def test_analysis(self, jhu_data, population_data):
scenario = Scenario(jhu_data, population_data, country="Italy")
with pytest.raises(KeyError):
scenario.simulate(name="Main", show_figure=False)
with pytest.raises(ValueError):
scenario.estimate(model=SIRF)
# S-R trend analysis
scenario.trend(show_figure=False)
warnings.filterwarnings("ignore", category=UserWarning)
scenario.trend(show_figure=True)
# Parameter estimation of SIR-F model
with pytest.raises(ValueError):
scenario.param_history(targets=["Rt"], show_figure=False)
with pytest.raises(ValueError):
scenario.estimate(model=SIRF, tau=1440)
scenario.estimate(model=SIRF)
# History of estimation
scenario.estimate_history(phase="1st")
with pytest.raises(KeyError):
scenario.estimate_history(phase="0th")
# Accuracy of estimation
scenario.estimate_accuracy(phase="1st")
with pytest.raises(KeyError):
scenario.estimate_accuracy(phase="0th")
# Prediction
scenario.add(name="Main", days=100)
scenario.simulate(name="Main", show_figure=False)
scenario.simulate(name="Main", show_figure=True)
scenario.param_history(targets=["Rt"], show_figure=False)
scenario.param_history(targets=["Rt"], divide_by_first=False)
scenario.param_history(targets=["Rt"], show_box_plot=False)
with pytest.raises(KeyError):
scenario.param_history(targets=["Rt", "Value"])
with pytest.raises(KeyError):
scenario.param_history(targets=["Rt"], box_plot=False)
# New scenario
sigma_new = scenario.get("sigma", phase="last") * 2
with pytest.raises(KeyError):
scenario.get("value")
warnings.filterwarnings("ignore", category=DeprecationWarning)
scenario.add_phase(name="New medicines", days=100, sigma=sigma_new)
# Summarize scenarios
summary_df = scenario.summary()
assert isinstance(summary_df, pd.DataFrame)
desc_df = scenario.describe()
assert isinstance(desc_df, pd.DataFrame)
# Estimation errors
with pytest.raises(TypeError):
scenario.estimate(SIRF, phases="1st")
with pytest.raises(KeyError):
scenario.estimate(SIRF, phases=["100th"])
def test_estimate(self, jhu_data, population_data, country):
warnings.simplefilter("ignore", category=UserWarning)
# Setting
snl = Scenario(jhu_data, population_data, country)
snl.first_date = "01Apr2020"
snl.last_date = "01Aug2020"
with pytest.raises(ValueError):
snl.estimate(SIR)
snl.trend(show_figure=False)
with pytest.raises(AttributeError):
snl.estimate_history(phase="last")
# Parameter estimation
with pytest.raises(KeyError):
snl.estimate(SIR, phases=["30th"])
with pytest.raises(ValueError):
snl.estimate(model=SIR, tau=1440)
snl.estimate(SIR, timeout=1, timeout_iteration=1)
# Estimation history
snl.estimate_history(phase="last")
# Estimation accuracy
snl.estimate_accuracy(phase="last")
# Get a value
snl.get(Term.RT)
with pytest.raises(KeyError):
snl.get("feeling")
def test_simulate(self, jhu_data, population_data, country):
warnings.simplefilter("ignore", category=UserWarning)
warnings.simplefilter("ignore", category=DeprecationWarning)
# Setting
snl = Scenario(jhu_data, population_data, country)
snl.first_date = "01Apr2020"
snl.last_date = "01May2020"
with pytest.raises(ValueError):
snl.simulate()
with pytest.raises(ValueError):
snl.track()
snl.trend(show_figure=False)
# Parameter estimation
with pytest.raises(ValueError):
# Deprecated
snl.param_history(["rho"])
all_phases = snl.summary().index.tolist()
snl.disable(all_phases[:-2])
with pytest.raises(NameError):
snl.simulate()
snl.estimate(SIRF, timeout=5, timeout_iteration=5)
# Simulation
snl.simulate(variables=[Term.C, Term.CI, Term.F, Term.R])
snl.simulate(phases=all_phases[-2:])
# Parameter history (Deprecated)
snl.param_history([Term.RT], divide_by_first=False)
snl.param_history(["rho"])
snl.param_history(["rho"], show_figure=False)
snl.param_history(["rho"], show_box_plot=False)
with pytest.raises(KeyError):
snl.param_history(["feeling"])
# Comparison of scenarios
snl.describe()
snl.track()
snl.history(target="Rt")
snl.history(target="sigma")
snl.history(target="rho", show_figure=False)
snl.history(target="Infected", phases=all_phases[-2:])
with pytest.raises(KeyError):
snl.history(target="temperature")
# Change rate of parameters
snl.history_rate(name="Main")
snl.history_rate(
name="Main", params=["theta", "kappa"], show_figure=False)
with pytest.raises(TypeError):
snl.history_rate(params="", name="Main")
# Add new scenario
snl.add(end_date="01Sep2020", name="New")
snl.describe()
def test_fit_predict(self, jhu_data, population_data, oxcgrt_data, country):
snl = Scenario(jhu_data, population_data, country)
snl.trend(show_figure=False)
with pytest.raises(UnExecutedError):
snl.fit(oxcgrt_data)
snl.estimate(SIRF, timeout=1, timeout_iteration=1)
# Fitting
with pytest.raises(UnExecutedError):
snl.predict()
info_dict = snl.fit(oxcgrt_data)
assert isinstance(info_dict, dict)
# Prediction
snl.predict()
assert Term.FUTURE in snl.summary()[Term.TENSE].unique()
# Fitting & predict
snl.fit_predict(oxcgrt_data)
assert Term.FUTURE in snl.summary()[Term.TENSE].unique()
def test_score(self, jhu_data, population_data, country):
snl = Scenario(jhu_data, population_data, country, tau=360)
snl.trend(show_figure=False)
snl.estimate(SIRF, timeout=1, timeout_iteration=1)
assert isinstance(snl.score(metrics="RMSLE"), float)
# Selected phases
df = snl.summary()
all_phases = df.index.tolist()
sel_score = snl.score(phases=all_phases[-2:])
# Selected past days (when the begging date is a start date)
beginning_date = df.loc[df.index[-2], Term.START]
past_days = Term.steps(beginning_date, snl.last_date, tau=1440)
assert snl.score(past_days=past_days) == sel_score
# Selected past days
snl.score(past_days=60)
with pytest.raises(ValueError):
snl.score(phases=["1st"], past_days=60)
def test_score(self, jhu_data, population_data, country):
snl = Scenario(jhu_data, population_data, country, tau=360)
snl.trend(show_figure=False)
snl.estimate(SIRF, timeout=10)
metrics_list = ["MAE", "MSE", "MSLE", "RMSE", "RMSLE"]
for metrics in metrics_list:
score = snl.score(metrics=metrics)
assert isinstance(score, float)
# Selected phases
df = snl.summary()
all_phases = df.index.tolist()
sel_score = snl.score(phases=all_phases[-2:])
# Selected past days (when the begging date is a start date)
beginning_date = df.loc[df.index[-2], Term.START]
past_days = Term.steps(beginning_date, snl.last_date, tau=1440)
assert snl.score(past_days=past_days) == sel_score
# Selected past days
snl.score(past_days=60)
def test_analysis(self, jhu_data, population_data):
scenario = Scenario(jhu_data, population_data, country="Italy")
# S-R trend analysis
scenario.trend(show_figure=False)
# Parameter estimation of SIR-F model
scenario.estimate(model=SIRF)
# Prediction
scenario.add_phase(name="Main", days=100)
scenario.simulate(name="Main", show_figure=False)
scenario.param_history(targets=["Rt"], name="Main", show_figure=False)
# New scenario
sigma_new = scenario.get("sigma", phase="last") * 2
scenario.add_phase(name="New medicines", days=100, sigma=sigma_new)
# Summarize scenarios
summary_df = scenario.summary()
assert isinstance(summary_df, pd.DataFrame)
desc_df = scenario.describe()
assert isinstance(desc_df, pd.DataFrame)
def test_score_error(self, jhu_data, population_data, country):
snl = Scenario(jhu_data, population_data, country)
with pytest.raises(ValueError):
snl.score()
snl.trend(show_figure=False)
with pytest.raises(NameError):
snl.score()
all_phases = snl.summary().index.tolist()
snl.disable(phases=all_phases[:-2])
snl.estimate(SIRF, timeout=10)
with pytest.raises(TypeError):
snl.score(phases="0th")
with pytest.raises(KeyError):
snl.score(phases=["100th"])
with pytest.raises(TypeError):
snl.score(variables="Infected")
with pytest.raises(KeyError):
snl.score(variables=["Susceptible"])
with pytest.raises(ValueError):
snl.score(metrics="Subjective evaluation")
with pytest.raises(ValueError):
snl.score(phases=["0th"], past_days=100)
def test_simulate(self, jhu_data, population_data, country):
warnings.simplefilter("ignore", category=UserWarning)
warnings.simplefilter("ignore", category=DeprecationWarning)
# Setting
snl = Scenario(jhu_data, population_data, country)
snl.first_date = "01Apr2020"
snl.last_date = "01Aug2020"
snl.trend(show_figure=False)
# Parameter estimation
with pytest.raises(ValueError):
# Deprecated
snl.param_history(["rho"])
snl.estimate(SIR)
# Simulation
snl.simulate()
# Parameter history (Deprecated)
snl.param_history([Term.RT], divide_by_first=False)
snl.param_history(["rho"])
snl.param_history(["rho"], show_figure=False)
snl.param_history(["rho"], show_box_plot=False)
with pytest.raises(KeyError):
snl.param_history(["feeling"])
# Comparison of scenarios
snl.describe()
snl.history(target="Rt")
snl.history(target="sigma")
snl.history(target="Infected")
with pytest.raises(KeyError):
snl.history(target="temperature")
# Change rate of parameters
snl.history_rate(name="Main")
with pytest.raises(TypeError):
snl.history_rate(params="", name="Main")
# Add new scenario
snl.add(end_date="01Sep2020", name="New")
snl.describe()
def test_estimate_tau(self, jhu_data, population_data, country):
# Setting
snl = Scenario(jhu_data, population_data, country)
snl.trend(show_figure=False)
with pytest.raises(ValueError):
snl.estimate(SIR, tau=1440)
def test_estimate(self, jhu_data, population_data, country):
warnings.simplefilter("ignore", category=UserWarning)
# Setting
snl = Scenario(jhu_data, population_data, country)
snl.first_date = "01Apr2020"
snl.last_date = "01Aug2020"
with pytest.raises(ValueError):
snl.estimate(SIR)
snl.trend(include_init_phase=True, show_figure=False)
snl.disable(phases=["0th"])
with pytest.raises(AttributeError):
snl.estimate_history(phase="1th")
# Parameter estimation
with pytest.raises(KeyError):
snl.estimate(SIR, phases=["30th"])
with pytest.raises(ValueError):
snl.estimate(model=SIR, tau=1440)
snl.enable(phases=["0th"])
with pytest.raises(TypeError):
snl.estimate(model=SIR, phases="1st")
with pytest.raises(ValueError):
snl.estimate(model=SIR, phases=["0th"])
snl.clear(include_past=True)
snl.trend(show_figure=False)
snl.estimate(SIR)
# Estimation history
snl.estimate_history(phase="1st")
# Estimation accuracy
snl.estimate_accuracy(phase="1st")
# Get a value
snl.get(Term.RT)
with pytest.raises(KeyError):
snl.get("feeling")