| import os | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.linear_model import Ridge, Lasso, LinearRegression | |
| from utils import seqs_to_onehot, read_fasta | |
| from predictors.base_predictors import BaseRegressionPredictor | |
| class HMMPredictor(BaseRegressionPredictor): | |
| """HMM likelihood as features in regression.""" | |
| def __init__(self, dataset_name, model_name='uniref100_b0.5', | |
| reg_coef=1e-8, **kwargs): | |
| super(HMMPredictor, self).__init__(dataset_name, reg_coef=reg_coef, | |
| **kwargs) | |
| seqs_path = os.path.join('data', dataset_name, 'seqs.fasta') | |
| hmm_seqs = read_fasta(seqs_path) | |
| id2seq = pd.Series(index=np.arange(len(hmm_seqs)), data=hmm_seqs, | |
| name='seq') | |
| hmm_data_path = os.path.join('inference', dataset_name, 'hmm', | |
| f'{model_name}.csv') | |
| ll = pd.read_csv(hmm_data_path)[['target', 'score_full']] | |
| ll['id'] = ll['target'].apply(lambda x: int(x.replace('id_', ''))) | |
| ll = ll.join(id2seq, on='id', how='left') | |
| self.seq2score_dict = dict(zip(ll.seq, ll.score_full)) | |
| def seq2score(self, seqs): | |
| scores = np.array([self.seq2score_dict.get(s, 0.0) for s in seqs]) | |
| return scores | |
| def seq2feat(self, seqs): | |
| return self.seq2score(seqs)[:, None] | |
| def predict_unsupervised(self, seqs): | |
| return self.seq2score(seqs) | |
| class BLOSUM62HMMPredictor(HMMPredictor): | |
| def __init__(self, dataset_name, reg_coef=1e-8, **kwargs): | |
| super(BLOSUM62Predictor, self).__init__(dataset_name, | |
| model_name='blosum62', reg_coef=reg_coef, **kwargs) | |