notebookc80a383385.ipynb

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      "name": "notebookc80a383385.ipynb",
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/annyfan/550/blob/master/notebookc80a383385.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEBZVPWyWxPi"
      },
      "source": [
        "# Imports\n",
        "import os\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import random\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OManZEihWxPl"
      },
      "source": [
        "# Machine Learning\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.tree import ExtraTreeRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.ensemble import BaggingRegressor\n",
        "\n",
        "# Metrics\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "from sklearn.metrics import mean_squared_log_error\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.preprocessing import PowerTransformer\n",
        "from sklearn.preprocessing import QuantileTransformer\n",
        "import sys"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myThf3-3W74J",
        "outputId": "d93c36c9-07dd-4c48-bbcc-9840f3124e51"
      },
      "source": [
        "!wget \"https://www.dropbox.com/s/qvo45vf0kc7j41d/ift6758-a20.zip?dl=0\" -O data.zip\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-12-11 15:03:03--  https://www.dropbox.com/s/qvo45vf0kc7j41d/ift6758-a20.zip?dl=0\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.65.18, 2620:100:6022:18::a27d:4212\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.65.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/qvo45vf0kc7j41d/ift6758-a20.zip [following]\n",
            "--2020-12-11 15:03:04--  https://www.dropbox.com/s/raw/qvo45vf0kc7j41d/ift6758-a20.zip\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://ucb10185d51f53d59d7d13fa9cfd.dl.dropboxusercontent.com/cd/0/inline/BE0tuOVlkmHkSTqiwoNQl6dHpz50Gcg11UE2lnp2WB3yWGdpXfXs1gj9Sd66dDYFjjHx35Ce1C87l8xHsdpP2uj-jfLB9nJUndvHbvgUPAtz1WC6IhyTvsEvi_eYqS5FEo4/file# [following]\n",
            "--2020-12-11 15:03:04--  https://ucb10185d51f53d59d7d13fa9cfd.dl.dropboxusercontent.com/cd/0/inline/BE0tuOVlkmHkSTqiwoNQl6dHpz50Gcg11UE2lnp2WB3yWGdpXfXs1gj9Sd66dDYFjjHx35Ce1C87l8xHsdpP2uj-jfLB9nJUndvHbvgUPAtz1WC6IhyTvsEvi_eYqS5FEo4/file\n",
            "Resolving ucb10185d51f53d59d7d13fa9cfd.dl.dropboxusercontent.com (ucb10185d51f53d59d7d13fa9cfd.dl.dropboxusercontent.com)... 162.125.65.15, 2620:100:6021:15::a27d:410f\n",
            "Connecting to ucb10185d51f53d59d7d13fa9cfd.dl.dropboxusercontent.com (ucb10185d51f53d59d7d13fa9cfd.dl.dropboxusercontent.com)|162.125.65.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /cd/0/inline2/BE3GnP0zKVc-1tK8jCL6UbBDr-yHv__yzTj3ARIgylTHTU_D1_v7yPlEXbTm9-ZYeXC8dw0qEPAgtpEe-6D8d5Jvx4UEP0geZfwSDiDPeAUAToZY7duw-XzDkmVxzxpZCgDvqPrhx-eOwbSsCRQp7gdxrKjNyPyER7uHQfBrLzhy9kitALx_5ryGERip0XUtRUTI6Knj_zyq30pX1_zwcdisx-vWj_cJ7SVQcV1TUSV8dxSdAgO8Ax5OTcWTt9p6VWsAkh4wfHIefHtnjhWdV0IlGh5HLrfasvPxaZu0m4Nu1cW1bx2weY-R_e2U-owHw3b6uzP0u8YVaoCMD_PkgMVuVQi2Ta6slPIJAfyEskxY_Q/file [following]\n",
            "--2020-12-11 15:03:05--  https://ucb10185d51f53d59d7d13fa9cfd.dl.dropboxusercontent.com/cd/0/inline2/BE3GnP0zKVc-1tK8jCL6UbBDr-yHv__yzTj3ARIgylTHTU_D1_v7yPlEXbTm9-ZYeXC8dw0qEPAgtpEe-6D8d5Jvx4UEP0geZfwSDiDPeAUAToZY7duw-XzDkmVxzxpZCgDvqPrhx-eOwbSsCRQp7gdxrKjNyPyER7uHQfBrLzhy9kitALx_5ryGERip0XUtRUTI6Knj_zyq30pX1_zwcdisx-vWj_cJ7SVQcV1TUSV8dxSdAgO8Ax5OTcWTt9p6VWsAkh4wfHIefHtnjhWdV0IlGh5HLrfasvPxaZu0m4Nu1cW1bx2weY-R_e2U-owHw3b6uzP0u8YVaoCMD_PkgMVuVQi2Ta6slPIJAfyEskxY_Q/file\n",
            "Reusing existing connection to ucb10185d51f53d59d7d13fa9cfd.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 15721515 (15M) [application/zip]\n",
            "Saving to: ‘data.zip’\n",
            "\n",
            "data.zip            100%[===================>]  14.99M  19.4MB/s    in 0.8s    \n",
            "\n",
            "2020-12-11 15:03:06 (19.4 MB/s) - ‘data.zip’ saved [15721515/15721515]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pkj-esV6W_3u",
        "outputId": "50dc6bca-255d-437a-b934-936c1193d711"
      },
      "source": [
        "!unzip data.zip"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "  inflating: train_profile_images/profile_images_train/TSQ7RFM1A4E57M33.png  \n",
            "  inflating: train_profile_images/profile_images_train/TT14BQPQ9C051M3A.png  \n",
            "  inflating: train_profile_images/profile_images_train/TT1EFKHH8GB631FT.png  \n",
            "  inflating: train_profile_images/profile_images_train/TT2F93SDA7A3MG1I.png  \n",
            "  inflating: train_profile_images/profile_images_train/TT361OIKGFT69RDD.png  \n",
            "  inflating: train_profile_images/profile_images_train/TT3945NLPAIEKALH.png  \n",
            "  inflating: train_profile_images/profile_images_train/TT8372GJ381MB1TP.png  \n",
            "  inflating: train_profile_images/profile_images_train/TT89FQHGFC6E9I2E.png  \n",
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            "  inflating: train_profile_images/profile_images_train/TTEJQG78NTFT472Q.png  \n",
            "  inflating: train_profile_images/profile_images_train/TTI54SH195E8ESPR.png  \n",
            "  inflating: train_profile_images/profile_images_train/TTJAK07L2HJ0NN86.png  \n",
            "  inflating: train_profile_images/profile_images_train/TTKEBSEPNT8R6D77.png  \n",
            "  inflating: train_profile_images/profile_images_train/TTLRSKA0IB6C2T1S.png  \n",
            "  inflating: train_profile_images/profile_images_train/TTOGI3B4JNMJ7541.png  \n",
            "  inflating: train_profile_images/profile_images_train/TTPSK50O1KKR677Q.png  \n",
            "  inflating: train_profile_images/profile_images_train/TTSJ9G6II47IEAD1.png  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIMtvpv3WxPj"
      },
      "source": [
        "#Visualize the count of number of survivors\n",
        "sns.set()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiLhbQMLWxPj"
      },
      "source": [
        "# Set random seeds (for reproducibility requirement)\n",
        "os.environ['PYTHONHASHSEED']=str(1)\n",
        "tf.random.set_seed(67)\n",
        "np.random.seed(67)\n",
        "random.seed(67)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aG6RbL8mWxPj"
      },
      "source": [
        "# Load data\n",
        "train_df = pd.read_csv('train.csv')\n",
        "test_df = pd.read_csv('test.csv')\n",
        "\n",
        "\n"
      ],
      "execution_count": 238,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2wPaDiVWxPk",
        "outputId": "81d5e225-a72f-4df7-87f7-ce62cfacd73c"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                                                0\n",
              "User Name                                         0\n",
              "Personal URL                                   4244\n",
              "Profile Cover Image Status                       90\n",
              "Profile Verification Status                       0\n",
              "Profile Text Color                               66\n",
              "Profile Page Color                               78\n",
              "Profile Theme Color                              72\n",
              "Is Profile View Size Customized?                  0\n",
              "UTC Offset                                      486\n",
              "Location                                       1461\n",
              "Location Public Visibility                        0\n",
              "User Language                                     0\n",
              "Profile Creation Timestamp                        0\n",
              "User Time Zone                                  486\n",
              "Num of Followers                                  0\n",
              "Num of People Following                           0\n",
              "Num of Status Updates                             0\n",
              "Num of Direct Messages                            0\n",
              "Profile Category                                  0\n",
              "Avg Daily Profile Visit Duration in seconds      77\n",
              "Avg Daily Profile Clicks                         76\n",
              "Profile Image                                     0\n",
              "Num of Profile Likes                              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mb4DK7c48rDM",
        "outputId": "97e201af-ec51-483c-ef07-83e187f67e00"
      },
      "source": [
        "train_df.loc[train_df['Num of Profile Likes'].idxmax()]"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                                                           D8E23RLRSGIEHRO6\n",
              "User Name                                                      lLvSEsGhtaCNaf\n",
              "Personal URL                                      https://blob/vrpslktps6.com\n",
              "Profile Cover Image Status                                                Set\n",
              "Profile Verification Status                                          Verified\n",
              "Profile Text Color                                                     f58ea8\n",
              "Profile Page Color                                                     ddeef6\n",
              "Profile Theme Color                                                    ffffff\n",
              "Is Profile View Size Customized?                                        False\n",
              "UTC Offset                                                              10800\n",
              "Location                                                       Moscow, Russia\n",
              "Location Public Visibility                                            Enabled\n",
              "User Language                                                              ru\n",
              "Profile Creation Timestamp                     Thu Mar 08 14:43:54 +0000 2012\n",
              "User Time Zone                                                      Volgograd\n",
              "Num of Followers                                                      1098185\n",
              "Num of People Following                                                    11\n",
              "Num of Status Updates                                                    3270\n",
              "Num of Direct Messages                                                    563\n",
              "Profile Category                                                      unknown\n",
              "Avg Daily Profile Visit Duration in seconds                             21.68\n",
              "Avg Daily Profile Clicks                                               6.4352\n",
              "Profile Image                                            D8E23RLRSGIEHRO6.png\n",
              "Num of Profile Likes                                                   550594\n",
              "Name: 6890, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZllB_dPLuG-k"
      },
      "source": [
        "\r\n",
        "# get the number of seconds to the unix epoch start.\r\n",
        "df_unix_sec = pd.to_datetime(train_df['Profile Creation Timestamp']).astype(int)/ 10**9"
      ],
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "jtmoLR5H7juP",
        "outputId": "08967b47-afa3-479b-8a1f-de962d30f8ef"
      },
      "source": [
        "fig, ax = plt.subplots()\r\n",
        "ax.scatter(x = df_unix_sec, y = train_df['Num of Profile Likes'])\r\n",
        "plt.ylabel('Num of Profile Likes', fontsize=13)\r\n",
        "plt.xlabel('profile_create_sec', fontsize=13)\r\n",
        "plt.show()\r\n",
        "\r\n"
      ],
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "TD9XbyB4_uLt",
        "outputId": "e8dbcda3-0457-4179-9a7f-c90c378b520f"
      },
      "source": [
        "# Num of Profile Likes分布图\r\n",
        "from scipy.stats import norm\r\n",
        "from scipy import stats\r\n",
        "sns.distplot(train_df['Num of Profile Likes'] , fit=norm)\r\n",
        "# SalePrice均值、均方差\r\n",
        "(mu, sigma) = norm.fit(train_df['Num of Profile Likes'])\r\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\r\n",
        "           loc='best')\r\n",
        "plt.ylabel('Frequency')\r\n",
        "plt.title('Num of Profile Likesn')\r\n",
        "\r\n",
        "# QQ-plot（验证某两组数据是否来自同一分布，离的距离）\r\n",
        "fig = plt.figure()\r\n",
        "res = stats.probplot(train_df['Num of Profile Likes'], plot=plt)\r\n",
        "plt.show()"
      ],
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WUSW0fPnyWtO+++477N271+KBiIioaRLTsrB133mUV+gBAHmFZdi67zwAyK6IRA/bU1P//v1x6NAhS2YhIiIL2Hk0XSigKuUVeuw8mi5RIvNEbQlV3VW1SklJCeLi4uDr62uVUERE1Hh5haYHEjA3XUqiSujpp5+GQqFA1f3vnJycEBQUhBUrVlg1HBERNZyHm4PJwvFwc5AgTd1EldD58+etnYOIiCxkzMBAo2NCAKC2U2LMwEAJU5km+johIiJqHqpOPmjWZ8cNHDhQGKy0LkeOHLFkHiIisoA+XX1kWTo1mS2hVatW2TIHERE1UXO5Nqg6syXUq1cvW+YgIqJGSkzLwlcHL6C4VCdMk/O1QdWJPiZ07tw5JCcn4+bNm8JZcgCH7SEiklLNC1Orq7o2SM4lJOpi1X/961+YMGECjh8/jk8++QQXL17EZ599hqtXr1o7HxER1cHUhanVyfHaoOpEldCmTZuwadMmbNiwAY6OjtiwYQM++OAD4VbfREQkjfpKRo7XBlUnqoTy8vKg1WorX6BUQq/XY+DAgTh8+LDoFU2fPh1hYWEIDw/HxIkTce7cOQDA5cuXMW7cOAwdOhTjxo3Db7/9JrzG1vOIiJqbukpGrtcGVSeqhHx8fPD7778DADp16oT//ve/SE5Ohr29vegVRUVFITY2FjExMZgyZQreeustAMDChQsxceJExMfHY+LEiYiMjBReY+t5RETNzZiBgVDb1f5V7upkh788+4isjwcBIkvopZdeQnp65cB306dPx9y5c/GXv/wFM2bMEL2iNm3aCI+LioqgUCiQl5eHs2fPIjQ0FAAQGhqKs2fPIj8/3+bziIiaoz5dffCXZx8Rtog83Bzw8sgu+HD2k7IvIKCes+Nmz56NMWPGIDw8HEplZV8NHDgQJ06cwN27d+Hi4tKglS1YsAA//PADDAYDNm3ahMzMTHh7e0OlUgEAVCoVvLy8kJmZCYPBYNN57u7uDfosRERy0VwuTDWlzhLy9vbGggULYDAYEBoaitGjR+ORRx6BWq2GWq1u8MqWLVsGAIiJicHKlSub7endHh6uRs89PduYWVI+mLHp5J4PYEZLkXtGuecDxGess4TeeustRERE4NixY4iNjcX48ePRsWNHjB49GiNHjkT79u0bFS48PByRkZHw8fFBdnY2dDodVCoVdDodcnJy4OvrC4PBYNN5DZGXVwS9vvJaKU/PNsjNvd2o78FWmLHp5J4PYEZLkXtGuecDamdUKhW1/ngX5tX3ZkqlEgMHDsTq1avx/fffY/LkyTh8+DAGDx6MV155RVSg4uJiZGZmCs8TEhLQtm1beHh4ICgoCHFxcQCAuLg4BAUFwd3d3ebziIjI9hSG6sMfiJCbm4vdu3dj27ZtuHXrFk6dOlXva27cuIHp06ejpKQESqUSbdu2xbx589C1a1ekp6cjIiIChYWFcHNzQ1RUFB588EEAsPk8sbglZHlyzyj3fAAzWorcM8o9H9CwLSFRJVRWVoYDBw4gJiYGSUlJ0Gq1CA8PxzPPPANnZ2fLJW8mWEKWJ/eMcs8HMKOlyD2j3PMBDSuhOo8JJSUlISYmBgcOHICnpydGjRqFpUuXws/Pz7KJiYiowZrjqNk11VlCM2fOxPDhw7Fp0yYEBwfbKhMREdWj5sClzWXU7JrqLKEffvihUadiExGRdZkauLQ5jJpdU51nx7GAiIjkydzApXIfNbsmUcP2EBGRvJgbuFTuo2bXxBIiImqGTA1c2hxGza6JNwQiImqGqo77tNiz4yZOnAiFQlHvG2zbts2igYiISJzmPHBpFbMl9Kc//Ul4fPXqVXzzzTcYPXo0/Pz8kJGRgZiYGDz33HM2CUlERC2T2RIaPXq08Pj555/H5s2b8cc//lGYNnLkSLz11luYNWuWdRMSEVGLJerEhPT0dHTs2NFoWkBAAH799VerhCIiotZBVAn17NkTERER+O2331BaWorLly9jwYIF0Gq11s5HREQtmKgSWrFiBYDK22H36NEDI0eOhMFgwHvvvWfVcERE1LKJOkVbo9Fg7dq10Ov1yM/Ph7u7u3C7byIiosYS3STp6enYuHEjPvroIyiVSvz66684f/68NbMREVELJ6qE9u3bhz//+c/Izs5GTEwMgMq7pVbtpiMiImoMUbvjPvzwQ2zZsgWPPPII9u3bBwB45JFHuCVERERNImpLKD8/H507dwYAYRQFhUIhakQFIiIic0SVUNeuXbF7926jaXv37sWjjz5qlVBERNQ6iNodt2DBAkydOhX/+c9/cOfOHUydOhWXL1/Gp59+au18RETUgtVbQgaDAWq1GnFxcfjuu+8waNAg+Pr6YtCgQXBxcbFFRiIiaqHqLSGFQoGRI0fi1KlTGD58uC0yERFRKyHqmFBQUBAuX75s7SxERNTKiDom1KtXL7z88ssYPXo0fHx8jM6KGzt2rNXCERFRyyaqhE6dOgV/f3+cOHHCaLpCoWAJERFRo4kqoS+++KJJK7l58yb+/ve/4+rVq1Cr1XjggQewZMkSuLu74/Tp04iMjERZWRn8/f2xatUqeHh4AIDN5xERkW2JHjvu5s2biImJwaZNmwAA2dnZyMrKEvVahUKBl156CfHx8dizZw86dOiA999/H3q9HnPnzkVkZCTi4+Oh1Wrx/vvvA4DN5xERke2JKqETJ05g2LBh2LNnDzZs2AAAuHLlChYtWiRqJRqNBr179xae9+jRAxkZGUhNTYWDg4NwX6Lx48dj//79AGDzeUREzUliWhbmfvQDpqxIwNyPfkBimriNArkRVULvvfce1q1bh82bN8POrnIPXvfu3XHmzJkGr1Cv12P79u0ICQlBZmYm/Pz8hHnu7u7Q6/UoKCiw+TwiouYiMS0LW/edR15hGQAgr7AMW/edb5ZFJOqY0PXr19GnTx8A98eOs7e3h06na/AKly5dCmdnZ0yaNAkHDx5s8OvlwMPD1ei5p2cbiZKIx4xNJ/d8ADNaitwzxnx/GeUVeqNp5RV6xHx/GWGD/ihRKmNiv0NRJRQYGIhjx45hwIABwrQff/wRDz/8cINCRUVF4cqVK4iOjoZSqYSvry8yMjKE+fn5+VAqldBoNDaf1xB5eUXQ6w0AKr/o3NzbDXq9rTFj08k9H8CMliL3jJ6ebZB7s8TkvNybJbLIXvM7VCoVtf54F+aJecOIiAjMmTMH8+bNQ2lpKSIjIxEREYG5c+eKDrVmzRqkpqZiw4YNUKvVAIBu3bqhtLQUycnJAIAdO3Zg2LBhkswjImouPNwcGjRdzhQGg8EgZsHs7GzExsYiIyMDvr6+CAsLg4+Pj6iVXLp0CaGhoejUqRMcHR0BAAEBAdiwYQNOnTqFhQsXGp0y3b59ewCw+TyxuCVkeXLPKPd8ADNaitwzenq2QeyRS9i677zRLjm1nRJ/efYR9Okq7veyNTVkS0h0CdF9LCHLk3tGuecDmNFS5J6xKl9iWhZ2Hk1HXmEZPNwcMGZgoCwKCGhYCZk9JjR37lxRN61buXJlIyISEVFT9OnqI5vSaQqzJfTAAw8Ij2/evIldu3Zh8ODB8Pf3R0ZGBg4fPozRo0fbJCQREVU68r9r2BKXJsstoMYwW0IzZ84UHk+dOhUff/yxcJEnACQnJ2Pjxo3WTUdERILEtCx8vv8Cyu5WXh5TdX0QgGZbRKLOjjt9+jS6d+9uNK179+5ISUmxSigiIqpt59F0oYCqlFfosfNoukSJmk5UCXXp0gVr1qxBaWkpAKC0tBRr165FUFCQVcMREdF9VSMkiJ3eHIi6WHX58uWYM2cOtFot3NzcUFhYiG7dunHwTyIiG3J1skNRSUWt6c3x+qAq9ZaQTqfD8ePHsXXrVuTn5yMnJweenp5GY7AREZF1JaZloaS0dgHZqRQYMzBQgkSWUe/uOJVKhRUrVsDBwQG+vr7o3r07C4iIyMa2H7oInYmrOh3slc32pARA5DGhwYMHIyEhwdpZiIiohsS0LLy+7qjJ3XAAUFza8IGk5UTUMaGysjLMmjULwcHB8PHxMbqIlRerEhFZR9UtG2qOmF1dcz4eBIgsoYcffrjBI2YTEVHTbD90sc4CAtCsjwcBIkuo+oWrRERkfYlpWWZ3wVVxcVQ16+NBgIgSqqioQGxsLH744QcUFBRAo9Ggb9++CAsLg729vS0yEhG1OvVdgKq2U2Li051tlMZ66jwx4fbt2xg/fjxWrVoFe3t7dOnSBfb29li9ejXGjx+P27flO9IsEVFzVtcFqK5OdrK5bUNT1bkltHr1ari7u+Pzzz+Hs7OzML24uBhvvPEGVq9ejUWLFlk7IxFRq7Jq+ymz89o42+ODWQPMzm9u6twSOnToEBYtWmRUQADg4uKCyMhIHDp0yKrhiIhamy/iz+PclQKz86eF/z8bprG+OkuoqKgI3t7eJuf5+PigqKjIKqGIiFqjxLQsHE7JqHOZQY93sFEa26izhDp06IDjx4+bnJeYmIgOHVrWl0FEJKX6TkZQ1n+f0WanzhJ68cUXMW/ePMTHx0OvrzxXXa/XY//+/Zg/fz7+7//+zxYZiYhavMS0rHpHwx7Yo+UNmVbniQljxoxBQUEBIiIi8Le//Q0ajQYFBQWwt7fHjBkz8Nxzz9kqJxFRi5WYloVNcWfrXCboAQ1eGPqIjRLZTr3XCU2ZMgXPP/88UlJScPPmTbRr1w7BwcFwdXW1RT4iohZv+6GLMJgYnLTK4GC/FllAgMgRE1xdXTFgQMs5JZCISC7EjIzQUgsIEDmKNhERWZ6Y3XDNfYDS+rCEiIgkUt9uOKD5D1BaH5YQEZFE6tsNNzjYr0UMzVMXm5RQVFQUQkJC0LlzZ1y8eFGYfvnyZYwbNw5Dhw7FuHHj8Ntvv0k2j4hITlwcVS36WFAVm5TQkCFDsG3bNvj7+xtNX7hwISZOnIj4+HhMnDgRkZGRks0jIrI1F0eV2XktYYRsMWxSQlqtFr6+vkbT8vLycPbsWYSGhgIAQkNDcfbsWeTn59t8HhGRFHoFmR4WrTXshqsi6hRta8jMzIS3tzdUqsq/BFQqFby8vJCZmQmDwWDTee7u7hJ8A0TUmq3afsrkQKUt+ZogUyQroebMw8P4Ql1PzzYSJRGPGZtO7vkAZrQUa2Y88r9rWPPVKZg7KS75Qi7enNSzzvdoSd+hZCXk6+uL7Oxs6HQ6qFQq6HQ65OTkwNfXFwaDwabzGiovrwh6feV/Qp6ebZCbK++b+zFj08k9H8CMlmKtjIlpWfg07ix09ZySffvO3TrX3xy/Q6VSUeuPd2GerULV5OHhgaCgIMTFxQEA4uLiEBQUBHd3d5vPIyKypsS0LGzaU38BtUYKg6G+S6Wa7t1338WBAwdw48YNtGvXDhqNBnv37kV6ejoiIiJQWFgINzc3REVF4cEHHwQAm89rCG4JWZ7cM8o9H8CMlmKNjHM/+qHeEbKruDiqsP6vA83Ob47fYV1bQjYpoZaGJWR5cs8o93wAM1qKNTJOWZEgetmXR3ap88y45vgd1lVCPDGBiMiKVm0/JWo5lVKBKSOCWs2p2VVYQkREVpCYloVP9tQ9OGmVoAc0mDvhMSsnkieWEBGRhX0Rfx6HUzJELVvf7reWjiVERGRBb3+SiIy8ElHLtqaREcxhCRERWUBDdr9VaU0jI5jDEiIiaqSq638ac4rx4GA/i+dpjlhCRESN0JgtnyqtbXy4urCEiIgayNzgo/VxUiux4c1Blg/UjLGEiIhEashZbzVpXOyx5vUBFk7U/LGEiIjq0ZTyAVr3dUD1YQkREdWhIUPu1KQA8FIrvw6oPiwhIqIa3lx/DAXFd5v0Htz6EYclRERUzci/7W7S61v7CAgNxRIiIoJltn5YQA3HEiKiVu2lqATom3hDG+56azyWEBG1Sk054aCKn4cT3n25jwXSyIfBYIChrBS6oiLoiouhKyqCvrgYKo0Gzg93tvj6WEJE1GpYoniA5nPNj768XCgRXXGRUCz64qJ7zyun15wPna7We9m398QfVqyyeEaWEBG1WE29vqcmexXwz7khFns/sQwVFUJp3MrRo+h6jrCVoisqgv5OsfGWy73nhrvmj3Ep1GqoXFygdHGFysUFal+/+89dK6epXLJtTMwAABMfSURBVFyhdHGBytUV9u7uVvlsLCEialFeWZWAu7X/kG8SS+12M+h00N+5Y7TVYbSlUvP5vWUMZaXm31SlqiwMV1eoXFxh7+kJlesfqpWIK1SulY9VLq5Q3isYpVrd5M9jCSwhImq2LL2lY8qnEbW3fAx6PfQlJdW2PGqWyv3H93d/FUN/5475FSkURlsedhoNHPwDhNJQ3dticff3xO0KpbC1onBwhEKhsOI3YF0sISJqVix1XAcAYDBAbaiAo64MTroyOOnLKh/ry+DlYMDgR9oha/MnRlspVcdQYDB/Sp3S2dmoUNTe3kbPq2+5VG2pKB2doFAq642s8WyDu7m3LfcdSIwlRESy1NCysdNX3CuRcuNCuffYSVcGR335vf+/N11XDhX0Zt/zVq6j0a4suw7uleXh6gKVc+WxE6NicXGF0tkZCpWqqR+/1WAJEZGkapaN0qCDk64c7auXhVAo5UZbK066e6WjL4O9wfyBoLsKFUqUDihVOaBEpUaefVuUOjqgRFn5vFTlgJf+pIXSxRWeHbxxqxxQubhCYcdfkdbGb5iIrKLqIPy8tYeEojC1hTKu2jxHXRkcDBVm31MHBUpUDihROaBU6YAC+zYocXBAqUpdOb2qaJT3llGpUaJ0QIXS9K86UyccOHu2QXEL2t0ldywhIqqTQa+HvrSk8pqSewfhN24/cW93lqldXGVw0pfDUV8OAJhm4j31UKBUqRYKpUjlhFy1BqX3yqPkXnlUlU1loTigXGEHNPEgvKkTDUg6LCGiVmDKioRaB+Gr7+py0hkXSVWxVD1WwvggfFi1x6VKNUruFUqpygE37d3ulcf9krm/K8wBJUo1ypTqJpdJQ7B45KtVltDly5cRERGBgoICaDQaREVFoVOnTlZbX2JaFnYeTUdeYRk83BwwZmBgrUEOTS0DwGjao4EeOJOeh7zCMrg4qqBQKFBUUgGlAtAbIPw/tXxmD8LXOE5SVSwzRRyEL1PYVSsKB9x2cK62W+v+Lq7KQlELRWNQ1H9Gly2xcJoXhcFQx3mGLdTkyZPx3HPPYdSoUdi9eze++eYbfP7556Jfn5dXBP293/aenm2QW8f+48S0LGzddx7lFff/8avtlPjLs48IRWRqGZUCUCgVqNC1uh9Pq1J1EN54V5a5g/DlwmNxB+FNHSdRGx8zqXbsRKdonmd0WXrw0Pr+TUtN7vmA2hmVSgU8PFxNLtvqtoTy8vJw9uxZfPbZZwCA0NBQLF26FPn5+XC3wrAUO4+mG5ULAJRX6LHzaLpQQqaW0Rmq/oeaA4VBf//YiJmD8MIurkYfhHdFqYO7UCw1t1DqOwjfEnArp+Vpuf+1mpGZmQlvb2+o7p3Hr1Kp4OXlhczMTNElVLPRPT3bmF02v7DM7PSq15lbhiRgMMBBf9fkNSYmD8ILz8vNvmX1g/ClSrXZg/ClNY6dWOIgfHOkABC7epSkGer6Ny0Hcs8HiM/Y6krIEhqyO87dzQF5JkrG3c1BeJ25ZagJzB6ELxe2QmpewGjuIHx1pg7Cl1Y7k0sOB+Gbg/q2aKTc3ST33V1yzwdwd1ydfH19kZ2dDZ1OB5VKBZ1Oh5ycHPj6+lplfWMGBpo8JlR14oG5ZXhM6D47fYVRUdR3EL7qTC+xB+FLlWrkOLQzvYur2hlecjwILydidpU1h1+gZFutroQ8PDwQFBSEuLg4jBo1CnFxcQgKCrLK8SAARsd9zJ0dZ26ZmtOa+9lx5g/C19zF1fiD8Pn2bVHi6GC0tdKSDsJbEo+vkBy0yrPj0tPTERERgcLCQri5uSEqKgoPPvig6Nc3ZHecHFg6o0GvvzfU/P3h5vVVgzveqbznib7aDbOqBnzUl5ofjl6hUlWOweVSbTyuakPQV93zpGqMrqp7nthqOPrW+HO2BmZsOrnnA7g7rl6BgYH4+uuvpY4huepXwgt3Wqx+P5MiE9OKi+odjl5Zbdh5O40Gan9/4fn9kYONRxP2CvDEjRtFtvvwRCQLrbKEWprKe8KXGRdG1b1NiotQpC9HUe7N+/OLi6AvKobuTjGgN3/cROnkdG+r5N6dF729q92J0fRWitJJ3HD0NTXn+6EQUeOxhGRGX15eefMrYSukqNrzGru/quYVF8NQYf56E6WjI5TO97c87Np1rHOrROniApWzC4ejJyKrYwnZyN3cXNy5cK7e2/kays1fb6Kws7t3l8XK4yFqH9/K0nCuWSL3t1SUzi7w9nOX/T5kImqdWEI2krNjG4p/Ol35pOqe8PfKxL59e6ge6FSjRGochHdxgdLBQdoPQURkYSwhG/F9ZTp0hbegcnVt9veEJyKyFJaQjSjVaijbe0odg4hIVnj5NxERSYYlREREkmEJERGRZFhCREQkGZYQERFJhiVERESS4SnajaBUKup8LkfM2HRyzwcwo6XIPaPc8wHGGevK2ypv5UBERPLA3XFERCQZlhAREUmGJURERJJhCRERkWRYQkREJBmWEBERSYYlREREkmEJERGRZFhCREQkGZaQBSUlJSEoKAhffvml1FFq2bhxI0aOHInw8HCMGjUK3377rdSRalm8eDGGDRuGsLAwjB8/Hj///LPUkYzs3r0bI0eORJcuXWT1M758+TLGjRuHoUOHYty4cfjtt9+kjlRLVFQUQkJC0LlzZ1y8eFHqOLXcvHkTL7/8MoYOHYqRI0di5syZyM/PlzpWLdOnT0dYWBjCw8MxceJEnDt3TupIJv3jH/8Q/7M2kEXcvn3bMHbsWMO0adMMX3zxhdRxaiksLBQeZ2VlGYKDgw0FBQUSJqotISHBUF5eLjweMmSIxImMXbhwwXDp0iXD3LlzZfUzfuGFFwwxMTEGg8FgiImJMbzwwgsSJ6rt5MmThoyMDMPgwYMNFy5ckDpOLTdv3jQcP35ceL5ixQrD/PnzJUxkWvV/xwcPHjSEh4dLmMa01NRUw9SpU0X/rLklZCErVqzA1KlT0a5dO6mjmNSmTRvh8Z07d6BQKKDX6yVMVNvgwYNhb28PAOjRoweysrJklfHhhx/GQw89BKVSPv9s8vLycPbsWYSGhgIAQkNDcfbsWdn9Fa/VauHr6yt1DLM0Gg169+4tPO/RowcyMjIkTGRa9X/HRUVFUCjkNZBpeXk5lixZgkWLFol+DUfRtoCjR4/i9u3bGDZsGI4cOSJ1HLO2b9+OrVu3IisrC++9955sCxMAtm3bhkGDBsnqF74cZWZmwtvbGyqVCgCgUqng5eWFzMxMuLu7S5yuedLr9di+fTtCQkKkjmLSggUL8MMPP8BgMGDTpk1SxzHywQcfICwsDAEBAaJfwxISYfTo0Wb/Ktq/fz9Wr16Nzz77zMapjNWV8ccff4RKpcKECRMwYcIEXLhwAXPmzEGfPn1sWkRiMgLA3r17sWfPHmzbts1m2QDx+ahlW7p0KZydnTFp0iSpo5i0bNkyAEBMTAxWrlyJTz75ROJElVJSUpCamoo5c+Y06HUsIRF27dpldl5ycjJyc3Pxpz/9CUDlAc7Dhw+joKAAM2fOtFXEOjPW1LlzZ3h5eeHEiRMYOnSoFVMZE5Px4MGDWLt2LbZs2YL27dvbINV9DfkO5cLX1xfZ2dnQ6XRQqVTQ6XTIycmR9a4vOYuKisKVK1cQHR0t+63w8PBwREZG4ubNm7LYq3Hy5Emkp6djyJAhAICsrCxMnToVy5cvR//+/c2+jiXURFqtFomJicLziIgIdOvWTXZ/Rf3yyy946KGHAADXrl3DuXPnhOdycfjwYSxfvhyfffZZgzbnWzMPDw8EBQUhLi4Oo0aNQlxcHIKCgrgrrhHWrFmD1NRUfPzxx1Cr1VLHqaW4uBiFhYXCHxgJCQlo27YtNBqNxMkqTZs2DdOmTROeh4SEIDo6Gg8//HCdr+NN7SxMriU0e/Zs/PLLL7Czs4NKpcJLL72E4cOHSx3LyBNPPAF7e3ujX6BbtmyRxV95ABAXF4eVK1eisLAQ9vb2cHJywqeffip5maenpyMiIgKFhYVwc3NDVFQUHnzwQUkz1fTuu+/iwIEDuHHjBtq1aweNRoO9e/dKHUtw6dIlhIaGolOnTnB0dAQABAQEYMOGDRInu+/GjRuYPn06SkpKoFQq0bZtW8ybNw9du3aVOppJLCEiIpI9ee/0JCKiFo0lREREkmEJERGRZFhCREQkGZYQERFJhiVErcr69esbfEW3rcTGxmLKlClNfp/OnTvjypUrFkhkXZGRkcIp0ElJSXjyySclTkRS4MWq1KIEBwcLj0tKSqBWq4XhdhYvXixVrFp+//13DBkyBGlpabCzq/xnGBYWhrCwMImTWcfOnTvx9ddfY/v27cK0JUuWSJiI5IIlRC1KSkqK8DgkJATvvvsu+vbtK0xbv369TXJUVFQI5UJE5nF3HLU6d+/exd///ncEBwdjxIgRRjfPy87Oxuuvv44nnngCISEh+Pzzz4V55eXlWLZsGfr374/+/ftj2bJlKC8vB3B/d9LHH3+Mfv36Yf78+dDr9fj444/x1FNPoXfv3pg9ezYKCgoAQBhRo2fPnggODkZKSgp27tyJCRMmCOu7dOkSXnzxRfTq1Qt9+/ZFdHQ0AODMmTMYN24ctFot+vfvjyVLlgg56nPt2jVMmjQJwcHBePHFF7FkyRJh96SpXWIhISH48ccfRa23c+fO2L59O5555hlotVosXrwYBoMB6enpWLhwIU6fPo3g4GBotVoAlaOLrF271mTOun4OZ86cwZgxY/DYY4+hb9++WL58uajPTvLEEqJWJyEhASNGjEBycjJCQkKwdOlSAJVD+L/22mvo3LkzvvvuO2zduhVbt27FsWPHAFTenfann37C7t27ERsbi59//hkfffSR8L43btzArVu3cPjwYSxduhRffPEFDh06hC+//BLHjh1D27ZthV1QVXdmPXnyJFJSUox2IwKV94p58cUXMWDAABw7dgwHDhxAnz59AABKpRLz58/H8ePHsWPHDiQmJuKrr74S9dnnzJmDrl27IikpCdOnT2/QoK1i1nvkyBH85z//QWxsLPbt24djx44hMDAQixcvRo8ePZCSkoLk5OQ611Pfz2HZsmWYPHkyTp06hYMHD+LZZ58V/RlIflhC1Oo8/vjjGDhwIFQqFUaNGoXz588DAH7++Wfk5+dj5syZUKvV6NChA55//nnhVuh79uzBjBkz4OHhAXd3d8yYMQOxsbHC+yqVSsyaNQtqtRqOjo7YsWMH3njjDfj4+ECtVmPmzJmIj49HRUVFvRmPHDmC9u3bY8qUKXBwcICrqyu6d+8OAOjWrRt69OgBOzs7BAQEYNy4cTh58mS975mRkYGff/4Zs2fPhlqtRs+ePRt0zxwx63355Zfh5uYGPz8/9O7dW/huG6K+n4OdnR2uXr2K/Px8uLi4oEePHg1eB8kHd1pTq1P9FhGOjo4oKytDRUUFrl+/jpycHGF3EQDodDrheU5ODvz8/IR5fn5+yMnJEZ63a9cODg4OwvOMjAzMmDHD6JYASqUSeXl59WbMzMxEx44dTc67fPkyVqxYgdTUVJSUlECn04kaxDInJwdubm5wdnY2+gyZmZn1vlbsej09PYXHTk5OKC4uFvXe1dX3c1i2bBk+/PBDPPvsswgICMDMmTMxePDgBq+H5IElRHSPr68vAgICcODAAZPzvby8kJGRgT/+8Y8AKovCy8tLmF/zVss+Pj5477338Pjjj9d6r+vXr9ebpeov/5oWLVqELl26YPXq1XB1dcWWLVsQHx9f5/sBlQVRWFiIO3fuCEWUkZEh5HZyckJpaamwvE6nM7pNeGPXC9T+bupS38+hU6dOWLNmDfR6PQ4cOIBZs2YhKSnJqFyp+eDuOKJ7Hn30Ubi4uODjjz9GaWkpdDodLl68iDNnzgAARowYgY0bNyI/Px/5+fnYsGEDRo4cafb9JkyYgHXr1gmFk5+fj0OHDgEA3N3doVQqce3aNZOvHTRoEHJzc7FlyxaUl5ejqKgIP/30E4DK+8q4uLjAxcUF6enpRqc918Xf3x/dunXD+vXrUV5ejuTkZBw+fFiY/4c//AFlZWU4cuQI7t69i40bNxqdeNDY9QKV9z3Kzs4WdQJFfT+H3bt3Iz8/H0qlEm5ubgAg+xvQkXn8yRHdo1KpEB0djfPnz2PIkCF44okn8Pbbb6OoqAgAMH36dHTr1k24nqdr166YPn262febPHkyQkJCMGXKFAQHB+P5558XfpE6OTnh1VdfxYQJE6DVanH69Gmj17q6uuLTTz/F4cOH0a9fPwwdOhRJSUkAgHnz5iEuLg6PPfYY3nnnnQbdF2r16tX46aef0Lt3b2zYsAHh4eHCvDZt2mDhwoV4++238eSTT8LJyQk+Pj7C/Kas94knnsBDDz2E/v37o3fv3nUuW9/P4dixYxgxYgSCg4OxbNkyrF27VrgHEDU/vJ8QUSu2fv16XLlyBe+//77UUaiV4pYQERFJhiVERESS4e44IiKSDLeEiIhIMiwhIiKSDEuIiIgkwxIiIiLJsISIiEgyLCEiIpLM/wfTt+Lb7JECgQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "PV8KsSrg6T5j",
        "outputId": "b6a9c896-e0dd-4ea8-e6db-af8110e9ca28"
      },
      "source": [
        "# Num of Profile Likes分布图\r\n",
        "from scipy.stats import norm\r\n",
        "from scipy import stats\r\n",
        "sns.distplot(np.log(train_df['Num of Profile Likes'] +1), fit=norm)\r\n",
        "# SalePrice均值、均方差\r\n",
        "(mu, sigma) = norm.fit(train_df['Num of Profile Likes'])\r\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\r\n",
        "           loc='best')\r\n",
        "plt.ylabel('Frequency')\r\n",
        "plt.title('Num of Profile Likesn')\r\n",
        "\r\n",
        "# QQ-plot（验证某两组数据是否来自同一分布，离的距离）\r\n",
        "fig = plt.figure()\r\n",
        "res = stats.probplot(np.log(train_df['Num of Profile Likes']+1), plot=plt)\r\n",
        "plt.show()"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXlWcBn02Y8z"
      },
      "source": [
        "train_df = train_df[train_df['Num of Profile Likes']>0]"
      ],
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myAKSdO2BmaC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "outputId": "be9b628c-29b2-4a47-a739-b929eeecf228"
      },
      "source": [
        "# Num of Profile Likes分布图\r\n",
        "from scipy.stats import norm\r\n",
        "from scipy import stats\r\n",
        "sns.distplot(np.log(like_train['Num of Profile Likes']), fit=norm)\r\n",
        "# SalePrice均值、均方差\r\n",
        "(mu, sigma) = norm.fit(train_df['Num of Profile Likes'])\r\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\r\n",
        "           loc='best')\r\n",
        "plt.ylabel('Frequency')\r\n",
        "plt.title('Num of Profile Likesn')\r\n",
        "\r\n",
        "# QQ-plot（验证某两组数据是否来自同一分布，离的距离）\r\n",
        "fig = plt.figure()\r\n",
        "res = stats.probplot(np.log(like_train['Num of Profile Likes']), plot=plt)\r\n",
        "plt.show()"
      ],
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        },
        "id": "aaJZNbN4Doxm",
        "outputId": "eafdefee-97ba-48bb-915f-85a615322248"
      },
      "source": [
        "# 两文件数据量\r\n",
        "\r\n",
        "ntrain = train_df.shape[0]\r\n",
        "ntest = test_df.shape[0]\r\n",
        "y_train = train_df['Num of Profile Likes'].values\r\n",
        "# 连接两个表，将两个表的数据共同处理\r\n",
        "all_data = pd.concat((train_df, test_df)).reset_index(drop=True)\r\n",
        "# 去掉目标变量SalePrice列\r\n",
        "#all_data.drop(['Num of Profile Likes'], axis=1, inplace=True)\r\n",
        "#查看各变量与预测目标相关性，按照右边颜色条进行判断\r\n",
        "\r\n",
        "corrmat = train_df.corr()\r\n",
        "plt.subplots(figsize=(12,9))\r\n",
        "sns.heatmap(corrmat, vmax=0.9)\r\n",
        "plt.show()"
      ],
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GlHfkC8oGEyz",
        "outputId": "a9c73a6a-0568-42eb-c1f0-dbb38834096f"
      },
      "source": [
        "# 查看各变量缺失率及降序排序\r\n",
        "all_data_na = (all_data.isnull().sum() / len(all_data)) * 100\r\n",
        "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)[:30]\r\n",
        "missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\r\n",
        "print(\"missing_data:\", missing_data.head(20)) # 列出前20个\r\n",
        "\r\n",
        "# 填补缺失值，None\\0\\众数\\中位数\\指定值填充，或者去掉该列\r\n"
      ],
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "missing_data:                                              Missing Ratio\n",
            "Personal URL                                     58.125644\n",
            "Location                                         18.980433\n",
            "User Time Zone                                    6.076210\n",
            "UTC Offset                                        6.076210\n",
            "Profile Cover Image Status                        1.091658\n",
            "Profile Page Color                                1.071061\n",
            "Avg Daily Profile Clicks                          1.040165\n",
            "Avg Daily Profile Visit Duration in seconds       1.029866\n",
            "Profile Text Color                                1.029866\n",
            "Profile Theme Color                               0.937178\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Am4zrB-8y1HS",
        "outputId": "2290ff89-70e0-468f-e8b4-41a5e29857b7"
      },
      "source": [
        "from scipy.stats import skew\r\n",
        "numeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\r\n",
        "\r\n",
        "# 列出数据的偏斜度，降序排序\r\n",
        "skewed_feats = all_data[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\r\n",
        "print(\"\\nSkew in numerical features: \\n\")\r\n",
        "skewness = pd.DataFrame({'Skew' :skewed_feats})\r\n",
        "print(\"skew:\",skewness.head(10))\r\n"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Skew in numerical features: \n",
            "\n",
            "skew:                                                   Skew\n",
            "Num of Direct Messages                       91.643092\n",
            "Num of Status Updates                        21.113899\n",
            "Num of Followers                             15.748003\n",
            "Num of People Following                      11.976475\n",
            "Is Profile View Size Customized?              2.860706\n",
            "Avg Daily Profile Clicks                      1.866166\n",
            "UTC Offset                                    0.657441\n",
            "Avg Daily Profile Visit Duration in seconds   0.032626\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usYwWpp3ub46",
        "outputId": "b732ff7f-0670-4186-c53f-734b09d4af42"
      },
      "source": [
        "\r\n",
        "# 将偏斜度大于0.5的数值列做一个log转换，使之尽量符合正态分布，因为很多模型的假设数据服从正态分布\r\n",
        "skewness = skewness[abs(skewness.Skew) > 0.75]\r\n",
        "print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))\r\n",
        "\r\n",
        "from scipy.special import boxcox1p\r\n",
        "skewed_features = skewness.index\r\n",
        "lam = 0.15\r\n",
        "for feat in skewed_features:\r\n",
        "    all_data[feat] = boxcox1p(all_data[feat], lam)\r\n",
        "print(\"LabelEncoder：all_data.shape:\", all_data.shape)"
      ],
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 6 skewed numerical features to Box Cox transform\n",
            "LabelEncoder：all_data.shape: (9710, 23)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1W8kWN31WxPk"
      },
      "source": [
        "# Finalizing all the pre-processing steps we have seen above.\n",
        "def pre_process(data):\n",
        "\n",
        "  data.loc[data['Personal URL'].notnull(), 'URL']=1\n",
        "  data.loc[data['Personal URL'].isnull(), 'URL']=0\n",
        "    \n",
        "  data['Profile Cover Image Status'] = data['Profile Cover Image Status'].fillna('Not Set').replace(['Set', 'Not set', 'Not Set'], [1., 0., 0.])\n",
        "\n",
        "  data['Profile Verification Status'] = data['Profile Verification Status'].fillna('0').replace(['Not verified', 'Pending','Verified'], [0.,1., 2.])\n",
        "    \n",
        "  data['Profile Category'] = data['Profile Category'].replace(['business', 'celebrity','government', 'unknown'], [1., 2., 3., 0.])\n",
        "\n",
        "  data['Avg Daily Profile Clicks'] = data['Avg Daily Profile Clicks'].fillna(0.)\n",
        "    \n",
        "  data['Avg Daily Profile Visit Duration in seconds'] = data['Avg Daily Profile Visit Duration in seconds'].fillna(0.)\n",
        "\n",
        "  data['Profile Text Color'] = data['Profile Text Color'].fillna('000000')\n",
        "  data['Profile Page Color'] = data['Profile Page Color'].fillna('FFFFFF')\n",
        "  data['Profile Theme Color'] = data['Profile Theme Color'].fillna('FFFFFF')\n",
        "    \n",
        "  data['UTC Offset'] = data['UTC Offset'].fillna('0')\n",
        "  data['Location'] = data['Location'].fillna('unknown')\n",
        "  data['Location Public Visibility'] = data['Location Public Visibility'].replace(['Disabled', 'Enabled', 'enabled', 'disabled', '??', ' '], [0., 1., 1., 0., 0., 0.])\n",
        "  data['User Time Zone'] = data['User Time Zone'].fillna('unknown')\n",
        "  '''\n",
        "  Color: Convert 16-digit Hex Code to 0-255, Separate Columns RR-GG-BB, then will have numeric features\n",
        "  Profile Creation Timestamp: convert to long numeric (the year matters)'''\n",
        "\n",
        "  data['profile_text_r'] = data['Profile Text Color'].str[:2].apply(lambda x: int(x, 16))\n",
        "  data['profile_text_g'] = data['Profile Text Color'].str[2:4].apply(lambda x: int(x, 16))\n",
        "  data['profile_text_b'] = data['Profile Text Color'].str[4:].apply(lambda x: int(x, 16))\n",
        "\n",
        "  data['profile_page_r'] = data['Profile Page Color'].str[:2].apply(lambda x: int(x, 16))\n",
        "  data['profile_page_g'] = data['Profile Page Color'].str[2:4].apply(lambda x: int(x, 16))\n",
        "  data['profile_page_b'] = data['Profile Page Color'].str[4:].apply(lambda x: int(x, 16))\n",
        "\n",
        "  data['profile_theme_r'] = data['Profile Theme Color'].str[:2].apply(lambda x: int(x, 16))\n",
        "  data['profile_theme_g'] = data['Profile Theme Color'].str[2:4].apply(lambda x: int(x, 16))\n",
        "  data['profile_theme_b'] = data['Profile Theme Color'].str[4:].apply(lambda x: int(x, 16))\n",
        "\n",
        "  data['profile_create_sec'] = pd.to_datetime(data['Profile Creation Timestamp']).astype(int)/ 10**9\n",
        "\n",
        "  data['Profile Category'] = data['Profile Category'].replace([' '], [4.])\n",
        "\n",
        "  data['User Time Zone'] = LabelEncoder().fit_transform(data['User Time Zone'])\n",
        "  data['User Language'] = LabelEncoder().fit_transform(data['User Language'])\n",
        "\n",
        "  return data\n",
        "\n",
        "# 数据预处理完成后的总训练集、预测集\n",
        "train = all_data[:ntrain]\n",
        "test = all_data[ntrain:]\n",
        "\n",
        "train_df_p = pre_process(train)\n",
        "test_df_p = pre_process(test) \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lh3byiFs5d8i"
      },
      "source": [
        "train_df_p = train_df_p.drop(columns=['Personal URL','User Name', 'Id', 'Profile Text Color', 'Profile Page Color','Profile Theme Color', 'Location', \r\n",
        "                         'Profile Creation Timestamp', 'Profile Image'])\r\n",
        "test_df_p = test_df_p.drop(columns=['Personal URL','User Name', 'Id', 'Profile Text Color', 'Profile Page Color','Profile Theme Color', 'Location', \r\n",
        "                         'Profile Creation Timestamp', 'Profile Image'])"
      ],
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        },
        "id": "D6Lf5BkcHOJN",
        "outputId": "a4008c54-f27e-4f11-b19d-c589dbe8ade8"
      },
      "source": [
        "corrmat = train_df_p.corr()\r\n",
        "plt.subplots(figsize=(12,9))\r\n",
        "sns.heatmap(corrmat, vmax=0.9)\r\n",
        "plt.show()"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDP6LuofWxPl"
      },
      "source": [
        "\n",
        "cols_x = ['URL',\n",
        "          'Profile Cover Image Status',\n",
        "          'Profile Verification Status',\n",
        "          'Is Profile View Size Customized?',\n",
        "          'Location Public Visibility', \n",
        "          'User Language', \n",
        "          'profile_create_sec', \n",
        "          'Num of Followers', \n",
        "          'Num of People Following', \n",
        "          'Num of Status Updates', \n",
        "          'Num of Direct Messages',\n",
        "          'Profile Category',\n",
        "          'Avg Daily Profile Visit Duration in seconds', \n",
        "          'Avg Daily Profile Clicks', \n",
        "          'profile_text_r', \n",
        "          'profile_text_g', \n",
        "          'profile_text_b', \n",
        "          'profile_page_r', \n",
        "          'profile_page_g', \n",
        "          'profile_page_b', \n",
        "          'profile_theme_r', \n",
        "          'profile_theme_g', \n",
        "          'profile_theme_b' \n",
        "          ]\n",
        "cols_y = 'Num of Profile Likes'"
      ],
      "execution_count": 269,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3LQa4SqRxU5W"
      },
      "source": [
        "# frequency encoding\r\n",
        "# Color: Frequency Encoding (~1900 colors, they have different encodings)\r\n",
        "# User Time Zone: frequency encoding (duplicate feature with UTC-offset)\r\n",
        "# User Language: frequency encoding (28 unique)\r\n",
        "def trans_freq(x_freq,x_freq_col):\r\n",
        "  for name in x_freq.columns:\r\n",
        "    if name in x_freq_col:\r\n",
        "      fe = x_freq.groupby(name).size()/len(x_freq)\r\n",
        "      x_freq.loc[:, name] = x_freq[name].map(fe)\r\n",
        "    \r\n",
        "  return x_freq "
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7Dcy9nCvnFd"
      },
      "source": [
        "\n",
        "x = train_df_p[cols_x]\n",
        "y = train_df[cols_y]\n",
        "\n",
        "\n",
        "# split dataset to train and test\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.1, shuffle=True, random_state=76) #  To guarantee that the split will always be the same \n",
        "\n",
        "#one hot encoding + log transformation\n",
        "X_train_dum = pd.get_dummies(X_train)\n",
        "X_test_dum = pd.get_dummies(X_test)\n",
        "\n",
        "y_train_log = np.log(y_train)\n",
        "y_test_log = np.log(y_test)\n",
        "\n",
        "freq_col = [\n",
        "            #'profile_text_r', \n",
        "            #'profile_text_g', \n",
        "            #'profile_text_b', \n",
        "            #'profile_page_r', \n",
        "            #'profile_page_g', \n",
        "            #'profile_page_b', \n",
        "            #'profile_theme_r', \n",
        "            #'profile_theme_g', \n",
        "            #'profile_theme_b', \n",
        "            #'profile_create_sec',\n",
        "            #'Num of Followers',                          \n",
        "            #'Num of People Following'   ,                      \n",
        "            #'Num of Status Updates'   ,                         \n",
        "            #'Num of Direct Messages'  ,                                \n",
        "            ##'Avg Daily Profile Visit Duration in seconds',     \n",
        "            #'Avg Daily Profile Clicks',\n",
        "            'profile_create_sec'\n",
        "            ]\n",
        "\n",
        "# frequency encoding\n",
        "X_train_freq= trans_freq(X_train.copy(), freq_col)\n",
        "X_test_freq= trans_freq(X_test.copy(), freq_col)\n",
        "\n",
        "#再整理出一组标准化的数据，通过对比可以看出模型的效果有没有提高\n",
        "from sklearn.preprocessing import MaxAbsScaler\n",
        "scale_x=QuantileTransformer(n_quantiles=10, random_state=67)\n",
        "#x1=scale_x.fit_transform(x)\n",
        "#scale_y=StandardScaler()\n",
        "#y=np.array(y).reshape(-1,1)\n",
        "#y1=scale_y.fit_transform(y)\n",
        "#y1=y1.ravel()\n",
        "X_train1= scale_x.fit_transform(X_train)\n",
        "X_test1 = scale_x.fit_transform(X_test)\n",
        "#y_train1 = scale_y.fit_transform(np.array(y_train).reshape(-1,1)).ravel()\n",
        "#y_test1 = scale_y.fit_transform(np.array(y_test).reshape(-1,1)).ravel()"
      ],
      "execution_count": 270,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxXJVrg8MCTC"
      },
      "source": [
        "from sklearn.decomposition import PCA\r\n",
        "from sklearn.decomposition import KernelPCA\r\n",
        "pca = KernelPCA( svd_solver='full')\r\n",
        "pca.fit(X_train)\r\n",
        "print(pca.explained_variance_ratio_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BRwnr1kZpnzU"
      },
      "source": [
        "PCA not better #poly 7.11, PCA 6.39"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVnTAQDXMucA"
      },
      "source": [
        "pca = KernelPCA( kernel='rbf') #poly 7.11, PCA 6.39\r\n",
        "reduced_data = pca.fit_transform(X_train)\r\n",
        "r2 = RandomForestRegressor(n_estimators=100,random_state=67)\r\n",
        "r2 = r2.fit(reduced_data,y_train)\r\n",
        "regression.append(\"RandomForestRegressor\")\r\n",
        "\r\n",
        "print(mean_squared_log_error(y_test, r2.predict(pca.transform(X_test))))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tyLqkH8WxPl"
      },
      "source": [
        "result = pd.DataFrame(columns = (\"Regression\",\"Training\",\"Testing\"))\n",
        "regression, train_scores, test_scores = [],[],[]"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZMo6lBOaq4r",
        "outputId": "fbad0e91-7bde-4146-c094-b4792344bbab"
      },
      "source": [
        "r2 = RandomForestRegressor(n_estimators=100,random_state=67)\n",
        "r2 = r2.fit(X_train,y_train_log)\n",
        "regression.append(\"RandomForestRegressor\")\n",
        "ypred = r2.predict(X_test)\n",
        "ypred_original=np.exp(ypred)\n",
        "print(mean_squared_log_error(y_test, ypred_original))"
      ],
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.704949409857735\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MDWOOwM98BR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7272987d-b9c7-4b13-9a72-7a2bcb5de6bc"
      },
      "source": [
        "r2 = r2.fit(X_train_dum,y_train_log)\n",
        "ypred = r2.predict(X_test_dum)\n",
        "ypred_original=np.exp(ypred)\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error(y_test,ypred_original))\n",
        "\n",
        "r2 = r2.fit(X_train_freq,y_train_log)\n",
        "ypred = r2.predict(X_test_freq)\n",
        "ypred_original=np.exp(ypred) \n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error(y_test,ypred_original))\n",
        "\n",
        "r2 = r2.fit(X_train1,y_train_log)\n",
        "ypred = r2.predict(X_test1)\n",
        "ypred_original=np.exp(ypred)\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error( y_test, ypred_original))\n"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.7045707367576375\n",
            "2.719995750945171\n",
            "2.759965431380942\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kipI11Rf_RHV"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiVHJuf5uotR",
        "outputId": "7424727b-f544-43f7-a989-d51dc2e3e955"
      },
      "source": [
        "  models=[LinearRegression(),\n",
        "          KNeighborsRegressor(),\n",
        "          SVR(),\n",
        "          Ridge(),\n",
        "          Lasso(),\n",
        "          MLPRegressor(hidden_layer_sizes=(6, 4, 2,2),  activation='relu', solver='lbfgs', alpha=200, batch_size='auto',learning_rate='adaptive', learning_rate_init=0.0001, power_t=0.8, max_iter=5000, shuffle=True,random_state=67, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True,early_stopping=True,beta_1=0.9, beta_2=0.9, epsilon=1e-08),\n",
        "          DecisionTreeRegressor(),\n",
        "          ExtraTreeRegressor(),\n",
        "          RandomForestRegressor(n_estimators=100,random_state=67),\n",
        "          AdaBoostRegressor(),\n",
        "          GradientBoostingRegressor(),\n",
        "          BaggingRegressor(n_estimators=100) ,\n",
        "          XGBRegressor()\n",
        "          ]\n",
        "  models_str=['LinearRegression','KNNRegressor','SVR','Ridge','Lasso','MLPRegressor','DecisionTree','ExtraTree','RandomForest','AdaBoost','GradientBoost','Bagging',]\n",
        "  score_adapt=[]\n",
        "  for name,model in zip(models_str,models):\n",
        "\n",
        "    try:\n",
        "          print('---------------------------------')\n",
        "          #if name in ['LinearRegression','Ridge','ExtraTree']:   \n",
        "          print('开始训练模型：'+name+' 平滑处理')\n",
        "          model=model\n",
        "          model.fit(X_train_dum,y_train_log)\n",
        "          y_pred_log=model.predict(X_test_dum)   \n",
        "          ypred_original=np.exp(y_pred_log) \n",
        "          ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "          score=model.score(X_test_dum,y_test_log)\n",
        "          score_adapt.append(str(score)[:5])\n",
        "          print(name +' msle:',mean_squared_log_error(y_test, ypred_original))\n",
        "    except:\n",
        "          print(print(\"Unexpected error:\", sys.exc_info()[0]))\n",
        "    try:\n",
        "          print('---------------------------------')  \n",
        "          #elif name in ['SVR','MLPRegressor','Bagging','AdaBoost','KNNRegressor']:\n",
        "          print('开始训练模型：'+name+' 标准化处理')\n",
        "              \n",
        "          model.fit(X_train1,y_train_log)\n",
        "          y_pred1=model.predict(X_test1)\n",
        "          ypred_original=np.exp(y_pred1) \n",
        "          #ypred_original=scale_y.inverse_transform(y_pred1.reshape(-1, 1)).ravel()\n",
        "          ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "          score=model.score(X_test1,y_test1)\n",
        "          score_adapt.append(str(score)[:5])       \n",
        "          \n",
        "          print(name +' msle:',(mean_squared_log_error(y_test, ypred_original)))\n",
        "\n",
        "    except:\n",
        "          print(print(\"Unexpected error:\", sys.exc_info()[0]))\n",
        "    try:  \n",
        "          print('---------------------------------')      \n",
        "          print('开始训练模型：'+name+' 频率')\n",
        "         \n",
        "          model.fit(X_train_freq,y_train_log)\n",
        "          y_pred_log=model.predict(X_test_freq)\n",
        "          ypred_original=np.exp(y_pred_log) \n",
        "          ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "          score_adapt.append(str(score)[:5])    \n",
        "          print(name +' msle:'+str(mean_squared_log_error(y_test, ypred_original)))\n",
        "\n",
        "         \n",
        "    except:\n",
        "          print(print(\"Unexpected error:\", sys.exc_info()[0]))\n",
        "    try:    \n",
        "          print('---------------------------------') \n",
        "          print('开始训练模型：'+name+' 普通')\n",
        "         \n",
        "          model.fit(X_train,y_train)\n",
        "          y_pred = model.predict(X_test)\n",
        "          y_pred = np.around(np.maximum(y_pred, 0))\n",
        "          score=model.score(X_test,y_test)\n",
        "          score_adapt.append(str(score)[:5])   \n",
        "          print(name +' msle:'+str(mean_squared_log_error(y_test, y_pred)))\n",
        "\n",
        "          print('---------------------------------')\n",
        "    except:\n",
        "          print(print(\"Unexpected error:\", sys.exc_info()[0]))"
      ],
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---------------------------------\n",
            "开始训练模型：LinearRegression 平滑处理\n",
            "LinearRegression msle: 3.092671482827507\n",
            "---------------------------------\n",
            "开始训练模型：LinearRegression 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：LinearRegression 频率\n",
            "LinearRegression msle:3.094730039439707\n",
            "---------------------------------\n",
            "开始训练模型：LinearRegression 普通\n",
            "LinearRegression msle:7.494265995227434\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：KNNRegressor 平滑处理\n",
            "KNNRegressor msle: 5.451712128708891\n",
            "---------------------------------\n",
            "开始训练模型：KNNRegressor 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：KNNRegressor 频率\n",
            "KNNRegressor msle:5.286274662322619\n",
            "---------------------------------\n",
            "开始训练模型：KNNRegressor 普通\n",
            "KNNRegressor msle:6.950188489495186\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：SVR 平滑处理\n",
            "SVR msle: 4.987354402701752\n",
            "---------------------------------\n",
            "开始训练模型：SVR 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：SVR 频率\n",
            "SVR msle:4.603761741208924\n",
            "---------------------------------\n",
            "开始训练模型：SVR 普通\n",
            "SVR msle:5.070839549590266\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：Ridge 平滑处理\n",
            "Ridge msle: 3.0930293195331333\n",
            "---------------------------------\n",
            "开始训练模型：Ridge 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：Ridge 频率\n",
            "Ridge msle:3.0946484672624854\n",
            "---------------------------------\n",
            "开始训练模型：Ridge 普通\n",
            "Ridge msle:7.494565105136562\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：Lasso 平滑处理\n",
            "Lasso msle: 4.407881737586129\n",
            "---------------------------------\n",
            "开始训练模型：Lasso 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：Lasso 频率\n",
            "Lasso msle:4.406977924256154\n",
            "---------------------------------\n",
            "开始训练模型：Lasso 普通\n",
            "Lasso msle:7.496142898603166\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：MLPRegressor 平滑处理\n",
            "MLPRegressor msle: 4.94779837417132"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=9.18285e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=9.18285e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "---------------------------------\n",
            "开始训练模型：MLPRegressor 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：MLPRegressor 频率\n",
            "MLPRegressor msle:4.94779837417132\n",
            "---------------------------------\n",
            "开始训练模型：MLPRegressor 普通\n",
            "MLPRegressor msle:7.682330607515253\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：DecisionTree 平滑处理\n",
            "DecisionTree msle: 5.517797377028703\n",
            "---------------------------------\n",
            "开始训练模型：DecisionTree 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：DecisionTree 频率\n",
            "DecisionTree msle:5.534210783825747\n",
            "---------------------------------\n",
            "开始训练模型：DecisionTree 普通\n",
            "DecisionTree msle:5.6668613836889525\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：ExtraTree 平滑处理\n",
            "ExtraTree msle: 5.595586026931575\n",
            "---------------------------------\n",
            "开始训练模型：ExtraTree 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：ExtraTree 频率\n",
            "ExtraTree msle:5.387303039938088\n",
            "---------------------------------\n",
            "开始训练模型：ExtraTree 普通\n",
            "ExtraTree msle:5.785249528053445\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：RandomForest 平滑处理\n",
            "RandomForest msle: 2.7045707367576375\n",
            "---------------------------------\n",
            "开始训练模型：RandomForest 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：RandomForest 频率\n",
            "RandomForest msle:2.719995750945171\n",
            "---------------------------------\n",
            "开始训练模型：RandomForest 普通\n",
            "RandomForest msle:4.874073525079109\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：AdaBoost 平滑处理\n",
            "AdaBoost msle: 3.6881476198153886\n",
            "---------------------------------\n",
            "开始训练模型：AdaBoost 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：AdaBoost 频率\n",
            "AdaBoost msle:3.644388161326412\n",
            "---------------------------------\n",
            "开始训练模型：AdaBoost 普通\n",
            "AdaBoost msle:13.695076483484408\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：GradientBoost 平滑处理\n",
            "GradientBoost msle: 2.6788048652661836\n",
            "---------------------------------\n",
            "开始训练模型：GradientBoost 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：GradientBoost 频率\n",
            "GradientBoost msle:2.7075881665296078\n",
            "---------------------------------\n",
            "开始训练模型：GradientBoost 普通\n",
            "GradientBoost msle:5.992512445550514\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "开始训练模型：Bagging 平滑处理\n",
            "Bagging msle: 2.7072781416421976\n",
            "---------------------------------\n",
            "开始训练模型：Bagging 标准化处理\n",
            "Unexpected error: <class 'ValueError'>\n",
            "None\n",
            "---------------------------------\n",
            "开始训练模型：Bagging 频率\n",
            "Bagging msle:2.7636520852084017\n",
            "---------------------------------\n",
            "开始训练模型：Bagging 普通\n",
            "Bagging msle:4.859385142478937\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8Ewxx_7us8h",
        "outputId": "c9db23c4-09fc-4834-caf1-c523aa4f42ed"
      },
      "source": [
        "gdboost = GradientBoostingRegressor(random_state=76, loss = 'ls') #loss : {'ls', 'lad', 'huber', 'quantile'}, optional (default='ls')\n",
        "\n",
        "gdboost = gdboost.fit(X_train_dum,y_train_log)\n",
        "ypred = gdboost.predict(X_test_dum)\n",
        "ypred_original=np.exp(ypred)\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\n",
        "\n",
        "print(mean_squared_log_error(y_test,ypred_original))\n",
        "\n"
      ],
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.678572602193361\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbRQlav1S-MO",
        "outputId": "553e0b49-33d8-4237-a313-5d18b0df6f52"
      },
      "source": [
        "bagging = BaggingRegressor(n_estimators=500)\r\n",
        "bagging = bagging.fit(X_train_dum,y_train_log)\r\n",
        "ypred = bagging.predict(X_test_dum)\r\n",
        "ypred_original=np.exp(ypred)# - 1\r\n",
        "ypred_original = np.around(np.maximum(ypred_original, 0))\r\n",
        "\r\n",
        "print(mean_squared_log_error(y_test,ypred_original))"
      ],
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.7193863854961595\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6qtUiOoVfut"
      },
      "source": [
        "bagging = bagging.fit(x_dum,y_log)\r\n",
        "test_dum = pd.get_dummies(test_df_p[cols_x])\r\n",
        "\r\n",
        "predict_test = bagging.predict(test_dum)\r\n",
        "ypred_original=np.exp(predict_test) \r\n",
        "predict = np.around(np.maximum(ypred_original, 0))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6WbNGKfs6wn",
        "outputId": "2efe39bb-6941-4886-812d-9bfc34ac56bc"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "# Set the parameters by cross-validation\n",
        "tuned_parameters = [{\n",
        "  'loss': ['ls', 'lad', 'huber', 'quantile'],\n",
        "  'max_features':['auto', 'sqrt', 'log2']}\n",
        "]\n",
        "\n",
        "metric = 'neg_root_mean_squared_error'\n",
        "\n",
        "cv_strategy = KFold(n_splits=10, shuffle=True)\n",
        "\n",
        "grid_search = GridSearchCV(\n",
        "    gdboost, tuned_parameters, scoring=metric, cv=cv_strategy\n",
        ")\n",
        "grid_search.fit(X_train_dum, y_train_log);\n",
        "print('Finished!')"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Finished!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TFwmTon2UiV",
        "outputId": "7546579f-7456-4f06-94a9-805dae927114"
      },
      "source": [
        "print(\"Best parameters set found on development set:\")\n",
        "print()\n",
        "print(grid_search.best_params_)"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best parameters set found on development set:\n",
            "\n",
            "{'loss': 'ls', 'max_features': 'auto'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsHmxFgw3s-G"
      },
      "source": [
        "x_dum = pd.get_dummies(x)\r\n",
        "y_log = np.log(y)\r\n",
        "gdboost = gdboost.fit(x_dum,y_log)\r\n",
        "test_dum =  pd.get_dummies(test_df_p[cols_x])\r\n",
        "\r\n",
        "predict_test = gdboost.predict(test_dum)\r\n",
        "ypred_original=np.exp(predict_test) \r\n",
        "predict = np.around(np.maximum(ypred_original, 0))"
      ],
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1RvKNOB9IGf",
        "outputId": "6b44c3c5-1cbc-4e95-9f2a-85efab990dfb"
      },
      "source": [
        "predict"
      ],
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4855., 2486.,  309., ..., 4651., 1687.,  210.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNqatCjvWxPl"
      },
      "source": [
        "# Get indexes\n",
        "out = pd.DataFrame(test_df['Id'])\n",
        "\n",
        "# Make predictions\n",
        "out['Predicted'] = predict # TODO: real predictions"
      ],
      "execution_count": 279,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "OjWVbNYyWxPl",
        "outputId": "736732e2-65d0-4bd5-b552-914e52ec74ba"
      },
      "source": [
        "# Check output\n",
        "out[out['Predicted']  > 0]"
      ],
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>49I3SOKLI2CMNGP4</td>\n",
              "      <td>4855.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>727IRIR59A3P88LK</td>\n",
              "      <td>2486.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LN95SD15SRPCEE8F</td>\n",
              "      <td>309.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TB11I7F0PN033D4T</td>\n",
              "      <td>3098.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32PSGCK5PATHMR07</td>\n",
              "      <td>195.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2495</th>\n",
              "      <td>7SDJE48EFRPPNEJK</td>\n",
              "      <td>514.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2496</th>\n",
              "      <td>PRT8RDNG6E86518P</td>\n",
              "      <td>4516.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2497</th>\n",
              "      <td>SRNIBIK27BQ2M3PB</td>\n",
              "      <td>4651.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2498</th>\n",
              "      <td>6CP232J9R8N84702</td>\n",
              "      <td>1687.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2499</th>\n",
              "      <td>AROR24MH3M79EHBS</td>\n",
              "      <td>210.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2500 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Id  Predicted\n",
              "0     49I3SOKLI2CMNGP4     4855.0\n",
              "1     727IRIR59A3P88LK     2486.0\n",
              "2     LN95SD15SRPCEE8F      309.0\n",
              "3     TB11I7F0PN033D4T     3098.0\n",
              "4     32PSGCK5PATHMR07      195.0\n",
              "...                ...        ...\n",
              "2495  7SDJE48EFRPPNEJK      514.0\n",
              "2496  PRT8RDNG6E86518P     4516.0\n",
              "2497  SRNIBIK27BQ2M3PB     4651.0\n",
              "2498  6CP232J9R8N84702     1687.0\n",
              "2499  AROR24MH3M79EHBS      210.0\n",
              "\n",
              "[2500 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WciX9ywsWxPl"
      },
      "source": [
        "# Save output\n",
        "out.to_csv('predictions.csv', index=False)"
      ],
      "execution_count": 281,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RIXbi0q7BcDd"
      },
      "source": [
        "----------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qv914cdVFpwu"
      },
      "source": [
        "# Feature Engineering Ideas\n",
        "- Color: Convert 16-digit Hex Code to 0-255, Separate Columns RR-GG-BB, then will have numeric features\n",
        "- Color: Frequency Encoding (~1900 colors, they have different encodings)\n",
        "- User Time Zone: frequency encoding (duplicate feature with UTC-offset)\n",
        "- User Language: frequency encoding (28 unique)\n",
        "- Profile Creation Timestamp: convert to long numeric (the year matters)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ctq0opgIS2N2"
      },
      "source": [
        "# Comp550 Project Ideas\n",
        "- https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data\n",
        "- https://www.kaggle.com/c/quora-insincere-questions-classification/overview"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Y4JdyYtS1fF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}