def plot_history(self, ax=None, yscale=None):
"""Plot the gain/loss of every point in the order in which they were sampled."""
if not self._examples:
raise ValueError("no existing data available to be plotted")
iterations = range(1, len(self._examples) + 1)
metrics = ((list)(map(_coconut.operator.itemgetter(self._metric), (sorted_examples)(self._examples))))
return plot(iterations, metrics, ax=ax, yscale=yscale, title="History plot for {_coconut_format_0}".format(_coconut_format_0=(self._file_name)), xlabel="Number of trials $n$", ylabel="The {_coconut_format_0} on the $n$th trial".format(_coconut_format_0=(self._metric)))
def plot_convergence(self, ax=None, yscale=None):
"""Plot the best gain/loss over the history of optimization.
Based on skopt.plots.plot_convergence."""
if not self._examples:
raise ValueError("no existing data available to be plotted")
iterations = range(1, len(self._examples) + 1)
best_metrics = ((list)(map(_coconut.operator.itemgetter(self._metric), (running_best)((sorted_examples)(self._examples)))))
return plot(iterations, best_metrics, ax=ax, yscale=yscale, title="Convergence plot for {_coconut_format_0}".format(_coconut_format_0=(self._file_name)), xlabel="Number of trials $n$", ylabel="Best {_coconut_format_0} after $n$ trials".format(_coconut_format_0=(self._metric)))
def plot_partial_dependence_1D(self,
i_name,
ax=None,
yscale=None,
**kwargs):
"""Constructs a 1D partial dependence plot using self.partial_dependence."""
xi, yi = self.partial_dependence(i_name, **kwargs)
return plot(xi,
yi,
ax=ax,
yscale=yscale,
title="Partial dependence of {_coconut_format_0}".format(
_coconut_format_0=(i_name)),
xlabel="Values of {_coconut_format_0}".format(
_coconut_format_0=(i_name)),
ylabel="The loss at each point".format())