sharing-bike.ipynb

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   "source": [
    "#import necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler, OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, RepeatedKFold, GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge, Lasso, ElasticNet\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error, mean_squared_log_error, mean_absolute_error, mean_squared_log_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f71f7f6e",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "#loading dataset\n",
    "submission = pd.read_csv('../input/bike-sharing-demand/sampleSubmission.csv')\n",
    "train_data = pd.read_csv('../input/bike-sharing-demand/train.csv')\n",
    "test_data = pd.read_csv('../input/bike-sharing-demand/test.csv')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "8bd22fe1",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape:  (10886, 12)\n",
      "Test Shape:  (6493, 9)\n"
     ]
    }
   ],
   "source": [
    "print('Train Shape: ', train_data.shape)\n",
    "print('Test Shape: ', test_data.shape)"
   ]
  },
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   "execution_count": 4,
   "id": "44ab0db6",
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    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    }\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>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>2011-03-12 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>11.365</td>\n",
       "      <td>65</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8882</th>\n",
       "      <td>2012-08-12 11:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.34</td>\n",
       "      <td>33.335</td>\n",
       "      <td>42</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>167</td>\n",
       "      <td>282</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5740</th>\n",
       "      <td>2012-01-14 08:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.92</td>\n",
       "      <td>7.575</td>\n",
       "      <td>54</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>3</td>\n",
       "      <td>89</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8298</th>\n",
       "      <td>2012-07-07 03:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.34</td>\n",
       "      <td>35.605</td>\n",
       "      <td>74</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>4298</th>\n",
       "      <td>2011-10-11 03:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21.32</td>\n",
       "      <td>25.000</td>\n",
       "      <td>88</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                 datetime  season  holiday  workingday  weather   temp  \\\n",
       "1138  2011-03-12 02:00:00       1        0           0        1   9.84   \n",
       "8882  2012-08-12 11:00:00       3        0           0        1  30.34   \n",
       "5740  2012-01-14 08:00:00       1        0           0        1   4.92   \n",
       "8298  2012-07-07 03:00:00       3        0           0        1  30.34   \n",
       "4298  2011-10-11 03:00:00       4        0           1        2  21.32   \n",
       "\n",
       "       atemp  humidity  windspeed  casual  registered  count  \n",
       "1138  11.365        65    12.9980       0          14     14  \n",
       "8882  33.335        42    11.0014     167         282    449  \n",
       "5740   7.575        54     7.0015       3          89     92  \n",
       "8298  35.605        74     8.9981      12          19     31  \n",
       "4298  25.000        88     7.0015       2           1      3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.sample(5)"
   ]
  },
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     "exception": false,
     "start_time": "2021-09-02T05:55:23.334877",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X Shape:  (10886, 9)\n",
      "Train Y Shape:  (10886,)\n",
      "Test Shape:  (6493, 9)\n"
     ]
    }
   ],
   "source": [
    "X = train_data.iloc[:, 0:9]\n",
    "Y = train_data['count']\n",
    "\n",
    "print('Train X Shape: ', X.shape)\n",
    "print('Train Y Shape: ', Y.shape)\n",
    "print('Test Shape: ', test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46ec90e1",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2021-09-02T05:55:23.391379",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime      0\n",
       "season        0\n",
       "holiday       0\n",
       "workingday    0\n",
       "weather       0\n",
       "temp          0\n",
       "atemp         0\n",
       "humidity      0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "count         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check missing value\n",
    "train_data.isna().sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd99bb67",
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     "start_time": "2021-09-02T05:55:23.446716",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
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    "tags": []
   },
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(Y, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca63724e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:24.255061Z",
     "iopub.status.busy": "2021-09-02T05:55:24.254254Z",
     "iopub.status.idle": "2021-09-02T05:55:24.681004Z",
     "shell.execute_reply": "2021-09-02T05:55:24.681434Z",
     "shell.execute_reply.started": "2021-09-02T05:47:32.653195Z"
    },
    "papermill": {
     "duration": 0.453321,
     "end_time": "2021-09-02T05:55:24.681626",
     "exception": false,
     "start_time": "2021-09-02T05:55:24.228305",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1ea413de90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ejGCIj3/9X4+/dt+dq1ys7Nwtmp7HvubkGgkR7T5gBX4vIltFZL1zrGxMqDYBZc7jCqBuzHvrnWMnEJH1IrJFRLa0trZGq25jEtZIOExjd4DKomy3S5lSC8vyONDcl1RTkqMdwFep6qVEuhfuEZFrxr7otHbP6r+mqj6kqstUdVlpaXKukGTMuWjuHmIkrAm1/9tELJyex9BImMPtyTMlOaoBrKrHnN8twFPAcqBZRMoBnN8tzunHgKoxb690jhljzkJ91wAAFQm0A/JELJqeDyTXjbioBbCI5IhI3uhj4MPALuAZ4C7ntLuAp53HzwB3OqMhVgDd1v9rzNmr7xykNNdHpjfd7VKm1IKyXNIkuYaiRfMmXBnwlIiMXucnqvqciLwNPC4idwNHgHXO+c8CNwE1wADwmSjWZkxSUkmjsTvARRUFbpcy5TK96cwuyWFfU/LciItaAKtqLbBknOPtwMpxjitwT7TqMSYVDOVMJ5SE/b+jFk3PS6o1IWwmnDFJZCg/MnAo2fp/Ry0sy+dIxwADwyNulzIlLICNSSJDeRWU5iVf/++ohdPzUIUDzX1ulzIlLICNSRKBYIih3HIqk7T1C5EuCEiekRAWwMYkiR11XZDmSdr+X4CZ/myyvOlJMxLCjanIxpgo2FTbDqpJ2f9bXb2Xq1euBiC0eB2PPnuY57/9VQBKi4t48vHH3Cxv0iyAjUkSm2rb8Q604vOe53YpUy4YVtbe+wAAz+9p5nB7P2vXRp4/ef+X3CztnFgXhDFJIBAMse1oF77e5J886s/JYGA4RCAYcruUc2YBbEwS2H60i+GRMJk99W6XEnWjO3x09A+7XMm5swA2Jgm8cbCNNAFfX4PbpURdsRPA7RbAxph48HpNG0uqCkkLJX4onUlepgdvulgL2Bjjvp5AkJ313Vw1v8TtUmJCRCjKzrAANsa4763aDkJh5coUCWCIdENYABtjXPd6TRtZ3nSWzix0u5SY8edm0Dc0wlCCj4SwADYmwb1W08byOX58nuRc/2E8x0dCDCR2K9gC2JgE1tQdoKalL2X6f0cV5/iAxB8JYQFsTAJ7vaYNIKX6fwHyMz140oSOPgtgY4xLXq9pozgn4/gqYalCRPAnwY04C2BjEpSq8lpNGx+cX0JamrhdTsz5czKsC8IY446alj5aeoe4an6x26W4wp8TGQkRTvO6Xcqk2WpoxiSYtevuoLW9k95pF8OsD/Ht//Y1/s9wZH3c6v37Xa4udkanJAez/C5XMnkWwMYkmNb2Ttbe+wDP7GxA+4dZ99VvHn/tvjtXuVhZbPmTIICtC8KYBBQKK8c6B5npz3a7FNfkZ3lJTxNGMi2AjTEx1NwTYDgUpsqffLtfTFSaCEXZXmsBG2Ni62jHAABVRanbAobIhAwLYGNMTB1pH2B6fmbSbj8/Uf6cDEK+fPqHRtwuZVIsgI1JMCFPJk09AWYVp3brF967EXewtc/lSibHAtiYBBPInwnA7OIclytx3+hQtP3NFsDGmBgIFMwiy5tOWb7P7VJcV5DlhXCIAy29bpcyKRbAxiSQcFgJFMxkpj8bkdSbfnyytDTBG+ikJplbwCJy5USOGWOia1dDN2FvNrOt//c4z2AH+5O8BfzdCR4zxkTRK/taQZWZFsDHeQOd1HcOEkjA3TFOOxVZRK4APgiUishXx7yUD0xo/IuIpANbgGOqerOIzAF+ChQDW4FPqeqwiPiAjcBlQDvwCVU9fJZ/HmOS2iv7WsjobyE74zy3S4kb3kAnqnCorZ/F5flul3NWztQCzgByiQR13pifHuBjE7zGl4G9Y57/C/AtVZ0PdAJ3O8fvBjqd499yzjPGOLoGhtlR10Vm9xG3S4krnsFOIDGHop22BayqfwD+ICKPqOpZ/68uIpXAR4BvAl+VyF2D64E/c07ZAPx/wIPArc5jgCeA/yMioqp6ttc1Jhm9sq+VsEJm92G3S4krnkAnInCwpd/tUs7aRFdD84nIQ8Dsse9R1evP8L5vA39HpNUMkW6HLlUdnbZSD1Q4jyuAOudzR0Sk2zm/bewHish6YD3AzJkzJ1i+MYnvhb3NlOT6yOhvdruUuJKmISqLsqhJthbwGD8H/hP4PjChnm4RuRloUdWtInLtpKobh6o+BDwEsGzZMmsdm5QwPBLmD/tauemict542e1q4s+80lwOtiRvAI+o6oNn+dlXAreIyE1AJpEbd98BCkXE47SCK4FjzvnHgCqgXkQ8QAGRm3HGpLy3D3fQOzTCqvPLeMPtYuJMdfVeOv/we/pLL+SqlasZOzq6tLiIJx9/zLXazmSiAfwrEflr4ClgaPSgqnac6g2q+g3gGwBOC/hvVfWTIvJzIjfwfgrcBTztvOUZ5/mbzusvWf+vMRHP72nG50lLue3nJyIYVi6/9kZerG7hhi//G/lZ721R9OT9X3KxsjObaADf5fz++phjCsydxDX/HvipiPwPYDvwsHP8YeBHIlIDdAC3T+KzjUk6qsoLe5u5an4JWRmpvfrZqRRlR9aE6BgYPiGA492EAlhV55zLRVT1FeAV53EtsHyccwLAx8/lOsYko/3NfdR3DnLPdfPdLiVuFeVEQrezfzihFimaUACLyJ3jHVfVjVNbjjHmZC/sjYx6WLlomsuVxK8sbzqZnjQ6BhJrm/qJdkF8YMzjTGAlsI3IzDVjTBQ9v6eZJZUFTMvPdLuUuCUiFOVk0NkfdLuUszLRLogvjn0uIoVEbqIZY6KoqTvAjrou/vbDNvX4TPw5GdS2JtZkjMkuR9kPnFO/sDHmzH63uwmA1ReWu1xJ/CvKzmAwGEqoRXkm2gf8KyKjHiCyCM9i4PFoFWWMifjtrkYWTMtl/rRct0uJe8dvxA0MU16QGLtFT7QP+H+PeTwCHFHV+ijUY4xxtPUNsflQB1+w0Q8T4h8ditafOAE8oS4IZ1GeaiJrOhQBiXWr0ZgE9PvdzYTVuh8mKj/LS7oInQOJcyNuojtirAM2Exmnuw54S0QmuhylMWYSfrurkVnF2SwuzzvzyYY0EQqzvXT0J077cKJdEP8F+ICqtgCISCnwApFlI40xU6x7IMibB9v53NVzbe+3s1CUk0Fb79CZT4wTEx0FkTYavo72s3ivMeYsPb+3mZGwsubC6W6XklD82Rl0B4KMhMNulzIhE20BPycivwNGlxX6BPBsdEoyxvzmnQYqCrO4uLLA7VISSlGOF9XIN4jiXJ/b5ZzRmfaEmw+UqerXRWQtcJXz0pvAo9EuzphU1Nk/zMvVzeQ1beeaVf/6vter9+93oarE4B+zKE/CBzCRHS2+AaCqTwJPAojIRc5rfxLF2oxJSc/uagRJ4+Zbb2Na3vsXBbzvzlUuVJUYCp0ATpQpyWfqxy1T1XdPPugcmx2VioxJcc/saMAz2EFpArTg4k2GJ41cn4fOBFmU50wBXHia1xJjpLMxCaSxe5DNhzvI7thvox8myZ+TkTBD0c4UwFtE5PMnHxSRzwFbo1OSManr1zsbUYXsduvnnSx/dgadA8MkwoY6Z+oD/grwlIh8kvcCdxmQAfxpFOsyJiU9s7OBiysL6Hy72+1SElZRjpdgSOkbGjnzyS47bQtYVZtV9YPAfcBh5+c+Vb1CVZuiX54xqeNQWz/vHuvmliUz3C4loY1uT5QIU5Inuh7wy4Bthm1MFD21/RgicPPFM9jgdjEJzJ8zOhIi/vuBbTabMXEgHFZ+sbWeq+aXML3Adr44F9kZ6WQkyPZEFsDGxIFNh9o51jXIxy6rdLuUhCci+LMTYySEBbAxceCJrfXk+TzceIGt/TAVinK8CTEW2ALYGJf1D43w3K4mbl5STqY33e1ykkJRdgb9QyHCaV63SzktC2BjXPbsu40MDIes+2EKjd6IC2YVuVzJ6VkAG+OyJ7bWM6ckh0tnxndYJJLRRXlGMv0uV3J6FsDGuOhIez9vHergo5dW2NTjKZSf5SVNIJgZ3/+oWQAb46KfbD5Keprw8WVVbpeSVNLThIIsLyPWBWGMGc/wSJgnttSzctE0yvJt7O9U8+dkxH0LeKI7YhhjptiNn/8G7WXXsO2ph7h6wz+e8Jotun7uirIzOOgrIBgK402Pz7amBbAxLmnImkN+poc/+6uvva//1xZdP3f+nAxIS+doxwDzSnPdLmdc8fnPgjFJ7lBbP0P5VVxQUWA336JkdFGegy19LldyalELYBHJFJHNIrJTRHaLyH3O8Tki8paI1IjIz0Qkwznuc57XOK/PjlZtxrjtsc1HIRzigvJ8t0tJWkU5kUkYB1v7Xa7k1KLZAh4CrlfVJcAlwGoRWQH8C/AtVZ0PdAJ3O+ffDXQ6x7/lnGdM0hkcDvH4ljqyug6R47NewGjxedJJH+6jJhVbwBox+if3Oj8KXA884RzfANzmPL7VeY7z+kqx72YmCf1yxzG6BoLkNu90u5Sk5wl0crA1BQMYQETSRWQH0AI8DxwEulR1dKn6eqDCeVwB1AE4r3cDxeN85noR2SIiW1pbW6NZvjFTTlX5wWuHuGBGPr6+BrfLSXrewU4OtvTF7fZEUQ1gVQ2p6iVAJbAcWDQFn/mQqi5T1WWlpaXn+nHGxNRrNW0caOnjM1fOwb7eRZ8n0Env0AgtvUNulzKumIyCUNUuIjtqXAEUishox1clcMx5fAyoAnBeLwDaY1GfMbHyw9cPU5KbwZ8sKXe7lJTgHewAiNt+4GiOgigVkULncRZwA7CXSBB/zDntLuBp5/EzznOc11/SeP3eYMwk1Lb28VJ1C5+8fBY+jy07GQveQCdA3PYDR/MWbDmwQUTSiQT946r6axHZA/xURP4HsB142Dn/YeBHIlIDdAC3R7E2Y2LuB68fwpsufHLFTLdLSRlpwX7yfJ64bQFHLYBV9R1g6TjHa4n0B598PAB8PFr1GOOmlp4Aj2+p56OXVjItz9Z9iBUB5k7LjdsAtplwxsTAw68dYiQU5i8/NM/tUlLO/FILYGNSVtfAMD/edISbL57B7JIct8tJOfOn5dLSO0RPIOh2Ke9jAWxMlD3yxmH6h0P89XXW+nXD/GmRhXjicU0IC2Bjoqh/aIQfvn6YVYvLWDTd1n1ww7zSyLeOeOyGsInoxkTRI28cpnswyDu/+C5Xb2w+4TVb8zc2ZvqzyUhPoyYOh6JZABsTJZ39w/znKwfJ7Kzlji/+l/e9bmv+xoYnPY3ZJdkcbIm/VdGsC8KYKHnwDwfpGx6hsP5Nt0tJefOn5cblZAxrARsTBY3dgzzyxmHWLq1k8+YOt8tJWdXVe7l65Wq6Ky6np3wZV626CdEwpcVFPPn4Y26XZy1gY6LhOy8cAIW/uWGB26WktGBYWXvvA1xxw5+ApHHtPf/C2nsfoLW90+3SAAtgY6bc7oZuHt9Sx5+vmEVlUbbb5Ric/eGAjv5hlys5kQWwMVMoHFb+6y93UZSdwZdXWus3XozuD9cxYAFsTNJ6Yms924528Y2bFlOQ7XW7HOPwpqeRl+mxFrAxyaprYJj7n6vmA7OL+OilFWd+g4kpf04GnQPxNR3ZRkEYMwXWrruDfXlL6S9ZjPetR7jm5/90/DWbcBEf/NkZHOvsjqvtiSyAjZkCR0cK6C+9gMtmFXHVqv9+wms24SI+FOVkMBJWegMjZz45RqwLwphz1No7RMeclZTm+lgx1+92OeYU/NnxNxLCAtiYc6Cq/N0TO9H0DG68oAxPmv1fKl4dH4oWRyMh7G+LMefg4dcO8fK+VgrqXqc41+d2OeY0sjLSyfKm02ktYGMS38v7WvjnZ/ey+oLp5La843Y5ZgKKsr20WwAbk9j2NfXyxZ9sZ3F5Pv/+iSWI2wWZCfHnZtDRP0y8jIOwADbmLDX3BLh7w9tkZ6Tz/buWkZ1hg4kSRXGOj6GRMGFvfEwRtwA25iw09wS446FNdPYP8707l1FekOV2SeYsjN6IC2bGx2gV+6fbmAlq7glw+0ObaOkJsOGzy1lSVeh2SeYsFY8GcJYFsDEJo7a1j8888jZtvUNs+Oxyls2Oj/8Dm7OTnZFOpieNYFax26UAFsDGnNGm2nb+4kdbSU8TNt59OZfNKnK7JDNJIoI/N4M2awEbE3/WrrvjhMW6+0rOp3PWtXiGulnY+iqXzbrBxerMVCjO8dGY5UdVEXF3/IoFsDFjtLZ3svbeBwiGwry8r4W6xl5m+rO56cIF/ObfHnW7PDMF/DkZqCeT1t4hpuVnulqLBbAxJ+nsH+Y3uxpp7xtm+Ww/l8/1k+ZyS8lMndEbcfub+1wPYBuGZswYA0Xzeezto/QPjXDbJTO4Yl6xhW+S8R8P4F6XK7EWsElRJ/f1qqTRVXklffPXMD3Hx00XTScv03a0SEbZGemkBQc50GIBbIwrRvt6AfoCIzy7q5G+7gBDe17iY/esJz3t/a3e0S3Ox2OLricOEcE72MH+5j63S4leAItIFbARKAMUeEhVvyMifuBnwGzgMLBOVTslcjvyO8BNwADwaVXdFq36jAGo6xjgt7uaGAmHWXPhdB7b8HPS0/5i3HNHtzgfjy26nlg8gXYONPe6PhIimn3AI8DXVPV8YAVwj4icD9wLvKiqC4AXnecAa4AFzs964MEo1mYMuxu6+eWOY2RlpHP7B2ZyXlme2yWZGPEOdtITGKGld8jVOqIWwKraONqCVdVeYC9QAdwKbHBO2wDc5jy+FdioEZuAQhEpj1Z9JnWpKt0VK3hhbwuVRdmsW1Z5/MaMSQ3ewXbA/RtxMRkFISKzgaXAW0CZqjY6LzUR6aKASDjXjXlbvXPs5M9aLyJbRGRLa2tr9Io2SWloJMTf/GwHPTM+wAUz8rllyQx8nnS3yzIx5h3sAHC9HzjqASwiucAvgK+oas/Y1zSyPelZLc2pqg+p6jJVXVZaWjqFlZpk1z0Q5K4fbOaXOxooqH+TlYumjXuzzSS/9JFBinMy2N+UxC1gEfESCd9HVfVJ53DzaNeC87vFOX4MqBrz9krnmDHnrK5jgI/+5xtsO9LFd26/hPzGLa5PQzXuWlSeR3VTz5lPjKKoBbAzquFhYK+q/vuYl54B7nIe3wU8Peb4nRKxAuge01VhzKTtrOviT///12npCbDx7uXcesn7erZMClo0PZ99zb2Ewu7tjxHNccBXAp8C3hWRHc6xfwDuBx4XkbuBI8A657VniQxBqyEyDO0zUazNRNHJkxzGKi0u4snHH4tZLc/vaeZLj22nODeDn65fwfxpNtLBRCycnkcgGOZIez9zS3NdqSFqAayqr8Ept8paOc75CtwTrXpM7Iyd5HCyJ+//0pRe63Rhr3Mup6F0ORdVFPD9uz5AaZ7tWmzes3h6PhDZ3y/pAtiYWBgv7MNh5dUDreys7yaro5a2Lb9j7VMjJ5xjM9fMgrJc0gT2NvWy5iJ3RrxaAJukEgiGeHZXI3UdgwR2v8AXv/iXpMmN7zvPZq6ZTG86s0tyqG5070acBbCZlNN99XerddnSG+DZd5voC4ywavE0ntjwC9Lkr1ypxSSGxdPzefdYt2vXtwA2k3K6ft5Yty5VlR11Xbxe005WRjprL61gRmEWT8S0CpOIFk3P4zfvNtI/NEKOL/ZxaAFsElrQV8AvdzRwtGOAuSU5rDq/jCyvzWwzE7NwemRUzL7mXi6dGfu9/iyATdwbr7sjnOahd/pldF9wB77uANcuLOXiigKbXGHOyuLyyEiI6kYLYGPGNba7Y2gkxDv13Ww/2sVgMESwdjOfu/MT5Lrw9dEkvorCLHJ9HtdmxNnf2hRxqptmsZ4YMRkKHOscpLqph/3NfQyHwszyZ/OBOX6+v+GvyP38J90u0SSotDThvLJcql1aE8ICOEWc6qbZVE+MmCotvQE21Xbw2oFWGpd8mie21eNJE+ZPy2VpVaHrmyma5LGoPJ9f72xwZXF2C2DjuoHhEd6p72ZnXRc767vYWdfNsa5BAPIzPWT0NXPN1fOZW5JLhsf2kTVTa/H0PH7y1giN3QFmFGbF9NoWwCbmWnoDvHagjS1HOtl+tIv9YxZEmenPZunMQj5z5Wwum1XExZWFXHvDt1j08TUuV22S1SLnRtyehh4LYJOcegJB9jb20HT+7Sz/5osA5GV6uKSqkBsWz2PpzCKWVBXazhQm5haX5yMCuxt6WHV+2ZnfMIUsgE3UjITD1Lb2s7uhh6MdAwBkhIN8/caFfOi8Us4vzyfNFkQ3Lsv1eZhTksOuhtjPiLMANlMuraiCV/a1sK+pl8BImFyfh+Vz/Jxfns+L3/ku91z3ebdLNOYEF1UUsPlQR8yvawFspkQgGGJfUy97GnvIv+Uf2XWsh3nTcji/PJ8qfzZpNkHCxLELZxTw9I4G2vqGKMmN3bKlFsBJJNYL5IyEwhxuH2BfUy+H2vsJhZXSXB8Db/2Uv/n6P5BpU4JNgrigInIjbndDDx86L3Z7TVoAJ5FoL5AzEgpzsLWfzYfaaZ3/ER76Yy3BkJLlTeeiGQUsnpHHtLxM7vuPP5Dp/a/nfD1jYuWCGQUA7DrWbQGc6uJhS5/RLoXdDT3sauhmd0MP1Y09DI2EAUjPKub86fnMK82hqijbbqaZhFaQ5WWmP5vdMb4RZwEch2K5pc+oweEQrx5oZfOhDt4+3MGehh5GnLG5eZkeLpxRwKdWzOLCigKWVBXyqU98lOs/Mn6Np1NdvZerV65+3/FEmBJtksd4fw/b5q3mdw3TWPv0/47Z30UL4BSmqgTyKvjqz3bwu91N9A+H8HnSuKSqkM9fM5eLKwq4YEYBVf6s903RnGx7NxjWhJoSbZLTeH8P3z7cwRsH22nuGohZHRbAKepY1yBvHmynddFant/bzM0Xz+DWS2Zw2ewifB67eWZSzzRn09bh7JKYXdMCOMUMBkO8XN3CgZY+sjPSGdr8MyrT2nnjjyHeOOlc6xYwqaT0eABPi9k1LYCjLB5uqI062jHA7/c0MTgc4oq5xSydWcg/f+8VPrrxhXHPt24Bk0qyMzzk+jwEc2wURNJw44bayVQV34U38tT2YxRle7ll2QxbztGYcUzL83HUWsBmqoRVeWVfK1mX3cZ5ZbmsWlyGN92WdDRmPGX5mdRmFdE9GKQgyxv161kAJ7GRcJjndjVxsLWfwLu/Y/X198TtnmmnGp4G7m1zb1LP9ILIN8OddV1cE4MJGRbASSqsyu93N3OwtZ8PnVfK0xt+icgX3C7rlE41PA1iv829SV1l+T5QZdvRTgtgMznqdDscaOnj6vklXFJVyNOT+BxrlZpU4/Ok4x1sZ9vRrphczwI4CW2q7eDdY91cNquIS2dNfqtta5WaVJTR18SOo9MJhzXqU+ztbkyS2VHXxebDHVwwI58r5xW7XY4xCSejv4mewAi1bX1Rv5YFcBLp95/HH/a3Mq80h+sXTovbG27GxDNfXxMA2450Rf1aUQtgEfmBiLSIyK4xx/wi8ryIHHB+FznHRUQeEJEaEXlHRC6NVl3J6uV9LXTMWUVlYRarL5huq5MZM0meQCcFWV62HR1/AtVUimYL+BHg5Ds49wIvquoC4EXnOcAaYIHzsx54MIp1JZ2tRzr4qx9vxTvYzs1LyvHYOF9jJk2ApTML2R6DG3FRuwmnqq+KyOyTDt8KXOs83gC8Avy9c3yjqiqwSUQKRaRcVRujUduppgcn4toH+5p6+cwP36a8IIuhzc/gu+VKt0syJuFdOrOIb+3fT08gSH5m9CZkxHoURNmYUG0CRveArgDqxpxX7xyLSgCfanpwoq19UNcxwJ0/eIusjHQ2fnY5f/bbQbdLMiYpLJ1ZiGpkQsbVC6I3Hti176pOa1fP9n0isl5EtojIltbW1ihUlhhae4f41MNvEQiG2fjZy6nyZ7tdkjFJ45KqQkSifyMu1gHcLCLlAM7vFuf4MaBqzHmVzrH3UdWHVHWZqi4rLY3dqkXxpCcQ5NM/3ExTT4AffHoZC6fnuV2SMUklL9PLwrI8tkb5RlysA/gZ4C7n8V1wfILWM8CdzmiIFUB3tPp/E11Y0lm/cQv7mnp58M8v47JZfrdLMiYpLZ/jZ+vhDoKhcNSuEc1haI8BbwILRaReRO4G7gduEJEDwCrnOcCzQC1QA3wP+Oto1ZXIQmGlff4a3jrUwb+tW8J1C2O3bJ4xqWbF3GL6h0PsOha9jTqjOQrijlO8tHKccxW4J1q1JIOwKr/b3USgcA7/fNtF3HpJhdslGZPUls+JfLvcVNvB0pmTn9J/OjZgNAGoKi/ujWwjVHD0Nf7s8plul2RM0ivJ9bFgWi6batujdg0L4Dinqry6v409jT0sn+Mnv3m72yUZkzJWzC1mSxT7gS2A49ho+O6o72JpVSEr5tgNN2Ni6YPzIv3AO+u6ovL5FsBxSlV5aV/L8fC9ekGJLa5jTIx9cF4JaQKvHmiLyudbAMchlTSe39PMrmM9LJtVZOFrjEsKsr0sqSrktQPRmfRlARxnugeDtJ53C3uberlibjEfnFds4WuMi66eX8KOui66B4NT/tkWwHHkaPsAH33wDYZyZ/Dh88tYPsdv4WuMy64+r5Swwhs1U98NYQEcJ361s4GPPPBHWnoClO5/msXl+W6XZIwhsi5EfqaHl6pbznzyWbIAdllPIMjfPbGTLz62nfllufzmS1eT2TvuMhjGGBd409O4duE0XqpuIRQ+6/XDTss25XRJWJW+0gu47n+9QsfAMF+4bj5fXrUAry2mboyrxtsNfMC/gPZ5q1lz15f4/Y++O2XXsgB2QV3HAK8eaKVz9vV8oDSHR25ezkWVBW6XZYxh/N3Ah4IhHvpjLY1SMqXXsgCOoa6BYV6raeNgaz95mR6Ka37L4//zu3ajzZg45/OmM6Mwi+b+2VP6ufZ9NwaCoTCv1bTx401HOdoxwBXzirlzxSyyO2ssfI1JEB86r5TS/U+f+cSzYC3gKAvkzeDRt47SPRhkcXkeV84rIcdn/9mNSTQluT7SgwNT+pmWBFESDIX5n89W07rooxQAH720gsoi2zbIGPMeC+Ao6Ogf5p5Ht/FmbTu5zTv55CfW2ugGY8z7WCpMsf3Nvdz6H6+x9Wgn3/rEEoqOvmrha4wZlyXDFNpZ18W6//smgWCYn61fwZ8urXS7JGNMHLMuiCmyqbadux95G39uBj/53ArbJt4Yc0YWwFPg5eoW/vLHW6nyZ/Pjuy9nekGm2yUZYxKABfA5+tXOBr76+A7OK8tj42eXU5zrc7skY0yCSKk+4O7BIF/92Q46Zl/PmwfbqW3tm/ReT6rKf7xcwxcf284lVYU8tn6Fha8x5qykVAs4EAzx9pEOBgtm8fbhDhTwpAkLpuVycVXhhD9neCTMP/7yXR7fUs8tS2bwrx+7mExvetTqNsYkp5QK4LL8TP74d9dz9crV3PL1b9PYHWB/Sy/7mnrZ29RLxuJ1/GJrPR+5uPyUgbr9aCffePJdqpt6+dLKBfzNqgU2ndgYMykpFcBjedLTqPJnU+XP5qr5JVQ39vLazg6+9vOdfPPZvdx2SQVXzCumojCLYCjMvqZefv1uI6/ub2Vano+H71rGysVlbv8xjDEJLGUDeCyfJ50lVYXUPPoo//J/f8zGNw/zo02H+cHrh044r7wgk6/fuJA7r5hFXqbXpWqNMcnCAngMAa6cX8KV80sIBEPsbuimpWcIT3oaM/3ZnFeWa90NxpgpYwF8CpnedC6b5Xe7DGNMEkupYWjGGBNPLICNMcYlFsDGGOMSC2BjjHFJXAWwiKwWkX0iUiMi97pdjzHGRFPcBLCIpAP/AawBzgfuEJHz3a3KGGOiJ24CGFgO1KhqraoOAz8FbnW5JmOMiRpRVbdrAEBEPgasVtXPOc8/BVyuql846bz1wHrn6UJg3yQuVwK0nUO50WS1TV481xfPtUF81xfPtcHE6mtT1dUnH0y4iRiq+hDw0Ll8hohsUdVlU1TSlLLaJi+e64vn2iC+64vn2uDc6ounLohjQNWY55XOMWOMSUrxFMBvAwtEZI6IZAC3A8+4XJMxxkRN3HRBqOqIiHwB+B2QDvxAVXdH6XLn1IURZVbb5MVzffFcG8R3ffFcG5xDfXFzE84YY1JNPHVBGGNMSrEANsYYl6RUAMfzVGcR+YGItIjILrdrOZmIVInIyyKyR0R2i8iX3a5pLBHJFJHNIrLTqe8+t2s6mYiki8h2Efm127WMJSKHReRdEdkhIlvcrudkIlIoIk+ISLWI7BWRK9yuCUBEFjr/zUZ/ekTkK2f9OanSB+xMdd4P3ADUExl1cYeq7nG1MIeIXAP0ARtV9UK36xlLRMqBclXdJiJ5wFbgtjj6bydAjqr2iYgXeA34sqpucrm040Tkq8AyIF9Vb3a7nlEichhYpqpxOdFBRDYAf1TV7zujo7JVtcvlsk7gZMsxIhPHjpzNe1OpBRzXU51V9VWgw+06xqOqjaq6zXncC+wFKtyt6j0a0ec89To/cdOyEJFK4CPA992uJZGISAFwDfAwgKoOx1v4OlYCB882fCG1ArgCqBvzvJ44CpFEISKzgaXAWy6XcgLnK/4OoAV4XlXjqb5vA38HhF2uYzwK/F5EtjrT/OPJHKAV+KHTffN9Eclxu6hx3A48Npk3plIAm3MkIrnAL4CvqGqP2/WMpaohVb2EyAzK5SISF904InIz0KKqW92u5RSuUtVLiaxCeI/TFRYvPMClwIOquhToB+Lt3k0GcAvw88m8P5UC2KY6nwOnb/UXwKOq+qTb9ZyK8xX1ZeB9C5+45ErgFqev9afA9SLyY3dLeo+qHnN+twBPEemqixf1QP2YbzNPEAnkeLIG2KaqzZN5cyoFsE11niTnJtfDwF5V/Xe36zmZiJSKSKHzOIvIjdZqV4tyqOo3VLVSVWcT+Tv3kqr+uctlASAiOc5NVZyv9h8G4mYUjqo2AXUistA5tBKIixu/Y9zBJLsfII6mIkdbjKc6nzUReQy4FigRkXrgv6vqw+5WddyVwKeAd51+VoB/UNVn3SvpBOXABududBrwuKrG1XCvOFUGPBX59xUP8BNVfc7dkt7ni8CjTqOpFviMy/Uc5/yjdQPwF5P+jFQZhmaMMfEmlbogjDEmrlgAG2OMSyyAjTHGJRbAxhjjEgtgY4xxiQWwMZMkIl8RkWy36zCJy4ahGTNJ8b6SmIl/1gI2SU1E7hSRd5y1gn8kIrNF5CXn2IsiMtM57xER+diY9/U5v68VkVfGrEn7qER8CZgBvCwiL7vzpzOJLmVmwpnUIyIXAP8IfFBV20TED2wANqjqBhH5LPAAcNsZPmopcAHQALwOXKmqDzhr/F5nLWAzWdYCNsnseuDnowGpqh3AFcBPnNd/BFw1gc/ZrKr1qhoGdgCzp75Uk4osgI2JGMH5/4OIpAEZY14bGvM4hH1zNFPEAtgks5eAj4tIMYDTBfEGkVXJAD4J/NF5fBi4zHl8C5FdNc6kF8ibqmJN6rF/yU3SUtXdIvJN4A8iEgK2E1ld64ci8nUiuy2Mrq71PeBpEdkJPEdk8e8zeQh4TkQaVPW6qf8TmGRnw9CMMcYl1gVhjDEusQA2xhiXWAAbY4xLLICNMcYlFsDGGOMSC2BjjHGJBbAxxrjk/wGPEU/rKy1BSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(np.log(Y), kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89dcff28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:24.764750Z",
     "iopub.status.busy": "2021-09-02T05:55:24.758646Z",
     "iopub.status.idle": "2021-09-02T05:55:24.899452Z",
     "shell.execute_reply": "2021-09-02T05:55:24.898995Z",
     "shell.execute_reply.started": "2021-09-02T05:47:46.974204Z"
    },
    "papermill": {
     "duration": 0.194352,
     "end_time": "2021-09-02T05:55:24.899607",
     "exception": false,
     "start_time": "2021-09-02T05:55:24.705255",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='season', ylabel='Count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(X.season, bins=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d2f3dee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:25.082429Z",
     "iopub.status.busy": "2021-09-02T05:55:24.958251Z",
     "iopub.status.idle": "2021-09-02T05:55:25.473141Z",
     "shell.execute_reply": "2021-09-02T05:55:25.473604Z",
     "shell.execute_reply.started": "2021-09-02T05:47:58.327079Z"
    },
    "papermill": {
     "duration": 0.550113,
     "end_time": "2021-09-02T05:55:25.473777",
     "exception": false,
     "start_time": "2021-09-02T05:55:24.923664",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1eae62c9d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(X.temp, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f301faba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:25.526611Z",
     "iopub.status.busy": "2021-09-02T05:55:25.526007Z",
     "iopub.status.idle": "2021-09-02T05:55:25.909621Z",
     "shell.execute_reply": "2021-09-02T05:55:25.909082Z",
     "shell.execute_reply.started": "2021-09-02T05:48:11.094582Z"
    },
    "papermill": {
     "duration": 0.410958,
     "end_time": "2021-09-02T05:55:25.909768",
     "exception": false,
     "start_time": "2021-09-02T05:55:25.498810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1ea40e9b10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(X.atemp, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ee78803",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:25.970642Z",
     "iopub.status.busy": "2021-09-02T05:55:25.965128Z",
     "iopub.status.idle": "2021-09-02T05:55:26.422432Z",
     "shell.execute_reply": "2021-09-02T05:55:26.421965Z",
     "shell.execute_reply.started": "2021-09-02T05:48:22.508801Z"
    },
    "papermill": {
     "duration": 0.486955,
     "end_time": "2021-09-02T05:55:26.422589",
     "exception": false,
     "start_time": "2021-09-02T05:55:25.935634",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1e94303ad0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(X.windspeed, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "37410259",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:26.487145Z",
     "iopub.status.busy": "2021-09-02T05:55:26.486560Z",
     "iopub.status.idle": "2021-09-02T05:55:26.884815Z",
     "shell.execute_reply": "2021-09-02T05:55:26.884284Z",
     "shell.execute_reply.started": "2021-09-02T05:48:42.310670Z"
    },
    "papermill": {
     "duration": 0.435179,
     "end_time": "2021-09-02T05:55:26.884963",
     "exception": false,
     "start_time": "2021-09-02T05:55:26.449784",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1e941e5cd0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(X.humidity, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8825eaa1",
   "metadata": {
    "papermill": {
     "duration": 0.027513,
     "end_time": "2021-09-02T05:55:26.940522",
     "exception": false,
     "start_time": "2021-09-02T05:55:26.913009",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preprocessing & Feature Engineering with Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fa66e02e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:27.003882Z",
     "iopub.status.busy": "2021-09-02T05:55:27.003112Z",
     "iopub.status.idle": "2021-09-02T05:55:27.005842Z",
     "shell.execute_reply": "2021-09-02T05:55:27.006228Z",
     "shell.execute_reply.started": "2021-09-02T05:49:36.570673Z"
    },
    "papermill": {
     "duration": 0.038322,
     "end_time": "2021-09-02T05:55:27.006391",
     "exception": false,
     "start_time": "2021-09-02T05:55:26.968069",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "import calendar\n",
    "from datetime import datetime\n",
    "\n",
    "class ProcessDateTime(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        print('Transforming datetime...')\n",
    "        \n",
    "        x_copy = X.copy()\n",
    "        x_copy['month'] = x_copy.datetime.apply(lambda x : calendar.month_name[datetime.strptime(x,\"%Y-%m-%d %H:%M:%S\").weekday()])\n",
    "        x_copy['weekday'] = x_copy.datetime.apply(lambda x : calendar.day_name[datetime.strptime(x,\"%Y-%m-%d %H:%M:%S\").weekday()])\n",
    "        x_copy['hour'] = x_copy.datetime.apply(lambda x : datetime.strptime(x,\"%Y-%m-%d %H:%M:%S\").hour)\n",
    "        x_copy['minute'] = x_copy.datetime.apply(lambda x : datetime.strptime(x,\"%Y-%m-%d %H:%M:%S\").minute)\n",
    "        x_copy = x_copy.drop(['datetime'], axis=1)\n",
    "        \n",
    "        return x_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a20f04d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:27.067754Z",
     "iopub.status.busy": "2021-09-02T05:55:27.066798Z",
     "iopub.status.idle": "2021-09-02T05:55:27.586617Z",
     "shell.execute_reply": "2021-09-02T05:55:27.586030Z",
     "shell.execute_reply.started": "2021-09-02T05:49:47.310124Z"
    },
    "papermill": {
     "duration": 0.552207,
     "end_time": "2021-09-02T05:55:27.586748",
     "exception": false,
     "start_time": "2021-09-02T05:55:27.034541",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transforming datetime...\n"
     ]
    },
    {
     "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>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>May</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>May</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>May</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>May</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>May</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10881</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.58</td>\n",
       "      <td>19.695</td>\n",
       "      <td>50</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>February</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10882</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.76</td>\n",
       "      <td>17.425</td>\n",
       "      <td>57</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>February</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10883</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.94</td>\n",
       "      <td>15.910</td>\n",
       "      <td>61</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>February</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10884</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.94</td>\n",
       "      <td>17.425</td>\n",
       "      <td>61</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>February</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10885</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.12</td>\n",
       "      <td>16.665</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>February</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10886 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  holiday  workingday  weather   temp   atemp  humidity  \\\n",
       "0           1        0           0        1   9.84  14.395        81   \n",
       "1           1        0           0        1   9.02  13.635        80   \n",
       "2           1        0           0        1   9.02  13.635        80   \n",
       "3           1        0           0        1   9.84  14.395        75   \n",
       "4           1        0           0        1   9.84  14.395        75   \n",
       "...       ...      ...         ...      ...    ...     ...       ...   \n",
       "10881       4        0           1        1  15.58  19.695        50   \n",
       "10882       4        0           1        1  14.76  17.425        57   \n",
       "10883       4        0           1        1  13.94  15.910        61   \n",
       "10884       4        0           1        1  13.94  17.425        61   \n",
       "10885       4        0           1        1  13.12  16.665        66   \n",
       "\n",
       "       windspeed     month    weekday  hour  minute  \n",
       "0         0.0000       May   Saturday     0       0  \n",
       "1         0.0000       May   Saturday     1       0  \n",
       "2         0.0000       May   Saturday     2       0  \n",
       "3         0.0000       May   Saturday     3       0  \n",
       "4         0.0000       May   Saturday     4       0  \n",
       "...          ...       ...        ...   ...     ...  \n",
       "10881    26.0027  February  Wednesday    19       0  \n",
       "10882    15.0013  February  Wednesday    20       0  \n",
       "10883    15.0013  February  Wednesday    21       0  \n",
       "10884     6.0032  February  Wednesday    22       0  \n",
       "10885     8.9981  February  Wednesday    23       0  \n",
       "\n",
       "[10886 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('datetime', ProcessDateTime())\n",
    "])\n",
    "\n",
    "pipeline.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "953118ec",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "class ProcessSeasonWeather(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        print('Transforming season and weather...')\n",
    "        x_copy = X.copy()\n",
    "        x_copy['season'] = x_copy['season'].map({\n",
    "            1: 'Spring',\n",
    "            2: 'Summer',\n",
    "            3: 'Fall',\n",
    "            4: 'Winter'\n",
    "        })\n",
    "        x_copy['weather'] = x_copy['weather'].map({\n",
    "            1: \"Clear+FewClouds+PartlyCloudy,PartlyCloudy\",\n",
    "            2: \"Mist+Cloudy,Mist+BrokenClouds,Mist+FewClouds,Mist\",\n",
    "            3: \"LightSnow,LightRain+Thunderstorm+ScatteredClouds,LightRain+ScatteredClouds\",\n",
    "            4: \"HeavyRain+IcePallets+Thunderstorm+Mist,Snow+Fog\" \n",
    "        })\n",
    "        return x_copy\n",
    "pipeline = Pipeline([\n",
    "    ('datetime', ProcessDateTime()),\n",
    "    ('seasonweather', ProcessSeasonWeather())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f7477dd",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transforming datetime...\n",
      "Transforming season and weather...\n"
     ]
    },
    {
     "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>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear+FewClouds+PartlyCloudy,PartlyCloudy</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>May</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear+FewClouds+PartlyCloudy,PartlyCloudy</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>May</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear+FewClouds+PartlyCloudy,PartlyCloudy</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>May</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear+FewClouds+PartlyCloudy,PartlyCloudy</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>May</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear+FewClouds+PartlyCloudy,PartlyCloudy</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>May</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10881</th>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear+FewClouds+PartlyCloudy,PartlyCloudy</td>\n",
       "      <td>15.58</td>\n",
       "      <td>19.695</td>\n",
       "      <td>50</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>February</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10882</th>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear+FewClouds+PartlyCloudy,PartlyCloudy</td>\n",
       "      <td>14.76</td>\n",
       "      <td>17.425</td>\n",
       "      <td>57</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>February</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10883</th>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear+FewClouds+PartlyCloudy,PartlyCloudy</td>\n",
       "      <td>13.94</td>\n",
       "      <td>15.910</td>\n",
       "      <td>61</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>February</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10884</th>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear+FewClouds+PartlyCloudy,PartlyCloudy</td>\n",
       "      <td>13.94</td>\n",
       "      <td>17.425</td>\n",
       "      <td>61</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>February</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10885</th>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear+FewClouds+PartlyCloudy,PartlyCloudy</td>\n",
       "      <td>13.12</td>\n",
       "      <td>16.665</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>February</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10886 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  holiday  workingday                                    weather  \\\n",
       "0      Spring        0           0  Clear+FewClouds+PartlyCloudy,PartlyCloudy   \n",
       "1      Spring        0           0  Clear+FewClouds+PartlyCloudy,PartlyCloudy   \n",
       "2      Spring        0           0  Clear+FewClouds+PartlyCloudy,PartlyCloudy   \n",
       "3      Spring        0           0  Clear+FewClouds+PartlyCloudy,PartlyCloudy   \n",
       "4      Spring        0           0  Clear+FewClouds+PartlyCloudy,PartlyCloudy   \n",
       "...       ...      ...         ...                                        ...   \n",
       "10881  Winter        0           1  Clear+FewClouds+PartlyCloudy,PartlyCloudy   \n",
       "10882  Winter        0           1  Clear+FewClouds+PartlyCloudy,PartlyCloudy   \n",
       "10883  Winter        0           1  Clear+FewClouds+PartlyCloudy,PartlyCloudy   \n",
       "10884  Winter        0           1  Clear+FewClouds+PartlyCloudy,PartlyCloudy   \n",
       "10885  Winter        0           1  Clear+FewClouds+PartlyCloudy,PartlyCloudy   \n",
       "\n",
       "        temp   atemp  humidity  windspeed     month    weekday  hour  minute  \n",
       "0       9.84  14.395        81     0.0000       May   Saturday     0       0  \n",
       "1       9.02  13.635        80     0.0000       May   Saturday     1       0  \n",
       "2       9.02  13.635        80     0.0000       May   Saturday     2       0  \n",
       "3       9.84  14.395        75     0.0000       May   Saturday     3       0  \n",
       "4       9.84  14.395        75     0.0000       May   Saturday     4       0  \n",
       "...      ...     ...       ...        ...       ...        ...   ...     ...  \n",
       "10881  15.58  19.695        50    26.0027  February  Wednesday    19       0  \n",
       "10882  14.76  17.425        57    15.0013  February  Wednesday    20       0  \n",
       "10883  13.94  15.910        61    15.0013  February  Wednesday    21       0  \n",
       "10884  13.94  17.425        61     6.0032  February  Wednesday    22       0  \n",
       "10885  13.12  16.665        66     8.9981  February  Wednesday    23       0  \n",
       "\n",
       "[10886 rows x 12 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c872c22",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class DummyEncoding(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        print('Dummy encoding...')\n",
    "        x_copy = X.copy()\n",
    "        x_copy = pd.get_dummies(x_copy)\n",
    "        return x_copy\n",
    "\n",
    "    \n",
    "class RemoveFeature(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, features=[]):\n",
    "        self._features = features\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        print('Removing features...')\n",
    "        x_copy = X.copy()\n",
    "        for f in self._features:\n",
    "            if f in x_copy.columns:\n",
    "                x_copy = x_copy.drop([f], axis=1)\n",
    "        return x_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2496c151",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:28.382161Z",
     "iopub.status.busy": "2021-09-02T05:55:28.381583Z",
     "iopub.status.idle": "2021-09-02T05:55:28.384100Z",
     "shell.execute_reply": "2021-09-02T05:55:28.384622Z",
     "shell.execute_reply.started": "2021-09-02T05:50:36.623246Z"
    },
    "papermill": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('datetime', ProcessDateTime()),\n",
    "    ('seasonweather', ProcessSeasonWeather()),\n",
    "    ('dummyencode', DummyEncoding()),\n",
    "    ('removefeature', RemoveFeature(features=['windspeed']))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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    {
     "name": "stdout",
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     "text": [
      "Transforming datetime...\n",
      "Transforming season and weather...\n",
      "Dummy encoding...\n",
      "Removing features...\n"
     ]
    },
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       "<p>10886 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       holiday  workingday   temp   atemp  humidity  hour  minute  \\\n",
       "0            0           0   9.84  14.395        81     0       0   \n",
       "1            0           0   9.02  13.635        80     1       0   \n",
       "2            0           0   9.02  13.635        80     2       0   \n",
       "3            0           0   9.84  14.395        75     3       0   \n",
       "4            0           0   9.84  14.395        75     4       0   \n",
       "...        ...         ...    ...     ...       ...   ...     ...   \n",
       "10881        0           1  15.58  19.695        50    19       0   \n",
       "10882        0           1  14.76  17.425        57    20       0   \n",
       "10883        0           1  13.94  15.910        61    21       0   \n",
       "10884        0           1  13.94  17.425        61    22       0   \n",
       "10885        0           1  13.12  16.665        66    23       0   \n",
       "\n",
       "       season_Fall  season_Spring  season_Summer  ...  month_June  \\\n",
       "0                0              1              0  ...           0   \n",
       "1                0              1              0  ...           0   \n",
       "2                0              1              0  ...           0   \n",
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       "...            ...            ...            ...  ...         ...   \n",
       "10881            0              0              0  ...           0   \n",
       "10882            0              0              0  ...           0   \n",
       "10883            0              0              0  ...           0   \n",
       "10884            0              0              0  ...           0   \n",
       "10885            0              0              0  ...           0   \n",
       "\n",
       "       month_March  month_May  weekday_Friday  weekday_Monday  \\\n",
       "0                0          1               0               0   \n",
       "1                0          1               0               0   \n",
       "2                0          1               0               0   \n",
       "3                0          1               0               0   \n",
       "4                0          1               0               0   \n",
       "...            ...        ...             ...             ...   \n",
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       "10882            0          0               0               0   \n",
       "10883            0          0               0               0   \n",
       "10884            0          0               0               0   \n",
       "10885            0          0               0               0   \n",
       "\n",
       "       weekday_Saturday  weekday_Sunday  weekday_Thursday  weekday_Tuesday  \\\n",
       "0                     1               0                 0                0   \n",
       "1                     1               0                 0                0   \n",
       "2                     1               0                 0                0   \n",
       "3                     1               0                 0                0   \n",
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       "10883                 0               0                 0                0   \n",
       "10884                 0               0                 0                0   \n",
       "10885                 0               0                 0                0   \n",
       "\n",
       "       weekday_Wednesday  \n",
       "0                      0  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      0  \n",
       "4                      0  \n",
       "...                  ...  \n",
       "10881                  1  \n",
       "10882                  1  \n",
       "10883                  1  \n",
       "10884                  1  \n",
       "10885                  1  \n",
       "\n",
       "[10886 rows x 29 columns]"
      ]
     },
     "execution_count": 20,
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  {
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    "pipeline = Pipeline([\n",
    "    ('datetime', ProcessDateTime()),\n",
    "    ('seasonweather', ProcessSeasonWeather()),\n",
    "    ('dummyencode', DummyEncoding()),\n",
    "    ('removefeature', RemoveFeature(features=['windspeed'])),\n",
    "    ('scaler', StandardScaler())\n",
    "])"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transforming datetime...\n",
      "Transforming season and weather...\n",
      "Dummy encoding...\n",
      "Removing features...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.17149048, -1.46067232, -1.33366069, ..., -0.40792009,\n",
       "        -0.40577304, -0.40761367],\n",
       "       [-0.17149048, -1.46067232, -1.43890721, ..., -0.40792009,\n",
       "        -0.40577304, -0.40761367],\n",
       "       [-0.17149048, -1.46067232, -1.43890721, ..., -0.40792009,\n",
       "        -0.40577304, -0.40761367],\n",
       "       ...,\n",
       "       [-0.17149048,  0.68461625, -0.80742813, ..., -0.40792009,\n",
       "        -0.40577304,  2.45330341],\n",
       "       [-0.17149048,  0.68461625, -0.80742813, ..., -0.40792009,\n",
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       "        -0.40577304,  2.45330341]])"
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     },
     "execution_count": 22,
     "metadata": {},
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   "source": [
    "pipeline.fit_transform(X)"
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  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e410bab8",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transforming datetime...\n",
      "Transforming season and weather...\n",
      "Dummy encoding...\n",
      "Removing features...\n",
      "Transforming datetime...\n",
      "Transforming season and weather...\n",
      "Dummy encoding...\n",
      "Removing features...\n",
      "Transforming datetime...\n",
      "Transforming season and weather...\n",
      "Dummy encoding...\n",
      "Removing features...\n"
     ]
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('datetime', ProcessDateTime()),\n",
    "    ('seasonweather', ProcessSeasonWeather()),\n",
    "    ('dummyencode', DummyEncoding()),\n",
    "    ('removefeature', RemoveFeature(['windspeed'])),\n",
    "    ('scaler', MinMaxScaler())\n",
    "])\n",
    "\n",
    "pipeline.fit(X)\n",
    "X = pipeline.transform(X)\n",
    "X_test = pipeline.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886, 29)\n",
      "(6493, 29)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "78c873ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:31.244525Z",
     "iopub.status.busy": "2021-09-02T05:55:31.243913Z",
     "iopub.status.idle": "2021-09-02T05:55:31.279885Z",
     "shell.execute_reply": "2021-09-02T05:55:31.280322Z",
     "shell.execute_reply.started": "2021-09-02T05:51:32.603930Z"
    },
    "papermill": {
     "duration": 0.072054,
     "end_time": "2021-09-02T05:55:31.280480",
     "exception": false,
     "start_time": "2021-09-02T05:55:31.208426",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "        0    1         2         3     4         5    6    7    8    9   ...  \\\n",
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       "3      0.0  0.0  0.224490  0.305068  0.75  0.130435  0.0  0.0  1.0  0.0  ...   \n",
       "4      0.0  0.0  0.224490  0.305068  0.75  0.173913  0.0  0.0  1.0  0.0  ...   \n",
       "...    ...  ...       ...       ...   ...       ...  ...  ...  ...  ...  ...   \n",
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       "\n",
       "        19   20   21   22   23   24   25   26   27   28  \n",
       "0      0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  \n",
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       "\n",
       "[10886 rows x 29 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d7fede0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:31.352152Z",
     "iopub.status.busy": "2021-09-02T05:55:31.351568Z",
     "iopub.status.idle": "2021-09-02T05:55:31.353638Z",
     "shell.execute_reply": "2021-09-02T05:55:31.354064Z",
     "shell.execute_reply.started": "2021-09-02T05:51:43.421848Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Modeling\n",
    "lr = LinearRegression()\n",
    "sgd = SGDRegressor()\n",
    "rr = Ridge()\n",
    "ls = Lasso()\n",
    "en = ElasticNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3ebb4bf1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:31.425396Z",
     "iopub.status.busy": "2021-09-02T05:55:31.424823Z",
     "iopub.status.idle": "2021-09-02T05:55:31.429098Z",
     "shell.execute_reply": "2021-09-02T05:55:31.429589Z",
     "shell.execute_reply.started": "2021-09-02T05:51:50.488737Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2021-09-02T05:55:31.387931",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['explained_variance', 'r2', 'max_error', 'neg_median_absolute_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_root_mean_squared_error', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance', 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted', 'balanced_accuracy', 'average_precision', 'neg_log_loss', 'neg_brier_score', 'adjusted_rand_score', 'homogeneity_score', 'completeness_score', 'v_measure_score', 'mutual_info_score', 'adjusted_mutual_info_score', 'normalized_mutual_info_score', 'fowlkes_mallows_score', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'jaccard', 'jaccard_macro', 'jaccard_micro', 'jaccard_samples', 'jaccard_weighted'])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.metrics.SCORERS.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2d3cc99f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:55:31.499665Z",
     "iopub.status.busy": "2021-09-02T05:55:31.499129Z",
     "iopub.status.idle": "2021-09-02T05:57:34.295479Z",
     "shell.execute_reply": "2021-09-02T05:57:34.294693Z",
     "shell.execute_reply.started": "2021-09-02T05:52:04.517233Z"
    },
    "papermill": {
     "duration": 122.832309,
     "end_time": "2021-09-02T05:57:34.295671",
     "exception": false,
     "start_time": "2021-09-02T05:55:31.463362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py:765: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  self.best_estimator_.fit(X, y, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6388.530988457536, tolerance: 2.413447000052981\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5049.756067315204, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5181.046721170828, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5051.681886918483, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5172.775359451367, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5090.307584138897, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6534.389846801199, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6669.738617763438, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6551.907093510309, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6674.3406828636635, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6589.6311836676705, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7236.426039298538, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7379.704072807081, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7263.4340269615495, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7380.508722738865, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7302.194567560895, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7661.843469171262, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7810.831897068874, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7694.151999454534, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7807.390521526989, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7734.2973841622315, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7950.437059328428, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8103.448247334951, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7986.033798395105, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8096.46597702388, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8027.440511123132, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8160.303506983179, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8316.220816315139, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8198.102907094079, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8306.382659346269, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8240.579659011824, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8320.367185136214, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8478.445058958492, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8359.7247484973, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8466.291427013628, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8403.09752608765, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8446.775478249836, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8606.501758836761, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8487.280475395359, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8592.445425880434, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8531.406073232016, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8549.301289299392, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8710.31365466261, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8590.677670520134, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8694.671000038905, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8635.441089132264, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8634.228159325223, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8796.263352565024, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8676.282608950565, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8779.28004938543, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8721.591649966242, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8705.790943217615, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8868.653141016024, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8748.383590387031, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8850.522879151962, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8794.163854924805, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8766.953647581535, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8930.494165893777, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8809.980564940839, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8911.371954476313, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8856.171453126384, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8819.85613391982, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8983.96003240145, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8863.238376799056, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8963.97166698462, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8909.790026423456, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8866.083845737481, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9030.660852385008, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8909.760316649108, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9009.909727315355, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8956.63128848746, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8906.83759225616, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9071.815808816407, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8950.76024280104, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9050.387889353302, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8997.915769130288, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8943.04423919253, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108.365682317934, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8987.174753808447, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9086.333188096056, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9034.58540468027, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8975.43107407753, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9141.048456563029, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9019.738523197295, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9118.47263044756, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9067.379181915332, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9004.57708321308, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9170.451450562561, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9049.036228005043, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9147.384668960529, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9096.885296485454, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9030.94912178804, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9197.048089964053, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9075.539167302872, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9173.535492125917, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9123.577943643824, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9054.92794482605, tolerance: 1.9128235045682356\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9221.224367842897, tolerance: 1.9465363697926663\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9099.631608190628, tolerance: 1.921938657746484\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9197.305128955573, tolerance: 1.9399715486973217\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9147.843794406706, tolerance: 1.9323556238485873\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py:765: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  self.best_estimator_.fit(X, y, **fit_params)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:531: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6388.530988457536, tolerance: 2.413447000052981\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "#crossvalidation\n",
    "cv = RepeatedKFold(n_splits=5, n_repeats=1, random_state=27)\n",
    "\n",
    "grid_ridge_lasso = {\n",
    "    'alpha': np.arange(0, 1, 0.05)\n",
    "}\n",
    "\n",
    "grid_elastic = {\n",
    "    'alpha': np.arange(0, 1, 0.05),\n",
    "    'l1_ratio': np.arange(0, 1, 0.05)\n",
    "}\n",
    "\n",
    "lr_score = cross_val_score(lr, X, np.log(Y+0.0001), cv=cv, scoring='neg_mean_squared_log_error')\n",
    "sgd_score = cross_val_score(sgd, X, np.log(Y+0.0001), cv=cv, scoring='neg_mean_squared_log_error')\n",
    "\n",
    "rr_search = GridSearchCV(rr, grid_ridge_lasso, cv=cv, scoring='neg_mean_squared_log_error')\n",
    "rr_score = rr_search.fit(X, np.log(Y+0.0001))\n",
    "\n",
    "ls_search = GridSearchCV(ls, grid_ridge_lasso, cv=cv, scoring='neg_mean_squared_log_error')\n",
    "ls_score = ls_search.fit(X, np.log(Y+0.0001))\n",
    "\n",
    "en_search = GridSearchCV(en, grid_elastic, cv=cv, scoring='neg_mean_squared_log_error')\n",
    "en_score = en_search.fit(X, np.log(Y+0.0001))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7f9c158b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:57:34.463903Z",
     "iopub.status.busy": "2021-09-02T05:57:34.463039Z",
     "iopub.status.idle": "2021-09-02T05:57:34.467173Z",
     "shell.execute_reply": "2021-09-02T05:57:34.466692Z",
     "shell.execute_reply.started": "2021-09-02T05:54:07.997253Z"
    },
    "papermill": {
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     "end_time": "2021-09-02T05:57:34.467300",
     "exception": false,
     "start_time": "2021-09-02T05:57:34.377424",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.13664070169138626\n",
      "-0.07448596356847402\n",
      "-0.07477886455495521\n",
      "-0.0747767401929157\n",
      "-0.0747767401929157\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(lr_score))\n",
    "print(np.mean(sgd_score))\n",
    "\n",
    "print(rr_score.best_score_)\n",
    "print(ls_score.best_score_)\n",
    "print(en_score.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2df92193",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:57:34.634918Z",
     "iopub.status.busy": "2021-09-02T05:57:34.633931Z",
     "iopub.status.idle": "2021-09-02T05:57:34.639609Z",
     "shell.execute_reply": "2021-09-02T05:57:34.640488Z",
     "shell.execute_reply.started": "2021-09-02T05:54:19.069698Z"
    },
    "papermill": {
     "duration": 0.092334,
     "end_time": "2021-09-02T05:57:34.640763",
     "exception": false,
     "start_time": "2021-09-02T05:57:34.548429",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 13.37740867,  15.41881537,  17.0608271 , ..., 104.76117895,\n",
       "       124.75724479, 120.63443231])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(rr_score.best_estimator_.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "44c4de60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:57:34.861297Z",
     "iopub.status.busy": "2021-09-02T05:57:34.860319Z",
     "iopub.status.idle": "2021-09-02T05:57:34.866278Z",
     "shell.execute_reply": "2021-09-02T05:57:34.867108Z",
     "shell.execute_reply.started": "2021-09-02T05:54:27.605862Z"
    },
    "papermill": {
     "duration": 0.09309,
     "end_time": "2021-09-02T05:57:34.867383",
     "exception": false,
     "start_time": "2021-09-02T05:57:34.774293",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "predictions = np.exp(rr_score.best_estimator_.predict(X_test))\n",
    "predictions = predictions.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b33e1e0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:57:35.086373Z",
     "iopub.status.busy": "2021-09-02T05:57:35.085800Z",
     "iopub.status.idle": "2021-09-02T05:57:35.088779Z",
     "shell.execute_reply": "2021-09-02T05:57:35.089195Z",
     "shell.execute_reply.started": "2021-09-02T05:54:34.096009Z"
    },
    "papermill": {
     "duration": 0.088007,
     "end_time": "2021-09-02T05:57:35.089344",
     "exception": false,
     "start_time": "2021-09-02T05:57:35.001337",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 13,  15,  17, ..., 104, 124, 120])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "590a4248",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-02T05:57:35.255623Z",
     "iopub.status.busy": "2021-09-02T05:57:35.254704Z",
     "iopub.status.idle": "2021-09-02T05:57:35.272275Z",
     "shell.execute_reply": "2021-09-02T05:57:35.271713Z",
     "shell.execute_reply.started": "2021-09-02T05:54:53.666076Z"
    },
    "papermill": {
     "duration": 0.102108,
     "end_time": "2021-09-02T05:57:35.272413",
     "exception": false,
     "start_time": "2021-09-02T05:57:35.170305",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.DataFrame({\n",
    "    'datetime': test_data.datetime,\n",
    "    'count': predictions\n",
    "})\n",
    "submission.to_csv('/kaggle/working/submission.csv', index=False)"
   ]
  }
 ],
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