Visualize Objective (Unified 1D/2D)
VisualizerObj.RdThis class provides a unified interface for visualizing objective functions and optimization traces on both 1D and 2D objectives. It automatically detects the dimensionality and creates appropriate visualizations using ggplot2.
Super class
vistool::Visualizer -> VisualizerObj
Methods
Method new()
Creates a new instance of this [R6][R6::R6Class] class.
Usage
VisualizerObj$new(
objective,
x1_limits = NULL,
x2_limits = NULL,
padding = 0,
n_points = 100L,
type = NULL
)Arguments
objective(`Objective`)
The objective to optimize.x1_limits(`numeric(2)`)
Limits for the first dimension. For 1D objectives, this controls the x-axis range. If NULL, will be determined from objective bounds.x2_limits(`numeric(2)`)
Limits for the second dimension (2D objectives only). Ignored for 1D objectives. If NULL, will be determined from objective bounds.padding(`numeric(1)`)
A margin that is added to x1limits and x2limits. The x1 margin is calculated by `max(x1lmits) - min(x1limits) * padding`.padding(`numeric(1)`)
A margin that is added to x1limits and x2limits. The x1 margin is calculated by `max(x1lmits) - min(x1limits) * padding`.n_points(`integer(1)`)
The number of generated point per dimension. Note that a grid of `npoints^2` values is generated and evaluated by `objective$eval(x)` to plot the surface.n_points(`integer(1)`)
The number of generated point per dimension. Note that a grid of `npoints^2` values is generated and evaluated by `objective$eval(x)` to plot the surface.type(`character(1)`)
Optional visualization type ("1d" or "2d") to override automatic dimension detection. Useful when objective dimensions are unknown but visualization type is explicitly specified.
Method add_optimization_trace()
Add optimization trace to the plot.
Usage
VisualizerObj$add_optimization_trace(
optimizer,
line_color = NULL,
line_width = NULL,
line_type = "solid",
npoints = NULL,
npmax = NULL,
name = NULL,
add_marker_at = 1,
marker_size = 3,
marker_shape = 16,
marker_color = NULL,
show_start_end = TRUE,
alpha = NULL
)Arguments
optimizer(`Optimizer`)
The optimizer to add to the plot. Must have been run and contain archive data.line_color(`character(1)`)
Color of the optimization trace line. If NULL, uses automatic color.line_width(`numeric(1)`)
Width of the trace line. If NULL, uses theme$line_width.line_type(`character(1)`)
Type of the trace line. One of "solid", "dashed", "dotted". Default is "solid".npoints(`integer(1)`)
Number of points to show from the trace. If NULL, shows all points.npmax(`integer(1)`)
Maximum number of points to show. If NULL, no limit.name(`character(1)`)
Name for the trace (used in legends). If NULL, uses optimizer ID.add_marker_at(`integer()`)
Iteration numbers where to add markers. For 2D plots only.marker_size(`numeric(1)`)
Size of markers. Default is 3.marker_shape(`numeric(1)` or `character(1)`)
Shape of markers. Default is 16 (filled circle).marker_color(`character(1)`)
Color of markers. If NULL, uses line color.show_start_end(`logical(1)`)
Whether to highlight start and end points. Default is TRUE for 2D plots.alpha(`numeric(1)`)
Alpha transparency. If NULL, uses theme$alpha.
Method plot()
Create and return the ggplot2 plot with model-specific layers.