def plot_model(self, field, strict_recalc=False, sample=1.0):
    '\n        Plot dependency graph from the model.\n        It also take into account times, so plot becomes much more resilient, cause not every value treats as equal\n        (more occurences means more power)\n        field must be list of exactly one feature\n        strict_recalc=True ignores precalculated `part_dep_df` and calculate it anyway\n        sample is a coef to len(df). Lower if kernel use to shut down on that\n        '
    cached = self.get_pd(feature=self._list_to_key(field))
    if ((strict_recalc == False) and isNotNone(cached)):
        pd_table = cached
    else:
        pd_table = self._get_part_dep_one(fields=field)
    clear_output()
    field = field[0]
    dep_var = f'{self._get_dep_var()}'
    rearr = []
    for (var, fee, times) in zip(pd_table[field], pd_table[dep_var], pd_table['times']):
        for i in range(int(times)):
            rearr.append([var, fee])
    rearr = pd.DataFrame(rearr, columns=[field, dep_var])
    rearr = rearr.sample(int((len(rearr) * sample)))
    return (ggplot(rearr, aes(field, dep_var)) + stat_smooth(se=True, method='loess'))
Plot dependency graph from the model.
It also take into account times, so plot becomes much more resilient, cause not every value treats as equal
(more occurences means more power)
field must be list of exactly one feature
strict_recalc=True ignores precalculated `part_dep_df` and calculate it anyway
sample is a coef to len(df). Lower if kernel use to shut down on that
def plot_model(self, field, strict_recalc=False, sample=1.0):
    '\n        Plot dependency graph from the model.\n        It also take into account times, so plot becomes much more resilient, cause not every value treats as equal\n        (more occurences means more power)\n        field must be list of exactly one feature\n        strict_recalc=True ignores precalculated `part_dep_df` and calculate it anyway\n        sample is a coef to len(df). Lower if kernel use to shut down on that\n        '
    cached = self.get_pd(feature=self._list_to_key(field))
    if ((strict_recalc == False) and isNotNone(cached)):
        pd_table = cached
    else:
        pd_table = self._get_part_dep_one(fields=field)
    clear_output()
    field = field[0]
    dep_var = f'{self._get_dep_var()}'
    rearr = []
    for (var, fee, times) in zip(pd_table[field], pd_table[dep_var], pd_table['times']):
        for i in range(int(times)):
            rearr.append([var, fee])
    rearr = pd.DataFrame(rearr, columns=[field, dep_var])
    rearr = rearr.sample(int((len(rearr) * sample)))
    return (ggplot(rearr, aes(field, dep_var)) + stat_smooth(se=True, method='loess'))