{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instructions\n",
    "\n",
    "Get data\n",
    "- Download the latest backup via `wget` from [Render Dashboard](https://dashboard.render.com/d/dpg-cgfrde82qv28tc0tavcg-a/recovery). Ask Martin for access. Name it something like `studio.sql.gz`\n",
    "- wget -O studio.sql.gz link\n",
    "- copy the link and `wget link`. Note this can take ~ 0.5hr to download\n",
    "- Unzip it with `gzip -d studio.sql.gz` (also takes a bit of time)\n",
    "\n",
    "Some of these need to be done once:\n",
    "- sudo apt-get install postgresql\n",
    "- sudo service postgresql start\n",
    "- sudo service postgresql status\n",
    "- export PATH=$PATH:/usr/lib/postgresql/12/bin (maybe?)\n",
    "- need to move it to local dir (pgdata), see: https://fitodic.github.io/how-to-change-postgresql-data-directory-on-linux\n",
    "- vim /etc/postgresql/12/main/pg_hba.conf and change to \n",
    "  - `local   all             all                                     trust` \n",
    "  - `local   all             all                                     trust` \n",
    "- sudo service postgresql restart\n",
    "- createdb -U postgres mydatabase  (this can take a while)\n",
    "\n",
    "Finally:\n",
    "Load it into postres via `psql -U postgres -d mydatabase -f studio.sql`\n",
    "This is taking forever now `6:54:43.33` \n",
    "\n",
    "Things that can be useful (some debugging mumble jumble for imgrating data disk):\n",
    "- psql -U postgres -d mydatabase\n",
    "- ALTER SYSTEM SET max_wal_size = '1GB';\n",
    "- SHOW max_wal_size;\n",
    "- pg_lsclusters\n",
    "- sudo pg_ctlcluster 12 main start\n",
    "- Check postgres user belongs to ssl-cert user group: \n",
    "- chown -R postgres:postgres pgdata\n",
    "- chmod -R u+rwx,g-rwx,o-rwx pgdata\n",
    "- sudo chown postgres.postgres /var/lib/postgresql/12/main/global/pg_internal.init\n",
    "- sudo rm -rf 12/main/global/pg_internal.init\n",
    "- sudo rm -rf /var/lib/postgresql/12/main/pg_logical/replorigin_checkpoint\n",
    "- sudo -i -u postgres\n",
    "- /usr/lib/postgresql/12/bin/pg_ctl restart -D /var/lib/postgresql/12/main\n",
    "\n",
    "\n",
    "Create user? (only first time)\n",
    "- psql -U postgres\n",
    "- CREATE ROLE tony WITH LOGIN PASSWORD '123';\n",
    "- \\q\n",
    "\n",
    "Then need to authenticate? (need to redo this after recreating a new database everytime) \n",
    "- psql -U postgres -d mydatabase\n",
    "- \\du\n",
    "- \\dt\n",
    "- \\l+ (check size)\n",
    "- SELECT COUNT(*) FROM bots_generatedclip;\n",
    "- GRANT ALL PRIVILEGES ON DATABASE mydatabase TO tony;\n",
    "- GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO tony;\n",
    "- \\q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T13:06:07.502343Z",
     "start_time": "2024-05-14T13:06:05.975298Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2993476/3788171299.py:3: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "# pip install psycopg2-binary\n",
    "# make sure sqlalchemy is >=2\n",
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "from suno_utils.audio import Audio\n",
    "from suno_utils.utils.s3 import open_from_s3\n",
    "import numpy as np\n",
    "import ast\n",
    "import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from preference_helper import *\n",
    "\n",
    "# %#load_ext autoreload\n",
    "# %#autoreload 2\n",
    "\n",
    "# engine = sqlalchemy.create_engine(\"postgresql://tony:123@localhost/mydatabase\")\n",
    "# alternative...\n",
    "engine = sqlalchemy.create_engine(\n",
    "    \"postgresql://studio_hga1_user:pJr5NeKjVZPxae5bp6am9qtLWVY8t5Ni@dpg-cgfrde82qv28tc0tavcg-d.replica-cyan.ohio-postgres.render.com/studio_hga1\"\n",
    ")\n",
    "# connection = engine.raw_connection()\n",
    "# %#load_ext autoreload\n",
    "# %#autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T13:06:07.505327Z",
     "start_time": "2024-05-14T13:06:07.503637Z"
    }
   },
   "outputs": [],
   "source": [
    "# # Read the sql file and execute the query\n",
    "# with open('/home/tony/Data/Preference/studio.sql', 'r') as query:\n",
    "#     # connection == the connection to your database, in your case prob_db\n",
    "#     df = pd.read_sql_query(query.read(), connection)\n",
    "\n",
    "# pd.read_sql_query(\n",
    "#     \"SELECT COUNT(*) FROM bots_generatedclip;\",\n",
    "#     engine,\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validate some info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T13:06:07.572804Z",
     "start_time": "2024-05-14T13:06:07.506338Z"
    }
   },
   "outputs": [],
   "source": [
    "cutoff_date = \"2024-04-26\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T13:06:08.535227Z",
     "start_time": "2024-05-14T13:06:07.574609Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['auth_group' 'auth_group_permissions' 'auth_permission' 'auth_user'\n",
      " 'auth_user_groups' 'auth_user_user_permissions' 'bots_actionlogging'\n",
      " 'bots_clipdiscordmessage' 'bots_clipprompt' 'bots_creditpack'\n",
      " 'bots_dailytheme' 'bots_discordinfo' 'bots_generatedclip'\n",
      " 'bots_generatedclipextra' 'bots_generatedplaylist'\n",
      " 'bots_generatedplaylistoverride' 'bots_generationrequest'\n",
      " 'bots_modeltype' 'bots_periodcreditusage' 'bots_playlist'\n",
      " 'bots_playlistclip' 'bots_profilefollow' 'bots_promptinspiration'\n",
      " 'bots_purchaseinfo' 'bots_usageplan' 'bots_userplaylistreaction'\n",
      " 'bots_userreaction' 'clips_clip' 'clips_clip_playlists' 'clips_clip_tags'\n",
      " 'clips_playlist' 'clips_tag' 'clips_team' 'clips_teammembership'\n",
      " 'clips_textprompt' 'clips_textprompt_tags' 'clips_usercliprating'\n",
      " 'clips_usertoken' 'configs_flag' 'configs_flag_groups'\n",
      " 'configs_flag_users' 'configs_sample' 'configs_switch' 'django_admin_log'\n",
      " 'django_content_type' 'django_migrations' 'django_session'\n",
      " 'pg_stat_statements' 'pg_stat_statements_info' 'waffle_flag'\n",
      " 'waffle_flag_groups' 'waffle_flag_users' 'waffle_sample' 'waffle_switch']\n"
     ]
    }
   ],
   "source": [
    "df_all_tables = pd.read_sql_query(\n",
    "    \"SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'\",\n",
    "    engine,\n",
    ")\n",
    "print(df_all_tables[\"table_name\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T13:07:15.926656Z",
     "start_time": "2024-05-14T13:06:08.536478Z"
    }
   },
   "outputs": [],
   "source": [
    "# bots_generatedclipextra\n",
    "# these are all the logged actions\n",
    "query = f\"\"\"\n",
    "SELECT * FROM bots_generatedclipextra\n",
    "WHERE updated_at>='{cutoff_date}'\n",
    "\"\"\"\n",
    "bots_action_df = pd.read_sql_query(query, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T13:26:44.098171Z",
     "start_time": "2024-05-14T13:07:15.928418Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75,099,786 rows\n"
     ]
    }
   ],
   "source": [
    "# ~ 4 min...X.x\n",
    "# bots_userreaction\n",
    "# id\tplay_count\tskip_count\tflagged\tflagged_reason\treaction_type\tupdated_at\tclip_id\tuser_id\n",
    "# this turns out to be much smaller ~ 570k\n",
    "query = f\"\"\"\n",
    "SELECT * FROM bots_userreaction\n",
    "WHERE updated_at>='{cutoff_date}' AND play_count>0\n",
    "\"\"\"\n",
    "reaction_df = pd.read_sql_query(query, engine)\n",
    "print(f\"{reaction_df.shape[0]:,} rows\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T13:26:45.291458Z",
     "start_time": "2024-05-14T13:26:44.099399Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3,607,221 rows\n"
     ]
    }
   ],
   "source": [
    "upvoted_df = reaction_df[reaction_df[\"reaction_type\"] == \"L\"].copy()\n",
    "print(f\"{upvoted_df.shape[0]:,} rows\")\n",
    "upvoted_ids = upvoted_df[\"clip_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T13:26:45.357183Z",
     "start_time": "2024-05-14T13:26:45.292735Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129,078 rows\n"
     ]
    }
   ],
   "source": [
    "flagged_df = reaction_df[reaction_df[\"flagged\"] == True].copy()\n",
    "print(f\"{flagged_df.shape[0]:,} rows\")\n",
    "flagged_ids = flagged_df[\"clip_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T13:26:45.363392Z",
     "start_time": "2024-05-14T13:26:45.358443Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reaction_type\n",
       "L    212\n",
       "D    205\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reaction_df.tail(n=10000)[\"reaction_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T13:26:45.428425Z",
     "start_time": "2024-05-14T13:26:45.364417Z"
    }
   },
   "outputs": [],
   "source": [
    "# these are continues, ~ 1,485k (much more than likes) ~ takes 3.5 mins\n",
    "# columns are:\n",
    "# 'id', 'created_at', 'updated_at', 'time_used', 'metadata', 'user_id',\n",
    "#        'status', 'discord_message_id', 'prompt_id', 'request_id',\n",
    "#        'is_generated', 's3_id', 'upvote_count', 'batch_index', 'model_name',\n",
    "#        'prompt_text', 'daily_theme_id', 'is_deleted', 'image_s3_id',\n",
    "#        'is_public', 'dislike_count', 'flag_count', 'play_count', 'skip_count',\n",
    "#        'title', 'is_public_approved', 'slug'\n",
    "\n",
    "# find all the complete clips -- this query takes ~ 10 sec\n",
    "# query = \"\"\"\n",
    "# SELECT COUNT(*) FROM bots_generatedclip\n",
    "# \"\"\"\n",
    "# clip_counts = pd.read_sql_query(query, engine)\n",
    "# all_total_clip_counts = clip_counts[\"count\"][0]\n",
    "# print(f\"all version total clips: {all_total_clip_counts}\")\n",
    "\n",
    "# query = \"\"\"\n",
    "# SELECT COUNT(*) FROM bots_generatedclip\n",
    "# WHERE status='complete' AND model_name::text LIKE '%%v3%%'\n",
    "# \"\"\"\n",
    "# clip_counts = pd.read_sql_query(query, engine)\n",
    "# all_total_clip_counts = clip_counts[\"count\"][0]\n",
    "# print(f\"v3 version total clips: {all_total_clip_counts}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T17:55:01.987150Z",
     "start_time": "2024-05-14T13:26:45.429449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75,950,220 rows\n"
     ]
    }
   ],
   "source": [
    "# ~ 1h 25 mins...or, 3 days takes ~ 15 mins\n",
    "# TODO: query only v3 here....\n",
    "# This is still a lot...we will have to do this in steps very soon\n",
    "# Some data eng required, disk is much cheaper\n",
    "# the generated clips table has play count issues (we need to read it without filtering on playcounts)\n",
    "# AND model_name::text LIKE '%%v3%%' AND play_count>=1\n",
    "query = f\"\"\"\n",
    "SELECT * FROM bots_generatedclip\n",
    "WHERE status='complete' AND created_at>='{cutoff_date}' \n",
    "\"\"\"\n",
    "total_clip_df = pd.read_sql_query(query, engine)\n",
    "print(f\"{total_clip_df.shape[0]:,} rows\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T17:55:26.540571Z",
     "start_time": "2024-05-14T17:55:01.988357Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3,690,319 clips\n"
     ]
    }
   ],
   "source": [
    "# get playlists\n",
    "query = \"\"\"\n",
    "SELECT * FROM bots_playlistclip\n",
    "\"\"\"\n",
    "playlist_clip_df = pd.read_sql_query(query, engine)\n",
    "print(f\"{playlist_clip_df.shape[0]:,} clips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T17:56:18.415856Z",
     "start_time": "2024-05-14T17:55:26.543366Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total clips: 73733637\n"
     ]
    }
   ],
   "source": [
    "# filter on versions\n",
    "clip_df = total_clip_df[\n",
    "    (total_clip_df[\"model_name\"].str.contains(\"v3\"))  # or v3...\n",
    "    & (total_clip_df[\"created_at\"] >= \"2024-02-20\")\n",
    "].copy()\n",
    "# print(f\"total v3 selected fraction = {clip_df.shape[0] / all_total_clip_counts}\")\n",
    "total_clip_counts = clip_df.shape[0]\n",
    "print(f\"total clips: {total_clip_counts}\")\n",
    "# 9,745,619 rows\n",
    "# total v3 selected fraction = 0.11235114663577185\n",
    "# total clips: 9745619"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proceed with feature engineering and cleaning up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T17:57:14.130115Z",
     "start_time": "2024-05-14T17:56:18.417075Z"
    }
   },
   "outputs": [],
   "source": [
    "# add clip is in playlist feature\n",
    "clip_df[\"is_in_playlist\"] = clip_df[\"id\"].isin(playlist_clip_df[\"clip_id\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T17:58:01.686425Z",
     "start_time": "2024-05-14T17:57:14.131932Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clips that have children 8901453 1378263 \n",
      " Average continues from clip =  6.458457493236051\n"
     ]
    }
   ],
   "source": [
    "def parse_parent_id(x):\n",
    "    if \"history\" not in x:\n",
    "        return None\n",
    "    out = x.get(\"history\", [])\n",
    "    if not isinstance(out, list) or len(out) == 0:\n",
    "        return None\n",
    "    out = out[0]\n",
    "    if isinstance(out, dict):\n",
    "        # this is the continued info, which is a dict with id and continue_at\n",
    "        return out[\"id\"]\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "\n",
    "clip_df[\"continued_parent\"] = clip_df[\"metadata\"].apply(lambda x: parse_parent_id(x))\n",
    "clip_history_df = clip_df[~clip_df[\"continued_parent\"].isna()].copy()\n",
    "continued_ids = clip_history_df[\"id\"]\n",
    "has_continued_children_ids = clip_history_df[\n",
    "    \"continued_parent\"\n",
    "]  # these are the parent's ids\n",
    "print(\n",
    "    \"clips that have children\",\n",
    "    len(has_continued_children_ids),\n",
    "    len(has_continued_children_ids.unique()),\n",
    "    \"\\n\",\n",
    "    \"Average continues from clip = \",\n",
    "    len(has_continued_children_ids) / len(has_continued_children_ids.unique()),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T17:58:37.540723Z",
     "start_time": "2024-05-14T17:58:01.687706Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "concat clips frac = 0.014458326530128983\n"
     ]
    }
   ],
   "source": [
    "# the nans are concats, we want to drop them for now\n",
    "concated_clips = clip_df[clip_df[\"request_id\"].isna()].copy()\n",
    "# need to kick them out...\n",
    "clip_df = clip_df[~clip_df[\"request_id\"].isna()]\n",
    "print(f\"concat clips frac = {concated_clips.shape[0] / total_clip_counts}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T17:58:43.803570Z",
     "start_time": "2024-05-14T17:58:37.542020Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model_name\n",
       "chirp-v3-engine-i     32775996\n",
       "chirp-v3-engine-s     32628125\n",
       "chirp-v3-engine-d      4540075\n",
       "chirp-v3-engine-v0     2722316\n",
       "chirp-v3-0                1057\n",
       "chirp-v3-alpha               3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[\"model_name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T17:59:12.861174Z",
     "start_time": "2024-05-14T17:58:43.804738Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(72667572, 28)\n",
      "(72666512, 28)\n"
     ]
    }
   ],
   "source": [
    "print(clip_df.shape)\n",
    "clip_df = clip_df[\n",
    "    clip_df[\"model_name\"].isin(\n",
    "        [\n",
    "            \"chirp-v3-engine-d\",\n",
    "            \"chirp-v3-engine-v0\",\n",
    "            \"chirp-v3-engine-i\",\n",
    "            # \"chirp-v3-engine-i-d\",\n",
    "            \"chirp-v3-engine-s\",\n",
    "            # \"chirp-v3-engine-s2\",\n",
    "            # \"chirp-v3-engine-s3\",\n",
    "            # \"chirp-v3-engine-i2\",\n",
    "            # \"chirp-v3-engine-u\",\n",
    "        ]\n",
    "    )\n",
    "]\n",
    "print(clip_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T17:59:18.347420Z",
     "start_time": "2024-05-14T17:59:12.862462Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_name\n",
      "chirp-v3-engine-i     32775996\n",
      "chirp-v3-engine-s     32628125\n",
      "chirp-v3-engine-d      4540075\n",
      "chirp-v3-engine-v0     2722316\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(clip_df[\"model_name\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T17:59:18.411043Z",
     "start_time": "2024-05-14T17:59:18.348680Z"
    }
   },
   "outputs": [],
   "source": [
    "# I fucking hate this but what can I do\n",
    "# DO NOT FILTER ON play counts yet..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:00:10.179058Z",
     "start_time": "2024-05-14T17:59:18.412047Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5224024, 9) 894237\n"
     ]
    }
   ],
   "source": [
    "# ~ only 1 min :) \n",
    "concat_reaction_df = reaction_df[reaction_df[\"clip_id\"].isin(concated_clips[\"id\"])].copy()\n",
    "print(concat_reaction_df.shape, concat_reaction_df['clip_id'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:00:20.264023Z",
     "start_time": "2024-05-14T18:00:10.180351Z"
    }
   },
   "outputs": [],
   "source": [
    "concat_total_play_reaction_df_sum = concat_reaction_df.groupby(\"clip_id\")[\n",
    "    \"play_count\"\n",
    "].sum()\n",
    "concat_total_play_reaction_df_sum_df = (\n",
    "    concat_total_play_reaction_df_sum.reset_index().rename(\n",
    "        columns={\"clip_id\": \"id\", \"play_count\": \"reaction_play_count\"}\n",
    "    )\n",
    ")\n",
    "concated_clips = concated_clips.merge(\n",
    "    concat_total_play_reaction_df_sum_df, on=\"id\", how=\"left\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:00:20.855957Z",
     "start_time": "2024-05-14T18:00:20.265396Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1066065, 29)\n",
      "(894237, 29)\n"
     ]
    }
   ],
   "source": [
    "print(concated_clips.shape)\n",
    "concated_clips = concated_clips[concated_clips[\"reaction_play_count\"] > 0]\n",
    "print(concated_clips.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:00:20.858680Z",
     "start_time": "2024-05-14T18:00:20.857259Z"
    }
   },
   "outputs": [],
   "source": [
    "# concated_clips[[\"upvote_count\", \"dislike_count\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:01:07.650856Z",
     "start_time": "2024-05-14T18:00:20.859696Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "894237it [00:46, 19138.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total concat unique clips are: 1913949 with error: 3, duplicate 190293\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# this is each clip. and the mapped start time of the clip\n",
    "concat_clips_ids = {}\n",
    "history_error_counter = 0\n",
    "history_duplicate_error_counter = 0\n",
    "for _, row in tqdm.tqdm(concated_clips.iterrows()):\n",
    "    if history_ids := row[\"metadata\"][\"concat_history\"]:\n",
    "        total_duration = row[\"metadata\"][\"duration\"]\n",
    "        start_s = 0\n",
    "        for history_id in history_ids:\n",
    "            # print(history_ids, row[\"metadata\"][\"duration\"])\n",
    "            if isinstance(history_id, dict) and \"id\" in history_id:\n",
    "                # the other key is `continue_at`\n",
    "                if history_id[\"id\"]:\n",
    "                    if history_id[\"id\"] in concat_clips_ids:\n",
    "                        if (\n",
    "                            row[\"upvote_count\"]\n",
    "                            < concat_clips_ids[history_id[\"id\"]][\"concat_likes\"]\n",
    "                        ):\n",
    "                            continue\n",
    "                        if (\n",
    "                            row[\"reaction_play_count\"]\n",
    "                            < concat_clips_ids[history_id[\"id\"]][\"concat_play_counts\"]\n",
    "                        ):\n",
    "                            continue\n",
    "                        history_duplicate_error_counter += 1\n",
    "                    concat_clips_ids[history_id[\"id\"]] = {\n",
    "                        \"total_start_s\": start_s,\n",
    "                        \"total_clip_s\": total_duration,\n",
    "                        \"concat_play_counts\": row[\"reaction_play_count\"],\n",
    "                        \"concat_in_playlist\": row[\"is_in_playlist\"],\n",
    "                        \"concat_likes\": row[\"upvote_count\"],\n",
    "                    }\n",
    "                else:\n",
    "                    history_error_counter += 1\n",
    "                # but we always update the start_s\n",
    "                start_s = history_id[\"continue_at\"]\n",
    "print(\n",
    "    \"total concat unique clips are:\",\n",
    "    len(concat_clips_ids),\n",
    "    f\"with error: {history_error_counter}, duplicate {history_duplicate_error_counter}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:01:08.569467Z",
     "start_time": "2024-05-14T18:01:07.652116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(283152, 3)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT *\n",
    "FROM auth_user_groups\n",
    "\"\"\"\n",
    "auth_user_df = pd.read_sql_query(query, engine)\n",
    "auth_user_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:01:10.193338Z",
     "start_time": "2024-05-14T18:01:08.570711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163014"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[\"is_pro_user\"] = clip_df[\"user_id\"].isin(auth_user_df[\"user_id\"].unique())\n",
    "clip_df[\"is_pro_user\"].value_counts()\n",
    "clip_df[\"user_id\"][clip_df[\"is_pro_user\"]].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:01:12.898471Z",
     "start_time": "2024-05-14T18:01:10.194603Z"
    }
   },
   "outputs": [],
   "source": [
    "# set user number of clips generated\n",
    "clip_df[\"user_n_clips\"] = clip_df[\"user_id\"].map(clip_df[\"user_id\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:01:56.373329Z",
     "start_time": "2024-05-14T18:01:12.899800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has upvoted upvoted\n",
      "False    69503739\n",
      "True      3162773\n",
      "Name: count, dtype: int64 upvoted\n",
      "False    0.956476\n",
      "True     0.043524\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# add upvoted column\n",
    "clip_df[\"upvoted\"] = clip_df[\"id\"].isin(upvoted_ids)\n",
    "print(\n",
    "    \"has upvoted\",\n",
    "    clip_df[\"upvoted\"].value_counts(),\n",
    "    clip_df[\"upvoted\"].value_counts(normalize=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:01:56.668071Z",
     "start_time": "2024-05-14T18:01:56.374627Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "upvote_count\n",
       "False    0.956114\n",
       "True     0.043886\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clip_df[\"upvote_count\"] >= 1).value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:03:05.774075Z",
     "start_time": "2024-05-14T18:01:56.669276Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2525944,)\n",
      "has deleted deleted\n",
      "False    70216958\n",
      "True      2449554\n",
      "Name: count, dtype: int64 deleted\n",
      "False    0.96629\n",
      "True     0.03371\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "deleted_ids = reaction_df[reaction_df[\"reaction_type\"] == \"D\"][\"clip_id\"]\n",
    "print(deleted_ids.shape)\n",
    "\n",
    "clip_df[\"deleted\"] = clip_df[\"id\"].isin(deleted_ids)\n",
    "print(\n",
    "    \"has deleted\",\n",
    "    clip_df[\"deleted\"].value_counts(),\n",
    "    clip_df[\"deleted\"].value_counts(normalize=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:04:47.073943Z",
     "start_time": "2024-05-14T18:03:05.775270Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has has_continued has_continued\n",
      "False    71564916\n",
      "True      1101596\n",
      "Name: count, dtype: int64 has_continued\n",
      "False    0.98484\n",
      "True     0.01516\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# add continued column -- uuid and str are not compatible X.x\n",
    "clip_df[\"has_continued\"] = (\n",
    "    clip_df[\"id\"].astype(str).isin(set(list(has_continued_children_ids)))\n",
    ")\n",
    "print(\n",
    "    \"has has_continued\",\n",
    "    clip_df[\"has_continued\"].value_counts(),\n",
    "    clip_df[\"has_continued\"].value_counts(normalize=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:06:15.661358Z",
     "start_time": "2024-05-14T18:04:47.075113Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is part of a concat part_of_concat\n",
      "False    70969529\n",
      "True      1696983\n",
      "Name: count, dtype: int64 part_of_concat\n",
      "False    0.976647\n",
      "True     0.023353\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# add concat column\n",
    "clip_df[\"part_of_concat\"] = clip_df[\"id\"].astype(str).isin(concat_clips_ids)\n",
    "print(\n",
    "    \"is part of a concat\",\n",
    "    clip_df[\"part_of_concat\"].value_counts(),\n",
    "    clip_df[\"part_of_concat\"].value_counts(normalize=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:07:28.035738Z",
     "start_time": "2024-05-14T18:06:15.662558Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has has_action has_action\n",
      "False    66540340\n",
      "True      6126172\n",
      "Name: count, dtype: int64 has_action\n",
      "False    0.915695\n",
      "True     0.084305\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# verify bots action are all non-empty\n",
    "# assert (\n",
    "#     bots_action_df[\n",
    "#         bots_action_df[\"download_audio_count\"]\n",
    "#         + bots_action_df[\"download_video_count\"]\n",
    "#         + bots_action_df[\"share_count\"]\n",
    "#         == 0\n",
    "#     ].shape[0]\n",
    "#     == 0\n",
    "# )\n",
    "action_mask = (\n",
    "    bots_action_df[\"download_audio_count\"]\n",
    "    + bots_action_df[\"download_video_count\"]\n",
    "    + bots_action_df[\"share_count\"]\n",
    ") >= 1\n",
    "has_action_ids = set(i for i in bots_action_df[action_mask][\"clip_id\"].unique())\n",
    "clip_df[\"has_action\"] = clip_df[\"id\"].isin(has_action_ids)\n",
    "print(\n",
    "    \"has has_action\",\n",
    "    clip_df[\"has_action\"].value_counts(),\n",
    "    clip_df[\"has_action\"].value_counts(normalize=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:08:01.857997Z",
     "start_time": "2024-05-14T18:07:28.036930Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has downvoted downvoted\n",
      "False    72546279\n",
      "True       120233\n",
      "Name: count, dtype: int64 downvoted\n",
      "False    0.998345\n",
      "True     0.001655\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# add downvoted column\n",
    "clip_df[\"downvoted\"] = clip_df[\"id\"].isin(flagged_ids)\n",
    "print(\n",
    "    \"has downvoted\",\n",
    "    clip_df[\"downvoted\"].value_counts(),\n",
    "    clip_df[\"downvoted\"].value_counts(normalize=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:08:10.903134Z",
     "start_time": "2024-05-14T18:08:01.859267Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_clips, 72666512, total preference, 9623483, pos 9716342, neg 70117902\n"
     ]
    }
   ],
   "source": [
    "# This is probably the most important cell of this notebook -- what are good labels, and not having good label makes it a bad label\n",
    "must_be_positive_mask = (\n",
    "    (clip_df[\"upvoted\"] == True)\n",
    "    | (clip_df[\"has_action\"] == True)\n",
    "    | (clip_df[\"part_of_concat\"] == True)\n",
    ")\n",
    "must_be_not_negative_mask = (clip_df[\"downvoted\"] == False) & (\n",
    "    clip_df[\"deleted\"] == False\n",
    ")\n",
    "mask = must_be_positive_mask & must_be_not_negative_mask\n",
    "print(\n",
    "    f\"total_clips, {clip_df.shape[0]}, total preference, {sum(mask)}, pos {sum(must_be_positive_mask)}, neg {sum(must_be_not_negative_mask)}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:12:14.162210Z",
     "start_time": "2024-05-14T18:08:10.904427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "liked 8106119 unliked 34871885\n",
      "6544541 requests have preference paired generations, 0.180\n"
     ]
    }
   ],
   "source": [
    "total_unique_requests = clip_df[\"request_id\"].nunique()\n",
    "liked_requests = clip_df[mask][\"request_id\"].unique()\n",
    "unliked_requests = clip_df[~mask][\"request_id\"].unique()\n",
    "has_liked_requests = set(liked_requests).intersection(set(unliked_requests))\n",
    "print(\"liked\", len(liked_requests), \"unliked\", len(unliked_requests))\n",
    "print(\n",
    "    f\"{len(has_liked_requests)} requests have preference paired generations, {len(has_liked_requests) / total_unique_requests:.3f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:12:14.165629Z",
     "start_time": "2024-05-14T18:12:14.163498Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6544541"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "requests = has_liked_requests\n",
    "len(requests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:12:14.241816Z",
     "start_time": "2024-05-14T18:12:14.166621Z"
    }
   },
   "outputs": [],
   "source": [
    "# this used to be a terrible bug...X.x\n",
    "assert mask.shape[0] == clip_df.shape[0]\n",
    "clip_df[\"preference\"] = mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:13:26.923096Z",
     "start_time": "2024-05-14T18:12:15.342703Z"
    }
   },
   "outputs": [],
   "source": [
    "# creation of interesting_clips\n",
    "interesting_clips = clip_df[clip_df[\"request_id\"].isin(requests)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:14:19.895356Z",
     "start_time": "2024-05-14T18:13:26.924491Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6544541 13089082\n"
     ]
    }
   ],
   "source": [
    "# get df of requests -- let's move on!\n",
    "print(interesting_clips[\"request_id\"].nunique(), interesting_clips[\"id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:16:01.837053Z",
     "start_time": "2024-05-14T18:14:19.896559Z"
    }
   },
   "outputs": [],
   "source": [
    "# some validations\n",
    "assert interesting_clips[interesting_clips[\"request_id\"].isna()].shape[0] == 0\n",
    "check_df = interesting_clips.groupby(\"request_id\")[\"id\"].nunique()\n",
    "check_df[check_df.values != 2]\n",
    "assert check_df[check_df.values != 2].shape[0] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:16:01.870612Z",
     "start_time": "2024-05-14T18:16:01.838358Z"
    }
   },
   "outputs": [],
   "source": [
    "# validation...\n",
    "# TODO: refactor this with above into a func\n",
    "interesting_clips_must_be_positive_mask = (\n",
    "    (interesting_clips[\"upvoted\"] == True)\n",
    "    | (interesting_clips[\"has_action\"] == True)\n",
    "    | (interesting_clips[\"part_of_concat\"] == True)\n",
    ")\n",
    "interesting_clips_must_be_not_negative_mask = (\n",
    "    interesting_clips[\"downvoted\"] == False\n",
    ") & (interesting_clips[\"deleted\"] == False)\n",
    "interesting_clips_mask = (\n",
    "    interesting_clips_must_be_positive_mask\n",
    "    & interesting_clips_must_be_not_negative_mask\n",
    ")\n",
    "assert interesting_clips_mask.eq(interesting_clips[\"preference\"]).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:16:01.904873Z",
     "start_time": "2024-05-14T18:16:01.871993Z"
    }
   },
   "outputs": [],
   "source": [
    "# # listen to some pairs\n",
    "# test_requests = interesting_clips[\"request_id\"].sample(10)\n",
    "\n",
    "# for i in range(1):\n",
    "#     rows = interesting_clips[interesting_clips[\"request_id\"] == test_requests.iloc[i]]\n",
    "#     assert rows.shape[0] == 2\n",
    "#     # Audio.from_s3(f\"s3://suno-data-uploads/studio/uploads/{row['s3_id']}.mp3\").play()\n",
    "#     # sort by likes\n",
    "#     rows = rows.sort_values(\"upvoted\", ascending=True)\n",
    "#     print(rows.iloc[0][\"prompt_text\"])\n",
    "#     print(rows.iloc[0][\"metadata\"])\n",
    "#     for _, row in rows.iterrows():\n",
    "#         print(row[\"id\"], row[\"preference\"], row[\"upvoted\"])\n",
    "#         Audio.from_s3(\n",
    "#             f\"s3://suno-data-uploads/studio/uploads/{row['s3_id']}.mp3\"\n",
    "#         ).play()\n",
    "#         with open_from_s3(\n",
    "#             f\"s3://suno-data-uploads/studio/uploads/{row['s3_id']}.npz\", as_binary=True\n",
    "#         ) as f:\n",
    "#             # read numpy array\n",
    "#             npz_a = np.load(f)\n",
    "#             if \"v1_raw\" in npz_a:\n",
    "#                 a = np.load(f)[\"v1_raw\"]\n",
    "#             elif \"v3.0_raw\" in npz_a:\n",
    "#                 a = np.load(f)[\"v3.0_raw\"]\n",
    "#             else:\n",
    "#                 print(\"npz_a\", npz_a)\n",
    "#                 raise ValueError\n",
    "#             print(a.shape)\n",
    "#     print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further cuts and selections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:16:01.970045Z",
     "start_time": "2024-05-14T18:16:01.906537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13089082\n"
     ]
    }
   ],
   "source": [
    "# interesting_clips[\"has_gpt_prompt\"] = interesting_clips[\"metadata\"].apply(\n",
    "#     lambda x: ast.literal_eval(str(x)).get(\"gpt_description_prompt\", None) is not None\n",
    "# )\n",
    "# print(len(interesting_clips))\n",
    "# interesting_clips = interesting_clips[~interesting_clips[\"has_gpt_prompt\"]]\n",
    "print(len(interesting_clips))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:16:02.366605Z",
     "start_time": "2024-05-14T18:16:01.971053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batch_index  preference\n",
       "0            False         3323335\n",
       "             True          3221206\n",
       "1            True          3323335\n",
       "             False         3221206\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interesting_clips.groupby(\"batch_index\")[\"preference\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:16:02.823955Z",
     "start_time": "2024-05-14T18:16:02.367805Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1718815,\n",
       " user_id\n",
       " 10415471    2600\n",
       " 1715957     2202\n",
       " 14524671    2152\n",
       " 2352038     2118\n",
       " 4220448     1872\n",
       "             ... \n",
       " 17673907       2\n",
       " 17672767       2\n",
       " 7168769        2\n",
       " 17674678       2\n",
       " 6104866        2\n",
       " Name: count, Length: 1718815, dtype: int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interesting_clips[\"user_id\"].nunique(), interesting_clips[\"user_id\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:16:04.611535Z",
     "start_time": "2024-05-14T18:16:02.825132Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model_name\n",
       "chirp-v3-engine-i     5952727\n",
       "chirp-v3-engine-s     5917342\n",
       "chirp-v3-engine-d      830781\n",
       "chirp-v3-engine-v0     388232\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interesting_clips[\"model_name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:16:04.980723Z",
     "start_time": "2024-05-14T18:16:04.612745Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time validation 2024-04-26 00:00:00.157431+00:00 2024-05-14 13:06:19.594026+00:00 2024-04-26 00:00:00.001597+00:00 MAX_TIME 2024-05-14 13:08:12.258566+00:00\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"time validation\",\n",
    "    interesting_clips[\"created_at\"].min(),\n",
    "    interesting_clips[\"created_at\"].max(),\n",
    "    clip_df[\"created_at\"].min(),\n",
    "    \"MAX_TIME\",\n",
    "    clip_df[\"created_at\"].max(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:16:57.987778Z",
     "start_time": "2024-05-14T18:16:04.981968Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6544541 13089082\n"
     ]
    }
   ],
   "source": [
    "print(interesting_clips[\"request_id\"].nunique(), interesting_clips[\"id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:18:32.142406Z",
     "start_time": "2024-05-14T18:16:57.989075Z"
    }
   },
   "outputs": [],
   "source": [
    "interesting_clips = interesting_clips.sort_values(by=[\"request_id\", \"preference\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:18:45.226233Z",
     "start_time": "2024-05-14T18:18:32.144202Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model_name\n",
       "chirp-v3-engine-i     0.090395\n",
       "chirp-v3-engine-s     0.090720\n",
       "chirp-v3-engine-d     0.099940\n",
       "chirp-v3-engine-v0    0.061711\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interesting_clips[interesting_clips[\"preference\"] == True][\n",
    "    \"model_name\"\n",
    "].value_counts() / clip_df[\"model_name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:19.750766Z",
     "start_time": "2024-05-14T18:18:45.227467Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-v3-engine-d_win_over_chirp-v3-engine-i, win ratio 0.519, counts 176605\n",
      "chirp-v3-engine-d_win_over_chirp-v3-engine-s, win ratio 0.517, counts 172179\n",
      "chirp-v3-engine-d_win_over_chirp-v3-engine-v0, win ratio 0.666, counts 104952\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-d, win ratio 0.481, counts 163785\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-i, win ratio 1.000, counts 13861\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-s, win ratio 0.499, counts 2785152\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-d, win ratio 0.483, counts 160544\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-i, win ratio 0.501, counts 2799463\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-s, win ratio 1.000, counts 2\n",
      "chirp-v3-engine-v0_win_over_chirp-v3-engine-d, win ratio 0.334, counts 52716\n",
      "chirp-v3-engine-v0_win_over_chirp-v3-engine-v0, win ratio 1.000, counts 115282\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_preferfence_counts(interesting_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:19.753549Z",
     "start_time": "2024-05-14T18:20:19.752007Z"
    }
   },
   "outputs": [],
   "source": [
    "# top_user_df = interesting_clips.groupby([\"user_id\"]).filter(lambda x: len(x) > 500)\n",
    "# get_preferfence_counts(top_user_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:21.372560Z",
     "start_time": "2024-05-14T18:20:19.754542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tony/anaconda3/envs/suno_env/lib/python3.10/site-packages/matplotlib/axes/_axes.py:6859: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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bhgMAACgLy6Hz7LPPasOGDbrrrrsUERHBxz8AAIByy3LofPnll5o1a5ZatGjhjXkAAABsY/kcnXr16qlq1aremAUAAMBWlkNnxIgRmjp1qn755RdvzAMAAGAby4euwsPDdf78eXXu3FlBQUEKCAgocv+mTZtsGw4AAKAsLIfO0KFDdeTIESUmJqp27dqcjAwAAMoty6GTnp6u9957T6Ghod6YBwAAwDaWz9Fp1KiRzp07541ZAAAAbGU5dIYNG6aJEydq48aNOn78uHJzc4v8AQAAKC8sH7oaMGCAJKl///5F1t1utxwOh7KysmwZDAAAoKwsh86CBQu8MQcAAIDtLIdOq1atvDEHAACA7SyHzubNmy95/1/+8pdSDwMAAGAny6HTr1+/Ymu/v5YO5+gAAIDyosx7dH799VdlZWVp+vTpSkxMtG0wAACAsrIcOtWqVSu21q5dOwUEBGjixIlasWKFLYMBAACUleXr6PyR4OBg/fTTT3ZtDgAAoMws79HZsWNHsbUjR45o9uzZfCwEAAAoVyyHTkxMjBwOh9xud5H1yMhIjR8/3rbBAAAAyspy6Kxbt67IbT8/P9WqVUuVK1e2bSgAAAA7WA6dBg0aeGMOAAAA21kOHUlKTU1VamqqXC6XCgsLi9yXlJRky2AAAABlZTl0Xn/9db3xxhsKCwtTnTp1ilwsEAAAoDyxHDpLly5VUlKSYmJivDAOAACAfSxfR+fXX39V8+bNvTELAACArSyHTu/evfXxxx97YxYAAABbWT50df78eSUnJys1NVVOp1OVKhXdxMiRI20bDgAAoCwsh86PP/7ouQLyzp07i9zHickAAKA8sRw6Cxcu9MYcAAAAtivVdXTKm/379+vZZ5+Vy+WSv7+/3nvvPVWpUsXXYwEAAB8zInRGjhypIUOGqGXLljpx4oQCAwN9PRIAACgHKnzo7Nq1S5UqVVLLli0lSTVq1PDtQAAAoNyw/Ovldtu8ebMGDRqk9u3by+l0KiUlpdhjFi9erI4dOyo8PFx9+vRRRkaG576ff/5ZVapU0aBBg9SjRw+9/fbbV3J8AABQjvk8dPLy8uR0OjVq1KiL3r969WolJSVp8ODBWrlypUJDQ5WQkCCXyyVJys/PV1pamkaNGqX33ntPGzZs0IYNG67ktwAAAMopn4dOhw4dlJiYqC5dulz0/nnz5qlv377q1auXbrrpJo0ePVpBQUF6//33JUn16tVTWFiYrr32WgUGBqpDhw7Kysq6kt8CAAAop3weOpdy4cIFZWZmqm3btp41Pz8/tW3bVunp6ZKk8PBwuVwunTx5UoWFhfr+++/VuHFjX40MAADKkXJ9MvLx48dVUFCg4ODgIuvBwcHas2ePJKlSpUpKTExUbGys3G632rVrpzvvvNMX4wIAgHKmXIdOSXXo0EEdOnTw9RgAAKCcKdeHrmrWrCl/f3/Pice/cblcql27to+mAgAAFUW5Dp3AwEA1bdpUqampnrXCwkKlpqYqKirKh5MBAICKwOeHrs6cOaN9+/Z5bh84cEBZWVmqXr266tevr/j4eI0YMUJhYWGKiIjQO++8o7Nnz6pnz54+nBoAAFQEPg+dbdu2KS4uznM7KSlJktSjRw9NnDhR3bp107FjxzRjxgzl5OSoSZMmmjNnDoeuAADAZfk8dFq3bq0ff/zxko+JjY1VbGzsFZoIAACYolyfowMAAFAWhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADBWJV8PAO/w83PIz89R6ucXFrpVWOi2cSIAAK48QsdAfn4OVa9RRZX8S7/DLr+gUCdP5BE7AIAKjdAxkJ+fQ5X8/fTU0nT9+0iu5effVPdqTf/fKPn5OQgdAECFRugY7N9HcpWZfcrXYwAA4DOcjAwAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIzFp5f/H4fDO9u9KtBfV1e2/jZfFejv+bq0s/nytQEA8KaS/nxyuN1ut3dHAQAA8A0OXQEAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjETpesnjxYnXs2FHh4eHq06ePMjIyfD2SUWbOnKlevXopKipKbdq00WOPPaY9e/b4eizjzZo1S06nU+PHj/f1KMY5fPiwhg8frtatWysiIkLR0dHaunWrr8cySkFBgV599VV17NhRERER6ty5s9544w3xAQFlt3nzZg0aNEjt27eX0+lUSkpKkfvdbremT5+u9u3bKyIiQv3799fevXuvyGyEjhesXr1aSUlJGjx4sFauXKnQ0FAlJCTI5XL5ejRjbNq0SX/961+VnJysefPmKT8/XwkJCcrLy/P1aMbKyMjQ0qVL5XQ6fT2KcU6ePKkHH3xQAQEBmj17tj755BONGDFC1atX9/VoRpk9e7aWLFmiF198UatXr9bw4cM1Z84cLVy40NejVXh5eXlyOp0aNWrURe+fPXu2Fi5cqJdeeknJycm66qqrlJCQoPPnz3t/ODds17t3b/fo0aM9twsKCtzt27d3z5w504dTmc3lcrlvvvlm96ZNm3w9ipFyc3PdXbt2dW/YsMEdGxvrHjdunK9HMsqUKVPcDz74oK/HMN7AgQPdI0eOLLL2+OOPu4cNG+ajicx08803uz///HPP7cLCQne7du3cc+bM8aydOnXKHRYW5l61apXX52GPjs0uXLigzMxMtW3b1rPm5+entm3bKj093YeTme306dOSxN+AvWTMmDHq0KFDkX+vYZ/169crLCxMTz75pNq0aaOYmBglJyf7eizjREVF6V//+pd++uknSdKOHTuUlpam22+/3ceTme3AgQPKyckp8v+PatWqqVmzZlfk52Ilr7/Cn8zx48dVUFCg4ODgIuvBwcGcQ+IlhYWFmjBhgpo3b66bb77Z1+MY55NPPtH27du1fPlyX49irP3792vJkiWKj4/XoEGDtHXrVo0bN04BAQHq0aOHr8czxsCBA5Wbm6t77rlH/v7+KigoUGJiou677z5fj2a0nJwcSbroz8WjR496/fUJHVR4o0eP1q5du/Tuu+/6ehTjHDx4UOPHj9fcuXNVuXJlX49jLLfbrbCwMA0dOlSSdMstt2jXrl1aunQpoWOjTz/9VB9//LGmTZumm266SVlZWUpKSlLdunV5nw1G6NisZs2a8vf3L3biscvlUu3atX00lbnGjBmjL7/8UosWLVJISIivxzFOZmamXC6Xevbs6VkrKCjQ5s2btXjxYm3dulX+/v4+nNAMderUUePGjYusNWrUSGvXrvXRRGaaPHmyBg4cqO7du0uSnE6nsrOzNXPmTELHi+rUqSPpPz8H69at61l3uVwKDQ31+utzjo7NAgMD1bRpU6WmpnrWCgsLlZqaqqioKB9OZha3260xY8bo888/1zvvvKPrr7/e1yMZ6dZbb9XHH3+sDz74wPMnLCxM0dHR+uCDD4gcmzRv3txz3shv9u7dqwYNGvhoIjOdO3dODoejyJq/vz+/Xu5l1113nerUqVPk52Jubq62bNlyRX4uskfHC+Lj4zVixAiFhYUpIiJC77zzjs6ePVvkb8Uom9GjR2vVqlV68803VbVqVc8x4GrVqikoKMjH05nj6quvLnbeU5UqVVSjRg3Oh7LR3/72Nz344IN6++23dc899ygjI0PJyckaM2aMr0czyp133qm3335b9evX9xy6mjdvnnr16uXr0Sq8M2fOaN++fZ7bBw4cUFZWlqpXr6769esrLi5Ob731lho2bKjrrrtO06dPV926ddW5c2evz+Zwk7JesWjRIv3zn/9UTk6OmjRpoueff17NmjXz9VjG+KNruSQlJRGUXtavXz+Fhobqueee8/UoRvniiy/08ssva+/evbruuusUHx+vvn37+noso+Tm5mr69OlKSUnxHEbp3r27Bg8erMDAQF+PV6Ft3LhRcXFxxdZ79OihiRMnyu12a8aMGUpOTtapU6fUokULjRo1SjfeeKPXZyN0AACAsThHBwAAGIvQAQAAxiJ0AACAsQgdAABgLEIHAAAYi9ABAADGInQAAICxCB0AAGAsQgeAbfr166fx48f7egwPt9utF154Qa1atZLT6VRWVlaxx6xYsUItW7b0wXQXV5L3sGPHjpo/f77nttPpVEpKipcnAyomPusKgLG+/vprrVy5UgsWLND111+vmjVrFntMt27d1KFDBx9MZ59vv/1W1atX9/UYQLlE6AAo1woKCuRwOOTnZ30H9P79+1WnTh01b978Dx8TFBRU4T8Itk6dOr4eASi3OHQFGKZfv34aN26cJk+erFatWqldu3Z67bXXPPcfOHCg2GGcU6dOyel0auPGjZL+8wF9TqdT33zzjWJiYhQREaG4uDi5XC599dVXuueee9S8eXMNGzZMZ8+eLfL6BQUFGjNmjFq0aKHWrVvr1Vdf1e8/Uu/ChQuaNGmSbrvtNkVGRqpPnz6e15X+/6GkdevWqVu3bgoPD1d2dvZFv9dNmzapd+/eCgsLU/v27TV16lTl5+dLkp555hmNHTtW2dnZcjqd6tix40W38d+Hrnbs2KF+/fopKipKzZs3V8+ePbV169bLvu+/bSclJUVdu3ZVeHi4EhISdPDgQc9jnnnmGT322GNFnjd+/Hj169fP0nv43/770NWhQ4c0dOhQtWrVSpGRkerZs6e2bNly2e8BMBGhAxho5cqVqlKlipKTk/X000/rjTfe0IYNGyxv5/XXX9cLL7ygpUuX6tChQxoyZIgWLFigadOmadasWfr222+1cOHCYq/t7++vZcuW6bnnntP8+fO1bNkyz/1jxoxRenq6XnnlFX300Ue6++67NWDAAO3du9fzmHPnzmn27NkaN26cVq1apeDg4GKzHT58WAMHDlR4eLg+/PBDvfTSS1q+fLneeustSdJzzz2nJ598UiEhIfr222+1fPnyEn3Pw4cPV0hIiJYvX64VK1bo4YcfVkBAQImee+7cOb311luaNGmSlixZolOnTikxMbFEz/29y72Hl3LmzBnFxsbq8OHDevPNN/Xhhx9qwIABKiwstDwHYAIOXQEGcjqdevzxxyVJN9xwgxYtWqTU1FS1a9fO0naGDBmiFi1aSJJ69+6tadOmKSUlRddff70k6a677tLGjRs1cOBAz3OuvfZaPfvss3I4HGrUqJF27typ+fPnq2/fvsrOztaKFSv0xRdfqF69epKkhIQEffPNN1qxYoWGDh0qSfr111/10ksvKTQ09A9ne/fddxUSEqIXX3xRDodDjRs31uHDhzV16lQNHjxY1apVU9WqVeXv72/p0E52drYSEhLUuHFjz/tXUr/++qtefPFFNWvWTJI0ceJEdevWTRkZGYqIiCjxdi71Hl7OqlWrdOzYMS1fvlw1atSQJDVs2LDErw2Yhj06gIGcTmeR23Xq1JHL5SrTdoKDg3XVVVd5IkeSateurWPHjhV5TrNmzeRwODy3IyMj9fPPP6ugoEA7d+5UQUGB7r77bkVFRXn+bN68Wfv27fM8JyAgoNj38N92796tqKioIq/VokUL5eXl6dChQ5a/19/Ex8fr+eefV//+/TVr1qwic11OpUqVFB4e7rnduHFjXXPNNdq9e7elGS71Hl5OVlaWbrnlFk/kAH927NEBDFSpUtH/tB0Oh+ccj99O6v39OR+/nddyqe04HI6LbtfKIZG8vDz5+/vr/fffl7+/f5H7qlSp4vk6KCioyA/6K+mJJ57Qvffeq6+++kpff/21ZsyYoVdeeUVdunQp87Z//8/hN3/03pdWRT+xGrAbe3SAP5latWpJknJycjxrF7u+TGllZGQUub1lyxY1bNhQ/v7+atKkiQoKCnTs2DE1bNiwyB+rvznUuHFjpaenFwmHtLQ0Va1aVSEhIWX6Hm688Ub1799fc+fOVdeuXfX++++X6Hn5+fnatm2b5/aePXt06tQpz2GwWrVqFXnfpYu/95d6Dy/ntxPNT5w4UaKZAdMROsCfTFBQkCIjIzVr1izt3r1bmzZt0quvvmrb9rOzs5WUlKQ9e/Zo1apVWrRokeLi4iT9JyCio6P1j3/8Q5999pn279+vjIwMzZw5U19++aWl13nooYd06NAhjR07Vrt371ZKSopee+01xcfHl+pX0aX/nEw8ZswYbdy4Ub/88ovS0tK0detWT6hcTkBAgMaOHastW7Zo27ZtGjlypCIjIz3n59x6663atm2bPvjgA+3du1czZszQrl27im3nUu/h5XTv3l21a9fW4MGDlZaWpv3792vt2rVKT08v+RsBGIRDV8Cf0IQJE/Tcc8+pZ8+euvHGG/X000/r73//uy3bjomJ0blz59SnTx/5+/srLi5ODzzwgOf+pKQkvfXWW5o4caKOHDmiGjVqKDIyUnfccYel16lXr55mzZqlyZMnKzk5WTVq1FDv3r316KOPlnp2Pz8/nThxQiNGjNDRo0dVs2ZNde3aVU8++WSJnh8UFKSHH35Yw4YN0+HDh9WyZcsiVzm+7bbb9Nhjj2nKlCk6f/68evXqpZiYGO3cubPIdi73Hl5KYGCg5s6dq0mTJmngwIEqKChQ48aNNWrUqJK/EYBBHO5LXZwBAFAiK1as0IQJE/T999/7ehQAv8OhKwAAYCwOXQFACQwYMEBpaWkXve+RRx5R3bp1r/BEAEqCQ1cAUAKHDx/WuXPnLnpf9erVuW4NUE4ROgAAwFicowMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAw1v8DoX7VZSXSp6MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(interesting_clips[\"dislike_count\"], bins=np.linspace(0, 10, 30))\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"number of dislike_count\")\n",
    "plt.ylabel(\"number of clips\")\n",
    "plt.show()\n",
    "plt.hist(interesting_clips[\"upvote_count\"], bins=np.linspace(0, 10, 30))\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"number of like_count\")\n",
    "plt.ylabel(\"number of clips\")\n",
    "plt.show()\n",
    "plt.hist(interesting_clips[\"is_public\"], bins=np.linspace(0, 10, 30))\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"number of is_public\")\n",
    "plt.ylabel(\"number of clips\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:22.030242Z",
     "start_time": "2024-05-14T18:20:21.373819Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(interesting_clips[\"user_id\"].value_counts(), bins=np.linspace(0, 1000, 100))\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"number of preferences clips\")\n",
    "plt.ylabel(\"number of users\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:22.033248Z",
     "start_time": "2024-05-14T18:20:22.031489Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:22.098085Z",
     "start_time": "2024-05-14T18:20:22.034302Z"
    }
   },
   "outputs": [],
   "source": [
    "# FUCK THIS FOR NOW\n",
    "# MAX_PREFERENCE_PER_USER = 400\n",
    "# grouped_interesting_clips = interesting_clips.groupby([\"user_id\"])\n",
    "# user_top_df = (\n",
    "#     interesting_clips.sort_values(\n",
    "#         [\"preference\", \"upvote_count\", \"part_of_concat\", \"is_in_playlist\"], ascending=False\n",
    "#     )\n",
    "#     .groupby(\"user_id\")\n",
    "#     .head(MAX_PREFERENCE_PER_USER)\n",
    "# )\n",
    "# print(user_top_df.shape, interesting_clips.shape)\n",
    "\n",
    "# user_top_requests = user_top_df[\"request_id\"].unique()\n",
    "# user_intersting_clips = interesting_clips[\n",
    "#     interesting_clips[\"request_id\"].isin(user_top_requests)\n",
    "# ].copy()\n",
    "# print(user_intersting_clips.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:26.034490Z",
     "start_time": "2024-05-14T18:20:22.099462Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13089082, 37)\n"
     ]
    }
   ],
   "source": [
    "user_intersting_clips = interesting_clips.copy()\n",
    "print(user_intersting_clips.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:26.746674Z",
     "start_time": "2024-05-14T18:20:26.035743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WrVtLktauXavk5GRJ0iWXXKLi4mLvVoezxro6AICWrFHr6CxdulQbNmzQ2rVrNWDAAEnSwYMH1blzZ2/XBwAA0GgeB50//vGPeu2113TTTTdpxIgRioiIkCT961//cnVpAQAA+AOPu64SEhL0xRdfqKysTJ06dXIdHzt2rNq2bevV4gAAAM5Go9bRsdvtbiFHkn7xi194pSAAAABv8TjoJCcnKyjo9NOTP/7447MqCE2v9to6rKsDALAyj4POzTff7Pa6qqpK27Zt0+eff66JEyd6rTB4X+21dWpUVTt19EgFYQcAYElnHXRqLFmyRHl5eWddEJpO3bV1WFcHAGB1Hs+6Op0BAwbogw8+8Nbt0IRq1tapWUgQAACr8lrQef/991lHBwAA+BWPu65GjhzpNhjZGKNDhw7p8OHDmjlzpleLAwAAOBseB50hQ4a4vQ4KCtJ5552nvn37qkePHl4rDM2HHc4BAFblcdBh007rqG8WlsRMLACAdTRqwUBYAzucAwCsjqADdjgHAFiW12ZdAQAA+JsGBZ3t27fL6XQ2dS0AAABe1aCgM2rUKJWWlkqSBg8e7PozrMtutyk4+OSPzXb6vc0AAPBnDRqj07FjR+3fv1+hoaH6+uuvZQyDVK2K/bAAAFbSoKAzbNgwpaamqkuXLgoKClJKSopstvobg9i9PLCxHxYAwEoaFHRmzZqloUOHqqioSLNnz9aYMWPUvn37pq4NPsRMLACAFTR4evmAAQMkSfn5+UpLS1OHDh2arCgAAABv8HgdnYyMDNefv/32W0nS+eef772K4JfYJgIAEIg8DjpOp1P/+7//q0WLFqmiokKS1L59e02YMEF33HHHacfuIDCxTQQAIJB5HHSeeuopvfnmm5o2bZri4+MlSRs3btRzzz2nyspKTZ061etFwnfYJgIAEMg8DjrLly/X7NmzNXjwYNexiIgIdevWTenp6QQdi2JwMgAgEHkcdI4ePapLLrnklOOXXHKJjh496pWiEBhqj9thzA4AwB95PKAmIiJCS5YsOeX4kiVLFBER4ZWi4N9qj9sJCWmvkJD26tS5HSsoAwD8jsctOvfee69uv/12rV27VrGxsZKkTZs26ZtvvtHf/vY3b9cHP8SiggCAQOFx0Onbt6/ef/99vfLKKyosLJQkDR06VL/5zW/UrVs3rxf4U44dO6bx48erurpa1dXVSktL09ixY5u9jpaIcTsAAH/ncdCRpG7duvnNoOP27dtryZIlatu2rSoqKnTttddq6NChCgkJ8XVpLQ5r7QAA/E2jgo4/sdvtatv25BovlZWVksSmo82MtXYAAP7K56v75eTkaPLkyerfv78cDodWrVp1yjVLlixRcnKyoqOjNWbMGG3ZssXt/LFjx3T99dcrKSlJEydO1Hnnnddc5UPuY3ZGZH6mEZmf6Q9LcxVstzFAGQDgUz4POhUVFXI4HJo5c2a951euXKmMjAxNmTJFy5cvV0REhCZOnKiSkhLXNR07dtQ777yjjz/+WCtWrNChQ4eaq3zUUjNmJ//AMdfiggAA+JJHQccYowMHDujEiRNeKyApKUlTp07V0KFD6z2/aNEijR07VikpKerZs6fS09PVpk0bZWVlnXJtWFiYIiIitGHDBq/Vh7Njt9sUHPzfH1p4AADNyeOgM2zYMH3zzTdNVY+byspK5efnKzEx0XXMZrMpMTFRubm5kqRDhw6prOxk68H333+vDRs26OKLL26W+nB69a21w3o7AIDm5tFgZJvNpgsvvFBHjhxponLclZaWqrq6WqGhoW7HQ0NDXVPbDxw4oBkzZsgYI2OMUlNT5XA4mqU+nB57ZAEA/IHHs66mTZumuXPn6q9//avCw8OboiaPxMTE6O233/Z1GTgN1toBAPiSx0Fn+vTpOn78uH71q1+pVatWatOmjdv59evXe624kJAQ2e12t4HHklRSUqKwsDCvfQ4AALAmj4POAw880BR11Kt169aKjIxUdna2hgwZIklyOp3Kzs5Wampqs9UB72IzUABAc/E46IwaNcqrBZSXl6uoqMj1ev/+/SooKFCnTp3UvXt3TZgwQdOnT1dUVJRiYmK0ePFiHT9+XKNHj/ZqHWh69S0syKKCAICm1KiVkYuKipSVlaV9+/bpwQcfVGhoqD755BN1795d//M//+PRvfLy8pSWluZ6nZGRIelkoJozZ46GDx+uw4cPKzMzU8XFxerVq5cWLlxI11UAYjNQAEBz8zjorF+/Xrfddpvi4+OVk5OjqVOnKjQ0VDt27FBWVpYyMzM9ul9CQoJ27NhxxmtSU1PpqrKQugOU2SMLANBUPF4Z+YknntDdd9+tRYsWqVWrVq7jl19+uTZt2uTN2mBxrLUDAGhqHrfofPnll3r88cdPOX7eeeeptLTUK0WhZWCtHQBAU/M46Jx77rkqLi7WBRdc4Ha8oKBA3bp181phaDlYawcA0FQ87roaMWKEHn/8cRUXFysoKEhOp1MbN27Uo48+qpEjRzZBiWiJau+RRTcWAKCxPG7RmTp1qh566CENHDhQ1dXVGjFihKqrq3XttdfqjjvuaIoa0YI0dAq6zRbkFoAYwAwAqI/HQad169aaPXu27rzzTu3cuVPl5eW69NJLddFFFzVBeWhpGjIF3WYLUqfO7RRca7YW6/EAAOrTqHV0JKl79+762c9+JkkKCqJrAd51pnE7NluQgu021uMBAPykRgWdN954Q4sXL9bevXslSRdddJFuvvlmjRkzxpu1AS6119qp+TODmAEAP8XjoPPMM8/o73//u1JTUxUbGytJ2rRpkx555BEdOHBAf/jDH7xdI1qw+sbsAADQUB4HnVdffVWzZs3Stdde6zo2ePBgORwOzZo1i6ADr6pvrZ2Bji6696oIH1cGAAgEHgedqqoqRUVFnXI8MjJS1dXVXikKqKt2N1WPLu19XA0AIFB4vI7Or371K7366qunHH/99dd13XXXeaUoAAAAb2hQi07NjuLSyRlWb7zxhtasWaPevXtLkrZs2aIDBw6wYCAAAPArDQo627Ztc3sdGRkpSSoqKpIkde7cWZ07d9bOnTu9XB4AAEDjNSjovPzyy01dB3DWak9Brw+rJwNAy9PoBQMBf3G6KejVTiN7rW0iWD0ZAFoej4POiRMn9PLLL2vdunUqKSmRMe7/0Vi+fLnXigMa4kxT0Fk9GQBaNo+DzgMPPKA1a9boqquuUkxMDNs/wG/UNwWd1ZMBoGXzOOisXr1aL7zwgi677LKmqAdoUnXH8TBuBwCszeOg061bN7Vvz4JtCCynG8fDuB0AsDaPFwycPn26Hn/8cX399ddNUQ/QJGqP4xmR+ZlGZH6mPyzNVbDdJpuN7lcAsCqPW3Sio6N14sQJDRkyRG3atFGrVq3czq9fv95rxQHexpgdAGhZPA4699xzjw4ePKipU6cqLCyMwcgIeLXH7TBmBwCsxeOgk5ubq9dee00REewejcBW37gdxuwAgLV4HHQuueQS/fDDD01RC9Cs6q6/w1o7AGA9Hg9GnjZtmubMmaN169aptLRUZWVlbj9AoKkZt1Oz2GBdNluQgoNtbj8MYAaAwOBxi86tt94qSRo/frzbcWOMgoKCVFBQ4JXCAF+pPWYnKChIHc5to+A66+/QxQUAgcHjoPPSSy81RR2Az51urR1JbttL0MUFAIHD46DTt2/fpqgD8Lkz7ZnFtHQACEweB52cnJwznv/lL3/Z6GIAf1DfnlkAgMDkcdC56aabTjlWey0dxugAAAB/cdYtOj/++KMKCgr0zDPPaOrUqV4rDAAA4Gx5HHTOPffcU47169dPrVq10pw5c7Rs2TKvFAYAAHC2PA46pxMaGqo9e/Z463aA37PbPVuGiu0lAKD5eRx0tm/ffsqxgwcP6m9/+xvbQqBFqG8aerXTyF5nEcG6x1h7BwCan8dBZ+TIkQoKCpIx7r+sY2Nj9fDDD3utMMBf1Z2GXjMFvb5p6WwvAQC+5XHQ+fjjj91e22w2nXfeeTrnnHO8VhQQCGqmoddMQa9vWjrr7wCAb3kcdH7+8583RR0AAABe16jByNnZ2crOzlZJSYmcTqfbuYyMDK8UBgAAcLY8DjrPPfec5s2bp6ioKHXp0sVtsUAAAAB/4nHQWbp0qTIyMjRy5MgmKMdz33zzjf70pz+ppKREdrtdd955p6655hpflwUAAPyAx0Hnxx9/VHx8fFPU0ih2u10PPPCAevXqpeLiYo0ePVpJSUlq166dr0sDAAA+5tmKZ5JuuOEGrVixoilqaZSuXbuqV69ekqQuXbooJCRER48e9XFVQP3sdpuCg0//Y7PRFQwA3uRxi86JEyf0+uuvKzs7Ww6HQ8HB7re4//77PbpfTk6OXnzxReXl5am4uFjz5s3TkCFD3K5ZsmSJXnzxRRUXFysiIkIzZsxQTEzMKffKy8uT0+nUz372M08fC2hS9S0yKDVsUUGbLcgtALHCMgA0nMdBZ8eOHa4VkL/88ku3c40ZmFxRUSGHw6GUlBTdddddp5xfuXKlMjIylJ6ert69e2vx4sWaOHGi3n//fYWGhrquO3LkiKZPn65Zs2Z5XAPQ1OouMig1bFFBmy1InTq3U3Ct7SZYYRkAGs7joPPyyy97tYCkpCQlJSWd9vyiRYs0duxYpaSkSJLS09O1evVqZWVladKkSZKkyspKTZkyRbfddptfjR8C6vJ0UUGbLUjBdhsrLANAI3k8Rqc5VVZWKj8/X4mJia5jNptNiYmJys3NlSQZY3Tffffp8ssv95uZYMDZqD2Op2bj0JowVNMaBABoGK/tXt4USktLVV1d7dZFJZ3cKb2wsFCStHHjRq1cuVIOh0OrVq2SJM2dO1cOh6PZ6wXOxunG8QAAGs+vg05D9OnTp94d1YFAc6ZxPACAxvHroBMSEiK73a6SkhK34yUlJQoLC/NRVUDTqm8cz0+pOzNL8t7sLGZ9AQhkfj1Gp3Xr1oqMjFR2drbrmNPpVHZ2tuLi4nxYGeA/amZmhYS0d/vp1LndWa/LU9+9vXFfAGguPm/RKS8vV1FRkev1/v37VVBQoE6dOql79+6aMGGCpk+frqioKMXExGjx4sU6fvy4Ro8e7cOqAd+y15pubrfb3GZmSfLa7CxmfQEIdD4POnl5eUpLS3O9rtn9fNSoUZozZ46GDx+uw4cPKzMzU8XFxerVq5cWLlxI1xVapDMNWK5vmnrtQHQ2XU5nmgIPAP7M50EnISFBO3bsOOM1qampSk1NbaaKAP/V0AHL9QUiVl0G0BL5POgA8NxPDViuG4hYdRlAS0XQASysbpfTmcb2MP4GgBURdIAWwNOxPQBgFQQdoAXwZDHCuq0+ABDICDpAC3KmsT1sQQHAigg6ACSxBQUAayLoAHDTmC0oAMBfEXQAnLX69tqqizV6APgCQQfAWalvPZ5qp5G9TvBhjR4AvkDQAXBW6u6HVTOupyn23gIATxF0AHhFzdiemnE9rM8DwB8QdAB4jLV2AAQKgg6ABjvbtXbqhiIGKANoagQdAA3W2LV2TheQ6g5Qrm/2FmEIwNkg6ADwmKdr7dQXkGoGKLdqZVd1tVNBQUHqcG4bt9lb0skwVPb9DzLmZNgh+ADwBEEHQLOpHZBO18pTOwz98qIQzbg2Up07t3OdZ5o6AE8QdAD4RN1WnpousLqtRbWvYZo6AE8RdAD4VN1p6We6BgA8RdABEHCYvQWgoQg6AAJGQ2dvAUANgg6AgNGQ2VsSLTwA/ougAyDg/NTsrfpaeOqu0VNfGGIdH8B6CDoAAlrdVp76ZmbVt8N6fYsV1r2mvusABBaCDgBLONPMrLo7rJ8uDNW+Rmr8ruu0DAH+g6ADoMVoyDT1s53KTssQ4F8IOgDgRd5sGQJw9gg6ANAEWOQQ8A8EHQCWVHtRwboLDPoDxvEAzYOgA8BSTreooD850zgedmoHvIugA8BS6ltUsGbDUF+raVmy222njONhp3agaRB0AFhS3V3Q69Nc3Vuna2Vip3ag6RF0ALQ4Z9O9Vd/YmtrqC0x1W5nO1MLEIGbAuwg6AFqcxnZvnW5sTbXTyH6G8FOjJsScroUJgPcRdAC0WA3p3pLOPLamJiA1pLXGG5itBXiGoAMAp9HQsTW1jzVla403V10mMKGlIOgAwGl4MramOXhr1WW2qUBLQtABgJ/gb2NrGjNguXYLTn1dcMzyglURdADAAmrP9qrbBXW6FhxmeKElIOgAQACrbxxR3S6oul1evu6CA5oTQQcAAljdcURn6oLyty44/BeDw5sOQQcALIBuqMDF4PCmZYmgM2XKFK1fv15XXHGFMjMzfV0OAPhcY7e3ONNYH+nUlgdaHc6et2bToX6WCDppaWlKSUnRW2+95etSAMCnGru9RUPH+tRteaDVwXtolWsalgg6CQkJWrduna/LAACv87RlprHbWzRkrE/dlgdaHRqG8Te+5fOgk5OToxdffFF5eXkqLi7WvHnzNGTIELdrlixZohdffFHFxcWKiIjQjBkzFBMT46OKAaDpnc3Go1LDt7c40/vO5hqcxPgb3/N50KmoqJDD4VBKSoruuuuuU86vXLlSGRkZSk9PV+/evbV48WJNnDhR77//vkJDQ31QMQA0vca2zHhbY8f6tBQ/1VrD+Bvf83nQSUpKUlJS0mnPL1q0SGPHjlVKSookKT09XatXr1ZWVpYmTZrUXGUCgE80tmXmbJ1ti1JL4ElrDa1gvuPzoHMmlZWVys/P1+233+46ZrPZlJiYqNzcXB9WBgDW5i8tSv6M1prA4NdBp7S0VNXV1ad0UYWGhqqwsND1evz48dq+fbuOHz+uAQMG6JlnnlFcXFxzlwsAltNULUqNHaDrj9Pbaa3xb34ddBrq73//u69LAAA0UGMH6DK9HY3h10EnJCREdrtdJSUlbsdLSkoUFhbmo6oAAGejsV0+TG9HY/j1EPrWrVsrMjJS2dnZrmNOp1PZ2dl0TQGAn7LZghQcbHP7qdtNJf23yyf/wDFX4GmImvc19D0Nrccq6j6vlZ+1IXzeolNeXq6ioiLX6/3796ugoECdOnVS9+7dNWHCBE2fPl1RUVGKiYnR4sWLdfz4cY0ePdqHVQMA6uNv68b4Wz1Nje69U/k86OTl5SktLc31OiMjQ5I0atQozZkzR8OHD9fhw4eVmZmp4uJi9erVSwsXLqTrCgD8kL/NRPK3epoa3Xun8nnQSUhI0I4dO854TWpqqlJTU5upIgDA2fLGTKS6M6waumBh7ffVvKch9fjjjK7GYibYf/k86AAAUNfpupya830tvcvHKgg6AAC/U1+XU0MWLKz7voYuckiXj3URdAAAfqOmq6m+LqfTLVhY335cNe/zdJHDul0+dbvLGtqdVfc5WhJ/6wIk6AAAfK4xe2s15X5cp7v3T3VntfQ9wvyxC5CgAwDwubp7azWky6kp9+Oq794N6c5qzHNYiT92ARJ0AAB+ozFdTk25w3tjZy81tuvMKvxp1lfL6zwEAAAtBi06AICzUt9g4EAQqHV7g78NGG5KBB0AQKME6sDbQK3bW/xxwHBTIugAABqlKQcDN6VArdtb/HHAcFMi6AAAzkpTDgZuSoFat7f404DhptSyOiUBAECLQosOAKBZWGW1YG89R2Pu05yDiK0yYJmgAwBoUlYZ/Out52jsfZpzELGVBiwTdAAATcoqqwV76zkae5/mHERspQHLBB0AQLOwymrB3noOb2082pSsMGA5sDtKAQAAzoCgAwAALIugAwAALIsxOgAAWJy39vWq+95AmHJO0AEAwKKaekp8IEw5J+gAAGBR3trXq777BMqUc4IOAAAW5619vQJxujmDkQEAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGUx6woAAD/VkIX+vLUYoFURdAAA8DMNWejPW4sBWh1BBwAAP9OQhf68tRig1RF0AADwUw1Z6M9biwFaFZ15AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsiwRdP7973/rqquu0rBhw/TGG2/4uhwAAOAnAn4LiKqqKs2ZM0cvvfSSOnTooNGjR2vIkCEKCQnxdWkAAMDHAr5FZ8uWLerZs6e6deum9u3ba8CAAVqzZo2vywIAAH7A50EnJydHkydPVv/+/eVwOLRq1apTrlmyZImSk5MVHR2tMWPGaMuWLa5zBw8eVLdu3Vyvu3Xrpu+++65ZagcAAP7N50GnoqJCDodDM2fOrPf8ypUrlZGRoSlTpmj58uWKiIjQxIkTVVJS0syVAgBgXXa7TcHBJ3/s9obHg8a+r7n4fIxOUlKSkpKSTnt+0aJFGjt2rFJSUiRJ6enpWr16tbKysjRp0iR17drVrQXnu+++U0xMTJPXDQCAFXTpcI6qnUYdO7Ztlvc1N58HnTOprKxUfn6+br/9dtcxm82mxMRE5ebmSpJiYmK0c+dOfffdd+rQoYM+/fRT3Xnnnb4qGQCAgNKxbbDstiD9YWmudh0skyQNdHTRvVdFNMn7mptfB53S0lJVV1crNDTU7XhoaKgKCwslScHBwZo+fbrS0tLkdDp16623MuMKAAAP7TpYpvwDxyRJPbq0b/L3NRe/DjoNNXjwYA0ePNjXZQAAAD/jf6OGagkJCZHdbj9l4HFJSYnCwsJ8VBUAAAgUfh10WrdurcjISGVnZ7uOOZ1OZWdnKy4uzoeVAQCAQODzrqvy8nIVFRW5Xu/fv18FBQXq1KmTunfvrgkTJmj69OmKiopSTEyMFi9erOPHj2v06NE+rBoAAAQCnwedvLw8paWluV5nZGRIkkaNGqU5c+Zo+PDhOnz4sDIzM1VcXKxevXpp4cKFdF0BAICf5POgk5CQoB07dpzxmtTUVKWmpjZTRQAAwCr8eowOAADA2SDoAAAAyyLoAAAAyyLoAAAAyyLoAAAAyyLoAAAAyyLoAAAAy/L5Ojr+Iiioae7btrVdHc45+dfcJtjmdqzu68Ze05T3bkk1WuU5fP351EiN/vT51OjbGtu2tquGt/8729D7BRljjHc/GgAAwD/QdQUAACyLoAMAACyLoAMAACyLoAMAACyLoAMAACyLoAMAACyLoAMAACyLoAMAACyLoAMAACyLoAMAACyLoNNElixZouTkZEVHR2vMmDHasmWLr0tqkJycHE2ePFn9+/eXw+HQqlWr3M4bY/TMM8+of//+iomJ0fjx47V37163a44cOaJp06YpPj5effr00QMPPKDy8vJmfIrTW7BggVJSUhQXF6crrrhCd955pwoLC92uOXHihNLT05WQkKC4uDj97ne/06FDh9yuOXDggCZNmqTevXvriiuu0KOPPqqqqqrmfJR6vfLKK7ruuusUHx+v+Ph4jRs3Tp988onrfCA/W31eeOEFORwOPfzww65jgfyMzz77rBwOh9vP1Vdf7TofyM9W47vvvtMf//hHJSQkKCYmRtddd522bt3qOh/ov2OSk5NP+Q4dDofS09MlBf53WF1draefflrJycmKiYnRkCFDNG/ePNXeTcrvvkMDr3v33XdNZGSkefPNN83OnTvNn//8Z9OnTx9z6NAhX5f2k1avXm2efPJJ8+GHH5rw8HDz0UcfuZ1fsGCBueyyy8xHH31kCgoKzOTJk01ycrL54YcfXNdMnDjRXH/99WbTpk0mJyfHDB061Nxzzz3N/Sj1uuWWW0xWVpb58ssvTUFBgbntttvMwIEDTXl5ueuav/zlLyYpKcmsXbvWbN261YwdO9aMGzfOdb6qqspce+21Zvz48Wbbtm1m9erVJiEhwTzxxBO+eCQ3H3/8sVm9erXZs2ePKSwsNE8++aSJjIw0X375pTEmsJ+trs2bN5tBgwaZ6667zsyePdt1PJCfMTMz04wYMcIcPHjQ9VNSUuI6H8jPZowxR44cMYMGDTL33Xef2bx5sykqKjKfffaZ+eqrr1zXBPrvmJKSErfvb82aNSY8PNx88cUXxpjA/w6ff/5507dvX/Pvf//b7Nu3z7z33nsmNjbWLF682HWNv32HBJ0mcMMNN5j09HTX6+rqatO/f3+zYMECH1blubpBx+l0mn79+pmFCxe6jh07dsxERUWZf/7zn8YYY3bt2mXCw8PNli1bXNd88sknxuFwmG+//bb5im+gkpISEx4ebtavX2+MOfk8kZGR5r333nNdU/NMubm5xpiTYTAiIsIUFxe7rnnllVdMfHy8OXHiRLPW3xC//OUvzeuvv26pZysrKzPDhg0za9asMampqa6gE+jPmJmZaa6//vp6zwX6sxljzGOPPWZ+/etfn/a8FX/HzJ492wwZMsQ4nU5LfIeTJk0y999/v9uxu+66y0ybNs0Y45/fIV1XXlZZWan8/HwlJia6jtlsNiUmJio3N9eHlZ29/fv3q7i42O3Zzj33XPXu3dv1bLm5uerYsaOio6Nd1yQmJspms/ll9933338vSerUqZMkKS8vTz/++KPbM/bo0UPdu3fXpk2bJEmbNm1SeHi4wsLCXNf0799fZWVl2rVrV/MV/xOqq6v17rvvqqKiQnFxcZZ6toceekhJSUluzyJZ4/v76quv1L9/fw0ePFjTpk3TgQMHJFnj2f71r38pKipKv//973XFFVdo5MiRev31113nrfY7prKyUu+8845SUlIUFBRkie8wLi5OX3zxhfbs2SNJ2r59uzZu3KgBAwZI8s/vMNjrd2zhSktLVV1drdDQULfjoaGhp4wFCTTFxcWSVO+z1fQxHzp0SOedd57b+eDgYHXq1Mn1fn/hdDr1yCOPKD4+XuHh4ZJO1t+qVSt17NjR7drQ0FBX/YcOHXL7JSTJ9dofnnHHjh268cYbdeLECbVr107z5s1Tz549VVBQEPDPJknvvvuutm3bpjfffPOUc4H+/cXExCgjI0MXX3yxiouLNW/ePP32t7/VihUrAv7ZJGnfvn169dVXNWHCBE2ePFlbt27V7Nmz1apVK40aNcpyv2NWrVql77//XqNGjZIU+P98StKkSZNUVlama665Rna7XdXV1Zo6daquv/56Sf753wmCDlqs9PR07dy5U6+88oqvS/Gqiy++WG+99Za+//57ffDBB5o+fbr+8Y9/+Losr/jmm2/08MMP6//+7/90zjnn+Locr0tKSnL9OSIiQr1799agQYP03nvvqU2bNj6szDuMMYqKitI999wjSbr00ku1c+dOLV261BUGrCQrK0sDBgxQt27dfF2K17z33ntasWKFnnjiCdf/QGVkZKhr165++x3SdeVlISEhstvtKikpcTteUlJySkoPNF26dJGkMz5bWFiYDh8+7Ha+qqpKR48edb3fHzz00ENavXq1Fi9erPPPP991PCwsTD/++KOOHTvmdn1JSYmr/rCwsFNmSdS89odnbN26tS688EJFRUVp2rRpioiI0EsvvWSJZ8vPz1dJSYlGjx6tSy+9VJdeeqnWr1+vl19+WZdeeqklnrG2jh076qKLLlJRUZElnq1Lly7q0aOH27FLLrnE1T1npd8xX3/9tdauXasbbrjBdcwK3+HcuXM1adIkjRgxQg6HQyNHjtTNN9+sBQsWSPLP75Cg42WtW7dWZGSksrOzXcecTqeys7MVFxfnw8rO3i9+8Qt16dLF7dnKysq0efNm17PFxcXp2LFjysvLc13zxRdfyOl0KiYmptlrrssYo4ceekgfffSRFi9erAsuuMDtfFRUlFq1auX2jIWFhTpw4IBiY2MlSbGxsfryyy/d/kVeu3atOnTooJ49ezbLc3jC6XSqsrLSEs92+eWXa8WKFXrrrbdcP1FRUbruuutcfw70Z6ytvLxc+/btU5cuXSzxbPHx8a6xHTX27t2rn//855Ks8TumxrJlyxQaGqqBAwe6jlnhO/zhhx8UFBTkdsxut7uml/vld+j14c0w7777romKijLLli0zu3btMjNmzDB9+vRxG0Xvr8rKysy2bdvMtm3bTHh4uFm0aJHZtm2b+frrr40xJ6cN9unTx6xatcps377d3HHHHfVOGxw5cqTZvHmz2bBhgxk2bJjfTP2cOXOmueyyy8y6devcpoAeP37cdc1f/vIXM3DgQJOdnW22bt1qxo0bV+/0z1tuucUUFBSYTz/91Fx++eV+Mf3z8ccfN+vXrzf79u0z27dvN48//rhxOBzm888/N8YE9rOdTu1ZV8YE9jPOmTPHrFu3zuzbt89s3LjRjB8/3iQkJLimmAfysxlzckmASy+91Dz//PNm79695p133jG9e/c2b7/9tuuaQP8dY8zJmbYDBw40jz322CnnAv07nD59urnyyitd08s//PBDk5CQYObOneu6xt++Q4JOE3n55ZfNwIEDTWRkpLnhhhvMpk2bfF1Sg3zxxRcmPDz8lJ/p06cbY05OHXz66adNYmKiiYqKMjfffLMpLCx0u0dpaam55557TGxsrImPjzf33XefKSsr88XjnKK+ZwsPDzdZWVmua3744Qfz17/+1fzyl780vXv3NlOmTDEHDx50u8/+/fvNrbfeamJiYkxCQoKZM2eO+fHHH5v7cU5x//33m0GDBpnIyEhz+eWXm5tvvtkVcowJ7Gc7nbpBJ5Cf8e677zb9+vUzkZGR5sorrzR333232xozgfxsNf71r3+Za6+91kRFRZmrr77avPbaa27nA/13jDHGfPbZZyY8PPyUuo0J/O/w+++/N7NnzzYDBw400dHRZvDgwebJJ590m/rub99hkDG1ljMEAACwEMboAAAAyyLoAAAAyyLoAAAAyyLoAAAAyyLoAAAAyyLoAAAAyyLoAAAAyyLoAAAAyyLoADijm266SQ8//LCvy3AxxmjGjBnq27evHA6HCgoKmr2G1157TUlJSYqIiNDf//73Zv98AA0X7OsCAMATn376qZYvX66XXnpJF1xwgUJCQpr188vKyjRr1izdd999GjZsmM4999xm/XwAniHoAGh21dXVCgoKks3meaNyzW7e8fHxXq2psrJSrVu3/snrDhw4oB9//FFJSUnq2rVroz/vxx9/VKtWrRr9fgANQ9cVEABuuukmzZ49W3PnzlXfvn3Vr18/Pfvss67z+/fvP6Ub59ixY3I4HFq3bp0kad26dXI4HPrss880cuRIxcTEKC0tTSUlJfrkk090zTXXKD4+XtOmTdPx48fdPr+6uloPPfSQLrvsMiUkJOjpp59W7W3yKisr9eijj+rKK69UbGysxowZ4/pcSVq2bJn69Omjjz/+WMOHD1d0dLQOHDhQ77OuX79eN9xwg6KiotS/f389/vjjqqqqkiTdd999mjVrlg4cOCCHw6Hk5OR671HzeatWrdKwYcMUHR2tiRMn6ptvvnFd8+yzz+pXv/qV3njjDSUnJysmJsb19/bggw/q8ssvV3x8vNLS0rR9+3bXfa+77jpJ0pAhQ+RwOLR//35J0qpVqzRq1ChFR0dr8ODBeu6551x1S5LD4dArr7yiyZMnKzY2VvPnz2/w+9544w1NmTJFvXv31rBhw/Txxx+7Pe/OnTt1++23Kz4+XnFxcfrNb36joqIi1/k33nhD11xzjaKjo3X11VdryZIlbt/dQw89pP79+ys6OlqDBg3SggUL6v17BQJSk2wVCsCrUlNTTXx8vHn22WfNnj17zPLly43D4XDtTL5v3z4THh5utm3b5nrP0aNHTXh4uPniiy+MMf/dmX7s2LFmw4YNJj8/3wwdOtSkpqaaW265xeTn55ucnBzTt29fs2DBArfPjo2NNbNnzza7d+82b7/9tundu7fbrtMPPvigGTdunMnJyTFfffWVWbhwoYmKijJ79uwxxhiTlZVlIiMjzbhx48zGjRvN7t27TUVFxSnP+e2335revXubv/71r2bXrl3mo48+MgkJCSYzM9MYY8yxY8fMc889ZwYMGGAOHjxoSkpK6v37qvm80aNHm//85z9m69at5oYbbjDjxo1zXZOZmWliY2PNxIkTTX5+vikoKDDGGDN+/Hhz++23my1btpg9e/aYOXPmmL59+5rS0lJz/Phxs3btWhMeHm42b95sDh48aKqqqkxOTo6Jj483y5YtM0VFRebzzz83gwYNMs8++6zr88LDw80VV1xh3nzzTVNUVGS+/vrrBr9vwIABZsWKFWbv3r1m1qxZJjY21pSWlrr+zvr27Wvuuusus2XLFlNYWGjefPNNs3v3bmOMMW+//bbp16+f+eCDD0xRUZH54IMPTN++fc2yZcuMMcYsXLjQJCUlmZycHLN//36Tk5NjVqxYcaZ/HIGAQtABAkBqaqr59a9/7XYsJSXFPPbYY8YYz4LO2rVrXdcsWLDAhIeHm6KiItexGTNmmFtuucXts6+55hrjdDpdxx577DFzzTXXGGOM+frrr02vXr3Mt99+61bfzTffbJ544gljzMngER4e7goTp/Pkk0+aq666yu2z/vGPf5jY2FhTXV1tjDFm0aJFZtCgQWe8T83nbdq0yXVs165droBizMmgExkZ6RaWaoLHiRMn3O43ZMgQs3TpUmOMMdu2bTPh4eFm3759bs86f/58t/e89dZbpl+/fq7X4eHh5uGHH3a7pqHve+qpp1yvy8vLTXh4uPnkk0+MMcY88cQTJjk52VRWVtb7dzFkyJBTgsu8efNcoW/WrFkmLS3N7e8csBLG6AABwuFwuL3u0qWLSkpKzuo+oaGhatu2rS644ALXsbCwMG3dutXtPb1791ZQUJDrdWxsrBYtWqTq6mp9+eWXqq6u1tVXX+32nsrKSnXu3Nn1ulWrVqc8Q127d+9WXFyc22dddtllqqio0Lfffqvu3bs3+DmDg4MVHR3tet2jRw917NhRu3fvdnVTde/eXeedd57rmh07dqiiokIJCQlu9/rhhx/cuoLq2r59u/7zn/+4uqOkk919J06c0PHjx9W2bVtJUlRUVKPeV/vvrV27durQoYMOHz4sSSooKFCfPn3qHe9TUVGhoqIiPfjgg5oxY4breFVVlWsQ9ahRo3TLLbfo6quv1pVXXqmBAweqf//+p31WINAQdIAAERzs/q9rUFCQa5xMzaBeU2vcTO1xHqe7T1BQUL33dTqdDa6roqJCdrtdWVlZstvtbufatWvn+nObNm3cAow/qAkSNcrLy9WlSxe9/PLLp1x7ptlVFRUV+t3vfqdhw4adcu6cc85x/bn234cn76sbYmp/R23atDljXZI0a9Ys9e7d2+1czT8zkZGR+vjjj/Xpp59q7dq1uvvuu5WYmKjMzMzT3hcIJAQdwAJqWiWKi4tdx7y5vsyWLVvcXm/evFkXXnih7Ha7evXqperqah0+fFh9+vQ5q8/p0aOHPvjgAxljXKFo48aNat++vc4//3yP7lVVVaW8vDxX601hYaGOHTumHj16nPY9kZGROnTokOx2u37xi180+LMuvfRS7dmzRxdeeKFHNTb2fbU5HA4tX7683llcYWFh6tq1q/bt26frr7/+tPfo0KGDhg8fruHDh+uqq67SrbfeqiNHjri1yAGBillXgAW0adNGsbGxeuGFF7R7926tX79eTz/9tNfuf+DAAWVkZKiwsFD//Oc/9Y9//ENpaWmSpIsvvljXXXed/vSnP+nDDz/Uvn37tGXLFi1YsECrV6/26HN+85vf6Ntvv9WsWbO0e/durVq1Ss8++6wmTJjg8VT0Vq1aadasWdq8ebPy8vJ0//33KzY21hV86pOYmKjY2FhNmTJFn3/+ufbv36///Oc/euqpp07pzqttypQpevvtt/Xcc89p586d2r17t95991099dRTZ6yxse+r7be//a3Kysp0zz33aOvWrdq7d6/eeustFRYWSpJ+//vf64UXXtBLL72kPXv2aMeOHcrKytKiRYskSYsWLdI///lP7d69W3v27NH777+vLl26qGPHjg2uAfBntOgAFvHII4/owQcf1OjRo3XxxRfr3nvv1S233OKVe48cOVI//PCDxowZI7vdrrS0NI0bN851PiMjQ88//7zmzJmjgwcPqnPnzoqNjdXAgQM9+pxu3brphRde0Ny5c/X666+rc+fOuuGGG3THHXd4XHObNm102223adq0afruu+/Up0+fn1zhOSgoSC+88IKefvpp3X///SotLVVYWJj69OmjsLCw077vyiuv1Pz58zVv3jz97W9/U3BwsC655BKNGTPmjJ/X2PfVFhISosWLF+uxxx7TTTfdJJvNpl69eumyyy6TJI0ZM0Zt2rTRiy++qLlz56pdu3YKDw/XzTffLElq3769Fi5cqK+++ko2m03R0dF64YUXGrXGEeCPgkztTn0AsIBly5bpkUce0YYNG3xdCgAfI7IDAADLIugAAADLousKAABYFi06AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsv4/CX+liMeRp30AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(user_intersting_clips[\"user_id\"].value_counts(), bins=np.linspace(0, 802, 100))\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"number of preferences\")\n",
    "plt.ylabel(\"number of users\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:26.749973Z",
     "start_time": "2024-05-14T18:20:26.747874Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finally 13089082 requests 6544541.0 frac 0.1775184641983685\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"finally\",\n",
    "    user_intersting_clips.shape[0],\n",
    "    \"requests\",\n",
    "    user_intersting_clips.shape[0] / 2,\n",
    "    \"frac\",\n",
    "    user_intersting_clips.shape[0] / total_clip_counts,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:28.863757Z",
     "start_time": "2024-05-14T18:20:26.751063Z"
    }
   },
   "outputs": [
    {
     "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>time_used</th>\n",
       "      <th>user_id</th>\n",
       "      <th>upvote_count</th>\n",
       "      <th>batch_index</th>\n",
       "      <th>dislike_count</th>\n",
       "      <th>flag_count</th>\n",
       "      <th>play_count</th>\n",
       "      <th>skip_count</th>\n",
       "      <th>user_n_clips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.308908e+07</td>\n",
       "      <td>1.308908e+07</td>\n",
       "      <td>1.308908e+07</td>\n",
       "      <td>13089082.0</td>\n",
       "      <td>1.308908e+07</td>\n",
       "      <td>1.308908e+07</td>\n",
       "      <td>1.308908e+07</td>\n",
       "      <td>13089082.0</td>\n",
       "      <td>1.308908e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.038188e+01</td>\n",
       "      <td>1.204055e+07</td>\n",
       "      <td>1.627246e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.415112e-02</td>\n",
       "      <td>1.291916e-03</td>\n",
       "      <td>3.206592e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.310715e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.958979e+01</td>\n",
       "      <td>4.806311e+06</td>\n",
       "      <td>3.762990e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.540528e-01</td>\n",
       "      <td>2.785932e-01</td>\n",
       "      <td>1.307187e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.488485e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.490858e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>-3.500000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.500000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.675840e+01</td>\n",
       "      <td>8.656384e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.419160e+01</td>\n",
       "      <td>1.406632e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.078938e+02</td>\n",
       "      <td>1.580710e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.860000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.883650e+02</td>\n",
       "      <td>1.767499e+07</td>\n",
       "      <td>1.140000e+02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>3.644920e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.217500e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          time_used       user_id  upvote_count  batch_index  dislike_count    flag_count    play_count  skip_count  user_n_clips\n",
       "count  1.308908e+07  1.308908e+07  1.308908e+07   13089082.0   1.308908e+07  1.308908e+07  1.308908e+07  13089082.0  1.308908e+07\n",
       "mean   8.038188e+01  1.204055e+07  1.627246e-01          0.5   2.415112e-02  1.291916e-03  3.206592e+00         0.0  2.310715e+02\n",
       "std    2.958979e+01  4.806311e+06  3.762990e-01          0.5   1.540528e-01  2.785932e-01  1.307187e+02         0.0  5.488485e+02\n",
       "min    1.490858e+00  5.000000e+00 -3.500000e+01          0.0  -3.500000e+01  0.000000e+00  0.000000e+00         0.0  2.000000e+00\n",
       "25%    5.675840e+01  8.656384e+06  0.000000e+00          0.0   0.000000e+00  0.000000e+00  1.000000e+00         0.0  1.800000e+01\n",
       "50%    8.419160e+01  1.406632e+07  0.000000e+00          0.5   0.000000e+00  0.000000e+00  2.000000e+00         0.0  5.600000e+01\n",
       "75%    1.078938e+02  1.580710e+07  0.000000e+00          1.0   0.000000e+00  0.000000e+00  4.000000e+00         0.0  1.860000e+02\n",
       "max    5.883650e+02  1.767499e+07  1.140000e+02          1.0   7.000000e+00  1.000000e+03  3.644920e+05         0.0  1.217500e+04"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_intersting_clips.describe()\n",
    "# 214202\n",
    "# 608730\n",
    "# 1376250\n",
    "# 4625842"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:28.909584Z",
     "start_time": "2024-05-14T18:20:28.864979Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time validation 2024-04-26 00:00:00.157431+00:00 2024-05-14 13:06:19.594026+00:00\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"time validation\",\n",
    "    user_intersting_clips[\"created_at\"].min(),\n",
    "    user_intersting_clips[\"created_at\"].max(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:31.368082Z",
     "start_time": "2024-05-14T18:20:28.910832Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time validation NaT\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"time validation\",\n",
    "    user_intersting_clips[user_intersting_clips[\"model_name\"] == \"chirp-v3-engine-u\"][\n",
    "        \"created_at\"\n",
    "    ].min(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:31.425272Z",
     "start_time": "2024-05-14T18:20:31.369319Z"
    }
   },
   "outputs": [],
   "source": [
    "date_cut = '2024-04-22 19:17:53' # v3 launch test time  # 2024-04-03 07:47:01.770988+00:00 t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:31.673039Z",
     "start_time": "2024-05-14T18:20:31.426316Z"
    }
   },
   "outputs": [],
   "source": [
    "user_intersting_clips[\"is_pro_user\"] = user_intersting_clips[\"user_id\"].isin(\n",
    "    auth_user_df[\"user_id\"].unique()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:20:31.676207Z",
     "start_time": "2024-05-14T18:20:31.674703Z"
    }
   },
   "outputs": [],
   "source": [
    "# def parse_for_tag(x):\n",
    "#     if \"tags\" not in x:\n",
    "#         return \"\"\n",
    "#     out = x.get(\"tags\", \"\")\n",
    "#     return out.lower() if out else \"\"\n",
    "\n",
    "# user_intersting_clips[\"tags\"] = user_intersting_clips[\"metadata\"].apply(parse_for_tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:22:19.759516Z",
     "start_time": "2024-05-14T18:20:31.677218Z"
    }
   },
   "outputs": [],
   "source": [
    "user_compare_mask = (\n",
    "    user_intersting_clips[\"created_at\"] >= date_cut\n",
    ") # & (user_intersting_clips[\"is_pro_user\"] == True)\n",
    "\n",
    "user_compare_mask = (\n",
    "    (user_intersting_clips[\"created_at\"] >= date_cut)\n",
    "    & (\n",
    "        user_intersting_clips[\"model_name\"].isin(\n",
    "            [\n",
    "                \"chirp-v3-engine-d\",\n",
    "                # \"chirp-v3-engine-v0\",\n",
    "                \"chirp-v3-engine-i\",\n",
    "                \"chirp-v3-engine-s\",\n",
    "            ]\n",
    "        )\n",
    "    )\n",
    "    # & (user_intersting_clips[\"is_pro_user\"] == True)\n",
    ")\n",
    "# user_compare_mask = (user_intersting_clips[\"created_at\"] >= date_cut) & (\n",
    "#     user_intersting_clips[\"tags\"].apply(lambda x: \"metal\" in x.lower())\n",
    "# )\n",
    "# # this is fucked up sometimes one box doesn't give prompt to one generation\n",
    "extra_compare_mask = user_intersting_clips[user_compare_mask][\"request_id\"].isin(\n",
    "    user_intersting_clips[user_compare_mask][\"request_id\"]\n",
    "    .value_counts()\n",
    "    .index[user_intersting_clips[user_compare_mask][\"request_id\"].value_counts() == 2]\n",
    ")\n",
    "\n",
    "user_compare_mask = user_compare_mask & extra_compare_mask\n",
    "\n",
    "# for _, row in user_intersting_clips[user_intersting_clips[\"request_id\"].astype(str) == \"87c45d24-68ae-45dd-b5b7-92cd70bd0ab5\"].iterrows():\n",
    "#     print(row[\"metadata\"])\n",
    "\n",
    "# for _, row in user_intersting_clips[user_intersting_clips[\"request_id\"].astype(str) == \"fa86f07f-4476-406f-b756-7166e0b08679\"].iterrows():\n",
    "#     print(row[\"metadata\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:22:20.023424Z",
     "start_time": "2024-05-14T18:22:19.761231Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:23:53.010388Z",
     "start_time": "2024-05-14T18:22:20.024523Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2993476/1362788945.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  user_intersting_clips[user_compare_mask]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-v3-engine-d_win_over_chirp-v3-engine-i, win ratio 0.519, counts 176605\n",
      "chirp-v3-engine-d_win_over_chirp-v3-engine-s, win ratio 0.517, counts 172179\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-d, win ratio 0.481, counts 163785\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-i, win ratio 1.000, counts 13861\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-s, win ratio 0.499, counts 2785152\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-d, win ratio 0.483, counts 160544\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-i, win ratio 0.501, counts 2799463\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-s, win ratio 1.000, counts 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_preferfence_counts(\n",
    "    user_intersting_clips[user_compare_mask]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:25:10.024356Z",
     "start_time": "2024-05-14T18:23:53.011639Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first gen\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2993476/177329422.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  user_intersting_clips[\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-v3-engine-d_win_over_chirp-v3-engine-i, win ratio 0.531, counts 143616\n",
      "chirp-v3-engine-d_win_over_chirp-v3-engine-s, win ratio 0.528, counts 139450\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-d, win ratio 0.469, counts 126738\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-i, win ratio 1.000, counts 11141\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-s, win ratio 0.495, counts 2199895\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-d, win ratio 0.472, counts 124856\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-i, win ratio 0.505, counts 2241443\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-s, win ratio 1.000, counts 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"first gen\")\n",
    "get_preferfence_counts(\n",
    "    user_intersting_clips[\n",
    "        (user_intersting_clips[\"continued_parent\"].isna()) & user_compare_mask\n",
    "    ],\n",
    "    title_name=\"first generation\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:25:31.385004Z",
     "start_time": "2024-05-14T18:25:10.025636Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2993476/563358240.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  user_intersting_clips[\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-v3-engine-d_win_over_chirp-v3-engine-i, win ratio 0.471, counts 32989\n",
      "chirp-v3-engine-d_win_over_chirp-v3-engine-s, win ratio 0.478, counts 32729\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-d, win ratio 0.529, counts 37047\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-i, win ratio 1.000, counts 2720\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-s, win ratio 0.512, counts 585257\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-d, win ratio 0.522, counts 35688\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-i, win ratio 0.488, counts 558020\n",
      "chirp-v3-engine-s_win_over_chirp-v3-engine-s, win ratio 1.000, counts 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_preferfence_counts(\n",
    "    user_intersting_clips[\n",
    "        (~user_intersting_clips[\"continued_parent\"].isna())\n",
    "        & user_compare_mask\n",
    "    ],\n",
    "    \"is continue\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean up SHIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:25:31.400706Z",
     "start_time": "2024-05-14T18:25:31.386289Z"
    }
   },
   "outputs": [],
   "source": [
    "# to get the right play conts, we need the right df..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:26:32.043491Z",
     "start_time": "2024-05-14T18:25:31.402288Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12972399, 9)\n"
     ]
    }
   ],
   "source": [
    "# ~ only 1 min :) \n",
    "partial_reaction_df = reaction_df[reaction_df[\"clip_id\"].isin(user_intersting_clips[\"id\"])].copy()\n",
    "print(partial_reaction_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:29:18.781929Z",
     "start_time": "2024-05-14T18:26:32.044722Z"
    }
   },
   "outputs": [],
   "source": [
    "total_play_reaction_df_sum = partial_reaction_df.groupby(\"clip_id\")[\"play_count\"].sum()\n",
    "total_play_reaction_df_sum_df = total_play_reaction_df_sum.reset_index().rename(\n",
    "    columns={\"clip_id\": \"id\", \"play_count\": \"reaction_play_count\"}\n",
    ")\n",
    "user_intersting_clips = user_intersting_clips.merge(\n",
    "    total_play_reaction_df_sum_df, on=\"id\", how=\"left\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:29:49.448131Z",
     "start_time": "2024-05-14T18:29:18.783733Z"
    }
   },
   "outputs": [],
   "source": [
    "def unpack_dict(x):\n",
    "    if v := concat_clips_ids.get(str(x)):\n",
    "        return v\n",
    "    else:\n",
    "        return {\n",
    "            \"total_start_s\": None,\n",
    "            \"total_clip_s\": None,\n",
    "            \"concat_play_counts\": None,\n",
    "            \"concat_in_playlist\": None,\n",
    "            \"concat_likes\": None,\n",
    "        }\n",
    "\n",
    "\n",
    "extra_cols = user_intersting_clips[\"id\"].apply(unpack_dict)\n",
    "extra_cols_df = pd.DataFrame.from_records(extra_cols.values, index=extra_cols.index)\n",
    "user_intersting_clips[\n",
    "    [\n",
    "        \"total_start_s\",\n",
    "        \"total_clip_s\",\n",
    "        \"concat_play_counts\",\n",
    "        \"concat_in_playlist\",\n",
    "        \"concat_likes\",\n",
    "    ]\n",
    "] = extra_cols_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:29:49.464043Z",
     "start_time": "2024-05-14T18:29:49.449908Z"
    }
   },
   "outputs": [],
   "source": [
    "# user_intersting_clips.to_csv(\"/app/suno/data/dpo/data_prep/user_df_eda.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:29:49.583772Z",
     "start_time": "2024-05-14T18:29:49.465021Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30441645945834855"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# too_much_data_mask = (\n",
    "#     (user_intersting_clips[\"preference\"] == False)\n",
    "#     & (\n",
    "#         (user_intersting_clips[\"dislike_count\"] >= 1) # single play is super catchy\n",
    "#         | (user_intersting_clips[\"flag_count\"] >= 1) # or the concat play is super catchy\n",
    "#     )\n",
    "# )\n",
    "# too_much_data_mask.sum() / ((user_intersting_clips[\"preference\"] == True).sum())\n",
    "too_much_data_mask = (\n",
    "    (user_intersting_clips[\"preference\"] == True)\n",
    "    & (\n",
    "        (\n",
    "            user_intersting_clips[\"reaction_play_count\"] >= 3\n",
    "        )  # single play is super catchy\n",
    "        | (\n",
    "            user_intersting_clips[\"concat_play_counts\"] >= 3\n",
    "        )  # or the concat play is super catchy\n",
    "    )\n",
    "    & (user_intersting_clips[\"user_n_clips\"] >= 40)\n",
    "    # & (user_intersting_clips[\"continued_parent\"].isna())\n",
    ")\n",
    "too_much_data_mask.sum() / ((user_intersting_clips[\"preference\"] == True).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:29:54.947582Z",
     "start_time": "2024-05-14T18:29:49.584999Z"
    }
   },
   "outputs": [],
   "source": [
    "final_good_enough_requests = user_intersting_clips[too_much_data_mask][\n",
    "    \"request_id\"\n",
    "].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:30:20.547822Z",
     "start_time": "2024-05-14T18:29:54.948956Z"
    }
   },
   "outputs": [],
   "source": [
    "final_interesting_clips = user_intersting_clips[\n",
    "    user_intersting_clips[\"request_id\"].isin(set(final_good_enough_requests))\n",
    "].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:30:20.913558Z",
     "start_time": "2024-05-14T18:30:20.549108Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_index  preference\n",
      "0            True          1015491\n",
      "             False          976775\n",
      "1            False         1015491\n",
      "             True           976775\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(final_interesting_clips.groupby(\"batch_index\")[\"preference\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:30:43.514234Z",
     "start_time": "2024-05-14T18:30:20.914823Z"
    }
   },
   "outputs": [],
   "source": [
    "assert (\n",
    "    final_interesting_clips[final_interesting_clips[\"request_id\"].isna()].shape[0] == 0\n",
    ")\n",
    "check_df = final_interesting_clips.groupby(\"request_id\")[\"id\"].nunique()\n",
    "check_df[check_df.values != 2]\n",
    "assert check_df[check_df.values != 2].shape[0] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-15T03:42:48.733141Z",
     "start_time": "2024-05-15T03:39:19.412749Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done (3984532, 43)\n"
     ]
    }
   ],
   "source": [
    "# final_interesting_clips.to_csv(\n",
    "#     \"/home/tony/Data/Preference/7b_v2/interesting_clips_20240514.csv\", index=False\n",
    "# )\n",
    "print(\"done\", final_interesting_clips.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:30:43.597403Z",
     "start_time": "2024-05-14T18:30:43.531531Z"
    }
   },
   "outputs": [],
   "source": [
    "# smaller_mask = final_interesting_clips[\"model_name\"].isin([\"chirp-v3-engine-d\", \"chirp-v3-engine-v0\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:30:43.664332Z",
     "start_time": "2024-05-14T18:30:43.598441Z"
    }
   },
   "outputs": [],
   "source": [
    "# final_interesting_clips[smaller_mask].to_csv(\n",
    "#     \"/home/tony/Data/Preference/7b_v2/interesting_clips_20240421_prev_model.csv\", index=False\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For faster processing once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:30:44.801865Z",
     "start_time": "2024-05-14T18:30:43.665779Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total unique users 3340317\n"
     ]
    }
   ],
   "source": [
    "print(\"total unique users\", clip_df[\"user_id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:31:37.800860Z",
     "start_time": "2024-05-14T18:30:44.803140Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>password</th>\n",
       "      <th>last_login</th>\n",
       "      <th>is_superuser</th>\n",
       "      <th>username</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>is_staff</th>\n",
       "      <th>is_active</th>\n",
       "      <th>date_joined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63359</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>False</td>\n",
       "      <td>conflictolog</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-09-27 16:18:42.432982+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73</td>\n",
       "      <td>pbkdf2_sha256$390000$t8YTwHzVLwnmpFzRLugqwH$Ff...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>False</td>\n",
       "      <td>daniel@dcgross.com</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>daniel@dcgross.com</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-05-23 22:19:27+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79526</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>False</td>\n",
       "      <td>cintaterpendamhldrive.com_46460</td>\n",
       "      <td></td>\n",
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       "      <td></td>\n",
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       "      <td>True</td>\n",
       "      <td>2023-10-08 16:09:02.545084+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>79875</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>False</td>\n",
       "      <td>_scballofc</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-10-08 22:18:24.206502+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5212171</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>False</td>\n",
       "      <td>mepok39157@sfpixel.com</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>mepok39157@sfpixel.com</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2024-03-03 15:48:49.314837+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                           password last_login  is_superuser                         username first_name last_name                   email  is_staff  is_active                      date_joined\n",
       "0    63359                                                           NaT         False                     conflictolog                                                  False       True 2023-09-27 16:18:42.432982+00:00\n",
       "1       73  pbkdf2_sha256$390000$t8YTwHzVLwnmpFzRLugqwH$Ff...        NaT         False               daniel@dcgross.com                           daniel@dcgross.com     False       True        2023-05-23 22:19:27+00:00\n",
       "2    79526                                                           NaT         False  cintaterpendamhldrive.com_46460                                                  False       True 2023-10-08 16:09:02.545084+00:00\n",
       "3    79875                                                           NaT         False                       _scballofc                                                  False       True 2023-10-08 22:18:24.206502+00:00\n",
       "4  5212171                                                           NaT         False           mepok39157@sfpixel.com                       mepok39157@sfpixel.com     False       True 2024-03-03 15:48:49.314837+00:00"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT *\n",
    "FROM auth_user\n",
    "\"\"\"\n",
    "user_df = pd.read_sql_query(query, engine)\n",
    "user_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:31:38.120472Z",
     "start_time": "2024-05-14T18:31:37.802067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: count, dtype: int64)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[clip_df[\"user_id\"] == 4688272][\"created_at\"].apply(lambda x: str(x)[:10]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:31:38.196174Z",
     "start_time": "2024-05-14T18:31:38.121705Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 37)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[clip_df[\"user_id\"] == 4688272].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:31:38.227460Z",
     "start_time": "2024-05-14T18:31:38.197366Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10415471,  9349315, 11510778])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_intersting_clips[user_intersting_clips[\"user_n_clips\"] > 10000][\"user_id\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:31:38.288743Z",
     "start_time": "2024-05-14T18:31:38.228661Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>password</th>\n",
       "      <th>last_login</th>\n",
       "      <th>is_superuser</th>\n",
       "      <th>username</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>is_staff</th>\n",
       "      <th>is_active</th>\n",
       "      <th>date_joined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999719</th>\n",
       "      <td>3877426</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>False</td>\n",
       "      <td>tbchappell803@gmail.com</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>tbchappell803@gmail.com</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2024-01-29 07:20:07.504738+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id password last_login  is_superuser                 username first_name last_name                    email  is_staff  is_active                      date_joined\n",
       "1999719  3877426                 NaT         False  tbchappell803@gmail.com                       tbchappell803@gmail.com     False       True 2024-01-29 07:20:07.504738+00:00"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_df[user_df[\"id\"] == 3877426]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:31:43.960592Z",
     "start_time": "2024-05-14T18:31:38.289939Z"
    }
   },
   "outputs": [
    {
     "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>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>time_used</th>\n",
       "      <th>metadata</th>\n",
       "      <th>user_id</th>\n",
       "      <th>status</th>\n",
       "      <th>discord_message_id</th>\n",
       "      <th>prompt_id</th>\n",
       "      <th>request_id</th>\n",
       "      <th>is_generated</th>\n",
       "      <th>s3_id</th>\n",
       "      <th>upvote_count</th>\n",
       "      <th>batch_index</th>\n",
       "      <th>model_name</th>\n",
       "      <th>prompt_text</th>\n",
       "      <th>daily_theme_id</th>\n",
       "      <th>is_deleted</th>\n",
       "      <th>image_s3_id</th>\n",
       "      <th>is_public</th>\n",
       "      <th>dislike_count</th>\n",
       "      <th>flag_count</th>\n",
       "      <th>play_count</th>\n",
       "      <th>skip_count</th>\n",
       "      <th>title</th>\n",
       "      <th>slug</th>\n",
       "      <th>is_in_playlist</th>\n",
       "      <th>continued_parent</th>\n",
       "      <th>is_pro_user</th>\n",
       "      <th>user_n_clips</th>\n",
       "      <th>upvoted</th>\n",
       "      <th>deleted</th>\n",
       "      <th>has_continued</th>\n",
       "      <th>part_of_concat</th>\n",
       "      <th>has_action</th>\n",
       "      <th>downvoted</th>\n",
       "      <th>preference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, created_at, updated_at, time_used, metadata, user_id, status, discord_message_id, prompt_id, request_id, is_generated, s3_id, upvote_count, batch_index, model_name, prompt_text, daily_theme_id, is_deleted, image_s3_id, is_public, dislike_count, flag_count, play_count, skip_count, title, slug, is_in_playlist, continued_parent, is_pro_user, user_n_clips, upvoted, deleted, has_continued, part_of_concat, has_action, downvoted, preference]\n",
       "Index: []"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[clip_df[\"model_name\"] == \"chirp-v3-0\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:31:49.613697Z",
     "start_time": "2024-05-14T18:31:43.961792Z"
    }
   },
   "outputs": [],
   "source": [
    "# wtf\n",
    "for i, x in enumerate(clip_df[clip_df[\"model_name\"] == \"chirp-v3-0\"][\"metadata\"]):\n",
    "    print(x)\n",
    "    if i > 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:31:49.771430Z",
     "start_time": "2024-05-14T18:31:49.615023Z"
    }
   },
   "outputs": [
    {
     "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>password</th>\n",
       "      <th>last_login</th>\n",
       "      <th>is_superuser</th>\n",
       "      <th>username</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>is_staff</th>\n",
       "      <th>is_active</th>\n",
       "      <th>date_joined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>pbkdf2_sha256$390000$Pj04K3OODhsKlkcmmVFNeI$1B...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>tongbaojia@gmail.com</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>tongbaojia@gmail.com</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-04-29 15:44:31+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                           password last_login  is_superuser              username first_name last_name                 email  is_staff  is_active               date_joined\n",
       "0   8  pbkdf2_sha256$390000$Pj04K3OODhsKlkcmmVFNeI$1B...       None         False  tongbaojia@gmail.com                       tongbaojia@gmail.com     False       True 2023-04-29 15:44:31+00:00"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT *\n",
    "FROM auth_user\n",
    "WHERE id=8\n",
    "\"\"\"\n",
    "# 3 keenan\n",
    "# 6 martin\n",
    "# 8 tony\n",
    "# 186417 georg\n",
    "test_user_df = pd.read_sql_query(query, engine)\n",
    "test_user_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find some weird generations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:33:28.688838Z",
     "start_time": "2024-05-14T18:31:49.772474Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10270753, 26)\n",
      "0.1352300625330644\n"
     ]
    }
   ],
   "source": [
    "no_reaction_clip_df = total_clip_df[~total_clip_df[\"id\"].isin(reaction_df[\"clip_id\"])].copy()\n",
    "print(no_reaction_clip_df.shape)\n",
    "print(no_reaction_clip_df.shape[0] / total_clip_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:33:41.756941Z",
     "start_time": "2024-05-14T18:33:28.690058Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3077166141100425\n"
     ]
    }
   ],
   "source": [
    "no_reaction_clip_df[\"no_reaction_count\"] = no_reaction_clip_df.groupby(\"user_id\")[\n",
    "    \"user_id\"\n",
    "].transform(\"count\")\n",
    "no_reaction_clip_df[\"is_pro_user\"] = no_reaction_clip_df[\"user_id\"].isin(\n",
    "    auth_user_df[\"user_id\"].unique()\n",
    ")\n",
    "no_reaction_clip_df[\"user_id\"].nunique()\n",
    "bot_user_mask = (no_reaction_clip_df[\"no_reaction_count\"] >= 20)\n",
    "# bot_user_mask = (no_reaction_clip_df[\"no_reaction_count\"] >= 20) & (\n",
    "#     no_reaction_clip_df[\"is_pro_user\"] == False\n",
    "# )\n",
    "sub_total_clip_df = total_clip_df[\n",
    "    total_clip_df[\"user_id\"].isin(\n",
    "        no_reaction_clip_df[bot_user_mask][\"user_id\"].unique()\n",
    "    )\n",
    "].copy()\n",
    "print(no_reaction_clip_df[bot_user_mask].shape[0] / sub_total_clip_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:33:53.039794Z",
     "start_time": "2024-05-14T18:33:41.758156Z"
    }
   },
   "outputs": [],
   "source": [
    "sub_total_clip_df[\"gen_count\"] = sub_total_clip_df.groupby(\"user_id\")[\"user_id\"].transform('count')\n",
    "user_id_no_reaction_dict = no_reaction_clip_df.set_index('user_id')['no_reaction_count'].to_dict()\n",
    "user_id_total_dict = sub_total_clip_df.set_index('user_id')['gen_count'].to_dict()\n",
    "\n",
    "user_ratio_dict = {}\n",
    "for user_id, total_gen in user_id_total_dict.items():\n",
    "    user_ratio = user_id_no_reaction_dict.get(user_id, 0) /total_gen\n",
    "    user_ratio_dict[user_id] = user_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:33:55.804397Z",
     "start_time": "2024-05-14T18:33:55.565457Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mKDk5WeXKldPUqVO1d+9ezZgxQ/fee29B1AgAAFDkXB5pkqRmzZpdcUdwAACAksrl0PTzzz/LZrOpZs2akqSUlBQtWbJEDRs2VNeuXd1eIAAAgCdweXpuyJAhWrdunSQpIyNDTz75pLZu3ao333xT06dPd3uBAAAAnsDl0JSWlqaQkBBJ0vLly+Xv76/58+dr0qRJSkxMdHuBAAAAnsDl0JSbm6vSpUtLkpKSktS2bVtJUv369ZWRkeHe6gAAADzEDe3TNH/+fG3cuFFJSUlq1aqVJOnYsWO65ZZb3F0fAACAR3A5NA0dOlSffPKJevXqpaioKAUGBkqSvvnmG8e0HQAAQEnj8qfn7r77bq1bt07Z2dmqUqWKo/3xxx9XuXLl3FocAACAp7ihfZq8vLycApMk1a1b1y0FAQAAeCKXQ1Pbtm1ls9muenzlypV/qiAAAABP5HJo+tvf/ub0ODc3Vzt27NDatWvVp08ftxUGAADgSf50aLpo3rx52rZt258uCAAAwBO5/Om5q2nVqpW+/PJLd50OAADAo7gtNH3xxRfs0wQAAEosl6fnOnbs6LQQ3BijzMxMHT9+XGPGjHFrcQAAAJ7C5dDUrl07p8c2m03VqlVT8+bN1aBBA7cVBgAA4ElcDk0DBgwoiDoAAAA8mtvWNAEAAJRkhCYAAAALCE0AAAAWWApNO3fuVH5+fkHXAgAA4LEshaZOnTrpxIkTkqT777/f8d8AAAA3C0uhqXLlyjp06JAk6fDhwzLGFGhRAAAAnsbSlgPt27dXz5495efnJ5vNpi5dushuv3LeWrlypVsLPHr0qP75z39qzZo1+u2333T77bdrwoQJCg4OlvT75ppTp07VwoULdfr0aYWHh2vs2LG64447HOc4efKkYmJi9J///Ed2u13t27fXyy+/rAoVKjj67Ny5U+PHj9fWrVtVrVo19ezZU88++6xbrwUAABRflkJTTEyM/vrXv+rgwYOKjY3VY4895hQ4CsqpU6fUvXt33X333Zo1a5aqVq2qn376SVWqVHH0mTVrlubOnav4+HjVrVtXU6ZMUZ8+fbRs2TKVKVNGkjR06FBlZGQoISFBOTk5GjlypF555RW9/vrrkqTs7Gz16dNHLVq00Lhx47R7926NHDlSlStXVteuXQv8OgEAgOezvLllq1atJEnbt29X7969VbFixQIr6qJZs2apZs2aiouLc7Tdeuutjv82xmjOnDnq37+/Y6fyiRMnKiIiQitWrFBUVJT27t2rNWvW6NNPP3WMTo0aNUp9+/bVsGHDVKNGDX3++efKycnRhAkTVLp0af3lL39RamqqEhISCE0AAEDSDWw5EBcX5whMv/zyi3755Re3F3XRN998o6CgIA0cOFAtWrRQx44dtWDBAsfxQ4cOKSMjQxEREY62SpUqqUmTJtq8ebMkafPmzapcubIjMElSRESE7Ha7UlJSJElbtmxRs2bNVLp0aUefyMhI7d+/X6dOnSqw6wMAAMWHy6EpPz9f06dPV9OmTdWmTRu1adNGzZo101tvveX2bQnS09P18ccf64477tDs2bPVvXt3xcbGKjExUZKUkZEhSfLx8XF6no+PjzIzMyVJmZmZqlatmtPxUqVKqUqVKo7nZ2ZmytfX16nPxccXzwMAAG5uLv/tuTfffFOffvqphgwZovDwcEnSpk2bNH36dF24cEGDBg1yW3HGGAUFBWnw4MGSpDvvvFNpaWmaP3++OnXq5LbXAQAAuB6XQ1NiYqJiY2N1//33O9oCAwNVo0YNjRs3zq2hyc/PTw0aNHBqq1+/vr788kvHcUnKyspS9erVHX2ysrIUGBgo6fcRo+PHjzudIzc3V6dOnXI839fX97IRpYuP/zgCBQAAbk4uT8+dOnVK9evXv6y9fv36bl//Ex4erv379zu1HThwQHXq1JEk1a1bV35+fkpOTnYcz87O1o8//qiwsDBJUlhYmE6fPq1t27Y5+qxbt075+fkKCQmRJIWGhmrjxo3Kyclx9ElKSlK9evWcPqkHAABuXi6HpsDAQM2bN++y9nnz5jlGd9zlb3/7m3788UfNmDFDP/30k5YsWaIFCxaoR48ekiSbzabevXvr7bff1sqVK7Vr1y4NGzZM1atXd3yarkGDBmrZsqVGjx6tlJQUbdq0STExMYqKilKNGjUkSdHR0fL29tbLL7+stLQ0LVu2THPmzNFTTz3l1usBAADFl824uL33999/r379+qlWrVoKDQ2V9Punz37++WfNmjVLzZo1c2uB//nPf/TGG2/owIEDqlu3rp566ik9/vjjjuMXN7dcsGCBTp8+raZNm2rMmDGqV6+eo8/FzS2/+eYbx+aWo0aNuurmllWrVlXPnj3Vt29fl+vNzPxVbJgOAIA1pUrZVbVqBUVNXaPtR05ftV/j2pW1dGBLnThxRrm57vvgmc0m+fpWstbX1dAk/b5L90cffaR9+/ZJ+n1qrkePHo6Rm5sZoQkAAOuKU2hyeSG4JNWoUcOtC74BAAA8nctrmgAAAG5GhCYAAAALCE0AAAAWuBSajDE6cuSIzp8/X1D1AAAAeCSXQ1P79u31888/F1Q9AAAAHsml0GS323X77bfr5MmTBVQOAACAZ3J5TdOQIUM0ceJE7d69uyDqAQAA8Egu79M0fPhw/fbbb3rkkUfk7e2tsmXLOh3//vvv3VYcAACAp3A5NI0cObIg6gAAAPBoLoemTp06FUQdAAAAHu2G9mk6ePCg3nzzTQ0ePFhZWVmSpNWrVystLc2txQEAAHgKl0PT999/r+joaKWkpOirr77S2bNnJUm7du3StGnT3F4gAACAJ3A5NL3++uv6xz/+oYSEBHl7ezva77nnHm3ZssWdtQEAAHgMl0PT7t271a5du8vaq1WrphMnTrilKAAAAE/jcmiqVKmSMjIyLmtPTU1VjRo13FIUAACAp3E5NEVFRWnSpEnKyMiQzWZTfn6+Nm3apNdee00dO3YsgBIBAACKnsuhadCgQapfv77uu+8+nT17VlFRUerZs6fCwsLUv3//gqgRAACgyLm8T1Pp0qUVGxur559/XmlpaTpz5ozuvPNO3XHHHQVQHgAAgGdwOTRdVLt2bdWqVUuSZLPZ3FYQAACAJ7qhzS0XLlyohx56SMHBwQoODtZDDz2khQsXurs2AAAAj+HySNOUKVP0/vvvq2fPngoNDZUkbdmyRRMmTNCRI0f0wgsvuLtGAACAIudyaPr4448VExOjhx56yNF2//33KyAgQDExMYQmAABQIrk8PZebm6ugoKDL2hs3bqy8vDy3FAUAAOBpXA5NjzzyiD7++OPL2hcsWKDo6Gi3FAUAAOBpLE3PxcXFOf7bZrNp4cKF+u6779SkSRNJUkpKio4cOcLmlgAAoMSyFJp27Njh9Lhx48aSpIMHD0qSbrnlFt1yyy1KS0tzc3kAAACewVJomjt3bkHXAQAA4NFuaJ8mAACAm43LWw6cP39ec+fO1fr165WVlSVjjNPxxMREtxUHAADgKVwOTSNHjtR3332nDh06KCQkhD+hAgAAbgouh6ZVq1bpnXfeUdOmTQuiHgAAAI/k8pqmGjVqqEKFCgVRCwAAgMdyOTQNHz5ckyZN0uHDhwuiHgAAAI/k8vRccHCwzp8/r3bt2qls2bLy9vZ2Ov7999+7rTgAAABP4XJoGjx4sI4dO6ZBgwbJ19eXheAAAOCm4HJo2rx5sz755BMFBgYWRD0AAAAeyeU1TfXr19e5c+cKohYAAACP5XJoGjJkiOLj47V+/XqdOHFC2dnZTv8AAABKIpen55555hlJ0pNPPunUboyRzWZTamqqWwoDAADwJC6Hpjlz5hREHQAAAB7N5dDUvHnzgqgDAADAo7kcmjZs2HDN43fdddcNFwMAAOCpXA5NvXr1uqzt0r2aWNMEAABKoj890pSTk6PU1FRNmTJFgwYNclthAAAAnsTl0FSpUqXL2u699155e3srPj5en332mVsKAwAA8CQu79N0NT4+Ptq/f7+7TgcAAOBRXB5p2rlz52Vtx44d06xZs/jTKgAAoMRyOTR17NhRNptNxhin9tDQUL366qtuKwwAAMCTuByaVq5c6fTYbrerWrVqKlOmjNuKAgAA8DQuh6Y6deoURB0AAAAezeXQJEnJyclKTk5WVlaW8vPznY7FxcW5pTAAAABP4nJomj59ut566y0FBQXJz8/PaWNLAACAksrl0DR//nzFxcWpY8eOBVDOtb3zzjt6/fXX1bt3b7388suSpPPnzys+Pl7Lli3ThQsXFBkZqTFjxsjX19fxvCNHjmjs2LFav369ypcvr44dO2rIkCEqVeq/l79+/XrFx8crLS1NtWrVUv/+/dW5c+dCv0YAAOCZXN6nKScnR+Hh4QVRyzWlpKRo/vz5CggIcGqfMGGC/vOf/2jy5MmaO3eujh07pgEDBjiO5+XlqV+/fsrJydH8+fMVHx+vxMRETZ061dEnPT1d/fr10913361///vf+tvf/qZRo0ZpzZo1hXZ9AADAs7kcmh599FEtWbKkIGq5qjNnzujFF19UbGysqlSp4mj/9ddftWjRIo0YMUItWrRQUFCQJkyYoM2bN2vLli2SpLVr12rPnj365z//qUaNGql169Z64YUXNG/ePF24cEHS76NndevW1YgRI9SgQQP17NlTHTp00Pvvv1+o1wkAADyXy9Nz58+f14IFC5ScnKyAgACnKS5Jeumll9xW3EXjx49X69atFRERobffftvRvm3bNuXk5CgiIsLR1qBBA9WuXVtbtmxRaGiotmzZIn9/f6fpusjISI0dO1Z79uzRnXfeqS1btqhFixZOrxkZGakJEya4/VoAAEDx5HJo2rVrl2Pn7927dzsdK4hF4UuXLtWOHTv06aefXnYsMzNT3t7eqly5slO7j4+PMjIyHH0uDUySHI+v1yc7O1vnzp1T2bJl3XY9AACgeHI5NM2dO7cg6riin3/+Wa+++qree+89Ns8EAABF6ob2aSos27dvV1ZWltOn2PLy8rRhwwbNmzdPs2fPVk5Ojk6fPu002pSVlSU/Pz9Jv48YpaSkOJ03MzNTkpz6XGy7tE/FihUZZQIAAJI8PDTdc889ly06f+mll1S/fn09++yzqlWrlry9vZWcnKwOHTpIkvbt26cjR44oNDRU0u9/E2/GjBnKysqSj4+PJCkpKUkVK1ZUw4YNHX2+/fZbp9dJSkpynAMAAMCjQ1PFihXl7+/v1Fa+fHndcsstjvYuXbooPj5eVapUUcWKFRUbG6uwsDBH4ImMjFTDhg01bNgwvfjii8rIyNDkyZP1xBNPqHTp0pKkbt26ad68eZo4caK6dOmidevWafny5Zo5c2ahXi8AAPBcHh2arBg5cqTsdrsGDhzotLnlRV5eXpoxY4bGjh2rrl27qly5curUqZMGDhzo6HPrrbdq5syZiouL05w5c1SzZk3FxsaqZcuWRXFJAADAA9mMMaaoiyhJMjN/FXcUAABrSpWyq2rVCoqaukbbj5y+ar/GtStr6cCWOnHijHJz86/az1U2m+TrW8lSX5c3twQAALgZEZoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMCCUkVdAAqX3W6T3W67Zp/8fKP8fFNIFQEAUDwQmm4idrtNVW4pr1Je1x5gzM3L16mTZwlOAABcgtB0E7HbbSrlZdcL8zdrz7HsK/ZpWL2ipnQLk91uIzQBAHAJQtNNaM+xbG0/crqoywAAoFhhITgAAIAFjDThiryus+5JYsE4AODmQmiCE7+KZZSXb1S5crnr9mXBOADgZuLRoWnmzJn66quvtG/fPpUtW1ZhYWEaOnSo6tev7+hz/vx5xcfHa9myZbpw4YIiIyM1ZswY+fr6OvocOXJEY8eO1fr161W+fHl17NhRQ4YMUalS/7389evXKz4+XmlpaapVq5b69++vzp07F+r1eoLK5UrJy2675mJxiQXjAICbj0eHpu+//15PPPGEgoODlZeXpzfeeEN9+vTR0qVLVb58eUnShAkTtHr1ak2ePFmVKlVSTEyMBgwYoPnz50uS8vLy1K9fP/n6+mr+/Pk6duyYhg8fLm9vbw0ePFiSlJ6ern79+qlbt26aNGmSkpOTNWrUKPn5+ally5ZFdv1FicXiAAA48+jQNHv2bKfH8fHxatGihbZv36677rpLv/76qxYtWqRJkyapRYsWkn4PUQ8++KC2bNmi0NBQrV27Vnv27FFCQoJ8fX3VqFEjvfDCC5o0aZIGDBig0qVLa/78+apbt65GjBghSWrQoIE2bdqk999//6YNTQAAwFmx+vTcr7/+KkmqUqWKJGnbtm3KyclRRESEo0+DBg1Uu3ZtbdmyRZK0ZcsW+fv7O03XRUZGKjs7W3v27HH0uRi6Lu1z8RwAAADFJjTl5+drwoQJCg8Pl7+/vyQpMzNT3t7eqly5slNfHx8fZWRkOPpcGpgkOR5fr092drbOnTtXINcDAACKF4+enrvUuHHjlJaWpo8++qioSwEAADehYjHSNH78eK1atUoffPCBatas6Wj39fVVTk6OTp92XrCclZUlPz8/R5/MzEyn4xcfX69PxYoVVbZsWbdfDwAAKH48OjQZYzR+/Hh9/fXX+uCDD3Trrbc6HQ8KCpK3t7eSk5Mdbfv27dORI0cUGhoqSQoNDdXu3buVlZXl6JOUlKSKFSuqYcOGjj7r1q1zOndSUpLjHLg6Ly+7SpW6+j+73VbUJQIA4BYePT03btw4/b//9//0r3/9SxUqVHCsQapUqZLKli2rSpUqqUuXLoqPj1eVKlVUsWJFxcbGKiwszBF4IiMj1bBhQw0bNkwvvviiMjIyNHnyZD3xxBMqXbq0JKlbt26aN2+eJk6cqC5dumjdunVavny5Zs6cWVSX7vGsboLJBpgAgJLCo0PTxx9/LEnq1auXU3tcXJxj48mRI0fKbrdr4MCBTptbXuTl5aUZM2Zo7Nix6tq1q8qVK6dOnTpp4MCBjj633nqrZs6cqbi4OM2ZM0c1a9ZUbGws2w1cg5VNMNkAEwBQknh0aNq1a9d1+5QpU0ZjxoxxCkp/VKdOHc2aNeua57n77ru1ePFiV0u86bEJJgDgZuHRa5oAAAA8BaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALPDoLQdQMnh5XTub5+cb9nECAHg8QhMKDLuGAwBKEkITCgy7hgMAShJCEwocu4YDAEoCFoIDAABYwEhTCWK322S32656/HoLsgEAwNURmkoIu92mKreUVymCEQAABYLQVELY7TaV8rJfc9H1fQF+erFDYCFXBgBAyUBoKmGutei6gV+FQq7GOvZyAgB4OkITihR7OQEAigtCE4oUezkBAIoLQhM8Ans5AQA8HR+1AgAAsICRJhQbLBYHABQlQhM8HovFAQCegNAEj8dicQCAJyA0odiwsljcyp+KYRoPAHAjCE0oEaxO4UlM4wEAbgyhCSWClSk8iWk8AMCNIzShRGG/JwBAQWGfJgAAAAsITQAAABYwPYebEhtlAgBcRWjCTYWNMgEAN4rQhJsKG2UCAG4UoQk3JXdslMkUHgDcXAhNwB8whQcAuBJCE/AHTOEBAK6E0ARcBVN4AIBLEZqAG8AUHgDcfAhNwA1wZQrP29tLeXn5Vz0Xo1EAUDwQmoA/4VpTeIxGAUDJQmgCCog7R6MkRqQAoKgRmoAC5o7RKIkRKQAoaoQmoAhZGY2SWB8FAJ6A0AR4gOttb8D6KAAoeoQmoBjg03oAUPQITUAx4q5P62X/ek7GXD04EawA4HKEJqCEsDIaddcdVTX6oca65Zby1zwXwQoALkdoAkqYa41GNfCrUKjBSiJcASg5CE3ATaiwgpXE4nQAJQehCcAV/dlgJbm2eac7MKoFoCARmgDcMHdtlZCXb+Rlt/3pPlanDAsTQQ4oOQhNxYTdbpP9Gj8wvLzshVgNYI2Vxen3BfjpxQ6Bf7qP1SlDK+HLaj+rQc4d05PX+x5w0c0e0qzcp5v9HuHGEZqKAbvdpiq3lFcpghGKqetN9bmrjzsCmtV+Vvq4a+8sV74HlOQ1ZNcLRDabTRUrlb3ufSrJ98gqwuWNITQVA3a7TaW87Ja+gQM3uz8bvqz2s9LHXXtneXnZr/s9QHLvGjIrPzAL8wevK8HRSpC1222FFgjcdZ/ceR4r99JKuLzZwhehqRix8g0cgOdw595ZUuGuIbtekHNlVMcd68ysBMeLvzxe7z5dPF9hcNfol7uDzvXupZUA7q73QHFaXkJo+oN58+Zp9uzZysjIUGBgoEaPHq2QkJCiLgtAMeauacXrcdcaMleCnLvOY3Wt2Z/95dGdwdKVfn82oFgJjVZHGi+GFHeMkkruew8UB4SmSyxbtkxxcXEaN26cmjRpog8++EB9+vTRF198IR8fn6IuD0AJ5s6R5MJcH+aOrSlcWUP2Z7krWLpat7sCirvOcz2u3KfC+qXAExCaLpGQkKDHH39cXbp0kSSNGzdOq1at0qJFi9S3b98irg4ACpe7gpw715C5S2GvfbsWVwLKnz2P1XNdVBjvgeK0vITQ9H8uXLig7du3q1+/fo42u92uiIgIbd682fJ5bNcfpb1hjWtXVrnSXlc81sCvYqH1KezXo27Pq4m6Pa+PJ9ZE3a71KVPKftU+pf9vSu3PnsfVc3lKH0mq7/vfcOXOn7WunMtmPGkXuCJ09OhRtWrVSvPnz1dYWJijfeLEidqwYYMWLlxYhNUBAICiVnyWrAMAABQhQtP/qVq1qry8vJSVleXUnpWVJV9f3yKqCgAAeApC0/8pXbq0GjdurOTkZEdbfn6+kpOTnabrAADAzYmF4Jd46qmnNHz4cAUFBSkkJEQffPCBfvvtN3Xu3LmoSwMAAEWM0HSJBx98UMePH9fUqVOVkZGhRo0a6d1332V6DgAA8Ok5AAAAK1jTBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITR5i3rx5atu2rYKDg/XYY48pJSXlmv2XL1+uBx54QMHBwYqOjtbq1asLqdLizZX7vGDBAvXo0UN33XWX7rrrLj355JPX/brgd66+ny9aunSpAgIC9PzzzxdwhSWDq/f59OnTGjdunCIjIxUUFKQOHTrwvcMCV+/z+++/rw4dOigkJEStW7fWhAkTdP78+UKqtnjasGGDnnvuOUVGRiogIEArVqy47nPWr1+vTp06KSgoSH/961/12WefFXyhBkVu6dKlpnHjxubTTz81aWlpZtSoUaZZs2YmMzPziv03bdpkGjVqZGbNmmX27Nlj3nzzTdO4cWOza9euQq68eHH1Pg8ePNh8+OGHZseOHWbPnj1mxIgRpmnTpuaXX34p5MqLF1fv80Xp6emmZcuWpkePHqZ///6FVG3x5ep9Pn/+vOncubN59tlnzcaNG016erpZv369SU1NLeTKixdX7/Pnn39ugoKCzOeff27S09PNmjVrzL333msmTJhQyJUXL6tWrTJvvPGG+eqrr4y/v7/5+uuvr9n/4MGDpkmTJiYuLs7s2bPHzJ071zRq1Mh8++23BVonockDPProo2bcuHGOx3l5eSYyMtLMnDnziv1feOEF07dvX6e2xx57zIwePbpA6yzuXL3Pf5Sbm2vCwsJMYmJiAVVYMtzIfc7NzTVdu3Y1CxYsMMOHDyc0WeDqff7oo4/M/fffby5cuFBYJZYIrt7ncePGmd69ezu1xcXFmW7duhVonSWJldA0ceJEExUV5dT2j3/8wzz99NMFWZpheq6IXbhwQdu3b1dERISjzW63KyIiQps3b77ic7Zs2aIWLVo4tUVGRmrLli0FWWqxdiP3+Y9+++035ebmqkqVKgVVZrF3o/f5rbfeko+Pjx577LHCKLPYu5H7/M033yg0NFTjx49XRESEHnroIc2YMUN5eXmFVXaxcyP3OSwsTNu3b3dM4aWnp2v16tVq3bp1odR8syiqn4PsCF7ETpw4oby8PPn4+Di1+/j4aN++fVd8TmZm5mW7lPv4+CgzM7PA6izubuQ+/9GkSZNUvXp1p2+gcHYj93njxo369NNPtXjx4kKosGS4kfucnp6udevWKTo6Wu+8844OHjyocePGKTc3VwMGDCiMsoudG7nP0dHROnHihHr06CFjjHJzc9WtWzc999xzhVHyTeNKPwd9fX2VnZ2tc+fOqWzZsgXyuow0ARa88847WrZsmaZPn64yZcoUdTklRnZ2toYNG6aYmBhVq1atqMsp0Ywx8vHxUUxMjIKCgvTggw/queee0/z584u6tBJl/fr1mjlzpsaMGaPPPvtM06dP1+rVq/XWW28VdWlwA0aailjVqlXl5eWlrKwsp/asrKyr/s07X1/fy0aVrtUfN3afL5o9e7beeecdJSQkKDAwsCDLLPZcvc/p6ek6fPiw+vfv72jLz8+XJN1555364osvdNtttxVs0cXQjbyf/fz8VKpUKXl5eTna6tevr4yMDF24cEGlS5cu0JqLoxu5z1OmTNHDDz/smGoOCAjQ2bNn9corr6h///6y2xmrcIcr/RzMzMxUxYoVC2yUSWKkqciVLl1ajRs3VnJysqMtPz9fycnJCgsLu+JzQkNDtW7dOqe2pKQkhYaGFmSpxdqN3GdJmjVrlv71r3/p3XffVXBwcGGUWqy5ep/r16+vJUuWaPHixY5/bdu21d13363FixerZs2ahVl+sXEj7+fw8HAdPHjQEUol6cCBA/Lz8yMwXcWN3Odz585dFowuBlXDn3p1m6L6OUho8gBPPfWUFixYoMTERO3du1djx47Vb7/9ps6dO0uShg0bptdff93Rv3fv3lqzZo3ee+897d27V9OmTdO2bdvUs2fPorqEYsHV+/zOO+9oypQpmjBhgurUqaOMjAxlZGTozJkzRXUJxYIr97lMmTLy9/d3+le5cmVVqFBB/v7+/DC/Blffz927d9fJkyf16quvav/+/Vq1apVmzpypJ554oqguoVhw9T63adNGH3/8sZYuXar09HR99913mjJlitq0aeM0ygdnZ86cUWpqqlJTUyVJhw4dUmpqqo4cOSJJev311zVs2DBH/27duik9PV0TJ07U3r17NW/ePC1fvlxPPvlkgdbJ9JwHePDBB3X8+HFNnTpVGRkZatSokd59913H8O/PP//s9JtLeHi4Jk2apMmTJ+uNN97QHXfcobfeekv+/v5FdQnFgqv3ef78+crJydHAgQOdzjNgwAD97//+b6HWXpy4ep9xY1y9z7Vq1dLs2bMVFxenhx9+WDVq1FDv3r317LPPFtUlFAuu3uf+/fvLZrNp8uTJOnr0qKpVq6Y2bdpo0KBBRXUJxcK2bdvUu3dvx+O4uDhJUqdOnRQfH6+MjAz9/PPPjuO33nqrZs6cqbi4OM2ZM0c1a9ZUbGysWrZsWaB12gzjhQAAANfFr3sAAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNKDGMMRo9erSaN2+ugIAAx3b8hWX9+vUKCAjQ6dOnC/V1r2TTpk2Kjo5W48aN9fzzzxd1OYXqs88+U7NmzYq6jMsU9vsjICBAK1asKJTXKqkOHTpUJN9L4LkITSgxvv32WyUmJmrGjBlau3at/vKXvxTYa/Xq1UuvvvqqU1tYWJjWrl2rSpUqFdjrWhUfH6/AwECtXLlS8fHxRV1OgWnbtq3ef/99p7YHH3xQX375ZdEU9H+u9P5wlytdM/68ESNGXPYLRq1atQr8ewmKF/72HEqM9PR0+fn5KTw8/Kp9Lly4UGB/BLZ06dLy8/MrkHO76uDBg+rWrZtq1qxZ1KW4zBijvLw8lSp1Y9+eypYtq7Jly7q5KtyMvLy8POb/aXgGRppQIowYMUIxMTE6cuSIAgIC1LZtW0m//8Y/fvx4vfrqq7r77rvVp08fSVJCQoKio6MVGhqq1q1ba+zYsTpz5ozTOTdt2qRevXqpSZMmuuuuu9SnTx+dOnVKI0aM0Pfff685c+YoICBAAQEBOnTo0BWnX7788ktFRUUpKChIbdu21Xvvvef0Gm3bttWMGTP00ksvKSwsTPfdd58++eSTa17rhQsXFBsbqxYtWig4OFjdu3dXSkqKpP9OJ5w8eVIjR45UQECAPvvssyue59ixY+rbt69CQkLUtm1bLVmy5LJRjNOnT+vll1/WPffco/DwcPXu3Vs7d+50HJ82bZoeeeQRLV68WG3btlXTpk01aNAgZWdnO/rk5+dr5syZatu2rUJCQvTwww/riy++cBy/eN9Wr16tzp07Kzg4WJs2bdLBgwfVv39/RUREKCwsTF26dFFSUpLjeb169dLhw4cVFxfn+DpIV56e++ijj9SuXTsFBQWpQ4cOWrx4sdPxgIAALVy4UH//+9/VpEkTtW/fXitXrnQcP3XqlIYMGaJ77rlHISEhat++vRYtWnTF+3q198dF27dvV+fOndWkSRN169ZN+/btcxy70Wu+mhMnTlz1mvLy8jRy5EjH16VDhw764IMPHMfXrl2r4ODgy6YTY2Njnf6w6saNG9WjRw+FhISodevWio2N1dmzZ69ak5X3zLXe41ezePFide7cWWFhYbr33ns1ZMgQZWVlOfVJS0tTv379FB4errCwMPXo0UMHDx7UtGnTlJiYqJUrVzru6/r16684Pff999/r0UcfVVBQkCIjIzVp0iTl5uY6jvfq1UuxsbGaOHGimjdvrnvvvVfTpk1zHDfGaNq0abrvvvsc54iNjb3mtcGDGKAEOH36tJk+fbpp1aqVOXbsmMnKyjLGGNOzZ08TGhpqXnvtNbN3716zd+9eY4wxCQkJJjk52aSnp5ukpCTToUMHM2bMGMf5duzYYYKCgsyYMWNMamqq2b17t5k7d67Jysoyp0+fNl27djWjRo0yx44dM8eOHTO5ublm3bp1xt/f35w6dcoYY8zWrVtNYGCgmT59utm3b59ZtGiRCQkJMYsWLXK8Tps2bUzz5s3Nhx9+aA4cOGBmzpxpAgMDHXVeSUxMjImMjDSrVq0yaWlpZvjw4eauu+4yJ06cMLm5uebYsWMmPDzcvP/+++bYsWPmt99+u+J5nnzySfPII4+YLVu2mG3btpmePXuakJAQk5CQ4NSnX79+JiUlxezfv9/Ex8eb5s2bmxMnThhjjJk6daoJDQ01AwYMMLt27TIbNmww9957r3njjTcc5/jXv/5lHnjgAfPtt9+agwcPmkWLFpmgoCCzfv16Y4xx3Lfo6Gizdu1a89NPP5kTJ06Y1NRU8/HHH5tdu3aZ/fv3mzfffNMEBwebw4cPG2OMOXHihGnVqpWZPn264+tgjDGLFi0yTZs2dbz+V199ZRo3bmw+/PBDs2/fPvPee++ZRo0ameTkZEcff39/06pVK7NkyRJz4MABExMTY0JDQx3XOW7cOPPII4+YlJQUk56ebr777juzcuXKK97X670/HnvsMbN+/XqTlpZmevToYbp27ep47o1e85Vc75ouXLhgpkyZYlJSUszBgwfNv//9b9OkSROzdOlSY4wxubm5JiIiwixYsMBxzj+2/fTTTyY0NNQkJCSY/fv3m02bNpmOHTuaESNGXLUuK++Za73Hr2bhwoVm1apV5uDBg2bz5s2ma9eu5plnnnEc/+WXX0zz5s3NgAEDTEpKitm3b5/59NNPzd69e012drZ54YUXTJ8+fRz39fz58yY9Pd34+/ubHTt2OM7RpEkTM3bsWLNnzx7z9ddfm7vvvttMnTrV8To9e/Y04eHhZtq0aWb//v0mMTHRBAQEmLVr1xpjjFm+fLkJDw83q1atMocPHzY//vij+eSTT656XfAshCaUGAkJCaZNmzZObT179jQdO3a87nOXL19umjdv7ng8ePBg061bt6v279mzp4mNjXVq+2NoGjx4sHnqqaec+rz22mvmwQcfdDxu06aNGTp0qONxfn6+adGihfnoo4+u+LpnzpwxjRs3Np9//rmj7cKFCyYyMtLMmjXL0da0aVOncPZHe/bsMf7+/iYlJcXRduDAAePv7+8ITRs2bDDh4eHm/PnzTs9t166dmT9/vjHm9x+ATZo0Mb/++qvTNT722GPGGGPOnz9vmjRpYn744Qenc4wcOdIMHjzYGPPf+/b1119ftd6LoqKizNy5cx2P27Rp4xTyjLk8NF0MMJcaOHCgefbZZx2P/f39zZtvvul4fObMGePv729Wr15tjDGmX79+1wwCf3St90dSUpKjbdWqVcbf39+cO3fuqueycs1Xcr1rupJx48aZ//3f/3U8jo2NNb1793Y8XrNmjQkKCnK8x0eOHGlGjx7tdI4NGzaYwMDAq17T9d4zVt/j15OSkmL8/f1Ndna2McaY119/3bRt29ZcuHDhiv2HDx9u+vfv79T2x9D0xhtvmA4dOpj8/HxHnw8//NCEhoaavLw8Y8zvX/vu3bs7nadLly7mn//8pzHGmPfee8+0b9/+qnXAs7GmCSVe48aNL2tLSkrSzJkztW/fPmVnZysvL0/nz5/Xb7/9pnLlyik1NVUPPPDAn3rdffv26f7773dqCw8P15w5c5SXlycvLy9Jcppisdls8vX1vWxa4aKDBw8qJyfHad2Wt7e3QkJCtHfvXsu17d+/X6VKlXK6N7fffruqVKnieLxr1y6dPXtWd999t9Nzz507p4MHDzoe16lTRxUrVnQ8rl69uqP+n376Sb/99puefvppp3Pk5OSoUaNGTm3BwcFOj8+cOaPp06dr1apVysjIUF5ens6dO6cjR45Yvk7p969D165dndoufh0udenXoXz58qpYsaKOHz8uSerevbsGDhyoHTt26N5771W7du2uuXbuWi59nYvrZbKyslS7dm23XbOVa5KkefPmadGiRTpy5IjOnz+vnJwcBQYGOo5HR0era9euOnr0qGrUqKElS5bovvvuU+XKlSVJO3fu1K5du7RkyRLHc4wxys/P16FDh9SgQYMr1nWt98yNvse3bdum6dOna+fOnTp16pSMMZKkn3/+WQ0bNlRqaqqaNWsmb29vS/fuSvbu3auwsDDZbDZHW9OmTXX27Fn98ssvql27tiRdNm3q5+fnuL4HHnhAH3zwgdq1a6eWLVuqdevWatOmzQ2v4UPh4quEEq9cuXJOjw8dOqR+/fqpe/fuGjRokKpUqaJNmzbp5ZdfVk5OjsqVK1eoC4n/+M3SZrM5vuEXpTNnzsjPz09z58697NilnxC80jf7i/VfXNsyc+ZM1ahRw6nPHxfk//Hr9NprrykpKUnDhw/XbbfdprJly2rgwIHKycm5sQu6jj/+MLXZbMrPz5cktW7dWv/5z3+0evVqfffdd3ryySf1xBNPaPjw4S6/zqX36+IP34uv4+5rvtY1LV26VK+99pqGDx+usLAwVahQQbNnz9aPP/7o6B8SEqLbbrtNy5YtU/fu3fX11187fRrz7Nmz6tatm3r16nXZa9eqVeuqdV3rPXMjzp49qz59+jjWGFWtWlU///yz+vTp47h3nvL/dK1atfTFF18oKSlJSUlJGjdunGbPnq25c+f+qUCHwkFowk1n+/btMsZoxIgRstt//yzE8uXLnfoEBAQoOTlZAwcOvOI5vL29HT98rqZ+/fr64YcfnNp++OEH3XHHHY5RJlfddttt8vb21g8//KA6depI+n3UZuvWrfrb3/5m+Tz16tVTbm6uduzYoaCgIEm/jwqdOnXK0adx48bKzMyUl5eX6tate0P1NmjQQKVLl9aRI0fUvHlzl567efNmderUSX/9618l/R7iDh8+7NTHla9Dp06dHG0//PCDGjZs6FI91apVU6dOndSpUyfNnz9fEydOvGposlLXlbjrmq344YcfFBYWpieeeMLRdukI4kXR0dFasmSJatSoIbvdrvvuu89x7M4779SePXt0++23/+l6LrqR9/i+fft08uRJDR061BHWtm3b5tQnICBAiYmJysnJuWI4sXJfGzRooC+//FLGGEfg3bRpkypUqODSJ1XLli2rtm3bqm3bturRo4f+53/+R7t3777iqDg8C5+ew03n9ttvV05OjubOnav09HQtXrxY8+fPd+rTt29fbd26VWPHjtXOnTu1d+9effTRR46pjTp16ujHH3/UoUOHdPz48St+s3366aeVnJyst956S/v371diYqLmzZt32VSVK8qXL6/u3btr4sSJ+vbbb7Vnzx6NHj1a586d06OPPmr5PA0aNFBERIReeeUVpaSkaMeOHRo9erTKli3r+GEQERGh0NBQ/f3vf9fatWt16NAh/fDDD3rzzTe1detWS69TsWJFPf3004qLi1NiYqIOHjyo7du3a+7cuUpMTLzmc2+//XZ9/fXXSk1N1c6dOzVkyJDL7nOdOnW0YcMGHT161Gna6VLPPPOMEhMT9dFHH+nAgQNKSEjQ119/7dLXYcqUKVqxYoV++uknpaWladWqVVederpY1/XeHwV5zVZfa9u2bVqzZo3279+vyZMnX/HrGh0dre3bt2vGjBnq0KGD0wjhs88+q82bN2v8+PFKTU3VgQMHtGLFCo0fP/6G67qR93jt2rXl7e3t+H965cqV+te//uXU54knnlB2drYGDx6srVu36sCBA1q8eLHj04t16tTRrl27tG/fPh0/fvyKo3s9evTQL7/8opiYGO3du1crVqzQtGnT9NRTTzl+Abuezz77TAsXLtTu3buVnp6uzz//XGXLlnVM7cGzEZpw0wkMDNRLL72kWbNm6aGHHtKSJUs0ePBgpz716tXTe++9p507d+qxxx5Tt27dtHLlSsew+9NPPy0vLy9FRUWpRYsWV1xz0rhxY02ePFnLli1TdHS0pk6dqoEDB6pz585/qv6hQ4eqQ4cOGjZsmDp16qSffvpJ7777rtN6JCtee+01+fj46IknntCAAQP0+OOPq0KFCipTpoyk36cU3nnnHd1111166aWX9MADD2jw4ME6fPiwfH19Lb/OP/7xDz3//POaOXOmHnzwQT3zzDNatWrVdUevRowYocqVK6tbt2567rnn1LJly8t+Ex84cKAOHz6sdu3aqUWLFlc8T7t27TRy5Ei99957euihhzR//nxNmDDhsrVa1+Lt7a033nhDDz/8sHr27Cm73a433njjqv2tvD8K8pqt6Natm9q3b69Bgwbp8ccf18mTJ9WjR4/L+t1+++0KCQnRrl27FB0d7XQsMDBQc+fO1YEDB9SjRw916tRJU6dOVfXq1W+4Lsn193i1atUUHx+vL774Qg8++KBmzZp12Shg1apV9cEHH+js2bPq1auXOnfurIULFzpGnR5//HHVq1dPXbp0UYsWLS4bJZakGjVq6J133lFKSooeeeQRjR07Vo8++qj69+9v+doqV66shQsXqnv37nr44YeVnJysGTNmqGrVqi7cIRQVm/GExRMAitwvv/yi1q1b6/333/9TP4wBoKRiTRNwk0pOTtbZs2fl7++vjIwM/fOf/1SdOnU88u+2AYAnIDQBN6nc3Fy9+eabSk9PV4UKFRQWFqZJkybxCR4AuAqm5wAAACxgITgAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAgv8Paq+eMjdnF/UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(user_ratio_dict.values(), bins=np.linspace(0, 1, 50))\n",
    "plt.xlabel(\"fraction of generations that have no actions\")\n",
    "plt.ylabel(\"number of users\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:33:53.298249Z",
     "start_time": "2024-05-14T18:33:53.279564Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10705\n"
     ]
    }
   ],
   "source": [
    "super_bad_user_id = set()\n",
    "for user_id, user_ratio in user_ratio_dict.items():\n",
    "    if user_ratio == 1.0:\n",
    "        # print(user_id)\n",
    "        super_bad_user_id.add(user_id)\n",
    "print(len(super_bad_user_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-14T18:33:58.006779Z",
     "start_time": "2024-05-14T18:33:55.805615Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025405746026805454"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_clip_df[total_clip_df[\"user_id\"].isin(super_bad_user_id)].shape[0] / total_clip_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}