{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:23.303355Z",
     "iopub.status.busy": "2025-09-12T13:05:23.303027Z",
     "iopub.status.idle": "2025-09-12T13:05:23.316268Z",
     "shell.execute_reply": "2025-09-12T13:05:23.315854Z",
     "shell.execute_reply.started": "2025-09-12T13:05:23.303336Z"
    }
   },
   "outputs": [],
   "source": [
    "# setup autoload\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.040680Z",
     "start_time": "2024-05-16T13:58:19.777010Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:23.317876Z",
     "iopub.status.busy": "2025-09-12T13:05:23.317760Z",
     "iopub.status.idle": "2025-09-12T13:05:26.080266Z",
     "shell.execute_reply": "2025-09-12T13:05:26.079731Z",
     "shell.execute_reply.started": "2025-09-12T13:05:23.317861Z"
    }
   },
   "outputs": [],
   "source": [
    "import ast\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "from collections import defaultdict\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from preference_data_preparation_diff import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from suno_utils.utils.s3 import download_s3_files\n",
    "from suno_utils.utils.text import read_json, read_jsonl, write_json, write_jsonl\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from suno_utils.audio import Audio\n",
    "from suno_analytics.preference_helper import get_preference_counts\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 500)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"display.width\", 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.082172Z",
     "start_time": "2024-05-16T13:58:21.041926Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:26.082281Z",
     "iopub.status.busy": "2025-09-12T13:05:26.082152Z",
     "iopub.status.idle": "2025-09-12T13:05:26.100198Z",
     "shell.execute_reply": "2025-09-12T13:05:26.099770Z",
     "shell.execute_reply.started": "2025-09-12T13:05:26.082267Z"
    }
   },
   "outputs": [],
   "source": [
    "OUT_DATA_DIR = \"/app/suno/data/dpo/diff_v6_t11_comb/\"\n",
    "os.makedirs(OUT_DATA_DIR, exist_ok=True)\n",
    "NPZ_DIR = \"/app/suno/data/dpo/diff_v6\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:53.962528Z",
     "start_time": "2024-05-16T13:58:21.105919Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:26.101843Z",
     "iopub.status.busy": "2025-09-12T13:05:26.101724Z",
     "iopub.status.idle": "2025-09-12T13:05:30.445608Z",
     "shell.execute_reply": "2025-09-12T13:05:30.445045Z",
     "shell.execute_reply.started": "2025-09-12T13:05:26.101830Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference data shape (148440, 89)\n",
      "unique users 37498\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle(\n",
    "    \"/home/tony/Data/Preference/up_v6/interesting_clips_up_u_6_20250323_full.pkl\"\n",
    ")  # , engine='python')\n",
    "print(\"Preference data shape\", df.shape)\n",
    "print(\"unique users\", df[\"user_id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:30.447501Z",
     "iopub.status.busy": "2025-09-12T13:05:30.447372Z",
     "iopub.status.idle": "2025-09-12T13:05:30.465366Z",
     "shell.execute_reply": "2025-09-12T13:05:30.464909Z",
     "shell.execute_reply.started": "2025-09-12T13:05:30.447485Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_public\n",
      "False    135888\n",
      "True      12552\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"is_public\"].value_counts())\n",
    "# # remove public for now cause fucking users\n",
    "# df = df[~df[\"is_public\"]]\n",
    "# print(df[\"is_public\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:30.466002Z",
     "iopub.status.busy": "2025-09-12T13:05:30.465863Z",
     "iopub.status.idle": "2025-09-12T13:05:30.575542Z",
     "shell.execute_reply": "2025-09-12T13:05:30.575101Z",
     "shell.execute_reply.started": "2025-09-12T13:05:30.465988Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"upsample_clip_id\"] = df[\"metadata\"].apply(lambda x: x.get(\"upsample_clip_id\", \"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.199480Z",
     "start_time": "2024-05-16T13:58:53.963687Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:30.576181Z",
     "iopub.status.busy": "2025-09-12T13:05:30.576043Z",
     "iopub.status.idle": "2025-09-12T13:05:38.150898Z",
     "shell.execute_reply": "2025-09-12T13:05:38.150316Z",
     "shell.execute_reply.started": "2025-09-12T13:05:30.576168Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "233813\n",
      "76801\n",
      "157012\n",
      "pre-downloaded df (148440, 90)\n",
      "downloaded df (148408, 90)\n",
      "vae downloaded df (148408, 90)\n"
     ]
    }
   ],
   "source": [
    "all_converted_paths = os.listdir(NPZ_DIR)\n",
    "print(len(all_converted_paths))\n",
    "\n",
    "converted_paths = set(\n",
    "    [f.replace(\".npz\", \"\") for f in all_converted_paths if \"vae\" not in f]\n",
    ")\n",
    "print(len(converted_paths))\n",
    "vae_converted_paths = set(\n",
    "    [f.replace(\"_vae.npz\", \"\") for f in all_converted_paths if \"vae\" in f]\n",
    ")\n",
    "print(len(vae_converted_paths))\n",
    "\n",
    "print(\"pre-downloaded df\", df.shape)\n",
    "df[df[\"upsample_clip_id\"].isin(converted_paths)].shape\n",
    "df = df[df[\"upsample_clip_id\"].isin(converted_paths)].copy()\n",
    "print(\"downloaded df\", df.shape)\n",
    "df[df[\"s3_id\"].isin(vae_converted_paths)].shape\n",
    "df = df[df[\"s3_id\"].isin(vae_converted_paths)].copy()\n",
    "print(\"vae downloaded df\", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.467253Z",
     "start_time": "2024-05-16T13:58:56.207647Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:38.151619Z",
     "iopub.status.busy": "2025-09-12T13:05:38.151468Z",
     "iopub.status.idle": "2025-09-12T13:05:38.196933Z",
     "shell.execute_reply": "2025-09-12T13:05:38.196472Z",
     "shell.execute_reply.started": "2025-09-12T13:05:38.151603Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_up\n",
       "True    148408\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"is_up\"] = df[\"model_name\"].str.contains(\"up\")\n",
    "df[\"is_up\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LET's do the data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.592883Z",
     "start_time": "2024-05-16T13:58:56.470781Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:38.197582Z",
     "iopub.status.busy": "2025-09-12T13:05:38.197438Z",
     "iopub.status.idle": "2025-09-12T13:05:38.294898Z",
     "shell.execute_reply": "2025-09-12T13:05:38.294386Z",
     "shell.execute_reply.started": "2025-09-12T13:05:38.197567Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preference  model_name     \n",
      "False       chirp-v4-up-u-6    74204\n",
      "True        chirp-v4-up-u-6    74204\n",
      "Name: count, dtype: int64\n",
      "(148408, 91)\n",
      "(148408, 91)\n"
     ]
    }
   ],
   "source": [
    "## for 13b this is easy for now\n",
    "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n",
    "print(df.shape)\n",
    "df = df[df[\"model_name\"].isin([\"chirp-v4-up-u-6\"])]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.909539Z",
     "start_time": "2024-05-16T13:58:56.595736Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:38.295580Z",
     "iopub.status.busy": "2025-09-12T13:05:38.295437Z",
     "iopub.status.idle": "2025-09-12T13:05:38.482512Z",
     "shell.execute_reply": "2025-09-12T13:05:38.481985Z",
     "shell.execute_reply.started": "2025-09-12T13:05:38.295565Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(148408, 91)\n",
      "(148408, 91)\n",
      "preference  model_name     \n",
      "False       chirp-v4-up-u-6    74204\n",
      "True        chirp-v4-up-u-6    74204\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df = df[\n",
    "    df[\"request_id\"].isin(\n",
    "        df[\"request_id\"].value_counts().index[df[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "]\n",
    "print(df.shape)\n",
    "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n",
    "assert df.shape[0] == df[\"request_id\"].nunique() * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:38.483177Z",
     "iopub.status.busy": "2025-09-12T13:05:38.483035Z",
     "iopub.status.idle": "2025-09-12T13:05:50.776282Z",
     "shell.execute_reply": "2025-09-12T13:05:50.775696Z",
     "shell.execute_reply.started": "2025-09-12T13:05:38.483162Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total pair quality scores: 78647\n",
      "Total unpacked pair quality scores: 910224\n"
     ]
    }
   ],
   "source": [
    "with open(\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"r\") as file:\n",
    "    full_pair_quality = json.load(file)\n",
    "print(\"Total pair quality scores:\", len(full_pair_quality))\n",
    "\n",
    "unpacked_pair_quality = {}\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\n",
    "    for clip_id, pair_quality in pairs_of_qualities.items():\n",
    "        unpacked_pair_quality[clip_id] = pair_quality\n",
    "print(\"Total unpacked pair quality scores:\", len(unpacked_pair_quality))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:50.777108Z",
     "iopub.status.busy": "2025-09-12T13:05:50.776868Z",
     "iopub.status.idle": "2025-09-12T13:05:56.138259Z",
     "shell.execute_reply": "2025-09-12T13:05:56.137683Z",
     "shell.execute_reply.started": "2025-09-12T13:05:50.777092Z"
    }
   },
   "outputs": [],
   "source": [
    "clip_diffs = []\n",
    "clip_ratios = []\n",
    "loudness_diff = []\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\n",
    "    mean_neg_scores = []\n",
    "    mean_pos_scores = []\n",
    "    neg_loudness = []\n",
    "    pos_loudness = []\n",
    "    for i, (clip_id, pair_quality) in enumerate(pairs_of_qualities.items()):\n",
    "        if i % 2 == 0:\n",
    "            mean_neg_scores.append(np.mean(pair_quality[\"ear_v2_quality_scores\"] if pair_quality else 0))\n",
    "            neg_loudness.append(pair_quality[\"abs_loudness_factor\"] if pair_quality else 0)\n",
    "        if i % 2 == 1:\n",
    "            mean_pos_scores.append(np.mean(pair_quality[\"ear_v2_quality_scores\"] if pair_quality else 0))\n",
    "            pos_loudness.append(pair_quality[\"abs_loudness_factor\"] if pair_quality else 0)\n",
    "    ratios = [(pos - neg) / (pos + 0.0001) for pos, neg in zip(mean_pos_scores, mean_neg_scores)]\n",
    "    pos_diffs = [(pos - prev_pos) / (prev_pos + 0.0001) for prev_pos, pos in zip(mean_pos_scores, mean_pos_scores[1:])]\n",
    "    neg_diffs = [(neg - prev_neg) / (prev_neg + 0.0001) for prev_neg, neg in zip(mean_neg_scores, mean_neg_scores[1:])]\n",
    "    loudness_diff.extend([pos_l - neg_l for (pos_l, neg_l) in zip(pos_loudness, neg_loudness)])\n",
    "    for i in range(1, len(ratios)):\n",
    "        clip_ratios.append(ratios[i])\n",
    "        clip_diffs.append(pos_diffs[i - 1] - neg_diffs[i - 1])\n",
    "x = clip_ratios\n",
    "y = clip_diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:56.139011Z",
     "iopub.status.busy": "2025-09-12T13:05:56.138859Z",
     "iopub.status.idle": "2025-09-12T13:05:57.242106Z",
     "shell.execute_reply": "2025-09-12T13:05:57.241606Z",
     "shell.execute_reply.started": "2025-09-12T13:05:56.138995Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0015148434117973127\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(loudness_diff, bins=np.linspace(-3, 3, 100))\n",
    "print(np.mean([l for l in loudness_diff if l != 0]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:57.243009Z",
     "iopub.status.busy": "2025-09-12T13:05:57.242676Z",
     "iopub.status.idle": "2025-09-12T13:05:57.951701Z",
     "shell.execute_reply": "2025-09-12T13:05:57.951206Z",
     "shell.execute_reply.started": "2025-09-12T13:05:57.242993Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the 2D histogram (heatmap)\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Create a 2D histogram\n",
    "bin_edges = np.linspace(-0.5, 0.5, 101)  # 30 bins from -1 to 1\n",
    "hist, x_edges, y_edges = np.histogram2d(\n",
    "    x, y, \n",
    "    bins=[bin_edges, bin_edges],  # Same bins for both x and y\n",
    "    range=[[-0.5, 0.5], [-0.5, 0.5]]      # Ensure range is from -1 to 1 for both axes\n",
    ")\n",
    "\n",
    "# Create a heatmap using pcolormesh for better control\n",
    "X, Y = np.meshgrid(x_edges[:-1], y_edges[:-1])\n",
    "plt.pcolormesh(X, Y, hist.T, cmap='viridis', shading='auto')\n",
    "\n",
    "# Add a color bar\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Counts', rotation=270, labelpad=20, fontsize=12)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Clip quality diff ratios', fontsize=12)\n",
    "plt.ylabel('Clip quality diff ratio difference with prev', fontsize=12)\n",
    "plt.title('2D Histogram (Heatmap) of Correlated Data', fontsize=14)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig('2d_histogram.png', dpi=300)  # Save to file (optional)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:57.952422Z",
     "iopub.status.busy": "2025-09-12T13:05:57.952267Z",
     "iopub.status.idle": "2025-09-12T13:05:59.765305Z",
     "shell.execute_reply": "2025-09-12T13:05:59.764812Z",
     "shell.execute_reply.started": "2025-09-12T13:05:57.952407Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1 ---> -0.11953252744657139\n",
      "0.2 ---> -0.07354777665790817\n",
      "0.5 ---> 0.0001426730134182208\n",
      "0.8 ---> 0.07380426103527324\n",
      "0.9 ---> 0.12047755535464598\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(clip_diffs, bins=np.linspace(-1, 1, 100))\n",
    "for percentage in [0.1, 0.2, 0.5, 0.8, 0.9]:\n",
    "    print(percentage, \"--->\", np.quantile(sorted(clip_diffs), percentage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:05:59.766036Z",
     "iopub.status.busy": "2025-09-12T13:05:59.765883Z",
     "iopub.status.idle": "2025-09-12T13:06:01.571851Z",
     "shell.execute_reply": "2025-09-12T13:06:01.571354Z",
     "shell.execute_reply.started": "2025-09-12T13:05:59.766021Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1 ---> -0.13923352906741976\n",
      "0.2 ---> -0.0800077638546069\n",
      "0.5 ---> 0.001993863020674336\n",
      "0.8 ---> 0.07901185201238375\n",
      "0.9 ---> 0.1290829524922543\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(clip_ratios, bins=np.linspace(-1, 1, 100))\n",
    "for percentage in [0.1, 0.2, 0.5, 0.8, 0.9]:\n",
    "    print(percentage, \"--->\", np.quantile(sorted(clip_ratios), percentage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:01.572594Z",
     "iopub.status.busy": "2025-09-12T13:06:01.572442Z",
     "iopub.status.idle": "2025-09-12T13:06:02.812484Z",
     "shell.execute_reply": "2025-09-12T13:06:02.811918Z",
     "shell.execute_reply.started": "2025-09-12T13:06:01.572578Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_audio_quality_measures(s3_id):\n",
    "    audio_quality = unpacked_pair_quality.get(s3_id, [])\n",
    "    if not audio_quality:\n",
    "        return [None for _ in range(11)]\n",
    "    return [\n",
    "        np.mean(audio_quality[\"ear_v2_quality_scores\"]), # float(audio_quality[\"ear_v2_quality_scores\"]),\n",
    "        float(audio_quality[\"shimmer_score\"]),\n",
    "        float(audio_quality[\"loudness_factor\"]),\n",
    "        audio_quality[\"spectral_character\"],\n",
    "        float(audio_quality[\"spectral_centroid\"]),\n",
    "        float(audio_quality[\"bass_ratio\"]),\n",
    "        float(audio_quality[\"mid_ratio\"]),\n",
    "        float(audio_quality[\"high_ratio\"]),\n",
    "        float(audio_quality[\"stereo_width\"]),\n",
    "        int(audio_quality[\"total_clips\"]),\n",
    "        float(audio_quality[\"clips_per_second\"]),\n",
    "        float(audio_quality[\"abs_loudness_factor\"])\n",
    "    ]\n",
    "\n",
    "\n",
    "df[\n",
    "    [\n",
    "        \"pair_quality\",\n",
    "        \"total_shimmer_score\",\n",
    "        \"loudness_factor\",\n",
    "        \"spectral_character\",\n",
    "        \"spectral_centroid\",\n",
    "        \"bass_ratio\",\n",
    "        \"mid_ratio\",\n",
    "        \"high_ratio\",\n",
    "        \"stereo_width\",\n",
    "        \"total_clips\",\n",
    "        \"clips_per_second\",\n",
    "        \"loudness_abs\"\n",
    "    ]\n",
    "] = pd.DataFrame(df[\"s3_id\"].apply(get_audio_quality_measures).tolist(), index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:02.813241Z",
     "iopub.status.busy": "2025-09-12T13:06:02.813088Z",
     "iopub.status.idle": "2025-09-12T13:06:03.279259Z",
     "shell.execute_reply": "2025-09-12T13:06:03.278679Z",
     "shell.execute_reply.started": "2025-09-12T13:06:02.813224Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(148408, 103)\n",
      "(147244, 103)\n",
      "(147244, 103)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df = df.dropna(subset=[\n",
    "        \"pair_quality\",\n",
    "        \"total_shimmer_score\",\n",
    "        \"loudness_factor\",\n",
    "        \"spectral_character\",\n",
    "        \"spectral_centroid\",\n",
    "        \"bass_ratio\",\n",
    "        \"mid_ratio\",\n",
    "        \"high_ratio\",\n",
    "        \"stereo_width\",\n",
    "        \"total_clips\",\n",
    "        \"clips_per_second\",\n",
    "        \"loudness_abs\"\n",
    "    ])\n",
    "print(df.shape)\n",
    "df = df[\n",
    "    df[\"request_id\"].isin(\n",
    "        df[\"request_id\"].value_counts().index[df[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:36.043975Z",
     "start_time": "2024-05-16T13:58:56.910958Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:03.279996Z",
     "iopub.status.busy": "2025-09-12T13:06:03.279841Z",
     "iopub.status.idle": "2025-09-12T13:06:03.313043Z",
     "shell.execute_reply": "2025-09-12T13:06:03.312550Z",
     "shell.execute_reply.started": "2025-09-12T13:06:03.279980Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique_requests 73622\n"
     ]
    }
   ],
   "source": [
    "# Let's use the old selection for now -- for quality assurance\n",
    "# expand the metadata columns -- this takes forever...~ 6 mins\n",
    "# test_slice = df[\"metadata\"].apply(lambda x: ast.literal_eval(str(x)))\n",
    "# test_slice = df[\"metadata\"]  # .apply(lambda x: custom_parse(x))\n",
    "# test_slice_series = test_slice.apply(pd.Series)\n",
    "# df = pd.concat([df, test_slice_series], axis=1, join=\"inner\")\n",
    "print(\"unique_requests\", df[\"request_id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:40.799375Z",
     "start_time": "2024-05-16T13:59:36.394236Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:03.317634Z",
     "iopub.status.busy": "2025-09-12T13:06:03.317261Z",
     "iopub.status.idle": "2025-09-12T13:06:03.887135Z",
     "shell.execute_reply": "2025-09-12T13:06:03.886567Z",
     "shell.execute_reply.started": "2025-09-12T13:06:03.317617Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    147244\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    73622\n",
      "True     73622\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-v4-up-u-6    147244\n",
      "Name: count, dtype: int64 preference  model_name     \n",
      "False       chirp-v4-up-u-6    73622\n",
      "True        chirp-v4-up-u-6    73622\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    147244\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# get the original duration of the clips, if they are concacted\n",
    "df[\"original_duration_s\"] = df[\"total_start_s\"] + df[\"duration\"]\n",
    "# classify the continue at behavoirs by the duration choice\n",
    "audio_prompt_id_to_continue_at = {}\n",
    "for _, row in df[~df[\"continued_parent\"].isna()].iterrows():\n",
    "    audio_prompt_id = row[\"continued_parent\"]\n",
    "    if audio_prompt_id not in audio_prompt_id_to_continue_at:\n",
    "        audio_prompt_id_to_continue_at[audio_prompt_id] = row[\"continue_at\"]\n",
    "    else:\n",
    "        # pick the max\n",
    "        audio_prompt_id = max(\n",
    "            audio_prompt_id_to_continue_at[audio_prompt_id], row[\"continue_at\"]\n",
    "        )\n",
    "print(len(audio_prompt_id_to_continue_at))\n",
    "df[\"has_continue_and_start_continue_at\"] = df[\"s3_id\"].apply(\n",
    "    lambda x: audio_prompt_id_to_continue_at.get(x)\n",
    ")\n",
    "# we want continue at to be at most of the clip...\n",
    "df[\"good_continue_at\"] = (\n",
    "    (df[\"has_continue_and_start_continue_at\"] / df[\"duration\"]) > 0.9\n",
    ") | df[\"has_continue_and_start_continue_at\"].isna()\n",
    "print(df[\"good_continue_at\"].value_counts())\n",
    "\n",
    "\n",
    "print(\n",
    "    \"\\n Check some basics... \\n\",\n",
    "    df[\"preference\"].value_counts(),\n",
    "    df[\"model_name\"].value_counts(),\n",
    "    df.groupby([\"preference\"])[\"model_name\"].value_counts(),\n",
    ")\n",
    "\n",
    "df = df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "df[\"duration_rel_diff\"] = df[\"duration\"].diff()\n",
    "df[\"play_rel_diff\"] = df[\"reaction_play_count\"].diff()\n",
    "print(df[\"task\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:03.887853Z",
     "iopub.status.busy": "2025-09-12T13:06:03.887704Z",
     "iopub.status.idle": "2025-09-12T13:06:04.138578Z",
     "shell.execute_reply": "2025-09-12T13:06:04.138065Z",
     "shell.execute_reply.started": "2025-09-12T13:06:03.887837Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos_diff_preference\n",
       "1.0    51716\n",
       "2.0    21906\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by=[\"request_id\", \"preference\", \"diff_preference\"])\n",
    "df[\"pos_diff_preference\"] = df[\"diff_preference\"].diff()\n",
    "df[df[\"preference\"]][\"pos_diff_preference\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:04.139264Z",
     "iopub.status.busy": "2025-09-12T13:06:04.139116Z",
     "iopub.status.idle": "2025-09-12T13:06:04.561985Z",
     "shell.execute_reply": "2025-09-12T13:06:04.561475Z",
     "shell.execute_reply.started": "2025-09-12T13:06:04.139248Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[df[\"preference\"]][\"loudness_abs\"], label=\"pos\", bins=np.linspace(-20, -5, 100), alpha=0.5)\n",
    "plt.hist(df[~df[\"preference\"]][\"loudness_abs\"], label=\"neg\", bins=np.linspace(-20, -5, 100), alpha=0.5)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:04.562705Z",
     "iopub.status.busy": "2025-09-12T13:06:04.562555Z",
     "iopub.status.idle": "2025-09-12T13:06:04.762623Z",
     "shell.execute_reply": "2025-09-12T13:06:04.762091Z",
     "shell.execute_reply.started": "2025-09-12T13:06:04.562690Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-11.999825496287714\n"
     ]
    }
   ],
   "source": [
    "print(df[(df[\"preference\"]) & (df[\"source\"] == \"web\") & (df[\"loudness_abs\"] < -11)][\"loudness_abs\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:04.763277Z",
     "iopub.status.busy": "2025-09-12T13:06:04.763130Z",
     "iopub.status.idle": "2025-09-12T13:06:05.266585Z",
     "shell.execute_reply": "2025-09-12T13:06:05.266100Z",
     "shell.execute_reply.started": "2025-09-12T13:06:04.763261Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-10.964647137879542\n",
      "-10.99629199808624\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[(df[\"preference\"]) & (df[\"source\"] == \"web\")][\"loudness_abs\"], label=\"web\", bins=np.linspace(-20, -5, 100), alpha=0.5)\n",
    "plt.hist(df[(df[\"preference\"]) & (df[\"source\"] != \"web\")][\"loudness_abs\"], label=\"mobile\", bins=np.linspace(-20, -5, 100), alpha=0.5)\n",
    "plt.legend()\n",
    "print(df[(df[\"preference\"]) & (df[\"source\"] == \"web\")][\"loudness_abs\"].mean())\n",
    "print(df[(df[\"preference\"]) & (df[\"source\"] != \"web\")][\"loudness_abs\"].mean())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:05.267327Z",
     "iopub.status.busy": "2025-09-12T13:06:05.267166Z",
     "iopub.status.idle": "2025-09-12T13:06:05.776739Z",
     "shell.execute_reply": "2025-09-12T13:06:05.776227Z",
     "shell.execute_reply.started": "2025-09-12T13:06:05.267310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "df[\"loudness_diff\"] = df[\"loudness_abs\"].diff() / df[\"loudness_abs\"]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"loudness_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"loudness_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['loudness_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Loudness difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:05.777525Z",
     "iopub.status.busy": "2025-09-12T13:06:05.777292Z",
     "iopub.status.idle": "2025-09-12T13:06:06.182123Z",
     "shell.execute_reply": "2025-09-12T13:06:06.181615Z",
     "shell.execute_reply.started": "2025-09-12T13:06:05.777508Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "df[\"shimmer_score_diff\"] = df[\"total_shimmer_score\"].diff()\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"shimmer_score_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"shimmer_score_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['shimmer_score_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Shimmer score difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:06.182825Z",
     "iopub.status.busy": "2025-09-12T13:06:06.182675Z",
     "iopub.status.idle": "2025-09-12T13:06:06.786014Z",
     "shell.execute_reply": "2025-09-12T13:06:06.785523Z",
     "shell.execute_reply.started": "2025-09-12T13:06:06.182809Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"pair_quality\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"pair_quality\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['pair_quality']):.2f}\",\n",
    "    bins=np.linspace(10, 30, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"pair_quality\"],\n",
    "    label=f\"neg, mean: {np.mean(df[df['preference']]['pair_quality']):.2f}\",\n",
    "    bins=np.linspace(10, 30, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Pair quality --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:06.786821Z",
     "iopub.status.busy": "2025-09-12T13:06:06.786580Z",
     "iopub.status.idle": "2025-09-12T13:06:06.804506Z",
     "shell.execute_reply": "2025-09-12T13:06:06.804047Z",
     "shell.execute_reply.started": "2025-09-12T13:06:06.786805Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"pair_quality_diff\"] = df[\"pair_quality\"].diff() / df[\"pair_quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:06.805106Z",
     "iopub.status.busy": "2025-09-12T13:06:06.804964Z",
     "iopub.status.idle": "2025-09-12T13:06:07.183920Z",
     "shell.execute_reply": "2025-09-12T13:06:07.183419Z",
     "shell.execute_reply.started": "2025-09-12T13:06:06.805092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"pair_quality_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"pair_quality_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['pair_quality_diff']):.2f}\",\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Pair quality diff --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:07.184618Z",
     "iopub.status.busy": "2025-09-12T13:06:07.184469Z",
     "iopub.status.idle": "2025-09-12T13:06:07.223104Z",
     "shell.execute_reply": "2025-09-12T13:06:07.222672Z",
     "shell.execute_reply.started": "2025-09-12T13:06:07.184603Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>request_id</th>\n",
       "      <th>pair_quality</th>\n",
       "      <th>preference</th>\n",
       "      <th>total_shimmer_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>6a37a2a6-bffb-4665-b571-64cfd06c06d3</td>\n",
       "      <td>00a20fbb-d7f3-428a-8ae0-be8995760e58</td>\n",
       "      <td>12.576633</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>890efbed-b1c7-4aed-9405-3449b3285eb4</td>\n",
       "      <td>00a20fbb-d7f3-428a-8ae0-be8995760e58</td>\n",
       "      <td>18.145600</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>dce6617b-5dae-4d9b-86ea-60fc08e520d9</td>\n",
       "      <td>00c209a9-1a1d-4a63-be4a-2e66073f9c16</td>\n",
       "      <td>13.545133</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>c64cc199-695e-4954-990d-c97a4dd424d1</td>\n",
       "      <td>00c209a9-1a1d-4a63-be4a-2e66073f9c16</td>\n",
       "      <td>18.734817</td>\n",
       "      <td>True</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>e7c75183-6888-45cf-a3d4-ab3ad2ceafbc</td>\n",
       "      <td>00e6f9b0-db8f-44d1-809c-b491ee5489f3</td>\n",
       "      <td>11.550150</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252897</th>\n",
       "      <td>22e413c0-515a-49ca-a702-1dd3c7f99216</td>\n",
       "      <td>fcc8ae68-8e4a-4a77-b664-e7cda460ceb2</td>\n",
       "      <td>24.939233</td>\n",
       "      <td>True</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254470</th>\n",
       "      <td>07dc963a-baee-4c5e-bfa4-d5b859dd6eaa</td>\n",
       "      <td>fe6edaea-d044-4509-a35b-8457cbb2a2de</td>\n",
       "      <td>18.197233</td>\n",
       "      <td>False</td>\n",
       "      <td>4.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254471</th>\n",
       "      <td>55559b73-3c9f-4734-b313-67f7103319a6</td>\n",
       "      <td>fe6edaea-d044-4509-a35b-8457cbb2a2de</td>\n",
       "      <td>23.270967</td>\n",
       "      <td>True</td>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255080</th>\n",
       "      <td>40b9b3af-a872-42e7-a8d6-4a893937087d</td>\n",
       "      <td>ff03b3d0-5357-4318-bc0a-0122d1d31281</td>\n",
       "      <td>16.218417</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255081</th>\n",
       "      <td>2f2b5bb2-fdcf-4f2d-866d-d95e7d44e783</td>\n",
       "      <td>ff03b3d0-5357-4318-bc0a-0122d1d31281</td>\n",
       "      <td>20.352867</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1574 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          id                            request_id  pair_quality  preference  total_shimmer_score\n",
       "664     6a37a2a6-bffb-4665-b571-64cfd06c06d3  00a20fbb-d7f3-428a-8ae0-be8995760e58     12.576633       False             0.000000\n",
       "665     890efbed-b1c7-4aed-9405-3449b3285eb4  00a20fbb-d7f3-428a-8ae0-be8995760e58     18.145600        True             0.000000\n",
       "796     dce6617b-5dae-4d9b-86ea-60fc08e520d9  00c209a9-1a1d-4a63-be4a-2e66073f9c16     13.545133       False             0.000000\n",
       "797     c64cc199-695e-4954-990d-c97a4dd424d1  00c209a9-1a1d-4a63-be4a-2e66073f9c16     18.734817        True             0.233333\n",
       "952     e7c75183-6888-45cf-a3d4-ab3ad2ceafbc  00e6f9b0-db8f-44d1-809c-b491ee5489f3     11.550150       False             0.000000\n",
       "...                                      ...                                   ...           ...         ...                  ...\n",
       "252897  22e413c0-515a-49ca-a702-1dd3c7f99216  fcc8ae68-8e4a-4a77-b664-e7cda460ceb2     24.939233        True             0.733333\n",
       "254470  07dc963a-baee-4c5e-bfa4-d5b859dd6eaa  fe6edaea-d044-4509-a35b-8457cbb2a2de     18.197233       False             4.466667\n",
       "254471  55559b73-3c9f-4734-b313-67f7103319a6  fe6edaea-d044-4509-a35b-8457cbb2a2de     23.270967        True             6.866667\n",
       "255080  40b9b3af-a872-42e7-a8d6-4a893937087d  ff03b3d0-5357-4318-bc0a-0122d1d31281     16.218417       False             0.000000\n",
       "255081  2f2b5bb2-fdcf-4f2d-866d-d95e7d44e783  ff03b3d0-5357-4318-bc0a-0122d1d31281     20.352867        True             0.000000\n",
       "\n",
       "[1574 rows x 5 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_requests = df[(df[\"pair_quality_diff\"] > 0.2) & (df[\"preference\"])][\"request_id\"].unique()\n",
    "df[df[\"request_id\"].isin(subset_requests)][[\"id\", \"request_id\", \"pair_quality\", \"preference\", \"total_shimmer_score\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:07.223838Z",
     "iopub.status.busy": "2025-09-12T13:06:07.223700Z",
     "iopub.status.idle": "2025-09-12T13:06:07.816851Z",
     "shell.execute_reply": "2025-09-12T13:06:07.816335Z",
     "shell.execute_reply.started": "2025-09-12T13:06:07.223824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said -1 means stero to mono; 1 means mono to stereo\n",
    "# only cut off the left side\n",
    "df[\"stereo_width_diff\"] = df[\"stereo_width\"].diff()\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"stereo_width_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"stereo_width_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['stereo_width_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Stereo Width Difference --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:07.817582Z",
     "iopub.status.busy": "2025-09-12T13:06:07.817432Z",
     "iopub.status.idle": "2025-09-12T13:06:08.859715Z",
     "shell.execute_reply": "2025-09-12T13:06:08.859203Z",
     "shell.execute_reply.started": "2025-09-12T13:06:07.817566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said no tails is good\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"spectral_centroid\"].dropna(), lookup_percentiles\n",
    ")\n",
    "percentiles = np.percentile(\n",
    "    df[~df[\"preference\"]][\"spectral_centroid\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"spectral_centroid\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['spectral_centroid']):.2f}\",\n",
    "    bins=np.linspace(0, 8000, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"spectral_centroid\"],\n",
    "    label=f\"neg, mean: {np.mean(df[~df['preference']]['spectral_centroid']):.2f}\",\n",
    "    bins=np.linspace(0, 8000, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\"Spectral Centroid\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:08.860439Z",
     "iopub.status.busy": "2025-09-12T13:06:08.860281Z",
     "iopub.status.idle": "2025-09-12T13:06:09.459062Z",
     "shell.execute_reply": "2025-09-12T13:06:09.458555Z",
     "shell.execute_reply.started": "2025-09-12T13:06:08.860423Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said no tails is good\n",
    "# take the relative centroid diff\n",
    "df[\"spectral_centroid_diff\"] = df[\"spectral_centroid\"].diff() / df[\"spectral_centroid\"]\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"spectral_centroid_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"spectral_centroid_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['spectral_centroid_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Spectral Centroid Difference ratio --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:09.460007Z",
     "iopub.status.busy": "2025-09-12T13:06:09.459625Z",
     "iopub.status.idle": "2025-09-12T13:06:10.090269Z",
     "shell.execute_reply": "2025-09-12T13:06:10.089691Z",
     "shell.execute_reply.started": "2025-09-12T13:06:09.459991Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    147244\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    73622\n",
      "True     73622\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-v4-up-u-6    147244\n",
      "Name: count, dtype: int64 preference  model_name     \n",
      "False       chirp-v4-up-u-6    73622\n",
      "True        chirp-v4-up-u-6    73622\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    147244\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# get the original duration of the clips, if they are concacted\n",
    "df[\"original_duration_s\"] = df[\"total_start_s\"] + df[\"duration\"]\n",
    "# classify the continue at behavoirs by the duration choice\n",
    "audio_prompt_id_to_continue_at = {}\n",
    "for _, row in df[~df[\"continued_parent\"].isna()].iterrows():\n",
    "    audio_prompt_id = row[\"continued_parent\"]\n",
    "    if audio_prompt_id not in audio_prompt_id_to_continue_at:\n",
    "        audio_prompt_id_to_continue_at[audio_prompt_id] = row[\"continue_at\"]\n",
    "    else:\n",
    "        # pick the max\n",
    "        audio_prompt_id = max(\n",
    "            audio_prompt_id_to_continue_at[audio_prompt_id], row[\"continue_at\"]\n",
    "        )\n",
    "print(len(audio_prompt_id_to_continue_at))\n",
    "df[\"has_continue_and_start_continue_at\"] = df[\"s3_id\"].apply(\n",
    "    lambda x: audio_prompt_id_to_continue_at.get(x)\n",
    ")\n",
    "# we want continue at to be at most of the clip...\n",
    "df[\"good_continue_at\"] = (\n",
    "    (df[\"has_continue_and_start_continue_at\"] / df[\"duration\"]) > 0.9\n",
    ") | df[\"has_continue_and_start_continue_at\"].isna()\n",
    "print(df[\"good_continue_at\"].value_counts())\n",
    "\n",
    "\n",
    "print(\n",
    "    \"\\n Check some basics... \\n\",\n",
    "    df[\"preference\"].value_counts(),\n",
    "    df[\"model_name\"].value_counts(),\n",
    "    df.groupby([\"preference\"])[\"model_name\"].value_counts(),\n",
    ")\n",
    "\n",
    "df = df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "df[\"duration_rel_diff\"] = df[\"duration\"].diff()\n",
    "df[\"play_rel_diff\"] = df[\"reaction_play_count\"].diff()\n",
    "print(df[\"task\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:10.091017Z",
     "iopub.status.busy": "2025-09-12T13:06:10.090864Z",
     "iopub.status.idle": "2025-09-12T13:06:11.969573Z",
     "shell.execute_reply": "2025-09-12T13:06:11.969069Z",
     "shell.execute_reply.started": "2025-09-12T13:06:10.091000Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"neg, mean: {np.mean(df[~df['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "percentiles = np.percentile(df[df[\"preference\"]][\"total_shimmer_score\"].dropna(), [50, 75, 90])\n",
    "for percentile in percentiles:\n",
    "    # print(percentile)\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "# plt.yscale(\"log\")\n",
    "plt.title(f\"Shimmer score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.035167Z",
     "start_time": "2024-05-16T13:59:40.801098Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:11.970416Z",
     "iopub.status.busy": "2025-09-12T13:06:11.970184Z",
     "iopub.status.idle": "2025-09-12T13:06:12.155444Z",
     "shell.execute_reply": "2025-09-12T13:06:12.154866Z",
     "shell.execute_reply.started": "2025-09-12T13:06:11.970400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative 67216 positive 48518\n",
      "total pair requests 73622 selected pair requests 43842 frac 0.596\n"
     ]
    }
   ],
   "source": [
    "normal_pos_play_count = 3\n",
    "# this is lower, cause a concat is probably already ensuring that it is good\n",
    "concat_pos_play_count = 2\n",
    "# this is a filter on the concated clip\n",
    "concat_total_play_count = 3\n",
    "\n",
    "neg_filter_selection_mask = (\n",
    "    (~df[\"preference\"])  # get basics aligned\n",
    "    & (df[\"reaction_play_count\"] >= 1)  # has to be played once\n",
    "    # & (df[\"play_count\"] <= 3)  # if it is actually bad, shouldn't be listened often\n",
    "    & (df[\"duration\"] >= 30)  # can't be too short, otherwise it is obvious\n",
    "    # & (df[\"duration\"] <= 60)  # can't be badly long\n",
    "    & (df[\"has_continue_and_start_continue_at\"].isna())  # won't have any continues\n",
    "    & (df[\"norm_play_frac\"] <= 2.1)\n",
    "    # & (df[\"sum_total_play_duration_5\"] >= 31)\n",
    "    # & (df[\"dislike_count\"] >= 1) # this is kinda strict\n",
    "    #     & (\n",
    "    #         (df_slice[\"is_in_playlist\"] == False)\n",
    "    #         & (df_slice[\"concat_in_playlist\"] == False)\n",
    "    #     )  # can't be part of a playlist -- otherwise there are some like signal in it?\n",
    ")\n",
    "pos_filter_selectin_mask = (\n",
    "    (df[\"preference\"])  # get basics aligned\n",
    "    & (\n",
    "        df[\"good_continue_at\"]\n",
    "    )  # if continue, needs to continue off a certain percentage\n",
    "    & (df[\"reaction_play_count\"] >= 1)\n",
    "    & (df[\"play_rel_diff\"] >= 0)  # this is more like quality assurance\n",
    "    & (df[\"duration\"] >= 30)  # can't be too short, otherwise it is obvious\n",
    "    & (df[\"dislike_count\"] == 0)  # can't have dislikes\n",
    "    & (df[\"flag_count\"] == 0)  # can't have issues\n",
    "    & (\n",
    "        (\n",
    "            (df[\"part_of_concat\"])\n",
    "            & (df[\"reaction_play_count\"] >= concat_pos_play_count)\n",
    "            & (df[\"concat_play_counts\"] >= concat_total_play_count)\n",
    "        )\n",
    "        | (\n",
    "            (~df[\"part_of_concat\"])\n",
    "            & (df[\"reaction_play_count\"] >= normal_pos_play_count)\n",
    "            & (df[\"norm_play_frac\"] >= 2.1)  # this is a bit of a luxury cut...\n",
    "            & (df[\"sum_total_play_duration_5\"] >= 31)\n",
    "        )\n",
    "    )\n",
    "    # & (df[\"norm_play_frac\"] >= 1.9)\n",
    "    # & (df[\"user_n_clips\"] >= 100)  # user needs to have genereated at least 20\n",
    "    # & (df[\"duration_rel_diff\"] < 10) # positive isn't just longer\n",
    "    # & ((df[\"task\"] == \"\") | (df[\"task\"] == \"extend\"))\n",
    "    & (\n",
    "        (df[\"upvote_count\"] >= 1)\n",
    "        | (df[\"reaction_play_count\"] >= 5)\n",
    "        | (df[\"concat_play_counts\"] >= 5)\n",
    "    )\n",
    "    # & (df[\"pos_diff_preference\"] == 2)\n",
    "    # & ((0 < df[\"similarity\"]) &  (df[\"similarity\"] <= 0.99))\n",
    "    # & (\n",
    "    #     (df[\"cer_diff_preference\"] < 0.25) & (df[\"cer\"] < 0.8)\n",
    "    # )  # cut on hoot cer difference and abs cer\n",
    "    # & (df[\"pair_quality\"] > 0.31)  # bottom 5%\n",
    "    # & ((df[\"total_shimmer_score\"] < 1) | (df[\"shimmer_score_diff\"] < 0.4))\n",
    "    # & (df[\"stereo_width_diff\"] > -0.2)  # cut off bottom 5%\n",
    "    # & (df[\"spectral_centroid_diff\"] < 0.25)  # crop off the top 5%\n",
    ")\n",
    "print(\n",
    "    \"negative\",\n",
    "    sum(neg_filter_selection_mask),\n",
    "    \"positive\",\n",
    "    sum(pos_filter_selectin_mask),\n",
    ")\n",
    "\n",
    "neg_filter_requests = df[neg_filter_selection_mask][\"request_id\"].unique()\n",
    "pos_filter_requests = df[pos_filter_selectin_mask][\"request_id\"].unique()\n",
    "# looking for very strong signal here:\n",
    "# listen to the positive/negative more than once\n",
    "# disliked one of the clips\n",
    "unique_requests = set(pos_filter_requests).intersection(neg_filter_requests)\n",
    "print(\n",
    "    \"total pair requests\",\n",
    "    df[\"request_id\"].nunique(),\n",
    "    \"selected pair requests\",\n",
    "    len(unique_requests),\n",
    "    f\"frac {len(unique_requests) / df['request_id'].nunique():.3f}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.250737Z",
     "start_time": "2024-05-16T13:59:41.036434Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:12.156199Z",
     "iopub.status.busy": "2025-09-12T13:06:12.156039Z",
     "iopub.status.idle": "2025-09-12T13:06:12.366844Z",
     "shell.execute_reply": "2025-09-12T13:06:12.366264Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.156183Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " requests 43842 clips 87684 total khrs 4.588; N gpus for 1000 iters 5.480; 4 gpus for x iters 1370.062; n unique users 24777 n pro users 16668\n"
     ]
    }
   ],
   "source": [
    "df_slice = df[df[\"request_id\"].isin(set(unique_requests))].copy()\n",
    "print(\n",
    "    f\"{os.path.basename(OUT_DATA_DIR)} requests\",\n",
    "    df_slice[\"request_id\"].nunique(),\n",
    "    \"clips\",\n",
    "    df_slice.shape[0],\n",
    "    f\"total khrs {sum(df_slice['duration'] / 3600 / 1000):.3f};\",\n",
    "    f\"N gpus for 1000 iters {df_slice.shape[0] / 8 / 2 / 1000:.3f};\",\n",
    "    f\"4 gpus for x iters {df_slice.shape[0] / 8 / 2 / 4:.3f};\",\n",
    "    f\"n unique users {df_slice['user_id'].nunique()}\",\n",
    "    f\"n pro users {df_slice[df_slice['is_pro_user']]['user_id'].nunique()}\",\n",
    ")\n",
    "# up t7 requests 37735 clips 75470 total khrs 3.964; N gpus for 1000 iters 4.717; 4 gpus for x iters 1179.219; n unique users 19093 n pro users 16217\n",
    "# up t17 requests 49262 clips 98524 total khrs 5.187; N gpus for 1000 iters 6.158; 4 gpus for x iters 1539.438; n unique users 24093 n pro users 20326\n",
    "# up v2 t1 requests 6008 clips 12016 total khrs 0.642; N gpus for 1000 iters 0.751; 4 gpus for x iters 187.750; n unique users 3846 n pro users 3645\n",
    "# up v2 t2 requests 10772 clips 21544 total khrs 1.154; N gpus for 1000 iters 1.347; 4 gpus for x iters 336.625; n unique users 6527 n pro users 6027\n",
    "# up v3 t10 requests 27102 clips 54204 total khrs 2.938; N gpus for 1000 iters 3.388; 4 gpus for x iters 846.938; n unique users 13932 n pro users 12187\n",
    "# up v4 t1  requests 3201 clips 6402 total khrs 0.343; N gpus for 1000 iters 0.400; 4 gpus for x iters 100.031; n unique users 2363 n pro users 2321\n",
    "# up v4 t2  requests 12818 clips 25636 total khrs 1.371; N gpus for 1000 iters 1.602; 4 gpus for x iters 400.562; n unique users 7753 n pro users 7524\n",
    "# up v4 t3  requests 15332 clips 30664 total khrs 1.637; N gpus for 1000 iters 1.917; 4 gpus for x iters 479.125; n unique users 8960 n pro users 8656\n",
    "# up v4 t4  requests 18368 clips 36736 total khrs 1.962; N gpus for 1000 iters 2.296; 4 gpus for x iters 574.000; n unique users 10427 n pro users 10049\n",
    "# up v4 t5  requests 22037 clips 44074 total khrs 2.347; N gpus for 1000 iters 2.755; 4 gpus for x iters 688.656; n unique users 12121 n pro users 11551\n",
    "# up v4 t6  requests 27374 clips 54748 total khrs 2.915; N gpus for 1000 iters 3.422; 4 gpus for x iters 855.438; n unique users 14463 n pro users 13678\n",
    "# up v4 t7  requests 31797 clips 63594 total khrs 3.384; N gpus for 1000 iters 3.975; 4 gpus for x iters 993.656; n unique users 16398 n pro users 15325\n",
    "# up v5 t1  requests 17035 clips 34070 total khrs 1.788; N gpus for 1000 iters 2.129; 4 gpus for x iters 532.344; n unique users 10858 n pro users 8065\n",
    "# up v5 t2  requests 20030 clips 40060 total khrs 2.108; N gpus for 1000 iters 2.504; 4 gpus for x iters 625.938; n unique users 12395 n pro users 9156\n",
    "# up v6 t1  requests 17941 clips 35882 total khrs 1.889; N gpus for 1000 iters 2.243; 4 gpus for x iters 560.656; n unique users 11471 n pro users 8374\n",
    "# up v6 t2  requests 23563 clips 47126 total khrs 2.473; N gpus for 1000 iters 2.945; 4 gpus for x iters 736.344; n unique users 14550 n pro users 10353\n",
    "# up v6 t3  requests 34761 clips 69522 total khrs 3.647; N gpus for 1000 iters 4.345; 4 gpus for x iters 1086.281; n unique users 20264 n pro users 14000\n",
    "# up v6 t4  requests 40471 clips 80942 total khrs 4.243; N gpus for 1000 iters 5.059; 4 gpus for x iters 1264.719; n unique users 23141 n pro users 15698\n",
    "# up v6 t10  requests 40471 clips 80942 total khrs 4.243; N gpus for 1000 iters 5.059; 4 gpus for x iters 1264.719; n unique users 23141 n pro users 15698\n",
    "# up v6 t11  requests 43842 clips 87684 total khrs 4.588; N gpus for 1000 iters 5.480; 4 gpus for x iters 1370.062; n unique users 24777 n pro users 16668"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.277006Z",
     "start_time": "2024-05-16T13:59:41.252105Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:12.367561Z",
     "iopub.status.busy": "2025-09-12T13:06:12.367407Z",
     "iopub.status.idle": "2025-09-12T13:06:12.401934Z",
     "shell.execute_reply": "2025-09-12T13:06:12.401450Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.367545Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive in playlist (11524, 114)\n"
     ]
    }
   ],
   "source": [
    "test_mask = (df_slice[\"preference\"]) & (\n",
    "    (df_slice[\"is_in_playlist\"]) | (df_slice[\"concat_in_playlist\"])\n",
    ")\n",
    "print(\"positive in playlist\", df_slice[test_mask].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.392244Z",
     "start_time": "2024-05-16T13:59:41.324472Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:12.402582Z",
     "iopub.status.busy": "2025-09-12T13:06:12.402443Z",
     "iopub.status.idle": "2025-09-12T13:06:12.417631Z",
     "shell.execute_reply": "2025-09-12T13:06:12.417177Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.402567Z"
    }
   },
   "outputs": [],
   "source": [
    "# save positive ids\n",
    "# positive_preference_ids = df_slice[df_slice[\"preference\"] == False][\"s3_id\"].to_json(orient='values')\n",
    "# with open('/home/tony/Data/Preference/7b_v2/7v_v20_full_recut_id_negative.json', 'w') as file:\n",
    "#     file.write(positive_preference_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:12.418464Z",
     "iopub.status.busy": "2025-09-12T13:06:12.418131Z",
     "iopub.status.idle": "2025-09-12T13:06:12.431681Z",
     "shell.execute_reply": "2025-09-12T13:06:12.431259Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.418449Z"
    }
   },
   "outputs": [],
   "source": [
    "# def modify_model_name(model_name, metadata):\n",
    "#     if (\n",
    "#         model_name.startswith(\"chirp-v3p5-engine-t\")\n",
    "#         or model_name.startswith(\"chirp-v3p5-engine-s\")\n",
    "#         or model_name.startswith(\"chirp-v4\")\n",
    "#         or model_name.startswith(\"chirp-v3p5-h-s-31\")\n",
    "#     ):\n",
    "#         if \"param_experiment\" in metadata:\n",
    "#             exp = metadata.get(\"param_experiment\", \"\")\n",
    "#             if exp:\n",
    "#                 return f\"{model_name}_{exp}\"\n",
    "#     return model_name\n",
    "\n",
    "# metrics_check_df_slice = df_slice.copy()\n",
    "# metrics_check_df_slice[\"model_name\"] = metrics_check_df_slice.apply(\n",
    "#     lambda row: modify_model_name(row[\"model_name\"], row[\"metadata\"]), axis=1\n",
    "# )\n",
    "# get_preference_counts(metrics_check_df_slice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:12.432280Z",
     "iopub.status.busy": "2025-09-12T13:06:12.432149Z",
     "iopub.status.idle": "2025-09-12T13:06:12.731209Z",
     "shell.execute_reply": "2025-09-12T13:06:12.730718Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.432267Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df_slice[df_slice[\"preference\"]][\"loudness_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[df_slice[\"preference\"]][\"loudness_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[df_slice['preference']]['loudness_diff']):.2f}\",\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Loudness difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:12.732101Z",
     "iopub.status.busy": "2025-09-12T13:06:12.731739Z",
     "iopub.status.idle": "2025-09-12T13:06:12.763151Z",
     "shell.execute_reply": "2025-09-12T13:06:12.762665Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.732085Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "web        44578\n",
      "android    23758\n",
      "ios        19348\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_slice[\"source\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T14:00:20.866354Z",
     "start_time": "2024-05-16T14:00:12.443344Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-12T13:06:12.763871Z",
     "iopub.status.busy": "2025-09-12T13:06:12.763734Z",
     "iopub.status.idle": "2025-09-12T13:06:12.975124Z",
     "shell.execute_reply": "2025-09-12T13:06:12.974548Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.763856Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'BREAK' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[43], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# df_slice.to_csv(\"/home/tony/Data/Preference/13b_v0/interesting_clips_v3p5_s_8_20240828_slice.csv\")\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mBREAK\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'BREAK' is not defined"
     ]
    }
   ],
   "source": [
    "# df_slice.to_csv(\"/home/tony/Data/Preference/13b_v0/interesting_clips_v3p5_s_8_20240828_slice.csv\")\n",
    "BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Need to kick out the ones has gpt prompt -- these are pairs with different text inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.932966Z",
     "start_time": "2024-05-16T13:59:41.932957Z"
    },
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.975615Z",
     "iopub.status.idle": "2025-09-12T13:06:12.975793Z",
     "shell.execute_reply": "2025-09-12T13:06:12.975708Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.975699Z"
    }
   },
   "outputs": [],
   "source": [
    "final_filtered_requests = df_slice[\"request_id\"].unique()\n",
    "print(len(final_filtered_requests))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934277Z",
     "start_time": "2024-05-16T13:59:41.934268Z"
    },
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.976234Z",
     "iopub.status.idle": "2025-09-12T13:06:12.976396Z",
     "shell.execute_reply": "2025-09-12T13:06:12.976316Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.976308Z"
    }
   },
   "outputs": [],
   "source": [
    "train_requests, val_requests = train_test_split(\n",
    "    sorted(list(final_filtered_requests)), test_size=0.01, random_state=42\n",
    ")\n",
    "print(len(train_requests), len(val_requests))\n",
    "\n",
    "train_df = df_slice[df_slice[\"request_id\"].isin(set(train_requests))].copy()\n",
    "val_df = df_slice[df_slice[\"request_id\"].isin(set(val_requests))].copy()\n",
    "train_df = train_df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "train_df = train_df  # .reset_index()\n",
    "val_df = val_df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "val_df = val_df  # .reset_index()\n",
    "\n",
    "print(train_df.shape, val_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.977037Z",
     "iopub.status.idle": "2025-09-12T13:06:12.977201Z",
     "shell.execute_reply": "2025-09-12T13:06:12.977123Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.977115Z"
    }
   },
   "outputs": [],
   "source": [
    "# BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actually make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934954Z",
     "start_time": "2024-05-16T13:59:41.934946Z"
    },
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.977666Z",
     "iopub.status.idle": "2025-09-12T13:06:12.977822Z",
     "shell.execute_reply": "2025-09-12T13:06:12.977748Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.977740Z"
    }
   },
   "outputs": [],
   "source": [
    "# val_df[[\"request_id\", \"metadata\", \"updated_at\", \"user_id\", \"preference\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.935620Z",
     "start_time": "2024-05-16T13:59:41.935613Z"
    },
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.978307Z",
     "iopub.status.idle": "2025-09-12T13:06:12.978456Z",
     "shell.execute_reply": "2025-09-12T13:06:12.978387Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.978379Z"
    }
   },
   "outputs": [],
   "source": [
    "total_duration = 0\n",
    "for i, row in tqdm(train_df.iterrows(), total=len(train_df)):\n",
    "    # we need to alternate between preference: neg, pos\n",
    "    # print(i, row)\n",
    "    try:\n",
    "        assert row[\"preference\"] == (i % 2 == 1)\n",
    "    except:\n",
    "        print(i, row)\n",
    "    total_duration += row[\"duration\"]\n",
    "print(\n",
    "    f\"{round(total_duration / 60 / 60):,} hours of {train_df.shape[0]} clips, {train_df.shape[0] / 8 / 2 / 1000} nodes, {train_df.shape[0] / 8 / 2 / 4} steps\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936268Z",
     "start_time": "2024-05-16T13:59:41.936260Z"
    },
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.978868Z",
     "iopub.status.idle": "2025-09-12T13:06:12.979012Z",
     "shell.execute_reply": "2025-09-12T13:06:12.978944Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.978937Z"
    }
   },
   "outputs": [],
   "source": [
    "make_dataset(\n",
    "    val_df,\n",
    "    OUT_DATA_DIR,\n",
    "    is_val=True,\n",
    "    npz_dir=NPZ_DIR,\n",
    "    do_extend_chunks=True,\n",
    "    clip_id_to_quality_scores=unpacked_pair_quality,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936964Z",
     "start_time": "2024-05-16T13:59:41.936957Z"
    },
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.979300Z",
     "iopub.status.idle": "2025-09-12T13:06:12.979440Z",
     "shell.execute_reply": "2025-09-12T13:06:12.979374Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.979367Z"
    }
   },
   "outputs": [],
   "source": [
    "make_dataset(\n",
    "    train_df,\n",
    "    OUT_DATA_DIR,\n",
    "    is_val=False,\n",
    "    npz_dir=NPZ_DIR,\n",
    "    do_extend_chunks=True,\n",
    "    clip_id_to_quality_scores=unpacked_pair_quality,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-29T19:46:47.549860Z",
     "start_time": "2024-01-29T19:46:47.548015Z"
    }
   },
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.937879Z",
     "start_time": "2024-05-16T13:59:41.937870Z"
    },
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.979949Z",
     "iopub.status.idle": "2025-09-12T13:06:12.980103Z",
     "shell.execute_reply": "2025-09-12T13:06:12.980030Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.980022Z"
    }
   },
   "outputs": [],
   "source": [
    "# verify\n",
    "metas_val = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_val.jsonl\"))\n",
    "print(len(metas_val) / 2)\n",
    "mm_semantic_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_semantic_val.bin\"), dtype=np.uint16, mode=\"r\"\n",
    ")\n",
    "mm_vae_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    ")\n",
    "\n",
    "\n",
    "mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "print(mm_vae_val.shape)\n",
    "\n",
    "mm_semantic_val = mm_semantic_val.reshape(-1, SEMANTIC_MEMMAP_SIZE)\n",
    "print(mm_semantic_val.shape)\n",
    "\n",
    "assert len(metas_val) == mm_vae_val.shape[0] == mm_semantic_val.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.980377Z",
     "iopub.status.idle": "2025-09-12T13:06:12.980520Z",
     "shell.execute_reply": "2025-09-12T13:06:12.980452Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.980444Z"
    }
   },
   "outputs": [],
   "source": [
    "# # load codec for decoding\n",
    "# from suno_utils.tasks.dac_vae_100hz_peaq import (  # NOTE: works for 25hz as well\n",
    "#     preload_models as preload_codec_models,\n",
    "#     decode as codec_decode,\n",
    "#     encode as codec_encode,\n",
    "#     get_embedding_rate,\n",
    "#     load_model as load_codec_model,\n",
    "# )\n",
    "\n",
    "# CODEC_FILEPATH = \"s3://suno-data/christian/25hz_vae_peaq_kl_0.005.pth\"\n",
    "# preload_codec_models(CODEC_FILEPATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.981088Z",
     "iopub.status.idle": "2025-09-12T13:06:12.981233Z",
     "shell.execute_reply": "2025-09-12T13:06:12.981165Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.981158Z"
    }
   },
   "outputs": [],
   "source": [
    "# # decode some audio\n",
    "idx = 108\n",
    "# # ensure even index\n",
    "assert idx % 2 == 0\n",
    "# print(metas_val[idx])\n",
    "# print(\"negative\")\n",
    "# audio = codec_decode(mm_vae_val[idx])\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(metas_val[idx + 1])\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(mm_vae_val[idx + 1])\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.981517Z",
     "iopub.status.idle": "2025-09-12T13:06:12.981655Z",
     "shell.execute_reply": "2025-09-12T13:06:12.981591Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.981584Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.982120Z",
     "iopub.status.idle": "2025-09-12T13:06:12.982271Z",
     "shell.execute_reply": "2025-09-12T13:06:12.982200Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.982193Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.equal(torch.tensor(mm_semantic_val[idx]), torch.tensor(mm_semantic_val[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941167Z",
     "start_time": "2024-05-16T13:59:41.941159Z"
    },
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.982696Z",
     "iopub.status.idle": "2025-09-12T13:06:12.982841Z",
     "shell.execute_reply": "2025-09-12T13:06:12.982773Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.982766Z"
    }
   },
   "outputs": [],
   "source": [
    "# original_npz_path = f\"/app/suno/data/dpo/7b_npz/{test_metas[idx]['id']}.npz\"\n",
    "# original_npz_path = \"/app/suno/data/dpo/7b_npz/729c3011-f672-4ccd-8d82-1cbf2b52ff69.npz\"\n",
    "# original_arr = np.load(original_npz_path)[\"v2_raw\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941801Z",
     "start_time": "2024-05-16T13:59:41.941793Z"
    },
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.983243Z",
     "iopub.status.idle": "2025-09-12T13:06:12.983390Z",
     "shell.execute_reply": "2025-09-12T13:06:12.983321Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.983314Z"
    }
   },
   "outputs": [],
   "source": [
    "def validation_on_metas(input_metas):\n",
    "    total_bad = 0\n",
    "    total_good = 0\n",
    "    for idx in range(len(input_metas)):\n",
    "        if idx % 2 == 0:\n",
    "            pos_idx = idx + 1\n",
    "            if input_metas[idx].get(\"tags\") != input_metas[pos_idx].get(\"tags\"):\n",
    "                # print(test_metas[idx].get(\"text\") == test_metas[pos_idx].get(\"text\"), test_metas[idx].get(\"tags\"), test_metas[pos_idx].get(\"tags\"))\n",
    "                total_bad += 1\n",
    "            else:\n",
    "                total_good += 1\n",
    "    print(total_good, total_bad)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.983803Z",
     "iopub.status.idle": "2025-09-12T13:06:12.983947Z",
     "shell.execute_reply": "2025-09-12T13:06:12.983877Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.983870Z"
    }
   },
   "outputs": [],
   "source": [
    "metas_tr = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_tr.jsonl\"))\n",
    "validation_on_metas(metas_tr)\n",
    "print(len(metas_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.984422Z",
     "iopub.status.idle": "2025-09-12T13:06:12.984563Z",
     "shell.execute_reply": "2025-09-12T13:06:12.984497Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.984490Z"
    }
   },
   "outputs": [],
   "source": [
    "sum(len(meta[\"tags\"][0]) == 0 for meta in metas_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.945972Z",
     "start_time": "2024-05-16T13:59:41.945964Z"
    },
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.984818Z",
     "iopub.status.idle": "2025-09-12T13:06:12.984966Z",
     "shell.execute_reply": "2025-09-12T13:06:12.984897Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.984890Z"
    }
   },
   "outputs": [],
   "source": [
    "!cd /home/tony/Work/tony/slurm/diffusion && sbatch run_diffusion.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.985288Z",
     "iopub.status.idle": "2025-09-12T13:06:12.985430Z",
     "shell.execute_reply": "2025-09-12T13:06:12.985364Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.985357Z"
    }
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "# Basic file copy\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/make_dataset_diff_upsample_v1_r5_comb.ipynb\",\n",
    "    os.path.join(OUT_DATA_DIR, \"make_dataset.ipynb\"),\n",
    ")\n",
    "print(\"Cache kept!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspections "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.985804Z",
     "iopub.status.idle": "2025-09-12T13:06:12.985945Z",
     "shell.execute_reply": "2025-09-12T13:06:12.985879Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.985872Z"
    }
   },
   "outputs": [],
   "source": [
    "# df[df[\"preference\"] & (df[\"shimmer_score_diff\"] > 3)][\n",
    "#     [\n",
    "#         \"index\",\n",
    "#         \"s3_id\",\n",
    "#         \"total_shimmer_score\",\n",
    "#         \"shimmer_score_diff\",\n",
    "#         \"request_id\",\n",
    "#         \"preference\",\n",
    "#     ]\n",
    "# ].tail()\n",
    "\n",
    "# df[df[\"preference\"] & (df[\"pair_quality\"] < 0.1)][\n",
    "#     [\n",
    "#         \"index\",\n",
    "#         \"s3_id\",\n",
    "#         \"total_shimmer_score\",\n",
    "#         \"shimmer_score_diff\",\n",
    "#         \"pair_quality\",\n",
    "#         \"request_id\",\n",
    "#         \"preference\",\n",
    "#     ]\n",
    "# ].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.986435Z",
     "iopub.status.idle": "2025-09-12T13:06:12.986579Z",
     "shell.execute_reply": "2025-09-12T13:06:12.986511Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.986504Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_pair_df = df[df[\"request_id\"] == \"621c8b02-a905-48f1-a2d5-a4e8423d1505\"]\n",
    "# print(\n",
    "#     test_pair_df[\n",
    "#         [\n",
    "#             \"s3_id\",\n",
    "#             \"total_shimmer_score\",\n",
    "#             \"pair_quality\",\n",
    "#             \"request_id\",\n",
    "#             \"preference\",\n",
    "#             \"prompt_text\",\n",
    "#         ]\n",
    "#     ]\n",
    "# )\n",
    "# negative_audio = Audio.from_s3(\n",
    "#     f\"s3://suno-data-uploads/studio/uploads/{test_pair_df['s3_id'].values[0]}.mp3\",\n",
    "#     n_channels=2,\n",
    "# )\n",
    "# print(\"negative\")\n",
    "# negative_audio.get_segment(0, 30).play()\n",
    "# positive_audio = Audio.from_s3(\n",
    "#     f\"s3://suno-data-uploads/studio/uploads/{test_pair_df['s3_id'].values[1]}.mp3\",\n",
    "#     n_channels=2,\n",
    "# )\n",
    "# print(\"positive\")\n",
    "# positive_audio.get_segment(0, 30).play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.987030Z",
     "iopub.status.idle": "2025-09-12T13:06:12.987173Z",
     "shell.execute_reply": "2025-09-12T13:06:12.987106Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.987099Z"
    }
   },
   "outputs": [],
   "source": [
    "# total_dict = {}\n",
    "# total_dict.update(pair_quality_dict)\n",
    "# total_dict.update(pair_quality_1_dict)\n",
    "# total_dict.update(pair_quality_2_dict)\n",
    "# total_dict.update(pair_quality_3_dict)\n",
    "# len(total_dict)\n",
    "# with open(\n",
    "#     os.path.join(\"/home/tony/Data/Preference/up_v1\", \"pair_quality.json\"), \"w\"\n",
    "# ) as fp:\n",
    "#     json.dump(total_dict, fp, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.987434Z",
     "iopub.status.idle": "2025-09-12T13:06:12.987571Z",
     "shell.execute_reply": "2025-09-12T13:06:12.987506Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.987499Z"
    }
   },
   "outputs": [],
   "source": [
    "# import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.988018Z",
     "iopub.status.idle": "2025-09-12T13:06:12.988166Z",
     "shell.execute_reply": "2025-09-12T13:06:12.988097Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.988090Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_arr = np.load(\"/home/tony/Data/test_npz/diffusion_input_tensor([ 18, 182]).npy\")\n",
    "# test_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.988729Z",
     "iopub.status.idle": "2025-09-12T13:06:12.988882Z",
     "shell.execute_reply": "2025-09-12T13:06:12.988809Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.988802Z"
    }
   },
   "outputs": [],
   "source": [
    "# mm_vae_val = np.memmap(\n",
    "#     os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    "# )\n",
    "\n",
    "# mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "# print(mm_vae_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.989144Z",
     "iopub.status.idle": "2025-09-12T13:06:12.989285Z",
     "shell.execute_reply": "2025-09-12T13:06:12.989218Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.989211Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(\"negative\")\n",
    "# audio = codec_decode(test_arr[0].T / 2.5)\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(test_arr[1].T / 2.5)\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.989760Z",
     "iopub.status.idle": "2025-09-12T13:06:12.989903Z",
     "shell.execute_reply": "2025-09-12T13:06:12.989837Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.989830Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(\"negative\")\n",
    "# audio = codec_decode(mm_vae_val[18])\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(mm_vae_val[19])\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.990266Z",
     "iopub.status.idle": "2025-09-12T13:06:12.990417Z",
     "shell.execute_reply": "2025-09-12T13:06:12.990346Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.990338Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "rng = torch.quasirandom.SobolEngine(1, scramble=True, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.990864Z",
     "iopub.status.idle": "2025-09-12T13:06:12.991010Z",
     "shell.execute_reply": "2025-09-12T13:06:12.990941Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.990934Z"
    }
   },
   "outputs": [],
   "source": [
    "t = rng.draw(4)[:, 0].to(torch.bfloat16)\n",
    "print(t)\n",
    "# Replace 1% of t with ones to ensure training on terminal SNR\n",
    "t = torch.where(torch.rand_like(t) < 0.5, torch.ones_like(t), t)\n",
    "print(t)\n",
    "t = torch.repeat_interleave(t, repeats=2, dim=0)\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.991346Z",
     "iopub.status.idle": "2025-09-12T13:06:12.991486Z",
     "shell.execute_reply": "2025-09-12T13:06:12.991421Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.991414Z"
    }
   },
   "outputs": [],
   "source": [
    "(t * 32).to(int) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.991749Z",
     "iopub.status.idle": "2025-09-12T13:06:12.991887Z",
     "shell.execute_reply": "2025-09-12T13:06:12.991822Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.991815Z"
    }
   },
   "outputs": [],
   "source": [
    "t[0] = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.992296Z",
     "iopub.status.idle": "2025-09-12T13:06:12.992441Z",
     "shell.execute_reply": "2025-09-12T13:06:12.992375Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.992368Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.round(t * 32) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.992896Z",
     "iopub.status.idle": "2025-09-12T13:06:12.993051Z",
     "shell.execute_reply": "2025-09-12T13:06:12.992979Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.992971Z"
    }
   },
   "outputs": [],
   "source": [
    "# import json\n",
    "# with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"r\") as f:\n",
    "#    result = json.load(f)\n",
    "# # result = {}\n",
    "# print(len(result))\n",
    "# for job_idx in range(8):\n",
    "#     with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality_{job_idx}.json\", \"r\") as fp:\n",
    "#         current_result = json.load(fp)\n",
    "#         result.update(current_result)\n",
    "# print(len(result))\n",
    "# with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.993493Z",
     "iopub.status.idle": "2025-09-12T13:06:12.993640Z",
     "shell.execute_reply": "2025-09-12T13:06:12.993570Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.993563Z"
    }
   },
   "outputs": [],
   "source": [
    "with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality_loudness.json\", \"r\") as f:\n",
    "    result_loundess = json.load(f)\n",
    "flat_loudness = {}\n",
    "for k, v in result_loundess.items():\n",
    "    flat_loudness.update(v)\n",
    "metas_val = read_jsonl(os.path.join(\"/app/suno/data/dpo/diff_v6_t3_comb/\", \"metas_tr.jsonl\"))\n",
    "neg_loudness = []\n",
    "pos_loudness = []\n",
    "for i, meta in enumerate(metas_val):\n",
    "    if i % 2 == 0:\n",
    "        neg_loudness.append(flat_loudness[meta[\"id_x\"]][\"abs_loudness_factor\"] if meta[\"id_x\"] in flat_loudness else 0)\n",
    "    else:\n",
    "        pos_loudness.append(flat_loudness[meta[\"id_x\"]][\"abs_loudness_factor\"]  if meta[\"id_x\"] in flat_loudness else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.993952Z",
     "iopub.status.idle": "2025-09-12T13:06:12.994095Z",
     "shell.execute_reply": "2025-09-12T13:06:12.994028Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.994021Z"
    }
   },
   "outputs": [],
   "source": [
    "print(len(pos_loudness), len(neg_loudness))\n",
    "plt.hist(pos_loudness, label=\"pos\", bins=np.linspace(-20, -5, 100), alpha=0.5)\n",
    "plt.hist(neg_loudness, label=\"neg\", bins=np.linspace(-20, -5, 100), alpha=0.5)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "diff_arr = [p - n for (p, n) in zip(pos_loudness, neg_loudness)]\n",
    "plt.hist(diff_arr, label=f\"diff {np.mean(diff_arr)}\", bins=np.linspace(-5, 5, 100), alpha=0.5)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.994362Z",
     "iopub.status.idle": "2025-09-12T13:06:12.994501Z",
     "shell.execute_reply": "2025-09-12T13:06:12.994436Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.994429Z"
    }
   },
   "outputs": [],
   "source": [
    "# import json\n",
    "# with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"r\") as f:\n",
    "#    result = json.load(f)\n",
    "# print(len(result))\n",
    "# print(len(result_loundess))\n",
    "# new_result = {}\n",
    "# for k, v in result.items():\n",
    "#     new_v = v.copy()\n",
    "#     for clip_id, contents in v.items():\n",
    "#         if contents and \"abs_loudness_factor\" not in contents:\n",
    "#             # print(contents, result_loundess[k][clip_id])\n",
    "#             try:\n",
    "#                 new_v[clip_id][\"abs_loudness_factor\"] =  result_loundess[k][clip_id][\"abs_loudness_factor\"]\n",
    "#             except:\n",
    "#                 # print(clip_id)\n",
    "#                 new_v[clip_id][\"abs_loudness_factor\"] = 0.0\n",
    "#             # break\n",
    "#     # print(k, v)\n",
    "#     # break\n",
    "#     new_result[k] = new_v\n",
    "# print(len(new_result))\n",
    "# with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(new_result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.995027Z",
     "iopub.status.idle": "2025-09-12T13:06:12.995181Z",
     "shell.execute_reply": "2025-09-12T13:06:12.995109Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.995102Z"
    }
   },
   "outputs": [],
   "source": [
    "# import torch\n",
    "# semantic_codes_chunk = torch.ones((1, 100))\n",
    "# semantic_skip_phase = 0\n",
    "# semantic_skip_factor = 4\n",
    "# mask = torch.ones_like(semantic_codes_chunk, dtype=torch.bool)\n",
    "# indices = (\n",
    "#     torch.arange(semantic_codes_chunk.size(1)) + semantic_skip_phase\n",
    "# ) % semantic_skip_factor == 0\n",
    "# mask[:, indices] = False\n",
    "# mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.995671Z",
     "iopub.status.idle": "2025-09-12T13:06:12.995818Z",
     "shell.execute_reply": "2025-09-12T13:06:12.995749Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.995741Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.996343Z",
     "iopub.status.idle": "2025-09-12T13:06:12.996486Z",
     "shell.execute_reply": "2025-09-12T13:06:12.996420Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.996413Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] < 0.5)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-09-12T13:06:12.996769Z",
     "iopub.status.idle": "2025-09-12T13:06:12.996905Z",
     "shell.execute_reply": "2025-09-12T13:06:12.996842Z",
     "shell.execute_reply.started": "2025-09-12T13:06:12.996835Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] > 2)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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