hp_search_dT.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNhIp3DhDduJ9tWq5+qnjAl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fabibo3/music-genre-classification/blob/main/hp_search_dT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IflI7FGwduxD",
        "outputId": "012d3c5b-fe98-4d05-abc8-411cde44b2f0"
      },
      "source": [
        "# Clone repo\r\n",
        "!rm -r music-genre-classification/\r\n",
        "!git clone https://github.com/fabibo3/music-genre-classification.git\r\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'music-genre-classification'...\n",
            "remote: Enumerating objects: 139, done.\u001b[K\n",
            "remote: Counting objects:   0% (1/139)\u001b[K\rremote: Counting objects:   1% (2/139)\u001b[K\rremote: Counting objects:   2% (3/139)\u001b[K\rremote: Counting objects:   3% (5/139)\u001b[K\rremote: Counting objects:   4% (6/139)\u001b[K\rremote: Counting objects:   5% (7/139)\u001b[K\rremote: Counting objects:   6% (9/139)\u001b[K\rremote: Counting objects:   7% (10/139)\u001b[K\rremote: Counting objects:   8% (12/139)\u001b[K\rremote: Counting objects:   9% (13/139)\u001b[K\rremote: Counting objects:  10% (14/139)\u001b[K\rremote: Counting objects:  11% (16/139)\u001b[K\rremote: Counting objects:  12% (17/139)\u001b[K\rremote: Counting objects:  13% (19/139)\u001b[K\rremote: Counting objects:  14% (20/139)\u001b[K\rremote: Counting objects:  15% (21/139)\u001b[K\rremote: Counting objects:  16% (23/139)\u001b[K\rremote: Counting objects:  17% (24/139)\u001b[K\rremote: Counting objects:  18% (26/139)\u001b[K\rremote: Counting objects:  19% (27/139)\u001b[K\rremote: Counting objects:  20% (28/139)\u001b[K\rremote: Counting objects:  21% (30/139)\u001b[K\rremote: Counting objects:  22% (31/139)\u001b[K\rremote: Counting objects:  23% (32/139)\u001b[K\rremote: Counting objects:  24% (34/139)\u001b[K\rremote: Counting objects:  25% (35/139)\u001b[K\rremote: Counting objects:  26% (37/139)\u001b[K\rremote: Counting objects:  27% (38/139)\u001b[K\rremote: Counting objects:  28% (39/139)\u001b[K\rremote: Counting objects:  29% (41/139)\u001b[K\rremote: Counting objects:  30% (42/139)\u001b[K\rremote: Counting objects:  31% (44/139)\u001b[K\rremote: Counting objects:  32% (45/139)\u001b[K\rremote: Counting objects:  33% (46/139)\u001b[K\rremote: Counting objects:  34% (48/139)\u001b[K\rremote: Counting objects:  35% (49/139)\u001b[K\rremote: Counting objects:  36% (51/139)\u001b[K\rremote: Counting objects:  37% (52/139)\u001b[K\rremote: Counting objects:  38% (53/139)\u001b[K\rremote: Counting objects:  39% (55/139)\u001b[K\rremote: Counting objects:  40% (56/139)\u001b[K\rremote: Counting objects:  41% (57/139)\u001b[K\rremote: Counting objects:  42% (59/139)\u001b[K\rremote: Counting objects:  43% (60/139)\u001b[K\rremote: Counting objects:  44% (62/139)\u001b[K\rremote: Counting objects:  45% (63/139)\u001b[K\rremote: Counting objects:  46% (64/139)\u001b[K\rremote: Counting objects:  47% (66/139)\u001b[K\rremote: Counting objects:  48% (67/139)\u001b[K\rremote: Counting objects:  49% (69/139)\u001b[K\rremote: Counting objects:  50% (70/139)\u001b[K\rremote: Counting objects:  51% (71/139)\u001b[K\rremote: Counting objects:  52% (73/139)\u001b[K\rremote: Counting objects:  53% (74/139)\u001b[K\rremote: Counting objects:  54% (76/139)\u001b[K\rremote: Counting objects:  55% (77/139)\u001b[K\rremote: Counting objects:  56% (78/139)\u001b[K\rremote: Counting objects:  57% (80/139)\u001b[K\rremote: Counting objects:  58% (81/139)\u001b[K\rremote: Counting objects:  59% (83/139)\u001b[K\rremote: Counting objects:  60% (84/139)\u001b[K\rremote: Counting objects:  61% (85/139)\u001b[K\rremote: Counting objects:  62% (87/139)\u001b[K\rremote: Counting objects:  63% (88/139)\u001b[K\rremote: Counting objects:  64% (89/139)\u001b[K\rremote: Counting objects:  65% (91/139)\u001b[K\rremote: Counting objects:  66% (92/139)\u001b[K\rremote: Counting objects:  67% (94/139)\u001b[K\rremote: Counting objects:  68% (95/139)\u001b[K\rremote: Counting objects:  69% (96/139)\u001b[K\rremote: Counting objects:  70% (98/139)\u001b[K\rremote: Counting objects:  71% (99/139)\u001b[K\rremote: Counting objects:  72% (101/139)\u001b[K\rremote: Counting objects:  73% (102/139)\u001b[K\rremote: Counting objects:  74% (103/139)\u001b[K\rremote: Counting objects:  75% (105/139)\u001b[K\rremote: Counting objects:  76% (106/139)\u001b[K\rremote: Counting objects:  77% (108/139)\u001b[K\rremote: Counting objects:  78% (109/139)\u001b[K\rremote: Counting objects:  79% (110/139)\u001b[K\rremote: Counting objects:  80% (112/139)\u001b[K\rremote: Counting objects:  81% (113/139)\u001b[K\rremote: Counting objects:  82% (114/139)\u001b[K\rremote: Counting objects:  83% (116/139)\u001b[K\rremote: Counting objects:  84% (117/139)\u001b[K\rremote: Counting objects:  85% (119/139)\u001b[K\rremote: Counting objects:  86% (120/139)\u001b[K\rremote: Counting objects:  87% (121/139)\u001b[K\rremote: Counting objects:  88% (123/139)\u001b[K\rremote: Counting objects:  89% (124/139)\u001b[K\rremote: Counting objects:  90% (126/139)\u001b[K\rremote: Counting objects:  91% (127/139)\u001b[K\rremote: Counting objects:  92% (128/139)\u001b[K\rremote: Counting objects:  93% (130/139)\u001b[K\rremote: Counting objects:  94% (131/139)\u001b[K\rremote: Counting objects:  95% (133/139)\u001b[K\rremote: Counting objects:  96% (134/139)\u001b[K\rremote: Counting objects:  97% (135/139)\u001b[K\rremote: Counting objects:  98% (137/139)\u001b[K\rremote: Counting objects:  99% (138/139)\u001b[K\rremote: Counting objects: 100% (139/139)\u001b[K\rremote: Counting objects: 100% (139/139), done.\u001b[K\n",
            "remote: Compressing objects:   1% (1/89)\u001b[K\rremote: Compressing objects:   2% (2/89)\u001b[K\rremote: Compressing objects:   3% (3/89)\u001b[K\rremote: Compressing objects:   4% (4/89)\u001b[K\rremote: Compressing objects:   5% (5/89)\u001b[K\rremote: Compressing objects:   6% (6/89)\u001b[K\rremote: Compressing objects:   7% (7/89)\u001b[K\rremote: Compressing objects:   8% (8/89)\u001b[K\rremote: Compressing objects:  10% (9/89)\u001b[K\rremote: Compressing objects:  11% (10/89)\u001b[K\rremote: Compressing objects:  12% (11/89)\u001b[K\rremote: Compressing objects:  13% (12/89)\u001b[K\rremote: Compressing objects:  14% (13/89)\u001b[K\rremote: Compressing objects:  15% (14/89)\u001b[K\rremote: Compressing objects:  16% (15/89)\u001b[K\rremote: Compressing objects:  17% (16/89)\u001b[K\rremote: Compressing objects:  19% (17/89)\u001b[K\rremote: Compressing objects:  20% (18/89)\u001b[K\rremote: Compressing objects:  21% (19/89)\u001b[K\rremote: Compressing objects:  22% (20/89)\u001b[K\rremote: Compressing objects:  23% (21/89)\u001b[K\rremote: Compressing objects:  24% (22/89)\u001b[K\rremote: Compressing objects:  25% (23/89)\u001b[K\rremote: Compressing objects:  26% (24/89)\u001b[K\rremote: Compressing objects:  28% (25/89)\u001b[K\rremote: Compressing objects:  29% (26/89)\u001b[K\rremote: Compressing objects:  30% (27/89)\u001b[K\rremote: Compressing objects:  31% (28/89)\u001b[K\rremote: Compressing objects:  32% (29/89)\u001b[K\rremote: Compressing objects:  33% (30/89)\u001b[K\rremote: Compressing objects:  34% (31/89)\u001b[K\rremote: Compressing objects:  35% (32/89)\u001b[K\rremote: Compressing objects:  37% (33/89)\u001b[K\rremote: Compressing objects:  38% (34/89)\u001b[K\rremote: Compressing objects:  39% (35/89)\u001b[K\rremote: Compressing objects:  40% (36/89)\u001b[K\rremote: Compressing objects:  41% (37/89)\u001b[K\rremote: Compressing objects:  42% (38/89)\u001b[K\rremote: Compressing objects:  43% (39/89)\u001b[K\rremote: Compressing objects:  44% (40/89)\u001b[K\rremote: Compressing objects:  46% (41/89)\u001b[K\rremote: Compressing objects:  47% (42/89)\u001b[K\rremote: Compressing objects:  48% (43/89)\u001b[K\rremote: Compressing objects:  49% (44/89)\u001b[K\rremote: Compressing objects:  50% (45/89)\u001b[K\rremote: Compressing objects:  51% (46/89)\u001b[K\rremote: Compressing objects:  52% (47/89)\u001b[K\rremote: Compressing objects:  53% (48/89)\u001b[K\rremote: Compressing objects:  55% (49/89)\u001b[K\rremote: Compressing objects:  56% (50/89)\u001b[K\rremote: Compressing objects:  57% (51/89)\u001b[K\rremote: Compressing objects:  58% (52/89)\u001b[K\rremote: Compressing objects:  59% (53/89)\u001b[K\rremote: Compressing objects:  60% (54/89)\u001b[K\rremote: Compressing objects:  61% (55/89)\u001b[K\rremote: Compressing objects:  62% (56/89)\u001b[K\rremote: Compressing objects:  64% (57/89)\u001b[K\rremote: Compressing objects:  65% (58/89)\u001b[K\rremote: Compressing objects:  66% (59/89)\u001b[K\rremote: Compressing objects:  67% (60/89)\u001b[K\rremote: Compressing objects:  68% (61/89)\u001b[K\rremote: Compressing objects:  69% (62/89)\u001b[K\rremote: Compressing objects:  70% (63/89)\u001b[K\rremote: Compressing objects:  71% (64/89)\u001b[K\rremote: Compressing objects:  73% (65/89)\u001b[K\rremote: Compressing objects:  74% (66/89)\u001b[K\rremote: Compressing objects:  75% (67/89)\u001b[K\rremote: Compressing objects:  76% (68/89)\u001b[K\rremote: Compressing objects:  77% (69/89)\u001b[K\rremote: Compressing objects:  78% (70/89)\u001b[K\rremote: Compressing objects:  79% (71/89)\u001b[K\rremote: Compressing objects:  80% (72/89)\u001b[K\rremote: Compressing objects:  82% (73/89)\u001b[K\rremote: Compressing objects:  83% (74/89)\u001b[K\rremote: Compressing objects:  84% (75/89)\u001b[K\rremote: Compressing objects:  85% (76/89)\u001b[K\rremote: Compressing objects:  86% (77/89)\u001b[K\rremote: Compressing objects:  87% (78/89)\u001b[K\rremote: Compressing objects:  88% (79/89)\u001b[K\rremote: Compressing objects:  89% (80/89)\u001b[K\rremote: Compressing objects:  91% (81/89)\u001b[K\rremote: Compressing objects:  92% (82/89)\u001b[K\rremote: Compressing objects:  93% (83/89)\u001b[K\rremote: Compressing objects:  94% (84/89)\u001b[K\rremote: Compressing objects:  95% (85/89)\u001b[K\rremote: Compressing objects:  96% (86/89)\u001b[K\rremote: Compressing objects:  97% (87/89)\u001b[K\rremote: Compressing objects:  98% (88/89)\u001b[K\rremote: Compressing objects: 100% (89/89)\u001b[K\rremote: Compressing objects: 100% (89/89), done.\u001b[K\n",
            "remote: Total 139 (delta 56), reused 108 (delta 39), pack-reused 0\u001b[K\n",
            "Receiving objects:   0% (1/139)   \rReceiving objects:   1% (2/139)   \rReceiving objects:   2% (3/139)   \rReceiving objects:   3% (5/139)   \rReceiving objects:   4% (6/139)   \rReceiving objects:   5% (7/139)   \rReceiving objects:   6% (9/139)   \rReceiving objects:   7% (10/139)   \rReceiving objects:   8% (12/139)   \rReceiving objects:   9% (13/139)   \rReceiving objects:  10% (14/139)   \rReceiving objects:  11% (16/139)   \rReceiving objects:  12% (17/139)   \rReceiving objects:  13% (19/139)   \rReceiving objects:  14% (20/139)   \rReceiving objects:  15% (21/139)   \rReceiving objects:  16% (23/139)   \rReceiving objects:  17% (24/139)   \rReceiving objects:  18% (26/139)   \rReceiving objects:  19% (27/139)   \rReceiving objects:  20% (28/139)   \rReceiving objects:  21% (30/139)   \rReceiving objects:  22% (31/139)   \rReceiving objects:  23% (32/139)   \rReceiving objects:  24% (34/139)   \rReceiving objects:  25% (35/139)   \rReceiving objects:  26% (37/139)   \rReceiving objects:  27% (38/139)   \rReceiving objects:  28% (39/139)   \rReceiving objects:  29% (41/139)   \rReceiving objects:  30% (42/139)   \rReceiving objects:  31% (44/139)   \rReceiving objects:  32% (45/139)   \rReceiving objects:  33% (46/139)   \rReceiving objects:  34% (48/139)   \rReceiving objects:  35% (49/139)   \rReceiving objects:  36% (51/139)   \rReceiving objects:  37% (52/139)   \rReceiving objects:  38% (53/139)   \rReceiving objects:  39% (55/139)   \rReceiving objects:  40% (56/139)   \rReceiving objects:  41% (57/139)   \rReceiving objects:  42% (59/139)   \rReceiving objects:  43% (60/139)   \rReceiving objects:  44% (62/139)   \rReceiving objects:  45% (63/139)   \rReceiving objects:  46% (64/139)   \rReceiving objects:  47% (66/139)   \rReceiving objects:  48% (67/139)   \rReceiving objects:  49% (69/139)   \rReceiving objects:  50% (70/139)   \rReceiving objects:  51% (71/139)   \rReceiving objects:  52% (73/139)   \rReceiving objects:  53% (74/139)   \rReceiving objects:  54% (76/139)   \rReceiving objects:  55% (77/139)   \rReceiving objects:  56% (78/139)   \rReceiving objects:  57% (80/139)   \rReceiving objects:  58% (81/139)   \rReceiving objects:  59% (83/139)   \rReceiving objects:  60% (84/139)   \rReceiving objects:  61% (85/139)   \rReceiving objects:  62% (87/139)   \rReceiving objects:  63% (88/139)   \rReceiving objects:  64% (89/139)   \rReceiving objects:  65% (91/139)   \rReceiving objects:  66% (92/139)   \rReceiving objects:  67% (94/139)   \rReceiving objects:  68% (95/139)   \rReceiving objects:  69% (96/139)   \rReceiving objects:  70% (98/139)   \rReceiving objects:  71% (99/139)   \rReceiving objects:  72% (101/139)   \rReceiving objects:  73% (102/139)   \rReceiving objects:  74% (103/139)   \rReceiving objects:  75% (105/139)   \rReceiving objects:  76% (106/139)   \rReceiving objects:  77% (108/139)   \rReceiving objects:  78% (109/139)   \rReceiving objects:  79% (110/139)   \rReceiving objects:  80% (112/139)   \rReceiving objects:  81% (113/139)   \rReceiving objects:  82% (114/139)   \rReceiving objects:  83% (116/139)   \rReceiving objects:  84% (117/139)   \rReceiving objects:  85% (119/139)   \rReceiving objects:  86% (120/139)   \rReceiving objects:  87% (121/139)   \rReceiving objects:  88% (123/139)   \rReceiving objects:  89% (124/139)   \rReceiving objects:  90% (126/139)   \rReceiving objects:  91% (127/139)   \rReceiving objects:  92% (128/139)   \rReceiving objects:  93% (130/139)   \rReceiving objects:  94% (131/139)   \rReceiving objects:  95% (133/139)   \rReceiving objects:  96% (134/139)   \rReceiving objects:  97% (135/139)   \rReceiving objects:  98% (137/139)   \rReceiving objects:  99% (138/139)   \rReceiving objects: 100% (139/139)   \rReceiving objects: 100% (139/139), 44.53 KiB | 14.84 MiB/s, done.\n",
            "Resolving deltas:   0% (0/56)   \rResolving deltas:  12% (7/56)   \rResolving deltas:  14% (8/56)   \rResolving deltas:  17% (10/56)   \rResolving deltas:  23% (13/56)   \rResolving deltas:  28% (16/56)   \rResolving deltas:  32% (18/56)   \rResolving deltas:  42% (24/56)   \rResolving deltas:  50% (28/56)   \rResolving deltas:  55% (31/56)   \rResolving deltas:  58% (33/56)   \rResolving deltas:  60% (34/56)   \rResolving deltas:  71% (40/56)   \rResolving deltas:  73% (41/56)   \rResolving deltas:  75% (42/56)   \rResolving deltas:  80% (45/56)   \rResolving deltas:  82% (46/56)   \rResolving deltas:  85% (48/56)   \rResolving deltas:  89% (50/56)   \rResolving deltas:  91% (51/56)   \rResolving deltas:  94% (53/56)   \rResolving deltas:  96% (54/56)   \rResolving deltas:  98% (55/56)   \rResolving deltas: 100% (56/56)   \rResolving deltas: 100% (56/56), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtG9skBue1VC",
        "outputId": "1755f6d8-52fe-4e44-e3f9-4c350d605caa"
      },
      "source": [
        "# check repo\r\n",
        "!ls music-genre-classification/"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "config\thp_search_dT.ipynb  README.md  src\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vW9xmYEbRBA",
        "outputId": "7f4e0660-8d76-410b-eeb8-41507d3bd94a"
      },
      "source": [
        "# Mount google drive\r\n",
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBjsTzK9fXIN"
      },
      "source": [
        "# Create experiments directory \r\n",
        "!mkdir music-genre-classification/experiments/"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKhVjWzRlF2d",
        "outputId": "03ea479c-f4f3-4451-a2c8-79ec83d51770"
      },
      "source": [
        "# Execute code \r\n",
        "!pip install catboost\r\n",
        "!python music-genre-classification/src/run.py --data_base_dir drive/MyDrive/data/ decision_tree_config.json "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: catboost in /usr/local/lib/python3.6/dist-packages (0.24.3)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.18.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.1.4)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Reading configuration files...\n",
            "Start experiment 1 of 1\n",
            "##################################################\n",
            "Preparing experiment \"dec-tree-cv2\"...\n",
            "##################################################\n",
            "Searching for best parameters...\n",
            "Shape of dataset: (4000, 518)\n",
            "Using 4000 training files\n",
            "Datasets created\n",
            "<IPython.core.display.HTML object>\n",
            "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))\n",
            "0:\tlearn: 2.0703911\ttest: 2.0713081\tbest: 2.0713081 (0)\n",
            "50:\tlearn: 1.7219705\ttest: 1.7606245\tbest: 1.7606245 (50)\n",
            "100:\tlearn: 1.5360104\ttest: 1.6049215\tbest: 1.6049215 (100)\n",
            "150:\tlearn: 1.4115517\ttest: 1.5068178\tbest: 1.5068178 (150)\n",
            "200:\tlearn: 1.3214488\ttest: 1.4415035\tbest: 1.4415035 (200)\n",
            "250:\tlearn: 1.2519462\ttest: 1.3945409\tbest: 1.3945409 (250)\n",
            "300:\tlearn: 1.1950356\ttest: 1.3595076\tbest: 1.3595076 (300)\n",
            "350:\tlearn: 1.1472479\ttest: 1.3319546\tbest: 1.3319546 (350)\n",
            "400:\tlearn: 1.1052202\ttest: 1.3083669\tbest: 1.3083669 (400)\n",
            "450:\tlearn: 1.0682350\ttest: 1.2893788\tbest: 1.2893788 (450)\n",
            "500:\tlearn: 1.0334943\ttest: 1.2720154\tbest: 1.2720154 (500)\n",
            "550:\tlearn: 1.0024092\ttest: 1.2569030\tbest: 1.2569030 (550)\n",
            "600:\tlearn: 0.9743818\ttest: 1.2439457\tbest: 1.2439457 (600)\n",
            "650:\tlearn: 0.9473391\ttest: 1.2318979\tbest: 1.2318979 (650)\n",
            "700:\tlearn: 0.9226425\ttest: 1.2216023\tbest: 1.2216023 (700)\n",
            "750:\tlearn: 0.8997643\ttest: 1.2123119\tbest: 1.2123119 (750)\n",
            "800:\tlearn: 0.8767108\ttest: 1.2036605\tbest: 1.2036605 (800)\n",
            "850:\tlearn: 0.8544648\ttest: 1.1952278\tbest: 1.1952278 (850)\n",
            "900:\tlearn: 0.8335212\ttest: 1.1876442\tbest: 1.1876442 (900)\n",
            "950:\tlearn: 0.8137716\ttest: 1.1807724\tbest: 1.1807724 (950)\n",
            "1000:\tlearn: 0.7950083\ttest: 1.1741274\tbest: 1.1741274 (1000)\n",
            "1050:\tlearn: 0.7770442\ttest: 1.1678537\tbest: 1.1678537 (1050)\n",
            "1100:\tlearn: 0.7594600\ttest: 1.1619650\tbest: 1.1619650 (1100)\n",
            "1150:\tlearn: 0.7430835\ttest: 1.1567194\tbest: 1.1567194 (1150)\n",
            "1200:\tlearn: 0.7272115\ttest: 1.1513866\tbest: 1.1513866 (1200)\n",
            "1250:\tlearn: 0.7117993\ttest: 1.1465283\tbest: 1.1465283 (1250)\n",
            "1300:\tlearn: 0.6969523\ttest: 1.1419474\tbest: 1.1419474 (1300)\n",
            "1350:\tlearn: 0.6830550\ttest: 1.1379122\tbest: 1.1379122 (1350)\n",
            "1400:\tlearn: 0.6694809\ttest: 1.1338018\tbest: 1.1338018 (1400)\n",
            "1450:\tlearn: 0.6559167\ttest: 1.1296841\tbest: 1.1296841 (1450)\n",
            "1500:\tlearn: 0.6432632\ttest: 1.1262768\tbest: 1.1262768 (1500)\n",
            "1550:\tlearn: 0.6309821\ttest: 1.1229168\tbest: 1.1229168 (1550)\n",
            "1600:\tlearn: 0.6191414\ttest: 1.1198333\tbest: 1.1198333 (1600)\n",
            "1650:\tlearn: 0.6074887\ttest: 1.1164777\tbest: 1.1164777 (1650)\n",
            "1700:\tlearn: 0.5960815\ttest: 1.1134447\tbest: 1.1134447 (1700)\n",
            "1750:\tlearn: 0.5855287\ttest: 1.1107041\tbest: 1.1107041 (1750)\n",
            "1800:\tlearn: 0.5748813\ttest: 1.1079799\tbest: 1.1079799 (1800)\n",
            "1850:\tlearn: 0.5647790\ttest: 1.1056054\tbest: 1.1056054 (1850)\n",
            "1900:\tlearn: 0.5549844\ttest: 1.1030993\tbest: 1.1030993 (1900)\n",
            "1950:\tlearn: 0.5452158\ttest: 1.1005884\tbest: 1.1005884 (1950)\n",
            "1999:\tlearn: 0.5359258\ttest: 1.0982406\tbest: 1.0982406 (1999)\ttotal: 4m 51s\tremaining: 0us\n",
            "Best iteration for lr 0.01: 1984 with val accuracy 0.61575\n",
            "<IPython.core.display.HTML object>\n",
            "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))\n",
            "0:\tlearn: 2.0262746\ttest: 2.0317732\tbest: 2.0317732 (0)\n",
            "50:\tlearn: 1.1937381\ttest: 1.3614214\tbest: 1.3614214 (50)\n",
            "100:\tlearn: 0.9766293\ttest: 1.2510968\tbest: 1.2510968 (100)\n",
            "150:\tlearn: 0.8407564\ttest: 1.1958501\tbest: 1.1958501 (150)\n",
            "200:\tlearn: 0.7332096\ttest: 1.1607011\tbest: 1.1607011 (200)\n",
            "250:\tlearn: 0.6493207\ttest: 1.1369488\tbest: 1.1369488 (250)\n",
            "300:\tlearn: 0.5806845\ttest: 1.1178734\tbest: 1.1178734 (300)\n",
            "350:\tlearn: 0.5221622\ttest: 1.1045061\tbest: 1.1045061 (350)\n",
            "400:\tlearn: 0.4739933\ttest: 1.0937003\tbest: 1.0937003 (400)\n",
            "450:\tlearn: 0.4312001\ttest: 1.0852092\tbest: 1.0852092 (450)\n",
            "500:\tlearn: 0.3940296\ttest: 1.0772497\tbest: 1.0772497 (500)\n",
            "550:\tlearn: 0.3605632\ttest: 1.0701626\tbest: 1.0701626 (550)\n",
            "600:\tlearn: 0.3319799\ttest: 1.0649930\tbest: 1.0649666 (599)\n",
            "650:\tlearn: 0.3063278\ttest: 1.0599519\tbest: 1.0599519 (650)\n",
            "700:\tlearn: 0.2825941\ttest: 1.0554278\tbest: 1.0554278 (700)\n",
            "750:\tlearn: 0.2622921\ttest: 1.0521279\tbest: 1.0521279 (750)\n",
            "800:\tlearn: 0.2438802\ttest: 1.0492654\tbest: 1.0492654 (800)\n",
            "850:\tlearn: 0.2268658\ttest: 1.0473327\tbest: 1.0471664 (845)\n",
            "900:\tlearn: 0.2113981\ttest: 1.0455086\tbest: 1.0455086 (900)\n",
            "950:\tlearn: 0.1977540\ttest: 1.0444496\tbest: 1.0444494 (943)\n",
            "1000:\tlearn: 0.1849881\ttest: 1.0421156\tbest: 1.0421156 (1000)\n",
            "1050:\tlearn: 0.1730421\ttest: 1.0404635\tbest: 1.0404368 (1048)\n",
            "1100:\tlearn: 0.1617951\ttest: 1.0386170\tbest: 1.0385323 (1096)\n",
            "1150:\tlearn: 0.1519748\ttest: 1.0375399\tbest: 1.0375072 (1149)\n",
            "1200:\tlearn: 0.1428032\ttest: 1.0360202\tbest: 1.0359890 (1199)\n",
            "1250:\tlearn: 0.1343068\ttest: 1.0353264\tbest: 1.0353264 (1250)\n",
            "1300:\tlearn: 0.1266716\ttest: 1.0345191\tbest: 1.0345191 (1300)\n",
            "1350:\tlearn: 0.1192052\ttest: 1.0334783\tbest: 1.0333714 (1347)\n",
            "1400:\tlearn: 0.1124352\ttest: 1.0329002\tbest: 1.0328395 (1399)\n",
            "1450:\tlearn: 0.1063784\ttest: 1.0322659\tbest: 1.0322659 (1450)\n",
            "1500:\tlearn: 0.1007933\ttest: 1.0323971\tbest: 1.0322250 (1452)\n",
            "1550:\tlearn: 0.0953755\ttest: 1.0322491\tbest: 1.0319165 (1537)\n",
            "1600:\tlearn: 0.0906271\ttest: 1.0320094\tbest: 1.0318826 (1594)\n",
            "1650:\tlearn: 0.0861210\ttest: 1.0317443\tbest: 1.0316486 (1647)\n",
            "1700:\tlearn: 0.0818893\ttest: 1.0318251\tbest: 1.0316486 (1647)\n",
            "1750:\tlearn: 0.0780230\ttest: 1.0324109\tbest: 1.0316486 (1647)\n",
            "1800:\tlearn: 0.0743254\ttest: 1.0326463\tbest: 1.0316486 (1647)\n",
            "1850:\tlearn: 0.0708620\ttest: 1.0326994\tbest: 1.0316486 (1647)\n",
            "1900:\tlearn: 0.0675263\ttest: 1.0328983\tbest: 1.0316486 (1647)\n",
            "1950:\tlearn: 0.0645529\ttest: 1.0326631\tbest: 1.0316486 (1647)\n",
            "1999:\tlearn: 0.0618714\ttest: 1.0323595\tbest: 1.0316486 (1647)\ttotal: 4m 42s\tremaining: 0us\n",
            "Best iteration for lr 0.060000000000000005: 1698 with val accuracy 0.63975\n",
            "<IPython.core.display.HTML object>\n",
            "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))\n",
            "0:\tlearn: 1.9842045\ttest: 1.9942796\tbest: 1.9942796 (0)\n",
            "50:\tlearn: 0.9981735\ttest: 1.2645333\tbest: 1.2645333 (50)\n",
            "100:\tlearn: 0.7600456\ttest: 1.1753879\tbest: 1.1753879 (100)\n",
            "150:\tlearn: 0.6108466\ttest: 1.1358467\tbest: 1.1358467 (150)\n",
            "200:\tlearn: 0.5058731\ttest: 1.1143986\tbest: 1.1143986 (200)\n",
            "250:\tlearn: 0.4280037\ttest: 1.1007302\tbest: 1.1007302 (250)\n",
            "300:\tlearn: 0.3635706\ttest: 1.0885320\tbest: 1.0885320 (300)\n",
            "350:\tlearn: 0.3122399\ttest: 1.0788417\tbest: 1.0788417 (350)\n",
            "400:\tlearn: 0.2721723\ttest: 1.0720248\tbest: 1.0720248 (400)\n",
            "450:\tlearn: 0.2380823\ttest: 1.0665722\tbest: 1.0665722 (450)\n",
            "500:\tlearn: 0.2097075\ttest: 1.0617794\tbest: 1.0617794 (500)\n",
            "550:\tlearn: 0.1851609\ttest: 1.0578572\tbest: 1.0578572 (550)\n",
            "600:\tlearn: 0.1650112\ttest: 1.0541487\tbest: 1.0541487 (600)\n",
            "650:\tlearn: 0.1462967\ttest: 1.0524137\tbest: 1.0522491 (647)\n",
            "700:\tlearn: 0.1305791\ttest: 1.0513453\tbest: 1.0511002 (696)\n",
            "750:\tlearn: 0.1173415\ttest: 1.0497656\tbest: 1.0497656 (750)\n",
            "800:\tlearn: 0.1058492\ttest: 1.0492276\tbest: 1.0490437 (793)\n",
            "850:\tlearn: 0.0955548\ttest: 1.0484280\tbest: 1.0482572 (848)\n",
            "900:\tlearn: 0.0870996\ttest: 1.0485984\tbest: 1.0481694 (866)\n",
            "950:\tlearn: 0.0796012\ttest: 1.0484893\tbest: 1.0479095 (922)\n",
            "1000:\tlearn: 0.0728650\ttest: 1.0482087\tbest: 1.0479095 (922)\n",
            "1050:\tlearn: 0.0670325\ttest: 1.0496445\tbest: 1.0479095 (922)\n",
            "1100:\tlearn: 0.0620694\ttest: 1.0508018\tbest: 1.0479095 (922)\n",
            "1150:\tlearn: 0.0574031\ttest: 1.0520171\tbest: 1.0479095 (922)\n",
            "1200:\tlearn: 0.0533889\ttest: 1.0535392\tbest: 1.0479095 (922)\n",
            "1250:\tlearn: 0.0497551\ttest: 1.0542537\tbest: 1.0479095 (922)\n",
            "1300:\tlearn: 0.0464523\ttest: 1.0558517\tbest: 1.0479095 (922)\n",
            "1350:\tlearn: 0.0433507\ttest: 1.0569861\tbest: 1.0479095 (922)\n",
            "1400:\tlearn: 0.0406399\ttest: 1.0583180\tbest: 1.0479095 (922)\n",
            "1450:\tlearn: 0.0381714\ttest: 1.0599071\tbest: 1.0479095 (922)\n",
            "1500:\tlearn: 0.0360376\ttest: 1.0618356\tbest: 1.0479095 (922)\n",
            "1550:\tlearn: 0.0340570\ttest: 1.0631471\tbest: 1.0479095 (922)\n",
            "1600:\tlearn: 0.0322617\ttest: 1.0640955\tbest: 1.0479095 (922)\n",
            "1650:\tlearn: 0.0306175\ttest: 1.0654365\tbest: 1.0479095 (922)\n",
            "1700:\tlearn: 0.0291269\ttest: 1.0668919\tbest: 1.0479095 (922)\n",
            "1750:\tlearn: 0.0277396\ttest: 1.0686745\tbest: 1.0479095 (922)\n",
            "1800:\tlearn: 0.0264715\ttest: 1.0702449\tbest: 1.0479095 (922)\n",
            "1850:\tlearn: 0.0252279\ttest: 1.0711929\tbest: 1.0479095 (922)\n",
            "1900:\tlearn: 0.0241286\ttest: 1.0719812\tbest: 1.0479095 (922)\n",
            "1950:\tlearn: 0.0230684\ttest: 1.0725322\tbest: 1.0479095 (922)\n",
            "1999:\tlearn: 0.0221303\ttest: 1.0737214\tbest: 1.0479095 (922)\ttotal: 4m 41s\tremaining: 0us\n",
            "Best iteration for lr 0.11: 1941 with val accuracy 0.64075\n",
            "<IPython.core.display.HTML object>\n",
            "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))\n",
            "0:\tlearn: 1.9443989\ttest: 1.9590459\tbest: 1.9590459 (0)\n",
            "50:\tlearn: 0.8769414\ttest: 1.2230191\tbest: 1.2230191 (50)\n",
            "100:\tlearn: 0.6264957\ttest: 1.1494461\tbest: 1.1494461 (100)\n",
            "150:\tlearn: 0.4801995\ttest: 1.1215330\tbest: 1.1214654 (149)\n",
            "200:\tlearn: 0.3815858\ttest: 1.1055164\tbest: 1.1055164 (200)\n",
            "250:\tlearn: 0.3106038\ttest: 1.0945052\tbest: 1.0945052 (250)\n",
            "300:\tlearn: 0.2542567\ttest: 1.0839226\tbest: 1.0834064 (298)\n",
            "350:\tlearn: 0.2106447\ttest: 1.0792573\tbest: 1.0792573 (350)\n",
            "400:\tlearn: 0.1770597\ttest: 1.0752096\tbest: 1.0745775 (398)\n",
            "450:\tlearn: 0.1489056\ttest: 1.0696928\tbest: 1.0696928 (450)\n",
            "500:\tlearn: 0.1268760\ttest: 1.0686261\tbest: 1.0686257 (497)\n",
            "550:\tlearn: 0.1092464\ttest: 1.0682500\tbest: 1.0676062 (537)\n",
            "600:\tlearn: 0.0944839\ttest: 1.0690702\tbest: 1.0676062 (537)\n",
            "650:\tlearn: 0.0820200\ttest: 1.0701215\tbest: 1.0676062 (537)\n",
            "700:\tlearn: 0.0724211\ttest: 1.0702204\tbest: 1.0676062 (537)\n",
            "750:\tlearn: 0.0644568\ttest: 1.0708546\tbest: 1.0676062 (537)\n",
            "800:\tlearn: 0.0578288\ttest: 1.0723820\tbest: 1.0676062 (537)\n",
            "850:\tlearn: 0.0521527\ttest: 1.0731192\tbest: 1.0676062 (537)\n",
            "900:\tlearn: 0.0470774\ttest: 1.0741541\tbest: 1.0676062 (537)\n",
            "950:\tlearn: 0.0427306\ttest: 1.0757001\tbest: 1.0676062 (537)\n",
            "1000:\tlearn: 0.0390463\ttest: 1.0789968\tbest: 1.0676062 (537)\n",
            "1050:\tlearn: 0.0358440\ttest: 1.0811296\tbest: 1.0676062 (537)\n",
            "1100:\tlearn: 0.0330515\ttest: 1.0836191\tbest: 1.0676062 (537)\n",
            "1150:\tlearn: 0.0306294\ttest: 1.0854952\tbest: 1.0676062 (537)\n",
            "1200:\tlearn: 0.0285083\ttest: 1.0873392\tbest: 1.0676062 (537)\n",
            "1250:\tlearn: 0.0266179\ttest: 1.0882671\tbest: 1.0676062 (537)\n",
            "1300:\tlearn: 0.0249423\ttest: 1.0900296\tbest: 1.0676062 (537)\n",
            "1350:\tlearn: 0.0233749\ttest: 1.0922437\tbest: 1.0676062 (537)\n",
            "1400:\tlearn: 0.0219964\ttest: 1.0945011\tbest: 1.0676062 (537)\n",
            "1450:\tlearn: 0.0207342\ttest: 1.0964336\tbest: 1.0676062 (537)\n",
            "1500:\tlearn: 0.0195984\ttest: 1.0982074\tbest: 1.0676062 (537)\n",
            "1550:\tlearn: 0.0185509\ttest: 1.1003282\tbest: 1.0676062 (537)\n",
            "1600:\tlearn: 0.0176090\ttest: 1.1019567\tbest: 1.0676062 (537)\n",
            "1650:\tlearn: 0.0167750\ttest: 1.1043713\tbest: 1.0676062 (537)\n",
            "1700:\tlearn: 0.0160134\ttest: 1.1053953\tbest: 1.0676062 (537)\n",
            "1750:\tlearn: 0.0152992\ttest: 1.1078250\tbest: 1.0676062 (537)\n",
            "1800:\tlearn: 0.0146511\ttest: 1.1093936\tbest: 1.0676062 (537)\n",
            "1850:\tlearn: 0.0140069\ttest: 1.1107461\tbest: 1.0676062 (537)\n",
            "1900:\tlearn: 0.0134159\ttest: 1.1129010\tbest: 1.0676062 (537)\n",
            "1950:\tlearn: 0.0128803\ttest: 1.1142326\tbest: 1.0676062 (537)\n",
            "1999:\tlearn: 0.0123871\ttest: 1.1155126\tbest: 1.0676062 (537)\ttotal: 4m 41s\tremaining: 0us\n",
            "Best iteration for lr 0.16000000000000003: 1847 with val accuracy 0.63475\n",
            "<IPython.core.display.HTML object>\n",
            "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))\n",
            "0:\tlearn: 1.9070771\ttest: 1.9262916\tbest: 1.9262916 (0)\n",
            "50:\tlearn: 0.7897741\ttest: 1.1980942\tbest: 1.1980942 (50)\n",
            "100:\tlearn: 0.5319289\ttest: 1.1438878\tbest: 1.1438878 (100)\n",
            "150:\tlearn: 0.3897292\ttest: 1.1234799\tbest: 1.1230899 (149)\n",
            "200:\tlearn: 0.2968913\ttest: 1.1092753\tbest: 1.1092753 (200)\n",
            "250:\tlearn: 0.2321208\ttest: 1.1002113\tbest: 1.1002113 (250)\n",
            "300:\tlearn: 0.1821229\ttest: 1.0941733\tbest: 1.0941733 (300)\n",
            "350:\tlearn: 0.1459599\ttest: 1.0924630\tbest: 1.0924460 (349)\n",
            "400:\tlearn: 0.1194082\ttest: 1.0896054\tbest: 1.0893711 (398)\n",
            "450:\tlearn: 0.0988612\ttest: 1.0881915\tbest: 1.0878276 (420)\n",
            "500:\tlearn: 0.0833553\ttest: 1.0893254\tbest: 1.0878276 (420)\n",
            "550:\tlearn: 0.0709395\ttest: 1.0901581\tbest: 1.0878276 (420)\n",
            "600:\tlearn: 0.0607889\ttest: 1.0935080\tbest: 1.0878276 (420)\n",
            "650:\tlearn: 0.0529656\ttest: 1.0952729\tbest: 1.0878276 (420)\n",
            "700:\tlearn: 0.0465021\ttest: 1.0965252\tbest: 1.0878276 (420)\n",
            "750:\tlearn: 0.0411547\ttest: 1.0985809\tbest: 1.0878276 (420)\n",
            "800:\tlearn: 0.0368646\ttest: 1.1001170\tbest: 1.0878276 (420)\n",
            "850:\tlearn: 0.0329944\ttest: 1.1026070\tbest: 1.0878276 (420)\n",
            "900:\tlearn: 0.0297349\ttest: 1.1056037\tbest: 1.0878276 (420)\n",
            "950:\tlearn: 0.0270775\ttest: 1.1074286\tbest: 1.0878276 (420)\n",
            "1000:\tlearn: 0.0248084\ttest: 1.1113318\tbest: 1.0878276 (420)\n",
            "1050:\tlearn: 0.0228144\ttest: 1.1123587\tbest: 1.0878276 (420)\n",
            "1100:\tlearn: 0.0210982\ttest: 1.1156657\tbest: 1.0878276 (420)\n",
            "1150:\tlearn: 0.0196384\ttest: 1.1186572\tbest: 1.0878276 (420)\n",
            "1200:\tlearn: 0.0183151\ttest: 1.1212034\tbest: 1.0878276 (420)\n",
            "1250:\tlearn: 0.0171844\ttest: 1.1229441\tbest: 1.0878276 (420)\n",
            "1300:\tlearn: 0.0161215\ttest: 1.1247862\tbest: 1.0878276 (420)\n",
            "1350:\tlearn: 0.0151384\ttest: 1.1264143\tbest: 1.0878276 (420)\n",
            "1400:\tlearn: 0.0142777\ttest: 1.1287165\tbest: 1.0878276 (420)\n",
            "1450:\tlearn: 0.0135067\ttest: 1.1303831\tbest: 1.0878276 (420)\n",
            "1500:\tlearn: 0.0128191\ttest: 1.1332336\tbest: 1.0878276 (420)\n",
            "1550:\tlearn: 0.0121845\ttest: 1.1359553\tbest: 1.0878276 (420)\n",
            "1600:\tlearn: 0.0116055\ttest: 1.1389719\tbest: 1.0878276 (420)\n",
            "1650:\tlearn: 0.0110828\ttest: 1.1404511\tbest: 1.0878276 (420)\n",
            "1700:\tlearn: 0.0105816\ttest: 1.1428898\tbest: 1.0878276 (420)\n",
            "1750:\tlearn: 0.0101265\ttest: 1.1450077\tbest: 1.0878276 (420)\n",
            "1800:\tlearn: 0.0097255\ttest: 1.1462673\tbest: 1.0878276 (420)\n",
            "1850:\tlearn: 0.0093175\ttest: 1.1483499\tbest: 1.0878276 (420)\n",
            "1900:\tlearn: 0.0089541\ttest: 1.1503011\tbest: 1.0878276 (420)\n",
            "1950:\tlearn: 0.0086134\ttest: 1.1510925\tbest: 1.0878276 (420)\n",
            "1999:\tlearn: 0.0082931\ttest: 1.1530429\tbest: 1.0878276 (420)\ttotal: 4m 39s\tremaining: 0us\n",
            "Best iteration for lr 0.21000000000000002: 1990 with val accuracy 0.6359999999999999\n",
            "<IPython.core.display.HTML object>\n",
            "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))\n",
            "0:\tlearn: 1.8724442\ttest: 1.8962225\tbest: 1.8962225 (0)\n",
            "50:\tlearn: 0.7154696\ttest: 1.1958402\tbest: 1.1958402 (50)\n",
            "100:\tlearn: 0.4649648\ttest: 1.1422854\tbest: 1.1422854 (100)\n",
            "150:\tlearn: 0.3266971\ttest: 1.1220404\tbest: 1.1217083 (148)\n",
            "200:\tlearn: 0.2401508\ttest: 1.1096178\tbest: 1.1092831 (197)\n",
            "250:\tlearn: 0.1830246\ttest: 1.1018503\tbest: 1.1018503 (250)\n",
            "300:\tlearn: 0.1415896\ttest: 1.0987311\tbest: 1.0982502 (287)\n",
            "350:\tlearn: 0.1119825\ttest: 1.0957750\tbest: 1.0954135 (347)\n",
            "400:\tlearn: 0.0896683\ttest: 1.0939743\tbest: 1.0935271 (395)\n",
            "450:\tlearn: 0.0728515\ttest: 1.0966524\tbest: 1.0935271 (395)\n",
            "500:\tlearn: 0.0603313\ttest: 1.0965704\tbest: 1.0935271 (395)\n",
            "550:\tlearn: 0.0507163\ttest: 1.0993999\tbest: 1.0935271 (395)\n",
            "600:\tlearn: 0.0432153\ttest: 1.1002388\tbest: 1.0935271 (395)\n",
            "650:\tlearn: 0.0374151\ttest: 1.1054923\tbest: 1.0935271 (395)\n",
            "700:\tlearn: 0.0328655\ttest: 1.1089722\tbest: 1.0935271 (395)\n",
            "750:\tlearn: 0.0291281\ttest: 1.1117405\tbest: 1.0935271 (395)\n",
            "800:\tlearn: 0.0260637\ttest: 1.1128901\tbest: 1.0935271 (395)\n",
            "850:\tlearn: 0.0234887\ttest: 1.1156933\tbest: 1.0935271 (395)\n",
            "900:\tlearn: 0.0213635\ttest: 1.1179897\tbest: 1.0935271 (395)\n",
            "950:\tlearn: 0.0195173\ttest: 1.1208866\tbest: 1.0935271 (395)\n",
            "1000:\tlearn: 0.0179352\ttest: 1.1239993\tbest: 1.0935271 (395)\n",
            "1050:\tlearn: 0.0164932\ttest: 1.1275587\tbest: 1.0935271 (395)\n",
            "1100:\tlearn: 0.0153235\ttest: 1.1304757\tbest: 1.0935271 (395)\n",
            "1150:\tlearn: 0.0143191\ttest: 1.1327654\tbest: 1.0935271 (395)\n",
            "1200:\tlearn: 0.0133246\ttest: 1.1362333\tbest: 1.0935271 (395)\n",
            "1250:\tlearn: 0.0124887\ttest: 1.1392180\tbest: 1.0935271 (395)\n",
            "1300:\tlearn: 0.0117114\ttest: 1.1412802\tbest: 1.0935271 (395)\n",
            "1350:\tlearn: 0.0110418\ttest: 1.1431241\tbest: 1.0935271 (395)\n",
            "1400:\tlearn: 0.0104345\ttest: 1.1464707\tbest: 1.0935271 (395)\n",
            "1450:\tlearn: 0.0098780\ttest: 1.1491720\tbest: 1.0935271 (395)\n",
            "1500:\tlearn: 0.0094075\ttest: 1.1510099\tbest: 1.0935271 (395)\n",
            "1550:\tlearn: 0.0089511\ttest: 1.1525616\tbest: 1.0935271 (395)\n",
            "1600:\tlearn: 0.0085361\ttest: 1.1549177\tbest: 1.0935271 (395)\n",
            "1650:\tlearn: 0.0081537\ttest: 1.1572625\tbest: 1.0935271 (395)\n",
            "1700:\tlearn: 0.0078074\ttest: 1.1594646\tbest: 1.0935271 (395)\n",
            "1750:\tlearn: 0.0074671\ttest: 1.1610487\tbest: 1.0935271 (395)\n",
            "1800:\tlearn: 0.0071555\ttest: 1.1626426\tbest: 1.0935271 (395)\n",
            "1850:\tlearn: 0.0068766\ttest: 1.1640807\tbest: 1.0935271 (395)\n",
            "1900:\tlearn: 0.0066087\ttest: 1.1659971\tbest: 1.0935271 (395)\n",
            "1950:\tlearn: 0.0063673\ttest: 1.1676104\tbest: 1.0935271 (395)\n",
            "1999:\tlearn: 0.0061375\ttest: 1.1699227\tbest: 1.0935271 (395)\ttotal: 4m 47s\tremaining: 0us\n",
            "Best iteration for lr 0.26: 1436 with val accuracy 0.6415000000000001\n",
            "<IPython.core.display.HTML object>\n",
            "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))\n",
            "0:\tlearn: 1.8406767\ttest: 1.8690158\tbest: 1.8690158 (0)\n",
            "50:\tlearn: 0.6501065\ttest: 1.1911592\tbest: 1.1911592 (50)\n",
            "100:\tlearn: 0.4053455\ttest: 1.1556336\tbest: 1.1556336 (100)\n",
            "150:\tlearn: 0.2720807\ttest: 1.1412184\tbest: 1.1412184 (150)\n",
            "200:\tlearn: 0.1942552\ttest: 1.1358161\tbest: 1.1354147 (198)\n",
            "250:\tlearn: 0.1434831\ttest: 1.1313966\tbest: 1.1313966 (250)\n",
            "300:\tlearn: 0.1074209\ttest: 1.1297208\tbest: 1.1297208 (300)\n",
            "350:\tlearn: 0.0825227\ttest: 1.1297792\tbest: 1.1288856 (344)\n",
            "400:\tlearn: 0.0659835\ttest: 1.1312209\tbest: 1.1288856 (344)\n",
            "450:\tlearn: 0.0537740\ttest: 1.1344628\tbest: 1.1288856 (344)\n",
            "500:\tlearn: 0.0447583\ttest: 1.1370412\tbest: 1.1288856 (344)\n",
            "550:\tlearn: 0.0375288\ttest: 1.1404745\tbest: 1.1288856 (344)\n",
            "600:\tlearn: 0.0322354\ttest: 1.1446609\tbest: 1.1288856 (344)\n",
            "650:\tlearn: 0.0280090\ttest: 1.1479375\tbest: 1.1288856 (344)\n",
            "700:\tlearn: 0.0245452\ttest: 1.1494296\tbest: 1.1288856 (344)\n",
            "750:\tlearn: 0.0218354\ttest: 1.1535578\tbest: 1.1288856 (344)\n",
            "800:\tlearn: 0.0194902\ttest: 1.1585744\tbest: 1.1288856 (344)\n",
            "850:\tlearn: 0.0175598\ttest: 1.1616676\tbest: 1.1288856 (344)\n",
            "900:\tlearn: 0.0159728\ttest: 1.1657894\tbest: 1.1288856 (344)\n",
            "950:\tlearn: 0.0146071\ttest: 1.1699263\tbest: 1.1288856 (344)\n",
            "1000:\tlearn: 0.0134423\ttest: 1.1733186\tbest: 1.1288856 (344)\n",
            "1050:\tlearn: 0.0124110\ttest: 1.1768262\tbest: 1.1288856 (344)\n",
            "1100:\tlearn: 0.0115288\ttest: 1.1800239\tbest: 1.1288856 (344)\n",
            "1150:\tlearn: 0.0107509\ttest: 1.1824063\tbest: 1.1288856 (344)\n",
            "1200:\tlearn: 0.0100397\ttest: 1.1853487\tbest: 1.1288856 (344)\n",
            "1250:\tlearn: 0.0094358\ttest: 1.1878445\tbest: 1.1288856 (344)\n",
            "1300:\tlearn: 0.0089015\ttest: 1.1911101\tbest: 1.1288856 (344)\n",
            "1350:\tlearn: 0.0084121\ttest: 1.1937122\tbest: 1.1288856 (344)\n",
            "1400:\tlearn: 0.0079570\ttest: 1.1951722\tbest: 1.1288856 (344)\n",
            "1450:\tlearn: 0.0075586\ttest: 1.1969101\tbest: 1.1288856 (344)\n",
            "1500:\tlearn: 0.0071951\ttest: 1.1991591\tbest: 1.1288856 (344)\n",
            "1550:\tlearn: 0.0068614\ttest: 1.2022660\tbest: 1.1288856 (344)\n",
            "1600:\tlearn: 0.0065562\ttest: 1.2046071\tbest: 1.1288856 (344)\n",
            "1650:\tlearn: 0.0062788\ttest: 1.2079059\tbest: 1.1288856 (344)\n",
            "1700:\tlearn: 0.0060152\ttest: 1.2100252\tbest: 1.1288856 (344)\n",
            "1750:\tlearn: 0.0057801\ttest: 1.2116900\tbest: 1.1288856 (344)\n",
            "1800:\tlearn: 0.0055519\ttest: 1.2135419\tbest: 1.1288856 (344)\n",
            "1850:\tlearn: 0.0053334\ttest: 1.2152661\tbest: 1.1288856 (344)\n",
            "1900:\tlearn: 0.0051355\ttest: 1.2170635\tbest: 1.1288856 (344)\n",
            "1950:\tlearn: 0.0049555\ttest: 1.2179757\tbest: 1.1288856 (344)\n",
            "1999:\tlearn: 0.0047947\ttest: 1.2197312\tbest: 1.1288856 (344)\ttotal: 4m 39s\tremaining: 0us\n",
            "Best iteration for lr 0.31000000000000005: 1901 with val accuracy 0.63575\n",
            "<IPython.core.display.HTML object>\n",
            "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))\n",
            "0:\tlearn: 1.8119066\ttest: 1.8448046\tbest: 1.8448046 (0)\n",
            "50:\tlearn: 0.6132189\ttest: 1.1939889\tbest: 1.1939889 (50)\n",
            "100:\tlearn: 0.3664025\ttest: 1.1610107\tbest: 1.1610107 (100)\n",
            "150:\tlearn: 0.2398711\ttest: 1.1487508\tbest: 1.1483744 (144)\n",
            "200:\tlearn: 0.1663145\ttest: 1.1436018\tbest: 1.1429519 (194)\n",
            "250:\tlearn: 0.1190844\ttest: 1.1452783\tbest: 1.1410876 (213)\n",
            "300:\tlearn: 0.0880994\ttest: 1.1433738\tbest: 1.1410876 (213)\n",
            "350:\tlearn: 0.0670196\ttest: 1.1467176\tbest: 1.1410876 (213)\n",
            "400:\tlearn: 0.0528116\ttest: 1.1475979\tbest: 1.1410876 (213)\n",
            "450:\tlearn: 0.0426846\ttest: 1.1494123\tbest: 1.1410876 (213)\n",
            "500:\tlearn: 0.0352593\ttest: 1.1532011\tbest: 1.1410876 (213)\n",
            "550:\tlearn: 0.0297584\ttest: 1.1584981\tbest: 1.1410876 (213)\n",
            "600:\tlearn: 0.0256876\ttest: 1.1617466\tbest: 1.1410876 (213)\n",
            "650:\tlearn: 0.0223649\ttest: 1.1657264\tbest: 1.1410876 (213)\n",
            "700:\tlearn: 0.0197663\ttest: 1.1690320\tbest: 1.1410876 (213)\n",
            "750:\tlearn: 0.0175779\ttest: 1.1717840\tbest: 1.1410876 (213)\n",
            "800:\tlearn: 0.0157609\ttest: 1.1756659\tbest: 1.1410876 (213)\n",
            "850:\tlearn: 0.0143060\ttest: 1.1782192\tbest: 1.1410876 (213)\n",
            "900:\tlearn: 0.0130303\ttest: 1.1816237\tbest: 1.1410876 (213)\n",
            "950:\tlearn: 0.0118884\ttest: 1.1855821\tbest: 1.1410876 (213)\n",
            "1000:\tlearn: 0.0109512\ttest: 1.1893893\tbest: 1.1410876 (213)\n",
            "1050:\tlearn: 0.0101450\ttest: 1.1923928\tbest: 1.1410876 (213)\n",
            "1100:\tlearn: 0.0094256\ttest: 1.1953141\tbest: 1.1410876 (213)\n",
            "1150:\tlearn: 0.0088139\ttest: 1.1989749\tbest: 1.1410876 (213)\n",
            "1200:\tlearn: 0.0082627\ttest: 1.2027177\tbest: 1.1410876 (213)\n",
            "1250:\tlearn: 0.0077529\ttest: 1.2062343\tbest: 1.1410876 (213)\n",
            "1300:\tlearn: 0.0073274\ttest: 1.2089611\tbest: 1.1410876 (213)\n",
            "1350:\tlearn: 0.0069232\ttest: 1.2113035\tbest: 1.1410876 (213)\n",
            "1400:\tlearn: 0.0065608\ttest: 1.2144851\tbest: 1.1410876 (213)\n",
            "1450:\tlearn: 0.0062421\ttest: 1.2162384\tbest: 1.1410876 (213)\n",
            "1500:\tlearn: 0.0059446\ttest: 1.2182779\tbest: 1.1410876 (213)\n",
            "1550:\tlearn: 0.0056762\ttest: 1.2201897\tbest: 1.1410876 (213)\n",
            "1600:\tlearn: 0.0054223\ttest: 1.2223251\tbest: 1.1410876 (213)\n",
            "1650:\tlearn: 0.0051956\ttest: 1.2248606\tbest: 1.1410876 (213)\n",
            "1700:\tlearn: 0.0049908\ttest: 1.2272764\tbest: 1.1410876 (213)\n",
            "1750:\tlearn: 0.0047946\ttest: 1.2298757\tbest: 1.1410876 (213)\n",
            "1800:\tlearn: 0.0046117\ttest: 1.2318852\tbest: 1.1410876 (213)\n",
            "1850:\tlearn: 0.0044437\ttest: 1.2339162\tbest: 1.1410876 (213)\n",
            "1900:\tlearn: 0.0042831\ttest: 1.2351487\tbest: 1.1410876 (213)\n",
            "1950:\tlearn: 0.0041332\ttest: 1.2368086\tbest: 1.1410876 (213)\n",
            "1999:\tlearn: 0.0039956\ttest: 1.2381539\tbest: 1.1410876 (213)\ttotal: 4m 39s\tremaining: 0us\n",
            "Best iteration for lr 0.36000000000000004: 1616 with val accuracy 0.631\n",
            "##################################################\n",
            "Best value: 0.6415000000000001\n",
            "Best parameters found:\n",
            "loss_function: MultiClass\n",
            "iterations: 2000\n",
            "custom_metric: Accuracy\n",
            "task_type: GPU\n",
            "devices: 0\n",
            "learning_rate: 0.26\n",
            "best_iteration: 1436\n",
            "Best model written to /content/music-genre-classification/experiments/dec-tree-cv2/tuned_parameters.csv\n",
            "##################################################\n",
            "Experiment dec-tree-cv2 finished.\n",
            "Error in atexit._run_exitfuncs:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/history.py\", line 786, in writeout_cache\n",
            "    self._writeout_input_cache(conn)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/history.py\", line 770, in _writeout_input_cache\n",
            "    (self.session_number,)+line)\n",
            "sqlite3.ProgrammingError: SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 140561930815232 and this is thread id 140564106766208.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C63udThjnukg"
      },
      "source": [
        "# Copy experiments folder to drive\r\n",
        "!cp -r music-genre-classification/experiments/ /content/drive/MyDrive/"
      ],
      "execution_count": 21,
      "outputs": []
    }
  ]
}