iup -> vcf (#320)

Resolves #315 
Resolves #321 
Increment to 0.2.0

Author: swo

README.md

@@ -1,12 +1,12 @@
 # Vaccination coverage forecasting
 
-_Formerly known as Immunization Uptake Projections, or `iup`._
+_Formerly known as Immunization Uptake Projections, or `vcf`._
 
 This repo represents an experimental prototype for forecasting the coverage of vaccinations.
 
 ## Getting started
 
-1. Read the docs at <https://cdcgov.github.io/cfa-immunization-uptake-projection>, or build them locally with `mkdocs serve`
+1. Read the docs at <https://cdcgov.github.io/cfa-vaccination-coverage-forecasting>, or build them locally with `mkdocs serve`
 1. This project uses [`uv`](https://docs.astral.sh/uv/) for environment and dependency management. Ensure you can `uv sync`. Use the uv-managed virtual environment (e.g., by prepending `uv run`).
 1. Run the [vignette](#vignette).
 
@@ -20,7 +20,7 @@ The vignette demonstrates a workflow using this package:
 
 ### Data source
 
-For convenience, the raw data are tracked in this repo under `data/`, which includes the script `get_nis.py`, used to collect that data with [`nis-py-api`](https://github.com/CDCgov/nis-py-api). These are estimates of season flu vaccination coverage, tracked monthly from the 2009/2010 to 2022/2023 seasons, from the [National Immunization Survey](https://www.cdc.gov/nis/about/index.html).
+For convenience, the raw data are tracked in this repo under `data/`, which includes the script `get_nis.py`, used to collect that data with [`nis-py-api`](https://github.com/CDCgov/nis-py-api). These are estimates of season flu vaccination coverage, tracked monthly from the 2009/2010 to 2022/2023 seasons, from the [National Immunization Survey](https://www.cdc.gov/nis/about/index.html) and [Behavioral Risk Factor Surveillance System](https://www.cdc.gov/brfss/index.html).
 
 ### Running the vignette
 
@@ -59,12 +59,6 @@ pred --> viz;
 scores --> viz;
 ```
 
-## Project admins
-
-- Scott Olesen (CDC/CFA/Predict) <ulp7@cdc.gov>
-
----
-
 ## Disclaimers
 
 ### General Disclaimer

docs/api.md

@@ -1,3 +1,3 @@
 # API reference
 
-::: iup
+::: vcf

mkdocs.yaml

@@ -5,7 +5,7 @@ nav:
   - lpl.md
   - api.md
 
-repo_url: https://github.com/CDCgov/cfa-immunization-uptake-projection
+repo_url: https://github.com/CDCgov/cfa-vaccination-coverage-forecasting
 repo_name: repo
 
 # advanced configuration ------------------------------------------------------

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
-name = "iup"
-version = "0.1.5"
+name = "vcf"
+version = "0.2.0"
 description = ""
 authors = [
     { name = "Scott Olesen", email = "ulp7@cdc.gov" },
@@ -37,9 +37,7 @@ requires = ["hatchling"]
 build-backend = "hatchling.build"
 
 [dependency-groups]
-dev = [
-    "pytest>=9.0.3",
-]
+dev = ["pytest>=9.0.3"]
 mkdocs = [
     "mkdocs>=1.6.1",
     "mkdocs-material>=9.7.1",

scripts/demo_rf.py

@@ -1,5 +1,3 @@
-"""Random Forest demonstration script for immunization uptake projection."""
-
 import datetime
 from pathlib import Path
 from typing import Tuple
@@ -10,7 +8,7 @@
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.preprocessing import OneHotEncoder
 
-from iup import to_season
+from vcf import to_season
 
 SEASON_START_MONTH = 8
 SEASON_END_MONTH = 4

scripts/eval.py

@@ -3,7 +3,7 @@
 import polars as pl
 import yaml
 
-import iup
+import vcf
 
 if __name__ == "__main__":
     p = argparse.ArgumentParser()
@@ -23,7 +23,7 @@
 
     # score the forecasts proper, only in the season that the forecasts were made
     forecast_season = pred.select(
-        iup.to_season(
+        vcf.to_season(
             pl.col("forecast_date"),
             season_start_month=season["start_month"],
             season_start_day=season["start_day"],
@@ -34,7 +34,7 @@
     assert len(forecast_season) == 1, "Can only score forecasts from one season"
     forecast_season = forecast_season[0]
 
-    eos_abs_diff = iup.eos_abs_diff(
+    eos_abs_diff = vcf.eos_abs_diff(
         obs=data.filter(pl.col("season") == pl.lit(forecast_season)),
         pred=pred.filter(pl.col("season") == pl.lit(forecast_season)),
         features=["season", "geography"],

scripts/fit.py

@@ -14,12 +14,12 @@
 import polars as pl
 import yaml
 
-import iup
+import vcf
 
 
 def fit_all_models(
     data: pl.DataFrame, forecast_date: dt.date, config: dict[str, Any]
-) -> dict[Tuple[str, dt.date], iup.CoverageModel]:
+) -> dict[Tuple[str, dt.date], vcf.CoverageModel]:
     """Run all forecasts.
 
     Args:
@@ -37,9 +37,9 @@ def fit_all_models(
     all_models = {}
     for config_model in config["models"]:
         model_name = config_model["name"]
-        model_class = getattr(iup, model_name)
+        model_class = getattr(vcf, model_name)
 
-        assert issubclass(model_class, iup.CoverageModel), (
+        assert issubclass(model_class, vcf.CoverageModel), (
             f"{model_name} is not a valid model type!"
         )
 

scripts/plot_preds.py

@@ -6,7 +6,7 @@
 import yaml
 from plot_data import AXIS_PERCENT
 
-import iup
+import vcf
 
 LINE_OPACITY = 0.4
 
@@ -39,7 +39,7 @@
     assert quantiles.issubset(all_quantiles)
 
     forecast_season = preds_raw.select(
-        iup.to_season(
+        vcf.to_season(
             pl.col("forecast_date"),
             season_start_month=season["start_month"],
             season_end_month=season["end_month"],

scripts/preprocess.py

@@ -5,7 +5,7 @@
 import polars as pl
 import yaml
 
-from iup import to_season
+from vcf import to_season
 
 
 def preprocess(

tests/test_rf.py

@@ -5,7 +5,7 @@
 import pytest
 from sklearn.ensemble import RandomForestRegressor
 
-import iup
+import vcf
 
 
 @pytest.fixture
@@ -38,7 +38,7 @@ def frame():
 
 @pytest.fixture
 def rf(frame):
-    return iup.RFModel(
+    return vcf.RFModel(
         data=frame,
         season={"start_month": 9, "start_day": 1, "end_month": 4, "end_day": 1},
         params={"n_estimators": 10},