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main_split.py
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : main.py
@Time : 2023/11/09 11:18:29
@Author : Cmf
@Version : 1.0
@Desc : None
'''
# here put the import lib
import click
import numpy as np
from functools import partial
from pathlib import Path
from ruamel.yaml import YAML
from torch.utils.data.dataloader import DataLoader
from logzero import logger
from tfnet.data_utils import *
from tfnet.datasets import TFBindDataset
from tfnet.models_split import Model
from tfnet.evaluation import output_eval, output_predict, CUTOFF
import pdb
# code
def train(model, get_data_fn, data_cnf, model_cnf, valid_data=None, class_weights_dict_list = None, random_state=1240):
logger.info(f'Start training model {model.model_path}')
valid_loader = DataLoader(TFBindDataset(valid_data, data_cnf['genome_fasta_file'], data_cnf['bigwig_file'], **model_cnf['padding']),
batch_size=model_cnf['valid']['batch_size'])
model.train(model_cnf, data_cnf, valid_loader, class_weights_dict_list, get_data_fn, **model_cnf['train'])
def test(model, model_cnf, test_data):
data_loader = DataLoader(TFBindDataset(test_data, **model_cnf['padding']),
batch_size=model_cnf['test']['batch_size'])
return model.predict(data_loader)
def generate_cv_id(length, num_groups=5):
base_size = length // num_groups
extra_size = length % num_groups
group_sizes = [base_size + 1 if i < extra_size else base_size for i in range(num_groups)]
#labels = np.repeat(np.arange(1, num_groups + 1), group_sizes)
labels = np.concatenate([np.full(size, i) for i, size in enumerate(group_sizes)])
return labels
@click.command()
@click.option('-d', '--data-cnf', type=click.Path(exists=True))
@click.option('-m', '--model-cnf', type=click.Path(exists=True))
@click.option('--mode', type=click.Choice(('train', 'eval', 'predict','5cv', 'loo', 'lomo')), default=None)
@click.option('-s', '--start-id', default=0)
@click.option('-n', '--num_models', default=20)
@click.option('-c', '--continue_train', is_flag=True)
def main(data_cnf, model_cnf, mode, start_id, num_models, continue_train):
yaml = YAML(typ='safe')
data_cnf, model_cnf = yaml.load(Path(data_cnf)), yaml.load(Path(model_cnf))
logger.info(f'check important parameter')
logger.info(f"Model Name: {model_cnf['name']}, tf number: {len(model_cnf['model']['all_tfs'])}, cutoff: {CUTOFF}")
model_name = model_cnf['name']
model_path = Path(model_cnf['path'])/f'{model_name}.pt'
res_path = Path(data_cnf['results'])/f'{model_name}'
Path(data_cnf['results']).mkdir(parents=True, exist_ok=True)
model_cnf.setdefault('ensemble', 20)
get_data_fn = partial(get_data_lazy, genome_fasta_file= data_cnf['genome_fasta_file'])
all_tfs = model_cnf['model']['all_tfs']
model_structure = model_cnf['model_structure']
if model_structure == 'DanQ':
from tfnet.networks_danq import Danq as Selected_Model
elif model_structure == 'scFAN':
from tfnet.networks_scfan import scFAN as Selected_Model
elif model_structure == 'DeepATT':
from tfnet.networks_deepatt import DeepATT as Selected_Model
elif model_structure == 'DeepFormer':
from tfnet.networks_deepformer import DeepFormer as Selected_Model
elif model_structure == 'TFNet3':
from tfnet.networks_tfnet3 import TFNet3 as Selected_Model
else:
raise ValueError(f"Unknown network type: {model_structure}")
classweights = model_cnf['classweights']
if classweights and ( mode != "eval" and mode != "predict"):
class_weights_dict_list = []
for index, train_data_split in enumerate(Path(data_cnf['train_prefix']).parent.glob(str(Path(data_cnf['train_prefix']).name)+"*")):
class_weights_dict_list.append(calculate_class_weights_dict(train_data_split))
else :
class_weights_dict = None
if mode == "train":
for model_id in range(start_id, start_id + num_models):
if continue_train:
logger.info(f'Continue train Mode')
model = Model(Selected_Model, model_path=model_path.with_stem(f'{model_path.stem}-{model_id}'), class_weights_dict_list = class_weights_dict_list,
**model_cnf['model'])
logger.info(f'Loading Model: {model_path.stem}-{model_id}')
model.load_model()
valid_data = get_data_fn(data_cnf['valid'])
train(model, get_data_fn, data_cnf, model_cnf, valid_data=valid_data, class_weights_dict_list = class_weights_dict_list)
else:
if not model_path.with_stem(f'{model_path.stem}-{model_id}').exists():
model = Model(Selected_Model, model_path=model_path.with_stem(f'{model_path.stem}-{model_id}'), class_weights_dict_list = class_weights_dict_list,
**model_cnf['model'])
valid_data = get_data_fn(data_cnf['valid'])
train(model, get_data_fn, data_cnf, model_cnf, valid_data=valid_data, class_weights_dict_list = class_weights_dict_list)
else:
logger.info(f'Model already exsit: {model_path.stem}-{model_id}')
elif mode == 'eval':
test_data = get_data_fn(data_cnf['test'])
chr, start, stop, targets_lists = [x[0] for x in test_data], [x[1] for x in test_data], [x[2] for x in test_data], [x[-1] for x in test_data]
scores_lists = []
for model_id in range(start_id, start_id + num_models):
model = Model(Selected_Model, model_path=model_path.with_stem(f'{model_path.stem}-{model_id}'), class_weights_dict_list = class_weights_dict_list,
**model_cnf['model'])
scores_lists.append(test(model, model_cnf, test_data=test_data))
output_eval(chr, start, stop, np.array(targets_lists), np.mean(scores_lists, axis=0), all_tfs, res_path)
elif mode == 'predict':
predict_data = get_data_fn(data_cnf['predict'])
predict_prefix = Path(data_cnf['predict']).stem
predict_path = Path(data_cnf['results'])/f'{model_name}.{predict_prefix}'
chr, start, stop, targets_lists = [x[0] for x in test_data], [x[1] for x in predict_data], [x[2] for x in predict_data], [x[-1] for x in predict_data]
scores_lists = []
for model_id in range(start_id, start_id + num_models):
model = Model(Selected_Model, model_path=model_path.with_stem(f'{model_path.stem}-{model_id}'), class_weights_dict_list = class_weights_dict_list,
**model_cnf['model'])
scores_lists.append(test(model, model_cnf, test_data=predict_data))
output_predict(chr, start, stop, np.mean(scores_lists, axis=0), predict_path)
elif mode == 'predict_list':
for index, file_path in enumerate(data_cnf['predict_list']):
predict_data = get_data_fn(data_cnf['predict_list'][index])
predict_prefix = Path(data_cnf['predict_list'][index]).stem
predict_path = Path(data_cnf['results'])/f'{model_name}.{predict_prefix}'
chr, start, stop, targets_lists = [x[0] for x in predict_data], [x[1] for x in predict_data], [x[2] for x in predict_data], [x[-1] for x in predict_data]
scores_lists = []
for model_id in range(start_id, start_id + num_models):
model = Model(Selected_Model, model_path=model_path.with_stem(f'{model_path.stem}-{model_id}'), class_weights_dict = class_weights_dict,
**model_cnf['model'])
scores_lists.append(test(model, data_cnf, model_cnf, test_data=predict_data))
output_predict(chr, start, stop, np.mean(scores_lists, axis=0), predict_path)
elif mode == '5cv':
data = np.asarray(get_data_fn(data_cnf['train']), dtype=object)
data_group_name, atac_signal, data_truth = [x[0] for x in data], [x[1] for x in data], [x[2] for x in data]
cv_id_len = data.shape[0]
# ---------------------- generate cv id for use rather than read a input ---------------------- #
cv_id = generate_cv_id(cv_id_len)
assert len(data) == len(cv_id)
scores_list = []
for model_id in range(start_id, start_id + num_models):
scores_ = np.empty(len(data)*len(all_tfs), dtype=np.float32).reshape(len(data), len(all_tfs))
for cv_ in range(5):
train_data, test_data = data[cv_id != cv_], data[cv_id == cv_]
model = Model(Selected_Model, model_path=model_path.with_stem(f'{model_path.stem}-{model_id}-CV{cv_}'), class_weights_dict = class_weights_dict,
**model_cnf['model'])
if not continue_train or not model.model_path.exists():
train(model, data_cnf, model_cnf, train_data=train_data, class_weights_dict = class_weights_dict)
scores_[cv_id == cv_] = test(model, model_cnf, test_data=test_data)
scores_list.append(scores_)
#pdb.set_trace()
#output_res(np.array(data_group_name)[cv_id == cv_], np.array(data_truth)[cv_id == cv_], np.mean(scores_[cv_id == cv_], axis=0),
output_eval(np.array(data_group_name)[cv_id == cv_], np.array(data_truth)[cv_id == cv_], scores_[cv_id == cv_],
res_path.with_name(f'{res_path.stem}-5CV'))
elif mode == 'loo' or mode == 'lomo':
data = np.asarray(get_data_fn(data_cnf['train']), dtype=object)
with open(data_cnf['cv_id']) as fp:
cv_id = np.asarray([int(line) for line in fp])
scores_list = []
for model_id in range(start_id, start_id + num_models):
group_names, group_names_, truth_, scores_ = np.asarray([x[0] for x in data]), [], [], []
for name_ in sorted(set(group_names)):
train_data, train_cv_id = data[group_names != name_], cv_id[group_names != name_]
test_data, test_cv_id = data[group_names == name_], cv_id[group_names == name_]
if len(test_data) > 30 and len([x[-1] for x in test_data if x[-1] >= CUTOFF]) >= 3:
for cv_ in range(5):
model = Model(Selected_Model,
model_path=model_path.with_stem(F'{model_path.stem}-{name_}-{model_id}-CV{cv_}'), class_weights_dict = class_weights_dict,
**model_cnf['model'])
if not model.model_path.exists() or not continue_train:
train(model, data_cnf, model_cnf, train_data[train_cv_id != cv_], class_weights_dict=class_weights_dict)
test_data_ = test_data[test_cv_id == cv_]
group_names_ += [x[0] for x in test_data_]
truth_ += [x[-1] for x in test_data_]
scores_ += test(model, model_cnf, test_data_).tolist()
scores_list.append(scores_)
output_eval(group_names_, truth_, np.mean(scores_list, axis=0), res_path.with_name(f'{res_path.stem}-LOMO'))
if __name__ == '__main__':
main()