Test-Supervised_Timbre_FreqAtt-h1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adult-submission",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import csv\n",
    "import sys, os.path\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import torch\n",
    "from torch import optim, nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "#from torch.utils.tensorboard import SummaryWriter\n",
    "#from torchsummary import summary\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "renewable-penguin",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our modules\n",
    "import sys\n",
    "sys.path.append('.')\n",
    "sys.path.append('..')\n",
    "\n",
    "from vae import configs, train, plot_utils, models\n",
    "from vae.data import build_dataloader\n",
    "from vae.latent_spaces import dimensionality_reduction, plot_spaces\n",
    "from vae.reconstructions import plot_reconstructions\n",
    "from vae.models import model_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "comparative-company",
   "metadata": {},
   "outputs": [],
   "source": [
    "def multi_acc(y_pred, y_test):\n",
    "    y_pred_softmax = torch.log_softmax(y_pred, dim = 1)\n",
    "    _, y_pred_tags = torch.max(y_pred_softmax, dim = 1)    \n",
    "    \n",
    "    correct_pred = (y_pred_tags == y_test).float()\n",
    "    acc = correct_pred.sum() / len(correct_pred)\n",
    "    \n",
    "    acc = torch.round(acc * 100)\n",
    "    \n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "verbal-scholarship",
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = ['violin', 'viola', 'cello', 'double-bass',\n",
    "                'clarinet', 'bass-clarinet', 'saxophone', 'flute', 'oboe', 'bassoon', 'contrabassoon',\n",
    "                'french-horn', 'trombone', 'trumpet', 'tuba', 'english-horn',\n",
    "                'guitar', 'mandolin', 'banjo', 'chromatic-percussion']\n",
    "\n",
    "chromatic_perc = ['agogo-bells', 'banana-shaker', 'bass-drum', 'bell-tree', 'cabasa', 'Chinese-hand-cymbals',\n",
    "                        'castanets', 'Chinese-cymbal', 'clash-cymbals', 'cowbell', 'djembe', 'djundjun', 'flexatone', 'guiro',\n",
    "                        'lemon-shaker',  'motor-horn',  'ratchet', 'sheeps-toenails', 'sizzle-cymbal', 'sleigh-bells', 'snare-drum',\n",
    "                        'spring-coil', 'squeaker', 'strawberry-shaker', 'surdo', 'suspended-cymbal', 'swanee-whistle',\n",
    "                        'tambourine', 'tam-tam', 'tenor-drum', 'Thai-gong', 'tom-toms', 'train-whistle', 'triangle',\n",
    "                        'vibraslap', 'washboard', 'whip', 'wind-chimes', 'woodblock']\n",
    "\n",
    "labels_list = [i for i in range(len(classes))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dominican-portfolio",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dict of classes\n",
    "classes_dict = {classes[i]: classes.index(classes[i]) for i in range(len(classes))}\n",
    "\n",
    "\n",
    "df = pd.DataFrame.from_dict(classes_dict, orient='index', columns=['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "prescribed-peoples",
   "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>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>violin</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>viola</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cello</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double-bass</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clarinet</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bass-clarinet</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saxophone</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flute</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oboe</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bassoon</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contrabassoon</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>french-horn</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trombone</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trumpet</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tuba</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>english-horn</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guitar</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mandolin</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banjo</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chromatic-percussion</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      class\n",
       "violin                    0\n",
       "viola                     1\n",
       "cello                     2\n",
       "double-bass               3\n",
       "clarinet                  4\n",
       "bass-clarinet             5\n",
       "saxophone                 6\n",
       "flute                     7\n",
       "oboe                      8\n",
       "bassoon                   9\n",
       "contrabassoon            10\n",
       "french-horn              11\n",
       "trombone                 12\n",
       "trumpet                  13\n",
       "tuba                     14\n",
       "english-horn             15\n",
       "guitar                   16\n",
       "mandolin                 17\n",
       "banjo                    18\n",
       "chromatic-percussion     19"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "smooth-finding",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of files in the training dataset: 2051\n"
     ]
    }
   ],
   "source": [
    "model_name = 'supervised_timbre'\n",
    "input = 'mel_cut'\n",
    "trained_epochs = 300\n",
    "\n",
    "test_dataset, test_dataloader = build_dataloader.build_testset(input, model_name)\n",
    "print('Number of files in the training dataset:', len(test_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "painful-wisconsin",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model_utils.import_model(model_name, input)\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model.to(device)\n",
    "checkpoint = torch.load(os.path.join(configs.ParamsConfig.TRAINED_MODELS_PATH, 'saved_model_' + str(trained_epochs) + \"epochs.pth\"))\n",
    "model.load_state_dict(checkpoint['model'])\n",
    "with torch.no_grad():\n",
    "    model.eval()\n",
    "    \n",
    "    y_pred_list = []\n",
    "    y_test_list = []\n",
    "    for sample_batch, y in test_dataloader:\n",
    "            sample_batch = sample_batch.to(device, dtype=torch.float)\n",
    "            y_test_list.append(y)\n",
    "            y_test_pred = model(sample_batch)\n",
    "            _, y_pred_tags = torch.max(y_test_pred, dim = 1)\n",
    "            y_pred_list.append(y_pred_tags.cpu().numpy())\n",
    "\n",
    "y_pred_list = [a.squeeze().tolist() for a in y_pred_list]\n",
    "y_test = [a.squeeze().tolist() for a in y_test_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "established-things",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of parameters: 122388\n",
      "AttentionTimbreEncoder(\n",
      "  (multihead_attn): MultiheadAttention(\n",
      "    (out_proj): _LinearWithBias(in_features=128, out_features=128, bias=True)\n",
      "  )\n",
      "  (fc_1): Linear(in_features=2816, out_features=20, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model_utils.show_total_params(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "convertible-arbitration",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert y_pred_list != y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "developmental-disney",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "          6,   0,   1,   0,   1,   0,  30],\n",
       "       [ 23,  43,   2,   0,  28,  10,  11,  15,   0,   0,   0,   0,   5,\n",
       "          0,   2,   0,   0,   0,   0,   7],\n",
       "       [  8,   0,  28,   1,  33,   6,   3,   6,   0,   3,   2,   0,  11,\n",
       "          2,  27,   1,   0,   0,   0,   2],\n",
       "       [ 12,   0,   3,  63,   2,  16,   0,   0,   0,   6,   0,   0,   3,\n",
       "          0,  23,   0,   0,   0,   0,   0],\n",
       "       [  9,   0,   1,   0,  63,  15,  14,   0,   0,  17,   0,   0,   0,\n",
       "          0,   0,   4,   0,   0,   0,   4],\n",
       "       [  1,   0,  16,   9,  39,  28,   5,   0,   0,  28,   0,   0,   9,\n",
       "          0,   4,   3,   0,   0,   0,   0],\n",
       "       [ 30,   0,   0,   0,  36,   9,  17,   0,   0,  16,   0,   0,   2,\n",
       "          0,   0,   0,   0,   0,   0,   0],\n",
       "       [ 26,   1,   0,   0,  67,   0,   0,  30,   0,   5,   0,   0,   1,\n",
       "          2,   0,   0,   0,   0,   0,   0],\n",
       "       [  2,   0,   0,   0,  32,   0,   1,  28,  19,   0,   0,   0,   1,\n",
       "          2,   0,   0,   0,   0,   0,   4],\n",
       "       [ 25,   0,   0,   0,   2,   7,   0,   0,   0,  53,   0,   4,  16,\n",
       "          0,   1,   0,   0,   0,   0,   0],\n",
       "       [  0,   3,  50,   3,   1,  10,   0,   0,   0,   0,   9,   0,   3,\n",
       "          0,  10,   0,   1,   0,   0,  16],\n",
       "       [ 49,  13,   6,   0,   0,   1,   0,   1,   0,   7,   0,   4,   8,\n",
       "          1,   8,   0,   0,   0,   0,   0],\n",
       "       [ 21,   1,   0,   0,   6,   8,   2,   0,   0,   5,   0,   0,  70,\n",
       "          3,   5,   0,   0,   1,   0,   3],\n",
       "       [  1,   1,   0,   0,  10,   2,   0,   3,   1,   0,   0,   0,   4,\n",
       "         51,   0,   0,   0,   0,   0,   0],\n",
       "       [  0,   0,   3,  19,   0,   0,   0,   0,   0,   1,   2,   0,   2,\n",
       "          0, 117,   0,   2,   0,   0,   0],\n",
       "       [  5,   0,   0,   0,  35,   0,   0,   0,   0,   0,   0,   6,  42,\n",
       "          0,   0,  16,   0,   0,   0,   0],\n",
       "       [  4,   2,   0,   0,   1,   7,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   1,   0,   0,   0,   1],\n",
       "       [  1,   0,   0,   0,   3,   0,   0,   3,   0,   0,   0,   0,   0,\n",
       "          1,   0,   0,   0,   0,   0,   4],\n",
       "       [  3,   0,   0,   0,   4,   2,   0,   1,   0,   0,   0,   0,   0,\n",
       "          1,   0,   0,   0,   0,   0,   0],\n",
       "       [  0,   2,   0,   4,   2,   0,   0,   0,   0,   0,   0,   0,   1,\n",
       "          0,   0,   0,   0,   0,   0,  11]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "confusion_matrix(y_test, y_pred_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "interesting-valuable",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_class = {v: k for k, v in classes_dict.items()}\n",
    "\n",
    "confusion_matrix_df = pd.DataFrame(confusion_matrix(y_test, y_pred_list)).rename(columns=idx_class, index=idx_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "average-material",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(figsize=(8, 6)) \n",
    "sns.heatmap(confusion_matrix_df, annot=True, cmap=\"YlGnBu\", ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "olympic-grove",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6)) \n",
    "sns.heatmap(confusion_matrix_df, annot=False, cmap=\"YlGnBu\", ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "behind-burning",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      0.39      0.33       225\n",
      "           1       0.64      0.29      0.40       146\n",
      "           2       0.26      0.21      0.23       133\n",
      "           3       0.64      0.49      0.56       128\n",
      "           4       0.15      0.50      0.23       127\n",
      "           5       0.23      0.20      0.21       142\n",
      "           6       0.27      0.15      0.20       110\n",
      "           7       0.32      0.23      0.27       132\n",
      "           8       0.95      0.21      0.35        89\n",
      "           9       0.34      0.49      0.40       108\n",
      "          10       0.69      0.08      0.15       106\n",
      "          11       0.29      0.04      0.07        98\n",
      "          12       0.37      0.56      0.45       125\n",
      "          13       0.74      0.70      0.72        73\n",
      "          14       0.59      0.80      0.68       146\n",
      "          15       0.62      0.15      0.25       104\n",
      "          16       0.00      0.00      0.00        16\n",
      "          17       0.00      0.00      0.00        12\n",
      "          18       0.00      0.00      0.00        11\n",
      "          19       0.13      0.55      0.22        20\n",
      "\n",
      "    accuracy                           0.35      2051\n",
      "   macro avg       0.38      0.30      0.29      2051\n",
      "weighted avg       0.43      0.35      0.33      2051\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seasonal-mattress",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "* https://towardsdatascience.com/pytorch-tabular-multiclass-classification-9f8211a123ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "removable-quarter",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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