{ "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": "2025-01-04T15:20:28.452472Z", "iopub.status.busy": "2025-01-04T15:20:28.452339Z", "iopub.status.idle": "2025-01-04T15:20:28.613284Z", "shell.execute_reply": "2025-01-04T15:20:28.612820Z", "shell.execute_reply.started": "2025-01-04T15:20:28.452456Z" } }, "outputs": [], "source": [ "# setup tailscale if you haven't\n", "# https://tailscale.com/kb/1031/install-linux\n", "# curl -fsSL https://tailscale.com/install.sh | sh\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": "2025-01-04T15:20:28.613933Z", "iopub.status.busy": "2025-01-04T15:20:28.613794Z", "iopub.status.idle": "2025-01-04T15:20:34.617591Z", "shell.execute_reply": "2025-01-04T15:20:34.617033Z", "shell.execute_reply.started": "2025-01-04T15:20:28.613919Z" } }, "outputs": [], "source": [ "# make sure sqlalchemy is >=2\n", "# pip install psycopg2-binary\n", "# pip install \"sqlalchemy>=2\"\n", "import os\n", "import datetime\n", "from collections import defaultdict, Counter\n", "import json\n", "from urllib.parse import quote\n", "\n", "import boto3\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import sqlalchemy\n", "from sqlalchemy.pool import QueuePool\n", "import tqdm\n", "from botocore.exceptions import ClientError\n", "from suno_analytics.preference_helper import get_preference_counts\n", "from suno_analytics.preference_data_selection import (\n", " gather_data,\n", " plot_clip_distribution,\n", " parse_metadata_for_basics,\n", " get_concat_clip_ids,\n", " validate_preference_data,\n", " run_bot_detection,\n", " print_out_value_counts_nicely,\n", " merge_concat_clips_with_reactions,\n", " plot_clip_basic_distributions,\n", ")\n", "\n", "\n", "# setup some pandas display stuff\n", "pd.set_option(\"display.max_rows\", 500)\n", "pd.set_option(\"display.max_columns\", 500)\n", "pd.set_option(\"display.width\", 1000)\n", "\n", "\n", "def get_secret():\n", " secret_name = \"app-user-main-db-secret\"\n", " region_name = \"us-east-2\"\n", " # Create a Secrets Manager client\n", " session = boto3.session.Session()\n", " client = session.client(service_name=\"secretsmanager\", region_name=region_name)\n", " try:\n", " get_secret_value_response = client.get_secret_value(SecretId=secret_name)\n", " except ClientError as e:\n", " raise e\n", " secret = get_secret_value_response[\"SecretString\"]\n", " return json.loads(secret)\n", "\n", "\n", "my_secrets = get_secret()\n", "\n", "# alternative...\n", "engine = sqlalchemy.create_engine(\n", " \"postgresql://suno:%s@suno-main-postgres-prod-analytics.cnfvffydbwvc.us-east-2.rds.amazonaws.com/suno_main\"\n", " % quote(my_secrets[\"password\"]),\n", ")\n", "\n", "\n", "home_dir = os.path.expanduser(\"~\")\n", "snow_password_path = os.path.join(home_dir, \".aws\", \"snow_pw.txt\")\n", "if os.path.exists(snow_password_path):\n", " # !pip install snowflake\n", " from snowflake.core import Root\n", " from snowflake.snowpark import Session\n", "\n", " with open(snow_password_path, \"r\") as fp:\n", " fp_lines = fp.readlines()\n", " snow_password = fp_lines[0].strip()\n", " snow_username = fp_lines[1].strip()\n", "\n", " CONNECTION_PARAMETERS = {\n", " \"account\": \"fu90569.us-east-2.aws\",\n", " \"user\": snow_username,\n", " \"password\": snow_password,\n", " \"role\": \"ACCOUNTADMIN\",\n", " \"database\": \"SUNO_PROD\",\n", " \"warehouse\": \"SUNO_PROD_LARGE\",\n", " \"schema\": \"PROD\",\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Validate some info" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:11:08.550447Z", "start_time": "2024-05-26T00:11:08.397196Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:20:34.618441Z", "iopub.status.busy": "2025-01-04T15:20:34.618191Z", "iopub.status.idle": "2025-01-04T15:20:34.636521Z", "shell.execute_reply": "2025-01-04T15:20:34.636045Z", "shell.execute_reply.started": "2025-01-04T15:20:34.618426Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2025-01-06 13:00:00\n" ] } ], "source": [ "# there are 4 hr time difference between eastern time and utc\n", "# now there are 5 hr time difference?! WTF\n", "# cutoff_date = \"2024-08-26 21:00:00\" # v4-t3 out\n", "# cutoff_date = \"2024-10-09 15:20:00\" # 30b t5 out\n", "# cutoff_date = \"2024-10-31 16:00:00\" # 30b t6 out\n", "# cutoff_date = \"2024-11-01 20:00:00\" # staff v4\n", "# cutoff_date = \"2024-11-10 00:00:00\" # 13b s31 scan\n", "# cutoff_date = \"2024-11-10 04:00:00\" # 30b t6-1 out\n", "# cutoff_date = \"2024-11-11 04:00:00\" # 30b t6-2 out\n", "# cutoff_date = \"2024-11-11 14:00:00\" # 30b t5 out\n", "# cutoff_date = \"2024-11-12 13:00:00\" # 30b t6-2 out\n", "# cutoff_date = \"2024-11-13 08:00:00\" # 30b t6-2 out\n", "# cutoff_date = \"2024-11-15 04:00:00\" # upsample exps\n", "# cutoff_date = \"2024-11-16 13:00:00\" # upsample exps\n", "# cutoff_date = \"2024-11-18 01:30:00\" # upsample codec minz 2\n", "# cutoff_date = \"2024-11-19 16:00:00\" # v4 stage set\n", "# cutoff_date = \"2024-11-21 20:00:00\" # inference tests\n", "# cutoff_date = \"2024-11-22 20:00:00\" # inference tests\n", "# cutoff_date = \"2024-11-25 01:30:00\" # diff dpo v2\n", "# cutoff_date = \"2024-11-25 18:00:00\" # diff dpo v2\n", "# cutoff_date = \"2024-12-01 16:00:00\" # diff dpo v9\n", "# cutoff_date = \"2024-12-05 13:00:00\" # diff dpo v17\n", "# cutoff_date = \"2024-12-06 16:00:00\" # diff v2 out\n", "# cutoff_date = \"2024-12-10 20:30:00\" # diff v2 t2 out\n", "# cutoff_date = \"2024-12-11 14:00:00\" # diff v3 out\n", "# cutoff_date = \"2024-12-13 23:00:00\" # diff v1 20 test\n", "# cutoff_date = \"2024-12-14 21:05:00\" # diff v1 20 test\n", "# cutoff_date = \"2024-12-15 06:00:00\" # diff v1 20 test\n", "# cutoff_date = \"2024-12-16 13:00:00\" # diff v1 20 test\n", "# cutoff_date = \"2024-12-16 20:40:00\" # v4 s32 out\n", "# cutoff_date = \"2024-12-17 20:00:00\" # test\n", "# cutoff_date = \"2025-01-03 18:00:00\" # s32 c1, t6 c16\n", "# cutoff_date = \"2025-01-04 01:30:00\" # up v3 t4\n", "cutoff_date = \"2025-01-06 13:00:00\" # s32 c2, t6 c17\n", "\n", "# cutoff_date = (\n", "# (datetime.datetime.now() - datetime.timedelta(hours=2))\n", "# .astimezone(datetime.timezone.utc)\n", "# .strftime(\"%Y-%m-%d %H:%M:%S\")1\n", "# )\n", "print(cutoff_date)\n", "\n", "target_model_name = \"chirp-v3p5-engine-t-6\"" ] }, { "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": "2025-01-04T15:20:34.637155Z", "iopub.status.busy": "2025-01-04T15:20:34.637019Z", "iopub.status.idle": "2025-01-04T15:20:34.956555Z", "shell.execute_reply": "2025-01-04T15:20:34.956111Z", "shell.execute_reply.started": "2025-01-04T15:20:34.637142Z" } }, "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": { "execution": { "iopub.execute_input": "2025-01-04T15:20:34.957177Z", "iopub.status.busy": "2025-01-04T15:20:34.957041Z", "iopub.status.idle": "2025-01-04T15:34:30.641830Z", "shell.execute_reply": "2025-01-04T15:34:30.641250Z", "shell.execute_reply.started": "2025-01-04T15:20:34.957164Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start gather data from 2025-01-06 13:00:00\n", "Bots Action: 464,575 rows\n", " ---- Execution time: 6.48 seconds\n", "Reactions: 669,391 rows\n", " ---- Execution time: 7.18 seconds\n", "Total Clips: 445,263 rows\n", " ---- Execution time: 120.65 seconds\n", "Playlist Clips: 16,305 rows\n", " ---- Execution time: 0.74 seconds\n", "Authenticated Users: 996,084 rows\n", " ---- Execution time: 4.24 seconds\n", "Discord Info: 426,993 rows\n", "subscription_status\n", "active 407352\n", "past_due 19641\n", "Name: count, dtype: int64\n", " ---- Execution time: 16.95 seconds\n" ] } ], "source": [ "gathered_data = gather_data(engine, cutoff_date)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:34:30.643477Z", "iopub.status.busy": "2025-01-04T15:34:30.643310Z", "iopub.status.idle": "2025-01-04T15:34:31.439179Z", "shell.execute_reply": "2025-01-04T15:34:31.438686Z", "shell.execute_reply.started": "2025-01-04T15:34:30.643461Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "finished query!\n" ] } ], "source": [ "# this serves as a break point\n", "print(\"finished query!\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:34:31.439923Z", "iopub.status.busy": "2025-01-04T15:34:31.439781Z", "iopub.status.idle": "2025-01-04T15:34:31.486340Z", "shell.execute_reply": "2025-01-04T15:34:31.485898Z", "shell.execute_reply.started": "2025-01-04T15:34:31.439909Z" } }, "outputs": [], "source": [ "# unpack the information\n", "bots_action_df = gathered_data[\"bots_action_df\"]\n", "reaction_df = gathered_data[\"reaction_df\"]\n", "total_clip_df = gathered_data[\"total_clip_df\"]\n", "playlist_clip_df = gathered_data[\"playlist_clip_df\"]\n", "auth_user_df = gathered_data[\"auth_user_df\"]\n", "discord_info_df = gathered_data[\"discord_info_df\"]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:34:31.486975Z", "iopub.status.busy": "2025-01-04T15:34:31.486837Z", "iopub.status.idle": "2025-01-04T15:34:36.348859Z", "shell.execute_reply": "2025-01-04T15:34:36.348268Z", "shell.execute_reply.started": "2025-01-04T15:34:31.486962Z" } }, "outputs": [], "source": [ "# parse out the necessary metadata early\n", "total_clip_df[\n", " [\"continued_parent\", \"duration\", \"source\", \"clip_type\", \"task\", \"edited_clip_id\"]\n", "] = pd.DataFrame(\n", " total_clip_df[\"metadata\"].map(parse_metadata_for_basics).tolist(),\n", " index=total_clip_df.index,\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:34:36.349630Z", "iopub.status.busy": "2025-01-04T15:34:36.349477Z", "iopub.status.idle": "2025-01-04T15:34:39.823771Z", "shell.execute_reply": "2025-01-04T15:34:39.823231Z", "shell.execute_reply.started": "2025-01-04T15:34:36.349614Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total clips: 445263\n", "gen: 424256 (95.28%)\n", "upsample: 11251 (2.53%)\n", "upload: 4878 (1.10%)\n", "concat: 2495 (0.56%)\n", "stem: 1155 (0.26%)\n", "edit_crop: 620 (0.14%)\n", "concat_infilling: 608 (0.14%)\n", "total without model: 6050\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Total number of hours: 2\n", "Average clips per hour: 222631.50\n", "Max clips in an hour: 247525\n", "Min clips in an hour: 197738\n" ] } ], "source": [ "# filter on versions\n", "clip_df = total_clip_df.copy()\n", "total_clip_counts = clip_df.shape[0]\n", "print(f\"total clips: {total_clip_counts}\")\n", "print_out_value_counts_nicely(clip_df, \"clip_type\")\n", "# check the number of audio uploads\n", "upload_clip_df = total_clip_df[total_clip_df[\"clip_type\"] == \"upload\"].copy()\n", "stem_clip_df = total_clip_df[total_clip_df[\"clip_type\"] == \"stem\"].copy()\n", "print(\"total without model:\", (total_clip_df[\"model_name\"] == \"\").sum())\n", "\n", "# Call the function\n", "plot_clip_distribution(total_clip_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Proceed with feature engineering and cleaning up" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:34:39.824501Z", "iopub.status.busy": "2025-01-04T15:34:39.824346Z", "iopub.status.idle": "2025-01-04T15:34:39.999490Z", "shell.execute_reply": "2025-01-04T15:34:39.998924Z", "shell.execute_reply.started": "2025-01-04T15:34:39.824485Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of upvoates: 96,089 rows\n", "number of flagged reports: 1,782 rows\n" ] } ], "source": [ "upvoted_df = reaction_df[reaction_df[\"reaction_type\"] == \"L\"].copy()\n", "print(f\"number of upvoates: {upvoted_df.shape[0]:,} rows\")\n", "upvoted_ids = upvoted_df[\"clip_id\"]\n", "\n", "flagged_df = reaction_df[reaction_df[\"flagged\"]].copy()\n", "print(f\"number of flagged reports: {flagged_df.shape[0]:,} rows\")\n", "flagged_ids = flagged_df[\"clip_id\"]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:34:40.000194Z", "iopub.status.busy": "2025-01-04T15:34:40.000047Z", "iopub.status.idle": "2025-01-04T15:34:40.327244Z", "shell.execute_reply": "2025-01-04T15:34:40.326701Z", "shell.execute_reply.started": "2025-01-04T15:34:40.000179Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reactions fraction by pro user:\n", "False: 377082 (56.33%)\n", "True: 292309 (43.67%)\n", "------------\n", "Clip generated fraction by pro user:\n", "False: 236673 (53.15%)\n", "True: 208590 (46.85%)\n" ] } ], "source": [ "# this is probably the right way to figure out the pro user group\n", "pro_users = set(discord_info_df[\"user_id\"].unique())\n", "reaction_df[\"is_pro_user\"] = reaction_df[\"user_id\"].isin(pro_users)\n", "clip_df[\"is_pro_user\"] = clip_df[\"user_id\"].isin(pro_users)\n", "\n", "# this is very interesting....\n", "# reaction check\n", "print(\"Reactions fraction by pro user:\")\n", "print_out_value_counts_nicely(reaction_df, \"is_pro_user\")\n", "print(\"------------\")\n", "# clip check\n", "print(\"Clip generated fraction by pro user:\")\n", "print_out_value_counts_nicely(clip_df, \"is_pro_user\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:34:40.328096Z", "iopub.status.busy": "2025-01-04T15:34:40.327800Z", "iopub.status.idle": "2025-01-04T15:34:40.355526Z", "shell.execute_reply": "2025-01-04T15:34:40.355054Z", "shell.execute_reply.started": "2025-01-04T15:34:40.328080Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stem parent ids: 570\n" ] } ], "source": [ "# find out the stem parent ids\n", "stem_parent_ids = set(\n", " stem_clip_df[\"metadata\"].apply(lambda x: x.get(\"stem_from_id\", \"xxx\"))\n", ")\n", "print(\"stem parent ids:\", len(stem_parent_ids))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:12.019778Z", "start_time": "2024-05-26T00:22:57.637371Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:34:40.356157Z", "iopub.status.busy": "2025-01-04T15:34:40.356018Z", "iopub.status.idle": "2025-01-04T15:34:44.871934Z", "shell.execute_reply": "2025-01-04T15:34:44.871364Z", "shell.execute_reply.started": "2025-01-04T15:34:40.356142Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clips in a splaylist:\n", "False: 441074 (99.06%)\n", "True: 4189 (0.94%)\n", "------------\n", "Clips has stem children:\n", "False: 444958 (99.93%)\n", "True: 305 (0.07%)\n" ] } ], "source": [ "# add clip is in playlist feature\n", "clip_df[\"is_in_playlist\"] = clip_df[\"id\"].isin(playlist_clip_df[\"clip_id\"].unique())\n", "print(\"Clips in a splaylist:\")\n", "print_out_value_counts_nicely(clip_df, \"is_in_playlist\")\n", "clip_df[\"has_stems\"] = clip_df[\"id\"].astype(str).isin(stem_parent_ids)\n", "print(\"------------\")\n", "print(\"Clips has stem children:\")\n", "print_out_value_counts_nicely(clip_df, \"has_stems\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:17.912428Z", "start_time": "2024-05-26T00:23:12.021726Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:34:44.872655Z", "iopub.status.busy": "2025-01-04T15:34:44.872504Z", "iopub.status.idle": "2025-01-04T15:34:45.673595Z", "shell.execute_reply": "2025-01-04T15:34:45.673010Z", "shell.execute_reply.started": "2025-01-04T15:34:44.872639Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clips that have children: 44327 \n", "clips that are parents: 9045 \n", " Average continues from clip = 4.9\n", "web: 381155 (86.20%)\n", "ios: 36349 (8.22%)\n", "android: 24656 (5.58%)\n" ] } ], "source": [ "# parse the metadata for histories and types\n", "clip_history_df = clip_df[~clip_df[\"continued_parent\"].isna()].copy()\n", "# these are the direct parent's ids -- not grandparents\n", "has_continued_children_ids = clip_history_df[\"continued_parent\"]\n", "print(\n", " \"clips that have children:\",\n", " len(has_continued_children_ids),\n", " \"\\nclips that are parents:\",\n", " has_continued_children_ids.nunique(),\n", " \"\\n\",\n", " \"Average continues from clip = \",\n", " round(\n", " len(has_continued_children_ids) / len(has_continued_children_ids.unique()), 2\n", " ),\n", ")\n", "# Get value counts\n", "print_out_value_counts_nicely(clip_df, \"source\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:22.092835Z", "start_time": "2024-05-26T00:23:17.914456Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:34:45.674317Z", "iopub.status.busy": "2025-01-04T15:34:45.674167Z", "iopub.status.idle": "2025-01-04T15:34:48.027954Z", "shell.execute_reply": "2025-01-04T15:34:48.027370Z", "shell.execute_reply.started": "2025-01-04T15:34:45.674301Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total uploads: 6050\n", "clips without request id: 9756\n", "clips without request id: 9756 clip_type\n", "upload 4878\n", "concat 2495\n", "stem 1155\n", "edit_crop 620\n", "concat_infilling 608\n", "Name: count, dtype: int64\n", "Clips without request id (concat, uploads...) frac = 0.00697\n" ] } ], "source": [ "print(\"total uploads:\", (clip_df[\"model_name\"] == \"\").sum())\n", "print(\"clips without request id:\", (clip_df[\"request_id\"].isna()).sum())\n", "# the nans are concats, we want to drop them for now\n", "concated_clips = clip_df[\n", " (clip_df[\"clip_type\"] == \"concat\") | (clip_df[\"clip_type\"] == \"concat_infilling\")\n", "].copy()\n", "non_request_clips = clip_df[clip_df[\"request_id\"].isna()].copy()\n", "print(\n", " \"clips without request id:\",\n", " non_request_clips.shape[0],\n", " non_request_clips[\"clip_type\"].value_counts(),\n", ")\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": 16, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:22.833148Z", "start_time": "2024-05-26T00:23:22.094796Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:34:48.028684Z", "iopub.status.busy": "2025-01-04T15:34:48.028532Z", "iopub.status.idle": "2025-01-04T15:34:48.501692Z", "shell.execute_reply": "2025-01-04T15:34:48.501208Z", "shell.execute_reply.started": "2025-01-04T15:34:48.028669Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chirp-v4-h-s-32: 207334 (47.61%)\n", "chirp-v3p5-engine-s-8: 139824 (32.11%)\n", "chirp-v4-h-t-6: 37573 (8.63%)\n", "chirp-v4-up-u-3: 11198 (2.57%)\n", "chirp-v3p5-engine-upload-4: 10790 (2.48%)\n", "chirp-v3p5-engine-t-6: 9110 (2.09%)\n", "chirp-v4: 4603 (1.06%)\n", "chirp-v3-5: 3738 (0.86%)\n", "chirp-v4-h-s-32-c-2: 2702 (0.62%)\n", "chirp-v4-engine-b: 2067 (0.47%)\n", "chirp-v3-engine-i: 2021 (0.46%)\n", "chirp-v4-h-t-6-c-17: 1377 (0.32%)\n", "chirp-v3p5-engine-b-r: 1065 (0.24%)\n", "chirp-v3p5-engine-b: 1032 (0.24%)\n", "chirp-v2-xxl-alpha: 614 (0.14%)\n", "chirp-v3p5-engine-short: 299 (0.07%)\n", "chirp-v3-0: 71 (0.02%)\n", "chirp-v3-5-short: 51 (0.01%)\n", "chirp-up: 21 (0.00%)\n", "chirp-v4-tau: 8 (0.00%)\n", "chirp-v3-5-upload: 7 (0.00%)\n", "chirp-v3-5-tau: 2 (0.00%)\n" ] } ], "source": [ "# check the model conts\n", "print_out_value_counts_nicely(clip_df, \"model_name\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:26.317699Z", "start_time": "2024-05-26T00:23:22.835074Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:34:48.502359Z", "iopub.status.busy": "2025-01-04T15:34:48.502215Z", "iopub.status.idle": "2025-01-04T15:34:52.907702Z", "shell.execute_reply": "2025-01-04T15:34:52.907122Z", "shell.execute_reply.started": "2025-01-04T15:34:48.502344Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pre-filter model type clip_df shape: (435507, 37)\n", "post-filter model type clip_df shape: (427006, 37)\n", "chirp-v4-h-s-32: 207334 (48.56%)\n", "chirp-v3p5-engine-s-8: 139824 (32.75%)\n", "chirp-v4-h-t-6: 37573 (8.80%)\n", "chirp-v4-up-u-3: 11198 (2.62%)\n", "chirp-v3p5-engine-upload-4: 10790 (2.53%)\n", "chirp-v3p5-engine-t-6: 9110 (2.13%)\n", "chirp-v4-h-s-32-c-2: 2702 (0.63%)\n", "chirp-v4-engine-b: 2067 (0.48%)\n", "chirp-v3-engine-i: 2021 (0.47%)\n", "chirp-v4-h-t-6-c-17: 1377 (0.32%)\n", "chirp-v3p5-engine-b-r: 1065 (0.25%)\n", "chirp-v3p5-engine-b: 1032 (0.24%)\n", "chirp-v2-xxl-alpha: 614 (0.14%)\n", "chirp-v3p5-engine-short: 299 (0.07%)\n" ] } ], "source": [ "print(\"pre-filter model type clip_df shape:\", clip_df.shape)\n", "clip_df = clip_df[\n", " (clip_df[\"model_name\"] != \"chirp-v3-5\")\n", " & (clip_df[\"model_name\"] != \"chirp-v3-0\")\n", " & (clip_df[\"model_name\"] != \"chirp-v3-5-tau\")\n", " & (clip_df[\"model_name\"] != \"chirp-v3-5-upload\")\n", " & (clip_df[\"model_name\"] != \"chirp-v3-5-short\")\n", " & (clip_df[\"model_name\"] != \"chirp-v4\")\n", " & (clip_df[\"model_name\"] != \"chirp-v4-tau\")\n", " & (clip_df[\"model_name\"] != \"chirp-up\")\n", "]\n", "print(\"post-filter model type clip_df shape:\", clip_df.shape)\n", "print_out_value_counts_nicely(clip_df, \"model_name\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:34:52.908398Z", "iopub.status.busy": "2025-01-04T15:34:52.908255Z", "iopub.status.idle": "2025-01-04T15:34:54.544222Z", "shell.execute_reply": "2025-01-04T15:34:54.543653Z", "shell.execute_reply.started": "2025-01-04T15:34:52.908383Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "concat reactions: 2575 unique concat clips: 2337\n", "total concats (3103, 40)\n", "check \n", " reaction_play_count reaction_upvote_count reaction_dislike_count\n", "count 2337.000000 2337.000000 2337.000000\n", "mean 1.889602 0.242191 0.011981\n", "std 3.720634 1.030967 0.108824\n", "min 1.000000 0.000000 0.000000\n", "25% 1.000000 0.000000 0.000000\n", "50% 1.000000 0.000000 0.000000\n", "75% 2.000000 0.000000 0.000000\n", "max 133.000000 40.000000 1.000000\n", "All concats 3103\n", "total concats with plays 2337\n" ] } ], "source": [ "concated_clips = merge_concat_clips_with_reactions(concated_clips, reaction_df)\n", "# TODO: why so many clips are concats without plays??? -- oh probably they concat multiple times?\n", "print(\"All concats\", concated_clips.shape[0])\n", "concated_clips = concated_clips[concated_clips[\"reaction_play_count\"] > 0]\n", "print(\"total concats with plays\", concated_clips.shape[0])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:38.974479Z", "start_time": "2024-05-26T00:23:34.385507Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:34:54.544945Z", "iopub.status.busy": "2025-01-04T15:34:54.544790Z", "iopub.status.idle": "2025-01-04T15:34:55.422702Z", "shell.execute_reply": "2025-01-04T15:34:55.422174Z", "shell.execute_reply.started": "2025-01-04T15:34:54.544929Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2337it [00:00, 18030.84it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "total concat unique clips are: 4472 with error: 15, duplicate 425 \n", " uploads are in concats 2 frac 0.000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "concat_clips_ids = get_concat_clip_ids(concated_clips, clip_df, upload_clip_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Features" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:40.236690Z", "start_time": "2024-05-26T00:23:39.995713Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:34:55.423357Z", "iopub.status.busy": "2025-01-04T15:34:55.423213Z", "iopub.status.idle": "2025-01-04T15:34:55.575137Z", "shell.execute_reply": "2025-01-04T15:34:55.574578Z", "shell.execute_reply.started": "2025-01-04T15:34:55.423343Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 427006.000000\n", "mean 26.191683\n", "std 42.330048\n", "min 1.000000\n", "25% 6.000000\n", "50% 10.000000\n", "75% 28.000000\n", "max 577.000000\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": 21, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:44.826860Z", "start_time": "2024-05-26T00:23:40.238330Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:34:55.578499Z", "iopub.status.busy": "2025-01-04T15:34:55.578113Z", "iopub.status.idle": "2025-01-04T15:34:57.212083Z", "shell.execute_reply": "2025-01-04T15:34:57.211508Z", "shell.execute_reply.started": "2025-01-04T15:34:55.578481Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has upvoted upvoted\n", "False 407054\n", "True 19952\n", "Name: count, dtype: int64 upvoted\n", "False 0.953275\n", "True 0.046725\n", "Name: proportion, dtype: float64 upvote_count\n", "False 0.95197\n", "True 0.04803\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": 22, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:48.404433Z", "start_time": "2024-05-26T00:23:44.857788Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:34:57.212765Z", "iopub.status.busy": "2025-01-04T15:34:57.212618Z", "iopub.status.idle": "2025-01-04T15:34:58.497200Z", "shell.execute_reply": "2025-01-04T15:34:58.496620Z", "shell.execute_reply.started": "2025-01-04T15:34:57.212751Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "downvoted fraction by category:\n", "False: 413267 (96.78%)\n", "True: 13739 (3.22%)\n" ] } ], "source": [ "disliked_ids = reaction_df[reaction_df[\"reaction_type\"] == \"D\"][\"clip_id\"].unique()\n", "\n", "clip_df[\"downvoted\"] = clip_df[\"id\"].isin(disliked_ids)\n", "print(\"downvoted fraction by category:\")\n", "print_out_value_counts_nicely(clip_df, \"downvoted\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:56.590022Z", "start_time": "2024-05-26T00:23:48.405668Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:34:58.497922Z", "iopub.status.busy": "2025-01-04T15:34:58.497772Z", "iopub.status.idle": "2025-01-04T15:35:01.784707Z", "shell.execute_reply": "2025-01-04T15:35:01.784128Z", "shell.execute_reply.started": "2025-01-04T15:34:58.497907Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has_continued fraction by category:\n", "False: 424580 (99.43%)\n", "True: 2426 (0.57%)\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", "print_out_value_counts_nicely(clip_df, \"has_continued\")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:01.785418Z", "iopub.status.busy": "2025-01-04T15:35:01.785269Z", "iopub.status.idle": "2025-01-04T15:35:01.821958Z", "shell.execute_reply": "2025-01-04T15:35:01.821483Z", "shell.execute_reply.started": "2025-01-04T15:35:01.785403Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has upvoted in exp 0.04673\n", "has downvoted out of exp 0.03218\n" ] } ], "source": [ "print(\n", " \"has upvoted in exp\",\n", " # clip_df[in_exp_mask][\"upvoted\"].value_counts(),\n", " round(clip_df[\"upvoted\"].value_counts(normalize=True)[True], 5),\n", " # (clip_df[clip_df[\"in_fe_exp\"]][\"upvote_count\"] >= 1).value_counts(normalize=True),\n", ")\n", "print(\n", " \"has downvoted out of exp\",\n", " # clip_df[out_exp_mask][\"downvoted\"].value_counts(),\n", " round(clip_df[\"downvoted\"].value_counts(normalize=True)[True], 5),\n", ")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:04.704306Z", "start_time": "2024-05-26T00:23:56.591353Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:01.822620Z", "iopub.status.busy": "2025-01-04T15:35:01.822475Z", "iopub.status.idle": "2025-01-04T15:35:04.798298Z", "shell.execute_reply": "2025-01-04T15:35:04.797725Z", "shell.execute_reply.started": "2025-01-04T15:35:01.822606Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "part_of_concat fraction by category:\n", "False: 424271 (99.36%)\n", "True: 2735 (0.64%)\n", "------------\n", "Model distribution for part_of_concat clips:\n", "chirp-v4-h-s-32: 50.53%\n", "chirp-v4-h-t-6: 18.98%\n", "chirp-v3p5-engine-s-8: 16.93%\n", "chirp-v3p5-engine-t-6: 4.24%\n", "chirp-v3p5-engine-upload-4: 3.95%\n", "chirp-v4-up-u-3: 1.86%\n", "chirp-v4-h-t-6-c-17: 1.35%\n", "chirp-v4-engine-b: 0.91%\n", "chirp-v4-h-s-32-c-2: 0.69%\n", "chirp-v3-engine-i: 0.33%\n", "chirp-v3p5-engine-b: 0.11%\n", "chirp-v3p5-engine-b-r: 0.07%\n", "chirp-v2-xxl-alpha: 0.04%\n" ] } ], "source": [ "# add concat column\n", "clip_df[\"part_of_concat\"] = clip_df[\"id\"].astype(str).isin(concat_clips_ids)\n", "print(\"part_of_concat fraction by category:\")\n", "print_out_value_counts_nicely(clip_df, \"part_of_concat\")\n", "\n", "print(\"------------\")\n", "print(\"Model 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": 26, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:09.954233Z", "start_time": "2024-05-26T00:24:04.705554Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:04.799035Z", "iopub.status.busy": "2025-01-04T15:35:04.798875Z", "iopub.status.idle": "2025-01-04T15:35:06.924560Z", "shell.execute_reply": "2025-01-04T15:35:06.923985Z", "shell.execute_reply.started": "2025-01-04T15:35:04.799019Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has_action fraction by category:\n", "False: 423235 (99.12%)\n", "True: 3771 (0.88%)\n" ] } ], "source": [ "# verify bots action are all non-empty\n", "action_mask = (\n", " bots_action_df[\"download_audio_count\"]\n", " + bots_action_df[\"download_video_count\"]\n", " + bots_action_df[\"download_audio_wav_count\"]\n", " # + bots_action_df[\"share_count\"] # will remove share cause it can be negative, just can be...\n", ") >= 1\n", "has_action_ids = set(i for i in bots_action_df[action_mask][\"clip_id\"].unique())\n", "clip_df[\"has_action\"] = clip_df[\"id\"].isin(has_action_ids)\n", "print(\"has_action fraction by category:\")\n", "print_out_value_counts_nicely(clip_df, \"has_action\")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:14.579841Z", "start_time": "2024-05-26T00:24:09.955568Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:06.925280Z", "iopub.status.busy": "2025-01-04T15:35:06.925121Z", "iopub.status.idle": "2025-01-04T15:35:08.165509Z", "shell.execute_reply": "2025-01-04T15:35:08.164933Z", "shell.execute_reply.started": "2025-01-04T15:35:06.925265Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flagged fraction by category:\n", "False: 426289 (99.83%)\n", "True: 717 (0.17%)\n" ] } ], "source": [ "# add downvoted column\n", "clip_df[\"flagged\"] = clip_df[\"id\"].isin(flagged_ids)\n", "print(\"flagged fraction by category:\")\n", "print_out_value_counts_nicely(clip_df, \"flagged\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:08.166186Z", "iopub.status.busy": "2025-01-04T15:35:08.166039Z", "iopub.status.idle": "2025-01-04T15:35:08.195316Z", "shell.execute_reply": "2025-01-04T15:35:08.194848Z", "shell.execute_reply.started": "2025-01-04T15:35:08.166171Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deleted fraction by category:\n", "False: 364735 (85.42%)\n", "True: 62271 (14.58%)\n" ] } ], "source": [ "clip_df[\"deleted\"] = clip_df[\"is_deleted\"]\n", "print(\"deleted fraction by category:\")\n", "print_out_value_counts_nicely(clip_df, \"deleted\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:15.382315Z", "start_time": "2024-05-26T00:24:14.581073Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:08.195972Z", "iopub.status.busy": "2025-01-04T15:35:08.195827Z", "iopub.status.idle": "2025-01-04T15:35:08.514373Z", "shell.execute_reply": "2025-01-04T15:35:08.513882Z", "shell.execute_reply.started": "2025-01-04T15:35:08.195958Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total clips: 427,006\n", "Must be positive: 26,934 (6.31%)\n", "Definitely not negative: 351,568 (82.33%)\n", "Must be negative: 75,438 (17.67%)\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[\"flagged\"]) | (clip_df[\"deleted\"])\n", ")\n", "total_clips_count = clip_df.shape[0]\n", "must_be_positive_count = sum(must_be_positive_mask)\n", "definitely_not_negative_count = sum(must_be_not_negative_mask)\n", "must_be_negative_count = sum(must_be_negative_mask)\n", "\n", "print(\n", " f\"Total clips: {total_clips_count:,}\\n\"\n", " f\"Must be positive: {must_be_positive_count:,} ({must_be_positive_count/total_clips_count:.2%})\\n\"\n", " f\"Definitely not negative: {definitely_not_negative_count:,} ({definitely_not_negative_count/total_clips_count:.2%})\\n\"\n", " f\"Must be negative: {must_be_negative_count:,} ({must_be_negative_count/total_clips_count:.2%})\"\n", ")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:31.095990Z", "start_time": "2024-05-26T00:24:15.383572Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:08.515028Z", "iopub.status.busy": "2025-01-04T15:35:08.514888Z", "iopub.status.idle": "2025-01-04T15:35:13.198153Z", "shell.execute_reply": "2025-01-04T15:35:13.197564Z", "shell.execute_reply.started": "2025-01-04T15:35:08.515013Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Liked requests: 21,056\n", "Not liked requests: 213,387\n", "Requests with preference paired generations: 16,267\n", "Percentage of total unique requests: 7.46%\n" ] } ], "source": [ "mask = must_be_positive_mask & must_be_not_negative_mask\n", "total_unique_requests = clip_df[\"request_id\"].nunique()\n", "liked_requests = clip_df[mask][\"request_id\"].unique() # requests with at least 1 like\n", "unliked_requests = clip_df[~mask][\"request_id\"].unique() # requests without like\n", "has_liked_requests = set(liked_requests).intersection(\n", " set(unliked_requests)\n", ") # the request must have 1 like and one without like\n", "print(f\"Liked requests: {len(liked_requests):,}\")\n", "print(f\"Not liked requests: {len(unliked_requests):,}\")\n", "print(f\"Requests with preference paired generations: {len(has_liked_requests):,}\")\n", "print(\n", " f\"Percentage of total unique requests: {len(has_liked_requests) / total_unique_requests:.2%}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:13.198894Z", "iopub.status.busy": "2025-01-04T15:35:13.198737Z", "iopub.status.idle": "2025-01-04T15:35:16.209529Z", "shell.execute_reply": "2025-01-04T15:35:16.208946Z", "shell.execute_reply.started": "2025-01-04T15:35:13.198879Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Disliked requests: 45,268\n", "Not disliked requests: 186,754\n", "Requests with preference paired generations: 13,846\n", "Percentage of total unique requests: 6.35%\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": 32, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:31.099372Z", "start_time": "2024-05-26T00:24:31.097244Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:16.210273Z", "iopub.status.busy": "2025-01-04T15:35:16.210123Z", "iopub.status.idle": "2025-01-04T15:35:16.247034Z", "shell.execute_reply": "2025-01-04T15:35:16.246528Z", "shell.execute_reply.started": "2025-01-04T15:35:16.210258Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total selected pairs of requests: 26,450\n", "Percentage of total unique requests: 12.12%\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": 33, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:31.239254Z", "start_time": "2024-05-26T00:24:31.100389Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:16.247687Z", "iopub.status.busy": "2025-01-04T15:35:16.247544Z", "iopub.status.idle": "2025-01-04T15:35:16.297984Z", "shell.execute_reply": "2025-01-04T15:35:16.297501Z", "shell.execute_reply.started": "2025-01-04T15:35:16.247673Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference in preference counts:\n", "0: 326,129 (76.38%)\n", "-1: 75,438 (17.67%)\n", "1: 25,439 (5.96%)\n" ] } ], "source": [ "# this used to be a terrible bug...X.x\n", "assert mask.shape[0] == clip_df.shape[0]\n", "clip_df[\"pos_preference\"] = mask\n", "clip_df[\"neg_preference\"] = must_be_negative_mask\n", "# note that this is along the same row, so a positive clip can't be negative\n", "clip_df[\"diff_preference\"] = clip_df[\"pos_preference\"].astype(int) - clip_df[\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": 34, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:37.322031Z", "start_time": "2024-05-26T00:24:31.240829Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:16.298594Z", "iopub.status.busy": "2025-01-04T15:35:16.298456Z", "iopub.status.idle": "2025-01-04T15:35:18.273841Z", "shell.execute_reply": "2025-01-04T15:35:18.273250Z", "shell.execute_reply.started": "2025-01-04T15:35:16.298579Z" } }, "outputs": [], "source": [ "# creation of interesting_clips\n", "interesting_clips = clip_df[clip_df[\"request_id\"].isin(requests)].copy()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:18.274608Z", "iopub.status.busy": "2025-01-04T15:35:18.274456Z", "iopub.status.idle": "2025-01-04T15:35:19.496373Z", "shell.execute_reply": "2025-01-04T15:35:19.495862Z", "shell.execute_reply.started": "2025-01-04T15:35:18.274592Z" } }, "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 0002fd2d-05a6-4e1b-b844-dd7a03845c8e False True -1\n", "1 0002fd2d-05a6-4e1b-b844-dd7a03845c8e False False 0\n", "2 00038a1f-8b1a-4eda-85e1-e324ee205d73 False True -1\n", "3 00038a1f-8b1a-4eda-85e1-e324ee205d73 False False 0\n", "4 00048e9d-ec9a-4500-a66d-fdd1bc272b23 False False 0\n", "5 00048e9d-ec9a-4500-a66d-fdd1bc272b23 True False 1" ] }, "execution_count": 35, "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(n=6)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:19.497070Z", "iopub.status.busy": "2025-01-04T15:35:19.496924Z", "iopub.status.idle": "2025-01-04T15:35:19.519252Z", "shell.execute_reply": "2025-01-04T15:35:19.518754Z", "shell.execute_reply.started": "2025-01-04T15:35:19.497055Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference 0: 22,787 (43.08%)\n", "Difference 1: 16,267 (30.75%)\n", "Difference -1: 13,846 (26.17%)\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": 37, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:19.519907Z", "iopub.status.busy": "2025-01-04T15:35:19.519766Z", "iopub.status.idle": "2025-01-04T15:35:19.562042Z", "shell.execute_reply": "2025-01-04T15:35:19.561578Z", "shell.execute_reply.started": "2025-01-04T15:35:19.519893Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Value 1.0: 22,787 (86.15%)\n", "Value 2.0: 3,663 (13.85%)\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": 38, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:38.960130Z", "start_time": "2024-05-26T00:24:37.323369Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:19.562666Z", "iopub.status.busy": "2025-01-04T15:35:19.562524Z", "iopub.status.idle": "2025-01-04T15:35:20.883579Z", "shell.execute_reply": "2025-01-04T15:35:20.882967Z", "shell.execute_reply.started": "2025-01-04T15:35:19.562652Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of unique request_ids: 26,450\n", "Number of unique ids: 52,900\n", "Validation passed!\n" ] } ], "source": [ "# assign the labels now\n", "interesting_clips[\"preference\"] = interesting_clips.index % 2 == 1\n", "# get df of requests -- let's move on!\n", "print(f\"Number of unique request_ids: {interesting_clips['request_id'].nunique():,}\")\n", "print(f\"Number of unique ids: {interesting_clips['id'].nunique():,}\")\n", "validate_preference_data(interesting_clips)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:43.332222Z", "start_time": "2024-05-26T00:24:43.166461Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:20.884314Z", "iopub.status.busy": "2025-01-04T15:35:20.884165Z", "iopub.status.idle": "2025-01-04T15:35:20.904229Z", "shell.execute_reply": "2025-01-04T15:35:20.903781Z", "shell.execute_reply.started": "2025-01-04T15:35:20.884298Z" } }, "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": 40, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:20.904843Z", "iopub.status.busy": "2025-01-04T15:35:20.904698Z", "iopub.status.idle": "2025-01-04T15:35:27.968625Z", "shell.execute_reply": "2025-01-04T15:35:27.968033Z", "shell.execute_reply.started": "2025-01-04T15:35:20.904829Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of interesting clips: 52,900\n", "Unique request and clip counts in interesting_clips:\n", "Request IDs: 26,450\n", "Clip IDs: 52,900\n" ] } ], "source": [ "# need the reaction play counts\n", "# Filter reaction_df for relevant clip_ids\n", "partial_reaction_df = reaction_df[\n", " reaction_df[\"clip_id\"].isin(set(interesting_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", "interesting_clips = interesting_clips.merge(total_play_counts, on=\"id\", how=\"left\")\n", "interesting_clips = interesting_clips.merge(pro_play_counts, on=\"id\", how=\"left\")\n", "\n", "print(f\"Number of interesting clips: {len(interesting_clips):,}\")\n", "# Get unique counts for request_id and id\n", "unique_request_ids = interesting_clips[\"request_id\"].nunique()\n", "unique_clip_ids = interesting_clips[\"id\"].nunique()\n", "\n", "# Print the results in a formatted manner\n", "print(\"Unique request and clip counts in interesting_clips:\")\n", "print(f\"{'Request IDs:':<15} {unique_request_ids:,}\")\n", "print(f\"{'Clip IDs:':<15} {unique_clip_ids:,}\")" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:43.737067Z", "start_time": "2024-05-26T00:24:43.563216Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:27.969352Z", "iopub.status.busy": "2025-01-04T15:35:27.969198Z", "iopub.status.idle": "2025-01-04T15:35:28.002660Z", "shell.execute_reply": "2025-01-04T15:35:28.002167Z", "shell.execute_reply.started": "2025-01-04T15:35:27.969336Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preference counts and fractions by batch index:\n", "--------------------------------------------------\n", "Batch Index: 0\n", " Preference False: Count: 13,481 Fraction: 50.97%\n", " Preference True: Count: 12,969 Fraction: 49.03%\n", "\n", "Batch Index: 1\n", " Preference False: Count: 12,969 Fraction: 49.03%\n", " Preference True: Count: 13,481 Fraction: 50.97%\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}: Count: {count:,} Fraction: {fraction:.2%}\")\n", " print()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:44.304573Z", "start_time": "2024-05-26T00:24:43.973218Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:28.003326Z", "iopub.status.busy": "2025-01-04T15:35:28.003182Z", "iopub.status.idle": "2025-01-04T15:35:28.090096Z", "shell.execute_reply": "2025-01-04T15:35:28.089628Z", "shell.execute_reply.started": "2025-01-04T15:35:28.003311Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chirp-v4-h-s-32: 26076 (49.29%)\n", "chirp-v3p5-engine-s-8: 13402 (25.33%)\n", "chirp-v4-h-t-6: 6862 (12.97%)\n", "chirp-v4-up-u-3: 2398 (4.53%)\n", "chirp-v3p5-engine-t-6: 1804 (3.41%)\n", "chirp-v3p5-engine-upload-4: 1228 (2.32%)\n", "chirp-v4-h-s-32-c-2: 334 (0.63%)\n", "chirp-v4-h-t-6-c-17: 290 (0.55%)\n", "chirp-v4-engine-b: 190 (0.36%)\n", "chirp-v3-engine-i: 128 (0.24%)\n", "chirp-v3p5-engine-b: 74 (0.14%)\n", "chirp-v3p5-engine-b-r: 62 (0.12%)\n", "chirp-v3p5-engine-short: 26 (0.05%)\n", "chirp-v2-xxl-alpha: 26 (0.05%)\n" ] } ], "source": [ "print_out_value_counts_nicely(interesting_clips, \"model_name\")" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:44.533983Z", "start_time": "2024-05-26T00:24:44.305722Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:28.090727Z", "iopub.status.busy": "2025-01-04T15:35:28.090586Z", "iopub.status.idle": "2025-01-04T15:35:28.138323Z", "shell.execute_reply": "2025-01-04T15:35:28.137847Z", "shell.execute_reply.started": "2025-01-04T15:35:28.090714Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time Validation:\n", "--------------------\n", "Interesting Clips:\n", " Earliest: 2025-01-06 13:00:00.119837+00:00\n", " Latest: 2025-01-06 14:47:20.741190+00:00\n", "\n", "All Clips:\n", " Earliest: 2025-01-06 13:00:00.019271+00:00\n", " Latest: 2025-01-06 14:47:53.954804+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": 44, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:50.674838Z", "start_time": "2024-05-26T00:24:46.366677Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:28.138947Z", "iopub.status.busy": "2025-01-04T15:35:28.138807Z", "iopub.status.idle": "2025-01-04T15:35:28.914568Z", "shell.execute_reply": "2025-01-04T15:35:28.913983Z", "shell.execute_reply.started": "2025-01-04T15:35:28.138933Z" } }, "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": 45, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:51.600621Z", "start_time": "2024-05-26T00:24:50.676186Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:28.915314Z", "iopub.status.busy": "2025-01-04T15:35:28.915164Z", "iopub.status.idle": "2025-01-04T15:35:29.416196Z", "shell.execute_reply": "2025-01-04T15:35:29.415613Z", "shell.execute_reply.started": "2025-01-04T15:35:28.915299Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ratio of preferred clips to total clips for each model:\n", "------------------------------------------------------------\n", "chirp-v2-xxl-alpha 2.12% ± 0.58%\n", "chirp-v3-engine-i 3.17% ± 0.39%\n", "chirp-v3p5-engine-b 3.59% ± 0.58%\n", "chirp-v3p5-engine-b-r 2.91% ± 0.52%\n", "chirp-v3p5-engine-s-8 4.79% ± 0.06%\n", "chirp-v3p5-engine-short 4.35% ± 1.18%\n", "chirp-v3p5-engine-t-6 9.90% ± 0.31%\n", "chirp-v3p5-engine-upload-4 5.69% ± 0.22%\n", "chirp-v4-engine-b 4.60% ± 0.46%\n", "chirp-v4-h-s-32 6.29% ± 0.05%\n", "chirp-v4-h-s-32-c-2 6.29% ± 0.47%\n", "chirp-v4-h-t-6 9.10% ± 0.15%\n", "chirp-v4-h-t-6-c-17 11.26% ± 0.85%\n", "chirp-v4-up-u-3 10.71% ± 0.29%\n" ] } ], "source": [ "# Calculate the ratio of preferred clips to total clips for each model\n", "clip_df_model_counts = clip_df[\"model_name\"].value_counts()\n", "preference_ratio = (\n", " interesting_clips[interesting_clips[\"preference\"]][\"model_name\"].value_counts()\n", " / clip_df_model_counts\n", ")\n", "\n", "# Print the results in a formatted manner\n", "print(\"Ratio of preferred clips to total clips for each model:\")\n", "print(\"-\" * 60)\n", "for model, ratio in preference_ratio.items():\n", " n = clip_df_model_counts[model]\n", " uncertainty = (ratio * (1 - ratio) / n) ** 0.5\n", " print(f\"{model:<30} {ratio:.2%} ± {uncertainty:.2%}\")" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:56.302599Z", "start_time": "2024-05-26T00:24:51.601863Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:29.416913Z", "iopub.status.busy": "2025-01-04T15:35:29.416762Z", "iopub.status.idle": "2025-01-04T15:35:31.839499Z", "shell.execute_reply": "2025-01-04T15:35:31.838976Z", "shell.execute_reply.started": "2025-01-04T15:35:29.416896Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "len pos models: 26450\n", "differing counts: 624\n", "chirp-v2-xxl-alpha_win_over_chirp-v2-xxl-alpha, win ratio 1.000, (1.000, 1.000), counts 13, total 13.\n", "chirp-v3-engine-i_win_over_chirp-v3-engine-i, win ratio 1.000, (1.000, 1.000), counts 64, total 64.\n", "chirp-v3p5-engine-b-r_win_over_chirp-v3p5-engine-b-r, win ratio 1.000, (1.000, 1.000), counts 31, total 31.\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 37, total 37.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 6701, total 6701.\n", "chirp-v3p5-engine-short_win_over_chirp-v3p5-engine-short, win ratio 1.000, (1.000, 1.000), counts 13, total 13.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 902, total 902.\n", "chirp-v3p5-engine-upload-4_win_over_chirp-v3p5-engine-upload-4, win ratio 1.000, (1.000, 1.000), counts 614, total 614.\n", "chirp-v4-engine-b_win_over_chirp-v4-engine-b, win ratio 1.000, (1.000, 1.000), counts 95, total 95.\n", "chirp-v4-h-s-32-c-2_win_over_chirp-v4-h-s-32, win ratio 0.509, (0.455, 0.563), counts 170, total 334.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32, win ratio 1.000, (1.000, 1.000), counts 12871, total 12871.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32-c-2, win ratio 0.491, (0.437, 0.545), counts 164, total 334.\n", "chirp-v4-h-t-6-c-17_win_over_chirp-v4-h-t-6, win ratio 0.534, (0.477, 0.592), counts 155, total 290.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6, win ratio 1.000, (1.000, 1.000), counts 3286, total 3286.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6-c-17, win ratio 0.466, (0.408, 0.523), counts 135, total 290.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3, win ratio 1.000, (1.000, 1.000), counts 1199, total 1199.\n", "tournament players: ['chirp-v4-h-s-32', 'chirp-v4-h-s-32-c-2', 'chirp-v4-h-t-6', 'chirp-v4-h-t-6-c-17']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:01<00:00, 868.64it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v4-h-s-32: 996.9, (979, 1016)\n", "chirp-v4-h-s-32-c-2: 1003.1, (984, 1022)\n", "chirp-v4-h-t-6: 988.0, (967, 1008)\n", "chirp-v4-h-t-6-c-17: 1012.0, (992, 1034)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "get_preference_counts(interesting_clips)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:57.443236Z", "start_time": "2024-05-26T00:24:56.306473Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:31.840241Z", "iopub.status.busy": "2025-01-04T15:35:31.840088Z", "iopub.status.idle": "2025-01-04T15:35:33.707911Z", "shell.execute_reply": "2025-01-04T15:35:33.707436Z", "shell.execute_reply.started": "2025-01-04T15:35:31.840226Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<__array_function__ internals>:180: RuntimeWarning: Converting input from bool to for compatibility.\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_clip_basic_distributions(interesting_clips)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:58.153310Z", "start_time": "2024-05-26T00:24:57.858363Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:33.708618Z", "iopub.status.busy": "2025-01-04T15:35:33.708470Z", "iopub.status.idle": "2025-01-04T15:35:33.727089Z", "shell.execute_reply": "2025-01-04T15:35:33.726645Z", "shell.execute_reply.started": "2025-01-04T15:35:33.708603Z" } }, "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": 49, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:58.890520Z", "start_time": "2024-05-26T00:24:58.154350Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:33.727753Z", "iopub.status.busy": "2025-01-04T15:35:33.727607Z", "iopub.status.idle": "2025-01-04T15:35:35.726616Z", "shell.execute_reply": "2025-01-04T15:35:35.726017Z", "shell.execute_reply.started": "2025-01-04T15:35:33.727739Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of clips in interesting_clips:\n", "52,900\n", "Number of clips in user_interesting_clips:\n", "49,458\n", "Ratio of preferred clips to total clips for each model:\n", "------------------------------------------------------------\n", "chirp-v2-xxl-alpha nan% ± nan%\n", "chirp-v3-engine-i nan% ± nan%\n", "chirp-v3p5-engine-b 2.13% ± 0.45%\n", "chirp-v3p5-engine-b-r 1.41% ± 0.36%\n", "chirp-v3p5-engine-s-8 4.51% ± 0.06%\n", "chirp-v3p5-engine-short 3.01% ± 0.99%\n", "chirp-v3p5-engine-t-6 9.63% ± 0.31%\n", "chirp-v3p5-engine-upload-4 5.21% ± 0.21%\n", "chirp-v4-engine-b 3.77% ± 0.42%\n", "chirp-v4-h-s-32 5.89% ± 0.05%\n", "chirp-v4-h-s-32-c-2 5.96% ± 0.46%\n", "chirp-v4-h-t-6 8.71% ± 0.15%\n", "chirp-v4-h-t-6-c-17 10.68% ± 0.83%\n", "chirp-v4-up-u-3 9.47% ± 0.28%\n" ] } ], "source": [ "# subselect interesting clips\n", "interesting_clips_masks = (interesting_clips[\"model_name\"].str.contains(\"v3p5|v4\")) & (\n", " interesting_clips[\"reaction_play_count\"] > 0\n", ")\n", "# make sure we have pairs\n", "extra_compare_mask = interesting_clips[interesting_clips_masks][\"request_id\"].isin(\n", " interesting_clips[interesting_clips_masks][\"request_id\"]\n", " .value_counts()\n", " .index[interesting_clips[interesting_clips_masks][\"request_id\"].value_counts() == 2]\n", ")\n", "user_intersting_clips = interesting_clips[\n", " interesting_clips_masks & extra_compare_mask\n", "].copy()\n", "\n", "print(\"Number of clips in interesting_clips:\")\n", "print(f\"{interesting_clips.shape[0]:,}\")\n", "print(\"Number of clips in user_interesting_clips:\")\n", "print(f\"{user_intersting_clips.shape[0]:,}\")\n", "# Calculate the ratio of preferred clips to total clips for each model\n", "preference_ratio = (\n", " user_intersting_clips[user_intersting_clips[\"preference\"]][\n", " \"model_name\"\n", " ].value_counts()\n", " / clip_df_model_counts\n", ")\n", "\n", "# Print the results in a formatted manner\n", "print(\"Ratio of preferred clips to total clips for each model:\")\n", "print(\"-\" * 60)\n", "for model, ratio in preference_ratio.items():\n", " n = clip_df_model_counts[model]\n", " uncertainty = (ratio * (1 - ratio) / n) ** 0.5\n", " print(f\"{model:<30} {ratio:.2%} ± {uncertainty:.2%}\")" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:59.204547Z", "start_time": "2024-05-26T00:24:58.891851Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:35.727358Z", "iopub.status.busy": "2025-01-04T15:35:35.727204Z", "iopub.status.idle": "2025-01-04T15:35:36.022467Z", "shell.execute_reply": "2025-01-04T15:35:36.021994Z", "shell.execute_reply.started": "2025-01-04T15:35:35.727343Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "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": 51, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:24:59.207707Z", "start_time": "2024-05-26T00:24:59.205659Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:36.023146Z", "iopub.status.busy": "2025-01-04T15:35:36.022997Z", "iopub.status.idle": "2025-01-04T15:35:36.152241Z", "shell.execute_reply": "2025-01-04T15:35:36.151721Z", "shell.execute_reply.started": "2025-01-04T15:35:36.023130Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Summary of user_interesting_clips:\n", "Total requests: 49,458\n", "Unique clips: 24,729\n", "Fraction of total clips: 11.11%\n", "Time Validation:\n", "Earliest timestamp: 2025-01-06 13:00:00.119837+00:00\n", "Latest timestamp: 2025-01-06 14:46:42.297563+00:00\n", "Earliest timestamp: 2025-01-06 13:00:00.119837+00:00\n", "Latest timestamp: 2025-01-06 14:45:42.819895+00:00\n" ] } ], "source": [ "print(\"Summary of user_interesting_clips:\")\n", "print(f\"Total requests: {user_intersting_clips.shape[0]:,}\")\n", "print(f\"Unique clips: {user_intersting_clips.shape[0] // 2:,}\")\n", "print(\n", " f\"Fraction of total clips: {user_intersting_clips.shape[0] / total_clip_counts:.2%}\"\n", ")\n", "print(\"Time Validation:\")\n", "print(f\"Earliest timestamp: {user_intersting_clips['created_at'].min()}\")\n", "print(f\"Latest timestamp: {user_intersting_clips['created_at'].max()}\")\n", "model_to_test = target_model_name\n", "print(\n", " f\"Earliest timestamp: {user_intersting_clips[user_intersting_clips['model_name'] == model_to_test]['created_at'].min()}\"\n", ")\n", "print(\n", " f\"Latest timestamp: {user_intersting_clips[user_intersting_clips['model_name'] == model_to_test]['created_at'].max()}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:36.152909Z", "iopub.status.busy": "2025-01-04T15:35:36.152765Z", "iopub.status.idle": "2025-01-04T15:35:36.438201Z", "shell.execute_reply": "2025-01-04T15:35:36.437746Z", "shell.execute_reply.started": "2025-01-04T15:35:36.152895Z" } }, "outputs": [], "source": [ "def parse_for_instrumental(x):\n", " if \"make_instrumental\" not in x:\n", " return False\n", " out = x.get(\"make_instrumental\", False)\n", " if out is None:\n", " return False\n", " return out\n", "\n", "\n", "# from suno_analytics.preference_data_selection import parse_for_tag, parse_for_one_box\n", "# user_intersting_clips[\"tags\"] = user_intersting_clips[\"metadata\"].apply(parse_for_tag)\n", "# user_intersting_clips[\"is_onebox\"] = user_intersting_clips[\"metadata\"].apply(parse_for_one_box)\n", "user_intersting_clips[\"is_instrumental\"] = user_intersting_clips[\"metadata\"].apply(\n", " parse_for_instrumental\n", ")" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:00.893917Z", "start_time": "2024-05-26T00:25:00.485775Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:36.438853Z", "iopub.status.busy": "2025-01-04T15:35:36.438703Z", "iopub.status.idle": "2025-01-04T15:35:37.751996Z", "shell.execute_reply": "2025-01-04T15:35:37.751407Z", "shell.execute_reply.started": "2025-01-04T15:35:36.438838Z" } }, "outputs": [], "source": [ "user_compare_mask = (\n", " user_intersting_clips[\"created_at\"] >= cutoff_date\n", " # & (\n", " # (user_intersting_clips[\"model_name\"].str.startswith(\"chirp-v3p5-engine-t\"))\n", " # | (user_intersting_clips[\"model_name\"].str.startswith(\"chirp-v3p5-engine-s\"))\n", " # )\n", " # & (~user_intersting_clips[\"is_pro_user\"])\n", " # & (~user_intersting_clips[\"is_onebox\"])\n", " # & user_intersting_clips[\"is_instrumental\"]\n", ")\n", "# # this is fucked up sometimes one box doesn't give prompt to one generation\n", "extra_compare_mask = user_intersting_clips[user_compare_mask][\"request_id\"].isin(\n", " user_intersting_clips[user_compare_mask][\"request_id\"]\n", " .value_counts()\n", " .index[user_intersting_clips[user_compare_mask][\"request_id\"].value_counts() == 2]\n", ")\n", "\n", "user_compare_mask = user_compare_mask & extra_compare_mask" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:01.140472Z", "start_time": "2024-05-26T00:25:00.895575Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:37.752764Z", "iopub.status.busy": "2025-01-04T15:35:37.752613Z", "iopub.status.idle": "2025-01-04T15:35:41.762365Z", "shell.execute_reply": "2025-01-04T15:35:41.761760Z", "shell.execute_reply.started": "2025-01-04T15:35:37.752749Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(49458, 54)\n", "Model Name Value Counts and Fractions:\n", "chirp-v4-h-s-32: 18350 (37.10%)\n", "chirp-v3p5-engine-s-8: 12626 (25.53%)\n", "chirp-v4-h-t-6: 5481 (11.08%)\n", "chirp-v3p5-engine-t-6: 1641 (3.32%)\n", "chirp-v4-up-u-3: 1557 (3.15%)\n", "chirp-v3p5-engine-upload-4: 1124 (2.27%)\n", "chirp-v4-h-s-32_n_tag_3: 652 (1.32%)\n", "chirp-v4-h-s-32_temp_s_85: 633 (1.28%)\n", "chirp-v4-h-s-32_step_16: 631 (1.28%)\n", "chirp-v4-h-s-32_temp_s_90: 614 (1.24%)\n", "chirp-v4-h-s-32_text_1: 606 (1.23%)\n", "chirp-v4-h-s-32_temp_s_70: 600 (1.21%)\n", "chirp-v4-h-s-32_temp_s_75: 597 (1.21%)\n", "chirp-v4-h-s-32_step_16_text_1: 595 (1.20%)\n", "chirp-v4-h-s-32_step_8: 593 (1.20%)\n", "chirp-v4-h-s-32_step_10: 561 (1.13%)\n", "chirp-v4-h-s-32-c-2: 311 (0.63%)\n", "chirp-v4-h-t-6-c-17: 278 (0.56%)\n", "chirp-v4-engine-b: 156 (0.32%)\n", "chirp-v4-h-t-6_step_16_text_1: 149 (0.30%)\n", "chirp-v4-h-t-6_step_10: 148 (0.30%)\n", "chirp-v4-h-t-6_temp_s_70: 148 (0.30%)\n", "chirp-v4-h-t-6_text_1: 147 (0.30%)\n", "chirp-v4-h-t-6_temp_s_90: 141 (0.29%)\n", "chirp-v4-h-t-6_temp_s_80: 118 (0.24%)\n", "chirp-v4-h-t-6_step_16: 117 (0.24%)\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2: 113 (0.23%)\n", "chirp-v4-h-t-6_step_8: 113 (0.23%)\n", "chirp-v4-up-u-3_step_8: 99 (0.20%)\n", "chirp-v4-up-u-3_step_10: 98 (0.20%)\n", "chirp-v4-up-u-3_step_16: 98 (0.20%)\n", "chirp-v4-up-u-3_text_3: 66 (0.13%)\n", "chirp-v4-up-u-3_text_1: 55 (0.11%)\n", "chirp-v4-up-u-3_step_16_text_1: 53 (0.11%)\n", "chirp-v4-up-u-3_text_25: 49 (0.10%)\n", "chirp-v4-up-u-3_text_15: 47 (0.10%)\n", "chirp-v3p5-engine-b: 44 (0.09%)\n", "chirp-v3p5-engine-b-r: 30 (0.06%)\n", "chirp-v3p5-engine-short: 18 (0.04%)\n", "chirp-v4-h-s-32-c-2_step_16: 1 (0.00%)\n" ] } ], "source": [ "user_intersting_clips_3p5 = (\n", " user_intersting_clips[user_compare_mask].reset_index().copy()\n", ")\n", "\n", "\n", "def modify_model_name(model_name, metadata):\n", " if (\n", " model_name.startswith(\"chirp-v3p5-engine-t\")\n", " or model_name.startswith(\"chirp-v3p5-engine-s\")\n", " or model_name.startswith(\"chirp-v4\")\n", " or model_name.startswith(\"chirp-v3p5-h-s-31\")\n", " ):\n", " if \"param_experiment\" in metadata:\n", " exp = metadata.get(\"param_experiment\", \"\")\n", " if exp:\n", " return f\"{model_name}_{exp}\"\n", " return model_name\n", "\n", "\n", "user_intersting_clips_3p5[\"model_name\"] = user_intersting_clips_3p5.apply(\n", " lambda row: modify_model_name(row[\"model_name\"], row[\"metadata\"]), axis=1\n", ")\n", "user_intersting_clips_3p5 = user_intersting_clips_3p5.sort_values(\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", "print_out_value_counts_nicely(user_intersting_clips_3p5, \"model_name\")\n", "\n", "\n", "# georg_requst_ids = user_intersting_clips_3p5.groupby([\"user_id\"])[\"request_id\"].unique().apply(lambda x: x[0])\n", "# georg_clips = user_intersting_clips_3p5[user_intersting_clips_3p5[\"request_id\"].isin(georg_requst_ids)].copy()\n", "# print(georg_clips.shape, user_intersting_clips_3p5.shape)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:02.009450Z", "start_time": "2024-05-26T00:25:01.523515Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:41.763094Z", "iopub.status.busy": "2025-01-04T15:35:41.762943Z", "iopub.status.idle": "2025-01-04T15:35:48.009286Z", "shell.execute_reply": "2025-01-04T15:35:48.008751Z", "shell.execute_reply.started": "2025-01-04T15:35:41.763079Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "len pos models: 24729\n", "differing counts: 8430\n", "chirp-v3p5-engine-b-r_win_over_chirp-v3p5-engine-b-r, win ratio 1.000, (1.000, 1.000), counts 15, total 15.\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 22, total 22.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 6313, total 6313.\n", "chirp-v3p5-engine-short_win_over_chirp-v3p5-engine-short, win ratio 1.000, (1.000, 1.000), counts 9, total 9.\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.531, (0.439, 0.623), counts 60, total 113.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 764, total 764.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_12_tag_2, win ratio 0.469, (0.377, 0.561), counts 53, total 113.\n", "chirp-v3p5-engine-upload-4_win_over_chirp-v3p5-engine-upload-4, win ratio 1.000, (1.000, 1.000), counts 562, total 562.\n", "chirp-v4-engine-b_win_over_chirp-v4-engine-b, win ratio 1.000, (1.000, 1.000), counts 78, total 78.\n", "chirp-v4-h-s-32-c-2_win_over_chirp-v4-h-s-32, win ratio 0.518, (0.462, 0.573), counts 161, total 311.\n", "chirp-v4-h-s-32_n_tag_3_win_over_chirp-v4-h-s-32, win ratio 0.497, (0.459, 0.535), counts 324, total 652.\n", "chirp-v4-h-s-32_step_10_win_over_chirp-v4-h-s-32, win ratio 0.502, (0.460, 0.543), counts 281, total 560.\n", "chirp-v4-h-s-32_step_16_text_1_win_over_chirp-v4-h-s-32, win ratio 0.464, (0.424, 0.504), counts 276, total 595.\n", "chirp-v4-h-s-32_step_16_win_over_chirp-v4-h-s-32, win ratio 0.485, (0.446, 0.524), counts 306, total 631.\n", "chirp-v4-h-s-32_step_8_win_over_chirp-v4-h-s-32, win ratio 0.469, (0.429, 0.509), counts 278, total 593.\n", "chirp-v4-h-s-32_temp_s_70_win_over_chirp-v4-h-s-32, win ratio 0.540, (0.500, 0.580), counts 324, total 600.\n", "chirp-v4-h-s-32_temp_s_75_win_over_chirp-v4-h-s-32, win ratio 0.499, (0.459, 0.539), counts 298, total 597.\n", "chirp-v4-h-s-32_temp_s_85_win_over_chirp-v4-h-s-32, win ratio 0.499, (0.460, 0.538), counts 316, total 633.\n", "chirp-v4-h-s-32_temp_s_90_win_over_chirp-v4-h-s-32, win ratio 0.491, (0.451, 0.531), counts 301, total 613.\n", "chirp-v4-h-s-32_temp_s_90_win_over_chirp-v4-h-s-32_step_10, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v4-h-s-32_text_1_win_over_chirp-v4-h-s-32, win ratio 0.488, (0.449, 0.528), counts 296, total 606.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32, win ratio 1.000, (1.000, 1.000), counts 5979, total 5979.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32-c-2, win ratio 0.482, (0.427, 0.538), counts 150, total 311.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32-c-2_step_16, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_n_tag_3, win ratio 0.503, (0.465, 0.541), counts 328, total 652.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_10, win ratio 0.498, (0.457, 0.540), counts 279, total 560.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_16, win ratio 0.515, (0.476, 0.554), counts 325, total 631.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_16_text_1, win ratio 0.536, (0.496, 0.576), counts 319, total 595.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_8, win ratio 0.531, (0.491, 0.571), counts 315, total 593.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_70, win ratio 0.460, (0.420, 0.500), counts 276, total 600.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_75, win ratio 0.501, (0.461, 0.541), counts 299, total 597.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_85, win ratio 0.501, (0.462, 0.540), counts 317, total 633.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_90, win ratio 0.509, (0.469, 0.549), counts 312, total 613.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_text_1, win ratio 0.512, (0.472, 0.551), counts 310, total 606.\n", "chirp-v4-h-t-6-c-17_win_over_chirp-v4-h-t-6, win ratio 0.529, (0.470, 0.587), counts 147, total 278.\n", "chirp-v4-h-t-6_step_10_win_over_chirp-v4-h-t-6, win ratio 0.527, (0.447, 0.607), counts 78, total 148.\n", "chirp-v4-h-t-6_step_16_text_1_win_over_chirp-v4-h-t-6, win ratio 0.503, (0.423, 0.584), counts 75, total 149.\n", "chirp-v4-h-t-6_step_16_win_over_chirp-v4-h-t-6, win ratio 0.419, (0.329, 0.508), counts 49, total 117.\n", "chirp-v4-h-t-6_step_8_win_over_chirp-v4-h-t-6, win ratio 0.549, (0.457, 0.640), counts 62, total 113.\n", "chirp-v4-h-t-6_temp_s_70_win_over_chirp-v4-h-t-6, win ratio 0.615, (0.536, 0.693), counts 91, total 148.\n", "chirp-v4-h-t-6_temp_s_80_win_over_chirp-v4-h-t-6, win ratio 0.483, (0.393, 0.573), counts 57, total 118.\n", "chirp-v4-h-t-6_temp_s_90_win_over_chirp-v4-h-t-6, win ratio 0.496, (0.414, 0.579), counts 70, total 141.\n", "chirp-v4-h-t-6_text_1_win_over_chirp-v4-h-t-6, win ratio 0.463, (0.382, 0.543), counts 68, total 147.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6, win ratio 1.000, (1.000, 1.000), counts 2061, total 2061.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6-c-17, win ratio 0.471, (0.413, 0.530), counts 131, total 278.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_10, win ratio 0.473, (0.393, 0.553), counts 70, total 148.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_16, win ratio 0.581, (0.492, 0.671), counts 68, total 117.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_16_text_1, win ratio 0.497, (0.416, 0.577), counts 74, total 149.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_8, win ratio 0.451, (0.360, 0.543), counts 51, total 113.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_70, win ratio 0.385, (0.307, 0.464), counts 57, total 148.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_80, win ratio 0.517, (0.427, 0.607), counts 61, total 118.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_90, win ratio 0.504, (0.421, 0.586), counts 71, total 141.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_text_1, win ratio 0.537, (0.457, 0.618), counts 79, total 147.\n", "chirp-v4-up-u-3_step_10_win_over_chirp-v4-up-u-3, win ratio 0.520, (0.421, 0.619), counts 51, total 98.\n", "chirp-v4-up-u-3_step_16_text_1_win_over_chirp-v4-up-u-3, win ratio 0.453, (0.319, 0.587), counts 24, total 53.\n", "chirp-v4-up-u-3_step_16_win_over_chirp-v4-up-u-3, win ratio 0.469, (0.371, 0.568), counts 46, total 98.\n", "chirp-v4-up-u-3_step_8_win_over_chirp-v4-up-u-3, win ratio 0.394, (0.298, 0.490), counts 39, total 99.\n", "chirp-v4-up-u-3_text_15_win_over_chirp-v4-up-u-3, win ratio 0.468, (0.325, 0.611), counts 22, total 47.\n", "chirp-v4-up-u-3_text_1_win_over_chirp-v4-up-u-3, win ratio 0.491, (0.359, 0.623), counts 27, total 55.\n", "chirp-v4-up-u-3_text_25_win_over_chirp-v4-up-u-3, win ratio 0.490, (0.350, 0.630), counts 24, total 49.\n", "chirp-v4-up-u-3_text_3_win_over_chirp-v4-up-u-3, win ratio 0.576, (0.457, 0.695), counts 38, total 66.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3, win ratio 1.000, (1.000, 1.000), counts 496, total 496.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_step_10, win ratio 0.480, (0.381, 0.579), counts 47, total 98.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_step_16, win ratio 0.531, (0.432, 0.629), counts 52, total 98.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_step_16_text_1, win ratio 0.547, (0.413, 0.681), counts 29, total 53.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_step_8, win ratio 0.606, (0.510, 0.702), counts 60, total 99.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_text_1, win ratio 0.509, (0.377, 0.641), counts 28, total 55.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_text_15, win ratio 0.532, (0.389, 0.675), counts 25, total 47.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_text_25, win ratio 0.510, (0.370, 0.650), counts 25, total 49.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_text_3, win ratio 0.424, (0.305, 0.543), counts 28, total 66.\n", "tournament players: ['chirp-v3p5-engine-t-6', 'chirp-v3p5-engine-t-6_s_max_t_12_tag_2', 'chirp-v4-h-s-32', 'chirp-v4-h-s-32-c-2', 'chirp-v4-h-s-32-c-2_step_16', 'chirp-v4-h-s-32_n_tag_3', 'chirp-v4-h-s-32_step_10', 'chirp-v4-h-s-32_step_16', 'chirp-v4-h-s-32_step_16_text_1', 'chirp-v4-h-s-32_step_8', 'chirp-v4-h-s-32_temp_s_70', 'chirp-v4-h-s-32_temp_s_75', 'chirp-v4-h-s-32_temp_s_85', 'chirp-v4-h-s-32_temp_s_90', 'chirp-v4-h-s-32_text_1', 'chirp-v4-h-t-6', 'chirp-v4-h-t-6-c-17', 'chirp-v4-h-t-6_step_10', 'chirp-v4-h-t-6_step_16', 'chirp-v4-h-t-6_step_16_text_1', 'chirp-v4-h-t-6_step_8', 'chirp-v4-h-t-6_temp_s_70', 'chirp-v4-h-t-6_temp_s_80', 'chirp-v4-h-t-6_temp_s_90', 'chirp-v4-h-t-6_text_1', 'chirp-v4-up-u-3', 'chirp-v4-up-u-3_step_10', 'chirp-v4-up-u-3_step_16', 'chirp-v4-up-u-3_step_16_text_1', 'chirp-v4-up-u-3_step_8', 'chirp-v4-up-u-3_text_1', 'chirp-v4-up-u-3_text_15', 'chirp-v4-up-u-3_text_25', 'chirp-v4-up-u-3_text_3']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:07<00:00, 130.13it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-t-6: 989.2, (957, 1021)\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2: 1010.8, (980, 1043)\n", "chirp-v4-h-s-32: 1118.0, (997, 1159)\n", "chirp-v4-h-s-32-c-2: 1130.3, (989, 1189)\n", "chirp-v4-h-s-32-c-2_step_16: -382.9, (-868, 1000)\n", "chirp-v4-h-s-32_n_tag_3: 1115.9, (983, 1172)\n", "chirp-v4-h-s-32_step_10: 1118.6, (982, 1171)\n", "chirp-v4-h-s-32_step_16: 1107.6, (973, 1162)\n", "chirp-v4-h-s-32_step_16_text_1: 1092.9, (956, 1146)\n", "chirp-v4-h-s-32_step_8: 1096.3, (959, 1147)\n", "chirp-v4-h-s-32_temp_s_70: 1145.9, (1011, 1199)\n", "chirp-v4-h-s-32_temp_s_75: 1117.4, (982, 1169)\n", "chirp-v4-h-s-32_temp_s_85: 1117.5, (982, 1169)\n", "chirp-v4-h-s-32_temp_s_90: 1112.4, (978, 1162)\n", "chirp-v4-h-s-32_text_1: 1110.0, (975, 1162)\n", "chirp-v4-h-t-6: 994.1, (976, 1011)\n", "chirp-v4-h-t-6-c-17: 1014.2, (976, 1056)\n", "chirp-v4-h-t-6_step_10: 1012.9, (959, 1067)\n", "chirp-v4-h-t-6_step_16: 937.2, (876, 1001)\n", "chirp-v4-h-t-6_step_16_text_1: 996.4, (942, 1047)\n", "chirp-v4-h-t-6_step_8: 1028.0, (970, 1094)\n", "chirp-v4-h-t-6_temp_s_70: 1075.3, (1026, 1134)\n", "chirp-v4-h-t-6_temp_s_80: 982.3, (918, 1042)\n", "chirp-v4-h-t-6_temp_s_90: 991.6, (936, 1046)\n", "chirp-v4-h-t-6_text_1: 968.0, (916, 1023)\n", "chirp-v4-up-u-3: 1010.8, (983, 1039)\n", "chirp-v4-up-u-3_step_10: 1024.9, (959, 1089)\n", "chirp-v4-up-u-3_step_16: 989.5, (921, 1057)\n", "chirp-v4-up-u-3_step_16_text_1: 977.7, (888, 1064)\n", "chirp-v4-up-u-3_step_8: 935.9, (872, 1000)\n", "chirp-v4-up-u-3_text_1: 1004.9, (918, 1095)\n", "chirp-v4-up-u-3_text_15: 988.5, (890, 1078)\n", "chirp-v4-up-u-3_text_25: 1003.9, (911, 1101)\n", "chirp-v4-up-u-3_text_3: 1063.9, (982, 1147)\n" ] }, { "data": { "image/png": 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15FB4eKguXPjfWsPRpcIUXbqEz3M/2X5Es9fuz/f6YW2u0f1ta/k8FwAAWIfhPADA1rKW0mlXJ0oD3t2iV+9qrNjKZcglt8h5VmyIsGqjgFW5wXqyX4bzV8+qoSbgS1YNye9sEqObrq0gSdp3IkVPfb5LCd3rq1aFCEn+O5oJAABYh+E8AABANlZsiHDS0QnSpQHTseT8B/AmnexXurSkTta/sXRprXt//xtbxYqhZs4NAjkFaoNASor//obIDWymVUPy6NIlcv2t1qoQEZCN4dmZ/Lsll98tufbOdGIuzMdwHgAAIAg45egEyZrldKzYICD9b6377EcnZK11v2rPCc3qG2fUgN6KoWawLAXicrnkdudzeAa5tsoMliG5VUz+3ZLL75Zce2c6MRfmYzgPAACAgLJiOR2r1td32lr3Vgw1g2UpkJIlQy3Zq85JuVb16jRO+t2S63vBsryZyf/GwZLrpF6tzIX5GM4DAAAgoJx0sl8r17p3ynI6Tt/LGQCCSbAczQQAdsFwHgAAAAHnlJP9WrXWvdOW0wEABIdgOZoJAOyC4TwAAAAcwYr19a1a695py+kEg0AtQ5tzyYgSJUJ18WLgl4ywYtldlvoNDCf9bsn1vWA5msnkf+NgyXVSr1bmwnwM5wEAAAA/sWqteyuX03Gq8+cDsw5tsCwZEah+rc50Iif9bsk1V6B6zbnBNKdAbTDlcWtuLszHcB4AAADwE6vWurdqOR0nK1bMpcz8toj4ULAsGRGofq3OdCIn/W7JNVegeg2WDaY8bs3NhfkYzgMAAAB+YtVa91Ytp+MkOfeWDA8P1YUL/l9eJliWjAgPD1VKSmA38liR6URO+t0GKteq54ucnPQYClSvwbLBlMetubkwH8N5AAAAwI+sWOvequV0nCRY9pYEEPx4vjBXsGwwBWBfDOcBAAAAw1i1nI6TBMveksDVCpa9ugPBql55vgAA5IfhPAAAAGAYq5bTcRKn7y0ZiHV3cw5Sw8JClJqa7vnepKGxlYJhr+5AreNsVa/B8nxh8nrZTn++sOJ3a9Xfk9NyYT6G8wAAAICBrFhOR5JSUjM8GwUkafTHP7JRwAA5B185+WPwFQxDYycIhr26s++97k/B0KuVAvXvbAWnP19Y8bu16u/JabkwH8N5AAAAAD6RkpqhB+Zv81pO59T5dM1em6hVe05oVt84vwzo2SDgf1YMvpw+SA2UYNirOySkmNLTM/2eEwy9WilQ/85WcPrzhRW/W6v+npyWC/MxnAcAAADgE+9vOphrnXtJynRLu5OS9f6mgxrapqZPM63aIOA0Vgy+nD5IdZKwsBClp+d/ZAZ8w+R/Z6c/X1jxu7Xq78lpuTAfw3kAAADAT7IvBbLvRIrX/yXz1sBduP1IrsF8lkz3pet9PZy3YoNAFiftse/0wRcAAIA/MJwHAAAA/CSvpUCe+nyX52vT1sC93JrkBbm+KKzYICBZu8e+VRsFrMh10gYQACionOcBCQ8P9VoT3bSN/4DJGM4DAAAAfpJ9KRBJCg0LUVpquud709bAjS4VpmPJlz9pqK9ZsUFAsm6PfSvX9Q90rhM3gDhVRgbrOPtDzgFuXq9BDHDtKRhOgGvV49ZpuTAfw3kAAADAT/JaCsRkdzSJ0ey1iXnuyV7Mdel6X7Nig4Bk3R77Vm0UsCLXaRtAsrKduFHg4sX0K98IhRYMA1z4RzCcANeqx63TcmG+YlYXAMB5Nm7cqOHDh6tdu3aqX7++VqxY4XW92+3WjBkz1K5dOzVp0kT33Xef9u3b53Wb06dP65FHHlHz5s11ww036IknntC5c+e8brNz507169dPjRs3VocOHTR79mx/txYUjidf1M7fz2rn72e91jfOuux48kWLKwQA5woNNXe4Jkn9WlRXvYqlVczlfXkxl1SvYmn1a1Hd55l3NInJlZc91x8bBCTr9tgvyEYBU3Kt6rUgGwX8IWujwOy1iTp1/tIQKGujwAPztyklNcMvuVbI/n515+9ntSfbe1Xer/rOnU1iNLd/M83t30wJ3etLkhK61/dcdqefnh/hf9GlSyi2chnFVi7jGchnnQcktnKZgOwYYNV7GqflwnzsOQ8g4FJSUlS/fn3dddddGjVqVK7rZ8+erblz5+rFF19U9erVNWPGDA0ZMkSfffaZSpS49CZj3LhxSkpK0n/+8x+lpaXpiSee0NNPP62XX35ZkpScnKwhQ4aoTZs2evbZZ7V792498cQTKlu2rPr27RvQfgPNaesbA4CdhIYWV1qaOQO2nCLCimtW3zjPnr+nzqerfMkQv+75269Fda3acyLXMNWfGwQk6/bYt2qjgBW5wbwBxJSjE7IEeo999ugODE7kDH+y6j2N03JhPobzAAKuQ4cO6tChQ57Xud1uzZkzRyNGjFCXLl0kSS+99JLatm2rFStWqEePHtq7d69Wr16tBQsWqHHjxpKkiRMn6v7779f48eNVuXJlLVmyRGlpaXr++ecVFham6667Tjt27NB//vMf44fzOdc3zuvkQAAA+EtEWHENbVNT7epEacC7W/TqXY39OgiyYoOAZM0SPpJ1GwWsyHXaBhAnndy4e4PK+mpnkhJPnVf2ll2SapYvqe4NKvs0LzsnnVDZqlynLs9kOk5CC/gHw3kAQeXgwYNKSkpS27ZtPZeVKVNGcXFx2rJli3r06KEtW7aobNmynsG8JLVt21bFihXT9u3b1bVrV23dulU33HCDwsL+96GtXbt2mj17tv744w+VK1cuoH0FUs49ZCIiwpSS4p8PkU6V/Y1p9qWDsvDGFAACK9AbBCTr9ti3aqOAFblO2wDipJMbL995TPtPew/mJcktaf/p81q+85gxGyKclmvlORvgX1Yd8WLVRgE2RiBQGM4DCCpJSUmSpAoVKnhdXqFCBR0/flySdPz4cUVFRXldHxISonLlynl+/vjx46pe3ftDcXR0tOe6wgznS5b83wew9PQMpaZmKCysuEJC/vemMi0tQ2lpGSpRIkTFi//vdB6pqelKT89UeHioimVbEPfChTRlZrpVsmSYXNnWyT1/Pk1ut1sREd4f+lJSUuVyuVSyZKjnMrdbOn8+VcWKuRQe/r/LMzPdunAhTSEhxRQWdqmeiIgwZWRk6uLFdIWGFvdaL88fPWXVEx4eKpfL5fOesmTvKXtmWFhxv/6ePlmTqNlrE736yb500MibauvBDtdedU+X+z1l3VdWf4H628veg697ypLX7yl7TqAeT9kz/dGTlPfvKXtuIJ4jsoT+/69DQop73b+/n/dcrv/1nT3X188R2X9PWV+Hhnr3Gojn8oyMDK/L/fUckb2nLNn/jQPx+uQq5v279edzRFZPefXqr+eIiAjpnUEt9Nb3+/Th1kM6lZKuqIhQ9WtZQ/1bVldxt/zyHDGkfW2t2ntCu4/lsVGg0qWNAv54jvhr25qX3RhxX5trfP54umyvFUvrLzfU8MvjqXezavrXd7/lu1GgT/NqXrm+ejxdaaNAxdIlPD/jy8fTlfbYX/TjUY3ufF2Resrvb68wmb58jnh/beJlN0R8tP2IRtxUp0g9Xe5v74MthwqU6+vX3I+2HylQri9fc+dvPVygTH+85p5LTdecdfv1/sYDki7tsf+XVtdoYOtrVCosxO+vuVlfh4aF+OU5Ir/fU5bsr33+eM39y401dUvDKpKk3UfP6sllO/TC7derbqXSki49RxUr5vL5a+7SjQf0f9/+pvwMb1dLQ268xuevuVfKHdampu5vW9Pvn3P98dkdwYXhPABcwfnzqV5veiQpNfXSi2JO+Z3BPfsW9pz3nZe89nR3u915Xp6Zmffl6emZSk/PfXnWC3dOV9tTXnuT7zz0h+d2ee1Z4MuesnIuXEjz9OGv39OdTaropmuj8rytdKnXnPX7+veUVWt6+qXbBOpvL/v9BfJvL3tuoB5P2XMC+XjK/nUgnyPS/v9gMz09I8/b++vx5P7/T7AXLqTlyvXX817W12lpeffqz8fTxYsZknL/O/r78STl/W/sz8eTOzPv362/H095ZUr+eTwVk7z22J9xZ6NLe+y7fdtT9t+TS9Ksuy+/jI8/niOKS5ddPihELp8/nq7Ua8nQYn55jujbtKq+3pWU74aIu+Oq5nn/V/t4utKRAr0aV8n1M754PF1pj/yk5Is+fzwVJdMXj6crbRT4cNNBDbrBewcfXzyePt56uFC5vnrN/XDTwQLn+uo1d8GWQ4Xq1VevucdPn89zj/3/+/a/WrHjmNce+75+zc25jM+IeVvyXMbHn6+5Wffvj+cI6dLvqVRxl0qVC/dkSVL1siVU6/9fJl36t/VVT1m/p/gGldTmmkhJlz5nPvX5LiV0r+85EW7W0Uy+fs0taK5dPru7XFKpUuzpH4wYzgMIKhUrVpQknThxQpUqVfJcfuLECcXGxkq6tAf8yZMnvX4uPT1df/zxh+fno6OjPXvaZ8n6PmsPeqfI2ovc35x0ItqcSwcF6t8YgP3xfAFfsGIZH6tyrcq04jwGTjq5sdOWDnJSrpOWZ5JYxicQrDqxMSdURqAwnAcQVKpXr66KFStq7dq1atCggSQpOTlZ27Zt07333itJatasmc6cOaOffvpJjRo1kiStW7dOmZmZatKkiSSpadOmmj59utLS0hQaeumwsTVr1qh27dpGrzefl5CQwAyCrDgRbbCs/R6of2Or+nVaLuBPpj9fwFxO+5ty0kYBzifgfT259suUrDuhslUbBQCYg+E8gIA7d+6c9u//3x7WBw8e1I4dO1SuXDlVrVpVAwcO1D//+U/VrFlT1atX14wZM1SpUiV16dJFknTttdeqffv2euqpp/Tss88qLS1NCQkJ6tGjhypXrixJio+P1+uvv64nn3xSw4YN06+//qo5c+ZowoQJlvTsBFaciNZJe+tL1vXrpFynDZ9gLqc9P8L/eE4ODKec3LhbbCUt33FMiae8TwrrknRNZEl1i62U349eFSedUNmqXKt6tWqPfas2Ckj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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "get_preference_counts(user_intersting_clips_3p5)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:02.332947Z", "start_time": "2024-05-26T00:25:02.010694Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:48.010018Z", "iopub.status.busy": "2025-01-04T15:35:48.009859Z", "iopub.status.idle": "2025-01-04T15:35:50.717035Z", "shell.execute_reply": "2025-01-04T15:35:50.716502Z", "shell.execute_reply.started": "2025-01-04T15:35:48.010002Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "first gen\n", "len pos models: 16802\n", "differing counts: 5438\n", "chirp-v3p5-engine-b-r_win_over_chirp-v3p5-engine-b-r, win ratio 1.000, (1.000, 1.000), counts 12, total 12.\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 6, total 6.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 5792, total 5792.\n", "chirp-v4-engine-b_win_over_chirp-v4-engine-b, win ratio 1.000, (1.000, 1.000), counts 34, total 34.\n", "chirp-v4-h-s-32-c-2_win_over_chirp-v4-h-s-32, win ratio 0.506, (0.445, 0.566), counts 133, total 263.\n", "chirp-v4-h-s-32_n_tag_3_win_over_chirp-v4-h-s-32, win ratio 0.504, (0.462, 0.545), counts 287, total 570.\n", "chirp-v4-h-s-32_step_10_win_over_chirp-v4-h-s-32, win ratio 0.496, (0.450, 0.541), counts 232, total 468.\n", "chirp-v4-h-s-32_step_16_text_1_win_over_chirp-v4-h-s-32, win ratio 0.466, (0.423, 0.510), counts 235, total 504.\n", "chirp-v4-h-s-32_step_16_win_over_chirp-v4-h-s-32, win ratio 0.482, (0.440, 0.525), counts 258, total 535.\n", "chirp-v4-h-s-32_step_8_win_over_chirp-v4-h-s-32, win ratio 0.452, (0.409, 0.495), counts 230, total 509.\n", "chirp-v4-h-s-32_temp_s_70_win_over_chirp-v4-h-s-32, win ratio 0.547, (0.503, 0.590), counts 275, total 503.\n", "chirp-v4-h-s-32_temp_s_75_win_over_chirp-v4-h-s-32, win ratio 0.508, (0.464, 0.552), counts 255, total 502.\n", "chirp-v4-h-s-32_temp_s_85_win_over_chirp-v4-h-s-32, win ratio 0.501, (0.459, 0.543), counts 273, total 545.\n", "chirp-v4-h-s-32_temp_s_90_win_over_chirp-v4-h-s-32, win ratio 0.480, (0.437, 0.523), counts 252, total 525.\n", "chirp-v4-h-s-32_temp_s_90_win_over_chirp-v4-h-s-32_step_10, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v4-h-s-32_text_1_win_over_chirp-v4-h-s-32, win ratio 0.494, (0.451, 0.537), counts 253, total 512.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32, win ratio 1.000, (1.000, 1.000), counts 5065, total 5065.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32-c-2, win ratio 0.494, (0.434, 0.555), counts 130, total 263.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32-c-2_step_16, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_n_tag_3, win ratio 0.496, (0.455, 0.538), counts 283, total 570.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_10, win ratio 0.504, (0.459, 0.550), counts 236, total 468.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_16, win ratio 0.518, (0.475, 0.560), counts 277, total 535.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_16_text_1, win ratio 0.534, (0.490, 0.577), counts 269, total 504.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_8, win ratio 0.548, (0.505, 0.591), counts 279, total 509.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_70, win ratio 0.453, (0.410, 0.497), counts 228, total 503.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_75, win ratio 0.492, (0.448, 0.536), counts 247, total 502.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_85, win ratio 0.499, (0.457, 0.541), counts 272, total 545.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_90, win ratio 0.520, (0.477, 0.563), counts 273, total 525.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_text_1, win ratio 0.506, (0.463, 0.549), counts 259, total 512.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6, win ratio 1.000, (1.000, 1.000), counts 455, total 455.\n", "tournament players: ['chirp-v4-h-s-32', 'chirp-v4-h-s-32-c-2', 'chirp-v4-h-s-32-c-2_step_16', 'chirp-v4-h-s-32_n_tag_3', 'chirp-v4-h-s-32_step_10', 'chirp-v4-h-s-32_step_16', 'chirp-v4-h-s-32_step_16_text_1', 'chirp-v4-h-s-32_step_8', 'chirp-v4-h-s-32_temp_s_70', 'chirp-v4-h-s-32_temp_s_75', 'chirp-v4-h-s-32_temp_s_85', 'chirp-v4-h-s-32_temp_s_90', 'chirp-v4-h-s-32_text_1']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:02<00:00, 450.40it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v4-h-s-32: 1159.7, (996, 1181)\n", "chirp-v4-h-s-32-c-2: 1163.6, (981, 1209)\n", "chirp-v4-h-s-32-c-2_step_16: -870.6, (-1090, 1000)\n", "chirp-v4-h-s-32_n_tag_3: 1162.1, (986, 1194)\n", "chirp-v4-h-s-32_step_10: 1155.9, (977, 1191)\n", "chirp-v4-h-s-32_step_16: 1147.3, (969, 1179)\n", "chirp-v4-h-s-32_step_16_text_1: 1136.2, (959, 1168)\n", "chirp-v4-h-s-32_step_8: 1126.1, (947, 1158)\n", "chirp-v4-h-s-32_temp_s_70: 1192.2, (1014, 1227)\n", "chirp-v4-h-s-32_temp_s_75: 1165.2, (985, 1199)\n", "chirp-v4-h-s-32_temp_s_85: 1160.3, (983, 1191)\n", "chirp-v4-h-s-32_temp_s_90: 1146.4, (969, 1177)\n", "chirp-v4-h-s-32_text_1: 1155.6, (978, 1189)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "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", " & (user_intersting_clips_3p5[\"task\"] == \"\")\n", "].copy()\n", "if first_gen_slice_df.shape[0] > 0:\n", " get_preference_counts(\n", " first_gen_slice_df,\n", " title_name=\"first generation\",\n", " )" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:02.574659Z", "start_time": "2024-05-26T00:25:02.334214Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:35:50.717760Z", "iopub.status.busy": "2025-01-04T15:35:50.717607Z", "iopub.status.idle": "2025-01-04T15:35:52.781118Z", "shell.execute_reply": "2025-01-04T15:35:52.780608Z", "shell.execute_reply.started": "2025-01-04T15:35:50.717745Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is continue\n", "len pos models: 2050\n", "differing counts: 723\n", "chirp-v3p5-engine-b-r_win_over_chirp-v3p5-engine-b-r, win ratio 1.000, (1.000, 1.000), counts 3, total 3.\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 3, total 3.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 520, total 520.\n", "chirp-v3p5-engine-upload-4_win_over_chirp-v3p5-engine-upload-4, win ratio 1.000, (1.000, 1.000), counts 34, total 34.\n", "chirp-v4-engine-b_win_over_chirp-v4-engine-b, win ratio 1.000, (1.000, 1.000), counts 24, total 24.\n", "chirp-v4-h-s-32-c-2_win_over_chirp-v4-h-s-32, win ratio 0.545, (0.376, 0.715), counts 18, total 33.\n", "chirp-v4-h-s-32_n_tag_3_win_over_chirp-v4-h-s-32, win ratio 0.484, (0.362, 0.607), counts 31, total 64.\n", "chirp-v4-h-s-32_step_10_win_over_chirp-v4-h-s-32, win ratio 0.541, (0.427, 0.654), counts 40, total 74.\n", "chirp-v4-h-s-32_step_16_text_1_win_over_chirp-v4-h-s-32, win ratio 0.426, (0.309, 0.544), counts 29, total 68.\n", "chirp-v4-h-s-32_step_16_win_over_chirp-v4-h-s-32, win ratio 0.486, (0.369, 0.603), counts 34, total 70.\n", "chirp-v4-h-s-32_step_8_win_over_chirp-v4-h-s-32, win ratio 0.576, (0.457, 0.695), counts 38, total 66.\n", "chirp-v4-h-s-32_temp_s_70_win_over_chirp-v4-h-s-32, win ratio 0.473, (0.359, 0.587), counts 35, total 74.\n", "chirp-v4-h-s-32_temp_s_75_win_over_chirp-v4-h-s-32, win ratio 0.416, (0.306, 0.526), counts 32, total 77.\n", "chirp-v4-h-s-32_temp_s_85_win_over_chirp-v4-h-s-32, win ratio 0.492, (0.369, 0.616), counts 31, total 63.\n", "chirp-v4-h-s-32_temp_s_90_win_over_chirp-v4-h-s-32, win ratio 0.507, (0.388, 0.627), counts 34, total 67.\n", "chirp-v4-h-s-32_text_1_win_over_chirp-v4-h-s-32, win ratio 0.478, (0.358, 0.597), counts 32, total 67.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32, win ratio 1.000, (1.000, 1.000), counts 686, total 686.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32-c-2, win ratio 0.455, (0.285, 0.624), counts 15, total 33.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_n_tag_3, win ratio 0.516, (0.393, 0.638), counts 33, total 64.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_10, win ratio 0.459, (0.346, 0.573), counts 34, total 74.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_16, win ratio 0.514, (0.397, 0.631), counts 36, total 70.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_16_text_1, win ratio 0.574, (0.456, 0.691), counts 39, total 68.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_step_8, win ratio 0.424, (0.305, 0.543), counts 28, total 66.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_70, win ratio 0.527, (0.413, 0.641), counts 39, total 74.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_75, win ratio 0.584, (0.474, 0.694), counts 45, total 77.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_85, win ratio 0.508, (0.384, 0.631), counts 32, total 63.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_temp_s_90, win ratio 0.493, (0.373, 0.612), counts 33, total 67.\n", "chirp-v4-h-s-32_win_over_chirp-v4-h-s-32_text_1, win ratio 0.522, (0.403, 0.642), counts 35, total 67.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6, win ratio 1.000, (1.000, 1.000), counts 57, total 57.\n", "tournament players: ['chirp-v4-h-s-32', 'chirp-v4-h-s-32-c-2', 'chirp-v4-h-s-32_n_tag_3', 'chirp-v4-h-s-32_step_10', 'chirp-v4-h-s-32_step_16', 'chirp-v4-h-s-32_step_16_text_1', 'chirp-v4-h-s-32_step_8', 'chirp-v4-h-s-32_temp_s_70', 'chirp-v4-h-s-32_temp_s_75', 'chirp-v4-h-s-32_temp_s_85', 'chirp-v4-h-s-32_temp_s_90', 'chirp-v4-h-s-32_text_1']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:01<00:00, 644.19it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v4-h-s-32: 1004.4, (979, 1030)\n", "chirp-v4-h-s-32-c-2: 1036.1, (930, 1160)\n", "chirp-v4-h-s-32_n_tag_3: 993.6, (910, 1084)\n", "chirp-v4-h-s-32_step_10: 1032.7, (961, 1111)\n", "chirp-v4-h-s-32_step_16: 994.5, (913, 1079)\n", "chirp-v4-h-s-32_step_16_text_1: 953.0, (869, 1031)\n", "chirp-v4-h-s-32_step_8: 1057.5, (982, 1140)\n", "chirp-v4-h-s-32_temp_s_70: 985.6, (906, 1066)\n", "chirp-v4-h-s-32_temp_s_75: 945.2, (869, 1019)\n", "chirp-v4-h-s-32_temp_s_85: 998.9, (912, 1082)\n", "chirp-v4-h-s-32_temp_s_90: 1009.6, (928, 1084)\n", "chirp-v4-h-s-32_text_1: 988.9, (900, 1075)\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"is continue\")\n", "get_preference_counts(\n", " user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"extend\")],\n", " \"is extend\",\n", ")" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:52.781820Z", "iopub.status.busy": "2025-01-04T15:35:52.781670Z", "iopub.status.idle": "2025-01-04T15:35:54.852817Z", "shell.execute_reply": "2025-01-04T15:35:54.852290Z", "shell.execute_reply.started": "2025-01-04T15:35:52.781804Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is cover\n", "len pos models: 1757\n", "differing counts: 704\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 2, total 2.\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.446, (0.316, 0.577), counts 25, total 56.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 364, total 364.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_12_tag_2, win ratio 0.554, (0.423, 0.684), counts 31, total 56.\n", "chirp-v4-engine-b_win_over_chirp-v4-engine-b, win ratio 1.000, (1.000, 1.000), counts 6, total 6.\n", "chirp-v4-h-t-6-c-17_win_over_chirp-v4-h-t-6, win ratio 0.483, (0.393, 0.573), counts 57, total 118.\n", "chirp-v4-h-t-6_step_10_win_over_chirp-v4-h-t-6, win ratio 0.553, (0.441, 0.664), counts 42, total 76.\n", "chirp-v4-h-t-6_step_16_text_1_win_over_chirp-v4-h-t-6, win ratio 0.561, (0.441, 0.680), counts 37, total 66.\n", "chirp-v4-h-t-6_step_16_win_over_chirp-v4-h-t-6, win ratio 0.451, (0.314, 0.588), counts 23, total 51.\n", "chirp-v4-h-t-6_step_8_win_over_chirp-v4-h-t-6, win ratio 0.468, (0.344, 0.592), counts 29, total 62.\n", "chirp-v4-h-t-6_temp_s_70_win_over_chirp-v4-h-t-6, win ratio 0.605, (0.498, 0.711), counts 49, total 81.\n", "chirp-v4-h-t-6_temp_s_80_win_over_chirp-v4-h-t-6, win ratio 0.541, (0.416, 0.666), counts 33, total 61.\n", "chirp-v4-h-t-6_temp_s_90_win_over_chirp-v4-h-t-6, win ratio 0.591, (0.472, 0.710), counts 39, total 66.\n", "chirp-v4-h-t-6_text_1_win_over_chirp-v4-h-t-6, win ratio 0.463, (0.343, 0.582), counts 31, total 67.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6, win ratio 1.000, (1.000, 1.000), counts 681, total 681.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6-c-17, win ratio 0.517, (0.427, 0.607), counts 61, total 118.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_10, win ratio 0.447, (0.336, 0.559), counts 34, total 76.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_16, win ratio 0.549, (0.412, 0.686), counts 28, total 51.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_16_text_1, win ratio 0.439, (0.320, 0.559), counts 29, total 66.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_8, win ratio 0.532, (0.408, 0.656), counts 33, total 62.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_70, win ratio 0.395, (0.289, 0.502), counts 32, total 81.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_80, win ratio 0.459, (0.334, 0.584), counts 28, total 61.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_90, win ratio 0.409, (0.290, 0.528), counts 27, total 66.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_text_1, win ratio 0.537, (0.418, 0.657), counts 36, total 67.\n", "tournament players: ['chirp-v3p5-engine-t-6', 'chirp-v3p5-engine-t-6_s_max_t_12_tag_2', 'chirp-v4-h-t-6', 'chirp-v4-h-t-6-c-17', 'chirp-v4-h-t-6_step_10', 'chirp-v4-h-t-6_step_16', 'chirp-v4-h-t-6_step_16_text_1', 'chirp-v4-h-t-6_step_8', 'chirp-v4-h-t-6_temp_s_70', 'chirp-v4-h-t-6_temp_s_80', 'chirp-v4-h-t-6_temp_s_90', 'chirp-v4-h-t-6_text_1']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:01<00:00, 647.02it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-t-6: 1018.7, (975, 1065)\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2: 981.3, (935, 1025)\n", "chirp-v4-h-t-6: 984.9, (958, 1013)\n", "chirp-v4-h-t-6-c-17: 973.1, (913, 1036)\n", "chirp-v4-h-t-6_step_10: 1021.6, (951, 1107)\n", "chirp-v4-h-t-6_step_16: 950.7, (855, 1039)\n", "chirp-v4-h-t-6_step_16_text_1: 1027.2, (945, 1109)\n", "chirp-v4-h-t-6_step_8: 962.4, (875, 1046)\n", "chirp-v4-h-t-6_temp_s_70: 1058.9, (986, 1134)\n", "chirp-v4-h-t-6_temp_s_80: 1013.4, (932, 1098)\n", "chirp-v4-h-t-6_temp_s_90: 1048.8, (969, 1132)\n", "chirp-v4-h-t-6_text_1: 958.9, (879, 1043)\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"is cover\")\n", "get_preference_counts(\n", " user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"cover\")],\n", " \"is cover\",\n", ")" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:54.853531Z", "iopub.status.busy": "2025-01-04T15:35:54.853378Z", "iopub.status.idle": "2025-01-04T15:35:56.898056Z", "shell.execute_reply": "2025-01-04T15:35:56.897553Z", "shell.execute_reply.started": "2025-01-04T15:35:54.853515Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is infill\n", "len pos models: 383\n", "differing counts: 140\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.286, (-0.049, 0.620), counts 2, total 7.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 63, total 63.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_12_tag_2, win ratio 0.714, (0.380, 1.049), counts 5, total 7.\n", "chirp-v4-engine-b_win_over_chirp-v4-engine-b, win ratio 1.000, (1.000, 1.000), counts 2, total 2.\n", "chirp-v4-h-t-6-c-17_win_over_chirp-v4-h-t-6, win ratio 0.719, (0.563, 0.875), counts 23, total 32.\n", "chirp-v4-h-t-6_step_10_win_over_chirp-v4-h-t-6, win ratio 0.500, (0.154, 0.846), counts 4, total 8.\n", "chirp-v4-h-t-6_step_16_text_1_win_over_chirp-v4-h-t-6, win ratio 0.667, (0.428, 0.905), counts 10, total 15.\n", "chirp-v4-h-t-6_step_16_win_over_chirp-v4-h-t-6, win ratio 0.438, (0.194, 0.681), counts 7, total 16.\n", "chirp-v4-h-t-6_step_8_win_over_chirp-v4-h-t-6, win ratio 0.818, (0.590, 1.046), counts 9, total 11.\n", "chirp-v4-h-t-6_temp_s_70_win_over_chirp-v4-h-t-6, win ratio 0.700, (0.416, 0.984), counts 7, total 10.\n", "chirp-v4-h-t-6_temp_s_80_win_over_chirp-v4-h-t-6, win ratio 0.500, (0.190, 0.810), counts 5, total 10.\n", "chirp-v4-h-t-6_temp_s_90_win_over_chirp-v4-h-t-6, win ratio 0.421, (0.199, 0.643), counts 8, total 19.\n", "chirp-v4-h-t-6_text_1_win_over_chirp-v4-h-t-6, win ratio 0.500, (0.217, 0.783), counts 6, total 12.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6, win ratio 1.000, (1.000, 1.000), counts 178, total 178.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6-c-17, win ratio 0.281, (0.125, 0.437), counts 9, total 32.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_10, win ratio 0.500, (0.154, 0.846), counts 4, total 8.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_16, win ratio 0.562, (0.319, 0.806), counts 9, total 16.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_16_text_1, win ratio 0.333, (0.095, 0.572), counts 5, total 15.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_8, win ratio 0.182, (-0.046, 0.410), counts 2, total 11.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_70, win ratio 0.300, (0.016, 0.584), counts 3, total 10.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_80, win ratio 0.500, (0.190, 0.810), counts 5, total 10.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_90, win ratio 0.579, (0.357, 0.801), counts 11, total 19.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_text_1, win ratio 0.500, (0.217, 0.783), counts 6, total 12.\n", "tournament players: ['chirp-v3p5-engine-t-6', 'chirp-v3p5-engine-t-6_s_max_t_12_tag_2', 'chirp-v4-h-t-6', 'chirp-v4-h-t-6-c-17', 'chirp-v4-h-t-6_step_10', 'chirp-v4-h-t-6_step_16', 'chirp-v4-h-t-6_step_16_text_1', 'chirp-v4-h-t-6_step_8', 'chirp-v4-h-t-6_temp_s_70', 'chirp-v4-h-t-6_temp_s_80', 'chirp-v4-h-t-6_temp_s_90', 'chirp-v4-h-t-6_text_1']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:02<00:00, 483.63it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-t-6: 1079.6, (926, 2566)\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2: 920.4, (-565, 1074)\n", "chirp-v4-h-t-6: 940.7, (601, 1035)\n", "chirp-v4-h-t-6-c-17: 1103.7, (724, 1331)\n", "chirp-v4-h-t-6_step_10: 940.7, (381, 1236)\n", "chirp-v4-h-t-6_step_16: 897.1, (497, 1115)\n", "chirp-v4-h-t-6_step_16_text_1: 1061.1, (675, 1353)\n", "chirp-v4-h-t-6_step_8: 1202.0, (863, 3819)\n", "chirp-v4-h-t-6_temp_s_70: 1087.9, (687, 3702)\n", "chirp-v4-h-t-6_temp_s_80: 940.7, (505, 1220)\n", "chirp-v4-h-t-6_temp_s_90: 885.4, (482, 1089)\n", "chirp-v4-h-t-6_text_1: 940.7, (509, 1206)\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"is infill\")\n", "get_preference_counts(\n", " user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"infill\")],\n", " \"is infill\",\n", ")" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:56.898776Z", "iopub.status.busy": "2025-01-04T15:35:56.898618Z", "iopub.status.idle": "2025-01-04T15:35:58.945796Z", "shell.execute_reply": "2025-01-04T15:35:58.945270Z", "shell.execute_reply.started": "2025-01-04T15:35:56.898760Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is artist\n", "len pos models: 1655\n", "differing counts: 628\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 11, total 11.\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.660, (0.529, 0.791), counts 33, total 50.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 337, total 337.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_12_tag_2, win ratio 0.340, (0.209, 0.471), counts 17, total 50.\n", "chirp-v4-engine-b_win_over_chirp-v4-engine-b, win ratio 1.000, (1.000, 1.000), counts 10, total 10.\n", "chirp-v4-h-t-6-c-17_win_over_chirp-v4-h-t-6, win ratio 0.523, (0.437, 0.610), counts 67, total 128.\n", "chirp-v4-h-t-6_step_10_win_over_chirp-v4-h-t-6, win ratio 0.500, (0.378, 0.623), counts 32, total 64.\n", "chirp-v4-h-t-6_step_16_text_1_win_over_chirp-v4-h-t-6, win ratio 0.412, (0.295, 0.529), counts 28, total 68.\n", "chirp-v4-h-t-6_step_16_win_over_chirp-v4-h-t-6, win ratio 0.380, (0.245, 0.515), counts 19, total 50.\n", "chirp-v4-h-t-6_step_8_win_over_chirp-v4-h-t-6, win ratio 0.600, (0.448, 0.752), counts 24, total 40.\n", "chirp-v4-h-t-6_temp_s_70_win_over_chirp-v4-h-t-6, win ratio 0.614, (0.488, 0.740), counts 35, total 57.\n", "chirp-v4-h-t-6_temp_s_80_win_over_chirp-v4-h-t-6, win ratio 0.404, (0.264, 0.545), counts 19, total 47.\n", "chirp-v4-h-t-6_temp_s_90_win_over_chirp-v4-h-t-6, win ratio 0.411, (0.282, 0.540), counts 23, total 56.\n", "chirp-v4-h-t-6_text_1_win_over_chirp-v4-h-t-6, win ratio 0.456, (0.338, 0.574), counts 31, total 68.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6, win ratio 1.000, (1.000, 1.000), counts 669, total 669.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6-c-17, win ratio 0.477, (0.390, 0.563), counts 61, total 128.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_10, win ratio 0.500, (0.378, 0.623), counts 32, total 64.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_16, win ratio 0.620, (0.485, 0.755), counts 31, total 50.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_16_text_1, win ratio 0.588, (0.471, 0.705), counts 40, total 68.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_step_8, win ratio 0.400, (0.248, 0.552), counts 16, total 40.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_70, win ratio 0.386, (0.260, 0.512), counts 22, total 57.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_80, win ratio 0.596, (0.455, 0.736), counts 28, total 47.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_temp_s_90, win ratio 0.589, (0.460, 0.718), counts 33, total 56.\n", "chirp-v4-h-t-6_win_over_chirp-v4-h-t-6_text_1, win ratio 0.544, (0.426, 0.662), counts 37, total 68.\n", "tournament players: ['chirp-v3p5-engine-t-6', 'chirp-v3p5-engine-t-6_s_max_t_12_tag_2', 'chirp-v4-h-t-6', 'chirp-v4-h-t-6-c-17', 'chirp-v4-h-t-6_step_10', 'chirp-v4-h-t-6_step_16', 'chirp-v4-h-t-6_step_16_text_1', 'chirp-v4-h-t-6_step_8', 'chirp-v4-h-t-6_temp_s_70', 'chirp-v4-h-t-6_temp_s_80', 'chirp-v4-h-t-6_temp_s_90', 'chirp-v4-h-t-6_text_1']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:01<00:00, 620.39it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-t-6: 942.4, (880, 990)\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2: 1057.6, (1010, 1122)\n", "chirp-v4-h-t-6: 1014.0, (985, 1043)\n", "chirp-v4-h-t-6-c-17: 1030.3, (969, 1091)\n", "chirp-v4-h-t-6_step_10: 1014.0, (933, 1099)\n", "chirp-v4-h-t-6_step_16: 929.0, (826, 1017)\n", "chirp-v4-h-t-6_step_16_text_1: 952.1, (873, 1037)\n", "chirp-v4-h-t-6_step_8: 1084.5, (976, 1209)\n", "chirp-v4-h-t-6_temp_s_70: 1094.7, (1008, 1195)\n", "chirp-v4-h-t-6_temp_s_80: 946.7, (844, 1040)\n", "chirp-v4-h-t-6_temp_s_90: 951.3, (854, 1039)\n", "chirp-v4-h-t-6_text_1: 983.3, (903, 1063)\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"is artist\")\n", "get_preference_counts(\n", " user_intersting_clips_3p5[\n", " (user_intersting_clips_3p5[\"task\"] == \"artist_consistency\")\n", " ],\n", " \"is artist\",\n", ")" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:35:58.946534Z", "iopub.status.busy": "2025-01-04T15:35:58.946379Z", "iopub.status.idle": "2025-01-04T15:36:01.345922Z", "shell.execute_reply": "2025-01-04T15:36:01.345397Z", "shell.execute_reply.started": "2025-01-04T15:35:58.946518Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "upsample\n", "len pos models: 1061\n", "differing counts: 565\n", "chirp-v4-up-u-3_step_10_win_over_chirp-v4-up-u-3, win ratio 0.520, (0.421, 0.619), counts 51, total 98.\n", "chirp-v4-up-u-3_step_16_text_1_win_over_chirp-v4-up-u-3, win ratio 0.453, (0.319, 0.587), counts 24, total 53.\n", "chirp-v4-up-u-3_step_16_win_over_chirp-v4-up-u-3, win ratio 0.469, (0.371, 0.568), counts 46, total 98.\n", "chirp-v4-up-u-3_step_8_win_over_chirp-v4-up-u-3, win ratio 0.394, (0.298, 0.490), counts 39, total 99.\n", "chirp-v4-up-u-3_text_15_win_over_chirp-v4-up-u-3, win ratio 0.468, (0.325, 0.611), counts 22, total 47.\n", "chirp-v4-up-u-3_text_1_win_over_chirp-v4-up-u-3, win ratio 0.491, (0.359, 0.623), counts 27, total 55.\n", "chirp-v4-up-u-3_text_25_win_over_chirp-v4-up-u-3, win ratio 0.490, (0.350, 0.630), counts 24, total 49.\n", "chirp-v4-up-u-3_text_3_win_over_chirp-v4-up-u-3, win ratio 0.576, (0.457, 0.695), counts 38, total 66.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3, win ratio 1.000, (1.000, 1.000), counts 496, total 496.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_step_10, win ratio 0.480, (0.381, 0.579), counts 47, total 98.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_step_16, win ratio 0.531, (0.432, 0.629), counts 52, total 98.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_step_16_text_1, win ratio 0.547, (0.413, 0.681), counts 29, total 53.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_step_8, win ratio 0.606, (0.510, 0.702), counts 60, total 99.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_text_1, win ratio 0.509, (0.377, 0.641), counts 28, total 55.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_text_15, win ratio 0.532, (0.389, 0.675), counts 25, total 47.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_text_25, win ratio 0.510, (0.370, 0.650), counts 25, total 49.\n", "chirp-v4-up-u-3_win_over_chirp-v4-up-u-3_text_3, win ratio 0.424, (0.305, 0.543), counts 28, total 66.\n", "tournament players: ['chirp-v4-up-u-3', 'chirp-v4-up-u-3_step_10', 'chirp-v4-up-u-3_step_16', 'chirp-v4-up-u-3_step_16_text_1', 'chirp-v4-up-u-3_step_8', 'chirp-v4-up-u-3_text_1', 'chirp-v4-up-u-3_text_15', 'chirp-v4-up-u-3_text_25', 'chirp-v4-up-u-3_text_3']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:01<00:00, 687.49it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v4-up-u-3: 1010.8, (984, 1041)\n", "chirp-v4-up-u-3_step_10: 1025.0, (961, 1094)\n", "chirp-v4-up-u-3_step_16: 989.5, (923, 1051)\n", "chirp-v4-up-u-3_step_16_text_1: 977.9, (889, 1068)\n", "chirp-v4-up-u-3_step_8: 936.0, (868, 1008)\n", "chirp-v4-up-u-3_text_1: 1004.5, (909, 1094)\n", "chirp-v4-up-u-3_text_15: 988.6, (894, 1085)\n", "chirp-v4-up-u-3_text_25: 1003.7, (904, 1096)\n", "chirp-v4-up-u-3_text_3: 1063.9, (986, 1152)\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"upsample\")\n", "upsample_slice_df = user_intersting_clips_3p5[\n", " (user_intersting_clips_3p5[\"task\"] == \"upsample\")\n", "].copy()\n", "if upsample_slice_df.shape[0] > 0:\n", " get_preference_counts(\n", " upsample_slice_df,\n", " title_name=\"upsample\",\n", " )" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:36:01.346614Z", "iopub.status.busy": "2025-01-04T15:36:01.346465Z", "iopub.status.idle": "2025-01-04T15:36:01.366532Z", "shell.execute_reply": "2025-01-04T15:36:01.366062Z", "shell.execute_reply.started": "2025-01-04T15:36:01.346598Z" } }, "outputs": [], "source": [ "# print(\"instrumental\")\n", "# instrumental_gen_slice_df = user_intersting_clips_3p5[\n", "# user_intersting_clips_3p5[\"is_instrumental\"]\n", "# ].copy()\n", "# if instrumental_gen_slice_df.shape[0] > 0:\n", "# get_preference_counts(\n", "# instrumental_gen_slice_df,\n", "# title_name=\"instrumental\",\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": 63, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:08.095035Z", "start_time": "2024-05-26T00:25:07.782738Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:36:01.367160Z", "iopub.status.busy": "2025-01-04T15:36:01.367019Z", "iopub.status.idle": "2025-01-04T15:36:02.075594Z", "shell.execute_reply": "2025-01-04T15:36:02.075009Z", "shell.execute_reply.started": "2025-01-04T15:36:01.367146Z" } }, "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": 64, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:36:02.076338Z", "iopub.status.busy": "2025-01-04T15:36:02.076190Z", "iopub.status.idle": "2025-01-04T15:36:02.780988Z", "shell.execute_reply": "2025-01-04T15:36:02.780398Z", "shell.execute_reply.started": "2025-01-04T15:36:02.076322Z" } }, "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": 65, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:08.361849Z", "start_time": "2024-05-26T00:25:08.199583Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:36:02.781762Z", "iopub.status.busy": "2025-01-04T15:36:02.781609Z", "iopub.status.idle": "2025-01-04T15:36:02.805736Z", "shell.execute_reply": "2025-01-04T15:36:02.805227Z", "shell.execute_reply.started": "2025-01-04T15:36:02.781746Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Positive proportion of data meeting criteria: 25.33%\n", "Negative proportion of data meeting criteria: 100.00%\n" ] } ], "source": [ "pos_too_much_data_mask = (\n", " (user_intersting_clips[\"preference\"])\n", " & (\n", " (\n", " user_intersting_clips[\"reaction_play_count\"] >= 3\n", " ) # single play is super catchy\n", " | (\n", " user_intersting_clips[\"concat_play_counts\"] >= 3\n", " ) # or the concat play is super catchy\n", " )\n", " # & (user_intersting_clips[\"user_n_clips\"] >= 40)\n", " # & (user_intersting_clips[\"continued_parent\"].isna())\n", ")\n", "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\"] >= 40)\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": 66, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:08.523643Z", "start_time": "2024-05-26T00:25:08.367348Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:36:02.810611Z", "iopub.status.busy": "2025-01-04T15:36:02.810305Z", "iopub.status.idle": "2025-01-04T15:36:03.659530Z", "shell.execute_reply": "2025-01-04T15:36:03.658940Z", "shell.execute_reply.started": "2025-01-04T15:36:02.810595Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model Name Value Counts for Preferred Clips:\n", "----------------------------------------------------------------------\n", "Model Count Fraction\n", "----------------------------------------------------------------------\n", "chirp-v4-h-s-32 3,034 48.43%\n", "chirp-v3p5-engine-s-8 1,673 26.70%\n", "chirp-v4-h-t-6 813 12.98%\n", "chirp-v4-up-u-3 360 5.75%\n", "chirp-v3p5-engine-t-6 169 2.70%\n", "chirp-v3p5-engine-upload-4 139 2.22%\n", "chirp-v4-h-s-32-c-2 29 0.46%\n", "chirp-v4-h-t-6-c-17 25 0.40%\n", "chirp-v4-engine-b 14 0.22%\n", "chirp-v3p5-engine-b 4 0.06%\n", "chirp-v3p5-engine-b-r 3 0.05%\n", "chirp-v3p5-engine-short 2 0.03%\n", "----------------------------------------------------------------------\n", "Total 6,265 100.00%\n" ] } ], "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()\n", "# Get the value counts of model_name for preferred clips\n", "model_counts = final_interesting_clips[final_interesting_clips[\"preference\"]][\n", " \"model_name\"\n", "].value_counts()\n", "\n", "# Print the results in a nicely formatted way\n", "total_count = model_counts.sum()\n", "print(\"Model Name Value Counts for Preferred Clips:\")\n", "print(\"-\" * 70)\n", "print(f\"{'Model':<30} {'Count':>10} {'Fraction':>15}\")\n", "print(\"-\" * 70)\n", "for model, count in model_counts.items():\n", " fraction = count / total_count\n", " print(f\"{model:<30} {count:>10,d} {fraction:>15.2%}\")\n", "print(\"-\" * 70)\n", "print(f\"{'Total':<30} {total_count:>10,d} {1:>15.2%}\")" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:09.198504Z", "start_time": "2024-05-26T00:25:08.885507Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:36:03.660251Z", "iopub.status.busy": "2025-01-04T15:36:03.660102Z", "iopub.status.idle": "2025-01-04T15:36:03.886566Z", "shell.execute_reply": "2025-01-04T15:36:03.886012Z", "shell.execute_reply.started": "2025-01-04T15:36:03.660237Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Validation passed!\n" ] } ], "source": [ "validate_preference_data(final_interesting_clips)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:36:03.887471Z", "iopub.status.busy": "2025-01-04T15:36:03.887117Z", "iopub.status.idle": "2025-01-04T15:36:03.922759Z", "shell.execute_reply": "2025-01-04T15:36:03.922276Z", "shell.execute_reply.started": "2025-01-04T15:36:03.887455Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of rows in final_interesting_clips for model chirp-v3p5-engine-t-6:\n", "338\n", "done (12530, 59)\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\"] == target_model_name\n", "].shape[0]\n", "\n", "# Print the row count in a nicely formatted way\n", "print(f\"Number of rows in final_interesting_clips for model {target_model_name}:\")\n", "print(f\"{row_count:,}\")\n", "print(\"done\", final_interesting_clips.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# For faster processing once" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:10.265241Z", "start_time": "2024-05-26T00:25:09.934743Z" }, "execution": { "iopub.execute_input": "2025-01-04T15:36:03.923407Z", "iopub.status.busy": "2025-01-04T15:36:03.923262Z", "iopub.status.idle": "2025-01-04T15:36:03.992762Z", "shell.execute_reply": "2025-01-04T15:36:03.992284Z", "shell.execute_reply.started": "2025-01-04T15:36:03.923392Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Unique Users:\n", "--------------------\n", "58,334\n", "--------------------\n", "Series([], Name: count, dtype: int64)\n", "(0, 48)\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)\n", "\n", "# This can take a while cause we have a lot of users...\n", "# query = \"\"\"\n", "# SELECT *\n", "# FROM auth_user\n", "# \"\"\"\n", "# user_df = pd.read_sql_query(query, engine)\n", "# user_df.head()\n", "\n", "test_user_id = 4688272\n", "print(\n", " clip_df[clip_df[\"user_id\"] == test_user_id][\"created_at\"]\n", " .apply(lambda x: str(x)[:10])\n", " .value_counts()\n", ")\n", "print(clip_df[clip_df[\"user_id\"] == test_user_id].shape)\n", "query = \"\"\"\n", "SELECT *\n", "FROM auth_user\n", "WHERE id=27205089\n", "\"\"\"\n", "# 3 keenan\n", "# 6 martin\n", "# 8 tony -- that's me!\n", "# 186417 georg\n", "# test_user_df = pd.read_sql_query(query, engine)\n", "# test_user_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Find some weird generations" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:36:03.993410Z", "iopub.status.busy": "2025-01-04T15:36:03.993266Z", "iopub.status.idle": "2025-01-04T15:36:11.890052Z", "shell.execute_reply": "2025-01-04T15:36:11.889496Z", "shell.execute_reply.started": "2025-01-04T15:36:03.993395Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of no_reaction_clip_df:\n", "(94520, 37)\n", "\n", "Proportion of clips without reactions:\n", "21.23%\n", "Ratio of clips without reactions to total clips from the same users:\n", "0.7891\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "free 298\n", "pro 593\n", "Ratio of clips from super bad users: 0.72%\n", "DONE\n" ] } ], "source": [ "not_known_bot_gens_mask = clip_df[\"model_name\"] != \"chirp-v3p5-engine-b\"\n", "total_clip_df[\"is_pro_user\"] = total_clip_df[\"user_id\"].isin(pro_users)\n", "run_bot_detection(\n", " total_clip_df,\n", " reaction_df,\n", " write_to_file=True,\n", " cut_off_freq=0.95,\n", " min_generations_for_no_reaction=10,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Alpha testing user selection" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:36:11.890797Z", "iopub.status.busy": "2025-01-04T15:36:11.890639Z", "iopub.status.idle": "2025-01-04T15:36:11.911108Z", "shell.execute_reply": "2025-01-04T15:36:11.910649Z", "shell.execute_reply.started": "2025-01-04T15:36:11.890781Z" } }, "outputs": [], "source": [ "# Alpha testing user selection\n", "# we focus on the folks who are good good\n", "\n", "# # 0526 is v2 -- prod\n", "# # 0529 is v4 -- still good IMO, more data\n", "# early_v3p5_data = pd.read_csv(\"/home/tony/Data/Preference/13b_v0/interesting_clips_20240529.csv\")\n", "\n", "# print(\"uqniue users for vp5\", early_v3p5_data[\"user_id\"].nunique())\n", "\n", "# early_v3_data = pd.read_csv(\"/home/tony/Data/Preference/7b_v0_interesting_clips.csv\")\n", "\n", "# print(\"uqniue users for v3\", early_v3_data[\"user_id\"].nunique())\n", "\n", "# early_v2_data = pd.read_csv(\"/home/tony/Data/Preference/3b_v0_interesting_clips.csv\")\n", "\n", "# print(\"uqniue users for v2\", early_v2_data[\"user_id\"].nunique())\n", "\n", "# intersection_user_ids_super = set(early_v3p5_data[\"user_id\"].unique()).intersection(set(early_v3_data[\"user_id\"].unique())).intersection(set(early_v2_data[\"user_id\"].unique()))\n", "\n", "# intersection_user_ids_v3_on = set(early_v3p5_data[\"user_id\"].unique()).intersection(set(early_v3_data[\"user_id\"].unique())).difference(intersection_user_ids_super)\n", "\n", "# print(len(intersection_user_ids_super), len(intersection_user_ids_v3_on))\n", "\n", "# super_user_df = user_df[user_df[\"id\"].isin(intersection_user_ids_super)].copy()\n", "# print(super_user_df.shape)\n", "# v3_onward_user_df = user_df[user_df[\"id\"].isin(intersection_user_ids_v3_on)].copy()\n", "# print(v3_onward_user_df.shape)\n", "# super_user_df.to_csv(\"/home/tony/Data/Preference/alpha_users/super_user.csv\", index=False)\n", "# v3_onward_user_df.to_csv(\"/home/tony/Data/Preference/alpha_users/v3_onward_user.csv\", index=False)\n", "# print(\"Done!!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# User generated clips lifetime filter" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:36:11.911749Z", "iopub.status.busy": "2025-01-04T15:36:11.911608Z", "iopub.status.idle": "2025-01-04T15:37:56.828797Z", "shell.execute_reply": "2025-01-04T15:37:56.828196Z", "shell.execute_reply.started": "2025-01-04T15:36:11.911735Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(8191149, 25)\n", "1857618\n" ] } ], "source": [ "query = \"\"\"\n", "SELECT *\n", "FROM bots_userstats\n", "WHERE total_clips>=20\n", "\"\"\"\n", "user_stats_df = pd.read_sql_query(query, engine)\n", "print(user_stats_df.shape)\n", "user_stats_df[\"total_clips\"].describe()\n", "top_users = user_stats_df[user_stats_df[\"total_clips\"] >= 100][\"user_id\"].unique()\n", "print(len(top_users))" ] }, { "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": 73, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:37:56.829527Z", "iopub.status.busy": "2025-01-04T15:37:56.829375Z", "iopub.status.idle": "2025-01-04T15:37:58.559477Z", "shell.execute_reply": "2025-01-04T15:37:58.558822Z", "shell.execute_reply.started": "2025-01-04T15:37:56.829511Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROD\n" ] } ], "source": [ "if not os.path.exists(snow_password_path):\n", " raise Exception(\"you are not authorized to access snowflake -- please setup\")\n", "\n", "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": 74, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:37:58.560340Z", "iopub.status.busy": "2025-01-04T15:37:58.560172Z", "iopub.status.idle": "2025-01-04T15:37:58.638288Z", "shell.execute_reply": "2025-01-04T15:37:58.637707Z", "shell.execute_reply.started": "2025-01-04T15:37:58.560323Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(338, 59)\n", "(338, 59)\n" ] } ], "source": [ "# select the df we want to squery for play counts\n", "subset_v4_clips_df_all = final_interesting_clips[\n", " final_interesting_clips[\"model_name\"] == target_model_name\n", "].copy()\n", "print(subset_v4_clips_df_all.shape)\n", "pair_request_mask = subset_v4_clips_df_all[\"request_id\"].isin(\n", " subset_v4_clips_df_all[\"request_id\"]\n", " .value_counts()\n", " .index[subset_v4_clips_df_all[\"request_id\"].value_counts() == 2]\n", ")\n", "subset_v4_clips_df = subset_v4_clips_df_all[pair_request_mask].copy()\n", "print(subset_v4_clips_df.shape)\n", "v4_clip_ids = list(str(s) for s in subset_v4_clips_df[\"id\"].unique())" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:37:58.639207Z", "iopub.status.busy": "2025-01-04T15:37:58.639056Z", "iopub.status.idle": "2025-01-04T15:38:09.345532Z", "shell.execute_reply": "2025-01-04T15:38:09.344849Z", "shell.execute_reply.started": "2025-01-04T15:37:58.639192Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/1 [00:00 2\u001b[0;31m subset_v4_clips_df_test = subset_v4_clips_df.merge(\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdf_snow_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"str_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"left\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m )\n\u001b[1;32m 5\u001b[0m subset_v4_clips_df_test[\"norm_play_frac\"] = (\n", "\u001b[0;32m~/anaconda3/envs/suno_env_dev/lib/python3.10/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m 10828\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mMergeValidate\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10829\u001b[0m ) -> DataFrame:\n\u001b[1;32m 10830\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10831\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10832\u001b[0;31m return merge(\n\u001b[0m\u001b[1;32m 10833\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10834\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10835\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/suno_env_dev/lib/python3.10/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m 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\u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1913\u001b[0m \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1914\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'str_id'" ] } ], "source": [ "subset_v4_clips_df[\"str_id\"] = subset_v4_clips_df[\"id\"].astype(str)\n", "subset_v4_clips_df_test = subset_v4_clips_df.merge(\n", " df_snow_test, on=\"str_id\", how=\"left\"\n", ")\n", "subset_v4_clips_df_test[\"norm_play_frac\"] = (\n", " subset_v4_clips_df_test[\"total_play_time\"].fillna(0)\n", " / subset_v4_clips_df_test[\"duration\"]\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:09.427964Z", "iopub.status.busy": "2025-01-04T15:38:09.427814Z", "iopub.status.idle": "2025-01-04T15:38:10.255576Z", "shell.execute_reply": "2025-01-04T15:38:10.255024Z", "shell.execute_reply.started": "2025-01-04T15:38:09.427950Z" } }, "outputs": [], "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": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:10.256368Z", "iopub.status.busy": "2025-01-04T15:38:10.256214Z", "iopub.status.idle": "2025-01-04T15:38:10.499155Z", "shell.execute_reply": "2025-01-04T15:38:10.498421Z", "shell.execute_reply.started": "2025-01-04T15:38:10.256353Z" } }, "outputs": [], "source": [ "play_duration_mask = (\n", " subset_v4_clips_df_test[\"preference\"]\n", " & (subset_v4_clips_df_test[\"norm_play_frac\"] >= 0.95)\n", " & (subset_v4_clips_df_test[\"total_play_time\"] >= 10)\n", " & (subset_v4_clips_df_test[\"user_id\"].isin(top_users))\n", " & (\n", " (subset_v4_clips_df_test[\"reaction_play_count\"] >= 3)\n", " | (subset_v4_clips_df_test[\"concat_play_counts\"] >= 3)\n", " )\n", ") | (\n", " (~subset_v4_clips_df_test[\"preference\"])\n", " & (subset_v4_clips_df_test[\"norm_play_frac\"] <= 3.1)\n", " & (subset_v4_clips_df_test[\"total_play_time\"] >= 3)\n", " & (subset_v4_clips_df_test[\"user_id\"].isin(top_users))\n", ")\n", "# Calculate the fraction of clips that pass the play duration cut\n", "frac_pass_play_duration = play_duration_mask.sum() / subset_v4_clips_df_test.shape[0]\n", "\n", "# Print the result with a formatted string\n", "print(\n", " f\"Fraction of clips that pass the play duration cut: {frac_pass_play_duration:.4f}\"\n", ")\n", "\n", "# Get unique request IDs that pass the play duration criteria\n", "unique_requests_pass_play_durations = subset_v4_clips_df_test[play_duration_mask][\n", " \"request_id\"\n", "].unique()\n", "\n", "# Print the number of unique requests that pass the play duration criteria\n", "print(\n", " f\"Number of unique requests passing play duration criteria: {len(unique_requests_pass_play_durations)}\"\n", ")\n", "\n", "# Calculate the fraction of unique requests that pass play duration criteria\n", "fraction_requests_pass = (\n", " len(unique_requests_pass_play_durations)\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": 80, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:10.500041Z", "iopub.status.busy": "2025-01-04T15:38:10.499869Z", "iopub.status.idle": "2025-01-04T15:38:10.531827Z", "shell.execute_reply": "2025-01-04T15:38:10.531226Z", "shell.execute_reply.started": "2025-01-04T15:38:10.500024Z" } }, "outputs": [], "source": [ "subset_v4_clips_df_pass_duration = subset_v4_clips_df_test[play_duration_mask].copy()\n", "play_duration_mask_request_mask = subset_v4_clips_df_pass_duration[\"request_id\"].isin(\n", " subset_v4_clips_df_pass_duration[\"request_id\"]\n", " .value_counts()\n", " .index[subset_v4_clips_df_pass_duration[\"request_id\"].value_counts() == 2]\n", ")\n", "final_subset_v4_clips_df = subset_v4_clips_df_pass_duration[\n", " play_duration_mask_request_mask\n", "].copy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:10.532747Z", "iopub.status.busy": "2025-01-04T15:38:10.532431Z", "iopub.status.idle": "2025-01-04T15:38:10.564958Z", "shell.execute_reply": "2025-01-04T15:38:10.564404Z", "shell.execute_reply.started": "2025-01-04T15:38:10.532730Z" } }, "outputs": [], "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": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:10.565716Z", "iopub.status.busy": "2025-01-04T15:38:10.565568Z", "iopub.status.idle": "2025-01-04T15:38:10.606059Z", "shell.execute_reply": "2025-01-04T15:38:10.605534Z", "shell.execute_reply.started": "2025-01-04T15:38:10.565702Z" } }, "outputs": [], "source": [ "(\n", " subset_v4_clips_df_test[\"task\"].value_counts(),\n", " final_subset_v4_clips_df[\"task\"].value_counts(),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:10.606973Z", "iopub.status.busy": "2025-01-04T15:38:10.606672Z", "iopub.status.idle": "2025-01-04T15:38:10.643676Z", "shell.execute_reply": "2025-01-04T15:38:10.643123Z", "shell.execute_reply.started": "2025-01-04T15:38:10.606957Z" } }, "outputs": [], "source": [ "# final_subset_v4_clips_df.to_pickle(\n", "# \"/home/tony/Data/Preference/30b_v5/interesting_clips_v4_t_5_20241016_full.pkl\",\n", "# )\n", "print(final_subset_v4_clips_df.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:10.644578Z", "iopub.status.busy": "2025-01-04T15:38:10.644430Z", "iopub.status.idle": "2025-01-04T15:38:10.805157Z", "shell.execute_reply": "2025-01-04T15:38:10.804633Z", "shell.execute_reply.started": "2025-01-04T15:38:10.644564Z" } }, "outputs": [], "source": [ "clip_df[\"task\"].value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:10.806128Z", "iopub.status.busy": "2025-01-04T15:38:10.805752Z", "iopub.status.idle": "2025-01-04T15:38:12.381125Z", "shell.execute_reply": "2025-01-04T15:38:12.380355Z", "shell.execute_reply.started": "2025-01-04T15:38:10.806112Z" } }, "outputs": [], "source": [ "n_created_cover = clip_df[clip_df[\"task\"] == \"cover\"][\"user_id\"].nunique()\n", "n_pro_created = clip_df[clip_df[\"is_pro_user\"]][\"user_id\"].nunique()\n", "print(\n", " \"cover usage\",\n", " n_created_cover,\n", " n_pro_created,\n", " round(n_created_cover / n_pro_created, 4),\n", ")\n", "n_created_artist = clip_df[clip_df[\"task\"] == \"artist_consistency\"][\"user_id\"].nunique()\n", "print(\n", " \"artist usage\",\n", " n_created_artist,\n", " n_pro_created,\n", " round(n_created_artist / n_pro_created, 4),\n", ")\n", "n_created_infill = clip_df[clip_df[\"task\"] == \"infill\"][\"user_id\"].nunique()\n", "print(\n", " \"infill usage\",\n", " n_created_infill,\n", " n_pro_created,\n", " round(n_created_infill / n_pro_created, 4),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# FE change exp " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:12.382337Z", "iopub.status.busy": "2025-01-04T15:38:12.381892Z", "iopub.status.idle": "2025-01-04T15:38:12.412746Z", "shell.execute_reply": "2025-01-04T15:38:12.412146Z", "shell.execute_reply.started": "2025-01-04T15:38:12.382318Z" } }, "outputs": [], "source": [ "print_out_value_counts_nicely(clip_df[clip_df[\"has_stems\"]], \"model_name\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:12.413554Z", "iopub.status.busy": "2025-01-04T15:38:12.413391Z", "iopub.status.idle": "2025-01-04T15:38:12.735912Z", "shell.execute_reply": "2025-01-04T15:38:12.735257Z", "shell.execute_reply.started": "2025-01-04T15:38:12.413539Z" } }, "outputs": [], "source": [ "print_out_value_counts_nicely(clip_df[clip_df[\"task\"] == \"cover\"], \"model_name\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:12.736767Z", "iopub.status.busy": "2025-01-04T15:38:12.736603Z", "iopub.status.idle": "2025-01-04T15:38:12.938470Z", "shell.execute_reply": "2025-01-04T15:38:12.937842Z", "shell.execute_reply.started": "2025-01-04T15:38:12.736751Z" } }, "outputs": [], "source": [ "print_out_value_counts_nicely(clip_df[clip_df[\"task\"] == \"infill\"], \"model_name\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:12.939279Z", "iopub.status.busy": "2025-01-04T15:38:12.939122Z", "iopub.status.idle": "2025-01-04T15:38:13.226572Z", "shell.execute_reply": "2025-01-04T15:38:13.225913Z", "shell.execute_reply.started": "2025-01-04T15:38:12.939264Z" } }, "outputs": [], "source": [ "print_out_value_counts_nicely(\n", " clip_df[clip_df[\"task\"] == \"artist_consistency\"], \"model_name\"\n", ")" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:13.227567Z", "iopub.status.busy": "2025-01-04T15:38:13.227233Z", "iopub.status.idle": "2025-01-04T15:38:13.248247Z", "shell.execute_reply": "2025-01-04T15:38:13.247695Z", "shell.execute_reply.started": "2025-01-04T15:38:13.227551Z" } }, "outputs": [], "source": [ "# clip_df[(clip_df[\"task\"] == \"artist_consistency\") & (clip_df[\"model_name\"] == \"chirp-v3p5-engine-s-8\")][[\"model_name\", \"s3_id\", \"task\"]].tail(n=20)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:13.249022Z", "iopub.status.busy": "2025-01-04T15:38:13.248870Z", "iopub.status.idle": "2025-01-04T15:38:13.288470Z", "shell.execute_reply": "2025-01-04T15:38:13.287953Z", "shell.execute_reply.started": "2025-01-04T15:38:13.249008Z" } }, "outputs": [], "source": [ "# clip_df[(clip_df[\"task\"] == \"infill\") & (clip_df[\"model_name\"] == \"chirp-v3p5-engine-s-8\")][[\"model_name\", \"s3_id\", \"task\"]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:13.289224Z", "iopub.status.busy": "2025-01-04T15:38:13.289072Z", "iopub.status.idle": "2025-01-04T15:38:13.547691Z", "shell.execute_reply": "2025-01-04T15:38:13.547155Z", "shell.execute_reply.started": "2025-01-04T15:38:13.289210Z" } }, "outputs": [], "source": [ "clip_infill_tags = clip_df[(clip_df[\"task\"] == \"infill\")][\"metadata\"].apply(\n", " lambda x: x.get(\"tags\", \"\")\n", ")\n", "clip_infill_tags.apply(lambda x: len(x.strip()) == 0).sum() / clip_infill_tags.shape" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:13.548614Z", "iopub.status.busy": "2025-01-04T15:38:13.548310Z", "iopub.status.idle": "2025-01-04T15:38:13.565694Z", "shell.execute_reply": "2025-01-04T15:38:13.565174Z", "shell.execute_reply.started": "2025-01-04T15:38:13.548598Z" } }, "outputs": [], "source": [ "# alpha_df = pd.read_csv(\"/home/tony/Data/Preference/alpha_users/alpha_users_20241111.csv\")\n", "# input_users = \", \".join([str(x) for x in alpha_df[\"0\"].values])\n", "# query = f\"\"\"\n", "# SELECT *\n", "# FROM bots_discordinfo\n", "# WHERE user_id IN ({input_users});\n", "# \"\"\"\n", "# test_user_discord_df = pd.read_sql_query(query, engine)\n", "# print(test_user_discord_df.shape)\n", "# test_user_df.to_csv(\"alpha_users_20241111_full.csv\")" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:13.566419Z", "iopub.status.busy": "2025-01-04T15:38:13.566271Z", "iopub.status.idle": "2025-01-04T15:38:13.606664Z", "shell.execute_reply": "2025-01-04T15:38:13.606137Z", "shell.execute_reply.started": "2025-01-04T15:38:13.566404Z" } }, "outputs": [], "source": [ "# user_intersting_clips[user_intersting_clips[\"request_id\"].isin(user_intersting_clips[user_intersting_clips[\"model_name\"] == \"chirp-v4-up-u-1-c-3\"][\"request_id\"])][[\"s3_id\", \"model_name\", \"preference\"]].head(n=10)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:13.607394Z", "iopub.status.busy": "2025-01-04T15:38:13.607243Z", "iopub.status.idle": "2025-01-04T15:38:13.645788Z", "shell.execute_reply": "2025-01-04T15:38:13.645274Z", "shell.execute_reply.started": "2025-01-04T15:38:13.607380Z" } }, "outputs": [], "source": [ "# user_intersting_clips[\n", "# user_intersting_clips[\"request_id\"].isin(user_intersting_clips[user_intersting_clips[\"model_name\"] == \"chirp-v4-up-u-1-c-4\"][\"request_id\"])\n", "# ].to_pickle(\"/home/tony/Data/Preference/up_v1/interesting_clips_up_cycle_debug_c4.pkl\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:13.646497Z", "iopub.status.busy": "2025-01-04T15:38:13.646354Z", "iopub.status.idle": "2025-01-04T15:38:13.685972Z", "shell.execute_reply": "2025-01-04T15:38:13.685461Z", "shell.execute_reply.started": "2025-01-04T15:38:13.646483Z" } }, "outputs": [], "source": [ "upsample_slice_df[\"user_id\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "execution": { "iopub.execute_input": "2025-01-04T15:38:13.686649Z", "iopub.status.busy": "2025-01-04T15:38:13.686506Z", "iopub.status.idle": "2025-01-04T15:38:13.726950Z", "shell.execute_reply": "2025-01-04T15:38:13.726428Z", "shell.execute_reply.started": "2025-01-04T15:38:13.686635Z" } }, "outputs": [], "source": [ "# upsample_slice_df = user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"upsample\")].copy()\n", "# if upsample_slice_df.shape[0] > 0:\n", "# get_preference_counts(\n", "# upsample_slice_df[upsample_slice_df[\"user_id\"].isin([428185])],\n", "# title_name=\"upsample\",\n", "# )" ] }, { "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": "3.10.15" }, "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 }