def plot_feature_importance(df: pd.DataFrame, title: str) -> AxPlotConfig:
if df.empty:
raise NoDataError("No Data on Feature Importances found.")
df.set_index(df.columns[0], inplace=True)
data = [
go.Bar(y=df.index,
x=df[column_name],
name=column_name,
orientation="h") for column_name in df.columns
]
fig = subplots.make_subplots(
rows=len(df.columns),
cols=1,
subplot_titles=df.columns,
print_grid=False,
shared_xaxes=True,
)
for idx, item in enumerate(data):
fig.append_trace(item, idx + 1, 1)
fig.layout.showlegend = False
fig.layout.margin = go.layout.Margin(
l=8 * min(max(len(idx) for idx in df.index), 75) # noqa E741
)
fig.layout.title = title
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def interact_empirical_model_validation(batch: BatchTrial, data: Data) -> AxPlotConfig:
"""Compare the model predictions for the batch arms against observed data.
Relies on the model predictions stored on the generator_runs of batch.
Args:
batch: Batch on which to perform analysis.
data: Observed data for the batch.
Returns:
AxPlotConfig for the plot.
"""
insample_data: Dict[str, PlotInSampleArm] = {}
metric_names = list(data.df["metric_name"].unique())
for struct in batch.generator_run_structs:
generator_run = struct.generator_run
if generator_run.model_predictions is None:
continue
for i, arm in enumerate(generator_run.arms):
arm_data = {
"name": arm.name_or_short_signature,
"y": {},
"se": {},
"parameters": arm.parameters,
"y_hat": {},
"se_hat": {},
"context_stratum": None,
}
predictions = generator_run.model_predictions
for _, row in data.df[
data.df["arm_name"] == arm.name_or_short_signature
].iterrows():
metric_name = row["metric_name"]
# pyre-fixme[16]: Optional type has no attribute `__setitem__`.
arm_data["y"][metric_name] = row["mean"]
# pyre-fixme[16]: Item `None` of `Union[None, Dict[typing.Any,
# typing.Any], Dict[str, typing.Union[None, bool, float, int, str]],
# str]` has no attribute `__setitem__`.
arm_data["se"][metric_name] = row["sem"]
# pyre-fixme[16]: `Optional` has no attribute `__getitem__`.
arm_data["y_hat"][metric_name] = predictions[0][metric_name][i]
# pyre-fixme[16]: Item `None` of `Union[None, Dict[typing.Any,
# typing.Any], Dict[str, typing.Union[None, bool, float, int, str]],
# str]` has no attribute `__setitem__`.
arm_data["se_hat"][metric_name] = predictions[1][metric_name][
metric_name
][i]
# pyre-fixme[6]: Expected `Optional[Dict[str, Union[float, str]]]` for 1s...
insample_data[arm.name_or_short_signature] = PlotInSampleArm(**arm_data)
if not insample_data:
raise ValueError("No model predictions present on the batch.")
plot_data = PlotData(
metrics=metric_names,
in_sample=insample_data,
out_of_sample=None,
status_quo_name=None,
)
fig = _obs_vs_pred_dropdown_plot(data=plot_data, rel=False)
fig["layout"]["title"] = "Cross-validation"
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def interact_batch_comparison(
observations: List[Observation],
experiment: Experiment,
batch_x: int,
batch_y: int,
rel: bool = False,
status_quo_name: Optional[str] = None,
) -> AxPlotConfig:
"""Compare repeated arms from two trials; select metric via dropdown.
Args:
observations: List of observations to compute comparison.
batch_x: Index of batch for x-axis.
batch_y: Index of bach for y-axis.
rel: Whether to relativize data against status_quo arm.
status_quo_name: Name of the status_quo arm.
"""
if isinstance(experiment, MultiTypeExperiment):
observations = convert_mt_observations(observations, experiment)
if not status_quo_name and experiment.status_quo:
status_quo_name = not_none(experiment.status_quo).name
plot_data = _get_batch_comparison_plot_data(
observations,
batch_x,
batch_y,
rel=rel,
status_quo_name=status_quo_name)
fig = _obs_vs_pred_dropdown_plot(
data=plot_data,
rel=rel,
xlabel="Batch {}".format(batch_x),
ylabel="Batch {}".format(batch_y),
)
fig["layout"]["title"] = "Repeated arms across trials"
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def plot_relative_feature_importance(model: ModelBridge) -> AxPlotConfig:
"""Create a stacked bar chart of feature importances per metric"""
importances = []
for metric_name in sorted(model.metric_names):
try:
vals: Dict[str, Any] = model.feature_importances(metric_name)
vals["index"] = metric_name
importances.append(vals)
except Exception:
logger.warning(
"Model for {} does not support feature importances.".format(
metric_name))
df = pd.DataFrame(importances)
df.set_index("index", inplace=True)
df = df.div(df.sum(axis=1), axis=0)
data = [
go.Bar(y=df.index,
x=df[column_name],
name=column_name,
orientation="h") for column_name in df.columns
]
layout = go.Layout(
margin=go.layout.Margin(l=250), # noqa E741
barmode="grouped",
yaxis={"title": ""},
xaxis={"title": "Relative Feature importance"},
showlegend=False,
title="Relative Feature Importance per Metric",
)
fig = go.Figure(data=data, layout=layout)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def plot_feature_importance_by_metric(model: ModelBridge) -> AxPlotConfig:
"""Wrapper method to convert plot_feature_importance_by_metric_plotly to
AxPlotConfig"""
return AxPlotConfig(
data=plot_feature_importance_by_metric_plotly(model),
plot_type=AxPlotTypes.GENERIC,
)
def optimization_trace_all_methods(
y_dict: Dict[str, np.ndarray],
optimum: Optional[float] = None,
title: str = "",
ylabel: str = "",
trace_colors: List[Tuple[int]] = DISCRETE_COLOR_SCALE,
optimum_color: Tuple[int] = COLORS.ORANGE.value,
) -> AxPlotConfig:
"""Plots a comparison of optimization traces with 2-SEM bands for multiple
methods on the same problem.
Args:
y: a mapping of method names to (r x t) arrays, where r is the number
of runs in the test, and t is the number of trials.
optimum: value of the optimal objective.
title: Title for this plot.
ylabel: Label for y axis
trace_colors: tuples of 3 int values representing
RGB colors to use for different methods shown in the combination plot.
Defaults to Ax discrete color scale.
optimum_color: tuple of 3 int values representing an RGB color.
Defaults to orange.
Returns:
AxPlotConfig: plot of the comparison of optimization traces with IQR
"""
data: List[go.Scatter] = []
for i, (method, y) in enumerate(y_dict.items()):
# If there are more traces than colors, start reusing colors.
color = trace_colors[i % len(trace_colors)]
trace = mean_trace_scatter(y=y, trace_color=color, legend_label=method)
# pyre-fixme[23]: Unable to unpack single value, 2 were expected.
lower, upper = sem_range_scatter(y=y,
trace_color=color,
legend_label=method)
data.extend([lower, trace, upper])
if optimum is not None:
num_iterations = max(y.shape[1] for y in y_dict.values())
data.append(
optimum_objective_scatter(
optimum=optimum,
num_iterations=num_iterations,
optimum_color=optimum_color,
))
layout = go.Layout( # pyre-ignore[16]: ...graph_objs` has no attr. `Layout`
title=title,
showlegend=True,
yaxis={"title": ylabel},
xaxis={"title": "Iteration"},
)
return AxPlotConfig(
# pyre-ignore[16]: ...graph_objs` has no attr. `Figure`
data=go.Figure(layout=layout, data=data),
plot_type=AxPlotTypes.GENERIC,
)
def plot_contour(
model: ModelBridge,
param_x: str,
param_y: str,
metric_name: str,
generator_runs_dict: TNullableGeneratorRunsDict = None,
relative: bool = False,
density: int = 50,
slice_values: Optional[Dict[str, Any]] = None,
lower_is_better: bool = False,
) -> AxPlotConfig:
"""Plot predictions for a 2-d slice of the parameter space.
Args:
model: ModelBridge that contains model for predictions
param_x: Name of parameter that will be sliced on x-axis
param_y: Name of parameter that will be sliced on y-axis
metric_name: Name of metric to plot
generator_runs_dict: A dictionary {name: generator run} of generator runs
whose arms will be plotted, if they lie in the slice.
relative: Predictions relative to status quo
density: Number of points along slice to evaluate predictions.
slice_values: A dictionary {name: val} for the fixed values of the
other parameters. If not provided, then the status quo values will
be used if there is a status quo, otherwise the mean of numeric
parameters or the mode of choice parameters.
lower_is_better: Lower values for metric are better.
"""
if param_x == param_y:
raise ValueError("Please select different parameters for x- and y-dimensions.")
data, f_plt, sd_plt, grid_x, grid_y, scales = _get_contour_predictions(
model=model,
x_param_name=param_x,
y_param_name=param_y,
metric=metric_name,
generator_runs_dict=generator_runs_dict,
density=density,
slice_values=slice_values,
)
config = {
"arm_data": data,
"blue_scale": BLUE_SCALE,
"density": density,
"f": f_plt,
"green_scale": GREEN_SCALE,
"green_pink_scale": GREEN_PINK_SCALE,
"grid_x": grid_x,
"grid_y": grid_y,
"lower_is_better": lower_is_better,
"metric": metric_name,
"rel": relative,
"sd": sd_plt,
"xvar": param_x,
"yvar": param_y,
"x_is_log": scales["x"],
"y_is_log": scales["y"],
}
return AxPlotConfig(config, plot_type=AxPlotTypes.CONTOUR)
def optimization_trace_single_method(
y: np.ndarray,
optimum: Optional[float] = None,
model_transitions: Optional[List[int]] = None,
title: str = "",
ylabel: str = "",
hover_labels: Optional[List[str]] = None,
trace_color: Tuple[int] = COLORS.STEELBLUE.value,
optimum_color: Tuple[int] = COLORS.ORANGE.value,
generator_change_color: Tuple[int] = COLORS.TEAL.value,
optimization_direction: Optional[str] = "passthrough",
plot_trial_points: bool = False,
trial_points_color: Tuple[int] = COLORS.LIGHT_PURPLE.value,
) -> AxPlotConfig:
"""Plots an optimization trace with mean and 2 SEMs
Args:
y: (r x t) array; result to plot, with r runs and t trials
optimum: value of the optimal objective
model_transitions: iterations, before which generators
changed
title: title for this plot.
ylabel: label for the Y-axis.
hover_labels: optional, text to show on hover; list where the i-th value
corresponds to the i-th value in the value of the `y` argument.
trace_color: tuple of 3 int values representing an RGB color for plotting
running optimum. Defaults to blue.
optimum_color: tuple of 3 int values representing an RGB color.
Defaults to orange.
generator_change_color: tuple of 3 int values representing
an RGB color. Defaults to teal.
optimization_direction: str, "minimize" will plot running minimum,
"maximize" will plot running maximum, "passthrough" (default) will plot
y as lines, None does not plot running optimum)
plot_trial_points: bool, whether to plot the objective for each trial, as
supplied in y (default False for backward compatibility)
trial_points_color: tuple of 3 int values representing an RGB color for
plotting trial points. Defaults to light purple.
Returns:
AxPlotConfig: plot of the optimization trace with IQR
"""
return AxPlotConfig(
data=optimization_trace_single_method_plotly(
y=y,
optimum=optimum,
model_transitions=model_transitions,
title=title,
ylabel=ylabel,
hover_labels=hover_labels,
trace_color=trace_color,
optimum_color=optimum_color,
generator_change_color=generator_change_color,
optimization_direction=optimization_direction,
plot_trial_points=plot_trial_points,
trial_points_color=trial_points_color,
),
plot_type=AxPlotTypes.GENERIC,
)
def plot_feature_importance_by_feature(
model: ModelBridge, relative: bool = True, caption: str = ""
) -> AxPlotConfig:
"""Wrapper method to convert plot_feature_importance_by_feature_plotly to
AxPlotConfig"""
return AxPlotConfig(
data=plot_feature_importance_by_feature_plotly(model, relative, caption),
plot_type=AxPlotTypes.GENERIC,
)
def plot_marginal_effects(model: ModelBridge, metric: str) -> AxPlotConfig:
"""
Calculates and plots the marginal effects -- the effect of changing one
factor away from the randomized distribution of the experiment and fixing it
at a particular level.
Args:
model: Model to use for estimating effects
metric: The metric for which to plot marginal effects.
Returns:
AxPlotConfig of the marginal effects
"""
plot_data, _, _ = get_plot_data(model, {}, {metric})
arm_dfs = []
for arm in plot_data.in_sample.values():
arm_df = pd.DataFrame(arm.parameters, index=[arm.name])
arm_df["mean"] = arm.y_hat[metric]
arm_df["sem"] = arm.se_hat[metric]
arm_dfs.append(arm_df)
effect_table = marginal_effects(pd.concat(arm_dfs, 0))
varnames = effect_table["Name"].unique()
data: List[Any] = []
for varname in varnames:
var_df = effect_table[effect_table["Name"] == varname]
data += [
# pyre-ignore[16]
go.Bar(
x=var_df["Level"],
y=var_df["Beta"],
error_y={
"type": "data",
"array": var_df["SE"]
},
name=varname,
)
]
fig = tools.make_subplots(
cols=len(varnames),
rows=1,
subplot_titles=list(varnames),
print_grid=False,
shared_yaxes=True,
)
for idx, item in enumerate(data):
fig.append_trace(item, 1, idx + 1)
fig.layout.showlegend = False
# fig.layout.margin = go.Margin(l=2, r=2)
fig.layout.title = "Marginal Effects by Factor"
fig.layout.yaxis = {
"title": "% better than experiment average",
"hoverformat": ".{}f".format(DECIMALS),
}
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def plot_slice(
model: ModelBridge,
param_name: str,
metric_name: str,
generator_runs_dict: TNullableGeneratorRunsDict = None,
relative: bool = False,
density: int = 50,
slice_values: Optional[Dict[str, Any]] = None,
fixed_features: Optional[ObservationFeatures] = None,
) -> AxPlotConfig:
"""Plot predictions for a 1-d slice of the parameter space.
Args:
model: ModelBridge that contains model for predictions
param_name: Name of parameter that will be sliced
metric_name: Name of metric to plot
generator_runs_dict: A dictionary {name: generator run} of generator runs
whose arms will be plotted, if they lie in the slice.
relative: Predictions relative to status quo
density: Number of points along slice to evaluate predictions.
slice_values: A dictionary {name: val} for the fixed values of the
other parameters. If not provided, then the status quo values will
be used if there is a status quo, otherwise the mean of numeric
parameters or the mode of choice parameters. Ignored if
fixed_features is specified.
fixed_features: An ObservationFeatures object containing the values of
features (including non-parameter features like context) to be set
in the slice.
"""
pd, cntp, f_plt, rd, grid, _, _, _, fv, sd_plt, ls = _get_slice_predictions(
model=model,
param_name=param_name,
metric_name=metric_name,
generator_runs_dict=generator_runs_dict,
relative=relative,
density=density,
slice_values=slice_values,
fixed_features=fixed_features,
)
config = {
"arm_data": pd,
"arm_name_to_parameters": cntp,
"f": f_plt,
"fit_data": rd,
"grid": grid,
"metric": metric_name,
"param": param_name,
"rel": relative,
"setx": fv,
"sd": sd_plt,
"is_log": ls,
}
return AxPlotConfig(config, plot_type=AxPlotTypes.SLICE)
def plot_contour(
model: ModelBridge,
param_x: str,
param_y: str,
metric_name: str,
generator_runs_dict: TNullableGeneratorRunsDict = None,
relative: bool = False,
density: int = 50,
slice_values: Optional[Dict[str, Any]] = None,
lower_is_better: bool = False,
fixed_features: Optional[ObservationFeatures] = None,
trial_index: Optional[int] = None,
) -> AxPlotConfig:
"""Plot predictions for a 2-d slice of the parameter space.
Args:
model: ModelBridge that contains model for predictions
param_x: Name of parameter that will be sliced on x-axis
param_y: Name of parameter that will be sliced on y-axis
metric_name: Name of metric to plot
generator_runs_dict: A dictionary {name: generator run} of generator runs
whose arms will be plotted, if they lie in the slice.
relative: Predictions relative to status quo
density: Number of points along slice to evaluate predictions.
slice_values: A dictionary {name: val} for the fixed values of the
other parameters. If not provided, then the status quo values will
be used if there is a status quo, otherwise the mean of numeric
parameters or the mode of choice parameters.
lower_is_better: Lower values for metric are better.
fixed_features: An ObservationFeatures object containing the values of
features (including non-parameter features like context) to be set
in the slice.
Returns:
AxPlotConfig: contour plot of objective vs. parameter values
"""
return AxPlotConfig(
data=plot_contour_plotly(
model=model,
param_x=param_x,
param_y=param_y,
metric_name=metric_name,
generator_runs_dict=generator_runs_dict,
relative=relative,
density=density,
slice_values=slice_values,
lower_is_better=lower_is_better,
fixed_features=fixed_features,
trial_index=trial_index,
),
plot_type=AxPlotTypes.GENERIC,
)
def get_running_trials_per_minute(
experiment: Experiment,
show_until_latest_end_plus_timedelta: timedelta = FIVE_MINUTES,
) -> AxPlotConfig:
trial_runtimes: List[Tuple[int, datetime, Optional[datetime]]] = [
(
trial.index,
not_none(trial._time_run_started),
trial.
_time_completed, # Time trial was completed, failed, or abandoned.
) for trial in experiment.trials.values()
if trial._time_run_started is not None
]
earliest_start = min(tr[1] for tr in trial_runtimes)
latest_end = max(
not_none(tr[2]) for tr in trial_runtimes if tr[2] is not None)
running_during = {
ts: [
t[0] # Trial index.
for t in trial_runtimes
# Trial is running during a given timestamp if:
# 1) it's run start time is at/before the timestamp,
# 2) it's completion time has not yet come or is after the timestamp.
if t[1] <= ts and (True if t[2] is None else not_none(t[2]) >= ts)
]
for ts in timestamps_in_range(
earliest_start,
latest_end + show_until_latest_end_plus_timedelta,
timedelta(seconds=60),
)
}
num_running_at_ts = {
ts: len(trials)
for ts, trials in running_during.items()
}
scatter = go.Scatter(
x=list(num_running_at_ts.keys()),
y=[num_running_at_ts[ts] for ts in num_running_at_ts],
)
return AxPlotConfig(
data=go.Figure(
layout=go.Layout(
title="Number of running trials during experiment"),
data=[scatter],
),
plot_type=AxPlotTypes.GENERIC,
)
def interact_cross_validation(
cv_results: List[CVResult], show_context: bool = True
) -> AxPlotConfig:
"""Interactive cross-validation (CV) plotting; select metric via dropdown.
Note: uses the Plotly version of dropdown (which means that all data is
stored within the notebook).
Args:
cv_results: cross-validation results.
show_context: if True, show context on hover.
"""
data = _get_cv_plot_data(cv_results)
fig = _obs_vs_pred_dropdown_plot(data=data, rel=False, show_context=show_context)
fig["layout"]["title"] = "Cross-validation"
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def scatter_plot_with_pareto_frontier(
Y: np.ndarray,
Y_pareto: np.ndarray,
metric_x: str,
metric_y: str,
reference_point: Tuple[float, float],
minimize: bool = True,
) -> AxPlotConfig:
return AxPlotConfig(
data=scatter_plot_with_pareto_frontier_plotly(
Y=Y,
Y_pareto=Y_pareto,
metric_x=metric_x,
metric_y=metric_y,
reference_point=reference_point,
),
plot_type=AxPlotTypes.GENERIC,
)
def plot_parallel_coordinates(
experiment: Experiment,
ignored_names: Optional[List[str]] = None) -> AxPlotConfig:
"""Plot trials as a parallel coordinates graph
Args:
experiment: Experiment containing trials to plot
ignored_names: Metrics present in the experiment data we wish to exclude from
the final plot. By default we ignore ["generation_method", "trial_status",
"arm_name"]
Returns:
AxPlotConfig: parellel coordinates plot of all experiment trials
"""
fig = plot_parallel_coordinates_plotly(experiment=experiment,
ignored_names=ignored_names)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def interact_slice(
model: ModelBridge,
generator_runs_dict: TNullableGeneratorRunsDict = None,
relative: bool = False,
density: int = 50,
slice_values: Optional[Dict[str, Any]] = None,
fixed_features: Optional[ObservationFeatures] = None,
trial_index: Optional[int] = None,
) -> AxPlotConfig:
"""Create interactive plot with predictions for a 1-d slice of the parameter
space.
Args:
model: ModelBridge that contains model for predictions
generator_runs_dict: A dictionary {name: generator run} of generator runs
whose arms will be plotted, if they lie in the slice.
relative: Predictions relative to status quo
density: Number of points along slice to evaluate predictions.
slice_values: A dictionary {name: val} for the fixed values of the
other parameters. If not provided, then the status quo values will
be used if there is a status quo, otherwise the mean of numeric
parameters or the mode of choice parameters. Ignored if
fixed_features is specified.
fixed_features: An ObservationFeatures object containing the values of
features (including non-parameter features like context) to be set
in the slice.
Returns:
AxPlotConfig: interactive plot of objective vs. parameter
"""
return AxPlotConfig(
data=interact_slice_plotly(
model=model,
generator_runs_dict=generator_runs_dict,
relative=relative,
density=density,
slice_values=slice_values,
fixed_features=fixed_features,
trial_index=trial_index,
),
plot_type=AxPlotTypes.GENERIC,
)
def plot_bandit_rollout(experiment: Experiment) -> AxPlotConfig:
"""Plot bandit rollout from ane experiement."""
categories: List[str] = []
arms: Dict[str, Dict[str, Any]] = {}
data = []
index = 0
for trial in sorted(experiment.trials.values(),
key=lambda trial: trial.index):
if not isinstance(trial, BatchTrial):
raise ValueError(
"Bandit rollout graph is not supported for BaseTrial."
) # pragma: no cover
category = f"Round {trial.index}"
categories.append(category)
for arm, weight in trial.normalized_arm_weights(total=100).items():
if arm.name not in arms:
arms[arm.name] = {
"index": index,
"name": arm.name,
"x": [],
"y": [],
"text": [],
}
index += 1
arms[arm.name]["x"].append(category)
arms[arm.name]["y"].append(weight)
arms[arm.name]["text"].append("{:.2f}%".format(weight))
for key in arms.keys():
data.append(arms[key])
# pyre-fixme[6]: Expected `typing.Tuple[...g.Tuple[int, int, int]`.
colors = [rgba(c) for c in MIXED_SCALE]
config = {"data": data, "categories": categories, "colors": colors}
return AxPlotConfig(config, plot_type=AxPlotTypes.BANDIT_ROLLOUT)
def interact_cross_validation(
cv_results: List[CVResult], show_context: bool = True
) -> AxPlotConfig:
"""Interactive cross-validation (CV) plotting; select metric via dropdown.
Note: uses the Plotly version of dropdown (which means that all data is
stored within the notebook).
Args:
cv_results: cross-validation results.
show_context: if True, show context on hover.
Returns an AxPlotConfig
"""
return AxPlotConfig(
data=interact_cross_validation_plotly(
cv_results=cv_results, show_context=show_context
),
plot_type=AxPlotTypes.GENERIC,
)
def optimization_trace_single_method(
y: np.ndarray,
optimum: Optional[float] = None,
model_transitions: Optional[List[int]] = None,
title: str = "",
ylabel: str = "",
hover_labels: Optional[List[str]] = None,
trace_color: Tuple[int] = COLORS.STEELBLUE.value,
optimum_color: Tuple[int] = COLORS.ORANGE.value,
generator_change_color: Tuple[int] = COLORS.TEAL.value,
) -> AxPlotConfig:
"""Plots an optimization trace with mean and 2 SEMs
Args:
y: (r x t) array; result to plot, with r runs and t trials
optimum: value of the optimal objective
model_transitions: iterations, before which generators
changed
title: title for this plot.
ylabel: label for the Y-axis.
hover_labels: optional, text to show on hover; list where the i-th value
corresponds to the i-th value in the value of the `y` argument.
trace_color: tuple of 3 int values representing an RGB color.
Defaults to orange.
optimum_color: tuple of 3 int values representing an RGB color.
Defaults to orange.
generator_change_color: tuple of 3 int values representing
an RGB color. Defaults to orange.
Returns:
AxPlotConfig: plot of the optimization trace with IQR
"""
trace = mean_trace_scatter(y=y,
trace_color=trace_color,
hover_labels=hover_labels)
lower, upper = sem_range_scatter(y=y, trace_color=trace_color)
layout = go.Layout(
title=title,
showlegend=True,
yaxis={"title": ylabel},
xaxis={"title": "Iteration"},
)
data = [lower, trace, upper]
if optimum is not None:
data.append(
optimum_objective_scatter(optimum=optimum,
num_iterations=y.shape[1],
optimum_color=optimum_color))
if model_transitions is not None: # pragma: no cover
y_lower = np.min(np.percentile(y, 25, axis=0))
y_upper = np.max(np.percentile(y, 75, axis=0))
if optimum is not None and optimum < y_lower:
y_lower = optimum
if optimum is not None and optimum > y_upper:
y_upper = optimum
data.extend(
model_transitions_scatter(
model_transitions=model_transitions,
y_range=[y_lower, y_upper],
generator_change_color=generator_change_color,
))
return AxPlotConfig(data=go.Figure(layout=layout, data=data),
plot_type=AxPlotTypes.GENERIC)
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 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 plot_feature_importance_by_feature(model: ModelBridge,
relative: bool = True) -> AxPlotConfig:
"""One plot per metric, showing importances by feature."""
traces = []
dropdown = []
for i, metric_name in enumerate(sorted(model.metric_names)):
try:
importances = model.feature_importances(metric_name)
except NotImplementedError:
logger.warning(
f"Model for {metric_name} does not support feature importances."
)
continue
df = pd.DataFrame([{
"Factor": factor,
"Importance": importance
} for factor, importance in importances.items()])
if relative:
df["Importance"] = df["Importance"].div(df["Importance"].sum())
df = df.sort_values("Importance")
traces.append(
go.Bar(
name="Importance",
orientation="h",
visible=i == 0,
x=df["Importance"],
y=df["Factor"],
))
is_visible = [False] * len(sorted(model.metric_names))
is_visible[i] = True
dropdown.append({
"args": ["visible", is_visible],
"label": metric_name,
"method": "restyle"
})
if not traces:
raise NotImplementedError("No traces found for metric")
updatemenus = [{
"x": 0,
"y": 1,
"yanchor": "top",
"xanchor": "left",
"buttons": dropdown,
"pad": {
"t": -40
}, # hack to put dropdown below title regardless of number of features
}]
features = traces[0].y
title = ("Relative Feature Importances"
if relative else "Absolute Feature Importances")
layout = go.Layout(
height=200 + len(features) * 20,
hovermode="closest",
margin=go.layout.Margin(l=8 *
min(max(len(idx)
for idx in features), 75) # noqa E741
),
showlegend=False,
title=title,
updatemenus=updatemenus,
)
if relative:
layout.update({"xaxis": {"tickformat": ".0%"}})
fig = go.Figure(data=traces, layout=layout)
plot_fi = AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
return plot_fi
def plot_bandit_rollout(experiment: Experiment) -> AxPlotConfig:
"""Plot bandit rollout from ane experiement."""
categories: List[str] = []
arms: Dict[str, Dict[str, Any]] = {}
data = []
index = 0
for trial in sorted(experiment.trials.values(),
key=lambda trial: trial.index):
if not isinstance(trial, BatchTrial):
raise ValueError(
"Bandit rollout graph is not supported for BaseTrial."
) # pragma: no cover
category = f"Round {trial.index}"
categories.append(category)
for arm, weight in trial.normalized_arm_weights(total=100).items():
if arm.name not in arms:
arms[arm.name] = {
"index": index,
"name": arm.name,
"x": [],
"y": [],
"text": [],
}
index += 1
arms[arm.name]["x"].append(category)
arms[arm.name]["y"].append(weight)
arms[arm.name]["text"].append("{:.2f}%".format(weight))
for key in arms.keys():
data.append(arms[key])
# pyre-fixme[6]: Expected `typing.Tuple[...g.Tuple[int, int, int]`.
colors = [rgba(c) for c in MIXED_SCALE]
layout = go.Layout( # pyre-ignore[16]
title="Rollout Process<br>Bandit Weight Graph",
xaxis={
"title": "Rounds",
"zeroline": False,
"categoryorder": "array",
"categoryarray": categories,
},
yaxis={"title": "Percent", "showline": False},
barmode="stack",
showlegend=False,
margin={"r": 40},
)
bandit_config = {"type": "bar", "hoverinfo": "name+text", "width": 0.5}
bandits = [
dict(bandit_config,
marker={"color": colors[d["index"] % len(colors)]},
**d) for d in data
]
for bandit in bandits:
del bandit[
"index"] # Have to delete index or figure creation causes error
fig = go.Figure(data=bandits, layout=layout) # pyre-ignore[16]
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
def plot_slice(
model: ModelBridge,
param_name: str,
metric_name: str,
generator_runs_dict: TNullableGeneratorRunsDict = None,
relative: bool = False,
density: int = 50,
slice_values: Optional[Dict[str, Any]] = None,
) -> AxPlotConfig:
"""Plot predictions for a 1-d slice of the parameter space.
Args:
model: ModelBridge that contains model for predictions
param_name: Name of parameter that will be sliced
metric_name: Name of metric to plot
generator_runs_dict: A dictionary {name: generator run} of generator runs
whose arms will be plotted, if they lie in the slice.
relative: Predictions relative to status quo
density: Number of points along slice to evaluate predictions.
slice_values: A dictionary {name: val} for the fixed values of the
other parameters. If not provided, then the status quo values will
be used if there is a status quo, otherwise the mean of numeric
parameters or the mode of choice parameters.
"""
if generator_runs_dict is None:
generator_runs_dict = {}
parameter = get_range_parameter(model, param_name)
grid = get_grid_for_parameter(parameter, density)
plot_data, raw_data, cond_name_to_parameters = get_plot_data(
model=model, generator_runs_dict=generator_runs_dict, metric_names={metric_name}
)
fixed_values = get_fixed_values(model, slice_values)
prediction_features = []
for x in grid:
parameters = fixed_values.copy()
parameters[param_name] = x
# Here we assume context is None
prediction_features.append(ObservationFeatures(parameters=parameters))
f, cov = model.predict(prediction_features)
f_plt = f[metric_name]
sd_plt = np.sqrt(cov[metric_name][metric_name])
config = {
"arm_data": plot_data,
"arm_name_to_parameters": cond_name_to_parameters,
"f": f_plt,
"fit_data": raw_data,
"grid": grid,
"metric": metric_name,
"param": param_name,
"rel": relative,
"setx": fixed_values,
"sd": sd_plt,
"is_log": parameter.log_scale,
}
return AxPlotConfig(config, plot_type=AxPlotTypes.SLICE)
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 plot_feature_importance(df: pd.DataFrame, title: str) -> AxPlotConfig:
"""Wrapper method to convert plot_feature_importance_plotly to
AxPlotConfig"""
return AxPlotConfig(
data=plot_feature_importance_plotly(df, title), plot_type=AxPlotTypes.GENERIC
)
def optimization_times(
fit_times: Dict[str, List[float]],
gen_times: Dict[str, List[float]],
title: str = "",
) -> AxPlotConfig:
"""Plots wall times for each method as a bar chart.
Args:
fit_times: A map from method name to a list of the model fitting times.
gen_times: A map from method name to a list of the gen times.
title: Title for this plot.
Returns: AxPlotConfig with the plot
"""
# Compute means and SEs
methods = list(fit_times.keys())
fit_res: Dict[str, Union[str, List[float]]] = {"name": "Fitting"}
fit_res["mean"] = [np.mean(fit_times[m]) for m in methods]
fit_res["2sems"] = [
2 * np.std(fit_times[m]) / np.sqrt(len(fit_times[m])) for m in methods
]
gen_res: Dict[str, Union[str, List[float]]] = {"name": "Generation"}
gen_res["mean"] = [np.mean(gen_times[m]) for m in methods]
gen_res["2sems"] = [
2 * np.std(gen_times[m]) / np.sqrt(len(gen_times[m])) for m in methods
]
total_mean: List[float] = []
total_2sems: List[float] = []
for m in methods:
totals = np.array(fit_times[m]) + np.array(gen_times[m])
total_mean.append(np.mean(totals))
total_2sems.append(2 * np.std(totals) / np.sqrt(len(totals)))
total_res: Dict[str, Union[str, List[float]]] = {
"name": "Total",
"mean": total_mean,
"2sems": total_2sems,
}
# Construct plot
data: List[go.Bar] = []
for i, res in enumerate([fit_res, gen_res, total_res]):
data.append(
go.Bar(
x=methods,
y=res["mean"],
text=res["name"],
textposition="auto",
error_y={
"type": "data",
"array": res["2sems"],
"visible": True
},
marker={
"color": rgba(DISCRETE_COLOR_SCALE[i]),
"line": {
"color": "rgb(0,0,0)",
"width": 1.0
},
},
opacity=0.6,
name=res["name"],
))
layout = go.Layout(
title=title,
showlegend=False,
yaxis={"title": "Time"},
xaxis={"title": "Method"},
)
return AxPlotConfig(data=go.Figure(layout=layout, data=data),
plot_type=AxPlotTypes.GENERIC)
def plot_slice_plotly(
model: ModelBridge,
param_name: str,
metric_name: str,
generator_runs_dict: TNullableGeneratorRunsDict = None,
relative: bool = False,
density: int = 50,
slice_values: Optional[Dict[str, Any]] = None,
fixed_features: Optional[ObservationFeatures] = None,
trial_index: Optional[int] = None,
) -> go.Figure:
"""Plot predictions for a 1-d slice of the parameter space.
Args:
model: ModelBridge that contains model for predictions
param_name: Name of parameter that will be sliced
metric_name: Name of metric to plot
generator_runs_dict: A dictionary {name: generator run} of generator runs
whose arms will be plotted, if they lie in the slice.
relative: Predictions relative to status quo
density: Number of points along slice to evaluate predictions.
slice_values: A dictionary {name: val} for the fixed values of the
other parameters. If not provided, then the status quo values will
be used if there is a status quo, otherwise the mean of numeric
parameters or the mode of choice parameters. Ignored if
fixed_features is specified.
fixed_features: An ObservationFeatures object containing the values of
features (including non-parameter features like context) to be set
in the slice.
Returns:
go.Figure: plot of objective vs. parameter value
"""
pd, cntp, f_plt, rd, grid, _, _, _, fv, sd_plt, ls = _get_slice_predictions(
model=model,
param_name=param_name,
metric_name=metric_name,
generator_runs_dict=generator_runs_dict,
relative=relative,
density=density,
slice_values=slice_values,
fixed_features=fixed_features,
trial_index=trial_index,
)
config = {
"arm_data": pd,
"arm_name_to_parameters": cntp,
"f": f_plt,
"fit_data": rd,
"grid": grid,
"metric": metric_name,
"param": param_name,
"rel": relative,
"setx": fv,
"sd": sd_plt,
"is_log": ls,
}
config = AxPlotConfig(config, plot_type=AxPlotTypes.GENERIC).data
arm_data = config["arm_data"]
arm_name_to_parameters = config["arm_name_to_parameters"]
f = config["f"]
fit_data = config["fit_data"]
grid = config["grid"]
metric = config["metric"]
param = config["param"]
rel = config["rel"]
setx = config["setx"]
sd = config["sd"]
is_log = config["is_log"]
traces = slice_config_to_trace(
arm_data,
arm_name_to_parameters,
f,
fit_data,
grid,
metric,
param,
rel,
setx,
sd,
is_log,
True,
)
# layout
xrange = axis_range(grid, is_log)
xtype = "log" if is_log else "linear"
layout = {
"hovermode": "closest",
"xaxis": {
"anchor": "y",
"autorange": False,
"exponentformat": "e",
"range": xrange,
"tickfont": {
"size": 11
},
"tickmode": "auto",
"title": param,
"type": xtype,
},
"yaxis": {
"anchor": "x",
"tickfont": {
"size": 11
},
"tickmode": "auto",
"title": metric,
},
}
return go.Figure(data=traces, layout=layout)
def interact_slice_plotly(
model: ModelBridge,
generator_runs_dict: TNullableGeneratorRunsDict = None,
relative: bool = False,
density: int = 50,
slice_values: Optional[Dict[str, Any]] = None,
fixed_features: Optional[ObservationFeatures] = None,
trial_index: Optional[int] = None,
) -> go.Figure:
"""Create interactive plot with predictions for a 1-d slice of the parameter
space.
Args:
model: ModelBridge that contains model for predictions
generator_runs_dict: A dictionary {name: generator run} of generator runs
whose arms will be plotted, if they lie in the slice.
relative: Predictions relative to status quo
density: Number of points along slice to evaluate predictions.
slice_values: A dictionary {name: val} for the fixed values of the
other parameters. If not provided, then the status quo values will
be used if there is a status quo, otherwise the mean of numeric
parameters or the mode of choice parameters. Ignored if
fixed_features is specified.
fixed_features: An ObservationFeatures object containing the values of
features (including non-parameter features like context) to be set
in the slice.
Returns:
go.Figure: interactive plot of objective vs. parameter
"""
if generator_runs_dict is None:
generator_runs_dict = {}
metric_names = list(model.metric_names)
# Populate `pbuttons`, which allows the user to select 1D slices of parameter
# space with the chosen parameter on the x-axis.
range_parameters = get_range_parameters(model)
param_names = [parameter.name for parameter in range_parameters]
pbuttons = []
init_traces = []
xaxis_init_format = {}
first_param_bool = True
should_replace_slice_values = fixed_features is not None
for param_name in param_names:
pbutton_data_args = {"x": [], "y": [], "error_y": []}
parameter = get_range_parameter(model, param_name)
grid = get_grid_for_parameter(parameter, density)
plot_data_dict = {}
raw_data_dict = {}
sd_plt_dict: Dict[str, Dict[str, np.ndarray]] = {}
cond_name_to_parameters_dict = {}
is_log_dict: Dict[str, bool] = {}
if should_replace_slice_values:
slice_values = not_none(fixed_features).parameters
else:
fixed_features = ObservationFeatures(parameters={})
fixed_values = get_fixed_values(model, slice_values, trial_index)
prediction_features = []
for x in grid:
predf = deepcopy(not_none(fixed_features))
predf.parameters = fixed_values.copy()
predf.parameters[param_name] = x
prediction_features.append(predf)
f, cov = model.predict(prediction_features)
for metric_name in metric_names:
pd, cntp, f_plt, rd, _, _, _, _, _, sd_plt, ls = _get_slice_predictions(
model=model,
param_name=param_name,
metric_name=metric_name,
generator_runs_dict=generator_runs_dict,
relative=relative,
density=density,
slice_values=slice_values,
fixed_features=fixed_features,
)
plot_data_dict[metric_name] = pd
raw_data_dict[metric_name] = rd
cond_name_to_parameters_dict[metric_name] = cntp
sd_plt_dict[metric_name] = np.sqrt(cov[metric_name][metric_name])
is_log_dict[metric_name] = ls
config = {
"arm_data": plot_data_dict,
"arm_name_to_parameters": cond_name_to_parameters_dict,
"f": f,
"fit_data": raw_data_dict,
"grid": grid,
"metrics": metric_names,
"param": param_name,
"rel": relative,
"setx": fixed_values,
"sd": sd_plt_dict,
"is_log": is_log_dict,
}
config = AxPlotConfig(config, plot_type=AxPlotTypes.GENERIC).data
arm_data = config["arm_data"]
arm_name_to_parameters = config["arm_name_to_parameters"]
f = config["f"]
fit_data = config["fit_data"]
grid = config["grid"]
metrics = config["metrics"]
param = config["param"]
rel = config["rel"]
setx = config["setx"]
sd = config["sd"]
is_log = config["is_log"]
# layout
xrange = axis_range(grid, is_log[metrics[0]])
xtype = "log" if is_log_dict[metrics[0]] else "linear"
for i, metric in enumerate(metrics):
cur_visible = i == 0
metric = metrics[i]
traces = slice_config_to_trace(
arm_data[metric],
arm_name_to_parameters[metric],
f[metric],
fit_data[metric],
grid,
metric,
param,
rel,
setx,
sd[metric],
is_log[metric],
cur_visible,
)
pbutton_data_args["x"] += [trace["x"] for trace in traces]
pbutton_data_args["y"] += [trace["y"] for trace in traces]
pbutton_data_args["error_y"] += [{
"type": "data",
"array": trace["error_y"]["array"],
"visible": True,
"color": "black",
} if "error_y" in trace and "array" in trace["error_y"] else []
for trace in traces]
if first_param_bool:
init_traces.extend(traces)
pbutton_args = [
pbutton_data_args,
{
"xaxis.title": param_name,
"xaxis.range": xrange,
"xaxis.type": xtype,
},
]
pbuttons.append({
"args": pbutton_args,
"label": param_name,
"method": "update"
})
if first_param_bool:
xaxis_init_format = {
"anchor": "y",
"autorange": False,
"exponentformat": "e",
"range": xrange,
"tickfont": {
"size": 11
},
"tickmode": "auto",
"title": param_name,
"type": xtype,
}
first_param_bool = False
# Populate mbuttons, which allows the user to select which metric to plot
mbuttons = []
for i, metric in enumerate(metrics):
trace_cnt = 3 + len(arm_data[metric]["out_of_sample"].keys())
visible = [False] * (len(metrics) * trace_cnt)
for j in range(i * trace_cnt, (i + 1) * trace_cnt):
visible[j] = True
mbuttons.append({
"method": "update",
"args": [{
"visible": visible
}, {
"yaxis.title": metric
}],
"label": metric,
})
layout = {
"title":
"Predictions for a 1-d slice of the parameter space",
"annotations": [
{
"showarrow": False,
"text": "Choose metric:",
"x": 0.225,
"xanchor": "right",
"xref": "paper",
"y": -0.455,
"yanchor": "bottom",
"yref": "paper",
},
{
"showarrow": False,
"text": "Choose parameter:",
"x": 0.225,
"xanchor": "right",
"xref": "paper",
"y": -0.305,
"yanchor": "bottom",
"yref": "paper",
},
],
"updatemenus": [
{
"y": -0.35,
"x": 0.25,
"xanchor": "left",
"yanchor": "top",
"buttons": mbuttons,
"direction": "up",
},
{
"y": -0.2,
"x": 0.25,
"xanchor": "left",
"yanchor": "top",
"buttons": pbuttons,
"direction": "up",
},
],
"hovermode":
"closest",
"xaxis":
xaxis_init_format,
"yaxis": {
"anchor": "x",
"autorange": True,
"tickfont": {
"size": 11
},
"tickmode": "auto",
"title": metrics[0],
},
}
return go.Figure(data=init_traces, layout=layout)