{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Select Preference Data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:11:04.757824Z", "start_time": "2024-05-26T00:11:04.555293Z" }, "execution": { "iopub.execute_input": "2024-11-01T02:46:11.584280Z", "iopub.status.busy": "2024-11-01T02:46:11.584128Z", "iopub.status.idle": "2024-11-01T02:46:11.740530Z", "shell.execute_reply": "2024-11-01T02:46:11.740074Z", "shell.execute_reply.started": "2024-11-01T02:46:11.584265Z" } }, "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": "2024-11-01T02:46:11.741170Z", "iopub.status.busy": "2024-11-01T02:46:11.741036Z", "iopub.status.idle": "2024-11-01T02:46:17.933619Z", "shell.execute_reply": "2024-11-01T02:46:17.933048Z", "shell.execute_reply.started": "2024-11-01T02:46:11.741157Z" } }, "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 = \"rds!cluster-a3b66c33-40a7-47dd-bd6e-32b1c17c9124\"\n", " region_name = \"us-east-2\"\n", " # Create a Secrets Manager client\n", " session = boto3.session.Session()\n", " client = session.client(service_name=\"secretsmanager\", region_name=region_name)\n", " try:\n", " get_secret_value_response = client.get_secret_value(SecretId=secret_name)\n", " except ClientError as e:\n", " raise e\n", " secret = get_secret_value_response[\"SecretString\"]\n", " return json.loads(secret)\n", "\n", "\n", "my_secrets = get_secret()\n", "\n", "# alternative...\n", "engine = sqlalchemy.create_engine(\n", " \"postgresql://postgres:%s@suno-main-pgdb-prod-instance-1.cnfvffydbwvc.us-east-2.rds.amazonaws.com/suno_main\"\n", " % quote(my_secrets[\"password\"]),\n", ")\n", "\n", "\n", "home_dir = os.path.expanduser(\"~\")\n", "snow_password_path = os.path.join(home_dir, \".aws\", \"snow_pw.txt\")\n", "if os.path.exists(snow_password_path):\n", " # !pip install snowflake\n", " from snowflake.core import Root\n", " from snowflake.snowpark import Session\n", "\n", " with open(snow_password_path, \"r\") as fp:\n", " fp_lines = fp.readlines()\n", " snow_password = fp_lines[0].strip()\n", " snow_username = fp_lines[1].strip()\n", "\n", " CONNECTION_PARAMETERS = {\n", " \"account\": \"fu90569.us-east-2.aws\",\n", " \"user\": snow_username,\n", " \"password\": snow_password,\n", " \"role\": \"ACCOUNTADMIN\",\n", " \"database\": \"SUNO_PROD\",\n", " \"warehouse\": \"SUNO_PROD_LARGE\",\n", " \"schema\": \"PROD\",\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Validate some info" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:11:08.550447Z", "start_time": "2024-05-26T00:11:08.397196Z" }, "execution": { "iopub.execute_input": "2024-11-01T02:46:17.934418Z", "iopub.status.busy": "2024-11-01T02:46:17.934186Z", "iopub.status.idle": "2024-11-01T02:46:17.951979Z", "shell.execute_reply": "2024-11-01T02:46:17.951492Z", "shell.execute_reply.started": "2024-11-01T02:46:17.934402Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-10-31 03:55:00\n" ] } ], "source": [ "# there are 4 hr time difference between eastern time and utc\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 = \"2024-10-14 20:20:00\" # 30b t5-1 out\n", "# cutoff_date = \"2024-10-15 00:20:00\" # 30b t5-1-b40 out\n", "# cutoff_date = \"2024-10-15 19:00:00\" # 30b t5-2 out\n", "# cutoff_date = \"2024-10-16 13:00:00\" # 30b t5-3 out\n", "# cutoff_date = \"2024-10-17 12:30:00\" # 30b t5-4 out\n", "# cutoff_date = \"2024-10-17 17:20:00\" # 30b t5-4 out\n", "# cutoff_date = \"2024-10-18 18:20:00\" # 30b t5-6 out\n", "# cutoff_date = \"2024-10-21 01:30:00\" # 30b t5-7 out\n", "# cutoff_date = \"2024-10-22 12:30:00\" # 30b t5-8 out\n", "# cutoff_date = \"2024-10-24 1:00:00\" # 30b t5-9 out\n", "# cutoff_date = \"2024-10-25 01:00:00\" # 30b t5-10 out\n", "# cutoff_date = \"2024-10-28 03:00:00\" # 30b t5-13 out\n", "# cutoff_date = \"2024-10-30 15:20:00\" # 30b t5-15 out\n", "cutoff_date = \"2024-10-31 03:55:00\" # hybrid out\n", "# cutoff_date = (datetime.datetime.now() - datetime.timedelta(hours=2)).astimezone(datetime.timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S\")\n", "print(cutoff_date)\n", "\n", "target_model_name = \"chirp-v3p5-engine-t-5\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:11:09.349857Z", "start_time": "2024-05-26T00:11:08.551408Z" }, "execution": { "iopub.execute_input": "2024-11-01T02:46:17.953190Z", "iopub.status.busy": "2024-11-01T02:46:17.953044Z", "iopub.status.idle": "2024-11-01T02:46:18.233397Z", "shell.execute_reply": "2024-11-01T02:46:18.232920Z", "shell.execute_reply.started": "2024-11-01T02:46:17.953176Z" } }, "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": "2024-11-01T02:46:18.234007Z", "iopub.status.busy": "2024-11-01T02:46:18.233865Z", "iopub.status.idle": "2024-11-01T03:01:30.172983Z", "shell.execute_reply": "2024-11-01T03:01:30.172380Z", "shell.execute_reply.started": "2024-11-01T02:46:18.233993Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start gather data from 2024-10-31 03:55:00\n", "Bots Action: 4,664,876 rows\n", " ---- Execution time: 67.11 seconds\n", "Reactions: 5,491,030 rows\n", " ---- Execution time: 58.59 seconds\n", "Total Clips: 4,674,972 rows\n", " ---- Execution time: 757.64 seconds\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/tony/Work/glockenspiel/suno_analytics/suno_analytics/preference_data_selection.py:64: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " playlist_clip_df = pd.concat([playlist_clip_df, chunk], ignore_index=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Playlist Clips: 110,312 rows\n", " ---- Execution time: 1.38 seconds\n", "Authenticated Users: 898,372 rows\n", " ---- Execution time: 2.04 seconds\n", "Discord Info: 331,561 rows\n", "subscription_status\n", "active 309621\n", "past_due 21940\n", "Name: count, dtype: int64\n", " ---- Execution time: 25.16 seconds\n" ] } ], "source": [ "gathered_data = gather_data(engine, cutoff_date)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:01:30.173740Z", "iopub.status.busy": "2024-11-01T03:01:30.173586Z", "iopub.status.idle": "2024-11-01T03:01:31.445900Z", "shell.execute_reply": "2024-11-01T03:01:31.445409Z", "shell.execute_reply.started": "2024-11-01T03:01:30.173725Z" } }, "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": "2024-11-01T03:01:31.446544Z", "iopub.status.busy": "2024-11-01T03:01:31.446402Z", "iopub.status.idle": "2024-11-01T03:01:31.492676Z", "shell.execute_reply": "2024-11-01T03:01:31.492234Z", "shell.execute_reply.started": "2024-11-01T03:01:31.446529Z" } }, "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": "2024-11-01T03:01:31.493276Z", "iopub.status.busy": "2024-11-01T03:01:31.493133Z", "iopub.status.idle": "2024-11-01T03:01:42.270947Z", "shell.execute_reply": "2024-11-01T03:01:42.270327Z", "shell.execute_reply.started": "2024-11-01T03:01:31.493262Z" } }, "outputs": [], "source": [ "# parse out the necessary metadata early\n", "total_clip_df[[\"continued_parent\", \"duration\", \"source\", \"clip_type\", \"task\"]] = (\n", " pd.DataFrame(\n", " total_clip_df[\"metadata\"].map(parse_metadata_for_basics).tolist(),\n", " index=total_clip_df.index,\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:01:42.271770Z", "iopub.status.busy": "2024-11-01T03:01:42.271611Z", "iopub.status.idle": "2024-11-01T03:01:49.975219Z", "shell.execute_reply": "2024-11-01T03:01:49.974667Z", "shell.execute_reply.started": "2024-11-01T03:01:42.271755Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total clips: 4674972\n", "gen: 4519540 (96.68%)\n", "stem: 74212 (1.59%)\n", "upload: 44264 (0.95%)\n", "concat: 26857 (0.57%)\n", "edit_crop: 5522 (0.12%)\n", "concat_infilling: 4577 (0.10%)\n", "total without model: 118602\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Total number of hours: 24\n", "Average clips per hour: 194790.50\n", "Max clips in an hour: 251037\n", "Min clips in an hour: 14138\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": "2024-11-01T03:01:49.975953Z", "iopub.status.busy": "2024-11-01T03:01:49.975795Z", "iopub.status.idle": "2024-11-01T03:01:50.200430Z", "shell.execute_reply": "2024-11-01T03:01:50.199841Z", "shell.execute_reply.started": "2024-11-01T03:01:49.975937Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of upvoates: 642,614 rows\n", "number of flagged reports: 9,631 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": "2024-11-01T03:01:50.201156Z", "iopub.status.busy": "2024-11-01T03:01:50.201007Z", "iopub.status.idle": "2024-11-01T03:01:50.567669Z", "shell.execute_reply": "2024-11-01T03:01:50.567108Z", "shell.execute_reply.started": "2024-11-01T03:01:50.201141Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reactions fraction by pro user:\n", "False: 3471754 (63.23%)\n", "True: 2019276 (36.77%)\n", "------------\n", "Clip generated fraction by pro user:\n", "False: 2639451 (56.46%)\n", "True: 2035521 (43.54%)\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": "2024-11-01T03:01:50.568395Z", "iopub.status.busy": "2024-11-01T03:01:50.568246Z", "iopub.status.idle": "2024-11-01T03:01:50.682659Z", "shell.execute_reply": "2024-11-01T03:01:50.682172Z", "shell.execute_reply.started": "2024-11-01T03:01:50.568380Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stem parent ids: 36946\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": "2024-11-01T03:01:50.695985Z", "iopub.status.busy": "2024-11-01T03:01:50.695670Z", "iopub.status.idle": "2024-11-01T03:01:58.665521Z", "shell.execute_reply": "2024-11-01T03:01:58.664943Z", "shell.execute_reply.started": "2024-11-01T03:01:50.695967Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clips in a splaylist:\n", "False: 4636925 (99.19%)\n", "True: 38047 (0.81%)\n", "------------\n", "Clips has stem children:\n", "False: 4639584 (99.24%)\n", "True: 35388 (0.76%)\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": "2024-11-01T03:01:58.666206Z", "iopub.status.busy": "2024-11-01T03:01:58.666060Z", "iopub.status.idle": "2024-11-01T03:02:00.630394Z", "shell.execute_reply": "2024-11-01T03:02:00.629801Z", "shell.execute_reply.started": "2024-11-01T03:01:58.666191Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clips that have children: 480648 \n", "clips that are parents: 85124 \n", " Average continues from clip = 5.65\n", "web: 4421002 (95.21%)\n", "ios: 222536 (4.79%)\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": "2024-11-01T03:02:00.631125Z", "iopub.status.busy": "2024-11-01T03:02:00.630975Z", "iopub.status.idle": "2024-11-01T03:02:06.048352Z", "shell.execute_reply": "2024-11-01T03:02:06.047736Z", "shell.execute_reply.started": "2024-11-01T03:02:00.631110Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total uploads: 118602\n", "clips without request id: 155432\n", "clips without request id: 155432 clip_type\n", "stem 74212\n", "upload 44264\n", "concat 26857\n", "edit_crop 5522\n", "concat_infilling 4577\n", "Name: count, dtype: int64\n", "Clips without request id (concat, uploads...) frac = 0.00672\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": "2024-11-01T03:02:06.049093Z", "iopub.status.busy": "2024-11-01T03:02:06.048940Z", "iopub.status.idle": "2024-11-01T03:02:06.813163Z", "shell.execute_reply": "2024-11-01T03:02:06.812673Z", "shell.execute_reply.started": "2024-11-01T03:02:06.049079Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chirp-v3p5-engine-s-8: 3489458 (77.21%)\n", "chirp-v3p5-engine-t-6: 259939 (5.75%)\n", "chirp-v3p5-engine-upload-4: 196076 (4.34%)\n", "chirp-v3p5-engine-b: 153153 (3.39%)\n", "chirp-v3p5-engine-t-5-15: 92863 (2.05%)\n", "chirp-v3-engine-i: 88386 (1.96%)\n", "chirp-v3p5-engine-s-31: 87082 (1.93%)\n", "chirp-v3p5-h-s-31: 56256 (1.24%)\n", "chirp-v3p5-engine-s-29-6: 22938 (0.51%)\n", "chirp-v3p5-engine-t-5: 20719 (0.46%)\n", "chirp-v2-xxl-alpha: 18257 (0.40%)\n", "chirp-v3-5: 11263 (0.25%)\n", "chirp-v3p5-engine-short: 11197 (0.25%)\n", "chirp-v3p5-h-t-6: 6282 (0.14%)\n", "chirp-v3p5-engine-ft-1: 5128 (0.11%)\n", "chirp-v3-0: 249 (0.01%)\n", "chirp-v3-5-short: 149 (0.00%)\n", "chirp-v3-5-tau: 102 (0.00%)\n", "chirp-v3-5-upload: 38 (0.00%)\n", "chirp-v3-5|chirp-v3-0: 3 (0.00%)\n", "chirp-v3.5-0: 1 (0.00%)\n", "chirp-v3.5: 1 (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": "2024-11-01T03:02:06.813821Z", "iopub.status.busy": "2024-11-01T03:02:06.813677Z", "iopub.status.idle": "2024-11-01T03:02:13.733769Z", "shell.execute_reply": "2024-11-01T03:02:13.733196Z", "shell.execute_reply.started": "2024-11-01T03:02:06.813807Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pre-filter model type clip_df shape: (4519540, 36)\n", "post-filter model type clip_df shape: (4507739, 36)\n", "chirp-v3p5-engine-s-8: 3489458 (77.41%)\n", "chirp-v3p5-engine-t-6: 259939 (5.77%)\n", "chirp-v3p5-engine-upload-4: 196076 (4.35%)\n", "chirp-v3p5-engine-b: 153153 (3.40%)\n", "chirp-v3p5-engine-t-5-15: 92863 (2.06%)\n", "chirp-v3-engine-i: 88386 (1.96%)\n", "chirp-v3p5-engine-s-31: 87082 (1.93%)\n", "chirp-v3p5-h-s-31: 56256 (1.25%)\n", "chirp-v3p5-engine-s-29-6: 22938 (0.51%)\n", "chirp-v3p5-engine-t-5: 20719 (0.46%)\n", "chirp-v2-xxl-alpha: 18257 (0.41%)\n", "chirp-v3p5-engine-short: 11197 (0.25%)\n", "chirp-v3p5-h-t-6: 6282 (0.14%)\n", "chirp-v3p5-engine-ft-1: 5128 (0.11%)\n", "chirp-v3-5|chirp-v3-0: 3 (0.00%)\n", "chirp-v3.5-0: 1 (0.00%)\n", "chirp-v3.5: 1 (0.00%)\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", "]\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": "2024-11-01T03:02:13.734504Z", "iopub.status.busy": "2024-11-01T03:02:13.734345Z", "iopub.status.idle": "2024-11-01T03:02:15.771170Z", "shell.execute_reply": "2024-11-01T03:02:15.770596Z", "shell.execute_reply.started": "2024-11-01T03:02:13.734489Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "concat reactions: 39154 unique concat clips: 22860\n", "total concats (31434, 39)\n", "check \n", " reaction_play_count reaction_upvote_count reaction_dislike_count\n", "count 22860.000000 22860.000000 22860.000000\n", "mean 6.513386 0.301444 0.019291\n", "std 364.263825 1.294707 0.147376\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% 3.000000 0.000000 0.000000\n", "max 52036.000000 73.000000 8.000000\n", "All concats 31434\n", "total concats with plays 22860\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": "2024-11-01T03:02:15.771880Z", "iopub.status.busy": "2024-11-01T03:02:15.771724Z", "iopub.status.idle": "2024-11-01T03:02:17.090265Z", "shell.execute_reply": "2024-11-01T03:02:17.089699Z", "shell.execute_reply.started": "2024-11-01T03:02:15.771865Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "6549it [00:00, 17445.86it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "52377a7b-e058-40e9-a993-08ce425847de chirp-v3p5-engine-s-8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "12138it [00:00, 18296.70it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "3e03c8a8-0b34-4e4a-82f4-32ae771108d3 chirp-v3p5-engine-upload-4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "19559it [00:01, 18472.70it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "5ae26497-a1da-4607-b06e-9abe00349aee chirp-v3p5-engine-s-8\n", "78dfde48-83e4-4c03-82f4-4df466037b27 chirp-v3p5-engine-s-8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "22860it [00:01, 17849.03it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1cea90ab-4089-43e5-9a72-355b04bbda37 chirp-v3p5-engine-s-8\n", "3cef7ab9-6bad-409f-9f5a-00be8e2ef94d chirp-v3p5-engine-upload-4\n", "e4d9cd62-2ea2-45bf-b480-73f8663c7102 chirp-v3p5-engine-s-8\n", "total concat unique clips are: 43729 with error: 1, duplicate 4241 \n", " uploads are in concats 96 frac 0.002\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": "2024-11-01T03:02:17.090987Z", "iopub.status.busy": "2024-11-01T03:02:17.090833Z", "iopub.status.idle": "2024-11-01T03:02:17.344747Z", "shell.execute_reply": "2024-11-01T03:02:17.344150Z", "shell.execute_reply.started": "2024-11-01T03:02:17.090972Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 4.507739e+06\n", "mean 1.343430e+02\n", "std 4.045928e+02\n", "min 1.000000e+00\n", "25% 1.000000e+01\n", "50% 1.200000e+01\n", "75% 7.600000e+01\n", "max 5.012000e+03\n", "Name: user_n_clips, dtype: float64\n" ] } ], "source": [ "# set user number of clips generated\n", "clip_df[\"user_n_clips\"] = clip_df[\"user_id\"].map(clip_df[\"user_id\"].value_counts())\n", "print(clip_df[\"user_n_clips\"].describe())" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:23:44.826860Z", "start_time": "2024-05-26T00:23:40.238330Z" }, "execution": { "iopub.execute_input": "2024-11-01T03:02:17.345485Z", "iopub.status.busy": "2024-11-01T03:02:17.345332Z", "iopub.status.idle": "2024-11-01T03:02:21.119889Z", "shell.execute_reply": "2024-11-01T03:02:21.119283Z", "shell.execute_reply.started": "2024-11-01T03:02:17.345470Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has upvoted upvoted\n", "False 4295294\n", "True 212445\n", "Name: count, dtype: int64 upvoted\n", "False 0.952871\n", "True 0.047129\n", "Name: proportion, dtype: float64 upvote_count\n", "False 0.951426\n", "True 0.048574\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": "2024-11-01T03:02:21.120613Z", "iopub.status.busy": "2024-11-01T03:02:21.120467Z", "iopub.status.idle": "2024-11-01T03:02:23.627332Z", "shell.execute_reply": "2024-11-01T03:02:23.626751Z", "shell.execute_reply.started": "2024-11-01T03:02:21.120598Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "downvoted fraction by category:\n", "False: 4336466 (96.20%)\n", "True: 171273 (3.80%)\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": "2024-11-01T03:02:23.628070Z", "iopub.status.busy": "2024-11-01T03:02:23.627919Z", "iopub.status.idle": "2024-11-01T03:02:29.571016Z", "shell.execute_reply": "2024-11-01T03:02:29.570442Z", "shell.execute_reply.started": "2024-11-01T03:02:23.628055Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has_continued fraction by category:\n", "False: 4477755 (99.33%)\n", "True: 29984 (0.67%)\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": "2024-11-01T03:02:29.571752Z", "iopub.status.busy": "2024-11-01T03:02:29.571601Z", "iopub.status.idle": "2024-11-01T03:02:29.619629Z", "shell.execute_reply": "2024-11-01T03:02:29.619148Z", "shell.execute_reply.started": "2024-11-01T03:02:29.571736Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has upvoted in exp 0.04713\n", "has downvoted out of exp 0.038\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": "2024-11-01T03:02:29.620290Z", "iopub.status.busy": "2024-11-01T03:02:29.620146Z", "iopub.status.idle": "2024-11-01T03:02:35.375685Z", "shell.execute_reply": "2024-11-01T03:02:35.375098Z", "shell.execute_reply.started": "2024-11-01T03:02:29.620276Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "part_of_concat fraction by category:\n", "False: 4477980 (99.34%)\n", "True: 29759 (0.66%)\n", "------------\n", "Model distribution for part_of_concat clips:\n", "chirp-v3p5-engine-s-8: 61.86%\n", "chirp-v3p5-engine-t-6: 11.72%\n", "chirp-v3p5-engine-upload-4: 9.88%\n", "chirp-v3p5-engine-t-5-15: 6.39%\n", "chirp-v3-engine-i: 2.57%\n", "chirp-v3p5-engine-s-31: 2.18%\n", "chirp-v3p5-h-s-31: 1.85%\n", "chirp-v3p5-engine-t-5: 1.42%\n", "chirp-v3p5-engine-b: 1.07%\n", "chirp-v3p5-engine-s-29-6: 0.59%\n", "chirp-v3p5-h-t-6: 0.21%\n", "chirp-v2-xxl-alpha: 0.15%\n", "chirp-v3p5-engine-ft-1: 0.10%\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": "2024-11-01T03:02:35.376437Z", "iopub.status.busy": "2024-11-01T03:02:35.376287Z", "iopub.status.idle": "2024-11-01T03:02:39.156012Z", "shell.execute_reply": "2024-11-01T03:02:39.155422Z", "shell.execute_reply.started": "2024-11-01T03:02:35.376422Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "has_action fraction by category:\n", "False: 4470983 (99.18%)\n", "True: 36756 (0.82%)\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": "2024-11-01T03:02:39.156725Z", "iopub.status.busy": "2024-11-01T03:02:39.156576Z", "iopub.status.idle": "2024-11-01T03:02:41.644153Z", "shell.execute_reply": "2024-11-01T03:02:41.643581Z", "shell.execute_reply.started": "2024-11-01T03:02:39.156709Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flagged fraction by category:\n", "False: 4500893 (99.85%)\n", "True: 6846 (0.15%)\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": "2024-11-01T03:02:41.644885Z", "iopub.status.busy": "2024-11-01T03:02:41.644736Z", "iopub.status.idle": "2024-11-01T03:02:41.680058Z", "shell.execute_reply": "2024-11-01T03:02:41.679583Z", "shell.execute_reply.started": "2024-11-01T03:02:41.644870Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deleted fraction by category:\n", "False: 3899792 (86.51%)\n", "True: 607947 (13.49%)\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": "2024-11-01T03:02:41.680719Z", "iopub.status.busy": "2024-11-01T03:02:41.680576Z", "iopub.status.idle": "2024-11-01T03:02:42.223491Z", "shell.execute_reply": "2024-11-01T03:02:42.223024Z", "shell.execute_reply.started": "2024-11-01T03:02:41.680704Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total clips: 4,507,739\n", "Must be positive: 279,422 (6.20%)\n", "Definitely not negative: 3,738,321 (82.93%)\n", "Must be negative: 769,418 (17.07%)\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": "2024-11-01T03:02:42.224139Z", "iopub.status.busy": "2024-11-01T03:02:42.223998Z", "iopub.status.idle": "2024-11-01T03:02:55.701617Z", "shell.execute_reply": "2024-11-01T03:02:55.701021Z", "shell.execute_reply.started": "2024-11-01T03:02:42.224124Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Liked requests: 216,106\n", "Not liked requests: 2,216,089\n", "Requests with preference paired generations: 167,357\n", "Percentage of total unique requests: 7.39%\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": "2024-11-01T03:02:55.702379Z", "iopub.status.busy": "2024-11-01T03:02:55.702222Z", "iopub.status.idle": "2024-11-01T03:03:04.262482Z", "shell.execute_reply": "2024-11-01T03:03:04.261879Z", "shell.execute_reply.started": "2024-11-01T03:02:55.702363Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Disliked requests: 454,876\n", "Not disliked requests: 1,944,962\n", "Requests with preference paired generations: 135,000\n", "Percentage of total unique requests: 5.96%\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": "2024-11-01T03:03:04.263224Z", "iopub.status.busy": "2024-11-01T03:03:04.263068Z", "iopub.status.idle": "2024-11-01T03:03:04.315272Z", "shell.execute_reply": "2024-11-01T03:03:04.314751Z", "shell.execute_reply.started": "2024-11-01T03:03:04.263207Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total selected pairs of requests: 263,867\n", "Percentage of total unique requests: 11.65%\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": "2024-11-01T03:03:04.315952Z", "iopub.status.busy": "2024-11-01T03:03:04.315803Z", "iopub.status.idle": "2024-11-01T03:03:04.412597Z", "shell.execute_reply": "2024-11-01T03:03:04.412046Z", "shell.execute_reply.started": "2024-11-01T03:03:04.315937Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference in preference counts:\n", "0: 3,474,862 (77.09%)\n", "-1: 769,418 (17.07%)\n", "1: 263,459 (5.84%)\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": "2024-11-01T03:03:04.413309Z", "iopub.status.busy": "2024-11-01T03:03:04.413142Z", "iopub.status.idle": "2024-11-01T03:03:07.957353Z", "shell.execute_reply": "2024-11-01T03:03:07.956735Z", "shell.execute_reply.started": "2024-11-01T03:03:04.413294Z" } }, "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": "2024-11-01T03:03:07.958102Z", "iopub.status.busy": "2024-11-01T03:03:07.957951Z", "iopub.status.idle": "2024-11-01T03:03:10.145651Z", "shell.execute_reply": "2024-11-01T03:03:10.145122Z", "shell.execute_reply.started": "2024-11-01T03:03:07.958087Z" } }, "outputs": [ { "data": { "text/html": [ "
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request_idpos_preferenceneg_preferencediff_preference
000003b63-4194-4cdb-b204-dfc854087b09FalseFalse0
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\n", "
" ], "text/plain": [ " request_id pos_preference neg_preference diff_preference\n", "0 00003b63-4194-4cdb-b204-dfc854087b09 False False 0\n", "1 00003b63-4194-4cdb-b204-dfc854087b09 True False 1\n", "2 000050c9-5318-489f-a329-5737ff6f6a31 False False 0\n", "3 000050c9-5318-489f-a329-5737ff6f6a31 True False 1\n", "4 0000a08c-73af-4ffd-b5fd-5c97c1a467fd False True -1\n", "5 0000a08c-73af-4ffd-b5fd-5c97c1a467fd 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": "2024-11-01T03:03:10.146358Z", "iopub.status.busy": "2024-11-01T03:03:10.146210Z", "iopub.status.idle": "2024-11-01T03:03:10.170157Z", "shell.execute_reply": "2024-11-01T03:03:10.169653Z", "shell.execute_reply.started": "2024-11-01T03:03:10.146342Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference 0: 225,377 (42.71%)\n", "Difference 1: 167,357 (31.71%)\n", "Difference -1: 135,000 (25.58%)\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": "2024-11-01T03:03:10.170945Z", "iopub.status.busy": "2024-11-01T03:03:10.170805Z", "iopub.status.idle": "2024-11-01T03:03:10.219648Z", "shell.execute_reply": "2024-11-01T03:03:10.219171Z", "shell.execute_reply.started": "2024-11-01T03:03:10.170931Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Value 1.0: 225,377 (85.41%)\n", "Value 2.0: 38,490 (14.59%)\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": "2024-11-01T03:03:10.220273Z", "iopub.status.busy": "2024-11-01T03:03:10.220131Z", "iopub.status.idle": "2024-11-01T03:03:12.798570Z", "shell.execute_reply": "2024-11-01T03:03:12.797972Z", "shell.execute_reply.started": "2024-11-01T03:03:10.220258Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of unique request_ids: 263,867\n", "Number of unique ids: 527,734\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": "2024-11-01T03:03:12.799313Z", "iopub.status.busy": "2024-11-01T03:03:12.799159Z", "iopub.status.idle": "2024-11-01T03:03:12.819808Z", "shell.execute_reply": "2024-11-01T03:03:12.819287Z", "shell.execute_reply.started": "2024-11-01T03:03:12.799297Z" } }, "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": "2024-11-01T03:03:12.824610Z", "iopub.status.busy": "2024-11-01T03:03:12.824290Z", "iopub.status.idle": "2024-11-01T03:03:24.316780Z", "shell.execute_reply": "2024-11-01T03:03:24.316169Z", "shell.execute_reply.started": "2024-11-01T03:03:12.824593Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of interesting clips: 527,734\n", "Unique request and clip counts in interesting_clips:\n", "Request IDs: 263,867\n", "Clip IDs: 527,734\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": "2024-11-01T03:03:24.317517Z", "iopub.status.busy": "2024-11-01T03:03:24.317366Z", "iopub.status.idle": "2024-11-01T03:03:24.355114Z", "shell.execute_reply": "2024-11-01T03:03:24.354428Z", "shell.execute_reply.started": "2024-11-01T03:03:24.317501Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preference counts and fractions by batch index:\n", "--------------------------------------------------\n", "Batch Index: 0\n", " Preference False: Count: 135,699 Fraction: 51.43%\n", " Preference True: Count: 128,168 Fraction: 48.57%\n", "\n", "Batch Index: 1\n", " Preference False: Count: 128,168 Fraction: 48.57%\n", " Preference True: Count: 135,699 Fraction: 51.43%\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": "2024-11-01T03:03:24.355813Z", "iopub.status.busy": "2024-11-01T03:03:24.355667Z", "iopub.status.idle": "2024-11-01T03:03:24.533380Z", "shell.execute_reply": "2024-11-01T03:03:24.532890Z", "shell.execute_reply.started": "2024-11-01T03:03:24.355798Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chirp-v3p5-engine-s-8: 390527 (74.00%)\n", "chirp-v3p5-engine-t-6: 47799 (9.06%)\n", "chirp-v3p5-engine-upload-4: 26930 (5.10%)\n", "chirp-v3p5-engine-t-5-15: 19731 (3.74%)\n", "chirp-v3p5-engine-s-31: 12342 (2.34%)\n", "chirp-v3p5-h-s-31: 8464 (1.60%)\n", "chirp-v3-engine-i: 6586 (1.25%)\n", "chirp-v3p5-engine-t-5: 4373 (0.83%)\n", "chirp-v3p5-engine-b: 3516 (0.67%)\n", "chirp-v3p5-engine-s-29-6: 3415 (0.65%)\n", "chirp-v2-xxl-alpha: 1230 (0.23%)\n", "chirp-v3p5-engine-short: 1206 (0.23%)\n", "chirp-v3p5-h-t-6: 1061 (0.20%)\n", "chirp-v3p5-engine-ft-1: 554 (0.10%)\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": "2024-11-01T03:03:24.534050Z", "iopub.status.busy": "2024-11-01T03:03:24.533907Z", "iopub.status.idle": "2024-11-01T03:03:24.593363Z", "shell.execute_reply": "2024-11-01T03:03:24.592890Z", "shell.execute_reply.started": "2024-11-01T03:03:24.534035Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time Validation:\n", "--------------------\n", "Interesting Clips:\n", " Earliest: 2024-10-31 03:55:00.394836+00:00\n", " Latest: 2024-11-01 02:47:34.001065+00:00\n", "\n", "All Clips:\n", " Earliest: 2024-10-31 03:55:00.016687+00:00\n", " Latest: 2024-11-01 02:48:10.554025+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": "2024-11-01T03:03:24.594024Z", "iopub.status.busy": "2024-11-01T03:03:24.593876Z", "iopub.status.idle": "2024-11-01T03:03:26.047440Z", "shell.execute_reply": "2024-11-01T03:03:26.046830Z", "shell.execute_reply.started": "2024-11-01T03:03:24.594009Z" } }, "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": "2024-11-01T03:03:26.048221Z", "iopub.status.busy": "2024-11-01T03:03:26.048068Z", "iopub.status.idle": "2024-11-01T03:03:26.992943Z", "shell.execute_reply": "2024-11-01T03:03:26.992351Z", "shell.execute_reply.started": "2024-11-01T03:03:26.048205Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ratio of preferred clips to total clips for each model:\n", "------------------------------------------------------------\n", "chirp-v2-xxl-alpha 3.37% ± 0.13%\n", "chirp-v3-5|chirp-v3-0 nan% ± nan%\n", "chirp-v3-engine-i 3.73% ± 0.06%\n", "chirp-v3.5 nan% ± nan%\n", "chirp-v3.5-0 nan% ± nan%\n", "chirp-v3p5-engine-b 1.15% ± 0.03%\n", "chirp-v3p5-engine-ft-1 5.40% ± 0.32%\n", "chirp-v3p5-engine-s-29-6 6.81% ± 0.17%\n", "chirp-v3p5-engine-s-31 7.09% ± 0.09%\n", "chirp-v3p5-engine-s-8 5.58% ± 0.01%\n", "chirp-v3p5-engine-short 5.39% ± 0.21%\n", "chirp-v3p5-engine-t-5 10.40% ± 0.21%\n", "chirp-v3p5-engine-t-5-15 10.64% ± 0.10%\n", "chirp-v3p5-engine-t-6 9.20% ± 0.06%\n", "chirp-v3p5-engine-upload-4 6.87% ± 0.06%\n", "chirp-v3p5-h-s-31 9.15% ± 0.12%\n", "chirp-v3p5-h-t-6 6.05% ± 0.30%\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": "2024-11-01T03:03:26.993674Z", "iopub.status.busy": "2024-11-01T03:03:26.993521Z", "iopub.status.idle": "2024-11-01T03:03:31.594760Z", "shell.execute_reply": "2024-11-01T03:03:31.594202Z", "shell.execute_reply.started": "2024-11-01T03:03:26.993658Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "len pos models: 263867\n", "differing counts: 12322\n", "chirp-v2-xxl-alpha_win_over_chirp-v2-xxl-alpha, win ratio 1.000, (1.000, 1.000), counts 615, total 615.\n", "chirp-v3-engine-i_win_over_chirp-v3-engine-i, win ratio 1.000, (1.000, 1.000), counts 3293, total 3293.\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 1758, total 1758.\n", "chirp-v3p5-engine-ft-1_win_over_chirp-v3p5-engine-ft-1, win ratio 1.000, (1.000, 1.000), counts 277, total 277.\n", "chirp-v3p5-engine-s-29-6_win_over_chirp-v3p5-h-s-31, win ratio 0.458, (0.441, 0.474), counts 1563, total 3415.\n", "chirp-v3p5-engine-s-31_win_over_chirp-v3p5-engine-s-31, win ratio 1.000, (1.000, 1.000), counts 6171, total 6171.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 193526, total 193526.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-h-s-31, win ratio 0.321, (0.306, 0.337), counts 1116, total 3474.\n", "chirp-v3p5-engine-short_win_over_chirp-v3p5-engine-short, win ratio 1.000, (1.000, 1.000), counts 603, total 603.\n", "chirp-v3p5-engine-t-5-15_win_over_chirp-v3p5-engine-t-5, win ratio 0.504, (0.488, 0.520), counts 1936, total 3839.\n", "chirp-v3p5-engine-t-5-15_win_over_chirp-v3p5-engine-t-5-15, win ratio 1.000, (1.000, 1.000), counts 7946, total 7946.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-5, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-5-15, win ratio 0.496, (0.480, 0.512), counts 1903, total 3839.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-6, win ratio 0.472, (0.429, 0.514), counts 251, total 532.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-5, win ratio 0.528, (0.486, 0.571), counts 281, total 532.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 23633, total 23633.\n", "chirp-v3p5-engine-upload-4_win_over_chirp-v3p5-engine-upload-4, win ratio 1.000, (1.000, 1.000), counts 13465, total 13465.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-engine-s-29-6, win ratio 0.542, (0.526, 0.559), counts 1852, total 3415.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-engine-s-8, win ratio 0.679, (0.663, 0.694), counts 2358, total 3474.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-h-s-31, win ratio 1.000, (1.000, 1.000), counts 257, total 257.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-h-t-6, win ratio 0.642, (0.613, 0.671), counts 681, total 1061.\n", "chirp-v3p5-h-t-6_win_over_chirp-v3p5-h-s-31, win ratio 0.358, (0.329, 0.387), counts 380, total 1061.\n", "tournament players: ['chirp-v3p5-engine-s-29-6', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-t-5', 'chirp-v3p5-engine-t-5-15', 'chirp-v3p5-engine-t-6', 'chirp-v3p5-h-s-31', 'chirp-v3p5-h-t-6']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:02<00:00, 451.20it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-s-29-6: 301.7, (145, 1044)\n", "chirp-v3p5-engine-s-8: 201.2, (45, 943)\n", "chirp-v3p5-engine-t-5: 1971.2, (984, 2181)\n", "chirp-v3p5-engine-t-5-15: 1974.2, (985, 2184)\n", "chirp-v3p5-engine-t-6: 1990.8, (997, 2196)\n", "chirp-v3p5-h-s-31: 331.1, (174, 1070)\n", "chirp-v3p5-h-t-6: 229.8, (75, 977)\n" ] }, { "data": { "image/png": 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Acp+T0QEA4N9k/vz5Gj58uAoXLqwiRYpY7dJuMpko1AJI591339WZM2fUo0cPfkkCkCW+vr7p/qgDABmZMGGCzGazpkyZomXLlun111+Xo6Oj1q9fr+TkZPXr109Tp07VsGHDjI4KQHTUAoBN1a1bVy+//LK6detmdBQAeQSdcQCya8OGDZoxY4ZGjBihhx56yOg4AOxY7dq1NWXKFAUFBeny5cuqWbOmZsyYoVq1akmSNm3apKFDh2rNmjUGJwUg0VELADZ15coVNW7c2OgYAPIQOuMAZFefPn108+ZNNWzYUG5ubunmS4aHhxuUDIC9uXr1qooVKyZJ8vT0VL58+eTn52c5X7JkScXExBgVD8AdKNQCgA09++yz2rBhg15++WWjowDIIwYPHqyPP/6YzjgAWTZ48GCjIwDII7y9vRUTEyNfX19JUtu2bVWoUCHL+atXrypfvnxGxQNwBwq1AGBDJUuW1MSJE7Vz506VL19eTk7W/zP76quvGpQMgL2iMw5AdrVs2dLoCADyiMDAQG3fvl3VqlWTJPXv39/qfEREhCpUqGBENAAZYEYtANhQvXr1Mj1nMpm0du3aXEwDIC9YtGjRXc9TkAGQkZMnT+qnn37SqVOnNGTIEHl7e2v9+vXy8/NTuXLljI4HII/YtWuX3NzcVL58eaOjABCFWgAAAADIU8LDw9W1a1eFhIRo69at+uWXXxQQEKBp06Zpz549mjRpktERAdiZW7duydXV1egYAO7BwegAAAAA/3UnT57Up59+qr59+yo2NlaStH79eh0+fNjgZADs0ccff6zevXtrxowZVuNSatasqR07dhgXDIDdqlWrlt59911t3LhRKSkpRscBkAlm1ALAAxozZox69eold3d3jRkz5q5rBw0alEupAOQVd3bG9enTR97e3jp48KB++uknOuMApHPo0CFNmDAh3XEvLy9dunTJgEQA7N24ceO0bNky9ejRQwUKFFDjxo313HPPqWrVqkZHA5AGhVoAeED79u1TUlKS5XZmTCZTbkUCkIfc7ozr2LGjgoODLcdr1qypH374wcBkAOxVgQIFFBMTo4CAAKvj+/fvV7FixQxKBcCeNWzYUA0bNlRcXJxWrVqlFStWqE2bNgoICFBYWJh69uxpdEQAYkYtAACAoYKDg7V06VIFBARY3T59+rQaN26s3bt3Gx0RgJ0ZN26cdu7cqYkTJ6pRo0ZatGiRLly4oIEDB6pFixYUXABkyZEjR9S/f38dPHhQ+/fvNzoOADGjFgAAwFC3O+PuRGccgMz06dNHpUuX1tNPP60bN26oadOmateunYKDg9W9e3ej4wGwY7du3dLKlSvVo0cPtWzZUleuXFHnzp2NjgXg/9FRCwA29Oabb2Y44sBkMsnFxUUlS5ZUs2bNVLp0aQPSAbBHdMYBuF9nzpzRoUOHdP36dVWqVEmlSpUyOhIAO/Xnn39q+fLlWrNmjZycnNSoUSOFhYUpNDTU6GgA0qBQCwA29O6772rNmjUqWLCgKleuLEnau3evrl27pieeeEIHDhxQVFSUZs6cqUceecTgtADsQUJCgkaOHKlFixYpOTlZTk5OSk5OVrNmzTR27Fg5OjoaHRGAHYuIiFDVqlXl4uJidBQAdqx69ep6+umnFRYWpqeeekrOzs5GRwKQAQq1AGBDEyZMUFxcnIYNGyYHh9TpMikpKRo9erTy58+vPn366P3339fhw4c1Z84cg9MCsCd0xgG4HyEhIVqyZEm6jcUAIK24uDh5eHhIks6ePauiRYtafl8BYD/4rgQAG1q4cKE6dOhg9UOPg4OD2rVrp3nz5slkMqlt27Y6fPiwgSkB2CNfX195eHioQYMGFGkBZBl9NwCy4naRVpKaNGmiqKgoA9MAyAyFWgCwoeTkZB07dizd8WPHjiklJUWS5OrqmuEcWwDo2rWrzp07Z3QMAADwL8YfeAD75WR0AAD4N2nevLmGDBmiU6dOqUqVKpKkPXv2aOrUqWrevLkkaevWrSpbtqyRMQHYKX5xApBdI0eOlLe3t9ExAACADVCoBQAbGjRokLy9vfX111/rwoULkqQiRYrotddeU9euXSVJTzzxhOrUqWNkTAAA8C8RFhZmdAQAecwbb7yhQoUKGR0DQAbYTAwAckhcXJwk63lQAHA3y5YtU/369eXu7m50FAB2KDY21qp7dv/+/Zo5c6YiIyNVtGhRtW3bVo899piBCQHkBbfLQIxjA+wPM2oBIId4eHhQpAWQLWFhYRRpAWSqdu3aio2NlSRt27ZNL774oqKjoxUSEqK4uDh16tRJW7duNTglAHu1YMECNWvWTFWrVlXVqlXVrFkzLViwwOhYANJg9AEA2NCFCxc0btw4bdq0SRcvXkw3b3L//v0GJQNgb+iMA5BdaX+umDJlip577jl9+OGHlmOjR4/WlClT9N133xkRD4AdmzhxombOnKl27dopKChIkrRjxw59+OGHio6OVq9evYwNCEAShVoAsKl3331XZ86cUY8ePVS0aFGj4wCwY7Vr19aGDRvk7e2tbdu26dVXX1VwcLBCQkJ04MABderUSTNnzlRoaKjRUQHYoUOHDuntt9+2Ota6dWu1b9/eoEQA7NmcOXM0atQoNWvWzHKsfv36qlChgkaNGkWhFrATFGoBwIYiIiL0448/qmLFikZHAWDn6IwDcD+uX78uV1dXubq6ysXFxeqcq6ur4uPjDUoGwJ4lJSWpSpUq6Y5XrlxZycnJBiQCkBFm1AKADfn6+qYbdwAA93Lo0CG1bt3a6ljr1q118OBBgxIBsFeNGjVSaGiooqKitGfPHqtzhw8f5hM9ADLUvHlzzZkzJ93x+fPnKywszIBEADJCRy0A2NDgwYP18ccfa8SIEXrooYeMjgPAztEZByA7vv/+e6v7Pj4+VvdPnz6d7o8+AHDbwoULtXHjRlWvXl2StGvXLkVHR6tFixYaM2aMZd2gQYOMigj851GoBQAb6tOnj27evKmGDRvKzc1Nzs7OVufDw8MNSgbAHjVq1EhS6hiEPXv2qFKlSpZzdMYBuFONGjXuer5Dhw65lARAXnPo0CHLzxknT56UJHl6esrT01OHDh2yrDOZTIbkA5CKQi0A2NDgwYONjgAgj6AzDsCD2r17t44ePSpJKlOmjKpWrWpwIgD2atasWUZHAJAFJjPDFAEAAAAgzzh79qz69u2rbdu2qWDBgpKkq1evKjg4WJ9++qmKFy9ucEIA9iQxMVHVq1fX4sWLVb58eaPjALgLNhMDABs7efKkPv30U/Xt21exsbGSpPXr1+vw4cMGJwNgz3bv3q3Fixdr8eLF2r17t9FxANixIUOGKCkpSStXrlR4eLjCw8O1cuVKmc1mDRkyxOh4AOyMs7OzfH19lZKSYnQUAPdARy0A2FB4eLi6du2qkJAQbd26Vb/88osCAgI0bdo07dmzR5MmTTI6IgA7Q2ccgOyqVq2a5s6dazXXWpL27Nmjtm3baufOnQYlA2CvFixYoNWrV2v8+PHy9PQ0Og6ATDCjFgBs6OOPP1bv3r3VsWNHBQcHW47XrFlTP/zwg4HJANirtJ1xpUuXliQdO3ZMgwcP1pAhQ/TNN98YnBCAvfH19VVSUlK64ykpKWxCCCBDs2fPVmRkpOrUqSM/Pz+5u7tbnV+0aJFByQCkRaEWAGzo0KFDmjBhQrrjXl5eunTpkgGJANi7rVu3au7cuZYirSSVLl1a7733ntq2bWtgMgD26p133tGoUaM0bNgwywZiu3fv1ujRozVw4ECD0wGwRw0aNDA6AoAsoFALADZUoEABxcTEKCAgwOr4/v37VaxYMYNSAbBndMYByK5Bgwbp5s2bat26tRwdHSVJycnJcnR01ODBgzV48GDL2vDwcKNiArAjPXv2NDoCgCygUAsANtS0aVNNmDBBEydOlMlkUkpKiiIiIjRu3Di1aNHC6HgA7BCdcQCyK20hFgAA/HuwmRgA2FBCQoJGjhypRYsWKTk5WU5OTkpOTlazZs00duxYS9cLANwWGhqqmzdvWrrhpH864+6cH0dnHAAAyKoaNWro119/lZeXl0JDQ2UymTJdy88YgH2gUAsAOeDMmTM6dOiQrl+/rkqVKqlUqVJGRwJgp7KzeUfLli1zMAmAvCLtH3YkaefOnUpISFBQUJCcnZ0NTAbAnixatEhNmzaVi4vLPX/e4GcMwD5QqAWAHBIREaGqVavKxcXF6CgAAOBf4Pz58+rVq5d27typkJAQff755xowYIDWr18vSSpZsqRmzZrFfGsAAPIoCrUAkENCQkK0ZMmSdBuLAUBadMYByKoBAwbo5MmT6tatm5YtW6YzZ87I0dFRn3zyiZKTk9WvXz9VrFhRw4YNMzoqADuUkpKiyMhIxcbG6s5SUGhoqEGpAKTFZmIAkEP4OxiAu6EzDkB2/fXXX5oyZYqCgoIUEhKimjVrasaMGSpWrJgk6e2339bQoUMNTgnAHu3YsUP9+vVTdHR0ut9TTCaT9u/fb1AyAGlRqAUAADDAhAkTZDabNWXKFC1btkyvv/66HB0dtX79ektn3NSpU+mMA2Bx9epVS1HW09NT+fLlk5+fn+V8yZIlFRMTY1Q8AHbs/fffV5UqVTRt2jT5+PjcdWMxAMahUAsAOWTkyJHy9vY2OgYAO0VnHIDs8vb2VkxMjHx9fSVJbdu2VaFChSznr169qnz58hkVD4Adi4yM1KRJk1SyZEmjowC4CwejAwDAv1VYWJjc3d2NjgHATtEZByC7AgMDtX37dsv9/v37y9PT03I/IiJCFSpUMCAZAHtXrVo1RUZGGh0DwD3QUQsANhAbG2vVPbt//37NnDlTkZGRKlq0qNq2bavHHnvMwIQA7A2dcQCy68svv7zr+apVq7IhEACLAwcOWG63b99e48aN04ULF1S+fHk5OVmXgwIDA3M7HoAMmMzsdgMAD6xixYrasGGDvL29tW3bNr366qsKDg5W1apVdeDAAW3ZskUzZ87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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": "2024-11-01T03:03:31.595496Z", "iopub.status.busy": "2024-11-01T03:03:31.595340Z", "iopub.status.idle": "2024-11-01T03:03:32.787397Z", "shell.execute_reply": "2024-11-01T03:03:32.786901Z", "shell.execute_reply.started": "2024-11-01T03:03:31.595480Z" } }, "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": "2024-11-01T03:03:32.788112Z", "iopub.status.busy": "2024-11-01T03:03:32.787964Z", "iopub.status.idle": "2024-11-01T03:03:32.807583Z", "shell.execute_reply": "2024-11-01T03:03:32.807133Z", "shell.execute_reply.started": "2024-11-01T03:03:32.788096Z" } }, "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": "2024-11-01T03:03:32.808237Z", "iopub.status.busy": "2024-11-01T03:03:32.808099Z", "iopub.status.idle": "2024-11-01T03:03:36.766784Z", "shell.execute_reply": "2024-11-01T03:03:36.766181Z", "shell.execute_reply.started": "2024-11-01T03:03:32.808222Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of clips in interesting_clips:\n", "527,734\n", "Number of clips in user_interesting_clips:\n", "489,030\n", "Ratio of preferred clips to total clips for each model:\n", "------------------------------------------------------------\n", "chirp-v2-xxl-alpha nan% ± nan%\n", "chirp-v3-5|chirp-v3-0 nan% ± nan%\n", "chirp-v3-engine-i nan% ± nan%\n", "chirp-v3.5 nan% ± nan%\n", "chirp-v3.5-0 nan% ± nan%\n", "chirp-v3p5-engine-b 0.99% ± 0.03%\n", "chirp-v3p5-engine-ft-1 4.99% ± 0.30%\n", "chirp-v3p5-engine-s-29-6 6.02% ± 0.16%\n", "chirp-v3p5-engine-s-31 6.70% ± 0.08%\n", "chirp-v3p5-engine-s-8 5.23% ± 0.01%\n", "chirp-v3p5-engine-short 4.25% ± 0.19%\n", "chirp-v3p5-engine-t-5 9.98% ± 0.21%\n", "chirp-v3p5-engine-t-5-15 10.31% ± 0.10%\n", "chirp-v3p5-engine-t-6 8.78% ± 0.06%\n", "chirp-v3p5-engine-upload-4 6.63% ± 0.06%\n", "chirp-v3p5-h-s-31 8.69% ± 0.12%\n", "chirp-v3p5-h-t-6 5.68% ± 0.29%\n" ] } ], "source": [ "# subselect interesting clips\n", "interesting_clips_masks = (interesting_clips[\"model_name\"].str.contains(\"v3p5\")) & (\n", " interesting_clips[\"reaction_play_count\"] > 0\n", ")\n", "# make sure we have pairs\n", "extra_compare_mask = interesting_clips[interesting_clips_masks][\"request_id\"].isin(\n", " interesting_clips[interesting_clips_masks][\"request_id\"]\n", " .value_counts()\n", " .index[interesting_clips[interesting_clips_masks][\"request_id\"].value_counts() == 2]\n", ")\n", "user_intersting_clips = interesting_clips[\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": "2024-11-01T03:03:36.767531Z", "iopub.status.busy": "2024-11-01T03:03:36.767381Z", "iopub.status.idle": "2024-11-01T03:03:38.331669Z", "shell.execute_reply": "2024-11-01T03:03:38.331192Z", "shell.execute_reply.started": "2024-11-01T03:03:36.767515Z" } }, "outputs": [ { "data": { "image/png": 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er1y5UgcOHFCnTp08w+rXr68NGzZ4apCkjz76qMQtu32prVOnTiouLtaiRYu8hs+bN08ul8tr+aeiU6dOOnDggJYvX+4ZVlRUpAULFiguLk7nn3++X/Pt3LmzNmzYoE2bNnkNz8zM1Ntvv60mTZqoZs2akqTs7GwVFRV5TZeUlKSwsDDPz/Syyy5TeHi4Zs6cWeLoiWVZOnTokF91BsLll1+uffv2afHixSXG5eXlKScn56TvLy4u1lNPPVViXFFRUZk+L5dffrm+/fZbrV69usS4zMzMEj/fk6levbrOP/98LVmypMSpnkd//uHh4erWrZvefffdUpuuv59aeGw+UVFRatSokSzLUmFhoU+1ATATR6YAQH/e8GD79u0qLi7WwYMHtXbtWn3++eeqU6eOZs+efcK/UM+aNUvr169X586dVbduXaWnp+ull15S7dq1PddenHHGGapcubJeeeUVVapUSXFxcWrRooXXESJfVKlSRddee6369OnjuTV6gwYNvG7f3q9fP7377ru64YYbdPnllystLU1vv/12iVMWfamtS5cuatu2raZPn67du3crOTlZn3/+uT744ANdd911Jzwd0hcDBgzQq6++qvHjx+uHH35Q3bp19e677+qbb77RPffcU+KarbIaMWKEVq5cqdTUVA0YMEBnnXWW9u/fr6VLl2r//v165JFHPNN++eWXmjRpkrp3764zzzxTxcXFevPNNz2/rEt//uxuu+02TZs2Tbt371bXrl1VqVIl7dq1S6tWrVL//v01bNiwgPxMfHXVVVdpxYoVmjBhgtauXavWrVuruLhY27dv18qVK/Xcc895Paj6WBdccIEGDBigOXPmaPPmzerQoYMiIyP122+/aeXKlbr33nvVvXv3E9YwbNgwffjhh7rpppvUu3dvNW3aVLm5udq6daveffddffDBBz7fyv6+++7TNddco969e2vAgAGqV6+edu/erY8//thzJHns2LFau3at+vfvr379+qlx48Y6fPiwfvjhB61Zs0ZfffWVp74aNWqodevWSkxM1Pbt27Vw4UJ17tzZ788YALPQTAGApBkzZkj6845xVatWVVJSku655x716dPnpL9UdenSRbt379aSJUt06NAhVatWTRdccIHGjBnjuVFBZGSkpkyZoscff1wPPPCAioqKNHnyZL+bqZtuuklbtmzRM888oyNHjqhdu3aaMGGC1x3eLrroIo0fP15z587VI488ombNmunpp5/Wo48+6jUvX2oLCwvT7NmzNWPGDC1fvlxvvPGG6tatq7vuukvXX3+9X+tSmpiYGC1YsEBTp07V0qVLlZ2drYYNG2ry5Mk+3w3v72rUqKHXXntNTz75pFasWKH09HTFx8erVatWmj59us4991zPtMnJyerYsaM++ugj7du3T7GxsUpOTtazzz6rli1beqYbMWKEzjzzTM2bN0+zZs2S9Of1SB06dCjxwONgCgsL06xZszRv3jy9+eabev/99xUbG6t69epp0KBBZTotcdKkSWrWrJleeeUVTZ8+XeHh4apbt66uvPLK4z6L7O9iY2O1YMECzZkzRytXrtSyZcsUHx+vM88802v78EVKSooWL16sJ554Qi+//LLy8/NVp04dXX755Z5pjuY8a9Ysvf/++3r55ZdVtWpVNW7cWHfeeadnugEDBujtt9/W3LlzlZOTo9q1a2vQoEEaNWqUz3UBMJPLcuoqYAAAAACowLhmCgAAAAD8QDMFAAAAAH6gmQIAAAAAP9BMAQAAAIAfaKYAAAAAwA80UwAAAADgB5opAAAAAPADzRQAAAAA+CHC6QLKi/T0LAX78cUJCbHKysoN7kIRdORsDrI2AzmbgZzNQdZmKGvOLpeUmJhQ5vnSTP1/lqWgN1NHl4vQR87mIGszkLMZyNkcZG0GO3J2WRYfH0k6eDD4R6bCwlxyu/nxhzpyNgdZm4GczUDO5iBrM5Q1Z5dLqlGj7EemuGbKQWy4ZiBnc5C1GcjZDORsDrI2g10500w5qHLlWKdLQBCQsznI2gzkbAZyNgdZm8GunGmmAAAAAMAPNFMAAAAA4AeaKQAAAADwA3fz+/+cuJsfAAAAgPKDu/lVIGFhLqdLQBCQsznI2gzkbAZyNgdZm8GunEPiob1dunRRpUqVFBYWpsqVK2vBggVOl1Qm8fExyszkiduhjpzNQdZmIGczkLM5yNoMduUcEs2UJL3yyiuqVKmS02UAAAAAMASn+QEAAACAHxxvptatW6ebbrpJHTt2VHJyslatWlVimkWLFqlLly5q3ry5+vXrp40bN5aYZtCgQerbt6/eeuutYJQdENz7wwzkbA6yNgM5m4GczUHWZrArZ8ebqZycHCUnJ2vChAmljl++fLkmT56s0aNHa+nSpUpJSdGwYcOUnp7umebll1/WG2+8odmzZ2vOnDn66aefglX+KcnKynO6BAQBOZuDrM1AzmYgZ3OQtRnsytnxa6Y6d+6szp07H3f83Llz1b9/f/Xt21eSNHHiRH388cdasmSJRowYIUk67bTTJEm1atVSp06d9OOPPyolJcWnOhISYj3/LiwsUm5uoWJjIxUZ+dePKD+/UPn5RYqLi1JERLhneG5ugQoLixUfH62wsL/605ycfBUVuZWQECOX6687iGRn58nttlSlSqzX7dgzM3MVFuZSfHyMZ5hlWcrKylNERJji4qI9w91ut7Kz8xUZGa7Y2CjP8KKiYuXkFCg6OkLR0ZFBX6fKlf/6ObJOf65TXl6hXC5XSK1TKOYUiHU6OjyU1ikUczrVdXK5JMsKrXUKxZxOdZ0qVYr27KNDZZ1CMadArFOlSlEKD/+r9lBYp1DM6VTX6ej0ZV2nsipXz5lKTk7WrFmz1LVrV0lSQUGBWrZsqRkzZniGSdK4ceOUmZmp2bNnKycnR263W/Hx8Tpy5IgGDRqkBx54QC1atPBp2U48Z6py5VjuHmMAcjYHWZuBnM1AzuYgazOUNWdfnzPl+JGpEzl06JCKi4uVmJjoNTwxMVHbt2+XJKWnp2v06NGS/ux0+/Xr53MjBQAAAAC+KtfNVFnUr1+/Qt10AgAAAEBoKNfNVLVq1RQeHu51swnpz6NRNWrUcKiqwHG73SWG7dq1UxkZ6aVMHRjVqyeqXr36ts0fJZWWM0ITWZuBnM1AzuYgazPYlXO5bqaioqLUtGlTrVmzxnPNlNvt1po1a5SamupwdacuOzvf6/WuXTvVrn0b5efZd95udEys1nyxnoYqiI7NGaGLrM1AzmYgZ3OQtRnsytnxZurIkSNKS0vzvN61a5c2b96sKlWqqE6dOho6dKjGjRunZs2aqUWLFpo/f75yc3PVp08fB6sOjMjIcBUWFnteZ2SkKz8vV4m9xioyMfDNTmH6TqW/M00ZGek0U0F0bM4IXWRtBnI2Azmbg6zNYFfOjjdTmzZt0uDBgz2vJ0+eLEnq3bu3pkyZoh49eigjI0MzZszQgQMH1KRJEz333HMhcZpfbGyUCgtLHoWKTKyv6NqNHagIdjhezgg9ZG0GcjYDOZuDrM1gV86ON1Nt27bVli1bTjhNampqSJzWBwAAACB0hJ18EgAAAADAsWimHFRUxPm5JiBnc5C1GcjZDORsDrI2g10500w5KCenwOkSEATkbA6yNgM5m4GczUHWZrArZ5opB0VHO37JGoKAnM1B1mYgZzOQsznI2gx25Uwz5aDo6EinS0AQkLM5yNoM5GwGcjYHWZvBrpxppgAAAADADzRTAAAAAOAHmikHFRYWOV0CgoCczUHWZiBnM5CzOcjaDHblTDPloNzcQqdLQBCQsznI2gzkbAZyNgdZm8GunGmmHBQbywWPJiBnc5C1GcjZDORsDrI2g10500w5KDKSW3GagJzNQdZmIGczkLM5yNoMduVMMwUAAAAAfqCZAgAAAAA/0Ew5KD+fCx5NQM7mIGszkLMZyNkcZG0Gu3KmmXJQfj634jQBOZuDrM1AzmYgZ3OQtRnsyplmykFxcVFOl4AgIGdzkLUZyNkM5GwOsjaDXTnTTDkoIiLc6RIQBORsDrI2AzmbgZzNQdZmsCtnmikAAAAA8APNFAAAAAD4gWbKQbm5BU6XgCAgZ3OQtRnI2QzkbA6yNoNdOdNMOaiwsNjpEhAE5GwOsjYDOZuBnM1B1mawK2eaKQfFx0c7XQKCgJzNQdZmIGczkLM5yNoMduVMM+WgsDB+/CYgZ3OQtRnI2QzkbA6yNoNdOfPpAQAAAAA/0EwBAAAAgB9ophyUk5PvdAkIAnI2B1mbgZzNQM7mIGsz2JUzzZSDiorcTpeAICBnc5C1GcjZDORsDrI2g10500w5KCEhxukSEATkbA6yNgM5m4GczUHWZrArZ5opB7lcLqdLQBCQsznI2gzkbAZyNgdZm8GunGmmAAAAAMAPNFMAAAAA4AeaKQdlZ+c5XQKCgJzNQdZmIGczkLM5yNoMduVMM+Ugt9tyugQEATmbg6zNQM5mIGdzkLUZ7MqZZspBlSvHOl0CgoCczUHWZiBnM5CzOcjaDHblTDMFAAAAAH6gmQIAAAAAP9BMAQAAAIAfaKYclJmZ63QJCAJyNgdZm4GczUDO5iBrM9iVM82Ug8LCeOK2CcjZHGRtBnI2Azmbg6zNYFfONFMOio+PcboEBAE5m4OszUDOZiBnc5C1GezKmWYKAAAAAPxAMwUAAAAAfqCZcpBl8cRtE5CzOcjaDORsBnI2B1mbwa6caaYclJWV53QJCAJyNgdZm4GczUDO5iBrM9iVM82UgyIi+PGbgJzNQdZmIGczkLM5yNoMduXMp8dBcXHRTpeAICBnc5C1GcjZDORsDrI2g10500wBAAAAgB9opgAAAADADzRTDnK73U6XgCAgZ3OQtRnI2QzkbA6yNoNdOdNMOSg7O9/pEhAE5GwOsjYDOZuBnM1B1mawK2eaKQdFRoY7XQKCgJzNQdZmIGczkLM5yNoMduVMM+Wg2Ngop0tAEJCzOcjaDORsBnI2B1mbwa6caaYAAAAAwA80UwAAAADgB5opBxUVFTtdAoKAnM1B1mYgZzOQsznI2gx25Uwz5aCcnAKnS0AQkLM5yNoM5GwGcjYHWZvBrpxpphwUHR3hdAkIAnI2B1mbgZzNQM7mIGsz2JUzzZSDoqMjnS4BQUDO5iBrM5CzGcjZHGRtBrtyppkCAAAAAD/QTAEAAACAH2imHFRYWOR0CQgCcjYHWZuBnM1AzuYgazPYlTPNlINycwudLgFBQM7mIGszkLMZyNkcZG0Gu3KmmXJQbCwXPJqAnM1B1mYgZzOQsznI2gx25Uwz5aDISG7FaQJyNgdZm4GczUDO5iBrM9iVM80UAAAAAPiBZgoAAAAA/EAz5aD8fC54NAE5m4OszUDOZiBnc5C1GezKmWbKQfn53IrTBORsDrI2AzmbgZzNQdZmsCtnmikHxcVFOV0CgoCczUHWZiBnM5CzOcjaDHblTDPloIiIcKdLQBCQsznI2gzkbAZyNgdZm8GunGmmAAAAAMAPIdNM5ebm6uKLL9ajjz7qdCkAAAAADBAyzdTTTz+tc8891+kyfJKbW+B0CQgCcjYHWZuBnM1AzuYgazPYlXNINFO//fabtm/frk6dOjldik8KC4udLgFBQM7mIGszkLMZyNkcZG0Gu3J2vJlat26dbrrpJnXs2FHJyclatWpViWkWLVqkLl26qHnz5urXr582btzoNf7RRx/VHXfcEaySAyY+PtrpEhAE5GwOsjYDOZuBnM1B1mawK2fHm6mcnBwlJydrwoQJpY5fvny5Jk+erNGjR2vp0qVKSUnRsGHDlJ6eLklatWqVzjzzTDVs2DCYZQdEWJjjP34EATmbg6zNQM5mIGdzkLUZ7Mo5wpa5+qBz587q3LnzccfPnTtX/fv3V9++fSVJEydO1Mcff6wlS5ZoxIgR2rBhg5YvX653331XR44cUVFRkSpVqqSbb77ZpzoSEmI9/y4sLFJubqFiYyMVGfnXjyg/v1D5+UWKi4vyur1ibm6BCguLFR8f7RVUTk6+iorcSkiIkcvl8gzPzs6T220pIiJMlSv/tdxgqVQp2rPcQK/TseuTmZmrsDCX4uNjPMMsy1JWVp4iIsIUF/fXXwncbreys/MVGRmu2Ni/ngVQVFSsnJwCRUdHKDo60jM8WDmd6jpJCrl1CsWcArFOYWF/1hVK6xSKOZ3qOh397g6ldQrFnE51nf6+jw6VdQrFnAKxTuHhLq/lhsI6hWJOp7pOR1ejrOtUVi7Lsiy/3mmD5ORkzZo1S127dpUkFRQUqGXLlpoxY4ZnmCSNGzdOmZmZmj17ttf733jjDW3btk3jxo3zedkHD2Yp2D+JypVjlZmZ63m9ceN36tq1k2pf919F124c8OXl//6zfp9/m1at+lQtWrQM+PxRumNzRugiazOQsxnI2RxkbYay5uxySTVqJJR5vuX6uOahQ4dUXFysxMREr+GJiYk6ePCgQ1UFTk5OvtMlIAjI2RxkbQZyNgM5m4OszWBXzo6f5hdIffr0cboEnxQVuZ0uAUFAzuYgazOQsxnI2RxkbQa7ci7XR6aqVaum8PBwz80mjkpPT1eNGjUcqipwEhJiTj4RKjxyNgdZm4GczUDO5iBrM9iVc7lupqKiotS0aVOtWbPGM8ztdmvNmjVq1aqVg5UFxt8vfkPoImdzkLUZyNkM5GwOsjaDXTk7fprfkSNHlJaW5nm9a9cubd68WVWqVFGdOnU0dOhQjRs3Ts2aNVOLFi00f/585ebmVrhT+gAAAACEFsebqU2bNmnw4MGe15MnT5Yk9e7dW1OmTFGPHj2UkZGhGTNm6MCBA2rSpImee+65kDjNDwAAAEDF5Xgz1bZtW23ZsuWE06Smpio1NTVIFQVPdnae0yUgCMjZHGRtBnI2Azmbg6zNYFfO5fqaqVDndpebR3zBRuRsDrI2AzmbgZzNQdZmsCtnmikH+fukZVQs5GwOsjYDOZuBnM1B1mawK2eaKQAAAADwA80UAAAAAPiBZgoAAAAA/EAz5aDMzFynS0AQkLM5yNoM5GwGcjYHWZvBrpxpphwUFsYTt01AzuYgazOQsxnI2RxkbQa7cqaZclB8fIzTJSAIyNkcZG0GcjYDOZuDrM1gV840UwAAAADgB5opAAAAAPADzZSDLIsnbpuAnM1B1mYgZzOQsznI2gx25Uwz5aCsrDynS0AQkLM5yNoM5GwGcjYHWZvBrpxpphwUEcGP3wTkbA6yNgM5m4GczUHWZrArZz49DoqLi3a6BAQBOZuDrM1AzmYgZ3OQtRnsyplmCgAAAAD8QDMFAAAAAH6gmXKQ2+12ugQEATmbg6zNQM5mIGdzkLUZ7MqZZspB2dn5TpeAICBnc5C1GcjZDORsDrI2g105R9gyV5RJZGS4CguLg77cbdu22Dbv6tUTVa9efdvmXxE5lTOCj6zNQM5mIGdzkLUZ7MqZZspBsbFRKizMDdryirMPSS6XRo4cbtsyomNiteaL9TRUfxPsnOEcsjYDOZuBnM1B1mawK2eaKYO487Mly1Jir7GKTAx8s1OYvlPp70xTRkY6zRQAAABCHs2UgSIT6yu6dmOnywAAAAAqNG5A4aCiIs7PNQE5m4OszUDOZiBnc5C1GezKmWbKQTk5BU6XgCAgZ3OQtRnI2QzkbA6yNoNdOdNMOSg6mrMsTUDO5iBrM5CzGcjZHGRtBrtypplyUHR0pNMlIAjI2RxkbQZyNgM5m4OszWBXzjRTAAAAAOAHn453ud1uffXVV1q/fr327NmjvLw8Va9eXU2aNFH79u11+umn21UnAAAAAJQrZToylZeXp6eeekqdO3fWiBEjtHr1amVlZSksLEw7duzQk08+qUsuuUTDhw/Xd999Z3PJoaOwsMjpEhAE5GwOsjYDOZuBnM1B1mawK+cyHZnq1q2bWrZsqYceekjt27dXZGTJcw53796td955R3fccYduuukm9e/fP+DFhprc3EKnS0AQkLM5yNoM5GwGcjYHWZvBrpzL1Ey98MILatSo0QmnqVu3rm688UZdf/312rt3b0CKC3WxsZFswAYgZ3OQtRnI2QzkbA6yNoNdOZfpNL+TNVJ/FxkZqTPOOMPvgkwSGcmtOE1AzuYgazOQsxnI2RxkbQa7cvbrbn7r16/XnXfeqQEDBmjfvn2SpGXLlmn9+vUBLQ4AAAAAyiufm6l3331Xw4YNU0xMjH788UcVFPz5NOHs7GzNmTMn4AUCAAAAQHnkczM1e/ZsTZw4UQ899JAiIv46XNa6dWv9+OOPAS0u1OXnc36uCcjZHGRtBnI2Azmbg6zNYFfOPjdTv/76q9q0aVNieEJCgjIzMwNSlCny87kVpwnI2RxkbQZyNgM5m4OszWBXzj43UzVq1FBaWlqJ4V9//bXq168fkKJMERcX5XQJCAJyNgdZm4GczUDO5iBrM9iVs8/NVP/+/fXwww9rw4YNcrlc2rdvn9566y09+uijuuaaa+yoMWRFRIQ7XQKCgJzNQdZmIGczkLM5yNoMduXs8z0CR4wYIbfbrSFDhig3N1epqamKiorS9ddfr0GDBtlRIwAAAACUOz43Uy6XSyNHjtSwYcOUlpamnJwcNWrUSJUqVbKjPgAAAAAol/x+elVUVJQaN24cyFqMk5tb4HQJCAJyNgdZm4GczUDO5iBrM9iVc5maqZtvvrnMM5w5c6bfxZimsLDY6RIQBORsDrI2AzmbgZzNQdZmsCvnMjVTCQkJtizcdPHx0crOzne6DNiMnM1B1mYgZzOQsznI2gx25VymZmry5MkBXzCksDCfb6aICoiczUHWZiBnM5CzOcjaDHblzKcHAAAAAPzg1w0oVq5cqRUrVmjv3r0qLCz0Grd06dKAFAYAAAAA5ZnPR6ZefPFF3X333apRo4Z+/PFHNW/eXFWrVtXOnTvVqVMnO2oMWTk5nJ9rAnI2B1mbgZzNQM7mIGsz2JWzz0emXnrpJT344IPq1auX3njjDQ0fPlz169fXE088ocOHD9tRY8gqKnI7XQKCgJzNQdZmIGczkLM5yNoMduXs85GpvXv3qlWrVpKkmJgYHTlyRJJ01VVX6X//+19gqwtxCQkxTpeAICBnc5C1GcjZDORsDrI2g105+9xM1ahRw3ME6vTTT9d3330nSdq1a5csywpocaHO5XI5XQKCgJzNQdZmIGczkLM5yNoMduXs82l+F154oT788EOdc8456tu3ryZPnqx3331XmzZt0qWXXmpHjQAAAABQ7vjcTD344INyu/8853DgwIGqWrWqvv32W3Xp0kUDBgwIeIEAAAAAUB753EyFhYV5PfSqZ8+e6tmzZ0CLMkV2dp7TJSAIyNkcZG0GcjYDOZuDrM1gV84+XzO1ZMkSrVixosTwFStW8IwpH7ndXGNmAnI2B1mbgZzNQM7mIGsz2JWzz83UM888o2rVqpUYnpiYqKeffjogRZmicuVYp0tAEJCzOcjaDORsBnI2B1mbwa6cfW6m9uzZo3r16pUYXqdOHe3duzcgRQEAAABAeedzM5WYmKgtW7aUGP7TTz+patWqgagJAAAAAMo9n29A0bNnTz388MOqVKmSzj//fEnSV199pUceeYQbUUCStG1byWY7kKpXT1S9evVtXQYAAABwMj43U7feeqt2796tIUOGKCLiz7e73W5dddVVuv322wNeYCjLzMx1uoSAKs4+JLlcGjlyuK3LiY6J1Zov1leYhirUcsbxkbUZyNkM5GwOsjaDXTn73ExFRUXpv//9r3777Tdt3rxZMTExSkpKUt26de2oL6SFhblC6g4y7vxsybKU2GusIhPtaXQK03cq/Z1pyshIrzDNVKjljOMjazOQsxnI2RxkbQa7cva5mTrqzDPP1Jlnnqni4mJt3bpV8fHxqlKlSiBrC3nx8TEh+deQyMT6iq7d2Okyyo1QzRklkbUZyNkM5GwOsjaDXTn7fAOKhx9+WK+99pokqbi4WKmpqerdu7f+8Y9/aO3atQEvEAAAAADKI5+bqXfffVcpKSmSpI8++kg7d+7UihUrdN1112n69OkBLxAAAAAAyiOfm6lDhw6pZs2akqRPPvlEl19+uRo2bKi+fftq69atAS8wlFkW5+eagJzNQdZmIGczkLM5yNoMduXsczNVo0YN/fzzzyouLtbq1avVoUMHSVJeXp7Cw8MDXmAoy8rKc7oEBAE5m4OszUDOZiBnc5C1GezK2ecbUPTp00e33XabatasKZfLpfbt20uSNmzYoLPOOivgBYayiIgwFRW5nS4DNiNnc5C1GcjZDORsDrI2g105+9xMjRkzRmeffbZ+//13de/eXVFRUZKk8PBwDR9u7/OFQk1cXDR3jzEAOZuDrM1AzmYgZ3OQtRnsytmvW6N37969xLDevXufcjEAAAAAUFH4fM0UAAAAAIBmylFuN+fnmoCczUHWZiBnM5CzOcjaDHblTDPloOzsfKdLQBCQsznI2gzkbAZyNgdZm8GunP26Zqo8yczM1JAhQ1RcXKzi4mINHjxY/fv3d7qsMomMDFdhYbHTZcBm5GwOsjYDOZuBnM1B1mawK2efm6ns7OzjjouKivLc3S9YKlWqpEWLFik2NlY5OTnq1auXLr30UlWrVi2odfgjNjZKhYXcPSbUkbM5yNoM5GwGcjYHWZvBrpx9bqbatGkjl8t13PG1a9dW7969dfPNNysszP6zCMPDwxUbGytJKigokMSTrAEAAADYz+dmasqUKZo+fbp69+6tFi1aSJI2btyoZcuWaeTIkcrIyNALL7ygqKgo3XTTTSed37p16/T8889r06ZNOnDggGbNmqWuXbt6TbNo0SI9//zzOnDggFJSUvTvf//bs2zpz1P9UlNTtWPHDt11112qXr26r6sFAAAAAD7xuZlaunSpxo0bpx49eniGdenSRUlJSXr11Vc1f/58nX766Xr66afL1Ezl5OQoOTlZffv21c0331xi/PLlyzV58mRNnDhR5557rubPn69hw4Zp5cqVSkxMlCRVrlxZb731lg4ePKibb75Z3bp1U40aNXxdtaArKuL8XBOQsznI2gzkbAZyNgdZm8GunH1upr799ltNnDixxPBzzjlH3333nSTpvPPO0969e8s0v86dO6tz587HHT937lz1799fffv2lSRNnDhRH3/8sZYsWaIRI0Z4TVujRg2lpKRo/fr1pT5Y+EQSEmI9/y4sLFJubqFiYyMVGfnXjyg/v1D5+UWKi4tSRES4Z3huboEKC4sVHx/tdWpjTk6+iorcSkiI8To1Mjs7T263pYiIcFWu/NdyUXaVKkUrNjYyKDkdm1FmZq7CwlyKj4/xDLMsS1lZeYqICFNcXLRnuNvtVnZ2viIjwxUb+9f1hEVFxcrJKVB0dISioyM9w4P12WOd7FsnSSG3TqGY06muU+XKsSG3TlLo5XQq6/T3fXSorFMo5hSIdbIs7+WGwjqFYk6BWCdJZV6nsvK5mTr99NP1+uuv68477/Qa/vrrr6t27dqSpD/++EOVK1f2q6C/Kygo0A8//KAbb7zRMywsLEzt27fXt99+K0k6ePCgYmJiFB8fr6ysLK1fv17XXHONz8vKysrVsZda5eYWKje3sMS0OTkFpc7jeLdczMrKK3X40Q8BfHfkSL4nG7tzyswsebGi222VOryoyF1ieHR0hPLzi0q96DE/v6jUz0B5XydJKiwsZp2OWafo6D+/UkNpnY5inf5ap6Pb9FGhsE7HYp3cpe6jK/o6hWJOgVin482/Iq9TKOZ0qusUHR2hoiL3SdfJ5ZKioxNKnaY0PjdTd911l2699VZ9+umnat68uSRp06ZN2r59u2bMmCFJ+v77771OA/TXoUOHVFxc7Dmd76jExERt375dkrRnzx79+9//lmVZsixLqampSk5OPuVlB0N0dCTNlAHI2RxkbQZyNgM5m4OszWBXzj43U5dccolWrFihV199Vb/99pskqVOnTpo1a5bq1asnSbr22msDWuSJtGjRQm+++WbQlgcAAAAAkp8P7a1fv36J0/zsUK1aNYWHhys9Pd1reHp6eoW4wQQAAACA0OVXM5WZmanXX39dv/zyiyTp7LPPVt++fZWQUPbzC8siKipKTZs21Zo1azy3S3e73VqzZo1SU1MDuiwnFBZySNkE5GwOsjYDOZuBnM1B1mawK2efn6r7/fff69JLL9W8efN0+PBhHT58WHPnzlXXrl31ww8/+FzAkSNHtHnzZm3evFmStGvXLm3evFl79uyRJA0dOlSLFy/W0qVL9csvv+iBBx5Qbm6u+vTp4/OyypvSLqJD6CFnc5C1GcjZDORsDrI2g105+3xkavLkyerSpYsefPBBRUT8+faioiLdd999euSRR7Ro0SKf5rdp0yYNHjzYa/6S1Lt3b02ZMkU9evRQRkaGZsyYoQMHDqhJkyZ67rnnQuI0v6O39kZoI2dzkLUZyNkM5GwOsjaDXTn73Ext2rTJq5GSpIiICN1www2eZ0H5om3bttqyZcsJp0lNTQ2J0/qOFRkZwcZrAHI2B1mbgZzNQM7mIGsz2JWzz6f5xcfHl/pA3r1796pSpUoBKQoAAAAAyjufm6kePXro3nvv1fLly7V3717t3btX//vf/3TfffepZ8+edtQIAAAAAOWOXw/tPfr/4uLiP2cSEaFrrrkmKLdLDyX5+RxSNgE5m4OszUDOZiBnc5C1GezK2edmKioqSvfdd5/Gjh2rtLQ0SdIZZ5yh2NjYgBcX6njathnI2RxkbQZyNgM5m4OszWBXzj6f5ndUbGyskpOTlZycTCPlp7i4KKdLQBCQsznI2gzkbAZyNgdZm8GunMt0ZOrmm28u8wxnzpzpdzGmiYgId7oEBAE5m4OszUDOZiBnc5C1GezKuUzNVEJCgi0LB/y1bduJb6d/KqpXT1S9evVtmz8AAABCQ5maqaMP0gWcVpx9SHK5NHLkcNuWER0TqzVfrKehAgAAwAn5fAMKBE5uboHTJVQ47vxsybKU2GusIhMD3+wUpu9U+jvTlJGRHrBmipzNQdZmIGczkLM5yNoMduVcpmZq2LBhGjNmjFq2bHnC6bKzs/XSSy+pUqVKGjhwYCDqC2mFhcVOl1BhRSbWV3Ttxk6XUSbkbA6yNgM5m4GczUHWZrAr5zI1U927d9eYMWOUkJCgiy++WM2aNVOtWrUUHR2tzMxM/fzzz/r666/16aefqnPnzp5nUeHE4uOjlZ2d73QZsBk5m4OszUDOZiBnc5C1GezKuUzNVL9+/XTVVVdpxYoVWrFihRYvXqysrCxJksvlUuPGjdWxY0e9/vrratSoUcCLDFVhYX7fmR4VCDmbg6zNQM5mIGdzkLUZ7Mq5zNdMRUVF6aqrrtJVV10lScrKylJeXp6qVq2qyMhIW4oDAAAAgPLK7xtQJCQkcMt0AAAAAMbiuKaDcnI4P9cE5GwOsjYDOZuBnM1B1mawK2eaKQcVFbmdLgFBQM7mIGszkLMZyNkcZG0Gu3KmmXJQQkKM0yUgCMjZHGRtBnI2Azmbg6zNYFfONFMOcrlcTpeAICBnc5C1GcjZDORsDrI2g105+3wDir1798rlcql27dqSpI0bN+rtt99W48aNNWDAgIAXCAAAAADlkc9HpsaOHasvv/xSknTgwAENHTpU33//vaZPn66ZM2cGvEAAAAAAKI98bqa2bdumFi1aSJJWrFihs88+W6+88oqmTp2qpUuXBrzAUJadned0CQgCcjYHWZuBnM1AzuYgazPYlbPPzVRRUZGioqIkSV988YW6dOkiSTrrrLN04MCBwFYX4txuy+kSEATkbA6yNgM5m4GczUHWZrArZ5+bqcaNG+uVV17R+vXr9cUXX6hTp06SpP3796tq1aqBri+kVa4c63QJCAJyNgdZm4GczUDO5iBrM9iVs8/N1J133qlXX31VgwYNUs+ePZWSkiJJ+vDDDz2n/wEAAABAqPPpbn6WZal+/fr66KOPVFxcrCpVqnjG9e/fX7GxdPYAAAAAzODTkSnLsnTZZZfp4MGDXo2UJNWrV0+JiYkBLQ4AAAAAyiufmqmwsDA1aNBAf/zxh03lmCUzM9fpEhAE5GwOsjYDOZuBnM1B1mawK2e/njP12GOPaevWrXbUY5SwMJ64bQJyNgdZm4GczUDO5iBrM9iVs0/XTEnSuHHjlJubq6uuukqRkZGKiYnxGv/VV18FrLhQFx8fw19DDEDO5iBrM5CzGcjZHGRtBrty9rmZuueeewJeBAAAAABUND43U71797ajDgAAAACoUHy+ZkqS0tLSNH36dN1xxx1KT0+XJH3yySfatm1bQIsLdZbFE7dNQM7mIGszkLMZyNkcZG0Gu3L2uZn66quvdMUVV2jjxo167733lJOTI0nasmWLnnzyyYAXGMqysvKcLgFBQM7mIGszkLMZyNkcZG0Gu3L2+TS/adOm6bbbbtPQoUPVqlUrz/ALL7xQCxcuDGhxoS4iIkxFRW6ny0Aptm3bErB5hYeHqbj4r5yrV09UvXr1AzZ/lB9s02YgZzOQsznI2gx25exzM7V161ZNnTq1xPDq1avr0KFDASnKFHFx0dw9ppwpzj4kuVwaOXK4bcuIjonVmi/W01CFILZpM5CzGcjZHGRtBrty9rmZSkhI0IEDB1S/vvcvgps3b9Zpp50WsMIAJ7jzsyXLUmKvsYpMDHyzU5i+U+nvTFNGRjrNFAAAQAXnczPVs2dPTZ06VU888YRcLpfcbre+/vprPfroo7r66qttKBEIvsjE+oqu3djpMgAAAFCO+XwDittvv11nnXWW/vGPfygnJ0c9e/ZUamqqWrVqpZEjR9pRY8hyuzk/FwglbNNmIGczkLM5yNoMduXs85GpqKgoPfTQQxo9erS2bt2qI0eO6JxzztGZZ55pQ3mhLTs73+kSAAQQ27QZyNkM5GwOsjaDXTn73Ewddfrpp+v0009XcXGxtm7dqsOHD6tKlSqBrC3kRUaGq7Cw2OkyAAQI27QZyNkM5GwOsjaDXTn7fJrfww8/rNdee02SVFxcrNTUVPXu3Vv/+Mc/tHbt2oAXGMpiY6OcLgFAALFNm4GczUDO5iBrM9iVs8/N1LvvvquUlBRJ0kcffaSdO3dqxYoVuu666zR9+vSAFwgAAAAA5ZHPzdShQ4dUs2ZNSdInn3yiyy+/XA0bNlTfvn21devWgBcIAAAAAOWRz81UjRo19PPPP6u4uFirV69Whw4dJEl5eXkKDw8PeIGhrKiI83OBUMI2bQZyNgM5m4OszWBXzj7fgKJPnz667bbbVLNmTblcLrVv316StGHDBp111lkBLzCU5eQUOF0CgABimzYDOZuBnM1B1mawK2efm6kxY8bo7LPP1u+//67u3bsrKurPi7nCw8M1fPjwgBcYyqKjI5SfX+R0GQAChG3aDORsBnI2B1mbwa6c/bo1evfu3UsM69279ykXY5ro6Eg2XiCEsE2bgZzNQM7mIGsz2JWzz83UzJkzTzj+5ptv9rsYAAAAAKgofG6mVq1a5fW6qKhIu3btUnh4uM444wyaKQAAAABG8LmZWrZsWYlh2dnZGj9+vLp27RqImoxRWMghZSCUsE2bgZzNQM7mIGsz2JWzz7dGL018fLzGjBmjGTNmBGJ2xsjNLXS6BAABxDZtBnI2Azmbg6zNYFfOAWmmJCkrK0tZWVmBmp0RYmMjnS4BQACxTZuBnM1AzuYgazPYlbPPp/m9+OKLXq8ty9KBAwf05ptvqlOnTgErzASRkRH8NQQIIWzTZiBnM5CzOcjaDHbl7HMzNW/ePK/XYWFhql69unr37q0RI0YEqi4AAAAAKNd8bqY+/PBDO+oAjLJt2xZb51+9eqLq1atv6zIAAABM59dDexEY+fkcUjZNcfYhyeXSyJHDbV1OdEys1nyxnoYqyNimzUDOZiBnc5C1GezKmWbKQTxt2zzu/GzJspTYa6wiE+1pdArTdyr9nWnKyEinmQoytmkzkLMZyNkcZG0Gu3KmmXJQXFyUcnIKnC4DDohMrK/o2o2dLgMBxjZtBnI2Azmbg6zNYFfOAbs1OnwXERHudAkAAoht2gzkbAZyNgdZm8GunMvUTPXu3VuHDx+WJM2cOVO5ubm2FAMAAAAAFUWZmqlffvnF00DNmjVLOTk5thYFAAAAAOVdma6ZatKkie6++26dd955sixLzz//vOLi4kqd9uabbw5ogaEsN5fzc4FQwjZtBnI2Azmbg6zNYFfOZWqmJk+erCeffFIfffSRXC6XVq9erfDwkucdulwumikfFBYWO10CgABimzYDOZuBnM1B1mawK+cyNVNnnXWWpk+fLklKSUnRvHnzlJiYaEtBJomPj1Z2dr7TZQAIELZpM5CzGcjZHGRtBrty9vnW6D/99FPAizBVWBg3UwRCCdu0GcjZDORsDrI2g105+/WcqbS0NM2fP1+//PKLJKlx48YaPHiwzjjjjIAWBwAAAADllc8t2urVq9WjRw9t3LhRycnJSk5O1oYNG9SzZ099/vnndtQIAAAAAOWOz0empk2bpiFDhujOO+/0Gj516lRNnTpVHTp0CFhxoS4nh/NzgVDCNm0GcjYDOZuDrM1gV84+H5n65Zdf9M9//rPE8L59++rnn38OSFGmKCpyO10CgABimzYDOZuBnM1B1mawK2efm6nq1atr8+bNJYZv3ryZO/z5KCEhxukSAAQQ27QZyNkM5GwOsjaDXTn7fJpfv379dP/992vnzp1q3bq1JOmbb77Rs88+qyFDhgS6vpPau3ev7rrrLqWnpys8PFyjRo3S5ZdfHvQ6/OFyuZwuAUAAsU2bgZzNQM7mIGsz2JWzz83U6NGjFR8frxdeeEGPP/64JKlWrVq6+eabNXjw4IAXeDLh4eG655571KRJEx04cEB9+vRR586dFRcXF/RaAAAAAJjD52bK5XJpyJAhGjJkiLKzsyVJ8fHxAS+srGrVqqVatWpJkmrWrKlq1arp8OHDNFMAAAAAbHVKT6+Kj48/5UZq3bp1uummm9SxY0clJydr1apVJaZZtGiRunTpoubNm6tfv37auHFjqfPatGmT3G63Tj/99FOqKViys/OcLgFAALFNm4GczUDO5iBrM9iVs+OPfM7JyVFycrImTJhQ6vjly5dr8uTJGj16tJYuXaqUlBQNGzZM6enpXtP98ccfGjdunCZNmhSMsgPC7bacLgFAALFNm4GczUDO5iBrM9iVs+PNVOfOnXX77bfr0ksvLXX83Llz1b9/f/Xt21eNGzfWxIkTFRMToyVLlnimKSgo0OjRozV8+HDPTTEqgsqVY50uAUAAsU2bgZzNQM7mIGsz2JWzz9dMBVNBQYF++OEH3XjjjZ5hYWFhat++vb799ltJkmVZGj9+vC688EJdffXVfi8rIeGvH3BhYZFycwsVGxupyMi/fkT5+YXKzy9SXFyUIiLCPcNzcwtUWFis+PhohYX91Z/m5OSrqMithIQYrzuIZGfnye22FBERxgYMWx3vs3fs5y4zM1dhYS7Fx/9121DLspSVlaeIiDDFxUV7hrvdbmVn5ysyMlyxsVGe4UVFxcrJKVB0dISioyM9w4O1PZWHdQoL+7OuUFqnUMzpVNfp6Hd3KK1TKOZ0quv09310qKxTKOYUiHUKD3d5LTcU1ikUczrVdTq6GmVdp7LyqZkqLCzUDTfcoIkTJ+rMM8/0a4G+OHTokIqLi0s8vyoxMVHbt2+XJH399ddavny51/VWjz32mJKTk31aVlZWrqxjjv7l5hYqN7ewxLQ5OQWlziM7u/QnK2dllX6OZlGRW5mZuT7VCfjieJ+90j53brdV6vDjfU4LC4tVWFhyeH5+kfLzi0oMt3t7Kg/rdPSLOJTW6SjW6a91qlw51quuUFinY7FO7lLXq6KvUyjmFIh1Ki4uff4VeZ1CMadTXaej++iTrZPLJUVHJ5Q6TWl8aqYiIyO1ZcsWX95iuzZt2uinn35yugwAAAAAhvH5NL8rr7xSr7/+uu6880476vFSrVo1hYeHl7jZRHp6umrUqGH78u3GUSnYads2+/7wUb16ourVq2/b/CsqtmkzkLMZyNkcZG0Gu3L2uZkqLi7Wyy+/rC+++ELNmjVTbKz3+YV33313wIqLiopS06ZNtWbNGnXt2lXSn+dZrlmzRqmpqQFbjlPCwlzcQQYBV5x9SHK5NHLkcNuWER0TqzVfrKehOgbbtBnI2QzkbA6yNoNdOfvcTG3dulXnnHOOJOnXX3/1Gvf3i7nK6siRI0pLS/O83rVrlzZv3qwqVaqoTp06Gjp0qMaNG6dmzZqpRYsWmj9/vnJzc9WnTx+fl1XexMfH8NcQBJw7P1uyLCX2GqvIxMA3O4XpO5X+zjRlZKTTTB2DbdoM5GwGcjYHWZvBrpx9bqYWLFgQ0AI2bdqkwYMHe15PnjxZktS7d29NmTJFPXr0UEZGhmbMmKEDBw6oSZMmeu6550LiND/ATpGJ9RVdu7HTZQAAAIQsv2+NvmPHDqWlpen8889XTEyMLMvy68hU27ZtT3pTi9TU1JA4rQ8AAABA6PC5mTp06JBuu+02rV27Vi6XS++9957q16+ve+65R1WqVNH48ePtqDMkWcfeix1AhcY2bQZyNgM5m4OszWBXzmEnn8Tb5MmTFRERoY8//lgxMX89mKtHjx5avXp1QIsLdce7zz2Aiolt2gzkbAZyNgdZm8GunH0+MvX555/r+eefV+3atb2Gn3nmmdqzZ0/ACjNBRESYiorcTpcB+IVbr5fENm0GcjYDOZuDrM1gV84+N1M5OTleR6SO+uOPPxQVFRWQokwRFxfN3WNQ4XDr9eNjmzYDOZuBnM1B1mawK2efm6k2bdpo2bJluu222zzD3G63nnvuObVt2zaQtQEoh7j1OgAAwJ98bqb+7//+T0OGDNGmTZtUWFio//znP/r55591+PBhvfzyy3bUCKAc4tbrAADAdD43U0lJSXr33Xe1cOFCVapUSTk5Obr00ks1cOBA1apVy44aQ5bbzfm5QChhmzYDOZuBnM1B1mawK2e/njOVkJCgkSNHBroW42Rn5ztdAoAAYps2AzmbgZzNQdZmsCtnv5qpw4cP6/XXX9cvv/wiSWrcuLH69OmjqlWrBrK2kBcZGa7CwmKnywAQIGzTZiBnM5CzOcjaDHbl7PNzptatW6cuXbpowYIFyszMVGZmphYsWKBLLrlE69atC3iBoSw2lrsfAqGEbdoM5GwGcjYHWZvBrpx9PjI1adIk9ejRQw888IDCw8MlScXFxZo4caImTZqkt99+O+BFAgAAAEB54/ORqR07dmjo0KGeRkqSwsPDNWTIEO3YsSOgxQEAAABAeeVzM3XOOedo+/btJYZv375dKSkpASnKFEVFnJ8LhBK2aTOQsxnI2RxkbQa7ci7TaX4//fST59+DBw/Www8/rB07dujcc8+VJG3YsEGLFi3SnXfeaUuRoSonp8DpEgAEENu0GcjZDORsDrI2g105l6mZuvrqq+VyuWRZlmfYf/7znxLTjR07Vj169AhcdSEuOjpC+flFTpcBIEDYps1AzmYgZ3OQtRnsyrlMzdQHH3wQ8AVDio6OZOMFQgjbtBnI2QzkbA6yNoNdOZepmapbt27AFwwAAAAAFZlfD+3dt2+fvv76a2VkZMjtdnuNGzx4cEAKAwAAAIDyzOdm6o033tD999+vyMhIVatWzWucy+WimfJBYSGHlIFQwjZtBnI2Azmbg6zNYFfOPjdTTzzxhEaPHq0bb7xRYWE+31kdf5ObW+h0CQACiG3aDORsBnI2B1mbwa6cfe6G8vLy1LNnTxqpAIiNjXS6BAABxDZtBnI2Azmbg6zNYFfOPh+Z6tu3r1auXKkRI0bYUY9RIiMj+GsIcBzbtm2xdf7VqyeqXr36AZ0n27QZyNkM5GwOsjaDXTn73EyNHTtWN954o1avXq2kpCRFRHjP4u677w5YcQDMU5x9SHK5NHLkcFuXEx0TqzVfrA94QwUAAMzhczM1Z84cffbZZ2rYsGGJcS6XKyBFATCXOz9bsiwl9hqryER7Gp3C9J1Kf2eaMjLSaaYAAIDffG6m5s6dq0ceeUR9+vSxox6j5OdzSBk4nsjE+oqu3djpMnzCNm0GcjYDOZuDrM1gV84+30UiKipKrVu3tqMW4/C0bSC0sE2bgZzNQM7mIGsz2JWzz83U4MGDtXDhQjtqMU5cXJTTJQAIILZpM5CzGcjZHGRtBrty9vk0v40bN+rLL7/URx99pLPPPrvEDShmzpwZsOJCXUREuNMlAAggtmkzkLMZyNkcZG0Gu3L2uZmqXLmyLrvsMjtqAQAAAIAKw+dmavLkyXbUAQAAAAAVis/XTCFwcnMLnC4BQACxTZuBnM1AzuYgazPYlbPPR6a6dOlywudJffDBB6dUkEkKC4udLgFAALFNm4GczUDO5iBrM9iVs8/N1HXXXef1uqioSD/++KM+++wzDRs2LGCFmSA+PlrZ2flOlwEgQNimzUDOZiBnc5C1GezK+ZSbqaMWLVqkTZs2nXJBJgkL4yxLIJSwTZuBnM1AzuYgazPYlXPA5tqpUye9++67gZodAAAAAJRrAWumVq5cqapVqwZqdgAAAABQrvl8mt/VV1/tdQMKy7J08OBBZWRkaMKECQEtLtTl5HB+LhBK2KbNQM5mIGdzkLUZ7MrZ52aqa9euXq9dLpeqV6+uCy64QI0aNQpYYSYoKnI7XQKAAGKbNgM5m4GczUHWZrArZ5+bqZtvvtmOOoyUkBCjrKw8p8sAECBs02YgZzOQsznI2gx25cztSxx0oud1Aah42KbNQM5mIGdzkLUZ7Mq5zEemUlJSTlqEy+XSjz/+eMpFAQAAAEB5V+ZmaubMmccd991332nBggVyuznnFAAAAIAZytxMHXvjCUnavn27pk2bpo8++khXXHGFbrnlloAWF+qyszk/FwglbNNmIGczkLM5yNoMduXs8w0oJGnfvn168skntWzZMnXs2FHLli1TUlJSoGsLeW635XQJAAKIbdoM5GwGcjYHWZvBrpx9aqaysrL09NNPa+HChWrSpInmzZunNm3a2FKYCSpXjlVmZq7TZQAIELZpM5CzGcjZHGRtBrtyLnMz9eyzz+q5555TjRo1NG3atFJP+wMAAAAAU5S5mZo2bZpiYmJ0xhlnaNmyZVq2bFmp053oRhUAAAAAECrK3ExdffXV3IcfQEjZtm1LQOdXqVK0jhzJlyRVr56oevXqB3T+AACgfClzMzVlyhQ76zAS5+cCzijOPiS5XBo5crhty4iOidWaL9bTUIUgvrvNQM7mIGsz2JWzX3fzQ2CEhbm4gwzgAHd+tmRZSuw1VpGJgW92CtN3Kv2dacrISKeZCkF8d5uBnM1B1mawK2eaKQfFx8fw1xDAQZGJ9RVdu7HTZaCC4bvbDORsDrI2g105hwV8jgAAAABgAJopAAAAAPADzZSDLIvzcwGgouG72wzkbA6yNoNdOdNMOSgrK8/pEgAAPuK72wzkbA6yNoNdOdNMOSgigh8/AFQ0fHebgZzNQdZmsCtnPj0OiouLdroEAICP+O42Azmbg6zNYFfONFMAAAAA4AeaKQAAAADwA82Ug9xut9MlAAB8xHe3GcjZHGRtBrtypplyUHZ2vtMlAAB8xHe3GcjZHGRtBrtypplyUGRkuNMlAAB8xHe3GcjZHGRtBrtypplyUGxslNMlAAB8xHe3GcjZHGRtBrtyppkCAAAAAD/QTAEAAACAH2imHFRUVOx0CQAAH/HdbQZyNgdZm8GunGmmHJSTU+B0CQAAH/HdbQZyNgdZm8GunGmmHBQdHeF0CQAAH/HdbQZyNgdZm8GunGmmHBQdHel0CQAAH/HdbQZyNgdZm8GunGmmAAAAAMAPNFMAAAAA4IeQaKZGjx6t888/X7fccovTpfiksLDI6RIAAD7iu9sM5GwOsjaDXTmHRDM1ePBgPfroo06X4bPc3EKnSwAA+IjvbjOQsznI2gx25RwSzVTbtm1VqVIlp8vwWWwsFzwCQEXDd7cZyNkcZG0Gu3J2vJlat26dbrrpJnXs2FHJyclatWpViWkWLVqkLl26qHnz5urXr582btzoQKWBFxnJrTgBoKLhu9sM5GwOsjaDXTk73kzl5OQoOTlZEyZMKHX88uXLNXnyZI0ePVpLly5VSkqKhg0bpvT09CBXCgAAAAB/cbwV79y5szp37nzc8XPnzlX//v3Vt29fSdLEiRP18ccfa8mSJRoxYkSwygQAn23btsW2eVevnqh69erbNn8AAHByjjdTJ1JQUKAffvhBN954o2dYWFiY2rdvr2+//Tagy0pIiPX8u7CwSLm5hYqNjfQ6JJifX6j8/CLFxUUpIiLcMzw3t0CFhcWKj49WWNhfB/tycvJVVORWQkKMXC6XZ3h2dp7cbkthYS5VrvzXcgGEhuLsQ5LLpZEjh9u2jJjYWG3csEn169dXVlaeIiLCFBcX7RnvdruVnZ2vyMhwxcZGeYYXFRUrJ6dA0dERXg8wDNb33rHfeZmZuQoLcyk+PsYzzLKscr1OR7+7Q2mdQjGnU12nv++jQ2WdQjGnQKyTZbm9lhsK6xSKOZ3qOh29m19Z16msynUzdejQIRUXFysxMdFreGJiorZv3+55PWTIEP3000/Kzc1Vp06d9MQTT6hVq1Y+LSsrK1eW5T0sN7ew1Dt/5OQUlDqP7Oz848w7r9Thf/yR41ONACoGd362ZFlK7DVWkYmBP3pUmL5T6e9MU1rablWtWlOSVFTkVmZmbslpC4tVWFhyeH5+kfLzS94m1u7vvdJqdLutUoezTqyT5Nw6lbaPrujrFIo5BWKdsrJKr7Eir1Mo5hSsdXK5pOjohFKnKU25bqbKat68eU6X4Je4uKjjfgAAVHyRifUVXbux02UgwPjuNgM5m4OszWBXzuW6mapWrZrCw8NL3GwiPT1dNWrUcKiqwPn7IUkA8BXXZDmD724zkLM5yNoMduVcrpupqKgoNW3aVGvWrFHXrl0l/Xme5Zo1a5SamupwdQDgjGBckxUdE6s1X6ynoQIA4AQcb6aOHDmitLQ0z+tdu3Zp8+bNqlKliurUqaOhQ4dq3LhxatasmVq0aKH58+crNzdXffr0cbBqAHBOsK7JyshIp5kCAOAEHG+mNm3apMGDB3teT548WZLUu3dvTZkyRT169FBGRoZmzJihAwcOqEmTJnruuedC4jS/3FzOzwXgP67Jcgbf3WYgZ3OQtRnsytnxZqpt27basuXE5/2npqaG5Gl9hYXFTpcAAPAR391mIGdzkLUZ7Mo57OSTwC7x8dEnnwgAUK7w3W0GcjYHWZvBrpxpphz09weJAQAqBr67zUDO5iBrM9iVM58eAAAAAPADzRQAAAAA+MHxG1CYLCcn3+kSACBk7dq1UxkZ6Sef0Efh4WEqLnbzYOMQxz7aHGRtBrtypplyUFGR2+kSACAk7dq1U+3at1F+Xq5ty+DBxqGNfbQ5yNoMduVMM+WghIQYZWXlOV0GAIScjIx05efl8mBj+I19tDnI2gx25Uwz5SCXy+V0CQAQ0niwMfzFPtocZG0Gu3LmBhQAAAAA4AeaKQAAAADwA82Ug7KzOT8XAIDyiH20OcjaDHblTDPlILfbcroEAABQCvbR5iBrM9iVM82UgypXjnW6BAAAUAr20eYgazPYlTPNFAAAAAD4gWYKAAAAAPxAMwUAAAAAfqCZclBmZq7TJQAAgFKwjzYHWZvBrpxpphwUFsYTtwEAKI/YR5uDrM1gV840Uw6Kj49xugQAAFAK9tHmIGsz2JUzzRQAAAAA+IFmCgAAAAD8QDPlIMviidsAAJRH7KPNQdZmsCtnmikHZWXlOV0CAAAoBftoc5C1GezKmWbKQRER/PgBACiP2Eebg6zNYFfOfHocFBcX7XQJAACgFOyjzUHWZrAr5whb5goAwEns2rVTGRnptsx727YttswXAIC/o5kCAATdrl071a59G+Xn2fNEegAAgoFmykFut9vpEgDAERkZ6crPy1Vir7GKTKwf8Pnnbl+vw6sXBny+MAf7aHOQtRnsyplmykHZ2flOlwAAjopMrK/o2o0DPt/C9J0BnyfMwj7aHGRtBrty5gYUDoqMDHe6BAAAUAr20eYgazPYlTPNlINiY6OcLgEAAJSCfbQ5yNoMduVMMwUAAAAAfqCZAgAAAAA/0Ew5qKio2OkSAABAKdhHm4OszWBXzjRTDsrJKXC6BAAAUAr20eYgazPYlTO3RndQdHSE8vOLnC4DAEq1bduWCjnvULJr105lZKTbNv/q1RNVr17gn/MVCoK1jyZj5/H7mBnsyplmykHR0ZFsvADKneLsQ5LLpZEjhztditF27dqpdu3bKD8v17ZlRMfEas0X6/lluxTB2EeTcfnA72NmsCtnmikAgBd3frZkWUrsNVaRifb8Apa7fb0Or15oy7xDRUZGuvLzcm3LoTB9p9LfmaaMjHR+0XYIGQMVH80UAKBUkYn1FV27sS3zLkzfact8Q5GdOaB8IGOg4uIGFA4qLOSQMgAA5RH7aHOQtRnsyplmykG5uYVOlwAAAErBPtocZG0Gu3KmmXJQbGyk0yUAAIBSsI82B1mbwa6caaYcFBnJJWsAAJRH7KPNQdZmsCtnmikAAAAA8APNFAAAAAD4gWbKQfn5XPAIAEB5xD7aHGRtBrtypplyEE/bBgCgfGIfbQ6yNoNdOdNMOSguLsrpEgAAQCnYR5uDrM1gV840Uw6KiAh3ugQAAFAK9tHmIGsz2JUzzRQAAAAA+IEb6wMAYLBt27bYNu/q1RNVr1592+YPAE6jmXJQbm6B0yUAAAxVnH1Icrk0cuRw25YRHROrNV+sr5ANFftoc5C1GezKmWbKQYWFxU6XAAAwlDs/W7IsJfYaq8jEwDc7hek7lf7ONGVkpFfIZop9tDnI2gx25Uwz5aD4+GhlZ+c7XQYAwGCRifUVXbux02WUO+yjzUHWZrArZ25A4aCwMH78AACUR+yjzUHWZrArZz49AAAAAOAHmikAAAAA8APNlINycjg/FwCA8oh9tDnI2gx25Uwz5aCiIrfTJQAAgFKwjzYHWZvBrpxpphyUkBDjdAkAAKAU7KPNQdZmsCtnmikHuVwup0sAAAClYB9tDrI2g10500wBAAAAgB9opgAAAADADzRTDsrOznO6BAAAUAr20eYgazPYlTPNlIPcbsvpEgAAQCnYR5uDrM1gV840Uw6qXDnW6RIAAEAp2Eebg6zNYFfONFMAAAAA4AeaKQAAAADwA80UAAAAAPiBZspBmZm5TpcAAABKwT7aHGRtBrtypplyUFgYT9wGAKA8Yh9tDrI2g105h0Qz9dFHH6lbt2667LLL9NprrzldTpnFx8c4XQIAACgF+2hzkLUZ7Mo5wpa5BlFRUZGmTJmiF198UfHx8erTp4+6du2qatWqOV0aAAAAgBBW4Y9Mbdy4UY0bN9Zpp52mSpUqqVOnTvr888+dLgsAAABAiHO8mVq3bp1uuukmdezYUcnJyVq1alWJaRYtWqQuXbqoefPm6tevnzZu3OgZt3//fp122mme16eddpr27dsXlNpPlWXxxG0AAMoj9tHmIGsz2JWz481UTk6OkpOTNWHChFLHL1++XJMnT9bo0aO1dOlSpaSkaNiwYUpPTw9ypYGXlZXndAkAAKAU7KPNQdZmsCtnx6+Z6ty5szp37nzc8XPnzlX//v3Vt29fSdLEiRP18ccfa8mSJRoxYoRq1arldSRq3759atGihe11B0JERJiKitxOlwEA8NO2bVsq5Lxxckf30bt27VRGhj1/wA1WxnYvp3r1RNWrV9/WZdiJ38dOzs7tQArOZ8iunB1vpk6koKBAP/zwg2688UbPsLCwMLVv317ffvutJKlFixbatm2b9u3bp/j4eH366acaNWqUz8tKSIj1/LuwsEi5uYWKjY1UZORfP6L8/ELl5xcpLi5KERHhnuG5uQUqLCxWfHy0wsL+OtiXk5OvoiK3EhJi5HL9dTvG7Ow8ud2WKleOZeMFgAqoOPuQ5HJp5MjhTpdS7lWqFP3/93fFyskpUHR0hKKjIz3jg7nP/bvMzFyFhbm87vBlWZaysvIUERGmypVjtX37b2rfoY3ycivmc4iC9TmNjonVd99uUMOGDT3DgpVTXFy0Z7jb7VZ2dr4iI8MVGxvlGX6yz15CQoyKi/86Bczpz14g1imQ29POnTtt3w5iYmP17TcblJhY27Z1crmkw4dzy5xTWZXrZurQoUMqLi5WYmKi1/DExERt375dkhQREaFx48Zp8ODBcrvduuGGG/y6k19WVq6OPZUyN7dQubmFJabNySkodR7Z2fnHmXfphxWLitw8KA4AKiB3frZkWUrsNVaRifb8NTV3+3odXr3QlnkH05Ej+V77uvz8IuXnF5WYzu59bmn7W7fbKnV4UZFbRUVupaXtVl5urm05251xMD6nhek7lf7ONO3e/bsSE2uXGG93TqUNLywsVmFhyeHH++wVF5c+f6c+e4FYp0BuT3ZvB8d+huxap6PN0slycrmk6OiEMtdfrpupsrrkkkt0ySWXOF0GAMAwkYn1FV27sS3zLkzfact84Tu7cg5WxnZ+TmEOPkelc/wGFCdSrVo1hYeHl7jZRHp6umrUqOFQVYHjdnOKHwAA5RH7aHOQtRnsyrlcN1NRUVFq2rSp1qxZ4xnmdru1Zs0atWrVysHKAuN4h1QBAICz2Eebg6zNYFfOjp/md+TIEaWlpXle79q1S5s3b1aVKlVUp04dDR06VOPGjVOzZs3UokULzZ8/X7m5uerTp4+DVQdGZGS4CguLnS4DAAAcIzIy/OQTISTw+5gZ7MrZ8WZq06ZNGjx4sOf15MmTJUm9e/fWlClT1KNHD2VkZGjGjBk6cOCAmjRpoueeey4kTvOLjY0q9WJCAADgrL/fOQ2hjd/HzGBXzo43U23bttWWLSd+/kFqaqpSU1ODVBEAAAAAnFy5vmYKAAAAAMormikHFRVxfi4AAOUR+2hzkLUZ7MqZZspBx3vIGAAAcBb7aHOQtRnsyplmykHR0Y5fsgYAAErBPtocZG0Gu3KmmXJQdHSk0yUAAIBSsI82B1mbwa6caaYAAAAAwA80UwAAAADgB5opBxUWFjldAgAAKAX7aHOQtRnsyplmykG5uYVOlwAAAErBPtocZG0Gu3KmmXJQbCwXPAIAUB6xjzYHWZvBrpxpphwUGcmtOAEAKI/YR5uDrM1gV840UwAAAADgB1rx/8/lcn654eHhSkhIUHxMlD0PFouNVkFFnn8wllHR5x+MZVT0+QdjGczf+WVU9PkHYxk2zz8yJkoJCQkKDw93bB97qtgvn1wo5Cw593tgRWD3dhDMz1BZ5u9rDS7Lsiz/ygEAAAAAc3GaHwAAAAD4gWYKAAAAAPxAMwUAAAAAfqCZAgAAAAA/0EwBAAAAgB9opgAAAADADzRTAAAAAOAHmikAAAAA8APNFAAAAAD4gWYKAAAAAPxAM+WARYsWqUuXLmrevLn69eunjRs3Ol0STtGTTz6p5ORkr/+6d+/uGZ+fn6+JEyeqbdu2atWqlcaMGaODBw86WDHKYt26dbrpppvUsWNHJScna9WqVV7jLcvSE088oY4dO6pFixYaMmSIfvvtN69p/vjjD40dO1atW7dWmzZtdM899+jIkSNBXAuczMlyHj9+fInte9iwYV7TkHP5N2fOHPXt21etWrVSu3btNGrUKG3fvt1rmrJ8V+/Zs0cjRozQueeeq3bt2unRRx9VUVFRMFcFJ1CWnAcNGlRim77//vu9piHn8u+ll17SFVdcodatW6t169YaMGCAPvnkE8/4YG3PNFNBtnz5ck2ePFmjR4/W0qVLlZKSomHDhik9Pd3p0nCKzj77bH322Wee/1566SXPuEceeUQfffSR/vvf/2rBggXav3+/br75ZgerRVnk5OQoOTlZEyZMKHX8s88+qwULFuiBBx7Q4sWLFRsbq2HDhik/P98zzZ133qmff/5Zc+fO1dNPP63169eX2GnDWSfLWZIuuugir+378ccf9xpPzuXfV199pYEDB2rx4sWaO3euioqKNGzYMOXk5HimOdl3dXFxsW688UYVFhbqlVde0ZQpU7R06VLNmDHDiVVCKcqSsyT179/fa5u+6667POPIuWKoXbu27rzzTr3xxhtasmSJLrzwQo0ePVrbtm2TFMTt2UJQ/fOf/7QmTpzoeV1cXGx17NjRmjNnjoNV4VTNmDHDuvLKK0sdl5mZaTVt2tRasWKFZ9jPP/9sJSUlWd9++22QKsSpSkpKst5//33Pa7fbbXXo0MF67rnnPMMyMzOtZs2aWe+8845lWX/lvHHjRs80n3zyiZWcnGz9/vvvwSseZXZszpZlWePGjbNGjhx53PeQc8WUnp5uJSUlWV999ZVlWWX7rv7444+tlJQU68CBA55pXnrpJat169ZWfn5+UOtH2Rybs2VZVmpqqvXQQw8d9z3kXHGdf/751uLFi4O6PXNkKogKCgr0ww8/qH379p5hYWFhat++vb799lsHK0Mg7NixQx07dtQll1yisWPHas+ePZKkTZs2qbCw0Cv3Ro0aqU6dOvruu+8cqhanateuXTpw4IBXrgkJCTr33HM92/O3336rypUrq3nz5p5p2rdvr7CwME7vrWC++uortWvXTt26ddOECRN06NAhzzhyrpiysrIkSVWqVJFUtu/q7777TklJSapRo4Znmo4dOyo7O1s///xz8IpHmR2b81Fvv/222rZtq169emnatGnKzc31jCPniqe4uFj/+9//lJOTo1atWgV1e44I2FrgpA4dOqTi4mIlJiZ6DU9MTCxxPi8qlhYtWmjy5Mlq2LChDhw4oFmzZmngwIF6++23dfDgQUVGRqpy5cpe70lMTNSBAwccqhin6mh2pW3PR8/JPnjwoKpXr+41PiIiQlWqVCH7CuSiiy7SpZdeqnr16mnnzp16/PHHNXz4cL366qsKDw8n5wrI7XbrkUceUevWrZWUlCRJZfquPnjwoNcvXpI8r8m6/CktZ0nq1auX6tSpo1q1amnLli2aOnWqfv31V82cOVMSOVckW7Zs0b/+9S/l5+crLi5Os2bNUuPGjbV58+agbc80U0AAdO7c2fPvlJQUnXvuubr44ou1YsUKxcTEOFgZgFPVs2dPz7+PXqzetWtXz9EqVDwTJ07Utm3bvK5tReg5Xs4DBgzw/Ds5OVk1a9bUkCFDlJaWpjPOOCPYZeIUNGzYUMuWLVNWVpbeffddjRs3TgsXLgxqDZzmF0TVqlVTeHh4iZtNpKenl+iMUbFVrlxZZ555ptLS0lSjRg0VFhYqMzPTa5r09HTVrFnToQpxqo5md6LtuUaNGsrIyPAaX1RUpMOHD5N9BVa/fn1Vq1ZNO3bskETOFc2kSZP08ccfa/78+apdu7ZneFm+q2vUqFHibmBHX5N1+XK8nEtz7rnnSpLXNk3OFUNUVJQaNGigZs2aaezYsUpJSdGLL74Y1O2ZZiqIoqKi1LRpU61Zs8YzzO12a82aNWrVqpWDlSHQjhw5op07d6pmzZpq1qyZIiMjvXLfvn279uzZo5YtWzpXJE5JvXr1VLNmTa9cs7OztWHDBs/23KpVK2VmZmrTpk2eab788ku53W61aNEi6DUjMH7//Xf98ccfnp0tOVcMlmVp0qRJev/99zV//nzVr1/fa3xZvqtbtmyprVu3ev0R5YsvvlB8fLwaN24clPXAiZ0s59Js3rxZ0l+/QJNzxeV2u1VQUBDU7ZnT/IJs6NChGjdunJo1a6YWLVpo/vz5ys3NVZ8+fZwuDafg0Ucf1cUXX6w6depo//79evLJJxUWFqZevXopISFBffv21ZQpU1SlShXFx8froYceUqtWrWimyrkjR44oLS3N83rXrl3avHmzqlSpojp16mjw4MGaPXu2GjRooHr16umJJ55QrVq11LVrV0l/Xux60UUX6d///rcmTpyowsJCPfjgg+rZs6dOO+00p1YLxzhRzlWqVNHMmTPVrVs31ahRQzt37tR//vMfNWjQQBdddJEkcq4oJk6cqHfeeUdPPfWUKlWq5LkmIiEhQTExMWX6ru7YsaMaN26su+66S//3f/+nAwcO6L///a8GDhyoqKgoB9cOR50s57S0NL399tvq3Lmzqlatqi1btmjy5Mk6//zzlZKSIomcK4pp06apU6dOOv3003XkyBG98847+uqrr/T8888HdXt2WZZl2bSOOI6FCxfq+eef14EDB9SkSRPdd999nkPMqJhuv/12rVu3Tn/88YeqV6+u8847T7fffrvn3Ov8/HxNmTJF//vf/1RQUKCOHTtqwoQJnC5Qzq1du1aDBw8uMbx3796aMmWKLMvSjBkztHjxYmVmZuq8887ThAkT1LBhQ8+0f/zxhx588EF9+OGHCgsL02WXXab77rtPlSpVCuaq4AROlPMDDzyg0aNH68cff1RWVpZq1aqlDh066NZbb/U6PZucy7/k5ORSh0+ePNnzB82yfFfv3r1bDzzwgL766ivFxsaqd+/eGjt2rCIi+Pt0eXCynPfu3av/+7//07Zt25STk6PTTz9dXbt21ahRoxQfH++ZnpzLv3vuuUdffvml9u/fr4SEBCUnJ2v48OHq0KGDpOBtzzRTAAAAAOAHrpkCAAAAAD/QTAEAAACAH2imAAAAAMAPNFMAAAAA4AeaKQAAAADwA80UAAAAAPiBZgoAAAAA/EAzBQAAAAB+oJkCgBCza9cuJScna/PmzU6X4vHLL7+of//+at68ua666qqgLz83N1djxoxR69atlZycrMzMzKDXAAAIPTRTABBg48ePV3Jysp555hmv4atWrVJycrJDVTnrySefVGxsrFauXKl58+YFfflLly7V+vXr9corr+izzz5TQkJC0GsAAIQemikAsEF0dLSeffZZHT582OlSAqagoMDv96alpem8885T3bp1Va1ataDXtHPnTjVq1EhJSUmqWbOmXC6Xz8sqLi6W2+32+X2h6lQ+D4FWWFjodAkADEUzBQA2aN++vWrUqKE5c+Ycd5onn3yyxClv8+bNU5cuXTyvx48fr1GjRunpp59W+/bt1aZNG82cOVNFRUV69NFHdcEFF6hTp05asmRJiflv375d//rXv9S8eXP16tVLX331ldf4rVu36oYbblCrVq3Uvn17/d///Z8yMjI84wcNGqRJkybp4YcfVtu2bTVs2LBS18PtdmvmzJnq1KmTmjVrpquuukqffvqpZ3xycrJ++OEHzZo1S8nJyXryySdLnc/R5U2aNEnnnXee2rZtq//+97+yLMszTZcuXTRr1izdddddat26te6//35J0vr163XttdeqRYsW6ty5sx566CHl5OR45vvCCy9o3bp1Sk5O1qBBgyT92Qw8+uijuuiii9SyZUv169dPa9eu9SzrjTfeUJs2bfTBBx+oR48eat68ufbs2VPm961evVqXX365WrVqpWHDhmn//v1e6/v666+rZ8+eatasmTp27KhJkyZ5xmVmZuree+/VhRdeqNatW2vw4MH66aefPON/+uknDRo0SK1atVLr1q3Vp08fff/996X+XI9m8NJLL+mGG25QixYtdMkll2jlypVe0+zdu1e33nqr2rRpowsuuEAjR47Url27POOPfhZnz56tjh07qnv37qUu6+h0f/fwww97fu6StHLlSl1xxRVq0aKF2rZtqyFDhnjykqTXXntNl19+uZo3b67u3btr0aJFnnFHT2Ndvny5UlNT1bx5c7399tvHXXcAsBPNFADYICwsTHfccYcWLlyo33///ZTm9eWXX2r//v1auHChxo8fryeffFI33nijqlSposWLF+tf//qXJkyYUGI5jz32mIYOHaply5apZcuWuummm3To0CFJf/6yft111+mcc87R66+/rueee07p6em67bbbvOaxdOlSRUZG6uWXX9bEiRNLre/FF1/U3LlzNW7cOL311lvq2LGjRo0apd9++02S9Nlnn+nss8/W9ddfr88++0zXX3/9cdd16dKlCg8P12uvvaZ7771X8+bN02uvveY1zQsvvKCUlBQtW7ZMo0aNUlpamoYPH67LLrtMb731lqZPn66vv/5aDz74oKQ/m9b+/furVatW+uyzzzzN3KRJk/Ttt99q+vTpeuutt9S9e3fdcMMNnrolKS8vT88++6weeughvfPOO0pMTCzz+1544QU99thjWrhwofbu3atHH33UM/6ll17SpEmT1L9/f7399tt66qmndMYZZ3jG33rrrUpPT9ezzz6rN954Q02bNtV1112nP/74Q5J05513qnbt2nr99df1xhtvaPjw4YqMjDzuz1WSnnjiCXXr1k1vvvmmrrjiCt1xxx365ZdfJP15ZGfYsGGqVKmSFi1apJdffllxcXG64YYbvI5ArVmzRr/++qvmzp17wj8UnMj+/fs1duxY9e3bV8uXL9eLL76oSy+91NM0v/XWW3riiSd0++23a/ny5brjjjs0Y8YMLV261Gs+U6dO1eDBg7V8+XJ17NjRr1oA4JRZAICAGjdunDVy5EjLsiyrf//+1t13321ZlmW9//77VlJSkme6GTNmWFdeeaXXe+fOnWtdfPHFXvO6+OKLreLiYs+wbt26Wddee63ndVFRkdWyZUvrnXfesSzLsnbu3GklJSVZc+bM8UxTWFhoderUyXrmmWcsy7KsWbNmWddff73Xsvfu3WslJSVZ27dvtyzLslJTU62rr776pOvbsWNHa/bs2V7D+vbtaz3wwAOe11deeaU1Y8aME84nNTXVuvzyyy232+0Z9p///Me6/PLLPa8vvvhia9SoUV7vu+eee6x///vfXsPWrVtnpaSkWHl5eZZlWdZDDz1kpaamesbv3r3batKkifX77797ve+6666zpk2bZlmWZS1ZssRKSkqyNm/e7Nf7duzY4Rm/cOFCq3379p7XHTt2tB5//PFSfw7r1q2zWrdubeXn53sN79q1q/XKK69YlmVZrVq1st54441S31+apKQk6/777/ca1q9fP2vChAmWZVnWsmXLrG7dunn97PPz860WLVpYq1evtizrz89i+/btS9R1rL9//o/6+89/06ZNVlJSkrVr165S39+1a1fr7bff9ho2a9Ysa8CAAZZl/fX5njdv3knWGgDsF+F0MwcAoezOO+/Uddddd9xT5MqicePGCgv760SCGjVq6Oyzz/a8Dg8PV9WqVZWenu71vlatWnn+HRERoWbNmmn79u2S/jxNbO3atV7THJWWlqaGDRtKkpo2bXrC2rKzs7V//361bt3aa3jr1q29Tksrq3PPPdfreqaWLVtq7ty5Ki4uVnh4uCSpWbNmXu/56aeftGXLFq9TvSzLktvt1q5du9SoUaMSy9m6dauKi4tLnKpWUFCgqlWrel5HRkZ63TSkrO+LjY31OtJUq1YtTz7p6enav3+/2rVrV+rPYMuWLcrJyVHbtm29hufl5SktLU2SNHToUN13331688031b59e3Xv3t1reaU5NuuWLVt67vj4008/KS0trUSO+fn5nmVKUlJSkqKiok64nJNJSUlRu3btdMUVV6hjx47q2LGjunXrpipVqignJ0dpaWm699579e9//9vznqKiohI3DTn2cwAATqCZAgAbnX/++erYsaOmTZumPn36eI1zuVxe1wNJf/7SeKyICO+vapfLVeowX26OkJOTo4svvlh33nlniXE1a9b0/Ds2NrbM8wyWY2vKycnRv/71L69rco46/fTTS51HTk6OwsPDtWTJEk+TdlRcXJzn3zExMV7NXVnfV1o+R7OOjo4+0erpyJEjqlmzphYsWFBi3NGGYsyYMerVq5c++eQTffrpp5oxY4amT5+uSy+99ITzPp6cnBw1bdpUU6dOLTGuevXqnn+X5fNwss91eHi45s6dq2+++Uaff/65FixYoOnTp2vx4sWe+T/44IM699xzvebx9z8oSN4/bwBwCs0UANhs7Nixuvrqqz1He46qXr26Dh48KMuyPL+wB/LZUN99953OP/98SX/+MvvDDz9o4MCBkv484vTuu++qbt26JX7x90V8fLxq1aqlb775RhdccIFn+DfffKMWLVr4PL+NGzd6vd6wYYMaNGhQonH5u3POOUc///yzGjRoUOblNGnSRMXFxcrIyFCbNm1sf9/fxcfHq27dulqzZo0uvPDCEuObNm2qgwcPKjw8XPXq1TvufBo2bKiGDRtqyJAhuuOOO7RkyZITNlPfffedrr76as/rDRs2qEmTJp5lrlixQomJiYqPj/drvY6qXr26tm3b5jVs8+bNXtd0uVwunXfeeTrvvPM0evRoXXzxxVq1apWGDh2qWrVqaefOnbryyitPqQ4ACAZuQAEANktOTtYVV1xR4khD27ZtlZGRoWeffVZpaWlatGiRVq9eHbDlvvTSS3r//ff1yy+/aNKkSTp8+LD69u0rSbr22mt1+PBh3XHHHdq4caPS0tK0evVq3X333SouLvZpOcOGDdOzzz6r5cuXa/v27Zo6dap++uknDR482Oea9+zZo8mTJ2v79u165513tHDhwpPOZ/jw4fr22281adIkbd68Wb/99ptWrVrldXe8YzVs2FBXXHGF7rrrLr333nvauXOnNm7cqDlz5ujjjz8O+PuONWbMGM2dO1cvvviifvvtN/3www+ez0f79u3VsmVLjR49Wp999pl27dqlb775RtOnT9f333+vvLw8TZo0SWvXrtXu3bv19ddf6/vvvy/1dMa/W7lypV5//XX9+uuvmjFjhjZu3KjU1FRJ0hVXXKFq1app5MiRWr9+vXbu3Km1a9fqoYce8vkGKhdeeKE2bdqkZcuW6bffftOMGTO8mqsNGzbo6aef1vfff689e/bovffeU0ZGhs466yxJ0i233KJnnnlGL774on799Vdt2bJFS5Ys0dy5c32qAwCCgSNTABAEt9xyi5YvX+41rFGjRpowYYLmzJmj2bNn67LLLtP111+vxYsXB2SZY8eO1TPPPKPNmzerQYMGmj17tueUrdNOO00vv/yypk6dqmHDhqmgoEB16tTRRRddVOJ0qpMZPHiwsrOzNWXKFGVkZKhRo0Z66qmndOaZZ/pc89VXX628vDz169dP4eHhGjx4sAYMGHDC96SkpGjBggX673//q2uvvVaSVL9+ffXo0eOE75s8ebJmz56tKVOmaP/+/apatapatmypf/zjH7a87+969+6t/Px8zZs3T4899piqVq3quQ7L5XLpmWee0X//+1/dfffdOnTokGrUqKE2bdqoRo0aCgsL0x9//KFx48bp4MGDqlatmi677DLdcsstJ1zmmDFjtHz5ck2cOFE1a9bUtGnT1LhxY0l/nr63cOFCTZ06VTfffLOOHDmi0047Te3atfP5SNVFF12kUaNG6T//+Y/y8/PVt29fXX311dq6daukP4/MrVu3TvPnz1d2drbq1Kmj8ePHq3PnzpKkfv36KSYmRs8//7wee+wxxcXFKSkpSdddd51PdQBAMLisY09sBgDAAYMGDVJKSoruvfdep0sJOcnJyZo1a5a6du3qdCkAEFI4zQ8AAAAA/EAzBQAAAAB+4DQ/AAAAAPADR6YAAAAAwA80UwAAAADgB5opAAAAAPADzRQAAAAA+IFmCgAAAAD8QDMFAAAAAH6gmQIAAAAAP9BMAQAAAIAf/h/+DlOEROfdXwAAAABJRU5ErkJggg==", "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": "2024-11-01T03:03:38.332367Z", "iopub.status.busy": "2024-11-01T03:03:38.332218Z", "iopub.status.idle": "2024-11-01T03:03:38.614916Z", "shell.execute_reply": "2024-11-01T03:03:38.614399Z", "shell.execute_reply.started": "2024-11-01T03:03:38.332351Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Summary of user_interesting_clips:\n", "Total requests: 489,030\n", "Unique clips: 244,515\n", "Fraction of total clips: 10.46%\n", "Time Validation:\n", "Earliest timestamp: 2024-10-31 03:55:00.394836+00:00\n", "Latest timestamp: 2024-11-01 02:46:59.335843+00:00\n", "Earliest timestamp: 2024-10-31 03:55:08.983445+00:00\n", "Latest timestamp: 2024-10-31 16:49:53.528831+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": "2024-11-01T03:03:38.615584Z", "iopub.status.busy": "2024-11-01T03:03:38.615439Z", "iopub.status.idle": "2024-11-01T03:03:38.634474Z", "shell.execute_reply": "2024-11-01T03:03:38.634019Z", "shell.execute_reply.started": "2024-11-01T03:03:38.615569Z" } }, "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", " 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(parse_for_instrumental)" ] }, { "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": "2024-11-01T03:03:38.635103Z", "iopub.status.busy": "2024-11-01T03:03:38.634962Z", "iopub.status.idle": "2024-11-01T03:03:41.334839Z", "shell.execute_reply": "2024-11-01T03:03:41.334092Z", "shell.execute_reply.started": "2024-11-01T03:03:38.635088Z" } }, "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": "2024-11-01T03:03:41.335618Z", "iopub.status.busy": "2024-11-01T03:03:41.335465Z", "iopub.status.idle": "2024-11-01T03:03:48.222770Z", "shell.execute_reply": "2024-11-01T03:03:48.222163Z", "shell.execute_reply.started": "2024-11-01T03:03:41.335602Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(489030, 52)\n", "Model Name Value Counts and Fractions:\n", "chirp-v3p5-engine-s-8: 365950 (74.83%)\n", "chirp-v3p5-engine-t-6: 36776 (7.52%)\n", "chirp-v3p5-engine-upload-4: 25998 (5.32%)\n", "chirp-v3p5-engine-t-5-15: 19130 (3.91%)\n", "chirp-v3p5-engine-s-31: 11672 (2.39%)\n", "chirp-v3p5-h-s-31: 7842 (1.60%)\n", "chirp-v3p5-engine-t-5: 4182 (0.86%)\n", "chirp-v3p5-engine-s-29-6: 3119 (0.64%)\n", "chirp-v3p5-engine-b: 3042 (0.62%)\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2: 2232 (0.46%)\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2: 2225 (0.45%)\n", "chirp-v3p5-engine-t-6_s_30_t_12_tag_2: 2217 (0.45%)\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2: 2171 (0.44%)\n", "chirp-v3p5-h-t-6: 1010 (0.21%)\n", "chirp-v3p5-engine-short: 952 (0.19%)\n", "chirp-v3p5-engine-ft-1: 512 (0.10%)\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 model_name.startswith(\"chirp-v3p5-engine-t\") or model_name.startswith(\n", " \"chirp-v3p5-engine-s\"\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": "2024-11-01T03:03:48.223513Z", "iopub.status.busy": "2024-11-01T03:03:48.223361Z", "iopub.status.idle": "2024-11-01T03:03:53.214387Z", "shell.execute_reply": "2024-11-01T03:03:53.213811Z", "shell.execute_reply.started": "2024-11-01T03:03:48.223498Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "len pos models: 244515\n", "differing counts: 20381\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 1521, total 1521.\n", "chirp-v3p5-engine-ft-1_win_over_chirp-v3p5-engine-ft-1, win ratio 1.000, (1.000, 1.000), counts 256, total 256.\n", "chirp-v3p5-engine-s-29-6_win_over_chirp-v3p5-h-s-31, win ratio 0.442, (0.425, 0.460), counts 1380, total 3119.\n", "chirp-v3p5-engine-s-31_win_over_chirp-v3p5-engine-s-31, win ratio 1.000, (1.000, 1.000), counts 5836, total 5836.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 181359, total 181359.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-h-s-31, win ratio 0.302, (0.286, 0.318), counts 975, total 3231.\n", "chirp-v3p5-engine-short_win_over_chirp-v3p5-engine-short, win ratio 1.000, (1.000, 1.000), counts 476, total 476.\n", "chirp-v3p5-engine-t-5-15_win_over_chirp-v3p5-engine-t-5, win ratio 0.503, (0.487, 0.519), counts 1854, total 3684.\n", "chirp-v3p5-engine-t-5-15_win_over_chirp-v3p5-engine-t-5-15, win ratio 1.000, (1.000, 1.000), counts 7723, total 7723.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-5, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-5-15, win ratio 0.497, (0.481, 0.513), counts 1830, total 3684.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-6, win ratio 0.478, (0.434, 0.522), counts 237, total 496.\n", "chirp-v3p5-engine-t-6_s_30_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.539, (0.518, 0.560), counts 1194, total 2216.\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.525, (0.504, 0.546), counts 1167, total 2223.\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2_win_over_chirp-v3p5-engine-t-6_s_max_t_12_tag_2, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.537, (0.516, 0.558), counts 1165, total 2170.\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2_win_over_chirp-v3p5-engine-t-6_s_30_t_12_tag_2, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.531, (0.511, 0.552), counts 1185, total 2230.\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2_win_over_chirp-v3p5-engine-t-6_s_max_t_14_tag_2, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-5, win ratio 0.522, (0.478, 0.566), counts 259, total 496.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 13720, total 13720.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_30_t_12_tag_2, win ratio 0.461, (0.440, 0.482), counts 1022, total 2216.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_12_tag_2, win ratio 0.475, (0.454, 0.496), counts 1056, total 2223.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_13_tag_2, win ratio 0.463, (0.442, 0.484), counts 1005, total 2170.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_14_tag_2, win ratio 0.469, (0.448, 0.489), counts 1045, total 2230.\n", "chirp-v3p5-engine-upload-4_win_over_chirp-v3p5-engine-upload-4, win ratio 1.000, (1.000, 1.000), counts 12999, total 12999.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-engine-s-29-6, win ratio 0.558, (0.540, 0.575), counts 1739, total 3119.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-engine-s-8, win ratio 0.698, (0.682, 0.714), counts 2256, total 3231.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-h-s-31, win ratio 1.000, (1.000, 1.000), counts 241, total 241.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-h-t-6, win ratio 0.647, (0.617, 0.676), counts 653, total 1010.\n", "chirp-v3p5-h-t-6_win_over_chirp-v3p5-h-s-31, win ratio 0.353, (0.324, 0.383), counts 357, total 1010.\n", "tournament players: ['chirp-v3p5-engine-s-29-6', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-t-5', 'chirp-v3p5-engine-t-5-15', 'chirp-v3p5-engine-t-6', 'chirp-v3p5-engine-t-6_s_30_t_12_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_12_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_13_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_14_tag_2', 'chirp-v3p5-h-s-31', 'chirp-v3p5-h-t-6']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:02<00:00, 393.98it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-s-29-6: -131.0, (-244, 1040)\n", "chirp-v3p5-engine-s-8: -236.6, (-352, 936)\n", "chirp-v3p5-engine-t-5: 1640.5, (960, 1703)\n", "chirp-v3p5-engine-t-5-15: 1642.7, (960, 1708)\n", "chirp-v3p5-engine-t-6: 1655.8, (984, 1722)\n", "chirp-v3p5-engine-t-6_s_30_t_12_tag_2: 1682.7, (1007, 1751)\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2: 1673.2, (997, 1737)\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2: 1681.7, (1004, 1751)\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2: 1677.7, (1001, 1743)\n", "chirp-v3p5-h-s-31: -90.9, (-207, 1079)\n", "chirp-v3p5-h-t-6: -195.8, (-309, 982)\n" ] }, { "data": { "image/png": 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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": "2024-11-01T03:03:53.215140Z", "iopub.status.busy": "2024-11-01T03:03:53.214987Z", "iopub.status.idle": "2024-11-01T03:03:57.273645Z", "shell.execute_reply": "2024-11-01T03:03:57.273082Z", "shell.execute_reply.started": "2024-11-01T03:03:53.215124Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "first gen\n", "len pos models: 180621\n", "differing counts: 6709\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 1217, total 1217.\n", "chirp-v3p5-engine-ft-1_win_over_chirp-v3p5-engine-ft-1, win ratio 1.000, (1.000, 1.000), counts 215, total 215.\n", "chirp-v3p5-engine-s-29-6_win_over_chirp-v3p5-h-s-31, win ratio 0.439, (0.421, 0.457), counts 1255, total 2857.\n", "chirp-v3p5-engine-s-31_win_over_chirp-v3p5-engine-s-31, win ratio 1.000, (1.000, 1.000), counts 5259, total 5259.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 162429, total 162429.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-h-s-31, win ratio 0.294, (0.278, 0.311), counts 868, total 2949.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 4584, total 4584.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-engine-s-29-6, win ratio 0.561, (0.543, 0.579), counts 1602, total 2857.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-engine-s-8, win ratio 0.706, (0.689, 0.722), counts 2081, total 2949.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-h-s-31, win ratio 1.000, (1.000, 1.000), counts 208, total 208.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-h-t-6, win ratio 0.657, (0.626, 0.688), counts 593, total 902.\n", "chirp-v3p5-h-t-6_win_over_chirp-v3p5-h-s-31, win ratio 0.343, (0.312, 0.374), counts 309, total 902.\n", "tournament players: ['chirp-v3p5-engine-s-29-6', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-engine-t-6', 'chirp-v3p5-h-s-31', 'chirp-v3p5-h-t-6']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:02<00:00, 486.12it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-s-29-6: 629.3, (598, 1043)\n", "chirp-v3p5-engine-s-8: 519.9, (490, 934)\n", "chirp-v3p5-engine-t-6: 2620.5, (1000, 2742)\n", "chirp-v3p5-h-s-31: 671.8, (642, 1083)\n", "chirp-v3p5-h-t-6: 558.5, (527, 978)\n" ] }, { "data": { "image/png": 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Xr17pti1durSqVauWYX+p/xHg1g7qqZnNZrtNqF588cUsjTMt9/t5AQDAg0GACQAA/hFKliypunXrateuXfrjjz8UEREhX19f/frrr7bNYLJbfXnixAklJydLkvLmzSs/P79Mr6lataot9EpOTtbx48dVq1Yt2/nUO18HBARk2p/JZFKNGjW0fv36dNvs37/f9vnSpUt67733Mu1Xur1JjtVq1aVLlx5ogLljxw7t2LEjwzaFCxfWxIkT9eSTT2bYLrPATLJ/RydOnMjyO7olOjraIYzOjtT3T0hIyPL9Q0NDbZ8vXbqUYYCZlfeQekOc9evXa/fu3ZleExMTY/uc2S7vlStXzrQ/b29v2+db65GmduzYMdsu6nny5LGtN3o37vfzAgCAB4MAEwAA/GO0b99eu3btUnJyslatWqXevXvrp59+kpQSBGY3wLx27Zrtc/HixdPcIOROTk5OKlasmE6fPu3QhyS7ncyLFy+epXFk1u7y5cu2zydOnNCJEyey1G9qd+5S/aCZTCbly5dPPj4+qlq1qho3bqz//ve/mU6zllKmmGcm9Tvas2eP9uzZk+0xXr9+/a4DzNT3DwsLs035z47M/oyy+x7SW38yI6l3hk9LVkLw1NPLk5KSHM5HRkbaPhcrVizd6ehZcb+fFwAAPBgEmAAA4B/jqaee0oQJE3Tjxg2tWLFC7du317Zt2yRJtWvXznQX5zvFxcXZPqe16Ud6UrdN3YeUsiFLdvvMrF3qirG7davS9EGZPHmy3VqI9yIrIWdOvKO0wrasehB/RlmZFp9WxWNOjiErIX9mUv83c7eB8S33+3kBAMCDQYAJAAD+MfLnz6/mzZtr9erVOnHihN5//31b6NShQ4ds95d63cUbN25k+brUbe9cuzF1IJPVPjNrlzrg7Natm955550s9ftvkvodjRo1Sj179sy1+zdr1kxz5sx5oPdPPY5bYeqPP/6Y4aZUuSX1fzOpA/+78TA8LwAAyBy7kAMAgH+U1EHl8uXLJaVUpj311FPZ7iv1BjuXLl2S1WrN9BqLxaJLly6l2YdkP803q+vrpe4vLYULF7Z9Tj39FrelfkcRERG5ev/c/DNKvcFTbryHrEj9ri5dunRPla8Pw/MCAIDMEWACAIB/lIYNG8rX19fuWPPmze9qgxo/Pz85OztLSpnWmpW1JY8fP26rGnN2dnbYdKVq1aq2z6k3dkmP1WrVgQMHMmzj7+9v+7xv374sBa3/Nqnf0b3uKH43Um9Ec+zYsXuuLMyJceTGe8iKqlWr2qbD37hxI9Pf/4w8DM8LAAAyR4AJAAD+UZydndW2bVu7Y3czfVxKmZJevXp12/cff/wx02uWLl1q++zv7++whl9gYKDt89atWxUVFZVhfzt37sy0ArN27dry9PSUlFKxtmnTpkzH+W/zxBNP2DaD2bdvn44fP/5A71+6dGlVqFBBkpSYmGj3e/Igpd7RfdmyZbbdvo3Ezc3N7r+Tu9nw6JaH4XkBAEDmCDABAMA/zquvvqqlS5fafho1anTXfXXt2tX2+Ztvvskw+Dp8+LCWLFli+/7ss886tGnUqJFtV/EbN25o+vTp6fZ38+ZNTZkyJdMxurm5qUePHrbv48aNU3h4eKbX3fJvmHZetGhRtWvXTlJKVevw4cOzvMGLxWKx2z3+bvXt29f2eebMmdnaLT6npj+3atVKZcuWtfU5duzYLFfsxsXFPbDK0V69etk+r1mzRmvWrLmrfh6W5wUAABkjwAQAAP84np6eeuyxx2w/t6aB3422bdvapoEnJibqpZde0s6dOx3a/fHHH+rbt69tvb5q1aqpTZs2Du2cnZ31+uuv274vXbpUEydOdKgMi4iIUL9+/XT8+HG5urpmOs5evXqpUqVKkqTw8HB17txZv/zyiywWS5rtr169qiVLlqhjx46aP39+pv3/E7zxxhu25QVOnDihZ555Rr///nu67S9duqQFCxboqaee0s8//3zP92/Xrp3q168vKSUce/7557V48WKZzeY028fGxmrlypXq1q2bxo8ff8/3l1J+/8aOHWv7b2L58uV6+eWXderUqXSvOXbsmKZPn64nn3xSYWFhOTKOzDRs2NBu3dphw4YpKCgozQ2tLBaLdu7cqddee81ht/eH5XkBAEDG2IUcAAAgA25ubvrwww/14osv6urVq4qIiFCPHj1UpUoV23qWx44ds6vMLFSokD744IN0g8eOHTtqy5Yt+uWXXyRJCxcu1E8//aTAwEB5e3vr4sWLCg4OltlsVqlSpdS8eXN99dVXGY4zX758mjNnjnr27KmwsDBFRETojTfeUMGCBRUQEKDChQvLarUqOjpaf/31l86dO2cLN2+Fav90RYsW1ezZs/Xyyy/r2rVrOnPmjPr06aOiRYvK399fPj4+SkxM1LVr1xQSEpLj4ZWzs7Nmzpyp3r176+jRo4qNjdWYMWM0ffp0BQQEqGjRonJ2dlZ0dLTOnDmj06dP2wLxVq1a5dg4GjZsqLFjx2rs2LFKTk7W1q1btW3bNlWsWFF+fn7Kly+fEhISFBERoePHj+dI9endmDhxoi5cuKCDBw8qOTlZs2bN0vz581WrVi0VK1ZMVqtV4eHhOnz4sG0phrSqKx+W5wUAAOkjwAQAAMhEhQoV9O233+rNN9/U0aNHJaVs1pPWdPJq1app5syZKlOmTIZ9Tp8+XR4eHrZ1NaOjo7V+/Xq7NuXLl1dQUFCWq/9Kly6tZcuWacyYMVq3bp2sVquuXbumzZs3p3uNp6enKleunKX+/wn8/f21bNkyvf3229qxY4eklIrVDRs2pHtN4cKFbdOQ71XBggX13XffafLkyVq6dKmSkpIUGxubYSWoh4eHqlWrliP3v+V///ufypQpozFjxujs2bOyWq0KCQlRSEhIutdUqlRJXl5eOTqOjOTPn1+LFi3SxIkTtWzZMiUnJys+Pj7dd+Xu7i4np7QnmD0MzwsAANJHgAkAAJAF5cqV07Jly7R27VqtX79eBw8etFVq+fj4qEaNGmrVqpVatWolk8mUaX+urq6aMmWK2rdvr++//1579+7VlStX5OXlpTJlyujpp59W586dlS9fvmyN09vbWx999JFOnjypNWvWKDg4WGFhYYqKipKTk5M8PT1VpkwZPfroo2rYsKEef/xx247P/xYlS5bUggULtG/fPq1du1a7d+/WpUuXdP36dTk7O8vb21tly5ZV9erV1ahRI9WrV8+2AVBO8PDw0Lhx49S3b1+tXLlSO3fu1NmzZxUVFSWLxaICBQqodOnSqlKliurXr6/GjRsrf/78OXb/W+rXr6+ff/5ZGzdu1G+//aYDBw4oMjJSsbGx8vDwUOHChVW+fHnVrFlTjRs3tlUcP0geHh4aP368evbsqZ9++kk7duzQ+fPnFR0dLVdXV/n6+srPz08NGzZU69atM3xPD8PzAgCAtJmsWV3FGgAAAAAAAAAeMDbxAQAAAAAAAGBYBJgAAAAAAAAADIsAEwAAAAAAAIBhEWACAAAAAAAAMCwCTAAAAAAAAACGRYAJAAAAAAAAwLAIMAEAAAAAAAAYFgEmAAAAAAAAAMMiwAQAAAAAAABgWASYAAAAAAAAAAyLABMAAAAAAACAYbnk9gAAAAAeBgcPHtQ333yjvXv3KiIiQjdu3LCdO3HihCRp1qxZCgoKkiQNGDBAAwcOzJWxArnNz8/P9vnWfx8AAAB3iwATAAAgE99++63Gjx8vi8WS20MBAAAA/nUIMAEAADJw/vx5TZw40RZeli5dWjVq1JCXl1cujyz7wsLC1Lx5c0lSyZIltWnTplweER4GVFMCAIDcRoAJAACQgTVr1igpKUmS1KhRI82dO1cuLvwVCgAAAHhQ+Ns3AABABo4cOWL73L59+wzDy4EDB7LuJSAqNQEAQM5iF3IAAIAMXL9+3fbZ19c3F0cCAAAA/DsRYAIAAGTg1vRxSXJy4q9OAAAAwINmslqt1tweBAAAgJGMHDlSP/74Y5baLly4UIGBgZKkWbNmKSgoSJI0YMCANKeTL1++XKNGjZIkdezYUVOmTFFycrLWrl2r1atX6+TJk4qIiNDNmzf1ySefqEWLFrZrExMT9fPPP2vDhg06duyYrl69qps3b8rd3V2FCxdWmTJl5O/vr6ZNm8rf3z/Ne2ZFTkz/PXjwoBYvXqzg4GBFREQob968KlWqlP7zn/+oS5cuKliwYJrvIiv9rl69WsHBwQoPD1dsbKy8vLxUrlw5NW7cWF27ds10g6VmzZrp/PnzkqRff/1VpUqV0qVLl7R48WJt2rRJFy5cUFJSkooVK6bHH39cvXv3VsmSJbP87ImJiVqzZo02b96sw4cP6+rVq7JarfLx8VFAQICefvpptWjRQiaTKd0+goOD1b17d0lSvXr1tGjRIknSli1b9NNPP+nw4cOKiIhQfHy8Ro0apZ49e9rdf+fOndqxY4cOHTqkM2fOKDo6WiaTSd7e3qpcubIaNWqkLl26KF++fJnePytuvcdbsrvxT0hIiJYvX64dO3bo4sWLiouLk7e3t8qVK6cnnnjC9juTkfR+nzZs2KBly5bp+PHjioyMVIECBVS1alW1b99e7dq1y/DPAQAAGANrYAIAAOSi8PBwDR48WHv27Mmw3ZkzZ/Taa6/p1KlTDufi4+MVGhqq0NBQ/f7775o9e7bWr1+vsmXL3q9hp8tqtWratGlasGCBbed2Sbp586auXbumQ4cO6euvv9bHH3+crX6jo6P17rvvat26dQ7nIiMjFRkZqd27d2vevHkaP368nnrqqSz3vXHjRo0cOVIxMTF2x8+cOaMzZ85o6dKl+uijj/Tkk09m2ldwcLDeeecdhYaGOpw7f/68zp8/rzVr1iggIEAff/yxihYtmqUxxsTEaNSoUdqwYUOG7S5evKgOHTooKioqzfPh4eEKDw/Xtm3bNGfOHH344Yd6/PHHszSG+yEpKUlTpkzRt99+q+TkZLtzERERioiI0K5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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": "2024-11-01T03:03:57.274394Z", "iopub.status.busy": "2024-11-01T03:03:57.274237Z", "iopub.status.idle": "2024-11-01T03:03:59.234596Z", "shell.execute_reply": "2024-11-01T03:03:59.234046Z", "shell.execute_reply.started": "2024-11-01T03:03:57.274378Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is continue\n", "len pos models: 33854\n", "differing counts: 649\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 189, total 189.\n", "chirp-v3p5-engine-ft-1_win_over_chirp-v3p5-engine-ft-1, win ratio 1.000, (1.000, 1.000), counts 40, total 40.\n", "chirp-v3p5-engine-s-29-6_win_over_chirp-v3p5-h-s-31, win ratio 0.477, (0.417, 0.538), counts 125, total 262.\n", "chirp-v3p5-engine-s-31_win_over_chirp-v3p5-engine-s-31, win ratio 1.000, (1.000, 1.000), counts 566, total 566.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 18792, total 18792.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-h-s-31, win ratio 0.379, (0.323, 0.436), counts 107, total 282.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 587, total 587.\n", "chirp-v3p5-engine-upload-4_win_over_chirp-v3p5-engine-upload-4, win ratio 1.000, (1.000, 1.000), counts 12999, total 12999.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-engine-s-29-6, win ratio 0.523, (0.462, 0.583), counts 137, total 262.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-engine-s-8, win ratio 0.621, (0.564, 0.677), counts 175, total 282.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-h-s-31, win ratio 1.000, (1.000, 1.000), counts 32, total 32.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-h-t-6, win ratio 0.543, (0.448, 0.638), counts 57, total 105.\n", "chirp-v3p5-h-t-6_win_over_chirp-v3p5-h-s-31, win ratio 0.457, (0.362, 0.552), counts 48, total 105.\n", "tournament players: ['chirp-v3p5-engine-s-29-6', 'chirp-v3p5-engine-s-8', 'chirp-v3p5-h-s-31', 'chirp-v3p5-h-t-6']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:01<00:00, 786.34it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-s-29-6: 1016.9, (981, 1057)\n", "chirp-v3p5-engine-s-8: 947.3, (906, 984)\n", "chirp-v3p5-h-s-31: 1032.8, (1010, 1055)\n", "chirp-v3p5-h-t-6: 1003.0, (951, 1055)\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": "2024-11-01T03:03:59.235323Z", "iopub.status.busy": "2024-11-01T03:03:59.235169Z", "iopub.status.idle": "2024-11-01T03:04:01.450443Z", "shell.execute_reply": "2024-11-01T03:04:01.449914Z", "shell.execute_reply.started": "2024-11-01T03:03:59.235306Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is cover\n", "len pos models: 20687\n", "differing counts: 8861\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 54, total 54.\n", "chirp-v3p5-engine-t-5-15_win_over_chirp-v3p5-engine-t-5, win ratio 0.507, (0.490, 0.524), counts 1600, total 3156.\n", "chirp-v3p5-engine-t-5-15_win_over_chirp-v3p5-engine-t-5-15, win ratio 1.000, (1.000, 1.000), counts 6592, total 6592.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-5, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-5-15, win ratio 0.493, (0.476, 0.510), counts 1556, total 3156.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-6, win ratio 0.472, (0.424, 0.520), counts 194, total 411.\n", "chirp-v3p5-engine-t-6_s_30_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.542, (0.515, 0.568), counts 731, total 1349.\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.522, (0.495, 0.549), counts 686, total 1314.\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2_win_over_chirp-v3p5-engine-t-6_s_max_t_12_tag_2, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.513, (0.486, 0.541), counts 655, total 1276.\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2_win_over_chirp-v3p5-engine-t-6_s_30_t_12_tag_2, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.527, (0.501, 0.554), counts 714, total 1354.\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2_win_over_chirp-v3p5-engine-t-6_s_max_t_14_tag_2, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-5, win ratio 0.528, (0.480, 0.576), counts 217, total 411.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 5177, total 5177.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_30_t_12_tag_2, win ratio 0.458, (0.432, 0.485), counts 618, total 1349.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_12_tag_2, win ratio 0.478, (0.451, 0.505), counts 628, total 1314.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_13_tag_2, win ratio 0.487, (0.459, 0.514), counts 621, total 1276.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_14_tag_2, win ratio 0.473, (0.446, 0.499), counts 640, total 1354.\n", "tournament players: ['chirp-v3p5-engine-t-5', 'chirp-v3p5-engine-t-5-15', 'chirp-v3p5-engine-t-6', 'chirp-v3p5-engine-t-6_s_30_t_12_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_12_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_13_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_14_tag_2']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:01<00:00, 656.85it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-t-5: 975.0, (949, 999)\n", "chirp-v3p5-engine-t-5-15: 979.8, (952, 1005)\n", "chirp-v3p5-engine-t-6: 994.5, (984, 1006)\n", "chirp-v3p5-engine-t-6_s_30_t_12_tag_2: 1023.4, (1003, 1043)\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2: 1009.8, (991, 1030)\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2: 1004.0, (987, 1026)\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2: 1013.5, (995, 1033)\n" ] }, { "data": { "image/png": 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v50a0AAAL8dBDD+XSSy/NFltska222irbb799fv/732fVVVdNknzta1/LGWecUeEqAYDlmfkGAO/nSnsAgIV45513Sr8w9+7dO127ds3qq69e2r/OOutkypQplSoPAGgHzDcAeD+hPQDAQvTt27fFL8lf+MIX0qtXr9Lzd955J127dq1EaQBAO2G+AcD7Ce0BABZi0KBBeeKJJ0rPv/Wtb6V3796l52PHjs3AgQMrUBkA0F6YbwDwflVNTU1NlS4CAGB59PTTT6dLly7ZeOONK10KANBOmW8ArHiE9gAAizBv3rx07ty50mUAAO2Y+QYAzSyPAwCwCDvssENOPfXU/POf/0xjY2OlywEA2iHzDQCaudIeAGAR/va3v+WWW27J/fffnx49emSPPfbIPvvsk8GDB1e6NACgnTDfAKCZ0B4AYDHNnDkzf/3rX3Pbbbfl4YcfzlprrZW99947J5xwQqVLAwDaCfMNAIT2AABL4MUXX8y3vvWtPPfccxk3blylywEA2iHzDYAVU3WlCwAAWF7Mmzcv99xzT2699dY8+OCD6devX0aOHFnpsgCAdsR8AwBX2gMALMKDDz6YW2+9NXfffXeqq6uz++67Z++9986wYcMqXRoA0E6YbwDQTGgPALAIQ4cOzSc+8Ynsvffe2XnnndOpU6dKlwQAtDPmGwA0E9oDACzCzJkz07179yTJ66+/nv79+6dDhw4VrgoAaE/MNwBo5r/+AACL0PwLdJJ8+tOfzqRJkypYDQDQHplvANBMaA8A8CH4kCIAsKyZbwCs2IT2AAAAAABQEEJ7AIAP4ZhjjkmvXr0qXQYA0I6ZbwCs2NyIFgDgQ2ieOlVVVVW4EgCgvTLfAFixudIeAGAxXHvttdlrr70yePDgDB48OHvttVeuvfbaSpcFALQj5hsAJEl1pQsAACi6iy66KH/4wx9y6KGHZosttkiSPPnkk/nBD36Q1157LV//+tcrWyAAsNwz3wCgmeVxAAAWYfvtt893v/vd7LXXXi2233rrrfne976XRx55pEKVAQDthfkGAM0sjwMAsAj19fXZfPPNW23fbLPN0tDQUIGKAID2xnwDgGZCewCARdh3331z1VVXtdp+zTXXZO+9965ARQBAe2O+AUAzy+MAACzC9773vdx0000ZMGBAhg4dmiR5+umn89prr2W//fZLdfV7twk67bTTKlUmALAcM98AoJkb0QIALMLzzz+fTTfdNEnyyiuvJEl69+6d3r175/nnny8dV1VVVZH6AIDln/kGAM1caQ8AAAAAAAVhTXsAgA9QV1eXTTfdtMUVbgAAS5P5BgALEtoDAHyATp06ZcCAAWlsbKx0KQBAO2W+AcCChPYAAItwzDHH5Cc/+UnefvvtSpcCALRT5hsANLOmPQDAIuy3336ZMGFC6uvrs/rqq6dbt24t9t94440VqgwAaC/MNwBoVl3pAgAAim7EiBGVLgEAaOfMNwBo5kp7AAAAAAAoCGvaAwAAAABAQVgeBwCgDdtuu23uvPPOrLzyyhk2bFiqqqoWeuyYMWPKWBkA0F6YbwDQFqE9AEAbTjvttHTv3j1Jcvrpp1e4GgCgPTLfAKAt1rQHAAAAAICCcKU9AMBiaGxszIQJEzJt2rS8/5qHYcOGVagqAKA9Md8AIBHaAwAs0pNPPpmTTjopr732WqtfoKuqqjJu3LgKVQYAtBfmGwA0szwOAMAi7Lvvvll33XXzta99Lausskqrm8T16NGjQpUBAO2F+QYAzYT2AACLsMUWW+Qvf/lL1llnnUqXAgC0U+YbADTrUOkCAACKbsiQIZkwYUKlywAA2jHzDQCaWdMeAKAN48ePLz0+7LDDcv7552fq1KnZeOONU13dcgo1aNCgcpcHALQD5hsAtMXyOAAAbRg0aFCqqqpa3QiuWfM+N4YDAJaU+QYAbRHaAwC0YdKkSYt97BprrLEMKwEA2ivzDQDaIrQHAFiEX//61+nbt28++9nPtth+3XXXZfr06Tn66KMrVBkA0F6YbwDQzI1oAQAWYdSoUVl//fVbbd9oo41y9dVXV6AiAKC9Md8AoJnQHgBgEaZMmZJVVlml1faVV145U6ZMqUBFAEB7Y74BQDOhPQDAIgwYMCCPP/54q+1jx45N//79K1ARANDemG8A0Ky60gUAABTd5z73ufzgBz9IfX19tt9++yTJ6NGj86Mf/ShHHnlkhasDANoD8w0AmrkRLQDAIjQ1NeXCCy/MFVdckbq6uiRJ586d8+UvfzknnHBChasDANoD8w0AmgntAQAW06xZs/Kf//wnXbp0ybrrrpuamppKlwQAtDPmGwAI7QEAAAAAoCDciBYAAAAAAApCaA8AAAAAAAUhtAcAAAAAgIIQ2gMAAAAAQEEI7QEAAAAAoCCE9gAAAItp4sSJGThwYEaOHFnpUgAAaKeE9gAAAAAAUBBCewAAAAAAKIjqShcAAADQHk2aNCk///nP8+CDD+att97KyiuvnOHDh+eEE07I6quv3uLYN998M7/5zW/ywAMP5PXXX09NTU1WWWWVDBs2LN/+9rfT54IIqAAA1+JJREFUo0eP0rG1tbX585//nJtvvjkvvfRSqqqqsskmm2TkyJHZbbfdyj1MAACWMqE9AADAUvbSSy/lkEMOyfTp07PLLrtko402ygsvvJDrr78+9913X6688sqst956SZI5c+bk4IMPzqRJk/Kxj30sI0aMSF1dXSZOnJibb745I0eOLIX2tbW1GTlyZMaMGZNNNtkkn/3sZ1NXV5f7778/xx13XM4444wceuihlRw6AAAfkdAeAABgKTvrrLMyffr0nHvuuTnooINK2//85z/n3HPPzdlnn50//vGPSZLRo0dn4sSJOeKII3L66ae3OM+sWbPSqVOn0vOf//znGTNmTI477rh87WtfS1VVVZJk5syZOeKII/LDH/4wn/zkJ7PqqquWYZQAACwL1rQHAABYil577bU88sgj2XDDDXPggQe22HfwwQdn/fXXz8MPP5zJkye32NelS5dW51pppZVSU1OTJGlsbMxVV12Vtddeu0VgnyTdu3fP8ccfn7q6uvztb39bBqMCAKBcXGkPAACwFI0bNy5JMmzYsBbBepJ06NAhw4YNy3//+9+MGzcuAwYMyLBhw7LKKqvkN7/5TcaPH59PfOIT2XbbbbPBBhu0eP1LL72UGTNmpH///rn00ktbve/06dOTJP/973+X4egAAFjWhPYAAABL0cyZM5Mk/fr1a3P/Kqus0uK4Hj165JprrsnFF1+c++67L/fff3+SZMCAATnqqKPyhS98IUny9ttvJ0leeOGFvPDCCwt9/zlz5iyVcQAAUBlCewAAgKWoe/fuSZKpU6e2uX/KlCktjkuS1VdfPT/84Q/T2NiY5557Lv/4xz9yxRVX5Nxzz02vXr2y1157lY7ffffdc/HFFy/jUQAAUCnWtAcAAFiKNtlkkyTJY489lqamphb7mpqa8thjj7U4bkEdOnTIJptskqOOOio/+clPkiT33ntvkmSDDTZI9+7d88wzz6Surm5ZDgEAgAoS2gMAACxFq6++erbbbru88MILue6661rsGzVqVP7zn/9k++23z4ABA5LMX+6mravym7d17tw5SVJdXZ2DDz44kyZNyvnnn99mcP/8889n2rRpS3tIAACUUVXT+y/9AAAAoE0TJ07Mbrvtlv79++djH/tYm8esv/76GTFiRA455JC8/fbb2XXXXbPhhhvmhRdeyL333puVV145V155ZdZbb70kyR/+8If86Ec/ylZbbZV11103vXv3zquvvlq6wv7Pf/5zBg8enCSpra3NMccck3/+859Ze+21s80226Rv375544038vzzz2f8+PEZNWpUtthii7L8ewAAsPQJ7QEAABZTc2j/QbbddttcccUVmTRpUi699NI8+OCDeeutt9KnT598/OMfzwknnJA11lijdPx//vOfXH311Xnsscfy2muvZfbs2Vl11VWzzTbb5Mtf/nI23HDDFudvaGjIddddl5tuuinPP/98amtr069fv2ywwQbZbbfdsu+++6Zbt27LZPwAACx7QnsAAAAAACgIa9oDAAAAAEBBCO0BAAAAAKAghPYAAAAAAFAQQnsAAAAAACgIoT0AAAAAABSE0B4AAAAAAApCaA8AAAAAAAUhtAcAAAAAgIIQ2gMAAAAAQEEI7QEAAAAAoCCE9gAAAAAAUBBCewAAAAAAKAihPQAAAAAAFITQHgAAAAAACkJoDwAAAAAABSG0BwAAAACAghDaAwAAAABAQQjtAQAAAACgIIT2AAAAAABQEEJ7AAAAAAAoCKE9AAAAAAAUhNAeAAAAAAAKQmgPAAAAAAAFIbQHAAAAAICCENoDAAAAAEBBCO0BAAAAAKAghPYAAAAAAFAQQnsAAAAAACgIoT0AAAAAABSE0B4AAAAAAApCaA8AAAAAAAUhtAcAAAAAgIIQ2gMAAAAAQEEI7QEAAAAAoCCE9gAAAAAAUBBCewAAAAAAKAihPQAAAAAAFITQHgAAAAAACkJoDwAAAAAABSG0BwAAAACAghDaAwAAAABAQQjtAQAAAACgIIT2AAAAAABQEEJ7AAAAAAAoCKE9AAAAAAAUhNAeAAAAAAAKQmgPAAAAAAAFIbQHAAAAAICCENoDAAAAAEBBCO0BAAAAAKAghPYAAAAAAFAQQnsAAAAAACgIoT0AAAAAABSE0B4AAAAAAApCaA8AAAAAAAUhtAcAAAAAgIIQ2gMAAAAAQEEI7QEAAAAAoCCE9gAAAAAAUBBCewAAAAAAKAihPQAAAAAAFITQHgAAAAAACkJoDwAAAAAABSG0BwAAAACAghDaAwAAAABAQQjtAQAAAACgIIT2AAAAAABQEEJ7AAAAAAAoCKE9AAAAAAAUhNAeAAAAAAAKQmgPAAAAAAAFIbQHAAAAAICCENoDAAAAAEBBCO0BAAAAAKAghPYAAAAAAFAQQnsAAAAAACgIoT0AAAAAABSE0B4AAAAAAApCaA8AAAAAAAUhtAcAAAAAgIIQ2gMAAAAAQEEI7QEAAAAAoCCE9gAAAAAAUBBCewAAAAAAKAihPQAAAAAAFITQHgAAAAAACkJoDwAAAAAABSG0BwAAAACAghDaAwAAAABAQQjtAQAAAACgIIT2AAAAAABQEEJ7AAAAAAAoCKE9AAAAAAAUhNAeAAAAAAAKQmgPAAAAAAAFIbQHAAAAAICCENoDAAAAAEBBCO0BAAAAAKAghPYAAAAAAFAQQnsAAAAAACgIoT0AAAAAABSE0B4AAAAAAApCaA8AAAAAAAUhtAcAAAAAgIIQ2gMAAAAAQEEI7QEAAAAAoCCE9gAAAAAAUBBCewAAAAAAKAihPQAAAAAAFITQHgAAAAAACkJoDwAAAAAABSG0BwAAAACAghDaAwAAAABAQQjtAQAAAACgIIT2AAAAAABQEEJ7AAAAAAAoCKE9AAAAAAAUhNAeAAAAAAAKQmgPAAAAAAAFIbQHAAAAAICCENoDAAAAAEBBCO0BAAAAAKAghPYAAAAAAFAQQnsAAAAAACgIoT0AAAAAABSE0B4AAAAAAApCaA8AAAAAAAUhtAcAAAAAgIIQ2gMAAAAAQEEI7QEAAAAAoCCE9gAAAAAAUBBCewAAAAAAKAihPQAAAAAAFITQHgAAAAAACkJoDwAAAAAABSG0BwAAAACAghDaAwAAAABAQQjtAQAAAACgIIT2AAAAAABQEEJ7AAAAAAAoCKE9AAAAAAAUhNAeAAAAAAAKQmgPAAAAAAAFIbQHAAAAAICCENoDAAAAAEBBCO0BAAAAAKAghPYAAAAAAFAQQnsAAAAAACgIoT0AAAAAABSE0B4AAFhsl1xySQYOHJiBAwfmkksuKdv77rrrrqX3nThxYtneFwAAyq260gUAAMDy6rDDDsuYMWNabPv5z3+eESNGLPY5zj///Pzf//1fi20nnHBCvvrVry6VGmnbrrvumkmTJi3Raxf29VmwH5bm1/Ddd9/Nfffdl/vvvz/jx4/PtGnTMnPmzPTu3Tv9+vXLlltumU984hP52Mc+lurqZfsr3jPPPJNbbrklTz31VF555ZW88847qaqqSrdu3bLaaqtlnXXWyeabb56tttoqW2yxxTKvBwCgPTKDAgCApegvf/nLYof2DQ0NueWWW5ZxRSyv6urq8uc//zm//OUv8/bbb7faP2XKlEyZMiXjxo3LlVdemfXWWy8nn3xydt1116Vey+uvv54zzzwz999/f5v7a2tr8/bbb2f8+PH561//miTp0aNHHnnkkXTs2HGp1wMA0J4J7QEAYCm67777MmPGjPTq1WuRx/7zn//MlClTylAVH2SHHXbI+uuvv9jHDxkyZBlWM9/MmTNz3HHH5ZFHHmmxfeONN856662XXr16ZerUqRk3blwmT56cJHnppZdy7LHH5sgjj8zJJ5+cqqqqpVLLpEmTcuihh+a1114rbevZs2eGDBmS/v37p1OnTnn77bfz0ksv5T//+U8aGhqSzP+EQFNT01KpAQBgRSK0BwCApWDDDTfMiy++mLq6utx222055JBDFvmav/zlL61eT/nts88+OeCAAypdRsmcOXNy2GGH5dlnny1t++QnP5mTTjop6623Xotjm5qa8uijj+b73/9+xo8fnyT5v//7v8yaNSvnnnvuUqnn9NNPLwX23bt3z3e+853svffe6dSpU6tjZ86cmfvuuy+33nprHnjggaXy/gAAKxo3ogUAgKXg05/+dCnEXDCMX5iZM2fm7rvvTpJssskm2XjjjZdpfSw//vd//7dFYH/yySfn0ksvbRXYJ0lVVVW23XbbXH/99fnkJz9Z2j5q1KjcfPPNH7mWp59+Og8//HDpvX71q1/lgAMOaDOwT+aH+nvvvXd+/etf56677rI0DgDAEhDaAwDAUrDyyivn4x//eJLkySefzMsvv/yBx99xxx2ZO3dukmS//fZbxtWxvBg9enSuu+660vPDDjssI0eOXOTrqqur85Of/CSbbbZZads555yTd9555yPV889//rP0eOjQoRk2bNhiv3attdZaakv0AACsSCyPAwAAS8l+++2Xe++9N0ly00035Rvf+MZCj22+Gr+6ujp77713nnrqqQ/1Xk1NTbnzzjvzt7/9LU8//XSmTZuWJOnbt2+GDh2aT37yk9l9990/VGj68MMP59prr83jjz+eqVOnplevXll77bWz55575oADDkjXrl0/VI3NRo8enTvuuCNjx47NlClTMnv27PTu3TsDBw7MLrvsks9+9rPp0qXLEp27vfnd735XerzqqqvmxBNPXOzX1tTU5Hvf+14+85nPpKmpKTNnzsyoUaNy1FFHLXE9b7zxRunx6quvvsTnWZT7778/99xzTx5//PFMmTIlM2fOTNeuXbPWWmtl8ODB2XnnnbPzzjununrhv8Iure+Jww47LGPGjEmSXH755dluu+3y5ptv5oYbbsjdd9+dyZMnZ/r06VlppZXy2GOPtXp98xJZ9913X5555plMnz49TU1NWXnllbPFFltkjz32yIgRIxbre/Ppp5/OTTfdlCeeeCITJ07MrFmzUl1dnZ49e2b11VfPJptsku222y6f+MQn0q1bt0WeDwBYPgjtAQBgKdlll13Sq1evzJgxIzfffHO+/vWvtxnMTZw4sRT2DR8+PH379v1Q7/Pyyy/nm9/8ZoslVJrNnj07r776am699dZsttlmueiii7LWWmt94Pnq6+tz5pln5vrrr2+xfcqUKZkyZUrGjh2bK6+8MpdccsmHqnPy5Mk5+eSTSwFoW+f+xz/+kV//+tf56U9/mm222eZDnb+9mThxYh588MHS84MOOuhDB7GbbbZZhg0bVvo3v+qqqz5SaN+hw3sfzp44ceISn2dhXnjhhZx66ql55plnWu1799138+yzz+bZZ5/NqFGj8ulPfzo//elP2zzP0v6eWNDdd9+d008/PTNmzFjksY888ki++93v5pVXXmm1b9KkSZk0aVJuu+22bLHFFrn44ouz6qqrtnme+vr6nHvuuRk1alSrfQ0NDaXvn6eeeipXX311jjnmmHzzm99c7DEBAMUmtAcAgKWkpqYme+yxR66++upMmjQpjz76aLbddttWx910001pampKkuy7774f6j3+85//5NBDD8306dNL2zbeeONssskmqaqqyrPPPpvnn38+SfLvf/87n//85/OnP/2pzfXQm51yyim59dZbS8979uyZ7bbbLr17987kyZPzyCOP5MUXX8zRRx+dXXfddbHrPOKIIzJlypQk89dD33TTTbPhhhumS5cueeONN/Loo49m1qxZefPNN/OlL30pl112WbbffvsP9e/Rnrz/jxt77733Ep1nn332KZ2rOSheY401luhca6+9dunxM888k9GjR2eHHXZYonO93yOPPJJjjz02s2bNKm1bffXVM3jw4PTu3TuzZ8/OSy+9lOeeey51dXWZN29em+dZFt8TzZ544olceumlqaurS+/evTNs2LD06dMn06ZNy7hx41oce8cdd+Tb3/526urqkiRdunTJ0KFDs8Yaa6RDhw55+eWX8+STT6a+vj5PPvlkDjrooFx33XXp169fq/e94IILWgT2q666aoYMGZKVV145jY2Nefvtt/Piiy/mpZdeWuQYAIDlj9AeAACWov322y9XX311kvnhfFuhffMNQnv27Jnddtttsc9dW1ubE088sRRO9u3bNxdeeGF23HHHFsf94x//yLe+9a289dZbmTp1ak466aSMGjWqzZuH3nTTTS0C+0MPPTTf/va3WyxX8+abb+bb3/52Hn744Vx55ZWLrHP27Nn56le/Wgrsd9ppp5xxxhktAuBk/s14L7zwwlx11VWpra3Nt771rdxxxx3p0aPHYv+btCdjx44tPe7Tp0+rf6/FNWTIkFbnXdLQfpdddsn555+fxsbGNDY25thjj82XvvSl7LfffllnnXWW6JzJ/E9hfP3rXy8F9muuuWbOOuus7LTTTq2OnTFjRu64445MmDCh1b5l8T2xoEsuuSQNDQ35+te/nqOOOqrF8bW1taXHzZ8YqKurS1VVVb70pS/l2GOPTc+ePVuc79VXX80pp5ySsWPHZvLkyTnttNNy2WWXtTjmrbfeyp///OckSceOHfP9738/++23X5uf2nnzzTfz17/+1fJSANDOCO0BAGAp2nLLLbPuuuvm5Zdfzl//+teceeaZLQK1xx9/vBQ+7rHHHuncufNin/uWW27J+PHjkySdOnXKb3/722y66aatjhs+fHh+85vf5OCDD059fX3+/e9/57bbbmt1w9vGxsb87Gc/Kz0/4IADcsYZZ7Q6X//+/fPrX/86Bx54YJ577rlF1vn73/8+//nPf5Ikn/zkJ3PxxRe3WGalWffu3XP22Wdn7ty5ufHGGzNlypRcddVVOfrooxf5HkvTzTff3ObyLAtz5plnLpM6Jk2aVHq80UYbLfF5Ntxww3To0CGNjY2tzvthrbPOOvnCF76QK664IkkyZ86c/OIXv8gvfvGLrLHGGhk6dGg233zzDB06NEOGDElNTc1inffHP/5x3nrrrSTJGmuskVGjRrV5xXmS9OrVK5///Ofb3Le0vyfer76+Pt/4xjdy7LHHttq34Fj/93//t3Rj6VNPPTVf/OIX2zzfWmutld/+9rf53Oc+lxdffDEPPPBAnnrqqQwdOrR0TPPV+Eny6U9/Ovvvv/9C6+vfv38OO+ywDxwDALD8EdoDAMBStu++++aiiy7KzJkzc/fdd2evvfYq7bvppptaHPdhLLhcxuc///k2w8lmQ4YMyec+97lcddVVSeavbf7+gPLBBx/M5MmTk8xfyuPkk09e6Pm6dOmSU045JUceeeQH1lhXV1e6SrimpibnnHNOm4H9gr75zW+Wlgy65ZZbyh7ajx49OqNHj17s45dVaL/gmum9evVa4vN07NgxK620Ut59991W510Sp556aurr60u91Kx56Z3bb789yfyv98c+9rF89rOfzYgRIxZ6vjfeeCN33HFH6fnZZ5+90MB+UZb298T79e/ff5H3BBg/fnwefvjhJMmmm26aI4444gOP79atW4477rjSTYZvueWWFqH9zJkzS49XXnnlDzwXANA+ffDsGQAA+ND23Xff0lIWC4b0tbW1pbBy7bXXztZbb73Y55w5c2aLq8E/+9nPLvI1n/vc50qP//Wvf2X27Nkt9j/yyCOlxzvvvHP69OnzgefbcccdF3rjzGbPPPNMpk2bliTZYYcdFusmu6uuumrWX3/9JPOXGWkOm1c0C67t3rVr1490rgVvYLtgCLwkqqurc/bZZ+fqq6/OiBEjFrqkTG1tbe67774cf/zx+fznP5/XXnutzeMeeuih0pXk6667bptL4iyOZfE98X677757qqs/+Fq3+++/v/R4zz33bHMZm/db8N4NCy6LlCQDBgwoPf7b3/5W+n4CAFYcrrQHAIClbI011siwYcMyZsyYPPTQQ5kyZUpWWWWV3HPPPXnnnXeSfPir7J977rk0NDQkmR/IDhw4cJGv2WSTTdKtW7fMnj07DQ0NGT9+fLbaaqvS/gVvpLnFFlss8nxVVVUZOnRo7rrrroUe8+STT5Yev/766zn33HMXed4kpX+XpqamvP7662Vd1/68887LAQccULb3W5iVVlqp9HjOnDkf6VwLhtHdu3f/SOdqtuWWW+bnP/953n333Tz66KMZO3Zs/v3vf+fZZ59tdTX/E088kQMPPDDXX399qz/0LNgjbd3zYXEti++J99t8880Xec4nnnii9PiRRx5Z6B8rFtR8I+okpU+7NBs6dGgGDBiQyZMn57XXXsuee+6ZAw44ILvuuuuHWoIIAFh+Ce0BAGAZ2HfffTNmzJg0NDTklltuyZFHHpm//OUvSeaH3x82tG9e/zuZfyXu4lzN26FDh6y22mr573//2+ocSUo372w+5+JY1HFvvvlm6fFzzz23WGvgv99HXc5lebXgkjgf5d+goaGhxVX7H2Wpnbb06NEju+66a3bdddck8wPoZ599NjfffHNGjRpV+oPDlClTctZZZ+VXv/pVi9cveOX4WmuttcR1LIvvifdb1KdPkpY9/8ADDyzy+Pdr/oNVs06dOuWCCy7IV77ylcyePTtvvfVWfve73+V3v/tdOnfunM033zzDhg3LTjvtlK222mqxxg0ALF8sjwMAAMvApz71qdISJzfddFOmTZuWBx98MEmy9dZbf+iwckmXTlnw2AXPkbS8Gntxz7mo45bG0jbNV0+vaNZYY43S4xdeeGGJz/Piiy+WbkL7/vMuC1VVVdlss81y2mmn5YYbbsgqq6xS2nfffffl1VdfbXH8gn244DI+H9ay+J54vwVvIr0wH3X5obb6fdttt83NN9+c/fbbr0UN8+bNy9ixY/OrX/0qhxxySD71qU/l7rvv/kjvDwAUjyvtAQBgGejevXt222233HrrrXnuuedy4YUXltbxXtTNL9uypEunLHjsgudIWgami3vORR23YCB62GGH5bvf/e5inZdkq622ynXXXZdk/hXgEyZMyDrrrPOhz/P000+3eP5h7p3wUa2//vo59dRTc9JJJ5W2jR07tsUfqRbsw0WtKf9BlsX3xJJYsOcvvfTSfPKTn/zI50zmfwrh/PPPz1lnnZWxY8dm7Nixefzxx/PUU09l7ty5SZKXX345xx9/fE499dR86UtfWirvCwBUnivtAQBgGVkwnL/hhhuSJJ07d86nPvWpD32uBZfpeP3111usib0wjY2Nef3119s8R5KsvPLKpcfvX1d7YRY8X1v69etXejx16tTFOifzvX9991tuuWWJznPzzTeXHq+xxhrL/Er79/v4xz/e4vmCy8ckaXFz4okTJy7x+yyL74klsWDPT5ky5SOf7/26deuWj3/84/nGN76Ryy+/PI888kguuuiibLzxxqVjfvzjH+eNN95Y6u8NAFSG0B4AAJaRHXfcscVSIUmy2267LdFNVgcOHJiOHTsmmb+kx+KsFT9+/PjSlcwdO3bMoEGDWuzfZJNNSo8XvDnowjQ1NeWpp576wGOGDBlSevzEE08sVpDKfGuttVaGDx9een7NNdd86CvR//3vf+fRRx8tPT/44IOXWn2Lq3Pnzi2ev//GqQve9PiRRx5Z4vdZFt8TS2LBnn/88cc/8vkWpUuXLvnUpz6VK664ovQHg7q6utLyWwDA8k9oDwAAy0jHjh2z9957t9i2JEvjJPOX29l8881Lz2+88cZFvqZ5qZVkfrD4/vXDt9tuu9LjBx54IG+//fYHnu/hhx9e5JX2W2+9dXr27Jlk/tXP99577yLr5D0jR44sPX7jjTfyk5/8ZLFfW1dXlzPOOKP0h5Lu3bvnoIMOWuo1Lsr48eNbPF999dVbPN9xxx1TXT1/pdaXX355icPmZfE9sSR22WWX0uO//e1vZfuESe/evbPVVluVni94g18AYPkmtAcAgGXo2GOPzXXXXVf634JXUn9YCwawf/7zn1uFowt65plnMmrUqNLzz3/+862OGT58eAYMGJBk/jrfP/rRjxZ6vnnz5uWHP/zhImusqanJEUccUXp+zjnnfKhlO1b0JXV23HHHHHDAAaXnV1xxRf7v//5vka+rr6/PiSeemH//+9+lbWeddVbpDyhL6oYbbsidd9652J+YaGhoyCWXXFJ63qlTp+ywww4tjll11VWzxx57tKhzSb/uS/t7YkkMGTKktLTR3Llzc/LJJ6e2tnaxXltbW5sZM2a02PbWW28t9nsvuKzVgstdAQDLN6E9AAAsQz179szgwYNL/2tezmNJ7L333qXlPOrq6vLlL385Dz/8cKvjHnrooRx11FGlG99uttlm2XPPPVsd17Fjx3z9618vPb/uuuvy/e9/P/PmzWtx3JQpU3LMMcdk/Pjx6dSp0yLr/NKXvpSNNtooyfyrxT/zmc/kjjvuSGNjY5vHT58+PaNGjcr++++f3/3ud4s8f3t3xhlnZODAgaXn559/fr761a/mpZdeanVsU1NTHn300Xz2s5/NXXfdVdp+0EEHZZ999vnItfz3v//N17/+9Xz605/OZZddlkmTJi302BdffDFHH310/vGPf5S2ff7zn29zOaiTTjopvXv3TpJMmjQpBx100EKvuH/nnXcyatSoXHDBBa32Le3viSV1xhlnlK7a/+c//5lDDz30A5eSeumll/Lzn/88u+66a6sldf70pz9l3333zZVXXrnQNfJnzZqVn/70p/nXv/6VZP738kf5gyAAUCzVlS4AAABYPDU1NfnJT36SQw89NNOnT8+UKVNyxBFHZNCgQaX16ceNG9fiauO+ffvmxz/+8ULD9v333z/3339/7rjjjiTJ5Zdfnr/85S/Zbrvt0rt370yePDmPPPJIamtrs+aaa2a33XbLH//4xw+sc6WVVsovf/nLfPGLX8zEiRMzZcqUfOMb30ifPn2yxRZbpF+/fmlqasqMGTPy4osvZsKECaVAf/vtt18a/1Qfys0335xnnnlmsY9fbbXVcvTRR3/gMVdffXXuvvvuxT7n1772tey2225J5t949E9/+lOOP/74jBkzJkly11135a677srAgQOz/vrrp0ePHpk+fXqeffbZvPbaay3O9aUvfSmnnHLKYr/34vjvf/+bCy+8MBdeeGFWW221DBw4MCuvvHJqamoyY8aMvPDCC/nPf/7T4jVbbrllvvnNb7Z5vgEDBuRnP/tZjjvuuMyePTsTJ07Ml7/85ayxxhoZPHhwevXqldmzZ+fll1/O+PHjU1dXV/r3WdCy+J5YEhtvvHF+8pOf5Jvf/GbmzJmTp556KgceeGDWXnvtbLrppunVq1dqa2szbdq0PPfcc4v89Mn48eNzzjnn5Nxzz83aa6+djTbaKH369El9fX2mTJmSxx9/vMX9Do466qjSp2YAgOWf0B4AAJYjG2ywQa688sqceOKJefbZZ5PMD/jaWhZks802y89+9rOsvfbaH3jOH/3oR+nSpUtpTfAZM2a0uGo7SdZff/1ceumluf322xerzrXWWivXX399zjrrrPz1r39NU1NT3nrrrdx3330LfU3Pnj2z8cYbL9b5l6bRo0dn9OjRi338oEGDFhnaT5069UMt+fL+JVJ69uyZ3/3ud/nTn/6UX/3qV6X9zz333EJvuLreeuvlW9/6VkaMGLHY77soW2yxRTbccMO8+OKLpW2vv/76B97boLq6Oocccki++c1vfuCa8TvssEOuuuqqnHLKKaX+nTRp0kKv5l/YuZbF98SS2GWXXXL11Vfn9NNPLy1T9Morr+SVV15Z6GvWWGONrLbaai22rbTSSqXHTU1NmTBhQiZMmNDm6zt16pRjjjkmJ5xwwlIYAQBQFEJ7AABYzqy33nq5/vrrc+edd+auu+7K008/nenTpyeZv6710KFDs/vuu2f33XdPVVXVIs/XqVOn/PCHP8y+++6ba665Jo8//nimTZuWXr16Ze21184ee+yRz3zmMy3CxMXRu3fvXHTRRXn++edz22235ZFHHsnEiRPz9ttvp0OHDunZs2fpSuQdd9wxH/vYx9K5c+cl+jdpj2pqanLkkUfmc5/7XO65557cf//9GT9+fKZPn55Zs2alV69e6devX7bYYovssssuGT58eOkGr0vLiBEjMmLEiLzyyit55JFH8sQTT+S///1vJk6cmHfeeScNDQ3p1q1bVl555Wy88cbZeuut8+lPfzr9+/dfrPMPGjQoN910U+6+++7cfffdefLJJzN16tTMmTMn3bt3z5prrpkhQ4Zkl112ycc//vGFnmdpf08sqUGDBuWGG27IP/7xj9x99915/PHH8+abb+bdd99NTU1N+vTpk/XWWy9Dhw7N8OHDs+WWW7aq58gjj8z//M//5KGHHsoTTzyR5557LpMmTcqsWbNSVVWVnj17Zv3118/222+f/fbbL2usscYyGw8AUBlVTYt7RyEAAAAAAGCZciNaAAAAAAAoCKE9AAAAAAAUhNAeAAAAAAAKQmgPAAAAAAAFIbQHAAAAAICCENoDAAAAAEBBCO0BAAAAAKAghPYAAAAAAFAQQnsAAAAAACgIoT0AAAAAABSE0B4AAAAAAApCaA8AAAAAAAUhtAcAAAAAgIIQ2gMAAAAAQEEI7QEAAAAAoCCE9gAAAAAAUBBCewAAAAAAKAihPQAAAAAAFITQHgAAAAAACkJoDwAAAAAABVFd6QIAim7atHfT1FTpKpZc1641mTOnttJlQBL9SHHoRYpEP1Ik+pGi0IsUyfLej1VVSd++PSpdBh+C0B5gEZqaslyH9snyXz/ti36kKPQiRaIfKRL9SFHoRYpEP1JOlscBAAAAAICCENoDtHOuBqBI9CNFoRcpEv1IkehHikIvUiT6kXKramrSdgAfZOrU5XtNewAAAGDFVVWV9OtnTfvliSvtAdq5Dh2qKl0ClOhHikIvUiT6kSLRjxSFXqRI9CPlJrQHaOe6dOlU6RKgRD9SFHqRItGPFIl+pCj0IkWiHyk3oT0AAAAAABSE0B4AAAAAAApCaA/QzjU2uosuxaEfKQq9SJHoR4pEP1IUepEi0Y+UW1VTU5OuA/gAU6e+G/+lBAAAAJZHVVVJv349Kl0GH4Ir7QHauepq/6mnOPQjRaEXKRL9SJHoR4pCL1Ik+pFy03EA7VxNTXWlS4AS/UhR6EWKRD9SJPqRotCLFIl+pNyE9gAAAAAAUBBCewAAAAAAKAihPUA719DQWOkSoEQ/UhR6kSLRjxSJfqQo9CJFoh8pt6qmpqamShcBUGRTp74b/6UEAAAAlkdVVUm/fj0qXQYfgivtAdq5Tp06VroEKNGPFIVepEj0I0WiHykKvUiR6EfKTWgP0M6ZXFAk+pGi0IsUiX6kSPQjRaEXKRL9SLkJ7QEAAAAAoCCE9gAAAAAAUBBCe4B2rr6+odIlQIl+pCj0IkWiHykS/UhR6EWKRD9SbkJ7gHauttbkguLQjxSFXqRI9CNFoh8pCr1IkehHyk1oD9DO1dS4YQ7FoR8pCr1IkehHikQ/UhR6kSLRj5RbdaULAGDpmjpzXqbOqi0979KlU+bOrSs977dSTfp171yJ0iDV1R1dpUIh6EWKRD9SJPqRotCLFIl+pNyE9gDtzA1PT85lo19Z6P6jdlg7R++4bvkKAgAAAGCxCe0B2pkDhgzIThv0TZK8PG12zrjjuXxvj4FZt2+3JPOvtAcAAACgmIT2AO1Mv+6dWy1/s27fbhm0ao8KVQTvqavzkVKKQS9SJPqRItGPFIVepEj0I+XmRrQAQNmY7FIUepEi0Y8UiX6kKPQiRaIfKTehPQBQNp07+5AfxaAXKRL9SJHoR4pCL1Ik+pFyE9oDAGXTsaOpB8WgFykS/UiR6EeKQi9SJPqRctNxAAAAAABQEEJ7AAAAAAAoCKE9AFA2tbX1lS4BkuhFikU/UiT6kaLQixSJfqTchPYAQNnU1zdWugRIohcpFv1IkehHikIvUiT6kXIT2gMAZdOlS6dKlwBJ9CLFoh8pEv1IUehFikQ/Um5CewCgbDp0qKp0CZBEL1Is+pEi0Y8UhV6kSPQj5Sa0B8ru0UcfzTHHHJPhw4dn4MCBufvuu1vsb2pqykUXXZThw4dnyJAh+eIXv5iXX365zXPV1tZm3333zcCBAzNu3LgW+8aPH59DDjkkgwcPzs4775zLLrtsWQ0JAAAAAJYKoT1QdrNnz87AgQNz1llntbn/sssuyxVXXJGzzz4711xzTbp27ZqRI0dm3rx5rY694IIL0r9//1bbZ86cmZEjR2b11VfPDTfckJNPPjmXXnppRo0atdTHAwAAAABLS3WlCwBWPDvvvHN23nnnNvc1NTXl8ssvz7HHHpsRI0YkmR/M77jjjrn77ruz5557lo69//77889//jOXXHJJHnjggRbnufnmm1NXV5cf/OAHqampyUYbbZRx48bl97//fQ466KBlNzjgA82dW1fpEiCJXqRY9CNFoh8pCr1IkehHys2V9kChTJw4MVOmTMmOO+5Y2tajR48MHTo0TzzxRGnb1KlTc8YZZ+SCCy5Ily5dWp3nySefzDbbbJOamprStuHDh+ell17KjBkzlu0ggIVqbGyqdAmQRC9SLPqRItGPFIVepEj0I+XmSnugUKZMmZIk6du3b4vtffv2zdSpU5PMvxr/1FNPzec///kMHjw4EydObHWeqVOnZs0112yxrV+/fqV9vXr1WuyaunZ9L/ivr29IbW1Damo6prq6Y2l7XV1D6uoa0rlzdTp2fO/vobW19amvb0yXLp1a3Lhm7ty6NDY2pWvXmlQtcD+bOXPq0tTUlG7d3nvPJJk9uzZVVVXp2vW9O9Y3NSVz5tSmQ4eqFneyb2xsyty5damu7lDa3qVLp3TuXJ158+rTqVPHdOr0Xu3L25hqat770dXQ0GhMy9mYunWrSV1dQ7saU3v8Oq0IY+rYsWMaGhra1Zja49dpRRlThw5VmTlzXrsaU3v8Oq0oY+rUqWNmzZrXrsaUtL+v04owps6dq0vzxvYypvb4dVpRxtSzZ9fU1ze0izGxfBDaA8udK664IrNmzcpXvvKVsrzfnDm1aXrfH9Vra+f/sHy/efPq2zzHwj5KN2dObZvbZ89uvb2pqanN7Y2NbW+vr28sve/cuXWl2pp/oL/f8jKm+vrW241p+RrT+1/THsb0fsZU/DF169ax3Y0paX9fp2TFGFPzL/ztaUzNjGn5G1O3bjWpr29M0n7GtCBjWn7G1KFDVav3Xt7H1BZjWj7GVF/f+veYZPkZU1VVstJKndvcTzFZHgcolFVWWSVJMm3atBbbp02bVrpS/uGHH86TTz6ZwYMHZ9NNN83//M//JEk+85nP5JRTTkky/6r65ivzmzU/bz4PAAAAABSNK+2BQllzzTWzyiqrZPTo0dlkk02SJDNnzsxTTz2Vgw8+OEny3e9+N9/4xjdKr3nzzTczcuTI/PSnP83QoUOTJFtssUV+9rOfpa6uLp06zf/42UMPPZT11lvvQy2NAwAAAADlJLQHym7WrFl55ZVXSs8nTpyYcePGpVevXll99dVz+OGH55e//GXWWWedrLnmmrnooovSv3//jBgxIkmy+uqrtzhft27dkiRrr712VltttSTJ3nvvnZ///Of5zne+k6OOOiovvPBCLr/88px22mllGiXQljlz2v64KZSbXqRI9CNFoh8pCr1IkehHyk1oD5TdM888k8MPP7z0/LzzzkuS7L///vnhD3+Yo446KnPmzMmZZ56Zd955J1tvvXV++9vfpnPnxV9/rUePHvnd736Xc889NwcccED69OmT4447LgcddNBSHw+w+Jref4MIqBC9SJHoR4pEP1IUepEi0Y+UW1WTrgP4QFOnvtvqRrTLi/FvvJvD/vRErjh0ywxatUely4F061bT5s2XoNz0IkWiHykS/UhR6EWKZHnvx6qqpF8/mcDyxI1oAQAAAACgIIT2AAAAAABQEEJ7AAAAAAAoCKE9AFA2y/M6kLQvepEi0Y8UiX6kKPQiRaIfKTehPQBQNlVVVZUuAZLoRYpFP1Ik+pGi0IsUiX6k3IT2AEDZdO3aqdIlQBK9SLHoR4pEP1IUepEi0Y+Um9AeAAAAAAAKQmgPAAAAAAAFIbQHAMqmqanSFcB8epEi0Y8UiX6kKPQiRaIfKTehPQBQNnPm1Fa6BEiiFykW/UiR6EeKQi9SJPqRchPaAwBl06FDVaVLgCR6kWLRjxSJfqQo9CJFoh8pN6E9AFA2Xbp0qnQJkEQvUiz6kSLRjxSFXqRI9CPlJrQHAAAAAICCENoDAAAAAEBBCO0BgLJpbGyqdAmQRC9SLPqRItGPFIVepEj0I+UmtAcAymbu3LpKlwBJ9CLFoh8pEv1IUehFikQ/Um5CewCgbKqrTT0oBr1IkehHikQ/UhR6kSLRj5SbjgMAyqamprrSJUASvUix6EeKRD9SFHqRItGPlJvQHgAAAAAACkJoDwAAAAAABSG0BwDKpqGhsdIlQBK9SLHoR4pEP1IUepEi0Y+Um9AeACibefPqK10CJNGLFIt+pEj0I0WhFykS/Ui5Ce0BgLLp1KljpUuAJHqRYtGPFIl+pCj0IkWiHyk3oT0AUDYmuxSFXqRI9CNFoh8pCr1IkehHyk1oDwAAAAAABSG0BwAAAACAghDaAwBlU1/fUOkSIIlepFj0I0WiHykKvUiR6EfKrbrSBQAA7dfUmfMydVbtQvf3W6km/bp3LmNFMF9trV+8KA79SJHoR4pCL1Ik+pFyE9oDAMvMDU9PzmWjX1no/qN2WDtH77hu+QqC/6+mpqNfvigM/UiR6EeKQi9SJPqRchPaAwDLzAFDBmSnDfomSV6eNjtn3PFcvrfHwKzbt1uS+VfaQyVUV/vFi+LQjxSJfqQo9CJFoh8pN6E9ALDM9OveudXyN+v27ZZBq/aoUEUAAABQbG5ECwAAAAAABSG0BwBghVNX5+PNFId+pEj0I0WhFykS/Ui5Ce0BAFjh+MWLItGPFIl+pCj0IkWiHyk3oT0AACuczp3d2oni0I8UiX6kKPQiRaIfKTehPQAAK5yOHU2DKQ79SJHoR4pCL1Ik+pFy03EAAAAAAFAQQnsAAAAAACgIoT0AACuc2tr6SpcAJfqRItGPFIVepEj0I+XmLgpAmyZMmJDHH388r7/+et5666107do1ffr0ycCBA7PlllumS5culS4RAJZYfX1jpUuAEv1IkehHikIvUiT6kXIT2gMlkydPzrXXXpsbb7wxr7/+epKkqampxTFVVVXp2LFjhg8fnoMOOiif+MQnUlVVVYlyAWCJdenSKXPn1lW6DEiiHykW/UhR6EWKRD9SbkJ7INOnT8/FF1+c6667LvX19VlnnXWyzz77ZPPNN0/fvn3Tu3fvzJ07NzNmzMhLL72UJ598Mg8//HDuv//+rLPOOvn2t7+d3XbbrdLDAIDF1qGDPzhTHPqRItGPFIVepEj0I+UmtAcyYsSIdOjQIYcddlj22WefbLLJJot8zezZs/PXv/411157bU444YSccsop+eIXv7jsiwUAAACAdkxoD+Twww/PkUcemZ49ey72a7p165b9998/+++/f0aPHp2ZM2cuwwoBAAAAYMUgtAfyjW984yO9focddlg6hQBAmViTlCLRjxSJfqQo9CJFoh8ptw6VLgAAAMqtsbFp0QdBmehHikQ/UhR6kSLRj5Sb0B4AgBVO1641lS4BSvQjRaIfKQq9SJHoR8rN8jhAkuSoo4760K+pqqrKb37zm2VQDQAsXVNnzsvUWbWl5126dGrxMed+K9WkX/fOlSgNUlVV6QrgPfqRotCLFIl+pNyE9kCS5MEHH/zQr6nyUwuA5cQNT0/OZaNfWej+o3ZYO0fvuG75CgIAAFgIoT2QJLnnnnsqXQIALDMHDBmQnTbomyR5edrsnHHHc/neHgOzbt9uSeZfaQ8AAFAEQnsgSbLGGmtUugQAWGb6de/cavmbdft2y6BVe1SoInjPnDl1iz4IykQ/UhR6kSLRj5SbG9ECAABUUFNTU6VLgBL9SFHoRYpEP1JuQnsgSTJ79uz8z//8Tz7/+c+nrm7hf0Gura3NwQcfnE996lOZO3duGSsEAGifunWzPBPFoR8pCr1IkehHyk1oDyRJbrjhhrz66qs56aST0qlTp4UeV1NTk5NOOikvv/xyrr/++jJWCAAAAADtn9AeSDL/RrQbbLBBhg0btshjt9lmm2y88ca56667ylAZAAAAAKw4hPZAkmT8+PHZZpttFvv4rbbaKs8///wyrAgAAAAAVjxCeyBJ8u6776Z3796LfXyvXr3y7rvvLruCAABWELNn11a6BCjRjxSFXqRI9CPlJrQHkiQrrbRS3n777cU+fsaMGVlppZWWXUEAACuIqqqqSpcAJfqRotCLFIl+pNyE9kCSZL311stjjz222Mc/9thjWW+99ZZhRQAAK4auXTtVugQo0Y8UhV6kSPQj5Sa0B5IkO+20U/7zn//ktttuW+Sxt99+e1588cV84hOfWPaFAQAAAMAKRGgPJEkOPfTQ9OzZM9/97ndzww03LPS4G2+8Md/5znfSu3fvHHLIIWWsEAAAAADav+pKFwAUQ8+ePfOzn/0sxx57bL7zne/k0ksvzbBhw7LaaqslSd54442MGTMmkydPTufOnfOzn/0sPXv2rHDVAADLv6amSlcA79GPFIVepEj0I+UmtAdKdthhh1x99dX53//93zz22GP5y1/+0uqYYcOG5Tvf+U4GDRpUgQoBANqfOXNqK10ClOhHikIvUiT6kXIT2gMtDBo0KH/605/yyiuv5PHHH8+UKVOSJKusskq22mqrrL322hWuEACgfenQoSqNjS7hoxj0I0WhFykS/Ui5Ce2BNq299toCegCAMujSpVNmz3YFH8WgHykKvUiR6EfKzY1oAQAAAACgIIT2AAAAAABQEEJ7AACACrJGLkWiHykKvUiR6EfKTWgPAABQQXPn1lW6BCjRjxSFXqRI9CPlJrQHAACooOpqv5ZRHPqRotCLFIl+pNyqK10AAADAimTqzHmZOqu29LxLl04truDrt1JN+nXvXInSIDU11amvr130gbCM6UWKRD9SbkJ7YJHq6+sza9asrLTSSqmu9p8NAICP4oanJ+ey0a8sdP9RO6ydo3dct3wFAQBQKNI3oE0NDQ254oorcsMNN+TFF19MU1NTqqqqstFGG2X//ffPoYceKsAHAFgCBwwZkJ026JskeXna7Jxxx3P53h4Ds27fbknmX2kPAMCKS+IGtDJr1qyMHDkyTz31VDp06JABAwakX79+mTp1al588cWcf/75+etf/5rf/e536datW6XLBQBYrvTr3rnV8jfr9u2WQav2qFBF8J6GhsZKlwBJ9CLFoh8pN6E90MrFF1+cJ598MnvttVdOPPHErL766qV9r732Wn784x/ntttuy8UXX5xTTz21gpUCAABL07x59ZUuAZLoRYpFP1Jubn0MtHLHHXdk8803z4UXXtgisE+S1VdfPT/+8Y+z2Wab5fbbb69QhQAAwLLQqVPHSpcASfQixaIfKTehPdDK22+/nR133PEDj9lxxx0zY8aMMlUEAACUg2CKotCLFIl+pNyE9kAr66yzTqZNm/aBx0yfPj1rr712mSoCAAAAgBWD0B5o5fDDD8/tt9+eF154oc39zz33XG6//fYcccQRZa4MAAAAANo3N6IFWll33XWz/fbb5zOf+Uz222+/bL311unXr1+mTp2asWPH5qabbsrw4cOzzjrr5NFHH23x2mHDhlWoahY0u7YhV46dmGuemJQk+dr1/8qBW66RQ7ZeM91qfKwPAIC21dc3VLoESKIXKRb9SLkJ7YFWDjvssFRVVaWpqSnXXHNNrr322tK+pqamJMl9992X++67r9Vrx40bV7Y6advs2oZ8ZdRTeX7KzDTO/3LlrTn1uWz0hNz/4rT8+qChgnsAANpUWyuYohj0IkWiHyk3oT3QyvHHH5+qqqpKl8ESunLsxBaBfbPGpuT5KTNz5diJ+fIO61SmOAAACq2mpqNwikLQixSJfqTchPZAK1/96lcrXQIfwY1PT24V2DdrbJq/X2gPAEBbqqsFUxSDXqRI9CPl5ka0AO3M1Fm1H2k/AAAAAJUjtAdoZ/qtVPOR9gMAAABQOZbHAdo0efLk/PKXv8xDDz2UN998M3V1da2OqaqqyrPPPluB6vgg+w8ZkMtGT2hziZwOVfP3AwBAW+rqLP9AMehFikQ/Um5Ce6CVV199NZ/73OfyzjvvZMMNN0xtbW1WX331dO7cOa+++mrq6+szaNCg9OjRo9Kl0oZDtl4z9784rdXNaDtUJRuv0j2HbL1m5YoDAKDQBFMUhV6kSPQj5WZ5HKCVSy+9NDNnzswf/vCH3HzzzUmSAw44IHfccUfuvffe7LrrrpkzZ04uvvjiCldKW7rVdMyvDxqao3ZYJ326zv/bbJ+u1Tlqh3Xy64OGpltNxwpXCABAUXXu7No+ikEvUiT6kXIT2gOtPPTQQ9lpp52y7bbbttrXv3///OxnP0uS/PSnPy1zZSyubjUd8+Ud1snFnxmcJLn4M4Pz5R3WEdgDAPCBOnYUE1AMepEi0Y+Um44DWnnrrbey/vrrl55XV1dnzpw5pec1NTXZcccdc99991WiPAAAAABot4T2QCt9+vRpEdL37t07kyZNanFMx44d8+6775a7NAAAAABo14T2QCvrrrtuXnnlldLzIUOG5B//+EdeffXVJMn06dPz17/+NWuttValSgQAAJaB2tr6SpcASfQixaIfKTehPdDKxz/+8TzyyCN55513kiRHHHFEZs2alX322Sef+cxnsvvuu2fq1Kk57LDDKlwpAACwNNXXN1a6BEiiFykW/Ui5Ce2BVg455JBcccUV6dBh/n8itttuu/zkJz/J6quvnhdeeCF9+/bNd7/73Rx44IEVrhQAAFiaunTpVOkSIIlepFj0I+VWXekCgOLp3r17hg4d2mLbHnvskT322KNCFQEAAOXQoUNVpUuAJHqRYtGPlJsr7YFWDj/88PzsZz+rdBkAAAAAsMIR2gOtPP3002lstF4bAAAAAJSb0B5oZf3118+kSZOW2fkfffTRHHPMMRk+fHgGDhyYu+++u8X+pqamXHTRRRk+fHiGDBmSL37xi3n55ZdL+ydOnJjTTz89u+66a4YMGZIRI0bk4osvTm1tbYvzjB8/PoccckgGDx6cnXfeOZdddtkyGxMAALQHc+fWVboESKIXKRb9SLkJ7YFWDj300Nx777158cUXl8n5Z8+enYEDB+ass85qc/9ll12WK664ImeffXauueaadO3aNSNHjsy8efOSJP/973/T1NSUc889N7fddltOO+20XH311fnpT39aOsfMmTMzcuTIrL766rnhhhty8skn59JLL82oUaOWyZgAAKA9aGxsqnQJkEQvUiz6kXJzI1qglbXWWivbbrttDjzwwBx00EEZPHhw+vXrl6qq1jdeGTZs2Ic+/84775ydd965zX1NTU25/PLLc+yxx2bEiBFJkgsuuCA77rhj7r777uy5557ZaaedstNOO7Wo96WXXspVV12VU045JUly8803p66uLj/4wQ9SU1OTjTbaKOPGjcvvf//7HHTQQR+6ZgAAWBF07VqTOXNqF30gLGN6kSLRj5Sb0B5o5bDDDktVVVWampry+9//vs2wvtm4ceOW6ntPnDgxU6ZMyY477lja1qNHjwwdOjRPPPFE9txzzzZf9+6776ZXr16l508++WS22Wab1NTUlLYNHz48l112WWbMmNHiWAAAYL4PmPpDWelFikQ/Um5Ce6CV448//gOD+mVpypQpSZK+ffu22N63b99MnTq1zddMmDAhf/rTn0pX2SfJ1KlTs+aaa7Y4rl+/fqV9Hya079r1veC/vr4htbUNqanpmOrqjqXtdXUNqatrSOfO1enY8b2Vx2pr61Nf35guXTqlQ4f3/k3nzq1LY2NTunatafHDf86cujQ1NaVbt/feM0lmz65NVVVVunbtVNrW1JTMmVObDh2q0qXLe9sbG5syd25dqqs7lLZ36dIpnTtXZ968+nTq1DGdOr1X+/I2ppqa9350NTQ0GtNyNKbmurt06ZRu3WraxZja49dpRRhT8+NOnTq2eN/leUzt8eu0ooxpwfO1lzEl7e/rtCKNqVOnjqmu7tCuxpS0v6/TijCmBX9Ot5cxtcev04oypvfPG5fnMbF8ENoDrXz1q1+tdAmL7Y033siXv/zlfOpTn8qBBx64TN5jzpzaNL1v+bra2vk/LN9v3rz6Ns+xsJvWLOzjdbNnt97e1NTU5vbGxra319c3lt537ty6Um3NP9Dfb3kZU3196+3GtHyMqb6+oVTTgq9bnsfUHr9OK8KYmh/X1TW0eZ7lcUzN2tPXqVl7H9OC9baXMS3ImJa/Mc3/w3pjkvYzpgUZ0/Izpg4dqlq99/I+prYY0/IxpoXNG5eXMVVVJSut1LnN/RSTG9ECrTz66KN57bXXPvCYyZMn59FHH13q773KKqskSaZNm9Zi+7Rp00pXyjd74403cvjhh2fLLbfM9773vRb7+vXr1+rK/Obn7z8PAAAw35w5bQdWUG56kSLRj5Sb0B5o5fDDD88NN9zwgcfcdNNNOfzww5f6e6+55ppZZZVVMnr06NK2mTNn5qmnnsqWW25Z2tYc2G+22WY577zz0qFDy/+cbbHFFnnsscdSV/feD9aHHnoo6623nvXsAQBgIZre/xFTqBC9SJHoR8pNaA+0sjg/jBobG5d43ftZs2Zl3LhxpZvYTpw4MePGjctrr72WqqqqHH744fnlL3+Ze+65J88991xOPvnk9O/fPyNGjEgyP7A/7LDDMmDAgJxyyimZPn16pkyZUloPP0n23nvvdOrUKd/5znfywgsv5Pbbb8/ll1+eL33pS0tUMwAArAjevz4zVIpepEj0I+VmTXtgiUyYMCE9evRYotc+88wzLa7SP++885Ik+++/f374wx/mqKOOypw5c3LmmWfmnXfeydZbb53f/va36dx5/vpr//znPzNhwoRMmDAhO+20U4tzP/fcc0mSHj165He/+13OPffcHHDAAenTp0+OO+64HHTQQUtUMwAAAACUg9AeSJKcdtppLZ7fc889mTRpUqvjGhsbM3ny5Dz22GOtAvPFtd1225XC9bZUVVXl61//er7+9a+3uf+AAw7IAQccsMj3GTRoUK688solqhEAAAAAKkFoDyRJbrzxxtLjqqqqFsvXvF9VVVUGDx7cKugHAAAAAD4aoT2QZP6V9cn89exHjBiRI444os0bzXbs2DE9e/ZMt27dyl0iAACwjM2eXVvpEiCJXqRY9CPlJrQHkiRrrLFG6fF5552XTTbZpMU2AACg/auqqkpTU1OlywC9SKHoR8pNaA+0sv/++7e5vampKRMmTEjnzp0zYMCAMlcFAAAsa127dnJFKYWgFykS/Ui5dah0AUDx3HXXXTn55JMzY8aM0raJEydmn332yR577JFdd9013/zmN9PQ0FDBKgEAAACg/RHaA61cddVVGTduXHr16lXadt555+WFF17Idtttl4EDB+bOO+/M9ddfX8EqAQAAAKD9EdoDrbz44osZMmRI6fnMmTNz//3359Of/nT+8Ic/5Nprr80GG2wgtAcAgHbGks0UhV6kSPQj5Sa0B1qZMWNG+vXrV3o+duzY1NfXZ88990ySdOrUKTvuuGNeeeWVSpUIAAAsA3PmWLOZYtCLFIl+pNyE9kAr3bt3z9tvv116/sgjj6RDhw7ZZpttStuqq6szZ86cClQHAAAsKx06VFW6BEiiFykW/Ui5Ce2BVtZff/3cd999eeutt/LOO+/k1ltvzWabbdZijfvXXnstffv2rWCVAADA0talS6dKlwBJ9CLFoh8pN6E90Mphhx2WN998MzvvvHM+8YlPZMqUKTn44INbHPPUU09l0KBBFaoQAAAAANqn6koXABTP7rvvnjPPPDPXXXddkmTPPffMAQccUNo/ZsyYzJw5Mx//+McrVSIAAAAAtEtCe6BNhxxySA455JA292277bZ59NFHy1wRAACwrDU2NlW6BEiiFykW/Ui5WR4HaOXSSy9dZCj/2GOP5dJLLy1TRQAAQDnMnVtX6RIgiV6kWPQj5Sa0B1q59NJL88gjj3zgMY8++mh+/vOfl6kiAACgHKqrxQQUg16kSPQj5abjgCVSV1eXjh07VroMAABgKaqpsYouxaAXKRL9SLkJ7YE2VVVVLXRfbW1tHnvssay88splrAgAAAAA2j9/JgKSJLvttluL53/84x9zww03tDqusbExb731VubNm5fPfe5z5SoPAAAAAFYIQnsgSdLU9N6d0KuqqtLU1NRiW7Pq6upsuOGG2X777XPccceVs0QAAGAZa2horHQJkEQvUiz6kXIT2gNJknvvvbf0eNCgQTniiCNywgknVLAiAACg3ObNq690CZBEL1Is+pFyE9oDrdxzzz3p2bNnpcsAAADKrFOnjqmra6h0GaAXKRT9SLkJ7YFW1lhjjdLj+vr6vPTSS5k5c2a6d++e9dZbL9XV/tMBAADtkWCKotCLFIl+pNwkb0Cb3n777Vx44YW59dZbM2/evNL2Ll26ZK+99sqJJ56YPn36VLBCAAAAAGh/hPZAK2+//XYOOuigTJgwIb169crWW2+d/v37Z8qUKXnmmWdy7bXXZsyYMRk1alR69+5d6XIBAAAAoN0Q2gOt/OIXv8iECRMycuTIHH/88enWrVtp35w5c/KLX/wil112WX71q1/l1FNPrWClwPJgdm1Drhw7Mdc8MSlJ8rXr/5UDt1wjh2y9ZrrVdKxwdQDAgurrLf9AMehFikQ/Um4dKl0AUDz33HNPtt1223z7299uEdgnSdeuXXPSSSdl2223zd/+9rcKVQgsL2bXNuQro57KZaMn5K059UmSt+bU57LRE/KVUU9ldq3JLwAUSa2fzRSEXqRI9CPlJrQHWnnzzTez5ZZbfuAxW265Zd58880yVQQsr64cOzHPT5mZxqaW2xubkuenzMyVYydWpjAAoE01PgVHQehFikQ/Um5Ce6CVHj16ZNKkSR94zKRJk9KjR48yVQQsr258enKrwL5ZY9P8/QBAcVRXC6YoBr1IkehHyk1oD7QybNiw3HnnnXnooYfa3D969Ojceeed2XbbbctcGbC8mTqr9iPtBwAAgBWNG9ECrZxwwgm5//77M3LkyOy8884ZNmxY+vbtm2nTpmXMmDF54IEH0qVLlxx//PGVLhUouH4r1eTNmQsP5vutVFPGagAAAKD4hPZAKxtttFF++9vf5rTTTsvf//73/P3vf09VVVWamuavcbH22mvnvPPOy0YbbVThSoGi23/IgFw2ekKbS+R0qJq/HwAojro6N1ukGPQiRaIfKTehPdCmbbbZJnfddVfGjh2bcePGZebMmenevXs22WSTbL311qmqqqp0icBy4JCt18z9L05rdTPaDlXJxqt0zyFbr1m54gCAVgRTFIVepEj0I+UmtAcWqqqqKttss0222WabSpcCLKe61XTMrw8amivHTsw1T0zKW3Pq06drdQ7cco0csvWa6Vbjhk4AUCSdO1dn3rz6SpcBepFC0Y+Um9Ae+ED19fV56aWXSlfar7feeqmu9p8OYPF1q+mYL++wToavv3IO+9MTufgzgzNo1R6VLgsAaEPHjh0qXQIk0YsUi36k3CRvQJvefvvtXHjhhbn11lszb9680vYuXbpkr732yoknnpg+ffpUsEIAAAAAaH+E9kArb7/9dg466KBMmDAhvXr1ytZbb53+/ftnypQpeeaZZ3LttddmzJgxGTVqVHr37l3pcgEAAACg3RDaA6384he/yIQJEzJy5Mgcf/zx6datW2nfnDlz8otf/CKXXXZZfvWrX+XUU0+tYKUAAMDSVFtrzWaKQS9SJPqRcrMgE9DKPffck2233Tbf/va3WwT2SdK1a9ecdNJJ2XbbbfO3v/2tQhUCAADLQn19Y6VLgCR6kWLRj5Sb0B5o5c0338yWW275gcdsueWWefPNN8tUEQAAUA5dunSqdAmQRC9SLPqRchPaA6306NEjkyZN+sBjJk2alB49epSpIgAAoBw6dKiqdAmQRC9SLPqRchPaA60MGzYsd955Zx566KE2948ePTp33nlntt122zJXBgAAAADtmxvRAq2ccMIJuf/++zNy5MjsvPPOGTZsWPr27Ztp06ZlzJgxeeCBB9KlS5ccf/zxlS4VAAAAANoVoT3QykYbbZTf/va3Oe200/L3v/89f//731NVVZWmpqYkydprr53zzjsvG220UYUrBQAAlqa5c+sqXQIk0YsUi36k3IT2QJu22Wab3HXXXRk7dmzGjRuXmTNnpnv37tlkk02y9dZbp6rKem4AANDeNDY2VboESKIXKRb9SLkJ7YGFqqqqyjbbbJNtttmm0qUAwEc2u7YhV46dmGuemH+z9a9d/68cuOUaOWTrNdOtpmOFqwMohq5dazJnTm2lywC9SKHoR8pNaA98oLfeeivjx48vXWk/aNCg9OnTp9JlAcCHMru2IV8Z9VSenzIzzRdKvTWnPpeNnpD7X5yWXx80VHAPkMQHaikKvUiR6EfKTWgPtGnixIn5/ve/n/vvv7+0ln0y/+r7T3ziEzn99NOz5pprVrBCAFh8V46d2CKwb9bYlDw/ZWauHDsxX95hncoUBwAAsAChPdDKK6+8koMPPjjTpk3LOuusk6222ir9+vXL1KlT88QTT+Tee+/NU089lauvvjprrbVWpcsFgEW68enJrQL7Zo1N8/cL7QEAgCIQ2gOtXHjhhZk+fXrOOeecHHjggS1uOtvU1JRRo0blnHPOyY9+9KNcfPHFFawUABbP1FkfvAbpovYDrCjmzKmrdAmQRC9SLPqRcutQ6QKA4hk9enR23XXXHHTQQS0C+2T+8jif//zn84lPfCKjR4+uUIUA8OH0W6nmI+0HWFEsuDQmVJJepEj0I+UmtAdaaWhoyIYbbviBx2y88cZpaGgoU0UA8NHsP2RAOizkBmIdqubvByDp1s0fMSkGvUiR6EfKTWgPtLLZZpvlxRdf/MBjXnjhhWy++eZlqggAPppDtl4zG6/SvVVw36Eq2XiV7jlkazdXBwAAikFoD7TyjW98Iw888ECuvfbaNvePGjUq//jHP/KNb3yjvIUBwBLqVtMxvz5oaI7aYZ306Tr/tk59ulbnqB3Wya8PGppuNR0rXCEAAMB8bkQL5NJLL221bbvttsuZZ56Z//u//8tWW22Vfv36ZerUqXn88cfz8ssvZ/jw4Rk9enS22mqrClQMAB9et5qO+fIO62T4+ivnsD89kYs/MziDVu1R6bIAAABaENoDbYb2zV566aW89NJLrbY/+OCD+cc//pHjjz9+WZYGAACU0ezZtZUuAZLoRYpFP1JuQnsgl19+eaVLAAAACqCqqipNTU2VLgP0IoWiHyk3oT2QbbfdttIlAAAABdC1aydXlFIIepEi0Y+UmxvRAgAAAABAQQjtAQAAAACgIIT2AAAAQJLEks0UhV6kSPQj5Sa0BwAAAJIkc+ZYs5li0IsUiX6k3IT2AAAAQJKkQ4eqSpcASfQixaIfKTehPQAAAJAk6dKlU6VLgCR6kWLRj5Sb0B4AAAAAAAqiutIFAMU3Z86c3HLLLfnvf/+bqqqqbLDBBtlrr73SpUuXSpcGAAAAAO2K0B74QE8//XSOPfbYTJ8+PX369EltbW1mzpyZiy66KL/5zW+yySabVLpEAABgKWlsbKp0CZBEL1Is+pFyszwO8IHOPvvsbLzxxvn73/+ehx56KI899lj+/Oc/p6mpKeecc06lywMAAJaiuXPrKl0CJNGLFIt+pNxcaQ8kSW677bbsueeerbY/99xz+e1vf5tVV121tG3rrbfOpz/96Vx99dXlLBEAAFjKps6cl6mzakvPO3bskIaGxtLzfivVpF/3zpUojRVcdXWH1Nc3LvpAKAP9SLkJ7YEkySmnnJJbbrkl55xzTouAftVVV83f//737LDDDqVts2fPziOPPJLVVlutEqUCAABLyQ1PT85lo19Z6P6jdlg7R++4bvkKgv+vpqY69fW1iz4QykA/Um5CeyBJcuONN+a73/1uPv3pT+fEE0/MF77whSTJCSeckNNPPz0PPPBABg0alNra2jz22GOZMWNGzj///ApXDQAAfBQHDBmQnTbomyR5edrsnHHHc/neHgOzbt9uSeZfaQ8AlJfQHkiSbLTRRrn66qtzxRVX5Mc//nFuueWWfP/7388BBxyQddddN1dccUVefvnlJMnw4cNz+OGHZ+jQoZUtGgAA+Ej6de/cavmbdft2y6BVe1SoIgBAaA+UVFVV5fDDD8+IESNy9tlnZ7/99svRRx+dY445Jj/96U8rXR4AAADt1Pvvr9Cppjp1tfWl5+6vQCUteK8PKAehPdDK6quvnt/85je55ZZbct555+WOO+7I//7v/2arrbaqdGkAAAC0Q+6vQJHNm1e/6INgKRLaAwu19957Z/jw4TnvvPNy6KGH5qCDDspJJ52U7t27V7o0AAAA2hH3V6DIOnXqmLq6hkqXwQpEaA+U/Pvf/861116byZMnZ8CAATnwwAOz6aab5oILLsg+++yTs88+O/fee2/OPPPM7LbbbpUuFwAAgHbC/RUoMqE95dah0gUAxXDvvffmwAMPzO2335633nord9xxRz73uc/l73//e5L5N5+99dZb86lPfSpf+9rX8vWvfz3Tpk2rbNEAAMup2bUN+e3oCfna9f9Kknzt+n/lt6MnZHatQAAAYEUntAeSJBdffHE22GCD3Hvvvbnmmmty7733ZoMNNshFF11UOqZLly457bTTctVVV+Wll17KHnvsUcGKAQCWT7NrG/KVUU/lstET8tac+WvkvjWnPpeNnpCvjHpKcA8AsIIT2gNJkgkTJmT48OGl9epXWmmlDB8+PBMmTGh17JAhQ3LjjTdm5MiR5S4TAGC5d+XYiXl+ysw0NrXc3tiUPD9lZq4cO7EyhQEAbaqv9wd1yktoDyRJ1llnnYwePTpz585NksydOzcPPfRQ1lprrTaP79ixY77yla+Us0QAgHbhxqcntwrsmzU2zd8PABRHrU/BUWZuRAskSb761a/mhBNOyM4775z11lsvL7/8cmbMmJFLLrmk0qUBALQrU2fVfqT9AEB51dR0FNxTVq60B5Iku+22W0aNGpXdd989PXv2zP/8z/9k1KhRGTFiRKVLAwBoV/qtVPOR9gMA5VVd3bHSJbCCcaU9UDJkyJAMGTKk0mUAALRr+w8ZkMtGT2hziZwOVfP3AwCw4nKlPQAAQBkdsvWa2XiV7ulQ1XJ7h6pk41W655Ct16xMYQAAFIIr7YFFmj59ep5++unMnj07a665ZgYPHpyqqqpFvxAAgFa61XTMrw8amivHTsw1T0zKW3Pq06drdQ7cco0csvWa6VbjI/gAUCR1ddazp7yE9kCS5KabbsqgQYMyaNCg0rampqZccMEFueKKK9LQ8N4PqHXXXTc/+tGPsvnmm1eiVBZh6sx5pRvYvTxtdov/T+avk9uve+eK1AYAzNetpmO+vMM6Gb7+yjnsT0/k4s8MzqBVe1S6LACgDUJ7yk1oDyRJTj311JxwwgktQvvzzz8/f/jDH7Lyyitnt912S58+ffLkk09mzJgxGTlyZG6++easuuqqFayattzw9ORcNvqVFtvOuOO50uOjdlg7R++4bpmrAgAAgOVT587VmTevvtJlsAIR2gNteuONN/KnP/0pG2ywQf74xz+mX79+pX1//OMfc9555+UPf/hDTjnllApWSVsOGDIgO23Qt/S8S5dOmTu3rvS830o1lSgLAAAAlksdO7otKOUltAfa9PDDD6ehoSEnnXRSi8A+SY444oj85S9/yQMPPCC0L6B+3Tu3WP6mW7eazJ5dW8GKAAAAAFhc/kwEtOmNN95IkmyxxRZt7h86dGhee+21MlYEAAAAAO2f0B5oU+fO86/U7tatW5v7u3btmsbGxnKWxBKqrbXuHgAAACwpv1dTbpbHAUrGjBmTSy+9NEny8ssvJ0kmTZqUDTbYoNWxb7zxRnr37l3G6lhS9fX+uAIAAABLyu/VlJvQHigZM2ZMxowZ02Lbfffd12Zo/8wzz2S99dYrV2l8BO+/ES0AAACw+PxeTbkJ7YEkyeWXX97m9pVXXrnVtn//+9+pq6vL8OHDl3VZLAUdOlRVugQAAABYbvm9mnIT2gNJkm233Xaxj91ss81y7733LsNqAAAAAGDF5Ea0AAAAAABQEK60B9pUV1eXV199Ne+++26qqqrSt2/frLHGGpUuiyVg3T0AAABYcn6vptyE9kALt99+e6666qo88cQTaWhoaLGvd+/e2XPPPXPUUUdl1VVXrVCFfFiNjU2VLgEAAACWW36vptwsjwMkSRobG/ONb3wjJ510Uh599NHU19enqakpTU1NGTBgQDbccMPMmjUrf/rTn7L33ntn9OjRS/xejz76aI455pgMHz48AwcOzN13391if1NTUy666KIMHz48Q4YMyRe/+MW8/PLLLY55++23c9JJJ2WrrbbKNttsk9NPPz2zZs1qccz48eNzyCGHZPDgwdl5551z2WWXLXHNy7OuXWsqXQIAAAAst/xeTbkJ7YEkyeWXX54777wzu+66a2666aY89thjuemmmzJixIjMnDkzP/vZz/LYY4/lggsuSKdOnXLcccfl1VdfXaL3mj17dgYOHJizzjqrzf2XXXZZrrjiipx99tm55ppr0rVr14wcOTLz5s0rHfOtb30rL774Yn7/+9/nV7/6VR577LGceeaZpf0zZ87MyJEjs/rqq+eGG27IySefnEsvvTSjRo1aopqXZ1Vucg8AAABLzO/VlJvQHkiSXH/99dl4441zySWXZNCgQenevXsGDRqUiy66KKuttlp+9KMfpaamJvvss08uv/zyNDQ05De/+c0SvdfOO++cb37zm/nkJz/Zal9TU1Muv/zyHHvssRkxYkQGDRqUCy64IG+++Wbpivz//Oc/efDBB/O///u/GTp0aLbZZpt897vfzW233ZY33ngjSXLzzTenrq4uP/jBD7LRRhtlzz33zGGHHZbf//73S/6PBAAAAADLmNAeSJK88sor2XHHHdOhQ8v/LHTs2DE77LBDHnvssdK2DTbYILvsskv++c9/LvU6Jk6cmClTpmTHHXcsbevRo0eGDh2aJ554IknyxBNPpGfPnhk8eHDpmOban3766STJk08+mW222SY1Ne99hG348OF56aWXMmPGjKVeNwAAAAAsDW5ECyRJampq8s4777S579133211U9p11103995771KvY8qUKUmSvn37ttjet2/fTJ06NUkyderUrLzyyi32V1dXp1evXqXXT506NWuuuWaLY/r161fa16tXr8WuacG16+rrG1Jb25Camo6pru5Y2l5X15C6uoZ07lydjh3f+8NHbW196usb06VLp3To8N7n6ebOrUtjY1O6dq1p8TG7OXPq0tTUlG7dWq6XN3t2baqqqtK1a6fStqamZM6c2nToUJUuXd7b3tjYlLlz61Jd3SE1NdWpqqpKt241aWhozLx59enUqWM6dXqv9uVxTM2MafkaU3PdXbp0SrduNe1iTO3x67QijKn5cadOHVu87/I8pvb4dVpRxrTg+drLmJL293VaUcbU/B7N42sPY2rWnr5OK8KYms/dPG9sD2Nqj1+nFWlMSVrUvzyPieWD0B5Ikmy22Wb529/+lq997WtZbbXVSttff/31/O1vf8vGG2/c4vi33347K620UrnLrIg5c2rT9L4bxdfWzv9h+X7z5tW3eY65c+sWeu62zJ7dentTU1Ob2xsb295eX9+Y+vrW25t/oL+fMRlTsmzHVF/fUKppwdctz2Nqj1+nFWFMzY/r6hraPM/yOKZm7enr1Ky9j2nBetvLmBZkTMvXmJrH0NDQ2OL5+y1PY3o/Y1o+xlRb+15PLvj+y/OY2uPXaUUaU1u1LGx7EcdUVZWstFLnNvdTTEJ7IEly5JFH5uijj86+++6bAw88MGussUYmTpyY6667LjNnzsyhhx7a4vh//etf2XDDDZd6HausskqSZNq0aenfv39p+7Rp0zJo0KAk86+Ynz59eovX1dfXZ8aMGaXX9+vXr3RlfrPm581X3K8ounWrWegEAwAAAPhgfq+m3KxpDyRJdtppp5x++umZM2dOLrvsspxzzjn57W9/m3fffTfHHnts9t5779KxM2fOzGabbdYqyF8a1lxzzayyyioZPXp0i/d76qmnsuWWWyZJttxyy7zzzjt55plnSsc8/PDDaWxszJAhQ5IkW2yxRR577LHU1b33V/KHHnoo66233odaGgcAAAAAysmV9kDJ4Ycfnk996lN54IEHMnXq1PTp0ycf+9jHWq0N371793zve99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" ] }, "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": "2024-11-01T03:04:01.451179Z", "iopub.status.busy": "2024-11-01T03:04:01.451026Z", "iopub.status.idle": "2024-11-01T03:04:03.562525Z", "shell.execute_reply": "2024-11-01T03:04:03.562024Z", "shell.execute_reply.started": "2024-11-01T03:04:01.451163Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is infill\n", "len pos models: 3968\n", "differing counts: 1747\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 33, total 33.\n", "chirp-v3p5-engine-t-5-15_win_over_chirp-v3p5-engine-t-5, win ratio 0.482, (0.439, 0.525), counts 253, total 525.\n", "chirp-v3p5-engine-t-5-15_win_over_chirp-v3p5-engine-t-5-15, win ratio 1.000, (1.000, 1.000), counts 1128, total 1128.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-5-15, win ratio 0.518, (0.475, 0.561), counts 272, total 525.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-6, win ratio 0.506, (0.400, 0.612), counts 43, total 85.\n", "chirp-v3p5-engine-t-6_s_30_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.535, (0.477, 0.594), counts 151, total 282.\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.491, (0.431, 0.550), counts 132, total 269.\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.543, (0.486, 0.600), counts 158, total 291.\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.451, (0.394, 0.508), counts 133, total 295.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-5, win ratio 0.494, (0.388, 0.600), counts 42, total 85.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 1060, total 1060.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_30_t_12_tag_2, win ratio 0.465, (0.406, 0.523), counts 131, total 282.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_12_tag_2, win ratio 0.509, (0.450, 0.569), counts 137, total 269.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_13_tag_2, win ratio 0.457, (0.400, 0.514), counts 133, total 291.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_14_tag_2, win ratio 0.549, (0.492, 0.606), counts 162, total 295.\n", "tournament players: ['chirp-v3p5-engine-t-5', 'chirp-v3p5-engine-t-5-15', 'chirp-v3p5-engine-t-6', 'chirp-v3p5-engine-t-6_s_30_t_12_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_12_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_13_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_14_tag_2']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:01<00:00, 647.59it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-t-5: 1002.7, (950, 1058)\n", "chirp-v3p5-engine-t-5-15: 990.2, (929, 1047)\n", "chirp-v3p5-engine-t-6: 998.6, (974, 1024)\n", "chirp-v3p5-engine-t-6_s_30_t_12_tag_2: 1023.3, (979, 1062)\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2: 992.2, (943, 1036)\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2: 1028.6, (990, 1072)\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2: 964.4, (920, 1007)\n" ] }, { "data": { "image/png": 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4HL0RlcHQHQAcZM2aNVq9erWys7M1dOjQwPG8vDzFxMQYTAY3Onr0qLKyslRUVKQ9e/YEjtesWVPTpk0zmAxuVVxcrJEjR5qOAUiiHmEPixYt0qJFi7R3716lpKQEjufl5en//b//ZzAZ3GrixImBT+0mJycHHQNCgd6IqsDQHQAcpFmzZkpKStKnn36qpKSkwPHY2FglJiYaTAY36tGjh3r06KF33nlH3bp1u+D7Xn/9dW5IhJBo0aKFDh48qPj4eNNRAOoRtvDjH/9Y1157rSZOnKixY8cGjsfGxqply5YGk8Gt+MQu7IDeiKrAjVQBwIFycnJUv379C74+Y8YMjR49OoSJgAvjJlkIlUGDBmn37t1q166doqKiAsfT09MNpoJbUY+oTkaPHq0ZM2aYjgEHO/uJ3S1btujmm28OHD+7LeGrr75qMB1QMXojLoYr3QHAgS42cJek999/P0RJgG/H3/8RKj/96U/105/+1HQMQBL1iOrlP//5j+kIcDg+sYvqiN6Ii2HoDgAuxJATdmJZlukIcInz9+QETKMeAeCcVq1aqVWrVrr11lsDFxDl5uaqTp06hpMBwPfD0B0AXIghJwA3mTdvngYPHnzBG/iev1cncLlRjwBwYed/YnfgwIFsQQig2mLoDgAAjOKTF7jcYmJiJEm1atUynASgHgHgu2KNCKA6Y+gOAC7EAhZ2MmLECNMR4HD9+/eXJA0dOtRwEoB6RPXk8XhMR4ALNW3a1HQE4KLojbgYy8/kBQAcraK9EHNycr71ZqtAVVi2bJmys7PVvXt3tWnTJnD8xRdf1K9//WuDyeBG6enpQcdq166tdu3aKSEhwUAiuBn1CAAX5vV6FRkZaToGELBo0aLAH86B74I/yQCAg+zZs0d9+/ZVSkqKPv/8cz3wwAPq2rWrbr31VmVlZQXex8AdoTBz5ky99tprOnHihH7961/rb3/7W+C1tWvXGkwGt/ryyy+1aNEiHT58WEeOHNGiRYu0ZcsWjR49WhkZGabjwWWoR9jB/v379Ytf/EJJSUmaNm2aiouLA6/de++9BpPBrbKysvSTn/xEPXr0kCTt3r1b06dPN5wKbrN+/fqg/82aNSvwGPgu2F4GABxk6tSpGjZsmE6dOqUHHnhAv/3tb/XSSy/prbfe0vTp0zVv3jzTEeEi77zzjpYsWaLIyEg9+OCD+s1vfqOioiL9+te/ZosjGJGbm6tly5YpLi5OknTs2DGNGTNGf//735WWlqYBAwYYTgg3oR5hB5MmTVLPnj11ww03KCMjQ7/85S/1v//7v4qNjS03gAdCZerUqZo4caKmTJkiSWrTpo1+//vfa8yYMYaTwU0efvhhtWvXThEREYFjeXl5mj9/vizLUlJSksF0qC640h0AHCQ/P189evRQamqq/H6/kpOTJUk9evTQ8ePHzYaD6/j9/sDHghs2bKj58+dr48aNeu6552RZluF0cKMjR44EBpySFBcXp6NHj6pu3boKD+daFIQW9Qg7OH78uNLS0tS2bVtNnz5dt956qwYOHKi8vDx+VsOI06dPq2PHjoHnlmWVG3wCoTB16lRJ0tixY7VgwQItWLBAcXFxWrBgAZ9Gw3fG0B0AHOT8q4dvvvnmC74GhEJ0dLSys7MDz2NjYzV37ly9++67+vzzzw0mg1s1btxY6enpOnTokA4dOqT09HQ1atRIPp+P4RJCjnqEHRQVFZV7/uCDD+quu+7SL3/5S50+fdpQKrhZeHi4SkpKAn3w0KFD3KwSIdevXz89/fTTmjFjhtLT0/nZjO+FzgUADhIXF6f8/HxJ0lNPPRU4fvToUUVFRZmKBZcaOXJkoB7PqlmzpubNm6ehQ4caSgU3e/LJJ/X5558rOTlZycnJ+vzzzzVt2jSVlpaW65lAKFCPsIPmzZvr3XffLXdsyJAh6tOnj/bv328oFdwsLS1NDz/8sHJycvTss88qLS1N999/v+lYcKErr7xS8+bNU0xMjNLS0uT1ek1HQjVj+bn0EQAcLy8vT3l5eYqPjzcdBQAAADZxdoh0dju48x05ckSNGzcOdSRA27Zt0/r16+X3+3XbbbeV224GMOHzzz/Xxx9/rPvuu890FFQjDN0BAEDIPfvssxoxYoTpGHChnTt3av/+/fL5fIFjZ+9/AYQa9QgAAOBMDN0BwCXuvPNOrVu3znQMQJJ066236u233zYdAy4zYcIEvffee2rdunVgf1jLsvTnP//ZcDK4EfUIu2PtCBOys7M1Z84cHThwQKWlpYHj3LwSdkFvxHcVbjoAAKDqZGVlXfA1boaFUEtJSanwuN/v1/Hjx0OcBpA2b96sNWvWcI8L2AL1CDtg7Qi7GT58uBITE5WWlqawsDDTceBS9EZUBYbuAOAgycnJuvLKK1XRh5hOnjwZ+kBwtQMHDujpp59WdHR0ueN+v5+tZWBEw4YNK9y3GDCBeoQdsHaE3RQXF2vkyJGmY8Dl6I2oCgzdAcBB4uPj9corr1R406tu3boZSAQ3a926tWrVqqUOHToEvRYREWEgEdwuISFBjzzyiO66665yVxcnJSUZTAW3oh5hB6wdYTctWrTQwYMHFR8fbzoKXIzeiKrA0B0AHOS2227TgQMHWBzAFp588knFxsZW+Br7IMKE3bt3S5JeffXVwDHLshhywgjqEXbA2hF2k5OTo759+6pdu3bl/iCZnp5uMBXcht6IqsCNVAHAhbxeLx9ph23MmDFDo0ePNh0DAABcAGtHhEpmZmaFxy90ryDAJHojLoYr3QHAhe69994LLmiBUHv//fdNR4BLlJaWKiMjQ/v379fEiRO1f/9+ffXVV0pMTDQdDS5EPaI6Ye2IUGG4juqE3oiLYegOAC7Eh5xgJ9QjQuWJJ55QWVmZtm7dKkmqW7euhg8frqVLlxpOBjeiHlGd8LMal9u8efM0ePBgTZs2rcLXx44dG+JEwLejN+JiGLoDgAtZlmU6AhBAPSJUdu7cqeXLlys5OVmSVLt2bZWWlpoNBdeiHlGd8LMal1tMTIwkqVatWoaTAN8dvREXw9AdAAAArnD+DdkkyefzcYUSjKEeAeCc/v37S5KGDh1qOAkAVA2G7gDgQvxSDzuhHhEqLVu21PLly1VWVqZ9+/Zpzpw56tSpk+lYcCnqEdUJP6sRKunp6UHHateurXbt2ikhIcFAIuDC6I24GI/pAACAqpeTk3PRYzfccEMo48DlJk6ceNFj8+bNC10YuNqjjz6qrVu36tixY+rfv788Ho9GjRplOhZcinqEnbB2hF18+eWXWrRokQ4fPqwjR45o0aJF2rJli0aPHq2MjAzT8eAy9EZUhuXnzzIA4DgpKSlBd1Gv6BgQChXVXt++fbV8+XJDiQAAwPlYO8IuhgwZoqeeekpxcXGSpGPHjmnMmDF65plnlJaWptWrVxtOCDehN6Iy2F4GABzE6/XK6/XK5/MpPz8/cDwvL08FBQUGk8GN1qxZo9WrVys7O7vc/px5eXmBm2UBpgwbNkyzZs0yHQOQRD3CHNaOsJsjR44EBu6SFBcXp6NHj6pu3boKD2eEhdCgN6Iq0LEAwEFeeuklpaeny7IsdezYMXA8NjZWgwYNMpgMbtSsWTMlJSXp008/VVJSUuB4bGysEhMTDSYDpOzsbNMRgADqEaawdoTdNG7cWOnp6erXr58kacmSJWrUqJF8Pp8syzKcDm5Bb0RVYHsZAHCgCRMmaNKkSRd83ev1KjIyMoSJ4GY5OTmqX7/+BV+fMWOGRo8eHcJEgJScnKxly5aZjgFIoh5hHmtH2MXXX3+tKVOm6IMPPpAkde7cWePGjVPdunW1d+9etWzZ0nBCuAm9EZXB0B0AXIh96GAn1CNMyMvLU61atUzHACRRj7A/flYDQDB6Iy6G7WUAwIX4eyvshHpEqB06dEi7d+9WixYtdPXVV5uOA5ejHlEd8LMaobRz507t379fPp8vcCw5OdlcIOAC6I24GI/pAACA0GM/RNgJ9YjL7fztiz788EP169dPixYtUlpamt58802DyeBG1COqI35WI1QmTJig3/3ud1q3bp02bNigDRs2aOPGjaZjARWiN+JiuNIdAAAAjvb5558HHj///PNKT09Xhw4dtHfvXo0cOVK33367wXRwG+oRAC5s8+bNWrNmjaKiokxHAYBK4Up3AHAhPgYHO6EecbmdfxXSyZMn1aFDB0nSNddcU+6j60AoUI+ojvhZjVBp2LAhN6ZEtUFvxMVwpTsAuNANN9xgOgJc5MCBAzp48KAkKT4+Xk2aNCn3+rx580zEgoscPnxY06ZNk9/v14kTJ+Tz+RQWFiZJDDkRctQjqiPWjgiVhIQEPfLII7rrrrvKXe2elJRkMBVQMXojLoYr3QHAgV588cVyz0tKSjRp0qTA8/MfA5fLF198obvvvlv33XefZs6cqZkzZ+q+++7T3XffXW57hfr16xtMCTdIS0tTrVq1VLt2bd1zzz06efKkJOnIkSNq06aN2XBwHeoRdsTaEXaxe/dunTx5Uq+++qrmz5+v+fPn629/+5vpWHApeiMqw/LzWQgAcJwHH3xQZWVlmjFjhnJzczV8+HC1b99e48ePNx0NLnLPPffo/vvv15133lnu+Nq1a/W///u/Wrx4saFkAADgfKwdASAYvRGVwdAdABxq7ty5WrhwocrKyvToo4+qZ8+epiPBZe68806tW7fukl8DLhev16v58+fr/fffl2VZSkxM1MCBA9k7FkZQj7Ab1o6wg9LSUmVkZGj//v2aOHGi9u/fr6+++kqJiYmmo8Gl6I34vtheBgAcqKSkRAcPHlStWrVkWZaKi4tNR4IL1atXT8uWLVNZWVngWFlZmTIzM1WvXj2DyeBWEyZM0LZt2/Tzn/9caWlp2rFjhyZMmGA6FlyKeoSdsHaEXTzxxBP68ssv9eGHH0qS6tatqxkzZhhOBbeiN6IyuJEqADhQ//791bZtW/3jH//QsWPH9Lvf/U4ffvih/vjHP5qOBhd56qmn9Pjjj2vKlClq2LChJOnrr7/Wddddp2nTphlOBzfavn27Xn/9dVmWJUnq3r27evfubTgV3Ip6hJ2wdoRd7Ny5U8uXL1dycrIkqXbt2iotLTUbCq5Fb0RlMHQHAAcaOHCg+vTpI0mKj4/Xyy+/rKefftpwKrjN1Vdfrb/97W/KycnRoUOHJElXXHEFN06FMXXr1lVRUZGio6Mlndneg09dwBTqEXbC2hF2ERUVVe65z+cTuyLDFHojKoM93QEAQMjk5uaqTp06pmPApcaNG6d//etfgb04161bp+uvv14tW7aUJA0YMMBkPLgM9QgAwcaPH6+OHTtq7ty5mjVrlubMmaOoqChuXAmg2uFKdwBwoKKiIi1YsEBZWVnl9p1LT083mApus2fPHj366KPyeDyaPn26ZsyYoQ8//FD16tXTCy+8oFatWpmOCJfx+/1q27atsrOzJUlt2rRRWVmZ9uzZYzgZ3Ih6hJ2wdoRdPProo3rqqad07Ngx9e/fX7fffrtGjRplOhZcit6IyuBKdwBwoFGjRik2NlbvvvuuBg0apMzMTHXs2FHjxo0zHQ0u8vOf/1wDBw7UqVOnNGvWLP32t79VcnKy3nrrLb3yyiuaN2+e6YgAAECsHQGgIvRGVIbHdAAAQNX77LPPNHHiRNWsWVO/+MUvlJGRoU8++cR0LLhMfn6+evToodTUVPn9/sANsXr06KHjx4+bDQfXycrKUlZWliRp7969mj9/vjZv3mw4FdyKeoTdsHaEHQ0bNsx0BLgcvRGVwfYyAOBAZ29AFB4eroKCAsXGxionJ8dwKrjN+R+mu/nmmy/4GnC5LViwQPPmzVNpaamGDBmi5cuXKyEhQa+++qoGDRqk/v37m44IF6EeYUesHWFHZ7ffAkyhN6IyGLoDgAPVqVNHubm56tq1q4YMGaJ69erpBz/4gelYcJm4uDjl5+crNjZWTz31VOD40aNHAwtYIBRee+01rVq1SqdPn1aPHj20du1axcfHKycnR4MHD2bIiZCiHmFHrB1hR1ykAdPojagM9nQHAAfy+XwKCwuT3+/XihUrlJeXp+TkZMXGxpqOBigvL095eXmKj4+XJHm9XkVGRhpOBSdLTk7WsmXLJEk/+clPtGrVqsBrKSkpyszMNJQMbkQ9wo5YO8KO8vLyVKtWLdMx4GL0RlQGV7oDgAOFhYVJkizLUt++fQ2nAcqrVatWuV+g7r33XoZMuKx8Pl/g8Tf3h/V6vaGOA5ejHmFHrB1hN4cOHdLu3bvVokULXX311abjwKXojagMhu4A4EAfffSRnnrqKe3fv1+lpaWB49u2bTOYCqgYH7rD5da/f//AVkd33nln4PgXX3yhG2+80WAyuBH1CDti7QjTRo8erRkzZkiSPvzwQ40YMUKtW7fWZ599pgkTJuj22283nBBuRG9EZbC9DAA40J133qkRI0YoISEh8Nd5SWrcuLHBVEDF2E4BAACzWDvCtPO33ho4cKAeeeQRdejQQXv37tXIkSO1ZMkSswHhSvRGVAZXugOAA8XGxqpnz56mYwCAraSkpOjuu+/WT37yE9WpU8d0HLgc9Qg7Ye0I0yzLCjw+efKkOnToIEm65pprym3LBYQSvRGV4TEdAABQ9e68804tW7aMvWFRLfChO4TK2LFj9a9//Ut33HGHhg8frk2bNlF/MIZ6hJ2wdoRphw8f1rRp0/THP/5RJ06cKDdoZ+gOU+iNqAy2lwEAB3rrrbc0evRoFRUVSToz1LQsS3v27DGcDAg2YcIETZo0yXQMuEhBQYHWrl2rzMxMHThwQG+//bbpSHAx6hF2wNoRpqWnp5d7ft9996lBgwY6cuSInn32WT355JOGksHN6I2oDIbuAOBASUlJmjJliq6//np5POc+1BQTE2MwFdxq0aJF6t27t2rVqqVJkyZp586dGjt2rG666SbT0eBSx44d07Jly7R06VKFhYVp5cqVpiPBxahH2AFrRwAIRm9EZbCnOwA4UIMGDZSYmGg6BiBJWrhwofr376+tW7fq888/14gRI/TUU09p8eLFpqPBZd544w0tXbpUO3fuVM+ePfXkk08qISHBdCy4FPUIO2HtCLvwer2aP3++3n//fVmWpcTERA0cOFCRkZGmo8GF6I2oDK50BwAHeuGFFxQbG6u77rpLUVFRgeOxsbEGU8GtUlJSlJmZqdmzZ6tRo0a65557AseAUBo8eLBSU1N1++23l+uNgAnUI+yEtSPsYuzYsTpx4oTuvvtuSdLSpUtVp04dTZs2zXAyuBG9EZXB0B0AHKhVq1aBx5ZlsfccjEpNTdX999+v5557Ti+++KKuvPJK/eQnP9GqVatMR4OLZWZmKiUlxXQMQBL1CPNYO8Iuevbsqddff12WZUk6cxPV3r17a+3atYaTwY3ojagMtpcBAAfKysq66Oter5ePaCJkJkyYoBdffFH33HOPrrzySv33v/9V586dTceCy2VkZDDkhG1QjzCNtSPsom7duioqKlJ0dLSkM7VXr149w6ngVvRGVIbn298CAHCae++913QEuMgNN9yg5557Tr/85S8lSddee60ee+yxwOszZswwFQ0uxoc9YSfUI+yOtSNCpVmzZvrZz36m2bNna/bs2br33nvVrFkzZWRkKCMjw3Q8oBx6Iy6GK90BwIX45R528v7775uOABcaOHCg6QhAAPUIu2PtiFDx+/1q27atsrOzJUlt2rRRWVkZ23nAluiNuBiG7gDgQmf3SATsgMUqQungwYP66KOP5PF4dPDgQcXHx5uOBBejHlFdsHZEqHDDVFQn9EZcDNvLAAAAo1isIlRWrlyplJQUvfnmm1q3bp1SU1O1evVq07HgUtQjAJSXlZUV2EN77969mj9/vjZv3mw4FQB8P1zpDgAuxJXFANxo9uzZWrx4sZo0aSJJys7O1v3336/evXsbTgY3oh5RnbB2xOW2YMECzZs3T6WlpRoyZIiWL1+uhIQEvfrqqxo0aJD69+9vOiIQhN6Ii2HoDgAudMMNN5iOAASwWEWoREdHBwacknTVVVcpOjraYCK4GfWI6oS1Iy631157TatWrdLp06fVo0cPrV27VvHx8crJydHgwYMZusOW6I24GLaXAQAHWrRokfLy8iRJkyZNUmpqqj766KPA65MmTTIVDS6Wk5OjzZs36+jRo+WOz5s3z1AiuE23bt00a9YsHT58WIcOHVJ6erq6d++u/Px85efnm44Hl6EeYSesHWFaWFiYatasqUaNGqlp06aBe1zUr1+frQhhDL0RlWH5ubwMABynT58+WrlypbZu3apnn31Wv/71r/XnP/9ZixcvNh0NLjJmzBj9/ve/V4MGDbR582aNGDFCV111lb766itNnjxZPXr0MB0RLtOqVasLvmZZlvbs2RPCNHA76hF2wtoRpp2tQUlat26d7rzzzsBrvXv35p4XMILeiMpgexkAcKDw8DPt/YMPPlDfvn314x//WM8884zhVHCbrKwsNWjQQNKZvYv/+te/qnXr1jpw4ICGDRvG0B0hd/bmbIAdUI+wE9aOMK1///7Kz89XbGxsuYH7F198oRtvvNFgMrgZvRGVwfYyAOBAlmVpzZo1ev3119WlSxdJUklJieFUcJvi4uLA48LCQrVu3VqS1KRJE/l8PlOx4FI+n0+9evUyHQOQRD3Cflg7wrS0tDTFxsYGHW/evLmeeOIJA4kAeiMqh6E7ADjQhAkTtGrVKt1zzz268sor9d///ledO3c2HQsuc8stt2jKlCk6ffq0unTpohUrVsjv9+udd95RvXr1TMeDy4SFhal+/foqLCw0HQWgHmE7rB1hFykpKVq4cKFyc3NNRwHojagU9nQHABeaMWOGRo8ebToGHM7r9WrmzJlasmSJ6tSpo4MHDyosLEydO3fWxIkT1aRJE9MR4TLjxo3Tp59+qp49eyomJiZwfMCAAQZTwa2oR1QnrB0RKlu2bNHSpUu1ceNGJSYmql+/frrlllu4mSpsid6Ii2FPdwBwoffff990BLhAZGSkxo0bpxEjRmj//v3y+Xy64ooruModxvj9frVu3Vr79u0zHQWgHlGtsHZEqHTq1EmdOnVSQUGB1q5dq5deeknjx4/X22+/bToaEITeiIth6A4ALsSHnBBK0dHRatmypSQpMzNTKSkphhPBraZNm2Y6AhBAPaI6Ye2IUCsoKFBOTo6OHz+uWrVqmY4DVIjeiIth6A4ALsTHM2FKRkYGQ3eE3JYtW9SpUyetX7++wteTkpJCnAhuRj2iOmLtiFB54403tHTpUu3cuVM9e/bUk08+qYSEBNOxgArRG3ExDN0BAEDIcDUITFixYoU6deqk+fPnB71mWRZDToQU9QgAF7Zo0SKlpqbqz3/+s6KiokzHAYDvjaE7ALgQg0+YMnDgQNMR4EJTpkyRJC1YsMBwEoB6RPXE2hGhMm/evMBjtiWE3dEbcTGWnwoBAMfKycnRZ599pubNm6tRo0bljtevX99gMrjJxIkTNXHixG89BoTC0aNHlZ2dLZ/PFzh20003GUwEN6MeYTesHWEnKSkpyszMNB0DoDfie+FKdwBwkDFjxuj3v/+9GjRooM2bN2vEiBG66qqr9NVXX2ny5Mnq0aOHJLEwQEjt3Lkz6Nj27dsNJIHbPf/885o7d66aNGkij8cj6cx2HosXLzacDG5EPcIOWDvCzrhGFKbQG1EVGLoDgINkZWWpQYMGkqTZs2frr3/9q1q3bq0DBw5o2LBhgcUBEApr1qzR6tWrlZ2draFDhwaO5+XlKSYmxmAyuNWSJUv05ptvql69eqajANQjbIG1I+yMbQlhCr0RVYGhOwA4SHFxceBxYWGhWrduLUlq0qRJuY+uA6HQrFkzJSUl6dNPPy13Y8DY2FglJiYaTAa3iouLY8AJ26AeYQesHWFHBw8e1EcffSSPx6ODBw8qPj7edCS4DL0RVcFjOgAAoOrccsstmjJlik6fPq0uXbpoxYoV8vv9euedd/jFHiHXqlUrpaamasmSJUpJSVFKSopuu+023X777YqNjTUdDy6SlZWlrKwsdenSRVOnTtWuXbsCx7KyskzHg8tQj7AT1o6wm5UrVyolJUVvvvmm1q1bp9TUVK1evdp0LLgMvRFVgRupAoCDeL1ezZw5U0uWLFGdOnV08OBBhYWFqXPnzpo4caKaNGliOiJcjhtiwYTbbrvtgq9ZlqX169eHMA3cjnqEnbB2hN307NlTc+bMCdRedna27r//fq1du9ZwMrgJvRFVgaE7ADhQYWGh9u/fL5/PpyuuuIK/xsM2kpOTtWzZMtMx4FLr169Xx44dVadOHUlSbm6utm3bpu7duxtOBjeiHmEnrB1hFxVdoMFFGzCF3ojKYHsZAHCg6OhotWzZUtddd53efvtt03GAgKZNm5qOABf7y1/+EhhwSlLt2rX1l7/8xWAiuBn1CDth7Qi76Natm2bNmqXDhw/r0KFDSk9PV/fu3ZWfn6/8/HzT8eAy9EZUBjdSBQCHy8jIUEpKiukYgLxeLwMl2IplWdwMC7ZBPcIuWDvCpBdeeEGSNHv27HLHn3vuOVmWpT179piIBdAbcckYugOAw7GLGEzLysrSqFGjdOrUKb377rvavXu31qxZozFjxpiOBpepWbOmtm3bpg4dOkiStm7dqpo1axpOBbeiHmFXrB1hEjeUhl3RG3GpGLoDgMMNHDjQdAS43NSpUzVx4kRNmTJFktSmTRv9/ve/Z+iOkBs9erSGDh2qZs2aSZL27t0bdCUdECrUI+yKtSNM8fl86tOnj9asWWM6ChCE3ohLxZ7uAOBAEydODDxOTk4OOgaE0unTp9WxY8fAc8uyFBERYTAR3Kp9+/Zas2aN7r//ft1///1avXq1EhISTMeCS1GPsBPWjrCDsLAw1a9fX4WFhaajAJLojagcrnQHAAfauXNn0LHt27cbSAJI4eHhKikpkWVZkqRDhw7J4+Hv/jCjTp066tatm+kYgCTqEfbB2hF20bRpU913333q2bOnYmJiAscHDBhgMBXcit6IymDoDgAOsmbNGq1evVrZ2dkaOnRo4HheXl65RSsQSmlpaXr44YeVk5OjZ599VitXrtSoUaNMxwIAwPVYO8Ju/H6/WrdurX379pmOAhejN6IqMHQHAAdp1qyZkpKS9OmnnyopKSlwPDY2VomJiQaTwc369u2rJk2aaP369SopKdH06dPLbTcDAADMYO0Iu5k2bZrpCAC9EVXC8nP7XQBwnJycHNWvX1+SlJubqzp16hhOBAAAALti7QjTtmzZok6dOmn9+vUVvn7+4BMIFXojKoMr3QHAgc4uDKQzd1nPzMw0mAZul52drTlz5ujAgQMqLS0NHM/IyDCYCgAAnMXaEaatWLFCnTp10vz584NesyyLoTuMoDeiMhi6A4DD8YEmmDZ8+HAlJiYqLS1NYWFhpuMAAICLYO0IE6ZMmSJJWrBggeEkQMXojbhUDN0BwOGaNm1qOgJcrri4WCNHjjQdAwAAfAesHWHa0aNHlZ2dLZ/PFzh20003GUwE0Btx6djTHQAczOv1KjIy0nQMuNzIkSM1cuRIxcfHm44CAAAugrUjTHv++ec1d+5cNWnSRB6PR9KZ7WUWL15sOBncjN6I74Mr3QHAgbKysjRq1CidOnVK7777rnbv3q01a9ZozJgxpqPBhXJyctS3b1+1a9dOUVFRgePp6ekGUwEAgLNYO8IulixZojfffFP16tUzHQWgN6JSPKYDAACq3tSpUzVx4sTAjV/atGmjd955x3AquNVPf/pTjRs3Tr169VJSUlLgfwAAwB5YO8Iu4uLiGLjDNuiNqAyudAcABzp9+rQ6duwYeG5ZliIiIgwmgpulpKSYjgAAAC6CtSNMy8rKkiR16dJFU6dOVZ8+fcpt59GqVStT0eBi9EZUBkN3AHCg8PBwlZSUyLIsSdKhQ4cCeyICoTJv3jwNHjxY06ZNq/D1sWPHhjgRAACoCGtHmPbQQw+Ve75+/frAY8uyyj0HQoXeiMpg6A4ADpSWlqaHH35YOTk5evbZZ7Vy5UqNGjXKdCy4TExMjCSpVq1ahpMAAICLYe0I0zZs2CDpzLC9Y8eOqlOnjiQpNzdX27ZtMxkNLkZvRGVYfr/fbzoEAKDqbdu2TevXr5ff79dtt91W7mNxAAAAwPlYO8IO+vbtq+XLlwee+/1+paamKjMz02AquBm9Ed8XV7oDgEN16NBBHTp0MB0DUHp6etCx2rVrq127dkpISDCQCAAAfBNrR9iRZVny+XymY8DF6I34vhi6A4ADZWdna86cOTpw4IBKS0sDxzMyMgymglt9+eWX2rJli2699VZZlqWNGzeqXbt2WrhwodLS0jRgwADTEQEAcDXWjrCLmjVratu2bYEh59atW1WzZk3DqeBW9EZUBkN3AHCg4cOHKzExUWlpaQoLCzMdBy6Xm5urZcuWKS4uTpJ07NgxjRkzRn//+98ZugMAYAOsHWEXo0eP1tChQ9WsWTNJ0t69ezV79mzDqeBW9EZUBkN3AHCg4uJijRw50nQMQJJ05MiRwMBdkuLi4nT06FHVrVtX4eEsRQAAMI21I+yiffv2WrNmjXbs2BF4Xrt2bbOh4Fr0RlSGx3QAAEDVa9GihQ4ePGg6BiBJaty4sdLT03Xo0CEdOnRI6enpatSokXw+nyzLMh0PAADXY+0IO6lTp466deumbt26MXCHUfRGVIbl9/v9pkMAAKrWoEGDtHv3brVr105RUVGB4xXd0BK43L7++mtNmTJFH3zwgSSpc+fOGjdunOrWrau9e/eqZcuWhhMCAOBurB0BIBi9EZXB0B0AHCgzM7PC4ykpKSFOAgAAALtj7QgAweiNqAyG7gAA4LLbuXOn9u/fL5/PFziWnJxsLhAAAAAAAJcJdy8DAAeZN2+eBg8erGnTplX4+tixY0OcCJAmTJig9957T61bt5bHc+Z2MpZlMXQHAMAw1o4AEIzeiKrA0B0AHCQmJkaSVKtWLcNJgHM2b96sNWvWlNsHEQAAmMfaEQCC0RtRFdheBgAAXFZpaWl6+eWXZVmW6SgAAAAAAFx2DN0BwIEqupt67dq11a5dOyUkJBhIBDd76qmnlJ2drbvuuqvc1e5JSUkGUwEAgLNYOwJAMHojKoPtZQDAgb788ktt2bJFt956qyzL0saNG9WuXTstXLhQaWlpGjBggOmIcJHdu3dLkl599dXAMcuyGLoDAGATrB0BIBi9EZXB0B0AHCg3N1fLli1TXFycJOnYsWMaM2aM/v73v7M4QMgtWLDAdAQAAHARrB0BIBi9EZXhMR0AAFD1jhw5ElgYSFJcXJyOHj2qunXrKjycv7citEpLSzVv3jxNnDhRkrR//35t3rzZbCgAABDA2hEAgtEbURkM3QHAgRo3bqz09HQdOnRIhw4dUnp6uho1aiSfz8fNLBFyTzzxhL788kt9+OGHkqS6detqxowZhlMBAICzWDsCQDB6IyqDG6kCgAN9/fXXmjJlij744ANJUufOnTVu3DjVrVtXe/fuVcuWLQ0nhJv07dtXy5cvV3JyspYtWyZJ+ulPf6oVK1aYDQYAACSxdgSAitAbURkM3QEAwGX1s5/9TK+99lpg6O7z+ZScnKyVK1eajgYAAAAAQJVjAyIAcKidO3dq//798vl8gWPJycnmAsG1WrZsqeXLl6usrEz79u3TnDlz1KlTJ9OxAADAeVg7AkAweiO+L650BwAHmjBhgt577z21bt1aHs+Z23dYlqU///nPhpPBjU6fPq2nnnpKb731lvx+v26//XaNHTtW0dHRpqMBAACxdgSAitAbURkM3QHAge644w6tXLlSUVFRpqMAAADA5lg7AkAweiMqw2M6AACg6jVs2FCRkZGmYwBBhg0bZjoCAAD4BtaOABCM3ojKYE93AHCghIQEPfLII7rrrrvK/VU+KSnJYCpAys7ONh0BAAB8A2tHAAhGb0RlMHQHAAfavXu3JOnVV18NHLMsi8UBjGNXOwAA7Ie1IwAEozeiMtjTHQAAhExeXp5q1aplOgYAAAAAAJcNe7oDgAOVlpZq3rx5mjhxoiRp//792rx5s9lQcL1Dhw7pgw8+0L59+0xHAQAA52HtCADB6I2oDIbuAOBATzzxhL788kt9+OGHkqS6detqxowZhlPBbUaPHh14/OGHH6pfv35atGiR0tLS9OabbxpMBgAAzsfaEQCC0RtRGQzdAcCBdu7cqSlTpgRu9lK7dm2VlpYaTgW3+fzzzwOPn3/+eaWnp2vu3Ll6+eWX9cILLxhMBgAAzsfaEQCC0RtRGQzdAcCBzr+zuiT5fD5uYImQsywr8PjkyZPq0KGDJOmaa66Rz+czFQsAAHwDa0cACEZvRGWEmw4AAKh6LVu21PLly1VWVqZ9+/Zpzpw56tSpk+lYcJnDhw9r2rRp8vv9OnHihHw+n8LCwiSJoTsAADbC2hEAgtEbURlc6Q4ADvToo49q69atOnbsmPr37y+Px6NRo0aZjgWXSUtLU61atVS7dm3dc889OnnypCTpyJEjatOmjdlwAAAggLUjAASjN6IyLD+fiwAAAAAAAAAAoEpwpTsAONywYcNMR4DLeb1evfTSSxo4cKAGDRqkl156SV6v13QsAABQAdaOABCM3ohLxdAdABwuOzvbdAS43IQJE7Rt2zb9/Oc/V1pamnbs2KEJEyaYjgUAACrA2hEAgtEbcam4kSoAOBy7iMG07du36/XXX5dlWZKk7t27q3fv3oZTAQCAirB2BIBg9EZcKq50BwCHW7BggekIcLm6deuqqKgo8Nzr9apevXoGEwEAgAth7QgAweiNuFTcSBUAHOzQoUPavXu3WrRooauvvtp0HLjUuHHj9K9//Us9e/aUJK1bt07XX3+9WrZsKUkaMGCAyXgAAOD/sHYEgGD0RnwfXOkOAA4yevTowOMPP/xQ/fr106JFi5SWlqY333zTYDK4md/vV9u2bZWdna3s7Gy1adNGZWVl2rNnj/bs2WM6HgAArsXaEQCC0RtRFdjTHQAc5PPPPw88fv7555Wenq4OHTpo7969GjlypG6//XaD6eBW06ZNMx0BAABUgLUjAASjN6IqcKU7ADjI2RtVStLJkyfVoUMHSdI111wjn89nKhZcLCsrS1lZWZKkvXv3av78+dq8ebPhVAAAQGLtCAAVoTeiKnClOwA4yOHDhzVt2jT5/X6dOHFCPp9PYWFhksTiACG3YMECzZs3T6WlpRoyZIiWL1+uhIQEvfrqqxo0aJD69+9vOiIAAK7G2hEAgtEbURUYugOAg6SlpQUe33PPPTp58qQaNGigI0eOqE2bNgaTwY1ee+01rVq1SqdPn1aPHj20du1axcfHKycnR4MHD2boDgCAYawdASAYvRFVwfL7/X7TIQAAgPMkJydr2bJlkqSf/OQnWrVqVeC1lJQUZWZmGkoGAAAAAMDlw5XuAOBAXq9X8+fP1/vvvy/LspSYmKiBAwcqMjLSdDS4yPkfvRw2bFi517xeb6jjAACAC2DtCADB6I2oDK50BwAHGjt2rE6cOKG7775bkrR06VLVqVNH06ZNM5wMbrJw4UL17dtXsbGx5Y5/8cUX+tvf/qYnnnjCUDIAAHA+1o4AEIzeiMpg6A4ADtSzZ0+9/vrrgbuu+3w+9e7dW2vXrjWcDAAAAHbD2hEAgtEbURke0wEAAFWvbt26KioqCjz3er2qV6+ewURws5SUFC1cuFC5ubmmowAAgAqwdgSAYPRGVAZXugOAA40bN07/+te/1LNnT0nSunXrdP3116tly5aSpAEDBpiMB5fZsmWLli5dqo0bNyoxMVH9+vXTLbfcErhiBAAAmMXaEQCC0RtRGdxIFQAcyO/3q23btsrOzpYktWnTRmVlZdqzZ4/hZHCjTp06qVOnTiooKNDatWv10ksvafz48Xr77bdNRwMAAGLtCAAVoTeiMrjSHQAAhMSxY8e0bNkyLV26VGFhYVq5cqXpSAAAAAAAVDmG7gDgMFlZWZKkVq1aae/evXr77bfVsmVLJSYmGk4Gt3rjjTe0dOlS7dy5Uz179lRKSooSEhJMxwIAAGLtCAAVoTeishi6A4CDLFiwQPPmzVNpaamGDBmi5cuXKyEhQR988IEGDRqk/v37m44IFxo8eLBSU1N1++23KyoqynQcAADwf1g7AkAweiOqAkN3AHCQPn36aNGiRTp9+rR69OihtWvXKj4+Xjk5ORo8eLCWLVtmOiJcLjMzUykpKaZjAAAAsXYEgIrQG1EVPKYDAACqTlhYmGrWrKlGjRqpadOmio+PlyTVr19flmUZTgdIGRkZpiMAAID/w9oRAILRG1EVGLoDgIP4fL7A42HDhpV7zev1hjoOEIQP2AEAYB+sHQEgGL0RVYGhOwA4SP/+/ZWfny9JuvPOOwPHv/jiC914442mYgEBAwcONB0BAAD8H9aOABCM3oiqwJ7uAADgsjt48KA++ugjWZaljh07Bj6iCQAAAACA03ClOwA4UEpKihYuXKjc3FzTUQCtXLlSKSkpevPNN7Vu3TqlpqZq9erVpmMBAID/w9oRAILRG1EZXOkOAA60ZcsWLV26VBs3blRiYqL69eunW265hZu+wIiePXtqzpw5atKkiSQpOztb999/v9auXWs4GQAAkFg7AkBF6I2oDIbuAOBgBQUFWrt2rTIzM3XgwAG9/fbbpiPBhVJSUpSZmfmtxwAAgFmsHQEgGL0R3wfbywCAgxUUFCgnJ0fHjx9XrVq1TMeBS3Xr1k2zZs3S4cOHdejQIaWnp6t79+7Kz88P3KAIAACYx9oRAILRG/F9cKU7ADjQG2+8oaVLl2rnzp3q2bOnUlJSlJCQYDoWXKpVq1YXfM2yLO3ZsyeEaQAAwDexdgSAYPRGVAZDdwBwoMGDBys1NVW33367oqKiTMcBAACAjbF2BIBg9EZUBkN3AHC4zMxMpaSkmI4Bl/L5fOrTp4/WrFljOgoAAPgOWDsCQDB6Iy4Ve7oDgMNlZGSYjgAXCwsLU/369VVYWGg6CgAA+A5YOwJAMHojLlW46QAAgMuLDzTBtKZNm+q+++5Tz549FRMTEzg+YMAAg6kAAEBFWDsCQDB6Iy4VQ3cAcLiBAweajgCX8/v9at26tfbt22c6CgAA+BasHQEgGL0Rl4o93QHAoQ4ePKiPPvpIlmWpY8eOio+PNx0JAAAANsXaEQCC0RvxfTF0BwAHWrlypaZMmaKbbrpJfr9fW7du1fjx49W7d2/T0eAiW7ZsUadOnbR+/foKX09KSgpxIgAAUBHWjgAQjN6IymB7GQBwoNmzZ2vx4sVq0qSJJCk7O1v3338/iwOE1IoVK9SpUyfNnz8/6DXLshi6AwBgE6wdASAYvRGVwdAdABwoOjo6sDCQpKuuukrR0dEGE8GNpkyZIklasGCB4SQAAOBiWDsCQDB6IyqD7WUAwIH+9Kc/KSwsTPfcc4/8fr+WLFmisrIyDR48WJIUGxtrOCHc5ujRo8rOzpbP5wscu+mmmwwmAgAAZ7F2BIBg9EZUBkN3AHCgVq1aXfA1y7K0Z8+eEKaB2z3//POaO3eumjRpIo/HI+lMHS5evNhwMgAAILF2BICK0BtRGQzdAQDAZdWjRw/94x//UL169UxHAQAAAADgsvOYDgAAqFo+n0+9evUyHQMIiIuLY+AOAIBNsXYEgGD0RlQWN1IFAIcJCwtT/fr1VVhYyE1eYFRWVpYkqUuXLpo6dar69OmjyMjIwOsX+7gmAAAIDdaOABCM3ojKYugOAA7UtGlT3XffferZs6diYmICxwcMGGAwFdzmoYceKvd8/fr1gceWZZV7DgAAzGHtCADB6I2oDIbuAOBAfr9frVu31r59+0xHgYtt2LBB0plhe8eOHVWnTh1JUm5urrZt22YyGgAAOA9rRwAIRm9EZXAjVQAAcFn17dtXy5cvDzz3+/1KTU1VZmamwVQAAAAAAFweXOkOAA6yZcsWderU6YLbdiQlJYU4ERDMsiz5fD7TMQAAcD3WjgAQjN6IqsDQHQAcZMWKFerUqZPmz58f9JplWSwOYETNmjW1bds2dejQQZK0detW1axZ03AqAADA2hEAgtEbURXYXgYAAFxW27dv19ChQ9WsWTNJ0t69ezV79mwlJCQYTgYAAAAAQNVj6A4ADnX06FFlZ2eX28bjpptuMpgIbpabm6sdO3ZIktq3b6/atWubDQQAAMph7QgAweiN+L4YugOAAz3//POaO3eumjRpIo/HI+nMx+AWL15sOBkAAADshrUjAASjN6Iy2NMdABxoyZIlevPNN1WvXj3TUQAAAGBzrB0BIBi9EZXhMR0AAFD14uLiWBgAAADgO2HtCADB6I2oDLaXAQAHycrKkiS98cYbysvLU58+fRQZGRl4vVWrVqaiAQAAwGZYOwJAMHojqgJDdwBwkNtuu+2Cr1mWpfXr14cwDQAAAOyMtSMABKM3oiowdAcAB1q/fr06duyoOnXqSJJyc3O1bds2de/e3XAyAAAA2A1rRwAIRm9EZbCnOwA40F/+8pfAwkCSateurb/85S8GEwEAAMCuWDsCQDB6IyqDoTsAuIBlWfL5fKZjAAAAoBpg7QgAweiNuBQM3QHAgWrWrKlt27YFnm/dulU1a9Y0mAgAAAB2xdoRAILRG1EZ7OkOAA60fft2DR06VM2aNZMk7d27V7Nnz1ZCQoLhZAAAALAb1o4AEIzeiMpg6A4ADpWbm6sdO3ZIktq3b6/atWubDQQAAADbYu0IAMHojfi+GLoDAAAAAAAAAFBF2NMdAAAAAAAAAIAqwtAdAAAAAAAAAIAqwtAdAAAAAAAAAIAqwtAdAAAAAAAAAIAqwtAdAAAAAAAAAIAqwtAdAAAAAAAAAIAqwtAdAAAAAAAAAIAq8v8B/LebFQQDZ9cAAAAASUVORK5CYII=", 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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": "2024-11-01T03:04:03.563243Z", "iopub.status.busy": "2024-11-01T03:04:03.563093Z", "iopub.status.idle": "2024-11-01T03:04:06.680821Z", "shell.execute_reply": "2024-11-01T03:04:06.680307Z", "shell.execute_reply.started": "2024-11-01T03:04:03.563227Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is artist\n", "len pos models: 4909\n", "differing counts: 2415\n", "chirp-v3p5-engine-b_win_over_chirp-v3p5-engine-b, win ratio 1.000, (1.000, 1.000), counts 28, total 28.\n", "chirp-v3p5-engine-ft-1_win_over_chirp-v3p5-engine-ft-1, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-engine-s-31_win_over_chirp-v3p5-engine-s-31, win ratio 1.000, (1.000, 1.000), counts 11, total 11.\n", "chirp-v3p5-engine-s-8_win_over_chirp-v3p5-engine-s-8, win ratio 1.000, (1.000, 1.000), counts 138, total 138.\n", "chirp-v3p5-engine-t-5-15_win_over_chirp-v3p5-engine-t-5, win ratio 0.333, (-0.200, 0.867), counts 1, total 3.\n", "chirp-v3p5-engine-t-5-15_win_over_chirp-v3p5-engine-t-5-15, win ratio 1.000, (1.000, 1.000), counts 3, total 3.\n", "chirp-v3p5-engine-t-5_win_over_chirp-v3p5-engine-t-5-15, win ratio 0.667, (0.133, 1.200), counts 2, total 3.\n", "chirp-v3p5-engine-t-6_s_30_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.533, (0.493, 0.574), counts 312, total 585.\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.545, (0.507, 0.584), counts 349, total 640.\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.584, (0.544, 0.623), counts 352, total 603.\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2_win_over_chirp-v3p5-engine-t-6, win ratio 0.582, (0.542, 0.622), counts 338, total 581.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6, win ratio 1.000, (1.000, 1.000), counts 2312, total 2312.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_30_t_12_tag_2, win ratio 0.467, (0.426, 0.507), counts 273, total 585.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_12_tag_2, win ratio 0.455, (0.416, 0.493), counts 291, total 640.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_13_tag_2, win ratio 0.416, (0.377, 0.456), counts 251, total 603.\n", "chirp-v3p5-engine-t-6_win_over_chirp-v3p5-engine-t-6_s_max_t_14_tag_2, win ratio 0.418, (0.378, 0.458), counts 243, total 581.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-h-s-31, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n", "chirp-v3p5-h-s-31_win_over_chirp-v3p5-h-t-6, win ratio 1.000, (1.000, 1.000), counts 3, total 3.\n", "tournament players: ['chirp-v3p5-engine-t-5', 'chirp-v3p5-engine-t-5-15', 'chirp-v3p5-engine-t-6', 'chirp-v3p5-engine-t-6_s_30_t_12_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_12_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_13_tag_2', 'chirp-v3p5-engine-t-6_s_max_t_14_tag_2', 'chirp-v3p5-h-s-31', 'chirp-v3p5-h-t-6']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:02<00:00, 395.81it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ELOs\n", "chirp-v3p5-engine-t-5: 1060.2, (-178, 3417)\n", "chirp-v3p5-engine-t-5-15: 939.8, (-1195, 2179)\n", "chirp-v3p5-engine-t-6: 965.8, (955, 976)\n", "chirp-v3p5-engine-t-6_s_30_t_12_tag_2: 989.0, (965, 1013)\n", "chirp-v3p5-engine-t-6_s_max_t_12_tag_2: 997.4, (976, 1022)\n", "chirp-v3p5-engine-t-6_s_max_t_13_tag_2: 1024.6, (1001, 1050)\n", "chirp-v3p5-engine-t-6_s_max_t_14_tag_2: 1023.2, (1000, 1050)\n", "chirp-v3p5-h-s-31: 2255.6, (1000, 4693)\n", "chirp-v3p5-h-t-6: -255.6, (-2650, 1000)\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": "markdown", "metadata": {}, "source": [ "# Clean up SHIT\n", "\n", "to get the right play conts, we need the right df..." ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:08.095035Z", "start_time": "2024-05-26T00:25:07.782738Z" }, "execution": { "iopub.execute_input": "2024-11-01T03:04:06.681571Z", "iopub.status.busy": "2024-11-01T03:04:06.681413Z", "iopub.status.idle": "2024-11-01T03:04:07.844426Z", "shell.execute_reply": "2024-11-01T03:04:07.843838Z", "shell.execute_reply.started": "2024-11-01T03:04:06.681555Z" } }, "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": 62, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:04:07.845186Z", "iopub.status.busy": "2024-11-01T03:04:07.845034Z", "iopub.status.idle": "2024-11-01T03:04:09.047824Z", "shell.execute_reply": "2024-11-01T03:04:09.047209Z", "shell.execute_reply.started": "2024-11-01T03:04:07.845170Z" } }, "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": 63, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:08.361849Z", "start_time": "2024-05-26T00:25:08.199583Z" }, "execution": { "iopub.execute_input": "2024-11-01T03:04:09.048617Z", "iopub.status.busy": "2024-11-01T03:04:09.048461Z", "iopub.status.idle": "2024-11-01T03:04:09.075772Z", "shell.execute_reply": "2024-11-01T03:04:09.075238Z", "shell.execute_reply.started": "2024-11-01T03:04:09.048601Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Positive proportion of data meeting criteria: 32.40%\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": 64, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:08.523643Z", "start_time": "2024-05-26T00:25:08.367348Z" }, "execution": { "iopub.execute_input": "2024-11-01T03:04:09.076448Z", "iopub.status.busy": "2024-11-01T03:04:09.076302Z", "iopub.status.idle": "2024-11-01T03:04:10.663501Z", "shell.execute_reply": "2024-11-01T03:04:10.662900Z", "shell.execute_reply.started": "2024-11-01T03:04:09.076433Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model Name Value Counts for Preferred Clips:\n", "----------------------------------------------------------------------\n", "Model Count Fraction\n", "----------------------------------------------------------------------\n", "chirp-v3p5-engine-s-8 57,392 72.44%\n", "chirp-v3p5-engine-t-6 8,038 10.15%\n", "chirp-v3p5-engine-upload-4 3,895 4.92%\n", "chirp-v3p5-engine-t-5-15 3,767 4.75%\n", "chirp-v3p5-engine-s-31 2,046 2.58%\n", "chirp-v3p5-h-s-31 1,881 2.37%\n", "chirp-v3p5-engine-t-5 811 1.02%\n", "chirp-v3p5-engine-s-29-6 588 0.74%\n", "chirp-v3p5-engine-b 386 0.49%\n", "chirp-v3p5-engine-short 218 0.28%\n", "chirp-v3p5-h-t-6 116 0.15%\n", "chirp-v3p5-engine-ft-1 88 0.11%\n", "----------------------------------------------------------------------\n", "Total 79,226 100.00%\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": 65, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:09.198504Z", "start_time": "2024-05-26T00:25:08.885507Z" }, "execution": { "iopub.execute_input": "2024-11-01T03:04:10.664257Z", "iopub.status.busy": "2024-11-01T03:04:10.664101Z", "iopub.status.idle": "2024-11-01T03:04:11.081147Z", "shell.execute_reply": "2024-11-01T03:04:11.080580Z", "shell.execute_reply.started": "2024-11-01T03:04:10.664241Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Validation passed!\n" ] } ], "source": [ "validate_preference_data(final_interesting_clips)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:04:11.081895Z", "iopub.status.busy": "2024-11-01T03:04:11.081743Z", "iopub.status.idle": "2024-11-01T03:04:11.147002Z", "shell.execute_reply": "2024-11-01T03:04:11.146508Z", "shell.execute_reply.started": "2024-11-01T03:04:11.081879Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of rows in final_interesting_clips for model chirp-v3p5-engine-t-5:\n", "1,631\n", "done (158452, 57)\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": 67, "metadata": { "ExecuteTime": { "end_time": "2024-05-26T00:25:10.265241Z", "start_time": "2024-05-26T00:25:09.934743Z" }, "execution": { "iopub.execute_input": "2024-11-01T03:04:11.147656Z", "iopub.status.busy": "2024-11-01T03:04:11.147512Z", "iopub.status.idle": "2024-11-01T03:04:11.281409Z", "shell.execute_reply": "2024-11-01T03:04:11.280937Z", "shell.execute_reply.started": "2024-11-01T03:04:11.147641Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Unique Users:\n", "--------------------\n", "398,173\n", "--------------------\n", "Series([], Name: count, dtype: int64)\n", "(0, 47)\n" ] }, { "data": { "text/html": [ "
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idpasswordlast_loginis_superuserusernamefirst_namelast_nameemailis_staffis_activedate_joined
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" ], "text/plain": [ " id password last_login is_superuser username first_name last_name email is_staff is_active date_joined\n", "0 27205089 None False perkintonpatricia441@gmail.com perkintonpatricia441@gmail.com False True 2024-07-08 16:16:06.847119+00:00" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate the number of unique users\n", "total_unique_users = clip_df[\"user_id\"].nunique()\n", "\n", "# Print the result in a nicely formatted way\n", "print(\"Total Unique Users:\")\n", "print(\"-\" * 20)\n", "print(f\"{total_unique_users:,}\")\n", "print(\"-\" * 20)\n", "\n", "# This can take a while cause we have a lot of users...\n", "# query = \"\"\"\n", "# SELECT *\n", "# FROM auth_user\n", "# \"\"\"\n", "# user_df = pd.read_sql_query(query, engine)\n", "# user_df.head()\n", "\n", "test_user_id = 4688272\n", "print(\n", " clip_df[clip_df[\"user_id\"] == test_user_id][\"created_at\"]\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": 68, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:04:11.282089Z", "iopub.status.busy": "2024-11-01T03:04:11.281942Z", "iopub.status.idle": "2024-11-01T03:04:23.832582Z", "shell.execute_reply": "2024-11-01T03:04:23.831997Z", "shell.execute_reply.started": "2024-11-01T03:04:11.282075Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of no_reaction_clip_df:\n", "(830151, 47)\n", "\n", "Proportion of clips without reactions:\n", "19.06%\n", "Ratio of clips without reactions to total clips from the same users:\n", "0.7462\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "free 9625\n", "pro 1705\n", "Ratio of clips from super bad users: 2.47%\n", "DONE\n" ] } ], "source": [ "not_known_bot_gens_mask = clip_df[\"model_name\"] != \"chirp-v3p5-engine-b\"\n", "run_bot_detection(clip_df[not_known_bot_gens_mask], reaction_df, write_to_file=True, cut_off_freq=0.95, min_generations_for_no_reaction=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Alpha testing user selection" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:04:23.833333Z", "iopub.status.busy": "2024-11-01T03:04:23.833178Z", "iopub.status.idle": "2024-11-01T03:04:23.853935Z", "shell.execute_reply": "2024-11-01T03:04:23.853484Z", "shell.execute_reply.started": "2024-11-01T03:04:23.833318Z" } }, "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": 70, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:04:23.854548Z", "iopub.status.busy": "2024-11-01T03:04:23.854408Z", "iopub.status.idle": "2024-11-01T03:05:58.894739Z", "shell.execute_reply": "2024-11-01T03:05:58.893951Z", "shell.execute_reply.started": "2024-11-01T03:04:23.854533Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6205818, 25)\n", "1418386\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": 71, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:05:58.895773Z", "iopub.status.busy": "2024-11-01T03:05:58.895485Z", "iopub.status.idle": "2024-11-01T03:06:00.850838Z", "shell.execute_reply": "2024-11-01T03:06:00.850158Z", "shell.execute_reply.started": "2024-11-01T03:05:58.895753Z" } }, "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": 72, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:06:00.851969Z", "iopub.status.busy": "2024-11-01T03:06:00.851514Z", "iopub.status.idle": "2024-11-01T03:06:00.918291Z", "shell.execute_reply": "2024-11-01T03:06:00.917701Z", "shell.execute_reply.started": "2024-11-01T03:06:00.851948Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1631, 57)\n", "(0, 57)\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": 73, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:06:00.919064Z", "iopub.status.busy": "2024-11-01T03:06:00.918905Z", "iopub.status.idle": "2024-11-01T03:06:00.953316Z", "shell.execute_reply": "2024-11-01T03:06:00.952719Z", "shell.execute_reply.started": "2024-11-01T03:06:00.919049Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "snow_batch_size = 100_000\n", "snow_results = []\n", "for clip_ids_chunk in tqdm.tqdm(\n", " [\n", " v4_clip_ids[i : i + snow_batch_size]\n", " for i in range(0, len(v4_clip_ids), snow_batch_size)\n", " ]\n", "):\n", " id_query_str = \",\".join(\"'\" + x + \"'\" for x in clip_ids_chunk)\n", " print(f\"Number of clip IDs in this chunk: {len(clip_ids_chunk)}\")\n", " print(f\"Length of the ID query string: {len(id_query_str)}\")\n", "\n", " session_query = snow_session.sql(\n", " f\"\"\" select *\n", " from ML_SONG_SUMMARY_INFO\n", " where p_date = DATE(SYSDATE() - INTERVAL '2 HOUR')\n", " and p_hour = hour(SYSDATE() - INTERVAL '2 HOUR')\n", " and song_id in ({id_query_str})\n", " order by p_hour desc;\"\"\"\n", " )\n", " temp_df_snow_test = pd.DataFrame(session_query.collect())\n", " snow_results.append(temp_df_snow_test)\n", "print(len(snow_results))" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T03:06:00.954074Z", "iopub.status.busy": "2024-11-01T03:06:00.953928Z", "iopub.status.idle": "2024-11-01T03:06:01.367713Z", "shell.execute_reply": "2024-11-01T03:06:01.366969Z", "shell.execute_reply.started": "2024-11-01T03:06:00.954060Z" } }, "outputs": [ { "ename": "ValueError", "evalue": "No objects to concatenate", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[74], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_snow_test \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43msnow_results\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m df_snow_test \u001b[38;5;241m=\u001b[39m df_snow_test\u001b[38;5;241m.\u001b[39mrename(columns\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39mlower())\n\u001b[1;32m 3\u001b[0m df_snow_test \u001b[38;5;241m=\u001b[39m df_snow_test\u001b[38;5;241m.\u001b[39mrename(columns\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msong_id\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstr_id\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n", "File \u001b[0;32m~/anaconda3/envs/suno_env_dev/lib/python3.10/site-packages/pandas/core/reshape/concat.py:382\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 380\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 382\u001b[0m op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[43m \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 384\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 385\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 387\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n", "File \u001b[0;32m~/anaconda3/envs/suno_env_dev/lib/python3.10/site-packages/pandas/core/reshape/concat.py:445\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_integrity \u001b[38;5;241m=\u001b[39m verify_integrity\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;241m=\u001b[39m copy\n\u001b[0;32m--> 445\u001b[0m objs, keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_clean_keys_and_objs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[1;32m 448\u001b[0m ndims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ndims(objs)\n", "File \u001b[0;32m~/anaconda3/envs/suno_env_dev/lib/python3.10/site-packages/pandas/core/reshape/concat.py:507\u001b[0m, in \u001b[0;36m_Concatenator._clean_keys_and_objs\u001b[0;34m(self, objs, keys)\u001b[0m\n\u001b[1;32m 504\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m 506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 507\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 510\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs_list))\n", "\u001b[0;31mValueError\u001b[0m: No objects to concatenate" ] } ], "source": [ "df_snow_test = pd.concat(snow_results)\n", "df_snow_test = df_snow_test.rename(columns=lambda x: x.lower())\n", "df_snow_test = df_snow_test.rename(columns={\"song_id\": \"str_id\"})\n", "print(\"Shape of df_snow_test:\")\n", "print(f\"Rows: {df_snow_test.shape[0]}\")\n", "print(f\"Columns: {df_snow_test.shape[1]}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.368298Z", "iopub.status.idle": "2024-11-01T03:06:01.368502Z", "shell.execute_reply": "2024-11-01T03:06:01.368400Z", "shell.execute_reply.started": "2024-11-01T03:06:01.368391Z" } }, "outputs": [], "source": [ "subset_v4_clips_df[\"str_id\"] = subset_v4_clips_df[\"id\"].astype(str)\n", "subset_v4_clips_df_test = subset_v4_clips_df.merge(\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.status.busy": "2024-11-01T03:06:01.369166Z", "iopub.status.idle": "2024-11-01T03:06:01.369373Z", "shell.execute_reply": "2024-11-01T03:06:01.369269Z", "shell.execute_reply.started": "2024-11-01T03:06:01.369261Z" } }, "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.status.busy": "2024-11-01T03:06:01.369928Z", "iopub.status.idle": "2024-11-01T03:06:01.370114Z", "shell.execute_reply": "2024-11-01T03:06:01.370020Z", "shell.execute_reply.started": "2024-11-01T03:06:01.370012Z" } }, "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": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.370609Z", "iopub.status.idle": "2024-11-01T03:06:01.370767Z", "shell.execute_reply": "2024-11-01T03:06:01.370696Z", "shell.execute_reply.started": "2024-11-01T03:06:01.370688Z" } }, "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.status.busy": "2024-11-01T03:06:01.371513Z", "iopub.status.idle": "2024-11-01T03:06:01.371696Z", "shell.execute_reply": "2024-11-01T03:06:01.371606Z", "shell.execute_reply.started": "2024-11-01T03:06:01.371595Z" } }, "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.status.busy": "2024-11-01T03:06:01.372213Z", "iopub.status.idle": "2024-11-01T03:06:01.372367Z", "shell.execute_reply": "2024-11-01T03:06:01.372299Z", "shell.execute_reply.started": "2024-11-01T03:06:01.372292Z" } }, "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.status.busy": "2024-11-01T03:06:01.372864Z", "iopub.status.idle": "2024-11-01T03:06:01.373018Z", "shell.execute_reply": "2024-11-01T03:06:01.372945Z", "shell.execute_reply.started": "2024-11-01T03:06:01.372938Z" } }, "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.status.busy": "2024-11-01T03:06:01.373370Z", "iopub.status.idle": "2024-11-01T03:06:01.373520Z", "shell.execute_reply": "2024-11-01T03:06:01.373452Z", "shell.execute_reply.started": "2024-11-01T03:06:01.373445Z" } }, "outputs": [], "source": [ "clip_df[\"task\"].value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.374006Z", "iopub.status.idle": "2024-11-01T03:06:01.374153Z", "shell.execute_reply": "2024-11-01T03:06:01.374085Z", "shell.execute_reply.started": "2024-11-01T03:06:01.374078Z" } }, "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(\"cover usage\", n_created_cover, n_pro_created, round(n_created_cover / n_pro_created, 4))\n", "n_created_artist = clip_df[clip_df[\"task\"] == \"artist_consistency\"][\"user_id\"].nunique()\n", "print(\"artist usage\", n_created_artist, n_pro_created, round(n_created_artist / n_pro_created, 4))\n", "n_created_infill = clip_df[clip_df[\"task\"] == \"infill\"][\"user_id\"].nunique()\n", "print(\"infill usage\", n_created_infill, n_pro_created, round(n_created_infill / n_pro_created, 4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# FE change exp " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.374585Z", "iopub.status.idle": "2024-11-01T03:06:01.374731Z", "shell.execute_reply": "2024-11-01T03:06:01.374664Z", "shell.execute_reply.started": "2024-11-01T03:06:01.374656Z" } }, "outputs": [], "source": [ "# with open(\"user_id_to_exp_dict.json\", \"w\") as fp:\n", "# json.dump(user_id_to_exp_dict, fp)\n", "with open(\"user_id_to_exp_dict.json\", \"r\") as fp:\n", " user_id_to_exp_dict = json.load(fp)\n", "clip_df[\"in_fe_exp\"] = clip_df[\"user_id\"].apply(\n", " lambda x: user_id_to_exp_dict.get(str(x), False)\n", ")\n", "print(\"clip\", clip_df[\"in_fe_exp\"].sum(), clip_df.shape[0])\n", "print(\n", " \"user\",\n", " clip_df[clip_df[\"in_fe_exp\"]][\"user_id\"].nunique(),\n", " clip_df[\"user_id\"].nunique(),\n", ")\n", "\n", "create_exp_mask = clip_df[\n", " \"in_fe_exp\"\n", "] # & (clip_df[\"created_at\"] >= \"2024-09-25 00:00:00\")\n", "unique_users_in_exp = clip_df[create_exp_mask][\"user_id\"].unique()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.375254Z", "iopub.status.idle": "2024-11-01T03:06:01.375408Z", "shell.execute_reply": "2024-11-01T03:06:01.375336Z", "shell.execute_reply.started": "2024-11-01T03:06:01.375329Z" } }, "outputs": [], "source": [ "request_counts = clip_df[(clip_df[\"model_name\"] == \"chirp-v3p5-engine-s-8\")][\n", " \"request_id\"\n", "].value_counts()\n", "unique_count_2_requests = request_counts[request_counts == 2].index\n", "unique_count_2_requests = set(list(unique_count_2_requests))\n", "same_model_mask = clip_df[\"request_id\"].isin(unique_count_2_requests)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.375712Z", "iopub.status.idle": "2024-11-01T03:06:01.375863Z", "shell.execute_reply": "2024-11-01T03:06:01.375796Z", "shell.execute_reply.started": "2024-11-01T03:06:01.375788Z" } }, "outputs": [], "source": [ "out_exp_period_mask = clip_df[\"created_at\"] < \"2024-09-25 00:00:00\"\n", "in_exp_period_mask = clip_df[\"created_at\"] >= \"2024-09-25 00:00:00\"\n", "users_in_both = set(clip_df[out_exp_period_mask][\"user_id\"].unique()).intersection(\n", " clip_df[in_exp_period_mask][\"user_id\"].unique()\n", ")\n", "print(len(users_in_both))\n", "same_period_user_mask = clip_df[\"user_id\"].isin(users_in_both)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.376485Z", "iopub.status.idle": "2024-11-01T03:06:01.376644Z", "shell.execute_reply": "2024-11-01T03:06:01.376573Z", "shell.execute_reply.started": "2024-11-01T03:06:01.376565Z" } }, "outputs": [], "source": [ "def plot_hourly_upvote_rate(input_df, color, column_name, label):\n", " # Convert created_at to datetime and group by hour\n", " input_df = input_df.copy()\n", " input_df[\"created_at\"] = pd.to_datetime(input_df[\"created_at\"])\n", " hourly_upvote_rate = input_df.groupby(input_df[\"created_at\"].dt.floor(\"h\"))[\n", " column_name\n", " ].mean()\n", "\n", " # Compute the rate's error using standard error of the mean\n", " hourly_upvote_count = input_df.groupby(input_df[\"created_at\"].dt.floor(\"h\"))[\n", " column_name\n", " ].count()\n", " hourly_upvote_std = input_df.groupby(input_df[\"created_at\"].dt.floor(\"h\"))[\n", " column_name\n", " ].std()\n", " # hourly_upvote_rate_err = hourly_upvote_std / np.sqrt(hourly_upvote_count)\n", "\n", " # Plot the hourly upvote rate as a vertical bar plot with error bars\n", " plt.plot(hourly_upvote_rate, alpha=0.5, color=color, label=label)\n", "\n", " # Reduce the number of x-axis ticks\n", " # num_ticks = 10 # Adjust this number to get desired tick density\n", " # tick_indices = np.linspace(0, len(hourly_upvote_rate.index) - 1, num_ticks, dtype=int)\n", " # plt.xticks(tick_indices, [hourly_upvote_rate.index[i].strftime('%Y-%m-%d %H:00') for i in tick_indices], rotation=45, ha='right')\n", "\n", " # Calculate overall update rate and its error for comparison\n", " overall_update_rate = input_df[column_name].mean()\n", " overall_update_rate_err = input_df[column_name].std() / np.sqrt(len(input_df))\n", " print(\n", " f\"Overall update rate: {overall_update_rate:.4f} ± {overall_update_rate_err:.4f}\"\n", " )\n", "\n", "\n", "# Call the function with clip_df\n", "plt.figure(figsize=(12, 6))\n", "plot_hourly_upvote_rate(\n", " clip_df[(clip_df[\"in_fe_exp\"]) & (same_model_mask) & (same_period_user_mask)],\n", " color=\"blue\",\n", " column_name=\"upvoted\",\n", " label=\"exp\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(~clip_df[\"in_fe_exp\"]) & (same_model_mask) & (same_period_user_mask)],\n", " color=\"orange\",\n", " column_name=\"upvoted\",\n", " label=\"control\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(clip_df[\"in_fe_exp\"]) & (same_model_mask) & (~same_period_user_mask)],\n", " color=\"green\",\n", " column_name=\"upvoted\",\n", " label=\"exp diff user\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(~clip_df[\"in_fe_exp\"]) & (same_model_mask) & (~same_period_user_mask)],\n", " color=\"red\",\n", " column_name=\"upvoted\",\n", " label=\"control diff user\",\n", ")\n", "plt.title(\"Hourly Upvote Rate with Error Bars\")\n", "plt.xlabel(\"Date and Hour\")\n", "plt.ylabel(\"Upvote Rate\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.377249Z", "iopub.status.idle": "2024-11-01T03:06:01.377406Z", "shell.execute_reply": "2024-11-01T03:06:01.377335Z", "shell.execute_reply.started": "2024-11-01T03:06:01.377327Z" } }, "outputs": [], "source": [ "# Call the function with clip_df\n", "plt.figure(figsize=(12, 6))\n", "plot_hourly_upvote_rate(\n", " clip_df[(clip_df[\"in_fe_exp\"]) & (same_model_mask) & (same_period_user_mask)],\n", " color=\"blue\",\n", " column_name=\"downvoted\",\n", " label=\"exp\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(~clip_df[\"in_fe_exp\"]) & (same_model_mask) & (same_period_user_mask)],\n", " color=\"orange\",\n", " column_name=\"downvoted\",\n", " label=\"control\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(clip_df[\"in_fe_exp\"]) & (same_model_mask) & (~same_period_user_mask)],\n", " color=\"green\",\n", " column_name=\"downvoted\",\n", " label=\"exp diff user\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(~clip_df[\"in_fe_exp\"]) & (same_model_mask) & (~same_period_user_mask)],\n", " color=\"red\",\n", " column_name=\"downvoted\",\n", " label=\"control diff user\",\n", ")\n", "plt.title(\"Hourly Downvote Rate with Error Bars\")\n", "plt.xlabel(\"Date and Hour\")\n", "plt.ylabel(\"Downvote Rate\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.377971Z", "iopub.status.idle": "2024-11-01T03:06:01.378126Z", "shell.execute_reply": "2024-11-01T03:06:01.378054Z", "shell.execute_reply.started": "2024-11-01T03:06:01.378047Z" } }, "outputs": [], "source": [ "out_exp_period_mask = clip_df[\"created_at\"] < \"2024-10-03 22:00:00\"\n", "in_exp_period_mask = clip_df[\"created_at\"] >= \"2024-10-03 22:00:00\"\n", "users_in_both = set(clip_df[out_exp_period_mask][\"user_id\"].unique()).intersection(\n", " clip_df[in_exp_period_mask][\"user_id\"].unique()\n", ")\n", "print(len(users_in_both))\n", "same_period_user_mask = clip_df[\"user_id\"].isin(users_in_both)\n", "plt.figure(figsize=(12, 6))\n", "plot_hourly_upvote_rate(\n", " clip_df[(clip_df[\"in_fe_exp\"]) & (same_model_mask) & (same_period_user_mask)],\n", " color=\"blue\",\n", " column_name=\"upvoted\",\n", " label=\"exp\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(~clip_df[\"in_fe_exp\"]) & (same_model_mask) & (same_period_user_mask)],\n", " color=\"orange\",\n", " column_name=\"upvoted\",\n", " label=\"control\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(clip_df[\"in_fe_exp\"]) & (same_model_mask) & (~same_period_user_mask)],\n", " color=\"green\",\n", " column_name=\"upvoted\",\n", " label=\"exp diff user\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(~clip_df[\"in_fe_exp\"]) & (same_model_mask) & (~same_period_user_mask)],\n", " color=\"red\",\n", " column_name=\"upvoted\",\n", " label=\"control diff user\",\n", ")\n", "plt.title(\"Hourly Upvote Rate with Error Bars\")\n", "plt.xlabel(\"Date and Hour\")\n", "plt.ylabel(\"Upvote Rate\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()\n", "# Call the function with clip_df\n", "plt.figure(figsize=(12, 6))\n", "plot_hourly_upvote_rate(\n", " clip_df[(clip_df[\"in_fe_exp\"]) & (same_model_mask) & (same_period_user_mask)],\n", " color=\"blue\",\n", " column_name=\"downvoted\",\n", " label=\"exp\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(~clip_df[\"in_fe_exp\"]) & (same_model_mask) & (same_period_user_mask)],\n", " color=\"orange\",\n", " column_name=\"downvoted\",\n", " label=\"control\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(clip_df[\"in_fe_exp\"]) & (same_model_mask) & (~same_period_user_mask)],\n", " color=\"green\",\n", " column_name=\"downvoted\",\n", " label=\"exp diff user\",\n", ")\n", "plot_hourly_upvote_rate(\n", " clip_df[(~clip_df[\"in_fe_exp\"]) & (same_model_mask) & (~same_period_user_mask)],\n", " color=\"red\",\n", " column_name=\"downvoted\",\n", " label=\"control diff user\",\n", ")\n", "plt.title(\"Hourly Downvote Rate with Error Bars\")\n", "plt.xlabel(\"Date and Hour\")\n", "plt.ylabel(\"Downvote Rate\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.378624Z", "iopub.status.idle": "2024-11-01T03:06:01.378774Z", "shell.execute_reply": "2024-11-01T03:06:01.378702Z", "shell.execute_reply.started": "2024-11-01T03:06:01.378695Z" } }, "outputs": [], "source": [ "print_out_value_counts_nicely(clip_df[clip_df[\"has_stems\"]], \"model_name\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.379277Z", "iopub.status.idle": "2024-11-01T03:06:01.379429Z", "shell.execute_reply": "2024-11-01T03:06:01.379359Z", "shell.execute_reply.started": "2024-11-01T03:06:01.379352Z" } }, "outputs": [], "source": [ "user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"cover\")][\"model_name\"].value_counts() # [[\"model_name\", \"s3_id\"]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.380081Z", "iopub.status.idle": "2024-11-01T03:06:01.380236Z", "shell.execute_reply": "2024-11-01T03:06:01.380164Z", "shell.execute_reply.started": "2024-11-01T03:06:01.380157Z" } }, "outputs": [], "source": [ "user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"infill\")][\"model_name\"].value_counts() # [[\"model_name\", \"s3_id\"]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.380880Z", "iopub.status.idle": "2024-11-01T03:06:01.381037Z", "shell.execute_reply": "2024-11-01T03:06:01.380965Z", "shell.execute_reply.started": "2024-11-01T03:06:01.380957Z" } }, "outputs": [], "source": [ "test_request_ids = user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"infill\") & (user_intersting_clips_3p5[\"model_name\"] == \"chirp-v3p5-engine-t-5_s_max_t_13_tag_3\")][\"request_id\"].unique()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.381512Z", "iopub.status.idle": "2024-11-01T03:06:01.381658Z", "shell.execute_reply": "2024-11-01T03:06:01.381591Z", "shell.execute_reply.started": "2024-11-01T03:06:01.381583Z" } }, "outputs": [], "source": [ "user_intersting_clips_3p5[user_intersting_clips_3p5[\"request_id\"].isin(test_request_ids)][[\"s3_id\", \"preference\", \"model_name\"]].tail(n=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.383371Z", "iopub.status.idle": "2024-11-01T03:06:01.383539Z", "shell.execute_reply": "2024-11-01T03:06:01.383464Z", "shell.execute_reply.started": "2024-11-01T03:06:01.383456Z" } }, "outputs": [], "source": [ "print_out_value_counts_nicely(clip_df[clip_df[\"task\"] == \"cover\"], \"model_name\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.383941Z", "iopub.status.idle": "2024-11-01T03:06:01.384098Z", "shell.execute_reply": "2024-11-01T03:06:01.384025Z", "shell.execute_reply.started": "2024-11-01T03:06:01.384017Z" } }, "outputs": [], "source": [ "print_out_value_counts_nicely(clip_df[clip_df[\"task\"] == \"infill\"], \"model_name\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.384653Z", "iopub.status.idle": "2024-11-01T03:06:01.384824Z", "shell.execute_reply": "2024-11-01T03:06:01.384745Z", "shell.execute_reply.started": "2024-11-01T03:06:01.384737Z" } }, "outputs": [], "source": [ "# plot_clip_distribution(clip_df[(clip_df[\"task\"] == \"infill\") & (clip_df[\"model_name\"] == \"chirp-v3p5-engine-s-8\")])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.385354Z", "iopub.status.idle": "2024-11-01T03:06:01.385508Z", "shell.execute_reply": "2024-11-01T03:06:01.385435Z", "shell.execute_reply.started": "2024-11-01T03:06:01.385428Z" } }, "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.status.busy": "2024-11-01T03:06:01.385866Z", "iopub.status.idle": "2024-11-01T03:06:01.386010Z", "shell.execute_reply": "2024-11-01T03:06:01.385943Z", "shell.execute_reply.started": "2024-11-01T03:06:01.385935Z" } }, "outputs": [], "source": [ "clip_infill_tags = clip_df[(clip_df[\"task\"] == \"infill\")][\"metadata\"].apply(lambda x: x.get(\"tags\", \"\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.status.busy": "2024-11-01T03:06:01.386560Z", "iopub.status.idle": "2024-11-01T03:06:01.386713Z", "shell.execute_reply": "2024-11-01T03:06:01.386641Z", "shell.execute_reply.started": "2024-11-01T03:06:01.386634Z" } }, "outputs": [], "source": [ "clip_infill_tags.apply(lambda x: len(x.strip()) == 0).sum() / clip_infill_tags.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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 }