Evaluation.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/anonyblank/ml-zoomcamp-hw/blob/main/Evaluation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "execution_count": 467,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3oblgTryBQyk",
        "outputId": "33d6dbc6-3a21-42cb-cfa6-6f99fcbaf619"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-10-09 15:00:14--  https://raw.githubusercontent.com/alexeygrigorev/mlbookcamp-code/master/chapter-02-car-price/data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1475504 (1.4M) [text/plain]\n",
            "Saving to: ‘data.csv.16’\n",
            "\n",
            "\rdata.csv.16           0%[                    ]       0  --.-KB/s               \rdata.csv.16         100%[===================>]   1.41M  --.-KB/s    in 0.04s   \n",
            "\n",
            "2023-10-09 15:00:14 (33.9 MB/s) - ‘data.csv.16’ saved [1475504/1475504]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget \"https://raw.githubusercontent.com/alexeygrigorev/mlbookcamp-code/master/chapter-02-car-price/data.csv\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "NY8UFeJqBklz"
      },
      "execution_count": 468,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "selected_columns = [\n",
        "  \"Make\",\n",
        "  \"Model\",\n",
        "  \"Year\",\n",
        "  \"Engine HP\",\n",
        "  \"Engine Cylinders\",\n",
        "  \"Transmission Type\",\n",
        "  \"Vehicle Style\",\n",
        "  \"highway MPG\",\n",
        "  \"city mpg\",\n",
        "  \"MSRP\"\n",
        "]"
      ],
      "metadata": {
        "id": "accMIWE_B3ut"
      },
      "execution_count": 469,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datasets = pd.read_csv('data.csv')"
      ],
      "metadata": {
        "id": "6qZCEMt5CWjl"
      },
      "execution_count": 470,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datasets = datasets[selected_columns]\n",
        "datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GDyqT6G8CiGV",
        "outputId": "80ef445b-8fee-4929-8b7a-cf651157f82a"
      },
      "execution_count": 471,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Make       Model  Year  Engine HP  Engine Cylinders  \\\n",
              "0          BMW  1 Series M  2011      335.0               6.0   \n",
              "1          BMW    1 Series  2011      300.0               6.0   \n",
              "2          BMW    1 Series  2011      300.0               6.0   \n",
              "3          BMW    1 Series  2011      230.0               6.0   \n",
              "4          BMW    1 Series  2011      230.0               6.0   \n",
              "...        ...         ...   ...        ...               ...   \n",
              "11909    Acura         ZDX  2012      300.0               6.0   \n",
              "11910    Acura         ZDX  2012      300.0               6.0   \n",
              "11911    Acura         ZDX  2012      300.0               6.0   \n",
              "11912    Acura         ZDX  2013      300.0               6.0   \n",
              "11913  Lincoln      Zephyr  2006      221.0               6.0   \n",
              "\n",
              "      Transmission Type  Vehicle Style  highway MPG  city mpg   MSRP  \n",
              "0                MANUAL          Coupe           26        19  46135  \n",
              "1                MANUAL    Convertible           28        19  40650  \n",
              "2                MANUAL          Coupe           28        20  36350  \n",
              "3                MANUAL          Coupe           28        18  29450  \n",
              "4                MANUAL    Convertible           28        18  34500  \n",
              "...                 ...            ...          ...       ...    ...  \n",
              "11909         AUTOMATIC  4dr Hatchback           23        16  46120  \n",
              "11910         AUTOMATIC  4dr Hatchback           23        16  56670  \n",
              "11911         AUTOMATIC  4dr Hatchback           23        16  50620  \n",
              "11912         AUTOMATIC  4dr Hatchback           23        16  50920  \n",
              "11913         AUTOMATIC          Sedan           26        17  28995  \n",
              "\n",
              "[11914 rows x 10 columns]"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "datasets.columns = datasets.columns.str.lower().str.replace(\" \", \"_\")"
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        "id": "qAWsN28PC0o9"
      },
      "execution_count": 472,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datasets = datasets.fillna(0)"
      ],
      "metadata": {
        "id": "Vp5tAmk-DETI"
      },
      "execution_count": 473,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datasets['above_average'] = (datasets.msrp.values >= datasets.msrp.values.mean()).astype(int)"
      ],
      "metadata": {
        "id": "xWTtbn_eDlXd"
      },
      "execution_count": 474,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "EH5VjqIXEU59",
        "outputId": "47de3da0-968a-4048-8d32-b14600583bb1"
      },
      "execution_count": 475,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          make       model  year  engine_hp  engine_cylinders  \\\n",
              "0          BMW  1 Series M  2011      335.0               6.0   \n",
              "1          BMW    1 Series  2011      300.0               6.0   \n",
              "2          BMW    1 Series  2011      300.0               6.0   \n",
              "3          BMW    1 Series  2011      230.0               6.0   \n",
              "4          BMW    1 Series  2011      230.0               6.0   \n",
              "...        ...         ...   ...        ...               ...   \n",
              "11909    Acura         ZDX  2012      300.0               6.0   \n",
              "11910    Acura         ZDX  2012      300.0               6.0   \n",
              "11911    Acura         ZDX  2012      300.0               6.0   \n",
              "11912    Acura         ZDX  2013      300.0               6.0   \n",
              "11913  Lincoln      Zephyr  2006      221.0               6.0   \n",
              "\n",
              "      transmission_type  vehicle_style  highway_mpg  city_mpg   msrp  \\\n",
              "0                MANUAL          Coupe           26        19  46135   \n",
              "1                MANUAL    Convertible           28        19  40650   \n",
              "2                MANUAL          Coupe           28        20  36350   \n",
              "3                MANUAL          Coupe           28        18  29450   \n",
              "4                MANUAL    Convertible           28        18  34500   \n",
              "...                 ...            ...          ...       ...    ...   \n",
              "11909         AUTOMATIC  4dr Hatchback           23        16  46120   \n",
              "11910         AUTOMATIC  4dr Hatchback           23        16  56670   \n",
              "11911         AUTOMATIC  4dr Hatchback           23        16  50620   \n",
              "11912         AUTOMATIC  4dr Hatchback           23        16  50920   \n",
              "11913         AUTOMATIC          Sedan           26        17  28995   \n",
              "\n",
              "       above_average  \n",
              "0                  1  \n",
              "1                  1  \n",
              "2                  0  \n",
              "3                  0  \n",
              "4                  0  \n",
              "...              ...  \n",
              "11909              1  \n",
              "11910              1  \n",
              "11911              1  \n",
              "11912              1  \n",
              "11913              0  \n",
              "\n",
              "[11914 rows x 11 columns]"
            ],
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              "      <th>make</th>\n",
              "      <th>model</th>\n",
              "      <th>year</th>\n",
              "      <th>engine_hp</th>\n",
              "      <th>engine_cylinders</th>\n",
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              "      <th>above_average</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
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              "      <td>BMW</td>\n",
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              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>40650</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>36350</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>29450</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>34500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11909</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>46120</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11910</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>56670</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11911</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>50620</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11912</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2013</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>50920</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11913</th>\n",
              "      <td>Lincoln</td>\n",
              "      <td>Zephyr</td>\n",
              "      <td>2006</td>\n",
              "      <td>221.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>26</td>\n",
              "      <td>17</td>\n",
              "      <td>28995</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11914 rows × 11 columns</p>\n",
              "</div>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c6e7df49-bb6e-4ffa-ba0c-373f13bcb31d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 475
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "full_train_datasets , test_datasets = train_test_split(datasets, test_size=0.2, random_state=1)\n",
        "train_datasets , val_datasets = train_test_split(full_train_datasets, test_size=0.25, random_state=1)"
      ],
      "metadata": {
        "id": "DaiUnWaVIvPA"
      },
      "execution_count": 476,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_train_datasets = full_train_datasets.reset_index(drop=True)\n",
        "train_datasets = train_datasets.reset_index(drop=True)\n",
        "val_datasets = val_datasets.reset_index(drop=True)\n",
        "test_datasets = test_datasets.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "RNg-gNeyJ5Cy"
      },
      "execution_count": 477,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numerical = [\n",
        "  'engine_hp',\n",
        "  'engine_cylinders',\n",
        "  'highway_mpg',\n",
        "  'city_mpg'\n",
        "]"
      ],
      "metadata": {
        "id": "Puzpodo4KxCY"
      },
      "execution_count": 478,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score"
      ],
      "metadata": {
        "id": "XqP72tacL4g3"
      },
      "execution_count": 479,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "9BRRmKi6M-b_"
      },
      "execution_count": 480,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "auc_scores = {}\n",
        "for col in numerical:\n",
        "  model = LogisticRegression()\n",
        "  model.fit(train_datasets[[col]], train_datasets['above_average'])\n",
        "  pred_labels = model.predict_proba(val_datasets[[col]])[:, 1]\n",
        "  auc = roc_auc_score(val_datasets['above_average'], pred_labels)\n",
        "  if(auc < 0.5):\n",
        "    auc_scores[col] = -auc\n",
        "    continue\n",
        "  auc_scores[col] = auc\n",
        ""
      ],
      "metadata": {
        "id": "S5flieqdMbzH"
      },
      "execution_count": 481,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the variable with the highest AUC\n",
        "highest_auc_variable = max(auc_scores, key=auc_scores.get)\n",
        "\n",
        "print(\"AUC Scores for Numerical Variables:\")\n",
        "for variable, auc in auc_scores.items():\n",
        "    print(f\"{variable}: {auc:.4f}\")\n",
        "\n",
        "print(f\"The numerical variable with the highest AUC is: {highest_auc_variable}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPt15tc-RV7H",
        "outputId": "0cd9421a-b201-4bf0-9479-b332453fd961"
      },
      "execution_count": 482,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC Scores for Numerical Variables:\n",
            "engine_hp: 0.9153\n",
            "engine_cylinders: 0.7691\n",
            "highway_mpg: 0.6286\n",
            "city_mpg: 0.6700\n",
            "The numerical variable with the highest AUC is: engine_hp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction import DictVectorizer"
      ],
      "metadata": {
        "id": "IC61OG45UMp1"
      },
      "execution_count": 483,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical = list(train_datasets.dtypes[train_datasets.dtypes == 'object'].index)\n",
        "categorical"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V8kp1J7jSeVk",
        "outputId": "8458a54e-c57c-4faa-cb0c-8de2fc6cc891"
      },
      "execution_count": 484,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['make', 'model', 'transmission_type', 'vehicle_style']"
            ]
          },
          "metadata": {},
          "execution_count": 484
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dicts = train_datasets[categorical].to_dict(orient='records')"
      ],
      "metadata": {
        "id": "SOthy5fiUpgX"
      },
      "execution_count": 485,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dv = DictVectorizer(sparse=False)\n",
        "train_features = dv.fit_transform(train_dicts)\n",
        "train_labels = train_datasets['above_average']"
      ],
      "metadata": {
        "id": "ZY6HF22sXfXb"
      },
      "execution_count": 486,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression(solver='liblinear', C=1.0, max_iter=1000)\n",
        "model.fit(train_features, train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "XkikjzN_X1fs",
        "outputId": "1dd04027-ae93-4ee8-a05e-ec7b3b8683b1"
      },
      "execution_count": 487,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000, solver='liblinear')"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 487
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_dicts = val_datasets[categorical].to_dict(orient='records')\n",
        "val_features = dv.transform(val_dicts)\n",
        "val_labels = val_datasets['above_average']\n",
        "\n",
        "val_pred_labels = model.predict_proba(val_features)[:, 1]\n",
        "\n",
        "auc = roc_auc_score(val_labels, val_pred_labels)\n",
        "\n",
        "np.round(auc, 3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEoKmH4oYbCD",
        "outputId": "14276cd9-e6e6-4792-98a6-b1637e98ec18"
      },
      "execution_count": 488,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.972"
            ]
          },
          "metadata": {},
          "execution_count": 488
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores = []\n",
        "for threshold in np.arange(0.0, 1.0, 0.01):\n",
        "  actual_positive = val_labels == 1 #1\n",
        "  actual_negative = val_labels == 0 #0\n",
        "\n",
        "  pred_positive = val_pred_labels >= threshold #1\n",
        "  pred_negative = val_pred_labels < threshold #0\n",
        "\n",
        "  tp = (actual_positive & pred_positive).sum()\n",
        "  tn = (actual_negative & pred_negative).sum()\n",
        "\n",
        "  fp = (actual_negative & pred_positive).sum()\n",
        "  fn = (actual_positive & pred_negative).sum()\n",
        "  scores.append((threshold, tp, tn, fp, fn))\n"
      ],
      "metadata": {
        "id": "UHa9PS7PZhz1"
      },
      "execution_count": 489,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p_r_datasets = pd.DataFrame(scores, columns=['threshold', 'tp', 'tn', 'fp', 'fn'])\n",
        "p_r_datasets['precision'] = p_r_datasets.tp / ( p_r_datasets.tp + p_r_datasets.fp )\n",
        "p_r_datasets['recall'] = p_r_datasets.tp / ( p_r_datasets.tp + p_r_datasets.fn )"
      ],
      "metadata": {
        "id": "AZm_TzaQcSsQ"
      },
      "execution_count": 490,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(p_r_datasets.threshold, p_r_datasets.precision, label=\"Precision\")\n",
        "plt.plot(p_r_datasets.threshold, p_r_datasets.recall, label=\"Recall\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "pMsAHUExgJta",
        "outputId": "dd944264-a9f6-4693-f622-b49639d9a6c5"
      },
      "execution_count": 491,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7e4a7f5dff70>"
            ]
          },
          "metadata": {},
          "execution_count": 491
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "# Initialize an array of thresholds from 0.0 to 1.0 with a step of 0.01\n",
        "thresholds = np.arange(0.0, 1.01, 0.01)\n",
        "\n",
        "# Initialize lists to store precision and recall values\n",
        "precision_values = []\n",
        "recall_values = []\n",
        "\n",
        "# Calculate precision and recall for each threshold\n",
        "for threshold in thresholds:\n",
        "    y_pred_thresholded = (val_pred_labels >= threshold).astype(int)\n",
        "    precision, recall, _ = precision_recall_curve(val_datasets['above_average'], y_pred_thresholded)\n",
        "    precision_values.append(precision[1])  # Precision at index 1 corresponds to positive class\n",
        "    recall_values.append(recall[1])  # Recall at index 1 corresponds to positive class\n",
        "\n",
        "# Plot precision and recall curves\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(thresholds, precision_values, label='Precision')\n",
        "plt.plot(thresholds, recall_values, label='Recall')\n",
        "plt.xlabel('Threshold')\n",
        "plt.ylabel('Precision / Recall')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "k8QohJ8iwFxS",
        "outputId": "7a6a3c61-4cb2-4878-8ca5-e7fec4ef1e27"
      },
      "execution_count": 492,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p_r_datasets['F1 Scores'] = (2 * (p_r_datasets.precision * p_r_datasets.precision)) / (p_r_datasets.precision + p_r_datasets.precision)"
      ],
      "metadata": {
        "id": "tG2Gr_NKwRHG"
      },
      "execution_count": 493,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "# Initialize an array of thresholds from 0.0 to 1.0 with a step of 0.01\n",
        "thresholds = np.arange(0.0, 1.01, 0.01)\n",
        "\n",
        "# Initialize a list to store F1 scores\n",
        "f1_scores = []\n",
        "\n",
        "# Calculate F1 score for each threshold\n",
        "for threshold in thresholds:\n",
        "    y_pred_thresholded = (val_pred_labels >= threshold).astype(int)\n",
        "    f1 = f1_score(val_datasets['above_average'], y_pred_thresholded)\n",
        "    f1_scores.append(f1)\n",
        "\n",
        "# Find the threshold at which F1 is maximal\n",
        "max_f1_threshold = thresholds[np.argmax(f1_scores)]\n",
        "max_f1_score = max(f1_scores)\n",
        "\n",
        "print(f\"The threshold at which F1 is maximal is: {max_f1_threshold:.2f}\")\n",
        "print(f\"The maximal F1 score is: {max_f1_score:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEFgcZgvyDbn",
        "outputId": "55cf2291-aaec-415f-dc1d-b87f8ad54f8b"
      },
      "execution_count": 494,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The threshold at which F1 is maximal is: 0.51\n",
            "The maximal F1 score is: 0.87\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold"
      ],
      "metadata": {
        "id": "7FDgALKrzFNw"
      },
      "execution_count": 495,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kfold = KFold(n_splits=5, shuffle=True, random_state=1)"
      ],
      "metadata": {
        "id": "Zjn5VFMk0hLy"
      },
      "execution_count": 496,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import auc\n",
        "scores = []\n",
        "for train_index , val_index in kfold.split(full_train_datasets):\n",
        "  train_data = full_train_datasets.iloc[train_index]\n",
        "  val_data = full_train_datasets.iloc[val_index]\n",
        "\n",
        "  train_dicts = train_data[categorical+numerical].to_dict(orient='records')\n",
        "  val_dicts = val_data[categorical+numerical].to_dict(orient='records')\n",
        "\n",
        "  dv = DictVectorizer(sparse=False)\n",
        "\n",
        "  train_x = dv.fit_transform(train_dicts)\n",
        "  val_x = dv.transform(val_dicts)\n",
        "\n",
        "  train_y = train_data.above_average.values\n",
        "  val_y = val_data.above_average.values\n",
        "\n",
        "  model = LogisticRegression(solver='liblinear', C=1.0, max_iter=1000)\n",
        "  model.fit(train_x, train_y)\n",
        "\n",
        "  val_pred_y = model.predict_proba(val_x)[:, 1]\n",
        "\n",
        "  auc = roc_auc_score(val_y, val_pred_y)\n",
        "\n",
        "  scores.append(auc)\n",
        "  # print('auc',)"
      ],
      "metadata": {
        "id": "4rcyMmOx0lC7"
      },
      "execution_count": 497,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.round(np.std(scores), 3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uAkLi2q7NuX",
        "outputId": "b70fdf49-dc5c-47f5-ebfe-7b5ada291359"
      },
      "execution_count": 498,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.002"
            ]
          },
          "metadata": {},
          "execution_count": 498
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "# Define the list of C values to iterate over\n",
        "C_values = [0.01, 0.1, 0.5, 10]\n",
        "\n",
        "# Initialize a dictionary to store mean and std scores for each C\n",
        "scores = {}\n",
        "\n",
        "# Initialize K-Fold cross-validation\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=1)\n",
        "\n",
        "# Iterate over different C values\n",
        "for C in C_values:\n",
        "    # Initialize lists to store AUC scores for each fold\n",
        "    auc_scores = []\n",
        "\n",
        "    # Define the model with the current C value\n",
        "    lr = LogisticRegression(solver='liblinear', C=C, max_iter=1000)\n",
        "\n",
        "    # Iterate over different folds\n",
        "    for train_idx, val_idx in kf.split(full_train_datasets):\n",
        "        train_data_fold = full_train_datasets.iloc[train_idx]\n",
        "        val_data_fold = full_train_datasets.iloc[val_idx]\n",
        "\n",
        "        # Apply one-hot-encoding using DictVectorizer\n",
        "        train_dicts = train_data_fold[categorical].to_dict(orient='records')\n",
        "        val_dicts = val_data_fold[categorical].to_dict(orient='records')\n",
        "\n",
        "        dv = DictVectorizer(sparse=False)\n",
        "        X_train_fold = dv.fit_transform(train_dicts)\n",
        "        X_val_fold = dv.transform(val_dicts)\n",
        "\n",
        "        # Train the model\n",
        "        lr.fit(X_train_fold, train_data_fold['above_average'])\n",
        "\n",
        "        # Predict probabilities on the validation set\n",
        "        val_predictions_fold = lr.predict_proba(X_val_fold)[:, 1]\n",
        "\n",
        "        # Calculate AUC on the validation dataset\n",
        "        auc_fold = roc_auc_score(val_data_fold['above_average'], val_predictions_fold)\n",
        "\n",
        "        auc_scores.append(auc_fold)\n",
        "\n",
        "    # Calculate the mean and standard deviation of AUC scores for the current C\n",
        "    mean_score = np.mean(auc_scores)\n",
        "    std_score = np.std(auc_scores)\n",
        "\n",
        "    # Store the mean and std scores in the dictionary\n",
        "    scores[C] = (mean_score, std_score)\n",
        "\n",
        "# Find the C value with the best mean score (ties broken by lowest std)\n",
        "best_C = min(scores, key=lambda k: (scores[k][0], -scores[k][1]))\n",
        "\n",
        "best_mean_score, best_std_score = scores[best_C]\n",
        "\n",
        "print(f\"The best C value is: {best_C}\")\n",
        "print(f\"The best mean score is: {best_mean_score:.3f}\")\n",
        "print(f\"The corresponding std is: {best_std_score:.3f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzp9E7A58TBi",
        "outputId": "066b36a1-83e8-4f73-e153-db8488290470"
      },
      "execution_count": 499,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best C value is: 0.01\n",
            "The best mean score is: 0.903\n",
            "The corresponding std is: 0.010\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f77J3Pzz-aHt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}