Create logic.py

logic.py

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+import pandas as pd
+from arango import ArangoClient
+from tqdm import tqdm
+import numpy as np
+import itertools
+import requests
+import sys
+import oasis
+from arango import ArangoClient
+
+import torch
+import torch.nn.functional as F
+from torch.nn import Linear
+from arango import ArangoClient
+import torch_geometric.transforms as T
+from torch_geometric.nn import SAGEConv, to_hetero
+from torch_geometric.transforms import RandomLinkSplit, ToUndirected
+from sentence_transformers import SentenceTransformer
+from torch_geometric.data import HeteroData
+import yaml
+
+#-------------------------------------------------------------------------------------------
+# Functions
+
+# performs user and movie mappings
+def node_mappings(path, index_col):
+    df = pd.read_csv(path, index_col=index_col)
+    mapping = {index: i for i, index in enumerate(df.index.unique())}
+
+    return mapping
+
+
+def convert_int(x):
+    try:
+        return int(x)
+    except:
+        return np.nan
+
+
+def remove_movies(m_id):
+    '''
+    # Remove ids which dont have meta data information
+    '''
+    no_metadata = []
+    for idx in range(len(m_id)):
+        tmdb_id = id_map.loc[id_map['movieId'] == m_id[idx]]
+  
+        if tmdb_id.size == 0:
+            no_metadata.append(m_id[idx])
+            #print('No Meta data information at:', m_id[idx])
+    return no_metadata
+
+
+#-------------------------------------------------------------------------------------------
+
+def make_graph():
+    metadata_path = './sampled_movie_dataset/movies_metadata.csv'
+    df = pd.read_csv(metadata_path)
+    df = df.drop([19730, 29503, 35587])
+    df['id'] = df['id'].astype('int')
+
+    links_small = pd.read_csv('./sampled_movie_dataset/links_small.csv')
+    links_small = links_small[links_small['tmdbId'].notnull()]['tmdbId'].astype('int') # selecting tmdbId coloumn from links_small file
+
+    sampled_md = df[df['id'].isin(links_small)]
+    sampled_md['tagline'] = sampled_md['tagline'].fillna('')
+    sampled_md['description'] = sampled_md['overview'] + sampled_md['tagline']
+    sampled_md['description'] = sampled_md['description'].fillna('')
+    sampled_md = sampled_md.reset_index()
+
+    indices = pd.Series(sampled_md.index, index=sampled_md['title'])
+    ind_gen = pd.Series(sampled_md.index, index=sampled_md['genres'])
+
+    ratings_path = './sampled_movie_dataset/ratings_small.csv'
+    ratings_df = pd.read_csv(ratings_path)
+    m_id = ratings_df['movieId'].tolist()
+    m_id = list(dict.fromkeys(m_id))
+
+    user_mapping = node_mappings(ratings_path, index_col='userId')
+    movie_mapping = node_mappings(ratings_path, index_col='movieId')
+
+    id_map = pd.read_csv('./sampled_movie_dataset/links_small.csv')[['movieId', 'tmdbId']]
+    id_map['tmdbId'] = id_map['tmdbId'].apply(convert_int)
+    id_map.columns = ['movieId', 'id']
+    id_map = id_map.merge(sampled_md[['title', 'id']], on='id').set_index('title') # tmbdid is same (of links_small) as of id in sampled_md
+    indices_map = id_map.set_index('id')
+
+    no_metadata = remove_movies(m_id)
+
+    ## remove ids which dont have meta data information
+    for element in no_metadata:
+        if element in m_id:
+            print("ids with no metadata information:",element)
+            m_id.remove(element)
+
+    # create new movie_mapping dict with only m_ids having metadata information
+    movie_mappings = {}
+    for idx, m in enumerate(m_id):
+        movie_mappings[m] = idx
+
+    return movie_mappings
+
+    
+    
+def load_data_to_ArangoDB():
+    
+
+
+
+