KNNFundRatings.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:3058: DtypeWarning: Columns (29,31,32,64,78,101) 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": [
    {
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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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       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95</td>\n",
       "      <td>16.58</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>-0.01</td>\n",
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       "      <td>1.33</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.84</td>\n",
       "      <td>16.38</td>\n",
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       "      <td>32.55</td>\n",
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       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.19</td>\n",
       "      <td>8.08</td>\n",
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       "      <td>9</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.86</td>\n",
       "      <td>7.34</td>\n",
       "      <td>1.35</td>\n",
       "      <td>8.40</td>\n",
       "      <td>63.07</td>\n",
       "      <td>3.90</td>\n",
       "      <td>15.47</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.87</td>\n",
       "      <td>6.24</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       0.00                  0.0            0.00           0.00   \n",
       "1       0.00                  0.0            0.00           0.00   \n",
       "2       0.00                  0.0            0.00           0.00   \n",
       "3       0.00                  0.0            0.00           0.00   \n",
       "4       0.00                  0.0            0.00           0.00   \n",
       "5       0.00                  0.0            0.00           0.00   \n",
       "6      17.94                  0.0            0.78          10.04   \n",
       "7       0.00                  0.0            0.00           0.00   \n",
       "8       0.00                  0.0            0.00           0.00   \n",
       "9       1.78                  0.0            0.17           5.86   \n",
       "\n",
       "   maturity_bond  b_rating  a_rating  aaa_rating  aa_rating  bbb_rating  ...  \\\n",
       "0            NaN      0.00      0.00        0.00       0.00        0.00  ...   \n",
       "1            NaN      0.00      0.00        0.00       0.00        0.00  ...   \n",
       "2            NaN      0.00      0.00        0.00       0.00        0.00  ...   \n",
       "3            NaN      0.00      0.00        0.00       0.00        0.00  ...   \n",
       "4            NaN      0.00      0.00        0.00       0.00        0.00  ...   \n",
       "5            NaN      0.00      0.00        0.00       0.00        0.00  ...   \n",
       "6            NaN     21.07      8.53        7.44       1.64       32.55  ...   \n",
       "7            NaN      0.00      0.00        0.00       0.00        0.00  ...   \n",
       "8            NaN      0.00      0.00        0.00       0.00        0.00  ...   \n",
       "9           7.34      1.35      8.40       63.07       3.90       15.47  ...   \n",
       "\n",
       "   10_years_beta_fund  10yrs_treynor_ratio_fund  \\\n",
       "0                 NaN                       NaN   \n",
       "1                1.08                     15.57   \n",
       "2                0.95                     16.58   \n",
       "3                0.84                     16.38   \n",
       "4                 NaN                       NaN   \n",
       "5                1.22                      8.44   \n",
       "6                0.49                      7.08   \n",
       "7                 NaN                       NaN   \n",
       "8                0.91                     -0.23   \n",
       "9                0.90                     11.14   \n",
       "\n",
       "   10_years_return_mean_annual_category  10yrs_treynor_ratio_category  \\\n",
       "0                                  0.01                          0.21   \n",
       "1                                  0.01                          0.15   \n",
       "2                                  0.01                          0.15   \n",
       "3                                  0.01                          0.14   \n",
       "4                                  0.01                          0.08   \n",
       "5                                  0.01                          0.09   \n",
       "6                                  0.00                          0.06   \n",
       "7                                  0.01                          0.14   \n",
       "8                                  0.01                          0.08   \n",
       "9                                  0.01                         -0.14   \n",
       "\n",
       "   10_years_return_fund  10_years_alpha_category  10_years_beta_category  \\\n",
       "0                  0.00                     0.06                    0.01   \n",
       "1                 17.25                    -0.01                    0.01   \n",
       "2                 16.21                    -0.01                    0.01   \n",
       "3                 14.12                    -0.02                    0.01   \n",
       "4                  0.00                    -0.02                    0.01   \n",
       "5                 10.77                     0.00                    0.01   \n",
       "6                  3.90                    -0.01                    0.01   \n",
       "7                  0.00                    -0.02                    0.01   \n",
       "8                  0.58                    -0.01                    0.01   \n",
       "9                 10.53                     0.01                    0.00   \n",
       "\n",
       "   10years_category_std  10_years_return_mean_annual_fund  \\\n",
       "0                  0.13                               NaN   \n",
       "1                  0.14                              1.42   \n",
       "2                  0.14                              1.33   \n",
       "3                  0.13                              1.16   \n",
       "4                  0.12                               NaN   \n",
       "5                  0.10                              0.91   \n",
       "6                  0.07                              0.33   \n",
       "7                  0.13                               NaN   \n",
       "8                  0.16                              0.19   \n",
       "9                  0.05                              0.87   \n",
       "\n",
       "   10_years_return_category  \n",
       "0                     14.30  \n",
       "1                     15.94  \n",
       "2                     15.94  \n",
       "3                     13.68  \n",
       "4                     11.53  \n",
       "5                     11.07  \n",
       "6                      4.77  \n",
       "7                     13.68  \n",
       "8                      8.08  \n",
       "9                      6.24  \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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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>bb_rating</td>\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",
       "      <td>us_govt_bond_rating</td>\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",
       "      <td>below_b_rating</td>\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",
       "      <td>others_rating</td>\n",
       "      <td>24886.0</td>\n",
       "      <td>1.666769</td>\n",
       "      <td>6.885158</td>\n",
       "      <td>-68.21</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>maturity_bond</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10_years_alpha_category</td>\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",
       "      <td>10_years_beta_category</td>\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",
       "      <td>10years_category_std</td>\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",
       "      <td>10_years_return_mean_annual_fund</td>\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",
       "      <td>10_years_return_category</td>\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, 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 0x10cad09d0>"
      ]
     },
     "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 0x1a221b9910>"
      ]
     },
     "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        0.0                  0.0             0.0            0.0   \n",
       " 10962        0.0                  0.0             0.0            0.0   \n",
       " 8422         0.0                  0.0             0.0            0.0   \n",
       " 3081         0.0                  0.0             0.0            0.0   \n",
       " 10287        0.0                  0.0             0.0            0.0   \n",
       " ...          ...                  ...             ...            ...   \n",
       " 13123        0.0                  0.0             0.0            0.0   \n",
       " 19648        0.0                  0.0             0.0            0.0   \n",
       " 9845         0.0                  0.0             0.0            0.0   \n",
       " 10799        0.0                  0.0             0.0            0.0   \n",
       " 2732         0.0                  0.0             0.0            0.0   \n",
       " \n",
       "        maturity_bond  b_rating  a_rating  aaa_rating  aa_rating  bbb_rating  \\\n",
       " 22331           7.29       0.0       0.0         0.0        0.0         0.0   \n",
       " 10962           7.29       0.0       0.0         0.0        0.0         0.0   \n",
       " 8422            7.29       0.0       0.0         0.0        0.0         0.0   \n",
       " 3081            7.29       0.0       0.0         0.0        0.0         0.0   \n",
       " 10287           7.29       0.0       0.0         0.0        0.0         0.0   \n",
       " ...              ...       ...       ...         ...        ...         ...   \n",
       " 13123           7.29       0.0       0.0         0.0        0.0         0.0   \n",
       " 19648           7.29       0.0       0.0         0.0        0.0         0.0   \n",
       " 9845            7.29       0.0       0.0         0.0        0.0         0.0   \n",
       " 10799           7.29       0.0       0.0         0.0        0.0         0.0   \n",
       " 2732            7.29       0.0       0.0         0.0        0.0         0.0   \n",
       " \n",
       "        ...  10yrs_sharpe_ratio_category  10_years_beta_fund  \\\n",
       " 22331  ...                         0.01                1.01   \n",
       " 10962  ...                         0.01                1.01   \n",
       " 8422   ...                         0.01                1.10   \n",
       " 3081   ...                         0.01                1.01   \n",
       " 10287  ...                         0.01                1.21   \n",
       " ...    ...                          ...                 ...   \n",
       " 13123  ...                         0.01                1.02   \n",
       " 19648  ...                         0.01                1.01   \n",
       " 9845   ...                         0.01                0.98   \n",
       " 10799  ...                         0.01                0.92   \n",
       " 2732   ...                         0.01                0.96   \n",
       " \n",
       "        10_years_return_mean_annual_category  10yrs_treynor_ratio_category  \\\n",
       " 22331                                  0.01                          0.14   \n",
       " 10962                                  0.01                          0.11   \n",
       " 8422                                   0.01                          0.12   \n",
       " 3081                                   0.01                          0.14   \n",
       " 10287                                  0.01                          0.13   \n",
       " ...                                     ...                           ...   \n",
       " 13123                                  0.01                          0.13   \n",
       " 19648                                  0.01                          0.06   \n",
       " 9845                                   0.01                          0.07   \n",
       " 10799                                  0.01                          0.19   \n",
       " 2732                                   0.01                          0.23   \n",
       " \n",
       "        10_years_return_fund  10_years_alpha_category  10_years_beta_category  \\\n",
       " 22331                  0.00                   -0.010                    0.01   \n",
       " 10962                  0.00                   -0.020                    0.01   \n",
       " 8422                  11.89                   -0.030                    0.01   \n",
       " 3081                   0.00                    0.040                    0.01   \n",
       " 10287                 16.12                   -0.020                    0.01   \n",
       " ...                     ...                      ...                     ...   \n",
       " 13123                 14.26                    0.020                    0.01   \n",
       " 19648                  0.00                   -0.010                    0.01   \n",
       " 9845                   6.16                   -0.020                    0.01   \n",
       " 10799                 17.76                    0.055                    0.01   \n",
       " 2732                   9.57                    0.055                    0.01   \n",
       " \n",
       "        10years_category_std  10_years_return_mean_annual_fund  \\\n",
       " 22331                  0.13                              0.90   \n",
       " 10962                  0.10                              0.90   \n",
       " 8422                   0.17                              1.06   \n",
       " 3081                   0.14                              0.90   \n",
       " 10287                  0.18                              1.38   \n",
       " ...                     ...                               ...   \n",
       " 13123                  0.16                              1.23   \n",
       " 19648                  0.07                              0.90   \n",
       " 9845                   0.10                              0.53   \n",
       " 10799                  0.18                              1.51   \n",
       " 2732                   0.07                              0.78   \n",
       " \n",
       "        10_years_return_category  \n",
       " 22331                     14.54  \n",
       " 10962                      7.08  \n",
       " 8422                      14.67  \n",
       " 3081                      11.79  \n",
       " 10287                     16.24  \n",
       " ...                         ...  \n",
       " 13123                     13.14  \n",
       " 19648                      4.77  \n",
       " 9845                       8.38  \n",
       " 10799                     17.24  \n",
       " 2732                       9.49  \n",
       " \n",
       " [18750 rows x 108 columns],\n",
       "        bb_rating  us_govt_bond_rating  below_b_rating  others_rating  \\\n",
       " 14149       6.05                  0.0            0.00           0.80   \n",
       " 8946        0.00                  0.0            0.00           0.00   \n",
       " 22378       0.00                  0.0            0.00           0.00   \n",
       " 12162       0.00                  0.0            0.00           0.00   \n",
       " 4879        6.05                  0.0            0.25           0.80   \n",
       " ...          ...                  ...             ...            ...   \n",
       " 8487        0.00                  0.0            0.00           0.00   \n",
       " 863         0.00                  0.0            0.00           0.00   \n",
       " 8463        6.05                  0.0            0.25           0.48   \n",
       " 6258        0.00                  0.0            0.00           0.00   \n",
       " 4183        1.20                  0.0            0.00           0.00   \n",
       " \n",
       "        maturity_bond  b_rating  a_rating  aaa_rating  aa_rating  bbb_rating  \\\n",
       " 14149           7.29       0.0   20.6937        8.54      7.475        8.22   \n",
       " 8946            7.29       0.0    0.0000        0.00      0.000        0.00   \n",
       " 22378           7.29       0.0    0.0000        0.00      0.000        0.00   \n",
       " 12162           7.29       0.0    0.0000        0.00      0.000        0.00   \n",
       " 4879            7.29       0.7    0.0000        0.00      0.000        7.90   \n",
       " ...              ...       ...       ...         ...        ...         ...   \n",
       " 8487            7.29       0.0    0.0000        0.00      0.000        0.00   \n",
       " 863             7.29       0.0    0.0000        0.00      0.000        0.00   \n",
       " 8463            7.29       0.7   11.4000       42.79      6.010       18.81   \n",
       " 6258            7.29       0.0    0.0000        0.00      0.000        0.00   \n",
       " 4183            7.29       0.7    0.0000       46.05      0.000        7.39   \n",
       " \n",
       "        ...  10yrs_sharpe_ratio_category  10_years_beta_fund  \\\n",
       " 14149  ...                         0.01                1.08   \n",
       " 8946   ...                         0.01                1.01   \n",
       " 22378  ...                         0.01                0.96   \n",
       " 12162  ...                         0.01                1.05   \n",
       " 4879   ...                         0.01                0.96   \n",
       " ...    ...                          ...                 ...   \n",
       " 8487   ...                         0.01                1.01   \n",
       " 863    ...                         0.01                1.01   \n",
       " 8463   ...                         0.01                1.21   \n",
       " 6258   ...                         0.01                1.01   \n",
       " 4183   ...                         0.01                0.96   \n",
       " \n",
       "        10_years_return_mean_annual_category  10yrs_treynor_ratio_category  \\\n",
       " 14149                                  0.01                         0.040   \n",
       " 8946                                   0.01                         0.140   \n",
       " 22378                                  0.01                        -0.035   \n",
       " 12162                                  0.01                         0.150   \n",
       " 4879                                   0.01                         0.230   \n",
       " ...                                     ...                           ...   \n",
       " 8487                                   0.01                         0.150   \n",
       " 863                                    0.01                         0.150   \n",
       " 8463                                   0.01                         0.080   \n",
       " 6258                                   0.01                         0.140   \n",
       " 4183                                   0.01                         0.020   \n",
       " \n",
       "        10_years_return_fund  10_years_alpha_category  10_years_beta_category  \\\n",
       " 14149                  5.42                   -0.010                    0.01   \n",
       " 8946                  18.82                   -0.020                    0.01   \n",
       " 22378                 -7.20                   -0.020                    0.01   \n",
       " 12162                 15.77                   -0.010                    0.01   \n",
       " 4879                  10.23                    0.055                    0.01   \n",
       " ...                     ...                      ...                     ...   \n",
       " 8487                   0.00                   -0.010                    0.01   \n",
       " 863                   17.13                   -0.010                    0.01   \n",
       " 8463                  11.06                   -0.020                    0.01   \n",
       " 6258                   0.00                   -0.010                    0.01   \n",
       " 4183                   1.51                    0.000                    0.01   \n",
       " \n",
       "        10years_category_std  10_years_return_mean_annual_fund  \\\n",
       " 14149                  0.04                             0.450   \n",
       " 8946                   0.16                             1.525   \n",
       " 22378                  0.15                             0.290   \n",
       " 12162                  0.14                             1.310   \n",
       " 4879                   0.07                             0.840   \n",
       " ...                     ...                               ...   \n",
       " 8487                   0.14                             0.900   \n",
       " 863                    0.14                             1.400   \n",
       " 8463                   0.12                             0.920   \n",
       " 6258                   0.13                             0.900   \n",
       " 4183                   0.01                             0.290   \n",
       " \n",
       "        10_years_return_category  \n",
       " 14149                      4.22  \n",
       " 8946                      15.70  \n",
       " 22378                     -3.06  \n",
       " 12162                     15.94  \n",
       " 4879                       9.49  \n",
       " ...                         ...  \n",
       " 8487                      15.94  \n",
       " 863                       15.94  \n",
       " 8463                      11.71  \n",
       " 6258                      14.54  \n",
       " 4183                       1.23  \n",
       " \n",
       " [6250 rows x 108 columns],\n",
       "        bb_rating  us_govt_bond_rating  below_b_rating  others_rating  \\\n",
       " 14149       6.05                  0.0            0.00            0.8   \n",
       " 8946        0.00                  0.0            0.00            0.0   \n",
       " 22378       0.00                  0.0            0.00            0.0   \n",
       " 12162       0.00                  0.0            0.00            0.0   \n",
       " 4879        6.05                  0.0            0.25            0.8   \n",
       " ...          ...                  ...             ...            ...   \n",
       " 4022        6.05                  0.0            0.25            0.8   \n",
       " 17601       6.05                  0.0            0.25            0.8   \n",
       " 4718        6.05                  0.0            0.25            0.8   \n",
       " 9376        0.00                  0.0            0.00            0.0   \n",
       " 8019        6.05                  0.0            0.25            0.8   \n",
       " \n",
       "        maturity_bond  b_rating  a_rating  aaa_rating  aa_rating  bbb_rating  \\\n",
       " 14149           7.29       0.0   20.6937        8.54      7.475        8.22   \n",
       " 8946            7.29       0.0    0.0000        0.00      0.000        0.00   \n",
       " 22378           7.29       0.0    0.0000        0.00      0.000        0.00   \n",
       " 12162           7.29       0.0    0.0000        0.00      0.000        0.00   \n",
       " 4879            7.29       0.7    0.0000        0.00      0.000        7.90   \n",
       " ...              ...       ...       ...         ...        ...         ...   \n",
       " 4022            7.29       0.7    0.0000        0.00      0.000        1.36   \n",
       " 17601           7.29       0.7   11.0600       35.18      7.475       22.40   \n",
       " 4718            7.29       0.7    0.0000        0.00      0.000        6.01   \n",
       " 9376            7.29       0.0    0.0000        0.00      0.000        0.00   \n",
       " 8019            7.29       0.7    7.8800       46.05      4.190       12.96   \n",
       " \n",
       "        ...  10yrs_sharpe_ratio_category  10_years_beta_fund  \\\n",
       " 14149  ...                         0.01                1.08   \n",
       " 8946   ...                         0.01                1.01   \n",
       " 22378  ...                         0.01                0.96   \n",
       " 12162  ...                         0.01                1.05   \n",
       " 4879   ...                         0.01                0.96   \n",
       " ...    ...                          ...                 ...   \n",
       " 4022   ...                         0.01                1.01   \n",
       " 17601  ...                         0.01                1.01   \n",
       " 4718   ...                         0.01                0.96   \n",
       " 9376   ...                         0.01                1.01   \n",
       " 8019   ...                         0.01                1.01   \n",
       " \n",
       "        10_years_return_mean_annual_category  10yrs_treynor_ratio_category  \\\n",
       " 14149                                  0.01                         0.040   \n",
       " 8946                                   0.01                         0.140   \n",
       " 22378                                  0.01                        -0.035   \n",
       " 12162                                  0.01                         0.150   \n",
       " 4879                                   0.01                         0.230   \n",
       " ...                                     ...                           ...   \n",
       " 4022                                   0.01                         0.230   \n",
       " 17601                                  0.01                         0.080   \n",
       " 4718                                   0.01                         0.245   \n",
       " 9376                                   0.01                         0.080   \n",
       " 8019                                   0.01                         0.080   \n",
       " \n",
       "        10_years_return_fund  10_years_alpha_category  10_years_beta_category  \\\n",
       " 14149                  5.42                   -0.010                    0.01   \n",
       " 8946                  18.82                   -0.020                    0.01   \n",
       " 22378                 -7.20                   -0.020                    0.01   \n",
       " 12162                 15.77                   -0.010                    0.01   \n",
       " 4879                  10.23                    0.055                    0.01   \n",
       " ...                     ...                      ...                     ...   \n",
       " 4022                   0.00                    0.055                    0.01   \n",
       " 17601                  0.00                   -0.020                    0.01   \n",
       " 4718                   6.90                    0.055                    0.01   \n",
       " 9376                   0.00                   -0.020                    0.01   \n",
       " 8019                   0.00                   -0.010                    0.01   \n",
       " \n",
       "        10years_category_std  10_years_return_mean_annual_fund  \\\n",
       " 14149                  0.04                             0.450   \n",
       " 8946                   0.16                             1.525   \n",
       " 22378                  0.15                             0.290   \n",
       " 12162                  0.14                             1.310   \n",
       " 4879                   0.07                             0.840   \n",
       " ...                     ...                               ...   \n",
       " 4022                   0.07                             0.900   \n",
       " 17601                  0.13                             0.900   \n",
       " 4718                   0.05                             0.570   \n",
       " 9376                   0.13                             0.900   \n",
       " 8019                   0.10                             0.900   \n",
       " \n",
       "        10_years_return_category  \n",
       " 14149                      4.22  \n",
       " 8946                      15.70  \n",
       " 22378                     -3.06  \n",
       " 12162                     15.94  \n",
       " 4879                       9.49  \n",
       " ...                         ...  \n",
       " 4022                       9.49  \n",
       " 17601                     12.56  \n",
       " 4718                       6.69  \n",
       " 9376                      12.71  \n",
       " 8019                      10.79  \n",
       " \n",
       " [5000 rows x 108 columns],\n",
       " 22331    3\n",
       " 10962    2\n",
       " 8422     2\n",
       " 3081     3\n",
       " 10287    3\n",
       "         ..\n",
       " 13123    4\n",
       " 19648    0\n",
       " 9845     3\n",
       " 10799    2\n",
       " 2732     3\n",
       " Name: greatstone_rating, Length: 18750, dtype: int64,\n",
       " 14149    5\n",
       " 8946     5\n",
       " 22378    3\n",
       " 12162    3\n",
       " 4879     4\n",
       "         ..\n",
       " 8487     4\n",
       " 863      3\n",
       " 8463     3\n",
       " 6258     3\n",
       " 4183     3\n",
       " Name: greatstone_rating, Length: 6250, dtype: int64,\n",
       " 14149    5\n",
       " 8946     5\n",
       " 22378    3\n",
       " 12162    3\n",
       " 4879     4\n",
       "         ..\n",
       " 4022     4\n",
       " 17601    0\n",
       " 4718     3\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.          0.8312647  ... -0.45569111 -0.29777564\n",
      "  -0.28955799]\n",
      " [ 0.          0.          0.         ...  0.          0.\n",
      "   0.        ]\n",
      " [ 0.8312647   0.          1.00005334 ... -0.41402338 -0.26070691\n",
      "  -0.24513939]\n",
      " ...\n",
      " [-0.45569111  0.         -0.41402338 ...  1.00005334  0.57960194\n",
      "   0.69296258]\n",
      " [-0.29777564  0.         -0.26070691 ...  0.57960194  1.00005334\n",
      "   0.72718427]\n",
      " [-0.28955799  0.         -0.24513939 ...  0.69296258  0.72718427\n",
      "   1.00005334]]\n",
      "Eigenvectors \n",
      "[[-0.0886354 +0.j  0.07507357+0.j  0.05220408+0.j ...  0.        +0.j\n",
      "   0.        +0.j  0.        +0.j]\n",
      " [ 0.        +0.j  0.        +0.j  0.        +0.j ...  0.        +0.j\n",
      "   0.        +0.j  0.        +0.j]\n",
      " [-0.07718416+0.j  0.07672382+0.j  0.06468846+0.j ...  0.        +0.j\n",
      "   0.        +0.j  0.        +0.j]\n",
      " ...\n",
      " [ 0.15361356+0.j -0.08417116+0.j -0.03694897+0.j ...  0.        +0.j\n",
      "   0.        +0.j  0.        +0.j]\n",
      " [ 0.14742886+0.j  0.04828036+0.j -0.06235358+0.j ...  0.        +0.j\n",
      "   0.        +0.j  0.        +0.j]\n",
      " [ 0.15699036+0.j  0.07862422+0.j -0.01954287+0.j ...  0.        +0.j\n",
      "   0.        +0.j  0.        +0.j]]\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",
      "[ 2.94850103e+01+0.00000000e+00j  1.13513559e+01+0.00000000e+00j\n",
      "  7.88538283e+00+0.00000000e+00j  5.12740963e+00+0.00000000e+00j\n",
      "  4.80178558e+00+0.00000000e+00j  2.98451281e+00+0.00000000e+00j\n",
      "  2.21891073e+00+0.00000000e+00j  2.16987781e+00+0.00000000e+00j\n",
      "  1.92485318e+00+0.00000000e+00j  1.80208360e+00+0.00000000e+00j\n",
      "  1.73750461e+00+0.00000000e+00j  1.57271644e+00+0.00000000e+00j\n",
      "  1.23583292e+00+0.00000000e+00j  1.08090140e+00+0.00000000e+00j\n",
      "  1.01954510e+00+0.00000000e+00j  1.00933047e+00+0.00000000e+00j\n",
      "  9.56845525e-01+0.00000000e+00j  9.03303334e-01+0.00000000e+00j\n",
      "  8.48396212e-01+0.00000000e+00j  8.17111099e-01+0.00000000e+00j\n",
      "  7.51047629e-01+0.00000000e+00j  7.30345418e-01+0.00000000e+00j\n",
      "  6.92033392e-01+0.00000000e+00j  6.55771040e-01+0.00000000e+00j\n",
      "  6.24082940e-01+0.00000000e+00j  6.03791053e-01+0.00000000e+00j\n",
      "  5.98151048e-01+0.00000000e+00j  5.87406811e-01+0.00000000e+00j\n",
      "  5.57142228e-01+0.00000000e+00j  5.28748633e-01+0.00000000e+00j\n",
      "  5.05093549e-01+0.00000000e+00j  4.92663551e-01+0.00000000e+00j\n",
      "  4.64481433e-01+0.00000000e+00j  4.80176194e-01+0.00000000e+00j\n",
      "  4.51889784e-01+0.00000000e+00j  4.31148769e-01+0.00000000e+00j\n",
      "  4.04880665e-01+0.00000000e+00j  3.97378632e-01+0.00000000e+00j\n",
      "  4.08240396e-01+0.00000000e+00j  3.84461485e-01+0.00000000e+00j\n",
      "  3.66682470e-01+0.00000000e+00j  3.79213514e-01+0.00000000e+00j\n",
      "  3.49912935e-01+0.00000000e+00j  3.38862724e-01+0.00000000e+00j\n",
      "  3.25465633e-01+0.00000000e+00j  3.01106908e-01+0.00000000e+00j\n",
      "  3.08518379e-01+0.00000000e+00j  2.94392419e-01+0.00000000e+00j\n",
      "  2.77905949e-01+0.00000000e+00j  2.74088675e-01+0.00000000e+00j\n",
      "  2.58523449e-01+0.00000000e+00j  2.50033890e-01+0.00000000e+00j\n",
      "  2.33097744e-01+0.00000000e+00j  2.13624692e-01+0.00000000e+00j\n",
      "  2.24088327e-01+0.00000000e+00j  2.21422310e-01+0.00000000e+00j\n",
      "  1.98281005e-01+0.00000000e+00j  1.92616988e-01+0.00000000e+00j\n",
      "  1.81526274e-01+0.00000000e+00j  1.89461236e-01+0.00000000e+00j\n",
      "  1.72074891e-01+0.00000000e+00j  1.67937862e-01+0.00000000e+00j\n",
      "  2.46148318e-03+0.00000000e+00j  4.90568241e-03+0.00000000e+00j\n",
      "  8.22556262e-03+0.00000000e+00j  1.05521723e-02+0.00000000e+00j\n",
      "  1.64091912e-01+0.00000000e+00j  1.59814233e-01+0.00000000e+00j\n",
      "  1.44040629e-02+0.00000000e+00j  1.69491955e-02+0.00000000e+00j\n",
      "  1.53559808e-01+0.00000000e+00j  2.02898819e-02+0.00000000e+00j\n",
      "  2.24046702e-02+0.00000000e+00j  1.43817179e-01+0.00000000e+00j\n",
      "  3.22594585e-02+0.00000000e+00j  3.52450337e-02+0.00000000e+00j\n",
      "  3.70046280e-02+0.00000000e+00j  4.41798185e-02+0.00000000e+00j\n",
      "  4.22673487e-02+0.00000000e+00j  1.17234149e-01+0.00000000e+00j\n",
      "  1.14638025e-01+0.00000000e+00j  1.05371488e-01+0.00000000e+00j\n",
      "  1.25865285e-01+0.00000000e+00j  5.76685988e-02+0.00000000e+00j\n",
      "  7.24443895e-02+0.00000000e+00j  2.66515265e-02+0.00000000e+00j\n",
      "  6.81784872e-02+0.00000000e+00j  6.05142241e-02+0.00000000e+00j\n",
      "  9.95512440e-02+0.00000000e+00j  8.32500516e-02+0.00000000e+00j\n",
      "  8.00068096e-02+0.00000000e+00j  6.42592530e-02+0.00000000e+00j\n",
      "  1.37783485e-01+0.00000000e+00j  2.83682079e-02+0.00000000e+00j\n",
      "  1.33601242e-01+0.00000000e+00j  9.17295552e-02+0.00000000e+00j\n",
      "  1.30107843e-01+0.00000000e+00j  9.52352208e-02+0.00000000e+00j\n",
      " -7.10092962e-18+2.36759408e-18j -7.10092962e-18-2.36759408e-18j\n",
      "  0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j\n",
      "  0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j\n",
      "  0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j\n",
      "  0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j]\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 [ 29.48343776+0.00000000e+00j  40.83418823+0.00000000e+00j\n",
      "  48.71915051+0.00000000e+00j  53.84628668+0.00000000e+00j\n",
      "  58.64781617+0.00000000e+00j  61.6321698 +0.00000000e+00j\n",
      "  63.85096219+0.00000000e+00j  66.02072428+0.00000000e+00j\n",
      "  67.9454748 +0.00000000e+00j  69.74746229+0.00000000e+00j\n",
      "  71.48487424+0.00000000e+00j  73.0575068 +0.00000000e+00j\n",
      "  74.29327381+0.00000000e+00j  75.37411757+0.00000000e+00j\n",
      "  76.39360829+0.00000000e+00j  77.40288493+0.00000000e+00j\n",
      "  78.35967943+0.00000000e+00j  79.26293458+0.00000000e+00j\n",
      "  80.11128555+0.00000000e+00j  80.92835307+0.00000000e+00j\n",
      "  81.67936064+0.00000000e+00j  82.40966711+0.00000000e+00j\n",
      "  83.10166359+0.00000000e+00j  83.75739966+0.00000000e+00j\n",
      "  84.38144931+0.00000000e+00j  84.98520816+0.00000000e+00j\n",
      "  85.58332731+0.00000000e+00j  86.17070279+0.00000000e+00j\n",
      "  86.72781531+0.00000000e+00j  87.25653574+0.00000000e+00j\n",
      "  87.76160235+0.00000000e+00j  88.25423963+0.00000000e+00j\n",
      "  88.73439021+0.00000000e+00j  89.19884687+0.00000000e+00j\n",
      "  89.65071256+0.00000000e+00j  90.08183833+0.00000000e+00j\n",
      "  90.49005695+0.00000000e+00j  90.89491602+0.00000000e+00j\n",
      "  91.29227346+0.00000000e+00j  91.67671444+0.00000000e+00j\n",
      "  92.05590773+0.00000000e+00j  92.42257065+0.00000000e+00j\n",
      "  92.77246492+0.00000000e+00j  93.11130957+0.00000000e+00j\n",
      "  93.43675785+0.00000000e+00j  93.74525977+0.00000000e+00j\n",
      "  94.04635062+0.00000000e+00j  94.34072734+0.00000000e+00j\n",
      "  94.61861846+0.00000000e+00j  94.89269252+0.00000000e+00j\n",
      "  95.15120218+0.00000000e+00j  95.40122274+0.00000000e+00j\n",
      "  95.63430805+0.00000000e+00j  95.85838443+0.00000000e+00j\n",
      "  96.07979493+0.00000000e+00j  96.29340822+0.00000000e+00j\n",
      "  96.49167866+0.00000000e+00j  96.68428537+0.00000000e+00j\n",
      "  96.8737365 +0.00000000e+00j  97.05525309+0.00000000e+00j\n",
      "  97.22731881+0.00000000e+00j  97.39524771+0.00000000e+00j\n",
      "  97.55933087+0.00000000e+00j  97.71913658+0.00000000e+00j\n",
      "  97.8726882 +0.00000000e+00j  98.01649771+0.00000000e+00j\n",
      "  98.15427385+0.00000000e+00j  98.28786796+0.00000000e+00j\n",
      "  98.41796887+0.00000000e+00j  98.54382744+0.00000000e+00j\n",
      "  98.66105533+0.00000000e+00j  98.77568725+0.00000000e+00j\n",
      "  98.88105311+0.00000000e+00j  98.98059905+0.00000000e+00j\n",
      "  99.07582919+0.00000000e+00j  99.16755385+0.00000000e+00j\n",
      "  99.25079946+0.00000000e+00j  99.33080201+0.00000000e+00j\n",
      "  99.40324253+0.00000000e+00j  99.47141738+0.00000000e+00j\n",
      "  99.53567321+0.00000000e+00j  99.59618421+0.00000000e+00j\n",
      "  99.65384973+0.00000000e+00j  99.69802719+0.00000000e+00j\n",
      "  99.74029229+0.00000000e+00j  99.77729494+0.00000000e+00j\n",
      "  99.81253809+0.00000000e+00j  99.84479583+0.00000000e+00j\n",
      "  99.87316253+0.00000000e+00j  99.89981263+0.00000000e+00j\n",
      "  99.92221611+0.00000000e+00j  99.94250491+0.00000000e+00j\n",
      "  99.9594532 +0.00000000e+00j  99.97385649+0.00000000e+00j\n",
      "  99.9844081 +0.00000000e+00j  99.99263323+0.00000000e+00j\n",
      "  99.99753865+0.00000000e+00j 100.        +0.00000000e+00j\n",
      " 100.        +0.00000000e+00j 100.        +0.00000000e+00j\n",
      " 100.        +0.00000000e+00j 100.        +0.00000000e+00j\n",
      " 100.        +0.00000000e+00j 100.        +0.00000000e+00j\n",
      " 100.        +0.00000000e+00j 100.        +0.00000000e+00j\n",
      " 100.        +2.36746781e-18j 100.        +0.00000000e+00j]\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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chai3/opt/anaconda3/lib/python3.7/site-packages/matplotlib/transforms.py:796: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  points = np.array(args, dtype=float).reshape(2, 2)\n",
      "/Users/chai3/opt/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "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": [
      "[[ 8.87279713e-02 -1.73472348e-18  7.75555953e-02 ... -1.53372799e-01\n",
      "  -1.47410143e-01 -1.57081062e-01]\n",
      " [-7.76613994e-02 -0.00000000e+00 -7.93129448e-02 ...  8.50430746e-02\n",
      "  -4.75187861e-02 -7.90243593e-02]\n",
      " [-4.96248499e-02 -2.70616862e-16 -6.12787727e-02 ...  3.60934427e-02\n",
      "   6.21542052e-02  1.97052636e-02]\n",
      " ...\n",
      " [ 1.22623407e-02 -1.88628394e-16  3.65451983e-03 ... -1.06699801e-03\n",
      "   1.83737415e-01  7.41117611e-02]\n",
      " [-1.21197661e-02 -2.45973211e-17 -3.05130764e-02 ...  1.21856443e-02\n",
      "  -2.75724090e-02 -1.81987392e-01]\n",
      " [-1.20440466e-03  1.80073760e-17  7.97101828e-03 ...  2.32173881e-02\n",
      "   1.09538507e-01 -2.67455889e-01]]\n",
      "[0.29476097 0.11293048 0.07857572 0.05096042 0.04807558 0.03025799\n",
      " 0.02201864 0.02165187 0.01928627 0.01802802 0.017464   0.01568021\n",
      " 0.01240385 0.01082523 0.01017038 0.01009773 0.00965755 0.00909453\n",
      " 0.00854367 0.00818677 0.00751866 0.00731272 0.00691135 0.00655844\n",
      " 0.0063016  0.00610879 0.00597475 0.0059451  0.00560281 0.0052663\n",
      " 0.00506854 0.00498076 0.00476518 0.00466149 0.00453625 0.00431879\n",
      " 0.00409203 0.00403764 0.00396033 0.00386847 0.00380858 0.0036879\n",
      " 0.00349292 0.00341133 0.00327896 0.00307893 0.00302414 0.00296734\n",
      " 0.00280113 0.00273946 0.00257083 0.00252757 0.00234332 0.00224904\n",
      " 0.00223105 0.00214697 0.00199183 0.00192276 0.00190052 0.00183473\n",
      " 0.0017336  0.00168038 0.00165375 0.00160504 0.00154259 0.00144601\n",
      " 0.00139635 0.00134116 0.00130809 0.00125793 0.0011607  0.00113561\n",
      " 0.00106124 0.00100183 0.00095242 0.00091265 0.00084    0.00079956\n",
      " 0.00072508 0.0006801  0.00064324 0.00060471 0.00057696 0.00044517\n",
      " 0.00042675]\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": [],
   "source": [
    "# #Before PCA-SVC\n",
    "# from sklearn.svm import SVC\n",
    "# svc = SVC(C=10,gamma=0.01)\n",
    "# svc.fit(x_train, y_train)\n",
    "# svc.score(x_train, y_train), svc.score(x_cv, y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6512533333333334, 0.56192)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNNModel = KNeighborsClassifier(n_neighbors = 9, metric = 'minkowski', p = 2)\n",
    "KNNModel.fit(x_train, y_train)\n",
    "KNNModel.score(x_train, y_train), KNNModel.score(x_cv, y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.563"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNNModel.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [],
   "source": [
    "greatstone_rating=KNNModel.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 = KNNModel.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": 40,
   "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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_dist = {\"n_estimators\":[55],\n",
    "              \"max_depth\":[None],\n",
    "              \"max_features\": sp_randint(40,45),\n",
    "              \"min_samples_split\": [16],\n",
    "              \"min_samples_leaf\": [5],\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [ \"entropy\"]}\n",
    "\n",
    "param_list={\"n_neighbors\":sp_randint(50,75),\n",
    "             #\"algorithm\": sp_randint('ball_tree','kd_tree', 'brute'),\n",
    "             \"leaf_size\":sp_randint(20,40),\n",
    "             \"p\":sp_randint(1,2),\n",
    "              \"metric\":['minkowski']}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run randomized search\n",
    "samples = 10  # number of random samples \n",
    "randomCV = RandomizedSearchCV(KNNModel, param_distributions=param_list, n_iter=samples,verbose=10) #default cv = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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 10 candidates, totalling 30 fits\n",
      "[CV] leaf_size=23, metric=minkowski, n_neighbors=50, p=1 .............\n",
      "[CV]  leaf_size=23, metric=minkowski, n_neighbors=50, p=1, score=0.533, total=  16.0s\n",
      "[CV] leaf_size=23, metric=minkowski, n_neighbors=50, p=1 .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   16.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  leaf_size=23, metric=minkowski, n_neighbors=50, p=1, score=0.530, total=  16.0s\n",
      "[CV] leaf_size=23, metric=minkowski, n_neighbors=50, p=1 .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   32.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  leaf_size=23, metric=minkowski, n_neighbors=50, p=1, score=0.525, total=  16.2s\n",
      "[CV] leaf_size=35, metric=minkowski, n_neighbors=57, p=1 .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   48.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  leaf_size=35, metric=minkowski, n_neighbors=57, p=1, score=0.530, total=  14.6s\n",
      "[CV] leaf_size=35, metric=minkowski, n_neighbors=57, p=1 .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  1.0min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  leaf_size=35, metric=minkowski, n_neighbors=57, p=1, score=0.527, total=  14.6s\n",
      "[CV] leaf_size=35, metric=minkowski, n_neighbors=57, p=1 .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  1.3min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  leaf_size=35, metric=minkowski, n_neighbors=57, p=1, score=0.524, total=  14.8s\n",
      "[CV] leaf_size=29, metric=minkowski, n_neighbors=56, p=1 .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:  1.5min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  leaf_size=29, metric=minkowski, n_neighbors=56, p=1, score=0.531, total=  14.8s\n",
      "[CV] leaf_size=29, metric=minkowski, n_neighbors=56, p=1 .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:  1.8min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  leaf_size=29, metric=minkowski, n_neighbors=56, p=1, score=0.527, total=  14.7s\n",
      "[CV] leaf_size=29, metric=minkowski, n_neighbors=56, p=1 .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:  2.0min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  leaf_size=29, metric=minkowski, n_neighbors=56, p=1, score=0.523, total=  14.8s\n",
      "[CV] leaf_size=31, metric=minkowski, n_neighbors=68, p=1 .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:  2.3min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  leaf_size=31, metric=minkowski, n_neighbors=68, p=1, score=0.528, total=  15.1s\n",
      "[CV] leaf_size=31, metric=minkowski, n_neighbors=68, p=1 .............\n",
      "[CV]  leaf_size=31, metric=minkowski, n_neighbors=68, p=1, score=0.523, total=  15.1s\n",
      "[CV] leaf_size=31, metric=minkowski, n_neighbors=68, p=1 .............\n",
      "[CV]  leaf_size=31, metric=minkowski, n_neighbors=68, p=1, score=0.520, total=  15.1s\n",
      "[CV] leaf_size=36, metric=minkowski, n_neighbors=65, p=1 .............\n",
      "[CV]  leaf_size=36, metric=minkowski, n_neighbors=65, p=1, score=0.528, total=  15.0s\n",
      "[CV] leaf_size=36, metric=minkowski, n_neighbors=65, p=1 .............\n",
      "[CV]  leaf_size=36, metric=minkowski, n_neighbors=65, p=1, score=0.525, total=  15.1s\n",
      "[CV] leaf_size=36, metric=minkowski, n_neighbors=65, p=1 .............\n",
      "[CV]  leaf_size=36, metric=minkowski, n_neighbors=65, p=1, score=0.521, total=  15.2s\n",
      "[CV] leaf_size=39, metric=minkowski, n_neighbors=69, p=1 .............\n",
      "[CV]  leaf_size=39, metric=minkowski, n_neighbors=69, p=1, score=0.526, total=  15.1s\n",
      "[CV] leaf_size=39, metric=minkowski, n_neighbors=69, p=1 .............\n",
      "[CV]  leaf_size=39, metric=minkowski, n_neighbors=69, p=1, score=0.523, total=  15.1s\n",
      "[CV] leaf_size=39, metric=minkowski, n_neighbors=69, p=1 .............\n",
      "[CV]  leaf_size=39, metric=minkowski, n_neighbors=69, p=1, score=0.519, total=  15.2s\n",
      "[CV] leaf_size=29, metric=minkowski, n_neighbors=50, p=1 .............\n",
      "[CV]  leaf_size=29, metric=minkowski, n_neighbors=50, p=1, score=0.533, total=  14.5s\n",
      "[CV] leaf_size=29, metric=minkowski, n_neighbors=50, p=1 .............\n",
      "[CV]  leaf_size=29, metric=minkowski, n_neighbors=50, p=1, score=0.530, total=  14.5s\n",
      "[CV] leaf_size=29, metric=minkowski, n_neighbors=50, p=1 .............\n",
      "[CV]  leaf_size=29, metric=minkowski, n_neighbors=50, p=1, score=0.525, total=  14.6s\n",
      "[CV] leaf_size=38, metric=minkowski, n_neighbors=54, p=1 .............\n",
      "[CV]  leaf_size=38, metric=minkowski, n_neighbors=54, p=1, score=0.532, total=  14.7s\n",
      "[CV] leaf_size=38, metric=minkowski, n_neighbors=54, p=1 .............\n",
      "[CV]  leaf_size=38, metric=minkowski, n_neighbors=54, p=1, score=0.525, total=  14.8s\n",
      "[CV] leaf_size=38, metric=minkowski, n_neighbors=54, p=1 .............\n",
      "[CV]  leaf_size=38, metric=minkowski, n_neighbors=54, p=1, score=0.523, total=  14.8s\n",
      "[CV] leaf_size=27, metric=minkowski, n_neighbors=55, p=1 .............\n",
      "[CV]  leaf_size=27, metric=minkowski, n_neighbors=55, p=1, score=0.531, total=  14.7s\n",
      "[CV] leaf_size=27, metric=minkowski, n_neighbors=55, p=1 .............\n",
      "[CV]  leaf_size=27, metric=minkowski, n_neighbors=55, p=1, score=0.526, total=  14.7s\n",
      "[CV] leaf_size=27, metric=minkowski, n_neighbors=55, p=1 .............\n",
      "[CV]  leaf_size=27, metric=minkowski, n_neighbors=55, p=1, score=0.522, total=  14.9s\n",
      "[CV] leaf_size=34, metric=minkowski, n_neighbors=70, p=1 .............\n",
      "[CV]  leaf_size=34, metric=minkowski, n_neighbors=70, p=1, score=0.527, total=  15.4s\n",
      "[CV] leaf_size=34, metric=minkowski, n_neighbors=70, p=1 .............\n",
      "[CV]  leaf_size=34, metric=minkowski, n_neighbors=70, p=1, score=0.522, total=  15.0s\n",
      "[CV] leaf_size=34, metric=minkowski, n_neighbors=70, p=1 .............\n",
      "[CV]  leaf_size=34, metric=minkowski, n_neighbors=70, p=1, score=0.518, total= 4.0min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 11.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "                   estimator=KNeighborsClassifier(algorithm='auto',\n",
       "                                                  leaf_size=30,\n",
       "                                                  metric='minkowski',\n",
       "                                                  metric_params=None,\n",
       "                                                  n_jobs=None, n_neighbors=9,\n",
       "                                                  p=2, weights='uniform'),\n",
       "                   iid='warn', n_iter=10, n_jobs=None,\n",
       "                   param_distributions={'leaf_size': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a25ec3f90>,\n",
       "                                        'metric': ['minkowski'],\n",
       "                                        'n_neighbors': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a25ec3a90>,\n",
       "                                        'p': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a25ec6110>},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=10)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomCV.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Best cross-validation accuracy: 0.53\n",
      " Best parameters:  {'leaf_size': 23, 'metric': 'minkowski', 'n_neighbors': 50, 'p': 1}\n",
      " Test set accuracy: 0.54\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': [5,10,14], 'svc__gamma': [0.05,0.06,0.07,], 'svc__kernel':['rbf']} \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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