{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "from suno_utils.utils.text import read_jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "{'clip_a_id': 'christian/data/upsample_100hz_v4_t_5_20241018/v1/502a5f3c-0b3d-4949-bf68-9844e09361e2-832201', 'clip_b_id': 'christian/data/upsample_100hz_v4_t_5_20241018/v1/502a5f3c-0b3d-4949-bf68-9844e09361e2-804140', 'clip_a_url': 'https://suno-annotation-public.s3.amazonaws.com/upsample-compare-ab-preference-20241022/502a5f3c-0b3d-4949-bf68-9844e09361e2-832201.mp3', 'clip_b_url': 'https://suno-annotation-public.s3.amazonaws.com/upsample-compare-ab-preference-20241022/502a5f3c-0b3d-4949-bf68-9844e09361e2-trimmed.mp3', 'orig_pref': 'a'}\n",
      "{'clip_a_id': 'christian/data/upsample_100hz_v4_t_5_20241018/v1/c1234ca6-865c-4900-a6c3-18bb3314bc18-151716', 'clip_b_id': 'christian/data/upsample_100hz_v4_t_5_20241018/v1/c1234ca6-865c-4900-a6c3-18bb3314bc18-486792', 'clip_a_url': 'https://suno-annotation-public.s3.amazonaws.com/upsample-compare-ab-preference-20241022/c1234ca6-865c-4900-a6c3-18bb3314bc18-151716.mp3', 'clip_b_url': 'https://suno-annotation-public.s3.amazonaws.com/upsample-compare-ab-preference-20241022/c1234ca6-865c-4900-a6c3-18bb3314bc18-trimmed.mp3', 'orig_pref': 'a'}\n",
      "{'clip_a_id': 'christian/data/upsample_100hz_v4_t_5_20241018/v1/aea3979f-e0a6-4e29-9e51-e38ddbb0f375-129635', 'clip_b_id': 'christian/data/upsample_100hz_v4_t_5_20241018/v1/aea3979f-e0a6-4e29-9e51-e38ddbb0f375-197978', 'clip_a_url': 'https://suno-annotation-public.s3.amazonaws.com/upsample-compare-ab-preference-20241022/aea3979f-e0a6-4e29-9e51-e38ddbb0f375-trimmed.mp3', 'clip_b_url': 'https://suno-annotation-public.s3.amazonaws.com/upsample-compare-ab-preference-20241022/aea3979f-e0a6-4e29-9e51-e38ddbb0f375-129635.mp3', 'orig_pref': 'b'}\n",
      "195 103 49 51\n",
      "total: 298\n",
      "a preference: 0.65\n",
      "b preference: 0.35\n",
      "with 100% consensus: 0.49 (100)\n",
      "a consensus: 0.82 (49)\n",
      "b consensus: 0.18 (49)\n"
     ]
    }
   ],
   "source": [
    "#label_filepath = \"/home/christian/code/christian/labelbox/music-ab-preference/Export v2 project - music-ab-preference - 10_23_2024.ndjson\"\n",
    "#label_filepath = \"/home/christian/code/christian/labelbox/music-ab-preference/Export v2 project - ab-music-preference-v2 (test) - 10_29_2024.ndjson\"\n",
    "label_filepath = \"/home/christian/code/christian/labelbox/music-ab-preference/Export v2 project - ab-music-preference-v2 (test) - 10_29_2024 (2).ndjson\"\n",
    "data = read_jsonl(label_filepath)\n",
    "\n",
    "metadata_filepath = \"/home/christian/code/christian/labelbox/music-ab-preference/metadata-upsample-compare-ab-preference-20241022.json\"\n",
    "metadata = json.load(open(metadata_filepath, \"r\"))\n",
    " \n",
    "# each element is a row in the labelbox export\n",
    "# we want to get the s3 id and which item had the preference (A or B)\n",
    "# we also want to connect this with the two generations\n",
    "# in this test \"A\" is the upsampled and \"B\" is the original from sunoGPT\n",
    "a_count = 0 # upsampled\n",
    "b_count = 0 # gpt\n",
    "consensus_count = 0\n",
    "no_consensus_count = 0\n",
    "\n",
    "a_consensus_count = 0\n",
    "b_consensus_count = 0\n",
    "\n",
    "results = {}\n",
    "\n",
    "for elem in data:\n",
    "    # get the s3 id\n",
    "    s3_id = elem[\"data_row\"][\"global_key\"]\n",
    "\n",
    "    # check in metadata\n",
    "    if s3_id in metadata:\n",
    "        row_metadata = metadata[s3_id]\n",
    "        ref_preference = row_metadata[\"orig_pref\"]\n",
    "        print(row_metadata)\n",
    "    else:\n",
    "        raise ValueError(f\"S3 ID {s3_id} not found in metadata\")\n",
    "    \n",
    "    results[s3_id] = {\n",
    "        \"labels\": [],\n",
    "        \"a_clip_url\": row_metadata[\"clip_a_url\"] if ref_preference == \"a\" else row_metadata[\"clip_b_url\"],\n",
    "        \"b_clip_url\": row_metadata[\"clip_b_url\"] if ref_preference == \"a\" else row_metadata[\"clip_a_url\"]\n",
    "    }\n",
    "\n",
    "    # extract the preference\n",
    "    details = elem[\"data_row\"][\"details\"]\n",
    "    project_id = list(elem[\"projects\"].keys())[0]\n",
    "    project = elem[\"projects\"][project_id]\n",
    "    if len(project[\"labels\"]) > 0:\n",
    "        labels = []\n",
    "        for val in project[\"labels\"]:\n",
    "            if len(val[\"annotations\"][\"classifications\"]) > 0:\n",
    "                # we also want to check for consensus among all labels                 \n",
    "                preference = val[\"annotations\"][\"classifications\"][0][\"radio_answer\"][\"value\"]\n",
    "                # there are four cases, the user always selects A or B\n",
    "                # however, sometimes A refers to the upsampled (A) and sometimes it refers to the original (B)\n",
    "                # we need to keep track of which is which\n",
    "                if preference == \"A\":\n",
    "                    if ref_preference == \"a\":\n",
    "                        a_count += 1\n",
    "                        actual_preference = \"a\"\n",
    "                    else:\n",
    "                        b_count += 1\n",
    "                        actual_preference = \"b\"\n",
    "                elif preference == \"B\":\n",
    "                    if ref_preference == \"a\":\n",
    "                        b_count += 1\n",
    "                        actual_preference = \"b\"\n",
    "                    else:\n",
    "                        a_count += 1\n",
    "                        actual_preference = \"a\"\n",
    "                # check for consensus\n",
    "                labels.append(preference)\n",
    "                results[s3_id][\"labels\"].append(actual_preference)\n",
    "        if len(labels) > 1:\n",
    "            # check that all labels are the same\n",
    "            consensus = all(label == labels[0] for label in labels)\n",
    "            if consensus:\n",
    "                if labels[0] == \"A\":\n",
    "                    if ref_preference == \"a\":\n",
    "                        a_consensus_count += 1\n",
    "                    else:\n",
    "                        b_consensus_count += 1\n",
    "                elif labels[0] == \"B\":\n",
    "                    if ref_preference == \"a\":\n",
    "                        b_consensus_count += 1\n",
    "                    else:\n",
    "                        a_consensus_count += 1\n",
    "            else:\n",
    "                no_consensus_count += 1\n",
    "        else:\n",
    "            no_consensus_count += 1\n",
    "\n",
    "    else:\n",
    "        preference = None\n",
    "\n",
    "    if consensus:\n",
    "        consensus_count += 1    \n",
    "\n",
    "\n",
    "print(a_count, b_count, consensus_count, no_consensus_count)\n",
    "print(f\"total: {a_count + b_count}\")\n",
    "print(f\"a preference: {a_count / (a_count + b_count):0.2f}\")\n",
    "print(f\"b preference: {b_count / (a_count + b_count):0.2f}\")\n",
    "print(f\"with 100% consensus: {consensus_count / (consensus_count + no_consensus_count):0.2f} ({consensus_count + no_consensus_count})\")\n",
    "print(f\"a consensus: {a_consensus_count / (a_consensus_count + b_consensus_count):0.2f} ({a_consensus_count + b_consensus_count})\")\n",
    "print(f\"b consensus: {b_consensus_count / (a_consensus_count + b_consensus_count):0.2f} ({a_consensus_count + b_consensus_count})\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython\n",
    "\n",
    "count = 0\n",
    "for key, value in results.items():\n",
    "    # check where all labels are the same\n",
    "    if all(label == value[\"labels\"][0] for label in value[\"labels\"]):\n",
    "        if value[\"labels\"][0] == \"b\":\n",
    "            print(value[\"a_clip_url\"], value[\"b_clip_url\"])\n",
    "            IPython.display.display(IPython.display.Audio(value[\"a_clip_url\"]))\n",
    "            IPython.display.display(IPython.display.Audio(value[\"b_clip_url\"]))\n",
    "            count += 1\n",
    "\n",
    "print(count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "# now that we have a_count and b_count, lets' calculate the statistics \n",
    "def analyze_preference_test(a_counts, b_counts):\n",
    "    \"\"\"\n",
    "    Analyze results of preference test between two systems.\n",
    "    \n",
    "    Parameters:\n",
    "    a_counts (int): Number of preferences for system A (original)\n",
    "    b_counts (int): Number of preferences for system B (improved)\n",
    "    \n",
    "    Returns:\n",
    "    dict: Dictionary containing analysis results\n",
    "    \"\"\"\n",
    "    # Total number of comparisons\n",
    "    n_total = a_counts + b_counts\n",
    "    \n",
    "    # Calculate preference rate\n",
    "    preference_rate = b_counts / n_total\n",
    "    \n",
    "    # Calculate preference above chance (deviation from 50%)\n",
    "    preference_above_chance = (preference_rate - 0.5) * 100\n",
    "    \n",
    "    # Perform binomial test\n",
    "    p_value = stats.binomtest(b_counts, n_total, p=0.5).pvalue\n",
    "    \n",
    "    # Calculate confidence interval (95%)\n",
    "    ci = stats.binomtest(b_counts, n_total).proportion_ci()\n",
    "    ci_above_chance = (ci.low - 0.5) * 100, (ci.high - 0.5) * 100\n",
    "    \n",
    "    results = {\n",
    "        'total_comparisons': n_total,\n",
    "        'preference_rate': preference_rate * 100,  # as percentage\n",
    "        'preference_above_chance': preference_above_chance,\n",
    "        'p_value': p_value,\n",
    "        'significant': p_value < 0.05,\n",
    "        'ci_preference': (ci.low * 100, ci.high * 100),\n",
    "        'ci_above_chance': ci_above_chance\n",
    "    }\n",
    "    \n",
    "    return results\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_comparisons: 298\n",
      "preference_rate: 65.44\n",
      "preference_above_chance: 15.44\n",
      "p_value: 1.0820540098197794e-07\n",
      "significant: True\n",
      "ci_preference: 59.74 - 70.83\n",
      "ci_above_chance: 9.74 - 20.83\n"
     ]
    }
   ],
   "source": [
    "results = analyze_preference_test(b_count, a_count)\n",
    "for key, value in results.items():\n",
    "    if type(value) == float:\n",
    "        print(f\"{key}: {value:0.2f}\")\n",
    "    elif type(value) == tuple:\n",
    "        print(f\"{key}: {value[0]:0.2f} - {value[1]:0.2f}\")\n",
    "    else:\n",
    "        print(f\"{key}: {value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_comparisons: 49\n",
      "preference_rate: 81.63\n",
      "preference_above_chance: 31.63\n",
      "p_value: 9.263546409954415e-06\n",
      "significant: True\n",
      "ci_preference: 67.98 - 91.24\n",
      "ci_above_chance: 17.98 - 41.24\n"
     ]
    }
   ],
   "source": [
    "results = analyze_preference_test(b_consensus_count, a_consensus_count)\n",
    "for key, value in results.items():\n",
    "    if type(value) == float:\n",
    "        print(f\"{key}: {value:0.2f}\")\n",
    "    elif type(value) == tuple:\n",
    "        print(f\"{key}: {value[0]:0.2f} - {value[1]:0.2f}\")\n",
    "    else:\n",
    "        print(f\"{key}: {value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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