Add Dockerfile and build-and-run-model workflow for CI model runs

.github/workflows/build-and-run-model.yaml

@@ -0,0 +1,38 @@
+# Workflow that builds a Docker image containing the model code,
+# pushes it to the GitHub Container Registry, and then optionally uses
+# that container image to run the model using an AWS Batch job.
+#
+# Images are built on every commit to a PR or main branch in order to ensure
+# that the build continues to work properly, but Batch jobs are gated behind
+# a `deploy` environment that requires manual approval from a
+# @ccao-data/core-team member.
+
+name: build-and-run-model
+
+on:
+  pull_request:
+    types: [opened, reopened, synchronize, closed]
+  workflow_dispatch:
+  push:
+    branches: [master]
+
+jobs:
+  build-and-run-model:
+    permissions:
+      # contents:read and id-token:write permissions are needed to interact
+      # with GitHub's OIDC Token endpoint so that we can authenticate with AWS
+      contents: read
+      id-token: write
+      # While packages:write is usually not required for workflows, it is
+      # required in order to allow the reusable called workflow to push to
+      # GitHub Container Registry
+      packages: write
+    uses: ccao-data/actions/.github/workflows/build-and-run-batch-job.yaml@jeancochrane/add-batch-and-terraform-workflows-and-actions
+    with:
+      ref: jeancochrane/add-batch-and-terraform-workflows-and-actions
+      vcpu: "16.0"
+      memory: "65536"
+      role-duration-seconds: 14400  # Worst-case time for a full model run
+    secrets:
+      AWS_IAM_ROLE_TO_ASSUME_ARN: ${{ secrets.AWS_IAM_ROLE_TO_ASSUME_ARN }}
+      AWS_ACCOUNT_ID: ${{ secrets.AWS_ACCOUNT_ID }}

Dockerfile

@@ -0,0 +1,45 @@
+FROM rocker/r-ver:4.3.1
+
+# Use PPM for binary installs
+ENV RENV_CONFIG_REPOS_OVERRIDE "https://packagemanager.posit.co/cran/__linux__/jammy/latest"
+ENV RENV_PATHS_LIBRARY renv/library
+
+# Install system dependencies
+RUN apt-get update && apt-get install --no-install-recommends -y \
+    libcurl4-openssl-dev libssl-dev libxml2-dev libgit2-dev git \
+    libudunits2-dev python3-dev python3-pip libgdal-dev libgeos-dev \
+    libproj-dev libfontconfig1-dev libharfbuzz-dev libfribidi-dev pandoc
+
+# Install pipenv for Python dependencies
+RUN pip install pipenv
+
+# Copy pipenv files into the image. The reason this is a separate step from
+# the later step that adds files from the working directory is because we want
+# to avoid having to reinstall dependencies every time a file in the directory
+# changes, as Docker will bust the cache of every layer following a layer that
+# needs to change
+COPY Pipfile .
+COPY Pipfile.lock .
+
+# Install Python dependencies
+RUN pipenv install --system --deploy
+
+# Copy R bootstrap files into the image
+COPY renv.lock .
+COPY .Rprofile .
+COPY renv/ renv/
+
+# Install R dependencies
+RUN Rscript -e 'renv::restore()'
+
+# Copy the directory into the container
+ADD ./ model-condo-avm/
+
+# Copy R dependencies into the app directory
+RUN rm -Rf model-condo-avm/renv
+RUN mv renv model-condo-avm/
+
+# Set the working directory to the app dir
+WORKDIR model-condo-avm/
+
+CMD dvc pull && dvc repro

Pipfile

@@ -4,11 +4,7 @@ verify_ssl = true
 name = "pypi"
 
 [packages]
-numpy = "*"
-pandas = "*"
-scipy = "*"
-scikit-learn = "*"
-dvc = "*"
+"dvc[s3]" = "*"
 
 [dev-packages]
 

README.Rmd

@@ -1,6 +1,6 @@
 ---
 title: "Table of Contents"
-output: 
+output:
   github_document:
     toc: true
     toc_depth: 3
@@ -46,7 +46,7 @@ The repository itself contains the [code](./pipeline) and [data](./input) for th
 
 ## Differences Compared to the Residential Model
 
-The Cook County Assessor's Office ***does not track characteristic data for condominiums***. Like most assessors nationwide, our office staff cannot enter buildings to observe property characteristics. For condos, this means we cannot observe amenities, quality, or any other interior characteristics. 
+The Cook County Assessor's Office ***does not track characteristic data for condominiums***. Like most assessors nationwide, our office staff cannot enter buildings to observe property characteristics. For condos, this means we cannot observe amenities, quality, or any other interior characteristics.
 
 The only information our office has about individual condominium units is their age, location, sale date/price, and percentage of ownership. This makes modeling condos particularly challenging, as the number of usable features is quite small. Fortunately, condos have two qualities which make modeling a bit easier:
 
@@ -106,11 +106,9 @@ ccao::vars_dict %>%
     )
   ) %>%
   mutate(`Unique to Condo Model` = ifelse(
-    var_name_model != "loc_tax_municipality_name" & (
-      var_name_model %in% condo_unique_preds |
-        `Feature Name` %in%
-          c("Condominium Building Year Built", "Condominium % Ownership")
-    ),
+    var_name_model %in% condo_unique_preds |
+      `Feature Name` %in%
+        c("Condominium Building Year Built", "Condominium % Ownership"),
     "X", ""
   )) %>%
   arrange(desc(`Unique to Condo Model`), Category) %>%
@@ -124,7 +122,7 @@ For the most part, condos are valued the same way as single- and multi-family re
 
 However, because the CCAO has so [little information about individual units](#differences-compared-to-the-residential-model), we must rely on the [condominium percentage of ownership](#features-used) to differentiate between units in a building. This feature is effectively the proportion of the building's overall value held by a unit. It is created when a condominium declaration is filed with the County (usually by the developer of the building). The critical assumption underlying the condo valuation process is that percentage of ownership correlates with current market value.
 
-Percentage of ownership is used in two ways: 
+Percentage of ownership is used in two ways:
 
 1. It is used directly as a predictor/feature in the regression model to estimate differing unit values within the same building.
 2. It is used to reapportion unit values directly i.e. the value of a unit is ultimately equal to `% of ownership * total building value`.
@@ -186,7 +184,7 @@ The condo model relies on sales within the same building to calculate [strata](#
 
 Fortunately, buildings without any recent sales are relatively rare, as condos have a higher turnover rate than single and multi-family property. Smaller buildings with low turnover are the most likely to not have recent sales.
 
-### Buildings Without Sales 
+### Buildings Without Sales
 
 When no sales have occurred in a building in the 5 years prior to assessment, the building's strata features are imputed. The model will look at nearby buildings that have similar unit counts/age and then try to assign an appropriate strata to the target building.
 

README.md

@@ -152,12 +152,12 @@ the 2023 assessment model.
 | Percent Population Mobility, Moved From Within Same County in Past Year | acs5           | numeric   |                       |
 | Longitude                                                               | loc            | numeric   |                       |
 | Latitude                                                                | loc            | numeric   |                       |
+| Municipality Name                                                       | loc            | character |                       |
 | FEMA Special Flood Hazard Area                                          | loc            | logical   |                       |
 | First Street Factor                                                     | loc            | numeric   |                       |
 | First Street Risk Direction                                             | loc            | numeric   |                       |
 | School Elementary District GEOID                                        | loc            | character |                       |
 | School Secondary District GEOID                                         | loc            | character |                       |
-| Municipality Name                                                       | loc            | character |                       |
 | CMAP Walkability Score (No Transit)                                     | loc            | numeric   |                       |
 | CMAP Walkability Total Score                                            | loc            | numeric   |                       |
 | Airport Noise DNL                                                       | loc            | numeric   |                       |

dvc.yaml

@@ -47,7 +47,7 @@ stages:
         cache: false
     - output/workflow/recipe/model_workflow_recipe.rds:
         cache: false
-    frozen: true
+
   assess:
     cmd: Rscript pipeline/02-assess.R
     desc: >

params.yaml

@@ -178,7 +178,7 @@ model:
       "char_full_baths",
       "loc_longitude",
       "loc_latitude",
-      "loc_tax_municipality_name",
+      "loc_cook_municipality_name",
       "loc_env_flood_fema_sfha",
       "loc_env_flood_fs_factor",
       "loc_env_flood_fs_risk_direction",
@@ -244,7 +244,7 @@ model:
     categorical: [
       "meta_township_code",
       "meta_nbhd_code",
-      "loc_tax_municipality_name",
+      "loc_cook_municipality_name",
       "loc_school_elementary_district_geoid",
       "loc_school_secondary_district_geoid",
       "time_sale_quarter_of_year",

pipeline/01-train.R

@@ -347,7 +347,7 @@ test %>%
   select(
     meta_year, meta_pin, meta_pin10, meta_class, meta_card_num, meta_lline_num,
     meta_triad_code, meta_township_code, meta_nbhd_code,
-    loc_tax_municipality_name, loc_ward_num, loc_census_puma_geoid,
+    loc_cook_municipality_name, loc_ward_num, loc_census_puma_geoid,
     loc_census_tract_geoid, loc_school_elementary_district_geoid,
     loc_school_secondary_district_geoid, loc_school_unified_district_geoid,
     char_unit_sf, char_building_sf, char_full_baths, char_half_baths,

pipeline/02-assess.R

@@ -293,7 +293,7 @@ assessment_data_pin <- assessment_data_merged %>%
     # Keep locations, prior year values, and indicators
     loc_longitude, loc_latitude,
     starts_with(c(
-      "loc_property_", "loc_tax_", "loc_ward_", "loc_chicago_",
+      "loc_property_", "loc_cook_", "loc_ward_", "loc_chicago_",
       "loc_census", "loc_school_", "prior_", "ind_"
     )),
     meta_pin10_5yr_num_sale,

pipeline/03-evaluate.R

@@ -52,7 +52,7 @@ col_rename_dict <- c(
   "geography_id" = "meta_township_code",
   "geography_id" = "meta_township_name",
   "geography_id" = "meta_nbhd_code",
-  "geography_id" = "loc_tax_municipality_name",
+  "geography_id" = "loc_cook_municipality_name",
   "geography_id" = "loc_ward_num",
   "geography_id" = "loc_census_puma_geoid",
   "geography_id" = "loc_census_tract_geoid",
@@ -80,7 +80,7 @@ if (run_type == "full") {
   assessment_data_pin <- read_parquet(paths$output$assessment_pin$local) %>%
     select(
       meta_pin, meta_class, meta_triad_code, meta_township_code, meta_nbhd_code,
-      loc_tax_municipality_name, loc_ward_num, loc_census_puma_geoid,
+      loc_cook_municipality_name, loc_ward_num, loc_census_puma_geoid,
       loc_census_tract_geoid, loc_school_elementary_district_geoid,
       loc_school_secondary_district_geoid, loc_school_unified_district_geoid,
       char_total_bldg_sf, char_unit_sf, prior_far_tot, prior_near_tot,
@@ -343,7 +343,7 @@ gen_agg_stats_quantile <- function(data, truth, estimate,
 # class or no class option for each level
 geographies_quosures <- rlang::quos(
   meta_township_code,
-  meta_nbhd_code, loc_tax_municipality_name,
+  meta_nbhd_code, loc_cook_municipality_name,
   loc_ward_num, loc_census_puma_geoid, loc_census_tract_geoid,
   loc_school_elementary_district_geoid, loc_school_secondary_district_geoid,
   loc_school_unified_district_geoid,

pipeline/05-finalize.R

@@ -9,6 +9,7 @@
 suppressPackageStartupMessages({
   library(arrow)
   library(aws.s3)
+  library(aws.ec2metadata)
   library(ccao)
   library(dplyr)
   library(here)

renv.lock

@@ -172,6 +172,17 @@
       ],
       "Hash": "89761c5b8e6ce66e7176cbb70a892df8"
     },
+    "aws.ec2metadata": {
+      "Package": "aws.ec2metadata",
+      "Version": "0.2.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "curl",
+        "jsonlite"
+      ],
+      "Hash": "2e630add89f47540d9da2240710e2702"
+    },
     "aws.s3": {
       "Package": "aws.s3",
       "Version": "0.3.21",