def tile_fitted(
model: ModelBridge,
generator_runs_dict: TNullableGeneratorRunsDict = None,
rel: bool = True,
show_arm_details_on_hover: bool = False,
show_CI: bool = True,
arm_noun: str = "arm",
metrics: Optional[List[str]] = None,
fixed_features: Optional[ObservationFeatures] = None,
) -> AxPlotConfig:
"""Tile version of fitted outcome plots.
Args:
model: model to use for predictions.
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.
show_CI: if True, render confidence intervals.
arm_noun: noun to use instead of "arm" (e.g. group)
metrics: List of metric names to restrict to when plotting.
fixed_features: Fixed features to use when making model predictions.
"""
metrics = metrics or list(model.metric_names)
nrows = int(np.ceil(len(metrics) / 2))
ncols = min(len(metrics), 2)
# make subplots (plot per row)
fig = tools.make_subplots(
rows=nrows,
cols=ncols,
print_grid=False,
shared_xaxes=False,
shared_yaxes=False,
subplot_titles=tuple(metrics),
horizontal_spacing=0.05,
vertical_spacing=0.30 / nrows,
)
name_order_args: Dict[str, Any] = {}
name_order_axes: Dict[str, Dict[str, Any]] = {}
effect_order_args: Dict[str, Any] = {}
for i, metric in enumerate(metrics):
data = _single_metric_traces(
model,
metric,
generator_runs_dict,
rel,
showlegend=i == 0,
show_arm_details_on_hover=show_arm_details_on_hover,
show_CI=show_CI,
arm_noun=arm_noun,
fixed_features=fixed_features,
)
# order arm name sorting arm numbers within batch
names_by_arm = sorted(
np.unique(np.concatenate([d["x"] for d in data])),
key=lambda x: arm_name_to_tuple(x),
)
# get arm names sorted by effect size
names_by_effect = list(
OrderedDict.fromkeys(
np.concatenate([d["x"] for d in data]).flatten().take(
np.argsort(
np.concatenate([d["y"] for d in data]).flatten()))))
# options for ordering arms (x-axis)
# Note that xaxes need to be references as xaxis, xaxis2, xaxis3, etc.
# for the purposes of updatemenus argument (dropdown) in layout.
# However, when setting the initial ordering layout, the keys should be
# xaxis1, xaxis2, xaxis3, etc. Note the discrepancy for the initial
# axis.
label = "" if i == 0 else i + 1
name_order_args["xaxis{}.categoryorder".format(label)] = "array"
name_order_args["xaxis{}.categoryarray".format(label)] = names_by_arm
effect_order_args["xaxis{}.categoryorder".format(label)] = "array"
effect_order_args["xaxis{}.categoryarray".format(
label)] = names_by_effect
name_order_axes["xaxis{}".format(i + 1)] = {
"categoryorder": "array",
"categoryarray": names_by_arm,
"type": "category",
}
name_order_axes["yaxis{}".format(i + 1)] = {
"ticksuffix": "%" if rel else "",
"zerolinecolor": "red",
}
for d in data:
fig.append_trace(d, int(np.floor(i / ncols)) + 1, i % ncols + 1)
order_options = [
{
"args": [name_order_args],
"label": "Name",
"method": "relayout"
},
{
"args": [effect_order_args],
"label": "Effect Size",
"method": "relayout"
},
]
# 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
fig["layout"].update(
margin={"t": 0},
hovermode="closest",
updatemenus=[{
"x": 0.15,
"y": 1 + 0.40 / nrows,
"buttons": order_options,
"xanchor": "left",
"yanchor": "middle",
}],
font={"size": 10},
width=650 if ncols == 1 else 950,
height=300 * nrows,
legend={
"orientation": "h",
"x": 0,
"y": 1 + 0.20 / nrows,
"xanchor": "left",
"yanchor": "middle",
},
**name_order_axes,
)
# append dropdown annotations
fig["layout"]["annotations"] += (
{
"x": 0.5,
"y": 1 + 0.40 / nrows,
"xref": "paper",
"yref": "paper",
"font": {
"size": 14
},
"text": "Predicted Outcomes",
"showarrow": False,
"xanchor": "center",
"yanchor": "middle",
},
{
"x": 0.05,
"y": 1 + 0.40 / nrows,
"xref": "paper",
"yref": "paper",
"text": "Sort By",
"showarrow": False,
"xanchor": "left",
"yanchor": "middle",
},
)
fig = resize_subtitles(figure=fig, size=10)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def plot_fitted(
model: ModelBridge,
metric: str,
generator_runs_dict: TNullableGeneratorRunsDict = None,
rel: bool = True,
custom_arm_order: Optional[List[str]] = None,
custom_arm_order_name: str = "Custom",
show_CI: bool = True,
) -> AxPlotConfig:
"""Plot fitted metrics.
Args:
model: model to use for predictions.
metric: metric to plot predictions for.
generator_runs_dict: a mapping from
generator run name to generator run.
rel: if True, use relative effects. Default is True.
custom_arm_order: a list of arm names in the
order corresponding to how they should be plotted on the x-axis.
If not None, this is the default ordering.
custom_arm_order_name: name for custom ordering to
show in the ordering dropdown. Default is 'Custom'.
show_CI: if True, render confidence intervals.
"""
traces = _single_metric_traces(model,
metric,
generator_runs_dict,
rel,
show_CI=show_CI)
# order arm name sorting arm numbers within batch
names_by_arm = sorted(
np.unique(np.concatenate([d["x"] for d in traces])),
key=lambda x: arm_name_to_tuple(x),
)
# get arm names sorted by effect size
names_by_effect = list(
OrderedDict.fromkeys(
np.concatenate([d["x"] for d in traces]).flatten().take(
np.argsort(np.concatenate([d["y"]
for d in traces]).flatten()))))
# options for ordering arms (x-axis)
xaxis_categoryorder = "array"
xaxis_categoryarray = names_by_arm
order_options = [
{
"args": [{
"xaxis.categoryorder": "array",
"xaxis.categoryarray": names_by_arm
}],
"label":
"Name",
"method":
"relayout",
},
{
"args": [{
"xaxis.categoryorder": "array",
"xaxis.categoryarray": names_by_effect
}],
"label":
"Effect Size",
"method":
"relayout",
},
]
# if a custom order has been passed, default to that
if custom_arm_order is not None:
xaxis_categoryorder = "array"
xaxis_categoryarray = custom_arm_order
order_options = [{
"args": [{
"xaxis.categoryorder": "array",
"xaxis.categoryarray": custom_arm_order,
}],
"label":
custom_arm_order_name,
"method":
"relayout",
}
# Union[List[str...
] + order_options
layout = go.Layout(
title="Predicted Outcomes",
hovermode="closest",
updatemenus=[{
"x": 1.25,
"y": 0.67,
"buttons": list(order_options),
"yanchor": "middle",
"xanchor": "left",
}],
yaxis={
"zerolinecolor": "red",
"title": "{}{}".format(metric, " (%)" if rel else ""),
},
xaxis={
"tickangle": 45,
"categoryorder": xaxis_categoryorder,
"categoryarray": xaxis_categoryarray,
},
annotations=[{
"x": 1.18,
"y": 0.72,
"xref": "paper",
"yref": "paper",
"text": "Sort By",
"showarrow": False,
"yanchor": "middle",
}],
font={"size": 10},
)
fig = go.Figure(data=traces, layout=layout)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
