backward_catboost_byday_0727 (2).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "backward_catboost_byday_0727.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJzUo1U8WlDA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "outputId": "b48c37d6-95e5-4fe2-db1b-b504159fdf8a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSEsuy4BuRjr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.chdir(\"/content/drive/My Drive/data\")\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "dat = pd.read_csv(\"final_data0723_3.csv\", encoding='CP949')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KB2InuW9Auga",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dat = dat[('2019-07-02'<=dat['date']) & (dat['date']<'2020-06-30')]. dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tdwo3YqLw3r1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "4b8187dc-ca81-45cd-91ce-55efb88e3e46"
      },
      "source": [
        "! pip install catboost"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b2/aa/e61819d04ef2bbee778bf4b3a748db1f3ad23512377e43ecfdc3211437a0/catboost-0.23.2-cp36-none-manylinux1_x86_64.whl (64.8MB)\n",
            "\u001b[K     |████████████████████████████████| 64.8MB 64kB/s \n",
            "\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.18.5)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.0.5)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.8.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.23.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OxkB464sEgBs",
        "colab": {}
      },
      "source": [
        "from catboost import CatBoostRegressor\n",
        "cb_model = CatBoostRegressor(iterations=1000,\n",
        "                          loss_function='RMSE',\n",
        "                          eval_metric='RMSE',#task_type=\"GPU\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FD_fjmW8bKis",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "muqqR5U-uli3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ahqyb8KprzRs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdPjlzGm2Jx2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "day_index=[]\n",
        "for i in range(int(dat.shape[0]/24)):\n",
        "  day_index .append(i)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNyrZ50ZpFrT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tr_idx =[]\n",
        "te_idx =[]\n",
        "for i, (tr_day_index, te_day_index) in enumerate(kf.split(day_index)):\n",
        "  tr_idx.append(tr_day_index)\n",
        "  te_idx.append(te_day_index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lb35kbA8vJWk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e6c86c4e-9bbe-4121-c73a-68ffb5400f39"
      },
      "source": [
        "tr_idx"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
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          "height": 557
        },
        "outputId": "ac11cb09-b3f4-4a18-b52f-9659eb07ecf4"
      },
      "source": [
        "te_idx"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "         98, 104, 107, 112, 123, 125, 130, 131, 143, 148, 155, 169, 178,\n",
              "        180, 182, 184, 187, 188, 201, 207, 209, 222, 227, 236, 256, 270,\n",
              "        271, 274, 276, 279, 283, 284, 297, 298, 304, 308, 310, 315, 320,\n",
              "        321, 328, 331, 335, 336, 341, 344]),\n",
              " array([  9,  25,  28,  31,  32,  38,  39,  42,  47,  53,  57,  70,  72,\n",
              "         82,  84,  87,  88,  91,  99, 105, 115, 117, 119, 121, 127, 128,\n",
              "        147, 151, 163, 165, 172, 174, 177, 183, 185, 192, 193, 195, 197,\n",
              "        202, 203, 211, 242, 243, 244, 251, 257, 262, 265, 267, 273, 277,\n",
              "        285, 288, 290, 291, 292, 309, 311, 314, 317, 319, 323, 326, 330,\n",
              "        332, 333, 334, 337, 346, 348, 359])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i75bA-D7nLwn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fold_train=[]\n",
        "for i in range(5):\n",
        "  fold = []\n",
        "  for j in range(len(tr_idx[i])):\n",
        "    for k in range(24):\n",
        "      tmp = tr_idx[i][j] * 24 + k \n",
        "      fold.append(tmp)\n",
        "  fold_train.append(list(fold))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67aR9rqd9E75",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "735f0aa6-7bc7-4a1d-bf27-3a412058e8d8"
      },
      "source": [
        "print(fold_train[1])"
      ],
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwahtQfHyVsq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fold_test=[]\n",
        "for i in range(5):\n",
        "  fold = []\n",
        "  for j in range(len(te_idx[i])):\n",
        "    for k in range(24):\n",
        "      tmp = te_idx[i][j] * 24 + k \n",
        "      fold.append(tmp)\n",
        "  fold_test.append(list(fold))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bIIsVPhF9bZ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "aa4adbf1-4f0a-4709-c19a-9b424116cf57"
      },
      "source": [
        "print(fold_test[1])"
      ],
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t70jhBaiGuVx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cv_cat(train):\n",
        "  result = []\n",
        "  X = train[train.columns.difference(['y'])]\n",
        "  y = train.loc[:, 'y']\n",
        "  for i in range(5):\n",
        "    train_index = fold_train[i]\n",
        "    test_index = fold_test[i]\n",
        "    y_train, y_valid = y.iloc[train_index], y.iloc[test_index]\n",
        "    X_train, X_valid = X.iloc[train_index,:], X.iloc[test_index,:]\n",
        "    cat_fit = cb_model.fit(X_train, y_train, cat_features=list(train.select_dtypes(include=['object', 'int64']).columns))\n",
        "    pred = cat_fit.predict(X_valid)\n",
        "    true = y_valid\n",
        "    error = mean_squared_error(true, pred, squared=False)\n",
        "    result.append(error)\n",
        "  return (np.mean(result)) \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Imq3AX3i1Z91",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = dat[dat.columns.difference(['y'])]\n",
        "y = dat.loc[:, 'y']\n",
        "train_index = fold_train[i]\n",
        "test_index = fold_test[i]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGF2DrA-8VCh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "variable_list = list(dat.columns)\n",
        "variable = [e for e in variable_list if e not in ('y','date','day','wday','ymd' )]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4OY1sTL8NzQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from itertools import combinations"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8y6myPzGUwc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = dat.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cySeEVCB6h-i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i = len(variable)\n",
        "best_df =pd.DataFrame()\n",
        "while i>5:\n",
        "  variable_df = pd.DataFrame()\n",
        "  temp_comb = list(combinations(variable, i))\n",
        "  variable_df ['variable'] = temp_comb\n",
        "  variable_df['rmse'] =0\n",
        "  for k in range(len(temp_comb)):\n",
        "    var_name = list(temp_comb[k])\n",
        "    var = var_name + ['y']\n",
        "    train_cv = train.loc[:,var]\n",
        "    variable_df.iloc[k, 1] = cv_cat(train_cv)\n",
        " \n",
        "  idx = np.where(min(variable_df['rmse']))\n",
        "  min_variable = variable_df.iloc[idx].iloc[0,0]\n",
        "  best_df = best_df.append(variable_df.iloc[idx])\n",
        "  best_df.to_csv('/content/drive/My Drive/data/backward_stepwise_add.csv')\n",
        "  variable = list(min_variable)\n",
        "  i=i-1\n",
        "  \n",
        " "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLtmYNOiIVv2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "best_idx = np.where(min(best_df['rmse']))\n",
        "print('best variable:',best_df.iloc[best_idx].iloc[0,0])"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}