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| 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) | ||
|
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||
| 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) |
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