def test_create_abcsmc(data_api):
inf.ABCSMC(
data_api.read_table("human/abcsmc-parameters", "abcsmc-parameters"),
data_api.read_table("human/historical-deaths", "historical-deaths"),
data_api.read_table("human/compartment-transition", "compartment-transition"),
data_api.read_table("human/population", "population"),
data_api.read_table("human/commutes", "commutes"),
data_api.read_table("human/mixing-matrix", "mixing-matrix"),
data_api.read_table("human/infection-probability", "infection-probability"),
data_api.read_table("human/initial-infections", "initial-infections"),
data_api.read_table("human/infectious-compartments", "infectious-compartments"),
data_api.read_table("human/trials", "trials"),
data_api.read_table("human/start-end-date", "start-end-date"),
data_api.read_table("human/movement-multipliers", "movement-multipliers"),
data_api.read_table("human/stochastic-mode", "stochastic-mode"),
data_api.read_table("human/random-seed", "random-seed"),
)
def test_create_abcsmc_too_many_infection_probas(data_api):
with pytest.raises(AssertionError):
inf.ABCSMC(
data_api.read_table("human/abcsmc-parameters", "abcsmc-parameters"),
data_api.read_table("human/historical-deaths", "historical-deaths"),
data_api.read_table("human/compartment-transition", "compartment-transition"),
data_api.read_table("human/population", "population"),
data_api.read_table("human/commutes", "commutes"),
data_api.read_table("human/mixing-matrix", "mixing-matrix"),
pd.DataFrame([{"Date": "2020-01-01", "Value": 0.5}, {"Date": "2020-01-02", "Value": 0.5}]),
data_api.read_table("human/initial-infections", "initial-infections"),
data_api.read_table("human/infectious-compartments", "infectious-compartments"),
data_api.read_table("human/trials", "trials"),
data_api.read_table("human/start-end-date", "start-end-date"),
data_api.read_table("human/movement-multipliers", "movement-multipliers"),
data_api.read_table("human/stochastic-mode", "stochastic-mode"),
data_api.read_table("human/random-seed", "random-seed"),
)
def test_create_abcsmc_invalid_abc_smc_params(data_api, param_index):
parameters = data_api.read_table("human/abcsmc-parameters", "abcsmc-parameters")
parameters.at[param_index, "Value"] = 0
with pytest.raises(AssertionError):
inf.ABCSMC(
parameters,
data_api.read_table("human/historical-deaths", "historical-deaths"),
data_api.read_table("human/compartment-transition", "compartment-transition"),
data_api.read_table("human/population", "population"),
data_api.read_table("human/commutes", "commutes"),
data_api.read_table("human/mixing-matrix", "mixing-matrix"),
data_api.read_table("human/infection-probability", "infection-probability"),
data_api.read_table("human/initial-infections", "initial-infections"),
data_api.read_table("human/infectious-compartments", "infectious-compartments"),
data_api.read_table("human/trials", "trials"),
data_api.read_table("human/start-end-date", "start-end-date"),
data_api.read_table("human/movement-multipliers", "movement-multipliers"),
data_api.read_table("human/stochastic-mode", "stochastic-mode"),
data_api.read_table("human/random-seed", "random-seed"),
)
def test_ABCSMC_run_model_with_issues(mock_warn_logger, data_api):
abcsmc = inf.ABCSMC(
data_api.read_table("human/abcsmc-parameters", "abcsmc-parameters"),
data_api.read_table("human/historical-deaths", "historical-deaths"),
data_api.read_table("human/compartment-transition", "compartment-transition"),
data_api.read_table("human/population", "population").iloc[:10],
data_api.read_table("human/commutes", "commutes"),
data_api.read_table("human/mixing-matrix", "mixing-matrix"),
pd.DataFrame([{"Date": "2020-01-01", "Value": 0.5}]),
data_api.read_table("human/initial-infections", "initial-infections"),
data_api.read_table("human/infectious-compartments", "infectious-compartments"),
data_api.read_table("human/trials", "trials"),
data_api.read_table("human/start-end-date", "start-end-date"),
data_api.read_table("human/movement-multipliers", "movement-multipliers"),
data_api.read_table("human/stochastic-mode", "stochastic-mode"),
data_api.read_table("human/random-seed", "random-seed"),
)
particle = inf.Particle.generate_from_priors(abcsmc)
abcsmc.run_model(particle)
mock_warn_logger.assert_has_calls([call("We had %s issues when running the model:", 36)])
def abcsmc(data_api): # pylint: disable=redefined-outer-name
yield inference.ABCSMC(
data_api.read_table("human/abcsmc-parameters", "abcsmc-parameters"),
data_api.read_table("human/historical-deaths", "historical-deaths"),
data_api.read_table("human/compartment-transition",
"compartment-transition"),
data_api.read_table("human/population", "population"),
data_api.read_table("human/commutes", "commutes"),
data_api.read_table("human/mixing-matrix", "mixing-matrix"),
pd.DataFrame([{
"Date": "2020-01-01",
"Value": 0.5
}]),
data_api.read_table("human/initial-infections", "initial-infections"),
data_api.read_table("human/infectious-compartments",
"infectious-compartments"),
data_api.read_table("human/trials", "trials"),
data_api.read_table("human/start-end-date", "start-end-date"),
data_api.read_table("human/movement-multipliers",
"movement-multipliers"),
data_api.read_table("human/stochastic-mode", "stochastic-mode"),
data_api.read_table("human/random-seed", "random-seed"),
)