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main.py
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import platform
import os
import argparse
import random
import time
import pandas as pd
import numpy as np
import torch
import neuralnetwork
if platform.system() == 'Windows':
webDriveFolder = "W:/staff-umbrella/JGMasters/2122-mathijs-de-wolf/feature_sets/"
outputFolder = ""
else:
webDriveFolder = "/tudelft.net/staff-umbrella/JGMasters/2122-mathijs-de-wolf/feature_sets/"
outputFolder = "/tudelft.net/staff-umbrella/JGMasters/2122-mathijs-de-wolf/output/"
def setup_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
def init_argparse() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
usage="%(prog)s [OPTION] [FILE]...",
description="Run neuralnetwork"
)
parser.add_argument("-t", "--testset")
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--num-epochs", type=int, default=1)
parser.add_argument("-l", "--layers", type=int, nargs='*', default=[])
parser.add_argument('-C', '--cancer', choices=['BRCA', 'CESC', 'COAD', 'KIRC', 'LAML', 'LUAD', 'SKCM', 'OV'])
parser.add_argument("--output-file", type=str, default=None)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--lr", type=float, default=0.01)
parser.add_argument("--knn", type=int, default=5)
parser.add_argument("--miner", action='store_true')
parser.add_argument("--contrastive", action='store_true')
parser.add_argument("--exclude", type=str, default='')
parser.add_argument("--vis-genes", action='store_true')
parser.add_argument("--vis-embeddings", action='store_true')
parser.add_argument("--save-embeddings", action='store_true')
parser.add_argument("file")
return parser
if __name__ == '__main__':
parser = init_argparse()
args = parser.parse_args()
dataPath = args.file
if not os.path.exists(dataPath):
dataPath = webDriveFolder + dataPath
if not os.path.exists(dataPath):
raise FileNotFoundError('The dataset does not exist')
layers = [x for x in args.layers if x > 0]
if args.output_file:
outputFolder = outputFolder + args.output_file + "/"
else:
outputFile = "experiment-"
i = 0
while os.path.exists(outputFolder + outputFile + str(i)):
i += 1
outputFolder = outputFolder + outputFile + str(i) + '/'
time.sleep(random.random())
if not os.path.exists(outputFolder):
os.mkdir(outputFolder)
if args.lr > 1:
args.lr = 1 / args.lr
with open(outputFolder + 'settings.txt', 'a') as f:
f.write('\n'.join([
outputFolder,
'number of epochs: '+str(args.num_epochs),
'batch size: '+str(args.batch_size),
'trainingset: '+args.file,
'testset: '+str(args.testset),
'cancer: '+str(args.cancer),
'layers: '+' '.join([str(x) for x in layers]),
'learning rate: '+str(args.lr),
'seed: '+str(args.seed),
'knn: '+str(args.knn),
'miner: '+str(args.miner),
'loss: '+('contrastive' if args.contrastive else 'triplet'),
'excluded gene: '+str(args.exclude),
''
]))
setup_seed(args.seed)
dataset = pd.read_csv(dataPath).fillna(0)
if args.cancer is not None:
dataset = dataset[dataset['cancer']==args.cancer]
dataset = dataset[(dataset['seq1']!=-1.0) & (dataset['seq1']!=0.0)]
train_dataset = dataset[(dataset['gene1']!=args.exclude) & (dataset['gene2']!=args.exclude)]
test_dataset = dataset[(dataset['gene1']==args.exclude) | (dataset['gene2']==args.exclude)]
idx_0 = train_dataset[train_dataset['class'] == 0]
idx_1 = train_dataset[train_dataset['class'] == 1]
if len(idx_0) < len(idx_1):
idx_1 = idx_1.sample(len(idx_0))
if len(idx_0) > len(idx_1):
idx_0 = idx_0.sample(len(idx_1))
train_dataset = pd.concat([idx_0, idx_1])
train_dataset = pd.concat([train_dataset, test_dataset[test_dataset['class'] == 0].iloc[:40, :], test_dataset[test_dataset['class'] == 1].iloc[:10, :]])
test_dataset = pd.concat([test_dataset[test_dataset['class'] == 0].iloc[40:, :], test_dataset[test_dataset['class'] == 1].iloc[10:, :]])
flags = {
'visualize_genes': args.vis_genes,
'visualize_embeddings': args.vis_embeddings,
'save_embeddings': args.save_embeddings,
'miner': args.miner,
'contrastive': args.contrastive
}
neuralnetwork.main(outputPath=outputFolder, dataset=train_dataset, testset=test_dataset, num_epochs=args.num_epochs, batch_size=args.batch_size, layers=layers, seed=args.seed, learning_rate=args.lr, flags_in=flags, knn=args.knn)