From c9e54f67a8c6d1f701007688d331e4df7a4d079c Mon Sep 17 00:00:00 2001 From: Ben Stabler Date: Mon, 11 May 2020 18:40:50 -0700 Subject: [PATCH] Develop (#120) * Package updates (#109) * ActivitySim 0.9.2; Pandas 1.0 * Freeze ortools package below 7.5 * Add info about zero-person households (#106) This addresses the documentation needs arising from #104 Co-authored-by: Ben Stabler * Fixes (#110) * ActivitySim 0.9.2; Pandas 1.0 * Freeze ortools package below 7.5 * fix issue #103 * fix issue #102 * Update setup.py (#112) * Pypi cleanup (#114) * pypi cleanup * remove rst file * Add validation notebook (#119) Co-authored-by: Blake Co-authored-by: Greg Macfarlane --- README.rst | 8 - scripts/README.md | 4 +- scripts/calm_verification.yaml | 148 ++++ scripts/columnMapPopSim_CALM.csv | 32 - scripts/validation.ipynb | 1192 +++++++++++++++++++++++++++++ scripts/validationPopulationSim.R | 270 ------- setup.py | 4 - 7 files changed, 1342 insertions(+), 316 deletions(-) delete mode 100644 README.rst create mode 100644 scripts/calm_verification.yaml delete mode 100644 scripts/columnMapPopSim_CALM.csv create mode 100644 scripts/validation.ipynb delete mode 100644 scripts/validationPopulationSim.R diff --git a/README.rst b/README.rst deleted file mode 100644 index c6149e5..0000000 --- a/README.rst +++ /dev/null @@ -1,8 +0,0 @@ -PopulationSim -============= - -PopulationSim is an open platform for population synthesis. It emerged -from Oregon DOT's desire to build a shared, open, platform that could be -easily adapted for statewide, regional, and urban transporation planning -needs. PopulationSim is implemented in the -[ActivitySim](https://github.com/activitysim/activitysim) framework. diff --git a/scripts/README.md b/scripts/README.md index e8f3bdc..3a30495 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -1,5 +1,5 @@ ## Scripts - - validationPopulationSim.R - R validation script to generate advanced summary statistics and validation plots. This validation script takes summaries and outputs from a PopulationSim run. The script is configured to run for the CALM region example and includes notes on inputs and configuration settings - - columnMapPopSim_CALM.csv - CSV file to specify the controls for which the summaries should be generated \ No newline at end of file + - validation.ipynb - Jupyter validation script to generate advanced summary statistics and validation plots. This validation script takes summaries and outputs from a PopulationSim run. The script is configured to run for the CALM region example and includes notes on inputs and configuration settings + - calm_verification.yaml - YAML file to specify the controls for which the summaries should be generated diff --git a/scripts/calm_verification.yaml b/scripts/calm_verification.yaml new file mode 100644 index 0000000..9b2a9bc --- /dev/null +++ b/scripts/calm_verification.yaml @@ -0,0 +1,148 @@ +# directory of target run of PopulationSim, containing data/output directories +popsim_dir: ../example_calm +# folder to save outputs +validation_dir: calm_validation_results +scenario: Base +region: CALM +geographies: data/geo_cross_walk.csv +group_geographies: + - REGION + - PUMA + - TRACT + - TAZ +seed_households: data/seed_households.csv +seed_cols: + geog: PUMA + geog_weight: WGTP + hh_id: hhnum +expanded_hhid: output/expanded_household_ids.csv +expanded_hhid_col: hh_id +summaries: + - output/summary_TAZ_PUMA.csv + - output/summary_TRACT.csv + - output/summary_TAZ.csv +aggregate_summaries: + - name: Total Households + geography: TAZ + control: num_hh_control + result: num_hh_result + - name: 'Household Size: 1 person HH' + geography: TAZ + control: hh_size_1_control + result: hh_size_1_result + - name: 'Household Size: 2 person HH' + geography: TAZ + control: hh_size_2_control + result: hh_size_2_result + - name: 'Household Size: 3 person HH' + geography: TAZ + control: hh_size_3_control + result: hh_size_3_result + - name: 'Household Size: 4+ person HH' + geography: TAZ + control: hh_size_4_plus_control + result: hh_size_4_plus_result + - name: 'Householder Age: 15-24 years' + geography: TAZ + control: hh_age_15_24_control + result: hh_age_15_24_result + - name: 'Householder Age: 25-54 years' + geography: TAZ + control: hh_age_25_54_control + result: hh_age_25_54_result + - name: 'Householder Age: 55-64 years' + geography: TAZ + control: hh_age_55_64_control + result: hh_age_55_64_result + - name: 'Householder Age: >65 years' + geography: TAZ + control: hh_age_65_plus_control + result: hh_age_65_plus_result + - name: 'Household Income: (-Inf,21297)' + geography: TAZ + control: hh_inc_15_control + result: hh_inc_15_result + - name: 'Household Income: [21297,42593)' + geography: TAZ + control: hh_inc_15_30_control + result: hh_inc_15_30_result + - name: 'Household Income: [42593,85185)' + geography: TAZ + control: hh_inc_30_60_control + result: hh_inc_30_60_result + - name: 'Household Income: [85185,+Inf)' + geography: TAZ + control: hh_inc_60_plus_control + result: hh_inc_60_plus_result + - name: Students by family + geography: TAZ + control: students_by_family_housing_type_control + result: students_by_family_housing_type_result + - name: Students by non-family + geography: TAZ + control: students_by_nonfamily_housing_type_control + result: students_by_nonfamily_housing_type_result + - name: SF + geography: TRACT + control: hh_by_type_sf_control + result: hh_by_type_sf_result + - name: MF + geography: TRACT + control: hh_by_type_mf_control + result: hh_by_type_mf_result + - name: MH + geography: TRACT + control: hh_by_type_mh_control + result: hh_by_type_mh_result + - name: Dup + geography: TRACT + control: hh_by_type_dup_control + result: hh_by_type_dup_result + - name: 'Household Workers: 0 worker HH' + geography: TRACT + control: hh_wrks_0_control + result: hh_wrks_0_result + - name: 'Household Workers: 1 worker HH' + geography: TRACT + control: hh_wrks_1_control + result: hh_wrks_1_result + - name: 'Household Workers: 2 worker HH' + geography: TRACT + control: hh_wrks_2_control + result: hh_wrks_2_result + - name: 'Household Workers: 3+ worker HH' + geography: TRACT + control: hh_wrks_3_plus_control + result: hh_wrks_3_plus_result + - name: Occupation Type 1 + geography: REGION + control: persons_occ_1_control + result: persons_occ_1_result + - name: Occupation Type 2 + geography: REGION + control: persons_occ_2_control + result: persons_occ_2_result + - name: Occupation Type 3 + geography: REGION + control: persons_occ_3_control + result: persons_occ_3_result + - name: Occupation Type 4 + geography: REGION + control: persons_occ_4_control + result: persons_occ_4_result + - name: Occupation Type 5 + geography: REGION + control: persons_occ_5_control + result: persons_occ_5_result + - name: Occupation Type 6 + geography: REGION + control: persons_occ_6_control + result: persons_occ_6_result + - name: Occupation Type 7 + geography: REGION + control: persons_occ_7_control + result: persons_occ_7_result + - name: Occupation Type 8 + geography: REGION + control: persons_occ_8_control + result: persons_occ_8_result diff --git a/scripts/columnMapPopSim_CALM.csv b/scripts/columnMapPopSim_CALM.csv deleted file mode 100644 index 5839a48..0000000 --- a/scripts/columnMapPopSim_CALM.csv +++ /dev/null @@ -1,32 +0,0 @@ -NAME,GEOGRAPHY,CONTROL,SUMMARY -TAZ - Total Households,TAZ,num_hh_control,num_hh_result -TAZ - Household Size: 1 person HH,TAZ,hh_size_1_control,hh_size_1_result -TAZ - Household Size: 2 person HH,TAZ,hh_size_2_control,hh_size_2_result -TAZ - Household Size: 3 person HH,TAZ,hh_size_3_control,hh_size_3_result -TAZ - Household Size: 4+ person HH,TAZ,hh_size_4_plus_control,hh_size_4_plus_result -TAZ - Householder Age: 15-24 years,TAZ,hh_age_15_24_control,hh_age_15_24_result -TAZ - Householder Age: 25-54 years,TAZ,hh_age_25_54_control,hh_age_25_54_result -TAZ - Householder Age: 55-64 years,TAZ,hh_age_55_64_control,hh_age_55_64_result -TAZ - Householder Age: >65 years,TAZ,hh_age_65_plus_control,hh_age_65_plus_result -"TAZ - Household Income: (-Inf,21297)",TAZ,hh_inc_15_control,hh_inc_15_result -"TAZ - Household Income: [21297,42593)",TAZ,hh_inc_15_30_control,hh_inc_15_30_result -"TAZ - Household Income: [42593,85185)",TAZ,hh_inc_30_60_control,hh_inc_30_60_result -"TAZ - Household Income: [85185,+Inf)",TAZ,hh_inc_60_plus_control,hh_inc_60_plus_result -TAZ - Students by family,TAZ,students_by_family_housing_type_control,students_by_family_housing_type_result -TAZ - Students by non-family,TAZ,students_by_nonfamily_housing_type_control,students_by_nonfamily_housing_type_result -TRACT - SF,TRACT,hh_by_type_sf_control,hh_by_type_sf_result -TRACT - MF,TRACT,hh_by_type_mf_control,hh_by_type_mf_result -TRACT - MH,TRACT,hh_by_type_mh_control,hh_by_type_mh_result -TRACT - Dup,TRACT,hh_by_type_dup_control,hh_by_type_dup_result -TRACT - Household Workers: 0 worker HH,TRACT,hh_wrks_0_control,hh_wrks_0_result -TRACT - Household Workers: 1 worker HH,TRACT,hh_wrks_1_control,hh_wrks_1_result -TRACT - Household Workers: 2 worker HH,TRACT,hh_wrks_2_control,hh_wrks_2_result -TRACT - Household Workers: 3+ worker HH,TRACT,hh_wrks_3_plus_control,hh_wrks_3_plus_result -REGION - Occupation Type 1,REGION,persons_occ_1_control,persons_occ_1_result -REGION - Occupation Type 2,REGION,persons_occ_2_control,persons_occ_2_result -REGION - Occupation Type 3,REGION,persons_occ_3_control,persons_occ_3_result -REGION - Occupation Type 4,REGION,persons_occ_4_control,persons_occ_4_result -REGION - Occupation Type 5,REGION,persons_occ_5_control,persons_occ_5_result -REGION - Occupation Type 6,REGION,persons_occ_6_control,persons_occ_6_result -REGION - Occupation Type 7,REGION,persons_occ_7_control,persons_occ_7_result -REGION - Occupation Type 8,REGION,persons_occ_8_control,persons_occ_8_result diff --git a/scripts/validation.ipynb b/scripts/validation.ipynb new file mode 100644 index 0000000..10ba5e2 --- /dev/null +++ b/scripts/validation.ipynb @@ -0,0 +1,1192 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PopulationSim validation script\n", + "\n", + "This notebook helps visualize and summarize the results of a PopulationSim model run.\n", + "\n", + "## Inputs\n", + "\n", + "This script uses several core input/output files from PopulationSim. A `.yaml` configuration helper file allows the user of this notebook to pick and choose which summaries to calculate. This configuration file can be rewritten to handle different regions without the need to rewite the script itself. The example file provided here is for the Corvallis-Albany-Lebanon Modeling (CALM) region in Oregon.\n", + "\n", + "### config" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "region_yaml = 'calm_verification.yaml' # default" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It contains several model-specific parameters:\n", + "\n", + "- `popsim_dir:` the path to the main model run, containing popsim's data/output files,\n", + "- `scenario:` and `region:` for labeling output plots\n", + "- `summaries:` the list of `summary_*.csv` output files to use for validation\n", + "- `aggregate_summaries:` a list of desired control vs. results comparison columns to calculate from the summary files\n", + "\n", + "See the example `region_yaml` for more.\n", + "\n", + "### CSV file (optional)\n", + "\n", + "Additional inputs parameters may be given via CSV.\n", + "\n", + "E.x.\n", + "- POPSIMDIR\n", + "- VALID_DIR\n", + "\n", + "Both input files may either be given directly in the notebook below, or on the command line (download the script by choosing `File` > `Download as` > `Python (.py)`):\n", + "\n", + "```script\n", + "python validation.py calm_verification.yaml [parameters.csv]\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outputs\n", + "\n", + "- A CSV summary of statistics for each control group in `aggregate_summaries`\n", + "- A plot of the frequency distribution for each control group\n", + "- A plot of the standard deviation for each control group\n", + "- A CSV summary of aggregated statistics for a given geography (e.g. \"PUMA\")\n", + "- A plot of the household expansion factor distribution for the given geography\n", + "\n", + "Requests for further outputs options and configurations are welcome via GitHub!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import packages\n", + "\n", + "Required packages. The notebook requires `jupyter`, while the script needs only the following." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "try:\n", + " import yaml\n", + " import pandas as pd\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + "except ImportError:\n", + " packages = 'pyyaml pandas numpy matplotlib'\n", + " ret = os.system(f'conda install {packages}')\n", + " if ret != 0:\n", + " os.system(f'pip install {packages}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configure matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from matplotlib import cycler\n", + "plt.style.use('ggplot')\n", + "plt.rcParams['lines.linewidth'] = 1.5\n", + "plt.rcParams['axes.prop_cycle'] = cycler(color=['b', 'g', 'r', 'y'])\n", + "plt.rcParams['savefig.dpi'] = 200\n", + "plt.rcParams['figure.constrained_layout.use'] = True\n", + "plt.rcParams['figure.constrained_layout.h_pad'] = 0.5\n", + "plt.rcParams['figure.figsize'] = (8,15)\n", + "plt.rcParams['hist.bins'] = 25" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load input parameters\n", + "\n", + "Read in the input file(s). If you encounter errors at this stage, be sure that\n", + "1. the paths to the files are correct\n", + "2. the YAML is well-structured (fields can't contain special characters like ':')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "parameters = {}\n", + "\n", + "# ignore sys.argv if running validation notebook\n", + "is_script = 'ipykernel' not in sys.modules\n", + "\n", + "if is_script and len(sys.argv) > 1:\n", + " if os.path.isfile(sys.argv[1]):\n", + " region_yaml = sys.argv[1]\n", + "\n", + "with open(region_yaml) as f:\n", + " parameters.update(yaml.safe_load(f))\n", + "\n", + "if is_script and len(sys.argv) > 2:\n", + " if os.path.isfile(sys.argv[2]):\n", + " parameters_csv = sys.argv[2]\n", + " parameters.update(pd.read_csv(parameters_csv, header=None, index_col=0, comment='#').to_dict()[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "aggregate_summaries:\n", + "- control: num_hh_control\n", + " geography: TAZ\n", + " name: Total Households\n", + " result: num_hh_result\n", + "- control: hh_size_1_control\n", + " geography: TAZ\n", + " name: 'Household Size: 1 person HH'\n", + " result: hh_size_1_result\n", + "- control: hh_size_2_control\n", + " geography: TAZ\n", + " name: 'Household Size: 2 person HH'\n", + " result: hh_size_2_result\n", + "- control: hh_size_3_control\n", + " geography: TAZ\n", + " name: 'Household Size: 3 person HH'\n", + " result: hh_size_3_result\n", + "- control: hh_size_4_plus_control\n", + " geography: TAZ\n", + " name: 'Household Size: 4+ person HH'\n", + " result: hh_size_4_plus_result\n", + "- control: hh_age_15_24_control\n", + " geography: TAZ\n", + " name: 'Householder Age: 15-24 years'\n", + " result: hh_age_15_24_result\n", + "- control: hh_age_25_54_control\n", + " geography: TAZ\n", + " name: 'Householder Age: 25-54 years'\n", + " result: hh_age_25_54_result\n", + "- control: hh_age_55_64_control\n", + " geography: TAZ\n", + " name: 'Householder Age: 55-64 years'\n", + " result: hh_age_55_64_result\n", + "- control: hh_age_65_plus_control\n", + " geography: TAZ\n", + " name: 'Householder Age: >65 years'\n", + " result: hh_age_65_plus_result\n", + "- control: hh_inc_15_control\n", + " geography: TAZ\n", + " name: 'Household Income: (-Inf,21297)'\n", + " result: hh_inc_15_result\n", + "- control: hh_inc_15_30_control\n", + " geography: TAZ\n", + " name: 'Household Income: [21297,42593)'\n", + " result: hh_inc_15_30_result\n", + "- control: hh_inc_30_60_control\n", + " geography: TAZ\n", + " name: 'Household Income: [42593,85185)'\n", + " result: hh_inc_30_60_result\n", + "- control: hh_inc_60_plus_control\n", + " geography: TAZ\n", + " name: 'Household Income: [85185,+Inf)'\n", + " result: hh_inc_60_plus_result\n", + "- control: students_by_family_housing_type_control\n", + " geography: TAZ\n", + " name: Students by family\n", + " result: students_by_family_housing_type_result\n", + "- control: students_by_nonfamily_housing_type_control\n", + " geography: TAZ\n", + " name: Students by non-family\n", + " result: students_by_nonfamily_housing_type_result\n", + "- control: hh_by_type_sf_control\n", + " geography: TRACT\n", + " name: SF\n", + " result: hh_by_type_sf_result\n", + "- control: hh_by_type_mf_control\n", + " geography: TRACT\n", + " name: MF\n", + " result: hh_by_type_mf_result\n", + "- control: hh_by_type_mh_control\n", + " geography: TRACT\n", + " name: MH\n", + " result: hh_by_type_mh_result\n", + "- control: hh_by_type_dup_control\n", + " geography: TRACT\n", + " name: Dup\n", + " result: hh_by_type_dup_result\n", + "- control: hh_wrks_0_control\n", + " geography: TRACT\n", + " name: 'Household Workers: 0 worker HH'\n", + " result: hh_wrks_0_result\n", + "- control: hh_wrks_1_control\n", + " geography: TRACT\n", + " name: 'Household Workers: 1 worker HH'\n", + " result: hh_wrks_1_result\n", + "- control: hh_wrks_2_control\n", + " geography: TRACT\n", + " name: 'Household Workers: 2 worker HH'\n", + " result: hh_wrks_2_result\n", + "- control: hh_wrks_3_plus_control\n", + " geography: TRACT\n", + " name: 'Household Workers: 3+ worker HH'\n", + " result: hh_wrks_3_plus_result\n", + "- control: persons_occ_1_control\n", + " geography: REGION\n", + " name: Occupation Type 1\n", + " result: persons_occ_1_result\n", + "- control: persons_occ_2_control\n", + " geography: REGION\n", + " name: Occupation Type 2\n", + " result: persons_occ_2_result\n", + "- control: persons_occ_3_control\n", + " geography: REGION\n", + " name: Occupation Type 3\n", + " result: persons_occ_3_result\n", + "- control: persons_occ_4_control\n", + " geography: REGION\n", + " name: Occupation Type 4\n", + " result: persons_occ_4_result\n", + "- control: persons_occ_5_control\n", + " geography: REGION\n", + " name: Occupation Type 5\n", + " result: persons_occ_5_result\n", + "- control: persons_occ_6_control\n", + " geography: REGION\n", + " name: Occupation Type 6\n", + " result: persons_occ_6_result\n", + "- control: persons_occ_7_control\n", + " geography: REGION\n", + " name: Occupation Type 7\n", + " result: persons_occ_7_result\n", + "- control: persons_occ_8_control\n", + " geography: REGION\n", + " name: Occupation Type 8\n", + " result: persons_occ_8_result\n", + "expanded_hhid: output/expanded_household_ids.csv\n", + "expanded_hhid_col: hh_id\n", + "geographies: data/geo_cross_walk.csv\n", + "group_geographies:\n", + "- REGION\n", + "- PUMA\n", + "- TRACT\n", + "- TAZ\n", + "popsim_dir: ../example_calm\n", + "region: CALM\n", + "scenario: Base\n", + "seed_cols:\n", + " geog: PUMA\n", + " geog_weight: WGTP\n", + " hh_id: hhnum\n", + "seed_households: data/seed_households.csv\n", + "summaries:\n", + "- output/summary_TAZ_PUMA.csv\n", + "- output/summary_TRACT.csv\n", + "- output/summary_TAZ.csv\n", + "validation_dir: calm_validation_results\n", + "\n" + ] + } + ], + "source": [ + "print(yaml.dump(parameters))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `parameters` var contains all the input information needed for the validation script. Values may be updated either in the source files (recommended), or here in the notebook by updating keys of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "popsim_dir = parameters.get('POPSIMDIR') or parameters.get('popsim_dir')\n", + "validation_dir = parameters.get('VALID_DIR') or parameters.get('validation_dir')\n", + "geography_file = parameters.get('geographies')\n", + "use_geographies = parameters.get('group_geographies')\n", + "summary_files = parameters.get('summaries', [])\n", + "aggregate_list = parameters.get('aggregate_summaries', [])\n", + "scenario = parameters.get('scenario')\n", + "region = parameters.get('region')\n", + "exp_hh_file = parameters.get('expanded_hhid')\n", + "exp_hh_id_col = parameters.get('expanded_hhid_col')\n", + "seed_hh_file = parameters.get('seed_households')\n", + "seed_hh_cols = parameters.get('seed_cols')\n", + "\n", + "if not os.path.isdir(validation_dir):\n", + " os.mkdir(validation_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summaries\n", + "Create the master summary DataFrame from `parameters['summaries']`, listed either in the input YAML or above. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geographyidnum_hh_controlhh_size_1_controlhh_size_2_controlhh_size_3_controlhh_size_4_plus_controlhh_age_15_24_controlhh_age_25_54_controlhh_age_55_64_control...hh_by_type_mh_diffhh_by_type_dup_diffpersons_occ_1_diffpersons_occ_2_diffpersons_occ_3_diffpersons_occ_4_diffpersons_occ_5_diffpersons_occ_6_diffpersons_occ_7_diffpersons_occ_8_diff
0PUMA60062041171562270195241266072583022211049...3.0-13.051.045.013.039.027.013.01.05.0
0TRACT100292176210865285454531711463...0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
1TRACT20223026449093314182571013392...0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
2TRACT400329882413285346122081595684...0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
3TRACT500129430857018623018482361...0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

5 rows × 95 columns

\n", + "
" + ], + "text/plain": [ + " geography id num_hh_control hh_size_1_control hh_size_2_control \\\n", + "0 PUMA 600 62041 17156 22701 \n", + "0 TRACT 100 2921 762 1086 \n", + "1 TRACT 202 2302 644 909 \n", + "2 TRACT 400 3298 824 1328 \n", + "3 TRACT 500 1294 308 570 \n", + "\n", + " hh_size_3_control hh_size_4_plus_control hh_age_15_24_control \\\n", + "0 9524 12660 7258 \n", + "0 528 545 453 \n", + "1 331 418 257 \n", + "2 534 612 208 \n", + "3 186 230 18 \n", + "\n", + " hh_age_25_54_control hh_age_55_64_control ... hh_by_type_mh_diff \\\n", + "0 30222 11049 ... 3.0 \n", + "0 1711 463 ... 0.0 \n", + "1 1013 392 ... 0.0 \n", + "2 1595 684 ... 0.0 \n", + "3 482 361 ... 0.0 \n", + "\n", + " hh_by_type_dup_diff persons_occ_1_diff persons_occ_2_diff \\\n", + "0 -13.0 51.0 45.0 \n", + "0 0.0 NaN NaN \n", + "1 0.0 NaN NaN \n", + "2 0.0 NaN NaN \n", + "3 0.0 NaN NaN \n", + "\n", + " persons_occ_3_diff persons_occ_4_diff persons_occ_5_diff \\\n", + "0 13.0 39.0 27.0 \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "\n", + " persons_occ_6_diff persons_occ_7_diff persons_occ_8_diff \n", + "0 13.0 1.0 5.0 \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "\n", + "[5 rows x 95 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summary_df = pd.DataFrame()\n", + "for summary_file in summary_files:\n", + " filepath = os.path.join(popsim_dir, summary_file)\n", + " df = pd.read_csv(filepath)\n", + " summary_df = summary_df.append(df)\n", + "\n", + "summary_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['PUMA', 'TRACT', 'TAZ'], dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summary_df.geography.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geographyidnum_hh_controlhh_size_1_controlhh_size_2_controlhh_size_3_controlhh_size_4_plus_controlhh_age_15_24_controlhh_age_25_54_controlhh_age_55_64_control...hh_by_type_mh_diffhh_by_type_dup_diffpersons_occ_1_diffpersons_occ_2_diffpersons_occ_3_diffpersons_occ_4_diffpersons_occ_5_diffpersons_occ_6_diffpersons_occ_7_diffpersons_occ_8_diff
777TAZ129061824141513316...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
778TAZ1291407137131256...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
779TAZ129219643673452210346...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
780TAZ129378811427314925238481136...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
0REGION162041171562270195241266072583022211049...3.0-13.051.045.013.039.027.013.01.05.0
\n", + "

5 rows × 95 columns

\n", + "
" + ], + "text/plain": [ + " geography id num_hh_control hh_size_1_control hh_size_2_control \\\n", + "777 TAZ 1290 61 8 24 \n", + "778 TAZ 1291 40 7 13 \n", + "779 TAZ 1292 196 43 67 \n", + "780 TAZ 1293 788 114 273 \n", + "0 REGION 1 62041 17156 22701 \n", + "\n", + " hh_size_3_control hh_size_4_plus_control hh_age_15_24_control \\\n", + "777 14 15 1 \n", + "778 7 13 1 \n", + "779 34 52 2 \n", + "780 149 252 38 \n", + "0 9524 12660 7258 \n", + "\n", + " hh_age_25_54_control hh_age_55_64_control ... hh_by_type_mh_diff \\\n", + "777 33 16 ... NaN \n", + "778 25 6 ... NaN \n", + "779 103 46 ... NaN \n", + "780 481 136 ... NaN \n", + "0 30222 11049 ... 3.0 \n", + "\n", + " hh_by_type_dup_diff persons_occ_1_diff persons_occ_2_diff \\\n", + "777 NaN NaN NaN \n", + "778 NaN NaN NaN \n", + "779 NaN NaN NaN \n", + "780 NaN NaN NaN \n", + "0 -13.0 51.0 45.0 \n", + "\n", + " persons_occ_3_diff persons_occ_4_diff persons_occ_5_diff \\\n", + "777 NaN NaN NaN \n", + "778 NaN NaN NaN \n", + "779 NaN NaN NaN \n", + "780 NaN NaN NaN \n", + "0 13.0 39.0 27.0 \n", + "\n", + " persons_occ_6_diff persons_occ_7_diff persons_occ_8_diff \n", + "777 NaN NaN NaN \n", + "778 NaN NaN NaN \n", + "779 NaN NaN NaN \n", + "780 NaN NaN NaN \n", + "0 13.0 1.0 5.0 \n", + "\n", + "[5 rows x 95 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def meta_geog_df(meta_geog):\n", + " geography_df = pd.read_csv(os.path.join(popsim_dir, geography_file))\n", + " geog = use_geographies[use_geographies.index(meta_geog) + 1] # next geography in list\n", + " meta_df = geography_df[[meta_geog, geog]].drop_duplicates(ignore_index=True)\n", + " geog_df = summary_df[summary_df.geography == geog].copy()\n", + " geog_df['geography'] = meta_geog\n", + " geog_df['id'] = geog_df.id.map(meta_df.set_index(geog).to_dict()[meta_geog])\n", + " \n", + " return geog_df\n", + " \n", + "for geog in use_geographies:\n", + " if not geog in summary_df.geography.unique():\n", + " summary_df = summary_df.append(meta_geog_df(geog))\n", + "\n", + "summary_df.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Handle each control vs. result comparison specified in `parameters['aggregate_summaries']`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def process_control(name, geography, control, result):\n", + " \"\"\"\n", + " Global\n", + " ------\n", + " summary_df: pandas DataFrame\n", + " \n", + " Parameters\n", + " ----------\n", + " name: str, output plot title\n", + " geography: str, groupby geography\n", + " control: str, control column name in summary table\n", + " result: str, result column name in summary table\n", + " \n", + " Returns\n", + " -------\n", + " \n", + " stats: pandas Series of statistics, aggregated by geography/control/result\n", + " frequencies: pandas Series of control vs. results differences\n", + " \"\"\"\n", + "\n", + " sub_df = summary_df[summary_df.geography == geography][[control, result]].dropna(axis=0, how='any')\n", + " \n", + " observed = sub_df[control]\n", + " non_zero_observed = observed[observed > 0]\n", + " predicted = sub_df[result]\n", + " difference = predicted - observed\n", + " pc_difference = (difference/non_zero_observed)*100\n", + " rmse = (difference ** 2).mean() ** 0.5\n", + " \n", + " frequencies = non_zero_observed.groupby(difference).count()\n", + "\n", + " stats = pd.Series({\n", + " 'name': name,\n", + " 'geography': geography,\n", + " 'observed': observed.sum(),\n", + " 'predicted': predicted.sum(),\n", + " 'difference': difference.sum(),\n", + " 'pc_difference': (difference.sum()/observed.sum())*100,\n", + " 'mean_pc_difference': pc_difference.mean(),\n", + " 'N': non_zero_observed.shape[0],\n", + " 'rmse': rmse.sum(),\n", + " 'std': pc_difference.std(),\n", + " })\n", + "\n", + " return stats, frequencies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot each comparison and save outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig_w = 3\n", + "fig_l = int(len(aggregate_list) / fig_w) + 1\n", + "\n", + "summary_fig, axes = plt.subplots(fig_l, fig_w, figsize=(fig_w * 5,fig_l*5))\n", + "\n", + "stats = []\n", + "for params, ax in zip(aggregate_list, axes.ravel()):\n", + " s, f = process_control(**params)\n", + " stats.append(s)\n", + " \n", + " ax.set_title(f\"{params['geography']} - {params['name']}\")\n", + " ax.set_ylabel('Frequency'); ax.set_xlabel('Difference: control vs. result')\n", + " ax.scatter(f.index, f)\n", + "\n", + "summary_fig.savefig(os.path.join(validation_dir, 'frequencies.pdf'))\n", + "stats_df = pd.DataFrame(stats)\n", + "stats_df.to_csv(os.path.join(validation_dir, f'{scenario}_{region}_popsim_stats.csv'), index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate the standard deviation for each comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "std_fig = plt.figure()\n", + "std_fig.suptitle('PopulationSim Controls Percentage Difference')\n", + "plt.errorbar(stats_df['mean_pc_difference'],\n", + " stats_df['name'],\n", + " xerr=stats_df['std'],\n", + " linestyle='None',\n", + " marker='.')\n", + "\n", + "std_fig.savefig(os.path.join(validation_dir, f'{scenario}_{region}_popsim_convergence_stdev.pdf'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Uniformity\n", + "\n", + "Examine the household weight distribution by geography using `expanded_hhid` and `seed_households`\n", + "```yaml\n", + "seed_cols:\n", + " geog: PUMA\n", + " geog_weight: WGTP\n", + " hh_id: hh_id\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "seed_cols = (seed_hh_cols.values())\n", + "geog = seed_hh_cols.get('geog')\n", + "geog_weight = seed_hh_cols.get('geog_weight')\n", + "hh_id = seed_hh_cols.get('hh_id')\n", + "\n", + "expanded_hhids = pd.read_csv(os.path.join(popsim_dir, exp_hh_file), usecols=[exp_hh_id_col])\n", + "seed_hh_df = pd.read_csv(os.path.join(popsim_dir, seed_hh_file), usecols=seed_cols, index_col=hh_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Weight each household by its frequency in the household file, and by the given weight column. Group by geography and summarize." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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", + "
WZNEXPEXP_MINEXP_MAXRMSE
PUMA
6007753662041.048410.8001570.012.01.101312
\n", + "
" + ], + "text/plain": [ + " W Z N EXP EXP_MIN EXP_MAX RMSE\n", + "PUMA \n", + "600 77536 62041.0 4841 0.800157 0.0 12.0 1.101312" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weight_mask = seed_hh_df[geog_weight] > 0\n", + "weight = expanded_hhids[exp_hh_id_col].value_counts()[weight_mask]\n", + "expansion_factor = (weight/seed_hh_df[geog_weight]).fillna(0)\n", + "\n", + "df = pd.DataFrame({\n", + " geog: seed_hh_df[geog],\n", + " geog_weight: seed_hh_df[geog_weight],\n", + " 'weight': weight,\n", + " 'ef': expansion_factor,\n", + "})\n", + "\n", + "geog_group = df.groupby(geog)\n", + "geog_final_weight = geog_group.sum()['weight']\n", + "expansion = geog_final_weight/geog_group.sum()[geog_weight]\n", + "\n", + "expansion.name = 'avg_expansion'\n", + "df = df.join(expansion, on=geog)\n", + "df['diff_sq'] = (df['avg_expansion'] - df['ef']) ** 2\n", + "rmse = df.groupby(geog).mean()['diff_sq'] ** 0.5\n", + "\n", + "uniformity_df = pd.DataFrame({\n", + " 'W': geog_group.sum()[geog_weight],\n", + " 'Z': geog_group.sum()['weight'],\n", + " 'N': geog_group.count()[geog_weight],\n", + " 'EXP': expansion,\n", + " 'EXP_MIN': geog_group.min()['ef'],\n", + " 'EXP_MAX': geog_group.max()['ef'],\n", + " 'RMSE': rmse, \n", + "})\n", + "\n", + "uniformity_df.to_csv(os.path.join(validation_dir, 'uniformity.csv'))\n", + "uniformity_df" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geogs = df[geog].unique()\n", + "geog_fig = plt.figure(figsize=(10*len(geogs), 10))\n", + "\n", + "for i, g in enumerate(geogs):\n", + " geog_df = df[df[geog] == g]\n", + " counts, bins = np.histogram(geog_df['ef'])\n", + " ax = geog_fig.add_subplot(1, len(geogs), i+1)\n", + " ax.set_title(f'{geog} {g}')\n", + " ax.set_ylabel('Percentage'); ax.set_xlabel('Expansion Factor Range')\n", + " ax.hist(bins[:-1], bins, weights=counts*100/len(geog_df))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/scripts/validationPopulationSim.R b/scripts/validationPopulationSim.R deleted file mode 100644 index f120d06..0000000 --- a/scripts/validationPopulationSim.R +++ /dev/null @@ -1,270 +0,0 @@ -############################################################################################################## -# Validation script for PopulationSim -# -# binny.paul@rsginc.com, Jan 2018 -# -# This script uses summary outputs from a PopulationSim run to generate validation summaries and plots -# -# User needs to specify the following inputs and settings:- -# -# PopulationSim working directory [the directory containing data, configs and output folders] -# Validation directory [the directory where you would want all your validation summaries] -# scenario name -# region name -# List of geographies - from highest to lower geography [e.g., REGION > PUMA > TAZ > MAZ] -# First Meta, second Seed and then all Sub-Seed from the highest to the lowest -# Plot geographies - any geography other than Seed geography. Plots will be generated only for the -# geographies listed here for the controls for that geography -# Geographic crosswalk file name [assumed to be inside the data folder in PopulationSim working dir] -# Column Map CSV - CSV file to specify the controls for which the summaries should be generated. FOllowing -# columns need to be specified: -# NAME : Name of the control to be used for labels -# GEOGRAPHY : Geography at which the control was specified -# CONTROL : Control value column name in the summary_GEOGRAPHY file [summary_PUMA file for Meta controls] -# SUMMARY : Estimate/Result value column name in the summary_GEOGRAPHY file [summary_PUMA file for Meta controls] -# -# Input seed household sample file -# List of column names in the input seed household sample (seed_col) for following: -# Seed geography name -# Unique household ID -# Initial household weight -# -# Column name of the unique HH ID (expanded_hhid_col) in the expanded household id file (expanded_household_ids.csv). -# This is the column name assigned to the unique household ID in the initial seed sample in the -# PopulationSim YAML settings file. -# -# The user should also configure the PopulationSim to produce following summary files in the output folder: -# expanded_household_ids.csv -# summary_GEOGRAPHY.csv (for all sub-seed geographies, e.g., summary_TRACT.csv) -# summary_LOWEST_PUMA.csv (PUMA level summaries for the lowest gepgraphy, e.g., summary_TAZ_PUMA.csv) -# -# -# List of outputs:- -# -# CSV summary Statistics file - It has the following statistics: -# controlName - Name of the control -# geography - Geography at which the control is specified -# Observed - Regional total specified -# Predicted - Regional total synthesized -# Difference - Predicted - Observed -# pcDifference - Perecntage difference at a regional level -# N - Number of geographies (MAZ/TAZ/META) with non-zero control -# RMSE - Percentage root mean square error for the control at the specified geography -# SDEV - Standard deviation of precentage difference -# -# Plots (JPEGs) - convergence plots: -# -# Plot showing mean percentage difference and STDEV for each control -# Plot showing frequency distribution of differences b/w target and estimate for each control -# Plot showing expansion factor distribution -# -############################################################################################################## - - -### User Inputs [Read command line arguments] -popSimDir <- "C:/ODOT_PopSyn/PopulationSimTest/example_calm" -validationDir <- "C:/ODOT_PopSyn/PopulationSimTest/validation_calm" - -scenario <- "Base" -region <- "CALM" - -geographyList <- c("REGION", "PUMA", "TRACT", "TAZ") -plotGeographies <- c("REGION", "TRACT", "TAZ") - -geogXWalk <- read.csv(paste(popSimDir, "data/geo_cross_walk.csv", sep = "/")) -columnMap <- read.csv(paste(validationDir, "columnMapPopSim_CALM.csv", sep = "/")) - -seed_households <- read.csv(paste(popSimDir, "data/seed_households.csv", sep = "/")) -seed_col <- c("PUMA", "hhnum", "WGTP") - -expanded_hhid <- read.csv(paste(popSimDir, "output/expanded_household_ids.csv", sep = "/")) -expanded_hhid_col <- c("hh_id") - -# This is currently configured for 2 sub-seed geography -# User should add more read lines when more geographies is involved -# The nummber at the end of summary file name indicates the geographic index -# Example, summary3 is the name for summary_TRACT which is the 3rd geography in the geography list -summary2 <- read.csv(paste(popSimDir, "output/summary_TAZ_PUMA.csv", sep = "/")) -summary3 <- read.csv(paste(popSimDir, "output/summary_TRACT.csv", sep = "/")) -summary4 <- read.csv(paste(popSimDir, "output/summary_TAZ.csv", sep = "/")) - -summary2$meta_geog <- geogXWalk$REGION[match(summary2$id, geogXWalk$PUMA)] -summary1 <- summary2 -summary1$geography <- geographyList[1] -summary1$id <- summary1$meta_geog - -### Install all required R packages to the user specified directory [Make sure the R library directory has write permissions for the user] -if (!"RODBC" %in% installed.packages()) install.packages("RODBC", repos='http://cran.us.r-project.org') -if (!"dplyr" %in% installed.packages()) install.packages("dplyr", repos='http://cran.us.r-project.org') -if (!"ggplot2" %in% installed.packages()) install.packages("ggplot2", repos='http://cran.us.r-project.org') -if (!"tidyr" %in% installed.packages()) install.packages("tidyr", repos='http://cran.us.r-project.org') -if (!"scales" %in% installed.packages()) install.packages("scales", repos='http://cran.us.r-project.org') -if (!"hydroGOF" %in% installed.packages()) install.packages("hydroGOF", repos='http://cran.us.r-project.org') - -### Load libraries -R_PKGS <- c("RODBC", "dplyr", "ggplot2", "tidyr", "scales", "hydroGOF") -lib_sink <- suppressWarnings(suppressMessages(lapply(R_PKGS, library, character.only = TRUE))) - -setwd(validationDir) - -### Function to process each control -procControl <- function(geography, controlName, controlID, summaryID){ - - - #Fetching data - geoIndex <- which(geographyList == geography) - ev1 <- paste("sub_summary <- summary", geoIndex, sep = "") - eval(parse(text = ev1)) - - controls <- sub_summary[, c(which("id" == names(sub_summary)),which(controlID == names(sub_summary)))] - synthesized <- sub_summary[, c(which("id" == names(sub_summary)),which(summaryID == names(sub_summary)))] - colnames(controls) <- c("GEOGRAPHY", "CONTROL") - colnames(synthesized) <- c("GEOGRAPHY", "SYNTHESIZED") - - # Meta controls are grouped by PUMAs, aggregation is required - if(geoIndex==1){ - # aggregate control to meta geography - controls <- controls %>% - group_by(GEOGRAPHY) %>% - summarise(CONTROL = sum(CONTROL)) %>% - ungroup() - - # aggregate synthesized to meta geography - synthesized <- synthesized %>% - group_by(GEOGRAPHY) %>% - summarise(SYNTHESIZED = sum(SYNTHESIZED)) %>% - ungroup() - } - - #Fetch and process each control for getting convergance statistics - compareData <- left_join(controls, synthesized, by="GEOGRAPHY") %>% - mutate(CONTROL = as.numeric(CONTROL)) %>% - mutate(SYNTHESIZED = ifelse(is.na(SYNTHESIZED), 0, SYNTHESIZED)) %>% - mutate(DIFFERENCE = SYNTHESIZED - CONTROL) %>% - mutate(pcDIFFERENCE = ifelse(CONTROL > 0,(DIFFERENCE/CONTROL)*100,NA)) - - #Calculate statistics - Observed <- sum(compareData$CONTROL) - Predicted <- sum(compareData$SYNTHESIZED) - Difference <- Predicted - Observed - pcDifference <- (Difference/Observed)*100 - N <- sum(compareData$CONTROL > 0) - PRMSE <- (((sum((compareData$CONTROL - compareData$SYNTHESIZED)^2)/(sum(compareData$CONTROL > 0) - 1))^0.5)/sum(compareData$CONTROL))*sum(compareData$CONTROL > 0)*100 - meanPCDiff <- mean(compareData$pcDIFFERENCE, na.rm=TRUE) - SDEV <- sd(compareData$pcDIFFERENCE, na.rm=TRUE) - stats <- data.frame(controlName, geography, Observed, Predicted, Difference, pcDifference, N, PRMSE, meanPCDiff, SDEV) - - #Preparing data for difference frequency plot - freqPlotData <- compareData %>% - filter(CONTROL > 0) %>% - group_by(DIFFERENCE) %>% - summarise(FREQUENCY = n()) - - if(geography %in% plotGeographies){ - #computing plotting parameters - xaxisLimit <- max(abs(freqPlotData$DIFFERENCE)) + 10 - plotTitle <- paste("Frequency Plot: Syn - Control totals for", controlName, sep = " ") - - #Frequency Plot - p1 <- ggplot(freqPlotData, aes(x=DIFFERENCE,y=FREQUENCY))+ - geom_point(colour="coral") + - coord_cartesian(xlim = c(-xaxisLimit, xaxisLimit)) + - geom_vline(xintercept=c(0), colour = "steelblue")+ - labs(title = plotTitle) - ggsave(paste("plots/",controlID,".png",sep=""), width=9,height=6) - } - - cat("\n Processed Control: ", controlName) - - return(stats) -} - -myRMSE <- function(FINALEXPANSIONS, AVERAGEEXPANSION, N){ - EXPECTED <- rep(AVERAGEEXPANSION,N) - ACTUAL <- FINALEXPANSIONS - return(rmse(ACTUAL, EXPECTED, na.rm=TRUE)) -} - -#Create plot directory -dir.create('plots', showWarnings = FALSE) - -### Computing convergance statistics and write out results -stats <- apply(columnMap, 1, function(x) procControl(x["GEOGRAPHY"], x["NAME"], x["CONTROL"],x["SUMMARY"])) - -stats <- do.call(rbind,stats) -write.csv(stats, paste(scenario, "PopulationSim stats.csv"), row.names = FALSE) - -### Convergance plot -p2 <- ggplot(stats, aes(x = controlName, y=meanPCDiff)) + - geom_point(shape = 15, colour = "steelblue", size = 2)+ - geom_errorbar(data = stats, aes(ymin=-SDEV,ymax=SDEV), width=0.2, colour = "steelblue") + - scale_x_discrete(limits=rev(levels(stats$controlName))) + - geom_hline(yintercept=c(0)) + - labs(x = NULL, y="Percentage Difference [+/- SDEV]", title = gsub("Region",region,"Region PopulationSim Controls Validation")) + - coord_flip(ylim = c(-50, 50)) + - theme_bw() + - theme(plot.title=element_text(size=12, lineheight=.9, face="bold", vjust=1)) - -ggsave(file=paste(scenario, "PopulationSim Convergance-sdev.jpeg"), width=8,height=10) - -### Convergance plot -p3 <- ggplot(stats, aes(x = controlName, y=meanPCDiff)) + - geom_point(shape = 15, colour = "steelblue", size = 2)+ - geom_errorbar(data = stats, aes(ymin=-PRMSE,ymax=PRMSE), width=0.2, colour = "steelblue") + - scale_x_discrete(limits=rev(levels(stats$controlName))) + - geom_hline(yintercept=c(0)) + - labs(x = NULL, y="Percentage Difference [+/- PRMSE]", title = gsub("Region",region,"Region PopulationSim Controls Validation")) + - coord_flip(ylim = c(-50, 50)) + - theme_bw() + - theme(plot.title=element_text(size=12, lineheight=.9, face="bold", vjust=1)) - -ggsave(file=paste(scenario, "PopulationSim Convergance-PRMSE.jpeg"), width=8,height=10) - - -### Uniformity Analysis -summary_hhid <- expanded_hhid %>% - mutate(FINALWEIGHT = 1) %>% - select(FINALWEIGHT, expanded_hhid_col) %>% - group_by(hh_id) %>% - summarise(FINALWEIGHT = sum(FINALWEIGHT)) - -uniformity <- seed_households[seed_households$WGTP>0, seed_col] %>% - left_join(summary_hhid, by = c("hhnum" = "hh_id")) %>% - mutate(FINALWEIGHT = ifelse(is.na(FINALWEIGHT), 0, FINALWEIGHT)) %>% - mutate(EXPANSIONFACTOR = FINALWEIGHT/WGTP) %>% - mutate(EFBIN = cut(EXPANSIONFACTOR,seq(0,max(EXPANSIONFACTOR)+0.5,0.5),right=FALSE, include.lowest=FALSE)) - -uAnalysisPUMA <- group_by(uniformity, PUMA, EFBIN) - -efPlotData <- summarise(uAnalysisPUMA, PC = n()) %>% - mutate(PC=PC/sum(PC)) - -ggplot(efPlotData, aes(x=EFBIN, y=PC)) + - geom_bar(colour="black", fill="#DD8888", width=.7, stat="identity") + - guides(fill=FALSE) + - xlab("RANGE OF EXPANSION FACTOR") + ylab("PERCENTAGE") + - ggtitle("EXPANSION FACTOR DISTRIBUTION BY PUMA") + - facet_wrap(~PUMA, ncol=6) + - theme_bw()+ - theme(axis.title.x = element_text(face="bold"), - axis.title.y = element_text(face="bold"), - axis.text.x = element_text(angle=90, size=5), - axis.text.y = element_text(size=5)) + - scale_y_continuous(labels = percent_format()) - -ggsave("plots/EF-Distribution.png", width=15,height=10) - -uAnalysisPUMA <- group_by(uniformity, PUMA) - -uAnalysisPUMA <- summarize(uAnalysisPUMA - ,W = sum(WGTP) - ,Z = sum(FINALWEIGHT) - ,N = n() - ,EXP = Z/W - ,EXP_MIN = min(EXPANSIONFACTOR) - ,EXP_MAX = max(EXPANSIONFACTOR) - ,RMSE = myRMSE(EXPANSIONFACTOR, EXP, N)) -write.csv(uAnalysisPUMA, "uniformity.csv", row.names=FALSE) - - -### Finish \ No newline at end of file diff --git a/setup.py b/setup.py index 1f543a5..066d571 100644 --- a/setup.py +++ b/setup.py @@ -3,9 +3,6 @@ from setuptools import setup, find_packages -with open('README.rst') as file: - long_description = file.read() - setup( name='populationsim', version='0.4.1', @@ -22,7 +19,6 @@ 'Programming Language :: Python :: 3.7', 'License :: OSI Approved :: BSD License' ], - long_description=long_description, packages=find_packages(exclude=['*.tests']), include_package_data=True, python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*',