Jupyter_Overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2b720a30",
   "metadata": {},
   "source": [
    "# Python Application Chapter "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e61e0b4",
   "metadata": {},
   "source": [
    "## Managing the imports and implementation of the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be330c25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from tqdm import tnrange,tqdm_notebook\n",
    "from sklearn import linear_model\n",
    "import itertools\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import Lasso,Ridge, LinearRegression\n",
    "from sklearn.metrics import mean_squared_error as m\n",
    "from scipy import stats\n",
    "import random\n",
    "\n",
    "df = pd.read_csv(\"NBA_salaries_and_stats.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8310c11b",
   "metadata": {},
   "source": [
    "## Using only variables that are not dependent on each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3afd4b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[['+/-', '3P%', 'AGE', 'AST', 'BLK', 'DD2' , 'FG%',\n",
    "        'FP', 'FT%', 'GP', 'MIN' , 'PF',\n",
    "       'PTS', 'REB', 'STL', 'TD3', 'TOV',  'salary']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f2b093",
   "metadata": {},
   "source": [
    "## Implementing created methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06f33119",
   "metadata": {},
   "outputs": [],
   "source": [
    "from underlying_functions import General as G\n",
    "from underlying_functions import Regularization as R\n",
    "from underlying_functions import Combined as C\n",
    "from underlying_functions import Subset as S"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8817dbf8",
   "metadata": {},
   "source": [
    "## Subset selection processes\n",
    "* **best subset**\n",
    "* **forward stepwise selection**\n",
    "* **backward stepwise elimination**\n",
    "* **information criterion selection**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62ffb252",
   "metadata": {},
   "source": [
    "## Working with the subset methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c73be91",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a training and testing dataframe\n",
    "df_train= df.sample(frac=0.7,random_state=1)\n",
    "df_test=df.drop(df_train.index)\n",
    "#comparing the three methods with the first 14 variables of the dataset\n",
    "df_train_auswahl=df_train.iloc[:,:14]\n",
    "df_train_auswahl['salary']=df_train['salary']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0f7727",
   "metadata": {},
   "source": [
    "## **`best subset`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94d2a6aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_variables</th>\n",
       "      <th>RSS</th>\n",
       "      <th>R_squared</th>\n",
       "      <th>variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.089929e+16</td>\n",
       "      <td>0.497275</td>\n",
       "      <td>[FP]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8.291898e+15</td>\n",
       "      <td>0.617540</td>\n",
       "      <td>[AGE, FP]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7.826176e+15</td>\n",
       "      <td>0.639021</td>\n",
       "      <td>[AGE, FG%, FP]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>7.631272e+15</td>\n",
       "      <td>0.648011</td>\n",
       "      <td>[3P%, AGE, FG%, FP]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7.487532e+15</td>\n",
       "      <td>0.654641</td>\n",
       "      <td>[+/-, 3P%, AGE, FG%, FP]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>7.383353e+15</td>\n",
       "      <td>0.659446</td>\n",
       "      <td>[+/-, 3P%, AGE, FG%, FP, PTS]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>7.284740e+15</td>\n",
       "      <td>0.663995</td>\n",
       "      <td>[+/-, 3P%, AGE, DD2, FG%, FP, PTS]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>7.247789e+15</td>\n",
       "      <td>0.665699</td>\n",
       "      <td>[+/-, 3P%, AGE, DD2, FG%, FP, GP, PTS]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>7.225291e+15</td>\n",
       "      <td>0.666737</td>\n",
       "      <td>[+/-, 3P%, AGE, DD2, FG%, FP, GP, PF, PTS]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>7.217586e+15</td>\n",
       "      <td>0.667092</td>\n",
       "      <td>[+/-, 3P%, AGE, DD2, FG%, FP, FT%, GP, PF, PTS]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>7.203990e+15</td>\n",
       "      <td>0.667719</td>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, FP, GP, PF...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>7.193943e+15</td>\n",
       "      <td>0.668183</td>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, FP, FT%, G...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>7.185583e+15</td>\n",
       "      <td>0.668568</td>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, FT%, GP, M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>7.183632e+15</td>\n",
       "      <td>0.668658</td>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, FP, FT%, G...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    number_of_variables           RSS  R_squared  \\\n",
       "0                     1  1.089929e+16   0.497275   \n",
       "1                     2  8.291898e+15   0.617540   \n",
       "2                     3  7.826176e+15   0.639021   \n",
       "3                     4  7.631272e+15   0.648011   \n",
       "4                     5  7.487532e+15   0.654641   \n",
       "5                     6  7.383353e+15   0.659446   \n",
       "6                     7  7.284740e+15   0.663995   \n",
       "7                     8  7.247789e+15   0.665699   \n",
       "8                     9  7.225291e+15   0.666737   \n",
       "9                    10  7.217586e+15   0.667092   \n",
       "10                   11  7.203990e+15   0.667719   \n",
       "11                   12  7.193943e+15   0.668183   \n",
       "12                   13  7.185583e+15   0.668568   \n",
       "13                   14  7.183632e+15   0.668658   \n",
       "\n",
       "                                            variables  \n",
       "0                                                [FP]  \n",
       "1                                           [AGE, FP]  \n",
       "2                                      [AGE, FG%, FP]  \n",
       "3                                 [3P%, AGE, FG%, FP]  \n",
       "4                            [+/-, 3P%, AGE, FG%, FP]  \n",
       "5                       [+/-, 3P%, AGE, FG%, FP, PTS]  \n",
       "6                  [+/-, 3P%, AGE, DD2, FG%, FP, PTS]  \n",
       "7              [+/-, 3P%, AGE, DD2, FG%, FP, GP, PTS]  \n",
       "8          [+/-, 3P%, AGE, DD2, FG%, FP, GP, PF, PTS]  \n",
       "9     [+/-, 3P%, AGE, DD2, FG%, FP, FT%, GP, PF, PTS]  \n",
       "10  [+/-, 3P%, AGE, AST, BLK, DD2, FG%, FP, GP, PF...  \n",
       "11  [+/-, 3P%, AGE, AST, BLK, DD2, FG%, FP, FT%, G...  \n",
       "12  [+/-, 3P%, AGE, AST, BLK, DD2, FG%, FT%, GP, M...  \n",
       "13  [+/-, 3P%, AGE, AST, BLK, DD2, FG%, FP, FT%, G...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculating the best subset selection process and saving it as df_bs\n",
    "df_bs=S.best_subset(df_train_auswahl,output='salary')\n",
    "#filter df_bs after the subsets with the minimal RSS and displaying the results in the end\n",
    "df_bs_min_rss=df_bs[df_bs['min_RSS']==df_bs['RSS']][['number_of_variables','RSS','R_squared','variables']].reset_index()\n",
    "df_bs_min_rss=df_bs_min_rss.drop(['index'], axis=1)\n",
    "display(df_bs_min_rss)#this diplays the Figure 4.2.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9d765ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#the following plots show all subsets in blue and the best subset choice in red \n",
    "#with their corresponding RSS/R-squared values\n",
    "fig=plt.figure(figsize=(20,9))\n",
    "ax=fig.add_subplot(1,2,1)\n",
    "ax.scatter(df_bs.number_of_variables,df_bs.RSS,alpha=0.2,color='darkblue')\n",
    "ax.set_xlabel('Number of variables')\n",
    "ax.set_ylabel('RSS')\n",
    "ax.plot(df_bs.number_of_variables,df_bs.min_RSS,color='r',label='Best Subset')\n",
    "ax.legend()\n",
    "ax=fig.add_subplot(1,2,2)\n",
    "ax.scatter(df_bs.number_of_variables,df_bs.R_squared,alpha=0.2,color='darkblue')\n",
    "ax.set_xlabel('Number of variables')\n",
    "ax.set_ylabel('R_squared')\n",
    "ax.plot(df_bs.number_of_variables,df_bs.max_R_squared,color='r',label='Best Subset')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c76f7ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>r_squaredadj</th>\n",
       "      <th>variables</th>\n",
       "      <th>mse</th>\n",
       "      <th>nf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MIN AIC</th>\n",
       "      <td>6930.911736</td>\n",
       "      <td>6957.417383</td>\n",
       "      <td>0.819272</td>\n",
       "      <td>[+/-, 3P%, AGE, DD2, FG%, FP, GP, PTS]</td>\n",
       "      <td>3.457007e+13</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIN BIC</th>\n",
       "      <td>6930.911736</td>\n",
       "      <td>6957.417383</td>\n",
       "      <td>0.819272</td>\n",
       "      <td>[+/-, 3P%, AGE, DD2, FG%, FP, GP, PTS]</td>\n",
       "      <td>3.457007e+13</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAX ADJ. R-sq.</th>\n",
       "      <td>6932.041588</td>\n",
       "      <td>6965.173647</td>\n",
       "      <td>0.819963</td>\n",
       "      <td>[+/-, 3P%, AGE, DD2, FG%, FP, FT%, GP, PF, PTS]</td>\n",
       "      <td>3.356248e+13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIN MSE TEST</th>\n",
       "      <td>6935.228785</td>\n",
       "      <td>6974.987257</td>\n",
       "      <td>0.818804</td>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, FP, FT%, G...</td>\n",
       "      <td>3.286815e+13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        aic          bic  r_squaredadj  \\\n",
       "                                                         \n",
       "MIN AIC         6930.911736  6957.417383      0.819272   \n",
       "MIN BIC         6930.911736  6957.417383      0.819272   \n",
       "MAX ADJ. R-sq.  6932.041588  6965.173647      0.819963   \n",
       "MIN MSE TEST    6935.228785  6974.987257      0.818804   \n",
       "\n",
       "                                                        variables  \\\n",
       "                                                                    \n",
       "MIN AIC                    [+/-, 3P%, AGE, DD2, FG%, FP, GP, PTS]   \n",
       "MIN BIC                    [+/-, 3P%, AGE, DD2, FG%, FP, GP, PTS]   \n",
       "MAX ADJ. R-sq.    [+/-, 3P%, AGE, DD2, FG%, FP, FT%, GP, PF, PTS]   \n",
       "MIN MSE TEST    [+/-, 3P%, AGE, AST, BLK, DD2, FG%, FP, FT%, G...   \n",
       "\n",
       "                         mse  nf  \n",
       "                                  \n",
       "MIN AIC         3.457007e+13   8  \n",
       "MIN BIC         3.457007e+13   8  \n",
       "MAX ADJ. R-sq.  3.356248e+13  10  \n",
       "MIN MSE TEST    3.286815e+13  12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "#showing the optimal subset for each information criterion\n",
    "df1,df2,df3=S.ic(df_bs_min_rss['variables'],df_train_auswahl,df_test,output='salary')\n",
    "print(display(df2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f06acf1",
   "metadata": {},
   "source": [
    "## **`forward stepwise selection`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "afcc7192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>RSS</th>\n",
       "      <th>R_squared</th>\n",
       "      <th>number of variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[FP]</td>\n",
       "      <td>1.089929e+16</td>\n",
       "      <td>0.497275</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[FP, AGE]</td>\n",
       "      <td>8.291898e+15</td>\n",
       "      <td>0.617540</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[FP, AGE, FG%]</td>\n",
       "      <td>7.826176e+15</td>\n",
       "      <td>0.639021</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[FP, AGE, FG%, 3P%]</td>\n",
       "      <td>7.631272e+15</td>\n",
       "      <td>0.648011</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-]</td>\n",
       "      <td>7.487532e+15</td>\n",
       "      <td>0.654641</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS]</td>\n",
       "      <td>7.383353e+15</td>\n",
       "      <td>0.659446</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2]</td>\n",
       "      <td>7.284740e+15</td>\n",
       "      <td>0.663995</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP]</td>\n",
       "      <td>7.247789e+15</td>\n",
       "      <td>0.665699</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF]</td>\n",
       "      <td>7.225291e+15</td>\n",
       "      <td>0.666737</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%]</td>\n",
       "      <td>7.217586e+15</td>\n",
       "      <td>0.667092</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%...</td>\n",
       "      <td>7.211987e+15</td>\n",
       "      <td>0.667350</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%...</td>\n",
       "      <td>7.193943e+15</td>\n",
       "      <td>0.668183</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%...</td>\n",
       "      <td>7.187313e+15</td>\n",
       "      <td>0.668488</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            variables           RSS  \\\n",
       "1                                                [FP]  1.089929e+16   \n",
       "2                                           [FP, AGE]  8.291898e+15   \n",
       "3                                      [FP, AGE, FG%]  7.826176e+15   \n",
       "4                                 [FP, AGE, FG%, 3P%]  7.631272e+15   \n",
       "5                            [FP, AGE, FG%, 3P%, +/-]  7.487532e+15   \n",
       "6                       [FP, AGE, FG%, 3P%, +/-, PTS]  7.383353e+15   \n",
       "7                  [FP, AGE, FG%, 3P%, +/-, PTS, DD2]  7.284740e+15   \n",
       "8              [FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP]  7.247789e+15   \n",
       "9          [FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF]  7.225291e+15   \n",
       "10    [FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%]  7.217586e+15   \n",
       "11  [FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%...  7.211987e+15   \n",
       "12  [FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%...  7.193943e+15   \n",
       "13  [FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%...  7.187313e+15   \n",
       "\n",
       "    R_squared  number of variables  \n",
       "1    0.497275                    1  \n",
       "2    0.617540                    2  \n",
       "3    0.639021                    3  \n",
       "4    0.648011                    4  \n",
       "5    0.654641                    5  \n",
       "6    0.659446                    6  \n",
       "7    0.663995                    7  \n",
       "8    0.665699                    8  \n",
       "9    0.666737                    9  \n",
       "10   0.667092                   10  \n",
       "11   0.667350                   11  \n",
       "12   0.668183                   12  \n",
       "13   0.668488                   13  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_fss=S.forward_stepwise(df_train_auswahl,output='salary')\n",
    "display(df_fss)#this diplays the Figure 4.3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "07286e2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>r_squaredadj</th>\n",
       "      <th>variables</th>\n",
       "      <th>mse</th>\n",
       "      <th>nf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MIN AIC</th>\n",
       "      <td>6930.911736</td>\n",
       "      <td>6957.417383</td>\n",
       "      <td>0.819272</td>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP]</td>\n",
       "      <td>3.457007e+13</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIN BIC</th>\n",
       "      <td>6930.911736</td>\n",
       "      <td>6957.417383</td>\n",
       "      <td>0.819272</td>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP]</td>\n",
       "      <td>3.457007e+13</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAX ADJ. R-sq.</th>\n",
       "      <td>6932.041588</td>\n",
       "      <td>6965.173647</td>\n",
       "      <td>0.819963</td>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%]</td>\n",
       "      <td>3.356248e+13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIN MSE TEST</th>\n",
       "      <td>6935.228785</td>\n",
       "      <td>6974.987257</td>\n",
       "      <td>0.818804</td>\n",
       "      <td>[FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%...</td>\n",
       "      <td>3.286815e+13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        aic          bic  r_squaredadj  \\\n",
       "                                                         \n",
       "MIN AIC         6930.911736  6957.417383      0.819272   \n",
       "MIN BIC         6930.911736  6957.417383      0.819272   \n",
       "MAX ADJ. R-sq.  6932.041588  6965.173647      0.819963   \n",
       "MIN MSE TEST    6935.228785  6974.987257      0.818804   \n",
       "\n",
       "                                                        variables  \\\n",
       "                                                                    \n",
       "MIN AIC                    [FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP]   \n",
       "MIN BIC                    [FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP]   \n",
       "MAX ADJ. R-sq.    [FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%]   \n",
       "MIN MSE TEST    [FP, AGE, FG%, 3P%, +/-, PTS, DD2, GP, PF, FT%...   \n",
       "\n",
       "                         mse  nf  \n",
       "                                  \n",
       "MIN AIC         3.457007e+13   8  \n",
       "MIN BIC         3.457007e+13   8  \n",
       "MAX ADJ. R-sq.  3.356248e+13  10  \n",
       "MIN MSE TEST    3.286815e+13  12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculating and showing the optimal subset for each information criterion\n",
    "df1,df2,df3=S.ic(df_fss['variables'],df_train,df_test,output='salary')\n",
    "display(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad5c20c",
   "metadata": {},
   "source": [
    "## **`backward stepwise elimination`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6abe7101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>RSS</th>\n",
       "      <th>R_squared</th>\n",
       "      <th>Eliminated</th>\n",
       "      <th>Number_of_variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, FT%, GP, M...</td>\n",
       "      <td>7.185583e+15</td>\n",
       "      <td>0.668568</td>\n",
       "      <td>FP</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, MIN, P...</td>\n",
       "      <td>7.194414e+15</td>\n",
       "      <td>0.668161</td>\n",
       "      <td>FT%</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PF, PT...</td>\n",
       "      <td>7.207181e+15</td>\n",
       "      <td>0.667572</td>\n",
       "      <td>MIN</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PTS, REB]</td>\n",
       "      <td>7.222090e+15</td>\n",
       "      <td>0.666884</td>\n",
       "      <td>PF</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PTS]</td>\n",
       "      <td>7.253175e+15</td>\n",
       "      <td>0.665450</td>\n",
       "      <td>REB</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, PTS]</td>\n",
       "      <td>7.280694e+15</td>\n",
       "      <td>0.664181</td>\n",
       "      <td>GP</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[+/-, 3P%, AGE, AST, DD2, FG%, PTS]</td>\n",
       "      <td>7.427065e+15</td>\n",
       "      <td>0.657430</td>\n",
       "      <td>BLK</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[+/-, 3P%, AGE, DD2, FG%, PTS]</td>\n",
       "      <td>7.550906e+15</td>\n",
       "      <td>0.651718</td>\n",
       "      <td>AST</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[3P%, AGE, DD2, FG%, PTS]</td>\n",
       "      <td>7.771981e+15</td>\n",
       "      <td>0.641521</td>\n",
       "      <td>+/-</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[3P%, AGE, DD2, PTS]</td>\n",
       "      <td>8.141361e+15</td>\n",
       "      <td>0.624483</td>\n",
       "      <td>FG%</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[AGE, DD2, PTS]</td>\n",
       "      <td>8.406759e+15</td>\n",
       "      <td>0.612242</td>\n",
       "      <td>3P%</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[AGE, PTS]</td>\n",
       "      <td>9.031645e+15</td>\n",
       "      <td>0.583419</td>\n",
       "      <td>DD2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[PTS]</td>\n",
       "      <td>1.190574e+16</td>\n",
       "      <td>0.450853</td>\n",
       "      <td>AGE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Variables           RSS  \\\n",
       "1   [+/-, 3P%, AGE, AST, BLK, DD2, FG%, FT%, GP, M...  7.185583e+15   \n",
       "2   [+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, MIN, P...  7.194414e+15   \n",
       "3   [+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PF, PT...  7.207181e+15   \n",
       "4   [+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PTS, REB]  7.222090e+15   \n",
       "5        [+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PTS]  7.253175e+15   \n",
       "6            [+/-, 3P%, AGE, AST, BLK, DD2, FG%, PTS]  7.280694e+15   \n",
       "7                 [+/-, 3P%, AGE, AST, DD2, FG%, PTS]  7.427065e+15   \n",
       "8                      [+/-, 3P%, AGE, DD2, FG%, PTS]  7.550906e+15   \n",
       "9                           [3P%, AGE, DD2, FG%, PTS]  7.771981e+15   \n",
       "10                               [3P%, AGE, DD2, PTS]  8.141361e+15   \n",
       "11                                    [AGE, DD2, PTS]  8.406759e+15   \n",
       "12                                         [AGE, PTS]  9.031645e+15   \n",
       "13                                              [PTS]  1.190574e+16   \n",
       "\n",
       "    R_squared Eliminated  Number_of_variables  \n",
       "1    0.668568         FP                   13  \n",
       "2    0.668161        FT%                   12  \n",
       "3    0.667572        MIN                   11  \n",
       "4    0.666884         PF                   10  \n",
       "5    0.665450        REB                    9  \n",
       "6    0.664181         GP                    8  \n",
       "7    0.657430        BLK                    7  \n",
       "8    0.651718        AST                    6  \n",
       "9    0.641521        +/-                    5  \n",
       "10   0.624483        FG%                    4  \n",
       "11   0.612242        3P%                    3  \n",
       "12   0.583419        DD2                    2  \n",
       "13   0.450853        AGE                    1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_be=S.backwards_elimination(df_train_auswahl,output='salary')\n",
    "display(df_be)#this display is shown as Fig. 4.4.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "10db012b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>r_squaredadj</th>\n",
       "      <th>variables</th>\n",
       "      <th>mse</th>\n",
       "      <th>nf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MIN AIC</th>\n",
       "      <td>6932.757739</td>\n",
       "      <td>6962.576593</td>\n",
       "      <td>0.818478</td>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PTS]</td>\n",
       "      <td>3.348882e+13</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIN BIC</th>\n",
       "      <td>6939.480169</td>\n",
       "      <td>6959.359404</td>\n",
       "      <td>0.809681</td>\n",
       "      <td>[+/-, 3P%, AGE, DD2, FG%, PTS]</td>\n",
       "      <td>3.630277e+13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAX ADJ. R-sq.</th>\n",
       "      <td>6932.757739</td>\n",
       "      <td>6962.576593</td>\n",
       "      <td>0.818478</td>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PTS]</td>\n",
       "      <td>3.348882e+13</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIN MSE TEST</th>\n",
       "      <td>6935.596386</td>\n",
       "      <td>6972.041652</td>\n",
       "      <td>0.817634</td>\n",
       "      <td>[+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PF, PT...</td>\n",
       "      <td>3.278870e+13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        aic          bic  r_squaredadj  \\\n",
       "                                                         \n",
       "MIN AIC         6932.757739  6962.576593      0.818478   \n",
       "MIN BIC         6939.480169  6959.359404      0.809681   \n",
       "MAX ADJ. R-sq.  6932.757739  6962.576593      0.818478   \n",
       "MIN MSE TEST    6935.596386  6972.041652      0.817634   \n",
       "\n",
       "                                                        variables  \\\n",
       "                                                                    \n",
       "MIN AIC              [+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PTS]   \n",
       "MIN BIC                            [+/-, 3P%, AGE, DD2, FG%, PTS]   \n",
       "MAX ADJ. R-sq.       [+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PTS]   \n",
       "MIN MSE TEST    [+/-, 3P%, AGE, AST, BLK, DD2, FG%, GP, PF, PT...   \n",
       "\n",
       "                         mse  nf  \n",
       "                                  \n",
       "MIN AIC         3.348882e+13   9  \n",
       "MIN BIC         3.630277e+13   6  \n",
       "MAX ADJ. R-sq.  3.348882e+13   9  \n",
       "MIN MSE TEST    3.278870e+13  11  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculating and showing the optimal subset for each information criterion\n",
    "df1,df2,df3=S.ic(df_be['Variables'],df_train,df_test,output='salary')\n",
    "display(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b59680",
   "metadata": {},
   "source": [
    "## Comparing the subset selections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f3a92de",
   "metadata": {},
   "source": [
    "* **This plot shows the best subset selection for the three information criterion (Figure 2.5.1)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fbfb0abf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1728x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1,df2,df3=S.ic(df_bs_min_rss['variables'],df_train_auswahl,df_test,output='salary')\n",
    "fig=plt.figure(figsize=(24,5))\n",
    "ax=fig.add_subplot(1,3,1)\n",
    "ax.scatter(df1.nf,df1.r_squaredadj,alpha=0.7,color='darkblue')\n",
    "ax.set_xlabel('NUMBER OF PARAMETERS')\n",
    "ax.set_ylabel('adj. R-squared')\n",
    "ax.plot(df1.nf,df1.r_squaredadj,color='r',label='Best Subset Selections')\n",
    "ax.scatter(df2.iloc[2].nf,df2.iloc[2].r_squaredadj,s=300,c='g',marker='x',label='Max. adj. R-squared ')\n",
    "ax.axvline(df3['One SE adj R sq'],c='r',linestyle='--',label='One Sd.E. selection: {f}'.format(f=df3['One SE adj R sq']))\n",
    "ax.legend()\n",
    "\n",
    "ax=fig.add_subplot(1,3,2)\n",
    "ax.scatter(df1.nf,df1.aic,alpha=0.7,color='darkblue')\n",
    "ax.set_xlabel('NUMBER OF PARAMETERS')\n",
    "ax.set_ylabel('AIC')\n",
    "ax.plot(df1.nf,df1.aic,color='r',label='Best Subset Selections')\n",
    "ax.scatter(df2.iloc[0].nf,df2.iloc[0].aic,s=300,c='g',marker='x',label='Min. AIC')\n",
    "ax.axvline(df3['One SE AIC'],c='r',linestyle='--',label='One Sd.E. selection: {f}'.format(f=df3['One SE AIC']))\n",
    "ax.legend()\n",
    "\n",
    "ax=fig.add_subplot(1,3,3)\n",
    "ax.scatter(df1.nf,df1.bic,alpha=0.7,color='darkblue')\n",
    "ax.set_xlabel('NUMBER OF PARAMETERS')\n",
    "ax.set_ylabel('BIC')\n",
    "ax.plot(df1.nf,df1.bic,color='r',label='Best Subset Selections')\n",
    "ax.scatter(df2.iloc[1].nf,df2.iloc[1].bic,s=300,c='g',marker='x',label='Min. BIC')\n",
    "ax.axvline(df3['One SE BIC'],c='r',linestyle='--',label='One Sd.E. selection: {f}'.format(f=df3['One SE BIC']))\n",
    "ax.legend()\n",
    "#plt.savefig('abschnitt.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92341ac",
   "metadata": {},
   "source": [
    "* **We implement a method to frequently plot our results by reusing code**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6944d1d8",
   "metadata": {},
   "source": [
    "* **The following plot shows the subset selections for the three information criterion and the directly estimated test mean squared error for all three subset selection methods(Figure 4.5.2)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a9f46d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1_be,df2_be,df3_be=S.ic(df_be['Variables'],df_train,df_test,output='salary')\n",
    "df1_fss,df2_fss,df3_fss=S.ic(df_fss['variables'],df_train,df_test,output='salary')\n",
    "df1_bs,df2_bs,df3_bs=S.ic(df_bs_min_rss['variables'],df_train,df_test,output='salary')\n",
    "\n",
    "S.ic_plot(df1_bs,df2_bs,df3_bs,df1_fss,df2_fss,df3_fss,df1_be,df2_be,df3_be,\n",
    "          one='Best Subset',two='Forward Stepwise',three='Backward Elimination',pngname='Figure 4.5.2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed9ab0e6",
   "metadata": {},
   "source": [
    "## Comparison with created data\n",
    "1. Implementation of the data generating method\n",
    "2. Creating the data\n",
    "3. Using the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4276301",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating data set 1 with 15 observations\n",
    "X1,y1=G.dataset_declaration(15,related=0,mu=0,sigma=1,noise=12)\n",
    "df_self1=pd.DataFrame(X1)\n",
    "df_self1['pred']=y1\n",
    "df_train1= df_self1.sample(frac=0.75,random_state=1)\n",
    "df_test1=df_self1.drop(df_train1.index)\n",
    "\n",
    "#creating data set 2 with 150 observations\n",
    "X2,y2=G.dataset_declaration(150,related=0,mu=0,sigma=1,noise=12)\n",
    "df_self2=pd.DataFrame(X2)\n",
    "df_self2['pred']=y2\n",
    "df_train2= df_self2.sample(frac=0.75,random_state=1)\n",
    "df_test2=df_self2.drop(df_train2.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3de05f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#computing the best subset selections\n",
    "df_bs_1=S.best_subset(df_train1,output='pred')\n",
    "df_bs_1_rss=df_bs_1[df_bs_1['min_RSS']==df_bs_1['RSS']][['number_of_variables','RSS','R_squared','variables']].reset_index()\n",
    "df_bs_1_rss=df_bs_1_rss.drop(['index'], axis=1)\n",
    "df_bs_2=S.best_subset(df_train2,output='pred')\n",
    "df_bs_2_rss=df_bs_2[df_bs_2['min_RSS']==df_bs_2['RSS']][['number_of_variables','RSS','R_squared','variables']].reset_index()\n",
    "df_bs_2_rss=df_bs_2_rss.drop(['index'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12ecdad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_8d511\" style='display:inline'>\n",
       "  <caption>15 observations and 12 unrelated predictors</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8d511_level0_col0\" class=\"col_heading level0 col0\" >number_of_variables</th>\n",
       "      <th id=\"T_8d511_level0_col1\" class=\"col_heading level0 col1\" >RSS</th>\n",
       "      <th id=\"T_8d511_level0_col2\" class=\"col_heading level0 col2\" >R_squared</th>\n",
       "      <th id=\"T_8d511_level0_col3\" class=\"col_heading level0 col3\" >variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_8d511_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_8d511_row0_col1\" class=\"data row0 col1\" >3.547795</td>\n",
       "      <td id=\"T_8d511_row0_col2\" class=\"data row0 col2\" >0.370055</td>\n",
       "      <td id=\"T_8d511_row0_col3\" class=\"data row0 col3\" >[8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_8d511_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_8d511_row1_col1\" class=\"data row1 col1\" >1.511968</td>\n",
       "      <td id=\"T_8d511_row1_col2\" class=\"data row1 col2\" >0.731536</td>\n",
       "      <td id=\"T_8d511_row1_col3\" class=\"data row1 col3\" >[8, 9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_8d511_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_8d511_row2_col1\" class=\"data row2 col1\" >0.763195</td>\n",
       "      <td id=\"T_8d511_row2_col2\" class=\"data row2 col2\" >0.864487</td>\n",
       "      <td id=\"T_8d511_row2_col3\" class=\"data row2 col3\" >[8, 9, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_8d511_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_8d511_row3_col1\" class=\"data row3 col1\" >0.377244</td>\n",
       "      <td id=\"T_8d511_row3_col2\" class=\"data row3 col2\" >0.933017</td>\n",
       "      <td id=\"T_8d511_row3_col3\" class=\"data row3 col3\" >[7, 8, 9, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_8d511_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_8d511_row4_col1\" class=\"data row4 col1\" >0.212006</td>\n",
       "      <td id=\"T_8d511_row4_col2\" class=\"data row4 col2\" >0.962356</td>\n",
       "      <td id=\"T_8d511_row4_col3\" class=\"data row4 col3\" >[1, 7, 8, 9, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_8d511_row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "      <td id=\"T_8d511_row5_col1\" class=\"data row5 col1\" >0.108424</td>\n",
       "      <td id=\"T_8d511_row5_col2\" class=\"data row5 col2\" >0.980748</td>\n",
       "      <td id=\"T_8d511_row5_col3\" class=\"data row5 col3\" >[0, 4, 5, 6, 7, 10]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_8d511_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_8d511_row6_col1\" class=\"data row6 col1\" >0.029149</td>\n",
       "      <td id=\"T_8d511_row6_col2\" class=\"data row6 col2\" >0.994824</td>\n",
       "      <td id=\"T_8d511_row6_col3\" class=\"data row6 col3\" >[0, 2, 3, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_8d511_row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "      <td id=\"T_8d511_row7_col1\" class=\"data row7 col1\" >0.002092</td>\n",
       "      <td id=\"T_8d511_row7_col2\" class=\"data row7 col2\" >0.999629</td>\n",
       "      <td id=\"T_8d511_row7_col3\" class=\"data row7 col3\" >[0, 3, 4, 5, 6, 7, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_8d511_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_8d511_row8_col1\" class=\"data row8 col1\" >0.000029</td>\n",
       "      <td id=\"T_8d511_row8_col2\" class=\"data row8 col2\" >0.999995</td>\n",
       "      <td id=\"T_8d511_row8_col3\" class=\"data row8 col3\" >[0, 3, 4, 5, 6, 7, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_8d511_row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "      <td id=\"T_8d511_row9_col1\" class=\"data row9 col1\" >0.000000</td>\n",
       "      <td id=\"T_8d511_row9_col2\" class=\"data row9 col2\" >1.000000</td>\n",
       "      <td id=\"T_8d511_row9_col3\" class=\"data row9 col3\" >[1, 3, 4, 5, 6, 7, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_8d511_row10_col0\" class=\"data row10 col0\" >11</td>\n",
       "      <td id=\"T_8d511_row10_col1\" class=\"data row10 col1\" >0.000000</td>\n",
       "      <td id=\"T_8d511_row10_col2\" class=\"data row10 col2\" >1.000000</td>\n",
       "      <td id=\"T_8d511_row10_col3\" class=\"data row10 col3\" >[0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d511_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_8d511_row11_col0\" class=\"data row11 col0\" >12</td>\n",
       "      <td id=\"T_8d511_row11_col1\" class=\"data row11 col1\" >0.000000</td>\n",
       "      <td id=\"T_8d511_row11_col2\" class=\"data row11 col2\" >1.000000</td>\n",
       "      <td id=\"T_8d511_row11_col3\" class=\"data row11 col3\" >[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       " <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_88d9c\" style='display:inline'>\n",
       "  <caption>150 observations and 12 unrelated predictors</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_88d9c_level0_col0\" class=\"col_heading level0 col0\" >number_of_variables</th>\n",
       "      <th id=\"T_88d9c_level0_col1\" class=\"col_heading level0 col1\" >RSS</th>\n",
       "      <th id=\"T_88d9c_level0_col2\" class=\"col_heading level0 col2\" >R_squared</th>\n",
       "      <th id=\"T_88d9c_level0_col3\" class=\"col_heading level0 col3\" >variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_88d9c_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_88d9c_row0_col1\" class=\"data row0 col1\" >93.852560</td>\n",
       "      <td id=\"T_88d9c_row0_col2\" class=\"data row0 col2\" >0.114785</td>\n",
       "      <td id=\"T_88d9c_row0_col3\" class=\"data row0 col3\" >[1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_88d9c_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_88d9c_row1_col1\" class=\"data row1 col1\" >92.307680</td>\n",
       "      <td id=\"T_88d9c_row1_col2\" class=\"data row1 col2\" >0.129356</td>\n",
       "      <td id=\"T_88d9c_row1_col3\" class=\"data row1 col3\" >[1, 9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_88d9c_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_88d9c_row2_col1\" class=\"data row2 col1\" >90.349466</td>\n",
       "      <td id=\"T_88d9c_row2_col2\" class=\"data row2 col2\" >0.147826</td>\n",
       "      <td id=\"T_88d9c_row2_col3\" class=\"data row2 col3\" >[1, 9, 10]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_88d9c_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_88d9c_row3_col1\" class=\"data row3 col1\" >88.620445</td>\n",
       "      <td id=\"T_88d9c_row3_col2\" class=\"data row3 col2\" >0.164134</td>\n",
       "      <td id=\"T_88d9c_row3_col3\" class=\"data row3 col3\" >[1, 5, 9, 10]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_88d9c_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_88d9c_row4_col1\" class=\"data row4 col1\" >87.754329</td>\n",
       "      <td id=\"T_88d9c_row4_col2\" class=\"data row4 col2\" >0.172303</td>\n",
       "      <td id=\"T_88d9c_row4_col3\" class=\"data row4 col3\" >[1, 5, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_88d9c_row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "      <td id=\"T_88d9c_row5_col1\" class=\"data row5 col1\" >86.720168</td>\n",
       "      <td id=\"T_88d9c_row5_col2\" class=\"data row5 col2\" >0.182057</td>\n",
       "      <td id=\"T_88d9c_row5_col3\" class=\"data row5 col3\" >[1, 5, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_88d9c_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_88d9c_row6_col1\" class=\"data row6 col1\" >86.146696</td>\n",
       "      <td id=\"T_88d9c_row6_col2\" class=\"data row6 col2\" >0.187466</td>\n",
       "      <td id=\"T_88d9c_row6_col3\" class=\"data row6 col3\" >[1, 3, 5, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_88d9c_row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "      <td id=\"T_88d9c_row7_col1\" class=\"data row7 col1\" >85.785405</td>\n",
       "      <td id=\"T_88d9c_row7_col2\" class=\"data row7 col2\" >0.190874</td>\n",
       "      <td id=\"T_88d9c_row7_col3\" class=\"data row7 col3\" >[1, 3, 5, 7, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_88d9c_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_88d9c_row8_col1\" class=\"data row8 col1\" >85.552805</td>\n",
       "      <td id=\"T_88d9c_row8_col2\" class=\"data row8 col2\" >0.193068</td>\n",
       "      <td id=\"T_88d9c_row8_col3\" class=\"data row8 col3\" >[1, 2, 3, 5, 7, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_88d9c_row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "      <td id=\"T_88d9c_row9_col1\" class=\"data row9 col1\" >85.383686</td>\n",
       "      <td id=\"T_88d9c_row9_col2\" class=\"data row9 col2\" >0.194663</td>\n",
       "      <td id=\"T_88d9c_row9_col3\" class=\"data row9 col3\" >[1, 2, 3, 5, 6, 7, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_88d9c_row10_col0\" class=\"data row10 col0\" >11</td>\n",
       "      <td id=\"T_88d9c_row10_col1\" class=\"data row10 col1\" >85.366069</td>\n",
       "      <td id=\"T_88d9c_row10_col2\" class=\"data row10 col2\" >0.194829</td>\n",
       "      <td id=\"T_88d9c_row10_col3\" class=\"data row10 col3\" >[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88d9c_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_88d9c_row11_col0\" class=\"data row11 col0\" >12</td>\n",
       "      <td id=\"T_88d9c_row11_col1\" class=\"data row11 col1\" >85.361460</td>\n",
       "      <td id=\"T_88d9c_row11_col2\" class=\"data row11 col2\" >0.194873</td>\n",
       "      <td id=\"T_88d9c_row11_col3\" class=\"data row11 col3\" >[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#displaying our results -> Figure 4.5.3\n",
    "from IPython.display import display_html\n",
    "df1=df_bs_1_rss\n",
    "df2=df_bs_2_rss\n",
    "df1_styler = df1.style.set_table_attributes(\"style='display:inline'\").set_caption('15 observations and 12 unrelated predictors')\n",
    "df2_styler = df2.style.set_table_attributes(\"style='display:inline'\").set_caption('150 observations and 12 unrelated predictors')\n",
    "space = \"\\xa0\" \n",
    "display_html(df1_styler._repr_html_()+space+df2_styler._repr_html_(), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "094dd27e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_be=S.backwards_elimination(df_train1,output='pred')\n",
    "df1_be,df2_be,df3_be=S.ic(df_be['Variables'],df_train1,df_test1,output='pred')\n",
    "df_fss=S.forward_stepwise(df_train1,output='pred')\n",
    "df1_fss,df2_fss,df3_fss=S.ic(df_fss['variables'],df_train1,df_test1,output='pred')\n",
    "df1_bs,df2_bs,df3_bs=S.ic(df_bs_1_rss['variables'],df_train1,df_test1,output='pred')\n",
    "#plotting Figure 4.5.4\n",
    "S.ic_plot(df1_bs,df2_bs,df3_bs,df1_fss,df2_fss,df3_fss,df1_be,df2_be,df3_be,\n",
    "          one='Best Subset',two='Forward',three='Backward',pngname='own data 15 observations')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76925fe3",
   "metadata": {},
   "source": [
    "## Shrinkage processes\n",
    "* Ridge regression\n",
    "* Lasso regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "853fbacc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=np.arange(0.0,0.05,0.00001)#these are all lambda values we will be setting as alpha into lasso or ridge\n",
    "df=(df-df.mean())/df.std()#standardizing the data set\n",
    "\n",
    "df_train= df.sample(frac=0.75,random_state=1)\n",
    "df_test=df.drop(df_train.index)\n",
    "min_mse,y_pos,mse_list,r_squared_list,df_p=R.best_lambda(a,df_train,df_test,output='salary',methode='ridge')\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12,5))\n",
    "fig.tight_layout(pad=0.01, w_pad=3, h_pad=1.0)\n",
    "\n",
    "random.seed(1)\n",
    "get_colors = lambda n: list(map(lambda i: \"#\" + \"%06x\" % random.randint(0, 0xFFFFFF),range(n)))\n",
    "color=get_colors(df_p.shape[1])\n",
    "\n",
    "ax[0].set_prop_cycle(color=color)\n",
    "for i in df_p.columns:\n",
    "    ax[0].plot(a,df_p[i],label=i)\n",
    "ax[0].axvline(x=y_pos, color='r',linestyle='--',label= \"λ: {f}\".format(f=y_pos))\n",
    "ax[0].set_ylim(-1,1)\n",
    "ax[0].set_ylabel(\"β coefficients\")\n",
    "ax[0].set_xlabel(\"λ\")\n",
    "ax[1].set_prop_cycle(color=color)\n",
    "\n",
    "min_mse,y_pos,mse_list,r_squared_list,df_p=R.best_lambda(a,df_train,df_test,output='salary',methode='lasso')\n",
    "for i in df_p.columns:\n",
    "    ax[1].plot(a,df_p[i],label=i)\n",
    "ax[1].set_ylim(-1,1)\n",
    "ax[1].axvline(x=y_pos, color='r',linestyle='--')\n",
    "ax[1].legend(bbox_to_anchor=(0.8,0.5), loc=\"upper center\",ncol=2)\n",
    "ax[1].set_ylabel(\"β coefficients\")\n",
    "ax[1].set_xlabel(\"λ\")\n",
    "plt.tight_layout()\n",
    "#plt.savefig('Figure 4 6 2.png')\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b260b96e",
   "metadata": {},
   "source": [
    "# Creating multiple splits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583e1c4a",
   "metadata": {},
   "source": [
    "* **First for the standardized NBA data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "34cbef7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=(df-df.mean())/df.std()\n",
    "df = pd.read_csv(\"NBA_salaries_and_stats.csv\", sep=\",\")\n",
    "df=df[['+/-', '3P%', 'AGE', 'AST', 'BLK', 'DD2' , 'FG%',\n",
    "        'FP', 'FT%', 'GP', 'MIN' , 'PF',\n",
    "       'PTS', 'REB', 'STL', 'TD3', 'TOV',  'salary']]#new data\n",
    "df=(df-df.mean())/df.std()\n",
    "\n",
    "count=15\n",
    "a_r=np.arange(0,1.,0.01)\n",
    "a_l=np.arange(0,0.02,0.0001)\n",
    "ridge,lasso,y_train,X_train,y_test,X_test=R.mult_split(df,a_r,a_l,count=count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9ebb70ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12,5))\n",
    "from operator import add\n",
    "fig.tight_layout()\n",
    "\n",
    "ridgel=[b[2]for b in ridge]\n",
    "ridge_mse_mean=np.sum([b[2]for b in ridge],0)/count\n",
    "\n",
    "\n",
    "ridge_y_pos_mean=np.sum([b[1] for b in ridge])/count\n",
    "ridge_min_mse_mean=np.sum([b[0] for b in ridge])/count\n",
    "\n",
    "for b in ridge:\n",
    "    min_mse,y_pos,mse_list,r_squared_list,df_p=b   \n",
    "    ax[0].plot(a_r,mse_list,color='b',alpha=0.3)\n",
    "    ax[0].scatter(y_pos,min_mse,color='r',alpha=0.3)\n",
    "\n",
    "ax[0].plot(a_r,ridge_mse_mean,color='g',label='Mean',alpha=1.0)\n",
    "ax[0].scatter(ridge_y_pos_mean,ridge_min_mse_mean,color='Navy',label='MEAN MIN MSE at λ= '+str(round(ridge_y_pos_mean,5)),alpha=1.0)\n",
    "ax[0].set_ylim(0.2,0.7)   \n",
    "ax[0].set_xlabel('λ')\n",
    "ax[0].set_ylabel('MSE')\n",
    "ax[0].legend()\n",
    "ax[0].set_title('Ridge')\n",
    " \n",
    "lasso_mse_mean=np.sum([b[2]for b in lasso],0)/count\n",
    "lasso_y_pos_mean=np.sum([b[1] for b in lasso])/count\n",
    "lasso_min_mse_mean=np.sum([b[0] for b in lasso])/count\n",
    "\n",
    "for b in lasso:\n",
    "    min_mse,y_pos,mse_list,r_squared_list,df_p=b   \n",
    "    ax[1].plot(a_l,mse_list,color='b',alpha=0.3)\n",
    "    ax[1].scatter(y_pos,min_mse,color='r',alpha=0.3)\n",
    "\n",
    "ax[1].plot(a_l,lasso_mse_mean,color='g',label='Mean',alpha=1.0)\n",
    "ax[1].scatter(lasso_y_pos_mean,lasso_min_mse_mean,color='Navy',label='MEAN MIN MSE at λ= '+str(round(lasso_y_pos_mean,5)),alpha=1.0)\n",
    "ax[1].set_ylim(0.2,0.7)  \n",
    "ax[1].set_xlabel('λ')\n",
    "ax[1].set_ylabel('MSE')\n",
    "ax[1].legend(loc='upper left')\n",
    "ax[1].set_title('Lasso')\n",
    "#plt.savefig('ridge_lasso_multisplit.png')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab0d24a",
   "metadata": {},
   "source": [
    "* **Then for a created data set -> Figure 4.6.3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "32f2266d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=G.dataset_declaration(15,related=5,mu=0,sigma=1,noise=12)\n",
    "df_self=pd.DataFrame(X)\n",
    "df_self['pred']=y\n",
    "df_train= df_self.sample(frac=0.75,random_state=1)\n",
    "df_test=df_self.drop(df_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8e6eff90",
   "metadata": {},
   "outputs": [],
   "source": [
    "count=15\n",
    "a_r=np.arange(0,2,0.01)\n",
    "a_l=np.arange(0,0.001,0.00001)\n",
    "ridge,lasso,y_train,X_train,y_test,X_test=R.mult_split(df_self,a_r,a_l,count=count,output='pred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "967c4d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12,5))\n",
    "from operator import add\n",
    "fig.tight_layout()\n",
    "\n",
    "ridgel=[b[2]for b in ridge]\n",
    "ridge_mse_mean=np.sum([b[2]for b in ridge],0)/count\n",
    "\n",
    "\n",
    "ridge_y_pos_mean=np.sum([b[1] for b in ridge])/count\n",
    "ridge_min_mse_mean=np.sum([b[0] for b in ridge])/count\n",
    "\n",
    "for b in ridge:\n",
    "    min_mse,y_pos,mse_list,r_squared_list,df_p=b   \n",
    "    ax[0].plot(a_r,mse_list,color='b',alpha=0.3)\n",
    "    ax[0].scatter(y_pos,min_mse,color='r',alpha=0.3)\n",
    "\n",
    "ax[0].plot(a_r,ridge_mse_mean,color='g',label='Mean',alpha=1.0)\n",
    "ax[0].scatter(ridge_y_pos_mean,ridge_min_mse_mean,color='Navy',label='MEAN MIN MSE at λ= '+str(round(ridge_y_pos_mean,5)),alpha=1.0)\n",
    "#ax[0].set_ylim(0.2,0.7)   \n",
    "ax[0].set_xlabel('λ')\n",
    "ax[0].set_ylabel('MSE')\n",
    "ax[0].legend()\n",
    "ax[0].set_title('Ridge')\n",
    " \n",
    "lasso_mse_mean=np.sum([b[2]for b in lasso],0)/count\n",
    "lasso_y_pos_mean=np.sum([b[1] for b in lasso])/count\n",
    "lasso_min_mse_mean=np.sum([b[0] for b in lasso])/count\n",
    "\n",
    "for b in lasso:\n",
    "    min_mse,y_pos,mse_list,r_squared_list,df_p=b   \n",
    "    ax[1].plot(a_l,mse_list,color='b',alpha=0.3)\n",
    "    ax[1].scatter(y_pos,min_mse,color='r',alpha=0.3)\n",
    "\n",
    "ax[1].plot(a_l,lasso_mse_mean,color='g',label='Mean',alpha=1.0)\n",
    "ax[1].scatter(lasso_y_pos_mean,lasso_min_mse_mean,color='Navy',label='MEAN MIN MSE at λ= '+str(round(lasso_y_pos_mean,5)),alpha=1.0)\n",
    "#ax[1].set_ylim(0.2,0.7)  \n",
    "ax[1].set_xlabel('λ')\n",
    "ax[1].set_ylabel('MSE')\n",
    "ax[1].legend(loc='upper left')\n",
    "ax[1].set_title('Lasso')\n",
    "#plt.savefig('Figure 4 6 3.png')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cce832b",
   "metadata": {},
   "source": [
    "# Comparing the results of each method \n",
    "* **selection: its main purpose is to determines the variable selection property of the lasso regression**\n",
    "* **min_mse: calculates the test mean squarred error estimate for a given subset dataframe**\n",
    "* **mult_split_be: computes the backwards elimination and forward stepwise selection for multiple splits**\n",
    "* **choice: counts the number of included variables in the given subset dataframe**\n",
    "* **meanf: determines the minimal test mse mean and the mean number of non-zero variables over different splits**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e078ce51",
   "metadata": {},
   "source": [
    "* **exemplary output of the selection method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2a4072bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"NBA_salaries_and_stats.csv\", sep=\",\")\n",
    "df=df[['+/-', '3P%', 'AGE', 'AST', 'BLK', 'DD2' , 'FG%',\n",
    "        'FP', 'FT%', 'GP', 'MIN' , 'PF',\n",
    "       'PTS', 'REB', 'STL', 'TD3', 'TOV',  'salary']]#new data\n",
    "df=(df-df.mean())/df.std()\n",
    "\n",
    "count=15\n",
    "a_r=np.arange(0,2.,0.01)\n",
    "a_l=np.arange(0,0.02,0.0001)\n",
    "ridge,lasso,y_train,X_train,y_test,X_test=R.mult_split(df,a_r,a_l,count=count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8fb79b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>+/-</th>\n",
       "      <th>3P%</th>\n",
       "      <th>TD3</th>\n",
       "      <th>REB</th>\n",
       "      <th>PTS</th>\n",
       "      <th>PF</th>\n",
       "      <th>MIN</th>\n",
       "      <th>GP</th>\n",
       "      <th>FT%</th>\n",
       "      <th>FG%</th>\n",
       "      <th>BLK</th>\n",
       "      <th>AGE</th>\n",
       "      <th>TOV</th>\n",
       "      <th>DD2</th>\n",
       "      <th>STL</th>\n",
       "      <th>AST</th>\n",
       "      <th>FP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Count Lasso</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0           +/- 3P% TD3 REB PTS  PF MIN  GP FT% FG% BLK AGE TOV DD2 STL AST FP\n",
       "Count Lasso  15  15  15  15  15  15  15  15  15  15  15  15  15  14  14  10  9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>+/-</th>\n",
       "      <th>3P%</th>\n",
       "      <th>AGE</th>\n",
       "      <th>AST</th>\n",
       "      <th>BLK</th>\n",
       "      <th>DD2</th>\n",
       "      <th>FG%</th>\n",
       "      <th>FP</th>\n",
       "      <th>FT%</th>\n",
       "      <th>GP</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>REB</th>\n",
       "      <th>STL</th>\n",
       "      <th>TD3</th>\n",
       "      <th>TOV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>0.111735</td>\n",
       "      <td>-0.129521</td>\n",
       "      <td>0.363136</td>\n",
       "      <td>0.242529</td>\n",
       "      <td>0.183107</td>\n",
       "      <td>0.082580</td>\n",
       "      <td>-0.176255</td>\n",
       "      <td>-0.715386</td>\n",
       "      <td>-0.024123</td>\n",
       "      <td>-0.035202</td>\n",
       "      <td>0.086365</td>\n",
       "      <td>-0.068421</td>\n",
       "      <td>0.887085</td>\n",
       "      <td>0.268452</td>\n",
       "      <td>0.119114</td>\n",
       "      <td>0.117256</td>\n",
       "      <td>-0.027987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>0.081546</td>\n",
       "      <td>-0.141124</td>\n",
       "      <td>0.386106</td>\n",
       "      <td>-0.028300</td>\n",
       "      <td>0.078535</td>\n",
       "      <td>-0.104453</td>\n",
       "      <td>-0.202448</td>\n",
       "      <td>0.287158</td>\n",
       "      <td>-0.006552</td>\n",
       "      <td>-0.039926</td>\n",
       "      <td>-0.084231</td>\n",
       "      <td>-0.030625</td>\n",
       "      <td>0.579751</td>\n",
       "      <td>0.093341</td>\n",
       "      <td>-0.044662</td>\n",
       "      <td>0.079489</td>\n",
       "      <td>0.019102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>0.070791</td>\n",
       "      <td>-0.045016</td>\n",
       "      <td>0.304042</td>\n",
       "      <td>0.569770</td>\n",
       "      <td>0.384314</td>\n",
       "      <td>0.068870</td>\n",
       "      <td>-0.187536</td>\n",
       "      <td>-2.013206</td>\n",
       "      <td>-0.031574</td>\n",
       "      <td>-0.012675</td>\n",
       "      <td>-0.028168</td>\n",
       "      <td>-0.127082</td>\n",
       "      <td>1.677588</td>\n",
       "      <td>0.663770</td>\n",
       "      <td>0.294209</td>\n",
       "      <td>0.116950</td>\n",
       "      <td>-0.055301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>0.088131</td>\n",
       "      <td>-0.097095</td>\n",
       "      <td>0.333454</td>\n",
       "      <td>0.365315</td>\n",
       "      <td>0.229429</td>\n",
       "      <td>-0.048474</td>\n",
       "      <td>-0.149423</td>\n",
       "      <td>-0.981328</td>\n",
       "      <td>-0.009759</td>\n",
       "      <td>-0.052424</td>\n",
       "      <td>-0.011262</td>\n",
       "      <td>-0.084500</td>\n",
       "      <td>1.072366</td>\n",
       "      <td>0.470870</td>\n",
       "      <td>0.119405</td>\n",
       "      <td>0.104088</td>\n",
       "      <td>-0.017956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>0.122990</td>\n",
       "      <td>-0.089090</td>\n",
       "      <td>0.306231</td>\n",
       "      <td>0.584766</td>\n",
       "      <td>0.382815</td>\n",
       "      <td>0.009753</td>\n",
       "      <td>-0.180334</td>\n",
       "      <td>-1.816177</td>\n",
       "      <td>-0.016230</td>\n",
       "      <td>-0.010513</td>\n",
       "      <td>-0.040333</td>\n",
       "      <td>-0.133995</td>\n",
       "      <td>1.566680</td>\n",
       "      <td>0.690075</td>\n",
       "      <td>0.266231</td>\n",
       "      <td>0.093264</td>\n",
       "      <td>-0.123211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>0.037798</td>\n",
       "      <td>-0.106480</td>\n",
       "      <td>0.360174</td>\n",
       "      <td>0.679535</td>\n",
       "      <td>0.421448</td>\n",
       "      <td>0.034656</td>\n",
       "      <td>-0.159532</td>\n",
       "      <td>-2.372540</td>\n",
       "      <td>-0.000470</td>\n",
       "      <td>-0.000603</td>\n",
       "      <td>0.014749</td>\n",
       "      <td>-0.084929</td>\n",
       "      <td>1.861252</td>\n",
       "      <td>0.781559</td>\n",
       "      <td>0.334596</td>\n",
       "      <td>-0.030958</td>\n",
       "      <td>-0.141133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>0.053481</td>\n",
       "      <td>-0.110810</td>\n",
       "      <td>0.345391</td>\n",
       "      <td>0.263815</td>\n",
       "      <td>0.263442</td>\n",
       "      <td>0.064058</td>\n",
       "      <td>-0.178299</td>\n",
       "      <td>-0.569250</td>\n",
       "      <td>-0.013654</td>\n",
       "      <td>-0.008478</td>\n",
       "      <td>-0.009585</td>\n",
       "      <td>-0.112815</td>\n",
       "      <td>0.894023</td>\n",
       "      <td>0.212955</td>\n",
       "      <td>0.096257</td>\n",
       "      <td>0.062033</td>\n",
       "      <td>-0.037656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "      <td>0.064864</td>\n",
       "      <td>-0.085869</td>\n",
       "      <td>0.333803</td>\n",
       "      <td>0.091658</td>\n",
       "      <td>0.134648</td>\n",
       "      <td>-0.056126</td>\n",
       "      <td>-0.154134</td>\n",
       "      <td>0.174365</td>\n",
       "      <td>0.017906</td>\n",
       "      <td>-0.069183</td>\n",
       "      <td>-0.096712</td>\n",
       "      <td>-0.116062</td>\n",
       "      <td>0.426548</td>\n",
       "      <td>0.184051</td>\n",
       "      <td>0.020384</td>\n",
       "      <td>0.050939</td>\n",
       "      <td>0.181053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>0.056499</td>\n",
       "      <td>-0.088043</td>\n",
       "      <td>0.364391</td>\n",
       "      <td>-0.033309</td>\n",
       "      <td>0.129503</td>\n",
       "      <td>-0.071910</td>\n",
       "      <td>-0.166120</td>\n",
       "      <td>0.046345</td>\n",
       "      <td>-0.004473</td>\n",
       "      <td>-0.022114</td>\n",
       "      <td>0.043170</td>\n",
       "      <td>-0.163509</td>\n",
       "      <td>0.446908</td>\n",
       "      <td>0.227368</td>\n",
       "      <td>0.014110</td>\n",
       "      <td>0.157623</td>\n",
       "      <td>0.125689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17</td>\n",
       "      <td>0.037585</td>\n",
       "      <td>-0.078732</td>\n",
       "      <td>0.290078</td>\n",
       "      <td>0.214063</td>\n",
       "      <td>0.133551</td>\n",
       "      <td>0.055026</td>\n",
       "      <td>-0.078179</td>\n",
       "      <td>-0.005731</td>\n",
       "      <td>0.007079</td>\n",
       "      <td>-0.056462</td>\n",
       "      <td>-0.071197</td>\n",
       "      <td>-0.137710</td>\n",
       "      <td>0.591579</td>\n",
       "      <td>0.074274</td>\n",
       "      <td>0.031503</td>\n",
       "      <td>-0.007996</td>\n",
       "      <td>0.065574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17</td>\n",
       "      <td>0.080364</td>\n",
       "      <td>-0.104378</td>\n",
       "      <td>0.318085</td>\n",
       "      <td>0.574807</td>\n",
       "      <td>0.349153</td>\n",
       "      <td>-0.022968</td>\n",
       "      <td>-0.176736</td>\n",
       "      <td>-1.853055</td>\n",
       "      <td>-0.011743</td>\n",
       "      <td>-0.001127</td>\n",
       "      <td>-0.059698</td>\n",
       "      <td>-0.098765</td>\n",
       "      <td>1.646548</td>\n",
       "      <td>0.687173</td>\n",
       "      <td>0.259450</td>\n",
       "      <td>0.055238</td>\n",
       "      <td>-0.070819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17</td>\n",
       "      <td>0.078388</td>\n",
       "      <td>-0.095700</td>\n",
       "      <td>0.334294</td>\n",
       "      <td>-0.019908</td>\n",
       "      <td>0.111453</td>\n",
       "      <td>-0.018600</td>\n",
       "      <td>-0.185609</td>\n",
       "      <td>0.071371</td>\n",
       "      <td>-0.024127</td>\n",
       "      <td>-0.012137</td>\n",
       "      <td>0.032272</td>\n",
       "      <td>-0.189954</td>\n",
       "      <td>0.446354</td>\n",
       "      <td>0.163172</td>\n",
       "      <td>0.068588</td>\n",
       "      <td>0.096828</td>\n",
       "      <td>0.124689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17</td>\n",
       "      <td>0.067116</td>\n",
       "      <td>-0.058942</td>\n",
       "      <td>0.311498</td>\n",
       "      <td>0.997947</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.022469</td>\n",
       "      <td>-0.148866</td>\n",
       "      <td>-3.488111</td>\n",
       "      <td>-0.008400</td>\n",
       "      <td>-0.016841</td>\n",
       "      <td>-0.030103</td>\n",
       "      <td>-0.070981</td>\n",
       "      <td>2.455584</td>\n",
       "      <td>1.144568</td>\n",
       "      <td>0.471665</td>\n",
       "      <td>0.021550</td>\n",
       "      <td>-0.105876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17</td>\n",
       "      <td>0.067411</td>\n",
       "      <td>-0.078415</td>\n",
       "      <td>0.332560</td>\n",
       "      <td>0.211055</td>\n",
       "      <td>0.205110</td>\n",
       "      <td>0.115413</td>\n",
       "      <td>-0.121801</td>\n",
       "      <td>-0.599679</td>\n",
       "      <td>-0.022566</td>\n",
       "      <td>-0.030410</td>\n",
       "      <td>-0.067164</td>\n",
       "      <td>-0.105783</td>\n",
       "      <td>0.938051</td>\n",
       "      <td>0.216985</td>\n",
       "      <td>0.106328</td>\n",
       "      <td>0.061880</td>\n",
       "      <td>0.057062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17</td>\n",
       "      <td>0.026111</td>\n",
       "      <td>-0.081417</td>\n",
       "      <td>0.262332</td>\n",
       "      <td>0.123949</td>\n",
       "      <td>0.054577</td>\n",
       "      <td>0.039402</td>\n",
       "      <td>-0.156737</td>\n",
       "      <td>0.141268</td>\n",
       "      <td>0.034464</td>\n",
       "      <td>-0.016531</td>\n",
       "      <td>-0.093998</td>\n",
       "      <td>-0.063133</td>\n",
       "      <td>0.509876</td>\n",
       "      <td>0.086032</td>\n",
       "      <td>0.030666</td>\n",
       "      <td>0.059098</td>\n",
       "      <td>0.043345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Count       +/-       3P%       AGE       AST       BLK       DD2  \\\n",
       "0      17  0.111735 -0.129521  0.363136  0.242529  0.183107  0.082580   \n",
       "1      17  0.081546 -0.141124  0.386106 -0.028300  0.078535 -0.104453   \n",
       "2      17  0.070791 -0.045016  0.304042  0.569770  0.384314  0.068870   \n",
       "3      17  0.088131 -0.097095  0.333454  0.365315  0.229429 -0.048474   \n",
       "4      17  0.122990 -0.089090  0.306231  0.584766  0.382815  0.009753   \n",
       "5      17  0.037798 -0.106480  0.360174  0.679535  0.421448  0.034656   \n",
       "6      17  0.053481 -0.110810  0.345391  0.263815  0.263442  0.064058   \n",
       "7      17  0.064864 -0.085869  0.333803  0.091658  0.134648 -0.056126   \n",
       "8      17  0.056499 -0.088043  0.364391 -0.033309  0.129503 -0.071910   \n",
       "9      17  0.037585 -0.078732  0.290078  0.214063  0.133551  0.055026   \n",
       "10     17  0.080364 -0.104378  0.318085  0.574807  0.349153 -0.022968   \n",
       "11     17  0.078388 -0.095700  0.334294 -0.019908  0.111453 -0.018600   \n",
       "12     17  0.067116 -0.058942  0.311498  0.997947  0.508371  0.022469   \n",
       "13     17  0.067411 -0.078415  0.332560  0.211055  0.205110  0.115413   \n",
       "14     17  0.026111 -0.081417  0.262332  0.123949  0.054577  0.039402   \n",
       "\n",
       "         FG%        FP       FT%        GP       MIN        PF       PTS  \\\n",
       "0  -0.176255 -0.715386 -0.024123 -0.035202  0.086365 -0.068421  0.887085   \n",
       "1  -0.202448  0.287158 -0.006552 -0.039926 -0.084231 -0.030625  0.579751   \n",
       "2  -0.187536 -2.013206 -0.031574 -0.012675 -0.028168 -0.127082  1.677588   \n",
       "3  -0.149423 -0.981328 -0.009759 -0.052424 -0.011262 -0.084500  1.072366   \n",
       "4  -0.180334 -1.816177 -0.016230 -0.010513 -0.040333 -0.133995  1.566680   \n",
       "5  -0.159532 -2.372540 -0.000470 -0.000603  0.014749 -0.084929  1.861252   \n",
       "6  -0.178299 -0.569250 -0.013654 -0.008478 -0.009585 -0.112815  0.894023   \n",
       "7  -0.154134  0.174365  0.017906 -0.069183 -0.096712 -0.116062  0.426548   \n",
       "8  -0.166120  0.046345 -0.004473 -0.022114  0.043170 -0.163509  0.446908   \n",
       "9  -0.078179 -0.005731  0.007079 -0.056462 -0.071197 -0.137710  0.591579   \n",
       "10 -0.176736 -1.853055 -0.011743 -0.001127 -0.059698 -0.098765  1.646548   \n",
       "11 -0.185609  0.071371 -0.024127 -0.012137  0.032272 -0.189954  0.446354   \n",
       "12 -0.148866 -3.488111 -0.008400 -0.016841 -0.030103 -0.070981  2.455584   \n",
       "13 -0.121801 -0.599679 -0.022566 -0.030410 -0.067164 -0.105783  0.938051   \n",
       "14 -0.156737  0.141268  0.034464 -0.016531 -0.093998 -0.063133  0.509876   \n",
       "\n",
       "         REB       STL       TD3       TOV  \n",
       "0   0.268452  0.119114  0.117256 -0.027987  \n",
       "1   0.093341 -0.044662  0.079489  0.019102  \n",
       "2   0.663770  0.294209  0.116950 -0.055301  \n",
       "3   0.470870  0.119405  0.104088 -0.017956  \n",
       "4   0.690075  0.266231  0.093264 -0.123211  \n",
       "5   0.781559  0.334596 -0.030958 -0.141133  \n",
       "6   0.212955  0.096257  0.062033 -0.037656  \n",
       "7   0.184051  0.020384  0.050939  0.181053  \n",
       "8   0.227368  0.014110  0.157623  0.125689  \n",
       "9   0.074274  0.031503 -0.007996  0.065574  \n",
       "10  0.687173  0.259450  0.055238 -0.070819  \n",
       "11  0.163172  0.068588  0.096828  0.124689  \n",
       "12  1.144568  0.471665  0.021550 -0.105876  \n",
       "13  0.216985  0.106328  0.061880  0.057062  \n",
       "14  0.086032  0.030666  0.059098  0.043345  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k,d=R.selection(lasso,y_train,X_train,count=count,threshold=0.9,method='Lasso')\n",
    "display(k)\n",
    "k,d=R.selection(ridge,y_train,X_train,count=count,threshold=0.9,method='Ridge')\n",
    "display(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e714024",
   "metadata": {},
   "source": [
    "# Combining the methods on the standardized NBA data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "79cf4579",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"NBA_salaries_and_stats.csv\", sep=\",\")\n",
    "df=df[['+/-', '3P%', 'AGE', 'AST', 'BLK', 'DD2' , 'FG%',\n",
    "        'FP', 'FT%', 'GP', 'MIN' , 'PF',\n",
    "       'PTS', 'REB', 'STL', 'TD3', 'TOV',  'salary']]\n",
    "df=(df-df.mean())/df.std()\n",
    "op='salary'\n",
    "\n",
    "count=15\n",
    "a_r=np.arange(0,2.,0.01)\n",
    "a_l=np.arange(0,2.,0.01)\n",
    "#calculating the methods over multiple splits\n",
    "ridge,lasso,y_train,X_train,y_test,X_test=R.mult_split(df,a_r,a_l,count=count)\n",
    "backwards,forwards,df_train,df_test,be_mse,fss_mse=C.mult_split_be(df,count=count,frac=0.7,output=op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "427b869f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PTS AGE BLK FG% 3P% REB +/- AST TD3  PF  FP STL TOV  GP DD2  \\\n",
       "Count backwards  15  15  10  10  10  10   9   9   9   9   6   6   3   2   2   \n",
       "Count forwards    9  15   8  11   9   5   8   4   8  10  14   4   6   4   1   \n",
       "Count Ridge      15  15  15  15  15  15  15  15  15  15  15  15  15  15  15   \n",
       "Count Lasso      15  15  15  15  15  14  15  11  15  15  15  13  14  15  12   \n",
       "\n",
       "                MIN FT%  \n",
       "Count backwards   1   1  \n",
       "Count forwards    4   1  \n",
       "Count Ridge      15  15  \n",
       "Count Lasso      14  13  "
      ]
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       "                PTS AGE BLK FG% 3P% +/- TD3  PF\n",
       "Count backwards  15  15  10  10  10   9   9   9\n",
       "Count forwards    9  15   8  11   9   8   8  10\n",
       "Count Ridge      15  15  15  15  15  15  15  15\n",
       "Count Lasso      15  15  15  15  15  15  15  15"
      ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_mse_mean</th>\n",
       "      <th>nf_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Backwards</th>\n",
       "      <td>0.369060</td>\n",
       "      <td>8.466670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forwards</th>\n",
       "      <td>0.372180</td>\n",
       "      <td>8.066670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>0.369841</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>0.381770</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           min_mse_mean    nf_mean\n",
       "Backwards      0.369060   8.466670\n",
       "Forwards       0.372180   8.066670\n",
       "Ridge          0.369841  15.000000\n",
       "Lasso          0.381770  14.176471"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=['+/-', '3P%', 'AGE', 'AST', 'BLK', 'DD2' , 'FG%',\n",
    "        'FP', 'FT%', 'GP', 'MIN' , 'PF',\n",
    "       'PTS', 'REB', 'STL', 'TD3', 'TOV',  'salary']\n",
    "df_be=C.choice(be_mse,cols,'backwards',count=count,output=op)\n",
    "df_fss=C.choice(fss_mse,cols,'forwards',count=count,output=op)\n",
    "\n",
    "\n",
    "df_lasso=R.selection(lasso,y_train,X_train,count=count,threshold=0.9,method='Lasso')[0]\n",
    "\n",
    "df_ridge=R.selection(ridge,y_train,X_train,count=count,threshold=0.9,method='Ridge')[0]\n",
    "\n",
    "df_sub=pd.concat([df_be,df_fss,df_ridge,df_lasso],join=\"inner\")\n",
    "df_sub=df_sub.sort_values(by='Count backwards',axis=1,ascending=False)\n",
    "display(df_sub)\n",
    "#display the variables that are included in 50% of the model\n",
    "display(df_sub[df_sub>0.5*count].dropna(axis=1))\n",
    "\n",
    "df_mean=C.meanf(be_mse,fss_mse,lasso,ridge,df_lasso,df_ridge)\n",
    "df_sub.index=df_mean.index\n",
    "display(df_mean)\n",
    "df_sub['mean min MSE']=df_mean['min_mse_mean']\n",
    "df_sub['mean number variables']=df_mean['nf_mean']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f78c830",
   "metadata": {},
   "source": [
    "* **Figure 4.7.1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9aecf18a",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PTS</th>\n",
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       "      <th>mean min MSE</th>\n",
       "      <th>mean number variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Backwards</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.369060</td>\n",
       "      <td>8.466670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forwards</th>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.372180</td>\n",
       "      <td>8.066670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0.369841</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>0.381770</td>\n",
       "      <td>14.176471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PTS AGE BLK FG% 3P% REB +/- AST TD3  PF  FP STL TOV  GP DD2 MIN FT%  \\\n",
       "Backwards  15  15  10  10  10  10   9   9   9   9   6   6   3   2   2   1   1   \n",
       "Forwards    9  15   8  11   9   5   8   4   8  10  14   4   6   4   1   4   1   \n",
       "Ridge      15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15  15   \n",
       "Lasso      15  15  15  15  15  14  15  11  15  15  15  13  14  15  12  14  13   \n",
       "\n",
       "           mean min MSE  mean number variables  \n",
       "Backwards      0.369060               8.466670  \n",
       "Forwards       0.372180               8.066670  \n",
       "Ridge          0.369841              15.000000  \n",
       "Lasso          0.381770              14.176471  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_sub)"
   ]
  }
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