{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select Preference Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:04.757824Z",
     "start_time": "2024-05-26T00:11:04.555293Z"
    },
    "execution": {
     "iopub.execute_input": "2024-08-25T15:47:50.213624Z",
     "iopub.status.busy": "2024-08-25T15:47:50.213134Z",
     "iopub.status.idle": "2024-08-25T15:47:50.363280Z",
     "shell.execute_reply": "2024-08-25T15:47:50.362775Z",
     "shell.execute_reply.started": "2024-08-25T15:47:50.213602Z"
    }
   },
   "outputs": [],
   "source": [
    "# setup tailscale if you haven't\n",
    "# https://tailscale.com/kb/1031/install-linux\n",
    "!sudo tailscale up --accept-routes=true\n",
    "\n",
    "# setup autoload\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:08.392310Z",
     "start_time": "2024-05-26T00:11:04.759383Z"
    },
    "execution": {
     "iopub.execute_input": "2024-08-25T15:47:50.941284Z",
     "iopub.status.busy": "2024-08-25T15:47:50.940953Z",
     "iopub.status.idle": "2024-08-25T15:47:53.580587Z",
     "shell.execute_reply": "2024-08-25T15:47:53.579958Z",
     "shell.execute_reply.started": "2024-08-25T15:47:50.941267Z"
    }
   },
   "outputs": [],
   "source": [
    "# pip install psycopg2-binary\n",
    "# make sure sqlalchemy is >=2\n",
    "# pip install \"sqlalchemy>=2\"\n",
    "import os\n",
    "import datetime\n",
    "from collections import defaultdict, Counter\n",
    "import json\n",
    "from urllib.parse import quote\n",
    "\n",
    "import boto3\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "import tqdm\n",
    "from botocore.exceptions import ClientError\n",
    "from suno_analytics.preference_helper import get_preference_counts\n",
    "from suno_analytics.preference_data_selection import (\n",
    "    gather_data,\n",
    "    plot_clip_distribution,\n",
    "    parse_metadata_for_basics,\n",
    "    get_concat_clip_ids,\n",
    "    validate_preference_data,\n",
    "    run_bot_detection,\n",
    "    print_out_value_counts_nicely,\n",
    "    merge_concat_clips_with_reactions,\n",
    "    plot_clip_basic_distributions,\n",
    ")\n",
    "\n",
    "\n",
    "# setup some pandas display stuff\n",
    "pd.set_option(\"display.max_rows\", 500)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"display.width\", 1000)\n",
    "\n",
    "\n",
    "def get_secret():\n",
    "    secret_name = \"rds!cluster-a3b66c33-40a7-47dd-bd6e-32b1c17c9124\"\n",
    "    region_name = \"us-east-2\"\n",
    "    # Create a Secrets Manager client\n",
    "    session = boto3.session.Session()\n",
    "    client = session.client(service_name=\"secretsmanager\", region_name=region_name)\n",
    "    try:\n",
    "        get_secret_value_response = client.get_secret_value(SecretId=secret_name)\n",
    "    except ClientError as e:\n",
    "        raise e\n",
    "    secret = get_secret_value_response[\"SecretString\"]\n",
    "    return json.loads(secret)\n",
    "\n",
    "\n",
    "my_secrets = get_secret()\n",
    "\n",
    "# alternative...\n",
    "engine = sqlalchemy.create_engine(\n",
    "    \"postgresql://postgres:%s@suno-main-pgdb-prod-analytics.cnfvffydbwvc.us-east-2.rds.amazonaws.com/suno_main\"\n",
    "    % quote(my_secrets[\"password\"])\n",
    ")\n",
    "\n",
    "\n",
    "home_dir = os.path.expanduser(\"~\")\n",
    "snow_password_path = os.path.join(home_dir, \".aws\", \"snow_pw.txt\")\n",
    "if os.path.exists(snow_password_path):\n",
    "    # !pip install snowflake\n",
    "    from snowflake.core import Root\n",
    "    from snowflake.snowpark import Session\n",
    "\n",
    "    with open(snow_password_path, \"r\") as fp:\n",
    "        fp_lines = fp.readlines()\n",
    "        snow_password = fp_lines[0].strip()\n",
    "        snow_username = fp_lines[1].strip()\n",
    "\n",
    "    CONNECTION_PARAMETERS = {\n",
    "        \"account\": \"fu90569.us-east-2.aws\",\n",
    "        \"user\": snow_username,\n",
    "        \"password\": snow_password,\n",
    "        \"role\": \"ACCOUNTADMIN\",\n",
    "        \"database\": \"SUNO_PROD\",\n",
    "        \"warehouse\": \"SUNO_PROD_LARGE\",\n",
    "        \"schema\": \"PROD\",\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validate some info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:08.550447Z",
     "start_time": "2024-05-26T00:11:08.397196Z"
    },
    "execution": {
     "iopub.execute_input": "2024-08-25T15:47:53.893087Z",
     "iopub.status.busy": "2024-08-25T15:47:53.892389Z",
     "iopub.status.idle": "2024-08-25T15:47:53.910672Z",
     "shell.execute_reply": "2024-08-25T15:47:53.910112Z",
     "shell.execute_reply.started": "2024-08-25T15:47:53.893067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-08-25 14:47:53\n"
     ]
    }
   ],
   "source": [
    "# there are 4 hr time difference between eastern time and utc\n",
    "# cutoff_date = \"2024-08-09 21:00:00\"  # v4-t2 out\n",
    "#cutoff_date = \"2024-08-24 18:30:00\"  # v4-t2-12 out\n",
    "# cutoff_date = \"2024-08-25 06:40:00\"  # v4-t2-10 out\n",
    "cutoff_date = (datetime.datetime.now() - datetime.timedelta(hours=1)).astimezone(datetime.timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "print(cutoff_date)\n",
    "\n",
    "target_model_name = \"chirp-v3p5-engine-t-2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:09.349857Z",
     "start_time": "2024-05-26T00:11:08.551408Z"
    },
    "execution": {
     "iopub.execute_input": "2024-08-25T15:47:54.816863Z",
     "iopub.status.busy": "2024-08-25T15:47:54.816291Z",
     "iopub.status.idle": "2024-08-25T15:47:55.049232Z",
     "shell.execute_reply": "2024-08-25T15:47:55.048673Z",
     "shell.execute_reply.started": "2024-08-25T15:47:54.816842Z"
    }
   },
   "outputs": [],
   "source": [
    "df_all_tables = pd.read_sql_query(\n",
    "    \"SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'\",\n",
    "    engine,\n",
    ")\n",
    "# should have all the basic table names here\n",
    "assert df_all_tables[\"table_name\"].nunique() >= 61"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T15:47:55.796606Z",
     "iopub.status.busy": "2024-08-25T15:47:55.796152Z",
     "iopub.status.idle": "2024-08-25T15:47:55.814839Z",
     "shell.execute_reply": "2024-08-25T15:47:55.814377Z",
     "shell.execute_reply.started": "2024-08-25T15:47:55.796588Z"
    }
   },
   "outputs": [],
   "source": [
    "# generated_clip_query = f\"\"\"\n",
    "# SELECT COUNT(*) AS total_rows FROM bots_generatedclip\n",
    "# \"\"\"\n",
    "# total_clip_df = pd.read_sql_query(generated_clip_query, engine)\n",
    "# total_clip_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T15:48:00.389566Z",
     "iopub.status.busy": "2024-08-25T15:48:00.389019Z"
    }
   },
   "outputs": [],
   "source": [
    "generated_clip_query = f\"\"\"\n",
    "SELECT id, model_name, created_at, \n",
    "       CAST(JSON_EXTRACT_PATH_TEXT(metadata::json, 'duration') AS FLOAT) AS duration \n",
    "FROM bots_generatedclip\n",
    "WHERE status='complete'\n",
    "\"\"\"\n",
    "total_clip_df = pd.read_sql_query(generated_clip_query, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T14:04:08.049217Z",
     "iopub.status.busy": "2024-08-26T14:04:08.048463Z",
     "iopub.status.idle": "2024-08-26T14:04:08.395235Z",
     "shell.execute_reply": "2024-08-26T14:04:08.394775Z",
     "shell.execute_reply.started": "2024-08-26T14:04:08.049197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720129300, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_clip_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T14:04:09.833179Z",
     "iopub.status.busy": "2024-08-26T14:04:09.833020Z",
     "iopub.status.idle": "2024-08-26T14:04:09.850856Z",
     "shell.execute_reply": "2024-08-26T14:04:09.850447Z",
     "shell.execute_reply.started": "2024-08-26T14:04:09.833164Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720129300, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_clip_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T14:04:37.626235Z",
     "iopub.status.busy": "2024-08-26T14:04:37.625829Z",
     "iopub.status.idle": "2024-08-26T15:09:21.126247Z",
     "shell.execute_reply": "2024-08-26T15:09:21.125684Z",
     "shell.execute_reply.started": "2024-08-26T14:04:37.626214Z"
    }
   },
   "outputs": [],
   "source": [
    "total_clip_df.to_csv(\"big_dump.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T15:41:44.090605Z",
     "iopub.status.busy": "2024-08-26T15:41:44.090186Z",
     "iopub.status.idle": "2024-08-26T15:42:03.015579Z",
     "shell.execute_reply": "2024-08-26T15:42:03.014973Z",
     "shell.execute_reply.started": "2024-08-26T15:41:44.090585Z"
    }
   },
   "outputs": [],
   "source": [
    "# first_slice = total_clip_df.sample(n=1000000).copy()\n",
    "first_slice = total_clip_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T15:42:03.016783Z",
     "iopub.status.busy": "2024-08-26T15:42:03.016609Z",
     "iopub.status.idle": "2024-08-26T15:42:03.345393Z",
     "shell.execute_reply": "2024-08-26T15:42:03.344967Z",
     "shell.execute_reply.started": "2024-08-26T15:42:03.016766Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((720129300, 4),\n",
       " Index(['id', 'model_name', 'created_at', 'duration'], dtype='object'))"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_slice.shape, first_slice.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T15:42:03.346151Z",
     "iopub.status.busy": "2024-08-26T15:42:03.346010Z",
     "iopub.status.idle": "2024-08-26T16:16:16.087817Z",
     "shell.execute_reply": "2024-08-26T16:16:16.087229Z",
     "shell.execute_reply.started": "2024-08-26T15:42:03.346136Z"
    }
   },
   "outputs": [],
   "source": [
    "first_slice['date'] = pd.to_datetime(first_slice['created_at']).dt.strftime('%Y-%m-%d')\n",
    "first_slice['duration_h'] = first_slice['duration'] / 3600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T16:16:16.089239Z",
     "iopub.status.busy": "2024-08-26T16:16:16.089071Z",
     "iopub.status.idle": "2024-08-26T16:16:51.504627Z",
     "shell.execute_reply": "2024-08-26T16:16:51.504064Z",
     "shell.execute_reply.started": "2024-08-26T16:16:16.089222Z"
    }
   },
   "outputs": [],
   "source": [
    "daily_df = first_slice.groupby('date')['duration_h'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T16:16:51.505538Z",
     "iopub.status.busy": "2024-08-26T16:16:51.505382Z",
     "iopub.status.idle": "2024-08-26T16:16:51.527722Z",
     "shell.execute_reply": "2024-08-26T16:16:51.527297Z",
     "shell.execute_reply.started": "2024-08-26T16:16:51.505521Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2023-05-12', '2024-08-25', (463, 2))"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df[\"date\"].min(), daily_df[\"date\"].max(), daily_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T16:16:51.528637Z",
     "iopub.status.busy": "2024-08-26T16:16:51.528493Z",
     "iopub.status.idle": "2024-08-26T16:16:51.564846Z",
     "shell.execute_reply": "2024-08-26T16:16:51.564415Z",
     "shell.execute_reply.started": "2024-08-26T16:16:51.528622Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T16:16:51.565567Z",
     "iopub.status.busy": "2024-08-26T16:16:51.565425Z",
     "iopub.status.idle": "2024-08-26T16:16:51.603063Z",
     "shell.execute_reply": "2024-08-26T16:16:51.602635Z",
     "shell.execute_reply.started": "2024-08-26T16:16:51.565553Z"
    }
   },
   "outputs": [],
   "source": [
    "daily_df['date'] = pd.to_datetime(daily_df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T17:26:06.340348Z",
     "iopub.status.busy": "2024-08-26T17:26:06.339944Z",
     "iopub.status.idle": "2024-08-26T17:26:06.360553Z",
     "shell.execute_reply": "2024-08-26T17:26:06.360093Z",
     "shell.execute_reply.started": "2024-08-26T17:26:06.340327Z"
    }
   },
   "outputs": [],
   "source": [
    "# daily_df.to_csv(\"hello_georg.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-26T17:29:45.094704Z",
     "iopub.status.busy": "2024-08-26T17:29:45.094320Z",
     "iopub.status.idle": "2024-08-26T17:29:46.009700Z",
     "shell.execute_reply": "2024-08-26T17:29:46.009230Z",
     "shell.execute_reply.started": "2024-08-26T17:29:45.094684Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the bar plot\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.bar(daily_df['date'], daily_df['duration_h'] / 24 / 365)\n",
    "\n",
    "# Customize the plot\n",
    "plt.title('Total generated audios by Date', fontsize=16)\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "plt.ylabel('Total generated years of audio', fontsize=12)\n",
    "\n",
    "start_date = pd.to_datetime('2023-11-01')  # Adjust this to your desired start date\n",
    "end_date = pd.to_datetime('2024-09-01')    # Adjust this to your desired end date\n",
    "plt.xlim(start_date, end_date)\n",
    "\n",
    "# Format x-axis\n",
    "# plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "# plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())\n",
    "\n",
    "# Rotate and align the tick labels so they look better\n",
    "# plt.gcf().autofmt_xdate()\n",
    "\n",
    "# # Add grid lines for better readability\n",
    "plt.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# # Customize tick labels\n",
    "# plt.tick_params(axis='both', which='major', labelsize=10)\n",
    "\n",
    "# # Add value labels on top of each bar\n",
    "# for i, v in enumerate(daily_df['duration_h']):\n",
    "#     plt.text(daily_df['date'].iloc[i], v, f'{v:.0f}', ha='center', va='bottom')\n",
    "\n",
    "# # Adjust layout and display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query the DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-23T21:05:56.430863Z",
     "iopub.status.busy": "2024-08-23T21:05:56.430716Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start gather data from 2024-08-25 06:40:00\n",
      "Bots Action: 1,348,514 rows\n",
      " ---- Execution time: 25.36 seconds\n",
      "Reactions: 1,683,798 rows\n",
      " ---- Execution time: 21.08 seconds\n",
      "Total Clips: 1,396,292 rows\n",
      " ---- Execution time: 246.37 seconds\n",
      "Playlist Clips: 18,777 rows\n",
      " ---- Execution time: 0.55 seconds\n",
      "Authenticated Users: 373,604 rows\n",
      " ---- Execution time: 0.90 seconds\n",
      "Discord Info: 299,962 rows\n",
      "subscription_status\n",
      "active      284050\n",
      "past_due     15912\n",
      "Name: count, dtype: int64\n",
      " ---- Execution time: 11.71 seconds\n"
     ]
    }
   ],
   "source": [
    "gathered_data = gather_data(engine, cutoff_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unpack the information\n",
    "bots_action_df = gathered_data[\"bots_action_df\"]\n",
    "reaction_df = gathered_data[\"reaction_df\"]\n",
    "total_clip_df = gathered_data[\"total_clip_df\"]\n",
    "playlist_clip_df = gathered_data[\"playlist_clip_df\"]\n",
    "auth_user_df = gathered_data[\"auth_user_df\"]\n",
    "discord_info_df = gathered_data[\"discord_info_df\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parse out the necessary metadata early\n",
    "total_clip_df[[\"continued_parent\", \"duration\", \"source\", \"clip_type\"]] = pd.DataFrame(\n",
    "    total_clip_df[\"metadata\"].map(parse_metadata_for_basics).tolist(), index=total_clip_df.index\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total clips: 1396292\n",
      "total without model: 46822\n",
      "uploads: 12079\n",
      "stems: 34743\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of hours: 9\n",
      "Average clips per hour: 155143.56\n",
      "Max clips in an hour: 213674\n",
      "Min clips in an hour: 51296\n"
     ]
    }
   ],
   "source": [
    "# filter on versions\n",
    "clip_df = total_clip_df.copy()\n",
    "total_clip_counts = clip_df.shape[0]\n",
    "print(f\"total clips: {total_clip_counts}\")\n",
    "# check the number of audio uploads\n",
    "upload_clip_df = total_clip_df[total_clip_df[\"clip_type\"] == \"upload\"].copy()\n",
    "stem_clip_df = total_clip_df[total_clip_df[\"clip_type\"] == \"stem\"].copy()\n",
    "print(\"total without model:\", (total_clip_df[\"model_name\"] == \"\").sum())\n",
    "print(\"uploads:\", upload_clip_df.shape[0])\n",
    "print(\"stems:\", stem_clip_df.shape[0])\n",
    "\n",
    "# Call the function\n",
    "plot_clip_distribution(total_clip_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proceed with feature engineering and cleaning up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of upvoates: 172,078 rows\n",
      "number of flagged reports: 2,649 rows\n"
     ]
    }
   ],
   "source": [
    "upvoted_df = reaction_df[reaction_df[\"reaction_type\"] == \"L\"].copy()\n",
    "print(f\"number of upvoates: {upvoted_df.shape[0]:,} rows\")\n",
    "upvoted_ids = upvoted_df[\"clip_id\"]\n",
    "\n",
    "flagged_df = reaction_df[reaction_df[\"flagged\"]].copy()\n",
    "print(f\"number of flagged reports: {flagged_df.shape[0]:,} rows\")\n",
    "flagged_ids = flagged_df[\"clip_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactions fraction by pro user:\n",
      "False: 1136953 (67.52%)\n",
      "True: 546845 (32.48%)\n",
      "Reactions fraction by pro user:\n",
      "False: 827714 (59.28%)\n",
      "True: 568578 (40.72%)\n"
     ]
    }
   ],
   "source": [
    "# this is probably the right way to figure out the pro user group\n",
    "pro_users = set(discord_info_df[\"user_id\"].unique())\n",
    "reaction_df[\"is_pro_user\"] = reaction_df[\"user_id\"].isin(pro_users)\n",
    "clip_df[\"is_pro_user\"] = clip_df[\"user_id\"].isin(pro_users)\n",
    "\n",
    "# this is very interesting....\n",
    "# reaction check\n",
    "print(\"Reactions fraction by pro user:\")\n",
    "print_out_value_counts_nicely(reaction_df, \"is_pro_user\")\n",
    "# clip check\n",
    "print(\"Reactions fraction by pro user:\")\n",
    "print_out_value_counts_nicely(clip_df, \"is_pro_user\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stem parent ids: 17335\n"
     ]
    }
   ],
   "source": [
    "# find out the stem parent ids\n",
    "stem_parent_ids = set(stem_clip_df[\"metadata\"].apply(lambda x: x.get(\"stem_from_id\", \"xxx\")))\n",
    "print(\"stem parent ids:\", len(stem_parent_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:12.019778Z",
     "start_time": "2024-05-26T00:22:57.637371Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clips in a splaylist:\n",
      "False: 1387767 (99.39%)\n",
      "True: 8525 (0.61%)\n",
      "Clips has stem children:\n",
      "False: 1379703 (98.81%)\n",
      "True: 16589 (1.19%)\n"
     ]
    }
   ],
   "source": [
    "# add clip is in playlist feature\n",
    "clip_df[\"is_in_playlist\"] = clip_df[\"id\"].isin(playlist_clip_df[\"clip_id\"].unique())\n",
    "print(\"Clips in a splaylist:\")\n",
    "print_out_value_counts_nicely(clip_df, \"is_in_playlist\")\n",
    "clip_df[\"has_stems\"] = clip_df[\"id\"].astype(str).isin(stem_parent_ids)\n",
    "print(\"Clips has stem children:\")\n",
    "print_out_value_counts_nicely(clip_df, \"has_stems\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:17.912428Z",
     "start_time": "2024-05-26T00:23:12.021726Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clips that have children: 144153 \n",
      "clips that are parents: 27431 \n",
      " Average continues from clip =  5.26\n",
      "web: 1370901 (99.18%)\n",
      "ios: 11343 (0.82%)\n"
     ]
    }
   ],
   "source": [
    "# parse the metadata for histories and types\n",
    "clip_history_df = clip_df[~clip_df[\"continued_parent\"].isna()].copy()\n",
    "# these are the direct parent's ids -- not grandparents\n",
    "has_continued_children_ids = clip_history_df[\"continued_parent\"]\n",
    "print(\n",
    "    \"clips that have children:\",\n",
    "    len(has_continued_children_ids),\n",
    "    \"\\nclips that are parents:\",\n",
    "    has_continued_children_ids.nunique(),\n",
    "    \"\\n\",\n",
    "    \"Average continues from clip = \",\n",
    "    round(len(has_continued_children_ids) / len(has_continued_children_ids.unique()), 2),\n",
    ")\n",
    "# Get value counts\n",
    "print_out_value_counts_nicely(clip_df, \"source\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:22.092835Z",
     "start_time": "2024-05-26T00:23:17.914456Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total uploads: 46822\n",
      "clips without request id: 56992\n",
      "clips without request id: 56992 clip_type\n",
      "stem         34743\n",
      "upload       12079\n",
      "concat       10168\n",
      "edit_crop        2\n",
      "Name: count, dtype: int64\n",
      "Clips without request id (concat, uploads...) frac = 0.00728\n"
     ]
    }
   ],
   "source": [
    "print(\"total uploads:\", (clip_df[\"model_name\"] == \"\").sum())\n",
    "print(\"clips without request id:\", (clip_df[\"request_id\"].isna()).sum())\n",
    "# the nans are concats, we want to drop them for now\n",
    "concated_clips = clip_df[clip_df[\"clip_type\"] == \"concat\"].copy()\n",
    "non_request_clips = clip_df[clip_df[\"request_id\"].isna()].copy()\n",
    "print(\"clips without request id:\", non_request_clips.shape[0], non_request_clips[\"clip_type\"].value_counts())\n",
    "# need to kick them out...\n",
    "clip_df = clip_df[~clip_df[\"request_id\"].isna()]\n",
    "print(\n",
    "    f\"Clips without request id (concat, uploads...) frac = {concated_clips.shape[0] / total_clip_counts:.5f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:22.833148Z",
     "start_time": "2024-05-26T00:23:22.094796Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-v3p5-engine-s-8: 1023867 (76.45%)\n",
      "chirp-v3p5-engine-upload-4: 62202 (4.64%)\n",
      "chirp-v3p5-engine-t-2: 58312 (4.35%)\n",
      "chirp-v3p5-engine-s-24: 58015 (4.33%)\n",
      "chirp-v3p5-engine-t-2-10: 57966 (4.33%)\n",
      "chirp-v3-engine-i: 34650 (2.59%)\n",
      "chirp-v3p5-engine-b: 29858 (2.23%)\n",
      "chirp-v2-xxl-alpha: 4902 (0.37%)\n",
      "chirp-v3-5: 4849 (0.36%)\n",
      "chirp-v2-engine-msft-60s: 3880 (0.29%)\n",
      "chirp-v3p5-engine-ft-1: 332 (0.02%)\n",
      "chirp-v3p5-engine-short: 272 (0.02%)\n",
      "chirp-v3-0: 189 (0.01%)\n",
      "chirp-v3-5-short: 6 (0.00%)\n"
     ]
    }
   ],
   "source": [
    "# check the model conts\n",
    "print_out_value_counts_nicely(clip_df, \"model_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:26.317699Z",
     "start_time": "2024-05-26T00:23:22.835074Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-filter model type clip_df shape: (1339300, 33)\n",
      "post-filter model type clip_df shape: (1334262, 33)\n",
      "chirp-v3p5-engine-s-8: 1023867 (76.74%)\n",
      "chirp-v3p5-engine-upload-4: 62202 (4.66%)\n",
      "chirp-v3p5-engine-t-2: 58312 (4.37%)\n",
      "chirp-v3p5-engine-s-24: 58015 (4.35%)\n",
      "chirp-v3p5-engine-t-2-10: 57966 (4.34%)\n",
      "chirp-v3-engine-i: 34650 (2.60%)\n",
      "chirp-v3p5-engine-b: 29858 (2.24%)\n",
      "chirp-v2-xxl-alpha: 4902 (0.37%)\n",
      "chirp-v2-engine-msft-60s: 3880 (0.29%)\n",
      "chirp-v3p5-engine-ft-1: 332 (0.02%)\n",
      "chirp-v3p5-engine-short: 272 (0.02%)\n",
      "chirp-v3-5-short: 6 (0.00%)\n"
     ]
    }
   ],
   "source": [
    "print(\"pre-filter model type clip_df shape:\", clip_df.shape)\n",
    "clip_df = clip_df[(clip_df[\"model_name\"] != \"chirp-v3-5\") & (clip_df[\"model_name\"] != \"chirp-v3-0\")]\n",
    "print(\"post-filter model type clip_df shape:\", clip_df.shape)\n",
    "print_out_value_counts_nicely(clip_df, \"model_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "concat reactions: 8562 unique concat clips: 6419\n",
      "total concats (10168, 36)\n",
      "check \n",
      "        reaction_play_count  reaction_upvote_count  reaction_dislike_count\n",
      "count          6419.000000            6419.000000             6419.000000\n",
      "mean              2.525783               0.220595                0.010438\n",
      "std              26.393192               0.539444                0.101639\n",
      "min               1.000000               0.000000                0.000000\n",
      "25%               1.000000               0.000000                0.000000\n",
      "50%               1.000000               0.000000                0.000000\n",
      "75%               2.000000               0.000000                0.000000\n",
      "max            2081.000000              18.000000                1.000000\n",
      "All concats 10168\n",
      "total concats with plays 6419\n"
     ]
    }
   ],
   "source": [
    "concated_clips = merge_concat_clips_with_reactions(concated_clips, reaction_df)\n",
    "# TODO: why so many clips are concats without plays??? -- oh probably they concat multiple times?\n",
    "print(\"All concats\", concated_clips.shape[0])\n",
    "concated_clips = concated_clips[concated_clips[\"reaction_play_count\"] > 0]\n",
    "print(\"total concats with plays\", concated_clips.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:38.974479Z",
     "start_time": "2024-05-26T00:23:34.385507Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6419it [00:00, 18157.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total concat unique clips are: 12600 with error: 0, duplicate 1415 \n",
      " uploads are in concats 964 frac 0.080\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "concat_clips_ids = get_concat_clip_ids(concated_clips, clip_df, upload_clip_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:40.236690Z",
     "start_time": "2024-05-26T00:23:39.995713Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1.334262e+06\n",
      "mean     9.896511e+01\n",
      "std      2.990241e+02\n",
      "min      1.000000e+00\n",
      "25%      8.000000e+00\n",
      "50%      1.000000e+01\n",
      "75%      4.800000e+01\n",
      "max      3.880000e+03\n",
      "Name: user_n_clips, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# set user number of clips generated\n",
    "clip_df[\"user_n_clips\"] = clip_df[\"user_id\"].map(clip_df[\"user_id\"].value_counts())\n",
    "print(clip_df[\"user_n_clips\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:44.826860Z",
     "start_time": "2024-05-26T00:23:40.238330Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has upvoted upvoted\n",
      "False    1278037\n",
      "True       56225\n",
      "Name: count, dtype: int64 upvoted\n",
      "False    0.957861\n",
      "True     0.042139\n",
      "Name: proportion, dtype: float64 upvote_count\n",
      "False    0.957526\n",
      "True     0.042474\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",
    "    (clip_df[\"upvote_count\"] >= 1).value_counts(normalize=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:48.404433Z",
     "start_time": "2024-05-26T00:23:44.857788Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downvoted fraction by category:\n",
      "False: 1294668 (97.03%)\n",
      "True: 39594 (2.97%)\n"
     ]
    }
   ],
   "source": [
    "disliked_ids = reaction_df[reaction_df[\"reaction_type\"] == \"D\"][\"clip_id\"].unique()\n",
    "\n",
    "clip_df[\"downvoted\"] = clip_df[\"id\"].isin(disliked_ids)\n",
    "print(\"downvoted fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"downvoted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:56.590022Z",
     "start_time": "2024-05-26T00:23:48.405668Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has_continued fraction by category:\n",
      "False: 1324500 (99.27%)\n",
      "True: 9762 (0.73%)\n"
     ]
    }
   ],
   "source": [
    "# add continued column -- uuid and str are not compatible X.x\n",
    "clip_df[\"has_continued\"] = clip_df[\"id\"].astype(str).isin(set(list(has_continued_children_ids)))\n",
    "print(\"has_continued fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"has_continued\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:04.704306Z",
     "start_time": "2024-05-26T00:23:56.591353Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "part_of_concat fraction by category:\n",
      "False: 1325781 (99.36%)\n",
      "True: 8481 (0.64%)\n",
      "\n",
      "Model distribution for part_of_concat clips:\n",
      "chirp-v3p5-engine-s-8: 68.28%\n",
      "chirp-v3p5-engine-upload-4: 11.41%\n",
      "chirp-v3-engine-i: 5.97%\n",
      "chirp-v3p5-engine-t-2: 5.62%\n",
      "chirp-v3p5-engine-t-2-10: 5.36%\n",
      "chirp-v3p5-engine-s-24: 3.02%\n",
      "chirp-v2-xxl-alpha: 0.22%\n",
      "chirp-v3p5-engine-b: 0.08%\n",
      "chirp-v3p5-engine-ft-1: 0.02%\n"
     ]
    }
   ],
   "source": [
    "# add concat column\n",
    "clip_df[\"part_of_concat\"] = clip_df[\"id\"].astype(str).isin(concat_clips_ids)\n",
    "print(\"part_of_concat fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"part_of_concat\")\n",
    "\n",
    "print(\"\\nModel distribution for part_of_concat clips:\")\n",
    "for model, fraction in (\n",
    "    clip_df[clip_df[\"part_of_concat\"]][\"model_name\"].value_counts(normalize=True).items()\n",
    "):\n",
    "    print(f\"{model}: {fraction:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:09.954233Z",
     "start_time": "2024-05-26T00:24:04.705554Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has_action fraction by category:\n",
      "False: 1326177 (99.39%)\n",
      "True: 8085 (0.61%)\n"
     ]
    }
   ],
   "source": [
    "# verify bots action are all non-empty\n",
    "action_mask = (\n",
    "    bots_action_df[\"download_audio_count\"]\n",
    "    + bots_action_df[\"download_video_count\"]\n",
    "    + bots_action_df[\"download_audio_wav_count\"]\n",
    "    # + bots_action_df[\"share_count\"] # will remove share cause it can be negative, just can be...\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(\"has_action fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"has_action\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:14.579841Z",
     "start_time": "2024-05-26T00:24:09.955568Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flagged fraction by category:\n",
      "False: 1332528 (99.87%)\n",
      "True: 1734 (0.13%)\n"
     ]
    }
   ],
   "source": [
    "# add downvoted column\n",
    "clip_df[\"flagged\"] = clip_df[\"id\"].isin(flagged_ids)\n",
    "print(\"flagged fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"flagged\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deleted fraction by category:\n",
      "False: 1149653 (86.16%)\n",
      "True: 184609 (13.84%)\n"
     ]
    }
   ],
   "source": [
    "clip_df[\"deleted\"] = clip_df[\"is_deleted\"]\n",
    "print(\"deleted fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"deleted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:15.382315Z",
     "start_time": "2024-05-26T00:24:14.581073Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total clips: 1,334,262\n",
      "Must be positive: 73,072 (5.48%)\n",
      "Definitely not negative: 1,111,181 (83.28%)\n",
      "Must be negative: 223,081 (16.72%)\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\"])\n",
    "    | (clip_df[\"has_action\"])\n",
    "    | (clip_df[\"part_of_concat\"])\n",
    "    | (clip_df[\"is_in_playlist\"])\n",
    ")\n",
    "must_be_not_negative_mask = (~clip_df[\"downvoted\"]) & (~clip_df[\"deleted\"]) & (~clip_df[\"flagged\"])\n",
    "must_be_negative_mask = (clip_df[\"downvoted\"]) | (clip_df[\"flagged\"]) | (clip_df[\"deleted\"])\n",
    "total_clips_count = clip_df.shape[0]\n",
    "must_be_positive_count = sum(must_be_positive_mask)\n",
    "definitely_not_negative_count = sum(must_be_not_negative_mask)\n",
    "must_be_negative_count = sum(must_be_negative_mask)\n",
    "\n",
    "print(\n",
    "    f\"Total clips: {total_clips_count:,}\\n\"\n",
    "    f\"Must be positive: {must_be_positive_count:,} ({must_be_positive_count/total_clips_count:.2%})\\n\"\n",
    "    f\"Definitely not negative: {definitely_not_negative_count:,} ({definitely_not_negative_count/total_clips_count:.2%})\\n\"\n",
    "    f\"Must be negative: {must_be_negative_count:,} ({must_be_negative_count/total_clips_count:.2%})\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:31.095990Z",
     "start_time": "2024-05-26T00:24:15.383572Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liked requests: 57,331\n",
      "Not liked requests: 661,315\n",
      "Requests with preference paired generations: 45,486\n",
      "Percentage of total unique requests: 6.76%\n"
     ]
    }
   ],
   "source": [
    "mask = must_be_positive_mask & must_be_not_negative_mask\n",
    "total_unique_requests = clip_df[\"request_id\"].nunique()\n",
    "liked_requests = clip_df[mask][\"request_id\"].unique()  # requests with at least 1 like\n",
    "unliked_requests = clip_df[~mask][\"request_id\"].unique()  # requests without like\n",
    "has_liked_requests = set(liked_requests).intersection(\n",
    "    set(unliked_requests)\n",
    ")  # the request must have 1 like and one without like\n",
    "print(f\"Liked requests: {len(liked_requests):,}\")\n",
    "print(f\"Not liked requests: {len(unliked_requests):,}\")\n",
    "print(f\"Requests with preference paired generations: {len(has_liked_requests):,}\")\n",
    "print(f\"Percentage of total unique requests: {len(has_liked_requests) / total_unique_requests:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disliked requests: 132,757\n",
      "Not disliked requests: 581,162\n",
      "Requests with preference paired generations: 40,759\n",
      "Percentage of total unique requests: 6.05%\n"
     ]
    }
   ],
   "source": [
    "# introduce a negative preference count\n",
    "has_disliked_half_requests = clip_df[must_be_negative_mask][\n",
    "    \"request_id\"\n",
    "].unique()  # requests with at least 1 dislike\n",
    "not_have_disliked_requests = clip_df[~must_be_negative_mask][\n",
    "    \"request_id\"\n",
    "].unique()  # request without dislike\n",
    "has_disliked_requests = set(has_disliked_half_requests).intersection(\n",
    "    set(not_have_disliked_requests)\n",
    ")  # the request must have 1 dislike and one without dislike\n",
    "print(f\"Disliked requests: {len(has_disliked_half_requests):,}\")\n",
    "print(f\"Not disliked requests: {len(not_have_disliked_requests):,}\")\n",
    "print(f\"Requests with preference paired generations: {len(has_disliked_requests):,}\")\n",
    "print(f\"Percentage of total unique requests: {len(has_disliked_requests) / total_unique_requests:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:31.099372Z",
     "start_time": "2024-05-26T00:24:31.097244Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total selected pairs of requests: 75,544\n",
      "Percentage of total unique requests: 11.22%\n"
     ]
    }
   ],
   "source": [
    "requests = has_liked_requests.union(has_disliked_requests)\n",
    "print(f\"Total selected pairs of requests: {len(requests):,}\")\n",
    "print(f\"Percentage of total unique requests: {len(requests) / total_unique_requests:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:31.239254Z",
     "start_time": "2024-05-26T00:24:31.100389Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference in preference counts:\n",
      "0: 1,042,459 (78.13%)\n",
      "-1: 223,081 (16.72%)\n",
      "1: 68,722 (5.15%)\n"
     ]
    }
   ],
   "source": [
    "# this used to be a terrible bug...X.x\n",
    "assert mask.shape[0] == clip_df.shape[0]\n",
    "clip_df[\"pos_preference\"] = mask\n",
    "clip_df[\"neg_preference\"] = must_be_negative_mask\n",
    "# note that this is along the same row, so a positive clip can't be negative\n",
    "clip_df[\"diff_preference\"] = clip_df[\"pos_preference\"].astype(int) - clip_df[\"neg_preference\"].astype(\n",
    "    int\n",
    ")\n",
    "print(\"Difference in preference counts:\")\n",
    "value_counts = clip_df[\"diff_preference\"].value_counts()\n",
    "total = value_counts.sum()\n",
    "for value, count in value_counts.items():\n",
    "    fraction = count / total\n",
    "    print(f\"{value}: {count:,} ({fraction:.2%})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:37.322031Z",
     "start_time": "2024-05-26T00:24:31.240829Z"
    }
   },
   "outputs": [],
   "source": [
    "# creation of interesting_clips\n",
    "interesting_clips = clip_df[clip_df[\"request_id\"].isin(requests)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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>request_id</th>\n",
       "      <th>pos_preference</th>\n",
       "      <th>neg_preference</th>\n",
       "      <th>diff_preference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000061aa-2991-4b50-84be-a73af2ac9bc9</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000061aa-2991-4b50-84be-a73af2ac9bc9</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000c9a7-88e6-4938-ba84-83b8dee022d9</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000c9a7-88e6-4938-ba84-83b8dee022d9</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0001227c-14d2-48d2-94e5-a9ebc9c3a27d</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0001227c-14d2-48d2-94e5-a9ebc9c3a27d</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             request_id  pos_preference  neg_preference  diff_preference\n",
       "0  000061aa-2991-4b50-84be-a73af2ac9bc9           False           False                0\n",
       "1  000061aa-2991-4b50-84be-a73af2ac9bc9            True           False                1\n",
       "2  0000c9a7-88e6-4938-ba84-83b8dee022d9           False           False                0\n",
       "3  0000c9a7-88e6-4938-ba84-83b8dee022d9            True           False                1\n",
       "4  0001227c-14d2-48d2-94e5-a9ebc9c3a27d           False           False                0\n",
       "5  0001227c-14d2-48d2-94e5-a9ebc9c3a27d            True           False                1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interesting_clips = interesting_clips.sort_values(by=[\"request_id\", \"diff_preference\"]).reset_index()\n",
    "interesting_clips[[\"request_id\", \"pos_preference\", \"neg_preference\", \"diff_preference\"]].head(n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference 0: 64,843 (42.92%)\n",
      "Difference 1: 45,486 (30.11%)\n",
      "Difference -1: 40,759 (26.98%)\n"
     ]
    }
   ],
   "source": [
    "# this is a mix now\n",
    "value_counts = interesting_clips[\"diff_preference\"].value_counts()\n",
    "total = value_counts.sum()\n",
    "for value, count in value_counts.items():\n",
    "    fraction = count / total\n",
    "    print(f\"Difference {value}: {count:,} ({fraction:.2%})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value 1.0: 64,843 (85.83%)\n",
      "Value 2.0: 10,701 (14.17%)\n"
     ]
    }
   ],
   "source": [
    "diff_series = interesting_clips[\"diff_preference\"].diff()\n",
    "value_counts = diff_series[1::2].value_counts()\n",
    "total = value_counts.sum()\n",
    "for value, count in value_counts.items():\n",
    "    fraction = count / total\n",
    "    print(f\"Value {value}: {count:,} ({fraction:.2%})\")\n",
    "# 1 is pos, not neg pair or nothing, neg; 2 is pos / neg (hence the larger difference)\n",
    "# there are only two values for this positive pair\n",
    "assert diff_series[1::2].nunique() == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:38.960130Z",
     "start_time": "2024-05-26T00:24:37.323369Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique request_ids: 75,544\n",
      "Number of unique ids: 151,088\n",
      "Validation passed!\n"
     ]
    }
   ],
   "source": [
    "# assign the labels now\n",
    "interesting_clips[\"preference\"] = interesting_clips.index % 2 == 1\n",
    "# get df of requests -- let's move on!\n",
    "print(f\"Number of unique request_ids: {interesting_clips['request_id'].nunique():,}\")\n",
    "print(f\"Number of unique ids: {interesting_clips['id'].nunique():,}\")\n",
    "validate_preference_data(interesting_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:43.332222Z",
     "start_time": "2024-05-26T00:24:43.166461Z"
    }
   },
   "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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of interesting clips: 151,088\n",
      "Unique request and clip counts in interesting_clips:\n",
      "Request IDs:    75,544\n",
      "Clip IDs:       151,088\n"
     ]
    }
   ],
   "source": [
    "# need the reaction play counts\n",
    "# Filter reaction_df for relevant clip_ids\n",
    "partial_reaction_df = reaction_df[reaction_df[\"clip_id\"].isin(set(interesting_clips[\"id\"]))].copy()\n",
    "\n",
    "# Calculate total play counts\n",
    "total_play_counts = partial_reaction_df.groupby(\"clip_id\")[\"play_count\"].sum().reset_index()\n",
    "total_play_counts = total_play_counts.rename(\n",
    "    columns={\"clip_id\": \"id\", \"play_count\": \"reaction_play_count\"}\n",
    ")\n",
    "\n",
    "# Calculate pro user play counts\n",
    "pro_play_counts = (\n",
    "    partial_reaction_df[partial_reaction_df[\"is_pro_user\"]]\n",
    "    .groupby(\"clip_id\")[\"play_count\"]\n",
    "    .sum()\n",
    "    .reset_index()\n",
    ")\n",
    "pro_play_counts = pro_play_counts.rename(\n",
    "    columns={\"clip_id\": \"id\", \"play_count\": \"reaction_pro_play_count\"}\n",
    ")\n",
    "\n",
    "# Merge with user_intersting_clips\n",
    "interesting_clips = interesting_clips.merge(total_play_counts, on=\"id\", how=\"left\")\n",
    "interesting_clips = interesting_clips.merge(pro_play_counts, on=\"id\", how=\"left\")\n",
    "\n",
    "print(f\"Number of interesting clips: {len(interesting_clips):,}\")\n",
    "# Get unique counts for request_id and id\n",
    "unique_request_ids = interesting_clips[\"request_id\"].nunique()\n",
    "unique_clip_ids = interesting_clips[\"id\"].nunique()\n",
    "\n",
    "# Print the results in a formatted manner\n",
    "print(\"Unique request and clip counts in interesting_clips:\")\n",
    "print(f\"{'Request IDs:':<15} {unique_request_ids:,}\")\n",
    "print(f\"{'Clip IDs:':<15} {unique_clip_ids:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:43.737067Z",
     "start_time": "2024-05-26T00:24:43.563216Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference counts and fractions by batch index:\n",
      "--------------------------------------------------\n",
      "Batch Index: 0\n",
      "  Preference False: Count: 38,591 Fraction: 51.08%\n",
      "  Preference True: Count: 36,953 Fraction: 48.92%\n",
      "\n",
      "Batch Index: 1\n",
      "  Preference False: Count: 36,953 Fraction: 48.92%\n",
      "  Preference True: Count: 38,591 Fraction: 51.08%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "preference_counts = interesting_clips.groupby(\"batch_index\")[\"preference\"].value_counts()\n",
    "total_counts = preference_counts.groupby(level=0).sum()\n",
    "\n",
    "print(\"Preference counts and fractions by batch index:\")\n",
    "print(\"-\" * 50)\n",
    "for batch_index in [0, 1]:\n",
    "    print(f\"Batch Index: {batch_index}\")\n",
    "    for preference in [False, True]:\n",
    "        count = preference_counts[batch_index, preference]\n",
    "        fraction = count / total_counts[batch_index]\n",
    "        print(f\"  Preference {preference}: Count: {count:,} Fraction: {fraction:.2%}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:44.304573Z",
     "start_time": "2024-05-26T00:24:43.973218Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-v3p5-engine-s-8: 117059 (77.48%)\n",
      "chirp-v3p5-engine-upload-4: 8448 (5.59%)\n",
      "chirp-v3p5-engine-t-2-10: 7353 (4.87%)\n",
      "chirp-v3p5-engine-s-24: 7338 (4.86%)\n",
      "chirp-v3p5-engine-t-2: 7288 (4.82%)\n",
      "chirp-v3-engine-i: 3078 (2.04%)\n",
      "chirp-v2-xxl-alpha: 386 (0.26%)\n",
      "chirp-v3p5-engine-b: 86 (0.06%)\n",
      "chirp-v3p5-engine-ft-1: 30 (0.02%)\n",
      "chirp-v3p5-engine-short: 22 (0.01%)\n"
     ]
    }
   ],
   "source": [
    "print_out_value_counts_nicely(interesting_clips, \"model_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:44.533983Z",
     "start_time": "2024-05-26T00:24:44.305722Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Validation:\n",
      "--------------------\n",
      "Interesting Clips:\n",
      "  Earliest: 2024-08-25 06:40:00.726724+00:00\n",
      "  Latest:   2024-08-25 14:19:30.677038+00:00\n",
      "\n",
      "All Clips:\n",
      "  Earliest: 2024-08-25 06:40:00.031990+00:00\n",
      "  Latest:   2024-08-25 14:20:33.884353+00:00\n"
     ]
    }
   ],
   "source": [
    "print(\"Time Validation:\")\n",
    "print(\"-\" * 20)\n",
    "print(\"Interesting Clips:\")\n",
    "print(f\"  Earliest: {interesting_clips['created_at'].min()}\")\n",
    "print(f\"  Latest:   {interesting_clips['created_at'].max()}\")\n",
    "print(\"\\nAll Clips:\")\n",
    "print(f\"  Earliest: {clip_df['created_at'].min()}\")\n",
    "print(f\"  Latest:   {clip_df['created_at'].max()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:50.674838Z",
     "start_time": "2024-05-26T00:24:46.366677Z"
    }
   },
   "outputs": [],
   "source": [
    "# make sure we sort here before proceed\n",
    "interesting_clips = interesting_clips.sort_values(by=[\"request_id\", \"preference\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:51.600621Z",
     "start_time": "2024-05-26T00:24:50.676186Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ratio of preferred clips to total clips for each model:\n",
      "------------------------------------------------------------\n",
      "chirp-v2-engine-msft-60s       nan% ± nan%\n",
      "chirp-v2-xxl-alpha             3.94% ± 0.28%\n",
      "chirp-v3-5-short               nan% ± nan%\n",
      "chirp-v3-engine-i              4.44% ± 0.11%\n",
      "chirp-v3p5-engine-b            0.14% ± 0.02%\n",
      "chirp-v3p5-engine-ft-1         4.52% ± 1.14%\n",
      "chirp-v3p5-engine-s-24         6.48% ± 0.10%\n",
      "chirp-v3p5-engine-s-8          5.77% ± 0.02%\n",
      "chirp-v3p5-engine-short        4.04% ± 1.19%\n",
      "chirp-v3p5-engine-t-2          5.62% ± 0.10%\n",
      "chirp-v3p5-engine-t-2-10       5.92% ± 0.10%\n",
      "chirp-v3p5-engine-upload-4     6.79% ± 0.10%\n"
     ]
    }
   ],
   "source": [
    "# Calculate the ratio of preferred clips to total clips for each model\n",
    "preference_ratio = (\n",
    "    interesting_clips[interesting_clips[\"preference\"]][\"model_name\"].value_counts()\n",
    "    / clip_df[\"model_name\"].value_counts()\n",
    ")\n",
    "\n",
    "# Print the results in a formatted manner\n",
    "print(\"Ratio of preferred clips to total clips for each model:\")\n",
    "print(\"-\" * 60)\n",
    "for model, ratio in preference_ratio.items():\n",
    "    n = clip_df[\"model_name\"].value_counts()[model]\n",
    "    uncertainty = (ratio * (1 - ratio) / n) ** 0.5\n",
    "    print(f\"{model:<30} {ratio:.2%} ± {uncertainty:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:56.302599Z",
     "start_time": "2024-05-26T00:24:51.601863Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len pos models: 75544\n",
      "differing counts: 18665\n",
      "chirp-v2-xxl-alpha_win_over_chirp-v2-xxl-alpha, win ratio 1.000, counts 193\n",
      "chirp-v3-engine-i_win_over_chirp-v3-engine-i, win ratio 1.000, counts 1539\n",
      "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, counts 43\n",
      "chirp-v3p5-engine-ft-1_win_over_chirp-v3p5-engine-ft-1, win ratio 1.000, counts 15\n",
      "chirp-v3p5-engine-s-24_win_over_chirp-v3p5-engine-s-8, win ratio 0.513, counts 3761\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-24, win ratio 0.487, counts 3577\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, counts 50854\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-t-2, win ratio 0.589, counts 2341\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-t-2-10, win ratio 0.563, counts 2274\n",
      "chirp-v3p5-engine-short_win_over_chirp-v3p5-engine-short, win ratio 1.000, counts 11\n",
      "chirp-v3p5-engine-t-2-10_win_over_chirp-v3p5-engine-s-8, win ratio 0.437, counts 1765\n",
      "chirp-v3p5-engine-t-2-10_win_over_chirp-v3p5-engine-t-2, win ratio 0.504, counts 1669\n",
      "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-s-8, win ratio 0.411, counts 1633\n",
      "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2-10, win ratio 0.496, counts 1645\n",
      "chirp-v3p5-engine-upload-4_win_over_chirp-v3p5-engine-upload-4, win ratio 1.000, counts 4224\n",
      "tournament players: ['chirp-v3p5-engine-s-24', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-t-2', 'chirp-v3p5-engine-t-2-10']\n",
      "mle_elos: {'chirp-v3p5-engine-s-24': 1033.1463763393967, 'chirp-v3p5-engine-s-8': 1024.4325244512463, 'chirp-v3p5-engine-t-2': 966.9760413842878, 'chirp-v3p5-engine-t-2-10': 975.4450578250694}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:01<00:00, 735.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model names: ['chirp-v2-xxl-alpha', 'chirp-v3-engine-i', 'chirp-v3p5-engine-b', 'chirp-v3p5-engine-ft-1', 'chirp-v3p5-engine-s-24', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-short', 'chirp-v3p5-engine-t-2', 'chirp-v3p5-engine-t-2-10', 'chirp-v3p5-engine-upload-4']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_preference_counts(interesting_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:57.443236Z",
     "start_time": "2024-05-26T00:24:56.306473Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tony/anaconda3/envs/suno_env/lib/python3.10/site-packages/matplotlib/axes/_axes.py:7001: 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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",
      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_clip_basic_distributions(interesting_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:58.153310Z",
     "start_time": "2024-05-26T00:24:57.858363Z"
    }
   },
   "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": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:58.890520Z",
     "start_time": "2024-05-26T00:24:58.154350Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clips in interesting_clips:\n",
      "151,088\n",
      "Number of clips in user_interesting_clips:\n",
      "139,458\n",
      "Ratio of preferred clips to total clips for each model:\n",
      "------------------------------------------------------------\n",
      "chirp-v2-engine-msft-60s       nan% ± nan%\n",
      "chirp-v2-xxl-alpha             nan% ± nan%\n",
      "chirp-v3-5-short               nan% ± nan%\n",
      "chirp-v3-engine-i              nan% ± nan%\n",
      "chirp-v3p5-engine-b            0.12% ± 0.02%\n",
      "chirp-v3p5-engine-ft-1         3.92% ± 1.06%\n",
      "chirp-v3p5-engine-s-24         6.12% ± 0.10%\n",
      "chirp-v3p5-engine-s-8          5.44% ± 0.02%\n",
      "chirp-v3p5-engine-short        4.04% ± 1.19%\n",
      "chirp-v3p5-engine-t-2          5.29% ± 0.09%\n",
      "chirp-v3p5-engine-t-2-10       5.63% ± 0.10%\n",
      "chirp-v3p5-engine-upload-4     6.57% ± 0.10%\n"
     ]
    }
   ],
   "source": [
    "# subselect interesting clips\n",
    "interesting_clips_masks = (interesting_clips[\"model_name\"].str.contains(\"v3p5\")) & (\n",
    "    interesting_clips[\"reaction_play_count\"] > 0\n",
    ")\n",
    "# make sure we have pairs\n",
    "extra_compare_mask = interesting_clips[interesting_clips_masks][\"request_id\"].isin(\n",
    "    interesting_clips[interesting_clips_masks][\"request_id\"]\n",
    "    .value_counts()\n",
    "    .index[interesting_clips[interesting_clips_masks][\"request_id\"].value_counts() == 2]\n",
    ")\n",
    "user_intersting_clips = interesting_clips[interesting_clips_masks & extra_compare_mask].copy()\n",
    "\n",
    "print(\"Number of clips in interesting_clips:\")\n",
    "print(f\"{interesting_clips.shape[0]:,}\")\n",
    "print(\"Number of clips in user_interesting_clips:\")\n",
    "print(f\"{user_intersting_clips.shape[0]:,}\")\n",
    "# Calculate the ratio of preferred clips to total clips for each model\n",
    "preference_ratio = (\n",
    "    user_intersting_clips[user_intersting_clips[\"preference\"]][\"model_name\"].value_counts()\n",
    "    / clip_df[\"model_name\"].value_counts()\n",
    ")\n",
    "\n",
    "# Print the results in a formatted manner\n",
    "print(\"Ratio of preferred clips to total clips for each model:\")\n",
    "print(\"-\" * 60)\n",
    "for model, ratio in preference_ratio.items():\n",
    "    n = clip_df[\"model_name\"].value_counts()[model]\n",
    "    uncertainty = (ratio * (1 - ratio) / n) ** 0.5\n",
    "    print(f\"{model:<30} {ratio:.2%} ± {uncertainty:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:59.204547Z",
     "start_time": "2024-05-26T00:24:58.891851Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the number of preferences per user\n",
    "preferences_per_user = user_intersting_clips[\"user_id\"].value_counts()\n",
    "\n",
    "# Determine the maximum number of preferences\n",
    "max_preferences = preferences_per_user.max()\n",
    "\n",
    "# Choose bins using Sturges' rule, but ensure a minimum of 15 bins and a maximum of 30\n",
    "n_bins = max(30, min(100, int(np.ceil(np.log2(len(preferences_per_user)) + 1))))\n",
    "\n",
    "# Calculate bin edges using a linear scale\n",
    "bin_edges = np.linspace(preferences_per_user.min(), max_preferences, n_bins)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(preferences_per_user, bins=bin_edges, edgecolor=\"black\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"Number of preferences per user\")\n",
    "plt.ylabel(\"Number of users (log scale)\")\n",
    "plt.title(\"Distribution of User Preferences\")\n",
    "plt.grid(axis=\"both\", linestyle=\"--\", alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:59.207707Z",
     "start_time": "2024-05-26T00:24:59.205659Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of user_interesting_clips:\n",
      "Total requests: 139,458\n",
      "Unique clips: 69,729\n",
      "Fraction of total clips: 9.99%\n",
      "Time Validation:\n",
      "Earliest timestamp: 2024-08-25 06:40:00.726724+00:00\n",
      "Latest timestamp:   2024-08-25 14:19:14.799807+00:00\n",
      "Earliest timestamp: 2024-08-25 06:40:56.111727+00:00\n",
      "Latest timestamp:   2024-08-25 14:18:21.750892+00:00\n"
     ]
    }
   ],
   "source": [
    "print(\"Summary of user_interesting_clips:\")\n",
    "print(f\"Total requests: {user_intersting_clips.shape[0]:,}\")\n",
    "print(f\"Unique clips: {user_intersting_clips.shape[0] // 2:,}\")\n",
    "print(f\"Fraction of total clips: {user_intersting_clips.shape[0] / total_clip_counts:.2%}\")\n",
    "print(\"Time Validation:\")\n",
    "print(f\"Earliest timestamp: {user_intersting_clips['created_at'].min()}\")\n",
    "print(f\"Latest timestamp:   {user_intersting_clips['created_at'].max()}\")\n",
    "model_to_test = target_model_name\n",
    "print(\n",
    "    f\"Earliest timestamp: {user_intersting_clips[user_intersting_clips['model_name'] == model_to_test]['created_at'].min()}\"\n",
    ")\n",
    "print(\n",
    "    f\"Latest timestamp:   {user_intersting_clips[user_intersting_clips['model_name'] == model_to_test]['created_at'].max()}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:00.484734Z",
     "start_time": "2024-05-26T00:25:00.377280Z"
    }
   },
   "outputs": [],
   "source": [
    "# from suno_analytics.preference_data_selection import parse_for_tag, parse_for_one_box\n",
    "# user_intersting_clips[\"tags\"] = user_intersting_clips[\"metadata\"].apply(parse_for_tag)\n",
    "# user_intersting_clips[\"is_onebox\"] = user_intersting_clips[\"metadata\"].apply(parse_for_one_box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:00.893917Z",
     "start_time": "2024-05-26T00:25:00.485775Z"
    }
   },
   "outputs": [],
   "source": [
    "user_compare_mask = (\n",
    "    (user_intersting_clips[\"created_at\"] >= cutoff_date)\n",
    "    # & (\n",
    "    #     (user_intersting_clips[\"model_name\"].str.startswith(\"chirp-v3p5-engine-t\"))\n",
    "    #     | (user_intersting_clips[\"model_name\"].str.startswith(\"chirp-v3p5-engine-s\"))\n",
    "    # )\n",
    "    # & (user_intersting_clips[\"is_pro_user\"])\n",
    "    # & (~user_intersting_clips[\"is_onebox\"])\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:01.140472Z",
     "start_time": "2024-05-26T00:25:00.895575Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(139458, 49)\n",
      "Model Name Value Counts and Fractions:\n",
      "chirp-v3p5-engine-s-8: 110422 (79.18%)\n",
      "chirp-v3p5-engine-upload-4: 8168 (5.86%)\n",
      "chirp-v3p5-engine-t-2-10: 6971 (5.00%)\n",
      "chirp-v3p5-engine-t-2: 6894 (4.94%)\n",
      "chirp-v3p5-engine-s-24: 6885 (4.94%)\n",
      "chirp-v3p5-engine-b: 70 (0.05%)\n",
      "chirp-v3p5-engine-ft-1: 26 (0.02%)\n",
      "chirp-v3p5-engine-short: 22 (0.02%)\n"
     ]
    }
   ],
   "source": [
    "user_intersting_clips_3p5 = user_intersting_clips[user_compare_mask].reset_index().copy()\n",
    "\n",
    "\n",
    "def modify_model_name(model_name, metadata):\n",
    "    if model_name.startswith(\"chirp-v3p5-engine-t\"):\n",
    "        if \"param_experiment\" in metadata:\n",
    "            exp = metadata.get(\"param_experiment\", \"\")\n",
    "            if exp:\n",
    "                return f\"{model_name}_{exp}\"\n",
    "    return model_name\n",
    "\n",
    "\n",
    "user_intersting_clips_3p5[\"model_name\"] = user_intersting_clips_3p5.apply(\n",
    "    lambda row: modify_model_name(row[\"model_name\"], row[\"metadata\"]), axis=1\n",
    ")\n",
    "user_intersting_clips_3p5 = user_intersting_clips_3p5.sort_values(by=[\"request_id\", \"preference\"])\n",
    "print(user_intersting_clips_3p5.shape)\n",
    "model_counts = user_intersting_clips_3p5[\"model_name\"].value_counts()\n",
    "model_fracs = model_counts / model_counts.sum()\n",
    "\n",
    "print(\"Model Name Value Counts and Fractions:\")\n",
    "print_out_value_counts_nicely(user_intersting_clips_3p5, \"model_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:02.009450Z",
     "start_time": "2024-05-26T00:25:01.523515Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len pos models: 69729\n",
      "differing counts: 17617\n",
      "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, counts 35\n",
      "chirp-v3p5-engine-ft-1_win_over_chirp-v3p5-engine-ft-1, win ratio 1.000, counts 13\n",
      "chirp-v3p5-engine-s-24_win_over_chirp-v3p5-engine-s-8, win ratio 0.516, counts 3551\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-24, win ratio 0.484, counts 3334\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, counts 47969\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-t-2, win ratio 0.591, counts 2224\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-t-2-10, win ratio 0.563, counts 2161\n",
      "chirp-v3p5-engine-short_win_over_chirp-v3p5-engine-short, win ratio 1.000, counts 11\n",
      "chirp-v3p5-engine-t-2-10_win_over_chirp-v3p5-engine-s-8, win ratio 0.437, counts 1677\n",
      "chirp-v3p5-engine-t-2-10_win_over_chirp-v3p5-engine-t-2, win ratio 0.506, counts 1586\n",
      "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-s-8, win ratio 0.409, counts 1537\n",
      "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2-10, win ratio 0.494, counts 1547\n",
      "chirp-v3p5-engine-upload-4_win_over_chirp-v3p5-engine-upload-4, win ratio 1.000, counts 4084\n",
      "tournament players: ['chirp-v3p5-engine-s-24', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-t-2', 'chirp-v3p5-engine-t-2-10']\n",
      "mle_elos: {'chirp-v3p5-engine-s-24': 1035.232253271856, 'chirp-v3p5-engine-s-8': 1024.277678667314, 'chirp-v3p5-engine-t-2': 965.1444256368015, 'chirp-v3p5-engine-t-2-10': 975.3456424240278}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:01<00:00, 748.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model names: ['chirp-v3p5-engine-b', 'chirp-v3p5-engine-ft-1', 'chirp-v3p5-engine-s-24', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-short', 'chirp-v3p5-engine-t-2', 'chirp-v3p5-engine-t-2-10', 'chirp-v3p5-engine-upload-4']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_preference_counts(user_intersting_clips_3p5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:02.332947Z",
     "start_time": "2024-05-26T00:25:02.010694Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first gen\n",
      "len pos models: 58687\n",
      "differing counts: 15744\n",
      "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, counts 27\n",
      "chirp-v3p5-engine-ft-1_win_over_chirp-v3p5-engine-ft-1, win ratio 1.000, counts 9\n",
      "chirp-v3p5-engine-s-24_win_over_chirp-v3p5-engine-s-8, win ratio 0.531, counts 3288\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-24, win ratio 0.469, counts 2908\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, counts 42896\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-t-2, win ratio 0.602, counts 2020\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-t-2-10, win ratio 0.578, counts 1984\n",
      "chirp-v3p5-engine-short_win_over_chirp-v3p5-engine-short, win ratio 1.000, counts 11\n",
      "chirp-v3p5-engine-t-2-10_win_over_chirp-v3p5-engine-s-8, win ratio 0.422, counts 1451\n",
      "chirp-v3p5-engine-t-2-10_win_over_chirp-v3p5-engine-t-2, win ratio 0.514, counts 1415\n",
      "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-s-8, win ratio 0.398, counts 1338\n",
      "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2-10, win ratio 0.486, counts 1340\n",
      "tournament players: ['chirp-v3p5-engine-s-24', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-t-2', 'chirp-v3p5-engine-t-2-10']\n",
      "mle_elos: {'chirp-v3p5-engine-s-24': 1047.4553941553936, 'chirp-v3p5-engine-s-8': 1026.1203125708216, 'chirp-v3p5-engine-t-2': 957.0318968119434, 'chirp-v3p5-engine-t-2-10': 969.3923964618415}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:01<00:00, 728.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model names: ['chirp-v3p5-engine-b', 'chirp-v3p5-engine-ft-1', 'chirp-v3p5-engine-s-24', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-short', 'chirp-v3p5-engine-t-2', 'chirp-v3p5-engine-t-2-10']\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"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"first gen\")\n",
    "first_gen_slice_df = user_intersting_clips_3p5[\n",
    "    (user_intersting_clips_3p5[\"continued_parent\"].isna())\n",
    "].copy()\n",
    "if first_gen_slice_df.shape[0] > 0:\n",
    "    get_preference_counts(\n",
    "        user_intersting_clips_3p5[(user_intersting_clips_3p5[\"continued_parent\"].isna())],\n",
    "        title_name=\"first generation\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:02.574659Z",
     "start_time": "2024-05-26T00:25:02.334214Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is continue\n",
      "len pos models: 11042\n",
      "differing counts: 1873\n",
      "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, counts 8\n",
      "chirp-v3p5-engine-ft-1_win_over_chirp-v3p5-engine-ft-1, win ratio 1.000, counts 4\n",
      "chirp-v3p5-engine-s-24_win_over_chirp-v3p5-engine-s-8, win ratio 0.382, counts 263\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-24, win ratio 0.618, counts 426\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, counts 5073\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-t-2, win ratio 0.506, counts 204\n",
      "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-t-2-10, win ratio 0.439, counts 177\n",
      "chirp-v3p5-engine-t-2-10_win_over_chirp-v3p5-engine-s-8, win ratio 0.561, counts 226\n",
      "chirp-v3p5-engine-t-2-10_win_over_chirp-v3p5-engine-t-2, win ratio 0.452, counts 171\n",
      "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-s-8, win ratio 0.494, counts 199\n",
      "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2-10, win ratio 0.548, counts 207\n",
      "chirp-v3p5-engine-upload-4_win_over_chirp-v3p5-engine-upload-4, win ratio 1.000, counts 4084\n",
      "tournament players: ['chirp-v3p5-engine-s-24', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-t-2', 'chirp-v3p5-engine-t-2-10']\n",
      "mle_elos: {'chirp-v3p5-engine-s-24': 927.6668641075615, 'chirp-v3p5-engine-s-8': 1011.4516410501809, 'chirp-v3p5-engine-t-2': 1033.1403471128274, 'chirp-v3p5-engine-t-2-10': 1027.7411477294302}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:01<00:00, 727.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model names: ['chirp-v3p5-engine-b', 'chirp-v3p5-engine-ft-1', 'chirp-v3p5-engine-s-24', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-t-2', 'chirp-v3p5-engine-t-2-10', 'chirp-v3p5-engine-upload-4']\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"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"is continue\")\n",
    "get_preference_counts(\n",
    "    user_intersting_clips_3p5[(~user_intersting_clips_3p5[\"continued_parent\"].isna())],\n",
    "    \"is continue\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean up SHIT\n",
    "\n",
    "to get the right play conts, we need the right df..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:08.095035Z",
     "start_time": "2024-05-26T00:25:07.782738Z"
    }
   },
   "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",
    "            \"concat_dislikes\": 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",
    "        \"concat_dislikes\",\n",
    "    ]\n",
    "] = extra_cols_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:08.361849Z",
     "start_time": "2024-05-26T00:25:08.199583Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive proportion of data meeting criteria: 69.56%\n",
      "Negative proportion of data meeting criteria: 97.08%\n"
     ]
    }
   ],
   "source": [
    "pos_too_much_data_mask = (\n",
    "    (user_intersting_clips[\"preference\"])\n",
    "    & (\n",
    "        (user_intersting_clips[\"reaction_play_count\"] >= 2)  # single play is super catchy\n",
    "        | (user_intersting_clips[\"concat_play_counts\"] >= 2)  # or the concat play is super catchy\n",
    "    )\n",
    "    & (user_intersting_clips[\"user_n_clips\"] >= 4)\n",
    "    # & (user_intersting_clips[\"continued_parent\"].isna())\n",
    ")\n",
    "neg_too_much_data_mask = (\n",
    "    (~user_intersting_clips[\"preference\"])\n",
    "    & (user_intersting_clips[\"reaction_play_count\"] >= 1)  # single play is super catchy\n",
    "    & (user_intersting_clips[\"user_n_clips\"] >= 4)\n",
    "    # & (user_intersting_clips[\"continued_parent\"].isna())\n",
    ")\n",
    "# Calculate and print the proportion of data that meets our criteria\n",
    "pos_proportion = pos_too_much_data_mask.sum() / user_intersting_clips.shape[0] * 2\n",
    "print(f\"Positive proportion of data meeting criteria: {pos_proportion:.2%}\")\n",
    "neg_proportion = neg_too_much_data_mask.sum() / user_intersting_clips.shape[0] * 2\n",
    "print(f\"Negative proportion of data meeting criteria: {neg_proportion:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:08.523643Z",
     "start_time": "2024-05-26T00:25:08.367348Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name Value Counts for Preferred Clips:\n",
      "----------------------------------------------------------------------\n",
      "Model                               Count        Fraction\n",
      "----------------------------------------------------------------------\n",
      "chirp-v3p5-engine-s-8              38,807          80.01%\n",
      "chirp-v3p5-engine-upload-4          2,800           5.77%\n",
      "chirp-v3p5-engine-s-24              2,484           5.12%\n",
      "chirp-v3p5-engine-t-2-10            2,286           4.71%\n",
      "chirp-v3p5-engine-t-2               2,095           4.32%\n",
      "chirp-v3p5-engine-b                    20           0.04%\n",
      "chirp-v3p5-engine-ft-1                 10           0.02%\n",
      "chirp-v3p5-engine-short                 3           0.01%\n",
      "----------------------------------------------------------------------\n",
      "Total                              48,505         100.00%\n"
     ]
    }
   ],
   "source": [
    "final_good_enough_requests = set(\n",
    "    user_intersting_clips[pos_too_much_data_mask][\"request_id\"].unique()\n",
    ").intersection(set(user_intersting_clips[neg_too_much_data_mask][\"request_id\"].unique()))\n",
    "final_interesting_clips = user_intersting_clips[\n",
    "    user_intersting_clips[\"request_id\"].isin(final_good_enough_requests)\n",
    "].copy()\n",
    "# Get the value counts of model_name for preferred clips\n",
    "model_counts = final_interesting_clips[final_interesting_clips[\"preference\"]][\n",
    "    \"model_name\"\n",
    "].value_counts()\n",
    "\n",
    "# Print the results in a nicely formatted way\n",
    "total_count = model_counts.sum()\n",
    "print(\"Model Name Value Counts for Preferred Clips:\")\n",
    "print(\"-\" * 70)\n",
    "print(f\"{'Model':<30} {'Count':>10} {'Fraction':>15}\")\n",
    "print(\"-\" * 70)\n",
    "for model, count in model_counts.items():\n",
    "    fraction = count / total_count\n",
    "    print(f\"{model:<30} {count:>10,d} {fraction:>15.2%}\")\n",
    "print(\"-\" * 70)\n",
    "print(f\"{'Total':<30} {total_count:>10,d} {1:>15.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:09.198504Z",
     "start_time": "2024-05-26T00:25:08.885507Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation passed!\n"
     ]
    }
   ],
   "source": [
    "validate_preference_data(final_interesting_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in final_interesting_clips for model chirp-v3p5-engine-t-2:\n",
      "4,766\n",
      "done (97010, 54)\n"
     ]
    }
   ],
   "source": [
    "# Get the number of rows for final_interesting_clips with the specific model\n",
    "row_count = final_interesting_clips[final_interesting_clips[\"model_name\"] == target_model_name].shape[0]\n",
    "\n",
    "# Print the row count in a nicely formatted way\n",
    "print(f\"Number of rows in final_interesting_clips for model {target_model_name}:\")\n",
    "print(f\"{row_count:,}\")\n",
    "print(\"done\", final_interesting_clips.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For faster processing once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:10.265241Z",
     "start_time": "2024-05-26T00:25:09.934743Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Unique Users:\n",
      "--------------------\n",
      "152,230\n",
      "--------------------\n",
      "Series([], Name: count, dtype: int64)\n",
      "(0, 44)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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>27205089</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>perkintonpatricia441@gmail.com</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>perkintonpatricia441@gmail.com</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2024-07-08 16:16:06.847119+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  27205089                None         False  perkintonpatricia441@gmail.com                       perkintonpatricia441@gmail.com     False       True 2024-07-08 16:16:06.847119+00:00"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the number of unique users\n",
    "total_unique_users = clip_df[\"user_id\"].nunique()\n",
    "\n",
    "# Print the result in a nicely formatted way\n",
    "print(\"Total Unique Users:\")\n",
    "print(\"-\" * 20)\n",
    "print(f\"{total_unique_users:,}\")\n",
    "print(\"-\" * 20)\n",
    "\n",
    "# This can take a while cause we have a lot of users...\n",
    "# query = \"\"\"\n",
    "# SELECT *\n",
    "# FROM auth_user\n",
    "# \"\"\"\n",
    "# user_df = pd.read_sql_query(query, engine)\n",
    "# user_df.head()\n",
    "\n",
    "test_user_id = 4688272\n",
    "print(\n",
    "    clip_df[clip_df[\"user_id\"] == test_user_id][\"created_at\"].apply(lambda x: str(x)[:10]).value_counts()\n",
    ")\n",
    "print(clip_df[clip_df[\"user_id\"] == test_user_id].shape)\n",
    "query = \"\"\"\n",
    "SELECT *\n",
    "FROM auth_user\n",
    "WHERE id=27205089\n",
    "\"\"\"\n",
    "# 3 keenan\n",
    "# 6 martin\n",
    "# 8 tony -- that's me!\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": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of no_reaction_clip_df:\n",
      "(325256, 44)\n",
      "\n",
      "Proportion of clips without reactions:\n",
      "24.38%\n",
      "Ratio of clips without reactions to total clips from the same users:\n",
      "0.9176\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "free 6\n",
      "pro 594\n",
      "Ratio of clips from super bad users: 0.35%\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "run_bot_detection(clip_df, reaction_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alpha testing user selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alpha testing user selection\n",
    "# we focus on the folks who are good good\n",
    "\n",
    "# # 0526 is v2 -- prod\n",
    "# # 0529 is v4 -- still good IMO, more data\n",
    "# early_v3p5_data = pd.read_csv(\"/home/tony/Data/Preference/13b_v0/interesting_clips_20240529.csv\")\n",
    "\n",
    "# print(\"uqniue users for vp5\", early_v3p5_data[\"user_id\"].nunique())\n",
    "\n",
    "# early_v3_data = pd.read_csv(\"/home/tony/Data/Preference/7b_v0_interesting_clips.csv\")\n",
    "\n",
    "# print(\"uqniue users for v3\", early_v3_data[\"user_id\"].nunique())\n",
    "\n",
    "# early_v2_data = pd.read_csv(\"/home/tony/Data/Preference/3b_v0_interesting_clips.csv\")\n",
    "\n",
    "# print(\"uqniue users for v2\", early_v2_data[\"user_id\"].nunique())\n",
    "\n",
    "# intersection_user_ids_super = set(early_v3p5_data[\"user_id\"].unique()).intersection(set(early_v3_data[\"user_id\"].unique())).intersection(set(early_v2_data[\"user_id\"].unique()))\n",
    "\n",
    "# intersection_user_ids_v3_on = set(early_v3p5_data[\"user_id\"].unique()).intersection(set(early_v3_data[\"user_id\"].unique())).difference(intersection_user_ids_super)\n",
    "\n",
    "# print(len(intersection_user_ids_super), len(intersection_user_ids_v3_on))\n",
    "\n",
    "# super_user_df = user_df[user_df[\"id\"].isin(intersection_user_ids_super)].copy()\n",
    "# print(super_user_df.shape)\n",
    "# v3_onward_user_df = user_df[user_df[\"id\"].isin(intersection_user_ids_v3_on)].copy()\n",
    "# print(v3_onward_user_df.shape)\n",
    "# super_user_df.to_csv(\"/home/tony/Data/Preference/alpha_users/super_user.csv\", index=False)\n",
    "# v3_onward_user_df.to_csv(\"/home/tony/Data/Preference/alpha_users/v3_onward_user.csv\", index=False)\n",
    "# print(\"Done!!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-21T19:35:17.755108Z",
     "iopub.status.busy": "2024-06-21T19:35:17.754937Z",
     "iopub.status.idle": "2024-06-21T19:35:17.774581Z",
     "shell.execute_reply": "2024-06-21T19:35:17.774106Z",
     "shell.execute_reply.started": "2024-06-21T19:35:17.755091Z"
    }
   },
   "source": [
    "# Snow flake access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(snow_password_path):\n",
    "    raise Exception(\"you are not authorized to access snowflake -- please setup\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROD\n"
     ]
    }
   ],
   "source": [
    "snow_session = Session.builder.configs(CONNECTION_PARAMETERS).create()\n",
    "\n",
    "snow_root = Root(snow_session)\n",
    "snow_schema = snow_root.databases[\"SUNO_PROD\"].schemas[\"PROD\"]\n",
    "print(snow_schema.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the df we want to squery for play counts\n",
    "subset_v4_clips_df = final_interesting_clips[\n",
    "    final_interesting_clips[\"model_name\"] == target_model_name\n",
    "].copy()\n",
    "\n",
    "v4_clip_ids = list(str(s) for s in subset_v4_clips_df[\"id\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 4766\n",
      "Length of the ID query string: 185873\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:03<00:00,  3.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "snow_batch_size = 100_000\n",
    "snow_results = []\n",
    "for clip_ids_chunk in tqdm.tqdm(\n",
    "    [v4_clip_ids[i : i + snow_batch_size] for i in range(0, len(v4_clip_ids), snow_batch_size)]\n",
    "):\n",
    "    id_query_str = \",\".join(\"'\" + x + \"'\" for x in clip_ids_chunk)\n",
    "    print(f\"Number of clip IDs in this chunk: {len(clip_ids_chunk)}\")\n",
    "    print(f\"Length of the ID query string: {len(id_query_str)}\")\n",
    "\n",
    "    session_query = snow_session.sql(\n",
    "        f\"\"\" select *\n",
    "        from ML_SONG_SUMMARY_INFO\n",
    "        where p_date = DATE(SYSDATE() - INTERVAL '2 HOUR')\n",
    "        and p_hour = hour(SYSDATE() - INTERVAL '2 HOUR')\n",
    "        and song_id in ({id_query_str})\n",
    "        order by p_hour desc;\"\"\"\n",
    "    )\n",
    "    temp_df_snow_test = pd.DataFrame(session_query.collect())\n",
    "    snow_results.append(temp_df_snow_test)\n",
    "print(len(snow_results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of df_snow_test:\n",
      "Rows: 3781\n",
      "Columns: 10\n"
     ]
    }
   ],
   "source": [
    "df_snow_test = pd.concat(snow_results)\n",
    "df_snow_test = df_snow_test.rename(columns=lambda x: x.lower())\n",
    "df_snow_test = df_snow_test.rename(columns={\"song_id\": \"str_id\"})\n",
    "print(\"Shape of df_snow_test:\")\n",
    "print(f\"Rows: {df_snow_test.shape[0]}\")\n",
    "print(f\"Columns: {df_snow_test.shape[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_v4_clips_df[\"str_id\"] = subset_v4_clips_df[\"id\"].astype(str)\n",
    "subset_v4_clips_df_test = subset_v4_clips_df.merge(df_snow_test, on=\"str_id\", how=\"left\")\n",
    "subset_v4_clips_df_test[\"norm_play_frac\"] = (\n",
    "    subset_v4_clips_df_test[\"total_play_time\"].fillna(0) / subset_v4_clips_df_test[\"duration\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with two subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# First subplot: Total play duration\n",
    "pos_play_time = subset_v4_clips_df_test[subset_v4_clips_df_test[\"preference\"]][\"total_play_time\"]\n",
    "neg_play_time = subset_v4_clips_df_test[~subset_v4_clips_df_test[\"preference\"]][\"total_play_time\"]\n",
    "\n",
    "pos_play_time.hist(\n",
    "    bins=np.linspace(0, 400, 100),\n",
    "    alpha=0.5,\n",
    "    label=f\"pos (mean={pos_play_time.mean():.2f}, median={pos_play_time.median():.2f})\",\n",
    "    ax=ax1,\n",
    ")\n",
    "neg_play_time.hist(\n",
    "    bins=np.linspace(0, 400, 100),\n",
    "    alpha=0.5,\n",
    "    label=f\"neg (mean={neg_play_time.mean():.2f}, median={neg_play_time.median():.2f})\",\n",
    "    ax=ax1,\n",
    ")\n",
    "ax1.legend()\n",
    "ax1.set_xlabel(\"Total play duration in seconds\")\n",
    "ax1.set_ylabel(\"counts\")\n",
    "ax1.set_title(\"Play duration comparison\")\n",
    "\n",
    "# Second subplot: Normalized play fraction\n",
    "pos_norm_play_frac = subset_v4_clips_df_test[subset_v4_clips_df_test[\"preference\"]][\"norm_play_frac\"]\n",
    "neg_norm_play_frac = subset_v4_clips_df_test[~subset_v4_clips_df_test[\"preference\"]][\"norm_play_frac\"]\n",
    "\n",
    "pos_norm_play_frac.hist(\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    "    label=f\"pos (mean={pos_norm_play_frac.mean():.2f}, median={pos_norm_play_frac.median():.2f})\",\n",
    "    ax=ax2,\n",
    ")\n",
    "neg_norm_play_frac.hist(\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    "    label=f\"neg (mean={neg_norm_play_frac.mean():.2f}, median={neg_norm_play_frac.median():.2f})\",\n",
    "    ax=ax2,\n",
    ")\n",
    "ax2.legend()\n",
    "ax2.set_xlabel(\"Normalized play counts (play duration/duration)\")\n",
    "ax2.set_ylabel(\"Log counts\")\n",
    "ax2.set_yscale(\"log\")\n",
    "ax2.set_title(\"Normalized play duration comparison (Log scale)\")\n",
    "\n",
    "# Adjust layout and display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of clips that pass the play duration cut: 0.6313\n",
      "Number of unique requests passing play duration criteria: 3009\n",
      "Fraction of unique requests that pass play duration criteria: 0.6313\n"
     ]
    }
   ],
   "source": [
    "play_duration_mask = (\n",
    "    subset_v4_clips_df_test[\"preference\"]\n",
    "    & (subset_v4_clips_df_test[\"norm_play_frac\"] >= 0.95)\n",
    "    & (subset_v4_clips_df_test[\"total_play_time\"] >= 10)\n",
    ") | (\n",
    "    (~subset_v4_clips_df_test[\"preference\"])\n",
    "    & (subset_v4_clips_df_test[\"norm_play_frac\"] <= 3.1)\n",
    "    & (subset_v4_clips_df_test[\"total_play_time\"] >= 10)\n",
    ")\n",
    "# Calculate the fraction of clips that pass the play duration cut\n",
    "frac_pass_play_duration = play_duration_mask.sum() / subset_v4_clips_df_test.shape[0]\n",
    "\n",
    "# Print the result with a formatted string\n",
    "print(f\"Fraction of clips that pass the play duration cut: {frac_pass_play_duration:.4f}\")\n",
    "\n",
    "# Get unique request IDs that pass the play duration criteria\n",
    "unique_requests_pass_play_durations = subset_v4_clips_df_test[play_duration_mask][\"request_id\"].unique()\n",
    "\n",
    "# Print the number of unique requests that pass the play duration criteria\n",
    "print(\n",
    "    f\"Number of unique requests passing play duration criteria: {len(unique_requests_pass_play_durations)}\"\n",
    ")\n",
    "\n",
    "# Calculate the fraction of unique requests that pass play duration criteria\n",
    "fraction_requests_pass = (\n",
    "    len(unique_requests_pass_play_durations) / subset_v4_clips_df_test[\"request_id\"].nunique()\n",
    ")\n",
    "\n",
    "# Print the result with a formatted string\n",
    "print(f\"Fraction of unique requests that pass play duration criteria: {fraction_requests_pass:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_v4_clips_df_pass_duration = subset_v4_clips_df_test[play_duration_mask].copy()\n",
    "play_duration_mask_request_mask = subset_v4_clips_df_pass_duration[\"request_id\"].isin(\n",
    "    subset_v4_clips_df_pass_duration[\"request_id\"]\n",
    "    .value_counts()\n",
    "    .index[subset_v4_clips_df_pass_duration[\"request_id\"].value_counts() == 2]\n",
    ")\n",
    "final_subset_v4_clips_df = subset_v4_clips_df_pass_duration[play_duration_mask_request_mask].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique request IDs: 0, Total 4766\n"
     ]
    }
   ],
   "source": [
    "# Count and print the number of unique request IDs\n",
    "unique_request_count = final_subset_v4_clips_df[\"request_id\"].nunique()\n",
    "print(\n",
    "    f\"Number of unique request IDs: {unique_request_count:,}, Total {subset_v4_clips_df_test['request_id'].nunique()}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 65)\n"
     ]
    }
   ],
   "source": [
    "# final_subset_v4_clips_df.to_pickle(\n",
    "#     \"/home/tony/Data/Preference/30b_v1/interesting_clips_v4_t_2_20240812_full.pkl\",\n",
    "# )\n",
    "print(final_subset_v4_clips_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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