main_multiple_prediction.py

from src import fastqc_extract, data_preparation, feature_engineering, ml_model
#import sys

def main (data_dir, model_dir, exports_dir, organism):
    if organism not in ['Ecoli', 'Efcm', 'Sau']:
        organism = 'complete_data'
    ngs_reads = fastqc_extract.import_all_reads(data_dir, exports_dir, include_metadata=False)
    filenames = ngs_reads.index
    ngs_reads = data_preparation.prepare_fastqc_data(ngs_reads)
    ngs_reads = feature_engineering.apply_feature_engineering(ngs_reads, exports_dir)

    pred = ml_model.predict_evaluation(ngs_reads, model_dir+'/model_rf_'+organism+'.pkl')

    with open(exports_dir+'/evaluations.txt', 'w') as f:
        for id, prediction in enumerate(pred):
            f.write(filenames[id]+': ')
            if prediction == 0:
                print(filenames[id]+ ': ugly')
                f.write('ugly\n')
            elif prediction == 1:
                print(filenames[id]+ ': good')
                f.write('good\n')

if __name__ == "__main__":
    data_dir = 'test_data/all' #sys.argv[1]
    model_dir = 'models' #sys.argv[2]
    exports_dir = 'exports_evaluation_multiple' #sys.argv[3]
    organism = 'complete_data' #sys.argv[4]
    main(data_dir, model_dir, exports_dir, organism)