pLM4ACE_template_for_other_bioactivity.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95NTckuFZZzm"
      },
      "source": [
        "### requirements for the following codings\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aaap3SIwvVcZ"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UO71IBS6ZgZV"
      },
      "outputs": [],
      "source": [
        "### working environments and packages required \n",
        "!pip install fair-esm==2.0.0\n",
        "!pip install torch==1.13.0+cu116\n",
        "!pip install pandas==1.3.5\n",
        "!pip install numpy==1.21.6\n",
        "!pip install sklearn==1.0.2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m91cA0H5w_eY"
      },
      "source": [
        "### peptide embeddings with esm2_t6_8M_UR50D pretrained models\n",
        "6 layers, 8M parameters, dataset: UR50/D 2021_04, embedding dimension: 320\n",
        "mode download URL: https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t6_8M_UR50D.pt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pl7XVx5HZsHf"
      },
      "outputs": [],
      "source": [
        "def esm_embeddings(peptide_sequence_list):\n",
        "  # NOTICE: ESM for embeddings is quite RAM usage, if your sequence is too long, \n",
        "  #         or you have too many sequences for transformation in a single converting, \n",
        "  #         you conputer might automatically kill the job.\n",
        "  import torch\n",
        "  import esm\n",
        "  import pandas as pd\n",
        "  import collections\n",
        "  # load the model\n",
        "  # NOTICE: if the model was not downloaded in your local environment, it will automatically download it.\n",
        "  model, alphabet = esm.pretrained.esm2_t6_8M_UR50D()\n",
        "  batch_converter = alphabet.get_batch_converter()\n",
        "  model.eval()  # disables dropout for deterministic results\n",
        "\n",
        "  # load the peptide sequence list into the bach_converter\n",
        "  batch_labels, batch_strs, batch_tokens = batch_converter(peptide_sequence_list)\n",
        "  batch_lens = (batch_tokens != alphabet.padding_idx).sum(1)\n",
        "  ## batch tokens are the embedding results of the whole data set\n",
        "\n",
        "  # Extract per-residue representations (on CPU)\n",
        "  with torch.no_grad():\n",
        "      # Here we export the last layer of the EMS model output as the representation of the peptides\n",
        "      # model'esm2_t6_8M_UR50D' only has 6 layers, and therefore repr_layers parameters is equal to 6\n",
        "      results = model(batch_tokens, repr_layers=[6], return_contacts=True)  \n",
        "  token_representations = results[\"representations\"][6]\n",
        "\n",
        "  # Generate per-sequence representations via averaging\n",
        "  # NOTE: token 0 is always a beginning-of-sequence token, so the first residue is token 1.\n",
        "  sequence_representations = []\n",
        "  for i, tokens_len in enumerate(batch_lens):\n",
        "      sequence_representations.append(token_representations[i, 1 : tokens_len - 1].mean(0))\n",
        "  # save dataset\n",
        "  # sequence_representations is a list and each element is a tensor\n",
        "  embeddings_results = collections.defaultdict(list)\n",
        "  for i in range(len(sequence_representations)):\n",
        "      # tensor can be transformed as numpy sequence_representations[0].numpy() or sequence_representations[0].to_list\n",
        "      each_seq_rep = sequence_representations[i].tolist()\n",
        "      for each_element in each_seq_rep:\n",
        "          embeddings_results[i].append(each_element)\n",
        "  embeddings_results = pd.DataFrame(embeddings_results).T\n",
        "  return embeddings_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RddxugbsdR1Y"
      },
      "source": [
        "### data loading and embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n6NOFoREw-40"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LNlD8pvizH84"
      },
      "outputs": [],
      "source": [
        "# training dataset loading\n",
        "dataset = pd.read_excel('whole_sample_dataset.xlsx',na_filter = False) # take care the NA sequence problem\n",
        "sequence_list = dataset['sequence'] \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9_ciOTsgG0lN"
      },
      "outputs": [],
      "source": [
        "# loading the y dataset for model development \n",
        "y = dataset['label']\n",
        "y = np.array(y) # transformed as np.array for CNN model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ePUhLwBUwQIi"
      },
      "outputs": [],
      "source": [
        "peptide_sequence_list = []\n",
        "for seq in sequence_list:\n",
        "    format_seq = [seq,seq] # the setting is just following the input format setting in ESM model, [name,sequence]\n",
        "    tuple_sequence = tuple(format_seq)\n",
        "    peptide_sequence_list.append(tuple_sequence) # build a summarize list variable including all the sequence information\n",
        "\n",
        "# employ ESM model for converting and save the converted data in csv format\n",
        "embeddings_results = esm_embeddings(peptide_sequence_list)\n",
        "embeddings_results.to_csv('whole_sample_dataset_esm2_t6_8M_UR50D_unified_320_dimension.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xk13-JbBXAph"
      },
      "outputs": [],
      "source": [
        "# assign the dataset \n",
        "X_data_name = 'whole_sample_dataset_esm2_t6_8M_UR50D_unified_320_dimension.csv'\n",
        "X = pd.read_csv(X_data_name,header=0,delimiter=',',index_col = 0)\n",
        "X = np.array(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9EzJGXsk1w-Z"
      },
      "source": [
        "### t-SNE of original dataset "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQuWwSm3rUcJ",
        "outputId": "b38860dd-5839-451c-d148-1f541e21bae8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import seaborn as sns\n",
        "import pandas as pd  \n",
        "tsne = TSNE(n_components=2, verbose=0, perplexity= 50, learning_rate='auto',n_iter = 5000,random_state=123)\n",
        "z = tsne.fit_transform(X) \n",
        "df = pd.DataFrame()\n",
        "df[\"comp-1\"] = z[:,0]\n",
        "df[\"comp-2\"] = z[:,1]\n",
        "y_new_label=[]\n",
        "for i in y:\n",
        "    if i == 0:\n",
        "        y_new_label.append('Active')\n",
        "    if i == 1:\n",
        "        y_new_label.append('Inactive')\n",
        "df[\"y\"] = y_new_label\n",
        "graph = sns.scatterplot(data=df, x=\"comp-1\", y=\"comp-2\", hue=y_new_label,\n",
        "                palette='BrBG_r', legend='full')\n",
        "graph_for_output = graph.get_figure()\n",
        "graph_for_output.savefig('1385_before_CleanLab.png', dpi=600)\n",
        "df.to_excel('1385_before_CleanLab.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "oHaSJkNGmOJa",
        "outputId": "809ba47a-aea9-4f13-f4bf-8af277a38a12"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import seaborn as sns\n",
        "import pandas as pd  \n",
        "tsne = TSNE(n_components=2, verbose=0, perplexity= 50, learning_rate='auto',n_iter = 5000,random_state=123)\n",
        "z = tsne.fit_transform(X) \n",
        "df = pd.DataFrame()\n",
        "df[\"comp-1\"] = z[:,0]\n",
        "df[\"comp-2\"] = z[:,1]\n",
        "y_new_label=[]\n",
        "for i in y:\n",
        "    if i == 0:\n",
        "        y_new_label.append('Active')\n",
        "    if i == 1:\n",
        "        y_new_label.append('Inactive')\n",
        "df[\"y\"] = y_new_label\n",
        "graph = sns.scatterplot(data=df, x=\"comp-1\", y=\"comp-2\", hue=y_new_label,\n",
        "                palette='BrBG_r', legend='full')\n",
        "graph_for_output = graph.get_figure()\n",
        "graph_for_output.savefig('1.AHT_t-SNE_1and2.png', dpi=300)\n",
        "df.to_excel('1.AHT_t-SNE.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "_1eWMHpkrWVm",
        "outputId": "7a001e40-3b40-47b3-c924-4863ab202905"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import seaborn as sns\n",
        "import pandas as pd  \n",
        "tsne = TSNE(n_components=2, verbose=0, perplexity= 5, learning_rate='auto',n_iter = 5000,random_state=123)\n",
        "z = tsne.fit_transform(X) \n",
        "df = pd.DataFrame()\n",
        "df[\"comp-1\"] = z[:,0]\n",
        "df[\"comp-2\"] = z[:,1]\n",
        "y_new_label=[]\n",
        "for i in y:\n",
        "    if i == 0:\n",
        "        y_new_label.append('Active')\n",
        "    if i == 1:\n",
        "        y_new_label.append('Inactive')\n",
        "df[\"y\"] = y_new_label\n",
        "graph = sns.scatterplot(data=df, x=\"comp-1\", y=\"comp-2\", hue=y_new_label,\n",
        "                palette='BrBG_r', legend='full')\n",
        "graph_for_output = graph.get_figure()\n",
        "graph_for_output.savefig('1.AHT_t-SNE_1and2.png', dpi=300)\n",
        "df.to_excel('1.AHT_t-SNE.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NsunBsWcfXUB",
        "outputId": "fed8da79-3aff-43c1-ab4e-6165b65883f1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1386,)\n",
            "(1386, 320)\n",
            "592\n",
            "794\n",
            "0\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "print(y.shape)\n",
        "print(X.shape)\n",
        "print(np.count_nonzero(y==0))\n",
        "print(np.count_nonzero(y==1))\n",
        "print(np.count_nonzero(y==2))\n",
        "print(np.count_nonzero(y==3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dN8Xh24_RHtp"
      },
      "source": [
        "### CleanLab for data cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "owAmxIvMRkue",
        "outputId": "51d665fd-328c-4927-c9c2-2e67255da4c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting cleanlab\n",
            "  Downloading cleanlab-2.2.0-py3-none-any.whl (157 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m157.5/157.5 KB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.53.0 in /usr/local/lib/python3.8/dist-packages (from cleanlab) (4.64.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from cleanlab) (2.2.0)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.8/dist-packages (from cleanlab) (1.21.6)\n",
            "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from cleanlab) (1.3.5)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.8/dist-packages (from cleanlab) (1.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.0.0->cleanlab) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.0.0->cleanlab) (2022.7.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.18->cleanlab) (3.1.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.18->cleanlab) (1.7.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.18->cleanlab) (1.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0.0->cleanlab) (1.15.0)\n",
            "Installing collected packages: cleanlab\n",
            "Successfully installed cleanlab-2.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install cleanlab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HK21s3x7_bYb"
      },
      "outputs": [],
      "source": [
        "# option 1\n",
        "from cleanlab.classification import CleanLearning\n",
        "from cleanlab.filter import find_label_issues\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "label_issues_info = CleanLearning(clf=LogisticRegression(max_iter=2000)).find_label_issues(X, y)\n",
        "# find the index of the label\n",
        "label = label_issues_info['is_label_issue']\n",
        "label_issue_index=[]\n",
        "for i in range(label_issues_info.shape[0]):\n",
        "    if label[i] == True:\n",
        "        label_issue_index.append(i)\n",
        "\n",
        "# delete all the wrong labels\n",
        "X= np.array(X)\n",
        "y = np.array(y)\n",
        "# remvoe the highest voted data and the second high voted data\n",
        "y_new = np.delete(y, label_issue_index,axis=0)\n",
        "X_new= np.delete(X, label_issue_index,axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xIfnDe97TEhl"
      },
      "outputs": [],
      "source": [
        "# save numpy array as csv file\n",
        "from numpy import savetxt\n",
        "# save to csv file\n",
        "savetxt('X_Cleaned.csv', X_new, delimiter=',')\n",
        "savetxt('y_Cleaned.csv', y_new, delimiter=',')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eOgwG39J15b"
      },
      "source": [
        "### load CleanLab processed data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iQjLpn9zV53L"
      },
      "outputs": [],
      "source": [
        "# load numpy array from csv file\n",
        "from numpy import loadtxt\n",
        "import numpy as np\n",
        "# load array\n",
        "X_new = loadtxt('X_Cleaned.csv', delimiter=',')\n",
        "y_new = loadtxt('y_Cleaned.csv', delimiter=',')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vnRH5gaJCHF",
        "outputId": "98c37667-6de1-4f21-b0ed-18f30897c3cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1020, 320)\n",
            "(1020,)\n",
            "394\n",
            "626\n"
          ]
        }
      ],
      "source": [
        "print(X_new.shape)\n",
        "print(y_new.shape)\n",
        "print(np.count_nonzero(y_new==0))\n",
        "print(np.count_nonzero(y_new==1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzMyShvqxxAy"
      },
      "source": [
        "### final dataset profile"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZCS0kBMmx3Hs"
      },
      "outputs": [],
      "source": [
        "# extract all the sequence information of X_new after CleanLab\n",
        "x_new_seq = []\n",
        "for i in range(X_new.shape[0]):\n",
        "    a=0\n",
        "    for j in range(X.shape[0]):\n",
        "        if False not in (X_new[i,:] == X[j,:]):\n",
        "            x_new_seq.append(sequence_list[j])\n",
        "            a= a+1\n",
        "            if a ==2:\n",
        "              print(i,j)\n",
        "pd.DataFrame(x_new_seq).to_csv('sequence_info_after_CleanLab.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wn01dehF2QQX"
      },
      "outputs": [],
      "source": [
        "# collect all the sequence length info\n",
        "len_seq = []\n",
        "for seq in x_new_seq:\n",
        "    len_seq.append(len(seq))\n",
        "# count their number \n",
        "sum_len_seq = []\n",
        "for i in range(30): \n",
        "    sum_len_seq.append(len_seq.count(i+1))\n",
        "# print the number of each length peptide\n",
        "sum_len_seq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e77nUZbc4HQ3"
      },
      "outputs": [],
      "source": [
        "# original dataset distribution \n",
        "\n",
        "# collect all the sequence length info\n",
        "len_seq = []\n",
        "for seq in sequence_list:\n",
        "    len_seq.append(len(seq))\n",
        "# count their number \n",
        "sum_len_seq = []\n",
        "for i in range(30): \n",
        "    sum_len_seq.append(len_seq.count(i+1))\n",
        "# print the number of each length peptide\n",
        "sum_len_seq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDxnvWKb1rTP"
      },
      "source": [
        "### Cross-validation for hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ljJnDTp4Pd6"
      },
      "outputs": [],
      "source": [
        "import statistics\n",
        "from sklearn.metrics import balanced_accuracy_score\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RZWpqaGg1yoU"
      },
      "outputs": [],
      "source": [
        "# dataset splitting \n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train_whole, X_ind_test, y_train_whole, y_ind_test =  train_test_split( X_new, y_new, test_size=0.2, random_state=1111)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5P1fK2FJApwf"
      },
      "source": [
        "#### logistic regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZl8OlLhSdl-",
        "outputId": "43fb344f-175f-437c-cab5-58007b63200d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LogisticRegression(max_iter=2000, penalty='none') 0.888 0.037\n"
          ]
        }
      ],
      "source": [
        "# logistic regresion non penality\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "acc_cv_mean_collection=[]\n",
        "C_para = [0.5]\n",
        "from sklearn.model_selection import StratifiedKFold, KFold\n",
        "for i in C_para:\n",
        "    acc_cv_collection = []\n",
        "    clf = LogisticRegression(penalty= 'none', max_iter = 2000)  # need to tune the hyperparameters\n",
        "    skf = StratifiedKFold(n_splits=10)\n",
        "    for train, test in skf.split(X_train_whole, y_train_whole):\n",
        "        X_train, X_valid, y_train, y_valid = np.take(X_train_whole, train.tolist(), axis=0), np.take(X_train_whole,\n",
        "                                                                                                     test.tolist(),\n",
        "                                                                                                     axis=0), np.take(\n",
        "            y_train_whole, train.tolist(), axis=0), np.take(y_train_whole, test.tolist(), axis=0)\n",
        "        clf.fit(X_train, y_train)\n",
        "        y_valid_pred = clf.predict(X_valid)\n",
        "        acc_cv = balanced_accuracy_score(y_valid, y_valid_pred)\n",
        "        acc_cv_collection.append(acc_cv)\n",
        "    acc_cv_mean_collection.append(round(statistics.mean(acc_cv_collection),3))\n",
        "    print(clf,round(statistics.mean(acc_cv_collection),3), round(statistics.stdev(acc_cv_collection),3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3zkmofkS7Wi",
        "outputId": "69794462-2705-4a76-e769-0751ff1f71d3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LogisticRegression(C=0.5, max_iter=5000, penalty='l1', solver='saga') 0.789 0.067\n",
            "LogisticRegression(C=1, max_iter=5000, penalty='l1', solver='saga') 0.806 0.064\n",
            "LogisticRegression(C=1.5, max_iter=5000, penalty='l1', solver='saga') 0.835 0.047\n",
            "LogisticRegression(C=2, max_iter=5000, penalty='l1', solver='saga') 0.838 0.041\n",
            "LogisticRegression(C=2.5, max_iter=5000, penalty='l1', solver='saga') 0.85 0.038\n",
            "LogisticRegression(C=4, max_iter=5000, penalty='l1', solver='saga') 0.88 0.045\n",
            "LogisticRegression(C=6, max_iter=5000, penalty='l1', solver='saga') 0.892 0.043\n",
            "LogisticRegression(C=8, max_iter=5000, penalty='l1', solver='saga') 0.887 0.04\n",
            "LogisticRegression(C=13, max_iter=5000, penalty='l1', solver='saga') 0.885 0.041\n",
            "LogisticRegression(C=14, max_iter=5000, penalty='l1', solver='saga') 0.885 0.041\n",
            "LogisticRegression(C=15, max_iter=5000, penalty='l1', solver='saga') 0.885 0.043\n",
            "LogisticRegression(C=16, max_iter=5000, penalty='l1', solver='saga') 0.885 0.043\n",
            "LogisticRegression(C=20, max_iter=5000, penalty='l1', solver='saga') 0.882 0.043\n",
            "LogisticRegression(C=30, max_iter=5000, penalty='l1', solver='saga') 0.887 0.045\n",
            "LogisticRegression(C=40, max_iter=5000, penalty='l1', solver='saga') 0.888 0.04\n",
            "LogisticRegression(C=50, max_iter=5000, penalty='l1', solver='saga') 0.885 0.043\n",
            "LogisticRegression(C=60, max_iter=5000, penalty='l1', solver='saga') 0.887 0.043\n",
            "LogisticRegression(C=70, max_iter=5000, penalty='l1', solver='saga') 0.886 0.04\n",
            "LogisticRegression(C=80, max_iter=5000, penalty='l1', solver='saga') 0.886 0.04\n",
            "LogisticRegression(C=90, max_iter=5000, penalty='l1', solver='saga') 0.887 0.041\n",
            "LogisticRegression(C=100, max_iter=5000, penalty='l1', solver='saga') 0.886 0.041\n",
            "LogisticRegression(C=125, max_iter=5000, penalty='l1', solver='saga') 0.888 0.042\n"
          ]
        }
      ],
      "source": [
        "# logistic regresion l1 penality\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "acc_cv_mean_collection=[]\n",
        "C_para = [0.5,1,1.5,2,2.5,4,6,8,13,14,15,16,20,30,40, 50,60,70, 80, 90 ,100,125]\n",
        "from sklearn.model_selection import StratifiedKFold, KFold\n",
        "for i in C_para:\n",
        "    acc_cv_collection = []\n",
        "    clf = LogisticRegression(penalty= 'l1', solver= 'saga', C=i, max_iter = 5000)  # need to tune the hyperparameters\n",
        "    skf = StratifiedKFold(n_splits=10)\n",
        "    for train, test in skf.split(X_train_whole, y_train_whole):\n",
        "        X_train, X_valid, y_train, y_valid = np.take(X_train_whole, train.tolist(), axis=0), np.take(X_train_whole,\n",
        "                                                                                                     test.tolist(),\n",
        "                                                                                                     axis=0), np.take(\n",
        "            y_train_whole, train.tolist(), axis=0), np.take(y_train_whole, test.tolist(), axis=0)\n",
        "        clf.fit(X_train, y_train)\n",
        "        y_valid_pred = clf.predict(X_valid)\n",
        "        acc_cv = balanced_accuracy_score(y_valid, y_valid_pred)\n",
        "        acc_cv_collection.append(acc_cv)\n",
        "    acc_cv_mean_collection.append(round(statistics.mean(acc_cv_collection),3))\n",
        "    print(clf,round(statistics.mean(acc_cv_collection),3), round(statistics.stdev(acc_cv_collection),3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtfLMPXiSVdm",
        "outputId": "7aee32f4-d860-437f-a69a-abba492afd70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression(C=0.5, max_iter=5000) 0.812 0.072\n",
            "LogisticRegression(C=1, max_iter=5000) 0.833 0.063\n",
            "LogisticRegression(C=1.5, max_iter=5000) 0.833 0.054\n",
            "LogisticRegression(C=2, max_iter=5000) 0.849 0.046\n",
            "LogisticRegression(C=2.5, max_iter=5000) 0.854 0.046\n",
            "LogisticRegression(C=4, max_iter=5000) 0.858 0.044\n",
            "LogisticRegression(C=6, max_iter=5000) 0.87 0.05\n",
            "LogisticRegression(C=8, max_iter=5000) 0.877 0.05\n",
            "LogisticRegression(C=13, max_iter=5000) 0.881 0.043\n",
            "LogisticRegression(C=14, max_iter=5000) 0.881 0.043\n",
            "LogisticRegression(C=15, max_iter=5000) 0.881 0.043\n",
            "LogisticRegression(C=16, max_iter=5000) 0.88 0.041\n",
            "LogisticRegression(C=20, max_iter=5000) 0.885 0.042\n",
            "LogisticRegression(C=30, max_iter=5000) 0.89 0.043\n",
            "LogisticRegression(C=40, max_iter=5000) 0.886 0.047\n",
            "LogisticRegression(C=50, max_iter=5000) 0.885 0.046\n",
            "LogisticRegression(C=60, max_iter=5000) 0.885 0.046\n",
            "LogisticRegression(C=70, max_iter=5000) 0.888 0.044\n",
            "LogisticRegression(C=80, max_iter=5000) 0.886 0.044\n",
            "LogisticRegression(C=90, max_iter=5000) 0.888 0.045\n",
            "LogisticRegression(C=100, max_iter=5000) 0.886 0.044\n",
            "LogisticRegression(C=125, max_iter=5000) 0.888 0.044\n",
            "LogisticRegression(C=500, max_iter=5000) 0.889 0.04\n",
            "LogisticRegression(C=1000, max_iter=5000) 0.891 0.038\n",
            "LogisticRegression(C=2000, max_iter=5000) 0.892 0.042\n",
            "LogisticRegression(C=4000, max_iter=5000) 0.891 0.044\n",
            "LogisticRegression(C=10000, max_iter=5000) 0.889 0.044\n"
          ]
        }
      ],
      "source": [
        "# logistic regresion\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "acc_cv_mean_collection=[]\n",
        "C_para = [0.5,1,1.5,2,2.5,4,6,8,13,14,15,16,20,30,40, 50,60,70, 80, 90 ,100,125, 500, 1000,2000,4000,10000]\n",
        "from sklearn.model_selection import StratifiedKFold, KFold\n",
        "for i in C_para:\n",
        "    acc_cv_collection = []\n",
        "    clf = LogisticRegression(C=i, max_iter = 5000)  # need to tune the hyperparameters\n",
        "    skf = StratifiedKFold(n_splits=10)\n",
        "    for train, test in skf.split(X_train_whole, y_train_whole):\n",
        "        X_train, X_valid, y_train, y_valid = np.take(X_train_whole, train.tolist(), axis=0), np.take(X_train_whole,\n",
        "                                                                                                     test.tolist(),\n",
        "                                                                                                     axis=0), np.take(\n",
        "            y_train_whole, train.tolist(), axis=0), np.take(y_train_whole, test.tolist(), axis=0)\n",
        "        clf.fit(X_train, y_train)\n",
        "        y_valid_pred = clf.predict(X_valid)\n",
        "        acc_cv = balanced_accuracy_score(y_valid, y_valid_pred)\n",
        "        acc_cv_collection.append(acc_cv)\n",
        "    acc_cv_mean_collection.append(round(statistics.mean(acc_cv_collection),3))\n",
        "    print(clf,round(statistics.mean(acc_cv_collection),3), round(statistics.stdev(acc_cv_collection),3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YjMK3qEHAvLV"
      },
      "source": [
        "#### random forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "fsAq7RSnGugd",
        "outputId": "ef21fa92-42f3-45dc-9de6-b73e5d8c5836"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RandomForestClassifier(max_depth=1, max_features='log2', n_estimators=80) 0.507 0.012\n",
            "RandomForestClassifier(max_depth=1, max_features='sqrt', n_estimators=80) 0.568 0.041\n",
            "RandomForestClassifier(max_depth=2, max_features='log2', n_estimators=80) 0.66 0.062\n",
            "RandomForestClassifier(max_depth=2, max_features='sqrt', n_estimators=80) 0.677 0.058\n",
            "RandomForestClassifier(max_depth=3, max_features='log2', n_estimators=80) 0.696 0.076\n",
            "RandomForestClassifier(max_depth=3, max_features='sqrt', n_estimators=80) 0.722 0.074\n",
            "RandomForestClassifier(max_depth=4, max_features='log2', n_estimators=80) 0.718 0.067\n",
            "RandomForestClassifier(max_depth=4, max_features='sqrt', n_estimators=80) 0.745 0.074\n",
            "RandomForestClassifier(max_depth=1, max_features='log2', n_estimators=160) 0.51 0.011\n",
            "RandomForestClassifier(max_depth=1, max_features='sqrt', n_estimators=160) 0.569 0.041\n",
            "RandomForestClassifier(max_depth=2, max_features='log2', n_estimators=160) 0.661 0.062\n",
            "RandomForestClassifier(max_depth=2, max_features='sqrt', n_estimators=160) 0.689 0.06\n",
            "RandomForestClassifier(max_depth=3, max_features='log2', n_estimators=160) 0.698 0.067\n",
            "RandomForestClassifier(max_depth=3, max_features='sqrt', n_estimators=160) 0.714 0.08\n",
            "RandomForestClassifier(max_depth=4, max_features='log2', n_estimators=160) 0.723 0.069\n",
            "RandomForestClassifier(max_depth=4, max_features='sqrt', n_estimators=160) 0.731 0.083\n",
            "RandomForestClassifier(max_depth=1, max_features='log2', n_estimators=320) 0.505 0.008\n",
            "RandomForestClassifier(max_depth=1, max_features='sqrt', n_estimators=320) 0.565 0.036\n",
            "RandomForestClassifier(max_depth=2, max_features='log2', n_estimators=320) 0.662 0.058\n",
            "RandomForestClassifier(max_depth=2, max_features='sqrt', n_estimators=320) 0.692 0.065\n",
            "RandomForestClassifier(max_depth=3, max_features='log2', n_estimators=320) 0.701 0.079\n",
            "RandomForestClassifier(max_depth=3, max_features='sqrt', n_estimators=320) 0.716 0.078\n",
            "RandomForestClassifier(max_depth=4, max_features='log2', n_estimators=320) 0.718 0.075\n",
            "RandomForestClassifier(max_depth=4, max_features='sqrt', n_estimators=320) 0.74 0.079\n",
            "RandomForestClassifier(max_depth=1, max_features='log2', n_estimators=480) 0.505 0.008\n",
            "RandomForestClassifier(max_depth=1, max_features='sqrt', n_estimators=480) 0.563 0.03\n",
            "RandomForestClassifier(max_depth=2, max_features='log2', n_estimators=480) 0.667 0.058\n",
            "RandomForestClassifier(max_depth=2, max_features='sqrt', n_estimators=480) 0.692 0.072\n",
            "RandomForestClassifier(max_depth=3, max_features='log2', n_estimators=480) 0.709 0.077\n",
            "RandomForestClassifier(max_depth=3, max_features='sqrt', n_estimators=480) 0.715 0.073\n",
            "RandomForestClassifier(max_depth=4, max_features='log2', n_estimators=480) 0.724 0.082\n",
            "RandomForestClassifier(max_depth=4, max_features='sqrt', n_estimators=480) 0.734 0.076\n",
            "RandomForestClassifier(max_depth=1, max_features='log2', n_estimators=640) 0.507 0.008\n",
            "RandomForestClassifier(max_depth=1, max_features='sqrt', n_estimators=640) 0.566 0.04\n",
            "RandomForestClassifier(max_depth=2, max_features='log2', n_estimators=640) 0.662 0.063\n",
            "RandomForestClassifier(max_depth=2, max_features='sqrt', n_estimators=640) 0.693 0.061\n",
            "RandomForestClassifier(max_depth=3, max_features='log2', n_estimators=640) 0.71 0.084\n",
            "RandomForestClassifier(max_depth=3, max_features='sqrt', n_estimators=640) 0.716 0.077\n",
            "RandomForestClassifier(max_depth=4, max_features='log2', n_estimators=640) 0.72 0.082\n",
            "RandomForestClassifier(max_depth=4, max_features='sqrt', n_estimators=640) 0.733 0.078\n",
            "RandomForestClassifier(max_depth=1, max_features='log2', n_estimators=1280) 0.507 0.008\n",
            "RandomForestClassifier(max_depth=1, max_features='sqrt', n_estimators=1280) 0.561 0.037\n",
            "RandomForestClassifier(max_depth=2, max_features='log2', n_estimators=1280) 0.659 0.058\n",
            "RandomForestClassifier(max_depth=2, max_features='sqrt', n_estimators=1280) 0.691 0.068\n",
            "RandomForestClassifier(max_depth=3, max_features='log2', n_estimators=1280) 0.705 0.078\n",
            "RandomForestClassifier(max_depth=3, max_features='sqrt', n_estimators=1280) 0.718 0.075\n",
            "RandomForestClassifier(max_depth=4, max_features='log2', n_estimators=1280) 0.716 0.079\n",
            "RandomForestClassifier(max_depth=4, max_features='sqrt', n_estimators=1280) 0.729 0.074\n"
          ]
        }
      ],
      "source": [
        "# random forest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import StratifiedKFold, KFold\n",
        "\n",
        "acc_cv_mean_collection = []\n",
        "n_estimatorsint = [80, 160, 320, 480, 640, 1280]\n",
        "max_depthint = [1, 2, 3, 4]\n",
        "max_features = ['log2', 'sqrt']\n",
        "\n",
        "for i in n_estimatorsint:\n",
        "    for j in max_depthint:\n",
        "        for p in max_features:\n",
        "            acc_cv_collection = []\n",
        "            clf = RandomForestClassifier(n_estimators=i, max_depth=j, max_features=p)\n",
        "            from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold\n",
        "            skf = StratifiedKFold(n_splits=10)\n",
        "            for train, test in skf.split(X_train_whole, y_train_whole):\n",
        "                X_train, X_valid, y_train, y_valid = np.take(X_train_whole, train.tolist(), axis=0), np.take(X_train_whole,\n",
        "                                                                                                                test.tolist(),\n",
        "                                                                                                                axis=0), np.take(\n",
        "                    y_train_whole, train.tolist(), axis=0), np.take(y_train_whole, test.tolist(), axis=0)\n",
        "                clf.fit(X_train, y_train)\n",
        "                y_valid_pred = clf.predict(X_valid)\n",
        "                acc_cv = balanced_accuracy_score(y_valid, y_valid_pred)\n",
        "                acc_cv_collection.append(acc_cv)\n",
        "            acc_cv_mean_collection.append(round(statistics.mean(acc_cv_collection),3))\n",
        "            print(clf,round(statistics.mean(acc_cv_collection),3), round(statistics.stdev(acc_cv_collection),3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cp1Han6zB1vU"
      },
      "source": [
        "#### KNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tG3BrQXo44in",
        "outputId": "705efb81-acc1-4997-f73e-297d66f0a9b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNeighborsClassifier(leaf_size=10, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=2) 0.727 0.045\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=2, weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=2,\n",
            "                     weights='distance') 0.723 0.062\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=3) 0.735 0.027\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=3, weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=3,\n",
            "                     weights='distance') 0.739 0.026\n",
            "KNeighborsClassifier(leaf_size=10) 0.73 0.022\n",
            "KNeighborsClassifier(leaf_size=20) 0.73 0.022\n",
            "KNeighborsClassifier() 0.73 0.022\n",
            "KNeighborsClassifier(leaf_size=40) 0.73 0.022\n",
            "KNeighborsClassifier(leaf_size=50) 0.73 0.022\n",
            "KNeighborsClassifier(leaf_size=60) 0.73 0.022\n",
            "KNeighborsClassifier(leaf_size=70) 0.73 0.022\n",
            "KNeighborsClassifier(leaf_size=80) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree') 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree') 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='brute') 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70) 0.73 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80) 0.73 0.022\n",
            "KNeighborsClassifier(leaf_size=10, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(leaf_size=20, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(leaf_size=40, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(leaf_size=50, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(leaf_size=60, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(leaf_size=70, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(leaf_size=80, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='brute', weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, weights='distance') 0.738 0.022\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=7) 0.755 0.042\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=7, weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=7,\n",
            "                     weights='distance') 0.761 0.039\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=11) 0.767 0.044\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=11, weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=11,\n",
            "                     weights='distance') 0.779 0.039\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=13) 0.766 0.044\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=13, weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=13,\n",
            "                     weights='distance') 0.776 0.04\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=17) 0.769 0.034\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=17, weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=17,\n",
            "                     weights='distance') 0.773 0.034\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=19) 0.76 0.046\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=19, weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=19,\n",
            "                     weights='distance') 0.765 0.048\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=23) 0.749 0.054\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=23, weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=23,\n",
            "                     weights='distance') 0.758 0.052\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=29) 0.746 0.053\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=29, weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=29,\n",
            "                     weights='distance') 0.751 0.052\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=31) 0.741 0.052\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=31, weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=31,\n",
            "                     weights='distance') 0.75 0.054\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=37) 0.741 0.066\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=37, weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=37,\n",
            "                     weights='distance') 0.748 0.055\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=41) 0.741 0.074\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=41, weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=41,\n",
            "                     weights='distance') 0.745 0.071\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=43) 0.741 0.069\n",
            "KNeighborsClassifier(leaf_size=10, n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(leaf_size=20, n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(leaf_size=40, n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(leaf_size=50, n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(leaf_size=60, n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(leaf_size=70, n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(leaf_size=80, n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=10, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='ball_tree', n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=50, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=60, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=70, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='ball_tree', leaf_size=80, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=10, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=50, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=60, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=70, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='kd_tree', leaf_size=80, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=10, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=20, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=43, weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=40, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=50, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=60, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=70, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n",
            "KNeighborsClassifier(algorithm='brute', leaf_size=80, n_neighbors=43,\n",
            "                     weights='distance') 0.749 0.065\n"
          ]
        }
      ],
      "source": [
        "# KNN\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import StratifiedKFold, KFold\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "acc_cv_mean_collection = []\n",
        "n_neighbors = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43]\n",
        "weights = ['uniform', 'distance']\n",
        "algorithm = ['auto', 'ball_tree', 'kd_tree', 'brute']\n",
        "leaf_size = [10, 20, 30, 40, 50, 60, 70, 80]\n",
        "for i in n_neighbors:\n",
        "    for j in weights:\n",
        "        for k in algorithm:\n",
        "            for p in leaf_size:\n",
        "                acc_cv_collection = []\n",
        "                clf = KNeighborsClassifier(n_neighbors=i, weights=j, algorithm=k,\n",
        "                                           leaf_size=p)  # need to tune the hyperparameters\n",
        "                from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold\n",
        "                skf = StratifiedKFold(n_splits=10)\n",
        "                for train, test in skf.split(X_train_whole, y_train_whole):\n",
        "                    X_train, X_valid, y_train, y_valid = np.take(X_train_whole, train.tolist(), axis=0), np.take(X_train_whole,\n",
        "                                                                                                                test.tolist(),\n",
        "                                                                                                                axis=0), np.take(\n",
        "                        y_train_whole, train.tolist(), axis=0), np.take(y_train_whole, test.tolist(), axis=0)\n",
        "                    clf.fit(X_train, y_train)\n",
        "                    y_valid_pred = clf.predict(X_valid)\n",
        "                    acc_cv = balanced_accuracy_score(y_valid, y_valid_pred)\n",
        "                    # print(acc_cv)\n",
        "                    acc_cv_collection.append(acc_cv)\n",
        "                acc_cv_mean_collection.append(round(statistics.mean(acc_cv_collection),3))\n",
        "                print(clf,round(statistics.mean(acc_cv_collection),3), round(statistics.stdev(acc_cv_collection),3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qoJpjw-uCihR"
      },
      "source": [
        "#### SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUUyFzZiGq0j",
        "outputId": "83a772c3-6389-4ac0-eca2-87c7f2f564c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSVC(C=0.0001, max_iter=80000, tol=1e-05) 0.5 0.0\n",
            "LinearSVC(C=0.01, max_iter=80000, tol=1e-05) 0.772 0.075\n",
            "LinearSVC(C=0.1, max_iter=80000, tol=1e-05) 0.828 0.055\n",
            "LinearSVC(C=0.5, max_iter=80000, tol=1e-05) 0.872 0.046\n",
            "LinearSVC(C=1.1, max_iter=80000, tol=1e-05) 0.878 0.044\n",
            "LinearSVC(C=1.3, max_iter=80000, tol=1e-05) 0.883 0.045\n",
            "LinearSVC(C=10, max_iter=80000, tol=1e-05) 0.89 0.041\n",
            "LinearSVC(C=20, max_iter=80000, tol=1e-05) 0.893 0.041\n",
            "LinearSVC(C=30, max_iter=80000, tol=1e-05) 0.89 0.041\n",
            "LinearSVC(C=40, max_iter=80000, tol=1e-05) 0.885 0.039\n",
            "LinearSVC(C=50, max_iter=80000, tol=1e-05) 0.884 0.037\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSVC(C=60, max_iter=80000, tol=1e-05) 0.883 0.036\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSVC(C=70, max_iter=80000, tol=1e-05) 0.887 0.035\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSVC(C=80, max_iter=80000, tol=1e-05) 0.888 0.036\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSVC(C=90, max_iter=80000, tol=1e-05) 0.889 0.038\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSVC(C=100, max_iter=80000, tol=1e-05) 0.892 0.038\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSVC(C=300, max_iter=80000, tol=1e-05) 0.894 0.042\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# LinearSVC\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.model_selection import StratifiedKFold, KFold\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "acc_cv_mean_collection = []\n",
        "C = [0.0001, 0.01, 0.1,  0.5, 1.1, 1.3, 10,20, 30, 40, 50,60, 70, 80, 90, 100, 300]\n",
        "degree = [1]\n",
        "tol = [1e-5]\n",
        "for i in C:\n",
        "    for k in degree:\n",
        "        for p in tol:\n",
        "            acc_cv_collection = []\n",
        "            clf = LinearSVC(C=i, tol=p, max_iter= 80000)  # need to tune the hyperparameters\n",
        "            from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold\n",
        "            skf = StratifiedKFold(n_splits=10)\n",
        "            for train, test in skf.split(X_train_whole, y_train_whole):\n",
        "                X_train, X_valid, y_train, y_valid = np.take(X_train_whole, train.tolist(), axis=0), np.take(X_train_whole,\n",
        "                                                                                                            test.tolist(),\n",
        "                                                                                                            axis=0), np.take(\n",
        "                    y_train_whole, train.tolist(), axis=0), np.take(y_train_whole, test.tolist(), axis=0)\n",
        "                clf.fit(X_train, y_train)\n",
        "                y_valid_pred = clf.predict(X_valid)\n",
        "                acc_cv = balanced_accuracy_score(y_valid, y_valid_pred)\n",
        "                acc_cv_collection.append(acc_cv)\n",
        "            acc_cv_mean_collection.append(round(statistics.mean(acc_cv_collection),3))\n",
        "            print(clf,round(statistics.mean(acc_cv_collection),3), round(statistics.stdev(acc_cv_collection),3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2V4Lif5bG0tU",
        "outputId": "0ba38718-b56f-4f9e-b29a-52a279e69ee9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC(C=0.001, degree=1, kernel='linear') 0.5 0.0\n",
            "SVC(C=0.001, kernel='linear') 0.5 0.0\n",
            "SVC(C=0.001, degree=5, kernel='linear') 0.5 0.0\n",
            "SVC(C=0.001, degree=7, kernel='linear') 0.5 0.0\n",
            "SVC(C=0.001, degree=9, kernel='linear') 0.5 0.0\n",
            "SVC(C=0.001, degree=1, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.001, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.001, degree=5, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.001, degree=7, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.001, degree=9, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.001, degree=1) 0.5 0.0\n",
            "SVC(C=0.001) 0.5 0.0\n",
            "SVC(C=0.001, degree=5) 0.5 0.0\n",
            "SVC(C=0.001, degree=7) 0.5 0.0\n",
            "SVC(C=0.001, degree=9) 0.5 0.0\n",
            "SVC(C=0.001, degree=1, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.001, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.001, degree=5, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.001, degree=7, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.001, degree=9, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.01, degree=1, kernel='linear') 0.515 0.018\n",
            "SVC(C=0.01, kernel='linear') 0.515 0.018\n",
            "SVC(C=0.01, degree=5, kernel='linear') 0.515 0.018\n",
            "SVC(C=0.01, degree=7, kernel='linear') 0.515 0.018\n",
            "SVC(C=0.01, degree=9, kernel='linear') 0.515 0.018\n",
            "SVC(C=0.01, degree=1, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.01, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.01, degree=5, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.01, degree=7, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.01, degree=9, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.01, degree=1) 0.5 0.0\n",
            "SVC(C=0.01) 0.5 0.0\n",
            "SVC(C=0.01, degree=5) 0.5 0.0\n",
            "SVC(C=0.01, degree=7) 0.5 0.0\n",
            "SVC(C=0.01, degree=9) 0.5 0.0\n",
            "SVC(C=0.01, degree=1, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.01, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.01, degree=5, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.01, degree=7, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.01, degree=9, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.1, degree=1, kernel='linear') 0.791 0.062\n",
            "SVC(C=0.1, kernel='linear') 0.791 0.062\n",
            "SVC(C=0.1, degree=5, kernel='linear') 0.791 0.062\n",
            "SVC(C=0.1, degree=7, kernel='linear') 0.791 0.062\n",
            "SVC(C=0.1, degree=9, kernel='linear') 0.791 0.062\n",
            "SVC(C=0.1, degree=1, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.1, kernel='poly') 0.5 0.0\n",
            "SVC(C=0.1, degree=5, kernel='poly') 0.518 0.018\n",
            "SVC(C=0.1, degree=7, kernel='poly') 0.582 0.028\n",
            "SVC(C=0.1, degree=9, kernel='poly') 0.647 0.044\n",
            "SVC(C=0.1, degree=1) 0.5 0.0\n",
            "SVC(C=0.1) 0.5 0.0\n",
            "SVC(C=0.1, degree=5) 0.5 0.0\n",
            "SVC(C=0.1, degree=7) 0.5 0.0\n",
            "SVC(C=0.1, degree=9) 0.5 0.0\n",
            "SVC(C=0.1, degree=1, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.1, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.1, degree=5, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.1, degree=7, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.1, degree=9, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.5, degree=1, kernel='linear') 0.825 0.05\n",
            "SVC(C=0.5, kernel='linear') 0.825 0.05\n",
            "SVC(C=0.5, degree=5, kernel='linear') 0.825 0.05\n",
            "SVC(C=0.5, degree=7, kernel='linear') 0.825 0.05\n",
            "SVC(C=0.5, degree=9, kernel='linear') 0.825 0.05\n",
            "SVC(C=0.5, degree=1, kernel='poly') 0.556 0.018\n",
            "SVC(C=0.5, kernel='poly') 0.76 0.073\n",
            "SVC(C=0.5, degree=5, kernel='poly') 0.766 0.067\n",
            "SVC(C=0.5, degree=7, kernel='poly') 0.763 0.068\n",
            "SVC(C=0.5, degree=9, kernel='poly') 0.767 0.064\n",
            "SVC(C=0.5, degree=1) 0.717 0.08\n",
            "SVC(C=0.5) 0.717 0.08\n",
            "SVC(C=0.5, degree=5) 0.717 0.08\n",
            "SVC(C=0.5, degree=7) 0.717 0.08\n",
            "SVC(C=0.5, degree=9) 0.717 0.08\n",
            "SVC(C=0.5, degree=1, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.5, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.5, degree=5, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.5, degree=7, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=0.5, degree=9, kernel='sigmoid') 0.5 0.0\n",
            "SVC(C=1.1, degree=1, kernel='linear') 0.863 0.045\n",
            "SVC(C=1.1, kernel='linear') 0.863 0.045\n",
            "SVC(C=1.1, degree=5, kernel='linear') 0.863 0.045\n",
            "SVC(C=1.1, degree=7, kernel='linear') 0.863 0.045\n",
            "SVC(C=1.1, degree=9, kernel='linear') 0.863 0.045\n",
            "SVC(C=1.1, degree=1, kernel='poly') 0.742 0.094\n",
            "SVC(C=1.1, kernel='poly') 0.79 0.068\n",
            "SVC(C=1.1, degree=5, kernel='poly') 0.798 0.06\n",
            "SVC(C=1.1, degree=7, kernel='poly') 0.798 0.062\n",
            "SVC(C=1.1, degree=9, kernel='poly') 0.799 0.061\n",
            "SVC(C=1.1, degree=1) 0.777 0.065\n",
            "SVC(C=1.1) 0.777 0.065\n",
            "SVC(C=1.1, degree=5) 0.777 0.065\n",
            "SVC(C=1.1, degree=7) 0.777 0.065\n",
            "SVC(C=1.1, degree=9) 0.777 0.065\n",
            "SVC(C=1.1, degree=1, kernel='sigmoid') 0.553 0.02\n",
            "SVC(C=1.1, kernel='sigmoid') 0.553 0.02\n",
            "SVC(C=1.1, degree=5, kernel='sigmoid') 0.553 0.02\n",
            "SVC(C=1.1, degree=7, kernel='sigmoid') 0.553 0.02\n",
            "SVC(C=1.1, degree=9, kernel='sigmoid') 0.553 0.02\n",
            "SVC(C=1.3, degree=1, kernel='linear') 0.878 0.046\n",
            "SVC(C=1.3, kernel='linear') 0.878 0.046\n",
            "SVC(C=1.3, degree=5, kernel='linear') 0.878 0.046\n",
            "SVC(C=1.3, degree=7, kernel='linear') 0.878 0.046\n",
            "SVC(C=1.3, degree=9, kernel='linear') 0.878 0.046\n",
            "SVC(C=1.3, degree=1, kernel='poly') 0.757 0.078\n",
            "SVC(C=1.3, kernel='poly') 0.791 0.065\n",
            "SVC(C=1.3, degree=5, kernel='poly') 0.801 0.059\n",
            "SVC(C=1.3, degree=7, kernel='poly') 0.798 0.058\n",
            "SVC(C=1.3, degree=9, kernel='poly') 0.806 0.06\n",
            "SVC(C=1.3, degree=1) 0.788 0.064\n",
            "SVC(C=1.3) 0.788 0.064\n",
            "SVC(C=1.3, degree=5) 0.788 0.064\n",
            "SVC(C=1.3, degree=7) 0.788 0.064\n",
            "SVC(C=1.3, degree=9) 0.788 0.064\n",
            "SVC(C=1.3, degree=1, kernel='sigmoid') 0.612 0.03\n",
            "SVC(C=1.3, kernel='sigmoid') 0.612 0.03\n",
            "SVC(C=1.3, degree=5, kernel='sigmoid') 0.612 0.03\n",
            "SVC(C=1.3, degree=7, kernel='sigmoid') 0.612 0.03\n",
            "SVC(C=1.3, degree=9, kernel='sigmoid') 0.612 0.03\n",
            "SVC(C=5, degree=1, kernel='linear') 0.886 0.054\n",
            "SVC(C=5, kernel='linear') 0.886 0.054\n",
            "SVC(C=5, degree=5, kernel='linear') 0.886 0.054\n",
            "SVC(C=5, degree=7, kernel='linear') 0.886 0.054\n",
            "SVC(C=5, degree=9, kernel='linear') 0.886 0.054\n",
            "SVC(C=5, degree=1, kernel='poly') 0.797 0.064\n",
            "SVC(C=5, kernel='poly') 0.828 0.049\n",
            "SVC(C=5, degree=5, kernel='poly') 0.835 0.045\n",
            "SVC(C=5, degree=7, kernel='poly') 0.839 0.045\n",
            "SVC(C=5, degree=9, kernel='poly') 0.843 0.044\n",
            "SVC(C=5, degree=1) 0.826 0.052\n",
            "SVC(C=5) 0.826 0.052\n",
            "SVC(C=5, degree=5) 0.826 0.052\n",
            "SVC(C=5, degree=7) 0.826 0.052\n",
            "SVC(C=5, degree=9) 0.826 0.052\n",
            "SVC(C=5, degree=1, kernel='sigmoid') 0.787 0.067\n",
            "SVC(C=5, kernel='sigmoid') 0.787 0.067\n",
            "SVC(C=5, degree=5, kernel='sigmoid') 0.787 0.067\n",
            "SVC(C=5, degree=7, kernel='sigmoid') 0.787 0.067\n",
            "SVC(C=5, degree=9, kernel='sigmoid') 0.787 0.067\n",
            "SVC(C=10, degree=1, kernel='linear') 0.893 0.037\n",
            "SVC(C=10, kernel='linear') 0.893 0.037\n",
            "SVC(C=10, degree=5, kernel='linear') 0.893 0.037\n",
            "SVC(C=10, degree=7, kernel='linear') 0.893 0.037\n",
            "SVC(C=10, degree=9, kernel='linear') 0.893 0.037\n",
            "SVC(C=10, degree=1, kernel='poly') 0.816 0.061\n",
            "SVC(C=10, kernel='poly') 0.836 0.041\n",
            "SVC(C=10, degree=5, kernel='poly') 0.85 0.036\n",
            "SVC(C=10, degree=7, kernel='poly') 0.865 0.037\n",
            "SVC(C=10, degree=9, kernel='poly') 0.867 0.044\n",
            "SVC(C=10, degree=1) 0.834 0.045\n",
            "SVC(C=10) 0.834 0.045\n",
            "SVC(C=10, degree=5) 0.834 0.045\n",
            "SVC(C=10, degree=7) 0.834 0.045\n",
            "SVC(C=10, degree=9) 0.834 0.045\n",
            "SVC(C=10, degree=1, kernel='sigmoid') 0.796 0.064\n",
            "SVC(C=10, kernel='sigmoid') 0.796 0.064\n",
            "SVC(C=10, degree=5, kernel='sigmoid') 0.796 0.064\n",
            "SVC(C=10, degree=7, kernel='sigmoid') 0.796 0.064\n",
            "SVC(C=10, degree=9, kernel='sigmoid') 0.796 0.064\n",
            "SVC(C=30, degree=1, kernel='linear') 0.888 0.04\n",
            "SVC(C=30, kernel='linear') 0.888 0.04\n",
            "SVC(C=30, degree=5, kernel='linear') 0.888 0.04\n",
            "SVC(C=30, degree=7, kernel='linear') 0.888 0.04\n",
            "SVC(C=30, degree=9, kernel='linear') 0.888 0.04\n",
            "SVC(C=30, degree=1, kernel='poly') 0.85 0.051\n",
            "SVC(C=30, kernel='poly') 0.879 0.038\n",
            "SVC(C=30, degree=5, kernel='poly') 0.871 0.041\n",
            "SVC(C=30, degree=7, kernel='poly') 0.868 0.046\n",
            "SVC(C=30, degree=9, kernel='poly') 0.875 0.042\n",
            "SVC(C=30, degree=1) 0.863 0.041\n",
            "SVC(C=30) 0.863 0.041\n",
            "SVC(C=30, degree=5) 0.863 0.041\n",
            "SVC(C=30, degree=7) 0.863 0.041\n",
            "SVC(C=30, degree=9) 0.863 0.041\n",
            "SVC(C=30, degree=1, kernel='sigmoid') 0.819 0.067\n",
            "SVC(C=30, kernel='sigmoid') 0.819 0.067\n",
            "SVC(C=30, degree=5, kernel='sigmoid') 0.819 0.067\n",
            "SVC(C=30, degree=7, kernel='sigmoid') 0.819 0.067\n",
            "SVC(C=30, degree=9, kernel='sigmoid') 0.819 0.067\n",
            "SVC(C=50, degree=1, kernel='linear') 0.888 0.039\n",
            "SVC(C=50, kernel='linear') 0.888 0.039\n",
            "SVC(C=50, degree=5, kernel='linear') 0.888 0.039\n",
            "SVC(C=50, degree=7, kernel='linear') 0.888 0.039\n",
            "SVC(C=50, degree=9, kernel='linear') 0.888 0.039\n",
            "SVC(C=50, degree=1, kernel='poly') 0.876 0.044\n",
            "SVC(C=50, kernel='poly') 0.878 0.031\n",
            "SVC(C=50, degree=5, kernel='poly') 0.87 0.043\n",
            "SVC(C=50, degree=7, kernel='poly') 0.875 0.038\n",
            "SVC(C=50, degree=9, kernel='poly') 0.879 0.051\n",
            "SVC(C=50, degree=1) 0.882 0.039\n",
            "SVC(C=50) 0.882 0.039\n",
            "SVC(C=50, degree=5) 0.882 0.039\n",
            "SVC(C=50, degree=7) 0.882 0.039\n",
            "SVC(C=50, degree=9) 0.882 0.039\n",
            "SVC(C=50, degree=1, kernel='sigmoid') 0.818 0.052\n",
            "SVC(C=50, kernel='sigmoid') 0.818 0.052\n",
            "SVC(C=50, degree=5, kernel='sigmoid') 0.818 0.052\n",
            "SVC(C=50, degree=7, kernel='sigmoid') 0.818 0.052\n",
            "SVC(C=50, degree=9, kernel='sigmoid') 0.818 0.052\n",
            "SVC(C=100, degree=1, kernel='linear') 0.892 0.04\n",
            "SVC(C=100, kernel='linear') 0.892 0.04\n",
            "SVC(C=100, degree=5, kernel='linear') 0.892 0.04\n",
            "SVC(C=100, degree=7, kernel='linear') 0.892 0.04\n",
            "SVC(C=100, degree=9, kernel='linear') 0.892 0.04\n",
            "SVC(C=100, degree=1, kernel='poly') 0.876 0.047\n",
            "SVC(C=100, kernel='poly') 0.872 0.036\n",
            "SVC(C=100, degree=5, kernel='poly') 0.875 0.045\n",
            "SVC(C=100, degree=7, kernel='poly') 0.872 0.042\n",
            "SVC(C=100, degree=9, kernel='poly') 0.875 0.042\n",
            "SVC(C=100, degree=1) 0.878 0.032\n",
            "SVC(C=100) 0.878 0.032\n",
            "SVC(C=100, degree=5) 0.878 0.032\n",
            "SVC(C=100, degree=7) 0.878 0.032\n",
            "SVC(C=100, degree=9) 0.878 0.032\n",
            "SVC(C=100, degree=1, kernel='sigmoid') 0.798 0.05\n",
            "SVC(C=100, kernel='sigmoid') 0.798 0.05\n",
            "SVC(C=100, degree=5, kernel='sigmoid') 0.798 0.05\n",
            "SVC(C=100, degree=7, kernel='sigmoid') 0.798 0.05\n",
            "SVC(C=100, degree=9, kernel='sigmoid') 0.798 0.05\n",
            "SVC(C=300, degree=1, kernel='linear') 0.892 0.04\n",
            "SVC(C=300, kernel='linear') 0.892 0.04\n",
            "SVC(C=300, degree=5, kernel='linear') 0.892 0.04\n",
            "SVC(C=300, degree=7, kernel='linear') 0.892 0.04\n",
            "SVC(C=300, degree=9, kernel='linear') 0.892 0.04\n",
            "SVC(C=300, degree=1, kernel='poly') 0.897 0.042\n",
            "SVC(C=300, kernel='poly') 0.883 0.036\n",
            "SVC(C=300, degree=5, kernel='poly') 0.886 0.034\n",
            "SVC(C=300, degree=7, kernel='poly') 0.874 0.039\n",
            "SVC(C=300, degree=9, kernel='poly') 0.875 0.042\n",
            "SVC(C=300, degree=1) 0.879 0.038\n",
            "SVC(C=300) 0.879 0.038\n",
            "SVC(C=300, degree=5) 0.879 0.038\n",
            "SVC(C=300, degree=7) 0.879 0.038\n",
            "SVC(C=300, degree=9) 0.879 0.038\n",
            "SVC(C=300, degree=1, kernel='sigmoid') 0.774 0.038\n",
            "SVC(C=300, kernel='sigmoid') 0.774 0.038\n",
            "SVC(C=300, degree=5, kernel='sigmoid') 0.774 0.038\n",
            "SVC(C=300, degree=7, kernel='sigmoid') 0.774 0.038\n",
            "SVC(C=300, degree=9, kernel='sigmoid') 0.774 0.038\n"
          ]
        }
      ],
      "source": [
        "# SVM\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import StratifiedKFold, KFold\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "acc_cv_mean_collection = []\n",
        "C = [0.001, 0.01, 0.1,  0.5, 1.1, 1.3, 5, 10, 30, 50, 100, 300]\n",
        "kernel = ['linear', 'poly', 'rbf', 'sigmoid']\n",
        "degree = [1, 3, 5, 7, 9]\n",
        "tol = [1e-5]\n",
        "for i in C:\n",
        "    for j in kernel:\n",
        "        for k in degree:\n",
        "            for p in tol:\n",
        "                acc_cv_collection = []\n",
        "                clf = SVC(C=i, kernel=j, degree=k)  # need to tune the hyperparameters\n",
        "                from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold\n",
        "                skf = StratifiedKFold(n_splits=10)\n",
        "                for train, test in skf.split(X_train_whole, y_train_whole):\n",
        "                    X_train, X_valid, y_train, y_valid = np.take(X_train_whole, train.tolist(), axis=0), np.take(X_train_whole,\n",
        "                                                                                                                test.tolist(),\n",
        "                                                                                                                axis=0), np.take(\n",
        "                        y_train_whole, train.tolist(), axis=0), np.take(y_train_whole, test.tolist(), axis=0)\n",
        "                    clf.fit(X_train, y_train)\n",
        "                    y_valid_pred = clf.predict(X_valid)\n",
        "                    acc_cv = balanced_accuracy_score(y_valid, y_valid_pred)\n",
        "                    acc_cv_collection.append(acc_cv)\n",
        "                acc_cv_mean_collection.append(round(statistics.mean(acc_cv_collection),3))\n",
        "                print(clf,round(statistics.mean(acc_cv_collection),3), round(statistics.stdev(acc_cv_collection),3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUm2yu6cCqi7"
      },
      "source": [
        "#### MLP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KBZ7o-bEb38a",
        "outputId": "cb80092e-715d-4c38-c23e-6ceaa677c11d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(1,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.58 0.134\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(2,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.666 0.154\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(3,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.754 0.112\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(4,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.74 0.113\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(6,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.771 0.095\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(8,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.773 0.068\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(12,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.803 0.05\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(16,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.821 0.09\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(18,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.794 0.105\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(20,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.81 0.049\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(22,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.818 0.061\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(24,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.81 0.067\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(26,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.783 0.072\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(28,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.8 0.083\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(32,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.816 0.065\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(64,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.839 0.036\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(128,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.802 0.084\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(240,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.838 0.058\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(320,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.849 0.039\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(640,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.868 0.042\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(1280,), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.858 0.041\n"
          ]
        }
      ],
      "source": [
        "# MLP one layer\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "hidden_layers_1 = [1,2,3,4,6,8,12,16, 18, 20, 22, 24,26,28,32, 64, 128, 240, 320, 640,1280]\n",
        "\n",
        "acc_cv_mean_collection = []\n",
        "for i in hidden_layers_1:\n",
        "    acc_cv_collection = []\n",
        "    clf = MLPClassifier(hidden_layer_sizes=(i,),early_stopping= True,n_iter_no_change= 40, max_iter = 3000)  # need to tune the hyperparameters\n",
        "    from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold\n",
        "    skf = StratifiedKFold(n_splits=10)\n",
        "    for train, test in skf.split(X_train_whole, y_train_whole):\n",
        "        X_train, X_valid, y_train, y_valid = np.take(X_train_whole, train.tolist(), axis=0), np.take(X_train_whole,\n",
        "                                                                                                     test.tolist(),\n",
        "                                                                                                     axis=0), np.take(\n",
        "            y_train_whole, train.tolist(), axis=0), np.take(y_train_whole, test.tolist(), axis=0)\n",
        "        clf.fit(X_train, y_train)\n",
        "        y_valid_pred = clf.predict(X_valid)\n",
        "        y_train_pred = clf.predict(X_train)\n",
        "        # print(balanced_accuracy_score(y_train, y_train_pred))\n",
        "        acc_cv = balanced_accuracy_score(y_valid, y_valid_pred)\n",
        "        acc_cv_collection.append(acc_cv)\n",
        "    acc_cv_mean_collection.append(round(statistics.mean(acc_cv_collection),3))\n",
        "    print(clf,round(statistics.mean(acc_cv_collection),3),  round(statistics.stdev(acc_cv_collection),3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ke2HDzyFciIi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e03a1a2e-d5fe-44be-d6eb-c1bda0503533"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(12, 2), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.627 0.168\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(12, 8), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.821 0.061\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(12, 12), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.819 0.082\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(12, 24), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.812 0.078\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(12, 32), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.824 0.076\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(12, 64), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.834 0.044\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(12, 128), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.854 0.065\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(12, 256), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.841 0.036\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(24, 2), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.754 0.149\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(24, 8), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.817 0.081\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(24, 12), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.831 0.065\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(24, 24), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.862 0.024\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(24, 32), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.817 0.051\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(24, 64), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.845 0.063\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(24, 128), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.855 0.047\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(24, 256), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.848 0.045\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(32, 2), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.678 0.176\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(32, 8), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.825 0.06\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(32, 12), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.852 0.054\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(32, 24), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.837 0.036\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(32, 32), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.851 0.037\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(32, 64), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.858 0.027\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(32, 128), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.844 0.051\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(32, 256), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.859 0.049\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(64, 2), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.684 0.166\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(64, 8), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.811 0.076\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(64, 12), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.846 0.048\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(64, 24), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.859 0.031\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(64, 32), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.852 0.023\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(64, 64), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.867 0.045\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(64, 128), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.853 0.051\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(64, 256), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.855 0.055\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(128, 2), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.683 0.194\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(128, 8), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.86 0.026\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(128, 12), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.827 0.067\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(128, 24), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.837 0.037\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(128, 32), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.856 0.03\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(128, 64), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.861 0.031\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(128, 128), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.866 0.043\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(128, 256), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.84 0.06\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(256, 2), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.778 0.154\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(256, 8), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.86 0.041\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(256, 12), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.825 0.117\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(256, 24), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.846 0.047\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(256, 32), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.831 0.049\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(256, 64), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.853 0.06\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(256, 128), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.852 0.05\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(256, 256), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.857 0.047\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(320, 2), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.664 0.179\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(320, 8), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.853 0.069\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(320, 12), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.852 0.024\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(320, 24), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.841 0.065\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(320, 32), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.841 0.048\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(320, 64), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.871 0.025\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(320, 128), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.851 0.045\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(320, 256), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.853 0.036\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(640, 2), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.634 0.177\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(640, 8), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.842 0.033\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(640, 12), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.846 0.044\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(640, 24), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.837 0.074\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(640, 32), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.824 0.087\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(640, 64), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.848 0.047\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(640, 128), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.875 0.035\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(640, 256), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.874 0.033\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(1280, 2), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.648 0.192\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(1280, 8), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.837 0.12\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(1280, 12), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.882 0.021\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(1280, 24), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.849 0.049\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(1280, 32), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.875 0.026\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(1280, 64), max_iter=3000,\n",
            "              n_iter_no_change=40) 0.874 0.019\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(1280, 128),\n",
            "              max_iter=3000, n_iter_no_change=40) 0.872 0.045\n",
            "MLPClassifier(early_stopping=True, hidden_layer_sizes=(1280, 256),\n",
            "              max_iter=3000, n_iter_no_change=40) 0.841 0.051\n"
          ]
        }
      ],
      "source": [
        "# MLP two layer\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "hidden_layers_1 = [12,24,32,64,128,256,320,640,1280]\n",
        "hidden_layers_2 = [2,8,12,24,32,64,128,256]\n",
        "acc_cv_mean_collection = []\n",
        "for i in hidden_layers_1:\n",
        "    for j in hidden_layers_2:\n",
        "        acc_cv_collection = []\n",
        "        clf = MLPClassifier(hidden_layer_sizes=(i,j),early_stopping = True,n_iter_no_change= 40,  max_iter = 3000)  # need to tune the hyperparameters\n",
        "        from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold\n",
        "        skf = StratifiedKFold(n_splits=10)\n",
        "        for train, test in skf.split(X_train_whole, y_train_whole):\n",
        "            X_train, X_valid, y_train, y_valid = np.take(X_train_whole, train.tolist(), axis=0), np.take(X_train_whole,\n",
        "                                                                                                                    test.tolist(),\n",
        "                                                                                                                    axis=0), np.take(\n",
        "            y_train_whole, train.tolist(), axis=0), np.take(y_train_whole, test.tolist(), axis=0)\n",
        "            clf.fit(X_train, y_train)\n",
        "            y_valid_pred = clf.predict(X_valid)\n",
        "            acc_cv = balanced_accuracy_score(y_valid, y_valid_pred)\n",
        "            acc_cv_collection.append(acc_cv)\n",
        "        acc_cv_mean_collection.append(round(statistics.mean(acc_cv_collection),3))\n",
        "        print(clf,round(statistics.mean(acc_cv_collection),3), round(statistics.stdev(acc_cv_collection),3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pIZ278ZDOwmA"
      },
      "source": [
        "### t-SNE graph \n",
        "change the perplexity "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "iV2DQH5fq7gq",
        "outputId": "9d29ece2-11a4-490f-9b12-a50d5805bcb8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import seaborn as sns\n",
        "import pandas as pd  \n",
        "tsne = TSNE(n_components=2, verbose=0, perplexity= 10, learning_rate='auto',n_iter = 5000,random_state=123)\n",
        "z = tsne.fit_transform(X_new) \n",
        "df = pd.DataFrame()\n",
        "df[\"comp-1\"] = z[:,0]\n",
        "df[\"comp-2\"] = z[:,1]\n",
        "y_new_label=[]\n",
        "for i in y_new:\n",
        "    if i == 0:\n",
        "        y_new_label.append('Active')\n",
        "    if i == 1:\n",
        "        y_new_label.append('Inactive')\n",
        "df[\"y\"] = y_new_label\n",
        "graph = sns.scatterplot(data=df, x=\"comp-1\", y=\"comp-2\", hue=y_new_label,\n",
        "                palette='BrBG_r', legend='full')\n",
        "graph_for_output = graph.get_figure()\n",
        "graph_for_output.savefig('1020 after CleanLab.png', dpi=600)\n",
        "df.to_excel('1020 after CleanLab.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "AuYFtNqDQMxs",
        "outputId": "db329f7d-67a9-4aeb-935f-e5798d781122"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import seaborn as sns\n",
        "import pandas as pd  \n",
        "tsne = TSNE(n_components=2, verbose=0, perplexity= 5, learning_rate='auto',n_iter = 5000,random_state=123)\n",
        "z = tsne.fit_transform(X_new) \n",
        "df = pd.DataFrame()\n",
        "df[\"comp-1\"] = z[:,0]\n",
        "df[\"comp-2\"] = z[:,1]\n",
        "y_new_label=[]\n",
        "for i in y_new:\n",
        "    if i == 0:\n",
        "        y_new_label.append('Active')\n",
        "    if i == 1:\n",
        "        y_new_label.append('Inactive')\n",
        "df[\"y\"] = y_new_label\n",
        "graph = sns.scatterplot(data=df, x=\"comp-1\", y=\"comp-2\", hue=y_new_label,\n",
        "                palette='BrBG_r', legend='full')\n",
        "graph_for_output = graph.get_figure()\n",
        "graph_for_output.savefig('1.AHT_t-SNE_1and2.png', dpi=300)\n",
        "df.to_excel('1.AHT_t-SNE.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "pgJuzGVFXToj",
        "outputId": "6f8df903-3b30-4430-b9d4-600a7caba280"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import seaborn as sns\n",
        "import pandas as pd  \n",
        "tsne = TSNE(n_components=2, verbose=0, perplexity= 500, learning_rate='auto',n_iter = 5000,random_state=123)\n",
        "z = tsne.fit_transform(X_new) \n",
        "df = pd.DataFrame()\n",
        "df[\"comp-1\"] = z[:,0]\n",
        "df[\"comp-2\"] = z[:,1]\n",
        "y_new_label=[]\n",
        "for i in y_new:\n",
        "    if i == 0:\n",
        "        y_new_label.append('Active')\n",
        "    if i == 1:\n",
        "        y_new_label.append('Inactive')\n",
        "df[\"y\"] = y_new_label\n",
        "graph = sns.scatterplot(data=df, x=\"comp-1\", y=\"comp-2\", hue=y_new_label,\n",
        "                palette='BrBG_r', legend='full')\n",
        "graph_for_output = graph.get_figure()\n",
        "graph_for_output.savefig('1.AHT_t-SNE_1and2.png', dpi=300)\n",
        "df.to_excel('1.AHT_t-SNE.xlsx')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4Ru7iocM6aE"
      },
      "source": [
        "### model development with best hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AQbu1XxtNFUG"
      },
      "outputs": [],
      "source": [
        "import statistics\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "import math\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ah-bSWvcM-86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "494dc713-5ffa-414e-f806-c9840012d1a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.883 ± 0.017\n",
            "0.845 ± 0.041\n",
            "0.92 ± 0.025\n",
            "0.77 ± 0.032\n"
          ]
        }
      ],
      "source": [
        "# logistic regression for final test \n",
        "\n",
        "# result collection list\n",
        "ACC_collecton = []\n",
        "BACC_collecton = []\n",
        "Sn_collecton = []\n",
        "Sp_collecton = []\n",
        "MCC_collecton = []\n",
        "for i in range(10):\n",
        "    # dataset splitting\n",
        "    X_train_whole, X_ind_test, y_train_whole, y_ind_test =  train_test_split( X_new, y_new, test_size=0.2, random_state=i)\n",
        "    # clf = LogisticRegression(penalty= 'none', C=i, max_iter = 2000)\n",
        "    clf = LogisticRegression(C=2000, max_iter=5000)\n",
        "    clf.fit(X_train_whole,y_train_whole)# fitting model \n",
        "    y_pred = clf.predict(X_ind_test)    # predict results\n",
        "    y_true = y_ind_test                 # asign values for confusiong matrix calculation\n",
        "    TP, FP, FN, TN = confusion_matrix(y_true, y_pred).ravel() # shape [ [True-Positive, False-positive], [False-negative, True-negative] ]\n",
        "    Sn_collecton.append(TP/(TP+FN))\n",
        "    Sp_collecton.append(TN/(TN+FP))\n",
        "    MCC = (TP*TN-FP*FN)/math.pow(((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)),0.5)\n",
        "    MCC_collecton.append(MCC)\n",
        "    BACC_collecton.append(0.5*TP/(TP+FN)+0.5*TN/(TN+FP))\n",
        "    \n",
        "print(round(statistics.mean(BACC_collecton),3),'±',round(statistics.stdev(BACC_collecton),3))\n",
        "print(round(statistics.mean(Sn_collecton),3),'±',round(statistics.stdev(Sn_collecton),3))\n",
        "print(round(statistics.mean(Sp_collecton),3),'±',round(statistics.stdev(Sp_collecton),3))\n",
        "print(round(statistics.mean(MCC_collecton),3),'±',round(statistics.stdev(MCC_collecton),3))\n",
        "# print(round(mean(AUC_collecton),3),'±',round(stdev(AUC_collecton),3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "31Axqp_POnjm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4da785e-e5da-443f-a640-1ddfd0676ed4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.79 ± 0.03\n",
            "0.814 ± 0.04\n",
            "0.765 ± 0.041\n",
            "0.511 ± 0.062\n"
          ]
        }
      ],
      "source": [
        "# random forest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "# result collection list\n",
        "ACC_collecton = []\n",
        "BACC_collecton = []\n",
        "Sn_collecton = []\n",
        "Sp_collecton = []\n",
        "MCC_collecton = []\n",
        "for i in range(10):\n",
        "    # dataset splitting\n",
        "    X_train_whole, X_ind_test, y_train_whole, y_ind_test =  train_test_split( X_new, y_new, test_size=0.2, random_state=i)\n",
        "    clf = RandomForestClassifier(max_depth=4, max_features='sqrt', n_estimators=320)\n",
        "    clf.fit(X_train_whole,y_train_whole)# fitting model \n",
        "    y_pred = clf.predict(X_ind_test)    # predict results\n",
        "    y_true = y_ind_test                 # asign values for confusiong matrix calculation\n",
        "    TP, FP, FN, TN = confusion_matrix(y_true, y_pred).ravel() # shape [ [True-Positive, False-positive], [False-negative, True-negative] ]\n",
        "    Sn_collecton.append(TP/(TP+FN))\n",
        "    Sp_collecton.append(TN/(TN+FP))\n",
        "    MCC = (TP*TN-FP*FN)/math.pow(((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)),0.5)\n",
        "    MCC_collecton.append(MCC)\n",
        "    BACC_collecton.append(0.5*TP/(TP+FN)+0.5*TN/(TN+FP))\n",
        "    \n",
        "print(round(statistics.mean(BACC_collecton),3),'±',round(statistics.stdev(BACC_collecton),3))\n",
        "print(round(statistics.mean(Sn_collecton),3),'±',round(statistics.stdev(Sn_collecton),3))\n",
        "print(round(statistics.mean(Sp_collecton),3),'±',round(statistics.stdev(Sp_collecton),3))\n",
        "print(round(statistics.mean(MCC_collecton),3),'±',round(statistics.stdev(MCC_collecton),3))\n",
        "# print(round(mean(AUC_collecton),3),'±',round(stdev(AUC_collecton),3))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rg3bw9xjOsJh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27e85d23-c093-4640-e663-7658d38496a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.748 ± 0.031\n",
            "0.663 ± 0.033\n",
            "0.832 ± 0.042\n",
            "0.505 ± 0.066\n"
          ]
        }
      ],
      "source": [
        "# KNN\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "# result collection list\n",
        "ACC_collecton = []\n",
        "BACC_collecton = []\n",
        "Sn_collecton = []\n",
        "Sp_collecton = []\n",
        "MCC_collecton = []\n",
        "for i in range(10):\n",
        "    # dataset splitting\n",
        "    X_train_whole, X_ind_test, y_train_whole, y_ind_test =  train_test_split( X_new, y_new, test_size=0.2, random_state=i)\n",
        "    clf = KNeighborsClassifier(n_neighbors=11, weights='distance') \n",
        "    clf.fit(X_train_whole,y_train_whole)# fitting model \n",
        "    y_pred = clf.predict(X_ind_test)    # predict results\n",
        "    y_true = y_ind_test                 # asign values for confusiong matrix calculation\n",
        "    TP, FP, FN, TN = confusion_matrix(y_true, y_pred).ravel() # shape [ [True-Positive, False-positive], [False-negative, True-negative] ]\n",
        "    Sn_collecton.append(TP/(TP+FN))\n",
        "    Sp_collecton.append(TN/(TN+FP))\n",
        "    MCC = (TP*TN-FP*FN)/math.pow(((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)),0.5)\n",
        "    MCC_collecton.append(MCC)\n",
        "    BACC_collecton.append(0.5*TP/(TP+FN)+0.5*TN/(TN+FP))\n",
        "\n",
        "print(round(statistics.mean(BACC_collecton),3),'±',round(statistics.stdev(BACC_collecton),3))\n",
        "print(round(statistics.mean(Sn_collecton),3),'±',round(statistics.stdev(Sn_collecton),3))\n",
        "print(round(statistics.mean(Sp_collecton),3),'±',round(statistics.stdev(Sp_collecton),3))\n",
        "print(round(statistics.mean(MCC_collecton),3),'±',round(statistics.stdev(MCC_collecton),3))\n",
        "# print(round(mean(AUC_collecton),3),'±',round(stdev(AUC_collecton),3))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kGAR7eT2OmvK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a072cc3-f889-4448-b036-5e2292c90473"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.867 ± 0.02\n",
            "0.825 ± 0.041\n",
            "0.91 ± 0.027\n",
            "0.74 ± 0.038\n"
          ]
        }
      ],
      "source": [
        "# SVM\n",
        "from sklearn.svm import SVC\n",
        "# result collection list\n",
        "ACC_collecton = []\n",
        "BACC_collecton = []\n",
        "Sn_collecton = []\n",
        "Sp_collecton = []\n",
        "MCC_collecton = []\n",
        "for i in range(10):\n",
        "    # dataset splitting\n",
        "    X_train_whole, X_ind_test, y_train_whole, y_ind_test =  train_test_split( X_new, y_new, test_size=0.2, random_state=i)\n",
        "    clf = SVC(C=300, degree=5, kernel='linear')\n",
        "    clf.fit(X_train_whole,y_train_whole)# fitting model \n",
        "    y_pred = clf.predict(X_ind_test)    # predict results\n",
        "    y_true = y_ind_test                 # asign values for confusiong matrix calculation\n",
        "    TP, FP, FN, TN = confusion_matrix(y_true, y_pred).ravel() # shape [ [True-Positive, False-positive], [False-negative, True-negative] ]\n",
        "    Sn_collecton.append(TP/(TP+FN))\n",
        "    Sp_collecton.append(TN/(TN+FP))\n",
        "    MCC = (TP*TN-FP*FN)/math.pow(((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)),0.5)\n",
        "    MCC_collecton.append(MCC)\n",
        "    BACC_collecton.append(0.5*TP/(TP+FN)+0.5*TN/(TN+FP))\n",
        "    \n",
        "print(round(statistics.mean(BACC_collecton),3),'±',round(statistics.stdev(BACC_collecton),3))\n",
        "print(round(statistics.mean(Sn_collecton),3),'±',round(statistics.stdev(Sn_collecton),3))\n",
        "print(round(statistics.mean(Sp_collecton),3),'±',round(statistics.stdev(Sp_collecton),3))\n",
        "print(round(statistics.mean(MCC_collecton),3),'±',round(statistics.stdev(MCC_collecton),3))\n",
        "# print(round(mean(AUC_collecton),3),'±',round(stdev(AUC_collecton),3))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q-1l-PbzOHDo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "601e721c-442c-4786-ab66-fa00290d5e7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.855 ± 0.024\n",
            "0.815 ± 0.036\n",
            "0.895 ± 0.037\n",
            "0.711 ± 0.054\n"
          ]
        }
      ],
      "source": [
        "# MLP one layer\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "# result collection list\n",
        "ACC_collecton = []\n",
        "BACC_collecton = []\n",
        "Sn_collecton = []\n",
        "Sp_collecton = []\n",
        "MCC_collecton = []\n",
        "for i in range(10):\n",
        "    # dataset splitting\n",
        "    X_train_whole, X_ind_test, y_train_whole, y_ind_test =  train_test_split( X_new, y_new, test_size=0.2, random_state=i)\n",
        "    clf = MLPClassifier(early_stopping=True, hidden_layer_sizes=(1280, 32), max_iter=3000, n_iter_no_change=40) \n",
        "    clf.fit(X_train_whole,y_train_whole)# fitting model \n",
        "    y_pred = clf.predict(X_ind_test)    # predict results\n",
        "    y_true = y_ind_test                 # asign values for confusiong matrix calculation\n",
        "    TP, FP, FN, TN = confusion_matrix(y_true, y_pred).ravel() # shape [ [True-Positive, False-positive], [False-negative, True-negative] ]\n",
        "    Sn_collecton.append(TP/(TP+FN))\n",
        "    Sp_collecton.append(TN/(TN+FP))\n",
        "    MCC = (TP*TN-FP*FN)/math.pow(((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)),0.5)\n",
        "    MCC_collecton.append(MCC)\n",
        "    BACC_collecton.append(0.5*TP/(TP+FN)+0.5*TN/(TN+FP))\n",
        "    \n",
        "print(round(statistics.mean(BACC_collecton),3),'±',round(statistics.stdev(BACC_collecton),3))\n",
        "print(round(statistics.mean(Sn_collecton),3),'±',round(statistics.stdev(Sn_collecton),3))\n",
        "print(round(statistics.mean(Sp_collecton),3),'±',round(statistics.stdev(Sp_collecton),3))\n",
        "print(round(statistics.mean(MCC_collecton),3),'±',round(statistics.stdev(MCC_collecton),3))\n",
        "# print(round(mean(AUC_collecton),3),'±',round(stdev(AUC_collecton),3))\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
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      "provenance": []
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    "kernelspec": {
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      "name": "python3"
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    "language_info": {
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