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)
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 table_view_plot(
experiment: Experiment,
data: Data,
use_empirical_bayes: bool = True,
only_data_frame: bool = False,
arm_noun: str = "arm",
):
"""Table of means and confidence intervals.
Table is of the form:
+-------+------------+-----------+
| arm | metric_1 | metric_2 |
+=======+============+===========+
| 0_0 | mean +- CI | ... |
+-------+------------+-----------+
| 0_1 | ... | ... |
+-------+------------+-----------+
"""
model_func = get_empirical_bayes_thompson if use_empirical_bayes else get_thompson
model = model_func(experiment=experiment, data=data)
# We don't want to include metrics from a collection,
# or the chart will be too big to read easily.
# Example:
# experiment.metrics = {
# 'regular_metric': Metric(),
# 'collection_metric: CollectionMetric()', # collection_metric =[metric1, metric2]
# }
# model.metric_names = [regular_metric, metric1, metric2] # "exploded" out
# We want to filter model.metric_names and get rid of metric1, metric2
metric_names = [
metric_name for metric_name in model.metric_names
if metric_name in experiment.metrics
]
metric_name_to_lower_is_better = {
metric_name: experiment.metrics[metric_name].lower_is_better
for metric_name in metric_names
}
plot_data, _, _ = get_plot_data(
model=model,
generator_runs_dict={},
# pyre-fixme[6]: Expected `Optional[typing.Set[str]]` for 3rd param but got
# `List[str]`.
metric_names=metric_names,
)
if plot_data.status_quo_name:
status_quo_arm = plot_data.in_sample.get(plot_data.status_quo_name)
rel = True
else:
status_quo_arm = None
rel = False
records = []
colors = []
records_with_mean = []
records_with_ci = []
for metric_name in metric_names:
arm_names, _, ys, ys_se = _error_scatter_data(
# pyre-fixme[6]: Expected
# `List[typing.Union[ax.plot.base.PlotInSampleArm,
# ax.plot.base.PlotOutOfSampleArm]]` for 1st param but got
# `List[ax.plot.base.PlotInSampleArm]`.
arms=list(plot_data.in_sample.values()),
y_axis_var=PlotMetric(metric_name, pred=True, rel=rel),
x_axis_var=None,
status_quo_arm=status_quo_arm,
)
results_by_arm = list(zip(arm_names, ys, ys_se))
colors.append([
get_color(
x=y,
ci=Z * y_se,
rel=rel,
# pyre-fixme[6]: Expected `bool` for 4th param but got
# `Optional[bool]`.
reverse=metric_name_to_lower_is_better[metric_name],
) for (_, y, y_se) in results_by_arm
])
records.append([
"{:.3f} ± {:.3f}".format(y, Z * y_se)
for (_, y, y_se) in results_by_arm
])
records_with_mean.append(
{arm_name: y
for (arm_name, y, _) in results_by_arm})
records_with_ci.append(
{arm_name: Z * y_se
for (arm_name, _, y_se) in results_by_arm})
if only_data_frame:
return tuple(
pd.DataFrame.from_records(records, index=metric_names)
for records in [records_with_mean, records_with_ci])
def transpose(m):
return [[m[j][i] for j in range(len(m))] for i in range(len(m[0]))]
records = [[name.replace(":", " : ")
for name in metric_names]] + transpose(records)
colors = [["#ffffff"] * len(metric_names)] + transpose(colors)
# pyre-fixme[6]: Expected `List[str]` for 1st param but got `List[float]`.
header = [f"<b>{x}</b>" for x in [f"{arm_noun}s"] + arm_names]
column_widths = [300] + [150] * len(arm_names)
trace = go.Table(
header={
"values": header,
"align": ["left"]
},
cells={
"values": records,
"align": ["left"],
"fill": {
"color": colors
}
},
columnwidth=column_widths,
)
layout = go.Layout(
width=sum(column_widths),
margin=go.layout.Margin(l=0, r=20, b=20, t=20, pad=4), # noqa E741
)
fig = go.Figure(data=[trace], layout=layout)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def _single_metric_traces(
model: ModelBridge,
metric: str,
generator_runs_dict: TNullableGeneratorRunsDict,
rel: bool,
show_arm_details_on_hover: bool = True,
showlegend: bool = True,
show_CI: bool = True,
arm_noun: str = "arm",
fixed_features: Optional[ObservationFeatures] = None,
) -> Traces:
"""Plot scatterplots with errors for a single metric (y-axis).
Arms are plotted on the x-axis.
Args:
model: model to draw predictions from.
metric: name of metric to plot.
generator_runs_dict: a mapping from
generator run name to generator run.
rel: if True, plot relative predictions.
show_arm_details_on_hover: if True, display
parameterizations of arms on hover. Default is True.
show_legend: if True, show legend for trace.
show_CI: if True, render confidence intervals.
arm_noun: noun to use instead of "arm" (e.g. group)
fixed_features: Fixed features to use when making model predictions.
"""
plot_data, _, _ = get_plot_data(model,
generator_runs_dict or {}, {metric},
fixed_features=fixed_features)
status_quo_arm = (
None if plot_data.status_quo_name is None
# pyre-fixme[6]: Expected `str` for 1st param but got `Optional[str]`.
else plot_data.in_sample.get(plot_data.status_quo_name))
traces = [
_error_scatter_trace(
# Expected `List[Union[PlotInSampleArm, PlotOutOfSampleArm]]`
# for 1st anonymous parameter to call
# `ax.plot.scatter._error_scatter_trace` but got
# `List[PlotInSampleArm]`.
# pyre-fixme[6]:
list(plot_data.in_sample.values()),
x_axis_var=None,
y_axis_var=PlotMetric(metric, pred=True, rel=rel),
status_quo_arm=status_quo_arm,
legendgroup="In-sample",
showlegend=showlegend,
show_arm_details_on_hover=show_arm_details_on_hover,
show_CI=show_CI,
arm_noun=arm_noun,
)
]
# Candidates
for i, (generator_run_name, cand_arms) in enumerate(
(plot_data.out_of_sample or {}).items(), start=1):
traces.append(
_error_scatter_trace(
list(cand_arms.values()),
x_axis_var=None,
y_axis_var=PlotMetric(metric, pred=True, rel=rel),
status_quo_arm=status_quo_arm,
name=generator_run_name,
color=DISCRETE_COLOR_SCALE[i],
legendgroup=generator_run_name,
showlegend=showlegend,
show_arm_details_on_hover=show_arm_details_on_hover,
show_CI=show_CI,
arm_noun=arm_noun,
))
return traces
def lattice_multiple_metrics(
model: ModelBridge,
generator_runs_dict: TNullableGeneratorRunsDict = None,
rel: bool = True,
show_arm_details_on_hover: bool = False,
) -> AxPlotConfig:
"""Plot raw values or predictions of combinations of two metrics for arms.
Args:
model: model to draw predictions from.
generator_runs_dict: a mapping from
generator run name to generator run.
rel: if True, use relative effects. Default is True.
show_arm_details_on_hover: if True, display
parameterizations of arms on hover. Default is False.
"""
metrics = model.metric_names
fig = tools.make_subplots(
rows=len(metrics),
cols=len(metrics),
print_grid=False,
shared_xaxes=False,
shared_yaxes=False,
)
plot_data, _, _ = get_plot_data(
model, generator_runs_dict if generator_runs_dict is not None else {},
metrics)
status_quo_arm = (
None if plot_data.status_quo_name is None
# pyre-fixme[6]: Expected `str` for 1st param but got `Optional[str]`.
else plot_data.in_sample.get(plot_data.status_quo_name))
# iterate over all combinations of metrics and generate scatter traces
for i, o1 in enumerate(metrics, start=1):
for j, o2 in enumerate(metrics, start=1):
if o1 != o2:
# in-sample observed and predicted
obs_insample_trace = _error_scatter_trace(
# Expected `List[Union[PlotInSampleArm,
# PlotOutOfSampleArm]]` for 1st anonymous parameter to call
# `ax.plot.scatter._error_scatter_trace` but got
# `List[PlotInSampleArm]`.
# pyre-fixme[6]:
list(plot_data.in_sample.values()),
x_axis_var=PlotMetric(o1, pred=False, rel=rel),
y_axis_var=PlotMetric(o2, pred=False, rel=rel),
status_quo_arm=status_quo_arm,
showlegend=(i == 1 and j == 2),
legendgroup="In-sample",
visible=False,
show_arm_details_on_hover=show_arm_details_on_hover,
)
predicted_insample_trace = _error_scatter_trace(
# Expected `List[Union[PlotInSampleArm,
# PlotOutOfSampleArm]]` for 1st anonymous parameter to call
# `ax.plot.scatter._error_scatter_trace` but got
# `List[PlotInSampleArm]`.
# pyre-fixme[6]:
list(plot_data.in_sample.values()),
x_axis_var=PlotMetric(o1, pred=True, rel=rel),
y_axis_var=PlotMetric(o2, pred=True, rel=rel),
status_quo_arm=status_quo_arm,
legendgroup="In-sample",
showlegend=(i == 1 and j == 2),
visible=True,
show_arm_details_on_hover=show_arm_details_on_hover,
)
fig.append_trace(obs_insample_trace, j, i)
fig.append_trace(predicted_insample_trace, j, i)
# iterate over models here
for k, (generator_run_name, cand_arms) in enumerate(
(plot_data.out_of_sample or {}).items(), start=1):
fig.append_trace(
_error_scatter_trace(
list(cand_arms.values()),
x_axis_var=PlotMetric(o1, pred=True, rel=rel),
y_axis_var=PlotMetric(o2, pred=True, rel=rel),
status_quo_arm=status_quo_arm,
name=generator_run_name,
color=DISCRETE_COLOR_SCALE[k],
showlegend=(i == 1 and j == 2),
legendgroup=generator_run_name,
show_arm_details_on_hover=show_arm_details_on_hover,
),
j,
i,
)
else:
# if diagonal is set to True, add box plots
fig.append_trace(
go.Box(
y=[arm.y[o1] for arm in plot_data.in_sample.values()],
name=None,
marker={"color": rgba(COLORS.STEELBLUE.value)},
showlegend=False,
legendgroup="In-sample",
visible=False,
hoverinfo="none",
),
j,
i,
)
fig.append_trace(
go.Box(
y=[
arm.y_hat[o1]
for arm in plot_data.in_sample.values()
],
name=None,
marker={"color": rgba(COLORS.STEELBLUE.value)},
showlegend=False,
legendgroup="In-sample",
hoverinfo="none",
),
j,
i,
)
for k, (generator_run_name, cand_arms) in enumerate(
(plot_data.out_of_sample or {}).items(), start=1):
fig.append_trace(
go.Box(
y=[arm.y_hat[o1] for arm in cand_arms.values()],
name=None,
marker={"color": rgba(DISCRETE_COLOR_SCALE[k])},
showlegend=False,
legendgroup=generator_run_name,
hoverinfo="none",
),
j,
i,
)
fig["layout"].update(
height=800,
width=960,
font={"size": 10},
hovermode="closest",
legend={
"orientation": "h",
"x": 0,
"y": 1.05,
"xanchor": "left",
"yanchor": "middle",
},
updatemenus=[
{
"x":
0.35,
"y":
1.08,
"xanchor":
"left",
"yanchor":
"middle",
"buttons": [
{
"args": [{
"error_x.width": 0,
"error_x.thickness": 0,
"error_y.width": 0,
"error_y.thickness": 0,
}],
"label":
"No",
"method":
"restyle",
},
{
"args": [{
"error_x.width": 4,
"error_x.thickness": 2,
"error_y.width": 4,
"error_y.thickness": 2,
}],
"label":
"Yes",
"method":
"restyle",
},
],
},
{
"x":
0.1,
"y":
1.08,
"xanchor":
"left",
"yanchor":
"middle",
"buttons": [
{
"args": [{
"visible":
(([False, True] +
[True] * len(plot_data.out_of_sample or {})) *
(len(metrics)**2))
}],
"label":
"Modeled",
"method":
"restyle",
},
{
"args": [{
"visible":
(([True, False] +
[False] * len(plot_data.out_of_sample or {})) *
(len(metrics)**2))
}],
"label":
"In-sample",
"method":
"restyle",
},
],
},
],
annotations=[
{
"x": 0.02,
"y": 1.1,
"xref": "paper",
"yref": "paper",
"text": "Type",
"showarrow": False,
"yanchor": "middle",
"xanchor": "left",
},
{
"x": 0.30,
"y": 1.1,
"xref": "paper",
"yref": "paper",
"text": "Show CI",
"showarrow": False,
"yanchor": "middle",
"xanchor": "left",
},
],
)
# add metric names to axes - add to each subplot if boxplots on the
# diagonal and axes are not shared; else, add to the leftmost y-axes
# and bottom x-axes.
for i, o in enumerate(metrics):
pos_x = len(metrics) * len(metrics) - len(metrics) + i + 1
pos_y = 1 + (len(metrics) * i)
fig["layout"]["xaxis{}".format(pos_x)].update(title=_wrap_metric(o),
titlefont={"size": 10})
fig["layout"]["yaxis{}".format(pos_y)].update(title=_wrap_metric(o),
titlefont={"size": 10})
# do not put x-axis ticks for boxplots
boxplot_xaxes = []
for trace in fig["data"]:
if trace["type"] == "box":
# stores the xaxes which correspond to boxplot subplots
# since we use xaxis1, xaxis2, etc, in plotly.py
boxplot_xaxes.append("xaxis{}".format(trace["xaxis"][1:]))
else:
# clear all error bars since default is no CI
trace["error_x"].update(width=0, thickness=0)
trace["error_y"].update(width=0, thickness=0)
for xaxis in boxplot_xaxes:
fig["layout"][xaxis]["showticklabels"] = False
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def _multiple_metric_traces(
model: ModelBridge,
metric_x: str,
metric_y: str,
generator_runs_dict: TNullableGeneratorRunsDict,
rel_x: bool,
rel_y: bool,
fixed_features: Optional[ObservationFeatures] = None,
) -> Traces:
"""Plot traces for multiple metrics given a model and metrics.
Args:
model: model to draw predictions from.
metric_x: metric to plot on the x-axis.
metric_y: metric to plot on the y-axis.
generator_runs_dict: a mapping from
generator run name to generator run.
rel_x: if True, use relative effects on metric_x.
rel_y: if True, use relative effects on metric_y.
fixed_features: Fixed features to use when making model predictions.
"""
plot_data, _, _ = get_plot_data(
model,
generator_runs_dict if generator_runs_dict is not None else {},
{metric_x, metric_y},
fixed_features=fixed_features,
)
status_quo_arm = (
None if plot_data.status_quo_name is None
# pyre-fixme[6]: Expected `str` for 1st param but got `Optional[str]`.
else plot_data.in_sample.get(plot_data.status_quo_name))
traces = [
_error_scatter_trace(
# Expected `List[Union[PlotInSampleArm, PlotOutOfSampleArm]]`
# for 1st anonymous parameter to call
# `ax.plot.scatter._error_scatter_trace` but got
# `List[PlotInSampleArm]`.
# pyre-fixme[6]:
list(plot_data.in_sample.values()),
x_axis_var=PlotMetric(metric_x, pred=False, rel=rel_x),
y_axis_var=PlotMetric(metric_y, pred=False, rel=rel_y),
status_quo_arm=status_quo_arm,
visible=False,
),
_error_scatter_trace(
# Expected `List[Union[PlotInSampleArm, PlotOutOfSampleArm]]`
# for 1st anonymous parameter to call
# `ax.plot.scatter._error_scatter_trace` but got
# `List[PlotInSampleArm]`.
# pyre-fixme[6]:
list(plot_data.in_sample.values()),
x_axis_var=PlotMetric(metric_x, pred=True, rel=rel_x),
y_axis_var=PlotMetric(metric_y, pred=True, rel=rel_y),
status_quo_arm=status_quo_arm,
visible=True,
),
]
for i, (generator_run_name, cand_arms) in enumerate(
(plot_data.out_of_sample or {}).items(), start=1):
traces.append(
_error_scatter_trace(
list(cand_arms.values()),
x_axis_var=PlotMetric(metric_x, pred=True, rel=rel_x),
y_axis_var=PlotMetric(metric_y, pred=True, rel=rel_y),
status_quo_arm=status_quo_arm,
name=generator_run_name,
color=DISCRETE_COLOR_SCALE[i],
))
return traces
def table_view_plot(
experiment: Experiment,
data: Data,
use_empirical_bayes: bool = True,
only_data_frame: bool = False,
):
"""Table of means and confidence intervals.
Table is of the form:
+-------+------------+-----------+
| arm | metric_1 | metric_2 |
+=======+============+===========+
| 0_0 | mean +- CI | ... |
+-------+------------+-----------+
| 0_1 | ... | ... |
+-------+------------+-----------+
"""
model_func = get_empirical_bayes_thompson if use_empirical_bayes else get_thompson
model = model_func(experiment=experiment, data=data)
metric_name_to_lower_is_better = {
metric.name: metric.lower_is_better
for metric in experiment.metrics.values()
}
plot_data, _, _ = get_plot_data(model=model,
generator_runs_dict={},
metric_names=model.metric_names)
if plot_data.status_quo_name:
status_quo_arm = plot_data.in_sample.get(plot_data.status_quo_name)
rel = True
else:
status_quo_arm = None
rel = False
results = {}
records_with_mean = []
records_with_ci = []
for metric_name in model.metric_names:
arms, _, ys, ys_se = _error_scatter_data(
arms=list(plot_data.in_sample.values()),
y_axis_var=PlotMetric(metric_name, True),
x_axis_var=None,
rel=rel,
status_quo_arm=status_quo_arm,
)
# results[metric] will hold a list of tuples, one tuple per arm
tuples = list(zip(arms, ys, ys_se))
results[metric_name] = tuples
# used if only_data_frame == True
records_with_mean.append({arm: y for (arm, y, _) in tuples})
records_with_ci.append({arm: y_se for (arm, _, y_se) in tuples})
if only_data_frame:
return tuple(
pd.DataFrame.from_records(records,
index=model.metric_names).transpose()
for records in [records_with_mean, records_with_ci])
# cells and colors are both lists of lists
# each top-level list corresponds to a column,
# so the first is a list of arms
cells = [[f"<b>{x}</b>" for x in arms]]
colors = [["#ffffff"] * len(arms)]
metric_names = []
for metric_name, list_of_tuples in sorted(results.items()):
cells.append([
"{:.3f} ± {:.3f}".format(y, Z * y_se)
for (_, y, y_se) in list_of_tuples
])
metric_names.append(metric_name.replace(":", " : "))
color_vec = []
for (_, y, y_se) in list_of_tuples:
color_vec.append(
get_color(
x=y,
ci=Z * y_se,
rel=rel,
reverse=metric_name_to_lower_is_better[metric_name],
))
colors.append(color_vec)
header = ["arms"] + metric_names
header = [f"<b>{x}</b>" for x in header]
trace = go.Table(
header={
"values": header,
"align": ["left"]
},
cells={
"values": cells,
"align": ["left"],
"fill": {
"color": colors
}
},
)
layout = go.Layout(
height=min([400, len(arms) * 20 + 200]),
width=175 * len(header),
margin=go.Margin(l=0, r=20, b=20, t=20, pad=4), # noqa E741
)
fig = go.Figure(data=[trace], layout=layout)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
