diff --git a/.github/workflows/buildwheels.yml b/.github/workflows/deploy.yml similarity index 50% rename from .github/workflows/buildwheels.yml rename to .github/workflows/deploy.yml index bf65c9c..b7146bc 100644 --- a/.github/workflows/buildwheels.yml +++ b/.github/workflows/deploy.yml @@ -1,13 +1,16 @@ -name: Build Wheels +name: Build and Deploy to PyPI on: + workflow_dispatch: + pull_request: push: - tags: - - "v*.*.*" + release: + types: + - published jobs: build_wheels: - name: Build wheels on ${{ matrix.os }} + name: Build wheels for ${{ matrix.os }} runs-on: ${{ matrix.os }} strategy: matrix: @@ -28,38 +31,58 @@ jobs: persist-credentials: false - name: Install uv - uses: astral-sh/setup-uv@v7 + uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b with: - version: "0.10.11" + version: "0.11.20" - - name: Build wheels - uses: pypa/cibuildwheel@v3.4.0 - # env: - # CIBW_BUILD_FRONTEND: value - # ... - # with: - # package-dir: . - # output-dir: wheelhouse - # config-file: "{package}/pyproject.toml" + - name: Build Wheels + run: uvx cibuildwheel==4.0.0 + + # - name: Build wheels + # uses: pypa/cibuildwheel@v3.4.0 + # env: + # CIBW_BUILD_FRONTEND: value + # ... + # with: + # package-dir: . + # output-dir: wheelhouse + # config-file: "{package}/pyproject.toml" - uses: actions/upload-artifact@v4 with: name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }} path: ./wheelhouse/*.whl - publish: + build_sdist: + name: Build Source Distribution + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + with: + persist-credentials: false + - name: Build sdist + run: uvx build --sdist + - uses: actions/upload-artifact@v4 + with: + name: cibw-sdist + path: dist/*.tar.gz + + upload_pypi: name: Publish to PyPI - needs: build_wheels + needs: [build_wheels, build_sdist] runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/respondpy/ permissions: id-token: write - + if: github.event_name == 'release' && github.event.action == 'published' steps: - name: Download wheel artifacts uses: actions/download-artifact@v5 with: path: dist - pattern: cibw-wheels-* + pattern: cibw-* merge-multiple: true - name: Publish distributions to PyPI with attestations diff --git a/CMakeLists.txt b/CMakeLists.txt index 15c00c2..046082e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -45,7 +45,7 @@ set(Python_ARTIFACTS_INTERACTIVE TRUE) FetchContent_Declare( pybind11 GIT_REPOSITORY https://github.com/pybind/pybind11.git - GIT_TAG f5fbe867d2d26e4a0a9177a51f6e568868ad3dc8 # v3.0.1 + GIT_TAG d03662f0984f652b60e7ddce53d3868002275197 # v3.0.4 FIND_PACKAGE_ARGS NAMES pybind11) FetchContent_Declare( @@ -62,7 +62,7 @@ set(SPDLOG_INSTALL ON) FetchContent_Declare( respond GIT_REPOSITORY https://github.com/SyndemicsLab/respond.git - GIT_TAG 538f5b228f7b8781562057d390c378f0775b50fb # v2.3.1 + GIT_TAG f54256ce2b27ea09a19bfdf2395d46c2f04e99c8 # feature/documentation-and-logging OVERRIDE_FIND_PACKAGE ) set(RESPOND_BUILD_DOCS OFF) diff --git a/benchmarks/conftest.py b/benchmarks/conftest.py new file mode 100644 index 0000000..675af5d --- /dev/null +++ b/benchmarks/conftest.py @@ -0,0 +1,242 @@ +################################################################################ +# File: conftest.py # +# Project: respondpy # +# Created Date: 2026-05-06 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-05-06 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +""" +Shared fixtures for respondpy benchmarks. + +The database is created once per session (scope="session") so DB setup cost +is paid only once across all benchmark functions. +""" + +from __future__ import annotations + +import sqlite3 +from configparser import ConfigParser +from pathlib import Path + +import pytest + + +# --------------------------------------------------------------------------- +# Schema & seed data +# --------------------------------------------------------------------------- + +_DB_SCHEMA = """ +DROP TABLE IF EXISTS "intervention"; +CREATE TABLE "intervention" ( + "id" INTEGER NOT NULL UNIQUE, + "name" TEXT NOT NULL UNIQUE, + PRIMARY KEY("id" AUTOINCREMENT) +); +DROP TABLE IF EXISTS "behavior"; +CREATE TABLE "behavior" ( + "id" INTEGER NOT NULL UNIQUE, + "name" TEXT NOT NULL UNIQUE, + PRIMARY KEY("id" AUTOINCREMENT) +); +DROP TABLE IF EXISTS "background_mortality"; +CREATE TABLE "background_mortality" ( + "sample" INTEGER NOT NULL, + "time" INTEGER NOT NULL, + "probability" REAL NOT NULL DEFAULT 0.0, + PRIMARY KEY("sample","time") +); +DROP TABLE IF EXISTS "behavior_transition"; +CREATE TABLE "behavior_transition" ( + "sample" INTEGER NOT NULL, + "intervention" INTEGER NOT NULL, + "time" INTEGER NOT NULL, + "initial_behavior" INTEGER NOT NULL, + "new_behavior" INTEGER NOT NULL, + "probability" REAL NOT NULL DEFAULT 0.0, + PRIMARY KEY("sample","intervention","time","initial_behavior","new_behavior"), + FOREIGN KEY("initial_behavior") REFERENCES "behavior"("id"), + FOREIGN KEY("intervention") REFERENCES "intervention"("id"), + FOREIGN KEY("new_behavior") REFERENCES "behavior"("id") +); +DROP TABLE IF EXISTS "initial_population"; +CREATE TABLE "initial_population" ( + "sample" INTEGER NOT NULL, + "intervention" INTEGER NOT NULL, + "behavior" INTEGER NOT NULL, + "count" REAL NOT NULL DEFAULT 0.0, + PRIMARY KEY("sample","intervention","behavior") +); +DROP TABLE IF EXISTS "intervention_transition"; +CREATE TABLE "intervention_transition" ( + "sample" INTEGER NOT NULL, + "behavior" INTEGER NOT NULL, + "time" INTEGER NOT NULL, + "initial_intervention" INTEGER NOT NULL, + "new_intervention" INTEGER NOT NULL, + "probability" REAL NOT NULL DEFAULT 0.0, + PRIMARY KEY("sample","behavior","initial_intervention","new_intervention","time"), + FOREIGN KEY("behavior") REFERENCES "behavior"("id"), + FOREIGN KEY("initial_intervention") REFERENCES "intervention"("id"), + FOREIGN KEY("new_intervention") REFERENCES "intervention"("id") +); +DROP TABLE IF EXISTS "overdose"; +CREATE TABLE "overdose" ( + "intervention" INTEGER NOT NULL, + "sample" INTEGER NOT NULL, + "behavior" INTEGER NOT NULL, + "time" INTEGER NOT NULL, + "probability" REAL NOT NULL DEFAULT 0.0, + PRIMARY KEY("intervention","sample","behavior","time"), + FOREIGN KEY("behavior") REFERENCES "behavior"("id"), + FOREIGN KEY("intervention") REFERENCES "intervention"("id") +); +DROP TABLE IF EXISTS "overdose_fatality"; +CREATE TABLE "overdose_fatality" ( + "sample" INTEGER NOT NULL, + "intervention" INTEGER NOT NULL, + "behavior" INTEGER NOT NULL, + "time" INTEGER NOT NULL, + "probability" REAL NOT NULL DEFAULT 0.0, + PRIMARY KEY("sample","intervention","behavior","time"), + FOREIGN KEY("behavior") REFERENCES "behavior"("id"), + FOREIGN KEY("intervention") REFERENCES "intervention"("id") +); +DROP TABLE IF EXISTS "population_change"; +CREATE TABLE "population_change" ( + "sample" INTEGER NOT NULL, + "intervention" INTEGER NOT NULL, + "behavior" INTEGER NOT NULL, + "time" INTEGER NOT NULL, + "count" REAL NOT NULL DEFAULT 0.0, + PRIMARY KEY("sample","intervention","behavior","time"), + FOREIGN KEY("behavior") REFERENCES "behavior"("id"), + FOREIGN KEY("intervention") REFERENCES "intervention"("id") +); +DROP TABLE IF EXISTS "smr"; +CREATE TABLE "smr" ( + "sample" INTEGER NOT NULL, + "intervention" INTEGER NOT NULL, + "behavior" INTEGER NOT NULL, + "time" INTEGER NOT NULL, + "ratio" REAL NOT NULL DEFAULT 1.0, + PRIMARY KEY("sample","time","behavior","intervention"), + FOREIGN KEY("behavior") REFERENCES "behavior"("id"), + FOREIGN KEY("intervention") REFERENCES "intervention"("id") +); +DROP TABLE IF EXISTS "cohort"; +CREATE TABLE "cohort" ( + "id" INTEGER NOT NULL UNIQUE, + "description" TEXT, + "background_mortality_sample" INTEGER NOT NULL, + "behavior_transition_sample" INTEGER NOT NULL, + "initial_population_sample" INTEGER NOT NULL, + "intervention_transition_sample" INTEGER NOT NULL, + "overdose_sample" INTEGER NOT NULL, + "overdose_fatality_sample" INTEGER NOT NULL, + "population_change_sample" INTEGER NOT NULL, + "smr_sample" INTEGER NOT NULL, + PRIMARY KEY("id" AUTOINCREMENT) +); +DROP TABLE IF EXISTS "demographics"; +CREATE TABLE "demographics" ( + "id" INTEGER NOT NULL UNIQUE, + "type" TEXT NOT NULL, + "value" TEXT NOT NULL, + PRIMARY KEY("id" AUTOINCREMENT) +); +DROP TABLE IF EXISTS "cohort_demographics"; +CREATE TABLE "cohort_demographics" ( + "cohort_id" INTEGER NOT NULL, + "demographic_id" INTEGER NOT NULL, + PRIMARY KEY("cohort_id","demographic_id"), + FOREIGN KEY("cohort_id") REFERENCES "cohort"("id"), + FOREIGN KEY("demographic_id") REFERENCES "demographics"("id") +); +""" + +_SEED_DATA = """ +INSERT INTO cohort (id, description, background_mortality_sample, behavior_transition_sample, initial_population_sample, intervention_transition_sample, overdose_sample, overdose_fatality_sample, population_change_sample, smr_sample) + VALUES (1, "Benchmark Cohort", 1, 1, 1, 1, 1, 1, 1, 1); + +INSERT INTO intervention (id, name) + VALUES (1, "no_treatment"), (2, "early_buprenorphine"), (3, "buprenorphine"), (4, "post_buprenorphine"); + +INSERT INTO behavior (id, name) + VALUES (1, "active_injection"), (2, "nonactive_injection"); + +INSERT INTO initial_population (sample, intervention, behavior, count) + VALUES (1,1,1,100),(1,1,2,150),(1,2,1,200),(1,2,2,250),(1,3,1,0),(1,3,2,0),(1,4,1,0),(1,4,2,0); + +INSERT INTO population_change (sample, intervention, behavior, time, count) + VALUES (1,1,1,1,100),(1,1,2,1,150),(1,2,1,1,200),(1,2,2,1,250),(1,3,1,1,0),(1,3,2,1,0),(1,4,1,1,0),(1,4,2,1,0); + +INSERT INTO intervention_transition (sample, behavior, time, initial_intervention, new_intervention, probability) + VALUES (1,1,1,1,1,0.8),(1,2,1,1,1,0.7),(1,1,1,1,2,0.2),(1,2,1,1,2,0.3), + (1,1,1,1,3,0.0),(1,2,1,1,3,0.0),(1,1,1,1,4,0.0),(1,2,1,1,4,0.0), + (1,1,1,2,1,0.0),(1,2,1,2,1,0.0),(1,1,1,2,2,0.7),(1,2,1,2,2,0.6), + (1,1,1,2,3,0.2),(1,2,1,2,3,0.1),(1,1,1,2,4,0.1),(1,2,1,2,4,0.3), + (1,1,1,3,1,0.0),(1,2,1,3,1,0.0),(1,1,1,3,2,0.0),(1,2,1,3,2,0.0), + (1,1,1,3,3,0.8),(1,2,1,3,3,0.8),(1,1,1,3,4,0.2),(1,2,1,3,4,0.2), + (1,1,1,4,1,0.8),(1,2,1,4,1,0.8),(1,1,1,4,2,0.0),(1,2,1,4,2,0.0), + (1,1,1,4,3,0.0),(1,2,1,4,3,0.0),(1,1,1,4,4,0.2),(1,2,1,4,4,0.2); + +INSERT INTO behavior_transition (sample, intervention, time, initial_behavior, new_behavior, probability) + VALUES (1,1,1,1,1,0.8),(1,1,1,1,2,0.2),(1,1,1,2,1,0.1),(1,1,1,2,2,0.9), + (1,2,1,1,1,0.9),(1,2,1,1,2,0.1),(1,2,1,2,1,0.7),(1,2,1,2,2,0.3), + (1,3,1,1,1,0.3),(1,3,1,1,2,0.7),(1,3,1,2,1,0.4),(1,3,1,2,2,0.6), + (1,4,1,1,1,0.3),(1,4,1,1,2,0.7),(1,4,1,2,1,0.2),(1,4,1,2,2,0.8); + +INSERT INTO smr (sample, intervention, behavior, time, ratio) + VALUES (1,1,1,1,2.0),(1,1,2,1,2.1),(1,2,1,1,2.0),(1,2,2,1,2.1), + (1,3,1,1,2.0),(1,3,2,1,2.1),(1,4,1,1,2.0),(1,4,2,1,2.1); + +INSERT INTO background_mortality (sample, time, probability) + VALUES (1,1,0.25); + +INSERT INTO overdose (sample, intervention, behavior, time, probability) + VALUES (1,1,1,1,0.8),(1,1,2,1,0.7),(1,2,1,1,0.8),(1,2,2,1,0.7), + (1,3,1,1,0.8),(1,3,2,1,0.7),(1,4,1,1,0.8),(1,4,2,1,0.7); + +INSERT INTO overdose_fatality (sample, intervention, behavior, time, probability) + VALUES (1,1,1,1,0.1),(1,1,2,1,0.2),(1,2,1,1,0.1),(1,2,2,1,0.2), + (1,3,1,1,0.1),(1,3,2,1,0.2),(1,4,1,1,0.1),(1,4,2,1,0.2); +""" + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + +@pytest.fixture(scope="session") +def benchmark_db(tmp_path_factory) -> Path: + """Create and seed a SQLite database once for the entire benchmark session.""" + db_path = tmp_path_factory.mktemp("bench-data") / "benchmark.db" + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + cursor.executescript(_DB_SCHEMA) + cursor.executescript(_SEED_DATA) + conn.commit() + conn.close() + return db_path + + +@pytest.fixture(scope="session") +def benchmark_config() -> ConfigParser: + """Fixed simulation config used across all benchmarks.""" + cfg = ConfigParser() + cfg["simulation"] = { + "duration": "52", + "parameter_change_times": "52", + "stratify_entering_cohort": "false", + } + cfg["output"] = { + "build_summary_stats": "true", + "save_state_history": "true", + "timesteps_to_report": "52", + } + return cfg diff --git a/benchmarks/test_bench_end_to_end.py b/benchmarks/test_bench_end_to_end.py new file mode 100644 index 0000000..f24273a --- /dev/null +++ b/benchmarks/test_bench_end_to_end.py @@ -0,0 +1,147 @@ +################################################################################ +# File: bench_end_to_end.py # +# Project: respondpy # +# Created Date: 2026-05-06 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-05-06 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +""" +End-to-end benchmarks for the respondpy simulation pipeline. + +Three phases are benchmarked individually and as a combined pipeline: + 1. load — read data from SQLite and construct the Simulation + 2. run — execute all model transitions for the full duration + 3. write — export all model histories to CSV files + +Run with: + uv run pytest benchmarks/ -v --benchmark-only +Or via the nox session: + uv run nox -s benchmark +""" + +from __future__ import annotations + +from configparser import ConfigParser +from pathlib import Path + +import polars as pl +import pytest + +from respondpy import Simulation, build_simulation + +# --------------------------------------------------------------------------- +# CSV history writer (local to benchmarks — not part of the distributed lib) +# --------------------------------------------------------------------------- + + +def _write_histories_to_csv(sim: Simulation, out_dir: Path) -> None: + """Write each model's densified state histories to a separate CSV file. + + Output format per file: + history_name, timestep, state_0, state_1, ..., state_n + + Files are named ``_histories.csv`` and written to *out_dir*. + """ + histories = sim.get_model_histories() + for model_name, model_hist in histories.items(): + rows: list[dict] = [] + for hist_name, state_vectors in model_hist.items(): + for t, vec in enumerate(state_vectors): + row = {"history_name": hist_name, "timestep": t} + row.update({f"state_{i}": float(v) for i, v in enumerate(vec)}) + rows.append(row) + if rows: + pl.DataFrame(rows).write_csv( + out_dir / f"{model_name}_histories.csv") + + +# --------------------------------------------------------------------------- +# Phase 1 — load +# --------------------------------------------------------------------------- + +@pytest.mark.benchmark +def test_bench_load_data(benchmark, benchmark_db, benchmark_config): + """Benchmark SQLite data loading and Simulation construction. + + Measures the time to call build_simulation() from scratch on each round, + including all database reads and model/transition setup. + """ + benchmark.pedantic( + build_simulation, + args=([1], benchmark_db, benchmark_config), + rounds=10, + iterations=1, + ) + + +# --------------------------------------------------------------------------- +# Phase 2 — run +# --------------------------------------------------------------------------- + +@pytest.mark.benchmark +def test_bench_run_model(benchmark, benchmark_db, benchmark_config): + """Benchmark sim.run() in isolation. + + The Simulation is rebuilt fresh before each round so that history state + from a prior run does not carry over and inflate subsequent measurements. + """ + def _setup(): + sim = build_simulation([1], benchmark_db, benchmark_config) + return (sim,), {} + + benchmark.pedantic( + lambda sim: sim.run(), + setup=_setup, + rounds=10, + iterations=1, + ) + + +# --------------------------------------------------------------------------- +# Phase 3 — write +# --------------------------------------------------------------------------- + +@pytest.mark.benchmark +def test_bench_write_histories(benchmark, benchmark_db, benchmark_config, tmp_path): + """Benchmark CSV history export in isolation. + + The Simulation is built and run once in setup; only the CSV write is timed. + """ + def _setup(): + sim = build_simulation([1], benchmark_db, benchmark_config) + sim.run() + return (sim, tmp_path), {} + + benchmark.pedantic( + _write_histories_to_csv, + setup=_setup, + rounds=10, + iterations=1, + ) + + +# --------------------------------------------------------------------------- +# Combined — end-to-end +# --------------------------------------------------------------------------- + +@pytest.mark.benchmark +def test_bench_end_to_end(benchmark, benchmark_db, benchmark_config, tmp_path): + """Benchmark the full pipeline: load → run → write CSV. + + Use this figure for direct comparison with RESPONDv1 wall-clock timings. + """ + def _pipeline(): + sim = build_simulation([1], benchmark_db, benchmark_config) + sim.run() + _write_histories_to_csv(sim, tmp_path) + + benchmark.pedantic( + _pipeline, + rounds=10, + iterations=1, + ) diff --git a/noxfile.py b/noxfile.py index eff8540..a43a7fd 100644 --- a/noxfile.py +++ b/noxfile.py @@ -21,3 +21,25 @@ def pylint(session: nox.Session) -> None: session.env["PYTHONPATH"] = "src" session.install("pylint", "numpy", "polars") session.run("pylint", "src/respondpy") + + +@nox.session(python=["3.12"], venv_backend="uv") +def benchmark(session: nox.Session) -> None: + """Run end-to-end benchmarks (not included in normal test runs). + + Results are printed to stdout. Optionally pass --benchmark-save= + to persist results for later comparison: + + uv run nox -s benchmark -- --benchmark-save=baseline + """ + session.env["PYTHONPATH"] = "build/respondpy" + session.install("--group=benchmark", ".") + session.run( + "pytest", + "benchmarks/", + "--benchmark-only", + "--benchmark-columns=min,mean,stddev,rounds", + "--benchmark-sort=name", + "-v", + *session.posargs, + ) diff --git a/pyproject.toml b/pyproject.toml index c29cbdb..35ad74e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "scikit_build_core.build" [project] name = "respondpy" -version = "0.1.2" +version = "0.2.0" description = "The Syndemic Lab's RESPOND Simulation Python Extension Module." readme = "README.md" requires-python = ">=3.10" @@ -17,10 +17,9 @@ keywords = ["simulation", "Opioid Use Disorder", "Syndemics"] license = "AGPL-3.0-or-later" license-files = ["LICENSE.txt"] dependencies = [ - "nox>=2026.2.9", "numpy", "polars>=1.37.1", - "pytest-cov>=7.0.0", + "setuptools-scm>=10.0.5", ] [project.urls] @@ -43,8 +42,10 @@ dev = [ { include-group = "test" }, "ipykernel", "nbconvert", + "nox>=2026.2.9", "polars>=1.37.1", "pybind11-stubgen>=2.5.5", + "pylint>=4.0.5", "pytest>=9.0.2", "setuptools_scm", "typer", @@ -63,6 +64,7 @@ examples = [] test = [ "cloudpickle", "pytest-benchmark", + "pytest-cov>=7.0.0", "pytest>=6.0", "pytest-benchmark", "numpy", @@ -74,6 +76,10 @@ github = [ "setuptools_scm", "pytest-github-actions-annotate-failures" ] +benchmark = [ + { include-group = "test" }, + "polars>=1.37.1", +] [[tool.scikit-build.generate]] path = "respondpy/version.py" @@ -111,7 +117,8 @@ required_plugins = ["pytest-benchmark"] log_level = "DEBUG" markers = [ "smoke: Marks tests as smoke tests to respond import accuracy.", - "unit: Marks tests as unit tests to verify basic functionality." + "unit: Marks tests as unit tests to verify basic functionality.", + "benchmark: Marks benchmarks for end-to-end performance measurement." ] [tool.mypy] @@ -137,25 +144,19 @@ test-command = "python -m pytest -n auto --benchmark-disable tests" test-sources = ["pyproject.toml", "tests"] test-skip = [ "cp310-win_arm64", - "cp310-musllinux_*", - "pp310-manylinux_aarch64", - "pp310-macosx_arm64", "cp31*-musllinux_*", # Threading test crashes - "cp313*", # polars-runtime-32 lacks wheels for 3.13 and 3.14 + "cp313*", # polars-runtime-32 has issues with abi3 on 3.13 and above "cp314*", "pp*", # polars-runtime-32 unsupported on PyPy 3.11 (pp73) - "gp311_242-macosx_x86_64", - "gp311_242-manylinux_aarch64", - "gp311_242-win*", # pytest crashes with module 'io' has no attribute '_WindowsConsoleIO' - "gp312_*", # No numpy wheels yet "cp314*-ios_*", # No numpy wheels yet ] -enable = ["cpython-freethreading", "pypy"] +enable = ["pypy"] environment-pass = ["SETUPTOOLS_SCM_PRETEND_VERSION"] environment.PIP_ONLY_BINARY = "numpy" environment.PIP_PREFER_BINARY = "1" environment.UV_ONLY_BINARY = "numpy" environment.UV_PREFER_BINARY = "1" +environment.CMAKE_BUILD_PARALLEL_LEVEL = "3" [tool.cibuildwheel.pyodide] build-frontend = {name = "build", args = ["--exports", "whole_archive"]} @@ -187,6 +188,8 @@ omit = [ [tool.pylint] py-version = "3.12" +# Shim added to make sure pylint can find our modules since the pybind11 is messing with uv virtual environments. This just adds the src directory to the path before pylint runs but shouldn't impact any distribution details. +init-hook = "import os,sys; sys.path.insert(0, os.path.join(os.getcwd(), 'src'))" ignore-patterns = ['.*\.pyi'] ignore = "version.py" extension-pkg-allow-list = ["respondpy._core"] diff --git a/src/register_history.cpp b/src/register_history.cpp index 027535d..79b91bd 100644 --- a/src/register_history.cpp +++ b/src/register_history.cpp @@ -4,7 +4,7 @@ // Created Date: 2026-02-09 // // Author: Matthew Carroll // // ----- // -// Last Modified: 2026-02-09 // +// Last Modified: 2026-05-06 // // Modified By: Matthew Carroll // // ----- // // Copyright (c) 2026 Syndemics Lab at Boston Medical Center // @@ -21,10 +21,26 @@ using namespace respond; // NOLINTNEXTLINE(misc-use-internal-linkage) void register_history(py::module &m) { + py::enum_(m, "HistoryMode") + .value("Snapshot", HistoryMode::Snapshot) + .value("Accumulated", HistoryMode::Accumulated) + .export_values(); + + m.def("get_default_history_mode", &GetDefaultHistoryMode, + py::arg("name"), + "Return the default HistoryMode for a named history. Accumulated " + "histories track intervention_admission, total_overdose, " + "fatal_overdose, and background_death; all others are Snapshot."); + py::class_(m, "History") .def(py::init(), - pybind11::arg("name") = "state", - pybind11::arg("log_name") = "console") + py::arg("name") = "state", + py::arg("log_name") = "console") + .def(py::init(), + py::arg("name"), + py::arg("log_name"), + py::arg("mode"), + "Construct a History with an explicit recording mode.") .def("__copy__", [](const History &self) { return History(self); }) .def("get_state_map", &History::GetStateMap, "Get the state map (timestep -> state vector).") @@ -32,15 +48,43 @@ void register_history(py::module &m) { "Get the name of the history object.") .def("get_log_name", &History::GetLogName, "Get the log name used for logging.") + .def("get_history_mode", &History::GetHistoryMode, + "Get the recording mode (Snapshot or Accumulated).") .def("get_state_as_vector", &History::GetStateAsVector, - "Get the state as a vector, padding missing timesteps with zero " - "vectors.") - .def("add_state", &History::AddState, pybind11::arg("state"), - pybind11::arg("timestep") = -1, + "Get the state as a dense vector, padding missing timesteps with " + "zero vectors.") + .def("get_recorded_timesteps", &History::GetRecordedTimesteps, + "Get the raw recorded timestep indices without gap-filling.") + .def("get_recorded_states", &History::GetRecordedStates, + "Get the raw recorded state vectors without gap-filling.") + .def("has_pending_state", &History::HasPendingState, + "Return True when an accumulated history has a pending aggregate " + "not yet flushed.") + .def("get_pending_state", &History::GetPendingState, + "Get the pending accumulated state vector, or an empty vector if " + "none exists.") + .def("get_latest_recorded_timestep", &History::GetLatestRecordedTimestep, + "Get the largest recorded timestep, or -1 if history is empty.") + .def("add_state", &History::AddState, + py::arg("state"), + py::arg("timestep") = -1, "Add a state vector at a given timestep (-1 for auto-increment).") + .def("record_snapshot", &History::RecordSnapshot, + py::arg("state"), + py::arg("timestep"), + "Record a snapshot state vector at a concrete timestep.") + .def("accumulate_state", &History::AccumulateState, + py::arg("state"), + "Add a per-step contribution to an accumulated history.") + .def("flush_pending_state", &History::FlushPendingState, + py::arg("timestep"), + py::arg("state_size") = 0, + "Flush the pending accumulated state into a recorded timestep. " + "Records a zero vector of state_size if nothing is pending.") .def("clear", &History::Clear, "Clear all stored state history.") .def("__eq__", &History::operator==, - "Check equality of History objects (name, log_name, and state).") + "Check equality of History objects (name, log_name, mode, state, " + "and pending state).") .def("__ne__", &History::operator!=, "Check inequality of History objects."); } \ No newline at end of file diff --git a/src/register_logging.cpp b/src/register_logging.cpp index f965b35..41bc8c1 100644 --- a/src/register_logging.cpp +++ b/src/register_logging.cpp @@ -4,7 +4,7 @@ // Created Date: 2026-01-08 // // Author: Matthew Carroll // // ----- // -// Last Modified: 2026-01-08 // +// Last Modified: 2026-05-06 // // Modified By: Matthew Carroll // // ----- // // Copyright (c) 2026 Syndemics Lab at Boston Medical Center // @@ -30,9 +30,50 @@ void register_logging(py::module &m) { .value("kExists", respond::CreationStatus::kExists) .value("kNotCreated", respond::CreationStatus::kNotCreated) .export_values(); + py::enum_(m, "LogPattern") + .value("kSimple", respond::LogPattern::kSimple) + .value("kStandard", respond::LogPattern::kStandard) + .value("kDetailed", respond::LogPattern::kDetailed) + .value("kThreadSafe", respond::LogPattern::kThreadSafe) + .export_values(); m.def("create_file_logger", &respond::CreateFileLogger, "Creates a File Logger for use with RESPOND."); + m.def("create_shared_file_sink", &respond::CreateSharedFileSink, + py::arg("filepath"), + "Create a shared file sink for thread-safe concurrent logging from " + "multiple models or threads to the same output file. Call before " + "create_shared_logger()."); + m.def("create_shared_logger", &respond::CreateSharedLogger, + py::arg("logger_name"), + "Create a logger that writes to the shared file sink. Requires " + "create_shared_file_sink() to be called first."); + + m.def("set_log_pattern", &respond::SetLogPattern, + py::arg("pattern"), + "Set the logging pattern template for all subsequent logger " + "creations."); + m.def("get_log_pattern", &respond::GetLogPattern, + "Get the current logging pattern template."); + m.def("set_flush_interval", &respond::SetFlushInterval, + py::arg("seconds"), + "Set the global flush interval in seconds (0 to disable " + "auto-flush)."); + m.def("flush_all_loggers", &respond::FlushAllLoggers, + "Flush all active loggers, ensuring buffered output is written."); + + m.def("check_logger_exists", &respond::CheckLoggerExists, + py::arg("logger_name"), + "Check if a logger with the given name exists. Returns " + "CreationStatus.kExists or kNotCreated."); + m.def("get_logger_info", &respond::GetLoggerInfo, + py::arg("logger_name"), + "Retrieve a string with details about a logger (name, file path, " + "level, thread info)."); + m.def("set_logger_level", &respond::SetLoggerLevel, + py::arg("logger_name"), py::arg("level"), + "Set the logging level for a specific logger. level: 0=trace, " + "1=debug, 2=info, 3=warn, 4=error, 5=critical."); m.def("log_info", &respond::LogInfo, "Logs an info message to the log."); m.def("log_warning", &respond::LogWarning, diff --git a/src/register_model.cpp b/src/register_model.cpp index 2a112bc..e5f8995 100644 --- a/src/register_model.cpp +++ b/src/register_model.cpp @@ -33,6 +33,22 @@ void register_model(py::module &m) { .def("get_model_name", &Model::GetModelName) .def("get_log_name", &Model::GetLogName) .def("create_default_histories", &Model::CreateDefaultHistories) + .def("clear_histories", &Model::ClearHistories, + "Clear all history records and reset history tracking state.") + .def("set_history_capture_interval", + &Model::SetHistoryCaptureInterval, + py::arg("interval"), + "Set the global history capture interval. Records every " + "interval timesteps; values less than 1 default to full capture.") + .def("get_history_capture_interval", + &Model::GetHistoryCaptureInterval, + "Get the active capture interval. A value of 1 means full " + "capture.") + .def("set_final_timestep", &Model::SetFinalTimestep, + py::arg("final_timestep"), + "Set the final timestep that must always be recorded.") + .def("get_final_timestep", &Model::GetFinalTimestep, + "Get the configured final simulation timestep, or -1 if unset.") .def("__repr__", [](const Model &m) { return "GetHistories()) { + model_hist[py::str(kv.first)] = + kv.second.GetStateAsVector(); + } + result[py::str(model->GetModelName())] = model_hist; + } + return result; + }, + "Get densified state histories for all models. Returns " + "dict[model_name, dict[history_name, list[ndarray]]].") + .def("get_model_sparse_histories", + [](const Simulation &self) { + py::dict result; + for (const auto &model : self.GetModels()) { + py::dict model_hist; + for (const auto &kv : model->GetHistories()) { + model_hist[py::str(kv.first)] = kv.second; + } + result[py::str(model->GetModelName())] = model_hist; + } + return result; + }, + "Get sparse History objects for all models. Returns " + "dict[model_name, dict[history_name, History]].") .def("get_model_history_names", &Simulation::GetModelHistoryNames) .def("get_log_name", &Simulation::GetLogName) .def("__repr__", diff --git a/src/register_transition.cpp b/src/register_transition.cpp index fa281a2..86ab2aa 100644 --- a/src/register_transition.cpp +++ b/src/register_transition.cpp @@ -4,7 +4,7 @@ // Created Date: 2026-02-02 // // Author: Matthew Carroll // // ----- // -// Last Modified: 2026-02-09 // +// Last Modified: 2026-05-06 // // Modified By: Matthew Carroll // // ----- // // Copyright (c) 2026 Syndemics Lab at Boston Medical Center // @@ -12,6 +12,11 @@ #include +#include +#include + +#include +#include #include #include @@ -23,7 +28,26 @@ void register_transition(py::module &m) { py::class_ t(m, "Transition"); t.def(py::init(&TransitionFactory::CreateTransition), py::arg("type"), py::arg("log_name") = "console") - .def("execute", &Transition::Execute) + .def("execute", + [](const Transition &self, const Eigen::VectorXd &state, + py::object hist_obj) { + std::map h; + if (hist_obj.is_none()) { + LogWarning(self.GetLogName(), + "execute() called without a history map. " + "History will not be recorded. Pass the " + "model's history map for expected behavior."); + } else { + h = hist_obj.cast>(); + } + auto result = self.Execute(state, h); + return py::make_tuple(result, h); + }, + py::arg("state"), py::arg("history") = py::none(), + "Execute the transition on the given state. Returns " + "(state_result, history_map). Pass the model's history map for " + "expected behavior; omitting it will issue a warning and the " + "returned history map will be empty.") .def("add_transition_matrix", &Transition::AddTransitionMatrix) .def("get_transition_name", &Transition::GetTransitionName) .def("clear_transition_matrices", &Transition::ClearTransitionMatrices) diff --git a/src/respondpy/__init__.py b/src/respondpy/__init__.py index 19da146..67ac460 100644 --- a/src/respondpy/__init__.py +++ b/src/respondpy/__init__.py @@ -4,75 +4,54 @@ # Created Date: 2025-08-04 # # Author: Matthew Carroll # # ----- # -# Last Modified: 2026-02-26 # +# Last Modified: 2026-06-10 # # Modified By: Matthew Carroll # # ----- # # Copyright (c) 2025-2026 Syndemics Lab at Boston Medical Center # ################################################################################ from __future__ import annotations -from .version import version as __version__ # pylint: disable=import-error,no-name-in-module -from .data.parameters import Parameter +from .version import version as __version__ # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] -from .execute.build_simulation import build_simulation -from .execute.build_transition import get_data_array +from . import data -from .io.reading import ( - get_parameter_by_id_and_time, get_state_names, get_behaviors, get_interventions, get_cohorts, get_behavior_table, get_intervention_table, get_sample_ids_by_table +from .cost_effectiveness import ( + discount, cwise_product, cwise_min, calculate_life_years ) -from .io.writing import insert_parameter, insert_cohort +from .history import History -from ._core.cost_effectiveness import discount, cwise_product, cwise_min, calculate_life_years # pylint: disable=import-error,no-name-in-module - -from ._core.history import History # pylint: disable=import-error,no-name-in-module - -from ._core.logging import CreationStatus, kError, kExists, kNotCreated, kSuccess, LogType, kInfo, kWarn, kDebug, create_file_logger, log_debug, log_error, log_info, log_warning # pylint: disable=import-error,no-name-in-module - -from ._core.model import Model # pylint: disable=import-error,no-name-in-module +from .model import ( + Model, build_model, add_transitions_to_model, build_model_transitions +) -from ._core.simulation import Simulation # pylint: disable=import-error,no-name-in-module +from .simulation import ( + Simulation, build_simulation +) -from ._core.transition import Transition # pylint: disable=import-error,no-name-in-module +from .transition import ( + Transition, transition_factory, build_timestep_transition +) __all__ = [ - "Parameter", # data.parameters - "build_simulation", - "get_data_array", - "get_parameter_by_id_and_time", # io.reading - "get_state_names", - "get_behaviors", - "get_interventions", - "get_behavior_table", - "get_intervention_table", - "get_cohorts", - "get_sample_ids_by_table", - "insert_parameter", # io.writing - "insert_cohort", - "discount", # _core.cost_effectiveness + "data", + "discount", "cwise_product", "cwise_min", "calculate_life_years", - "History", # _core.history - "CreationStatus", # _core.logging - "kError", - "kExists", - "kNotCreated", - "kSuccess", - "LogType", - "kInfo", - "kWarn", - "kDebug", - "create_file_logger", - "log_debug", - "log_error", - "log_info", - "log_warning", - "Model", # _core.model - "Simulation", # _core.simulation - "Transition" # _core.transition + "History", + "Model", + "build_model", + "add_transitions_to_model", + "build_model_transitions", + "Simulation", + "build_simulation", + "Transition", + "transition_factory", + "build_timestep_transition" ] def __dir__() -> list[str]: + """Return tab-completion symbols for the package namespace.""" return __all__ diff --git a/src/respondpy/_core/__init__.pyi b/src/respondpy/_core/__init__.pyi index 469b1a0..509178a 100644 --- a/src/respondpy/_core/__init__.pyi +++ b/src/respondpy/_core/__init__.pyi @@ -1,3 +1,54 @@ -from . import cost_effectiveness, history, logging, model, simulation, transition -__all__: list[str] = ['cost_effectiveness', 'history', - 'logging', 'model', 'simulation', 'transition'] +################################################################################ +# File: __init__.pyi # +# Project: respondpy # +# Created Date: 2026-02-13 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-05 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +from __future__ import annotations + +from .cost_effectiveness import discount, cwise_product, cwise_min, calculate_life_years # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] + +from .history import History # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] + +from .logging import CreationStatus, LogType, create_file_logger, kDebug, kError, kExists, kInfo, kNotCreated, kSuccess, kWarn, log_debug, log_error, log_info, log_warning # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] + +from .model import Model # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] + +from .simulation import Simulation # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] + +from .transition import Transition # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] + +__all__: list[str] = [ + 'discount', + 'cwise_product', + 'cwise_min', + 'calculate_life_years', + 'CreationStatus', + 'LogType', + 'create_file_logger', + 'kDebug', + 'kError', + 'kExists', + 'kInfo', + 'kNotCreated', + 'kSuccess', + 'kWarn', + 'log_debug', + 'log_error', + 'log_info', + 'log_warning', + 'History', + 'Model', + 'Simulation', + 'Transition' +] + + +def __dir__() -> list[str]: + return __all__ diff --git a/src/respondpy/_core/logging.pyi b/src/respondpy/_core/logging.pyi index e212010..78e47d7 100644 --- a/src/respondpy/_core/logging.pyi +++ b/src/respondpy/_core/logging.pyi @@ -4,7 +4,7 @@ # Created Date: 2026-02-09 # # Author: Matthew Carroll # # ----- # -# Last Modified: 2026-02-10 # +# Last Modified: 2026-06-01 # # Modified By: Matthew Carroll # # ----- # # Copyright (c) 2026 Syndemics Lab at Boston Medical Center # @@ -12,8 +12,12 @@ from __future__ import annotations import typing -__all__: list[str] = ['CreationStatus', 'LogType', 'create_file_logger', 'kDebug', 'kError', 'kExists', - 'kInfo', 'kNotCreated', 'kSuccess', 'kWarn', 'log_debug', 'log_error', 'log_info', 'log_warning'] + +__all__: list[str] = [ + 'CreationStatus', 'LogType', 'create_file_logger', 'kDebug', 'kError', + 'kExists', 'kInfo', 'kNotCreated', 'kSuccess', 'kWarn', 'log_debug', + 'log_error', 'log_info', 'log_warning' +] class CreationStatus: @@ -28,9 +32,10 @@ class CreationStatus: kNotCreated """ - __members__: typing.ClassVar[dict[str, CreationStatus] - # value = {'kError': , 'kSuccess': , 'kExists': , 'kNotCreated': } - ] + __members__: typing.ClassVar[ + dict[str, CreationStatus] + # value = {'kError': , 'kSuccess': , 'kExists': , 'kNotCreated': } + ] # value = kError: typing.ClassVar[CreationStatus] # value = diff --git a/src/respondpy/_core/simulation.pyi b/src/respondpy/_core/simulation.pyi index 43b5b36..295fea4 100644 --- a/src/respondpy/_core/simulation.pyi +++ b/src/respondpy/_core/simulation.pyi @@ -4,7 +4,7 @@ # Created Date: 2026-02-09 # # Author: Matthew Carroll # # ----- # -# Last Modified: 2026-02-12 # +# Last Modified: 2026-06-01 # # Modified By: Matthew Carroll # # ----- # # Copyright (c) 2026 Syndemics Lab at Boston Medical Center # @@ -16,7 +16,6 @@ from collections.abc import Sequence import numpy import numpy.typing -import respondpy._core.history import respondpy._core.model import typing @@ -50,6 +49,9 @@ class Simulation: def get_model_histories(self) -> Sequence[dict[str, Sequence[typing.Annotated[numpy.typing.NDArray[numpy.float64], "[m, 1]"]]]]: ... + def get_model_sparse_histories(self) -> dict[str, dict[str, typing.Any]]: + ... + def get_model_history_names(self) -> Sequence[tuple[str, str]]: ... diff --git a/src/respondpy/_utils.py b/src/respondpy/_utils.py new file mode 100644 index 0000000..a975885 --- /dev/null +++ b/src/respondpy/_utils.py @@ -0,0 +1,25 @@ +################################################################################ +# File: _utils.py # +# Project: respondpy # +# Created Date: 2026-06-05 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-05 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ +from __future__ import annotations + + +def str_to_int_list(config_string: str, *, delimiter: str = ',') -> list[int]: + """Parse a delimited integer string into a list of integers. + + Whitespace around each token is stripped before conversion. + + :param config_string: Delimited string of integer-like values. + :param delimiter: Token delimiter used to split ``config_string``. + :returns: Parsed integers in input order. + :raises ValueError: If any token cannot be converted to ``int``. + """ + return [int(x.strip()) for x in config_string.split(delimiter)] diff --git a/src/respondpy/cost_effectiveness.py b/src/respondpy/cost_effectiveness.py new file mode 100644 index 0000000..760dd4c --- /dev/null +++ b/src/respondpy/cost_effectiveness.py @@ -0,0 +1,22 @@ +################################################################################ +# File: cost_effectiveness.py # +# Project: respondpy # +# Created Date: 2026-06-05 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-10 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +from __future__ import annotations + +from ._core.cost_effectiveness import calculate_life_years, cwise_min, cwise_product, discount # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] + +__all__: list[str] = [ + "calculate_life_years", + "cwise_min", + "cwise_product", + "discount" +] diff --git a/src/respondpy/data/__init__.py b/src/respondpy/data/__init__.py new file mode 100644 index 0000000..e1683ae --- /dev/null +++ b/src/respondpy/data/__init__.py @@ -0,0 +1,42 @@ +################################################################################ +# File: __init__.py # +# Project: respondpy # +# Created Date: 2026-06-04 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-10 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +from __future__ import annotations + +from .parameters import ParameterType, Parameter + +from .state_vectors import build_constant_state_vector +from .logic_conditions import ( + verify_no_nulls, verify_no_duplicates, validate_time_list +) +from .transition_matrices import ( + build_constant_transition, update_retention_probability, verify_transition_probability +) +from .input import Input + +__all__ = [ + "ParameterType", + "Parameter", + "update_retention_probability", + "verify_transition_probability", + "verify_no_nulls", + "verify_no_duplicates", + "build_constant_transition", + "build_constant_state_vector", + "validate_time_list", + "Input" +] + + +def __dir__() -> list[str]: + """Return tab-completion symbols for the data namespace.""" + return __all__ diff --git a/src/respondpy/data/database_helpers.py b/src/respondpy/data/database_helpers.py new file mode 100644 index 0000000..cf7a7f6 --- /dev/null +++ b/src/respondpy/data/database_helpers.py @@ -0,0 +1,116 @@ +################################################################################ +# File: database_helpers.py # +# Project: respondpy # +# Created Date: 2026-06-05 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-09 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +import polars as pl + + +def _sort_state_vector( + lf: pl.LazyFrame, + behaviors: list[tuple[int, str]], + interventions: list[tuple[int, str]] +) -> pl.LazyFrame: + """Sort a state-vector dataframe by intervention and behavior ids. + + :param lf: State-vector LazyFrame with ``intervention`` and ``behavior``. + :param behaviors: Ordered ``(id, name)`` behavior tuples. + :param interventions: Ordered ``(id, name)`` intervention tuples. + :returns: Sorted LazyFrame in deterministic state order. + :raises ValueError: If required state columns are missing. + """ + s = lf.collect_schema().names() + if 'intervention' not in s or 'behavior' not in s: + raise ValueError( + f"Invalid columns provided when attempting to sort state vector: {s}") + behav = pl.LazyFrame( + behaviors, schema=["b_id", "b_name"], orient='row') + + inter = pl.LazyFrame( + interventions, schema=["i_id", "i_name"], orient='row') + + # Sort first by behaviors, then by interventions + return lf.join( + behav, left_on="behavior", right_on="b_name", how="inner" + ).sort(pl.col("b_id")).drop("b_id").join( + inter, left_on="intervention", right_on="i_name", how="inner" + ).sort(pl.col("i_id")).drop("i_id") + + +def _sort_transition_matrix( + lf: pl.LazyFrame, + behaviors: list[tuple[int, str]], + interventions: list[tuple[int, str]] +) -> pl.LazyFrame: + """Sort a transition-matrix dataframe into deterministic state order. + + :param lf: Transition LazyFrame with initial and next state columns. + :param behaviors: Ordered ``(id, name)`` behavior tuples. + :param interventions: Ordered ``(id, name)`` intervention tuples. + :returns: Sorted LazyFrame for stable downstream reshaping/comparison. + :raises ValueError: If required transition columns are missing. + """ + if 'intervention' not in lf.columns or 'behavior' not in lf.columns or 'next_intervention' not in lf.columns or 'next_behavior' not in lf.columns: + raise ValueError( + f"Invalid columns provided when attempting to sort transition matrix: {lf.columns}") + behav = pl.LazyFrame(behaviors, schema=["b_id", "b_name"]) + inter = pl.LazyFrame(interventions, schema=["i_id", "i_name"]) + + # Sort order: + # 1. Next Behavior + # 2. Next Behavior + # 3. Initial Behavior + # 4. Initial Intervention + # e.g. [active_injection, no_treatment, active_injection, no_treatment], [active_injection, no_treatment, active_injection, buprenorphine], [active_injection, no_treatment, active_injection, methadone], etc. + return lf.drop("i_id").join( + behav, left_on="next_behavior", right_on="b_name", how="inner" + ).sort(pl.col("b_id")).drop("b_id").join( + inter, left_on="next_intervention", right_on="i_name", how="inner" + ).sort(pl.col("i_id")).drop("i_id").join( + behav, left_on="behavior", right_on="b_name", how="inner" + ).sort(pl.col("b_id")).drop("b_id").join( + inter, left_on="intervention", right_on="i_name", how="inner" + ).sort(pl.col("i_id")) + + +def sort_dataframes( + lf: pl.LazyFrame, + behaviors: list[tuple[int, str]], + interventions: list[tuple[int, str]] +) -> pl.LazyFrame: + """Sort known RESPOND shapes while leaving other schemas unchanged. + + Dataframes with 3 columns are treated as state vectors, and dataframes with + 4 columns as transition matrices. + + :param lf: LazyFrame to sort. + :param behaviors: Ordered ``(id, name)`` behavior tuples. + :param interventions: Ordered ``(id, name)`` intervention tuples. + :returns: Sorted LazyFrame when shape is recognized, otherwise input. + """ + if len(lf.collect_schema().names()) == 3: + return _sort_state_vector(lf, behaviors, interventions) + if len(lf.collect_schema().names()) == 4: + return _sort_transition_matrix(lf, behaviors, interventions) + return lf + + +def get_column_order(col_name: str, values: list[str]) -> str: + """Get the SQL ORDER BY clause for a given column and list of values. + + :param col_name: Column expression used in generated CASE conditions. + :param values: Ordered values representing desired sort priority. + :returns: SQL CASE expression suitable for ORDER BY clauses. + """ + order_clause = "CASE\n" + for idx, v in enumerate(values): + order_clause += f"WHEN {col_name} = \'{v}\' THEN {idx}\n" + order_clause += f"ELSE {len(values)}\n END" + return order_clause diff --git a/src/respondpy/data/input.py b/src/respondpy/data/input.py new file mode 100644 index 0000000..ae49fb9 --- /dev/null +++ b/src/respondpy/data/input.py @@ -0,0 +1,460 @@ +################################################################################ +# File: input.py # +# Project: respondpy # +# Created Date: 2026-06-05 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-11 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +import sqlite3 +from pathlib import Path +from typing import Literal +from operator import itemgetter +from configparser import ConfigParser + +import numpy as np +import polars as pl + +from .database_helpers import sort_dataframes +from .parameters import Parameter, ParameterType +from .transition_matrices import build_constant_transition, update_retention_probability, combine_dataframes +from .state_vectors import build_constant_state_vector + + +class Input: + """Data access object for RESPOND SQLite and simulation configuration. + + This class resolves cohort-linked sample ids, loads raw parameter tables, + and returns either raw rows or model-ready numpy arrays. + """ + + def __init__( + self, + *, + path: str | Path | None = None, + db_name: str = "input.db", + conf_name: str = "sim.conf", + db_path: str | Path | None = None, + conf_path: str | Path | None = None + ) -> None: + """Create an Input data source from a base path or explicit files. + + :param path: Directory containing both database and config files. + :param db_name: Database filename used when ``path`` is provided. + :param conf_name: Config filename used when ``path`` is provided. + :param db_path: Explicit database file path. + :param conf_path: Explicit config file path. + :raises ValueError: If required path arguments are incomplete. + :raises FileNotFoundError: If database or config files do not exist. + """ + if path is not None: + if isinstance(path, str): + path = Path(path) + self._db_path = path / db_name + self._conf_path = path / conf_name + elif db_path is not None and conf_path is not None: + if isinstance(db_path, str): + db_path = Path(db_path) + if isinstance(conf_path, str): + conf_path = Path(conf_path) + self._db_path = db_path + self._conf_path = conf_path + else: + raise ValueError( + "Must provide either a path or both a db_path and conf_path!" + ) + + if not self._db_path.exists(): + raise FileNotFoundError( + f"Database file not found at {self._db_path}!") + if not self._conf_path.exists(): + raise FileNotFoundError( + f"Config file not found at {self._conf_path}!") + + self._db_path = db_path + self._connection = sqlite3.connect(str(self._db_path)) + + self._config = ConfigParser() + self._config.read(self._conf_path) + + self.states: dict[str, list] = {} + self.interventions: list[str] | None = None + self.behaviors: list[str] | None = None + + def __repr__(self) -> str: + return f"Input(db_path={self._db_path}, conf_path={self._conf_path})" + + @property + def config(self) -> ConfigParser: + """Return parsed simulation configuration.""" + return self._config + + # Helper Functions for Database Operations + + def _get_connection(self) -> sqlite3.Connection: + if not self._connection: + raise ConnectionError("No database connection established.") + return self._connection + + def _check_valid_list(self, l: list[tuple], tuple_items: int) -> bool: + if len(l) == 0: + return False + if len(l[0]) != tuple_items: + return False + return True + + def _connect_and_executemany(self, data: list[tuple], stmt: str) -> None: + n_question_marks = stmt.count("?") + if not self._check_valid_list(data, n_question_marks): + raise ValueError( + f"Data provided does not match the expected format for the SQL statement! Expected list of tuples with {n_question_marks} items each. Provided data: {data}" + ) + con = self._get_connection() + cur = con.cursor() + cur.executemany(stmt, data) + con.commit() + + def _connect_and_fetchall( + self, + stmt: str, + params: tuple = () + ) -> tuple[list, list[tuple]]: + con = self._get_connection() + cur = con.cursor() + cur.execute(stmt, params) + col_names = [d[0] for d in cur.description] + return col_names, cur.fetchall() + + # Helper Functions for Retrieving Data from Database + + def _get_single_state_table( + self, + state: Literal["intervention", "behavior"] + ) -> list[tuple[int, str]]: + if state in self.states: + return self.states[state] + + stmt = f"SELECT id, name FROM {state} ORDER BY id" + _, results = self._connect_and_fetchall(stmt) + self.states[state] = [(row[0], row[1]) for row in results] + return self.states[state] + + def _get_sample_ids_by_table( + self, + table_name: str + ) -> list[int]: + + con = self._get_connection() + cur = con.cursor() + + # Check 2 things: + # 1. Does the table exist + # 2. Is the table_name a valid SQL string (i.e. prevent SQL injection + # later) + cur.execute(""" + SELECT count(name) FROM sqlite_master WHERE type='table' AND name=? + """, (table_name,)) + if cur.fetchone()[0] != 1: + raise ValueError( + f"The specified table does not exist: {table_name}") + + # Be very careful here, this works and isn't gonna get SQL Injection attacked because we verify the table_name in the check above. + stmt = f"SELECT DISTINCT sample FROM {table_name}" + cur.execute(stmt) + result = [row[0] for row in cur.fetchall()] + return result + + def _get_sample_id_for_parameter( + self, + param: Parameter, + cohort_id: int = 1 + ) -> int: + col_name = param.get_cohort_column_name() + stmt = f"SELECT {col_name} FROM cohort WHERE id = ?" + _, result = self._connect_and_fetchall(stmt, (str(cohort_id),)) + if len(result) == 0 or len(result[0]) == 0: + raise ValueError( + f"No sample ID found for parameter {param} and cohort ID {cohort_id}!" + ) + return result[0][0] + + def _select_parameter_raw( + self, + param: Parameter, + sample_id: int = 1, + time: int = 1 + ) -> pl.LazyFrame: + stmt = param.get_select_statement( + self.get_interventions(), + self.get_behaviors() + ) + if not param.is_time_varying(): + cols, vals = self._connect_and_fetchall( + stmt, (str(sample_id),)) + lzdf = pl.LazyFrame( + vals, schema=cols, orient='row' + ).with_columns(sample=sample_id) + else: + cols, vals = self._connect_and_fetchall( + stmt, (str(sample_id), str(time))) + lzdf = pl.LazyFrame( + vals, schema=cols, orient='row' + ).with_columns( + sample=sample_id, + time=time + ) + + return lzdf + + def _extract_values( + self, + param: Parameter, + lf: pl.LazyFrame, + *, + n: int = 64 # 16 interventions * 4 behaviors + ) -> np.ndarray: + """Convert extracted rows to a model-ready state vector or matrix. + + :param param: Parameter descriptor controlling output shape. + :param lf: LazyFrame containing extracted values. + :param n: Number of states in the model. + :returns: ``(n, 1)`` state vector or ``(n, n)`` transition matrix. + :raises ValueError: If ``param`` cannot be mapped to either shape. + """ + val_col_name = param.get_value_column_name() + if param.is_state_vector_operation(): + vec = lf.select(pl.col(val_col_name)).collect().to_numpy() + return vec.reshape(n, 1) + if param.is_transition_matrix_operation(): + return lf.select( + pl.col(val_col_name) + ).collect().to_numpy().reshape(n, n) + raise ValueError( + "Invalid parameter applied when attempting to extract parameters!") + + def _zero_invalid_transitions( + self, + param: Parameter, + transition_matrix: pl.DataFrame + ) -> pl.DataFrame: + """Set structurally invalid transition probabilities to zero. + + For intervention transitions, behavior changes are invalid. For behavior + transitions, intervention changes are invalid. + + :param param: Parameter descriptor identifying transition type. + :param transition_matrix: Transition rows to sanitize. + :returns: Transition dataframe with invalid rows forced to zero. + """ + if param == ParameterType.INTERVENTION_TRANSITION_PROBABILITY: + m = transition_matrix.with_columns( + pl.when( + pl.col("new_behavior") != pl.col("initial_behavior") + ).then( + pl.lit(0.0) + ).otherwise( + pl.col("probability") + ).alias("probability") + ) + elif param == ParameterType.BEHAVIOR_TRANSITION_PROBABILITY: + m = transition_matrix.with_columns( + pl.when( + pl.col("new_intervention") != pl.col( + "initial_intervention") + ).then( + pl.lit(0.0) + ).otherwise( + pl.col("probability") + ).alias("probability") + ) + else: + m = transition_matrix + return m + + def _get_parameter_filled( + self, + param: Parameter, + sample_id: int = 1, + time: int = 1 + ) -> pl.LazyFrame: + """Return a complete parameter table with missing rows backfilled. + + State-vector parameters are completed from a constant state vector. + Transition parameters are completed from a constant transition matrix, + then normalized for retention probabilities. + + :param param: Parameter descriptor to extract. + :param sample_id: Sample id selected from the cohort table. + :param time: Timestep used for time-varying parameters. + :returns: Complete and consistently ordered parameter rows. + :raises ValueError: If required transition state columns are missing. + """ + lf = self._select_parameter_raw(param, sample_id, time) + if param == ParameterType.INTERVENTION_TRANSITION_PROBABILITY: + lf = lf.rename({"behavior": "initial_behavior"}) + elif param == ParameterType.BEHAVIOR_TRANSITION_PROBABILITY: + lf = lf.rename({"intervention": "initial_intervention"}) + + n_rows = lf.select(pl.len()).collect().item() + + n_interventions = len(self.get_interventions()) + n_behaviors = len(self.get_behaviors()) + n_states = n_interventions * n_behaviors + + complete_state_vector = ( + param.is_state_vector_operation() and n_rows == n_states + ) + complete_transition = ( + n_rows == n_states * n_states and param in [ + ParameterType.INTERVENTION_TRANSITION_PROBABILITY, ParameterType.BEHAVIOR_TRANSITION_PROBABILITY] + ) + + if complete_state_vector or complete_transition: + return lf + + if param.is_state_vector_operation(): + return combine_dataframes( + build_constant_state_vector( + self.get_interventions(), self.get_behaviors(), sample_id=sample_id, time=time + ).lazy(), + lf, + value_col=param.get_value_column_name() + ) + temp = build_constant_transition( + self.get_interventions(), + self.get_behaviors(), + sample_id=sample_id, + time=time + ) + + res = combine_dataframes( + temp, lf, value_col=param.get_value_column_name() + ) + + init_col = param.get_initial_state_column_name() + next_col = param.get_next_state_column_name() + if init_col is None or next_col is None: + raise ValueError( + f"Parameter {param} is missing the initial or next state column names required for transition matrix operations!" + ) + + res = self._zero_invalid_transitions(param, res.collect()) + + res = update_retention_probability( + res, + init_col, + next_col, + probability_column=param.get_value_column_name() + ) + + return sort_dataframes( + res.lazy(), + self._get_single_state_table("intervention"), + self._get_single_state_table("behavior") + ) + + # External Functions + + def get_interventions(self) -> list[str]: + """Return ordered intervention names from the database.""" + if self.interventions is None: + self.interventions = list(map( + itemgetter(1), + self._get_single_state_table(state="intervention") + )) + return self.interventions + + def get_behaviors(self) -> list[str]: + """Return ordered behavior names from the database.""" + if self.behaviors is None: + self.behaviors = list(map( + itemgetter(1), + self._get_single_state_table(state="behavior") + )) + return self.behaviors + + def get_state_names(self) -> list[tuple[str, str]]: + """Return ordered state-name tuples as ``(intervention, behavior)``. + + :returns: State label pairs sorted by intervention id then behavior id. + """ + if "combination" in self.states: + return self.states["combination"] + + stmt = """ + SELECT i.name AS intervention, b.name AS behavior + FROM intervention AS i + CROSS JOIN behavior AS b + ORDER BY i.id, b.id + """ + + _, results = self._connect_and_fetchall(stmt) + self.states["combination"] = [(row[0], row[1]) for row in results] + return self.states["combination"] + + def get_cohorts(self) -> tuple[list[str], list]: + """Return raw cohort table data. + + :returns: Tuple of column names and row tuples from ``cohort``. + """ + stmt = "SELECT * FROM cohort;" + col_names, results = self._connect_and_fetchall(stmt) + return col_names, results + + def get_cohort_ids(self) -> list[int]: + """Return a list of cohort ids from the cohort table.""" + col_names, results = self.get_cohorts() + id_index = col_names.index("id") + return [row[id_index] for row in results] + + def insert_cohorts(self, data: list) -> None: + """Insert cohort rows into the cohort table. + + :param data: Row tuples matching cohort insert statement order. + """ + sql_stmt = """ + INSERT INTO cohort(description, background_mortality_sample, behavior_transition_sample, initial_population_sample, intervention_transition_sample, overdose_sample, overdose_fatality_sample, population_change_sample, smr_sample) VALUES(?, ?, ?, ?, ?, ?, ?, ?, ?) + """ + self._connect_and_executemany(data, sql_stmt) + + def select_parameter( + self, + param: Parameter, + cohort_id: int = 1, + time: int = 1, + raw: bool = False + ) -> np.ndarray: + """Select parameter data for a cohort and optional timestep. + + :param param: Parameter descriptor to extract. + :param cohort_id: Cohort id used to resolve sample ids. + :param time: Timestep for time-varying parameters. + :param raw: When ``True``, return raw table rows as numpy values. + :returns: Raw rows or model-ready shaped numpy array. + """ + sample_id = self._get_sample_id_for_parameter(param, cohort_id) + if raw: + return self._select_parameter_raw( + param, sample_id, time).collect().to_numpy() + + return self._extract_values( + param, + self._get_parameter_filled(param, sample_id, time), + n=len(self.get_state_names()) + ) + + def insert_parameter( + self, + param: Parameter, + data: list, + ) -> None: + """Insert parameter rows using parameter-specific SQL. + + :param param: Parameter descriptor choosing target table and schema. + :param data: Row tuples matching ``param`` insert statement order. + """ + return self._connect_and_executemany(data, param.get_insert_statement()) diff --git a/src/respondpy/data/logic_conditions.py b/src/respondpy/data/logic_conditions.py new file mode 100644 index 0000000..84cb187 --- /dev/null +++ b/src/respondpy/data/logic_conditions.py @@ -0,0 +1,78 @@ +################################################################################ +# File: logic_conditions.py # +# Project: respondpy # +# Created Date: 2026-06-04 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-09 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +import polars as pl + +from respondpy.data.parameters import ParameterType + + +def verify_no_nulls( + df: pl.DataFrame, + sample_id: int, + p: ParameterType, + *, + col_to_check: str = "probability" +) -> None: + """Validate that a required value column contains no null values. + + :param df: Data to validate. + :param sample_id: Sample id used only for error context. + :param p: Parameter type used only for error context. + :param col_to_check: Column expected to be fully populated. + :raises ValueError: If any null value is found. + """ + if df.select(pl.col(col_to_check).is_null().any()).item(): + raise ValueError( + f"Null transition probabilities found for sample {sample_id} and parameter {p}" + ) + + +def verify_no_duplicates( + df: pl.DataFrame, + key_columns: list[str], + sample_id: int, + p: ParameterType +) -> None: + """Validate that key columns uniquely identify transition rows. + + :param df: Data to validate. + :param key_columns: Columns that must uniquely identify each row. + :param sample_id: Sample id used only for error context. + :param p: Parameter type used only for error context. + :raises ValueError: If duplicated keys are found. + """ + if df.select( + pl.struct(key_columns).is_duplicated().any() + ).item(): + raise ValueError( + f"Duplicate transition rows found for sample {sample_id} and parameter {p}" + ) + + +def validate_time_list(ct_list: list[int]) -> list[int]: + """Validate and normalize configured parameter change times. + + The value ``1`` is removed because timestep 1 is always explicitly built in + model construction. + + :param ct_list: Parsed integer values from + ``simulation.parameter_change_times``. + :returns: Validated and ascending change-time list. + :raises ValueError: If any value is less than or equal to zero. + """ + if any(num <= 0 for num in ct_list): + raise ValueError( + "The config file contains zero or a negative number in the `parameter_change_times` list!") + ct_list.sort(reverse=True) + if ct_list[-1] == 1: + ct_list.pop() + return ct_list[::-1] diff --git a/src/respondpy/data/parameters.py b/src/respondpy/data/parameters.py index 9e8fb97..6acf792 100644 --- a/src/respondpy/data/parameters.py +++ b/src/respondpy/data/parameters.py @@ -4,7 +4,7 @@ # Created Date: 2026-01-15 # # Author: Matthew Carroll # # ----- # -# Last Modified: 2026-01-15 # +# Last Modified: 2026-06-10 # # Modified By: Matthew Carroll # # ----- # # Copyright (c) 2026 Syndemics Lab at Boston Medical Center # @@ -12,8 +12,12 @@ from enum import Enum +from .database_helpers import get_column_order + + +class ParameterType(Enum): + """Supported parameter families used in RESPOND.""" -class Parameter(Enum): INITIAL_COHORT = 1 MIGRATION_COHORT = 2 INTERVENTION_TRANSITION_PROBABILITY = 3 @@ -22,3 +26,246 @@ class Parameter(Enum): OVERDOSE_FATALITY_PROBABILITY = 6 BACKGROUND_DEATH_PROBABILITY = 7 STANDARD_MORTALITY_RATIO = 8 + + +class Parameter(): + """Typed wrapper around ParameterType with SQL and schema helpers.""" + + def __init__( + self, + parameter_type: ParameterType + ) -> None: + """Create a parameter descriptor. + + :param parameter_type: Enumerated parameter family. + """ + self.__parameter_type = parameter_type + + def __eq__(self, other) -> bool: + if isinstance(other, Parameter): + return self.__parameter_type == other.get_parameter_type() + if isinstance(other, ParameterType): + return self.__parameter_type == other + return False + + def __repr__(self) -> str: + return f"Parameter(parameter_type={self.__parameter_type})" + + def get_parameter_type(self) -> ParameterType: + """Return the wrapped parameter type.""" + return self.__parameter_type + + def is_time_varying(self) -> bool: + """Return whether this parameter is indexed by timestep.""" + if self.__parameter_type == ParameterType.INITIAL_COHORT: + return False + return True + + def is_transition_matrix_operation(self) -> bool: + """Return whether this parameter maps to transition-matrix data.""" + if self.__parameter_type in [ParameterType.INTERVENTION_TRANSITION_PROBABILITY, ParameterType.BEHAVIOR_TRANSITION_PROBABILITY]: + return True + return False + + def is_state_vector_operation(self) -> bool: + """Return whether this parameter maps to state-vector data.""" + return not self.is_transition_matrix_operation() + + def get_value_column_name(self) -> str: + """Return the numeric value column name used for this parameter. + + :returns: One of ``count``, ``probability``, or ``ratio``. + :raises ValueError: If the parameter type is not implemented. + """ + match self.__parameter_type: + case ParameterType.STANDARD_MORTALITY_RATIO: + return "ratio" + case ParameterType.BACKGROUND_DEATH_PROBABILITY | ParameterType.OVERDOSE_PROBABILITY | ParameterType.OVERDOSE_FATALITY_PROBABILITY: + return "probability" + case ParameterType.INTERVENTION_TRANSITION_PROBABILITY | ParameterType.BEHAVIOR_TRANSITION_PROBABILITY: + return "probability" + case ParameterType.INITIAL_COHORT | ParameterType.MIGRATION_COHORT: + return "count" + case _: + raise ValueError( + f"ParameterType value supplied is not implemented in the cohort table! ParameterType: {self.__parameter_type}." + ) + + def get_initial_state_column_name(self) -> str | None: + """Return the origin-state column name for transition parameters. + + :returns: Origin-state column name, or ``None`` for non-transition + parameters. + """ + match self.__parameter_type: + case ParameterType.INTERVENTION_TRANSITION_PROBABILITY: + return "initial_intervention" + case ParameterType.BEHAVIOR_TRANSITION_PROBABILITY: + return "initial_behavior" + case _: + return None + + def get_next_state_column_name(self) -> str | None: + """Return the destination-state column name for transition parameters. + + :returns: Destination-state column name, or ``None`` for non-transition + parameters. + """ + match self.__parameter_type: + case ParameterType.INTERVENTION_TRANSITION_PROBABILITY: + return "new_intervention" + case ParameterType.BEHAVIOR_TRANSITION_PROBABILITY: + return "new_behavior" + case _: + return None + + def get_cohort_column_name(self) -> str: + """Return cohort-table sample column used by this parameter. + + :returns: Column name in the ``cohort`` table. + :raises ValueError: If the parameter type is not implemented. + """ + match self.__parameter_type: + case ParameterType.INITIAL_COHORT: + return "initial_population_sample" + case ParameterType.MIGRATION_COHORT: + return "population_change_sample" + case ParameterType.INTERVENTION_TRANSITION_PROBABILITY: + return "intervention_transition_sample" + case ParameterType.BEHAVIOR_TRANSITION_PROBABILITY: + return "behavior_transition_sample" + case ParameterType.OVERDOSE_PROBABILITY: + return "overdose_sample" + case ParameterType.OVERDOSE_FATALITY_PROBABILITY: + return "overdose_fatality_sample" + case ParameterType.STANDARD_MORTALITY_RATIO: + return "smr_sample" + case ParameterType.BACKGROUND_DEATH_PROBABILITY: + return "background_mortality_sample" + case _: + raise ValueError( + f"ParameterType value supplied is not implemented in the cohort table! ParameterType: {self.__parameter_type}." + ) + + def get_insert_statement(self) -> str: + """Return the INSERT SQL template for this parameter table. + + :returns: Parameter-specific SQL INSERT statement. + :raises ValueError: If the parameter type is not implemented. + """ + match self.__parameter_type: + case ParameterType.INITIAL_COHORT: + return "INSERT INTO initial_population VALUES (?, ?, ?, ?)" + case ParameterType.MIGRATION_COHORT: + return "INSERT INTO population_change VALUES (?, ?, ?, ?, ?)" + case ParameterType.INTERVENTION_TRANSITION_PROBABILITY: + return "INSERT INTO intervention_transition VALUES (?, ?, ?, ?, ?, ?)" + case ParameterType.BEHAVIOR_TRANSITION_PROBABILITY: + return "INSERT INTO behavior_transition VALUES (?, ?, ?, ?, ?, ?)" + case ParameterType.OVERDOSE_PROBABILITY: + return "INSERT INTO overdose VALUES (?, ?, ?, ?, ?)" + case ParameterType.OVERDOSE_FATALITY_PROBABILITY: + return "INSERT INTO overdose_fatality VALUES (?, ?, ?, ?, ?)" + case ParameterType.BACKGROUND_DEATH_PROBABILITY: + return "INSERT INTO background_mortality VALUES (?, ?, ?)" + case ParameterType.STANDARD_MORTALITY_RATIO: + return "INSERT INTO smr VALUES (?, ?, ?, ?, ?)" + case _: + raise ValueError( + f"ParameterType value supplied is not implemented in the cohort table! ParameterType: {self.__parameter_type}." + ) + + def get_select_statement( + self, interventions: list[str], behaviors: list[str] + ) -> str: + """Return parameter-specific SELECT SQL with deterministic ordering. + + :param interventions: Ordered intervention names used in ``ORDER BY``. + :param behaviors: Ordered behavior names used in ``ORDER BY``. + :returns: SQL query string for parameter extraction. + :raises ValueError: If SELECT generation is not implemented. + """ + match self.__parameter_type: + case ParameterType.INITIAL_COHORT: + return f""" + SELECT i.name AS intervention, b.name AS behavior, count + FROM initial_population AS ip + INNER JOIN intervention AS i ON ip.intervention = i.id + INNER JOIN behavior AS b ON ip.behavior = b.id + WHERE sample = ? + ORDER BY + {get_column_order("i.name", interventions)}, {get_column_order("b.name", behaviors)} + """ + case ParameterType.MIGRATION_COHORT: + return f""" + SELECT i.name AS intervention, b.name AS behavior, count + FROM population_change AS pc + INNER JOIN intervention AS i ON pc.intervention = i.id + INNER JOIN behavior AS b ON pc.behavior = b.id + WHERE sample = ? AND time = ? + ORDER BY + {get_column_order("i.name", interventions)}, {get_column_order("b.name", behaviors)} + """ + case ParameterType.INTERVENTION_TRANSITION_PROBABILITY: + return f""" + SELECT ii.name AS initial_intervention, ni.name AS new_intervention, b.name AS behavior, probability + FROM intervention_transition AS it + INNER JOIN intervention AS ii ON it.initial_intervention = ii.id + INNER JOIN intervention AS ni ON it.new_intervention = ni.id + INNER JOIN behavior AS b ON it.behavior = b.id + WHERE sample = ? AND time = ? + ORDER BY + {get_column_order("ii.name", interventions)}, {get_column_order("ni.name", interventions)}, {get_column_order("b.name", behaviors)} + """ + case ParameterType.BEHAVIOR_TRANSITION_PROBABILITY: + return f""" + SELECT i.name AS intervention, ib.name AS initial_behavior, nb.name AS new_behavior, probability + FROM behavior_transition AS it + INNER JOIN intervention AS i ON it.intervention = i.id + INNER JOIN behavior AS ib ON it.initial_behavior = ib.id + INNER JOIN behavior AS nb ON it.new_behavior = nb.id + WHERE sample = ? AND time = ? + ORDER BY + {get_column_order("i.name", interventions)},{get_column_order("ib.name", behaviors)}, + {get_column_order("nb.name", behaviors)} + """ + case ParameterType.OVERDOSE_FATALITY_PROBABILITY: + return f""" + SELECT i.name AS intervention, b.name AS behavior, probability + FROM overdose_fatality AS fod + INNER JOIN intervention AS i ON fod.intervention = i.id + INNER JOIN behavior AS b ON fod.behavior = b.id + WHERE sample = ? AND time = ? + ORDER BY + {get_column_order("i.name", interventions)}, {get_column_order("b.name", behaviors)} + """ + case ParameterType.OVERDOSE_PROBABILITY: + return f""" + SELECT i.name AS intervention, b.name AS behavior, probability + FROM overdose AS od + INNER JOIN intervention AS i ON od.intervention = i.id + INNER JOIN behavior AS b ON od.behavior = b.id + WHERE sample = ? AND time = ? + ORDER BY + {get_column_order("i.name", interventions)}, {get_column_order("b.name", behaviors)} + """ + case ParameterType.BACKGROUND_DEATH_PROBABILITY: + return """ + SELECT probability + FROM background_mortality + WHERE sample = ? AND time = ? + """ + case ParameterType.STANDARD_MORTALITY_RATIO: + return f""" + SELECT i.name AS intervention, b.name AS behavior, ratio + FROM smr + INNER JOIN intervention AS i ON smr.intervention = i.id + INNER JOIN behavior AS b ON smr.behavior = b.id + WHERE sample = ? AND time = ? + ORDER BY + {get_column_order("i.name", interventions)}, {get_column_order("b.name", behaviors)} + """ + case _: + raise ValueError( + f"Select statement generation is only implemented for transition probability parameters! ParameterType: {self.__parameter_type}." + ) diff --git a/src/respondpy/data/state_vectors.py b/src/respondpy/data/state_vectors.py new file mode 100644 index 0000000..2af5e5e --- /dev/null +++ b/src/respondpy/data/state_vectors.py @@ -0,0 +1,48 @@ +################################################################################ +# File: state_vectors.py # +# Project: respondpy # +# Created Date: 2026-06-04 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-09 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +import polars as pl + + +def build_constant_state_vector( + interventions: list, + behaviors: list, + *, + sample_id: int = 1, + time: int = 1, + value_column: str = "probability", + constant: float = 0.0 +) -> pl.DataFrame: + """Build a fully populated constant-valued state vector table. + + :param interventions: Ordered intervention names. + :param behaviors: Ordered behavior names. + :param sample_id: Sample identifier written into output rows. + :param time: Timestep written into output rows. + :param value_column: Name of the generated value column. + :param constant: Constant value assigned to every state row. + :returns: Cross-product dataframe with one row per intervention-behavior + state. + """ + inter = pl.DataFrame({"intervention": interventions}) + + behav = pl.DataFrame({"behavior": behaviors}) + + df = inter.join( + behav, how="cross" + ).with_columns( + pl.lit(sample_id).alias("sample"), + pl.lit(time).alias("time"), + pl.lit(constant).alias(value_column) + ).select(["sample", "intervention", "behavior", "time", value_column]) + + return df diff --git a/src/respondpy/data/transition_matrices.py b/src/respondpy/data/transition_matrices.py new file mode 100644 index 0000000..9120e97 --- /dev/null +++ b/src/respondpy/data/transition_matrices.py @@ -0,0 +1,368 @@ +################################################################################ +# File: transition_matrices.py # +# Project: respondpy # +# Created Date: 2026-06-02 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-10 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +import polars as pl +import numpy as np + +def _require_columns( + frame: pl.LazyFrame | pl.DataFrame, columns: list[str] +) -> None: + if isinstance(frame, pl.LazyFrame): + frame_cols = set(frame.collect_schema().names()) + else: + frame_cols = set(frame.columns) + missing = [c for c in columns if c not in frame_cols] + if missing: + raise ValueError(f"Missing required columns: {missing}") + + +def _validate_probability_column( + frame: pl.DataFrame, + probability_column: str, +) -> None: + _require_columns(frame, [probability_column]) + has_null = frame.select(pl.col(probability_column).is_null().any()).item() + if has_null: + raise ValueError(f"Null values found in {probability_column}") + + +def _ensure_no_duplicate_keys( + frame: pl.LazyFrame | pl.DataFrame, + key_columns: list[str] | None = None +) -> None: + if not key_columns: + key_columns = frame.collect_schema().names() + else: + _require_columns(frame, key_columns) + + # We have to collect because we need to verify the contents of the dataframe + if isinstance(frame, pl.LazyFrame): + frame = frame.collect() + has_duplicates = frame.select( + pl.struct(key_columns).is_duplicated().any()).item() + if has_duplicates: + raise ValueError( + f"Duplicate transition rows found for key columns: {key_columns}" + ) + + +def _default_transition_shape( + frame: pl.LazyFrame | pl.DataFrame, +) -> tuple[list[str], list[str], list[str], list[str]]: + default_cols = { + "sample", + "time", + "initial_intervention", + "new_intervention", + "initial_behavior", + "new_behavior" + } + + if isinstance(frame, pl.LazyFrame): + cols = set(frame.collect_schema().names()) + else: + cols = set(frame.columns) + + if default_cols.issubset(cols): + from_cols = ['initial_intervention', 'initial_behavior'] + to_cols = ['new_intervention', 'new_behavior'] + sample_cols = ["sample", "time"] + grouping_cols = sample_cols + from_cols + unique_conditions = sample_cols + from_cols + to_cols + return grouping_cols, from_cols, to_cols, unique_conditions + + raise ValueError("Unable to find default transition shape in columns.") + + +def build_constant_transition( + interventions: list[str], + behaviors: list[str], + *, + sample_id: int = 1, + time: int = 1, + constant: float = 0.0 +) -> pl.LazyFrame: + """Build a complete constant-valued transition matrix table. + + :param interventions: Ordered intervention names. + :param behaviors: Ordered behavior names. + :param sample_id: Sample identifier written into output rows. + :param time: Timestep written into output rows. + :param constant: Constant probability assigned before normalization. + :returns: LazyFrame containing all from/to state combinations. + :raises ValueError: If the generated matrix is not square. + """ + init_behav = pl.LazyFrame({"initial_behavior": behaviors}) + new_behav = pl.LazyFrame({"new_behavior": behaviors}) + init_inter = pl.LazyFrame({"initial_intervention": interventions}) + new_inter = pl.LazyFrame({"new_intervention": interventions}) + + result = init_behav.join( + new_behav, how="cross" + ).join( + init_inter, how="cross" + ).join( + new_inter, how="cross" + ).with_columns( + sample=pl.lit(sample_id), + time=pl.lit(time), + probability=pl.lit(constant), + ).select( + [ + "sample", + "time", + "initial_behavior", + "new_behavior", + "initial_intervention", + "new_intervention", + "probability", + ] + ) + + n_states = len(interventions) * len(behaviors) + + if result.select(pl.len()).collect().item() != n_states**2: + raise ValueError( + "The resulting transition matrix is not square. Check that the number of interventions and behaviors is correct." + ) + + return result + + +def combine_dataframes( + complete_df: pl.LazyFrame, + raw_data_df: pl.LazyFrame, + *, + value_col: str = "probability" +) -> pl.LazyFrame: + """Overlay raw sampled values onto a complete template dataframe. + + Values in ``raw_data_df`` take precedence over template values where keys + match. + + :param complete_df: Fully enumerated dataframe used as fallback. + :param raw_data_df: Observed values to merge into template. + :param value_col: Name of numeric value column to collapse. + :returns: Combined LazyFrame with a single ``value_col``. + :raises ValueError: If join key columns are incompatible. + """ + join_cols = raw_data_df.collect_schema().names() + join_cols.remove(value_col) + if not set( + join_cols + ).issubset(set(complete_df.collect_schema().names())): + raise ValueError( + f"The raw data dataframe must contain a subset of columns of the complete dataframe in order to combine them! Complete dataframe columns: {complete_df.collect_schema().names()}, raw data dataframe columns: {raw_data_df.collect_schema().names()}" + ) + # We want to join on all columns except the value column, and then collapse the value column so that if there is a value in raw_data_df it takes precedence, but if there isn't then we keep the value from complete_df. This allows us to fill in missing values with the constant dataframes. + joined_lf = complete_df.join( + raw_data_df, on=join_cols, how="left", suffix="_new") + collapsed_df = joined_lf.with_columns( + pl.when( + pl.col(f"{value_col}_new").is_null() + ).then( + pl.col(value_col) + ).otherwise( + pl.col(f"{value_col}_new") + ).alias(value_col) + ).select(pl.all().exclude(f"{value_col}_new")) + return collapsed_df + + +def update_retention_probability( + transition_matrix: pl.LazyFrame | pl.DataFrame, + transition_columns: str | list[str], + new_columns: str | list[str], + *, + probability_column: str = "probability", + group_columns: list[str] | None = None, + unique_key_columns: list[str] | None = None, + tolerance: float = 1e-12, + forbid_negative_retention: bool = True, +) -> pl.DataFrame: + """Set retention probabilities so each transition group sums to one. + + Retention rows are those where origin-state columns equal destination-state + columns. Their values are replaced by ``1 - sum(non_retention)`` per group. + + :param transition_matrix: Transition rows to normalize. + :param transition_columns: Origin-state column(s). + :param new_columns: Destination-state column(s). + :param probability_column: Probability column to update. + :param group_columns: Grouping keys defining one origin state/time/sample. + :param unique_key_columns: Columns expected to uniquely identify rows. + :param tolerance: Floating-point tolerance for validation checks. + :param forbid_negative_retention: If ``True``, reject negative retention. + :returns: Transition dataframe with updated retention probabilities. + :raises ValueError: If schema validation fails or probabilities cannot be + normalized. + """ + if group_columns is None or unique_key_columns is None: + group_cols, from_cols, to_cols, constraints = _default_transition_shape( + transition_matrix) + else: + group_cols = group_columns + from_cols = transition_columns if isinstance( + transition_columns, list) else [transition_columns] + to_cols = new_columns if isinstance( + new_columns, list) else [new_columns] + constraints = [] + + _require_columns(transition_matrix, constraints + [probability_column]) + _ensure_no_duplicate_keys(transition_matrix, constraints) + + if len(to_cols) != len(from_cols): + raise ValueError( + "The number of transition from columns does not match the number of transition to columns!") + + # we have to collect the LazyFrame here because we need to count rows + if isinstance(transition_matrix, pl.LazyFrame): + transition_matrix = transition_matrix.collect() + + n_states = np.square( + np.prod([transition_matrix.n_unique(c) for c in from_cols]) + ) + + if transition_matrix.select(pl.len()).item() != n_states: + required_cols = ['initial_intervention', + 'initial_behavior', 'sample', 'time'] + if not set(required_cols).issubset(set(transition_matrix.columns)): + raise ValueError( + "Transition matrix is missing required columns for completion. Please provide a complete transition matrix or ensure that 'initial_intervention' and 'initial_behavior' columns are present for automatic completion." + ) + inter = transition_matrix['initial_intervention'].unique().to_list() + behav = transition_matrix['initial_behavior'].unique().to_list() + sample = transition_matrix['sample'].unique().item() + time = transition_matrix['time'].unique().item() + + completed = combine_dataframes( + build_constant_transition( + inter, behav, sample_id=sample, time=time + ), transition_matrix.lazy(), value_col=probability_column).collect() + else: + completed = transition_matrix + + from_to_pairs = [(from_cols, to_cols)] + + _validate_probability_column(transition_matrix, probability_column) + + is_retention = pl.lit(True) + for origin_cols, destination_cols in from_to_pairs: + for origin_col, destination_col in zip(origin_cols, destination_cols): + is_retention = is_retention & ( + pl.col(destination_col) == pl.col(origin_col) + ) + + non_retention = ( + completed + .filter(~is_retention) + .group_by(group_cols) # Add new_behavior (if intervention) + .agg(pl.col(probability_column).sum().alias("__non_retention_sum")) + ) + + retention_targets = ( + completed.select(group_cols) + .unique() + .join(non_retention, on=group_cols, how="left") + .with_columns( + pl.col("__non_retention_sum").fill_null(0.0), + (1.0 - pl.col("__non_retention_sum")).alias("__retention_target"), + ) + .drop("__non_retention_sum") + ) + + if forbid_negative_retention: + has_negative = retention_targets.select( + pl.col("__retention_target").lt(-tolerance).any() + ).item() + if has_negative: + raise ValueError( + "Cannot update retention probabilities: non-retention probabilities exceed 1 in at least one transition group." + ) + + retention_targets = retention_targets.with_columns( + pl.when(pl.col("__retention_target").abs().le(tolerance)) + .then(0.0) + .otherwise(pl.col("__retention_target")) + .alias("__retention_target") + ) + + result = ( + completed.join(retention_targets, on=group_cols, how="left") + .with_columns( + pl.when(is_retention) + .then(pl.col("__retention_target")) + .otherwise(pl.col(probability_column)) + .alias(probability_column) + ) + .drop("__retention_target") + ) + + # final invariant check; this keeps the function idempotent and defensive + if not verify_transition_probability( + result, + from_cols, + probability_column=probability_column, + group_columns=group_cols, + unique_key_columns=constraints, + tolerance=tolerance, + ): + raise ValueError( + "Retention update failed to normalize probabilities to 1 for all transition groups." + ) + + return result + + +def verify_transition_probability( + transition_matrix: pl.DataFrame, + transition_columns: str | list[str], + *, + probability_column: str = "probability", + group_columns: list[str] | None = None, + unique_key_columns: list[str] | None = None, + tolerance: float = 1e-12, +) -> bool: + """Check that transition probabilities sum to one within each group. + + :param transition_matrix: Transition rows to verify. + :param transition_columns: Origin-state column(s), used with grouping. + :param probability_column: Probability column to sum. + :param group_columns: Explicit grouping keys. If omitted, defaults are + inferred. + :param unique_key_columns: Explicit unique-key columns. If omitted, + defaults are inferred. + :param tolerance: Absolute tolerance for checking sums against ``1.0``. + :returns: ``True`` when all transition groups sum to one. + """ + if group_columns is None or unique_key_columns is None: + group_cols, _, _, constraints = _default_transition_shape( + transition_matrix) + else: + group_cols = group_columns + constraints = transition_matrix.columns + constraints.remove(probability_column) + + if isinstance(transition_columns, str): + transition_columns = [transition_columns] + + _require_columns(transition_matrix, group_cols) + _validate_probability_column(transition_matrix, probability_column) + _ensure_no_duplicate_keys(transition_matrix, constraints) + + grouped = transition_matrix.group_by(group_cols).agg( + pl.col(probability_column).sum().alias("prob_sum") + ) + + return grouped.select( + (pl.col("prob_sum") - 1.0).abs().le(tolerance).all() + ).item() diff --git a/src/respondpy/execute/build_simulation.py b/src/respondpy/execute/build_simulation.py deleted file mode 100644 index b9de0f8..0000000 --- a/src/respondpy/execute/build_simulation.py +++ /dev/null @@ -1,220 +0,0 @@ -################################################################################ -# File: build_simulation.py # -# Project: respondpy # -# Created Date: 2026-02-26 # -# Author: Matthew Carroll # -# ----- # -# Last Modified: 2026-02-26 # -# Modified By: Matthew Carroll # -# ----- # -# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # -################################################################################ - -from pathlib import Path -from configparser import ConfigParser -from collections.abc import Sequence - -import polars as pl -import numpy as np - -from respondpy._core.transition import Transition # pylint: disable=import-error,no-name-in-module -from respondpy._core.model import Model # pylint: disable=import-error,no-name-in-module -from respondpy._core.simulation import Simulation # pylint: disable=import-error,no-name-in-module -from respondpy.data.parameters import Parameter -from respondpy.io.reading import get_cohorts -from respondpy.execute.build_transition import get_data_array - - -def _get_list_of_ints(config_string: str) -> list[int]: - """Converts a comma-separated string into a list of integers - - Args: - config_string (str): A comma-separated string - - Returns: - list[int]: The list of integers from the string - """ - return [int(x.strip()) for x in config_string.split(',')] - - -def _build_single_transition(name: str, tran_matrices: list[np.ndarray], *, log_name: str = "console") -> Transition: - t = Transition(name, log_name) - for tm in tran_matrices: - t.add_transition_matrix(tm) - return t - - -def _build_timestep( - timestep: int, - db: str | Path, - sample_ids: pl.DataFrame -) -> list[Transition]: - """Helper function to build a single timestep because a timestep consists of the same transitions. - - Args: - timestep (int): The integer for the timestep we want to build. - db (str | Path): The location of the database file. - sample_ids (pl.DataFrame): The cohort with the sample ids. - - Returns: - list[Transition]: The list of transitions that make the timestep. - """ - - migration = _build_single_transition("migration", [ - get_data_array(Parameter.MIGRATION_COHORT, - db, sample_ids, timestep).squeeze() - ]) - - inter = _build_single_transition("intervention", [ - get_data_array( - Parameter.INTERVENTION_TRANSITION_PROBABILITY, db, sample_ids, timestep - ).T - ]) - - behav = _build_single_transition("behavior", [ - get_data_array( - Parameter.BEHAVIOR_TRANSITION_PROBABILITY, db, sample_ids, timestep - ).T - ]) - - overd = _build_single_transition("overdose", [ - get_data_array( - Parameter.OVERDOSE_PROBABILITY, db, sample_ids, timestep - ).squeeze(), - get_data_array( - Parameter.OVERDOSE_FATALITY_PROBABILITY, db, sample_ids, timestep - ).squeeze() - ]) - - morta = _build_single_transition("background_death", [ - get_data_array( - Parameter.STANDARD_MORTALITY_RATIO, db, sample_ids, timestep - ).squeeze() * get_data_array( - Parameter.BACKGROUND_DEATH_PROBABILITY, db, sample_ids, timestep - ).squeeze() - ]) - - return [migration, inter, behav, overd, morta] - - -def _add_transitions( - model: Model, - t_transition: list[Transition] -) -> Model: - """Helper function to add the transitions to the Markov model. - - Args: - model (Model): The model to add the transitions to. - t_transition (list[Transition]): A timestep (i.e. a list of transitions) - - Returns: - rpy.Markov: The Markov model with the transitions added. - """ - for t in t_transition: - model.add_transition(t) - return model - - -def _valid_change_times(ct_list: list[int]) -> list[int]: - """A validation function that validates and cleans up the change times from the config file. - - Args: - ct_list (list[int]): A parsed list of integers from the `simulation.parameter_change_times` entry in the `sim.conf` - - Raises: - ValueError: Invalid values provided in the config file. - - Returns: - list[int]: The cleaned config values. - """ - if any(num <= 0 for num in ct_list): - raise ValueError( - "The config file contains zero or a negative number in the `parameter_change_times` list!") - ct_list.sort(reverse=True) - if ct_list[-1] == 1: - ct_list.pop() - return ct_list - - -def _build_transitions( - model: Model, - db: str | Path, - config: ConfigParser, - sample_ids: pl.DataFrame -) -> Model: - """Helper function to build the transition list for the Markov model. - - Args: - model (Model): The model we are intended to add transitions to. - db (str | Path): The string or Path object to the database file. - config (ConfigParser): The object containing the config data. - sample_ids (pl.DataFrame): The cohort sample containing all the sample ids. - - Returns: - rpy.Markov: The Markov model with the transitions added for the entire duration. - """ - # Add the first timestep - ct_val = 1 - transition = _build_timestep(ct_val, db, sample_ids) - _add_transitions(model, transition) - duration = config.getint('simulation', 'duration') - change_times = _valid_change_times( - _get_list_of_ints(config.get('simulation', 'parameter_change_times')) - ) - - # we start at 2 because 0 is in the initial state, 1 is the first transition (added above), and now we look for more transitions. If there is no other change times then we just make copies. - for i in range(1, duration): - if change_times and i == change_times[-1]: - ct_val = change_times.pop() - transition = _build_timestep(ct_val, db, sample_ids) - _add_transitions(model, transition) - else: - _add_transitions(model, transition.copy()) - return model - - -def _build_new_model( - db: str | Path, - config: ConfigParser, - sample_ids: pl.DataFrame, - *, - name: str = "markov", - log_name: str = "console" -) -> Model: - m = Model(name, log_name) - init_pop = get_data_array( - Parameter.INITIAL_COHORT, db, sample_ids).squeeze() - m.set_state(init_pop) - m = _build_transitions(m, db, config, sample_ids) - return m - - -def build_simulation( - cohort_ids: Sequence[int], - db: str | Path, - config: ConfigParser, - *, - log_name: str = "console" -) -> Simulation: - """Build a new simulation object. This function takes the cohorts, the database, and the config file then applies them to a new simulation. It creates a new model for each cohort id and adds it to the simulation returning the result. - - Args: - cohort_ids (Sequence[int]): A sequence of ints identifying the cohorts. - db (str | Path): A database containing the data for the simulation. - config (ConfigParser): The `sim.conf` file used during the simulation. - log_name (str, optional): The name of the logger to apply to the simulation. Defaults to "console". - - Returns: - Simulation: The new simulation object. - """ - s = Simulation(log_name) - col_names, cohorts = get_cohorts(db) - for cohort_id in cohort_ids: - sample_ids = pl.DataFrame( - cohorts, schema=col_names, orient='row' - ).filter(pl.col("id") == cohort_id) - s.add_model(_build_new_model( - db, config, sample_ids, log_name=log_name - )) - - return s diff --git a/src/respondpy/execute/build_transition.py b/src/respondpy/execute/build_transition.py deleted file mode 100644 index c6b34ef..0000000 --- a/src/respondpy/execute/build_transition.py +++ /dev/null @@ -1,445 +0,0 @@ -################################################################################ -# File: build_transition.py # -# Project: respondpy # -# Created Date: 2026-02-26 # -# Author: Matthew Carroll # -# ----- # -# Last Modified: 2026-02-26 # -# Modified By: Matthew Carroll # -# ----- # -# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # -################################################################################ -from pathlib import Path -import itertools - -import polars as pl -import numpy as np - -import respondpy as rpy - -# Internal Functions - - -def _validate_time(time: int | None, p: rpy.Parameter) -> int: - """Helper function to validate a good time value for the specified parameter. If the parameter is - - Args: - time (int | None): The time variable to check. - p (rpy.Parameter): The parameter to validate time against. - - Raises: - ValueError: If time is None and the parameter is not the initial cohort, warn the user they need a time. - - Returns: - int: The parameter time or 0 if None and the parameter is an initial cohort. - """ - if time is None: - if p != rpy.Parameter.INITIAL_COHORT: - raise ValueError( - f"Parameter being extracted requires a time value be provided, but none was found: {p}") - return 0 - return time - - -def _get_sample_column_string(p: rpy.Parameter) -> str: - """Getter for the column name in cohort representing the sample ID based on the parameter specified. - - Args: - p (rpy.Parameter): The parameter we want the column name for. - - Raises: - ValueError: Invalid parameter specified. - - Returns: - str: The column name in the cohort table for the parameter sample. - """ - if p == rpy.Parameter.INITIAL_COHORT: - return "initial_population_sample" - if p == rpy.Parameter.MIGRATION_COHORT: - return "population_change_sample" - if p == rpy.Parameter.INTERVENTION_TRANSITION_PROBABILITY: - return "intervention_transition_sample" - if p == rpy.Parameter.BEHAVIOR_TRANSITION_PROBABILITY: - return "behavior_transition_sample" - if p == rpy.Parameter.OVERDOSE_PROBABILITY: - return "overdose_sample" - if p == rpy.Parameter.OVERDOSE_FATALITY_PROBABILITY: - return "overdose_fatality_sample" - if p == rpy.Parameter.STANDARD_MORTALITY_RATIO: - return "smr_sample" - if p == rpy.Parameter.BACKGROUND_DEATH_PROBABILITY: - return "background_mortality_sample" - raise ValueError( - f"Parameter value supplied is not implemented in the cohort table! {p}.") - - -def _get_parameter_data( - p: rpy.Parameter, - db: str | Path, - sample_ids: pl.DataFrame, - time: int -) -> pl.LazyFrame: - """Getter for the parameter data from the database. - - Args: - p (rpy.Parameter): The parameter to retrieve from the database. - db (str | Path): The path to the database file. - sample_ids (pl.DataFrame): The cohort table containing the sample IDs. - time (int): The time point to get data for. - - Returns: - pl.LazyFrame: A LazyFrame containing the parameter data from the database. The columns match the columns of the select statement, a state vector returns 3 columns ['intervention', 'behavior', 'value'] pattern where a transition matrix returns 4 columns ['init_intervention', 'init_behaivor' 'next_parameter', 'value']. Constant values return the single column. - """ - sid_name = _get_sample_column_string(p) - ret_cols, ret_values = rpy.get_parameter_by_id_and_time( - p, db, sample_ids.select(pl.col(sid_name)).item(), time - ) - return pl.LazyFrame(ret_values, schema=ret_cols, orient='row') - - -def _fill_missing_values( - lf: pl.LazyFrame, - p: rpy.Parameter, - db: str | Path, - sample_ids: pl.DataFrame, - time: int -) -> pl.LazyFrame: - """Helper function to fill missing values for a sample. This is applied when we only want to draw a specific subset of values from a transition matrix and hold the rest constant (e.g. intervention transitions where we calibrate uptake and fix loss to follow up). - - Args: - lf (pl.LazyFrame): LazyFrame containing the sampled parameters - p (rpy.Parameter): Parameter of the data the LazyFrame contains - db (str | Path): Path to the database file - sample_ids (pl.DataFrame): The row of sample ids from the cohort table - time (int): Timestep for the sampled parameters - - Raises: - ValueError: Too many transitions found in the sample - ValueError: The first sample does not have fixed values - - Returns: - pl.LazyFrame: A set of data composed of both fixed and sampled parameters. A state vector returns a 3 column ['intervention', 'behavior', 'value'] pattern where a transition matrix returns 4 columns ['init_intervention', 'init_behavior' 'next_parameter', 'value']. Constant values return the single column. - """ - # quick exit if background mortality since it is a cohort constant - if p == rpy.Parameter.BACKGROUND_DEATH_PROBABILITY: - return lf - sid_name = _get_sample_column_string(p) - - num_states = len(rpy.get_state_names(db)) - if p == rpy.Parameter.INTERVENTION_TRANSITION_PROBABILITY: - num_states *= len(rpy.get_interventions(db)) - # check the height of a dataframe is the size of the intervention list in the db - elif p == rpy.Parameter.BEHAVIOR_TRANSITION_PROBABILITY: - num_states *= len(rpy.get_behaviors(db)) - # check the height of a dataframe is the size of the behavior list in the db - - sample_id = sample_ids.select(pl.col(sid_name)).item() - height = lf.collect().height - if height == num_states: - return lf - if height > num_states: - raise ValueError( - f"Too many transition values found for sample {sample_id} and parameter {p}") - - # Below assumes the dataframe has only part of the data and we must load more from fixed samples - if sample_id == 1: - raise ValueError( - f"The first sample in the database for parameter {p} does not have {num_states} values!") - one_cols, one_values = rpy.get_parameter_by_id_and_time(p, db, 1, time) - return _fill_gaps_with_sample(lf, pl.LazyFrame(one_values, schema=one_cols, orient='row')) - - -def _build_transition_matrix_from_partial( - partial: pl.LazyFrame, - db: str | Path, - *, - on_cols: list | None = None -) -> pl.LazyFrame: - """Helper function to build a transition matrix from the samples. This only executes if the partial has 4 columns (the indicator for a transition matrix). This function is needed because both transition matrices (interventions and behaviors) are sparse matrices. - - Note: partial has columns dependent on the parameter, but the only transition matrices are intervention and behavior. Thus the columns must be either: - - ['initial_intervention', 'new_intervention', 'behavior', 'probability'] - - ['intervention', 'initial_behavior', 'new_behavior', 'probability'] - - Args: - partial (pl.LazyFrame): The sample from the database with both sampled and fixed values. - db (str | Path): A path to the database file. - on_cols (list | None, optional): What columns to join on. If None is provided, defaults to ['intervention', 'behavior']. - - Raises: - ValueError: Invalid number of column names have the "new_" prefix. - NotImplementedError: A column other than intervention or behavior has the "new_" prefix. - - Returns: - pl.LazyFrame: Either the original partial LazyFrame or a LazyFrame containing columns ['intervention', 'behavior' 'next_intervention', 'next_behavior', 'probability']. - """ - # quick exit if there aren't 4 columns in the partial (it isn't a transition matrix) - if len(partial.collect_schema().names()) != 4: - return partial - - if not on_cols: - on_cols = ["intervention", "behavior"] - - lf = _get_zero_transition_matrix(db) - - # collecting into a temporary variable so we don't materialize the partial back in the calling function - temp = partial.collect() - temp.columns = pl.Series( - temp.columns).str.strip_prefix("initial_").to_list() - - parameter_cols = pl.Series(temp.columns).str.starts_with("new_").to_list() - - if sum(parameter_cols) != 1: - raise ValueError( - f"Invalid number of new parameters specified in DataFrame. Columns found: {parameter_cols}") - p_col = str(temp.columns[parameter_cols.index(True)]) - - left = on_cols.copy() - right = on_cols.copy() - if p_col.endswith("behavior"): - left.extend(["next_behavior", "next_intervention"]) - right.extend([p_col, "intervention"]) - # expr = ((pl.col("intervention") == pl.col("next_intervention")) & - # (pl.col(p_col) == pl.col("next_behavior"))) - - elif p_col.endswith("intervention"): - left.extend(["next_intervention", "next_behavior"]) - right.extend([p_col, "behavior"]) - - # expr = ((pl.col("behavior") == pl.col("next_behavior")) & - # (pl.col(p_col) == pl.col("next_intervention"))) - else: - raise NotImplementedError( - "Only behavior and intervention parameters are allowed to be transition matrices!") - - lf = lf.join(temp.lazy(), left_on=left, right_on=right, how="left") - lf = lf.with_columns( - pl.col("probability").fill_null(0.0) - ).drop("values").select( - ["intervention", "behavior", "next_intervention", - "next_behavior", "probability"] - ) - - return lf - - -def _sort_dataframes(lf: pl.LazyFrame, db: str | Path) -> pl.LazyFrame: - """Generalized sorting function to direct the sorting order. Only LazyFrames with state vectors (3 columns) or transition matrices (4 columns)are sorted, all other LazyFrames are returned as is. - - Args: - lf (pl.LazyFrame): LazyFrame to sort. - db (str | Path): A path to the database file. - - Returns: - pl.LazyFrame: A sorted LazyFrame. - """ - if len(lf.collect_schema().names()) == 3: - return _sort_state_vector(lf, db) - if len(lf.collect_schema().names()) == 4: - return _sort_transition_matrix(lf, db) - return lf - - -def _extract_values( - p: rpy.Parameter, - lf: pl.LazyFrame, - *, - n: int = 64 # 16 interventions * 4 behaviors -) -> np.ndarray: - """Helper function to convert the dataframe rows to a numpy array reshaped into either a numpy state vector [n x 1] or transition matrix [n x n]. - - Args: - p (rpy.Parameter): The parameter we are extracting data for - results (pl.LazyFrame): The dataframe rows - n (int, optional): The number of states in the state vector. Defaults to 64, assuming 16 interventions and 4 behaviors. - - Raises: - ValueError: Invalid parameter provided. - - Returns: - np.ndarray: A numpy matrix of either [n x 1] or [n x n] - """ - if p in [rpy.Parameter.INITIAL_COHORT, rpy.Parameter.MIGRATION_COHORT]: - values = lf.select(pl.col("count")).collect().to_numpy().reshape(n, 1) - elif p in [rpy.Parameter.INTERVENTION_TRANSITION_PROBABILITY, - rpy.Parameter.BEHAVIOR_TRANSITION_PROBABILITY]: - values = lf.select(pl.col("probability") - ).collect().to_numpy().reshape(n, n) - elif p in [rpy.Parameter.OVERDOSE_PROBABILITY, - rpy.Parameter.OVERDOSE_FATALITY_PROBABILITY]: - values = lf.select(pl.col("probability") - ).collect().to_numpy().reshape(n, 1) - elif p == rpy.Parameter.STANDARD_MORTALITY_RATIO: - values = lf.select(pl.col("ratio")).collect().to_numpy().reshape(n, 1) - elif p == rpy.Parameter.BACKGROUND_DEATH_PROBABILITY: - values = np.repeat(lf.select(pl.col("probability") - ).collect().to_numpy(), n).reshape(n, 1) - else: - raise ValueError( - "Invalid parameter applied when attempting to extract parameters!") - - return values - - -def _sort_state_vector(lf: pl.LazyFrame, db: str | Path) -> pl.LazyFrame: - """Sorting function for a state vector. Expects the columns ['intervention' 'behavior']. - - Args: - lf (pl.LazyFrame): A sorted LazyFrame - db (str | Path): A path to the database file. - - Raises: - ValueError: Invalid column names. - - Returns: - pl.LazyFrame: A sorted LazyFrame (sorted by intervention, behavior). - """ - s = lf.collect_schema().names() - if 'intervention' not in s or 'behavior' not in s: - raise ValueError( - f"Invalid columns provided when attempting to sort state vector: {s}") - behaviors = pl.LazyFrame( - rpy.get_behavior_table(db), schema=["b_id", "b_name"], orient='row') - - interventions = pl.LazyFrame( - rpy.get_intervention_table(db), schema=["i_id", "i_name"], orient='row') - - # Sort first by behaviors, then by interventions - return lf.join( - behaviors, left_on="behavior", right_on="b_name", how="inner" - ).sort(pl.col("b_id")).drop("b_id").join( - interventions, left_on="intervention", right_on="i_name", how="inner" - ).sort(pl.col("i_id")).drop("i_id") - - -def _sort_transition_matrix(lf: pl.LazyFrame, db: str | Path) -> pl.LazyFrame: - """Sorting function for the transition matrix. It expects the columns ['intervention', 'behavior', 'next_intervention', 'next_behavior'] - - Args: - lf (pl.LazyFrame): The LazyFrame to sort. - db (str | Path): A path to the database file. - - Raises: - ValueError: Invalid column names. - - Returns: - pl.LazyFrame: A sorted LazyFrame (sorted by intervention, behavior, next_intervention, next_behavior). - """ - if 'intervention' not in lf.columns or 'behavior' not in lf.columns or 'next_intervention' not in lf.columns or 'next_behavior' not in lf.columns: - raise ValueError( - f"Invalid columns provided when attempting to sort transition matrix: {lf.columns}") - behaviors = pl.LazyFrame( - rpy.get_behavior_table(db), schema=["b_id", "b_name"]) - - interventions = pl.LazyFrame( - rpy.get_intervention_table(db), schema=["i_id", "i_name"]) - - # Sort order: - # 1. Next Behavior - # 2. Next Behavior - # 3. Initial Behavior - # 4. Initial Intervention - # e.g. [active_injection, no_treatment, active_injection, no_treatment], [active_injection, no_treatment, active_injection, buprenorphine], [active_injection, no_treatment, active_injection, methadone], etc. - return lf.drop("i_id").join( - behaviors, left_on="next_behavior", right_on="b_name", how="inner" - ).sort(pl.col("b_id")).drop("b_id").join( - interventions, left_on="next_intervention", right_on="i_name", how="inner" - ).sort(pl.col("i_id")).drop("i_id").join( - behaviors, left_on="behavior", right_on="b_name", how="inner" - ).sort(pl.col("b_id")).drop("b_id").join( - interventions, left_on="intervention", right_on="i_name", how="inner" - ).sort(pl.col("i_id")) - - -def _fill_gaps_with_sample( - partial_sample: pl.LazyFrame, - filling_sample: pl.LazyFrame, - *, - value_name: str = "probability" -) -> pl.LazyFrame: - """Helper function to fill a set of sampled parameters with fixed parameters to make a full LazyFrame. - - Args: - partial_sample (pl.LazyFrame): The LazyFrame containing the varied parameters within a sample. - filling_sample (pl.LazyFrame): The LazyFrame containing an entire sample (fixed and varied parameters). - value_name (str, optional): The column name of the parameter. Defaults to "probability". - - Raises: - ValueError: The column names do not match between the partial and filling samples. - - Returns: - pl.LazyFrame: A LazyFrame containing both fixed and varied parameters for the sample. A state vector returns a 3 column ['intervention', 'behavior', 'value'] pattern where a transition matrix returns 4 columns ['init_intervention', 'init_behavior' 'next_parameter', 'value']. Constant values return the single column. - """ - if partial_sample.collect_schema().names() != filling_sample.collect_schema().names(): - raise ValueError( - f"Partially filled sample and filling sample do not have matching columns!\nPartial: {partial_sample.columns}\nFilling: {filling_sample.columns}") - - col_list = [c for c in partial_sample.collect_schema().names() - if c != value_name] - joined = filling_sample.join(partial_sample, on=col_list, how='left') - - joined = joined.with_columns( - pl.when(pl.col(f"{value_name}_right").is_not_null()) - .then(pl.col(f"{value_name}_right")) - .otherwise(pl.col(value_name)).alias("result") - ).drop([value_name, f"{value_name}_right"]).rename( - {"result": value_name} - ) - return joined - - -def _get_zero_transition_matrix(db: str | Path) -> pl.LazyFrame: - """Getter for setting up a fully zero transition matrix. - - Args: - db (str | Path): File path to the database. - - Returns: - pl.LazyFrame: A LazyFrame depicting a zero transition matrix. Columns are: ['intervention', 'behavior', 'next_intervention', 'next_behavior', 'values'] - """ - state_names = rpy.get_state_names(db) - name_product = itertools.product(state_names, state_names) - flat_names = [list(itertools.chain.from_iterable(t)) for t in name_product] - - try: - lf = pl.LazyFrame( - flat_names, orient='row', - schema=["intervention", "behavior", - "next_intervention", "next_behavior"] - ).with_columns( - pl.lit(0.0).alias("values") - ) - except Exception as e: - raise e - return lf - - -# External Functions - - -def get_data_array( - p: rpy.Parameter, - db: str | Path, - sample_ids: pl.DataFrame, - time: int | None = None -) -> np.ndarray: - """Helper function used to extract transitions from the database based on the cohort sample and the corresponding sample IDs. - - Args: - p (rpy.Parameter): The parameter to extract. - db (str | Path): The string or Path object to the database. - sample_ids (pl.DataFrame): The cohort sample containing the sample IDs. - time (int | None): The timestep we are using to extract the transition. None is only valid when the initial cohort is being extracted. - - Raises: - ValueError: Unimplemented Enum value. - - Returns: - np.ndarray: The transition value as a numpy array. - """ - time = _validate_time(time, p) - lf = _get_parameter_data(p, db, sample_ids, time) - lf = _fill_missing_values(lf, p, db, sample_ids, time) - lf = _build_transition_matrix_from_partial(lf, db) - lf = _sort_dataframes(lf, db) - return _extract_values(p, lf, n=len(rpy.get_state_names(db))) diff --git a/src/respondpy/history.py b/src/respondpy/history.py new file mode 100644 index 0000000..6a870ac --- /dev/null +++ b/src/respondpy/history.py @@ -0,0 +1,17 @@ +################################################################################ +# File: history.py # +# Project: respondpy # +# Created Date: 2026-06-05 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-10 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +from __future__ import annotations + +from ._core.history import History # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] + +__all__: list[str] = ['History'] diff --git a/src/respondpy/io/reading.py b/src/respondpy/io/reading.py deleted file mode 100644 index e3d8116..0000000 --- a/src/respondpy/io/reading.py +++ /dev/null @@ -1,548 +0,0 @@ -################################################################################ -# File: reading.py # -# Project: respondpy # -# Created Date: 2025-11-20 # -# Author: Matthew Carroll # -# ----- # -# Last Modified: 2026-01-28 # -# Modified By: Matthew Carroll # -# ----- # -# Copyright (c) 2025-2026 Syndemics Lab at Boston Medical Center # -################################################################################ - -from pathlib import Path -from typing import Literal -import sqlite3 - -from respondpy.data.parameters import Parameter - -# Internal Functions - - -def _get_states( - db: str | Path, - state: Literal["intervention", "behavior"] -) -> list[str]: - """Getter for the state tables from the SQLite database. - - Args: - db (str, sqlite3.Connection): Path to the SQLite database OR the sqlite3 - connection. - state (str, optional): Either "intervention" or "behavior". The specific - state to look for in the database. Defaults to "intervention". - - Raises: - ValueError: If the state is not "intervention" or "behavior" raise an - error. This prevents SQL injections. - - Returns: - list[str]: List of possible state names from the table in the SQLite - database. - """ - stmt = f"SELECT name FROM {state} ORDER BY id" - con = sqlite3.connect(db) - cur = con.cursor() - cur.execute(stmt) - result = [row[0] for row in cur.fetchall()] - con.close() - return result - - -def _get_state_table( - db: str | Path, - state: Literal["intervention", "behavior"] -) -> list[tuple[int, str]]: - """Getter for the state tables from the SQLite database. - - Args: - db (str, sqlite3.Connection): Path to the SQLite database OR the sqlite3 - connection. - state (str, optional): Either "intervention" or "behavior". The specific - state to look for in the database. Defaults to "intervention". - - Raises: - ValueError: If the state is not "intervention" or "behavior" raise an - error. This prevents SQL injections. - - Returns: - list[str]: List of possible state names from the table in the SQLite - database. - """ - stmt = f"SELECT id, name FROM {state} ORDER BY id" - con = sqlite3.connect(db) - cur = con.cursor() - cur.execute(stmt) - result = [(row[0], row[1]) for row in cur.fetchall()] - con.close() - return result - - -def _get_column_order( - col_name: str, - values: list[str] -) -> str: - """Get the SQL ORDER BY clause for a given column and list of values. - - Args: - col_name (str): column in the SQLite table to order by. - values (list[str]): List of values to order by. - - Returns: - str: String representing the SQL ORDER BY clause. - """ - order_clause = "CASE\n" - for idx, v in enumerate(values): - order_clause += f"WHEN {col_name} = \'{v}\' THEN {idx}\n" - order_clause += f"ELSE {len(values)}\n END" - return order_clause - - -def _get_initial_cohort_by_id( - db: str | Path, - sample_id: int = 1 -) -> tuple[list, list]: - """Get the initial cohort from the database as a numpy array. - Args: - db (str, Path): Path to the SQLite database. - sample_id (int, optional): Sample ID to retrieve. Defaults to 1. - Returns: - tuple[list, list]: Initial cohort as a numpy array. - """ - i_order = _get_column_order( - "i.name", _get_states(db, state="intervention")) - b_order = _get_column_order( - "b.name", _get_states(db, state="behavior")) - con = sqlite3.connect(db) - cur = con.cursor() - stmt = f""" - SELECT i.name AS intervention, b.name AS behavior, count - FROM initial_population AS ip - INNER JOIN intervention AS i ON ip.intervention = i.id - INNER JOIN behavior AS b ON ip.behavior = b.id - WHERE sample = ? - ORDER BY - {i_order}, {b_order} - """ - cur.execute(stmt, str(sample_id)) - col_names = [d[0] for d in cur.description] - result = cur.fetchall() - con.close() - return col_names, result - - -def _get_migration_by_id_and_timestep( - db: str | Path, - sample_id: int = 1, - time: int = 52 -) -> tuple[list, list]: - """Get the change in population from the database as a numpy array. - Args: - db (str, Path): Path to the SQLite database. - sample_id (int, optional): Sample ID to retrieve. Defaults to 1. - time (int, optiona): Time point to retrieve. Defaults to 52. - Returns: - tuple[list, list]: Initial cohort as a numpy array. - """ - i_order = _get_column_order( - "i.name", _get_states(db, state="intervention")) - b_order = _get_column_order( - "b.name", _get_states(db, state="behavior")) - con = sqlite3.connect(db) - cur = con.cursor() - stmt = f""" - SELECT i.name AS intervention, b.name AS behavior, count - FROM population_change AS pc - INNER JOIN intervention AS i ON pc.intervention = i.id - INNER JOIN behavior AS b ON pc.behavior = b.id - WHERE sample = ? AND time = ? - ORDER BY - {i_order}, {b_order} - """ - cur.execute(stmt, (str(sample_id), str(time))) - col_names = [d[0] for d in cur.description] - result = cur.fetchall() - con.close() - return col_names, result - - -def _get_intervention_trans_by_id_and_time( - db: str | Path, - sample_id: int = 1, - time: int = 52 -) -> tuple[list, list]: - """Get the intervention transitions from the database as a numpy array. - - Args: - db (str, Path): Path to the SQLite database. - sample_id (int, optional): Sample ID to retrieve. Defaults to 1. - time (int, optiona): Time point to retrieve. Defaults to 52. - - Returns: - tuple[list, list]: Intervention transitions as a numpy array. - """ - ii_order = _get_column_order( - "ii.name", _get_states(db, state="intervention")) - ni_order = _get_column_order( - "ni.name", _get_states(db, state="intervention")) - b_order = _get_column_order( - "b.name", _get_states(db, state="behavior")) - con = sqlite3.connect(db) - cur = con.cursor() - stmt = f""" - SELECT ii.name AS initial_intervention, ni.name AS new_intervention, b.name AS behavior, probability - FROM intervention_transition AS it - INNER JOIN intervention AS ii ON it.initial_intervention = ii.id - INNER JOIN intervention AS ni ON it.new_intervention = ni.id - INNER JOIN behavior AS b ON it.behavior = b.id - WHERE sample = ? AND time = ? - ORDER BY - {ii_order}, {ni_order}, {b_order} - """ - cur.execute(stmt, (str(sample_id), str(time))) - col_names = [d[0] for d in cur.description] - result = cur.fetchall() - con.close() - return col_names, result - - -def _get_behavior_trans_by_id_and_time( - db: str | Path, - sample_id: int = 1, - time: int = 52 -) -> tuple[list, list]: - """Get the behavior transitions from the database as a numpy array. - - Args: - db (str, Path): Path to the SQLite database. - sample_id (int, optional): Sample ID to retrieve. Defaults to 1. - time (int, optiona): Time point to retrieve. Defaults to 52. - - Returns: - tuple[list, list]: behavior transitions as a numpy array. - """ - i_order = _get_column_order( - "i.name", _get_states(db, state="intervention")) - ib_order = _get_column_order( - "ib.name", _get_states(db, state="behavior")) - nb_order = _get_column_order( - "nb.name", _get_states(db, state="behavior")) - con = sqlite3.connect(db) - cur = con.cursor() - stmt = f""" - SELECT i.name AS intervention, ib.name AS initial_behavior, nb.name AS new_behavior, probability - FROM behavior_transition AS it - INNER JOIN intervention AS i ON it.intervention = i.id - INNER JOIN behavior AS ib ON it.initial_behavior = ib.id - INNER JOIN behavior AS nb ON it.new_behavior = nb.id - WHERE sample = ? AND time = ? - ORDER BY - {i_order}, {ib_order}, {nb_order} - """ - cur.execute(stmt, (str(sample_id), str(time))) - col_names = [d[0] for d in cur.description] - result = cur.fetchall() - con.close() - return col_names, result - - -def _get_od_by_id_and_time( - db: str | Path, - sample_id: int = 1, - time: int = 52 -) -> tuple[list, list]: - """Get the overdose probabilities from the database as a numpy array. - - Args: - db (str, Path): Path to the SQLite database. - sample_id (int, optional): Sample ID to retrieve. Defaults to 1. - time (int, optiona): Time point to retrieve. Defaults to 52. - - Returns: - tuple[list, list]: behavior transitions as a numpy array. - """ - i_order = _get_column_order( - "i.name", _get_states(db, state="intervention")) - b_order = _get_column_order( - "b.name", _get_states(db, state="behavior")) - con = sqlite3.connect(db) - cur = con.cursor() - stmt = f""" - SELECT i.name AS intervention, b.name AS behavior, probability - FROM overdose AS od - INNER JOIN intervention AS i ON od.intervention = i.id - INNER JOIN behavior AS b ON od.behavior = b.id - WHERE sample = ? AND time = ? - ORDER BY - {i_order}, {b_order} - """ - cur.execute(stmt, (str(sample_id), str(time))) - col_names = [d[0] for d in cur.description] - result = cur.fetchall() - con.close() - return col_names, result - - -def _get_fod_by_id_and_time( - db: str | Path, - sample_id: int = 1, - time: int = 52 -) -> tuple[list, list]: - """Get the fatal overdose probabilities from the database as a numpy array. - - Args: - db (str, Path): Path to the SQLite database. - sample_id (int, optional): Sample ID to retrieve. Defaults to 1. - time (int, optiona): Time point to retrieve. Defaults to 52. - - Returns: - tuple[list, list]: behavior transitions as a numpy array. - """ - i_order = _get_column_order( - "i.name", _get_states(db, state="intervention")) - b_order = _get_column_order( - "b.name", _get_states(db, state="behavior")) - con = sqlite3.connect(db) - cur = con.cursor() - cur.execute( - f""" - SELECT i.name AS intervention, b.name AS behavior, probability - FROM overdose_fatality AS fod - INNER JOIN intervention AS i ON fod.intervention = i.id - INNER JOIN behavior AS b ON fod.behavior = b.id - WHERE sample = ? AND time = ? - ORDER BY - {i_order}, {b_order} - """, (str(sample_id), str(time))) - col_names = [d[0] for d in cur.description] - result = cur.fetchall() - con.close() - return col_names, result - - -def _get_bg_death_by_id_and_time( - db: str | Path, - sample_id: int = 1, - time: int = 52 -) -> tuple[list, list]: - """Get the background mortality probabilities from the database as a numpy array. - - Args: - db (str, Path): Path to the SQLite database. - sample_id (int, optional): Sample ID to retrieve. Defaults to 1. - time (int, optiona): Time point to retrieve. Defaults to 52. - - Returns: - tuple[list, list]: behavior transitions as a numpy array. - """ - con = sqlite3.connect(db) - cur = con.cursor() - stmt = """ - SELECT probability - FROM background_mortality - WHERE sample = ? AND time = ? - """ - cur.execute(stmt, (str(sample_id), str(time))) - col_names = [d[0] for d in cur.description] - result = cur.fetchall() - con.close() - return col_names, result - - -def _get_smr_by_id_and_time( - db: str | Path, - sample_id: int = 1, - time: int = 52 -) -> tuple[list, list]: - """Get the SMRs from the database as a numpy array. - - Args: - db (str, Path): Path to the SQLite database. - sample_id (int, optional): Sample ID to retrieve. Defaults to 1. - time (int, optiona): Time point to retrieve. Defaults to 52. - - Returns: - tuple[list, list]: behavior transitions as a numpy array. - """ - i_order = _get_column_order( - "i.name", _get_states(db, state="intervention")) - b_order = _get_column_order( - "b.name", _get_states(db, state="behavior")) - con = sqlite3.connect(db) - cur = con.cursor() - stmt = f""" - SELECT i.name AS intervention, b.name AS behavior, ratio - FROM smr - INNER JOIN intervention AS i ON smr.intervention = i.id - INNER JOIN behavior AS b ON smr.behavior = b.id - WHERE sample = ? AND time = ? - ORDER BY - {i_order}, {b_order} - """ - cur.execute(stmt, (str(sample_id), str(time))) - col_names = [d[0] for d in cur.description] - result = cur.fetchall() - con.close() - return col_names, result - -# External Functions - - -def get_sample_ids_by_table( - db: str | Path, - table_name: str -) -> list[int]: - if not Path(db).is_file(): - raise FileNotFoundError( - f"The database file was not found when attempting to retrieve sample ids by table! File specified: {db}.") - - con = sqlite3.connect(db) - cur = con.cursor() - - # Check 2 things: - # 1. Does the table exist - # 2. Is the table_name a valid SQL string (i.e. prevent SQL injection - # later) - cur.execute(""" - SELECT count(name) FROM sqlite_master WHERE type='table' AND name=? - """, (table_name,)) - if cur.fetchone()[0] != 1: - con.close() - raise ValueError(f"The specified table does not exist: {table_name}") - - # Be very careful here, this works and isn't gonna get SQL Injection attacked because we verify the table_name in the check above. - stmt = f"SELECT DISTINCT sample FROM {table_name}" - cur.execute(stmt) - result = [row[0] for row in cur.fetchall()] - con.close() - return result - - -def get_parameter_by_id_and_time( - param: Parameter, - db: str | Path, - sample_id: int, - time: int -) -> tuple[list, list]: - if not Path(db).is_file(): - raise FileNotFoundError( - f"The database file was not found when attempting to retrieve parameters by id and time! File specified: {db}.") - - if param == Parameter.INITIAL_COHORT: - return _get_initial_cohort_by_id(db, sample_id) - if param == Parameter.MIGRATION_COHORT: - return _get_migration_by_id_and_timestep(db, sample_id, time) - if param == Parameter.INTERVENTION_TRANSITION_PROBABILITY: - return _get_intervention_trans_by_id_and_time(db, sample_id, time) - if param == Parameter.BEHAVIOR_TRANSITION_PROBABILITY: - return _get_behavior_trans_by_id_and_time(db, sample_id, time) - if param == Parameter.STANDARD_MORTALITY_RATIO: - return _get_smr_by_id_and_time(db, sample_id, time) - if param == Parameter.BACKGROUND_DEATH_PROBABILITY: - return _get_bg_death_by_id_and_time(db, sample_id, time) - if param == Parameter.OVERDOSE_PROBABILITY: - return _get_od_by_id_and_time(db, sample_id, time) - if param == Parameter.OVERDOSE_FATALITY_PROBABILITY: - return _get_fod_by_id_and_time(db, sample_id, time) - raise ValueError( - f"Attempting to extract invalid parameter of value {param}!") - - -def get_interventions(db: str | Path) -> list[str]: - """Get the list of interventions the describe the model. - - Args: - db (str | Path): Path to the SQLite database. - - Returns: - list[str]: A list of intervention names. - """ - return _get_states(db, state="intervention") - - -def get_behaviors(db: str | Path) -> list[str]: - """Get the list of behaviors the describe the model. - - Args: - db (str | Path): Path to the SQLite database. - - Returns: - list[str]: A list of behavior names. - """ - return _get_states(db, state="behavior") - - -def get_intervention_table(db: str | Path) -> list[tuple[int, str]]: - """Return the intervention table as a list of id, name tuples - - Args: - db (str | Path): Path to the SQLite database. - - Returns: - list[tuple[int, str]]: A list of tuples containing id, name interventions. - """ - return _get_state_table(db, state="intervention") - - -def get_behavior_table(db: str | Path) -> list[tuple[int, str]]: - """Return the intervention table as a list of id, name tuples - - Args: - db (str | Path): Path to the SQLite database. - - Returns: - list[tuple[int, str]]: A list of tuples containing id, name behaviors. - """ - return _get_state_table(db, state="behavior") - - -def get_state_names( - db: str | Path -) -> list[tuple[str, str]]: - """Get the intervention and behavior names in a list of tuples sorted by intervention and behavior ID. - - Args: - db (str, Path): Path to the SQLite database. - Returns: - list[tuple[str]]: List of tuples containing intervention, behavior combinations that form the state names. - """ - if not Path(db).is_file(): - raise FileNotFoundError( - f"The database file was not found when attempting to retrieve state names! File specified: {db}.") - con = sqlite3.connect(db) - cur = con.cursor() - cur.execute( - """ - SELECT i.name AS intervention, b.name AS behavior - FROM intervention AS i - CROSS JOIN behavior AS b - ORDER BY i.id, b.id - """ - ) - result = cur.fetchall() - con.close() - return result - - -def get_cohorts(db: str | Path) -> tuple[list[str], list]: - """Get all the cohorts from the database. - - Args: - db (str | Path): Path to the database. - - Returns: - tuple[list[str], list]: a tuple of the column names and then a list of the cohort details. - """ - if not Path(db).is_file(): - raise FileNotFoundError( - f"The database file was not found when attempting to retrieve state names! File specified: {db}.") - con = sqlite3.connect(db) - cur = con.cursor() - cur.execute( - """ - SELECT * FROM cohort; - """ - ) - column_names = [description[0] for description in cur.description] - result = cur.fetchall() - con.close() - return column_names, result diff --git a/src/respondpy/io/writing.py b/src/respondpy/io/writing.py deleted file mode 100644 index 42320bb..0000000 --- a/src/respondpy/io/writing.py +++ /dev/null @@ -1,130 +0,0 @@ -################################################################################ -# File: writing.py # -# Project: respondpy # -# Created Date: 2026-01-15 # -# Author: Matthew Carroll # -# ----- # -# Last Modified: 2026-01-28 # -# Modified By: Matthew Carroll # -# ----- # -# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # -################################################################################ - -import sqlite3 -from pathlib import Path - -from respondpy.data.parameters import Parameter - -# Internal Functions - - -def _check_valid_list(l: list[tuple], tuple_items: int) -> bool: - if len(l) == 0: - return False - if len(l[0]) != tuple_items: - return False - return True - - -def _connect_and_execute(data: list[tuple], db: str | Path, stmt: str) -> None: - con = sqlite3.connect(db) - cur = con.cursor() - cur.executemany(stmt, data) - con.commit() - con.close() - - -def _insert_overdose(data: list[tuple], db: str | Path) -> None: - if not _check_valid_list(data, 5): - return - sql_stmt = "INSERT INTO overdose VALUES (?, ?, ?, ?, ?)" - _connect_and_execute(data, db, sql_stmt) - - -def _insert_overdose_fatality(data: list[tuple], db: str | Path) -> None: - if not _check_valid_list(data, 5): - return - sql_stmt = "INSERT INTO overdose_fatality VALUES (?, ?, ?, ?, ?)" - _connect_and_execute(data, db, sql_stmt) - - -def _insert_background_mortality(data: list[tuple], db: str | Path) -> None: - if not _check_valid_list(data, 3): - return - sql_stmt = "INSERT INTO background_mortality VALUES (?, ?, ?)" - _connect_and_execute(data, db, sql_stmt) - - -def _insert_intervention_transition(data: list[tuple], db: str | Path) -> None: - if not _check_valid_list(data, 6): - return - sql_stmt = "INSERT INTO intervention_transition VALUES (?, ?, ?, ?, ?, ?)" - _connect_and_execute(data, db, sql_stmt) - - -def _insert_behavior_transition(data: list[tuple], db: str | Path) -> None: - if not _check_valid_list(data, 6): - return - sql_stmt = "INSERT INTO behavior_transition VALUES (?, ?, ?, ?, ?, ?)" - _connect_and_execute(data, db, sql_stmt) - - -def _insert_smr(data: list[tuple], db: str | Path) -> None: - if not _check_valid_list(data, 5): - return - sql_stmt = "INSERT INTO smr VALUES (?, ?, ?, ?, ?)" - _connect_and_execute(data, db, sql_stmt) - - -def _insert_sample_count(data: list[tuple], db: str | Path) -> None: - if not _check_valid_list(data, 4): - return - sql_stmt = "INSERT INTO initial_population VALUES (?, ?, ?, ?)" - print(data) - _connect_and_execute(data, db, sql_stmt) - - -def _insert_migration_count(data: list[tuple], db: str | Path) -> None: - if not _check_valid_list(data, 5): - return - sql_stmt = "INSERT INTO population_change VALUES (?, ?, ?, ?, ?)" - _connect_and_execute(data, db, sql_stmt) - -# External Functions - - -def insert_cohort(data: list, db: str | Path) -> None: - if not _check_valid_list(data, 9): - return - sql_stmt = """ - INSERT INTO cohort (description, background_mortality_sample, behavior_transition_sample, initial_population_sample,intervention_transition_sample, overdose_sample, overdose_fatality_sample, population_change_sample, smr_sample) VALUES (?,?,?,?,?,?,?,?,?) - """ - _connect_and_execute(data, db, sql_stmt) - - -def insert_parameter( - data: list, - db: str | Path, - param: Parameter -) -> None: - if not Path(db).is_file(): - raise FileNotFoundError( - f"The specified database could not be found: {db}") - if param == Parameter.INITIAL_COHORT: - return _insert_sample_count(data, db) - if param == Parameter.MIGRATION_COHORT: - return _insert_migration_count(data, db) - if param == Parameter.INTERVENTION_TRANSITION_PROBABILITY: - return _insert_intervention_transition(data, db) - if param == Parameter.BEHAVIOR_TRANSITION_PROBABILITY: - return _insert_behavior_transition(data, db) - if param == Parameter.STANDARD_MORTALITY_RATIO: - return _insert_smr(data, db) - if param == Parameter.BACKGROUND_DEATH_PROBABILITY: - return _insert_background_mortality(data, db) - if param == Parameter.OVERDOSE_PROBABILITY: - return _insert_overdose(data, db) - if param == Parameter.OVERDOSE_FATALITY_PROBABILITY: - return _insert_overdose_fatality(data, db) - raise ValueError( - f"Attempting to insert invalid parameter of value {param}!") diff --git a/src/respondpy/model.py b/src/respondpy/model.py new file mode 100644 index 0000000..1c2cd1a --- /dev/null +++ b/src/respondpy/model.py @@ -0,0 +1,100 @@ +################################################################################ +# File: model.py # +# Project: respondpy # +# Created Date: 2026-06-05 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-09 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ +from __future__ import annotations + +from .data.input import Input +from .data.logic_conditions import validate_time_list +from .data.parameters import Parameter, ParameterType +from .transition import Transition, build_timestep_transition +from ._core.model import Model # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] +from ._utils import str_to_int_list + +__all__: list[str] = [ + 'Model', 'build_model', + 'add_transitions_to_model', 'build_model_transitions' +] + + +def build_model( + input_data: Input, + cohort_id: int = 1, + *, + name: str = "markov", + log_name: str = "console" +) -> Model: + """Build a Model with initialized state and configured transitions. + + :param input_data: Loaded input data and simulation configuration. + :param cohort_id: Cohort identifier used to resolve sampled parameters. + :param name: Model name passed to the core model constructor. + :param log_name: Logger name used by the underlying core model. + :returns: A model ready to be added to a simulation. + """ + m = Model(name, log_name) + init_pop = input_data.select_parameter( + Parameter(ParameterType.INITIAL_COHORT), cohort_id, time=1).squeeze() + m.set_state(init_pop) + m = build_model_transitions(m, input_data, cohort_id) + return m + + +def build_model_transitions( + model: Model, + input_data: Input, + cohort_id: int +) -> Model: + """Populate a model with per-timestep transitions for full duration. + + The first transition block is built from timestep 1. Additional timestep + transition blocks are either copied or rebuilt at configured + ``parameter_change_times`` values. + + :param model: Model instance to mutate. + :param input_data: Loaded input data and simulation configuration. + :param cohort_id: Cohort identifier used to resolve sampled parameters. + :returns: The same model instance, with transitions appended. + """ + # Add the first timestep + ct_val = 1 + transition = build_timestep_transition(ct_val, input_data, cohort_id) + add_transitions_to_model(model, transition) + duration = input_data.config.getint('simulation', 'duration') + change_times = validate_time_list( + str_to_int_list(input_data.config.get( + 'simulation', 'parameter_change_times')) + ) + + # we start at 2 because 0 is in the initial state, 1 is the first transition (added above), and now we look for more transitions. If there is no other change times then we just make copies. + for i in range(1, duration): + if change_times and i == change_times[-1]: + ct_val = change_times.pop() + transition = build_timestep_transition( + ct_val, input_data, cohort_id) + add_transitions_to_model(model, transition) + else: + add_transitions_to_model(model, transition.copy()) + return model + + +def add_transitions_to_model( + model: Model, + t_transition: list[Transition] +) -> Model: + """Append one timestep's transitions to a model. + + :param model: Model to update. + :param t_transition: Transition objects for one simulation timestep. + :returns: The same model instance, for chaining. + """ + for t in t_transition: + model.add_transition(t) + return model diff --git a/src/respondpy/simulation.py b/src/respondpy/simulation.py new file mode 100644 index 0000000..bbce8cf --- /dev/null +++ b/src/respondpy/simulation.py @@ -0,0 +1,49 @@ +################################################################################ +# File: simulation.py # +# Project: respondpy # +# Created Date: 2026-06-05 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-11 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +from __future__ import annotations +from collections.abc import Sequence + +from .model import build_model +from .data import Input +from ._core.simulation import Simulation # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] + +__all__: list[str] = ['Simulation', 'build_simulation'] + + +def build_simulation( + input_data: Input, + *, + cohort_ids: Sequence[int] | None = None, + log_name: str = "console" +) -> Simulation: + """Build a simulation containing one model per cohort id. + + :param cohort_ids: Cohort identifiers to include in the simulation. + :param input_data: Loaded input data and simulation configuration. + :param log_name: Logger name used by the underlying core simulation/model. + :returns: A simulation object populated with cohort-specific models. + """ + input_cohort_ids = input_data.get_cohort_ids() + if cohort_ids is None: + cohort_ids = input_cohort_ids + else: + missing_cohorts = set(cohort_ids) - set(input_cohort_ids) + if missing_cohorts: + raise ValueError( + f"Cohort IDs {missing_cohorts} not found in input data." + ) + s = Simulation(log_name) + for cohort_id in cohort_ids: + s.add_model(build_model(input_data, cohort_id, log_name=log_name)) + + return s diff --git a/src/respondpy/transition.py b/src/respondpy/transition.py new file mode 100644 index 0000000..376efff --- /dev/null +++ b/src/respondpy/transition.py @@ -0,0 +1,114 @@ +################################################################################ +# File: transition.py # +# Project: respondpy # +# Created Date: 2026-06-05 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-09 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +from __future__ import annotations + +import numpy as np + +from .data.parameters import Parameter, ParameterType +from .data.input import Input +from ._core.transition import Transition # pylint: disable=E0611,E0401 # type: ignore[reportMissingModuleSource] + +__all__: list[str] = [ + 'Transition', 'transition_factory', 'build_timestep_transition' +] + + +def transition_factory( + name: str, + tran_matrices: list[np.ndarray], + *, + log_name: str = "console" +) -> Transition: + """Create a transition and load its ordered transition matrices. + + :param name: Transition name used by the core model. + :param tran_matrices: Matrix/vector operands consumed in execution order. + :param log_name: Logger name used by the underlying core transition. + :returns: A transition ready to be attached to a model. + """ + t = Transition(name, log_name) + for tm in tran_matrices: + t.add_transition_matrix(tm) + return t + + +def build_timestep_transition( + timestep: int, + input_data: Input, + cohort_id: int +) -> list[Transition]: + """Build the full ordered transition set for one timestep. + + The returned transitions are: migration, intervention change, behavior + change, overdose, and background mortality. + + :param timestep: Simulation timestep to sample. + :param input_data: Loaded input data and simulation configuration. + :param cohort_id: Cohort identifier used to resolve sampled parameters. + :returns: Transition list for exactly one model timestep. + """ + + migration = transition_factory( + "migration", [ + input_data.select_parameter( + Parameter(ParameterType.MIGRATION_COHORT), cohort_id, timestep) + ]) + + inter = transition_factory( + "intervention", [ + input_data.select_parameter( + Parameter(ParameterType.INTERVENTION_TRANSITION_PROBABILITY), + cohort_id, + timestep + ).T] + ) + + behav = transition_factory( + "behavior", [ + input_data.select_parameter( + Parameter(ParameterType.BEHAVIOR_TRANSITION_PROBABILITY), + cohort_id, + timestep + ).T] + ) + + overd = transition_factory( + "overdose", [ + input_data.select_parameter( + Parameter(ParameterType.OVERDOSE_PROBABILITY), + cohort_id, + timestep + ).squeeze(), + input_data.select_parameter( + Parameter(ParameterType.OVERDOSE_FATALITY_PROBABILITY), + cohort_id, + timestep + ).squeeze() + ] + ) + + morta = transition_factory( + "background_death", [ + input_data.select_parameter( + Parameter(ParameterType.BACKGROUND_DEATH_PROBABILITY), + cohort_id, + timestep + ).squeeze() * input_data.select_parameter( + Parameter(ParameterType.STANDARD_MORTALITY_RATIO), + cohort_id, + timestep + ).squeeze() + ] + ) + + return [migration, inter, behav, overd, morta] diff --git a/tests/test_data_database_helpers.py b/tests/test_data_database_helpers.py new file mode 100644 index 0000000..a86b032 --- /dev/null +++ b/tests/test_data_database_helpers.py @@ -0,0 +1,179 @@ +################################################################################ +# File: test_data_database_helpers.py # +# Project: respondpy # +# Created Date: 2026-06-10 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-10 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + + +from __future__ import annotations + +import polars as pl +import pytest + +import respondpy.data as rpydata +from respondpy.data.database_helpers import ( + _sort_state_vector, + _sort_transition_matrix, + get_column_order, + sort_dataframes, +) + + +@pytest.mark.unit +def test_data_module_dir_matches_exports() -> None: + assert sorted(rpydata.__dir__()) == sorted(rpydata.__all__) + + +@pytest.mark.unit +def test_validate_time_list_removes_initial_timestep() -> None: + assert rpydata.validate_time_list([1, 52, 104]) == [52, 104] + + +@pytest.mark.unit +def test_validate_time_list_rejects_non_positive_values() -> None: + with pytest.raises(ValueError, match="zero or a negative number"): + rpydata.validate_time_list([52, 0]) + + +@pytest.mark.unit +def test_sort_transition_matrix_requires_columns() -> None: + with pytest.raises(ValueError, match="Invalid columns provided"): + _sort_transition_matrix( + pl.DataFrame( + {"foo": [1], "bar": [2], "baz": [3], "probability": [0.1]} + ), + [(1, "b1")], + [(1, "i1")], + ) + + +@pytest.mark.unit +def test_get_column_order_builds_case_clause() -> None: + clause = get_column_order("i.name", ["no_treatment", "buprenorphine"]) + assert "WHEN i.name = 'no_treatment' THEN 0" in clause + assert "WHEN i.name = 'buprenorphine' THEN 1" in clause + assert "ELSE 2" in clause + + +@pytest.mark.unit +def test_sort_dataframes_unrecognized_shape_returns_input() -> None: + lf = pl.LazyFrame({"a": [1], "b": [2]}) + out = sort_dataframes(lf, [(1, "b1")], [(1, "i1")]).collect() + assert out.columns == ["a", "b"] + assert out.row(0) == (1, 2) + + +@pytest.mark.unit +def test_sort_state_vector_requires_columns() -> None: + with pytest.raises(ValueError, match="Invalid columns provided"): + _sort_state_vector( + pl.LazyFrame({"foo": [1], "bar": [2], "count": [3]}), + [(1, "b1")], + [(1, "i1")], + ).collect() + + +@pytest.mark.unit +def test_sort_state_vector_happy_path() -> None: + lf = pl.LazyFrame( + { + "intervention": ["i2", "i1", "i2", "i1"], + "behavior": ["b2", "b2", "b1", "b1"], + "count": [40, 20, 30, 10], + } + ) + + out = _sort_state_vector( + lf, + behaviors=[(1, "b1"), (2, "b2")], + interventions=[(1, "i1"), (2, "i2")], + ).collect() + + assert out.columns == ["intervention", "behavior", "count"] + assert out.select("count").to_series().to_list() == [20, 10, 40, 30] + + +@pytest.mark.unit +@pytest.mark.filterwarnings( + "ignore:Determining the column names of a LazyFrame requires resolving its schema.*:polars.exceptions.PerformanceWarning" +) +def test_sort_transition_matrix_happy_path() -> None: + lf = pl.LazyFrame( + { + "intervention": ["i2", "i1", "i2", "i1"], + "behavior": ["b2", "b1", "b1", "b2"], + "next_intervention": ["i1", "i2", "i2", "i1"], + "next_behavior": ["b1", "b2", "b1", "b2"], + "probability": [0.1, 0.2, 0.3, 0.4], + # Current implementation drops this column before re-adding it. + "i_id": [0, 0, 0, 0], + } + ) + + out = _sort_transition_matrix( + lf, + behaviors=[[1, 2], ["b1", "b2"]], + interventions=[[1, 2], ["i1", "i2"]], + ).collect() + + assert out.height == 4 + assert "probability" in out.columns + + +@pytest.mark.unit +def test_sort_dataframes_calls_state_vector_sort(monkeypatch: pytest.MonkeyPatch) -> None: + lf = pl.LazyFrame( + { + "intervention": ["i1"], + "behavior": ["b1"], + "count": [1.0], + } + ) + + def fake_sort_state_vector( + _lf: pl.LazyFrame, + _behaviors: list[tuple[int, str]], + _interventions: list[tuple[int, str]], + ) -> pl.LazyFrame: + return pl.LazyFrame({"sentinel": [1]}) + + monkeypatch.setattr( + "respondpy.data.database_helpers._sort_state_vector", + fake_sort_state_vector, + ) + + out = sort_dataframes(lf, [(1, "b1")], [(1, "i1")]).collect() + assert out.columns == ["sentinel"] + + +@pytest.mark.unit +def test_sort_dataframes_calls_transition_matrix_sort(monkeypatch: pytest.MonkeyPatch) -> None: + lf = pl.LazyFrame( + { + "intervention": ["i1"], + "behavior": ["b1"], + "next_intervention": ["i1"], + "next_behavior": ["b1"], + } + ) + + def fake_sort_transition_matrix( + _lf: pl.LazyFrame, + _behaviors: list[tuple[int, str]], + _interventions: list[tuple[int, str]], + ) -> pl.LazyFrame: + return pl.LazyFrame({"sentinel": [2]}) + + monkeypatch.setattr( + "respondpy.data.database_helpers._sort_transition_matrix", + fake_sort_transition_matrix, + ) + + out = sort_dataframes(lf, [(1, "b1")], [(1, "i1")]).collect() + assert out.columns == ["sentinel"] diff --git a/tests/test_data_input.py b/tests/test_data_input.py new file mode 100644 index 0000000..f225a9d --- /dev/null +++ b/tests/test_data_input.py @@ -0,0 +1,353 @@ +################################################################################ +# File: test_data_input.py # +# Project: respondpy # +# Created Date: 2026-06-10 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-11 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +import sqlite3 +from configparser import ConfigParser +from pathlib import Path + +import pytest +import numpy as np +import polars as pl + +import respondpy.data as rpydata + + +@pytest.fixture +def make_tempdir(tmp_path_factory): + temp_dir = tmp_path_factory.mktemp("test-data") + yield temp_dir + + +@pytest.fixture +def setup_db(make_tempdir, db_schema, insert_complete_sample): + """Fixture to execute before all tests to setup_db the dummy database + + Yields: + _type_: _description_ + """ + temp_dir = make_tempdir + mem_str = temp_dir / "input.db" + conn = sqlite3.connect(mem_str) + cursor = conn.cursor() + cursor.executescript(db_schema) + cursor.executescript(insert_complete_sample) + conn.commit() + conn.close() + yield mem_str + + +@pytest.fixture +def setup_config(make_tempdir): + temp_dir = make_tempdir + mem_str = temp_dir / "sim.conf" + cfg = ConfigParser() + cfg['simulation'] = { + 'duration': '52', + 'parameter_change_times': '52', + 'stratify_entering_cohort': 'false' + } + + cfg['output'] = { + 'build_summary_stats': 'true', + 'save_state_history': 'true', + 'timesteps_to_report': '52', + } + + with mem_str.open('w') as configfile: + cfg.write(configfile) + + yield mem_str + + +@pytest.fixture +def setup_data(setup_db, setup_config): + """Pytest fixture to setup the data + + Args: + setup_db (_type_): _description_ + setup_config (_type_): _description_ + + Yields: + _type_: path to database and a ConfigParser + """ + yield setup_db, setup_config + + +@pytest.mark.unit +def test_input_initialization(setup_data): + db_path, config_path = setup_data + input_data = rpydata.Input(db_path=db_path, conf_path=config_path) + assert input_data is not None + + +@pytest.mark.unit +def test_input_initialization_single_path(setup_data): + db_path, config_path = setup_data + p = Path(db_path).parent + input_data = rpydata.Input(path=p) + assert input_data is not None + + +@pytest.mark.unit +def test_input_initialization_invalid_db(setup_config): + config_path = setup_config + with pytest.raises(FileNotFoundError, match="Database file not found"): + rpydata.Input(db_path="invalid.db", conf_path=config_path) + + +@pytest.mark.unit +def test_input_initialization_invalid_conf(setup_db): + db_path = setup_db + with pytest.raises(FileNotFoundError, match="Config file not found at"): + rpydata.Input(db_path=db_path, conf_path="invalid.conf") + + +@pytest.fixture +def input_data(setup_data): + db_path, config_path = setup_data + input_data = rpydata.Input(db_path=db_path, conf_path=config_path) + yield input_data + + +@pytest.mark.unit +def test_repr(input_data): + repr_str = repr(input_data) + assert "Input" in repr_str + assert "db_path" in repr_str + assert "conf_path" in repr_str + + +@pytest.mark.unit +def test_get_interventions(input_data): + interventions = input_data.get_interventions() + expected_interventions = [ + 'no_treatment', 'early_buprenorphine', 'buprenorphine', 'post_buprenorphine'] + assert isinstance(interventions, list) + assert set(interventions) == set(expected_interventions) + + +@pytest.mark.unit +def test_get_behaviors(input_data): + behaviors = input_data.get_behaviors() + expected_behaviors = ['active_injection', 'nonactive_injection'] + assert isinstance(behaviors, list) + assert set(behaviors) == set(expected_behaviors) + + +@pytest.mark.unit +def test_get_cohorts(input_data): + col_names, results = input_data.get_cohorts() + expected_col_names = [ + "id", "description", "background_mortality_sample", "behavior_transition_sample", "initial_population_sample", + "intervention_transition_sample", "overdose_sample", "overdose_fatality_sample", "population_change_sample", "smr_sample" + ] + expected_results = [1, "Test Cohort 1", 1, 1, 1, 1, 1, 1, 1, 1] + assert set(expected_col_names) == set(col_names) + assert expected_results == list(results[0]) + + +@pytest.mark.unit +def test_get_cohort_ids(input_data): + ids = input_data.get_cohort_ids() + expected_ids = [1] + assert expected_ids == ids + + +@pytest.mark.unit +def test_select_parameter_intervention(input_data): + param = rpydata.Parameter( + rpydata.ParameterType.INTERVENTION_TRANSITION_PROBABILITY) + result = input_data.select_parameter(param, cohort_id=1, time=1) + assert isinstance(result, np.ndarray) + print(result) + + +@pytest.mark.unit +def test_select_parameter_behavior(input_data): + param = rpydata.Parameter( + rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY) + result = input_data.select_parameter(param, cohort_id=1, time=1) + assert isinstance(result, np.ndarray) + print(result) + + +@pytest.mark.unit +def test_select_parameter_initial_cohort(input_data): + param = rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT) + result = input_data.select_parameter(param, cohort_id=1, time=1) + assert isinstance(result, np.ndarray) + print(result) + + +@pytest.mark.unit +def test_select_parameter_migrating_cohort(input_data): + param = rpydata.Parameter( + rpydata.ParameterType.MIGRATION_COHORT) + result = input_data.select_parameter(param, cohort_id=1, time=1) + assert isinstance(result, np.ndarray) + print(result) + + +@pytest.mark.unit +def test_select_parameter_overdose(input_data): + param = rpydata.Parameter( + rpydata.ParameterType.OVERDOSE_PROBABILITY) + result = input_data.select_parameter(param, cohort_id=1, time=1) + assert isinstance(result, np.ndarray) + print(result) + + +@pytest.mark.unit +def test_select_parameter_fatal_overdose(input_data): + param = rpydata.Parameter( + rpydata.ParameterType.OVERDOSE_FATALITY_PROBABILITY) + result = input_data.select_parameter(param, cohort_id=1, time=1) + assert isinstance(result, np.ndarray) + print(result) + + +@pytest.mark.unit +def test_select_parameter_background_mortality(input_data): + param = rpydata.Parameter( + rpydata.ParameterType.BACKGROUND_DEATH_PROBABILITY) + result = input_data.select_parameter(param, cohort_id=1, time=1) + assert isinstance(result, np.ndarray) + print(result) + + +@pytest.mark.unit +def test_select_parameter_smr(input_data): + param = rpydata.Parameter( + rpydata.ParameterType.STANDARD_MORTALITY_RATIO) + result = input_data.select_parameter(param, cohort_id=1, time=1) + assert isinstance(result, np.ndarray) + print(result) + + +@pytest.mark.unit +def test_input_initialization_requires_complete_paths(setup_db): + db_path = setup_db + with pytest.raises(ValueError, match="Must provide either a path"): + rpydata.Input(db_path=db_path) + + +@pytest.mark.unit +def test_get_connection_raises_when_missing(input_data): + input_data._connection = None + with pytest.raises(ConnectionError, match="No database connection established"): + input_data._get_connection() + + +@pytest.mark.unit +def test_check_valid_list_edge_cases(input_data): + assert input_data._check_valid_list([], 2) is False + assert input_data._check_valid_list([(1, 2)], 3) is False + assert input_data._check_valid_list([(1, 2)], 2) is True + + +@pytest.mark.unit +def test_connect_and_executemany_rejects_bad_shapes(input_data): + with pytest.raises(ValueError, match="Expected list of tuples with 2 items each"): + input_data._connect_and_executemany( + [(1,)], + "INSERT INTO intervention(id, name) VALUES (?, ?)" + ) + + +@pytest.mark.unit +def test_get_sample_ids_by_table_valid_and_invalid(input_data): + assert input_data._get_sample_ids_by_table("initial_population") == [1] + with pytest.raises(ValueError, match="does not exist"): + input_data._get_sample_ids_by_table("not_a_real_table") + + +@pytest.mark.unit +def test_get_sample_id_for_parameter_missing_cohort_raises(input_data): + with pytest.raises(ValueError, match="No sample ID found"): + input_data._get_sample_id_for_parameter( + rpydata.Parameter(rpydata.ParameterType.INITIAL_COHORT), + cohort_id=999, + ) + + +@pytest.mark.unit +def test_select_parameter_raw_returns_numpy(input_data): + result = input_data.select_parameter( + rpydata.Parameter(rpydata.ParameterType.INITIAL_COHORT), + cohort_id=1, + time=1, + raw=True, + ) + assert isinstance(result, np.ndarray) + assert result.shape[0] > 0 + + +@pytest.mark.unit +def test_extract_values_rejects_invalid_parameter(input_data): + class InvalidParameter: + def get_value_column_name(self): + return "probability" + + def is_state_vector_operation(self): + return False + + def is_transition_matrix_operation(self): + return False + + with pytest.raises(ValueError, match="Invalid parameter applied"): + input_data._extract_values( + InvalidParameter(), + pl.LazyFrame({"probability": [1.0]}), + n=1, + ) + + +@pytest.mark.unit +def test_zero_invalid_transitions_passthrough_for_non_transition_param(input_data): + transition_matrix = pl.DataFrame( + { + "initial_intervention": ["A"], + "new_intervention": ["B"], + "initial_behavior": ["X"], + "new_behavior": ["Y"], + "probability": [0.5], + } + ) + + out = input_data._zero_invalid_transitions( + rpydata.Parameter(rpydata.ParameterType.INITIAL_COHORT), + transition_matrix, + ) + assert out.equals(transition_matrix) + + +@pytest.mark.unit +def test_insert_cohorts_adds_new_row(input_data): + before_cols, before_rows = input_data.get_cohorts() + assert "id" in before_cols + + input_data.insert_cohorts([ + ("Second Cohort", 1, 1, 1, 1, 1, 1, 1, 1) + ]) + + _, after_rows = input_data.get_cohorts() + assert len(after_rows) == len(before_rows) + 1 + + +@pytest.mark.unit +def test_insert_parameter_adds_sample_row(input_data): + parameter = rpydata.Parameter(rpydata.ParameterType.INITIAL_COHORT) + input_data.insert_parameter(parameter, [(2, 1, 1, 42.0)]) + + sample_ids = input_data._get_sample_ids_by_table("initial_population") + assert 2 in sample_ids diff --git a/tests/test_data_logic_conditions.py b/tests/test_data_logic_conditions.py new file mode 100644 index 0000000..22de1b7 --- /dev/null +++ b/tests/test_data_logic_conditions.py @@ -0,0 +1,107 @@ +################################################################################ +# File: test_data_logic_conditions.py # +# Project: respondpy # +# Created Date: 2026-06-09 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-09 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +import pytest +import polars as pl + +import respondpy.data as rpydata + + +@pytest.mark.unit +def test_verify_no_nulls_raise_error() -> None: + df = pl.DataFrame({ + "probability": [0.1, 0.2, None] + }) + + with pytest.raises(ValueError, match="Null transition probabilities found for sample 1 and parameter ParameterType.INITIAL_COHORT"): + rpydata.verify_no_nulls( + df, sample_id=1, p=rpydata.ParameterType.INITIAL_COHORT + ) + + +@pytest.mark.unit +def test_verify_no_nulls_pass() -> None: + df = pl.DataFrame({ + "probability": [0.1, 0.2, 0.3] + }) + + rpydata.verify_no_nulls( + df, sample_id=1, p=rpydata.ParameterType.INITIAL_COHORT + ) + assert True # If no error is raised, the test passes + + +@pytest.mark.unit +def test_verify_no_duplicates_raise_error() -> None: + df = pl.DataFrame({ + "state_from": ["A", "A", "B"], + "state_to": ["B", "B", "C"], + "probability": [0.1, 0.1, 0.2] + }) + + with pytest.raises(ValueError, match="Duplicate transition rows found for sample 1 and parameter ParameterType.INITIAL_COHORT"): + rpydata.verify_no_duplicates( + df, + key_columns=["state_from", "state_to"], + sample_id=1, + p=rpydata.ParameterType.INITIAL_COHORT + ) + + +@pytest.mark.unit +def test_verify_no_duplicates_pass() -> None: + df = pl.DataFrame({ + "state_from": ["A", "B", "C"], + "state_to": ["B", "C", "D"], + "probability": [0.1, 0.2, 0.3] + }) + + rpydata.verify_no_duplicates( + df, + key_columns=["state_from", "state_to"], + sample_id=1, + p=rpydata.ParameterType.INITIAL_COHORT + ) + assert True # If no error is raised, the test passes + + +@pytest.mark.unit +def validate_time_list_negative_number() -> None: + with pytest.raises(ValueError, match="The config file contains zero or a negative number in the `parameter_change_times` list!"): + rpydata.validate_time_list([10, -5, 20]) + + +@pytest.mark.unit +def validate_time_list_zero() -> None: + with pytest.raises(ValueError, match="The config file contains zero or a negative number in the `parameter_change_times` list!"): + rpydata.validate_time_list([10, 0, 20]) + + +@pytest.mark.unit +def validate_time_list_one_value() -> None: + input_list = [1] + expected_output = [] + assert rpydata.validate_time_list(input_list) == expected_output + + +@pytest.mark.unit +def validate_time_list_valid() -> None: + input_list = [1, 52, 104] + expected_output = [52, 104] + assert rpydata.validate_time_list(input_list) == expected_output + + +@pytest.mark.unit +def validate_time_list_no_one() -> None: + input_list = [52, 104] + expected_output = [52, 104] + assert rpydata.validate_time_list(input_list) == expected_output diff --git a/tests/test_data_parameters.py b/tests/test_data_parameters.py new file mode 100644 index 0000000..d06a20f --- /dev/null +++ b/tests/test_data_parameters.py @@ -0,0 +1,362 @@ +################################################################################ +# File: test_data_parameters.py # +# Project: respondpy # +# Created Date: 2026-06-09 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-09 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +import pytest + +import respondpy.data as rpydata + + +@pytest.mark.unit +def test_parameter_constructor_and_equality() -> None: + """Test the Parameter constructor.""" + assert rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT + ) == rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT + ) + + +@pytest.mark.unit +def test_parameter_inequality() -> None: + """Test the Parameter constructor.""" + assert rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT + ) != rpydata.Parameter( + rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY + ) + + +@pytest.mark.unit +def test_parameter_equality_with_enum() -> None: + """Test the Parameter constructor.""" + assert rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT + ) == rpydata.ParameterType.INITIAL_COHORT + + +@pytest.mark.unit +def test_parameter_inequality_with_enum() -> None: + """Test the Parameter constructor.""" + assert rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT + ) != rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY + + +@pytest.mark.unit +def test_parameter_is_state_vector_operation() -> None: + """Test the Parameter is_state_vector_operation method.""" + assert rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT + ).is_state_vector_operation() is True + + +@pytest.mark.unit +def test_parameter_is_not_state_vector_operation() -> None: + """Test the Parameter is_state_vector_operation method.""" + assert rpydata.Parameter( + rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY + ).is_state_vector_operation() is False + + +@pytest.mark.unit +def test_parameter_is_transition_matrix_operation() -> None: + """Test the Parameter is_transition_matrix_operation method.""" + assert rpydata.Parameter( + rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY + ).is_transition_matrix_operation() is True + + +@pytest.mark.unit +def test_parameter_is_not_transition_matrix_operation() -> None: + """Test the Parameter is_transition_matrix_operation method.""" + assert rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT + ).is_transition_matrix_operation() is False + + +@pytest.mark.unit +@pytest.mark.parametrize( + ("parameter_type", "expected"), + [ + (rpydata.ParameterType.INITIAL_COHORT, "count"), + (rpydata.ParameterType.MIGRATION_COHORT, "count"), + (rpydata.ParameterType.INTERVENTION_TRANSITION_PROBABILITY, "probability"), + (rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY, "probability"), + (rpydata.ParameterType.OVERDOSE_PROBABILITY, "probability"), + (rpydata.ParameterType.OVERDOSE_FATALITY_PROBABILITY, "probability"), + (rpydata.ParameterType.BACKGROUND_DEATH_PROBABILITY, "probability"), + (rpydata.ParameterType.STANDARD_MORTALITY_RATIO, "ratio"), + ], +) +def test_value_column_name( + parameter_type: rpydata.ParameterType, + expected: str +) -> None: + """Test that the value column names are correct.""" + assert rpydata.Parameter( + parameter_type + ).get_value_column_name() == expected + + +@pytest.mark.unit +def test_get_initial_state_column_name() -> None: + """Test that the initial state column name in the cohort table are correct.""" + assert rpydata.Parameter( + rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY + ).get_initial_state_column_name() == "initial_behavior" + + +@pytest.mark.unit +def test_get_initial_state_column_name_for_intervention() -> None: + """Test transition initial-state name for intervention transitions.""" + assert rpydata.Parameter( + rpydata.ParameterType.INTERVENTION_TRANSITION_PROBABILITY + ).get_initial_state_column_name() == "initial_intervention" + + +@pytest.mark.unit +def test_get_initial_state_column_name_is_none() -> None: + """Test that the initial state column name in the cohort table are correct.""" + assert rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT + ).get_initial_state_column_name() is None + + +@pytest.mark.unit +def test_get_next_state_column_name() -> None: + """Test that the new state column name in the cohort table are correct.""" + assert rpydata.Parameter( + rpydata.ParameterType.INTERVENTION_TRANSITION_PROBABILITY + ).get_next_state_column_name() == "new_intervention" + + +@pytest.mark.unit +def test_get_next_state_column_name_for_behavior() -> None: + """Test transition next-state name for behavior transitions.""" + assert rpydata.Parameter( + rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY + ).get_next_state_column_name() == "new_behavior" + + +@pytest.mark.unit +def test_get_next_state_column_name_is_none() -> None: + """Test that the new state column name in the cohort table are correct.""" + assert rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT + ).get_next_state_column_name() is None + + +@pytest.mark.unit +def test_parameter_repr() -> None: + """Test repr output for debugging readability.""" + p = rpydata.Parameter(rpydata.ParameterType.INITIAL_COHORT) + assert repr(p) == "Parameter(parameter_type=ParameterType.INITIAL_COHORT)" + + +@pytest.mark.unit +def test_get_parameter_type() -> None: + """Test parameter type getter.""" + p = rpydata.Parameter(rpydata.ParameterType.OVERDOSE_PROBABILITY) + assert p.get_parameter_type() == rpydata.ParameterType.OVERDOSE_PROBABILITY + + +@pytest.mark.unit +def test_parameter_equality_with_non_parameter_type() -> None: + """Ensure equality against unrelated objects returns False.""" + p = rpydata.Parameter(rpydata.ParameterType.INITIAL_COHORT) + assert (p == "INITIAL_COHORT") is False + + +@pytest.mark.unit +@pytest.mark.parametrize( + ("parameter_type", "expected"), + [ + (rpydata.ParameterType.INITIAL_COHORT, False), + (rpydata.ParameterType.MIGRATION_COHORT, True), + (rpydata.ParameterType.INTERVENTION_TRANSITION_PROBABILITY, True), + (rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY, True), + (rpydata.ParameterType.OVERDOSE_PROBABILITY, True), + (rpydata.ParameterType.OVERDOSE_FATALITY_PROBABILITY, True), + (rpydata.ParameterType.BACKGROUND_DEATH_PROBABILITY, True), + (rpydata.ParameterType.STANDARD_MORTALITY_RATIO, True), + ], +) +def test_is_time_varying(parameter_type: rpydata.ParameterType, expected: bool) -> None: + """Test time-varying behavior for all parameter types.""" + assert rpydata.Parameter(parameter_type).is_time_varying() is expected + + +@pytest.mark.unit +@pytest.mark.parametrize( + ("parameter_type", "expected"), + [ + (rpydata.ParameterType.INITIAL_COHORT, "initial_population_sample"), + (rpydata.ParameterType.MIGRATION_COHORT, "population_change_sample"), + (rpydata.ParameterType.INTERVENTION_TRANSITION_PROBABILITY, + "intervention_transition_sample"), + (rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY, + "behavior_transition_sample"), + (rpydata.ParameterType.OVERDOSE_PROBABILITY, "overdose_sample"), + (rpydata.ParameterType.OVERDOSE_FATALITY_PROBABILITY, + "overdose_fatality_sample"), + (rpydata.ParameterType.STANDARD_MORTALITY_RATIO, "smr_sample"), + (rpydata.ParameterType.BACKGROUND_DEATH_PROBABILITY, + "background_mortality_sample"), + ], +) +def test_get_cohort_column_name(parameter_type: rpydata.ParameterType, expected: str) -> None: + """Test cohort sample column mapping for each parameter type.""" + assert rpydata.Parameter( + parameter_type).get_cohort_column_name() == expected + + +@pytest.mark.unit +@pytest.mark.parametrize( + ("parameter_type", "expected"), + [ + (rpydata.ParameterType.INITIAL_COHORT, + "INSERT INTO initial_population VALUES (?, ?, ?, ?)"), + (rpydata.ParameterType.MIGRATION_COHORT, + "INSERT INTO population_change VALUES (?, ?, ?, ?, ?)"), + (rpydata.ParameterType.INTERVENTION_TRANSITION_PROBABILITY, + "INSERT INTO intervention_transition VALUES (?, ?, ?, ?, ?, ?)"), + (rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY, + "INSERT INTO behavior_transition VALUES (?, ?, ?, ?, ?, ?)"), + (rpydata.ParameterType.OVERDOSE_PROBABILITY, + "INSERT INTO overdose VALUES (?, ?, ?, ?, ?)"), + (rpydata.ParameterType.OVERDOSE_FATALITY_PROBABILITY, + "INSERT INTO overdose_fatality VALUES (?, ?, ?, ?, ?)"), + (rpydata.ParameterType.BACKGROUND_DEATH_PROBABILITY, + "INSERT INTO background_mortality VALUES (?, ?, ?)"), + (rpydata.ParameterType.STANDARD_MORTALITY_RATIO, + "INSERT INTO smr VALUES (?, ?, ?, ?, ?)"), + ], +) +def test_get_insert_statement(parameter_type: rpydata.ParameterType, expected: str) -> None: + """Test insert statement mapping for each parameter type.""" + assert rpydata.Parameter(parameter_type).get_insert_statement() == expected + + +@pytest.mark.unit +def test_get_select_statement_initial_cohort() -> None: + """Test select statement shape for initial cohort parameters.""" + sql = rpydata.Parameter( + rpydata.ParameterType.INITIAL_COHORT + ).get_select_statement(["none", "methadone"], ["stable", "active"]) + assert "FROM initial_population" in sql + assert "WHERE sample = ?" in sql + assert "WHEN i.name = 'none' THEN 0" in sql + assert "WHEN b.name = 'active' THEN 1" in sql + + +@pytest.mark.unit +def test_get_select_statement_migration_cohort() -> None: + """Test select statement shape for migration cohort parameters.""" + sql = rpydata.Parameter( + rpydata.ParameterType.MIGRATION_COHORT + ).get_select_statement(["none"], ["stable"]) + assert "FROM population_change" in sql + assert "WHERE sample = ? AND time = ?" in sql + + +@pytest.mark.unit +def test_get_select_statement_intervention_transition() -> None: + """Test select statement shape for intervention transition parameters.""" + sql = rpydata.Parameter( + rpydata.ParameterType.INTERVENTION_TRANSITION_PROBABILITY + ).get_select_statement(["none", "methadone"], ["stable", "active"]) + assert "FROM intervention_transition" in sql + assert "ii.name AS initial_intervention" in sql + assert "ni.name AS new_intervention" in sql + + +@pytest.mark.unit +def test_get_select_statement_behavior_transition() -> None: + """Test select statement shape for behavior transition parameters.""" + sql = rpydata.Parameter( + rpydata.ParameterType.BEHAVIOR_TRANSITION_PROBABILITY + ).get_select_statement(["none", "methadone"], ["stable", "active"]) + assert "FROM behavior_transition" in sql + assert "ib.name AS initial_behavior" in sql + assert "nb.name AS new_behavior" in sql + + +@pytest.mark.unit +def test_get_select_statement_overdose_fatality() -> None: + """Test select statement shape for overdose fatality parameters.""" + sql = rpydata.Parameter( + rpydata.ParameterType.OVERDOSE_FATALITY_PROBABILITY + ).get_select_statement(["none"], ["stable"]) + assert "FROM overdose_fatality" in sql + assert "probability" in sql + + +@pytest.mark.unit +def test_get_select_statement_overdose_probability() -> None: + """Test select statement shape for overdose probability parameters.""" + sql = rpydata.Parameter( + rpydata.ParameterType.OVERDOSE_PROBABILITY + ).get_select_statement(["none"], ["stable"]) + assert "FROM overdose AS od" in sql + assert "probability" in sql + + +@pytest.mark.unit +def test_get_select_statement_background_death() -> None: + """Test select statement shape for background death parameters.""" + sql = rpydata.Parameter( + rpydata.ParameterType.BACKGROUND_DEATH_PROBABILITY + ).get_select_statement(["none"], ["stable"]) + assert "FROM background_mortality" in sql + assert "WHERE sample = ? AND time = ?" in sql + + +@pytest.mark.unit +def test_get_select_statement_standard_mortality_ratio() -> None: + """Test select statement shape for standard mortality ratio parameters.""" + sql = rpydata.Parameter( + rpydata.ParameterType.STANDARD_MORTALITY_RATIO + ).get_select_statement(["none"], ["stable"]) + assert "FROM smr" in sql + assert "ratio" in sql + + +@pytest.mark.unit +def test_get_value_column_name_invalid_type_raises() -> None: + """Test invalid parameter type handling for value-column lookup.""" + p = rpydata.Parameter("invalid") # type: ignore[arg-type] + with pytest.raises(ValueError, match="not implemented"): + p.get_value_column_name() + + +@pytest.mark.unit +def test_get_cohort_column_name_invalid_type_raises() -> None: + """Test invalid parameter type handling for cohort-column lookup.""" + p = rpydata.Parameter("invalid") # type: ignore[arg-type] + with pytest.raises(ValueError, match="not implemented"): + p.get_cohort_column_name() + + +@pytest.mark.unit +def test_get_insert_statement_invalid_type_raises() -> None: + """Test invalid parameter type handling for insert statement generation.""" + p = rpydata.Parameter("invalid") # type: ignore[arg-type] + with pytest.raises(ValueError, match="not implemented"): + p.get_insert_statement() + + +@pytest.mark.unit +def test_get_select_statement_invalid_type_raises() -> None: + """Test invalid parameter type handling for select statement generation.""" + p = rpydata.Parameter("invalid") # type: ignore[arg-type] + with pytest.raises(ValueError, match="only implemented"): + p.get_select_statement(["none"], ["stable"]) diff --git a/tests/test_data_state_vectors.py b/tests/test_data_state_vectors.py new file mode 100644 index 0000000..0886948 --- /dev/null +++ b/tests/test_data_state_vectors.py @@ -0,0 +1,27 @@ +################################################################################ +# File: test_data_state_vectors.py # +# Project: respondpy # +# Created Date: 2026-06-09 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-10 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +import pytest + +import respondpy.data as rpydata + + +@pytest.mark.unit +def test_build_constant_state_vector() -> None: + """Test the build_constant_state_vector function.""" + inter = ["intervention_1", "intervention_2"] + behav = ["behavior_1", "behavior_2", "behavior_3"] + state_vector = rpydata.build_constant_state_vector(inter, behav) + print(state_vector) + assert state_vector.shape == (6, 5) + assert state_vector.select("probability").to_numpy().sum() == 0 + assert all(state_vector["probability"] == 0.0) diff --git a/tests/test_data_transition_matrices.py b/tests/test_data_transition_matrices.py new file mode 100644 index 0000000..56d6418 --- /dev/null +++ b/tests/test_data_transition_matrices.py @@ -0,0 +1,246 @@ +################################################################################ +# File: test_data_transition_matrices.py # +# Project: respondpy # +# Created Date: 2026-06-09 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-10 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +import pytest +import polars as pl + +import respondpy.data as rpydata + +from respondpy.data.transition_matrices import ( + _ensure_no_duplicate_keys, + _require_columns, + combine_dataframes, + update_retention_probability, + verify_transition_probability, +) + + +@pytest.mark.unit +def test_build_constant_transition_intervention() -> None: + inter = [f"I{i}" for i in range(16)] + behav = [f"B{j}" for j in range(4)] + df = rpydata.build_constant_transition(inter, behav).collect() + assert df.shape == ((16 * 4)**2, 7) + assert set(df.columns) == { + "sample", "time", "initial_intervention", "new_intervention", "initial_behavior", "new_behavior", "probability"} + + +@pytest.mark.unit +def test_update_retention_probability_valid_one() -> None: + df = pl.DataFrame({ + "sample": [1, 1, 1, 1], + "time": [0, 0, 0, 0], + "initial_intervention": ["A", "A", "B", "B"], + "new_intervention": ["A", "B", "A", "B"], + "initial_behavior": ["X", "X", "X", "X"], + "new_behavior": ["X", "X", "X", "X"], + "probability": [0.0, 0.2, 1.0, 0.0] + }) + + updated_df = rpydata.update_retention_probability( + df, "initial_intervention", "new_intervention") + + expected_probabilities = [0.8, 0.2, 1.0, 0.0] + assert updated_df["probability"].to_list() == expected_probabilities + + +@pytest.mark.unit +def test_update_retention_probability_valid_two() -> None: + df = pl.DataFrame({ + "sample": [1, 1, 1, 1], + "time": [0, 0, 0, 0], + "initial_intervention": ["A", "A", "A", "A"], + "new_intervention": ["A", "A", "A", "A"], + "initial_behavior": ["X", "X", "Y", "Y"], + "new_behavior": ["X", "Y", "X", "Y"], + "probability": [0.6, 0.2, 1.0, 0.0] + }) + + updated_df = rpydata.update_retention_probability( + df, "initial_intervention", "new_intervention") + + expected_probabilities = [0.8, 0.2, 1.0, 0.0] + assert updated_df["probability"].to_list() == expected_probabilities + + +@pytest.mark.unit +def test_update_retention_probability_valid_three() -> None: + df = pl.DataFrame({ + "sample": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], + "time": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + "initial_intervention": [ + "A", "A", "A", "A", "A", "A", "A", "A", + "B", "B", "B", "B", "B", "B", "B", "B" + ], + "new_intervention": [ + "A", "A", "A", "A", "B", "B", "B", "B", + "A", "A", "A", "A", "B", "B", "B", "B" + ], + "initial_behavior": [ + "X", "X", "Y", "Y", "X", "X", "Y", "Y", + "X", "X", "Y", "Y", "X", "X", "Y", "Y" + ], + "new_behavior": [ + "X", "Y", "X", "Y", "X", "Y", "X", "Y", + "X", "Y", "X", "Y", "X", "Y", "X", "Y" + ], + "probability": [ + 0.6, 0.0, 0.0, 0.4, 0.3, 0.0, 0.0, 0.5, + 0.6, 0.0, 0.0, 0.2, 0.6, 0.0, 0.0, 1.0 + ] + # rx, z, z, ry, tx, z, z, ty + # tx, z, z, ty, rx, z, z, ry + }) + from_cols = ["initial_intervention", "initial_behavior"] + to_cols = ["new_intervention", "new_behavior"] + updated_df = rpydata.update_retention_probability(df, from_cols, to_cols) + + expected_probabilities = [0.7, 0.0, 0.0, 0.5, 0.3, 0.0, 0.0, 0.5, + 0.6, 0.0, 0.0, 0.2, 0.4, 0.0, 0.0, 0.8] + assert updated_df["probability"].to_list() == expected_probabilities + + +@pytest.mark.unit +def test_update_retention_probability_valid_four() -> None: + df = pl.DataFrame({ + "sample": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], + "time": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + "initial_intervention": [ + "A", "A", "A", "A", "A", "A", "A", "A", + "B", "B", "B", "B", "B", "B", "B", "B" + ], + "new_intervention": [ + "A", "A", "A", "A", "B", "B", "B", "B", + "A", "A", "A", "A", "B", "B", "B", "B" + ], + "initial_behavior": [ + "X", "X", "Y", "Y", "X", "X", "Y", "Y", + "X", "X", "Y", "Y", "X", "X", "Y", "Y" + ], + "new_behavior": [ + "X", "Y", "X", "Y", "X", "Y", "X", "Y", + "X", "Y", "X", "Y", "X", "Y", "X", "Y" + ], + "probability": [ + 0.6, 0.3, 0.2, 0.4, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.2, 0.2, 0.1, 1.0 + ] + # rx, tx, ty, ry, z, z, z, z + # z, z, z, z, rx, tx, ty, ry + }) + + updated_df = rpydata.update_retention_probability( + df, "initial_intervention", "new_intervention") + + expected_probabilities = [0.7, 0.3, 0.2, 0.8, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.8, 0.2, 0.1, 0.9] + assert updated_df["probability"].to_list() == expected_probabilities + + +@pytest.mark.unit +def test_require_columns_raises_on_missing_columns() -> None: + with pytest.raises(ValueError, match="Missing required columns"): + _require_columns(pl.DataFrame({"a": [1]}), ["a", "b"]) + + +@pytest.mark.unit +def test_ensure_no_duplicate_keys_handles_lazyframe() -> None: + dup = pl.LazyFrame({"k": [1, 1], "v": [10, 10]}) + with pytest.raises(ValueError, match="Duplicate transition rows found"): + _ensure_no_duplicate_keys(dup, ["k", "v"]) + + +@pytest.mark.unit +def test_combine_dataframes_requires_compatible_schema() -> None: + complete = pl.LazyFrame({"sample": [1], "time": [1], "probability": [0.0]}) + raw = pl.LazyFrame({"missing_col": [1], "probability": [0.5]}) + + with pytest.raises(ValueError, match="must contain a subset of columns"): + combine_dataframes(complete, raw).collect() + + +@pytest.mark.unit +def test_update_retention_probability_completes_missing_rows() -> None: + # Missing the retention row for initial_behavior == new_behavior == "Y". + partial = pl.DataFrame( + { + "sample": [1, 1, 1], + "time": [1, 1, 1], + "initial_intervention": ["A", "A", "A"], + "new_intervention": ["A", "A", "A"], + "initial_behavior": ["X", "X", "Y"], + "new_behavior": ["X", "Y", "X"], + "probability": [0.6, 0.4, 0.1], + } + ) + + updated = update_retention_probability( + partial, + ["initial_intervention", "initial_behavior"], + ["new_intervention", "new_behavior"], + ) + + assert updated.height == 4 + retention_y = updated.filter( + (pl.col("initial_behavior") == "Y") + & (pl.col("new_behavior") == "Y") + )["probability"].item() + assert retention_y == pytest.approx(0.9) + assert verify_transition_probability( + updated, + ["initial_intervention", "initial_behavior"], + ) + + +@pytest.mark.unit +def test_update_retention_probability_with_custom_group_columns() -> None: + custom = pl.DataFrame( + { + "cohort": [1, 1, 1, 1], + "src": ["A", "A", "B", "B"], + "dst": ["A", "B", "A", "B"], + "probability": [0.0, 0.2, 0.7, 0.0], + } + ) + + updated = update_retention_probability( + custom, + "src", + "dst", + group_columns=["cohort", "src"], + unique_key_columns=["cohort", "src", "dst"], + ) + + assert updated.filter((pl.col("src") == "A") & (pl.col("dst") == "A"))[ + "probability"].item() == pytest.approx(0.8) + assert updated.filter((pl.col("src") == "B") & (pl.col("dst") == "B"))[ + "probability"].item() == pytest.approx(0.3) + + +@pytest.mark.unit +def test_verify_transition_probability_false_when_rows_do_not_sum_to_one() -> None: + not_normalized = pl.DataFrame( + { + "sample": [1, 1], + "time": [1, 1], + "initial_intervention": ["A", "A"], + "new_intervention": ["A", "B"], + "initial_behavior": ["X", "X"], + "new_behavior": ["X", "X"], + "probability": [0.2, 0.2], + } + ) + + assert not verify_transition_probability( + not_normalized, + ["initial_intervention", "initial_behavior"], + ) diff --git a/tests/test_model.py b/tests/test_model.py new file mode 100644 index 0000000..e8697fc --- /dev/null +++ b/tests/test_model.py @@ -0,0 +1,150 @@ +################################################################################ +# File: test_model.py # +# Project: respondpy # +# Created Date: 2026-06-09 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-10 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +from __future__ import annotations + + +import sqlite3 +from configparser import ConfigParser + +import pytest + +import respondpy as rpy + + +class DummyTransition: + def __init__(self, label: str) -> None: + self.label = label + + def copy(self) -> DummyTransition: + return DummyTransition(self.label) + + +class DummyModel: + def __init__(self) -> None: + self.transitions: list[DummyTransition] = [] + + def add_transition(self, transition: DummyTransition) -> None: + self.transitions.append(transition) + + +class DummyInput: + def __init__(self, duration: int, parameter_change_times: str) -> None: + cfg = ConfigParser() + cfg["simulation"] = { + "duration": str(duration), + "parameter_change_times": parameter_change_times, + } + self.config = cfg + + +@pytest.fixture +def setup_db(tmp_path_factory, db_schema, insert_complete_sample): + """Fixture to execute before all tests to setup_db the dummy database + + Yields: + _type_: _description_ + """ + temp_dir = tmp_path_factory.mktemp("test-data") + mem_str = temp_dir / "input.db" + conn = sqlite3.connect(mem_str) + cursor = conn.cursor() + cursor.executescript(db_schema) + cursor.executescript(insert_complete_sample) + conn.commit() + conn.close() + yield mem_str + + +@pytest.fixture +def setup_config(tmp_path_factory): + temp_dir = tmp_path_factory.mktemp("test-data") + mem_str = temp_dir / "sim.conf" + cfg = ConfigParser() + cfg['simulation'] = { + 'duration': '52', + 'parameter_change_times': '52', + 'stratify_entering_cohort': 'false' + } + + cfg['output'] = { + 'build_summary_stats': 'true', + 'save_state_history': 'true', + 'timesteps_to_report': '52', + } + + with mem_str.open('w') as configfile: + cfg.write(configfile) + + yield mem_str + + +@pytest.fixture +def setup_data(setup_db, setup_config): + """Pytest fixture to setup the data + + Args: + setup_db (_type_): _description_ + setup_config (_type_): _description_ + + Yields: + _type_: path to database and a ConfigParser + """ + yield setup_db, setup_config + + +@pytest.mark.unit +def test_build_model(setup_data) -> None: + db, cfg = setup_data + inp = rpy.data.Input(db_path=db, conf_path=cfg) + m = rpy.build_model(inp, 1) + assert isinstance(m, rpy.Model) + + +@pytest.mark.unit +def test_build_model_transitions_rebuilds_on_change_times( + monkeypatch: pytest.MonkeyPatch, +) -> None: + calls: list[int] = [] + + def fake_build_timestep_transition( + timestep: int, + _input_data: DummyInput, + _cohort_id: int, + ) -> list[DummyTransition]: + calls.append(timestep) + return [DummyTransition(f"t{timestep}")] + + monkeypatch.setattr(rpy.model, "build_timestep_transition", + fake_build_timestep_transition) + + model = DummyModel() + data = DummyInput(duration=4, parameter_change_times="1,3") + + out = rpy.model.build_model_transitions(model, data, cohort_id=1) + + assert out is model + # Initial build at timestep 1, then rebuild when i == 3. + assert calls == [1, 3] + # One transition for each timestep in duration. + assert len(model.transitions) == 4 + + +@pytest.mark.unit +def test_add_transitions_to_model_returns_same_instance() -> None: + model = DummyModel() + transitions = [DummyTransition("a"), DummyTransition("b")] + + out = rpy.model.add_transitions_to_model(model, transitions) + + assert out is model + assert [t.label for t in model.transitions] == ["a", "b"] diff --git a/tests/test_reading.py b/tests/test_reading.py deleted file mode 100644 index 8bc40d4..0000000 --- a/tests/test_reading.py +++ /dev/null @@ -1,248 +0,0 @@ -################################################################################ -# File: test_reading.py # -# Project: respondpy # -# Created Date: 2025-11-24 # -# Author: Matthew Carroll # -# ----- # -# Last Modified: 2026-02-04 # -# Modified By: Matthew Carroll # -# ----- # -# Copyright (c) 2025-2026 Syndemics Lab at Boston Medical Center # -################################################################################ -import sqlite3 - -import pytest - -from respondpy import get_parameter_by_id_and_time, get_state_names, get_cohorts, get_interventions, get_intervention_table, get_behaviors, get_behavior_table, get_sample_ids_by_table -from respondpy import Parameter - - -@pytest.fixture(scope="module", autouse=True) -def setup(tmp_path_factory, db_schema, db_intervention_insert, db_behavior_insert, db_cohort_insert, db_population_insert, db_migration_insert, db_intervention_transition_insert, db_behavior_transition_insert, db_smr_insert, db_bgm_insert, db_od_insert, db_fod_insert): - """Fixture to execute before all tests to setup the dummy database - - Yields: - _type_: _description_ - """ - temp_dir = tmp_path_factory.mktemp("test-data") - mem_str = temp_dir / "input.db" - conn = sqlite3.connect(mem_str) - cursor = conn.cursor() - cursor.executescript(db_schema) - cursor.execute(db_intervention_insert) - cursor.execute(db_behavior_insert) - cursor.execute(db_cohort_insert) - cursor.execute(db_population_insert) - cursor.execute(db_migration_insert) - cursor.execute(db_intervention_transition_insert) - cursor.execute(db_behavior_transition_insert) - cursor.execute(db_smr_insert) - cursor.execute(db_bgm_insert) - cursor.execute(db_od_insert) - cursor.execute(db_fod_insert) - conn.commit() - conn.close() - return mem_str - - -@pytest.mark.unit -class TestReading: - """Test Class for the file reading.py - """ - - def test_get_state_names(self, setup): - """Test the ability to get the state names from the database - """ - db_file = setup - expected = [ - ("no_treatment", "active_injection"), - ("no_treatment", "nonactive_injection"), - ("buprenorphine", "active_injection"), - ("buprenorphine", "nonactive_injection") - ] - assert expected == get_state_names(db_file) - - def test_get_state_names_empty(self): - """Test that a FileNotFoundError is raised when an invalid path is passed in. - """ - with pytest.raises(FileNotFoundError, match="The database file was not found when attempting to retrieve state names! File specified:"): - get_state_names("") - - def test_get_initial_cohort(self, setup): - """Test the ability to get the init cohort from the database - """ - db_file = setup - expected_cols = ["intervention", "behavior", "count"] - expected_vals = [ - ("no_treatment", "active_injection", 100), - ("no_treatment", "nonactive_injection", 150), - ("buprenorphine", "active_injection", 200), - ("buprenorphine", "nonactive_injection", 250) - ] - r_cols, r_vals = get_parameter_by_id_and_time( - Parameter.INITIAL_COHORT, db_file, 1, 0) - assert (expected_cols, expected_vals) == (r_cols, r_vals) - - def test_get_migrating_cohort(self, setup): - """Test the ability to get the init cohort from the database - """ - db_file = setup - expected_cols = ["intervention", "behavior", "count"] - expected_vals = [ - ("no_treatment", "active_injection", 100), - ("no_treatment", "nonactive_injection", 150), - ("buprenorphine", "active_injection", 200), - ("buprenorphine", "nonactive_injection", 250) - ] - r_cols, r_vals = get_parameter_by_id_and_time( - Parameter.MIGRATION_COHORT, db_file, 1, 1) - assert (expected_cols, expected_vals) == (r_cols, r_vals) - - def test_get_intervention_transitions(self, setup): - """Test the ability to get the init cohort from the database - """ - db_file = setup - expected_cols = ["initial_intervention", - "new_intervention", "behavior", "probability"] - expected_vals = [ - ("no_treatment", "no_treatment", "active_injection", 0.75), - ("no_treatment", "no_treatment", "nonactive_injection", 0.75), - ("no_treatment", "buprenorphine", "active_injection", 0.25), - ("no_treatment", "buprenorphine", "nonactive_injection", 0.25), - ("buprenorphine", "no_treatment", "active_injection", 0.0), - ("buprenorphine", "no_treatment", "nonactive_injection", 0.2), - ("buprenorphine", "buprenorphine", "active_injection", 1.0), - ("buprenorphine", "buprenorphine", "nonactive_injection", 0.8), - ] - r_cols, r_vals = get_parameter_by_id_and_time( - Parameter.INTERVENTION_TRANSITION_PROBABILITY, db_file, 1, 1) - assert (expected_cols, expected_vals) == (r_cols, r_vals) - - def test_get_behavior_transitions(self, setup): - """Test the ability to get the init cohort from the database - """ - db_file = setup - expected_cols = ["intervention", "initial_behavior", - "new_behavior", "probability"] - expected_vals = [ - ("no_treatment", "active_injection", "active_injection", 0.75), - ("no_treatment", "active_injection", "nonactive_injection", 0.25), - ("no_treatment", "nonactive_injection", "active_injection", 0.0), - ("no_treatment", "nonactive_injection", "nonactive_injection", 1.0), - ("buprenorphine", "active_injection", "active_injection", 0.75), - ("buprenorphine", "active_injection", "nonactive_injection", 0.25), - ("buprenorphine", "nonactive_injection", "active_injection", 0.2), - ("buprenorphine", "nonactive_injection", "nonactive_injection", 0.8), - ] - r_cols, r_vals = get_parameter_by_id_and_time( - Parameter.BEHAVIOR_TRANSITION_PROBABILITY, db_file, 1, 1) - assert (expected_cols, expected_vals) == (r_cols, r_vals) - - def test_get_smrs(self, setup): - """Test the ability to get the init cohort from the database - """ - db_file = setup - expected_cols = ["intervention", "behavior", "ratio"] - expected_vals = [ - ("no_treatment", "active_injection", 2.0), - ("no_treatment", "nonactive_injection", 2.1), - ("buprenorphine", "active_injection", 2.2), - ("buprenorphine", "nonactive_injection", 2.3) - ] - r_cols, r_vals = get_parameter_by_id_and_time( - Parameter.STANDARD_MORTALITY_RATIO, db_file, 1, 1) - assert (expected_cols, expected_vals) == (r_cols, r_vals) - - def test_get_background_mortality(self, setup): - """Test the ability to get the init cohort from the database - """ - db_file = setup - expected_cols = ["probability"] - expected_vals = [(0.25,)] - r_cols, r_vals = get_parameter_by_id_and_time( - Parameter.BACKGROUND_DEATH_PROBABILITY, db_file, 1, 1) - assert (expected_cols, expected_vals) == (r_cols, r_vals) - - def test_get_overdose(self, setup): - """Test the ability to get the init cohort from the database - """ - db_file = setup - expected_cols = ["intervention", "behavior", "probability"] - expected_vals = [ - ("no_treatment", "active_injection", 0.8), - ("no_treatment", "nonactive_injection", 0.7), - ("buprenorphine", "active_injection", 0.6), - ("buprenorphine", "nonactive_injection", 0.5) - ] - r_cols, r_vals = get_parameter_by_id_and_time( - Parameter.OVERDOSE_PROBABILITY, db_file, 1, 1) - assert (expected_cols, expected_vals) == (r_cols, r_vals) - - def test_get_fatal_overdose(self, setup): - """Test the ability to get the init cohort from the database - """ - db_file = setup - expected_cols = ["intervention", "behavior", "probability"] - expected_vals = [ - ("no_treatment", "active_injection", 0.1), - ("no_treatment", "nonactive_injection", 0.2), - ("buprenorphine", "active_injection", 0.3), - ("buprenorphine", "nonactive_injection", 0.4) - ] - r_cols, r_vals = get_parameter_by_id_and_time( - Parameter.OVERDOSE_FATALITY_PROBABILITY, db_file, 1, 1) - assert (expected_cols, expected_vals) == (r_cols, r_vals) - - def test_get_parameter_invalid_file(self): - """Test the ability to get the init cohort from the database - """ - with pytest.raises(FileNotFoundError, match="The database file was not found when attempting to retrieve parameters by id and time! File specified:"): - get_parameter_by_id_and_time( - Parameter.OVERDOSE_FATALITY_PROBABILITY, "", 1, 1) - - def test_get_cohorts(self, setup): - """Test the ability to get cohorts from the database - """ - db_file = setup - expected_cols = ["id", "description", "background_mortality_sample", "behavior_transition_sample", "initial_population_sample", - "intervention_transition_sample", "overdose_sample", "overdose_fatality_sample", "population_change_sample", "smr_sample"] - expected_vals = [(1, "Test Cohort 1", 1, 1, 1, 1, 1, 1, 1, 1)] - r_cols, r_vals = get_cohorts(db_file) - assert (expected_cols, expected_vals) == (r_cols, r_vals) - - def test_get_interventions(self, setup): - db_file = setup - expected = ["no_treatment", "buprenorphine"] - result = get_interventions(db_file) - assert result == expected - - def test_get_behaviors(self, setup): - db_file = setup - expected = ["active_injection", "nonactive_injection"] - result = get_behaviors(db_file) - assert result == expected - - def test_get_intervention_table(self, setup): - db_file = setup - expected = [(1, "no_treatment"), (2, "buprenorphine")] - result = get_intervention_table(db_file) - assert result == expected - - def test_get_behavior_table(self, setup): - db_file = setup - expected = [(1, "active_injection"), (2, "nonactive_injection")] - result = get_behavior_table(db_file) - assert result == expected - - def test_get_sample_ids_by_table(self, setup): - samples = get_sample_ids_by_table(setup, "behavior_transition") - assert samples == [1] - - def test_get_sample_ids_no_table(self, setup): - with pytest.raises(ValueError, match="The specified table does not exist:"): - get_sample_ids_by_table(setup, "falalalala") - - def test_get_sample_ids_sql_injection(self, setup): - with pytest.raises(ValueError, match="The specified table does not exist:"): - get_sample_ids_by_table( - setup, "behavior_transition; SELECT * FROM behavior_transition;") diff --git a/tests/test_simulation.py b/tests/test_simulation.py new file mode 100644 index 0000000..8bac6e7 --- /dev/null +++ b/tests/test_simulation.py @@ -0,0 +1,114 @@ +################################################################################ +# File: test_simulation.py # +# Project: respondpy # +# Created Date: 2026-02-26 # +# Author: Matthew Carroll # +# ----- # +# Last Modified: 2026-06-11 # +# Modified By: Matthew Carroll # +# ----- # +# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # +################################################################################ + +# pylint: disable=attribute-defined-outside-init, redefined-outer-name + +# Disabling comments from pylint because pytest fixtures don't follow the same convention. + +import sqlite3 +from configparser import ConfigParser + +import pytest + +import respondpy as rpy + + +@pytest.fixture +def setup_db(tmp_path_factory, db_schema, insert_complete_sample): + """Fixture to execute before all tests to setup_db the dummy database + + Yields: + _type_: _description_ + """ + temp_dir = tmp_path_factory.mktemp("test-data") + mem_str = temp_dir / "input.db" + conn = sqlite3.connect(mem_str) + cursor = conn.cursor() + cursor.executescript(db_schema) + cursor.executescript(insert_complete_sample) + conn.commit() + conn.close() + yield mem_str + + +@pytest.fixture +def setup_config(tmp_path_factory): + temp_dir = tmp_path_factory.mktemp("test-data") + mem_str = temp_dir / "sim.conf" + cfg = ConfigParser() + cfg['simulation'] = { + 'duration': '52', + 'parameter_change_times': '52', + 'stratify_entering_cohort': 'false' + } + + cfg['output'] = { + 'build_summary_stats': 'true', + 'save_state_history': 'true', + 'timesteps_to_report': '52', + } + + with mem_str.open('w') as configfile: + cfg.write(configfile) + + yield mem_str + + +@pytest.fixture +def setup_data(setup_db, setup_config): + """Pytest fixture to setup the data + + Args: + setup_db (_type_): _description_ + setup_config (_type_): _description_ + + Yields: + _type_: path to database and a ConfigParser + """ + yield setup_db, setup_config + + +@pytest.mark.unit +def test_build_simulation(setup_data): + """Basic unit test to verify the simulation can be built + + Args: + setup_data (_type_): _description_ + """ + db, cfg = setup_data + inp = rpy.data.Input(db_path=db, conf_path=cfg) + sim = rpy.build_simulation(inp) + assert len(sim.get_models()) == 1 + + +def test_build_simulation_with_cohort_id(setup_data): + """Basic unit test to verify the simulation can be built + + Args: + setup_data (_type_): _description_ + """ + db, cfg = setup_data + inp = rpy.data.Input(db_path=db, conf_path=cfg) + sim = rpy.build_simulation(inp, cohort_ids=[1]) + assert len(sim.get_models()) == 1 + + +def test_build_simulation_with_invalid_cohort_id(setup_data): + """Basic unit test to verify the simulation can be built + + Args: + setup_data (_type_): _description_ + """ + db, cfg = setup_data + inp = rpy.data.Input(db_path=db, conf_path=cfg) + with pytest.raises(ValueError, match="Cohort IDs"): + rpy.build_simulation(inp, cohort_ids=[2]) diff --git a/tests/test_smoke.py b/tests/test_smoke.py index c45c39f..122d522 100644 --- a/tests/test_smoke.py +++ b/tests/test_smoke.py @@ -4,7 +4,7 @@ # Created Date: 2026-01-08 # # Author: Matthew Carroll # # ----- # -# Last Modified: 2026-02-12 # +# Last Modified: 2026-06-09 # # Modified By: Matthew Carroll # # ----- # # Copyright (c) 2026 Syndemics Lab at Boston Medical Center # @@ -23,6 +23,20 @@ import respondpy as rpy +@pytest.mark.smoke +def test_import() -> None: + """Test that we can import the module.""" + import respondpy + assert respondpy.__version__ is not None + + +@pytest.mark.smoke +def test_data_import() -> None: + import respondpy.data + assert respondpy.data.__all__ is not None + assert isinstance(respondpy.__dir__(), list) + + @pytest.mark.smoke def test_model() -> None: state = np.array([10, 20, 30]) @@ -72,3 +86,29 @@ def test_one_step() -> None: print(model.get_state()) expected = [0.76715528791564891, 0.72320370216816077, 1.037712429738102] np.testing.assert_almost_equal(model.get_state(), expected) + + +@pytest.mark.smoke +def test_simulation_sparse_histories() -> None: + """Verify get_model_sparse_histories returns a name-keyed dict of History objects.""" + state = np.array([10.0, 20.0, 30.0]) + model = rpy.Model("markov", "console") + model.set_state(state) + model.create_default_histories() + + sim = rpy.Simulation() + sim.add_model(model) + sim.run() + + sparse = sim.get_model_sparse_histories() + + assert isinstance(sparse, dict), "Expected a dict keyed by model name" + assert "markov" in sparse, "Expected 'markov' model name as key" + + model_histories = sparse["markov"] + assert isinstance( + model_histories, dict), "Expected inner dict keyed by history name" + + for hist in model_histories.values(): + assert isinstance( + hist, rpy.History), "Expected History values in inner dict" diff --git a/tests/test_execute.py b/tests/test_transition.py similarity index 73% rename from tests/test_execute.py rename to tests/test_transition.py index b22cd7b..edbb261 100644 --- a/tests/test_execute.py +++ b/tests/test_transition.py @@ -1,25 +1,21 @@ ################################################################################ -# File: test_execute.py # +# File: test_transition.py # # Project: respondpy # -# Created Date: 2026-02-26 # +# Created Date: 2026-06-09 # # Author: Matthew Carroll # # ----- # -# Last Modified: 2026-02-26 # +# Last Modified: 2026-06-10 # # Modified By: Matthew Carroll # # ----- # # Copyright (c) 2026 Syndemics Lab at Boston Medical Center # ################################################################################ -# pylint: disable=attribute-defined-outside-init, redefined-outer-name - -# Disabling comments from pylint because pytest fixtures don't follow the same convention. - import sqlite3 from configparser import ConfigParser import pytest -from respondpy import build_simulation +import respondpy as rpy @pytest.fixture @@ -41,7 +37,9 @@ def setup_db(tmp_path_factory, db_schema, insert_complete_sample): @pytest.fixture -def setup_config(): +def setup_config(tmp_path_factory): + temp_dir = tmp_path_factory.mktemp("test-data") + mem_str = temp_dir / "sim.conf" cfg = ConfigParser() cfg['simulation'] = { 'duration': '52', @@ -55,7 +53,10 @@ def setup_config(): 'timesteps_to_report': '52', } - yield cfg + with mem_str.open('w') as configfile: + cfg.write(configfile) + + yield mem_str @pytest.fixture @@ -73,12 +74,9 @@ def setup_data(setup_db, setup_config): @pytest.mark.unit -def test_build_simulation(setup_data): - """Basic unit test to verify the simulation can be built - - Args: - setup_data (_type_): _description_ - """ +def test_build_timestep_transition(setup_data) -> None: db, cfg = setup_data - sim = build_simulation([1], db, cfg) - assert len(sim.get_models()) == 1 + inp = rpy.data.Input(db_path=db, conf_path=cfg) + transitions = rpy.build_timestep_transition(1, inp, 1) + assert isinstance(transitions, list) + assert all(isinstance(t, rpy.Transition) for t in transitions) diff --git a/tests/test_writing.py b/tests/test_writing.py deleted file mode 100644 index 0e705f3..0000000 --- a/tests/test_writing.py +++ /dev/null @@ -1,328 +0,0 @@ -################################################################################ -# File: test_writing.py # -# Project: respondpy # -# Created Date: 2026-01-16 # -# Author: Matthew Carroll # -# ----- # -# Last Modified: 2026-02-04 # -# Modified By: Matthew Carroll # -# ----- # -# Copyright (c) 2026 Syndemics Lab at Boston Medical Center # -################################################################################ - -import sqlite3 -from pathlib import Path - -import pytest -import polars as pl - -from respondpy import insert_parameter, Parameter, insert_cohort - - -@pytest.fixture(scope="module", autouse=True) -def setup(tmp_path_factory, db_schema, db_cohort_insert, db_intervention_insert, db_behavior_insert): - """Fixture to execute before all tests to setup the dummy database - - Yields: - _type_: _description_ - """ - temp_dir = tmp_path_factory.mktemp("test-data") - mem_str = temp_dir / "input.db" - conn = sqlite3.connect(mem_str) - cursor = conn.cursor() - cursor.executescript(db_schema) - cursor.execute(db_cohort_insert) - cursor.execute(db_intervention_insert) - cursor.execute(db_behavior_insert) - conn.commit() - conn.close() - return mem_str - - -def delete(db_file, table_name): - conn = sqlite3.connect(db_file) - cursor = conn.cursor() - cursor.execute(f"DELETE FROM {table_name};") - conn.commit() - conn.close() - - -@pytest.mark.unit -class TestWriting: - """Test Class for the file writing.py - """ - - def _count_rows(self, db_file: str | Path, table: str) -> int: - con = sqlite3.connect(db_file) - cur = con.cursor() - stmt = f"SELECT COUNT(*) FROM {table}" - cur.execute(stmt) - result = cur.fetchone()[0] - con.close() - return result - - def test_insert_initial_cohort(self, setup): - db_file = setup - df = pl.DataFrame({ - "sample": [1, 1, 1, 1], - "intervention": [1, 1, 2, 2], - "behavior": [1, 2, 1, 2], - "count": [100, 200, 300, 400] - }) - insert_parameter(df.rows(), - db_file, Parameter.INITIAL_COHORT) - - assert self._count_rows(db_file, "initial_population") == df.height - - def test_insert_migrating_cohort(self, setup): - db_file = setup - df = pl.DataFrame({ - "sample": [1, 1, 1, 1], - "intervention": [1, 1, 2, 2], - "behavior": [1, 2, 1, 2], - "time": [1, 1, 1, 1], - "count": [100, 200, 300, 400] - }) - insert_parameter(df.rows(), - db_file, Parameter.MIGRATION_COHORT) - - assert self._count_rows(db_file, "population_change") == df.height - - def test_insert_intervention_transition(self, setup): - db_file = setup - df = pl.DataFrame({ - "sample": [1, 1, 1, 1, 1, 1, 1, 1], - "behavior": [1, 2, 1, 2, 1, 2, 1, 2], - "time": [1, 1, 1, 1, 1, 1, 1, 1], - "initial_intervention": [1, 1, 2, 2, 1, 1, 2, 2], - "new_intervention": [1, 1, 1, 1, 2, 2, 2, 2], - "probability": [.25, .25, .25, .25, .25, .25, .25, .25] - }) - insert_parameter(df.rows(), - db_file, Parameter.INTERVENTION_TRANSITION_PROBABILITY) - - assert self._count_rows( - db_file, "intervention_transition") == df.height - - def test_insert_behavior_transition(self, setup): - db_file = setup - df = pl.DataFrame({ - "sample": [1, 1, 1, 1, 1, 1, 1, 1], - "intervention": [1, 2, 1, 2, 1, 2, 1, 2], - "time": [1, 1, 1, 1, 1, 1, 1, 1], - "initial_behavior": [1, 1, 2, 2, 1, 1, 2, 2], - "new_behavior": [1, 1, 1, 1, 2, 2, 2, 2], - "probability": [.25, .25, .25, .25, .25, .25, .25, .25] - }) - insert_parameter(df.rows(), - db_file, Parameter.BEHAVIOR_TRANSITION_PROBABILITY) - - assert self._count_rows( - db_file, "behavior_transition") == df.height - - def test_insert_smr(self, setup): - db_file = setup - df = pl.DataFrame({ - "sample": [1, 1, 1, 1], - "intervention": [1, 1, 2, 2], - "behavior": [1, 2, 1, 2], - "time": [1, 1, 1, 1], - "ratio": [100, 200, 300, 400] - }) - insert_parameter(df.rows(), - db_file, Parameter.STANDARD_MORTALITY_RATIO) - - assert self._count_rows(db_file, "smr") == df.height - - def test_insert_background_mortality(self, setup): - db_file = setup - df = pl.DataFrame({ - "sample": [1], - "time": [1], - "ratio": [100] - }) - insert_parameter(df.rows(), - db_file, Parameter.BACKGROUND_DEATH_PROBABILITY) - - assert self._count_rows(db_file, "background_mortality") == df.height - - def test_insert_overdose(self, setup): - db_file = setup - df = pl.DataFrame({ - "intervention": [1, 1, 2, 2], - "sample": [1, 1, 1, 1], - "behavior": [1, 2, 1, 2], - "time": [1, 1, 1, 1], - "probability": [100, 200, 300, 400] - }) - insert_parameter(df.rows(), - db_file, Parameter.OVERDOSE_PROBABILITY) - - assert self._count_rows(db_file, "overdose") == df.height - - def test_insert_fatal_overdose(self, setup): - db_file = setup - df = pl.DataFrame({ - "sample": [1, 1, 1, 1], - "intervention": [1, 1, 2, 2], - "behavior": [1, 2, 1, 2], - "time": [1, 1, 1, 1], - "probability": [100, 200, 300, 400] - }) - insert_parameter(df.rows(), - db_file, Parameter.OVERDOSE_FATALITY_PROBABILITY) - - assert self._count_rows(db_file, "overdose_fatality") == df.height - - def test_insert_invalid_file(self): - """Test the ability to get the init cohort from the database - """ - with pytest.raises(FileNotFoundError, match="The specified database could not be found:"): - insert_parameter([], "", - Parameter.OVERDOSE_FATALITY_PROBABILITY) - - def test_insert_cohort(self, setup): - df = pl.DataFrame({ - "description": ["sample 1", "sample 2"], - "background_mortality_sample": [1, 1], - "behavior_transition_sample": [1, 1], - "initial_population_sample": [1, 1], - "intervention_transition_sample": [1, 1], - "overdose_sample": [1, 1], - "overdose_fatality_sample": [1, 1], - "population_change_sample": [1, 1], - "smr_sample": [1, 1] - }) - - insert_cohort(df.rows(), setup) - # since a cohort is already present we +1 the height - assert self._count_rows(setup, "cohort") == df.height+1 - - -@pytest.mark.unit -class TestInvalidWriting: - """Class to test the invalid writing attempts - """ - - def _count_rows(self, db_file: str | Path, table: str) -> int: - con = sqlite3.connect(db_file) - cur = con.cursor() - stmt = f"SELECT COUNT(*) FROM {table}" - cur.execute(stmt) - result = cur.fetchone()[0] - con.close() - return result - - def test_insert_initial_cohort(self, setup): - db_file = setup - delete(db_file, "initial_population") - df = pl.DataFrame({ - "sample": [1, 1, 1, 1], - "intervention": [1, 1, 2, 2], - "behavior": [1, 2, 1, 2] - }) - - insert_parameter(df.rows(), - db_file, Parameter.INITIAL_COHORT) - - assert self._count_rows(db_file, "initial_population") == 0 - - def test_insert_migrating_cohort(self, setup): - db_file = setup - delete(db_file, "population_change") - df = pl.DataFrame({ - "sample": [1, 1, 1, 1], - "intervention": [1, 1, 2, 2], - "behavior": [1, 2, 1, 2], - "time": [1, 1, 1, 1] - }) - insert_parameter(df.rows(), - db_file, Parameter.MIGRATION_COHORT) - - assert self._count_rows(db_file, "population_change") == 0 - - def test_insert_intervention_transition(self, setup): - db_file = setup - delete(db_file, "intervention_transition") - df = pl.DataFrame({ - "sample": [1, 1, 1, 1, 1, 1, 1, 1], - "behavior": [1, 2, 1, 2, 1, 2, 1, 2], - "time": [1, 1, 1, 1, 1, 1, 1, 1], - "initial_intervention": [1, 1, 2, 2, 1, 1, 2, 2], - "new_intervention": [1, 1, 1, 1, 2, 2, 2, 2] - }) - insert_parameter(df.rows(), - db_file, Parameter.INTERVENTION_TRANSITION_PROBABILITY) - - assert self._count_rows( - db_file, "intervention_transition") == 0 - - def test_insert_behavior_transition(self, setup): - db_file = setup - delete(db_file, "behavior_transition") - df = pl.DataFrame({ - "sample": [1, 1, 1, 1, 1, 1, 1, 1], - "intervention": [1, 2, 1, 2, 1, 2, 1, 2], - "time": [1, 1, 1, 1, 1, 1, 1, 1], - "initial_behavior": [1, 1, 2, 2, 1, 1, 2, 2], - "new_behavior": [1, 1, 1, 1, 2, 2, 2, 2] - }) - insert_parameter(df.rows(), - db_file, Parameter.BEHAVIOR_TRANSITION_PROBABILITY) - - assert self._count_rows( - db_file, "behavior_transition") == 0 - - def test_insert_smr(self, setup): - db_file = setup - delete(db_file, "smr") - df = pl.DataFrame({ - "sample": [1, 1, 1, 1], - "intervention": [1, 1, 2, 2], - "behavior": [1, 2, 1, 2], - "time": [1, 1, 1, 1] - }) - insert_parameter(df.rows(), - db_file, Parameter.STANDARD_MORTALITY_RATIO) - - assert self._count_rows(db_file, "smr") == 0 - - def test_insert_background_mortality(self, setup): - db_file = setup - delete(db_file, "background_mortality") - df = pl.DataFrame({ - "sample": [1], - "time": [1] - }) - insert_parameter(df.rows(), - db_file, Parameter.BACKGROUND_DEATH_PROBABILITY) - - assert self._count_rows(db_file, "background_mortality") == 0 - - def test_insert_overdose(self, setup): - db_file = setup - delete(db_file, "overdose") - df = pl.DataFrame({ - "intervention": [1, 1, 2, 2], - "sample": [1, 1, 1, 1], - "behavior": 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