models.ipynb

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    "colab": {
      "name": "models.ipynb",
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      "collapsed_sections": [
        "vk8Vm-xulsKP",
        "KQTMeBvclsKQ",
        "BKYSbBb6lsKQ",
        "dbAM76h4lsKR",
        "VOsebf1AlsKR"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0W5ioaCclsKM"
      },
      "source": [
        "# Medical Exam Recommendation Based on Symptoms\n",
        "\n",
        "Grid Search with:\n",
        "- Decision Tree Classifier\n",
        "- Random Forest Classifier\n",
        "- KNN\n",
        "- Nayve Bayes\n",
        "- SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjNFLUVklsKO"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "\n",
        "from sklearn import ensemble\n",
        "from sklearn import multioutput\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "from sklearn import naive_bayes\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLojRnhiluh_",
        "outputId": "7206ec75-da93-41be-aa18-a339c10cbc28"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_v9LhqVlwIL"
      },
      "source": [
        "import sys\n",
        "sys.path.insert(0, '/content/drive/My Drive/LEI')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Rdd96GglyxS"
      },
      "source": [
        "!unzip -q '/content/drive/My Drive/LEI/Data'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vk8Vm-xulsKP"
      },
      "source": [
        "#### Load Symptom Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JFvUAsflsKP"
      },
      "source": [
        "data = pd.read_csv('./Data/final.csv', sep = ';')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wHLwEaNZ21hw",
        "outputId": "ba145313-7172-4b3e-d4b4-b09430e9d0bf"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Blood Tests</th>\n",
              "      <th>Skin biopsy</th>\n",
              "      <th>Patch test</th>\n",
              "      <th>Complete blood count (CBC)</th>\n",
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              "      <th>Enlarged Thyroid</th>\n",
              "      <th>Brittle Nails</th>\n",
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              "      <th>Abnormal Menstruation</th>\n",
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              "      <th>Hip Joint Pain</th>\n",
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              "      <th>Red Sore Around Nose</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4680 rows × 233 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Blood Tests  Skin biopsy  ...  Red Sore Around Nose  Yellow  Crust Ooze\n",
              "0               1            0  ...                     0                   0\n",
              "1               1            0  ...                     0                   0\n",
              "2               1            0  ...                     0                   0\n",
              "3               1            0  ...                     0                   0\n",
              "4               1            0  ...                     0                   0\n",
              "...           ...          ...  ...                   ...                 ...\n",
              "4675            0            0  ...                     0                   0\n",
              "4676            1            0  ...                     0                   0\n",
              "4677            1            0  ...                     0                   0\n",
              "4678            0            1  ...                     0                   0\n",
              "4679            0            0  ...                     1                   1\n",
              "\n",
              "[4680 rows x 233 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQTMeBvclsKQ"
      },
      "source": [
        "####  Load Symptom Severity "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yV-tlSAflsKQ"
      },
      "source": [
        "severity = pd.read_csv('./Data/Symptom-severity.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BKYSbBb6lsKQ"
      },
      "source": [
        "####  Load Exam Mapping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVCz_Ci_lsKR"
      },
      "source": [
        "mapping = pd.read_csv('./Data/map.csv', sep = ';')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dbAM76h4lsKR"
      },
      "source": [
        "# Split data for train and test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0Z23nfXlsKR"
      },
      "source": [
        "symptomCount = 131\n",
        "testCount = 102\n",
        "\n",
        "[train,test] = train_test_split(data,random_state=420)\n",
        "trainA = np.array(train)\n",
        "trainAX = trainA[:,testCount:]\n",
        "trainAY = trainA[:,:testCount]\n",
        "\n",
        "trainX=train.iloc[:,testCount:]\n",
        "trainY=train.iloc[:,:testCount]\n",
        "\n",
        "testX=test.iloc[:,testCount:]\n",
        "testY=test.iloc[:,:testCount]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VOsebf1AlsKR"
      },
      "source": [
        "# Grid Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWiaNZ_-lsKS"
      },
      "source": [
        "def realAccuracies(predictions, labels):\n",
        "    thresh =(predictions.max()+ predictions.min())/2\n",
        "    predictions[predictions>thresh]=1\n",
        "    predictions[predictions<=thresh]=0\n",
        "    \n",
        "    correctByLine=[]\n",
        "    for i in range(len(testY)):\n",
        "        matches = predictions[i]==labels[i]\n",
        "        correctByLine.append(len(matches[matches==True])/len(matches))\n",
        "        \n",
        "    return([len(np.nonzero([x for x in correctByLine if x==1])[0]),\n",
        "            len(np.nonzero([x for x in correctByLine if x>0.99])[0]),\n",
        "            len(np.nonzero([x for x in correctByLine if x>0.95])[0]),\n",
        "            len(np.nonzero([x for x in correctByLine if x>0.90])[0]),\n",
        "            len(np.nonzero([x for x in correctByLine if x!=1])[0])])\n",
        "\n",
        "\n",
        "def getRealAcc(clf):\n",
        "    predicted = clf.predict(testX)\n",
        "    accs =  realAccuracies(predicted, testY.to_numpy())\n",
        "    print('')\n",
        "    print(accs[3],\" classified 90% correctly\")\n",
        "    print(accs[2],\" classified 95% correctly\")\n",
        "    print(accs[1],\" classified 99% correctly\")\n",
        "    print(accs[0],\" classified 100% correctly\")\n",
        "    print(accs[4],\" classified incorrectly (Not 100%)\")\n",
        "    return predicted"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJx30a0HlsKS"
      },
      "source": [
        "def gridSearch(model, param_grid, X_train, y_train, X_test, y_test):        \n",
        "    clf = GridSearchCV(model, param_grid, refit=True, verbose=3)\n",
        "    clf.fit(X_train,y_train)\n",
        "    print(clf.best_params_)\n",
        "    predictions = clf.predict(testX)\n",
        "    predicted = getRealAcc(clf)\n",
        "    return ;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crVFDibtlsKT"
      },
      "source": [
        "# Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AuiA5Lj4lsKU",
        "outputId": "aa3d6ea0-1d1c-4161-d3c4-506c3b539ea6"
      },
      "source": [
        "grid_params_randomForest = {\n",
        "    'n_estimators' : [10,20,30,50,100,200,1000],\n",
        "    'max_depth' : [1, 10, 20, None],\n",
        "    'bootstrap': [True, False],\n",
        "}\n",
        "\n",
        "gridSearch(RandomForestClassifier(), grid_params_randomForest, trainX, trainY, testX, testY)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 56 candidates, totalling 280 fits\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=10 ....................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  bootstrap=True, max_depth=1, n_estimators=10, score=0.027, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=10 ....................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  bootstrap=True, max_depth=1, n_estimators=10, score=0.021, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=10 ....................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  bootstrap=True, max_depth=1, n_estimators=10, score=0.017, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=10 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=10, score=0.028, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=10 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=10, score=0.078, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=20 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=20, score=0.027, total=   0.5s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=20 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=20, score=0.021, total=   0.5s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=20 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=20, score=0.017, total=   0.5s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=20 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=20, score=0.028, total=   0.5s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=20 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=20, score=0.037, total=   0.5s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=30 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=30, score=0.027, total=   0.7s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=30 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=30, score=0.021, total=   0.7s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=30 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=30, score=0.017, total=   0.7s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=30 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=30, score=0.028, total=   0.7s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=30 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=30, score=0.037, total=   0.7s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=50 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=50, score=0.027, total=   1.2s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=50 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=50, score=0.021, total=   1.2s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=50 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=50, score=0.017, total=   1.2s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=50 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=50, score=0.028, total=   1.2s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=50 ....................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=50, score=0.037, total=   1.2s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=100 ...................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=100, score=0.027, total=   2.3s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=100 ...................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=100, score=0.021, total=   2.4s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=100 ...................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=100, score=0.017, total=   2.3s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=100 ...................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=100, score=0.028, total=   2.3s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=100 ...................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=100, score=0.037, total=   2.3s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=200 ...................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=200, score=0.027, total=   4.6s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=200 ...................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=200, score=0.021, total=   4.6s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=200 ...................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=200, score=0.017, total=   4.7s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=200 ...................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=200, score=0.028, total=   4.6s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=200 ...................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=200, score=0.037, total=   4.6s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=1000 ..................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=1000, score=0.027, total=  23.0s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=1000 ..................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=1000, score=0.021, total=  22.9s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=1000 ..................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=1000, score=0.017, total=  23.0s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=1000 ..................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=1000, score=0.028, total=  22.8s\n",
            "[CV] bootstrap=True, max_depth=1, n_estimators=1000 ..................\n",
            "[CV]  bootstrap=True, max_depth=1, n_estimators=1000, score=0.037, total=  23.0s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=10 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=10, score=0.526, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=10 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=10, score=0.573, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=10 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=10, score=0.557, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=10 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=10, score=0.526, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=10 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=10, score=0.600, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=20 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=20, score=0.591, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=20 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=20, score=0.575, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=20 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=20, score=0.567, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=20 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=20, score=0.523, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=20 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=20, score=0.620, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=30 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=30, score=0.573, total=   0.8s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=30 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=30, score=0.594, total=   0.8s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=30 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=30, score=0.585, total=   0.8s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=30 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=30, score=0.536, total=   0.8s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=30 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=30, score=0.607, total=   0.8s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=50 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=50, score=0.581, total=   1.4s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=50 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=50, score=0.608, total=   1.4s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=50 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=50, score=0.568, total=   1.4s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=50 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=50, score=0.564, total=   1.4s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=50 ...................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=50, score=0.614, total=   1.4s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=100 ..................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=100, score=0.635, total=   2.7s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=100 ..................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=100, score=0.551, total=   2.7s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=100 ..................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=100, score=0.587, total=   2.7s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=100 ..................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=100, score=0.564, total=   2.7s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=100 ..................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=100, score=0.597, total=   2.7s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=200 ..................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=200, score=0.604, total=   5.3s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=200 ..................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=200, score=0.611, total=   5.4s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=200 ..................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=200, score=0.618, total=   5.4s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=200 ..................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=200, score=0.551, total=   5.4s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=200 ..................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=200, score=0.597, total=   5.4s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=1000 .................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=1000, score=0.571, total=  26.7s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=1000 .................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=1000, score=0.605, total=  26.4s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=1000 .................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=1000, score=0.597, total=  26.5s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=1000 .................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=1000, score=0.548, total=  26.7s\n",
            "[CV] bootstrap=True, max_depth=10, n_estimators=1000 .................\n",
            "[CV]  bootstrap=True, max_depth=10, n_estimators=1000, score=0.590, total=  27.0s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=10 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=10, score=0.974, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=10 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=10, score=0.934, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=10 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=10, score=0.917, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=10 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=10, score=0.987, total=   0.4s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=10 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=10, score=0.986, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=20 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=20, score=0.974, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=20 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=20, score=0.983, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=20 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=20, score=0.959, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=20 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=20, score=0.983, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=20 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=20, score=0.999, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=30 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=30, score=0.984, total=   0.9s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=30 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=30, score=0.989, total=   0.9s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=30 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=30, score=0.989, total=   0.9s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=30 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=30, score=0.936, total=   0.9s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=30 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=30, score=0.967, total=   0.9s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=50 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=50, score=0.981, total=   1.5s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=50 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=50, score=0.977, total=   1.5s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=50 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=50, score=0.984, total=   1.5s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=50 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=50, score=0.979, total=   1.5s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=50 ...................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=50, score=0.993, total=   1.5s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=100 ..................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=100, score=0.956, total=   3.0s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=100 ..................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=100, score=0.987, total=   3.0s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=100 ..................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=100, score=0.993, total=   2.9s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=100 ..................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=100, score=0.986, total=   2.9s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=100 ..................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=100, score=0.994, total=   2.9s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=200 ..................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=200, score=0.979, total=   5.8s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=200 ..................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=200, score=0.990, total=   5.8s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=200 ..................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=200, score=0.994, total=   5.8s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=200 ..................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=200, score=0.989, total=   5.8s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=200 ..................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=200, score=0.996, total=   5.8s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=1000 .................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=1000, score=0.987, total=  28.8s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=1000 .................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=1000, score=1.000, total=  28.6s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=1000 .................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=1000, score=0.990, total=  28.8s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=1000 .................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=1000, score=0.989, total=  28.6s\n",
            "[CV] bootstrap=True, max_depth=20, n_estimators=1000 .................\n",
            "[CV]  bootstrap=True, max_depth=20, n_estimators=1000, score=1.000, total=  28.8s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=10 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=10, score=0.994, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=10 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=10, score=1.000, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=10 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=10, score=1.000, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=10 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=10, score=1.000, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=10 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=10, score=1.000, total=   0.3s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=20 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=20, score=1.000, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=20 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=20, score=1.000, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=20 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=20, score=1.000, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=20 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=20, score=1.000, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=20 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=20, score=1.000, total=   0.6s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=30 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=30, score=1.000, total=   0.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=30 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=30, score=1.000, total=   0.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=30 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=30, score=1.000, total=   0.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=30 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=30, score=1.000, total=   0.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=30 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=30, score=1.000, total=   0.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=50 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=50, score=1.000, total=   1.5s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=50 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=50, score=1.000, total=   1.5s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=50 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=50, score=1.000, total=   1.5s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=50 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=50, score=1.000, total=   1.5s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=50 .................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=50, score=1.000, total=   1.5s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=100 ................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=100, score=1.000, total=   3.0s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=100 ................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=100, score=1.000, total=   3.0s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=100 ................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=100, score=1.000, total=   3.0s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=100 ................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=100, score=1.000, total=   2.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=100 ................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=100, score=1.000, total=   2.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=200 ................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=200, score=1.000, total=   5.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=200 ................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=200, score=1.000, total=   5.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=200 ................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=200, score=1.000, total=   5.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=200 ................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=200, score=1.000, total=   5.9s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=200 ................\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=200, score=1.000, total=   6.0s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=1000 ...............\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=1000, score=1.000, total=  29.4s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=1000 ...............\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=1000, score=1.000, total=  29.5s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=1000 ...............\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=1000, score=1.000, total=  29.4s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=1000 ...............\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=1000, score=1.000, total=  29.2s\n",
            "[CV] bootstrap=True, max_depth=None, n_estimators=1000 ...............\n",
            "[CV]  bootstrap=True, max_depth=None, n_estimators=1000, score=1.000, total=  29.2s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=10 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=10, score=0.027, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=10 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=10, score=0.021, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=10 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=10, score=0.017, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=10 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=10, score=0.028, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=10 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=10, score=0.037, total=   0.2s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=20 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=20, score=0.027, total=   0.5s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=20 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=20, score=0.021, total=   0.5s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=20 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=20, score=0.017, total=   0.5s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=20 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=20, score=0.028, total=   0.5s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=20 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=20, score=0.037, total=   0.5s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=30 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=30, score=0.027, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=30 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=30, score=0.021, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=30 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=30, score=0.017, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=30 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=30, score=0.028, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=30 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=30, score=0.037, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=50 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=50, score=0.027, total=   1.2s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=50 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=50, score=0.021, total=   1.2s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=50 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=50, score=0.017, total=   1.2s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=50 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=50, score=0.028, total=   1.2s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=50 ...................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=50, score=0.037, total=   1.2s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=100 ..................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=100, score=0.027, total=   2.3s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=100 ..................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=100, score=0.021, total=   2.3s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=100 ..................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=100, score=0.017, total=   2.3s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=100 ..................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=100, score=0.028, total=   2.3s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=100 ..................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=100, score=0.037, total=   2.3s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=200 ..................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=200, score=0.027, total=   4.5s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=200 ..................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=200, score=0.021, total=   4.6s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=200 ..................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=200, score=0.017, total=   4.5s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=200 ..................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=200, score=0.028, total=   4.5s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=200 ..................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=200, score=0.037, total=   4.5s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=1000 .................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=1000, score=0.027, total=  22.5s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=1000 .................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=1000, score=0.021, total=  22.4s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=1000 .................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=1000, score=0.017, total=  22.6s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=1000 .................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=1000, score=0.028, total=  22.4s\n",
            "[CV] bootstrap=False, max_depth=1, n_estimators=1000 .................\n",
            "[CV]  bootstrap=False, max_depth=1, n_estimators=1000, score=0.037, total=  22.6s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=10 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=10, score=0.556, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=10 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=10, score=0.563, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=10 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=10, score=0.608, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=10 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=10, score=0.538, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=10 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=10, score=0.590, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=20 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=20, score=0.516, total=   0.6s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=20 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=20, score=0.580, total=   0.6s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=20 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=20, score=0.620, total=   0.6s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=20 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=20, score=0.506, total=   0.6s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=20 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=20, score=0.563, total=   0.6s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=30 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=30, score=0.608, total=   0.9s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=30 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=30, score=0.580, total=   0.9s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=30 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=30, score=0.524, total=   0.9s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=30 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=30, score=0.546, total=   0.9s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=30 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=30, score=0.593, total=   0.9s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=50 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=50, score=0.632, total=   1.5s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=50 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=50, score=0.566, total=   1.5s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=50 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=50, score=0.568, total=   1.4s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=50 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=50, score=0.551, total=   1.4s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=50 ..................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=50, score=0.650, total=   1.4s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=100 .................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=100, score=0.593, total=   2.9s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=100 .................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=100, score=0.605, total=   2.9s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=100 .................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=100, score=0.605, total=   2.9s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=100 .................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=100, score=0.534, total=   2.8s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=100 .................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=100, score=0.587, total=   2.9s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=200 .................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=200, score=0.567, total=   5.8s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=200 .................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=200, score=0.563, total=   5.7s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=200 .................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=200, score=0.623, total=   5.8s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=200 .................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=200, score=0.516, total=   5.8s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=200 .................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=200, score=0.613, total=   5.7s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=1000 ................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=1000, score=0.556, total=  28.5s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=1000 ................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=1000, score=0.573, total=  28.3s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=1000 ................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=1000, score=0.587, total=  28.4s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=1000 ................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=1000, score=0.548, total=  28.2s\n",
            "[CV] bootstrap=False, max_depth=10, n_estimators=1000 ................\n",
            "[CV]  bootstrap=False, max_depth=10, n_estimators=1000, score=0.593, total=  28.3s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=10 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=10, score=0.989, total=   0.4s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=10 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=10, score=0.989, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=10 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=10, score=0.960, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=10 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=10, score=0.947, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=10 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=10, score=0.949, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=20 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=20, score=0.981, total=   0.6s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=20 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=20, score=0.990, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=20 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=20, score=0.944, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=20 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=20, score=0.980, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=20 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=20, score=0.967, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=30 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=30, score=0.973, total=   1.0s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=30 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=30, score=0.981, total=   1.0s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=30 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=30, score=0.989, total=   1.0s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=30 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=30, score=0.966, total=   1.0s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=30 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=30, score=0.974, total=   1.0s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=50 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=50, score=0.960, total=   1.6s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=50 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=50, score=0.997, total=   1.6s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=50 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=50, score=0.987, total=   1.6s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=50 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=50, score=0.989, total=   1.6s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=50 ..................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=50, score=0.990, total=   1.6s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=100 .................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=100, score=0.991, total=   3.2s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=100 .................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=100, score=0.997, total=   3.1s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=100 .................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=100, score=0.976, total=   3.2s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=100 .................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=100, score=0.979, total=   3.2s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=100 .................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=100, score=0.991, total=   3.1s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=200 .................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=200, score=0.986, total=   6.3s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=200 .................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=200, score=0.997, total=   6.3s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=200 .................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=200, score=0.986, total=   6.3s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=200 .................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=200, score=0.994, total=   6.3s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=200 .................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=200, score=0.994, total=   6.3s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=1000 ................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=1000, score=0.989, total=  31.4s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=1000 ................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=1000, score=0.999, total=  31.5s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=1000 ................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=1000, score=0.989, total=  31.5s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=1000 ................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=1000, score=0.989, total=  31.4s\n",
            "[CV] bootstrap=False, max_depth=20, n_estimators=1000 ................\n",
            "[CV]  bootstrap=False, max_depth=20, n_estimators=1000, score=0.997, total=  31.4s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=10 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=10, score=1.000, total=   0.3s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=10 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=10, score=1.000, total=   0.4s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=10 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=10, score=1.000, total=   0.4s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=10 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=10, score=1.000, total=   0.4s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=10 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=10, score=1.000, total=   0.4s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=20 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=20, score=1.000, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=20 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=20, score=1.000, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=20 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=20, score=1.000, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=20 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=20, score=1.000, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=20 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=20, score=1.000, total=   0.7s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=30 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=30, score=1.000, total=   1.0s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=30 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=30, score=1.000, total=   1.0s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=30 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=30, score=1.000, total=   1.0s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=30 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=30, score=1.000, total=   1.0s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=30 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=30, score=1.000, total=   1.0s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=50 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=50, score=1.000, total=   1.7s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=50 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=50, score=1.000, total=   1.7s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=50 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=50, score=1.000, total=   1.7s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=50 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=50, score=1.000, total=   1.7s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=50 ................\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=50, score=1.000, total=   1.7s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=100 ...............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=100, score=1.000, total=   3.3s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=100 ...............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=100, score=1.000, total=   3.3s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=100 ...............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=100, score=1.000, total=   3.3s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=100 ...............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=100, score=1.000, total=   3.2s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=100 ...............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=100, score=1.000, total=   3.3s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=200 ...............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=200, score=1.000, total=   6.5s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=200 ...............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=200, score=1.000, total=   6.5s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=200 ...............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=200, score=1.000, total=   6.5s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=200 ...............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=200, score=1.000, total=   6.5s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=200 ...............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=200, score=1.000, total=   6.5s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=1000 ..............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=1000, score=1.000, total=  32.5s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=1000 ..............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=1000, score=1.000, total=  32.5s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=1000 ..............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=1000, score=1.000, total=  32.6s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=1000 ..............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=1000, score=1.000, total=  32.6s\n",
            "[CV] bootstrap=False, max_depth=None, n_estimators=1000 ..............\n",
            "[CV]  bootstrap=False, max_depth=None, n_estimators=1000, score=1.000, total=  32.5s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 280 out of 280 | elapsed: 26.3min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'bootstrap': True, 'max_depth': None, 'n_estimators': 20}\n",
            "\n",
            "1170  classified 90% correctly\n",
            "1170  classified 95% correctly\n",
            "1170  classified 99% correctly\n",
            "1170  classified 100% correctly\n",
            "0  classified incorrectly (Not 100%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e99k5g5QlsKU"
      },
      "source": [
        "# Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvZU4eEUlsKV",
        "outputId": "d0a75a76-8144-4edd-9a67-b13d40926c90"
      },
      "source": [
        "grid_params_decisionTree = {\n",
        "    'criterion' : ['gini', 'entropy'],\n",
        "    'splitter' : ['best', 'random'],\n",
        "    'max_depth' : [1, 10, 20, None],\n",
        "}\n",
        "\n",
        "gridSearch(DecisionTreeClassifier(), grid_params_decisionTree, trainX, trainY, testX, testY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
            "[CV] criterion=gini, max_depth=1, splitter=best ......................\n",
            "[CV]  criterion=gini, max_depth=1, splitter=best, score=0.060, total=   0.0s\n",
            "[CV] criterion=gini, max_depth=1, splitter=best ......................\n",
            "[CV]  criterion=gini, max_depth=1, splitter=best, score=0.037, total=   0.0s\n",
            "[CV] criterion=gini, max_depth=1, splitter=best ......................\n",
            "[CV]  criterion=gini, max_depth=1, splitter=best, score=0.044, total=   0.0s\n",
            "[CV] criterion=gini, max_depth=1, splitter=best ......................\n",
            "[CV]  criterion=gini, max_depth=1, splitter=best, score=0.053, total=   0.0s\n",
            "[CV] criterion=gini, max_depth=1, splitter=best ......................\n",
            "[CV]  criterion=gini, max_depth=1, splitter=best, score=0.074, total=   0.0s\n",
            "[CV] criterion=gini, max_depth=1, splitter=random ....................\n",
            "[CV]  criterion=gini, max_depth=1, splitter=random, score=0.060, total=   0.0s\n",
            "[CV] criterion=gini, max_depth=1, splitter=random ....................\n",
            "[CV]  criterion=gini, max_depth=1, splitter=random, score=0.037, total=   0.0s\n",
            "[CV] criterion=gini, max_depth=1, splitter=random ....................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  criterion=gini, max_depth=1, splitter=random, score=0.044, total=   0.0s\n",
            "[CV] criterion=gini, max_depth=1, splitter=random ....................\n",
            "[CV]  criterion=gini, max_depth=1, splitter=random, score=0.053, total=   0.0s\n",
            "[CV] criterion=gini, max_depth=1, splitter=random ....................\n",
            "[CV]  criterion=gini, max_depth=1, splitter=random, score=0.074, total=   0.0s\n",
            "[CV] criterion=gini, max_depth=10, splitter=best .....................\n",
            "[CV]  criterion=gini, max_depth=10, splitter=best, score=0.509, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=10, splitter=best .....................\n",
            "[CV]  criterion=gini, max_depth=10, splitter=best, score=0.620, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=10, splitter=best .....................\n",
            "[CV]  criterion=gini, max_depth=10, splitter=best, score=0.623, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=10, splitter=best .....................\n",
            "[CV]  criterion=gini, max_depth=10, splitter=best, score=0.566, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=10, splitter=best .....................\n",
            "[CV]  criterion=gini, max_depth=10, splitter=best, score=0.611, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=10, splitter=random ...................\n",
            "[CV]  criterion=gini, max_depth=10, splitter=random, score=0.509, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=10, splitter=random ...................\n",
            "[CV]  criterion=gini, max_depth=10, splitter=random, score=0.620, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=10, splitter=random ...................\n",
            "[CV]  criterion=gini, max_depth=10, splitter=random, score=0.621, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=10, splitter=random ...................\n",
            "[CV]  criterion=gini, max_depth=10, splitter=random, score=0.566, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=10, splitter=random ...................\n",
            "[CV]  criterion=gini, max_depth=10, splitter=random, score=0.613, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=20, splitter=best .....................\n",
            "[CV]  criterion=gini, max_depth=20, splitter=best, score=0.893, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=20, splitter=best .....................\n",
            "[CV]  criterion=gini, max_depth=20, splitter=best, score=0.897, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=20, splitter=best .....................\n",
            "[CV]  criterion=gini, max_depth=20, splitter=best, score=0.868, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=20, splitter=best .....................\n",
            "[CV]  criterion=gini, max_depth=20, splitter=best, score=0.830, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=20, splitter=best .....................\n",
            "[CV]  criterion=gini, max_depth=20, splitter=best, score=0.896, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=20, splitter=random ...................\n",
            "[CV]  criterion=gini, max_depth=20, splitter=random, score=0.892, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=20, splitter=random ...................\n",
            "[CV]  criterion=gini, max_depth=20, splitter=random, score=0.897, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=20, splitter=random ...................\n",
            "[CV]  criterion=gini, max_depth=20, splitter=random, score=0.869, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=20, splitter=random ...................\n",
            "[CV]  criterion=gini, max_depth=20, splitter=random, score=0.828, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=20, splitter=random ...................\n",
            "[CV]  criterion=gini, max_depth=20, splitter=random, score=0.895, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=None, splitter=best ...................\n",
            "[CV]  criterion=gini, max_depth=None, splitter=best, score=0.994, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=None, splitter=best ...................\n",
            "[CV]  criterion=gini, max_depth=None, splitter=best, score=1.000, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=None, splitter=best ...................\n",
            "[CV]  criterion=gini, max_depth=None, splitter=best, score=1.000, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=None, splitter=best ...................\n",
            "[CV]  criterion=gini, max_depth=None, splitter=best, score=1.000, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=None, splitter=best ...................\n",
            "[CV]  criterion=gini, max_depth=None, splitter=best, score=1.000, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=None, splitter=random .................\n",
            "[CV]  criterion=gini, max_depth=None, splitter=random, score=0.994, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=None, splitter=random .................\n",
            "[CV]  criterion=gini, max_depth=None, splitter=random, score=1.000, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=None, splitter=random .................\n",
            "[CV]  criterion=gini, max_depth=None, splitter=random, score=1.000, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=None, splitter=random .................\n",
            "[CV]  criterion=gini, max_depth=None, splitter=random, score=1.000, total=   0.1s\n",
            "[CV] criterion=gini, max_depth=None, splitter=random .................\n",
            "[CV]  criterion=gini, max_depth=None, splitter=random, score=1.000, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=1, splitter=best ...................\n",
            "[CV]  criterion=entropy, max_depth=1, splitter=best, score=0.083, total=   0.0s\n",
            "[CV] criterion=entropy, max_depth=1, splitter=best ...................\n",
            "[CV]  criterion=entropy, max_depth=1, splitter=best, score=0.088, total=   0.0s\n",
            "[CV] criterion=entropy, max_depth=1, splitter=best ...................\n",
            "[CV]  criterion=entropy, max_depth=1, splitter=best, score=0.075, total=   0.0s\n",
            "[CV] criterion=entropy, max_depth=1, splitter=best ...................\n",
            "[CV]  criterion=entropy, max_depth=1, splitter=best, score=0.081, total=   0.0s\n",
            "[CV] criterion=entropy, max_depth=1, splitter=best ...................\n",
            "[CV]  criterion=entropy, max_depth=1, splitter=best, score=0.073, total=   0.0s\n",
            "[CV] criterion=entropy, max_depth=1, splitter=random .................\n",
            "[CV]  criterion=entropy, max_depth=1, splitter=random, score=0.083, total=   0.0s\n",
            "[CV] criterion=entropy, max_depth=1, splitter=random .................\n",
            "[CV]  criterion=entropy, max_depth=1, splitter=random, score=0.088, total=   0.0s\n",
            "[CV] criterion=entropy, max_depth=1, splitter=random .................\n",
            "[CV]  criterion=entropy, max_depth=1, splitter=random, score=0.075, total=   0.0s\n",
            "[CV] criterion=entropy, max_depth=1, splitter=random .................\n",
            "[CV]  criterion=entropy, max_depth=1, splitter=random, score=0.081, total=   0.0s\n",
            "[CV] criterion=entropy, max_depth=1, splitter=random .................\n",
            "[CV]  criterion=entropy, max_depth=1, splitter=random, score=0.073, total=   0.0s\n",
            "[CV] criterion=entropy, max_depth=10, splitter=best ..................\n",
            "[CV]  criterion=entropy, max_depth=10, splitter=best, score=0.801, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=10, splitter=best ..................\n",
            "[CV]  criterion=entropy, max_depth=10, splitter=best, score=0.812, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=10, splitter=best ..................\n",
            "[CV]  criterion=entropy, max_depth=10, splitter=best, score=0.830, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=10, splitter=best ..................\n",
            "[CV]  criterion=entropy, max_depth=10, splitter=best, score=0.825, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=10, splitter=best ..................\n",
            "[CV]  criterion=entropy, max_depth=10, splitter=best, score=0.862, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=10, splitter=random ................\n",
            "[CV]  criterion=entropy, max_depth=10, splitter=random, score=0.801, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=10, splitter=random ................\n",
            "[CV]  criterion=entropy, max_depth=10, splitter=random, score=0.812, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=10, splitter=random ................\n",
            "[CV]  criterion=entropy, max_depth=10, splitter=random, score=0.830, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=10, splitter=random ................\n",
            "[CV]  criterion=entropy, max_depth=10, splitter=random, score=0.823, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=10, splitter=random ................\n",
            "[CV]  criterion=entropy, max_depth=10, splitter=random, score=0.865, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=20, splitter=best ..................\n",
            "[CV]  criterion=entropy, max_depth=20, splitter=best, score=0.980, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=20, splitter=best ..................\n",
            "[CV]  criterion=entropy, max_depth=20, splitter=best, score=0.977, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=20, splitter=best ..................\n",
            "[CV]  criterion=entropy, max_depth=20, splitter=best, score=0.986, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=20, splitter=best ..................\n",
            "[CV]  criterion=entropy, max_depth=20, splitter=best, score=0.977, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=20, splitter=best ..................\n",
            "[CV]  criterion=entropy, max_depth=20, splitter=best, score=0.977, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=20, splitter=random ................\n",
            "[CV]  criterion=entropy, max_depth=20, splitter=random, score=0.980, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=20, splitter=random ................\n",
            "[CV]  criterion=entropy, max_depth=20, splitter=random, score=0.977, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=20, splitter=random ................\n",
            "[CV]  criterion=entropy, max_depth=20, splitter=random, score=0.984, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=20, splitter=random ................\n",
            "[CV]  criterion=entropy, max_depth=20, splitter=random, score=0.977, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=20, splitter=random ................\n",
            "[CV]  criterion=entropy, max_depth=20, splitter=random, score=0.977, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=None, splitter=best ................\n",
            "[CV]  criterion=entropy, max_depth=None, splitter=best, score=1.000, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=None, splitter=best ................\n",
            "[CV]  criterion=entropy, max_depth=None, splitter=best, score=1.000, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=None, splitter=best ................\n",
            "[CV]  criterion=entropy, max_depth=None, splitter=best, score=1.000, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=None, splitter=best ................\n",
            "[CV]  criterion=entropy, max_depth=None, splitter=best, score=1.000, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=None, splitter=best ................\n",
            "[CV]  criterion=entropy, max_depth=None, splitter=best, score=1.000, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=None, splitter=random ..............\n",
            "[CV]  criterion=entropy, max_depth=None, splitter=random, score=1.000, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=None, splitter=random ..............\n",
            "[CV]  criterion=entropy, max_depth=None, splitter=random, score=1.000, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=None, splitter=random ..............\n",
            "[CV]  criterion=entropy, max_depth=None, splitter=random, score=1.000, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=None, splitter=random ..............\n",
            "[CV]  criterion=entropy, max_depth=None, splitter=random, score=1.000, total=   0.1s\n",
            "[CV] criterion=entropy, max_depth=None, splitter=random ..............\n",
            "[CV]  criterion=entropy, max_depth=None, splitter=random, score=1.000, total=   0.1s\n",
            "{'criterion': 'entropy', 'max_depth': None, 'splitter': 'best'}\n",
            "\n",
            "1170  classified 90% correctly\n",
            "1170  classified 95% correctly\n",
            "1170  classified 99% correctly\n",
            "1170  classified 100% correctly\n",
            "0  classified incorrectly (Not 100%)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    4.8s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2c9z7FZLlsKV"
      },
      "source": [
        "# KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzBy816AlsKV",
        "outputId": "4b16a353-fe5f-4b35-9e66-29ae39162f78"
      },
      "source": [
        "grid_params_knn = {\n",
        "    'n_neighbors' : [3,5,7,11,13,15,17,25],\n",
        "    'algorithm' : ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
        "    'weights' : ['uniform', 'distance'],\n",
        "    'metric' : ['euclidean', 'manhattan'],\n",
        "}\n",
        "\n",
        "gridSearch(KNeighborsClassifier(), grid_params_knn, trainX, trainY, testX, testY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 128 candidates, totalling 640 fits\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=3, weights=uniform \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   2.0s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=3, weights=uniform \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.0s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=3, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=3, weights=uniform \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    3.9s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=5, weights=uniform, score=0.990, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=5, weights=uniform, score=0.993, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=5, weights=uniform, score=0.991, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=5, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=5, weights=uniform, score=0.986, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=7, weights=uniform, score=0.984, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=7, weights=uniform, score=0.977, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=7, weights=uniform, score=0.983, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=7, weights=uniform, score=0.993, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=7, weights=uniform, score=0.977, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=11, weights=uniform, score=0.976, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=11, weights=uniform, score=0.964, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=11, weights=uniform, score=0.972, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=11, weights=uniform, score=0.986, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=11, weights=uniform, score=0.970, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=13, weights=uniform, score=0.973, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=13, weights=uniform, score=0.960, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=13, weights=uniform, score=0.967, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=13, weights=uniform, score=0.979, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=13, weights=uniform, score=0.967, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=15, weights=uniform, score=0.972, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=15, weights=uniform, score=0.959, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=15, weights=uniform, score=0.967, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=15, weights=uniform, score=0.979, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=15, weights=uniform, score=0.962, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=17, weights=uniform, score=0.964, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=17, weights=uniform, score=0.949, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=17, weights=uniform, score=0.966, total=   2.0s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=17, weights=uniform, score=0.974, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=17, weights=uniform, score=0.957, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=25, weights=uniform, score=0.963, total=   2.0s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=25, weights=uniform, score=0.949, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=25, weights=uniform, score=0.963, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=25, weights=uniform, score=0.970, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=25, weights=uniform, score=0.953, total=   1.9s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=auto, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=auto, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.8s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.8s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=5, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=5, weights=uniform, score=0.993, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=7, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=7, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=7, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=7, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=7, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=11, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=11, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=11, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=11, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=11, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=13, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=13, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=15, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=15, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   2.0s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=17, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=17, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=17, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=17, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=17, weights=uniform, score=0.994, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=25, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=25, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=25, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=25, weights=uniform, score=0.997, total=   2.0s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=25, weights=uniform, score=0.994, total=   1.9s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=auto, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=auto, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=uniform, score=0.990, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=uniform, score=0.993, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=uniform, score=0.991, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=uniform, score=0.986, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=uniform, score=0.984, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=uniform, score=0.977, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=uniform, score=0.983, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=uniform, score=0.993, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=uniform, score=0.977, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=uniform, score=0.976, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=uniform, score=0.964, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=uniform, score=0.972, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=uniform, score=0.986, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=uniform, score=0.970, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=uniform, score=0.973, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=uniform, score=0.960, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=uniform, score=0.967, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=uniform, score=0.979, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=uniform, score=0.967, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=uniform, score=0.972, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=uniform, score=0.959, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=uniform, score=0.967, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=uniform, score=0.979, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=uniform, score=0.962, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=uniform, score=0.967, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=uniform, score=0.949, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=uniform, score=0.966, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=uniform, score=0.974, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=uniform, score=0.957, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=uniform, score=0.963, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=uniform, score=0.949, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=uniform, score=0.963, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=uniform, score=0.970, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=uniform, score=0.953, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=uniform, score=0.993, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=uniform, score=0.997, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=uniform, score=0.997, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=uniform, score=0.996, total=   2.0s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=uniform, score=0.994, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=uniform, score=0.994, total=   1.9s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=ball_tree, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=uniform, score=0.990, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=uniform, score=0.993, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=uniform, score=0.991, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=uniform, score=0.986, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=uniform, score=0.984, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=uniform, score=0.977, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=uniform, score=0.983, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=uniform, score=0.993, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=uniform, score=0.977, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=uniform, score=0.976, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=uniform, score=0.964, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=uniform, score=0.972, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=uniform, score=0.986, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=uniform, score=0.970, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=uniform, score=0.973, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=uniform, score=0.960, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=uniform, score=0.967, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=uniform, score=0.979, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=uniform, score=0.967, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=uniform, score=0.972, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=uniform, score=0.959, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=uniform, score=0.967, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=uniform, score=0.979, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=uniform, score=0.962, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=uniform, score=0.964, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=uniform, score=0.949, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=uniform, score=0.966, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=uniform, score=0.974, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=uniform, score=0.957, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=uniform, score=0.963, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=uniform, score=0.949, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=uniform, score=0.963, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=uniform, score=0.970, total=   2.0s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=uniform, score=0.953, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=uniform, score=0.993, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=uniform, score=1.000, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=uniform, score=0.999, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=uniform, score=0.994, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.3s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=uniform, score=0.997, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=uniform, score=0.994, total=   1.9s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=kd_tree, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.4s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=3, weights=uniform, score=0.997, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=3, weights=uniform, score=1.000, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=3, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=5, weights=uniform, score=0.990, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=5, weights=uniform, score=0.993, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=5, weights=uniform, score=0.991, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=5, weights=uniform, score=0.996, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=5, weights=uniform, score=0.986, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=5, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=7, weights=uniform, score=0.984, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=7, weights=uniform, score=0.977, total=   1.8s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=7, weights=uniform, score=0.983, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=7, weights=uniform, score=0.996, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=7, weights=uniform, score=0.977, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=7, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=11, weights=uniform, score=0.976, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=11, weights=uniform, score=0.966, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=11, weights=uniform, score=0.972, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=11, weights=uniform, score=0.989, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=11, weights=uniform, score=0.972, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=11, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=13, weights=uniform, score=0.973, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=13, weights=uniform, score=0.960, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=13, weights=uniform, score=0.967, total=   1.8s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=13, weights=uniform, score=0.979, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=13, weights=uniform, score=0.966, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=13, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=15, weights=uniform, score=0.972, total=   1.8s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=15, weights=uniform, score=0.959, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=15, weights=uniform, score=0.967, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=15, weights=uniform, score=0.979, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=15, weights=uniform, score=0.962, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=15, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=17, weights=uniform, score=0.964, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=17, weights=uniform, score=0.950, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=17, weights=uniform, score=0.966, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=17, weights=uniform, score=0.974, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=17, weights=uniform, score=0.959, total=   1.8s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=17, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=25, weights=uniform, score=0.963, total=   1.8s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=25, weights=uniform, score=0.949, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=25, weights=uniform, score=0.963, total=   1.8s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=25, weights=uniform, score=0.970, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=25, weights=uniform, score=0.953, total=   1.7s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=euclidean, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=brute, metric=euclidean, n_neighbors=25, weights=distance, score=1.000, total=   0.1s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=3, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=3, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=3, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=3, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=5, weights=uniform, score=0.999, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=5, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=5, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=5, weights=uniform, score=0.993, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=5, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=5, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=7, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=7, weights=uniform, score=0.997, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=7, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=7, weights=uniform, score=1.000, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=7, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=7, weights=uniform, score=0.996, total=   1.9s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=7, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=7, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=11, weights=uniform, score=0.999, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=11, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=11, weights=uniform, score=0.997, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=11, weights=uniform, score=0.999, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=11, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=11, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=11, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=11, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=13, weights=uniform, score=0.999, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=13, weights=uniform, score=0.999, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=13, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=13, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=13, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=13, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=15, weights=uniform, score=0.997, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=15, weights=uniform, score=0.999, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=15, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=15, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=15, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=15, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=17, weights=uniform, score=0.997, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=17, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=17, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=17, weights=uniform, score=0.997, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=17, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=17, weights=uniform, score=0.994, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=17, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=17, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=25, weights=uniform, score=0.997, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=25, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=25, weights=uniform, score=0.996, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=25, weights=uniform, score=0.997, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=25, weights=uniform \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=25, weights=uniform, score=0.994, total=   1.8s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.2s\n",
            "[CV] algorithm=brute, metric=manhattan, n_neighbors=25, weights=distance \n",
            "[CV]  algorithm=brute, metric=manhattan, n_neighbors=25, weights=distance, score=1.000, total=   0.2s\n",
            "{'algorithm': 'auto', 'metric': 'euclidean', 'n_neighbors': 3, 'weights': 'distance'}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 640 out of 640 | elapsed: 11.5min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "1170  classified 90% correctly\n",
            "1170  classified 95% correctly\n",
            "1170  classified 99% correctly\n",
            "1170  classified 100% correctly\n",
            "0  classified incorrectly (Not 100%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEWYPkwMlsKV"
      },
      "source": [
        "# Nayve Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hd-R9aOnlsKW"
      },
      "source": [
        "grid_params_gaussian = {\n",
        "    'estimator__var_smoothing': [1e-11, 1e-10, 1e-9]\n",
        "}\n",
        "\n",
        "gaussianNB = multioutput.MultiOutputClassifier(naive_bayes.GaussianNB())\n",
        "#print('Gaussian Params:',  gaussianNB.get_params().keys())\n",
        "\n",
        "nSteps = 10\n",
        "grid_params_bernoulli = {\n",
        "    'estimator__alpha': np.array(range(nSteps))/nSteps,\n",
        "    'estimator__fit_prior':[True,False]\n",
        "}\n",
        "\n",
        "bernoulli = multioutput.MultiOutputClassifier(naive_bayes.BernoulliNB())\n",
        "#print('Bernoulli Params:', bernoulli.get_params().keys())\n",
        "\n",
        "grid_params_multinominal = {\n",
        "    'estimator__alpha': np.array(range(nSteps))/nSteps,\n",
        "}\n",
        "\n",
        "multinominal = multioutput.MultiOutputClassifier(naive_bayes.MultinomialNB())\n",
        "#print('Multinominal Params:', multinominal.get_params().keys())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ybl2hlzDlsKX",
        "outputId": "6d91e2f5-d546-4d0e-adf4-81071fb4eff4"
      },
      "source": [
        "gridSearch(gaussianNB, grid_params_gaussian, trainX, trainY, testX, testY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n",
            "[CV] estimator__var_smoothing=1e-11 ..................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ...... estimator__var_smoothing=1e-11, score=0.859, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-11 ..................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.6s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ...... estimator__var_smoothing=1e-11, score=0.895, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-11 ..................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ...... estimator__var_smoothing=1e-11, score=0.855, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-11 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-11, score=0.866, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-11 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-11, score=0.877, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-10 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-10, score=0.859, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-10 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-10, score=0.895, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-10 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-10, score=0.855, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-10 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-10, score=0.866, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-10 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-10, score=0.877, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-09 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-09, score=0.859, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-09 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-09, score=0.895, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-09 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-09, score=0.855, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-09 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-09, score=0.866, total=   0.6s\n",
            "[CV] estimator__var_smoothing=1e-09 ..................................\n",
            "[CV] ...... estimator__var_smoothing=1e-09, score=0.877, total=   0.6s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    9.0s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'estimator__var_smoothing': 1e-11}\n",
            "\n",
            "1170  classified 90% correctly\n",
            "1142  classified 95% correctly\n",
            "1076  classified 99% correctly\n",
            "1011  classified 100% correctly\n",
            "159  classified incorrectly (Not 100%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lq9WkxYalsKX",
        "outputId": "949cb8fc-d26c-4943-94ae-af3c24578103"
      },
      "source": [
        "gridSearch(bernoulli, grid_params_bernoulli, trainX, trainY, testX, testY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
            "[CV] estimator__alpha=0.0, estimator__fit_prior=True .................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=0.0, estimator__fit_prior=True, score=0.909, total=   0.6s\n",
            "[CV] estimator__alpha=0.0, estimator__fit_prior=True .................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.6s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=0.0, estimator__fit_prior=True, score=0.944, total=   0.5s\n",
            "[CV] estimator__alpha=0.0, estimator__fit_prior=True .................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=0.0, estimator__fit_prior=True, score=0.910, total=   0.5s\n",
            "[CV] estimator__alpha=0.0, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.0, estimator__fit_prior=True, score=0.932, total=   0.5s\n",
            "[CV] estimator__alpha=0.0, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.0, estimator__fit_prior=True, score=0.925, total=   0.5s\n",
            "[CV] estimator__alpha=0.0, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.0, estimator__fit_prior=False, score=0.907, total=   0.5s\n",
            "[CV] estimator__alpha=0.0, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.0, estimator__fit_prior=False, score=0.939, total=   0.5s\n",
            "[CV] estimator__alpha=0.0, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.0, estimator__fit_prior=False, score=0.913, total=   0.5s\n",
            "[CV] estimator__alpha=0.0, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.0, estimator__fit_prior=False, score=0.929, total=   0.5s\n",
            "[CV] estimator__alpha=0.0, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.0, estimator__fit_prior=False, score=0.917, total=   0.5s\n",
            "[CV] estimator__alpha=0.1, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.1, estimator__fit_prior=True, score=0.909, total=   0.5s\n",
            "[CV] estimator__alpha=0.1, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.1, estimator__fit_prior=True, score=0.943, total=   0.5s\n",
            "[CV] estimator__alpha=0.1, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.1, estimator__fit_prior=True, score=0.910, total=   0.5s\n",
            "[CV] estimator__alpha=0.1, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.1, estimator__fit_prior=True, score=0.929, total=   0.5s\n",
            "[CV] estimator__alpha=0.1, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.1, estimator__fit_prior=True, score=0.925, total=   0.5s\n",
            "[CV] estimator__alpha=0.1, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.1, estimator__fit_prior=False, score=0.893, total=   0.5s\n",
            "[CV] estimator__alpha=0.1, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.1, estimator__fit_prior=False, score=0.925, total=   0.5s\n",
            "[CV] estimator__alpha=0.1, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.1, estimator__fit_prior=False, score=0.879, total=   0.5s\n",
            "[CV] estimator__alpha=0.1, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.1, estimator__fit_prior=False, score=0.913, total=   0.5s\n",
            "[CV] estimator__alpha=0.1, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.1, estimator__fit_prior=False, score=0.907, total=   0.5s\n",
            "[CV] estimator__alpha=0.2, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.2, estimator__fit_prior=True, score=0.900, total=   0.5s\n",
            "[CV] estimator__alpha=0.2, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.2, estimator__fit_prior=True, score=0.937, total=   0.5s\n",
            "[CV] estimator__alpha=0.2, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.2, estimator__fit_prior=True, score=0.906, total=   0.5s\n",
            "[CV] estimator__alpha=0.2, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.2, estimator__fit_prior=True, score=0.917, total=   0.5s\n",
            "[CV] estimator__alpha=0.2, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.2, estimator__fit_prior=True, score=0.919, total=   0.5s\n",
            "[CV] estimator__alpha=0.2, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.2, estimator__fit_prior=False, score=0.850, total=   0.5s\n",
            "[CV] estimator__alpha=0.2, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.2, estimator__fit_prior=False, score=0.895, total=   0.5s\n",
            "[CV] estimator__alpha=0.2, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.2, estimator__fit_prior=False, score=0.863, total=   0.5s\n",
            "[CV] estimator__alpha=0.2, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.2, estimator__fit_prior=False, score=0.892, total=   0.5s\n",
            "[CV] estimator__alpha=0.2, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.2, estimator__fit_prior=False, score=0.896, total=   0.5s\n",
            "[CV] estimator__alpha=0.3, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.3, estimator__fit_prior=True, score=0.900, total=   0.5s\n",
            "[CV] estimator__alpha=0.3, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.3, estimator__fit_prior=True, score=0.954, total=   0.5s\n",
            "[CV] estimator__alpha=0.3, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.3, estimator__fit_prior=True, score=0.906, total=   0.5s\n",
            "[CV] estimator__alpha=0.3, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.3, estimator__fit_prior=True, score=0.917, total=   0.5s\n",
            "[CV] estimator__alpha=0.3, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.3, estimator__fit_prior=True, score=0.937, total=   0.5s\n",
            "[CV] estimator__alpha=0.3, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.3, estimator__fit_prior=False, score=0.829, total=   0.5s\n",
            "[CV] estimator__alpha=0.3, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.3, estimator__fit_prior=False, score=0.859, total=   0.5s\n",
            "[CV] estimator__alpha=0.3, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.3, estimator__fit_prior=False, score=0.830, total=   0.5s\n",
            "[CV] estimator__alpha=0.3, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.3, estimator__fit_prior=False, score=0.868, total=   0.5s\n",
            "[CV] estimator__alpha=0.3, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.3, estimator__fit_prior=False, score=0.860, total=   0.5s\n",
            "[CV] estimator__alpha=0.4, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.4, estimator__fit_prior=True, score=0.900, total=   0.5s\n",
            "[CV] estimator__alpha=0.4, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.4, estimator__fit_prior=True, score=0.953, total=   0.5s\n",
            "[CV] estimator__alpha=0.4, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.4, estimator__fit_prior=True, score=0.905, total=   0.5s\n",
            "[CV] estimator__alpha=0.4, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.4, estimator__fit_prior=True, score=0.917, total=   0.5s\n",
            "[CV] estimator__alpha=0.4, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.4, estimator__fit_prior=True, score=0.934, total=   0.5s\n",
            "[CV] estimator__alpha=0.4, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.4, estimator__fit_prior=False, score=0.805, total=   0.5s\n",
            "[CV] estimator__alpha=0.4, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.4, estimator__fit_prior=False, score=0.833, total=   0.5s\n",
            "[CV] estimator__alpha=0.4, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.4, estimator__fit_prior=False, score=0.822, total=   0.5s\n",
            "[CV] estimator__alpha=0.4, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.4, estimator__fit_prior=False, score=0.845, total=   0.5s\n",
            "[CV] estimator__alpha=0.4, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.4, estimator__fit_prior=False, score=0.849, total=   0.5s\n",
            "[CV] estimator__alpha=0.5, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.5, estimator__fit_prior=True, score=0.897, total=   0.5s\n",
            "[CV] estimator__alpha=0.5, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.5, estimator__fit_prior=True, score=0.947, total=   0.5s\n",
            "[CV] estimator__alpha=0.5, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.5, estimator__fit_prior=True, score=0.897, total=   0.5s\n",
            "[CV] estimator__alpha=0.5, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.5, estimator__fit_prior=True, score=0.937, total=   0.5s\n",
            "[CV] estimator__alpha=0.5, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.5, estimator__fit_prior=True, score=0.932, total=   0.5s\n",
            "[CV] estimator__alpha=0.5, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.5, estimator__fit_prior=False, score=0.771, total=   0.5s\n",
            "[CV] estimator__alpha=0.5, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.5, estimator__fit_prior=False, score=0.801, total=   0.5s\n",
            "[CV] estimator__alpha=0.5, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.5, estimator__fit_prior=False, score=0.805, total=   0.5s\n",
            "[CV] estimator__alpha=0.5, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.5, estimator__fit_prior=False, score=0.809, total=   0.5s\n",
            "[CV] estimator__alpha=0.5, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.5, estimator__fit_prior=False, score=0.826, total=   0.5s\n",
            "[CV] estimator__alpha=0.6, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.6, estimator__fit_prior=True, score=0.876, total=   0.5s\n",
            "[CV] estimator__alpha=0.6, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.6, estimator__fit_prior=True, score=0.944, total=   0.5s\n",
            "[CV] estimator__alpha=0.6, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.6, estimator__fit_prior=True, score=0.897, total=   0.5s\n",
            "[CV] estimator__alpha=0.6, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.6, estimator__fit_prior=True, score=0.930, total=   0.5s\n",
            "[CV] estimator__alpha=0.6, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.6, estimator__fit_prior=True, score=0.932, total=   0.5s\n",
            "[CV] estimator__alpha=0.6, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.6, estimator__fit_prior=False, score=0.764, total=   0.5s\n",
            "[CV] estimator__alpha=0.6, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.6, estimator__fit_prior=False, score=0.789, total=   0.5s\n",
            "[CV] estimator__alpha=0.6, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.6, estimator__fit_prior=False, score=0.762, total=   0.5s\n",
            "[CV] estimator__alpha=0.6, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.6, estimator__fit_prior=False, score=0.802, total=   0.5s\n",
            "[CV] estimator__alpha=0.6, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.6, estimator__fit_prior=False, score=0.812, total=   0.5s\n",
            "[CV] estimator__alpha=0.7, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.7, estimator__fit_prior=True, score=0.873, total=   0.5s\n",
            "[CV] estimator__alpha=0.7, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.7, estimator__fit_prior=True, score=0.944, total=   0.5s\n",
            "[CV] estimator__alpha=0.7, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.7, estimator__fit_prior=True, score=0.897, total=   0.5s\n",
            "[CV] estimator__alpha=0.7, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.7, estimator__fit_prior=True, score=0.934, total=   0.5s\n",
            "[CV] estimator__alpha=0.7, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.7, estimator__fit_prior=True, score=0.929, total=   0.5s\n",
            "[CV] estimator__alpha=0.7, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.7, estimator__fit_prior=False, score=0.742, total=   0.5s\n",
            "[CV] estimator__alpha=0.7, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.7, estimator__fit_prior=False, score=0.744, total=   0.5s\n",
            "[CV] estimator__alpha=0.7, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.7, estimator__fit_prior=False, score=0.762, total=   0.5s\n",
            "[CV] estimator__alpha=0.7, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.7, estimator__fit_prior=False, score=0.778, total=   0.5s\n",
            "[CV] estimator__alpha=0.7, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.7, estimator__fit_prior=False, score=0.796, total=   0.5s\n",
            "[CV] estimator__alpha=0.8, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.8, estimator__fit_prior=True, score=0.872, total=   0.5s\n",
            "[CV] estimator__alpha=0.8, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.8, estimator__fit_prior=True, score=0.915, total=   0.5s\n",
            "[CV] estimator__alpha=0.8, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.8, estimator__fit_prior=True, score=0.897, total=   0.5s\n",
            "[CV] estimator__alpha=0.8, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.8, estimator__fit_prior=True, score=0.933, total=   0.5s\n",
            "[CV] estimator__alpha=0.8, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.8, estimator__fit_prior=True, score=0.923, total=   0.5s\n",
            "[CV] estimator__alpha=0.8, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.8, estimator__fit_prior=False, score=0.741, total=   0.5s\n",
            "[CV] estimator__alpha=0.8, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.8, estimator__fit_prior=False, score=0.739, total=   0.5s\n",
            "[CV] estimator__alpha=0.8, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.8, estimator__fit_prior=False, score=0.749, total=   0.5s\n",
            "[CV] estimator__alpha=0.8, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.8, estimator__fit_prior=False, score=0.779, total=   0.5s\n",
            "[CV] estimator__alpha=0.8, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.8, estimator__fit_prior=False, score=0.785, total=   0.5s\n",
            "[CV] estimator__alpha=0.9, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.9, estimator__fit_prior=True, score=0.870, total=   0.5s\n",
            "[CV] estimator__alpha=0.9, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.9, estimator__fit_prior=True, score=0.890, total=   0.5s\n",
            "[CV] estimator__alpha=0.9, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.9, estimator__fit_prior=True, score=0.868, total=   0.5s\n",
            "[CV] estimator__alpha=0.9, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.9, estimator__fit_prior=True, score=0.933, total=   0.5s\n",
            "[CV] estimator__alpha=0.9, estimator__fit_prior=True .................\n",
            "[CV]  estimator__alpha=0.9, estimator__fit_prior=True, score=0.923, total=   0.5s\n",
            "[CV] estimator__alpha=0.9, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.9, estimator__fit_prior=False, score=0.691, total=   0.5s\n",
            "[CV] estimator__alpha=0.9, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.9, estimator__fit_prior=False, score=0.729, total=   0.5s\n",
            "[CV] estimator__alpha=0.9, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.9, estimator__fit_prior=False, score=0.741, total=   0.5s\n",
            "[CV] estimator__alpha=0.9, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.9, estimator__fit_prior=False, score=0.765, total=   0.5s\n",
            "[CV] estimator__alpha=0.9, estimator__fit_prior=False ................\n",
            "[CV]  estimator__alpha=0.9, estimator__fit_prior=False, score=0.759, total=   0.5s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:   50.5s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'estimator__alpha': 0.0, 'estimator__fit_prior': True}\n",
            "\n",
            "1170  classified 90% correctly\n",
            "1144  classified 95% correctly\n",
            "1104  classified 99% correctly\n",
            "1084  classified 100% correctly\n",
            "86  classified incorrectly (Not 100%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddyFnaU0lsKY",
        "outputId": "14762a25-123a-41d8-a891-88b9396ee4b9"
      },
      "source": [
        "gridSearch(multinominal, grid_params_multinominal, trainX, trainY, testX, testY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
            "[CV] estimator__alpha=0.0 ............................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................ estimator__alpha=0.0, score=0.905, total=   0.3s\n",
            "[CV] estimator__alpha=0.0 ............................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................ estimator__alpha=0.0, score=0.940, total=   0.2s\n",
            "[CV] estimator__alpha=0.0 ............................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................ estimator__alpha=0.0, score=0.905, total=   0.2s\n",
            "[CV] estimator__alpha=0.0 ............................................\n",
            "[CV] ................ estimator__alpha=0.0, score=0.927, total=   0.3s\n",
            "[CV] estimator__alpha=0.0 ............................................\n",
            "[CV] ................ estimator__alpha=0.0, score=0.919, total=   0.3s\n",
            "[CV] estimator__alpha=0.1 ............................................\n",
            "[CV] ................ estimator__alpha=0.1, score=0.811, total=   0.2s\n",
            "[CV] estimator__alpha=0.1 ............................................\n",
            "[CV] ................ estimator__alpha=0.1, score=0.829, total=   0.2s\n",
            "[CV] estimator__alpha=0.1 ............................................\n",
            "[CV] ................ estimator__alpha=0.1, score=0.792, total=   0.3s\n",
            "[CV] estimator__alpha=0.1 ............................................\n",
            "[CV] ................ estimator__alpha=0.1, score=0.771, total=   0.2s\n",
            "[CV] estimator__alpha=0.1 ............................................\n",
            "[CV] ................ estimator__alpha=0.1, score=0.806, total=   0.2s\n",
            "[CV] estimator__alpha=0.2 ............................................\n",
            "[CV] ................ estimator__alpha=0.2, score=0.736, total=   0.2s\n",
            "[CV] estimator__alpha=0.2 ............................................\n",
            "[CV] ................ estimator__alpha=0.2, score=0.746, total=   0.3s\n",
            "[CV] estimator__alpha=0.2 ............................................\n",
            "[CV] ................ estimator__alpha=0.2, score=0.691, total=   0.2s\n",
            "[CV] estimator__alpha=0.2 ............................................\n",
            "[CV] ................ estimator__alpha=0.2, score=0.702, total=   0.2s\n",
            "[CV] estimator__alpha=0.2 ............................................\n",
            "[CV] ................ estimator__alpha=0.2, score=0.734, total=   0.2s\n",
            "[CV] estimator__alpha=0.3 ............................................\n",
            "[CV] ................ estimator__alpha=0.3, score=0.655, total=   0.2s\n",
            "[CV] estimator__alpha=0.3 ............................................\n",
            "[CV] ................ estimator__alpha=0.3, score=0.698, total=   0.2s\n",
            "[CV] estimator__alpha=0.3 ............................................\n",
            "[CV] ................ estimator__alpha=0.3, score=0.662, total=   0.2s\n",
            "[CV] estimator__alpha=0.3 ............................................\n",
            "[CV] ................ estimator__alpha=0.3, score=0.650, total=   0.2s\n",
            "[CV] estimator__alpha=0.3 ............................................\n",
            "[CV] ................ estimator__alpha=0.3, score=0.670, total=   0.2s\n",
            "[CV] estimator__alpha=0.4 ............................................\n",
            "[CV] ................ estimator__alpha=0.4, score=0.628, total=   0.2s\n",
            "[CV] estimator__alpha=0.4 ............................................\n",
            "[CV] ................ estimator__alpha=0.4, score=0.679, total=   0.2s\n",
            "[CV] estimator__alpha=0.4 ............................................\n",
            "[CV] ................ estimator__alpha=0.4, score=0.603, total=   0.2s\n",
            "[CV] estimator__alpha=0.4 ............................................\n",
            "[CV] ................ estimator__alpha=0.4, score=0.620, total=   0.3s\n",
            "[CV] estimator__alpha=0.4 ............................................\n",
            "[CV] ................ estimator__alpha=0.4, score=0.640, total=   0.3s\n",
            "[CV] estimator__alpha=0.5 ............................................\n",
            "[CV] ................ estimator__alpha=0.5, score=0.611, total=   0.2s\n",
            "[CV] estimator__alpha=0.5 ............................................\n",
            "[CV] ................ estimator__alpha=0.5, score=0.648, total=   0.2s\n",
            "[CV] estimator__alpha=0.5 ............................................\n",
            "[CV] ................ estimator__alpha=0.5, score=0.566, total=   0.2s\n",
            "[CV] estimator__alpha=0.5 ............................................\n",
            "[CV] ................ estimator__alpha=0.5, score=0.618, total=   0.2s\n",
            "[CV] estimator__alpha=0.5 ............................................\n",
            "[CV] ................ estimator__alpha=0.5, score=0.597, total=   0.2s\n",
            "[CV] estimator__alpha=0.6 ............................................\n",
            "[CV] ................ estimator__alpha=0.6, score=0.597, total=   0.2s\n",
            "[CV] estimator__alpha=0.6 ............................................\n",
            "[CV] ................ estimator__alpha=0.6, score=0.631, total=   0.3s\n",
            "[CV] estimator__alpha=0.6 ............................................\n",
            "[CV] ................ estimator__alpha=0.6, score=0.564, total=   0.2s\n",
            "[CV] estimator__alpha=0.6 ............................................\n",
            "[CV] ................ estimator__alpha=0.6, score=0.587, total=   0.2s\n",
            "[CV] estimator__alpha=0.6 ............................................\n",
            "[CV] ................ estimator__alpha=0.6, score=0.580, total=   0.2s\n",
            "[CV] estimator__alpha=0.7 ............................................\n",
            "[CV] ................ estimator__alpha=0.7, score=0.583, total=   0.3s\n",
            "[CV] estimator__alpha=0.7 ............................................\n",
            "[CV] ................ estimator__alpha=0.7, score=0.591, total=   0.2s\n",
            "[CV] estimator__alpha=0.7 ............................................\n",
            "[CV] ................ estimator__alpha=0.7, score=0.543, total=   0.3s\n",
            "[CV] estimator__alpha=0.7 ............................................\n",
            "[CV] ................ estimator__alpha=0.7, score=0.558, total=   0.2s\n",
            "[CV] estimator__alpha=0.7 ............................................\n",
            "[CV] ................ estimator__alpha=0.7, score=0.573, total=   0.2s\n",
            "[CV] estimator__alpha=0.8 ............................................\n",
            "[CV] ................ estimator__alpha=0.8, score=0.553, total=   0.3s\n",
            "[CV] estimator__alpha=0.8 ............................................\n",
            "[CV] ................ estimator__alpha=0.8, score=0.538, total=   0.3s\n",
            "[CV] estimator__alpha=0.8 ............................................\n",
            "[CV] ................ estimator__alpha=0.8, score=0.528, total=   0.2s\n",
            "[CV] estimator__alpha=0.8 ............................................\n",
            "[CV] ................ estimator__alpha=0.8, score=0.543, total=   0.3s\n",
            "[CV] estimator__alpha=0.8 ............................................\n",
            "[CV] ................ estimator__alpha=0.8, score=0.561, total=   0.3s\n",
            "[CV] estimator__alpha=0.9 ............................................\n",
            "[CV] ................ estimator__alpha=0.9, score=0.540, total=   0.3s\n",
            "[CV] estimator__alpha=0.9 ............................................\n",
            "[CV] ................ estimator__alpha=0.9, score=0.533, total=   0.3s\n",
            "[CV] estimator__alpha=0.9 ............................................\n",
            "[CV] ................ estimator__alpha=0.9, score=0.503, total=   0.3s\n",
            "[CV] estimator__alpha=0.9 ............................................\n",
            "[CV] ................ estimator__alpha=0.9, score=0.536, total=   0.2s\n",
            "[CV] estimator__alpha=0.9 ............................................\n",
            "[CV] ................ estimator__alpha=0.9, score=0.528, total=   0.3s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   12.5s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'estimator__alpha': 0.0}\n",
            "\n",
            "1170  classified 90% correctly\n",
            "1165  classified 95% correctly\n",
            "1080  classified 99% correctly\n",
            "1074  classified 100% correctly\n",
            "96  classified incorrectly (Not 100%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wvoy6LKGlsKY"
      },
      "source": [
        "# SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzxWirDtlsKY"
      },
      "source": [
        "grid_params_svc = {\n",
        "    'estimator__kernel': ['poly','rbf','linear'],\n",
        "    'estimator__gamma': [1,0.1,0.01,0.001],\n",
        "    'estimator__C': [0.1,1, 10, 100],\n",
        "    'estimator__class_weight': ['balanced', None]\n",
        "} "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQ81hAAglsKZ",
        "outputId": "7fe29d50-eca3-4acd-b326-2c9888bb7dd2"
      },
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "svc = OneVsRestClassifier(SVC()).fit(trainX.to_numpy(), trainY.to_numpy())\n",
        "gridSearch(svc, grid_params_svc, trainX, trainY.to_numpy(), testX, testY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 96 candidates, totalling 480 fits\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.2s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    8.3s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.972, total=  27.5s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.976, total=  27.4s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.981, total=  27.3s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.976, total=  27.3s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.979, total=  27.3s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=0.989, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=0.993, total=   3.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=0.997, total=   3.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=0.996, total=   3.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=0.984, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=0.825, total=  10.7s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=0.793, total=  10.4s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=0.856, total=  11.3s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=0.838, total=  10.8s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=0.819, total=  10.6s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.973, total=  17.8s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.977, total=  17.6s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.983, total=  17.7s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.990, total=  17.8s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.977, total=  17.7s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=0.989, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=0.993, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=0.997, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=0.996, total=   3.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=0.984, total=   3.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.346, total=  59.4s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.395, total=  59.8s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.386, total= 1.0min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.395, total=  59.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.348, total=  58.8s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.828, total=  24.2s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.852, total=  24.2s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.869, total=  24.8s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.858, total=  24.6s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.828, total=  24.4s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=0.989, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=0.993, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=0.997, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=0.996, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=0.984, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.000, total= 2.4min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.000, total= 2.4min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.000, total= 2.4min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.000, total= 2.4min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.000, total= 2.4min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.121, total= 1.2min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.141, total= 1.2min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.144, total= 1.2min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.165, total= 1.2min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.154, total= 1.2min\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=0.989, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=0.993, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=0.997, total=   2.8s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=0.996, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=0.984, total=   3.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.832, total=  22.2s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.819, total=  21.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.839, total=  22.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.809, total=  22.1s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.835, total=  21.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=0.991, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=0.986, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=0.991, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=0.993, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=0.989, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.845, total=   5.8s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.821, total=   5.7s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.876, total=   6.0s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.838, total=   5.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.819, total=   5.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.905, total=  14.7s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.885, total=  14.7s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.906, total=  14.8s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.903, total=  14.8s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.893, total=  14.7s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=0.991, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=0.986, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=0.991, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=0.993, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=0.989, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.407, total=  11.3s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.416, total=  11.3s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.425, total=  11.2s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.419, total=  11.3s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.403, total=  11.1s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.709, total=  12.4s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.708, total=  12.2s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.728, total=  12.3s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.729, total=  12.3s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.708, total=  12.2s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=0.991, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=0.986, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=0.991, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=0.993, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=0.989, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.027, total=  12.2s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.021, total=  12.1s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.017, total=  12.1s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.028, total=  12.3s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.037, total=  12.1s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.078, total=  15.7s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.098, total=  15.4s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.073, total=  15.4s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.084, total=  15.5s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.073, total=  15.4s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=0.991, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=0.986, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=0.991, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=0.993, total=   2.9s\n",
            "[CV] estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=0.1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=0.989, total=   2.9s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.989, total=  18.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.997, total=  18.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  18.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  18.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.5s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=0.999, total=   5.7s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=0.990, total=   5.7s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   5.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=0.997, total=   5.7s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   5.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.993, total=  10.9s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.996, total=  10.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.999, total=  11.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  11.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.997, total=  11.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.5s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.5s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.640, total=  27.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.644, total=  26.9s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.701, total=  28.2s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.682, total=  27.7s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.627, total=  27.5s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.989, total=   5.7s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.981, total=   5.7s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.989, total=   5.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.993, total=   5.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.983, total=   5.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.5s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.000, total= 2.3min\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.000, total= 2.3min\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.000, total= 2.3min\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.000, total= 2.3min\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.000, total= 2.3min\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.785, total=  24.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.762, total=  23.7s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.789, total=  24.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.821, total=  24.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.802, total=  24.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.5s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.989, total=  18.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.997, total=  18.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  18.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  18.5s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  18.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.972, total=   4.7s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.953, total=   4.6s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.983, total=   4.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.959, total=   4.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.962, total=   4.7s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.994, total=  11.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.993, total=  11.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.999, total=  11.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  11.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.996, total=  11.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.5s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.664, total=   8.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.648, total=   7.9s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.711, total=   8.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.695, total=   8.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.658, total=   7.9s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.976, total=   4.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.966, total=   4.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.969, total=   4.9s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.986, total=   4.9s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.973, total=   4.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.027, total=  12.6s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.021, total=  12.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.017, total=  12.3s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.028, total=  12.5s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.037, total=  12.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.682, total=  12.1s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.631, total=  11.8s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.701, total=  12.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.664, total=  12.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.637, total=  12.0s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=1, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.989, total=  18.0s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.997, total=  18.1s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  18.0s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  18.0s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.2s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.994, total=  10.7s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.997, total=  10.6s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.7s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.7s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.6s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.754, total=  14.6s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.748, total=  14.2s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.816, total=  15.3s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.778, total=  14.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.745, total=  14.6s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.997, total=   2.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=0.993, total=   2.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.5s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.061, total= 1.9min\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.057, total= 1.8min\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.066, total= 1.9min\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.094, total= 1.8min\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.083, total= 1.8min\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.974, total=   6.0s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.972, total=   5.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.980, total=   5.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.987, total=   5.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.979, total=   5.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.989, total=  17.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.997, total=  17.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=0.994, total=   4.1s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.1s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.994, total=  10.5s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.997, total=  10.6s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.6s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.6s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.6s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.783, total=   6.3s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.762, total=   6.2s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.829, total=   6.5s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.798, total=   6.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.771, total=   6.2s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=0.996, total=   2.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.094, total=  12.7s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.097, total=  12.7s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.087, total=  12.6s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.101, total=  12.7s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.097, total=  12.6s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.967, total=   4.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.962, total=   4.8s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.969, total=   4.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.980, total=   4.8s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.960, total=   4.9s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=10, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.989, total=  17.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=0.997, total=  17.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.8s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.994, total=  10.5s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=0.997, total=  10.6s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.5s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.6s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.6s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.825, total=  10.3s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.793, total=  10.1s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.856, total=  10.8s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.838, total=  10.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=poly, score=0.819, total=  10.3s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.346, total=  58.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.395, total=  58.6s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.386, total=  58.8s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.395, total=  58.3s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=poly, score=0.348, total=  57.3s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.993, total=   2.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.997, total=   2.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=rbf, score=0.993, total=   2.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=balanced, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.989, total=  17.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=0.997, total=  17.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.8s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=rbf, score=1.000, total=  17.8s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   3.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=poly, score=1.000, total=   4.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.994, total=  10.5s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=0.997, total=  10.6s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.6s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.5s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=rbf, score=1.000, total=  10.6s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.1, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.845, total=   5.8s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.821, total=   5.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.876, total=   5.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.838, total=   5.8s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=poly, score=0.819, total=   5.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.6s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=rbf, score=1.000, total=   2.7s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.01, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.407, total=  11.1s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.416, total=  11.2s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.425, total=  11.2s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.419, total=  11.2s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=poly, score=0.403, total=  11.0s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=1.000, total=   2.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.999, total=   2.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=rbf, score=0.996, total=   2.9s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n",
            "[CV] estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear \n",
            "[CV]  estimator__C=100, estimator__class_weight=None, estimator__gamma=0.001, estimator__kernel=linear, score=1.000, total=   2.4s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 480 out of 480 | elapsed: 104.9min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'estimator__C': 0.1, 'estimator__class_weight': 'balanced', 'estimator__gamma': 1, 'estimator__kernel': 'poly'}\n",
            "\n",
            "1170  classified 90% correctly\n",
            "1170  classified 95% correctly\n",
            "1170  classified 99% correctly\n",
            "1170  classified 100% correctly\n",
            "0  classified incorrectly (Not 100%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jznfapM_Dz5g"
      },
      "source": [
        "# Run and save the best Random Forest Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFQsMPXRDzLF"
      },
      "source": [
        "model = RandomForestClassifier(bootstrap = True, max_depth= None, n_estimators = 20) \n",
        "\n",
        "model.fit(trainX, trainY)\n",
        "\n",
        "# save the model to disk\n",
        "filename = 'bestRFT.sav'\n",
        "pickle.dump(model, open(filename, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52Jw5XKUEAvt",
        "outputId": "2afe9e17-5e21-4964-c7a1-8f41aba41ddc"
      },
      "source": [
        "# load the model from disk\n",
        "loaded_model = pickle.load(open(filename, 'rb'))\n",
        "result = loaded_model.score(testX, testY)\n",
        "print(result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mF8_2uxbC8ne"
      },
      "source": [
        "# Run and save the best Decicion Tree "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Puet_m2PC69A"
      },
      "source": [
        "model = DecisionTreeClassifier(criterion= 'entropy', max_depth = None, splitter= 'best') \n",
        "\n",
        "model.fit(trainX, trainY)\n",
        "\n",
        "# save the model to disk\n",
        "filename = 'bestDT.sav'\n",
        "pickle.dump(model, open(filename, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjNStTDxDvk3",
        "outputId": "7e006086-7835-4993-d5f0-6c6217ec7d00"
      },
      "source": [
        "# load the model from disk\n",
        "loaded_model = pickle.load(open(filename, 'rb'))\n",
        "result = loaded_model.score(testX, testY)\n",
        "print(result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1Pp0WVvAdiy"
      },
      "source": [
        "# Run and save the best KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dh4QnOeVAgFD"
      },
      "source": [
        "model = KNeighborsClassifier(algorithm = 'auto', metric='euclidean', n_neighbors = 3, weights = 'distance') \n",
        "\n",
        "model.fit(trainX, trainY)\n",
        "\n",
        "# save the model to disk\n",
        "filename = 'bestKnn.sav'\n",
        "pickle.dump(model, open(filename, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81T70HHCCT65",
        "outputId": "5a1f87cb-d1f9-41f8-d0ba-499539d15be9"
      },
      "source": [
        "# load the model from disk\n",
        "loaded_model = pickle.load(open(filename, 'rb'))\n",
        "result = loaded_model.score(testX, testY)\n",
        "print(result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}