main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import librosa\n",
    "import os\n",
    "import numpy as np\n",
    "import re\n",
    "from pydub import AudioSegment, effects\n",
    "import noisereduce as nr\n",
    "import logging\n",
    "\n",
    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/songs_wav.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sanitize_filename(filename):\n",
    "    \"\"\"\n",
    "    Removes or replaces invalid characters from a filename or folder name.\n",
    "    \n",
    "    Args:\n",
    "        filename (str): The original filename or folder name.\n",
    "        \n",
    "    Returns:\n",
    "        str: The sanitized filename or folder name.\n",
    "    \"\"\"\n",
    "    return re.sub(r'[<>:\"/\\\\|?*]', '', filename)\n",
    "\n",
    "def normalized_yt_title(title):\n",
    "    \"\"\"\n",
    "    Normalizes a YouTube title by converting it to lowercase, replacing spaces with underscores,\n",
    "    and removing non-alphanumeric characters except underscores.\n",
    "    \n",
    "    Args:\n",
    "        title (str): The original title of the YouTube video.\n",
    "        \n",
    "    Returns:\n",
    "        str: The normalized title.\n",
    "    \"\"\"\n",
    "    text = title.lower()\n",
    "    text = text.replace(' ', '_')\n",
    "    text = re.sub(r'[^a-z0-9_]', '', text)\n",
    "    text = re.sub(r'_{2,}', '_', text)\n",
    "    \n",
    "    return text\n",
    "\n",
    "def remove_silence(audio, threshold=20):\n",
    "    \"\"\"\n",
    "    Removes silent sections from an audio signal based on a decibel threshold.\n",
    "    \n",
    "    Args:\n",
    "        audio (ndarray): The audio signal as a numpy array.\n",
    "        threshold (int): The decibel threshold to trim silence. Defaults to 20.\n",
    "        \n",
    "    Returns:\n",
    "        ndarray: The trimmed audio signal.\n",
    "    \"\"\"\n",
    "    return librosa.effects.trim(audio, top_db=threshold)[0]\n",
    "\n",
    "def normalize_audio(audio):\n",
    "    \"\"\"\n",
    "    Normalizes the audio signal to a standard volume level.\n",
    "    \n",
    "    Args:\n",
    "        audio (AudioSegment): The audio segment to normalize.\n",
    "        \n",
    "    Returns:\n",
    "        AudioSegment: The normalized audio segment.\n",
    "    \"\"\"\n",
    "    return effects.normalize(audio)\n",
    "\n",
    "def noise_reduction(audio, noise_profile):\n",
    "    \"\"\"\n",
    "    Reduces noise from an audio signal using a noise profile.\n",
    "    \n",
    "    Args:\n",
    "        audio (ndarray): The audio signal as a numpy array.\n",
    "        noise_profile (ndarray): The noise profile as a numpy array.\n",
    "        \n",
    "    Returns:\n",
    "        ndarray: The noise-reduced audio signal.\n",
    "    \"\"\"\n",
    "    return nr.reduce_noise(audio_clip=audio, noise_clip=noise_profile)\n",
    "\n",
    "split_result = []\n",
    "\n",
    "def split_songs_to_segments(df, output_base_folder='datasets/30s_segments'):\n",
    "    \"\"\"\n",
    "    Splits songs into 30-second segments and saves them as individual files.\n",
    "    \n",
    "    Args:\n",
    "        df (DataFrame): A pandas DataFrame containing song metadata with 'wav_path' and 'nama_lagu' columns.\n",
    "        output_base_folder (str): The base folder to save the segments. Defaults to 'datasets/30s_segments'.\n",
    "        \n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "    for index, row in df.iterrows():\n",
    "        wav_path = row['wav_path']\n",
    "        nama_lagu = row['nama_lagu']\n",
    "        \n",
    "        os.makedirs(f\"{output_base_folder}/{normalized_yt_title(nama_lagu)}\", exist_ok=True)\n",
    "        \n",
    "        audio = AudioSegment.from_wav(wav_path)\n",
    "        audio = audio.set_channels(1)\n",
    "        \n",
    "        audio = normalize_audio(audio)\n",
    "        \n",
    "        total_duration = len(audio) / 1000\n",
    "        segment_duration = 30\n",
    "        num_segments = int(total_duration // segment_duration)\n",
    "        \n",
    "        for i in range(num_segments):\n",
    "            start_time = i * segment_duration * 1000 \n",
    "            end_time = (i + 1) * segment_duration * 1000\n",
    "            segment = audio[start_time:end_time]\n",
    "            \n",
    "            segment_file = f\"{output_base_folder}/{normalized_yt_title(nama_lagu)}/segment_{index}_{i}.wav\"\n",
    "            segment.export(segment_file, format=\"wav\")\n",
    "            \n",
    "            split_result.append({\n",
    "                'title': nama_lagu,\n",
    "                '30s_path': segment_file\n",
    "            })\n",
    "        print(f\"Saved {num_segments} segments for {nama_lagu} at: {output_base_folder}/{normalized_yt_title(nama_lagu)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 12 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 4 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 10 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 9 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 6 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 10 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 9 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 10 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 4 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 4 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 4 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 6 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 10 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 12 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 4 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 10 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 9 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 6 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 10 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 9 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 10 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 4 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 4 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 4 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 6 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 10 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 11 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 3 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 8 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 12 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 4 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 10 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 9 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 6 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 5 segments for Kicir-kicir at: datasets/30s_segments/kicirkicir\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 9 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 9 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 5 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 5 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 2 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 5 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 5 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 2 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 2 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 10 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 9 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 9 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 5 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 5 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 4 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 2 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 5 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 5 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 2 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 7 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 8 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 6 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
      "Saved 2 segments for Ampar-ampar Pisang at: datasets/30s_segments/amparampar_pisang\n",
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      "Saved 5 segments for Yamko Rambe Yamko at: datasets/30s_segments/yamko_rambe_yamko\n",
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      "Saved 12 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 13 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 6 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 12 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 13 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 6 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 5 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 12 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 13 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 6 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 12 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 6 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 2 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 13 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 6 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 8 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 10 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 9 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 5 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 7 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n",
      "Saved 12 segments for Bengawan Solo at: datasets/30s_segments/bengawan_solo\n"
     ]
    }
   ],
   "source": [
    "split_songs_to_segments(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save split result to csv\n",
    "split_result_df = pd.DataFrame(split_result)\n",
    "split_result_df.to_csv('data/30s_segments.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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>title</th>\n",
       "      <th>30s_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kicir-kicir</td>\n",
       "      <td>datasets/30s_segments/kicirkicir/segment_0_0.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kicir-kicir</td>\n",
       "      <td>datasets/30s_segments/kicirkicir/segment_0_1.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kicir-kicir</td>\n",
       "      <td>datasets/30s_segments/kicirkicir/segment_0_2.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kicir-kicir</td>\n",
       "      <td>datasets/30s_segments/kicirkicir/segment_0_3.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kicir-kicir</td>\n",
       "      <td>datasets/30s_segments/kicirkicir/segment_0_4.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         title                                          30s_path\n",
       "0  Kicir-kicir  datasets/30s_segments/kicirkicir/segment_0_0.wav\n",
       "1  Kicir-kicir  datasets/30s_segments/kicirkicir/segment_0_1.wav\n",
       "2  Kicir-kicir  datasets/30s_segments/kicirkicir/segment_0_2.wav\n",
       "3  Kicir-kicir  datasets/30s_segments/kicirkicir/segment_0_3.wav\n",
       "4  Kicir-kicir  datasets/30s_segments/kicirkicir/segment_0_4.wav"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_res = pd.read_csv('data/30s_segments.csv')\n",
    "df_res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def noise(data):\n",
    "    \"\"\"\n",
    "    Adds random noise to an audio signal to augment data.\n",
    "\n",
    "    Args:\n",
    "        data (ndarray): The audio signal as a numpy array.\n",
    "\n",
    "    Returns:\n",
    "        ndarray: The audio signal with added noise.\n",
    "    \"\"\"\n",
    "    noise_amp = 0.05 * np.random.uniform() * np.amax(data)\n",
    "    data = data.astype('float64') + noise_amp * np.random.normal(size=data.shape[0])\n",
    "    return data\n",
    "\n",
    "def stretch(data, rate=1.0):\n",
    "    \"\"\"\n",
    "    Stretches or compresses the audio by a given rate.\n",
    "\n",
    "    Args:\n",
    "        data (ndarray): The audio signal as a numpy array.\n",
    "        rate (float): The rate to stretch the audio. A value > 1 speeds it up, < 1 slows it down.\n",
    "\n",
    "    Returns:\n",
    "        ndarray: The time-stretched audio signal.\n",
    "    \"\"\"\n",
    "    data = librosa.effects.time_stretch(data, rate)\n",
    "    return data\n",
    "\n",
    "def pitch_shift(data, sample_rate):\n",
    "    \"\"\"\n",
    "    Shifts the pitch of an audio signal.\n",
    "\n",
    "    Args:\n",
    "        data (ndarray): The audio signal as a numpy array.\n",
    "        sample_rate (int): The sampling rate of the audio signal.\n",
    "\n",
    "    Returns:\n",
    "        ndarray: The pitch-shifted audio signal.\n",
    "    \"\"\"\n",
    "    bins_per_octave = 12\n",
    "    pitch_pm = 2\n",
    "    pitch_change =  pitch_pm * 2*(np.random.uniform())\n",
    "    data = librosa.effects.pitch_shift(data.astype('float64'), \n",
    "                                       sample_rate, n_steps=pitch_change, \n",
    "                                       bins_per_octave=bins_per_octave)\n",
    "    return data\n",
    "\n",
    "def speedNpitch(data):\n",
    "    \"\"\"\n",
    "    Adjusts both the speed and pitch of an audio signal.\n",
    "\n",
    "    Args:\n",
    "        data (ndarray): The audio signal as a numpy array.\n",
    "\n",
    "    Returns:\n",
    "        ndarray: The audio signal with modified speed and pitch.\n",
    "    \"\"\"\n",
    "    length_change = np.random.uniform(low=0.8, high = 1)\n",
    "    speed_fac = 1.2  / length_change\n",
    "    tmp = np.interp(np.arange(0,len(data),speed_fac),np.arange(0,len(data)),data)\n",
    "    minlen = min(data.shape[0], tmp.shape[0])\n",
    "    data *= 0\n",
    "    data[0:minlen] = tmp[0:minlen]\n",
    "    return data\n",
    "\n",
    "def pad_or_truncate(data, length):\n",
    "    \"\"\"\n",
    "    Pads or truncates an audio signal to the specified length.\n",
    "\n",
    "    Args:\n",
    "        data (ndarray): The audio signal as a numpy array.\n",
    "        length (int): The desired length of the audio signal.\n",
    "\n",
    "    Returns:\n",
    "        ndarray: The audio signal padded or truncated to the specified length.\n",
    "    \"\"\"\n",
    "    if len(data) > length:\n",
    "        return data[:length]\n",
    "    elif len(data) < length:\n",
    "        return np.pad(data, (0, length - len(data)), 'constant')\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract the feature using mfcc\n",
    "extract_res = []\n",
    "def mfcc_features(data):\n",
    "    \"\"\"\n",
    "    Extract MFCC (Mel-frequency cepstral coefficients) features from the dataset.\n",
    "\n",
    "    Args:\n",
    "        data (pandas.DataFrame): A DataFrame containing paths to audio files and their titles.\n",
    "\n",
    "    Returns:\n",
    "        list: A list of dictionaries with titles, paths, and extracted features.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        for index, row in data.iterrows():\n",
    "            audio, sample_rate = librosa.load(row['30s_path'])\n",
    "            mfcc_def = np.mean(librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40).T, axis=0)\n",
    "\n",
    "            augmentations = [\n",
    "                lambda x: noise(x),\n",
    "                lambda x: speedNpitch(x)\n",
    "            ]\n",
    "\n",
    "            extract_res.append({\n",
    "                'title': row['title'],\n",
    "                '30s_path': row['30s_path'],\n",
    "                'features': mfcc_def\n",
    "            })\n",
    "\n",
    "            for aug in augmentations:\n",
    "                augmented_data = aug(audio)\n",
    "                mfccs = np.mean(librosa.feature.mfcc(y=augmented_data, sr=sample_rate, n_mfcc=40).T, axis=0)\n",
    "                extract_res.append({\n",
    "                    'title': row['title'],\n",
    "                    '30s_path': row['30s_path'],\n",
    "                    'features': mfccs\n",
    "                })\n",
    "        return extract_res\n",
    "    except Exception as e:\n",
    "        logging.error(f\"Error {e} encountered while parsing file: {data['30s_path']}\")\n",
    "        return None \n",
    "\n",
    "def chroma_features(file_path):\n",
    "    \"\"\"\n",
    "    Extract chroma features from an audio file.\n",
    "\n",
    "    Args:\n",
    "        file_path (str): Path to the audio file.\n",
    "\n",
    "    Returns:\n",
    "        numpy.ndarray: Processed chroma features.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        audio, sample_rate = librosa.load(file_path) \n",
    "        chroma = librosa.feature.chroma_stft(y=audio, sr=sample_rate)\n",
    "        chroma_processed = np.mean(chroma.T,axis=0)\n",
    "    except Exception as e:\n",
    "        logging.error(f\"Error encountered while parsing file: {file_path}\")\n",
    "        return None \n",
    "     \n",
    "    return chroma_processed\n",
    "\n",
    "def mel_features(file_path):\n",
    "    \"\"\"\n",
    "    Extract Mel-spectrogram features from an audio file.\n",
    "\n",
    "    Args:\n",
    "        file_path (str): Path to the audio file.\n",
    "\n",
    "    Returns:\n",
    "        numpy.ndarray: Processed Mel-spectrogram features.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        audio, sample_rate = librosa.load(file_path) \n",
    "        mel = librosa.feature.melspectrogram(y=audio, sr=sample_rate)\n",
    "        mel_processed = np.mean(mel.T,axis=0)\n",
    "    except Exception as e:\n",
    "        logging.error(f\"Error encountered while parsing file: {file_path}\")\n",
    "        return None \n",
    "     \n",
    "    return mel_processed\n",
    "\n",
    "def combined_features(file_path):\n",
    "    \"\"\"\n",
    "    Combine MFCC and chroma features from an audio file.\n",
    "\n",
    "    Args:\n",
    "        file_path (str): Path to the audio file.\n",
    "\n",
    "    Returns:\n",
    "        numpy.ndarray: Concatenated features of MFCC and chroma.\n",
    "    \"\"\"\n",
    "    mfcc = mfcc_features(file_path)\n",
    "    chroma = chroma_features(file_path)\n",
    "    # mel = mel_features(file_path)\n",
    "    \n",
    "    return np.concatenate((mfcc, chroma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = mfcc_features(df_res)\n",
    "df_features = pd.DataFrame(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "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>title</th>\n",
       "      <th>30s_path</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kicir-kicir</td>\n",
       "      <td>datasets/30s_segments/kicirkicir/segment_0_0.wav</td>\n",
       "      <td>[-239.42424, 98.03014, 7.972566, 15.686206, 5....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kicir-kicir</td>\n",
       "      <td>datasets/30s_segments/kicirkicir/segment_0_0.wav</td>\n",
       "      <td>[-88.61130580150618, 33.69310555147082, 15.662...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kicir-kicir</td>\n",
       "      <td>datasets/30s_segments/kicirkicir/segment_0_0.wav</td>\n",
       "      <td>[-312.61584, 65.76842, 0.18668926, 9.734197, 5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kicir-kicir</td>\n",
       "      <td>datasets/30s_segments/kicirkicir/segment_0_1.wav</td>\n",
       "      <td>[-158.31898, 93.41291, 22.756842, 37.474716, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kicir-kicir</td>\n",
       "      <td>datasets/30s_segments/kicirkicir/segment_0_1.wav</td>\n",
       "      <td>[-155.2345679165902, 90.69286100612311, 23.906...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         title                                          30s_path  \\\n",
       "0  Kicir-kicir  datasets/30s_segments/kicirkicir/segment_0_0.wav   \n",
       "1  Kicir-kicir  datasets/30s_segments/kicirkicir/segment_0_0.wav   \n",
       "2  Kicir-kicir  datasets/30s_segments/kicirkicir/segment_0_0.wav   \n",
       "3  Kicir-kicir  datasets/30s_segments/kicirkicir/segment_0_1.wav   \n",
       "4  Kicir-kicir  datasets/30s_segments/kicirkicir/segment_0_1.wav   \n",
       "\n",
       "                                            features  \n",
       "0  [-239.42424, 98.03014, 7.972566, 15.686206, 5....  \n",
       "1  [-88.61130580150618, 33.69310555147082, 15.662...  \n",
       "2  [-312.61584, 65.76842, 0.18668926, 9.734197, 5...  \n",
       "3  [-158.31898, 93.41291, 22.756842, 37.474716, 1...  \n",
       "4  [-155.2345679165902, 90.69286100612311, 23.906...  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.to_csv('data/30s_segments_features.csv', index=False)\n",
    "df_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "\n",
    "def print_score(clf, X_train, y_train, X_test, y_test, train=True):    \n",
    "    if train:\n",
    "        pred = clf.predict(X_train)\n",
    "        clf_report = classification_report(y_train, pred, zero_division=1)\n",
    "        print(\"Train Result:\\n================================================\")\n",
    "        print(f\"Accuracy Score: {accuracy_score(y_train, pred) * 100:.2f}%\")\n",
    "        print(f\"F1 Score: {f1_score(y_train, pred, average='weighted', zero_division=1) * 100:.2f}%\")\n",
    "        print(f\"Precision Score: {precision_score(y_train, pred, average='weighted', zero_division=1) * 100:.2f}%\")\n",
    "        print(f\"Recall Score: {recall_score(y_train, pred, average='weighted', zero_division=1) * 100:.2f}%\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"CLASSIFICATION REPORT:\\n{clf_report}\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"Confusion Matrix: \\n {confusion_matrix(y_train, pred)}\\n\")\n",
    "        \n",
    "    elif train==False:\n",
    "        pred = clf.predict(X_test)\n",
    "        clf_report = classification_report(y_test, pred, zero_division=1)\n",
    "        print(\"Test Result:\\n================================================\")        \n",
    "        print(f\"Accuracy Score: {accuracy_score(y_test, pred) * 100:.2f}%\")\n",
    "        print(f\"F1 Score: {f1_score(y_test, pred, average='weighted', zero_division=1) * 100:.2f}%\")\n",
    "        print(f\"Precision Score: {precision_score(y_test, pred, average='weighted', zero_division=1) * 100:.2f}%\")\n",
    "        print(f\"Recall Score: {recall_score(y_test, pred, average='weighted', zero_division=1) * 100:.2f}%\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"CLASSIFICATION REPORT:\\n{clf_report}\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"Confusion Matrix: \\n {confusion_matrix(y_test, pred)}\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Prepare the data\n",
    "X = []\n",
    "y = []\n",
    "\n",
    "for index, row in df_features.iterrows():\n",
    "    label = row['title']\n",
    "    features = row['features']\n",
    "    \n",
    "    X.append(features)\n",
    "    y.append(label)\n",
    "    \n",
    "X = np.array(X)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (9738, 40), y shape: (9738,)\n"
     ]
    }
   ],
   "source": [
    "print(f\"X shape: {X.shape}, y shape: {y.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Train a model\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "================================================\n",
      "Accuracy Score: 100.00%\n",
      "F1 Score: 100.00%\n",
      "Precision Score: 100.00%\n",
      "Recall Score: 100.00%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "Ampar-ampar Pisang       1.00      1.00      1.00      1401\n",
      "             Apuse       1.00      1.00      1.00      1434\n",
      "     Bengawan Solo       1.00      1.00      1.00      2072\n",
      "       Kicir-kicir       1.00      1.00      1.00      1593\n",
      " Yamko Rambe Yamko       1.00      1.00      1.00      1290\n",
      "\n",
      "          accuracy                           1.00      7790\n",
      "         macro avg       1.00      1.00      1.00      7790\n",
      "      weighted avg       1.00      1.00      1.00      7790\n",
      "\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[1401    0    0    0    0]\n",
      " [   0 1434    0    0    0]\n",
      " [   0    0 2072    0    0]\n",
      " [   0    0    0 1593    0]\n",
      " [   0    0    0    0 1290]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "print_score(model, X_train, y_train, X_test, y_test, train=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Result:\n",
      "================================================\n",
      "Accuracy Score: 94.30%\n",
      "F1 Score: 94.30%\n",
      "Precision Score: 94.49%\n",
      "Recall Score: 94.30%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "Ampar-ampar Pisang       0.98      0.93      0.95       354\n",
      "             Apuse       0.97      0.95      0.96       354\n",
      "     Bengawan Solo       0.89      0.99      0.94       496\n",
      "       Kicir-kicir       0.96      0.96      0.96       414\n",
      " Yamko Rambe Yamko       0.94      0.87      0.91       330\n",
      "\n",
      "          accuracy                           0.94      1948\n",
      "         macro avg       0.95      0.94      0.94      1948\n",
      "      weighted avg       0.94      0.94      0.94      1948\n",
      "\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[329   2   7   7   9]\n",
      " [  1 335  12   3   3]\n",
      " [  0   1 489   3   3]\n",
      " [  1   3  12 396   2]\n",
      " [  6   4  29   3 288]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_score(model, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred, labels=model.classes_)\n",
    "\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=model.classes_, yticklabels=model.classes_)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM - Supervised ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Train an SVM model\n",
    "svm_model = SVC(kernel='linear', random_state=42, C=1, probability=True)\n",
    "svm_model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "================================================\n",
      "Accuracy Score: 65.06%\n",
      "F1 Score: 64.89%\n",
      "Precision Score: 65.18%\n",
      "Recall Score: 65.06%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "Ampar-ampar Pisang       0.70      0.65      0.67      1401\n",
      "             Apuse       0.66      0.61      0.64      1434\n",
      "     Bengawan Solo       0.63      0.76      0.69      2072\n",
      "       Kicir-kicir       0.64      0.62      0.63      1593\n",
      " Yamko Rambe Yamko       0.64      0.56      0.60      1290\n",
      "\n",
      "          accuracy                           0.65      7790\n",
      "         macro avg       0.65      0.64      0.64      7790\n",
      "      weighted avg       0.65      0.65      0.65      7790\n",
      "\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[ 913   65  203  125   95]\n",
      " [ 122  878  222  134   78]\n",
      " [  78  124 1574  159  137]\n",
      " [  99  166  245  980  103]\n",
      " [  99   93  240  135  723]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the SVM model\n",
    "print_score(svm_model, X_train, y_train, X_test, y_test, train=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Result:\n",
      "================================================\n",
      "Accuracy Score: 62.73%\n",
      "F1 Score: 62.56%\n",
      "Precision Score: 62.79%\n",
      "Recall Score: 62.73%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "Ampar-ampar Pisang       0.64      0.61      0.63       354\n",
      "             Apuse       0.64      0.61      0.63       354\n",
      "     Bengawan Solo       0.62      0.74      0.67       496\n",
      "       Kicir-kicir       0.64      0.58      0.61       414\n",
      " Yamko Rambe Yamko       0.60      0.55      0.57       330\n",
      "\n",
      "          accuracy                           0.63      1948\n",
      "         macro avg       0.63      0.62      0.62      1948\n",
      "      weighted avg       0.63      0.63      0.63      1948\n",
      "\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[217  19  48  39  31]\n",
      " [ 37 217  55  28  17]\n",
      " [ 16  39 366  35  40]\n",
      " [ 41  45  55 242  31]\n",
      " [ 28  20  69  33 180]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print_score(svm_model, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "y_pred = svm_model.predict(X_test)\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred, labels=svm_model.classes_)\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=svm_model.classes_, yticklabels=svm_model.classes_)\n",
    "\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Train a Decision Tree model\n",
    "dt_model = DecisionTreeClassifier(random_state=42)\n",
    "dt_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "================================================\n",
      "Accuracy Score: 100.00%\n",
      "F1 Score: 100.00%\n",
      "Precision Score: 100.00%\n",
      "Recall Score: 100.00%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "Ampar-ampar Pisang       1.00      1.00      1.00      1401\n",
      "             Apuse       1.00      1.00      1.00      1434\n",
      "     Bengawan Solo       1.00      1.00      1.00      2072\n",
      "       Kicir-kicir       1.00      1.00      1.00      1593\n",
      " Yamko Rambe Yamko       1.00      1.00      1.00      1290\n",
      "\n",
      "          accuracy                           1.00      7790\n",
      "         macro avg       1.00      1.00      1.00      7790\n",
      "      weighted avg       1.00      1.00      1.00      7790\n",
      "\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[1401    0    0    0    0]\n",
      " [   0 1434    0    0    0]\n",
      " [   0    0 2072    0    0]\n",
      " [   0    0    0 1593    0]\n",
      " [   0    0    0    0 1290]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the Decision Tree model on the training data\n",
    "print_score(dt_model, X_train, y_train, X_test, y_test, train=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Result:\n",
      "================================================\n",
      "Accuracy Score: 79.16%\n",
      "F1 Score: 79.20%\n",
      "Precision Score: 79.29%\n",
      "Recall Score: 79.16%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "Ampar-ampar Pisang       0.82      0.82      0.82       354\n",
      "             Apuse       0.86      0.80      0.83       354\n",
      "     Bengawan Solo       0.78      0.81      0.79       496\n",
      "       Kicir-kicir       0.80      0.80      0.80       414\n",
      " Yamko Rambe Yamko       0.71      0.72      0.72       330\n",
      "\n",
      "          accuracy                           0.79      1948\n",
      "         macro avg       0.79      0.79      0.79      1948\n",
      "      weighted avg       0.79      0.79      0.79      1948\n",
      "\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[290   7  20  17  20]\n",
      " [ 20 282  19  15  18]\n",
      " [ 10  17 400  31  38]\n",
      " [ 12  10  38 331  23]\n",
      " [ 21  13  36  21 239]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the Decision Tree model on the test data\n",
    "print_score(dt_model, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "y_pred = dt_model.predict(X_test)\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred, labels=dt_model.classes_)\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=dt_model.classes_, yticklabels=dt_model.classes_)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# knn\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Train a KNN model\n",
    "knn_model = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "knn_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "================================================\n",
      "Accuracy Score: 92.34%\n",
      "F1 Score: 92.34%\n",
      "Precision Score: 92.43%\n",
      "Recall Score: 92.34%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "Ampar-ampar Pisang       0.87      0.94      0.90      1401\n",
      "             Apuse       0.94      0.92      0.93      1434\n",
      "     Bengawan Solo       0.94      0.94      0.94      2072\n",
      "       Kicir-kicir       0.95      0.93      0.94      1593\n",
      " Yamko Rambe Yamko       0.92      0.87      0.89      1290\n",
      "\n",
      "          accuracy                           0.92      7790\n",
      "         macro avg       0.92      0.92      0.92      7790\n",
      "      weighted avg       0.92      0.92      0.92      7790\n",
      "\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[1323   14   22   14   28]\n",
      " [  68 1314   19   12   21]\n",
      " [  40   19 1957   20   36]\n",
      " [  35   33   32 1477   16]\n",
      " [  58   22   54   34 1122]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_score(knn_model, X_train, y_train, X_test, y_test, train=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Result:\n",
      "================================================\n",
      "Accuracy Score: 86.60%\n",
      "F1 Score: 86.60%\n",
      "Precision Score: 86.69%\n",
      "Recall Score: 86.60%\n",
      "_______________________________________________\n",
      "CLASSIFICATION REPORT:\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "Ampar-ampar Pisang       0.82      0.89      0.85       354\n",
      "             Apuse       0.89      0.87      0.88       354\n",
      "     Bengawan Solo       0.88      0.88      0.88       496\n",
      "       Kicir-kicir       0.89      0.87      0.88       414\n",
      " Yamko Rambe Yamko       0.85      0.80      0.82       330\n",
      "\n",
      "          accuracy                           0.87      1948\n",
      "         macro avg       0.86      0.86      0.86      1948\n",
      "      weighted avg       0.87      0.87      0.87      1948\n",
      "\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[316   9   7   5  17]\n",
      " [ 22 309  10   6   7]\n",
      " [ 18   7 437  15  19]\n",
      " [ 15  15  18 361   5]\n",
      " [ 16   8  25  17 264]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_score(knn_model, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "y_pred = knn_model.predict(X_test)\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred, labels=knn_model.classes_)\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=knn_model.classes_, yticklabels=knn_model.classes_)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save all models\n",
    "import pickle\n",
    "\n",
    "# Save the models\n",
    "with open('models/random_forest_model.pkl', 'wb') as file:\n",
    "    pickle.dump(model, file)\n",
    "    \n",
    "with open('models/svm_model.pkl', 'wb') as file:\n",
    "    pickle.dump(svm_model, file)\n",
    "    \n",
    "with open('models/decision_tree_model.pkl', 'wb') as file:\n",
    "    pickle.dump(dt_model, file)\n",
    "    \n",
    "with open('models/knn_model.pkl', 'wb') as file:\n",
    "    pickle.dump(knn_model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mfcc_feature(file_path):\n",
    "    \"\"\"\n",
    "    Extract MFCC (Mel-frequency cepstral coefficients) features from an audio file.\n",
    "\n",
    "    Args:\n",
    "        file_path (str): Path to the audio file.\n",
    "\n",
    "    Returns:\n",
    "        numpy.ndarray: Processed MFCC features.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        audio, sample_rate = librosa.load(file_path)\n",
    "        mfcc = np.mean(librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40).T, axis=0)\n",
    "    except Exception as e:\n",
    "        logging.error(f\"Error encountered while parsing file: {file_path}\")\n",
    "        return None \n",
    "     \n",
    "    return mfcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test with external data\n",
    "def predict_song(model, file_path):\n",
    "    \"\"\"\n",
    "    Predicts the genre of a song using a trained model.\n",
    "\n",
    "    Args:\n",
    "        model: The trained model to use for prediction.\n",
    "        file_path (str): The path to the audio file to predict.\n",
    "\n",
    "    Returns:\n",
    "        str: The predicted genre of the song.\n",
    "        float: The probability of the predicted genre.\n",
    "    \"\"\"\n",
    "    features = mfcc_feature(file_path)\n",
    "    \n",
    "    if (features is None):\n",
    "        return None, None\n",
    "    \n",
    "    prediction = model.predict([features])\n",
    "    probability = model.predict_proba([features])\n",
    "    \n",
    "    return prediction[0], np.max(probability)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_53791/1820902920.py:12: UserWarning: PySoundFile failed. Trying audioread instead.\n",
      "  audio, sample_rate = librosa.load(file_path)\n",
      "/home/katow/.pyenv/versions/3.10.15/lib/python3.10/site-packages/librosa/core/audio.py:184: FutureWarning: librosa.core.audio.__audioread_load\n",
      "\tDeprecated as of librosa version 0.10.0.\n",
      "\tIt will be removed in librosa version 1.0.\n",
      "  y, sr_native = __audioread_load(path, offset, duration, dtype)\n",
      "/tmp/ipykernel_53791/1820902920.py:12: UserWarning: PySoundFile failed. Trying audioread instead.\n",
      "  audio, sample_rate = librosa.load(file_path)\n",
      "/home/katow/.pyenv/versions/3.10.15/lib/python3.10/site-packages/librosa/core/audio.py:184: FutureWarning: librosa.core.audio.__audioread_load\n",
      "\tDeprecated as of librosa version 0.10.0.\n",
      "\tIt will be removed in librosa version 1.0.\n",
      "  y, sr_native = __audioread_load(path, offset, duration, dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted song: Bengawan Solo\n",
      "Probability: 23.00%\n",
      "Predicted song: Ampar-ampar Pisang\n",
      "Probability: 100.00%\n",
      "Predicted song: Apuse\n",
      "Probability: 80.00%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_53791/1820902920.py:12: UserWarning: PySoundFile failed. Trying audioread instead.\n",
      "  audio, sample_rate = librosa.load(file_path)\n",
      "/home/katow/.pyenv/versions/3.10.15/lib/python3.10/site-packages/librosa/core/audio.py:184: FutureWarning: librosa.core.audio.__audioread_load\n",
      "\tDeprecated as of librosa version 0.10.0.\n",
      "\tIt will be removed in librosa version 1.0.\n",
      "  y, sr_native = __audioread_load(path, offset, duration, dtype)\n"
     ]
    }
   ],
   "source": [
    "# svm_pred = predict_song(svm_model, 'test/Yamke Rambo.m4a')\n",
    "# print(f\"Predicted song: {svm_pred}\")\n",
    "\n",
    "rf_pred, rf_prob = predict_song(model, 'test/Yamke Rambo.m4a')\n",
    "print(f\"Predicted song: {rf_pred}\")\n",
    "print(f\"Probability: {rf_prob * 100:.2f}%\")\n",
    "\n",
    "dt_pred, dt_prob = predict_song(dt_model, 'test/Yamke Rambo.m4a')\n",
    "print(f\"Predicted song: {dt_pred}\")\n",
    "print(f\"Probability: {dt_prob * 100:.2f}%\")\n",
    "\n",
    "knn_pred, knn_prob = predict_song(knn_model, 'test/Yamke Rambo.m4a')\n",
    "print(f\"Predicted song: {knn_pred}\")\n",
    "print(f\"Probability: {knn_prob * 100:.2f}%\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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