PodcastMix_audio_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v42bY4DcunxQ"
   },
   "source": [
    "# Audio Process\n",
    "This notebooks needs the podcastmix subset to be in the same directory of this notebook. If you run all the cells from FinalProject_dataset_creation notebook the dataset should already be in the required location and you skip the cell below and go directly to the \"!pip install requirements\" cell. However, if you wanted to save some time and directly download the dataset, without running the FinalProject_dataset_creation notebook, you must run the cell below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d-DQZoN_c7C8"
   },
   "source": [
    "# Install required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mdbhgNdXsRHO",
    "outputId": "7669e1c1-1feb-464a-be7d-462b240f2d35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting torchaudio\n",
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      "Collecting torch==1.4.0 (from torchaudio)\n",
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    }
   ],
   "source": [
    "!pip install pytorch-lightning\n",
    "!pip install asteroid\n",
    "!pip install ffmpeg\n",
    "!pip install torchaudio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LXeMWcwuejn9"
   },
   "source": [
    "# Analize data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z6qGRVOAeo9N"
   },
   "source": [
    "## Speaker dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "H8xXxOIifS_j"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "plt.rcParams[\"font.size\"] = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "id": "H9fZ904cfUkh",
    "outputId": "f58c7c26-3e75-46dd-e6cc-8768a16121cc"
   },
   "outputs": [
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       "       speech_ID speaker_id  speaker_age speaker_gender speaker_accent  \\\n",
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       "\n",
       "                                         speech_path  length subset  \n",
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       "2  podcastmix/podcastmix-synth/train/speech/p298_...  163744  train  \n",
       "3  podcastmix/podcastmix-synth/train/speech/p298_...  139362  train  \n",
       "4  podcastmix/podcastmix-synth/train/speech/p298_...  194040  train  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv('podcastmix/podcastmix-synth/metadata/train/speech.csv')\n",
    "train_data['subset'] = 'train'\n",
    "val_data = pd.read_csv('podcastmix/podcastmix-synth/metadata/val/speech.csv')\n",
    "val_data['subset'] = 'val'\n",
    "test_data = pd.read_csv('podcastmix/podcastmix-synth/metadata/test/speech.csv')\n",
    "test_data['subset'] = 'test'\n",
    "data = pd.concat([train_data, val_data, test_data], ignore_index = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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      "text/plain": [
       "          speech_ID speaker_id  speaker_age speaker_gender speaker_accent  \\\n",
       "0     p228_167_mic1       p228           22              F        English   \n",
       "1     p228_275_mic1       p228           22              F        English   \n",
       "2     p228_331_mic1       p228           22              F        English   \n",
       "3     p228_288_mic1       p228           22              F        English   \n",
       "4     p228_123_mic1       p228           22              F        English   \n",
       "...             ...        ...          ...            ...            ...   \n",
       "4502  p376_124_mic1       p376           22              M         Indian   \n",
       "4503  p376_120_mic1       p376           22              M         Indian   \n",
       "4504  p376_232_mic1       p376           22              M         Indian   \n",
       "4505  p376_318_mic1       p376           22              M         Indian   \n",
       "4506  p376_272_mic1       p376           22              M         Indian   \n",
       "\n",
       "                                            speech_path  length subset  \n",
       "0     podcastmix/podcastmix-synth/val/speech/p228_16...  186991    val  \n",
       "1     podcastmix/podcastmix-synth/val/speech/p228_27...  118595    val  \n",
       "2     podcastmix/podcastmix-synth/val/speech/p228_33...   99130    val  \n",
       "3     podcastmix/podcastmix-synth/val/speech/p228_28...  141405    val  \n",
       "4     podcastmix/podcastmix-synth/val/speech/p228_12...  124362    val  \n",
       "...                                                 ...     ...    ...  \n",
       "4502  podcastmix/podcastmix-synth/val/speech/p376_12...  133795    val  \n",
       "4503  podcastmix/podcastmix-synth/val/speech/p376_12...  117496    val  \n",
       "4504  podcastmix/podcastmix-synth/val/speech/p376_23...   92141    val  \n",
       "4505  podcastmix/podcastmix-synth/val/speech/p376_31...  112434    val  \n",
       "4506  podcastmix/podcastmix-synth/val/speech/p376_27...  130742    val  \n",
       "\n",
       "[4507 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "speakers_dict = {}\n",
    "for speaker_id in val_data.speaker_id.unique():\n",
    "    speakers_dict[speaker_id] = val_data.loc[\n",
    "        val_data['speaker_id'] == speaker_id\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "speakers_dict\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "list.remove(x): x not in list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-764c75452c1e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlist_of_speakers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspeakers_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlist_of_speakers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'p330'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mnon_speaker_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_of_speakers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
     ]
    }
   ],
   "source": [
    "list_of_speakers = list(speakers_dict.keys())\n",
    "list_of_speakers.remove('p330')\n",
    "non_speaker_id = random.sample(list_of_speakers, 1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'non_speaker_id' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-d5ffa81e4931>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnon_speaker_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'non_speaker_id' is not defined"
     ]
    }
   ],
   "source": [
    "non_speaker_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 162
    },
    "id": "ue6aY3ljfRXg",
    "outputId": "3f1ee53b-dbdd-4c6e-a945-ed061efbb4b0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>22.681918</td>\n",
       "      <td>148306.386319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val</th>\n",
       "      <td>23.148214</td>\n",
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      "text/plain": [
       "        speaker_age         length\n",
       "subset                            \n",
       "test      22.092708  142846.089373\n",
       "train     22.681918  148306.386319\n",
       "val       23.148214  156838.304637"
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    "data.groupby(\"subset\").mean()"
   ]
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   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "raauivAckrxw",
    "outputId": "2e0ac4ba-e820-4e15-df73-66c36119cb07"
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    {
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       "  <thead>\n",
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       "      <th>speaker_age</th>\n",
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       "      <td>3.086560</td>\n",
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       "      <th>val</th>\n",
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       "        speaker_age        length\n",
       "subset                           \n",
       "test       2.223169  51873.323341\n",
       "train      3.086560  52443.353473\n",
       "val        2.238795  56578.046693"
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    "data.groupby(\"subset\").std()"
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  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "BNJfduZEgz3h",
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speech_ID</th>\n",
       "      <th>speaker_id</th>\n",
       "      <th>speaker_age</th>\n",
       "      <th>speaker_gender</th>\n",
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       "  <tbody>\n",
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       "      <th>American</th>\n",
       "      <td>8575</td>\n",
       "      <td>8575</td>\n",
       "      <td>8575</td>\n",
       "      <td>8575</td>\n",
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       "      <td>8575</td>\n",
       "      <td>8575</td>\n",
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       "    <tr>\n",
       "      <th>Australian</th>\n",
       "      <td>823</td>\n",
       "      <td>823</td>\n",
       "      <td>823</td>\n",
       "      <td>823</td>\n",
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       "      <td>823</td>\n",
       "      <td>823</td>\n",
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       "    <tr>\n",
       "      <th>British</th>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canadian</th>\n",
       "      <td>3151</td>\n",
       "      <td>3151</td>\n",
       "      <td>3151</td>\n",
       "      <td>3151</td>\n",
       "      <td>3151</td>\n",
       "      <td>3151</td>\n",
       "      <td>3151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>13636</td>\n",
       "      <td>13636</td>\n",
       "      <td>13636</td>\n",
       "      <td>13636</td>\n",
       "      <td>13636</td>\n",
       "      <td>13636</td>\n",
       "      <td>13636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indian</th>\n",
       "      <td>1164</td>\n",
       "      <td>1164</td>\n",
       "      <td>1164</td>\n",
       "      <td>1164</td>\n",
       "      <td>1164</td>\n",
       "      <td>1164</td>\n",
       "      <td>1164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irish</th>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewZealand</th>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorthernIrish</th>\n",
       "      <td>2580</td>\n",
       "      <td>2580</td>\n",
       "      <td>2580</td>\n",
       "      <td>2580</td>\n",
       "      <td>2580</td>\n",
       "      <td>2580</td>\n",
       "      <td>2580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scottish</th>\n",
       "      <td>7611</td>\n",
       "      <td>7611</td>\n",
       "      <td>7611</td>\n",
       "      <td>7611</td>\n",
       "      <td>7611</td>\n",
       "      <td>7611</td>\n",
       "      <td>7611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SouthAfrican</th>\n",
       "      <td>1687</td>\n",
       "      <td>1687</td>\n",
       "      <td>1687</td>\n",
       "      <td>1687</td>\n",
       "      <td>1687</td>\n",
       "      <td>1687</td>\n",
       "      <td>1687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Welsh</th>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                speech_ID  speaker_id  speaker_age  speaker_gender  \\\n",
       "speaker_accent                                                       \n",
       "American             8575        8575         8575            8575   \n",
       "Australian            823         823          823             823   \n",
       "British               398         398          398             398   \n",
       "Canadian             3151        3151         3151            3151   \n",
       "English             13636       13636        13636           13636   \n",
       "Indian               1164        1164         1164            1164   \n",
       "Irish                3623        3623         3623            3623   \n",
       "NewZealand            422         422          422             422   \n",
       "NorthernIrish        2580        2580         2580            2580   \n",
       "Scottish             7611        7611         7611            7611   \n",
       "SouthAfrican         1687        1687         1687            1687   \n",
       "Unknown               410         410          410             410   \n",
       "Welsh                 375         375          375             375   \n",
       "\n",
       "                speech_path  length  subset  \n",
       "speaker_accent                               \n",
       "American               8575    8575    8575  \n",
       "Australian              823     823     823  \n",
       "British                 398     398     398  \n",
       "Canadian               3151    3151    3151  \n",
       "English               13636   13636   13636  \n",
       "Indian                 1164    1164    1164  \n",
       "Irish                  3623    3623    3623  \n",
       "NewZealand              422     422     422  \n",
       "NorthernIrish          2580    2580    2580  \n",
       "Scottish               7611    7611    7611  \n",
       "SouthAfrican           1687    1687    1687  \n",
       "Unknown                 410     410     410  \n",
       "Welsh                   375     375     375  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"speaker_accent\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>F</th>\n",
       "      <td>25619</td>\n",
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       "      <td>25619</td>\n",
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       "      <td>25619</td>\n",
       "      <td>25619</td>\n",
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       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>18836</td>\n",
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       "      <td>18836</td>\n",
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      ],
      "text/plain": [
       "                speech_ID  speaker_id  speaker_age  speaker_accent  \\\n",
       "speaker_gender                                                       \n",
       "F                   25619       25619        25619           25619   \n",
       "M                   18836       18836        18836           18836   \n",
       "\n",
       "                speech_path  length  subset  \n",
       "speaker_gender                               \n",
       "F                     25619   25619   25619  \n",
       "M                     18836   18836   18836  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"speaker_gender\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>speech_ID</th>\n",
       "      <th>speaker_id</th>\n",
       "      <th>speaker_age</th>\n",
       "      <th>speaker_accent</th>\n",
       "      <th>speech_path</th>\n",
       "      <th>length</th>\n",
       "      <th>subset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speaker_gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>21948</td>\n",
       "      <td>21948</td>\n",
       "      <td>21948</td>\n",
       "      <td>21948</td>\n",
       "      <td>21948</td>\n",
       "      <td>21948</td>\n",
       "      <td>21948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>13502</td>\n",
       "      <td>13502</td>\n",
       "      <td>13502</td>\n",
       "      <td>13502</td>\n",
       "      <td>13502</td>\n",
       "      <td>13502</td>\n",
       "      <td>13502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                speech_ID  speaker_id  speaker_age  speaker_accent  \\\n",
       "speaker_gender                                                       \n",
       "F                   21948       21948        21948           21948   \n",
       "M                   13502       13502        13502           13502   \n",
       "\n",
       "                speech_path  length  subset  \n",
       "speaker_gender                               \n",
       "F                     21948   21948   21948  \n",
       "M                     13502   13502   13502  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(\"speaker_gender\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set\n",
      "F 21948\n",
      "M 13502\n"
     ]
    }
   ],
   "source": [
    "print(\"Train set\")\n",
    "print(\"F\", train_data.groupby(\"speaker_gender\").count().iloc(0)[0][0])\n",
    "print(\"M\", train_data.groupby(\"speaker_gender\").count().iloc(0)[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set\n",
      "F 1234\n",
      "M 3264\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set\")\n",
    "print(\"F\", test_data.groupby(\"speaker_gender\").count().iloc(0)[0][0])\n",
    "print(\"M\", test_data.groupby(\"speaker_gender\").count().iloc(0)[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val set\n",
      "F 2437\n",
      "M 2070\n"
     ]
    }
   ],
   "source": [
    "print(\"Val set\")\n",
    "print(\"F\", val_data.groupby(\"speaker_gender\").count().iloc(0)[0][0])\n",
    "print(\"M\", val_data.groupby(\"speaker_gender\").count().iloc(0)[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "Ynui7R0ig2KA",
    "outputId": "9fb0e3f3-62d6-406b-e5f3-b333aeb9dfc0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56f28007f0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.figsize=(8,8)\n",
    "fig.edgecolor='black'\n",
    "ax.set_xlabel('Accent')\n",
    "ax.set_ylabel('Number of speakers')\n",
    "ax.set_title('Number of speakers per accent')\n",
    "data['speaker_accent'].value_counts().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Irish'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   4410\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4411\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value_at\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4412\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_at\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_at\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.get_value_at\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.validate_indexer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-407974b58a82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdictionary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'speaker_accent'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mdictionary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'speaker_accent'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-17-407974b58a82>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdictionary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'speaker_accent'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mdictionary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'speaker_accent'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    869\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    870\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 871\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   4417\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4418\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4419\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4420\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4421\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   4403\u001b[0m         \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"getitem\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4404\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4405\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"tz\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4406\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4407\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Irish'"
     ]
    }
   ],
   "source": [
    "dictionary = data['speaker_accent'].value_counts()\n",
    "x = list(dictionary.keys())\n",
    "p = [dictionary[key] for key in x]\n",
    "dictionary = test_data['speaker_accent'].value_counts()\n",
    "q = [dictionary[key] for key in x]\n",
    "dictionary = val_data['speaker_accent'].value_counts()\n",
    "r = [dictionary[key] for key in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'q' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-b5747ffe32f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0myvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mrects1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mzvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mrects2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'g'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mkvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'q' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 13\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.27       # the width of the bars\n",
    "\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "yvals = p\n",
    "rects1 = ax.bar(ind, yvals, width, color='r')\n",
    "zvals = q\n",
    "rects2 = ax.bar(ind+width, zvals, width, color='g')\n",
    "kvals = r\n",
    "rects3 = ax.bar(ind+width*2, kvals, width, color='b')\n",
    "\n",
    "ax.set_title('Number of speakers per accent per set')\n",
    "ax.set_ylabel('Number of speakers')\n",
    "ax.set_xlabel('Accents per set')\n",
    "ax.set_xticks(ind+width)\n",
    "ax.set_xticklabels( (x) )\n",
    "ax.legend( (rects1[0], rects2[0], rects3[0]), ('Train', 'Test', 'Val') )\n",
    "\n",
    "def autolabel(rects):\n",
    "    for rect in rects:\n",
    "        h = rect.get_height()\n",
    "        ax.text(rect.get_x()+rect.get_width()/2., 1.05*h, '%d'%int(h),\n",
    "                ha='center', va='bottom')\n",
    "\n",
    "#autolabel(rects1)\n",
    "#autolabel(rects2)\n",
    "#autolabel(rects3)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56f1849280>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['speaker_accent'].value_counts().plot(kind='bar', title='Train accents')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56f17b34c0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data['speaker_accent'].value_counts().plot(kind='bar', title='Test accents')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56f1786bb0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_data['speaker_accent'].value_counts().plot(kind='bar', title='Val accents')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 310
    },
    "id": "PmM0IVY-eli_",
    "outputId": "beeb6730-b868-4c8e-f3a8-142e70904ad7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Gender frequency')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "pd.crosstab(data.speaker_gender, data.subset).plot(kind=\"bar\")\n",
    "plt.title(\"Gender frequency grouped by subset\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylabel(\"Gender frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "id": "PSuOxWF3nheu",
    "outputId": "888bd2fb-52bd-4e99-a5f6-04d1e9f6f7da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "data.speaker_age.hist()\n",
    "plt.title(\"Age histogram\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "-8sHbRFqnqKc",
    "outputId": "954f6acd-7d62-4e63-96ee-42d18265c312"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56f15e1d00>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.speaker_age, data.subset).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ikw9ncso6WP"
   },
   "source": [
    "# For the music:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Jamendo/metadata.json') as file:\n",
    "    json_file = json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = list(json_file.keys())\n",
    "artists = {}\n",
    "for song_id in keys:\n",
    "    song = json_file.get(song_id)\n",
    "    artist_id = song['artist_id']\n",
    "    if artist_id in artists.keys():\n",
    "        artists[artist_id].append(song_id)\n",
    "    else:\n",
    "        artists[artist_id] = [song_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2777"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(artists.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_dict = {artist:len(artists[artist]) for artist in artists}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'14': 233,\n",
       " '343607': 191,\n",
       " '359034': 186,\n",
       " '7907': 177,\n",
       " '375935': 158,\n",
       " '7872': 143,\n",
       " '560': 138,\n",
       " '7805': 133,\n",
       " '7063': 129,\n",
       " '338247': 122,\n",
       " '339574': 101,\n",
       " '587': 101,\n",
       " '2740': 94,\n",
       " '341642': 89,\n",
       " '2034': 86,\n",
       " '353807': 85,\n",
       " '3944': 85,\n",
       " '338649': 71,\n",
       " '357359': 69,\n",
       " '4812': 69,\n",
       " '3799': 68,\n",
       " '336426': 64,\n",
       " '3370': 58,\n",
       " '1435': 57,\n",
       " '332': 57,\n",
       " '340121': 57,\n",
       " '344353': 57,\n",
       " '343767': 55,\n",
       " '349976': 55,\n",
       " '341323': 54,\n",
       " '347734': 53,\n",
       " '7561': 53,\n",
       " '342904': 53,\n",
       " '87': 52,\n",
       " '3786': 51,\n",
       " '460469': 50,\n",
       " '2485': 50,\n",
       " '5261': 48,\n",
       " '361044': 48,\n",
       " '5240': 48,\n",
       " '346808': 48,\n",
       " '104': 47,\n",
       " '5721': 47,\n",
       " '8004': 47,\n",
       " '339989': 46,\n",
       " '348835': 45,\n",
       " '876': 44,\n",
       " '354648': 43,\n",
       " '451073': 42,\n",
       " '345952': 42,\n",
       " '365833': 42,\n",
       " '337939': 42,\n",
       " '348064': 41,\n",
       " '475967': 41,\n",
       " '696': 41,\n",
       " '365142': 40,\n",
       " '811': 40,\n",
       " '4819': 39,\n",
       " '441585': 38,\n",
       " '2278': 38,\n",
       " '438603': 38,\n",
       " '2741': 38,\n",
       " '350716': 38,\n",
       " '8236': 38,\n",
       " '341681': 37,\n",
       " '341528': 37,\n",
       " '702': 37,\n",
       " '371777': 36,\n",
       " '2208': 36,\n",
       " '4056': 36,\n",
       " '341226': 35,\n",
       " '6682': 35,\n",
       " '958': 35,\n",
       " '340092': 35,\n",
       " '340202': 35,\n",
       " '336855': 34,\n",
       " '350065': 34,\n",
       " '3917': 33,\n",
       " '338447': 33,\n",
       " '976': 33,\n",
       " '368794': 33,\n",
       " '357612': 32,\n",
       " '341355': 32,\n",
       " '7625': 32,\n",
       " '7057': 32,\n",
       " '341415': 31,\n",
       " '337766': 31,\n",
       " '352428': 31,\n",
       " '5385': 31,\n",
       " '349991': 31,\n",
       " '2177': 31,\n",
       " '223': 31,\n",
       " '125': 30,\n",
       " '1235': 30,\n",
       " '2189': 30,\n",
       " '872': 30,\n",
       " '350817': 30,\n",
       " '364561': 30,\n",
       " '336871': 29,\n",
       " '470592': 29,\n",
       " '675': 29,\n",
       " '1428': 29,\n",
       " '341393': 29,\n",
       " '346032': 29,\n",
       " '427108': 28,\n",
       " '2824': 28,\n",
       " '15': 28,\n",
       " '341040': 28,\n",
       " '353120': 27,\n",
       " '353332': 27,\n",
       " '314': 27,\n",
       " '368762': 27,\n",
       " '338047': 27,\n",
       " '3769': 27,\n",
       " '346554': 27,\n",
       " '1333': 26,\n",
       " '440512': 26,\n",
       " '347916': 26,\n",
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       " '448643': 7,\n",
       " '363330': 7,\n",
       " '432338': 7,\n",
       " '439258': 7,\n",
       " '366006': 7,\n",
       " '338550': 7,\n",
       " '492789': 7,\n",
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       " '350452': 7,\n",
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       " '377564': 7,\n",
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       " '365823': 7,\n",
       " '508529': 7,\n",
       " '508493': 7,\n",
       " '462394': 7,\n",
       " '1119': 7,\n",
       " '2391': 7,\n",
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       " '202': 7,\n",
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       " '360365': 6,\n",
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       " '1706': 6,\n",
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       " '354405': 6,\n",
       " '492815': 6,\n",
       " '480019': 6,\n",
       " '247': 6,\n",
       " '339053': 6,\n",
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       " '359737': 6,\n",
       " '490499': 6,\n",
       " '342619': 6,\n",
       " '995': 6,\n",
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       " '360780': 5,\n",
       " '348487': 5,\n",
       " '365009': 5,\n",
       " '423766': 5,\n",
       " '375130': 5,\n",
       " '367405': 5,\n",
       " '355673': 5,\n",
       " '442305': 5,\n",
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       " '362848': 5,\n",
       " '421422': 5,\n",
       " '352350': 5,\n",
       " '464776': 5,\n",
       " '430396': 5,\n",
       " '471736': 5,\n",
       " '457223': 5,\n",
       " '491348': 5,\n",
       " '345816': 5,\n",
       " '339246': 5,\n",
       " '473685': 5,\n",
       " '366820': 5,\n",
       " '360461': 5,\n",
       " '2223': 5,\n",
       " '339724': 5,\n",
       " '2309': 5,\n",
       " '340444': 5,\n",
       " '454720': 5,\n",
       " '337983': 5,\n",
       " '442526': 5,\n",
       " '346555': 5,\n",
       " '471328': 5,\n",
       " '441907': 5,\n",
       " '499004': 5,\n",
       " '485181': 5,\n",
       " '450911': 5,\n",
       " '438966': 5,\n",
       " '337690': 5,\n",
       " '456650': 5,\n",
       " '440773': 5,\n",
       " '6568': 5,\n",
       " '338426': 5,\n",
       " '443173': 5,\n",
       " '433311': 5,\n",
       " '455057': 5,\n",
       " '349925': 5,\n",
       " '454463': 5,\n",
       " '440730': 5,\n",
       " '425281': 5,\n",
       " '455059': 5,\n",
       " '2350': 5,\n",
       " ...}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{k: v for k, v in sorted(count_dict.items(), key=lambda item: item[1], reverse=True)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'433460'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set({k: v for k, v in sorted(count_dict.items(), key=lambda item: item[1])}.keys()))[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "id": "wiCkjS7WpDei",
    "outputId": "ee1d8780-e66c-4f41-88d6-4ab6f273441e"
   },
   "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>music_ID</th>\n",
       "      <th>jamendo_id</th>\n",
       "      <th>name</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>artist_id</th>\n",
       "      <th>album_name</th>\n",
       "      <th>license_ccurl</th>\n",
       "      <th>releasedate</th>\n",
       "      <th>image</th>\n",
       "      <th>vocalinstrumental</th>\n",
       "      <th>lang</th>\n",
       "      <th>gender</th>\n",
       "      <th>acousticelectric</th>\n",
       "      <th>speed</th>\n",
       "      <th>tags</th>\n",
       "      <th>music_path</th>\n",
       "      <th>length</th>\n",
       "      <th>subset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11751</td>\n",
       "      <td>11751</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Tom La Meche</td>\n",
       "      <td>696</td>\n",
       "      <td>Blog</td>\n",
       "      <td>http://creativecommons.org/licenses/by-nc-nd/3.0/</td>\n",
       "      <td>2006-04-22</td>\n",
       "      <td>https://images.jamendo.com/albums/s1/1741/cove...</td>\n",
       "      <td>instrumental</td>\n",
       "      <td>NaN</td>\n",
       "      <td>neutral</td>\n",
       "      <td>electric</td>\n",
       "      <td>medium</td>\n",
       "      <td>{\"genres\": [\"jazz\"/ \"nujazz\"]/ \"instruments\": ...</td>\n",
       "      <td>podcastmix/podcastmix-synth/train/music/11751....</td>\n",
       "      <td>12304128</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5086</td>\n",
       "      <td>5086</td>\n",
       "      <td>Indian Dream</td>\n",
       "      <td>Tom La Meche</td>\n",
       "      <td>696</td>\n",
       "      <td>Personnel</td>\n",
       "      <td>http://creativecommons.org/licenses/by-nc-sa/2.5/</td>\n",
       "      <td>2005-12-02</td>\n",
       "      <td>https://images.jamendo.com/albums/s0/786/cover...</td>\n",
       "      <td>instrumental</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>medium</td>\n",
       "      <td>{\"genres\": [\"jazz\"/ \"nujazz\"]/ \"instruments\": ...</td>\n",
       "      <td>podcastmix/podcastmix-synth/train/music/5086.flac</td>\n",
       "      <td>10362691</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11750</td>\n",
       "      <td>11750</td>\n",
       "      <td>Eluder</td>\n",
       "      <td>Tom La Meche</td>\n",
       "      <td>696</td>\n",
       "      <td>Blog</td>\n",
       "      <td>http://creativecommons.org/licenses/by-nc-sa/2.5/</td>\n",
       "      <td>2006-04-22</td>\n",
       "      <td>https://images.jamendo.com/albums/s1/1741/cove...</td>\n",
       "      <td>instrumental</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>medium</td>\n",
       "      <td>{\"genres\": [\"jazz\"/ \"blues\"/ \"nujazz\"]/ \"instr...</td>\n",
       "      <td>podcastmix/podcastmix-synth/train/music/11750....</td>\n",
       "      <td>4639844</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5083</td>\n",
       "      <td>5083</td>\n",
       "      <td>Rencontre du 7ième type</td>\n",
       "      <td>Tom La Meche</td>\n",
       "      <td>696</td>\n",
       "      <td>Personnel</td>\n",
       "      <td>http://creativecommons.org/licenses/by-nc-sa/2.5/</td>\n",
       "      <td>2005-12-02</td>\n",
       "      <td>https://images.jamendo.com/albums/s0/786/cover...</td>\n",
       "      <td>instrumental</td>\n",
       "      <td>NaN</td>\n",
       "      <td>neutral</td>\n",
       "      <td>electric</td>\n",
       "      <td>medium</td>\n",
       "      <td>{\"genres\": [\"experimental\"/ \"electronic\"]/ \"in...</td>\n",
       "      <td>podcastmix/podcastmix-synth/train/music/5083.flac</td>\n",
       "      <td>7290629</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48726</td>\n",
       "      <td>48726</td>\n",
       "      <td>Interlude interrompu</td>\n",
       "      <td>Tom La Meche</td>\n",
       "      <td>696</td>\n",
       "      <td>Où se cachent...</td>\n",
       "      <td>http://creativecommons.org/licenses/by-nc-nd/3.0/</td>\n",
       "      <td>2007-06-04</td>\n",
       "      <td>https://images.jamendo.com/albums/s5/5905/cove...</td>\n",
       "      <td>instrumental</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>medium</td>\n",
       "      <td>{\"genres\": [\"jazz\"/ \"ambient\"]/ \"instruments\":...</td>\n",
       "      <td>podcastmix/podcastmix-synth/train/music/48726....</td>\n",
       "      <td>6036822</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   music_ID  jamendo_id                     name   artist_name  artist_id  \\\n",
       "0     11751       11751                    Urban  Tom La Meche        696   \n",
       "1      5086        5086             Indian Dream  Tom La Meche        696   \n",
       "2     11750       11750                   Eluder  Tom La Meche        696   \n",
       "3      5083        5083  Rencontre du 7ième type  Tom La Meche        696   \n",
       "4     48726       48726     Interlude interrompu  Tom La Meche        696   \n",
       "\n",
       "         album_name                                      license_ccurl  \\\n",
       "0              Blog  http://creativecommons.org/licenses/by-nc-nd/3.0/   \n",
       "1         Personnel  http://creativecommons.org/licenses/by-nc-sa/2.5/   \n",
       "2              Blog  http://creativecommons.org/licenses/by-nc-sa/2.5/   \n",
       "3         Personnel  http://creativecommons.org/licenses/by-nc-sa/2.5/   \n",
       "4  Où se cachent...  http://creativecommons.org/licenses/by-nc-nd/3.0/   \n",
       "\n",
       "  releasedate                                              image  \\\n",
       "0  2006-04-22  https://images.jamendo.com/albums/s1/1741/cove...   \n",
       "1  2005-12-02  https://images.jamendo.com/albums/s0/786/cover...   \n",
       "2  2006-04-22  https://images.jamendo.com/albums/s1/1741/cove...   \n",
       "3  2005-12-02  https://images.jamendo.com/albums/s0/786/cover...   \n",
       "4  2007-06-04  https://images.jamendo.com/albums/s5/5905/cove...   \n",
       "\n",
       "  vocalinstrumental lang   gender acousticelectric   speed  \\\n",
       "0      instrumental  NaN  neutral         electric  medium   \n",
       "1      instrumental  NaN      NaN              NaN  medium   \n",
       "2      instrumental  NaN      NaN              NaN  medium   \n",
       "3      instrumental  NaN  neutral         electric  medium   \n",
       "4      instrumental  NaN      NaN              NaN  medium   \n",
       "\n",
       "                                                tags  \\\n",
       "0  {\"genres\": [\"jazz\"/ \"nujazz\"]/ \"instruments\": ...   \n",
       "1  {\"genres\": [\"jazz\"/ \"nujazz\"]/ \"instruments\": ...   \n",
       "2  {\"genres\": [\"jazz\"/ \"blues\"/ \"nujazz\"]/ \"instr...   \n",
       "3  {\"genres\": [\"experimental\"/ \"electronic\"]/ \"in...   \n",
       "4  {\"genres\": [\"jazz\"/ \"ambient\"]/ \"instruments\":...   \n",
       "\n",
       "                                          music_path    length subset  \n",
       "0  podcastmix/podcastmix-synth/train/music/11751....  12304128  train  \n",
       "1  podcastmix/podcastmix-synth/train/music/5086.flac  10362691  train  \n",
       "2  podcastmix/podcastmix-synth/train/music/11750....   4639844  train  \n",
       "3  podcastmix/podcastmix-synth/train/music/5083.flac   7290629  train  \n",
       "4  podcastmix/podcastmix-synth/train/music/48726....   6036822  train  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load music\n",
    "train_data = pd.read_csv('podcastmix/podcastmix-synth/metadata/train/music.csv')\n",
    "train_data['subset'] = 'train'\n",
    "val_data = pd.read_csv('podcastmix/podcastmix-synth/metadata/val/music.csv')\n",
    "val_data['subset'] = 'val'\n",
    "test_data = pd.read_csv('podcastmix/podcastmix-synth/metadata/test/music.csv')\n",
    "test_data['subset'] = 'test'\n",
    "data_csv = pd.concat([train_data, val_data, test_data], ignore_index = True)\n",
    "data_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19370"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2208"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.artist_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "278"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.artist_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "277"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_data.artist_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'funkybass': 1, 'clarinette': 1, 'blackbird': 1, 'beatles': 3, 'thebeatles': 1, 'theshins': 1, 'intimiste': 2, 'stereophonics': 1, 'hardtechno': 6, 'synthi': 9, 'delicate': 1, 'possibility': 1, '90sfeel': 1, 'interlude': 2, 'blue': 2, 'livedrums': 1, 'asia': 1, 'cafemusic': 1, 'irishfolk': 1, 'delirium': 1, 'riff': 2, 'knopfler': 2, 'tangoelectronico': 1, 'femalevokals': 1, 'sweden': 1, 'adrenaline': 1, 'arizona': 1, 'melancoly': 3, 'classicmusic': 1, 'chilloutloungeinstrumental': 1, 'klaviermusik': 6, 'poetic': 1, 'clasical': 1, 'synthsizers': 1, 'drummachines': 3, 'voodoo': 10, 'haiti': 2, 'sommer': 1, 'filmtv': 1, 'deepchords': 1, 'downtrempo': 1, 'downtwmpo': 1, 'fullon': 1, 'milonga': 1, 'ritmico': 1, 'son': 1, 'alegre': 1, 'sins': 12, 'atheism': 1, 'angry': 2, 'intimate': 1, 'mellow': 1, 'outerspace': 1, 'electricguitarsolo': 1, 'synthetizers': 2, 'trio': 9, 'videohive': 8, 'smile': 3, 'original': 9, 'instumental': 1, 'growl': 7, 'tambourin': 2, 'dense': 1, 'heavyclassical': 2, 'hangdrum': 1, 'belong': 1, 'train': 1, 'berlinschool': 2, 'backgrounds': 1, 'bliss': 1, 'slide': 2, 'basquiat': 1, 'bouzouki': 1, 'rythmn': 15, 'merenguereggaeton': 1, 'songwriters': 1, 'pisstake': 1, 'los': 1, 'happynewyear': 1, 'christmasmusic': 1, 'fender': 1, 'ranchero': 2, 'barroco': 1, 'trova': 3, 'espiritual': 1, 'guajiro': 1, 'altcountry': 3, 'fiesta': 1, 'wellbeing': 1, 'diatonic': 5, 'neosoul': 1, 'lightness': 1, 'caribbean': 2, 'ballroom': 1, 'funeral': 1, 'york': 1, 'eastern': 10, 'neoclassique': 2, 'alternattive': 1, 'ralph': 1, 'government': 1, 'tribute': 1, 'heavenly': 2, 'separation': 1, 'fly': 1, 'documentaries': 2, 'commerical': 1, 'pianist': 2, 'vampire': 1, 'january': 1, 'soudntrack': 1, 'white': 1, 'shakedb': 1, 'mad': 1, 'lilnoah': 1, 'word': 1, 'childhood': 1, 'discofunk': 1, 'del': 1, 'stupefiant': 12, 'politics': 1, 'canadian': 11, 'canada': 1, 'all4one': 1, 'nyc': 1, 'black': 4, 'prague': 8, 'eurotechno': 4, 'donkey': 1, 'crack': 1, 'italodance': 1, 'geek': 1, 'netherlands': 1, 'slavic': 1, 'fidule': 1, 'soprano': 1, 'thundering': 1, 'ambientmusic': 1, 'peace': 3, 'musclecar': 1, 'charleston': 1, 'femalesinger': 1, 'dj7guard': 1, 'virtuoso': 1, 'moviescore': 4, 'bluesharp': 1, 'pesma': 1, '2013': 1, 'discohouse': 1, 'politic': 6, 'barroom': 1, 'montage': 2, 'relationships': 3, 'conflict': 1, 'walking': 1, 'synthipop': 1, 'broadcasting': 1, 'invasion': 3, 'atmo': 1, 'wave': 2, 'heavylistening': 10, 'jig': 1, 'holyday': 1, 'beiger': 4, 'norinebraun': 1, 'norine': 2, 'jazzz': 10, 'kiss': 3, 'pain': 1, 'girl': 1, 'orignal': 1, 'spring': 1, 'scene': 1, 'sunrise': 2, 'balearic': 5, 'instrumentall': 4, 'athmospheric': 2, 'computergamemusic': 1, 'carnival': 1, 'soaring': 1, 'south': 3, 'withintemptation': 3, 'winter': 2, 'helloween': 1, 'sonataarctica': 1, 'dreambient': 1, 'meditativ': 2, 'funkfusion': 1, 'nksinternational': 13, 'ukrainian': 6, 'relationship': 5, 'spark': 1, 'moviemusic': 2, 'sound': 2, 'slash': 2, 'radiohead': 8, 'birthday': 1, 'hhardcore': 1, 'crossover': 4, 'industrialcrossover': 1, 'chilling': 1, 'thepianolady': 1, 'bronca': 1, 'psyche': 10, 'electronics': 3, 'scary': 3, 'thriller': 1, 'jeuvideo': 1, 'tiple': 1, 'loungetrance': 1, 'indianclassical': 1, 'suspenseful': 1, 'newageambient': 1, 'wild': 1, 'poptronica': 1, 'bluesjazz': 2, 'excellent': 5, 'better': 2, 'gratitude': 1, 'buddha': 1, 'beauty': 1, 'scabeater': 2, 'loving': 2, 'southern': 1, 'worship': 1, 'ramones': 1, 'rockblues': 1, 'sigurros': 1, 'boastfull': 1, 'beatmaker': 12, 'triphopjazz': 2, 'swinghop': 8, 'jazzyhiphop': 1, 'mao': 1, 'progresivo': 6, 'eternaldream': 1, 'educational': 4, 'edgy': 1, 'synthsizer': 1, 'noises': 1, 'proud': 1, 'sarcastic': 1, 'toby': 1, 'clapping': 1, 'bop': 1, 'retrowave': 3, 'reposant': 1, 'naturel': 1, 'moving': 1, 'hype': 1, 'dreamdance': 1, 'liquidfunk': 1, 'ethnique': 7, 'tourisme': 1, 'botticelli': 1, 'revolution': 3, 'rupture': 1, 'chirurgie': 1, 'profs': 1, 'mere': 2, 'engagement': 1, 'autoroute': 1, 'caca': 1, 'presse': 1, 'immigration': 1, 'chanteuse': 1, 'realite': 1, 'routine': 1, 'erection': 1, 'franchouille': 1, 'supernova': 1, 'poèmesymphonique': 1, 'classicall': 1, 'lied': 1, 'indianinstruments': 1, 'arcangelosimonetti': 1, 'open': 2, 'beethoven': 2, 'schumann': 11, 'fashionshow': 1, 'clubsound': 1, 'exciting': 3, 'respect': 1, 'afrique': 7, 'speedgarage': 7, 'live': 2, 'music2000': 1, 'freestyle': 1, 'encouraging': 1, 'school': 1, 'zimmer': 1, 'sringsslow': 1, 'middle': 5, 'tunisien': 3, 'tunisie': 1, 'tunisia': 1, 'chipsound': 10, 'bitpop': 3, 'cpc': 5, 'emogrunge': 7, 'nightclub': 1, 'entre': 1, 'energico': 1, 'rnbhiphop': 1, '2015': 3, 'jazzclassic': 1, 'scratching': 4, 'breaks': 3, 'turntables': 5, 'hiphopundergroud': 4, 'soundrack': 1, 'softmusic': 1, 'soulfunk': 1, 'dancebeat': 1, 'slowjam': 1, 'deceit': 1, 'femalvocal': 1, 'rocking': 2, 'hiphoper': 1, 'viejitasperobonitas': 1, 'bandams': 1, 'balada': 1, 'jazzsamba': 3, 'line': 1, 'digital': 1, 'ambienthouse': 1, 'belgique': 9, 'tr808': 1, 'kraftwerk': 1, 'symphony': 2, 'instrospective': 1, 'age': 13, 'aggresive': 1, 'misery': 1, 'django': 1, 'choice': 1, 'moby': 1, 'worldmusic': 1, 'electronicrock': 1, 'think': 1, 'change': 1, 'romantico': 1, 'undergroundrap': 1, 'latinpop': 2, 'trains': 1, 'support': 1, 'jamesbland': 1, 'solstice': 1, 'faith': 1, 'sandrafluke': 1, 'acceptance': 1, 'strangers': 1, 'medicalmarijuana': 1, 'day': 1, 'puro': 1, 'spaceambient': 1, 'four': 1, 'scandinavian': 1, 'repente': 1, 'sambajazz': 1, 'fife': 1, 'coco': 2, 'xote': 1, 'junina': 1, 'melodious': 1, 'waltzy': 2, 'baiao': 2, 'nordeste': 1, '7stringguitar': 1, 'cavaco': 1, 'afrobrazilian': 1, 'clarineta': 1, 'toada': 1, 'saxsoprano': 1, 'mediterraneo': 2, 'ninetyfiverecords': 1, 'pignamadenoob': 1, 'vintage': 4, 'leftfield': 1, 'dums': 1, 'anglaise': 1, 'devilish': 2, 'flo': 4, 'heavyrock': 5, 'aspiring': 1, 'violent': 1, 'arenarock': 1, 'acousticpiano': 1, 'double': 1, 'crossoverprog': 1, 'mashup': 1, 'trippop': 1, 'lqiuid': 1, 'moonimean': 2, 'mistery': 12, 'friendly': 1, 'triplets': 1, 'impact': 2, 'airy': 1, 'evil': 1, 'productivity': 1, 'jolly': 2, 'cuivres': 4, 'djent': 9, 'progressivemetalcore': 1, 'anthem': 3, 'soccer': 2, 'teenpop': 4, 'nudisco': 1, 'disney': 1, 'dubnbass': 2, 'surfer': 1, 'alcohal': 1, 'slap': 1, 'reflexing': 1, 'hopefull': 1, 'celta': 9, 'srings': 1, 'popsoul': 1, 'here': 1, 'chorale': 13, 'enfants': 3, 'oldies': 7, 'modernclassical': 2, 'soleil': 4, 'groumf': 3, 'metisse': 10, 'flstudio': 2, 'blockbuster': 1, 'top40': 1, 'burlesque': 1, 'hebrew': 2, 'judeospanish': 1, 'hardtekno': 9, 'hello': 1, 'more': 1, 'monkey': 1, 'dress': 1, 'moms': 1, 'lie': 1, 'drumms': 1, 'jazzblues': 1, 'clubmusic': 1, 'tecnopop': 1, 'ukhiphop': 2, 'frac14frac34tilde': 1, 'ambientfolk': 1, 'acid': 10, 'kateorange': 2, 'avicii': 1, 'marriage': 1, 'friends': 1, 'robertbrowning': 1, 'couple': 1, 'boats': 1, 'skareggae': 1, 'reggaepop': 1, 'technologic': 1, 'progressivo': 5, 'nora': 1, 'minnesota': 1, 'concept': 6, 'existentialism': 1, 'jarre': 1, 'energisch': 1, 'deutschsprachig': 1, '2016': 2, 'sensuality': 1, 'instrumentalbeat': 2, 'bollywood': 4, 'rappers': 1, 'closer': 1, 'indonesia': 1, 'sporting': 1, 'man': 1, 'beyondthewhite': 2, 'happyhouse': 1, 'turntab': 1, 'acoustick': 1, 'violao': 1, 'sonata': 7, 'guitaracoustique': 1, 'classica': 1, 'fuga': 1, 'instrumetal': 1, 'goal': 1, 'caja': 1, 'primer': 1, 'face': 1, 'sorry': 2, 'heartache': 1, 'eerie': 1, 'romantique': 1, 'indien': 2, 'ancient': 1, 'youth': 1, 'jeuxvideo': 1, 'technorock': 1, 'speed': 1, 'musical': 1, 'montero': 1, 'flight': 1, 'droptuning': 1, 'vocalguitarbassdrum': 1, 'microsound': 1, 'burn': 1, 'rythme': 1, 'hybridtrailer': 2, 'alternatives': 1, 'weihnachtsmusik': 1, 'rhythms': 9, 'seaside': 1, 'singers': 1, 'sensitive': 2, 'ludovicoeinaudi': 1, 'gentle': 1, 'blackout': 1, 'lowtempo': 1, 'liszt': 2, 'rockurbano': 1, 'whitestripes': 1, 'wymiar': 1, 'nowy': 1, 'fonk': 4, 'poppy': 1, 'bluesy': 1, 'curse': 1, 'strangefetish': 1, 'vainadurateam': 1, '3men2yelbory': 1, 'gamescore': 3, 'rockguitariff': 1, 'darkpop': 1, 'stringsensemble': 1, 'roses': 1, 'genova': 1, 'beach': 1, 'travelling': 3, 'lovestory': 1, 'california': 1, 'deejay': 1, 'rockcristiano': 1, 'pink': 1, 'emotronic': 1, 'brokenkites': 1, 'coquin': 1, 'lyric': 7, 'strangezero': 4, 'exprimental': 13, 'downtempoelectro': 6, 'downtemp': 1, 'erotic': 2, 'shopping': 1, 'trompete': 2, 'popmixtape': 9, 'popmixtap': 1, 'hiphoppop': 1, 'technoambient': 3, 'ganja': 1, 'indiedance': 3, 'lgbt': 1, 'passion': 1, 'glory': 1, 'gun': 1, 'drumsynth': 1, 'yuanan': 4, 'raggamuffin': 1, 'digitalreggae': 1, 'rootsy': 1, 'background': 1}\n"
     ]
    }
   ],
   "source": [
    "genres_train_count = {}\n",
    "vartags_train_count = {}\n",
    "for i in range(len(train_data)):\n",
    "    row = train_data.iloc[i]\n",
    "    tags = json.loads(row[\"tags\"].replace('/',','))\n",
    "    genres = tags['genres']\n",
    "    vartags = tags['vartags']\n",
    "    for genre in genres:\n",
    "        if genre not in genres_train_count:\n",
    "            genres_train_count[genre] = 1\n",
    "        else:\n",
    "            genres_train_count[genre] += 1\n",
    "    for vartag in vartags:\n",
    "        if vartag not in vartags_train_count:\n",
    "            vartags_train_count[vartag] = 1\n",
    "        else:\n",
    "            vartags_train_count[vartag] += 1\n",
    "\n",
    "print(genres_train_count)\n",
    "print(vartags_train_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rock': 382, 'latin': 45, 'poprock': 15, 'rocknroll': 9, 'idm': 26, 'house': 16, 'metal': 43, 'electronic': 317, 'dubstep': 8, 'filmscore': 140, 'tribal': 1, 'pop': 405, 'rap': 38, 'folk': 68, 'dance': 80, 'world': 57, 'funk': 16, 'hiphop': 61, 'indie': 138, 'salsa': 14, 'cumbia': 8, 'reggaeton': 2, 'experimental': 16, 'jazz': 32, 'chillout': 36, 'spokenword': 1, 'soul': 39, 'gospel': 1, 'electropop': 22, 'blues': 39, 'newage': 45, 'singersongwriter': 38, 'rnb': 24, 'corporate': 4, 'downtempo': 9, 'classical': 91, 'neoclassical': 4, 'reggae': 43, 'ragga': 4, 'heavymetal': 7, 'punk': 3, 'ambient': 62, 'oriental': 3, 'deathmetal': 4, 'middleeastern': 2, 'jungle': 4, 'bossanova': 2, 'dreampop': 9, 'electrorock': 6, 'indierock': 12, 'alternativehiphop': 1, 'triphop': 28, 'darkwave': 20, 'trance': 49, 'techno': 22, 'chansonfrancaise': 9, 'darkambient': 5, 'chillwave': 1, 'electrohouse': 1, 'disco': 3, 'celtic': 4, 'swing': 6, 'classicrock': 8, 'alternativerock': 4, '8bit': 20, 'acidjazz': 15, 'breakbeat': 3, 'samba': 2, 'synthpop': 16, 'americana': 2, 'surfrock': 9, 'symphonic': 18, 'waltz': 3, 'hardcore': 15, 'shoegaze': 5, 'trailer': 3, 'baroque': 9, 'intro': 4, 'production': 4, 'dancehall': 1, 'newwave': 8, 'indiepop': 19, 'flamenco': 4, 'gangstarap': 2, 'gothic': 32, 'metalcore': 5, 'postrock': 2, 'country': 15, 'freejazz': 2, 'afrocubanjazz': 1, 'merengue': 1, 'dub': 1, 'ska': 1, 'tango': 1, 'rockabilly': 1, 'bluesrock': 6, 'garage': 5, 'asian': 1, 'grunge': 1, 'tropicalhouse': 4, 'industrial': 10, 'trap': 1, 'lofi': 1, 'drumnbass': 2, 'emo': 1, 'postgrunge': 4, 'hardrock': 4, 'bolero': 1, 'jazzfusion': 1, 'southernrock': 2, 'britpop': 1, 'electrofunk': 1, 'nujazz': 1, 'western': 1, 'avantgarde': 2, 'rumba': 1, 'latinjazz': 1, 'gypsy': 1, 'afrobeat': 1, 'progressivehouse': 2, 'edm': 3, 'eurodance': 1, 'progressivemetal': 4, 'progressiverock': 3, 'mathcore': 1, 'speedmetal': 1}\n",
      "{'vocal': 837, 'voice': 872, 'alternativo': 5, '60s': 30, '80s': 39, 'httpswwwfacebookcomjonathancaizajonyflowdj': 26, '70s': 5, 'screaming': 1, 'gamemusic': 3, 'acoustic': 508, 'force': 1, 'urban': 16, 'militant': 1, 'neutral': 266, 'immigrant': 1, 'africain': 1, 'upbeat': 31, 'concience': 1, 'political': 2, 'paix': 2, 'children': 5, 'monde': 1, 'ballad': 16, 'romantic': 13, 'love': 54, 'fingerpicking': 1, 'nature': 7, 'mort': 1, 'injustice': 1, 'fast': 40, 'adventure': 3, 'travel': 7, 'desert': 1, 'island': 4, 'single': 1, 'ustedsemellevolavida': 1, 'estoscelos': 1, 'harveys': 2, 'trompeta': 1, 'education': 2, 'tibetan': 1, 'peaceful': 179, 'christmas': 26, 'holiday': 6, 'universe': 1, 'elegant': 1, 'vocals': 13, 'gentle': 1, 'lounge': 24, 'sophisticated': 1, 'kiss': 3, 'original': 1, 'happy': 108, 'energetic': 129, 'peace': 1, 'heaven': 1, 'indiefolk': 1, 'sad': 30, 'nyc': 1, 'hardkore': 1, 'antisexista': 1, 'instrumental': 80, 'abstract': 2, 'melancholic': 22, 'freddie': 1, 'oldskool': 1, 'advertising': 17, 'sentimental': 8, 'soft': 36, 'cinematic': 11, 'emotional': 26, 'contemporary': 14, 'caribbean': 4, 'adult': 1, 'notte': 1, 'bambini': 1, 'gamelan': 1, 'meditative': 13, 'orientallounge': 1, 'taiwan': 1, 'soundscape': 1, 'noise': 2, 'guitarsolo': 3, 'space': 10, 'chilloutinstrumental': 1, 'film': 29, 'atmospheric': 22, 'anthem': 1, 'endcredits': 1, 'travelling': 3, 'beauty': 1, 'epic': 24, 'action': 7, 'actionmusic': 1, 'release': 1, 'sleep': 1, 'arpeggios': 3, 'journey': 2, 'uplifting': 17, 'family': 1, 'crime': 1, 'santa': 1, 'ethno': 8, 'documentary': 3, 'relaxe': 1, 'funny': 6, 'brave': 1, 'religious': 2, 'dreamy': 17, 'dark': 16, 'history': 1, 'psy': 2, 'goa': 2, 'lovesongs': 1, 'aggressive': 3, 'noenminombre': 1, 'comedy': 2, 'sarkozy': 1, 'rocking': 1, 'harmonious': 3, 'fun': 17, 'percusion': 1, 'discohouse': 1, 'fusions': 1, '90s': 7, 'groovy': 25, 'bigbeat': 3, 'dirtyelectro': 1, 'sport': 2, 'peacefull': 1, 'quirky': 3, 'mystery': 1, 'suspense': 1, 'fitness': 2, 'videogame': 4, 'scifi': 5, 'uptempo': 2, 'jangly': 6, 'thebeatles': 1, 'tender': 1, 'summer': 8, 'happybirthday': 1, 'calming': 1, 'thebeachboys': 1, 'haunting': 2, 'rem': 1, 'beatles': 3, 'loving': 1, 'autore': 1, 'c64': 17, 'demoscene': 1, 'brasil': 1, 'psicotropicodelia': 1, 'portugueseguitar': 1, 'clap': 3, 'kick': 2, 'singer': 1, 'blunt': 1, 'victim': 1, 'whimsical': 1, 'commercial': 4, 'celebrate': 1, 'lover': 1, 'sensitive': 1, 'uprightbass': 1, 'moore': 1, 'matthew': 1, 'braveheart': 4, 'gamesoundtrack': 1, 'dramatic': 7, 'breath': 1, 'arpeggio': 3, 'woodwinds': 2, 'passion': 1, 'prepare': 1, 'sonata': 1, 'retro': 4, 'nostalgy': 1, 'nightmare': 2, 'fantastic': 2, 'calmness': 1, 'voices': 6, 'uilleann': 1, 'cymbals': 1, 'thoughts': 2, 'concerto': 1, 'sequencer': 2, 'ending': 1, 'kingdom': 1, 'thunder': 1, 'medieval': 4, 'powerfull': 1, 'lead': 1, 'englishhorn': 1, 'fairy': 1, 'psychedelic': 7, 'psychedelica': 1, 'sample': 12, 'ancient': 9, 'symphony': 1, 'jingles': 1, 'hollywood': 1, 'system': 4, 'lion': 7, 'voz': 8, 'worlds': 1, 'familia': 3, 'roots': 2, 'pianojazz': 2, 'rush': 1, 'echoing': 1, 'stripped': 4, 'gwb': 6, 'guitarmusic': 1, 'good': 1, 'people': 1, 'plane': 1, 'frustration': 1, 'nostalgia': 1, 'sexy': 6, 'congas': 1, 'boombap': 1, 'mix': 2, 'restaurant': 1, 'reggaepop': 1, 'balade': 1, 'healinglove2017healinglove': 1, 'akkustisch': 5, 'chiefkeef': 1, 'noises': 1, 'instumental': 4, 'sountrack': 1, 'flight': 2, 'tltctronic': 1, 'office': 1, 'lunatic': 1, 'fairytale': 2, 'silentmovie': 1, 'intensive': 1, 'cartoonish': 1, 'heartwarming': 1, 'hippy': 1, 'minimal': 11, 'bear': 1, 'anime': 1, 'dymanic': 1, 'party': 4, 'knight': 1, 'motivational': 6, 'teenpop': 1, 'niñez': 1, 'salsaparaelmundo': 2, 'folcklore': 1, 'romantica': 1, 'tambor': 1, 'salsaromantica': 3, 'salsapop': 1, 'folclor': 2, 'merengueton': 1, 'comunista': 1, 'pulp': 3, 'surfguitar': 2, 'beachparty': 1, 'sunshine': 1, 'nostalgic': 5, 'tragic': 1, 'musiquedefilm': 5, 'nostalgie': 6, 'rock80': 1, 'hop': 5, 'happyhouse': 1, 'surfmusic': 1, 'guitarelectro': 1, 'jessiek': 1, 'pianorock': 5, 'steelguitar': 1, 'slideguitars': 2, 'county': 1, 'hope': 1, 'moviemusic': 1, 'sunny': 3, 'synthetic': 1, 'revolt': 1, 'vitamin': 1, 'sainton': 2, 'crying': 1, 'vulnerable': 2, 'violent': 1, 'heart': 1, 'banger': 1, 'synthop': 3, 'driveelectricguitars': 1, 'inspirational': 2, 'advertisement': 1, 'breakdance': 1, 'improvisation': 3, 'bigband': 1, 'upliftingadvertising': 1, 'breakup': 1, 'malevocal': 1, 'moody': 1, 'acustica': 1, 'pops': 1, 'poppop': 1, 'balad': 1, 'popbalad': 1, 'kinks': 1, 'rage': 1, 'direstraits': 1, 'pigbass': 1, 'bonamassa': 1, 'popculture': 1, 'sarcastic': 2, 'countryside': 1, 'connection': 1, 'easy': 3, 'talkingheads': 1, 'mediaeval': 1, 'twosome': 1, 'montypython': 1, 'generalcontimplation': 1, 'emancipation': 1, 'contimplating': 1, 'building': 1, 'wedding': 1, 'longing': 1, 'coldplay': 1, 'adaption': 1, 'change': 1, 'holidays': 1, 'rollingstones': 1, 'snow': 1, 'dances': 1, 'bombo': 2, 'ledzeppelin': 1, 'synthrock': 1, 'electrometal': 1, 'phantasmagoria': 1, 'depressiv': 1, 'wave': 3, 'elektrorock': 1, 'chor': 1, 'dreams': 1, 'alterntive': 1, 'new': 8, 'hit': 1, 'manchester': 2, 'newave': 1, 'celebration': 1, 'cumbias': 2, 'salsaton': 1, 'cumbiarapida': 1, 'sonora': 4, 'bailar': 1, 'romantika': 1, 'step': 1, 'soulmusic': 3, 'поп': 1, 'djent': 2, 'pianosoloimprovised': 1, 'improvisations': 1, 'southern': 1, 'modernmelodicdeathmetal': 1, 'drummachines': 1, 'radio': 1, 'fashion': 2, '90sfeel': 1, 'summerhit': 1, 'politic': 1, 'relationship': 2, 'instrospective': 1, 'slowrock': 1, 'introspective': 1, 'encouraging': 1, 'stay': 1, 'liquid': 1, 'liquidjazz': 1, 'countryfolk': 1, 'horror': 1, 'naughty': 1, 'hawaii': 1, 'obsession': 1, 'harmony': 1, 'jazzfolk': 1, 'salento': 1, 'song': 1, 'tammurriata': 1, 'lonelyness': 1, 'missing': 2, 'active': 2, 'guetta': 1, 'commercials': 1, 'filmsoundtrack': 3, 'houseprogressive': 1, 'rockroll': 9, 'progrock': 3, 'elctronica': 5, 'vanda': 4, 'rockindependent': 1, 'phychedelic': 1, 'funkygroovy': 2, 'ukepop': 1, 'mots': 1, 'tvmusic': 1, 'tvfilm': 1, 'breathtaking': 1, 'atmosphericmusic': 1, 'slow': 1, 'poetry': 1, 'melancolico': 1, 'nylon': 1, 'chorinho': 1, 'trebeats': 1, 'newpop': 2, 'chelsea': 1, 'adultrock': 2, 'maya': 1}\n"
     ]
    }
   ],
   "source": [
    "genres_test_count = {}\n",
    "vartags_test_count = {}\n",
    "for i in range(len(test_data)):\n",
    "    row = test_data.iloc[i]\n",
    "    tags = json.loads(row[\"tags\"].replace('/',','))\n",
    "    genres = tags['genres']\n",
    "    vartags = tags['vartags']\n",
    "    for genre in genres:\n",
    "        if genre not in genres_test_count:\n",
    "            genres_test_count[genre] = 1\n",
    "        else:\n",
    "            genres_test_count[genre] += 1\n",
    "    for vartag in vartags:\n",
    "        if vartag not in vartags_test_count:\n",
    "            vartags_test_count[vartag] = 1\n",
    "        else:\n",
    "            vartags_test_count[vartag] += 1\n",
    "\n",
    "print(genres_test_count)\n",
    "print(vartags_test_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'classical': 112, 'electronic': 340, 'filmscore': 248, 'newage': 83, 'chillout': 86, 'idm': 6, 'drumnbass': 13, 'neoclassical': 2, 'pop': 266, 'rock': 333, 'country': 16, 'downtempo': 32, 'triphop': 23, 'jazz': 69, 'oriental': 5, 'singersongwriter': 31, 'folk': 44, 'indiepop': 16, 'trance': 26, 'world': 49, 'tribal': 4, 'ambient': 136, 'reggae': 48, 'dubstep': 8, 'experimental': 22, 'spokenword': 3, 'chansonfrancaise': 2, 'indie': 110, 'hiphop': 107, 'nujazz': 8, 'acidjazz': 4, 'latin': 20, 'loop': 1, 'production': 3, 'metal': 43, 'alternativemetal': 1, 'ragga': 12, 'soul': 6, 'funk': 24, 'rnb': 25, 'darkwave': 1, 'electronica': 5, 'industrial': 1, 'surfrock': 2, 'rocknroll': 3, 'garage': 2, 'alternativerock': 7, 'blues': 26, 'swing': 9, 'rap': 48, 'techno': 20, 'punk': 13, 'chamber': 1, 'dance': 44, 'house': 21, 'disco': 3, 'trap': 1, 'gothic': 8, 'shoegaze': 8, 'postrock': 7, 'deathmetal': 7, 'reggaeton': 2, 'indierock': 9, 'synthpop': 3, 'flamenco': 2, 'bossanova': 3, 'psychedelicrock': 1, 'corporate': 2, 'african': 3, 'drone': 8, 'darkambient': 9, 'dub': 18, 'jungle': 1, 'intro': 12, 'rockabilly': 1, 'indian': 8, 'classicrock': 2, 'symphonic': 42, 'trailer': 9, 'western': 2, 'choral': 1, 'asian': 5, 'heavymetal': 3, 'edm': 2, 'afrobeat': 1, 'poprock': 4, 'americana': 5, 'electrorock': 7, 'jazzfusion': 1, 'christian': 2, 'jazzfunk': 2, 'waltz': 1, 'bluesrock': 2, 'ragtime': 1, 'rocksteady': 2, 'newwave': 17, 'tango': 1, 'musicbed': 1, 'progressiverock': 1, 'chillhop': 2, 'manouche': 1, 'gypsy': 1, 'emo': 1, 'deephouse': 2, 'bluegrass': 2, 'hardrock': 3, 'gospel': 1, 'hardcore': 15, 'numetal': 1, 'celtic': 2, 'breakbeat': 10, 'electropop': 21, 'dreampop': 2, 'eurodance': 1, 'latinjazz': 1, 'smoothjazz': 1, 'salsa': 3, 'ska': 4, 'dancehall': 1, 'britpop': 4}\n",
      "{'soft': 51, 'neoclassic': 9, 'documentary': 14, 'soundart': 1, 'easy': 1, 'emotional': 88, 'beatiful': 13, 'acoustic': 441, 'voice': 693, 'lounge': 61, 'peaceful': 155, 'melancholic': 21, 'instrumental': 102, 'vocal': 660, 'minimal': 19, 'solo': 1, 'neutral': 254, 'county': 5, 'happy': 73, 'dark': 31, 'softrock': 1, 'love': 45, 'malevocals': 2, 'epic': 119, 'activism': 1, 'rufuswainwright': 1, 'children': 17, 'videogame': 7, 'action': 41, 'education': 5, 'nature': 11, 'energetic': 127, 'etnic': 1, 'fashion': 5, 'advertising': 7, 'meditative': 6, 'political': 6, 'sad': 38, 'heart': 1, 'sport': 4, 'videogames': 3, 'sleep': 4, 'wedding': 4, 'christmas': 8, 'lie': 1, 'cinematic': 30, 'horror': 9, 'tear': 2, 'travel': 9, 'adventure': 17, 'song': 8, '60s': 7, 'fast': 28, 'inspirational': 17, 'young': 1, 'imaginary': 1, 'summer': 4, 'psychedelic': 13, 'rockalternative': 1, 'groovy': 11, '2018': 1, 'wolrd': 7, 'scat': 2, 'villancico': 3, 'holiday': 11, 'seasonal': 1, 'birthday': 2, 'trippy': 6, 'feelgood': 1, 'uplifting': 31, 'acouticguitar': 1, 'slideguitars': 3, 'retro': 1, 'protopunk': 1, 'nobackbone': 1, 'electricguitarsolo': 1, 'acoustica': 1, 'jazzmanouche': 1, 'ironic': 1, 'ballad': 17, 'beatlesque': 1, 'hiphopundergroud': 4, 'nostalgic': 4, 'upbeat': 40, 'banger': 1, 'mp3': 2, 'radio': 1, 'contemporary': 13, 'ethno': 13, 'chinese': 1, 'coffee': 1, 'party': 3, 'happyhouse': 1, 'hit': 1, 'discofunk': 2, 'urban': 5, 'film': 64, 'soundscape': 15, 'independant': 3, 'text': 5, 'textesfrancais': 1, 'aggressif': 1, 'yamaha': 4, 'swampy': 1, 'dreamy': 23, 'drake': 1, 'lyrical': 8, 'abstract': 2, 'weed': 1, '90srap': 1, 'rhodes': 1, '2014': 1, 'media': 1, 'colaars': 3, 'inlove': 1, 'indirock': 1, 'trompetas': 1, 'romantic': 7, 'symphonicmetal': 1, 'congas': 1, 'motivational': 6, 'advertise': 1, 'laura': 1, 'dirving': 1, 'royaltyfree': 2, 'girl': 1, 'people': 1, 'musiquedefilm': 1, 'space': 14, 'atmospheric': 15, 'slow': 7, 'italodance': 1, 'traditional': 1, 'balance': 1, 'punq': 1, 'atari': 1, 'fireworks': 1, 'catalan': 1, 'debut': 5, 'turnoffthemagic': 1, 'album': 1, 'beatles': 3, 'stringquartet': 1, 'orchestration': 1, 'friendship': 1, 'flowerpower': 1, 'crime': 13, 'musiquedefilms': 6, 'theme': 1, 'choeurs': 5, 'scifi': 8, 'military': 7, 'orchestral': 5, 'modern': 3, 'operatic': 1, 'themes': 4, 'thematic': 1, 'toms': 1, 'emotions': 4, 'pizzicato': 1, 'cellos': 1, 'depaysant': 1, 'batman': 1, 'bourne': 1, 'orchesta': 2, 'intense': 6, 'superhero': 2, 'tribute': 1, 'boy': 1, 'actions': 1, 'actionmusic': 1, 'romantique': 1, 'francophone': 1, 'robotic': 1, 'trompette': 1, 'tragic': 2, 'orchestal': 1, 'spiritual': 4, 'pirates': 1, 'japanese': 4, 'spying': 1, 'adventura': 1, 'symphony': 1, 'comedy': 4, 'landscapes': 1, 'climax': 1, 'white': 1, 'progresive': 1, 'clubdance': 1, 'trancecore': 1, 'relationships': 1, 'step': 1, 'personal': 1, 'soulmusic': 6, 'mood': 1, 'sensual': 1, 'fun': 3, 'groovey': 1, 'recovery': 1, 'religious': 2, 'lively': 4, 'tenacity': 1, 'strange': 5, 'poetic': 1, 'understanding': 1, 'sentimental': 6, 'memories': 1, 'relief': 1, 'fight': 1, 'pipo': 7, 'pegoraro': 1, 'folksy': 3, 'folky': 1, 'cavaquinho': 2, 'peace': 1, 'distorsion': 1, 'exciting': 1, 'fuzz': 2, 'animals': 1, 'violas': 1, 'storymusic': 11, 'guitarsolo': 1, 'dramatic': 1, 'acoustisque': 1, 'soulpop': 3, 'jazzpop': 1, 'soulfunk': 1, 'slowtempo': 2, 'slowstyle': 1, 'popjazz': 1, 'sleepmusic': 4, '90s': 2, 'djumbe': 1, 'instrumentale': 1, 'altcountry': 4, 'irlandais': 1, 'dobro': 1, 'smoothpiano': 1, 'pianopop': 1, 'jazzpiano': 1, 'classicalpiano': 1, 'instru': 1, 'serrano': 1, 'theweeknd': 1, 'other': 11, 'malevocal': 4, 'milonga': 1, 'mystery': 2, 'long': 1, 'improvisation': 1, 'dreams': 2, 'improvization': 1, 'frozen': 1, 'commercial': 1, 'lovely': 1, 'electronicpop': 1, 'wendy': 1, 'goovy': 2, 'celebration': 1, 'cordial': 2, 'inspire': 1, 'inspired': 1, 'explicit': 2, 'sylenth1': 1, 'beatz': 1, 'noel': 1, 'percu': 1, 'poetry': 1, 'more': 1, 'onirique': 16, 'softmusik': 5, 'musesoftware': 3, 'rammstein': 1, 'resoblues': 1, 'fusionrockinstrumental': 1, 'singer': 12, 'rootsreggae': 10, 'roots': 10, 'nogreaterlove': 1, 'underground': 10, 'aggressive': 3, 'drama': 1, 'medieval': 1, 'serene': 1, 'quirky': 3, 'tunguska': 3, 'soundfilm': 1, 'cinematique': 1, 'poland': 11, 'guitarmusic': 2, 'europop': 1, 'marxmallows': 1, 'cantautor': 1, 'dreamymood': 1, 'goodmood': 1, 'melancoly': 1, 'pianorock': 2, 'partymusik': 1, 'sexy': 3, 'clubing': 1, 'komodo': 2, 'accoustique': 1, 'brazilian': 2, 'refreshing': 1, 'classica': 5, 'contemporaryclassical': 1, 'lonely': 1, 'lessxmore': 4, 'new': 1, 'age': 2, 'lovers': 2, 'rasta': 1, 'funkyhouse': 1, 'british': 3, 'coldplay': 2, 'windinstruments': 1, 'soundsystem': 1, 'wurlitzer': 1, 'mystical': 1, 'naruto': 1, 'oldie': 1, 'marihuana': 1, 'charty': 1, 'tempo': 2, 'precussion': 1, 'global': 2, 'electrotechno': 2, 'middleeast': 1, 'dubstephop': 2, 'technorap': 1, 'goodvibes': 2, 'vocals': 1, 'earth': 1, 'adult': 2, 'fitness': 2, 'colors': 1, 'funkhop': 1, 'spokenwords': 1, 'moody': 1, 'electrorap': 2, 'techodance': 1, 'danceremix': 1, 'hindi': 1, 'rapping': 4, 'dancemix': 1, 'empowering': 1, 'singing': 1, 'electrodark': 1, 'enduring': 1, 'elecronic': 2, 'chords': 1, 'ying': 1, 'electopop': 1, 'hiphopeletronicapop': 1, 'tuning': 1, 'beyonce': 1, 'rapper': 1, 'ethnicfusion': 1, 'dancechill': 1, 'racism': 1, 'singers': 1, 'hipsoul': 1, 'flyinghigh': 1, 'big': 9, 'fishingshow': 1, 'spa': 2, 'fastcountry': 1}\n"
     ]
    }
   ],
   "source": [
    "genres_val_count = {}\n",
    "vartags_val_count = {}\n",
    "for i in range(len(val_data)):\n",
    "    row = val_data.iloc[i]\n",
    "    tags = json.loads(row[\"tags\"].replace('/',','))\n",
    "    genres = tags['genres']\n",
    "    vartags = tags['vartags']\n",
    "    for genre in genres:\n",
    "        if genre not in genres_val_count:\n",
    "            genres_val_count[genre] = 1\n",
    "        else:\n",
    "            genres_val_count[genre] += 1\n",
    "    for vartag in vartags:\n",
    "        if vartag not in vartags_val_count:\n",
    "            vartags_val_count[vartag] = 1\n",
    "        else:\n",
    "            vartags_val_count[vartag] += 1\n",
    "\n",
    "print(genres_val_count)\n",
    "print(vartags_val_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'jazz': 889, 'nujazz': 56, 'blues': 246, 'experimental': 338, 'electronic': 3212, 'ambient': 1119, 'chillout': 717, 'jungle': 66, 'funk': 305, 'world': 632, 'latinjazz': 17, 'bossanova': 40, 'folk': 1046, 'classical': 1102, 'rock': 3588, 'metal': 719, 'indie': 825, 'spacerock': 3, 'alternativerock': 90, 'artrock': 4, 'classicrock': 56, 'newage': 481, 'dance': 682, 'synthpop': 49, 'reggae': 370, 'dub': 94, 'lofi': 17, 'shoegaze': 15, 'indiepop': 68, 'indierock': 103, 'house': 301, 'edm': 51, 'trance': 350, 'pop': 2973, 'soul': 144, 'techno': 320, 'hiphop': 742, 'rap': 259, 'latin': 236, 'filmscore': 1328, 'psytrance': 25, 'celtic': 100, 'corporate': 202, 'eurodance': 35, 'rnb': 200, 'samba': 13, 'jazzfunk': 29, 'manouche': 8, 'gypsy': 24, 'synthwave': 13, 'newwave': 37, 'postrock': 57, 'progressiverock': 24, 'drumnbass': 142, 'punk': 248, 'singersongwriter': 414, 'country': 210, 'indietronica': 13, 'symphonic': 101, 'breakbeat': 106, 'bebop': 13, 'heavymetal': 41, 'chillwave': 4, 'electropop': 150, 'reggaeton': 22, 'grunge': 81, 'downtempo': 320, 'poprock': 110, 'surfrock': 23, 'western': 13, 'bluesrock': 29, 'african': 64, 'ska': 92, 'acidjazz': 53, 'industrial': 97, 'dubstep': 70, 'hardrock': 21, 'flamenco': 44, '8bit': 90, 'indian': 38, 'asian': 29, 'christian': 32, 'electrorock': 21, 'stoner': 19, 'breakcore': 4, 'jazzfusion': 29, 'avantgarde': 17, 'triphop': 396, 'balkan': 7, 'idm': 43, 'middleeastern': 15, 'swing': 115, 'salsa': 30, 'rumba': 3, 'rocknroll': 49, 'glitch': 5, 'chansonfrancaise': 126, 'americana': 40, 'tango': 5, 'alternativemetal': 2, 'chamber': 8, 'gothic': 70, 'ragga': 32, 'darkambient': 50, 'southernrock': 16, 'waltz': 19, 'britpop': 8, 'disco': 62, 'cumbia': 11, 'trap': 15, 'neoclassical': 39, 'intro': 36, 'electronica': 54, 'electrofunk': 8, 'musicbed': 24, 'production': 49, 'hardcore': 92, 'smoothjazz': 28, 'dancehall': 13, 'baroque': 52, 'oriental': 46, 'gangstarap': 8, 'electroswing': 23, 'deathmetal': 57, 'deathcore': 3, 'deephouse': 33, 'bluegrass': 18, 'electrohouse': 8, 'americannative': 3, 'drone': 18, 'tribal': 31, 'thrashmetal': 9, 'acidhouse': 4, 'blackmetal': 5, 'cabaret': 9, 'ragtime': 10, 'poppunk': 16, 'emo': 26, 'postpunk': 5, 'gospel': 18, 'chillhop': 14, 'trailer': 46, 'garage': 30, 'metalcore': 36, 'ambiant': 7, 'spokenword': 5, 'rocksteady': 4, 'mambo': 8, 'merengue': 11, 'grime': 1, 'freejazz': 6, 'darkwave': 35, 'loop': 8, 'jingle': 9, 'grindcore': 3, 'dreampop': 23, 'logo': 3, 'powermetal': 12, 'progressivemetal': 5, 'bolero': 5, 'skapunk': 2, 'psychedelicrock': 3, 'futurebass': 2, 'numetal': 7, 'march': 3, 'bachata': 1, 'rockabilly': 5, 'countryrock': 4, 'fado': 1, 'industrialmetal': 1, 'choral': 4, 'zouk': 1, 'sludge': 1, 'afrobeat': 2, 'alternativehiphop': 1, 'afrocubanjazz': 1, 'tropicalhouse': 4, 'postgrunge': 4, 'progressivehouse': 2, 'mathcore': 1, 'speedmetal': 1}\n",
      "{'improvisation': 81, 'upbeat': 488, 'lounge': 738, 'peaceful': 1703, 'neutral': 2598, 'soft': 473, 'instrumental': 1105, 'fast': 486, 'urban': 63, 'meditative': 179, 'ethno': 101, 'jam': 2, 'groovy': 213, 'acoustic': 4419, 'vocal': 6810, 'voice': 7093, 'happy': 874, 'energetic': 1371, 'chansons': 1, 'atmospheric': 125, 'emotional': 475, 'romantic': 172, 'love': 338, 'heartskin': 1, 'space': 108, 'whisper': 2, 'zion': 1, 'summer': 91, 'melancholic': 171, 'nostalgic': 48, 'slow': 59, 'other': 52, 'elements': 1, 'danceable': 1, 'sad': 435, 'between': 1, 'blood': 1, 'stage': 1, 'synrhesizer': 1, 'estrenos2015rapreggaeton': 1, 'joyeux': 5, 'epic': 322, 'cinematic': 185, 'documetary': 1, 'psychedelic': 123, 'trippy': 41, 'bandasonora': 12, 'film': 325, 'contemporary': 96, 'sentimental': 70, 'news': 11, 'adult': 13, 'action': 107, 'dark': 301, 'adventure': 123, '90s': 36, 'retro': 61, 'easy': 18, 'dreamy': 183, 'acoutsic': 3, 'rythmic': 5, 'warm': 1, 'rhythmic': 6, 'rhytmic': 2, 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'birthday': 3, 'hhardcore': 1, 'crossover': 4, 'industrialcrossover': 1, 'chilling': 1, 'thepianolady': 1, 'bronca': 1, 'psyche': 10, 'electronics': 3, 'scary': 3, 'thriller': 1, 'jeuvideo': 1, 'tiple': 1, 'loungetrance': 1, 'indianclassical': 1, 'suspenseful': 1, 'newageambient': 1, 'wild': 1, 'poptronica': 1, 'bluesjazz': 2, 'excellent': 5, 'better': 2, 'gratitude': 1, 'buddha': 1, 'beauty': 2, 'scabeater': 2, 'loving': 3, 'southern': 2, 'worship': 1, 'ramones': 1, 'rockblues': 1, 'sigurros': 1, 'boastfull': 1, 'beatmaker': 12, 'triphopjazz': 2, 'swinghop': 8, 'jazzyhiphop': 1, 'mao': 1, 'progresivo': 6, 'eternaldream': 1, 'educational': 4, 'edgy': 1, 'synthsizer': 1, 'noises': 2, 'proud': 1, 'sarcastic': 3, 'toby': 1, 'clapping': 1, 'bop': 1, 'retrowave': 3, 'reposant': 1, 'naturel': 1, 'moving': 1, 'hype': 1, 'dreamdance': 1, 'liquidfunk': 1, 'ethnique': 7, 'tourisme': 1, 'botticelli': 1, 'revolution': 3, 'rupture': 1, 'chirurgie': 1, 'profs': 1, 'mere': 2, 'engagement': 1, 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'mashup': 1, 'trippop': 1, 'lqiuid': 1, 'moonimean': 2, 'mistery': 12, 'friendly': 1, 'triplets': 1, 'impact': 2, 'airy': 1, 'evil': 1, 'productivity': 1, 'jolly': 2, 'cuivres': 4, 'djent': 11, 'progressivemetalcore': 1, 'anthem': 4, 'soccer': 2, 'teenpop': 5, 'nudisco': 1, 'disney': 1, 'dubnbass': 2, 'surfer': 1, 'alcohal': 1, 'slap': 1, 'reflexing': 1, 'hopefull': 1, 'celta': 9, 'srings': 1, 'popsoul': 1, 'here': 1, 'chorale': 13, 'enfants': 3, 'oldies': 7, 'modernclassical': 2, 'soleil': 4, 'groumf': 3, 'metisse': 10, 'flstudio': 2, 'blockbuster': 1, 'top40': 1, 'burlesque': 1, 'hebrew': 2, 'judeospanish': 1, 'hardtekno': 9, 'hello': 1, 'more': 2, 'monkey': 1, 'dress': 1, 'moms': 1, 'lie': 2, 'drumms': 1, 'jazzblues': 1, 'clubmusic': 1, 'tecnopop': 1, 'ukhiphop': 2, 'frac14frac34tilde': 1, 'ambientfolk': 1, 'acid': 10, 'kateorange': 2, 'avicii': 1, 'marriage': 1, 'friends': 1, 'robertbrowning': 1, 'couple': 1, 'boats': 1, 'skareggae': 1, 'reggaepop': 2, 'technologic': 1, 'progressivo': 5, 'nora': 1, 'minnesota': 1, 'concept': 6, 'existentialism': 1, 'jarre': 1, 'energisch': 1, 'deutschsprachig': 1, '2016': 2, 'sensuality': 1, 'instrumentalbeat': 2, 'bollywood': 4, 'rappers': 1, 'closer': 1, 'indonesia': 1, 'sporting': 1, 'man': 1, 'beyondthewhite': 2, 'happyhouse': 3, 'turntab': 1, 'acoustick': 1, 'violao': 1, 'sonata': 8, 'guitaracoustique': 1, 'classica': 6, 'fuga': 1, 'instrumetal': 1, 'goal': 1, 'caja': 1, 'primer': 1, 'face': 1, 'sorry': 2, 'heartache': 1, 'eerie': 1, 'romantique': 2, 'indien': 2, 'ancient': 10, 'youth': 1, 'jeuxvideo': 1, 'technorock': 1, 'speed': 1, 'musical': 1, 'montero': 1, 'flight': 3, 'droptuning': 1, 'vocalguitarbassdrum': 1, 'microsound': 1, 'burn': 1, 'rythme': 1, 'hybridtrailer': 2, 'alternatives': 1, 'weihnachtsmusik': 1, 'rhythms': 9, 'seaside': 1, 'singers': 2, 'sensitive': 3, 'ludovicoeinaudi': 1, 'gentle': 2, 'blackout': 1, 'lowtempo': 1, 'liszt': 2, 'rockurbano': 1, 'whitestripes': 1, 'wymiar': 1, 'nowy': 1, 'fonk': 4, 'poppy': 1, 'bluesy': 1, 'curse': 1, 'strangefetish': 1, 'vainadurateam': 1, '3men2yelbory': 1, 'gamescore': 3, 'rockguitariff': 1, 'darkpop': 1, 'stringsensemble': 1, 'roses': 1, 'genova': 1, 'beach': 1, 'travelling': 6, 'lovestory': 1, 'california': 1, 'deejay': 1, 'rockcristiano': 1, 'pink': 1, 'emotronic': 1, 'brokenkites': 1, 'coquin': 1, 'lyric': 7, 'strangezero': 4, 'exprimental': 13, 'downtempoelectro': 6, 'downtemp': 1, 'erotic': 2, 'shopping': 1, 'trompete': 2, 'popmixtape': 9, 'popmixtap': 1, 'hiphoppop': 1, 'technoambient': 3, 'ganja': 1, 'indiedance': 3, 'lgbt': 1, 'passion': 2, 'glory': 1, 'gun': 1, 'drumsynth': 1, 'yuanan': 4, 'raggamuffin': 1, 'digitalreggae': 1, 'rootsy': 1, 'background': 1, 'soundart': 1, 'beatiful': 13, 'activism': 1, 'rufuswainwright': 1, 'etnic': 1, 'videogames': 3, 'young': 1, 'imaginary': 1, '2018': 1, 'wolrd': 7, 'scat': 2, 'villancico': 3, 'acouticguitar': 1, 'slideguitars': 5, 'protopunk': 1, 'nobackbone': 1, 'acoustica': 1, 'ironic': 1, 'beatlesque': 1, 'banger': 2, 'mp3': 2, 'coffee': 1, 'hit': 2, 'textesfrancais': 1, 'aggressif': 1, 'yamaha': 4, 'swampy': 1, 'weed': 1, '90srap': 1, 'media': 1, 'colaars': 3, 'inlove': 1, 'indirock': 1, 'trompetas': 1, 'symphonicmetal': 1, 'congas': 2, 'advertise': 1, 'laura': 1, 'dirving': 1, 'royaltyfree': 2, 'people': 2, 'balance': 1, 'punq': 1, 'fireworks': 1, 'catalan': 1, 'debut': 5, 'turnoffthemagic': 1, 'orchestration': 1, 'friendship': 1, 'flowerpower': 1, 'musiquedefilms': 6, 'theme': 1, 'military': 7, 'orchestral': 5, 'operatic': 1, 'themes': 4, 'thematic': 1, 'toms': 1, 'cellos': 1, 'batman': 1, 'bourne': 1, 'superhero': 2, 'boy': 1, 'actions': 1, 'actionmusic': 2, 'francophone': 1, 'robotic': 1, 'tragic': 3, 'orchestal': 1, 'japanese': 4, 'spying': 1, 'adventura': 1, 'landscapes': 1, 'climax': 1, 'trancecore': 1, 'personal': 1, 'soulmusic': 9, 'recovery': 1, 'tenacity': 1, 'understanding': 1, 'relief': 1, 'pipo': 7, 'pegoraro': 1, 'folksy': 3, 'cavaquinho': 2, 'fuzz': 2, 'violas': 1, 'storymusic': 11, 'acoustisque': 1, 'soulpop': 3, 'jazzpop': 1, 'slowtempo': 2, 'slowstyle': 1, 'popjazz': 1, 'sleepmusic': 4, 'djumbe': 1, 'instrumentale': 1, 'irlandais': 1, 'smoothpiano': 1, 'jazzpiano': 1, 'classicalpiano': 1, 'instru': 1, 'serrano': 1, 'theweeknd': 1, 'dreams': 3, 'improvization': 1, 'wendy': 1, 'goovy': 2, 'cordial': 2, 'inspired': 1, 'sylenth1': 1, 'beatz': 1, 'percu': 1, 'onirique': 16, 'softmusik': 5, 'musesoftware': 3, 'rammstein': 1, 'resoblues': 1, 'fusionrockinstrumental': 1, 'nogreaterlove': 1, 'serene': 1, 'tunguska': 3, 'cinematique': 1, 'marxmallows': 1, 'cantautor': 1, 'dreamymood': 1, 'goodmood': 1, 'partymusik': 1, 'clubing': 1, 'komodo': 2, 'refreshing': 1, 'lonely': 1, 'lessxmore': 4, 'rasta': 1, 'british': 3, 'windinstruments': 1, 'soundsystem': 1, 'naruto': 1, 'oldie': 1, 'marihuana': 1, 'charty': 1, 'tempo': 2, 'precussion': 1, 'global': 2, 'electrotechno': 2, 'middleeast': 1, 'dubstephop': 2, 'technorap': 1, 'goodvibes': 2, 'earth': 1, 'colors': 1, 'funkhop': 1, 'spokenwords': 1, 'electrorap': 2, 'techodance': 1, 'danceremix': 1, 'rapping': 4, 'dancemix': 1, 'electrodark': 1, 'enduring': 1, 'chords': 1, 'ying': 1, 'electopop': 1, 'tuning': 1, 'beyonce': 1, 'rapper': 1, 'ethnicfusion': 1, 'dancechill': 1, 'racism': 1, 'hipsoul': 1, 'flyinghigh': 1, 'fishingshow': 1, 'fastcountry': 1, 'httpswwwfacebookcomjonathancaizajonyflowdj': 26, 'screaming': 1, 'gamemusic': 3, 'force': 1, 'militant': 1, 'immigrant': 1, 'africain': 1, 'concience': 1, 'paix': 2, 'monde': 1, 'fingerpicking': 1, 'mort': 1, 'injustice': 1, 'single': 1, 'ustedsemellevolavida': 1, 'estoscelos': 1, 'harveys': 2, 'trompeta': 1, 'tibetan': 1, 'universe': 1, 'sophisticated': 1, 'heaven': 1, 'indiefolk': 1, 'hardkore': 1, 'antisexista': 1, 'freddie': 1, 'oldskool': 1, 'notte': 1, 'bambini': 1, 'gamelan': 1, 'orientallounge': 1, 'taiwan': 1, 'noise': 2, 'chilloutinstrumental': 1, 'endcredits': 1, 'arpeggios': 3, 'santa': 1, 'relaxe': 1, 'brave': 1, 'lovesongs': 1, 'noenminombre': 1, 'sarkozy': 1, 'harmonious': 3, 'fusions': 1, 'dirtyelectro': 1, 'jangly': 6, 'happybirthday': 1, 'thebeachboys': 1, 'rem': 1, 'autore': 1, 'demoscene': 1, 'brasil': 1, 'portugueseguitar': 1, 'kick': 2, 'blunt': 1, 'victim': 1, 'whimsical': 1, 'lover': 1, 'moore': 1, 'matthew': 1, 'braveheart': 4, 'gamesoundtrack': 1, 'breath': 1, 'woodwinds': 2, 'prepare': 1, 'nostalgy': 1, 'nightmare': 2, 'calmness': 1, 'uilleann': 1, 'cymbals': 1, 'thoughts': 2, 'concerto': 1, 'sequencer': 2, 'ending': 1, 'kingdom': 1, 'thunder': 1, 'powerfull': 1, 'lead': 1, 'englishhorn': 1, 'fairy': 1, 'psychedelica': 1, 'jingles': 1, 'lion': 7, 'voz': 8, 'worlds': 1, 'familia': 3, 'rush': 1, 'echoing': 1, 'stripped': 4, 'gwb': 6, 'good': 1, 'plane': 1, 'frustration': 1, 'boombap': 1, 'restaurant': 1, 'healinglove2017healinglove': 1, 'akkustisch': 5, 'chiefkeef': 1, 'tltctronic': 1, 'office': 1, 'lunatic': 1, 'silentmovie': 1, 'intensive': 1, 'cartoonish': 1, 'heartwarming': 1, 'hippy': 1, 'bear': 1, 'dymanic': 1, 'knight': 1, 'niñez': 1, 'salsaparaelmundo': 2, 'folcklore': 1, 'tambor': 1, 'salsaromantica': 3, 'salsapop': 1, 'folclor': 2, 'merengueton': 1, 'comunista': 1, 'pulp': 3, 'surfguitar': 2, 'beachparty': 1, 'sunshine': 1, 'nostalgie': 6, 'rock80': 1, 'surfmusic': 1, 'guitarelectro': 1, 'jessiek': 1, 'steelguitar': 1, 'hope': 1, 'revolt': 1, 'vitamin': 1, 'sainton': 2, 'crying': 1, 'vulnerable': 2, 'synthop': 3, 'driveelectricguitars': 1, 'breakdance': 1, 'bigband': 1, 'upliftingadvertising': 1, 'pops': 1, 'poppop': 1, 'balad': 1, 'popbalad': 1, 'kinks': 1, 'direstraits': 1, 'pigbass': 1, 'bonamassa': 1, 'popculture': 1, 'countryside': 1, 'connection': 1, 'talkingheads': 1, 'mediaeval': 1, 'twosome': 1, 'montypython': 1, 'generalcontimplation': 1, 'emancipation': 1, 'contimplating': 1, 'building': 1, 'adaption': 1, 'rollingstones': 1, 'bombo': 2, 'ledzeppelin': 1, 'synthrock': 1, 'electrometal': 1, 'phantasmagoria': 1, 'depressiv': 1, 'elektrorock': 1, 'chor': 1, 'alterntive': 1, 'manchester': 2, 'newave': 1, 'cumbias': 2, 'salsaton': 1, 'cumbiarapida': 1, 'sonora': 4, 'bailar': 1, 'romantika': 1, 'pianosoloimprovised': 1, 'improvisations': 1, 'modernmelodicdeathmetal': 1, 'summerhit': 1, 'slowrock': 1, 'stay': 1, 'liquidjazz': 1, 'naughty': 1, 'hawaii': 1, 'obsession': 1, 'jazzfolk': 1, 'salento': 1, 'tammurriata': 1, 'lonelyness': 1, 'missing': 2, 'active': 2, 'guetta': 1, 'commercials': 1, 'filmsoundtrack': 3, 'houseprogressive': 1, 'rockroll': 9, 'progrock': 3, 'elctronica': 5, 'vanda': 4, 'rockindependent': 1, 'phychedelic': 1, 'ukepop': 1, 'mots': 1, 'tvmusic': 1, 'tvfilm': 1, 'breathtaking': 1, 'atmosphericmusic': 1, 'melancolico': 1, 'nylon': 1, 'chorinho': 1, 'trebeats': 1, 'newpop': 2, 'chelsea': 1, 'adultrock': 2, 'maya': 1}\n"
     ]
    }
   ],
   "source": [
    "genres_train_count = {}\n",
    "vartags_train_count = {}\n",
    "for i in range(len(data_csv)):\n",
    "    row = data_csv.iloc[i]\n",
    "    tags = json.loads(row[\"tags\"].replace('/',','))\n",
    "    genres = tags['genres']\n",
    "    vartags = tags['vartags']\n",
    "    for genre in genres:\n",
    "        if genre not in genres_train_count:\n",
    "            genres_train_count[genre] = 1\n",
    "        else:\n",
    "            genres_train_count[genre] += 1\n",
    "    for vartag in vartags:\n",
    "        if vartag not in vartags_train_count:\n",
    "            vartags_train_count[vartag] = 1\n",
    "        else:\n",
    "            vartags_train_count[vartag] += 1\n",
    "\n",
    "print(genres_train_count)\n",
    "print(vartags_train_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_test_genres = dict( sorted(genres_test_count.items(), key=operator.itemgetter(1),reverse=True))\n",
    "sorted_test_vartags = dict( sorted(vartags_test_count.items(), key=operator.itemgetter(1),reverse=True))\n",
    "sorted_train_genres = dict( sorted(genres_train_count.items(), key=operator.itemgetter(1),reverse=True))\n",
    "sorted_train_vartags = dict( sorted(vartags_train_count.items(), key=operator.itemgetter(1),reverse=True))\n",
    "sorted_val_genres = dict( sorted(genres_val_count.items(), key=operator.itemgetter(1),reverse=True))\n",
    "sorted_val_vartags = dict( sorted(vartags_val_count.items(), key=operator.itemgetter(1),reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = list(sorted_train_genres.keys())[0:20]\n",
    "p = [sorted_train_genres[key] for key in x]\n",
    "q = [sorted_test_genres[key] for key in x]\n",
    "r = [sorted_val_genres[key] for key in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 20\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.27       # the width of the bars\n",
    "\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "yvals = p\n",
    "rects1 = ax.bar(ind, yvals, width, color='r')\n",
    "zvals = q\n",
    "rects2 = ax.bar(ind+width, zvals, width, color='g')\n",
    "kvals = r\n",
    "rects3 = ax.bar(ind+width*2, kvals, width, color='b')\n",
    "\n",
    "ax.set_title('Number of songs per genre per set')\n",
    "ax.set_ylabel('Number of songs')\n",
    "ax.set_xlabel('Genres per set')\n",
    "ax.set_xticks(ind+width)\n",
    "ax.set_xticklabels( (x) )\n",
    "ax.legend( (rects1[0], rects2[0], rects3[0]), ('Train', 'Test', 'Val') )\n",
    "\n",
    "def autolabel(rects):\n",
    "    for rect in rects:\n",
    "        h = rect.get_height()\n",
    "        ax.text(rect.get_x()+rect.get_width()/2., 1.05*h, '%d'%int(h),\n",
    "                ha='center', va='bottom')\n",
    "\n",
    "#autolabel(rects1)\n",
    "#autolabel(rects2)\n",
    "#autolabel(rects3)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = list(sorted_train_vartags.keys())[0:20]\n",
    "p = [sorted_train_vartags[key] for key in x]\n",
    "q = [sorted_test_vartags[key] for key in x]\n",
    "r = [sorted_val_vartags[key] for key in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 20\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.27       # the width of the bars\n",
    "\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "yvals = p\n",
    "rects1 = ax.bar(ind, yvals, width, color='r')\n",
    "zvals = q\n",
    "rects2 = ax.bar(ind+width, zvals, width, color='g')\n",
    "kvals = r\n",
    "rects3 = ax.bar(ind+width*2, kvals, width, color='b')\n",
    "\n",
    "# ax.set_title('Number of songs per vartag per set')\n",
    "ax.set_ylabel('Number of songs', fontsize=22)\n",
    "# ax.set_xlabel('Vartags per set')\n",
    "ax.set_xticks(ind+width)\n",
    "ax.set_xticklabels( (x) )\n",
    "ax.legend( (rects1[0], rects2[0], rects3[0]), ('Train', 'Test', 'Val'),prop={\"size\":18} )\n",
    "\n",
    "def autolabel(rects):\n",
    "    for rect in rects:\n",
    "        h = rect.get_height()\n",
    "        ax.text(rect.get_x()+rect.get_width()/2., 1.05*h, '%d'%int(h),\n",
    "                ha='center', va='bottom')\n",
    "\n",
    "#autolabel(rects1)\n",
    "#autolabel(rects2)\n",
    "#autolabel(rects3)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.rcParams['font.size'] = '22'\n",
    "plt.rcParams['font.family'] = 'Serif'\n",
    "plt.rcParams['font.serif'] = 'Times New Roman'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sorted_vartags' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-78-f579792dba81>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msorted_vartags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mmax_genres\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msorted_vartags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mmax_genres\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'vertical'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sorted_vartags' is not defined"
     ]
    }
   ],
   "source": [
    "plt.bar(list(sorted_vartags.keys())[0:max_genres], list(sorted_vartags.values())[0:max_genres])\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 348
    },
    "id": "ogluaQj5Cp9r",
    "outputId": "501f6055-0ae6-444f-f0d8-276ff7655d52"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1dcfbcf730>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data_csv.vocalinstrumental, data_csv.subset).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "id": "9sDw46-LCxzU",
    "outputId": "dd9150ac-ee08-42ca-af47-98fe49be9e61"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1dce17dbe0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data_csv.gender, data_csv.subset).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 327
    },
    "id": "oCjgzPR9C40c",
    "outputId": "839b9a55-9c25-4229-ab03-d1e38d828cda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1dce0e4160>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEmCAYAAACOMEBlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAdgklEQVR4nO3df7xVdZ3v8ddbPPFDUBEOBBz1nHqgplBHPBJezUQzMC3oMVnHMqm4nq45ozXpAM1Y2EDDXLs+vDRJ1xoTJ0sZzYEJNZQBscLoQCC/NChRjhgcGDVQIcXP/WN/oe1hc37gOXvjXu/n47Efe63v/q61PtvozeK71v4uRQRmZpYNR5S6ADMzKx6HvplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZciRpS6gLf3794/q6upSl2Fm9rayfPny7RFR2bL9sA/96upqGhsbS12GmdnbiqRnCrV7eMfMLEMc+mZmGeLQNzPLkMN+TN/MrKNee+01mpqa2L17d6lL6XI9evSgqqqKioqKdvV36JtZ2WlqaqJPnz5UV1cjqdTldJmIYMeOHTQ1NVFTU9OubTy8Y2ZlZ/fu3fTr16+sAx9AEv369evQv2gc+mZWlso98Pfp6Pd06JuZHYKpU6fy7W9/+y3v55ZbbuGVV17phIrax2P6Zta5ph5T5OO9VNzjdbJbbrmFyy+/nF69ehXleD7TNzNLXn75ZS6++GLe9773MWzYMO655x6qq6vZvn07AI2NjZx33nn7+69atYrzzz+foUOH8v3vfx+A559/nnPPPZfa2lqGDRvGY489BsCCBQs466yzGDFiBJdeeim7du1i5syZbNmyhdGjRzN69OiifEeHvplZ8tBDDzF48GBWrVrFmjVrGDt2bKv9n3jiCebPn8/SpUv55je/yZYtW/jxj3/MmDFjWLlyJatWraK2tpbt27czbdo0HnnkEVasWEFdXR0333wz11xzDYMHD2bRokUsWrSoKN/RwztmZsnw4cO57rrrmDRpEpdccgkf+MAHWu0/btw4evbsSc+ePRk9ejTLli3jzDPP5Atf+AKvvfYa48ePp7a2lkcffZR169Zx9tlnA/DnP/+Zs846qxhf6QAOfTOz5KSTTmL58uU88MADTJkyhQ9/+MMceeSRvPHGGwAH3BrZ8s4ZSZx77rksWbKE+fPn89nPfpbrr7+evn37cuGFF/KTn/ykaN/lYNo1vCPpWEn3SnpS0npJZ0k6TtLDkjak9755/adI2ijpKUlj8trPkLQ6fTZTWbmnyszeFrZs2UKvXr24/PLLue6661ixYgXV1dUsX74cgPvuu+9N/efOncvu3bvZsWMHixcv5swzz+SZZ55hwIABXHnllUycOJEVK1YwatQofvnLX7Jx40YAXnnlFX73u98B0KdPH3bu3Fm079jeM/3/CzwUEZ+Q9A6gF/A1YGFEzJA0GZgMTJJ0KlAPnAYMBh6RdFJE7AVmAQ3A48ADwFjgwU79RmZmh2j16tVcf/31HHHEEVRUVDBr1ixeffVVJk6cyLe+9S3e//73v6n/yJEjufjii3n22We54YYbGDx4MLNnz+amm26ioqKC3r17c+edd1JZWckdd9zBZZddxp49ewCYNm0aJ510Eg0NDVx00UUMGjSoKOP6iojWO0hHA6uAd0VeZ0lPAedFxPOSBgGLI+JkSVMAIuKfUr+fA1OBTcCiiDgltV+Wtv9ia8evq6sLz6dv9jZyGNyyuX79et7znvcUt44SKvR9JS2PiLqWfdszvPMuoBn4oaTfSvqBpKOAgRHxPEB6H5D6DwE2523flNqGpOWW7WZmViTtCf0jgRHArIg4HXiZ3FDOwRQap49W2g/cgdQgqVFSY3NzcztKNDOz9mhP6DcBTRHx67R+L7m/BLamYR3S+7a8/sfnbV8FbEntVQXaDxARt0VEXUTUVVYe8IhHMzM7RG2GfkT8Edgs6eTUdAGwDpgHTEhtE4C5aXkeUC+pu6QaYCiwLA0B7ZQ0Kt21c0XeNmZmVgTtvXvnb4C70p07fwA+T+4vjDmSJgLPApcCRMRaSXPI/cXwOnB1unMH4CrgDqAnubt2fOeOmVkRtSv0I2IlcMBVYHJn/YX6TwemF2hvBIZ1pEAzM+s8nnvHzKyTvfjii9x6662HtG1XT7XsaRjMrOxVT57fqfvbNOPiVj/fF/pf+tKXOrzvrp5q2aFvZtbJJk+ezO9//3tqa2u58MILGTBgAHPmzGHPnj18/OMf58Ybb+Tll1/mk5/8JE1NTezdu5cbbriBrVu37p9quX///l3yC12HvplZJ5sxYwZr1qxh5cqVLFiwgHvvvZdly5YREXzsYx9jyZIlNDc3M3jwYObPz/0r5KWXXuKYY47h5ptvZtGiRfTv379LavOYvplZF1qwYAELFizg9NNPZ8SIETz55JNs2LCB4cOH88gjjzBp0iQee+wxjjmmONNX+EzfzKwLRQRTpkzhi188cJqxltM4f/3rX+/yenymb2bWyfKnSx4zZgy33347u3btAuC5555j27ZtBadxbrltV/CZvplZJ+vXrx9nn302w4YN46KLLuLTn/70/idl9e7dmx/96Eds3LjxgGmcgS6farnNqZVLzVMrm73NeGrlouvsqZXNzKxMOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZp3sUKdW/shHPsKLL77YBRX9hX+cZWblr7N/O1DgtwH5Dja18t69e+nWrdtBt3vggQc6pbzWOPTNzDpZ/tTKFRUV9O7dm0GDBrFy5UrWrVvH+PHj2bx5M7t37+baa6+loaEBgOrqahobG9m1axcXXXQR55xzDr/61a8YMmQIc+fOpWfPnm+5Ng/vmJl1shkzZvDud7+blStXctNNN7Fs2TKmT5/OunXrALj99ttZvnw5jY2NzJw5kx07dhywjw0bNnD11Vezdu1ajj32WO67775Oqc1n+mZmXWzkyJHU1NTsX585cyb3338/AJs3b2bDhg3069fvTdvU1NRQW1sLwBlnnMGmTZs6pRaHvplZFzvqqKP2Ly9evJhHHnmEpUuX0qtXL8477zx27959wDbdu3ffv9ytWzdeffXVTqnFwztmZp2stemRX3rpJfr27UuvXr148sknefzxx4tam8/0zcw6Wf7Uyj179mTgwIH7Pxs7dizf+973eO9738vJJ5/MqFGjilqbp1Y2s87lqZWLrtOnVpa0SdJqSSslNaa24yQ9LGlDeu+b13+KpI2SnpI0Jq/9jLSfjZJmStIhf0szM+uwjozpj46I2ry/OSYDCyNiKLAwrSPpVKAeOA0YC9wqad+vEWYBDcDQ9Br71r+CmZm111u5kDsOmJ2WZwPj89rvjog9EfE0sBEYKWkQcHRELI3cmNKdeduYmVkRtDf0A1ggabmkhtQ2MCKeB0jvA1L7EGBz3rZNqW1IWm7ZbmZmRdLeu3fOjogtkgYAD0t6spW+hcbpo5X2A3eQ+4ulAeCEE05oZ4lmZtaWdp3pR8SW9L4NuB8YCWxNQzak922pexNwfN7mVcCW1F5VoL3Q8W6LiLqIqKusrGz/tzEzs1a1GfqSjpLUZ98y8GFgDTAPmJC6TQDmpuV5QL2k7pJqyF2wXZaGgHZKGpXu2rkibxszs8zq3bt30Y7VnuGdgcD96e7KI4EfR8RDkn4DzJE0EXgWuBQgItZKmgOsA14Hro6IvWlfVwF3AD2BB9PLzKxLDZ89vFP3t3rC6k7dXzG1GfoR8QfgfQXadwAXHGSb6cD0Au2NwLCOl2lm9vYxadIkTjzxxP3z6U+dOhVJLFmyhBdeeIHXXnuNadOmMW7cuKLX5rl3zMw6WX19Pffcc8/+9Tlz5vD5z3+e+++/nxUrVrBo0SK++tWvUooZETz3jplZJzv99NPZtm0bW7Zsobm5mb59+zJo0CC+8pWvsGTJEo444giee+45tm7dyjvf+c6i1ubQNzPrAp/4xCe49957+eMf/0h9fT133XUXzc3NLF++nIqKCqqrqwtOqdzVHPpmZl2gvr6eK6+8ku3bt/Poo48yZ84cBgwYQEVFBYsWLeKZZ54pSV0OfTOzLnDaaaexc+dOhgwZwqBBg/jMZz7DRz/6Uerq6qitreWUU04pSV0OfTMre6W6xXL16r8ct3///ixdurRgv127dhWrJN+9Y2aWJQ59M7MMceibmWWIQ9/MytLh/ijYztLR7+nQN7Oy06NHD3bs2FH2wR8R7Nixgx49erR7G9+9Y2Zlp6qqiqamJpqbm0tdSpfr0aMHVVVVbXdMHPpmVnYqKiqoqakpdRmHJQ/vmJlliEPfzCxDHPpmZhni0DczyxCHvplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ0u7Ql9RN0m8l/SytHyfpYUkb0nvfvL5TJG2U9JSkMXntZ0hanT6bKUmd+3XMzKw1HTnTvxZYn7c+GVgYEUOBhWkdSacC9cBpwFjgVknd0jazgAZgaHqNfUvVm5lZh7Qr9CVVARcDP8hrHgfMTsuzgfF57XdHxJ6IeBrYCIyUNAg4OiKWRm6S6zvztjEzsyJo75n+LcDfAW/ktQ2MiOcB0vuA1D4E2JzXrym1DUnLLdvNzKxI2gx9SZcA2yJieTv3WWicPlppL3TMBkmNkhqz8BAEM7Niac+Z/tnAxyRtAu4Gzpf0I2BrGrIhvW9L/ZuA4/O2rwK2pPaqAu0HiIjbIqIuIuoqKys78HXMzKw1bYZ+REyJiKqIqCZ3gfa/IuJyYB4wIXWbAMxNy/OAekndJdWQu2C7LA0B7ZQ0Kt21c0XeNmZmVgRv5XGJM4A5kiYCzwKXAkTEWklzgHXA68DVEbE3bXMVcAfQE3gwvczMrEg6FPoRsRhYnJZ3ABccpN90YHqB9kZgWEeLNDOzzuFf5JqZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhjj0zcwyxKFvZpYhDn0zswxpM/Ql9ZC0TNIqSWsl3Zjaj5P0sKQN6b1v3jZTJG2U9JSkMXntZ0hanT6bKUld87XMzKyQ9pzp7wHOj4j3AbXAWEmjgMnAwogYCixM60g6FagHTgPGArdK6pb2NQtoAIam19hO/C5mZtaGNkM/cnal1Yr0CmAcMDu1zwbGp+VxwN0RsScingY2AiMlDQKOjoilERHAnXnbmJlZEbRrTF9SN0krgW3AwxHxa2BgRDwPkN4HpO5DgM15mzeltiFpuWW7mZkVSbtCPyL2RkQtUEXurH1YK90LjdNHK+0H7kBqkNQoqbG5ubk9JZqZWTt06O6diHgRWExuLH5rGrIhvW9L3ZqA4/M2qwK2pPaqAu2FjnNbRNRFRF1lZWVHSjQzs1a05+6dSknHpuWewIeAJ4F5wITUbQIwNy3PA+oldZdUQ+6C7bI0BLRT0qh0184VeduYmVkRHNmOPoOA2ekOnCOAORHxM0lLgTmSJgLPApcCRMRaSXOAdcDrwNURsTft6yrgDqAn8GB6mZlZkbQZ+hHxBHB6gfYdwAUH2WY6ML1AeyPQ2vUAMzPrQv5FrplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0Dczy5A2Q1/S8ZIWSVovaa2ka1P7cZIelrQhvffN22aKpI2SnpI0Jq/9DEmr02czJalrvpaZmRXSnjP914GvRsR7gFHA1ZJOBSYDCyNiKLAwrZM+qwdOA8YCt0rqlvY1C2gAhqbX2E78LmZm1oY2Qz8ino+IFWl5J7AeGAKMA2anbrOB8Wl5HHB3ROyJiKeBjcBISYOAoyNiaUQEcGfeNmZmVgQdGtOXVA2cDvwaGBgRz0PuLwZgQOo2BNict1lTahuSllu2m5lZkbQ79CX1Bu4DvhwRf2qta4G2aKW90LEaJDVKamxubm5viWZm1oZ2hb6kCnKBf1dE/DQ1b01DNqT3bam9CTg+b/MqYEtqryrQfoCIuC0i6iKirrKysr3fxczM2tCeu3cE/CuwPiJuzvtoHjAhLU8A5ua110vqLqmG3AXbZWkIaKekUWmfV+RtY2ZmRXBkO/qcDXwWWC1pZWr7GjADmCNpIvAscClARKyVNAdYR+7On6sjYm/a7irgDqAn8GB6mZlZkbQZ+hHxCwqPxwNccJBtpgPTC7Q3AsM6UqCZmXUe/yLXzCxDHPpmZhni0DczyxCHvplZhjj0zcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxD2gx9SbdL2iZpTV7bcZIelrQhvffN+2yKpI2SnpI0Jq/9DEmr02czJanzv46ZmbWmPWf6dwBjW7RNBhZGxFBgYVpH0qlAPXBa2uZWSd3SNrOABmBoerXcp5mZdbEj2+oQEUskVbdoHgecl5ZnA4uBSan97ojYAzwtaSMwUtIm4OiIWAog6U5gPPDgW/4GWTT1mCIf76XiHs/MusyhjukPjIjnAdL7gNQ+BNic168ptQ1Jyy3bC5LUIKlRUmNzc/MhlmhmZi119oXcQuP00Up7QRFxW0TURURdZWVlpxVnZpZ1hxr6WyUNAkjv21J7E3B8Xr8qYEtqryrQbmZmRXSooT8PmJCWJwBz89rrJXWXVEPugu2yNAS0U9KodNfOFXnbmJlZkbR5IVfST8hdtO0vqQn4BjADmCNpIvAscClARKyVNAdYB7wOXB0Re9OuriJ3J1BPchdwfRHXzKzI2nP3zmUH+eiCg/SfDkwv0N4IDOtQdWZm1qn8i1wzswxp80zfzN7eqifPL+rxNvUo6uGsgxz6Zva2Nnz28KIeb/WE1UU9Xmfz8I6ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhjj0zcwyxKFvZpYhfkautcnPIDUrHw79TlI9eX7RjrWpR9EOZWZlpujDO5LGSnpK0kZJk4t9fDOzLCtq6EvqBnwXuAg4FbhM0qnFrMHMLMuKfaY/EtgYEX+IiD8DdwPjilyDmVlmFXtMfwiwOW+9CXh/y06SGoCGtLpL0lNFqO1tQ4e+aX9ge8c3W3PoRzwE+txb+IZWcv7zedg4sVBjsUO/0H+tOKAh4jbgtq4vJ1skNUZEXanrMCvEfz6Lo9jDO03A8XnrVcCWItdgZpZZxQ793wBDJdVIegdQD8wrcg1mZplV1OGdiHhd0l8DPwe6AbdHxNpi1pBxHjKzw5n/fBaBIg4YUjczszLluXfMzDLEoW9mliEOfTOzDHHom5lliEO/zEmaLenYvPW+km4vZU1mAJJGSeqTt95H0gG/0LfO5dAvf++NiBf3rUTEC8DpJazHbJ9ZwK689ZdTm3Uhh375O0JS330rko7Dz1Gww4Mi757xiHgD/9nscv4PXP7+D/ArSfem9UuB6SWsx2yfP0i6hr+c3X8J+EMJ68kE/zgrA9IzC84nN+HdwohYV+KSzJA0AJhJ7s9mAAuBL0fEtpIWVuYc+mVK0tER8ac0nHOAiPjvYtdkZqXn0C9Tkn4WEZdIepo3T18tICLiXSUqzTJO0t9FxP+W9B0KT61+TQnKygyP6ZepiLgkvdeUuhazFtan98aSVpFRPtMvc5IWRsQFbbWZFVN6XvaMiLi+1LVkjc/0y5SkHkAvoH+6ZXPfU8uOBgaXrDAzICL2Sjqj1HVkkUO/fH0R+DK5gF/OX0L/T8B3S1WUWZ7fSpoH/Du5H2YBEBE/LV1J5c/DO2VO0t9ExHdKXYdZS5J+WKA5IuILRS8mQ3ymX/7+KKlPROyU9A/ACGBaRKwodWGWeT+IiF/mN0g6u1TFZIWnYSh/N6TAPwcYA8zG85vY4aHQv0D9r9Iu5jP98rc3vV8MzIqIuZKmlrAeyzhJZwH/A6iU9Ld5Hx1N7tnZ1oV8pl/+npP0/4BPAg9I6o7/d7fSegfQm9xJZ5+815+AT5SwrkzwhdwyJ6kXMBZYHREbJA0ChkfEghKXZhkn6cSIeKbUdWSNz/jKX39yv3zcI+kEoAJ4srQlmQHwgwIP+Pl5KQvKAo/pl7/55OY3EdADqAGeAk4rZVFmQP+WD/hJM29aF3Lol7mIGJ6/LmkEuR9umZXaG5JOiIhnITfcQ4EJ2KxzOfQzJiJWSDqz1HWYAX8P/ELSo2n9XKChhPVkgi/klrkWt8QdQe7HWf0iYkyJSjLbT1J/YBS54celEbG9xCWVPV/ILX/5t8R1JzfGP66kFZkBkkTuzrIREfGfQC9JI0tcVtnzmX5GSOpDbl6TXaWuxQxA0izgDeD8iHhPmg12QUR4+LEL+Uy/zEkaJum3wBpgraTlkoaVui4z4P0RcTWwG3J375D74ZZ1IYd++bsN+NuIODEiTgS+mtrMSu219DCVAJBUSe7M37qQQ7/8HRURi/atRMRi4KjSlWO230zgfmCApOnAL4Bvlbak8ucx/TIn6X5gBfBvqelyoC4ixpeuKrMcSacAF5C7e2dhRKxvYxN7ixz6ZS5dHLsROCc1LQFuTOOnZkUn6bjWPo+I/y5WLVnk0DezopL0NH+ZGiQ/gETuDrN3laSwjPCYfpmT9LAntbLDSUTUpGB/N/AN4I60/kHgspIWlwEO/fJ3wKRWgCe1ssPBd8n9Gndf0O8E/qV05WSDQ7/8vZGmVAZAUjWe1MoOD75PvwQ84Vr586RWdrjyffol4Au5GZDmKG8AVpKbU39bRCwpbVWWdZI+A3yK3CSAs8k9KvEfIuLfS1pYmXPolzlJ/xO4FqgiF/qjyM1meH5JCzPD9+mXgkO/zElaDZwJPB4Rten/ZDdGxKdKXJqZlYAv5Ja/3RGxG0BS94h4Eji5xDWZWYn4Qm75a0r36f8H8LCkF4AtJa7JzErEwzsZIumDwDHAQxHx51LXY2bF59A3M8sQj+mbmWWIQ9/MLEMc+mYtSPqypF556w/kT1rXgf18TtIhzSUj6WttfH5INZl5TN+sBUmbyD1oZvtb3M/n0n7++hC23RURvQu0i9z/bz1dgR0Sn+nbYU/Sf6QHuq+V1JDaxkpaIWmVpIWp7bjU9wlJj0t6b2qfKum6vP2tkVQt6ShJ89M+1kj6lKRrgMHAIkmLUv9Nkvqn5SvS/ldJ+rfUVinpPkm/Sa+zC3yHgn0k9Zb0Q0mr037/StIMoKeklZLuSrWul3QruaegHd9WTWYHFRF++XVYv4Dj0ntPYA0wENgM1LT4/DvAN9Ly+cDKtDwVuC5vf2uAauCvgO/ntR+T3jeRm5Ka/HXgNOCpfZ/lHffHwDlp+QRgfVr+HPAvbfT5Z+CWvGP1Te+78tqqyU1ENqq9Nfnl18Fe/nGWvR1cI+njafl4cpPHLYmIp+FNj9c7h1yQExH/JamfpGNa2e9q4NuS/hn4WUQ81kYd5wP3Rhr2yTvuh4BTcyMvABwtqU+LbQ/W50NA/b7GOPhjLJ+JiMc7UJNZQQ59O6xJOo9cMJ4VEa9IWgysovBUEirQFsDrvHkoswdARPxO0hnAR4B/krQgIr7ZWjkUfhbBEam+V1vU3p4+B9tnSy93sCazgjymb4e7Y4AXUuCfQm6W0O7AByXVwJsetL0E+ExqOw/YHhF/IjcUMiK1jwD2bTcYeCUifgR8e18fck9wanmmDrAQ+KSkfi2OuwDYf7FWUm2BbQ/Wp2V737T4mqSKg/1HaUdNZgU59O1w9xBwpKQngH8EHgeayQ3x/FTSKuCe1HcqUJf6zgAmpPb7gOMkrQSuAn6X2ocDy1L73wPTUvttwIP7LuTuExFrgenAo+m4N6ePrtl3XEnrgP9V4HscrM80oG+6kLwKGJ1XwxOS7mrtP04rNZkV5Fs2zcwyxGf6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEP+P3z/7H227U0wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data_csv.acousticelectric, data_csv.subset).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "id": "UffZbx2ODLM9",
    "outputId": "3dc82865-08d5-466f-b798-e2c5d5c20922"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1dce05a0a0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data_csv.speed, data_csv.subset).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1937"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(val_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = pd.read_csv('../../Downloads/experiments-defense-epochs/ConvTasNet_model/exp-92-epochs-LogL1/tmp/ConvTasNet_model/eval/tmp/all_metrics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "without_index = metrics.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = without_index.boxplot(column=['sdr', 'sir', 'sar'])\n",
    "title_boxplot = 'ConvTasNet 92 epochs LogL1'\n",
    "plt.title( title_boxplot )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = pd.read_csv('../../Desktop/exp-44100-51-e-LogL2/UNet_model/exp-44100-51-e-LogL2/tmp/UNet_model/eval/tmp/all_metrics.csv')\n",
    "without_index = metrics.drop(['Unnamed: 0'], axis=1)\n",
    "without_index.boxplot(column=['sdr', 'sir', 'sar'])\n",
    "title_boxplot = 'UNet 51 epochs LogL1'\n",
    "plt.title( title_boxplot )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "1932    False\n",
       "1933    False\n",
       "1934    False\n",
       "1935    False\n",
       "1936    False\n",
       "Name: sdr, Length: 1937, dtype: bool"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "without_index[\"sdr\"]>18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Unnamed: 0</th>\n",
       "      <th>input_si_sdr</th>\n",
       "      <th>input_sdr</th>\n",
       "      <th>input_sir</th>\n",
       "      <th>input_sar</th>\n",
       "      <th>input_stoi</th>\n",
       "      <th>si_sdr</th>\n",
       "      <th>sdr</th>\n",
       "      <th>sir</th>\n",
       "      <th>sar</th>\n",
       "      <th>stoi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.047440</td>\n",
       "      <td>0.078760</td>\n",
       "      <td>0.078760</td>\n",
       "      <td>152.945241</td>\n",
       "      <td>0.633346</td>\n",
       "      <td>12.902286</td>\n",
       "      <td>13.443705</td>\n",
       "      <td>21.619584</td>\n",
       "      <td>14.195148</td>\n",
       "      <td>0.844430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.080529</td>\n",
       "      <td>-0.051287</td>\n",
       "      <td>-0.051287</td>\n",
       "      <td>152.369325</td>\n",
       "      <td>0.663587</td>\n",
       "      <td>14.986272</td>\n",
       "      <td>15.317878</td>\n",
       "      <td>24.992563</td>\n",
       "      <td>15.897885</td>\n",
       "      <td>0.657941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.030053</td>\n",
       "      <td>0.055214</td>\n",
       "      <td>0.055214</td>\n",
       "      <td>152.658040</td>\n",
       "      <td>0.707160</td>\n",
       "      <td>11.419689</td>\n",
       "      <td>12.174802</td>\n",
       "      <td>18.860195</td>\n",
       "      <td>13.286260</td>\n",
       "      <td>0.810261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.003112</td>\n",
       "      <td>0.007539</td>\n",
       "      <td>0.007539</td>\n",
       "      <td>152.839531</td>\n",
       "      <td>0.570262</td>\n",
       "      <td>12.879701</td>\n",
       "      <td>13.178356</td>\n",
       "      <td>23.780115</td>\n",
       "      <td>13.643451</td>\n",
       "      <td>0.668016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.294288</td>\n",
       "      <td>0.796949</td>\n",
       "      <td>0.796949</td>\n",
       "      <td>151.785503</td>\n",
       "      <td>0.612120</td>\n",
       "      <td>5.157646</td>\n",
       "      <td>7.263264</td>\n",
       "      <td>15.786073</td>\n",
       "      <td>11.433932</td>\n",
       "      <td>0.580975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>1932</td>\n",
       "      <td>-0.013608</td>\n",
       "      <td>0.034553</td>\n",
       "      <td>0.034553</td>\n",
       "      <td>152.200640</td>\n",
       "      <td>0.559282</td>\n",
       "      <td>12.923563</td>\n",
       "      <td>13.123399</td>\n",
       "      <td>23.161099</td>\n",
       "      <td>13.604595</td>\n",
       "      <td>0.682884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>1933</td>\n",
       "      <td>0.131602</td>\n",
       "      <td>0.190430</td>\n",
       "      <td>0.190430</td>\n",
       "      <td>151.875353</td>\n",
       "      <td>0.605793</td>\n",
       "      <td>11.486515</td>\n",
       "      <td>12.124985</td>\n",
       "      <td>19.377347</td>\n",
       "      <td>13.124973</td>\n",
       "      <td>0.757391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>1934</td>\n",
       "      <td>-0.113163</td>\n",
       "      <td>-0.090300</td>\n",
       "      <td>-0.090300</td>\n",
       "      <td>152.243809</td>\n",
       "      <td>0.625846</td>\n",
       "      <td>7.924043</td>\n",
       "      <td>8.958798</td>\n",
       "      <td>15.010290</td>\n",
       "      <td>10.741752</td>\n",
       "      <td>0.758234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>1935</td>\n",
       "      <td>0.100544</td>\n",
       "      <td>0.136763</td>\n",
       "      <td>0.136763</td>\n",
       "      <td>152.594378</td>\n",
       "      <td>0.706906</td>\n",
       "      <td>5.974904</td>\n",
       "      <td>6.663296</td>\n",
       "      <td>13.175760</td>\n",
       "      <td>8.237348</td>\n",
       "      <td>0.715374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>1936</td>\n",
       "      <td>0.117654</td>\n",
       "      <td>0.318621</td>\n",
       "      <td>0.318621</td>\n",
       "      <td>151.908505</td>\n",
       "      <td>0.637973</td>\n",
       "      <td>12.810923</td>\n",
       "      <td>13.129133</td>\n",
       "      <td>22.906519</td>\n",
       "      <td>13.678809</td>\n",
       "      <td>0.694621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1937 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  input_si_sdr  input_sdr  input_sir   input_sar  input_stoi  \\\n",
       "0              0      0.047440   0.078760   0.078760  152.945241    0.633346   \n",
       "1              1     -0.080529  -0.051287  -0.051287  152.369325    0.663587   \n",
       "2              2      0.030053   0.055214   0.055214  152.658040    0.707160   \n",
       "3              3     -0.003112   0.007539   0.007539  152.839531    0.570262   \n",
       "4              4      0.294288   0.796949   0.796949  151.785503    0.612120   \n",
       "...          ...           ...        ...        ...         ...         ...   \n",
       "1932        1932     -0.013608   0.034553   0.034553  152.200640    0.559282   \n",
       "1933        1933      0.131602   0.190430   0.190430  151.875353    0.605793   \n",
       "1934        1934     -0.113163  -0.090300  -0.090300  152.243809    0.625846   \n",
       "1935        1935      0.100544   0.136763   0.136763  152.594378    0.706906   \n",
       "1936        1936      0.117654   0.318621   0.318621  151.908505    0.637973   \n",
       "\n",
       "         si_sdr        sdr        sir        sar      stoi  \n",
       "0     12.902286  13.443705  21.619584  14.195148  0.844430  \n",
       "1     14.986272  15.317878  24.992563  15.897885  0.657941  \n",
       "2     11.419689  12.174802  18.860195  13.286260  0.810261  \n",
       "3     12.879701  13.178356  23.780115  13.643451  0.668016  \n",
       "4      5.157646   7.263264  15.786073  11.433932  0.580975  \n",
       "...         ...        ...        ...        ...       ...  \n",
       "1932  12.923563  13.123399  23.161099  13.604595  0.682884  \n",
       "1933  11.486515  12.124985  19.377347  13.124973  0.757391  \n",
       "1934   7.924043   8.958798  15.010290  10.741752  0.758234  \n",
       "1935   5.974904   6.663296  13.175760   8.237348  0.715374  \n",
       "1936  12.810923  13.129133  22.906519  13.678809  0.694621  \n",
       "\n",
       "[1937 rows x 11 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(10000, 3),\n",
    "                  columns=['Col1', 'Col2', 'Col3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.397668</td>\n",
       "      <td>-0.875097</td>\n",
       "      <td>0.120277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.869511</td>\n",
       "      <td>-1.491615</td>\n",
       "      <td>-1.065536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.777149</td>\n",
       "      <td>0.616518</td>\n",
       "      <td>0.860068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.146586</td>\n",
       "      <td>0.924310</td>\n",
       "      <td>-1.037584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.094283</td>\n",
       "      <td>0.645252</td>\n",
       "      <td>-0.175847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>0.609031</td>\n",
       "      <td>-0.404769</td>\n",
       "      <td>-1.129228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>-0.766866</td>\n",
       "      <td>0.154695</td>\n",
       "      <td>-1.507295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>0.793994</td>\n",
       "      <td>0.182377</td>\n",
       "      <td>-1.875492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>1.641159</td>\n",
       "      <td>-0.688853</td>\n",
       "      <td>0.146205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>1.261842</td>\n",
       "      <td>0.855726</td>\n",
       "      <td>0.078842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Col1      Col2      Col3\n",
       "0     0.397668 -0.875097  0.120277\n",
       "1     1.869511 -1.491615 -1.065536\n",
       "2     0.777149  0.616518  0.860068\n",
       "3     1.146586  0.924310 -1.037584\n",
       "4     0.094283  0.645252 -0.175847\n",
       "...        ...       ...       ...\n",
       "9995  0.609031 -0.404769 -1.129228\n",
       "9996 -0.766866  0.154695 -1.507295\n",
       "9997  0.793994  0.182377 -1.875492\n",
       "9998  1.641159 -0.688853  0.146205\n",
       "9999  1.261842  0.855726  0.078842\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f208b72c850>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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