{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Instructions\n", "\n", "Get data\n", "- Download the latest backup via `wget` from [Render Dashboard](https://dashboard.render.com/d/dpg-cgfrde82qv28tc0tavcg-a/recovery). Ask Martin for access. Name it something like `studio.sql.gz`\n", "- wget -O studio.sql.gz link\n", "- copy the link and `wget link`. Note this can take ~ 0.5hr to download\n", "- Unzip it with `gzip -d studio.sql.gz` (also takes a bit of time)\n", "\n", "Some of these need to be done once:\n", "- sudo apt-get install postgresql\n", "- sudo service postgresql start\n", "- sudo service postgresql status\n", "- export PATH=$PATH:/usr/lib/postgresql/12/bin (maybe?)\n", "- need to move it to local dir (pgdata), see: https://fitodic.github.io/how-to-change-postgresql-data-directory-on-linux\n", "- vim /etc/postgresql/12/main/pg_hba.conf and change to \n", " - `local all all trust` \n", " - `local all all trust` \n", "- sudo service postgresql restart\n", "- createdb -U postgres mydatabase (this can take a while)\n", "\n", "Finally:\n", "Load it into postres via `psql -U postgres -d mydatabase -f studio.sql` ~ 4-5 hrs\n", "\n", "Things that can be useful (some debugging mumble jumble for imgrating data disk):\n", "- psql -U postgres -d mydatabase\n", "- ALTER SYSTEM SET max_wal_size = '1GB';\n", "- SHOW max_wal_size;\n", "- pg_lsclusters\n", "- sudo pg_ctlcluster 12 main start\n", "- Check postgres user belongs to ssl-cert user group: \n", "- chown -R postgres:postgres pgdata\n", "- chmod -R u+rwx,g-rwx,o-rwx pgdata\n", "- sudo chown postgres.postgres /var/lib/postgresql/12/main/global/pg_internal.init\n", "- sudo rm -rf 12/main/global/pg_internal.init\n", "- sudo rm -rf /var/lib/postgresql/12/main/pg_logical/replorigin_checkpoint\n", "- sudo -i -u postgres\n", "- /usr/lib/postgresql/12/bin/pg_ctl restart -D /var/lib/postgresql/12/main\n", "\n", "\n", "Create user? (only first time)\n", "- psql -U postgres\n", "- CREATE ROLE tony WITH LOGIN PASSWORD '123';\n", "- \\q\n", "\n", "Then need to authenticate? (need to redo this after recreating a new database everytime) \n", "- psql -U postgres -d mydatabase\n", "- \\du\n", "- \\dt\n", "- \\l+ (check size)\n", "- SELECT COUNT(*) FROM bots_generatedclip;\n", "- GRANT ALL PRIVILEGES ON DATABASE mydatabase TO tony;\n", "- GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO tony;\n", "- \\q" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:14:19.959075Z", "start_time": "2024-03-07T21:14:19.327168Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2672261/2917122343.py:3: DeprecationWarning: \n", "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", "but was not found to be installed on your system.\n", "If this would cause problems for you,\n", "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", " \n", " import pandas as pd\n" ] } ], "source": [ "# pip install psycopg2-binary\n", "# make sure sqlalchemy is >=2\n", "import pandas as pd\n", "import sqlalchemy\n", "from suno_utils.audio import Audio\n", "from suno_utils.utils.s3 import open_from_s3\n", "import numpy as np\n", "import ast\n", "import tqdm\n", "\n", "engine = sqlalchemy.create_engine(\"postgresql://tony:123@localhost/mydatabase\")\n", "# connection = engine.raw_connection()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:14:19.962010Z", "start_time": "2024-03-07T21:14:19.960329Z" } }, "outputs": [], "source": [ "# # Read the sql file and execute the query\n", "# with open('/home/tony/Data/Preference/studio.sql', 'r') as query:\n", "# # connection == the connection to your database, in your case prob_db\n", "# df = pd.read_sql_query(query.read(), connection)\n", "\n", "# pd.read_sql_query(\n", "# \"SELECT COUNT(*) FROM bots_generatedclip;\",\n", "# engine,\n", "# )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Validate some info" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:14:20.048733Z", "start_time": "2024-03-07T21:14:19.963006Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['auth_group' 'auth_user_groups' 'bots_generationrequest'\n", " 'clips_teammembership' 'waffle_flag_users' 'django_session' 'auth_user'\n", " 'auth_user_user_permissions' 'bots_actionlogging' 'bots_generatedclip'\n", " 'bots_clipdiscordmessage' 'bots_clipprompt' 'bots_discordinfo'\n", " 'bots_dailytheme' 'bots_generatedclipextra'\n", " 'bots_generatedplaylistoverride' 'bots_generatedplaylist'\n", " 'bots_creditpack' 'bots_playlist' 'bots_periodcreditusage'\n", " 'bots_playlistclip' 'bots_purchaseinfo' 'bots_modeltype'\n", " 'bots_userreaction' 'clips_clip_playlists' 'clips_playlist'\n", " 'clips_clip_tags' 'clips_tag' 'clips_team' 'clips_textprompt'\n", " 'clips_textprompt_tags' 'clips_usercliprating' 'clips_usertoken'\n", " 'configs_flag' 'configs_flag_users' 'django_admin_log' 'configs_sample'\n", " 'configs_switch' 'django_migrations' 'waffle_sample' 'waffle_switch'\n", " 'auth_permission' 'auth_group_permissions' 'django_content_type'\n", " 'bots_usageplan' 'clips_clip' 'configs_flag_groups' 'waffle_flag'\n", " 'waffle_flag_groups' 'pg_stat_statements']\n" ] } ], "source": [ "df_all_tables = pd.read_sql_query(\n", " \"SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'\",\n", " engine,\n", ")\n", "print(df_all_tables[\"table_name\"].values)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:14:32.297602Z", "start_time": "2024-03-07T21:14:20.051200Z" } }, "outputs": [], "source": [ "# bots_generatedclipextra\n", "# these are all the logged actions\n", "query = \"\"\"\n", "SELECT * FROM bots_generatedclipextra\n", "\"\"\"\n", "bots_action_df = pd.read_sql_query(query, engine)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:14:46.785024Z", "start_time": "2024-03-07T21:14:32.299852Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2,659,501 rows\n" ] }, { "data": { "text/html": [ "
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idplay_countskip_countflaggedflagged_reasonreaction_typeupdated_atclip_iduser_id
09926430TruePreset: BugL2023-12-04 23:44:23.378496+00:003c132508-631f-4073-b182-17d7a0c7d700122584
12988053210TruePreset: BugNone2024-02-08 01:38:26.934799+00:0030dc7b99-8d48-4806-8c24-06d5628371401329657
2541078710TruePreset: BugNone2023-12-24 09:05:44.410633+00:0074cc4a3a-d92a-48d3-ba85-ab2e5fd6d11b1697722
31040520520TruePreset: BugD2024-01-02 22:15:21.870637+00:004a3f5e22-b05d-453a-a417-2c43c08120c4383906
4833700740TruePreset: BugNone2023-12-29 11:51:50.878478+00:00e90a59ee-9165-432e-9f4f-3b4a39d9ba392134808
..............................
1249925127007510TruePreset: Bug: Erreur : différence entre le text...None2024-03-07 10:30:15.109270+00:004d53dd1c-5a20-4462-b90a-87b6cdfb8ca05131865
1249935126981310TruePreset: Bug: No vocals.None2024-03-07 10:29:54.969015+00:00ee34213d-16ad-41b7-b87d-5a3ed714222e5380953
1249945126869810TruePreset: Bug: No Output - it´s blankNone2024-03-07 10:28:26.909881+00:00f033263c-928c-4da9-be04-dc86a555784b3325692
1249955127009120TruePreset: Bug: No vocals.None2024-03-07 10:30:16.085474+00:003cee96a5-10ee-45e2-9bb7-b2823ace843c5380953
1249965127188110TruePreset: Bug: No lyrics Refound!!None2024-03-07 10:32:36.978396+00:007ab1ea27-6bfd-453b-bfca-5c0c05268e335286728
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124997 rows × 9 columns

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" ], "text/plain": [ " id play_count skip_count flagged \\\n", "0 99264 3 0 True \n", "1 29880532 1 0 True \n", "2 5410787 1 0 True \n", "3 10405205 2 0 True \n", "4 8337007 4 0 True \n", "... ... ... ... ... \n", "124992 51270075 1 0 True \n", "124993 51269813 1 0 True \n", "124994 51268698 1 0 True \n", "124995 51270091 2 0 True \n", "124996 51271881 1 0 True \n", "\n", " flagged_reason reaction_type \\\n", "0 Preset: Bug L \n", "1 Preset: Bug None \n", "2 Preset: Bug None \n", "3 Preset: Bug D \n", "4 Preset: Bug None \n", "... ... ... \n", "124992 Preset: Bug: Erreur : différence entre le text... None \n", "124993 Preset: Bug: No vocals. None \n", "124994 Preset: Bug: No Output - it´s blank None \n", "124995 Preset: Bug: No vocals. None \n", "124996 Preset: Bug: No lyrics Refound!! None \n", "\n", " updated_at clip_id \\\n", "0 2023-12-04 23:44:23.378496+00:00 3c132508-631f-4073-b182-17d7a0c7d700 \n", "1 2024-02-08 01:38:26.934799+00:00 30dc7b99-8d48-4806-8c24-06d562837140 \n", "2 2023-12-24 09:05:44.410633+00:00 74cc4a3a-d92a-48d3-ba85-ab2e5fd6d11b \n", "3 2024-01-02 22:15:21.870637+00:00 4a3f5e22-b05d-453a-a417-2c43c08120c4 \n", "4 2023-12-29 11:51:50.878478+00:00 e90a59ee-9165-432e-9f4f-3b4a39d9ba39 \n", "... ... ... \n", "124992 2024-03-07 10:30:15.109270+00:00 4d53dd1c-5a20-4462-b90a-87b6cdfb8ca0 \n", "124993 2024-03-07 10:29:54.969015+00:00 ee34213d-16ad-41b7-b87d-5a3ed714222e \n", "124994 2024-03-07 10:28:26.909881+00:00 f033263c-928c-4da9-be04-dc86a555784b \n", "124995 2024-03-07 10:30:16.085474+00:00 3cee96a5-10ee-45e2-9bb7-b2823ace843c \n", "124996 2024-03-07 10:32:36.978396+00:00 7ab1ea27-6bfd-453b-bfca-5c0c05268e33 \n", "\n", " user_id \n", "0 122584 \n", "1 1329657 \n", "2 1697722 \n", "3 383906 \n", "4 2134808 \n", "... ... \n", "124992 5131865 \n", "124993 5380953 \n", "124994 3325692 \n", "124995 5380953 \n", "124996 5286728 \n", "\n", "[124997 rows x 9 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = \"\"\"\n", "SELECT * FROM bots_userreaction\n", "WHERE flagged=TRUE AND play_count>0\n", "\"\"\"\n", "flagged_df = pd.read_sql_query(query, engine)\n", "print(f\"{flagged_df.shape[0]:,} rows\")\n", "flagged_ids = flagged_df[\"clip_id\"]\n", "flagged_df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:15:09.876447Z", "start_time": "2024-03-07T21:14:48.218463Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all version total clips: 64881678\n", "v2 version total clips: 54960207\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", "WHERE status='complete'\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 '%%v2%%'\n", "\"\"\"\n", "clip_counts = pd.read_sql_query(query, engine)\n", "all_total_clip_counts = clip_counts[\"count\"][0]\n", "print(f\"v2 version total clips: {all_total_clip_counts}\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:44:42.396520Z", "start_time": "2024-03-07T21:15:09.877802Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "54,960,207 rows\n", "total v2 selected fraction = 1.0\n", "total clips: 54960207\n" ] } ], "source": [ "# ~ 1 mins\n", "# TODO: query only v2 here....somehow model_name doesn't like string filter?\n", "query = \"\"\"\n", "SELECT * FROM bots_generatedclip\n", "WHERE status='complete' AND model_name::text LIKE '%%v2%%'\n", "\"\"\"\n", "clip_df = pd.read_sql_query(query, engine)\n", "print(f\"{clip_df.shape[0]:,} rows\")\n", "# filter on versions\n", "clip_df = clip_df[\n", " (clip_df[\"model_name\"].str.contains(\"v2\")) # or v3...\n", " # & (clip_df[\"created_at\"] >= \"2024-02-20\")\n", "]\n", "print(f\"total v2 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}\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:45:11.923754Z", "start_time": "2024-03-07T21:44:42.397856Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clips that have children 4113109 653964 \n", " Average continues from clip = 6.289503703567781\n" ] } ], "source": [ "def parse_parent_id(x):\n", " if \"history\" not in x:\n", " return None\n", " out = x.get(\"history\", [])[0]\n", " if isinstance(out, dict):\n", " # this is the continued info, which is a dict with id and continue_at\n", " return out[\"id\"]\n", " else:\n", " return None\n", "\n", "\n", "clip_df[\"continued_parent\"] = clip_df[\"metadata\"].apply(lambda x: parse_parent_id(x))\n", "clip_history_df = clip_df[~clip_df[\"continued_parent\"].isna()].copy()\n", "continued_ids = clip_history_df[\"id\"]\n", "has_continued_children_ids = clip_history_df[\n", " \"continued_parent\"\n", "] # these are the parent's ids\n", "print(\n", " \"clips that have children\",\n", " len(has_continued_children_ids),\n", " len(has_continued_children_ids.unique()),\n", " \"\\n\",\n", " \"Average continues from clip = \",\n", " len(has_continued_children_ids) / len(has_continued_children_ids.unique()),\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:45:45.267112Z", "start_time": "2024-03-07T21:45:11.925189Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "concat clips frac = 0.017197660845782475\n" ] } ], "source": [ "# the nans are concats, we want to drop them for now\n", "concated_clips = clip_df[clip_df[\"request_id\"].isna()].copy()\n", "# need to kick them out...\n", "clip_df = clip_df[~clip_df[\"request_id\"].isna()]\n", "print(f\"concat clips frac = {concated_clips.shape[0] / total_clip_counts}\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:46:22.171847Z", "start_time": "2024-03-07T21:45:45.268452Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "945187it [00:36, 25615.71it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "total concat unique clips are: 1098871\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# this is each clip. and the mapped start time of the clip\n", "concat_clips_ids = {}\n", "for _, row in tqdm.tqdm(concated_clips.iterrows()):\n", " if \"duration\" not in row[\"metadata\"] or \"concat_history\" not in row[\"metadata\"]:\n", " continue\n", " if history_ids := row[\"metadata\"][\"concat_history\"]:\n", " total_duration = row[\"metadata\"][\"duration\"]\n", " start_s = 0\n", " for history_id in history_ids:\n", " # print(history_ids, row[\"metadata\"][\"duration\"])\n", " if isinstance(history_id, dict) and \"id\" in history_id:\n", " # the other key is `continue_at`\n", " concat_clips_ids[history_id[\"id\"]] = {\"total_start_s\": start_s, \"total_clip_s\": total_duration}\n", " if \"continue_at\" in history_id:\n", " start_s = history_id[\"continue_at\"]\n", "print(\"total concat unique clips are:\", len(concat_clips_ids))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Features" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:47:07.755598Z", "start_time": "2024-03-07T21:46:22.173141Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has upvoted upvoted\n", "False 51976923\n", "True 2038097\n", "Name: count, dtype: int64\n" ] } ], "source": [ "# add upvoted column\n", "clip_df[\"upvoted\"] = clip_df[\"id\"].isin(upvoted_ids)\n", "print(\"has upvoted\", clip_df[\"upvoted\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:48:20.545058Z", "start_time": "2024-03-07T21:47:07.758545Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has has_continued has_continued\n", "False 53454973\n", "True 560047\n", "Name: count, dtype: int64\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 has_continued\", clip_df[\"has_continued\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:49:29.076444Z", "start_time": "2024-03-07T21:48:20.546372Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is part of a concat part_of_concat\n", "False 52985630\n", "True 1029390\n", "Name: count, dtype: int64\n" ] } ], "source": [ "# add concat column\n", "clip_df[\"part_of_concat\"] = clip_df[\"id\"].astype(str).isin(concat_clips_ids)\n", "print(\"is part of a concat\", clip_df[\"part_of_concat\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:49:49.103107Z", "start_time": "2024-03-07T21:49:29.077742Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has downvoted downvoted\n", "False 53902159\n", "True 112861\n", "Name: count, dtype: int64\n" ] } ], "source": [ "# add downvoted column\n", "clip_df[\"downvoted\"] = clip_df[\"id\"].isin(flagged_ids)\n", "print(\"has downvoted\", clip_df[\"downvoted\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:50:53.699013Z", "start_time": "2024-03-07T21:49:49.104405Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has has_action has_action\n", "False 52943991\n", "True 1071029\n", "Name: count, dtype: int64\n" ] } ], "source": [ "# verify bots action are all non-empty\n", "assert (\n", " bots_action_df[\n", " bots_action_df[\"download_audio_count\"]\n", " + bots_action_df[\"download_video_count\"]\n", " + bots_action_df[\"share_count\"]\n", " == 0\n", " ].shape[0]\n", " == 0\n", ")\n", "action_mask = (\n", " bots_action_df[\"download_audio_count\"] + bots_action_df[\"download_video_count\"]\n", ") >= 1\n", "has_action_ids = set(i for i in bots_action_df[action_mask][\"clip_id\"].unique())\n", "clip_df[\"has_action\"] = clip_df[\"id\"].isin(has_action_ids)\n", "print(\"has has_action\", clip_df[\"has_action\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:51:00.308530Z", "start_time": "2024-03-07T21:50:53.700334Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total mask 3734909, pos 3788958, neg 50762601\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\"]) | (clip_df[\"has_action\"]) | (clip_df[\"part_of_concat\"])\n", ")\n", "must_be_not_negative_mask = (~clip_df[\"downvoted\"]) & (clip_df[\"dislike_count\"] < 1)\n", "mask = must_be_positive_mask & must_be_not_negative_mask\n", "print(\n", " f\"total mask {sum(mask)}, pos {sum(must_be_positive_mask)}, neg {sum(must_be_not_negative_mask)}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:54:37.771478Z", "start_time": "2024-03-07T21:51:00.309817Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "liked 3165300 unliked 27305788\n", "2538961 requests have preference paired generations, 0.091\n" ] } ], "source": [ "total_unique_requests = clip_df[\"request_id\"].nunique()\n", "liked_requests = clip_df[mask][\"request_id\"].unique()\n", "unliked_requests = clip_df[~mask][\"request_id\"].unique()\n", "has_liked_requests = set(liked_requests).intersection(set(unliked_requests))\n", "print(\"liked\", len(liked_requests), \"unliked\", len(unliked_requests))\n", "print(\n", " f\"{len(has_liked_requests)} requests have preference paired generations, {len(has_liked_requests) / total_unique_requests:.3f}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:54:37.775261Z", "start_time": "2024-03-07T21:54:37.772941Z" } }, "outputs": [ { "data": { "text/plain": [ "2538961" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "requests = has_liked_requests\n", "len(requests)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:55:37.865953Z", "start_time": "2024-03-07T21:54:37.776237Z" } }, "outputs": [], "source": [ "# creation of interesting_clips\n", "interesting_clips = clip_df[clip_df[\"request_id\"].isin(requests)].copy()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:56:33.316010Z", "start_time": "2024-03-07T21:55:37.867356Z" } }, "outputs": [ { "data": { "text/plain": [ "model_name\n", "chirp-v2-xxl-alpha 4089238\n", "chirp-v2-engine-v13 907370\n", "chirp-v2-engine-v12 42382\n", "chirp-v2-xxl-test 25152\n", "chirp-v2-engine-v8 9004\n", "chirp-v2-engine-jingle 3382\n", "chirp-v2-engine-v0 1778\n", "chirp-v2-engine-dev 6\n", "Name: count, dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clip_df[clip_df[\"request_id\"].isin(requests)][\"model_name\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:56:52.431131Z", "start_time": "2024-03-07T21:56:33.317298Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5078312, 33)\n", "(5078312, 33)\n", "2538961 5078312\n" ] } ], "source": [ "# get df of requests -- let's move on!\n", "print(interesting_clips.shape)\n", "interesting_clips = interesting_clips[\n", " (interesting_clips[\"model_name\"].str.contains(\"v2\")) # or v3...\n", " # & (interesting_clips[\"created_at\"] >= \"2024-02-14\")\n", "]\n", "print(interesting_clips.shape)\n", "print(interesting_clips[\"request_id\"].nunique(), interesting_clips[\"id\"].nunique())" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:58:01.866380Z", "start_time": "2024-03-07T21:56:52.432449Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "152\n", "before remove bad requests (5078312, 33)\n", "remove bad requests (5077618, 33)\n" ] } ], "source": [ "# some validations\n", "check_df = interesting_clips.groupby(\"request_id\")[\"id\"].nunique()\n", "bad_requests = check_df[check_df.values != 2].index.unique()\n", "print(len(bad_requests))\n", "print(\"before remove bad requests\", interesting_clips.shape)\n", "interesting_clips = interesting_clips[~interesting_clips[\"request_id\"].isin(bad_requests)]\n", "print(\"remove bad requests\", interesting_clips.shape)\n", "assert interesting_clips[interesting_clips[\"request_id\"].isna()].shape[0] == 0\n", "check_df = interesting_clips.groupby(\"request_id\")[\"id\"].nunique()\n", "assert check_df[check_df.values != 2].shape[0] == 0" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:58:04.499706Z", "start_time": "2024-03-07T21:58:01.867788Z" } }, "outputs": [], "source": [ "interesting_clips[\"preference\"] = mask" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T21:58:10.324285Z", "start_time": "2024-03-07T21:58:04.501104Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[verse]\n", "Robot w klubie, bierze partnerkę w obroty\n", "Wszyscy się dziwią, jak on tańczy tak swobodnie (oo-yeah)\n", "Kroki perfekcyjne, ruchy precyzyjne\n", "Robi show na parkiecie, wszystkich zachwyca (ooh-yeah)\n", "\n", "[Chorus]\n", "Party ibiza całą noc!\n", "Imprezujmy, pełna moc!\n", "Wojenne machiny szaleją!\n", "Tańczą, śpiewają i chleją! YEEAH!\n", "\n", "{'tags': 'dance electronic', 'type': 'gen', 'prompt': '[verse]\\nRobot w klubie, bierze partnerkę w obroty\\nWszyscy się dziwią, jak on tańczy tak swobodnie (oo-yeah)\\nKroki perfekcyjne, ruchy precyzyjne\\nRobi show na parkiecie, wszystkich zachwyca (ooh-yeah)\\n\\n[Chorus]\\nParty ibiza całą noc!\\nImprezujmy, pełna moc!\\nWojenne machiny szaleją!\\nTańczą, śpiewają i chleją! YEEAH!\\n', 'stream': False, 'duration': 37.64, 'gpt_prompt': None, 'refund_credits': False, 'gpt_description_prompt': None}\n", "80b1bdc3-c320-40e1-a633-140be560fe1e False False\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(941, 9)\n", "6f958f69-8d51-4926-9efa-0313623d3ce1 True True\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(758, 9)\n", "\n" ] } ], "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": 26, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:37.583361Z", "start_time": "2024-03-07T21:58:10.325618Z" } }, "outputs": [], "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", ")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:39.942370Z", "start_time": "2024-03-07T22:03:37.584874Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5077618\n", "4087314\n" ] } ], "source": [ "print(len(interesting_clips))\n", "interesting_clips = interesting_clips[~interesting_clips[\"has_gpt_prompt\"]]\n", "print(len(interesting_clips))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:40.062729Z", "start_time": "2024-03-07T22:03:39.943697Z" } }, "outputs": [ { "data": { "text/plain": [ "batch_index preference\n", "0 False 1057614\n", " True 986043\n", "1 True 1057614\n", " False 986043\n", "Name: count, dtype: int64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interesting_clips.groupby(\"batch_index\")[\"preference\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:40.171615Z", "start_time": "2024-03-07T22:03:40.064065Z" } }, "outputs": [ { "data": { "text/plain": [ "(371694,\n", " user_id\n", " 3338997 4024\n", " 1715957 3936\n", " 449847 3842\n", " 2435788 3168\n", " 58123 2928\n", " ... \n", " 3240813 2\n", " 2293011 2\n", " 2242629 2\n", " 4813963 2\n", " 4210752 2\n", " Name: count, Length: 371694, dtype: int64)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interesting_clips[\"user_id\"].nunique(), interesting_clips[\"user_id\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:40.738359Z", "start_time": "2024-03-07T22:03:40.173018Z" } }, "outputs": [ { "data": { "text/plain": [ "model_name\n", "chirp-v2-xxl-alpha 4059208\n", "chirp-v2-engine-v12 19022\n", "chirp-v2-engine-v8 6136\n", "chirp-v2-engine-v0 1774\n", "chirp-v2-engine-jingle 1144\n", "chirp-v2-engine-v13 20\n", "chirp-v2-engine-dev 6\n", "chirp-v2-xxl-test 4\n", "Name: count, dtype: int64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interesting_clips[\"model_name\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:40.810809Z", "start_time": "2024-03-07T22:03:40.739702Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " id password last_login is_superuser username first_name \\\n", "0 186417 None False studio@kucsko.com \n", "\n", " last_name email is_staff is_active \\\n", "0 studio@kucsko.com False True \n", "\n", " date_joined \n", "0 2023-11-19 14:44:37.434106+00:00 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = \"\"\"\n", "SELECT *\n", "FROM auth_user\n", "WHERE id=186417\n", "\"\"\"\n", "user_df = pd.read_sql_query(query, engine)\n", "user_df" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:40.861217Z", "start_time": "2024-03-07T22:03:40.811732Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done\n" ] } ], "source": [ "print(\"Done\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:40.936907Z", "start_time": "2024-03-07T22:03:40.862355Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time validation 2023-11-10 23:44:08.661114+00:00 2024-03-07 10:34:01.908468+00:00\n" ] } ], "source": [ "print(\n", " \"time validation\",\n", " interesting_clips[\"created_at\"].min(),\n", " interesting_clips[\"created_at\"].max(),\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:54.196758Z", "start_time": "2024-03-07T22:03:40.938031Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2043657 4087314\n" ] } ], "source": [ "print(interesting_clips[\"request_id\"].nunique(), interesting_clips[\"id\"].nunique())" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:54.472797Z", "start_time": "2024-03-07T22:03:54.198072Z" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:54.476764Z", "start_time": "2024-03-07T22:03:54.474212Z" } }, "outputs": [ { "data": { "text/plain": [ "Index(['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', 'continued_parent', 'upvoted',\n", " 'has_continued', 'part_of_concat', 'downvoted', 'has_action',\n", " 'preference', 'has_gpt_prompt'],\n", " dtype='object')" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interesting_clips.columns" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:54.555027Z", "start_time": "2024-03-07T22:03:54.477899Z" } }, "outputs": [ { "data": { "text/html": [ "
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idcreated_atupdated_attime_usedmetadatauser_idstatusdiscord_message_idprompt_idrequest_id...is_public_approvedslugcontinued_parentupvotedhas_continuedpart_of_concatdownvotedhas_actionpreferencehas_gpt_prompt
353398339034e115-ebe0-46cb-bcbd-561172ab34202023-12-16 03:40:12.451813+00:002023-12-16 03:51:08.876496+00:0062.948608{'tags': 'hyperpop,jpop,hiphop', 'type': 'gen'...1221796completeNoneNone762f08e0-7b5f-4da7-8210-b197da618cdf...FalseNoneNoneTrueFalseFalseTrueFalseFalseFalse
4592282363b14fa9-39d1-41fe-b9f3-4d673b216e1b2023-12-22 06:27:55.976283+00:002023-12-22 06:31:42.111419+00:0011.746102{'tags': 'trap edm', 'type': 'gen', 'prompt': ...1631136completeNoneNone59a3300a-6676-4a54-b54b-9e367c677fb8...FalseNoneNoneTrueTrueTrueTrueFalseFalseFalse
5239191271a23d94-a498-47fa-8c3a-f05eba90b8112023-12-26 14:59:29.309454+00:002023-12-26 15:32:52.981661+00:0063.115511{'tags': 'j-pop', 'type': 'gen', 'prompt': '「二...1310221completeNoneNonee98a1641-eb55-4fb5-b13e-6d527dfdadd4...FalseNoneNoneTrueTrueFalseFalseFalseTrueFalse
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" ], "text/plain": [ " id \\\n", "35339833 9034e115-ebe0-46cb-bcbd-561172ab3420 \n", "45922823 63b14fa9-39d1-41fe-b9f3-4d673b216e1b \n", "52391912 71a23d94-a498-47fa-8c3a-f05eba90b811 \n", "\n", " created_at updated_at \\\n", "35339833 2023-12-16 03:40:12.451813+00:00 2023-12-16 03:51:08.876496+00:00 \n", "45922823 2023-12-22 06:27:55.976283+00:00 2023-12-22 06:31:42.111419+00:00 \n", "52391912 2023-12-26 14:59:29.309454+00:00 2023-12-26 15:32:52.981661+00:00 \n", "\n", " time_used metadata \\\n", "35339833 62.948608 {'tags': 'hyperpop,jpop,hiphop', 'type': 'gen'... \n", "45922823 11.746102 {'tags': 'trap edm', 'type': 'gen', 'prompt': ... \n", "52391912 63.115511 {'tags': 'j-pop', 'type': 'gen', 'prompt': '「二... \n", "\n", " user_id status discord_message_id prompt_id \\\n", "35339833 1221796 complete None None \n", "45922823 1631136 complete None None \n", "52391912 1310221 complete None None \n", "\n", " request_id ... is_public_approved slug \\\n", "35339833 762f08e0-7b5f-4da7-8210-b197da618cdf ... False None \n", "45922823 59a3300a-6676-4a54-b54b-9e367c677fb8 ... False None \n", "52391912 e98a1641-eb55-4fb5-b13e-6d527dfdadd4 ... False None \n", "\n", " continued_parent upvoted has_continued part_of_concat downvoted \\\n", "35339833 None True False False True \n", "45922823 None True True True True \n", "52391912 None True True False False \n", "\n", " has_action preference has_gpt_prompt \n", "35339833 False False False \n", "45922823 False False False \n", "52391912 False True False \n", "\n", "[3 rows x 35 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interesting_clips[interesting_clips[\"play_count\"] > 10000]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:55.890935Z", "start_time": "2024-03-07T22:03:54.556095Z" } }, "outputs": [ { "data": { "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(interesting_clips[\"play_count\"], bins=np.linspace(0, 100, 50))\n", "plt.yscale(\"log\")\n", "plt.xlabel(\"number of play counts\")\n", "plt.ylabel(\"number of clips\")\n", "plt.show()\n", "plt.hist(interesting_clips[\"dislike_count\"], bins=np.linspace(0, 10, 30))\n", "plt.yscale(\"log\")\n", "plt.xlabel(\"number of dislike_count\")\n", "plt.ylabel(\"number of clips\")\n", "plt.show()\n", "plt.hist(interesting_clips[\"upvote_count\"], bins=np.linspace(0, 10, 30))\n", "plt.yscale(\"log\")\n", "plt.xlabel(\"number of like_count\")\n", "plt.ylabel(\"number of clips\")\n", "plt.show()\n", "plt.hist(interesting_clips[\"is_public\"], bins=np.linspace(0, 10, 30))\n", "plt.yscale(\"log\")\n", "plt.xlabel(\"number of is_public\")\n", "plt.ylabel(\"number of clips\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:56.272759Z", "start_time": "2024-03-07T22:03:55.892347Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(interesting_clips[\"user_id\"].value_counts(), bins=np.linspace(0, 1000, 100))\n", "plt.yscale(\"log\")\n", "plt.xlabel(\"number of preferences\")\n", "plt.ylabel(\"number of users\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:03:56.278933Z", "start_time": "2024-03-07T22:03:56.277082Z" } }, "outputs": [], "source": [ "MAX_PREFERENCE_PER_USER = 100\n", "grouped_interesting_clips = interesting_clips.groupby([\"user_id\"])" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:04.436207Z", "start_time": "2024-03-07T22:03:56.280005Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3289458, 35) (4087314, 35)\n" ] } ], "source": [ "user_top_df = (\n", " interesting_clips.sort_values(\n", " [\"preference\", \"play_count\", \"part_of_concat\", \"time_used\"], ascending=False\n", " )\n", " .groupby(\"user_id\")\n", " .head(100)\n", ")\n", "print(user_top_df.shape, interesting_clips.shape)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:17.063225Z", "start_time": "2024-03-07T22:04:04.437549Z" } }, "outputs": [], "source": [ "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()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:17.067203Z", "start_time": "2024-03-07T22:04:17.064795Z" } }, "outputs": [ { "data": { "text/plain": [ "(3627548, 35)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_intersting_clips.shape" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:17.468528Z", "start_time": "2024-03-07T22:04:17.068297Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(user_intersting_clips[\"user_id\"].value_counts(), bins=np.linspace(0, 200, 100))\n", "plt.yscale(\"log\")\n", "plt.xlabel(\"number of preferences\")\n", "plt.ylabel(\"number of users\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:17.472150Z", "start_time": "2024-03-07T22:04:17.469875Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "finally 3627548 requests 1813774.0 frac 0.06600317207684461\n" ] } ], "source": [ "print(\n", " \"finally\",\n", " user_intersting_clips.shape[0],\n", " \"requests\",\n", " user_intersting_clips.shape[0] / 2,\n", " \"frac\",\n", " user_intersting_clips.shape[0] / total_clip_counts,\n", ")" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:25.133019Z", "start_time": "2024-03-07T22:04:17.473288Z" } }, "outputs": [], "source": [ "def unpack_dict(x):\n", " if v := concat_clips_ids.get(str(x)):\n", " return v\n", " else:\n", " return {\"total_start_s\": None, \"total_clip_s\": None}\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[[\"total_start_s\", \"total_clip_s\"]] = extra_cols_df" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:25.990634Z", "start_time": "2024-03-07T22:04:25.134427Z" } }, "outputs": [ { "data": { "text/html": [ "
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time_useduser_idupvote_countbatch_indexdislike_countflag_countplay_countskip_counttotal_start_stotal_clip_s
count3.627548e+063.627548e+063.627548e+063627548.03.627548e+063.627548e+063.627548e+063627548.0658032.000000675962.000000
mean5.595100e+012.883371e+062.549066e-010.55.827821e-021.473171e-033.413960e+000.047.538045179.744326
std1.483660e+011.611455e+067.523956e-010.52.366234e-014.702016e-029.571878e+010.044.26221286.667165
min4.322083e+003.000000e+00-4.000000e+000.0-7.000000e+000.000000e+000.000000e+000.00.0000006.040000
25%5.311131e+011.479019e+060.000000e+000.00.000000e+000.000000e+001.000000e+000.00.000000135.560000
50%6.021580e+013.013130e+060.000000e+000.50.000000e+000.000000e+002.000000e+000.053.000000161.400000
75%6.345773e+014.371231e+061.000000e+001.00.000000e+000.000000e+004.000000e+000.079.400000210.160000
max2.659650e+025.387744e+069.960000e+021.05.100000e+015.200000e+011.792790e+050.02330.2000002450.160000
\n", "
" ], "text/plain": [ " time_used user_id upvote_count batch_index dislike_count \\\n", "count 3.627548e+06 3.627548e+06 3.627548e+06 3627548.0 3.627548e+06 \n", "mean 5.595100e+01 2.883371e+06 2.549066e-01 0.5 5.827821e-02 \n", "std 1.483660e+01 1.611455e+06 7.523956e-01 0.5 2.366234e-01 \n", "min 4.322083e+00 3.000000e+00 -4.000000e+00 0.0 -7.000000e+00 \n", "25% 5.311131e+01 1.479019e+06 0.000000e+00 0.0 0.000000e+00 \n", "50% 6.021580e+01 3.013130e+06 0.000000e+00 0.5 0.000000e+00 \n", "75% 6.345773e+01 4.371231e+06 1.000000e+00 1.0 0.000000e+00 \n", "max 2.659650e+02 5.387744e+06 9.960000e+02 1.0 5.100000e+01 \n", "\n", " flag_count play_count skip_count total_start_s total_clip_s \n", "count 3.627548e+06 3.627548e+06 3627548.0 658032.000000 675962.000000 \n", "mean 1.473171e-03 3.413960e+00 0.0 47.538045 179.744326 \n", "std 4.702016e-02 9.571878e+01 0.0 44.262212 86.667165 \n", "min 0.000000e+00 0.000000e+00 0.0 0.000000 6.040000 \n", "25% 0.000000e+00 1.000000e+00 0.0 0.000000 135.560000 \n", "50% 0.000000e+00 2.000000e+00 0.0 53.000000 161.400000 \n", "75% 0.000000e+00 4.000000e+00 0.0 79.400000 210.160000 \n", "max 5.200000e+01 1.792790e+05 0.0 2330.200000 2450.160000 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_intersting_clips.describe()\n", "# 214202\n", "# 3132852" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:25.993552Z", "start_time": "2024-03-07T22:04:25.991892Z" } }, "outputs": [], "source": [ "# user_intersting_clips.to_csv(\"/home/tony/Data/Preference/7b_v0/interesting_clips.csv\", index=False)\n", "# print(\"done\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# For faster processing once" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:26.385687Z", "start_time": "2024-03-07T22:04:25.994576Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3989492 184635\n" ] } ], "source": [ "neg_filter_selection_mask = interesting_clips[\"play_count\"] > 0\n", "pos_filter_selectin_mask = interesting_clips[\"play_count\"] >= 10\n", "print(sum(neg_filter_selection_mask), sum(pos_filter_selectin_mask))\n", "# v1: 176273 1171102\n", "# v2: 229891 1616473" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:39.450486Z", "start_time": "2024-03-07T22:04:26.386840Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "prev total 2043657 filtered 155253\n" ] } ], "source": [ "neg_filter_requests = interesting_clips[neg_filter_selection_mask][\n", " \"request_id\"\n", "].unique()\n", "pos_filter_requests = interesting_clips[pos_filter_selectin_mask][\"request_id\"].unique()\n", "# looking for very strong signal here:\n", "# listen to the positive/negative more than once\n", "# disliked one of the clips\n", "unique_requests = set(pos_filter_requests).intersection(neg_filter_requests)\n", "print(\"prev total\", interesting_clips[\"request_id\"].nunique(), \"filtered\", len(unique_requests))\n", "# v1 853102 135360\n", "# v2 1170736 176676" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:43.262322Z", "start_time": "2024-03-07T22:04:39.451909Z" } }, "outputs": [], "source": [ "final_interesting_clips = interesting_clips[\n", " interesting_clips[\"request_id\"].isin(set(unique_requests))\n", "].copy()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:43.276774Z", "start_time": "2024-03-07T22:04:43.263743Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "batch_index preference\n", "0 True 80691\n", " False 74562\n", "1 False 80691\n", " True 74562\n", "Name: count, dtype: int64\n" ] } ], "source": [ "print(final_interesting_clips.groupby(\"batch_index\")[\"preference\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:44.698510Z", "start_time": "2024-03-07T22:04:43.277872Z" } }, "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": 54, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:44.701594Z", "start_time": "2024-03-07T22:04:44.699911Z" } }, "outputs": [], "source": [ "# final_interesting_clips.shape" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:44.762481Z", "start_time": "2024-03-07T22:04:44.702655Z" } }, "outputs": [], "source": [ "# final_interesting_clips.to_csv(\"/home/tony/Data/Preference/v4/interesting_clips.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:45.630018Z", "start_time": "2024-03-07T22:04:44.763454Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all 2603912, filtered 371694, fully filtered 57231\n" ] } ], "source": [ "print(\n", " f'all {clip_df[\"user_id\"].nunique()}, filtered {interesting_clips[\"user_id\"].nunique()}, fully filtered {final_interesting_clips[\"user_id\"].nunique()}'\n", ")" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:45.696606Z", "start_time": "2024-03-07T22:04:45.631418Z" } }, "outputs": [ { "data": { "text/html": [ "
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created_atmetadata
11688662023-11-23 21:09:47.821813+00:00{'tags': 'punk rock energetic rebellious', 'ty...
11688682023-11-23 21:09:47.821877+00:00{'tags': 'punk rock energetic rebellious', 'ty...
11689972023-11-23 21:14:04.590008+00:00{'tags': 'trumpet solo', 'type': 'gen', 'promp...
11690032023-11-23 21:14:04.590075+00:00{'tags': 'trumpet solo', 'type': 'gen', 'promp...
99593312023-12-19 04:06:21.122242+00:00{'tags': 'groovy pop funky', 'type': 'gen', 'p...
99759602023-12-19 04:06:21.122181+00:00{'tags': 'pop-funk groovy upbeat', 'type': 'ge...
101573842023-12-19 04:07:43.024240+00:00{'tags': 'upbeat pop', 'type': 'gen', 'prompt'...
102200862023-12-19 04:07:43.024312+00:00{'tags': 'indie pop mellow dreamy', 'type': 'g...
102387822023-12-19 04:08:42.742915+00:00{'tags': 'pop upbeat', 'type': 'gen', 'prompt'...
102509302023-12-19 04:08:42.742855+00:00{'tags': 'acoustic light pop', 'type': 'gen', ...
117621732023-12-20 22:27:42.417080+00:00{'tags': 'upbeat pop electronic', 'type': 'gen...
117877682023-12-20 22:27:42.417022+00:00{'tags': 'upbeat pop', 'type': 'gen', 'prompt'...
118487622023-12-20 22:29:14.779739+00:00{'tags': 'smooth jazz sultry', 'type': 'gen', ...
118804492023-12-20 22:29:14.779807+00:00{'tags': 'sultry jazz smooth', 'type': 'gen', ...
119821182023-12-19 18:44:13.473790+00:00{'tags': 'indie pop dreamy atmospheric', 'type...
119860972023-12-19 18:44:13.473869+00:00{'tags': 'upbeat pop', 'type': 'gen', 'prompt'...
124354052023-12-20 22:59:50.673570+00:00{'tags': 'rap upbeat', 'type': 'gen', 'prompt'...
124446552023-12-20 22:59:50.673628+00:00{'tags': 'playful rap', 'type': 'gen', 'prompt...
\n", "
" ], "text/plain": [ " created_at \\\n", "1168866 2023-11-23 21:09:47.821813+00:00 \n", "1168868 2023-11-23 21:09:47.821877+00:00 \n", "1168997 2023-11-23 21:14:04.590008+00:00 \n", "1169003 2023-11-23 21:14:04.590075+00:00 \n", "9959331 2023-12-19 04:06:21.122242+00:00 \n", "9975960 2023-12-19 04:06:21.122181+00:00 \n", "10157384 2023-12-19 04:07:43.024240+00:00 \n", "10220086 2023-12-19 04:07:43.024312+00:00 \n", "10238782 2023-12-19 04:08:42.742915+00:00 \n", "10250930 2023-12-19 04:08:42.742855+00:00 \n", "11762173 2023-12-20 22:27:42.417080+00:00 \n", "11787768 2023-12-20 22:27:42.417022+00:00 \n", "11848762 2023-12-20 22:29:14.779739+00:00 \n", "11880449 2023-12-20 22:29:14.779807+00:00 \n", "11982118 2023-12-19 18:44:13.473790+00:00 \n", "11986097 2023-12-19 18:44:13.473869+00:00 \n", "12435405 2023-12-20 22:59:50.673570+00:00 \n", "12444655 2023-12-20 22:59:50.673628+00:00 \n", "\n", " metadata \n", "1168866 {'tags': 'punk rock energetic rebellious', 'ty... \n", "1168868 {'tags': 'punk rock energetic rebellious', 'ty... \n", "1168997 {'tags': 'trumpet solo', 'type': 'gen', 'promp... \n", "1169003 {'tags': 'trumpet solo', 'type': 'gen', 'promp... \n", "9959331 {'tags': 'groovy pop funky', 'type': 'gen', 'p... \n", "9975960 {'tags': 'pop-funk groovy upbeat', 'type': 'ge... \n", "10157384 {'tags': 'upbeat pop', 'type': 'gen', 'prompt'... \n", "10220086 {'tags': 'indie pop mellow dreamy', 'type': 'g... \n", "10238782 {'tags': 'pop upbeat', 'type': 'gen', 'prompt'... \n", "10250930 {'tags': 'acoustic light pop', 'type': 'gen', ... \n", "11762173 {'tags': 'upbeat pop electronic', 'type': 'gen... \n", "11787768 {'tags': 'upbeat pop', 'type': 'gen', 'prompt'... \n", "11848762 {'tags': 'smooth jazz sultry', 'type': 'gen', ... \n", "11880449 {'tags': 'sultry jazz smooth', 'type': 'gen', ... \n", "11982118 {'tags': 'indie pop dreamy atmospheric', 'type... \n", "11986097 {'tags': 'upbeat pop', 'type': 'gen', 'prompt'... \n", "12435405 {'tags': 'rap upbeat', 'type': 'gen', 'prompt'... \n", "12444655 {'tags': 'playful rap', 'type': 'gen', 'prompt... " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clip_df[clip_df[\"user_id\"] == 2088][[\"created_at\", \"metadata\"]]" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:04:54.143519Z", "start_time": "2024-03-07T22:04:45.697888Z" } }, "outputs": [], "source": [ "query = \"\"\"\n", "SELECT *\n", "FROM auth_user\n", "\"\"\"\n", "user_df = pd.read_sql_query(query, engine)\n", "user_df.head()\n", "\n", "query = \"\"\"\n", "SELECT *\n", "FROM auth_user_groups\n", "\"\"\"\n", "auth_user_df = pd.read_sql_query(query, engine)\n", "auth_user_df.head()\n", "joined_user = pd.merge(user_df, auth_user_df, left_on='id', right_on='user_id')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:05:09.563165Z", "start_time": "2024-03-07T22:04:54.144971Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pro gens 11762015 pro gen ratio 0.21775452457483122\n" ] } ], "source": [ "pro_user_df = joined_user[joined_user[\"group_id\"] == 4].copy()\n", "pro_user_ids = pro_user_df[\"user_id\"].unique()\n", "pro_clips_df = clip_df[clip_df[\"user_id\"].isin(pro_user_ids)].copy()\n", "\n", "print(\n", " \"pro gens\",\n", " pro_clips_df.shape[0],\n", " \"pro gen ratio\",\n", " pro_clips_df.shape[0] / clip_df.shape[0],\n", ")" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:05:43.500507Z", "start_time": "2024-03-07T22:05:09.564477Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "877501 requests have preference paired generations, 0.148\n" ] } ], "source": [ "total_unique_pro_requests = pro_clips_df[\"request_id\"].nunique()\n", "pro_has_liked_requests = set(has_liked_requests).intersection(\n", " pro_clips_df[\"request_id\"].unique()\n", ")\n", "print(\n", " f\"{len(pro_has_liked_requests)} requests have preference paired generations, {len(pro_has_liked_requests) / total_unique_pro_requests:.3f}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:05:43.503519Z", "start_time": "2024-03-07T22:05:43.501805Z" } }, "outputs": [], "source": [ "# from collections import Counter\n", "# c_tag = Counter()\n", "# for _, row in tqdm.tqdm(clip_df.iterrows(), total=clip_df.shape[0]):\n", "# test_d = ast.literal_eval(str(row[\"metadata\"]))\n", "# tags = test_d.get(\"tags\", \"\")\n", "# if tags:\n", "# c_tag[tags.lower().strip()] += 1" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:05:43.569359Z", "start_time": "2024-03-07T22:05:43.504605Z" } }, "outputs": [ { "data": { "text/plain": [ "0.02290348129094463" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1237132/clip_df.shape[0]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:05:44.221820Z", "start_time": "2024-03-07T22:05:43.570297Z" } }, "outputs": [ { "ename": "NameError", "evalue": "name 'c_tag' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[63], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[43mc_tag\u001b[49m))\n", "\u001b[0;31mNameError\u001b[0m: name 'c_tag' is not defined" ] } ], "source": [ "print(len(c_tag))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:05:44.223047Z", "start_time": "2024-03-07T22:05:44.223037Z" } }, "outputs": [], "source": [ "# c_tag.most_common(1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:05:44.224063Z", "start_time": "2024-03-07T22:05:44.224054Z" } }, "outputs": [], "source": [ "# with open(\"user_tag_freq.json\", \"w\") as fp:\n", "# json.dump(c_tag, fp)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:05:44.224700Z", "start_time": "2024-03-07T22:05:44.224691Z" } }, "outputs": [], "source": [ "c_tag_unique = Counter()\n", "for k, v in c_tag.items():\n", " n_k = k.replace(\"(\", \"\").replace(\")\", \"\").replace(\",\", \"\")\n", " c_tag_unique[n_k] += v" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:05:44.225370Z", "start_time": "2024-03-07T22:05:44.225362Z" } }, "outputs": [], "source": [ "c_tag_unique.most_common(1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2024-03-07T22:05:44.225969Z", "start_time": "2024-03-07T22:05:44.225961Z" } }, "outputs": [], "source": [ "print(\"TWT\")" ] }, { "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": "3.10.13" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }