def __init__(self, start_date, end_date, population):
self._ensure_date_order(start_date, end_date, name="end_date")
self._start_date = start_date
self._end_date = end_date
self._population = self._ensure_population(population)
# Summary of information
self.info_dict = {
self.START: start_date,
self.END: end_date,
self.N: population,
self.ODE: None,
self.RT: None
}
self._ode_dict = {self.TAU: None}
self.day_param_dict = {}
self.est_dict = {
**{metric: None
for metric in Evaluator.metrics()}, self.TRIALS: None,
self.RUNTIME: None
}
# Init
self._id_dict = None
self._enabled = True
self._model = None
self._record_df = pd.DataFrame()
self.y0_dict = {}
self._estimator = None
def __init__(self, model, first_date, tau=None, metric="RMSLE", n_jobs=-1):
self._model = self._ensure_subclass(model, ModelBase, name="model")
self._first = self._ensure_date(first_date, name="first_date")
self._metric = self._ensure_selectable(metric,
Evaluator.metrics(),
name="metric")
self._n_jobs = cpu_count(
) if n_jobs == -1 else self._ensure_natural_int(n_jobs, name="n_jobs")
# Tau value [min] or None
self._tau = self._ensure_tau(tau, accept_none=True)
# {"0th": output of self.add()}
self._info_dict = {}
def __init__(self, model, data, tau, metric, quantiles):
self._model = self._ensure_subclass(model, ModelBase, name="model")
self._ensure_dataframe(data, name="data", columns=self.DSIFR_COLUMNS)
self._tau = self._ensure_tau(tau, accept_none=False)
self._metric = self._ensure_selectable(metric, Evaluator.metrics(), name="metric")
# time steps (index), variables of the model
df = model.convert(data, tau)
self._taufree_df = df.copy()
# Initial values
self._y0_dict = df.iloc[0].to_dict()
# Total population
self._population = df.iloc[0].sum()
# Step numbers
self._step_n = df.index.max()
# Parameter range
self._range_dict = model.guess(data, tau, q=quantiles)
# Max values of the variables
self._max_dict = {v: df[v].max() for v in model.VARIABLES}