nb_numerical_distance_measure_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from IPython.display import display, Markdown\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "from scipy.stats import ks_2samp\n",
    "from utils import ( \n",
    "                    numerical_data_distribution_plot,\n",
    "                    create_drift_metric_comparison_table_numerical,\n",
    "                    create_bin_strategy_comparison_table_numerical,\n",
    "                    create_comparison_with_evidently_table_numerical,\n",
    "                    create_sample_size_comparison_table_numerical,\n",
    "                    create_sample_size_bin_comparison_table_numerical,\n",
    "             )\n",
    "from yaml import safe_load\n",
    "config = safe_load(open('config.yml','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "observations = 9_000\n",
    "distribution_selection = '1b'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 9000\n",
      "Distribution Selection: 1b\n"
     ]
    }
   ],
   "source": [
    "print(f'Observations: {observations}')\n",
    "print(f'Distribution Selection: {distribution_selection}')\n",
    "\n",
    "if distribution_selection == '1a':\n",
    "    description = 'Identical Normal Distributions - No Drift'\n",
    "    x = np.random.normal(50, 15, observations) # reference\n",
    "    y = x\n",
    "\n",
    "elif distribution_selection == '1b':\n",
    "    description = 'Normal Distributions - Minimal Drift'\n",
    "    x = np.random.normal(50, 0, observations) # reference\n",
    "    y = np.random.normal(51, 0, observations) # small drift \n",
    "\n",
    "elif distribution_selection == '1c':\n",
    "    description = 'Normal Distributions - Small Drift'\n",
    "    x = np.random.normal(50, 15, observations) # reference\n",
    "    y = np.random.normal(53, 15, int(observations*0.1)) # small drift \n",
    "\n",
    "elif distribution_selection == '1d':\n",
    "    description = 'Normal Distributions - Large Drift'\n",
    "    x = np.random.normal(50, 15, observations) # reference\n",
    "    y = np.random.normal(60, 20, observations) # small drift \n",
    "\n",
    "elif distribution_selection == '1e':\n",
    "    description = 'Normal Distributions - Very Large Drift'\n",
    "    x = np.random.normal(50, 15, observations) # reference\n",
    "    y = np.random.normal(200, 15, observations) # small drift \n",
    "\n",
    "elif distribution_selection == '1f':\n",
    "    description = 'Normal Distributions - Extreme Drift'\n",
    "    x = np.random.normal(50, 15, observations) # reference\n",
    "    y = np.random.normal(100, 15, observations) # small drift \n",
    "\n",
    "elif distribution_selection == '1g':\n",
    "    description = 'Normal Distributions - No Drift - Different Scale'\n",
    "    x = np.random.normal(50000, 1500, observations) # reference\n",
    "    y = np.random.normal(50000, 1500, observations) # small drift \n",
    "\n",
    "elif distribution_selection == '2a':\n",
    "    description = 'Normal Distributions with Outliers - Small Drift'\n",
    "    x = np.random.normal(50, 15, observations) # reference\n",
    "    y = np.append(np.random.normal(50, 15, observations),np.random.normal(125,1,int(observations/100))) # same as reference with small amout of outliers\n",
    "\n",
    "elif distribution_selection == '2b':\n",
    "    description = 'Normal Distributions with Outliers - Some Drift'\n",
    "    x = np.random.normal(50, 15, observations) # reference\n",
    "    y = np.append(np.random.normal(50, 15, observations),np.random.normal(125,1,int(observations/80))) # same as reference with a some of outliers\n",
    "\n",
    "elif distribution_selection == '2c':\n",
    "    description = 'Normal Distributions with Outliers - Drift'\n",
    "    x = np.random.normal(50, 15, observations) # reference\n",
    "    y = np.append(np.random.normal(50, 15, observations),np.random.normal(125,1,int(observations/60))) # same as reference with some outliers\n",
    "\n",
    "elif distribution_selection == '2d':\n",
    "    description = 'Normal Distributions with Outliers - Drift'\n",
    "    x = np.random.normal(50, 15, observations) # reference\n",
    "    y = np.append(np.random.normal(50, 15, observations),np.random.normal(125,1,int(observations/40))) # same as reference with some outliers\n",
    "\n",
    "elif distribution_selection == '3a':\n",
    "    description = 'Identical Uniform Distributions - No Drift'\n",
    "    x = np.random.uniform(10,50, observations) # reference\n",
    "    y = x\n",
    "\n",
    "elif distribution_selection == '3b':\n",
    "    description = 'Uniform Distributions with small excess in the upper range - Small Drift'\n",
    "    x = np.random.uniform(10,50, observations) # reference\n",
    "    y = np.append(np.random.uniform(10,50, int(observations*0.95)),np.random.uniform(20,50, int(observations*0.05)))  # small drift \n",
    "\n",
    "elif distribution_selection == '3c':\n",
    "    description = 'Uniform Distributions with small excess in the upper range - Some Drift'\n",
    "    x = np.random.uniform(10,50, observations) # reference\n",
    "    y = np.append(np.random.uniform(10,50, int(observations*0.7)),np.random.uniform(20,50, int(observations*0.3)))  # some drift \n",
    "\n",
    "elif distribution_selection == '3d':\n",
    "    description = 'Uniform Distributions With Different Range - Drift'\n",
    "    x = np.random.uniform(10,50, observations) # reference\n",
    "    y = np.random.uniform(10,60, observations) # reference\n",
    "\n",
    "elif distribution_selection == '4a':\n",
    "    description = 'Single Value - No Drift'\n",
    "    x = np.full(observations,10) # single value\n",
    "    y = np.full(observations,10) # same single value\n",
    "\n",
    "elif distribution_selection == '4b':\n",
    "    description = 'Single Value with some outliers - Drift'\n",
    "    x = np.full(observations,10) # single value\n",
    "    y = np.append(np.full(observations,10), np.full(int(observations/50),10.01)) # same single value with some outliers\n",
    "\n",
    "elif distribution_selection == '4c':\n",
    "    description = 'Differnt Single Values - Drift'\n",
    "    x = np.full(observations,10) # single value\n",
    "    y = np.full(observations,10.01) # single value\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# <strong>Normal Distributions - Minimal Drift</strong><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Markdown('# <strong>{}</strong><br/>'.format(description))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = numerical_data_distribution_plot(x,y,bin_strategy='stu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Distance</th>\n",
       "      <th>Drift Detection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jensen-Shannon Distance</th>\n",
       "      <td>1.0</td>\n",
       "      <td>drift detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normed Wasserstein Distance</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>drift detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normed Wasserstein Distance on Prob Dist</th>\n",
       "      <td>0.0</td>\n",
       "      <td>no drift</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSI</th>\n",
       "      <td>18.2102</td>\n",
       "      <td>drift detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D_inf</th>\n",
       "      <td>1.0</td>\n",
       "      <td>drift detected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KS Test</th>\n",
       "      <td>0.0</td>\n",
       "      <td>drift detected</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Distance Drift Detection\n",
       "Jensen-Shannon Distance                       1.0  drift detected\n",
       "Normed Wasserstein Distance                1000.0  drift detected\n",
       "Normed Wasserstein Distance on Prob Dist      0.0        no drift\n",
       "PSI                                       18.2102  drift detected\n",
       "D_inf                                         1.0  drift detected\n",
       "KS Test                                       0.0  drift detected"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=create_drift_metric_comparison_table_numerical(x,y)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>stu</th>\n",
       "      <th>evi</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of Bins</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jensen-Shannon Distance</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normed Wasserstein Distance</th>\n",
       "      <td>1000.0000</td>\n",
       "      <td>1000.0000</td>\n",
       "      <td>1000.0000</td>\n",
       "      <td>1000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSI</th>\n",
       "      <td>23.0259</td>\n",
       "      <td>23.0259</td>\n",
       "      <td>23.0259</td>\n",
       "      <td>23.0259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D_inf</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KS Test</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   stu        evi        min        max\n",
       "Number of Bins                 18.0000    19.0000    18.0000    18.0000\n",
       "Jensen-Shannon Distance         1.0000     1.0000     1.0000     1.0000\n",
       "Normed Wasserstein Distance  1000.0000  1000.0000  1000.0000  1000.0000\n",
       "PSI                            23.0259    23.0259    23.0259    23.0259\n",
       "D_inf                           1.0000     1.0000     1.0000     1.0000\n",
       "KS Test                         0.0000     0.0000     0.0000     0.0000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = create_bin_strategy_comparison_table_numerical(x,y)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Local</th>\n",
       "      <th>Evidently</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jensen-Shannon Distance</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wasserstein Distance</th>\n",
       "      <td>1000.0000</td>\n",
       "      <td>1000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSI</th>\n",
       "      <td>18.4188</td>\n",
       "      <td>18.4188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Local  Evidently\n",
       "Jensen-Shannon Distance     1.0000     0.8326\n",
       "Wasserstein Distance     1000.0000  1000.0000\n",
       "PSI                        18.4188    18.4188"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = create_comparison_with_evidently_table_numerical(x,y)\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Jensen-Shannon Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>stu</th>\n",
       "      <th>evi</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Full Sample</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/10th Sample</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/100th Sample</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/1000th Sample</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/10,000th Sample</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   stu  evi  min  max\n",
       "Full Sample        1.0  1.0  1.0  1.0\n",
       "1/10th Sample      1.0  1.0  1.0  1.0\n",
       "1/100th Sample     1.0  1.0  1.0  1.0\n",
       "1/1000th Sample    1.0  1.0  1.0  1.0\n",
       "1/10,000th Sample  1.0  1.0  1.0  1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = create_sample_size_comparison_table_numerical(x,y,\"jensen_shannon_distance_numerical\", output='distance')\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Test Wasserstein Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>stu</th>\n",
       "      <th>evi</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Full Sample</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/10th Sample</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/100th Sample</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/1000th Sample</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/10,000th Sample</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      stu     evi     min     max\n",
       "Full Sample        1000.0  1000.0  1000.0  1000.0\n",
       "1/10th Sample      1000.0  1000.0  1000.0  1000.0\n",
       "1/100th Sample     1000.0  1000.0  1000.0  1000.0\n",
       "1/1000th Sample    1000.0  1000.0  1000.0  1000.0\n",
       "1/10,000th Sample  1000.0  1000.0  1000.0  1000.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = create_sample_size_comparison_table_numerical(x,y,\"normed_wasserstein_distance_numerical\", output='distance')\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test PSI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "        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>stu</th>\n",
       "      <th>evi</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Full Sample</th>\n",
       "      <td>23.0259</td>\n",
       "      <td>23.0259</td>\n",
       "      <td>23.0259</td>\n",
       "      <td>23.0259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/10th Sample</th>\n",
       "      <td>20.7263</td>\n",
       "      <td>20.7269</td>\n",
       "      <td>20.7263</td>\n",
       "      <td>20.7269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/100th Sample</th>\n",
       "      <td>18.4673</td>\n",
       "      <td>18.4992</td>\n",
       "      <td>18.4673</td>\n",
       "      <td>18.4992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/1000th Sample</th>\n",
       "      <td>16.6112</td>\n",
       "      <td>17.3013</td>\n",
       "      <td>16.6112</td>\n",
       "      <td>17.3013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/10,000th Sample</th>\n",
       "      <td>17.6041</td>\n",
       "      <td>29.5764</td>\n",
       "      <td>17.6041</td>\n",
       "      <td>29.5764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       stu      evi      min      max\n",
       "Full Sample        23.0259  23.0259  23.0259  23.0259\n",
       "1/10th Sample      20.7263  20.7269  20.7263  20.7269\n",
       "1/100th Sample     18.4673  18.4992  18.4673  18.4992\n",
       "1/1000th Sample    16.6112  17.3013  16.6112  17.3013\n",
       "1/10,000th Sample  17.6041  29.5764  17.6041  29.5764"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = create_sample_size_comparison_table_numerical(x,y,\"psi_numerical\", output='distance')\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test D_inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>stu</th>\n",
       "      <th>evi</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Full Sample</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/10th Sample</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/100th Sample</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/1000th Sample</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/10,000th Sample</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   stu  evi  min  max\n",
       "Full Sample        1.0  1.0  1.0  1.0\n",
       "1/10th Sample      1.0  1.0  1.0  1.0\n",
       "1/100th Sample     1.0  1.0  1.0  1.0\n",
       "1/1000th Sample    1.0  1.0  1.0  1.0\n",
       "1/10,000th Sample  1.0  1.0  1.0  1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = create_sample_size_comparison_table_numerical(x,y,\"d_inf_numerical\", output='distance')\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test KS Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "        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>stu</th>\n",
       "      <th>evi</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Full Sample</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/10th Sample</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/100th Sample</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/1000th Sample</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/10,000th Sample</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   stu  evi  min  max\n",
       "Full Sample        0.0  0.0  0.0  0.0\n",
       "1/10th Sample      0.0  0.0  0.0  0.0\n",
       "1/100th Sample     0.0  0.0  0.0  0.0\n",
       "1/1000th Sample    0.0  0.0  0.0  0.0\n",
       "1/10,000th Sample  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = create_sample_size_comparison_table_numerical(x,y,\"two_sample_ks_test_numerical\", output='distance')\n",
    "df"
   ]
  }
 ],
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