-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
8 changed files
with
368 additions
and
2 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Empty file.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,108 @@ | ||
import torch | ||
import torch.nn as nn | ||
from transformers import BertModel, BertTokenizer | ||
import pandas as pd | ||
from torch.nn.functional import cosine_similarity | ||
import random | ||
import numpy as np | ||
|
||
# Set seeds for reproducibility | ||
random.seed(42) | ||
np.random.seed(42) | ||
torch.manual_seed(42) | ||
torch.backends.cudnn.deterministic = True | ||
torch.backends.cudnn.benchmark = False | ||
|
||
|
||
class TextEnhancedKGE(nn.Module): | ||
def __init__(self, entity_dim, relation_dim, entity_to_idx, relation_to_idx, bert_model_name='bert-base-uncased'): | ||
super(TextEnhancedKGE, self).__init__() | ||
self.entity_embeddings = nn.Embedding(len(entity_to_idx), entity_dim) | ||
self.relation_embeddings = nn.Embedding(len(relation_to_idx), relation_dim) | ||
self.sentence_projection = nn.Linear(768, relation_dim) | ||
self.bert_model = BertModel.from_pretrained(bert_model_name) | ||
self.bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name) | ||
self.score_layer = nn.Linear(entity_dim * 2 + relation_dim, 1) | ||
self.combined_projection = nn.Linear(entity_dim * 2 + relation_dim, 768) | ||
|
||
nn.init.xavier_uniform_(self.entity_embeddings.weight) | ||
nn.init.xavier_uniform_(self.relation_embeddings.weight) | ||
nn.init.xavier_uniform_(self.sentence_projection.weight) | ||
nn.init.xavier_uniform_(self.combined_projection.weight) | ||
|
||
def sentence_to_embedding(self, sentences): | ||
inputs = self.bert_tokenizer(sentences, return_tensors="pt", padding=True, truncation=True, max_length=500) | ||
outputs = self.bert_model(**inputs) | ||
return outputs.last_hidden_state[:, 0, :].squeeze() | ||
|
||
def forward(self, heads, relations, tails, sentences): | ||
print("Heads: ", heads) | ||
print("Relations: ", relations) | ||
print("Tensors: ", tails) | ||
print("Sets: ", sentences) | ||
head_embeddings = self.entity_embeddings(heads) | ||
print("head embeddings", head_embeddings) | ||
relation_embeddings = self.relation_embeddings(relations) | ||
tail_embeddings = self.entity_embeddings(tails) | ||
|
||
sentence_embeddings = self.sentence_to_embedding(sentences) | ||
projected_sentences = self.sentence_projection(sentence_embeddings) | ||
|
||
score = self.calculate_score(head_embeddings, relation_embeddings, tail_embeddings, projected_sentences) | ||
return score | ||
|
||
def calculate_score(self, head_embeddings, relation_embeddings, tail_embeddings, sentence_embeddings): | ||
combined_embeddings = torch.cat([head_embeddings, relation_embeddings + sentence_embeddings, tail_embeddings], dim=1) | ||
return self.score_layer(combined_embeddings) | ||
|
||
def query(self, query_text, heads, relations, tails, sentences): | ||
query_embedding = self.sentence_to_embedding([query_text]).unsqueeze(0) | ||
sentence_embeddings = self.sentence_to_embedding(sentences) | ||
projected_sentences = self.sentence_projection(sentence_embeddings) | ||
|
||
head_embeddings = self.entity_embeddings(heads) | ||
relation_embeddings = self.relation_embeddings(relations) | ||
tail_embeddings = self.entity_embeddings(tails) | ||
|
||
relation_plus_sentence = relation_embeddings + projected_sentences | ||
combined_embeddings = torch.cat([head_embeddings, relation_plus_sentence, tail_embeddings], dim=1) | ||
|
||
if combined_embeddings.shape[-1] != query_embedding.shape[-1]: | ||
combined_embeddings = self.combined_projection(combined_embeddings) | ||
|
||
similarities = cosine_similarity(query_embedding, combined_embeddings, dim=-1) | ||
return similarities | ||
|
||
# Load your data | ||
data = pd.read_csv('my_subgraph_data.csv') | ||
print(data.head()) | ||
print(data.columns) | ||
# Create mappings | ||
entity_to_idx = {entity: idx for idx, entity in enumerate(pd.concat([data['Node Start'], data['Node End']]).unique())} | ||
relation_to_idx = {relation: idx for idx, relation in enumerate(data['Relationship Type'].unique())} | ||
print('Creating entity_to_idx and relation_to_idx', entity_to_idx) | ||
print('2nd indx', relation_to_idx) | ||
|
||
# Initialize the model | ||
model = TextEnhancedKGE( | ||
entity_dim=100, | ||
relation_dim=100, | ||
entity_to_idx=entity_to_idx, | ||
relation_to_idx=relation_to_idx | ||
) | ||
|
||
# Prepare data for the model | ||
heads = torch.LongTensor(data['Node Start'].map(entity_to_idx).values) | ||
relations = torch.LongTensor(data['Relationship Type'].map(relation_to_idx).values) | ||
tails = torch.LongTensor(data['Node End'].map(entity_to_idx).values) | ||
sentences = data['Sentence'].tolist() | ||
|
||
# Calculate scores | ||
scores = model(heads, relations, tails, sentences) | ||
print(scores) | ||
|
||
# User query | ||
query_text = "How does hydraulic fracturing enhance porosity?" | ||
similarities = model.query(query_text, heads, relations, tails, sentences) | ||
top_matches = similarities.topk(10) # Get the top 10 matches as per revised requirement | ||
print(top_matches) |
Oops, something went wrong.