feature-extraction/3. AAC-feature-vectors.ipynb

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        "<a href=\"https://colab.research.google.com/github/faezesarlakifar/AllerTrans/blob/main/feature-extraction/3.%20AAC-feature-vectors.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "## We utilized the [source code of the ProAll-D research study](https://data.mendeley.com/datasets/tjmt97xpjf/1) to define descriptors and extract AAC feature vectors. ❤"
      ],
      "metadata": {
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      "source": [
        "# @markdown config\n",
        "!pip install Bio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "8e-LdS7hZoFq"
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      "source": [
        "# @title Import Necessaries\n",
        "import pandas as pd\n",
        "from Bio import SeqIO\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define Descriptors"
      ],
      "metadata": {
        "id": "uYVESIii5pyB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vlJN4-ficTpk"
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      "source": [
        "E_descriptors = {'A': [0.008,0.134,-0.475,-0.039,0.181],\n",
        "                 'R': [0.171,-0.361,0.107,-0.258,-0.364],\n",
        "                 'N': [0.255,0.038,0.117,0.118,-0.055],\n",
        "                 'D': [0.303,-0.057,-0.014,0.225,0.156],\n",
        "                 'C': [-0.132,0.174,0.07,0.565,-0.374],\n",
        "                 'Q': [0.149,-0.184,0.03,0.035,-0.112],\n",
        "                 'E': [0.221,-0.280,-0.315,0.157,0.303],\n",
        "                 'G': [0.218,0.562,-0.024,0.018,0.106],\n",
        "                 'H': [0.023,-0.177,0.041,0.28,-0.021],\n",
        "                 'I': [-0.353,0.071,-0.088,-0.195,-0.107],\n",
        "                 'L': [-0.267,0.018,-0.265,-0.274,0.206],\n",
        "                 'K': [0.243,-0.339,-0.044,-0.325,-0.027],\n",
        "                 'M': [-0.239,-0.141,-0.155,0.321,0.077],\n",
        "                 'F': [-0.329,-0.023,0.072,-0.002,0.208],\n",
        "                 'P': [0.173,0.286,0.407,-0.215,0.384],\n",
        "                 'S': [0.199,0.238,-0.015,-0.068,-0.196],\n",
        "                 'T': [0.068,0.147,-0.015,-0.132,-0.274],\n",
        "                 'W': [-0.296,-0.186,0.389,0.083,0.297],\n",
        "                 'Y': [-0.141,-0.057,0.425,-0.096,-0.091],\n",
        "                 'V': [-0.274,0.136,-0.187,-0.196,-0.299]}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(E_descriptors)"
      ],
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        "outputId": "e006af83-4180-4a38-b8d2-998388b45f52",
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            "text/plain": [
              "20"
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          "metadata": {},
          "execution_count": 2
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    {
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      "execution_count": null,
      "metadata": {
        "id": "Fo8xpfZ0d6NB"
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      "source": [
        "allergen_label = 1\n",
        "non_allergen_label = 0\n",
        "\n",
        "# j_list is 0 to 4 because we have 5 E Descriptors\n",
        "j_list = [0,1,2,3,4]\n",
        "\n",
        "# lag is set to 5, as the in our data the length of shortest sequence is 5\n",
        "lag = [1,2,3,4,5]\n",
        "\n",
        "# we need this, exclusively for calculating cross covariance\n",
        "k_list = [0,1,2,3,4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "TaQJ5gSObpZh"
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      "source": [
        "# replacing bases in sequences with E descriptors\n",
        "def represent_as_E_descriptors(allergens):\n",
        "    allergens_E_descriptors = {}\n",
        "    for key in allergens:\n",
        "        allergens_E_descriptors[key] = []\n",
        "\n",
        "    for key in allergens_E_descriptors:\n",
        "        for base in allergens[key]:\n",
        "            allergens_E_descriptors[key].extend(E_descriptors[base])\n",
        "\n",
        "    return allergens_E_descriptors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mxELDh25hi0S"
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      "source": [
        "def represent_as_E_descriptors(sequence):\n",
        "\n",
        "  E = []\n",
        "\n",
        "  for base in sequence:\n",
        "    E.extend(E_descriptors[base])\n",
        "\n",
        "  return E"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ye6qDfwqbtKp"
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      "source": [
        "# converting E descriptors as a 2-D array, one row corresponding each type of descriptor, for each sequence\n",
        "def convert_to_2D(descriptors):\n",
        "    E1 = []\n",
        "    E2 = []\n",
        "    E3 = []\n",
        "    E4 = []\n",
        "    E5 = []\n",
        "    for i in range(0,len(descriptors),5):\n",
        "        E1.append(descriptors[i])\n",
        "        E2.append(descriptors[i+1])\n",
        "        E3.append(descriptors[i+2])\n",
        "        E4.append(descriptors[i+3])\n",
        "        E5.append(descriptors[i+4])\n",
        "\n",
        "    E = [E1,E2,E3,E4,E5]\n",
        "    E = np.array(E)\n",
        "    return E"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculate auto-covariance and cross-covariance"
      ],
      "metadata": {
        "id": "eqQaWgXq50wx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "1zz3saETb3Xx"
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      "source": [
        "def calculate_auto_covariance(j_list, lag, n, E):\n",
        "    auto_covariance = {}\n",
        "    for j in j_list:\n",
        "        for l in lag:\n",
        "            sum = 0\n",
        "            for i in range(0,n-l):\n",
        "                sum += (E[j,i] * E[j,i+l])/(n-l)\n",
        "            key = 'AC'+str(j+1)+str(j+1)+str(l)\n",
        "            auto_covariance[key] = sum\n",
        "    #print(auto_covariance)\n",
        "    return auto_covariance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IcnpWb8ScEg4"
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      "outputs": [],
      "source": [
        "def calculate_cross_covariance(j_list, k_list, lag, n, E):\n",
        "    cross_covariance = {}\n",
        "    for j in j_list:\n",
        "        for k in k_list:\n",
        "            if j == k:\n",
        "                continue\n",
        "            else:\n",
        "                for l in lag:\n",
        "                    sum = 0\n",
        "                    for i in range(0,n-l):\n",
        "                        sum += (E[j,i] * E[k,i+l])/(n-l)\n",
        "                    key = 'AC'+str(j+1)+str(k+1)+str(l)\n",
        "                    cross_covariance[key] = sum\n",
        "    #print(cross_covariance)\n",
        "    return cross_covariance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TITj6zePcpWy",
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      "source": [
        "# @title Load fasta files\n",
        "sequences_valid_pos = SeqIO.parse(\"algpred2_validation_positive.fasta\", \"fasta\")\n",
        "sequences_valid_neg = SeqIO.parse(\"algpred2_validation_negative.fasta\", \"fasta\")\n",
        "sequences_train_pos = SeqIO.parse(\"algpred2_train_positive.fasta\", \"fasta\")\n",
        "sequences_train_neg = SeqIO.parse(\"algpred2_train_negative.fasta\", \"fasta\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Extract AAC features"
      ],
      "metadata": {
        "id": "mS2fYO3p6aFX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "def AAC_feature_extractor(sequences, label):\n",
        "    # Extract features\n",
        "    feature_rows = []\n",
        "\n",
        "    for record in sequences:\n",
        "\n",
        "      sequence = str(record.seq)\n",
        "      ID = record.id\n",
        "\n",
        "      E = represent_as_E_descriptors(sequence)\n",
        "      E_2D = convert_to_2D(E)\n",
        "\n",
        "      n = len(E)//5\n",
        "      auto_cov = calculate_auto_covariance(j_list, lag, n, E_2D)\n",
        "      cross_cov = calculate_cross_covariance(j_list, k_list, lag, n, E_2D)\n",
        "\n",
        "      row = {**auto_cov, **cross_cov, \"Label\": label, \"id\": ID}\n",
        "\n",
        "      feature_rows.append(row)\n",
        "\n",
        "    # Create dataframe\n",
        "    df = pd.DataFrame(feature_rows)\n",
        "\n",
        "    # Write CSV\n",
        "    df.to_csv(\"output.csv\", index=False)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IwBxJvrhizDh"
      },
      "outputs": [],
      "source": [
        "df_valid_pos = AAC_feature_extractor(sequences_valid_pos, 1)\n",
        "df_valid_neg = AAC_feature_extractor(sequences_valid_neg, 0)\n",
        "df_train_pos = AAC_feature_extractor(sequences_train_pos, 1)\n",
        "df_train_neg = AAC_feature_extractor(sequences_train_neg, 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Merge and Save final feature vectors"
      ],
      "metadata": {
        "id": "I1KOMScS6PaP"
      }
    },
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "      AC111     AC112     AC113     AC114     AC115     AC221     AC222  \\\n",
              "0  0.001886  0.001307  0.000734 -0.002880  0.000974 -0.001117 -0.000485   \n",
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              "2  0.004823  0.003761  0.003679  0.000331  0.000563  0.009573  0.004162   \n",
              "3  0.003292 -0.001578  0.002206  0.000291  0.003010  0.001688 -0.001249   \n",
              "4  0.003209 -0.000163  0.001873 -0.001637  0.003451  0.000507 -0.001969   \n",
              "\n",
              "      AC223     AC224     AC225  ...     AC533     AC534     AC535     AC541  \\\n",
              "0  0.001613 -0.001169  0.002591  ... -0.000073  0.000695 -0.003275 -0.004461   \n",
              "1  0.001067 -0.001771  0.002240  ... -0.000084 -0.000580 -0.002765 -0.004671   \n",
              "2  0.002779  0.005414  0.002088  ...  0.000390 -0.004398 -0.001783 -0.004326   \n",
              "3 -0.005438 -0.008225 -0.007999  ...  0.000138  0.002905  0.002810 -0.003673   \n",
              "4 -0.005502 -0.007855 -0.007506  ...  0.001537  0.003198  0.001952 -0.003549   \n",
              "\n",
              "      AC542     AC543     AC544     AC545  Label      id  \n",
              "0  0.001704  0.000248  0.002862 -0.004252      1  P_7275  \n",
              "1  0.001425 -0.000495  0.003653 -0.004523      1  P_7276  \n",
              "2 -0.002584 -0.001865 -0.002965 -0.002761      1  P_9753  \n",
              "3 -0.000583  0.000653  0.003675  0.003971      1   P_269  \n",
              "4 -0.001662 -0.000398  0.002570  0.003676      1   P_270  \n",
              "\n",
              "[5 rows x 127 columns]"
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          },
          "execution_count": 14,
          "metadata": {},
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      "source": [
        "df_valid_pos.head()"
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              "      <th>AC111</th>\n",
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              "      AC111     AC112     AC113     AC114     AC115     AC221     AC222  \\\n",
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              "\n",
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              "4 -0.005192 -0.004026  0.003001  0.004504      0  N_262032  \n",
              "\n",
              "[5 rows x 127 columns]"
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          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
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              "      <td>0.008322</td>\n",
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              "      <td>0.001906</td>\n",
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              "      <td>-0.000094</td>\n",
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              "      AC111     AC112     AC113     AC114     AC115     AC221     AC222  \\\n",
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              "1  0.000490 -0.001669  0.004654  0.002188  0.000739  0.003232  0.014459   \n",
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              "4 -0.002693 -0.007448 -0.000248  0.000191  0.002411  0.004641  0.008922   \n",
              "\n",
              "      AC223     AC224     AC225  ...     AC533     AC534     AC535     AC541  \\\n",
              "0  0.004305  0.003685  0.003290  ... -0.001084  0.002212  0.001524  0.005349   \n",
              "1  0.008256  0.003990  0.001906  ... -0.000709  0.005907 -0.002400  0.000959   \n",
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              "3  0.006981  0.007383  0.004335  ... -0.005407  0.003977 -0.009576 -0.000270   \n",
              "4  0.007195  0.004451  0.001105  ... -0.007051  0.004516 -0.009173 -0.001007   \n",
              "\n",
              "      AC542     AC543     AC544     AC545  Label    id  \n",
              "0  0.001335  0.001866  0.001508 -0.002260      1  P_13  \n",
              "1 -0.005614 -0.001501 -0.003449 -0.005833      1  P_14  \n",
              "2  0.001405  0.001562 -0.002815  0.002598      1  P_17  \n",
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              "4 -0.003475 -0.000834 -0.006173 -0.000575      1  P_47  \n",
              "\n",
              "[5 rows x 127 columns]"
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          "metadata": {},
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              "      <td>0.010085</td>\n",
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              "      <td>0.016387</td>\n",
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              "      <td>-0.002816</td>\n",
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              "      <td>-0.014087</td>\n",
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              "      <td>0.015468</td>\n",
              "      <td>-0.005976</td>\n",
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              "      <td>0.006989</td>\n",
              "      <td>0.001822</td>\n",
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              "      <td>0.011145</td>\n",
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              "      <td>0.018016</td>\n",
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              "      <td>-0.008602</td>\n",
              "      <td>-0.004531</td>\n",
              "      <td>0.001568</td>\n",
              "      <td>0.000676</td>\n",
              "      <td>0.000378</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.017616</td>\n",
              "      <td>0.000081</td>\n",
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              "      <td>0.000028</td>\n",
              "      <td>-0.002588</td>\n",
              "      <td>-0.004809</td>\n",
              "      <td>0.004280</td>\n",
              "      <td>-0.006289</td>\n",
              "      <td>-0.003343</td>\n",
              "      <td>-0.007770</td>\n",
              "      <td>...</td>\n",
              "      <td>0.003017</td>\n",
              "      <td>0.008521</td>\n",
              "      <td>0.008130</td>\n",
              "      <td>0.005102</td>\n",
              "      <td>-0.000075</td>\n",
              "      <td>-0.009144</td>\n",
              "      <td>-0.011554</td>\n",
              "      <td>0.002463</td>\n",
              "      <td>0</td>\n",
              "      <td>N_284262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.003154</td>\n",
              "      <td>0.012648</td>\n",
              "      <td>0.013564</td>\n",
              "      <td>0.003795</td>\n",
              "      <td>0.014369</td>\n",
              "      <td>-0.003671</td>\n",
              "      <td>0.019171</td>\n",
              "      <td>-0.011904</td>\n",
              "      <td>0.001491</td>\n",
              "      <td>-0.001205</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000375</td>\n",
              "      <td>0.000262</td>\n",
              "      <td>0.010304</td>\n",
              "      <td>0.002175</td>\n",
              "      <td>-0.011979</td>\n",
              "      <td>0.003908</td>\n",
              "      <td>-0.000656</td>\n",
              "      <td>-0.006815</td>\n",
              "      <td>0</td>\n",
              "      <td>N_458959</td>\n",
              "    </tr>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "    </script>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "\n",
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              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
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              "    background-color: #434B5C;\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-8999dd82-d738-4094-ad59-9c3bf3a392ff button');\n",
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              "</div>\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "      AC111     AC112     AC113     AC114     AC115     AC221     AC222  \\\n",
              "0 -0.014873 -0.010485  0.009514 -0.010916  0.010085  0.009439  0.007081   \n",
              "1  0.002802  0.000336  0.002092  0.004080  0.002587 -0.001384  0.019427   \n",
              "2 -0.001318 -0.014087  0.005753  0.015468 -0.005976  0.007195  0.006989   \n",
              "3  0.017616  0.000081 -0.001344  0.000028 -0.002588 -0.004809  0.004280   \n",
              "4  0.003154  0.012648  0.013564  0.003795  0.014369 -0.003671  0.019171   \n",
              "\n",
              "      AC223     AC224     AC225  ...     AC533     AC534     AC535     AC541  \\\n",
              "0  0.014015  0.016387  0.006417  ...  0.008354 -0.001640 -0.011870  0.002486   \n",
              "1 -0.008919  0.004669 -0.007166  ...  0.004029  0.005986  0.003112  0.007879   \n",
              "2  0.001822 -0.001131  0.011145  ...  0.018016 -0.000941 -0.002570 -0.008602   \n",
              "3 -0.006289 -0.003343 -0.007770  ...  0.003017  0.008521  0.008130  0.005102   \n",
              "4 -0.011904  0.001491 -0.001205  ...  0.000375  0.000262  0.010304  0.002175   \n",
              "\n",
              "      AC542     AC543     AC544     AC545  Label        id  \n",
              "0 -0.007811  0.005816  0.009159  0.003974      0   N_15653  \n",
              "1  0.000025 -0.006039 -0.002816  0.011744      0  N_153477  \n",
              "2 -0.004531  0.001568  0.000676  0.000378      0   N_52129  \n",
              "3 -0.000075 -0.009144 -0.011554  0.002463      0  N_284262  \n",
              "4 -0.011979  0.003908 -0.000656 -0.006815      0  N_458959  \n",
              "\n",
              "[5 rows x 127 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train_neg.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-juwMgJHm3cK",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Merge Validation Dataframes\n",
        "df_valid_AAC = pd.concat([df_valid_neg, df_valid_pos], ignore_index=True, axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "PR5laJYXn9Y4",
        "outputId": "7433caed-b567-4c3a-bd6c-2cfce30dbfce"
      },
      "outputs": [
        {
          "data": {
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              "    <tr>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1344</th>\n",
              "      <td>0.003772</td>\n",
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              "      <td>-0.002938</td>\n",
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              "      <td>0.003998</td>\n",
              "      <td>0.011075</td>\n",
              "      <td>0.007395</td>\n",
              "      <td>0.005330</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>N_456440</td>\n",
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              "    padding: 0 0 0 0;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "</style>\n",
              "\n",
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            ],
            "text/plain": [
              "         AC111     AC112     AC113     AC114     AC115     AC221     AC222  \\\n",
              "3456  0.002624  0.006470  0.002608  0.004967  0.004951  0.019992  0.021952   \n",
              "3203 -0.006661 -0.003126  0.002786 -0.004657  0.004363 -0.007840  0.004575   \n",
              "3542  0.003352  0.000733 -0.001895  0.000282  0.009902  0.003358  0.004605   \n",
              "2278 -0.004833  0.005966 -0.003482  0.001975 -0.000652  0.008895  0.009987   \n",
              "1344  0.003772  0.000644  0.001577  0.002191 -0.002938  0.004852  0.003998   \n",
              "\n",
              "         AC223     AC224     AC225  ...     AC533     AC534     AC535  \\\n",
              "3456  0.022755  0.021410  0.022945  ...  0.002324  0.004584  0.002054   \n",
              "3203 -0.006647  0.001494 -0.003906  ...  0.005176 -0.003489  0.002211   \n",
              "3542  0.005894  0.004258  0.006740  ... -0.001510 -0.000469 -0.001925   \n",
              "2278  0.006889  0.008297  0.005965  ...  0.003785 -0.001776 -0.000406   \n",
              "1344  0.011075  0.007395  0.005330  ...  0.000919  0.002051 -0.003082   \n",
              "\n",
              "         AC541     AC542     AC543     AC544     AC545  Label        id  \n",
              "3456 -0.003368 -0.000529 -0.005784 -0.003549 -0.003501      1    P_9442  \n",
              "3203 -0.004341  0.002747 -0.005422  0.000788 -0.003550      1    P_6308  \n",
              "3542 -0.000277  0.000897 -0.001555 -0.000389  0.001086      1    P_5609  \n",
              "2278 -0.001597  0.002127 -0.008022 -0.002905 -0.001883      1    P_3900  \n",
              "1344  0.004117  0.003029  0.000473  0.003191  0.006430      0  N_456440  \n",
              "\n",
              "[5 rows x 127 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_valid_AAC = df_valid_AAC.sample(frac = 1)\n",
        "df_valid_AAC.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5llfp2pAoLxH"
      },
      "outputs": [],
      "source": [
        "df_valid_AAC.to_csv(\"df_valid_AAC.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "V2t-OrSwopBg"
      },
      "outputs": [],
      "source": [
        "# @title Merge Validation Dataframes\n",
        "df_train_AAC = pd.concat([df_train_neg, df_train_pos], ignore_index=True, axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "D6TOeSMEotdZ",
        "outputId": "3901522c-0c5e-43d8-d837-bf4a7527dba6"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AC111</th>\n",
              "      <th>AC112</th>\n",
              "      <th>AC113</th>\n",
              "      <th>AC114</th>\n",
              "      <th>AC115</th>\n",
              "      <th>AC221</th>\n",
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              "      <th>AC223</th>\n",
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              "      <th>AC535</th>\n",
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              "      <th>15086</th>\n",
              "      <td>0.001287</td>\n",
              "      <td>-0.003737</td>\n",
              "      <td>0.004184</td>\n",
              "      <td>0.004934</td>\n",
              "      <td>0.002597</td>\n",
              "      <td>0.005124</td>\n",
              "      <td>0.004059</td>\n",
              "      <td>-0.002822</td>\n",
              "      <td>-0.001064</td>\n",
              "      <td>0.009070</td>\n",
              "      <td>...</td>\n",
              "      <td>0.007424</td>\n",
              "      <td>0.007778</td>\n",
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              "      <td>0.003703</td>\n",
              "      <td>0.011624</td>\n",
              "      <td>0.005927</td>\n",
              "      <td>0.004203</td>\n",
              "      <td>-0.000232</td>\n",
              "      <td>1</td>\n",
              "      <td>P_2716</td>\n",
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              "    <tr>\n",
              "      <th>705</th>\n",
              "      <td>-0.010647</td>\n",
              "      <td>0.001622</td>\n",
              "      <td>-0.000106</td>\n",
              "      <td>-0.005017</td>\n",
              "      <td>0.002413</td>\n",
              "      <td>-0.001609</td>\n",
              "      <td>-0.000589</td>\n",
              "      <td>0.000810</td>\n",
              "      <td>-0.001232</td>\n",
              "      <td>-0.000431</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.003907</td>\n",
              "      <td>0.003002</td>\n",
              "      <td>0.001764</td>\n",
              "      <td>0.001258</td>\n",
              "      <td>0.001528</td>\n",
              "      <td>-0.004643</td>\n",
              "      <td>-0.002053</td>\n",
              "      <td>-0.000387</td>\n",
              "      <td>0</td>\n",
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              "      <th>6510</th>\n",
              "      <td>-0.000433</td>\n",
              "      <td>-0.018975</td>\n",
              "      <td>0.003073</td>\n",
              "      <td>0.008589</td>\n",
              "      <td>-0.004803</td>\n",
              "      <td>0.011586</td>\n",
              "      <td>-0.005543</td>\n",
              "      <td>0.005068</td>\n",
              "      <td>0.012254</td>\n",
              "      <td>0.001695</td>\n",
              "      <td>...</td>\n",
              "      <td>0.002947</td>\n",
              "      <td>-0.002481</td>\n",
              "      <td>0.001814</td>\n",
              "      <td>0.005288</td>\n",
              "      <td>-0.005832</td>\n",
              "      <td>0.003081</td>\n",
              "      <td>-0.000134</td>\n",
              "      <td>-0.000444</td>\n",
              "      <td>0</td>\n",
              "      <td>N_175149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1524</th>\n",
              "      <td>-0.000518</td>\n",
              "      <td>0.000346</td>\n",
              "      <td>-0.000777</td>\n",
              "      <td>0.007440</td>\n",
              "      <td>-0.000433</td>\n",
              "      <td>-0.002089</td>\n",
              "      <td>-0.000176</td>\n",
              "      <td>-0.002413</td>\n",
              "      <td>-0.002200</td>\n",
              "      <td>0.001625</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.000372</td>\n",
              "      <td>0.000034</td>\n",
              "      <td>-0.000289</td>\n",
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              "      <td>-0.001492</td>\n",
              "      <td>-0.002195</td>\n",
              "      <td>0.001643</td>\n",
              "      <td>0</td>\n",
              "      <td>N_393732</td>\n",
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              "    <tr>\n",
              "      <th>10256</th>\n",
              "      <td>0.004715</td>\n",
              "      <td>0.008496</td>\n",
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              "      <td>-0.002975</td>\n",
              "      <td>0.002222</td>\n",
              "      <td>0.009298</td>\n",
              "      <td>0.007544</td>\n",
              "      <td>0.002571</td>\n",
              "      <td>0.005287</td>\n",
              "      <td>...</td>\n",
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              "      <td>-0.003422</td>\n",
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              "      <td>1</td>\n",
              "      <td>P_2439</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "<p>5 rows × 127 columns</p>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      const charts = await google.colab.kernel.invokeFunction(\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b699e33a-d54a-4059-ae20-49ac54446293 button');\n",
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              "</div>\n",
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            ],
            "text/plain": [
              "          AC111     AC112     AC113     AC114     AC115     AC221     AC222  \\\n",
              "15086  0.001287 -0.003737  0.004184  0.004934  0.002597  0.005124  0.004059   \n",
              "705   -0.010647  0.001622 -0.000106 -0.005017  0.002413 -0.001609 -0.000589   \n",
              "6510  -0.000433 -0.018975  0.003073  0.008589 -0.004803  0.011586 -0.005543   \n",
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              "10256  0.004715  0.008496  0.003084  0.002396 -0.002975  0.002222  0.009298   \n",
              "\n",
              "          AC223     AC224     AC225  ...     AC533     AC534     AC535  \\\n",
              "15086 -0.002822 -0.001064  0.009070  ...  0.007424  0.007778  0.004185   \n",
              "705    0.000810 -0.001232 -0.000431  ... -0.003907  0.003002  0.001764   \n",
              "6510   0.005068  0.012254  0.001695  ...  0.002947 -0.002481  0.001814   \n",
              "1524  -0.002413 -0.002200  0.001625  ... -0.002254  0.000372  0.000034   \n",
              "10256  0.007544  0.002571  0.005287  ... -0.002114 -0.000066  0.002943   \n",
              "\n",
              "          AC541     AC542     AC543     AC544     AC545  Label        id  \n",
              "15086  0.003703  0.011624  0.005927  0.004203 -0.000232      1    P_2716  \n",
              "705    0.001258  0.001528 -0.004643 -0.002053 -0.000387      0   N_36306  \n",
              "6510   0.005288 -0.005832  0.003081 -0.000134 -0.000444      0  N_175149  \n",
              "1524  -0.000289 -0.005155 -0.001492 -0.002195  0.001643      0  N_393732  \n",
              "10256 -0.000839 -0.002706 -0.004998 -0.003422 -0.003153      1    P_2439  \n",
              "\n",
              "[5 rows x 127 columns]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train_AAC = df_train_AAC.sample(frac = 1)\n",
        "df_train_AAC.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "KzA6zmilo3hg"
      },
      "outputs": [],
      "source": [
        "df_train_AAC.to_csv(\"df_train_AAC.csv\", index=False)"
      ]
    }
  ],
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