def _obs_vs_pred_dropdown_plot(
data: PlotData,
rel: bool,
show_context: bool = False,
xlabel: str = "Actual Outcome",
ylabel: str = "Predicted Outcome",
) -> Dict[str, Any]:
"""Plot a dropdown plot of observed vs. predicted values from a model.
Args:
data: a name tuple storing observed and predicted data
from a model.
rel: if True, plot metrics relative to the status quo.
show_context: Show context on hover.
xlabel: Label for x-axis.
ylabel: Label for y-axis.
"""
traces = []
metric_dropdown = []
if rel and data.status_quo_name is not None:
if show_context:
raise ValueError(
"This plot does not support both context and relativization at "
"the same time.")
# pyre-fixme[6]: Expected `str` for 1st param but got `Optional[str]`.
status_quo_arm = data.in_sample[data.status_quo_name]
else:
status_quo_arm = None
for i, metric in enumerate(data.metrics):
y_raw, se_raw, y_hat, se_hat = _error_scatter_data(
# Expected `List[typing.Union[PlotInSampleArm,
# ax.plot.base.PlotOutOfSampleArm]]` for 1st anonymous
# parameter to call `ax.plot.scatter._error_scatter_data` but got
# `List[PlotInSampleArm]`.
# pyre-fixme[6]:
list(data.in_sample.values()),
y_axis_var=PlotMetric(metric, True),
x_axis_var=PlotMetric(metric, False),
rel=rel,
status_quo_arm=status_quo_arm,
)
min_, max_ = _get_min_max_with_errors(y_raw, y_hat, se_raw or [],
se_hat)
traces.append(_diagonal_trace(min_, max_, visible=(i == 0)))
traces.append(
_error_scatter_trace(
# Expected `List[typing.Union[PlotInSampleArm,
# ax.plot.base.PlotOutOfSampleArm]]` for 1st parameter
# `arms` to call `ax.plot.scatter._error_scatter_trace`
# but got `List[PlotInSampleArm]`.
# pyre-fixme[6]:
arms=list(data.in_sample.values()),
hoverinfo="text",
rel=rel,
show_arm_details_on_hover=True,
show_CI=True,
show_context=show_context,
status_quo_arm=status_quo_arm,
visible=(i == 0),
x_axis_label=xlabel,
x_axis_var=PlotMetric(metric, False),
y_axis_label=ylabel,
y_axis_var=PlotMetric(metric, True),
))
# only the first two traces are visible (corresponding to first outcome
# in dropdown)
is_visible = [False] * (len(data.metrics) * 2)
is_visible[2 * i] = True
is_visible[2 * i + 1] = True
# on dropdown change, restyle
metric_dropdown.append({
"args": ["visible", is_visible],
"label": metric,
"method": "restyle"
})
updatemenus = [
{
"x": 0,
"y": 1.125,
"yanchor": "top",
"xanchor": "left",
"buttons": metric_dropdown,
},
{
"buttons": [
{
"args": [{
"error_x.width": 4,
"error_x.thickness": 2,
"error_y.width": 4,
"error_y.thickness": 2,
}],
"label":
"Yes",
"method":
"restyle",
},
{
"args": [{
"error_x.width": 0,
"error_x.thickness": 0,
"error_y.width": 0,
"error_y.thickness": 0,
}],
"label":
"No",
"method":
"restyle",
},
],
"x":
1.125,
"xanchor":
"left",
"y":
0.8,
"yanchor":
"middle",
},
]
layout = go.Layout(
annotations=[{
"showarrow": False,
"text": "Show CI",
"x": 1.125,
"xanchor": "left",
"xref": "paper",
"y": 0.9,
"yanchor": "middle",
"yref": "paper",
}],
xaxis={
"title": xlabel,
"zeroline": False,
"mirror": True,
"linecolor": "black",
"linewidth": 0.5,
},
yaxis={
"title": ylabel,
"zeroline": False,
"mirror": True,
"linecolor": "black",
"linewidth": 0.5,
},
showlegend=False,
hovermode="closest",
updatemenus=updatemenus,
width=530,
height=500,
)
return go.Figure(data=traces, layout=layout)
def tile_cross_validation(
cv_results: List[CVResult],
show_arm_details_on_hover: bool = True,
show_context: bool = True,
) -> AxPlotConfig:
"""Tile version of CV plots; sorted by 'best fitting' outcomes.
Plots are sorted in decreasing order using the p-value of a Fisher exact
test statistic.
Args:
cv_results: cross-validation results.
include_measurement_error: if True, include
measurement_error metrics in plot.
show_arm_details_on_hover: if True, display
parameterizations of arms on hover. Default is True.
show_context: if True (default), display context on
hover.
"""
data = _get_cv_plot_data(cv_results)
metrics = data.metrics
# make subplots (2 plots per row)
nrows = int(np.ceil(len(metrics) / 2))
ncols = min(len(metrics), 2)
fig = tools.make_subplots(
rows=nrows,
cols=ncols,
print_grid=False,
subplot_titles=tuple(metrics),
horizontal_spacing=0.15,
vertical_spacing=0.30 / nrows,
)
for i, metric in enumerate(metrics):
y_hat = []
se_hat = []
y_raw = []
se_raw = []
for arm in data.in_sample.values():
y_hat.append(arm.y_hat[metric])
se_hat.append(arm.se_hat[metric])
y_raw.append(arm.y[metric])
se_raw.append(arm.se[metric])
min_, max_ = _get_min_max_with_errors(y_raw, y_hat, se_raw, se_hat)
fig.append_trace(_diagonal_trace(min_, max_),
int(np.floor(i / 2)) + 1, i % 2 + 1)
fig.append_trace(
_error_scatter_trace(
# Expected `List[typing.Union[PlotInSampleArm,
# ax.plot.base.PlotOutOfSampleArm]]` for 1st anonymous
# parameter to call `ax.plot.scatter._error_scatter_trace` but
# got `List[PlotInSampleArm]`.
# pyre-fixme[6]:
list(data.in_sample.values()),
y_axis_var=PlotMetric(metric, True),
x_axis_var=PlotMetric(metric, False),
y_axis_label="Predicted",
x_axis_label="Actual",
hoverinfo="text",
show_arm_details_on_hover=show_arm_details_on_hover,
show_context=show_context,
),
int(np.floor(i / 2)) + 1,
i % 2 + 1,
)
# if odd number of plots, need to manually remove the last blank subplot
# generated by `tools.make_subplots`
if len(metrics) % 2 == 1:
del fig["layout"]["xaxis{}".format(nrows * ncols)]
del fig["layout"]["yaxis{}".format(nrows * ncols)]
# allocate 400 px per plot (equal aspect ratio)
fig["layout"].update(
title="Cross-Validation", # What should I replace this with?
hovermode="closest",
width=800,
height=400 * nrows,
font={"size": 10},
showlegend=False,
)
# update subplot title size and the axis labels
for i, ant in enumerate(fig["layout"]["annotations"]):
ant["font"].update(size=12)
fig["layout"]["xaxis{}".format(i + 1)].update(title="Actual Outcome",
mirror=True,
linecolor="black",
linewidth=0.5)
fig["layout"]["yaxis{}".format(i + 1)].update(
title="Predicted Outcome",
mirror=True,
linecolor="black",
linewidth=0.5)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
