GridSearchCV(HYPER_PARMETER_TUNING).ipynb

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        "<a href=\"https://colab.research.google.com/github/Mathurshab2210/Ml_with_Mathur/blob/main/GridSearchCV(HYPER_PARMETER_TUNING).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "zkIBglrrtJcD"
      },
      "outputs": [],
      "source": [
        "from sklearn import svm, datasets\n",
        "iris = datasets.load_iris()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
        "df['flower'] = iris.target\n",
        "df['flower'] = df['flower'].apply(lambda x: iris.target_names[x])\n",
        "df[47:150]"
      ],
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        "outputId": "f3382fab-2357-49b1-b9f9-692fb8e62e9f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "47                 4.6               3.2                1.4               0.2   \n",
              "48                 5.3               3.7                1.5               0.2   \n",
              "49                 5.0               3.3                1.4               0.2   \n",
              "50                 7.0               3.2                4.7               1.4   \n",
              "51                 6.4               3.2                4.5               1.5   \n",
              "..                 ...               ...                ...               ...   \n",
              "145                6.7               3.0                5.2               2.3   \n",
              "146                6.3               2.5                5.0               1.9   \n",
              "147                6.5               3.0                5.2               2.0   \n",
              "148                6.2               3.4                5.4               2.3   \n",
              "149                5.9               3.0                5.1               1.8   \n",
              "\n",
              "         flower  \n",
              "47       setosa  \n",
              "48       setosa  \n",
              "49       setosa  \n",
              "50   versicolor  \n",
              "51   versicolor  \n",
              "..          ...  \n",
              "145   virginica  \n",
              "146   virginica  \n",
              "147   virginica  \n",
              "148   virginica  \n",
              "149   virginica  \n",
              "\n",
              "[103 rows x 5 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
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              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>5.3</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>versicolor</td>\n",
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              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>6.4</td>\n",
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              "      <td>4.5</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>103 rows × 5 columns</p>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3)\n",
        "model = svm.SVC(kernel='rbf',C=30,gamma='auto')\n",
        "model.fit(X_train,y_train)\n",
        "model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJY5BNtTte7n",
        "outputId": "903d22ea-6a0e-4ded-b0b3-fa4481954318"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9555555555555556"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "cross_val_score(svm.SVC(kernel='linear',C=10,gamma='auto'),iris.data, iris.target, cv=5)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxqK4LvYtpVw",
        "outputId": "d5c35930-63c0-463e-d7c4-fa47cb537a0b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.        , 1.        , 0.9       , 0.96666667, 1.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kernels = ['rbf', 'linear']\n",
        "C = [1,10,20]\n",
        "avg_scores = {}\n",
        "for kval in kernels:\n",
        "    for cval in C:\n",
        "        cv_scores = cross_val_score(svm.SVC(kernel=kval,C=cval,gamma='auto'),iris.data, iris.target, cv=5)\n",
        "        avg_scores[kval + '_' + str(cval)] = np.average(cv_scores)\n",
        "\n",
        "avg_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fA2V0GS5tvXD",
        "outputId": "fee6d662-93f5-49d6-902f-fb597f105d02"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rbf_1': 0.9800000000000001,\n",
              " 'rbf_10': 0.9800000000000001,\n",
              " 'rbf_20': 0.9666666666666668,\n",
              " 'linear_1': 0.9800000000000001,\n",
              " 'linear_10': 0.9733333333333334,\n",
              " 'linear_20': 0.9666666666666666}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use GridSearchCV"
      ],
      "metadata": {
        "id": "sRnEegUyuf_k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "clf = GridSearchCV(svm.SVC(gamma='auto'), {\n",
        "    'C': [1,10,20],\n",
        "    'kernel': ['rbf','linear']\n",
        "}, cv=5, return_train_score=False)\n",
        "clf.fit(iris.data, iris.target)\n",
        "clf.cv_results_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZ2MXH0buU-P",
        "outputId": "67826202-7bca-4e71-d3c2-f1b04b405413"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mean_fit_time': array([0.00308342, 0.00118937, 0.00232668, 0.00111737, 0.00123281,\n",
              "        0.00258074]),\n",
              " 'std_fit_time': array([2.47373328e-03, 1.37915007e-04, 2.12994704e-03, 6.90437482e-05,\n",
              "        4.00195084e-05, 3.09721956e-03]),\n",
              " 'mean_score_time': array([0.00284824, 0.00062885, 0.00070066, 0.00056515, 0.00063996,\n",
              "        0.00059261]),\n",
              " 'std_score_time': array([3.90022460e-03, 5.80294561e-05, 1.06444900e-04, 4.38108266e-05,\n",
              "        3.09949288e-05, 8.14993521e-05]),\n",
              " 'param_C': masked_array(data=[1, 1, 10, 10, 20, 20],\n",
              "              mask=[False, False, False, False, False, False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'param_kernel': masked_array(data=['rbf', 'linear', 'rbf', 'linear', 'rbf', 'linear'],\n",
              "              mask=[False, False, False, False, False, False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'params': [{'C': 1, 'kernel': 'rbf'},\n",
              "  {'C': 1, 'kernel': 'linear'},\n",
              "  {'C': 10, 'kernel': 'rbf'},\n",
              "  {'C': 10, 'kernel': 'linear'},\n",
              "  {'C': 20, 'kernel': 'rbf'},\n",
              "  {'C': 20, 'kernel': 'linear'}],\n",
              " 'split0_test_score': array([0.96666667, 0.96666667, 0.96666667, 1.        , 0.96666667,\n",
              "        1.        ]),\n",
              " 'split1_test_score': array([1., 1., 1., 1., 1., 1.]),\n",
              " 'split2_test_score': array([0.96666667, 0.96666667, 0.96666667, 0.9       , 0.9       ,\n",
              "        0.9       ]),\n",
              " 'split3_test_score': array([0.96666667, 0.96666667, 0.96666667, 0.96666667, 0.96666667,\n",
              "        0.93333333]),\n",
              " 'split4_test_score': array([1., 1., 1., 1., 1., 1.]),\n",
              " 'mean_test_score': array([0.98      , 0.98      , 0.98      , 0.97333333, 0.96666667,\n",
              "        0.96666667]),\n",
              " 'std_test_score': array([0.01632993, 0.01632993, 0.01632993, 0.03887301, 0.03651484,\n",
              "        0.0421637 ]),\n",
              " 'rank_test_score': array([1, 1, 1, 4, 5, 6], dtype=int32)}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(clf.cv_results_)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "oYnxq103uimU",
        "outputId": "d1c1deec-832d-49ac-cc55-ff4f72ba48a7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
              "0       0.003083      0.002474         0.002848        0.003900       1   \n",
              "1       0.001189      0.000138         0.000629        0.000058       1   \n",
              "2       0.002327      0.002130         0.000701        0.000106      10   \n",
              "3       0.001117      0.000069         0.000565        0.000044      10   \n",
              "4       0.001233      0.000040         0.000640        0.000031      20   \n",
              "5       0.002581      0.003097         0.000593        0.000081      20   \n",
              "\n",
              "  param_kernel                         params  split0_test_score  \\\n",
              "0          rbf      {'C': 1, 'kernel': 'rbf'}           0.966667   \n",
              "1       linear   {'C': 1, 'kernel': 'linear'}           0.966667   \n",
              "2          rbf     {'C': 10, 'kernel': 'rbf'}           0.966667   \n",
              "3       linear  {'C': 10, 'kernel': 'linear'}           1.000000   \n",
              "4          rbf     {'C': 20, 'kernel': 'rbf'}           0.966667   \n",
              "5       linear  {'C': 20, 'kernel': 'linear'}           1.000000   \n",
              "\n",
              "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
              "0                1.0           0.966667           0.966667                1.0   \n",
              "1                1.0           0.966667           0.966667                1.0   \n",
              "2                1.0           0.966667           0.966667                1.0   \n",
              "3                1.0           0.900000           0.966667                1.0   \n",
              "4                1.0           0.900000           0.966667                1.0   \n",
              "5                1.0           0.900000           0.933333                1.0   \n",
              "\n",
              "   mean_test_score  std_test_score  rank_test_score  \n",
              "0         0.980000        0.016330                1  \n",
              "1         0.980000        0.016330                1  \n",
              "2         0.980000        0.016330                1  \n",
              "3         0.973333        0.038873                4  \n",
              "4         0.966667        0.036515                5  \n",
              "5         0.966667        0.042164                6  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_C</th>\n",
              "      <th>param_kernel</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.003083</td>\n",
              "      <td>0.002474</td>\n",
              "      <td>0.002848</td>\n",
              "      <td>0.003900</td>\n",
              "      <td>1</td>\n",
              "      <td>rbf</td>\n",
              "      <td>{'C': 1, 'kernel': 'rbf'}</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.016330</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.001189</td>\n",
              "      <td>0.000138</td>\n",
              "      <td>0.000629</td>\n",
              "      <td>0.000058</td>\n",
              "      <td>1</td>\n",
              "      <td>linear</td>\n",
              "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.016330</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.002327</td>\n",
              "      <td>0.002130</td>\n",
              "      <td>0.000701</td>\n",
              "      <td>0.000106</td>\n",
              "      <td>10</td>\n",
              "      <td>rbf</td>\n",
              "      <td>{'C': 10, 'kernel': 'rbf'}</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.016330</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.001117</td>\n",
              "      <td>0.000069</td>\n",
              "      <td>0.000565</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>10</td>\n",
              "      <td>linear</td>\n",
              "      <td>{'C': 10, 'kernel': 'linear'}</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.038873</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.001233</td>\n",
              "      <td>0.000040</td>\n",
              "      <td>0.000640</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>20</td>\n",
              "      <td>rbf</td>\n",
              "      <td>{'C': 20, 'kernel': 'rbf'}</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.036515</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.002581</td>\n",
              "      <td>0.003097</td>\n",
              "      <td>0.000593</td>\n",
              "      <td>0.000081</td>\n",
              "      <td>20</td>\n",
              "      <td>linear</td>\n",
              "      <td>{'C': 20, 'kernel': 'linear'}</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.042164</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77d8e35f-3c7a-4eb5-b4c7-574c448a001f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-77d8e35f-3c7a-4eb5-b4c7-574c448a001f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['param_C','param_kernel','mean_test_score']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "srxAG7zIumD5",
        "outputId": "66e46b79-ec8b-4094-89ff-89de477a592e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  param_C param_kernel  mean_test_score\n",
              "0       1          rbf         0.980000\n",
              "1       1       linear         0.980000\n",
              "2      10          rbf         0.980000\n",
              "3      10       linear         0.973333\n",
              "4      20          rbf         0.966667\n",
              "5      20       linear         0.966667"
            ],
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              "      <div>\n",
              "<style scoped>\n",
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              "      <th>mean_test_score</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>1</td>\n",
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              "      <td>rbf</td>\n",
              "      <td>0.966667</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>20</td>\n",
              "      <td>linear</td>\n",
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              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2215788e-56ea-4f04-adf1-a6bc89f45fb0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "clf.best_params_\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3L_QdH8u9-r",
        "outputId": "dd310609-f509-44df-af9b-3bb97b8df501"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1, 'kernel': 'rbf'}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3j4WTdqvET6",
        "outputId": "a4c76af6-75f6-4b4f-f85c-0084673c9ab4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9800000000000001"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dir(clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHf8KSf7vIyZ",
        "outputId": "bdc478a6-829c-448f-efe5-7d14c00adaec"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__abstractmethods__',\n",
              " '__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__getstate__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__le__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__setstate__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_abc_impl',\n",
              " '_check_feature_names',\n",
              " '_check_n_features',\n",
              " '_check_refit_for_multimetric',\n",
              " '_estimator_type',\n",
              " '_format_results',\n",
              " '_get_param_names',\n",
              " '_get_tags',\n",
              " '_more_tags',\n",
              " '_pairwise',\n",
              " '_repr_html_',\n",
              " '_repr_html_inner',\n",
              " '_repr_mimebundle_',\n",
              " '_required_parameters',\n",
              " '_run_search',\n",
              " '_select_best_index',\n",
              " '_validate_data',\n",
              " 'best_estimator_',\n",
              " 'best_index_',\n",
              " 'best_params_',\n",
              " 'best_score_',\n",
              " 'classes_',\n",
              " 'cv',\n",
              " 'cv_results_',\n",
              " 'decision_function',\n",
              " 'error_score',\n",
              " 'estimator',\n",
              " 'fit',\n",
              " 'get_params',\n",
              " 'inverse_transform',\n",
              " 'multimetric_',\n",
              " 'n_features_in_',\n",
              " 'n_jobs',\n",
              " 'n_splits_',\n",
              " 'param_grid',\n",
              " 'pre_dispatch',\n",
              " 'predict',\n",
              " 'predict_log_proba',\n",
              " 'predict_proba',\n",
              " 'refit',\n",
              " 'refit_time_',\n",
              " 'return_train_score',\n",
              " 'score',\n",
              " 'score_samples',\n",
              " 'scorer_',\n",
              " 'scoring',\n",
              " 'set_params',\n",
              " 'transform',\n",
              " 'verbose']"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "rs = RandomizedSearchCV(svm.SVC(gamma='auto'), {\n",
        "        'C': [1,10,20],\n",
        "        'kernel': ['rbf','linear']\n",
        "    }, \n",
        "    cv=5, \n",
        "    return_train_score=False, \n",
        "    n_iter=5\n",
        ")\n",
        "rs.fit(iris.data, iris.target)\n",
        "pd.DataFrame(rs.cv_results_)[['param_C','param_kernel','mean_test_score']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LqyGPtHWvLOl",
        "outputId": "65618d99-5f71-44b4-9a54-719fb9099217"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  param_C param_kernel  mean_test_score\n",
              "0       1       linear         0.980000\n",
              "1       1          rbf         0.980000\n",
              "2      20          rbf         0.966667\n",
              "3      20       linear         0.966667\n",
              "4      10          rbf         0.980000"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>param_C</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>linear</td>\n",
              "      <td>0.980000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>rbf</td>\n",
              "      <td>0.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20</td>\n",
              "      <td>rbf</td>\n",
              "      <td>0.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20</td>\n",
              "      <td>linear</td>\n",
              "      <td>0.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>rbf</td>\n",
              "      <td>0.980000</td>\n",
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              "  </tbody>\n",
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              "          const element = document.querySelector('#df-3bacac3e-e774-43e6-bbe3-5ad59ce750c5');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dir(rs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEXOM6rS1JHF",
        "outputId": "c3ec4181-ed56-40d7-f836-33602dd9acfa"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__abstractmethods__',\n",
              " '__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__getstate__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__le__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__setstate__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_abc_impl',\n",
              " '_check_feature_names',\n",
              " '_check_n_features',\n",
              " '_check_refit_for_multimetric',\n",
              " '_estimator_type',\n",
              " '_format_results',\n",
              " '_get_param_names',\n",
              " '_get_tags',\n",
              " '_more_tags',\n",
              " '_pairwise',\n",
              " '_repr_html_',\n",
              " '_repr_html_inner',\n",
              " '_repr_mimebundle_',\n",
              " '_required_parameters',\n",
              " '_run_search',\n",
              " '_select_best_index',\n",
              " '_validate_data',\n",
              " 'best_estimator_',\n",
              " 'best_index_',\n",
              " 'best_params_',\n",
              " 'best_score_',\n",
              " 'classes_',\n",
              " 'cv',\n",
              " 'cv_results_',\n",
              " 'decision_function',\n",
              " 'error_score',\n",
              " 'estimator',\n",
              " 'fit',\n",
              " 'get_params',\n",
              " 'inverse_transform',\n",
              " 'multimetric_',\n",
              " 'n_features_in_',\n",
              " 'n_iter',\n",
              " 'n_jobs',\n",
              " 'n_splits_',\n",
              " 'param_distributions',\n",
              " 'pre_dispatch',\n",
              " 'predict',\n",
              " 'predict_log_proba',\n",
              " 'predict_proba',\n",
              " 'random_state',\n",
              " 'refit',\n",
              " 'refit_time_',\n",
              " 'return_train_score',\n",
              " 'score',\n",
              " 'score_samples',\n",
              " 'scorer_',\n",
              " 'scoring',\n",
              " 'set_params',\n",
              " 'transform',\n",
              " 'verbose']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model_params = {\n",
        "    'svm': {\n",
        "        'model': svm.SVC(gamma='auto'),\n",
        "        'params' : {\n",
        "            'C': [1,10,20],\n",
        "            'kernel': ['rbf','linear']\n",
        "        }  \n",
        "    },\n",
        "    'random_forest': {\n",
        "        'model': RandomForestClassifier(),\n",
        "        'params' : {\n",
        "            'n_estimators': [1,5,10]\n",
        "        }\n",
        "    },\n",
        "    'logistic_regression' : {\n",
        "        'model': LogisticRegression(solver='liblinear',multi_class='auto'),\n",
        "        'params': {\n",
        "            'C': [1,5,10]\n",
        "        }\n",
        "    }\n",
        "}\n",
        "scores = []\n",
        "\n",
        "for model_name, mp in model_params.items():\n",
        "    clf =   RandomizedSearchCV(mp['model'], mp['params'], cv=5, return_train_score=False)\n",
        "    clf.fit(iris.data, iris.target)\n",
        "    scores.append({\n",
        "        'model': model_name,\n",
        "        'best_score': clf.best_score_,\n",
        "        'best_params': clf.best_params_\n",
        "    })\n",
        "    \n",
        "df = pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Unev7ovr1Mnp",
        "outputId": "13c443e6-b489-4570-8446-ce827c66b63a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:296: UserWarning: The total space of parameters 6 is smaller than n_iter=10. Running 6 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  UserWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:296: UserWarning: The total space of parameters 3 is smaller than n_iter=10. Running 3 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  UserWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:296: UserWarning: The total space of parameters 3 is smaller than n_iter=10. Running 3 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 model  best_score                best_params\n",
              "0                  svm    0.980000  {'kernel': 'rbf', 'C': 1}\n",
              "1        random_forest    0.960000        {'n_estimators': 5}\n",
              "2  logistic_regression    0.966667                   {'C': 5}"
            ],
            "text/html": [
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>best_score</th>\n",
              "      <th>best_params</th>\n",
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              "      <th>0</th>\n",
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              "      <td>{'kernel': 'rbf', 'C': 1}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>random_forest</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>{'n_estimators': 5}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>logistic_regression</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>{'C': 5}</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4a3662de-eed5-4829-a4dc-d8e419bc7679');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
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    {
      "cell_type": "code",
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      },
      "execution_count": null,
      "outputs": []
    }
  ]
}