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Created October 26, 2016 03:57
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Exampville.ipynb
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# Welcome to Exampville\nThe fine people of Exampville are excited to see what kind of travel model you can build for them. \n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Simulating the Travel Survey\n\nWe begin by generating a simulated travel survey for the people of Exampville. The latest version of Larch includes a helpful simulation tool, which allows us to scale the size of Examplville to the amount of time we want to spend processing the example (and the power of our computer). Although we can expect better model estimates when the sample size is larger, we're mostly concerned with the technical operation of Larch and not with getting the best model fit we can, so we'll simulate only a small amount of data."
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "import larch, numpy, pandas, os\nfrom larch.roles import P,X\n\nfrom larch.examples.exampville import builder\nnumpy.set_printoptions(linewidth=160)\n\nnZones = 15\ntransit_scope = (4,15)\nn_HH = 2000\n\ndirectory='/tmp/exampville_1'\nif not os.path.exists(directory):\n os.makedirs(directory)\nos.chdir(directory)\n\ndirectory, omx, f_hh, f_pp, f_tour = builder(\n nZones=nZones, \n transit_scope=transit_scope, \n n_HH=n_HH,\n directory=directory,\n)\n\nf_hh.export_idco(os.path.join(directory,'exampville_hh.csv'))\nf_pp.export_idco(os.path.join(directory,'exampville_person.csv'))\nf_tour.export_idco(os.path.join(directory,'exampville_tours.csv'))",
"execution_count": 1,
"outputs": [
{
"text": "[09:31:16 PM]root: Connected log to stream <ipykernel.iostream.OutStream object at 0x103ca2b38>\n[09:31:16 PM]larch.exampville: EXAMPVILLE Builder (Year 1)\n[09:31:16 PM]larch.exampville: simulating a survey of 2000 households\n[09:31:16 PM]larch.exampville: traveling among 15 travel analysis zones\n[09:31:16 PM]larch.exampville: Zones\n[09:31:16 PM]larch.exampville: Skims\n[09:31:16 PM]larch.exampville: HHs\n[09:31:16 PM]larch.exampville: People\n[09:31:16 PM]larch.exampville: Tours\n[09:31:16 PM]larch.exampville: Choice Probability\n[09:31:18 PM]larch.exampville: Choices\n[09:31:18 PM]larch.exampville: Output\n[09:31:18 PM]larch.exampville: EXAMPVILLE Completed Builder (Year 1)\n[09:31:18 PM]larch.exampville: SKIMS : /tmp/exampville_1/exampville.omx\n[09:31:18 PM]larch.exampville: HHs : /tmp/exampville_1/exampville_hh.h5\n[09:31:18 PM]larch.exampville: Persons: /tmp/exampville_1/exampville_person.h5\n[09:31:18 PM]larch.exampville: Tours : /tmp/exampville_1/exampville_tours.h5\n",
"output_type": "stream",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "omx",
"execution_count": 2,
"outputs": [
{
"execution_count": 2,
"data": {
"text/plain": "<larch.OMX> /tmp/exampville_1/exampville.omx\n | shape:(15, 15)\n | data:\n | AUTO_TIME (float64)\n | DIST (float64)\n | RAIL_FARE (float64)\n | RAIL_TIME (float64)\n | lookup:\n | EMPLOYMENT (15 float64)\n | EMP_NONRETAIL (15 float64)\n | EMP_RETAIL (15 float64)\n | TAZID (15 int64)"
},
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "f_hh.info(1)",
"execution_count": 3,
"outputs": [
{
"execution_count": 3,
"data": {
"text/plain": " \u001b[32m\u001b[1mDT Content\u001b[0m\n┌────────┬─────┬─────┬───────────────┐\n│Variable│dtype│Shape│Original Source│\n├────────┼─────┼─────┼───────────────┤\n╞ idco ══════════════════════════════╡\n│HHSIZE │int64│(2000,) │\n│HOMETAZ │int64│(2000,) │\n│INCOME │int64│(2000,) │\n└────────┴─────┴─────┴───────────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx1\" reftxt=\"DT Content\" toclevel=\"2\"></a><h2>DT Content</h2><div><table><thead><tr><th>Variable</th><th>dtype</th><th>Shape</th><th>Original Source</th></tr></thead><tbody><tr><td class=\"parameter_category\" colspan=\"4\">idco</td><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td>HHSIZE</td><td>int64</td><td colspan=\"2\">(2000,)</td><div class=\"dummycell\"></div></tr><tr><td>HOMETAZ</td><td>int64</td><td colspan=\"2\">(2000,)</td><div class=\"dummycell\"></div></tr><tr><td>INCOME</td><td>int64</td><td colspan=\"2\">(2000,)</td><div class=\"dummycell\"></div></tr></tbody><tfoot></tfoot></table></div></div>"
},
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "f_pp.info(1)",
"execution_count": 4,
"outputs": [
{
"execution_count": 4,
"data": {
"text/plain": " \u001b[32m\u001b[1mDT Content\u001b[0m\n┌────────────┬─────┬─────┬───────────────┐\n│Variable │dtype│Shape│Original Source│\n├────────────┼─────┼─────┼───────────────┤\n╞ idco ══════════════════════════════════╡\n│AGE │int64│(3462,) │\n│HHID │int64│(3462,) │\n│N_OTHERTOURS│int64│(3462,) │\n│N_TOTALTOURS│int64│(3462,) │\n│N_WORKTOURS │int64│(3462,) │\n│WORKS │int64│(3462,) │\n└────────────┴─────┴─────┴───────────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx2\" reftxt=\"DT Content\" toclevel=\"2\"></a><h2>DT Content</h2><div><table><thead><tr><th>Variable</th><th>dtype</th><th>Shape</th><th>Original Source</th></tr></thead><tbody><tr><td class=\"parameter_category\" colspan=\"4\">idco</td><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td>AGE</td><td>int64</td><td colspan=\"2\">(3462,)</td><div class=\"dummycell\"></div></tr><tr><td>HHID</td><td>int64</td><td colspan=\"2\">(3462,)</td><div class=\"dummycell\"></div></tr><tr><td>N_OTHERTOURS</td><td>int64</td><td colspan=\"2\">(3462,)</td><div class=\"dummycell\"></div></tr><tr><td>N_TOTALTOURS</td><td>int64</td><td colspan=\"2\">(3462,)</td><div class=\"dummycell\"></div></tr><tr><td>N_WORKTOURS</td><td>int64</td><td colspan=\"2\">(3462,)</td><div class=\"dummycell\"></div></tr><tr><td>WORKS</td><td>int64</td><td colspan=\"2\">(3462,)</td><div class=\"dummycell\"></div></tr></tbody><tfoot></tfoot></table></div></div>"
},
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "f_tour.info(1)",
"execution_count": 5,
"outputs": [
{
"execution_count": 5,
"data": {
"text/plain": " \u001b[32m\u001b[1mDT Content\u001b[0m\n┌────────┬─────┬─────┬───────────────┐\n│Variable│dtype│Shape│Original Source│\n├────────┼─────┼─────┼───────────────┤\n╞ idco ══════════════════════════════╡\n│DTAZ │int64│(6123,) │\n│HHID │int64│(6123,) │\n│PERSONID│int64│(6123,) │\n│TOURMODE│int64│(6123,) │\n│TOURPURP│int64│(6123,) │\n└────────┴─────┴─────┴───────────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx3\" reftxt=\"DT Content\" toclevel=\"2\"></a><h2>DT Content</h2><div><table><thead><tr><th>Variable</th><th>dtype</th><th>Shape</th><th>Original Source</th></tr></thead><tbody><tr><td class=\"parameter_category\" colspan=\"4\">idco</td><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td>DTAZ</td><td>int64</td><td colspan=\"2\">(6123,)</td><div class=\"dummycell\"></div></tr><tr><td>HHID</td><td>int64</td><td colspan=\"2\">(6123,)</td><div class=\"dummycell\"></div></tr><tr><td>PERSONID</td><td>int64</td><td colspan=\"2\">(6123,)</td><div class=\"dummycell\"></div></tr><tr><td>TOURMODE</td><td>int64</td><td colspan=\"2\">(6123,)</td><div class=\"dummycell\"></div></tr><tr><td>TOURPURP</td><td>int64</td><td colspan=\"2\">(6123,)</td><div class=\"dummycell\"></div></tr></tbody><tfoot></tfoot></table></div></div>"
},
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Compiling the Mode Choice Data\n\nThe Exampville data output contains a set of files similar to what we might find for a real travel survey: network skims, and tables of households, persons, and tours. We'll need to merge these tables to create a composite dataset for mode choice model estimation."
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "flog = larch.logging.flogger('ModeData')\n\n\n### MODE CHOICE DATA\n\n# Define numbers and names for modes\nDA = 1\nSR = 2\nWalk = 3\nBike = 4\nTransit = 5\n\nd = larch.DT() \n# By omitting a filename here, we create a temporary HDF5 store.\n\nd.set_alternatives( [DA,SR,Walk,Bike,Transit], ['DA','SR','Walk','Bike','Transit'] )\n\nflog('merging survey datasets')\nd.new_caseids( f_tour.caseids() )\nd.merge_into_idco(f_tour, \"caseid\")\nd.merge_into_idco(f_pp, \"PERSONID\")\nd.merge_into_idco(f_hh, \"HHID\")\n\nflog('merging skims')\n# Create a new variables with zero-based home TAZ numbers\nd.new_idco(\"HOMETAZi\", \"HOMETAZ-1\", dtype=int)\nd.new_idco(\"DTAZi\", \"DTAZ-1\", dtype=int)\n\n# Pull in plucked data from Matrix file\nd.pluck_into_idco(omx, \"HOMETAZi\", \"DTAZi\")\n# This command is new as of Larch 3.3.15\n# It loads all the matrix DATA from an OMX based on OTAZ and DTAZ \n# columns that already exist in the DT\n\nflog('prep data')\nd.choice_idco = {\n DA: 'TOURMODE==1',\n SR: 'TOURMODE==2',\n Walk: 'TOURMODE==3',\n Bike: 'TOURMODE==4',\n Transit: 'TOURMODE==5',\n}\n# Alternately: d.choice_idco = {i:'TOURMODE=={}'.format(i) for i in [1,2,3,4,5]}\n\n\n\nd.avail_idco = {\n DA: 'AGE>=16',\n SR: '1',\n Walk: 'DIST<=3',\n Bike: 'DIST<=15',\n Transit: 'RAIL_TIME>0',\n}\n\n# Let's define some variables for clarity.\nd.new_idco(\"WALKTIME\", \"DIST / 2.5 * 60 * (DIST<=3)\") # 2.5 mph, 60 minutes per hour, max 3 miles\nd.new_idco(\"BIKETIME\", \"DIST / 12 * 60 * (DIST<=15)\") # 12 mph, 60 minutes per hour, max 15 miles\nd.new_idco(\"CARCOST\", \"DIST * 0.20\") # 20 cents per mile\n\nd.info(1)",
"execution_count": 6,
"outputs": [
{
"text": "[09:31:19 PM]larch: merging survey datasets\n[09:31:19 PM]larch: merging skims\n[09:31:19 PM]larch: prep data\n",
"output_type": "stream",
"name": "stdout"
},
{
"execution_count": 6,
"data": {
"text/plain": " \u001b[32m\u001b[1mDT Content\u001b[0m\n┌────────────┬───────┬───────┬──────────────────────────────────────┐\n│Variable │dtype │Shape │Original Source │\n├────────────┼───────┼───────┼──────────────────────────────────────┤\n╞ idco ═════════════════════════════════════════════════════════════╡\n│AGE │int64 │(6123,)│/tmp/exampville_1/exampville_person.h5│\n│AUTO_TIME │float64│(6123,)│/tmp/exampville_1/exampville.omx │\n│BIKETIME │float64│(6123,) │\n│CARCOST │float64│(6123,) │\n│DIST │float64│(6123,)│/tmp/exampville_1/exampville.omx │\n│DTAZ │int64 │(6123,)│/tmp/exampville_1/exampville_tours.h5 │\n│DTAZi │int64 │(6123,) │\n│HHID │int64 │(6123,)│/tmp/exampville_1/exampville_tours.h5 │\n│HHSIZE │int64 │(6123,)│/tmp/exampville_1/exampville_hh.h5 │\n│HOMETAZ │int64 │(6123,)│/tmp/exampville_1/exampville_hh.h5 │\n│HOMETAZi │int64 │(6123,) │\n│INCOME │int64 │(6123,)│/tmp/exampville_1/exampville_hh.h5 │\n│N_OTHERTOURS│int64 │(6123,)│/tmp/exampville_1/exampville_person.h5│\n│N_TOTALTOURS│int64 │(6123,)│/tmp/exampville_1/exampville_person.h5│\n│N_WORKTOURS │int64 │(6123,)│/tmp/exampville_1/exampville_person.h5│\n│PERSONID │int64 │(6123,)│/tmp/exampville_1/exampville_tours.h5 │\n│RAIL_FARE │float64│(6123,)│/tmp/exampville_1/exampville.omx │\n│RAIL_TIME │float64│(6123,)│/tmp/exampville_1/exampville.omx │\n│TOURMODE │int64 │(6123,)│/tmp/exampville_1/exampville_tours.h5 │\n│TOURPURP │int64 │(6123,)│/tmp/exampville_1/exampville_tours.h5 │\n│WALKTIME │float64│(6123,) │\n│WORKS │int64 │(6123,)│/tmp/exampville_1/exampville_person.h5│\n│caseid │int64 │(6123,)│/tmp/exampville_1/exampville_tours.h5 │\n╞ idca ═════════════════════════════════════════════════════════════╡\n│_avail_ │<stack>│<stack> │\n│_choice_ │<stack>│<stack> │\n└────────────┴───────┴───────┴──────────────────────────────────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx4\" reftxt=\"DT Content\" toclevel=\"2\"></a><h2>DT Content</h2><div><table><thead><tr><th>Variable</th><th>dtype</th><th>Shape</th><th>Original Source</th></tr></thead><tbody><tr><td class=\"parameter_category\" colspan=\"4\">idco</td><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td>AGE</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_person.h5</td></tr><tr><td>AUTO_TIME</td><td>float64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville.omx</td></tr><tr><td>BIKETIME</td><td>float64</td><td colspan=\"2\">(6123,)</td><div class=\"dummycell\"></div></tr><tr><td>CARCOST</td><td>float64</td><td colspan=\"2\">(6123,)</td><div class=\"dummycell\"></div></tr><tr><td>DIST</td><td>float64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville.omx</td></tr><tr><td>DTAZ</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_tours.h5</td></tr><tr><td>DTAZi</td><td>int64</td><td colspan=\"2\">(6123,)</td><div class=\"dummycell\"></div></tr><tr><td>HHID</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_tours.h5</td></tr><tr><td>HHSIZE</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_hh.h5</td></tr><tr><td>HOMETAZ</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_hh.h5</td></tr><tr><td>HOMETAZi</td><td>int64</td><td colspan=\"2\">(6123,)</td><div class=\"dummycell\"></div></tr><tr><td>INCOME</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_hh.h5</td></tr><tr><td>N_OTHERTOURS</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_person.h5</td></tr><tr><td>N_TOTALTOURS</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_person.h5</td></tr><tr><td>N_WORKTOURS</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_person.h5</td></tr><tr><td>PERSONID</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_tours.h5</td></tr><tr><td>RAIL_FARE</td><td>float64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville.omx</td></tr><tr><td>RAIL_TIME</td><td>float64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville.omx</td></tr><tr><td>TOURMODE</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_tours.h5</td></tr><tr><td>TOURPURP</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_tours.h5</td></tr><tr><td>WALKTIME</td><td>float64</td><td colspan=\"2\">(6123,)</td><div class=\"dummycell\"></div></tr><tr><td>WORKS</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_person.h5</td></tr><tr><td>caseid</td><td>int64</td><td>(6123,)</td><td>/tmp/exampville_1/exampville_tours.h5</td></tr><tr><td class=\"parameter_category\" colspan=\"4\">idca</td><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td>_avail_</td><td>&lt;stack&gt;</td><td colspan=\"2\">&lt;stack&gt;</td><div class=\"dummycell\"></div></tr><tr><td>_choice_</td><td>&lt;stack&gt;</td><td colspan=\"2\">&lt;stack&gt;</td><div class=\"dummycell\"></div></tr></tbody><tfoot></tfoot></table></div></div>"
},
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Mode Choice Model Estimation"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "flog = larch.logging.flogger('ModeModel')\n\nd.exclude_idco(\"TOURPURP != 1\")\nflog(\"nCases={}\", d.nCases())\n\n### MODE CHOICE MODEL\n\nm = larch.Model(d)\nm.title = \"Exampville Work Tour Mode Choice v1\"\n\n# In order to create a shadow (alias) parameter, the source parameter must already exist,\n# so we create it explicitly.\nm.parameter(\"InVehTime\")\n\n# Then we can create the shadow, as a multiple of the original parameter.\nm.shadow_parameter.NonMotorTime = P.InVehTime * 2\n\n\n\nm.utility[DA] = (\n + P.InVehTime * X.AUTO_TIME\n + P.Cost * X.CARCOST # dollars per mile\n)\n\nm.utility[SR] = (\n + P.ASC_SR\n + P.InVehTime * X.AUTO_TIME\n + P.Cost * (X.CARCOST * 0.5) # dollars per mile, half share\n + P(\"HighInc:SR\") * X(\"INCOME>75000\")\n)\n\nm.utility[Walk] = (\n + P.ASC_Walk\n + P.NonMotorTime * X.WALKTIME \n + P(\"HighInc:Walk\") * X(\"INCOME>75000\")\n)\n\nm.utility[Bike] = (\n + P.ASC_Bike\n + P.NonMotorTime * X.BIKETIME\n + P(\"HighInc:Bike\") * X(\"INCOME>75000\")\n)\n\nm.utility[Transit] = (\n + P.ASC_Transit\n + P.InVehTime * X.RAIL_TIME\n + P.Cost * X.RAIL_FARE\n + P(\"HighInc:Transit\") * X(\"INCOME>75000\")\n)\n\nCar = m.new_nest('Nest:Car', children=[DA,SR])\nNonMotor = m.new_nest('Nest:NonMotor', children=[Walk,Bike])\nMotor = m.new_nest('Nest:Motorized', children=[Car,Transit])\n\nfrom larch.util.categorize import Categorizer\n\nm.parameter_groups = (\n Categorizer(\"Level of Service\",\n \".*Time.*\",\n \".*Cost.*\",\n ),\n Categorizer(\"Alternative Specific Constants\",\n \"ASC.*\",\n ),\n Categorizer(\"Income\",\n \".*HighInc.*\",\n \".*LowInc.*\",\n ),\n Categorizer(\"Logsum Parameters\",\n \"Nest.*\",\n ),\n)\n\nm.maximize_loglike()\n",
"execution_count": 7,
"outputs": [
{
"text": "[09:31:20 PM]larch: nCases=1897\n[09:31:20 PM]larch.Model: Model.setUp...\n[09:31:20 PM]larch.Model: Model.setUp complete\n[09:31:20 PM]larch.Model: Using <larch.util.optimize.OptimizeTechnique SLSQP>\n[09:31:21 PM]larch.Model: Optimization terminated successfully. [SLSQP]\n[09:31:21 PM]larch.Model: Ending leg (success) using SLSQP at [-0.11274266 0.74170258 0.86969656 0.84439805 -0.34073929 -1.3943154 -1.75623615 0.73177238 -0.79580814 -1.66675159 -1.0525934 -1.67706803 -1.24100479]\n[09:31:21 PM]larch.Model: Preliminary Results\n+-----------------+---------------+----------+------+----------+\n Parameter Estimated Value Std Error t-Stat Null Value \n+-----------------+---------------+----------+------+----------+\n \n= Level of Service =============================================\n InVehTime -0.1127 nan nan 0 \n NonMotorTime -0.2255 = InVehTime * 2 \n Cost -0.3407 nan nan 0 \n= Alternative Specific Constants ===============================\n ASC_SR -1.394 nan nan 0 \n ASC_Walk 0.7318 nan nan 0 \n ASC_Bike -1.667 nan nan 0 \n ASC_Transit -1.677 nan nan 0 \n= Income =======================================================\n HighInc SR -1.756 nan nan 0 \n Walk -0.7958 nan nan 0 \n Bike -1.053 nan nan 0 \n Transit -1.241 nan nan 0 \n= Logsum Parameters ============================================\n Nest Car 0.7417 nan nan 1 \n NonMotor 0.8697 nan nan 1 \n Motorized 0.8444 nan nan 1 \n+-----------------+---------------+----------+------+----------+\n[09:31:23 PM]larch.Model: Final Results\n+-----------------+---------------+----------+------+----------+\n Parameter Estimated Value Std Error t-Stat Null Value \n+-----------------+---------------+----------+------+----------+\n \n= Level of Service =============================================\n InVehTime -0.1127 0.01356 -8.31 0 \n NonMotorTime -0.2255 = InVehTime * 2 \n Cost -0.3407 0.1812 -1.88 0 \n= Alternative Specific Constants ===============================\n ASC_SR -1.394 0.9541 -1.46 0 \n ASC_Walk 0.7318 0.3015 2.43 0 \n ASC_Bike -1.667 0.2739 -6.09 0 \n ASC_Transit -1.677 0.4977 -3.37 0 \n= Income =======================================================\n HighInc SR -1.756 1.29 -1.36 0 \n Walk -0.7958 0.4189 -1.90 0 \n Bike -1.053 0.4631 -2.27 0 \n Transit -1.241 0.4508 -2.75 0 \n= Logsum Parameters ============================================\n Nest Car 0.7417 0.5384 -0.48 1 \n NonMotor 0.8697 0.1835 -0.71 1 \n Motorized 0.8444 0.2487 -0.63 1 \n+-----------------+---------------+----------+------+----------+\n",
"output_type": "stream",
"name": "stdout"
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{
"execution_count": 7,
"data": {
"text/plain": " messages: Optimization terminated successfully. [SLSQP]:\n | SLSQP:Optimization terminated successfully.\n ctol: 1.679247820710903e-10\n fun: 963.8426415309915\n installed_memory: '8.0 GiB'\n jac: array([ -3.62739805e-04, -2.89772577e-04, -2.03761185e-05, -3.39222995e-05, 1.11003855e-05, -1.03199375e-04, -4.70356292e-05, -1.15896332e-06,\n | 3.85922970e-06, -1.88205941e-05, -5.68643253e-06, -4.56436771e-06, -9.30401952e-06, 0.00000000e+00])\n loglike: -963.8426415309915\n loglike_null: -2592.698004024908\n message: 'Optimization terminated successfully. [SLSQP]'\n nit: 42\n niter: [('SLSQP', 42)]\n peak_memory_usage: '131.94921875 MiB'\n stats: <larch.core.runstats, success in 0:01.3>\n status: 0\n success: True\n x: array([-0.11274266, 0.74170258, 0.86969656, 0.84439805, -0.34073929, -1.3943154 , -1.75623615, 0.73177238, -0.79580814, -1.66675159, -1.0525934 ,\n | -1.67706803, -1.24100479])"
},
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"output_type": "execute_result"
}
]
},
{
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"cell_type": "code",
"source": "## Save results to a report\nm.xhtml_report(filename=os.path.join(directory,'mode_choice_model.html'), cats='**')\n\n\n\n## View Mode Choice Model Results\n# Here we lump together a bunch of report sections into a single report that will\n# display neatly in a jupyter notebook.\nsections = ['title','params','ll',\n 'nesting_tree',\n 'latest','UTILITYSPEC','PROBABILITYSPEC',\n 'DATA', \n 'excludedcases','NOTES','options',\n 'possible_overspecification']\nm.jupyter(*sections)",
"execution_count": 8,
"outputs": [
{
"metadata": {},
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div class=\"page_header\"><h1>Exampville Work Tour Mode Choice v1</h1></div>"
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"text/plain": " \u001b[32m\u001b[1mModel Parameter Estimates\u001b[0m\n┌─────────────────┬───────────────┬──────────┬──────┬──────────┐\n│Parameter │Estimated Value│Std Error │t-Stat│Null Value│\n├─────────────────┼───────────────┼──────────┼──────┼──────────┤\n│ │ │ │ │ │ │\n╞ Level of Service ════════════════════════════════════════════╡\n│InVehTime │-0.1127 │ 0.01356 │-8.31 │ 0 │\n│NonMotorTime │-0.2255 │= InVehTime * 2 │\n│Cost │-0.3407 │ 0.1812 │-1.88 │ 0 │\n╞ Alternative Specific Constants ══════════════════════════════╡\n│ASC_SR │-1.394 │ 0.9541 │-1.46 │ 0 │\n│ASC_Walk │ 0.7318 │ 0.3015 │ 2.43 │ 0 │\n│ASC_Bike │-1.667 │ 0.2739 │-6.09 │ 0 │\n│ASC_Transit │-1.677 │ 0.4977 │-3.37 │ 0 │\n╞ Income ══════════════════════════════════════════════════════╡\n│HighInc│SR │-1.756 │ 1.29 │-1.36 │ 0 │\n│ │Walk │-0.7958 │ 0.4189 │-1.90 │ 0 │\n│ │Bike │-1.053 │ 0.4631 │-2.27 │ 0 │\n│ │Transit │-1.241 │ 0.4508 │-2.75 │ 0 │\n╞ Logsum Parameters ═══════════════════════════════════════════╡\n│Nest │Car │ 0.7417 │ 0.5384 │-0.48 │ 1 │\n│ │NonMotor │ 0.8697 │ 0.1835 │-0.71 │ 1 │\n│ │Motorized│ 0.8444 │ 0.2487 │-0.63 │ 1 │\n└─────────────────┴───────────────┴──────────┴──────┴──────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx39\" reftxt=\"Parameter Estimates\" toclevel=\"2\"></a><h2>Model Parameter Estimates</h2><div><table><thead><tr><th class=\"param_label\" colspan=\"2\">Parameter</th><div class=\"dummycell\"></div><th class=\"estimated_value\">Estimated Value</th><th class=\"std_err\">Std Error</th><th class=\"tstat\">t-Stat</th><th class=\"null_value\">Null Value</th></tr></thead><tbody><tr><td class=\"param_label\" colspan=\"6\"></td><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td class=\"param_label parameter_category\" colspan=\"6\"><a class=\"toc\" name=\"rx35\" reftxt=\"Level of Service\" toclevel=\"3\"></a>Level of Service</td><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramInVehTime\"></a>InVehTime</td><div class=\"dummycell\"></div><td class=\"estimated_value\">-0.1127 </td><td class=\"std_err\"> 0.01356 </td><td class=\"tstat\">-8.31</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramNonMotorTime\"></a>NonMotorTime</td><div class=\"dummycell\"></div><td class=\"estimated_value\">-0.2255 </td><td class=\"std_err\" colspan=\"3\">= InVehTime * 2</td><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramCost\"></a>Cost</td><div class=\"dummycell\"></div><td class=\"estimated_value\">-0.3407 </td><td class=\"std_err\"> 0.1812 </td><td class=\"tstat\">-1.88</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label parameter_category\" colspan=\"6\"><a class=\"toc\" name=\"rx36\" reftxt=\"Alternative Specific Constants\" toclevel=\"3\"></a>Alternative Specific Constants</td><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramASC_SR\"></a>ASC_SR</td><div class=\"dummycell\"></div><td class=\"estimated_value\">-1.394 </td><td class=\"std_err\"> 0.9541 </td><td class=\"tstat\">-1.46</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramASC_Walk\"></a>ASC_Walk</td><div class=\"dummycell\"></div><td class=\"estimated_value\"> 0.7318 </td><td class=\"std_err\"> 0.3015 </td><td class=\"tstat\"> 2.43</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramASC_Bike\"></a>ASC_Bike</td><div class=\"dummycell\"></div><td class=\"estimated_value\">-1.667 </td><td class=\"std_err\"> 0.2739 </td><td class=\"tstat\">-6.09</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramASC_Transit\"></a>ASC_Transit</td><div class=\"dummycell\"></div><td class=\"estimated_value\">-1.677 </td><td class=\"std_err\"> 0.4977 </td><td class=\"tstat\">-3.37</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label parameter_category\" colspan=\"6\"><a class=\"toc\" name=\"rx37\" reftxt=\"Income\" toclevel=\"3\"></a>Income</td><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td class=\"param_label\" rowspan=\"4\" style=\"vertical-align:top;\">HighInc</td><td class=\"param_label\"><a name=\"paramSR\"></a>SR</td><td class=\"estimated_value\">-1.756 </td><td class=\"std_err\"> 1.29 </td><td class=\"tstat\">-1.36</td><td class=\"null_value\"> 0 </td></tr><tr><div class=\"dummycell\"></div><td class=\"param_label\"><a name=\"paramWalk\"></a>Walk</td><td class=\"estimated_value\">-0.7958 </td><td class=\"std_err\"> 0.4189 </td><td class=\"tstat\">-1.90</td><td class=\"null_value\"> 0 </td></tr><tr><div class=\"dummycell\"></div><td class=\"param_label\"><a name=\"paramBike\"></a>Bike</td><td class=\"estimated_value\">-1.053 </td><td class=\"std_err\"> 0.4631 </td><td class=\"tstat\">-2.27</td><td class=\"null_value\"> 0 </td></tr><tr><div class=\"dummycell\"></div><td class=\"param_label\"><a name=\"paramTransit\"></a>Transit</td><td class=\"estimated_value\">-1.241 </td><td class=\"std_err\"> 0.4508 </td><td class=\"tstat\">-2.75</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label parameter_category\" colspan=\"6\"><a class=\"toc\" name=\"rx38\" reftxt=\"Logsum Parameters\" toclevel=\"3\"></a>Logsum Parameters</td><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div><div class=\"dummycell\"></div></tr><tr><td class=\"param_label\" rowspan=\"3\" style=\"vertical-align:top;\">Nest</td><td class=\"param_label\"><a name=\"paramCar\"></a>Car</td><td class=\"estimated_value\"> 0.7417 </td><td class=\"std_err\"> 0.5384 </td><td class=\"tstat\">-0.48</td><td class=\"null_value\"> 1 </td></tr><tr><div class=\"dummycell\"></div><td class=\"param_label\"><a name=\"paramNonMotor\"></a>NonMotor</td><td class=\"estimated_value\"> 0.8697 </td><td class=\"std_err\"> 0.1835 </td><td class=\"tstat\">-0.71</td><td class=\"null_value\"> 1 </td></tr><tr><div class=\"dummycell\"></div><td class=\"param_label\"><a name=\"paramMotorized\"></a>Motorized</td><td class=\"estimated_value\"> 0.8444 </td><td class=\"std_err\"> 0.2487 </td><td class=\"tstat\">-0.63</td><td class=\"null_value\"> 1 </td></tr></tbody><tfoot></tfoot></table></div></div>"
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"text/plain": " \u001b[32m\u001b[1mModel Estimation Statistics\u001b[0m\n┌──────────────────────────────────┬─────────┬────────┐\n│Statistic │Aggregate│Per Case│\n├──────────────────────────────────┼─────────┼────────┤\n│Number of Cases │ 1897 │\n│Log Likelihood at Convergence │-963.84 │-0.51 │\n│Log Likelihood at Null Parameters │-2592.70 │-1.37 │\n│Rho Squared w.r.t. Null Parameters│ 0.628 │\n└──────────────────────────────────┴─────────┴────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx40\" reftxt=\"Estimation Statistics\" toclevel=\"2\"></a><h2>Model Estimation Statistics</h2><div><table><thead><tr><th>Statistic</th><th>Aggregate</th><th>Per Case</th></tr></thead><tbody><tr><td>Number of Cases</td><td class=\"statistics_bridge centered_cell\" colspan=\"2\">1897</td><div class=\"dummycell\"></div></tr><tr><td>Log Likelihood at Convergence</td><td>-963.84</td><td>-0.51</td></tr><tr><td>Log Likelihood at Null Parameters</td><td>-2592.70</td><td>-1.37</td></tr><tr><td>Rho Squared w.r.t. Null Parameters</td><td class=\"statistics_bridge centered_cell\" colspan=\"2\">0.628</td><div class=\"dummycell\"></div></tr></tbody><tfoot></tfoot></table></div></div>"
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"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div xmlns=\"http://www.w3.org/2000/svg\" class=\"nesting_graph\"><a class=\"toc\" name=\"rx41\" reftxt=\"Nesting Structure\" toclevel=\"2\"></a><h2>Nesting Structure</h2><svg height=\"288pt\" viewBox=\"0.00 0.00 407.87 288.00\" width=\"408pt\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(0.957447 0.957447) rotate(0) translate(4 296.8)\">\n<title>Tree</title>\n<polygon fill=\"white\" points=\"-4,4 -4,-296.8 422,-296.8 422,4 -4,4\" stroke=\"none\"></polygon>\n<g class=\"cluster\" id=\"clust1\"><title>cluster_elemental</title>\n<path d=\"M20,-8C20,-8 398,-8 398,-8 404,-8 410,-14 410,-20 410,-20 410,-72.8 410,-72.8 410,-78.8 404,-84.8 398,-84.8 398,-84.8 20,-84.8 20,-84.8 14,-84.8 8,-78.8 8,-72.8 8,-72.8 8,-20 8,-20 8,-14 14,-8 20,-8\" fill=\"#eeeeee\" stroke=\"#cccccc\"></path>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"209\" y=\"-16.2\">Elemental Alternatives</text>\n</g>\n\n<g class=\"node\" id=\"node1\"><title>1</title>\n<path d=\"M318.034,-76.8C318.034,-76.8 285.966,-76.8 285.966,-76.8 279.966,-76.8 273.966,-70.8 273.966,-64.8 273.966,-64.8 273.966,-52.8 273.966,-52.8 273.966,-46.8 279.966,-40.8 285.966,-40.8 285.966,-40.8 318.034,-40.8 318.034,-40.8 324.034,-40.8 330.034,-46.8 330.034,-52.8 330.034,-52.8 330.034,-64.8 330.034,-64.8 330.034,-70.8 324.034,-76.8 318.034,-76.8\" fill=\"none\" stroke=\"black\"></path>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"281.983\" y=\"-55.6\">DA </text>\n<text fill=\"#999999\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"305.696\" y=\"-55.6\">(1)</text>\n</g>\n\n<g class=\"node\" id=\"node2\"><title>2</title>\n<path d=\"M390,-76.8C390,-76.8 360,-76.8 360,-76.8 354,-76.8 348,-70.8 348,-64.8 348,-64.8 348,-52.8 348,-52.8 348,-46.8 354,-40.8 360,-40.8 360,-40.8 390,-40.8 390,-40.8 396,-40.8 402,-46.8 402,-52.8 402,-52.8 402,-64.8 402,-64.8 402,-70.8 396,-76.8 390,-76.8\" fill=\"none\" stroke=\"black\"></path>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"356.53\" y=\"-55.6\">SR </text>\n<text fill=\"#999999\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"377.149\" y=\"-55.6\">(2)</text>\n</g>\n\n<g class=\"node\" id=\"node3\"><title>3</title>\n<path d=\"M150.141,-76.8C150.141,-76.8 107.859,-76.8 107.859,-76.8 101.859,-76.8 95.8588,-70.8 95.8588,-64.8 95.8588,-64.8 95.8588,-52.8 95.8588,-52.8 95.8588,-46.8 101.859,-40.8 107.859,-40.8 107.859,-40.8 150.141,-40.8 150.141,-40.8 156.141,-40.8 162.141,-46.8 162.141,-52.8 162.141,-52.8 162.141,-64.8 162.141,-64.8 162.141,-70.8 156.141,-76.8 150.141,-76.8\" fill=\"none\" stroke=\"black\"></path>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"103.93\" y=\"-55.6\">Walk </text>\n<text fill=\"#999999\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"137.749\" y=\"-55.6\">(3)</text>\n</g>\n\n<g class=\"node\" id=\"node4\"><title>4</title>\n<path d=\"M66.2636,-76.8C66.2636,-76.8 27.7364,-76.8 27.7364,-76.8 21.7364,-76.8 15.7364,-70.8 15.7364,-64.8 15.7364,-64.8 15.7364,-52.8 15.7364,-52.8 15.7364,-46.8 21.7364,-40.8 27.7364,-40.8 27.7364,-40.8 66.2636,-40.8 66.2636,-40.8 72.2636,-40.8 78.2636,-46.8 78.2636,-52.8 78.2636,-52.8 78.2636,-64.8 78.2636,-64.8 78.2636,-70.8 72.2636,-76.8 66.2636,-76.8\" fill=\"none\" stroke=\"black\"></path>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"23.8685\" y=\"-55.6\">Bike </text>\n<text fill=\"#999999\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"53.8103\" y=\"-55.6\">(4)</text>\n</g>\n\n<g class=\"node\" id=\"node5\"><title>5</title>\n<path d=\"M243.976,-76.8C243.976,-76.8 192.024,-76.8 192.024,-76.8 186.024,-76.8 180.024,-70.8 180.024,-64.8 180.024,-64.8 180.024,-52.8 180.024,-52.8 180.024,-46.8 186.024,-40.8 192.024,-40.8 192.024,-40.8 243.976,-40.8 243.976,-40.8 249.976,-40.8 255.976,-46.8 255.976,-52.8 255.976,-52.8 255.976,-64.8 255.976,-64.8 255.976,-70.8 249.976,-76.8 243.976,-76.8\" fill=\"none\" stroke=\"black\"></path>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"188.263\" y=\"-55.6\">Transit </text>\n<text fill=\"#999999\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"231.416\" y=\"-55.6\">(5)</text>\n</g>\n\n<g class=\"node\" id=\"node6\"><title>0</title>\n<ellipse cx=\"173\" cy=\"-274.8\" fill=\"none\" rx=\"29.6358\" ry=\"18\" stroke=\"black\"></ellipse>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"173\" y=\"-270.6\">Root</text>\n</g>\n\n<g class=\"node\" id=\"node7\"><title>7</title>\n<path d=\"M178.471,-148.8C178.471,-148.8 77.5285,-148.8 77.5285,-148.8 71.5285,-148.8 65.5285,-142.8 65.5285,-136.8 65.5285,-136.8 65.5285,-124.8 65.5285,-124.8 65.5285,-118.8 71.5285,-112.8 77.5285,-112.8 77.5285,-112.8 178.471,-112.8 178.471,-112.8 184.471,-112.8 190.471,-118.8 190.471,-124.8 190.471,-124.8 190.471,-136.8 190.471,-136.8 190.471,-142.8 184.471,-148.8 178.471,-148.8\" fill=\"none\" stroke=\"black\"></path>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"73.7647\" y=\"-127.6\">Nest:NonMotor </text>\n<text fill=\"#999999\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"165.914\" y=\"-127.6\">(7)</text>\n</g>\n\n<g class=\"edge\" id=\"edge1\"><title>0-&gt;7</title>\n<path d=\"M163.724,-257.54C158.199,-247.276 151.425,-233.585 147,-220.8 139.992,-200.551 135.077,-176.778 132.004,-158.898\" fill=\"none\" stroke=\"black\"></path>\n<polygon fill=\"black\" points=\"135.432,-158.173 130.366,-148.867 128.523,-159.301 135.432,-158.173\" stroke=\"black\"></polygon>\n</g>\n\n<g class=\"node\" id=\"node8\"><title>8</title>\n<path d=\"M267.684,-220.8C267.684,-220.8 168.316,-220.8 168.316,-220.8 162.316,-220.8 156.316,-214.8 156.316,-208.8 156.316,-208.8 156.316,-196.8 156.316,-196.8 156.316,-190.8 162.316,-184.8 168.316,-184.8 168.316,-184.8 267.684,-184.8 267.684,-184.8 273.684,-184.8 279.684,-190.8 279.684,-196.8 279.684,-196.8 279.684,-208.8 279.684,-208.8 279.684,-214.8 273.684,-220.8 267.684,-220.8\" fill=\"none\" stroke=\"black\"></path>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"164.158\" y=\"-199.6\">Nest:Motorized </text>\n<text fill=\"#999999\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"255.521\" y=\"-199.6\">(8)</text>\n</g>\n\n<g class=\"edge\" id=\"edge2\"><title>0-&gt;8</title>\n<path d=\"M183.437,-257.565C188.789,-249.24 195.433,-238.904 201.462,-229.525\" fill=\"none\" stroke=\"black\"></path>\n<polygon fill=\"black\" points=\"204.528,-231.228 206.992,-220.924 198.64,-227.443 204.528,-231.228\" stroke=\"black\"></polygon>\n</g>\n\n<g class=\"edge\" id=\"edge5\"><title>7-&gt;3</title>\n<path d=\"M128.247,-112.497C128.357,-104.783 128.49,-95.5125 128.613,-86.9124\" fill=\"none\" stroke=\"black\"></path>\n<polygon fill=\"black\" points=\"132.112,-86.9533 128.756,-76.9043 125.113,-86.8533 132.112,-86.9533\" stroke=\"black\"></polygon>\n</g>\n\n<g class=\"edge\" id=\"edge6\"><title>7-&gt;4</title>\n<path d=\"M107.978,-112.497C97.8744,-103.765 85.464,-93.0405 74.4825,-83.5503\" fill=\"none\" stroke=\"black\"></path>\n<polygon fill=\"black\" points=\"76.6468,-80.7948 66.7922,-76.9043 72.0698,-86.0912 76.6468,-80.7948\" stroke=\"black\"></polygon>\n</g>\n\n<g class=\"edge\" id=\"edge7\"><title>8-&gt;5</title>\n<path d=\"M218,-184.671C218,-160.47 218,-116.011 218,-87.1932\" fill=\"none\" stroke=\"black\"></path>\n<polygon fill=\"black\" points=\"221.5,-86.9892 218,-76.9893 214.5,-86.9893 221.5,-86.9892\" stroke=\"black\"></polygon>\n</g>\n\n<g class=\"node\" id=\"node9\"><title>6</title>\n<path d=\"M332.581,-148.8C332.581,-148.8 271.419,-148.8 271.419,-148.8 265.419,-148.8 259.419,-142.8 259.419,-136.8 259.419,-136.8 259.419,-124.8 259.419,-124.8 259.419,-118.8 265.419,-112.8 271.419,-112.8 271.419,-112.8 332.581,-112.8 332.581,-112.8 338.581,-112.8 344.581,-118.8 344.581,-124.8 344.581,-124.8 344.581,-136.8 344.581,-136.8 344.581,-142.8 338.581,-148.8 332.581,-148.8\" fill=\"none\" stroke=\"black\"></path>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"267.21\" y=\"-127.6\">Nest:Car </text>\n<text fill=\"#999999\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"320.469\" y=\"-127.6\">(6)</text>\n</g>\n\n<g class=\"edge\" id=\"edge8\"><title>8-&gt;6</title>\n<path d=\"M238.764,-184.497C249.241,-175.765 262.111,-165.04 273.5,-155.55\" fill=\"none\" stroke=\"black\"></path>\n<polygon fill=\"black\" points=\"276.033,-157.995 281.475,-148.904 271.552,-152.617 276.033,-157.995\" stroke=\"black\"></polygon>\n</g>\n\n<g class=\"edge\" id=\"edge3\"><title>6-&gt;1</title>\n<path d=\"M302,-112.497C302,-104.783 302,-95.5125 302,-86.9124\" fill=\"none\" stroke=\"black\"></path>\n<polygon fill=\"black\" points=\"305.5,-86.9043 302,-76.9043 298.5,-86.9044 305.5,-86.9043\" stroke=\"black\"></polygon>\n</g>\n\n<g class=\"edge\" id=\"edge4\"><title>6-&gt;2</title>\n<path d=\"M320.045,-112.497C329.062,-103.85 340.118,-93.2486 349.943,-83.827\" fill=\"none\" stroke=\"black\"></path>\n<polygon fill=\"black\" points=\"352.367,-86.3518 357.163,-76.9043 347.522,-81.2993 352.367,-86.3518\" stroke=\"black\"></polygon>\n</g>\n</g>\n</svg></div>"
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"text/plain": " \u001b[32m\u001b[1mLatest Estimation Run Statistics\u001b[0m\n┌────────────────────────────────────┬─────────────────────────────────────────────┐\n│Estimation Date │Tuesday, October 25 2016, 09:31:20 PM │\n│Results │success │\n│Message │Optimization terminated successfully. [SLSQP]│\n│Optimization Method │SLSQP │\n│Number of Iterations │42 │\n│Running Time│Total │1.299 seconds │\n│ │setup │0:00.12 │\n│ │null_likelihood │0:00.01 │\n│ │weight choice rebalance│0:00 │\n│ │weight autorescale │0:00 │\n│ │optimize:SLSQP │0:00.85 │\n│ │parameter covariance │0:00.33 │\n│ │cleanup │0:00 │\n│Notes │did not automatically rescale weights │\n│Computer │george.local │\n│Processor │Intel(R) Core(TM) i5-4258U CPU @ 2.40GHz │\n│Number of CPU Cores │4 │\n│Number of Threads Used │4 │\n│Installed Memory │8.0 GiB │\n│Peak Memory Usage │131.94921875 MiB │\n└────────────────────────────────────┴─────────────────────────────────────────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx42\" reftxt=\"Latest Estimation Run\" toclevel=\"2\"></a><h2>Latest Estimation Run Statistics</h2><div><table><thead></thead><tbody><tr><td colspan=\"2\">Estimation Date</td><div class=\"dummycell\"></div><td>Tuesday, October 25 2016, 09:31:20 PM</td></tr><tr><td colspan=\"2\">Results</td><div class=\"dummycell\"></div><td>success</td></tr><tr><td colspan=\"2\">Message</td><div class=\"dummycell\"></div><td>Optimization terminated successfully. [SLSQP]</td></tr><tr><td colspan=\"2\">Optimization Method</td><div class=\"dummycell\"></div><td>SLSQP</td></tr><tr><td colspan=\"2\">Number of Iterations</td><div class=\"dummycell\"></div><td>42</td></tr><tr><td rowspan=\"8\" style=\"vertical-align:top;\">Running Time</td><td>Total</td><td>1.299\tseconds</td></tr><tr><div class=\"dummycell\"></div><td>setup</td><td>0:00.12</td></tr><tr><div class=\"dummycell\"></div><td>null_likelihood</td><td>0:00.01</td></tr><tr><div class=\"dummycell\"></div><td>weight choice rebalance</td><td>0:00</td></tr><tr><div class=\"dummycell\"></div><td>weight autorescale</td><td>0:00</td></tr><tr><div class=\"dummycell\"></div><td>optimize:SLSQP</td><td>0:00.85</td></tr><tr><div class=\"dummycell\"></div><td>parameter covariance</td><td>0:00.33</td></tr><tr><div class=\"dummycell\"></div><td>cleanup</td><td>0:00</td></tr><tr><td colspan=\"2\">Notes</td><div class=\"dummycell\"></div><td>did not automatically rescale weights</td></tr><tr><td colspan=\"2\">Computer</td><div class=\"dummycell\"></div><td>george.local</td></tr><tr><td colspan=\"2\">Processor</td><div class=\"dummycell\"></div><td>Intel(R) Core(TM) i5-4258U CPU @ 2.40GHz</td></tr><tr><td colspan=\"2\">Number of CPU Cores</td><div class=\"dummycell\"></div><td>4</td></tr><tr><td colspan=\"2\">Number of Threads Used</td><div class=\"dummycell\"></div><td>4</td></tr><tr><td colspan=\"2\">Installed Memory</td><div class=\"dummycell\"></div><td>8.0 GiB</td></tr><tr><td colspan=\"2\">Peak Memory Usage</td><div class=\"dummycell\"></div><td>131.94921875 MiB</td></tr></tbody><tfoot></tfoot></table></div></div>"
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"text/html": "<div class=\"utilityspec\"><a class=\"toc\" name=\"rx43\" reftxt=\"Utility Specification\" toclevel=\"2\"></a><h2>Utility Specification</h2><a class=\"toc\" name=\"rx44\" reftxt=\"Resolved Utility\" toclevel=\"3\"></a><h3>Resolved Utility</h3><table class=\"floatinghead\"><thead><tr><th>Code</th><th>Alternative</th><th>Resolved Utility</th></tr></thead><tbody><tr><td>1</td><td>DA</td><td>- 0.1127*AUTO_TIME<br>- 0.3407*CARCOST</td></tr><tr><td>2</td><td>SR</td><td>- 1.394<br>- 0.1127*AUTO_TIME<br>- 0.3407*CARCOST*(0.5)<br>- 1.756*INCOME&gt;75000</td></tr><tr><td>3</td><td>Walk</td><td>  0.7318<br>- 0.2255*WALKTIME<br>- 0.7958*INCOME&gt;75000</td></tr><tr><td>4</td><td>Bike</td><td>- 1.667<br>- 0.2255*BIKETIME<br>- 1.053*INCOME&gt;75000</td></tr><tr><td>5</td><td>Transit</td><td>- 1.677<br>- 0.1127*RAIL_TIME<br>- 0.3407*RAIL_FARE<br>- 1.241*INCOME&gt;75000</td></tr><tr><th>Code</th><th>Nest</th><th>Resolved Utility</th></tr><tr><td>6</td><td>Nest:Car</td><td>0.7417 * log( exp(Utility[DA]/0.7417) + exp(Utility[SR]/0.7417) )</td></tr><tr><td>7</td><td>Nest:NonMotor</td><td>0.8697 * log( exp(Utility[Walk]/0.8697) + exp(Utility[Bike]/0.8697) )</td></tr><tr><td>8</td><td>Nest:Motorized</td><td>0.8444 * log( exp(Utility[Transit]/0.8444) + exp(Utility[Nest:Car]/0.8444) )</td></tr><tr><td>0</td><td>ROOT</td><td>log( exp(Utility[Nest:Motorized]) + exp(Utility[Nest:NonMotor]) )</td></tr></tbody></table><a class=\"toc\" name=\"rx45\" reftxt=\"Formulaic Utility\" toclevel=\"3\"></a><h3>Formulaic Utility</h3><table class=\"floatinghead\"><thead><tr><th>Code</th><th>Alternative</th><th>Formulaic Utility</th></tr></thead><tbody><tr><td>1</td><td>DA</td><td>  <a class=\"parameter_reference\" href=\"#paramInVehTime\">InVehTime</a>*AUTO_TIME<br>+ <a class=\"parameter_reference\" href=\"#paramCost\">Cost</a>*CARCOST</td></tr><tr><td>2</td><td>SR</td><td>  <a class=\"parameter_reference\" href=\"#paramASC_SR\">ASC_SR</a><br>+ <a class=\"parameter_reference\" href=\"#paramInVehTime\">InVehTime</a>*AUTO_TIME<br>+ <a class=\"parameter_reference\" href=\"#paramCost\">Cost</a>*CARCOST*(0.5)<br>+ <a class=\"parameter_reference\" href=\"#paramHighInc:SR\">HighInc:SR</a>*INCOME&gt;75000</td></tr><tr><td>3</td><td>Walk</td><td>  <a class=\"parameter_reference\" href=\"#paramASC_Walk\">ASC_Walk</a><br>+ <a class=\"parameter_reference\" href=\"#paramNonMotorTime\">NonMotorTime</a>*WALKTIME<br>+ <a class=\"parameter_reference\" href=\"#paramHighInc:Walk\">HighInc:Walk</a>*INCOME&gt;75000</td></tr><tr><td>4</td><td>Bike</td><td>  <a class=\"parameter_reference\" href=\"#paramASC_Bike\">ASC_Bike</a><br>+ <a class=\"parameter_reference\" href=\"#paramNonMotorTime\">NonMotorTime</a>*BIKETIME<br>+ <a class=\"parameter_reference\" href=\"#paramHighInc:Bike\">HighInc:Bike</a>*INCOME&gt;75000</td></tr><tr><td>5</td><td>Transit</td><td>  <a class=\"parameter_reference\" href=\"#paramASC_Transit\">ASC_Transit</a><br>+ <a class=\"parameter_reference\" href=\"#paramInVehTime\">InVehTime</a>*RAIL_TIME<br>+ <a class=\"parameter_reference\" href=\"#paramCost\">Cost</a>*RAIL_FARE<br>+ <a class=\"parameter_reference\" href=\"#paramHighInc:Transit\">HighInc:Transit</a>*INCOME&gt;75000</td></tr><tr><th>Code</th><th>Nest</th><th>Formulaic Utility</th></tr><tr><td>6</td><td>Nest:Car</td><td><a class=\"parameter_reference\" href=\"#paramNest:Car\">Nest:Car</a> * log( exp(Utility[DA]/<a class=\"parameter_reference\" href=\"#paramNest:Car\">Nest:Car</a>) + exp(Utility[SR]/<a class=\"parameter_reference\" href=\"#paramNest:Car\">Nest:Car</a>) )</td></tr><tr><td>7</td><td>Nest:NonMotor</td><td><a class=\"parameter_reference\" href=\"#paramNest:NonMotor\">Nest:NonMotor</a> * log( exp(Utility[Walk]/<a class=\"parameter_reference\" href=\"#paramNest:NonMotor\">Nest:NonMotor</a>) + exp(Utility[Bike]/<a class=\"parameter_reference\" href=\"#paramNest:NonMotor\">Nest:NonMotor</a>) )</td></tr><tr><td>8</td><td>Nest:Motorized</td><td><a class=\"parameter_reference\" href=\"#paramNest:Motorized\">Nest:Motorized</a> * log( exp(Utility[Transit]/<a class=\"parameter_reference\" href=\"#paramNest:Motorized\">Nest:Motorized</a>) + exp(Utility[Nest:Car]/<a class=\"parameter_reference\" href=\"#paramNest:Motorized\">Nest:Motorized</a>) )</td></tr><tr><td>0</td><td>ROOT</td><td>log( exp(Utility[Nest:Motorized]) + exp(Utility[Nest:NonMotor]) )</td></tr></tbody></table></div>"
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"text/html": "<div class=\"probabilityspec\"><a class=\"toc\" name=\"rx46\" reftxt=\"Probability Specification\" toclevel=\"2\"></a><h2>Probability Specification</h2><a class=\"toc\" name=\"rx47\" reftxt=\"Resolved Probability\" toclevel=\"3\"></a><h3>Resolved Probability</h3><table class=\"floatinghead\"><thead><tr><th>Code</th><th>Alternative</th><th>Resolved Probability</th></tr></thead><tbody><tr><td>1</td><td>DA</td><td>exp(Utility[DA]/0.7417)/exp(Utility[Nest:Car]/0.7417) * exp(Utility[Nest:Car]/0.8444)/exp(Utility[Nest:Motorized]/0.8444) * exp(Utility[Nest:Motorized])/exp(Utility[ROOT])</td></tr><tr><td>2</td><td>SR</td><td>exp(Utility[SR]/0.7417)/exp(Utility[Nest:Car]/0.7417) * exp(Utility[Nest:Car]/0.8444)/exp(Utility[Nest:Motorized]/0.8444) * exp(Utility[Nest:Motorized])/exp(Utility[ROOT])</td></tr><tr><td>3</td><td>Walk</td><td>exp(Utility[Walk]/0.8697)/exp(Utility[Nest:NonMotor]/0.8697) * exp(Utility[Nest:NonMotor])/exp(Utility[ROOT])</td></tr><tr><td>4</td><td>Bike</td><td>exp(Utility[Bike]/0.8697)/exp(Utility[Nest:NonMotor]/0.8697) * exp(Utility[Nest:NonMotor])/exp(Utility[ROOT])</td></tr><tr><td>5</td><td>Transit</td><td>exp(Utility[Transit]/0.8444)/exp(Utility[Nest:Motorized]/0.8444) * exp(Utility[Nest:Motorized])/exp(Utility[ROOT])</td></tr></tbody></table><a class=\"toc\" name=\"rx48\" reftxt=\"Formulaic Probability\" toclevel=\"3\"></a><h3>Formulaic Probability</h3><table class=\"floatinghead\"><thead><tr><th>Code</th><th>Alternative</th><th>Formulaic Probability</th></tr></thead><tbody><tr><td>1</td><td>DA</td><td>exp(Utility[DA]/<a class=\"parameter_reference\" href=\"#paramNest:Car\">Nest:Car</a>)/exp(Utility[Nest:Car]/<a class=\"parameter_reference\" href=\"#paramNest:Car\">Nest:Car</a>) * exp(Utility[Nest:Car]/<a class=\"parameter_reference\" href=\"#paramNest:Motorized\">Nest:Motorized</a>)/exp(Utility[Nest:Motorized]/<a class=\"parameter_reference\" href=\"#paramNest:Motorized\">Nest:Motorized</a>) * exp(Utility[Nest:Motorized])/exp(Utility[ROOT])</td></tr><tr><td>2</td><td>SR</td><td>exp(Utility[SR]/<a class=\"parameter_reference\" href=\"#paramNest:Car\">Nest:Car</a>)/exp(Utility[Nest:Car]/<a class=\"parameter_reference\" href=\"#paramNest:Car\">Nest:Car</a>) * exp(Utility[Nest:Car]/<a class=\"parameter_reference\" href=\"#paramNest:Motorized\">Nest:Motorized</a>)/exp(Utility[Nest:Motorized]/<a class=\"parameter_reference\" href=\"#paramNest:Motorized\">Nest:Motorized</a>) * exp(Utility[Nest:Motorized])/exp(Utility[ROOT])</td></tr><tr><td>3</td><td>Walk</td><td>exp(Utility[Walk]/<a class=\"parameter_reference\" href=\"#paramNest:NonMotor\">Nest:NonMotor</a>)/exp(Utility[Nest:NonMotor]/<a class=\"parameter_reference\" href=\"#paramNest:NonMotor\">Nest:NonMotor</a>) * exp(Utility[Nest:NonMotor])/exp(Utility[ROOT])</td></tr><tr><td>4</td><td>Bike</td><td>exp(Utility[Bike]/<a class=\"parameter_reference\" href=\"#paramNest:NonMotor\">Nest:NonMotor</a>)/exp(Utility[Nest:NonMotor]/<a class=\"parameter_reference\" href=\"#paramNest:NonMotor\">Nest:NonMotor</a>) * exp(Utility[Nest:NonMotor])/exp(Utility[ROOT])</td></tr><tr><td>5</td><td>Transit</td><td>exp(Utility[Transit]/<a class=\"parameter_reference\" href=\"#paramNest:Motorized\">Nest:Motorized</a>)/exp(Utility[Nest:Motorized]/<a class=\"parameter_reference\" href=\"#paramNest:Motorized\">Nest:Motorized</a>) * exp(Utility[Nest:Motorized])/exp(Utility[ROOT])</td></tr></tbody></table></div>"
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"text/html": "<div class=\"data_statistics\"><a class=\"toc\" name=\"rx49\" reftxt=\"Choice and Availability\" toclevel=\"2\"></a><h2>Choice and Availability</h2><table><thead><tr><th>Code</th><th>Alternative</th><th># Avail</th><th># Chosen</th><th>Availability Condition</th></tr></thead><tbody><tr><td>1</td><td>DA </td><td>1897 </td><td>1573 </td><td>AGE&gt;=16</td></tr><tr><td>2</td><td>SR </td><td>1897 </td><td>171 </td><td>1</td></tr><tr><td>3</td><td>Walk </td><td>662 </td><td>47 </td><td>DIST&lt;=3</td></tr><tr><td>4</td><td>Bike </td><td>1882 </td><td>28 </td><td>DIST&lt;=15</td></tr><tr><td>5</td><td>Transit </td><td>1245 </td><td>78 </td><td>RAIL_TIME&gt;0</td></tr></tbody></table></div>"
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"text/plain": " \u001b[32m\u001b[1mExcluded Cases\u001b[0m\n┌─┬─────────────┬───────────┬────────────────┬─────────────────┐\n│ │Criteria │Data Source│# Cases Excluded│# Cases Remaining│\n├─┼─────────────┼───────────┼────────────────┼─────────────────┤\n│0│TOURPURP != 1│idco │4226 │1897 │\n└─┴─────────────┴───────────┴────────────────┴─────────────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx50\" reftxt=\"Excluded Cases\" toclevel=\"2\"></a><h2>Excluded Cases</h2><div><table><thead><tr><th> </th><th>Criteria</th><th>Data Source</th><th># Cases Excluded</th><th># Cases Remaining</th></tr></thead><tbody><tr><td>0</td><td>TOURPURP != 1</td><td>idco</td><td>4226</td><td>1897</td></tr></tbody><tfoot></tfoot></table></div></div>"
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"text/html": "<div class=\"options\"><a class=\"toc\" name=\"rx51\" reftxt=\"Options\" toclevel=\"2\"></a><h2>Options</h2><table><tr><td>author</td><td>jpn</td></tr><tr><td>autocreate_parameters</td><td>True</td></tr><tr><td>calc_null_likelihood</td><td>True</td></tr><tr><td>calc_std_errors</td><td>True</td></tr><tr><td>enforce_bounds</td><td>True</td></tr><tr><td>enforce_constraints</td><td>True</td></tr><tr><td>enforce_network_constraints</td><td>False</td></tr><tr><td>force_finite_diff_grad</td><td>False</td></tr><tr><td>force_recalculate</td><td>False</td></tr><tr><td>gradient_diagnostic</td><td>0</td></tr><tr><td>hessian_diagnostic</td><td>0</td></tr><tr><td>idca_avail_ratio_floor</td><td>0.1</td></tr><tr><td>ignore_bad_constraints</td><td>False</td></tr><tr><td>log_turns</td><td>False</td></tr><tr><td>mute_nan_warnings</td><td>True</td></tr><tr><td>null_disregards_holdfast</td><td>True</td></tr><tr><td>save_db_hash</td><td>False</td></tr><tr><td>suspend_xylem_rebuild</td><td>False</td></tr><tr><td>teardown_after_estimate</td><td>True</td></tr><tr><td>threads</td><td>4</td></tr><tr><td>weight_autorescale</td><td>True</td></tr><tr><td>weight_choice_rebalance</td><td>True</td></tr></table></div>"
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"text/plain": " \u001b[32m\u001b[1mChoice Distributions by Data Attributes\u001b[0m\n┌─────────────┬───────────┬──────┬──────┬──────┬─────┬───────┬──────┐\n│Variable │Value[s] │DA │SR │Walk │Bike │Transit│Count │\n├─────────────┼───────────┼──────┼──────┼──────┼─────┼───────┼──────┤\n│AUTO_TIME │> 8.6 │83.31%│10.81%│0.00% │0.16%│5.72% │629.0 │\n│ │4.1 to 8.6 │86.88%│9.72% │0.00% │0.77%│2.62% │648.0 │\n│ │< 4.1 │78.39%│6.45% │7.58% │3.55%│4.03% │620.0 │\n│CARCOST │> 1.3 │84.31%│11.89%│0.00% │0.00%│3.80% │631.0 │\n│ │0.39 to 1.3│86.80%│8.50% │0.00% │0.61%│4.10% │659.0 │\n│ │< 0.39 │77.27%│6.59% │7.74% │3.95%│4.45% │607.0 │\n│CARCOST*(0.5)│> 0.64 │84.31%│11.89%│0.00% │0.00%│3.80% │631.0 │\n│ │0.2 to 0.64│86.80%│8.50% │0.00% │0.61%│4.10% │659.0 │\n│ │< 0.2 │77.27%│6.59% │7.74% │3.95%│4.45% │607.0 │\n│INCOME>75000 │Yes │94.20%│1.73% │1.60% │0.74%│1.73% │810.0 │\n│ │No │74.52%│14.44%│3.13% │2.02%│5.89% │1087.0│\n│WALKTIME │> 37 │85.78%│7.80% │0.00% │2.29%│4.13% │218.0 │\n│ │23 to 37 │84.82%│8.48% │0.45% │2.23%│4.02% │224.0 │\n│ │< 23 │63.64%│4.55% │20.91%│6.36%│4.55% │220.0 │\n│ │= 0 │85.51%│10.12%│0.00% │0.32%│4.05% │1235.0│\n│BIKETIME │> 31 │84.22%│12.08%│0.00% │0.00%│3.70% │621.0 │\n│ │9.8 to 31 │86.70%│8.56% │0.00% │0.61%│4.13% │654.0 │\n│ │< 9.8 │77.27%│6.59% │7.74% │3.95%│4.45% │607.0 │\n│ │= 0 │93.33%│0.00% │0.00% │0.00%│6.67% │15.0 │\n│RAIL_TIME │> 7.7 │85.17%│7.93% │0.00% │0.00%│6.91% │391.0 │\n│ │2.6 to 7.7 │83.00%│8.83% │0.00% │1.32%│6.84% │453.0 │\n│ │< 2.6 │75.56%│6.23% │8.73% │4.49%│4.99% │401.0 │\n│ │= 0 │86.04%│11.50%│1.84% │0.61%│0.00% │652.0 │\n│RAIL_FARE │0.0 │86.04%│11.50%│1.84% │0.61%│0.00% │652.0 │\n│ │1.5 │81.29%│7.71% │2.81% │1.93%│6.27% │1245.0│\n└─────────────┴───────────┴──────┴──────┴──────┴─────┴───────┴──────┘",
"text/html": "<div><a class=\"toc\" name=\"rx52\" reftxt=\"Choice Distributions\" toclevel=\"2\"></a><h2>Choice Distributions by Data Attributes</h2><div><table><thead><tr><th>Variable</th><th>Value[s]</th><th>DA</th><th>SR</th><th>Walk</th><th>Bike</th><th>Transit</th><th>Count</th></tr></thead><tbody><tr><td rowspan=\"3\" style=\"vertical-align:top;\">AUTO_TIME</td><td>&gt; 8.6</td><td>83.31%</td><td>10.81%</td><td>0.00%</td><td>0.16%</td><td>5.72%</td><td>629.0</td></tr><tr><div class=\"dummycell\"></div><td>4.1 to 8.6</td><td>86.88%</td><td>9.72%</td><td>0.00%</td><td>0.77%</td><td>2.62%</td><td>648.0</td></tr><tr><div class=\"dummycell\"></div><td>&lt; 4.1</td><td>78.39%</td><td>6.45%</td><td>7.58%</td><td>3.55%</td><td>4.03%</td><td>620.0</td></tr><tr><td rowspan=\"3\" style=\"vertical-align:top;\">CARCOST</td><td>&gt; 1.3</td><td>84.31%</td><td>11.89%</td><td>0.00%</td><td>0.00%</td><td>3.80%</td><td>631.0</td></tr><tr><div class=\"dummycell\"></div><td>0.39 to 1.3</td><td>86.80%</td><td>8.50%</td><td>0.00%</td><td>0.61%</td><td>4.10%</td><td>659.0</td></tr><tr><div class=\"dummycell\"></div><td>&lt; 0.39</td><td>77.27%</td><td>6.59%</td><td>7.74%</td><td>3.95%</td><td>4.45%</td><td>607.0</td></tr><tr><td rowspan=\"3\" style=\"vertical-align:top;\">CARCOST*(0.5)</td><td>&gt; 0.64</td><td>84.31%</td><td>11.89%</td><td>0.00%</td><td>0.00%</td><td>3.80%</td><td>631.0</td></tr><tr><div class=\"dummycell\"></div><td>0.2 to 0.64</td><td>86.80%</td><td>8.50%</td><td>0.00%</td><td>0.61%</td><td>4.10%</td><td>659.0</td></tr><tr><div class=\"dummycell\"></div><td>&lt; 0.2</td><td>77.27%</td><td>6.59%</td><td>7.74%</td><td>3.95%</td><td>4.45%</td><td>607.0</td></tr><tr><td rowspan=\"2\" style=\"vertical-align:top;\">INCOME&gt;75000</td><td>Yes</td><td>94.20%</td><td>1.73%</td><td>1.60%</td><td>0.74%</td><td>1.73%</td><td>810.0</td></tr><tr><div class=\"dummycell\"></div><td>No</td><td>74.52%</td><td>14.44%</td><td>3.13%</td><td>2.02%</td><td>5.89%</td><td>1087.0</td></tr><tr><td rowspan=\"4\" style=\"vertical-align:top;\">WALKTIME</td><td>&gt; 37</td><td>85.78%</td><td>7.80%</td><td>0.00%</td><td>2.29%</td><td>4.13%</td><td>218.0</td></tr><tr><div class=\"dummycell\"></div><td>23 to 37</td><td>84.82%</td><td>8.48%</td><td>0.45%</td><td>2.23%</td><td>4.02%</td><td>224.0</td></tr><tr><div class=\"dummycell\"></div><td>&lt; 23</td><td>63.64%</td><td>4.55%</td><td>20.91%</td><td>6.36%</td><td>4.55%</td><td>220.0</td></tr><tr><div class=\"dummycell\"></div><td>= 0</td><td>85.51%</td><td>10.12%</td><td>0.00%</td><td>0.32%</td><td>4.05%</td><td>1235.0</td></tr><tr><td rowspan=\"4\" style=\"vertical-align:top;\">BIKETIME</td><td>&gt; 31</td><td>84.22%</td><td>12.08%</td><td>0.00%</td><td>0.00%</td><td>3.70%</td><td>621.0</td></tr><tr><div class=\"dummycell\"></div><td>9.8 to 31</td><td>86.70%</td><td>8.56%</td><td>0.00%</td><td>0.61%</td><td>4.13%</td><td>654.0</td></tr><tr><div class=\"dummycell\"></div><td>&lt; 9.8</td><td>77.27%</td><td>6.59%</td><td>7.74%</td><td>3.95%</td><td>4.45%</td><td>607.0</td></tr><tr><div class=\"dummycell\"></div><td>= 0</td><td>93.33%</td><td>0.00%</td><td>0.00%</td><td>0.00%</td><td>6.67%</td><td>15.0</td></tr><tr><td rowspan=\"4\" style=\"vertical-align:top;\">RAIL_TIME</td><td>&gt; 7.7</td><td>85.17%</td><td>7.93%</td><td>0.00%</td><td>0.00%</td><td>6.91%</td><td>391.0</td></tr><tr><div class=\"dummycell\"></div><td>2.6 to 7.7</td><td>83.00%</td><td>8.83%</td><td>0.00%</td><td>1.32%</td><td>6.84%</td><td>453.0</td></tr><tr><div class=\"dummycell\"></div><td>&lt; 2.6</td><td>75.56%</td><td>6.23%</td><td>8.73%</td><td>4.49%</td><td>4.99%</td><td>401.0</td></tr><tr><div class=\"dummycell\"></div><td>= 0</td><td>86.04%</td><td>11.50%</td><td>1.84%</td><td>0.61%</td><td>0.00%</td><td>652.0</td></tr><tr><td rowspan=\"2\" style=\"vertical-align:top;\">RAIL_FARE</td><td>0.0</td><td>86.04%</td><td>11.50%</td><td>1.84%</td><td>0.61%</td><td>0.00%</td><td>652.0</td></tr><tr><div class=\"dummycell\"></div><td>1.5</td><td>81.29%</td><td>7.71%</td><td>2.81%</td><td>1.93%</td><td>6.27%</td><td>1245.0</td></tr></tbody><tfoot></tfoot></table></div></div>"
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"text/plain": " \u001b[32m\u001b[1mUtility idCO Data\u001b[0m\n┌─────────────┬───────┬────────┬─────────┬───────┬─────┬─────────────┬─────────┬────────────┐\n│Data │Mean │Std.Dev.│Minimum │Maximum│Zeros│Mean(NonZero)│Positives│Distribution│\n├─────────────┼───────┼────────┼─────────┼───────┼─────┼─────────────┼─────────┼────────────┤\n│AUTO_TIME │7.7198 │5.637 │2 │49.678 │0 │7.7198 │1897 │ │\n│CARCOST │0.97796│0.71928 │0.0040437│3.621 │0 │0.97796 │1897 │ │\n│1 │1 │0 │1 │1 │0 │1 │1897 │ │\n│CARCOST*(0.5)│0.48898│0.35964 │0.0020218│1.8105 │0 │0.48898 │1897 │ │\n│INCOME>75000 │0.42699│0.49464 │0 │1 │1087 │1 │810 │ │\n│WALKTIME │10.386 │17.018 │0 │71.245 │1235 │29.763 │662 │ │\n│BIKETIME │23.819 │17.419 │0 │73.871 │15 │24.009 │1882 │ │\n│RAIL_TIME │4.1426 │5.4032 │0 │28.821 │652 │6.312 │1245 │ │\n│RAIL_FARE │0.98445│0.71241 │0 │1.5 │652 │1.5 │1245 │ │\n└─────────────┴───────┴────────┴─────────┴───────┴─────┴─────────────┴─────────┴────────────┘\n \u001b[36mGraphs are represented as pie charts if the data element has 4 or fewer distinct values.\u001b[0m\n \u001b[36mHistograms are green if the displayed range truncates some extreme outliers.\u001b[0m\n \u001b[36mHistograms are orange if the zeros are numerous and have been excluded.\u001b[0m\n\n \u001b[32m\u001b[1mUtility idCO Data by Alternative\u001b[0m\n┌───────────┬─────────────┬────────┬────────┬────────┬────────────────────┬──────────────────┬───────────────┬───────┬───────────┬────────────┐\n│Alternative│Data │Filter │Mean │Std.Dev.│Minimum │Maximum │Mean (Nonzeros)│# Zeros│# Positives│Distribution│\n├───────────┼─────────────┼────────┼────────┼────────┼────────────────────┼──────────────────┼───────────────┼───────┼───────────┼────────────┤\n│DA │AUTO_TIME │Chosen │7.8347 │5.607 │2.0 │49.67825818294802 │7.8347 │0 │1573 │ │\n│ │ │Unchosen│7.1621 │5.7478 │2.0 │33.84280990076548 │7.1621 │0 │324 │ │\n│ │CARCOST │Chosen │1.0037 │0.70675 │0.004043679488065144│3.6210315487992397│1.0037 │0 │1573 │ │\n│ │ │Unchosen│0.85277 │0.765 │0.004043679488065144│3.02732395233208 │0.85277 │0 │324 │ │\n│SR │AUTO_TIME │Chosen │8.4195 │5.3067 │2.0 │31.692757725069843│8.4195 │0 │171 │ │\n│ │ │Unchosen│7.6505 │5.664 │2.0 │49.67825818294802 │7.6505 │0 │1726 │ │\n│ │CARCOST*(0.5)│Chosen │0.55414 │0.3506 │0.011827442586893323│1.4774221314319649│0.55414 │0 │171 │ │\n│ │ │Unchosen│0.48253 │0.35988 │0.002021839744032572│1.8105157743996199│0.48253 │0 │1726 │ │\n│ │INCOME>75000 │Chosen │0.081871│0.27417 │0.0 │1.0 │1 │157 │14 │ │\n│ │ │Unchosen│0.46118 │0.49849 │0.0 │1.0 │1 │930 │796 │ │\n│Walk │INCOME>75000 │Chosen │0.2766 │0.44731 │0.0 │1.0 │1 │34 │13 │ │\n│ │ │Unchosen│0.40325 │0.49055 │0.0 │1.0 │1 │367 │248 │ │\n│ │WALKTIME │Chosen │3.8568 │4.4628 │0.4852415385678172 │23.486840213586337│3.8568 │0 │47 │ │\n│ │ │Unchosen│31.743 │14.69 │0.4852415385678172 │71.24491157605924 │31.743 │0 │615 │ │\n│Bike │INCOME>75000 │Chosen │0.21429 │0.41033 │0.0 │1.0 │1 │22 │6 │ │\n│ │ │Unchosen│0.43042 │0.49514 │0.0 │1.0 │1 │1056 │798 │ │\n│ │BIKETIME │Chosen │7.2267 │6.8186 │0.1010919872016286 │25.112680595697487│7.2267 │0 │28 │ │\n│ │ │Unchosen│24.263 │17.344 │0.1010919872016286 │73.87110657159823 │24.263 │0 │1854 │ │\n│Transit │INCOME>75000 │Chosen │0.17949 │0.38376 │0.0 │1.0 │1 │64 │14 │ │\n│ │ │Unchosen│0.43702 │0.49602 │0.0 │1.0 │1 │657 │510 │ │\n│ │RAIL_TIME │Chosen │7.1633 │5.8676 │0.17160166627168116 │28.820795984663313│7.1633 │0 │78 │ │\n│ │ │Unchosen│6.2551 │5.5223 │0.17160166627168116 │28.820795984663313│6.2551 │0 │1167 │ │\n│ │RAIL_FARE │Chosen │1.5 │0 │1.5 │1.5 │1.5 │0 │78 │ │\n│ │ │Unchosen│1.5 │0 │1.5 │1.5 │1.5 │0 │1167 │ │\n└───────────┴─────────────┴────────┴────────┴────────┴────────────────────┴──────────────────┴───────────────┴───────┴───────────┴────────────┘\n \u001b[36mGraphs are represented as pie charts if the data element has 4 or fewer distinct values.\u001b[0m\n \u001b[36mHistograms are green if the displayed range truncates some extreme outliers.\u001b[0m\n \u001b[36mHistograms are orange if the zeros are numerous and have been excluded.\u001b[0m",
"text/html": "<div xmlns=\"http://www.w3.org/2000/svg\"><a class=\"toc\" name=\"rx53\" reftxt=\"Data Stats\" toclevel=\"2\"></a><h2>Various Data Statistics</h2><div><div><a class=\"toc\" name=\"rx54\" reftxt=\"Utility idCO Data\" toclevel=\"3\"></a><h3>Utility idCO Data</h3><div><table><thead><tr><th>Data</th><th>Mean</th><th>Std.Dev.</th><th>Minimum</th><th>Maximum</th><th>Zeros</th><th>Mean(NonZero)</th><th>Positives</th><th>Distribution</th></tr></thead><tbody><tr><td>AUTO_TIME</td><td>7.7198</td><td>5.637</td><td>2</td><td>49.678</td><td>0</td><td>7.7198</td><td>1897</td><td class=\"figure\"><svg height=\"14pt\" version=\"1.1\" viewBox=\"0 0 54 14\" width=\"54pt\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;stroke-miterlimit:100000;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 14.4 L 54 14.4 L 54 0 L 0 0 z \" style=\"fill:#ffffff;\"></path>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path 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reftxt=\"Utility idCO Data by Alternative\" toclevel=\"3\"></a><h3>Utility idCO Data by Alternative</h3><div><table><thead><tr><th>Alternative</th><th>Data</th><th>Filter</th><th>Mean</th><th>Std.Dev.</th><th>Minimum</th><th>Maximum</th><th>Mean (Nonzeros)</th><th># Zeros</th><th># Positives</th><th>Distribution</th></tr></thead><tbody><tr><td rowspan=\"4\" style=\"vertical-align:top;\">DA</td><td>AUTO_TIME</td><td>Chosen</td><td>7.8347</td><td>5.607</td><td>2.0</td><td>49.67825818294802</td><td>7.8347</td><td>0</td><td>1573</td><td class=\"figure\"><svg height=\"14pt\" version=\"1.1\" viewBox=\"0 0 54 14\" width=\"54pt\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;stroke-miterlimit:100000;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 14.4 L 54 14.4 L 54 0 L 0 0 z \" style=\"fill:#ffffff;\"></path>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path clip-path=\"url(#p3b30b048a6)\" d=\"M 3.6 14.4 L 6.091956 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25.118203 14.213758 26.054505 14.4 27 14.4 C 27.945495 14.4 28.881797 14.213758 29.755321 13.851933 C 30.628845 13.490107 31.422603 12.959735 32.091169 12.291169 C 32.759735 11.622603 33.290107 10.828845 33.651933 9.955321 C 34.013758 9.081797 34.2 8.145495 34.2 7.2 M 27 7.2 M 34.2 7.2 z \" style=\"fill:#23c0f1;\"></path>\n </g>\n <g id=\"matplotlib.axis_1\"></g>\n <g id=\"matplotlib.axis_2\"></g>\n </g>\n </g>\n</svg></td></tr><tr><div class=\"dummycell\"></div><td></td><td>Unchosen</td><td>1.5</td><td>0</td><td>1.5</td><td>1.5</td><td>1.5</td><td>0</td><td>1167</td><td class=\"figure\"><svg height=\"14pt\" version=\"1.1\" viewBox=\"0 0 54 14\" width=\"54pt\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;stroke-miterlimit:100000;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 14.4 L 54 14.4 L 54 0 L 0 0 z \" style=\"fill:#ffffff;\"></path>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path d=\"M 34.2 7.2 C 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id=\"matplotlib.axis_2\"></g>\n </g>\n </g>\n</svg></td></tr></tbody><tfoot></tfoot><caption>Graphs are represented as pie charts if the data element has 4 or fewer distinct values.<br>Histograms are green if the displayed range truncates some extreme outliers.<br>Histograms are orange if the zeros are numerous and have been excluded.</caption></table></div></div></div></div>"
},
"output_type": "display_data"
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"metadata": {
"trusted": true,
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"cell_type": "code",
"source": "## Saving the model\nm_filename = os.path.join(directory, 'exampville_workmodechoice_v1.larchmodel')\nm.save(m_filename)\nm_filename",
"execution_count": 11,
"outputs": [
{
"execution_count": 11,
"data": {
"text/plain": "'/tmp/exampville_1/exampville_workmodechoice_v1.larchmodel'"
},
"metadata": {},
"output_type": "execute_result"
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"cell_type": "code",
"source": "m2 = larch.Model.load(m_filename)\nprint(m2)",
"execution_count": 12,
"outputs": [
{
"text": "============================================================================================\nExampville Work Tour Mode Choice v1\n============================================================================================\nModel Parameter Estimates\n--------------------------------------------------------------------------------------------\nParameter \tInitValue \tFinalValue \tStdError \tt-Stat \tNullValue \n~~ Level of Service ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nInVehTime \t 0 \t-0.112743 \t 0.0135618 \t-8.31328 \t 0 \nCost \t 0 \t-0.340739 \t 0.181214 \t-1.88032 \t 0 \n~~ Alternative Specific Constants ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nASC_SR \t 0 \t-1.39432 \t 0.954061 \t-1.46145 \t 0 \nASC_Walk \t 0 \t 0.731772 \t 0.301454 \t 2.42747 \t 0 \nASC_Bike \t 0 \t-1.66675 \t 0.273898 \t-6.08529 \t 0 \nASC_Transit \t 0 \t-1.67707 \t 0.497688 \t-3.36972 \t 0 \n~~ Income ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nHighInc:SR \t 0 \t-1.75624 \t 1.28965 \t-1.3618 \t 0 \nHighInc:Walk \t 0 \t-0.795808 \t 0.418918 \t-1.89967 \t 0 \nHighInc:Bike \t 0 \t-1.05259 \t 0.463062 \t-2.27312 \t 0 \nHighInc:Transit\t 0 \t-1.24099 \t 0.450795 \t-2.7529 \t 0 \n~~ Logsum Parameters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nNest:Car \t 1 \t 0.741703 \t 0.538446 \t-0.479709 \t 1 \nNest:NonMotor \t 1 \t 0.869697 \t 0.183454 \t-0.710277 \t 1 \nNest:Motorized \t 1 \t 0.844398 \t 0.248671 \t-0.625735 \t 1 \n============================================================================================\nModel Estimation Statistics\n--------------------------------------------------------------------------------------------\nLog Likelihood at Convergence \t-963.84\nLog Likelihood at Null Parameters \t-2592.70\n--------------------------------------------------------------------------------------------\nRho Squared w.r.t. Null Parameters\t0.628\n============================================================================================\nLatest Estimation Run Statistics\n--------------------------------------------------------------------------------------------\nNumber of Iterations \t42\nRunning Time \t1.301\tseconds\n- setup \t0:00.12\n- null_likelihood \t0:00.01\n- weight choice rebalance\t0:00\n- weight autorescale \t0:00\n- optimize:SLSQP \t0:00.85\n- parameter covariance \t0:00.33\n- cleanup \t0:00\nNotes \tdid not automatically rescale weights\nResults \tsuccess\n============================================================================================\n",
"output_type": "stream",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Building the Mode Choice Logsums"
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"source": "flog = larch.logging.flogger('modelogsum')\n\n# First create a new blank array for the logsums.\nd.new_idca('MODECHOICELOGSUM', numpy.zeros([d.nAllCases(), nZones], dtype=numpy.float32))\nscreen_idx = d.get_screen_indexes()\n\nm.setUp()\n# If the model was just estimated, this is unnecessary; otherwise this is necessary\n# to allocate the needed arrays for storing data and results.\n\n# Loop over all TAZ indexes based on the number of zones\nfor dtazi in range(nZones):\n # First we pull in replacement data from the skims,\n # replacing the data for the actually chosen destination with alternative\n # data from the proposed destination\n flog('pull data for DTAZi {}',dtazi)\n d.pluck_into_idco(omx, \"HOMETAZi\", dtazi, overwrite=True)\n \n # We need to refresh these derived variables too, as they are derived\n # from other data columns we just overloaded...\n d.new_idco(\"WALKTIME\", \"DIST / 2.5 * 60 * (DIST<=3)\", overwrite=True) # 2.5 mph, 60 minutes per hour, max 3 miles\n d.new_idco(\"BIKETIME\", \"DIST / 12 * 60 * (DIST<=15)\", overwrite=True) # 12 mph, 60 minutes per hour, max 15 miles\n d.new_idco(\"CARCOST\", \"DIST * 0.20\", overwrite=True) # 20 cents per mile\n \n # Then we will load the relevant data into the Model\n flog('load data for DTAZi {}',dtazi)\n m.provision()\n \n # Then we calculate and extract the logsums using the model and store\n # them in the correct indexes of the relevant column in the output array\n flog('calculate logsums DTAZi {}',dtazi)\n s = m.logsums()\n flog('write logsums DTAZi {}',dtazi)\n d.idca.MODECHOICELOGSUM[screen_idx, dtazi] = s\n\n",
"execution_count": 13,
"outputs": [
{
"text": "[09:32:12 PM]larch.Model: Model.setUp...\n[09:32:12 PM]larch.Model: Model.setUp complete\n[09:32:12 PM]larch: pull data for DTAZi 0\n[09:32:12 PM]larch: load data for DTAZi 0\n[09:32:12 PM]larch: calculate logsums DTAZi 0\n[09:32:12 PM]larch: write logsums DTAZi 0\n[09:32:13 PM]larch: pull data for DTAZi 1\n[09:32:13 PM]larch: load data for DTAZi 1\n[09:32:13 PM]larch: calculate logsums DTAZi 1\n[09:32:13 PM]larch: write logsums DTAZi 1\n[09:32:13 PM]larch: pull data for DTAZi 2\n[09:32:14 PM]larch: load data for DTAZi 2\n[09:32:14 PM]larch: calculate logsums DTAZi 2\n[09:32:14 PM]larch: write logsums DTAZi 2\n[09:32:14 PM]larch: pull data for DTAZi 3\n[09:32:15 PM]larch: load data for DTAZi 3\n[09:32:15 PM]larch: calculate logsums DTAZi 3\n[09:32:15 PM]larch: write logsums DTAZi 3\n[09:32:15 PM]larch: pull data for DTAZi 4\n[09:32:15 PM]larch: load data for DTAZi 4\n[09:32:16 PM]larch: calculate logsums DTAZi 4\n[09:32:16 PM]larch: write logsums DTAZi 4\n[09:32:16 PM]larch: pull data for DTAZi 5\n[09:32:17 PM]larch: load data for DTAZi 5\n[09:32:17 PM]larch: calculate logsums DTAZi 5\n[09:32:17 PM]larch: write logsums DTAZi 5\n[09:32:17 PM]larch: pull data for DTAZi 6\n[09:32:17 PM]larch: load data for DTAZi 6\n[09:32:18 PM]larch: calculate logsums DTAZi 6\n[09:32:18 PM]larch: write logsums DTAZi 6\n[09:32:18 PM]larch: pull data for DTAZi 7\n[09:32:18 PM]larch: load data for DTAZi 7\n[09:32:18 PM]larch: calculate logsums DTAZi 7\n[09:32:18 PM]larch: write logsums DTAZi 7\n[09:32:18 PM]larch: pull data for DTAZi 8\n[09:32:19 PM]larch: load data for DTAZi 8\n[09:32:19 PM]larch: calculate logsums DTAZi 8\n[09:32:19 PM]larch: write logsums DTAZi 8\n[09:32:19 PM]larch: pull data for DTAZi 9\n[09:32:19 PM]larch: load data for DTAZi 9\n[09:32:20 PM]larch: calculate logsums DTAZi 9\n[09:32:20 PM]larch: write logsums DTAZi 9\n[09:32:20 PM]larch: pull data for DTAZi 10\n[09:32:20 PM]larch: load data for DTAZi 10\n[09:32:20 PM]larch: calculate logsums DTAZi 10\n[09:32:20 PM]larch: write logsums DTAZi 10\n[09:32:21 PM]larch: pull data for DTAZi 11\n[09:32:21 PM]larch: load data for DTAZi 11\n[09:32:21 PM]larch: calculate logsums DTAZi 11\n[09:32:21 PM]larch: write logsums DTAZi 11\n[09:32:21 PM]larch: pull data for DTAZi 12\n[09:32:22 PM]larch: load data for DTAZi 12\n[09:32:22 PM]larch: calculate logsums DTAZi 12\n[09:32:22 PM]larch: write logsums DTAZi 12\n[09:32:22 PM]larch: pull data for DTAZi 13\n[09:32:23 PM]larch: load data for DTAZi 13\n[09:32:23 PM]larch: calculate logsums DTAZi 13\n[09:32:23 PM]larch: write logsums DTAZi 13\n[09:32:23 PM]larch: pull data for DTAZi 14\n[09:32:23 PM]larch: load data for DTAZi 14\n[09:32:23 PM]larch: calculate logsums DTAZi 14\n[09:32:23 PM]larch: write logsums DTAZi 14\n",
"output_type": "stream",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Destination Choice"
},
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"trusted": true,
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"cell_type": "code",
"source": "d.set_alternatives( numpy.arange(1,nZones+1) )\n\n# Due to a limitation of HDF5, we are not able to \n# give a choice_idco dictionary for models with lots of alternatives,\n# as there is a hard limit on the quantity of data in the header for\n# each array. This limitation will probably be circumvented in a\n# future version of Larch, but for now we must manually create and \n# store an entire idca array.\nch = numpy.zeros([d.nAllCases(), nZones ], dtype=numpy.float32)\ndtaz = d.idco.DTAZ[:]\nch[range(dtaz.shape[0]), dtaz-1] = 1\nd.new_idca_from_array('_choice_', ch, overwrite=True)\n\n# We're also gonna overwrite the _avail_ array, so that\n# all destinations are available for all tours.\nd.new_idca_from_array('_avail_', numpy.ones([d.nAllCases(), nZones ], dtype=bool), overwrite=True)\n\n# We attached the lookups from the OMX, which are OTAZ-generic\nd.idca.add_external_omx(omx, rowindexnode=None, n_alts=nZones)\n\n# We'll erase the OMX data we imported before in idco format...\nd.delete_data('EMPLOYMENT')\nd.delete_data('EMP_NONRETAIL')\nd.delete_data('EMP_RETAIL')\n\n# and replace it with full rows in idca format.\nd.idca.add_external_omx(omx, rowindexnode=d.idco.HOMETAZi, n_alts=nZones)\n\n",
"execution_count": 14,
"outputs": [
{
"text": "/Users/jpn/anaconda/local/larch/util/groupnode.py:217: UserWarning: the name \"TAZID\" already exists\n warnings.warn('the name \"{}\" already exists'.format(full_lname))\n",
"output_type": "stream",
"name": "stderr"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Destination Choice Model"
},
{
"metadata": {
"trusted": true,
"collapsed": false
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"cell_type": "code",
"source": "### DEST CHOICE MODEL\n\nm = larch.Model(d)\n\nm.title = \"Exampville Work Tour Destination Choice\"\n\nfrom larch.util.piecewise import log_and_linear_function\ndist_func = log_and_linear_function('DIST', baseparam='Distance')\n\nm.utility.ca = (\n + P.ModeChoiceLogSum * X.MODECHOICELOGSUM\n + dist_func\n# + P.Distance * X.DIST\n# + P.Log1pDist * X(\"log1p(DIST)\")\n)\n\nm.quantity = (\n + P(\"EmpRetail_HighInc\") * X('EMP_RETAIL') * X(\"INCOME>50000\")\n + P(\"EmpNonRetail_HighInc\") * X('EMP_NONRETAIL') * X(\"INCOME>50000\")\n + P(\"EmpRetail_LowInc\") * X('EMP_RETAIL') * X(\"INCOME<=50000\")\n + P(\"EmpNonRetail_LowInc\") * X('EMP_NONRETAIL') * X(\"INCOME<=50000\")\n\n)\n\nm.quantity_scale = P.Theta\nm.parameter.Theta(value=0.5, min=0.001, max=1.0, null_value=1.0)\n\nm.parameter.EmpRetail_HighInc(holdfast=1, value=0)\nm.parameter.EmpRetail_LowInc(holdfast=1, value=0)\n\nm.computed_factor_figure_with_derivative(\n dist_func.evaluator1d('U'),\n max_x=20,\n header=\"Utility by Distance\",\n xaxis_label=\"Freeflow Highway Distance (miles)\",\n yaxis_label=\"Utility\",\n short_header=\"U by Distance\",\n )\n\nm.maximize_loglike()\nm.xhtml_report(filename=os.path.join(directory,'destination_choice_model.html'), cats='**')",
"execution_count": 15,
"outputs": [
{
"text": "[09:32:24 PM]larch.Model: Model.setUp...\n[09:32:24 PM]larch.Model: Model.setUp complete\n[09:32:24 PM]larch.Model: Using <larch.util.optimize.OptimizeTechnique SLSQP>\n[09:32:24 PM]larch.Model: Optimization terminated successfully. [SLSQP]\n[09:32:24 PM]larch.Model: Ending leg (success) using SLSQP at [ 1.05287419e+00 -9.35971236e-04 -5.20771849e-02 8.11518368e-01 0.00000000e+00 0.00000000e+00 4.64005976e-01 -8.17847501e-01]\n[09:32:25 PM]larch.Model: Preliminary Results\n+--------------------+---------------+----------+------+----------+\n Parameter Estimated Value Std Error t-Stat Null Value \n+--------------------+---------------+----------+------+----------+\n ModeChoiceLogSum 1.053 nan nan 0 \n Distance -0.000936 nan nan 0 \n logDistanceP1 -0.05208 nan nan 0 \n Theta 0.8115 nan nan 1 \n EmpRetail_HighInc 0 fixed value 0 \n EmpRetail_LowInc 0 fixed value 0 \n EmpNonRetail_HighInc 0.464 nan nan 0 \n EmpNonRetail_LowInc -0.8178 nan nan 0 \n+--------------------+---------------+----------+------+----------+\n[09:32:26 PM]larch.Model: Final Results\n+--------------------+---------------+----------+------+----------+\n Parameter Estimated Value Std Error t-Stat Null Value \n+--------------------+---------------+----------+------+----------+\n ModeChoiceLogSum 1.053 0.08195 12.85 0 \n Distance -0.000936 0.02497 -0.04 0 \n logDistanceP1 -0.05208 0.1165 -0.45 0 \n Theta 0.8115 0.04343 -4.34 1 \n EmpRetail_HighInc 0 fixed value 0 \n EmpRetail_LowInc 0 fixed value 0 \n EmpNonRetail_HighInc 0.464 0.2182 2.13 0 \n EmpNonRetail_LowInc -0.8178 0.3048 -2.68 0 \n+--------------------+---------------+----------+------+----------+\n",
"output_type": "stream",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
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"cell_type": "code",
"source": "m.jupyter('title','params','ll','latest','UTILITYSPEC','PROBABILITYSPEC',\n 'DATA', 'excludedcases', 'NOTES')",
"execution_count": 17,
"outputs": [
{
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"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div class=\"page_header\"><h1>Exampville Work Tour Destination Choice</h1></div>"
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"data": {
"text/plain": " \u001b[32m\u001b[1mModel Parameter Estimates\u001b[0m\n┌────────────────────┬───────────────┬──────────┬──────┬──────────┐\n│Parameter │Estimated Value│Std Error │t-Stat│Null Value│\n├────────────────────┼───────────────┼──────────┼──────┼──────────┤\n│ModeChoiceLogSum │ 1.053 │ 0.08195 │ 12.85│ 0 │\n│Distance │-0.000936 │ 0.02497 │-0.04 │ 0 │\n│logDistanceP1 │-0.05208 │ 0.1165 │-0.45 │ 0 │\n│Theta │ 0.8115 │ 0.04343 │-4.34 │ 1 │\n│EmpRetail_HighInc │ 0 │fixed value │ 0 │\n│EmpRetail_LowInc │ 0 │fixed value │ 0 │\n│EmpNonRetail_HighInc│ 0.464 │ 0.2182 │ 2.13 │ 0 │\n│EmpNonRetail_LowInc │-0.8178 │ 0.3048 │-2.68 │ 0 │\n└────────────────────┴───────────────┴──────────┴──────┴──────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx71\" reftxt=\"Parameter Estimates\" toclevel=\"2\"></a><h2>Model Parameter Estimates</h2><div><table><thead><tr><th class=\"param_label\" colspan=\"2\">Parameter</th><div class=\"dummycell\"></div><th class=\"estimated_value\">Estimated Value</th><th class=\"std_err\">Std Error</th><th class=\"tstat\">t-Stat</th><th class=\"null_value\">Null Value</th></tr></thead><tbody><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramModeChoiceLogSum\"></a>ModeChoiceLogSum</td><div class=\"dummycell\"></div><td class=\"estimated_value\"> 1.053 </td><td class=\"std_err\"> 0.08195 </td><td class=\"tstat\"> 12.85</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramDistance\"></a>Distance</td><div class=\"dummycell\"></div><td class=\"estimated_value\">-0.000936 </td><td class=\"std_err\"> 0.02497 </td><td class=\"tstat\">-0.04</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramlogDistanceP1\"></a>logDistanceP1</td><div class=\"dummycell\"></div><td class=\"estimated_value\">-0.05208 </td><td class=\"std_err\"> 0.1165 </td><td class=\"tstat\">-0.45</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramTheta\"></a>Theta</td><div class=\"dummycell\"></div><td class=\"estimated_value\"> 0.8115 </td><td class=\"std_err\"> 0.04343 </td><td class=\"tstat\">-4.34</td><td class=\"null_value\"> 1 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramEmpRetail_HighInc\"></a>EmpRetail_HighInc</td><div class=\"dummycell\"></div><td class=\"estimated_value\"> 0 </td><td class=\"std_err\" colspan=\"2\">fixed value</td><div class=\"dummycell\"></div><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramEmpRetail_LowInc\"></a>EmpRetail_LowInc</td><div class=\"dummycell\"></div><td class=\"estimated_value\"> 0 </td><td class=\"std_err\" colspan=\"2\">fixed value</td><div class=\"dummycell\"></div><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramEmpNonRetail_HighInc\"></a>EmpNonRetail_HighInc</td><div class=\"dummycell\"></div><td class=\"estimated_value\"> 0.464 </td><td class=\"std_err\"> 0.2182 </td><td class=\"tstat\"> 2.13</td><td class=\"null_value\"> 0 </td></tr><tr><td class=\"param_label\" colspan=\"2\"><a name=\"paramEmpNonRetail_LowInc\"></a>EmpNonRetail_LowInc</td><div class=\"dummycell\"></div><td class=\"estimated_value\">-0.8178 </td><td class=\"std_err\"> 0.3048 </td><td class=\"tstat\">-2.68</td><td class=\"null_value\"> 0 </td></tr></tbody><tfoot></tfoot></table></div></div>"
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"data": {
"text/plain": " \u001b[32m\u001b[1mModel Estimation Statistics\u001b[0m\n┌──────────────────────────────────┬─────────┬────────┐\n│Statistic │Aggregate│Per Case│\n├──────────────────────────────────┼─────────┼────────┤\n│Number of Cases │ 1897 │\n│Log Likelihood at Convergence │-3670.60 │-1.93 │\n│Log Likelihood at Null Parameters │-4493.08 │-2.37 │\n│Rho Squared w.r.t. Null Parameters│ 0.183 │\n└──────────────────────────────────┴─────────┴────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx72\" reftxt=\"Estimation Statistics\" toclevel=\"2\"></a><h2>Model Estimation Statistics</h2><div><table><thead><tr><th>Statistic</th><th>Aggregate</th><th>Per Case</th></tr></thead><tbody><tr><td>Number of Cases</td><td class=\"statistics_bridge centered_cell\" colspan=\"2\">1897</td><div class=\"dummycell\"></div></tr><tr><td>Log Likelihood at Convergence</td><td>-3670.60</td><td>-1.93</td></tr><tr><td>Log Likelihood at Null Parameters</td><td>-4493.08</td><td>-2.37</td></tr><tr><td>Rho Squared w.r.t. Null Parameters</td><td class=\"statistics_bridge centered_cell\" colspan=\"2\">0.183</td><div class=\"dummycell\"></div></tr></tbody><tfoot></tfoot></table></div></div>"
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"data": {
"text/plain": " \u001b[32m\u001b[1mLatest Estimation Run Statistics\u001b[0m\n┌────────────────────────────────────┬─────────────────────────────────────────────┐\n│Estimation Date │Tuesday, October 25 2016, 09:32:24 PM │\n│Results │success │\n│Message │Optimization terminated successfully. [SLSQP]│\n│Optimization Method │SLSQP │\n│Number of Iterations │17 │\n│Running Time│Total │0.886 seconds │\n│ │setup │0:00.32 │\n│ │null_likelihood │0:00.01 │\n│ │weight choice rebalance│0:00 │\n│ │weight autorescale │0:00 │\n│ │optimize:SLSQP │0:00.44 │\n│ │parameter covariance │0:00.13 │\n│ │cleanup │0:00 │\n│Notes │did not automatically rescale weights │\n│Computer │george.local │\n│Processor │Intel(R) Core(TM) i5-4258U CPU @ 2.40GHz │\n│Number of CPU Cores │4 │\n│Number of Threads Used │4 │\n│Installed Memory │8.0 GiB │\n│Peak Memory Usage │146.3046875 MiB │\n└────────────────────────────────────┴─────────────────────────────────────────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx73\" reftxt=\"Latest Estimation Run\" toclevel=\"2\"></a><h2>Latest Estimation Run Statistics</h2><div><table><thead></thead><tbody><tr><td colspan=\"2\">Estimation Date</td><div class=\"dummycell\"></div><td>Tuesday, October 25 2016, 09:32:24 PM</td></tr><tr><td colspan=\"2\">Results</td><div class=\"dummycell\"></div><td>success</td></tr><tr><td colspan=\"2\">Message</td><div class=\"dummycell\"></div><td>Optimization terminated successfully. [SLSQP]</td></tr><tr><td colspan=\"2\">Optimization Method</td><div class=\"dummycell\"></div><td>SLSQP</td></tr><tr><td colspan=\"2\">Number of Iterations</td><div class=\"dummycell\"></div><td>17</td></tr><tr><td rowspan=\"8\" style=\"vertical-align:top;\">Running Time</td><td>Total</td><td>0.886\tseconds</td></tr><tr><div class=\"dummycell\"></div><td>setup</td><td>0:00.32</td></tr><tr><div class=\"dummycell\"></div><td>null_likelihood</td><td>0:00.01</td></tr><tr><div class=\"dummycell\"></div><td>weight choice rebalance</td><td>0:00</td></tr><tr><div class=\"dummycell\"></div><td>weight autorescale</td><td>0:00</td></tr><tr><div class=\"dummycell\"></div><td>optimize:SLSQP</td><td>0:00.44</td></tr><tr><div class=\"dummycell\"></div><td>parameter covariance</td><td>0:00.13</td></tr><tr><div class=\"dummycell\"></div><td>cleanup</td><td>0:00</td></tr><tr><td colspan=\"2\">Notes</td><div class=\"dummycell\"></div><td>did not automatically rescale weights</td></tr><tr><td colspan=\"2\">Computer</td><div class=\"dummycell\"></div><td>george.local</td></tr><tr><td colspan=\"2\">Processor</td><div class=\"dummycell\"></div><td>Intel(R) Core(TM) i5-4258U CPU @ 2.40GHz</td></tr><tr><td colspan=\"2\">Number of CPU Cores</td><div class=\"dummycell\"></div><td>4</td></tr><tr><td colspan=\"2\">Number of Threads Used</td><div class=\"dummycell\"></div><td>4</td></tr><tr><td colspan=\"2\">Installed Memory</td><div class=\"dummycell\"></div><td>8.0 GiB</td></tr><tr><td colspan=\"2\">Peak Memory Usage</td><div class=\"dummycell\"></div><td>146.3046875 MiB</td></tr></tbody><tfoot></tfoot></table></div></div>"
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"text/html": "<div class=\"utilityspec\"><a class=\"toc\" name=\"rx74\" reftxt=\"Utility Specification\" toclevel=\"2\"></a><h2>Utility Specification</h2><a class=\"toc\" name=\"rx75\" reftxt=\"Resolved Utility\" toclevel=\"3\"></a><h3>Resolved Utility</h3><table class=\"floatinghead\"><thead><tr><th>Code</th><th>Alternative</th><th>Resolved Utility</th></tr></thead><tbody><tr><td>*</td><td>all elemental alternatives</td><td>  1.053*MODECHOICELOGSUM<br>- 0.000936*DIST<br>- 0.05208*log1p(DIST)<br>+ 0.8115 * log(<br>      1*EMP_RETAIL*(INCOME&gt;50000)<br>    + 1.59*EMP_NONRETAIL*(INCOME&gt;50000)<br>    + 1*(EMP_RETAIL*(INCOME&lt;=50000))<br>    + 0.4414*(EMP_NONRETAIL*(INCOME&lt;=50000))<br>)</td></tr></tbody></table><a class=\"toc\" name=\"rx76\" reftxt=\"Formulaic Utility\" toclevel=\"3\"></a><h3>Formulaic Utility</h3><table class=\"floatinghead\"><thead><tr><th>Code</th><th>Alternative</th><th>Formulaic Utility</th></tr></thead><tbody><tr><td>*</td><td>all elemental alternatives</td><td>  <a class=\"parameter_reference\" href=\"#paramModeChoiceLogSum\">ModeChoiceLogSum</a>*MODECHOICELOGSUM<br>+ <a class=\"parameter_reference\" href=\"#paramDistance\">Distance</a>*DIST<br>+ <a class=\"parameter_reference\" href=\"#paramlogDistanceP1\">logDistanceP1</a>*log1p(DIST)<br>+ <a class=\"parameter_reference\" href=\"#paramTheta\">Theta</a> * log(<br>      exp(<a class=\"parameter_reference\" href=\"#paramEmpRetail_HighInc\">EmpRetail_HighInc</a>)*EMP_RETAIL*(INCOME&gt;50000)<br>    + exp(<a class=\"parameter_reference\" href=\"#paramEmpNonRetail_HighInc\">EmpNonRetail_HighInc</a>)*EMP_NONRETAIL*(INCOME&gt;50000)<br>    + exp(<a class=\"parameter_reference\" href=\"#paramEmpRetail_LowInc\">EmpRetail_LowInc</a>)*(EMP_RETAIL*(INCOME&lt;=50000))<br>    + exp(<a class=\"parameter_reference\" href=\"#paramEmpNonRetail_LowInc\">EmpNonRetail_LowInc</a>)*(EMP_NONRETAIL*(INCOME&lt;=50000))<br>)</td></tr></tbody></table></div>"
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"text/html": "<div class=\"probabilityspec\"><a class=\"toc\" name=\"rx77\" reftxt=\"Probability Specification\" toclevel=\"2\"></a><h2>Probability Specification</h2><a class=\"toc\" name=\"rx78\" reftxt=\"Resolved Probability\" toclevel=\"3\"></a><h3>Resolved Probability</h3><table class=\"floatinghead\"><thead><tr><th>Code</th><th>Alternative</th><th>Resolved Probability</th></tr></thead><tbody><tr><td>1</td><td>a1</td><td>exp(Utility[a1])/exp(Utility[ROOT])</td></tr><tr><td>2</td><td>a2</td><td>exp(Utility[a2])/exp(Utility[ROOT])</td></tr><tr><td>3</td><td>a3</td><td>exp(Utility[a3])/exp(Utility[ROOT])</td></tr><tr><td>4</td><td>a4</td><td>exp(Utility[a4])/exp(Utility[ROOT])</td></tr><tr><td>5</td><td>a5</td><td>exp(Utility[a5])/exp(Utility[ROOT])</td></tr><tr><td>6</td><td>a6</td><td>exp(Utility[a6])/exp(Utility[ROOT])</td></tr><tr><td>7</td><td>a7</td><td>exp(Utility[a7])/exp(Utility[ROOT])</td></tr><tr><td>8</td><td>a8</td><td>exp(Utility[a8])/exp(Utility[ROOT])</td></tr><tr><td>9</td><td>a9</td><td>exp(Utility[a9])/exp(Utility[ROOT])</td></tr><tr><td>10</td><td>a10</td><td>exp(Utility[a10])/exp(Utility[ROOT])</td></tr><tr><td>11</td><td>a11</td><td>exp(Utility[a11])/exp(Utility[ROOT])</td></tr><tr><td>12</td><td>a12</td><td>exp(Utility[a12])/exp(Utility[ROOT])</td></tr><tr><td>13</td><td>a13</td><td>exp(Utility[a13])/exp(Utility[ROOT])</td></tr><tr><td>14</td><td>a14</td><td>exp(Utility[a14])/exp(Utility[ROOT])</td></tr><tr><td>15</td><td>a15</td><td>exp(Utility[a15])/exp(Utility[ROOT])</td></tr></tbody></table><a class=\"toc\" name=\"rx79\" reftxt=\"Formulaic Probability\" toclevel=\"3\"></a><h3>Formulaic Probability</h3><table class=\"floatinghead\"><thead><tr><th>Code</th><th>Alternative</th><th>Formulaic Probability</th></tr></thead><tbody><tr><td>1</td><td>a1</td><td>exp(Utility[a1])/exp(Utility[ROOT])</td></tr><tr><td>2</td><td>a2</td><td>exp(Utility[a2])/exp(Utility[ROOT])</td></tr><tr><td>3</td><td>a3</td><td>exp(Utility[a3])/exp(Utility[ROOT])</td></tr><tr><td>4</td><td>a4</td><td>exp(Utility[a4])/exp(Utility[ROOT])</td></tr><tr><td>5</td><td>a5</td><td>exp(Utility[a5])/exp(Utility[ROOT])</td></tr><tr><td>6</td><td>a6</td><td>exp(Utility[a6])/exp(Utility[ROOT])</td></tr><tr><td>7</td><td>a7</td><td>exp(Utility[a7])/exp(Utility[ROOT])</td></tr><tr><td>8</td><td>a8</td><td>exp(Utility[a8])/exp(Utility[ROOT])</td></tr><tr><td>9</td><td>a9</td><td>exp(Utility[a9])/exp(Utility[ROOT])</td></tr><tr><td>10</td><td>a10</td><td>exp(Utility[a10])/exp(Utility[ROOT])</td></tr><tr><td>11</td><td>a11</td><td>exp(Utility[a11])/exp(Utility[ROOT])</td></tr><tr><td>12</td><td>a12</td><td>exp(Utility[a12])/exp(Utility[ROOT])</td></tr><tr><td>13</td><td>a13</td><td>exp(Utility[a13])/exp(Utility[ROOT])</td></tr><tr><td>14</td><td>a14</td><td>exp(Utility[a14])/exp(Utility[ROOT])</td></tr><tr><td>15</td><td>a15</td><td>exp(Utility[a15])/exp(Utility[ROOT])</td></tr></tbody></table></div>"
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"text/html": "<div class=\"data_statistics\"><a class=\"toc\" name=\"rx80\" reftxt=\"Choice and Availability\" toclevel=\"2\"></a><h2>Choice and Availability</h2><table><thead><tr><th>Code</th><th>Alternative</th><th># Avail</th><th># Chosen</th><th>Availability Condition</th></tr></thead><tbody><tr><td>1</td><td>a1 </td><td>1897 </td><td>10 </td><td>n/a</td></tr><tr><td>2</td><td>a2 </td><td>1897 </td><td>24 </td><td>n/a</td></tr><tr><td>3</td><td>a3 </td><td>1897 </td><td>42 </td><td>n/a</td></tr><tr><td>4</td><td>a4 </td><td>1897 </td><td>69 </td><td>n/a</td></tr><tr><td>5</td><td>a5 </td><td>1897 </td><td>183 </td><td>n/a</td></tr><tr><td>6</td><td>a6 </td><td>1897 </td><td>256 </td><td>n/a</td></tr><tr><td>7</td><td>a7 </td><td>1897 </td><td>248 </td><td>n/a</td></tr><tr><td>8</td><td>a8 </td><td>1897 </td><td>305 </td><td>n/a</td></tr><tr><td>9</td><td>a9 </td><td>1897 </td><td>259 </td><td>n/a</td></tr><tr><td>10</td><td>a10 </td><td>1897 </td><td>249 </td><td>n/a</td></tr><tr><td>11</td><td>a11 </td><td>1897 </td><td>151 </td><td>n/a</td></tr><tr><td>12</td><td>a12 </td><td>1897 </td><td>69 </td><td>n/a</td></tr><tr><td>13</td><td>a13 </td><td>1897 </td><td>17 </td><td>n/a</td></tr><tr><td>14</td><td>a14 </td><td>1897 </td><td>11 </td><td>n/a</td></tr><tr><td>15</td><td>a15 </td><td>1897 </td><td>4 </td><td>n/a</td></tr></tbody></table></div>"
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"text/plain": " \u001b[32m\u001b[1mExcluded Cases\u001b[0m\n┌─┬─────────────┬───────────┬────────────────┬─────────────────┐\n│ │Criteria │Data Source│# Cases Excluded│# Cases Remaining│\n├─┼─────────────┼───────────┼────────────────┼─────────────────┤\n│0│TOURPURP != 1│idco │4226 │1897 │\n└─┴─────────────┴───────────┴────────────────┴─────────────────┘",
"text/html": "<div><a class=\"toc\" name=\"rx81\" reftxt=\"Excluded Cases\" toclevel=\"2\"></a><h2>Excluded Cases</h2><div><table><thead><tr><th> </th><th>Criteria</th><th>Data Source</th><th># Cases Excluded</th><th># Cases Remaining</th></tr></thead><tbody><tr><td>0</td><td>TOURPURP != 1</td><td>idco</td><td>4226</td><td>1897</td></tr></tbody><tfoot></tfoot></table></div></div>"
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