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binary_classification.sh
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binary_classification.sh
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#!/bin/bash
#SBATCH --partition main ### specify partition name where to run a job
#SBATCH --time 7-00:00:00 ### limit the time of job running
#SBATCH --job-name slgc_binary_classification ### name of the job
#SBATCH --output job-%A_%a.out ### output log for running job - %A for array job ID, %a for array index
#SBATCH --mail-user=leorro@post.bgu.ac.il ### user's email for sending job status messages
#SBATCH --mail-type=ALL ### conditions for sending the email
#SBATCH --mem=128G ### amount of RAM memory
#SBATCH --cpus-per-task=16 ### number of CPU cores per task
#SBATCH --array=0-19 ### define job array with indices
### Array of Python commands
commands=(
"python -u -m binary_classification --agg_func mean --n_iterations 100 --n_permutations 1000 --model_type logistic_regression"
"python -u -m binary_classification --agg_func mean --n_iterations 100 --n_permutations 1000 --model_type decision_tree"
"python -u -m binary_classification --agg_func mean --n_iterations 100 --n_permutations 1000 --model_type random_forest"
"python -u -m binary_classification --agg_func mean --n_iterations 100 --n_permutations 1000 --model_type lightgbm"
"python -u -m binary_classification --agg_func mean --n_iterations 100 --n_permutations 1000 --model_type xgboost"
"python -u -m binary_classification --agg_func max --n_iterations 100 --n_permutations 1000 --model_type logistic_regression"
"python -u -m binary_classification --agg_func max --n_iterations 100 --n_permutations 1000 --model_type decision_tree"
"python -u -m binary_classification --agg_func max --n_iterations 100 --n_permutations 1000 --model_type random_forest"
"python -u -m binary_classification --agg_func max --n_iterations 100 --n_permutations 1000 --model_type lightgbm"
"python -u -m binary_classification --agg_func max --n_iterations 100 --n_permutations 1000 --model_type xgboost"
"python -u -m binary_classification --agg_func min --n_iterations 100 --n_permutations 1000 --model_type logistic_regression"
"python -u -m binary_classification --agg_func min --n_iterations 100 --n_permutations 1000 --model_type decision_tree"
"python -u -m binary_classification --agg_func min --n_iterations 100 --n_permutations 1000 --model_type random_forest"
"python -u -m binary_classification --agg_func min --n_iterations 100 --n_permutations 1000 --model_type lightgbm"
"python -u -m binary_classification --agg_func min --n_iterations 100 --n_permutations 1000 --model_type xgboost"
"python -u -m binary_classification --agg_func median --n_iterations 100 --n_permutations 1000 --model_type logistic_regression"
"python -u -m binary_classification --agg_func median --n_iterations 100 --n_permutations 1000 --model_type decision_tree"
"python -u -m binary_classification --agg_func median --n_iterations 100 --n_permutations 1000 --model_type random_forest"
"python -u -m binary_classification --agg_func median --n_iterations 100 --n_permutations 1000 --model_type lightgbm"
"python -u -m binary_classification --agg_func median --n_iterations 100 --n_permutations 1000 --model_type xgboost"
)
### Load modules and activate conda environment
module load anaconda
source activate tfgpu_jup
### Change to the working directory
cd "/sise/assafzar-group/assafzar/leor/delta_tissue_slgc/Nondestructive Spatial Lipidomics for Glioma Classification - Tissue Similarity and Grading/SLGC-tissue-similarity-and-grading/"
### Run the command corresponding to the array index
eval ${commands[$SLURM_ARRAY_TASK_ID]}