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calculator.py
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calculator.py
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import collections
import os
import pickle
import textwrap
import types
from typing import ValuesView
import numpy as np
import torch
from torch_geometric.data.dataloader import DataLoader
from tqdm import tqdm
from delfta.download import get_model_weights
from delfta.net import EGNN
from delfta.net_utils import (
MODEL_HPARAMS,
MULTITASK_ENDPOINTS,
QMUGS_ATOM_DICT,
DeltaDataset,
)
import openbabel
from delfta.utils import LOGGER, MODEL_PATH
from delfta.xtb import run_xtb_calc
_ALLTASKS = ["E_form", "E_homo", "E_lumo", "E_gap", "dipole", "charges"]
class DelftaCalculator:
def __init__(
self,
tasks="all",
delta=True,
force3d=False,
addh=False,
xtbopt=False,
verbose=True,
progress=True,
) -> None:
"""Main calculator class for predicting DFT observables.
Parameters
----------
tasks : str, optional
A list of tasks to predict. Available tasks include
`[E_form, E_homo, E_lumo, E_gap, dipole, charges]`, by default "all".
delta : bool, optional
Whether to use delta-learning models, by default True
force3d : bool, optional
Whether to assign 3D coordinates to molecules lacking them, by default False
addh : bool, optional
Whether to add hydrogens to molecules lacking them, by default False
xtbopt : bool, optional
Whether to perform GFN2-xTB geometry optimization, by default False
verbose : bool, optional
Enables/disables stdout logger, by default True
progress : bool, optional
Enables/disables progress bars in prediction, by default True
"""
if tasks == "all" or tasks == ["all"]:
tasks = _ALLTASKS
self.tasks = tasks
self.delta = delta
self.multitasks = [task for task in self.tasks if task in MULTITASK_ENDPOINTS]
self.force3d = force3d
self.addh = addh
self.xtbopt = xtbopt
self.verbose = verbose
self.progress = progress
with open(os.path.join(MODEL_PATH, "norm.pt"), "rb") as handle:
self.norm = pickle.load(handle)
self.models = []
for task in tasks:
if task in MULTITASK_ENDPOINTS:
task_name = "multitask"
elif task == "charges":
task_name = "charges"
elif task == "E_form":
task_name = "single_energy"
else:
raise ValueError(f"Task name `{task}` not recognised")
if self.delta:
task_name += "_delta"
else:
task_name += "_direct"
self.models.append(task_name)
self.models = list(set(self.models))
def _3dcheck(self, mol):
"""Checks whether `mol` has 3d coordinates assigned. If
`self.force3d=True` these will be computed for
those lacking them using the MMFF94 force-field as
available on pybel.
Parameters
----------
mol : pybel.Molecule
An OEChem molecule object
Returns
-------
bool
`True` if `mol` has a 3d conformation, `False` otherwise.
"""
if mol.dim != 3:
if self.force3d:
mol.make3D()
return False
return True
def _atomtypecheck(self, mol):
"""Checks whether the atom types in `mol` are supported
by the QMugs database
Parameters
----------
mol : pybel.Molecule
An OEChem molecule object
Returns
-------
bool
`True` if all atoms have valid atom types, `False` otherwise.
"""
for atom in mol.atoms:
if atom.atomicnum not in QMUGS_ATOM_DICT:
return False
return True
def _chargecheck(self, mol):
"""Checks whether the overall charge on `mol` is neutral.
Parameters
----------
mol : pybel.Molecule
An OEChem molecule object
Returns
-------
bool
`True` is overall `mol` charge is 0, `False` otherwise.
"""
if mol.charge != 0:
return True
else:
return False
def _hydrogencheck(self, mol):
"""Checks whether `mol` has assigned hydrogens. If `self.addh=True`
these will be added if lacking.
Parameters
----------
mol : pybel.Molecule
An OEChem molecule object
Returns
-------
bool
Whether `mol` has assigned hydrogens.
"""
atomicnums = set([atom.atomicnum for atom in mol.atoms])
if 1 not in atomicnums:
if self.addh:
mol.addh()
return False
else:
return True
def _validate_mols(self, mols):
if len(mols) == 0:
raise ValueError("No molecules provided.")
correct_types = [isinstance(mol, openbabel.pybel.Molecule) for mol in mols]
not_empty = [len(mol.atoms) > 0 for mol in mols]
valid = [(a and b) for a, b in zip(correct_types, not_empty)]
if not all(valid):
idx = [i for i, elem in enumerate(valid) if not elem]
raise ValueError(f"Invalid molecules at idx {idx}.")
def _preprocess(self, mols):
"""Performs a series of preprocessing checks on a list of molecules `mols`,
including 3d-conformation existence, validity of atom types, neutral charge
and hydrogen addition.
Parameters
----------
mols: [pybel.Molecule]
A list of OEChem molecule objects
Returns
-------
[pybel.Molecule]
A list of processed OEChem molecule objects
"""
self._validate_mols(mols)
idx_no3d = []
idx_non_valid_atypes = []
idx_charged = []
idx_noh = []
for idx, mol in enumerate(mols):
has_3d = self._3dcheck(mol)
if not has_3d:
idx_no3d.append(idx)
is_atype_valid = self._atomtypecheck(mol)
if not is_atype_valid:
idx_non_valid_atypes.append(idx)
is_charged = self._chargecheck(mol)
if is_charged:
idx_charged.append(idx)
has_h = self._hydrogencheck(mol)
if not has_h:
idx_noh.append(idx)
if idx_no3d:
if self.force3d:
if self.verbose:
LOGGER.info(
f"Assigned MMFF94 coordinates to molecules with idx. {idx_no3d}"
)
else:
raise ValueError(
textwrap.fill(
textwrap.dedent(
f"""
Molecules at position {idx_no3d} have no 3D conformations available.
Either provide a mol with one or re-run calculator with `force3D=True`.
"""
)
)
)
if idx_non_valid_atypes:
raise ValueError(
textwrap.fill(
textwrap.dedent(
f"""
Found non-supported atomic no. in molecules
at position {idx_non_valid_atypes}. This application currently supports only
the atom types used in the QMugs database, namely those with
the following atomic numbers {list(QMUGS_ATOM_DICT.keys())}.
"""
)
)
)
if idx_charged:
raise ValueError(
textwrap.fill(
textwrap.dedent(
f"""
Found molecules with a non-zero atomic formal charge at
positions {idx_charged}. This application currently does not support
prediction for charged molecules.
"""
)
)
)
if idx_noh:
if self.addh:
LOGGER.info(
f"Added hydrogens for non-protonated molecules at position {idx_noh}"
)
else:
raise ValueError(
textwrap.fill(
textwrap.dedent(
f"""
No hydrogens present for molecules at position {idx_noh}. Please add
them manually or re-run the calculator with argument `addh=True`.
"""
)
)
)
return mols
def _get_preds(self, loader, model):
"""Returns predictions for the data contained in `loader` of a
pyTorch `model`.
Parameters
----------
loader : delfta.net_utils.DeltaDataset
A `delfta.net_utils.DeltaDataset` instance.
model : delfta.net.EGNN
A `delfta.net.EGNN` instance.
Returns
-------
numpy.ndarray
Model predictions.
numpy.ndarray
Graph-specific indexes for node-level predictions.
"""
y_hats = []
g_ptrs = []
if self.progress:
loader = tqdm(loader)
with torch.no_grad():
for batch in loader:
y_hats.append(model(batch).numpy())
g_ptrs.append(batch.ptr.numpy())
return y_hats, g_ptrs
def _get_xtb_props(self, mols):
"""Runs the GFN2-xTB binary and returns observables
Parameters
----------
mols : [pybel.Molecule]
A list of OEChem molecule instances.
Returns
-------
dict
A dictionary containing the requested properties for
`mols`.
"""
xtb_props = collections.defaultdict(list)
if self.verbose:
LOGGER.info("Now running xTB...")
if self.progress:
mol_progress = tqdm(mols)
else:
mol_progress = mols
for mol in mol_progress:
xtb_out = run_xtb_calc(mol, opt=self.xtbopt)
for prop, val in xtb_out.items():
xtb_props[prop].append(val)
return xtb_props
def _inv_scale(self, preds, norm_dict):
"""Inverse min-max scaling transformation
Parameters
----------
preds : np.ndarray
Normalized predictions
norm_dict : dict
A dictionary containing scale and location values for
inverse normalization.
Returns
-------
numpy.ndarray
Unnormalized predictions in their original scale
and location.
"""
return preds * norm_dict["scale"] + norm_dict["location"]
def _predict_batch(self, generator, batch_size):
"""Utility method for prediction using OEChem generators
(e.g. those used for reading sdf or xyz files)
Parameters
----------
generator : pybel.filereader
A pybel.filereader instance
batch_size : int
Batch size used for prediction. Defaults to the same one
used under `self.predict`.
Returns
-------
dict
Requested DFT-predicted properties.
"""
preds_batch = []
done_flag = False
done_so_far = 0
while not done_flag:
mols = []
for _ in range(batch_size):
try:
mol = next(generator)
mols.append(mol)
done_so_far += 1
except StopIteration:
done_flag = True
break
if self.progress:
print(f"Done computing for {done_so_far} molecules...")
preds_batch.append(self.predict(mols, batch_size))
pred_keys = preds_batch[0].keys()
preds = collections.defaultdict(list)
for pred_k in pred_keys:
for batch in preds_batch:
if pred_k == "charges":
preds[pred_k].extend(batch[pred_k])
else:
preds[pred_k].extend(batch[pred_k].tolist())
if pred_k != "charges":
preds[pred_k] = np.array(preds[pred_k], dtype=np.float32)
return dict(preds)
def predict(self, input_, batch_size=32):
"""Main prediction method for DFT observables.
Parameters
----------
input_ : None
Either a list of OEChem Molecule instances or a pybel filereader generator instance.
batch_size : int, optional
Batch size used for prediction, by default 32
Returns
-------
dict
Requested DFT-predicted properties.
"""
if isinstance(input_, list):
mols = self._preprocess(input_)
elif isinstance(input_, types.GeneratorType):
return self._predict_batch(input_, batch_size)
else:
raise ValueError(
f"Invalid input. Expected list or generator, but got {type(input_)}."
)
data = DeltaDataset(mols)
loader = DataLoader(data, batch_size=batch_size, shuffle=False)
preds = {}
for _, model_name in enumerate(self.models):
if self.verbose:
LOGGER.info(f"Now running network for model {model_name}...")
model_param = MODEL_HPARAMS[model_name]
model = EGNN(
n_outputs=model_param.n_outputs, global_prop=model_param.global_prop
).eval()
weights = get_model_weights(model_name)
model.load_state_dict(weights)
y_hat, g_ptr = self._get_preds(loader, model)
if "charges" in model_name:
atom_y_hats = []
for batch_idx, batch_ptr in enumerate(g_ptr):
atom_y_hats.extend(
[
y_hat[batch_idx][batch_ptr[idx] : batch_ptr[idx + 1]]
for idx in range(len(batch_ptr) - 1)
]
)
preds[model_name] = atom_y_hats
else:
y_hat = np.vstack(y_hat)
if "multitask" in model_name:
if "direct" in model_name:
y_hat = self._inv_scale(y_hat, self.norm["direct"])
else:
y_hat = self._inv_scale(y_hat, self.norm["delta"])
preds[model_name] = y_hat
preds_filtered = {}
for model_name in preds.keys():
mname = model_name.rsplit("_", maxsplit=1)[0]
if mname == "single_energy":
preds_filtered["E_form"] = preds[model_name].squeeze()
elif mname == "multitask":
for task in self.multitasks:
preds_filtered[task] = preds[model_name][
:, MULTITASK_ENDPOINTS[task]
]
elif mname == "charges":
preds_filtered["charges"] = preds[model_name]
if self.delta:
xtb_props = self._get_xtb_props(mols)
for prop, delta_arr in preds_filtered.items():
if prop == "charges":
preds_filtered[prop] = [
d_arr + np.array(xtb_arr)
for d_arr, xtb_arr in zip(delta_arr, xtb_props[prop])
]
else:
preds_filtered[prop] = delta_arr + np.array(
xtb_props[prop], dtype=np.float32
)
return preds_filtered
if __name__ == "__main__":
import glob
from delfta.utils import DATA_PATH
from openbabel.pybel import readfile
mol_files = glob.glob(os.path.join(DATA_PATH, "test_data", "example_files_1.sdf"))
mols = [next(readfile("sdf", mol_file)) for mol_file in mol_files]
calc_delta = DelftaCalculator()
# Verbose passing of arguments. We could've used "all" as well
predictions_delta = calc_delta.predict(mols, batch_size=32)
a = 2