def plot(self, columns=None, loc=None, iloc=None, **kwargs):
""""
A wrapper around plotting. Matplotlib plot arguments can be passed in, plus:
Parameters
-----------
columns: string or list-like, optional
If not empty, plot a subset of columns from the ``cumulative_hazards_``. Default all.
loc:
iloc: slice, optional
specify a location-based subsection of the curves to plot, ex:
``.plot(iloc=slice(0,10))`` will plot the first 10 time points.
"""
from matplotlib import pyplot as plt
assert loc is None or iloc is None, "Cannot set both loc and iloc in call to .plot"
def shaded_plot(ax, x, y, y_upper, y_lower, **kwargs):
base_line, = ax.plot(x, y, drawstyle="steps-post", **kwargs)
ax.fill_between(x, y_lower, y2=y_upper, alpha=0.25, color=base_line.get_color(), linewidth=1.0, step="post")
def create_df_slicer(loc, iloc):
get_method = "loc" if loc is not None else "iloc"
if iloc is None and loc is None:
user_submitted_ix = slice(0, None)
else:
user_submitted_ix = loc if loc is not None else iloc
return lambda df: getattr(df, get_method)[user_submitted_ix]
subset_df = create_df_slicer(loc, iloc)
if not columns:
columns = self.cumulative_hazards_.columns
else:
columns = _to_list(columns)
set_kwargs_ax(kwargs)
ax = kwargs.pop("ax")
x = subset_df(self.cumulative_hazards_).index.values.astype(float)
for column in columns:
y = subset_df(self.cumulative_hazards_[column]).values
index = subset_df(self.cumulative_hazards_[column]).index
y_upper = subset_df(self.confidence_intervals_[column].loc["upper-bound"]).values
y_lower = subset_df(self.confidence_intervals_[column].loc["lower-bound"]).values
shaded_plot(ax, x, y, y_upper, y_lower, label=column, **kwargs)
plt.hlines(0, index.min() - 1, index.max(), color="k", linestyles="--", alpha=0.5)
ax.legend()
return ax
def plot(self, columns=None, loc=None, iloc=None, **kwargs):
""""
A wrapper around plotting. Matplotlib plot arguments can be passed in, plus:
Parameters
-----------
columns: string or list-like, optional
If not empty, plot a subset of columns from the ``cumulative_hazards_``. Default all.
loc:
iloc: slice, optional
specify a location-based subsection of the curves to plot, ex:
``.plot(iloc=slice(0,10))`` will plot the first 10 time points.
"""
from matplotlib import pyplot as plt
assert loc is None or iloc is None, "Cannot set both loc and iloc in call to .plot"
def shaded_plot(ax, x, y, y_upper, y_lower, **kwargs):
base_line, = ax.plot(x, y, drawstyle="steps-post", **kwargs)
ax.fill_between(x, y_lower, y2=y_upper, alpha=0.25, color=base_line.get_color(), linewidth=1.0, step="post")
def create_df_slicer(loc, iloc):
get_method = "loc" if loc is not None else "iloc"
if iloc is None and loc is None:
user_submitted_ix = slice(0, None)
else:
user_submitted_ix = loc if loc is not None else iloc
return lambda df: getattr(df, get_method)[user_submitted_ix]
subset_df = create_df_slicer(loc, iloc)
if not columns:
columns = self.cumulative_hazards_.columns
else:
columns = _to_list(columns)
set_kwargs_ax(kwargs)
ax = kwargs.pop("ax")
x = subset_df(self.cumulative_hazards_).index.values.astype(float)
for column in columns:
y = subset_df(self.cumulative_hazards_[column]).values
index = subset_df(self.cumulative_hazards_[column]).index
y_upper = subset_df(self.confidence_intervals_[column].loc["upper-bound"]).values
y_lower = subset_df(self.confidence_intervals_[column].loc["lower-bound"]).values
shaded_plot(ax, x, y, y_upper, y_lower, label=column, **kwargs)
plt.hlines(0, index.min() - 1, index.max(), color="k", linestyles="--", alpha=0.5)
ax.legend()
return ax
def plot(self, columns=None, parameter=None, **errorbar_kwargs):
"""
Produces a visual representation of the coefficients, including their standard errors and magnitudes.
Parameters
----------
columns : list, optional
specify a subset of the columns to plot
errorbar_kwargs:
pass in additional plotting commands to matplotlib errorbar command
Returns
-------
ax: matplotlib axis
the matplotlib axis that be edited.
"""
from matplotlib import pyplot as plt
set_kwargs_ax(errorbar_kwargs)
ax = errorbar_kwargs.pop("ax")
errorbar_kwargs.setdefault("c", "k")
errorbar_kwargs.setdefault("fmt", "s")
errorbar_kwargs.setdefault("markerfacecolor", "white")
errorbar_kwargs.setdefault("markeredgewidth", 1.25)
errorbar_kwargs.setdefault("elinewidth", 1.25)
errorbar_kwargs.setdefault("capsize", 3)
z = inv_normal_cdf(1 - self.alpha / 2)
params_ = self.params_.copy()
standard_errors_ = self.standard_errors_.copy()
if columns is not None:
params_ = params_.loc[:, columns]
standard_errors_ = standard_errors_.loc[:, columns]
if parameter is not None:
params_ = params_.loc[parameter]
standard_errors_ = standard_errors_.loc[parameter]
columns = params_.index
hazards = params_.loc[columns].to_frame(name="coefs")
hazards["se"] = z * standard_errors_.loc[columns]
if isinstance(hazards.index, pd.MultiIndex):
hazards = hazards.groupby(level=0, group_keys=False).apply(
lambda x: x.sort_values(by="coefs", ascending=True))
else:
hazards = hazards.sort_values(by="coefs", ascending=True)
yaxis_locations = list(range(len(columns)))
ax.errorbar(hazards["coefs"],
yaxis_locations,
xerr=hazards["se"],
**errorbar_kwargs)
best_ylim = ax.get_ylim()
ax.vlines(0,
-2,
len(columns) + 1,
linestyles="dashed",
linewidths=1,
alpha=0.65)
ax.set_ylim(best_ylim)
if isinstance(columns[0], tuple):
tick_labels = ["%s: %s" % (c, p) for (p, c) in hazards.index]
else:
tick_labels = [i for i in hazards.index]
plt.yticks(yaxis_locations, tick_labels)
plt.xlabel("log(accelerated failure rate) (%g%% CI)" %
((1 - self.alpha) * 100))
return ax
def plot(self, columns=None, parameters=None, **errorbar_kwargs):
"""
Produces a visual representation of the coefficients, including their standard errors and magnitudes.
Parameters
----------
columns : list, optional
specify a subset of the columns (variables from the training data) to plot
parameter : list, optional
specify a subset of the parameters to plot
errorbar_kwargs:
pass in additional plotting commands to matplotlib errorbar command
Returns
-------
ax: matplotlib axis
the matplotlib axis that be edited.
"""
from matplotlib import pyplot as plt
set_kwargs_ax(errorbar_kwargs)
ax = errorbar_kwargs.pop("ax")
errorbar_kwargs.setdefault("c", "k")
errorbar_kwargs.setdefault("fmt", "s")
errorbar_kwargs.setdefault("markerfacecolor", "white")
errorbar_kwargs.setdefault("markeredgewidth", 1.25)
errorbar_kwargs.setdefault("elinewidth", 1.25)
errorbar_kwargs.setdefault("capsize", 3)
z = inv_normal_cdf(1 - self.alpha / 2)
params_ = self.params_.copy()
standard_errors_ = self.standard_errors_.copy()
if columns is not None:
assert isinstance(columns, list), "columns must be a list"
params_ = params_.loc[:, columns]
standard_errors_ = standard_errors_.loc[:, columns]
if parameters is not None:
assert isinstance(parameters, list), "parameter must be a list"
params_ = params_.loc[parameters]
standard_errors_ = standard_errors_.loc[parameters]
columns = params_.index
hazards = params_.loc[columns].to_frame(name="coefs")
hazards["se"] = z * standard_errors_.loc[columns]
hazards = hazards.swaplevel(1, 0).sort_index()
yaxis_locations = list(range(len(columns) - 1, -1, -1))
ax.errorbar(hazards["coefs"], yaxis_locations, xerr=hazards["se"], **errorbar_kwargs)
# set zero hline
best_ylim = ax.get_ylim()
ax.vlines(0, -2, len(columns) + 1, linestyles="dashed", linewidths=1, alpha=0.65)
ax.set_ylim(best_ylim)
if isinstance(columns[0], tuple):
tick_labels = ["%s: %s" % (p, c) for (p, c) in hazards.index]
else:
tick_labels = [i for i in hazards.index]
plt.yticks(yaxis_locations, tick_labels)
plt.xlabel("log(accelerated failure rate) (%g%% CI)" % ((1 - self.alpha) * 100))
return ax
