#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Rhizome
# Version beta 0.0, August 2023
# Property of IBM Research, Accelerated Discovery
#
"""
PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE.
THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE.
"""
""" Title """
__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
__copyright__ = "(c) Copyright IBM Corp. 2018"
__version__ = "0.1"
__date__ = "Jan 1 2018"
import numpy as np
import torch
import torch.nn.functional as F
from graph_grammar.graph_grammar.hrg import ProductionRuleCorpus
from torch import nn
from torch.autograd import Variable
class MolecularProdRuleEmbedding(nn.Module):
''' molecular fingerprint layer
'''
def __init__(self, prod_rule_corpus, layer2layer_activation, layer2out_activation,
out_dim=32, element_embed_dim=32,
num_layers=3, padding_idx=None, use_gpu=False):
super().__init__()
if padding_idx is not None:
assert padding_idx == -1, 'padding_idx must be -1.'
self.prod_rule_corpus = prod_rule_corpus
self.layer2layer_activation = layer2layer_activation
self.layer2out_activation = layer2out_activation
self.out_dim = out_dim
self.element_embed_dim = element_embed_dim
self.num_layers = num_layers
self.padding_idx = padding_idx
self.use_gpu = use_gpu
self.layer2layer_list = []
self.layer2out_list = []
if self.use_gpu:
self.atom_embed = torch.randn(self.prod_rule_corpus.num_edge_symbol,
self.element_embed_dim, requires_grad=True).cuda()
self.bond_embed = torch.randn(self.prod_rule_corpus.num_node_symbol,
self.element_embed_dim, requires_grad=True).cuda()
self.ext_id_embed = torch.randn(self.prod_rule_corpus.num_ext_id,
self.element_embed_dim, requires_grad=True).cuda()
for _ in range(num_layers):
self.layer2layer_list.append(nn.Linear(self.element_embed_dim, self.element_embed_dim).cuda())
self.layer2out_list.append(nn.Linear(self.element_embed_dim, self.out_dim).cuda())
else:
self.atom_embed = torch.randn(self.prod_rule_corpus.num_edge_symbol,
self.element_embed_dim, requires_grad=True)
self.bond_embed = torch.randn(self.prod_rule_corpus.num_node_symbol,
self.element_embed_dim, requires_grad=True)
self.ext_id_embed = torch.randn(self.prod_rule_corpus.num_ext_id,
self.element_embed_dim, requires_grad=True)
for _ in range(num_layers):
self.layer2layer_list.append(nn.Linear(self.element_embed_dim, self.element_embed_dim))
self.layer2out_list.append(nn.Linear(self.element_embed_dim, self.out_dim))
def forward(self, prod_rule_idx_seq):
''' forward model for mini-batch
Parameters
----------
prod_rule_idx_seq : (batch_size, length)
Returns
-------
Variable, shape (batch_size, length, out_dim)
'''
batch_size, length = prod_rule_idx_seq.shape
if self.use_gpu:
out = Variable(torch.zeros((batch_size, length, self.out_dim))).cuda()
else:
out = Variable(torch.zeros((batch_size, length, self.out_dim)))
for each_batch_idx in range(batch_size):
for each_idx in range(length):
if int(prod_rule_idx_seq[each_batch_idx, each_idx]) == len(self.prod_rule_corpus.prod_rule_list):
continue
else:
each_prod_rule = self.prod_rule_corpus.prod_rule_list[int(prod_rule_idx_seq[each_batch_idx, each_idx])]
layer_wise_embed_dict = {each_edge: self.atom_embed[
each_prod_rule.rhs.edge_attr(each_edge)['symbol_idx']]
for each_edge in each_prod_rule.rhs.edges}
layer_wise_embed_dict.update({each_node: self.bond_embed[
each_prod_rule.rhs.node_attr(each_node)['symbol_idx']]
for each_node in each_prod_rule.rhs.nodes})
for each_node in each_prod_rule.rhs.nodes:
if 'ext_id' in each_prod_rule.rhs.node_attr(each_node):
layer_wise_embed_dict[each_node] \
= layer_wise_embed_dict[each_node] \
+ self.ext_id_embed[each_prod_rule.rhs.node_attr(each_node)['ext_id']]
for each_layer in range(self.num_layers):
next_layer_embed_dict = {}
for each_edge in each_prod_rule.rhs.edges:
v = layer_wise_embed_dict[each_edge]
for each_node in each_prod_rule.rhs.nodes_in_edge(each_edge):
v = v + layer_wise_embed_dict[each_node]
next_layer_embed_dict[each_edge] = self.layer2layer_activation(self.layer2layer_list[each_layer](v))
out[each_batch_idx, each_idx, :] \
= out[each_batch_idx, each_idx, :] + self.layer2out_activation(self.layer2out_list[each_layer](v))
for each_node in each_prod_rule.rhs.nodes:
v = layer_wise_embed_dict[each_node]
for each_edge in each_prod_rule.rhs.adj_edges(each_node):
v = v + layer_wise_embed_dict[each_edge]
next_layer_embed_dict[each_node] = self.layer2layer_activation(self.layer2layer_list[each_layer](v))
out[each_batch_idx, each_idx, :]\
= out[each_batch_idx, each_idx, :] + self.layer2out_activation(self.layer2out_list[each_layer](v))
layer_wise_embed_dict = next_layer_embed_dict
return out
class MolecularProdRuleEmbeddingLastLayer(nn.Module):
''' molecular fingerprint layer
'''
def __init__(self, prod_rule_corpus, layer2layer_activation, layer2out_activation,
out_dim=32, element_embed_dim=32,
num_layers=3, padding_idx=None, use_gpu=False):
super().__init__()
if padding_idx is not None:
assert padding_idx == -1, 'padding_idx must be -1.'
self.prod_rule_corpus = prod_rule_corpus
self.layer2layer_activation = layer2layer_activation
self.layer2out_activation = layer2out_activation
self.out_dim = out_dim
self.element_embed_dim = element_embed_dim
self.num_layers = num_layers
self.padding_idx = padding_idx
self.use_gpu = use_gpu
self.layer2layer_list = []
self.layer2out_list = []
if self.use_gpu:
self.atom_embed = nn.Embedding(self.prod_rule_corpus.num_edge_symbol, self.element_embed_dim).cuda()
self.bond_embed = nn.Embedding(self.prod_rule_corpus.num_node_symbol, self.element_embed_dim).cuda()
for _ in range(num_layers+1):
self.layer2layer_list.append(nn.Linear(self.element_embed_dim, self.element_embed_dim).cuda())
self.layer2out_list.append(nn.Linear(self.element_embed_dim, self.out_dim).cuda())
else:
self.atom_embed = nn.Embedding(self.prod_rule_corpus.num_edge_symbol, self.element_embed_dim)
self.bond_embed = nn.Embedding(self.prod_rule_corpus.num_node_symbol, self.element_embed_dim)
for _ in range(num_layers+1):
self.layer2layer_list.append(nn.Linear(self.element_embed_dim, self.element_embed_dim))
self.layer2out_list.append(nn.Linear(self.element_embed_dim, self.out_dim))
def forward(self, prod_rule_idx_seq):
''' forward model for mini-batch
Parameters
----------
prod_rule_idx_seq : (batch_size, length)
Returns
-------
Variable, shape (batch_size, length, out_dim)
'''
batch_size, length = prod_rule_idx_seq.shape
if self.use_gpu:
out = Variable(torch.zeros((batch_size, length, self.out_dim))).cuda()
else:
out = Variable(torch.zeros((batch_size, length, self.out_dim)))
for each_batch_idx in range(batch_size):
for each_idx in range(length):
if int(prod_rule_idx_seq[each_batch_idx, each_idx]) == len(self.prod_rule_corpus.prod_rule_list):
continue
else:
each_prod_rule = self.prod_rule_corpus.prod_rule_list[int(prod_rule_idx_seq[each_batch_idx, each_idx])]
if self.use_gpu:
layer_wise_embed_dict = {each_edge: self.atom_embed(
Variable(torch.LongTensor(
[each_prod_rule.rhs.edge_attr(each_edge)['symbol_idx']]
), requires_grad=False).cuda())
for each_edge in each_prod_rule.rhs.edges}
layer_wise_embed_dict.update({each_node: self.bond_embed(
Variable(
torch.LongTensor([
each_prod_rule.rhs.node_attr(each_node)['symbol_idx']]),
requires_grad=False).cuda()
) for each_node in each_prod_rule.rhs.nodes})
else:
layer_wise_embed_dict = {each_edge: self.atom_embed(
Variable(torch.LongTensor(
[each_prod_rule.rhs.edge_attr(each_edge)['symbol_idx']]
), requires_grad=False))
for each_edge in each_prod_rule.rhs.edges}
layer_wise_embed_dict.update({each_node: self.bond_embed(
Variable(
torch.LongTensor([
each_prod_rule.rhs.node_attr(each_node)['symbol_idx']]),
requires_grad=False)
) for each_node in each_prod_rule.rhs.nodes})
for each_layer in range(self.num_layers):
next_layer_embed_dict = {}
for each_edge in each_prod_rule.rhs.edges:
v = layer_wise_embed_dict[each_edge]
for each_node in each_prod_rule.rhs.nodes_in_edge(each_edge):
v += layer_wise_embed_dict[each_node]
next_layer_embed_dict[each_edge] = self.layer2layer_activation(self.layer2layer_list[each_layer](v))
for each_node in each_prod_rule.rhs.nodes:
v = layer_wise_embed_dict[each_node]
for each_edge in each_prod_rule.rhs.adj_edges(each_node):
v += layer_wise_embed_dict[each_edge]
next_layer_embed_dict[each_node] = self.layer2layer_activation(self.layer2layer_list[each_layer](v))
layer_wise_embed_dict = next_layer_embed_dict
for each_edge in each_prod_rule.rhs.edges:
out[each_batch_idx, each_idx, :] = self.layer2out_activation(self.layer2out_list[self.num_layers](v))
for each_edge in each_prod_rule.rhs.edges:
out[each_batch_idx, each_idx, :] = self.layer2out_activation(self.layer2out_list[self.num_layers](v))
return out
class MolecularProdRuleEmbeddingUsingFeatures(nn.Module):
''' molecular fingerprint layer
'''
def __init__(self, prod_rule_corpus, layer2layer_activation, layer2out_activation,
out_dim=32, num_layers=3, padding_idx=None, use_gpu=False):
super().__init__()
if padding_idx is not None:
assert padding_idx == -1, 'padding_idx must be -1.'
self.feature_dict, self.feature_dim = prod_rule_corpus.construct_feature_vectors()
self.prod_rule_corpus = prod_rule_corpus
self.layer2layer_activation = layer2layer_activation
self.layer2out_activation = layer2out_activation
self.out_dim = out_dim
self.num_layers = num_layers
self.padding_idx = padding_idx
self.use_gpu = use_gpu
self.layer2layer_list = []
self.layer2out_list = []
if self.use_gpu:
for each_key in self.feature_dict:
self.feature_dict[each_key] = self.feature_dict[each_key].to_dense().cuda()
for _ in range(num_layers):
self.layer2layer_list.append(nn.Linear(self.feature_dim, self.feature_dim).cuda())
self.layer2out_list.append(nn.Linear(self.feature_dim, self.out_dim).cuda())
else:
for _ in range(num_layers):
self.layer2layer_list.append(nn.Linear(self.feature_dim, self.feature_dim))
self.layer2out_list.append(nn.Linear(self.feature_dim, self.out_dim))
def forward(self, prod_rule_idx_seq):
''' forward model for mini-batch
Parameters
----------
prod_rule_idx_seq : (batch_size, length)
Returns
-------
Variable, shape (batch_size, length, out_dim)
'''
batch_size, length = prod_rule_idx_seq.shape
if self.use_gpu:
out = Variable(torch.zeros((batch_size, length, self.out_dim))).cuda()
else:
out = Variable(torch.zeros((batch_size, length, self.out_dim)))
for each_batch_idx in range(batch_size):
for each_idx in range(length):
if int(prod_rule_idx_seq[each_batch_idx, each_idx]) == len(self.prod_rule_corpus.prod_rule_list):
continue
else:
each_prod_rule = self.prod_rule_corpus.prod_rule_list[int(prod_rule_idx_seq[each_batch_idx, each_idx])]
edge_list = sorted(list(each_prod_rule.rhs.edges))
node_list = sorted(list(each_prod_rule.rhs.nodes))
adj_mat = torch.FloatTensor(each_prod_rule.rhs_adj_mat(edge_list + node_list).todense() + np.identity(len(edge_list)+len(node_list)))
if self.use_gpu:
adj_mat = adj_mat.cuda()
layer_wise_embed = [
self.feature_dict[each_prod_rule.rhs.edge_attr(each_edge)['symbol']]
for each_edge in edge_list]\
+ [self.feature_dict[each_prod_rule.rhs.node_attr(each_node)['symbol']]
for each_node in node_list]
for each_node in each_prod_rule.ext_node.values():
layer_wise_embed[each_prod_rule.rhs.num_edges + node_list.index(each_node)] \
= layer_wise_embed[each_prod_rule.rhs.num_edges + node_list.index(each_node)] \
+ self.feature_dict[('ext_id', each_prod_rule.rhs.node_attr(each_node)['ext_id'])]
layer_wise_embed = torch.stack(layer_wise_embed)
for each_layer in range(self.num_layers):
message = adj_mat @ layer_wise_embed
next_layer_embed = self.layer2layer_activation(self.layer2layer_list[each_layer](message))
out[each_batch_idx, each_idx, :] \
= out[each_batch_idx, each_idx, :] \
+ self.layer2out_activation(self.layer2out_list[each_layer](message)).sum(dim=0)
layer_wise_embed = next_layer_embed
return out