XGBoost_K_Cross_Validation+mrmr_2_.ipynb

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    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "import pandas as pd "
      ],
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      "execution_count": null,
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        "from google.colab import files\n",
        "uploaded = files.upload()"
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              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
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          "name": "stdout",
          "text": [
            "Saving colon - labled-1 C.csv to colon - labled-1 C.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "df = pd.read_csv(io.BytesIO(uploaded['colon - labled-1 C.csv']))"
      ],
      "metadata": {
        "id": "nEghRXyS4me8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "y = df.Class\n",
        "X = df.drop('Class', axis=1)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2)\n",
        "print(\"\\nX_train:\\n\")\n",
        "print(X_train.head())\n",
        "print(X_train.shape)\n",
        "\n",
        "print(\"\\nX_test:\\n\")\n",
        "print(X_test.head())\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_fvqxA1dW_f",
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      "execution_count": null,
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          "text": [
            "\n",
            "X_train:\n",
            "\n",
            "    Unnamed: 0     H55933     R39465    R39465_     R85482     U14973  \\\n",
            "52          53  7666.6750  6409.7390  6489.8640  2451.1180  4176.2120   \n",
            "31          32  5012.0200  1383.4886  1269.6487  3168.7124  1945.4929   \n",
            "4            5  3230.3286  3694.4500  3400.7400  3463.5857  2181.4202   \n",
            "24          25  8865.4590  5447.1865  4887.0576  3782.5945  3927.0107   \n",
            "0            1  8589.4160  5468.2407  4263.4077  4064.9358  1997.8930   \n",
            "\n",
            "       R02593     T51496     H80240     T65938  ...     X67699    R70790  \\\n",
            "52  4654.6357  2321.5225  3520.3162  4477.4510  ...  109.18690  32.20375   \n",
            "31  2726.8037  3300.5938  1672.8600  4185.6226  ...  107.97976  96.21375   \n",
            "4   2922.7820  2069.2463  2948.5750  3303.3710  ...  126.46429  76.60375   \n",
            "24  6138.9710  4379.5950  5385.0300  5130.4077  ...  159.17976  86.30500   \n",
            "0   5282.3250  2169.7200  2773.4211  7526.3860  ...   99.11072  67.56125   \n",
            "\n",
            "       L11706     T90549     D17390      M33210     H18490     H40891  \\\n",
            "52  601.84750  117.00375   62.32000  167.308330   13.26750   52.09250   \n",
            "31  365.51376   34.02000  125.69625  562.308350  127.43625  171.84250   \n",
            "4   161.35000   61.70125   54.56375  223.359530   73.09875   57.59875   \n",
            "24  253.40250  185.46625   77.13375    5.888095   57.80875  118.89375   \n",
            "0   259.91250  138.89874   88.23250   39.667860   67.82875   75.67750   \n",
            "\n",
            "      R77780    T49647  \n",
            "52  47.71750  30.42000  \n",
            "31  36.49500  48.37500  \n",
            "4    7.48875  31.81250  \n",
            "24  44.87625  56.00000  \n",
            "0   83.52250  28.70125  \n",
            "\n",
            "[5 rows x 2001 columns]\n",
            "(49, 2001)\n",
            "\n",
            "X_test:\n",
            "\n",
            "    Unnamed: 0     H55933     R39465    R39465_     R85482     U14973  \\\n",
            "58          59  9112.3720  6824.4863  5982.8460  3147.0430  4847.3080   \n",
            "8            9  9330.6790  7017.2295  4723.7827  9491.5340  5346.5415   \n",
            "23          24  1914.6775  2119.1113  1595.4088  1186.0304  2090.3882   \n",
            "18          19  5382.3940  3848.4430  3372.4888  4444.5660  2464.9023   \n",
            "19          20  4412.4760  1752.3955  1280.3239  5200.3447  2369.0608   \n",
            "\n",
            "        R02593     T51496     H80240     T65938  ...      X67699    R70790  \\\n",
            "58  11222.6820  6333.4727  4299.5024  4644.4424  ...  129.404750  35.87875   \n",
            "8    1557.1430  1969.0800  2295.4026  6880.3460  ...  111.138100  42.14625   \n",
            "23   1087.7500  1248.1638  1895.0950  1179.9350  ...   54.009525  37.18500   \n",
            "18   1304.5571  1062.6975  2903.3250  4866.8086  ...   80.970240  29.93500   \n",
            "19   2047.9500  2104.7588  2938.8674  2628.5938  ...  181.219060  69.33750   \n",
            "\n",
            "       L11706     T90549     D17390      M33210    H18490     H40891  \\\n",
            "58  342.65000   84.75125   60.27375  143.877380   8.99625  106.87875   \n",
            "8   277.85376  176.10374  149.15625  209.911900  99.91500  122.44125   \n",
            "23   62.79750    5.92375    5.91625   21.309525  10.00625    5.84875   \n",
            "18  155.76750   50.89500   43.65750  118.461900  22.04750   62.68625   \n",
            "19  124.29500   21.40375   67.86625  106.057140  26.40875   32.32500   \n",
            "\n",
            "      R77780    T49647  \n",
            "58  23.20250  32.16625  \n",
            "8   40.39125  26.84750  \n",
            "23   5.84250  19.05500  \n",
            "18   6.00000  25.10500  \n",
            "19  39.60875  36.04250  \n",
            "\n",
            "[5 rows x 2001 columns]\n",
            "(13, 2001)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mrmr_selection\n",
        "from mrmr import mrmr_classif\n",
        "from sklearn.datasets import make_classification\n",
        "selected_features = mrmr_classif(X, y, K = 500)\n",
        "print(selected_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iECUl316bPvG",
        "outputId": "b0847e69-28a1-4852-e2b3-199fd87ec276"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mrmr_selection\n",
            "  Downloading mrmr_selection-0.2.5-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from mrmr_selection) (1.7.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from mrmr_selection) (2.11.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from mrmr_selection) (4.64.1)\n",
            "Collecting sklearn\n",
            "  Downloading sklearn-0.0.post1.tar.gz (3.6 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting category-encoders\n",
            "  Downloading category_encoders-2.6.0-py2.py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.2/81.2 KB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.8/dist-packages (from mrmr_selection) (1.21.6)\n",
            "Requirement already satisfied: pandas>=1.0.3 in /usr/local/lib/python3.8/dist-packages (from mrmr_selection) (1.3.5)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from mrmr_selection) (1.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.0.3->mrmr_selection) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.0.3->mrmr_selection) (2022.7)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.8/dist-packages (from category-encoders->mrmr_selection) (0.5.3)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.8/dist-packages (from category-encoders->mrmr_selection) (1.0.2)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.8/dist-packages (from category-encoders->mrmr_selection) (0.12.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2->mrmr_selection) (2.0.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from patsy>=0.5.1->category-encoders->mrmr_selection) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.20.0->category-encoders->mrmr_selection) (3.1.0)\n",
            "Building wheels for collected packages: sklearn\n",
            "  Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sklearn: filename=sklearn-0.0.post1-py3-none-any.whl size=2344 sha256=90696a1e8473721381e1d80521ca1aa572cbf2a25f86b5f43b02763b4f128a7a\n",
            "  Stored in directory: /root/.cache/pip/wheels/14/25/f7/1cc0956978ae479e75140219088deb7a36f60459df242b1a72\n",
            "Successfully built sklearn\n",
            "Installing collected packages: sklearn, category-encoders, mrmr_selection\n",
            "Successfully installed category-encoders-2.6.0 mrmr_selection-0.2.5 sklearn-0.0.post1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 500/500 [03:51<00:00,  2.16it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['M63391', 'D13641', 'J02854', 'M76378__', 'H55916', 'Z50753', 'H08393', 'R87126', 'M76378', 'T62947', 'M76378_', 'X63629', 'T71025', 'T92451', 'J05032', 'R84411', 'R80427', 'U25138', 'X12671', 'T47377', 'H43887', 'T56604', 'H64489', 'M26697', 'M82919', 'H40095', 'M36634', 'T57468', 'X86693', 'M22382', 'U09587', 'X12369', 'T95018', 'H23544', 'T60155', 'T57619', 'H06524', 'X55715', 'Z24727', 'R08183', 'M80815', 'X14958', 'R78934', 'D43950', 'H55758', 'U19969', 'H67764', 'X70326', 'T60778', 'U09564', 'L07648', 'X12466', 'T67077', 'T51261', 'H40560', 'M64110', 'L11706', 'R36977', 'R54097', 'H77597', 'H05803', 'L05144', 'T58861', 'T83368', 'R62549', 'R44301', 'M26383', 'T59878', 'U14631', 'T51023', 'T51529', 'X56597', 'R67358', 'D00860', 'R15447', 'U32519', 'T86473', 'U01038', 'T48804', 'M91463', 'T52185', 'Z49269_', 'T51571', 'X74295', 'U30825', 'X83301', 'D29808', 'U17899', 'R60877', 'U22055', 'X54942', 'Z49269', 'T90350', 'T61609', 'T61661_', 'T79152', 'D31716', 'T70062', 'H17434', 'M36981', 'T94350', 'R64115', 'X15880', 'H65355', 'R10066', 'D42047', 'M87789', 'T63370', 'M15841', 'D31885', 'H20426', 'X16356', 'X87159', 'L12723', 'U14971', 'U31525', 'U21090', 'M94203', 'T98555', 'H70425', 'H41129', 'D38551', 'T51534', 'M34344', 'X15882', 'R42501', 'H24030', 'L10284', 'D26129', 'U26312', 'T49732', 'M96839', 'T89666', 'X54941', 'D13315_', 'T51858', 'J00231', 'T62972', 'M96233', 'M58050', 'R44418', 'H40137', 'M34175__', 'T56940', 'H72234', 'T51493', 'R08021', 'T61661', 'T51539', 'R97912', 'R46753', 'Z48541', 'T96873', 'T86749', 'T95048', 'T72938', 'T57483', 'D16431', 'M83667', 'Z11502', 'R34698', 'M28128', 'U04953', 'M35531', 'T63133', 'H89087', 'U02493', 'Z25521', 'M26252', 'R48303', 'X06614', 'U05040', 'D21261', 'H11084', 'R42127', 'R75843', 'M28882', 'X75208', 'T50797', 'X15183', 'T47144', 'X68277', 'H40269', 'M18216', 'L08069', 'T90280', 'X53586', 'M94630', 'R99907', 'T72863', 'X74795', 'H78386', 'R81170', 'M69135', 'L28010', 'X68314', 'M19045', 'M92287', 'R41873', 'R44884', 'T40578', 'T40454', 'X60489', 'X06614_', 'T72175', 'R33367', 'D59253', 'X72727', 'T68848', 'T94993', 'Y00345', 'T50321', 'H01346', 'J04102', 'H87344', 'J04621', 'M16937', 'R42244', 'T67406', 'D25217', 'R44770', 'X74330', 'M88279', 'T41204', 'D63874', 'D14662', 'T71001', 'D00596', 'R34876', 'T47383', 'R16156', 'M94556', 'X68688', 'R88740', 'H25136', 'R11485', 'K03460', 'M86553', 'H20709', 'R50158', 'H17646', 'X89986', 'T55248', 'X12496', 'M37583', 'T57633', 'R37428', 'X55187', 'X07290', 'R93738', 'T54276', 'M92843', 'R01182', 'D29641', 'D14520', 'D00763', 'T72889', 'R65697', 'T69748', 'D15049', 'R23889', 'D00762', 'L06132', 'M64445', 'M31303', 'L38503', 'control4', 'control5', 'L19437', 'control6', 'U37012', 'control7', 'H65823', 'T56244', 'M34175_', 'T53549', 'X66839', 'M76558', 'X01060', 'T56690', 'R49565', 'M29065', 'U10324', 'H05899', 'T72879', 'U07664', 'M34175', 'T52015', 'H79136', 'H62828', 'L03840', 'R98842', 'H24401', 'R40717_', 'J03210', 'X55362', 'T48904', 'T63496', 'J04813', 'M84721', 'M86737', 'X87342', 'M26683', 'M28373', 'T65790', 'T63591', 'R96357', 'T84049', 'M22490', 'L09604', 'control8', 'X73902', 'X67155', 'R39465_', 'T69446', 'M94132', 'T47584', 'T57780', 'L24203', 'X13482', 'U31215', 'T60437', 'Y00264', 'X07767', 'M65028', 'R86975', 'M81637', 'D13627', 'R01124', 'T51558', 'H20819', 'U24166', 'R20554', 'R56401', 'X69141', 'control9', 'R80612', 'M14200', 'X62153', 'H87473', 'H61410', 'U28686', 'M85289', 'U21049', 'R54422', 'T57630', 'H20512', 'R28373', 'H22579_', 'T65003', 'R22197', 'T84481', 'R67999', 'control10', 'X65873', 'H65182', 'M83751', 'M35878', 'M93651', 'T53889', 'T52642', 'R54941', 'H16991', 'M65105', 'L38951', 'H81068', 'T70058', 'control11', 'R56399', 'X57351', 'U12255_', 'M23115', 'M86752', 'D14665', 'M23419_', 'H64807', 'U34074', 'M59040', 'X81817', 'T51849', 'M86400', 'R41866', 'R71585', 'D13315', 'X82103', 'T93094', 'U29092', 'U06698', 'R40717', 'H38185', 'D16294', 'X70944', 'T64012', 'T61602', 'L41559', 'J04794', 'M64231', 'R49416', 'R78927', 'Z17240', 'X16354', 'T50334_', 'L37936', 'X81372', 'D14663', 'R43914', 'T78104', 'X72018', 'T58645', 'X89985', 'T54364', 'T40645', 'H81558', 'R73660', 'R52081', 'T54341', 'T51613', 'T47645', 'J04026', 'T80178', 'T65938', 'Y00971', 'U07695_', 'R45529', 'D13665', 'R70030', 'T53694', 'R44052', 'T65758', 'T49423', 'M14200_', 'X51417', 'T47719', 'J00073', 'U10362', 'T94579', 'T65740', 'D16469', 'R21547', 'M88108', 'U07695__', 'M97676', 'T48102', 'T92259', 'X64229', 'J03569', 'D14812', 'L27476', 'H45977', 'H06245', 'H86060', 'M90516', 'T57780_', 'R27369', 'H72965', 'R49459', 'T57686', 'T60318', 'R39209', 'M22632', 'U26710', 'K02268', 'U07695', 'M81933', 'Z18948', 'M77698', 'H73908', 'U05291', 'M21339', 'D55673', 'R37464', 'H39654', 'T49941', 'U26401', 'T71574', 'M63239', 'H23098', 'D43947', 'D17400', 'T72503', 'T73092', 'U12255', 'L33930', 'U37673_', 'D00761', 'R67343', 'L13385', 'H87135', 'T53396_', 'T58731', 'Y00711', 'R59202', 'R43913', 'T62220', 'J03040', 'X69295', 'L05485', 'X73882', 'R59583', 'R62945']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train)\n",
        "print(y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZMuUNvobdu-",
        "outputId": "1a76906e-2a6a-483c-94c4-8ae6daae010e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "52    1\n",
            "31    1\n",
            "4     1\n",
            "24    1\n",
            "0     1\n",
            "12    1\n",
            "2     1\n",
            "50    0\n",
            "60    1\n",
            "7     0\n",
            "42    0\n",
            "28    1\n",
            "1     0\n",
            "55    1\n",
            "9     0\n",
            "49    0\n",
            "57    1\n",
            "29    1\n",
            "11    0\n",
            "40    1\n",
            "51    1\n",
            "45    1\n",
            "38    0\n",
            "16    1\n",
            "54    0\n",
            "3     0\n",
            "15    0\n",
            "22    1\n",
            "27    1\n",
            "44    1\n",
            "13    0\n",
            "47    0\n",
            "46    1\n",
            "21    0\n",
            "17    0\n",
            "39    1\n",
            "61    0\n",
            "43    1\n",
            "32    1\n",
            "25    1\n",
            "33    1\n",
            "53    0\n",
            "36    1\n",
            "37    1\n",
            "30    1\n",
            "20    1\n",
            "5     0\n",
            "34    1\n",
            "56    1\n",
            "Name: Class, dtype: int64\n",
            "(49,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "X_mrmr = X_train[selected_features]\n",
        "print(X_mrmr)\n",
        "print(X_mrmr.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sMLAw8A5vxSt",
        "outputId": "048ed14f-3531-404a-fbef-04ea46154056"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        M63391     D13641      J02854    M76378__     H55916     Z50753  \\\n",
            "52   660.12750   78.82500    76.66125   183.95750  155.82625  190.50000   \n",
            "31   812.82120  213.29500   111.43875   306.00250   32.75250  149.71190   \n",
            "4    584.73250  147.08750   124.02000   361.31250  131.11874  104.64167   \n",
            "24   332.36124  216.87875   102.75875   187.59500   91.63375  193.57143   \n",
            "0    552.65875   85.31500   309.49374   389.76500  113.10000  282.27856   \n",
            "12   632.74630  165.05125   244.73750   494.24750  307.12250  260.17380   \n",
            "2    137.97125   40.35875   333.62500   164.73250   74.45500  217.13333   \n",
            "50   754.19000  133.45250    55.17750    85.09375  122.06250  246.65475   \n",
            "60   272.92874  113.26750    58.47750    52.95500   75.13125  198.93095   \n",
            "7   3886.99240   42.21000   541.53250  1781.31750   33.81625  388.40714   \n",
            "42  5832.31740  409.52000  2008.51370  1550.91130  171.30500  828.64526   \n",
            "28   448.47375  664.26373   413.35500   281.86000  272.97500  340.16428   \n",
            "1   2314.94870  166.79875  1644.42610  1779.98750  106.00750  761.37854   \n",
            "55  2787.04250  102.72500   697.32750   733.76874   33.86750  341.26428   \n",
            "9   4322.87900  301.47375   774.27875  2514.76760   64.13750  445.74405   \n",
            "49  4642.06350  178.75626  2202.90380  1063.71500  141.72250  294.60477   \n",
            "57   175.22874   69.23500    88.56625    42.74000  175.82124  143.87024   \n",
            "29   413.32500  452.30250   216.26125   393.58374  267.07126  383.35477   \n",
            "11  3552.98400  111.00875  2221.75120  3321.14870  136.14750  477.91310   \n",
            "40   335.69000   58.37500   182.51875    84.99375  141.97750   96.27024   \n",
            "51  1082.38120  224.17000    21.08000   339.10000  104.74375  305.23810   \n",
            "45   867.55000  381.04874   103.12750   288.40000  252.37875  422.79047   \n",
            "38  2689.20500  115.64000  1214.46370  1109.59250   18.07500  471.22263   \n",
            "16   185.32250   87.81625    49.02125   111.71000   43.17250  180.89761   \n",
            "54   221.18375  142.64500   148.65875    42.12875   41.43625  231.79762   \n",
            "3    987.79877   26.45375   973.16876   723.40125  126.10125  321.62024   \n",
            "15   183.62125   60.13625   277.54376   314.35250   62.68750  208.61429   \n",
            "22   182.00125  157.11750    16.04250    93.28375   18.17875   75.36786   \n",
            "27   329.86874  333.84125   178.57375   215.50125  225.21875  246.92381   \n",
            "44  2517.47120  462.47125  1494.58750   749.40750  212.16500  615.35360   \n",
            "13  1872.49130   77.29625  1236.29880  1491.04610   74.71125  319.44406   \n",
            "47  1931.14250  303.26376   992.03125   328.36750  134.26625  713.69640   \n",
            "46   686.53625  675.20624    99.58000   182.13500  228.16875  217.02380   \n",
            "21  2417.87740  216.98000  1224.73250  1392.77000  119.90750  444.50000   \n",
            "17   412.07250   38.92750    89.84000   258.26750   19.81875  632.94403   \n",
            "39   500.42500  286.07250   165.18500    39.38125   97.52875  236.26666   \n",
            "61  2699.19240  124.87750   953.95374   549.09500   42.14000  337.37262   \n",
            "43   846.97500  891.84250   228.12000   275.65250  196.16500  392.74167   \n",
            "32   287.17374  149.10750    43.59250   264.90375   63.95875  150.56786   \n",
            "25   103.42625  243.63250    85.42625   105.71625  143.81000  150.99286   \n",
            "33   779.63873  262.99250   170.10374   167.89125  177.16500  237.79167   \n",
            "53  3134.93870  129.05624  1343.42110   935.20374  160.75375  357.82740   \n",
            "36   250.28125  437.22876   401.69000   180.72874  156.71875  246.99524   \n",
            "37   228.02875  220.16500    51.14625   220.48125  130.42375   79.71191   \n",
            "30   471.47876  268.16376   664.96250   566.89624  206.53876  274.54642   \n",
            "20   188.36626  165.60750    81.27125   185.09750  135.34875  325.47025   \n",
            "5   2030.87740   44.65875   325.04500  1386.82250   35.55500  196.59525   \n",
            "34   505.99000   86.83500   181.66500   287.45126  146.32124  156.99524   \n",
            "56   657.41000   32.78250   374.20000   107.52375  274.79500  147.25833   \n",
            "\n",
            "       H08393      R87126      M76378     T62947  ...     Y00711     R59202  \\\n",
            "52  139.57250   216.84500   392.52500   97.48000  ...  200.09375   70.51500   \n",
            "31  107.41125   175.81500   663.10767   72.17125  ...  203.25125   41.00250   \n",
            "4   129.95750   147.90625   464.30963  120.19500  ...  244.58750   57.41125   \n",
            "24  143.99750   287.23000   343.97693  266.45500  ...  362.05374   92.07500   \n",
            "0    87.71000   624.42630   475.27884   74.53250  ...  345.50876   61.89500   \n",
            "12  152.88000   304.48874   528.86346   72.15875  ...   75.56000   83.38125   \n",
            "2    20.73500   338.46000   209.21922   33.96000  ...  108.18500   29.54250   \n",
            "50   44.32125   360.72000   211.98077   28.09875  ...  295.79750   56.13375   \n",
            "60   63.32125   103.10375   191.33846  117.42875  ...  352.82500   49.74500   \n",
            "7    45.74625   598.49750  1849.61730   43.55500  ...   96.41750   61.48875   \n",
            "42   38.14625  2161.89750  1094.67310  147.95750  ...  618.76500  110.16625   \n",
            "28  159.58624   286.00250   473.99808  155.33624  ...  345.59500   58.11750   \n",
            "1    53.14750  1342.96750  1648.45960   66.07000  ...  754.86880   54.72625   \n",
            "55   75.66875   371.53625  1746.38090  123.63000  ...   82.26875   83.29000   \n",
            "9    63.85250   906.42880  2510.98460  108.63500  ...  112.91000  108.77875   \n",
            "49   66.94000  1777.96620  2570.95970   75.92750  ...  335.79126   94.36625   \n",
            "57   47.65000   114.88000   134.11923   56.25875  ...  190.72500   18.78250   \n",
            "29  115.21750   241.35625   597.59424  258.50500  ...  447.32126   78.49500   \n",
            "11   68.18125  1747.85500  2679.89040  152.76500  ...  493.27625  103.65250   \n",
            "40   50.67750    93.07750   196.51154   49.33250  ...  128.20876   26.12125   \n",
            "51  375.80750   356.83000   527.05770  259.76250  ...  171.39250  144.99625   \n",
            "45  337.64500   443.05500   453.28845  244.12250  ...  133.98375  120.63625   \n",
            "38   78.09500   437.01126  1723.44810   87.12750  ...   85.48875   92.26750   \n",
            "16   61.74000   193.85000   194.14809  109.64250  ...  168.31750   64.84875   \n",
            "54   48.45250   153.23000   105.08462   18.16250  ...  320.25876   42.55375   \n",
            "3    27.58000   660.61000   576.64040   46.42750  ...  138.96875   73.46375   \n",
            "15   61.59875   418.39874   608.62690   83.50500  ...  110.72750   54.58500   \n",
            "22   80.11875    79.30000   118.08269  176.40000  ...   69.82500   61.44500   \n",
            "27  220.04375   254.69376   289.83078  137.89500  ...  753.21500   66.20875   \n",
            "44   89.42500  1051.74380  1811.18850  192.99500  ...  687.38000  191.67625   \n",
            "13   32.11250  1231.10620  1422.48270   79.43750  ...   99.68375   82.42250   \n",
            "47   45.33875   481.00876   558.24615   23.60625  ...  238.73625   35.15625   \n",
            "46  273.52750   170.08875   656.87310  478.26750  ...   46.37250  144.02625   \n",
            "21   73.73000  1270.96880  1513.11910   66.57250  ...  436.64000  106.12625   \n",
            "17   11.85625   620.47125   373.10000   46.93000  ...   66.36875   51.58375   \n",
            "39  182.19750   201.14750   226.80000  235.14500  ...  882.99000   84.74500   \n",
            "61   82.04750   596.04500  1240.58460  154.01250  ...  317.80124  152.60750   \n",
            "43  160.85625   302.89500   541.35190  275.35750  ...  764.19620   75.17750   \n",
            "32  112.94125   147.20000   436.23846  139.58500  ...  505.62625   28.44500   \n",
            "25   71.58250   102.50750   149.65192   53.75750  ...  558.96875   15.51750   \n",
            "33  127.94875   241.73875   472.14615  147.13750  ...  640.05630   55.16625   \n",
            "53   80.68375   580.72750  2307.57500  142.64000  ...  170.95125  167.98250   \n",
            "36  109.72125   280.29126   362.34040  114.17875  ...  439.84125   14.14625   \n",
            "37  192.03250    92.31750   282.71540  240.33624  ...  414.76874   21.06500   \n",
            "30   93.69875   261.01126   739.27310  290.05875  ...  531.58875   82.09875   \n",
            "20   89.76000   489.83374   271.71155   98.69875  ...  476.08624   53.39500   \n",
            "5    56.02750   387.24250  1453.34420   70.93000  ...   57.53625   78.91125   \n",
            "34   90.83250   214.05750   364.57886  107.95375  ...  431.92624   23.76625   \n",
            "56   63.61250   248.54124   442.29422   52.68875  ...   98.96375   76.94250   \n",
            "\n",
            "        R43913      T62220      J03040     X69295      L05485      X73882  \\\n",
            "52   303.63250  2365.45360  1005.74750   94.22750   57.754760   34.454760   \n",
            "31   383.73750   812.37620  2652.23400  112.86500   11.769048   31.126190   \n",
            "4    162.71250  1073.41000  1348.64870   45.62875  104.976190   39.844048   \n",
            "24   353.61000  2014.99620   367.49625  122.10875  283.963100   64.236910   \n",
            "0    509.20500  1582.03500   771.33124  108.68875  144.970250   41.158333   \n",
            "12   229.94624  1173.63240  1266.82390   89.94125  149.507140   33.065475   \n",
            "2    126.45375   807.39374  1278.77880   39.97500  183.911900   24.025000   \n",
            "50   341.45374  1357.16250   769.94250   92.69125  164.478580   74.284520   \n",
            "60   408.51624   861.10626  1091.49370  112.65625   99.311905   26.720238   \n",
            "7    209.40125   786.10126   852.85876   79.49125  118.065480   19.019049   \n",
            "42   872.90375  3036.15750  1701.73000   74.77375  209.948800   32.239285   \n",
            "28   934.83000  3425.46120  3040.23750  179.74500  123.953570  117.705950   \n",
            "1    534.20750  3218.32130  1042.66880  121.77000  194.090480   26.500000   \n",
            "55   195.68750  1533.62260  1245.29130  102.08250  102.184520   17.827381   \n",
            "9    281.96250  2423.36620  1087.02750   74.97750   99.694046   68.908330   \n",
            "49   341.17374  2133.67500  1831.02370   99.67125  232.373810   58.720238   \n",
            "57   223.53000  1325.21250   497.52000   57.64125   68.372620   24.125000   \n",
            "29  1061.34000  1866.77000  2631.38870  172.64500  162.120240   88.623810   \n",
            "11   692.32500  2905.36870   760.13370   96.50000  166.766660   31.022620   \n",
            "40   186.23875  1485.52000   465.77000   36.31250  150.102390   16.744047   \n",
            "51   549.48000  1814.33750   802.73750  175.26000  181.173810  118.152380   \n",
            "45  1025.11250  1994.44620  1621.80500  247.77250  199.302380   86.022620   \n",
            "38   176.47375  2547.32740   916.15875   24.84500  190.565480   50.367860   \n",
            "16   287.28876   870.00370   753.95624   69.35625   56.048810   32.915478   \n",
            "54   318.47000  1180.19000  3111.50630  108.54750   83.755950   21.623810   \n",
            "3    227.17625  3034.57130   201.79625   48.86875  260.235720   11.880952   \n",
            "15   186.97500  1716.65380   165.94750   37.23500   31.594048    6.555953   \n",
            "22   164.63875  1157.45000   625.86880   48.52000   63.335712   14.488095   \n",
            "27   538.00500  2059.73240  1378.58620  132.98875  207.854770   67.186905   \n",
            "44   701.13000  4026.87740  1696.64250  219.97750  428.042850   84.289280   \n",
            "13   201.35750  1912.94380   249.94000   63.27000  148.569050   32.853573   \n",
            "47   280.37250  2852.43260  1375.31750   61.93500  193.141660   43.829760   \n",
            "46   726.10250  2092.24760  1083.04880  219.29875  184.445240   99.580956   \n",
            "21   420.72125  3128.27000  1571.58130  152.49500  220.083330   59.613094   \n",
            "17   183.39500  1397.41880   124.96625   45.96625   41.366670   13.992857   \n",
            "39   587.46000  1932.92000  1227.95120  102.69125  138.461900   80.920235   \n",
            "61   385.62250  1911.40120  1176.87870  125.59500  149.433330   12.213095   \n",
            "43  1159.93630  1604.93380  1690.38000  244.47874   77.205956  123.057140   \n",
            "32   318.78876   781.05500   284.03375   67.31375   43.332140   57.747620   \n",
            "25   347.95000  1367.61250   926.12000   79.94625   79.450000   37.426190   \n",
            "33   295.10250  1346.93370  2614.38870  138.79250  227.002380   45.591667   \n",
            "53   171.74250  2675.09380   895.57370  117.72500  242.925000   48.270237   \n",
            "36   410.79500  1573.75240   904.70123   35.46250   59.195236    8.342857   \n",
            "37   283.16125   720.53125   566.95750   36.80125   33.280952   53.735714   \n",
            "30   460.09375  3636.58000  5519.81740  204.77374  154.047620   16.652382   \n",
            "20   719.77500  2938.27880   735.34375  152.09125  158.960710   44.361904   \n",
            "5     66.39125   916.71250   280.61874   26.68250  103.552380   10.488095   \n",
            "34   417.14624  1879.32250  1025.24500  100.60875   72.316666   38.711906   \n",
            "56   100.17625  1964.08370  2745.68630    8.14750   59.809525   11.085714   \n",
            "\n",
            "       R59583      R62945  \n",
            "52  138.89375   101.39875  \n",
            "31  190.30000   247.84375  \n",
            "4    28.52250    22.07125  \n",
            "24  184.56876   118.87250  \n",
            "0   113.69500    98.47250  \n",
            "12  238.80000   103.80750  \n",
            "2   100.80625    51.04375  \n",
            "50  142.15375   197.62875  \n",
            "60  235.47874   100.01500  \n",
            "7    57.88750    63.61125  \n",
            "42  357.01126   174.81625  \n",
            "28  610.17126   191.23875  \n",
            "1    80.99125    76.40750  \n",
            "55  232.15125    68.31000  \n",
            "9    86.20125    63.59875  \n",
            "49  249.82625   154.55875  \n",
            "57   68.93250    53.29875  \n",
            "29  370.28876  1086.95250  \n",
            "11  165.49625   117.54000  \n",
            "40   69.76750    57.52875  \n",
            "51  163.97250  1201.38880  \n",
            "45  386.55750   921.68130  \n",
            "38  102.89125    60.87750  \n",
            "16   64.13625   104.82250  \n",
            "54  101.02625    80.68250  \n",
            "3   122.44375    97.97375  \n",
            "15  110.08625    16.16750  \n",
            "22   35.56875     6.38250  \n",
            "27  157.13500   165.08000  \n",
            "44  348.45750   565.25250  \n",
            "13  116.52500    25.21250  \n",
            "47  202.83000   459.38750  \n",
            "46  249.71875   143.79875  \n",
            "21  109.86125    68.76000  \n",
            "17   74.25375    53.29000  \n",
            "39  212.62500   212.33000  \n",
            "61  203.23125   135.34375  \n",
            "43  467.65125  2586.04130  \n",
            "32  218.30000   322.49750  \n",
            "25  172.66250    92.96375  \n",
            "33  368.92624    57.89375  \n",
            "53  207.72250    46.12500  \n",
            "36  264.95874   124.58375  \n",
            "37   93.95500   103.06375  \n",
            "30  431.98500   203.92250  \n",
            "20  124.65250   108.38250  \n",
            "5    23.74000    26.41500  \n",
            "34  174.96250   123.30375  \n",
            "56   46.13750    45.56500  \n",
            "\n",
            "[49 rows x 500 columns]\n",
            "(49, 500)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.to_numpy()\n",
        "X_test = X_test.to_numpy()\n",
        "X_mrmr = X_mrmr.to_numpy()"
      ],
      "metadata": {
        "id": "q-YysNZElbiH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainX = X_train[:,1:].astype('float32')\n",
        "testX = X_test[:,1:].astype('float32')\n",
        "mrmrX = X_mrmr[:,1:].astype('float32')"
      ],
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "fepRy7p_7Jpv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "X_mrmr = scaler.fit_transform(X_mrmr)"
      ],
      "metadata": {
        "id": "orIUCUWQss3K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "model = xgb.XGBClassifier()\n",
        "import numpy as np\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "kf = KFold(n_splits=10)\n",
        "\n",
        "for i in enumerate(kf.split(X)):\n",
        "    if i == 9:\n",
        "        model.fit(X_mrmr, y_train) \n",
        "        accuracy = model.score(X_test, y_test)\n",
        "        print(f\"Fold {i}: accuracy={accuracy:.2f}\")\n",
        "\n",
        "    else:\n",
        "        model.fit(X_train, y_train) \n",
        "        accuracy = model.score(X_test, y_test)\n",
        "        print(f\"Fold {i}: accuracy={accuracy:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2243b927-f0a8-4069-dec2-9095fc3b8527",
        "id": "tX_-9v6Z1bTw"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold (0, (array([ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
            "       24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,\n",
            "       41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,\n",
            "       58, 59, 60, 61]), array([0, 1, 2, 3, 4, 5, 6]))): accuracy=0.77\n",
            "Fold (1, (array([ 0,  1,  2,  3,  4,  5,  6, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
            "       24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,\n",
            "       41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,\n",
            "       58, 59, 60, 61]), array([ 7,  8,  9, 10, 11, 12, 13]))): accuracy=0.77\n",
            "Fold (2, (array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 20, 21, 22,\n",
            "       23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n",
            "       40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
            "       57, 58, 59, 60, 61]), array([14, 15, 16, 17, 18, 19]))): accuracy=0.77\n",
            "Fold (3, (array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
            "       17, 18, 19, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n",
            "       40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
            "       57, 58, 59, 60, 61]), array([20, 21, 22, 23, 24, 25]))): accuracy=0.77\n",
            "Fold (4, (array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
            "       17, 18, 19, 20, 21, 22, 23, 24, 25, 32, 33, 34, 35, 36, 37, 38, 39,\n",
            "       40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
            "       57, 58, 59, 60, 61]), array([26, 27, 28, 29, 30, 31]))): accuracy=0.77\n",
            "Fold (5, (array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
            "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38, 39,\n",
            "       40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
            "       57, 58, 59, 60, 61]), array([32, 33, 34, 35, 36, 37]))): accuracy=0.77\n",
            "Fold (6, (array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
            "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
            "       34, 35, 36, 37, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
            "       57, 58, 59, 60, 61]), array([38, 39, 40, 41, 42, 43]))): accuracy=0.77\n",
            "Fold (7, (array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
            "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
            "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 50, 51, 52, 53, 54, 55, 56,\n",
            "       57, 58, 59, 60, 61]), array([44, 45, 46, 47, 48, 49]))): accuracy=0.77\n",
            "Fold (8, (array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
            "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
            "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 56,\n",
            "       57, 58, 59, 60, 61]), array([50, 51, 52, 53, 54, 55]))): accuracy=0.77\n",
            "Fold (9, (array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
            "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
            "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
            "       51, 52, 53, 54, 55]), array([56, 57, 58, 59, 60, 61]))): accuracy=0.77\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''#Accuracy without mrmr\n",
        "from numpy import loadtxt\n",
        "import xgboost\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "model = xgboost.XGBClassifier()\n",
        "kfold = KFold(n_splits=10)\n",
        "results = cross_val_score(model, X, y, cv=kfold)\n",
        "print(\"Accuracy: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "alS5rYNH7qN_",
        "outputId": "00419d40-954b-4f5e-d025-56bac4bfa82e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'#Accuracy without mrmr\\nfrom numpy import loadtxt\\nimport xgboost\\nfrom sklearn.model_selection import KFold\\nfrom sklearn.model_selection import cross_val_score\\nmodel = xgboost.XGBClassifier()\\nkfold = KFold(n_splits=10)\\nresults = cross_val_score(model, X, y, cv=kfold)\\nprint(\"Accuracy: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''#Model 2 \n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.feature_selection import SelectKBest, mutual_info_classif\n",
        "\n",
        "mrmr = SelectKBest(score_func=mutual_info_classif, k=500)\n",
        "X_mrmr = mrmr.fit_transform(X, y)\n",
        "\n",
        "xgb_model = xgb.XGBClassifier()\n",
        "\n",
        "k = 10\n",
        "for i in range(k):\n",
        "    if i < k :\n",
        "        scores = cross_val_score(xgb_model, X, y, cv=k)\n",
        "    else:\n",
        "        scores = cross_val_score(xgb_model, X_mrmr, y, cv=k)\n",
        "\n",
        "    print(\"Fold\", i+1, \"score:\", scores[i])'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "YaF78_wmBbWw",
        "outputId": "af8e6397-3f88-44bd-cdcf-108ce01d10e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'#Model 2 \\nimport xgboost as xgb\\nfrom sklearn.model_selection import cross_val_score\\nfrom sklearn.feature_selection import SelectKBest, mutual_info_classif\\n\\nmrmr = SelectKBest(score_func=mutual_info_classif, k=500)\\nX_mrmr = mrmr.fit_transform(X, y)\\n\\nxgb_model = xgb.XGBClassifier()\\n\\nk = 10\\nfor i in range(k):\\n    if i < k :\\n        scores = cross_val_score(xgb_model, X, y, cv=k)\\n    else:\\n        scores = cross_val_score(xgb_model, X_mrmr, y, cv=k)\\n\\n    print(\"Fold\", i+1, \"score:\", scores[i])'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    }
  ]
}