FundRatings_SVC-Bagging.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing all required libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chai3/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (29,31,32,64,78,101,117) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#Reading the data file\n",
    "FundRating=pd.read_csv('Rating.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bb_rating</th>\n",
       "      <th>us_govt_bond_rating</th>\n",
       "      <th>below_b_rating</th>\n",
       "      <th>others_rating</th>\n",
       "      <th>maturity_bond</th>\n",
       "      <th>b_rating</th>\n",
       "      <th>a_rating</th>\n",
       "      <th>aaa_rating</th>\n",
       "      <th>aa_rating</th>\n",
       "      <th>bbb_rating</th>\n",
       "      <th>...</th>\n",
       "      <th>10_years_beta_fund</th>\n",
       "      <th>10yrs_treynor_ratio_fund</th>\n",
       "      <th>10_years_return_mean_annual_category</th>\n",
       "      <th>10yrs_treynor_ratio_category</th>\n",
       "      <th>10_years_return_fund</th>\n",
       "      <th>10_years_alpha_category</th>\n",
       "      <th>10_years_beta_category</th>\n",
       "      <th>10years_category_std</th>\n",
       "      <th>10_years_return_mean_annual_fund</th>\n",
       "      <th>10_years_return_category</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>7.31</td>\n",
       "      <td>11.36</td>\n",
       "      <td>11.13</td>\n",
       "      <td>13.04</td>\n",
       "      <td>30.89</td>\n",
       "      <td>9.90</td>\n",
       "      <td>18.48</td>\n",
       "      <td>...</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.68</td>\n",
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       "      <td>3.87</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.33</td>\n",
       "      <td>3.95</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.09</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>9.46</td>\n",
       "      <td>20.01</td>\n",
       "      <td>16.67</td>\n",
       "      <td>29.57</td>\n",
       "      <td>1.47</td>\n",
       "      <td>15.23</td>\n",
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       "      <td>0.01</td>\n",
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       "      <td>1.47</td>\n",
       "      <td>15.23</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.24</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.09</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.83</td>\n",
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       "      <td>1.47</td>\n",
       "      <td>15.23</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>9.46</td>\n",
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       "      <td>1.47</td>\n",
       "      <td>15.23</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.01</td>\n",
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       "      <td>15.23</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.09</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.09</td>\n",
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       "      <td>2.13</td>\n",
       "      <td>1.83</td>\n",
       "      <td>9.46</td>\n",
       "      <td>20.01</td>\n",
       "      <td>16.67</td>\n",
       "      <td>29.57</td>\n",
       "      <td>1.47</td>\n",
       "      <td>15.23</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.24</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.13</td>\n",
       "      <td>1.83</td>\n",
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       "      <td>16.67</td>\n",
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       "      <td>15.23</td>\n",
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       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.24</td>\n",
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       "<p>10 rows × 126 columns</p>\n",
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      ],
      "text/plain": [
       "   bb_rating  us_govt_bond_rating  below_b_rating  others_rating  \\\n",
       "0       9.15                  0.0            0.10           7.31   \n",
       "1      13.09                  0.0            2.13           1.83   \n",
       "2      13.09                  0.0            2.13           1.83   \n",
       "3      13.09                  0.0            2.13           1.83   \n",
       "4      13.09                  0.0            2.13           1.83   \n",
       "5      13.09                  0.0            2.13           1.83   \n",
       "6      13.09                  0.0            2.13           1.83   \n",
       "7      13.09                  0.0            2.13           1.83   \n",
       "8      13.09                  0.0            2.13           1.83   \n",
       "9      13.09                  0.0            2.13           1.83   \n",
       "\n",
       "   maturity_bond  b_rating  a_rating  aaa_rating  aa_rating  bbb_rating  ...  \\\n",
       "0          11.36     11.13     13.04       30.89       9.90       18.48  ...   \n",
       "1           9.46     20.01     16.67       29.57       1.47       15.23  ...   \n",
       "2           9.46     20.01     16.67       29.57       1.47       15.23  ...   \n",
       "3           9.46     20.01     16.67       29.57       1.47       15.23  ...   \n",
       "4           9.46     20.01     16.67       29.57       1.47       15.23  ...   \n",
       "5           9.46     20.01     16.67       29.57       1.47       15.23  ...   \n",
       "6           9.46     20.01     16.67       29.57       1.47       15.23  ...   \n",
       "7           9.46     20.01     16.67       29.57       1.47       15.23  ...   \n",
       "8           9.46     20.01     16.67       29.57       1.47       15.23  ...   \n",
       "9           9.46     20.01     16.67       29.57       1.47       15.23  ...   \n",
       "\n",
       "   10_years_beta_fund  10yrs_treynor_ratio_fund  \\\n",
       "0                1.28                      2.68   \n",
       "1                 NaN                       NaN   \n",
       "2                 NaN                       NaN   \n",
       "3                 NaN                       NaN   \n",
       "4                 NaN                       NaN   \n",
       "5                 NaN                       NaN   \n",
       "6                 NaN                       NaN   \n",
       "7                 NaN                       NaN   \n",
       "8                 NaN                       NaN   \n",
       "9                 NaN                       NaN   \n",
       "\n",
       "   10_years_return_mean_annual_category  10yrs_treynor_ratio_category  \\\n",
       "0                                  0.00                          0.11   \n",
       "1                                  0.01                          0.07   \n",
       "2                                  0.01                          0.07   \n",
       "3                                  0.01                          0.07   \n",
       "4                                  0.01                          0.07   \n",
       "5                                  0.01                          0.07   \n",
       "6                                  0.01                          0.07   \n",
       "7                                  0.01                          0.07   \n",
       "8                                  0.01                          0.07   \n",
       "9                                  0.01                          0.07   \n",
       "\n",
       "   10_years_return_fund  10_years_alpha_category  10_years_beta_category  \\\n",
       "0                  3.87                     0.01                    0.01   \n",
       "1                  0.00                     0.04                    0.01   \n",
       "2                  0.00                     0.04                    0.01   \n",
       "3                  0.00                     0.04                    0.01   \n",
       "4                  0.00                     0.04                    0.01   \n",
       "5                  0.00                     0.04                    0.01   \n",
       "6                  0.00                     0.04                    0.01   \n",
       "7                  0.00                     0.04                    0.01   \n",
       "8                  0.00                     0.04                    0.01   \n",
       "9                  0.00                     0.04                    0.01   \n",
       "\n",
       "   10years_category_std  10_years_return_mean_annual_fund  \\\n",
       "0                  0.06                              0.33   \n",
       "1                  0.08                               NaN   \n",
       "2                  0.08                               NaN   \n",
       "3                  0.08                               NaN   \n",
       "4                  0.08                               NaN   \n",
       "5                  0.08                               NaN   \n",
       "6                  0.08                               NaN   \n",
       "7                  0.08                               NaN   \n",
       "8                  0.08                               NaN   \n",
       "9                  0.08                               NaN   \n",
       "\n",
       "   10_years_return_category  \n",
       "0                      3.95  \n",
       "1                      8.24  \n",
       "2                      8.24  \n",
       "3                      8.24  \n",
       "4                      8.24  \n",
       "5                      8.24  \n",
       "6                      8.24  \n",
       "7                      8.24  \n",
       "8                      8.24  \n",
       "9                      8.24  \n",
       "\n",
       "[10 rows x 126 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FundRating.head(10)\n",
    "#From below data, we can see that most of the values are numeric. We do have NAN values present in the data set\n",
    "#Let's analyze it further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data pre-processing – Perform all the necessary pre-processing on the data ready to be fed to an Unsupervised algorithm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25000 entries, 0 to 24999\n",
      "Columns: 126 entries, bb_rating to 10_years_return_category\n",
      "dtypes: float64(109), int64(1), object(16)\n",
      "memory usage: 24.0+ MB\n"
     ]
    }
   ],
   "source": [
    "FundRating.info()\n",
    "#The data set has 846 rows and 19 columns, all are numeric except column 'class' which is of type object. \n",
    "#This indicates that there is categorical variable which needs to represented in numeric format.\n",
    "#From below data it is evident that not all 846 rows has values, some cells are empty "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "    <tr>\n",
       "      <th>bb_rating</th>\n",
       "      <td>24886.0</td>\n",
       "      <td>3.473804</td>\n",
       "      <td>8.299747</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.45</td>\n",
       "      <td>80.47</td>\n",
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       "    <tr>\n",
       "      <th>us_govt_bond_rating</th>\n",
       "      <td>24886.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>below_b_rating</th>\n",
       "      <td>24886.0</td>\n",
       "      <td>0.827525</td>\n",
       "      <td>2.700019</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>42.30</td>\n",
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       "    <tr>\n",
       "      <th>others_rating</th>\n",
       "      <td>24886.0</td>\n",
       "      <td>1.666769</td>\n",
       "      <td>6.885158</td>\n",
       "      <td>-68.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maturity_bond</th>\n",
       "      <td>8093.0</td>\n",
       "      <td>7.765444</td>\n",
       "      <td>4.148558</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.46</td>\n",
       "      <td>7.29</td>\n",
       "      <td>8.92</td>\n",
       "      <td>29.30</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_years_alpha_category</th>\n",
       "      <td>24894.0</td>\n",
       "      <td>0.001136</td>\n",
       "      <td>0.027795</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_years_beta_category</th>\n",
       "      <td>24894.0</td>\n",
       "      <td>0.009076</td>\n",
       "      <td>0.005493</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10years_category_std</th>\n",
       "      <td>24894.0</td>\n",
       "      <td>0.109399</td>\n",
       "      <td>0.055202</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_years_return_mean_annual_fund</th>\n",
       "      <td>16416.0</td>\n",
       "      <td>0.858579</td>\n",
       "      <td>0.458993</td>\n",
       "      <td>-3.64</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_years_return_category</th>\n",
       "      <td>24885.0</td>\n",
       "      <td>9.679289</td>\n",
       "      <td>4.926417</td>\n",
       "      <td>-24.99</td>\n",
       "      <td>6.44</td>\n",
       "      <td>9.97</td>\n",
       "      <td>14.12</td>\n",
       "      <td>18.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count      mean       std    min   25%  \\\n",
       "bb_rating                         24886.0  3.473804  8.299747   0.00  0.00   \n",
       "us_govt_bond_rating               24886.0  0.000000  0.000000   0.00  0.00   \n",
       "below_b_rating                    24886.0  0.827525  2.700019  -0.02  0.00   \n",
       "others_rating                     24886.0  1.666769  6.885158 -68.21  0.00   \n",
       "maturity_bond                      8093.0  7.765444  4.148558   0.00  5.46   \n",
       "...                                   ...       ...       ...    ...   ...   \n",
       "10_years_alpha_category           24894.0  0.001136  0.027795  -0.11 -0.02   \n",
       "10_years_beta_category            24894.0  0.009076  0.005493  -0.02  0.01   \n",
       "10years_category_std              24894.0  0.109399  0.055202   0.00  0.06   \n",
       "10_years_return_mean_annual_fund  16416.0  0.858579  0.458993  -3.64  0.53   \n",
       "10_years_return_category          24885.0  9.679289  4.926417 -24.99  6.44   \n",
       "\n",
       "                                   50%    75%     max  \n",
       "bb_rating                         0.00   2.45   80.47  \n",
       "us_govt_bond_rating               0.00   0.00    0.00  \n",
       "below_b_rating                    0.00   0.10   42.30  \n",
       "others_rating                     0.00   0.33  100.00  \n",
       "maturity_bond                     7.29   8.92   29.30  \n",
       "...                                ...    ...     ...  \n",
       "10_years_alpha_category           0.00   0.01    0.10  \n",
       "10_years_beta_category            0.01   0.01    0.12  \n",
       "10years_category_std              0.12   0.15    0.34  \n",
       "10_years_return_mean_annual_fund  0.90   1.20    3.28  \n",
       "10_years_return_category          9.97  14.12   18.72  \n",
       "\n",
       "[110 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FundRating.describe().transpose()\n",
    "#Inference: From below data we can conclude that outlier values  and negetive values are present in data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking variables one bye one and proceeding with required data treatments in below steps.\n",
    "# Also we will perform univariate/bivariate (independent variable/dependent variable) analysis to understand the \n",
    "#each attributes and choose carefully which all attributes have to be a part of the analysis and why"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "FundRating=FundRating.replace(\",\", \"\",regex=True)\n",
    "FundRating=FundRating.replace(\">\", \"\",regex=True)\n",
    "FundRating=FundRating.replace(\"<\",\"\",regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# FundRating[\"pb_ratio\"]=np.where(FundRating[\"pb_ratio\"]>(5.085), (5.085), FundRating[\"pb_ratio\"])\n",
    "# FundRating[\"pb_ratio\"]=np.where(FundRating[\"pb_ratio\"]<(-2.155), (-2.155), FundRating[\"pb_ratio\"])\n",
    "\n",
    "# FundRating[\"fund_ratio_net_annual_expense\"]=np.where(FundRating[\"fund_ratio_net_annual_expense\"]>(2.52), (2.52), FundRating[\"fund_ratio_net_annual_expense\"])\n",
    "# FundRating[\"fund_ratio_net_annual_expense\"]=np.where(FundRating[\"fund_ratio_net_annual_expense\"]<(-0.3600), (-0.3600), FundRating[\"fund_ratio_net_annual_expense\"])\n",
    "\n",
    "# FundRating[\"portfolio_cyclical_consumer_allocation\"]=np.where(FundRating[\"portfolio_cyclical_consumer_allocation\"]>(33.0), (33.0), FundRating[\"portfolio_cyclical_consumer_allocation\"])\n",
    "# FundRating[\"portfolio_cyclical_consumer_allocation\"]=np.where(FundRating[\"portfolio_cyclical_consumer_allocation\"]<(-19.799), (-19.799), FundRating[\"portfolio_cyclical_consumer_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_utils_allocation\"]=np.where(FundRating[\"portfolio_utils_allocation\"]>(8.35), (8.35), FundRating[\"portfolio_utils_allocation\"])\n",
    "# FundRating[\"portfolio_utils_allocation\"]=np.where(FundRating[\"portfolio_utils_allocation\"]<(-5.01), (-5.01), FundRating[\"portfolio_utils_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_property_allocation\"]=np.where(FundRating[\"portfolio_property_allocation\"]>(11.08125), (11.08125), FundRating[\"portfolio_property_allocation\"])\n",
    "# FundRating[\"portfolio_property_allocation\"]=np.where(FundRating[\"portfolio_property_allocation\"]<(-6.6487), (-6.6487), FundRating[\"portfolio_property_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_healthcare_allocation\"]=np.where(FundRating[\"portfolio_healthcare_allocation\"]>(33.875), (33.875), FundRating[\"portfolio_healthcare_allocation\"])\n",
    "# FundRating[\"portfolio_healthcare_allocation\"]=np.where(FundRating[\"portfolio_healthcare_allocation\"]<(-20.3250), (-20.3250), FundRating[\"portfolio_healthcare_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_consumer_defence_allocation\"]=np.where(FundRating[\"portfolio_consumer_defence_allocation\"]>(19.675), (19.675), FundRating[\"portfolio_consumer_defence_allocation\"])\n",
    "# FundRating[\"portfolio_consumer_defence_allocation\"]=np.where(FundRating[\"portfolio_consumer_defence_allocation\"]<(-11.805), (-11.805), FundRating[\"portfolio_consumer_defence_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_energy_allocation\"]=np.where(FundRating[\"portfolio_energy_allocation\"]>(15.5562), (15.5562), FundRating[\"portfolio_energy_allocation\"])\n",
    "# FundRating[\"portfolio_energy_allocation\"]=np.where(FundRating[\"portfolio_energy_allocation\"]<(-9.3337), (-9.3337), FundRating[\"portfolio_energy_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_materials_basic_allocation\"]=np.where(FundRating[\"portfolio_materials_basic_allocation\"]>(12.625), (12.625), FundRating[\"portfolio_materials_basic_allocation\"])\n",
    "# FundRating[\"portfolio_materials_basic_allocation\"]=np.where(FundRating[\"portfolio_materials_basic_allocation\"]<(-7.5749), (-7.5749), FundRating[\"portfolio_materials_basic_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_tech_allocation\"]=np.where(FundRating[\"portfolio_tech_allocation\"]>(48.97499), (48.97499), FundRating[\"portfolio_tech_allocation\"])\n",
    "# FundRating[\"portfolio_tech_allocation\"]=np.where(FundRating[\"portfolio_tech_allocation\"]<(-29.3849), (-29.3849), FundRating[\"portfolio_tech_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_industrials_allocation\"]=np.where(FundRating[\"portfolio_industrials_allocation\"]>(31.825), (31.825), FundRating[\"portfolio_industrials_allocation\"])\n",
    "# FundRating[\"portfolio_industrials_allocation\"]=np.where(FundRating[\"portfolio_industrials_allocation\"]<(-19.095), (-19.095), FundRating[\"portfolio_industrials_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_financial_services\"]=np.where(FundRating[\"portfolio_financial_services\"]>(44.75), (44.75), FundRating[\"portfolio_financial_services\"])\n",
    "# FundRating[\"portfolio_financial_services\"]=np.where(FundRating[\"portfolio_financial_services\"]<(-26.8499), (-26.8499), FundRating[\"portfolio_financial_services\"])\n",
    "\n",
    "# FundRating[\"portfolio_communication_allocation\"]=np.where(FundRating[\"portfolio_communication_allocation\"]>(8.5), (8.5), FundRating[\"portfolio_communication_allocation\"])\n",
    "# FundRating[\"portfolio_communication_allocation\"]=np.where(FundRating[\"portfolio_communication_allocation\"]<(-5.1), (-5.1), FundRating[\"portfolio_communication_allocation\"])\n",
    "    \n",
    "# FundRating[\"duration_bond\"]=np.where(FundRating[\"duration_bond\"]>(4.8), (4.8), FundRating[\"duration_bond\"])\n",
    "# FundRating[\"duration_bond\"]=np.where(FundRating[\"duration_bond\"]<(4.8), (4.8), FundRating[\"duration_bond\"])\n",
    "\n",
    "# FundRating[\"bbb_rating\"]=np.where(FundRating[\"bbb_rating\"]>(28.3499), (28.3499), FundRating[\"bbb_rating\"])\n",
    "# FundRating[\"bbb_rating\"]=np.where(FundRating[\"bbb_rating\"]<(-17.009), (-17.009), FundRating[\"bbb_rating\"])\n",
    "\n",
    "# FundRating[\"aa_rating\"]=np.where(FundRating[\"aa_rating\"]>(7.4750), (7.4750), FundRating[\"aa_rating\"])\n",
    "# FundRating[\"aa_rating\"]=np.where(FundRating[\"aa_rating\"]<(-4.485), (-4.485), FundRating[\"aa_rating\"])\n",
    "\n",
    "# FundRating[\"aaa_rating\"]=np.where(FundRating[\"aaa_rating\"]>(46.0500), (46.0500), FundRating[\"aaa_rating\"])\n",
    "# FundRating[\"aaa_rating\"]=np.where(FundRating[\"aaa_rating\"]<(-27.6300), (-27.6300), FundRating[\"aaa_rating\"])\n",
    "\n",
    "# FundRating[\"a_rating\"]=np.where(FundRating[\"a_rating\"]>(20.6937), (20.6937), FundRating[\"a_rating\"])\n",
    "# FundRating[\"a_rating\"]=np.where(FundRating[\"a_rating\"]<(-12.4162), (-12.4162), FundRating[\"a_rating\"])\n",
    "\n",
    "# FundRating[\"maturity_bond\"]=np.where(FundRating[\"maturity_bond\"]>(7.29), (7.29), FundRating[\"maturity_bond\"])\n",
    "# FundRating[\"maturity_bond\"]=np.where(FundRating[\"maturity_bond\"]<(7.29), (7.29), FundRating[\"maturity_bond\"])\n",
    "\n",
    "# FundRating[\"others_rating\"]=np.where(FundRating[\"others_rating\"]>(0.8), (0.8), FundRating[\"others_rating\"])\n",
    "# FundRating[\"others_rating\"]=np.where(FundRating[\"others_rating\"]<(-0.48), (-0.48), FundRating[\"others_rating\"])\n",
    "\n",
    "# FundRating[\"2013_return_fund\"]=np.where(FundRating[\"2013_return_fund\"]>(47.7037), (47.7037), FundRating[\"2013_return_fund\"])\n",
    "# FundRating[\"2013_return_fund\"]=np.where(FundRating[\"2013_return_fund\"]<(-13.1862), (-13.1862), FundRating[\"2013_return_fund\"])\n",
    "\n",
    "# FundRating[\"fund_return_3months\"]=np.where(FundRating[\"fund_return_3months\"]>(26.2000), (26.2000), FundRating[\"fund_return_3months\"])\n",
    "# FundRating[\"fund_return_3months\"]=np.where(FundRating[\"fund_return_3months\"]<(-9.0), (-9.0), FundRating[\"fund_return_3months\"])\n",
    "\n",
    "# FundRating[\"ytd_return_category\"]=np.where(FundRating[\"ytd_return_category\"]>(24.82), (24.82), FundRating[\"ytd_return_category\"])\n",
    "# FundRating[\"ytd_return_category\"]=np.where(FundRating[\"ytd_return_category\"]<(-6.8599), (-6.8599), FundRating[\"ytd_return_category\"])\n",
    "\n",
    "# FundRating[\"2017_category_return\"]=np.where(FundRating[\"2017_category_return\"]>(44.045), (44.045), FundRating[\"2017_category_return\"])\n",
    "# FundRating[\"2017_category_return\"]=np.where(FundRating[\"2017_category_return\"]<(-16.0750), (-16.0750), FundRating[\"2017_category_return\"])\n",
    "\n",
    "# FundRating[\"1_year_return_fund\"]=np.where(FundRating[\"1_year_return_fund\"]>(12.795), (12.795), FundRating[\"1_year_return_fund\"])\n",
    "# FundRating[\"1_year_return_fund\"]=np.where(FundRating[\"1_year_return_fund\"]<(-7.7250), (-7.7250), FundRating[\"1_year_return_fund\"])\n",
    "\n",
    "# FundRating[\"2015_return_fund\"]=np.where(FundRating[\"2015_return_fund\"]>(4.79), (4.79), FundRating[\"2015_return_fund\"])\n",
    "# FundRating[\"2015_return_fund\"]=np.where(FundRating[\"2015_return_fund\"]<(-7.21), (-7.21), FundRating[\"2015_return_fund\"])\n",
    "\n",
    "# FundRating[\"portfolio_convertable\"]=np.where(FundRating[\"portfolio_convertable\"]>(0.1750), (0.1750), FundRating[\"portfolio_convertable\"])\n",
    "# FundRating[\"portfolio_convertable\"]=np.where(FundRating[\"portfolio_convertable\"]<(-0.1050), (-0.1050), FundRating[\"portfolio_convertable\"])\n",
    "\n",
    "# FundRating[\"3_months_return_category\"]=np.where(FundRating[\"3_months_return_category\"]>(24.82), (24.82), FundRating[\"3_months_return_category\"])\n",
    "# FundRating[\"3_months_return_category\"]=np.where(FundRating[\"3_months_return_category\"]<(-6.8599), (-6.8599), FundRating[\"3_months_return_category\"])\n",
    "\n",
    "# FundRating[\"portfolio_others\"]=np.where(FundRating[\"portfolio_others\"]>(0.7000), (0.7000), FundRating[\"portfolio_others\"])\n",
    "# FundRating[\"portfolio_others\"]=np.where(FundRating[\"portfolio_others\"]<(-0.4200), (-0.4200), FundRating[\"portfolio_others\"])\n",
    "\n",
    "# FundRating[\"2016_return_fund\"]=np.where(FundRating[\"2016_return_fund\"]>(19.7200), (19.7200), FundRating[\"2016_return_fund\"])\n",
    "# FundRating[\"2016_return_fund\"]=np.where(FundRating[\"2016_return_fund\"]<(-7.160), (-7.160), FundRating[\"2016_return_fund\"])\n",
    "\n",
    "# FundRating[\"stock_percent_of_portfolio\"]=np.where(FundRating[\"stock_percent_of_portfolio\"]>(243.11), (243.11), FundRating[\"stock_percent_of_portfolio\"])\n",
    "# FundRating[\"stock_percent_of_portfolio\"]=np.where(FundRating[\"stock_percent_of_portfolio\"]<(-144.95), (-144.95), FundRating[\"stock_percent_of_portfolio\"])\n",
    "\n",
    "# FundRating[\"2016_return_category\"]=np.where(FundRating[\"2016_return_category\"]>(18.28), (18.28), FundRating[\"2016_return_category\"])\n",
    "# FundRating[\"2016_return_category\"]=np.where(FundRating[\"2016_return_category\"]<(-5.7999), (-5.7999), FundRating[\"2016_return_category\"])\n",
    "\n",
    "# FundRating[\"2011_return_fund\"]=np.where(FundRating[\"2011_return_fund\"]>(2.945), (2.945), FundRating[\"2011_return_fund\"])\n",
    "# FundRating[\"2011_return_fund\"]=np.where(FundRating[\"2011_return_fund\"]<(-4.055), (-4.055), FundRating[\"2011_return_fund\"])\n",
    "\n",
    "# FundRating[\"2010_return_fund\"]=np.where(FundRating[\"2010_return_fund\"]>(13.5962), (13.5962), FundRating[\"2010_return_fund\"])\n",
    "# FundRating[\"2010_return_fund\"]=np.where(FundRating[\"2010_return_fund\"]<(12.6), (12.6), FundRating[\"2010_return_fund\"])\n",
    "\n",
    "# FundRating[\"fund_return_3years\"]=np.where(FundRating[\"fund_return_3years\"]>(21.2299), (21.2299), FundRating[\"fund_return_3years\"])\n",
    "# FundRating[\"fund_return_3years\"]=np.where(FundRating[\"fund_return_3years\"]<(-8.2099), (-8.2099), FundRating[\"fund_return_3years\"])\n",
    "\n",
    "# FundRating[\"2012_fund_return\"]=np.where(FundRating[\"2012_fund_return\"]>(18.4049), (18.4049), FundRating[\"2012_fund_return\"])\n",
    "# FundRating[\"2012_fund_return\"]=np.where(FundRating[\"2012_fund_return\"]<(8.5250), (8.5250), FundRating[\"2012_fund_return\"])\n",
    "\n",
    "# FundRating[\"2018_return_fund\"]=np.where(FundRating[\"2018_return_fund\"]>(10.73), (10.73), FundRating[\"2018_return_fund\"])\n",
    "# FundRating[\"2018_return_fund\"]=np.where(FundRating[\"2018_return_fund\"]<(-22.79), (-22.79), FundRating[\"2018_return_fund\"])\n",
    "\n",
    "# FundRating[\"2017_return_fund\"]=np.where(FundRating[\"2017_return_fund\"]>(42.2237), (42.2237), FundRating[\"2017_return_fund\"])\n",
    "# FundRating[\"2017_return_fund\"]=np.where(FundRating[\"2017_return_fund\"]<(-14.1062), (-14.1062), FundRating[\"2017_return_fund\"])\n",
    "\n",
    "# FundRating[\"category_ratio_net_annual_expense\"]=np.where(FundRating[\"category_ratio_net_annual_expense\"]>(1.7349), (1.7349), FundRating[\"category_ratio_net_annual_expense\"])\n",
    "# FundRating[\"category_ratio_net_annual_expense\"]=np.where(FundRating[\"category_ratio_net_annual_expense\"]<(0.2550), (0.2550), FundRating[\"category_ratio_net_annual_expense\"])\n",
    "\n",
    "# FundRating[\"category_return_2015\"]=np.where(FundRating[\"category_return_2015\"]>(1.4950), (1.4950), FundRating[\"category_return_2015\"])\n",
    "# FundRating[\"category_return_2015\"]=np.where(FundRating[\"category_return_2015\"]<(-5.025), (-5.025), FundRating[\"category_return_2015\"])\n",
    "\n",
    "# FundRating[\"1_month_fund_return\"]=np.where(FundRating[\"1_month_fund_return\"]>(3.75), (3.75), FundRating[\"1_month_fund_return\"])\n",
    "# FundRating[\"1_month_fund_return\"]=np.where(FundRating[\"1_month_fund_return\"]<(-1.69), (-1.69), FundRating[\"1_month_fund_return\"])\n",
    "\n",
    "# FundRating[\"bond_percentage_of_porfolio\"]=np.where(FundRating[\"bond_percentage_of_porfolio\"]>(159.25), (159.25), FundRating[\"bond_percentage_of_porfolio\"])\n",
    "# FundRating[\"bond_percentage_of_porfolio\"]=np.where(FundRating[\"bond_percentage_of_porfolio\"]<(-95.5500), (-95.5500), FundRating[\"bond_percentage_of_porfolio\"])\n",
    "\n",
    "# FundRating[\"portfolio_preferred\"]=np.where(FundRating[\"portfolio_preferred\"]>(0.025), (0.025), FundRating[\"portfolio_preferred\"])\n",
    "# FundRating[\"portfolio_preferred\"]=np.where(FundRating[\"portfolio_preferred\"]<(-0.015), (-0.015), FundRating[\"portfolio_preferred\"])\n",
    "\n",
    "# FundRating[\"2010_return_category\"]=np.where(FundRating[\"2010_return_category\"]>(14.115), (14.115), FundRating[\"2010_return_category\"])\n",
    "# FundRating[\"2010_return_category\"]=np.where(FundRating[\"2010_return_category\"]<(13.115), (13.115), FundRating[\"2010_return_category\"])\n",
    "\n",
    "# FundRating[\"2013_category_return\"]=np.where(FundRating[\"2013_category_return\"]>(52.13), (52.13), FundRating[\"2013_category_return\"])\n",
    "# FundRating[\"2013_category_return\"]=np.where(FundRating[\"2013_category_return\"]<(-19.71), (-19.71), FundRating[\"2013_category_return\"])\n",
    "\n",
    "# FundRating[\"3_years_alpha_fund\"]=np.where(FundRating[\"3_years_alpha_fund\"]>(4.6599), (4.6599), FundRating[\"3_years_alpha_fund\"])\n",
    "# FundRating[\"3_years_alpha_fund\"]=np.where(FundRating[\"3_years_alpha_fund\"]<(-5.8999), (-5.8999), FundRating[\"3_years_alpha_fund\"])\n",
    "\n",
    "# FundRating[\"3years_category_std\"]=np.where(FundRating[\"3years_category_std\"]>(0.265), (0.265), FundRating[\"3years_category_std\"])\n",
    "# FundRating[\"3years_category_std\"]=np.where(FundRating[\"3years_category_std\"]<(-0.095), (-0.095), FundRating[\"3years_category_std\"])\n",
    "\n",
    "# FundRating[\"3yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"3yrs_sharpe_ratio_fund\"]>(1.6849), (1.6849), FundRating[\"3yrs_sharpe_ratio_fund\"])\n",
    "# FundRating[\"3yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"3yrs_sharpe_ratio_fund\"]<(-0.2749), (-0.2749), FundRating[\"3yrs_sharpe_ratio_fund\"])\n",
    "\n",
    "# FundRating[\"3yrs_treynor_ratio_category\"]=np.where(FundRating[\"3yrs_treynor_ratio_category\"]>(0.1750), (0.1750), FundRating[\"3yrs_treynor_ratio_category\"])\n",
    "# FundRating[\"3yrs_treynor_ratio_category\"]=np.where(FundRating[\"3yrs_treynor_ratio_category\"]<(-0.0250), (-0.0250), FundRating[\"3yrs_treynor_ratio_category\"])\n",
    "\n",
    "# FundRating[\"3_years_return_mean_annual_fund\"]=np.where(FundRating[\"3_years_return_mean_annual_fund\"]>(1.635), (1.635), FundRating[\"3_years_return_mean_annual_fund\"])\n",
    "# FundRating[\"3_years_return_mean_annual_fund\"]=np.where(FundRating[\"3_years_return_mean_annual_fund\"]<(-0.405), (-0.405), FundRating[\"3_years_return_mean_annual_fund\"])\n",
    "\n",
    "# FundRating[\"fund_beta_3years\"]=np.where(FundRating[\"fund_beta_3years\"]>(1.6150), (1.6150), FundRating[\"fund_beta_3years\"])\n",
    "# FundRating[\"fund_beta_3years\"]=np.where(FundRating[\"fund_beta_3years\"]<(0.3), (0.3), FundRating[\"fund_beta_3years\"])\n",
    "\n",
    "# FundRating[\"3years_fund_r_squared\"]=np.where(FundRating[\"3years_fund_r_squared\"]>(131.43), (131.43), FundRating[\"3years_fund_r_squared\"])\n",
    "# FundRating[\"3years_fund_r_squared\"]=np.where(FundRating[\"3years_fund_r_squared\"]<(26.55), (26.55), FundRating[\"3years_fund_r_squared\"])\n",
    "\n",
    "# FundRating[\"3years_fund_std\"]=np.where(FundRating[\"3years_fund_std\"]>(23.68), (23.68), FundRating[\"3years_fund_std\"])\n",
    "# FundRating[\"3years_fund_std\"]=np.where(FundRating[\"3years_fund_std\"]<(-6.9599), (-6.9599), FundRating[\"3years_fund_std\"])\n",
    "\n",
    "# FundRating[\"category_beta_3years\"]=np.where(FundRating[\"category_beta_3years\"]>(0.01), (0.01), FundRating[\"category_beta_3years\"])\n",
    "# FundRating[\"category_beta_3years\"]=np.where(FundRating[\"category_beta_3years\"]<(0.01), (0.01), FundRating[\"category_beta_3years\"])\n",
    "\n",
    "# FundRating[\"fund_return_3years\"]=np.where(FundRating[\"fund_return_3years\"]>(21.2299), (21.2299), FundRating[\"fund_return_3years\"])\n",
    "# FundRating[\"fund_return_3years\"]=np.where(FundRating[\"fund_return_3years\"]<(-8.2099), (-8.2099), FundRating[\"fund_return_3years\"])\n",
    "\n",
    "# FundRating[\"3_years_alpha_category\"]=np.where(FundRating[\"3_years_alpha_category\"]>(0.015), (0.015), FundRating[\"3_years_alpha_category\"])\n",
    "# FundRating[\"3_years_alpha_category\"]=np.where(FundRating[\"3_years_alpha_category\"]<(-0.025), (-0.025), FundRating[\"3_years_alpha_category\"])\n",
    "\n",
    "# FundRating[\"3_years_return_mean_annual_category\"]=np.where(FundRating[\"3_years_return_mean_annual_category\"]>(0.025), (0.025), FundRating[\"3_years_return_mean_annual_category\"])\n",
    "# FundRating[\"3_years_return_mean_annual_category\"]=np.where(FundRating[\"3_years_return_mean_annual_category\"]<(-0.015), (-0.015), FundRating[\"3_years_return_mean_annual_category\"])\n",
    "\n",
    "# FundRating[\"3yrs_sharpe_ratio_category\"]=np.where(FundRating[\"3yrs_sharpe_ratio_category\"]>(0.025), (0.025), FundRating[\"3yrs_sharpe_ratio_category\"])\n",
    "# FundRating[\"3yrs_sharpe_ratio_category\"]=np.where(FundRating[\"3yrs_sharpe_ratio_category\"]<(-0.015), (-0.015), FundRating[\"3yrs_sharpe_ratio_category\"])\n",
    "\n",
    "# FundRating[\"3years_category_r_squared\"]=np.where(FundRating[\"3years_category_r_squared\"]>(1.28), (1.28), FundRating[\"3years_category_r_squared\"])\n",
    "# FundRating[\"3years_category_r_squared\"]=np.where(FundRating[\"3years_category_r_squared\"]<(0.24), (0.24), FundRating[\"3years_category_r_squared\"])\n",
    "\n",
    "# FundRating[\"3_years_return_category\"]=np.where(FundRating[\"3_years_return_category\"]>(18.485), (18.485), FundRating[\"3_years_return_category\"])\n",
    "# FundRating[\"3_years_return_category\"]=np.where(FundRating[\"3_years_return_category\"]<(-4.1149), (-4.1149), FundRating[\"3_years_return_category\"])\n",
    "\n",
    "# FundRating[\"category_r_squared_5years\"]=np.where(FundRating[\"category_r_squared_5years\"]>(1.2650), (1.2650), FundRating[\"category_r_squared_5years\"])\n",
    "# FundRating[\"category_r_squared_5years\"]=np.where(FundRating[\"category_r_squared_5years\"]<(0.265), (0.265), FundRating[\"category_r_squared_5years\"])\n",
    "\n",
    "# FundRating[\"5yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"5yrs_sharpe_ratio_fund\"]>(1.1549), (1.1549), FundRating[\"5yrs_sharpe_ratio_fund\"])\n",
    "# FundRating[\"5yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"5yrs_sharpe_ratio_fund\"]<(-0.08499), (-0.08499), FundRating[\"5yrs_sharpe_ratio_fund\"])\n",
    "\n",
    "# FundRating[\"5years_fund_r_squared\"]=np.where(FundRating[\"5years_fund_r_squared\"]>(125.6749), (125.6749), FundRating[\"5years_fund_r_squared\"])\n",
    "# FundRating[\"5years_fund_r_squared\"]=np.where(FundRating[\"5years_fund_r_squared\"]<(33.475), (33.475), FundRating[\"5years_fund_r_squared\"])\n",
    "\n",
    "# FundRating[\"5years_fund_std\"]=np.where(FundRating[\"5years_fund_std\"]>(22.4837), (22.4837), FundRating[\"5years_fund_std\"])\n",
    "# FundRating[\"5years_fund_std\"]=np.where(FundRating[\"5years_fund_std\"]<(-4.5662), (-4.5662), FundRating[\"5years_fund_std\"])\n",
    "\n",
    "# FundRating[\"5yrs_sharpe_ratio_category\"]=np.where(FundRating[\"5yrs_sharpe_ratio_category\"]>(0.025), (0.025), FundRating[\"5yrs_sharpe_ratio_category\"])\n",
    "# FundRating[\"5yrs_sharpe_ratio_category\"]=np.where(FundRating[\"5yrs_sharpe_ratio_category\"]<(-0.015), (-0.015), FundRating[\"5yrs_sharpe_ratio_category\"])\n",
    "\n",
    "# FundRating[\"5_years_alpha_fund\"]=np.where(FundRating[\"5_years_alpha_fund\"]>(3.560), (3.560), FundRating[\"5_years_alpha_fund\"])\n",
    "# FundRating[\"5_years_alpha_fund\"]=np.where(FundRating[\"5_years_alpha_fund\"]<(-4.84), (-4.84), FundRating[\"5_years_alpha_fund\"])\n",
    "\n",
    "# FundRating[\"5_years_beta_fund\"]=np.where(FundRating[\"5_years_beta_fund\"]>(1.4150), (1.4150), FundRating[\"5_years_beta_fund\"])\n",
    "# FundRating[\"5_years_beta_fund\"]=np.where(FundRating[\"5_years_beta_fund\"]<(0.5), (0.5), FundRating[\"5_years_beta_fund\"])\n",
    "\n",
    "# FundRating[\"5_years_return_mean_annual_fund\"]=np.where(FundRating[\"5_years_return_mean_annual_fund\"]>(1.0599), (1.0599), FundRating[\"5_years_return_mean_annual_fund\"])\n",
    "# FundRating[\"5_years_return_mean_annual_fund\"]=np.where(FundRating[\"5_years_return_mean_annual_fund\"]<(-0.2199), (-0.2199), FundRating[\"5_years_return_mean_annual_fund\"])\n",
    "\n",
    "# FundRating[\"5_years_return_mean_annual_category\"]=np.where(FundRating[\"5_years_return_mean_annual_category\"]>(0.025), (0.025), FundRating[\"5_years_return_mean_annual_category\"])\n",
    "# FundRating[\"5_years_return_mean_annual_category\"]=np.where(FundRating[\"5_years_return_mean_annual_category\"]<(-0.015), (-0.015), FundRating[\"5_years_return_mean_annual_category\"])\n",
    "\n",
    "# FundRating[\"5yrs_treynor_ratio_category\"]=np.where(FundRating[\"5yrs_treynor_ratio_category\"]>(0.12), (0.12), FundRating[\"5yrs_treynor_ratio_category\"])\n",
    "# FundRating[\"5yrs_treynor_ratio_category\"]=np.where(FundRating[\"5yrs_treynor_ratio_category\"]<(-0.0399), (-0.0399), FundRating[\"5yrs_treynor_ratio_category\"])\n",
    "\n",
    "# FundRating[\"5_years_return_fund\"]=np.where(FundRating[\"5_years_return_fund\"]>(14.375), (14.375), FundRating[\"5_years_return_fund\"])\n",
    "# FundRating[\"5_years_return_fund\"]=np.where(FundRating[\"5_years_return_fund\"]<(-6.625), (-6.625), FundRating[\"5_years_return_fund\"])\n",
    "\n",
    "# FundRating[\"5_years_alpha_category\"]=np.where(FundRating[\"5_years_alpha_category\"]>(0.03), (0.03), FundRating[\"5_years_alpha_category\"])\n",
    "# FundRating[\"5_years_alpha_category\"]=np.where(FundRating[\"5_years_alpha_category\"]<(-0.05), (-0.05), FundRating[\"5_years_alpha_category\"])\n",
    "\n",
    "# FundRating[\"5_years_beta_category\"]=np.where(FundRating[\"5_years_beta_category\"]>(0.01), (0.01), FundRating[\"5_years_beta_category\"])\n",
    "# FundRating[\"5_years_beta_category\"]=np.where(FundRating[\"5_years_beta_category\"]<(0.01), (0.01), FundRating[\"5_years_beta_category\"])\n",
    "\n",
    "# FundRating[\"5years_category_std\"]=np.where(FundRating[\"5years_category_std\"]>(0.25), (0.25), FundRating[\"5years_category_std\"])\n",
    "# FundRating[\"5years_category_std\"]=np.where(FundRating[\"5years_category_std\"]<(-0.0699), (-0.0699), FundRating[\"5years_category_std\"])\n",
    "\n",
    "# FundRating[\"10years_fund_r_squared\"]=np.where(FundRating[\"10years_fund_r_squared\"]>(105.2350), (105.2350), FundRating[\"10years_fund_r_squared\"])\n",
    "# FundRating[\"10years_fund_r_squared\"]=np.where(FundRating[\"10years_fund_r_squared\"]<(66), (66), FundRating[\"10years_fund_r_squared\"])\n",
    "\n",
    "# FundRating[\"10yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"10yrs_sharpe_ratio_fund\"]>(1.225), (1.225), FundRating[\"10yrs_sharpe_ratio_fund\"])\n",
    "# FundRating[\"10yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"10yrs_sharpe_ratio_fund\"]<(0.7050), (0.7050), FundRating[\"10yrs_sharpe_ratio_fund\"])\n",
    "\n",
    "# FundRating[\"10_years_alpha_fund\"]=np.where(FundRating[\"10_years_alpha_fund\"]>(2.085), (2.085), FundRating[\"10_years_alpha_fund\"])\n",
    "# FundRating[\"10_years_alpha_fund\"]=np.where(FundRating[\"10_years_alpha_fund\"]<(-2.675), (-2.675), FundRating[\"10_years_alpha_fund\"])\n",
    "\n",
    "# FundRating[\"10years_fund_std\"]=np.where(FundRating[\"10years_fund_std\"]>(20.6299), (20.6299), FundRating[\"10years_fund_std\"])\n",
    "# FundRating[\"10years_fund_std\"]=np.where(FundRating[\"10years_fund_std\"]<(3.2), (3.2), FundRating[\"10years_fund_std\"])\n",
    "\n",
    "# FundRating[\"10yrs_sharpe_ratio_category\"]=np.where(FundRating[\"10yrs_sharpe_ratio_category\"]>(0.01), (0.01), FundRating[\"10yrs_sharpe_ratio_category\"])\n",
    "# FundRating[\"10yrs_sharpe_ratio_category\"]=np.where(FundRating[\"10yrs_sharpe_ratio_category\"]<(0.01), (0.01), FundRating[\"10yrs_sharpe_ratio_category\"])\n",
    "\n",
    "# FundRating[\"10_years_beta_fund\"]=np.where(FundRating[\"10_years_beta_fund\"]>(1.210), (1.210), FundRating[\"10_years_beta_fund\"])\n",
    "# FundRating[\"10_years_beta_fund\"]=np.where(FundRating[\"10_years_beta_fund\"]<(0.8099), (0.8099), FundRating[\"10_years_beta_fund\"])\n",
    "\n",
    "# FundRating[\"10_years_return_mean_annual_category\"]=np.where(FundRating[\"10_years_return_mean_annual_category\"]>(0.01), (0.01), FundRating[\"10_years_return_mean_annual_category\"])\n",
    "# FundRating[\"10_years_return_mean_annual_category\"]=np.where(FundRating[\"10_years_return_mean_annual_category\"]<(0.01), (0.01), FundRating[\"10_years_return_mean_annual_category\"])\n",
    "\n",
    "# FundRating[\"10yrs_treynor_ratio_category\"]=np.where(FundRating[\"10yrs_treynor_ratio_category\"]>(0.2450), (0.2450), FundRating[\"10yrs_treynor_ratio_category\"])\n",
    "# FundRating[\"10yrs_treynor_ratio_category\"]=np.where(FundRating[\"10yrs_treynor_ratio_category\"]<(-0.035), (-0.035), FundRating[\"10yrs_treynor_ratio_category\"])\n",
    "\n",
    "# FundRating[\"10_years_return_fund\"]=np.where(FundRating[\"10_years_return_fund\"]>(30.9), (30.9), FundRating[\"10_years_return_fund\"])\n",
    "# FundRating[\"10_years_return_fund\"]=np.where(FundRating[\"10_years_return_fund\"]<(-18.54), (-18.54), FundRating[\"10_years_return_fund\"])\n",
    "\n",
    "# FundRating[\"10_years_alpha_category\"]=np.where(FundRating[\"10_years_alpha_category\"]>(0.055), (0.055), FundRating[\"10_years_alpha_category\"])\n",
    "# FundRating[\"10_years_alpha_category\"]=np.where(FundRating[\"10_years_alpha_category\"]<(-0.065), (-0.065), FundRating[\"10_years_alpha_category\"])\n",
    "\n",
    "\n",
    "# FundRating[\"10_years_beta_category\"]=np.where(FundRating[\"10_years_beta_category\"]>(0.01), (0.01), FundRating[\"10_years_beta_category\"])\n",
    "# FundRating[\"10_years_beta_category\"]=np.where(FundRating[\"10_years_beta_category\"]<(0.01), (0.01), FundRating[\"10_years_beta_category\"])\n",
    "\n",
    "# FundRating[\"10years_category_std\"]=np.where(FundRating[\"10years_category_std\"]>(0.2850), (0.2850), FundRating[\"10years_category_std\"])\n",
    "# FundRating[\"10years_category_std\"]=np.where(FundRating[\"10years_category_std\"]<(-0.0750), (-0.0750), FundRating[\"10years_category_std\"])\n",
    "\n",
    "# FundRating[\"10_years_return_mean_annual_fund\"]=np.where(FundRating[\"10_years_return_mean_annual_fund\"]>(1.5250), (1.5250), FundRating[\"10_years_return_mean_annual_fund\"])\n",
    "# FundRating[\"10_years_return_mean_annual_fund\"]=np.where(FundRating[\"10_years_return_mean_annual_fund\"]<(0.29), (0.29), FundRating[\"10_years_return_mean_annual_fund\"])\n",
    "\n",
    "# FundRating[\"10_years_return_category\"]=np.where(FundRating[\"10_years_return_category\"]>(25.6399), (25.6399), FundRating[\"10_years_return_category\"])\n",
    "# FundRating[\"10_years_return_category\"]=np.where(FundRating[\"10_years_return_category\"]<(-5.0799), (-5.0799), FundRating[\"10_years_return_category\"])\n",
    "\n",
    "# FundRating[\"fund_return_3years.1\"]=np.where(FundRating[\"fund_return_3years.1\"]>(21.2299), (21.2299), FundRating[\"fund_return_3years.1\"])\n",
    "# FundRating[\"fund_return_3years.1\"]=np.where(FundRating[\"fund_return_3years.1\"]<(-8.2099), (-8.2099), FundRating[\"fund_return_3years.1\"])\n",
    "\n",
    "# FundRating[\"10years_category_r_squared\"]=np.where(FundRating[\"10years_category_r_squared\"]>(1.235), (1.235), FundRating[\"10years_category_r_squared\"])\n",
    "# FundRating[\"10years_category_r_squared\"]=np.where(FundRating[\"10years_category_r_squared\"]<(0.4), (0.4), FundRating[\"10years_category_r_squared\"])\n",
    "\n",
    "# FundRating[\"category_return_1month\"]=np.where(FundRating[\"category_return_1month\"]>(2.76), (2.76), FundRating[\"category_return_1month\"])\n",
    "# FundRating[\"category_return_1month\"]=np.where(FundRating[\"category_return_1month\"]<(-0.9199), (-0.9199), FundRating[\"category_return_1month\"])\n",
    "\n",
    "# FundRating[\"2014_return_fund\"]=np.where(FundRating[\"2014_return_fund\"]>(12.645), (12.645), FundRating[\"2014_return_fund\"])\n",
    "# FundRating[\"2014_return_fund\"]=np.where(FundRating[\"2014_return_fund\"]<(-2.275), (-2.275), FundRating[\"2014_return_fund\"])\n",
    "\n",
    "# FundRating[\"years_down\"]=np.where(FundRating[\"years_down\"]>(7.0), (7.0), FundRating[\"years_down\"])\n",
    "# FundRating[\"years_down\"]=np.where(FundRating[\"years_down\"]<(-1.0), (-1.0), FundRating[\"years_down\"])\n",
    "\n",
    "\n",
    "# FundRating[\"ytd_return_fund\"]=np.where(FundRating[\"ytd_return_fund\"]>(25.93), (25.93), FundRating[\"ytd_return_fund\"])\n",
    "# FundRating[\"ytd_return_fund\"]=np.where(FundRating[\"ytd_return_fund\"]<(-8.39), (-8.39), FundRating[\"ytd_return_fund\"])\n",
    "\n",
    "# FundRating[\"2011_return_category\"]=np.where(FundRating[\"2011_return_category\"]>(1.815), (1.815), FundRating[\"2011_return_category\"])\n",
    "# FundRating[\"2011_return_category\"]=np.where(FundRating[\"2011_return_category\"]<(-5.025), (-5.025), FundRating[\"2011_return_category\"])\n",
    "\n",
    "# FundRating[\"cash_percent_of_portfolio\"]=np.where(FundRating[\"cash_percent_of_portfolio\"]>(15.715), (15.715), FundRating[\"cash_percent_of_portfolio\"])\n",
    "# FundRating[\"cash_percent_of_portfolio\"]=np.where(FundRating[\"cash_percent_of_portfolio\"]<(-7.445), (-7.445), FundRating[\"cash_percent_of_portfolio\"])\n",
    "\n",
    "# FundRating[\"category_return_1year\"]=np.where(FundRating[\"category_return_1year\"]>(10.3099), (10.3099),FundRating[\"category_return_1year\"])\n",
    "# FundRating[\"category_return_1year\"]=np.where(FundRating[\"category_return_1year\"]<(-5.1299), (-5.1299),FundRating[\"category_return_1year\"])\n",
    "\n",
    "# FundRating[\"2018_return_category\"]=np.where(FundRating[\"2018_return_category\"]>(8.0950),(8.0950), FundRating[\"2018_return_category\"])\n",
    "# FundRating[\"2018_return_category\"]=np.where(FundRating[\"2018_return_category\"]<(-19.0650),(-19.0650),FundRating[\"2018_return_category\"])\n",
    "\n",
    "# FundRating[\"years_up\"]=np.where(FundRating[\"years_up\"]>(25.5), (25.5), FundRating[\"years_up\"])\n",
    "# FundRating[\"years_up\"]=np.where(FundRating[\"years_up\"]<(-10.5), (-10.5), FundRating[\"years_up\"])\n",
    "\n",
    "\n",
    "# FundRating[\"2012_return_category\"]=np.where(FundRating[\"2012_return_category\"]>(17.8550), (17.8550), FundRating[\"2012_return_category\"])\n",
    "# FundRating[\"2012_return_category\"]=np.where(FundRating[\"2012_return_category\"]<(10.13), (10.14), FundRating[\"2012_return_category\"])\n",
    "\n",
    "# FundRating[\"bb_rating\"]=np.where(FundRating[\"bb_rating\"]>(2.42), (6.05), FundRating[\"bb_rating\"])\n",
    "# FundRating[\"bb_rating\"]=np.where(FundRating[\"bb_rating\"]<(-3.63), (-3.63), FundRating[\"bb_rating\"])\n",
    "\n",
    "# FundRating[\"below_b_rating\"]=np.where(FundRating[\"below_b_rating\"]>(0.25),(0.25),FundRating[\"below_b_rating\"])\n",
    "# FundRating[\"below_b_rating\"]=np.where(FundRating[\"below_b_rating\"]<(-0.1500), (-0.1500), FundRating[\"below_b_rating\"])\n",
    "\n",
    "# FundRating[\"2014_category_return\"]=np.where(FundRating[\"2014_category_return\"]>(12.7399),(12.7399),FundRating[\"2014_category_return\"])\n",
    "# FundRating[\"2014_category_return\"]=np.where(FundRating[\"2014_category_return\"]< (-3.1799), (-3.1799), FundRating[\"2014_category_return\"])\n",
    "\n",
    "# FundRating[\"total_assets\"]=np.where(FundRating[\"total_assets\"]>(3908895000.0),(3908895000.0),FundRating[\"total_assets\"])\n",
    "# FundRating[\"total_assets\"]=np.where(FundRating[\"total_assets\"]<(-2194825000.0), (-2194825000.0), FundRating[\"total_assets\"])\n",
    "\n",
    "# FundRating[\"return_ytd\"]=np.where(FundRating[\"return_ytd\"]>(25.93),(25.93),FundRating['return_ytd'])\n",
    "# FundRating[\"return_ytd\"]=np.where(FundRating[\"return_ytd\"]<(-8.39),(-8.39), FundRating['return_ytd'])\n",
    "\n",
    "# FundRating[\"yield\"]=np.where(FundRating[\"yield\"]>(5.8800),(5.8800),FundRating['yield'])\n",
    "# FundRating[\"yield\"]=np.where(FundRating[\"yield\"]<(-2.7600),(-2.7600), FundRating['yield'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # FundRating=replace_outlier(FundRating, \"bb_rating\")\n",
    "# df_columns = FundRating.columns\n",
    "# for i in range(df_columns.size):\n",
    "#     print(df_columns[i])\n",
    "#     FundRating=replace_outlier(FundRating, df_columns[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  import numpy as np\n",
    "# FundRating[\"pb_ratio\"]=np.where(FundRating[\"pb_ratio\"]>(4.5), FundRating[\"pb_ratio\"].median(), FundRating[\"pb_ratio\"])\n",
    "# FundRating[\"pb_ratio\"]=np.where(FundRating[\"pb_ratio\"]<(-2.155), FundRating[\"pb_ratio\"].median(), FundRating[\"pb_ratio\"])\n",
    "\n",
    "# FundRating[\"fund_ratio_net_annual_expense\"]=np.where(FundRating[\"fund_ratio_net_annual_expense\"]>(2.52), FundRating[\"fund_ratio_net_annual_expense\"].median(), FundRating[\"fund_ratio_net_annual_expense\"])\n",
    "# FundRating[\"fund_ratio_net_annual_expense\"]=np.where(FundRating[\"fund_ratio_net_annual_expense\"]<(-0.3600), FundRating[\"fund_ratio_net_annual_expense\"].median(), FundRating[\"fund_ratio_net_annual_expense\"])\n",
    "\n",
    "# FundRating[\"portfolio_cyclical_consumer_allocation\"]=np.where(FundRating[\"portfolio_cyclical_consumer_allocation\"]>(33.0), FundRating[\"portfolio_cyclical_consumer_allocation\"].median(), FundRating[\"portfolio_cyclical_consumer_allocation\"])\n",
    "# FundRating[\"portfolio_cyclical_consumer_allocation\"]=np.where(FundRating[\"portfolio_cyclical_consumer_allocation\"]<(-19.799), FundRating[\"portfolio_cyclical_consumer_allocation\"].median(), FundRating[\"portfolio_cyclical_consumer_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_utils_allocation\"]=np.where(FundRating[\"portfolio_utils_allocation\"]>(8.35), FundRating[\"portfolio_utils_allocation\"].median(), FundRating[\"portfolio_utils_allocation\"])\n",
    "# FundRating[\"portfolio_utils_allocation\"]=np.where(FundRating[\"portfolio_utils_allocation\"]<(-5.01), FundRating[\"portfolio_utils_allocation\"].median(), FundRating[\"portfolio_utils_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_property_allocation\"]=np.where(FundRating[\"portfolio_property_allocation\"]>(11.08125), FundRating[\"portfolio_property_allocation\"].median(), FundRating[\"portfolio_property_allocation\"])\n",
    "# FundRating[\"portfolio_property_allocation\"]=np.where(FundRating[\"portfolio_property_allocation\"]<(-6.6487), FundRating[\"portfolio_property_allocation\"].median(), FundRating[\"portfolio_property_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_healthcare_allocation\"]=np.where(FundRating[\"portfolio_healthcare_allocation\"]>(33.875), FundRating[\"portfolio_healthcare_allocation\"].median(), FundRating[\"portfolio_healthcare_allocation\"])\n",
    "# FundRating[\"portfolio_healthcare_allocation\"]=np.where(FundRating[\"portfolio_healthcare_allocation\"]<(-20.3250), FundRating[\"portfolio_healthcare_allocation\"].median(), FundRating[\"portfolio_healthcare_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_consumer_defence_allocation\"]=np.where(FundRating[\"portfolio_consumer_defence_allocation\"]>(19.675), FundRating[\"portfolio_consumer_defence_allocation\"].median(), FundRating[\"portfolio_consumer_defence_allocation\"])\n",
    "# FundRating[\"portfolio_consumer_defence_allocation\"]=np.where(FundRating[\"portfolio_consumer_defence_allocation\"]<(-11.805), FundRating[\"portfolio_consumer_defence_allocation\"].median(), FundRating[\"portfolio_consumer_defence_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_energy_allocation\"]=np.where(FundRating[\"portfolio_energy_allocation\"]>(15.5562), FundRating[\"portfolio_energy_allocation\"].median(), FundRating[\"portfolio_energy_allocation\"])\n",
    "# FundRating[\"portfolio_energy_allocation\"]=np.where(FundRating[\"portfolio_energy_allocation\"]<(-9.3337), FundRating[\"portfolio_energy_allocation\"].median(), FundRating[\"portfolio_energy_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_materials_basic_allocation\"]=np.where(FundRating[\"portfolio_materials_basic_allocation\"]>(12.625), FundRating[\"portfolio_materials_basic_allocation\"].median(), FundRating[\"portfolio_materials_basic_allocation\"])\n",
    "# FundRating[\"portfolio_materials_basic_allocation\"]=np.where(FundRating[\"portfolio_materials_basic_allocation\"]<(-7.5749), FundRating[\"portfolio_materials_basic_allocation\"].median(), FundRating[\"portfolio_materials_basic_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_tech_allocation\"]=np.where(FundRating[\"portfolio_tech_allocation\"]>(48.97499), FundRating[\"portfolio_tech_allocation\"].median(), FundRating[\"portfolio_tech_allocation\"])\n",
    "# FundRating[\"portfolio_tech_allocation\"]=np.where(FundRating[\"portfolio_tech_allocation\"]<(-29.3849), FundRating[\"portfolio_tech_allocation\"].median(), FundRating[\"portfolio_tech_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_industrials_allocation\"]=np.where(FundRating[\"portfolio_industrials_allocation\"]>(31.825), FundRating[\"portfolio_industrials_allocation\"].median(), FundRating[\"portfolio_industrials_allocation\"])\n",
    "# FundRating[\"portfolio_industrials_allocation\"]=np.where(FundRating[\"portfolio_industrials_allocation\"]<(-19.095), FundRating[\"portfolio_industrials_allocation\"].median(), FundRating[\"portfolio_industrials_allocation\"])\n",
    "\n",
    "# FundRating[\"portfolio_financial_services\"]=np.where(FundRating[\"portfolio_financial_services\"]>(44.75), (44.75), FundRating[\"portfolio_financial_services\"])\n",
    "# FundRating[\"portfolio_financial_services\"]=np.where(FundRating[\"portfolio_financial_services\"]<(-26.8499), (-26.8499), FundRating[\"portfolio_financial_services\"])\n",
    "\n",
    "# FundRating[\"portfolio_communication_allocation\"]=np.where(FundRating[\"portfolio_communication_allocation\"]>(8.5), (8.5), FundRating[\"portfolio_communication_allocation\"])\n",
    "# FundRating[\"portfolio_communication_allocation\"]=np.where(FundRating[\"portfolio_communication_allocation\"]<(-5.1), (-5.1), FundRating[\"portfolio_communication_allocation\"])\n",
    "    \n",
    "# FundRating[\"duration_bond\"]=np.where(FundRating[\"duration_bond\"]>(4.8), FundRating[\"duration_bond\"].median(), FundRating[\"duration_bond\"])\n",
    "# FundRating[\"duration_bond\"]=np.where(FundRating[\"duration_bond\"]<(4.8), FundRating[\"duration_bond\"].median(), FundRating[\"duration_bond\"])\n",
    "\n",
    "# FundRating[\"bbb_rating\"]=np.where(FundRating[\"bbb_rating\"]>(28.3499), FundRating[\"bbb_rating\"].median(), FundRating[\"bbb_rating\"])\n",
    "# FundRating[\"bbb_rating\"]=np.where(FundRating[\"bbb_rating\"]<(-17.009), FundRating[\"bbb_rating\"].median(), FundRating[\"bbb_rating\"])\n",
    "\n",
    "# FundRating[\"aa_rating\"]=np.where(FundRating[\"aa_rating\"]>(7.4750), FundRating[\"aa_rating\"].median(), FundRating[\"aa_rating\"])\n",
    "# FundRating[\"aa_rating\"]=np.where(FundRating[\"aa_rating\"]<(-4.485), FundRating[\"aa_rating\"].median(), FundRating[\"aa_rating\"])\n",
    "\n",
    "# FundRating[\"aaa_rating\"]=np.where(FundRating[\"aaa_rating\"]>(46.0500), FundRating[\"aaa_rating\"].median(), FundRating[\"aaa_rating\"])\n",
    "# FundRating[\"aaa_rating\"]=np.where(FundRating[\"aaa_rating\"]<(-27.6300), FundRating[\"aaa_rating\"].median(), FundRating[\"aaa_rating\"])\n",
    "\n",
    "# FundRating[\"a_rating\"]=np.where(FundRating[\"a_rating\"]>(20.6937), FundRating[\"a_rating\"].median(), FundRating[\"a_rating\"])\n",
    "# FundRating[\"a_rating\"]=np.where(FundRating[\"a_rating\"]<(-12.4162), FundRating[\"a_rating\"].median(), FundRating[\"a_rating\"])\n",
    "\n",
    "# FundRating[\"maturity_bond\"]=np.where(FundRating[\"maturity_bond\"]>(7.29), FundRating[\"maturity_bond\"].median(), FundRating[\"maturity_bond\"])\n",
    "# FundRating[\"maturity_bond\"]=np.where(FundRating[\"maturity_bond\"]<(7.29), FundRating[\"maturity_bond\"].median(), FundRating[\"maturity_bond\"])\n",
    "\n",
    "# FundRating[\"others_rating\"]=np.where(FundRating[\"others_rating\"]>(0.8), FundRating[\"others_rating\"].median(), FundRating[\"others_rating\"])\n",
    "# FundRating[\"others_rating\"]=np.where(FundRating[\"others_rating\"]<(-0.48), FundRating[\"others_rating\"].median(), FundRating[\"others_rating\"])\n",
    "\n",
    "# FundRating[\"2013_return_fund\"]=np.where(FundRating[\"2013_return_fund\"]>(47.7037), FundRating[\"2013_return_fund\"].median(), FundRating[\"2013_return_fund\"])\n",
    "# FundRating[\"2013_return_fund\"]=np.where(FundRating[\"2013_return_fund\"]<(-13.1862), FundRating[\"2013_return_fund\"].median(), FundRating[\"2013_return_fund\"])\n",
    "\n",
    "# FundRating[\"fund_return_3months\"]=np.where(FundRating[\"fund_return_3months\"]>(26.2000), FundRating[\"fund_return_3months\"].median(), FundRating[\"fund_return_3months\"])\n",
    "# FundRating[\"fund_return_3months\"]=np.where(FundRating[\"fund_return_3months\"]<(-9.0), FundRating[\"fund_return_3months\"].median(), FundRating[\"fund_return_3months\"])\n",
    "\n",
    "# FundRating[\"ytd_return_category\"]=np.where(FundRating[\"ytd_return_category\"]>(24.82), FundRating[\"ytd_return_category\"].median(), FundRating[\"ytd_return_category\"])\n",
    "# FundRating[\"ytd_return_category\"]=np.where(FundRating[\"ytd_return_category\"]<(-6.8599), FundRating[\"ytd_return_category\"].median(), FundRating[\"ytd_return_category\"])\n",
    "\n",
    "# FundRating[\"2017_category_return\"]=np.where(FundRating[\"2017_category_return\"]>(44.045), FundRating[\"2017_category_return\"].median(), FundRating[\"2017_category_return\"])\n",
    "# FundRating[\"2017_category_return\"]=np.where(FundRating[\"2017_category_return\"]<(-16.0750), FundRating[\"2017_category_return\"].median(), FundRating[\"2017_category_return\"])\n",
    "\n",
    "# FundRating[\"1_year_return_fund\"]=np.where(FundRating[\"1_year_return_fund\"]>(12.795), FundRating[\"1_year_return_fund\"].median(), FundRating[\"1_year_return_fund\"])\n",
    "# FundRating[\"1_year_return_fund\"]=np.where(FundRating[\"1_year_return_fund\"]<(-7.7250), FundRating[\"1_year_return_fund\"].median(), FundRating[\"1_year_return_fund\"])\n",
    "\n",
    "# FundRating[\"2015_return_fund\"]=np.where(FundRating[\"2015_return_fund\"]>(4.79), FundRating[\"2015_return_fund\"].median(), FundRating[\"2015_return_fund\"])\n",
    "# FundRating[\"2015_return_fund\"]=np.where(FundRating[\"2015_return_fund\"]<(-7.21), FundRating[\"2015_return_fund\"].median(), FundRating[\"2015_return_fund\"])\n",
    "\n",
    "# FundRating[\"portfolio_convertable\"]=np.where(FundRating[\"portfolio_convertable\"]>(0.1750), FundRating[\"portfolio_convertable\"].median(), FundRating[\"portfolio_convertable\"])\n",
    "# FundRating[\"portfolio_convertable\"]=np.where(FundRating[\"portfolio_convertable\"]<(-0.1050), FundRating[\"portfolio_convertable\"].median(), FundRating[\"portfolio_convertable\"])\n",
    "\n",
    "# FundRating[\"3_months_return_category\"]=np.where(FundRating[\"3_months_return_category\"]>(24.82), FundRating[\"3_months_return_category\"].median(), FundRating[\"3_months_return_category\"])\n",
    "# FundRating[\"3_months_return_category\"]=np.where(FundRating[\"3_months_return_category\"]<(-6.8599), FundRating[\"3_months_return_category\"].median(), FundRating[\"3_months_return_category\"])\n",
    "\n",
    "# FundRating[\"portfolio_others\"]=np.where(FundRating[\"portfolio_others\"]>(0.7000), FundRating[\"portfolio_others\"].median(), FundRating[\"portfolio_others\"])\n",
    "# FundRating[\"portfolio_others\"]=np.where(FundRating[\"portfolio_others\"]<(-0.4200), FundRating[\"portfolio_others\"].median(), FundRating[\"portfolio_others\"])\n",
    "\n",
    "# FundRating[\"2016_return_fund\"]=np.where(FundRating[\"2016_return_fund\"]>(19.7200), FundRating[\"2016_return_fund\"].median(), FundRating[\"2016_return_fund\"])\n",
    "# FundRating[\"2016_return_fund\"]=np.where(FundRating[\"2016_return_fund\"]<(-7.160), FundRating[\"2016_return_fund\"].median(), FundRating[\"2016_return_fund\"])\n",
    "\n",
    "# FundRating[\"stock_percent_of_portfolio\"]=np.where(FundRating[\"stock_percent_of_portfolio\"]>(243.11), FundRating[\"stock_percent_of_portfolio\"].median(), FundRating[\"stock_percent_of_portfolio\"])\n",
    "# FundRating[\"stock_percent_of_portfolio\"]=np.where(FundRating[\"stock_percent_of_portfolio\"]<(-144.95), FundRating[\"stock_percent_of_portfolio\"].median(), FundRating[\"stock_percent_of_portfolio\"])\n",
    "\n",
    "# FundRating[\"2016_return_category\"]=np.where(FundRating[\"2016_return_category\"]>(18.28), FundRating[\"2016_return_category\"].median(), FundRating[\"2016_return_category\"])\n",
    "# FundRating[\"2016_return_category\"]=np.where(FundRating[\"2016_return_category\"]<(-5.7999), FundRating[\"2016_return_category\"].median(), FundRating[\"2016_return_category\"])\n",
    "\n",
    "# FundRating[\"2011_return_fund\"]=np.where(FundRating[\"2011_return_fund\"]>(2.945), FundRating[\"2011_return_fund\"].median(), FundRating[\"2011_return_fund\"])\n",
    "# FundRating[\"2011_return_fund\"]=np.where(FundRating[\"2011_return_fund\"]<(-4.055), FundRating[\"2011_return_fund\"].median(), FundRating[\"2011_return_fund\"])\n",
    "\n",
    "# FundRating[\"2010_return_fund\"]=np.where(FundRating[\"2010_return_fund\"]>(13.5962), FundRating[\"2010_return_fund\"].median(), FundRating[\"2010_return_fund\"])\n",
    "# FundRating[\"2010_return_fund\"]=np.where(FundRating[\"2010_return_fund\"]<(12.6), FundRating[\"2010_return_fund\"].median(), FundRating[\"2010_return_fund\"])\n",
    "\n",
    "# FundRating[\"fund_return_3years\"]=np.where(FundRating[\"fund_return_3years\"]>(21.2299), FundRating[\"fund_return_3years\"].median(), FundRating[\"fund_return_3years\"])\n",
    "# FundRating[\"fund_return_3years\"]=np.where(FundRating[\"fund_return_3years\"]<(-8.2099), FundRating[\"fund_return_3years\"].median(), FundRating[\"fund_return_3years\"])\n",
    "\n",
    "# FundRating[\"2012_fund_return\"]=np.where(FundRating[\"2012_fund_return\"]>(18.4049), FundRating[\"2012_fund_return\"].median(), FundRating[\"2012_fund_return\"])\n",
    "# FundRating[\"2012_fund_return\"]=np.where(FundRating[\"2012_fund_return\"]<(8.5250), FundRating[\"2012_fund_return\"].median(), FundRating[\"2012_fund_return\"])\n",
    "    \n",
    "# FundRating[\"2018_return_fund\"]=np.where(FundRating[\"2018_return_fund\"]>(10.73), FundRating[\"2018_return_fund\"].median(), FundRating[\"2018_return_fund\"])\n",
    "# FundRating[\"2018_return_fund\"]=np.where(FundRating[\"2018_return_fund\"]<(-22.79), FundRating[\"2018_return_fund\"].median(), FundRating[\"2018_return_fund\"])\n",
    "\n",
    "# FundRating[\"2017_return_fund\"]=np.where(FundRating[\"2017_return_fund\"]>(42.2237), FundRating[\"2017_return_fund\"].median(), FundRating[\"2017_return_fund\"])\n",
    "# FundRating[\"2017_return_fund\"]=np.where(FundRating[\"2017_return_fund\"]<(-14.1062), FundRating[\"2017_return_fund\"].median(), FundRating[\"2017_return_fund\"])\n",
    "\n",
    "# FundRating[\"category_ratio_net_annual_expense\"]=np.where(FundRating[\"category_ratio_net_annual_expense\"]>(1.7349), FundRating[\"category_ratio_net_annual_expense\"].median(), FundRating[\"category_ratio_net_annual_expense\"])\n",
    "# FundRating[\"category_ratio_net_annual_expense\"]=np.where(FundRating[\"category_ratio_net_annual_expense\"]<(0.2550), FundRating[\"category_ratio_net_annual_expense\"].median(), FundRating[\"category_ratio_net_annual_expense\"])\n",
    "\n",
    "# FundRating[\"category_return_2015\"]=np.where(FundRating[\"category_return_2015\"]>(1.4950), FundRating[\"category_return_2015\"].median(), FundRating[\"category_return_2015\"])\n",
    "# FundRating[\"category_return_2015\"]=np.where(FundRating[\"category_return_2015\"]<(-5.025), FundRating[\"category_return_2015\"].median(), FundRating[\"category_return_2015\"])\n",
    "\n",
    "# FundRating[\"1_month_fund_return\"]=np.where(FundRating[\"1_month_fund_return\"]>(3.75), FundRating[\"1_month_fund_return\"].median(), FundRating[\"1_month_fund_return\"])\n",
    "# FundRating[\"1_month_fund_return\"]=np.where(FundRating[\"1_month_fund_return\"]<(-1.69), FundRating[\"1_month_fund_return\"].median(), FundRating[\"1_month_fund_return\"])\n",
    "\n",
    "# FundRating[\"bond_percentage_of_porfolio\"]=np.where(FundRating[\"bond_percentage_of_porfolio\"]>(159.25), FundRating[\"bond_percentage_of_porfolio\"].median(), FundRating[\"bond_percentage_of_porfolio\"])\n",
    "# FundRating[\"bond_percentage_of_porfolio\"]=np.where(FundRating[\"bond_percentage_of_porfolio\"]<(-95.5500), FundRating[\"bond_percentage_of_porfolio\"].median(), FundRating[\"bond_percentage_of_porfolio\"])\n",
    "\n",
    "# FundRating[\"portfolio_preferred\"]=np.where(FundRating[\"portfolio_preferred\"]>(0.025), FundRating[\"portfolio_preferred\"].median(), FundRating[\"portfolio_preferred\"])\n",
    "# FundRating[\"portfolio_preferred\"]=np.where(FundRating[\"portfolio_preferred\"]<(-0.015), FundRating[\"portfolio_preferred\"].median(), FundRating[\"portfolio_preferred\"])\n",
    "\n",
    "# FundRating[\"2010_return_category\"]=np.where(FundRating[\"2010_return_category\"]>(14.115), FundRating[\"2010_return_category\"].median(), FundRating[\"2010_return_category\"])\n",
    "# FundRating[\"2010_return_category\"]=np.where(FundRating[\"2010_return_category\"]<(13.115), FundRating[\"2010_return_category\"].median(), FundRating[\"2010_return_category\"])\n",
    "\n",
    "# FundRating[\"2013_category_return\"]=np.where(FundRating[\"2013_category_return\"]>(52.13), FundRating[\"2013_category_return\"].median(), FundRating[\"2013_category_return\"])\n",
    "# FundRating[\"2013_category_return\"]=np.where(FundRating[\"2013_category_return\"]<(-19.71), FundRating[\"2013_category_return\"].median(), FundRating[\"2013_category_return\"])\n",
    "\n",
    "# FundRating[\"3_years_alpha_fund\"]=np.where(FundRating[\"3_years_alpha_fund\"]>(4.6599), FundRating[\"3_years_alpha_fund\"].median(), FundRating[\"3_years_alpha_fund\"])\n",
    "# FundRating[\"3_years_alpha_fund\"]=np.where(FundRating[\"3_years_alpha_fund\"]<(-5.8999), FundRating[\"3_years_alpha_fund\"].median(), FundRating[\"3_years_alpha_fund\"])\n",
    "\n",
    "# FundRating[\"3years_category_std\"]=np.where(FundRating[\"3years_category_std\"]>(0.265), FundRating[\"3years_category_std\"].median(), FundRating[\"3years_category_std\"])\n",
    "# FundRating[\"3years_category_std\"]=np.where(FundRating[\"3years_category_std\"]<(-0.095), FundRating[\"3years_category_std\"].median(), FundRating[\"3years_category_std\"])\n",
    "\n",
    "# FundRating[\"3yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"3yrs_sharpe_ratio_fund\"]>(1.6849), FundRating[\"3yrs_sharpe_ratio_fund\"].median(), FundRating[\"3yrs_sharpe_ratio_fund\"])\n",
    "# FundRating[\"3yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"3yrs_sharpe_ratio_fund\"]<(-0.2749), FundRating[\"3yrs_sharpe_ratio_fund\"].median(), FundRating[\"3yrs_sharpe_ratio_fund\"])\n",
    "\n",
    "# FundRating[\"3yrs_treynor_ratio_category\"]=np.where(FundRating[\"3yrs_treynor_ratio_category\"]>(0.1750), FundRating[\"3yrs_treynor_ratio_category\"].median(), FundRating[\"3yrs_treynor_ratio_category\"])\n",
    "# FundRating[\"3yrs_treynor_ratio_category\"]=np.where(FundRating[\"3yrs_treynor_ratio_category\"]<(-0.0250), FundRating[\"3yrs_treynor_ratio_category\"].median(), FundRating[\"3yrs_treynor_ratio_category\"])\n",
    "\n",
    "# FundRating[\"3_years_return_mean_annual_fund\"]=np.where(FundRating[\"3_years_return_mean_annual_fund\"]>(1.635), FundRating[\"3_years_return_mean_annual_fund\"].median(), FundRating[\"3_years_return_mean_annual_fund\"])\n",
    "# FundRating[\"3_years_return_mean_annual_fund\"]=np.where(FundRating[\"3_years_return_mean_annual_fund\"]<(-0.405), FundRating[\"3_years_return_mean_annual_fund\"].median(), FundRating[\"3_years_return_mean_annual_fund\"])\n",
    "\n",
    "# FundRating[\"fund_beta_3years\"]=np.where(FundRating[\"fund_beta_3years\"]>(1.6150), FundRating[\"fund_beta_3years\"].median(), FundRating[\"fund_beta_3years\"])\n",
    "# FundRating[\"fund_beta_3years\"]=np.where(FundRating[\"fund_beta_3years\"]<(0.3), FundRating[\"fund_beta_3years\"].median(), FundRating[\"fund_beta_3years\"])\n",
    "\n",
    "# FundRating[\"3years_fund_r_squared\"]=np.where(FundRating[\"3years_fund_r_squared\"]>(131.43), FundRating[\"3years_fund_r_squared\"].median(), FundRating[\"3years_fund_r_squared\"])\n",
    "# FundRating[\"3years_fund_r_squared\"]=np.where(FundRating[\"3years_fund_r_squared\"]<(26.55), FundRating[\"3years_fund_r_squared\"].median(), FundRating[\"3years_fund_r_squared\"])\n",
    "\n",
    "# FundRating[\"3years_fund_std\"]=np.where(FundRating[\"3years_fund_std\"]>(23.68), FundRating[\"3years_fund_std\"].median(), FundRating[\"3years_fund_std\"])\n",
    "# FundRating[\"3years_fund_std\"]=np.where(FundRating[\"3years_fund_std\"]<(-6.9599), FundRating[\"3years_fund_std\"].median(), FundRating[\"3years_fund_std\"])\n",
    "\n",
    "# FundRating[\"category_beta_3years\"]=np.where(FundRating[\"category_beta_3years\"]>(0.01), FundRating[\"category_beta_3years\"].median(), FundRating[\"category_beta_3years\"])\n",
    "# FundRating[\"category_beta_3years\"]=np.where(FundRating[\"category_beta_3years\"]<(0.01), FundRating[\"category_beta_3years\"].median(), FundRating[\"category_beta_3years\"])\n",
    "\n",
    "# FundRating[\"fund_return_3years\"]=np.where(FundRating[\"fund_return_3years\"]>(21.2299), FundRating[\"fund_return_3years\"].median(), FundRating[\"fund_return_3years\"])\n",
    "# FundRating[\"fund_return_3years\"]=np.where(FundRating[\"fund_return_3years\"]<(-8.2099), FundRating[\"fund_return_3years\"].median(), FundRating[\"fund_return_3years\"])\n",
    "\n",
    "# FundRating[\"3_years_alpha_category\"]=np.where(FundRating[\"3_years_alpha_category\"]>(0.015), FundRating[\"3_years_alpha_category\"].median(), FundRating[\"3_years_alpha_category\"])\n",
    "# FundRating[\"3_years_alpha_category\"]=np.where(FundRating[\"3_years_alpha_category\"]<(-0.025), FundRating[\"3_years_alpha_category\"].median(), FundRating[\"3_years_alpha_category\"])\n",
    "\n",
    "# FundRating[\"3_years_return_mean_annual_category\"]=np.where(FundRating[\"3_years_return_mean_annual_category\"]>(0.025), FundRating[\"3_years_return_mean_annual_category\"].median(), FundRating[\"3_years_return_mean_annual_category\"])\n",
    "# FundRating[\"3_years_return_mean_annual_category\"]=np.where(FundRating[\"3_years_return_mean_annual_category\"]<(-0.015), FundRating[\"3_years_return_mean_annual_category\"].median(), FundRating[\"3_years_return_mean_annual_category\"])\n",
    "\n",
    "# FundRating[\"3yrs_sharpe_ratio_category\"]=np.where(FundRating[\"3yrs_sharpe_ratio_category\"]>(0.025), FundRating[\"3yrs_sharpe_ratio_category\"].median(), FundRating[\"3yrs_sharpe_ratio_category\"])\n",
    "# FundRating[\"3yrs_sharpe_ratio_category\"]=np.where(FundRating[\"3yrs_sharpe_ratio_category\"]<(-0.015), FundRating[\"3yrs_sharpe_ratio_category\"].median(), FundRating[\"3yrs_sharpe_ratio_category\"])\n",
    "\n",
    "# FundRating[\"3years_category_r_squared\"]=np.where(FundRating[\"3years_category_r_squared\"]>(1.28), FundRating[\"3years_category_r_squared\"].median(), FundRating[\"3years_category_r_squared\"])\n",
    "# FundRating[\"3years_category_r_squared\"]=np.where(FundRating[\"3years_category_r_squared\"]<(0.24), FundRating[\"3years_category_r_squared\"].median(), FundRating[\"3years_category_r_squared\"])\n",
    "\n",
    "# FundRating[\"3_years_return_category\"]=np.where(FundRating[\"3_years_return_category\"]>(18.485), FundRating[\"3_years_return_category\"].median(), FundRating[\"3_years_return_category\"])\n",
    "# FundRating[\"3_years_return_category\"]=np.where(FundRating[\"3_years_return_category\"]<(-4.1149), FundRating[\"3_years_return_category\"].median(), FundRating[\"3_years_return_category\"])\n",
    "\n",
    "# FundRating[\"category_r_squared_5years\"]=np.where(FundRating[\"category_r_squared_5years\"]>(1.2650), FundRating[\"category_r_squared_5years\"].median(), FundRating[\"category_r_squared_5years\"])\n",
    "# FundRating[\"category_r_squared_5years\"]=np.where(FundRating[\"category_r_squared_5years\"]<(0.265), FundRating[\"category_r_squared_5years\"].median(), FundRating[\"category_r_squared_5years\"])\n",
    "\n",
    "# FundRating[\"5yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"5yrs_sharpe_ratio_fund\"]>(1.1549), FundRating[\"5yrs_sharpe_ratio_fund\"].median(), FundRating[\"5yrs_sharpe_ratio_fund\"])\n",
    "# FundRating[\"5yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"5yrs_sharpe_ratio_fund\"]<(-0.08499), FundRating[\"5yrs_sharpe_ratio_fund\"].median(), FundRating[\"5yrs_sharpe_ratio_fund\"])\n",
    "\n",
    "# FundRating[\"5years_fund_r_squared\"]=np.where(FundRating[\"5years_fund_r_squared\"]>(125.6749), FundRating[\"5years_fund_r_squared\"].median(), FundRating[\"5years_fund_r_squared\"])\n",
    "# FundRating[\"5years_fund_r_squared\"]=np.where(FundRating[\"5years_fund_r_squared\"]<(33.475), FundRating[\"5years_fund_r_squared\"].median(), FundRating[\"5years_fund_r_squared\"])\n",
    "\n",
    "# FundRating[\"5years_fund_std\"]=np.where(FundRating[\"5years_fund_std\"]>(22.4837), FundRating[\"5years_fund_std\"].median(), FundRating[\"5years_fund_std\"])\n",
    "# FundRating[\"5years_fund_std\"]=np.where(FundRating[\"5years_fund_std\"]<(-4.5662), FundRating[\"5years_fund_std\"].median(), FundRating[\"5years_fund_std\"])\n",
    "\n",
    "# FundRating[\"5yrs_sharpe_ratio_category\"]=np.where(FundRating[\"5yrs_sharpe_ratio_category\"]>(0.025), FundRating[\"5yrs_sharpe_ratio_category\"].median(), FundRating[\"5yrs_sharpe_ratio_category\"])\n",
    "# FundRating[\"5yrs_sharpe_ratio_category\"]=np.where(FundRating[\"5yrs_sharpe_ratio_category\"]<(-0.015), FundRating[\"5yrs_sharpe_ratio_category\"].median(), FundRating[\"5yrs_sharpe_ratio_category\"])\n",
    "\n",
    "# FundRating[\"5_years_alpha_fund\"]=np.where(FundRating[\"5_years_alpha_fund\"]>(3.560), FundRating[\"5_years_alpha_fund\"].median(), FundRating[\"5_years_alpha_fund\"])\n",
    "# FundRating[\"5_years_alpha_fund\"]=np.where(FundRating[\"5_years_alpha_fund\"]<(-4.84), FundRating[\"5_years_alpha_fund\"].median(), FundRating[\"5_years_alpha_fund\"])\n",
    "\n",
    "# FundRating[\"5_years_beta_fund\"]=np.where(FundRating[\"5_years_beta_fund\"]>(1.4150), FundRating[\"5_years_beta_fund\"].median(), FundRating[\"5_years_beta_fund\"])\n",
    "# FundRating[\"5_years_beta_fund\"]=np.where(FundRating[\"5_years_beta_fund\"]<(0.5), FundRating[\"5_years_beta_fund\"].median(), FundRating[\"5_years_beta_fund\"])\n",
    "\n",
    "# FundRating[\"5_years_return_mean_annual_fund\"]=np.where(FundRating[\"5_years_return_mean_annual_fund\"]>(1.0599), FundRating[\"5_years_return_mean_annual_fund\"].median(), FundRating[\"5_years_return_mean_annual_fund\"])\n",
    "# FundRating[\"5_years_return_mean_annual_fund\"]=np.where(FundRating[\"5_years_return_mean_annual_fund\"]<(-0.2199), FundRating[\"5_years_return_mean_annual_fund\"].median(), FundRating[\"5_years_return_mean_annual_fund\"])\n",
    "\n",
    "# FundRating[\"5_years_return_mean_annual_category\"]=np.where(FundRating[\"5_years_return_mean_annual_category\"]>(0.025), FundRating[\"5_years_return_mean_annual_category\"].median(), FundRating[\"5_years_return_mean_annual_category\"])\n",
    "# FundRating[\"5_years_return_mean_annual_category\"]=np.where(FundRating[\"5_years_return_mean_annual_category\"]<(-0.015), FundRating[\"5_years_return_mean_annual_category\"].median(), FundRating[\"5_years_return_mean_annual_category\"])\n",
    "\n",
    "# FundRating[\"5yrs_treynor_ratio_category\"]=np.where(FundRating[\"5yrs_treynor_ratio_category\"]>(0.12), FundRating[\"5yrs_treynor_ratio_category\"].median(), FundRating[\"5yrs_treynor_ratio_category\"])\n",
    "# FundRating[\"5yrs_treynor_ratio_category\"]=np.where(FundRating[\"5yrs_treynor_ratio_category\"]<(-0.0399), FundRating[\"5yrs_treynor_ratio_category\"].median(), FundRating[\"5yrs_treynor_ratio_category\"])\n",
    "\n",
    "# FundRating[\"5_years_return_fund\"]=np.where(FundRating[\"5_years_return_fund\"]>(14.375), FundRating[\"5_years_return_fund\"].median(), FundRating[\"5_years_return_fund\"])\n",
    "# FundRating[\"5_years_return_fund\"]=np.where(FundRating[\"5_years_return_fund\"]<(-6.625), FundRating[\"5_years_return_fund\"].median(), FundRating[\"5_years_return_fund\"])\n",
    "\n",
    "# FundRating[\"5_years_alpha_category\"]=np.where(FundRating[\"5_years_alpha_category\"]>(0.03), FundRating[\"5_years_alpha_category\"].median(), FundRating[\"5_years_alpha_category\"])\n",
    "# FundRating[\"5_years_alpha_category\"]=np.where(FundRating[\"5_years_alpha_category\"]<(-0.05), FundRating[\"5_years_alpha_category\"].median(), FundRating[\"5_years_alpha_category\"])\n",
    "\n",
    "# FundRating[\"5_years_beta_category\"]=np.where(FundRating[\"5_years_beta_category\"]>(0.01), FundRating[\"5_years_beta_category\"].median(), FundRating[\"5_years_beta_category\"])\n",
    "# FundRating[\"5_years_beta_category\"]=np.where(FundRating[\"5_years_beta_category\"]<(0.01), FundRating[\"5_years_beta_category\"].median(), FundRating[\"5_years_beta_category\"])\n",
    "\n",
    "# FundRating[\"5years_category_std\"]=np.where(FundRating[\"5years_category_std\"]>(0.25), FundRating[\"5years_category_std\"].median(), FundRating[\"5years_category_std\"])\n",
    "# FundRating[\"5years_category_std\"]=np.where(FundRating[\"5years_category_std\"]<(-0.0699), FundRating[\"5years_category_std\"].median(), FundRating[\"5years_category_std\"])\n",
    "\n",
    "# FundRating[\"10years_fund_r_squared\"]=np.where(FundRating[\"10years_fund_r_squared\"]>(105.2350), FundRating[\"10years_fund_r_squared\"].median(), FundRating[\"10years_fund_r_squared\"])\n",
    "# FundRating[\"10years_fund_r_squared\"]=np.where(FundRating[\"10years_fund_r_squared\"]<(66), FundRating[\"10years_fund_r_squared\"].median(), FundRating[\"10years_fund_r_squared\"])\n",
    "\n",
    "# FundRating[\"10yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"10yrs_sharpe_ratio_fund\"]>(1.225), FundRating[\"10yrs_sharpe_ratio_fund\"].median(), FundRating[\"10yrs_sharpe_ratio_fund\"])\n",
    "# FundRating[\"10yrs_sharpe_ratio_fund\"]=np.where(FundRating[\"10yrs_sharpe_ratio_fund\"]<(0.7050), FundRating[\"10yrs_sharpe_ratio_fund\"].median(), FundRating[\"10yrs_sharpe_ratio_fund\"])\n",
    "\n",
    "# FundRating[\"10_years_alpha_fund\"]=np.where(FundRating[\"10_years_alpha_fund\"]>(2.085), FundRating[\"10_years_alpha_fund\"].median(), FundRating[\"10_years_alpha_fund\"])\n",
    "# FundRating[\"10_years_alpha_fund\"]=np.where(FundRating[\"10_years_alpha_fund\"]<(-2.675), FundRating[\"10_years_alpha_fund\"].median(), FundRating[\"10_years_alpha_fund\"])\n",
    "\n",
    "# FundRating[\"10years_fund_std\"]=np.where(FundRating[\"10years_fund_std\"]>(20.6299), FundRating[\"10years_fund_std\"].median(), FundRating[\"10years_fund_std\"])\n",
    "# FundRating[\"10years_fund_std\"]=np.where(FundRating[\"10years_fund_std\"]<(3.2), FundRating[\"10years_fund_std\"].median(), FundRating[\"10years_fund_std\"])\n",
    "\n",
    "# FundRating[\"10yrs_sharpe_ratio_category\"]=np.where(FundRating[\"10yrs_sharpe_ratio_category\"]>(0.01), FundRating[\"10yrs_sharpe_ratio_category\"].median(), FundRating[\"10yrs_sharpe_ratio_category\"])\n",
    "# FundRating[\"10yrs_sharpe_ratio_category\"]=np.where(FundRating[\"10yrs_sharpe_ratio_category\"]<(0.01), FundRating[\"10yrs_sharpe_ratio_category\"].median(), FundRating[\"10yrs_sharpe_ratio_category\"])\n",
    "\n",
    "# FundRating[\"10_years_beta_fund\"]=np.where(FundRating[\"10_years_beta_fund\"]>(1.210), FundRating[\"10_years_beta_fund\"].median(), FundRating[\"10_years_beta_fund\"])\n",
    "# FundRating[\"10_years_beta_fund\"]=np.where(FundRating[\"10_years_beta_fund\"]<(0.8099), FundRating[\"10_years_beta_fund\"].median(), FundRating[\"10_years_beta_fund\"])\n",
    "\n",
    "# FundRating[\"10_years_return_mean_annual_category\"]=np.where(FundRating[\"10_years_return_mean_annual_category\"]>(0.01), FundRating[\"10_years_return_mean_annual_category\"].median(), FundRating[\"10_years_return_mean_annual_category\"])\n",
    "# FundRating[\"10_years_return_mean_annual_category\"]=np.where(FundRating[\"10_years_return_mean_annual_category\"]<(0.01), FundRating[\"10_years_return_mean_annual_category\"].median(), FundRating[\"10_years_return_mean_annual_category\"])\n",
    "\n",
    "# FundRating[\"10yrs_treynor_ratio_category\"]=np.where(FundRating[\"10yrs_treynor_ratio_category\"]>(0.2450), FundRating[\"10yrs_treynor_ratio_category\"].median(), FundRating[\"10yrs_treynor_ratio_category\"])\n",
    "# FundRating[\"10yrs_treynor_ratio_category\"]=np.where(FundRating[\"10yrs_treynor_ratio_category\"]<(-0.035), FundRating[\"10yrs_treynor_ratio_category\"].median(), FundRating[\"10yrs_treynor_ratio_category\"])\n",
    "\n",
    "# FundRating[\"10_years_return_fund\"]=np.where(FundRating[\"10_years_return_fund\"]>(30.9), FundRating[\"10_years_return_fund\"].median(), FundRating[\"10_years_return_fund\"])\n",
    "# FundRating[\"10_years_return_fund\"]=np.where(FundRating[\"10_years_return_fund\"]<(-18.54), FundRating[\"10_years_return_fund\"].median(), FundRating[\"10_years_return_fund\"])\n",
    "\n",
    "# FundRating[\"10_years_alpha_category\"]=np.where(FundRating[\"10_years_alpha_category\"]>(0.055), FundRating[\"10_years_alpha_category\"].median(), FundRating[\"10_years_alpha_category\"])\n",
    "# FundRating[\"10_years_alpha_category\"]=np.where(FundRating[\"10_years_alpha_category\"]<(-0.065), FundRating[\"10_years_alpha_category\"].median(), FundRating[\"10_years_alpha_category\"])\n",
    "\n",
    "\n",
    "# FundRating[\"10_years_beta_category\"]=np.where(FundRating[\"10_years_beta_category\"]>(0.01), FundRating[\"10_years_beta_category\"].median(), FundRating[\"10_years_beta_category\"])\n",
    "# FundRating[\"10_years_beta_category\"]=np.where(FundRating[\"10_years_beta_category\"]<(0.01), FundRating[\"10_years_beta_category\"].median(), FundRating[\"10_years_beta_category\"])\n",
    "\n",
    "# FundRating[\"10years_category_std\"]=np.where(FundRating[\"10years_category_std\"]>(0.2850), FundRating[\"10years_category_std\"].median(), FundRating[\"10years_category_std\"])\n",
    "# FundRating[\"10years_category_std\"]=np.where(FundRating[\"10years_category_std\"]<(-0.0750), FundRating[\"10years_category_std\"].median(), FundRating[\"10years_category_std\"])\n",
    "\n",
    "# FundRating[\"10_years_return_mean_annual_fund\"]=np.where(FundRating[\"10_years_return_mean_annual_fund\"]>(1.5250), FundRating[\"10_years_return_mean_annual_fund\"].median(), FundRating[\"10_years_return_mean_annual_fund\"])\n",
    "# FundRating[\"10_years_return_mean_annual_fund\"]=np.where(FundRating[\"10_years_return_mean_annual_fund\"]<(0.29), FundRating[\"10_years_return_mean_annual_fund\"].median(), FundRating[\"10_years_return_mean_annual_fund\"])\n",
    "\n",
    "# FundRating[\"10_years_return_category\"]=np.where(FundRating[\"10_years_return_category\"]>(25.6399), FundRating[\"10_years_return_category\"].median(), FundRating[\"10_years_return_category\"])\n",
    "# FundRating[\"10_years_return_category\"]=np.where(FundRating[\"10_years_return_category\"]<(-5.0799), FundRating[\"10_years_return_category\"].median(), FundRating[\"10_years_return_category\"])\n",
    "\n",
    "# FundRating[\"fund_return_3years.1\"]=np.where(FundRating[\"fund_return_3years.1\"]>(21.2299), FundRating[\"fund_return_3years.1\"].median(), FundRating[\"fund_return_3years.1\"])\n",
    "# FundRating[\"fund_return_3years.1\"]=np.where(FundRating[\"fund_return_3years.1\"]<(-8.2099), FundRating[\"fund_return_3years.1\"].median(), FundRating[\"fund_return_3years.1\"])\n",
    "\n",
    "# FundRating[\"10years_category_r_squared\"]=np.where(FundRating[\"10years_category_r_squared\"]>(1.235), FundRating[\"10years_category_r_squared\"].median(), FundRating[\"10years_category_r_squared\"])\n",
    "# FundRating[\"10years_category_r_squared\"]=np.where(FundRating[\"10years_category_r_squared\"]<(0.4), FundRating[\"10years_category_r_squared\"].median(), FundRating[\"10years_category_r_squared\"])\n",
    "\n",
    "# FundRating[\"category_return_1month\"]=np.where(FundRating[\"category_return_1month\"]>(2.76), FundRating[\"category_return_1month\"].median(), FundRating[\"category_return_1month\"])\n",
    "# FundRating[\"category_return_1month\"]=np.where(FundRating[\"category_return_1month\"]<(-0.9199), FundRating[\"category_return_1month\"].median(), FundRating[\"category_return_1month\"])\n",
    "\n",
    "# FundRating[\"2014_return_fund\"]=np.where(FundRating[\"2014_return_fund\"]>(12.645), FundRating[\"2014_return_fund\"].median(), FundRating[\"2014_return_fund\"])\n",
    "# FundRating[\"2014_return_fund\"]=np.where(FundRating[\"2014_return_fund\"]<(-2.275), FundRating[\"2014_return_fund\"].median(), FundRating[\"2014_return_fund\"])\n",
    "\n",
    "# FundRating[\"years_down\"]=np.where(FundRating[\"years_down\"]>(7.0), FundRating[\"years_down\"].median(), FundRating[\"years_down\"])\n",
    "# FundRating[\"years_down\"]=np.where(FundRating[\"years_down\"]<(-1.0), FundRating[\"years_down\"].median(), FundRating[\"years_down\"])\n",
    "\n",
    "\n",
    "# FundRating[\"ytd_return_fund\"]=np.where(FundRating[\"ytd_return_fund\"]>(25.93), FundRating[\"ytd_return_fund\"].median(), FundRating[\"ytd_return_fund\"])\n",
    "# FundRating[\"ytd_return_fund\"]=np.where(FundRating[\"ytd_return_fund\"]<(-8.39), FundRating[\"ytd_return_fund\"].median(), FundRating[\"ytd_return_fund\"])\n",
    "\n",
    "# FundRating[\"2011_return_category\"]=np.where(FundRating[\"2011_return_category\"]>(1.815), FundRating[\"2011_return_category\"].median(), FundRating[\"2011_return_category\"])\n",
    "# FundRating[\"2011_return_category\"]=np.where(FundRating[\"2011_return_category\"]<(-5.025), FundRating[\"2011_return_category\"].median(), FundRating[\"2011_return_category\"])\n",
    "\n",
    "# FundRating[\"cash_percent_of_portfolio\"]=np.where(FundRating[\"cash_percent_of_portfolio\"]>(15.715), FundRating[\"cash_percent_of_portfolio\"].median(), FundRating[\"cash_percent_of_portfolio\"])\n",
    "# FundRating[\"cash_percent_of_portfolio\"]=np.where(FundRating[\"cash_percent_of_portfolio\"]<(-7.445), FundRating[\"cash_percent_of_portfolio\"].median(), FundRating[\"cash_percent_of_portfolio\"])\n",
    "\n",
    "# FundRating[\"category_return_1year\"]=np.where(FundRating[\"category_return_1year\"]>(10.3099), FundRating[\"category_return_1year\"].median(), FundRating[\"category_return_1year\"])\n",
    "# FundRating[\"category_return_1year\"]=np.where(FundRating[\"category_return_1year\"]<(-5.1299), FundRating[\"category_return_1year\"].median(), FundRating[\"category_return_1year\"])\n",
    "\n",
    "# FundRating[\"2018_return_category\"]=np.where(FundRating[\"2018_return_category\"]>(8.0950), FundRating[\"2018_return_category\"].median(), FundRating[\"2018_return_category\"])\n",
    "# FundRating[\"2018_return_category\"]=np.where(FundRating[\"2018_return_category\"]<(-19.0650),FundRating[\"2018_return_category\"].median(), FundRating[\"2018_return_category\"])\n",
    "\n",
    "# FundRating[\"years_up\"]=np.where(FundRating[\"years_up\"]>(25.5), FundRating[\"years_up\"].median(), FundRating[\"years_up\"])\n",
    "# FundRating[\"years_up\"]=np.where(FundRating[\"years_up\"]<(-10.5), FundRating[\"years_up\"].median(), FundRating[\"years_up\"])\n",
    "\n",
    "\n",
    "# FundRating[\"2012_return_category\"]=np.where(FundRating[\"2012_return_category\"]>(17.8550), FundRating[\"2012_return_category\"].median(), FundRating[\"2012_return_category\"])\n",
    "# FundRating[\"2012_return_category\"]=np.where(FundRating[\"2012_return_category\"]<(10.13), FundRating[\"2012_return_category\"].median(), FundRating[\"2012_return_category\"])\n",
    "\n",
    "# FundRating[\"bb_rating\"]=np.where(FundRating[\"bb_rating\"]>(2.42), FundRating[\"bb_rating\"].median(), FundRating[\"bb_rating\"])\n",
    "# FundRating[\"bb_rating\"]=np.where(FundRating[\"bb_rating\"]<(-3.63), FundRating[\"bb_rating\"].median(), FundRating[\"bb_rating\"])\n",
    "\n",
    "# FundRating[\"below_b_rating\"]=np.where(FundRating[\"below_b_rating\"]>(0.25), FundRating[\"below_b_rating\"].median(),FundRating[\"below_b_rating\"])\n",
    "# FundRating[\"below_b_rating\"]=np.where(FundRating[\"below_b_rating\"]<(-0.1500), FundRating[\"below_b_rating\"].median(), FundRating[\"below_b_rating\"])\n",
    "\n",
    "# FundRating[\"2014_category_return\"]=np.where(FundRating[\"2014_category_return\"]>(12.7399), FundRating[\"2014_category_return\"].median(), FundRating[\"2014_category_return\"])\n",
    "# FundRating[\"2014_category_return\"]=np.where(FundRating[\"2014_category_return\"]< (-3.1799), FundRating[\"2014_category_return\"].median(), FundRating[\"2014_category_return\"])\n",
    "\n",
    "# FundRating[\"total_assets\"]=np.where(FundRating[\"total_assets\"]>(3908895000.0),FundRating[\"total_assets\"].median(),FundRating[\"total_assets\"])\n",
    "# FundRating[\"total_assets\"]=np.where(FundRating[\"total_assets\"]<(-2194825000.0), FundRating[\"total_assets\"].median(), FundRating[\"total_assets\"])\n",
    "\n",
    "# FundRating[\"return_ytd\"]=np.where(FundRating[\"return_ytd\"]>(25.93), FundRating['return_ytd'].median(), FundRating['return_ytd'])\n",
    "# FundRating[\"return_ytd\"]=np.where(FundRating[\"return_ytd\"]<(-8.39), FundRating['return_ytd'].median(), FundRating['return_ytd'])\n",
    "\n",
    "# FundRating[\"yield\"]=np.where(FundRating[\"yield\"]>(5.8800), FundRating['yield'].median(), FundRating['yield'])\n",
    "# FundRating[\"yield\"]=np.where(FundRating[\"yield\"]<(-2.7600), FundRating['yield'].median(), FundRating['yield'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FundRating1=FundRating.drop(['greatstone_rating','id','inception_date','fund_id','category','fund_name','currency','parent_company'], axis=1)\n",
    "#cols = list(FundRating1.columns)\n",
    "#cols.drop([\"id\",\"inception_date\",\"fund_id\",\"category\",\"fund_name\",\"currency\",\"parent_company\"])\n",
    "#FundRating1[cols]\n",
    "# now iterate over the remaining columns and create a new zscore column\n",
    "#for col in cols:\n",
    "  #  col_zscore = col + '_zscore'\n",
    " #   FundRating1[col_zscore] = (FundRating1[col] - FundRating1[col].mean())/FundRating1[col].std(ddof=0)\n",
    "#FundRating1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True,\n",
       " bb_rating                             114\n",
       " us_govt_bond_rating                   114\n",
       " below_b_rating                        114\n",
       " others_rating                         114\n",
       " maturity_bond                       16907\n",
       "                                     ...  \n",
       " 10_years_alpha_category               106\n",
       " 10_years_beta_category                106\n",
       " 10years_category_std                  106\n",
       " 10_years_return_mean_annual_fund     8584\n",
       " 10_years_return_category              115\n",
       " Length: 126, dtype: int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the presence of missing values.\n",
    "FundRating.isnull().values.any(),FundRating.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107fc3f90>"
      ]
     },
     "execution_count": 14,
     "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": [
    "#Let's reconfirm the understanding with heatmap\n",
    "sns.heatmap(FundRating.isnull(),yticklabels=False,cbar=False,cmap='viridis')\n",
    "#From below graph, it is evident that values are missing (indicated by yellow bar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Labelling the variable based on size of the vehicle\n",
    "cleanup_nums = { \"greatstone_rating\": {0:\"zero\", 1:\"one\" , 2:\"two\", 3:\"three\", 4: \"four\", 5:\"five\" }\n",
    "                \n",
    "               }\n",
    "#Replacing in the data set\n",
    "FundRating.replace(cleanup_nums, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.base import TransformerMixin\n",
    "\n",
    "class DataFrameImputer(TransformerMixin):\n",
    "\n",
    "    def __init__(self):\n",
    "        \"\"\"Impute missing values.\n",
    "\n",
    "        Columns of dtype object are imputed with the most frequent value \n",
    "        in column.\n",
    "\n",
    "        Columns of other types are imputed with mean of column.\n",
    "\n",
    "        \"\"\"\n",
    "    def fit(self, FundRating, y=None):\n",
    "\n",
    "        self.fill = pd.Series([FundRating[c].value_counts().index[0]\n",
    "            if FundRating[c].dtype == np.dtype('O') else FundRating[c].median() for c in FundRating],\n",
    "            index=FundRating.columns)\n",
    "\n",
    "        return self\n",
    "\n",
    "    def transform(self, FundRating, y=None):\n",
    "        return FundRating.fillna(self.fill)\n",
    "    \n",
    "    \n",
    "    #X = pd.DataFrame(data)\n",
    "#xt = DataFrameImputer().fit_transform(FundRating)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "FundRating = DataFrameImputer().fit_transform(FundRating)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False, Index([], dtype='object'))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FundRating.isnull().values.any(), FundRating.isna().values.any(), FundRating.columns[FundRating.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d822cd0>"
      ]
     },
     "execution_count": 19,
     "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": [
    "#Let's reconfirm the understanding with heatmap\n",
    "sns.heatmap(FundRating.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data clean up and analysis is completed. We are good to proceed further.\n",
    "#Overall inference from the EDA is summarized below:\n",
    "#Presence of nan values taken care\n",
    "#Outliers treatment taken care"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the data into train and test \n",
    "#(Suggestion: specify “random state” if you are using train_test_split from Sklearn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25000 entries, 0 to 24999\n",
      "Columns: 108 entries, bb_rating to 10_years_return_category\n",
      "dtypes: float64(107), int64(1)\n",
      "memory usage: 20.6 MB\n"
     ]
    }
   ],
   "source": [
    "FundRating1=FundRating\n",
    "FundRating=FundRating.drop(['greatstone_rating','fund_size','investment_class','mmc','pc_ratio','pe_ratio','ps_ratio.1','return_ytd','10yrs_treynor_ratio_fund','5yrs_treynor_ratio_fund','3yrs_treynor_ratio_fund','ps_ratio','inception_date','fund_id','category','fund_name','currency','parent_company'],axis=1)\n",
    "#FundRating=FundRating.replace(\",\", \"\")\n",
    "FundRating.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Labelling the variable based on size of the vehicle\n",
    "cleanup_nums = { \"investment_class\": {\"Blend\":1, \"Growth\":2 ,\"Value\":3 },\n",
    "                \n",
    "                 \"fund_size\":  {\"Small\": 1, \"Medium\": 2 ,\"Large\":3 }\n",
    "                \n",
    "                #\"greatstone_rating\": {\"zero\":0, \"one\":1 , \"two\":2, \"three\":3, \"four\":4, \"five\":5 }\n",
    "               }\n",
    "#Replacing in the data set\n",
    "FundRating.replace(cleanup_nums, inplace=True)\n",
    "\n",
    "#FundRating['class'].unique()\n",
    "#Inference: Labelling and replacement is reflecting successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bb_rating\n",
      "us_govt_bond_rating\n",
      "below_b_rating\n",
      "others_rating\n",
      "maturity_bond\n",
      "b_rating\n",
      "a_rating\n",
      "aaa_rating\n",
      "aa_rating\n",
      "bbb_rating\n",
      "duration_bond\n",
      "portfolio_communication_allocation\n",
      "portfolio_financial_services\n",
      "portfolio_industrials_allocation\n",
      "portfolio_tech_allocation\n",
      "portfolio_materials_basic_allocation\n",
      "portfolio_energy_allocation\n",
      "portfolio_consumer_defence_allocation\n",
      "portfolio_healthcare_allocation\n",
      "portfolio_property_allocation\n",
      "id\n",
      "portfolio_utils_allocation\n",
      "portfolio_cyclical_consumer_allocation\n",
      "fund_ratio_net_annual_expense\n",
      "pb_ratio\n",
      "total_assets\n",
      "yield\n",
      "2014_category_return\n",
      "2012_return_category\n",
      "years_up\n",
      "2018_return_category\n",
      "category_return_1year\n",
      "cash_percent_of_portfolio\n",
      "2011_return_category\n",
      "ytd_return_fund\n",
      "years_down\n",
      "2014_return_fund\n",
      "category_return_1month\n",
      "2013_return_fund\n",
      "fund_return_3months\n",
      "ytd_return_category\n",
      "2017_category_return\n",
      "1_year_return_fund\n",
      "2015_return_fund\n",
      "portfolio_convertable\n",
      "3_months_return_category\n",
      "portfolio_others\n",
      "2016_return_fund\n",
      "stock_percent_of_portfolio\n",
      "2016_return_category\n",
      "2011_return_fund\n",
      "2010_return_fund\n",
      "fund_return_3years\n",
      "2012_fund_return\n",
      "2018_return_fund\n",
      "2017_return_fund\n",
      "category_ratio_net_annual_expense\n",
      "category_return_2015\n",
      "1_month_fund_return\n",
      "bond_percentage_of_porfolio\n",
      "portfolio_preferred\n",
      "2010_return_category\n",
      "2013_category_return\n",
      "3_years_alpha_fund\n",
      "3years_category_std\n",
      "3yrs_sharpe_ratio_fund\n",
      "3yrs_treynor_ratio_category\n",
      "3_years_return_mean_annual_fund\n",
      "fund_beta_3years\n",
      "3years_fund_r_squared\n",
      "3years_fund_std\n",
      "category_beta_3years\n",
      "fund_return_3years.1\n",
      "3_years_alpha_category\n",
      "3_years_return_mean_annual_category\n",
      "3yrs_sharpe_ratio_category\n",
      "3years_category_r_squared\n",
      "3_years_return_category\n",
      "category_r_squared_5years\n",
      "5yrs_sharpe_ratio_fund\n",
      "5_years_alpha_fund\n",
      "5years_fund_r_squared\n",
      "5years_fund_std\n",
      "5yrs_sharpe_ratio_category\n",
      "5_years_beta_fund\n",
      "5_years_return_mean_annual_fund\n",
      "5_years_return_mean_annual_category\n",
      "5yrs_treynor_ratio_category\n",
      "5_years_return_fund\n",
      "5_years_alpha_category\n",
      "5_years_beta_category\n",
      "5years_category_std\n",
      "5_years_return_category\n",
      "10years_category_r_squared\n",
      "10yrs_sharpe_ratio_fund\n",
      "10_years_alpha_fund\n",
      "10years_fund_r_squared\n",
      "10years_fund_std\n",
      "10yrs_sharpe_ratio_category\n",
      "10_years_beta_fund\n",
      "10_years_return_mean_annual_category\n",
      "10yrs_treynor_ratio_category\n",
      "10_years_return_fund\n",
      "10_years_alpha_category\n",
      "10_years_beta_category\n",
      "10years_category_std\n",
      "10_years_return_mean_annual_fund\n",
      "10_years_return_category\n"
     ]
    }
   ],
   "source": [
    "def replace_outlier(FundRating_in, col_name):\n",
    "    q1 = FundRating_in[col_name].quantile(0.25)\n",
    "    q3 = FundRating_in[col_name].quantile(0.75)\n",
    "    iqr = q3-q1 #Interquartile range\n",
    "    fence_low = q1-1.5*iqr\n",
    "    fence_high = q3+1.5*iqr\n",
    "    FundRating_in[col_name]=np.where((FundRating_in[col_name] < fence_low), FundRating_in[col_name].quantile(0.25), FundRating_in[col_name])\n",
    "    FundRating_in[col_name]=np.where((FundRating_in[col_name] > fence_high), FundRating_in[col_name].quantile(0.75), FundRating_in[col_name])\n",
    "    #     FundRating_in[col_name]=np.where(FundRating_in.loc[FundRating_in[col_name] > fence_high], FundRating_in[col_name].median(),FundRating_in[col_name])\n",
    "    #median=FundRating[col_name].median()\n",
    "    #FundRating_in[col_name] = np.where((FundRating_in[col_name] > fence_high) | (FundRating_in[col_name] < fence_low), median, FundRating_in[col_name])\n",
    "    return FundRating_in\n",
    "    \n",
    "df_columns = FundRating.columns\n",
    "for i in range(df_columns.size):\n",
    "    print(df_columns[i])\n",
    "    FundRating=replace_outlier(FundRating, df_columns[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanup_nums = {\n",
    "                \"greatstone_rating\": {\"zero\":0, \"one\":1 , \"two\":2, \"three\":3, \"four\":4, \"five\":5 }\n",
    "               }\n",
    "#Replacing in the data set\n",
    "FundRating1.replace(cleanup_nums, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the data into train and test \n",
    "#(Suggestion: specify “random state” if you are using train_test_split from Sklearn)\n",
    "\n",
    "#Importing required libaries\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "\n",
    "#Drop the target 'class' from data\n",
    "#x=FundRating.drop('greatstone_rating',axis=1)\n",
    "x=FundRating\n",
    "y=FundRating1['greatstone_rating']\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "# x_train,x_val,x_test,y_train,y_val,y_test=train_test_split(x,y,train_size=0.50, val_size=0.30, test_size=0.20,random_state=0)\n",
    "# x_train, x_val, x_test\n",
    "# y_train,y_val, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(       bb_rating  us_govt_bond_rating  below_b_rating  others_rating  \\\n",
       " 22331       0.00                  0.0            0.00           0.00   \n",
       " 10962       3.64                  0.0            0.00           0.32   \n",
       " 8422        0.02                  0.0            0.00           0.00   \n",
       " 3081        0.00                  0.0            0.00           0.00   \n",
       " 10287       5.64                  0.0            0.10           0.32   \n",
       " ...          ...                  ...             ...            ...   \n",
       " 13123       2.42                  0.0            0.10           0.32   \n",
       " 19648       2.26                  0.0            0.10           0.32   \n",
       " 9845        2.24                  0.0            0.02           0.52   \n",
       " 10799       2.19                  0.0            0.15           0.00   \n",
       " 2732        0.00                  0.0            0.00           0.00   \n",
       " \n",
       "        maturity_bond  b_rating  a_rating  aaa_rating  aa_rating  bbb_rating  \\\n",
       " 22331           7.29      0.00    0.0000        0.00       0.00        0.00   \n",
       " 10962           7.29      0.00   12.6000       15.19       2.99        1.03   \n",
       " 8422            7.29      0.00    8.2775        1.47       2.99       11.34   \n",
       " 3081            7.29      0.00    0.0000        0.00       0.00        0.00   \n",
       " 10287           7.29      1.54    8.2775       30.55       5.42       11.34   \n",
       " ...              ...       ...       ...         ...        ...         ...   \n",
       " 13123           7.29      0.70    7.3900       16.51       2.99       15.38   \n",
       " 19648           7.29      0.70    3.3900       18.42       1.70       17.52   \n",
       " 9845            7.29      0.02    8.2775       21.80       6.96       11.34   \n",
       " 10799           7.29      0.42    0.0000       18.42       2.99        2.81   \n",
       " 2732            7.29      0.00    0.0000        0.00       0.00        0.00   \n",
       " \n",
       "        ...  10yrs_sharpe_ratio_category  10_years_beta_fund  \\\n",
       " 22331  ...                         0.01                1.13   \n",
       " 10962  ...                         0.01                1.06   \n",
       " 8422   ...                         0.01                0.96   \n",
       " 3081   ...                         0.01                1.01   \n",
       " 10287  ...                         0.01                1.03   \n",
       " ...    ...                          ...                 ...   \n",
       " 13123  ...                         0.01                0.86   \n",
       " 19648  ...                         0.01                1.06   \n",
       " 9845   ...                         0.01                0.96   \n",
       " 10799  ...                         0.01                1.01   \n",
       " 2732   ...                         0.01                1.06   \n",
       " \n",
       "        10_years_return_mean_annual_category  10yrs_treynor_ratio_category  \\\n",
       " 22331                                  0.01                          0.15   \n",
       " 10962                                  0.01                          0.04   \n",
       " 8422                                   0.01                          0.04   \n",
       " 3081                                   0.01                          0.08   \n",
       " 10287                                  0.01                          0.05   \n",
       " ...                                     ...                           ...   \n",
       " 13123                                  0.01                          0.10   \n",
       " 19648                                  0.01                          0.08   \n",
       " 9845                                   0.01                          0.04   \n",
       " 10799                                  0.01                          0.05   \n",
       " 2732                                   0.01                          0.13   \n",
       " \n",
       "        10_years_return_fund  10_years_alpha_category  10_years_beta_category  \\\n",
       " 22331                 11.13                    -0.01                    0.01   \n",
       " 10962                  5.75                    -0.01                    0.01   \n",
       " 8422                   4.61                     0.01                    0.01   \n",
       " 3081                   0.00                    -0.02                    0.01   \n",
       " 10287                  5.96                     0.01                    0.01   \n",
       " ...                     ...                      ...                     ...   \n",
       " 13123                  8.79                     0.00                    0.01   \n",
       " 19648                 11.25                    -0.02                    0.01   \n",
       " 9845                   4.34                     0.01                    0.01   \n",
       " 10799                  0.00                     0.01                    0.01   \n",
       " 2732                  16.70                    -0.02                    0.01   \n",
       " \n",
       "        10years_category_std  10_years_return_mean_annual_fund  \\\n",
       " 22331                  0.14                              1.00   \n",
       " 10962                  0.04                              0.48   \n",
       " 8422                   0.02                              0.41   \n",
       " 3081                   0.13                              0.90   \n",
       " 10287                  0.03                              0.49   \n",
       " ...                     ...                               ...   \n",
       " 13123                  0.07                              0.73   \n",
       " 19648                  0.12                              0.96   \n",
       " 9845                   0.02                              0.36   \n",
       " 10799                  0.03                              0.90   \n",
       " 2732                   0.18                              1.43   \n",
       " \n",
       "        10_years_return_category  \n",
       " 22331                     15.94  \n",
       " 10962                      4.13  \n",
       " 8422                       2.70  \n",
       " 3081                      12.56  \n",
       " 10287                      4.56  \n",
       " ...                         ...  \n",
       " 13123                      7.87  \n",
       " 19648                     11.71  \n",
       " 9845                       2.70  \n",
       " 10799                      4.56  \n",
       " 2732                      16.24  \n",
       " \n",
       " [18750 rows x 108 columns],\n",
       "        bb_rating  us_govt_bond_rating  below_b_rating  others_rating  \\\n",
       " 14149       0.00                  0.0             0.0           0.32   \n",
       " 8946        0.00                  0.0             0.0           0.00   \n",
       " 22378       4.19                  0.0             0.1           0.09   \n",
       " 12162       2.42                  0.0             0.1           0.32   \n",
       " 4879        0.00                  0.0             0.0           0.00   \n",
       " ...          ...                  ...             ...            ...   \n",
       " 8487        0.00                  0.0             0.0           0.00   \n",
       " 863         0.00                  0.0             0.0           0.00   \n",
       " 8463        0.00                  0.0             0.0           0.00   \n",
       " 6258        0.00                  0.0             0.0           0.00   \n",
       " 4183        0.00                  0.0             0.0           0.00   \n",
       " \n",
       "        maturity_bond  b_rating  a_rating  aaa_rating  aa_rating  bbb_rating  \\\n",
       " 14149           7.29       0.0     13.99       42.62       2.99        3.46   \n",
       " 8946            7.29       0.0      0.00        0.00       0.00        0.00   \n",
       " 22378           7.29       0.7     11.40       18.42       6.67       17.11   \n",
       " 12162           7.29       0.7      8.50       19.86       1.22       13.82   \n",
       " 4879            7.29       0.0      0.00        0.00       0.00        0.00   \n",
       " ...              ...       ...       ...         ...        ...         ...   \n",
       " 8487            7.29       0.0      0.00        0.00       0.00        0.00   \n",
       " 863             7.29       0.0      0.00        0.00       0.00        0.00   \n",
       " 8463            7.29       0.0      0.00        0.00       0.00        0.00   \n",
       " 6258            7.29       0.0      0.00        0.00       0.00        0.00   \n",
       " 4183            7.29       0.0      0.00        0.00       0.00        0.00   \n",
       " \n",
       "        ...  10yrs_sharpe_ratio_category  10_years_beta_fund  \\\n",
       " 14149  ...                         0.01                1.01   \n",
       " 8946   ...                         0.01                1.01   \n",
       " 22378  ...                         0.01                1.12   \n",
       " 12162  ...                         0.01                1.01   \n",
       " 4879   ...                         0.01                1.01   \n",
       " ...    ...                          ...                 ...   \n",
       " 8487   ...                         0.01                1.01   \n",
       " 863    ...                         0.01                1.16   \n",
       " 8463   ...                         0.01                0.96   \n",
       " 6258   ...                         0.01                1.05   \n",
       " 4183   ...                         0.01                0.96   \n",
       " \n",
       "        10_years_return_mean_annual_category  10yrs_treynor_ratio_category  \\\n",
       " 14149                                  0.01                          0.08   \n",
       " 8946                                   0.01                          0.10   \n",
       " 22378                                  0.01                          0.09   \n",
       " 12162                                  0.01                         -0.02   \n",
       " 4879                                   0.01                          0.11   \n",
       " ...                                     ...                           ...   \n",
       " 8487                                   0.01                          0.08   \n",
       " 863                                    0.01                          0.11   \n",
       " 8463                                   0.01                          0.16   \n",
       " 6258                                   0.01                          0.14   \n",
       " 4183                                   0.01                          0.07   \n",
       " \n",
       "        10_years_return_fund  10_years_alpha_category  10_years_beta_category  \\\n",
       " 14149                  0.00                    -0.01                    0.01   \n",
       " 8946                   5.90                     0.00                    0.01   \n",
       " 22378                 11.27                     0.00                    0.01   \n",
       " 12162                  0.00                     0.04                    0.01   \n",
       " 4879                   0.00                    -0.02                    0.01   \n",
       " ...                     ...                      ...                     ...   \n",
       " 8487                   0.00                     0.00                    0.01   \n",
       " 863                   14.15                    -0.04                    0.01   \n",
       " 8463                 -17.34                    -0.01                    0.01   \n",
       " 6258                  13.39                    -0.02                    0.01   \n",
       " 4183                   0.73                     0.01                    0.01   \n",
       " \n",
       "        10years_category_std  10_years_return_mean_annual_fund  \\\n",
       " 14149                  0.11                              0.90   \n",
       " 8946                   0.12                              0.90   \n",
       " 22378                  0.09                              0.93   \n",
       " 12162                  0.05                              0.90   \n",
       " 4879                   0.10                              0.90   \n",
       " ...                     ...                               ...   \n",
       " 8487                   0.19                              0.90   \n",
       " 863                    0.18                              1.22   \n",
       " 8463                   0.24                              0.75   \n",
       " 6258                   0.16                              1.14   \n",
       " 4183                   0.04                              0.75   \n",
       " \n",
       "        10_years_return_category  \n",
       " 14149                     11.09  \n",
       " 8946                       9.97  \n",
       " 22378                      9.98  \n",
       " 12162                      6.69  \n",
       " 4879                       7.08  \n",
       " ...                         ...  \n",
       " 8487                       8.92  \n",
       " 863                       14.12  \n",
       " 8463                       6.44  \n",
       " 6258                      15.70  \n",
       " 4183                       1.48  \n",
       " \n",
       " [6250 rows x 108 columns],\n",
       "        bb_rating  us_govt_bond_rating  below_b_rating  others_rating  \\\n",
       " 14149       0.00                  0.0             0.0           0.32   \n",
       " 8946        0.00                  0.0             0.0           0.00   \n",
       " 22378       4.19                  0.0             0.1           0.09   \n",
       " 12162       2.42                  0.0             0.1           0.32   \n",
       " 4879        0.00                  0.0             0.0           0.00   \n",
       " ...          ...                  ...             ...            ...   \n",
       " 4022        0.00                  0.0             0.0           0.00   \n",
       " 17601       2.42                  0.0             0.1           0.32   \n",
       " 4718        0.00                  0.0             0.0           0.00   \n",
       " 9376        0.00                  0.0             0.0           0.00   \n",
       " 8019        1.51                  0.0             0.0           0.32   \n",
       " \n",
       "        maturity_bond  b_rating  a_rating  aaa_rating  aa_rating  bbb_rating  \\\n",
       " 14149           7.29      0.00     13.99       42.62       2.99        3.46   \n",
       " 8946            7.29      0.00      0.00        0.00       0.00        0.00   \n",
       " 22378           7.29      0.70     11.40       18.42       6.67       17.11   \n",
       " 12162           7.29      0.70      8.50       19.86       1.22       13.82   \n",
       " 4879            7.29      0.00      0.00        0.00       0.00        0.00   \n",
       " ...              ...       ...       ...         ...        ...         ...   \n",
       " 4022            7.29      0.00      0.00       18.42       0.00        0.00   \n",
       " 17601           7.29      0.70     16.15       35.47       6.61       12.44   \n",
       " 4718            7.29      0.00      0.00        0.00       0.00        0.00   \n",
       " 9376            7.29      0.00      0.00        0.00       0.00        0.00   \n",
       " 8019            7.29      0.43      7.84       18.42       7.04       21.85   \n",
       " \n",
       "        ...  10yrs_sharpe_ratio_category  10_years_beta_fund  \\\n",
       " 14149  ...                         0.01                1.01   \n",
       " 8946   ...                         0.01                1.01   \n",
       " 22378  ...                         0.01                1.12   \n",
       " 12162  ...                         0.01                1.01   \n",
       " 4879   ...                         0.01                1.01   \n",
       " ...    ...                          ...                 ...   \n",
       " 4022   ...                         0.01                1.01   \n",
       " 17601  ...                         0.01                1.01   \n",
       " 4718   ...                         0.01                1.01   \n",
       " 9376   ...                         0.01                1.02   \n",
       " 8019   ...                         0.01                0.97   \n",
       " \n",
       "        10_years_return_mean_annual_category  10yrs_treynor_ratio_category  \\\n",
       " 14149                                  0.01                          0.08   \n",
       " 8946                                   0.01                          0.10   \n",
       " 22378                                  0.01                          0.09   \n",
       " 12162                                  0.01                         -0.02   \n",
       " 4879                                   0.01                          0.11   \n",
       " ...                                     ...                           ...   \n",
       " 4022                                   0.01                          0.09   \n",
       " 17601                                  0.01                          0.08   \n",
       " 4718                                   0.01                          0.14   \n",
       " 9376                                   0.01                          0.05   \n",
       " 8019                                   0.01                          0.05   \n",
       " \n",
       "        10_years_return_fund  10_years_alpha_category  10_years_beta_category  \\\n",
       " 14149                  0.00                    -0.01                    0.01   \n",
       " 8946                   5.90                     0.00                    0.01   \n",
       " 22378                 11.27                     0.00                    0.01   \n",
       " 12162                  0.00                     0.04                    0.01   \n",
       " 4879                   0.00                    -0.02                    0.01   \n",
       " ...                     ...                      ...                     ...   \n",
       " 4022                   0.00                    -0.01                    0.01   \n",
       " 17601                  0.00                    -0.02                    0.01   \n",
       " 4718                   0.00                    -0.02                    0.01   \n",
       " 9376                   4.34                     0.01                    0.01   \n",
       " 8019                   4.75                     0.01                    0.01   \n",
       " \n",
       "        10years_category_std  10_years_return_mean_annual_fund  \\\n",
       " 14149                  0.11                              0.90   \n",
       " 8946                   0.12                              0.90   \n",
       " 22378                  0.09                              0.93   \n",
       " 12162                  0.05                              0.90   \n",
       " 4879                   0.10                              0.90   \n",
       " ...                     ...                               ...   \n",
       " 4022                   0.09                              0.90   \n",
       " 17601                  0.12                              0.90   \n",
       " 4718                   0.16                              0.90   \n",
       " 9376                   0.03                              0.36   \n",
       " 8019                   0.03                              0.39   \n",
       " \n",
       "        10_years_return_category  \n",
       " 14149                     11.09  \n",
       " 8946                       9.97  \n",
       " 22378                      9.98  \n",
       " 12162                      6.69  \n",
       " 4879                       7.08  \n",
       " ...                         ...  \n",
       " 4022                       7.75  \n",
       " 17601                     12.02  \n",
       " 4718                      15.70  \n",
       " 9376                       4.56  \n",
       " 8019                       4.56  \n",
       " \n",
       " [5000 rows x 108 columns],\n",
       " 22331    1\n",
       " 10962    5\n",
       " 8422     3\n",
       " 3081     1\n",
       " 10287    3\n",
       "         ..\n",
       " 13123    4\n",
       " 19648    3\n",
       " 9845     4\n",
       " 10799    4\n",
       " 2732     3\n",
       " Name: greatstone_rating, Length: 18750, dtype: int64,\n",
       " 14149    3\n",
       " 8946     0\n",
       " 22378    4\n",
       " 12162    3\n",
       " 4879     0\n",
       "         ..\n",
       " 8487     1\n",
       " 863      3\n",
       " 8463     3\n",
       " 6258     2\n",
       " 4183     3\n",
       " Name: greatstone_rating, Length: 6250, dtype: int64,\n",
       " 14149    3\n",
       " 8946     0\n",
       " 22378    4\n",
       " 12162    3\n",
       " 4879     0\n",
       "         ..\n",
       " 4022     1\n",
       " 17601    4\n",
       " 4718     4\n",
       " 9376     3\n",
       " 8019     3\n",
       " Name: greatstone_rating, Length: 5000, dtype: int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_svmlight_files\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,train_size=0.8,random_state=0)\n",
    "x_train, x_cv, y_train, y_cv = train_test_split(x,y,test_size = 0.25,train_size =0.75,random_state=0)\n",
    "x_train, x_cv,x_test,y_train,y_cv, y_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Do data Scaling/Normalization if required\n",
    "#This is needed we have variables in different scale ex: scaled_variance.1 etc\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train= sc.fit_transform(x_train)\n",
    "x_cv= sc.fit_transform(x_cv)\n",
    "x_test = sc.fit_transform(x_test)\n",
    "x=sc.fit_transform(x)\n",
    "#y=sc.fit_transform(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use PCA from Scikit learn, extract Principal Components that capture about 95% of the variance in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance Matrix \n",
      "%s [[ 1.00005334  0.          0.73605327 ... -0.45076793 -0.35747073\n",
      "  -0.29231193]\n",
      " [ 0.          0.          0.         ...  0.          0.\n",
      "   0.        ]\n",
      " [ 0.73605327  0.          1.00005334 ... -0.41338523 -0.30947979\n",
      "  -0.24978274]\n",
      " ...\n",
      " [-0.45076793  0.         -0.41338523 ...  1.00005334  0.55257107\n",
      "   0.73245153]\n",
      " [-0.35747073  0.         -0.30947979 ...  0.55257107  1.00005334\n",
      "   0.66343671]\n",
      " [-0.29231193  0.         -0.24978274 ...  0.73245153  0.66343671\n",
      "   1.00005334]]\n",
      "Eigenvectors \n",
      "[[-0.08793344 -0.13118343 -0.09306851 ...  0.          0.\n",
      "   0.        ]\n",
      " [ 0.          0.          0.         ...  0.          0.\n",
      "   0.        ]\n",
      " [-0.07814703 -0.13004672 -0.09599143 ...  0.          0.\n",
      "   0.        ]\n",
      " ...\n",
      " [ 0.15553698  0.09011656 -0.00282645 ...  0.          0.\n",
      "   0.        ]\n",
      " [ 0.13912118 -0.0115364   0.07007566 ...  0.          0.\n",
      "   0.        ]\n",
      " [ 0.15875077 -0.06576649  0.04623367 ...  0.          0.\n",
      "   0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# generating the covariance matrix and the eigen values for the PCA analysis\n",
    "cov_matrix = np.cov(x_train.T) # the relevanat covariance matrix\n",
    "print('Covariance Matrix \\n%s', cov_matrix)\n",
    "\n",
    "#generating the eigen values and the eigen vectors\n",
    "e_vals, e_vecs = np.linalg.eig(cov_matrix)\n",
    "print('Eigenvectors \\n%s' %e_vecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Eigenvalues \n",
      "[2.94690901e+01 1.01332484e+01 7.77448308e+00 4.48294872e+00\n",
      " 4.28055667e+00 3.19229044e+00 2.15190483e+00 2.09472315e+00\n",
      " 1.85245791e+00 1.69335798e+00 1.64581559e+00 1.41586268e+00\n",
      " 1.19819065e+00 1.06873613e+00 1.00563276e+00 9.65523849e-01\n",
      " 9.35695449e-01 8.95239707e-01 8.75882881e-01 8.10261991e-01\n",
      " 7.87005926e-01 7.50927880e-01 7.11182964e-01 6.95256359e-01\n",
      " 6.76947225e-01 6.43523739e-01 6.35508910e-01 6.07518932e-01\n",
      " 5.86174154e-01 5.50253122e-01 5.53110613e-01 5.21264888e-01\n",
      " 5.04586927e-01 4.86153721e-01 4.77708981e-01 4.60424403e-01\n",
      " 4.47323819e-01 4.32771323e-01 4.16460036e-01 4.10722505e-01\n",
      " 3.99168841e-01 3.86347790e-01 3.79488146e-01 3.73203677e-01\n",
      " 3.53555142e-01 3.42284687e-01 3.40755750e-01 8.90369548e-03\n",
      " 1.55349610e-02 2.14692768e-02 2.06760183e-02 2.79655638e-02\n",
      " 3.59933105e-02 3.31052843e-01 4.34206799e-02 4.26601855e-02\n",
      " 3.25240303e-01 4.97598460e-02 5.61597486e-02 6.13455811e-02\n",
      " 3.16461820e-01 6.58692495e-02 7.20592596e-02 7.55384063e-02\n",
      " 8.14829498e-02 8.35916939e-02 3.09351588e-01 2.98257146e-01\n",
      " 9.65090081e-02 1.10023620e-01 1.04122117e-01 2.73425081e-01\n",
      " 2.84541782e-01 2.67744598e-01 1.23299007e-01 1.25729475e-01\n",
      " 1.31827567e-01 2.64572329e-01 1.37573163e-01 2.86540684e-01\n",
      " 2.20423402e-01 2.37423860e-01 2.10565461e-01 1.40076269e-01\n",
      " 2.53952866e-01 2.41265416e-01 1.96427011e-01 1.98523845e-01\n",
      " 1.86112616e-01 1.55067784e-01 1.76680897e-01 1.69905592e-01\n",
      " 1.56036931e-01 2.44754115e-01 1.63265440e-01 1.54112843e-16\n",
      " 1.03714273e-17 2.05252462e-01 2.56967277e-01 1.72396363e-01\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "print('\\nEigenvalues \\n%s' %e_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cumulative Variance Explained [ 29.46751846  39.60022639  47.37429483  51.85700447  56.13733285\n",
      "  59.32945303  61.48124309  63.57585452  65.42821363  67.12148129\n",
      "  68.76720911  70.18299628  71.38112303  72.44980216  73.45538129\n",
      "  74.42085364  75.35649919  76.25169115  77.12752732  77.93774609\n",
      "  78.72471005  79.47559788  80.18674291  80.88196219  81.55887331\n",
      "  82.20236273  82.83783774  83.44532428  84.03146717  84.58454828\n",
      "  85.13477205  85.65600914  86.16056916  86.64669695  87.12438045\n",
      "  87.5847803   88.03208026  88.4648285   88.88126633  89.29196693\n",
      "  89.69111448  90.07744167  90.45690957  90.83009334  91.18362963\n",
      "  91.52589606  91.86663364  92.19766883  92.52289178  92.83933673\n",
      "  93.14867181  93.44691305  93.73343845  94.01796506  94.29137556\n",
      "  94.55910588  94.8236641   95.08061767  95.33455699  95.57929805\n",
      "  95.8205506   96.0579618   96.27837344  96.48892767  96.69416919\n",
      "  96.89268245  97.08909898  97.27520167  97.45187315  97.62426031\n",
      "  97.79415684  97.95741358  98.11344219  98.2685017   98.4085705\n",
      "  98.54613632  98.67795686  98.80367963  98.92697206  99.03698981\n",
      "  99.14110638  99.23761024  99.32119747  99.40267608  99.47821045\n",
      "  99.55026587  99.61613161  99.67747392  99.73363067  99.78338786\n",
      "  99.82680623  99.86946414  99.90545553  99.9334196   99.95488773\n",
      "  99.97556265  99.99109678 100.         100.         100.\n",
      " 100.         100.         100.         100.         100.\n",
      " 100.         100.         100.        ]\n"
     ]
    }
   ],
   "source": [
    "# \"cumulative variance explained\" analysis \n",
    "tot = sum(e_vals)\n",
    "var_exp = [( i /tot ) * 100 for i in sorted(e_vals, reverse=True)]\n",
    "cum_var_exp = np.cumsum(var_exp)\n",
    "print(\"Cumulative Variance Explained\", cum_var_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the variance expalained by the principal components and the cumulative variance explained.\n",
    "plt.figure(figsize=(10 , 5))\n",
    "plt.bar(range(1, e_vals.size + 1), var_exp, alpha = 0.5, align = 'center', label = 'Individual explained variance')\n",
    "plt.step(range(1, e_vals.size + 1), cum_var_exp, where='mid', label = 'Cumulative explained variance')\n",
    "plt.ylabel('Explained Variance Ratio')\n",
    "plt.xlabel('Principal Components')\n",
    "plt.legend(loc = 'best')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#Inference: From below graph we can learn that ~95% data variance can be captured \n",
    "#when we take only ~ 60 variables from the original data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8.84825695e-02 -0.00000000e+00  7.84793754e-02 ... -1.55075152e-01\n",
      "  -1.39091407e-01 -1.58679810e-01]\n",
      " [-1.28485929e-01  1.11022302e-16 -1.28136579e-01 ...  8.99601096e-02\n",
      "  -1.11106352e-02 -6.51111630e-02]\n",
      " [-9.32351176e-02  4.44089210e-16 -9.65323215e-02 ... -1.08923140e-03\n",
      "   6.91113213e-02  4.54508327e-02]\n",
      " ...\n",
      " [-8.79676048e-03  7.38096390e-17  1.44637759e-02 ... -2.77987808e-01\n",
      "   4.64796461e-02  1.49613090e-01]\n",
      " [ 2.65841631e-03  4.05041722e-16 -1.97318374e-02 ...  1.89648059e-02\n",
      "   1.52238868e-01  8.97453811e-03]\n",
      " [-6.60535012e-03 -6.26215811e-17 -1.43774726e-02 ...  1.27952157e-01\n",
      "   5.32544194e-02  4.36728116e-03]]\n",
      "[0.2952843  0.10156645 0.07702095 0.04500944 0.04232903 0.03198961\n",
      " 0.02132798 0.02079127 0.01855249 0.01696477 0.01646714 0.01410756\n",
      " 0.01194819 0.01061775 0.01006595 0.00960442 0.00940098 0.0089203\n",
      " 0.00877868 0.00815088 0.00794127 0.00752488 0.00708351 0.00693533\n",
      " 0.00673298 0.00647589 0.00637339 0.00612907 0.00585071 0.00555636\n",
      " 0.00550695 0.00522894 0.00505944 0.00487846 0.00472064 0.00464579\n",
      " 0.00448863 0.00430787 0.00416029 0.00413347 0.00397787 0.00388472\n",
      " 0.00380694 0.00376516 0.00353827 0.00343305 0.0034129  0.00332481\n",
      " 0.00325567 0.00317772 0.00308225 0.00299291 0.00289586 0.00285883\n",
      " 0.00277091 0.00265447 0.00264269 0.0025597  0.00253849 0.00245363\n",
      " 0.002423   0.00237581 0.00220253 0.00212591 0.00205102 0.00199693\n",
      " 0.00196694 0.00186069 0.00179424 0.00172765 0.00171448 0.0016336\n",
      " 0.00157127 0.00154406 0.00142153 0.00139153 0.00132389 0.00125612\n",
      " 0.00123285 0.00110645 0.00103365 0.00095529 0.00083859 0.00081713\n",
      " 0.00075576]\n"
     ]
    }
   ],
   "source": [
    "#Proceeding with 6 components\n",
    "pca_new = PCA(n_components=85)\n",
    "#Fitting PCA into train data\n",
    "pca_new.fit(x_train)\n",
    "#Fitting PCA into test data\n",
    "pca_new.fit(x_cv)\n",
    "pca_new.fit(x_test)\n",
    "pca_new.fit(x)\n",
    "print(pca_new.components_)\n",
    "print(pca_new.explained_variance_ratio_)\n",
    "Xpca_train_new = pca_new.transform(x_train)\n",
    "Xpca_cv_new = pca_new.transform(x_cv)\n",
    "Xpca_test_new = pca_new.transform(x_test)\n",
    "xpca=pca_new.transform(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9906133333333333, 0.63024)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "bgcl = BaggingClassifier(n_estimators=50, max_samples= .7, bootstrap=True, oob_score=True, random_state=22)\n",
    "bgcl = bgcl.fit(x_train, y_train)\n",
    "bgcl.score(x_train, y_train),bgcl.score(x_cv, y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.629"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgcl.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [],
   "source": [
    "greatstone_rating=bgcl.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 5, 4, ..., 3, 3, 4])"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greatstone_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [],
   "source": [
    "greatstone_rating1 = bgcl.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 4, ..., 1, 2, 3])"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greatstone_rating1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [],
   "source": [
    "greatstone_rating1 = pd.DataFrame(greatstone_rating1, columns=['greatstone_rating']).to_csv('submissionSVC7.csv')\n",
    "#FundRating1=pd.DataFrame(FundRating1, columns=['fund_id']).to_csv('submissionSVC4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "greatstone_rating = pd.DataFrame(greatstone_rating, columns=['greatstone_rating']).to_csv('submissionSVC.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chai3/opt/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.85504, 0.66512)"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#After PCA-SVC\n",
    "svc.fit(Xpca_train_new, y_train)\n",
    "svc.score(Xpca_train_new, y_train), svc.score(Xpca_cv_new, y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6614"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.score(Xpca_test_new, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "greatstone_rating = svc.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 5 4 ... 3 3 3]\n"
     ]
    }
   ],
   "source": [
    "print (greatstone_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "greatstone_rating = pd.DataFrame(greatstone_rating, columns=['greatstone_rating']).to_csv('sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.decomposition import PCA \n",
    "from sklearn.pipeline import Pipeline \n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint as sp_randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_dist = {\"n_estimators\":sp_randint(50,75),\n",
    "              \"max_samples\":sp_randint(0,25),\n",
    "              \"bootstrap\": [True],\n",
    "              \"oob_score\":[True], \n",
    "              \"random_state\":[22]\n",
    "             }\n",
    "#max_samples= .7, bootstrap=True, oob_score=True\n",
    "# #param_grid = {\"C\":[10],\n",
    "#               \"gamma\":sp_randint(0.001,0.06), \n",
    "#               \"kernel\":['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']\n",
    "#              } \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run randomized search\n",
    "samples = 20  # number of random samples \n",
    "randomCV = RandomizedSearchCV(bgcl, param_distributions=param_dist,n_iter=samples,verbose=10) #default cv = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chai3/opt/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n",
      "[CV] bootstrap=True, max_samples=13, n_estimators=52, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=13, n_estimators=52, oob_score=True, random_state=22, score=0.465, total=   1.9s\n",
      "[CV] bootstrap=True, max_samples=13, n_estimators=52, oob_score=True, random_state=22 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.9s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_samples=13, n_estimators=52, oob_score=True, random_state=22, score=0.472, total=   1.8s\n",
      "[CV] bootstrap=True, max_samples=13, n_estimators=52, oob_score=True, random_state=22 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    3.7s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_samples=13, n_estimators=52, oob_score=True, random_state=22, score=0.469, total=   1.8s\n",
      "[CV] bootstrap=True, max_samples=18, n_estimators=57, oob_score=True, random_state=22 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    5.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_samples=18, n_estimators=57, oob_score=True, random_state=22, score=0.463, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=18, n_estimators=57, oob_score=True, random_state=22 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    7.4s remaining:    0.0s\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_samples=18, n_estimators=57, oob_score=True, random_state=22, score=0.475, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=18, n_estimators=57, oob_score=True, random_state=22 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    9.4s remaining:    0.0s\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_samples=18, n_estimators=57, oob_score=True, random_state=22, score=0.474, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=23, n_estimators=56, oob_score=True, random_state=22 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:   11.4s remaining:    0.0s\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_samples=23, n_estimators=56, oob_score=True, random_state=22, score=0.475, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=23, n_estimators=56, oob_score=True, random_state=22 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:   13.4s remaining:    0.0s\n"
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    },
    {
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     "text": [
      "[CV]  bootstrap=True, max_samples=23, n_estimators=56, oob_score=True, random_state=22, score=0.475, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=23, n_estimators=56, oob_score=True, random_state=22 \n"
     ]
    },
    {
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     "output_type": "stream",
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      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:   15.4s remaining:    0.0s\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_samples=23, n_estimators=56, oob_score=True, random_state=22, score=0.474, total=   1.9s\n",
      "[CV] bootstrap=True, max_samples=3, n_estimators=55, oob_score=True, random_state=22 \n"
     ]
    },
    {
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     "output_type": "stream",
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      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:   17.4s remaining:    0.0s\n"
     ]
    },
    {
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     "text": [
      "[CV]  bootstrap=True, max_samples=3, n_estimators=55, oob_score=True, random_state=22, score=0.474, total=   1.8s\n",
      "[CV] bootstrap=True, max_samples=3, n_estimators=55, oob_score=True, random_state=22 \n",
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      "[CV] bootstrap=True, max_samples=3, n_estimators=55, oob_score=True, random_state=22 \n",
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      "[CV] bootstrap=True, max_samples=8, n_estimators=54, oob_score=True, random_state=22 \n",
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      "[CV] bootstrap=True, max_samples=8, n_estimators=54, oob_score=True, random_state=22 \n",
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      "[CV]  bootstrap=True, max_samples=8, n_estimators=54, oob_score=True, random_state=22, score=0.473, total=   1.8s\n",
      "[CV] bootstrap=True, max_samples=12, n_estimators=73, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=12, n_estimators=73, oob_score=True, random_state=22, score=0.472, total=   2.5s\n",
      "[CV] bootstrap=True, max_samples=12, n_estimators=73, oob_score=True, random_state=22 \n",
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      "[CV] bootstrap=True, max_samples=12, n_estimators=73, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=12, n_estimators=73, oob_score=True, random_state=22, score=0.474, total=   2.5s\n",
      "[CV] bootstrap=True, max_samples=23, n_estimators=64, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=23, n_estimators=64, oob_score=True, random_state=22, score=0.473, total=   2.2s\n",
      "[CV] bootstrap=True, max_samples=23, n_estimators=64, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=23, n_estimators=64, oob_score=True, random_state=22, score=0.477, total=   2.2s\n",
      "[CV] bootstrap=True, max_samples=23, n_estimators=64, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=23, n_estimators=64, oob_score=True, random_state=22, score=0.473, total=   2.2s\n",
      "[CV] bootstrap=True, max_samples=20, n_estimators=60, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=20, n_estimators=60, oob_score=True, random_state=22, score=0.473, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=20, n_estimators=60, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=20, n_estimators=60, oob_score=True, random_state=22, score=0.475, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=20, n_estimators=60, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=20, n_estimators=60, oob_score=True, random_state=22, score=0.474, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=6, n_estimators=62, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=6, n_estimators=62, oob_score=True, random_state=22, score=0.474, total=   2.2s\n",
      "[CV] bootstrap=True, max_samples=6, n_estimators=62, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=6, n_estimators=62, oob_score=True, random_state=22, score=0.474, total=   2.1s\n",
      "[CV] bootstrap=True, max_samples=6, n_estimators=62, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=6, n_estimators=62, oob_score=True, random_state=22, score=0.473, total=   2.1s\n",
      "[CV] bootstrap=True, max_samples=15, n_estimators=66, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=15, n_estimators=66, oob_score=True, random_state=22, score=0.472, total=   2.3s\n",
      "[CV] bootstrap=True, max_samples=15, n_estimators=66, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=15, n_estimators=66, oob_score=True, random_state=22, score=0.474, total=   2.3s\n",
      "[CV] bootstrap=True, max_samples=15, n_estimators=66, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=15, n_estimators=66, oob_score=True, random_state=22, score=0.473, total=   2.3s\n",
      "[CV] bootstrap=True, max_samples=17, n_estimators=51, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=17, n_estimators=51, oob_score=True, random_state=22, score=0.459, total=   1.7s\n",
      "[CV] bootstrap=True, max_samples=17, n_estimators=51, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=17, n_estimators=51, oob_score=True, random_state=22, score=0.475, total=   1.8s\n",
      "[CV] bootstrap=True, max_samples=17, n_estimators=51, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=17, n_estimators=51, oob_score=True, random_state=22, score=0.474, total=   1.8s\n",
      "[CV] bootstrap=True, max_samples=11, n_estimators=67, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=11, n_estimators=67, oob_score=True, random_state=22, score=0.473, total=   2.3s\n",
      "[CV] bootstrap=True, max_samples=11, n_estimators=67, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=11, n_estimators=67, oob_score=True, random_state=22, score=0.469, total=   2.3s\n",
      "[CV] bootstrap=True, max_samples=11, n_estimators=67, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=11, n_estimators=67, oob_score=True, random_state=22, score=0.473, total=   2.3s\n",
      "[CV] bootstrap=True, max_samples=11, n_estimators=57, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=11, n_estimators=57, oob_score=True, random_state=22, score=0.473, total=   1.9s\n",
      "[CV] bootstrap=True, max_samples=11, n_estimators=57, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=11, n_estimators=57, oob_score=True, random_state=22, score=0.472, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=11, n_estimators=57, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=11, n_estimators=57, oob_score=True, random_state=22, score=0.474, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=16, n_estimators=63, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=16, n_estimators=63, oob_score=True, random_state=22, score=0.473, total=   2.3s\n",
      "[CV] bootstrap=True, max_samples=16, n_estimators=63, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=16, n_estimators=63, oob_score=True, random_state=22, score=0.474, total=   2.2s\n",
      "[CV] bootstrap=True, max_samples=16, n_estimators=63, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=16, n_estimators=63, oob_score=True, random_state=22, score=0.474, total=   2.2s\n",
      "[CV] bootstrap=True, max_samples=15, n_estimators=70, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=15, n_estimators=70, oob_score=True, random_state=22, score=0.472, total=   2.5s\n",
      "[CV] bootstrap=True, max_samples=15, n_estimators=70, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=15, n_estimators=70, oob_score=True, random_state=22, score=0.474, total=   2.6s\n",
      "[CV] bootstrap=True, max_samples=15, n_estimators=70, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=15, n_estimators=70, oob_score=True, random_state=22, score=0.470, total=   2.8s\n",
      "[CV] bootstrap=True, max_samples=24, n_estimators=70, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=24, n_estimators=70, oob_score=True, random_state=22, score=0.467, total=   2.6s\n",
      "[CV] bootstrap=True, max_samples=24, n_estimators=70, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=24, n_estimators=70, oob_score=True, random_state=22, score=0.477, total=   2.7s\n",
      "[CV] bootstrap=True, max_samples=24, n_estimators=70, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=24, n_estimators=70, oob_score=True, random_state=22, score=0.471, total=   2.7s\n",
      "[CV] bootstrap=True, max_samples=19, n_estimators=59, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=19, n_estimators=59, oob_score=True, random_state=22, score=0.471, total=   2.1s\n",
      "[CV] bootstrap=True, max_samples=19, n_estimators=59, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=19, n_estimators=59, oob_score=True, random_state=22, score=0.475, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=19, n_estimators=59, oob_score=True, random_state=22 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, max_samples=19, n_estimators=59, oob_score=True, random_state=22, score=0.475, total=   2.0s\n",
      "[CV] bootstrap=True, max_samples=1, n_estimators=53, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=1, n_estimators=53, oob_score=True, random_state=22, score=0.474, total=   1.8s\n",
      "[CV] bootstrap=True, max_samples=1, n_estimators=53, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=1, n_estimators=53, oob_score=True, random_state=22, score=0.474, total=   1.8s\n",
      "[CV] bootstrap=True, max_samples=1, n_estimators=53, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=1, n_estimators=53, oob_score=True, random_state=22, score=0.474, total=   1.8s\n",
      "[CV] bootstrap=True, max_samples=6, n_estimators=74, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=6, n_estimators=74, oob_score=True, random_state=22, score=0.474, total=   2.6s\n",
      "[CV] bootstrap=True, max_samples=6, n_estimators=74, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=6, n_estimators=74, oob_score=True, random_state=22, score=0.474, total=   3.0s\n",
      "[CV] bootstrap=True, max_samples=6, n_estimators=74, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=6, n_estimators=74, oob_score=True, random_state=22, score=0.473, total=   2.6s\n",
      "[CV] bootstrap=True, max_samples=9, n_estimators=60, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=9, n_estimators=60, oob_score=True, random_state=22, score=0.474, total=   2.3s\n",
      "[CV] bootstrap=True, max_samples=9, n_estimators=60, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=9, n_estimators=60, oob_score=True, random_state=22, score=0.474, total=   2.1s\n",
      "[CV] bootstrap=True, max_samples=9, n_estimators=60, oob_score=True, random_state=22 \n",
      "[CV]  bootstrap=True, max_samples=9, n_estimators=60, oob_score=True, random_state=22, score=0.471, total=   2.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  60 out of  60 | elapsed:  2.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "                   estimator=BaggingClassifier(base_estimator=None,\n",
       "                                               bootstrap=True,\n",
       "                                               bootstrap_features=False,\n",
       "                                               max_features=1.0,\n",
       "                                               max_samples=0.7, n_estimators=50,\n",
       "                                               n_jobs=None, oob_score=True,\n",
       "                                               random_state=22, verbose=0,\n",
       "                                               warm_start=False),\n",
       "                   iid='warn', n_iter=20, n_jobs=None,\n",
       "                   param_distributions={'bootstrap': [True],\n",
       "                                        'max_samples': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a20c54050>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a22618350>,\n",
       "                                        'oob_score': [True],\n",
       "                                        'random_state': [22]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=10)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomCV.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Best cross-validation accuracy: 0.47\n",
      " Best parameters:  {'bootstrap': True, 'max_samples': 16, 'n_estimators': 57}\n",
      " Test set accuracy: 0.46\n"
     ]
    }
   ],
   "source": [
    "print(\" Best cross-validation accuracy: {:.2f}\". format( randomCV.best_score_)) \n",
    "print(\" Best parameters: \", randomCV.best_params_) \n",
    "print(\" Test set accuracy: {:.2f}\". format( randomCV.score( x_cv, y_cv)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "#pipe_svc = Pipeline([('scl', StandardScaler()), ('pca', PCA()), ('svc', SVC())]) \n",
    "pipe_svc = Pipeline([('scl', StandardScaler()),('rfcl', RandomForestClassifier())]) \n",
    "#param_grid = {'pca__n_components':[65],'svc__C': [0.001, 0.01, 0.1, 1,5,6,7,8,9,10], 'svc__gamma': [0.001,0.01,0.06,0.5,0.1,1,2,3,4,10], 'svc__kernel':['rbf','poly']} \n",
    "\n",
    "param_grid = {'svc__C': [10,],\n",
    "              'svc__gamma': [0.05,0.06,0.07,], \n",
    "              'svc__kernel':['rbf']\n",
    "             } \n",
    "\n",
    "grid = GridSearchCV( pipe_svc , param_grid = param_grid, cv = 5,verbose=10) \n",
    "\n",
    "grid.fit( x_train, y_train) \n",
    "\n",
    "print(\" Best cross-validation accuracy: {:.2f}\". format( grid.best_score_)) \n",
    "print(\" Best parameters: \", grid.best_params_) \n",
    "print(\" Test set accuracy: {:.2f}\". format( grid.score( x_cv, y_cv)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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