[ML]GridSearch_RandomSearch.ipynb

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        "# GridSearch, RandomSearch 비교하기\n",
        "[자료 출처](https://towardsdatascience.com/python-implementation-of-grid-search-and-random-search-for-hyperparameter-optimization-2d6a82ebf75c)\n",
        "\n",
        "- dataset : heard_disesase (다양한 feature에 대한 binary classification)"
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            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "5   57    1   0       140   192    0  ...      0      0.4      1   0     1       1\n",
              "6   56    0   1       140   294    0  ...      0      1.3      1   0     2       1\n",
              "7   44    1   1       120   263    0  ...      0      0.0      2   0     3       1\n",
              "8   52    1   2       172   199    1  ...      0      0.5      2   0     3       1\n",
              "9   57    1   2       150   168    0  ...      0      1.6      2   0     2       1\n",
              "\n",
              "[10 rows x 14 columns]"
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      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df=pd.read_csv('./heart_disease.csv')\n",
        "df.head(10)"
      ]
    },
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        "X = df.drop(columns='target')\n",
        "y = df['target']\n",
        "X.head()"
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        "id": "bZHthzOY8Anm",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8aea21a8-ad92-42ab-b11d-f1b1dd68ca6a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8aea21a8-ad92-42ab-b11d-f1b1dd68ca6a');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  thalach  exang  oldpeak  slope  ca  thal\n",
              "0   63    1   3       145   233    1  ...      150      0      2.3      0   0     1\n",
              "1   37    1   2       130   250    0  ...      187      0      3.5      0   0     2\n",
              "2   41    0   1       130   204    0  ...      172      0      1.4      2   0     2\n",
              "3   56    1   1       120   236    0  ...      178      0      0.8      2   0     2\n",
              "4   57    0   0       120   354    0  ...      163      1      0.6      2   0     2\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kYGm79hf9dYA",
        "outputId": "f8127b77-5ebb-4e62-9089-10f25f129eb4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# 10% 의 testset으로 split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, shuffle=True, random_state=0)\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# decision tree에 학습\n",
        "tree_clf = DecisionTreeClassifier(random_state =42 )\n",
        "\n",
        "tree_clf.fit(X_train,y_train)\n",
        "y_pred_train = tree_clf.predict(X_train) #yhat\n",
        "y_pred = tree_clf.predict(X_test) #yhat\n",
        "y_true = y_test\n",
        "\n",
        "# accuracy score module 활용\n",
        "from sklearn.metrics import accuracy_score \n",
        "acc_train = accuracy_score(y_train, y_pred_train)\n",
        "acc_test = accuracy_score(y_true, y_pred)\n",
        "print(f'Train accuracy : {acc_train}')\n",
        "print(f'Test accuracy : {acc_test}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cX-cF8IQ9hpm",
        "outputId": "c5342481-a751-4496-e828-6cbec3c906f1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train accuracy : 1.0\n",
            "Test accuracy : 0.7096774193548387\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cf_matrix = confusion_matrix(y_true, y_pred)\n",
        "print(\"\\nTest confusion_matrix\")\n",
        "\n",
        "sns.heatmap(cf_matrix,annot=True,cmap='Blues')\n",
        "\n",
        "plt.xlabel(\"Predicted\", fontsize = 12)\n",
        "plt.ylabel(\"True\", fontsize = 12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "klgcYVFcBR6l",
        "outputId": "41de8026-5bf6-4b07-8d6f-8dea23387f28"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test confusion_matrix\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'True')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##📌 GridSearch\n",
        "- python에서 `scikit-learn`의 `GridSerachCV` 모듈를 통해 구현 가능\n",
        "### parameters\n",
        "- estimator : 측정에 이용되는 model\n",
        "- param_grid : gridsearch에 사용되는 search space\n",
        "- scoring : 성능 측정 metric. 'accuracy', 'roc_auc', 'r2', 'neg_mean_squared_error' 등\n",
        "(이외의 scoring parameter는 [여기](https://scikit-learn.org/stable/modules/model_evaluation.html) 서 확인\n",
        "- n_jobs : gridsearch 시, multicore processor일 경우 병렬처리할 횟수\n",
        "- cv : cross-validation 시 fold 수\n",
        "\n",
        "\n",
        "### ✒gs = GridSearchCV(~)인스턴스 선언 시\n",
        "\n",
        "- gs.best_params_ : 가장 좋은 parameter\n",
        "\n",
        "- gs.best_score_ : 그 점수\n",
        "\n",
        "- gs.best_estimator_ : 가장 좋은 모델\n"
      ],
      "metadata": {
        "id": "QIwNaxp9HO0J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "# hyperparameter space 정의함\n",
        "param_space = { 'max_depth' : [2,3,4,6,8,10,12,15,20],\n",
        "          'min_samples_leaf' :[1,2,4,6,8,10,20,30],\n",
        "          'min_samples_split':[1,2,3,4,5,6,8,10]\n",
        "          }\n",
        "\n",
        "# GridSearchCV 모듈 이용\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs = GridSearchCV(tree_clf, param_grid=param_space,scoring='accuracy', n_jobs=-1, cv=10, return_train_score = True)\n",
        "\n",
        "# model 대신 gs 객체에 fit 함\n",
        "gs.fit(X_train, y_train)\n",
        "print(\"Optimal hyperparameter combination:\", gs.best_params_)\n",
        "\n",
        "print(\"Mean cross-validated training accuracy score:\",\n",
        "      gs.best_score_)\n",
        "gs.best_estimator_.fit(X_train, y_train)\n",
        "y_pred = gs.best_estimator_.predict(X_test) # Predictions\n",
        "y_true = y_test # True values\n",
        "\n",
        "print(\"Test accuracy:\", np.round(accuracy_score(y_true, y_pred), 2))\n",
        "cf_matrix = confusion_matrix(y_true, y_pred)\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.heatmap(cf_matrix, annot=True, cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "\n",
        "end = time.time()\n",
        "diff = end - start\n",
        "print('Execution time of Grid Search (in Seconds):', diff)\n",
        "print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Sai31zJrEdhe",
        "outputId": "30539cab-7bed-48c8-fbf7-4065bbfec3f9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "720 fits failed out of a total of 5760.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "720 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/tree/_classes.py\", line 942, in fit\n",
            "    X_idx_sorted=X_idx_sorted,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/tree/_classes.py\", line 254, in fit\n",
            "    % self.min_samples_split\n",
            "ValueError: min_samples_split must be an integer greater than 1 or a float in (0.0, 1.0]; got the integer 1\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [       nan 0.74986772 0.74986772 0.74986772 0.74986772 0.74986772\n",
            " 0.74986772 0.74986772        nan 0.74986772 0.74986772 0.74986772\n",
            " 0.74986772 0.74986772 0.74986772 0.74986772        nan 0.74986772\n",
            " 0.74986772 0.74986772 0.74986772 0.74986772 0.74986772 0.74986772\n",
            "        nan 0.74986772 0.74986772 0.74986772 0.74986772 0.74986772\n",
            " 0.74986772 0.74986772        nan 0.74986772 0.74986772 0.74986772\n",
            " 0.74986772 0.74986772 0.74986772 0.74986772        nan 0.74986772\n",
            " 0.74986772 0.74986772 0.74986772 0.74986772 0.74986772 0.74986772\n",
            "        nan 0.75357143 0.75357143 0.75357143 0.75357143 0.75357143\n",
            " 0.75357143 0.75357143        nan 0.75357143 0.75357143 0.75357143\n",
            " 0.75357143 0.75357143 0.75357143 0.75357143        nan 0.74669312\n",
            " 0.74669312 0.74669312 0.74669312 0.74669312 0.74669312 0.74669312\n",
            "        nan 0.74669312 0.74669312 0.74669312 0.74669312 0.74669312\n",
            " 0.74669312 0.74669312        nan 0.74669312 0.74669312 0.74669312\n",
            " 0.74669312 0.74669312 0.74669312 0.74669312        nan 0.74669312\n",
            " 0.74669312 0.74669312 0.74669312 0.74669312 0.74669312 0.74669312\n",
            "        nan 0.74298942 0.74298942 0.74298942 0.74298942 0.74298942\n",
            " 0.74298942 0.74298942        nan 0.74298942 0.74298942 0.74298942\n",
            " 0.74298942 0.74298942 0.74298942 0.74298942        nan 0.75436508\n",
            " 0.75436508 0.75436508 0.75436508 0.75436508 0.75436508 0.75436508\n",
            "        nan 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857\n",
            " 0.74642857 0.74642857        nan 0.73558201 0.73558201 0.73558201\n",
            " 0.73558201 0.73558201 0.73558201 0.73558201        nan 0.73187831\n",
            " 0.73187831 0.73187831 0.73187831 0.73187831 0.73187831 0.73187831\n",
            "        nan 0.76124339 0.76124339 0.76124339 0.76124339 0.76124339\n",
            " 0.76124339 0.76124339        nan 0.77208995 0.77208995 0.77208995\n",
            " 0.77208995 0.77208995 0.77208995 0.77208995        nan 0.75727513\n",
            " 0.75727513 0.75727513 0.75727513 0.75727513 0.75727513 0.75727513\n",
            "        nan 0.74246032 0.74246032 0.74246032 0.74246032 0.74246032\n",
            " 0.74246032 0.74246032        nan 0.75436508 0.75436508 0.75436508\n",
            " 0.75436508 0.75436508 0.75436508 0.75436508        nan 0.74642857\n",
            " 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857\n",
            "        nan 0.73888889 0.74259259 0.74246032 0.73518519 0.73161376\n",
            " 0.73187831 0.73941799        nan 0.73902116 0.73902116 0.73902116\n",
            " 0.72420635 0.72063492 0.7207672  0.72830688        nan 0.74656085\n",
            " 0.74656085 0.74656085 0.74656085 0.74656085 0.74656085 0.75383598\n",
            "        nan 0.7723545  0.7723545  0.7723545  0.7723545  0.7723545\n",
            " 0.7723545  0.7723545         nan 0.74642857 0.74642857 0.74642857\n",
            " 0.74642857 0.74642857 0.74642857 0.74642857        nan 0.73531746\n",
            " 0.73531746 0.73531746 0.73531746 0.73531746 0.73531746 0.73531746\n",
            "        nan 0.75436508 0.75436508 0.75436508 0.75436508 0.75436508\n",
            " 0.75436508 0.75436508        nan 0.74642857 0.74642857 0.74642857\n",
            " 0.74642857 0.74642857 0.74642857 0.74642857        nan 0.76468254\n",
            " 0.77222222 0.76111111 0.75753968 0.75013228 0.7468254  0.7505291\n",
            "        nan 0.72804233 0.72804233 0.72804233 0.73174603 0.7244709\n",
            " 0.72103175 0.73214286        nan 0.76507937 0.76507937 0.76507937\n",
            " 0.76507937 0.76507937 0.76507937 0.76865079        nan 0.7723545\n",
            " 0.7723545  0.7723545  0.7723545  0.7723545  0.7723545  0.7723545\n",
            "        nan 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857\n",
            " 0.74642857 0.74642857        nan 0.73531746 0.73531746 0.73531746\n",
            " 0.73531746 0.73531746 0.73531746 0.73531746        nan 0.75436508\n",
            " 0.75436508 0.75436508 0.75436508 0.75436508 0.75436508 0.75436508\n",
            "        nan 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857\n",
            " 0.74642857 0.74642857        nan 0.76851852 0.76851852 0.75740741\n",
            " 0.75013228 0.74642857 0.73941799 0.73941799        nan 0.74285714\n",
            " 0.74285714 0.74285714 0.72433862 0.7207672  0.71732804 0.72843915\n",
            "        nan 0.76507937 0.76507937 0.76507937 0.76507937 0.76507937\n",
            " 0.76507937 0.76865079        nan 0.7723545  0.7723545  0.7723545\n",
            " 0.7723545  0.7723545  0.7723545  0.7723545         nan 0.74642857\n",
            " 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857\n",
            "        nan 0.73531746 0.73531746 0.73531746 0.73531746 0.73531746\n",
            " 0.73531746 0.73531746        nan 0.75436508 0.75436508 0.75436508\n",
            " 0.75436508 0.75436508 0.75436508 0.75436508        nan 0.74642857\n",
            " 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857\n",
            "        nan 0.76851852 0.76851852 0.75740741 0.75013228 0.74642857\n",
            " 0.73941799 0.73941799        nan 0.74285714 0.74285714 0.74285714\n",
            " 0.72433862 0.7207672  0.71732804 0.72843915        nan 0.76507937\n",
            " 0.76507937 0.76507937 0.76507937 0.76507937 0.76507937 0.76865079\n",
            "        nan 0.7723545  0.7723545  0.7723545  0.7723545  0.7723545\n",
            " 0.7723545  0.7723545         nan 0.74642857 0.74642857 0.74642857\n",
            " 0.74642857 0.74642857 0.74642857 0.74642857        nan 0.73531746\n",
            " 0.73531746 0.73531746 0.73531746 0.73531746 0.73531746 0.73531746\n",
            "        nan 0.75436508 0.75436508 0.75436508 0.75436508 0.75436508\n",
            " 0.75436508 0.75436508        nan 0.74642857 0.74642857 0.74642857\n",
            " 0.74642857 0.74642857 0.74642857 0.74642857        nan 0.76851852\n",
            " 0.76851852 0.75740741 0.75013228 0.74642857 0.73941799 0.73941799\n",
            "        nan 0.74285714 0.74285714 0.74285714 0.72433862 0.7207672\n",
            " 0.71732804 0.72843915        nan 0.76507937 0.76507937 0.76507937\n",
            " 0.76507937 0.76507937 0.76507937 0.76865079        nan 0.7723545\n",
            " 0.7723545  0.7723545  0.7723545  0.7723545  0.7723545  0.7723545\n",
            "        nan 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857\n",
            " 0.74642857 0.74642857        nan 0.73531746 0.73531746 0.73531746\n",
            " 0.73531746 0.73531746 0.73531746 0.73531746        nan 0.75436508\n",
            " 0.75436508 0.75436508 0.75436508 0.75436508 0.75436508 0.75436508\n",
            "        nan 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857\n",
            " 0.74642857 0.74642857        nan 0.76851852 0.76851852 0.75740741\n",
            " 0.75013228 0.74642857 0.73941799 0.73941799        nan 0.74285714\n",
            " 0.74285714 0.74285714 0.72433862 0.7207672  0.71732804 0.72843915\n",
            "        nan 0.76507937 0.76507937 0.76507937 0.76507937 0.76507937\n",
            " 0.76507937 0.76865079        nan 0.7723545  0.7723545  0.7723545\n",
            " 0.7723545  0.7723545  0.7723545  0.7723545         nan 0.74642857\n",
            " 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857\n",
            "        nan 0.73531746 0.73531746 0.73531746 0.73531746 0.73531746\n",
            " 0.73531746 0.73531746        nan 0.75436508 0.75436508 0.75436508\n",
            " 0.75436508 0.75436508 0.75436508 0.75436508        nan 0.74642857\n",
            " 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857 0.74642857]\n",
            "  category=UserWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the train scores are non-finite: [       nan 0.78431081 0.78431081 0.78431081 0.78431081 0.78431081\n",
            " 0.78431081 0.78431081        nan 0.78431081 0.78431081 0.78431081\n",
            " 0.78431081 0.78431081 0.78431081 0.78431081        nan 0.78431081\n",
            " 0.78431081 0.78431081 0.78431081 0.78431081 0.78431081 0.78431081\n",
            "        nan 0.78431081 0.78431081 0.78431081 0.78431081 0.78431081\n",
            " 0.78431081 0.78431081        nan 0.78431081 0.78431081 0.78431081\n",
            " 0.78431081 0.78431081 0.78431081 0.78431081        nan 0.78431081\n",
            " 0.78431081 0.78431081 0.78431081 0.78431081 0.78431081 0.78431081\n",
            "        nan 0.78267815 0.78267815 0.78267815 0.78267815 0.78267815\n",
            " 0.78267815 0.78267815        nan 0.78267815 0.78267815 0.78267815\n",
            " 0.78267815 0.78267815 0.78267815 0.78267815        nan 0.85458013\n",
            " 0.85458013 0.85458013 0.85458013 0.85458013 0.85458013 0.85458013\n",
            "        nan 0.85376213 0.85376213 0.85376213 0.85376213 0.85376213\n",
            " 0.85376213 0.85376213        nan 0.85376213 0.85376213 0.85376213\n",
            " 0.85376213 0.85376213 0.85376213 0.85376213        nan 0.85417029\n",
            " 0.85417029 0.85417029 0.85417029 0.85417029 0.85417029 0.85417029\n",
            "        nan 0.85335396 0.85335396 0.85335396 0.85335396 0.85335396\n",
            " 0.85335396 0.85335396        nan 0.85335396 0.85335396 0.85335396\n",
            " 0.85335396 0.85335396 0.85335396 0.85335396        nan 0.83374707\n",
            " 0.83374707 0.83374707 0.83374707 0.83374707 0.83374707 0.83374707\n",
            "        nan 0.78308799 0.78308799 0.78308799 0.78308799 0.78308799\n",
            " 0.78308799 0.78308799        nan 0.88807126 0.88807126 0.8876631\n",
            " 0.88725326 0.88725326 0.88643694 0.88643694        nan 0.88480428\n",
            " 0.88480428 0.88480428 0.88480428 0.88480428 0.88398796 0.88398796\n",
            "        nan 0.87132653 0.87132653 0.87132653 0.87132653 0.87132653\n",
            " 0.87132653 0.87132653        nan 0.86519739 0.86519739 0.86519739\n",
            " 0.86519739 0.86519739 0.86519739 0.86519739        nan 0.86315657\n",
            " 0.86315657 0.86315657 0.86315657 0.86315657 0.86315657 0.86315657\n",
            "        nan 0.85866511 0.85866511 0.85866511 0.85866511 0.85866511\n",
            " 0.85866511 0.85866511        nan 0.83374707 0.83374707 0.83374707\n",
            " 0.83374707 0.83374707 0.83374707 0.83374707        nan 0.78308799\n",
            " 0.78308799 0.78308799 0.78308799 0.78308799 0.78308799 0.78308799\n",
            "        nan 0.96364838 0.9611994  0.9558916  0.95221144 0.94690197\n",
            " 0.93463868 0.92482101        nan 0.94036467 0.94036467 0.94036467\n",
            " 0.93465038 0.92729675 0.91993643 0.9088993         nan 0.90686015\n",
            " 0.90686015 0.90686015 0.90686015 0.90686015 0.90686015 0.90072934\n",
            "        nan 0.88357812 0.88357812 0.88357812 0.88357812 0.88357812\n",
            " 0.88357812 0.88357812        nan 0.87295417 0.87295417 0.87295417\n",
            " 0.87295417 0.87295417 0.87295417 0.87295417        nan 0.86274674\n",
            " 0.86274674 0.86274674 0.86274674 0.86274674 0.86274674 0.86274674\n",
            "        nan 0.83374707 0.83374707 0.83374707 0.83374707 0.83374707\n",
            " 0.83374707 0.83374707        nan 0.78308799 0.78308799 0.78308799\n",
            " 0.78308799 0.78308799 0.78308799 0.78308799        nan 0.99428237\n",
            " 0.98489127 0.97713282 0.97059552 0.96037973 0.94198561 0.92890264\n",
            "        nan 0.95548009 0.95548009 0.95548009 0.94894948 0.9366912\n",
            " 0.92769823 0.91421211        nan 0.91094513 0.91094513 0.91094513\n",
            " 0.91094513 0.91094513 0.91094513 0.90318167        nan 0.88357812\n",
            " 0.88357812 0.88357812 0.88357812 0.88357812 0.88357812 0.88357812\n",
            "        nan 0.87295417 0.87295417 0.87295417 0.87295417 0.87295417\n",
            " 0.87295417 0.87295417        nan 0.86274674 0.86274674 0.86274674\n",
            " 0.86274674 0.86274674 0.86274674 0.86274674        nan 0.83374707\n",
            " 0.83374707 0.83374707 0.83374707 0.83374707 0.83374707 0.83374707\n",
            "        nan 0.78308799 0.78308799 0.78308799 0.78308799 0.78308799\n",
            " 0.78308799 0.78308799        nan 0.99918367 0.98938274 0.98080796\n",
            " 0.97345266 0.96201238 0.94239378 0.92890264        nan 0.95629642\n",
            " 0.95629642 0.95629642 0.94976581 0.93709936 0.92810639 0.91421211\n",
            "        nan 0.91094513 0.91094513 0.91094513 0.91094513 0.91094513\n",
            " 0.91094513 0.90318167        nan 0.88357812 0.88357812 0.88357812\n",
            " 0.88357812 0.88357812 0.88357812 0.88357812        nan 0.87295417\n",
            " 0.87295417 0.87295417 0.87295417 0.87295417 0.87295417 0.87295417\n",
            "        nan 0.86274674 0.86274674 0.86274674 0.86274674 0.86274674\n",
            " 0.86274674 0.86274674        nan 0.83374707 0.83374707 0.83374707\n",
            " 0.83374707 0.83374707 0.83374707 0.83374707        nan 0.78308799\n",
            " 0.78308799 0.78308799 0.78308799 0.78308799 0.78308799 0.78308799\n",
            "        nan 1.         0.98938274 0.98080796 0.97345266 0.96201238\n",
            " 0.94239378 0.92890264        nan 0.95629642 0.95629642 0.95629642\n",
            " 0.94976581 0.93709936 0.92810639 0.91421211        nan 0.91094513\n",
            " 0.91094513 0.91094513 0.91094513 0.91094513 0.91094513 0.90318167\n",
            "        nan 0.88357812 0.88357812 0.88357812 0.88357812 0.88357812\n",
            " 0.88357812 0.88357812        nan 0.87295417 0.87295417 0.87295417\n",
            " 0.87295417 0.87295417 0.87295417 0.87295417        nan 0.86274674\n",
            " 0.86274674 0.86274674 0.86274674 0.86274674 0.86274674 0.86274674\n",
            "        nan 0.83374707 0.83374707 0.83374707 0.83374707 0.83374707\n",
            " 0.83374707 0.83374707        nan 0.78308799 0.78308799 0.78308799\n",
            " 0.78308799 0.78308799 0.78308799 0.78308799        nan 1.\n",
            " 0.98938274 0.98080796 0.97345266 0.96201238 0.94239378 0.92890264\n",
            "        nan 0.95629642 0.95629642 0.95629642 0.94976581 0.93709936\n",
            " 0.92810639 0.91421211        nan 0.91094513 0.91094513 0.91094513\n",
            " 0.91094513 0.91094513 0.91094513 0.90318167        nan 0.88357812\n",
            " 0.88357812 0.88357812 0.88357812 0.88357812 0.88357812 0.88357812\n",
            "        nan 0.87295417 0.87295417 0.87295417 0.87295417 0.87295417\n",
            " 0.87295417 0.87295417        nan 0.86274674 0.86274674 0.86274674\n",
            " 0.86274674 0.86274674 0.86274674 0.86274674        nan 0.83374707\n",
            " 0.83374707 0.83374707 0.83374707 0.83374707 0.83374707 0.83374707\n",
            "        nan 0.78308799 0.78308799 0.78308799 0.78308799 0.78308799\n",
            " 0.78308799 0.78308799        nan 1.         0.98938274 0.98080796\n",
            " 0.97345266 0.96201238 0.94239378 0.92890264        nan 0.95629642\n",
            " 0.95629642 0.95629642 0.94976581 0.93709936 0.92810639 0.91421211\n",
            "        nan 0.91094513 0.91094513 0.91094513 0.91094513 0.91094513\n",
            " 0.91094513 0.90318167        nan 0.88357812 0.88357812 0.88357812\n",
            " 0.88357812 0.88357812 0.88357812 0.88357812        nan 0.87295417\n",
            " 0.87295417 0.87295417 0.87295417 0.87295417 0.87295417 0.87295417\n",
            "        nan 0.86274674 0.86274674 0.86274674 0.86274674 0.86274674\n",
            " 0.86274674 0.86274674        nan 0.83374707 0.83374707 0.83374707\n",
            " 0.83374707 0.83374707 0.83374707 0.83374707        nan 0.78308799\n",
            " 0.78308799 0.78308799 0.78308799 0.78308799 0.78308799 0.78308799]\n",
            "  category=UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal hyperparameter combination: {'max_depth': 6, 'min_samples_leaf': 6, 'min_samples_split': 2}\n",
            "\n",
            "Mean cross-validated training accuracy score: 0.7723544973544973\n",
            "Test accuracy: 0.87\n",
            "Execution time of Grid Search (in Seconds): 27.01742196083069\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##📌 RandomSearch\n",
        "- python에서 `scikit-learn`의 `GridSerachCV` 모듈를 통해 구현 가능\n",
        "### parameters(gridsearch와 겹치는 paramter 생략)\n",
        "- _n_iter_ : random하게 골라지는 hyperpareter 조합의 수를 특정합니다. 값이 10인 경우, size가 10인 hyperparameter 조합을 고려합니다. \n",
        "- _random_state_ : random 변수 생성 시 seed 값\n",
        "\n"
      ],
      "metadata": {
        "id": "9ZSN5PciOFdJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "start = time.time()\n",
        "\n",
        "# Defining 3-dimensional hyperparameter space as a Python dictionary\n",
        "hyperparameter_space = {'max_depth':[2,3,4,6,8,10,12,15,20], \n",
        "                        'min_samples_leaf':[1,2,4,6,8,10,20,30],\n",
        "                        'min_samples_split':[1,2,3,4,5,6,8,10]}\n",
        "\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "rs = RandomizedSearchCV(tree_clf, param_distributions=param_space,\n",
        "                        n_iter=10, scoring=\"accuracy\", random_state=0,\n",
        "                        n_jobs=-1, cv=10, return_train_score=True)\n",
        "\n",
        "rs.fit(X_train, y_train)\n",
        "print(\"Optimal hyperparameter combination:\", rs.best_params_)\n",
        "print()\n",
        "print(\"Mean cross-validated training accuracy score:\",\n",
        "      rs.best_score_)\n",
        "rs.best_estimator_.fit(X_train, y_train)\n",
        "y_pred = rs.best_estimator_.predict(X_test) # Predictions\n",
        "y_true = y_test # True values\n",
        "\n",
        "print(\"Test accuracy:\", np.round(accuracy_score(y_true, y_pred), 2))\n",
        "cf_matrix = confusion_matrix(y_true, y_pred)\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.heatmap(cf_matrix, annot=True, cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "\n",
        "end = time.time()\n",
        "diff = end - start\n",
        "print('Execution time of Random Search (in Seconds):', diff)\n",
        "print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "1MUXxBvtMoDP",
        "outputId": "ad1b1da5-2496-4539-b111-566de478496f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal hyperparameter combination: {'min_samples_split': 8, 'min_samples_leaf': 6, 'max_depth': 12}\n",
            "\n",
            "Mean cross-validated training accuracy score: 0.7723544973544973\n",
            "Test accuracy: 0.87\n",
            "Execution time of Random Search (in Seconds): 1.595146894454956\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "20 fits failed out of a total of 100.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "20 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/tree/_classes.py\", line 942, in fit\n",
            "    X_idx_sorted=X_idx_sorted,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/tree/_classes.py\", line 254, in fit\n",
            "    % self.min_samples_split\n",
            "ValueError: min_samples_split must be an integer greater than 1 or a float in (0.0, 1.0]; got the integer 1\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.7723545         nan        nan 0.74642857 0.7723545  0.76507937\n",
            " 0.75436508 0.73531746 0.75436508 0.74642857]\n",
            "  category=UserWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the train scores are non-finite: [0.88357812        nan        nan 0.78308799 0.88357812 0.91094513\n",
            " 0.83374707 0.86274674 0.83374707 0.87295417]\n",
            "  category=UserWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> ### 🧐정리\n",
        "- RandomSearch가 시간을 훨씬 더 단축시킴에도 비슷한 결과를 준다. Randomsearch를 우선적으로 사용하자.\n"
      ],
      "metadata": {
        "id": "uajCqxuSPGho"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ybuKsjotPzyf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}