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{
"cells": [
{
"cell_type": "markdown",
"id": "dd3a9f32-3b8c-46b5-9165-b243049d2f81",
"metadata": {},
"source": [
"# TMDB Network\n",
"\n",
"This notebook contains recipe to construct a heterogeneous network based on the fetched TMDB dataset."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "412ba4a1-7f05-4a65-97ae-9a235c30dae5",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-02T15:41:05.965764Z",
"start_time": "2024-12-02T15:41:05.188819Z"
}
},
"outputs": [],
"source": [
"import pickle\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "e3ab0c46-271e-4cae-adf4-f5de94f011bf",
"metadata": {},
"source": [
"## Input Data\n",
"\n",
"[The Movie Database (TMDB)](https://www.themoviedb.org/) is a popular online database and community platform that provides a vast collection of information about movies, TV shows, and other related content. We collected metadata about more than 7500 of the most popular action, romance, thriller and animation English movies from [TMDB's public API](https://developer.themoviedb.org/docs) on May 31, 2024. After meticulous data cleaning, we finally obtain 7,505 movies, 13,016 actors, and 3,891 directors.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "262e4aa2-3566-476f-a79f-29609a88139a",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-02T15:41:07.133497Z",
"start_time": "2024-12-02T15:41:06.904795Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of movies: 7505\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>title</th>\n",
" <th>overview</th>\n",
" <th>genre</th>\n",
" <th>release_date</th>\n",
" <th>actor</th>\n",
" <th>director</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>199753</td>\n",
" <td>Tom and Jerry's Giant Adventure</td>\n",
" <td>Tom And Jerry are among the last animals livin...</td>\n",
" <td>Animation</td>\n",
" <td>2013-08-04</td>\n",
" <td>[80416, 86434, 60739, 5129, 1065, 31531, 11444...</td>\n",
" <td>[23683, 1447452, 92317]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9560</td>\n",
" <td>A Walk in the Clouds</td>\n",
" <td>World War II vet Paul Sutton falls for a pregn...</td>\n",
" <td>Romance</td>\n",
" <td>1995-05-27</td>\n",
" <td>[7353, 62595, 3753, 85869, 6862, 152943, 6384,...</td>\n",
" <td>[77162, 1377239]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10875</td>\n",
" <td>The Fabulous Baker Boys</td>\n",
" <td>The lives of two struggling musicians, who hap...</td>\n",
" <td>Romance</td>\n",
" <td>1989-10-13</td>\n",
" <td>[7906, 8354, 43364, 152963, 1160, 57992, 1229,...</td>\n",
" <td>[2226, 2571810, 1966431]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>67609</td>\n",
" <td>Three Blind Mouseketeers</td>\n",
" <td>As the title implies, the three blind mice are...</td>\n",
" <td>Animation</td>\n",
" <td>1936-09-26</td>\n",
" <td>[31771, 5462]</td>\n",
" <td>[564041, 5446]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>24021</td>\n",
" <td>The Twilight Saga: Eclipse</td>\n",
" <td>Bella once again finds herself surrounded by d...</td>\n",
" <td>Romance</td>\n",
" <td>2010-06-23</td>\n",
" <td>[84224, 84225, 45827, 121868, 87310, 1475835, ...</td>\n",
" <td>[2045537, 1393423, 27571, 2476949, 113019]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id title \\\n",
"0 199753 Tom and Jerry's Giant Adventure \n",
"1 9560 A Walk in the Clouds \n",
"2 10875 The Fabulous Baker Boys \n",
"3 67609 Three Blind Mouseketeers \n",
"4 24021 The Twilight Saga: Eclipse \n",
"\n",
" overview genre release_date \\\n",
"0 Tom And Jerry are among the last animals livin... Animation 2013-08-04 \n",
"1 World War II vet Paul Sutton falls for a pregn... Romance 1995-05-27 \n",
"2 The lives of two struggling musicians, who hap... Romance 1989-10-13 \n",
"3 As the title implies, the three blind mice are... Animation 1936-09-26 \n",
"4 Bella once again finds herself surrounded by d... Romance 2010-06-23 \n",
"\n",
" actor \\\n",
"0 [80416, 86434, 60739, 5129, 1065, 31531, 11444... \n",
"1 [7353, 62595, 3753, 85869, 6862, 152943, 6384,... \n",
"2 [7906, 8354, 43364, 152963, 1160, 57992, 1229,... \n",
"3 [31771, 5462] \n",
"4 [84224, 84225, 45827, 121868, 87310, 1475835, ... \n",
"\n",
" director \n",
"0 [23683, 1447452, 92317] \n",
"1 [77162, 1377239] \n",
"2 [2226, 2571810, 1966431] \n",
"3 [564041, 5446] \n",
"4 [2045537, 1393423, 27571, 2476949, 113019] "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movies = pd.read_csv('TMDB.csv')\n",
"movies['actor'] = movies['actor'].apply(eval)\n",
"movies['director'] = movies['director'].apply(eval)\n",
"print('Number of movies:', movies.shape[0])\n",
"movies.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "92ae38e6-98cb-4c6a-b66f-7187208c9e30",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-02T15:41:08.778301Z",
"start_time": "2024-12-02T15:41:08.763644Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"13016"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"actor_id_map = {}\n",
"idx = 0\n",
"for actors in movies['actor']:\n",
" for actor in actors:\n",
" if actor not in actor_id_map:\n",
" actor_id_map[actor] = idx\n",
" idx += 1\n",
"len(actor_id_map)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "fec3f109-45dd-43a5-a35f-6679339a756d",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-02T15:41:10.396138Z",
"start_time": "2024-12-02T15:41:10.388693Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"3891"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"director_id_map = {}\n",
"idx = 0\n",
"for directors in movies['director']:\n",
" for director in directors:\n",
" if director not in director_id_map:\n",
" director_id_map[director] = idx\n",
" idx += 1\n",
"len(director_id_map)"
]
},
{
"cell_type": "markdown",
"id": "7c3f9713-edbc-4bad-859c-1431177aa617",
"metadata": {},
"source": [
"## Graph Construction\n",
"\n",
"```mermaid\n",
"---\n",
"title: The TMDB HIN\n",
"---\n",
"flowchart LR\n",
" Movie[\"<b>Movie</b><br>7,505 nodes<br>4 labels\"]\n",
" Actor[\"<b>Actor</b><br>13,016 nodes\"]\n",
" Director[\"<b>Director</b><br>3,891 nodes\"]\n",
" Actor --\"<b>performs</b><br>86,517 edges\"--- Movie\n",
" Director --\"<b>directs</b><br>18,341 edges\"--- Movie\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a01c16ab-1ba7-4ea5-a5c8-0d78e40fe400",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-02T15:41:12.596227Z",
"start_time": "2024-12-02T15:41:12.592281Z"
}
},
"outputs": [],
"source": [
"data = {}"
]
},
{
"cell_type": "markdown",
"id": "02713230-25d4-4554-bd0c-6aaa34602bc0",
"metadata": {},
"source": [
"### Movie-Actor Links"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "76c1a5df-c091-49be-b6f6-2569235d4115",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-02T15:41:14.498387Z",
"start_time": "2024-12-02T15:41:14.322988Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of movie-actor links: 86517\n"
]
}
],
"source": [
"movie_ids = []\n",
"actor_ids = []\n",
"for movie_id in range(len(movies)):\n",
" actors = movies.iloc[movie_id]['actor']\n",
" for actor in actors:\n",
" movie_ids.append(movie_id)\n",
" actor_ids.append(actor_id_map[actor])\n",
" \n",
"movie_ids = np.array(movie_ids, dtype=np.int16)\n",
"actor_ids = np.array(actor_ids, dtype=np.int16)\n",
"print('Number of movie-actor links:', movie_ids.shape[0])\n",
"data['movie-actor'] = (movie_ids, actor_ids)"
]
},
{
"cell_type": "markdown",
"id": "18ecde55-9e2e-4a0d-97ef-e0d1e5ebfd79",
"metadata": {},
"source": [
"### Movie-Director Links"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dc40996c-a04c-4c49-9346-c7c8b1fbc7fd",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-02T15:41:16.702077Z",
"start_time": "2024-12-02T15:41:16.550123Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of movie-director links: 18341\n"
]
}
],
"source": [
"movie_ids = []\n",
"director_ids = []\n",
"for movie_id in range(len(movies)):\n",
" directors = movies.iloc[movie_id]['director']\n",
" \n",
" for director in directors:\n",
" movie_ids.append(movie_id)\n",
" director_ids.append(director_id_map[director])\n",
" \n",
"movie_ids = np.array(movie_ids, dtype=np.int16)\n",
"director_ids = np.array(director_ids, dtype=np.int16)\n",
"print('Number of movie-director links:', movie_ids.shape[0])\n",
"data['movie-director'] = (movie_ids, director_ids)"
]
},
{
"cell_type": "markdown",
"id": "64fc0ee7-6dcb-406f-a98e-21214866d25d",
"metadata": {},
"source": [
"### Labels\n",
"\n",
"There are a total of 19 movie genres in TMDB. We selected four genres with low intercorrelation as our ground truth labels. Note that one movie usually has multiple genres. To improve label quality, we only collect movies whose genres just include one of {action, romance, thriller, animation}.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "72cb9ae1-7a8b-4231-82e2-d0ceea47b1eb",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-02T15:41:19.409614Z",
"start_time": "2024-12-02T15:41:19.298045Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = movies['genre'].value_counts().plot.barh()\n",
"ax.bar_label(ax.containers[0])\n",
"plt.title('Frequency of Movie Labels')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a0adb2d4-a0a1-415c-bb1a-a4c089477112",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-02T15:41:23.653792Z",
"start_time": "2024-12-02T15:41:23.647865Z"
}
},
"outputs": [],
"source": [
"movie_label_ids = {'Action': 0, 'Romance': 1, 'Thriller': 2, 'Animation': 3}\n",
"\n",
"movie_labels = movies['genre'].apply(lambda g: movie_label_ids[g]).values\n",
"movie_labels = movie_labels.astype(np.int8)\n",
"data['movie_labels'] = movie_labels"
]
},
{
"cell_type": "markdown",
"id": "970ce294-9848-4dc7-a2c9-0f3f4d6337aa",
"metadata": {},
"source": [
"## Node Features\n",
"\n",
"We generate embeddings as features for each node in the graph. Node embeddings are generated by passing the movie overviews through a Sentence-BERT model and obtaining a 384-embedding vector for each movie node.\n",
"\n",
"\n",
"According to [Sentence-Transformers docs](https://www.sbert.net/docs/pretrained_models.html), the **all-MiniLM-L6-v2** model provides the best quality. So we use it to generate node features.\n",
"\n",
"all-MiniLM-L6-v2\n",
"\n",
"Description:\tAll-round model tuned for many use-cases. Trained on a large and diverse dataset of over 1 billion training pairs.\n",
"Base Model:\tnreimers/MiniLM-L6-H384-uncased\n",
"Max Sequence Length:\t256\n",
"Dimensions:\t384\n",
"Normalized Embeddings:\ttrue\n",
"Suitable Score Functions:\tdot-product (util.dot_score), cosine-similarity (util.cos_sim), euclidean distance\n",
"Size:\t80 MB\n",
"Pooling:\tMean Pooling\n",
"Training Data:\t1B+ training pairs. For details, see model card.\n",
"Model Card:\thttps://huggingface.co/sentence-transformers/all-MiniLM-L6-v2\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7ee22ad2-b585-47b1-8399-3bb0b56fa4d3",
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-01T15:04:46.716504Z",
"start_time": "2024-06-01T15:04:46.404836Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"data": {
"text/plain": [
"SentenceTransformer(\n",
" (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel \n",
" (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})\n",
" (2): Normalize()\n",
")"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"from sentence_transformers import SentenceTransformer\n",
"\n",
"device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
"model = SentenceTransformer('../sentence-transformers/all-MiniLM-L6-v2', device=device)\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e05e1a93",
"metadata": {},
"outputs": [],
"source": [
"text = movies['overview']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3faccc3a-1091-4044-8e6a-1af1e6dfa147",
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-01T15:04:52.986932Z",
"start_time": "2024-06-01T15:04:49.969544Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Batches: 100%|██████████| 2/2 [00:04<00:00, 2.22s/it]\n"
]
}
],
"source": [
"feats = model.encode(text, batch_size=4096, show_progress_bar=True, convert_to_numpy=True)\n",
"data['movie_feats'] = feats"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "bd4dc536-4581-41ea-8c2a-a7d949ee7d29",
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-01T15:05:58.038410Z",
"start_time": "2024-06-01T15:05:58.031236Z"
}
},
"outputs": [],
"source": [
"movie_years = movies['release_date'].apply(lambda s: int(s[:4]))\n",
"movie_years = movie_years.values.astype(np.int16)\n",
"data['movie_years'] = movie_years"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f7334c3a1db74c5",
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-01T15:06:10.649671Z",
"start_time": "2024-06-01T15:06:10.647029Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'movie-actor': (array([ 0, 0, 0, ..., 7504, 7504, 7504], dtype=int16),\n",
" array([ 0, 1, 2, ..., 11870, 1733, 11794], dtype=int16)),\n",
" 'movie-director': (array([ 0, 0, 0, ..., 7503, 7503, 7504], dtype=int16),\n",
" array([ 0, 1, 2, ..., 3423, 966, 2890], dtype=int16)),\n",
" 'movie_labels': array([3, 1, 1, ..., 1, 1, 2], dtype=int8),\n",
" 'movie_feats': array([[ 0.00635284, 0.00649689, 0.01250827, ..., 0.06342042,\n",
" -0.01747945, 0.0134356 ],\n",
" [-0.14075027, 0.02825641, 0.02670695, ..., -0.12270895,\n",
" 0.08417314, 0.02486392],\n",
" [ 0.00014208, -0.02286632, 0.00615967, ..., -0.03311544,\n",
" 0.04735276, -0.07458566],\n",
" ...,\n",
" [ 0.01835816, 0.07484645, -0.08099765, ..., -0.00150019,\n",
" 0.01669764, 0.00456595],\n",
" [-0.00821487, -0.10434289, 0.01928608, ..., -0.06343049,\n",
" 0.05060194, -0.04229118],\n",
" [-0.06465845, 0.13461556, -0.01640793, ..., -0.06274845,\n",
" 0.04002513, -0.00751513]], dtype=float32),\n",
" 'movie_years': array([2013, 1995, 1989, ..., 1939, 1941, 1965], dtype=int16)}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "577568478c17d746",
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-01T15:06:19.797438Z",
"start_time": "2024-06-01T15:06:19.746897Z"
}
},
"outputs": [],
"source": [
"with open('tmdb.pkl', 'wb') as f:\n",
" pickle.dump(data, f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4174a1f7a7c8da1",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|