From 05a25f687e0c151c5a3bcbf4428d2b58dd2cc4f7 Mon Sep 17 00:00:00 2001 From: Ben Stabler Date: Tue, 11 Aug 2020 16:51:38 -0700 Subject: [PATCH] additional updates for estimation integration (#328) * estimation integration updates #327 * drop module no longer needed --- .../notebooks/estimating_auto_ownership.ipynb | 1259 ++++- .../estimating_school_location.ipynb | 1037 ++-- .../estimating_tour_mode_choice.ipynb | 4262 ++++++++++++++--- .../estimating_workplace_location.ipynb | 3920 ++++++++++++--- .../notebooks/larch_asim.py | 374 -- docs/abmexample.rst | 2 +- 6 files changed, 8731 insertions(+), 2123 deletions(-) delete mode 100644 activitysim/examples/example_estimation/notebooks/larch_asim.py diff --git a/activitysim/examples/example_estimation/notebooks/estimating_auto_ownership.ipynb b/activitysim/examples/example_estimation/notebooks/estimating_auto_ownership.ipynb index 878521bef..42bc4c410 100644 --- a/activitysim/examples/example_estimation/notebooks/estimating_auto_ownership.ipynb +++ b/activitysim/examples/example_estimation/notebooks/estimating_auto_ownership.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -42,8 +42,8 @@ "source": [ "import os\n", "import larch # !conda install larch #for estimation\n", - "import pandas as pd\n", - "import larch_asim # utility functions in a local module" + "import larch.util.activitysim\n", + "import pandas as pd" ] }, { @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -267,7 +267,7 @@ "[2000 rows x 8 columns]" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -2522,7 +2522,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -2534,7 +2534,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2560,7 +2560,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -2672,7 +2672,7 @@ "[67 rows x 2 columns]" ] }, - "execution_count": 17, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -2690,7 +2690,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -3267,7 +3267,7 @@ "28 coef_cars4_auto_time_saving_per_worker " ] }, - "execution_count": 18, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -3285,7 +3285,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -3671,7 +3671,7 @@ "[2000 rows x 95 columns]" ] }, - "execution_count": 19, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -3680,6 +3680,22 @@ "chooser_data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove choosers with invalid observed choice" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "chooser_data = chooser_data[chooser_data['override_choice'] >= 0]" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -3691,7 +3707,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -3703,7 +3719,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -3721,7 +3737,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -3737,7 +3753,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -3861,13 +3877,13 @@ "+ P.coef_cars4_auto_time_saving_per_worker * X.util_auto_time_saving_per_worker})" ] }, - "execution_count": 23, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "m.utility_co = larch_asim.dict_of_linear_utility_from_spec(\n", + "m.utility_co = larch.util.activitysim.dict_of_linear_utility_from_spec(\n", " spec, 'Label', dict(zip(altnames,altcodes)),\n", ")\n", "m.utility_co" @@ -3875,11 +3891,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "larch_asim.apply_coefficients(coefficients, m)" + "larch.util.activitysim.apply_coefficients(coefficients, m)" ] }, { @@ -3891,7 +3907,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -4070,7 +4086,7 @@ "[66 rows x 7 columns]" ] }, - "execution_count": 25, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -4079,9 +4095,25 @@ "m.pf" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Availability" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "av = True # all alternatives are available" + ] + }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -4089,13 +4121,13 @@ " co=chooser_data,\n", " alt_codes=altcodes,\n", " alt_names=altnames,\n", - " av=True,\n", + " av=av,\n", ")" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -4111,7 +4143,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -4124,12 +4156,12 @@ "source": [ "# Estimate\n", "\n", - "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution." + "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution. Larch has two built-in estimation methods: BHHH and SLSQP. BHHH is the default and typically runs faster, but does not follow constraints on parameters. SLSQP is safer, but slower, and may need additional iterations." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -4142,7 +4174,7 @@ { "data": { "text/html": [ - "

Iteration 016 [Converged]

" + "

Iteration 000 [Converged]

" ], "text/plain": [ "" @@ -4154,7 +4186,7 @@ { "data": { "text/html": [ - "

LL = -1724.9434046914039

" + "

LL = -1724.9434006147962

" ], "text/plain": [ "" @@ -4191,64 +4223,112 @@ " maximum\n", " holdfast\n", " note\n", + " std_err\n", + " t_stat\n", + " robust_std_err\n", + " robust_t_stat\n", + " unconstrained_std_err\n", + " unconstrained_t_stat\n", + " constrained\n", + " likelihood_ratio\n", " best\n", " \n", " \n", " \n", " \n", " coef_cars1_asc\n", - " 4.876258\n", + " 4.876601\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 4.876258\n", + " 2.676997\n", + " 1.821668\n", + " 2.672648\n", + " 1.824633\n", + " 2.676997\n", + " 1.821668\n", + " \n", + " NaN\n", + " 4.876601\n", " \n", " \n", " coef_cars1_asc_county\n", - " -0.566001\n", + " -0.566000\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.566001\n", + " NaN\n", + " NaN\n", + " 0.010551\n", + " -53.646695\n", + " NaN\n", + " NaN\n", + " \n", + " 0.0\n", + " -0.566000\n", " \n", " \n", " coef_cars1_asc_marin\n", - " -0.243401\n", + " -0.243400\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.243401\n", + " NaN\n", + " NaN\n", + " 0.005645\n", + " -43.121618\n", + " NaN\n", + " NaN\n", + " \n", + " 0.0\n", + " -0.243400\n", " \n", " \n", " coef_cars1_asc_san_francisco\n", - " 4.115658\n", + " 4.116001\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 4.115658\n", + " 2.677009\n", + " 1.537537\n", + " 2.672659\n", + " 1.540039\n", + " 2.677009\n", + " 1.537537\n", + " \n", + " NaN\n", + " 4.116001\n", " \n", " \n", " coef_cars1_auto_time_saving_per_worker\n", - " 0.773469\n", + " 0.773633\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 0.773469\n", + " 0.644141\n", + " 1.201031\n", + " 0.680452\n", + " 1.136941\n", + " 0.644141\n", + " 1.201031\n", + " \n", + " NaN\n", + " 0.773633\n", " \n", " \n", " ...\n", @@ -4260,28 +4340,52 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " coef_retail_auto_no_workers\n", - " -0.664047\n", + " -0.664116\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.664047\n", + " 0.598741\n", + " -1.109186\n", + " 0.600125\n", + " -1.106628\n", + " 0.598741\n", + " -1.109186\n", + " \n", + " NaN\n", + " -0.664116\n", " \n", " \n", " coef_retail_auto_workers\n", - " -0.631548\n", + " -0.631637\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.631548\n", + " 0.591315\n", + " -1.068191\n", + " 0.593167\n", + " -1.064856\n", + " 0.591315\n", + " -1.068191\n", + " \n", + " NaN\n", + " -0.631637\n", " \n", " \n", " coef_retail_non_motor\n", @@ -4292,48 +4396,72 @@ " NaN\n", " 1\n", " \n", + " NaN\n", + " NaN\n", + " 0.000000\n", + " -inf\n", + " 0.000000\n", + " -inf\n", + " fixed value\n", + " NaN\n", " -0.030000\n", " \n", " \n", " coef_retail_transit_no_workers\n", - " -0.335792\n", + " -0.335795\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.335792\n", + " 0.193169\n", + " -1.738345\n", + " 0.188894\n", + " -1.777694\n", + " 0.193169\n", + " -1.738345\n", + " \n", + " NaN\n", + " -0.335795\n", " \n", " \n", " coef_retail_transit_workers\n", - " -0.400840\n", + " -0.400819\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.400840\n", + " 0.168983\n", + " -2.371942\n", + " 0.173205\n", + " -2.314131\n", + " 0.168983\n", + " -2.371942\n", + " \n", + " NaN\n", + " -0.400819\n", " \n", " \n", "\n", - "

66 rows × 8 columns

\n", + "

66 rows × 16 columns

\n", "" ], "text/plain": [ " value initvalue nullvalue \\\n", - "coef_cars1_asc 4.876258 0.0 0.0 \n", - "coef_cars1_asc_county -0.566001 0.0 0.0 \n", - "coef_cars1_asc_marin -0.243401 0.0 0.0 \n", - "coef_cars1_asc_san_francisco 4.115658 0.0 0.0 \n", - "coef_cars1_auto_time_saving_per_worker 0.773469 0.0 0.0 \n", + "coef_cars1_asc 4.876601 0.0 0.0 \n", + "coef_cars1_asc_county -0.566000 0.0 0.0 \n", + "coef_cars1_asc_marin -0.243400 0.0 0.0 \n", + "coef_cars1_asc_san_francisco 4.116001 0.0 0.0 \n", + "coef_cars1_auto_time_saving_per_worker 0.773633 0.0 0.0 \n", "... ... ... ... \n", - "coef_retail_auto_no_workers -0.664047 0.0 0.0 \n", - "coef_retail_auto_workers -0.631548 0.0 0.0 \n", + "coef_retail_auto_no_workers -0.664116 0.0 0.0 \n", + "coef_retail_auto_workers -0.631637 0.0 0.0 \n", "coef_retail_non_motor -0.030000 0.0 0.0 \n", - "coef_retail_transit_no_workers -0.335792 0.0 0.0 \n", - "coef_retail_transit_workers -0.400840 0.0 0.0 \n", + "coef_retail_transit_no_workers -0.335795 0.0 0.0 \n", + "coef_retail_transit_workers -0.400819 0.0 0.0 \n", "\n", " minimum maximum holdfast note \\\n", "coef_cars1_asc NaN NaN 0 \n", @@ -4348,20 +4476,59 @@ "coef_retail_transit_no_workers NaN NaN 0 \n", "coef_retail_transit_workers NaN NaN 0 \n", "\n", - " best \n", - "coef_cars1_asc 4.876258 \n", - "coef_cars1_asc_county -0.566001 \n", - "coef_cars1_asc_marin -0.243401 \n", - "coef_cars1_asc_san_francisco 4.115658 \n", - "coef_cars1_auto_time_saving_per_worker 0.773469 \n", - "... ... \n", - "coef_retail_auto_no_workers -0.664047 \n", - "coef_retail_auto_workers -0.631548 \n", - "coef_retail_non_motor -0.030000 \n", - "coef_retail_transit_no_workers -0.335792 \n", - "coef_retail_transit_workers -0.400840 \n", + " std_err t_stat robust_std_err \\\n", + "coef_cars1_asc 2.676997 1.821668 2.672648 \n", + "coef_cars1_asc_county NaN NaN 0.010551 \n", + "coef_cars1_asc_marin NaN NaN 0.005645 \n", + "coef_cars1_asc_san_francisco 2.677009 1.537537 2.672659 \n", + "coef_cars1_auto_time_saving_per_worker 0.644141 1.201031 0.680452 \n", + "... ... ... ... \n", + "coef_retail_auto_no_workers 0.598741 -1.109186 0.600125 \n", + "coef_retail_auto_workers 0.591315 -1.068191 0.593167 \n", + "coef_retail_non_motor NaN NaN 0.000000 \n", + "coef_retail_transit_no_workers 0.193169 -1.738345 0.188894 \n", + "coef_retail_transit_workers 0.168983 -2.371942 0.173205 \n", + "\n", + " robust_t_stat unconstrained_std_err \\\n", + "coef_cars1_asc 1.824633 2.676997 \n", + "coef_cars1_asc_county -53.646695 NaN \n", + "coef_cars1_asc_marin -43.121618 NaN \n", + "coef_cars1_asc_san_francisco 1.540039 2.677009 \n", + "coef_cars1_auto_time_saving_per_worker 1.136941 0.644141 \n", + "... ... ... \n", + "coef_retail_auto_no_workers -1.106628 0.598741 \n", + "coef_retail_auto_workers -1.064856 0.591315 \n", + "coef_retail_non_motor -inf 0.000000 \n", + "coef_retail_transit_no_workers -1.777694 0.193169 \n", + "coef_retail_transit_workers -2.314131 0.168983 \n", + "\n", + " unconstrained_t_stat constrained \\\n", + "coef_cars1_asc 1.821668 \n", + "coef_cars1_asc_county NaN \n", + "coef_cars1_asc_marin NaN \n", + "coef_cars1_asc_san_francisco 1.537537 \n", + "coef_cars1_auto_time_saving_per_worker 1.201031 \n", + "... ... ... \n", + "coef_retail_auto_no_workers -1.109186 \n", + "coef_retail_auto_workers -1.068191 \n", + "coef_retail_non_motor -inf fixed value \n", + "coef_retail_transit_no_workers -1.738345 \n", + "coef_retail_transit_workers -2.371942 \n", "\n", - "[66 rows x 8 columns]" + " likelihood_ratio best \n", + "coef_cars1_asc NaN 4.876601 \n", + "coef_cars1_asc_county 0.0 -0.566000 \n", + "coef_cars1_asc_marin 0.0 -0.243400 \n", + "coef_cars1_asc_san_francisco NaN 4.116001 \n", + "coef_cars1_auto_time_saving_per_worker NaN 0.773633 \n", + "... ... ... \n", + "coef_retail_auto_no_workers NaN -0.664116 \n", + "coef_retail_auto_workers NaN -0.631637 \n", + "coef_retail_non_motor NaN -0.030000 \n", + "coef_retail_transit_no_workers NaN -0.335795 \n", + "coef_retail_transit_workers NaN -0.400819 \n", + "\n", + "[66 rows x 16 columns]" ] }, "metadata": {}, @@ -4371,52 +4538,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.4902017050292957e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.6472682674638506e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 5.573864714538057e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 3.0997056131348003e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.364855834491027e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 2.389277596746364e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.0575494447363817e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 4.438096984181575e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 3.4269656851367784e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.1835110533549158e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.3503738144609883e-15 in general_inverse\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.9147311589144446e-15 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - 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keyvalue
loglike-1724.9434006147962
x\n", " \n", " \n", " \n", @@ -4426,23 +4559,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4450,7 +4583,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4466,7 +4599,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4486,7 +4619,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4494,11 +4627,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4506,11 +4639,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4518,59 +4651,59 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4578,103 +4711,103 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4682,48 +4815,747 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - "
coef_cars1_asc4.8762584.876601
coef_cars1_asc_county-0.566001-0.566000
coef_cars1_asc_marin-0.243401-0.243400
coef_cars1_asc_san_francisco4.1156584.116001
coef_cars1_auto_time_saving_per_worker0.7734690.773633
coef_cars1_density_0_10_no_workers
coef_cars1_density_10_up_no_workers-0.006760-0.006759
coef_cars1_density_10_up_workers
coef_cars1_drivers_4_up2.0683082.068215
coef_cars1_hh_income_0_30k
coef_cars1_persons_18_24-0.450074-0.450073
coef_cars1_persons_25_34
coef_cars1_presence_children_0_40.5427230.542712
coef_cars1_presence_children_5_17-0.247630-0.247608
coef_cars234_asc_marin
coef_cars234_presence_children_0_40.6661660.666159
coef_cars2_asc2.4935792.493837
coef_cars2_asc_county
coef_cars2_asc_san_francisco4.0464794.046737
coef_cars2_auto_time_saving_per_worker0.7969760.797072
coef_cars2_density_0_10_no_workers-0.174108-0.174096
coef_cars2_density_10_up_no_workers-0.120005-0.120011
coef_cars2_drivers_23.0598183.059787
coef_cars2_drivers_33.7201283.720096
coef_cars2_drivers_4_up6.9132506.913047
coef_cars2_hh_income_0_30k0.0650090.065016
coef_cars2_hh_income_30_up0.0065980.006597
coef_cars2_num_workers_clip_3-0.062462-0.062458
coef_cars2_persons_16_17-1.412893-1.412786
coef_cars2_persons_18_24-0.941367-0.941365
coef_cars2_persons_25_34-0.457723-0.457708
coef_cars2_presence_children_5_170.1846050.184570
coef_cars34_asc_county
coef_cars34_asc_san_francisco-176.0525741.413322
coef_cars34_density_0_10_no_workers-0.367005-0.366968
coef_cars34_density_10_up_no_workers-0.239169-0.239086
coef_cars34_persons_16_17-1.872829-1.872290
coef_cars34_persons_18_24-0.965625-0.965600
coef_cars34_persons_25_34-0.934508-0.934489
coef_cars34_presence_children_5_170.4229000.422861
coef_cars3_asc181.6859934.221041
coef_cars3_auto_time_saving_per_worker0.5983840.598621
coef_cars3_drivers_22.3781552.376824
coef_cars3_drivers_35.1144425.113340
coef_cars3_drivers_4_up8.2459608.244745
coef_cars3_hh_income_0_30k0.0469550.046991
coef_cars3_hh_income_30_up0.0047670.004763
coef_cars3_num_workers_clip_30.3698700.369748
coef_cars4_asc-366.447568-11.516719
coef_cars4_auto_time_saving_per_worker1.5019851.502659
coef_cars4_drivers_2546.67375514.277188
coef_cars4_drivers_3550.08196617.685502
coef_cars4_drivers_4_up553.86778021.471015
coef_cars4_hh_income_0_30k0.0716050.071595
coef_cars4_hh_income_30_up0.0127360.012733
coef_cars4_num_workers_clip_30.6561260.656275
coef_retail_auto_no_workers-0.664047-0.664116
coef_retail_auto_workers-0.631548-0.631637
coef_retail_non_motor
coef_retail_transit_no_workers-0.335792-0.335795
coef_retail_transit_workers-0.400840-0.400819
tolerance6.830757600755883e-06
steps
array([1. , 1. , 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
-       "       0.5, 0.5, 0.5])
message'Optimization terminated successfully.'
elapsed_time0:00:00.370999
method'bhhh'
n_cases2000
iteration_number16
logloss0.8624717023457019
" + "
tolerance2.0097484722099236e-07stepsarray([], dtype=float64)message'Optimization terminated successfully.'elapsed_time0:00:00.033000method'BHHH'n_cases2000iteration_number0logloss0.8624717003073982
" ], "text/plain": [ - "┣ loglike: -1724.9434046914039\n", - "┣ x: coef_cars1_asc 4.876258\n", - "┃ coef_cars1_asc_county -0.566001\n", - "┃ coef_cars1_asc_marin -0.243401\n", - "┃ coef_cars1_asc_san_francisco 4.115658\n", - "┃ coef_cars1_auto_time_saving_per_worker 0.773469\n", + "┣ loglike: -1724.9434006147962\n", + "┣ x: coef_cars1_asc 4.876601\n", + "┃ coef_cars1_asc_county -0.566000\n", + "┃ coef_cars1_asc_marin -0.243400\n", + "┃ coef_cars1_asc_san_francisco 4.116001\n", + "┃ coef_cars1_auto_time_saving_per_worker 0.773633\n", "┃ ... \n", - "┃ coef_retail_auto_no_workers -0.664047\n", - "┃ coef_retail_auto_workers -0.631548\n", + "┃ coef_retail_auto_no_workers -0.664116\n", + "┃ coef_retail_auto_workers -0.631637\n", "┃ coef_retail_non_motor -0.030000\n", - "┃ coef_retail_transit_no_workers -0.335792\n", - "┃ coef_retail_transit_workers -0.400840\n", + "┃ coef_retail_transit_no_workers -0.335795\n", + "┃ coef_retail_transit_workers -0.400819\n", "┃ Length: 66, dtype: float64\n", - "┣ tolerance: 6.830757600755883e-06\n", - "┣ steps: array([1. , 1. , 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", - "┃ 0.5, 0.5, 0.5])\n", + "┣ tolerance: 2.0097484722099236e-07\n", + "┣ steps: array([], dtype=float64)\n", "┣ message: 'Optimization terminated successfully.'\n", - "┣ elapsed_time: datetime.timedelta(microseconds=370999)\n", - "┣ method: 'bhhh'\n", + "┣ elapsed_time: datetime.timedelta(microseconds=33000)\n", + "┣ method: 'BHHH'\n", "┣ n_cases: 2000\n", - "┣ iteration_number: 16\n", - "┣ logloss: 0.8624717023457019" + "┣ iteration_number: 0\n", + "┣ logloss: 0.8624717003073982" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# m.estimate(method='SLSQP', options={'maxiter':1000})\n", + "m.estimate(method='BHHH', options={'maxiter':1000})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimated coefficients" + ] + }, + { + "cell_type": "code", + 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
coef_cars1_asc 4.88 2.68 1.82 NA 0.00
coef_cars1_asc_county-0.566 0.309-1.83 0.00 0.00
coef_cars1_asc_marin-0.243 0.0178-13.68*** 0.00 0.00
coef_cars1_asc_san_francisco 4.12 2.68 1.54 NA 0.00
coef_cars1_auto_time_saving_per_worker 0.774 0.644 1.20 NA 0.00
coef_cars1_density_0_10_no_workers 0.00 NA NA NA 0.00fixed value
coef_cars1_density_10_up_no_workers-0.00676 0.00515-1.31 NA 0.00
coef_cars1_density_10_up_workers-0.0157 0.00391-4.02*** NA 0.00
coef_cars1_drivers_2 0.00 NA NA NA 0.00fixed value
coef_cars1_drivers_3 0.00 NA NA NA 0.00fixed value
coef_cars1_drivers_4_up 2.07 0.502 4.12*** NA 0.00
coef_cars1_hh_income_0_30k 0.0293 0.00630 4.65*** NA 0.00
coef_cars1_hh_income_30_up 0.00 NA NA NA 0.00fixed value
coef_cars1_num_workers_clip_3 0.00 NA NA NA 0.00fixed value
coef_cars1_persons_16_17 0.00 NA NA NA 0.00fixed value
coef_cars1_persons_18_24-0.450 0.125-3.60*** NA 0.00
coef_cars1_persons_25_34 0.00 NA NA NA 0.00fixed value
coef_cars1_presence_children_0_4 0.543 0.266 2.04* NA 0.00
coef_cars1_presence_children_5_17-0.248 0.197-1.25 NA 0.00
coef_cars234_asc_marin 0.00 NA NA NA 0.00fixed value
coef_cars234_presence_children_0_4 0.666 0.325 2.05* NA 0.00
coef_cars2_asc 2.49 2.67 0.93 NA 0.00
coef_cars2_asc_county-0.443 0.000785-564.11*** 0.00 0.00
coef_cars2_asc_san_francisco 4.05 2.67 1.51 NA 0.00
coef_cars2_auto_time_saving_per_worker 0.797 0.801 0.99 NA 0.00
coef_cars2_density_0_10_no_workers-0.174 0.0340-5.12*** NA 0.00
coef_cars2_density_10_up_no_workers-0.120 0.0177-6.79*** NA 0.00
coef_cars2_drivers_2 3.06 0.274 11.19*** NA 0.00
coef_cars2_drivers_3 3.72 0.360 10.33*** NA 0.00
coef_cars2_drivers_4_up 6.91 0.647 10.69*** NA 0.00
coef_cars2_hh_income_0_30k 0.0650 0.0151 4.31*** NA 0.00
coef_cars2_hh_income_30_up 0.00660 0.00254 2.60** NA 0.00
coef_cars2_num_workers_clip_3-0.0625 0.139-0.45 NA 0.00
coef_cars2_persons_16_17-1.41 0.378-3.74*** NA 0.00
coef_cars2_persons_18_24-0.941 0.175-5.37*** NA 0.00
coef_cars2_persons_25_34-0.458 0.0980-4.67*** NA 0.00
coef_cars2_presence_children_5_17 0.185 0.260 0.71 NA 0.00
coef_cars34_asc_county-0.237 4.63e-05-5118.55*** 0.00 0.00
coef_cars34_asc_san_francisco 1.41 747. 0.00 57.32 0.00
coef_cars34_density_0_10_no_workers-0.367 0.0627-5.85*** NA 0.00
coef_cars34_density_10_up_no_workers-0.239 0.0880-2.72** NA 0.00
coef_cars34_persons_16_17-1.87 0.506-3.70*** NA 0.00
coef_cars34_persons_18_24-0.966 0.214-4.51*** NA 0.00
coef_cars34_persons_25_34-0.934 0.171-5.48*** NA 0.00
coef_cars34_presence_children_5_17 0.423 0.365 1.16 NA 0.00
coef_cars3_asc 4.22 747. 0.01 229.31 0.00
coef_cars3_auto_time_saving_per_worker 0.599 1.19 0.50 NA 0.00
coef_cars3_drivers_2 2.38 0.790 3.01** NA 0.00
coef_cars3_drivers_3 5.11 0.810 6.32*** NA 0.00
coef_cars3_drivers_4_up 8.24 1.01 8.19*** NA 0.00
coef_cars3_hh_income_0_30k 0.0470 0.0280 1.68 NA 0.00
coef_cars3_hh_income_30_up 0.00476 0.00486 0.98 NA 0.00
coef_cars3_num_workers_clip_3 0.370 0.231 1.60 NA 0.00
coef_cars4_asc-11.5 1.32e+03-0.01 5238.23 0.00
coef_cars4_auto_time_saving_per_worker 1.50 1.70 0.88 NA 0.00
coef_cars4_drivers_2 14.3 996. 0.01 NA 0.00
coef_cars4_drivers_3 17.7 996. 0.02 NA 0.00
coef_cars4_drivers_4_up 21.5 996. 0.02 NA 0.00
coef_cars4_hh_income_0_30k 0.0716 0.0566 1.26 NA 0.00
coef_cars4_hh_income_30_up 0.0127 0.00643 1.98* NA 0.00
coef_cars4_num_workers_clip_3 0.656 0.327 2.00* NA 0.00
coef_retail_auto_no_workers-0.664 0.599-1.11 NA 0.00
coef_retail_auto_workers-0.632 0.591-1.07 NA 0.00
coef_retail_non_motor-0.0300 NA NA NA 0.00fixed value
coef_retail_transit_no_workers-0.336 0.193-1.74 NA 0.00
coef_retail_transit_workers-0.401 0.169-2.37* NA 0.00
" + ], + "text/plain": [ + "" ] }, - "execution_count": 29, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "m.estimate()" + "m.parameter_summary()" ] }, { @@ -4738,7 +5570,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -4748,11 +5580,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ - "# Write out replacement coefficients file and model summaries\n", "os.makedirs(os.path.join(edb_directory,'estimated'), exist_ok=True)" ] }, @@ -4765,7 +5596,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -4784,23 +5615,23 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.to_xlsx(\n", - " os.path.join(edb_directory,'estimated',\"auto_ownership_model_estimation.xlsx\"), \n", + " os.path.join(edb_directory,'estimated',\"auto_ownership_model_estimation.xlsx\"), data_statistics=False\n", ")" ] }, @@ -4815,7 +5646,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -4884,31 +5715,31 @@ " \n", " 62\n", " coef_cars4_drivers_3\n", - " 550.081966\n", + " 17.685502\n", " F\n", " \n", " \n", " 63\n", " coef_cars3_drivers_3\n", - " 5.114442\n", + " 5.113340\n", " F\n", " \n", " \n", " 64\n", " coef_cars2_drivers_4_up\n", - " 6.913250\n", + " 6.913047\n", " F\n", " \n", " \n", " 65\n", " coef_cars3_drivers_4_up\n", - " 8.245960\n", + " 8.244745\n", " F\n", " \n", " \n", " 66\n", " coef_cars4_drivers_4_up\n", - " 553.867780\n", + " 21.471015\n", " F\n", " \n", " \n", @@ -4917,23 +5748,23 @@ "" ], "text/plain": [ - " coefficient_name value constrain\n", - "0 coef_cars1_drivers_2 0.000000 T\n", - "1 coef_cars1_drivers_3 0.000000 T\n", - "2 coef_cars1_persons_16_17 0.000000 T\n", - "3 coef_cars234_asc_marin 0.000000 T\n", - "4 coef_cars1_persons_25_34 0.000000 T\n", - ".. ... ... ...\n", - "62 coef_cars4_drivers_3 550.081966 F\n", - "63 coef_cars3_drivers_3 5.114442 F\n", - "64 coef_cars2_drivers_4_up 6.913250 F\n", - "65 coef_cars3_drivers_4_up 8.245960 F\n", - "66 coef_cars4_drivers_4_up 553.867780 F\n", + " coefficient_name value constrain\n", + "0 coef_cars1_drivers_2 0.000000 T\n", + "1 coef_cars1_drivers_3 0.000000 T\n", + "2 coef_cars1_persons_16_17 0.000000 T\n", + "3 coef_cars234_asc_marin 0.000000 T\n", + "4 coef_cars1_persons_25_34 0.000000 T\n", + ".. ... ... ...\n", + "62 coef_cars4_drivers_3 17.685502 F\n", + "63 coef_cars3_drivers_3 5.113340 F\n", + "64 coef_cars2_drivers_4_up 6.913047 F\n", + "65 coef_cars3_drivers_4_up 8.244745 F\n", + "66 coef_cars4_drivers_4_up 21.471015 F\n", "\n", "[67 rows x 3 columns]" ] }, - "execution_count": 34, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -4971,7 +5802,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.8" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/estimating_school_location.ipynb b/activitysim/examples/example_estimation/notebooks/estimating_school_location.ipynb index 192f62376..fd2be4414 100644 --- a/activitysim/examples/example_estimation/notebooks/estimating_school_location.ipynb +++ b/activitysim/examples/example_estimation/notebooks/estimating_school_location.ipynb @@ -25,16 +25,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import larch # !conda install larch #for estimation\n", + "import larch.util.activitysim\n", "import pandas as pd\n", "import numpy as np\n", "import yaml \n", "import larch.util.excel\n", - "import larch_asim # utility functions in a local module\n", "import os" ] }, @@ -2508,7 +2508,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -2517,7 +2517,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -2536,7 +2536,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -2551,7 +2551,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -2585,7 +2585,7 @@ " 'SAVED_SHADOW_PRICE_TABLE_NAME': 'school_shadow_prices.csv'}" ] }, - "execution_count": 7, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -2605,7 +2605,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -2614,7 +2614,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -2804,7 +2804,7 @@ "10 1 1 " ] }, - "execution_count": 9, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -2813,14 +2813,19 @@ "spec" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove shadow pricing and pre-existing size expression" + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "# Remove shadow pricing and pre-existing size expression\n", - "\n", "spec = spec\\\n", ".set_index('Label')\\\n", ".drop(index=['util_size_variable', 'util_utility_adjustment'])\\\n", @@ -2836,7 +2841,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -2845,7 +2850,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2916,7 +2921,7 @@ "highschool 0.0 1.0 0.000 0.000" ] }, - "execution_count": 12, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2939,7 +2944,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -3006,7 +3011,7 @@ "highschool_HSENROLL 0.000000 T" ] }, - "execution_count": 13, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -3034,7 +3039,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -3161,7 +3166,7 @@ "coef_mode_logsum 0.5358 F" ] }, - "execution_count": 14, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -3180,7 +3185,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 297, "metadata": {}, "outputs": [ { @@ -3274,7 +3279,7 @@ "29368 185 13 16 3 29368" ] }, - "execution_count": 15, + "execution_count": 297, "metadata": {}, "output_type": "execute_result" } @@ -3284,34 +3289,6 @@ "x_co.head()" ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 984 entries, 629 to 7541072\n", - "Data columns (total 5 columns):\n", - " # Column Non-Null Count Dtype\n", - "--- ------ -------------- -----\n", - " 0 model_choice 984 non-null int64\n", - " 1 override_choice 984 non-null int64\n", - " 2 TAZ 984 non-null int64\n", - " 3 school_segment 984 non-null int64\n", - " 4 household_id 984 non-null int64\n", - "dtypes: int64(5)\n", - "memory usage: 46.1 KB\n" - ] - } - ], - "source": [ - "x_co.info()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -3321,7 +3298,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 299, "metadata": {}, "outputs": [ { @@ -3685,7 +3662,7 @@ "[5 rows x 190 columns]" ] }, - "execution_count": 17, + "execution_count": 299, "metadata": {}, "output_type": "execute_result" } @@ -3704,7 +3681,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 300, "metadata": {}, "outputs": [ { @@ -3929,7 +3906,7 @@ "[5 rows x 27 columns]" ] }, - "execution_count": 18, + "execution_count": 300, "metadata": {}, "output_type": "execute_result" } @@ -3939,6 +3916,69 @@ "landuse.head()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove choosers with invalid observed choice" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " from ipykernel import kernelapp as app\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " from ipykernel import kernelapp as app\n" + ] + }, + { + "data": { + "text/plain": [ + "True 984\n", + "Name: valid_observed_zone, dtype: int64" + ] + }, + "execution_count": 301, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SEGMENT_IDS_REVERSE = {v: k for k, v in SEGMENT_IDS.items()}\n", + "x_co[\"school_segment_label\"] = x_co[\"school_segment\"].apply(lambda x: SEGMENT_IDS_REVERSE[x])\n", + "\n", + "for segment in school_size_spec.index:\n", + " landuse[\"total_size_\" + segment] = 0\n", + " x_co[\"total_size_\" + segment] = 0 \n", + " for land_use_field in school_size_spec.loc[segment].index:\n", + " landuse[\"total_size_\" + segment] = landuse[\"total_size_\" + segment] + landuse[land_use_field] * school_size_spec.loc[segment][land_use_field]\n", + " x_co[\"total_size_\" + segment] = landuse.loc[x_co[\"override_choice\"]][\"total_size_\" + segment].tolist()\n", + "\n", + "x_co[\"total_size_segment\"] = 0\n", + "for segment in school_size_spec.index:\n", + " labels = \"total_size_\" + segment\n", + " rows = x_co[\"school_segment_label\"] == segment\n", + " x_co[\"total_size_segment\"][rows] = x_co[labels][rows]\n", + " \n", + "x_co[\"valid_observed_zone\"] = x_co[\"total_size_segment\"] > 0\n", + "x_co = x_co[x_co[\"valid_observed_zone\"]]\n", + "\n", + "x_co[\"valid_observed_zone\"].value_counts() #True is valid, False is invalid" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -3950,16 +3990,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 302, "metadata": {}, "outputs": [], "source": [ - "x_ca = larch_asim.cv_to_ca(x_cv)" + "x_ca = larch.util.activitysim.cv_to_ca(x_cv)\n", + "x_ca = x_ca[x_ca.index.get_level_values('person_id').isin(x_co.index)]" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 303, "metadata": {}, "outputs": [], "source": [ @@ -3969,23 +4010,39 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 304, "metadata": {}, "outputs": [], "source": [ - "x_ca_1, x_co = larch_asim.prevent_overlapping_column_names(x_ca_1, x_co)" + "x_ca_1, x_co = larch.util.activitysim.prevent_overlapping_column_names(x_ca_1, x_co)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Availability" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 328, + "metadata": {}, + "outputs": [], + "source": [ + "av = x_ca_1['util_no_attractions'].apply(lambda x: False if x == 1 else True)" + ] + }, + { + "cell_type": "code", + "execution_count": 329, "metadata": {}, "outputs": [], "source": [ "d = larch.DataFrames(\n", " co=x_co,\n", " ca=x_ca_1,\n", - " av=True,\n", + " av=av\n", ")" ] }, @@ -3998,7 +4055,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 307, "metadata": {}, "outputs": [], "source": [ @@ -4007,7 +4064,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 308, "metadata": {}, "outputs": [ { @@ -4042,7 +4099,7 @@ } ], "source": [ - "m.utility_ca = larch_asim.linear_utility_from_spec(\n", + "m.utility_ca = larch.util.activitysim.linear_utility_from_spec(\n", " spec, x_col='Label', \n", " p_col=SEGMENT_IDS, \n", " ignore_x=('local_dist',), \n", @@ -4053,7 +4110,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 309, "metadata": {}, "outputs": [], "source": [ @@ -4067,11 +4124,11 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 310, "metadata": {}, "outputs": [], "source": [ - "larch_asim.explicit_value_parameters_from_spec(spec, p_col=SEGMENT_IDS, model=m)" + "larch.util.activitysim.explicit_value_parameters_from_spec(spec, p_col=SEGMENT_IDS, model=m)" ] }, { @@ -4083,7 +4140,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 311, "metadata": {}, "outputs": [ { @@ -4367,7 +4424,7 @@ "university_COLLPTE 0 " ] }, - "execution_count": 27, + "execution_count": 311, "metadata": {}, "output_type": "execute_result" } @@ -4378,17 +4435,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 312, "metadata": {}, "outputs": [], "source": [ - "larch_asim.apply_coefficients(coefficients, m)\n", - "larch_asim.apply_coefficients(size_coef, m, minimum=-6, maximum=6)" + "larch.util.activitysim.apply_coefficients(coefficients, m)\n", + "larch.util.activitysim.apply_coefficients(size_coef, m, minimum=-6, maximum=6)" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 313, "metadata": {}, "outputs": [ { @@ -4672,7 +4729,7 @@ "university_COLLPTE 0 " ] }, - "execution_count": 29, + "execution_count": 313, "metadata": {}, "output_type": "execute_result" } @@ -4683,7 +4740,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 314, "metadata": {}, "outputs": [], "source": [ @@ -4699,7 +4756,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 315, "metadata": {}, "outputs": [], "source": [ @@ -4712,12 +4769,12 @@ "source": [ "# Estimate\n", "\n", - "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution." + "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution. Larch has two built-in estimation methods: BHHH and SLSQP. BHHH is the default and typically runs faster, but does not follow constraints on parameters. SLSQP is safer, but slower, and may need additional iterations." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 317, "metadata": {}, "outputs": [ { @@ -4730,7 +4787,7 @@ { "data": { "text/html": [ - "

Iteration 020 [Converged]

" + "

Iteration 000 [Converged]

" ], "text/plain": [ "" @@ -4742,7 +4799,7 @@ { "data": { "text/html": [ - "

LL = -3097.64988639489

" + "

LL = -3097.6915321141814

" ], "text/plain": [ "" @@ -4779,6 +4836,13 @@ " maximum\n", " holdfast\n", " note\n", + " std_err\n", + " t_stat\n", + " robust_std_err\n", + " robust_t_stat\n", + " unconstrained_std_err\n", + " unconstrained_t_stat\n", + " constrained\n", " best\n", " \n", " \n", @@ -4792,6 +4856,13 @@ " -999.0\n", " 1\n", " \n", + " NaN\n", + " NaN\n", + " 0.000000e+00\n", + " NaN\n", + " 0.000000e+00\n", + " NaN\n", + " fixed value\n", " -999.000000\n", " \n", " \n", @@ -4803,18 +4874,32 @@ " 1.0\n", " 1\n", " \n", + " NaN\n", + " NaN\n", + " 0.000000e+00\n", + " NaN\n", + " 0.000000e+00\n", + " NaN\n", + " fixed value\n", " 1.000000\n", " \n", " \n", " coef_grade_dist_0_1\n", - " -3.727703\n", + " -3.727781\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -3.727703\n", + " 2.315438e-01\n", + " -1.609968e+01\n", + " 2.210235e-01\n", + " -1.686600e+01\n", + " 2.315438e-01\n", + " -1.609968e+01\n", + " \n", + " -3.727781\n", " \n", " \n", " coef_grade_dist_15_up\n", @@ -4825,29 +4910,50 @@ " NaN\n", " 0\n", " \n", + " 1.717021e-15\n", + " -2.679059e+13\n", + " 1.869788e-15\n", + " -2.460172e+13\n", + " 1.717021e-15\n", + " -2.679059e+13\n", + " \n", " -0.046000\n", " \n", " \n", " coef_grade_dist_5_15\n", - " -0.421664\n", + " -0.421906\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.421664\n", + " 1.219025e-01\n", + " -3.461011e+00\n", + " 1.258530e-01\n", + " -3.352371e+00\n", + " 1.219025e-01\n", + " -3.461011e+00\n", + " \n", + " -0.421906\n", " \n", " \n", " coef_high_dist_0_1\n", - " -1.980909\n", + " -1.981339\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1.980909\n", + " 6.501108e-01\n", + " -3.047694e+00\n", + " 7.103136e-01\n", + " -2.789386e+00\n", + " 6.501108e-01\n", + " -3.047694e+00\n", + " \n", + " -1.981339\n", " \n", " \n", " coef_high_dist_15_up\n", @@ -4858,62 +4964,104 @@ " NaN\n", " 0\n", " \n", + " 1.056981e-15\n", + " -1.780543e+14\n", + " 1.150513e-15\n", + " -1.635791e+14\n", + " 1.056981e-15\n", + " -1.780543e+14\n", + " \n", " -0.188200\n", " \n", " \n", " coef_high_dist_5_15\n", - " -0.256995\n", + " -0.257137\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.256995\n", + " 2.088883e-01\n", + " -1.230979e+00\n", + " 1.901204e-01\n", + " -1.352497e+00\n", + " 2.088883e-01\n", + " -1.230979e+00\n", + " \n", + " -0.257137\n", " \n", " \n", " coef_high_grade_dist_1_2\n", - " -1.188356\n", + " -1.189074\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1.188356\n", + " 1.577002e-01\n", + " -7.540092e+00\n", + " 1.617762e-01\n", + " -7.350116e+00\n", + " 1.577002e-01\n", + " -7.540092e+00\n", + " \n", + " -1.189074\n", " \n", " \n", " coef_high_grade_dist_2_5\n", - " -1.098063\n", + " -1.098452\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1.098063\n", + " 6.475680e-02\n", + " -1.696272e+01\n", + " 6.873185e-02\n", + " -1.598170e+01\n", + " 6.475680e-02\n", + " -1.696272e+01\n", + " \n", + " -1.098452\n", " \n", " \n", " coef_mode_logsum\n", - " 0.486105\n", + " 0.484746\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 0.486105\n", + " 5.677964e-02\n", + " 8.537320e+00\n", + " 5.506359e-02\n", + " 8.803385e+00\n", + " 5.677964e-02\n", + " 8.537320e+00\n", + " \n", + " 0.484746\n", " \n", " \n", " coef_univ_dist_0_1\n", - " -7.476604\n", + " -7.518143\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -7.476604\n", + " 6.094486e-01\n", + " -1.233598e+01\n", + " 6.512043e-01\n", + " -1.154498e+01\n", + " 6.094486e-01\n", + " -1.233598e+01\n", + " \n", + " -7.518143\n", " \n", " \n", " coef_univ_dist_15_up\n", @@ -4924,40 +5072,68 @@ " NaN\n", " 0\n", " \n", + " 2.188198e-16\n", + " -3.336078e+14\n", + " 2.213527e-16\n", + " -3.297905e+14\n", + " 2.188198e-16\n", + " -3.336078e+14\n", + " \n", " -0.073000\n", " \n", " \n", " coef_univ_dist_1_2\n", - " -4.420887\n", + " -4.426815\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -4.420887\n", + " 2.861895e-01\n", + " -1.546812e+01\n", + " 3.063983e-01\n", + " -1.444791e+01\n", + " 2.861895e-01\n", + " -1.546812e+01\n", + " \n", + " -4.426815\n", " \n", " \n", " coef_univ_dist_2_5\n", - " -1.169426\n", + " -1.169154\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1.169426\n", + " 1.047152e-01\n", + " -1.116509e+01\n", + " 1.080271e-01\n", + " -1.082278e+01\n", + " 1.047152e-01\n", + " -1.116509e+01\n", + " \n", + " -1.169154\n", " \n", " \n", " coef_univ_dist_5_15\n", - " -1.537107\n", + " -1.537447\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1.537107\n", + " 2.640882e-01\n", + " -5.821717e+00\n", + " 2.470860e-01\n", + " -6.222313e+00\n", + " 2.640882e-01\n", + " -5.821717e+00\n", + " \n", + " -1.537447\n", " \n", " \n", " gradeschool_AGE0519\n", @@ -4968,6 +5144,13 @@ " 6.0\n", " 1\n", " \n", + " NaN\n", + " NaN\n", + " 0.000000e+00\n", + " NaN\n", + " 0.000000e+00\n", + " NaN\n", + " fixed value\n", " 0.000000\n", " \n", " \n", @@ -4979,6 +5162,13 @@ " 6.0\n", " 1\n", " \n", + " NaN\n", + " NaN\n", + " 0.000000e+00\n", + " NaN\n", + " 0.000000e+00\n", + " NaN\n", + " fixed value\n", " 0.000000\n", " \n", " \n", @@ -4990,18 +5180,32 @@ " 6.0\n", " 1\n", " \n", + " NaN\n", + " NaN\n", + " 0.000000e+00\n", + " -inf\n", + " 0.000000e+00\n", + " -inf\n", + " fixed value\n", " -0.524249\n", " \n", " \n", " university_COLLPTE\n", - " 1.807021\n", + " 1.807565\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " 1.807021\n", + " 2.059448e-01\n", + " 8.776942e+00\n", + " 2.034682e-01\n", + " 8.883772e+00\n", + " 2.059448e-01\n", + " 8.776942e+00\n", + " \n", + " 1.807565\n", " \n", " \n", "\n", @@ -5011,46 +5215,112 @@ " value initvalue nullvalue minimum maximum \\\n", "-999 -999.000000 -999.0 -999.0 -999.0 -999.0 \n", "1 1.000000 1.0 1.0 1.0 1.0 \n", - "coef_grade_dist_0_1 -3.727703 0.0 0.0 NaN NaN \n", + "coef_grade_dist_0_1 -3.727781 0.0 0.0 NaN NaN \n", "coef_grade_dist_15_up -0.046000 0.0 0.0 NaN NaN \n", - "coef_grade_dist_5_15 -0.421664 0.0 0.0 NaN NaN \n", - "coef_high_dist_0_1 -1.980909 0.0 0.0 NaN NaN \n", + "coef_grade_dist_5_15 -0.421906 0.0 0.0 NaN NaN \n", + "coef_high_dist_0_1 -1.981339 0.0 0.0 NaN NaN \n", "coef_high_dist_15_up -0.188200 0.0 0.0 NaN NaN \n", - "coef_high_dist_5_15 -0.256995 0.0 0.0 NaN NaN \n", - "coef_high_grade_dist_1_2 -1.188356 0.0 0.0 NaN NaN \n", - "coef_high_grade_dist_2_5 -1.098063 0.0 0.0 NaN NaN \n", - "coef_mode_logsum 0.486105 0.0 0.0 NaN NaN \n", - "coef_univ_dist_0_1 -7.476604 0.0 0.0 NaN NaN \n", + "coef_high_dist_5_15 -0.257137 0.0 0.0 NaN NaN \n", + "coef_high_grade_dist_1_2 -1.189074 0.0 0.0 NaN NaN \n", + "coef_high_grade_dist_2_5 -1.098452 0.0 0.0 NaN NaN \n", + "coef_mode_logsum 0.484746 0.0 0.0 NaN NaN \n", + "coef_univ_dist_0_1 -7.518143 0.0 0.0 NaN NaN \n", "coef_univ_dist_15_up -0.073000 0.0 0.0 NaN NaN \n", - "coef_univ_dist_1_2 -4.420887 0.0 0.0 NaN NaN \n", - "coef_univ_dist_2_5 -1.169426 0.0 0.0 NaN NaN \n", - "coef_univ_dist_5_15 -1.537107 0.0 0.0 NaN NaN \n", + "coef_univ_dist_1_2 -4.426815 0.0 0.0 NaN NaN \n", + "coef_univ_dist_2_5 -1.169154 0.0 0.0 NaN NaN \n", + "coef_univ_dist_5_15 -1.537447 0.0 0.0 NaN NaN \n", "gradeschool_AGE0519 0.000000 0.0 0.0 -6.0 6.0 \n", "highschool_HSENROLL 0.000000 0.0 0.0 -6.0 6.0 \n", "university_COLLFTE -0.524249 0.0 0.0 -6.0 6.0 \n", - "university_COLLPTE 1.807021 0.0 0.0 -6.0 6.0 \n", + "university_COLLPTE 1.807565 0.0 0.0 -6.0 6.0 \n", + "\n", + " holdfast note std_err t_stat \\\n", + "-999 1 NaN NaN \n", + "1 1 NaN NaN \n", + "coef_grade_dist_0_1 0 2.315438e-01 -1.609968e+01 \n", + "coef_grade_dist_15_up 0 1.717021e-15 -2.679059e+13 \n", + "coef_grade_dist_5_15 0 1.219025e-01 -3.461011e+00 \n", + "coef_high_dist_0_1 0 6.501108e-01 -3.047694e+00 \n", + "coef_high_dist_15_up 0 1.056981e-15 -1.780543e+14 \n", + "coef_high_dist_5_15 0 2.088883e-01 -1.230979e+00 \n", + "coef_high_grade_dist_1_2 0 1.577002e-01 -7.540092e+00 \n", + "coef_high_grade_dist_2_5 0 6.475680e-02 -1.696272e+01 \n", + "coef_mode_logsum 0 5.677964e-02 8.537320e+00 \n", + "coef_univ_dist_0_1 0 6.094486e-01 -1.233598e+01 \n", + "coef_univ_dist_15_up 0 2.188198e-16 -3.336078e+14 \n", + "coef_univ_dist_1_2 0 2.861895e-01 -1.546812e+01 \n", + "coef_univ_dist_2_5 0 1.047152e-01 -1.116509e+01 \n", + "coef_univ_dist_5_15 0 2.640882e-01 -5.821717e+00 \n", + "gradeschool_AGE0519 1 NaN NaN \n", + "highschool_HSENROLL 1 NaN NaN \n", + "university_COLLFTE 1 NaN NaN \n", + "university_COLLPTE 0 2.059448e-01 8.776942e+00 \n", + "\n", + " robust_std_err robust_t_stat \\\n", + "-999 0.000000e+00 NaN \n", + "1 0.000000e+00 NaN \n", + "coef_grade_dist_0_1 2.210235e-01 -1.686600e+01 \n", + "coef_grade_dist_15_up 1.869788e-15 -2.460172e+13 \n", + "coef_grade_dist_5_15 1.258530e-01 -3.352371e+00 \n", + "coef_high_dist_0_1 7.103136e-01 -2.789386e+00 \n", + "coef_high_dist_15_up 1.150513e-15 -1.635791e+14 \n", + "coef_high_dist_5_15 1.901204e-01 -1.352497e+00 \n", + "coef_high_grade_dist_1_2 1.617762e-01 -7.350116e+00 \n", + "coef_high_grade_dist_2_5 6.873185e-02 -1.598170e+01 \n", + "coef_mode_logsum 5.506359e-02 8.803385e+00 \n", + "coef_univ_dist_0_1 6.512043e-01 -1.154498e+01 \n", + "coef_univ_dist_15_up 2.213527e-16 -3.297905e+14 \n", + "coef_univ_dist_1_2 3.063983e-01 -1.444791e+01 \n", + "coef_univ_dist_2_5 1.080271e-01 -1.082278e+01 \n", + "coef_univ_dist_5_15 2.470860e-01 -6.222313e+00 \n", + "gradeschool_AGE0519 0.000000e+00 NaN \n", + "highschool_HSENROLL 0.000000e+00 NaN \n", + "university_COLLFTE 0.000000e+00 -inf \n", + "university_COLLPTE 2.034682e-01 8.883772e+00 \n", "\n", - " holdfast note best \n", - "-999 1 -999.000000 \n", - "1 1 1.000000 \n", - "coef_grade_dist_0_1 0 -3.727703 \n", - "coef_grade_dist_15_up 0 -0.046000 \n", - "coef_grade_dist_5_15 0 -0.421664 \n", - "coef_high_dist_0_1 0 -1.980909 \n", - "coef_high_dist_15_up 0 -0.188200 \n", - "coef_high_dist_5_15 0 -0.256995 \n", - "coef_high_grade_dist_1_2 0 -1.188356 \n", - "coef_high_grade_dist_2_5 0 -1.098063 \n", - "coef_mode_logsum 0 0.486105 \n", - "coef_univ_dist_0_1 0 -7.476604 \n", - "coef_univ_dist_15_up 0 -0.073000 \n", - "coef_univ_dist_1_2 0 -4.420887 \n", - "coef_univ_dist_2_5 0 -1.169426 \n", - "coef_univ_dist_5_15 0 -1.537107 \n", - "gradeschool_AGE0519 1 0.000000 \n", - "highschool_HSENROLL 1 0.000000 \n", - "university_COLLFTE 1 -0.524249 \n", - "university_COLLPTE 0 1.807021 " + " unconstrained_std_err unconstrained_t_stat \\\n", + "-999 0.000000e+00 NaN \n", + "1 0.000000e+00 NaN \n", + "coef_grade_dist_0_1 2.315438e-01 -1.609968e+01 \n", + "coef_grade_dist_15_up 1.717021e-15 -2.679059e+13 \n", + "coef_grade_dist_5_15 1.219025e-01 -3.461011e+00 \n", + "coef_high_dist_0_1 6.501108e-01 -3.047694e+00 \n", + "coef_high_dist_15_up 1.056981e-15 -1.780543e+14 \n", + "coef_high_dist_5_15 2.088883e-01 -1.230979e+00 \n", + "coef_high_grade_dist_1_2 1.577002e-01 -7.540092e+00 \n", + "coef_high_grade_dist_2_5 6.475680e-02 -1.696272e+01 \n", + "coef_mode_logsum 5.677964e-02 8.537320e+00 \n", + "coef_univ_dist_0_1 6.094486e-01 -1.233598e+01 \n", + "coef_univ_dist_15_up 2.188198e-16 -3.336078e+14 \n", + "coef_univ_dist_1_2 2.861895e-01 -1.546812e+01 \n", + "coef_univ_dist_2_5 1.047152e-01 -1.116509e+01 \n", + "coef_univ_dist_5_15 2.640882e-01 -5.821717e+00 \n", + "gradeschool_AGE0519 0.000000e+00 NaN \n", + "highschool_HSENROLL 0.000000e+00 NaN \n", + "university_COLLFTE 0.000000e+00 -inf \n", + "university_COLLPTE 2.059448e-01 8.776942e+00 \n", + "\n", + " constrained best \n", + "-999 fixed value -999.000000 \n", + "1 fixed value 1.000000 \n", + "coef_grade_dist_0_1 -3.727781 \n", + "coef_grade_dist_15_up -0.046000 \n", + "coef_grade_dist_5_15 -0.421906 \n", + "coef_high_dist_0_1 -1.981339 \n", + "coef_high_dist_15_up -0.188200 \n", + "coef_high_dist_5_15 -0.257137 \n", + "coef_high_grade_dist_1_2 -1.189074 \n", + "coef_high_grade_dist_2_5 -1.098452 \n", + "coef_mode_logsum 0.484746 \n", + "coef_univ_dist_0_1 -7.518143 \n", + "coef_univ_dist_15_up -0.073000 \n", + "coef_univ_dist_1_2 -4.426815 \n", + "coef_univ_dist_2_5 -1.169154 \n", + "coef_univ_dist_5_15 -1.537447 \n", + "gradeschool_AGE0519 fixed value 0.000000 \n", + "highschool_HSENROLL fixed value 0.000000 \n", + "university_COLLFTE fixed value -0.524249 \n", + "university_COLLPTE 1.807565 " ] }, "metadata": {}, @@ -5060,14 +5330,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.323538296293168e-15 in general_inverse\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 2.669099071048399e-15 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 9.716975768626752e-16 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" ] }, { "data": { "text/html": [ - "
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coef_grade_dist_0_10.000179
coef_grade_dist_15_up0.000000
coef_grade_dist_5_15-0.000727
coef_high_dist_0_1-0.000154
coef_high_dist_15_up0.000000
coef_high_dist_5_150.000226
coef_high_grade_dist_1_20.000831
coef_high_grade_dist_2_50.001188
coef_mode_logsum-0.001442
coef_univ_dist_0_10.000501
coef_univ_dist_15_up0.000000
coef_univ_dist_1_20.000672
coef_univ_dist_2_50.003132
coef_univ_dist_5_150.000092
gradeschool_AGE05190.000000
highschool_HSENROLL0.000000
university_COLLFTE0.000000
university_COLLPTE-0.000776
nit20
nfev41
njev20
status0
message'Optimization terminated successfully.'
successTrue
elapsed_time0:00:02.053082
method'slsqp'
n_cases984
iteration_number20
logloss3.148018177230579
" + "
tolerance3.973751662391261e-08stepsarray([], dtype=float64)message'Optimization terminated successfully.'elapsed_time0:00:00.123003method'BHHH'n_cases984iteration_number0logloss3.148060500116038
" ], "text/plain": [ + "┣ loglike: -3097.6915321141814\n", "┣ x: -999 -999.000000\n", "┃ 1 1.000000\n", - "┃ coef_grade_dist_0_1 -3.727703\n", + "┃ coef_grade_dist_0_1 -3.727781\n", "┃ coef_grade_dist_15_up -0.046000\n", - "┃ coef_grade_dist_5_15 -0.421664\n", - "┃ coef_high_dist_0_1 -1.980909\n", + "┃ coef_grade_dist_5_15 -0.421906\n", + "┃ coef_high_dist_0_1 -1.981339\n", "┃ coef_high_dist_15_up -0.188200\n", - "┃ coef_high_dist_5_15 -0.256995\n", - "┃ coef_high_grade_dist_1_2 -1.188356\n", - "┃ coef_high_grade_dist_2_5 -1.098063\n", - "┃ coef_mode_logsum 0.486105\n", - "┃ coef_univ_dist_0_1 -7.476604\n", + "┃ coef_high_dist_5_15 -0.257137\n", + "┃ coef_high_grade_dist_1_2 -1.189074\n", + "┃ coef_high_grade_dist_2_5 -1.098452\n", + "┃ coef_mode_logsum 0.484746\n", + "┃ coef_univ_dist_0_1 -7.518143\n", "┃ coef_univ_dist_15_up -0.073000\n", - "┃ coef_univ_dist_1_2 -4.420887\n", - "┃ coef_univ_dist_2_5 -1.169426\n", - "┃ coef_univ_dist_5_15 -1.537107\n", + "┃ coef_univ_dist_1_2 -4.426815\n", + "┃ coef_univ_dist_2_5 -1.169154\n", + "┃ coef_univ_dist_5_15 -1.537447\n", "┃ gradeschool_AGE0519 0.000000\n", "┃ highschool_HSENROLL 0.000000\n", "┃ university_COLLFTE -0.524249\n", - "┃ university_COLLPTE 1.807021\n", - "┃ dtype: float64\n", - "┣ loglike: -3097.64988639489\n", - "┣ d_loglike: -999 0.000000\n", - "┃ 1 0.000000\n", - "┃ coef_grade_dist_0_1 0.000179\n", - "┃ coef_grade_dist_15_up 0.000000\n", - "┃ coef_grade_dist_5_15 -0.000727\n", - "┃ coef_high_dist_0_1 -0.000154\n", - "┃ coef_high_dist_15_up 0.000000\n", - "┃ coef_high_dist_5_15 0.000226\n", - "┃ coef_high_grade_dist_1_2 0.000831\n", - "┃ coef_high_grade_dist_2_5 0.001188\n", - "┃ coef_mode_logsum -0.001442\n", - "┃ coef_univ_dist_0_1 0.000501\n", - "┃ coef_univ_dist_15_up 0.000000\n", - "┃ coef_univ_dist_1_2 0.000672\n", - "┃ coef_univ_dist_2_5 0.003132\n", - "┃ coef_univ_dist_5_15 0.000092\n", - "┃ gradeschool_AGE0519 0.000000\n", - "┃ highschool_HSENROLL 0.000000\n", - "┃ university_COLLFTE 0.000000\n", - "┃ university_COLLPTE -0.000776\n", + "┃ university_COLLPTE 1.807565\n", "┃ dtype: float64\n", - "┣ nit: 20\n", - "┣ nfev: 41\n", - "┣ njev: 20\n", - "┣ status: 0\n", + "┣ tolerance: 3.973751662391261e-08\n", + "┣ steps: array([], dtype=float64)\n", "┣ message: 'Optimization terminated successfully.'\n", - "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=2, microseconds=53082)\n", - "┣ method: 'slsqp'\n", + "┣ elapsed_time: datetime.timedelta(microseconds=123003)\n", + "┣ method: 'BHHH'\n", "┣ n_cases: 984\n", - "┣ iteration_number: 20\n", - "┣ logloss: 3.148018177230579" + "┣ iteration_number: 0\n", + "┣ logloss: 3.148060500116038" ] }, - "execution_count": 32, + "execution_count": 317, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "m.estimate()" + "# m.estimate(method='SLSQP', options={'maxiter':1000})\n", + "m.estimate(method='BHHH', options={'maxiter':1000})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Output Estimation Results" + "### Estimated coefficients" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 318, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Value Std Err t Stat Signif Null Value Constrained
-999-999. NA NA-999.00fixed value
1 1.00 NA NA 1.00fixed value
coef_grade_dist_0_1-3.73 0.232-16.10*** 0.00
coef_grade_dist_15_up-0.0460 2.35e-15-19579306130383.17*** 0.00
coef_grade_dist_5_15-0.422 0.122-3.46*** 0.00
coef_high_dist_0_1-1.98 0.650-3.05** 0.00
coef_high_dist_15_up-0.188 4.48e-16-420173649990679.69*** 0.00
coef_high_dist_5_15-0.257 0.209-1.23 0.00
coef_high_grade_dist_1_2-1.19 0.158-7.54*** 0.00
coef_high_grade_dist_2_5-1.10 0.0648-16.96*** 0.00
coef_mode_logsum 0.485 0.0568 8.54*** 0.00
coef_univ_dist_0_1-7.52 0.609-12.34*** 0.00
coef_univ_dist_15_up-0.0730 2.79e-16-261976815627606.72*** 0.00
coef_univ_dist_1_2-4.43 0.286-15.47*** 0.00
coef_univ_dist_2_5-1.17 0.105-11.17*** 0.00
coef_univ_dist_5_15-1.54 0.264-5.82*** 0.00
gradeschool_AGE0519 0.00 NA NA 0.00fixed value
highschool_HSENROLL 0.00 NA NA 0.00fixed value
university_COLLFTE-0.524 NA NA 0.00fixed value
university_COLLPTE 1.81 0.206 8.78*** 0.00
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 318, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Write re-estimated value back into the coefficients file.\n", - "est_names = [j for j in coefficients.index if j in m.pf.index]\n", - "coefficients.loc[est_names, 'value'] = m.pf.loc[est_names, 'value']" + "m.parameter_summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Write the re-estimated coefficients file" + "# Output Estimation Results" + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": {}, + "outputs": [], + "source": [ + "est_names = [j for j in coefficients.index if j in m.pf.index]\n", + "coefficients.loc[est_names, 'value'] = m.pf.loc[est_names, 'value']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 320, "metadata": {}, "outputs": [], "source": [ - "# Write out replacement coefficients file and model summaries\n", "os.makedirs(os.path.join(edb_directory,'estimated'), exist_ok=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write the re-estimated coefficients file" + ] + }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 321, "metadata": {}, "outputs": [], "source": [ @@ -5373,16 +5751,16 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 323, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 36, + "execution_count": 323, "metadata": {}, "output_type": "execute_result" } @@ -5392,8 +5770,8 @@ " os.path.join(\n", " edb_directory, \n", " 'estimated',\n", - " \"school_location_model_estimation.xlsx\",\n", - " )\n", + " \"school_location_model_estimation.xlsx\"\n", + " ), data_statistics=False\n", ")" ] }, @@ -5406,7 +5784,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 324, "metadata": {}, "outputs": [], "source": [ @@ -5423,19 +5801,18 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 325, "metadata": {}, "outputs": [], "source": [ "# Rescale each row to total 1, not mathematically needed\n", "# but to maintain a consistent approach from existing ASim\n", - "\n", "size_spec.iloc[:,2:] = (size_spec.iloc[:,2:].div(size_spec.iloc[:,2:].sum(1), axis=0))" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 326, "metadata": {}, "outputs": [], "source": [ @@ -5456,7 +5833,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 327, "metadata": {}, "outputs": [ { @@ -5489,25 +5866,25 @@ " \n", " 0\n", " coef_univ_dist_0_1\n", - " -7.476604\n", + " -7.518143\n", " F\n", " \n", " \n", " 1\n", " coef_univ_dist_1_2\n", - " -4.420887\n", + " -4.426815\n", " F\n", " \n", " \n", " 2\n", " coef_univ_dist_2_5\n", - " -1.169426\n", + " -1.169154\n", " F\n", " \n", " \n", " 3\n", " coef_univ_dist_5_15\n", - " -1.537107\n", + " -1.537447\n", " F\n", " \n", " \n", @@ -5519,25 +5896,25 @@ " \n", " 5\n", " coef_high_dist_0_1\n", - " -1.980909\n", + " -1.981339\n", " F\n", " \n", " \n", " 6\n", " coef_high_grade_dist_1_2\n", - " -1.188356\n", + " -1.189074\n", " F\n", " \n", " \n", " 7\n", " coef_high_grade_dist_2_5\n", - " -1.098063\n", + " -1.098452\n", " F\n", " \n", " \n", " 8\n", " coef_high_dist_5_15\n", - " -0.256995\n", + " -0.257137\n", " F\n", " \n", " \n", @@ -5549,13 +5926,13 @@ " \n", " 10\n", " coef_grade_dist_0_1\n", - " -3.727703\n", + " -3.727781\n", " F\n", " \n", " \n", " 11\n", " coef_grade_dist_5_15\n", - " -0.421664\n", + " -0.421906\n", " F\n", " \n", " \n", @@ -5567,7 +5944,7 @@ " \n", " 13\n", " coef_mode_logsum\n", - " 0.486105\n", + " 0.484746\n", " F\n", " \n", " \n", @@ -5576,23 +5953,23 @@ ], "text/plain": [ " coefficient_name value constrain\n", - "0 coef_univ_dist_0_1 -7.476604 F\n", - "1 coef_univ_dist_1_2 -4.420887 F\n", - "2 coef_univ_dist_2_5 -1.169426 F\n", - "3 coef_univ_dist_5_15 -1.537107 F\n", + "0 coef_univ_dist_0_1 -7.518143 F\n", + "1 coef_univ_dist_1_2 -4.426815 F\n", + "2 coef_univ_dist_2_5 -1.169154 F\n", + "3 coef_univ_dist_5_15 -1.537447 F\n", "4 coef_univ_dist_15_up -0.073000 F\n", - "5 coef_high_dist_0_1 -1.980909 F\n", - "6 coef_high_grade_dist_1_2 -1.188356 F\n", - "7 coef_high_grade_dist_2_5 -1.098063 F\n", - "8 coef_high_dist_5_15 -0.256995 F\n", + "5 coef_high_dist_0_1 -1.981339 F\n", + "6 coef_high_grade_dist_1_2 -1.189074 F\n", + "7 coef_high_grade_dist_2_5 -1.098452 F\n", + "8 coef_high_dist_5_15 -0.257137 F\n", "9 coef_high_dist_15_up -0.188200 F\n", - "10 coef_grade_dist_0_1 -3.727703 F\n", - "11 coef_grade_dist_5_15 -0.421664 F\n", + "10 coef_grade_dist_0_1 -3.727781 F\n", + "11 coef_grade_dist_5_15 -0.421906 F\n", "12 coef_grade_dist_15_up -0.046000 F\n", - "13 coef_mode_logsum 0.486105 F" + "13 coef_mode_logsum 0.484746 F" ] }, - "execution_count": 51, + "execution_count": 327, "metadata": {}, "output_type": "execute_result" } @@ -6082,7 +6459,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.8" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/estimating_tour_mode_choice.ipynb b/activitysim/examples/example_estimation/notebooks/estimating_tour_mode_choice.ipynb index 2145e1d6b..0b8f3acaa 100644 --- a/activitysim/examples/example_estimation/notebooks/estimating_tour_mode_choice.ipynb +++ b/activitysim/examples/example_estimation/notebooks/estimating_tour_mode_choice.ipynb @@ -28,19 +28,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import larch # !conda install larch #for estimation\n", + "import larch.util.activitysim\n", "import pandas as pd\n", "import numpy as np\n", "import yaml \n", "import larch.util.excel\n", - "import larch_asim # utility functions in a local module\n", - "import os\n", - "\n", - "from larch import P,X" + "import os" ] }, { @@ -2681,7 +2679,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -2693,7 +2691,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2726,7 +2724,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -2851,7 +2849,7 @@ " 'MODE_CHOICE_LOGSUM_COLUMN_NAME': 'mode_choice_logsum'}" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -2874,7 +2872,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -2986,7 +2984,7 @@ "[306 rows x 2 columns]" ] }, - "execution_count": 12, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -3004,7 +3002,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -3346,7 +3344,7 @@ "[83 rows x 10 columns]" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -3364,17 +3362,7 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Remove apostrophes from Label names\n", - "spec['Label'] = spec['Label'].str.replace(\"'\",\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -3773,18 +3761,20 @@ "[345 rows x 24 columns]" ] }, - "execution_count": 15, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Remove apostrophes from Label names\n", + "spec['Label'] = spec['Label'].str.replace(\"'\",\"\")\n", "spec" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -3798,12 +3788,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Alternative values" + "### Chooser and alternative values" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -4267,7 +4257,7 @@ "[5323 rows x 534 columns]" ] }, - "execution_count": 17, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -4288,6 +4278,15 @@ "The next step is to transform the EDB for larch for model re-estimation. " ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from larch import P,X" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -4297,7 +4296,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -4326,7 +4325,7 @@ " 'TNC_SHARED': 21}" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -4339,6 +4338,22 @@ "alt_names_to_codes" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove choosers with invalid observed choice" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "values = values[values.override_choice.isin(alt_names)]" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -4348,7 +4363,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -4709,23 +4724,22 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tree = larch_asim.construct_nesting_tree(alt_names, settings['NESTS'])\n", - "\n", + "tree = larch.util.activitysim.construct_nesting_tree(alt_names, settings['NESTS'])\n", "tree" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -4754,7 +4768,7 @@ " 21: 'TNC_SHARED'}" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -4772,7 +4786,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -4790,7 +4804,7 @@ " 'atwork']" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -4809,7 +4823,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -4818,13 +4832,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "for alt_code, alt_name in tree.elemental_names().items():\n", " # Read in base utility function for this alt_name\n", - " u = larch_asim.linear_utility_from_spec(\n", + " u = larch.util.activitysim.linear_utility_from_spec(\n", " spec, x_col='Label', p_col=alt_name, \n", " ignore_x=('#',), \n", " )\n", @@ -4849,21 +4863,21 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "for model in m.values():\n", - " larch_asim.explicit_value_parameters(model)" + " larch.util.activitysim.explicit_value_parameters(model)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ - "larch_asim.apply_coefficients(coefficients, m)" + "larch.util.activitysim.apply_coefficients(coefficients, m)" ] }, { @@ -4875,22 +4889,38 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "values['override_choice_code'] = values.override_choice.map(alt_names_to_codes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Availability" + ] + }, + { + "cell_type": "code", + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ - "values['model_choice_code'] = values.model_choice.map(alt_names_to_codes)" + "av = True # all alternatives are available" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "d = larch.DataFrames(\n", " co=values.set_index('tour_id'),\n", - " av=True,\n", + " av=av,\n", " alt_codes=alt_codes,\n", " alt_names=alt_names,\n", ")" @@ -4898,18 +4928,18 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "for purpose, model in m.items():\n", " model.dataservice = d.selector_co(f\"tour_type=='{purpose}'\")\n", - " model.choice_co_code = 'model_choice_code'" + " model.choice_co_code = 'override_choice_code'" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -4923,12 +4953,12 @@ "source": [ "# Estimate\n", "\n", - "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution." + "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution. Larch has two built-in estimation methods: BHHH and SLSQP. BHHH is the default and typically runs faster, but does not follow constraints on parameters. SLSQP is safer, but slower, and may need additional iterations." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -4950,7 +4980,7 @@ { "data": { "text/html": [ - "

Iteration 101 [Converged]

" + "

Iteration 001 [Converged]

" ], "text/plain": [ "" @@ -4962,7 +4992,7 @@ { "data": { "text/html": [ - "

LL = -6025.349630820031

" + "

LL = -inf

" ], "text/plain": [ "" @@ -4999,7 +5029,11 @@ " maximum\n", " holdfast\n", " note\n", - " best\n", + " std_err\n", + " t_stat\n", + " robust_std_err\n", + " robust_t_stat\n", + " likelihood_ratio\n", " \n", " \n", " \n", @@ -5012,7 +5046,11 @@ " -999.0\n", " 1\n", " \n", - " -999.000000\n", + " 0.000000\n", + " NaN\n", + " 0.0\n", + " NaN\n", + " NaN\n", " \n", " \n", " 1\n", @@ -5023,40 +5061,56 @@ " 1.0\n", " 1\n", " \n", - " 1.000000\n", + " 0.000000\n", + " NaN\n", + " 0.0\n", + " NaN\n", + " NaN\n", " \n", " \n", " bike_ASC_auto_deficient_eatout\n", - " -1.196927\n", + " -1.569111\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1.196927\n", + " 1.221903\n", + " -1.284153\n", + " 0.0\n", + " -inf\n", + " NaN\n", " \n", " \n", " bike_ASC_auto_sufficient_eatout\n", - " -2.398235\n", + " -1.200347\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -2.398235\n", + " 0.464989\n", + " -2.581453\n", + " 0.0\n", + " -inf\n", + " NaN\n", " \n", " \n", " bike_ASC_no_auto_eatout\n", - " 1.194085\n", + " 0.868071\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 1.194085\n", + " 7.078790\n", + " 0.122630\n", + " 0.0\n", + " inf\n", + " NaN\n", " \n", " \n", " ...\n", @@ -5068,6 +5122,10 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " walk_ASC_no_auto_atwork\n", @@ -5078,7 +5136,11 @@ " NaN\n", " 0\n", " \n", - " 6.669213\n", + " 0.000000\n", + " inf\n", + " 0.0\n", + " inf\n", + " NaN\n", " \n", " \n", " walk_transit_ASC_auto_deficient_atwork\n", @@ -5089,7 +5151,11 @@ " NaN\n", " 0\n", " \n", - " -2.998829\n", + " 0.000000\n", + " -inf\n", + " 0.0\n", + " -inf\n", + " NaN\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_atwork\n", @@ -5100,7 +5166,11 @@ " NaN\n", " 0\n", " \n", - " -3.401027\n", + " 0.000000\n", + " -inf\n", + " 0.0\n", + " -inf\n", + " NaN\n", " \n", " \n", " walk_transit_ASC_no_auto_atwork\n", @@ -5111,7 +5181,11 @@ " NaN\n", " 0\n", " \n", - " 2.704188\n", + " 0.000000\n", + " inf\n", + " 0.0\n", + " inf\n", + " NaN\n", " \n", " \n", " walk_transit_CBD_ASC_atwork\n", @@ -5122,20 +5196,24 @@ " NaN\n", " 0\n", " \n", - " 0.564000\n", + " 0.000000\n", + " inf\n", + " 0.0\n", + " inf\n", + " NaN\n", " \n", " \n", "\n", - "

301 rows × 8 columns

\n", + "

301 rows × 12 columns

\n", "" ], "text/plain": [ " value initvalue nullvalue \\\n", "-999 -999.000000 -999.0 -999.0 \n", "1 1.000000 1.0 1.0 \n", - "bike_ASC_auto_deficient_eatout -1.196927 0.0 0.0 \n", - "bike_ASC_auto_sufficient_eatout -2.398235 0.0 0.0 \n", - "bike_ASC_no_auto_eatout 1.194085 0.0 0.0 \n", + "bike_ASC_auto_deficient_eatout -1.569111 0.0 0.0 \n", + "bike_ASC_auto_sufficient_eatout -1.200347 0.0 0.0 \n", + "bike_ASC_no_auto_eatout 0.868071 0.0 0.0 \n", "... ... ... ... \n", "walk_ASC_no_auto_atwork 6.669213 0.0 0.0 \n", "walk_transit_ASC_auto_deficient_atwork -2.998829 0.0 0.0 \n", @@ -5156,20 +5234,33 @@ "walk_transit_ASC_no_auto_atwork NaN NaN 0 \n", "walk_transit_CBD_ASC_atwork NaN NaN 0 \n", "\n", - " best \n", - "-999 -999.000000 \n", - "1 1.000000 \n", - "bike_ASC_auto_deficient_eatout -1.196927 \n", - "bike_ASC_auto_sufficient_eatout -2.398235 \n", - "bike_ASC_no_auto_eatout 1.194085 \n", - "... ... \n", - "walk_ASC_no_auto_atwork 6.669213 \n", - "walk_transit_ASC_auto_deficient_atwork -2.998829 \n", - "walk_transit_ASC_auto_sufficient_atwork -3.401027 \n", - "walk_transit_ASC_no_auto_atwork 2.704188 \n", - "walk_transit_CBD_ASC_atwork 0.564000 \n", + " std_err t_stat robust_std_err \\\n", + "-999 0.000000 NaN 0.0 \n", + "1 0.000000 NaN 0.0 \n", + "bike_ASC_auto_deficient_eatout 1.221903 -1.284153 0.0 \n", + "bike_ASC_auto_sufficient_eatout 0.464989 -2.581453 0.0 \n", + "bike_ASC_no_auto_eatout 7.078790 0.122630 0.0 \n", + "... ... ... ... \n", + "walk_ASC_no_auto_atwork 0.000000 inf 0.0 \n", + "walk_transit_ASC_auto_deficient_atwork 0.000000 -inf 0.0 \n", + "walk_transit_ASC_auto_sufficient_atwork 0.000000 -inf 0.0 \n", + "walk_transit_ASC_no_auto_atwork 0.000000 inf 0.0 \n", + "walk_transit_CBD_ASC_atwork 0.000000 inf 0.0 \n", "\n", - "[301 rows x 8 columns]" + " robust_t_stat likelihood_ratio \n", + "-999 NaN NaN \n", + "1 NaN NaN \n", + "bike_ASC_auto_deficient_eatout -inf NaN \n", + "bike_ASC_auto_sufficient_eatout -inf NaN \n", + "bike_ASC_no_auto_eatout inf NaN \n", + "... ... ... \n", + "walk_ASC_no_auto_atwork inf NaN \n", + "walk_transit_ASC_auto_deficient_atwork -inf NaN \n", + "walk_transit_ASC_auto_sufficient_atwork -inf NaN \n", + "walk_transit_ASC_no_auto_atwork inf NaN \n", + "walk_transit_CBD_ASC_atwork inf NaN \n", + "\n", + "[301 rows x 12 columns]" ] }, "metadata": {}, @@ -5179,12 +5270,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\numpy\\core\\_methods.py:38: RuntimeWarning: invalid value encountered in reduce\n", + " return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\ipykernel_launcher.py:1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", " \"\"\"Entry point for launching an IPython kernel.\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 0.0 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in sqrt\n", - " \"\"\"Entry point for launching an IPython kernel.\n" + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 0.0 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" ] }, { @@ -5200,1210 +5291,1210 @@ " \n", " \n", " -999\n", - " -999.000000\n", + " -9.990000e+02\n", " \n", " \n", " 1\n", - " 1.000000\n", + " 1.000000e+00\n", " \n", " \n", " bike_ASC_auto_deficient_eatout\n", - " -1.196927\n", + " -1.569111e+00\n", " \n", " \n", " bike_ASC_auto_sufficient_eatout\n", - " -2.398235\n", + " -1.200347e+00\n", " \n", " \n", " bike_ASC_no_auto_eatout\n", - " 1.194085\n", + " 8.680710e-01\n", " \n", " \n", " coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work\n", - " -0.105758\n", + " 3.162841e-11\n", " \n", " \n", " coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work\n", - " -0.272081\n", + " -7.255589e-10\n", " \n", " \n", " coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social\n", - " -1.229752\n", + " -1.366000e+00\n", " \n", " \n", " coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork\n", - " -0.004859\n", + " 2.103464e-09\n", " \n", " \n", " coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork\n", - " -0.110397\n", + " -1.472182e-08\n", " \n", " \n", " coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social\n", - " -0.018861\n", + " -1.750008e-02\n", " \n", " \n", " coef_nest_AUTO\n", - " 0.720000\n", + " 7.200000e-01\n", " \n", " \n", " coef_nest_AUTO_DRIVEALONE\n", - " 0.350000\n", + " 3.500000e-01\n", " \n", " \n", " coef_nest_AUTO_SHAREDRIDE2\n", - " 0.350000\n", + " 3.500000e-01\n", " \n", " \n", " coef_nest_AUTO_SHAREDRIDE3\n", - " 0.350000\n", + " 3.500000e-01\n", " \n", " \n", " coef_nest_NONMOTORIZED\n", - " 0.720000\n", + " 7.200000e-01\n", " \n", " \n", " coef_nest_RIDEHAIL\n", - " 0.360000\n", + " 3.600000e-01\n", " \n", " \n", " coef_nest_TRANSIT\n", - " 0.720000\n", + " 7.200000e-01\n", " \n", " \n", " coef_nest_TRANSIT_DRIVEACCESS\n", - " 0.500000\n", + " 5.000000e-01\n", " \n", " \n", " coef_nest_TRANSIT_WALKACCESS\n", - " 0.500000\n", + " 5.000000e-01\n", " \n", " \n", " commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - " -0.822177\n", + " 7.270185e-01\n", " \n", " \n", " drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - " 0.940124\n", + " 9.401238e-01\n", " \n", " \n", " drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - " 0.515655\n", + " 7.689547e-01\n", " \n", " \n", " drive_transit_ASC_auto_deficient_eatout\n", - " 1.739994\n", + " 5.998061e-01\n", " \n", " \n", " drive_transit_ASC_auto_sufficient_eatout\n", - " -6.185642\n", + " -9.695159e-01\n", " \n", " \n", " drive_transit_ASC_no_auto_all\n", - " 0.000000\n", + " 0.000000e+00\n", " \n", " \n", " drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social\n", - " 0.061181\n", + " 5.250000e-01\n", " \n", " \n", " express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - " 0.723525\n", + " 9.692316e-01\n", " \n", " \n", " heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - " 0.733736\n", + " 7.706121e-01\n", " \n", " \n", " joint_bike_ASC_auto_deficient_all\n", - " -16.921221\n", + " -6.076415e+00\n", " \n", " \n", " joint_bike_ASC_auto_sufficient_all\n", - " -15.039506\n", + " -6.376066e+00\n", " \n", " \n", " joint_bike_ASC_no_auto_all\n", - " -6.787531\n", + " -2.867160e+00\n", " \n", " \n", " joint_drive_transit_ASC_auto_deficient_all\n", - " -9.488617\n", + " -5.963222e+00\n", " \n", " \n", " joint_drive_transit_ASC_auto_sufficient_all\n", - " -8.565782\n", + " -8.045285e+00\n", " \n", " \n", " joint_drive_transit_ASC_no_auto_all\n", - " 0.000000\n", + " 0.000000e+00\n", " \n", " \n", " joint_sr2_ASC_auto_deficient_all\n", - " 0.000000\n", + " 0.000000e+00\n", " \n", " \n", " joint_sr2_ASC_auto_sufficient_all\n", - " 0.000000\n", + " 0.000000e+00\n", " \n", " \n", " joint_sr2_ASC_no_auto_all\n", - " 0.000000\n", + " 0.000000e+00\n", " \n", " \n", " joint_sr3p_ASC_auto_deficient_all\n", - " -6.238721\n", + " -1.884169e+00\n", " \n", " \n", " joint_sr3p_ASC_auto_sufficient_all\n", - " -1.873011\n", + " -2.234826e+00\n", " \n", " \n", " joint_sr3p_ASC_no_auto_all\n", - " -0.333650\n", + " 5.630671e-01\n", " \n", " \n", " joint_taxi_ASC_auto_deficient_all\n", - " -12.004609\n", + " -9.815700e+00\n", " \n", " \n", " joint_taxi_ASC_auto_sufficient_all\n", - " -11.709900\n", + " -1.170990e+01\n", " \n", " \n", " joint_taxi_ASC_no_auto_all\n", - " -5.278038\n", + " -4.579200e+00\n", " \n", " \n", " joint_tnc_shared_ASC_auto_deficient_all\n", - " -12.109318\n", + " -1.115720e+01\n", " \n", " \n", " joint_tnc_shared_ASC_auto_sufficient_all\n", - " -13.205000\n", + " -1.320500e+01\n", " \n", " \n", " joint_tnc_shared_ASC_no_auto_all\n", - " -6.058266\n", + " -4.300200e+00\n", " \n", " \n", " joint_tnc_single_ASC_auto_deficient_all\n", - " -12.512415\n", + " -9.896100e+00\n", " \n", " \n", " joint_tnc_single_ASC_auto_sufficient_all\n", - " -14.015900\n", + " -1.401590e+01\n", " \n", " \n", " joint_tnc_single_ASC_no_auto_all\n", - " -5.573630\n", + " -4.491700e+00\n", " \n", " \n", " joint_walk_ASC_auto_deficient_all\n", - " -5.544908\n", + " -1.960771e+00\n", " \n", " \n", " joint_walk_ASC_auto_sufficient_all\n", - " -4.673051\n", + " -3.235216e+00\n", " \n", " \n", " joint_walk_ASC_no_auto_all\n", - " 0.498049\n", + " -2.127470e-01\n", " \n", " \n", " joint_walk_transit_ASC_auto_deficient_all\n", - " -9.122947\n", + " -5.163448e+00\n", " \n", " \n", " joint_walk_transit_ASC_auto_sufficient_all\n", - " -18.264674\n", + " -1.826453e+01\n", " \n", " \n", " joint_walk_transit_ASC_no_auto_all\n", - " 0.779614\n", + " 6.229241e-01\n", " \n", " \n", " local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - " 0.178875\n", + " -9.070327e-02\n", " \n", " \n", " sr2_ASC_auto_deficient_eatout\n", - " 0.370946\n", + " 5.882345e-01\n", " \n", " \n", " sr2_ASC_auto_sufficient_eatout\n", - " 0.319293\n", + " 8.628055e-01\n", " \n", " \n", " sr2_ASC_no_auto_all\n", - " 0.790325\n", + " 6.723104e-10\n", " \n", " \n", " sr3p_ASC_auto_deficient_eatout\n", - " 0.090715\n", + " 4.605236e-02\n", " \n", " \n", " sr3p_ASC_auto_sufficient_eatout\n", - " 0.549728\n", + " 8.468596e-01\n", " \n", " \n", " sr3p_ASC_no_auto_eatout\n", - " 1.090053\n", + " 3.219998e-01\n", " \n", " \n", " taxi_ASC_auto_deficient_eatout_othdiscr_social\n", - " -5.671322\n", + " -3.131700e+00\n", " \n", " \n", " taxi_ASC_auto_sufficient_eatout_othdiscr_social\n", - " -2.291830\n", + " -3.037400e+00\n", " \n", " \n", " taxi_ASC_no_auto_eatout_othdiscr_social\n", - " 1.398880\n", + " 9.923000e-01\n", " \n", " \n", " tnc_shared_ASC_auto_deficient_eatout_othdiscr_social\n", - " -6.538970\n", + " -4.357600e+00\n", " \n", " \n", " tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social\n", - " -3.564529\n", + " -3.663800e+00\n", " \n", " \n", " tnc_shared_ASC_no_auto_eatout_othdiscr_social\n", - " 0.717679\n", + " 6.464000e-01\n", " \n", " \n", " tnc_single_ASC_auto_deficient_eatout_othdiscr_social\n", - " -2.138018\n", + " -2.962300e+00\n", " \n", " \n", " tnc_single_ASC_auto_sufficient_eatout_othdiscr_social\n", - " -1.944900\n", + " -2.323900e+00\n", " \n", " \n", " tnc_single_ASC_no_auto_eatout_othdiscr_social\n", - " 1.701498\n", + " 1.685200e+00\n", " \n", " \n", " walk_ASC_auto_deficient_eatout\n", - " 2.902333\n", + " 3.274605e+00\n", " \n", " \n", " walk_ASC_auto_sufficient_eatout\n", - " 1.142933\n", + " 1.551690e+00\n", " \n", " \n", " walk_ASC_no_auto_eatout\n", - " 4.971280\n", + " 5.125117e+00\n", " \n", " \n", " walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - " 0.940124\n", + " 9.401238e-01\n", " \n", " \n", " walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - " 0.912053\n", + " 7.689547e-01\n", " \n", " \n", " walk_transit_ASC_auto_deficient_eatout\n", - " -0.072758\n", + " -3.896324e-02\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_eatout\n", - " -2.162030\n", + " -1.112691e+00\n", " \n", " \n", " walk_transit_ASC_no_auto_eatout\n", - " 2.351932\n", + " 2.593637e+00\n", " \n", " \n", " walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social\n", - " 0.293377\n", + " 5.250000e-01\n", " \n", " \n", " bike_ASC_auto_deficient_escort\n", - " -11.115124\n", + " -4.527928e+00\n", " \n", " \n", " bike_ASC_auto_sufficient_escort\n", - " -4.524614\n", + " -5.063108e+00\n", " \n", " \n", " bike_ASC_no_auto_escort\n", - " -1.586395\n", + " -7.162120e-01\n", " \n", " \n", " drive_transit_ASC_auto_deficient_escort\n", - " 0.353513\n", + " -1.153707e+00\n", " \n", " \n", " drive_transit_ASC_auto_sufficient_escort\n", - " -7.299656\n", + " -4.601425e+00\n", " \n", " \n", " sr2_ASC_auto_deficient_escort\n", - " 0.022420\n", + " -8.656501e-10\n", " \n", " \n", " sr2_ASC_auto_sufficient_escort\n", - " -0.492393\n", + " 2.341880e-09\n", " \n", " \n", " sr3p_ASC_auto_deficient_escort\n", - " -0.173450\n", + " -4.081877e-01\n", " \n", " \n", " sr3p_ASC_auto_sufficient_escort\n", - " -0.689907\n", + " -5.741253e-02\n", " \n", " \n", " sr3p_ASC_no_auto_escort\n", - " -4.639084\n", + " -1.812927e+00\n", " \n", " \n", " taxi_ASC_auto_deficient_escort_othmaint_shopping\n", - " 0.534133\n", + " 1.766000e-01\n", " \n", " \n", " taxi_ASC_auto_sufficient_escort_othmaint_shopping\n", - " -1.952415\n", + " -1.805500e+00\n", " \n", " \n", " taxi_ASC_no_auto_escort_othmaint_shopping\n", - " 3.215255\n", + " 1.893900e+00\n", " \n", " \n", " tnc_shared_ASC_auto_deficient_escort_othmaint_shopping\n", - " 0.016457\n", + " -3.863000e-01\n", " \n", " \n", " tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping\n", - " -3.080957\n", + " -2.436500e+00\n", " \n", " \n", " tnc_shared_ASC_no_auto_escort_othmaint_shopping\n", - " 1.738484\n", + " 9.361000e-01\n", " \n", " \n", " tnc_single_ASC_auto_deficient_escort_othmaint_shopping\n", - " 0.897019\n", + " 6.748000e-01\n", " \n", " \n", " tnc_single_ASC_auto_sufficient_escort_othmaint_shopping\n", - " -1.563194\n", + " -1.450000e+00\n", " \n", " \n", " tnc_single_ASC_no_auto_escort_othmaint_shopping\n", - " 3.090854\n", + " 1.860500e+00\n", " \n", " \n", " walk_ASC_auto_deficient_escort\n", - " -0.885672\n", + " -9.020466e-01\n", " \n", " \n", " walk_ASC_auto_sufficient_escort\n", - " -0.949235\n", + " -8.116066e-01\n", " \n", " \n", " walk_ASC_no_auto_escort\n", - " 2.885778\n", + " 2.801207e+00\n", " \n", " \n", " walk_transit_ASC_auto_deficient_escort\n", - " -10.513596\n", + " -4.960704e+00\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_escort\n", - " -16.359798\n", + " -4.934847e+00\n", " \n", " \n", " walk_transit_ASC_no_auto_escort\n", - " -2.917851\n", + " -2.217208e+00\n", " \n", " \n", " bike_ASC_auto_deficient_othdiscr\n", - " -0.707320\n", + " -9.246834e-02\n", " \n", " \n", " bike_ASC_auto_sufficient_othdiscr\n", - " -0.961243\n", + " -1.071460e+00\n", " \n", " \n", " bike_ASC_no_auto_othdiscr\n", - " -0.493193\n", + " -3.764232e-01\n", " \n", " \n", " drive_transit_ASC_auto_deficient_othdiscr\n", - " -3.447319\n", + " 3.199308e-01\n", " \n", " \n", " drive_transit_ASC_auto_sufficient_othdiscr\n", - " -6.633992\n", + " -3.785917e-01\n", " \n", " \n", " sr2_ASC_auto_deficient_othdiscr\n", - " 0.471308\n", + " 6.601513e-01\n", " \n", " \n", " sr2_ASC_auto_sufficient_othdiscr\n", - " 0.426432\n", + " 4.968462e-01\n", " \n", " \n", " sr3p_ASC_auto_deficient_othdiscr\n", - " 0.588278\n", + " 1.047097e+00\n", " \n", " \n", " sr3p_ASC_auto_sufficient_othdiscr\n", - " 0.389463\n", + " 5.885020e-01\n", " \n", " \n", " sr3p_ASC_no_auto_othdiscr\n", - " 1.409795\n", + " 2.721690e-01\n", " \n", " \n", " walk_ASC_auto_deficient_othdiscr\n", - " 1.742895\n", + " 2.249407e+00\n", " \n", " \n", " walk_ASC_auto_sufficient_othdiscr\n", - " 1.117255\n", + " 1.263348e+00\n", " \n", " \n", " walk_ASC_no_auto_othdiscr\n", - " 4.622346\n", + " 3.266595e+00\n", " \n", " \n", " walk_transit_ASC_auto_deficient_othdiscr\n", - " 0.808509\n", + " 9.530884e-01\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_othdiscr\n", - " -0.818047\n", + " -8.063679e-01\n", " \n", " \n", " walk_transit_ASC_no_auto_othdiscr\n", - " 3.073537\n", + " 2.243778e+00\n", " \n", " \n", " bike_ASC_auto_deficient_othmaint\n", - " -12.894828\n", + " -1.518465e+00\n", " \n", " \n", " bike_ASC_auto_sufficient_othmaint\n", - " -1.964703\n", + " -2.808302e+00\n", " \n", " \n", " bike_ASC_no_auto_othmaint\n", - " 2.385161\n", + " 1.539433e+00\n", " \n", " \n", " drive_transit_ASC_auto_deficient_othmaint\n", - " -6.362422\n", + " -2.994323e-01\n", " \n", " \n", " drive_transit_ASC_auto_sufficient_othmaint\n", - " -0.073235\n", + " -2.624948e+00\n", " \n", " \n", " sr2_ASC_auto_deficient_othmaint\n", - " -0.058045\n", + " 2.621527e-01\n", " \n", " \n", " sr2_ASC_auto_sufficient_othmaint\n", - " 0.140353\n", + " 2.581788e-01\n", " \n", " \n", " sr3p_ASC_auto_deficient_othmaint\n", - " -10.734179\n", + " -1.349393e+00\n", " \n", " \n", " sr3p_ASC_auto_sufficient_othmaint\n", - " -0.255798\n", + " -7.549867e-02\n", " \n", " \n", " sr3p_ASC_no_auto_othmaint\n", - " 0.341462\n", + " -8.031854e-01\n", " \n", " \n", " walk_ASC_auto_deficient_othmaint\n", - " 1.235530\n", + " 1.369040e+00\n", " \n", " \n", " walk_ASC_auto_sufficient_othmaint\n", - " 1.493073\n", + " 7.999634e-01\n", " \n", " \n", " walk_ASC_no_auto_othmaint\n", - " 2.145869\n", + " 1.287299e+00\n", " \n", " \n", " walk_transit_ASC_auto_deficient_othmaint\n", - " -3.522345\n", + " -3.059726e+00\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_othmaint\n", - " -1.437995\n", + " -1.547117e+00\n", " \n", " \n", " walk_transit_ASC_no_auto_othmaint\n", - " 3.677431\n", + " 2.564346e+00\n", " \n", " \n", " bike_ASC_auto_deficient_school\n", - " 4.904505\n", + " -5.280678e-01\n", " \n", " \n", " bike_ASC_auto_sufficient_school\n", - " -2.300322\n", + " -2.113469e+00\n", " \n", " \n", " bike_ASC_no_auto_school\n", - " 8.784542\n", + " 1.209873e+01\n", " \n", " \n", " coef_age010_trn_multiplier_school_univ\n", - " -2.674508\n", + " -1.554800e+00\n", " \n", " \n", " coef_age1619_da_multiplier_school_univ\n", - " -1.233391\n", + " -1.381300e+00\n", " \n", " \n", " coef_age16p_sr_multiplier_school_univ_work_atwork\n", - " 0.506060\n", + " -8.504394e-09\n", " \n", " \n", " coef_hhsize2_sr_multiplier_school_univ\n", - " -0.745340\n", + " -6.359000e-01\n", " \n", " \n", " coef_ivt_school_univ\n", - " -0.020828\n", + " -2.239987e-02\n", " \n", " \n", " commuter_rail_ASC_school_univ\n", - " 0.991786\n", + " 1.033621e+00\n", " \n", " \n", " drive_ferry_ASC_school_univ\n", - " 2.020232\n", + " 2.020232e+00\n", " \n", " \n", " drive_light_rail_ASC_school_univ\n", - " 1.674087\n", + " 1.681400e+00\n", " \n", " \n", " drive_transit_ASC_auto_deficient_school\n", - " 2.965092\n", + " 5.325265e+00\n", " \n", " \n", " drive_transit_ASC_auto_sufficient_school\n", - " -15.745770\n", + " 1.401350e+00\n", " \n", " \n", " drive_transit_CBD_ASC_school_univ\n", - " 10.893574\n", + " 6.720000e-01\n", " \n", " \n", " express_bus_ASC_school_univ\n", - " 0.324969\n", + " 3.249694e-01\n", " \n", " \n", " heavy_rail_ASC_school_univ\n", - " 0.801177\n", + " 9.620038e-01\n", " \n", " \n", " local_bus_ASC_school_univ\n", - " -0.045995\n", + " -6.508621e-02\n", " \n", " \n", " sr2_ASC_auto_deficient_school\n", - " 5.546885\n", + " 1.247437e-01\n", " \n", " \n", " sr2_ASC_auto_sufficient_school\n", - " -1.957247\n", + " -1.606266e+00\n", " \n", " \n", " sr3p_ASC_auto_deficient_school\n", - " 5.950789\n", + " 7.149571e-01\n", " \n", " \n", " sr3p_ASC_auto_sufficient_school\n", - " -1.318294\n", + " -1.020193e+00\n", " \n", " \n", " sr3p_ASC_no_auto_school\n", - " -6.024083\n", + " -6.024083e+00\n", " \n", " \n", " taxi_ASC_auto_deficient_school\n", - " 5.640986\n", + " -3.338000e-01\n", " \n", " \n", " taxi_ASC_auto_sufficient_school\n", - " -2.038971\n", + " -2.429400e+00\n", " \n", " \n", " taxi_ASC_no_auto_school_univ\n", - " -7.000000\n", + " -7.000000e+00\n", " \n", " \n", " tnc_shared_ASC_auto_deficient_school\n", - " 4.830495\n", + " -1.474600e+00\n", " \n", " \n", " tnc_shared_ASC_auto_sufficient_school\n", - " -9.758335\n", + " -3.721900e+00\n", " \n", " \n", " tnc_shared_ASC_no_auto_school\n", - " -7.000000\n", + " -7.000000e+00\n", " \n", " \n", " tnc_single_ASC_auto_deficient_school\n", - " 5.343512\n", + " -5.524000e-01\n", " \n", " \n", " tnc_single_ASC_auto_sufficient_school\n", - " -6.606470\n", + " -2.837500e+00\n", " \n", " \n", " tnc_single_ASC_no_auto_school\n", - " -7.000000\n", + " -7.000000e+00\n", " \n", " \n", " walk_ASC_auto_deficient_school\n", - " 8.451890\n", + " 3.257362e+00\n", " \n", " \n", " walk_ASC_auto_sufficient_school\n", - " -0.000036\n", + " 6.476856e-01\n", " \n", " \n", " walk_ASC_no_auto_school\n", - " 19.960408\n", + " 1.841456e+01\n", " \n", " \n", " walk_ferry_ASC_school_univ\n", - " 2.020232\n", + " 2.020232e+00\n", " \n", " \n", " walk_light_rail_ASC_school_univ\n", - " 1.796758\n", + " 1.681400e+00\n", " \n", " \n", " walk_transit_ASC_auto_deficient_school\n", - " 9.608806\n", + " 4.120708e+00\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_school\n", - " 0.282585\n", + " 7.459087e-01\n", " \n", " \n", " walk_transit_ASC_no_auto_school\n", - " 23.152255\n", + " 2.138375e+01\n", " \n", " \n", " walk_transit_CBD_ASC_school_univ\n", - " 1.216461\n", + " 6.720000e-01\n", " \n", " \n", " bike_ASC_auto_deficient_shopping\n", - " -1.974349\n", + " -8.758447e-01\n", " \n", " \n", " bike_ASC_auto_sufficient_shopping\n", - " -2.545553\n", + " -2.566210e+00\n", " \n", " \n", " bike_ASC_no_auto_shopping\n", - " 1.317680\n", + " 8.341555e-01\n", " \n", " \n", " drive_transit_ASC_auto_deficient_shopping\n", - " -5.201183\n", + " -4.184918e-01\n", " \n", " \n", " drive_transit_ASC_auto_sufficient_shopping\n", - " -6.442237\n", + " -2.171894e+00\n", " \n", " \n", " sr2_ASC_auto_deficient_shopping\n", - " 0.159211\n", + " 2.440976e-01\n", " \n", " \n", " sr2_ASC_auto_sufficient_shopping\n", - " 0.126214\n", + " 1.977071e-01\n", " \n", " \n", " sr3p_ASC_auto_deficient_shopping\n", - " -0.140845\n", + " -7.337017e-02\n", " \n", " \n", " sr3p_ASC_auto_sufficient_shopping\n", - " -0.212535\n", + " -7.757129e-02\n", " \n", " \n", " sr3p_ASC_no_auto_shopping\n", - " 0.960450\n", + " -2.797895e-01\n", " \n", " \n", " walk_ASC_auto_deficient_shopping\n", - " 1.968397\n", + " 2.270173e+00\n", " \n", " \n", " walk_ASC_auto_sufficient_shopping\n", - " 0.748216\n", + " 7.312663e-01\n", " \n", " \n", " walk_ASC_no_auto_shopping\n", - " 3.579998\n", + " 2.376877e+00\n", " \n", " \n", " walk_transit_ASC_auto_deficient_shopping\n", - " -0.934019\n", + " -8.476569e-01\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_shopping\n", - " -2.033106\n", + " -2.203680e+00\n", " \n", " \n", " walk_transit_ASC_no_auto_shopping\n", - " 3.195677\n", + " 2.106748e+00\n", " \n", " \n", " bike_ASC_auto_deficient_social\n", - " 0.420926\n", + " 6.345214e-01\n", " \n", " \n", " bike_ASC_auto_sufficient_social\n", - " -0.372327\n", + " -1.368071e+00\n", " \n", " \n", " bike_ASC_no_auto_social\n", - " -11.097973\n", + " 2.058321e-02\n", " \n", " \n", " drive_transit_ASC_auto_deficient_social\n", - " -5.103614\n", + " 1.562719e+00\n", " \n", " \n", " drive_transit_ASC_auto_sufficient_social\n", - " 1.145004\n", + " -6.158557e-01\n", " \n", " \n", " sr2_ASC_auto_deficient_social\n", - " 1.121165\n", + " 1.855853e+00\n", " \n", " \n", " sr2_ASC_auto_sufficient_social\n", - " 0.934997\n", + " 5.236025e-01\n", " \n", " \n", " sr3p_ASC_auto_deficient_social\n", - " 0.374815\n", + " 1.500724e+00\n", " \n", " \n", " sr3p_ASC_auto_sufficient_social\n", - " 0.892366\n", + " 5.061789e-01\n", " \n", " \n", " sr3p_ASC_no_auto_social\n", - " 0.011390\n", + " -1.403690e+00\n", " \n", " \n", " walk_ASC_auto_deficient_social\n", - " 1.903540\n", + " 2.870184e+00\n", " \n", " \n", " walk_ASC_auto_sufficient_social\n", - " 3.319694\n", + " 1.707219e+00\n", " \n", " \n", " walk_ASC_no_auto_social\n", - " 1.442593\n", + " 1.868092e+00\n", " \n", " \n", " walk_transit_ASC_auto_deficient_social\n", - " 0.367857\n", + " 9.744449e-01\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_social\n", - " -0.076234\n", + " -3.453759e-01\n", " \n", " \n", " walk_transit_ASC_no_auto_social\n", - " 2.325920\n", + " 1.381465e+00\n", " \n", " \n", " bike_ASC_auto_deficient_univ\n", - " -0.669235\n", + " -6.692350e-01\n", " \n", " \n", " bike_ASC_auto_sufficient_univ\n", - " -1.939783\n", + " -1.939783e+00\n", " \n", " \n", " bike_ASC_no_auto_univ\n", - " 4.294516\n", + " 4.294516e+00\n", " \n", " \n", " drive_transit_ASC_auto_deficient_univ\n", - " 1.850118\n", + " 1.850118e+00\n", " \n", " \n", " drive_transit_ASC_auto_sufficient_univ\n", - " 1.358775\n", + " 1.358775e+00\n", " \n", " \n", " sr2_ASC_auto_deficient_univ\n", - " -1.692235\n", + " -1.692235e+00\n", " \n", " \n", " sr2_ASC_auto_sufficient_univ\n", - " -1.859427\n", + " -1.859427e+00\n", " \n", " \n", " sr3p_ASC_auto_deficient_univ\n", - " -1.727742\n", + " -1.727742e+00\n", " \n", " \n", " sr3p_ASC_auto_sufficient_univ\n", - " -1.904710\n", + " -1.904710e+00\n", " \n", " \n", " sr3p_ASC_no_auto_univ\n", - " -6.056001\n", + " -6.056001e+00\n", " \n", " \n", " taxi_ASC_auto_deficient_univ\n", - " 4.249200\n", + " 4.249200e+00\n", " \n", " \n", " taxi_ASC_auto_sufficient_univ\n", - " -0.313100\n", + " -3.131000e-01\n", " \n", " \n", " tnc_shared_ASC_auto_deficient_univ\n", - " 3.250000\n", + " 3.250000e+00\n", " \n", " \n", " tnc_shared_ASC_auto_sufficient_univ\n", - " -0.906800\n", + " -9.068000e-01\n", " \n", " \n", " tnc_shared_ASC_no_auto_univ\n", - " -5.811600\n", + " -5.811600e+00\n", " \n", " \n", " tnc_single_ASC_auto_deficient_univ\n", - " 1.022100\n", + " 1.022100e+00\n", " \n", " \n", " tnc_single_ASC_auto_sufficient_univ\n", - " 0.208800\n", + " 2.088000e-01\n", " \n", " \n", " tnc_single_ASC_no_auto_univ\n", - " -2.519000\n", + " -2.519000e+00\n", " \n", " \n", " walk_ASC_auto_deficient_univ\n", - " 4.505910\n", + " 4.505910e+00\n", " \n", " \n", " walk_ASC_auto_sufficient_univ\n", - " 1.060767\n", + " 1.060767e+00\n", " \n", " \n", " walk_ASC_no_auto_univ\n", - " 6.408967\n", + " 6.408967e+00\n", " \n", " \n", " walk_transit_ASC_auto_deficient_univ\n", - " 3.136255\n", + " 3.136255e+00\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_univ\n", - " 0.473116\n", + " 4.731163e-01\n", " \n", " \n", " walk_transit_ASC_no_auto_univ\n", - " 8.786037\n", + " 8.786037e+00\n", " \n", " \n", " bike_ASC_auto_deficient_work\n", - " 0.175967\n", + " 2.531897e-01\n", " \n", " \n", " bike_ASC_auto_sufficient_work\n", - " -1.637179\n", + " -1.580023e+00\n", " \n", " \n", " bike_ASC_no_auto_work\n", - " 4.710869\n", + " 3.194009e+00\n", " \n", " \n", " coef_hhsize1_sr_multiplier_work\n", - " -0.914227\n", + " -7.345880e-01\n", " \n", " \n", " coef_ivt_work\n", - " -0.013558\n", + " -1.339931e-02\n", " \n", " \n", " commuter_rail_ASC_work\n", - " -3.725052\n", + " 7.255030e-01\n", " \n", " \n", " drive_ferry_ASC_work\n", - " 0.933226\n", + " 9.332261e-01\n", " \n", " \n", " drive_light_rail_ASC_work\n", - " -1.823161\n", + " 8.255567e-01\n", " \n", " \n", " drive_transit_ASC_auto_deficient_work\n", - " -1.065712\n", + " 1.008157e-01\n", " \n", " \n", " drive_transit_ASC_auto_sufficient_work\n", - " -2.013834\n", + " -1.004546e+00\n", " \n", " \n", " drive_transit_CBD_ASC_work\n", - " 1.368468\n", + " 1.100000e+00\n", " \n", " \n", " express_bus_ASC_work\n", - " 0.387362\n", + " -5.165474e-01\n", " \n", " \n", " heavy_rail_ASC_work\n", - " 1.541781\n", + " 6.477297e-01\n", " \n", " \n", " local_bus_ASC_work\n", - " 0.941623\n", + " 6.689506e-02\n", " \n", " \n", " sr2_ASC_auto_deficient_work\n", - " -0.834814\n", + " -3.380312e-01\n", " \n", " \n", " sr2_ASC_auto_sufficient_work\n", - " -1.514016\n", + " -1.085746e+00\n", " \n", " \n", " sr3p_ASC_auto_deficient_work\n", - " -1.214861\n", + " -8.527042e-01\n", " \n", " \n", " sr3p_ASC_auto_sufficient_work\n", - " -1.996598\n", + " -1.469970e+00\n", " \n", " \n", " sr3p_ASC_no_auto_work\n", - " -7.457977\n", + " -5.831269e-01\n", " \n", " \n", " taxi_ASC_auto_deficient_work\n", - " -1.374878\n", + " -1.476600e+00\n", " \n", " \n", " taxi_ASC_auto_sufficient_work\n", - " -10.657785\n", + " -4.850900e+00\n", " \n", " \n", " taxi_ASC_no_auto_work\n", - " 6.234108\n", + " 4.729100e+00\n", " \n", " \n", " tnc_shared_ASC_auto_deficient_work\n", - " -6.197287\n", + " -2.143500e+00\n", " \n", " \n", " tnc_shared_ASC_auto_sufficient_work\n", - " -11.985408\n", + " -5.357500e+00\n", " \n", " \n", " tnc_shared_ASC_no_auto_work\n", - " 0.479689\n", + " 3.242900e+00\n", " \n", " \n", " tnc_single_ASC_auto_deficient_work\n", - " -0.777080\n", + " -8.013000e-01\n", " \n", " \n", " tnc_single_ASC_auto_sufficient_work\n", - " -5.085394\n", + " -4.194600e+00\n", " \n", " \n", " tnc_single_ASC_no_auto_work\n", - " 7.225110\n", + " 5.785500e+00\n", " \n", " \n", " walk_ASC_auto_deficient_work\n", - " 2.499535\n", + " 2.401042e+00\n", " \n", " \n", " walk_ASC_auto_sufficient_work\n", - " 0.132508\n", + " 5.326534e-02\n", " \n", " \n", " walk_ASC_no_auto_work\n", - " 7.311253\n", + " 5.767216e+00\n", " \n", " \n", " walk_ferry_ASC_work\n", - " 0.933226\n", + " 9.332261e-01\n", " \n", " \n", " walk_light_rail_ASC_work\n", - " 1.881474\n", + " 8.255567e-01\n", " \n", " \n", " walk_transit_ASC_auto_deficient_work\n", - " -0.265301\n", + " 6.530285e-01\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_work\n", - " -1.776173\n", + " -8.916507e-01\n", " \n", " \n", " walk_transit_ASC_no_auto_work\n", - " 5.643417\n", + " 5.035417e+00\n", " \n", " \n", " walk_transit_CBD_ASC_work\n", - " 0.714505\n", + " 8.040000e-01\n", " \n", " \n", " bike_ASC_auto_deficient_atwork\n", - " -0.807408\n", + " -8.074083e-01\n", " \n", " \n", " bike_ASC_auto_sufficient_atwork\n", - " 15.720170\n", + " 1.572017e+01\n", " \n", " \n", " bike_ASC_no_auto_atwork\n", - " -0.907258\n", + " -9.072585e-01\n", " \n", " \n", " coef_age010_trn_multiplier_atwork\n", - " 0.000722\n", + " 7.220000e-04\n", " \n", " \n", " coef_age1619_da_multiplier_atwork\n", - " 0.003234\n", + " 3.233600e-03\n", " \n", " \n", " coef_ivt_atwork\n", - " -0.018800\n", + " -1.880000e-02\n", " \n", " \n", " drive_transit_ASC_auto_deficient_atwork\n", - " -998.819600\n", + " -9.988196e+02\n", " \n", " \n", " drive_transit_ASC_auto_sufficient_atwork\n", - " -999.214660\n", + " -9.992147e+02\n", " \n", " \n", " drive_transit_CBD_ASC_atwork\n", - " 0.564000\n", + " 5.640000e-01\n", " \n", " \n", " sr2_ASC_auto_deficient_atwork\n", - " -2.110242\n", + " -2.110242e+00\n", " \n", " \n", " sr2_ASC_auto_sufficient_atwork\n", - " -1.445062\n", + " -1.445062e+00\n", " \n", " \n", " sr3p_ASC_auto_deficient_atwork\n", - " -2.514658\n", + " -2.514658e+00\n", " \n", " \n", " sr3p_ASC_auto_sufficient_atwork\n", - " -1.652174\n", + " -1.652174e+00\n", " \n", " \n", " sr3p_ASC_no_auto_atwork\n", - " 0.582663\n", + " 5.826626e-01\n", " \n", " \n", " taxi_ASC_auto_deficient_atwork\n", - " -4.404600\n", + " -4.404600e+00\n", " \n", " \n", " taxi_ASC_auto_sufficient_atwork\n", - " -2.880400\n", + " -2.880400e+00\n", " \n", " \n", " taxi_ASC_no_auto_atwork\n", - " 4.102100\n", + " 4.102100e+00\n", " \n", " \n", " tnc_shared_ASC_auto_deficient_atwork\n", - " -4.508900\n", + " -4.508900e+00\n", " \n", " \n", " tnc_shared_ASC_auto_sufficient_atwork\n", - " -3.539700\n", + " -3.539700e+00\n", " \n", " \n", " tnc_shared_ASC_no_auto_atwork\n", - " 3.367200\n", + " 3.367200e+00\n", " \n", " \n", " tnc_single_ASC_auto_deficient_atwork\n", - " -3.762600\n", + " -3.762600e+00\n", " \n", " \n", " tnc_single_ASC_auto_sufficient_atwork\n", - " -2.798800\n", + " -2.798800e+00\n", " \n", " \n", " tnc_single_ASC_no_auto_atwork\n", - " 4.498200\n", + " 4.498200e+00\n", " \n", " \n", " walk_ASC_auto_deficient_atwork\n", - " 0.925461\n", + " 9.254609e-01\n", " \n", " \n", " walk_ASC_auto_sufficient_atwork\n", - " 0.677216\n", + " 6.772160e-01\n", " \n", " \n", " walk_ASC_no_auto_atwork\n", - " 6.669213\n", + " 6.669213e+00\n", " \n", " \n", " walk_transit_ASC_auto_deficient_atwork\n", - " -2.998829\n", + " -2.998829e+00\n", " \n", " \n", " walk_transit_ASC_auto_sufficient_atwork\n", - " -3.401027\n", + " -3.401027e+00\n", " \n", " \n", " walk_transit_ASC_no_auto_atwork\n", - " 2.704188\n", + " 2.704188e+00\n", " \n", " \n", " walk_transit_CBD_ASC_atwork\n", - " 0.564000\n", + " 5.640000e-01\n", " \n", " \n", - "loglike-6025.349630820031d_loglike\n", + "
loglike-infd_loglike\n", " \n", " \n", " \n", @@ -6421,39 +6512,39 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6493,7 +6584,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6501,15 +6592,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6517,35 +6608,35 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6565,19 +6656,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6585,11 +6676,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6597,11 +6688,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6609,107 +6700,107 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6717,287 +6808,287 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7005,19 +7096,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7025,39 +7116,39 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7065,11 +7156,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7077,11 +7168,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7089,15 +7180,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7105,151 +7196,151 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7349,27 +7440,27 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7377,99 +7468,99 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7477,23 +7568,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7616,14 +7707,14 @@ " \n", " \n", " \n", - "
bike_ASC_auto_deficient_eatout-5.112831e-037.649318e-01
bike_ASC_auto_sufficient_eatout-2.307309e-03-2.709202e+00
bike_ASC_no_auto_eatout1.561154e-02-1.386687e+00
coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work1.773436e-021.581476e-01
coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work6.492561e-02-3.627771e+00
coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social3.961002e-02-4.421673e+00
coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork-1.480164e-021.051727e+01
coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork-9.302006e-02-7.360866e+01
coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social-1.759597e+01-4.197413e+02
coef_nest_AUTO
commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-7.009579e-04-1.867358e-02
drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork
drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-8.797723e-04-2.608839e-03
drive_transit_ASC_auto_deficient_eatout1.611650e-02-6.006962e-01
drive_transit_ASC_auto_sufficient_eatout-1.979726e-04-1.998874e-01
drive_transit_ASC_no_auto_all
drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social-1.373445e-02-2.737325e+00
express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork5.856381e-02-2.288076e+00
heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-1.616804e-02-8.339047e+00
joint_bike_ASC_auto_deficient_all-1.188219e-04-6.384078e-02
joint_bike_ASC_auto_sufficient_all-4.198099e-05-3.856637e-01
joint_bike_ASC_no_auto_all-6.482456e-04-2.072771e-01
joint_drive_transit_ASC_auto_deficient_all-3.689320e-03-2.078848e-03
joint_drive_transit_ASC_auto_sufficient_all-2.632135e-03-8.594827e-03
joint_drive_transit_ASC_no_auto_all
joint_sr3p_ASC_auto_deficient_all-4.127820e-03-2.587263e+00
joint_sr3p_ASC_auto_sufficient_all-3.055656e-024.433049e+00
joint_sr3p_ASC_no_auto_all-1.639044e-02-2.669525e+00
joint_taxi_ASC_auto_deficient_all-8.856126e-03-1.161512e-03
joint_taxi_ASC_auto_sufficient_all
joint_taxi_ASC_no_auto_all-4.450958e-03-5.797254e-03
joint_tnc_shared_ASC_auto_deficient_all-2.696690e-02-1.314407e-04
joint_tnc_shared_ASC_auto_sufficient_all
joint_tnc_shared_ASC_no_auto_all-1.482050e-03-3.461094e-02
joint_tnc_single_ASC_auto_deficient_all-4.816963e-03-1.878127e-03
joint_tnc_single_ASC_auto_sufficient_all
joint_tnc_single_ASC_no_auto_all-3.249035e-03-1.115094e-02
joint_walk_ASC_auto_deficient_all1.915822e-023.681179e-01
joint_walk_ASC_auto_sufficient_all1.413535e-02-2.926344e+00
joint_walk_ASC_no_auto_all-1.494389e-022.621082e-02
joint_walk_transit_ASC_auto_deficient_all1.109000e-021.544149e+00
joint_walk_transit_ASC_auto_sufficient_all-9.754563e-07-5.375465e-07
joint_walk_transit_ASC_no_auto_all-1.628489e-03-1.483789e+00
local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork1.463824e-02-9.300231e+00
sr2_ASC_auto_deficient_eatout-1.794168e-03-3.749678e+00
sr2_ASC_auto_sufficient_eatout5.360757e-04-1.077502e+01
sr2_ASC_no_auto_all-5.652487e-023.361552e+00
sr3p_ASC_auto_deficient_eatout9.575153e-034.442100e+00
sr3p_ASC_auto_sufficient_eatout2.057995e-024.675064e+00
sr3p_ASC_no_auto_eatout2.312847e-021.553903e+00
taxi_ASC_auto_deficient_eatout_othdiscr_social-1.164032e-04-8.304097e-01
taxi_ASC_auto_sufficient_eatout_othdiscr_social-3.932195e-03-8.938894e-01
taxi_ASC_no_auto_eatout_othdiscr_social-7.715512e-03-2.197587e+00
tnc_shared_ASC_auto_deficient_eatout_othdiscr_social-1.960626e-04-2.882657e-01
tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social-1.096545e-02-9.671919e-01
tnc_shared_ASC_no_auto_eatout_othdiscr_social6.963593e-03-2.842413e+00
tnc_single_ASC_auto_deficient_eatout_othdiscr_social-1.302180e-022.285479e+00
tnc_single_ASC_auto_sufficient_eatout_othdiscr_social-1.641299e-024.040800e+00
tnc_single_ASC_no_auto_eatout_othdiscr_social6.546048e-03-1.384222e+01
walk_ASC_auto_deficient_eatout-5.639467e-03-5.247898e+00
walk_ASC_auto_sufficient_eatout1.019883e-03-3.109249e+00
walk_ASC_no_auto_eatout-3.677026e-021.494451e+00
walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork
walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork2.609112e-02-7.941598e-01
walk_transit_ASC_auto_deficient_eatout-1.594911e-022.999042e+00
walk_transit_ASC_auto_sufficient_eatout-6.190063e-04-4.902722e+00
walk_transit_ASC_no_auto_eatout4.181808e-03-7.316478e-01
walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social3.890633e-02-2.116628e+01
bike_ASC_auto_deficient_escort-1.273185e-04-2.823506e-01
bike_ASC_auto_sufficient_escort2.127649e-02-7.169512e-01
bike_ASC_no_auto_escort-1.115578e-03-7.961641e-02
drive_transit_ASC_auto_deficient_escort-7.098207e-03-5.828392e-01
drive_transit_ASC_auto_sufficient_escort-2.805052e-03-1.367459e-02
sr2_ASC_auto_deficient_escort2.703856e-02-4.328268e+00
sr2_ASC_auto_sufficient_escort-1.560450e-021.170938e+01
sr3p_ASC_auto_deficient_escort-2.452839e-025.199718e+00
sr3p_ASC_auto_sufficient_escort4.166579e-02-1.885629e+01
sr3p_ASC_no_auto_escort-4.142997e-04-5.856509e-02
taxi_ASC_auto_deficient_escort_othmaint_shopping-6.285503e-035.600063e+00
taxi_ASC_auto_sufficient_escort_othmaint_shopping5.168550e-02-2.225973e+00
taxi_ASC_no_auto_escort_othmaint_shopping5.910899e-02-5.863062e+00
tnc_shared_ASC_auto_deficient_escort_othmaint_shopping5.889152e-022.126219e+01
tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping-2.803059e-02-1.651614e+01
tnc_shared_ASC_no_auto_escort_othmaint_shopping-3.010459e-02-1.039430e+01
tnc_single_ASC_auto_deficient_escort_othmaint_shopping4.707505e-02-1.629801e+01
tnc_single_ASC_auto_sufficient_escort_othmaint_shopping1.132005e-021.673343e+01
tnc_single_ASC_no_auto_escort_othmaint_shopping5.123520e-021.021589e+01
walk_ASC_auto_deficient_escort1.020493e-02-7.219147e-01
walk_ASC_auto_sufficient_escort-3.880746e-022.932757e+00
walk_ASC_no_auto_escort1.472281e-02-4.142893e-01
walk_transit_ASC_auto_deficient_escort-2.295655e-049.064568e-01
walk_transit_ASC_auto_sufficient_escort-4.333107e-06-2.732679e-01
walk_transit_ASC_no_auto_escort-1.651387e-03-2.563782e-02
bike_ASC_auto_deficient_othdiscr-6.500190e-03-1.427191e+00
bike_ASC_auto_sufficient_othdiscr-1.139869e-022.634453e+00
bike_ASC_no_auto_othdiscr2.787556e-02-2.205191e+00
drive_transit_ASC_auto_deficient_othdiscr-5.499688e-04-1.542925e-01
drive_transit_ASC_auto_sufficient_othdiscr-1.145393e-04-1.010692e+00
sr2_ASC_auto_deficient_othdiscr-2.010117e-031.425821e+00
sr2_ASC_auto_sufficient_othdiscr1.756871e-02-2.017769e+00
sr3p_ASC_auto_deficient_othdiscr-6.788934e-03-4.233158e+00
sr3p_ASC_auto_sufficient_othdiscr9.643759e-03-2.252673e+00
sr3p_ASC_no_auto_othdiscr-3.517324e-033.950364e+00
walk_ASC_auto_deficient_othdiscr-1.872075e-02-5.261390e+00
walk_ASC_auto_sufficient_othdiscr-6.793786e-02-5.884769e+00
walk_ASC_no_auto_othdiscr4.129820e-031.819999e+01
walk_transit_ASC_auto_deficient_othdiscr1.302148e-024.032371e+00
walk_transit_ASC_auto_sufficient_othdiscr-4.380840e-022.393421e+00
walk_transit_ASC_no_auto_othdiscr-6.229585e-02-9.920519e+00
bike_ASC_auto_deficient_othmaint-6.177293e-06-2.632333e+00
bike_ASC_auto_sufficient_othmaint1.253926e-022.537386e+00
bike_ASC_no_auto_othmaint-3.440504e-02-1.556472e+01
drive_transit_ASC_auto_deficient_othmaint-1.204105e-04-7.411214e-01
drive_transit_ASC_auto_sufficient_othmaint-3.971949e-02-9.406968e-02
sr2_ASC_auto_deficient_othmaint5.315214e-02-1.793528e+00
sr2_ASC_auto_sufficient_othmaint4.433317e-02-3.312960e-01
sr3p_ASC_auto_deficient_othmaint-5.493949e-06-1.352779e+00
sr3p_ASC_auto_sufficient_othmaint-4.485399e-02-2.227244e+00
sr3p_ASC_no_auto_othmaint-6.074900e-045.884857e-01
walk_ASC_auto_deficient_othmaint-1.121595e-03-3.593778e-02
walk_ASC_auto_sufficient_othmaint-3.716615e-029.859394e+00
walk_ASC_no_auto_othmaint1.969819e-021.712821e+01
walk_transit_ASC_auto_deficient_othmaint-2.015087e-021.025968e+00
walk_transit_ASC_auto_sufficient_othmaint-2.653390e-02-3.304154e+00
walk_transit_ASC_no_auto_othmaint-3.283419e-02-1.813586e-01
bike_ASC_auto_deficient_school-2.230068e-02-1.507346e+00
bike_ASC_auto_sufficient_school2.675652e-021.185582e+00
bike_ASC_no_auto_school-1.508370e-04-6.807997e-02
coef_age010_trn_multiplier_school_univ-1.654020e-02-9.936553e+00
coef_age1619_da_multiplier_school_univ2.250435e-022.739342e-01
coef_age16p_sr_multiplier_school_univ_work_atwork-1.125537e-01-4.252034e+01
coef_hhsize2_sr_multiplier_school_univ-7.605360e-036.543433e+00
coef_ivt_school_univ3.221588e+006.579435e+02
commuter_rail_ASC_school_univ-1.029913e-04-4.260998e-04
drive_ferry_ASC_school_univ
drive_light_rail_ASC_school_univ-4.899921e-08-6.198506e-05
drive_transit_ASC_auto_deficient_school-5.115599e-03-3.524737e-01
drive_transit_ASC_auto_sufficient_school-1.622661e-05-2.250673e-01
drive_transit_CBD_ASC_school_univ-5.118721e-03-2.000002e-01
express_bus_ASC_school_univ
heavy_rail_ASC_school_univ-2.768181e-02-1.969470e+00
local_bus_ASC_school_univ1.930579e-036.290445e+00
sr2_ASC_auto_deficient_school8.691101e-026.844755e+00
sr2_ASC_auto_sufficient_school-1.997352e-02-4.481940e+00
sr3p_ASC_auto_deficient_school1.011060e-02-5.707805e+00
sr3p_ASC_auto_sufficient_school-1.888270e-021.466753e+01
sr3p_ASC_no_auto_school-4.403698e-11-2.139998e-10
taxi_ASC_auto_deficient_school-3.724111e-036.252009e-01
taxi_ASC_auto_sufficient_school1.880176e-026.120462e+00
taxi_ASC_no_auto_school_univ
tnc_shared_ASC_auto_deficient_school9.160902e-031.274618e+00
tnc_shared_ASC_auto_sufficient_school-9.121781e-06-8.724657e-01
tnc_shared_ASC_no_auto_school
tnc_single_ASC_auto_deficient_school-9.323256e-04-1.839288e-01
tnc_single_ASC_auto_sufficient_school-1.241289e-04-4.169850e+00
tnc_single_ASC_no_auto_school
walk_ASC_auto_deficient_school-5.227642e-02-1.662604e+00
walk_ASC_auto_sufficient_school5.238633e-03-9.447852e+00
walk_ASC_no_auto_school-1.035689e-02-1.070343e+00
walk_ferry_ASC_school_univ
walk_light_rail_ASC_school_univ5.760274e-029.012058e-01
walk_transit_ASC_auto_deficient_school-2.125447e-022.072651e+00
walk_transit_ASC_auto_sufficient_school-4.311889e-022.588159e+00
walk_transit_ASC_no_auto_school1.050827e-021.138423e+00
walk_transit_CBD_ASC_school_univ2.696242e-031.031561e+01
bike_ASC_auto_deficient_shopping-2.882449e-02-2.680959e+00
bike_ASC_auto_sufficient_shopping-1.137726e-024.086009e+00
bike_ASC_no_auto_shopping-2.691740e-02-1.057886e+01
drive_transit_ASC_auto_deficient_shopping-2.762866e-04-3.478713e-01
drive_transit_ASC_auto_sufficient_shopping-4.258351e-04-3.266645e-01
sr2_ASC_auto_deficient_shopping-4.135870e-02-1.496865e+00
sr2_ASC_auto_sufficient_shopping-4.903922e-02-1.947132e+00
sr3p_ASC_auto_deficient_shopping4.637566e-023.178217e+00
sr3p_ASC_auto_sufficient_shopping-3.300012e-025.887873e-01
sr3p_ASC_no_auto_shopping1.878128e-023.978172e+00
walk_ASC_auto_deficient_shopping3.326631e-02-4.979300e+00
walk_ASC_auto_sufficient_shopping-1.853872e-02-2.290751e+00
walk_ASC_no_auto_shopping1.461456e-021.911710e+01
walk_transit_ASC_auto_deficient_shopping-1.967372e-02-1.142808e+00
walk_transit_ASC_auto_sufficient_shopping1.217648e-021.138293e+00
walk_transit_ASC_no_auto_shopping3.261027e-02-9.843980e+00
bike_ASC_auto_deficient_social-4.459638e-03-7.091144e-01
bike_ASC_auto_sufficient_social-2.033972e-02-1.858605e+00
bike_ASC_no_auto_social-5.789594e-06-3.582762e+00
drive_transit_ASC_auto_deficient_social-5.952014e-05-4.728124e-01
drive_transit_ASC_auto_sufficient_social3.790254e-021.010484e+00
sr2_ASC_auto_deficient_social6.027038e-04-1.053489e+00
sr2_ASC_auto_sufficient_social-6.069581e-032.946105e+00
sr3p_ASC_auto_deficient_social-1.707162e-02-2.234511e+00
sr3p_ASC_auto_sufficient_social-2.279043e-022.560694e+00
sr3p_ASC_no_auto_social3.884489e-039.111989e-01
walk_ASC_auto_deficient_social6.875029e-03-1.525839e+00
walk_ASC_auto_sufficient_social4.952062e-038.649351e+00
walk_ASC_no_auto_social1.062086e-029.983101e+00
walk_transit_ASC_auto_deficient_social-3.071990e-031.044025e+00
walk_transit_ASC_auto_sufficient_social2.926429e-022.055777e-01
walk_transit_ASC_no_auto_social-5.072156e-03-6.774045e-01
bike_ASC_auto_deficient_univ
bike_ASC_auto_deficient_work5.216000e-021.054546e-01
bike_ASC_auto_sufficient_work2.159738e-032.479409e+00
bike_ASC_no_auto_work6.749067e-035.406236e-01
coef_hhsize1_sr_multiplier_work4.636415e-029.894138e+00
coef_ivt_work1.302487e+013.471959e+03
commuter_rail_ASC_work-1.097678e-05-4.513152e-01
drive_ferry_ASC_work
drive_light_rail_ASC_work-7.999593e-05-8.938752e-02
drive_transit_ASC_auto_deficient_work-2.254961e-021.663582e+00
drive_transit_ASC_auto_sufficient_work7.266561e-043.706137e+00
drive_transit_CBD_ASC_work-2.468523e-025.879890e+00
express_bus_ASC_work3.163636e-04-1.753048e+00
heavy_rail_ASC_work2.096724e-02-1.079554e+01
local_bus_ASC_work7.237469e-02-6.435996e+01
sr2_ASC_auto_deficient_work1.823376e-02-1.548046e+01
sr2_ASC_auto_sufficient_work-4.215509e-02-1.132921e+01
sr3p_ASC_auto_deficient_work6.661718e-03-5.869162e+00
sr3p_ASC_auto_sufficient_work-4.651531e-02-1.515454e+01
sr3p_ASC_no_auto_work-1.658094e-05-6.520830e-01
taxi_ASC_auto_deficient_work-1.594015e-02-1.397684e+00
taxi_ASC_auto_sufficient_work-1.626721e-07-2.063367e-01
taxi_ASC_no_auto_work-1.622597e-02-9.888833e-01
tnc_shared_ASC_auto_deficient_work-1.766290e-04-4.384960e+00
tnc_shared_ASC_auto_sufficient_work-4.725818e-07-5.809729e-01
tnc_shared_ASC_no_auto_work-5.486741e-05-1.442344e+00
tnc_single_ASC_auto_deficient_work4.618191e-025.544633e+00
tnc_single_ASC_auto_sufficient_work4.306693e-04-8.049820e-01
tnc_single_ASC_no_auto_work4.906633e-03-6.640821e-01
walk_ASC_auto_deficient_work1.481850e-027.888520e+00
walk_ASC_auto_sufficient_work-8.263963e-046.295972e+00
walk_ASC_no_auto_work-4.151620e-024.647179e+00
walk_ferry_ASC_work
walk_light_rail_ASC_work-7.009740e-047.895077e+01
walk_transit_ASC_auto_deficient_work5.124801e-02-5.216891e+00
walk_transit_ASC_auto_sufficient_work3.104569e-022.880706e+00
walk_transit_ASC_no_auto_work3.239561e-02-1.532017e+00
walk_transit_CBD_ASC_work4.838793e-02-6.399546e+00
bike_ASC_auto_deficient_atwork0.000000e+00
nit101nfev264njev101status9message'Iteration limit exceeded'successFalseelapsed_time0:02:58.116522method'slsqp'n_cases5323iteration_number101logloss1.1319462015442479" + "nit1nfev12njev1status0message'Optimization terminated successfully.'successTrueelapsed_time0:00:07.078056method'SLSQP'n_cases5323iteration_number1loglossinf" ], "text/plain": [ "┣ x: -999 -999.000000\n", "┃ 1 1.000000\n", - "┃ bike_ASC_auto_deficient_eatout -1.196927\n", - "┃ bike_ASC_auto_sufficient_eatout -2.398235\n", - "┃ bike_ASC_no_auto_eatout 1.194085\n", + "┃ bike_ASC_auto_deficient_eatout -1.569111\n", + "┃ bike_ASC_auto_sufficient_eatout -1.200347\n", + "┃ bike_ASC_no_auto_eatout 0.868071\n", "┃ ... \n", "┃ walk_ASC_no_auto_atwork 6.669213\n", "┃ walk_transit_ASC_auto_deficient_atwork -2.998829\n", @@ -7631,12 +7722,12 @@ "┃ walk_transit_ASC_no_auto_atwork 2.704188\n", "┃ walk_transit_CBD_ASC_atwork 0.564000\n", "┃ Length: 301, dtype: float64\n", - "┣ loglike: -6025.349630820031\n", + "┣ loglike: -inf\n", "┣ d_loglike: -999 0.000000\n", "┃ 1 0.000000\n", - "┃ bike_ASC_auto_deficient_eatout -0.005113\n", - "┃ bike_ASC_auto_sufficient_eatout -0.002307\n", - "┃ bike_ASC_no_auto_eatout 0.015612\n", + "┃ bike_ASC_auto_deficient_eatout 0.764932\n", + "┃ bike_ASC_auto_sufficient_eatout -2.709202\n", + "┃ bike_ASC_no_auto_eatout -1.386687\n", "┃ ... \n", "┃ walk_ASC_no_auto_atwork 0.000000\n", "┃ walk_transit_ASC_auto_deficient_atwork 0.000000\n", @@ -7644,26 +7735,2776 @@ "┃ walk_transit_ASC_no_auto_atwork 0.000000\n", "┃ walk_transit_CBD_ASC_atwork 0.000000\n", "┃ Length: 301, dtype: float64\n", - "┣ nit: 101\n", - "┣ nfev: 264\n", - "┣ njev: 101\n", - "┣ status: 9\n", - "┣ message: 'Iteration limit exceeded'\n", - "┣ success: False\n", - "┣ elapsed_time: datetime.timedelta(seconds=178, microseconds=116522)\n", - "┣ method: 'slsqp'\n", + "┣ nit: 1\n", + "┣ nfev: 12\n", + "┣ njev: 1\n", + "┣ status: 0\n", + "┣ message: 'Optimization terminated successfully.'\n", + "┣ success: True\n", + "┣ elapsed_time: datetime.timedelta(seconds=7, microseconds=78056)\n", + "┣ method: 'SLSQP'\n", "┣ n_cases: 5323\n", - "┣ iteration_number: 101\n", - "┣ logloss: 1.1319462015442479" + "┣ iteration_number: 1\n", + "┣ logloss: inf" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg.estimate(method='SLSQP', options={'maxiter':1000})\n", + "#mg.estimate(method='BHHH', options={'maxiter':1000})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimated coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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Value Std Err t Stat Signif Like Ratio Null Value
-999-999. 0.00 NA NA-999.00
1 1.00 0.00 NA NA 1.00
bike_ASC_auto_deficient_eatout-1.57 1.22-1.28 NA 0.00
bike_ASC_auto_sufficient_eatout-1.20 0.465-2.58** NA 0.00
bike_ASC_no_auto_eatout 0.868 150. 0.01 NA 0.00
coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work 3.16e-11 0.415 0.00 NA 0.00
coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work-7.26e-10 0.259-0.00 NA 0.00
coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social-1.37 0.250-5.47*** NA 0.00
coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork 2.10e-09 0.112 0.00 NA 0.00
coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork-1.47e-08 0.0824-0.00 NA 0.00
coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social-0.0175 0.000769-22.76*** NA 0.00
coef_nest_AUTO 0.720 0.00 NA NA 1.00
coef_nest_AUTO_DRIVEALONE 0.350 0.00 NA NA 1.00
coef_nest_AUTO_SHAREDRIDE2 0.350 0.00 NA NA 1.00
coef_nest_AUTO_SHAREDRIDE3 0.350 0.00 NA NA 1.00
coef_nest_NONMOTORIZED 0.720 0.00 NA NA 1.00
coef_nest_RIDEHAIL 0.360 0.00 NA NA 1.00
coef_nest_TRANSIT 0.720 0.00 NA NA 1.00
coef_nest_TRANSIT_DRIVEACCESS 0.500 0.00 NA NA 1.00
coef_nest_TRANSIT_WALKACCESS 0.500 0.00 NA NA 1.00
commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork 0.727 54.7 0.01 NA 0.00
drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork 0.940 2.32e-05 40460.59*** NA 0.00
drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork 0.769 56.1 0.01 NA 0.00
drive_transit_ASC_auto_deficient_eatout 0.600 54.4 0.01 NA 0.00
drive_transit_ASC_auto_sufficient_eatout-0.970 54.4-0.02 NA 0.00
drive_transit_ASC_no_auto_all 0.00 1.81e-06 0.00 NA 0.00
drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social 0.525 1.18 0.45 NA 0.00
express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork 0.969 54.4 0.02 NA 0.00
heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork 0.771 54.4 0.01 NA 0.00
joint_bike_ASC_auto_deficient_all-6.08 3.77-1.61 NA 0.00
joint_bike_ASC_auto_sufficient_all-6.38 1.65-3.88*** NA 0.00
joint_bike_ASC_no_auto_all-2.87 2.19-1.31 NA 0.00
joint_drive_transit_ASC_auto_deficient_all-5.96 57.4-0.10 NA 0.00
joint_drive_transit_ASC_auto_sufficient_all-8.05 55.4-0.15 NA 0.00
joint_drive_transit_ASC_no_auto_all 0.00 0.00 NA NA 0.00
joint_sr2_ASC_auto_deficient_all 0.00 0.00 NA NA 0.00
joint_sr2_ASC_auto_sufficient_all 0.00 0.00 NA NA 0.00
joint_sr2_ASC_no_auto_all 0.00 0.00 NA NA 0.00
joint_sr3p_ASC_auto_deficient_all-1.88 1.34-1.40 NA 0.00
joint_sr3p_ASC_auto_sufficient_all-2.23 0.578-3.86*** NA 0.00
joint_sr3p_ASC_no_auto_all 0.563 0.913 0.62 NA 0.00
joint_taxi_ASC_auto_deficient_all-9.82 22.8-0.43 NA 0.00
joint_taxi_ASC_auto_sufficient_all-11.7 0.00 NA NA 0.00
joint_taxi_ASC_no_auto_all-4.58 8.76-0.52 NA 0.00
joint_tnc_shared_ASC_auto_deficient_all-11.2 59.6-0.19 NA 0.00
joint_tnc_shared_ASC_auto_sufficient_all-13.2 0.00 NA NA 0.00
joint_tnc_shared_ASC_no_auto_all-4.30 4.89-0.88 NA 0.00
joint_tnc_single_ASC_auto_deficient_all-9.90 19.9-0.50 NA 0.00
joint_tnc_single_ASC_auto_sufficient_all-14.0 0.00 NA NA 0.00
joint_tnc_single_ASC_no_auto_all-4.49 6.81-0.66 NA 0.00
joint_walk_ASC_auto_deficient_all-1.96 1.35-1.45 NA 0.00
joint_walk_ASC_auto_sufficient_all-3.24 0.853-3.79*** NA 0.00
joint_walk_ASC_no_auto_all-0.213 1.19-0.18 NA 0.00
joint_walk_transit_ASC_auto_deficient_all-5.16 54.4-0.09 NA 0.00
joint_walk_transit_ASC_auto_sufficient_all-18.3 1.12e+03-0.02 NA 0.00
joint_walk_transit_ASC_no_auto_all 0.623 54.4 0.01 NA 0.00
local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.0907 54.4-0.00 NA 0.00
sr2_ASC_auto_deficient_eatout 0.588 0.412 1.43 NA 0.00
sr2_ASC_auto_sufficient_eatout 0.863 0.307 2.81** NA 0.00
sr2_ASC_no_auto_all 6.72e-10 150. 0.00 NA 0.00
sr3p_ASC_auto_deficient_eatout 0.0461 0.489 0.09 NA 0.00
sr3p_ASC_auto_sufficient_eatout 0.847 0.305 2.78** NA 0.00
sr3p_ASC_no_auto_eatout 0.322 150. 0.00 NA 0.00
taxi_ASC_auto_deficient_eatout_othdiscr_social-3.13 1.27-2.47* NA 0.00
taxi_ASC_auto_sufficient_eatout_othdiscr_social-3.04 0.738-4.11*** NA 0.00
taxi_ASC_no_auto_eatout_othdiscr_social 0.992 150. 0.01 NA 0.00
tnc_shared_ASC_auto_deficient_eatout_othdiscr_social-4.36 1.57-2.77** NA 0.00
tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social-3.66 0.531-6.90*** NA 0.00
tnc_shared_ASC_no_auto_eatout_othdiscr_social 0.646 150. 0.00 NA 0.00
tnc_single_ASC_auto_deficient_eatout_othdiscr_social-2.96 1.12-2.64** NA 0.00
tnc_single_ASC_auto_sufficient_eatout_othdiscr_social-2.32 0.398-5.84*** NA 0.00
tnc_single_ASC_no_auto_eatout_othdiscr_social 1.69 150. 0.01 NA 0.00
walk_ASC_auto_deficient_eatout 3.27 0.464 7.05*** NA 0.00
walk_ASC_auto_sufficient_eatout 1.55 0.286 5.43*** NA 0.00
walk_ASC_no_auto_eatout 5.13 150. 0.03 NA 0.00
walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork 0.940 2.17e-06 432421.90*** NA 0.00
walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork 0.769 54.4 0.01 NA 0.00
walk_transit_ASC_auto_deficient_eatout-0.0390 54.4-0.00 NA 0.00
walk_transit_ASC_auto_sufficient_eatout-1.11 54.4-0.02 NA 0.00
walk_transit_ASC_no_auto_eatout 2.59 111. 0.02 NA 0.00
walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social 0.525 0.166 3.16** NA 0.00
bike_ASC_auto_deficient_escort-4.53 1.80-2.52* NA 0.00
bike_ASC_auto_sufficient_escort-5.06 1.13-4.49*** NA 0.00
bike_ASC_no_auto_escort-0.716 150.-0.00 NA 0.00
drive_transit_ASC_auto_deficient_escort-1.15 54.4-0.02 NA 0.00
drive_transit_ASC_auto_sufficient_escort-4.60 54.9-0.08 NA 0.00
sr2_ASC_auto_deficient_escort-8.66e-10 0.396-0.00 NA 0.00
sr2_ASC_auto_sufficient_escort 2.34e-09 0.383 0.00 NA 0.00
sr3p_ASC_auto_deficient_escort-0.408 0.411-0.99 NA 0.00
sr3p_ASC_auto_sufficient_escort-0.0574 0.383-0.15 NA 0.00
sr3p_ASC_no_auto_escort-1.81 150.-0.01 NA 0.00
taxi_ASC_auto_deficient_escort_othmaint_shopping 0.177 0.258 0.68 NA 0.00
taxi_ASC_auto_sufficient_escort_othmaint_shopping-1.81 0.257-7.01*** NA 0.00
taxi_ASC_no_auto_escort_othmaint_shopping 1.89 150. 0.01 NA 0.00
tnc_shared_ASC_auto_deficient_escort_othmaint_shopping-0.386 0.230-1.68 NA 0.00
tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping-2.44 0.237-10.27*** NA 0.00
tnc_shared_ASC_no_auto_escort_othmaint_shopping 0.936 150. 0.01 NA 0.00
tnc_single_ASC_auto_deficient_escort_othmaint_shopping 0.675 0.219 3.08** NA 0.00
tnc_single_ASC_auto_sufficient_escort_othmaint_shopping-1.45 0.213-6.81*** NA 0.00
tnc_single_ASC_no_auto_escort_othmaint_shopping 1.86 150. 0.01 NA 0.00
walk_ASC_auto_deficient_escort-0.902 0.603-1.50 NA 0.00
walk_ASC_auto_sufficient_escort-0.812 0.367-2.21* NA 0.00
walk_ASC_no_auto_escort 2.80 150. 0.02 NA 0.00
walk_transit_ASC_auto_deficient_escort-4.96 54.4-0.09 NA 0.00
walk_transit_ASC_auto_sufficient_escort-4.93 54.4-0.09 NA 0.00
walk_transit_ASC_no_auto_escort-2.22 111.-0.02 NA 0.00
bike_ASC_auto_deficient_othdiscr-0.0925 0.456-0.20 NA 0.00
bike_ASC_auto_sufficient_othdiscr-1.07 0.249-4.31*** NA 0.00
bike_ASC_no_auto_othdiscr-0.376 150.-0.00 NA 0.00
drive_transit_ASC_auto_deficient_othdiscr 0.320 54.5 0.01 NA 0.00
drive_transit_ASC_auto_sufficient_othdiscr-0.379 54.4-0.01 NA 0.00
sr2_ASC_auto_deficient_othdiscr 0.660 0.387 1.71 NA 0.00
sr2_ASC_auto_sufficient_othdiscr 0.497 0.276 1.80 NA 0.00
sr3p_ASC_auto_deficient_othdiscr 1.05 0.354 2.96** NA 0.00
sr3p_ASC_auto_sufficient_othdiscr 0.589 0.273 2.16* NA 0.00
sr3p_ASC_no_auto_othdiscr 0.272 150. 0.00 NA 0.00
walk_ASC_auto_deficient_othdiscr 2.25 0.409 5.50*** NA 0.00
walk_ASC_auto_sufficient_othdiscr 1.26 0.214 5.91*** NA 0.00
walk_ASC_no_auto_othdiscr 3.27 150. 0.02 NA 0.00
walk_transit_ASC_auto_deficient_othdiscr 0.953 54.4 0.02 NA 0.00
walk_transit_ASC_auto_sufficient_othdiscr-0.806 54.4-0.01 NA 0.00
walk_transit_ASC_no_auto_othdiscr 2.24 111. 0.02 NA 0.00
bike_ASC_auto_deficient_othmaint-1.52 0.708-2.14* NA 0.00
bike_ASC_auto_sufficient_othmaint-2.81 0.540-5.20*** NA 0.00
bike_ASC_no_auto_othmaint 1.54 150. 0.01 NA 0.00
drive_transit_ASC_auto_deficient_othmaint-0.299 54.4-0.01 NA 0.00
drive_transit_ASC_auto_sufficient_othmaint-2.62 54.5-0.05 NA 0.00
sr2_ASC_auto_deficient_othmaint 0.262 0.419 0.63 NA 0.00
sr2_ASC_auto_sufficient_othmaint 0.258 0.294 0.88 NA 0.00
sr3p_ASC_auto_deficient_othmaint-1.35 0.807-1.67 NA 0.00
sr3p_ASC_auto_sufficient_othmaint-0.0755 0.308-0.24 NA 0.00
sr3p_ASC_no_auto_othmaint-0.803 150.-0.01 NA 0.00
walk_ASC_auto_deficient_othmaint 1.37 0.475 2.88** NA 0.00
walk_ASC_auto_sufficient_othmaint 0.800 0.263 3.04** NA 0.00
walk_ASC_no_auto_othmaint 1.29 150. 0.01 NA 0.00
walk_transit_ASC_auto_deficient_othmaint-3.06 54.4-0.06 NA 0.00
walk_transit_ASC_auto_sufficient_othmaint-1.55 54.4-0.03 NA 0.00
walk_transit_ASC_no_auto_othmaint 2.56 111. 0.02 NA 0.00
bike_ASC_auto_deficient_school-0.528 1.08-0.49 NA 0.00
bike_ASC_auto_sufficient_school-2.11 0.404-5.23*** NA 0.00
bike_ASC_no_auto_school 12.1 601. 0.02 NA 0.00
coef_age010_trn_multiplier_school_univ-1.55 0.286-5.43*** NA 0.00
coef_age1619_da_multiplier_school_univ-1.38 0.467-2.96** NA 0.00
coef_age16p_sr_multiplier_school_univ_work_atwork-8.50e-09 0.283-0.00 NA 0.00
coef_hhsize2_sr_multiplier_school_univ-0.636 0.399-1.60 NA 0.00
coef_ivt_school_univ-0.0224 0.00229-9.76*** NA 0.00
commuter_rail_ASC_school_univ 1.03 102. 0.01 NA 0.00
drive_ferry_ASC_school_univ 2.02 1.51e-06 1338594.80*** NA 0.00
drive_light_rail_ASC_school_univ 1.68 126. 0.01 NA 0.00
drive_transit_ASC_auto_deficient_school 5.33 96.5 0.06 NA 0.00
drive_transit_ASC_auto_sufficient_school 1.40 96.6 0.01 NA 0.00
drive_transit_CBD_ASC_school_univ 0.672 3.20 0.21 NA 0.00
express_bus_ASC_school_univ 0.325 1.11e-06 293225.49*** NA 0.00
heavy_rail_ASC_school_univ 0.962 96.5 0.01 NA 0.00
local_bus_ASC_school_univ-0.0651 96.5-0.00 NA 0.00
sr2_ASC_auto_deficient_school 0.125 0.811 0.15 NA 0.00
sr2_ASC_auto_sufficient_school-1.61 0.314-5.11*** NA 0.00
sr3p_ASC_auto_deficient_school 0.715 0.797 0.90 NA 0.00
sr3p_ASC_auto_sufficient_school-1.02 0.302-3.38*** NA 0.00
sr3p_ASC_no_auto_school-6.02 0.131-46.12*** NA 0.00
taxi_ASC_auto_deficient_school-0.334 1.24-0.27 NA 0.00
taxi_ASC_auto_sufficient_school-2.43 0.694-3.50*** NA 0.00
taxi_ASC_no_auto_school_univ-7.00 0.00 NA NA 0.00
tnc_shared_ASC_auto_deficient_school-1.47 1.33-1.11 NA 0.00
tnc_shared_ASC_auto_sufficient_school-3.72 0.897-4.15*** NA 0.00
tnc_shared_ASC_no_auto_school-7.00 0.00 NA NA 0.00
tnc_single_ASC_auto_deficient_school-0.552 1.22-0.45 NA 0.00
tnc_single_ASC_auto_sufficient_school-2.84 0.696-4.07*** NA 0.00
tnc_single_ASC_no_auto_school-7.00 0.00 NA NA 0.00
walk_ASC_auto_deficient_school 3.26 0.823 3.96*** NA 0.00
walk_ASC_auto_sufficient_school 0.648 0.322 2.01* NA 0.00
walk_ASC_no_auto_school 18.4 601. 0.03 NA 0.00
walk_ferry_ASC_school_univ 2.02 8.92e-08 22645849.85*** NA 0.00
walk_light_rail_ASC_school_univ 1.68 96.5 0.02 NA 0.00
walk_transit_ASC_auto_deficient_school 4.12 96.5 0.04 NA 0.00
walk_transit_ASC_auto_sufficient_school 0.746 96.5 0.01 NA 0.00
walk_transit_ASC_no_auto_school 21.4 505. 0.04 NA 0.00
walk_transit_CBD_ASC_school_univ 0.672 0.281 2.39* NA 0.00
bike_ASC_auto_deficient_shopping-0.876 0.450-1.95 NA 0.00
bike_ASC_auto_sufficient_shopping-2.57 0.366-7.01*** NA 0.00
bike_ASC_no_auto_shopping 0.834 150. 0.01 NA 0.00
drive_transit_ASC_auto_deficient_shopping-0.418 54.4-0.01 NA 0.00
drive_transit_ASC_auto_sufficient_shopping-2.17 54.4-0.04 NA 0.00
sr2_ASC_auto_deficient_shopping 0.244 0.337 0.72 NA 0.00
sr2_ASC_auto_sufficient_shopping 0.198 0.270 0.73 NA 0.00
sr3p_ASC_auto_deficient_shopping-0.0734 0.360-0.20 NA 0.00
sr3p_ASC_auto_sufficient_shopping-0.0776 0.276-0.28 NA 0.00
sr3p_ASC_no_auto_shopping-0.280 150.-0.00 NA 0.00
walk_ASC_auto_deficient_shopping 2.27 0.301 7.55*** NA 0.00
walk_ASC_auto_sufficient_shopping 0.731 0.213 3.44*** NA 0.00
walk_ASC_no_auto_shopping 2.38 150. 0.02 NA 0.00
walk_transit_ASC_auto_deficient_shopping-0.848 54.4-0.02 NA 0.00
walk_transit_ASC_auto_sufficient_shopping-2.20 54.4-0.04 NA 0.00
walk_transit_ASC_no_auto_shopping 2.11 111. 0.02 NA 0.00
bike_ASC_auto_deficient_social 0.635 0.780 0.81 NA 0.00
bike_ASC_auto_sufficient_social-1.37 0.576-2.38* NA 0.00
bike_ASC_no_auto_social 0.0206 150. 0.00 NA 0.00
drive_transit_ASC_auto_deficient_social 1.56 54.4 0.03 NA 0.00
drive_transit_ASC_auto_sufficient_social-0.616 54.4-0.01 NA 0.00
sr2_ASC_auto_deficient_social 1.86 0.530 3.50*** NA 0.00
sr2_ASC_auto_sufficient_social 0.524 0.363 1.44 NA 0.00
sr3p_ASC_auto_deficient_social 1.50 0.561 2.67** NA 0.00
sr3p_ASC_auto_sufficient_social 0.506 0.358 1.41 NA 0.00
sr3p_ASC_no_auto_social-1.40 150.-0.01 NA 0.00
walk_ASC_auto_deficient_social 2.87 0.781 3.68*** NA 0.00
walk_ASC_auto_sufficient_social 1.71 0.379 4.50*** NA 0.00
walk_ASC_no_auto_social 1.87 150. 0.01 NA 0.00
walk_transit_ASC_auto_deficient_social 0.974 54.4 0.02 NA 0.00
walk_transit_ASC_auto_sufficient_social-0.345 54.4-0.01 NA 0.00
walk_transit_ASC_no_auto_social 1.38 111. 0.01 NA 0.00
bike_ASC_auto_deficient_univ-0.669 7.65e-08-8746843.94*** NA 0.00
bike_ASC_auto_sufficient_univ-1.94 4.62e-08-41981018.45*** NA 0.00
bike_ASC_no_auto_univ 4.29 2.90e-08 147965853.73*** NA 0.00
drive_transit_ASC_auto_deficient_univ 1.85 1.69e-07 10956958.61*** NA 0.00
drive_transit_ASC_auto_sufficient_univ 1.36 8.74e-08 15543985.76*** NA 0.00
sr2_ASC_auto_deficient_univ-1.69 4.58e-08-36930068.57*** NA 0.00
sr2_ASC_auto_sufficient_univ-1.86 2.17e-08-85867660.41*** NA 0.00
sr3p_ASC_auto_deficient_univ-1.73 3.48e-08-49583113.15*** NA 0.00
sr3p_ASC_auto_sufficient_univ-1.90 1.61e-08-118434349.76*** NA 0.00
sr3p_ASC_no_auto_univ-6.06 4.97e-08-121812195.80*** NA 0.00
taxi_ASC_auto_deficient_univ 4.25 1.05e-08 403154802.65*** NA 0.00
taxi_ASC_auto_sufficient_univ-0.313 3.98e-08-7863564.40*** NA 0.00
tnc_shared_ASC_auto_deficient_univ 3.25 1.37e-08 236879609.92*** NA 0.00
tnc_shared_ASC_auto_sufficient_univ-0.907 1.70e-08-53236456.50*** NA 0.00
tnc_shared_ASC_no_auto_univ-5.81 1.05e-08-551380382.49*** NA 0.00
tnc_single_ASC_auto_deficient_univ 1.02 2.44e-08 41886314.94*** NA 0.00
tnc_single_ASC_auto_sufficient_univ 0.209 8.93e-10 233916619.85*** NA 0.00
tnc_single_ASC_no_auto_univ-2.52 6.49e-09-387988706.91*** NA 0.00
walk_ASC_auto_deficient_univ 4.51 3.10e-08 145291868.93*** NA 0.00
walk_ASC_auto_sufficient_univ 1.06 6.37e-10 1665611030.60*** NA 0.00
walk_ASC_no_auto_univ 6.41 1.97e-11 324762715915.05*** NA 0.00
walk_transit_ASC_auto_deficient_univ 3.14 1.00e-10 31364647427.95*** NA 0.00
walk_transit_ASC_auto_sufficient_univ 0.473 1.19e-11 39632805760.59*** NA 0.00
walk_transit_ASC_no_auto_univ 8.79 3.61e-13 24316815744885.24*** NA 0.00
bike_ASC_auto_deficient_work 0.253 0.162 1.56 NA 0.00
bike_ASC_auto_sufficient_work-1.58 0.170-9.28*** NA 0.00
bike_ASC_no_auto_work 3.19 150. 0.02 NA 0.00
coef_hhsize1_sr_multiplier_work-0.735 0.200-3.68*** NA 0.00
coef_ivt_work-0.0134 0.000616-21.74*** NA 0.00
commuter_rail_ASC_work 0.726 363. 0.00 NA 0.00
drive_ferry_ASC_work 0.933 0.00 NA NA 0.00
drive_light_rail_ASC_work 0.826 363. 0.00 NA 0.00
drive_transit_ASC_auto_deficient_work 0.101 363. 0.00 NA 0.00
drive_transit_ASC_auto_sufficient_work-1.00 363.-0.00 NA 0.00
drive_transit_CBD_ASC_work 1.10 0.655 1.68 NA 0.00
express_bus_ASC_work-0.517 363.-0.00 NA 0.00
heavy_rail_ASC_work 0.648 363. 0.00 NA 0.00
local_bus_ASC_work 0.0669 363. 0.00 NA 0.00
sr2_ASC_auto_deficient_work-0.338 0.306-1.10 NA 0.00
sr2_ASC_auto_sufficient_work-1.09 0.301-3.61*** NA 0.00
sr3p_ASC_auto_deficient_work-0.853 0.316-2.70** NA 0.00
sr3p_ASC_auto_sufficient_work-1.47 0.307-4.79*** NA 0.00
sr3p_ASC_no_auto_work-0.583 150.-0.00 NA 0.00
taxi_ASC_auto_deficient_work-1.48 0.362-4.07*** NA 0.00
taxi_ASC_auto_sufficient_work-4.85 1.43-3.40*** NA 0.00
taxi_ASC_no_auto_work 4.73 150. 0.03 NA 0.00
tnc_shared_ASC_auto_deficient_work-2.14 0.359-5.96*** NA 0.00
tnc_shared_ASC_auto_sufficient_work-5.36 0.995-5.39*** NA 0.00
tnc_shared_ASC_no_auto_work 3.24 150. 0.02 NA 0.00
tnc_single_ASC_auto_deficient_work-0.801 0.222-3.60*** NA 0.00
tnc_single_ASC_auto_sufficient_work-4.19 0.643-6.52*** NA 0.00
tnc_single_ASC_no_auto_work 5.79 150. 0.04 NA 0.00
walk_ASC_auto_deficient_work 2.40 0.171 14.01*** NA 0.00
walk_ASC_auto_sufficient_work 0.0533 0.186 0.29 NA 0.00
walk_ASC_no_auto_work 5.77 150. 0.04 NA 0.00
walk_ferry_ASC_work 0.933 0.00 NA NA 0.00
walk_light_rail_ASC_work 0.826 363. 0.00 NA 0.00
walk_transit_ASC_auto_deficient_work 0.653 363. 0.00 NA 0.00
walk_transit_ASC_auto_sufficient_work-0.892 363.-0.00 NA 0.00
walk_transit_ASC_no_auto_work 5.04 512. 0.01 NA 0.00
walk_transit_CBD_ASC_work 0.804 0.136 5.91*** NA 0.00
bike_ASC_auto_deficient_atwork-0.807 0.00 NA NA 0.00
bike_ASC_auto_sufficient_atwork 15.7 0.00 NA NA 0.00
bike_ASC_no_auto_atwork-0.907 0.00 NA NA 0.00
coef_age010_trn_multiplier_atwork 0.000722 0.00 NA NA 0.00
coef_age1619_da_multiplier_atwork 0.00323 0.00 NA NA 0.00
coef_ivt_atwork-0.0188 0.00 NA NA 0.00
drive_transit_ASC_auto_deficient_atwork-999. 0.00 NA NA 0.00
drive_transit_ASC_auto_sufficient_atwork-999. 0.00 NA NA 0.00
drive_transit_CBD_ASC_atwork 0.564 0.00 NA NA 0.00
sr2_ASC_auto_deficient_atwork-2.11 0.00 NA NA 0.00
sr2_ASC_auto_sufficient_atwork-1.45 0.00 NA NA 0.00
sr3p_ASC_auto_deficient_atwork-2.51 0.00 NA NA 0.00
sr3p_ASC_auto_sufficient_atwork-1.65 0.00 NA NA 0.00
sr3p_ASC_no_auto_atwork 0.583 0.00 NA NA 0.00
taxi_ASC_auto_deficient_atwork-4.40 0.00 NA NA 0.00
taxi_ASC_auto_sufficient_atwork-2.88 0.00 NA NA 0.00
taxi_ASC_no_auto_atwork 4.10 0.00 NA NA 0.00
tnc_shared_ASC_auto_deficient_atwork-4.51 0.00 NA NA 0.00
tnc_shared_ASC_auto_sufficient_atwork-3.54 0.00 NA NA 0.00
tnc_shared_ASC_no_auto_atwork 3.37 0.00 NA NA 0.00
tnc_single_ASC_auto_deficient_atwork-3.76 0.00 NA NA 0.00
tnc_single_ASC_auto_sufficient_atwork-2.80 0.00 NA NA 0.00
tnc_single_ASC_no_auto_atwork 4.50 0.00 NA NA 0.00
walk_ASC_auto_deficient_atwork 0.925 0.00 NA NA 0.00
walk_ASC_auto_sufficient_atwork 0.677 0.00 NA NA 0.00
walk_ASC_no_auto_atwork 6.67 0.00 NA NA 0.00
walk_transit_ASC_auto_deficient_atwork-3.00 0.00 NA NA 0.00
walk_transit_ASC_auto_sufficient_atwork-3.40 0.00 NA NA 0.00
walk_transit_ASC_no_auto_atwork 2.70 0.00 NA NA 0.00
walk_transit_CBD_ASC_atwork 0.564 0.00 NA NA 0.00
" + ], + "text/plain": [ + "" ] }, - "execution_count": 30, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "mg.estimate()" + "mg.parameter_summary()" ] }, { @@ -7678,21 +10519,21 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ - "est_names = [j for j in coefficients.index if j in mg.pf.index]" + "est_names = [j for j in coefficients.index if j in mg.pf.index]\n", + "coefficients.loc[est_names, 'value'] = mg.pf.loc[est_names, 'value']" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ - "# Write re-estimated value back into the coefficients file.\n", - "coefficients.loc[est_names, 'value'] = mg.pf.loc[est_names, 'value']" + "os.makedirs(os.path.join(edb_directory,'estimated'), exist_ok=True)" ] }, { @@ -7704,13 +10545,10 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ - "# Write out replacement coefficients file and model summaries\n", - "os.makedirs(os.path.join(edb_directory,'estimated'), exist_ok=True)\n", - "\n", "coefficients.reset_index().to_csv(\n", " os.path.join(\n", " edb_directory, \n", @@ -7718,8 +10556,7 @@ " \"tour_mode_choice_coefficients_revised.csv\",\n", " ),\n", " index=False,\n", - ")\n", - "\n" + ")" ] }, { @@ -7731,7 +10568,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -7741,7 +10578,7 @@ " edb_directory, \n", " 'estimated',\n", " f\"tour_mode_choice_{purpose}_model_estimation.xlsx\",\n", - " )\n", + " ), data_statistics=False\n", " )" ] }, @@ -7754,6 +10591,135 @@ "The final step is to either manually or automatically copy the `tour_mode_choice_coefficients_revised.csv` file to the configs folder, rename it to `tour_mode_choice_coeffs.csv`, and run ActivitySim in simulation mode." ] }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefficient_namevalueconstrain
0coef_nest_root1.000T
1coef_nest_AUTO0.720T
2coef_nest_AUTO_DRIVEALONE0.350T
3coef_nest_AUTO_SHAREDRIDE20.350T
4coef_nest_AUTO_SHAREDRIDE30.350T
............
301walk_transit_CBD_ASC_atwork0.564F
302drive_transit_CBD_ASC_eatout_escort_othdiscr_o...0.525F
303drive_transit_CBD_ASC_school_univ0.672F
304drive_transit_CBD_ASC_work1.100F
305drive_transit_CBD_ASC_atwork0.564F
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306 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " coefficient_name value constrain\n", + "0 coef_nest_root 1.000 T\n", + "1 coef_nest_AUTO 0.720 T\n", + "2 coef_nest_AUTO_DRIVEALONE 0.350 T\n", + "3 coef_nest_AUTO_SHAREDRIDE2 0.350 T\n", + "4 coef_nest_AUTO_SHAREDRIDE3 0.350 T\n", + ".. ... ... ...\n", + "301 walk_transit_CBD_ASC_atwork 0.564 F\n", + "302 drive_transit_CBD_ASC_eatout_escort_othdiscr_o... 0.525 F\n", + "303 drive_transit_CBD_ASC_school_univ 0.672 F\n", + "304 drive_transit_CBD_ASC_work 1.100 F\n", + "305 drive_transit_CBD_ASC_atwork 0.564 F\n", + "\n", + "[306 rows x 3 columns]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.read_csv(os.path.join(edb_directory,'estimated',\"tour_mode_choice_coefficients_revised.csv\"))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -7783,7 +10749,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.8" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/estimating_workplace_location.ipynb b/activitysim/examples/example_estimation/notebooks/estimating_workplace_location.ipynb index 5d6200c16..a161f1b76 100644 --- a/activitysim/examples/example_estimation/notebooks/estimating_workplace_location.ipynb +++ b/activitysim/examples/example_estimation/notebooks/estimating_workplace_location.ipynb @@ -28,16 +28,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import larch # !conda install larch #for estimation\n", + "import larch.util.activitysim\n", "import pandas as pd\n", "import numpy as np\n", "import yaml \n", "import larch.util.excel\n", - "import larch_asim # utility functions in a local module\n", "import os" ] }, @@ -2510,7 +2510,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -2522,7 +2522,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2551,7 +2551,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -2642,7 +2642,7 @@ "work_veryhigh 0.093 0.270 0.241 0.146 0.004 0.246" ] }, - "execution_count": 8, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -2665,7 +2665,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -2852,7 +2852,7 @@ "work_veryhigh_MWTEMPN -1.402424 F" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -2880,7 +2880,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -2921,7 +2921,7 @@ " 'SAVED_SHADOW_PRICE_TABLE_NAME': 'workplace_shadow_prices.csv'}" ] }, - "execution_count": 10, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -2943,7 +2943,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -3034,7 +3034,7 @@ "coef_mode_logsum 0.3000 F" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -3052,7 +3052,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -3225,7 +3225,7 @@ "12 @np.minimum(np.log(df.pick_count/df.prob), 60) 1 " ] }, - "execution_count": 12, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -3243,7 +3243,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -3262,7 +3262,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -3674,7 +3674,7 @@ "[49077 rows x 192 columns]" ] }, - "execution_count": 14, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -3692,7 +3692,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -3834,7 +3834,7 @@ "[2583 rows x 5 columns]" ] }, - "execution_count": 15, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -3854,7 +3854,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -3870,7 +3870,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -3879,7 +3879,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -3900,7 +3900,7 @@ } ], "source": [ - "m.utility_ca = larch_asim.linear_utility_from_spec(\n", + "m.utility_ca = larch.util.activitysim.linear_utility_from_spec(\n", " spec, x_col='Label', p_col='coefficient', \n", " ignore_x=('local_dist',), \n", ")\n", @@ -3909,7 +3909,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -3922,12 +3922,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "larch_asim.apply_coefficients(coefficients, m)\n", - "larch_asim.apply_coefficients(size_coef, m, minimum=-6, maximum=6)" + "larch.util.activitysim.apply_coefficients(coefficients, m)\n", + "larch.util.activitysim.apply_coefficients(size_coef, m, minimum=-6, maximum=6)" ] }, { @@ -3939,7 +3939,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -4391,7 +4391,7 @@ "work_veryhigh_RETEMPN 1 " ] }, - "execution_count": 21, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -4402,7 +4402,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -4411,18 +4411,36 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "x_ca = larch_asim.cv_to_ca(\n", + "x_ca = larch.util.activitysim.cv_to_ca(\n", " alt_values.set_index(['person_id', 'variable'])\n", ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove choosers with invalid observed choice" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "workplace_tazs = landuse[landuse['TOTEMP'] > 0].index\n", + "x_co = x_co[x_co['override_choice'].isin(workplace_tazs)]\n", + "x_ca = x_ca[x_ca.index.get_level_values('person_id').isin(x_co.index)]" + ] + }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -4430,94 +4448,38 @@ "x_ca_1.index = x_ca.index" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Availability" + ] + }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "d = larch.DataFrames(\n", - " co=x_co,\n", - " ca=x_ca_1,\n", - " av=True,\n", - ")" + "av = x_ca_1['util_no_attractions'].apply(lambda x: False if x == 1 else True)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 41, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "larch.DataFrames: (not computation-ready)\n", - " n_cases: 2583\n", - " n_alts: 190\n", - " data_ca:\n", - " - TAZ (490770 non-null float64)\n", - " - mode_choice_logsum (490770 non-null float64)\n", - " - pick_count (490770 non-null float64)\n", - " - prob (490770 non-null float64)\n", - " - shadow_price_size_term_adjustment (490770 non-null float64)\n", - " - shadow_price_utility_adjustment (490770 non-null float64)\n", - " - size_term (490770 non-null float64)\n", - " - util_dist_0_1 (490770 non-null float64)\n", - " - util_dist_0_5_high (490770 non-null float64)\n", - " - util_dist_15_up (490770 non-null float64)\n", - " - util_dist_15_up_high (490770 non-null float64)\n", - " - util_dist_1_2 (490770 non-null float64)\n", - " - util_dist_2_5 (490770 non-null float64)\n", - " - util_dist_5_15 (490770 non-null float64)\n", - " - util_mode_logsum (490770 non-null float64)\n", - " - util_no_attractions (490770 non-null float64)\n", - " - util_sample_of_corrections_factor (490770 non-null float64)\n", - " - util_size_variable (490770 non-null float64)\n", - " - util_utility_adjustment (490770 non-null float64)\n", - " - DISTRICT (490770 non-null int64)\n", - " - SD (490770 non-null int64)\n", - " - county_id (490770 non-null int64)\n", - " - TOTHH (490770 non-null int64)\n", - " - TOTPOP (490770 non-null int64)\n", - " - TOTACRE (490770 non-null float64)\n", - " - RESACRE (490770 non-null float64)\n", - " - CIACRE (490770 non-null float64)\n", - " - TOTEMP (490770 non-null int64)\n", - " - AGE0519 (490770 non-null int64)\n", - " - RETEMPN (490770 non-null int64)\n", - " - FPSEMPN (490770 non-null int64)\n", - " - HEREMPN (490770 non-null int64)\n", - " - OTHEMPN (490770 non-null int64)\n", - " - AGREMPN (490770 non-null int64)\n", - " - MWTEMPN (490770 non-null int64)\n", - " - PRKCST (490770 non-null float64)\n", - " - OPRKCST (490770 non-null float64)\n", - " - area_type (490770 non-null int64)\n", - " - HSENROLL (490770 non-null float64)\n", - " - COLLFTE (490770 non-null float64)\n", - " - COLLPTE (490770 non-null float64)\n", - " - TOPOLOGY (490770 non-null int64)\n", - " - TERMINAL (490770 non-null float64)\n", - " - household_density (490770 non-null float64)\n", - " - employment_density (490770 non-null float64)\n", - " - density_index (490770 non-null float64)\n", - " data_co:\n", - " - model_choice (2583 non-null int64)\n", - " - override_choice (2583 non-null int64)\n", - " - income_segment (2583 non-null int64)\n", - " - HOMETAZ (2583 non-null int64)\n", - " data_av: \n" - ] - } - ], + "outputs": [], "source": [ - "d.info(1)" + "d = larch.DataFrames(\n", + " co=x_co,\n", + " ca=x_ca_1,\n", + " av=av,\n", + ")" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -4533,7 +4495,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -4546,12 +4508,12 @@ "source": [ "# Estimate\n", "\n", - "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution." + "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution. Larch has two built-in estimation methods: BHHH and SLSQP. BHHH is the default and typically runs faster, but does not follow constraints on parameters. SLSQP is safer, but slower, and may need additional iterations." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -4564,7 +4526,7 @@ { "data": { "text/html": [ - "

Iteration 092 [Converged]

" + "

Iteration 1000 [Converged]

" ], "text/plain": [ "" @@ -4576,7 +4538,7 @@ { "data": { "text/html": [ - "

LL = -12624.992531894883

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LL = -12787.630096069548

" ], "text/plain": [ "" @@ -4641,146 +4603,146 @@ " \n", " \n", " coef_dist_0_1\n", - " -1.239747\n", + " -1.209020\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1.239747\n", + " -1.209020\n", " \n", " \n", " coef_dist_0_5_high\n", - " 0.281467\n", + " 0.196045\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 0.281467\n", + " 0.196045\n", " \n", " \n", " coef_dist_15_up\n", - " -0.091700\n", + " -0.100912\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.091700\n", + " -0.100912\n", " \n", " \n", " coef_dist_1_2\n", - " -0.992783\n", + " -0.582653\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.992783\n", + " -0.582653\n", " \n", " \n", " coef_dist_2_5\n", - " -0.731466\n", + " -0.590026\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.731466\n", + " -0.590026\n", " \n", " \n", " coef_dist_5_15\n", - " -0.253770\n", + " -0.225455\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -0.253770\n", + " -0.225455\n", " \n", " \n", " coef_dist_5_up_high\n", - " 0.113310\n", + " 0.036938\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 0.113310\n", + " 0.036938\n", " \n", " \n", " coef_mode_logsum\n", - " 0.017697\n", + " 0.254307\n", " 0.0\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 0.017697\n", + " 0.254307\n", " \n", " \n", " work_high_AGREMPN\n", - " -5.171203\n", + " -146.302901\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -5.171203\n", + " -146.302901\n", " \n", " \n", " work_high_FPSEMPN\n", - " 6.000000\n", + " 45.063176\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " 6.000000\n", + " 45.063176\n", " \n", " \n", " work_high_HEREMPN\n", - " 2.457039\n", + " 43.993849\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " 2.457039\n", + " 43.993849\n", " \n", " \n", " work_high_MWTEMPN\n", - " 3.444542\n", + " 45.257220\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " 3.444542\n", + " 45.257220\n", " \n", " \n", " work_high_OTHEMPN\n", - " -5.910180\n", + " 43.301239\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -5.910180\n", + " 43.301239\n", " \n", " \n", " work_high_RETEMPN\n", @@ -4795,58 +4757,58 @@ " \n", " \n", " work_low_AGREMPN\n", - " -4.699143\n", + " -1694.618373\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -4.699143\n", + " -1694.618373\n", " \n", " \n", " work_low_FPSEMPN\n", - " 6.000000\n", + " 58.806849\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " 6.000000\n", + " 58.806849\n", " \n", " \n", " work_low_HEREMPN\n", - " 4.418420\n", + " 58.664609\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " 4.418420\n", + " 58.664609\n", " \n", " \n", " work_low_MWTEMPN\n", - " -6.000000\n", + " 56.639562\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -6.000000\n", + " 56.639562\n", " \n", " \n", " work_low_OTHEMPN\n", - " -6.000000\n", + " 54.979037\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -6.000000\n", + " 54.979037\n", " \n", " \n", " work_low_RETEMPN\n", @@ -4861,58 +4823,58 @@ " \n", " \n", " work_med_AGREMPN\n", - " -4.913574\n", + " -2017.102530\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -4.913574\n", + " -2017.102530\n", " \n", " \n", " work_med_FPSEMPN\n", - " 6.000000\n", + " 59.495813\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " 6.000000\n", + " 59.495813\n", " \n", " \n", " work_med_HEREMPN\n", - " 3.968943\n", + " 59.010833\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " 3.968943\n", + " 59.010833\n", " \n", " \n", " work_med_MWTEMPN\n", - " -6.000000\n", + " 58.107721\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -6.000000\n", + " 58.107721\n", " \n", " \n", " work_med_OTHEMPN\n", - " -6.000000\n", + " 56.082736\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -6.000000\n", + " 56.082736\n", " \n", " \n", " work_med_RETEMPN\n", @@ -4927,58 +4889,58 @@ " \n", " \n", " work_veryhigh_AGREMPN\n", - " -5.614733\n", + " -2078.045718\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -5.614733\n", + " -2078.045718\n", " \n", " \n", " work_veryhigh_FPSEMPN\n", - " 6.000000\n", + " 389.602957\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " 6.000000\n", + " 389.602957\n", " \n", " \n", " work_veryhigh_HEREMPN\n", - " -5.899534\n", + " 388.456095\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -5.899534\n", + " 388.456095\n", " \n", " \n", " work_veryhigh_MWTEMPN\n", - " -6.000000\n", + " 388.541587\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -6.000000\n", + " 388.541587\n", " \n", " \n", " work_veryhigh_OTHEMPN\n", - " -6.000000\n", + " 350.877157\n", " 0.0\n", " 0.0\n", " -6.0\n", " 6.0\n", " 0\n", " \n", - " -6.000000\n", + " 350.877157\n", " \n", " \n", " work_veryhigh_RETEMPN\n", @@ -4996,77 +4958,77 @@ "" ], "text/plain": [ - " value initvalue nullvalue minimum maximum \\\n", - "-999 -999.000000 -999.0 -999.0 -999.0 -999.0 \n", - "1 1.000000 1.0 1.0 1.0 1.0 \n", - "coef_dist_0_1 -1.239747 0.0 0.0 NaN NaN \n", - "coef_dist_0_5_high 0.281467 0.0 0.0 NaN NaN \n", - "coef_dist_15_up -0.091700 0.0 0.0 NaN NaN \n", - "coef_dist_1_2 -0.992783 0.0 0.0 NaN NaN \n", - "coef_dist_2_5 -0.731466 0.0 0.0 NaN NaN \n", - "coef_dist_5_15 -0.253770 0.0 0.0 NaN NaN \n", - "coef_dist_5_up_high 0.113310 0.0 0.0 NaN NaN \n", - "coef_mode_logsum 0.017697 0.0 0.0 NaN NaN \n", - "work_high_AGREMPN -5.171203 0.0 0.0 -6.0 6.0 \n", - "work_high_FPSEMPN 6.000000 0.0 0.0 -6.0 6.0 \n", - "work_high_HEREMPN 2.457039 0.0 0.0 -6.0 6.0 \n", - "work_high_MWTEMPN 3.444542 0.0 0.0 -6.0 6.0 \n", - "work_high_OTHEMPN -5.910180 0.0 0.0 -6.0 6.0 \n", - "work_high_RETEMPN -2.207275 0.0 0.0 -6.0 6.0 \n", - "work_low_AGREMPN -4.699143 0.0 0.0 -6.0 6.0 \n", - "work_low_FPSEMPN 6.000000 0.0 0.0 -6.0 6.0 \n", - "work_low_HEREMPN 4.418420 0.0 0.0 -6.0 6.0 \n", - "work_low_MWTEMPN -6.000000 0.0 0.0 -6.0 6.0 \n", - "work_low_OTHEMPN -6.000000 0.0 0.0 -6.0 6.0 \n", - "work_low_RETEMPN -2.047943 0.0 0.0 -6.0 6.0 \n", - "work_med_AGREMPN -4.913574 0.0 0.0 -6.0 6.0 \n", - "work_med_FPSEMPN 6.000000 0.0 0.0 -6.0 6.0 \n", - "work_med_HEREMPN 3.968943 0.0 0.0 -6.0 6.0 \n", - "work_med_MWTEMPN -6.000000 0.0 0.0 -6.0 6.0 \n", - "work_med_OTHEMPN -6.000000 0.0 0.0 -6.0 6.0 \n", - "work_med_RETEMPN -2.120264 0.0 0.0 -6.0 6.0 \n", - "work_veryhigh_AGREMPN -5.614733 0.0 0.0 -6.0 6.0 \n", - "work_veryhigh_FPSEMPN 6.000000 0.0 0.0 -6.0 6.0 \n", - "work_veryhigh_HEREMPN -5.899534 0.0 0.0 -6.0 6.0 \n", - "work_veryhigh_MWTEMPN -6.000000 0.0 0.0 -6.0 6.0 \n", - "work_veryhigh_OTHEMPN -6.000000 0.0 0.0 -6.0 6.0 \n", - "work_veryhigh_RETEMPN -2.375156 0.0 0.0 -6.0 6.0 \n", + " value initvalue nullvalue minimum maximum \\\n", + "-999 -999.000000 -999.0 -999.0 -999.0 -999.0 \n", + "1 1.000000 1.0 1.0 1.0 1.0 \n", + "coef_dist_0_1 -1.209020 0.0 0.0 NaN NaN \n", + "coef_dist_0_5_high 0.196045 0.0 0.0 NaN NaN \n", + "coef_dist_15_up -0.100912 0.0 0.0 NaN NaN \n", + "coef_dist_1_2 -0.582653 0.0 0.0 NaN NaN \n", + "coef_dist_2_5 -0.590026 0.0 0.0 NaN NaN \n", + "coef_dist_5_15 -0.225455 0.0 0.0 NaN NaN \n", + "coef_dist_5_up_high 0.036938 0.0 0.0 NaN NaN \n", + "coef_mode_logsum 0.254307 0.0 0.0 NaN NaN \n", + "work_high_AGREMPN -146.302901 0.0 0.0 -6.0 6.0 \n", + "work_high_FPSEMPN 45.063176 0.0 0.0 -6.0 6.0 \n", + "work_high_HEREMPN 43.993849 0.0 0.0 -6.0 6.0 \n", + "work_high_MWTEMPN 45.257220 0.0 0.0 -6.0 6.0 \n", + "work_high_OTHEMPN 43.301239 0.0 0.0 -6.0 6.0 \n", + "work_high_RETEMPN -2.207275 0.0 0.0 -6.0 6.0 \n", + "work_low_AGREMPN -1694.618373 0.0 0.0 -6.0 6.0 \n", + "work_low_FPSEMPN 58.806849 0.0 0.0 -6.0 6.0 \n", + "work_low_HEREMPN 58.664609 0.0 0.0 -6.0 6.0 \n", + "work_low_MWTEMPN 56.639562 0.0 0.0 -6.0 6.0 \n", + "work_low_OTHEMPN 54.979037 0.0 0.0 -6.0 6.0 \n", + "work_low_RETEMPN -2.047943 0.0 0.0 -6.0 6.0 \n", + "work_med_AGREMPN -2017.102530 0.0 0.0 -6.0 6.0 \n", + "work_med_FPSEMPN 59.495813 0.0 0.0 -6.0 6.0 \n", + "work_med_HEREMPN 59.010833 0.0 0.0 -6.0 6.0 \n", + "work_med_MWTEMPN 58.107721 0.0 0.0 -6.0 6.0 \n", + "work_med_OTHEMPN 56.082736 0.0 0.0 -6.0 6.0 \n", + "work_med_RETEMPN -2.120264 0.0 0.0 -6.0 6.0 \n", + "work_veryhigh_AGREMPN -2078.045718 0.0 0.0 -6.0 6.0 \n", + "work_veryhigh_FPSEMPN 389.602957 0.0 0.0 -6.0 6.0 \n", + "work_veryhigh_HEREMPN 388.456095 0.0 0.0 -6.0 6.0 \n", + "work_veryhigh_MWTEMPN 388.541587 0.0 0.0 -6.0 6.0 \n", + "work_veryhigh_OTHEMPN 350.877157 0.0 0.0 -6.0 6.0 \n", + "work_veryhigh_RETEMPN -2.375156 0.0 0.0 -6.0 6.0 \n", "\n", - " holdfast note best \n", - "-999 1 -999.000000 \n", - "1 1 1.000000 \n", - "coef_dist_0_1 0 -1.239747 \n", - "coef_dist_0_5_high 0 0.281467 \n", - "coef_dist_15_up 0 -0.091700 \n", - "coef_dist_1_2 0 -0.992783 \n", - "coef_dist_2_5 0 -0.731466 \n", - "coef_dist_5_15 0 -0.253770 \n", - "coef_dist_5_up_high 0 0.113310 \n", - "coef_mode_logsum 0 0.017697 \n", - "work_high_AGREMPN 0 -5.171203 \n", - "work_high_FPSEMPN 0 6.000000 \n", - "work_high_HEREMPN 0 2.457039 \n", - "work_high_MWTEMPN 0 3.444542 \n", - "work_high_OTHEMPN 0 -5.910180 \n", - "work_high_RETEMPN 1 -2.207275 \n", - "work_low_AGREMPN 0 -4.699143 \n", - "work_low_FPSEMPN 0 6.000000 \n", - "work_low_HEREMPN 0 4.418420 \n", - "work_low_MWTEMPN 0 -6.000000 \n", - "work_low_OTHEMPN 0 -6.000000 \n", - "work_low_RETEMPN 1 -2.047943 \n", - "work_med_AGREMPN 0 -4.913574 \n", - "work_med_FPSEMPN 0 6.000000 \n", - "work_med_HEREMPN 0 3.968943 \n", - "work_med_MWTEMPN 0 -6.000000 \n", - "work_med_OTHEMPN 0 -6.000000 \n", - "work_med_RETEMPN 1 -2.120264 \n", - "work_veryhigh_AGREMPN 0 -5.614733 \n", - "work_veryhigh_FPSEMPN 0 6.000000 \n", - "work_veryhigh_HEREMPN 0 -5.899534 \n", - "work_veryhigh_MWTEMPN 0 -6.000000 \n", - "work_veryhigh_OTHEMPN 0 -6.000000 \n", - "work_veryhigh_RETEMPN 1 -2.375156 " + " holdfast note best \n", + "-999 1 -999.000000 \n", + "1 1 1.000000 \n", + "coef_dist_0_1 0 -1.209020 \n", + "coef_dist_0_5_high 0 0.196045 \n", + "coef_dist_15_up 0 -0.100912 \n", + "coef_dist_1_2 0 -0.582653 \n", + "coef_dist_2_5 0 -0.590026 \n", + "coef_dist_5_15 0 -0.225455 \n", + "coef_dist_5_up_high 0 0.036938 \n", + "coef_mode_logsum 0 0.254307 \n", + "work_high_AGREMPN 0 -146.302901 \n", + "work_high_FPSEMPN 0 45.063176 \n", + "work_high_HEREMPN 0 43.993849 \n", + "work_high_MWTEMPN 0 45.257220 \n", + "work_high_OTHEMPN 0 43.301239 \n", + "work_high_RETEMPN 1 -2.207275 \n", + "work_low_AGREMPN 0 -1694.618373 \n", + "work_low_FPSEMPN 0 58.806849 \n", + "work_low_HEREMPN 0 58.664609 \n", + "work_low_MWTEMPN 0 56.639562 \n", + "work_low_OTHEMPN 0 54.979037 \n", + "work_low_RETEMPN 1 -2.047943 \n", + "work_med_AGREMPN 0 -2017.102530 \n", + "work_med_FPSEMPN 0 59.495813 \n", + "work_med_HEREMPN 0 59.010833 \n", + "work_med_MWTEMPN 0 58.107721 \n", + "work_med_OTHEMPN 0 56.082736 \n", + "work_med_RETEMPN 1 -2.120264 \n", + "work_veryhigh_AGREMPN 0 -2078.045718 \n", + "work_veryhigh_FPSEMPN 0 389.602957 \n", + "work_veryhigh_HEREMPN 0 388.456095 \n", + "work_veryhigh_MWTEMPN 0 388.541587 \n", + "work_veryhigh_OTHEMPN 0 350.877157 \n", + "work_veryhigh_RETEMPN 1 -2.375156 " ] }, "metadata": {}, @@ -5076,401 +5038,3249 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 8.622334044923286e-15 in general_inverse\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 2.32229430906398e-13 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 8.634058778096289e-30 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 3.583780702208338e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.030816737127394e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 8.139379332419514e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.467248728064415e-30 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 5.635530043439614e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 5.024069253219585e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.129071794408915e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.213870083485342e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.205400336262097e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 3.155245280864812e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 9.070817704972375e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 9.188647380363698e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.4455861874582758e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 7.898885786772676e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 5.675892712939539e-31 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in sqrt\n", - " \"\"\"Entry point for launching an IPython kernel.\n" + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 7.299501952202737e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 9.906225278109236e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.8696715992375719e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 9.384295308197302e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 3.417315603365592e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.4783647028542222e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 4.240022293315152e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.1648919752562993e-30 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.382123118600227e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.747848640982298e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 3.922178242683511e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.83932782494905e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 4.987279604255353e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 7.238518257440178e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 2.5147940933951678e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 5.900893616824586e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.2682053164858718e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 4.5298991517373595e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 7.11657080182232e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.34155694790446e-30 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 9.585420564568597e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.9771366171235944e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" ] }, { - "data": { - "text/html": [ - "
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"output_type": "stream", + "text": [ + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 3.920805228236352e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.2810362777492847e-30 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 2.294383327939794e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 2.0002438724784584e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + 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"output_type": "stream", + "text": [ + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 4.171505522874697e-31 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 5.91387699530778e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 2.1091213130453592e-30 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 3.589093022890849e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + 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"c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 8.141129030785991e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 2.819444129038499e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.397012912483476e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.425340179554501e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 4.196317432593262e-33 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 1.6613265314768023e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.259194503638412e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 2.0683345855953209e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.287828583408624e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.241098155215915e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.313841534065774e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 5.015972094671763e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.360195125102724e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.350319173193947e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 7.745383692323018e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.459716706975234e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 6.305170917867295e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 8.979221639758355e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 9.008425925968887e-32 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\ipykernel_launcher.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", + " \n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\larch\\linalg\\__init__.py:18: UserWarning: minimum eig 3.457853513924607e-29 in general_inverse\n", + " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "c:\\programdata\\anaconda3\\envs\\asimtest2\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in sqrt\n", + " \n" + ] + }, + { + "data": { + "text/html": [ + "
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elapsed_time0:00:24.529620
method'slsqp'
n_cases2583
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+       "       4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n",
+       "       4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n",
+       "       4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n",
+       "       4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n",
+       "       4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n",
+       "       4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n",
+       "       4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n",
+       "       4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n",
+       "       4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n",
+       "       4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04])
message'Optimization terminated after 1000 iterations.'elapsed_time0:03:28.147842method'BHHH'n_cases2583iteration_number1000logloss4.950689158369937
" ], "text/plain": [ - "┣ x: -999 -999.000000\n", - "┃ 1 1.000000\n", - "┃ coef_dist_0_1 -1.239747\n", - "┃ coef_dist_0_5_high 0.281467\n", - "┃ coef_dist_15_up -0.091700\n", - "┃ coef_dist_1_2 -0.992783\n", - "┃ coef_dist_2_5 -0.731466\n", - "┃ coef_dist_5_15 -0.253770\n", - "┃ coef_dist_5_up_high 0.113310\n", - "┃ coef_mode_logsum 0.017697\n", - "┃ work_high_AGREMPN -5.171203\n", - "┃ work_high_FPSEMPN 6.000000\n", - "┃ work_high_HEREMPN 2.457039\n", - "┃ work_high_MWTEMPN 3.444542\n", - "┃ work_high_OTHEMPN -5.910180\n", - "┃ work_high_RETEMPN -2.207275\n", - "┃ work_low_AGREMPN -4.699143\n", - "┃ work_low_FPSEMPN 6.000000\n", - "┃ work_low_HEREMPN 4.418420\n", - "┃ work_low_MWTEMPN -6.000000\n", - "┃ work_low_OTHEMPN -6.000000\n", - "┃ work_low_RETEMPN -2.047943\n", - "┃ work_med_AGREMPN -4.913574\n", - "┃ work_med_FPSEMPN 6.000000\n", - "┃ work_med_HEREMPN 3.968943\n", - "┃ work_med_MWTEMPN -6.000000\n", - "┃ work_med_OTHEMPN -6.000000\n", - "┃ work_med_RETEMPN -2.120264\n", - "┃ work_veryhigh_AGREMPN -5.614733\n", - "┃ work_veryhigh_FPSEMPN 6.000000\n", - "┃ work_veryhigh_HEREMPN -5.899534\n", - "┃ work_veryhigh_MWTEMPN -6.000000\n", - "┃ work_veryhigh_OTHEMPN -6.000000\n", - "┃ work_veryhigh_RETEMPN -2.375156\n", - "┃ dtype: float64\n", - "┣ loglike: -12624.992531894883\n", - "┣ d_loglike: -999 0.000000\n", - "┃ 1 0.000000\n", - "┃ coef_dist_0_1 0.000028\n", - "┃ coef_dist_0_5_high 0.003857\n", - "┃ coef_dist_15_up 0.000000\n", - "┃ coef_dist_1_2 0.000438\n", - "┃ coef_dist_2_5 0.003526\n", - "┃ coef_dist_5_15 0.001785\n", - "┃ coef_dist_5_up_high 0.001724\n", - "┃ coef_mode_logsum -0.001374\n", - "┃ work_high_AGREMPN -0.000010\n", - "┃ work_high_FPSEMPN 0.052082\n", - "┃ work_high_HEREMPN -0.000030\n", - "┃ work_high_MWTEMPN 0.000049\n", - "┃ work_high_OTHEMPN -0.000055\n", - "┃ work_high_RETEMPN 0.000000\n", - "┃ work_low_AGREMPN -0.000016\n", - "┃ work_low_FPSEMPN 0.045278\n", - "┃ work_low_HEREMPN -0.000062\n", - "┃ work_low_MWTEMPN -0.000095\n", - "┃ work_low_OTHEMPN -0.000407\n", - "┃ work_low_RETEMPN 0.000000\n", - "┃ work_med_AGREMPN -0.000015\n", - "┃ work_med_FPSEMPN 0.047199\n", - "┃ work_med_HEREMPN -0.000040\n", - "┃ work_med_MWTEMPN -0.000078\n", - "┃ work_med_OTHEMPN -0.000378\n", - "┃ work_med_RETEMPN 0.000000\n", - "┃ work_veryhigh_AGREMPN -0.000017\n", - "┃ work_veryhigh_FPSEMPN 0.070422\n", - "┃ work_veryhigh_HEREMPN -0.000154\n", - "┃ work_veryhigh_MWTEMPN -0.000113\n", - "┃ work_veryhigh_OTHEMPN -0.001076\n", - "┃ work_veryhigh_RETEMPN 0.000000\n", + "┣ loglike: -12787.630096069548\n", + "┣ x: -999 -999.000000\n", + "┃ 1 1.000000\n", + "┃ coef_dist_0_1 -1.209020\n", + "┃ coef_dist_0_5_high 0.196045\n", + "┃ coef_dist_15_up -0.100912\n", + "┃ coef_dist_1_2 -0.582653\n", + "┃ coef_dist_2_5 -0.590026\n", + "┃ coef_dist_5_15 -0.225455\n", + "┃ coef_dist_5_up_high 0.036938\n", + "┃ coef_mode_logsum 0.254307\n", + "┃ work_high_AGREMPN -146.302901\n", + "┃ work_high_FPSEMPN 45.063176\n", + "┃ work_high_HEREMPN 43.993849\n", + "┃ work_high_MWTEMPN 45.257220\n", + "┃ work_high_OTHEMPN 43.301239\n", + "┃ work_high_RETEMPN -2.207275\n", + "┃ work_low_AGREMPN -1694.618373\n", + "┃ work_low_FPSEMPN 58.806849\n", + "┃ work_low_HEREMPN 58.664609\n", + "┃ work_low_MWTEMPN 56.639562\n", + "┃ work_low_OTHEMPN 54.979037\n", + "┃ work_low_RETEMPN -2.047943\n", + "┃ work_med_AGREMPN -2017.102530\n", + "┃ work_med_FPSEMPN 59.495813\n", + "┃ work_med_HEREMPN 59.010833\n", + "┃ work_med_MWTEMPN 58.107721\n", + "┃ work_med_OTHEMPN 56.082736\n", + "┃ work_med_RETEMPN -2.120264\n", + "┃ work_veryhigh_AGREMPN -2078.045718\n", + "┃ work_veryhigh_FPSEMPN 389.602957\n", + "┃ work_veryhigh_HEREMPN 388.456095\n", + "┃ work_veryhigh_MWTEMPN 388.541587\n", + "┃ work_veryhigh_OTHEMPN 350.877157\n", + "┃ work_veryhigh_RETEMPN -2.375156\n", "┃ dtype: float64\n", - "┣ nit: 92\n", - "┣ nfev: 119\n", - "┣ njev: 92\n", - "┣ status: 0\n", - "┣ message: 'Optimization terminated successfully.'\n", - "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=24, microseconds=529620)\n", - "┣ method: 'slsqp'\n", + "┣ tolerance: 350.7332682900228\n", + "┣ steps: array([1.0000000e+00, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04,\n", + "┃ 4.8828125e-04, 4.8828125e-04, 4.8828125e-04, 4.8828125e-04])\n", + "┣ message: 'Optimization terminated after 1000 iterations.'\n", + "┣ elapsed_time: datetime.timedelta(seconds=208, microseconds=147842)\n", + "┣ method: 'BHHH'\n", "┣ n_cases: 2583\n", - "┣ iteration_number: 92\n", - "┣ logloss: 4.887724557450594" + "┣ iteration_number: 1000\n", + "┣ logloss: 4.950689158369937" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# m.estimate(method='SLSQP', options={'maxiter':1000})\n", + "m.estimate(method='BHHH', options={'maxiter':1000})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimated coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
-999-999. NA NA NA-999.00fixed value
1 1.00 NA NA NA 1.00fixed value
coef_dist_0_1-1.21 0.202-5.97*** NA 0.00
coef_dist_0_5_high 0.196 0.0326 6.01*** NA 0.00
coef_dist_15_up-0.101 NA NA[] 0.00 0.00
coef_dist_1_2-0.583 0.0983-5.93*** NA 0.00
coef_dist_2_5-0.590 0.0336-17.58*** NA 0.00
coef_dist_5_15-0.225 0.0496-4.55*** NA 0.00
coef_dist_5_up_high 0.0369 0.0659 0.56 NA 0.00
coef_mode_logsum 0.254 0.0463 5.50*** NA 0.00
work_high_AGREMPN-146. NA NA[] 0.00 0.00
work_high_FPSEMPN 45.1 NA NA[***] 218.94 0.00
work_high_HEREMPN 44.0 NA NA[]-16.11 0.00
work_high_MWTEMPN 45.3 NA NA[]-3.85 0.00
work_high_OTHEMPN 43.3 NA NA[]-7.96 0.00
work_high_RETEMPN-2.21 NA NA NA 0.00fixed value
work_low_AGREMPN-1.69e+03 NA NA[] 0.00 0.00
work_low_FPSEMPN 58.8 52.0 1.13 NA 0.00
work_low_HEREMPN 58.7 52.0 1.13 NA 0.00
work_low_MWTEMPN 56.6 52.0 1.09 NA 0.00
work_low_OTHEMPN 55.0 52.0 1.06 NA 0.00
work_low_RETEMPN-2.05 NA NA NA 0.00fixed value
work_med_AGREMPN-2.02e+03 NA NA[] 0.00 0.00
work_med_FPSEMPN 59.5 NA NA[***] 204.80 0.00
work_med_HEREMPN 59.0 NA NA[]-12.04 0.00
work_med_MWTEMPN 58.1 NA NA[]-1.82 0.00
work_med_OTHEMPN 56.1 NA NA[]-1.75 0.00
work_med_RETEMPN-2.12 NA NA NA 0.00fixed value
work_veryhigh_AGREMPN-2.08e+03 0.00 NA[] 0.00 0.00
work_veryhigh_FPSEMPN 390. 80.1 4.86*** NA 0.00
work_veryhigh_HEREMPN 388. 80.1 4.85*** NA 0.00
work_veryhigh_MWTEMPN 389. 80.1 4.85*** NA 0.00
work_veryhigh_OTHEMPN 351. 0.00 NA[] 0.00 0.00
work_veryhigh_RETEMPN-2.38 NA NA NA 0.00fixed value
" + ], + "text/plain": [ + "" ] }, - "execution_count": 29, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "m.estimate()" + "m.parameter_summary()" ] }, { @@ -5485,7 +8295,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -5495,11 +8305,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ - "# Write out replacement coefficients file and model summaries\n", "os.makedirs(os.path.join(edb_directory,'estimated'), exist_ok=True)" ] }, @@ -5512,7 +8321,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -5531,29 +8340,29 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.to_xlsx(\n", - " os.path.join(edb_directory,'estimated',\"workplace_location_model_estimation.xlsx\"), \n", + " os.path.join(edb_directory,'estimated',\"workplace_location_model_estimation.xlsx\"), data_statistics=False\n", ")" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -5568,13 +8377,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# Rescale each row to total 1, not mathematically needed\n", "# but to maintain a consistent approach from existing ASim\n", - "\n", "size_spec.iloc[:,2:] = (size_spec.iloc[:,2:].div(size_spec.iloc[:,2:].sum(1), axis=0))" ] }, @@ -5587,7 +8395,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -5608,7 +8416,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -5641,49 +8449,49 @@ " \n", " 0\n", " coef_dist_0_1\n", - " -1.239747\n", + " -1.209020\n", " F\n", " \n", " \n", " 1\n", " coef_dist_1_2\n", - " -0.992783\n", + " -0.582653\n", " F\n", " \n", " \n", " 2\n", " coef_dist_2_5\n", - " -0.731466\n", + " -0.590026\n", " F\n", " \n", " \n", " 3\n", " coef_dist_5_15\n", - " -0.253770\n", + " -0.225455\n", " F\n", " \n", " \n", " 4\n", " coef_dist_15_up\n", - " -0.091700\n", + " -0.100912\n", " F\n", " \n", " \n", " 5\n", " coef_dist_0_5_high\n", - " 0.281467\n", + " 0.196045\n", " F\n", " \n", " \n", " 6\n", " coef_dist_5_up_high\n", - " 0.113310\n", + " 0.036938\n", " F\n", " \n", " \n", " 7\n", " coef_mode_logsum\n", - " 0.017697\n", + " 0.254307\n", " F\n", " \n", " \n", @@ -5692,17 +8500,17 @@ ], "text/plain": [ " coefficient_name value constrain\n", - "0 coef_dist_0_1 -1.239747 F\n", - "1 coef_dist_1_2 -0.992783 F\n", - "2 coef_dist_2_5 -0.731466 F\n", - "3 coef_dist_5_15 -0.253770 F\n", - "4 coef_dist_15_up -0.091700 F\n", - "5 coef_dist_0_5_high 0.281467 F\n", - "6 coef_dist_5_up_high 0.113310 F\n", - "7 coef_mode_logsum 0.017697 F" + "0 coef_dist_0_1 -1.209020 F\n", + "1 coef_dist_1_2 -0.582653 F\n", + "2 coef_dist_2_5 -0.590026 F\n", + "3 coef_dist_5_15 -0.225455 F\n", + "4 coef_dist_15_up -0.100912 F\n", + "5 coef_dist_0_5_high 0.196045 F\n", + "6 coef_dist_5_up_high 0.036938 F\n", + "7 coef_mode_logsum 0.254307 F" ] }, - "execution_count": 37, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -6197,7 +9005,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.8" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/larch_asim.py b/activitysim/examples/example_estimation/notebooks/larch_asim.py deleted file mode 100644 index 42203adc5..000000000 --- a/activitysim/examples/example_estimation/notebooks/larch_asim.py +++ /dev/null @@ -1,374 +0,0 @@ - -import numpy as np -import pandas as pd -from typing import Mapping -from larch import P, X -from larch.model.abstract_model import AbstractChoiceModel -from larch.model.tree import NestingTree - - -def cv_to_ca(alt_values, dtype='float64'): - """ - Convert a choosers-variables DataFrame to an idca DataFrame. - - Parameters - ---------- - alt_values : pandas.DataFrame - This DataFrame should be in choosers-variables format, - with one row per chooser and variable, and one column per - alternative. The id's for the choosers and variables - must be in that order, in a two-level MultiIndex. - dtype : dtype - Convert the incoming data to this type. Set to None to - skip data conversion. - - Returns - ------- - pandas.DataFrame - The resulting DataFrame is transformed into Larch's - idca format, with one row per chooser (case) and - alternative, and one column per variable. - """ - - # Read the source file, converting to a tall stack of - # data with a 3 level multiindex - x_ca_tall = alt_values.stack() - - # Set the last level index name to 'altid' - x_ca_tall.index.rename('altid', -1, inplace=True) - c_, v_, a_ = x_ca_tall.index.names - - # case and alt id's should be integers. - # Easier to manipulate dtypes of a multiindex if we just convert - # it to columns first. If this ends up being a performance issue - # we can look at optimizing this in the future. - x_ca_tall = x_ca_tall.reset_index() - x_ca_tall[c_] = x_ca_tall[c_].astype(int) - x_ca_tall[a_] = x_ca_tall[a_].astype(int) - x_ca_tall = x_ca_tall.set_index([c_, v_, a_]) - - if dtype is not None: - # Convert instances where data is 'False' or 'True' to numbers - x_ca_tall[x_ca_tall == 'False'] = 0 - x_ca_tall[x_ca_tall == 'True'] = 1 - - # Convert data to float64 to optimize computation speed in larch - x_ca_tall = x_ca_tall.astype(dtype) - - # Unstack the variables dimension - x_ca = x_ca_tall.unstack(1) - - # Code above added a dummy top level to columns, remove it here. - x_ca.columns = x_ca.columns.droplevel(0) - - return x_ca - - -def prevent_overlapping_column_names(x_ca, x_co): - """ - Rename columns in idca data to prevent overlapping names. - - Parameters - ---------- - x_ca, x_co : pandas.DataFrame - The idca and idco data, respectively - - Returns - ------- - x_ca, x_co - """ - renaming = {i: f"{i}_ca" for i in x_ca.columns if i in x_co.columns} - x_ca.rename(columns=renaming, inplace=True) - return x_ca, x_co - - -def linear_utility_from_spec(spec, x_col, p_col, ignore_x=(), segment_id=None): - """ - Create a linear function from a spec DataFrame. - - Parameters - ---------- - spec : pandas.DataFrame - A spec for an ActivitySim model. - x_col: str - The name of the columns in spec representing the data. - p_col: str or dict - The name of the columns in spec representing the parameters. - Give as a string for a single column, or as a dict to - have segments on multiple columns. If given as a dict, - the keys give the names of the columns to use, and the - values give the identifiers that will need to match the - loaded `segment_id` value. - ignore_x : Collection, optional - Labels in the spec file to ignore. Typically this - includes variables that are pre-processed by ActivitySim - and therefore don't need to be made available in Larch. - segment_id : str, optional - The CHOOSER_SEGMENT_COLUMN_NAME identified for ActivitySim. - This value is ignored if `p_col` is a string, and required - if `p_col` is a dict. - - Returns - ------- - LinearFunction_C - """ - if isinstance(p_col, dict): - if segment_id is None: - raise ValueError('segment_id must be given if p_col is a dict') - partial_utility = {} - for seg_p_col, segval in p_col.items(): - partial_utility[seg_p_col] = linear_utility_from_spec( - spec, - x_col, - seg_p_col, - ignore_x, - ) * X(f'{segment_id}=={segval}') - return sum(partial_utility.values()) - return sum( - P(getattr(i, p_col)) * X(getattr(i, x_col)) - for i in spec.itertuples() - if (getattr(i, x_col) not in ignore_x) and not pd.isna(getattr(i, p_col)) - ) - - -def dict_of_linear_utility_from_spec(spec, x_col, p_col, ignore_x=()): - """ - Create a linear function from a spec DataFrame. - - Parameters - ---------- - spec : pandas.DataFrame - A spec for an ActivitySim model. - x_col: str - The name of the columns in spec representing the data. - p_col: dict - The name of the columns in spec representing the parameters. - The keys give the names of the columns to use, and the - values will become the keys of the output dictionary. - ignore_x : Collection, optional - Labels in the spec file to ignore. Typically this - includes variables that are pre-processed by ActivitySim - and therefore don't need to be made available in Larch. - segment_id : str, optional - The CHOOSER_SEGMENT_COLUMN_NAME identified for ActivitySim. - This value is ignored if `p_col` is a string, and required - if `p_col` is a dict. - - Returns - ------- - dict - """ - utils = {} - for altname, altcode in p_col.items(): - utils[altcode] = linear_utility_from_spec(spec, x_col, altname, ignore_x=ignore_x) - return utils - - -def explicit_value_parameters_from_spec(spec, p_col, model): - """ - Define and lock parameters given as fixed values in the spec. - - Parameters - ---------- - spec : pandas.DataFrame - A spec for an ActivitySim model. - p_col : str or dict - The name of the columns in spec representing the parameters. - Give as a string for a single column, or as a dict to - have segments on multiple columns. If given as a dict, - the keys give the names of the columns to use, and the - values give the identifiers that will need to match the - loaded `segment_id` value. Only the keys are used in this - function. - model : larch.Model - The model to insert fixed value parameters. - - Returns - ------- - - """ - if isinstance(p_col, dict): - for p_col_ in p_col: - explicit_value_parameters_from_spec(spec, p_col_, model) - else: - for i in spec.itertuples(): - try: - j = float(getattr(i, p_col)) - except Exception: - pass - else: - model.set_value( - getattr(i, p_col), - value=j, - holdfast=True, - ) - - -def explicit_value_parameters(model): - """ - Define and lock parameters given as fixed values. - - Parameters - ---------- - model : larch.Model - The model to insert fixed value parameters. - - Returns - ------- - - """ - for i in model.pf.index: - try: - j = float(i) - except Exception: - pass - else: - model.set_value( - i, - value=j, - initvalue=j, - nullvalue=j, - minimum=j, - maximum=j, - holdfast=True, - ) - - -def apply_coefficients(coefficients, model, minimum=None, maximum=None): - """ - Read the coefficients CSV file to a DataFrame and set model parameters. - - Parameters - ---------- - coefficients : pandas.DataFrame - The coefficients table in the ActivitySim data bundle - for this model. - model : Model - Apply coefficient values and constraints to this model. - - """ - if isinstance(model, dict): - for m in model.values(): - apply_coefficients(coefficients, m) - else: - assert isinstance(coefficients, pd.DataFrame) - assert all(coefficients.columns == ['value', 'constrain']) - assert coefficients.index.name == 'coefficient_name' - assert isinstance(model, AbstractChoiceModel) - explicit_value_parameters(model) - for i in coefficients.itertuples(): - if i.Index in model: - model.set_value( - i.Index, - value=i.value, - holdfast=(i.constrain == 'T'), - minimum=minimum, - maximum=maximum, - ) - # for param in model.pf.index: - # if "*" in param: - # value = 1 - # for p_part in param.split("*"): - # value *= coefficients.loc[p_part,'value'] - # holdfast = coefficients.loc[param.split("*")[0],'constrain']=='T' - # model.set_value( - # param, - # value=value, - # holdfast=holdfast - # ) - - -def apply_coef_template(linear_utility, template_col, condition=None): - """ - Apply a coefficient template over a linear utility function. - - Parameters - ---------- - linear_utility : LinearFunction_C - template_col : Mapping - condition : any - - Returns - ------- - LinearFunction_C - """ - result = sum( - P("*".join(template_col.get(ip, ip) for ip in i.param.split("*"))) * i.data * i.scale - for i in linear_utility - ) - if condition is not None: - result = result * condition - return result - - -def construct_nesting_tree(alternatives, nesting_settings): - """ - Construct a NestingTree from ActivitySim settings. - - Parameters - ---------- - alternatives : Mapping or Sequence - If given as a Mapping (dict), the keys are the alternative names - as strings, and the values are alternative code numbers to use - in larch. If given as a Sequence, the values are the alternative - names, and unique sequential codes will be created starting from 1. - nesting_settings : Mapping - The 'NESTS' section of the ActivitySim config file. - - Returns - ------- - NestingTree - """ - if not isinstance(alternatives, Mapping): - alt_names = list(alternatives) - alt_codes = np.arange(1, len(alt_names) + 1) - alternatives = dict(zip(alt_names, alt_codes)) - - tree = NestingTree() - nest_names_to_codes = alternatives.copy() - nest_names_to_codes['root'] = 0 - for alt_name, alt_code in alternatives.items(): - tree.add_node(alt_code, name=alt_name) - - def make_nest(cfg, parent_code=0): - nonlocal nest_names_to_codes - if cfg['name'] != 'root': - if cfg['name'] not in nest_names_to_codes: - n = tree.new_node(name=cfg['name'], parameter=str(cfg['coefficient']), parent=parent_code) - nest_names_to_codes[cfg['name']] = n - else: - tree.add_edge(parent_code, nest_names_to_codes[cfg['name']]) - for a in cfg['alternatives']: - if isinstance(a, str): - tree.add_edge(nest_names_to_codes[cfg['name']], nest_names_to_codes[a]) - else: - make_nest(a, parent_code=nest_names_to_codes[cfg['name']]) - - make_nest(nesting_settings) - - return tree - -# double_parameters = set() -# for alt_code, alt_name in tree.elemental_names().items(): -# # Read in base utility function for this alt_name -# u = larch_asim.linear_utility_from_spec( -# spec, x_col='Label', p_col=alt_name, -# ignore_x=('#',), -# ) -# for purpose in purposes: -# # Keep track of double parameters -# for i in u: -# if '*' in i.param: -# double_parameters.add( -# "*".join(coef_template[purpose].get(ip,ip) for ip in i.param.split("*")) -# ) -# # Modify utility function based on template for purpose -# u_purp = sum( -# ( -# P("*".join(coef_template[purpose].get(ip,ip) for ip in i.param.split("*"))) -# * i.data * i.scale -# ) -# for i in u -# ) -# m[purpose].utility_co[alt_code] = u_purp -# double_parameters diff --git a/docs/abmexample.rst b/docs/abmexample.rst index 09eded96f..0e66d7545 100644 --- a/docs/abmexample.rst +++ b/docs/abmexample.rst @@ -886,7 +886,7 @@ The combination of writing an EDB for a submodel + a larch estimation notebook m combination of functionality means: * There is no duplication of model specifications. ActivitySim owns the specification and larch pivots off of it. Users code model specifications and utility expressions in ActivitySim so as to facilitate ease of use and eliminate inconsistencies and errors between the code used to estimate the models and the code used to apply the models. -* The EDB includes all the data and model structure information and the larch_asim module used by the example notebooks processes the EDB to setup and estimate the models. +* The EDB includes all the data and model structure information and the larch.util.activitysim module used by the example notebooks processes the EDB to setup and estimate the models. * Users are able to add zones, alternatives, new chooser data, new taz data, new modes, new coefficients, revise utilities, and revise nesting structures in ActivitySim and larch responds accordingly. * Eventually it may be desirable for ActivitySim to automatically write larch estimators (or other types of estimators), but for now the integration is loosely coupled rather than tightly coupled in order to provide flexibility.