protein_location (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "y5CP4AOZG-Nc"
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Downloading seaborn-0.11.2-py3-none-any.whl (292 kB)\n",
      "\u001b[K     |████████████████████████████████| 292 kB 4.7 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from seaborn) (1.23.1)\n",
      "Requirement already satisfied: pandas>=0.23 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from seaborn) (1.4.3)\n",
      "Requirement already satisfied: matplotlib>=2.2 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from seaborn) (3.5.2)\n",
      "Requirement already satisfied: scipy>=1.0 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from seaborn) (1.9.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (3.0.9)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (1.4.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (4.34.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (21.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (9.2.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from pandas>=0.23->seaborn) (2022.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.11.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yBwLRe5f9MKH"
   },
   "source": [
    "# Data Vizualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yBwLRe5f9MKH"
   },
   "source": [
    "## Vizualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "-s0Tm2B_GtEs"
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"train_protein.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 758
    },
    "id": "5bHZwRNXHKOm",
    "outputId": "a26b4cbc-e91b-4d8b-8ccc-f7dfe1360f3d"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>sequence</th>\n",
       "      <th>Kingdom</th>\n",
       "      <th>seq_len</th>\n",
       "      <th>Nucleus</th>\n",
       "      <th>Cytoplasm</th>\n",
       "      <th>Membrane</th>\n",
       "      <th>Cell membrane</th>\n",
       "      <th>Extracellular</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_0</td>\n",
       "      <td>MAAAAAAAAAAGAAGGRGSGPGRRRHLVPGAGGEAGEGAPGGAGDY...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>306</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_1</td>\n",
       "      <td>MAAAAAAAAALGVRLRDCCSRGAVLLLFFSLSPRPPAAAAWLLGLR...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_2</td>\n",
       "      <td>MAAAAAAAAATEQQGSNGPVKKSMREKAVERRNVNKEHNSNFKAGY...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_3</td>\n",
       "      <td>MAAAAAAAGAAGSAAPAAAAGAPGSGGAPSGSQGVLIGDRLYSGVL...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_4</td>\n",
       "      <td>MAAAAAAGPSPGSGPGDSPEGPEGEAPERRRKAHGMLKLYYGLSEG...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>782</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>train_5</td>\n",
       "      <td>MAAAAAAKIAPSMLSSDFANLAAEADRMVRLGADWLHMDIMDGHFV...</td>\n",
       "      <td>Viridiplantae</td>\n",
       "      <td>228</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>train_6</td>\n",
       "      <td>MAAAAAALSGAGTPPAGGGAGGGGAGGGGSPPGGWAVARLEGREFE...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>660</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>train_7</td>\n",
       "      <td>MAAAAAAPGGGGGEPRGTAGVVPVVPGEVEVVKGQPFDVGPRYTQL...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>380</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>train_8</td>\n",
       "      <td>MAAAAAASHLNLDALREVLECPICMESFTEEQLRPKLLHCGHTICR...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>655</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>train_9</td>\n",
       "      <td>MAAAAAEEGMEPRALQYEQTLMYGRYTQDLGAFAKEEAARIRLGGP...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>train_10</td>\n",
       "      <td>MAAAAAGAGSGPWAAQEKQFPPALLSFFIYNPRFGPREGQEENKIL...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>train_11</td>\n",
       "      <td>MAAAAAGTATSQRFFQSFSDALIDEDPQAALEELTKALEQKPDDAQ...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>365</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>train_12</td>\n",
       "      <td>MAAAAALSGAGAPPAGGGAGGGGSPPGGWAVARLEGREFEYLMKKR...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>651</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>train_13</td>\n",
       "      <td>MAAAAAMAEQESARNGGRNRGGVQRVEGKLRASVEKGDYYEAHQMY...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>327</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>train_14</td>\n",
       "      <td>MAAAAASAPQQLSDEELFSQLRRYGLSPGPVTESTRPVYLKKLKKL...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>911</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>train_15</td>\n",
       "      <td>MAAAAATAVGPGAGSAGVAGPGGAGPCATVSVFPGARLLTIGDANG...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>651</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>train_16</td>\n",
       "      <td>MAAAAATKILLCLPLLLLLSGWSRAGRADPHSLCYDITVIPKFRPG...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>train_17</td>\n",
       "      <td>MAAAAAVVTPSGLEDEVSRSRGEGAGEMVVERGPGAAYHMFVVMED...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>train_18</td>\n",
       "      <td>MAAAADSFSGGPAGVRLPRSPPLKVLAEQLRRDAEGGPGAWRLSRA...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>147</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>train_19</td>\n",
       "      <td>MAAAAECDVVMAATEPELLDDQEAKREAETFKEQGNAYYAKKDYNE...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>494</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID                                           sequence  \\\n",
       "0    train_0  MAAAAAAAAAAGAAGGRGSGPGRRRHLVPGAGGEAGEGAPGGAGDY...   \n",
       "1    train_1  MAAAAAAAAALGVRLRDCCSRGAVLLLFFSLSPRPPAAAAWLLGLR...   \n",
       "2    train_2  MAAAAAAAAATEQQGSNGPVKKSMREKAVERRNVNKEHNSNFKAGY...   \n",
       "3    train_3  MAAAAAAAGAAGSAAPAAAAGAPGSGGAPSGSQGVLIGDRLYSGVL...   \n",
       "4    train_4  MAAAAAAGPSPGSGPGDSPEGPEGEAPERRRKAHGMLKLYYGLSEG...   \n",
       "5    train_5  MAAAAAAKIAPSMLSSDFANLAAEADRMVRLGADWLHMDIMDGHFV...   \n",
       "6    train_6  MAAAAAALSGAGTPPAGGGAGGGGAGGGGSPPGGWAVARLEGREFE...   \n",
       "7    train_7  MAAAAAAPGGGGGEPRGTAGVVPVVPGEVEVVKGQPFDVGPRYTQL...   \n",
       "8    train_8  MAAAAAASHLNLDALREVLECPICMESFTEEQLRPKLLHCGHTICR...   \n",
       "9    train_9  MAAAAAEEGMEPRALQYEQTLMYGRYTQDLGAFAKEEAARIRLGGP...   \n",
       "10  train_10  MAAAAAGAGSGPWAAQEKQFPPALLSFFIYNPRFGPREGQEENKIL...   \n",
       "11  train_11  MAAAAAGTATSQRFFQSFSDALIDEDPQAALEELTKALEQKPDDAQ...   \n",
       "12  train_12  MAAAAALSGAGAPPAGGGAGGGGSPPGGWAVARLEGREFEYLMKKR...   \n",
       "13  train_13  MAAAAAMAEQESARNGGRNRGGVQRVEGKLRASVEKGDYYEAHQMY...   \n",
       "14  train_14  MAAAAASAPQQLSDEELFSQLRRYGLSPGPVTESTRPVYLKKLKKL...   \n",
       "15  train_15  MAAAAATAVGPGAGSAGVAGPGGAGPCATVSVFPGARLLTIGDANG...   \n",
       "16  train_16  MAAAAATKILLCLPLLLLLSGWSRAGRADPHSLCYDITVIPKFRPG...   \n",
       "17  train_17  MAAAAAVVTPSGLEDEVSRSRGEGAGEMVVERGPGAAYHMFVVMED...   \n",
       "18  train_18  MAAAADSFSGGPAGVRLPRSPPLKVLAEQLRRDAEGGPGAWRLSRA...   \n",
       "19  train_19  MAAAAECDVVMAATEPELLDDQEAKREAETFKEQGNAYYAKKDYNE...   \n",
       "\n",
       "          Kingdom  seq_len  Nucleus  Cytoplasm  Membrane  Cell membrane  \\\n",
       "0         Metazoa      306      1.0        1.0       0.0            0.0   \n",
       "1         Metazoa      951      0.0        0.0       1.0            1.0   \n",
       "2         Metazoa      320      0.0        1.0       1.0            1.0   \n",
       "3         Metazoa      520      1.0        0.0       0.0            0.0   \n",
       "4         Metazoa      782      0.0        0.0       0.0            0.0   \n",
       "5   Viridiplantae      228      0.0        1.0       0.0            0.0   \n",
       "6         Metazoa      660      1.0        0.0       0.0            0.0   \n",
       "7         Metazoa      380      0.0        1.0       1.0            1.0   \n",
       "8         Metazoa      655      0.0        1.0       0.0            0.0   \n",
       "9         Metazoa      898      0.0        0.0       1.0            1.0   \n",
       "10        Metazoa      482      0.0        0.0       1.0            0.0   \n",
       "11        Metazoa      365      1.0        1.0       0.0            0.0   \n",
       "12        Metazoa      651      1.0        1.0       0.0            0.0   \n",
       "13        Metazoa      327      0.0        1.0       0.0            0.0   \n",
       "14        Metazoa      911      1.0        0.0       1.0            0.0   \n",
       "15        Metazoa      651      1.0        1.0       0.0            0.0   \n",
       "16        Metazoa      246      0.0        0.0       1.0            1.0   \n",
       "17        Metazoa      429      0.0        1.0       0.0            0.0   \n",
       "18        Metazoa      147      1.0        0.0       0.0            0.0   \n",
       "19        Metazoa      494      1.0        1.0       0.0            0.0   \n",
       "\n",
       "    Extracellular  \n",
       "0             0.0  \n",
       "1             0.0  \n",
       "2             0.0  \n",
       "3             0.0  \n",
       "4             0.0  \n",
       "5             0.0  \n",
       "6             0.0  \n",
       "7             0.0  \n",
       "8             0.0  \n",
       "9             0.0  \n",
       "10            0.0  \n",
       "11            0.0  \n",
       "12            0.0  \n",
       "13            0.0  \n",
       "14            0.0  \n",
       "15            0.0  \n",
       "16            1.0  \n",
       "17            0.0  \n",
       "18            0.0  \n",
       "19            0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4hxrqsXIHONr",
    "outputId": "13a25366-93d2-44df-9567-320d789d5e70"
   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18206 entries, 0 to 18205\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   ID             18206 non-null  object \n",
      " 1   sequence       18206 non-null  object \n",
      " 2   Kingdom        18206 non-null  object \n",
      " 3   seq_len        18206 non-null  int64  \n",
      " 4   Nucleus        18206 non-null  float64\n",
      " 5   Cytoplasm      18206 non-null  float64\n",
      " 6   Membrane       18206 non-null  float64\n",
      " 7   Cell membrane  18206 non-null  float64\n",
      " 8   Extracellular  18206 non-null  float64\n",
      "dtypes: float64(5), int64(1), object(3)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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       "      <th></th>\n",
       "      <th>seq_len</th>\n",
       "      <th>Nucleus</th>\n",
       "      <th>Cytoplasm</th>\n",
       "      <th>Membrane</th>\n",
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       "      <td>18206.000000</td>\n",
       "      <td>18206.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>478.705262</td>\n",
       "      <td>0.341646</td>\n",
       "      <td>0.342305</td>\n",
       "      <td>0.307591</td>\n",
       "      <td>0.148248</td>\n",
       "      <td>0.113699</td>\n",
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       "      <td>242.541811</td>\n",
       "      <td>0.474274</td>\n",
       "      <td>0.474494</td>\n",
       "      <td>0.461509</td>\n",
       "      <td>0.355355</td>\n",
       "      <td>0.317454</td>\n",
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       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>279.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>473.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>648.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>1022.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "            seq_len       Nucleus     Cytoplasm      Membrane  Cell membrane  \\\n",
       "count  18206.000000  18206.000000  18206.000000  18206.000000   18206.000000   \n",
       "mean     478.705262      0.341646      0.342305      0.307591       0.148248   \n",
       "std      242.541811      0.474274      0.474494      0.461509       0.355355   \n",
       "min       40.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%      279.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "50%      473.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "75%      648.000000      1.000000      1.000000      1.000000       0.000000   \n",
       "max     1022.000000      1.000000      1.000000      1.000000       1.000000   \n",
       "\n",
       "       Extracellular  \n",
       "count   18206.000000  \n",
       "mean        0.113699  \n",
       "std         0.317454  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  "
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.describe()"
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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    },
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       "(18206, 9)"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SsurZbrgMh-G",
    "outputId": "99d3cb3c-444e-4438-e441-a8f6516f47ba"
   },
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    {
     "data": {
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       "18206"
      ]
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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    "data.sequence.nunique()"
   ]
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  {
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   "execution_count": 14,
   "metadata": {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>sequence</th>\n",
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       "      <th>9735</th>\n",
       "      <td>train_9735</td>\n",
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       "      <th>8863</th>\n",
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       "      <th>1332</th>\n",
       "      <td>train_1332</td>\n",
       "      <td>MAFTNYSTLNRAQLTFDYLHTNSTTHAFLFGALAELIDNARDADAT...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>1022</td>\n",
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       "    <tr>\n",
       "      <th>10316</th>\n",
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       "      <td>MLPYQAAVAMDYAGYQRQPTPGHPGSHMATMGSLGMPAVPFTHSWM...</td>\n",
       "      <td>Metazoa</td>\n",
       "      <td>1022</td>\n",
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       "    <tr>\n",
       "      <th>18054</th>\n",
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       "      <td>1022</td>\n",
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       "    <tr>\n",
       "      <th>17321</th>\n",
       "      <td>train_17321</td>\n",
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       "      <td>Viridiplantae</td>\n",
       "      <td>1022</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>16505</th>\n",
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       "<p>18206 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "                ID                                           sequence  \\\n",
       "9735    train_9735           MKWMSLVFLCGLLAMAVASPLNPGNVIINGDCRHCNVRGG   \n",
       "9047    train_9047           MKITSALVLLFAGVAFAQSADPNTNENKNVIHINSPSAAK   \n",
       "8863    train_8863         MKFFQAAALLLAMFAALANAEPVPQPGTVLIQTDNTQYIRTG   \n",
       "13740  train_13740         MSAEDLFTIQILCDQIELKLASIVINSNIKLQLKRKKKTQQL   \n",
       "6920    train_6920        MFTNSIKNLIIYLMPLMVTLMLLSVSFVDAGKKPSGPNPGGNN   \n",
       "...            ...                                                ...   \n",
       "1332    train_1332  MAFTNYSTLNRAQLTFDYLHTNSTTHAFLFGALAELIDNARDADAT...   \n",
       "10316  train_10316  MLPYQAAVAMDYAGYQRQPTPGHPGSHMATMGSLGMPAVPFTHSWM...   \n",
       "18054  train_18054  MYFCWGADSRELQRRRTAGSPGAELLQAASGERHSLLLLTNHRVLS...   \n",
       "17321  train_17321  MVIAVEGGFVHEEEEVDHPIRYLPLGRVYSSSAPCPLPKKPRSAED...   \n",
       "16505  train_16505  MTGKVNRDLFFKLASDLREERLHAAVALIKDLSALDLPDDAEEWSY...   \n",
       "\n",
       "             Kingdom  seq_len  Nucleus  Cytoplasm  Membrane  Cell membrane  \\\n",
       "9735         Metazoa       40      0.0        0.0       0.0            0.0   \n",
       "9047         Metazoa       40      0.0        0.0       0.0            0.0   \n",
       "8863         Metazoa       42      0.0        0.0       0.0            0.0   \n",
       "13740          Fungi       42      1.0        1.0       0.0            0.0   \n",
       "6920         Metazoa       43      0.0        0.0       0.0            0.0   \n",
       "...              ...      ...      ...        ...       ...            ...   \n",
       "1332         Metazoa     1022      1.0        0.0       0.0            0.0   \n",
       "10316        Metazoa     1022      1.0        0.0       0.0            0.0   \n",
       "18054        Metazoa     1022      0.0        1.0       0.0            0.0   \n",
       "17321  Viridiplantae     1022      1.0        0.0       0.0            0.0   \n",
       "16505          Fungi     1022      1.0        0.0       0.0            0.0   \n",
       "\n",
       "       Extracellular  \n",
       "9735             1.0  \n",
       "9047             1.0  \n",
       "8863             1.0  \n",
       "13740            0.0  \n",
       "6920             1.0  \n",
       "...              ...  \n",
       "1332             0.0  \n",
       "10316            0.0  \n",
       "18054            0.0  \n",
       "17321            0.0  \n",
       "16505            0.0  \n",
       "\n",
       "[18206 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=\"seq_len\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 619
    },
    "id": "E_tr462PPKyA",
    "outputId": "0fa228c7-c6af-49a1-df48-61ac73ece890"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Extracellular')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1584x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 5, figsize=(22, 8), sharey=False)\n",
    "fig.suptitle('proteins per class')\n",
    "\n",
    "\n",
    "sns.countplot(data.Nucleus , ax=axes[0])\n",
    "axes[0].set_title(data[\"Nucleus\"].name)\n",
    "\n",
    "sns.countplot(data[\"Cytoplasm\"], ax=axes[1])\n",
    "axes[1].set_title(data[\"Cytoplasm\"].name)\n",
    "\n",
    "sns.countplot(data[\"Membrane\"], ax=axes[2])\n",
    "axes[2].set_title(data[\"Membrane\"].name)\n",
    "\n",
    "sns.countplot(data[\"Cell membrane\"], ax=axes[3])\n",
    "axes[3].set_title(data[\"Cell membrane\"].name)\n",
    "\n",
    "sns.countplot(data[\"Extracellular\"], ax=axes[4])\n",
    "axes[4].set_title(data[\"Extracellular\"].name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "id": "G_tn4KaA0bXG",
    "outputId": "e08631b1-730f-49c6-ff1a-34f1dfee90d4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Kingdom', ylabel='count'>"
      ]
     },
     "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": [
    "sns.countplot(data.Kingdom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "LABEL_COLUMNS = data.columns.tolist()[4:]\n",
    "data[LABEL_COLUMNS].sum().sort_values().plot(kind=\"barh\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAAAPPLAAGLRPAMAAAQAPVVAAAWGVGARRGAALSSSARCRALRLSRGGGGGRDGWVPPPVVGRMPPRTLSVRCAASNGRITQQEFTEMAWQSIVSSPEVAKESKHQIVETEHLMKSLLEQRNGLARRIFSKAGVDNTRLLDATEKFIQRQPKVLGEDPGSMLGRDLEALIQRARDFKKEYGDSFVSVEHLVLGFAEDKRFGRQLFKDFQITVQSLKTAIESIRGKQNVIDQDPEGKYEALDKYGKDLTAMARQGKLDPVIGRDDEIRRCIQILSRRTKNNPVLIGEPGVGKTAIAEGLAQRIVQGDVPQALTNRRLIALDMGALIAGAKYRGEFEDRLKAVLKEVTDSDGQTILFIDEIHTVVGAGATNGAMDAGNLLKPMLGRGELRCIGATTLDEYRKYIEKDPALERRFQQVYVDQPSVEDTISILRGLRERYELHHGVRISDSALVAAALLSDRYISGRFLPDKAIDLVDESAAKLKMEITSKPTALDEIDRAVIKLEMERLSLTNDTDKASRDRLSRIEAELSLLKEKQKDLTEQWEREKSVMTKIQSIKEEIDRVNVEIQQAEREYDLNRAAELKYGSLNALQRQLQTTEKELDEYQSSGKSMLREEVTQDDIAEIVSRWTGIPVSKLKQSDREKLLYLEEELHKRVVGQDPAVKAVSEAIQRSRAGLSDPNRPIASFMFMGPTGVGKTELAKALAAFMFNTEEAVVRIDMSEYMEKHSVSRLIGAPPGYVGYEEGGQLTEAVRRRPYSIILFDEIEKAHGDVFNVFLQILDDGRVTDSQGRKVSFTNSIIIMTSNVGSQFILNMDEEGGSTDSAYENIKKRVMDAARSVFRPEFMNRIDEYIVFKPLEREQINSIVKLQLARVQKRIADRKIKLEVSPGAVEFLGSLGYDPNYGARPVKRVIQQYVENELAKGILRGDFKDEDSILVDTQVTVPSNGQLPQQKLVFHKMSEESAPAAAEDEKFLPAV\n",
      "\n",
      "{'Nucleus': 0.0, 'Cytoplasm': 0.0, 'Membrane': 0.0, 'Cell membrane': 0.0, 'Extracellular': 0.0}\n"
     ]
    }
   ],
   "source": [
    "sample_row = data.iloc[100]\n",
    "sample_comment = sample_row.sequence\n",
    "sample_labels = sample_row[LABEL_COLUMNS]\n",
    "\n",
    "print(sample_comment)\n",
    "print()\n",
    "print(sample_labels.to_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cYv0Hyur4fbw"
   },
   "source": [
    "# preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "tZlnr3DA6KFY"
   },
   "outputs": [],
   "source": [
    "data[\"sequence\"] = data[\"sequence\"].apply(lambda seq: \" \".join(seq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qYqTwAuE6dsv",
    "outputId": "bb7bde73-d621-4c71-d0dc-5ff8773c4477"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        M A A A A A A A A A A G A A G G R G S G P G R ...\n",
       "1        M A A A A A A A A A L G V R L R D C C S R G A ...\n",
       "2        M A A A A A A A A A T E Q Q G S N G P V K K S ...\n",
       "3        M A A A A A A A G A A G S A A P A A A A G A P ...\n",
       "4        M A A A A A A G P S P G S G P G D S P E G P E ...\n",
       "                               ...                        \n",
       "18201    M Y W V L L C G S I L L C C L S G A S A S P A ...\n",
       "18202    M Y Y F S R V A A R T F C C C I F F C L A T A ...\n",
       "18203    M Y Y G I S Q F S E A Y N K I L R N S S S H S ...\n",
       "18204    M Y Y Q N Q H Q G K N I L S S S R M H I T S E ...\n",
       "18205    M Y Y Y L E I E C E S P V K D I F T C N K R R ...\n",
       "Name: sequence, Length: 18206, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"sequence\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wol2Lg019ZtK"
   },
   "source": [
    "## trasforming sequences into embedings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "E06oQAXz9izh",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "489beadd-5c21-4702-be3f-e5eea472b7e7",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting transformers\n",
      "  Downloading transformers-4.21.2-py3-none-any.whl (4.7 MB)\n",
      "\u001b[K     |████████████████████████████████| 4.7 MB 4.6 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from transformers) (4.64.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from transformers) (21.3)\n",
      "Requirement already satisfied: numpy>=1.17 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from transformers) (1.23.1)\n",
      "Requirement already satisfied: requests in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from transformers) (2.28.1)\n",
      "Requirement already satisfied: filelock in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from transformers) (3.7.1)\n",
      "Collecting regex!=2019.12.17\n",
      "  Downloading regex-2022.8.17-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (765 kB)\n",
      "\u001b[K     |████████████████████████████████| 765 kB 89.3 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
      "  Downloading tokenizers-0.12.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
      "\u001b[K     |████████████████████████████████| 6.6 MB 34.8 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n",
      "  Downloading huggingface_hub-0.9.1-py3-none-any.whl (120 kB)\n",
      "\u001b[K     |████████████████████████████████| 120 kB 90.6 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting pyyaml>=5.1\n",
      "  Downloading PyYAML-6.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (661 kB)\n",
      "\u001b[K     |████████████████████████████████| 661 kB 92.6 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.3.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from packaging>=20.0->transformers) (3.0.9)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from requests->transformers) (2022.6.15)\n",
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      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from requests->transformers) (1.26.11)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from requests->transformers) (3.3)\n",
      "Installing collected packages: pyyaml, tokenizers, regex, huggingface-hub, transformers\n",
      "Successfully installed huggingface-hub-0.9.1 pyyaml-6.0 regex-2022.8.17 tokenizers-0.12.1 transformers-4.21.2\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1hnLdeXRJlYR",
    "outputId": "2ffe1d72-b7c7-4c72-f240-8029bf3643d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1022"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"seq_len\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "hYwG2cxG9QMQ"
   },
   "outputs": [],
   "source": [
    "from transformers import BertModel, BertTokenizer\n",
    "import re\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2sSp5YH09nKA",
    "outputId": "aa5e82c0-f8d9-44f1-cce9-afe9f1d83987"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at Rostlab/prot_bert were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained(\"Rostlab/prot_bert\", do_lower_case=False )\n",
    "model = BertModel.from_pretrained(\"Rostlab/prot_bert\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "bk8L1gyNRgi0"
   },
   "outputs": [],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "model = model.to(device)\n",
    "model = model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "pQbyR0_T9yeo"
   },
   "outputs": [],
   "source": [
    "encoded_sequences = tokenizer.batch_encode_plus(data.sequence.values, add_special_tokens=False, return_attention_mask=True, padding=True, return_tensors='pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f99IzyAj_vH5",
    "outputId": "8b9cb2e8-46b0-4554-a69d-a18b389b3b13"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_sequences.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f99IzyAj_vH5",
    "outputId": "8b9cb2e8-46b0-4554-a69d-a18b389b3b13"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([18206, 1022])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_sequences[\"input_ids\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f99IzyAj_vH5",
    "outputId": "8b9cb2e8-46b0-4554-a69d-a18b389b3b13"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([18206, 1022])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_sequences[\"attention_mask\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([21,  6,  6,  ...,  0,  0,  0]), tensor([1, 1, 1,  ..., 0, 0, 0]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_sequences[\"input_ids\"].squeeze()[0] , encoded_sequences[\"attention_mask\"].squeeze()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', 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'[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n"
     ]
    }
   ],
   "source": [
    "print(tokenizer.convert_ids_to_tokens(encoded_sequences[\"input_ids\"].squeeze()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "ekhbqz_uDhWc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[21,  6,  6,  ...,  0,  0,  0],\n",
       "         [21,  6,  6,  ...,  0,  0,  0],\n",
       "         [21,  6,  6,  ...,  0,  0,  0],\n",
       "         ...,\n",
       "         [21, 20, 20,  ...,  0,  0,  0],\n",
       "         [21, 20, 20,  ...,  0,  0,  0],\n",
       "         [21, 20, 20,  ...,  0,  0,  0]]),\n",
       " 0        M A A A A A A A A A A G A A G G R G S G P G R ...\n",
       " 1        M A A A A A A A A A L G V R L R D C C S R G A ...\n",
       " 2        M A A A A A A A A A T E Q Q G S N G P V K K S ...\n",
       " 3        M A A A A A A A G A A G S A A P A A A A G A P ...\n",
       " 4        M A A A A A A G P S P G S G P G D S P E G P E ...\n",
       "                                ...                        \n",
       " 18201    M Y W V L L C G S I L L C C L S G A S A S P A ...\n",
       " 18202    M Y Y F S R V A A R T F C C C I F F C L A T A ...\n",
       " 18203    M Y Y G I S Q F S E A Y N K I L R N S S S H S ...\n",
       " 18204    M Y Y Q N Q H Q G K N I L S S S R M H I T S E ...\n",
       " 18205    M Y Y Y L E I E C E S P V K D I F T C N K R R ...\n",
       " Name: sequence, Length: 18206, dtype: object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_sequences.input_ids, data[\"sequence\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gYpbFrLgRLxC",
    "outputId": "ba608a91-4f7e-4430-f376-38d2fba3b282"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_260/573842129.py:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_ids = torch.tensor(encoded_sequences['input_ids']).to(device)\n",
      "/tmp/ipykernel_260/573842129.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  attention_mask = torch.tensor(encoded_sequences['attention_mask']).to(device)\n"
     ]
    }
   ],
   "source": [
    "input_ids = torch.tensor(encoded_sequences['input_ids']).to(device)\n",
    "attention_mask = torch.tensor(encoded_sequences['attention_mask']).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "DkL-cjvmUAaK"
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "vjFv64kaRE56"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'torch' 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/tmp/ipykernel_299/2886003022.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\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[0m\u001b[1;32m      2\u001b[0m     \u001b[0membedding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\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;31mNameError\u001b[0m: name 'torch' is not defined"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    embedding = model(input_ids=input_ids,attention_mask=attention_mask)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w1PimDA_DwIR"
   },
   "outputs": [],
   "source": [
    "encoded_seq = encoded_sequences.input_ids.detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "07sYlwdrF1lw"
   },
   "outputs": [],
   "source": [
    "encoded_seq[0:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VMt6tlZAIpAT"
   },
   "outputs": [],
   "source": [
    "encoded_seq.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rWuNCl9NGwGy"
   },
   "outputs": [],
   "source": [
    "encoded_seq = pd.DataFrame(encoded_seq, columns=[\"sequence\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YR-7bBavG1vZ"
   },
   "outputs": [],
   "source": [
    "data[\"sequence_encoded\"]= "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P92ZrubO9BLo"
   },
   "source": [
    "# encoding kingdom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "a7-xZIZa0KWO"
   },
   "outputs": [],
   "source": [
    "data[\"Kingdom\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PtLxw65t6hA-"
   },
   "outputs": [],
   "source": [
    "Kingdom_Encoding = {\"Metazoa\": 0, \"Fungi\": 1, \"Viridiplantae\":2, \"Other\":4}\n",
    "\n",
    "data[\"Kingdom\"] = data[\"Kingdom\"].apply(lambda x: Kingdom_Encoding[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S5JEn8o381Pv"
   },
   "outputs": [],
   "source": [
    "data[\"Kingdom\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BHblFhQC86wH"
   },
   "outputs": [],
   "source": [
    "data[\"Kingdom\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_1SlMqrNOR7J"
   },
   "outputs": [],
   "source": [
    "features = np.array(data[:,:4], dtype=float)\n",
    "labels = data.iloc[:, 4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-ujMsaDREEMu"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CS50hkbuEKWx"
   },
   "outputs": [],
   "source": [
    "data1=pd.read_csv(\"/content/drive/MyDrive/AI hack Tunisia/protein/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ihAcji0r8-Dv"
   },
   "outputs": [],
   "source": [
    "label_encoder_class = LabelEncoder()\n",
    "data[\"class_label2\"] = label_encoder_class.fit_transform(data1[\"Kingdom\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kyMe5waYEVWY"
   },
   "outputs": [],
   "source": [
    "data[\"class_label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MQV9VrCpOaLp"
   },
   "outputs": [],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ac8Wo5Q6Q3Na"
   },
   "outputs": [],
   "source": [
    "test=pd.read_csv(\"/content/drive/MyDrive/AI hack Tunisia/protein/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lDWDCeloNSJb"
   },
   "outputs": [],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hKRhI5ZA4ZnL"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "85VKb8OCOVWV"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JVO3npbJOm6u"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w-wXSfZtOxPm"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "name": "protein location.ipynb",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "default:Python",
   "language": "python",
   "name": "conda-env-default-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}