import keras as ks
from kgcnn.layers.scale import get as get_scaler
from kgcnn.models.utils import update_model_kwargs
from kgcnn.models.casting import (template_cast_output, template_cast_list_input,
template_cast_list_input_docs, template_cast_output_docs)
from keras.backend import backend as backend_to_use
from kgcnn.layers.modules import Input
from ._model import model_disjoint
# Keep track of model version from commit date in literature.
# To be updated if model is changed in a significant way.
__model_version__ = "2023.11.15"
# Supported backends
__kgcnn_model_backend_supported__ = ["tensorflow", "torch", "jax"]
if backend_to_use() not in __kgcnn_model_backend_supported__:
raise NotImplementedError("Backend '%s' for model 'AttentiveFP' is not supported." % backend_to_use())
# Implementation of AttentiveFP in `keras` from paper:
# Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
# Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li,
# Xiaomin Luo, Kaixian Chen, Hualiang Jiang*, and Mingyue Zheng*
# Cite this: J. Med. Chem. 2020, 63, 16, 8749–8760
# Publication Date:August 13, 2019
# https://doi.org/10.1021/acs.jmedchem.9b00959
model_default = {
"name": "AttentiveFP",
"inputs": [
{"shape": (None,), "name": "node_number", "dtype": "int64"},
{"shape": (None,), "name": "edge_number", "dtype": "int64"},
{"shape": (None, 2), "name": "edge_indices", "dtype": "int64"},
{"shape": (), "name": "total_nodes", "dtype": "int64"},
{"shape": (), "name": "total_edges", "dtype": "int64"}
],
"input_tensor_type": "padded",
"cast_disjoint_kwargs": {},
"input_embedding": None, # deprecated
"input_node_embedding": {"input_dim": 95, "output_dim": 64},
"input_edge_embedding": {"input_dim": 5, "output_dim": 64},
"attention_args": {"units": 32},
"depthmol": 2,
"depthato": 2,
"dropout": 0.1,
"verbose": 10,
"output_embedding": "graph",
"output_scaling": None,
"output_to_tensor": True, # deprecated
"output_tensor_type": "padded",
"output_mlp": {"use_bias": [True, True, False], "units": [25, 10, 1],
"activation": ["relu", "relu", "sigmoid"]}
}
[docs]@update_model_kwargs(model_default, update_recursive=0, deprecated=["input_embedding", "output_to_tensor"])
def make_model(inputs: list = None,
cast_disjoint_kwargs: dict = None,
input_tensor_type: str = None,
input_node_embedding: dict = None,
input_edge_embedding: dict = None,
input_embedding: dict = None, # noqa
depthmol: int = None,
depthato: int = None,
dropout: float = None,
attention_args: dict = None,
name: str = None,
verbose: int = None, # noqa
output_embedding: str = None,
output_to_tensor: bool = None, # noqa
output_tensor_type: str = None,
output_scaling: dict = None,
output_mlp: dict = None
):
r"""Make `AttentiveFP <https://doi.org/10.1021/acs.jmedchem.9b00959>`__ graph network via functional API.
Default parameters can be found in :obj:`kgcnn.literature.AttentiveFP.model_default`.
**Model inputs**:
Model uses the list template of inputs and standard output template.
The supported inputs are :obj:`[nodes, edges, edge_indices, ...]`
with '...' indicating mask or id tensors following the template below:
%s
**Model outputs**:
The standard output template:
%s
Args:
inputs (list): List of dictionaries unpacked in :obj:`Input`. Order must match model definition.
cast_disjoint_kwargs (dict): Dictionary of arguments for casting layers if used.
input_tensor_type (str): Input type of graph tensor. Default is "padded".
input_embedding (dict): Deprecated in favour of `input_node_embedding` etc.
input_node_embedding (dict): Dictionary of arguments for nodes unpacked in :obj:`Embedding` layers.
input_edge_embedding (dict): Dictionary of arguments for edge unpacked in :obj:`Embedding` layers.
depthato (int): Number of graph embedding units or depth of the network.
depthmol (int): Number of graph embedding units or depth of the graph embedding.
dropout (float): Dropout to use.
attention_args (dict): Dictionary of layer arguments unpacked in :obj:`AttentiveHeadFP` layer. Units parameter
is also used in GRU-update and :obj:`PoolingNodesAttentive`.
name (str): Name of the model.
verbose (int): Level of print output.
output_embedding (str): Main embedding task for graph network. Either "node", "edge" or "graph".
output_to_tensor (bool): Deprecated in favour of `output_tensor_type` .
output_tensor_type (str): Output type of graph tensors such as nodes or edges. Default is "padded".
output_mlp (dict): Dictionary of layer arguments unpacked in the final classification :obj:`MLP` layer block.
Defines number of model outputs and activation.
output_scaling (dict): Dictionary of layer arguments unpacked in scaling layers.
Returns:
:obj:`keras.models.Model`
"""
# Make input
model_inputs = [Input(**x) for x in inputs]
di_inputs = template_cast_list_input(
model_inputs,
input_tensor_type=input_tensor_type,
cast_disjoint_kwargs=cast_disjoint_kwargs,
mask_assignment=[0, 0, 1],
index_assignment=[None, None, 0]
)
n, ed, disjoint_indices, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges = di_inputs
# Wrapping disjoint model.
out = model_disjoint(
[n, ed, disjoint_indices, batch_id_node, count_nodes],
use_node_embedding=("int" in inputs[0]['dtype']) if input_node_embedding is not None else False,
use_edge_embedding=("int" in inputs[1]['dtype']) if input_edge_embedding is not None else False,
input_node_embedding=input_node_embedding,
input_edge_embedding=input_edge_embedding,
depthmol=depthmol,
depthato=depthato,
dropout=dropout,
attention_args=attention_args,
output_embedding=output_embedding,
output_mlp=output_mlp
)
if output_scaling is not None:
scaler = get_scaler(output_scaling["name"])(**output_scaling)
out = scaler(out)
# Output embedding choice
out = template_cast_output(
[out, batch_id_node, batch_id_edge, node_id, edge_id, count_nodes, count_edges],
output_embedding=output_embedding, output_tensor_type=output_tensor_type,
input_tensor_type=input_tensor_type, cast_disjoint_kwargs=cast_disjoint_kwargs,
)
model = ks.models.Model(inputs=model_inputs, outputs=out, name=name)
model.__kgcnn_model_version__ = __model_version__
if output_scaling is not None:
def set_scale(*args, **kwargs):
scaler.set_scale(*args, **kwargs)
setattr(model, "set_scale", set_scale)
return model
make_model.__doc__ = make_model.__doc__ % (template_cast_list_input_docs, template_cast_output_docs)