6.1-MultinomialLogitAndProbitModels/01-biogeme-basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An introduction to Biogeme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biogeme Basics: Logit Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "import numpy as np\n",
    "import biogeme.database as db\n",
    "import biogeme.biogeme as bio\n",
    "import biogeme.models as models\n",
    "import biogeme.expressions as exp\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import Swissmetro data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas = pd.read_csv(\"../Data/swissmetro.dat\",sep='\\t')\n",
    "database = db.Database(\"data/swissmetro\", pandas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's see what this dataset has"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* dataset consists of survey data collected on the trains between St. Gallen and Geneva, Switzerland, during March 1998\n",
    "* It is necessary to obtain data from surveys of hypothetical markets/situations, which include the innovation, to assess the impact. \n",
    "* Survey data were collected on rail-based travels, interviewing 470 respondents. Due to data problems, only 441 are used here. A similar method for relevant car trips. A total of 1070 persons filled in the survey completely and were willing to participate in the second SP survey, which was generated using the same approach used for the rail interviews. 750 usable SP surveys were returned, from the license-plate based survey.\n",
    "* Nine stated choice situations were generated for each the respondents, offering three alternatives: rail, Swissmetro and car\n",
    "\n",
    "Bierlaire, M., Axhausen, K. and Abay, G. (2001), The acceptance of modal innovation: The case of Swissmetro, in ‘Proceedings of the Swiss Transport Research Conference’, Ascona, Switzerland."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/swissmetro_var1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/swissmetro_var2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "chart = sns.countplot(pandas['PURPOSE'])\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=30, horizontalalignment='right');\n",
    "chart.set_xticklabels(['Commuter', 'Shopping', 'Business', 'Leisure', 'Return from work','Return from shopping', 'Return from business','Return from leisure','Other']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use collumn names as variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "globals().update(database.variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exclude some unwanted entries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "exclude = (( PURPOSE != 1 ) * ( PURPOSE != 3 ) + ( CHOICE == 0 )) > 0\n",
    "\n",
    "database.remove(exclude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define some dummy variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "SM_COST = SM_CO * ( GA == 0 )\n",
    "TRAIN_COST = TRAIN_CO * ( GA == 0 )\n",
    "\n",
    "CAR_AV_SP = exp.DefineVariable ('CAR_AV_SP', CAR_AV * ( SP !=0 ), database)\n",
    "TRAIN_AV_SP = exp.DefineVariable ('TRAIN_AV_SP', TRAIN_AV * ( SP != 0 ), database)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Rescale some data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_TT_SCALED   = exp.DefineVariable('TRAIN_TT_SCALED',   TRAIN_TT / 100.0, database)\n",
    "TRAIN_COST_SCALED = exp.DefineVariable('TRAIN_COST_SCALED', TRAIN_COST / 100, database)\n",
    "SM_TT_SCALED      = exp.DefineVariable('SM_TT_SCALED',      SM_TT / 100.0   , database)\n",
    "SM_COST_SCALED    = exp.DefineVariable('SM_COST_SCALED',    SM_COST / 100   , database)\n",
    "CAR_TT_SCALED     = exp.DefineVariable('CAR_TT_SCALED',     CAR_TT / 100    , database)\n",
    "CAR_CO_SCALED     = exp.DefineVariable('CAR_CO_SCALED',     CAR_CO / 100    , database)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas = database.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "chart = sns.countplot(pandas['PURPOSE'])\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=30, horizontalalignment='right');\n",
    "chart.set_xticklabels(['Commuter', 'Business']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(10,5))\n",
    "chart = sns.countplot(pandas['TRAIN_AV'])\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=30, horizontalalignment='right');\n",
    "chart.set_xticklabels(['Yes']);\n",
    "chart.set(title='Train Availability', xlabel=\"Available\", ylabel = \"Count\");\n",
    "\n",
    "plt.figure(2, figsize=(10,5))\n",
    "chart = sns.countplot(pandas['CAR_AV'])\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=30, horizontalalignment='right');\n",
    "chart.set_xticklabels(['No', 'Yes']);\n",
    "chart.set(title='Car Availability', xlabel=\"Available\", ylabel = \"Count\");\n",
    "\n",
    "plt.figure(3, figsize=(10,5))\n",
    "chart = sns.countplot(pandas['SM_AV'])\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=30, horizontalalignment='right');\n",
    "chart.set_xticklabels(['Yes']);\n",
    "chart.set(title='Swissmetro Availability', xlabel=\"Available\", ylabel = \"Count\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>GROUP</th>\n",
       "      <th>SURVEY</th>\n",
       "      <th>SP</th>\n",
       "      <th>ID</th>\n",
       "      <th>PURPOSE</th>\n",
       "      <th>FIRST</th>\n",
       "      <th>TICKET</th>\n",
       "      <th>WHO</th>\n",
       "      <th>LUGGAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>MALE</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>GA</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>DEST</th>\n",
       "      <th>TRAIN_AV</th>\n",
       "      <th>CAR_AV</th>\n",
       "      <th>SM_AV</th>\n",
       "      <th>TRAIN_TT</th>\n",
       "      <th>TRAIN_CO</th>\n",
       "      <th>TRAIN_HE</th>\n",
       "      <th>SM_TT</th>\n",
       "      <th>SM_CO</th>\n",
       "      <th>SM_HE</th>\n",
       "      <th>SM_SEATS</th>\n",
       "      <th>CAR_TT</th>\n",
       "      <th>CAR_CO</th>\n",
       "      <th>CHOICE</th>\n",
       "      <th>CAR_AV_SP</th>\n",
       "      <th>TRAIN_AV_SP</th>\n",
       "      <th>TRAIN_TT_SCALED</th>\n",
       "      <th>TRAIN_COST_SCALED</th>\n",
       "      <th>SM_TT_SCALED</th>\n",
       "      <th>SM_COST_SCALED</th>\n",
       "      <th>CAR_TT_SCALED</th>\n",
       "      <th>CAR_CO_SCALED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6768.00000</td>\n",
       "      <td>6768.00000</td>\n",
       "      <td>6768.0</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.0</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.0</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.0</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.62367</td>\n",
       "      <td>0.62367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>517.174202</td>\n",
       "      <td>2.534574</td>\n",
       "      <td>0.555851</td>\n",
       "      <td>2.739362</td>\n",
       "      <td>1.630319</td>\n",
       "      <td>0.652926</td>\n",
       "      <td>2.896277</td>\n",
       "      <td>0.783245</td>\n",
       "      <td>2.389628</td>\n",
       "      <td>0.132979</td>\n",
       "      <td>12.376330</td>\n",
       "      <td>9.933511</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.828457</td>\n",
       "      <td>1.0</td>\n",
       "      <td>166.077423</td>\n",
       "      <td>490.885195</td>\n",
       "      <td>69.840426</td>\n",
       "      <td>84.507388</td>\n",
       "      <td>641.066489</td>\n",
       "      <td>20.011820</td>\n",
       "      <td>0.116430</td>\n",
       "      <td>123.154846</td>\n",
       "      <td>78.655881</td>\n",
       "      <td>2.127364</td>\n",
       "      <td>0.828457</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.660774</td>\n",
       "      <td>0.843561</td>\n",
       "      <td>0.845074</td>\n",
       "      <td>1.023025</td>\n",
       "      <td>1.231548</td>\n",
       "      <td>0.786559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.48450</td>\n",
       "      <td>0.48450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>273.550159</td>\n",
       "      <td>0.845184</td>\n",
       "      <td>0.496908</td>\n",
       "      <td>2.095906</td>\n",
       "      <td>0.721338</td>\n",
       "      <td>0.627874</td>\n",
       "      <td>0.985276</td>\n",
       "      <td>0.412065</td>\n",
       "      <td>0.944566</td>\n",
       "      <td>0.339577</td>\n",
       "      <td>9.862034</td>\n",
       "      <td>9.705719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.377010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.795646</td>\n",
       "      <td>1062.593533</td>\n",
       "      <td>37.397666</td>\n",
       "      <td>47.113140</td>\n",
       "      <td>1411.658237</td>\n",
       "      <td>8.170988</td>\n",
       "      <td>0.320764</td>\n",
       "      <td>91.718406</td>\n",
       "      <td>55.921803</td>\n",
       "      <td>0.616052</td>\n",
       "      <td>0.377010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.697956</td>\n",
       "      <td>0.642435</td>\n",
       "      <td>0.471131</td>\n",
       "      <td>0.785257</td>\n",
       "      <td>0.917184</td>\n",
       "      <td>0.559218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>301.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>563.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>751.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>21.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.060000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.760000</td>\n",
       "      <td>1.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>939.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1022.000000</td>\n",
       "      <td>5040.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>6720.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.220000</td>\n",
       "      <td>5.760000</td>\n",
       "      <td>7.960000</td>\n",
       "      <td>7.680000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            GROUP      SURVEY      SP           ID      PURPOSE        FIRST       TICKET          WHO      LUGGAGE          AGE         MALE       INCOME           GA       ORIGIN         DEST  TRAIN_AV       CAR_AV   SM_AV     TRAIN_TT     TRAIN_CO     TRAIN_HE        SM_TT        SM_CO        SM_HE     SM_SEATS       CAR_TT       CAR_CO       CHOICE    CAR_AV_SP  TRAIN_AV_SP  TRAIN_TT_SCALED  TRAIN_COST_SCALED  SM_TT_SCALED  SM_COST_SCALED  CAR_TT_SCALED  CAR_CO_SCALED\n",
       "count  6768.00000  6768.00000  6768.0  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000    6768.0  6768.000000  6768.0  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000  6768.000000       6768.0      6768.000000        6768.000000   6768.000000     6768.000000    6768.000000    6768.000000\n",
       "mean      2.62367     0.62367     1.0   517.174202     2.534574     0.555851     2.739362     1.630319     0.652926     2.896277     0.783245     2.389628     0.132979    12.376330     9.933511       1.0     0.828457     1.0   166.077423   490.885195    69.840426    84.507388   641.066489    20.011820     0.116430   123.154846    78.655881     2.127364     0.828457          1.0         1.660774           0.843561      0.845074        1.023025       1.231548       0.786559\n",
       "std       0.48450     0.48450     0.0   273.550159     0.845184     0.496908     2.095906     0.721338     0.627874     0.985276     0.412065     0.944566     0.339577     9.862034     9.705719       0.0     0.377010     0.0    69.795646  1062.593533    37.397666    47.113140  1411.658237     8.170988     0.320764    91.718406    55.921803     0.616052     0.377010          0.0         0.697956           0.642435      0.471131        0.785257       0.917184       0.559218\n",
       "min       2.00000     0.00000     1.0     1.000000     1.000000     0.000000     1.000000     0.000000     0.000000     1.000000     0.000000     0.000000     0.000000     1.000000     1.000000       1.0     0.000000     1.0    35.000000     9.000000    30.000000    12.000000    11.000000    10.000000     0.000000     0.000000     0.000000     1.000000     0.000000          1.0         0.350000           0.000000      0.120000        0.000000       0.000000       0.000000\n",
       "25%       2.00000     0.00000     1.0   301.750000     3.000000     0.000000     1.000000     1.000000     0.000000     2.000000     1.000000     2.000000     0.000000     2.000000     1.000000       1.0     1.000000     1.0   112.000000    60.000000    30.000000    55.000000    74.000000    10.000000     0.000000    70.000000    40.000000     2.000000     1.000000          1.0         1.120000           0.400000      0.550000        0.490000       0.700000       0.400000\n",
       "50%       3.00000     1.00000     1.0   563.500000     3.000000     1.000000     3.000000     2.000000     1.000000     3.000000     1.000000     3.000000     0.000000    17.000000     2.000000       1.0     1.000000     1.0   159.000000    94.000000    60.000000    77.000000   112.000000    20.000000     0.000000   120.000000    76.000000     2.000000     1.000000          1.0         1.590000           0.760000      0.770000        0.900000       1.200000       0.760000\n",
       "75%       3.00000     1.00000     1.0   751.250000     3.000000     1.000000     3.000000     2.000000     1.000000     3.000000     1.000000     3.000000     0.000000    22.000000    21.250000       1.0     1.000000     1.0   206.000000   166.000000   120.000000   105.000000   196.000000    30.000000     0.000000   176.000000   115.000000     3.000000     1.000000          1.0         2.060000           1.150000      1.050000        1.400000       1.760000       1.150000\n",
       "max       3.00000     1.00000     1.0   939.000000     3.000000     1.000000    10.000000     3.000000     3.000000     6.000000     1.000000     4.000000     1.000000    25.000000    25.000000       1.0     1.000000     1.0  1022.000000  5040.000000   120.000000   796.000000  6720.000000    30.000000     1.000000  1560.000000   520.000000     3.000000     1.000000          1.0        10.220000           5.760000      7.960000        7.680000      15.600000       5.200000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "pandas.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define the utility functions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align}\n",
    "V_1 & = \\beta_{Train} + \\beta_{time}X_{Train_{TT}} + \\beta_{cost}X_{Train_{cost}}\\\\\n",
    "V_2 & = \\beta_{SM} +  \\beta_{time}X_{SM_{TT}} + \\beta_{cost}X_{SM_{cost}}\\\\\n",
    "V_3 & = \\beta_{Car} + \\beta_{time}X_{Car_{TT}} + \\beta_{cost}X_{Car_{cost}}\\\\\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create parameters to be estimated**\n",
    "\n",
    "`Beta`\n",
    "1. name of parameter\n",
    "2. default value for the parameter\n",
    "3. lower bound\n",
    "4. upper bound\n",
    "5. flag indicating if parameter is to be estimated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ASC_CAR   = exp.Beta('ASC_CAR',0,None ,None ,0)\n",
    "ASC_TRAIN = exp.Beta('ASC_TRAIN',0,None ,None ,0)\n",
    "ASC_SM    = exp.Beta('ASC_SM',0,None ,None ,1)\n",
    "B_TIME    = exp.Beta('B_TIME',0,None ,None ,0)\n",
    "B_COST    = exp.Beta('B_COST',0,None ,None ,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define the utility functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "V1 = ASC_TRAIN + \\\n",
    "     B_TIME * TRAIN_TT_SCALED + \\\n",
    "     B_COST * TRAIN_COST_SCALED\n",
    "V2 = ASC_SM + \\\n",
    "     B_TIME * SM_TT_SCALED + \\\n",
    "     B_COST * SM_COST_SCALED\n",
    "V3 = ASC_CAR + \\\n",
    "     B_TIME * CAR_TT_SCALED + \\\n",
    "     B_COST * CAR_CO_SCALED"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Associate utility functions with alternatives and associate availability of alternatives**\n",
    "\n",
    "Create a python dictionary with all utility functions\n",
    "\n",
    "Create a python dictionary with availability of choices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "V = {1: V1,\n",
    "     2: V2,\n",
    "     3: V3}\n",
    "\n",
    "av = {1: TRAIN_AV_SP,\n",
    "      2: SM_AV,\n",
    "      3: CAR_AV_SP}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "logprob = models.loglogit(V, av, CHOICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define the Biogeme object**\n",
    "\n",
    "* Give the database with all variables\n",
    "* Give the log likelihood model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "biogeme  = bio.BIOGEME(database, logprob)\n",
    "\n",
    "biogeme.modelName = \"swissmetro_logit_basic\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Estimate the model**\n",
    "\n",
    "1. A `.html` can be generated with a report of the results and can be opened with a browser\n",
    "2. A `.pickle` file can also be generaetd with a snapshot with the results. This file can then be used in other scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTML file:    swissmetro_logit_basic.html\n",
      "Pickle file:  None\n"
     ]
    }
   ],
   "source": [
    "biogeme.generateHtml = True\n",
    "biogeme.generatePickle = False\n",
    "\n",
    "results = biogeme.estimate()\n",
    "\n",
    "print(f\"HTML file:    {results.data.htmlFileName}\")\n",
    "print(f\"Pickle file:  {results.data.pickleFileName }\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Print results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ASC_CAR   =\t-0.155\n",
      "ASC_TRAIN =\t-0.701\n",
      "B_COST    =\t-1.08\n",
      "B_TIME    =\t-1.28\n"
     ]
    }
   ],
   "source": [
    "betas = results.getBetaValues()\n",
    "for k,v in betas.items():\n",
    "    print(f\"{k:10}=\\t{v:.3g}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get the variance-covariance matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>ASC_CAR</th>\n",
       "      <th>ASC_TRAIN</th>\n",
       "      <th>B_COST</th>\n",
       "      <th>B_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ASC_CAR</th>\n",
       "      <td>0.0018693</td>\n",
       "      <td>0.00137693</td>\n",
       "      <td>0.000484751</td>\n",
       "      <td>-0.00143774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASC_TRAIN</th>\n",
       "      <td>0.00137693</td>\n",
       "      <td>0.00301114</td>\n",
       "      <td>8.21713e-06</td>\n",
       "      <td>-0.00225393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B_COST</th>\n",
       "      <td>0.000484751</td>\n",
       "      <td>8.21713e-06</td>\n",
       "      <td>0.00268634</td>\n",
       "      <td>0.000549897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B_TIME</th>\n",
       "      <td>-0.00143774</td>\n",
       "      <td>-0.00225393</td>\n",
       "      <td>0.000549897</td>\n",
       "      <td>0.00323573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ASC_CAR    ASC_TRAIN       B_COST       B_TIME\n",
       "ASC_CAR      0.0018693   0.00137693  0.000484751  -0.00143774\n",
       "ASC_TRAIN   0.00137693   0.00301114  8.21713e-06  -0.00225393\n",
       "B_COST     0.000484751  8.21713e-06   0.00268634  0.000549897\n",
       "B_TIME     -0.00143774  -0.00225393  0.000549897   0.00323573"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.getVarCovar()"
   ]
  }
 ],
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