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_summary(self, jhu_data, population_data, country): # Setting snl = Scenario(jhu_data, population_data, country) snl.first_date = "01Apr2020" snl.last_date = "01Aug2020" snl.trend(show_figure=False) # One scenario assert set(snl.summary().columns) == set( [Term.TENSE, Term.START, Term.END, Term.N]) # Show two scenarios snl.clear(name="New") cols = snl.summary().reset_index().columns assert set([Term.SERIES, Term.PHASE]).issubset(set(cols)) # Show selected scenario cols_sel = snl.summary(name="New").reset_index().columns assert not set([Term.SERIES, Term.PHASE]).issubset(set(cols_sel)) # Columns to show show_cols = [Term.N, Term.START] assert set(snl.summary(columns=show_cols).columns) == set(show_cols) with pytest.raises(TypeError): snl.summary(columns=Term.N) with pytest.raises(KeyError): snl.summary(columns=[Term.N, "Temperature"]) # To markdown snl.summary().to_markdown()
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")
def test_retrospective(self, jhu_data, population_data, country): # Setting snl = Scenario(jhu_data, population_data, country) snl.first_date = "01Apr2020" snl.last_date = "19Oct2020" snl.trend(show_figure=False) # Retrospective analysis snl.retrospective( "01Sep2020", model=SIR, control="Main", target="Retrospective")
def test_retrospective(self, jhu_data, population_data, country): # Setting snl = Scenario(jhu_data, population_data, country) snl.first_date = "01Apr2020" snl.last_date = "01Jun2020" snl.trend(show_figure=False) # Retrospective analysis snl.retrospective( "01May2020", model=SIRF, control="Main", target="Retrospective", timeout=1, timeout_iteration=1)
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_trend(self, jhu_data, population_data, country): # Setting snl = Scenario(jhu_data, population_data, country) snl.first_date = "01Apr2020" snl.last_date = "01Aug2020" # Deprecated warnings.simplefilter("error") with pytest.raises(DeprecationWarning): snl.trend(include_init_phase=False, show_figure=False) warnings.simplefilter("ignore") snl.trend(include_init_phase=False, show_figure=False) # S-R trend analysis snl.trend(show_figure=False) assert snl["Main"] with pytest.raises(ValueError): snl.trend(show_figure=False, n_points=3) # Disable/enable length = len(snl["Main"]) snl.disable(phases=["0th"], name="Main") assert len(snl["Main"]) == length - 1 snl.enable(phases=["0th"], name="Main") assert len(snl["Main"]) == length with pytest.raises(TypeError): snl.enable(phases="0th", name="Main") with pytest.raises(TypeError): snl.disable(phases="1st", name="Main")
def test_add_past_phases(self, jhu_data, population_data): scenario = Scenario(jhu_data, population_data, country="India") scenario.delete() # Phase series scenario.clear(name="Medicine") scenario.add(days=100) scenario.delete(name="Medicine") with pytest.raises(TypeError): scenario.delete(phase="0th") with pytest.raises(TypeError): scenario.summary(columns="Population") with pytest.raises(KeyError): scenario.summary(columns=["Value"]) # Range of past phases scenario.first_date = "01Mar2020" scenario.first_date scenario.last_date = "16Jul2020" scenario.last_date with pytest.raises(ValueError): scenario.first_date = "01Aug2020" with pytest.raises(ValueError): scenario.last_date = "01Feb2020" # With trend analysis scenario.trend(set_phases=True) with pytest.raises(ValueError): scenario.trend(set_phases=False, n_points=3) scenario.combine(phases=["3rd", "4th"]) scenario.separate(date="30May2020", phase="1st") scenario.delete(phases=["1st"]) scenario.trend(set_phases=False) trend_df = scenario.summary() assert len(trend_df) == 4 # add scenarios one by one scenario.clear(include_past=True) scenario.add(end_date="29May2020") scenario.add(end_date="05Jun2020").delete(phases=["0th"]) scenario.add(end_date="15Jun2020") scenario.add(end_date="04Jul2020") scenario.add() one_df = scenario.summary() assert len(one_df) == 4 # With 0th phase scenario.use_0th = True scenario.trend(set_phases=False, include_init_phase=True) scenario.use_0th = False scenario.trend(set_phases=True, include_init_phase=True) scenario.delete(phases=["0th"]) assert len(scenario.summary()) == 5 with pytest.raises(TypeError): scenario.delete(phases="1st")
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_edit(self, jhu_data, population_data, country): # Setting snl = Scenario(jhu_data, population_data, country) snl.first_date = "01Apr2020" snl.last_date = "01Aug2020" snl.trend(show_figure=False) # Combine length = len(snl["Main"]) snl.combine(["1st", "2nd"]) n_changed = int(population_data.value(country) * 0.98) snl.combine(["2nd", "3rd"], population=n_changed) assert len(snl["Main"]) == length - 2 # Separate snl.separate(date="01May2020") assert len(snl["Main"]) == length - 1
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_trend(self, jhu_data, population_data, country): # Setting snl = Scenario(jhu_data, population_data, country) snl.first_date = "01Apr2020" snl.last_date = "01Aug2020" snl.trend(show_figure=False) assert snl["Main"] with pytest.raises(ValueError): snl.trend(show_figure=False, n_points=3) # Disable/enable length = len(snl["Main"]) snl.disable(phases=["0th"], name="Main") assert len(snl["Main"]) == length - 1 snl.enable(phases=["0th"], name="Main") assert len(snl["Main"]) == length with pytest.raises(TypeError): snl.enable(phases="0th", name="Main") with pytest.raises(TypeError): snl.disable(phases="1st", name="Main")
def test_ode_two_phases(self, population_data): # Setting eg_tau = 1440 # Simulation example_data = ExampleData(tau=eg_tau) example_data.add(SIRF, step_n=30) example_data.add(SIRD, step_n=30) nondim_df = example_data.non_dim(SIRF) assert isinstance(nondim_df, pd.DataFrame) nondim_cols = [Term.TS, *list(SIRF.VAR_DICT.keys())] assert set(nondim_df.columns) == set(nondim_cols) clean_df = example_data.cleaned() assert isinstance(clean_df, pd.DataFrame) assert set(clean_df.columns) == set(Term.COLUMNS) dim_df = example_data.subset(SIRF) assert isinstance(dim_df, pd.DataFrame) assert set(dim_df.columns) == set(Term.NLOC_COLUMNS) # Scenario analysis population = SIRF.EXAMPLE["population"] population_data.update(population, country=SIRF.NAME) scenario = Scenario(example_data, population_data, country=SIRF.NAME) scenario.trend()
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)