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import os
import pandas as pd
from neo4j import GraphDatabase
from dotenv import load_dotenv
import numpy as np
# Load environment variables
load_dotenv()
# Neo4j connection details
NEO4J_URI = os.getenv('AURA_CONNECTION_URI')
NEO4J_USER = os.getenv('AURA_USERNAME')
NEO4J_PASS = os.getenv('AURA_PASSWORD')
if not all([NEO4J_URI, NEO4J_USER, NEO4J_PASS]):
raise ValueError("Missing required Neo4j credentials in .env file")
def restore_game_data_with_embeddings():
# Path to the CSV files
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
game_data_file = os.path.join(SCRIPT_DIR, "niners_output/schedule_with_result.csv")
embeddings_file = os.path.join(SCRIPT_DIR, "niners_output/schedule_with_result_embedding.csv")
print(f"Reading game data from: {game_data_file}")
print(f"Reading embeddings from: {embeddings_file}")
# Read the CSV files
game_df = pd.read_csv(game_data_file)
embeddings_df = pd.read_csv(embeddings_file)
# Get the embedding columns (all columns starting with 'dim_')
embedding_cols = [col for col in embeddings_df.columns if col.startswith('dim_')]
# Merge the game data with embeddings on game_id
merged_df = pd.merge(game_df, embeddings_df, on='game_id', how='left')
print(f"Merged {len(game_df)} games with {len(embeddings_df)} embeddings")
# Connect to Neo4j
driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASS))
def update_game_data(tx, game_id, game_data, embedding):
# First, create/update the game node with basic properties
tx.run("""
MERGE (g:Game {game_id: $game_id})
SET g.date = $date,
g.home_team = $home_team,
g.away_team = $away_team,
g.home_score = $home_score,
g.away_score = $away_score,
g.result = $result
""", game_id=game_id,
date=game_data['date'],
home_team=game_data['home_team'],
away_team=game_data['away_team'],
home_score=game_data['home_score'],
away_score=game_data['away_score'],
result=game_data['result'])
# Then set the vector embedding using the proper Neo4j vector operation
tx.run("""
MATCH (g:Game {game_id: $game_id})
CALL db.create.setNodeVectorProperty(g, 'gameEmbedding', $embedding)
YIELD node
RETURN node
""", game_id=game_id, embedding=embedding)
# Process each game and update Neo4j
with driver.session() as session:
for _, row in merged_df.iterrows():
# Convert embedding columns to list
embedding = row[embedding_cols].values.tolist()
# Create game data dictionary
game_data = {
'date': row['date'],
'home_team': row['home_team'],
'away_team': row['away_team'],
'home_score': row['home_score'],
'away_score': row['away_score'],
'result': row['result']
}
# Update the game data in Neo4j
session.execute_write(update_game_data, row['game_id'], game_data, embedding)
print("Finished updating game data in Neo4j")
driver.close()
if __name__ == "__main__":
restore_game_data_with_embeddings() |