diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 0000000..267f02b --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,33 @@ +name: Publish Release to PyPI + +on: + push: + tags: + - "v*.*.*" + +jobs: + run: + runs-on: ubuntu-latest + environment: + name: pypi + permissions: + id-token: write + contents: read + steps: + - name: Checkout code + uses: actions/checkout@v6 + - name: Install uv + uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b + with: + enable-cache: true + version: "0.11.18" + - name: Set up Python + run: uv python install + - name: Build + run: uv build + - name: Smoke test (wheel) + run: uv run --isolated --no-project --with dist/*.whl tests/smoke_test.py + - name: Smoke test (source distribution) + run: uv run --isolated --no-project --with dist/*.tar.gz tests/smoke_test.py + - name: Publish + run: uv publish diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..d32b077 --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,38 @@ +name: tests + +on: + push: + branches: + - master + pull_request: + branches: + - "*" + +concurrency: + group: tests-${{ github.ref }} + cancel-in-progress: true + +jobs: + test: + name: pytest (Python ${{ matrix.python-version }}) + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.12", "3.13"] + + steps: + - uses: actions/checkout@v6 + + - name: Install uv + uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b + with: + version: "0.11.18" + enable-cache: true + python-version: ${{ matrix.python-version }} + + - name: Install project + run: uv sync --locked + + - name: Run tests + run: uv run pytest diff --git a/.gitignore b/.gitignore index 2f8e46e..3ca51a6 100644 --- a/.gitignore +++ b/.gitignore @@ -40,3 +40,16 @@ uv.lock # Training outputs output/ scripts/output/ + +# Model training artifacts (regenerable; shipped copies live in src/estimint/data/) +models/**/*.parquet +models/**/*.pkl +models/**/*.model +models/**/plots/ +models/**/metrics/ + +# Test / coverage +.coverage +.coverage.* +htmlcov/ +.pytest_cache/ diff --git a/MANIFEST.in b/MANIFEST.in index 12fbd8a..851de40 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,4 +1,3 @@ include README.md -include requirements.txt recursive-include src/estimint/data * -recursive-include src/estimint/inst * +prune tests diff --git a/README.md b/README.md index a0b2a1c..e9b58dc 100644 --- a/README.md +++ b/README.md @@ -1,62 +1,77 @@ -# estiMINT (Python) +# estiMINT -Python port of the estiMINT R package for EIR (Entomological Inoculation Rate) estimation using machine learning. +Package for EIR (Entomological Inoculation Rate) estimation using machine learning. + +It estimates EIR from prevalence, converts between EIR and human biting rate (including the effect of changes in mosquito density), and turns a bednet specification (net type and resistance level) into the `dn0` killing parameter. ## Installation ```bash -pip install -e . +pip install estimint # core: inference only (numpy, pandas, xgboost, scipy) ``` -Or install dependencies directly: +Optional extras, by use case: ```bash -pip install -r requirements.txt +pip install "estimint[train]" # data prep + model training (duckdb, scikit-learn, pyarrow) +pip install "estimint[viz]" # plotting (matplotlib) +pip install "estimint[scenarios]" # run_scenarios pipeline (stateMINT emulator) +pip install "estimint[all]" +pip install "estimint[dev]" # test/lint/type-check toolchain ``` -## File Mapping (R → Python) +The `run_scenarios` pipeline also needs the stateMINT emulator (Python 3.12+). For now it +comes from the `mamba2-train` branch. With uv this is handled for you: + +```bash +uv sync --extra scenarios +``` -| R File | Python File | Description | -|--------|-------------|-------------| -| `estiMINT-package.R` | `__init__.py` | Package initialization and exports | -| `globals.R` | `globals.py` | Global variables and constants | -| `utils.R` | `utils.py` | Utility functions (metrics, QMAP, etc.) | -| `data_processing.R` | `data_processing.py` | Data loading and preprocessing | -| `models.R` | `models.py` | XGBoost model training | -| `train.R` | `train.py` | Main training pipeline with K-fold CV | -| `plotting.R` | `plotting.py` | Visualization functions | -| `storage.R` | `storage.py` | Model persistence and loading | -| `run.R` | `run.py` | Model inference | +With plain pip, install stateMINT from the branch yourself, then estiMINT: -## API Reference +```bash +pip install "git+https://github.com/mrc-ide/stateMINT.git@mamba2-train" +pip install estimint +``` -### Training +For local development with [uv](https://docs.astral.sh/uv/): -```python -from estimint import train_xgb_model - -model = train_xgb_model( - in_parquet="data/input.parquet", - out_dir="output/", - thr_lo=0.02, # Lower prevalence threshold - thr_hi=0.95, # Upper prevalence threshold - k_strata=16, # K-means strata for EIR - K=10, # CV folds - seed=42, - save_pkl=True, - save_plots=True, - save_artifacts=True -) +```bash +uv sync --all-extras --dev ``` +## Data & retraining pipeline + +All training data lives in `datasets/estimint_simulations_y9.parquet`. Two model folders +derive their views from it and train: + +``` +datasets/ # training data (see datasets/README.md) +models/ + prevalence/ # prev_y9 -> EIR (estiMINT_model.pkl) + hbr/ # HBR<->EIR sub-models (estiMINT_HBR_model.pkl, estiMINT_EIR_to_HBR_model.pkl) +``` + +Retrain a model end-to-end, e.g. the prevalence model: + +```bash +python models/prevalence/prepare.py # derive the training view from the parquet +python models/prevalence/train.py # train -> estiMINT_model.pkl + metrics/ + plots/ +``` + +The deployed models shipped with the package live in `src/estimint/data/` and are loaded by +name (`prevalence`, `hbr`, `eir_to_hbr`). This is independent of the training pipeline above. + +## API Reference + ### Inference ```python from estimint import load_xgb_model, run_xgb_model import pandas as pd -# Load model -model = load_xgb_model("output/models/estiMINT_model.pkl") +# Load a bundled model by name: "prevalence", "hbr", or "eir_to_hbr" +model = load_xgb_model("prevalence") # Prepare input data new_data = pd.DataFrame({ @@ -80,13 +95,80 @@ print(f"Predicted EIR: {eir_predictions[0]:.2f}") from estimint import load_xgb_model, run_xgb_model, set_global_model # Set global model once -model = load_xgb_model("output/models/estiMINT_model.pkl") +model = load_xgb_model("prevalence") set_global_model(model) # Run predictions without passing model predictions = run_xgb_model(new_data) # Uses global model ``` +### Bednet to dn0 + +Turn a bednet specification (a mix of net types and an insecticide resistance level) into +the `dn0` covariate, the probability a mosquito dies on contact, along with total ITN usage. + +```python +from estimint import calculate_dn0, net_types + +net_types() # ['pyrethroid_only', 'pyrethroid_pbo', 'pyrethroid_ppf', 'pyrethroid_pyrrole'] +res = calculate_dn0(0.5, py_only=0.4, py_pbo=0.3, py_pyrrole=0.2, py_ppf=0.1) +res.dn0, res.itn_use # weighted dn0, total net usage +``` + +### Run scenarios + +`run_scenarios` runs the whole pipeline in one call. You give it a list of scenarios and +get back a DataFrame. For each scenario it works out the bednet killing effect, estimates +the EIR (from prevalence, from biting rate, or taken directly), optionally adjusts for a +change in mosquito density, then runs the stateMINT emulator forward to the prevalence and +cases trajectories. + +This needs the [stateMINT](https://github.com/mrc-ide/stateMINT) package installed as well +as estiMINT. estiMINT only loads it when you call `run_scenarios`, and the model weights +download from HuggingFace. + +```python +from estimint import run_scenarios +from estimint.scenarios import Scenario + +scenarios = [ + Scenario(name="PBO nets, prevalence input, 60% more mosquitoes", + input="prevalence", value=0.30, + res_use=0.55, py_pbo=0.85, + Q0=0.90, phi=0.85, seasonal=1, irs=0.40, lsm=0.0, + mosquito_delta=0.60), + Scenario(name="Biting rate input, mixed nets", + input="hbr", value=250000.0, + res_use=0.45, py_only=0.30, py_ppf=0.20, + Q0=0.80, phi=0.82, seasonal=0, irs=0.0), + Scenario(name="EIR supplied directly, no nets", + input="eir", value=20.0, res_use=0.0, + Q0=0.88, phi=0.78, seasonal=1, irs=0.60), +] + +df = run_scenarios(scenarios) +print(df[["name", "eir_baseline", "eir_final", "prev_y9", "cases_endline"]]) +``` + +Every scenario is a `Scenario` and needs `name`, `res_use`, `input`, `value`, `Q0`, +`phi`, `seasonal` and `irs`. `lsm`, `routine` and `irs_future` default to 0 (note +`irs_future` does **not** default to `irs` — set it explicitly if you want IRS to +continue). **Current nets:** give a net-type usage mix (`py_only`, `py_pbo`, +`py_pyrrole`, `py_ppf` shares), or leave the net keys out for none; current and +future legs share the same `res_use`. **Future nets:** give `net_type_future` + +`itn_future` to switch net type; omit `net_type_future` and the future leg is zeroed +(it does **not** carry the current mix forward), or set `itn_future=0` to remove +nets explicitly. `mosquito_delta` only applies when `input` is `"prevalence"`. + +The returned DataFrame has one row per scenario. Alongside the inputs it gives the +estimated EIR (`eir_baseline`, and `eir_final` after any mosquito-density change) and the +stateMINT output. That output is year-9 prevalence (`prev_y9`), endline prevalence and +cases, and the full 157-step `prevalence` and `cases` series. What you do with it is up to +you. + +The `estimint.scenarios` module is also where the simulation-based inference and experiment +code will go. + ## Utility Functions ```python @@ -110,6 +192,9 @@ y_calibrated = predict_qmap_w(y_pred, cal) ## Data Processing +These functions need the training extras. Install them with `pip install "estimint[train]"`, +which adds duckdb and scikit-learn. + ```python from estimint import load_and_filter, make_value_weights, strata_and_split @@ -126,6 +211,28 @@ df["eir_log10"] = np.log10(df["eir"]) df = strata_and_split(df, k_strata=16, seed=42) ``` +## Testing + +```bash +uv sync --extra dev # or: pip install -e ".[dev]" +uv run pytest # or: pytest +``` + +This covers the metric and utility helpers, the EIR estimators (prevalence, HBR and direct +EIR), the mosquito-density HBR pipeline, and the bednet calculation. + +## CI and releases + +The test suite runs on every push and pull request across Python 3.10 to 3.14, defined in +[`.github/workflows/tests.yml`](.github/workflows/tests.yml). + +Releases publish to PyPI from [`.github/workflows/publish.yml`](.github/workflows/publish.yml). +It builds with `uv build` and uploads with `uv publish` using +[PyPI trusted publishing](https://docs.astral.sh/uv/guides/integration/github/#publishing-to-pypi), +so no token is stored. To cut a release, bump `version` in `pyproject.toml` and publish a +GitHub Release. The first time, register this repository as a trusted publisher in the PyPI +project settings. + ## Key Differences from R Version 1. **File format**: Models saved as `.pkl` (pickle) instead of `.rds` @@ -133,17 +240,6 @@ df = strata_and_split(df, k_strata=16, seed=42) 3. **Plotting**: Uses matplotlib instead of ggplot2 4. **Global model**: Use `set_global_model()` / `get_global_model()` instead of `.GlobalEnv` -## Dependencies - -- numpy >= 1.20.0 -- pandas >= 1.3.0 -- duckdb >= 0.8.0 -- xgboost >= 1.6.0 -- scikit-learn >= 1.0.0 -- matplotlib >= 3.4.0 -- requests >= 2.28.0 (optional, for model download) -- appdirs >= 1.4.0 (optional, for cache directory) - ## License MIT License diff --git a/datasets/README.md b/datasets/README.md new file mode 100644 index 0000000..93f6908 --- /dev/null +++ b/datasets/README.md @@ -0,0 +1,11 @@ +# datasets/ + +Training data for retraining the estiMINT models. Not shipped with the package. + +**`estimint_simulations_y9.parquet`** — 16,384 rows (4,096 parameter sets × 4 sims). +`prev_y9` and `hbr_y9` are year-9 means: prevalence and human biting rate averaged over +the 365 days of simulation year 9 (the year ending at the intervention on day 3285). +Columns: `parameter_index`, `simulation_index`, `eir`, `dn0_use`, `Q0`, `phi_bednets`, +`seasonal`, `itn_use`, `irs_use`, `prev_y9`, `hbr_y9`. + +Each model's `prepare.py` filters this source and sorts by key into its training view. diff --git a/datasets/estimint_simulations_y9.parquet b/datasets/estimint_simulations_y9.parquet new file mode 100644 index 0000000..69e7cbb Binary files /dev/null and b/datasets/estimint_simulations_y9.parquet differ diff --git a/models/hbr/README.md b/models/hbr/README.md new file mode 100644 index 0000000..db612df --- /dev/null +++ b/models/hbr/README.md @@ -0,0 +1,17 @@ +# models/hbr + +The HBR feature's two sub-models, both used by `estimate_eir_with_mosquito_delta` +(`src/estimint/hbr.py`) to answer "what happens to EIR if mosquito density changes by X%?". + +| Sub-model | Direction | Bundle name | File | +|---|---|---|---| +| `train_hbr_to_eir.py` | HBR + interventions → EIR | `hbr` | `estiMINT_HBR_model.pkl` | +| `train_eir_to_hbr.py` | EIR + interventions → HBR | `eir_to_hbr` | `estiMINT_EIR_to_HBR_model.pkl` | + +```bash +python models/hbr/prepare.py # source -> hbr_training.parquet + eir_to_hbr_training.parquet +python models/hbr/train_hbr_to_eir.py # -> estiMINT_HBR_model.pkl +python models/hbr/train_eir_to_hbr.py # -> estiMINT_EIR_to_HBR_model.pkl +``` + +Deployed copies live in `src/estimint/data/`. diff --git a/models/hbr/prepare.py b/models/hbr/prepare.py new file mode 100644 index 0000000..5535ff1 --- /dev/null +++ b/models/hbr/prepare.py @@ -0,0 +1,38 @@ +"""Derive the two HBR training views from datasets/estimint_simulations_y9.parquet. + +- hbr_training: HBR->EIR model (hbr_y9 > 0) +- eir_to_hbr_training: EIR->HBR model (prev_y9 >= 0.01 AND hbr_y9 > 0) + +Both sorted by key for a deterministic, reproducible view. +""" + +from pathlib import Path + +import pandas as pd + +HERE = Path(__file__).parent +SOURCE = HERE.parents[1] / "datasets" / "estimint_simulations_y9.parquet" + +KEYS = ["parameter_index", "simulation_index"] +COLS = KEYS + ["eir", "dn0_use", "Q0", "phi_bednets", "seasonal", "itn_use", "irs_use", "hbr_y9"] +MIN_PREVALENCE = 0.01 + + +def prepare(): + src = pd.read_parquet(SOURCE) + + hbr = src[src.hbr_y9 > 0][COLS].sort_values(KEYS).reset_index(drop=True) + assert len(hbr) == 16384, f"expected 16,384 hbr rows, got {len(hbr):,}" + assert not hbr.isna().any().any(), "unexpected NaN in hbr view" + hbr.to_parquet(HERE / "hbr_training.parquet", index=False) + print(f"hbr view: {len(hbr):,} rows -> models/hbr/hbr_training.parquet") + + e2h = src[(src.prev_y9 >= MIN_PREVALENCE) & (src.hbr_y9 > 0)][COLS].sort_values(KEYS).reset_index(drop=True) + assert len(e2h) == 12874, f"expected 12,874 eir_to_hbr rows, got {len(e2h):,}" + assert not e2h.isna().any().any(), "unexpected NaN in eir_to_hbr view" + e2h.to_parquet(HERE / "eir_to_hbr_training.parquet", index=False) + print(f"eir_to_hbr view: {len(e2h):,} rows -> models/hbr/eir_to_hbr_training.parquet") + + +if __name__ == "__main__": + prepare() diff --git a/scripts/train_eir_to_hbr.py b/models/hbr/train_eir_to_hbr.py similarity index 59% rename from scripts/train_eir_to_hbr.py rename to models/hbr/train_eir_to_hbr.py index 43a3f6f..4f087ff 100644 --- a/scripts/train_eir_to_hbr.py +++ b/models/hbr/train_eir_to_hbr.py @@ -1,14 +1,9 @@ -""" -Train estiMINT EIR-to-HBR model. - -This script: -1. Loads the prepared data (eir + interventions -> hbr_y9) -2. Trains XGBoost model using K-fold CV and QMAP calibration -3. Saves model to output/eir_to_hbr_retrained/ +"""Train the EIR->HBR model (eir + interventions -> hbr_y9). -This is the "reverse" model: given a baseline EIR and interventions, -predict what the human biting rate is. Used to derive baseline HBR -so users can apply percentage changes (e.g. "10% more mosquitoes"). +The reverse model: given baseline EIR and interventions, predict the human biting +rate so percentage mosquito-density changes can be applied. XGBoost with k-means +strata on log10(HBR), 10-fold CV, QMAP+scale calibration (no monotone constraint). +Reads models/hbr/eir_to_hbr_training.parquet; writes artifacts into this folder. """ import sys @@ -19,59 +14,36 @@ from pathlib import Path from sklearn.cluster import KMeans -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent / "src")) +sys.path.insert(0, str(Path(__file__).parents[2] / "src")) from estimint.utils import ( - ts, r2, rmse, mse, mae, median_ae, mae_rel, rmsle, - safe_div, smape, fit_qmap_w, predict_qmap_w, scale_pos + ts, r2, rmse, mae, fit_qmap_w, predict_qmap_w, scale_pos ) from estimint.data_processing import make_value_weights from estimint.plotting import plot_obs_pred -# Configuration -DATA_PATH = "/home/cosmo/Documents/Repos/estimint/output/eir_to_hbr_data/eir_to_hbr_training_data.csv" -OUTPUT_DIR = "/home/cosmo/Documents/Repos/estimint/output/eir_to_hbr_retrained" +HERE = Path(__file__).parent +DATA_PATH = HERE / "eir_to_hbr_training.parquet" +OUTPUT_DIR = HERE K_FOLDS = 10 K_STRATA = 16 SEED = 42 + def main(): - print("=" * 80) - print("Training estiMINT EIR-to-HBR Model (eir + interventions -> hbr_y9)") - print("=" * 80) - - data_path = Path(DATA_PATH) - if not data_path.exists(): - print(f"ERROR: Training data not found at {DATA_PATH}") - print("Please run prepare_eir_to_hbr_data.py first") - return 1 - - print(f"\nInput data: {data_path}") - print(f"Output dir: {OUTPUT_DIR}") - - # Create output directories - out_dir = Path(OUTPUT_DIR) - dir_models = out_dir / "models" - dir_plots = out_dir / "plots" - dir_metric = out_dir / "metrics" - dir_pred = out_dir / "predictions" - - for d in [dir_models, dir_plots, dir_metric, dir_pred]: + dir_plots = OUTPUT_DIR / "plots" + dir_metric = OUTPUT_DIR / "metrics" + for d in [dir_plots, dir_metric]: d.mkdir(parents=True, exist_ok=True) - # Load data ts("Loading training data...") - DT = pd.read_csv(data_path) - print(f"Loaded {len(DT):,} rows") + df = pd.read_parquet(DATA_PATH) + print(f"Loaded {len(df):,} rows") - # Features: eir + interventions. Target: hbr_y9 features = ["eir", "dn0_use", "Q0", "phi_bednets", "seasonal", "itn_use", "irs_use"] - # Transform HBR to log10 (large range, same reason as EIR) - DT["hbr_log10"] = np.log10(DT["hbr_y9"]) + df["hbr_log10"] = np.log10(df["hbr_y9"]) - # XGBoost parameters xgb_params = { "objective": "reg:squarederror", "eval_metric": "rmse", @@ -85,23 +57,21 @@ def main(): "seed": SEED, } - # Create strata using k-means on log10(HBR) ts("Creating %d strata on log10(HBR) and 70/15/15 split...", K_STRATA) np.random.seed(SEED) - hbr_log10 = DT["hbr_log10"].values.reshape(-1, 1) + hbr_log10 = df["hbr_log10"].values.reshape(-1, 1) km = KMeans(n_clusters=K_STRATA, n_init=50, max_iter=5000, random_state=SEED) km.fit(hbr_log10) centers = km.cluster_centers_.flatten() ord_idx = np.argsort(centers) id_map = {old_id: new_id + 1 for new_id, old_id in enumerate(ord_idx)} - DT["strat_bin"] = np.array([id_map[c] for c in km.labels_]) + df["strat_bin"] = np.array([id_map[c] for c in km.labels_]) - # Stratified split - DT["split"] = None - for b in sorted(DT["strat_bin"].unique()): - idx = DT[DT["strat_bin"] == b].index.tolist() + df["split"] = None + for b in sorted(df["strat_bin"].unique()): + idx = df[df["strat_bin"] == b].index.tolist() n_b = len(idx) n_tr = int(np.floor(0.70 * n_b)) n_val = int(np.floor(0.15 * n_b)) @@ -112,51 +82,48 @@ def main(): val_idx = idx[n_tr:n_tr + n_val] if n_val > 0 else [] te_idx = idx[n_tr + n_val:] - DT.loc[tr_idx, "split"] = "train" - DT.loc[val_idx, "split"] = "val" - DT.loc[te_idx, "split"] = "test" + df.loc[tr_idx, "split"] = "train" + df.loc[val_idx, "split"] = "val" + df.loc[te_idx, "split"] = "test" - DT["split"] = DT["split"].fillna("train") + df["split"] = df["split"].fillna("train") - # Hold-out test set - DT_test = DT[DT["split"] == "test"] - X_test = DT_test[features].values.astype(np.float64) - y_test = DT_test["hbr_log10"].values + df_test = df[df["split"] == "test"] + X_test = df_test[features].values.astype(np.float64) + y_test = df_test["hbr_log10"].values obs_hbr_test = np.power(10, y_test) - ts("Test set: %d rows", len(DT_test)) + ts("Test set: %d rows", len(df_test)) - # CV folds ts("Assigning %d-fold CV within TRAIN+VAL strata...", K_FOLDS) - DTcv = DT[DT["split"] != "test"].copy() + dfcv = df[df["split"] != "test"].copy() np.random.seed(SEED + 1) - DTcv["fold"] = 0 - for b in DTcv["strat_bin"].unique(): - mask = DTcv["strat_bin"] == b + dfcv["fold"] = 0 + for b in dfcv["strat_bin"].unique(): + mask = dfcv["strat_bin"] == b n_b = mask.sum() - idx = DTcv.index[mask].tolist() + idx = dfcv.index[mask].tolist() np.random.shuffle(idx) folds = np.tile(np.arange(1, K_FOLDS + 1), int(np.ceil(n_b / K_FOLDS)))[:n_b] np.random.shuffle(folds) - DTcv.loc[idx, "fold"] = folds + dfcv.loc[idx, "fold"] = folds - # K-fold CV training ts("Running %d-fold CV with early stopping...", K_FOLDS) - oof_pred_raw = np.full(len(DTcv), np.nan) + oof_pred_raw = np.full(len(dfcv), np.nan) best_iters = np.zeros(K_FOLDS, dtype=int) for k in range(1, K_FOLDS + 1): ts(" Fold %d / %d", k, K_FOLDS) - idx_val = DTcv["fold"] == k - idx_tr = DTcv["fold"] != k + idx_val = dfcv["fold"] == k + idx_tr = dfcv["fold"] != k - X_tr = DTcv.loc[idx_tr, features].values.astype(np.float64) - y_tr = DTcv.loc[idx_tr, "hbr_log10"].values - X_va = DTcv.loc[idx_val, features].values.astype(np.float64) - y_va = DTcv.loc[idx_val, "hbr_log10"].values + X_tr = dfcv.loc[idx_tr, features].values.astype(np.float64) + y_tr = dfcv.loc[idx_tr, "hbr_log10"].values + X_va = dfcv.loc[idx_val, features].values.astype(np.float64) + y_va = dfcv.loc[idx_val, "hbr_log10"].values w_tr = make_value_weights(np.power(10, y_tr), digits=3) w_va = make_value_weights(np.power(10, y_va), digits=3) @@ -177,16 +144,14 @@ def main(): pred_log10_va = mdl.predict(dva) oof_pred_raw[idx_val.values] = np.power(10, pred_log10_va) - obs_cv_raw = np.power(10, DTcv["hbr_log10"].values) + obs_cv_raw = np.power(10, dfcv["hbr_log10"].values) - # Fit calibrator ts("Fitting final calibrator (QMAP + positive scale) on OOF...") cal_oof = fit_qmap_w(oof_pred_raw, obs_cv_raw, ngrid=1024, round_digits=8) oof_pred_cal = predict_qmap_w(oof_pred_raw, cal_oof) a_oof = scale_pos(obs_cv_raw, oof_pred_cal) oof_pred_final = np.maximum(0, a_oof * oof_pred_cal) - # OOF metrics oof_metrics = pd.DataFrame({ "set": ["OOF_uncalibrated", "OOF_calibrated"], "R2": [r2(obs_cv_raw, oof_pred_raw), r2(obs_cv_raw, oof_pred_final)], @@ -197,14 +162,13 @@ def main(): oof_metrics.to_csv(dir_metric / f"hbr_OOF_metrics_K{K_FOLDS}CV.csv", index=False) print("\n" + str(oof_metrics)) - # Train final model ts("Training final model on TRAIN+VAL with nrounds = median(best_iteration)...") best_nrounds = int(np.round(np.median(best_iters))) print(f"Best nrounds: {best_nrounds}") - DT_trcv = DT[DT["split"] != "test"] - X_trcv = DT_trcv[features].values.astype(np.float64) - y_trcv = DT_trcv["hbr_log10"].values + df_trcv = df[df["split"] != "test"] + X_trcv = df_trcv[features].values.astype(np.float64) + y_trcv = df_trcv["hbr_log10"].values w_trcv = make_value_weights(np.power(10, y_trcv), digits=3) dtrcv = xgb.DMatrix(X_trcv, label=y_trcv, weight=w_trcv) @@ -215,16 +179,14 @@ def main(): num_boost_round=best_nrounds, verbose_eval=False, ) - xgb_final.save_model(str(dir_models / "hbr_xgb_FINAL.model")) + xgb_final.save_model(str(OUTPUT_DIR / "hbr_xgb_FINAL.model")) - # Predict on TEST dtest = xgb.DMatrix(X_test, label=y_test) pred_log10_test_raw = xgb_final.predict(dtest) pred_raw_test = np.power(10, pred_log10_test_raw) pred_hbr_test = predict_qmap_w(pred_raw_test, cal_oof) pred_hbr_test = np.maximum(0, a_oof * pred_hbr_test) - # Test metrics test_metrics = pd.DataFrame({ "set": ["Test"], "R2": [r2(obs_hbr_test, pred_hbr_test)], @@ -235,7 +197,6 @@ def main(): test_metrics.to_csv(dir_metric / "hbr_test_metrics.csv", index=False) print("\n" + str(test_metrics)) - # Plot plot_obs_pred( obs_hbr_test, pred_hbr_test, f"HBR — Observed vs Predicted (XGBoost, K={K_FOLDS} CV, QMAP+Scale, test)", @@ -243,7 +204,6 @@ def main(): xlab="Observed HBR", ylab="Predicted HBR" ) - # Model bundle cal_bundle = { "kind": "qmap+scale", "qmap": {"xq": cal_oof["xq"], "yq": cal_oof["yq"]}, @@ -256,9 +216,9 @@ def main(): "transform": "log10", "inverse": "pow10", "training_data": { - "source": "MINTelligence malaria_simulations_4096.duckdb + HBR_malaria_simulations_4096.duckdb", - "n_rows": len(DT), - "n_params": DT["parameter_index"].nunique() + "source": "datasets/estimint_simulations_y9.parquet (prev_y9 >= 0.01 AND hbr_y9 > 0)", + "n_rows": len(df), + "n_params": df["parameter_index"].nunique() }, "cv": { "K": K_FOLDS, @@ -276,17 +236,12 @@ def main(): "preprocess": preprocess, } - with open(dir_models / "estiMINT_EIR_to_HBR_model.pkl", "wb") as f: + with open(OUTPUT_DIR / "estiMINT_EIR_to_HBR_model.pkl", "wb") as f: pickle.dump(model_bundle, f, protocol=pickle.HIGHEST_PROTOCOL) - print("\n" + "=" * 80) - print("Training Complete!") - print("=" * 80) - print(f"\nModel saved to: {dir_models}/estiMINT_EIR_to_HBR_model.pkl") - print(f"Plots saved to: {dir_plots}/") - print(f"Metrics saved to: {dir_metric}/") - + print(f"\nModel saved to: {OUTPUT_DIR}/estiMINT_EIR_to_HBR_model.pkl") return 0 + if __name__ == "__main__": sys.exit(main()) diff --git a/scripts/train_on_hbr_data.py b/models/hbr/train_hbr_to_eir.py similarity index 56% rename from scripts/train_on_hbr_data.py rename to models/hbr/train_hbr_to_eir.py index f0350d1..b42ea80 100644 --- a/scripts/train_on_hbr_data.py +++ b/models/hbr/train_hbr_to_eir.py @@ -1,10 +1,8 @@ -""" -Train estiMINT HBR model using MINTelligence data. +"""Train the HBR->EIR model (hbr_y9 + interventions -> EIR). -This script: -1. Loads the prepared HBR data from prepare_hbr_data.py -2. Trains XGBoost model (hbr_y9 + interventions -> EIR) using K-fold CV and QMAP calibration -3. Saves model to output/hbr_retrained/ +Same pipeline as the prevalence model but with hbr_y9 as the monotone feature. +Reads models/hbr/hbr_training.parquet; writes the model, booster, metrics and plot +into this folder. """ import sys @@ -15,64 +13,37 @@ from pathlib import Path from sklearn.cluster import KMeans -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent / "src")) +sys.path.insert(0, str(Path(__file__).parents[2] / "src")) from estimint.utils import ( - ts, r2, rmse, mse, mae, median_ae, mae_rel, rmsle, - safe_div, smape, fit_qmap_w, predict_qmap_w, scale_pos + ts, r2, rmse, mae, fit_qmap_w, predict_qmap_w, scale_pos ) from estimint.data_processing import make_value_weights from estimint.plotting import plot_obs_pred -# Configuration -DATA_PATH = "/home/cosmo/Documents/Repos/estimint/output/hbr_data/hbr_training_data.csv" -OUTPUT_DIR = "/home/cosmo/Documents/Repos/estimint/output/hbr_retrained" +HERE = Path(__file__).parent +DATA_PATH = HERE / "hbr_training.parquet" +OUTPUT_DIR = HERE K_FOLDS = 10 K_STRATA = 16 SEED = 42 + def main(): - print("=" * 80) - print("Training estiMINT HBR Model (hbr_y9 + interventions -> EIR)") - print("=" * 80) - - # Check if data exists - data_path = Path(DATA_PATH) - if not data_path.exists(): - print(f"ERROR: Training data not found at {DATA_PATH}") - print("Please run prepare_hbr_data.py first") - return 1 - - print(f"\nInput data: {data_path}") - print(f"Output dir: {OUTPUT_DIR}") - - # Create output directories - out_dir = Path(OUTPUT_DIR) - dir_models = out_dir / "models" - dir_plots = out_dir / "plots" - dir_metric = out_dir / "metrics" - dir_pred = out_dir / "predictions" - - for d in [dir_models, dir_plots, dir_metric, dir_pred]: + dir_plots = OUTPUT_DIR / "plots" + dir_metric = OUTPUT_DIR / "metrics" + for d in [dir_plots, dir_metric]: d.mkdir(parents=True, exist_ok=True) - # Load data ts("Loading training data...") - DT = pd.read_csv(data_path) - print(f"Loaded {len(DT):,} rows") + df = pd.read_parquet(DATA_PATH) + print(f"Loaded {len(df):,} rows") - # Define features — same as prevalence model but with hbr_y9 instead of prev_y9 features = ["dn0_use", "Q0", "phi_bednets", "seasonal", "itn_use", "irs_use", "hbr_y9"] - # Transform EIR to log10 - DT["eir_log10"] = np.log10(DT["eir"]) + df["eir_log10"] = np.log10(df["eir"]) - # XGBoost parameters (same as prevalence model) - # Monotone constraint: hbr_y9 (index 6) must be positively correlated with EIR - # More mosquito bites → more infectious bites, all else equal - # Features: ["dn0_use", "Q0", "phi_bednets", "seasonal", "itn_use", "irs_use", "hbr_y9"] - # 0 0 0 0 0 0 +1 + # monotone constraint: hbr_y9 (index 6) positively correlated with EIR xgb_params = { "objective": "reg:squarederror", "eval_metric": "rmse", @@ -88,24 +59,21 @@ def main(): "monotone_constraints": "(0,0,0,0,0,0,1)", } - # Create strata using k-means on log10(EIR) ts("Creating %d strata on log10(EIR) and 70/15/15 split...", K_STRATA) np.random.seed(SEED) - eir_log10 = DT["eir_log10"].values.reshape(-1, 1) + eir_log10 = df["eir_log10"].values.reshape(-1, 1) km = KMeans(n_clusters=K_STRATA, n_init=50, max_iter=5000, random_state=SEED) km.fit(eir_log10) - # Reorder cluster IDs by center value centers = km.cluster_centers_.flatten() ord_idx = np.argsort(centers) id_map = {old_id: new_id + 1 for new_id, old_id in enumerate(ord_idx)} - DT["strat_bin"] = np.array([id_map[c] for c in km.labels_]) + df["strat_bin"] = np.array([id_map[c] for c in km.labels_]) - # Stratified split - DT["split"] = None - for b in sorted(DT["strat_bin"].unique()): - idx = DT[DT["strat_bin"] == b].index.tolist() + df["split"] = None + for b in sorted(df["strat_bin"].unique()): + idx = df[df["strat_bin"] == b].index.tolist() n_b = len(idx) n_tr = int(np.floor(0.70 * n_b)) n_val = int(np.floor(0.15 * n_b)) @@ -116,62 +84,55 @@ def main(): val_idx = idx[n_tr:n_tr + n_val] if n_val > 0 else [] te_idx = idx[n_tr + n_val:] - DT.loc[tr_idx, "split"] = "train" - DT.loc[val_idx, "split"] = "val" - DT.loc[te_idx, "split"] = "test" + df.loc[tr_idx, "split"] = "train" + df.loc[val_idx, "split"] = "val" + df.loc[te_idx, "split"] = "test" - DT["split"] = DT["split"].fillna("train") + df["split"] = df["split"].fillna("train") - # Hold-out test set - DT_test = DT[DT["split"] == "test"] - X_test = DT_test[features].values.astype(np.float64) - y_test = DT_test["eir_log10"].values + df_test = df[df["split"] == "test"] + X_test = df_test[features].values.astype(np.float64) + y_test = df_test["eir_log10"].values obs_eir_test = np.power(10, y_test) - ts("Test set: %d rows", len(DT_test)) + ts("Test set: %d rows", len(df_test)) - # CV folds on train+val ts("Assigning %d-fold CV within TRAIN+VAL strata...", K_FOLDS) - DTcv = DT[DT["split"] != "test"].copy() + dfcv = df[df["split"] != "test"].copy() np.random.seed(SEED + 1) - # Assign folds within each stratum - DTcv["fold"] = 0 - for b in DTcv["strat_bin"].unique(): - mask = DTcv["strat_bin"] == b + dfcv["fold"] = 0 + for b in dfcv["strat_bin"].unique(): + mask = dfcv["strat_bin"] == b n_b = mask.sum() - idx = DTcv.index[mask].tolist() + idx = dfcv.index[mask].tolist() np.random.shuffle(idx) folds = np.tile(np.arange(1, K_FOLDS + 1), int(np.ceil(n_b / K_FOLDS)))[:n_b] np.random.shuffle(folds) - DTcv.loc[idx, "fold"] = folds + dfcv.loc[idx, "fold"] = folds - # K-fold CV training ts("Running %d-fold CV with early stopping...", K_FOLDS) - oof_pred_raw = np.full(len(DTcv), np.nan) + oof_pred_raw = np.full(len(dfcv), np.nan) best_iters = np.zeros(K_FOLDS, dtype=int) for k in range(1, K_FOLDS + 1): ts(" Fold %d / %d", k, K_FOLDS) - idx_val = DTcv["fold"] == k - idx_tr = DTcv["fold"] != k + idx_val = dfcv["fold"] == k + idx_tr = dfcv["fold"] != k - X_tr = DTcv.loc[idx_tr, features].values.astype(np.float64) - y_tr = DTcv.loc[idx_tr, "eir_log10"].values - X_va = DTcv.loc[idx_val, features].values.astype(np.float64) - y_va = DTcv.loc[idx_val, "eir_log10"].values + X_tr = dfcv.loc[idx_tr, features].values.astype(np.float64) + y_tr = dfcv.loc[idx_tr, "eir_log10"].values + X_va = dfcv.loc[idx_val, features].values.astype(np.float64) + y_va = dfcv.loc[idx_val, "eir_log10"].values - # Compute weights w_tr = make_value_weights(np.power(10, y_tr), digits=3) w_va = make_value_weights(np.power(10, y_va), digits=3) - # Create DMatrix objects dtr = xgb.DMatrix(X_tr, label=y_tr, weight=w_tr) dva = xgb.DMatrix(X_va, label=y_va, weight=w_va) - # Train model mdl = xgb.train( params=xgb_params, dtrain=dtr, @@ -182,21 +143,17 @@ def main(): ) best_iters[k - 1] = mdl.best_iteration - - # Predict on validation fold pred_log10_va = mdl.predict(dva) oof_pred_raw[idx_val.values] = np.power(10, pred_log10_va) - obs_cv_raw = np.power(10, DTcv["eir_log10"].values) + obs_cv_raw = np.power(10, dfcv["eir_log10"].values) - # Fit calibrator on OOF predictions ts("Fitting final calibrator (QMAP + positive scale) on OOF...") cal_oof = fit_qmap_w(oof_pred_raw, obs_cv_raw, ngrid=1024, round_digits=8) oof_pred_cal = predict_qmap_w(oof_pred_raw, cal_oof) a_oof = scale_pos(obs_cv_raw, oof_pred_cal) oof_pred_final = np.maximum(0, a_oof * oof_pred_cal) - # Save OOF metrics oof_metrics = pd.DataFrame({ "set": ["OOF_uncalibrated", "OOF_calibrated"], "R2": [r2(obs_cv_raw, oof_pred_raw), r2(obs_cv_raw, oof_pred_final)], @@ -207,14 +164,13 @@ def main(): oof_metrics.to_csv(dir_metric / f"eir_OOF_metrics_K{K_FOLDS}CV.csv", index=False) print("\n" + str(oof_metrics)) - # Train final model on TRAIN+VAL ts("Training final model on TRAIN+VAL with nrounds = median(best_iteration)...") best_nrounds = int(np.round(np.median(best_iters))) print(f"Best nrounds: {best_nrounds}") - DT_trcv = DT[DT["split"] != "test"] - X_trcv = DT_trcv[features].values.astype(np.float64) - y_trcv = DT_trcv["eir_log10"].values + df_trcv = df[df["split"] != "test"] + X_trcv = df_trcv[features].values.astype(np.float64) + y_trcv = df_trcv["eir_log10"].values w_trcv = make_value_weights(np.power(10, y_trcv), digits=3) dtrcv = xgb.DMatrix(X_trcv, label=y_trcv, weight=w_trcv) @@ -225,16 +181,14 @@ def main(): num_boost_round=best_nrounds, verbose_eval=False, ) - xgb_final.save_model(str(dir_models / "eir_xgb_HBR_FINAL.model")) + xgb_final.save_model(str(OUTPUT_DIR / "eir_xgb_HBR_FINAL.model")) - # Predict on TEST and calibrate dtest = xgb.DMatrix(X_test, label=y_test) pred_log10_test_raw = xgb_final.predict(dtest) pred_raw_test = np.power(10, pred_log10_test_raw) pred_eir_test = predict_qmap_w(pred_raw_test, cal_oof) pred_eir_test = np.maximum(0, a_oof * pred_eir_test) - # Test set metrics test_metrics = pd.DataFrame({ "set": ["Test"], "R2": [r2(obs_eir_test, pred_eir_test)], @@ -245,7 +199,6 @@ def main(): test_metrics.to_csv(dir_metric / "eir_test_metrics.csv", index=False) print("\n" + str(test_metrics)) - # Save diagnostic plots plot_obs_pred( obs_eir_test, pred_eir_test, f"EIR — Observed vs Predicted (XGBoost HBR, K={K_FOLDS} CV, QMAP+Scale, test)", @@ -253,14 +206,12 @@ def main(): xlab="Observed EIR", ylab="Predicted EIR" ) - # Create calibration bundle cal_bundle = { "kind": "qmap+scale", "qmap": {"xq": cal_oof["xq"], "yq": cal_oof["yq"]}, "scale": a_oof } - # Create preprocessing metadata preprocess = { "features": features, "target": "eir", @@ -270,9 +221,9 @@ def main(): "note": "Trained on MINTelligence data, HBR > 0 (Im > 0)" }, "training_data": { - "source": "MINTelligence HBR_malaria_simulations_4096.duckdb", - "n_rows": len(DT), - "n_params": DT["parameter_index"].nunique() + "source": "datasets/estimint_simulations_y9.parquet (hbr_y9 > 0)", + "n_rows": len(df), + "n_params": df["parameter_index"].nunique() }, "cv": { "K": K_FOLDS, @@ -281,7 +232,6 @@ def main(): }, } - # Create model bundle model_bundle = { "class": "estiMINT_HBR_model", "booster": xgb_final, @@ -291,18 +241,12 @@ def main(): "preprocess": preprocess, } - # Save pickle bundle - with open(dir_models / "estiMINT_HBR_model.pkl", "wb") as f: + with open(OUTPUT_DIR / "estiMINT_HBR_model.pkl", "wb") as f: pickle.dump(model_bundle, f, protocol=pickle.HIGHEST_PROTOCOL) - print("\n" + "=" * 80) - print("Training Complete!") - print("=" * 80) - print(f"\nModel saved to: {dir_models}/estiMINT_HBR_model.pkl") - print(f"Plots saved to: {dir_plots}/") - print(f"Metrics saved to: {dir_metric}/") - + print(f"\nModel saved to: {OUTPUT_DIR}/estiMINT_HBR_model.pkl") return 0 + if __name__ == "__main__": sys.exit(main()) diff --git a/models/prevalence/README.md b/models/prevalence/README.md new file mode 100644 index 0000000..e43e317 --- /dev/null +++ b/models/prevalence/README.md @@ -0,0 +1,13 @@ +# models/prevalence + +Prevalence → EIR model — the default estiMINT emulator. + +Predicts **EIR** from year-9 prevalence (`prev_y9`) + 6 interventions. Bundled as +`prevalence` → `estiMINT_model.pkl`. + +```bash +python models/prevalence/prepare.py # datasets source -> training.parquet (prev_y9 >= 0.02) +python models/prevalence/train.py # -> estiMINT_model.pkl, eir_xgb_FINAL.model, metrics/, plots/ +``` + +Deployed copy lives in `src/estimint/data/estiMINT_model.pkl`. diff --git a/models/prevalence/prepare.py b/models/prevalence/prepare.py new file mode 100644 index 0000000..08b6675 --- /dev/null +++ b/models/prevalence/prepare.py @@ -0,0 +1,30 @@ +"""Derive the prevalence->EIR training view from datasets/estimint_simulations_y9.parquet. + +Filters prev_y9 >= 0.02 and sorts by key for a deterministic, reproducible view. +""" + +from pathlib import Path + +import pandas as pd + +ROOT = Path(__file__).parents[2] +SOURCE = ROOT / "datasets" / "estimint_simulations_y9.parquet" +OUT = Path(__file__).parent / "training.parquet" + +KEYS = ["parameter_index", "simulation_index"] +COLS = KEYS + ["eir", "dn0_use", "Q0", "phi_bednets", "seasonal", "itn_use", "irs_use", "prev_y9"] +MIN_PREVALENCE = 0.02 + + +def prepare(): + src = pd.read_parquet(SOURCE) + view = src[src.prev_y9 >= MIN_PREVALENCE][COLS].sort_values(KEYS).reset_index(drop=True) + + assert len(view) == 12429, f"expected 12,429 rows, got {len(view):,}" + assert not view.isna().any().any(), "unexpected NaN in prevalence view" + view.to_parquet(OUT, index=False) + print(f"prevalence view: {len(view):,} rows -> {OUT}") + + +if __name__ == "__main__": + prepare() diff --git a/scripts/train_on_mint_data.py b/models/prevalence/train.py similarity index 55% rename from scripts/train_on_mint_data.py rename to models/prevalence/train.py index 2c73df8..3cdb65c 100644 --- a/scripts/train_on_mint_data.py +++ b/models/prevalence/train.py @@ -1,10 +1,8 @@ -""" -Retrain estiMINT using MINTelligence data. +"""Train the prevalence->EIR model (prev_y9 + interventions -> EIR). -This script: -1. Loads the prepared data from prepare_mint_data.py -2. Trains XGBoost model using K-fold CV and QMAP calibration -3. Saves model to output/mint_retrained/ +XGBoost with k-means strata on log10(EIR), 10-fold CV, and QMAP+scale calibration. +Reads models/prevalence/training.parquet; writes the model, booster, metrics and plot +into this folder. """ import sys @@ -15,64 +13,37 @@ from pathlib import Path from sklearn.cluster import KMeans -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent / "src")) +sys.path.insert(0, str(Path(__file__).parents[2] / "src")) from estimint.utils import ( - ts, r2, rmse, mse, mae, median_ae, mae_rel, rmsle, - safe_div, smape, fit_qmap_w, predict_qmap_w, scale_pos + ts, r2, rmse, mae, fit_qmap_w, predict_qmap_w, scale_pos ) from estimint.data_processing import make_value_weights from estimint.plotting import plot_obs_pred -# Configuration -DATA_PATH = "/home/cosmo/Documents/Repos/estimint/output/mint_data/mint_training_data.csv" -OUTPUT_DIR = "/home/cosmo/Documents/Repos/estimint/output/mint_retrained" +HERE = Path(__file__).parent +DATA_PATH = HERE / "training.parquet" +OUTPUT_DIR = HERE K_FOLDS = 10 K_STRATA = 16 SEED = 42 + def main(): - print("=" * 80) - print("Training estiMINT on MINTelligence Data") - print("=" * 80) - - # Check if data exists - data_path = Path(DATA_PATH) - if not data_path.exists(): - print(f"ERROR: Training data not found at {DATA_PATH}") - print("Please run prepare_mint_data.py first") - return 1 - - print(f"\nInput data: {data_path}") - print(f"Output dir: {OUTPUT_DIR}") - - # Create output directories - out_dir = Path(OUTPUT_DIR) - dir_models = out_dir / "models" - dir_plots = out_dir / "plots" - dir_metric = out_dir / "metrics" - dir_pred = out_dir / "predictions" - - for d in [dir_models, dir_plots, dir_metric, dir_pred]: + dir_plots = OUTPUT_DIR / "plots" + dir_metric = OUTPUT_DIR / "metrics" + for d in [dir_plots, dir_metric]: d.mkdir(parents=True, exist_ok=True) - # Load data ts("Loading training data...") - DT = pd.read_csv(data_path) - print(f"Loaded {len(DT):,} rows") + df = pd.read_parquet(DATA_PATH) + print(f"Loaded {len(df):,} rows") - # Define features features = ["dn0_use", "Q0", "phi_bednets", "seasonal", "itn_use", "irs_use", "prev_y9"] - # Transform EIR to log10 - DT["eir_log10"] = np.log10(DT["eir"]) + df["eir_log10"] = np.log10(df["eir"]) - # XGBoost parameters - # Monotone constraint: prev_y9 (index 6) must be positively correlated with EIR - # Higher prevalence → more infected humans → more transmission → higher EIR - # Features: ["dn0_use", "Q0", "phi_bednets", "seasonal", "itn_use", "irs_use", "prev_y9"] - # 0 0 0 0 0 0 +1 + # monotone constraint: prev_y9 (index 6) positively correlated with EIR xgb_params = { "objective": "reg:squarederror", "eval_metric": "rmse", @@ -88,24 +59,21 @@ def main(): "monotone_constraints": "(0,0,0,0,0,0,1)", } - # Create strata using k-means on log10(EIR) ts("Creating %d strata on log10(EIR) and 70/15/15 split...", K_STRATA) np.random.seed(SEED) - eir_log10 = DT["eir_log10"].values.reshape(-1, 1) + eir_log10 = df["eir_log10"].values.reshape(-1, 1) km = KMeans(n_clusters=K_STRATA, n_init=50, max_iter=5000, random_state=SEED) km.fit(eir_log10) - # Reorder cluster IDs by center value centers = km.cluster_centers_.flatten() ord_idx = np.argsort(centers) id_map = {old_id: new_id + 1 for new_id, old_id in enumerate(ord_idx)} - DT["strat_bin"] = np.array([id_map[c] for c in km.labels_]) + df["strat_bin"] = np.array([id_map[c] for c in km.labels_]) - # Stratified split - DT["split"] = None - for b in sorted(DT["strat_bin"].unique()): - idx = DT[DT["strat_bin"] == b].index.tolist() + df["split"] = None + for b in sorted(df["strat_bin"].unique()): + idx = df[df["strat_bin"] == b].index.tolist() n_b = len(idx) n_tr = int(np.floor(0.70 * n_b)) n_val = int(np.floor(0.15 * n_b)) @@ -116,62 +84,55 @@ def main(): val_idx = idx[n_tr:n_tr + n_val] if n_val > 0 else [] te_idx = idx[n_tr + n_val:] - DT.loc[tr_idx, "split"] = "train" - DT.loc[val_idx, "split"] = "val" - DT.loc[te_idx, "split"] = "test" + df.loc[tr_idx, "split"] = "train" + df.loc[val_idx, "split"] = "val" + df.loc[te_idx, "split"] = "test" - DT["split"] = DT["split"].fillna("train") + df["split"] = df["split"].fillna("train") - # Hold-out test set - DT_test = DT[DT["split"] == "test"] - X_test = DT_test[features].values.astype(np.float64) - y_test = DT_test["eir_log10"].values + df_test = df[df["split"] == "test"] + X_test = df_test[features].values.astype(np.float64) + y_test = df_test["eir_log10"].values obs_eir_test = np.power(10, y_test) - ts("Test set: %d rows", len(DT_test)) + ts("Test set: %d rows", len(df_test)) - # CV folds on train+val ts("Assigning %d-fold CV within TRAIN+VAL strata...", K_FOLDS) - DTcv = DT[DT["split"] != "test"].copy() + dfcv = df[df["split"] != "test"].copy() np.random.seed(SEED + 1) - # Assign folds within each stratum - DTcv["fold"] = 0 - for b in DTcv["strat_bin"].unique(): - mask = DTcv["strat_bin"] == b + dfcv["fold"] = 0 + for b in dfcv["strat_bin"].unique(): + mask = dfcv["strat_bin"] == b n_b = mask.sum() - idx = DTcv.index[mask].tolist() + idx = dfcv.index[mask].tolist() np.random.shuffle(idx) folds = np.tile(np.arange(1, K_FOLDS + 1), int(np.ceil(n_b / K_FOLDS)))[:n_b] np.random.shuffle(folds) - DTcv.loc[idx, "fold"] = folds + dfcv.loc[idx, "fold"] = folds - # K-fold CV training ts("Running %d-fold CV with early stopping...", K_FOLDS) - oof_pred_raw = np.full(len(DTcv), np.nan) + oof_pred_raw = np.full(len(dfcv), np.nan) best_iters = np.zeros(K_FOLDS, dtype=int) for k in range(1, K_FOLDS + 1): ts(" Fold %d / %d", k, K_FOLDS) - idx_val = DTcv["fold"] == k - idx_tr = DTcv["fold"] != k + idx_val = dfcv["fold"] == k + idx_tr = dfcv["fold"] != k - X_tr = DTcv.loc[idx_tr, features].values.astype(np.float64) - y_tr = DTcv.loc[idx_tr, "eir_log10"].values - X_va = DTcv.loc[idx_val, features].values.astype(np.float64) - y_va = DTcv.loc[idx_val, "eir_log10"].values + X_tr = dfcv.loc[idx_tr, features].values.astype(np.float64) + y_tr = dfcv.loc[idx_tr, "eir_log10"].values + X_va = dfcv.loc[idx_val, features].values.astype(np.float64) + y_va = dfcv.loc[idx_val, "eir_log10"].values - # Compute weights w_tr = make_value_weights(np.power(10, y_tr), digits=3) w_va = make_value_weights(np.power(10, y_va), digits=3) - # Create DMatrix objects dtr = xgb.DMatrix(X_tr, label=y_tr, weight=w_tr) dva = xgb.DMatrix(X_va, label=y_va, weight=w_va) - # Train model mdl = xgb.train( params=xgb_params, dtrain=dtr, @@ -182,21 +143,17 @@ def main(): ) best_iters[k - 1] = mdl.best_iteration - - # Predict on validation fold pred_log10_va = mdl.predict(dva) oof_pred_raw[idx_val.values] = np.power(10, pred_log10_va) - obs_cv_raw = np.power(10, DTcv["eir_log10"].values) + obs_cv_raw = np.power(10, dfcv["eir_log10"].values) - # Fit calibrator on OOF predictions ts("Fitting final calibrator (QMAP + positive scale) on OOF...") cal_oof = fit_qmap_w(oof_pred_raw, obs_cv_raw, ngrid=1024, round_digits=8) oof_pred_cal = predict_qmap_w(oof_pred_raw, cal_oof) a_oof = scale_pos(obs_cv_raw, oof_pred_cal) oof_pred_final = np.maximum(0, a_oof * oof_pred_cal) - # Save OOF metrics oof_metrics = pd.DataFrame({ "set": ["OOF_uncalibrated", "OOF_calibrated"], "R2": [r2(obs_cv_raw, oof_pred_raw), r2(obs_cv_raw, oof_pred_final)], @@ -207,14 +164,13 @@ def main(): oof_metrics.to_csv(dir_metric / f"eir_OOF_metrics_K{K_FOLDS}CV.csv", index=False) print("\n" + str(oof_metrics)) - # Train final model on TRAIN+VAL ts("Training final model on TRAIN+VAL with nrounds = median(best_iteration)...") best_nrounds = int(np.round(np.median(best_iters))) print(f"Best nrounds: {best_nrounds}") - DT_trcv = DT[DT["split"] != "test"] - X_trcv = DT_trcv[features].values.astype(np.float64) - y_trcv = DT_trcv["eir_log10"].values + df_trcv = df[df["split"] != "test"] + X_trcv = df_trcv[features].values.astype(np.float64) + y_trcv = df_trcv["eir_log10"].values w_trcv = make_value_weights(np.power(10, y_trcv), digits=3) dtrcv = xgb.DMatrix(X_trcv, label=y_trcv, weight=w_trcv) @@ -225,16 +181,14 @@ def main(): num_boost_round=best_nrounds, verbose_eval=False, ) - xgb_final.save_model(str(dir_models / "eir_xgb_FINAL.model")) + xgb_final.save_model(str(OUTPUT_DIR / "eir_xgb_FINAL.model")) - # Predict on TEST and calibrate dtest = xgb.DMatrix(X_test, label=y_test) pred_log10_test_raw = xgb_final.predict(dtest) pred_raw_test = np.power(10, pred_log10_test_raw) pred_eir_test = predict_qmap_w(pred_raw_test, cal_oof) pred_eir_test = np.maximum(0, a_oof * pred_eir_test) - # Test set metrics test_metrics = pd.DataFrame({ "set": ["Test"], "R2": [r2(obs_eir_test, pred_eir_test)], @@ -245,7 +199,6 @@ def main(): test_metrics.to_csv(dir_metric / "eir_test_metrics.csv", index=False) print("\n" + str(test_metrics)) - # Save diagnostic plots plot_obs_pred( obs_eir_test, pred_eir_test, f"EIR — Observed vs Predicted (XGBoost, K={K_FOLDS} CV, QMAP+Scale, test)", @@ -253,14 +206,12 @@ def main(): xlab="Observed EIR", ylab="Predicted EIR" ) - # Create calibration bundle cal_bundle = { "kind": "qmap+scale", "qmap": {"xq": cal_oof["xq"], "yq": cal_oof["yq"]}, "scale": a_oof } - # Create preprocessing metadata preprocess = { "features": features, "target": "eir", @@ -271,9 +222,9 @@ def main(): "note": "Trained on MINTelligence data with prev >= 0.02" }, "training_data": { - "source": "MINTelligence malaria_simulations_4096.duckdb", - "n_rows": len(DT), - "n_params": DT["parameter_index"].nunique() + "source": "datasets/estimint_simulations_y9.parquet (prev_y9 >= 0.02)", + "n_rows": len(df), + "n_params": df["parameter_index"].nunique() }, "cv": { "K": K_FOLDS, @@ -282,7 +233,6 @@ def main(): }, } - # Create model bundle model_bundle = { "class": "estiMINT_model", "booster": xgb_final, @@ -292,27 +242,12 @@ def main(): "preprocess": preprocess, } - # Save pickle bundle - with open(dir_models / "estiMINT_model.pkl", "wb") as f: + with open(OUTPUT_DIR / "estiMINT_model.pkl", "wb") as f: pickle.dump(model_bundle, f, protocol=pickle.HIGHEST_PROTOCOL) - print("\n" + "=" * 80) - print("✓ Training Complete!") - print("=" * 80) - print(f"\nModel saved to: {dir_models}/estiMINT_model.pkl") - print(f"Plots saved to: {dir_plots}/") - print(f"Metrics saved to: {dir_metric}/") - - print("\n=== Next Steps ===") - print("To test the new model:") - print(f" export ESTIMINT_MODELS_DIR={dir_models}") - print(" cd /home/cosmo/Documents/Repos/MINTelligence/tst") - print(" python API_check.py") - print("\nTo compare with old model:") - print(" unset ESTIMINT_MODELS_DIR") - print(" python API_check.py") - + print(f"\nModel saved to: {OUTPUT_DIR}/estiMINT_model.pkl") return 0 + if __name__ == "__main__": sys.exit(main()) diff --git a/pyproject.toml b/pyproject.toml index 6d20c39..a394c56 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,13 +4,14 @@ build-backend = "setuptools.build_meta" [project] name = "estimint" -version = "1.3.1" -description = "EIR Estimation using Machine learning INTerventions - Python port" +version = "1.5.2" +description = "EIR Estimation using Machine learning interventions " readme = "README.md" license = "MIT" -requires-python = ">=3.9" +requires-python = ">=3.12" authors = [ {name = "Cosmo Santoni", email = "cosmo.santoni@imperial.ac.uk"}, + {name = "Anmol Thapar", email = "a.thapar@imperial.ac.uk"} ] keywords = [ "malaria", @@ -19,28 +20,33 @@ keywords = [ "xgboost", "epidemiology", ] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Science/Research", - "Operating System :: OS Independent", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Topic :: Scientific/Engineering :: Bio-Informatics", -] dependencies = [ "numpy>=1.20.0", "pandas>=1.3.0", - "duckdb>=0.8.0", "xgboost>=1.6.0", + "scipy>=1.7.0", +] + +[project.optional-dependencies] +train = [ + "duckdb>=0.8.0", "scikit-learn>=1.0.0", + "pyarrow>=10.0.0", +] +viz = [ "matplotlib>=3.4.0", ] +download = [ + "requests>=2.28.0", + "appdirs>=1.4.0", +] +scenarios = [ + "mintstate>=0.3.0" +] +all = ["estimint[train,viz,download,scenarios]"] -[project.optional-dependencies] +[dependency-groups] dev = [ "pytest>=7.0.0", "pytest-cov>=4.0.0", @@ -49,10 +55,6 @@ dev = [ "mypy>=1.0.0", "flake8>=6.0.0", ] -download = [ - "requests>=2.28.0", - "appdirs>=1.4.0", -] [project.urls] Homepage = "https://github.com/CosmoNaught/estiMINT" @@ -65,8 +67,8 @@ where = ["src"] [tool.setuptools.package-data] estimint = [ - "*.txt", - "*.csv", + "*.txt", + "*.csv", "*.pkl", "*.json", "data/*", @@ -77,14 +79,14 @@ estimint = [ [tool.black] line-length = 100 -target-version = ["py39", "py310", "py311", "py312"] +target-version = ["py312", "py313"] [tool.isort] profile = "black" line_length = 100 [tool.mypy] -python_version = "3.9" +python_version = "3.12" warn_return_any = true warn_unused_configs = true ignore_missing_imports = true diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 038d617..0000000 --- a/requirements.txt +++ /dev/null @@ -1,19 +0,0 @@ -# Core dependencies -numpy>=1.20.0 -pandas>=1.3.0 -duckdb>=0.8.0 -xgboost>=1.6.0 -scikit-learn>=1.0.0 -matplotlib>=3.4.0 - -# Optional: for model download functionality -requests>=2.28.0 -appdirs>=1.4.0 - -# Optional: development dependencies -# pytest>=7.0.0 -# pytest-cov>=4.0.0 -# black>=23.0.0 -# isort>=5.12.0 -# mypy>=1.0.0 -# flake8>=6.0.0 diff --git a/scripts/prepare_eir_to_hbr_data.py b/scripts/prepare_eir_to_hbr_data.py deleted file mode 100644 index 83de699..0000000 --- a/scripts/prepare_eir_to_hbr_data.py +++ /dev/null @@ -1,60 +0,0 @@ -""" -Prepare training data for EIR -> HBR model. - -This script: -1. Loads mint_training_data.csv (has eir, interventions, prev_y9) -2. Loads hbr_training_data.csv (has eir, interventions, hbr_y9) -3. Joins on (parameter_index, simulation_index) -4. Outputs training data for eir + interventions -> hbr_y9 model -""" - -import pandas as pd -from pathlib import Path - -# Configuration -PREV_DATA = "/home/cosmo/Documents/Repos/estimint/output/mint_data/mint_training_data.csv" -HBR_DATA = "/home/cosmo/Documents/Repos/estimint/output/hbr_data/hbr_training_data.csv" -OUTPUT_DIR = Path("/home/cosmo/Documents/Repos/estimint/output/eir_to_hbr_data") - - -def prepare_data(): - print("Loading prevalence training data...") - df_prev = pd.read_csv(PREV_DATA) - print(f" {len(df_prev):,} rows") - - print("Loading HBR training data...") - df_hbr = pd.read_csv(HBR_DATA) - print(f" {len(df_hbr):,} rows") - - # Join on (parameter_index, simulation_index) - print("\nJoining on (parameter_index, simulation_index)...") - df = df_prev.merge( - df_hbr[["parameter_index", "simulation_index", "hbr_y9"]], - on=["parameter_index", "simulation_index"], - how="inner" - ) - print(f"Joined: {len(df):,} rows") - - # Keep: eir, interventions, hbr_y9 (target) - cols = ["parameter_index", "simulation_index", - "eir", "dn0_use", "Q0", "phi_bednets", "seasonal", - "itn_use", "irs_use", "hbr_y9"] - df = df[cols].copy() - - # Stats - print("\n=== Data Statistics ===") - print(f"EIR range: [{df['eir'].min():.2f}, {df['eir'].max():.2f}]") - print(f"HBR range: [{df['hbr_y9'].min():.2f}, {df['hbr_y9'].max():.2f}]") - print(f"Rows: {len(df):,}") - - # Save - OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - csv_path = OUTPUT_DIR / "eir_to_hbr_training_data.csv" - df.to_csv(csv_path, index=False) - print(f"\nCSV saved to {csv_path}") - - return csv_path - - -if __name__ == "__main__": - prepare_data() diff --git a/scripts/prepare_hbr_data.py b/scripts/prepare_hbr_data.py deleted file mode 100644 index ecfc8a9..0000000 --- a/scripts/prepare_hbr_data.py +++ /dev/null @@ -1,136 +0,0 @@ -""" -Extract training data for HBR-based estiMINT model. - -This script: -1. Reads HBR_malaria_simulations_4096.duckdb (n_bitten, EIR_Anopheles, total_M, Im) -2. Reads malaria_simulations_4096.duckdb (intervention params + static EIR target) -3. Computes year 9 annual mean HBR = EIR_Anopheles * total_M / Im -4. Joins with intervention parameters -5. Saves as CSV for training -""" - -import duckdb -import pandas as pd -import numpy as np -from pathlib import Path - -# Configuration -HBR_DB_PATH = "/home/cosmo/Documents/Repos/MINT_DATA/HBR_malaria_simulations_4096.duckdb" -ORIG_DB_PATH = "/home/cosmo/Documents/Repos/MINT_DATA/malaria_simulations_4096.duckdb" -OUTPUT_DIR = Path("/home/cosmo/Documents/Repos/estimint/output/hbr_data") -INTERVENTION_DAY = 3285 # Day 3285 = 9 years -YEAR9_START = INTERVENTION_DAY - 365 # Day 2920 = start of year 9 - - -def extract_year9_data(): - """Extract year 9 HBR data from all simulations.""" - - # --- Step 1: Extract HBR from HBR database --- - print(f"Connecting to HBR DB: {HBR_DB_PATH}...") - conn_hbr = duckdb.connect(HBR_DB_PATH, read_only=True) - - total_rows, total_params = conn_hbr.execute(""" - SELECT COUNT(*) as total_rows, - COUNT(DISTINCT parameter_index) as unique_params - FROM simulation_results - """).fetchone() - print(f"HBR DB: {total_rows:,} rows, {total_params:,} parameter sets") - - print(f"\nExtracting year 9 mean HBR (days {YEAR9_START}-{INTERVENTION_DAY - 1})...") - print("HBR = EIR_Anopheles * total_M_Anopheles / Im_Anopheles_count") - - hbr_query = f""" - SELECT - parameter_index, - simulation_index, - AVG( - CASE WHEN Im_Anopheles_count > 0 - THEN EIR_Anopheles * total_M_Anopheles / Im_Anopheles_count - ELSE NULL - END - ) AS hbr_y9 - FROM simulation_results - WHERE timesteps >= {YEAR9_START} AND timesteps < {INTERVENTION_DAY} - GROUP BY parameter_index, simulation_index - """ - - print("Running HBR extraction query...") - df_hbr = conn_hbr.execute(hbr_query).fetchdf() - conn_hbr.close() - print(f"Extracted {len(df_hbr):,} rows from HBR DB") - - # --- Step 2: Extract intervention params from original database --- - print(f"\nConnecting to original DB: {ORIG_DB_PATH}...") - conn_orig = duckdb.connect(ORIG_DB_PATH, read_only=True) - - params_query = f""" - SELECT - parameter_index, - simulation_index, - MAX(eir) AS eir, - MAX(dn0_use) AS dn0_use, - MAX(Q0) AS Q0, - MAX(phi_bednets) AS phi_bednets, - MAX(seasonal) AS seasonal, - MAX(itn_use) AS itn_use, - MAX(irs_use) AS irs_use - FROM simulation_results - WHERE timesteps >= {YEAR9_START} AND timesteps < {INTERVENTION_DAY} - GROUP BY parameter_index, simulation_index - """ - - print("Running intervention params extraction query...") - df_params = conn_orig.execute(params_query).fetchdf() - conn_orig.close() - print(f"Extracted {len(df_params):,} rows from original DB") - - # --- Step 3: Join --- - print("\nJoining HBR with intervention parameters...") - df = df_params.merge(df_hbr, on=["parameter_index", "simulation_index"], how="inner") - print(f"Joined: {len(df):,} rows") - - # --- Step 4: Filter --- - # Remove rows where HBR is NaN (Im_Anopheles_count was 0 for all timesteps) - n_before = len(df) - df = df.dropna(subset=["hbr_y9"]) - n_nan = n_before - len(df) - if n_nan > 0: - print(f"Removed {n_nan} rows with NaN HBR (Im=0 throughout year 9)") - - # Remove rows with HBR <= 0 - n_before = len(df) - df = df[df["hbr_y9"] > 0].copy() - n_zero = n_before - len(df) - if n_zero > 0: - print(f"Removed {n_zero} rows with HBR <= 0") - - print(f"Final dataset: {len(df):,} rows") - - # --- Step 5: Statistics --- - print("\n=== Data Statistics ===") - print(f"EIR range: [{df['eir'].min():.2f}, {df['eir'].max():.2f}]") - print(f"HBR range: [{df['hbr_y9'].min():.2f}, {df['hbr_y9'].max():.2f}]") - print(f"dn0 range: [{df['dn0_use'].min():.4f}, {df['dn0_use'].max():.4f}]") - print(f"ITN range: [{df['itn_use'].min():.4f}, {df['itn_use'].max():.4f}]") - print(f"IRS range: [{df['irs_use'].min():.4f}, {df['irs_use'].max():.4f}]") - print(f"Seasonal: {df['seasonal'].value_counts().to_dict()}") - - # --- Step 6: Save --- - OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - - csv_path = OUTPUT_DIR / "hbr_training_data.csv" - df.to_csv(csv_path, index=False) - print(f"\nCSV saved to {csv_path}") - - try: - parquet_path = OUTPUT_DIR / "hbr_training_data.parquet" - df.to_parquet(parquet_path, index=False) - print(f"Parquet saved to {parquet_path}") - except ImportError: - pass - - return csv_path - - -if __name__ == "__main__": - extract_year9_data() diff --git a/scripts/prepare_mint_data.py b/scripts/prepare_mint_data.py deleted file mode 100644 index 6ac3512..0000000 --- a/scripts/prepare_mint_data.py +++ /dev/null @@ -1,95 +0,0 @@ -""" -Extract training data from MINTelligence DuckDB for estiMINT retraining. - -This script: -1. Reads malaria_simulations_4096.duckdb -2. Extracts year 9 annual mean prevalence (avg over days 2920-3284) for each parameter set -3. Filters prevalence >= 0.02 (per user policy) -4. Saves as parquet for estiMINT training -""" - -import duckdb -import pandas as pd -from pathlib import Path - -# Configuration -DUCKDB_PATH = "/home/cosmo/Documents/Repos/MINT_DATA/malaria_simulations_4096.duckdb" -OUTPUT_DIR = Path("/home/cosmo/Documents/Repos/estimint/output/mint_data") -INTERVENTION_DAY = 3285 # Day 3285 = 9 years -YEAR9_START = INTERVENTION_DAY - 365 # Day 2920 = start of year 9 -MIN_PREVALENCE = 0.02 # User policy: never allow prevalence < 2% - -def extract_year9_data(): - """Extract year 9 data from all simulations.""" - print(f"Connecting to {DUCKDB_PATH}...") - conn = duckdb.connect(DUCKDB_PATH) - - # Check data size - total_rows, total_params = conn.execute(""" - SELECT COUNT(*) as total_rows, - COUNT(DISTINCT parameter_index) as unique_params - FROM simulation_results - """).fetchone() - print(f"Database contains: {total_rows:,} rows, {total_params:,} parameter sets") - - # Extract year 9 annual mean prevalence for all parameter sets - # Average over the full year before intervention (days 2920-3284) - print(f"\nExtracting annual mean prevalence for year 9 (days {YEAR9_START}-{INTERVENTION_DAY - 1})...") - - query = f""" - SELECT - parameter_index, - simulation_index, - MAX(eir) AS eir, - MAX(dn0_use) AS dn0_use, - MAX(Q0) AS Q0, - MAX(phi_bednets) AS phi_bednets, - MAX(seasonal) AS seasonal, - MAX(itn_use) AS itn_use, - MAX(irs_use) AS irs_use, - AVG(prevalence) AS prev_y9 - FROM simulation_results - WHERE timesteps >= {YEAR9_START} AND timesteps < {INTERVENTION_DAY} - GROUP BY parameter_index, simulation_index - """ - - print("Running extraction query...") - df = conn.execute(query).fetchdf() - conn.close() - - print(f"Extracted {len(df):,} rows (one per parameter×simulation)") - - # Filter by prevalence - print(f"\nFiltering prevalence >= {MIN_PREVALENCE}...") - df_filtered = df[df['prev_y9'] >= MIN_PREVALENCE].copy() - print(f"After filter: {len(df_filtered):,} rows ({len(df) - len(df_filtered):,} removed)") - - # Show statistics - print("\n=== Data Statistics ===") - print(f"EIR range: [{df_filtered['eir'].min():.2f}, {df_filtered['eir'].max():.2f}]") - print(f"Prevalence range: [{df_filtered['prev_y9'].min():.4f}, {df_filtered['prev_y9'].max():.4f}]") - print(f"dn0 range: [{df_filtered['dn0_use'].min():.4f}, {df_filtered['dn0_use'].max():.4f}]") - print(f"ITN range: [{df_filtered['itn_use'].min():.4f}, {df_filtered['itn_use'].max():.4f}]") - print(f"IRS range: [{df_filtered['irs_use'].min():.4f}, {df_filtered['irs_use'].max():.4f}]") - print(f"Seasonal: {df_filtered['seasonal'].value_counts().to_dict()}") - - # Save as both CSV (for train_on_mint_data.py) and parquet - OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - - csv_path = OUTPUT_DIR / "mint_training_data.csv" - df_filtered.to_csv(csv_path, index=False) - print(f"\nCSV saved to {csv_path}") - - try: - parquet_path = OUTPUT_DIR / "mint_training_data.parquet" - df_filtered.to_parquet(parquet_path, index=False) - print(f"Parquet saved to {parquet_path}") - except ImportError: - pass - - output_path = csv_path - - return output_path - -if __name__ == "__main__": - extract_year9_data() diff --git a/src/estimint/__init__.py b/src/estimint/__init__.py index 022b0b9..0f23b4d 100644 --- a/src/estimint/__init__.py +++ b/src/estimint/__init__.py @@ -4,18 +4,15 @@ This package provides tools for training and running XGBoost models to predict Entomological Inoculation Rate (EIR) from malaria intervention data. -Equivalent to: estiMINT-package.R - -Dependencies (equivalent to R imports): -- pandas, polars (data.table equivalent) -- duckdb (DBI + duckdb) -- xgboost (xgboost) -- numpy (stats) -- matplotlib, seaborn (ggplot2) -- scikit-learn (stats::kmeans) +Dependencies +------------ +Core (inference): numpy, pandas, xgboost, scipy. +Optional extras: +- train: duckdb, scikit-learn, pyarrow (data prep + model training) +- viz: matplotlib (plotting) +- download: requests, appdirs (fetch published models) """ -__version__ = "1.3.1" __package_name__ = "estiMINT" # Public API exports @@ -53,10 +50,15 @@ bundle_model, ) -from .run import run_xgb_model +from .run import run_xgb_model, set_global_model, get_global_model from .hbr import estimate_eir_with_mosquito_delta +from .bednet import calculate_dn0, net_types, DN0Result + +from .scenarios import run_scenarios +from .types import Scenario, EirTarget + __all__ = [ # utils "ts", @@ -88,6 +90,16 @@ "bundle_model", # run "run_xgb_model", + "set_global_model", + "get_global_model", # hbr "estimate_eir_with_mosquito_delta", + # bednet + "calculate_dn0", + "net_types", + "DN0Result", + # scenarios + "run_scenarios", + "Scenario", + "EirTarget", ] diff --git a/src/estimint/bednet.py b/src/estimint/bednet.py new file mode 100644 index 0000000..8e5a2ff --- /dev/null +++ b/src/estimint/bednet.py @@ -0,0 +1,70 @@ +"""Map a bednet spec (net-type usage mix + resistance level) to dn0, via data/itn_dn0.csv.""" + +from __future__ import annotations + +from functools import lru_cache +from pathlib import Path +from typing import NamedTuple + +import numpy as np +import pandas as pd +from scipy.interpolate import UnivariateSpline + +_DATA = Path(__file__).parent / "data" / "itn_dn0.csv" + +# short alias -> canonical net-type name +_NET_TYPES = { + "py_only": "pyrethroid_only", + "py_pbo": "pyrethroid_pbo", + "py_pyrrole": "pyrethroid_pyrrole", + "py_ppf": "pyrethroid_ppf", +} +_NET_TYPES.update({v: v for v in _NET_TYPES.values()}) + + +class DN0Result(NamedTuple): + dn0: float + itn_use: float + + +@lru_cache(maxsize=1) +def _splines() -> dict[str, UnivariateSpline]: + """Interpolating resistance->dn0 spline per net type (loaded/fit once).""" + df = pd.read_csv(_DATA).sort_values("resistance") + return { + nt: UnivariateSpline(g["resistance"].to_numpy(), g["dn0"].to_numpy(), s=0) + for nt, g in df.groupby("net_type") + } + + +def net_types() -> list[str]: + """Net types available in the dn0 table.""" + return sorted(_splines()) + + +def calculate_dn0(resistance_level: float, **usage: float) -> DN0Result: + """Usage-weighted dn0 for a net-type mix at a resistance level. + + Net-type shares are keywords, e.g. ``calculate_dn0(0.5, py_only=0.4, py_pbo=0.6)``. + """ + if not usage: + raise ValueError("supply at least one = pair") + + mix: dict[str, float] = {} + for name, share in usage.items(): + canon = _NET_TYPES.get(name.lower()) + if canon is None: + raise ValueError(f"unknown net type: {name!r} (have {sorted(_NET_TYPES)})") + mix[canon] = mix.get(canon, 0.0) + share + + active = {nt: w for nt, w in mix.items() if w > 0} + if not active: + return DN0Result(0.0, 0.0) + + splines = _splines() + dn0 = float(np.average( + [float(splines[nt](resistance_level)) for nt in active], + weights=list(active.values()), + )) + itn_use = float(sum(w for nt, w in active.items() if nt.startswith("pyrethroid"))) + return DN0Result(dn0=dn0, itn_use=itn_use) diff --git a/src/estimint/data/itn_dn0.csv b/src/estimint/data/itn_dn0.csv new file mode 100644 index 0000000..b6815ee --- /dev/null +++ b/src/estimint/data/itn_dn0.csv @@ -0,0 +1,405 @@ +"resistance","dn0","net_type" +0,0.3376,"pyrethroid_only" +0.01,0.33695,"pyrethroid_only" +0.02,0.33625,"pyrethroid_only" +0.03,0.3353,"pyrethroid_only" +0.04,0.33435,"pyrethroid_only" +0.05,0.33345,"pyrethroid_only" +0.06,0.333,"pyrethroid_only" +0.07,0.332,"pyrethroid_only" +0.08,0.3311,"pyrethroid_only" +0.09,0.3306,"pyrethroid_only" +0.1,0.33,"pyrethroid_only" +0.11,0.329,"pyrethroid_only" +0.12,0.3282,"pyrethroid_only" +0.13,0.32745,"pyrethroid_only" +0.14,0.32665,"pyrethroid_only" +0.15,0.3255,"pyrethroid_only" +0.16,0.32475,"pyrethroid_only" +0.17,0.324,"pyrethroid_only" +0.18,0.3231,"pyrethroid_only" +0.19,0.322,"pyrethroid_only" +0.2,0.3207,"pyrethroid_only" +0.21,0.31985,"pyrethroid_only" +0.22,0.3189,"pyrethroid_only" +0.23,0.3181,"pyrethroid_only" +0.24,0.3171,"pyrethroid_only" +0.25,0.3163,"pyrethroid_only" +0.26,0.3154,"pyrethroid_only" +0.27,0.3143,"pyrethroid_only" +0.28,0.313,"pyrethroid_only" 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DataFrame with rows passing filters - 'DT_excluded': DataFrame with rows failing filters """ + try: + import duckdb + except ImportError: + raise ImportError( + "load_and_filter() requires duckdb. " + "Install the training extras: pip install estimint[train]" + ) + con = duckdb.connect(database=":memory:") try: @@ -141,9 +149,17 @@ def strata_and_split( pd.DataFrame DataFrame with added 'strat_bin' and 'split' columns """ + try: + from sklearn.cluster import KMeans + except ImportError: + raise ImportError( + "strata_and_split() requires scikit-learn. " + "Install the training extras: pip install estimint[train]" + ) + DT = DT.copy() np.random.seed(seed) - + # K-means clustering on log10(EIR) eir_log10 = DT["eir_log10"].values.reshape(-1, 1) km = KMeans(n_clusters=k_strata, n_init=50, max_iter=5000, random_state=seed) diff --git a/src/estimint/hbr.py b/src/estimint/hbr.py index b1fea14..894495b 100644 --- a/src/estimint/hbr.py +++ b/src/estimint/hbr.py @@ -11,9 +11,8 @@ 5. EIR_new = EIR_baseline * (EIR_scaled / EIR_roundtrip) """ -from typing import Dict, Any, Optional, Union +from typing import Dict, Any, Optional -import numpy as np import pandas as pd from .run import run_xgb_model diff --git a/src/estimint/models.py b/src/estimint/models.py index 788282c..c8850e1 100644 --- a/src/estimint/models.py +++ b/src/estimint/models.py @@ -4,7 +4,7 @@ Equivalent to: models.R """ -from typing import Dict, Any, Optional, Callable, List +from typing import Dict, Any, Optional import numpy as np import xgboost as xgb from numpy.typing import ArrayLike diff --git a/src/estimint/plotting.py b/src/estimint/plotting.py index 4533c01..ec26da5 100644 --- a/src/estimint/plotting.py +++ b/src/estimint/plotting.py @@ -4,13 +4,13 @@ Equivalent to: plotting.R """ -from typing import Optional from pathlib import Path import numpy as np -import matplotlib.pyplot as plt from numpy.typing import ArrayLike +# matplotlib imported lazily in plot_obs_pred (estimint[viz]). + def plot_obs_pred( obs: ArrayLike, @@ -45,9 +45,17 @@ def plot_obs_pred( None Saves plot to file """ + try: + import matplotlib.pyplot as plt + except ImportError: + raise ImportError( + "plot_obs_pred() requires matplotlib. " + "Install the plotting extras: pip install estimint[viz]" + ) + obs = np.asarray(obs) pred = np.asarray(pred) - + # Create figure fig, ax = plt.subplots(figsize=(7.5, 6), dpi=150) diff --git a/src/estimint/scenarios.py b/src/estimint/scenarios.py new file mode 100644 index 0000000..fd76d88 --- /dev/null +++ b/src/estimint/scenarios.py @@ -0,0 +1,174 @@ +"""One-call estiMINT -> stateMINT scenario runner (stateMINT imported lazily).""" + +from __future__ import annotations + +from typing import Any, Dict + +import numpy as np +import pandas as pd + +from .bednet import calculate_dn0, DN0Result +from .run import run_xgb_model +from .hbr import estimate_eir_with_mosquito_delta +from .storage import load_xgb_model +from .types import Scenario + +HF_REPO = "dide-ic/stateMINT" +# 157 windows of 14 days from day 2190; intervention at day 3285. +_ABS_T = 2190 + 14 * np.arange(157) +_IDX_Y9 = int(np.argmin(np.abs(_ABS_T - 3285))) + +_MODELS: Dict[str, Any] = {} +_EMULATORS: Dict[str, Dict[str, Any]] = {} + +_NET_KEYS = ( + "py_only", + "py_pbo", + "py_pyrrole", + "py_ppf", +) + + +def _bednet(scenario: Scenario): + """Current and future net (dn0, itn_use); returns (cur, net_now, net_next).""" + cur_nets = {net_type: getattr(scenario, net_type) for net_type in _NET_KEYS if getattr(scenario, net_type)} + + net_now = calculate_dn0(scenario.res_use, **cur_nets) if cur_nets else DN0Result(0.0, 0.0) + + net_type_future = scenario.net_type_future + if scenario.itn_future == 0.0 or not net_type_future: + net_next = DN0Result(0.0, 0.0) + else: + net_next = calculate_dn0(scenario.res_use, **{net_type_future: scenario.itn_future}) + return cur_nets, net_now, net_next + + +def _est_models() -> Dict[str, Any]: + if not _MODELS: + _MODELS["prevalence"] = load_xgb_model("prevalence") + _MODELS["hbr"] = load_xgb_model("hbr") + return _MODELS + + +def _emulators(hf_repo: str) -> Dict[str, Any]: + if hf_repo not in _EMULATORS: + try: + from stateMINT.model import Mamba2Regressor + except ImportError as e: + raise ImportError( + "run_scenarios needs stateMINT. Install it with: " + "uv sync --extra scenarios (or pip install " + '"git+https://github.com/mrc-ide/stateMINT.git@mamba2-train").' + ) from e + _EMULATORS[hf_repo] = { + p: Mamba2Regressor.from_pretrained(hf_repo, predictor=p) for p in ("prevalence", "cases") + } + return _EMULATORS[hf_repo] + + +_EIR_INPUT_COLS = {"prevalence": ("prev_y9", "prevalence"), "hbr": ("hbr_y9", "hbr")} + + +def _estimate_eir(scenario: Scenario, eir_models: Dict[str, Any]) -> Dict[str, Any]: + """scenario -> EIR + the stateMINT covariate dict.""" + cur_nets, net_now, net_next = _bednet(scenario) + dn0_use, itn_use = net_now.dn0, net_now.itn_use + dn0_future, itn_future = net_next.dn0, net_next.itn_use + + Q0, phi, seasonal = scenario.Q0, scenario.phi, scenario.seasonal + irs_use = scenario.irs + irs_future = scenario.irs_future + routine = scenario.routine + ppf = scenario.py_ppf + lsm = scenario.lsm + if ppf > 0: + lsm = min(ppf * 0.248 + lsm, 1.0) + feats = dict(dn0_use=dn0_use, Q0=Q0, phi_bednets=phi, seasonal=seasonal, itn_use=itn_use, irs_use=irs_use) + + # ── EIR by input mode ── + delta = scenario.mosquito_delta + hbr_baseline, hbr_new = np.nan, np.nan + input_mode, input_value = scenario.eir_target.input_mode, scenario.eir_target.input_value + + if input_mode == "eir": + eir_base = eir_final = input_value + elif delta and input_mode == "prevalence": + r = estimate_eir_with_mosquito_delta(prevalence=input_value, mosquito_delta=delta, **feats) + eir_base, eir_final = r["eir_baseline"], r["eir_new"] + hbr_baseline, hbr_new = r["hbr_baseline"], r["hbr_new"] + elif input_mode in _EIR_INPUT_COLS: + col, model_key = _EIR_INPUT_COLS[input_mode] + eir_base = eir_final = float( + run_xgb_model( + pd.DataFrame({**{k: [v] for k, v in feats.items()}, col: [input_value]}), + eir_models[model_key], + )[0] + ) + else: + raise ValueError(f"'input' must be 'prevalence', 'hbr' or 'eir'; got {input_mode!r}") + + cov = dict( + eir=eir_final, + dn0_use=dn0_use, + dn0_future=dn0_future, + Q0=Q0, + phi_bednets=phi, + seasonal=seasonal, + routine=routine, + itn_use=itn_use, + irs_use=irs_use, + itn_future=itn_future, + irs_future=irs_future, + lsm=lsm, + ) + row = dict( + name=scenario.name, + input_mode=input_mode, + net="+".join(cur_nets) or "none", + net_future=scenario.net_type_future or "none", + dn0_use=dn0_use, + itn_use=itn_use, + irs_use=irs_use, + dn0_future=dn0_future, + itn_future=itn_future, + irs_future=irs_future, + routine=routine, + lsm=lsm, + seasonal=seasonal, + eir_baseline=eir_base, + mosquito_delta=delta, + eir_final=eir_final, + hbr_baseline=hbr_baseline, + hbr_new=hbr_new, + ) + return {"row": row, "cov": cov} + + +def run_scenarios( + scenarios: list[Scenario], + *, + hf_repo: str = HF_REPO, +) -> pd.DataFrame: + """Run a list of scenarios through the estiMINT -> stateMINT pipeline.""" + + eir_models = _est_models() + emulator_models = _emulators(hf_repo) + + parts = [_estimate_eir(scenario, eir_models) for scenario in scenarios] + covs = [p["cov"] for p in parts] + prev = emulator_models["prevalence"].predict(covs) + cases = np.maximum(emulator_models["cases"].predict(covs), 0.0) + + return pd.DataFrame( + [ + { + **part["row"], + "prev_y9": float(p[_IDX_Y9]), + "prev_endline": float(p[-1]), + "cases_endline": float(c[-1]), + "prevalence": p, + "cases": c, + } + for part, p, c in zip(parts, prev, cases) + ] + ) diff --git a/src/estimint/train.py b/src/estimint/train.py index 2ed52db..447c74b 100644 --- a/src/estimint/train.py +++ b/src/estimint/train.py @@ -6,7 +6,7 @@ import pickle from pathlib import Path -from typing import Dict, Any, Optional, List +from typing import Dict, Any, Optional import numpy as np import pandas as pd diff --git a/src/estimint/types.py b/src/estimint/types.py new file mode 100644 index 0000000..10bc61a --- /dev/null +++ b/src/estimint/types.py @@ -0,0 +1,31 @@ +from typing import Literal +from dataclasses import dataclass + +Input_Mode = Literal["prevalence", "eir", "hbr"] + + +@dataclass +class EirTarget: + input_value: float + input_mode: Input_Mode = "prevalence" + + +@dataclass +class Scenario: + name: str + res_use: float + Q0: float + phi: float + seasonal: float + irs: float + eir_target: EirTarget + py_only: float = 0.0 + py_pbo: float = 0.0 + py_pyrrole: float = 0.0 + py_ppf: float = 0.0 + mosquito_delta: float = 0.0 + itn_future: float = 0.0 + net_type_future: str | None = None + irs_future: float = 0.0 + routine: float = 0.0 + lsm: float = 0.0 diff --git a/src/estimint/utils.py b/src/estimint/utils.py index d945b34..8e2a5e3 100644 --- a/src/estimint/utils.py +++ b/src/estimint/utils.py @@ -5,9 +5,8 @@ """ import sys -import os from datetime import datetime -from typing import Optional, Dict, Any, List, Union +from typing import Optional, Dict, Any, Union from pathlib import Path import numpy as np diff --git a/tests/smoke_test.py b/tests/smoke_test.py new file mode 100644 index 0000000..9f62637 --- /dev/null +++ b/tests/smoke_test.py @@ -0,0 +1,3 @@ +from estimint import run_scenarios + +print("import estimint: OK") diff --git a/tests/test_bednet.py b/tests/test_bednet.py new file mode 100644 index 0000000..34647c6 --- /dev/null +++ b/tests/test_bednet.py @@ -0,0 +1,52 @@ +"""Tests for the bednet -> dn0 estimator.""" + +import pytest + +from estimint import calculate_dn0, net_types +from estimint.bednet import DN0Result + + +class TestNetTypes: + def test_lists_four_canonical_types(self): + types = net_types() + assert types == [ + "pyrethroid_only", + "pyrethroid_pbo", + "pyrethroid_ppf", + "pyrethroid_pyrrole", + ] + + +class TestCalculateDn0: + def test_returns_dn0_result(self): + res = calculate_dn0(0.5, py_only=1.0) + assert isinstance(res, DN0Result) + assert 0.0 <= res.dn0 <= 1.0 + assert res.itn_use == 1.0 + + def test_short_and_canonical_names_agree(self): + assert calculate_dn0(0.5, py_only=1.0) == calculate_dn0(0.5, pyrethroid_only=1.0) + + def test_usage_weighted_average(self): + a = calculate_dn0(0.4, py_only=1.0).dn0 + b = calculate_dn0(0.4, py_pbo=1.0).dn0 + mixed = calculate_dn0(0.4, py_only=0.5, py_pbo=0.5).dn0 + assert mixed == pytest.approx((a + b) / 2) + + def test_itn_use_sums_pyrethroid_shares(self): + assert calculate_dn0(0.5, py_only=0.4, py_pbo=0.3, py_ppf=0.1).itn_use == pytest.approx(0.8) + + def test_resistance_lowers_dn0(self): + # plain pyrethroid nets lose efficacy as resistance rises + assert calculate_dn0(0.0, py_only=1.0).dn0 > calculate_dn0(1.0, py_only=1.0).dn0 + + def test_zero_mix_returns_zero(self): + assert calculate_dn0(0.5, py_only=0.0) == DN0Result(0.0, 0.0) + + def test_no_nets_raises(self): + with pytest.raises(ValueError): + calculate_dn0(0.5) + + def test_unknown_net_raises(self): + with pytest.raises(ValueError, match="unknown net type"): + calculate_dn0(0.5, py_supernet=1.0) diff --git a/tests/test_flows.py b/tests/test_flows.py new file mode 100644 index 0000000..e963b0d --- /dev/null +++ b/tests/test_flows.py @@ -0,0 +1,65 @@ +"""End-to-end flow tests: prevalence -> EIR, and the mosquito-delta HBR pipeline. + +These exercise the bundled models in src/estimint/data, so they run offline. +""" + +import pandas as pd + +from estimint import ( + load_xgb_model, + run_xgb_model, + estimate_eir_with_mosquito_delta, +) + +INTERVENTIONS = dict( + dn0_use=0.33, Q0=0.87, phi_bednets=0.82, + seasonal=0.0, itn_use=0.6, irs_use=0.0, +) + + +class TestPrevalenceToEir: + def test_predicts_positive_eir(self): + model = load_xgb_model("prevalence") + X = pd.DataFrame({"prev_y9": [0.30], **{k: [v] for k, v in INTERVENTIONS.items()}}) + eir = run_xgb_model(X, model) + assert len(eir) == 1 + assert eir[0] > 0 + + def test_higher_prevalence_gives_higher_eir(self): + model = load_xgb_model("prevalence") + rows = {"prev_y9": [0.10, 0.50], **{k: [v, v] for k, v in INTERVENTIONS.items()}} + eir = run_xgb_model(pd.DataFrame(rows), model) + assert eir[1] > eir[0] + + +class TestMosquitoDelta: + def _run(self, delta): + return estimate_eir_with_mosquito_delta( + prevalence=0.30, mosquito_delta=delta, **INTERVENTIONS + ) + + def test_returns_expected_keys(self): + res = self._run(0.25) + assert set(res) == { + "eir_baseline", "eir_new", "eir_multiplier", "hbr_baseline", "hbr_new", + } + + def test_zero_delta_is_identity(self): + res = self._run(0.0) + assert res["eir_new"] == res["eir_baseline"] + assert res["eir_multiplier"] == 1.0 + + def test_more_mosquitoes_raises_eir(self): + res = self._run(0.25) + assert res["eir_new"] > res["eir_baseline"] + assert res["eir_multiplier"] > 1.0 + assert res["hbr_new"] > res["hbr_baseline"] + + def test_fewer_mosquitoes_lowers_eir(self): + res = self._run(-0.50) + assert res["eir_new"] < res["eir_baseline"] + assert res["hbr_new"] < res["hbr_baseline"] + + def test_monotonic_in_delta(self): + eirs = [self._run(d)["eir_new"] for d in (-0.5, -0.25, 0.0, 0.25, 0.5, 1.0)] + assert eirs == sorted(eirs) diff --git a/tests/test_scenarios.py b/tests/test_scenarios.py new file mode 100644 index 0000000..bb18c34 --- /dev/null +++ b/tests/test_scenarios.py @@ -0,0 +1,143 @@ +"""Tests for the run_scenarios pipeline. + +The estiMINT half (_estimate_eir) is tested offline against the bundled models. +The full run_scenarios call also runs the stateMINT emulator, so it is skipped +unless stateMINT is installed. +""" + +from typing import Any + +import numpy as np +import pytest + +from estimint.scenarios import _estimate_eir, _est_models, run_scenarios +from estimint.types import EirTarget, Scenario + +INTV: dict[str, Any] = dict(Q0=0.87, phi=0.82, seasonal=0.0, irs=0.0) + + +def mk(**kwargs: Any) -> Scenario: + defaults: dict[str, Any] = dict(name="t", res_use=0.0, **INTV) + input_mode = kwargs.pop("input") + input_value = kwargs.pop("value") + defaults.update(kwargs) + return Scenario(eir_target=EirTarget(input_value, input_mode), **defaults) + + +@pytest.fixture(scope="module") +def est(): + return _est_models() + + +class TestEstimateEir: + def test_prevalence_input(self, est): + out = _estimate_eir(mk(input="prevalence", value=0.30), est) + assert out["row"]["eir_baseline"] > 0 + assert out["cov"]["eir"] == out["row"]["eir_final"] + # no nets, no delta + assert out["row"]["dn0_use"] == 0.0 + assert np.isnan(out["row"]["hbr_baseline"]) + + def test_eir_input_passes_through(self, est): + out = _estimate_eir(mk(input="eir", value=20.0), est) + assert out["row"]["eir_baseline"] == 20.0 + assert out["row"]["eir_final"] == 20.0 + + def test_hbr_input(self, est): + out = _estimate_eir(mk(input="hbr", value=250000.0), est) + assert out["row"]["eir_baseline"] > 0 + + def test_bednet_mix_matches_minte(self, est): + # net-type usage mix feeds calculate_dn0 directly, same as minte; itn_use + # is the sum of pyrethroid shares (NOT rescaled by coverage again). + out = _estimate_eir( + mk(input="prevalence", value=0.30, py_only=0.70, py_pbo=0.30, res_use=0.30), est) + assert out["row"]["dn0_use"] > 0 + assert out["row"]["itn_use"] == pytest.approx(1.00) + assert out["row"]["net"] == "py_only+py_pbo" + + def test_future_net_switch_is_separate_leg(self, est): + # net_type_future/itn_future drive the future leg, not current nets; + # the future leg shares the same res_use as current (no res_future field). + out = _estimate_eir( + mk(input="prevalence", value=0.30, py_only=0.50, res_use=0.30, + net_type_future="pyrethroid_pbo", itn_future=0.70), est)["row"] + assert out["itn_use"] == pytest.approx(0.50) # current: py_only=0.50 + assert out["itn_future"] == pytest.approx(0.70) # future: pbo=0.70 + assert out["dn0_use"] != out["dn0_future"] + assert out["net"] == "py_only" and out["net_future"] == "pyrethroid_pbo" + + def test_future_without_net_type_is_zeroed(self, est): + # no net_type_future named -> future leg is zeroed, even if itn_future is set + # (no carry-forward of the current mix; this is intentional, not a default) + out = _estimate_eir( + mk(input="prevalence", value=0.30, py_pbo=0.80, res_use=0.30, + itn_future=0.70), est)["row"] + assert out["dn0_future"] == 0.0 and out["itn_future"] == 0.0 + assert out["net_future"] == "none" + + def test_future_nets_removed(self, est): + # itn_future == 0 removes nets in the future leg + out = _estimate_eir( + mk(input="prevalence", value=0.30, py_only=0.60, res_use=0.30, + itn_future=0.0), est)["row"] + assert out["itn_use"] == pytest.approx(0.60) + assert out["dn0_future"] == 0.0 and out["itn_future"] == 0.0 + + def test_ppf_boosts_lsm(self, est): + # PPF nets add larviciding to LSM (minte: py_ppf * 0.248) + out = _estimate_eir( + mk(input="eir", value=15.0, py_ppf=0.50, res_use=0.30, lsm=0.10), est) + assert out["cov"]["lsm"] == pytest.approx(0.50 * 0.248 + 0.10) + + def test_irs_future_and_routine_are_inputs(self, est): + # irs_future and routine are separate scenario inputs, like minte + out = _estimate_eir( + mk(input="eir", value=15.0, irs=0.40, irs_future=0.10, routine=0.25), est)["cov"] + assert out["irs_use"] == 0.40 and out["irs_future"] == 0.10 + assert out["routine"] == 0.25 + + def test_irs_future_and_routine_defaults(self, est): + # irs_future is a static dataclass default (0.0); it does NOT carry irs + # forward automatically, even when irs is nonzero. + out = _estimate_eir(mk(input="eir", value=15.0, irs=0.40), est)["cov"] + assert out["irs_future"] == 0.0 + assert out["irs_use"] == 0.40 + assert out["routine"] == 0.0 + + def test_mosquito_delta_direction(self, est): + up = _estimate_eir(mk(input="prevalence", value=0.30, mosquito_delta=0.25), est) + down = _estimate_eir(mk(input="prevalence", value=0.30, mosquito_delta=-0.50), est) + assert up["row"]["eir_final"] > up["row"]["eir_baseline"] + assert down["row"]["eir_final"] < down["row"]["eir_baseline"] + assert up["row"]["hbr_new"] > up["row"]["hbr_baseline"] + + def test_covariate_dict_keys(self, est): + out = _estimate_eir(mk(input="eir", value=15.0, lsm=0.3), est) + assert set(out["cov"]) == { + "eir", "dn0_use", "dn0_future", "Q0", "phi_bednets", "seasonal", + "routine", "itn_use", "irs_use", "itn_future", "irs_future", "lsm", + } + assert out["cov"]["lsm"] == 0.3 + assert out["cov"]["dn0_future"] == out["cov"]["dn0_use"] + + def test_bad_input_raises(self, est): + with pytest.raises(ValueError, match="input"): + _estimate_eir(mk(input="nope", value=1.0), est) + + +class TestRunScenariosFullPipeline: + def test_end_to_end(self): + pytest.importorskip("stateMINT", reason="stateMINT not installed") + df = run_scenarios([ + Scenario(name="prev+delta", eir_target=EirTarget(0.30, "prevalence"), + py_only=0.60, res_use=0.55, net_type_future="pyrethroid_pbo", + itn_future=0.85, + Q0=0.90, phi=0.85, seasonal=1, irs=0.40, mosquito_delta=0.60), + Scenario(name="eir", eir_target=EirTarget(20.0, "eir"), res_use=0.0, + Q0=0.88, phi=0.78, seasonal=1, irs=0.60), + ]) + assert len(df) == 2 + assert {"eir_final", "prev_y9", "prevalence", "cases"} <= set(df.columns) + assert len(df.iloc[0]["prevalence"]) == 157 + assert (df["cases"].iloc[0] >= 0).all() diff --git a/uv.lock 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