{ "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": "2024-08-12T18:02:25.851789Z", "iopub.status.busy": "2024-08-12T18:02:25.851640Z", "iopub.status.idle": "2024-08-12T18:02:26.003487Z", "shell.execute_reply": "2024-08-12T18:02:26.003025Z", "shell.execute_reply.started": "2024-08-12T18:02:25.851766Z" } }, "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": "2024-08-12T18:02:26.004439Z", "iopub.status.busy": "2024-08-12T18:02:26.004297Z", "iopub.status.idle": "2024-08-12T18:02:29.034759Z", "shell.execute_reply": "2024-08-12T18:02:29.034106Z", "shell.execute_reply.started": "2024-08-12T18:02:26.004423Z" } }, "outputs": [], "source": [ "# pip install psycopg2-binary\n", "# make sure sqlalchemy is >=2\n", "import ast\n", "import json\n", "from datetime import datetime\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 preference_helper import *\n", "from preference_helper import get_preferfence_counts\n", "from suno_utils.audio import Audio\n", "from suno_utils.utils.s3 import open_from_s3\n", "\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", "# engine = sqlalchemy.create_engine(\"postgresql://tony:123@localhost/mydatabase\")\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", "# connection = engine.raw_connection()\n", "\n", "# !pip install snowflake\n", "from snowflake.core import Root\n", "from snowflake.snowpark import Session\n", "\n", "snow_password_path = \"/home/tony/.aws/snow_pw.txt\"\n", "with open(snow_password_path, \"r\") as fp:\n", " snow_password = fp.readlines()[0].strip()\n", "\n", "CONNECTION_PARAMETERS = {\n", " \"account\": \"fu90569.us-east-2.aws\",\n", " \"user\": \"TONY\",\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": "2024-08-12T18:02:29.035887Z", "iopub.status.busy": "2024-08-12T18:02:29.035614Z", "iopub.status.idle": "2024-08-12T18:02:29.053587Z", "shell.execute_reply": "2024-08-12T18:02:29.053034Z", "shell.execute_reply.started": "2024-08-12T18:02:29.035869Z" } }, "outputs": [], "source": [ "# cutoff_date = \"2024-05-24 05:14:04\" # v3.5 early\n", "# cutoff_date = \"2024-05-30 02:03:11\" # v3.5 - 6\n", "# cutoff_date = \"2024-06-02 01:55:17\"\n", "# cutoff_date = \"2024-06-04 13:21:36\"\n", "# cutoff_date = \"2024-06-12 13:00:00\" # v3.5 extend\n", "# cutoff_date = \"2024-06-18 00:00:00\" # user feedback out\n", "# cutoff_date = \"2024-06-24 16:34:00\" # current time\n", "# 2 cutoff_date = \"2024-06-26 03:50:00\" # s-11 out\n", "# cutoff_date = \"2024-06-27 03:50:00\" # s-8 out\n", "# cutoff_date = \"2024-06-27 03:50:00\" # s-12 out\n", "# cutoff_date = \" 2024-06-29 03:00:00\" # s-13 end\n", "# cutoff_date = \"2024-07-01 05:00:00\" # s-14 out\n", "# cutoff_date = \"2024-07-05 03:10:00\" # s-14 out\n", "# cutoff_date = \"2024-07-10 04:10:00\" # ft-1 out\n", "# cutoff_date = \"2024-07-10 14:45:00\" # no-top-p out\n", "# cutoff_date = \"2024-07-10 19:35:00\" # 2h ft end ~ 4hr difference\n", "# cutoff_date = \"2024-07-11 23:35:00\" # v4 first test\n", "# cutoff_date = \"2024-07-13 04:30:00\" # s-18/19 out\n", "# cutoff_date = \"2024-07-14 04:45:00\" # ft-2 out\n", "# cutoff_date = \"2024-07-15 12:45:00\" # test\n", "# cutoff_date = \"2024-07-16 00:00:00\" # v4 collection out\n", "# cutoff_date = \"2024-07-26 00:00:00\" # v4 collection out\n", "cutoff_date = \"2024-08-09 21:00:00\" # v4-t2 out" ] }, { "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": "2024-08-12T18:02:29.054573Z", "iopub.status.busy": "2024-08-12T18:02:29.054257Z", "iopub.status.idle": "2024-08-12T18:02:29.351277Z", "shell.execute_reply": "2024-08-12T18:02:29.350724Z", "shell.execute_reply.started": "2024-08-12T18:02:29.054554Z" } }, "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": "markdown", "metadata": {}, "source": [ "# Query the DB" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:11:16.899029Z", "start_time": "2024-05-26T00:11:09.351909Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:02:29.352125Z", "iopub.status.busy": "2024-08-12T18:02:29.351969Z", "iopub.status.idle": "2024-08-12T18:05:11.951400Z", "shell.execute_reply": "2024-08-12T18:05:11.950819Z", "shell.execute_reply.started": "2024-08-12T18:02:29.352108Z" } }, "outputs": [], "source": [ "# bots_generatedclipextra\n", "# 'clip_id', 'created_at', 'updated_at', 'download_audio_count', 'download_video_count', 'share_count', 'is_public_approved', 'inferred_language', 'download_audio_wav_count\n", "# these are all the logged actions in the prod db\n", "query = f\"\"\"\n", "SELECT * FROM bots_generatedclipextra\n", "WHERE updated_at>='{cutoff_date}'\n", "\"\"\"\n", "bots_action_df = pd.read_sql_query(query, engine)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:12:13.867775Z", "start_time": "2024-05-26T00:11:16.900416Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:05:11.953210Z", "iopub.status.busy": "2024-08-12T18:05:11.953043Z", "iopub.status.idle": "2024-08-12T18:07:56.113968Z", "shell.execute_reply": "2024-08-12T18:07:56.113386Z", "shell.execute_reply.started": "2024-08-12T18:05:11.953194Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13,161,650 rows\n" ] } ], "source": [ "# ~ 16 min...X.x\n", "# bots_userreaction\n", "# 'id', 'play_count', 'skip_count', 'flagged', 'flagged_reason', 'reaction_type', 'updated_at', 'clip_id', 'user_id', 'feedback_reason'\n", "# this turns out to be much smaller ~ 570k\n", "query = f\"\"\"\n", "SELECT * FROM bots_userreaction\n", "WHERE updated_at>='{cutoff_date}' AND play_count>0\n", "\"\"\"\n", "reaction_df = pd.read_sql_query(query, engine)\n", "print(f\"{reaction_df.shape[0]:,} rows\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:12:14.002431Z", "start_time": "2024-05-26T00:12:13.869298Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:07:56.114830Z", "iopub.status.busy": "2024-08-12T18:07:56.114665Z", "iopub.status.idle": "2024-08-12T18:07:56.827255Z", "shell.execute_reply": "2024-08-12T18:07:56.826696Z", "shell.execute_reply.started": "2024-08-12T18:07:56.114813Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of upvoates: 1,242,171 rows\n", "number of flagged reports: 19,936 rows\n", "reaction_type\n", "L 0.825\n", "D 0.175\n", "Name: proportion, dtype: float64\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\"]\n", "\n", "# check basic reaction -- the rate should be very low\n", "print(reaction_df.tail(n=10000)[\"reaction_type\"].value_counts(normalize=True))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:12:14.504607Z", "start_time": "2024-05-26T00:12:14.296331Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:07:56.828130Z", "iopub.status.busy": "2024-08-12T18:07:56.827968Z", "iopub.status.idle": "2024-08-12T18:11:21.639617Z", "shell.execute_reply": "2024-08-12T18:11:21.639041Z", "shell.execute_reply.started": "2024-08-12T18:07:56.828112Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "980,644 rows\n" ] } ], "source": [ "# these are continues, ~ 1,485k (much more than likes) ~ takes 3.5 mins\n", "# columns are:\n", "# 'id', 'created_at', 'updated_at', 'time_used', 'metadata', 'user_id',\n", "# 'status', 'discord_message_id', 'prompt_id', 'request_id',\n", "# 'is_generated', 's3_id', 'upvote_count', 'batch_index', 'model_name',\n", "# 'prompt_text', 'daily_theme_id', 'is_deleted', 'image_s3_id',\n", "# 'is_public', 'dislike_count', 'flag_count', 'play_count', 'skip_count',\n", "# 'title', 'is_public_approved', 'slug'\n", "\n", "# find all the complete clips -- this query takes ~ 10 sec\n", "# query = \"\"\"\n", "# SELECT COUNT(*) FROM bots_generatedclip\n", "# \"\"\"\n", "# clip_counts = pd.read_sql_query(query, engine)\n", "# all_total_clip_counts = clip_counts[\"count\"][0]\n", "# print(f\"all version total clips: {all_total_clip_counts}\")\n", "\n", "# query = \"\"\"\n", "# SELECT COUNT(*) FROM bots_generatedclip\n", "# WHERE status='complete' AND model_name::text LIKE '%%v3%%'\n", "# \"\"\"\n", "# clip_counts = pd.read_sql_query(query, engine)\n", "# all_total_clip_counts = clip_counts[\"count\"][0]\n", "# print(f\"v3 version total clips: {all_total_clip_counts}\")\n", "\n", "# ~ 1h 25 mins...or, 3 days takes ~ 15 mins\n", "# NOTE that we need to query everything cause contact / continue can come from another model\n", "\n", "# TODO: query only v3 here....\n", "# This is still a lot...we will have to do this in steps very soon\n", "# Some data eng required, disk is much cheaper\n", "# the generated clips table has play count issues (we need to read it without filtering on playcounts)\n", "# AND model_name::text LIKE '%%v3%%' AND play_count>=1\n", "# AND model_name::text LIKE '%%v3p5%%'\n", "# WHERE status='complete' AND created_at>='{cutoff_date}' AND model_name::text='chirp-v3p5-engine-t-1'\n", "query = f\"\"\"\n", "SELECT * FROM bots_generatedclip\n", "WHERE status='complete' AND created_at>='{cutoff_date}' AND model_name::text='chirp-v3p5-engine-t-2'\n", "\"\"\"\n", "total_clip_df = pd.read_sql_query(query, engine)\n", "print(f\"{total_clip_df.shape[0]:,} rows\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:22:56.476914Z", "start_time": "2024-05-26T00:22:06.973099Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:21.640458Z", "iopub.status.busy": "2024-08-12T18:11:21.640303Z", "iopub.status.idle": "2024-08-12T18:11:24.606898Z", "shell.execute_reply": "2024-08-12T18:11:24.606324Z", "shell.execute_reply.started": "2024-08-12T18:11:21.640442Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "147,645 playlists updated\n" ] } ], "source": [ "# get playlists\n", "query = f\"\"\"\n", "SELECT * FROM bots_playlistclip\n", "WHERE updated_at>='{cutoff_date}'\n", "\"\"\"\n", "playlist_clip_df = pd.read_sql_query(query, engine)\n", "print(f\"{playlist_clip_df.shape[0]:,} playlists updated\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:11:24.607793Z", "iopub.status.busy": "2024-08-12T18:11:24.607630Z", "iopub.status.idle": "2024-08-12T18:11:26.370227Z", "shell.execute_reply": "2024-08-12T18:11:26.369659Z", "shell.execute_reply.started": "2024-08-12T18:11:24.607776Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "authenticated users (494173, 3)\n" ] } ], "source": [ "query = \"\"\"\n", "SELECT *\n", "FROM auth_user_groups\n", "\"\"\"\n", "auth_user_df = pd.read_sql_query(query, engine)\n", "print(\"authenticated users\", auth_user_df.shape)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:11:26.371109Z", "iopub.status.busy": "2024-08-12T18:11:26.370952Z", "iopub.status.idle": "2024-08-12T18:11:40.442610Z", "shell.execute_reply": "2024-08-12T18:11:40.442028Z", "shell.execute_reply.started": "2024-08-12T18:11:26.371092Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "subscription_status\n", "active 279662\n", "past_due 13045\n", "Name: count, dtype: int64\n" ] } ], "source": [ "query = \"\"\"\n", "SELECT * FROM bots_discordinfo\n", "WHERE subscription_status IN ('active', 'past_due')\n", "\"\"\"\n", "df_discord_info = pd.read_sql_query(\n", " query,\n", " engine,\n", ")\n", "# current active subscribers?\n", "print(df_discord_info[\"subscription_status\"].value_counts())\n", "# this is probably the right way to figure out the pro user group\n", "pro_users = set(df_discord_info[\"user_id\"].unique())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:11:40.443479Z", "iopub.status.busy": "2024-08-12T18:11:40.443324Z", "iopub.status.idle": "2024-08-12T18:11:40.861667Z", "shell.execute_reply": "2024-08-12T18:11:40.861090Z", "shell.execute_reply.started": "2024-08-12T18:11:40.443463Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total clips: 980644\n", "total uploads: 0 (0, 26)\n" ] } ], "source": [ "# filter on versions\n", "# clip_df = total_clip_df[\n", "# ((total_clip_df[\"model_name\"].str.contains(\"v3\")) | (total_clip_df[\"model_name\"].str.contains(\"v4\"))) # or v3...\n", "# & (total_clip_df[\"created_at\"] >= \"2024-02-20\")\n", "# ].copy()\n", "clip_df = total_clip_df.copy()\n", "# print(f\"total v3 selected fraction = {clip_df.shape[0] / all_total_clip_counts}\")\n", "total_clip_counts = clip_df.shape[0]\n", "print(f\"total clips: {total_clip_counts}\")\n", "# check the number of audio uploads\n", "upload_clip_df = total_clip_df[total_clip_df[\"s3_id\"].str.startswith(\"m_\")].copy()\n", "print(\"total uploads:\", (total_clip_df[\"model_name\"] == \"\").sum(), upload_clip_df.shape)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:11:40.862520Z", "iopub.status.busy": "2024-08-12T18:11:40.862361Z", "iopub.status.idle": "2024-08-12T18:11:41.131798Z", "shell.execute_reply": "2024-08-12T18:11:41.131324Z", "shell.execute_reply.started": "2024-08-12T18:11:40.862504Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Pluse time series check\n", "(total_clip_df[\"created_at\"].dt.hour + total_clip_df[\"created_at\"].dt.day * 24).hist(\n", " bins=100\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Proceed with feature engineering and cleaning up" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:11:41.132608Z", "iopub.status.busy": "2024-08-12T18:11:41.132459Z", "iopub.status.idle": "2024-08-12T18:11:41.597801Z", "shell.execute_reply": "2024-08-12T18:11:41.597271Z", "shell.execute_reply.started": "2024-08-12T18:11:41.132592Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pro reactions fraction by category:\n", "False: 66.36%\n", "True: 33.64%\n", "Pro generation fraction by category:\n", "False: 60.84%\n", "True: 39.16%\n", "pro users with generations 42100\n" ] } ], "source": [ "# this is very interesting....\n", "# reaction check\n", "reaction_df[\"is_pro_user\"] = reaction_df[\"user_id\"].isin(pro_users)\n", "pro_reactions_frac = reaction_df[\"is_pro_user\"].value_counts(normalize=True)\n", "print(\"Pro reactions fraction by category:\")\n", "for category, fraction in pro_reactions_frac.items():\n", " print(f\"{category}: {fraction:.2%}\")\n", "# clip check\n", "clip_df[\"is_pro_user\"] = clip_df[\"user_id\"].isin(pro_users)\n", "pro_gen_frac = clip_df[\"is_pro_user\"].value_counts(normalize=True)\n", "print(\"Pro generation fraction by category:\")\n", "for category, fraction in pro_gen_frac.items():\n", " print(f\"{category}: {fraction:.2%}\")\n", "print(\n", " \"pro users with generations\", clip_df[\"user_id\"][clip_df[\"is_pro_user\"]].nunique()\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:12.019778Z", "start_time": "2024-05-26T00:22:57.637371Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:41.598629Z", "iopub.status.busy": "2024-08-12T18:11:41.598479Z", "iopub.status.idle": "2024-08-12T18:11:42.153850Z", "shell.execute_reply": "2024-08-12T18:11:42.153302Z", "shell.execute_reply.started": "2024-08-12T18:11:41.598613Z" } }, "outputs": [], "source": [ "# add clip is in playlist feature\n", "# check if a clip is in a playlist\n", "clip_df[\"is_in_playlist\"] = clip_df[\"id\"].isin(playlist_clip_df[\"clip_id\"].unique())" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:11:42.154743Z", "iopub.status.busy": "2024-08-12T18:11:42.154582Z", "iopub.status.idle": "2024-08-12T18:11:43.397016Z", "shell.execute_reply": "2024-08-12T18:11:43.396431Z", "shell.execute_reply.started": "2024-08-12T18:11:42.154726Z" } }, "outputs": [], "source": [ "def parse_parent_id(x):\n", " \"\"\"Find out a clip's parent id.\"\"\"\n", " if \"history\" not in x:\n", " return None\n", " out = x.get(\"history\", [])\n", " if not isinstance(out, list) or len(out) == 0:\n", " return None\n", " # take the last one cause we continue off the children?\n", " out = out[-1]\n", " if isinstance(out, dict):\n", " # this is the continued info, which is a dict with id and continue_at\n", " return out[\"id\"]\n", " else:\n", " return None\n", "\n", "\n", "def parse_duration(x):\n", " \"\"\"Find out a clip's duration.\"\"\"\n", " if \"duration\" not in x:\n", " return None\n", " return x.get(\"duration\")\n", "\n", "\n", "def parse_source(x):\n", " \"\"\"Find out a clip's source (web/ios).\"\"\"\n", " if \"source\" not in x:\n", " return None\n", " return x.get(\"source\")\n", "\n", "\n", "def parse_metadata_for_basics(x):\n", " \"\"\"Parse the metadata for basics.\"\"\"\n", " parent_id = parse_parent_id(x)\n", " duration = parse_duration(x)\n", " source = parse_source(x)\n", " return parent_id, duration, source\n", "\n", "\n", "clip_df[[\"continued_parent\", \"duration\", \"source\"]] = pd.DataFrame(\n", " clip_df[\"metadata\"].map(parse_metadata_for_basics).tolist(), index=clip_df.index\n", ")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:17.912428Z", "start_time": "2024-05-26T00:23:12.021726Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:43.397922Z", "iopub.status.busy": "2024-08-12T18:11:43.397764Z", "iopub.status.idle": "2024-08-12T18:11:43.527710Z", "shell.execute_reply": "2024-08-12T18:11:43.527136Z", "shell.execute_reply.started": "2024-08-12T18:11:43.397905Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clips that have children: 62547 \n", " clips that are parents: 24328 \n", " Average continues from clip = 2.57\n" ] } ], "source": [ "clip_history_df = clip_df[~clip_df[\"continued_parent\"].isna()].copy()\n", "continued_ids = clip_history_df[\"id\"]\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", " \"\\n clips that are parents:\",\n", " has_continued_children_ids.nunique(),\n", " \"\\n\",\n", " \"Average continues from clip = \",\n", " round(\n", " len(has_continued_children_ids) / len(has_continued_children_ids.unique()), 2\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:22.092835Z", "start_time": "2024-05-26T00:23:17.914456Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:43.528589Z", "iopub.status.busy": "2024-08-12T18:11:43.528424Z", "iopub.status.idle": "2024-08-12T18:11:44.130313Z", "shell.execute_reply": "2024-08-12T18:11:44.129735Z", "shell.execute_reply.started": "2024-08-12T18:11:43.528571Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total uploads: 0\n", "Clips without request id (concat, uploads...) frac = 0.00613\n" ] } ], "source": [ "print(\"total uploads:\", (clip_df[\"model_name\"] == \"\").sum())\n", "# the nans are concats, we want to drop them for now\n", "concated_clips = clip_df[clip_df[\"request_id\"].isna()].copy()\n", "# need to kick them out...\n", "clip_df = clip_df[~clip_df[\"request_id\"].isna()]\n", "print(\n", " f\"Clips without request id (concat, uploads...) frac = {concated_clips.shape[0] / total_clip_counts:.5f}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:22.833148Z", "start_time": "2024-05-26T00:23:22.094796Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:44.131175Z", "iopub.status.busy": "2024-08-12T18:11:44.131013Z", "iopub.status.idle": "2024-08-12T18:11:44.255240Z", "shell.execute_reply": "2024-08-12T18:11:44.254760Z", "shell.execute_reply.started": "2024-08-12T18:11:44.131158Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chirp-v3p5-engine-t-2 --> 1.00000\n" ] } ], "source": [ "# check the model conts\n", "value_counts = clip_df[\"model_name\"].value_counts()\n", "total_count = value_counts.sum()\n", "for model_name, count in value_counts.items():\n", " model_fraction = round(count / total_count, 5)\n", " print(f\"{model_name} --> {model_fraction:.5f}\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:26.317699Z", "start_time": "2024-05-26T00:23:22.835074Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:44.256014Z", "iopub.status.busy": "2024-08-12T18:11:44.255864Z", "iopub.status.idle": "2024-08-12T18:11:45.644218Z", "shell.execute_reply": "2024-08-12T18:11:45.643651Z", "shell.execute_reply.started": "2024-08-12T18:11:44.255998Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pre-filter model type clip_df shape: (974628, 31)\n", "post-filter model type clip_df shape: (974628, 31)\n" ] } ], "source": [ "print(\"pre-filter model type clip_df shape:\", clip_df.shape)\n", "clip_df = clip_df[\n", " (clip_df[\"model_name\"] == \"\")\n", " | (clip_df[\"model_name\"].str.startswith(\"chirp-v2\"))\n", " | (clip_df[\"model_name\"].str.startswith(\"chirp-v3-engine\"))\n", " | (clip_df[\"model_name\"].str.startswith(\"chirp-v3p5-engine\"))\n", "]\n", "print(\"post-filter model type clip_df shape:\", clip_df.shape)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:26.994407Z", "start_time": "2024-05-26T00:23:26.319359Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:45.647768Z", "iopub.status.busy": "2024-08-12T18:11:45.647348Z", "iopub.status.idle": "2024-08-12T18:11:45.851317Z", "shell.execute_reply": "2024-08-12T18:11:45.850778Z", "shell.execute_reply.started": "2024-08-12T18:11:45.647748Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model_name\n", "chirp-v3p5-engine-t-2 974628\n", "Name: count, dtype: int64\n" ] } ], "source": [ "print(clip_df[\"model_name\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:33.488829Z", "start_time": "2024-05-26T00:23:27.161663Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:45.852150Z", "iopub.status.busy": "2024-08-12T18:11:45.851992Z", "iopub.status.idle": "2024-08-12T18:11:52.031903Z", "shell.execute_reply": "2024-08-12T18:11:52.031328Z", "shell.execute_reply.started": "2024-08-12T18:11:45.852134Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "concat reactions: 25142 unique concat clips: 4826\n" ] } ], "source": [ "# ~ only 1 min :)\n", "# find all the concact clip reactions\n", "concat_reaction_df = reaction_df[\n", " reaction_df[\"clip_id\"].isin(concated_clips[\"id\"])\n", "].copy()\n", "print(\n", " \"concat reactions:\",\n", " concat_reaction_df.shape[0],\n", " \"unique concat clips:\",\n", " concat_reaction_df[\"clip_id\"].nunique(),\n", ")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:11:52.032765Z", "iopub.status.busy": "2024-08-12T18:11:52.032607Z", "iopub.status.idle": "2024-08-12T18:11:52.055163Z", "shell.execute_reply": "2024-08-12T18:11:52.054714Z", "shell.execute_reply.started": "2024-08-12T18:11:52.032747Z" } }, "outputs": [], "source": [ "concat_reaction_df[\"upvote_count\"] = concat_reaction_df[\"reaction_type\"] == \"L\"\n", "concat_reaction_df[\"dislike_count\"] = concat_reaction_df[\"reaction_type\"] == \"D\"" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:34.169887Z", "start_time": "2024-05-26T00:23:33.491114Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:52.055919Z", "iopub.status.busy": "2024-08-12T18:11:52.055770Z", "iopub.status.idle": "2024-08-12T18:11:52.133869Z", "shell.execute_reply": "2024-08-12T18:11:52.133410Z", "shell.execute_reply.started": "2024-08-12T18:11:52.055903Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total concats (6016, 34)\n", "check \n", " reaction_play_count reaction_upvote_count reaction_dislike_count\n", "count 4826.000000 4826.000000 4826.000000\n", "mean 8.181724 0.367178 0.016991\n", "std 159.368974 2.578322 0.165781\n", "min 1.000000 0.000000 0.000000\n", "25% 1.000000 0.000000 0.000000\n", "50% 2.000000 0.000000 0.000000\n", "75% 3.000000 0.750000 0.000000\n", "max 8517.000000 116.000000 6.000000\n" ] } ], "source": [ "concat_total_reaction_df_sum = concat_reaction_df.groupby(\"clip_id\")[\n", " [\"play_count\", \"upvote_count\", \"dislike_count\"]\n", "].sum()\n", "concat_total_reaction_df_sum_df = concat_total_reaction_df_sum.reset_index().rename(\n", " columns={\n", " \"clip_id\": \"id\",\n", " \"play_count\": \"reaction_play_count\",\n", " \"upvote_count\": \"reaction_upvote_count\",\n", " \"dislike_count\": \"reaction_dislike_count\",\n", " }\n", ")\n", "concated_clips = concated_clips.merge(\n", " concat_total_reaction_df_sum_df, on=\"id\", how=\"left\"\n", ")\n", "\n", "print(\"total concats\", concated_clips.shape)\n", "print(\n", " \"check \\n\",\n", " concated_clips[\n", " [\"reaction_play_count\", \"reaction_upvote_count\", \"reaction_dislike_count\"]\n", " ].describe(),\n", ")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:11:52.134590Z", "iopub.status.busy": "2024-08-12T18:11:52.134445Z", "iopub.status.idle": "2024-08-12T18:11:52.152670Z", "shell.execute_reply": "2024-08-12T18:11:52.152213Z", "shell.execute_reply.started": "2024-08-12T18:11:52.134574Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All concats 6016\n", "total concats with plays 4826\n" ] } ], "source": [ "# 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": 26, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:38.974479Z", "start_time": "2024-05-26T00:23:34.385507Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:52.153426Z", "iopub.status.busy": "2024-08-12T18:11:52.153281Z", "iopub.status.idle": "2024-08-12T18:11:52.456631Z", "shell.execute_reply": "2024-08-12T18:11:52.456159Z", "shell.execute_reply.started": "2024-08-12T18:11:52.153410Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "4826it [00:00, 18219.03it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "total concat unique clips are: 10462 with error: 0, duplicate 676 \n", " uploads are in concats 343 frac 343.000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# this is each clip. and the mapped start time of the clip\n", "# for a clip in the concat history, we want to know which part it is starting / ending\n", "concat_clips_ids = {}\n", "history_error_counter = 0\n", "history_duplicate_error_counter = 0\n", "for _, row in tqdm.tqdm(concated_clips.iterrows()):\n", " if concat_history_clips := row[\"metadata\"].get(\"concat_history\"):\n", " total_duration = row[\"metadata\"].get(\"duration\", 0)\n", " if total_duration == 0:\n", " print(row[\"metadata\"], row[\"model_name\"])\n", " continue\n", " start_s = 0\n", " for history_clip in concat_history_clips:\n", " if isinstance(history_clip, dict) and \"id\" in history_clip:\n", " # the other key is `continue_at`\n", " if history_clip[\"id\"]:\n", " # in case a clip ends up in multiple concats, we need to choose the optimal one\n", " if history_clip[\"id\"] in concat_clips_ids:\n", " # keep the highest upvote clip\n", " if (\n", " row[\"reaction_upvote_count\"]\n", " < concat_clips_ids[history_clip[\"id\"]][\"concat_likes\"]\n", " ):\n", " continue\n", " # then keep the highest play count clip\n", " if (\n", " row[\"reaction_play_count\"]\n", " < concat_clips_ids[history_clip[\"id\"]][\"concat_play_counts\"]\n", " ):\n", " continue\n", " # multi-seed to concats\n", " history_duplicate_error_counter += 1\n", " concat_clips_ids[history_clip[\"id\"]] = {\n", " \"total_start_s\": start_s,\n", " \"total_clip_s\": total_duration,\n", " \"concat_play_counts\": row[\"reaction_play_count\"],\n", " \"concat_in_playlist\": row[\"is_in_playlist\"],\n", " \"concat_likes\": row[\"reaction_upvote_count\"],\n", " \"concat_dislikes\": row[\"reaction_dislike_count\"],\n", " }\n", " else:\n", " history_error_counter += 1\n", " try:\n", " # but we always update the start_s -- but keep the relative orders\n", " if history_clip[\"continue_at\"] is None:\n", " # we need to go back and fetch the duration\n", " if history_clip[\"id\"] in clip_df[\"id\"]:\n", " start_s += clip_df[clip_df[\"id\"] == history_clip[\"id\"]][\n", " \"duration\"\n", " ].iloc[0]\n", " else:\n", " # This is wrong but what can we do...\n", " # this clip isn't kept in the query\n", " start_s = 0\n", " else:\n", " start_s += history_clip[\"continue_at\"]\n", " except:\n", " print(history_clip)\n", " raise\n", "\n", "n_unique_uploads_in_concats = len(\n", " set(i for i in concat_clips_ids if i.startswith(\"m_\"))\n", ")\n", "print(\n", " \"total concat unique clips are:\",\n", " len(concat_clips_ids),\n", " f\"with error: {history_error_counter}, duplicate {history_duplicate_error_counter}\",\n", " \"\\n\",\n", " \"uploads are in concats\",\n", " n_unique_uploads_in_concats,\n", " \"frac\",\n", " f\"{n_unique_uploads_in_concats / (upload_clip_df.shape[0] or 1):.3f}\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Features" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:40.236690Z", "start_time": "2024-05-26T00:23:39.995713Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:52.457374Z", "iopub.status.busy": "2024-08-12T18:11:52.457230Z", "iopub.status.idle": "2024-08-12T18:11:52.552789Z", "shell.execute_reply": "2024-08-12T18:11:52.552313Z", "shell.execute_reply.started": "2024-08-12T18:11:52.457358Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 974628.000000\n", "mean 13.777591\n", "std 24.591028\n", "min 1.000000\n", "25% 2.000000\n", "50% 4.000000\n", "75% 12.000000\n", "max 302.000000\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": 28, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:44.826860Z", "start_time": "2024-05-26T00:23:40.238330Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:52.553544Z", "iopub.status.busy": "2024-08-12T18:11:52.553392Z", "iopub.status.idle": "2024-08-12T18:11:53.849321Z", "shell.execute_reply": "2024-08-12T18:11:53.848770Z", "shell.execute_reply.started": "2024-08-12T18:11:52.553528Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has upvoted upvoted\n", "False 932709\n", "True 41919\n", "Name: count, dtype: int64 upvoted\n", "False 0.95699\n", "True 0.04301\n", "Name: proportion, dtype: float64 upvote_count\n", "False 0.956351\n", "True 0.043649\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": 29, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:48.404433Z", "start_time": "2024-05-26T00:23:44.857788Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:53.850180Z", "iopub.status.busy": "2024-08-12T18:11:53.850018Z", "iopub.status.idle": "2024-08-12T18:11:55.236588Z", "shell.execute_reply": "2024-08-12T18:11:55.236034Z", "shell.execute_reply.started": "2024-08-12T18:11:53.850164Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downvoted fraction by category:\n", "False: 95.64%\n", "True: 4.36%\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", "for category, fraction in clip_df[\"downvoted\"].value_counts(normalize=True).items():\n", " print(f\"{category}: {fraction:.2%}\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:56.590022Z", "start_time": "2024-05-26T00:23:48.405668Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:55.237440Z", "iopub.status.busy": "2024-08-12T18:11:55.237284Z", "iopub.status.idle": "2024-08-12T18:11:56.368023Z", "shell.execute_reply": "2024-08-12T18:11:56.367465Z", "shell.execute_reply.started": "2024-08-12T18:11:55.237423Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has_continued fraction by category:\n", "False: 99.80%\n", "True: 0.20%\n" ] } ], "source": [ "# add continued column -- uuid and str are not compatible X.x\n", "clip_df[\"has_continued\"] = (\n", " clip_df[\"id\"].astype(str).isin(set(list(has_continued_children_ids)))\n", ")\n", "print(\"has_continued fraction by category:\")\n", "for category, fraction in clip_df[\"has_continued\"].value_counts(normalize=True).items():\n", " print(f\"{category}: {fraction:.2%}\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:04.704306Z", "start_time": "2024-05-26T00:23:56.591353Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:56.368879Z", "iopub.status.busy": "2024-08-12T18:11:56.368725Z", "iopub.status.idle": "2024-08-12T18:11:57.422804Z", "shell.execute_reply": "2024-08-12T18:11:57.422253Z", "shell.execute_reply.started": "2024-08-12T18:11:56.368863Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "part_of_concat fraction by category:\n", "False: 99.48%\n", "True: 0.52%\n", "\n", "Model distribution for part_of_concat clips:\n", "chirp-v3p5-engine-t-2: 100.00%\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", "for category, fraction in (\n", " clip_df[\"part_of_concat\"].value_counts(normalize=True).items()\n", "):\n", " print(f\"{category}: {fraction:.2%}\")\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\"]\n", " .value_counts(normalize=True)\n", " .items()\n", "):\n", " print(f\"{model}: {fraction:.2%}\")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:09.954233Z", "start_time": "2024-05-26T00:24:04.705554Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:57.423658Z", "iopub.status.busy": "2024-08-12T18:11:57.423503Z", "iopub.status.idle": "2024-08-12T18:11:58.223756Z", "shell.execute_reply": "2024-08-12T18:11:58.223204Z", "shell.execute_reply.started": "2024-08-12T18:11:57.423641Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has_action fraction by category:\n", "False: 99.36%\n", "True: 0.64%\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", "for category, fraction in clip_df[\"has_action\"].value_counts(normalize=True).items():\n", " print(f\"{category}: {fraction:.2%}\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:14.579841Z", "start_time": "2024-05-26T00:24:09.955568Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:58.224625Z", "iopub.status.busy": "2024-08-12T18:11:58.224471Z", "iopub.status.idle": "2024-08-12T18:11:58.753554Z", "shell.execute_reply": "2024-08-12T18:11:58.753016Z", "shell.execute_reply.started": "2024-08-12T18:11:58.224609Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flagged fraction by category:\n", "False: 99.77%\n", "True: 0.23%\n" ] } ], "source": [ "# add downvoted column\n", "clip_df[\"flagged\"] = clip_df[\"id\"].isin(flagged_ids)\n", "print(\"flagged fraction by category:\")\n", "for category, fraction in clip_df[\"flagged\"].value_counts(normalize=True).items():\n", " print(f\"{category}: {fraction:.2%}\")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:11:58.754394Z", "iopub.status.busy": "2024-08-12T18:11:58.754242Z", "iopub.status.idle": "2024-08-12T18:11:58.779778Z", "shell.execute_reply": "2024-08-12T18:11:58.779316Z", "shell.execute_reply.started": "2024-08-12T18:11:58.754378Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deleted fraction by category:\n", "False: 82.24%\n", "True: 17.76%\n" ] } ], "source": [ "clip_df[\"deleted\"] = clip_df[\"is_deleted\"]\n", "print(\"deleted fraction by category:\")\n", "for category, fraction in clip_df[\"deleted\"].value_counts(normalize=True).items():\n", " print(f\"{category}: {fraction:.2%}\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:15.382315Z", "start_time": "2024-05-26T00:24:14.581073Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:58.780523Z", "iopub.status.busy": "2024-08-12T18:11:58.780379Z", "iopub.status.idle": "2024-08-12T18:11:58.955029Z", "shell.execute_reply": "2024-08-12T18:11:58.954562Z", "shell.execute_reply.started": "2024-08-12T18:11:58.780508Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total clips: 974,628\n", "Total preference: 49,279 (5.06%)\n", "Must be positive: 52,953 (5.43%)\n", "Definitely not negative: 760,699 (78.05%)\n", "Must be negative: 213,929 (21.95%)\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 = (\n", " (~clip_df[\"downvoted\"]) & (~clip_df[\"deleted\"]) & (~clip_df[\"flagged\"])\n", ")\n", "must_be_negative_mask = (\n", " (clip_df[\"downvoted\"]) | (clip_df[\"deleted\"]) | (clip_df[\"flagged\"])\n", ")\n", "mask = must_be_positive_mask & must_be_not_negative_mask\n", "total_clips_count = clip_df.shape[0]\n", "total_preference_count = sum(mask)\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\"Total preference: {total_preference_count:,} ({total_preference_count/total_clips_count:.2%})\\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": 36, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:31.095990Z", "start_time": "2024-05-26T00:24:15.383572Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:11:58.955787Z", "iopub.status.busy": "2024-08-12T18:11:58.955643Z", "iopub.status.idle": "2024-08-12T18:12:00.493905Z", "shell.execute_reply": "2024-08-12T18:12:00.493312Z", "shell.execute_reply.started": "2024-08-12T18:11:58.955772Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Liked requests: 40,850\n", "Not liked requests: 490,286\n", "Requests with preference paired generations: 31,186\n", "Percentage of total unique requests: 6.24%\n" ] } ], "source": [ "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(\n", " f\"Percentage of total unique requests: {len(has_liked_requests) / total_unique_requests:.2%}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:00.494768Z", "iopub.status.busy": "2024-08-12T18:12:00.494610Z", "iopub.status.idle": "2024-08-12T18:12:01.599092Z", "shell.execute_reply": "2024-08-12T18:12:01.598496Z", "shell.execute_reply.started": "2024-08-12T18:12:00.494750Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Disliked requests: 125,330\n", "Not disliked requests: 405,515\n", "Requests with preference paired generations: 30,895\n", "Percentage of total unique requests: 6.18%\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(\n", " f\"Percentage of total unique requests: {len(has_disliked_requests) / total_unique_requests:.2%}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:31.099372Z", "start_time": "2024-05-26T00:24:31.097244Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:01.599955Z", "iopub.status.busy": "2024-08-12T18:12:01.599792Z", "iopub.status.idle": "2024-08-12T18:12:01.626660Z", "shell.execute_reply": "2024-08-12T18:12:01.626194Z", "shell.execute_reply.started": "2024-08-12T18:12:01.599937Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total selected pairs of requests: 54,063\n", "Percentage of total unique requests: 10.81%\n" ] } ], "source": [ "requests = has_liked_requests.union(has_disliked_requests)\n", "print(f\"Total selected pairs of requests: {len(requests):,}\")\n", "print(\n", " f\"Percentage of total unique requests: {len(requests) / total_unique_requests:.2%}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:31.239254Z", "start_time": "2024-05-26T00:24:31.100389Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:01.627439Z", "iopub.status.busy": "2024-08-12T18:12:01.627290Z", "iopub.status.idle": "2024-08-12T18:12:01.673495Z", "shell.execute_reply": "2024-08-12T18:12:01.673034Z", "shell.execute_reply.started": "2024-08-12T18:12:01.627424Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference in preference counts:\n", "0: 711,420 (72.99%)\n", "-1: 213,929 (21.95%)\n", "1: 49,279 (5.06%)\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", "clip_df[\"diff_preference\"] = clip_df[\"pos_preference\"].astype(int) - clip_df[\n", " \"neg_preference\"\n", "].astype(int)\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": 40, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:37.322031Z", "start_time": "2024-05-26T00:24:31.240829Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:01.674221Z", "iopub.status.busy": "2024-08-12T18:12:01.674080Z", "iopub.status.idle": "2024-08-12T18:12:02.234166Z", "shell.execute_reply": "2024-08-12T18:12:02.233589Z", "shell.execute_reply.started": "2024-08-12T18:12:01.674206Z" } }, "outputs": [], "source": [ "# creation of interesting_clips\n", "interesting_clips = clip_df[clip_df[\"request_id\"].isin(requests)].copy()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:02.235035Z", "iopub.status.busy": "2024-08-12T18:12:02.234874Z", "iopub.status.idle": "2024-08-12T18:12:02.585506Z", "shell.execute_reply": "2024-08-12T18:12:02.584999Z", "shell.execute_reply.started": "2024-08-12T18:12:02.235013Z" } }, "outputs": [ { "data": { "text/html": [ "
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request_idpos_preferenceneg_preferencediff_preference
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" ], "text/plain": [ " request_id pos_preference neg_preference diff_preference\n", "0 00006487-4c98-4020-832d-b5c7a1c8e24d False True -1\n", "1 00006487-4c98-4020-832d-b5c7a1c8e24d False False 0\n", "2 00009593-1f35-48c7-ac8d-b5319aa0742c False True -1\n", "3 00009593-1f35-48c7-ac8d-b5319aa0742c False False 0\n", "4 0001449a-502d-4e2e-bc77-a6baec139c38 False False 0" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interesting_clips = interesting_clips.sort_values(\n", " by=[\"request_id\", \"diff_preference\"]\n", ").reset_index()\n", "interesting_clips[\n", " [\"request_id\", \"pos_preference\", \"neg_preference\", \"diff_preference\"]\n", "].head()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:02.586333Z", "iopub.status.busy": "2024-08-12T18:12:02.586176Z", "iopub.status.idle": "2024-08-12T18:12:02.608794Z", "shell.execute_reply": "2024-08-12T18:12:02.608312Z", "shell.execute_reply.started": "2024-08-12T18:12:02.586316Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference 0: 46,045 (42.58%)\n", "Difference 1: 31,186 (28.84%)\n", "Difference -1: 30,895 (28.57%)\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": 43, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:02.609546Z", "iopub.status.busy": "2024-08-12T18:12:02.609403Z", "iopub.status.idle": "2024-08-12T18:12:02.648098Z", "shell.execute_reply": "2024-08-12T18:12:02.647641Z", "shell.execute_reply.started": "2024-08-12T18:12:02.609531Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Value 1.0: 46,045 (85.17%)\n", "Value 2.0: 8,018 (14.83%)\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": 44, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:38.960130Z", "start_time": "2024-05-26T00:24:37.323369Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:02.648841Z", "iopub.status.busy": "2024-08-12T18:12:02.648699Z", "iopub.status.idle": "2024-08-12T18:12:02.823786Z", "shell.execute_reply": "2024-08-12T18:12:02.823323Z", "shell.execute_reply.started": "2024-08-12T18:12:02.648825Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of unique request_ids: 54,063\n", "Number of unique ids: 108,126\n" ] } ], "source": [ "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():,}\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:43.156873Z", "start_time": "2024-05-26T00:24:38.961258Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:02.824525Z", "iopub.status.busy": "2024-08-12T18:12:02.824382Z", "iopub.status.idle": "2024-08-12T18:12:03.068613Z", "shell.execute_reply": "2024-08-12T18:12:03.068105Z", "shell.execute_reply.started": "2024-08-12T18:12:02.824509Z" } }, "outputs": [], "source": [ "# some validations\n", "assert interesting_clips[interesting_clips[\"request_id\"].isna()].shape[0] == 0\n", "check_df = interesting_clips.groupby(\"request_id\")[\"id\"].nunique()\n", "check_df[check_df.values != 2]\n", "assert check_df[check_df.values != 2].shape[0] == 0" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:43.164669Z", "start_time": "2024-05-26T00:24:43.158223Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:03.069419Z", "iopub.status.busy": "2024-08-12T18:12:03.069270Z", "iopub.status.idle": "2024-08-12T18:12:03.087608Z", "shell.execute_reply": "2024-08-12T18:12:03.087157Z", "shell.execute_reply.started": "2024-08-12T18:12:03.069404Z" } }, "outputs": [], "source": [ "# validation...\n", "# TODO: refactor this with above into a func\n", "# interesting_clips_must_be_positive_mask = (\n", "# (interesting_clips[\"upvoted\"] == True)\n", "# | (interesting_clips[\"has_action\"] == True)\n", "# | (interesting_clips[\"part_of_concat\"] == True)\n", "# )\n", "# interesting_clips_must_be_not_negative_mask = (\n", "# interesting_clips[\"downvoted\"] == False\n", "# ) & (interesting_clips[\"deleted\"] == False)\n", "# interesting_clips_mask = (\n", "# interesting_clips_must_be_positive_mask\n", "# & interesting_clips_must_be_not_negative_mask\n", "# )\n", "# assert interesting_clips_mask.eq(interesting_clips[\"preference\"]).all()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:43.332222Z", "start_time": "2024-05-26T00:24:43.166461Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:03.088362Z", "iopub.status.busy": "2024-08-12T18:12:03.088214Z", "iopub.status.idle": "2024-08-12T18:12:03.129662Z", "shell.execute_reply": "2024-08-12T18:12:03.129232Z", "shell.execute_reply.started": "2024-08-12T18:12:03.088346Z" } }, "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": 48, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:43.562167Z", "start_time": "2024-05-26T00:24:43.333784Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:03.130387Z", "iopub.status.busy": "2024-08-12T18:12:03.130244Z", "iopub.status.idle": "2024-08-12T18:12:03.169826Z", "shell.execute_reply": "2024-08-12T18:12:03.169366Z", "shell.execute_reply.started": "2024-08-12T18:12:03.130372Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of interesting clips: 108,126\n" ] } ], "source": [ "# interesting_clips[\"has_gpt_prompt\"] = interesting_clips[\"metadata\"].apply(\n", "# lambda x: ast.literal_eval(str(x)).get(\"gpt_description_prompt\", None) is not None\n", "# )\n", "# print(len(interesting_clips))\n", "# interesting_clips = interesting_clips[~interesting_clips[\"has_gpt_prompt\"]]\n", "print(f\"Number of interesting clips: {len(interesting_clips):,}\")" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:43.737067Z", "start_time": "2024-05-26T00:24:43.563216Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:03.170580Z", "iopub.status.busy": "2024-08-12T18:12:03.170442Z", "iopub.status.idle": "2024-08-12T18:12:03.208701Z", "shell.execute_reply": "2024-08-12T18:12:03.208239Z", "shell.execute_reply.started": "2024-08-12T18:12:03.170565Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preference counts and fractions by batch index:\n", "--------------------------------------------------\n", "Batch Index: 0\n", " Preference False:\n", " Count: 27,079\n", " Fraction: 50.09%\n", " Preference True:\n", " Count: 26,984\n", " Fraction: 49.91%\n", "\n", "Batch Index: 1\n", " Preference False:\n", " Count: 26,984\n", " Fraction: 49.91%\n", " Preference True:\n", " Count: 27,079\n", " Fraction: 50.09%\n", "\n" ] } ], "source": [ "preference_counts = interesting_clips.groupby(\"batch_index\")[\n", " \"preference\"\n", "].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}:\")\n", " print(f\" Count: {count:,}\")\n", " print(f\" Fraction: {fraction:.2%}\")\n", " print()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:44.304573Z", "start_time": "2024-05-26T00:24:43.973218Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:03.209457Z", "iopub.status.busy": "2024-08-12T18:12:03.209307Z", "iopub.status.idle": "2024-08-12T18:12:03.263414Z", "shell.execute_reply": "2024-08-12T18:12:03.262988Z", "shell.execute_reply.started": "2024-08-12T18:12:03.209442Z" } }, "outputs": [ { "data": { "text/plain": [ "model_name\n", "chirp-v3p5-engine-t-2 108126\n", "Name: count, dtype: int64" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interesting_clips[\"model_name\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:44.533983Z", "start_time": "2024-05-26T00:24:44.305722Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:03.264153Z", "iopub.status.busy": "2024-08-12T18:12:03.264008Z", "iopub.status.idle": "2024-08-12T18:12:03.305709Z", "shell.execute_reply": "2024-08-12T18:12:03.305257Z", "shell.execute_reply.started": "2024-08-12T18:12:03.264138Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time Validation:\n", "--------------------\n", "Interesting Clips:\n", " Earliest: 2024-08-09 21:00:02.921849+00:00\n", " Latest: 2024-08-12 18:05:37.629803+00:00\n", "\n", "All Clips:\n", " Earliest: 2024-08-09 21:00:02.921849+00:00\n", " Latest: 2024-08-12 18:07:32.646939+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": 52, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:46.365344Z", "start_time": "2024-05-26T00:24:44.535249Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:03.306404Z", "iopub.status.busy": "2024-08-12T18:12:03.306261Z", "iopub.status.idle": "2024-08-12T18:12:03.473660Z", "shell.execute_reply": "2024-08-12T18:12:03.473194Z", "shell.execute_reply.started": "2024-08-12T18:12:03.306389Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unique counts in interesting_clips:\n", "Request IDs: 54,063\n", "Clip IDs: 108,126\n" ] } ], "source": [ "# 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 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": 53, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:50.674838Z", "start_time": "2024-05-26T00:24:46.366677Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:03.474387Z", "iopub.status.busy": "2024-08-12T18:12:03.474245Z", "iopub.status.idle": "2024-08-12T18:12:03.693177Z", "shell.execute_reply": "2024-08-12T18:12:03.692636Z", "shell.execute_reply.started": "2024-08-12T18:12:03.474371Z" } }, "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": 54, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:51.600621Z", "start_time": "2024-05-26T00:24:50.676186Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:03.694028Z", "iopub.status.busy": "2024-08-12T18:12:03.693872Z", "iopub.status.idle": "2024-08-12T18:12:03.833861Z", "shell.execute_reply": "2024-08-12T18:12:03.833346Z", "shell.execute_reply.started": "2024-08-12T18:12:03.694012Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ratio of preferred clips to total clips for each model:\n", "------------------------------------------------------------\n", "chirp-v3p5-engine-t-2 0.055470\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", " print(f\"{model:<30} {ratio:.6f}\")" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:56.302599Z", "start_time": "2024-05-26T00:24:51.601863Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:03.834659Z", "iopub.status.busy": "2024-08-12T18:12:03.834507Z", "iopub.status.idle": "2024-08-12T18:12:04.114372Z", "shell.execute_reply": "2024-08-12T18:12:04.113819Z", "shell.execute_reply.started": "2024-08-12T18:12:03.834642Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2, win ratio 1.000, counts 54063\n" ] } ], "source": [ "get_preferfence_counts(interesting_clips)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:57.443236Z", "start_time": "2024-05-26T00:24:56.306473Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:04.115217Z", "iopub.status.busy": "2024-08-12T18:12:04.115064Z", "iopub.status.idle": "2024-08-12T18:12:05.429889Z", "shell.execute_reply": "2024-08-12T18:12:05.429371Z", "shell.execute_reply.started": "2024-08-12T18:12:04.115200Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/tony/anaconda3/envs/suno_env/lib/python3.10/site-packages/matplotlib/axes/_axes.py:6862: RuntimeWarning: Converting input from bool to 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": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))\n", "\n", "ax1.hist(interesting_clips[\"dislike_count\"], bins=np.linspace(0, 10, 30))\n", "ax1.set_yscale(\"log\")\n", "ax1.set_xlabel(\"Number of dislike_count\")\n", "ax1.set_ylabel(\"Number of clips\")\n", "ax1.set_title(\"Dislike Count Distribution\")\n", "\n", "ax2.hist(interesting_clips[\"upvote_count\"], bins=np.linspace(0, 10, 30))\n", "ax2.set_yscale(\"log\")\n", "ax2.set_xlabel(\"Number of like_count\")\n", "ax2.set_ylabel(\"Number of clips\")\n", "ax2.set_title(\"Upvote Count Distribution\")\n", "\n", "ax3.hist(interesting_clips[\"is_public\"], bins=np.linspace(0, 10, 30))\n", "ax3.set_yscale(\"log\")\n", "ax3.set_xlabel(\"Number of is_public\")\n", "ax3.set_ylabel(\"Number of clips\")\n", "ax3.set_title(\"Public Clips Distribution\")\n", "\n", "ax4.hist(interesting_clips[\"user_id\"].value_counts(), bins=np.linspace(0, 1000, 100))\n", "ax4.set_yscale(\"log\")\n", "ax4.set_xlabel(\"Number of preferences clips\")\n", "ax4.set_ylabel(\"Number of users\")\n", "ax4.set_title(\"User Preferences Distribution\")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:58.153310Z", "start_time": "2024-05-26T00:24:57.858363Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:05.430773Z", "iopub.status.busy": "2024-08-12T18:12:05.430604Z", "iopub.status.idle": "2024-08-12T18:12:05.451748Z", "shell.execute_reply": "2024-08-12T18:12:05.451299Z", "shell.execute_reply.started": "2024-08-12T18:12:05.430755Z" } }, "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": 58, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:58.890520Z", "start_time": "2024-05-26T00:24:58.154350Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:05.452493Z", "iopub.status.busy": "2024-08-12T18:12:05.452346Z", "iopub.status.idle": "2024-08-12T18:12:05.582433Z", "shell.execute_reply": "2024-08-12T18:12:05.581874Z", "shell.execute_reply.started": "2024-08-12T18:12:05.452476Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of clips in user_interesting_clips:\n", "108,126\n" ] } ], "source": [ "# subselect interesting clips\n", "user_intersting_clips = interesting_clips[\n", " interesting_clips[\"model_name\"].str.contains(\"v3p5\") # general 3.5\n", " # interesting_clips[\"model_name\"].str.contains(\"upload\") # only uploads\n", "].copy()\n", "print(\"Number of clips in user_interesting_clips:\")\n", "print(f\"{user_intersting_clips.shape[0]:,}\")" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:59.204547Z", "start_time": "2024-05-26T00:24:58.891851Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:05.583332Z", "iopub.status.busy": "2024-08-12T18:12:05.583167Z", "iopub.status.idle": "2024-08-12T18:12:05.922787Z", "shell.execute_reply": "2024-08-12T18:12:05.922287Z", "shell.execute_reply.started": "2024-08-12T18:12:05.583315Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "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": 60, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:59.207707Z", "start_time": "2024-05-26T00:24:59.205659Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:05.923645Z", "iopub.status.busy": "2024-08-12T18:12:05.923460Z", "iopub.status.idle": "2024-08-12T18:12:05.944216Z", "shell.execute_reply": "2024-08-12T18:12:05.943727Z", "shell.execute_reply.started": "2024-08-12T18:12:05.923624Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Summary of user_interesting_clips:\n", "Total requests: 108,126\n", "Unique clips: 54,063\n", "Fraction of total clips: 11.03%\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(\n", " f\"Fraction of total clips: {user_intersting_clips.shape[0] / total_clip_counts:.2%}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:59.496996Z", "start_time": "2024-05-26T00:24:59.208750Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:05.944964Z", "iopub.status.busy": "2024-08-12T18:12:05.944820Z", "iopub.status.idle": "2024-08-12T18:12:06.024126Z", "shell.execute_reply": "2024-08-12T18:12:06.023699Z", "shell.execute_reply.started": "2024-08-12T18:12:05.944948Z" } }, "outputs": [ { "data": { "text/html": [ "
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indextime_useduser_idupvote_countbatch_indexdislike_countflag_countplay_countskip_countdurationuser_n_clipsdiff_preference
count108126.000000108126.0000001.081260e+05108126.000000108126.000000108126.000000108126.000000108126.000000108126.0108126.000000108126.000000108126.000000
mean500458.656207172.3742892.234868e+070.2449550.5000000.0935670.0068902.1946430.0187.05495810.6497420.002691
std281022.14146358.5973051.242516e+070.4668340.5000020.2912580.0827214.3448580.064.21782215.9281400.757728
min5.0000002.7357034.110000e+020.0000000.0000000.0000000.0000000.0000000.02.4800002.000000-1.000000
25%255902.250000130.1694631.117460e+070.0000000.0000000.0000000.0000001.0000000.0141.1700002.000000-1.000000
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75%745047.000000218.7280343.494457e+070.0000001.0000000.0000000.0000002.0000000.0240.00000012.0000001.000000
max980623.000000554.0949513.856791e+0730.0000001.0000002.0000001.000000678.0000000.0240.000000238.0000001.000000
\n", "
" ], "text/plain": [ " index time_used user_id upvote_count batch_index dislike_count flag_count play_count skip_count duration user_n_clips diff_preference\n", "count 108126.000000 108126.000000 1.081260e+05 108126.000000 108126.000000 108126.000000 108126.000000 108126.000000 108126.0 108126.000000 108126.000000 108126.000000\n", "mean 500458.656207 172.374289 2.234868e+07 0.244955 0.500000 0.093567 0.006890 2.194643 0.0 187.054958 10.649742 0.002691\n", "std 281022.141463 58.597305 1.242516e+07 0.466834 0.500002 0.291258 0.082721 4.344858 0.0 64.217822 15.928140 0.757728\n", "min 5.000000 2.735703 4.110000e+02 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 2.480000 2.000000 -1.000000\n", "25% 255902.250000 130.169463 1.117460e+07 0.000000 0.000000 0.000000 0.000000 1.000000 0.0 141.170000 2.000000 -1.000000\n", "50% 508506.500000 196.318399 2.406767e+07 0.000000 0.500000 0.000000 0.000000 1.000000 0.0 217.000000 4.000000 0.000000\n", "75% 745047.000000 218.728034 3.494457e+07 0.000000 1.000000 0.000000 0.000000 2.000000 0.0 240.000000 12.000000 1.000000\n", "max 980623.000000 554.094951 3.856791e+07 30.000000 1.000000 2.000000 1.000000 678.000000 0.0 240.000000 238.000000 1.000000" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_intersting_clips.describe()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:59.685498Z", "start_time": "2024-05-26T00:24:59.498024Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:06.024853Z", "iopub.status.busy": "2024-08-12T18:12:06.024710Z", "iopub.status.idle": "2024-08-12T18:12:06.041755Z", "shell.execute_reply": "2024-08-12T18:12:06.041285Z", "shell.execute_reply.started": "2024-08-12T18:12:06.024838Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time Validation:\n", "Earliest timestamp: 2024-08-09 21:00:02.921849+00:00\n", "Latest timestamp: 2024-08-12 18:05:37.629803+00:00\n" ] } ], "source": [ "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()}\")" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:00.484734Z", "start_time": "2024-05-26T00:25:00.377280Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:06.042497Z", "iopub.status.busy": "2024-08-12T18:12:06.042354Z", "iopub.status.idle": "2024-08-12T18:12:06.080066Z", "shell.execute_reply": "2024-08-12T18:12:06.079634Z", "shell.execute_reply.started": "2024-08-12T18:12:06.042482Z" } }, "outputs": [], "source": [ "# def parse_for_tag(x):\n", "# if \"tags\" not in x:\n", "# return \"\"\n", "# out = x.get(\"tags\", \"\")\n", "# return out.lower() if out else \"\"\n", "\n", "# def parse_for_one_box(x):\n", "# if \"gpt_description_prompt\" not in x:\n", "# return False\n", "# out = x.get(\"gpt_description_prompt\", \"\")\n", "# return out != None\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": 64, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:00.893917Z", "start_time": "2024-05-26T00:25:00.485775Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:06.080798Z", "iopub.status.busy": "2024-08-12T18:12:06.080656Z", "iopub.status.idle": "2024-08-12T18:12:06.650852Z", "shell.execute_reply": "2024-08-12T18:12:06.650277Z", "shell.execute_reply.started": "2024-08-12T18:12:06.080784Z" } }, "outputs": [], "source": [ "# v3 launch test time # 2024-04-03 07:47:01.770988+00:00 t1\n", "# v3.5 launch time: 2024-05-19 05:21:54\n", "# latest exp time: '2024-05-29 04:01:39'\n", "\n", "date_cut = \"2024-06-04 15:21:36\"\n", "\n", "# user_compare_mask = (\n", "# user_intersting_clips[\"created_at\"] >= date_cut\n", "# ) # & (user_intersting_clips[\"is_pro_user\"] == True)\n", "user_compare_mask = (\n", " (user_intersting_clips[\"created_at\"] >= date_cut)\n", " & (\n", " user_intersting_clips[\"model_name\"].isin(\n", " [\n", " \"chirp-v2-xxl-alpha\",\n", " \"chirp-v3-engine-i\",\n", " \"chirp-v3p5-engine-d\",\n", " \"chirp-v3p5-engine-s\",\n", " \"chirp-v3p5-engine-s-8\",\n", " \"chirp-v3p5-engine-s-14\",\n", " \"chirp-v3p5-engine-s-15\",\n", " \"chirp-v3p5-engine-s-18\",\n", " \"chirp-v3p5-engine-s-19\",\n", " \"chirp-v3p5-engine-s-20\",\n", " \"chirp-v3p5-engine-s-21\",\n", " \"chirp-v3p5-engine-s-8-paged\",\n", " \"chirp-v3p5-engine-ft\",\n", " \"chirp-v3p5-engine-ft-1\",\n", " \"chirp-v3p5-engine-s-8-no-top-p\",\n", " \"chirp-v3p5-engine-ft-2\",\n", " \"chirp-v3p5-engine-ft-3\",\n", " \"chirp-v3p5-engine-ft-4\",\n", " \"chirp-v3p5-engine-ft-5\",\n", " \"chirp-v3p5-engine-ft-6\",\n", " \"\",\n", " \"chirp-v3p5-engine-b\",\n", " \"chirp-v3p5-engine-upload\",\n", " \"chirp-v3p5-engine-upload-4\",\n", " \"chirp-v3p5-engine-t\",\n", " \"chirp-v3p5-engine-t-1\",\n", " \"chirp-v3p5-engine-t-1-5\",\n", " \"chirp-v3p5-engine-t-1-7\",\n", " \"chirp-v3p5-engine-t-1-fast\",\n", " \"chirp-v3p5-engine-t-1-fast-sem\",\n", " \"chirp-v3p5-engine-t-1-10\",\n", " \"chirp-v3p5-engine-t-1-11\",\n", " \"chirp-v3p5-engine-t-1-12\",\n", " \"chirp-v3p5-engine-t-1-13\",\n", " \"chirp-v3p5-engine-t-1-14\",\n", " \"chirp-v3p5-engine-t-2\",\n", " ]\n", " )\n", " )\n", " # & (user_intersting_clips[\"is_pro_user\"] == True)\n", " # & (user_intersting_clips[\"is_onebox\"] == True)\n", ")\n", "# user_compare_mask = (user_intersting_clips[\"created_at\"] >= date_cut) & (\n", "# user_intersting_clips[\"tags\"].apply(lambda x: not \"pop\" in x.lower())\n", "# )\n", "# # this is fucked up sometimes one box doesn't give prompt to one generation\n", "extra_compare_mask = user_intersting_clips[user_compare_mask][\"request_id\"].isin(\n", " user_intersting_clips[user_compare_mask][\"request_id\"]\n", " .value_counts()\n", " .index[user_intersting_clips[user_compare_mask][\"request_id\"].value_counts() == 2]\n", ")\n", "\n", "user_compare_mask = user_compare_mask & extra_compare_mask\n", "\n", "# for _, row in user_intersting_clips[user_intersting_clips[\"request_id\"].astype(str) == \"87c45d24-68ae-45dd-b5b7-92cd70bd0ab5\"].iterrows():\n", "# print(row[\"metadata\"])\n", "\n", "# for _, row in user_intersting_clips[user_intersting_clips[\"request_id\"].astype(str) == \"fa86f07f-4476-406f-b756-7166e0b08679\"].iterrows():\n", "# print(row[\"metadata\"])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:01.140472Z", "start_time": "2024-05-26T00:25:00.895575Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:06.651766Z", "iopub.status.busy": "2024-08-12T18:12:06.651601Z", "iopub.status.idle": "2024-08-12T18:12:07.104433Z", "shell.execute_reply": "2024-08-12T18:12:07.103866Z", "shell.execute_reply.started": "2024-08-12T18:12:06.651748Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(108126, 45)\n", "(108126, 45)\n", "Model Name Value Counts and Fractions:\n", "chirp-v3p5-engine-t-2: 58772 (54.36%)\n", "chirp-v3p5-engine-t-2_text_cfg_12: 10768 (9.96%)\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_13: 6901 (6.38%)\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_12: 6745 (6.24%)\n", "chirp-v3p5-engine-t-2_text_cfg_13: 6740 (6.23%)\n", "chirp-v3p5-engine-t-2_n_repeat_tags_3: 6009 (5.56%)\n", "chirp-v3p5-engine-t-2_text_cfg_11: 4126 (3.82%)\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max: 4100 (3.79%)\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_11: 3965 (3.67%)\n" ] } ], "source": [ "user_intersting_clips_3p5 = (\n", " user_intersting_clips[user_compare_mask].reset_index().copy()\n", ")\n", "\n", "\n", "def parse_inference_exp(x):\n", " # print(x)\n", " if \"param_experiment\" not in x:\n", " return \"\"\n", " out = x.get(\"param_experiment\", \"\")\n", " if out:\n", " return \"_\" + out\n", " return \"\"\n", "\n", "\n", "user_intersting_clips_3p5[\"model_name\"] = user_intersting_clips_3p5[\n", " \"model_name\"\n", "] + user_intersting_clips_3p5[\"metadata\"].apply(parse_inference_exp)\n", "user_intersting_clips_3p5 = user_intersting_clips_3p5.sort_values(\n", " by=[\"request_id\", \"preference\"]\n", ")\n", "print(user_intersting_clips_3p5.shape)\n", "\n", "# second_user_compare_mask = (\n", "# user_intersting_clips_3p5[\"model_name\"].str.contains(\"all\")\n", "# ) | (user_intersting_clips_3p5[\"model_name\"] == \"chirp-v3p5-engine-t-1\")\n", "# extra_compare_mask = user_intersting_clips[second_user_compare_mask][\"request_id\"].isin(\n", "# user_intersting_clips[second_user_compare_mask][\"request_id\"]\n", "# .value_counts()\n", "# .index[\n", "# user_intersting_clips[second_user_compare_mask][\"request_id\"].value_counts()\n", "# == 2\n", "# ]\n", "# )\n", "\n", "# second_user_compare_mask = second_user_compare_mask & extra_compare_mask\n", "\n", "# user_intersting_clips_3p5 = user_intersting_clips_3p5[second_user_compare_mask].copy()\n", "\n", "# user_intersting_clips_3p5 = user_intersting_clips_3p5.sort_values(\n", "# by=[\"request_id\", \"preference\"]\n", "# )\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", "for model, count in model_counts.items():\n", " frac = model_fracs[model]\n", " print(f\"{model}: {count} ({frac:.2%})\")" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:02.009450Z", "start_time": "2024-05-26T00:25:01.523515Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:07.109958Z", "iopub.status.busy": "2024-08-12T18:12:07.109548Z", "iopub.status.idle": "2024-08-12T18:12:07.780924Z", "shell.execute_reply": "2024-08-12T18:12:07.780410Z", "shell.execute_reply.started": "2024-08-12T18:12:07.109938Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chirp-v3p5-engine-t-2_n_repeat_tags_3_win_over_chirp-v3p5-engine-t-2, win ratio 0.510, counts 3065\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_11_win_over_chirp-v3p5-engine-t-2, win ratio 0.528, counts 2094\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_12_win_over_chirp-v3p5-engine-t-2, win ratio 0.532, counts 3586\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_13_win_over_chirp-v3p5-engine-t-2, win ratio 0.533, counts 3676\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_win_over_chirp-v3p5-engine-t-2, win ratio 0.519, counts 2127\n", "chirp-v3p5-engine-t-2_text_cfg_11_win_over_chirp-v3p5-engine-t-2, win ratio 0.509, counts 2102\n", "chirp-v3p5-engine-t-2_text_cfg_12_win_over_chirp-v3p5-engine-t-2, win ratio 0.533, counts 5734\n", "chirp-v3p5-engine-t-2_text_cfg_13_win_over_chirp-v3p5-engine-t-2, win ratio 0.526, counts 3543\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2, win ratio 1.000, counts 4709\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_n_repeat_tags_3, win ratio 0.490, counts 2944\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max, win ratio 0.481, counts 1973\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_11, win ratio 0.472, counts 1871\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_12, win ratio 0.468, counts 3159\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_13, win ratio 0.467, counts 3225\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_text_cfg_11, win ratio 0.491, counts 2024\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_text_cfg_12, win ratio 0.467, counts 5034\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_text_cfg_13, win ratio 0.474, counts 3197\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "get_preferfence_counts(user_intersting_clips_3p5)\n", "# user_intersting_clips[user_compare_mask]\n", "# )" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:02.332947Z", "start_time": "2024-05-26T00:25:02.010694Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:07.781740Z", "iopub.status.busy": "2024-08-12T18:12:07.781580Z", "iopub.status.idle": "2024-08-12T18:12:08.516660Z", "shell.execute_reply": "2024-08-12T18:12:08.516115Z", "shell.execute_reply.started": "2024-08-12T18:12:07.781722Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "first gen\n", "chirp-v3p5-engine-t-2_n_repeat_tags_3_win_over_chirp-v3p5-engine-t-2, win ratio 0.514, counts 2768\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_11_win_over_chirp-v3p5-engine-t-2, win ratio 0.530, counts 1889\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_12_win_over_chirp-v3p5-engine-t-2, win ratio 0.531, counts 3165\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_13_win_over_chirp-v3p5-engine-t-2, win ratio 0.531, counts 3227\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_win_over_chirp-v3p5-engine-t-2, win ratio 0.524, counts 1919\n", "chirp-v3p5-engine-t-2_text_cfg_11_win_over_chirp-v3p5-engine-t-2, win ratio 0.510, counts 1898\n", "chirp-v3p5-engine-t-2_text_cfg_12_win_over_chirp-v3p5-engine-t-2, win ratio 0.533, counts 5094\n", "chirp-v3p5-engine-t-2_text_cfg_13_win_over_chirp-v3p5-engine-t-2, win ratio 0.530, counts 3178\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2, win ratio 1.000, counts 4196\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_n_repeat_tags_3, win ratio 0.486, counts 2613\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max, win ratio 0.476, counts 1744\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_11, win ratio 0.470, counts 1673\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_12, win ratio 0.469, counts 2800\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_13, win ratio 0.469, counts 2853\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_text_cfg_11, win ratio 0.490, counts 1820\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_text_cfg_12, win ratio 0.467, counts 4463\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_text_cfg_13, win ratio 0.470, counts 2821\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" ] }, "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_preferfence_counts(\n", " user_intersting_clips_3p5[\n", " (user_intersting_clips_3p5[\"continued_parent\"].isna())\n", " ],\n", " title_name=\"first generation\",\n", " )" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:02.574659Z", "start_time": "2024-05-26T00:25:02.334214Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:08.517534Z", "iopub.status.busy": "2024-08-12T18:12:08.517373Z", "iopub.status.idle": "2024-08-12T18:12:08.895344Z", "shell.execute_reply": "2024-08-12T18:12:08.894850Z", "shell.execute_reply.started": "2024-08-12T18:12:08.517516Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is continue\n", "chirp-v3p5-engine-t-2_n_repeat_tags_3_win_over_chirp-v3p5-engine-t-2, win ratio 0.473, counts 297\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_11_win_over_chirp-v3p5-engine-t-2, win ratio 0.509, counts 205\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_12_win_over_chirp-v3p5-engine-t-2, win ratio 0.540, counts 421\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_13_win_over_chirp-v3p5-engine-t-2, win ratio 0.547, counts 449\n", "chirp-v3p5-engine-t-2_tag_cfg_steps_max_win_over_chirp-v3p5-engine-t-2, win ratio 0.476, counts 208\n", "chirp-v3p5-engine-t-2_text_cfg_11_win_over_chirp-v3p5-engine-t-2, win ratio 0.500, counts 204\n", "chirp-v3p5-engine-t-2_text_cfg_12_win_over_chirp-v3p5-engine-t-2, win ratio 0.528, counts 640\n", "chirp-v3p5-engine-t-2_text_cfg_13_win_over_chirp-v3p5-engine-t-2, win ratio 0.493, counts 365\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2, win ratio 1.000, counts 513\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_n_repeat_tags_3, win ratio 0.527, counts 331\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max, win ratio 0.524, counts 229\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_11, win ratio 0.491, counts 198\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_12, win ratio 0.460, counts 359\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_tag_cfg_steps_max_text_cfg_13, win ratio 0.453, counts 372\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_text_cfg_11, win ratio 0.500, counts 204\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_text_cfg_12, win ratio 0.472, counts 571\n", "chirp-v3p5-engine-t-2_win_over_chirp-v3p5-engine-t-2_text_cfg_13, win ratio 0.507, counts 376\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"is continue\")\n", "get_preferfence_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": 69, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:08.896303Z", "iopub.status.busy": "2024-08-12T18:12:08.896010Z", "iopub.status.idle": "2024-08-12T18:12:08.917152Z", "shell.execute_reply": "2024-08-12T18:12:08.916724Z", "shell.execute_reply.started": "2024-08-12T18:12:08.896284Z" } }, "outputs": [ { "data": { "text/plain": [ "(108126, 44)" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_intersting_clips.shape" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:07.682737Z", "start_time": "2024-05-26T00:25:02.593456Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:08.917879Z", "iopub.status.busy": "2024-08-12T18:12:08.917739Z", "iopub.status.idle": "2024-08-12T18:12:13.632666Z", "shell.execute_reply": "2024-08-12T18:12:13.632072Z", "shell.execute_reply.started": "2024-08-12T18:12:08.917864Z" } }, "outputs": [], "source": [ "# ~ only 1 min :)\n", "# Filter reaction_df for relevant clip_ids\n", "partial_reaction_df = reaction_df[\n", " reaction_df[\"clip_id\"].isin(user_intersting_clips[\"id\"])\n", "].copy()\n", "\n", "# Calculate total play counts\n", "total_play_counts = (\n", " partial_reaction_df.groupby(\"clip_id\")[\"play_count\"].sum().reset_index()\n", ")\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", "user_intersting_clips = user_intersting_clips.merge(\n", " total_play_counts, on=\"id\", how=\"left\"\n", ")\n", "user_intersting_clips = user_intersting_clips.merge(\n", " pro_play_counts, on=\"id\", how=\"left\"\n", ")" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:13.633569Z", "iopub.status.busy": "2024-08-12T18:12:13.633410Z", "iopub.status.idle": "2024-08-12T18:12:13.662106Z", "shell.execute_reply": "2024-08-12T18:12:13.661660Z", "shell.execute_reply.started": "2024-08-12T18:12:13.633552Z" } }, "outputs": [ { "data": { "text/plain": [ "count 50229.000000\n", "mean 0.075076\n", "std 2.154657\n", "min 0.000000\n", "25% 0.000000\n", "50% 0.000000\n", "75% 0.000000\n", "max 291.000000\n", "dtype: float64" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(\n", " user_intersting_clips[\"reaction_play_count\"]\n", " - user_intersting_clips[\"reaction_pro_play_count\"]\n", ").describe()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:08.095035Z", "start_time": "2024-05-26T00:25:07.782738Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:13.662877Z", "iopub.status.busy": "2024-08-12T18:12:13.662730Z", "iopub.status.idle": "2024-08-12T18:12:13.892799Z", "shell.execute_reply": "2024-08-12T18:12:13.892305Z", "shell.execute_reply.started": "2024-08-12T18:12:13.662861Z" } }, "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": 73, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:08.361849Z", "start_time": "2024-05-26T00:25:08.199583Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:13.893604Z", "iopub.status.busy": "2024-08-12T18:12:13.893455Z", "iopub.status.idle": "2024-08-12T18:12:13.915411Z", "shell.execute_reply": "2024-08-12T18:12:13.914909Z", "shell.execute_reply.started": "2024-08-12T18:12:13.893588Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Positive proportion of data meeting criteria: 41.86%\n", "Negative proportion of data meeting criteria: 66.28%\n" ] } ], "source": [ "pos_too_much_data_mask = (\n", " (user_intersting_clips[\"preference\"])\n", " & (\n", " (\n", " user_intersting_clips[\"reaction_play_count\"] >= 2\n", " ) # single play is super catchy\n", " | (\n", " user_intersting_clips[\"concat_play_counts\"] >= 2\n", " ) # 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": 74, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:08.523643Z", "start_time": "2024-05-26T00:25:08.367348Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:13.916163Z", "iopub.status.busy": "2024-08-12T18:12:13.916021Z", "iopub.status.idle": "2024-08-12T18:12:14.206916Z", "shell.execute_reply": "2024-08-12T18:12:14.206351Z", "shell.execute_reply.started": "2024-08-12T18:12:13.916147Z" } }, "outputs": [], "source": [ "final_good_enough_requests = set(\n", " user_intersting_clips[pos_too_much_data_mask][\"request_id\"].unique()\n", ").intersection(\n", " set(user_intersting_clips[neg_too_much_data_mask][\"request_id\"].unique())\n", ")\n", "final_interesting_clips = user_intersting_clips[\n", " user_intersting_clips[\"request_id\"].isin(final_good_enough_requests)\n", "].copy()" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:14.207832Z", "iopub.status.busy": "2024-08-12T18:12:14.207676Z", "iopub.status.idle": "2024-08-12T18:12:14.241658Z", "shell.execute_reply": "2024-08-12T18:12:14.241152Z", "shell.execute_reply.started": "2024-08-12T18:12:14.207816Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model Name Value Counts for Preferred Clips:\n", "----------------------------------------------------------------------\n", "Model Count Fraction\n", "----------------------------------------------------------------------\n", "chirp-v3p5-engine-t-2 22,373 100.00%\n", "----------------------------------------------------------------------\n", "Total 22,373 100.00%\n" ] } ], "source": [ "# 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": 76, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:14.242488Z", "iopub.status.busy": "2024-08-12T18:12:14.242340Z", "iopub.status.idle": "2024-08-12T18:12:14.288384Z", "shell.execute_reply": "2024-08-12T18:12:14.287952Z", "shell.execute_reply.started": "2024-08-12T18:12:14.242472Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_837698/1428848772.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " final_interesting_clips[final_interesting_clips[\"preference\"]][\n" ] }, { "data": { "text/plain": [ "(22373, 52)" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_interesting_clips[final_interesting_clips[\"preference\"]][\n", " (final_interesting_clips[\"reaction_play_count\"] >= 2) # single play is super catchy\n", " | (\n", " final_interesting_clips[\"concat_play_counts\"] >= 2\n", " ) # or the concat play is super catchy]\n", "].shape" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:14.289121Z", "iopub.status.busy": "2024-08-12T18:12:14.288976Z", "iopub.status.idle": "2024-08-12T18:12:14.315679Z", "shell.execute_reply": "2024-08-12T18:12:14.315258Z", "shell.execute_reply.started": "2024-08-12T18:12:14.289105Z" } }, "outputs": [ { "data": { "text/plain": [ "(44746, 52)" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_interesting_clips.shape" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:09.198504Z", "start_time": "2024-05-26T00:25:08.885507Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:14.316406Z", "iopub.status.busy": "2024-08-12T18:12:14.316267Z", "iopub.status.idle": "2024-08-12T18:12:14.460392Z", "shell.execute_reply": "2024-08-12T18:12:14.459928Z", "shell.execute_reply.started": "2024-08-12T18:12:14.316391Z" } }, "outputs": [], "source": [ "assert (\n", " final_interesting_clips[final_interesting_clips[\"request_id\"].isna()].shape[0] == 0\n", ")\n", "check_df = final_interesting_clips.groupby(\"request_id\")[\"id\"].nunique()\n", "check_df[check_df.values != 2]\n", "assert check_df[check_df.values != 2].shape[0] == 0" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:14.461129Z", "iopub.status.busy": "2024-08-12T18:12:14.460987Z", "iopub.status.idle": "2024-08-12T18:12:14.486156Z", "shell.execute_reply": "2024-08-12T18:12:14.485691Z", "shell.execute_reply.started": "2024-08-12T18:12:14.461114Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of rows in final_interesting_clips for model 'chirp-v3p5-engine-t-1':\n", "0\n" ] } ], "source": [ "# Get the number of rows for final_interesting_clips with the specific model\n", "row_count = final_interesting_clips[\n", " final_interesting_clips[\"model_name\"] == \"chirp-v3p5-engine-t-1\"\n", "].shape[0]\n", "\n", "# Print the row count in a nicely formatted way\n", "print(\"Number of rows in final_interesting_clips for model 'chirp-v3p5-engine-t-1':\")\n", "print(f\"{row_count:,}\")" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:36:45.167690Z", "start_time": "2024-05-26T00:36:45.164768Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:14.486877Z", "iopub.status.busy": "2024-08-12T18:12:14.486736Z", "iopub.status.idle": "2024-08-12T18:12:14.528734Z", "shell.execute_reply": "2024-08-12T18:12:14.528280Z", "shell.execute_reply.started": "2024-08-12T18:12:14.486861Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "done (44746, 52)\n" ] } ], "source": [ "# final_interesting_clips[\n", "# final_interesting_clips[\"model_name\"] == \"chirp-v3p5-engine-t-1\"\n", "# ].to_csv(\n", "# \"/home/tony/Data/Preference/30b_v0/interesting_clips_v4_t_1_20240724.csv\",\n", "# index=False,\n", "# )\n", "print(\"done\", final_interesting_clips.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# For faster processing once" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:10.265241Z", "start_time": "2024-05-26T00:25:09.934743Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:14.529467Z", "iopub.status.busy": "2024-08-12T18:12:14.529318Z", "iopub.status.idle": "2024-08-12T18:12:14.578342Z", "shell.execute_reply": "2024-08-12T18:12:14.577882Z", "shell.execute_reply.started": "2024-08-12T18:12:14.529451Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Unique Users:\n", "--------------------\n", "269,903\n", "--------------------\n" ] } ], "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)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:26:43.409660Z", "start_time": "2024-05-26T00:25:10.266379Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:14.579061Z", "iopub.status.busy": "2024-08-12T18:12:14.578916Z", "iopub.status.idle": "2024-08-12T18:12:14.605242Z", "shell.execute_reply": "2024-08-12T18:12:14.604810Z", "shell.execute_reply.started": "2024-08-12T18:12:14.579046Z" } }, "outputs": [], "source": [ "# 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()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:26:43.423857Z", "start_time": "2024-05-26T00:26:43.411248Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:14.605925Z", "iopub.status.busy": "2024-08-12T18:12:14.605779Z", "iopub.status.idle": "2024-08-12T18:12:14.646631Z", "shell.execute_reply": "2024-08-12T18:12:14.646158Z", "shell.execute_reply.started": "2024-08-12T18:12:14.605909Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Series([], Name: count, dtype: int64)\n", "(0, 42)\n" ] } ], "source": [ "test_user_id = 4688272\n", "print(\n", " clip_df[clip_df[\"user_id\"] == test_user_id][\"created_at\"]\n", " .apply(lambda x: str(x)[:10])\n", " .value_counts()\n", ")\n", "print(clip_df[clip_df[\"user_id\"] == test_user_id].shape)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:26:43.803559Z", "start_time": "2024-05-26T00:26:43.622922Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:14.647375Z", "iopub.status.busy": "2024-08-12T18:12:14.647226Z", "iopub.status.idle": "2024-08-12T18:12:14.680814Z", "shell.execute_reply": "2024-08-12T18:12:14.680390Z", "shell.execute_reply.started": "2024-08-12T18:12:14.647360Z" } }, "outputs": [ { "data": { "text/plain": [ "array([], dtype=int64)" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_intersting_clips[user_intersting_clips[\"user_n_clips\"] > 10000][\"user_id\"].unique()" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:26:45.770044Z", "start_time": "2024-05-26T00:26:44.912085Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:14.681525Z", "iopub.status.busy": "2024-08-12T18:12:14.681384Z", "iopub.status.idle": "2024-08-12T18:12:14.718939Z", "shell.execute_reply": "2024-08-12T18:12:14.718501Z", "shell.execute_reply.started": "2024-08-12T18:12:14.681510Z" } }, "outputs": [], "source": [ "# # wtf is going on with these requests\n", "# print(total_clip_df[total_clip_df[\"model_name\"] == \"chirp-v3-0\"].shape)\n", "# print(\n", "# total_clip_df[total_clip_df[\"model_name\"] == \"chirp-v3-0\"][\"user_id\"].value_counts()\n", "# )" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:26:46.088296Z", "start_time": "2024-05-26T00:26:45.772102Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:14.719656Z", "iopub.status.busy": "2024-08-12T18:12:14.719512Z", "iopub.status.idle": "2024-08-12T18:12:14.954419Z", "shell.execute_reply": "2024-08-12T18:12:14.953994Z", "shell.execute_reply.started": "2024-08-12T18:12:14.719640Z" } }, "outputs": [ { "data": { "text/html": [ "
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idpasswordlast_loginis_superuserusernamefirst_namelast_nameemailis_staffis_activedate_joined
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" ], "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": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "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": 87, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:27:00.130778Z", "start_time": "2024-05-26T00:26:46.089923Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:14.955156Z", "iopub.status.busy": "2024-08-12T18:12:14.955009Z", "iopub.status.idle": "2024-08-12T18:12:22.557018Z", "shell.execute_reply": "2024-08-12T18:12:22.556431Z", "shell.execute_reply.started": "2024-08-12T18:12:14.955142Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of no_reaction_clip_df:\n", "(169197, 26)\n", "\n", "Proportion of clips without reactions:\n", "17.25%\n" ] } ], "source": [ "no_reaction_clip_df = total_clip_df[\n", " ~total_clip_df[\"id\"].isin(reaction_df[\"clip_id\"])\n", "].copy()\n", "print(\"Shape of no_reaction_clip_df:\")\n", "print(no_reaction_clip_df.shape)\n", "print(\"\\nProportion of clips without reactions:\")\n", "print(f\"{no_reaction_clip_df.shape[0] / total_clip_df.shape[0]:.2%}\")" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:27:01.185878Z", "start_time": "2024-05-26T00:27:00.132882Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:22.557915Z", "iopub.status.busy": "2024-08-12T18:12:22.557750Z", "iopub.status.idle": "2024-08-12T18:12:23.180892Z", "shell.execute_reply": "2024-08-12T18:12:23.180299Z", "shell.execute_reply.started": "2024-08-12T18:12:22.557898Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ratio of clips without reactions to total clips from the same users:\n", "0.9703\n" ] } ], "source": [ "min_generations_for_no_reaction = 20\n", "inspection_date_cut = \"2024-05-10\"\n", "# inspection_date_cut = \"2024-06-18\"\n", "no_reaction_clip_df[\"no_reaction_count\"] = no_reaction_clip_df.groupby(\"user_id\")[\n", " \"user_id\"\n", "].transform(\"count\")\n", "no_reaction_clip_df[\"is_pro_user\"] = no_reaction_clip_df[\"user_id\"].isin(pro_users)\n", "no_reaction_clip_df[\"user_id\"].nunique()\n", "bot_user_mask = (\n", " (no_reaction_clip_df[\"no_reaction_count\"] >= min_generations_for_no_reaction)\n", " & (no_reaction_clip_df[\"created_at\"] >= inspection_date_cut)\n", " # & (no_reaction_clip_df[\"is_pro_user\"] == True)\n", ")\n", "sub_total_clip_df = total_clip_df[\n", " total_clip_df[\"user_id\"].isin(\n", " no_reaction_clip_df[bot_user_mask][\"user_id\"].unique()\n", " )\n", "].copy()\n", "sub_total_clip_df[\"is_pro_user\"] = sub_total_clip_df[\"user_id\"].isin(pro_users)\n", "ratio = no_reaction_clip_df[bot_user_mask].shape[0] / sub_total_clip_df.shape[0]\n", "print(\"Ratio of clips without reactions to total clips from the same users:\")\n", "print(f\"{ratio:.4f}\")" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:27:02.022423Z", "start_time": "2024-05-26T00:27:01.187814Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:23.181751Z", "iopub.status.busy": "2024-08-12T18:12:23.181594Z", "iopub.status.idle": "2024-08-12T18:12:23.316817Z", "shell.execute_reply": "2024-08-12T18:12:23.316261Z", "shell.execute_reply.started": "2024-08-12T18:12:23.181734Z" } }, "outputs": [], "source": [ "sub_total_clip_df[\"gen_count\"] = sub_total_clip_df.groupby(\"user_id\")[\n", " \"user_id\"\n", "].transform(\"count\")\n", "user_id_no_reaction_dict = no_reaction_clip_df.set_index(\"user_id\")[\n", " \"no_reaction_count\"\n", "].to_dict()\n", "user_id_total_dict = (\n", " sub_total_clip_df[~sub_total_clip_df[\"is_pro_user\"]]\n", " .set_index(\"user_id\")[\"gen_count\"]\n", " .to_dict()\n", ")\n", "pro_user_id_total_dict = (\n", " sub_total_clip_df[sub_total_clip_df[\"is_pro_user\"]]\n", " .set_index(\"user_id\")[\"gen_count\"]\n", " .to_dict()\n", ")\n", "\n", "user_ratio_dict = {}\n", "pro_user_ratio_dict = {}\n", "for user_id, total_gen in user_id_total_dict.items():\n", " user_ratio = user_id_no_reaction_dict.get(user_id, 0) / total_gen\n", " user_ratio_dict[user_id] = user_ratio\n", "for user_id, total_gen in pro_user_id_total_dict.items():\n", " user_ratio = user_id_no_reaction_dict.get(user_id, 0) / total_gen\n", " pro_user_ratio_dict[user_id] = user_ratio" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:27:02.191083Z", "start_time": "2024-05-26T00:27:02.024409Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:23.317716Z", "iopub.status.busy": "2024-08-12T18:12:23.317546Z", "iopub.status.idle": "2024-08-12T18:12:23.706635Z", "shell.execute_reply": "2024-08-12T18:12:23.706136Z", "shell.execute_reply.started": "2024-08-12T18:12:23.317699Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(\n", " user_ratio_dict.values(), bins=np.linspace(0, 1, 50), alpha=0.5, label=\"free user\"\n", ")\n", "plt.hist(\n", " pro_user_ratio_dict.values(),\n", " bins=np.linspace(0, 1, 50),\n", " alpha=0.5,\n", " label=\"pro user\",\n", ")\n", "plt.xlabel(\n", " f\"fraction of generations (min {min_generations_for_no_reaction}) that have no actions\"\n", ")\n", "plt.ylabel(\"number of users\")\n", "plt.yscale(\"log\")\n", "plt.legend()\n", "plt.title(f\"Potential bots since {max(cutoff_date, inspection_date_cut)}\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:27:02.229332Z", "start_time": "2024-05-26T00:27:02.192457Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:23.707474Z", "iopub.status.busy": "2024-08-12T18:12:23.707324Z", "iopub.status.idle": "2024-08-12T18:12:23.729296Z", "shell.execute_reply": "2024-08-12T18:12:23.728814Z", "shell.execute_reply.started": "2024-08-12T18:12:23.707458Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "free 11\n", "pro 1063\n" ] } ], "source": [ "super_bad_user_id = set()\n", "for user_id, user_ratio in user_ratio_dict.items():\n", " if user_ratio >= 0.99:\n", " super_bad_user_id.add(user_id)\n", "print(\"free\", len(super_bad_user_id))\n", "super_bad_pro_user_id = set()\n", "for user_id, user_ratio in pro_user_ratio_dict.items():\n", " if user_ratio >= 0.99:\n", " super_bad_pro_user_id.add(user_id)\n", "print(\"pro\", len(super_bad_pro_user_id))" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:23.730048Z", "iopub.status.busy": "2024-08-12T18:12:23.729900Z", "iopub.status.idle": "2024-08-12T18:12:23.768440Z", "shell.execute_reply": "2024-08-12T18:12:23.768015Z", "shell.execute_reply.started": "2024-08-12T18:12:23.730032Z" } }, "outputs": [], "source": [ "# curr_date = datetime.today().strftime(\"%Y_%m_%d\")\n", "# with open(f\"/home/tony/Data/bots/bad_user_{curr_date}.json\", \"w\") as fp:\n", "# json.dump(list(super_bad_user_id), fp)\n", "# with open(f\"/home/tony/Data/bots/bad_pro_user_{curr_date}.json\", \"w\") as fp:\n", "# json.dump(list(super_bad_pro_user_id), fp)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:27:02.725735Z", "start_time": "2024-05-26T00:27:02.230988Z" }, "execution": { "iopub.execute_input": "2024-08-12T18:12:23.769355Z", "iopub.status.busy": "2024-08-12T18:12:23.769021Z", "iopub.status.idle": "2024-08-12T18:12:23.828986Z", "shell.execute_reply": "2024-08-12T18:12:23.828517Z", "shell.execute_reply.started": "2024-08-12T18:12:23.769337Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ratio of clips from super bad users: 0.04%\n" ] } ], "source": [ "# Calculate the ratio of clips from super bad users\n", "bad_user_clip_ratio = (\n", " total_clip_df[total_clip_df[\"user_id\"].isin(super_bad_user_id)].shape[0]\n", " / total_clip_df.shape[0]\n", ")\n", "\n", "# Print the result nicely\n", "print(f\"Ratio of clips from super bad users: {bad_user_clip_ratio:.2%}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Alpha testing user selection" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:23.829729Z", "iopub.status.busy": "2024-08-12T18:12:23.829584Z", "iopub.status.idle": "2024-08-12T18:12:23.846609Z", "shell.execute_reply": "2024-08-12T18:12:23.846171Z", "shell.execute_reply.started": "2024-08-12T18:12:23.829714Z" } }, "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))" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:23.847367Z", "iopub.status.busy": "2024-08-12T18:12:23.847216Z", "iopub.status.idle": "2024-08-12T18:12:23.886529Z", "shell.execute_reply": "2024-08-12T18:12:23.886099Z", "shell.execute_reply.started": "2024-08-12T18:12:23.847351Z" } }, "outputs": [], "source": [ "# 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": 96, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:23.887268Z", "iopub.status.busy": "2024-08-12T18:12:23.887125Z", "iopub.status.idle": "2024-08-12T18:12:24.501821Z", "shell.execute_reply": "2024-08-12T18:12:24.501327Z", "shell.execute_reply.started": "2024-08-12T18:12:23.887253Z" } }, "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": 97, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:24.502630Z", "iopub.status.busy": "2024-08-12T18:12:24.502469Z", "iopub.status.idle": "2024-08-12T18:12:24.629467Z", "shell.execute_reply": "2024-08-12T18:12:24.628972Z", "shell.execute_reply.started": "2024-08-12T18:12:24.502609Z" } }, "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\"] == \"chirp-v3p5-engine-t-2\"\n", "].copy()\n", "\n", "v4_clip_ids = list(str(s) for s in subset_v4_clips_df[\"id\"].unique())" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:24.630433Z", "iopub.status.busy": "2024-08-12T18:12:24.630090Z", "iopub.status.idle": "2024-08-12T18:12:38.238745Z", "shell.execute_reply": "2024-08-12T18:12:38.238184Z", "shell.execute_reply.started": "2024-08-12T18:12:24.630414Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/1 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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indextime_useduser_idupvote_countbatch_indexdislike_countflag_countplay_countskip_countdurationuser_n_clipsdiff_preferencereaction_play_countreaction_pro_play_counttotal_start_stotal_clip_sconcat_play_countsconcat_likesconcat_dislikesid_ytotal_play_countauto_play_counttotal_play_timetotal_play_count_hourauto_play_count_hourtotal_play_time_hournorm_play_frac
count44746.00000044746.0000004.474600e+0444746.00000044746.00000044746.00000044746.00000044746.00000044746.044746.00000044746.00000044746.00000044745.00000027475.0000001996.0000001996.0000001996.0000001996.0000001996.0000004.326100e+0443261.00000043261.00000043261.00000043261.00000043261.00000043261.00000044746.000000
mean495897.372994170.2024002.099417e+070.2763820.5000000.0759840.0052523.0048940.0184.70047813.9647790.0584632.9533802.840692126.396012230.23162213.4594190.5410820.0115239.360946e+102.9102190.393102214.3079280.0580430.0081604.0311691.186294
std277342.79039059.3675761.234311e+070.5250460.5000060.2650610.0722806.1121270.065.09112917.2289220.7741365.3444155.023073120.421129113.534406211.7692363.1417340.1656384.764031e+074.3895742.000410495.4772300.4068660.14116939.3629442.444633
min122.0000002.9627954.110000e+020.0000000.0000000.0000000.0000000.0000000.02.4800004.000000-1.0000001.0000001.0000000.00000027.0799791.0000000.0000000.0000009.352603e+100.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%256990.000000119.7900049.620193e+060.0000000.0000000.0000000.0000001.0000000.0126.5200004.000000-1.0000001.0000001.00000045.000000165.0599791.0000000.0000000.0000009.356821e+101.0000000.00000045.2445110.0000000.0000000.0000000.243159
50%501025.000000194.6929332.242488e+070.0000000.5000000.0000000.0000002.0000000.0215.0000008.0000000.0000002.0000002.000000116.000000215.4799792.0000000.0000000.0000009.360945e+102.0000000.000000107.0400000.0000000.0000000.0000000.677052
75%733692.500000218.3058473.302287e+071.0000001.0000000.0000000.0000003.0000000.0240.00000016.0000001.0000003.0000003.000000189.000000278.1399794.0000001.0000000.0000009.365054e+103.0000000.000000241.9201310.0000000.0000000.0000001.371729
max980589.000000500.9866013.856206e+0730.0000001.0000002.0000001.000000678.0000000.0240.000000238.0000001.000000637.000000637.0000003268.4000003320.0000008517.00000076.0000006.0000009.369203e+10408.000000138.00000032510.47872526.00000015.0000003600.360000136.027108
\n", "" ], "text/plain": [ " index time_used user_id upvote_count batch_index dislike_count flag_count play_count skip_count duration user_n_clips diff_preference reaction_play_count reaction_pro_play_count total_start_s total_clip_s concat_play_counts concat_likes concat_dislikes id_y total_play_count auto_play_count total_play_time total_play_count_hour auto_play_count_hour total_play_time_hour norm_play_frac\n", "count 44746.000000 44746.000000 4.474600e+04 44746.000000 44746.000000 44746.000000 44746.000000 44746.000000 44746.0 44746.000000 44746.000000 44746.000000 44745.000000 27475.000000 1996.000000 1996.000000 1996.000000 1996.000000 1996.000000 4.326100e+04 43261.000000 43261.000000 43261.000000 43261.000000 43261.000000 43261.000000 44746.000000\n", "mean 495897.372994 170.202400 2.099417e+07 0.276382 0.500000 0.075984 0.005252 3.004894 0.0 184.700478 13.964779 0.058463 2.953380 2.840692 126.396012 230.231622 13.459419 0.541082 0.011523 9.360946e+10 2.910219 0.393102 214.307928 0.058043 0.008160 4.031169 1.186294\n", "std 277342.790390 59.367576 1.234311e+07 0.525046 0.500006 0.265061 0.072280 6.112127 0.0 65.091129 17.228922 0.774136 5.344415 5.023073 120.421129 113.534406 211.769236 3.141734 0.165638 4.764031e+07 4.389574 2.000410 495.477230 0.406866 0.141169 39.362944 2.444633\n", "min 122.000000 2.962795 4.110000e+02 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 2.480000 4.000000 -1.000000 1.000000 1.000000 0.000000 27.079979 1.000000 0.000000 0.000000 9.352603e+10 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000\n", "25% 256990.000000 119.790004 9.620193e+06 0.000000 0.000000 0.000000 0.000000 1.000000 0.0 126.520000 4.000000 -1.000000 1.000000 1.000000 45.000000 165.059979 1.000000 0.000000 0.000000 9.356821e+10 1.000000 0.000000 45.244511 0.000000 0.000000 0.000000 0.243159\n", "50% 501025.000000 194.692933 2.242488e+07 0.000000 0.500000 0.000000 0.000000 2.000000 0.0 215.000000 8.000000 0.000000 2.000000 2.000000 116.000000 215.479979 2.000000 0.000000 0.000000 9.360945e+10 2.000000 0.000000 107.040000 0.000000 0.000000 0.000000 0.677052\n", "75% 733692.500000 218.305847 3.302287e+07 1.000000 1.000000 0.000000 0.000000 3.000000 0.0 240.000000 16.000000 1.000000 3.000000 3.000000 189.000000 278.139979 4.000000 1.000000 0.000000 9.365054e+10 3.000000 0.000000 241.920131 0.000000 0.000000 0.000000 1.371729\n", "max 980589.000000 500.986601 3.856206e+07 30.000000 1.000000 2.000000 1.000000 678.000000 0.0 240.000000 238.000000 1.000000 637.000000 637.000000 3268.400000 3320.000000 8517.000000 76.000000 6.000000 9.369203e+10 408.000000 138.000000 32510.478725 26.000000 15.000000 3600.360000 136.027108" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subset_v4_clips_df_test.describe()" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:38.592729Z", "iopub.status.busy": "2024-08-12T18:12:38.592585Z", "iopub.status.idle": "2024-08-12T18:12:39.635569Z", "shell.execute_reply": "2024-08-12T18:12:39.635065Z", "shell.execute_reply.started": "2024-08-12T18:12:38.592713Z" } }, "outputs": [ { "data": { "image/png": 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ogcePHyM2NjajS03T0qVLcezYMcyaNQvlypUDkDRaICYmBi1atEBkZKTxRxRFVK5c2dhef/78OW7evIl27doZ21UAULt2beO5MqNQoUImb+g7Ozujbdu2uHHjBsLDw9M8jv2MzGE/4+3uZyRPXZZ6QfnMOHPmDLRaLXr16gVRfPX1aKdOneDs7Gz8+3ft2jVER0ejc+fOkMtfTXjTqlUruLm5ZZhPctmOHDlicaonADh48CDc3d3Ro0cPs7SU02qlvJ8vX75EbGws3nnnHYv3MqXff/8d77zzDlxdXU3+7gUHB0Ov15tNW5Zc5pRTwxHlNZx+iojoP5MnT0bp0qUhk8ng5eWF0qVLmzRuLHny5AkWLVqEo0eP4uXLlyZpcXFxxv+vXbs2ChYsiF9//RW1atWCwWDA3r170ahRIzg7O6d5/sjISCQmJqJkyZJmaaVLlzbraGZV8tD6lKKjo7FkyRLs27cPERERJmnZ7VQ+efLE2GFJqUyZMsb0lMNaU3asgFeNKktzmyaLjIxEXFwcypcvn6NlSW7EFy1a1Gy7wWBAbGysyRBpS/euVKlSSEhIQGRkJAoWLIjExESsXLkSO3fuxLNnz0zmLrVU55buW2o1atRA06ZNsWTJEvzwww+oUaMGGjdujFatWhk7cqGhoShUqJDZM1i2bFljekqprxkA3Nzc0r0vREREZDufffYZfv31V6xatQoTJ040Sw8NDYUoiiZfJgJAwYIF4erqatYWSK8N8jptpgsXLmDx4sX43//+h4SEBJP9Y2NjTYIKmXXixAksXboUAwcORNOmTY3bHzx4AADo3bu3xeOS20VPnjwBYLktV7p06Qy/RExWsmRJs7n+S5UqBSCp/gsWLGjxOPYzMof9DFNvaz9DysbaD8m/48n3J5lSqUSJEiWMZUzeL/XfSblcjuLFi2eYz4cffoiff/4ZEydOxLx581CrVi00adIEzZo1M37f8OjRI5QuXdokaGLJsWPHsHz5cty8edNknZWM1hN5+PAhbt26hVq1allMj4yMtLg9ry4OTwQwqEFEZBQUFGTxzZu06PV69OnTBy9fvkS/fv1QpkwZODo64tmzZ/jyyy9N3sKQyWRo1aoVtm3bhqlTp+LixYt4/vw5WrdubbXyp9XgSH5LxxI7OzuzbSNGjMClS5fQt29fVKhQAY6OjjAYDOjXr1+uLRSWVjApt/JPKa2yWLOMX3/9NXbu3InevXujSpUqcHFxgSAIJnMQp2TpvqUmCAIWLVqE//3vfzh27BhOnjyJ8ePHY926dfjpp5/g5OSU5XKm9daWLe4LERERZSzlaI0BAwakuV9mv7hK+ZZwatltMz169Agff/wxypQpgy+//BJFixaFQqHA8ePH8cMPP6T5ZnN6Hj9+jM8//xzBwcEYMWKExXznzJljMaCQ0VvquYH9jJzDfsab1c9IDvLk5Zes7O3tsWnTJvz111/4448/cPLkSezbtw8//fQTvv/++0z/zTl//jwGDRqEd999F1OmTEHBggWhUCiwY8cO7N27N91jDQYDateujX79+llMTw60JksOpHLdRMrLGNQgIsqm27dv48GDB5g9ezbatm1r3H769GmL+7dp0wbff/89jh49ihMnTsDDwyPDod0eHh6wt7fHw4cPzdLu379v8jl56GvqBl3ymyWZ8fLlS5w9exZDhw41WYgu+Y22lLLy1kaxYsXMygsA9+7dM6a/Lg8PDzg7O+POnTs2L0tKlu7dgwcP4ODgYFzw8MCBA2jbti2+/PJL4z5qtfq1pltIVqVKFVSpUgUjR47Enj17MGbMGOzbtw+dOnVC8eLFcfbsWcTFxZm8RZVcF5l584iIiIjytkGDBuHXX3/F6tWrzdKKFy8Og8GAhw8fGt+gBpIWX46JicmVtsDRo0eh0WiwfPlyk3ZYyumUsiIxMRFDhw6Fi4sL5s+fb/YFcYkSJQAAnp6eCA4OTvM8yWXJTDs8PQ8fPoQkSSZt5+S2dVr1y34G+xmZwX7Gq1EWISEhWT42+X7cu3fP+HcBADQaDUJCQox/H5L3e/ToEd577z3jfjqdDqGhofDz88swL1EUUatWLdSqVQvjxo3DihUrsGDBAvz111/GqbcuX74MrVZrMiVZSgcOHICdnR3Wrl1rMoXajh07Mszfx8cHKpUq3b95KSXXZ8p/F4jyGq6pQUSUTckdpJRvj0iShB9//NHi/v7+/vDz88P27dtx8OBBtGjRIsPhpTKZDHXq1MHhw4dNOg13797FqVOnTPZ1dnaGu7s7zp8/b7J98+bNmb6mtN4SWb9+vdk2BwcHAJkbKl6/fn1cuXIFly5dMm5TqVTYtm0bihcvnqV5idMiiiIaN26MY8eO4erVq2bpyfcpN8qS0qVLl0zmCH769CmOHDmC2rVrG+vbUr1v2LAh3bffMvLy5UuzN5sqVKgAAMahyvXq1YNer8emTZtM9vvhhx8gCALq1auX7fyJiIgob/Dx8UHr1q3x008/ma3hUL9+fQDmbb1169aZpOek5HZQ6mlxMvNFnSVTpkzBgwcPsGTJEovz3detWxfOzs5YuXKlxfUAkqdhKVSoECpUqIBdu3aZtHdPnz5ttjZCep4/f45Dhw4ZP8fFxWH37t2oUKFCmlNPsZ/BfkZmsJ8BFC5cGEWLFsW1a9eyfGxwcDAUCgU2bNhgcj3bt29HbGys8e9fQEAAChQogG3btpmsa7Jnzx6zqeEsiY6ONtuWur4++OADREVFmdUX8Or5kslkEATB5N6FhITgyJEjGZahefPmuHTpEk6ePGmWFhMTY7Zey/Xr1+Hi4pLhlGtEtsSRGkRE2VSmTBn4+Phg9uzZePbsGZydnXHgwIF0h762bdsWs2fPBoBMDwkfOnQoTp48iY8++gjdunWDXq/Hxo0bUa5cOdy6dctk306dOmHVqlWYMGECAgICcP78+Sy9Sebs7Ix3330Xa9asgVarReHChXH69GmLb75UqlQJALBgwQJ8+OGHUCgUaNCgARwdHc32HTBgAH777Tf0798fPXv2hJubG3bv3o2QkBAsXrw4w7VLMmvUqFE4ffo0evbsic6dO6Ns2bIIDw/H77//js2bN8PV1TXXypLM19cXffv2Rc+ePaFUKrFlyxYASfc12fvvv49ffvkFzs7OKFeuHP73v//hzJkzKFCgQLbz3bVrF7Zs2YLGjRvDx8cH8fHx2LZtG5ydnY2diIYNG6JmzZpYsGCB8S2j06dP48iRI+jdu7fZvLFERESUP3366af45ZdfcP/+fZMvqfz9/dGuXTv89NNPiImJwbvvvourV69i165daNy4sclbyTmldu3aUCgU+PTTT9G1a1fEx8fj559/hqenZ7oLaVvyxx9/YPfu3WjatClu3bpl0lZ2cnJC48aN4ezsjKlTp+KLL75A+/bt8eGHH8LDwwNPnjzB8ePHUa1aNUyePBlAUtty4MCB6N69Ozp06IDo6Ghs3LgR5cuXh0qlylSZSpUqhQkTJuDq1avw9PTEjh07EBERgZkzZ6Z5DPsZ7GdkBvsZSRo1aoRDhw6ZjYgCkoKUy5YtMzvG29sbrVu3xsCBA7FkyRL069cPDRs2xP3797F582YEBgYaf4+USiWGDh2Kr7/+Gr1790bz5s0RGhqKnTt3Zuo6li5divPnz6N+/fooXrw4IiIisHnzZhQpUgTvvPMOgKTf3927d2PmzJm4cuUK3nnnHSQkJODs2bPo1q0bGjdujPr162PdunXo168fWrZsaTyPj4+P2e9ran379sXRo0fx6aefol27dqhUqRISEhJw+/ZtHDhwAEeOHDGO7gGSFlFv0KAB19SgPI1BDSKibFIoFFixYgWmT5+OlStXws7ODk2aNMFHH32ENm3aWDymVatWmDt3LkqUKIGgoKBM5ePv74+1a9di5syZWLRoEYoUKYKhQ4ciPDzcrPEyePBgREZG4sCBA9i/fz/q1auHNWvWpLkgmCXz5s3D119/jc2bN0OSJNSuXRurV69G3bp1TfYLCgrC8OHDsXXrVpw8eRIGgwFHjhyx2Nnw8vLC1q1b8e2332Ljxo1Qq9Xw8/PDihUr8P7772e6bBkpXLgwtm3bhoULF2LPnj2Ii4tD4cKFUa9ePeMc0LlVlmTvvvsuqlSpgqVLl+LJkycoV64cZs6cCX9/f+M+EyZMgCiK2LNnD9RqNapVq2ZssGZXjRo1cPXqVezbtw8vXryAi4sLgoKCjM8fkPTW2fLly7Fo0SLs27cPO3fuRPHixfHFF1/gk08+ee1rJyIioryhZMmSaN26NXbt2mWWNn36dHh7e2PXrl04fPgwvLy8MHDgQJMpgnJSmTJlsGjRInz33XeYPXs2vLy80K1bN3h4eGD8+PFZOlfyKIsDBw7gwIEDJmnFixdH48aNASS1yQsVKoRVq1Zh7dq10Gg0KFy4MKpXr4727dsbj6lXrx4WLlyI7777DvPmzYOPjw9mzpyJI0eO4O+//85UmUqVKoVJkyZhzpw5uH//Pry9vbFgwQKztnVK7Gewn5EZ7Gck6dChAzZu3IgLFy6gevXqJmkRERFYuHCh2TG1atVC69atMXToUHh4eGDjxo2YOXMm3Nzc0LlzZ4waNcpkGqgePXpAkiSsW7cOs2fPhr+/P5YvX47p06dnuA5Jw4YNERoaih07diAqKgru7u6oUaOGcZo8IGkUxurVq7F8+XLs3bsXBw8eRIECBVCtWjXj9Fa1atXCjBkzsHr1anzzzTfw9vbGmDFjEBoammFQw8HBARs2bMDKlSvx+++/Y/fu3XB2dkapUqVMygEkjda6fft2lv/+EuU2QeLqnkREuSYyMhJ169bFZ599hsGDB9u6OJTD/Pz88NFHHxnf9iMiIiKit0fDhg1Rvnx5rFy5MsfzYj/j7cJ+hqnevXujUKFC+Pbbb3MtT4PBgFq1aqFJkyaYPn16ruWb02bMmIHz589j586dHKlBeRrX1CAiykW7du2CXq9P8w0rIiIiIiKirGI/g95mo0aNwv79+xEaGpoj51er1WbriOzevRvR0dGoUaNGjuRpC1FRUdi+fTtGjBjBgAbleZx+iogoF5w9exZ3797FihUr0LhxY3h7e9u6SERERERElM+xn0EEVK5cOVuLhWfW//73P8ycORPNmjVDgQIFcOPGDWzfvh2+vr5o1qxZjuWb29zd3U0WuifKyxjUICLKBcuWLcOlS5dQtWpVTJo0ydbFISIiIiKiNwD7GUQ5r3jx4ihSpAg2bNiAly9fws3NDW3atMGYMWOgVCptXTyitxLX1CAiIiIiIiIiIiIionyBa2oQEREREREREREREVG+wKAGERERERERERERERHlCwxqEBERERERERERERFRvsCgBhERERERERERERER5QtyWxcgL4mIiIUtlk0XBMDT08Vm+b9JWJfWwXq0DtajdbAerYP1aB2sR+tgPVpHXqjH5DKQZexfUF7A54GS8VmgZHwWKCU+D5QsLzwLme1fMKiRgiTBpr+8ts7/TcK6tA7Wo3WwHq2D9WgdrEfrYD1aB+vROliPeZet742t86e8hc8DJeOzQMn4LFBKfB4oWX54Fjj9FBERERERERERERER5QsMahARERERERERERERUb7AoAYREREREREREREREeULXFODiIgohxgMBuj1OlsXA0DSYluJiYnQajV5fm7MvIz1aB2sR+vIjXqUyeQQRb4HRURERK/kpX4OvT62zSlZfupfMKhBRERkZZIkISYmEgkJcbYuionISBEGg8HWxcj3WI/WwXq0jtyoRwcHZ7i6ekAQhBzNh4iIiPK2vNrPodfHtjklyy/9CwY1iIiIrCy5oe/s7A6l0i7PfBEokwnQ6/nqzetiPVoH69E6crIeJUmCRqNGXFwUAMDNzTNH8iEiIqL8Ia/2c+j1sW1OyfJL/4JBDSIiIisyGPTGhr6zs6uti2NCLheh0/Htm9fFerQO1qN15HQ9KpV2AIC4uCi4uLhzKioiIqK3VF7u59DrY9uckuWX/gV7JURERFak1+sBvPqHmogov0v+e8a5s4mIiN5e7OcQkbVYo3/BoAYREVEO4FBsInpT8O8ZERERJWO7gIhelzX+jjCoQURERERERERERERE+QKDGkRERLlEEABRFHLtJ7+8RPXo0QO0bt0UKlW8rYtCedzatSvx8cfdjZ9nzJiKceNG27BE+d+ff57Bxx93h8HAOZSJiIgo69jHsYx9nLfDkCEDsHDhPOPnjh1bYdu2zTYske1ER0ejZcsmeP78Wa7kx4XCiYiIcoEgAFpBRLwm9+akd1LKoYABkpRrWWbLihVL0aFDZzg6Otm6KFb36NEDfPvtTDx4cB/x8XHw9CyIJk2a4pNPBkAuT2qGHT9+FD/+uA6hoY+h0+ng7e2Drl0/QrNmLYznmTFjKvbv32ty7ho1amH+/MVp5t2xYyuEhT01296uXSeMHj0WMTEvsXbtSvz995949uwZChQogHr13ke/foPg7OxspRrIWcOHj4Fk4wd8w4Z1OH78GB4+fAA7OzsEBgZh0KCh8PEpZdxHrVZjyZLvcOTIQWi1GtSo8R5Gj/4SHh6exn1u3ryOFSuW4NatmwAEVKxYCYMGDUP58r5p5q1Wq7FgwXc4dCjt854//zfWrFmBu3f/hYODA5o1a4EBAz4zPn/vvReMNWtW4ODB/SbPHBEREVFG2MdJ25vcx1Gr1Zg7dyZu3bqJhw8fIDi4DmbOnJfhcY8ePcSyZQtx9eplaLU6lC1bDv37D0K1atUBAHfu3MbGjT/g6tXLiI6ORtGiRdGmTQd07twtpy/Jalav/hEODg42LcMvv+zEoUO/4/btW1Cp4rF//zG4uLike8z//ncRmzdvwK1bNxER8QLffDMX9eq9b7JPnTrVLR772WfD0L17LxQoUADNmrXA2rUrMW7cZGtdTpoY1CAiIsoFgiAgXqPDsX+eQ6XO+Ua/o50cDfwLwV0ps/mXvukJCwvDmTMnMXLk57YuSo6QyeRo1qwFfH394eLign//vY3Zs2dAkiQMHDgYAODi4opevT5ByZKloFAocPr0Scyc+RXc3T1Qs2Yt47lq1gzG+PGvGocKhTLdvFev/hEGg974+d69uxg5cjAaNGgEAHjxIhwvXoRj8OARKF26DMLCnuLbb2fixYtwTJ8+x5rVkGPyQvDl0qWLaN++E/z9K0Kv12PVqqUYOXIINm782dihWbx4Ps6cOYWvv54FJydnLFgwBxMmfI7ly78HAKhUKowePQx16tTD6NFjodPp8f33KzF69FDs3PmbMQCR2uLF83H27Ok0z3vnzm18/vlw9Or1CSZOnIbw8OeYO3cmDAYDhgwZYTxP8+YtsX37TwxqEBERUZawj2PZm97HMRgMsLOzQ8eOXfHHH0czfdwXX4xEiRIlsHDhCtjZ2WHbti344osR+Omn3fD09MKtWzfh7u6BSZO+QqFChXHt2hXMmTMDMpmIDh265OAVWY+7u7utiwC1OhE1awajZs1grFy5JFPHJCQkoFy58mjRojUmTLD83P7yy+8mn//88wxmzfoa9es3NG778MNW6NevJwYPHg5XV7fsX0QmMKhBRESUi1RqHeJyocGfHUOGDECZMmUBAAcO7INcLkfbth3Rr9+nxoW8YmJisHDhXJw+fRJarQZVqryDESPGoEQJHwBAWNhTzJ8/B1eu/A86nRZFihTD4MHDUKtWHYt5Hj16COXK+aJgwULGbfv27cGiRfMwadLXWLJkAZ4/f4ZatWpj4sSvcOzYYaxduxJxcXFo1uxDDB06CjKZDACg0WiwatUyHD58AHFxsShduiwGDRpqfPPn5ctozJ8/B5cvX0JsbAyKF/dGz5590KRJM5M6KFeuPJRKJfbs+QUKhQJt2rRH374Ds1WnxYt7o3hxb+PnIkWK4tKlC7h8+ZJxW3L5knXu3A2//74XV678zySooVQq4Onplem8UzeoN25cj+LFvVG16jsAgDJlymHGjG9NyjpgwGf4+utJ0Ol0aX6RntratStx8uRxdOzYBd9/vwqxsTFo2rQFRo78HFu3bsRPP22GwWBAp05d0bt3X+NxsbGxWLhwPk6dOg6NRgt//woYOnSUyciEDRt+wLZtm5GYmIiGDRujQAHTa5oxYyri4mKNb4b9+ecZrF+/Fvfv34UoyhAQEIjhw8cY78HTp0/QqVNrzJgxB9u3/4QbN67B29sHn38+DgEBQZmu25RSj5YZP34qWrVqglu3bqJKlWqIi4vD3r2/YMqU6XjnnXf/22cKPvqoI65du4qAgEA8evQAMTEv0bfvQBQuXAQA0KfPAPTu3RVhYU/h7V3CLN/k83711Tdpnvfo0UMoW7Y8+vTpDwDw9i6BQYOGYfLkcfjkk/7GNwdr166HBQvmIDQ0xOR5JSIiIsoM9nFMvel9HAcHB4wZMw4AcPXqZcTFxWZ4THR0NEJCHmHcuEkoV648AGDQoCHYtetn3Lt3F56eXmjZso3JMcWLe+Patas4fvxYloIaQ4YMQNmy5SCKMuzfvxcKhQL9+w9CkybNsGDBHBw7dgQeHh4YMeJz1KpV23jcvXv/YunSRbhy5RLs7R1Qo0ZNDB06GgUKFACQ9MX/3LkzceLEMTg6OqJr155meXfs2AqdO3dD585JU+Zu3boR+/btwZMnoXB1dUNwcF189tkwODo6Anj1XEybNhOLFs3D8+fPEBhYBePHT4GXV+b7fikl533x4vlMH1OrVm2TurAkdV/01KnjqFatukn/oUyZsvD0LIgTJ46hZcu2mS90NjCoQUREREb79/+Gli3bYPXq9fjnn5uYM2cGChcugtat2wEAvvlmKkJCHmP27PlwdHTC8uWL8fnnw7Fx48+Qy+WYP382tFotli5dDXt7ezx4cB8ODo5p5nflyiX4+1cw256YmIjt27di2rRvoFKpMGHC5xg/fgycnV3w7bcL8eRJKCZO/AKBgZXRqNEHAIAFC+bgwYN7mDbtG3h5FcTx48cwZswwrF+/FSVK+ECj0cDPrwJ69OgNR0cnnD17CtOnT0Hx4t6oWDEgRR3sRZcuH2HVqh9w7doVfPPNNAQFVca7774HABgxYohJUCK1woWLYuPGbRbTQkIe46+/zqJevQYW0yVJwoUL5/Do0UMMGjTUJO3SpQto2bIJXFxc8M4776J//0FwcyuQZjlS0mq1OHhwH7p0+cjYebMkPj4OTk5OmQ5oJAsNDcGff57BvHmLERoagkmTxuLJk1D4+PhgyZKVuHr1CmbO/ArVq9dEpUpJdT1+/BdQKpWYO3cRnJyc8csvOzFixCBs2bITrq5uOHLkENatW4VRo75AUFAV/P77Pmzf/hOKFSueZjkSExPQtetHKFu2PBISVFizZgXGjx+Ddes2QxRfLSW3atUyDB48At7eJbBq1TJMnToBW7fuglwuR1hYGHr27JTu9fbs2Qe9en2SZh0CgKurKwDg1q2b0Ol0qF69pnGfkiVLoXDhIrh+/QoCAgLh41MSbm5u2Lv3F/Tq9QkMBj327v0FpUqVRpEiRS3mk3zed99N+7wajQZKpemIHjs7O2g0avzzz01jZ7hIkSLw8PDE5cuXGNQgIiKiNw77ODnbx8kMNzc3+PiUxO+//wZfX38oFArs3r0T7u4e8PMzr6tk8fFxxnZ1Vuzf/xu6d++J1avX48iRg5g3bxZOnDiGevUaoGfPPvjpp82YPn0yduz4Dfb29oiNjcWwYYPQqlVbDBs2Cmp1IpYvX4zJk7/EokUrAABLly7E//53ETNnzoO7uwdWrlyK27dvoXx5vzTLIYoiRoz4HEWLFsOTJ6GYN28Wli1bhDFjvjTuk5iYiC1bNmDSpK8gCCK+/noSli79DlOmTAcAHDy4H99++0261zt37iJUrlw1y/WUXZGREThz5hQmTJhmllaxYkVcvvw/BjWIiIgo9xQuXBjDho2CIAjw8SmFu3f/xbZtm9G6dTs8fvwIp06dwPLlaxEYWBkAMGXK12jfvgVOnPgDDRs2xrNnYahfvyHKli0HABl+QRoWFgZ//4pm23U6HcaMGWc8/v33G+HAgX349deDcHR0ROnSZVC1anVcvHgejRp9gLCwMOzbtwc7duyFl1dBAED37j3x119nsW/fHgwcOBgFCxZC9+6v3qbp2LEr/v77Txw9etikwV+2bHl88skAAECJEj7YuXMbzp8/Z2zwjx8/GSpVQprXZCkg8Omnn+D27X+g0WjQunU79Ov3qUl6XFwc2rVrDo1GA5lMhlGjxhrzA4CaNWuhfv0GKFq0OEJDQ7Bq1VKMGTMMK1asM77FlZ4TJ/5AXFwcPvywVZr7REdH44cf1qBVq3YZni81STJg/PjJcHR0Mt6bx48fYu7chRBFET4+pbBp03pcvHgelSoF4PLl/+HGjevYs+eg8Uv3IUNG4OTJP3Ds2BG0adMeP/+8BS1atDE2hgcM+Aznz/8NjUaTZjnef7+Ryedx46agZcvGePDgHsqUKWfc3q1bDwQHJ71Z17fvQPTs2RmhoSEoWbIUvLy8sG5d+ov7pdWxMhgMWLRoHgIDKxvzi4iIgEKhMJvH1sPDAxEREQAAR0cnLF68EuPGjcH69WsBJI2qmD9/SZoBppTn1ekMFs9bs2Yt/PzzFhw69DsaNmyCyMgI/PDDmv+Of2FyPi8vL4trsBARERHld+zj5EwfJysEQcB33y3DuHFj8MEH9SCKIgoUcMe8eYvSbFtfvXoZR44cxLffLsxyfuXKlcfHH/cDkPRC0qZN6+HmVsAYyOrTpx92796Of/+9g4CAQOzY8RN8ff2MUwQDwLhxk9G+fQs8evQQXl4F8dtvv2DSpK9RvXoNAMDEiVPRrt2H6ZYjedQEABQtWgz9+w/C3LkzTYIaOp0On38+3vhctG/f2dhmB4A6deqZ3EtLChYsmJlqsZr9+/fC0dEJ9eubv6zn5VUQt2/fyvEyMKhBRERERhUrBpi8yR8QEIitWzdCr9fj4cP7kMlkJg0qN7cC8PEpiYcP7wNIakTPnTsT5879ierVa6J+/YbG4cWWqNWJZm+SA4C9vb1JZ8HDwxNFihQzDtNN2uaBqKgoAElDhfV6Pbp1a29yHo1GAze3pLk89Xo9NmxYh6NHDyE8PBw6nRYajQZ2dvYmx5Qta1peT08vREVFGj8XKlTI5EvkzEh+G+vff29j2bJF2LJlAz76qLcx3dHREevWbUZCggrnz5/DkiULUKxYceOb9I0bN01RvnIoW7YcunRpi0uXLhgb1en57bdfULNmsLEzlFp8fBw+/3w4SpUqk61h6En35tUiiB4eHpDJRJPRER4enoiOTqrHf/+9jYQEFVq0MA1CqNVqhIaGAAAePLiPNm1M72dAQCAuXryQZjkeP36ENWtW4MaN63j5MhqSlHSfnj0LMwlqpLzHycOoo6IiUbJkKcjlcovTPWXG/Pmzce/eXSxbtibjnVNQqxMxc+bXCAysjKlTZ0CvN2Dr1g34/PPhWLPmR7NnNLNq1HgPn302DHPnzsT06VOgUCjQu3c/XL58yeTeAEkjOBITE7OVDxEREVFexj5OzvRxskKSJMyfPxvu7u5YunQ17OzssWfPbowdOwqrV/9oNtXSvXv/Yty40ejTpz9q1HgvjbOmLeX1ymQyuLq6GYNSQFLdA0jRP7mDixfPo0mTumbnCg0NgVqthlarNXlOXF2TRp+k59y5v7Bx4w94+PAB4uPjodfrodGokZiYCHv7pHuU+rlIfW8cHZ3y3ILzv/32Kz74oBns7OzM0pTK3OlXMKhBREREVtOqVVvUqPEezp49hb///gsbNqzDkCEj0LFjV4v7FyhQALGx5nOwWnoTKPU2QRCMX1onJKggk8mwdu0GiKLpyIXkxZo3b96An3/egmHDRqNMmXJwcHDAokXzoNNpM5HPq4UIszM0O3mdhNKly8BgMGDOnBno2rWHcZSFKIrGL9LLl/fDw4f3sXHjD2brbSQrXtwbBQoUQEjI4wyDGmFhT3H+/N+YMcPy4t8qVTxGjx4GR0cnfPPNt9l6C8tSnVk6j8GQVI8JCSp4enph8eKVZvs4O7uYbcussWNHokiRohg7dgK8vArCYDCgV68u0GpN53hOWbbkDq7BkPQsZXf6qfnzZ+PMmVNYsmQVChUqbNzu6ekJrVaL2NhYk9EakZGR8PRM6kwdOvQ7wsKeYuXKdcZgw5QpM9C8eQOcPHncJKhl6bwODk4WzwsAXbv2QJcuHyEi4gVcXFzw9OlTrFy5xGwar5iYGLM1S4iIiIiIfRzg9aefunDhHM6cOYX9+4/CyckZAODn9yXOn/8L+/fvRc+eHxv3vX//HoYP/wytWrUzjrbIqoz6J6/6AMn9kwTUrl0XgwYNMzuXp6cXQkIeZ7kMT58+wdixI9G2bQf07/8ZXF1dceXK/zBr1tfQarXGoEZG9yavTT91+fIlPHr0ENOmzbSYHhsbkysLpjOoQUREREY3blw3+Xz9+jWUKOEDmUyGkiVLQ6/X48aNa8ah2S9fRuPRo4coVaq08ZjChYugbduOaNu2I1asWII9e3an2eAvX94PDx7ce+1yly/vB71ej6ioqDQbc1evXkadOvXRtGnSEGGDwYBHjx6hdOnSFvdPy+sOzTYYDNDpdCYNVUv7pDfN0vPnz/Dy5ctMLR7322+/wt3d3eJChvHxcRg1aigUCgVmz55v8U2bnODn54/IyAjIZDIULVrM4j6lSpXGjRvX0bx5S+O269evpXnO5Gdx7NiJxmfg8uX/ZblsWZ1+SpIkLFgwBydO/IHFi1eaBQv8/CpALpfjwoW/jdNjPXr0AM+ehaFSpaTFyRMTEyGKgskbhIKQ9Dm5o5Va8nnPnfvbuEZL6vOmPFfyKJ3Dhw+gUKHC8PX1N6Ynj5Dx9U17PmAiIiKi/Ip9nIzl9PRTyW/uC4LpaOGUQRwAuHfvLoYPH4TmzVuYTAWV03x9/XD8+FEUKVLU4rUWL+4NuVyOGzeuoUiRpBfWYmJi8PjxI1Sp8o7Fc966dRMGgwFDhow0vrh09OihLJctr00/tXfvL/Dzq4Dy5X0tpt+7dxdVq1quE2tiUIOIiIiMnj0Lw+LF89GmTXvcuvUPduz4CYMHjwCQNPdq3br1MXv2DHz++Xg4OjpixYolKFiwEOrWfR8AsHDhPLz3XjBKlPBBbGwsLl48j5Il025Q16hRC7NnT4der8/U2hBp8fEpiQ8+aI7p06dgyJARKF/eD9HRUbhw4RzKli2P4OA6KFGiBI4dO4KrVy/DxcUVP/20CVFREVlu8GdlaPbBg/shk8lRtmw5KBQK/PPPTaxcuRSNGn1gbCxv2LAO/v4VUKyYN7RaLc6ePY0DB/ZhzJhxAACVSoV161ajfv2G8PT0RGhoCJYtW4TixUugRo1axryGDx+EevXeR4cOXYzbDAYD9u3bg2bNWpo1zuPj4zBy5BCo1YmYPPlrxMfHGRe5LlDA/bXuR0aqV6+JgIBAjBs3Bp99NgwlSvjgxYtwnDlzCvXrN4C/f0V06tQVM2ZMg79/BQQGVsahQ7/j/v17aS4U7uLiCjc3N/z66054enrh2bMwrFixOMtly+r0U/Pmzcbhw79j5sx5cHR0NK5V4ezsDDs7ezg7O6NlyzZYvHgBXF3d4OjohO+++xYBAUEICAgEALz77ntYtmwR5s2bjY4du8BgMGDTph8gk8mMo3XCw59j+PBBmDhxGipWDDCed9GieXB2drF4XgDYvPlH1KwZDEEQcOLEMWzc+AO++mqWyf29fv0qFAolAgJMgyH05hIEmATRUpIkCenEXImIiPId9nEyltXpp+7fvwedTouYmJdQqVS4cydpDYXkRbNv3LiG6dOnYOHC5ShYsBACAoLg4uKCGTOm4OOP+8POzg579uzG06dPjC9f3bv3L4YNG4SaNd8zjjQGAFGU5fib/x06dMaePbsxdeoEfPRRL7i6uiEk5DGOHDmIsWMnwtHRES1btsGyZQvh5uYGd3d3rFq1zCxIk1Lx4iWg0+mwfftPqF27Lq5evYxfftmZ5bJldfqpiIgXiIyMME7re+/ev3B0dEThwkXg6po0bVnqvqNKpUJo6KvRKE+fhuLOnVtwcXEzBnGApD7ksWOHMWTICIt5JyYm4tatm7kSkGJQg14bO0VERJnnaJc7//RmN59mzVpArVajf//eEEUZOnbsarKuwbhxU7Bw4VyMHTsCWq0WlStXw7ffLjR+YW4w6DF//myEhz+Ho6MTatashWHDRqWZ33vvBUMmk+H8+b9Rs2atNPfLjPHjp2D9+rVYsuQ7hIc/h5tbAVSqFIjg4KR5UXv37osnT0IxatRQ2Nvbo3Xrdqhb933jF/k5QSaTYdOm9Xj8+BEACYULF0WHDp1NFoxLSEjAvHmz8fz5c9jZ2aFkyVKYPPlrNGr0wX/nEHH37h3s378XcXGx8PIqiHfffQ/9+39qMldvaGgIoqOjTfI/f/5vPHsWhhYtWpuV7datf3DjRtLIhy5d2pqk/fzzr8YRFB07tkLz5i2ztdZGWgRBwPz5i7F8+RJ88800REdHwcPDE1WqVIO7uwcAoFGjDxAaGoLlyxdBrdbg/fcbom3bDvj77z8tnlMURUyd+g0WLpyLXr26oESJkhgxYgyGDrVeuS3ZvXs7AJjlM378FOPC7EOHjoIgiJgw4QtotRrUqFELo0ePNe5bsmQpzJ49H99/vxqfftoHgiDC19cPc+cuNo7G0el0ePToocn8tEOHjoJMlvZ5AeDPP8/gxx+/h0ajRbly5TFz5jzUqlXbZJ/Dhw/ggw+aGYfA05tNEABHIQGiNsZiukHhChUc2IYnIqJMYx/H1JvexwGAzz8fjrCwp8bPffp8BAA4deo8gKQvtx89egidLmka2AIFCmDevMVYtWoZhg8fBJ1Oh9Kly2DmzHnGN/6PHTuC6OgoHDiwHwcO7Deeu0iRoti+fQ+ApCmdOnVqjUWLVqQ5VW92eHkVxPLla7F8+WKMHDkEWq0GRYoURc2atYyjLD77bDgSElQYO3YkHB2d0LXrR4iLS7uey5f3xdChI7Fp03qsXLkElStXw8CBgzF9+hSrlduS3bt3YN261cbPgwf3B2DaP0ndd/znnxsYNuxT4+fFixcAAJo3b4kJE6Yatx8+fBCSJKFx42YW8z558g8ULlwkV6bCEqT05j54y7x4EWuTxrsgAF5eLjbL/3VkqlMk5V6nKD/XZV7CerQO1qN15Ld61Go1iIh4Ck/PolAoXn3hLAiAVhARr9Glc7R1OSnlUEgGY73J5WK6b98MGTIA5cv7Yfjw0blUwiQ7dmzD6dMnMH/+klzNN7syqsc3SWJiIj78sBHmzl1o1U4D8HbVY0563XqMjo5G9+4dsGbNj2mOgknr7xrw6m80WZYX+xeiKMBJGwbD7UOAJt40UekE0bcJ4hVF0pz6jPKf/NaWopzDZ4GSZedZsNQeyAt9nIywj5M5+aVtfvHieYwf/zm2bfvFZEpYsp7XeRYGDPgYHTt2xQcfWA56JLNG/4IjNei1CIIAURuTbqdIUDimO284EdHbQJIABQxwV+bclD7meWa+sW9Lbdq0R1xcLFSq+CwNq6Wcd/HiebzzTnWrBzQo7wgLe4LRo8emGdCg/C1pbRbzbQAATTwktenbhZbHXhMREVnGPk7a2MfJGWfPnkavXn0Y0MiDoqOjUb9+AzRp0jRX8mNQg6yDnSIiogxJEhjktUAul6N37762LgZZEBxcB8HB5guM05vD378i/P0r2roYZGWCACAhCk7aCPNEUQY59NDkeqmIiOhNxD6OZezj5IzBg4fbugiUhgIFCuCjj3rnWn4MapCJtNbH4NoYRERvviVLVtm6CEREZBUCkBgD6fYhSKlHUzsXhODzDsyGcBAREb2B2MchejMxqEFG6a2PwQUDiYiIiIjyF8nSaGolp8AgIiIiovyNQQ0ySnN9DK6NQURERERERERERER5AIMaZC7VG10cmE5EREREREREREREeYFo6wIQERERERERERERERFlBoMaRERERERERERERESULzCoQURERERERERERERE+QKDGkRERLlEEABRFHLtR8hniyKtXr0cs2fPsHUxyMbWrl2Jjz/ubvw8Y8ZUjBs32oYlytuio6PQsmUTPH/+zNZFISIiorcQ+zjpYx/nzbdv3x40a/a+8XPq/szbRKvVomPHVvjnnxs5nhcXCiciIsoFggA4CgkQtTG5lqdB4QoVHCBJuZZltkVEvMDPP2/Fjz9utXVRcoRKFY/Vq1fgxIljiIqKgq+vH4YPH40KFSoBAHQ6HVatWoY//zyNJ09C4eTkjOrVa2DQoKHw8iqY5nnXrl2JdetWm2zz8SmJzZt3mO0rSRLGjBmOv/46g2++mYt69d636jXmlOHDx0Cy8UOc2Xq+du0KVq1ahhs3rkEUZShf3hfz5y+GnZ29xfNu2LAOx48fw8OHD2BnZ4fAwCAMGjQUPj6ljPv88stOHDr0O27fvgWVKh779x+Di4uLMb1AAXc0a9YCa9euxLhxk6130fT2EkQIggAx1etvkiTli39PiIgo97CPk743vY8DADt2bMOWLRsQGRmBsmXLY+TIz1GxYkCa++/btwfffDPNZJtSqcTRo2eMn2fMmIr9+/ea7FOjRi3Mn7/YuoXPId269UTHjl1sXQwcPXoYa9YsR1jYU3h7l8CgQUNRq1adNPe/fPl/WLFiER4+fIjExEQUKVIEbdq0R5cuHxn3yaj/olAo0K1bDyxfvhgLFy7P0etjUIOIiCgXCIIAURsDw+1DgCY+5zNUOkH0bQJB4WjzL4QzY8+e3QgICEKRIkVtXZQcMWvWdNy7dxeTJn0FL6+COHBgH0aM+AwbN/6MggULITExEbdv/4PevfuhfPnyiImJxcKFczF27CisXbsh3XOXLl0G3323zPhZJrPcvNu2bXO+e7MNAJydnW1dBAAZ1/O1a1cwevRQ9OjRByNGfA65XIY7d+5AENIeGH3p0kW0b98J/v4VodfrsWrVUowcOQQbN/4MBwcHAIBanYiaNYNRs2YwVq5cYvE8H37YCv369cTgwcPh6upmhault5ZMCblMgKMmDIDpvx356UskIiLKHezjpO9N7+McOXIQS5YswJgx41CxYgC2bduCUaOGYsuWHXB390jzOCcnJ5OXgwQLnZSaNYMxfvyrF3YUCqV1C5+DHB0dATjatAxXr17GtGkTMHDgYAQH18WhQ79j3Lgx+P77jShTppzFYxwc7NG+fWf4+flBobDDlSv/w7fffgN7ewe0adMeQOb6L02aNMeSJd/h3r27KFOmbI5dI4MaREREuUkTD0kdl+PZZOe76yFDBqBcufJQKpXYs+cXKBQKtGnTHn37DjTuExsbi6VLv8OpU8eh0Wjh718BQ4eOQvnyvsZ9fvhhDbZv/wlqtRqNGjWBm1sB/PXXWfzww+Y08z5y5CDatu1oVp6yZctBFGXYv38vFAoF+vcfhCZNmmHBgjk4duwIPDw8MGLE56hVq7bxuHv3/sXSpYtw5col2Ns7oEaNmhg6dDQKFCgAAPjzzzNYv34t7t+/C1GUISAgEMOHj0Hx4t4AgKdPn6BTp9aYMWMOtm//CTduXIO3tw8+/3wcAgKCslyvanUijh8/ipkz56FKlWoAgL59B+L06ZPYtWs7Bgz4DM7OziZfmAPAqFFfoH//3ggLC0ORIkXSPL9MJoenp1e6Zbhz5xa2bt2ENWt+RJs2zbJ8DWvXrsTJk8fRsWMXfP/9KsTGxqBp0xYYOfJzbN26ET/9tBkGgwGdOnVF7959jcdl5nnZsOEHbNu2GYmJiWjYsDEKFHA3yXvGjKmIi4vFzJnzAOT+/UuWUT0vWjQfHTt2Rc+eHxu3pRxxYUnqt83Gj5+KVq2a4Natm8ZnpXPnpKHrFy+eT/M8ZcqUhadnQZw4cQwtW7ZN/0KI0iNTQNDEQ7p7EpI6xZdT+exLJCIiymXs41j0JvdxAGDr1k1o1aotWrRoDQD4/PNxOHv2FPbu/dWkTZyaIAgZ9l+USkWG+6Rn3749WLRoHiZN+hpLlizA8+fPUKtWbUyc+BWOHTuMtWtXIi4uDs2afYihQ0dBJpMBADQaDVatWobDhw8gLi4WpUuXxaBBQ1GtWnWTc69ZswIvX0ajRo1aCAqqYpJ3ct8p+dm4efM6Vq5cijt3bkGn06F8eT8MHToKfn7+xmPq1KmOsWMn4syZU/j777MoWLAQhgwZgTp16mfr+n/+eStq1qyF7t17AQD69x+Ec+f+wo4d2/D55+MtHuPr6w9fX3/I5SJ0OgOKFi2G48eP4cqVS8agRmb6L66urggMrIwjRw6iTJlB2Sp/ZnBNDSIiIjLav38v7O0dsGrVDxg0aCh++GENzp3705g+adJYREVFYu7cRVi7dgN8ff0xYsQgxMS8BAAcPLgfP/64DoMGDcXatRtQuHAR7N69Pd08Y2Je4sGD+/D3r2ihPL/Bzc0Nq1evR4cOnTFv3ixMmjQWAQFB+P77jXj33fcwffpkJCYmAkjqkAwbNgi+vn5Ys2YD5s1bhMjISEye/KXxnImJCeja9SOsWbMBCxcugyAIGD9+DAwGg0neq1YtQ7duPbFu3WaUKOGDqVMnQKfTAQDCwsLQpEnddH9+/PF7AIBer4der4dSafp2kZ1d0tsvaYmLi4MgCHBxSX+kQkjII7Rp0wydOrXBtGkTERYWZpKemJiIadMmYtSoL16rYxAaGoI//zyDefMWY8qUGfjtt1/w+ecjEB7+HEuWrMSgQUOxevVyXL9+zXhMRs/LkSOHsG7dKgwc+BnWrv0Rnp5e2LUr/eclt+9fZuo5KioSN25cg7u7Oz799BO0avUBhgwZgMuX/5elOo6PT/oywNXVNUvHAUDFihWznB9RWiSNCpI6zviTK2/fEhER5RD2cV6xVhtZq9Xi9u1/UL16TeO5RVFE9eo1cP36lXTrJiEhAR06tET79i3w5ZejcO/eXbN9Ll26gJYtm6Bbt/aYO3cmXr6MTvecliQmJmL79q2YNu0bzJu3GJcuXcD48WNw9uxpfPvtQkya9BV++WUn/vjjiPGYBQvm4Pr1K5g27RusX78VDRo0xpgxw/D48SMAwPXr1zBr1tfo0KEz1q3bjGrVqmP9+rXplkOlUqF585ZYtmwtVq78Ad7eJfD558OhUpm2r9atW42GDRtj/fqteO+92pg2bZLxGQSQ4b359ttvjPteu3YF1avXMDl/zZq1cO3a1UzX3+3b/+DatSuoUuWdNPdJq/9SoUIlXL58KdN5ZQdHahAREZFR2bLl8cknAwAAJUr4YOfObTh//hzeffc9XL78P9y8eR179hwyfkE/ZMgInDz5B44dO4I2bdpj+/af0LJla+PbOn369Mfff/+JhISENPN89iwMkiTBy8v8C/dy5crj44/7AQB69uyDTZvWw82tAFq3bvff+fth9+7t+PffOwgICMSOHT/B19cPAwcONp5j3LjJaN++BR49eggfn5J4//1GJnmMGzcFLVs2xoMH90yG4nbr1gPBwUlzjvbtOxA9e3ZGaGgIypYtAy8vL6xbl/ZbWcCrhp2joxMCAoLwww9rUKpUabi7e+Dw4QO4fv2q8c2p1NRqNZYvX4zGjZvCySntoEbFigEYP34qfHxKIiLiBdatW43Bg/thw4af4OjoBABYtGgeAgKCULfu++mWNyOSZMD48ZPh6OiE0qXLoGrV6nj8+CHmzl0IURTh41MKmzatx8WL51GpUkCGz0uHDh3x889b0KJFG+PoggEDPsP5839Do9GkWY7XvX8lS5bK0v0DMq7n0NBQAMD336/G4MHDUb68L37//TeMGDEIP/74E0qU8Mmwfg0GAxYtmofAwMppDglPj5dXQdy+fSvLxxERERG96djHsX4f5+XLaOj1enh4mE4z5eHhgYcPH6R5vI9PSXz55SSUK1cecXFx2LJlIwYN+gQbNmxDoUKFASR9+V6/fgMULVocoaEhWLVqKcaMGYYVK9YZR1Rkhk6nw5gx44x9rvffb4QDB/bh118PwtHR0dinuXjxPBo1+gBhYWHYt28PduzYa1zXsHv3nvjrr7PYt28PBg4cjJ9/3oKaNWvho496G6/n2rXL+Ouvs2mW45133jX5/MUXE9CsWQNcunQRtWvXNW5v3rwlmjRJGlU/cOBgbN++FTduXMd77wUDQIb3xsnJyfj/kZERZlOAubt7IDIyIt1zAECrVs0QHR0FvV6PTz4ZgFat2lrcL73+i5dXQTx7FmbxOGthUIOIiIiMypYtb/LZ09MLUVGRAIB//72NhIQEtGhh2mBWq9UIDQ0BADx69BDt2pkOsa5YsRIuXEh76hy1Wg0AUCrt0i2PTCaDq6sbypZ91WDy8PAEAERHJ5fxDi5ePI8mTeoitdDQEPj4lMTjx4+wZs0K3LhxHS9fRkOSkt5eevYszKQxljLv5BEOSXVRBnK5HN7eJdK8ptQmTfoKM2d+hbZtm0Mmk8HX1w+NGzfFrVs3zfbV6XT/vXUlYcyYL81PlkLKIenlypVHxYoB6NixJY4ePYSWLdvi1KnjuHjxPL7/flOmy5qWIkWKGQMlQFKHRSYTIaZYTdjDwzPFvcj4eXnw4L5xKHOygIBAXLx4Ic1yvO79K1myVJbvX0b1nFyGNm3aGzu7vr7+uHDhHH777Vd8+umQDPOYP3827t27i2XL1mS6XCkplXbGt/mIiIiI6BX2cXKmj5MdAQFBJtNdBQZWxkcfdcQvv+xE//5JUxU1btw0RXnLoWzZcujSpS0uXbpgNvogPfb29iYvkXl4eP7Xp3FMsc0DUVFRAJKm+NLr9ejWzbR/otFo4OaWtG7dw4f3Ua9eA5P0SpWC0g1qREZGYPXq5bh06QKioiJhMBiQmJho9qV/ynvj4OAAJycn43MKIMfvTbKVK9ciNjYe169fxYoVS1C8uLcx2JJSev0XO7uc75swqEFERERGcrlp00AQBOP86QkJKnh6emHx4pVmxzk7u2Q7Tze3AgCA2NgYuLubrqdgqTwptyUvKmcwJJcxAbVr18WgQcPM8klutI8dOxJFihTF2LET4OVVEAaDAb16dYFWq0sz71f5JHUOwsLC0LNnp3Svq2fPPujV6xMAQPHi3liyZBUSEhIQHx8PLy8vTJ48DsWKFTc5RqfTYdKkLxEWFoZFi5anO0rDEhcXF5QoURIhIUkdsAsXziM0NATNm5s2vCdO/AJBQVWwZMmqTJ87o3uR7NW9yJnnxRb3L7XU9Zz8bJUqVdpkv5IlS2fqDaX582fjzJlTWLJklfENtayy9PtDREREROzjpJX367SR3dwKQCaTITIy0iQ9MjISnp6e6Z4jdXnKl/dDSMjjNPcpXtwbBQoUQEjI4ywFNSz1VSw/C0nXn5Cggkwmw9q1GyCKpiNCkhfBzo7p06ciJuYlhg8fjcKFi0KpVOLTT/tAp9Nmomyv1jKzFNRK6YMPmhvXy/Dw8DQJiABJwavkgFl6ihUrDp3OgLJlyyEyMgLff7/KLKiRUf8lJualcb2XnMKgBhEREWWKn58/IiMjIJPJULRoMYv7+PiUxD//3EDz5i2N227evJHueYsX94aTkxMePLgPH5+Sr1VGX18/HD9+FEWKFLXYiH35MhqPHj3E2LETUblyVQDI1joEWZ2+KJmDgwMcHBwQExODv/8+a9IxSQ5ohIQ8wqJFK40doaxQqVQIDQ1B06YfAgB69OiNVq3amOzTq1dXDB06ymSoc07IzPNSqlRp3Lhx3eR5SbkmR2q2vn/JUtdz0aLF4OVVEI8ePTTZ7/Hjh3jvvdqWTgEAkCQJCxbMwYkTf2Dx4pVmQa6suHfvLqpWTXu+WyIiIiIyxz7OK1lpIysUiv9GJv+NevXeB5AUHLlw4Rzat++c6Tz1ej3u3fvXZGR0as+fP8PLly8tTuVlTeXL+0Gv1yMqKspYj6mVLFkaN26Y9leuX09/nYqrVy9j9OixqFUradqvZ8/CEB0dneXyZWX6qYCAIJw/fw6dO3c3bjt37i8EBARmKU9JkqDVak0+Z6b/cv/+Xfj6+mUpr6xiUIOIiIgypXr1mqhUKRDjxo3BZ58NQ4kSPnjxIhxnzpxC/foN4O9fER07dsHs2dPh718RAQFBOHLkIO7evZPul7XJC8pdufI/Y4M4uzp06Iw9e3Zj6tQJ+OijXnB1dUNIyGMcOXIQY8dOhIuLK9zc3PDrrzvh6emFZ8/CsGLF4iznk9Wh2X/9dRaSJMHHpyRCQx9j6dJF8PEpZZyqSKfTYeLEL3D79i3Mnr0ABoMeEREvAACurm5QKBQAgOHDB6FevffRoUMXAMCSJd+hdu26KFKkKF68CMfatSshk4nGIduenl4WFwcvXLjIa32BnhkZPS8BAQHo1KkrZsyYBn//CggMrIxDh37H/fv30iybre5fRvUsCAK6d++JtWtXoly58ihf3g/79+/Fw4cPMX36HON5Ut+/efNm4/Dh3zFz5jw4Ojoa77mzszPs7OwBABERLxAZGWGc/uDevX/h6OiIwoWLwNU1aRh8YmIibt26aTLPMhERERFljH2cV7LaRu7a9SPMmDEV/v4VUaFCJWzbtvm/qbxaGff5+uvJKFiwkHE61nXrVqNSpUAUL+6NuLg4bN78I8LCwoxr7KlUKqxbtxr16zeEp6cnQkNDsGzZIhQvXgI1atTK8jVlhY9PSXzwQXNMnz4FQ4aMQPnyfoiOjsKFC+dQtmx5BAfXQceOXfHZZ32xefMG1K1bH3//fTbdqacAoESJEjhwYB/8/SsiPj4ey5YthJ2d+bRkGcnKvenUqSuGDBmALVs2Iji4Dg4fPoB//rmBL74Yb9xnxYolCA9/jkmTvgIA7NixDYULF0GZMmWg1xtw+fJFbNmyER07djEek5n+C5AUVOvX79MsX2NWMKhBRESUm5ROEHIpH2sTBAFz5y7EqlXL8M030xAdHQUPD09UqVLNuAjZBx80x5MnoVi69Duo1Ro0bNgYzZu3ws2b19M9d8uWbTFnzgx89tkwkzUassrLqyCWL1+L5csXY+TIIdBqNShSpChq1qwFURQhCAKmTv0GCxfORa9eXVCiREmMGDEGQ4cOzHaemREXF4eVK5Maja6urqhfvyEGDBhsfNMqPPw5Tp06AQDo06e7ybGLFq1AtWrVASTNmZvyrZ7w8GeYOnXCf8N73REUVBkrV/6Q5WmIOnZshebNW6JvX+vVQ2ael0aNPkBoaAiWL18EtVqD999viLZtO+Dvv/+0eE5RFG1y/zJTz507d4darcHixQsQE/MS5cr5YsGCpSbz+Ka+f7t3bwcAs/KPHz8FH37Y6r99dmDdutXGtMGD+5vtc/LkHyhcuEiab5QRERER5Sj2cSx60/s4jRp9gOjoKKxZswKRkREoV84X8+YtNpni6NmzMJNrj42NwezZ0xEZGQEXF1f4+fljxYq1KF26DABAJhNx9+4d7N+/F3FxsfDyKoh3330P/ft/alzIHQCGDBmAokWLYcKEqVa9pvHjp2D9+rVYsuQ7hIc/h5tbAVSqFIjg4KRR7gEBgfjiiwn4/vtVWLt2BapXr4Hevfti/fq018X78stJmDPnG3zySQ8UKlQYAwd+hqVLF1q13KkFBlbGlCkzsHr1MqxatRTe3iUwc+Zck/VVIiJemEyVK0kGrFy5BE+fPoFMJkPx4t4YNGioyRqImem/XLt2BXFxcWjQwHSdGmsTpJSTc73lXryIhS1qQxAALy8Xm+WfTBQFOGnDYLi2G5I67lX57JwhBrRFvKKIcT6/jI7J6LicklfqMr9jPVoH69E68ls9arUaREQ8hadnUSgUrxpdggA4CgkQtTG5VhaDwhUqycFYb3K5CJ3OkGv5Jxsx4jN4enpi0qSv09xHkiQMGNAbnTt3t7gIWV5iq3rMKYmJifjww0aYO3ehMXiSG960erQVuVzEJ5/0QseOXfHBBznzu5PW3zXg1d9ossxW/3aJogBPMQKqCz+bt9FdCsOuXF2obx6AlBibqTRbtOvJevJbW4pyDp8FSpadZ8FSeyAv9HFshX0c2+rQIemFrOQv0sl6XvdZmDx5HMqVK5/m+oSAdfoXHKlBRESUCyQJUMEBgsIxF/OUcr2xn5iYiN27t6NGjVqQyWQ4fPgAzp//GwsWLE33OEEQ8MUXE3D37r+5VFJKdvHiebzzTvVcDWiQ9URHR6F+/QZo0qSprYtCREREbxn2cdjHsYV79+7C2dkZzZq1sHVRKBWtVosyZcqiS5fuGe/8mhjUICIiyiWSlNQIf5MJAnD27Bn8+OM6aDRq+PiUxIwZc/DuuzUzPLZ8eT+UL5+zi4mRueDgOggOrmPrYlA2FSjgjo8+6m3rYhAREdFbin2c9LGPY31lypTF+vVbbV0MskChUODjj/vlSl4MahAREZHV2NnZY+HCZbYuBhERERERkVWwj0OU92R/lRoiIiIiIiIiIiIiIqJcxKAGERERERERERERERHlC5x+ijJHECEIAsRUYTBBEGxTHiKiPE6SDLYuAhGRVfDvGRERESVju4CIXpc1/o4wqEEZkykhlwlw1IQBSLX4kyiDHHpobFIwIqK8Ry5XQBBEvHwZAWfnApDJ5HkmAGwwCNDr3+xF/HID69E6WI/WkZP1KEkS9HodYmOjIQgi5HJFjuRDREREeV9e7ufQ62PbnJLll/4FgxqUMZkCgiYe0t2TkNTxpmnOBSH4vAPwHzIiIgBJI9g8PYvg5ctIvHz5wtbFMSGKIgwGvln1uliP1sF6tI7cqEel0h6urh784uJtl8bIbSCpgyrxexAiojdaXu7n0Otj25yS5Zf+BYMalGmSRgVJHWeyTVA62ag0RER5l1yugIdHIRgM+jzTMBQEwN3dCVFR8fzi6TWwHq2D9WgduVGPoihCFGUMaLzt0hu5DcCgcIUKDvx9JiJ6w+XFfg69PrbNKVl+6l8wqEFERJQDBEGATCaHTGbrkiQRBMDe3h4KhZYN1dfAerQO1qN1sB4p16Q3clvpBNG3CQSFIyQ+iEREb7y81s+h18c2JSXLT88CgxpvIUGwvMA338AjIiIiIqK0WBy5baOyEBEREdHbi0GNt4wgAI5CAkRtjHkiF/0mIiIiIiIiIiIiojyMQY23jCAIELUxMNw+BGi46DcRERERERERERER5R8MarytNPFc9JuIiIiIiIiIiIiI8hUGNShnCWLS6BDRdLMkSXl+wRkiIiIiIiIiIiIiylsY1KCcI1NCLhPgqAkDYBrBMChcoYIDAxtERERERERERERElGkMalDOkSkgaOIh3T0JSZ1i/Q6lE0TfJhAUjpAY1SAiIiIiIiIiIiKiTGJQg3KcpFGZrN/BZciJiIiIiIiIiIiIKDvEjHchIiIiIiIiIiIiIiKyPQY1iIiIiIiIiIiIiIgoX2BQg4iIiIiIiIiIiIiI8gUGNYiIiIiIiIiIiIiIKF9gUIOIiIiIiIiIiIiIiPIFBjWIiIiIiIiIiIiIiChfYFCDiIiIiIiIiIiIiIjyBQY1iIiIiIiIiIiIiIgoX5DbugD0lhJECIIA0UJYTZIkSFLuF4mIiIiIiIiIiIiI8jYGNSj3yZSQywQ4asIAmEcvDApXqODAwAYREREREWWKIACCIFhM40tTRERERG8WBjUo98kUEDTxkO6ehKSON01TOkH0bQJB4QiJPQ8iIiIiIsqAIACOQgJEbYzFdL40RURERPRmYVCDbEbSqCCp40y2WX63ioiIiIiIyDJBECBqY2C4fQjQ8KUpIiIiojcdgxpERERERESUfXllvTxNPF+aIiIiInoLMKhBRERERERE2cP18oiIiIgolzGoQURERERERNnD9fKIiIiIKJcxqEFERERERESvhevlEREREVFusTDrKRERERERERERERERUd7DkRpERERERET0RrO0kHmuLmJORERERFbDoAYRERERERHlf4IAQRBSbRIgQcBLrR56g2kEw0kphwIGBjaIiIiI8hkGNYiIiIiIiChfM0CAWm+ApDeYbBcNEgSDhNP3XyAiVm3c7mgnRwP/QnBXyriIOREREVE+w6AGERERERER5VuCIEBnkPAoUoWE2FiTNNcCchSTJCRo9IhT62xUQiIiIiKyJgY1iIiIiIiIKN/T6QzQpBqpoU31mYiIiIjyPzHjXYiIiIiIiIiIiIiIiGyPQQ0iIiIiIiIiIiIiIsoXbBrU0Ov1+O6779CwYUMEBQWhcePGWLp0qclCbZIkYeHChahTpw6CgoLw8ccf48GDBybniY6OxujRo1GtWjVUr14d48ePR3x8fC5fDRERERERERERERER5SSbBjVWr16NLVu2YPLkydi3bx/GjBmDNWvWYMOGDSb7bNiwAVOnTsW2bdvg4OCAvn37Qq1WG/cZM2YM/v33X6xbtw4rVqzA+fPnMXnyZFtcEhERERERERERERER5RCbBjUuXbqERo0a4f3334e3tzeaNWuGOnXq4MqVKwCSRmn8+OOPGDRoEBo3bgx/f3/MmTMHz58/x+HDhwEAd+/excmTJzF9+nRUrlwZ1atXx8SJE/Hbb7/h2bNntrw8IiIiIiIiIiIiIiKyIrktM69atSq2bduG+/fvo3Tp0vjnn39w4cIFfPnllwCAkJAQhIeHIzg42HiMi4sLKleujEuXLqFFixa4dOkSXF1dERgYaNwnODgYoijiypUraNKkSabLIwjWu7asSM43t/NPL7u00rJzTLbPl436sFVdvmlYj9bBerQO1qN1sB6tg/VoHaxH68gL9ch7SEREREREuc2mQY0BAwYgLi4OzZs3h0wmg16vx8iRI9G6dWsAQHh4OADA09PT5DhPT0+8ePECAPDixQt4eHiYpMvlcri5uRmPzyxPT5fsXopV5Fr+UQrAQQnI7Ey32ysAmQhHewUg2mW8Pbtp6R2jVAJ2Sti7O7/WJdr6Xr4pWI/WwXq0DtajdbAerYP1aB2sR+tgPVKeJogQBAFiqjkCJElCiqUUiYiIiIgyzaZBjf3792PPnj2YN28eypUrh5s3b2LmzJkoVKgQ2rVrl+vliYiItUnDWhCSOqO5kb8gCHDSamFI0AAp1iUBAMi0sNMboE7UAonqjLdnNy29Y+AEpV4PXdhdAKaVYVC6IkFySLeOcrMu32SsR+tgPVoH69E6WI/WwXq0DtajdeSFekwuw5smJiYGH3/8MfR6PfR6PXr16oXOnTvbulhZJwgQUg+nyc3hNTIl5DIBjpowmLXtFa5QIf22PRERERGRJTYNasyZMwcDBgxAixYtAAB+fn548uQJVq5ciXbt2qFgwYIAgIiICBQqVMh4XEREBPz9/QEAXl5eiIyMNDmvTqfDy5cvjcdnliTBpo3q3Mg/ZR8mdVYpuzdSJrZnNy3dY2QKCJp4SHdPQlLHv0pQOkH0bQIoHCFlopJsfS/fFKxH62A9Wgfr0TpYj9bBerQO1qN1sB6tz8nJCZs2bYKDgwNUKhVatmyJJk2awN3d3dZFyxRBADR6A9R6AyS9wSRN1BugzK2CZNC2FzLZticiIiIiSsmmC4UnJiaavTkkk8mMDVtvb28ULFgQZ8+eNabHxcXh8uXLqFq1KoCkdTliYmJw7do14z5//vknDAYDgoKCcuEqKCdIGhUkdZzxB5r4jA8iIiIiIrICmUwGBwcHAIBGowGAfPbluwCtXsKjSBVuh8Wa/IRGJ8CA9Ne2sza27YmIiIjImmwa1GjQoAFWrFiBP/74AyEhITh06BDWrVuHxo0bA0iaKqlXr15Yvnw5jhw5glu3buGLL75AoUKFjPuULVsWdevWxaRJk3DlyhVcuHABX3/9NVq0aIHChQvb8vKIiIiIiMgGzp07h08//RR16tSBn58fDh8+bLbPpk2b0LBhQwQGBqJTp064cuWKSXpMTAxat26N+vXro2/fvmbr+OUHOp0BGr3pjy7VyA0iIiIiovzGpkGNiRMnomnTppg2bRo+/PBDzJ49G126dMHw4cON+/Tv3x89evTA5MmT0bFjR6hUKqxZswZ2dq8WmJ47dy7KlCmD3r17Y8CAAahWrRq++uorW1wSERERERHZmEqlgp+fH6ZMmWIxfd++fZg5cyYGDx6MXbt2wd/fH3379kVERIRxH1dXV/z66684cuQI9uzZgxcvXuRW8YmIiIiIKB02XVPD2dkZEyZMwIQJE9LcRxAEDB8+3CTQkVqBAgUwb968nCgiERERERHlM/Xr10f9+vXTTF+3bh06d+6MDh06AACmTZuGP/74Azt27MCAAQNM9vXy8oK/vz/Onz+PZs2aZakcubkmt8V8M8g/veS00rJzTLrns1Ed5bUy5KTk63vTr5MyxmeBkvFZoJT4PFCyvPAsZDZvmwY1iIiIiIiIcpNGo8H169cxcOBA4zZRFBEcHIxLly4BAF68eAF7e3s4OzsjNjYW58+fR7du3bKcl6eni9XKnVXxzwC5XAaFwrTLJ5fLIAgCHOwVgGhnepC9ApCJcEydltb27KYplYCdAvbuzla40iTxz0TIFWlfr729HI66V+uiOChlsLdXwNPd0WplyOts+TxS3sJngZLxWaCU+DxQsvzwLDCoQUREREREb42oqCjo9Xp4enqabPf09MS9e/cAAE+ePMGkSZMgSRIkSUKPHj3g5+eX5bwiImJhi/XFZTIRSgA6nR5arc4kTafTQ5IkJCRqISWqUx2ohZ3eAHWiFkiZltb27KbpFRDVWsRHxFllAXZRFCHXG6DTpn29iYlaqFSvymAvKKHTGfDv0xgA5mVwVsqhgMEm9+91JL3dKJht8/BwRmRkLAxcUuWtJghJX1TZ6m8T5R18FiglPg+ULC88C8llyAiDGm8oQUiaust8O8eSERERERGlJygoCL/88strn0eSYJMOoTHPDPJOnSykkZbW9pRpAgApdV/jv89maSn+31YdZrlMhEqjw+k74VAlmgZCHO3kaOBfCO5KmVWCLrlFEAANRMRrdGZp8ZEqKCQR8nwYqCHrs9XfJsp7+CxQSnweKFl+eBYY1HgDCQLgKCRA1MaYJ4oyyKGHJveLRURERERkc+7u7pDJZCaLggNAREQEvLy8bFSqvCXpZX/zIITZdiS9NCUBUBskGPSmwwBkegOUgnmaoDfALqMFP3KJSq1HnNo8CJAfCYKAeI0Ox/55DlWqa/Jwc0CtUu75LlBDREREZAmDGm8gQRAgamNguH0I0MSbJjoXhODzDlf/ISIiIqK3klKpRKVKlXD27Fk0btwYAGAwGHD27Fn06NHDxqWzPUsBirSCEwAgM0iQAXgSnYCY6FiTNA8UQCmDZJbm4CKitEH6bxQ5v2C3NpVaZxaocdDobVQaIiIiIutjUONNpomHpI4z2SQonWxUGCIiIiKi3BEfH49Hjx4ZP4eEhODmzZtwc3NDsWLF0KdPH4wdOxYBAQEICgrC+vXrkZCQgPbt29uw1HmAIMAA8wBFWsEJY5okQas3QJMq4KH973PqNIWOCzsQERERUfYxqEFERERERG+Ua9euoVevXsbPM2fOBAC0a9cOs2bNwocffojIyEgsWrQI4eHhqFChAtasWcPpp/6TOgiRVnAiZVqWCWLSCHPRfAR50gLt2TstEREREb35GNQgIiIiIqI3Ss2aNXHr1q109+nRowenm7IRUWEHO7kAaMIsru9gULpAZXBgYIOIiIiILGJQg4iIiIiIiHKNQqGEqI1Hwp2T0KeaLldUOkHh3xSCwpELWhMRERGRRQxqEBERERERUa4RRAGSJCHk+QvEREeZpDm4uKG0LxcRJyIiIqK0MahBREREREREuc7SGh15ZRFxUcB/a36Yp3HNDyIiIiLbYlCDiIiIiIiI6D9KuQhRJkOkWg9Lo0WclHIoYGBgg4iIiMhGGNQgIiIiIiIi+o9cJkKl0eH0nXCoEnUmaY52cjTwLwR3pYxrfhARERHZCIMaRERERERERKmo1HrEqXUZ70hEREREuYpBDSIiIiIiIsozBCSvZyGYpXE9CyIiIiJiUIOIiIiIiIjyBJkoQBAFRGv00BvMFw3nehZERERExKAGERERERER5QmCKECnN+D0w3BExKhN0hzt5GhUoRDslHKT9SwEwXxEBxERERG9uRjUICIiIiIieosIACAIZsGAvBQaSLCwnoVSLkKUyRCp1gOQUmyX4JaLZROF5OmxTLdzaiwiIiKi3MGgRj4nCOZvJvFNJSIiIiIiskT2X19BrTfAoDed3klmkOBgi0JlklwmQqXR4fSdcKgSXwU8vD0c0ai4lNQ5ymFpBVYATo1FRERElFsY1MjHBAFwFBIgamNME0QZ5NBDY5tiERERERFRHiWIAiRJwpPoBMREx5qkeaAASuWFb+QFEQ5KAc52pt1VB6UMogCoUo3iSNDqc61oaQVWHO3kaOBfCO5KmcnUWERERERkfQxq5GOCIEDUxsBw+xCgiX+V4FwQgs87ufKmEhERERER5T9avQGaVCM1tHrzhblzm6iwg71CRA2POCS4mAYrHBQ6eCgcoZSLaRyde1IHVoiIiIgo9zCo8SbQxENSxxk/CkonGxaGiIiIiIgoe0SZAqImDrpbB5EQ89IkTeHqBrlra8hFvrxFRERE9DZjUIOIiIiIiIjyFG1iHDQq02l2tUqZjUpDRERERHkJgxpEREREREREr0kU/psi2MLsWJIkcQFxIiIiIithUIOIiIiIiIjoNSjlIkSZDJFqPQDz6IWTUg4FDAxsEBEREVkBgxpERERERESUPwgiHJQCnO1edWUdFDIIgj6dg3KeXCZCpdHh9J1wqBJNFxB3tJOjgX8huCtlkBjVICIiInptDGoQERERERFRnicq7GCvEFHDIw4JLq+CGM4OgJ0oQJbWAuKWAiFKGXJivXGVWo84tS7jHYmIiIgo2xjUICIiIiIiojxPlCkgauKgu3UQCTEvjdsdChaDGFQbgoUgRVqBEAeFDh4KRyjlFhbAICIiIqI8jUENIiIiIiIiyje0iXHQqGJefVa7pblvWoEQhasb5K6tIc+J4RpERERElKMY1CAiIiIiIqI3mlkgRCmzYWmIiIiI6HUwqEFERERERESUg0QBEAQBooXZriRJAtcPJyIiIso8BjWIiIiIiIiIcohSLkKUyRCp1gMwj144KeVQwMDABhEREVEmMahBRERERERElEPkMhEqjQ6n74RDlagzSXO0k6OBfyG4K2WQGNUgIiIiyhQGNYiIiIiIiIhymEqtR5xal/GORERERJQuCzN6EhERERERERERERER5T0MahARERERERERERERUb7AoAYREREREREREREREeULDGoQEREREREREREREVG+wIXCiYiIiIiIiGxEFABBECCmeuVQkiRIkm3KRERERJSXMahBREREREREZANKuQhRJkOkWg/ANILhbCeHIs2ohgRAMNkiCILlXYmIiIjeMAxqEBEREREREdmAXCZCpdHh9J1wqBJ1xu1ujkq8X6Ew4nQGpA52iAJgr5RDpdaZbJeJAgxgYIOIiIjefAxqEBEREREREdmQSq1HXIoghaOd3GKwAwA8XexQrZSHWVrydnDEBhEREb3hGNQgIiIiIiIiyoNSBzuApICHpbTk7URERERvOrZ6iIiIiIiIiN5waS1IDnBRciIiIspfGNQgIiIiIiIieoMpZGkvSA4ATko5FDAwsEFERET5AoMa+YDw3xs15ts5VyoRERERERGlTy4T0lyjw9FOjgb+heCulEFiVIOIiIjyAQY18jhBAByFBIjaGPNEUQY59NDkfrGIiIiIiIgon7G0RgcRERFRfsOgRh4nCAJEbQwMtw8BmnjTROeCEHzeSYp8EBERERERERERERG94RjUyC808ZDUcSabBKWTjQpDRERERERERERERJT7RFsXgIiIiIiIiIiIiIiIKDMY1CAiIiIiIiIiIiIionyBQQ0iIiIiIiIiIiIiIsoXGNQgIiIiIiIiIiIiIqJ8gQuFExERERER0dtHEOGgFOBsZ9otdlDKIAo2KhMRERERZYhBDSIiIiIiInqriAo72CtE1PCIQ4KL3iTNQaGDh8IRSjknNiAiIiLKixjUICIiIiIioreKKFNA1MRBd+sgEmJemqQpXN0gd20NOYdrEBEREeVJDGoQERERERHRW0mbGAeNKsZ0m1Jmo9IQERERUWZwPC0REREREREREREREeULDGoQEREREREREREREVG+wOmniIiIiIiIiOiNIgiAIJiviyJJEiTJBgUiIiIiq2FQg/IPQYQgCBAtjC9iw5SIiIiIiCh7xP8CAKn7Wvm1nyUIgFYQEa/RmaU5KeVQwJAvr4uIiIiSMKhB+YNMCblMgKMmDIB569OgdEOC5GD8LAgCkl/Kya8NcSIiIiIiopymlIsQZTJEqvVI3dfKrwEAQRAQr9Hh2D/PoVK/Cmw42snRwL8Q3JUySPntooiIiMiIQQ3KH2QKCJp4SHdPQlLHm6Y5uEPp1xCiLgaABEQp4KTVGpMNCleo4JDvGuJEREREREQ5TS4TodLocPpOOFSJb1YAQKXWIU5tPlqDiIiI8jcGNShfkTQqSOo4k22C0sk04OGghCFBk5SodILo2wSCwjHfNsSJiIiIiIhymkqtZwCAiIiI8gUGNeiNIWlUgDoOkNkBajUkAObLwhERERERERERERFRfsWgBhEREREREVFKgggHpQBnO9Mus4NSBpFvThERERHZFIMaRERERERERP8RFXawV4io4RGHBBe9SZqDQgcPhSOUctFGpSMiIiIiBjWIiIiIiIiI/iPKFBA1cdDdOoiEmJcmaQpXN8hdW0PO4RpERERENsOgBhEREREREVEq2sQ4aFQxptuUMhuVxjZEARAEAaKFgSmSJEGScr9MRERERAxq0JtPEC02xNkIJyIiIiIiskwpFyHKZIhU6wGYd5yclHIoYGCfioiIiHIdgxr0ZpMpIZcJcNSEIXVD3KBwhQoObIQTERERERGlIpeJUGl0OH0nHKpEnUmao50cDfwLwV0pg8QOFREREeUyBjXozSZTQNDEQ7p7EpI6/tV2pRNE3yYQFI5shBMREREREaVBpdYjTq3LeEciIiKiXMKgBr0VJI0KkjrO+FkA0pyWCuDUVERERERERERERER5EYMa9HZKZ1oqgFNTEREREREREREREeVFDGrQ2ymtaakATk1FRERERERERERElEcxqEFvtdTTUgH/TU1FREREREREaRIFcDpfIiIisgkGNYiIiIiIiIgo05RyEaJMhki1Hpam83VSyqGAgYENIiIiyhEMahARERERERFRpsllIlQaHU7fCYcqUWeS5mgnRwP/QnBXyjidLxEREeUIBjWIiIiIiIiIKMtUaj3i1LqMdyQiIiKyIguzXxIREREREREREREREeU9DGoQEREREREREREREVG+wOmniIiIiIiIiN4AogA4KmVIvUK3g0IGUcjdcgiCADHVa5SSJHHxcCIiInptDGoQERERERER5XMKmQgvRSJqF0qAVm8wSXO2N8BNoYBSnvOTNSjlIkSZDJFqPQDTCIaTUg4FDAxsEBER0WthUIOIiIiIiIgoswQRDkoBznavutMOytwdCWGJXCZAromF7tZBJMTFmKTZu7tDXq0l5LlQSLlMhEqjw+k74VAlvlpE3NFOjgb+heCulEFiVIOIiIheA4MaRERERERERJkgKuxgrxBRwyMOCS5643YHhQ4eCsdcGQmREW1CHDQq06CGzlGZ6+VQqfWIU+sy3pGIiIgoixjUICIiIiIiIsoEUaaAqIlLGg0R89K4XeHqBrlr61wZCUFERET0tmNQg4iIiIiIiCgLtImmoyG0SpkNS0NERET0dmFQg4iIiIiIiIhynCgAgiBAtDhLlwTA8kgXSZK4uDgREREZMahBRERERERERDlKKRchymSIVOuRFMB4RRQAe6UcqjTW4HBSyqGAgYENIiIiAsCgBhERERERERHlMLlMhEqjw+k74VAlmgYvPF3sUK2Uh8U0Rzs5GlUoBDulHFKqqAZHcBAREb2dGNQgIiIiIiIiolyhUusRpzYPXKSVlt4ID47gICIiejsxqEFEREREREREeVJaIzzSG8EhCJbX5iAiIqI3g8XluXLTs2fPMGbMGNSsWRNBQUFo1aoVrl69akyXJAkLFy5EnTp1EBQUhI8//hgPHjwwOUd0dDRGjx6NatWqoXr16hg/fjzi4+Nz+UqIiIiIiIiIKCckj+JI/tHpDcYRHFEa05+XWj0MaSw6/mqxcvMfxkKIiIjyB5uO1Hj58iW6deuGmjVrYvXq1XB3d8fDhw/h5uZm3Gf16tXYsGEDZs2aBW9vbyxcuBB9+/bFvn37YGdnBwAYM2YMwsPDsW7dOmi1WowfPx6TJ0/GvHnzbHVpRERERERERJRDMrNGR+ooRXpTWQGczoqIiCi/sGlQY/Xq1ShSpAhmzpxp3FaiRAnj/0uShB9//BGDBg1C48aNAQBz5sxBcHAwDh8+jBYtWuDu3bs4efIktm/fjsDAQADAxIkTMWDAAHzxxRcoXLhw7l4UEREREREREeWK9NboSC29QIijnRwN/AvBXSkzm86KiIiI8habBjWOHj2KOnXqYNiwYTh37hwKFy6M7t27o3PnzgCAkJAQhIeHIzg42HiMi4sLKleujEuXLqFFixa4dOkSXF1djQENAAgODoYoirhy5QqaNGmS6fLYaqhpcr4Z5Z9eclpp2Tkmr58v3WoSXv1XkCwnZakMb+nw48w+k5Q+1qN1sB6tg/VoHaxH62A9WkdeqEfeQyLKjywFQoiIiCj/sGlQ4/Hjx9iyZQv69OmDTz/9FFevXsX06dOhUCjQrl07hIeHAwA8PT1NjvP09MSLFy8AAC9evICHh4dJulwuh5ubm/H4zPL0dHmNq3l9aeYfpQAclIDMznS7vQKQiXC0VwCiXcbbs5uWF86XxWMcHeyyfz6lErBTwN7dGW87W/9OvClYj9bBerQO1qN1sB6tg/VoHaxHIiIiIiJ6m9g0qCFJEgICAjBq1CgAQMWKFXHnzh1s3boV7dq1y/XyRETE2mTuTEFI6oxayl8QBDhptTAkaAC12jRRpoWd3gB1ohZIVGe8PbtpeeF8mT1GrYajgx1UCeqkKVKzcz69AqJai/iIuLd22HF6zyRlHuvROliP1sF6tA7Wo3WwHq0jL9RjchmIiIiIiIhyi02DGgULFkTZsmVNtpUpUwYHDhwwpgNAREQEChUqZNwnIiIC/v7+AAAvLy9ERkaanEOn0+Hly5fG4zNLkmDTjrWl/FMO6U9dtJSj/aVMbM9uWl44X2aPMSZKSf/72mV4y79osfXvxJuC9WgdrEfrYD1aB+vROliP1sF6JMojBBEOSgHOqdZzcFDKIHKqNiIiIiKrsWlQo1q1arh//77JtgcPHqB48eIAAG9vbxQsWBBnz55FhQoVAABxcXG4fPkyunXrBgCoWrUqYmJicO3aNQQEBAAA/vzzTxgMBgQFBeXi1RAREREREdHbSFTYwV4hooZHHBJc9CZpDgodPBSOUMpFG5WOMksUkmZLEC3cKkmSGEAmIiLKI2wa1Ojduze6deuGFStWoHnz5rhy5Qq2bduGr776CkBSY6JXr15Yvnw5SpYsCW9vbyxcuBCFChVC48aNAQBly5ZF3bp1MWnSJEybNg1arRZff/01WrRogcKFC9vy8oiIiIiIiOgtIMoUEDVx0N06iISYlyZpClc3yF1bQ87hGnmaUi5ClMkQqdbDfDw/4KSUQwEDAxtERER5gE2DGkFBQViyZAnmz5+PpUuXwtvbG+PHj0fr1q2N+/Tv3x8JCQmYPHkyYmJi8M4772DNmjWws3u1uPPcuXPx9ddfo3fv3hBFER988AEmTpxoi0siIiIiIiKit5Q2MQ4aVYzpNqXMRqWhrJDLRKg0Opy+Ew5Vos4kzdFOjgb+heCulL216y4SERHlJTYNagBAgwYN0KBBgzTTBUHA8OHDMXz48DT3KVCgAObNm5cTxSMiIiIiIiKit4RKrUecWpfxjkRERGQznNSTiIiIiIiIiIiIiIjyBQY1iIiIiIiIiIiIiIgoX7D59FNERERERERERGR9gpA0rbfpNgGCAEiSxIXPiYgoX2JQg4iIiIiIiIjoDSMIgFYQEa95tUaIKlIFtUYPAHBSyqGAgYENIiLKdxjUICIiIiIiIiJ6wwiCgHiNDsf+eQ7Vf4ufOzgokZCggaOdHA38C8FdKYPEqAYREeUzDGoQEREREREREb2hVGod4v4LahhkMmOAg4iIKL/iQuFERERERERERERERJQvcKQGERERERERUU4SRDgoBTjbmXbBHZQyiEIaxxARERGRRQxqEBEREREREeUQUWEHe4WIGh5xSHDRm6Q5KHTwUDhCKeckCkRERESZxaAGERERERERUQ4RZQqImjjobh1EQsxLkzSFqxvkrq0h53ANIiIiokxjUIOIiIiIiIgoh2kT46BRxZhuU8psVBoiIiKi/ItjXImIiIiIiIiIiIiIKF9gUIOIiIiIiIiIiIiIiPIFBjWIiIiIiIiIiIiIiChfYFCDiIiIiIhs6sSJEzh//rzx86ZNm9CmTRuMHj0aL1++TOdIIqLcIQqAIAgQRdMfgWu8ExER5ToGNYiIiIiIyKa+/fZbxMfHAwBu3bqFWbNmoX79+ggJCcGsWbNsXDoietsp5SJEmQyRaj2iNKY/WkFkYIOIiCiXyW1dACIiIiIieruFhISgbNmyAICDBw+iQYMGGDVqFK5fv44BAwbYuHRE9LaTy0SoNDqcvhMOVaLOuN3RTo4G/oXgrpRBkiQblpCIiOjtwqAGkSWC+N/QYvMkSZLA9ioRERGR9SgUCiQmJgIAzpw5g7Zt2wIA3NzcEBcXZ8OSERG9olLrEafWZbwjERER5SgGNYhSkykhlwlw1IQBMI9eGBSuUMGBgQ0iIiIiK6lWrRpmzpyJatWq4erVq/juu+8AAA8ePECRIkVyvTxPnz7FF198gYiICMhkMnz22Wdo3rx5rpeD3hKCCAelAGe7V91zB6UMIqc0yhderbVhnsYX4oiIiHIGgxpEqckUEDTxkO6ehKSON01TOkH0bQJB4cjhxURERERWMnnyZEybNg0HDhzAlClTULhwYQBJC4jXrVs318sjk8kwfvx4VKhQAeHh4Wjfvj3q168PR0fHXC8LvdlEhR3sFSJqeMQhwUVv3O6g0MFD4QilnMtg5mUp19qw9EKck1IOBQwMbBAREVkZgxpEaZA0Kkhq0+kO+LIUERERkfUVK1YMK1euNNs+fvx4G5QGKFSoEAoVKgQAKFiwINzd3fHy5UsGNcjqRJkCoiYOulsHkRDz0rhd4eoGuWtryDlcI09La60NgOttEBER5SS+9pGHJA1ZNf0RBDZiiYiIiOjNVqFCBURERJhtj4qKQoUKFbJ8vnPnzuHTTz9FnTp14Ofnh8OHD5vts2nTJjRs2BCBgYHo1KkTrly5YvFc165dg8FgQNGiRbNcDqLM0ibGQaOKMf5oE7mWTH6SvNZGyh+VWpdiaipLfX1bl5qIiCj/YlAjDxAEAAlRcNKGmf046p5DDn2G5yAiIiIiyq/SeotZo9FAoVBk+XwqlQp+fn6YMmWKxfR9+/Zh5syZGDx4MHbt2gV/f3/07dvXLLASHR2NsWPH4quvvspyGYhemyDCQSmDs53c5IfrbeQPKaemitKY/2gFkYENIiKibOL0U3mCACTGQLp9CJIm1RoOzgUh+LwDtnaIiIiI6E3z448/Akh6k/nnn382md7JYDDg3LlzKFOmTJbPW79+fdSvXz/N9HXr1qFz587o0KEDAGDatGn4448/sGPHDgwYMABAUkBl8ODB6N+/P6pVq5blMgC2a8Ib82UXIt9Ka60NIOvrbfAxsI3MTk1laS2OnJL8tyH13yZ+3fD2SetZoLcTnwdKlheehczmzaBGHiJp4s3XcFA62ag0lCZB/G8IselmSZK4ABwRERFRFvzwww8AktpRW7duhZiigaVQKODt7Y1p06ZZNU+NRoPr169j4MCBxm2iKCI4OBiXLl0ylufLL7/Ee++9h7Zt22Y7L09Pl9ctbrbFPwPkchkUCtMun1wuAwQhS2nZOSavny8vlCG9NKWdPURtPKR/D0MbG2NyjL2LKxQF2qCgmwNkcplxewFnO4hiAuRy0SwfICl4aG8vh6POtNNib6+ATCbCwV4BQ4rfwbS2ZzctL5zPFmWQBBEGmcwkTRJF2Nsr4Ome8+v0qCJVcHBQmpTBwcEODkoZ7OwU8PTgWkFvM1v+O0V5D58HSpYfngUGNYiyQqaEXCbAUROG1G/UGBSuUMGBgQ0iIiKiTDp69CgAoGfPnliyZAnc3NxyPM+oqCjo9Xp4enqabPf09MS9e/cAABcuXMC+fftM1uOYM2cO/Pz8spRXRESsTdqGMpkIJQCdTg+t1vQNcZ1OD0hSltKyc0xeP19eKENmzpcYHwNVTJTJMXZ2drCTCwhyikaC8tUoDmcHDZSCAMkgmZwr+YVHSZKQmKiFSqU2OV+iXIBeb0BCohaqRG2G27OblhfOlxfKAAD2ghI6nQH/Po1B6n6ls1IOBQxW+dshCALUGj0SEjRQqXUQhKSARkKCGqJeDrVai4iIOC5k/hYShKQvLW317xTlLXweKFleeBaSy5ARBjWIskKmgKCJh3T3JCR1iqnClE4QfZtAUDiyQUhERESURRs2bLB1EUxUr14d//zzz2ufR5Jgkw6hMU82S99IokwBURMH3a2DSIh5adzuULAYxKDaZtM28DHIe9KamirltFTW6FeaPQuS6X9Tb6e3j63+naK8ic8DJcsPzwKDGkTZIGlUJlOFcdpBIiIiouzT6/XYuXMn/vzzT0RERMBgMJikJ6+9YQ3u7u6QyWRmi4JHRETAy8vLavkQ5TRtYhw0qldTU2nVGYx0EkQ4KAU425l+DcCFx21HpdYjTq3LeEciIiIywaAGERERERHZ1IwZM7Br1y7Ur18f5cuXh5CDqxMqlUpUqlQJZ8+eRePGjQEkLUp+9uxZ9OjRI8fyJbIlUW69hceJiIiIbI1BDSIiIiIisqnffvsN3333HerXr2+V88XHx+PRo0fGzyEhIbh58ybc3NxQrFgx9OnTB2PHjkVAQACCgoKwfv16JCQkoH379lbJnyivEeWWp6wCAIWrG+SurSHncI08QRSS1sIQLcSYJElKczoQ4b/jTLfxnhIR0ZspW0GNp0+fQhAEFClSBABw5coV7NmzB+XKlUOXLl2sWkAiIiIiInqzKRQK+Pj4WO18165dQ69evYyfZ86cCQBo164dZs2ahQ8//BCRkZFYtGgRwsPDUaFCBaxZs4bTT9EbL/WUVQCgVcpsVBpKTSkXIcpkiFTrYWk1FKc0FhEXBEAriIjXmE5lJRMFGDhZMhERvYGyFdQYPXo0OnfujLZt2yI8PBx9+vRB+f+3d+fxTdXZ/8ff994kbbpCW3DEDTdAxbI4yrAogqjjAi6M21dlRBR3ZxQU1HEARcFtRhl3VMYFBh0VfqKM64grCjpuqDAIKjiolJ02bdb7+6M2NG2StiFtkub1fDyq5N7cm3Nvk/Tee+7nnP3314IFC1RRUaHLL7882XECAAAAaKfOP/98PfHEE/rzn/+clDuL+/XrpxUrVsR9zjnnnEO5KQBpJVYDcSl+E3HDMFTlC+jN5evlqdejo7QwR327ljTuGA4AQIZLKKmxcuVKlZeXS5L+9a9/af/999fcuXP17rvvatKkSSQ1AAAAADTbxx9/rA8//FBvv/229t9/fzkckacp9957b4oiA4C2l2gDcY83ELFcXg4VxwEA7VNCf+ECgYBcLpck6f3339fQoUMlSfvss48qKiqSFx0AAACAdq+oqEhHH310qsMAAAAAkAESSmrst99+mjt3ro488ki9//77+uMf/yhJWr9+vTp06JDE8AAAAAC0d3U9LwAAAACgKWYiC40fP15PP/20zj33XJ1wwgnq0aOHJOnf//53uCwVAAAAAAAA0pNp1PbjMM3IH1pwAADSXUIjNfr166cPPvhAlZWVKi4uDk8//fTTlZeXl7TgAAAAALR/Q4cOjdsg/I033mjDaACg/XM5TJmWpU3eoKTIxuP5LoecCqlBP3IAANJGQkmNUaNG6d57741IaEhShw4ddOmll+qJJ55ISnAAAAAA2r/f//73EY8DgYC++uorvfvuuxozZkyKogKA9sthmfL4AnpvZYU8NZHNxYf06KyOLks2WQ0AQJpKKKmxZMkS+f3+RtO9Xq8+/vjjnQ4KAAAAQPZomNSoM3v2bC1btqyNowGA9LSjXFTk9Hgj3Zri8QZV6Q00/UQAANJIi5Iay5cvD//7m2++UUVFRfhxKBTSO++8o1122SV50QEAAADIWkcccYTuuusuGokDyHrxykVZpqGQaIQBAMgeLUpqnHzyyTIMQ4ZhRL2bKjc3V3/605+SFhwAAACA7PXyyy+rQ4cOqQ4DAFIuVrkoSSotzFHfriWiwzcAIFu0KKnxxhtvyLZtDRs2TP/85z9VUlISnud0OlVaWirLspIeJAAAAID2q+7mqTq2bWvDhg3atGmTJk2alMLIACC9RCsXlZeTUGVxAAAyVov+8u22226SIstQAQAAAMDOGDZsWMRjwzBUUlKiww47TPvuu2+KogIAAACQjhJO53/33Xf68MMPtXHjRoVCoYh5l19++U4HBgAAACA7cP4AAGhNhhG7obpt27LtqLMAAGkqoaTGM888o8mTJ6tjx44qKyuL+MNgGAYnJQAAAABaJBgM6vXXX9eqVaskSfvvv7+GDh1KeVsAaGPmLwkA02w8LxMTAIYh+Q1TVb5A1Pn5LoecCmXcdgFANksoqfHAAw/oj3/8o8aOHZvseAAAAABkme+//15jx47Vzz//rL333luS9PDDD+tXv/qVHn74Ye25554pjhBo5wxTbpehgnq9GdwuSyZ9p7OOy2HKtCxt8gYlNb7Kn4kJAMMwVOUL6M3l6+WJ0o9kSI/O6uiyZGfSRgFAlksoqbF161Ydd9xxyY4FAAAAQBaaOnWq9thjDz399NPq0KGDJGnz5s265pprNHXqVD388MOpDRBox0xnjnKdpg4rqVR1YTA83e0MqMSZJ5cjyu36aLcclimPL6D3VlbIU9O+EgAeb6BRk3UAQGZKKKnx29/+Vu+++67OOuusZMcDAAAAIMssXbo0IqEhSR07dtT48eM55wBamWk5ZfoqFVjxqqq3bQ1PdxYVy1E0Qg6Ga2QljzdIAgAAkLYSSmrstddeuueee/TZZ5+pW7ducjgiVzNq1KikBAcAAACg/XO5XKqqqmo0vaqqSk6nMwURAdnHX1Mpn2fbjscu+tmgZWI1487EPhwAgPSWUFLj6aefVl5enpYsWaIlS5ZEzDMMg6QGAAAAgGY78sgj9ec//1m33HKLysvLJUmfffaZJk+erKFDh6Y4OgBAU+I1487EPhwAgPSWUFLj3//+d7LjAAAAAJCl/vSnP2nChAk644wzwqPAg8Gghg4dqhtuuCHF0QEAmhKrGXem9+EAAKSnhJIaAAAAAJAsRUVFeuCBB/T9999r1apVkqR9991Xe+21V4ojAwC0BM24AQBtIaGkxnXXXRd3/rRp0xIKBgAAAED22muvvUhkAAAk0aMDABBbQkmNbdu2RTwOBAJauXKltm3bpt/85jdJCQwAAABAdrjiiit08MEHa+zYsRHTZ86cqS+++EIzZsxIUWQAgFSgRwcAIJ6Ekhr33Xdfo2mhUEiTJ0/WHnvssdNBAQAAAMgeS5cu1eWXX95o+hFHHKFZs2alICIAQCrRowMAEI+ZtBWZps477zw9/vjjyVolAAAAgCzg8XjkdDobTXc4HKqsrExBRACAaMxfSkKZZuRPtDJRTS1Tu1z816vr0VH346FfBwBASUxqSNLatWsVCPAHBgAAAEDzdevWTQsXLmw0feHChdpvv/1SEBEASZJhyu2yVJDjiPhxuyyZTVyMRvvjcpgyLUubvEFt9kX+bPUHFVLjN0W8ZTb7gvIbZpOJDQAAGkqo/FTDRuC2bauiokKLFi3SKaeckpTAAAAAAGSHSy+9VFdccYXWrl0b7tG3ePFivfTSS7rnnntSHB2QnUxnjnKdpg4rqVR1YTBintsZUIkzTy5HUu+TRJpzWKY8voDeW1khT03kDa2lhTnq27VEDTMU8ZahlBQAIFEJJTW++uqriMemaaqkpEQTJ07UyJEjkxIYAAAAgOwwdOhQ3XfffXrwwQf1yiuvKCcnR927d9esWbN02GGHpTo8ICuZllOmr1KBFa+qetvWiHnOomI5ikbIwXCNrOTxBlXpbZygaOkyAAAkKqGkxpNPPpnsOAAAAABksSOPPFJHHnlkqsMA0IC/plI+z7bIaS4rRdEAAAAkmNSos2nTJq1evVqStM8++6ikpCQpQQEAAAAAAAAAADSUUFLD4/Ho5ptv1v/7f/9PoVBIkmRZlk466STdeOONcrvdSQ0SAAAAAAAAMA3JMAyZUVq62LYt2nMAQPuXUFev6dOna+nSpXrggQf00Ucf6aOPPtL999+vpUuXavr06cmOEQAAAAAAAFnO5TBlWpY2eYPa7Gv84zfMhr3KAQDtUEIjNV555RXNmDFD/fr1C08bPHiwcnJy9Mc//lFTpkxJWoAAAAAAAACAwzLl8QX03soKeWoaNysf0qOzOros2QzXAIB2LaGkRk1NjcrKyhpNLy0tVU1NzU4HBQAAAAAAAETj8QZV6Q00/UQAQLuUUFKjd+/emjFjhm6//Xbl5ORIqk103Hvvverdu3cy4wPaBeOXmp/RUPMTAABku2nTpkWdbhiGcnJytOeee+qoo45Shw4d2jYwAAAAAGknoaTG9ddfrwsuuEBHHHGEevToIUlavny5XC6XHnvssaQGCGQ6w5DyjGqZ/m1R54ecRfLITWIDAABkra+++kpfffWVQqGQ9t57b0nSt99+K8uytM8++2jOnDm67bbbNGfOHO23334pjhYAAABAKiWU1OjevbteffVVLViwQKtXr5YknXjiiRo+fLhyc3OTGiCQ6QzDkOnfptB/X5N8VZEzXfkyux0tw5lHzU8AAJC16kZhTJs2TQUFBZKk7du364YbbtAhhxyi008/XePGjdO0adP06KOPpjhaAAAAAKmUUFLjoYceUmlpqU4//fSI6c8++6w2bdqksWPHJiU4IKMYZm0Cw2wwua7slK9Ktrcycl4bhQYAAJDOHn30Uc2aNSuc0JCkwsJCXXHFFTr//PP1+9//XpdddpnOP//8FEYJAEh35i+ln+ufl8cqBQ0AyFxm009p7Omnn9Y+++zTaPr++++vuXPn7nRQQMaxXHJYhvJ8PynfH/mTF1gvh4KpjhAAACBtVVZWauPGjY2mb9q0SZWVtTeFFBUVye/3t3VoAIAM4XKYMi1Lm7xBbfbt+NnqDyrELYUA0K4kNFKjoqJCnTp1ajS9pKREFRUVOx0UkHEspwxflexV78j2NigxVdBJxp6H1DbXAAAAQCNDhw7V9ddfr4kTJ+rggw+WJH3xxRe67bbbNGzYMEnS559/rq5du6YwSgCZxDSkPJel+s0L3S5LJqdl7ZbDMuXxBfTeygp5agLh6aWFOerbtYRzcgBoRxJKauy66676z3/+oz322CNi+scff6zOnTsnJTAgE9k+T+MSU678FEUDAACQGW666SZNmzZNV111lYLB2hGulmXplFNO0XXXXSdJ2meffXTLLbekMkwAGcJpmSpz1mhg52r5g6HwdLczoBJnnlyOhIpWIEN4vEFVenckNfJyErr0BQBIYwl9s5922mm69dZbFQgE9Jvf/EaStHjxYt1xxx3UuQUAAADQIvn5+Zo6daquu+46rV27VpK0xx57KD9/x80hBxxwQKrCA5BhHJYhh2+7AiteVXXltvB0Z1GxHEUj5GC4BgAAGS2hpMYFF1ygLVu2aMqUKeG6tjk5Obrgggt00UUXJTVAAAAAANkhPz9fHTp0CP8bAHaGv7pSPs+OpIbfZaUwGgAAkCwJJTUMw9A111yjSy+9VKtWrVJubq66du0ql8uV7PgAAAAAtHOhUEj333+/Zs2aJY/HI6k2qTF69GhdcsklMk1KxQBAe2QatdeYGn7NG/S/AADEsVOFBfPz81VeXp6sWAAAAABkob/+9a969tlnNW7cOPXt21dSbb++e++9Vz6fT1dddVWKIwQAJJvLYcq0LG3yBiXZEfMs01BIJDYAANHRLQkAAABASs2bN09Tp07VUUcdFZ7Wo0cP7bLLLpoyZQpJDSAdGabcLkMFDZowu12WaFmB5nBYpjy+gN5bWSFPTSBiXmlhjvp2LZEYsQEAiIKkBgAAAICU2rp1q/bZZ59G0/fZZx9t3bo1BREBiMd05ijXaeqwkkpVFwYj5rmdAZU48+RyUDYOzePxBlXpjUxq5OVwuQoAEBt/JQAAAACkVI8ePTR79mz96U9/ipg+e/Zs9ejRI0VRAYjFtJwyfZUKrHhV1dsiE4/OomI5ikbIwXANAADQSkhqAAAAAEipa665RhdddJHef/999e7dW5L06aef6scff9TMmTNTGxyAmPw1lfJ5tkVOc1kxn28aUp7LkuzI/gmUrAIAAC1BUgMAAABASh122GF6+eWXNWfOHK1evVqSdPTRR+v//u//tMsuu6Q4OgAtFqXfRpHboU7OGg3sXC1/MBTxdEpWIdMYhmTE6Pdh23bDvB0AIMlIagAAAABIuV122aVRQ/CffvpJN954o26++eYURQWgpWL12yhwS/lBr0L/fU3V2ylZhcxlGJLfMFXlC0Sdn+9yyKkQiQ0AaEUkNQAAAACkpS1btujZZ58lqQFkkFj9NtydusgsH6igt2Ulq4B0YxiGqnwBvbl8vTxRGpwP6dFZHV2WbLIaANBqSGoAAAAAAICkathvw+8tTmE0QGzmL6WkzCjVz+KVkvJ4A6r0Rh+tEUusslVtWbKqfgyGYWh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tUcNeF55NHnl9wbRoqh1PW/boSHexkjuSZNt2o6bjAAAAAAAAzUFSAwDUdDO1dG4s1x417HXhdrtUXe1Li6ba8aSiR0dbaOnok3jJHUnKdznkVIjEBgAAAAAAaDGSGgBQT7xmamh7db0uQpYljzeQMX0u2lOPjkRGn8RL7uTlODSkR2d1dFmyyWoAAAAAAIAWyswrLAAAoE3szOiTaMkdidJUAAAAAAAgcSQ1AABAk5I1+oTSVAAAAAAAYGeQ1AAAAG2mLUtTGUbsvh+MCAEAAAAAIDOR1ACQXQxTbpehggZ3mLudlgwjmKKggOwTqzRVshiG5DdMVfmivwYjQgAAAAAAyEwkNQBkDdOZo1ynqcNKKlVdGJnAKHBLOaYhy4x+VzeAzGIYhqp8Ab25fL08Ucpm0awcAAAAAIDMRFIDQNYwLadMX6UCK15V9batEfPcnbrILB8Yq98xgAzl8QZadUQIAAAAAABoWyQ1AGQdf02lfJ5tkdO8xSmKBgAAAAAAAEBzkdQAgFZiGpLbZTXu3+GyRJUrAAAAAAAAoOVIarRjhqSGtXS4jgq0DadlqsxZo8M6VqomP7L0jdsZUIkzTy6HmaLoAAAAAAAAgMxEUqOdsiV5Q7ZCwVDEdCtky52akICs4rAMOXzbFVzxqqq3bomY5ywqlqNohBwM1wCazTBqm39HY9u26PcNAAAAAEB2IKnRHhmGQpLWbanWti3bI2aVqIO6cuUHaDNR+3e4rBRFA2Qmw5D8hqkqX/SG3/kuh5wKkdgAAAAAACALkNRox/zBkHwNRmr4GzwGACDdGYahKl9Aby5fL483MrGRl+PQUQd0Vo7LIbteViPWqA4AAAAAAJDZSGoAwM4wTLldRuNm4E5LhhFMUVBA++TxBlTZIKnhcpgyLUubvEHVFl+sZZmGQnE6SZm/lLMyo7S2oZwVAAAAAADpi6QGACTIdOYo12nqsJJKVRdGJjAK3FKOacjkbnGgVTksUx5fQO+trJCnZkfCo7QwR327ltTWrmogViKkDuWsAAAA0lg63FgWKwaXJYcp5bksNTyYdLsspXVbxTjblNZxtwLTaPw7zMb9EA/7CKlGUgMAEmRaTpm+SgVWvKrqbVsj5rk7dZFZPlBmrL/oUQ4YW+MAINqBRmu9FpBKHm8wYhRHXk7sQ5xYiZC65Yb06KyOLiuinBUAAABSrzk3llmtfKITL4b8nJC6uKp0+C418gYi57mdAZU48+RyRBkqnGLxtimd424NTstUmbNGAztXR5Rwz7b9EA/7COmApAYA7KSozcC9xTGfH+uA0e0MqNSVrw55zqQkIWIdaNS9FgcbyHYNEyE7wzDi9fGwpSilsChzBQAA0DLNubGstQfLNxWDo2CgQv9tPM9ZVCxH0Qg50vDusnjblM5xtwaHZcjh2167Lyp3nOdn236Ih32EdEBSAwDaWKwDxpySTsovO1H9yzyqLm58d0y0hEe8ZEesAw2Jgw0gnlj9NmKN3DAMyW+YqvI1TpCYhpTrcjRqcC5JBTkOOWOsM9kJj1hJFxIrAAAgE7X0xrK2jiHqPJfVZrElKlPjbg3+6sh9ka37IR72EVKJpAYApEjDA8ZgUXHMu2NiJTyaM+Ki4YGGxMEGEEu8fhv5LodcCjVaxjAMVfkCenP5+kbJi7reHg1LXRXnuXTkAbuoMhBq9Dp1r5Wsvh7xki70DwEAAAAAZBqSGgCQZqLdHRMr4cGIC7Q30UZJxC7rlHyx+m3U9dpwxUkIeryBRuWs6np7ROv50VZ9PWIlXegfAgAAAADIRCQ1ACCDNEx4MOIC7UmsURKWaSgUpSdFa0pmv410eJ3a12qcdAEAAAAAINOQ1AAAtCrTkPJcVlKan6N9izVKoq6EU6t3fUwjsfp6SPTBAAAAAABkN5IaAJDpDFNul6GCnMivdLfTkmEEYyzUNpyWqTJnjQZ2rpY/GNmLoDn9QJCdopVqyibx+npI6d0HI1ZDcolkDAAAAAAgObLrKgEAtDOmM0e5TlOHlVSqujAygVHglnJMQ1YKh0M4LEMO3/baXiCVkX1CnEXFchSfrMJch3z+yNgZxYFsFmvEipTefTDiNSSX0jsZAwAAAADIHCQ1ACCDmZYzagNxSXJ36iKzfGBaVOzxV0dpfp6fFzMhwygOIHa/jdilqWwpSu+RRButt3TURayG5FJ6J2MAAAAAAJmFpAYAtAMNG4hLkt9bnKJomideQsZZVCxH0Qg5GK4BRIhVmso0pFyXo1EyQYrfaD1WgsQwJJ9tqDKBURc0JAcAAAAAtCaSGgCAlIqakHFZKYoGSG9NNVOPVrIqVqP1eL07LNOQPyS9xagLAAAAAECaIanRDhhSxIUK7msGkAqmIeW5LNW/dTsdmpUDyWLWK8dkGEb4T2+i5Z12Rqxm6tFKVsVqtB6vd0ddIsTji14CCwAAAACAVCGpkeFsSd6QrVAwFJ5mhWy5UxcSgCzktEyVOWs0sHO1/PW+j9KhWTmQDPVHNVRt8sjr25Gsi1feKRO0JBECAAAAAECqccaayQxDIUnrtlRr25bt4ckl6qCulIMA0IYcliGHb3ttf4zKHaWk0qlZObAz6o9qsA1T1dW+8LxY5Z2w8+I1K4/VGF2K3sgcAAAAANA+kNRoB/zBkHz17oyuf5c0AMRkmHK7DBXUuyPb7bLkMBuXkaqb19SAC391ZH+MdG9WDrSUxxtUyLQj+kxk46iGaA3Gk12GyzAkv2GqKkqz8niN0SWpIMchZ5SsBskOAAAAAMh82XcWDgCQ6cxRrtPUYSWVqi7cUUYnPyekLq4qHb5LjbyByF4YbmdAJc48uRxmw9UByCKxGownuwyXYRiq8gX0ZpRm5fEaoxfnuXTkAbuoMhBSwwbo+S6HnAq1SWIj1igTEisAAAAAsHNIagBAFjItp0xfZW25qG1bw9PdnbrIUTBQof9GTpckZ1GxHEUj5KA/BpDVYjUYb60yXB5vIGbPj1j9QKLFl5fj0JAendXRZclu5axCvFEmbZlYAQAAAID2iKQGAGQxf030clENp0uS32W1aWwA0lvDhMLOlOGKNqphZ8tZRUt4tJVYo0zaMrECAAAAAO0VSQ0AQHqq1/PD7bJkBh3N6usBILPEGtWQ7HJWzYkj2eWioo0yAQAAAADsHJIa6cQwGp9M//LYqPfv8Ky2iQoA2lzDnh8OR0CBQJC+HkA7FGtUQ2uVs4oeA+WiAAAAACBTkNRIA4Yh+YIheYMh2cFQxDwrGJLLkLwhW6GG80K23G0ZKAC0kYY9PxwOS4FAsM37epiGlOeyVP9qpttlyWE2nl43j5EkQGIajmrYmXJWLZUu5aJijRaRaDAOAAAAAHVIaqQFQ/6grTWbPKrevj1iTok6qGvI1rot1dq2Jco8zm4BtGN1vT1sp0N+f6C2r0e9slT1JTuh4LRMlTlrNLBztfz1ksr5OSF1cVXp8F1q5A0EI2NgJAkQk/nLBXuz3sfDiDZKNYVSWS4q3mgRiREjAAAAAFCHpEYaCQRC8jUYjVF3Ic0fjD0PANpMrISC05JhBGMslDwNy1JFxpDchILDMuTwba8dLVK5o2m6u1MXOQoGKvTf2lEk9bX1SBIgU7gcpkzL0iZvUFLtVXnPJo+8vmCb985IV7FGi0g0GAcAAACA+khqAACaJV5CocAt5ZiGrFa+mN+wLFV9zqJiOYpPVmGuQz7/jvh2tlyUv7p2tEj4sbe49v81kdMl1Y4kAdCIwzLl8QX03soKeWpqL9i73S5VV/vatHdGtNEiUuyST6lAc3EAAAAAiI+kBgCgWeIlFNydusgsH9gW1yQlRU8oBPPzoiZdKBcFpA+PNxi+YB+yLHm8gTbrnRFttEidREeLxOqBkU5JEgAAAABob0hqAABaJOoIhV9GL6RSrKQL5aIASNFHi9SJN1ok9ugOyWcbqozSA4OSWgAAAADQekhqAADalYZJF8pFAaiv/miROrFGizQ1usMfkt6K0gOjLUtqAQAAAEC2IakBAEAmitW0vRl9QgA0T3NGd3h8zU+SAAAAAAB2HmdcAABkmHhN2+kTAiRfS0Z3AAAAAABaF2djAAAkUxuMoIjXtJ0+IUB6i92jg88sAAAAADQHSQ0AAJKkrUdQxOwTEiux4qQ0FZBKTfXooLk4AAAAADSNpAYAAEnS5AiK4pNVmOuQz78j4ZHsHhjxEisFuSEVO51REyumIeW5LMmOvNBKjw4geZrToyNWc/FYIzwAAAAAINuQ1AAAZLc4oxoMIxhjofiijaAI5udFTTa4nQGVuvLVIc/ZOKGQQAzxEiu5HTvK0ffERqWpnJapMmeNBnaulj8YahADPTqAZGtpj464Izw8PhlGo68PJMGbb76p6dOny7ZtXXjhhTrttNNSHRIAAAAAkdQAAGSxuKMa3FKOachK0jCFWMmGnJJOyi87Uf3LPKouTl4M0RIrgTxX1Oc6LEMO3/ba2Cojl6FHB5B6sUZ45OU49NteuylPhhomO7BzAoGApk+frieeeEIFBQU69dRTNWzYMHXs2DHVoQEAAABZj6QGACBrxRvV4O7URWb5wFiVYBLWMNkQLCpu2xiijEypGxHir47RowNAWog2wgOt4/PPP9d+++2nXXbZRZJ0xBFH6L333tOJJ56Y4sgAAAAAkNQAAGS9qA23vcXtLgbTEX1kSrJHpQBAqi1dulSPPvqoli1bpoqKCt13330aNmxYxHNmz56tRx99VBUVFerRo4duvPFGlZeXS5LWr18fTmhI0i677KKff/65TbcBAAAAQHQUyAYAIEuYjnojUz59PvwT/OYtmUF/UkeE1DUeL8hxRPzQeBxAW/B4POrevbsmTZoUdf7ChQs1bdo0XXbZZZo3b5569OihMWPGaOPGjW0cKQAAAICWYqQGAABZpuGokCZHhMRqph4jQUHjcQCpNnjwYA0ePDjm/FmzZun000/XyJEjJUlTpkzRokWL9Nxzz2ns2LHq3LlzxMiMn3/+OTyKoyWSXcK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", "text/plain": [ "
" ] }, "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\"]][\n", " \"total_play_time\"\n", "]\n", "neg_play_time = subset_v4_clips_df_test[~subset_v4_clips_df_test[\"preference\"]][\n", " \"total_play_time\"\n", "]\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\"]][\n", " \"norm_play_frac\"\n", "]\n", "neg_norm_play_frac = subset_v4_clips_df_test[~subset_v4_clips_df_test[\"preference\"]][\n", " \"norm_play_frac\"\n", "]\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": 105, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:39.636429Z", "iopub.status.busy": "2024-08-12T18:12:39.636272Z", "iopub.status.idle": "2024-08-12T18:12:39.742379Z", "shell.execute_reply": "2024-08-12T18:12:39.741854Z", "shell.execute_reply.started": "2024-08-12T18:12:39.636413Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of unique requests passing play duration criteria: 13028\n", "Fraction of unique requests that pass play duration criteria: 0.5823\n" ] } ], "source": [ "pos_play_duration_mask = (\n", " subset_v4_clips_df_test[\"preference\"]\n", " & (subset_v4_clips_df_test[\"norm_play_frac\"] >= 0.95) # default 0.95\n", " & (subset_v4_clips_df_test[\"total_play_time\"] >= 10)\n", ")\n", "\n", "neg_play_duration_mask = (\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", "# Get unique request IDs that pass the play duration criteria\n", "pos_unique_requests_pass_play_durations = subset_v4_clips_df_test[\n", " pos_play_duration_mask\n", "][\"request_id\"].unique()\n", "neg_unique_requests_pass_play_durations = subset_v4_clips_df_test[\n", " neg_play_duration_mask\n", "][\"request_id\"].unique()\n", "unique_requests_pass_play_durations = set(\n", " pos_unique_requests_pass_play_durations\n", ").intersection(set(neg_unique_requests_pass_play_durations))\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)\n", " / subset_v4_clips_df_test[\"request_id\"].nunique()\n", ")\n", "\n", "# Print the result with a formatted string\n", "print(\n", " f\"Fraction of unique requests that pass play duration criteria: {fraction_requests_pass:.4f}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:39.743196Z", "iopub.status.busy": "2024-08-12T18:12:39.743031Z", "iopub.status.idle": "2024-08-12T18:12:39.925334Z", "shell.execute_reply": "2024-08-12T18:12:39.924809Z", "shell.execute_reply.started": "2024-08-12T18:12:39.743178Z" } }, "outputs": [], "source": [ "subset_v4_clips_df_pass_duration = subset_v4_clips_df_test[\n", " subset_v4_clips_df_test[\"request_id\"].isin(unique_requests_pass_play_durations)\n", "].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[\n", " play_duration_mask_request_mask\n", "].copy()" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:39.926144Z", "iopub.status.busy": "2024-08-12T18:12:39.925994Z", "iopub.status.idle": "2024-08-12T18:12:39.981517Z", "shell.execute_reply": "2024-08-12T18:12:39.981033Z", "shell.execute_reply.started": "2024-08-12T18:12:39.926127Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of unique request IDs: 13,028, Total 22373\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": 108, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:39.982242Z", "iopub.status.busy": "2024-08-12T18:12:39.982098Z", "iopub.status.idle": "2024-08-12T18:12:40.000047Z", "shell.execute_reply": "2024-08-12T18:12:39.999610Z", "shell.execute_reply.started": "2024-08-12T18:12:39.982226Z" } }, "outputs": [], "source": [ "# final_subset_v4_clips_df[final_subset_v4_clips_df[\"preference\"]].describe()" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:12:40.000754Z", "iopub.status.busy": "2024-08-12T18:12:40.000611Z", "iopub.status.idle": "2024-08-12T18:12:40.040307Z", "shell.execute_reply": "2024-08-12T18:12:40.039848Z", "shell.execute_reply.started": "2024-08-12T18:12:40.000739Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(26056, 63)\n" ] } ], "source": [ "print(final_subset_v4_clips_df.shape)" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "execution": { "iopub.execute_input": "2024-08-12T18:19:48.419614Z", "iopub.status.busy": "2024-08-12T18:19:48.419255Z", "iopub.status.idle": "2024-08-12T18:19:50.861535Z", "shell.execute_reply": "2024-08-12T18:19:50.860970Z", "shell.execute_reply.started": "2024-08-12T18:19:48.419594Z" } }, "outputs": [], "source": [ "final_subset_v4_clips_df.to_csv(\n", " \"/home/tony/Data/Preference/30b_v1/interesting_clips_v4_t_2_20240812.csv\",\n", " index=False,\n", ")\n", "# # print(final_subset_v4_clips_df.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }