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aggregate_eval_all_models_exchromosomes.py
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import os
import time
import numpy as np
from scipy.stats import pearsonr, spearmanr
output_dirs = [
'saved_models/dcen/spliceosome_train_checkpoint-85000',
'saved_models/AblationJuncBaseline/spliceosome_train_checkpoint-50000',
'saved_models/AblationMoreLayerBaseline/spliceai_train_checkpoint-120000',
'saved_models/spliceai_clsreg/spliceai_train_checkpoint-200000',
'saved_models/spliceai_onlycls/spliceai_train_checkpoint-70000',
'saved_models/spliceai_onlyreg/spliceai_train_checkpoint-200000',
]
results_filename = 'all_excludedchr_genes_1to250.txt'
first_id = 1
last_id = 250
patient_ids_to_eval = [i for i in range(first_id, last_id+1)]
tissue_types_to_eval = ['ADP',
'BLD',
'BRN',
'BRS',
'CLN',
'HRT',
'KDN',
'LVR',
'LNG',
'LMP',
'PRS',
'SKM',
'TST',
'THR']
chromosomes_to_eval = ['chr1',
'chr3',
'chr5',
'chr7',
'chr9',]
def time_to_human(time):
hrs = time//3600
mins = (time - hrs*3600)//60
secs = time - hrs*3600 - mins*60
print('Overall time elapsed: {} hrs {} mins {} seconds'.format(int(hrs), int(mins), round(secs)))
return hrs, mins, secs
def pearson_and_spearman(preds, labels):
pearson_corr, pearson_p = pearsonr(preds, labels)
spearman_corr, spearman_p = spearmanr(preds, labels)
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"pearson_p": pearson_p,
"spearmanr_p": spearman_p,
"corr": (pearson_corr + spearman_corr) / 2,
}
for output_dir in output_dirs:
print(f"***********Computing results for model: {output_dir}************")
all_pred_psi_acc_list = []
all_pred_psi_don_list = []
all_labels_psi_acc_list = []
all_labels_psi_don_list = []
start_time = time.time()
for patient_id in patient_ids_to_eval:
for tissue_type in tissue_types_to_eval:
for chromosome in chromosomes_to_eval:
print(f"***********Including files from: {tissue_type}{patient_id}_{chromosome}************")
eval_output_dir_prefix = f'{tissue_type}{patient_id}_{chromosome}'
pred_psi_acc_path = os.path.join(output_dir, eval_output_dir_prefix, 'pred_psi_acc.npy')
pred_psi_don_path = os.path.join(output_dir, eval_output_dir_prefix, 'pred_psi_don.npy')
labels_psi_acc_path = os.path.join(output_dir, eval_output_dir_prefix, 'labels_psi_acc.npy')
labels_psi_don_path = os.path.join(output_dir, eval_output_dir_prefix, 'labels_psi_don.npy')
if os.path.exists(pred_psi_acc_path) and os.path.exists(pred_psi_don_path) and os.path.exists(labels_psi_acc_path) and os.path.exists(labels_psi_don_path):
with open(pred_psi_acc_path, 'rb') as f:
pred_psi_acc_list = np.load(f)
all_pred_psi_acc_list.append(pred_psi_acc_list)
with open(pred_psi_don_path, 'rb') as f:
pred_psi_don_list = np.load(f)
all_pred_psi_don_list.append(pred_psi_don_list)
with open(labels_psi_acc_path, 'rb') as f:
labels_psi_acc_list = np.load(f)
all_labels_psi_acc_list.append(labels_psi_acc_list)
with open(labels_psi_don_path, 'rb') as f:
labels_psi_don_list = np.load(f)
all_labels_psi_don_list.append(labels_psi_don_list)
else:
print("Missing npy files in: ", eval_output_dir_prefix )
for more_sample_name in more_sample_names:
for chromosome in chromosomes_to_eval:
print(f"***********Including files from: {more_sample_name}_{chromosome}************")
eval_output_dir_prefix = f'{more_sample_name}_{chromosome}'
pred_psi_acc_path = os.path.join(output_dir, eval_output_dir_prefix, 'pred_psi_acc.npy')
pred_psi_don_path = os.path.join(output_dir, eval_output_dir_prefix, 'pred_psi_don.npy')
labels_psi_acc_path = os.path.join(output_dir, eval_output_dir_prefix, 'labels_psi_acc.npy')
labels_psi_don_path = os.path.join(output_dir, eval_output_dir_prefix, 'labels_psi_don.npy')
if os.path.exists(pred_psi_acc_path) and os.path.exists(pred_psi_don_path) and os.path.exists(labels_psi_acc_path) and os.path.exists(labels_psi_don_path):
with open(pred_psi_acc_path, 'rb') as f:
pred_psi_acc_list = np.load(f)
all_pred_psi_acc_list.append(pred_psi_acc_list)
with open(pred_psi_don_path, 'rb') as f:
pred_psi_don_list = np.load(f)
all_pred_psi_don_list.append(pred_psi_don_list)
with open(labels_psi_acc_path, 'rb') as f:
labels_psi_acc_list = np.load(f)
all_labels_psi_acc_list.append(labels_psi_acc_list)
with open(labels_psi_don_path, 'rb') as f:
labels_psi_don_list = np.load(f)
all_labels_psi_don_list.append(labels_psi_don_list)
else:
print("Missing npy files in: ", eval_output_dir_prefix )
if len(all_pred_psi_acc_list) > 0 and len(all_pred_psi_don_list) > 0:
print("***********Compiling all preds and labels***********")
all_pred_psi_acc_list = np.concatenate(all_pred_psi_acc_list)
all_pred_psi_don_list = np.concatenate(all_pred_psi_don_list)
all_labels_psi_acc_list = np.concatenate(all_labels_psi_acc_list)
all_labels_psi_don_list = np.concatenate(all_labels_psi_don_list)
all_pred_psi_ss_list = np.concatenate([all_pred_psi_acc_list, all_pred_psi_don_list])
all_labels_psi_ss_list = np.concatenate([all_labels_psi_acc_list, all_labels_psi_don_list])
print("***********Computing correlations***********")
acc_cor_result = pearson_and_spearman(all_pred_psi_acc_list, all_labels_psi_acc_list)
don_cor_result = pearson_and_spearman(all_pred_psi_don_list, all_labels_psi_don_list)
all_ss_cor_result = pearson_and_spearman(all_pred_psi_ss_list, all_labels_psi_ss_list)
result = {"spearmanr_acc": acc_cor_result['spearmanr'], "pearson_acc": acc_cor_result['pearson'],
"spearmanr_p_acc": acc_cor_result['spearmanr_p'], "pearson_p_acc": acc_cor_result['pearson_p'],
"spearmanr_don": don_cor_result['spearmanr'], "pearson_don": don_cor_result['pearson'],
"spearmanr_p_don": don_cor_result['spearmanr_p'], "pearson_p_don": don_cor_result['pearson_p'],
"spearmanr_ss": all_ss_cor_result['spearmanr'], "pearson_ss": all_ss_cor_result['pearson'],
"spearmanr_p_ss": all_ss_cor_result['spearmanr_p'], "pearson_p_ss": all_ss_cor_result['pearson_p']}
end_time = time.time()
output_eval_file = os.path.join(output_dir, results_filename)
with open(output_eval_file, "w") as writer:
print("***** Compiled correlation results {} *****".format(output_eval_file))
for key in sorted(result.keys()):
print(" {} = {}".format(key, str(result[key])))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write("*****\n patient_ids_to_eval: {} \n".format(str(patient_ids_to_eval)))
writer.write("*****\n tissue_types_to_eval: {} \n".format(str(tissue_types_to_eval)))
writer.write("*****\n chromosomes_to_eval: {} \n".format(str(chromosomes_to_eval)))
hrs, mins, secs = time_to_human(end_time - start_time)
writer.write("Overall time elapsed: {} hrs {} mins {} seconds".format(int(hrs), int(mins), round(secs)))
else:
print("No prediction npy files found for: ", output_dir, ", skipping.." )