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"""
description:
metrics to compute model performance
"""
import Bio
from Bio.Align import substitution_matrices
import numpy as np
import matplotlib.pyplot as plt
import torch
import re
import Stage3_source.animation_tools as ani_tools
' compute Blosum62 soft accuracy '
class blosum_soft_accuracy:
def __init__(self, ):
self.blosum62 = substitution_matrices.load("BLOSUM62")
self.alphabet = self.blosum62.alphabet
def blosum_acc(
self,
aa1: str,
aa2: str
) -> np.single:
row = self.blosum62.alphabet.index(aa1)
col = self.blosum62.alphabet.index(aa2)
substitution_scores = self.blosum62[row, :].values()
# Apply the softmax function to the substitution scores to get a prob dist.
probs = np.exp(substitution_scores)/np.sum(np.exp(substitution_scores))
# compute the soft acc. as the dot product of the prob dist. with a one-hot encoding
# of the amino acid ...
correct_aa = aa2
correct_index = self.alphabet.index(correct_aa)
one_hot = np.zeros_like(probs)
one_hot[correct_index] = 1
# normalize acc.
soft_acc = np.dot(probs, one_hot) / np.max(probs)
return soft_acc
def split_seq(self, seq: str) ->list:
# no_pads = seq.count("<PAD>")
# split_seq = ["<START>"] + list(seq.replace("<START>","").replace("<END>","").replace("<PAD>","")) + ["<END>"] + ["<PAD>"] * no_pads
split_seq = re.split(r'(-|<START>|<END>|<PAD>|(?<=\w)(?=\w))', seq)
#split_seq = re.findall(r'<START>|<END>|<PAD>|[A-Z]|-|\*', seq)
# remove empty strings and whitespace-only elements
split_seq = [char for char in split_seq if char and char.strip()]
return split_seq
def compute_soft_accuracy(
self,
seq1_list: list,
seq2_list: list
) -> float:
# make sure batch size matches
if len(seq1_list) == len(seq2_list):
self.batch_size = len(seq1_list)
else:
print("Please make sequence batch size equivalent...")
# make sure sequence length matches
if len(seq1_list[0]) == len(seq2_list[0]):
self.L = len(seq1_list[0])
else:
#print("Please make sequence length match...")
pass
avg_soft_acc_per_batch = 0
# loop over the batch of sequence
for seq1, seq2 in zip(seq1_list, seq2_list):
# split sequence into individual tokens
seq1 = self.split_seq(seq1)
seq2 = self.split_seq(seq2)
# set number of positions
self.L = len(seq2)
self.L_h = 0
self.L_s = 0
avg_soft_acc_per_seq = 0
avg_hard_acc_per_seq = 0
# loop over the amino acid positions
for aa1, aa2 in zip(seq1, seq2):
if (aa1 not in ['-', '<START>', '<END>', '<PAD>']) and (aa2 not in ['-', '<START>', '<END>', '<PAD>']):
self.L_s += 1
soft_acc = self.blosum_acc(aa1=aa1, aa2=aa2)
avg_soft_acc_per_seq += soft_acc
else:
self.L_h += 1
acc = 1*(aa1==aa2)
avg_hard_acc_per_seq += acc
# compute accuracy for soft positions
try:
avg_soft_acc_per_seq *= 1/self.L_s
except ZeroDivisionError:
#print("L_s cannot be zero. Setting avg_soft_acc_per_seq to zero.")
avg_soft_acc_per_seq = 0
# compute accuracy for hard positions
try:
avg_hard_acc_per_seq *= 1/self.L_h
except ZeroDivisionError:
#print("L_h cannot be zero. Setting avg_hard_acc_per_seq to zero.")
avg_hard_acc_per_seq = 0
# compute the average accuracy between soft and hard
if self.L_s == 0:
avg_soft_acc_per_batch += avg_hard_acc_per_seq
elif self.L_h == 0:
avg_soft_acc_per_batch += avg_soft_acc_per_seq
else:
avg_soft_acc_per_batch += (avg_soft_acc_per_seq + avg_hard_acc_per_seq)/2
avg_soft_acc_per_batch *= 1/self.batch_size
return avg_soft_acc_per_batch
def compute_ppl(probs: torch.Tensor) -> float:
batch_size, sequence_length, class_labels = probs.shape
# flatten batch and sequence dimensions into a single dimension
flattened_probs = probs.reshape(batch_size * sequence_length, class_labels)
# calc. perplexity for each sequence independently
ppl = []
for i in range(batch_size * sequence_length):
sequence_probs = flattened_probs[i]
# compute ppl per seq
sequence_ppl = torch.exp(-torch.sum(
sequence_probs * torch.log(sequence_probs)
)
)
ppl.append(sequence_ppl.item())
ppl = torch.tensor(ppl).view(batch_size, sequence_length) # ppl per sequence in a given batch
avg_ppl = ppl.mean().item() # average ppl per batch
return avg_ppl
def batch_compute_ppl(probs_list: list) -> float:
batch_prob = sum([
compute_ppl(probs=probs.unsqueeze(0).permute(0,2,1)) for probs in probs_list
]) / len(probs_list)
return batch_prob
def compute_hard_acc(
seq1: str,
seq2: str
) -> float:
hard_acc = sum([aa1 == aa2 for (aa1 ,aa2) in zip(seq1, seq2) if aa2 != '<PAD>'])
valid_length = len([aa2 for aa2 in seq2 if aa2 != '<PAD>'])
if valid_length == 0:
return 1.0
hard_acc /= valid_length
return hard_acc
#def compute_hard_acc(
# seq1: str,
# seq2: str
# ) -> float:
#
# hard_acc = sum([aa1 == aa2 for (aa1 ,aa2) in zip(seq1, seq2)])
# hard_acc *= 1/len(seq2)
# return hard_acc
def batch_hard_acc(seq1_list: list, seq2_list: list) -> float:
hard_acc = sum([
compute_hard_acc(seq1=seq1, seq2=seq2) for (seq1,seq2) in zip(seq1_list, seq2_list)
]) / len(seq2_list)
return hard_acc
def time_split_on_seq(
seq: torch.Tensor,
sample_seq_path: torch.Tensor,
idx: torch.Tensor
) -> (
list,
list,
list
):
if len(seq.shape) != 2:
batch_size, class_labels, _ = seq.shape
# collect list
current_seq, prev_seq, fut_seq = [], [], []
for ii in range(batch_size):
current_stack_probs, prev_stack_probs, fut_stack_probs = [], [], []
for jj in range(class_labels):
# current probs
current_stack_probs.append(
seq[ii,jj][
(sample_seq_path.cpu()[ii] == idx.cpu()[ii])
]
)
# prev probs
prev_stack_probs.append(
seq[ii,jj][
(sample_seq_path.cpu()[ii] < idx.cpu()[ii])
]
)
# future probs
fut_stack_probs.append(
seq[ii,jj][
(sample_seq_path.cpu()[ii] > idx.cpu()[ii])
]
)
current_seq.append(torch.stack(current_stack_probs))
prev_seq.append(torch.stack(prev_stack_probs))
fut_seq.append(torch.stack(fut_stack_probs))
else:
# split the sequences based on time indices
current_seq = [seq[ii][sample_seq_path[ii] == idx[ii]] for ii in range(seq.shape[0])]
prev_seq = [seq[ii][sample_seq_path[ii] < idx[ii]] for ii in range(seq.shape[0])]
fut_seq = [seq[ii][sample_seq_path[ii] > idx[ii]] for ii in range(seq.shape[0])]
return (
current_seq,
prev_seq,
fut_seq
)
@torch.no_grad()
def compute_acc_given_time_pos(
real_tokens: torch.Tensor,
sample_seq: torch.Tensor,
sample_path: torch.Tensor,
idx: torch.Tensor
) -> (
float,
float,
float,
float,
float,
float
):
# tokenizer
tokens = ['-', '<START>', 'A','C','D','E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y','<END>','<PAD>']
#tokens = ['<START>', 'A','C','D','E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y','<END>','<PAD>']
tokens = tokens + ['X', 'U', 'Z', 'B', 'O']
# split real tokens based on time indices
current_real_tokens, prev_real_tokens, fut_real_tokens = time_split_on_seq(
seq=real_tokens.cpu(),
sample_seq_path=sample_path.cpu(),
idx=idx.cpu()
)
# split sampled tokens based on time indices
current_sample_tokens, prev_sample_tokens, fut_sample_tokens = time_split_on_seq(
seq=sample_seq.cpu(),
sample_seq_path=sample_path.cpu(),
idx=idx.cpu()
)
# convert real sequences to characters
current_real_chars = [ani_tools.convert_num_to_char(tokens,seq_tokens) for seq_tokens in current_real_tokens]
prev_real_chars = [ani_tools.convert_num_to_char(tokens,seq_tokens) for seq_tokens in prev_real_tokens]
fut_real_chars = [ani_tools.convert_num_to_char(tokens,seq_tokens) for seq_tokens in fut_real_tokens]
# convert sample sequences to characters
current_sample_chars = [ani_tools.convert_num_to_char(tokens,seq_tokens) for seq_tokens in current_sample_tokens]
prev_sample_chars = [ani_tools.convert_num_to_char(tokens,seq_tokens) for seq_tokens in prev_sample_tokens]
fut_sample_chars = [ani_tools.convert_num_to_char(tokens,seq_tokens) for seq_tokens in fut_sample_tokens]
# drop empty entries in list (happens if t=0 or t=256)
# prev string sequences
prev_sample_chars = [item for item in prev_sample_chars if item]
prev_real_chars = [item for item in prev_real_chars if item]
# fut string sequences
fut_real_chars = [item for item in fut_real_chars if item]
fut_sample_chars = [item for item in fut_sample_chars if item]
# class object to copmute blosum62 soft acc.
soft_acc_tool = blosum_soft_accuracy()
# split real sequence
prev_real_split_chars = [
soft_acc_tool.split_seq(sample) for sample in prev_real_chars
]
fut_real_split_chars = [
soft_acc_tool.split_seq(sample) for sample in fut_real_chars
]
# split sample sequence
prev_sample_split_chars = [
soft_acc_tool.split_seq(sample) for sample in prev_sample_chars
]
fut_sample_split_chars = [
soft_acc_tool.split_seq(sample) for sample in fut_sample_chars
]
# compute hard and soft accuracy
' soft accuracy: '
# positions < t ( aa positions)
#prev_batch_soft_acc = soft_acc_tool.compute_soft_accuracy(
# seq1_list=prev_sample_chars,
# seq2_list=prev_real_chars
#)
# positions > t ( aa positions)
#fut_batch_soft_acc = soft_acc_tool.compute_soft_accuracy(
# seq1_list=fut_sample_chars,
# seq2_list=fut_real_chars
#)
# positions = t (aa positions)
#current_soft_acc = soft_acc_tool.compute_soft_accuracy(
#seq1_list=current_sample_chars,
#seq2_list=current_real_chars
#)
prev_batch_soft_acc, fut_batch_soft_acc, current_soft_acc = 0, 0, 0
' hard accuracy: '
# positions < t ( aa positions)
prev_batch_hard_acc = batch_hard_acc(
seq1_list=prev_sample_split_chars,
seq2_list=prev_real_split_chars
)
# positions > t ( aa positions)
fut_batch_hard_acc = batch_hard_acc(
seq1_list=fut_sample_split_chars,
seq2_list=fut_real_split_chars
)
# positions = t (aa positions)
current_hard_acc = compute_hard_acc(
seq1=current_sample_chars,
seq2=current_real_chars
)
return (
prev_batch_hard_acc,
prev_batch_soft_acc,
fut_batch_hard_acc,
fut_batch_soft_acc,
current_hard_acc,
current_soft_acc
)
@torch.no_grad()
def compute_ppl_given_time_pos(
probs: torch.Tensor,
sample_path: torch.Tensor,
idx: torch.Tensor
) -> (
float,
float,
float
):
current_probs, prev_probs, fut_probs = time_split_on_seq(
probs.cpu(),
sample_seq_path=sample_path.cpu(),
idx=idx.cpu()
)
# ppl at the current time position (aa_i = t)
# current_ppl = compute_ppl(probs=torch.stack(current_probs).permute(0,2,1))
current_ppl = batch_compute_ppl(probs_list=current_probs)
# ppl at the prev and fut time positions (aa_i < t and aa_i > t)
prev_ppl = batch_compute_ppl(probs_list=prev_probs)
fut_ppl = batch_compute_ppl(probs_list=fut_probs)
return (
current_ppl,
prev_ppl,
fut_ppl
)
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