Support_Vector_Machines.ipynb

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      "source": [
        "#Support Vector Machines on the Wine Dataset and California Housing Dataset"
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        "## SVM on the Wine Dataset\n",
        "- load the data\n",
        "- understand the data, view it etc\n",
        "- preprocess the data for SVM Classifiers\n",
        "-  compare results with SGD, Linear Regression?\n",
        "- Conclusion\n"
      ],
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      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split"
      ],
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        "id": "UEF9s5W1CSsf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_wine\n",
        "wine = load_wine (as_frame = True)"
      ],
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        "id": "39r5QYfS8Pgq"
      },
      "execution_count": null,
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      "cell_type": "code",
      "source": [
        "print(wine.DESCR)"
      ],
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          "text": [
            ".. _wine_dataset:\n",
            "\n",
            "Wine recognition dataset\n",
            "------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 178\n",
            "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
            "    :Attribute Information:\n",
            " \t\t- Alcohol\n",
            " \t\t- Malic acid\n",
            " \t\t- Ash\n",
            "\t\t- Alcalinity of ash  \n",
            " \t\t- Magnesium\n",
            "\t\t- Total phenols\n",
            " \t\t- Flavanoids\n",
            " \t\t- Nonflavanoid phenols\n",
            " \t\t- Proanthocyanins\n",
            "\t\t- Color intensity\n",
            " \t\t- Hue\n",
            " \t\t- OD280/OD315 of diluted wines\n",
            " \t\t- Proline\n",
            "\n",
            "    - class:\n",
            "            - class_0\n",
            "            - class_1\n",
            "            - class_2\n",
            "\t\t\n",
            "    :Summary Statistics:\n",
            "    \n",
            "    ============================= ==== ===== ======= =====\n",
            "                                   Min   Max   Mean     SD\n",
            "    ============================= ==== ===== ======= =====\n",
            "    Alcohol:                      11.0  14.8    13.0   0.8\n",
            "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
            "    Ash:                          1.36  3.23    2.36  0.27\n",
            "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
            "    Magnesium:                    70.0 162.0    99.7  14.3\n",
            "    Total Phenols:                0.98  3.88    2.29  0.63\n",
            "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
            "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
            "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
            "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
            "    Hue:                          0.48  1.71    0.96  0.23\n",
            "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
            "    Proline:                       278  1680     746   315\n",
            "    ============================= ==== ===== ======= =====\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
            "    :Creator: R.A. Fisher\n",
            "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            "    :Date: July, 1988\n",
            "\n",
            "This is a copy of UCI ML Wine recognition datasets.\n",
            "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
            "\n",
            "The data is the results of a chemical analysis of wines grown in the same\n",
            "region in Italy by three different cultivators. There are thirteen different\n",
            "measurements taken for different constituents found in the three types of\n",
            "wine.\n",
            "\n",
            "Original Owners: \n",
            "\n",
            "Forina, M. et al, PARVUS - \n",
            "An Extendible Package for Data Exploration, Classification and Correlation. \n",
            "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
            "Via Brigata Salerno, 16147 Genoa, Italy.\n",
            "\n",
            "Citation:\n",
            "\n",
            "Lichman, M. (2013). UCI Machine Learning Repository\n",
            "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
            "School of Information and Computer Science. \n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "  (1) S. Aeberhard, D. Coomans and O. de Vel, \n",
            "  Comparison of Classifiers in High Dimensional Settings, \n",
            "  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n",
            "  Mathematics and Statistics, James Cook University of North Queensland. \n",
            "  (Also submitted to Technometrics). \n",
            "\n",
            "  The data was used with many others for comparing various \n",
            "  classifiers. The classes are separable, though only RDA \n",
            "  has achieved 100% correct classification. \n",
            "  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
            "  (All results using the leave-one-out technique) \n",
            "\n",
            "  (2) S. Aeberhard, D. Coomans and O. de Vel, \n",
            "  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
            "  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
            "  Mathematics and Statistics, James Cook University of North Queensland. \n",
            "  (Also submitted to Journal of Chemometrics).\n",
            "\n"
          ]
        }
      ]
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      "source": [
        "len(wine.data), set(wine.target), wine.target_names"
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          "data": {
            "text/plain": [
              "(178, {0, 1, 2}, array(['class_0', 'class_1', 'class_2'], dtype='<U7'))"
            ]
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    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, random_state=42)"
      ],
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        "X_train.head()"
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              "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
              "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
              "100    12.08        2.08  1.70               17.5       97.0           2.23   \n",
              "122    12.42        4.43  2.73               26.5      102.0           2.20   \n",
              "154    12.58        1.29  2.10               20.0      103.0           1.48   \n",
              "51     13.83        1.65  2.60               17.2       94.0           2.45   \n",
              "\n",
              "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
              "2          3.24                  0.30             2.81             5.68  1.03   \n",
              "100        2.17                  0.26             1.40             3.30  1.27   \n",
              "122        2.13                  0.43             1.71             2.08  0.92   \n",
              "154        0.58                  0.53             1.40             7.60  0.58   \n",
              "51         2.99                  0.22             2.29             5.60  1.24   \n",
              "\n",
              "     od280/od315_of_diluted_wines  proline  \n",
              "2                            3.17   1185.0  \n",
              "100                          2.96    710.0  \n",
              "122                          3.12    365.0  \n",
              "154                          1.55    640.0  \n",
              "51                           3.37   1265.0  "
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</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-3485d44f-2574-42c5-8886-877f55188745 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 133,\n  \"fields\": [\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8299932196040789,\n        \"min\": 11.03,\n        \"max\": 14.83,\n        \"num_unique_values\": 103,\n        \"samples\": [\n          14.23,\n          14.38,\n          13.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"malic_acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0989046670683609,\n        \"min\": 0.89,\n        \"max\": 5.8,\n        \"num_unique_values\": 106,\n        \"samples\": [\n          2.67,\n          1.73,\n          1.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ash\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2806063195736037,\n        \"min\": 1.36,\n        \"max\": 3.23,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          2.6,\n          2.35,\n          2.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcalinity_of_ash\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.467312451899561,\n        \"min\": 10.6,\n        \"max\": 30.0,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          18.6,\n          16.0,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"magnesium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.999570890801495,\n        \"min\": 70.0,\n        \"max\": 162.0,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          80.0,\n          84.0,\n          119.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_phenols\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6456964550412675,\n        \"min\": 0.98,\n        \"max\": 3.88,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          3.4,\n          2.8,\n          2.86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flavanoids\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0055369721771192,\n        \"min\": 0.34,\n        \"max\": 5.08,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          3.06,\n          2.63,\n          2.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nonflavanoid_phenols\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12692276038648054,\n        \"min\": 0.13,\n        \"max\": 0.66,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          0.56,\n          0.4,\n          0.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"proanthocyanins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5769640789654897,\n        \"min\": 0.42,\n        \"max\": 3.58,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          0.55,\n          2.81,\n          1.82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color_intensity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.202515830912026,\n        \"min\": 1.74,\n        \"max\": 10.8,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          3.94,\n          3.85,\n          2.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23454460755329853,\n        \"min\": 0.48,\n        \"max\": 1.71,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          1.24,\n          1.0,\n          1.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"od280/od315_of_diluted_wines\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7299605634843737,\n        \"min\": 1.27,\n        \"max\": 4.0,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          3.63,\n          2.0,\n          3.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"proline\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 306.8675930104206,\n        \"min\": 278.0,\n        \"max\": 1547.0,\n        \"num_unique_values\": 101,\n        \"samples\": [\n          1060.0,\n          590.0,\n          278.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.head()"
      ],
      "metadata": {
        "id": "NDQnFyzO8PTP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea1d5794-29e3-4343-cb9b-38c335cd1dd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2      0\n",
              "100    1\n",
              "122    1\n",
              "154    2\n",
              "51     0\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lin_clf = make_pipeline(StandardScaler(),\n",
        "                        LinearSVC(max_iter=1_000_0000,random_state=42))\n",
        "lin_clf.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "id": "vpefZa3y8PQ3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "ad9c292c-a5dd-4298-9f5d-b05c8b6e15ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('linearsvc', LinearSVC(max_iter=10000000, random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;, LinearSVC(max_iter=10000000, random_state=42))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;, LinearSVC(max_iter=10000000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(max_iter=10000000, random_state=42)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lin_clf[1].n_iter_"
      ],
      "metadata": {
        "id": "PGzFJKA68POe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "972e548e-1c26-4602-b9d6-77bb46ee2ff6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "195"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_score(lin_clf, X_train, y_train).mean()"
      ],
      "metadata": {
        "id": "i0t5pR3u8PMH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "y_pred = lin_clf.predict(X_test)\n",
        "mean_squared_error(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XvwWzBZIgXz",
        "outputId": "17154237-64bf-4d8a-d313-abaaaa55aeda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.022222222222222223"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Kernelized SVM"
      ],
      "metadata": {
        "id": "W33pOugZIrSs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Using Kernelized SVM\n",
        "svm_clf = make_pipeline(StandardScaler(), SVC(random_state=42))\n",
        "cross_val_score(svm_clf, X_train, y_train).mean()"
      ],
      "metadata": {
        "id": "2IdMfU6m8PJv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbadc105-3e56-4c5c-fa58-9b2b5d24bd27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9698005698005698"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#hyper parameter tuning using randomsearch\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from scipy.stats import loguniform, uniform\n",
        "\n",
        "param_distrib = {\n",
        "    \"svc__gamma\": loguniform(0.001, 0.1),\n",
        "    \"svc__C\" : uniform(1, 10)\n",
        "}\n",
        "rnd_search = RandomizedSearchCV(svm_clf, param_distrib, random_state=42, cv=5, n_iter=100) #give kernel for RBF AND poly\n",
        "rnd_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "5U88xyVy8PHP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "outputId": "16e85cb5-4623-4434-8463-f9e6d61bff51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5,\n",
              "                   estimator=Pipeline(steps=[('standardscaler',\n",
              "                                              StandardScaler()),\n",
              "                                             ('svc', SVC(random_state=42))]),\n",
              "                   n_iter=100,\n",
              "                   param_distributions={'svc__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f108268c0>,\n",
              "                                        'svc__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f10826d70>},\n",
              "                   random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 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-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 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-10 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-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 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-10 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-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 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-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 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-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 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-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
              "                   estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,\n",
              "                                              StandardScaler()),\n",
              "                                             (&#x27;svc&#x27;, SVC(random_state=42))]),\n",
              "                   n_iter=100,\n",
              "                   param_distributions={&#x27;svc__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f108268c0&gt;,\n",
              "                                        &#x27;svc__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f10826d70&gt;},\n",
              "                   random_state=42)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5,\n",
              "                   estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,\n",
              "                                              StandardScaler()),\n",
              "                                             (&#x27;svc&#x27;, SVC(random_state=42))]),\n",
              "                   n_iter=100,\n",
              "                   param_distributions={&#x27;svc__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f108268c0&gt;,\n",
              "                                        &#x27;svc__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f10826d70&gt;},\n",
              "                   random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rnd_search.best_params_, rnd_search.best_score_"
      ],
      "metadata": {
        "id": "IUIgSxwa8PFA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fad0bc0-9cc2-49b4-a396-fd73bc259ba4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'svc__C': 9.925589984899778, 'svc__gamma': 0.011986281799901188},\n",
              " 0.9925925925925926)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rnd_search.score(X_test, y_test)"
      ],
      "metadata": {
        "id": "TeverArR8PCl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e553bad3-8ecf-4add-fff6-76fc4f73a383"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9777777777777777"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SVM on California housing dataset"
      ],
      "metadata": {
        "id": "4tnnk_iY8O_9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.svm import LinearSVR, SVR #doing regression lol\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "eYGfEhTlMDD3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "housing = fetch_california_housing()\n",
        "# df = pd.read_csv(\"/content/sample_data/california_housing_train.csv\")\n",
        "# X_train, y_train = df.drop(\"median_house_value\", axis=1), df[\"median_house_value\"]\n",
        "# if fetch california housing does not work"
      ],
      "metadata": {
        "id": "ITWEz5wq8O9O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = housing.data\n",
        "y = housing.target"
      ],
      "metadata": {
        "id": "3lX5gfuyLyRX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2)\n",
        "lin_svr = make_pipeline(StandardScaler(), LinearSVR(random_state=42, max_iter=5_000))\n",
        "lin_svr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "id": "YN29_KJ5Mawn",
        "outputId": "aa279b1c-f1b4-430e-d309-2d9008a1c507"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('linearsvr', LinearSVR(max_iter=5000, random_state=42))])"
            ],
            "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>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvr&#x27;, LinearSVR(max_iter=5000, random_state=42))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvr&#x27;, LinearSVR(max_iter=5000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVR</label><div class=\"sk-toggleable__content\"><pre>LinearSVR(max_iter=5000, random_state=42)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lin_svr[1].n_iter_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAOz65atOx6i",
        "outputId": "fcdfb87a-2f42-4491-c18d-ec4ab96d5cf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2519"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "y_pred = lin_svr.predict(X_train)\n",
        "mean_squared_error(y_train, y_pred), np.sqrt(mean_squared_error(y_train, y_pred))\n",
        "\n",
        "# $98_000 error lol"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63DQs6AxPZcK",
        "outputId": "9695dcf7-5648-4ef7-af19-de8b83887701"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9595484665813285, 0.979565447829459)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from scipy.stats import loguniform, uniform_direction\n",
        "\n",
        "svm_reg = make_pipeline(StandardScaler(), SVR())\n",
        "\n",
        "param_distrib = {\n",
        "    \"svr__gamma\": loguniform(0.001, 0.1),\n",
        "    \"svr__C\" : uniform(1, 10)\n",
        "}\n",
        "rnd_search_cv = RandomizedSearchCV(svm_reg, param_distrib, random_state=42, n_iter=100, cv=3)\n",
        "rnd_search_cv.fit(X_train[:2000], y_train[:2000]) # svm is O(m^2*n) i guess approx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "id": "xZYv7JoOPZYq",
        "outputId": "e2fd19c8-bd87-48bf-d7d6-bd6f78ee1bde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=3,\n",
              "                   estimator=Pipeline(steps=[('standardscaler',\n",
              "                                              StandardScaler()),\n",
              "                                             ('svr', SVR())]),\n",
              "                   n_iter=100,\n",
              "                   param_distributions={'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f107a2200>,\n",
              "                                        'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f107a0730>},\n",
              "                   random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3,\n",
              "                   estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,\n",
              "                                              StandardScaler()),\n",
              "                                             (&#x27;svr&#x27;, SVR())]),\n",
              "                   n_iter=100,\n",
              "                   param_distributions={&#x27;svr__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f107a2200&gt;,\n",
              "                                        &#x27;svr__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f107a0730&gt;},\n",
              "                   random_state=42)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3,\n",
              "                   estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,\n",
              "                                              StandardScaler()),\n",
              "                                             (&#x27;svr&#x27;, SVR())]),\n",
              "                   n_iter=100,\n",
              "                   param_distributions={&#x27;svr__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f107a2200&gt;,\n",
              "                                        &#x27;svr__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x781f107a0730&gt;},\n",
              "                   random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()), (&#x27;svr&#x27;, SVR())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rnd_search_cv.best_params_, rnd_search_cv.best_score_, rnd_search_cv.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TD3POzcPZWB",
        "outputId": "5a2210e4-5508-49b8-eb8f-2d7652b51a35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'svr__C': 4.63629602379294, 'svr__gamma': 0.08781408196485979},\n",
              " 0.7572774474774365,\n",
              " Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                 ('svr', SVR(C=4.63629602379294, gamma=0.08781408196485979))]))"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "-cross_val_score(rnd_search_cv.best_estimator_, X_train, y_train,\n",
        "                 cv=3, scoring=\"neg_mean_squared_error\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-PjLa9QPZS6",
        "outputId": "5c23404f-8c23-49d8-bcf1-835ea3105966"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.34992382, 0.32241241, 0.34923475])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rnd_search_cv.best_estimator_.predict(X_test)\n",
        "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
        "rmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0YhZfUNPZQS",
        "outputId": "999920fa-eb5f-4219-ce10-c2b8086302aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5854732265172238"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    }
  ]
}