def _score_tau(self, tau, data, quantile):
"""
Calculate score for the tau value.
Args:
tau (int): tau value [min]
data (pandas.DataFrame):
Index
reset index
Columns
- Date (pd.Timestamp): Observation date
- Susceptible(int): the number of susceptible cases
- Infected (int): the number of currently infected cases
- Fatal(int): the number of fatal cases
- Recovered (int): the number of recovered cases
quantile (float): quantile to guess ODE parameter values for the candidates of tau
"""
info_dict = self._info_dict.copy()
for (phase, phase_dict) in info_dict.items():
start, end = phase_dict[self.START], phase_dict[self.END]
df = data.loc[(start <= data[self.DATE])
& (data[self.DATE] <= end)]
info_dict[phase]["param"] = self._model.guess(df, tau, q=quantile)
solver = _MultiPhaseODESolver(self._model, self._first, tau)
sim_df = solver.simulate(*info_dict.values())
evaluator = Evaluator(data.set_index(self.DATE),
sim_df.set_index(self.DATE))
return evaluator.score(metric=self._metric)
def _score(self, tau, param_dict):
"""
Calculate score.
Args:
tau (int): tau value [min]
param_dict (dict[str, int or float]): dictionary of parameter values
Returns:
float: score
"""
self.tau = tau
self._set_taufree()
cols = [self.TS, *self.variables_evaluate]
rec_df = self.taufree_df.loc[:, cols]
sim_df = self._simulate(self.step_n, param_dict).loc[:, cols]
evaluator = Evaluator(rec_df, sim_df, on=self.TS)
return evaluator.score(metric=self._metric)
def _score(self, **kwargs):
"""
Objective function to minimize.
Score will be calculated the data and metric.
Args:
kwargs: values of non-dimensional model parameters, including rho and sigma
Returns:
float: score
"""
# Simulate with applied parameter values
solver = _ODESolver(model=self._model, **kwargs)
sim_df = solver.run(step_n=self._step_n, **self._y0_dict)
# The first variable (Susceptible) will be ignored in score calculation
taufree_df = self._taufree_df.loc[:, self._taufree_df.columns[1:]]
sim_df = sim_df.loc[:, sim_df.columns[1:]]
# Calculate score
evaluator = Evaluator(taufree_df, sim_df, how="inner", on=None)
return evaluator.score(metric=self._metric)
def score(self, variables=None, phases=None, y0_dict=None, **kwargs):
"""
Evaluate accuracy of phase setting and parameter estimation of selected enabled phases.
Args:
variables (list[str] or None): variables to use in calculation
phases (list[str] or None): phases to use in calculation
y0_dict(dict[str, float] or None): dictionary of initial values of variables
kwargs: keyword arguments of covsirphy.Evaluator.score()
Returns:
float: score with the specified metrics
Note:
If @variables is None, ["Infected", "Fatal", "Recovered"] will be used.
"Confirmed", "Infected", "Fatal" and "Recovered" can be used in @variables.
If @phases is None, all phases will be used.
"""
# Arguments
variables = variables or [self.CI, self.F, self.R]
variables = self._ensure_list(variables,
self.VALUE_COLUMNS,
name="variables")
# Disable the non-target phases
all_phases, _ = self.past_phases(phases=None)
target_phases, _ = self.past_phases(phases=phases)
ignored_phases = list(set(all_phases) - set(target_phases))
if ignored_phases:
self.disable(ignored_phases)
# Get the number of cases
rec_df, sim_df = self._compare_with_actual(variables=variables,
y0_dict=y0_dict)
# Calculate score
evaluator = Evaluator(rec_df, sim_df)
score = evaluator.score(**find_args(Evaluator.score, **kwargs))
# Enable the disabled non-target phases
if ignored_phases:
self.enable(ignored_phases)
return score
def score(self, change_points, metric):
"""
Calculate scores of the phases.
Args:
change_points (list[pandas.Timestamp]): list of change points
metric (str): metric name
Returns:
list[float]: scores for phases
Note:
Please refer to covsirphy.Evaluator.score() for metric names
"""
fit_df = self._fitting(change_points)
phases = [self.num2str(num) for num in range(len(change_points) + 1)]
scores = []
for phase in phases:
df = fit_df[[self.ACTUAL, phase]].dropna()
evaluator = Evaluator(df[self.ACTUAL], df[phase], how="all")
scores.append(evaluator.score(metric=metric))
return scores