def _make_label(mean: float, sem: float, name: str, is_relative: bool,
Z: Optional[float]) -> str:
estimate = str(round(mean, DECIMALS))
perc = "%" if is_relative else ""
ci = ("" if (Z is None or np.isnan(sem)) else _format_CI(
estimate=mean, sd=sem, relative=is_relative, zval=Z))
return f"{name}: {estimate}{perc} {ci}<br>"
def _make_label(mean: float, sem: float, name: str, is_relative: bool,
Z: Optional[float]) -> str:
return "{name}: {estimate}{perc} {ci}<br>".format(
name=name,
estimate=round(mean, DECIMALS),
ci="" if Z is None else _format_CI(
estimate=mean, sd=sem, relative=is_relative, zval=Z),
perc="%" if is_relative else "",
)
def _error_scatter_trace(
arms: List[Union[PlotInSampleArm, PlotOutOfSampleArm]],
y_axis_var: PlotMetric,
x_axis_var: Optional[PlotMetric] = None,
y_axis_label: Optional[str] = None,
x_axis_label: Optional[str] = None,
status_quo_arm: Optional[PlotInSampleArm] = None,
show_CI: bool = True,
name: str = "In-sample",
color: Tuple[int] = COLORS.STEELBLUE.value,
visible: bool = True,
legendgroup: Optional[str] = None,
showlegend: bool = True,
hoverinfo: str = "text",
show_arm_details_on_hover: bool = True,
show_context: bool = False,
arm_noun: str = "arm",
) -> Dict[str, Any]:
"""Plot scatterplot with error bars.
Args:
arms (List[Union[PlotInSampleArm, PlotOutOfSampleArm]]):
a list of in-sample or out-of-sample arms.
In-sample arms have observed data, while out-of-sample arms
just have predicted data. As a result,
when passing out-of-sample arms, pred must be True.
y_axis_var: name of metric for y-axis, along with whether
it is observed or predicted.
x_axis_var: name of metric for x-axis,
along with whether it is observed or predicted. If None, arm names
are automatically used.
y_axis_label: custom label to use for y axis.
If None, use metric name from `y_axis_var`.
x_axis_label: custom label to use for x axis.
If None, use metric name from `x_axis_var` if that is not None.
status_quo_arm: the status quo
arm. Necessary for relative metrics.
show_CI: if True, plot confidence intervals.
name: name of trace. Default is "In-sample".
color: color as rgb tuple. Default is
(128, 177, 211), which corresponds to COLORS.STEELBLUE.
visible: if True, trace is visible (default).
legendgroup: group for legends.
showlegend: if True, legend if rendered.
hoverinfo: information to show on hover. Default is
custom text.
show_arm_details_on_hover: if True, display
parameterizations of arms on hover. Default is True.
show_context: if True and show_arm_details_on_hover,
context will be included in the hover.
arm_noun: noun to use instead of "arm" (e.g. group)
"""
x, x_se, y, y_se = _error_scatter_data(
arms=arms,
y_axis_var=y_axis_var,
x_axis_var=x_axis_var,
status_quo_arm=status_quo_arm,
)
labels = []
arm_names = [a.name for a in arms]
# No relativization if no x variable.
rel_x = x_axis_var.rel if x_axis_var else False
rel_y = y_axis_var.rel
for i in range(len(arm_names)):
heading = f"<b>{arm_noun.title()} {arm_names[i]}</b><br>"
x_lab = ("{name}: {estimate}{perc} {ci}<br>".format(
name=x_axis_var.metric if x_axis_label is None else x_axis_label,
estimate=(round(x[i], DECIMALS) if isinstance(
x[i], numbers.Number) else x[i]),
ci="" if x_se is None else _format_CI(x[i], x_se[i], rel_x),
perc="%" if rel_x else "",
) if x_axis_var is not None else "")
y_lab = "{name}: {estimate}{perc} {ci}<br>".format(
name=y_axis_var.metric if y_axis_label is None else y_axis_label,
estimate=(round(y[i], DECIMALS) if isinstance(
y[i], numbers.Number) else y[i]),
ci="" if y_se is None else _format_CI(y[i], y_se[i], rel_y),
perc="%" if rel_y else "",
)
parameterization = (_format_dict(arms[i].parameters,
"Parameterization")
if show_arm_details_on_hover else "")
context = (
# Expected `Dict[str, Optional[Union[bool, float, str]]]` for 1st anonymous
# parameter to call `ax.plot.helper._format_dict` but got
# `Optional[Dict[str, Union[float, str]]]`.
# pyre-fixme[6]:
_format_dict(arms[i].context_stratum, "Context")
if show_arm_details_on_hover and show_context # noqa W503
and arms[i].context_stratum # noqa W503
else "")
labels.append("{arm_name}<br>{xlab}{ylab}{param_blob}{context}".format(
arm_name=heading,
xlab=x_lab,
ylab=y_lab,
param_blob=parameterization,
context=context,
))
i += 1
trace = go.Scatter(
x=x,
y=y,
marker={"color": rgba(color)},
mode="markers",
name=name,
text=labels,
hoverinfo=hoverinfo,
)
if show_CI:
if x_se is not None:
trace.update(
error_x={
"type": "data",
"array": np.multiply(x_se, Z),
"color": rgba(color, CI_OPACITY),
})
if y_se is not None:
trace.update(
error_y={
"type": "data",
"array": np.multiply(y_se, Z),
"color": rgba(color, CI_OPACITY),
})
if visible is not None:
trace.update(visible=visible)
if legendgroup is not None:
trace.update(legendgroup=legendgroup)
if showlegend is not None:
trace.update(showlegend=showlegend)
return trace
