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import logging
from typing import Dict
from anndata import AnnData
from omegaconf import DictConfig, OmegaConf
from ..callbacks.metrics import accuracy_score
from ..novelty_prediction.id_vs_ood_entropy_clf import compute_entropies
from ..novelty_prediction.id_vs_ood_nld_clf import compute_nlds
from ..processing.augmentation import DataAugmenter
from ..processing.seq_tokenizer import SeqTokenizer
from ..processing.splitter import *
from ..processing.splitter import DataSplitter
from ..score.score import (compute_score_benchmark, compute_score_tcga,
infere_additional_test_data)
from ..utils.file import load, save
from ..utils.utils import (instantiate_predictor, prepare_data_benchmark,
set_seed_and_device, sync_skorch_with_config)
logger = logging.getLogger(__name__)
def compute_cv(cfg:DictConfig,path:str,output_dir:str):
summary_pd = pd.DataFrame(index=np.arange(cfg["num_replicates"]),columns = ['B. Acc','Dur'])
for seed_no in range(cfg["num_replicates"]):
logger.info(f"Currently training replicate {seed_no}")
cfg["seed"] = seed_no
test_score,net = train(cfg,path=path,output_dir=output_dir)
convrg_epoch = np.where(net.history[:,'val_acc_best'])[0][-1]
convrg_dur = sum(net.history[:,'dur'][:convrg_epoch+1])
summary_pd.iloc[seed_no] = [test_score,convrg_dur]
save(path=path+'/summary_pd',data=summary_pd)
return
def train(cfg:Dict= None,path:str = None,output_dir:str = None):
if cfg['tensorboard']:
from ..callbacks.tbWriter import writer
#set seed
set_seed_and_device(cfg["seed"],cfg["device_number"])
dataset = load(cfg["train_config"].dataset_path_train)
if isinstance(dataset,AnnData):
dataset = dataset.var
else:
dataset.set_index('sequence',inplace=True)
#instantiate dataset class
if cfg["task"] in ["premirna","sncrna"]:
tokenizer = SeqTokenizer(dataset,cfg)
test_ad = load(cfg["train_config"].dataset_path_test)
#prepare data for training and inference
all_data = prepare_data_benchmark(tokenizer,test_ad,cfg)
else:
df = DataAugmenter(dataset,cfg).get_augmented_df()
tokenizer = SeqTokenizer(df,cfg)
all_data = DataSplitter(tokenizer,cfg).prepare_data_tcga()
#sync skorch config with params in train and model config
sync_skorch_with_config(cfg["model"]["skorch_model"],cfg)
# instantiate skorch model
net = instantiate_predictor(cfg["model"]["skorch_model"], cfg,path)
#train
#if train_split is none, then discard valid_ds
net.fit(all_data["train_data"],all_data["train_labels_numeric"],all_data["valid_ds"])
#log train and model HP to curr run dir
save(data=OmegaConf.to_container(cfg, resolve=True),path=path+'/meta/hp_settings.yaml')
#compute scores and log embedds
if cfg['task'] == 'tcga':
test_score = compute_score_tcga(net, all_data,path,cfg)
compute_nlds(output_dir)
compute_entropies(output_dir)
else:
test_score = compute_score_benchmark(net, path,all_data,accuracy_score,cfg)
#only for premirna
if "additional_testset" in all_data:
infere_additional_test_data(net,all_data["additional_testset"])
if cfg['tensorboard']:
writer.close()
return test_score,net
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