runup_regression_rrmse0.16.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7X8zm2d9TdbA"
      },
      "source": [
        "# 一、导入库，读取数据\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fqk4OuCwSzGA"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import math\n",
        "# 不要显示warning\n",
        "import warnings\n",
        "def ignore_warn(*args, **kwargs):\n",
        "    pass\n",
        "warnings.warn = ignore_warn #ignore annoying warning (from sklearn and seaborn)\n",
        "from scipy import stats\n",
        "from scipy.stats import norm, skew, uniform\n",
        "# pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x)) #Limiting floats output to 3 decimal points\n",
        "# from subprocess import check_output\n",
        "# print(check_output([\"ls\", \"../input\"]).decode(\"utf8\")) #check the files available in the directory\n",
        "from sklearn.preprocessing import StandardScaler,RobustScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV\n",
        "from sklearn import metrics\n",
        "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC,LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
        "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import make_scorer\n",
        "from xgboost import XGBRegressor\n",
        "from lightgbm import LGBMRegressor\n",
        "import xgboost as xgb\n",
        "\n",
        "# 将多个结果一起输出\n",
        "from IPython.core.interactiveshell import InteractiveShell\n",
        "InteractiveShell.ast_node_interactivity = \"all\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sE-EBJx_WDl8"
      },
      "outputs": [],
      "source": [
        "# @title 定义rrmse，输出精度的函数\n",
        "def relative_root_mean_squared_error(true, pred):\n",
        "    num = np.sum(np.square(true - pred))\n",
        "    den = np.sum(np.square(true))\n",
        "    squared_error = num/den\n",
        "    rrmse = np.sqrt(squared_error)\n",
        "    return rrmse\n",
        "\n",
        "def print_evaluate(true, predicted , show = True):\n",
        "    print(f'参与计算精度的数据个数是{len(true)}')\n",
        "    mae = metrics.mean_absolute_error(true, predicted)\n",
        "    mse = metrics.mean_squared_error(true, predicted)\n",
        "    rmse = np.sqrt(metrics.mean_squared_error(true, predicted))\n",
        "    # rmse = metrics.root_mean_squared_error(true, predicted)\n",
        "    rrmse = relative_root_mean_squared_error(true, predicted)\n",
        "    r2_square = metrics.r2_score(true, predicted)\n",
        "    if show == True:\n",
        "      print('MAE:{:.4f}'.format(mae))\n",
        "      print('MSE:{:.4f}'.format(mse))\n",
        "      print('RMSE:{:.4f}'.format(rmse))\n",
        "      print('RRMSE:{:.4f}'.format(rrmse))\n",
        "      print('R2 Square:{:.4f}'.format(r2_square))\n",
        "      print('__________________________________')\n",
        "\n",
        "def show_result(model,X_train, X_test, y_train, y_test,ylog):\n",
        "  # 注意！！预测的是log1p（setup），所以计算rrmse时要转换回原先的setup\n",
        "  test_pred = model.predict(X_test)\n",
        "  train_pred = model.predict(X_train)\n",
        "  if ylog == True:\n",
        "    test_pred = np.expm1(test_pred)\n",
        "    train_pred = np.expm1(train_pred)\n",
        "    y_train = np.expm1(y_train)\n",
        "    y_test = np.expm1(y_test)\n",
        "  print('Train set evaluation:\\n_____________________________________')\n",
        "  print_evaluate(y_train, train_pred)\n",
        "  print('Test set evaluation:\\n_____________________________________')\n",
        "  print_evaluate(y_test,test_pred)\n",
        "\n",
        "# 使用k折交叉验证，来看不同数据集在同一模型下所能达到的精度，避免由于测试集分割造成偶然性\n",
        "# 自定义rrmse作为评分指标\n",
        "\n",
        "rrmse_error = make_scorer(relative_root_mean_squared_error, greater_is_better=False)\n",
        "def rrmse_cv(model,X,y,n_folds = 5):\n",
        "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(X)\n",
        "    rmse= np.sqrt(-cross_val_score(model, X, y, scoring=\"neg_root_mean_squared_error\", cv = kf))\n",
        "    rrmse= -cross_val_score(model, X, y, scoring=rrmse_error, cv = kf)\n",
        "    print(\"rrmse score: {:.4f} ({:.4f})\\n\".format(rrmse.mean(), rrmse.std()))\n",
        "    print(\"rmse score: {:.4f} ({:.4f})\\n\".format(rmse.mean(), rmse.std()))\n",
        "\n",
        "def cal_lambda(data):\n",
        "  # 计算box-cox变换最佳的lambda值\n",
        "  best_lambda = None\n",
        "  best_transformed_data = None\n",
        "  best_kurtosis = float('inf')\n",
        "  for lam in np.arange(-2, 3, 0.05):\n",
        "      transformed_data = stats.boxcox(data, lmbda=lam)\n",
        "      # transformed_data = stats.boxcox(d_best['tan'].values, lmbda=lam)\n",
        "      kurtosis = stats.kurtosis(transformed_data)\n",
        "      if abs(kurtosis) < abs(best_kurtosis):\n",
        "          best_lambda = lam\n",
        "          best_transformed_data = transformed_data\n",
        "          best_kurtosis = kurtosis\n",
        "  print(best_lambda)\n",
        "\n",
        "def cal_final_metric(model,X,y):\n",
        "  # 得到模型后，使用所有有真实setup的数据（d1）计算整体rrmse\n",
        "  # 注意X应该已经标准化\n",
        "  y_pre = model.predict(X)\n",
        "  print_evaluate(y, y_pre , show = True)\n",
        "  print('每次使用请注意X是否需要做变形处理！！')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Bdz9yPPROwL",
        "outputId": "318863da-de0d-4c13-9daf-a4e533d8bf27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1441 entries, 0 to 1440\n",
            "Data columns (total 13 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   literature           1441 non-null   object \n",
            " 1   R2%                  1296 non-null   float64\n",
            " 2   setup                545 non-null    float64\n",
            " 3   Stt                  690 non-null    float64\n",
            " 4   Sinc                 690 non-null    float64\n",
            " 5   Sig                  690 non-null    float64\n",
            " 6   Hs(80m or offshore)  1441 non-null   float64\n",
            " 7   Tp                   1441 non-null   float64\n",
            " 8   tan                  1441 non-null   float64\n",
            " 9   D50                  1441 non-null   float64\n",
            " 10  dataset              1441 non-null   object \n",
            " 11  Hs0                  1441 non-null   float64\n",
            " 12  L0                   1441 non-null   float64\n",
            "dtypes: float64(11), object(2)\n",
            "memory usage: 146.5+ KB\n"
          ]
        }
      ],
      "source": [
        "# @title 读取训练数据(nolab)\n",
        "# csv_dir='https://raw.githubusercontent.com/3297269/ML_setup/master/alldata_new.csv'\n",
        "csv_dir = 'https://raw.githubusercontent.com/3297269/ML_setup/master/alldata_new_nolab.csv'\n",
        "all_data = pd.read_csv(csv_dir)\n",
        "all_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 读取18年数模结果作为测试集test18，评估机器学习模型结果,共56个数据。\n",
        "test_dir = 'https://raw.githubusercontent.com/3297269/ML_setup/master/test18.csv'\n",
        "test18 = pd.read_csv(test_dir)\n",
        "test18.drop(['H0rms','H0s','Tp','tanbeta','maxsetup'], axis=1, inplace=True) # 去掉存在空值的行\n",
        "test18.rename(columns={'H0s.1': 'Hs','tanbeta.1':'tan','maxsetup.1':'setup'}, inplace=True)\n",
        "test18.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "F4AHi0sy1o2j",
        "outputId": "d977aeda-0846-42fa-9264-e056873610ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Hs          L0        tan      setup\n",
              "count  56.000000   56.000000  56.000000  56.000000\n",
              "mean    2.263062   57.653878   0.033961   0.211990\n",
              "std     1.161963   66.633501   0.028720   0.132921\n",
              "min     0.307238    6.238874   0.005000   0.026822\n",
              "25%     1.459751   14.037466   0.020000   0.103878\n",
              "50%     2.546378   38.992961   0.020000   0.195423\n",
              "75%     3.152136   56.149864   0.040000   0.287088\n",
              "max     4.595400  305.704815   0.100000   0.550400"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hs</th>\n",
              "      <th>L0</th>\n",
              "      <th>tan</th>\n",
              "      <th>setup</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>56.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.263062</td>\n",
              "      <td>57.653878</td>\n",
              "      <td>0.033961</td>\n",
              "      <td>0.211990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.161963</td>\n",
              "      <td>66.633501</td>\n",
              "      <td>0.028720</td>\n",
              "      <td>0.132921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.307238</td>\n",
              "      <td>6.238874</td>\n",
              "      <td>0.005000</td>\n",
              "      <td>0.026822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.459751</td>\n",
              "      <td>14.037466</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.103878</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.546378</td>\n",
              "      <td>38.992961</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.195423</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.152136</td>\n",
              "      <td>56.149864</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.287088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.595400</td>\n",
              "      <td>305.704815</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.550400</td>\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a7ebf659-c60d-4140-a3d4-2afd78a97848 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"test18\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Hs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.06175993572802,\n        \"min\": 0.307237781,\n        \"max\": 56.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.2630623594821424,\n          2.54637831,\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"L0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 95.65765319751515,\n        \"min\": 6.238873769,\n        \"max\": 305.7048147,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          57.65387813441071,\n          38.99296106,\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.78650049039191,\n        \"min\": 0.005,\n        \"max\": 56.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          56.0,\n          0.03396071428571429,\n          0.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"setup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.723422970677397,\n        \"min\": 0.0268221,\n        \"max\": 56.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.2119903553571429,\n          0.1954235,\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P2iUSn2GYKZn"
      },
      "outputs": [],
      "source": [
        "# @title 看各个变量与setup的线性相关大小\n",
        "# corrmat = all_data.corr().sort_values(by=['setup'], ascending=False) # 按照setup列从大到小排序\n",
        "# plt.subplots(figsize=(12,9))\n",
        "# sns.heatmap(corrmat, vmax=0.9, cmap=\"Blues\", square=True, annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "sm0cgiqNU0e2",
        "outputId": "551d075b-65af-4143-973b-768be3a96f9c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               R2%       setup          tan          D50           Hs  \\\n",
              "count  1283.000000  532.000000  1283.000000  1283.000000  1283.000000   \n",
              "mean      2.498262    0.468733     0.121008    10.523903     2.046196   \n",
              "std       1.719663    0.282578     0.061800    18.028265     1.244248   \n",
              "min       0.137000    0.006000     0.009000     0.200000     0.127472   \n",
              "25%       1.248267    0.256000     0.088500     0.360000     1.138097   \n",
              "50%       2.022876    0.413000     0.108761     3.000000     1.916000   \n",
              "75%       3.584923    0.657750     0.126290    10.000000     2.450067   \n",
              "max      12.669592    1.549000     0.286551    50.000000     7.174100   \n",
              "\n",
              "                L0   setup_log       Hs_log   tan_square      L0_sqrt  \n",
              "count  1283.000000  532.000000  1283.000000  1283.000000  1283.000000  \n",
              "mean    169.232480    0.366754     1.038217     0.018459    12.580423  \n",
              "std      93.558133    0.186456     0.386391     0.020042     3.312701  \n",
              "min      21.413259    0.005982     0.119978     0.000081     4.627446  \n",
              "25%     108.378608    0.227932     0.759916     0.007832    10.410505  \n",
              "50%     156.077682    0.345715     1.070213     0.011829    12.493105  \n",
              "75%     192.649961    0.505461     1.238394     0.015949    13.879840  \n",
              "max     875.218380    0.935701     2.100971     0.082112    29.584090  "
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R2%</th>\n",
              "      <th>setup</th>\n",
              "      <th>tan</th>\n",
              "      <th>D50</th>\n",
              "      <th>Hs</th>\n",
              "      <th>L0</th>\n",
              "      <th>setup_log</th>\n",
              "      <th>Hs_log</th>\n",
              "      <th>tan_square</th>\n",
              "      <th>L0_sqrt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1283.000000</td>\n",
              "      <td>532.000000</td>\n",
              "      <td>1283.000000</td>\n",
              "      <td>1283.000000</td>\n",
              "      <td>1283.000000</td>\n",
              "      <td>1283.000000</td>\n",
              "      <td>532.000000</td>\n",
              "      <td>1283.000000</td>\n",
              "      <td>1283.000000</td>\n",
              "      <td>1283.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.498262</td>\n",
              "      <td>0.468733</td>\n",
              "      <td>0.121008</td>\n",
              "      <td>10.523903</td>\n",
              "      <td>2.046196</td>\n",
              "      <td>169.232480</td>\n",
              "      <td>0.366754</td>\n",
              "      <td>1.038217</td>\n",
              "      <td>0.018459</td>\n",
              "      <td>12.580423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.719663</td>\n",
              "      <td>0.282578</td>\n",
              "      <td>0.061800</td>\n",
              "      <td>18.028265</td>\n",
              "      <td>1.244248</td>\n",
              "      <td>93.558133</td>\n",
              "      <td>0.186456</td>\n",
              "      <td>0.386391</td>\n",
              "      <td>0.020042</td>\n",
              "      <td>3.312701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.137000</td>\n",
              "      <td>0.006000</td>\n",
              "      <td>0.009000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.127472</td>\n",
              "      <td>21.413259</td>\n",
              "      <td>0.005982</td>\n",
              "      <td>0.119978</td>\n",
              "      <td>0.000081</td>\n",
              "      <td>4.627446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.248267</td>\n",
              "      <td>0.256000</td>\n",
              "      <td>0.088500</td>\n",
              "      <td>0.360000</td>\n",
              "      <td>1.138097</td>\n",
              "      <td>108.378608</td>\n",
              "      <td>0.227932</td>\n",
              "      <td>0.759916</td>\n",
              "      <td>0.007832</td>\n",
              "      <td>10.410505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.022876</td>\n",
              "      <td>0.413000</td>\n",
              "      <td>0.108761</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.916000</td>\n",
              "      <td>156.077682</td>\n",
              "      <td>0.345715</td>\n",
              "      <td>1.070213</td>\n",
              "      <td>0.011829</td>\n",
              "      <td>12.493105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.584923</td>\n",
              "      <td>0.657750</td>\n",
              "      <td>0.126290</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.450067</td>\n",
              "      <td>192.649961</td>\n",
              "      <td>0.505461</td>\n",
              "      <td>1.238394</td>\n",
              "      <td>0.015949</td>\n",
              "      <td>13.879840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>12.669592</td>\n",
              "      <td>1.549000</td>\n",
              "      <td>0.286551</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>7.174100</td>\n",
              "      <td>875.218380</td>\n",
              "      <td>0.935701</td>\n",
              "      <td>2.100971</td>\n",
              "      <td>0.082112</td>\n",
              "      <td>29.584090</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    async function quickchart(key) {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"d0\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"R2%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 452.419705649939,\n        \"min\": 0.137,\n        \"max\": 1283.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.498261694883866,\n          2.022875787,\n          1283.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"setup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 187.90746990780357,\n        \"min\": 0.006,\n        \"max\": 532.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.468733082706767,\n          0.413,\n          532.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 453.5685045984298,\n        \"min\": 0.009,\n        \"max\": 1283.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.12100790841153547,\n          0.108760627,\n          1283.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"D50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 449.2481426813544,\n        \"min\": 0.2,\n        \"max\": 1283.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.523903351519875,\n          3.0,\n          1283.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 452.8009382336907,\n        \"min\": 0.12747223,\n        \"max\": 1283.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.0461961103990647,\n          1.916,\n          1283.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"L0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 458.6096187940471,\n        \"min\": 21.41325867,\n        \"max\": 1283.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          169.2324803463601,\n          156.0776823,\n          1283.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"setup_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 187.96059669044655,\n        \"min\": 0.005982071677547464,\n        \"max\": 532.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.3667542327014097,\n          0.3457151037023904,\n          532.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hs_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 453.27027748760685,\n        \"min\": 0.11997816237962836,\n        \"max\": 1283.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.0382174065272327,\n          1.0702128141464118,\n          1283.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tan_square\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 453.60110627320614,\n        \"min\": 8.099999999999999e-05,\n        \"max\": 1283.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.018459143925565358,\n          0.01182887398543313,\n          1283.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"L0_sqrt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 449.2911559112023,\n        \"min\": 3.3127011679539327,\n        \"max\": 1283.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.58042307317747,\n          12.493105390574435,\n          1283.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "# @title 简单处理读取的数据，去掉空值行，去掉setup≤0的行\n",
        "d0 = all_data.dropna(axis=0, how=\"any\", subset=['tan','Hs0','L0','R2%']).reset_index(drop=True) # 去掉存在空值的行\n",
        "d0.rename(columns={'Hs0': 'Hs'}, inplace=True)\n",
        "d0.drop(['Stt','Sinc','Sig','Tp','literature','Hs(80m or offshore)'], axis=1, inplace=True)\n",
        "d0 = d0.drop(d0[d0['setup'] <= 0].index)\n",
        "d0[\"setup_log\"] = np.log1p(d0[\"setup\"])\n",
        "d0[\"Hs_log\"] = np.log1p(d0[\"Hs\"])\n",
        "d0[\"tan_square\"] = np.power(d0[\"tan\"],2)\n",
        "d0[\"L0_sqrt\"] = np.sqrt(d0[\"L0\"])\n",
        "d0.describe() # 还剩1287个数据,其中755个没有setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1tYCrkzlHng1"
      },
      "outputs": [],
      "source": [
        "# @title 将d0分为两部分，有setup的数据集为d1，没有setup的数据集为d2\n",
        "d1 = d0.dropna(axis=0, how=\"any\", subset=['setup']).copy()\n",
        "d2 = d0[d0['setup'].isna()]\n",
        "d2['have_setup'] = 0\n",
        "d1['have_setup'] = 1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 在d1中取20%作为测试集\n",
        "d1_val = d1.sample(round(len(d1)*0.2))\n",
        "d1_train = d1.drop(d1_val.index)\n",
        "d1_all = d1.copy()\n",
        "d1 = d1_train.copy()"
      ],
      "metadata": {
        "id": "ZCkgm_IqN_u_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 比较d1与test18的分布\n",
        "# plt.hist(d1['setup'], density=True, bins=20, alpha=0.5, label='d1')\n",
        "# plt.hist(test18['setup'], density=True, bins=20, alpha=0.5, label='test18')\n",
        "\n",
        "# # 添加标签和图例\n",
        "# plt.xlabel('Value')\n",
        "# plt.ylabel('Frequency')\n",
        "# plt.title('Comparison of Data Distributions')\n",
        "# plt.legend()"
      ],
      "metadata": {
        "id": "R2xJhy6xx_2X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3Y9whs-sIVT"
      },
      "source": [
        "#二、使用全部有setup的532个数据（d1）第一次建模\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EjW6aDKhHxmB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6336b014-aabc-4f14-84c6-7ed1952a0a83"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='setup', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Hs', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='tan', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='L0', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "D50           1.989269\n",
            "Hs            0.787259\n",
            "L0            0.762216\n",
            "setup         0.696699\n",
            "tan_square    0.364037\n",
            "R2%           0.292282\n",
            "setup_log     0.253679\n",
            "Hs_log        0.125266\n",
            "L0_sqrt       0.097453\n",
            "have_setup    0.000000\n",
            "tan          -0.467508\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# @title 作图看d1各个特征的分布并计算峰度。d1数据整体分布较好，skew基本小于1，不用做变换\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.distplot(d1['setup'] , fit=norm);\n",
        "plt.subplot(2, 2, 2)\n",
        "sns.distplot(d1['Hs'] , fit=norm);\n",
        "plt.subplot(2, 2, 3)\n",
        "sns.distplot(d1['tan'] , fit=norm);\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.distplot(d1['L0'] , fit=norm);\n",
        "print(d1.skew().sort_values(ascending=False))\n",
        "plt.subplots_adjust(wspace=0.4, hspace=0.4)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 检查d1，可发现离群不严重。\n",
        "scalar = StandardScaler()\n",
        "d1_scale = pd.DataFrame(scalar.fit_transform(d1.drop('dataset', axis=1).values),columns=d1.drop('dataset', axis=1).columns)\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.boxplot(d1_scale)"
      ],
      "metadata": {
        "id": "k4r_o5S2kTUv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "outputId": "0a49b42d-ac11-4eab-e15e-d216a1e95f55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 使用optuna调参寻找最佳xgboost参数\n",
        "def optuna_xgb(X,y,n_trials=100):\n",
        "  import xgboost as xgb\n",
        "  try:\n",
        "    import optuna\n",
        "  except ImportError:\n",
        "    !pip install optuna -q\n",
        "    import optuna\n",
        "\n",
        "  def objective(trial):\n",
        "    dtrain = xgb.DMatrix(X, label=y)\n",
        "    param = {\n",
        "        \"verbosity\": 0,\n",
        "        \"objective\": \"reg:squarederror\",\n",
        "        \"eval_metric\": \"rmse\",\n",
        "        \"booster\": \"dart\",\n",
        "        # \"booster\": trial.suggest_categorical(\"booster\", [\"gbtree\", \"gblinear\", \"dart\"]),\n",
        "        \"grow_policy\": \"lossguide\",\n",
        "        # \"lambda\": trial.suggest_float(\"lambda\", 0.1, 1.0, log=True),\n",
        "        \"alpha\": trial.suggest_float(\"alpha\", 0.1, 1.0, log=True),\n",
        "        \"subsample\": trial.suggest_float(\"subsample\", 0.5, 1.0, step=5)\n",
        "    }\n",
        "    param[\"max_depth\"] = trial.suggest_int(\"max_depth\", 1, 5)\n",
        "    param[\"eta\"] = trial.suggest_float(\"eta\", 1e-8, 0.3, log=True)\n",
        "    param[\"gamma\"] = trial.suggest_float(\"gamma\", 1e-8, 1.0, log=True)\n",
        "    param[\"min_child_weight\"] = trial.suggest_int(\"min_child_weight\", 3, 10)\n",
        "\n",
        "    pruning_callback = optuna.integration.XGBoostPruningCallback(trial, \"test-rmse\")\n",
        "    history = xgb.cv(param, dtrain, nfold=5, metrics='rmse', num_boost_round=100, callbacks=[pruning_callback])\n",
        "    rmse = history[\"test-rmse-mean\"].values[-1]\n",
        "    return rmse\n",
        "\n",
        "  pruner = optuna.pruners.MedianPruner(n_warmup_steps=5)\n",
        "  study = optuna.create_study(pruner=pruner, direction=\"minimize\")\n",
        "  study.optimize(objective, n_trials=n_trials)\n",
        "  print(\"Number of finished trials: {}\".format(len(study.trials)))\n",
        "  print(\"Best trial:\")\n",
        "  trial = study.best_trial\n",
        "  print(\"  Value: {}\".format(trial.value))\n",
        "  print(\"  Params: \")\n",
        "  print(trial.params)\n",
        "  # from optuna.visualization import plot_param_importances\n",
        "  # plot_param_importances(study)\n",
        "  return trial.params"
      ],
      "metadata": {
        "id": "KgbD5CuP5Hc8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 不发生数据泄露的pipeline缩放\n",
        "# steps = list()\n",
        "# steps.append(('scaler', MinMaxScaler()))\n",
        "# steps.append(('model', LogisticRegression()))\n",
        "# pipeline = Pipeline(steps=steps)\n",
        "# # define the evaluation procedure\n",
        "# cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "# # evaluate the model using cross-validation\n",
        "# scores = cross_val_score(pipeline, X, y, scoring='accuracy', cv=cv, n_jobs=-1)"
      ],
      "metadata": {
        "id": "N30vjIUeu_X3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title optuna调参过程可视化\n",
        "# 可视化不同超参数的重要性\n",
        "# from optuna.visualization import plot_param_importances\n",
        "# plot_param_importances(study)\n",
        "\n",
        "# @title 可视化优化的过程\n",
        "# from optuna.visualization import plot_optimization_history\n",
        "# plot_optimization_history(study)\n",
        "\n",
        "# optuna.visualization.plot_slice(study)"
      ],
      "metadata": {
        "id": "2I1wToU7myFX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "HnzEme7IKJ5U",
        "outputId": "b29a9548-f0de-4bc1-8417-bb9efa7754c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(alpha=0.18838438950099032, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.22201054440466547,\n",
              "             eval_metric=None, feature_types=None, gamma=0.000396927657304194,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=3, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(alpha=0.18838438950099032, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.22201054440466547,\n",
              "             eval_metric=None, feature_types=None, gamma=0.000396927657304194,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=3, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(alpha=0.18838438950099032, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.22201054440466547,\n",
              "             eval_metric=None, feature_types=None, gamma=0.000396927657304194,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=3, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "此时模型在d1数据上的精度为：\n",
            "参与计算精度的数据个数是426\n",
            "MAE:0.0409\n",
            "MSE:0.0031\n",
            "RMSE:0.0556\n",
            "RRMSE:0.1021\n",
            "R2 Square:0.9604\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n",
            "此时模型在数模数据上的精度为：\n",
            "参与计算精度的数据个数是56\n",
            "MAE:0.1060\n",
            "MSE:0.0165\n",
            "RMSE:0.1285\n",
            "RRMSE:0.5149\n",
            "R2 Square:0.0482\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n",
            "此时模型在测试数据上的精度为：\n",
            "参与计算精度的数据个数是106\n",
            "MAE:0.1133\n",
            "MSE:0.0270\n",
            "RMSE:0.1642\n",
            "RRMSE:0.2950\n",
            "R2 Square:0.6870\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n"
          ]
        }
      ],
      "source": [
        "# @title 使用xgboost调优选择最佳参数后建模，模型最小rrmse=0.1167\n",
        "X = d1[['Hs','tan','L0']].values\n",
        "y = d1['setup'].values\n",
        "scalar1 = StandardScaler()\n",
        "X_scale = scalar1.fit_transform(X)\n",
        "\n",
        "# best_param_d1 = optuna_xgb(X_scale,y,n_trials=200)\n",
        "best_param_d1 = {'alpha': 0.18838438950099032, 'subsample': 0.5, 'max_depth': 5, 'eta': 0.22201054440466547, 'gamma': 0.000396927657304194, 'min_child_weight': 3}\n",
        "xgb1 = XGBRegressor(**best_param_d1)\n",
        "xgb1.fit(X_scale, y)\n",
        "# 得到最佳模型xgb1\n",
        "\n",
        "X_all = scalar1.transform(d1[['Hs','tan','L0']].values) # 注意！这里一定要用训练模型一样的缩放\n",
        "y = d1['setup'].values\n",
        "print('此时模型在d1数据上的精度为：')\n",
        "cal_final_metric(xgb1,X_all,y)\n",
        "\n",
        "print('此时模型在数模数据上的精度为：')\n",
        "cal_final_metric(xgb1,scalar1.transform(test18[['Hs','tan','L0']].values),test18['setup'].values)\n",
        "\n",
        "print('此时模型在测试数据上的精度为：')\n",
        "cal_final_metric(xgb1,scalar1.transform(d1_val[['Hs','tan','L0']].values),d1_val['setup'].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xqDAWz4Nt1_4",
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "# @title xgboost等树模型对数据分布不敏感，对特征做b-box变换不影响结果\n",
        "# X = d1[['Hs_log','tan_square','L0_sqrt']].values\n",
        "# y = d1['setup'].values\n",
        "# scalar2 = StandardScaler()\n",
        "# X_scale = scalar2.fit_transform(X)\n",
        "# best_param = {'learning_rate': 0.05, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0.8164256459214504, 'reg_lambda': 0.8970365433738731, 'subsample': 0.7}\n",
        "# xgb_best = XGBRegressor(**best_param)\n",
        "# xgb_best.fit(X_scale, y)\n",
        "# X = scalar2.transform(d1[['Hs_log','tan_square','L0_sqrt']].values) # 注意！这里一定要用训练模型一样的缩放\n",
        "# d1[\"setup_pre\"]  = xgb_best.predict(X)\n",
        "# print_evaluate(d1['setup'], d1[\"setup_pre\"] , show = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 三、利用runup数据补充标签\n",
        "** 原数据只有532个，为增加泛化性，进一步补充数据。**"
      ],
      "metadata": {
        "id": "jsa_XYGwP5TP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 建立新的xgboost模型，加入相关度非常高的R2%来建立预测模型\n",
        "X = d1[['Hs','tan','L0','R2%']].values\n",
        "y = d1['setup'].values\n",
        "# X = d1[['Hs_log','tan_square','L0_sqrt','R2%']].values\n",
        "# y = d1['setup_log'].values\n",
        "scalar2 = StandardScaler()\n",
        "X_scale = scalar2.fit_transform(X)\n",
        "\n",
        "# best_param_d2 = optuna_xgb(X_scale,y,n_trials=200)\n",
        "best_param_d2 = {'alpha': 0.2778268007054582, 'subsample': 0.5, 'max_depth': 5, 'eta': 0.20758145829749824, 'gamma': 1.372184691627056e-07, 'min_child_weight': 4}"
      ],
      "metadata": {
        "id": "XEYSZ8YSQUS1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "iH5SLOyC0QW_",
        "outputId": "6c658c86-9e59-409f-a60a-c0988e05c6c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(alpha=0.2778268007054582, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.20758145829749824,\n",
              "             eval_metric=None, feature_types=None, gamma=1.372184691627056e-07,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=4, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(alpha=0.2778268007054582, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.20758145829749824,\n",
              "             eval_metric=None, feature_types=None, gamma=1.372184691627056e-07,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=4, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(alpha=0.2778268007054582, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.20758145829749824,\n",
              "             eval_metric=None, feature_types=None, gamma=1.372184691627056e-07,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=4, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "使用包含runup在内的四个变量建模时，能达到的精度：\n",
            "参与计算精度的数据个数是426\n",
            "MAE:0.0364\n",
            "MSE:0.0026\n",
            "RMSE:0.0507\n",
            "RRMSE:0.0930\n",
            "R2 Square:0.9671\n",
            "__________________________________\n"
          ]
        }
      ],
      "source": [
        "# @title 使用新建的xgb2模型预测缺失的setup值。由于R2%与setup有非常高的相关关系，所以当特征含有R2%时，rrmse可以减小到0.0963\n",
        "# best_param = best_param_d2\n",
        "xgb2 = XGBRegressor(**best_param_d2)\n",
        "xgb2.fit(X_scale, y)\n",
        "\n",
        "X = scalar2.transform(d1[['Hs','tan','L0','R2%']].values) # 注意！这里一定要用训练模型一样的缩放\n",
        "setup_pre  = xgb2.predict(X)\n",
        "print('使用包含runup在内的四个变量建模时，能达到的精度：')\n",
        "print_evaluate(d1['setup'], setup_pre , show = True)\n",
        "\n",
        "# 这里的缩放应该与建模时的缩放相同\n",
        "X_no_setup = scalar2.transform(d2[['Hs','tan','L0','R2%']].values)\n",
        "d2_pred = xgb2.predict(X_no_setup)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 将预测的setup填补到数据中，得到新的数据集d3，d3的数据集比d1多了751个，增加了141%\n",
        "# 增加的d2本身与原d1数据分布不同，因此对于新增的数据给予小的权重\n",
        "\n",
        "d2['setup'] = d2_pred\n",
        "# d3=pd.concat([d1,d2])\n",
        "# d1.info()\n",
        "# d2.info()\n",
        "# d3.info()\n",
        "print(f'原数据{len(d1)}个')\n",
        "print(f'增补后数据{len(d3)}个')\n",
        "print(f'数据增加了{len(d3)-len(d1)}个')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRW2aQhxe90m",
        "outputId": "a09f4232-627b-4dc3-c965-cf32a770fe8f",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "原数据426个\n",
            "增补后数据1177个\n",
            "数据增加了751个\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 看d3数据分布\n",
        "# CHESIL 与 LOEBAR 两个数据集的setup相对较大，造成d3出现双峰值\n",
        "unique_datasets = d3[\"dataset\"].unique()\n",
        "d3_sorted = d3.sort_values(by=\"setup\")\n",
        "plt.figure(figsize=(12, 5))\n",
        "for dataset in unique_datasets:\n",
        "  subset = d3_sorted[d3_sorted[\"dataset\"] == dataset]\n",
        "  marker = 'o' if dataset in d1['dataset'].unique() else 'x'\n",
        "  plt.scatter(subset[\"setup\"], subset.index, label=f\"{dataset} ({len(subset)})\", alpha=0.4, marker=marker)\n",
        "\n",
        "plt.xlabel(\"Setup\")\n",
        "plt.ylabel(\"Index\")\n",
        "plt.title(\"d3 setup\")\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "MEl3FSX6wdSR",
        "outputId": "13834c28-1d76-4f9a-8baa-1881ea95c299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d350fac0>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d36ee560>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d350ffa0>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d36ee170>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d3540b80>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d350fdf0>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d350ffd0>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d36ec730>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d3540910>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d3541930>"
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          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d8309de0>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d350de40>"
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          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d36ee260>"
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          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d35424d0>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d3542950>"
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          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d3542a10>"
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          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d830b130>"
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          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d350c0a0>"
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          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d3543760>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d3543b80>"
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          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d3543b50>"
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          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d3574460>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Setup')"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Index')"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'd3 setup')"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7827d8114f70>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "MlVq_0pUZmGx",
        "outputId": "b21145a2-511a-46d6-be4d-bcaf0474d13f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# @title 检查d3是否存在outliner。绘制箱线图。从图中可以看出L0存在较为严重的偏离点，应作调整\n",
        "# 箱体代表了数据的四分位数范围，其中箱子的底部和顶部分别表示了数据的第一（Q1）和第三（Q3）四分位数。箱子中间的水平线表示数据的中位数（第二四分位数，Q2）\n",
        "# 须线通常延伸到数据中的最大和最小值，但不超过1.5倍的IQR（Interquartile Range，四分位数间距）\n",
        "# 超出须线末端范围的数据点被认为是异常值，通常用点的形式表示。\n",
        "scalar = StandardScaler()\n",
        "d3_scale = pd.DataFrame(scalar.fit_transform(d3.drop('dataset', axis=1).values),columns=d3.drop('dataset', axis=1).columns)\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(d3_scale)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mV_C2GG8qT9h"
      },
      "source": [
        "<img style='float;left;' src='https://img2022.cnblogs.com/blog/1401833/202208/1401833-20220830130351026-2048205807.png' width='30%'>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "ZIE_UFdxaplL",
        "outputId": "94e5e28e-956c-4bd0-fdd5-92c22dcf39b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'L0 before delete outliner')"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d336b460>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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goEBJSUkeb5WvinoZWA4fPuyzl7ABAIC6dfDgQV1wwQVeHVMvA0vZC8cOHjyo6OhoP1cDAACqIj8/X61atfJ4cWhV1cvAUnYbKDo6msACAEA9U53HOXjoFgAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWB6BBQAAWF69XDgOAAD4ntNl9OV/Tyjju+OSbEpt11RXXNhUQXb/v7ePwAIAALR0+xE9/N425RaWuLe9sPJbxUaG6KnBXTSgcws/VsctIQAAAt7S7Uc0duFXHmGlTG5hicYu/EpLtx/xQ2U/IbAAABDAnC6jxz7aUWm7aR/vlNNl6qCiihFYAAAIYOszc5SVX1xpuyN5RVqfmVMHFVWMwAIAQADLLiiqlba+RmABACCAxUeF10pbXyOwAAAQwHomxykxOqzSdi1iwtUzOa4OKqoYgQUAgAAWZLfpsUEXV9pu6s0pfl2PhcACAECAG9C5hebedpliI0PK7WsSGaK5t13m93VYWDgOAABoQOcWui4lkZVuAQCAtQXZberToZn6dGjm71LK4ZYQAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPAILAACwPK8Ci9Pp1JQpU5ScnKyIiAi1a9dOjz/+uIwx7jbGGD366KNq0aKFIiIilJaWpr1793qcJycnR8OHD1d0dLRiY2M1atQonTx50jefCAAANDheBZa//OUvmjNnjl544QV98803+stf/qKnn35as2bNcrd5+umnNXPmTM2dO1fr1q1To0aN1L9/fxUVFbnbDB8+XDt27NCyZcu0ePFirVq1SmPGjPHdpwIAAA2KzZw5PFKJm266SQkJCXrllVfc24YMGaKIiAgtXLhQxhglJSXpgQce0IMPPihJysvLU0JCghYsWKBhw4bpm2++UUpKijZs2KAePXpIkpYuXaobb7xR33//vZKSkiqtIz8/XzExMcrLy1N0dLS3nxkAAPhBTb6/vRph6d27t5YvX649e/ZIkrZu3ao1a9bohhtukCRlZmYqKytLaWlp7mNiYmLUq1cvZWRkSJIyMjIUGxvrDiuSlJaWJrvdrnXr1lV43eLiYuXn53v8AACAwBHsTeOHH35Y+fn56tixo4KCguR0OjV9+nQNHz5ckpSVlSVJSkhI8DguISHBvS8rK0vx8fGeRQQHKy4uzt3mbDNmzNC0adO8KRUAADQgXo2wLFq0SG+88YbefPNNffXVV3rttdf017/+Va+99lpt1SdJmjx5svLy8tw/Bw8erNXrAQAAa/FqhGXSpEl6+OGHNWzYMElSly5dtH//fs2YMUMjR45UYmKiJOno0aNq0aKF+7ijR4+qa9eukqTExERlZ2d7nLe0tFQ5OTnu488WFhamsLAwb0oFAAANiFcjLIWFhbLbPQ8JCgqSy+WSJCUnJysxMVHLly9378/Pz9e6deuUmpoqSUpNTVVubq42bdrkbrNixQq5XC716tWr2h8EAAA0XF6NsNx8882aPn26WrdurYsvvlibN2/Ws88+q9///veSJJvNpgkTJuiJJ55Qhw4dlJycrClTpigpKUm33HKLJKlTp04aMGCARo8erblz56qkpETjx4/XsGHDqjRDCAAABB6vAsusWbM0ZcoU3X333crOzlZSUpLuuusuPfroo+42f/jDH3Tq1CmNGTNGubm56tu3r5YuXarw8HB3mzfeeEPjx4/XtddeK7vdriFDhmjmzJm++1QAAKBB8WodFqtgHRYAAOqfmnx/ezXCAgAAGi6ny2h9Zo6yC4oUHxWunslxCrLb/F2WJAILAACQtHT7EU37eKeO5P30Kp0WMeGaenOKBnRucZ4j6wZvawYAIMAt3X5E4xZ+5RFWJCkrr0jjFn6lpduP+KmynxBYAAAIYE6X0bSPd6qiB1rLtk37eKecLv8+8kpgAQAggK3PzCk3snImI+lIXpHWZ+bUXVEVILAAABDAsgvOHVaq0662EFgAAAhg8VHhlTfyol1tIbAAABDAeibHqUVMuM41edmmH2cL9UyOq8uyyiGwAAAQwILsNk29OUWSyoWWst+n3pzi9/VYCCwAAAS4AZ1baM5tlykxxvO2T2JMuObcdpkl1mFh4TgAAKABnVvoupREVroFAADWFmS3KbVdU3+XUSFuCQEAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsjsAAAAMsL9ncBAADA/xylLr2esU/7cwrVJi5SI1LbKjTYOuMaBBYAAALcjCU7NW91plzmp23Tl3yj0Vcma/KNKf4r7AwEFgAAAtiMJTv10qrMcttdRu7tVggt1hnrAQAAdcpR6tK81eXDypnmrc6Uo9RVRxWdG4EFAIAA9XrGPo/bQBVxmR/b+RuBBQCAALU/p9Cn7WoTgQUAgADVJi7Sp+1qE4EFAIAANSK1rey287ex235s528EFgAAAlRosF2jr0w+b5vRVyZbYj0WpjUDABDAyqYsn70Oi90mS63DYjPGVPJ8sPXk5+crJiZGeXl5io6O9nc5AADUe3Wx0m1Nvr+9ruTQoUO67bbb1LRpU0VERKhLly7auHGje78xRo8++qhatGihiIgIpaWlae/evR7nyMnJ0fDhwxUdHa3Y2FiNGjVKJ0+e9LYUAADgI6HBdo268kL9+RedNerKCy1xG+hMXlXzww8/qE+fPgoJCdEnn3yinTt36v/+7//UpEkTd5unn35aM2fO1Ny5c7Vu3To1atRI/fv3V1FRkbvN8OHDtWPHDi1btkyLFy/WqlWrNGbMGN99KgAA0KB4dUvo4Ycf1hdffKHVq1dXuN8Yo6SkJD3wwAN68MEHJUl5eXlKSEjQggULNGzYMH3zzTdKSUnRhg0b1KNHD0nS0qVLdeONN+r7779XUlJSpXVwSwgAgPqnzm4JffTRR+rRo4d+9atfKT4+Xt26ddO8efPc+zMzM5WVlaW0tDT3tpiYGPXq1UsZGRmSpIyMDMXGxrrDiiSlpaXJbrdr3bp1FV63uLhY+fn5Hj8AACBweBVYvvvuO82ZM0cdOnTQp59+qnHjxunee+/Va6+9JknKysqSJCUkJHgcl5CQ4N6XlZWl+Ph4j/3BwcGKi4tztznbjBkzFBMT4/5p1aqVN2UDAIB6zqvA4nK5dNlll+nJJ59Ut27dNGbMGI0ePVpz586trfokSZMnT1ZeXp775+DBg7V6PQAAYC1eBZYWLVooJcVzPnanTp104MABSVJiYqIk6ejRox5tjh496t6XmJio7Oxsj/2lpaXKyclxtzlbWFiYoqOjPX4AAEDg8Cqw9OnTR7t37/bYtmfPHrVp00aSlJycrMTERC1fvty9Pz8/X+vWrVNqaqokKTU1Vbm5udq0aZO7zYoVK+RyudSrV69qfxAAANBwebXS7f3336/evXvrySef1NChQ7V+/Xq9/PLLevnllyVJNptNEyZM0BNPPKEOHTooOTlZU6ZMUVJSkm655RZJP47IDBgwwH0rqaSkROPHj9ewYcOqNEMIAAAEHq9Xul28eLEmT56svXv3Kjk5WRMnTtTo0aPd+40xmjp1ql5++WXl5uaqb9++mj17tn72s5+52+Tk5Gj8+PH6+OOPZbfbNWTIEM2cOVONGzeuUg1MawYAoP6pyfc3S/MDAIA6UadL8wMAANQ1AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALA8AgsAALC8YH8XAAAA/M/pMlqfmaPsgiLFR4WrZ3Kcguw2f5flRmABACDALd1+RNM+3qkjeUXubS1iwjX15hQN6NzCj5X9hFtCAAAEsKXbj2jcwq88wookZeUVadzCr7R0+xE/VeaJwAIAQIByuoweWLRVpoJ9ZdumfbxTTldFLeoWgQUAgAA14e2vdMrhPOd+I+lIXpHWZ+bUXVHnQGABACAAOUpdWvx1VpXaZhcUVd6olhFYAAAIQK9n7KvwVlBF4qPCa7WWqiCwAAAQgPbnFFapXWRokHomx9VyNZUjsAAAEIDaxEVWqd2NnRMtsR4LgQUAgAA0IrWtKsshNklPDr6kTuqpDIEFAIAAFBps1+grk8/bZsxVyQoNtkZUYKVbAAAC1OQbUyRJ81Zn6sylVuw2afSVye79VmAzxvh/NRgv5efnKyYmRnl5eYqOjvZ3OQAA1GuOUpdez9in/TmFahMXqRGpbWtlZKUm39+MsAAAEOBCg+0adeWF/i7jvKxxYwoAAOA8CCwAAMDyCCwAAMDyeIYFAIAAVlcP3NYUgQUAgAA1Y8nOclOapy/5xnJTmiUCCwAAAWnGkp16aVVmue0uI/d2K4UW6435AACAWuUodWne6vJh5UzzVmfKUeqqo4oqR2ABACDAvJ6xz+M2UEVc5sd2VkFgAQAgwOzPKfRpu7rAMywAAAQIp8to7d7j2ph5okrt28RF1nJFVUdgAQAgACzdfkQTF21VocNZpfZ2mzQitW3tFuUFAgsAAA3c0u1HNHbhV14dc22neEutx2KdSgAAgM85XUZTP9zu9XHbvs+Xs7Inc+sQgQUAgAZsfWaOjhY4vD4uK79I6zNzaqGi6iGwAADQgGUXFPnlWF8jsAAA0IDFR4X75VhfI7AAANCA9UyOU0JUqNfHJUaHq2dyXC1UVD0EFgAAGrAgu03TftHZ6+MeG5SiILutFiqqHgILAAAN3OYDP1S5bViwXXNvu0wDOreoxYq8xzosAAA0YFV50aEkpV4Yp7H92qlvh+aWGlkpQ2ABAKABq8qLDiUprVOC+l0UX/sFVRO3hAAAaMDe2/x9ldpZ6UWHFSGwAADQQDlKXdpxuKBKba30osOKEFgAAGigXlu7r0rtbLLWiw4rQmABAKCB2rCvakvrt24aaakXHVbE2tUBAIBqiwwNqlK7bq1ia7cQHyCwAADQQJWUllap3ZBuF9RyJTVHYAEAoAFylLr0r+3ZlbaLDA1S7w7N6qCimiGwAADQAP3xva+r1O7GzomWXCjubAQWAAAaGKfL6IPNh6rUNjKsfqwhWz+qBAAAlXK6jNbuPa4XV+5WaRVWt5Wsv/5KmRqNsDz11FOy2WyaMGGCe1tRUZHS09PVtGlTNW7cWEOGDNHRo0c9jjtw4IAGDhyoyMhIxcfHa9KkSSqt4oNBAACgvKXbj6jLY59qxPz1+nJfXpWPs/r6K2WqHVg2bNigl156SZdcconH9vvvv18ff/yx3nnnHX3++ec6fPiwBg8e7N7vdDo1cOBAORwOrV27Vq+99poWLFigRx99tPqfAgCAALZ0+xGNXfiVCh1Or45rUw/WXylTrSpPnjyp4cOHa968eWrSpIl7e15enl555RU9++yzuuaaa9S9e3fNnz9fa9eu1ZdffilJ+ve//62dO3dq4cKF6tq1q2644QY9/vjjevHFF+VwOCq8XnFxsfLz8z1+AADAj7eBpn64vVrH3tartY+rqT3VCizp6ekaOHCg0tLSPLZv2rRJJSUlHts7duyo1q1bKyMjQ5KUkZGhLl26KCEhwd2mf//+ys/P144dOyq83owZMxQTE+P+adWqVXXKBgCgwVmfmaOjBRX/B39lRvZO9nE1tcfrwPL222/rq6++0owZM8rty8rKUmhoqGJjYz22JyQkKCsry93mzLBStr9sX0UmT56svLw898/Bgwe9LRsAgAYpK7+oWse1jA2vN7eDJC9nCR08eFD33Xefli1bpvDw8NqqqZywsDCFhYXV2fUAAKgvck4WV+u4azrG+7iS2uVVtNq0aZOys7N12WWXKTg4WMHBwfr88881c+ZMBQcHKyEhQQ6HQ7m5uR7HHT16VImJiZKkxMTEcrOGyn4vawMAAKomNjK0Wsf98cYUH1dSu7wKLNdee622bdumLVu2uH969Oih4cOHu/9/SEiIli9f7j5m9+7dOnDggFJTUyVJqamp2rZtm7Kzf1oueNmyZYqOjlZKSv3qPAAA/C230PvnV65LiVdEFV+MaBVe3RKKiopS586dPbY1atRITZs2dW8fNWqUJk6cqLi4OEVHR+uee+5RamqqrrjiCknS9ddfr5SUFI0YMUJPP/20srKy9Kc//Unp6enc9gEAwEvvbf7eq/bXpcRr3u8ur6Vqao/PV7p97rnnZLfbNWTIEBUXF6t///6aPXu2e39QUJAWL16scePGKTU1VY0aNdLIkSP15z//2delAADQoJ12OLXjcEGV2qZd1FyzhnevdyMrZWzGmCou3msd+fn5iomJUV5enqKjo/1dDgAAfjHlg216/csDlbaLDA3Stsf6+/0lhzX5/q4/85kAAICHfScKq9SuTdNIv4eVmiKwAABQT+04VLV3BvVo06TyRhZHYAEAoB56fPF25RSWVKltfZvCXBECCwAA9Yyj1KVX1uyvUtufxTeutw/anonAAgBAPfPa2n1VbntFu6a1V0gdIrAAAFDPbNiXU+W2beIia7GSukNgAQCgnon04hbPiNS2tVdIHfL5wnEAAKB2OEpdmv/Fd/r6QNVGWG7snFiv3sh8PgQWAADqgRlLduqlVZlVbh9st2nWby+rxYrqFoEFAACLm/6vnZq3uuphRZJe+G23er9Y3JkaxjgRAAAN1EdfHfI6rNgkXdMxoXYK8hMCCwAAFjVjyU7du2iL18cZSa9n7PN1OX5FYAEAwIKWfH3Yq2dWzrY/p2rvGaovCCwAAFiM02V0z1uba3SOhrL+ShkCCwAAFvO3ZbvlNNU/3m5rOOuvlCGwAABgIU6X0ayV/63ROUZfmdxg1l8p07A+DQAA9dzQl9aqBoMruuuqZE1uAG9nPhvrsAAAYBGnHU5t2p/r9XEtosM0sndb/b7vhQ1uZKUMgQUAAIu46/WNXrUPCbJp1m+6aUDnFrVUkXUQWAAA8LPTDqfufG29vvhv1d/C3KxRiNY9cl2DWs32fAgsAAD40e8XrNeKXce8Pm7Wb7sHTFiRCCwAAPiFo9Slrn/+VIUOl9fHJkaHq2dyXC1UZV0EFgAA6lh1XmZ4pscGpQTU6IpEYAEAoE6NWrBBy3dlV/v4O3q3CYiHbM9GYAEAoI6MWrBOy3cdr9E5rr848MKKxMJxAADUien/2lHjsBKIz66UIbAAAFDLHKUuzVu9r8bnCcRnV8oQWAAAqGW/e+XLGh0fZJfm3nZZQD67UoZnWAAAqEWOUpe+zPyh2scP6ByvF3/bI2BHVsoQWAAAqEU3zVrl9TEhQdKk6zvq9j4N763L1UVgAQCgloz++wbtOXrKq2N+/rOmmv/7K2qpovqLwAIAQC047XBq2U7v1lu5o08bTb25cy1VVL8xzgQAQC3w9s3L/To0I6ycB4EFAAAfW7r9iFbt9W7NlbFXt6+lahoGAgsAAD7kdBlN/XC7V8dEhwcH7IJwVUVgAQDAh9Zn5uhogcOrY6b/skvAT1uuDIEFAAAfcZS69PqX3r2F+bqUeN18aVItVdRwMEsIAAAf+PPHO/TqF/u8OuaKtk0073eX105BDQyBBQCAGur3zArtP3Ha6+P+fifrrVQVt4QAAKiBUQvWVSuspCbHsYqtF+gpAACq6bTDqeW7vJu+XOa1Ub18XE3Dxi0hAAC8dNrh1J8Xb9d7m76v1vEtY8MZXfESgQUAAC+MWrBBy3d5t+T+2a7pGO+jagIHgQUAgCpwuoxSn/yPsk96t8ZKRf54Y4oPKgosBBYAACrx4ZZDuu/tLT4513Up8YoIDfLJuQIJgQUAgPMY9MJqff19vk/OdV1KPOuuVBOBBQCAc7jztfU1DiuNw4L0i0uT9KebLmZkpQYILAAAVOC0w6n/fHOsxue5P+1nGnXlhT6oKLAxpwoAgAo8uWRnjc9ht0kjUtvWvBgwwgIAgNNltGb3Mc1d9a2+zS5QoaNUp0pqft7RVyaz3oqPEFgAAAFt6fYjuvftLXKUunx2TpukMVclazLTl32GwAIACFhLtx/R2IVf+eRcUWFB6hAfpQGdE3V7H0ZWfI3AAgAISE6X0fg3ah5WgmzSi8Mv04DOLXxQFc6FwAIACEj3vrVJpaZm52jXLEL/nvhzBdltvikK58R4FQAg4JwsKtW/th2t8Xn+/ItLCCt1hBEWAEBAmbFkp15alVnj84QG23VFu6Y+qAhVQWABAAQER6lLt/2/DK3fl+uT8z1zK6MrdYnAAgBo8Kb/a6fmra75qEqZSy6I1i+6tvTZ+VA5AgsAoEEbtWCDlu/K9tn50jo11/8b2dNn50PVEFgAAA2So9SlG57/TP89frra54gIlkKCgxQXGaY+7ZvyAkM/IrAAABoER6lLr6z5r/656XsdOFEoRw0Xrh1yWUv939CuPqkNNUdgAQDUaz8uALdJn+yo+TTlM80YfIlPz4eaIbAAAOqd0w6nHvt4mz7ZlqX8IqfPz3/XVSytbzUEFgBAvXLnaxv0n2989xDt2e7ipYWWRGABANQbg2at1teH8mvl3B2aR+pf9/VjZMWiCCwAAEtzuozW7D6mSe9uVvap0lq5xugr2+qRgRfXyrnhGwQWAIBlLd1+RPe+vUWO0hpO+TmHy9vG6o07UxlVqQe8+hOaMWOGLr/8ckVFRSk+Pl633HKLdu/e7dGmqKhI6enpatq0qRo3bqwhQ4bo6FHPJ7cPHDiggQMHKjIyUvHx8Zo0aZJKS2snNQMA6qclXx/R2IVf1VpYGX1lW70ztg9hpZ7w6k/p888/V3p6ur788kstW7ZMJSUluv7663Xq1Cl3m/vvv18ff/yx3nnnHX3++ec6fPiwBg8e7N7vdDo1cOBAORwOrV27Vq+99poWLFigRx991HefCgBQry35+rDufvOrWjl3kE2a/dtu3AKqZ2zGGFPdg48dO6b4+Hh9/vnnuuqqq5SXl6fmzZvrzTff1K233ipJ2rVrlzp16qSMjAxdccUV+uSTT3TTTTfp8OHDSkhIkCTNnTtXDz30kI4dO6bQ0NBKr5ufn6+YmBjl5eUpOjq6uuUDAGqg7NmSuau+1bfZBSp0lMoYm4yMguw22WWT07jc2+w2nXd/2bZip0vFvp+pLJuke37eTvdddxEvLfSTmnx/1+gZlry8PElSXFycJGnTpk0qKSlRWlqau03Hjh3VunVrd2DJyMhQly5d3GFFkvr3769x48Zpx44d6tatW7nrFBcXq7i42P17fn7tPCEOADg3p8to7d7jWrRxv1Z/e1y5pytKFeaM/zVnbatsvzmrbc0lRIWpfXwjjbmqnfp2aE5QqceqHVhcLpcmTJigPn36qHPnzpKkrKwshYaGKjY21qNtQkKCsrKy3G3ODCtl+8v2VWTGjBmaNm1adUsFAFRD2QjKnM/36utD+SosqZ1nSWrDHX1aa+rNXfxdBnyo2oElPT1d27dv15o1a3xZT4UmT56siRMnun/Pz89Xq1atav26ABBIzrzFs+NwnvKL609AkaS2cZH6ba/Wur0Pq9Q2RNUKLOPHj9fixYu1atUqXXDBBe7tiYmJcjgcys3N9RhlOXr0qBITE91t1q9f73G+sllEZW3OFhYWprCwsOqUCgA4j7KQMv2TndqTfaryAyzqupR4zfvd5f4uA7XIq8BijNE999yj999/X5999pmSk5M99nfv3l0hISFavny5hgwZIknavXu3Dhw4oNTUVElSamqqpk+fruzsbMXHx0uSli1bpujoaKWksBQyAPiKo9Sl+V98p6Xbjmh/zimVOo2crp8ecjWSihrAihIs+hYYvAos6enpevPNN/Xhhx8qKirK/cxJTEyMIiIiFBMTo1GjRmnixImKi4tTdHS07rnnHqWmpuqKK66QJF1//fVKSUnRiBEj9PTTTysrK0t/+tOflJ6ezigKAJyHo9SlV9b8V+9uPKgjeUUy5tyzbRylRo5z3tHx7YOt/hARbNP9113E7Z8A4tW0Zput4qer58+fr9tvv13SjwvHPfDAA3rrrbdUXFys/v37a/bs2R63e/bv369x48bps88+U6NGjTRy5Eg99dRTCg6uWn5iWjOAQFD2RuKV32Qr51SJSut/zvCJi1s01r/u6+fvMlANNfn+rtE6LP5CYAFgJacdTv158XZ9sfeYfjjlkO0c64t4s+10iVS/HnmtG52TGmvxvYSV+spv67AAQCA5cx2SjftzlHe6VEUlpoJgcb71Rep2HZKGZFTfNppyU2d/lwE/IbAAwHmU3Zb5ZFuW8otqYfnVBibUJgUFqUYr3ZZtswfZ1SImXEMuu0C/73shz6oEOAILgIBW9iDrPzd9r+z8Io9ZNA6n5GTA47zCgmy6rE0Tje3HSrKoXQQWAAHlzOdNjuQVq+LFW0kp5xIVFqQO8VEa0DmRGTqoUwQWAA1e2eJo9y3arNzTDWDhkToUGWJXt9axvIsHfkdgAVAtZ45U5JwslsvLGTHePs9Q3W0lTp1nPRJUJD4qVHf2vZARFFgKgQXAeVX0jEfFU269nRFTnWOYbVNToXYp2P5TsAuy2RUSbFfruEbc5oGlEVgAlFMWUuZ+9p3yGsLa7Q1Y49BzjzAZmxQZGqL28Y24pYN6j8ACBJDzzYgp+5IrLHWplFsolhRil1rGRqpP+6b6000XKyI0yN8lAXWGwAI0YGcudLZ89zEVVvgwB7dRfM0mKTKk5s/osA4J8BMCC9AAOUpdevifW/XBlsNykUFqlV1S4/AgxUWGMfIB1CICC9BAlI2mPPbxdv33eKG/y2mwwoNtSmkRwwOqQB0jsAD13GmHU6P/vkFffHuCGzo+Ehn80y2akKBgNY8K1WBuywB+RWAB6pmyd9us/CZbx0+W8EbfGgqxS00bh+nnF8Xr0Zu5nQNYFYEFsBjebVM3Lm8bqzfuTGXEBKgnCCyAn5w5g2fTgR90sqhUhSXnmlJs/ZQSJCk0uOK39PpzpVtjbJJNigwNZnE0oB4jsABeqihonDkKUpUvYqdLOl1q/RByLpHBTLkFULcILIAXlnx9RBMXbVFRhcMg3iw5X7+EBdk0+LILeMYDgN8QWIAqOO1w6pez12hX1kl/l+JzZ86IKRsNstmC1Dg8mAdRAVgGgQX4n3Pd6qn4RX/1V2SIXb/o2pIgAqBeIbAAquxWT8MwuGuSnrr1Up41AVAvEVgQUMqmDL+78aCO5BXJGKNSl1TSQHNKUnSYZgy5hLf0Aqj3CCwIGI8v3qlX1mT6u4xaFx0epPE/78DUXQANCoEFDc7ZC6+5jFGhwzSo51DOFGKXWsZG8uI9AA0agQX13pkPy67YfUynHA0vmvBuGwCBjsCCesPpMlqz+5jmrvpW32YX6HSJs94vwHY2u6TIUKYUA8DZCCywtLKQMv2TndqTfcrf5ZxX2ShIVZecd8kwWgIAVURggaU4Sl2a/8V3WrrtiPZkF+iUw/qjJ6P6ttGUmzr7uwwAaNAILPCbM5892bg/R8dOlpzjxX/WEx8Vqjv7XshMHACoIwQW+MWHWw7pgUVbVeqy/ggKL/oDAP8jsKBOOV1Gaf/3mTJPFPq7lEpxqwcArIPAgmqpaMXY8z1g6jQulTglK844DguSwoODFBJsV+u4RhrQOZFbPQBgMQQWVKpsps6cz/fqm6wCnSp2quKZxOZ/Pzrrf6152yfELs367WUa0LmFv0sBAFSCwIJzOu1w6s7X1uuL/+b4u5QaC7dLISF2RYaGqH18I425qh3v1wGAeoTAArfTDqf+vHi7vth7TIdyi+W05sBIlTQKtevajgn6VY9W6t2+GcEEAOo5AksAqmjF2KISc47bPNYWLCk8lOXqAaChI7A0UGeOluScLJbrfw/BGklFpf6urmaSm0bqNz1b82AsAAQQAks9dPYISaGj1D0rx26TTpeogjcT18Phk/9pFGrXJRfEamw/njsBgEBFYKlnlm4/onvf3iJHuSVh628gOVOwTUqICVf31k14/gQA4EZgqQfKlrD/2/Ld2nggz9/l1IqEqBCtfiiNWzwAgAoRWCxu6fYjmrhoqwodTn+XUmtYURYAUBkCi4Ut+fqI7n7zK3+XUWWNQ8+/0q0xNskmRYYGs6IsAMArBBY/cpS6NP+L77R02xHtO3FSxaXGvcR9qdOoyMKDKhEhdiVEhatP+6b6000XKyI0yN8lAQAaMAJLHTk7nBQUu1TuuVk3az5A2yI6TE8NuYSZOgCAOkdgqQPT/7VT81Zn+rsMrwXbpQubNWIxNgCA3xFYatmoBRu0fFe2v8uoVLCkyPAgxUWGcZsHAGA5BBYfO+1w6rGPt2nlN9nKPlni73LcgiSFBktBdpuCbHaFBNt58BUAUG8QWHzEUerSwJmrtDf7lN9qiAiW7DbJGJvsQXa1iAnXEG7nAAAaAAJLNZUtjz/n87366mCeHH58tXFap+b6fyN7+u36AADUNgKLF8peKLhsR5aOn/LvGwSDbdKverTSozfzrAkAoOEjsFSibFn8B9/doqMFDn+XI0nqnNRYi+/t5+8yAACoMwSW8/h462FNXLRFJX683XM2lrEHAAQiAss5/H7Beq3YdazOrxtik8JCflzO3iWjkKBgNY8KZS0UAEBAI7CcxekyumL6Mh07VftTkoNtUkJMuLq3bqJf9Wil3u2bsYIsAAAVILCcYen2Ixq7sG5eNjj6yrZ6ZODFdXItAADqOwLL/9RVWAmySbN+0003XpJU69cCAKChILDox9tAE96qnbBil9Q8Kkzt4xtpzFXteHEgAADVQGCRNGv5HhU5fXvOYLv0/NCuuqlrS9+eGACAABTwgcXpMpq3xjdvUo6LDFHf9s14gBYAAB8L+MCyPjNHp4qrN7wSGiRd3jaOWz0AANSygA8s2QVFXh/TKaGx3kvvy5L4AADUkYAPLHGRoVVuGxli05apA1i8DQCAOhbwgWVXVn6V2sU3CtH6KdfXcjUAAKAiAT9UcPCH01VqN4B1UwAA8JuADyxt4iJ92g4AAPhewAeWEaltVdnkHrvtx3YAAMA/Aj6whAbbNfrK5PO2GX1lMg/aAgDgRwH/0K0kTb4xRZI0b3WmXOan7Xbbj2GlbD8AAPAPmzHGVN6sdrz44ot65plnlJWVpUsvvVSzZs1Sz549Kz0uPz9fMTExysvLU3R0tM/qcZS69HrGPu3PKVSbuEiNSG3LyAoAAD5Sk+9vv42w/OMf/9DEiRM1d+5c9erVS88//7z69++v3bt3Kz4+3i81hQbbNerKC/1ybQAAcG5+Gz549tlnNXr0aN1xxx1KSUnR3LlzFRkZqVdffdVfJQEAAIvyS2BxOBzatGmT0tLSfirEbldaWpoyMjLKtS8uLlZ+fr7HDwAACBx+CSzHjx+X0+lUQkKCx/aEhARlZWWVaz9jxgzFxMS4f1q1alVXpQIAAAuoF0+UTp48WXl5ee6fgwcP+rskAABQh/zy0G2zZs0UFBSko0ePemw/evSoEhMTy7UPCwtTWFhYXZUHAAAsxi8jLKGhoerevbuWL1/u3uZyubR8+XKlpqb6oyQAAGBhfpvWPHHiRI0cOVI9evRQz5499fzzz+vUqVO64447/FUSAACwKL8Fll//+tc6duyYHn30UWVlZalr165aunRpuQdxAQAA/LrSbXXV1kq3AACg9tTLlW5roixjsR4LAAD1R9n3dnXGSuplYCkoKJAk1mMBAKAeKigoUExMjFfH1MtbQi6XS4cPH1ZUVJRsNptPz52fn69WrVrp4MGD3G6qAvrLe/SZ9+gz79Bf3qPPvFedPjPGqKCgQElJSbLbvZuoXC9HWOx2uy644IJavUZ0dDR/ab1Af3mPPvMefeYd+st79Jn3vO0zb0dWytSLlW4BAEBgI7AAAADLI7CcJSwsTFOnTuVVAFVEf3mPPvMefeYd+st79Jn36rrP6uVDtwAAILAwwgIAACyPwAIAACyPwAIAACyPwAIAACyPwAIAACyPwHKGF198UW3btlV4eLh69eql9evX+7skv5gxY4Yuv/xyRUVFKT4+Xrfccot2797t0aaoqEjp6elq2rSpGjdurCFDhujo0aMebQ4cOKCBAwcqMjJS8fHxmjRpkkpLS+vyo/jNU089JZvNpgkTJri30WflHTp0SLfddpuaNm2qiIgIdenSRRs3bnTvN8bo0UcfVYsWLRQREaG0tDTt3bvX4xw5OTkaPny4oqOjFRsbq1GjRunkyZN1/VFqndPp1JQpU5ScnKyIiAi1a9dOjz/+uMdL5AK9v1atWqWbb75ZSUlJstls+uCDDzz2+6p/vv76a1155ZUKDw9Xq1at9PTTT9f2R6s15+uzkpISPfTQQ+rSpYsaNWqkpKQk/e53v9Phw4c9zlFnfWZgjDHm7bffNqGhoebVV181O3bsMKNHjzaxsbHm6NGj/i6tzvXv39/Mnz/fbN++3WzZssXceOONpnXr1ubkyZPuNmPHjjWtWrUyy5cvNxs3bjRXXHGF6d27t3t/aWmp6dy5s0lLSzObN282S5YsMc2aNTOTJ0/2x0eqU+vXrzdt27Y1l1xyibnvvvvc2+kzTzk5OaZNmzbm9ttvN+vWrTPfffed+fTTT823337rbvPUU0+ZmJgY88EHH5itW7eaQYMGmeTkZHP69Gl3mwEDBphLL73UfPnll2b16tWmffv25je/+Y0/PlKtmj59umnatKlZvHixyczMNO+8845p3Lix+dvf/uZuE+j9tWTJEvPII4+Y9957z0gy77//vsd+X/RPXl6eSUhIMMOHDzfbt283b731lomIiDAvvfRSXX1Mnzpfn+Xm5pq0tDTzj3/8w+zatctkZGSYnj17mu7du3uco676jMDyPz179jTp6enu351Op0lKSjIzZszwY1XWkJ2dbSSZzz//3Bjz41/ikJAQ884777jbfPPNN0aSycjIMMb8+A+B3W43WVlZ7jZz5swx0dHRpri4uG4/QB0qKCgwHTp0MMuWLTP9+vVzBxb6rLyHHnrI9O3b95z7XS6XSUxMNM8884x7W25urgkLCzNvvfWWMcaYnTt3Gklmw4YN7jaffPKJsdls5tChQ7VXvB8MHDjQ/P73v/fYNnjwYDN8+HBjDP11trO/fH3VP7NnzzZNmjTx+GfyoYceMhdddFEtf6LaV1HIO9v69euNJLN//35jTN32GbeEJDkcDm3atElpaWnubXa7XWlpacrIyPBjZdaQl5cnSYqLi5Mkbdq0SSUlJR791bFjR7Vu3drdXxkZGerSpYsSEhLcbfr376/8/Hzt2LGjDquvW+np6Ro4cKBH30j0WUU++ugj9ejRQ7/61a8UHx+vbt26ad68ee79mZmZysrK8uizmJgY9erVy6PPYmNj1aNHD3ebtLQ02e12rVu3ru4+TB3o3bu3li9frj179kiStm7dqjVr1uiGG26QRH9Vxlf9k5GRoauuukqhoaHuNv3799fu3bv1ww8/1NGn8Z+8vDzZbDbFxsZKqts+q5dva/a148ePy+l0enxRSFJCQoJ27drlp6qsweVyacKECerTp486d+4sScrKylJoaKj7L2yZhIQEZWVludtU1J9l+xqit99+W1999ZU2bNhQbh99Vt53332nOXPmaOLEifrjH/+oDRs26N5771VoaKhGjhzp/swV9cmZfRYfH++xPzg4WHFxcQ2uzx5++GHl5+erY8eOCgoKktPp1PTp0zV8+HBJor8q4av+ycrKUnJycrlzlO1r0qRJrdRvBUVFRXrooYf0m9/8xv125rrsMwILzis9PV3bt2/XmjVr/F2KpR08eFD33Xefli1bpvDwcH+XUy+4XC716NFDTz75pCSpW7du2r59u+bOnauRI0f6uTrrWbRokd544w29+eabuvjii7VlyxZNmDBBSUlJ9BdqXUlJiYYOHSpjjObMmeOXGrglJKlZs2YKCgoqN2Pj6NGjSkxM9FNV/jd+/HgtXrxYK1eu1AUXXODenpiYKIfDodzcXI/2Z/ZXYmJihf1Ztq+h2bRpk7Kzs3XZZZcpODhYwcHB+vzzzzVz5kwFBwcrISGBPjtLixYtlJKS4rGtU6dOOnDggKSfPvP5/rlMTExUdna2x/7S0lLl5OQ0uD6bNGmSHn74YQ0bNkxdunTRiBEjdP/992vGjBmS6K/K+Kp/Au2fU+mnsLJ//34tW7bMPboi1W2fEVgkhYaGqnv37lq+fLl7m8vl0vLly5WamurHyvzDGKPx48fr/fff14oVK8oN5XXv3l0hISEe/bV7924dOHDA3V+pqanatm2bx1/ksr/oZ39JNQTXXnuttm3bpi1btrh/evTooeHDh7v/P33mqU+fPuWmy+/Zs0dt2rSRJCUnJysxMdGjz/Lz87Vu3TqPPsvNzdWmTZvcbVasWCGXy6VevXrVwaeoO4WFhbLbPf+VHRQUJJfLJYn+qoyv+ic1NVWrVq1SSUmJu82yZct00UUXNcjbQWVhZe/evfrPf/6jpk2beuyv0z7z6hHdBuztt982YWFhZsGCBWbnzp1mzJgxJjY21mPGRqAYN26ciYmJMZ999pk5cuSI+6ewsNDdZuzYsaZ169ZmxYoVZuPGjSY1NdWkpqa695dN0b3++uvNli1bzNKlS03z5s0b7BTdipw5S8gY+uxs69evN8HBwWb69Olm79695o033jCRkZFm4cKF7jZPPfWUiY2NNR9++KH5+uuvzS9+8YsKp6F269bNrFu3zqxZs8Z06NChwUzTPdPIkSNNy5Yt3dOa33vvPdOsWTPzhz/8wd0m0PuroKDAbN682WzevNlIMs8++6zZvHmze0aLL/onNzfXJCQkmBEjRpjt27ebt99+20RGRtbbac3n6zOHw2EGDRpkLrjgArNlyxaP74MzZ/zUVZ8RWM4wa9Ys07p1axMaGmp69uxpvvzyS3+X5BeSKvyZP3++u83p06fN3XffbZo0aWIiIyPNL3/5S3PkyBGP8+zbt8/ccMMNJiIiwjRr1sw88MADpqSkpI4/jf+cHVjos/I+/vhj07lzZxMWFmY6duxoXn75ZY/9LpfLTJkyxSQkJJiwsDBz7bXXmt27d3u0OXHihPnNb35jGjdubKKjo80dd9xhCgoK6vJj1In8/Hxz3333mdatW5vw8HBz4YUXmkceecTjiyPQ+2vlypUV/rtr5MiRxhjf9c/WrVtN3759TVhYmGnZsqV56qmn6uoj+tz5+iwzM/Oc3wcrV650n6Ou+sxmzBnLJAIAAFgQz7AAAADLI7AAAADLI7AAAADLI7AAAADLI7AAAADLI7AAAADLI7AAAADLI7AAAADLI7AAAADLI7AAAADLI7AAAADL+/8ZymY/15/ovwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# @title 单独绘制L0的图\n",
        "plt.title(\"L0 before delete outliner\")\n",
        "plt.scatter(x=range(len(d3)),y=d3['L0'].sort_values())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LCPmCIZ4coOz"
      },
      "outputs": [],
      "source": [
        "# @title 删掉L0超过 均值+3倍标准差的数据\n",
        "mean = d3['L0'].mean()\n",
        "std_dev = d3['L0'].std()\n",
        "# 计算阈值\n",
        "threshold = mean + 3 * std_dev\n",
        "# 找出大于阈值的数据\n",
        "result = d3['L0'][d3['L0'] > threshold]\n",
        "d_best = d3.drop(d3[d3['L0'] > threshold].index)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 再绘制散点图与箱线图\n",
        "plt.title(\"L0 after delete outliner\")\n",
        "plt.scatter(x=range(len(d_best)),y=d_best['L0'].sort_values())\n",
        "\n",
        "scalar = StandardScaler()\n",
        "d_best_scale = pd.DataFrame(scalar.fit_transform(d_best.drop('dataset', axis=1).values),columns=d_best.drop('dataset', axis=1).columns)\n",
        "# plt.title(\"boxfig after delete outliner\")\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(d_best_scale)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WuRJKqr14ytA",
        "outputId": "1ea970b2-a405-406f-dd6b-c9bf27c6c4b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'L0 after delete outliner')"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7827d33b3700>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBcjncukjDta",
        "outputId": "2a7269d4-bd0e-46d2-9228-e1b05bb0e1cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tan_square    1.781548\n",
            "R2%           1.565915\n",
            "D50           1.518861\n",
            "Hs            1.164672\n",
            "tan           1.101734\n",
            "L0            1.081459\n",
            "have_setup    0.591157\n",
            "tan_box       0.573816\n",
            "L0_sqrt       0.338362\n",
            "setup_log     0.309756\n",
            "Hs_log        0.045264\n",
            "L0_box        0.026692\n",
            "Hs_box       -0.035426\n",
            "setup        -0.179479\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# @title box-cox变换\n",
        "d_best[\"Hs_box\"] = stats.boxcox(d_best['Hs'].values, lmbda=0.35)\n",
        "d_best[\"L0_box\"] = stats.boxcox(d_best['L0'].values, lmbda=0.3)\n",
        "d_best[\"tan_box\"] = stats.boxcox(d_best['tan'].values, lmbda=0.6)\n",
        "print(d_best.skew().sort_values(ascending=False))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wQGCe87pk4-5",
        "outputId": "a1861dee-8d2d-4c83-f394-e5981ea651d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1400 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# @title 绘制转换后的图\n",
        "plt.figure(figsize=(10, 14))\n",
        "plt.subplot(4, 3, 1)\n",
        "sns.distplot(d_best['setup'] , fit=norm);\n",
        "plt.subplot(4, 3, 4)\n",
        "sns.distplot(d_best['Hs'] , fit=norm);\n",
        "plt.subplot(4, 3, 7)\n",
        "sns.distplot(d_best['tan'] , fit=norm);\n",
        "plt.subplot(4, 3, 10)\n",
        "sns.distplot(d_best['L0'] , fit=norm);\n",
        "plt.subplot(4, 3, 2)\n",
        "sns.distplot(d_best['setup_log'] );\n",
        "plt.subplot(4, 3, 5)\n",
        "sns.distplot(d_best['Hs_log'] );\n",
        "plt.subplot(4, 3, 8)\n",
        "sns.distplot(d_best['tan_square']);\n",
        "plt.subplot(4, 3, 11)\n",
        "sns.distplot(d_best['L0_sqrt'] );\n",
        "# plt.subplot(4, 3, 3)\n",
        "# sns.distplot(d_best['setup_box'] );\n",
        "plt.subplot(4, 3, 6)\n",
        "sns.distplot(d_best['Hs_box'] );\n",
        "plt.subplot(4, 3, 9)\n",
        "sns.distplot(d_best['tan_box'] );\n",
        "plt.subplot(4, 3, 12)\n",
        "sns.distplot(d_best['L0_box'] );"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 使用增加伪标签并删除L0异常值的数据建模\n",
        "print(f'此时数据长度{len(d_best)}')\n",
        "X = d_best[['Hs','tan','L0']].values\n",
        "y = d_best['setup'].values\n",
        "scalar3 = StandardScaler()\n",
        "X_scale = scalar3.fit_transform(X)\n",
        "\n",
        "# best_param_d3 = optuna_xgb(X_scale,y)\n",
        "best_param_d3 = {'alpha': 0.113747487451855, 'subsample': 0.7374858747006843, 'max_depth': 5, 'eta': 0.29016633640281453, 'gamma': 9.56011350798386e-07, 'min_child_weight': 9}\n",
        "xgb3 = XGBRegressor(**best_param_d3)\n",
        "xgb3.fit(X_scale, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "a5PdpCdEnE6E",
        "outputId": "daad8617-dc1b-4bea-b946-12e50c449989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "此时数据长度1155\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(alpha=0.113747487451855, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.29016633640281453,\n",
              "             eval_metric=None, feature_types=None, gamma=9.56011350798386e-07,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=9, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(alpha=0.113747487451855, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.29016633640281453,\n",
              "             eval_metric=None, feature_types=None, gamma=9.56011350798386e-07,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=9, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(alpha=0.113747487451855, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.29016633640281453,\n",
              "             eval_metric=None, feature_types=None, gamma=9.56011350798386e-07,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=9, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0RUj6NE8ZuXP",
        "outputId": "e25432fc-808c-47f4-dd98-19ddde6996ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "此时模型在d1上的精度为：\n",
            "参与计算精度的数据个数是532\n",
            "MAE:0.0628\n",
            "MSE:0.0096\n",
            "RMSE:0.0979\n",
            "RRMSE:0.1790\n",
            "R2 Square:0.8796\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n",
            "此时模型在数模数据上的精度为：\n",
            "参与计算精度的数据个数是56\n",
            "MAE:0.1709\n",
            "MSE:0.0663\n",
            "RMSE:0.2575\n",
            "RRMSE:1.0317\n",
            "R2 Square:-2.8214\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n",
            "此时模型在测试集上的精度为：\n",
            "参与计算精度的数据个数是106\n",
            "MAE:0.0643\n",
            "MSE:0.0082\n",
            "RMSE:0.0908\n",
            "RRMSE:0.1631\n",
            "R2 Square:0.9043\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n"
          ]
        }
      ],
      "source": [
        "# @title 计算目前的模型rrmse，注意只能用有真实setup的。所有数据处理必须与训练的步骤一模一样\n",
        "# d1[\"Hs_box\"] = stats.boxcox(d1['Hs'].values, lmbda=0.6)\n",
        "# d1[\"L0_box\"] = stats.boxcox(d1['L0'].values, lmbda=0.65)\n",
        "# d1[\"tan_box\"] = stats.boxcox(d1['tan'].values, lmbda=0.55)\n",
        "# X = scalar3.transform(d1[['Hs_box','tan_box','L0_box']].values) # 注意！这里一定要用训练模型一样的缩放\n",
        "print('此时模型在d1上的精度为：')\n",
        "cal_final_metric(xgb3,scalar3.transform(d1_all[['Hs','tan','L0']].values),d1_all['setup'].values)\n",
        "\n",
        "print('此时模型在数模数据上的精度为：')\n",
        "cal_final_metric(xgb3,scalar3.transform(test18[['Hs','tan','L0']].values),test18['setup'].values)\n",
        "\n",
        "print('此时模型在测试集上的精度为：')\n",
        "cal_final_metric(xgb3,scalar3.transform(d1_val[['Hs','tan','L0']].values),d1_val['setup'].values)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 如果使用sample_weight\n",
        "w_10 = d_best['have_setup']*9+1 #(1:10)\n",
        "w_5 = d_best['have_setup']*4+1 #(1:5)\n",
        "w_3 = d_best['have_setup']*2+1 #(1:3)\n",
        "\n",
        "xgb4 = XGBRegressor(**best_param_d3)\n",
        "xgb4.fit(scalar3.transform(d_best[['Hs','tan','L0']].values),d_best['setup'].values ,sample_weight=w_3)\n",
        "\n",
        "print('此时模型在d1_all上的精度为：')\n",
        "cal_final_metric(xgb4,scalar3.transform(d1_all[['Hs','tan','L0']].values),d1_all['setup'].values)\n",
        "\n",
        "print('此时模型在数模数据上的精度为：')\n",
        "cal_final_metric(xgb4,scalar3.transform(test18[['Hs','tan','L0']].values),test18['setup'].values)\n",
        "\n",
        "print('此时模型在测试集上的精度为：')\n",
        "cal_final_metric(xgb4,scalar3.transform(d1_val[['Hs','tan','L0']].values),d1_val['setup'].values)\n",
        "\n",
        "# 使用sample_weight 会导致d1上的精度升高，数模上的精度降低，测试集上精度降低"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "eAaAWf6_YENL",
        "outputId": "52fb898a-5175-4eca-bdcf-df0ae43ec3a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(alpha=0.113747487451855, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.29016633640281453,\n",
              "             eval_metric=None, feature_types=None, gamma=9.56011350798386e-07,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=9, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(alpha=0.113747487451855, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.29016633640281453,\n",
              "             eval_metric=None, feature_types=None, gamma=9.56011350798386e-07,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=9, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(alpha=0.113747487451855, base_score=None, booster=None,\n",
              "             callbacks=None, colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eta=0.29016633640281453,\n",
              "             eval_metric=None, feature_types=None, gamma=9.56011350798386e-07,\n",
              "             grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "             min_child_weight=9, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "此时模型在d1_all上的精度为：\n",
            "参与计算精度的数据个数是532\n",
            "MAE:0.0448\n",
            "MSE:0.0068\n",
            "RMSE:0.0823\n",
            "RRMSE:0.1505\n",
            "R2 Square:0.9149\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n",
            "此时模型在数模数据上的精度为：\n",
            "参与计算精度的数据个数是56\n",
            "MAE:0.1877\n",
            "MSE:0.0682\n",
            "RMSE:0.2611\n",
            "RRMSE:1.0462\n",
            "R2 Square:-2.9293\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n",
            "此时模型在测试集上的精度为：\n",
            "参与计算精度的数据个数是106\n",
            "MAE:0.0405\n",
            "MSE:0.0037\n",
            "RMSE:0.0605\n",
            "RRMSE:0.1087\n",
            "R2 Square:0.9575\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 使用optuna调参寻找最佳lightgbm参数\n",
        "def optuna_lgbm(X,y,n_trials = 500):\n",
        "\n",
        "  import lightgbm as lgb\n",
        "  try:\n",
        "    import optuna\n",
        "  except ImportError:\n",
        "    !pip install optuna -q\n",
        "    import optuna\n",
        "\n",
        "  def objective(trial):\n",
        "    dtrain = lgb.Dataset(X, label=y)\n",
        "    param = {\n",
        "        'boosting_type': 'dart',\n",
        "        \"verbosity\": -1,\n",
        "        'objective': 'regression',\n",
        "        \"metric\": \"rmse\",\n",
        "        # \"reg_lambda\": trial.suggest_float(\"reg_lambda\", 1e-8, 1.0, log=True),\n",
        "        \"reg_alpha\": trial.suggest_float(\"reg_alpha\", 0.01, 10, log=True),\n",
        "        \"subsample\": trial.suggest_float(\"subsample\", 0.5, 1.0, step=5),\n",
        "        \"colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.3, 0.9),\n",
        "        \"min_child_samples\": trial.suggest_int(\"min_child_samples\", 15, 50)\n",
        "    }\n",
        "    param[\"num_leaves\"] = trial.suggest_int(\"num_leaves\", 2, 32)\n",
        "    param[\"max_depth\"] = trial.suggest_int(\"max_depth\", 2, 6)\n",
        "    param[\"eta\"] = trial.suggest_float(\"eta\", 1e-8, 0.2)\n",
        "    # param[\"min_child_weight\"] = trial.suggest_int(\"min_child_weight\", 3, 10)\n",
        "    param['feature_fraction'] = trial.suggest_float(\"feature_fraction\", 0.5, 1.0,step=20)\n",
        "    pruning_callback = optuna.integration.LightGBMPruningCallback(trial, \"valid rmse\")\n",
        "    history = lgb.cv(param, dtrain, folds=KFold(n_splits=5), metrics='rmse', num_boost_round=100, callbacks=[pruning_callback])\n",
        "    rmse = history[\"valid rmse-mean\"][-1]\n",
        "    return rmse\n",
        "\n",
        "  pruner = optuna.pruners.MedianPruner(n_warmup_steps=5)\n",
        "  study = optuna.create_study(pruner=pruner, direction=\"minimize\")\n",
        "  study.optimize(objective, n_trials=n_trials)\n",
        "  print(\"Number of finished trials: {}\".format(len(study.trials)))\n",
        "  print(\"Best trial:\")\n",
        "  trial = study.best_trial\n",
        "  print(\"  Value: {}\".format(trial.value))\n",
        "  print(\"  Params: \")\n",
        "  print(trial.params)\n",
        "  # from optuna.visualization import plot_param_importances\n",
        "  # plot_param_importances(study)\n",
        "  return trial.params"
      ],
      "metadata": {
        "id": "XqEQorRG5g5L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 使用lightgbm建模\n",
        "# best_param_d1_lgbm = optuna_lgbm(scalar3.transform(d_best[['Hs','tan','L0']].values), d_best['setup'].values,n_trials = 500)\n",
        "best_param_d1_lgbm = {'reg_alpha': 0.022509643876086862, 'subsample': 0.5, 'colsample_bytree': 0.74607510948986, 'min_child_samples': 16, 'num_leaves': 26, 'max_depth': 5, 'eta': 0.19495690018655532, 'feature_fraction': 0.5}\n",
        "lgbm1 = LGBMRegressor(verbose=-1,**best_param_d1_lgbm)\n",
        "lgbm1.fit(scalar3.transform(d_best[['Hs','tan','L0']].values), d_best['setup'].values)\n",
        "\n",
        "print('此时模型在d1_all上的精度为：')\n",
        "cal_final_metric(lgbm1,scalar3.transform(d1_all[['Hs','tan','L0']].values),d1_all['setup'].values)\n",
        "\n",
        "print('此时模型在数模数据上的精度为：')\n",
        "cal_final_metric(lgbm1,scalar3.transform(test18[['Hs','tan','L0']].values),test18['setup'].values)\n",
        "\n",
        "print('此时模型在测试集上的精度为：')\n",
        "cal_final_metric(lgbm1,scalar3.transform(d1_val[['Hs','tan','L0']].values),d1_val['setup'].values)"
      ],
      "metadata": {
        "id": "FK52jlqP7OCe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "outputId": "76eeef3c-820b-4a7d-8bec-918842974533"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMRegressor(colsample_bytree=0.74607510948986, eta=0.19495690018655532,\n",
              "              feature_fraction=0.5, max_depth=5, min_child_samples=16,\n",
              "              num_leaves=26, reg_alpha=0.022509643876086862, subsample=0.5,\n",
              "              verbose=-1)"
            ],
            "text/html": [
              "<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(colsample_bytree=0.74607510948986, eta=0.19495690018655532,\n",
              "              feature_fraction=0.5, max_depth=5, min_child_samples=16,\n",
              "              num_leaves=26, reg_alpha=0.022509643876086862, subsample=0.5,\n",
              "              verbose=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(colsample_bytree=0.74607510948986, eta=0.19495690018655532,\n",
              "              feature_fraction=0.5, max_depth=5, min_child_samples=16,\n",
              "              num_leaves=26, reg_alpha=0.022509643876086862, subsample=0.5,\n",
              "              verbose=-1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 112
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "此时模型在d1_all上的精度为：\n",
            "参与计算精度的数据个数是532\n",
            "MAE:0.1017\n",
            "MSE:0.0198\n",
            "RMSE:0.1409\n",
            "RRMSE:0.2575\n",
            "R2 Square:0.7509\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n",
            "此时模型在数模数据上的精度为：\n",
            "参与计算精度的数据个数是56\n",
            "MAE:0.1564\n",
            "MSE:0.0378\n",
            "RMSE:0.1944\n",
            "RRMSE:0.7788\n",
            "R2 Square:-1.1774\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n",
            "此时模型在测试集上的精度为：\n",
            "参与计算精度的数据个数是106\n",
            "MAE:0.1068\n",
            "MSE:0.0198\n",
            "RMSE:0.1409\n",
            "RRMSE:0.2531\n",
            "R2 Square:0.7696\n",
            "__________________________________\n",
            "每次使用请注意X是否需要做变形处理！！\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = scalar3.transform(d1_all[['Hs','tan','L0']].values)\n",
        "y = d1_all['setup'].values\n",
        "y_xgb = xgb3.predict(X)\n",
        "y_lgbm = lgbm1.predict(X)\n",
        "y_avg = y_xgb*0.5+y_lgbm*0.5\n",
        "print('此时模型在d1_all上的精度为：')\n",
        "print_evaluate(y,y_avg,show= True)\n",
        "\n",
        "X = scalar3.transform(test18[['Hs','tan','L0']].values)\n",
        "y = test18['setup'].values\n",
        "y_xgb = xgb3.predict(X)\n",
        "y_lgbm = lgbm1.predict(X)\n",
        "y_avg = y_xgb*0.5+y_lgbm*0.5\n",
        "print('此时模型在数模数据上的精度为：')\n",
        "print_evaluate(y,y_avg,show= True)\n",
        "\n",
        "X = scalar3.transform(d1_val[['Hs','tan','L0']].values)\n",
        "y = d1_val['setup'].values\n",
        "y_xgb = xgb3.predict(X)\n",
        "y_lgbm = lgbm1.predict(X)\n",
        "y_avg = y_xgb*0.5+y_lgbm*0.5\n",
        "print('此时模型在测试集上的精度为：')\n",
        "print_evaluate(y,y_avg,show= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YgJ1j5Vg5t7j",
        "outputId": "f61258a5-fec9-498c-bbf1-1edca7aa6ac2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "此时模型在d1_all上的精度为：\n",
            "参与计算精度的数据个数是532\n",
            "MAE:0.0766\n",
            "MSE:0.0125\n",
            "RMSE:0.1118\n",
            "RRMSE:0.2043\n",
            "R2 Square:0.8432\n",
            "__________________________________\n",
            "此时模型在数模数据上的精度为：\n",
            "参与计算精度的数据个数是56\n",
            "MAE:0.1584\n",
            "MSE:0.0479\n",
            "RMSE:0.2190\n",
            "RRMSE:0.8773\n",
            "R2 Square:-1.7629\n",
            "__________________________________\n",
            "此时模型在测试集上的精度为：\n",
            "参与计算精度的数据个数是106\n",
            "MAE:0.0812\n",
            "MSE:0.0116\n",
            "RMSE:0.1079\n",
            "RRMSE:0.1939\n",
            "R2 Square:0.8648\n",
            "__________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# https://github.com/shap/shap\n",
        "# SHAP（SHapley Additive exPlanations）是一种博弈论方法，用于解释任何机器学习模型的输出。\n",
        "#\n",
        "# try:\n",
        "#   import shap\n",
        "# except ImportError:\n",
        "#   !pip install shap -q\n",
        "#   import shap\n",
        "# explainer = shap.Explainer(xgb3,feature_names=['Hs_box','tan_box','L0_box'])\n",
        "# shap_values = explainer(X_scale)\n",
        "\n",
        "# # visualize the first prediction's explanation\n",
        "# # shap.plots.waterfall(shap_values[0])\n",
        "\n",
        "# # summarize the effects of all the features\n",
        "# shap.plots.beeswarm(shap_values)\n",
        "\n",
        "# # 只取每个特征的 SHAP 值的平均绝对值来获得标准条形图\n",
        "# shap.plots.bar(shap_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "Jo3BcM7bNlmr",
        "outputId": "1495fda1-9e98-4c36-b044-b4f77405f841"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/535.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m409.6/535.7 kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m535.7/535.7 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x270 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WdoTFFl1J2Ut"
      },
      "outputs": [],
      "source": [
        "# d1.to_csv('/content/drive/My Drive/MachineLearning/Data/setup/alldata_new_predict.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WurMwEDOi923"
      },
      "source": [
        "# 旧代码"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 使用lightgbm建模\n",
        "# 调参过程可以参考 https://www.cnblogs.com/bjwu/p/9307344.html\n",
        "# 以及 https://www.heywhale.com/mw/project/6585325cdcad99bb0a1f4686\n",
        "lgbm = LGBMRegressor(boosting_type='dart',objective='regression', max_depth=-1,\n",
        "                    lambda_l1=0.0001, lambda_l2=0, learning_rate=0.1,\n",
        "                    n_estimators=100, max_bin=200, min_child_samples=5,\n",
        "                    bagging_fraction=0.75, bagging_freq=5,\n",
        "                    bagging_seed=7, feature_fraction=0.8,\n",
        "                    feature_fraction_seed=7, verbose=-1,\n",
        "                    device = gpu,\n",
        "                    early_stopping_rounds=10)\n",
        "# DART（Dropouts meet Multiple Additive Regression Trees）是 LightGBM 中的一种树提升算法，它引入了随机失活（dropout）的思想，以增加模型的鲁棒性和泛化能力。\n",
        "# DART 适用于许多场景，特别是对于处理过拟合和提高模型的泛化性能有帮助的情况。\n",
        "# DART 引入了随机失活机制，可以减轻过拟合的问题，对于数据噪声较多或者样本量较小的情况有帮助。\n",
        "param_lst = {\n",
        "    'max_depth' : [2, 3, 4, 5],\n",
        "    'learning_rate' : [0.001, 0.01, 0.1, 0.2],\n",
        "    'n_estimators' : [100, 300, 500],\n",
        "    'lambda_l1' : uniform(0.01, 1),\n",
        "    'lambda_l2' : [0, 0.1, 0.5, 1],\n",
        "    'feature_fraction' : [0.4, 0.6, 0.8],\n",
        "    'num_leaves' : [15,20,25,30],\n",
        "    'subsample' : [0.8,0.9,1.0]\n",
        "}\n",
        "\n",
        "lightgbm = RandomizedSearchCV(estimator = lgbm, param_distributions = param_lst,\n",
        "                              n_iter = 100, scoring = 'neg_root_mean_squared_error',\n",
        "                              cv = 5\n",
        "                              )\n",
        "\n",
        "lightgbm_search = lightgbm.fit(X_scale, y)\n",
        "best_param = lightgbm_search.best_params_\n",
        "# best_param ={'n_estimators': 1000, 'min_child_samples': 5, 'max_depth': 4, 'learning_rate': 0.01, 'lambda_l2': 0.0001, 'lambda_l1': 0.01, 'feature_fraction': 0.8}\n",
        "lgbm_best = LGBMRegressor(verbose=-1,**best_param) # verbose=-1避免输出[Warning]\n",
        "print(best_param)\n",
        "lgbm_best.fit(X_scale, y)\n",
        "\n",
        "X = scalar3.transform(d1[['Hs_box','tan_box','L0_box']].values) # 注意！这里一定要用训练模型一样的缩放\n",
        "# X = scalar3.transform(d1[['Hs','tan','L0']].values) # 注意！这里一定要用训练模型一样的缩放\n",
        "d1[\"setup_pre\"]  = lgbm_best.predict(X)\n",
        "print_evaluate(d1['setup'], d1[\"setup_pre\"] , show = True)\n",
        "\n",
        "from lightgbm import plot_importance\n",
        "plot_importance(lgbm_best,3)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TxIQEKubmKwQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 可以使用sklearn-onnx来保存模型与pipeline\n",
        "# https://cloud.google.com/bigquery/docs/making-predictions-with-sklearn-models-in-onnx-format?hl=zh-cn"
      ],
      "metadata": {
        "id": "h0L4sNEziCIY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Grpr3q0Bzv-6"
      },
      "outputs": [],
      "source": [
        "# @title 比较几个数据集。\n",
        "# d1是所有包含setup数据，\n",
        "# d2是所有只缺setup的数据，\n",
        "# d3=d1+d2，\n",
        "# d4是d3去掉砾石海岸，\n",
        "# d5是d3去掉D50超过10的项，\n",
        "# d6是d4去掉D50 nan项\n",
        "\n",
        "d4 = d3.drop(d3[d3['D50'] >= 3].index)\n",
        "d5 = d3.drop(d3[d3['D50'] >= 10].index)\n",
        "d6 = d4.dropna(axis=0, how=\"any\", subset=['D50']).reset_index(drop=True) # 去掉存在空值的行\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36f94fdf-0382-4f56-8099-1d361d7b82f8",
        "id": "RfinLakFeAib"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rrmse score: 0.4084 (0.0713)\n",
            "\n",
            "rmse score: 0.4503 (0.0556)\n",
            "\n",
            "rrmse score: 0.3000 (0.0902)\n",
            "\n",
            "rmse score: 0.4484 (0.0377)\n",
            "\n",
            "rrmse score: 0.3455 (0.1221)\n",
            "\n",
            "rmse score: 0.4190 (0.0948)\n",
            "\n",
            "rrmse score: 0.3355 (0.1294)\n",
            "\n",
            "rmse score: 0.4496 (0.0414)\n",
            "\n",
            "rrmse score: 0.4201 (0.0655)\n",
            "\n",
            "rmse score: 0.4791 (0.0555)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# @title 计算交叉验证的rrmse，误差最小的是d3\n",
        "best_param = {'learning_rate': 0.05, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0.8164256459214504, 'reg_lambda': 0.8970365433738731, 'subsample': 0.7}\n",
        "xgb1 = XGBRegressor(**best_param)\n",
        "rrmse_cv(xgb1,d1[['Hs','tan','L0']].values, d1['setup'])\n",
        "rrmse_cv(xgb1,d3[['Hs','tan','L0']].values, d3['setup'])\n",
        "rrmse_cv(xgb1,d4[['Hs','tan','L0']].values, d4['setup'])\n",
        "rrmse_cv(xgb1,d5[['Hs','tan','L0']].values, d5['setup'])\n",
        "rrmse_cv(xgb1,d6[['Hs','tan','L0']].values, d6['setup'])\n",
        "# 比较四个数据集。d1是所有包含setup数据，d2是所有只缺setup的数据，d3=d1+d2，d4是d3去掉D50>=3，d5是d3去掉D50>=10的项，d6是d4去掉D50的nan项\n",
        "# d1 532个数据 score: 0.1486 (0.0177)\n",
        "# d3 1431个数据 score: 0.1523 (0.0515)\n",
        "# d4 764个数据 score: 0.1387 (0.0345)\n",
        "# d5 1092个数据 score: 0.1628 (0.0645)\n",
        "# d6 620个数据 score: 0.1612 (0.0232)\n",
        "# 表现最好的是d3\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "QYoNxTbm8GQn",
        "outputId": "fb55ca8e-857b-47e9-9c3f-b92bb5a32b60"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train set evaluation:\n",
            "_____________________________________\n",
            "MAE:0.0336\n",
            "MSE:0.0022\n",
            "RMSE:0.0465\n",
            "RRMSE:0.0837\n",
            "R2 Square:0.9739\n",
            "__________________________________\n",
            "Test set evaluation:\n",
            "_____________________________________\n",
            "MAE:0.1050\n",
            "MSE:0.0251\n",
            "RMSE:0.1585\n",
            "RRMSE:0.3004\n",
            "R2 Square:0.6542\n",
            "__________________________________\n"
          ]
        }
      ],
      "source": [
        "# d1只包含setup\n",
        "# RRMSE:0.2575\n",
        "X_train1, X_test1, y_trai1n, y_test1 = train_test_split(d1[['Hs_log','tan_square','L0_sqrt']].values, d1['setup_log'], test_size=0.3, random_state=42)\n",
        "pipeline1 = Pipeline([('std_scalar', StandardScaler())])\n",
        "X_train1 = pipeline1.fit_transform(X_train1)\n",
        "X_test1 = pipeline1.transform(X_test1)\n",
        "\n",
        "best_param = {'reg_lambda': 0.001, 'reg_alpha': 0.1, 'n_estimators': 3000, 'min_child_weight': 1, 'max_depth': 4, 'learning_rate': 0.3}\n",
        "xgb1 = XGBRegressor(**best_param)\n",
        "xgb1.fit(X_train1,y_train1)\n",
        "show_result(xgb1,X_train1, X_test1, y_train1, y_test1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "E2IEFNbC4krJ",
        "outputId": "3b341244-562f-400b-ef5d-a6d6b15099a9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train set evaluation:\n",
            "_____________________________________\n",
            "MAE:0.0206\n",
            "MSE:0.0013\n",
            "RMSE:0.0363\n",
            "RRMSE:0.0868\n",
            "R2 Square:0.9666\n",
            "__________________________________\n",
            "Test set evaluation:\n",
            "_____________________________________\n",
            "MAE:0.0525\n",
            "MSE:0.0132\n",
            "RMSE:0.1147\n",
            "RRMSE:0.2732\n",
            "R2 Square:0.6674\n",
            "__________________________________\n"
          ]
        }
      ],
      "source": [
        "# d3包含所有伪标签\n",
        "# RRMSE:0.2326\n",
        "X_train3, X_test3, y_train3, y_test3 = train_test_split(d3[['Hs_log','tan_square','L0_sqrt']].values, d3['setup_log'], test_size=0.3) #, random_state=42\n",
        "pipeline3 = Pipeline([('std_scalar', StandardScaler())])\n",
        "X_train3 = pipeline3.fit_transform(X_train3)\n",
        "X_test3 = pipeline3.transform(X_test3)\n",
        "\n",
        "best_param = {'reg_lambda': 0.001, 'reg_alpha': 0.1, 'n_estimators': 3000, 'min_child_weight': 1, 'max_depth': 4, 'learning_rate': 0.3}\n",
        "xgb3 = XGBRegressor(**best_param)\n",
        "xgb3.fit(X_train3,y_train3)\n",
        "show_result(xgb3,X_train3, X_test3, y_train3, y_test3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "zloUx7Eu5wj0",
        "outputId": "f365b61e-1593-4f35-bc21-b4efa6dff2e3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train set evaluation:\n",
            "_____________________________________\n",
            "MAE:0.0256\n",
            "MSE:0.0018\n",
            "RMSE:0.0427\n",
            "RRMSE:0.0861\n",
            "R2 Square:0.9673\n",
            "__________________________________\n",
            "Test set evaluation:\n",
            "_____________________________________\n",
            "MAE:0.0853\n",
            "MSE:0.0247\n",
            "RMSE:0.1572\n",
            "RRMSE:0.3084\n",
            "R2 Square:0.6039\n",
            "__________________________________\n"
          ]
        }
      ],
      "source": [
        "# d4是去掉砾石海岸伪标签的数据\n",
        "# RRMSE:0.2305\n",
        "X_train4, X_test4, y_train4, y_test4 = train_test_split(d4[['Hs_log','tan_square','L0_sqrt']].values, d4['setup_log'], test_size=0.3, random_state=42)\n",
        "pipeline4 = Pipeline([('std_scalar', StandardScaler())])\n",
        "X_train4 = pipeline4.fit_transform(X_train4)\n",
        "X_test4 = pipeline4.transform(X_test4)\n",
        "\n",
        "best_param = {'reg_lambda': 0.001, 'reg_alpha': 0.1, 'n_estimators': 3000, 'min_child_weight': 1, 'max_depth': 4, 'learning_rate': 0.3}\n",
        "xgb4 = XGBRegressor(**best_param)\n",
        "xgb4.fit(X_train4,y_train4)\n",
        "show_result(xgb4,X_train4, X_test4, y_train4, y_test4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "zN3edAWj-am0",
        "outputId": "c4dc1636-058b-4f61-c9e0-2a3576bc32de"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=3000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train set evaluation:\n",
            "_____________________________________\n",
            "MAE:0.0202\n",
            "MSE:0.0011\n",
            "RMSE:0.0327\n",
            "RRMSE:0.0734\n",
            "R2 Square:0.9749\n",
            "__________________________________\n",
            "Test set evaluation:\n",
            "_____________________________________\n",
            "MAE:0.0726\n",
            "MSE:0.0211\n",
            "RMSE:0.1452\n",
            "RRMSE:0.3044\n",
            "R2 Square:0.6031\n",
            "__________________________________\n"
          ]
        }
      ],
      "source": [
        "# RRMSE:0.2319\n",
        "X_train5, X_test5, y_train5, y_test5 = train_test_split(d5[['Hs_log','tan_square','L0_sqrt']].values, d5['setup_log'], test_size=0.3, random_state=44)\n",
        "pipeline5 = Pipeline([('std_scalar', StandardScaler())])\n",
        "X_train5 = pipeline5.fit_transform(X_train5)\n",
        "X_test5 = pipeline5.transform(X_test5)\n",
        "\n",
        "best_param = {'reg_lambda': 0.001, 'reg_alpha': 0.1, 'n_estimators': 3000, 'min_child_weight': 1, 'max_depth': 4, 'learning_rate': 0.3}\n",
        "xgb5 = XGBRegressor(**best_param)\n",
        "xgb5.fit(X_train5,y_train5)\n",
        "show_result(xgb5,X_train5, X_test5, y_train5, y_test5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "r9OQkj9v_6eX",
        "outputId": "77d2aaa5-6422-4dcc-9629-9351e9fd0cc1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'reg_lambda': 0.1, 'reg_alpha': 0.1, 'n_estimators': 500, 'min_child_weight': 5, 'max_depth': 4, 'learning_rate': 0.1}\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=5, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=500, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=5, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=500, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "             min_child_weight=5, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=500, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train set evaluation:\n",
            "_____________________________________\n",
            "MAE:0.0312\n",
            "MSE:0.0025\n",
            "RMSE:0.0503\n",
            "RRMSE:0.1014\n",
            "R2 Square:0.9547\n",
            "__________________________________\n",
            "Test set evaluation:\n",
            "_____________________________________\n",
            "MAE:0.0852\n",
            "MSE:0.0236\n",
            "RMSE:0.1537\n",
            "RRMSE:0.3015\n",
            "R2 Square:0.6214\n",
            "__________________________________\n"
          ]
        }
      ],
      "source": [
        "xgb = XGBRegressor(booster='gbtree', objective='reg:squarederror',tree_method=\"hist\", device=\"cuda\") # 使用GPU计算\n",
        "param_lst = {\n",
        "    'learning_rate' : [0.01, 0.1, 0.15, 0.3, 0.5],\n",
        "    'n_estimators' : [100, 500, 1000, 2000, 3000],\n",
        "    'max_depth' : [1, 2, 3, 4, 5],\n",
        "    'min_child_weight' : [1, 5, 10, 20],\n",
        "    'reg_alpha' : [0.001, 0.01, 0.1],\n",
        "    'reg_lambda' : [0.001, 0.01, 0.1]\n",
        "}\n",
        "# xgb_reg = GridSearchCV(estimator = xgb, param_grid = param_lst,\n",
        "#                               scoring = 'neg_root_mean_squared_error',\n",
        "#                               cv = 5)\n",
        "\n",
        "xgb_reg = RandomizedSearchCV(estimator = xgb, param_distributions = param_lst,\n",
        "                               n_iter = 200, scoring = rrmse_scorer,\n",
        "                               cv = 5)\n",
        "\n",
        "xgb_search = xgb_reg.fit(X_train4, y_train4)\n",
        "best_param = xgb_search.best_params_\n",
        "# 随机100次+GPU耗时7分钟\n",
        "# best_param = {'reg_lambda': 0.01, 'reg_alpha': 0.1, 'n_estimators': 2000, 'min_child_weight': 5, 'max_depth': 3, 'learning_rate': 0.1}\n",
        "xgb = XGBRegressor(**best_param)\n",
        "print(best_param)\n",
        "xgb.fit(X_train4,y_train4)\n",
        "show_result(xgb,X_train4, X_test4, y_train4, y_test4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJPrqvaiKxBP"
      },
      "source": [
        "Pearson Correlation\n",
        "展示各个变量的相关性。\n",
        "\n",
        "**完美**：如果值接近± 1，那么这就是一个完美的相关性：当一个变量增加时，另一个变量也趋向于增加（如果是正的）或减少（如果是负的）。\n",
        "\n",
        "**高度相关**：如果系数值介于± 0.50和± 1，那么就说这是一个很强的相关性。\n",
        "\n",
        "**中度相关**：如果值介于± 0.30和± 0.49，则称为中等相关性。\n",
        "\n",
        "**低度相关**：当值低于± 0.29时，则表示相关性很小。\n",
        "\n",
        "**无相关性**：当值为零时。\n",
        "\n",
        "需要注意的是，它只对线性关系敏感，如果关系是非线性的，即便两个变量具有一一对应的关系，相关系数系数也可能会接近0。例如x和x^2之间的Pearson相关系数是0，但是两个变量并不是独立的。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X_S2BXL7du4t"
      },
      "outputs": [],
      "source": [
        "# 因为后面会用到很多模型，所以可以把 cross validation 和 fit 的部分写入一个函数，这样每次把分类器投入到函数中训练，最后返回训练好的模型即可。\n",
        "\n",
        "def get_model(estimator, parameters, X_train, y_train, scoring):\n",
        "    model = GridSearchCV(estimator, param_grid=parameters, scoring=scoring)\n",
        "    model.fit(X_train, y_train)\n",
        "    return model.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "29cHDJa3fG1v"
      },
      "outputs": [],
      "source": [
        "# 学习曲线将展示出随着训练样本数量的增加，训练集和验证集的性能变化情况。如果训练集和验证集的性能曲线收敛且趋于平稳，说明模型已经充分学习数据；如果两者之间存在差距，可能存在过拟合或欠拟合问题。\n",
        "\n",
        "from sklearn.model_selection import learning_curve\n",
        "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
        "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5), scoring='accuracy'):\n",
        "    plt.figure(figsize=(10,6))\n",
        "    plt.title(title)\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    plt.xlabel(\"Training examples\")\n",
        "    plt.ylabel(scoring)\n",
        "    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, scoring=scoring,\n",
        "                                                            n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    plt.grid()\n",
        "\n",
        "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"r\")\n",
        "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
        "             label=\"Training score\")\n",
        "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "             label=\"Cross-validation score\")\n",
        "\n",
        "    plt.legend(loc=\"best\")\n",
        "    return plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8wJcJ5DEK1d3"
      },
      "outputs": [],
      "source": [
        "corrmat = d1.corr().sort_values(by=['setup'], ascending=False) # 按照setup列从大到小排序\n",
        "plt.subplots(figsize=(12,9))\n",
        "sns.heatmap(corrmat, vmax=0.9, cmap=\"Blues\", square=True, annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yjmoo7RloGU5"
      },
      "outputs": [],
      "source": [
        "# data_nonan[['D50']].sort_values('D50').value_counts(sort=0)\n",
        "# D50\n",
        "# 0.200    114\n",
        "# 0.220     13\n",
        "# 0.250     40\n",
        "# 0.280     12\n",
        "# 0.292     18\n",
        "# 0.340     12\n",
        "# 0.360    138\n",
        "# 0.400     42\n",
        "# 0.750     36\n",
        "# 0.900     66\n",
        "# 1.650     29\n",
        "# 2.000     12\n",
        "# dtype: int64"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yob1UCCEXJMF"
      },
      "source": [
        "PPS的方法适用的问题非常多，应用的方面也很广，此处我们将其归纳为下面几点：\n",
        "\n",
        "\n",
        "数据分析：PPS的方式可以表示两个数据之间的潜在关系（线性的非线性的），PPS的相关性图是非常好的一种可视化的解释方式；\n",
        "\n",
        "特征筛选，其特征筛选的方式和Pearson相关性的方式类似，如果我们变量与标签之间的PPS分数很低的话，那么该特征可能和我们的目标变量并不存在任何关系，包括线性的和非线性的关系，可以考虑直接删除处理；"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qcFq_wRFUeYX"
      },
      "outputs": [],
      "source": [
        "# 尝试使用ppscore探索变量之间的关系\n",
        "!pip install ppscore -q\n",
        "import ppscore as pps\n",
        "pps.score(all_data, \"setup\", \"tan\")\n",
        "plt.figure(figsize=[10,6])\n",
        "matrix_df = pps.matrix(all_data)[['x', 'y', 'ppscore']].pivot(columns='y', index='x', values='ppscore')\n",
        "sns.heatmap(matrix_df, vmin=0, vmax=1, cmap=\"Blues\", linewidths=0.5, annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v1Vn1Jd_v2mL"
      },
      "outputs": [],
      "source": [
        "sns.distplot(data_nonan['R2%'] , fit=norm);\n",
        "# R2% skew=0.269,不再做变换了。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pkXEN9AURkb3"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnIwCrasVjMh"
      },
      "source": [
        "根据绘图可以看出，setup和Hs正偏，tan负偏，L0略微正偏\n",
        "因此，对setup和Hs取对数，tan取平方，L0取平方根,重新绘图"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4u5aTmPBQaC7"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhRWaeQT8Qj7"
      },
      "source": [
        "打印出skew（偏度），可以看出，处理后的数据偏度更接近0，也就是更接近正态分布"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OkbAYS850bT6"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.scatter(d4['Hs'], d4['setup']);\n",
        "plt.xlabel('Hs')\n",
        "plt.ylabel('setup')\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.scatter(d4['L0'], d4['setup']);\n",
        "plt.xlabel('L0')\n",
        "plt.ylabel('setup')\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.scatter(d4['tan'], d4['setup']);\n",
        "plt.xlabel('tan')\n",
        "plt.ylabel('setup')\n",
        "# plt.subplots_adjust(wspace=0.4, hspace=0.4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WI4BKZ0x8Zf5"
      },
      "source": [
        "或者直接使用pairplot来绘制"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UF7-NPmd7dfu"
      },
      "outputs": [],
      "source": [
        "sns.pairplot(data_nonan, vars=['setup', 'Hs', 'L0','tan'], hue='setup')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQ0M9mcMdWKV"
      },
      "source": [
        "## outliner分析"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pAcmcsIfXoJD"
      },
      "outputs": [],
      "source": [
        "# 核密度分析\n",
        "# 核密度估计图(Kernel Density Estimation, KDE)，KDE是非参数检验，用于估计分布未知的密度函数，相比于直方图，它受bin影响更小，绘图呈现更平滑，易于对比数据分布。\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.kdeplot(d4['setup']);\n",
        "plt.subplot(2, 2, 2)\n",
        "sns.kdeplot(d4['Hs'] );\n",
        "plt.subplot(2, 2, 3)\n",
        "sns.kdeplot(d4['tan']);\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.kdeplot(d4['L0']);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "svIJEOcrY1ds"
      },
      "outputs": [],
      "source": [
        "# Hs、setup、tan均无outliner\n",
        "# L0有几个，不处理了。\n",
        "# Z-score Method - Detected Outliers: [ 26  28  29  30 368 369 370 371 372 373 374 375 376 377 378 379 380 381\n",
        "# 382 383 384 385 386 387 388 389 390 391 392 393]\n",
        "# from scipy.stats import zscore\n",
        "# z_scores = zscore(data_nonan['L0'])\n",
        "# threshold = 2  # 设定阈值\n",
        "# outliers = np.where(np.abs(z_scores) > threshold)[0]\n",
        "# print(\"Z-score Method - Detected Outliers:\", outliers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cOL_9lQWWaE7"
      },
      "outputs": [],
      "source": [
        "# def box_outliers(data, fea, scale):\n",
        "#     '''\n",
        "#     data: DataFrame，需要处理的数据表格\n",
        "#     fea: 需要处理的特征\n",
        "#     scale: 删除尺度\n",
        "#     return: 异常值删除后的数据表格\n",
        "#     '''\n",
        "#     Q1 = data[fea].quantile(0.25)\n",
        "#     Q3 = data[fea].quantile(0.75)\n",
        "#     IQR = Q3 - Q1\n",
        "#     data = data[(data[fea] >= Q1 - scale * IQR) & (data[fea] <= Q3 + scale * IQR)]\n",
        "#     return data\n",
        "\n",
        "# box_outliers(data_nonan, 'tan', 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J2bQCFKG-n-U"
      },
      "outputs": [],
      "source": [
        "data_nonan.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aV2Qp18wA4_2"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data_nonan[['Hs_log','tan_square','L0_sqrt']].values, data_nonan['setup_log'], test_size=0.3, random_state=42)\n",
        "X_train1, X_test1, y_train1, y_test1 = train_test_split(data_nonan[['Hs_log','tan_square','L0_sqrt','R2%']].values, data_nonan['setup_log'], test_size=0.3, random_state=42)\n",
        "X_train_original, X_test_original, y_train_original, y_test_original = train_test_split(data_nonan[['Hs','tan','L0']].values, data_nonan['setup'], test_size=0.3, random_state=42)\n",
        "print(\"训练集大小:\", len(X_train))\n",
        "print(\"测试集大小:\", len(X_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3r_GC6aFFR-6"
      },
      "source": [
        "## 归一化缩放数据\n",
        "注意这里不能同时标准化train和test，否则会发生数据泄露。应先标准化train再以同样方法标准化test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0H4BvLbyCQaX"
      },
      "outputs": [],
      "source": [
        "pipeline = Pipeline([('std_scalar', StandardScaler())])\n",
        "X_train = pipeline.fit_transform(X_train)\n",
        "X_test = pipeline.transform(X_test)\n",
        "\n",
        "pipeline0 = Pipeline([('std_scalar', StandardScaler())])\n",
        "X_train_original = pipeline0.fit_transform(X_train_original)\n",
        "X_test_original = pipeline0.transform(X_test_original)\n",
        "\n",
        "pipeline_1 = Pipeline([('rob_scalar', StandardScaler())])\n",
        "X_train1 = pipeline.fit_transform(X_train1)\n",
        "X_test1 = pipeline.transform(X_test1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q9bZow2CdWjA"
      },
      "outputs": [],
      "source": [
        "# 交叉验证\n",
        "cont_features = [col for col in train.columns if col.startswith(\"cont\")]\n",
        "len(cont_features)\n",
        "y = train[\"target\"]\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=1)\n",
        "oof = np.zeros(len(train))\n",
        "score_list = []\n",
        "fold = 1\n",
        "test_preds = []\n",
        "\n",
        "\n",
        "for train_index, test_index in kf.split(train):\n",
        "    X_train, X_val = train.iloc[train_index], train.iloc[test_index]\n",
        "    y_train, y_val = y.iloc[train_index], y.iloc[test_index]\n",
        "\n",
        "    y_pred_list = []\n",
        "    for seed in [1]:\n",
        "        dtrain = lgbm.Dataset(X_train[cont_features], y_train)\n",
        "        dvalid = lgbm.Dataset(X_val[cont_features], y_val)\n",
        "        print(seed)\n",
        "        params = {\"objective\": \"regression\",\n",
        "              \"metric\": \"rmse\",\n",
        "              \"verbosity\": -1,\n",
        "              \"boosting_type\": \"gbdt\",\n",
        "              \"feature_fraction\":0.5,\n",
        "              \"num_leaves\": 200,\n",
        "              \"lambda_l1\":2,\n",
        "              \"lambda_l2\":2,\n",
        "              \"learning_rate\":0.01,\n",
        "              'min_child_samples': 50,\n",
        "              \"bagging_fraction\":0.7,\n",
        "              \"bagging_freq\":1}\n",
        "        params[\"seed\"] = seed\n",
        "        model = lgbm.train(params,\n",
        "                        dtrain,\n",
        "                        valid_sets=[dtrain, dvalid],\n",
        "                        verbose_eval=100,\n",
        "                        num_boost_round=100000,\n",
        "                        early_stopping_rounds=100\n",
        "                    )\n",
        "\n",
        "        y_pred_list.append(model.predict(X_val[cont_features]))\n",
        "        test_preds.append(model.predict(test[cont_features]))\n",
        "\n",
        "    oof[test_index] = np.mean(y_pred_list,axis=0)\n",
        "    score = np.sqrt(mean_squared_error(y_val, oof[test_index]))\n",
        "    score_list.append(score)\n",
        "    print(f\"RMSE Fold-{fold} : {score}\")\n",
        "    fold+=1\n",
        "\n",
        "np.mean(score_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9g8U1Ztak7L"
      },
      "source": [
        "递归特征消除（RFE）将机器学习模型的实例和要使用的最终期望特征数作为输入。然后，它递归地减少要使用的特征的数量，采用的方法是使用机器学习模型精度作为度量对它们进行排序。\n",
        "创建一个 for 循环，其中输入特征的数量是我们的变量，这样就可以通过跟踪在每个循环迭代中注册的精度，找出我们的模型所需的最佳特征数量。使用 RFE 支持方法，我们可以找出被评估为最重要的特征的名称（rfe.support 返回一个布尔列表，其中 true 表示一个特征被视为重要，false 表示一个特征不重要）。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2KWAllkranCC"
      },
      "outputs": [],
      "source": [
        "\n",
        "# from sklearn.feature_selection import RFE\n",
        "\n",
        "# model = RandomForestClassifier(n_estimators=700)\n",
        "# rfe = RFE(model, 4)\n",
        "# start = time.process_time()\n",
        "# RFE_X_Train = rfe.fit_transform(X_Train,Y_Train)\n",
        "# RFE_X_Test = rfe.transform(X_Test)\n",
        "# rfe = rfe.fit(RFE_X_Train,Y_Train)\n",
        "# print(time.process_time() - start)\n",
        "# print(\"Overall Accuracy using RFE: \", rfe.score(RFE_X_Test,Y_Test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nPCcvK2uFZvt"
      },
      "outputs": [],
      "source": [
        "# #Validation function\n",
        "# def rmsle_cv(model,n_folds = 5):\n",
        "#     kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(X_train)\n",
        "#     rmse= np.sqrt(-cross_val_score(model, X_train, y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n",
        "#     return(rmse)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pfYvSbMrQ2Vc"
      },
      "source": [
        "使用GridSearchCV来寻找合适的超参数。RandomizedSearch是在一定范围内进行搜索，且要设置搜索的次数，其默认不会对所有的组合进行搜索。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tqxq3cjOGl7k"
      },
      "source": [
        "## 1、 线性回归"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "Dcr6lGdmQI3y",
        "outputId": "22666e4b-610c-4f15-bebd-ef13b9285bab"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'X_train' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-76-2f2297dfd7ac>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlin_reg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlin_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mshow_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlin_reg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# scores = {}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# score = rmsle_cv(lin_reg)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
          ]
        }
      ],
      "source": [
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X_train,y_train)\n",
        "show_result(lin_reg,X_train, X_test, y_train, y_test)\n",
        "# scores = {}\n",
        "# score = rmsle_cv(lin_reg)\n",
        "# print(\"LinearRegression: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
        "# scores['LinearRegression'] = (score.mean(), score.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jkQZfxvvx-gY"
      },
      "outputs": [],
      "source": [
        "# # 观察特征的重要性\n",
        "# importance = model.coef_\n",
        "# # summarize feature importance\n",
        "# for i,v in enumerate(importance):\n",
        "# print('Feature: %0d, Score: %.5f' % (i,v))\n",
        "# # plot feature importance\n",
        "# pyplot.bar([x for x in range(len(importance))], importance)\n",
        "# pyplot.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TB3q2VzKJxUs"
      },
      "source": [
        "如果采用原来的数据"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yxS93Yw1J3fV"
      },
      "outputs": [],
      "source": [
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X_train_original,y_train_original)\n",
        "model = lin_reg\n",
        "test_pred = model.predict(X_test_original)\n",
        "train_pred = model.predict(X_train_original)\n",
        "print('Train set evaluation:\\n_____________________________________')\n",
        "print_evaluate(y_train_original, train_pred)\n",
        "print('Test set evaluation:\\n_____________________________________')\n",
        "print_evaluate(y_test_original, test_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3c8Ahx41UkU"
      },
      "source": [
        "如果考虑R2%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7z9ryE-J1Y3f"
      },
      "outputs": [],
      "source": [
        "lin_reg1 = LinearRegression()\n",
        "lin_reg1.fit(X_train1,y_train1)\n",
        "model = lin_reg1\n",
        "test_pred = model.predict(X_test1)\n",
        "train_pred = model.predict(X_train1)\n",
        "print('Train set evaluation:\\n_____________________________________')\n",
        "print_evaluate(y_train1, train_pred)\n",
        "print('Test set evaluation:\\n_____________________________________')\n",
        "print_evaluate(y_test1, test_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nf9xjwps1x9a"
      },
      "outputs": [],
      "source": [
        "for i,j in zip(['Hs_log','tan_square','L0_sqrt','R2%'],lin_reg1.coef_):\n",
        "  print(f'{i}的系数是{j}\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltRkvHkXRc4-"
      },
      "source": [
        "## 2、LASSO岭回归\n",
        "L1正则化将系数w的l1范数作为惩罚项加到损失函数上，由于正则项非零，这就迫使那些弱的特征所对应的系数变成0。因此L1正则化往往会使学到的模型很稀疏（系数w经常为0），这个特性使得L1正则化成为一种很好的特征选择方法。\n",
        "Lasso能够挑出一些优质特征，同时让其他特征的系数趋于0。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-tqHNf7ZGvuh"
      },
      "outputs": [],
      "source": [
        "param_lst = {\n",
        "    'alpha' :  [1e-15, 1e-10, 1e-8, 9e-4, 7e-4, 5e-4, 3e-4, 1e-4, 1e-3, 5e-2, 1e-2, 0.1, 0.3, 1, 3, 5, 10, 15, 18, 20, 30, 50, 75, 100],\n",
        "}\n",
        "lasso0 = Lasso()\n",
        "lasso_reg = RandomizedSearchCV(estimator = lasso0, param_distributions = param_lst,\n",
        "                              n_iter = 100, scoring = 'neg_root_mean_squared_error',\n",
        "                              cv = 5)\n",
        "lasso_search = lasso_reg.fit(X_train, y_train)\n",
        "best_param = lasso_search.best_params_\n",
        "lasso = Lasso(**best_param)\n",
        "print(best_param)\n",
        "lasso.fit(X_train,y_train)\n",
        "show_result(lasso,X_train, X_test, y_train, y_test)\n",
        "# score = rmsle_cv(lasso)\n",
        "# print(\"LassoRegression: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
        "# scores['LassoRegression'] = (score.mean(), score.std())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQUTt4flY4rr"
      },
      "source": [
        "## 3、XGBOOST\n",
        "用xgb.plot_importance来评估每个特征的重要性"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IEkWH_UvYAHi"
      },
      "outputs": [],
      "source": [
        "# # 交叉验证\n",
        "# n_fold = 10\n",
        "# folds = KFold(n_splits=n_fold, shuffle=True, random_state=1314)\n",
        "\n",
        "# oof = np.zeros(len(X))\n",
        "# prediction = np.zeros(len(X_test))\n",
        "# for fold_n, (train_index, valid_index) in enumerate(folds.split(X)):\n",
        "#     print('第 ' + str(fold_n + 1) + '折：')\n",
        "#     X_train, X_valid = X[features].iloc[train_index], X[features].iloc[valid_index]\n",
        "#     y_train, y_valid = y[train_index], y[valid_index]\n",
        "\n",
        "#     model = lgb.LGBMRegressor(**params, n_estimators=60000, n_jobs=-1)\n",
        "#     model.fit(X_train, y_train,\n",
        "#               eval_set=[(X_train, y_train), (X_valid, y_valid)],\n",
        "#               eval_metric='rmae',\n",
        "#               verbose=1000,\n",
        "#               early_stopping_rounds=500)\n",
        "#     y_pred_valid = model.predict(X_valid)\n",
        "#     y_pred = model.predict(X_test, num_iteration=model.best_iteration_)\n",
        "#     oof[valid_index] = y_pred_valid.reshape(-1, )\n",
        "#     prediction += y_pred\n",
        "# prediction /= n_fold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3CbawjF3ZFpn"
      },
      "outputs": [],
      "source": [
        "# best:{'reg_lambda': 0.001, 'reg_alpha': 0.01, 'n_estimators': 100, 'min_child_weight': 1, 'max_depth': 5, 'learning_rate': 0.1}\n",
        "xgb = XGBRegressor(booster='gbtree', objective='reg:squarederror')\n",
        "\n",
        "param_lst = {\n",
        "    'learning_rate' : [0.01, 0.1, 0.15, 0.3, 0.5],\n",
        "    'n_estimators' : [100, 500, 1000, 2000, 3000],\n",
        "    'max_depth' : [1, 2, 3, 4, 5],\n",
        "    'min_child_weight' : [1, 5, 10, 20],\n",
        "    'reg_alpha' : [0.001, 0.01, 0.1],\n",
        "    'reg_lambda' : [0.001, 0.01, 0.1]\n",
        "}\n",
        "\n",
        "xgb_reg = RandomizedSearchCV(estimator = xgb, param_distributions = param_lst,\n",
        "                              n_iter = 100, scoring = 'neg_root_mean_squared_error',\n",
        "                              cv = 5)\n",
        "xgb_search = xgb_reg.fit(X_train, y_train)\n",
        "best_param = xgb_search.best_params_\n",
        "xgb = XGBRegressor(**best_param)\n",
        "print(best_param)\n",
        "xgb.fit(X_train,y_train)\n",
        "show_result(xgb,X_train, X_test, y_train, y_test)\n",
        "\n",
        "# 不使用伪标签时\n",
        "# Train set evaluation:\n",
        "# _____________________________________\n",
        "# MAE:0.0225\n",
        "# MSE:0.0009\n",
        "# RMSE:0.0299\n",
        "# RRMSE:0.0719\n",
        "# R2 Square:0.9748\n",
        "# __________________________________\n",
        "# Test set evaluation:\n",
        "# _____________________________________\n",
        "# MAE:0.0700\n",
        "# MSE:0.0106\n",
        "# RMSE:0.1032\n",
        "# RRMSE:0.2575\n",
        "# R2 Square:0.6761\n",
        "# __________________________________"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WApupJDgKQE6"
      },
      "source": [
        "如果采用原来的数据"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "324UN-PhKTD-"
      },
      "outputs": [],
      "source": [
        "best_param = {'reg_lambda': 0.001, 'reg_alpha': 0.1, 'n_estimators': 3000, 'min_child_weight': 1, 'max_depth': 4, 'learning_rate': 0.3}\n",
        "xgb = XGBRegressor(**best_param)\n",
        "xgb.fit(X_train_original,y_train_original)\n",
        "model = xgb\n",
        "test_pred = model.predict(X_test_original)\n",
        "train_pred = model.predict(X_train_original)\n",
        "print('Train set evaluation:\\n_____________________________________')\n",
        "print_evaluate(y_train_original, train_pred)\n",
        "print('Test set evaluation:\\n_____________________________________')\n",
        "print_evaluate(y_test_original, test_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yBU6AHAImWsM"
      },
      "outputs": [],
      "source": [
        "# @title 使用dtreeviz可视化结果\n",
        "\n",
        "# 参考 https://github.com/parrt/dtreeviz/blob/master/notebooks/dtreeviz_xgboost_visualisations.ipynb\n",
        "\n",
        "# %config InlineBackend.figure_format = 'retina' # Make visualizations look good\n",
        "# #%config InlineBackend.figure_format = 'svg'\n",
        "# %matplotlib inline\n",
        "\n",
        "# if 'google.colab' in sys.modules:\n",
        "#   !pip install -q dtreeviz\n",
        "#   !pip install -q xgboost==1.5.0\n",
        "\n",
        "\n",
        "# import xgboost as xgb\n",
        "# from xgboost import plot_importance, plot_tree, plotting\n",
        "\n",
        "# import dtreeviz\n",
        "# import graphviz\n",
        "# import matplotlib.pyplot as plt\n",
        "# from matplotlib.pylab import rcParams\n",
        "\n",
        "# import pandas as pd\n",
        "# import numpy as np\n",
        "\n",
        "# random_state = 1234 # get reproducible trees\n",
        "\n",
        "\n",
        "\n",
        "# features_reg = [\"Pclass\", \"Fare\", \"Sex_label\", \"Cabin_label\", \"Embarked_label\", \"Survived\"]\n",
        "# target_reg = \"Age\"\n",
        "\n",
        "# dtrain_reg = xgb.DMatrix(dataset[features_reg], dataset[target_reg])\n",
        "# params_reg = {\"max_depth\":3, \"eta\":0.05, \"objective\":\"reg:squarederror\", \"subsample\":1}\n",
        "# xgb_model_reg = xgb.train(params=params_reg, dtrain=dtrain_reg, num_boost_round=8)\n",
        "\n",
        "\n",
        "# viz_rmodel = dtreeviz.model(model=xgb_model_reg, tree_index=1,\n",
        "#                             X_train=dataset[features_reg],\n",
        "#                             y_train=dataset[target_reg],\n",
        "#                             feature_names=features_reg,\n",
        "#                             target_name=target_reg)\n",
        "\n",
        "# viz_rmodel.view()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_3nBSmiotNM9"
      },
      "outputs": [],
      "source": [
        "# @title 使用shap可视化结果\n",
        "\n",
        "# https://github.com/shap/shap\n",
        "\n",
        "# import xgboost\n",
        "# import shap\n",
        "\n",
        "# # train an XGBoost model\n",
        "# X, y = shap.datasets.california()\n",
        "# model = xgboost.XGBRegressor().fit(X, y)\n",
        "\n",
        "# # explain the model's predictions using SHAP\n",
        "# # (same syntax works for LightGBM, CatBoost, scikit-learn, transformers, Spark, etc.)\n",
        "# explainer = shap.Explainer(model)\n",
        "# shap_values = explainer(X)\n",
        "\n",
        "# # visualize the first prediction's explanation\n",
        "# shap.plots.waterfall(shap_values[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qof-Dps3Z2Wr"
      },
      "source": [
        "## 4、lightgbm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "brYtmAJMaMsk"
      },
      "outputs": [],
      "source": [
        "# {'n_estimators': 1000, 'min_child_samples': 5, 'max_depth': 4, 'learning_rate': 0.01, 'lambda_l2': 0.0001, 'lambda_l1': 0.01, 'feature_fraction': 0.8}\n",
        "# lgbm = LGBMRegressor(boosting_type='gbdt',objective='regression', max_depth=-1,\n",
        "#                     lambda_l1=0.0001, lambda_l2=0, learning_rate=0.1,\n",
        "#                     n_estimators=100, max_bin=200, min_child_samples=5,\n",
        "#                     bagging_fraction=0.75, bagging_freq=5,\n",
        "#                     bagging_seed=7, feature_fraction=0.8,\n",
        "#                     feature_fraction_seed=7, verbose=-1)\n",
        "\n",
        "# param_lst = {\n",
        "#     'max_depth' : [1, 2, 3, 4, 5],\n",
        "#     'learning_rate' : [0.001, 0.01, 0.1, 0.2],\n",
        "#     'n_estimators' : [100, 300, 500, 1000, 1500],\n",
        "#     'lambda_l1' : [0.0001, 0.001, 0.01],\n",
        "#     'lambda_l2' : [0, 0.0001, 0.001, 0.01],\n",
        "#     'feature_fraction' : [0.4, 0.6, 0.8],\n",
        "#     'min_child_samples' : [5, 10, 20, 25]\n",
        "# }\n",
        "\n",
        "# lightgbm = RandomizedSearchCV(estimator = lgbm, param_distributions = param_lst,\n",
        "#                               n_iter = 100, scoring = 'neg_root_mean_squared_error',\n",
        "#                               cv = 5)\n",
        "\n",
        "# lightgbm_search = lightgbm.fit(X_train, y_train)\n",
        "# best_param = lightgbm_search.best_params_\n",
        "best_param ={'n_estimators': 1000, 'min_child_samples': 5, 'max_depth': 4, 'learning_rate': 0.01, 'lambda_l2': 0.0001, 'lambda_l1': 0.01, 'feature_fraction': 0.8}\n",
        "lgbm = LGBMRegressor(verbose=-1,**best_param) # verbose=-1避免输出[Warning] No further splits with positive gain, best gain: -inf\n",
        "print(best_param)\n",
        "lgbm.fit(X_train,y_train)\n",
        "show_result(lgbm,X_train, X_test, y_train, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5hk42YJtaO8_"
      },
      "outputs": [],
      "source": [
        "# 使用R2%\n",
        "best_param = {'n_estimators': 1000, 'min_child_samples': 5, 'max_depth': 4, 'learning_rate': 0.01, 'lambda_l2': 0.0001, 'lambda_l1': 0.01, 'feature_fraction': 0.8}\n",
        "lgbm1 = LGBMRegressor(verbose=-1,**best_param) # verbose=-1避免输出[Warning] No further splits with positive gain, best gain: -inf\n",
        "lgbm1.fit(X_train1,y_train1)\n",
        "show_result(lgbm1,X_train1, X_test1, y_train1, y_test1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQY8w37dbAEz"
      },
      "source": [
        "## 5、catBOOST"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hPSzPohrbWfr"
      },
      "outputs": [],
      "source": [
        "!pip install catboost -q\n",
        "from catboost import CatBoostRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p2fi80y6bD9_"
      },
      "outputs": [],
      "source": [
        "# {'rsm': 0.5, 'random_strength': 0.25, 'n_estimators': 1600, 'min_child_samples': 10, 'max_depth': 5, 'learning_rate': 0.01, 'l2_leaf_reg': 0.01}\n",
        "# cb = CatBoostRegressor(loss_function='RMSE', logging_level='Silent')\n",
        "# param_lst = {\n",
        "#     'n_estimators' : [100, 300, 500, 1000, 1300, 1600],\n",
        "#     'learning_rate' : [0.0001, 0.001, 0.01, 0.1],\n",
        "#     'l2_leaf_reg' : [0.001, 0.01, 0.05],\n",
        "#     'random_strength' : [0.25, 0.5 ,1],\n",
        "#     'max_depth' : [1, 2, 3, 4, 5],\n",
        "#     'min_child_samples' : [2, 5, 10, 15, 20],\n",
        "#     'rsm' : [0.5, 0.7, 0.9],\n",
        "# }\n",
        "\n",
        "# catboost = RandomizedSearchCV(estimator = cb, param_distributions = param_lst,\n",
        "#                               n_iter = 100, scoring = 'neg_root_mean_squared_error',\n",
        "#                               cv = 5)\n",
        "\n",
        "# catboost_search = catboost.fit(X_train, y_train)\n",
        "# best_param = catboost_search.best_params_\n",
        "best_param ={'rsm': 0.5, 'random_strength': 0.25, 'n_estimators': 1600, 'min_child_samples': 10, 'max_depth': 5, 'learning_rate': 0.01, 'l2_leaf_reg': 0.01}\n",
        "cb = CatBoostRegressor(logging_level='Silent', **best_param)\n",
        "print(best_param)\n",
        "cb.fit(X_train,y_train)\n",
        "show_result(cb,X_train, X_test, y_train, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UmjmvN8qef_e"
      },
      "source": [
        "## 6、 KRR(KernelRidge)\n",
        "使用核技巧的岭回归（L2正则线性回归）"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AgBCJ4mrefe3"
      },
      "outputs": [],
      "source": [
        "# best = {'kernel': 'polynomial', 'degree': 3, 'coef0': 0.6, 'alpha': 0.25}\n",
        "from scipy.stats import loguniform\n",
        "param_lst = {\n",
        "    \"alpha\": [0.1, 0.15, 0.2, 0.25],\n",
        "    \"kernel\": ['polynomial'],\n",
        "    \"degree\": [1, 2, 3],\n",
        "    'coef0': [0.6 ,0.7 ,0.75 ,0.8 ,0.9 ]\n",
        "}\n",
        "krr = KernelRidge()\n",
        "krr_reg = RandomizedSearchCV(estimator = krr, param_distributions = param_lst,\n",
        "                              n_iter = 100, scoring = 'neg_root_mean_squared_error',\n",
        "                              cv = 5)\n",
        "krr_search = krr_reg.fit(X_train, y_train)\n",
        "best_param = krr_search.best_params_\n",
        "krr = KernelRidge(**best_param)\n",
        "print(best_param)\n",
        "krr.fit(X_train,y_train)\n",
        "show_result(krr,X_train, X_test, y_train, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NXNSeQYHdRR7"
      },
      "source": [
        "## 7、AutoGluon"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W3-5bkNPdW6c"
      },
      "outputs": [],
      "source": [
        "\n",
        "# import autogluon as ag\n",
        "# from autogluon import TabularPrediction as task\n",
        "\n",
        "# train_data = task.Dataset(file_path='https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')\n",
        "# train_data = train_data.head(500) # subsample 500 data points for faster demo\n",
        "# print(train_data.head())\n",
        "\n",
        "# dir = 'agModels-predictClass' # specifies folder where to store trained models\n",
        "# predictor = task.fit(train_data=train_data, label=label_column, output_directory=dir)\n",
        "\n",
        "# test_data = task.Dataset(file_path='https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')\n",
        "# y_test = test_data[label_column] # values to predict\n",
        "# test_data_nolab = test_data.drop(labels=[label_column],axis=1) # delete label column to prove we're not cheating\n",
        "# print(test_data_nolab.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "0sJsbX5___6_"
      },
      "outputs": [],
      "source": [
        "# @title 尝试添加属性后岭回归，效果很差\n",
        "\n",
        "# d_best[\"tan_exp\"] = np.exp(d_best[\"tan\"])\n",
        "# d_best[\"Hs_exp\"] = np.exp(d_best[\"Hs\"])\n",
        "# d_best[\"L0_exp\"] = np.exp(d_best[\"L0\"])\n",
        "\n",
        "# X = d_best[['Hs','tan','L0']].values\n",
        "# from sklearn.preprocessing import FunctionTransformer, PolynomialFeatures\n",
        "# poly_transformer = PolynomialFeatures(degree=2, include_bias=False)\n",
        "# X_poly = poly_transformer.fit_transform(X)\n",
        "# poly_feature_names = poly_transformer.get_feature_names_out(['Hs','tan','L0'])\n",
        "# for i, col_name in enumerate(poly_feature_names):\n",
        "#     d_best[col_name] = X_poly[:, i]\n",
        "# d_best.drop(['D50','R2%','L0_exp'], axis=1, inplace=True) # L0_exp太大了\n",
        "\n",
        "# X = d_best.drop(['setup_log','setup'], axis=1).values\n",
        "# y = d_best['setup_log'].values\n",
        "\n",
        "# pipeline = Pipeline([('scaler', StandardScaler()),('lasso', Lasso())])\n",
        "# param_lst = {\n",
        "#     'lasso__alpha' :  [1e-15, 1e-10, 1e-8, 9e-4, 7e-4, 5e-4, 3e-4, 1e-4, 1e-3, 5e-2, 1e-2, 0.1, 0.3, 1, 3, 5, 10, 15, 18, 20, 30, 50, 75, 100],\n",
        "# }\n",
        "# # 注意在pipeline中使用时应加上lasso__ 以表明参数是给lasso的\n",
        "# lasso0 = Lasso()\n",
        "# lasso_reg = GridSearchCV(estimator = pipeline, param_grid = param_lst,\n",
        "#                               scoring = 'neg_root_mean_squared_error',\n",
        "#                               cv = 5)\n",
        "# lasso_search = lasso_reg.fit(X, y)\n",
        "# best_param = lasso_search.best_params_\n",
        "# print(best_param)\n",
        "\n",
        "# lasso = Lasso(alpha = 0.05)\n",
        "# core = rrmse_cv(lasso,X,y,n_folds=5)\n",
        "# lasso.fit(X,y)\n",
        "\n",
        "# non_zero_coef_indices = np.where(lasso.coef_ != 0)[0]\n",
        "# non_zero_coefs = lasso.coef_[non_zero_coef_indices]\n",
        "\n",
        "# namelist = d_best.drop(['setup_log','setup'], axis=1).columns.tolist()\n",
        "# for name,coef in zip(namelist,lasso.coef_):\n",
        "#   if coef!=0:\n",
        "#     print('特征名',name)\n",
        "#     print('系数：{:.5f}'.format(coef))\n",
        "\n",
        "# d_best['y_pre'] = np.expm1(d_best['L0']*0.0002-d_best['Hs_exp']*0.00045+d_best['Hs^2']*0.0004+d_best['Hs L0']*0.00022) # log1p的逆运算\n",
        "# d_best['y_pre'].describe()\n",
        "# print_evaluate(d_best['setup'] , d_best['y_pre'] )\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 可视化dataframe\n",
        "# !pip install pygwalker -q\n",
        "# import pygwalker as pyg\n",
        "# pyg.walk(df)"
      ],
      "metadata": {
        "id": "_22N2kF6sWrw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title randomsearchcv\n",
        "# xgb = XGBRegressor(booster='gbtree', objective='reg:squarederror',tree_method='gpu_hist', device=\"cuda\") # 使用GPU计算\n",
        "# param_lst = {\n",
        "#     'learning_rate' : np.linspace(0.01,0.2,20),\n",
        "#     'n_estimators' : [100, 500, 1000, 2000],\n",
        "#     'max_depth' : [2, 3, 4, 5, 6],\n",
        "#     'min_child_weight' :[1, 3, 5, 7],\n",
        "#     'reg_alpha' : uniform(0.1, 1),\n",
        "#     'reg_lambda' : uniform(0.1, 1),\n",
        "#     'subsample' : [0.6,0.7,0.8,0.9,1.0]\n",
        "# }\n",
        "# xgb_reg = RandomizedSearchCV(estimator = xgb, param_distributions = param_lst,\n",
        "#                                n_iter = 300, scoring = 'neg_root_mean_squared_error',\n",
        "#                                cv = 3, n_jobs = -1,verbose=1)\n",
        "# xgb_search = xgb_reg.fit(X_scale, y)\n",
        "# best_param = xgb_search.best_params_\n",
        "# print(best_param)\n",
        "# print(-xgb_search.best_score_)\n",
        "\n",
        "# best_param = {'learning_rate': 0.05, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0.8164256459214504, 'reg_lambda': 0.8970365433738731, 'subsample': 0.7}\n",
        "\n",
        "# best_param = {'alpha': 0.001525987327068456, 'subsample': 0.7874775592304251, 'max_depth': 5, 'eta': 0.11197447881761548, 'gamma': 1.0404156476070348e-08, 'min_child_weight': 10}"
      ],
      "metadata": {
        "cellView": "form",
        "id": "uAPjeQhNjwr-"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
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    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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