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predict.py
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from logging import basicConfig, INFO, info, warning
import pandas as pd
import numpy as np
import pickle
import json
import re
from tap import Tap
from typing import List, Optional, Literal, Tuple, Union
import pickle
import io
import torch
import yaml
import sys
from utils import Data
from mapping import LADModel
class DataUnpickler(pickle.Unpickler):
def find_class(self, module, name):
if module == 'torch.storage' and name == '_load_from_bytes' and not torch.cuda.is_available():
return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
else:
return super().find_class(module, name)
def load_model(path: str, all_in_one:bool=False):
path = path + '.pt' if not path.endswith('pt') else path
if (torch.cuda.is_available()):
model = torch.load(path, weights_only=False)
else:
model = torch.load(path, map_location=torch.device('cpu'), weights_only=False)
if hasattr(model, 'ranknet_encoder'):
model.ranknet_encoder.embedding.gnn.device = torch.device('cpu')
model.ranknet_encoder.embedding.gnn.encoder[0].device = torch.device('cpu')
try:
model.encoder.encoder[0].device = torch.device('cpu')
except:
pass
if (all_in_one):
return model
path = re.sub(r'_ep\d+(\.pt)?$', '', re.sub(r'\.pt$', '', path)) # for ep_save
data = DataUnpickler(open(f'{path}_data.pkl', 'rb')).load()
config = json.load(open(f'{path}_config.json'))
return model, data, config
class PredictArgs(Tap):
input_compounds: str # TSV file with `smiles` and `rt` columns
input_metadata: str # yaml file with at least `column.name`, `eluent.A.pH`, and `column.t0` specified
model: str # model to load
gpu: bool = False # whether to use GPU for predictions
output_anchors: bool = False # include anchors in output
out: Optional[str] = None # where to write the output (TSV format). If not specified, output will be written to screen.
batch_size: int = 256 # adjust according to available VRAM
repo_root_folder: str = '../RepoRT/' # location of RepoRT, needed for HSM/Tanaka database
verbose: bool = False # more info on what is being done internally
if __name__ == '__main__':
args = PredictArgs().parse_args()
if (args.verbose):
basicConfig(level=INFO)
if (args.gpu):
torch.set_default_device('cuda')
# load model
info('load model...')
model = load_model(args.model, all_in_one=True)
data_args = model.extra_storage['data_args']
data_args['repo_root_folder'] = args.repo_root_folder
sysfeature_scaler = model.extra_storage['sysfeature_scaler']
info('load input data...')
d = Data(**data_args)
metadata = yaml.load(open(args.input_metadata), yaml.SafeLoader)
# flatten metadata
[metadata] = pd.json_normalize(metadata, sep='.').to_dict(orient='records')
original_input_columns = open(args.input_compounds).readlines()[0].strip().split('\t')
d.add_external_data(args.input_compounds, metadata=metadata,
remove_nan_rts=False, tab_mode=True,
isomeric=True, split_type='evaluate')
# TODO: warn about missing metadata (or even error?)
info('computing features')
d.compute_features(mode=None, add_descs=False)
info('computing graphs')
d.compute_graphs()
info('(fake) splitting data')
d.split_data((0, 0))
if (sysfeature_scaler is not None):
info('standardize data')
d.standardize(other_descriptor_scaler=None, other_sysfeature_scaler=sysfeature_scaler,
can_create_new_scaler=False)
((train_graphs, train_x, train_sys, train_y),
(val_graphs, val_x, val_sys, val_y),
(test_graphs, test_x, test_sys, test_y)) = d.get_split_data()
X = np.concatenate((train_x, test_x, val_x)).astype(np.float32)
X_sys = np.concatenate((train_sys, test_sys, val_sys)).astype(np.float32)
Y = np.concatenate((train_y, test_y, val_y))
info(f'done preprocessing. predicting ROIs...')
graphs = np.concatenate((train_graphs, test_graphs, val_graphs))
if (hasattr(model, 'add_sys_features') and model.add_sys_features):
from utils_newbg import sysfeature_graph
info('add system features to graphs')
smiles_list = d.df.iloc[np.concatenate((d.train_indices, d.test_indices, d.val_indices))]['smiles'].tolist()
assert len(graphs) == len(smiles_list)
from chemprop.features import set_extra_atom_fdim, set_extra_bond_fdim
if (model.add_sys_features_mode == 'bond'):
set_extra_bond_fdim(train_sys.shape[1])
elif (model.add_sys_features_mode == 'atom'):
set_extra_atom_fdim(train_sys.shape[1])
for i in range(len(graphs)):
graphs[i] = sysfeature_graph(smiles_list[i], graphs[i], X_sys[i],
bond_or_atom=model.add_sys_features_mode)
preds = model.predict(graphs, X, X_sys, batch_size=args.batch_size,
ret_features=False, prog_bar=args.verbose)
info(f'done predicting ROIs. predicting retention times...')
d.df['roi'] = preds[np.arange(len(d.df.rt))[ # restore correct order
np.argsort(np.concatenate([d.train_indices, d.test_indices, d.val_indices]))]]
# predict retention times right here
d.df['roi2'] = d.df.roi ** 2 # for LAD model
# anchors are all data points with annotated retention time, discarding the void volume
data_anchors = d.df.loc[d.df.rt > metadata['column.t0']]
data_to_predict = d.df.loc[pd.isna(d.df.rt)].copy()
info(f'building mapping using {len(data_anchors)} anchors, predicting {len(data_to_predict)} retention times...')
mapping_model = LADModel(data_anchors, ols_after=True, ols_discard_if_negative=True, ols_drop_mode='2*median')
data_to_predict['rt_pred'] = mapping_model.get_mapping(data_to_predict.roi)
# TODO: output anchors, too?
out_df = data_to_predict[
# [c for c in data_to_predict.columns if any(['smiles' in c, 'inchi' in c.lower(), 'name' in c, c.startswith('rt_pred'), c.startswith('id')])]
original_input_columns + ['rt_pred']
]
if (args.output_anchors):
out_df = pd.concat([data_anchors[original_input_columns], out_df])
if (args.out is None):
info(f'done. showing output.')
out_df.to_csv(sys.stdout, sep='\t')
else:
info(f'done. saving to {args.out}.')
out_df.to_csv(args.out, sep='\t')