walkthrough-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PSI Walkthrough\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from psi import calculate_psi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"white\")\n",
    "rs = np.random.RandomState(5)\n",
    "\n",
    "initial = rs.normal(size = 100)\n",
    "new = rs.normal(loc = 0.2, size = 120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f280e793c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.kdeplot(initial, shade=True)\n",
    "plot = sns.kdeplot(new, shade=True)\n",
    "plot.set(yticklabels=[], xticklabels = [])\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot.get_figure().savefig('images/distributions.png', format='png', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_range (input, min, max):\n",
    "    input += -(np.min(input))\n",
    "    input /= np.max(input) / (max - min)\n",
    "    input += min\n",
    "    return input\n",
    "\n",
    "buckets = 10\n",
    "raw_breakpoints = np.arange(0, buckets + 1) / (buckets) * 100\n",
    "breakpoints = scale_range(raw_breakpoints, np.min(initial), np.max(initial))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_counts = np.histogram(initial, breakpoints)[0]\n",
    "new_counts = np.histogram(new, breakpoints)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'Bucket': np.arange(1, 11), 'Breakpoint Value':breakpoints[1:], 'Initial Count':initial_counts, 'New Count':new_counts})\n",
    "df['Initial Percent'] = df['Initial Count'] / len(initial)\n",
    "df['New Percent'] = df['New Count'] / len(new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['New Percent'][df['New Percent'] == 0] = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Breakpoint Value</th>\n",
       "      <th>Bucket</th>\n",
       "      <th>Initial Count</th>\n",
       "      <th>New Count</th>\n",
       "      <th>Initial Percent</th>\n",
       "      <th>New Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.330642</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.801596</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.272550</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.743504</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.214458</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.314588</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.191667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.843633</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.372679</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.901725</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.430771</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Breakpoint Value  Bucket  Initial Count  New Count  Initial Percent  \\\n",
       "0         -2.330642       1              1          0             0.01   \n",
       "1         -1.801596       2              1          3             0.01   \n",
       "2         -1.272550       3              4          6             0.04   \n",
       "3         -0.743504       4              8         15             0.08   \n",
       "4         -0.214458       5             27         18             0.27   \n",
       "5          0.314588       6             22         23             0.22   \n",
       "6          0.843633       7             16         26             0.16   \n",
       "7          1.372679       8             12         14             0.12   \n",
       "8          1.901725       9              6          9             0.06   \n",
       "9          2.430771      10              3          3             0.03   \n",
       "\n",
       "   New Percent  \n",
       "0     0.001000  \n",
       "1     0.025000  \n",
       "2     0.050000  \n",
       "3     0.125000  \n",
       "4     0.150000  \n",
       "5     0.191667  \n",
       "6     0.216667  \n",
       "7     0.116667  \n",
       "8     0.075000  \n",
       "9     0.025000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "percents = df[['Initial Percent', 'New Percent', 'Bucket']] \\\n",
    "             .melt(id_vars=['Bucket']) \\\n",
    "             .rename(columns={'variable':'Population', 'value':'Percent'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>Bucket</th>\n",
       "      <th>Population</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Initial Percent</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Initial Percent</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Initial Percent</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Initial Percent</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Initial Percent</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bucket       Population  Percent\n",
       "0       1  Initial Percent     0.01\n",
       "1       2  Initial Percent     0.01\n",
       "2       3  Initial Percent     0.04\n",
       "3       4  Initial Percent     0.08\n",
       "4       5  Initial Percent     0.27"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f280bf4d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = sns.barplot(x=\"Bucket\", y=\"Percent\", hue=\"Population\", data=percents)\n",
    "p.set(xlabel='Bucket', ylabel='Population Percent')\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#p.get_figure().savefig('images/constant_bins_percents.png', format='png', dpi=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$PSI = \\sum{}\\Big(\\big(Actual \\% - Expected \\%\\big) \\times ln\\big(\\dfrac{Actual \\%}{Expected \\%}\\big)\\Big)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PSI'] = (df['New Percent'] - df['Initial Percent']) * np.log(df['New Percent'] / df['Initial Percent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Breakpoint Value</th>\n",
       "      <th>Bucket</th>\n",
       "      <th>Initial Count</th>\n",
       "      <th>New Count</th>\n",
       "      <th>Initial Percent</th>\n",
       "      <th>New Percent</th>\n",
       "      <th>PSI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.330642</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.020723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.801596</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.013744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.272550</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.002231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.743504</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.020083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.214458</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.070534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Breakpoint Value  Bucket  Initial Count  New Count  Initial Percent  \\\n",
       "0         -2.330642       1              1          0             0.01   \n",
       "1         -1.801596       2              1          3             0.01   \n",
       "2         -1.272550       3              4          6             0.04   \n",
       "3         -0.743504       4              8         15             0.08   \n",
       "4         -0.214458       5             27         18             0.27   \n",
       "\n",
       "   New Percent       PSI  \n",
       "0        0.001  0.020723  \n",
       "1        0.025  0.013744  \n",
       "2        0.050  0.002231  \n",
       "3        0.125  0.020083  \n",
       "4        0.150  0.070534  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15275591122393328"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df['PSI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(calculate_psi(initial, new, buckets=10, axis=1), 5) == np.round(np.sum(df['PSI']), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "breakpoints = np.stack([np.percentile(initial, b) for b in np.arange(0, buckets + 1) / (buckets) * 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_counts = np.histogram(initial, breakpoints)[0]\n",
    "new_counts = np.histogram(new, breakpoints)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'Bucket': np.arange(1, 11), 'Breakpoint Value':breakpoints[1:], 'Initial Count':initial_counts, 'New Count':new_counts})\n",
    "df['Initial Percent'] = df['Initial Count'] / len(initial)\n",
    "df['New Percent'] = df['New Count'] / len(new)\n",
    "df['New Percent'][df['New Percent'] == 0] = 0.001\n",
    "percents = df[['Initial Percent', 'New Percent', 'Bucket']] \\\n",
    "             .melt(id_vars=['Bucket']) \\\n",
    "             .rename(columns={'variable':'Population', 'value':'Percent'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f280be423c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = sns.barplot(x=\"Bucket\", y=\"Percent\", hue=\"Population\", data=percents)\n",
    "p.set(xlabel='Bucket', ylabel='Population Percent')\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#p.get_figure().savefig('images/percentile_bins_percents.png', format='png', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12854351349505216"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_psi(initial, new, buckettype='quantiles', buckets=10, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}