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# -*- coding: utf-8 -*-
import math
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
def make_splits(pfam_seed_file: str,
num_splits: int = 10,
rand_key: int = 6,
topk1_valid: int = 3,
topk2_valid: int = 8):
"""
split pfams into OOD validation set + {num_splits} folds
when I made splits:
num_splits = 10
rand_key = 6
topk1_valid = 3
topk2_valid = 8
inputs:
-------
- pfam_seed_file (str): the original seed file; use for getting file
prefix
- num_splits (int): number of folds for in-distribution train+test set
- rand_key (int): random key for numpy
- topk1_valid (int): when making OOD Valid set, how many of the
widest pfams to hold out
- topk2_valid (int): when making OOD Valid set, how many of the
widest pfams to hold out
returns:
--------
(None)
outputs:
--------
- split_pfam_meta_file: a version of the previous pfam_level_metadata_file,
but with split information added
- split_metadata_file: metadata about the splits
- plain text files for each fold, with names of pfams in the folds
"""
prefix = pfam_seed_file.split('.')[0]
pfam_level_metadata_file = f'{prefix}_PFAM-METADATA.tsv'
df = pd.read_csv(pfam_level_metadata_file,
sep='\t',
index_col=0)
def num_pairs(n):
return (n * (n-1) ) / 2
df['possible_pairs'] = num_pairs(df['msa_depth'])
############################
### PFAMS WITH CLAN LABELS #
############################
pfams_with_clans = df[~df['clan_name'].isna()]
def add_stat(colname, fn, new_lab):
clans_to_stat = {}
for clan in list(set(pfams_with_clans['clan_name'])):
subdf = pfams_with_clans[pfams_with_clans['clan_name'] == clan]
stat = fn(subdf[colname])
clans_to_stat[clan] = stat
del subdf, stat, clan
pfams_with_clans[new_lab] = pfams_with_clans['clan_name'].apply(lambda x: clans_to_stat[x])
### clan stats
# sizes
add_stat(colname = 'possible_pairs',
fn = sum,
new_lab = 'CLAN_possible_pairs')
# add average MSA width for the clan
add_stat(colname = 'msa_width',
fn = lambda x: x.mean(),
new_lab = 'CLAN-AVE_msa_width')
# add average percent indels for the clan
add_stat(colname = 'percent_gaps',
fn = lambda x: x.mean(),
new_lab = 'CLAN-AVE_percent_gaps')
# clan-level dataframe; remove any particularly large clans
col_lst = ['clan_name',
'CLAN_possible_pairs',
'CLAN-AVE_msa_width',
'CLAN-AVE_percent_gaps']
clan_meta = pfams_with_clans.drop_duplicates(subset = 'clan_name',
keep = 'first')[col_lst]
clan_meta = clan_meta[clan_meta['CLAN_possible_pairs'] < 800000]
# widest MSAs
widest = clan_meta.nlargest(n=topk1_valid,
columns='CLAN-AVE_msa_width',
keep='all')
# gappiest MSAs
gappiest = clan_meta.nlargest(n=topk2_valid,
columns='CLAN-AVE_percent_gaps',
keep='all')
##############################
### SPLIT OFF VALIDATION SET #
##############################
# how many pairs, if I extract these pfams?
validation_clans = list(set(widest['clan_name'].tolist() + gappiest['clan_name'].tolist()))
sub_df = clan_meta[clan_meta['clan_name'].isin(validation_clans)]
num_validation_pairs = sub_df['CLAN_possible_pairs'].sum()
perc_data = num_validation_pairs / df['possible_pairs'].sum()
validation_pfams = df[df['clan_name'].isin(validation_clans)]['pfam'].tolist()
remaining = pfams_with_clans[~pfams_with_clans['clan_name'].isin(validation_clans)]
del col_lst, clan_meta, widest, gappiest, sub_df
del perc_data
#################################################
### START SPILTTING PFAMS WITH CLANS INTO FOLDS #
#################################################
col_lst = ['clan_name',
'CLAN_possible_pairs',
'CLAN-AVE_msa_width',
'CLAN-AVE_percent_gaps']
clan_meta = remaining.drop_duplicates(subset = 'clan_name',
keep = 'first')[col_lst]
total_samples = clan_meta['CLAN_possible_pairs'].sum()
max_size = math.ceil(total_samples / num_splits)
train_test_clans = {i: [] for i in range(num_splits)}
train_test_pfams = {i: [] for i in range(num_splits)}
split_sizes = {i:0 for i in range(num_splits)}
# shuffle before assigning splits
clan_meta = clan_meta.sample(frac=1, random_state=rand_key).reset_index(drop=True)
to_place = []
split_ids = list(range(num_splits))
for clan in clan_meta['clan_name']:
sub_df = clan_meta[clan_meta['clan_name'] == clan]
pfams = pfams_with_clans[pfams_with_clans['clan_name']==clan]['pfam'].tolist()
num_pairs = sub_df['CLAN_possible_pairs'].sum()
# try placing in one of num_splits splits
placed = False
for i in split_ids:
if split_sizes[i] < max_size:
placed = True
train_test_clans[i].append(clan)
train_test_pfams[i] += pfams
split_sizes[i] += num_pairs
break
if not placed:
to_place.append(clan)
# rotate list before next iteration
split_ids = split_ids[1:] + split_ids[:1]
assert len(to_place) == 0
del col_lst, clan_meta, total_samples, max_size, to_place, split_ids
##############################################
### START SPILTTING PFAMS WITH NO CLAN LABEL #
##############################################
pfams_without_clans = df[df['clan_name'].isna()]
total_samples = pfams_without_clans['possible_pairs'].sum()
max_size = math.ceil(total_samples / num_splits)
more_train_test_pfams = {i: [] for i in range(num_splits)}
more_split_sizes = {i:0 for i in range(num_splits)}
# shuffle before assigning splits
pfams_without_clans = pfams_without_clans.sample(frac=1,
random_state=rand_key).reset_index(drop=True)
if len(pfams_without_clans) > 0:
to_place = []
split_ids = list(range(num_splits))
for _, row in pfams_without_clans.iterrows():
pfam_name = row['pfam']
num_pairs = row['possible_pairs']
# try placing in one of 5 splits
placed = False
for i in split_ids:
if more_split_sizes[i] < max_size:
placed = True
more_train_test_pfams[i].append( pfam_name )
more_split_sizes[i] += num_pairs
break
if not placed:
to_place.append(pfam_name)
# rotate list before next iteration
split_ids = split_ids[1:] + split_ids[:1]
assert len(to_place) == 0
del pfams_without_clans, total_samples, max_size, to_place, split_ids, row
del pfam_name, num_pairs, placed, i
#############################################################
### COMBINE PFAM AND CLAN LISTS; MAKE FINAL META DATAFRAMES #
#############################################################
checksum = 0
for i in range(num_splits):
train_test_pfams[i] = train_test_pfams[i]+more_train_test_pfams[i]
split_sizes[i] = split_sizes[i]+more_split_sizes[i]
checksum += split_sizes[i]
checksum +=num_validation_pairs
# make sure all pairs are accounted for
assert checksum == df['possible_pairs'].sum()
# add one-hot label to main dataframe (easier to check for duplication)
for i in range(num_splits):
pfams_in_split = train_test_pfams[i]
one_hot = df['pfam'].isin(pfams_in_split)
df[f'in_split{i}'] = one_hot
df['in_OOD_valid'] = df['pfam'].isin(validation_pfams)
del checksum, i, train_test_pfams, split_sizes, more_train_test_pfams
del more_split_sizes, pfams_in_split, one_hot, validation_pfams
del num_validation_pairs, validation_clans, train_test_clans
####################################################################
### FINAL CHECK OF SPLITS, OUTPUT METADATA AT PFAM AND CLAN LEVELS #
####################################################################
# make sure all pfams only belong to one split
col_lst = ['in_OOD_valid'] + [f'in_split{i}' for i in range(num_splits)]
sub_df = df[col_lst]
rowsum = sub_df.sum(axis=1)
assert (rowsum == 1).all()
print('no data bleed :)')
# metadata per split: num_pfams, num_clans (if any), num_seqs,
# num_possible_pairs, percent_of_dataset
split_meta = []
for col in col_lst:
sub_df = df[df[col]]
num_pfams = len(sub_df)
num_clans = len(list(set(sub_df['clan_name'].tolist())))
num_seqs = sub_df['msa_depth'].sum()
num_possible_pairs = sub_df['possible_pairs'].sum()
percent_by_seqs = num_seqs / df['msa_depth'].sum()
percent_by_pairs = num_possible_pairs / df['possible_pairs'].sum()
ave_msa_depth = sub_df['msa_depth'].mean()
ave_msa_width = sub_df['msa_width'].mean()
ave_percent_gaps = sub_df['percent_gaps'].mean()
out_dict = {'split': col,
'num_pfams': num_pfams,
'num_clans': num_clans,
'num_seqs': num_seqs,
'num_possible_pairs': num_possible_pairs,
'ave_msa_depth': ave_msa_depth,
'ave_msa_width': ave_msa_width,
'ave_percent_gaps': ave_percent_gaps,
'percent_by_seqs': percent_by_seqs,
'percent_by_pairs': percent_by_pairs}
split_meta.append(out_dict)
# also write pfams to separate file
sub_df['pfam'].to_csv(f'pfams_{col}.tsv', sep='\t', header=False, index=False)
split_meta = pd.DataFrame(split_meta)
del col_lst, sub_df, rowsum, col, num_pfams, num_clans, num_seqs
del num_possible_pairs, percent_by_seqs, percent_by_pairs
del ave_msa_depth, ave_msa_width, ave_percent_gaps, out_dict
# output metadata with split annotations
pfam_level_metadata_file = f'{prefix}_PFAM-METADATA.tsv'
split_pfam_meta_file = pfam_level_metadata_file.replace('METADATA','METADATA_withSplitLabels')
split_metadata_file = f'{prefix}_SPLIT-METADATA.tsv'
df.to_csv(split_pfam_meta_file, sep='\t')
split_meta.to_csv(split_metadata_file, sep='\t')
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