[NAIVE_BAYES]_Fabry_Disease_Code.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b7zzS7qqY-pu"
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      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sequences = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSTDD8GOkotLEsmiiSs6Axki4ddSNKKSvVCHMRtgNjpFgN4-IfjS0KhWmSLB6WZnz3aDeLjcQgKMfe8/pub?gid=0&single=true&output=csv') # FULL DATASET - 0.958\n",
        "# sequences = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSTDD8GOkotLEsmiiSs6Axki4ddSNKKSvVCHMRtgNjpFgN4-IfjS0KhWmSLB6WZnz3aDeLjcQgKMfe8/pub?gid=2084779562&single=true&output=csv') # ONLY CLASSIC - 0.958\n",
        "# sequences = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSTDD8GOkotLEsmiiSs6Axki4ddSNKKSvVCHMRtgNjpFgN4-IfjS0KhWmSLB6WZnz3aDeLjcQgKMfe8/pub?gid=560410691&single=true&output=csv') # ONLY LATE-ONSET\n",
        "# sequences = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSTDD8GOkotLEsmiiSs6Axki4ddSNKKSvVCHMRtgNjpFgN4-IfjS0KhWmSLB6WZnz3aDeLjcQgKMfe8/pub?gid=1383392897&single=true&output=csv') # ONLY AMENABLE\n",
        "# sequences = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSTDD8GOkotLEsmiiSs6Axki4ddSNKKSvVCHMRtgNjpFgN4-IfjS0KhWmSLB6WZnz3aDeLjcQgKMfe8/pub?gid=1443616605&single=true&output=csv') # ONLY NOT AMENABLE\n",
        "# sequences = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSTDD8GOkotLEsmiiSs6Axki4ddSNKKSvVCHMRtgNjpFgN4-IfjS0KhWmSLB6WZnz3aDeLjcQgKMfe8/pub?gid=1449239318&single=true&output=csv') # CLASS 0 MUTED\n",
        "# sequences = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSTDD8GOkotLEsmiiSs6Axki4ddSNKKSvVCHMRtgNjpFgN4-IfjS0KhWmSLB6WZnz3aDeLjcQgKMfe8/pub?gid=2116880999&single=true&output=csv') # CLASS 1 MUTED\n",
        "# sequences = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSTDD8GOkotLEsmiiSs6Axki4ddSNKKSvVCHMRtgNjpFgN4-IfjS0KhWmSLB6WZnz3aDeLjcQgKMfe8/pub?gid=693494787&single=true&output=csv') # CLASS 2 MUTED\n",
        "# sequences = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vSTDD8GOkotLEsmiiSs6Axki4ddSNKKSvVCHMRtgNjpFgN4-IfjS0KhWmSLB6WZnz3aDeLjcQgKMfe8/pub?gid=247111228&single=true&output=csv') # CLASS 3 MUTED\n",
        "sequences"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             sequences  class\n",
              "0    ACTCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...      0\n",
              "1    ACCCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...      0\n",
              "2    ACACAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...      0\n",
              "3    ACGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...      0\n",
              "4    CGTCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...      0\n",
              "..                                                 ...    ...\n",
              "315  ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...      3\n",
              "316  ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...      3\n",
              "317  ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...      3\n",
              "318  ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...      3\n",
              "319  ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...      3\n",
              "\n",
              "[320 rows x 2 columns]"
            ],
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              "      <th>0</th>\n",
              "      <td>ACTCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>ACCCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...</td>\n",
              "      <td>0</td>\n",
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              "      <th>2</th>\n",
              "      <td>ACACAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>ACGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CGTCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...</td>\n",
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              "      <th>315</th>\n",
              "      <td>ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGC...</td>\n",
              "      <td>3</td>\n",
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              "type": "dataframe",
              "variable_name": "sequences",
              "summary": "{\n  \"name\": \"sequences\",\n  \"rows\": 320,\n  \"fields\": [\n    {\n      \"column\": \"sequences\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 320,\n        \"samples\": [\n          \"ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGCTTCGCTTCCTGCCGCTCGTTTCCTGGGACATCCCTGGGGCTAGAGCACTGGACAATGGATTGGCAAGGACGCCTACCATGGGCTGGCTGCACTGGGAGCGCTTCATGTGCAACCTTGACTGCCAGGAAGAGCCAGATTCCTGCATCAGTGAGAAGCTCTTCATGGAGATGGCAGAGCTCATGGTCTCAGAAGGCTGGAAGGATGCAGGTTATGAGTACCTCTGCATTGATGACTGTTGGATGGCTCCCCAAAGAGATTCAGAAGGCAGACTTCAGGCAGACCCTCAGCGCTTTCCTCATGGGATTCGCCAGCTAGCTAATTATGTTCACAGCAAAGGACTGAAGCTAGGGATTTATGCAGATGTTGGAAATAAAACCTGCGCAGGCTTCCCTGGGAGTTTTGGATACTACGACATTGATGCCCAGACCTTTGCTGACTGGGGAGTAGATCTGCTAAAATTTGATGGTTGTTACTGTGACAGTTTGGAAAATTTGGCAGATGGTTATAAGCACATGTCCTTGGCCCTGAATAGGACTGGCAGAAGCATTGTGTACTCCTGTGAGTGGCCTCTTTATATGTGGCCCTTTCAAAAGCCCAATTATACAGAAATCCGACAGTACTGCAATCACTGGCGAAATTTTGCTGACATTGATGATTCCTGGAAAAGTATAAAGAGTATCTTGGACTGGACATCTTTTAACCAGGAGAGAATTGTTGATGTTGCTGGACCAGGGGGTTGGAATGACCCAGATATGTTAGTGATTGGCAACTTTGGCCTCAGCTGGAATCAGCAAGTAACTCAGATGGCCCTCTGGGCTATCATGGCTGCTCCTTTATTCATGTCTAATGACCTCCGACACATCAGCCCTCAAGCCAAAGCTCTCCTTCAGGATAAGGACGTAATTGCCATCAATCAGGACCCCTTGGGCAAGCAAGGGTACCAGCTTAGACAGGGAGACAACTTTGAAGTGTGGGAACGACCTCTCTCAGGCTTAGCCTGGGCTGTAGCTATGATAAACCGGCAGGAGATTGGTGGACCTCGCTCTTATACCATCGCAGTTGCTTCCCTGGGTAAAGGAGTGGCCTGTAATCCTGCCTGCTTCATCACACAGCTCCTCCCTGTGAAAAGGAAGCTAGGGTTCTATGAATGGACTTCAAGGTTAAGAAGTCACATAAATCCCACAGGCACTGTTTTGCTTCAGCTAGAAAATACAATGCAGATGTCATTAAAAGACTTACTTTAA\",\n          \"ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGCTTCGCTTCCTGGCCCTCGTTTCCTGGGACATCCCTGGGGCTAGAGCACTGGACAATGGATTGGCAAGGACGCCTACCATGGGCTGGCTGCACTGGGAGCGCTTCATGTGCAACCTTGACTGCCAGGAAGAGCCAGATTCCTGCATCAGTGAGAAGCTCTTCATGGAGATGGCAGAGCTCATGGTCTCAGAAGGCTGGAAGGATGCAGGTTATGAGTACCTCTGCATTGATGACTGTTGGATGGTGCCCCAAAGAGATTCAGAAGGCAGACTTCAGGCAGACCCTCAGCGCTTTCCTCATGGGATTCGCCAGCTAGCTAATTATGTTCACAGCAAAGGACTGAAGCTAGGGATTTATGCAGATGTTGGAAATAAAACCTGCGCAGGCTTCCCTGGGAGTTTTGGATACTACGACATTGATGCCCAGACCTTTGCTGACTGGGGAGTAGATCTGCTAAAATTTGATGGTTGTTACTGTGACAGTTTGGAAAATTTGGCAGATGGTTATAAGCACATGTCCTTGGCCCTGAATAGGACTGGCAGAAGCATTGTGTACTCCTGTGAGTGGCCTCTTTATATGTGGCCCTTTCAAAAGCCCAATTATACAGAAATCCGACAGTACTGCAATCACTGGCGAAATTTTGCTGACATTGATGATTCCTGGAAAAGTATAAAGAGTATCTTGGACTGGACATCTTTTAACCAGGAGAGAATTGTTGATGTTGCTGGACCAGGGGGTTGGAATGACCCAGATATGTTAGTGATTGGCAACTTTGGCCTCAGCTGGAATCAGCAAGTAACTCAGATGGCCCTCTGGGCTATCATGGCTGCTCCTTTATTCATGTCTAATGACCTCCGACACATCAGCCCTCAAGCCAAAGCTCTCCTTCAGGATAAGGACGTAATTGCCATCAATCAGGACCCCTTGGGCAAGCAAGGGTACCAGCTTAGACAGGGAGACAACTTTGAAGTGTGGGAACGACCTCTCTCAGGCTTAGCCTGGGCTGTAGCTATGATAAACCGGCAGGAGATTGGTGGACCTCGCTCTTATACCATCGCAGTTGCTTCCCTGGGTAAAGGAGTGGCCTGTAATCCTGCCTGCTTCATCACACAGCTCCTCCCTGTGAAAAGGAAGCTAGGGTTCTATGAATGGACTTCAAGGTTAAGAAGTCACATAAATCCCACAGGCACTGTTTTGCTTCAGCTAGAAAATACAATGCAGATGTCATTAAAAGACTTACTTTAA\",\n          \"ATGCAGCTGAGGAACCCAGAACTACATCTGGGCTGCGCGCTTGCGCTTCGCTTCCTGGCCCTCGTTTCCTGGGACATCCCTGGGGCTAGAGTACTGGACAATGGATTGGCAAGGACGCCTACCATGGGCTGGCTGCACTGGGAGCGCTTCATGTGCAACCTTGACTGCCAGGAAGAGCCAGATTCCTGCATCAGTGAGAAGCTCTTCATGGAGATGGCAGAGCTCATGGTCTCAGAAGGCTGGAAGGATGCAGGTTATGAGTACCTCTGCATTGATGACTGTTGGATGGCTCCCCAAAGAGATTCAGAAGGCAGACTTCAGGCAGACCCTCAGCGCTTTCCTCATGGGATTCGCCAGCTAGCTAATTATGTTCACAGCAAAGGACTGAAGCTAGGGATTTATGCAGATGTTGGAAATAAAACCTGCGCAGGCTTCCCTGGGAGTTTTGGATACTACGACATTGATGCCCAGACCTTTGCTGACTGGGGAGTAGATCTGCTAAAATTTGATGGTTGTTACTGTGACAGTTTGGAAAATTTGGCAGATGGTTATAAGCACATGTCCTTGGCCCTGAATAGGACTGGCAGAAGCATTGTGTACTCCTGTGAGTGGCCTCTTTATATGTGGCCCTTTCAAAAGCCCAATTATACAGAAATCCGACAGTACTGCAATCACTGGCGAAATTTTGCTGACATTGATGATTCCTGGAAAAGTATAAAGAGTATCTTGGACTGGACATCTTTTAACCAGGAGAGAATTGTTGATGTTGCTGGACCAGGGGGTTGGAATGACCCAGATATGTTAGTGATTGGCAACTTTGGCCTCAGCTGGAATCAGCAAGTAACTCAGATGGCCCTCTGGGCTATCATGGCTGCTCCTTTATTCATGTCTAATGACCTCCGACACATCAGCCCTCAAGCCAAAGCTCTCCTTCAGGATAAGGACGTAATTGCCATCAATCAGGACCCCTTGGGCAAGCAAGGGTACCAGCTTAGACAGGGAGACAACTTTGAAGTGTGGGAACGACCTCTCTCAGGCTTAGCCTGGGCTGTAGCTATGATAAACCGGCAGGAGATTGGTGGACCTCGCTCTTATACCATCGCAGTTGCTTCCCTGGGTAAAGGAGTGGCCTGTAATCCTGCCTGCTTCATCACACAGCTCCTCCCTGTGAAAAGGAAGCTAGGGTTCTATGAATGGACTTCAAGGTTAAGAAGTCACATAAATCCCACAGGCACTGTTTTGCTTCAGCTAGAAAATACAATGCAGATGTCATTAAAAGACTTACTTTAA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# k-mers function\n",
        "def getKmers(sequence, size=6):\n",
        "    return [sequence[x:x+size].lower() for x in range(len(sequence) - size + 1)]\n",
        "\n",
        "# finding sequences\n",
        "sequences['words'] = sequences.apply(lambda x: getKmers(x['sequences']), axis=1)\n",
        "sequences = sequences.drop('sequences', axis=1)\n",
        "sequences.head()\n",
        "\n",
        "# getting classes\n",
        "sequence_texts = list(sequences['words'])\n",
        "for item in range(len(sequence_texts)):\n",
        "    sequence_texts[item] = ' '.join(sequence_texts[item])\n",
        "y_data = sequences.iloc[:, 0].values\n",
        "\n",
        "# vectorization of nucleotide sequences\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(ngram_range=(8,8))\n",
        "X = cv.fit_transform(sequence_texts)\n",
        "\n",
        "# splitting data\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_data,\n",
        "                                                    test_size = 0.15,\n",
        "                                                    random_state = 42)\n",
        "\n",
        "# defining classifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "classifier = MultinomialNB(alpha=.01)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# making predictions\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
        "print(\"Confusion matrix\\n\")\n",
        "print(pd.crosstab(pd.Series(y_test, name='Actual'), pd.Series(y_pred, name='Predicted')))\n",
        "def get_metrics(y_test, y_predicted):\n",
        "    accuracy = accuracy_score(y_test, y_predicted)\n",
        "    precision = precision_score(y_test, y_predicted, average='weighted')\n",
        "    recall = recall_score(y_test, y_predicted, average='weighted')\n",
        "    f1 = f1_score(y_test, y_predicted, average='weighted')\n",
        "    return accuracy, precision, recall, f1\n",
        "accuracy, precision, recall, f1 = get_metrics(y_test, y_pred)\n",
        "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))"
      ],
      "metadata": {
        "id": "KL3HuPd4aPkF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "468e66df-0cc1-4513-8d1d-707f749e5253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix\n",
            "\n",
            "Predicted   0  1   2   3\n",
            "Actual                  \n",
            "0          15  0   0   0\n",
            "1           0  8   1   0\n",
            "2           0  1  12   0\n",
            "3           0  0   0  11\n",
            "accuracy = 0.958 \n",
            "precision = 0.958 \n",
            "recall = 0.958 \n",
            "f1 = 0.958\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Confusion Matrix\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Plot Confusion Matrix\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()\n",
        "\n",
        "# Compute metrics\n",
        "def get_metrics(y_test, y_predicted):\n",
        "    accuracy = accuracy_score(y_test, y_predicted)\n",
        "    precision = precision_score(y_test, y_predicted, average='weighted')\n",
        "    recall = recall_score(y_test, y_predicted, average='weighted')\n",
        "    f1 = f1_score(y_test, y_predicted, average='weighted')\n",
        "    return accuracy, precision, recall, f1\n",
        "\n",
        "accuracy, precision, recall, f1 = get_metrics(y_test, y_pred)\n",
        "\n",
        "# Metrics Bar Plot\n",
        "metrics = ['Accuracy', 'Precision', 'Recall', 'F1 Score']\n",
        "values = [accuracy, precision, recall, f1]\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.barplot(x=metrics, y=values, palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('Model Performance Metrics')\n",
        "plt.xlabel('Metric')\n",
        "plt.ylabel('Score')\n",
        "plt.show()\n",
        "\n",
        "# Print metrics\n",
        "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LrYTRmuBT9rU",
        "outputId": "a93df6b8-7680-4304-ae48-d8a37b05ed51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-a603fa383cce>:31: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=metrics, y=values, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy = 0.958 \n",
            "precision = 0.958 \n",
            "recall = 0.958 \n",
            "f1 = 0.958\n"
          ]
        }
      ]
    }
  ]
}