ax.plot¶

Base¶

class ax.plot.base.AxPlotConfig(data: Dict[str, Any], plot_type: enum.Enum)[source]¶

Bases: ax.plot.base._AxPlotConfigBase

Config for plots

class ax.plot.base.AxPlotTypes(value)[source]¶

Bases: enum.Enum

Enum of Ax plot types.

BANDIT_ROLLOUT = 4¶

CONTOUR = 0¶

GENERIC = 1¶

HTML = 6¶

INTERACT_CONTOUR = 3¶

INTERACT_SLICE = 5¶

SLICE = 2¶

class ax.plot.base.PlotData(metrics: List[str], in_sample: Dict[str, ax.plot.base.PlotInSampleArm], out_of_sample: Optional[Dict[str, Dict[str, ax.plot.base.PlotOutOfSampleArm]]], status_quo_name: Optional[str])[source]¶

Bases: tuple

Struct for plot data, including both in-sample and out-of-sample arms

property in_sample¶

Alias for field number 1

property metrics¶

Alias for field number 0

property out_of_sample¶

Alias for field number 2

property status_quo_name¶

Alias for field number 3

class ax.plot.base.PlotInSampleArm(name: str, parameters: Dict[str, Optional[Union[str, bool, float, int]]], y: Dict[str, float], y_hat: Dict[str, float], se: Dict[str, float], se_hat: Dict[str, float], context_stratum: Optional[Dict[str, Union[str, float]]])[source]¶

Bases: tuple

Struct for in-sample arms (both observed and predicted data)

property context_stratum¶

Alias for field number 6

property name¶

Alias for field number 0

property parameters¶

Alias for field number 1

property se¶

Alias for field number 4

property se_hat¶

Alias for field number 5

property y¶

Alias for field number 2

property y_hat¶

Alias for field number 3

class ax.plot.base.PlotMetric(metric: str, pred: bool, rel: bool)[source]¶

Bases: tuple

Struct for metric

property metric¶

Alias for field number 0

property pred¶

Alias for field number 1

property rel¶

Alias for field number 2

class ax.plot.base.PlotOutOfSampleArm(name: str, parameters: Dict[str, Optional[Union[str, bool, float, int]]], y_hat: Dict[str, float], se_hat: Dict[str, float], context_stratum: Optional[Dict[str, Union[str, float]]])[source]¶

Bases: tuple

Struct for out-of-sample arms (only predicted data)

property context_stratum¶

Alias for field number 4

property name¶

Alias for field number 0

property parameters¶

Alias for field number 1

property se_hat¶

Alias for field number 3

property y_hat¶

Alias for field number 2

Bandit Rollout¶

ax.plot.bandit_rollout.plot_bandit_rollout(experiment: ax.core.experiment.Experiment) → ax.plot.base.AxPlotConfig[source]¶

Plot bandit rollout from ane experiement.

Contour Plot¶

ax.plot.contour.interact_contour(model: ax.modelbridge.base.ModelBridge, metric_name: str, generator_runs_dict: Optional[Dict[str, ax.core.generator_run.GeneratorRun]] = None, relative: bool = False, density: int = 50, slice_values: Optional[Dict[str, Any]] = None, lower_is_better: bool = False, fixed_features: Optional[ax.core.observation.ObservationFeatures] = None, trial_index: Optional[int] = None) → ax.plot.base.AxPlotConfig[source]¶

Create interactive plot with predictions for a 2-d slice of the parameter space.

Parameters

• model – ModelBridge that contains model for predictions

• 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.

ax.plot.contour.plot_contour(model: ax.modelbridge.base.ModelBridge, param_x: str, param_y: str, metric_name: str, generator_runs_dict: Optional[Dict[str, ax.core.generator_run.GeneratorRun]] = None, relative: bool = False, density: int = 50, slice_values: Optional[Dict[str, Any]] = None, lower_is_better: bool = False, fixed_features: Optional[ax.core.observation.ObservationFeatures] = None, trial_index: Optional[int] = None) → ax.plot.base.AxPlotConfig[source]¶

Plot predictions for a 2-d slice of the parameter space.

Parameters

• 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.

ax.plot.contour.short_name(param_name: str) → str[source]¶

Feature Importances¶

ax.plot.feature_importances.plot_feature_importance(df: pandas.DataFrame, title: str) → ax.plot.base.AxPlotConfig[source]¶

ax.plot.feature_importances.plot_feature_importance_by_feature(model: ax.modelbridge.base.ModelBridge, relative: bool = True) → ax.plot.base.AxPlotConfig[source]¶

One plot per metric, showing importances by feature.

ax.plot.feature_importances.plot_feature_importance_by_metric(model: ax.modelbridge.base.ModelBridge) → ax.plot.base.AxPlotConfig[source]¶

One plot per feature, showing importances by metric.

ax.plot.feature_importances.plot_relative_feature_importance(model: ax.modelbridge.base.ModelBridge) → ax.plot.base.AxPlotConfig[source]¶

Create a stacked bar chart of feature importances per metric

Marginal Effects¶

ax.plot.marginal_effects.plot_marginal_effects(model: ax.modelbridge.base.ModelBridge, metric: str) → ax.plot.base.AxPlotConfig[source]¶

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.

Parameters

• model – Model to use for estimating effects

• metric – The metric for which to plot marginal effects.

Returns

AxPlotConfig of the marginal effects

Model Diagnostics¶

ax.plot.diagnostic.interact_batch_comparison(observations: List[ax.core.observation.Observation], experiment: ax.core.experiment.Experiment, batch_x: int, batch_y: int, rel: bool = False, status_quo_name: Optional[str] = None) → ax.plot.base.AxPlotConfig[source]¶

Compare repeated arms from two trials; select metric via dropdown.

Parameters

• 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.

ax.plot.diagnostic.interact_cross_validation(cv_results: List[ax.modelbridge.cross_validation.CVResult], show_context: bool = True) → ax.plot.base.AxPlotConfig[source]¶

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).

Parameters

• cv_results – cross-validation results.

• show_context – if True, show context on hover.

ax.plot.diagnostic.interact_empirical_model_validation(batch: ax.core.batch_trial.BatchTrial, data: ax.core.data.Data) → ax.plot.base.AxPlotConfig[source]¶

Compare the model predictions for the batch arms against observed data.

Relies on the model predictions stored on the generator_runs of batch.

Parameters

• batch – Batch on which to perform analysis.

• data – Observed data for the batch.

Returns

AxPlotConfig for the plot.

ax.plot.diagnostic.tile_cross_validation(cv_results: List[ax.modelbridge.cross_validation.CVResult], show_arm_details_on_hover: bool = True, show_context: bool = True) → ax.plot.base.AxPlotConfig[source]¶

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.

Parameters

• 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.

Pareto Plots¶

ax.plot.pareto_frontier.interact_pareto_frontier(frontier_list: List[ax.plot.pareto_utils.ParetoFrontierResults], CI_level: float = 0.9, show_parameterization_on_hover: bool = True) → ax.plot.base.AxPlotConfig[source]¶

Plot a pareto frontier from a list of objects

ax.plot.pareto_frontier.plot_pareto_frontier(frontier: ax.plot.pareto_utils.ParetoFrontierResults, CI_level: float = 0.9, show_parameterization_on_hover: bool = True) → ax.plot.base.AxPlotConfig[source]¶

Plot a Pareto frontier from a ParetoFrontierResults object.

Parameters

• frontier (ParetoFrontierResults) – The results of the Pareto frontier computation.

• CI_level (float, optional) – The confidence level, i.e. 0.95 (95%)

• show_parameterization_on_hover (bool, optional) – If True, show the parameterization of the points on the frontier on hover.

Returns

The resulting Plotly plot definition.

Return type

AEPlotConfig

class ax.plot.pareto_utils.COLORS(value)[source]¶

Bases: enum.Enum

An enumeration.

CORAL = (251, 128, 114)¶

LIGHT_PURPLE = (190, 186, 218)¶

ORANGE = (253, 180, 98)¶

PINK = (188, 128, 189)¶

STEELBLUE = (128, 177, 211)¶

TEAL = (141, 211, 199)¶

class ax.plot.pareto_utils.ParetoFrontierResults(param_dicts: List[Dict[str, Optional[Union[str, bool, float, int]]]], means: Dict[str, List[float]], sems: Dict[str, List[float]], primary_metric: str, secondary_metric: str, absolute_metrics: List[str], outcome_constraints: Optional[List[ax.core.outcome_constraint.OutcomeConstraint]])[source]¶

Bases: tuple

Container for results from Pareto frontier computation.

property absolute_metrics¶

Alias for field number 5

property means¶

Alias for field number 1

property outcome_constraints¶

Alias for field number 6

property param_dicts¶

Alias for field number 0

property primary_metric¶

Alias for field number 3

property secondary_metric¶

Alias for field number 4

property sems¶

Alias for field number 2

ax.plot.pareto_utils.compute_pareto_frontier(experiment: ax.core.experiment.Experiment, primary_objective: ax.core.metric.Metric, secondary_objective: ax.core.metric.Metric, data: Optional[ax.core.data.Data] = None, outcome_constraints: Optional[List[ax.core.outcome_constraint.OutcomeConstraint]] = None, absolute_metrics: Optional[List[str]] = None, num_points: int = 10, trial_index: Optional[int] = None, chebyshev: bool = True) → ax.plot.pareto_utils.ParetoFrontierResults[source]¶

Compute the Pareto frontier between two objectives. For experiments with batch trials, a trial index or data object must be provided.

Parameters

• experiment – The experiment to compute a pareto frontier for.

• primary_objective – The primary objective to optimize.

• secondary_objective – The secondary objective against which to trade off the primary objective.

• outcome_constraints – Outcome constraints to be respected by the optimization. Can only contain constraints on metrics that are not primary or secondary objectives.

• absolute_metrics – List of outcome metrics that should NOT be relativized w.r.t. the status quo (all other outcomes will be in % relative to status_quo).

• num_points – The number of points to compute on the Pareto frontier.

• chebyshev – Whether to use augmented_chebyshev_scalarization when computing Pareto Frontier points.

Returns

A NamedTuple with the following fields:

• param_dicts: The parameter dicts of the

  points generated on the Pareto Frontier.

• means: The posterior mean predictions of

  the model for each metric (same order as the param dicts).

• sems: The posterior sem predictions of

  the model for each metric (same order as the param dicts).

• primary_metric: The name of the primary metric.

• secondary_metric: The name of the secondary metric.

• absolute_metrics: List of outcome metrics that

  are NOT be relativized w.r.t. the status quo (all other metrics are in % relative to status_quo).

Return type

ParetoFrontierResults

ax.plot.pareto_utils.rgba(rgb_tuple: Tuple[float], alpha: float = 1) → str[source]¶

Convert RGB tuple to an RGBA string.

Scatter Plots¶

ax.plot.scatter.interact_fitted(model: ax.modelbridge.base.ModelBridge, generator_runs_dict: Optional[Dict[str, ax.core.generator_run.GeneratorRun]] = None, rel: bool = True, show_arm_details_on_hover: bool = True, show_CI: bool = True, arm_noun: str = 'arm', metrics: Optional[List[str]] = None, fixed_features: Optional[ax.core.observation.ObservationFeatures] = None, data_selector: Optional[Callable[[ax.core.observation.Observation], bool]] = None) → ax.plot.base.AxPlotConfig[source]¶

Interactive fitted outcome plots for each arm used in fitting the model.

Choose the outcome to plot using a dropdown.

Parameters

• 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 True.

• 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.

• data_selector – Function for selecting observations for plotting.

ax.plot.scatter.lattice_multiple_metrics(model: ax.modelbridge.base.ModelBridge, generator_runs_dict: Optional[Dict[str, ax.core.generator_run.GeneratorRun]] = None, rel: bool = True, show_arm_details_on_hover: bool = False, data_selector: Optional[Callable[[ax.core.observation.Observation], bool]] = None) → ax.plot.base.AxPlotConfig[source]¶

Plot raw values or predictions of combinations of two metrics for arms.

Parameters

• 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.

• data_selector – Function for selecting observations for plotting.

ax.plot.scatter.plot_fitted(model: ax.modelbridge.base.ModelBridge, metric: str, generator_runs_dict: Optional[Dict[str, ax.core.generator_run.GeneratorRun]] = None, rel: bool = True, custom_arm_order: Optional[List[str]] = None, custom_arm_order_name: str = 'Custom', show_CI: bool = True, data_selector: Optional[Callable[[ax.core.observation.Observation], bool]] = None) → ax.plot.base.AxPlotConfig[source]¶

Plot fitted metrics.

Parameters

• 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.

• data_selector – Function for selecting observations for plotting.

ax.plot.scatter.plot_multiple_metrics(model: ax.modelbridge.base.ModelBridge, metric_x: str, metric_y: str, generator_runs_dict: Optional[Dict[str, ax.core.generator_run.GeneratorRun]] = None, rel: bool = True, fixed_features: Optional[ax.core.observation.ObservationFeatures] = None, data_selector: Optional[Callable[[ax.core.observation.Observation], bool]] = None) → ax.plot.base.AxPlotConfig[source]¶

Plot raw values or predictions of two metrics for arms.

All arms used in the model are included in the plot. Additional arms can be passed through the generator_runs_dict argument.

Parameters

• 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 – if True, use relative effects. Default is True.

• data_selector – Function for selecting observations for plotting.

ax.plot.scatter.plot_objective_vs_constraints(model: ax.modelbridge.base.ModelBridge, objective: str, subset_metrics: Optional[List[str]] = None, generator_runs_dict: Optional[Dict[str, ax.core.generator_run.GeneratorRun]] = None, rel: bool = True, infer_relative_constraints: Optional[bool] = False, fixed_features: Optional[ax.core.observation.ObservationFeatures] = None, data_selector: Optional[Callable[[ax.core.observation.Observation], bool]] = None) → ax.plot.base.AxPlotConfig[source]¶

Plot the tradeoff between an objetive and all other metrics in a model.

All arms used in the model are included in the plot. Additional arms can be passed through via the generator_runs_dict argument.

Fixed features input can be used to override fields of the insample arms when making model predictions.

Parameters

• model – model to draw predictions from.

• objective – metric to optimize. Plotted on the x-axis.

• subset_metrics – list of metrics to plot on the y-axes if need a subset of all metrics in the model.

• generator_runs_dict – a mapping from generator run name to generator run.

• rel – if True, use relative effects. Default is True.

• infer_relative_constraints – if True, read relative spec from model’s optimization config. Absolute constraints will not be relativized; relative ones will be. Objectives will respect the rel parameter. Metrics that are not constraints will be relativized.

• fixed_features – Fixed features to use when making model predictions.

• data_selector – Function for selecting observations for plotting.

ax.plot.scatter.tile_fitted(model: ax.modelbridge.base.ModelBridge, generator_runs_dict: Optional[Dict[str, ax.core.generator_run.GeneratorRun]] = 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[ax.core.observation.ObservationFeatures] = None, data_selector: Optional[Callable[[ax.core.observation.Observation], bool]] = None) → ax.plot.base.AxPlotConfig[source]¶

Tile version of fitted outcome plots.

Parameters

• 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.

• data_selector – Function for selecting observations for plotting.

Slice Plot¶

ax.plot.slice.interact_slice(model: ax.modelbridge.base.ModelBridge, param_name: str, metric_name: str = '', generator_runs_dict: Optional[Dict[str, ax.core.generator_run.GeneratorRun]] = None, relative: bool = False, density: int = 50, slice_values: Optional[Dict[str, Any]] = None, fixed_features: Optional[ax.core.observation.ObservationFeatures] = None, trial_index: Optional[int] = None) → ax.plot.base.AxPlotConfig[source]¶

Create interactive plot with predictions for a 1-d slice of the parameter space.

Parameters

• 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.

ax.plot.slice.plot_slice(model: ax.modelbridge.base.ModelBridge, param_name: str, metric_name: str, generator_runs_dict: Optional[Dict[str, ax.core.generator_run.GeneratorRun]] = None, relative: bool = False, density: int = 50, slice_values: Optional[Dict[str, Any]] = None, fixed_features: Optional[ax.core.observation.ObservationFeatures] = None, trial_index: Optional[int] = None) → ax.plot.base.AxPlotConfig[source]¶

Plot predictions for a 1-d slice of the parameter space.

Parameters

• 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.

Table¶

ax.plot.table_view.get_color(x: float, ci: float, rel: bool, reverse: bool)[source]¶

Determine the color of the table cell.

ax.plot.table_view.table_view_plot(experiment: ax.core.experiment.Experiment, data: ax.core.data.Data, use_empirical_bayes: bool = True, only_data_frame: bool = False, arm_noun: str = 'arm')[source]¶

Table of means and confidence intervals.

Table is of the form:

arm	metric_1	metric_2
0_0	mean +- CI	…
0_1	…	…

Trace Plots¶

ax.plot.trace.get_running_trials_per_minute(experiment: ax.core.experiment.Experiment, show_until_latest_end_plus_timedelta: datetime.timedelta = datetime.timedelta(seconds=300)) → ax.plot.base.AxPlotConfig[source]¶

ax.plot.trace.mean_trace_scatter(y: numpy.ndarray, trace_color: Tuple[int] = (128, 177, 211), legend_label: str = 'mean', hover_labels: Optional[List[str]] = None) → plotly.graph_objs._scatter.Scatter[source]¶

Creates a graph object for trace of the mean of the given series across runs.

Parameters

• y – (r x t) array with results from r runs and t trials.

• trace_color – tuple of 3 int values representing an RGB color. Defaults to blue.

• legend_label – label for this trace.

• 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.

Returns

plotly graph object

Return type

go.Scatter

ax.plot.trace.model_transitions_scatter(model_transitions: List[int], y_range: List[float], generator_change_color: Tuple[int] = (141, 211, 199)) → List[plotly.graph_objs._scatter.Scatter][source]¶

Creates a graph object for the line(s) representing generator changes.

Parameters

• model_transitions – iterations, before which generators changed

• y_range – upper and lower values of the y-range of the plot

• generator_change_color – tuple of 3 int values representing an RGB color. Defaults to orange.

Returns

plotly graph objects for the lines representing generator

changes

Return type

go.Scatter

ax.plot.trace.optimization_times(fit_times: Dict[str, List[float]], gen_times: Dict[str, List[float]], title: str = '') → ax.plot.base.AxPlotConfig[source]¶

Plots wall times for each method as a bar chart.

Parameters

• 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

ax.plot.trace.optimization_trace_all_methods(y_dict: Dict[str, numpy.ndarray], optimum: Optional[float] = None, title: str = '', ylabel: str = '', hover_labels: Optional[List[str]] = None, trace_colors: List[Tuple[int]] = [(128, 177, 211), (251, 128, 114), (141, 211, 199), (188, 128, 189), (190, 186, 218), (253, 180, 98)], optimum_color: Tuple[int] = (253, 180, 98)) → ax.plot.base.AxPlotConfig[source]¶

Plots a comparison of optimization traces with 2-SEM bands for multiple methods on the same problem.

Parameters

• 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 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_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

plot of the comparison of optimization traces with IQR

Return type

AxPlotConfig

ax.plot.trace.optimization_trace_single_method(y: numpy.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] = (128, 177, 211), optimum_color: Tuple[int] = (253, 180, 98), generator_change_color: Tuple[int] = (141, 211, 199)) → ax.plot.base.AxPlotConfig[source]¶

Plots an optimization trace with mean and 2 SEMs

Parameters

• 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

plot of the optimization trace with IQR

Return type

AxPlotConfig

ax.plot.trace.optimum_objective_scatter(optimum: float, num_iterations: int, optimum_color: Tuple[int] = (253, 180, 98)) → plotly.graph_objs._scatter.Scatter[source]¶

Creates a graph object for the line representing optimal objective.

Parameters

• optimum – value of the optimal objective

• num_iterations – how many trials were in the optimization (used to determine the width of the plot)

• trace_color – tuple of 3 int values representing an RGB color. Defaults to orange.

Returns

plotly graph objects for the optimal objective line

Return type

go.Scatter

ax.plot.trace.sem_range_scatter(y: numpy.ndarray, trace_color: Tuple[int] = (128, 177, 211), legend_label: str = '') → Tuple[plotly.graph_objs._scatter.Scatter, plotly.graph_objs._scatter.Scatter][source]¶

Creates a graph object for trace of mean +/- 2 SEMs for y, across runs.

Parameters

• y – (r x t) array with results from r runs and t trials.

• trace_color – tuple of 3 int values representing an RGB color. Defaults to blue.

• legend_label – Label for the legend group.

Returns

plotly graph objects for lower and upper bounds

Return type

Tuple[go.Scatter]