Evaluate documentation
Visualization methods
Visualization methods
Methods for visualizing evaluations results:
Radar Plot
evaluate.visualization.radar_plot
< source >( data model_names invert_range = [] config = None fig = None )
Parameters
- data (
List[dict]) — the results (list of metric + value pairs). E.g. data = [{“accuracy”: 0.9, “precision”:0.8},{“accuracy”: 0.7, “precision”:0.6}] - names (
List[dict]) — model names. E.g. names = [“model1”, “model 2”, …] - invert_range (
List[dict], optional) — the metrics to invert (in cases when smaller is better, e.g. speed) E.g. invert_range=[“latency_in_seconds”] - config (
dict, optional) — a specification of the formatting configurations, namely:-
rad_ln_args (
dict, default{"visible": True}): The visibility of the radial (circle) lines. -
outer_ring (
dict, default{"visible": True}): The visibility of the outer ring. -
angle_ln_args (
dict, default{"visible": True}): The visibility of the angle lines. -
rgrid_tick_lbls_args (
dict, default{"fontsize": 12}): The font size of the tick labels on the scales. -
theta_tick_lbls (
dict, default{"fontsize": 12}): The font size of the variable labels on the plot. -
theta_tick_lbls_pad (
int, default3): The padding of the variable labels on the plot. -
theta_tick_lbls_brk_lng_wrds (
bool, defaultTrue): Whether long words in the label are broken up or not. -
theta_tick_lbls_txt_wrap (
int, default15): Text wrap for tick labels -
incl_endpoint (
bool, defaultFalse): Include value endpoints on calse -
marker (
str, default"o"): the shape of the marker used in the radar plot. -
markersize (
int, default3): the shape of the marker used in the radar plot. -
legend_loc (
str, default"upper right"): the location of the legend in the radar plot. Must be one of: ‘upper left’, ‘upper right’, ‘lower left’, ‘lower right’. -
bbox_to_anchor (
tuple, default(2, 1): anchor for the legend.
-
- fig (
matplotlib.figure.Figure, optional) — figure used to plot the radar plot.
Create a complex radar chart with different scales for each variable Source: https://towardsdatascience.com/how-to-create-and-visualize-complex-radar-charts-f7764d0f3652