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# -*- coding: utf-8 -*-
"""
Created on Thu Nov 30 12:39:48 2023
About:
======
Custom pytorch dataset object for giving pfam data to counts-based models
outputs:
========
1. sample_subCounts: substitution counts
2. sample_insCounts: insert counts
3. sample_delCounts: deleted char counts
4. sample_transCounts: transition counts
5. sample_time: time to use per sample, or None
6. sample_idx: pair index, to retrieve info from metadata_df
Data to be read:
=================
1. subCounts.npy: (num_pairs, A, A)
counts of emissions at match states across whole alignment length
(i.e. true matches and substitutions)
2. insCounts.npy: (num_pairs, A)
counts of emissions at insert states across whole alignment length
3. delCounts.npy: (num_pairs, A)
counts of bases that get deleted
4. transCounts.npy: (num_pairs, 3, 3) OR (num_pairs, 5, 5)
transition counts across whole alignment length
3x3 if encoding start and end states as match sites; 5x5 otherwise
if protein:
5. AAcounts.npy: (A, )
equilibrium counts from whole dataset
if dna:
5. NuclCounts.npy: (A,)
equilibrium counts from whole dataset
6. metadata.tsv: [PANDAS DATAFRAME]
metadata about each sample
lengths do NOT include any sentinel tokens!!!
7. pair-times.tsv: (B,)
if desired, branch length per sample
plain .tsv file with two columns; no header and no index
first column is pairID
second column is time
"""
import torch
from torch.utils.data import Dataset, DataLoader,default_collate
import numpy as np
import jax
from jax import numpy as jnp
from jax.tree_util import tree_map
import pandas as pd
###############################################################################
### pytorch collator ########################################################
###############################################################################
def _default_collate_to_jax_array(mat):
"""
kind of cumbersome, but conversion path is
tuple -> pytorch tensor -> numpy array -> jax array
"""
pytorch_tensor = default_collate(mat)
numpy_mat = pytorch_tensor.numpy()
return jnp.array( numpy_mat )
def jax_collator(batch):
"""
collator that can handle if time per sample is None
B = number of samples in the batch
A = alphabet size
S = number of transitions; 4 here: M, I, D, START/END
Returns
-------
collated_subCounts : ArrayLike, (B, A, A)
collated_insCounts : ArrayLike, (B, A)
collated_delCounts : ArrayLike, (B, A)
collated_transCounts : ArrayLike, (B, S, S)
collated_time : ArrayLike, (B,) OR None
collated_idx : ArrayLike, (B,)
"""
# unpack batch
out = list( zip(*batch) )
sample_subCounts = out[0]
sample_insCounts = out[1]
sample_delCounts = out[2]
sample_transCounts = out[3]
sample_time = out[4]
sample_idx = out[5]
del out
# handle most with default_collate
collated_sample_subCounts = _default_collate_to_jax_array( sample_subCounts )
collated_sample_insCounts = _default_collate_to_jax_array( sample_insCounts )
collated_sample_delCounts = _default_collate_to_jax_array( sample_delCounts )
collated_sample_transCounts = _default_collate_to_jax_array( sample_transCounts )
collated_idx = _default_collate_to_jax_array( sample_idx )
# handle time, which could be none
if (sample_time[0] is not None):
collated_times = _default_collate_to_jax_array( sample_time )
elif (sample_time[0] is None):
collated_times = None
return (collated_sample_subCounts,
collated_sample_insCounts,
collated_sample_delCounts,
collated_sample_transCounts,
collated_times,
collated_idx)
###############################################################################
### some helpers ############################################################
###############################################################################
def _safe_convert(mat):
"""
pytorch doesn't support uint16 :(
"""
# try int16 first
int16_dtype_min = -32768
int16_dtype_max = 32767
cond1 = mat.max() <= int16_dtype_max
cond2 = mat.min() >= int16_dtype_min
if cond1 and cond2:
return mat.astype('int16')
# otherwise, return int32
else:
return mat.astype('int32')
"""
def _five_state_to_three_state_transCounts(five_by_five_mat):
# turn start into M token; add to appropriate transition
# to M: 0
# to I: 1
# to D: 2
start_to_tok_trans = np.argwhere( five_by_five_mat[:,3,:]==1 )
tok_to_end_trans = np.argwhere( five_by_five_mat[:,:,4]==1 )
five_by_five_mat[start_to_tok_trans[:,0], 0, start_to_tok_trans[:,1]] += 1
five_by_five_mat[tok_to_end_trans[:,0], tok_to_end_trans[:,1], 0] += 1
three_by_three_mat = five_by_five_mat[:,:-2, :-2]
return three_by_three_mat
"""
###############################################################################
### Main dataset object #####################################################
###############################################################################
class CountsDset(Dataset):
def __init__(self,
data_dir: str,
split_prefixes: list,
t_per_sample: bool,
toss_alignments_longer_than = None,
emission_alphabet_size: int = 20,
subs_only: bool = False):
"""
Load training data from precomputed counts of events
Arguments
----------
data_dir : str
Where data is located
split_prefixes : List[str]
prefixes of the datasets to include
emission_alphabet_size : int
4 if DNA, 20 if proteins
t_per_sample : bool
True if you want to read a branch length per sample, False otherwise
toss_alignments_longer_than : int, None
Max alignment length to keep, if desired
DEFAULT VALUE: None
Attributes created
-------------------
self.emit_counts: used to store equilibrium counts of emissions
self.num_transitions: 4 if using M,I,D,START/END, 3 if not using sentinel tokens
self.subCounts: counts of emissions at match states
self.insCounts: counts of emissions at insert states
self.delCounts: counts of emissions at delete states
self.transCounts: counts of transitions between states
self.names_df: dataframe with metadata, for recording later
self.times: branch length per sample
"""
#######################################################################
### 1: ITERATE THROUGH SPLIT PREFIXES AND READ FILES ################
#######################################################################
### setup
# always read
subCounts_list = []
insCounts_list = []
delCounts_list = []
transCounts_list = []
metadata_list = []
# equilibrium distribution counts file
self.emit_counts = np.zeros(emission_alphabet_size, dtype=int)
if (emission_alphabet_size == 20) and (not subs_only):
counts_suffix = 'AAcounts'
elif (emission_alphabet_size == 20) and (subs_only):
counts_suffix = 'AAcounts_subsOnly'
elif (emission_alphabet_size == 4) and (not subs_only):
counts_suffix = 'NuclCounts'
elif (emission_alphabet_size == 4) and (subs_only):
counts_suffix = 'NuclCounts_subsOnly'
# optionally read times
if t_per_sample:
times_lst = []
### start iter
for split in split_prefixes:
##############
### metadata #
##############
cols_to_keep = ['pairID',
'ancestor',
'descendant',
'pfam',
'anc_seq_len',
'desc_seq_len',
'alignment_len',
'num_matches',
'num_ins',
'num_del']
meta_df = pd.read_csv( f'./{data_dir}/{split}_metadata.tsv',
sep='\t',
index_col=0,
usecols = cols_to_keep )
meta_df = meta_df.reset_index(drop = True)
#########################################################
### remove samples longer where #
### align_len + 2 > toss_alignments_longer_than #
### (plus 2 to mimic the behavior in neural database, #
### which has <bos> and <eos>) #
#########################################################
if (toss_alignments_longer_than is not None):
cond = (meta_df['alignment_len'] + 2) <= toss_alignments_longer_than
idxes_to_keep = list( meta_df[ cond ].index )
if len(idxes_to_keep) == 0:
raise RuntimeError(f"no samples to keep from {split}!")
meta_df = meta_df.iloc[idxes_to_keep]
# otherwise, keep everything
else:
idxes_to_keep = list( meta_df.index )
metadata_list.append(meta_df)
######################################
### counts of emissions, transitions #
######################################
# subEncoded
with open(f'./{data_dir}/{split}_subCounts.npy', 'rb') as f:
mat = _safe_convert( np.load(f)[idxes_to_keep, ...] )
subCounts_list.append( mat )
del mat
# insCounts
with open(f'./{data_dir}/{split}_insCounts.npy', 'rb') as f:
mat = _safe_convert( np.load(f)[idxes_to_keep, ...] )
insCounts_list.append( mat )
del mat
# delCounts
with open(f'./{data_dir}/{split}_delCounts.npy', 'rb') as f:
mat = _safe_convert( np.load(f)[idxes_to_keep, ...] )
delCounts_list.append( mat )
del mat
# transCounts
with open(f'./{data_dir}/{split}_transCounts_five_by_five.npy', 'rb') as f:
mat = _safe_convert( np.load(f)[idxes_to_keep, ...] )
#if bos_eos_as_match:
# mat = _five_state_to_three_state_transCounts(mat)
# self.num_transitions = 3
#elif not bos_eos_as_match:
mat = mat[:, :-1, [0,1,2,4]]
self.num_transitions = 4
transCounts_list.append( mat )
del mat
# counts (technically uses emissions from tossed samples...
# fix this later)
with open(f'./{data_dir}/{split}_{counts_suffix}.npy', 'rb') as f:
mat = _safe_convert( np.load(f) )
self.emit_counts += mat
del mat
#####################
### (optional) time #
#####################
if t_per_sample:
times = pd.read_csv(f'{data_dir}/{split}_pair-times.tsv',
sep='\t',
header=None,
names=['pairID','time'],
index_col=None)
times = times.iloc[idxes_to_keep]
times_lst += times['time'].tolist()
del times
del split
#######################################################################
### 2: CONCATENATE ALL DATA MATRICES ################################
#######################################################################
######################################
### counts of emissions, transitions #
######################################
self.subCounts = np.concatenate(subCounts_list, axis=0)
del subCounts_list
self.insCounts = np.concatenate(insCounts_list, axis=0)
del insCounts_list
self.delCounts = np.concatenate(delCounts_list, axis=0)
del delCounts_list
self.transCounts = np.concatenate(transCounts_list, axis=0)
del transCounts_list
##############
### metadata #
##############
self.names_df = pd.concat(metadata_list, axis=0)
self.names_df = self.names_df.reset_index(drop=True)
del metadata_list
#####################
### (optional) time #
#####################
if t_per_sample:
self.times = np.array(times_lst) #(B,)
del times_lst
else:
self.times = None
def __len__(self):
return self.subCounts.shape[0]
def __getitem__(self, idx):
sample_subCounts = self.subCounts[idx, ...]
sample_insCounts = self.insCounts[idx, ...]
sample_delCounts = self.delCounts[idx, ...]
sample_transCounts = self.transCounts[idx, ...]
if self.times is not None:
sample_time = self.times[idx]
else:
sample_time = None
sample_idx = idx
return (sample_subCounts,
sample_insCounts,
sample_delCounts,
sample_transCounts,
sample_time,
sample_idx)
def retrieve_sample_names(self, idxes):
# used the list of sample indices to query the original names_df
return self.names_df.iloc[idxes]
def retrieve_equil_dist(self):
return self.emit_counts / ( self.emit_counts.sum() )
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