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revert(splits): use random split to resolve distribution drift (#131) #132

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Aug 2, 2024
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revert(splits): use random split to resolve distribution drift (#131) #132

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ec81863
feat(config): DATA_BASENAME for issue tracking
jyaacoub Aug 2, 2024
b3e52a9
fix(splits): new random splits #113 #128 #131
jyaacoub Aug 2, 2024
1feff7c
Merge branch 'main' into v131
jyaacoub Aug 2, 2024
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220 changes: 57 additions & 163 deletions playground.py
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Original file line number Diff line number Diff line change
@@ -1,133 +1,40 @@
#%%
import os
import logging
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor
import pandas as pd
from src.utils.residue import Chain
from multiprocessing import Pool, cpu_count
from src.data_prep.datasets import BaseDataset

# df = pd.read_csv("/cluster/home/t122995uhn/projects/MutDTA/df_base.csv", index_col=0)
df = pd.read_csv("/cluster/home/t122995uhn/projects/MutDTA/df_base_filtered.csv", index_col=0)
logging.getLogger().setLevel(logging.DEBUG)

def process_protein_multiprocessing(args):
"""
Checks if protein has conf file and correct sequence, returns:
- None, None - if it has a conf file and is correct
- pid, None - is missing a conf file
- pid, seq - has a conf file but is not correct sequence.
"""
group_codes, code, pid, seq, af_conf_dir, is_pdbbind, files = args
MIN_MODEL_COUNT = 5

correct_seq = False
matching_code = None
af_confs = []
if is_pdbbind:
for c in group_codes:
af_fp = os.path.join(af_conf_dir, f'{c}.pdb')
if os.path.exists(af_fp):
af_confs = [af_fp]
matching_code = code
if Chain(af_fp).sequence == seq:
correct_seq = True
break

else:
af_confs = [os.path.join(af_conf_dir, f) for f in files if f.startswith(pid)]

if len(af_confs) == 0:
return pid, None

# either all models in one pdb file (alphaflow) or spread out across multiple files (AF2 msa subsampling)
model_count = len(af_confs) if len(af_confs) > 1 else 5# Chain.get_model_count(af_confs[0])

if model_count < MIN_MODEL_COUNT:
return pid, None
elif not correct_seq: # final check
af_seq = Chain(af_confs[0]).sequence
if seq != af_seq:
logging.debug(f'Mismatched sequence for {pid}')
# if matching_code == code: # something wrong here -> incorrect seq but for the right code?
# return pid, af_seq
return pid, matching_code

if matching_code != code:
return None, matching_code
return None, None

#%% check_missing_confs method
af_conf_dir:str = '/cluster/home/t122995uhn/projects/data/pdbbind/alphaflow_io/out_pdb_MD-distilled/'
is_pdbbind=True

df_unique:pd.DataFrame = df.drop_duplicates('prot_id')
df_pid_groups = df.groupby(['prot_id']).groups

missing = set()
mismatched = {}
# total of 3728 unique proteins with alphaflow confs (named by pdb ID)
files = None
if not is_pdbbind:
files = [f for f in os.listdir(af_conf_dir) if f.endswith('.pdb')]

with Pool(processes=cpu_count()) as pool:
tasks = [(df_pid_groups[pid], code, pid, seq, af_conf_dir, is_pdbbind, files) \
for code, (pid, seq) in df_unique[['prot_id', 'prot_seq']].iterrows()]

for pid, new_seq in tqdm(pool.imap_unordered(process_protein_multiprocessing, tasks),
desc='Filtering out proteins with missing PDB files for multiple confirmations',
total=len(tasks)):
if new_seq is not None:
mismatched[pid] = new_seq
elif pid is not None: # just pid -> missing af files
missing.add(pid)

print(len(missing),len(mismatched))

#%% make subsitutions for rows
df = pd.read_csv("/cluster/home/t122995uhn/projects/MutDTA/df_base.csv", index_col=0)
df_mismatched = pd.DataFrame.from_dict(mismatched, orient='index', columns=['code'])
df_mismatched_sub = df.loc[df_mismatched['code']][['prot_id', 'prot_seq']].reset_index()
df_mismatched = df_mismatched.merge(df_mismatched_sub, on='code')

df_mismatched = df_mismatched.merge(df_mismatched_sub, on='code')
dff = pd.read_csv("/cluster/home/t122995uhn/projects/MutDTA/df_base_filtered.csv")
dffm = dff.merge(df_mismatched, on='code')
#%%
from src.data_prep.datasets import BaseDataset
import pandas as pd
csv_p = "/cluster/home/t122995uhn/projects/data/PDBbindDataset/nomsa_binary_original_binary/full/XY.csv"

df = pd.read_csv(csv_p, index_col=0)
def get_test_oncokbs(train_df=pd.read_csv('/cluster/home/t122995uhn/projects/data/test/PDBbindDataset/nomsa_binary_original_binary/full/cleaned_XY.csv'),
oncokb_fp='/cluster/home/t122995uhn/projects/data/tcga/mart_export.tsv',
biomart='/cluster/home/t122995uhn/projects/downloads/oncoKB_DrugGenePairList.csv'):
#Get gene names for PDBbind
dfbm = pd.read_csv(oncokb_fp, sep='\t')
dfbm['PDB ID'] = dfbm['PDB ID'].str.lower()
train_df.reset_index(names='idx',inplace=True)

# %%
import os
from tqdm import tqdm
alphaflow_dir = "/cluster/home/t122995uhn/projects/data/pdbbind/alphaflow_io/out_pdb_MD-distilled/"
ln_dir = "/cluster/home/t122995uhn/projects/data/pdbbind/alphaflow_io/out_pid_ln/"
df_uni = train_df.merge(dfbm, how='inner', left_on='prot_id', right_on='UniProtKB/Swiss-Prot ID')
df_pdb = train_df.merge(dfbm, how='inner', left_on='code', right_on='PDB ID')

# identifying ovelap with oncokb
# df_all will have duplicate entries for entries with multiple gene names...
df_all = pd.concat([df_uni, df_pdb]).drop_duplicates(['idx', 'Gene name'])[['idx', 'code', 'Gene name']]

dfkb = pd.read_csv(biomart)
df_all_kb = df_all.merge(dfkb.drop_duplicates('gene'), left_on='Gene name', right_on='gene', how='inner')

trained_genes = set(df_all_kb.gene)

#Identify non-trained genes
return dfkb[~dfkb['gene'].isin(trained_genes)], dfkb[dfkb['gene'].isin(trained_genes)], dfkb


train_df = pd.read_csv('/cluster/home/t122995uhn/projects/data/test/PDBbindDataset/nomsa_binary_original_binary/train0/cleaned_XY.csv')
val_df = pd.read_csv('/cluster/home/t122995uhn/projects/data/test/PDBbindDataset/nomsa_binary_original_binary/val0/cleaned_XY.csv')

train_df = pd.concat([train_df, val_df])

get_test_oncokbs(train_df=train_df)

os.makedirs(ln_dir, exist_ok=True)

# First, check and remove any broken links in the destination directory
for link_file in tqdm(os.listdir(ln_dir), desc="Checking for broken links"):
ln_p = os.path.join(ln_dir, link_file)
if os.path.islink(ln_p) and not os.path.exists(ln_p):
print(f"Removing broken link: {ln_p}")
os.remove(ln_p)


# %% files are .pdb with 50 "models" in each
for file in tqdm(os.listdir(alphaflow_dir)):
if not file.endswith('.pdb'):
continue

code, _ = os.path.splitext(file)
pid = df.loc[code].prot_id
src, dst = f"{alphaflow_dir}/{file}", f"{ln_dir}/{pid}.pdb"
if not os.path.exists(dst):
os.symlink(src,dst)

#%%
########################################################################
Expand All @@ -140,48 +47,35 @@ def process_protein_multiprocessing(args):
cfg.logger.setLevel(logging.DEBUG)

splits = '/cluster/home/t122995uhn/projects/MutDTA/splits/davis/'
create_datasets(cfg.DATA_OPT.davis,
create_datasets([cfg.DATA_OPT.PDBbind, cfg.DATA_OPT.davis, cfg.DATA_OPT.kiba],
feat_opt=cfg.PRO_FEAT_OPT.nomsa,
edge_opt=[cfg.PRO_EDGE_OPT.aflow, cfg.PRO_EDGE_OPT.binary],
edge_opt=[cfg.PRO_EDGE_OPT.binary, cfg.PRO_EDGE_OPT.aflow],
ligand_features=[cfg.LIG_FEAT_OPT.original, cfg.LIG_FEAT_OPT.gvp],
ligand_edges=cfg.LIG_EDGE_OPT.binary, overwrite=True,
k_folds=5,
ligand_edges=cfg.LIG_EDGE_OPT.binary, overwrite=False,
k_folds=5,
test_prots_csv=f'{splits}/test.csv',
val_prots_csv=[f'{splits}/val{i}.csv' for i in range(5)],
data_root=os.path.abspath('../data/test/'))


val_prots_csv=[f'{splits}/val{i}.csv' for i in range(5)],)
# data_root=os.path.abspath('../data/test/'))

# %% Copy splits to commit them:
#from to:
import shutil
from_dir_p = '/cluster/home/t122995uhn/projects/data/v131/'
to_dir_p = '/cluster/home/t122995uhn/projects/MutDTA/splits/'
from_db = ['PDBbindDataset', 'DavisKibaDataset/kiba', 'DavisKibaDataset/davis']
to_db = ['pdbbind', 'kiba', 'davis']

from_db = [f'{from_dir_p}/{f}/nomsa_binary_original_binary/' for f in from_db]
to_db = [f'{to_dir_p}/{f}' for f in to_db]

for src, dst in zip(from_db, to_db):
for x in ['train', 'val']:
for i in range(5):
print(f"{src}/{x}{i}/XY.csv", f"{dst}/{x}{i}.csv")
shutil.copy(f"{src}/{x}{i}/XY.csv", f"{dst}/{x}{i}.csv")

print(f"{src}/test/XY.csv", f"{dst}/test.csv")
shutil.copy(f"{src}/test/XY.csv", f"{dst}/test.csv")


# %%
########################################################################
########################## VIOLIN PLOTTING #############################
########################################################################
import logging
from matplotlib import pyplot as plt

from src.analysis.figures import prepare_df, fig_combined, custom_fig

dft = prepare_df('./results/v115/model_media/model_stats.csv')
dfv = prepare_df('./results/v115/model_media/model_stats_val.csv')

models = {
'DG': ('nomsa', 'binary', 'original', 'binary'),
'esm': ('ESM', 'binary', 'original', 'binary'), # esm model
'aflow': ('nomsa', 'aflow', 'original', 'binary'),
# 'gvpP': ('gvp', 'binary', 'original', 'binary'),
'gvpL': ('nomsa', 'binary', 'gvp', 'binary'),
# 'aflow_ring3': ('nomsa', 'aflow_ring3', 'original', 'binary'),
'gvpL_aflow': ('nomsa', 'aflow', 'gvp', 'binary'),
# 'gvpL_aflow_rng3': ('nomsa', 'aflow_ring3', 'gvp', 'binary'),
#GVPL_ESMM_davis3D_nomsaF_aflowE_48B_0.00010636872718329864LR_0.23282479481785903D_2000E_gvpLF_binaryLE
# 'gvpl_esm_aflow': ('ESM', 'aflow', 'gvp', 'binary'),
}

fig, axes = fig_combined(dft, datasets=['davis'], fig_callable=custom_fig,
models=models, metrics=['cindex', 'mse'],
fig_scale=(10,5), add_stats=True, title_postfix=" test set performance", box=True, fold_labels=True)
plt.xticks(rotation=45)

fig, axes = fig_combined(dfv, datasets=['davis'], fig_callable=custom_fig,
models=models, metrics=['cindex', 'mse'],
fig_scale=(10,5), add_stats=True, title_postfix=" validation set performance", box=True, fold_labels=True)
plt.xticks(rotation=45)
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