ./scripts/run_without_fedbiomed.py --tag decline-age-sex-case-aparc-norm --n-rounds 3 --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS
# # # ./scripts/run_without_fedbiomed.py --tag age-sex-hc-aseg-55-norm --n-rounds 3 --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS
# # ./scripts/run_without_fedbiomed.py --tag age-sex-hc-aseg-norm --n-rounds 3 --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS
# # ./scripts/run_without_fedbiomed.py --tag age-sex-diag-case-hc-aparc-aseg-norm --n-rounds 3 --n-splits 10 --null 10 $DPATH_FL_DATA_LATEST $DPATH_FL_RESULTS