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e76b79a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
from collections import Counter
from pathlib import Path
import pickle
import random
import time
from nltk import ngrams
import numpy as np
# Code for loading constants for preprocessing
seq_encode = ['L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C']
BASE = Path('./utils/ngram_stats/')
ngram_list = []
for fn in ["monogram_seg.p", "bigram_seg.p", "trigram_seg.p", "quadgram_seg.p"]:
with open(BASE / fn, "rb") as f:
ngram_list.append(pickle.load(f))
# Recompute ngram frequency based off the valid sequences used for design
for i, ngram_dict in enumerate(ngram_list):
idx_dict = {}
for k, v in ngram_dict.items():
ids = []
error = False
for ki in k:
if ki not in seq_encode:
error = True
break
id = seq_encode.index(ki)
ids.append(id)
if error:
continue
ids = tuple(ids)
idx_dict[ids] = v
total = sum(idx_dict.values())
# Min value for ngram is 1e-5
idx_dict = {k: max(v / total, 1e-5) for k, v in idx_dict.items()}
ngram_list[i] = idx_dict
def encode(seq):
if isinstance(seq, np.ndarray):
return seq # already encoded
elif isinstance(seq, str):
return np.array([seq_encode.index(AA) for AA in seq])
else:
raise ValueError(f'Unknown seq type {seq}')
def compute_kl_div(seq, order):
# Inputs Args:
# Seq: N dimensional numpy array consisting of numbers between 0 and 19 (inclusive)
# Order: integer for order of ngram used (should be between 0 and 3 for now)
order_dict = ngram_list[order-1]
seq = encode(seq) # this is not the problem.
# Compute ngram frequency rate for the input sequence
tup_dict = Counter(ngrams(seq,n=order))
total = sum(tup_dict.values())
tup_dict = {k: v / total for k, v in tup_dict.items()}
p = np.array(list(tup_dict.values())) # observed probabilities of ngrams
q = np.array([order_dict.get(k, 1e-5) for k in tup_dict.keys()]) # learned ngram probabilities
return np.sum(p * np.log(p/q))
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