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import os |
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import numpy as np |
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import pandas as pd |
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from sklearn.linear_model import Ridge, Lasso, LinearRegression |
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from utils import seqs_to_onehot, get_wt_seq, read_fasta, seq2effect, mutant2seq |
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from predictors.base_predictors import BaseRegressionPredictor |
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class VaePredictor(BaseRegressionPredictor): |
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"deepseq vae prediction.""" |
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def __init__(self, dataset_name, reg_coef=1e-8, **kwargs): |
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super(VaePredictor, self).__init__(dataset_name, reg_coef=reg_coef, **kwargs) |
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path = os.path.join('inference', dataset_name, 'vae', 'elbo.npy') |
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if os.path.exists(path): |
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delta_elbo = np.loadtxt(path) |
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seqs_path = os.path.join('inference', dataset_name, 'vae', 'seqs.fasta') |
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seqs = read_fasta(seqs_path) |
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assert len(delta_elbo) == len(seqs), 'file length mismatch' |
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self.seq2score_dict = dict(zip(seqs, delta_elbo)) |
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else: |
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df = pd.read_csv(os.path.join('inference', dataset_name, 'DeepSequence', |
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'vae_predictions.csv')) |
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df = df[np.isfinite(df.mutation_effect_prediction_vae_ensemble)] |
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wtseqs, wtids = read_fasta(os.path.join('data', dataset_name, |
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'wt.fasta'), return_ids=True) |
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offset = int(wtids[0].split('/')[-1].split('-')[0]) |
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wt = wtseqs[0] |
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seqs = [mutant2seq(m, wt, offset) for m in df.mutant.values] |
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self.seq2score_dict = dict(zip(seqs, |
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df.mutation_effect_prediction_vae_ensemble)) |
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def seq2score(self, seqs): |
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scores = np.array([self.seq2score_dict.get(s, 0.0) for s in seqs]) |
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return scores |
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def seq2feat(self, seqs): |
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return self.seq2score(seqs)[:, None] |
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def predict_unsupervised(self, seqs): |
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return self.seq2score(seqs) |
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