def plot_metric_bubble_chart(
disparity_df,
metrics_list,
attribute,
fairness_threshold=1.25,
chart_height=None,
chart_width=Metric_Chart.full_width,
accessibility_mode=False,
):
"""Draws bubble chart to visualize the values of the selected metrics for a given attribute.
:param disparity_df: a dataframe generated by the Aequitas Bias class
:type disparity_df: pandas.core.frame.DataFrame
:param metrics_list: a list of the metrics of interest
:type metrics_list: list
:param attribute: an attribute to plot
:type attribute: str
:param fairness_threshold: a value for the maximum allowed disparity, defaults to 1.25
:type fairness_threshold: float, optional
:param chart_height: a value (in pixels) for the height of the chart
:type chart_height: int, optional
:param chart_width: a value (in pixels) for the width of the chart
:type chart_width: int, optional
:param accessibility_mode: a switch for the display of more accessible visual elements, defaults to False
:type accessibility_mode: bool, optional
:return: the full metrics chart
:rtype: Altair chart object
"""
(
plot_table,
metrics,
ref_group,
global_scales,
chart_height,
chart_width,
selection,
) = Initializer.prepare_bubble_chart(
disparity_df,
metrics_list,
attribute,
fairness_threshold,
chart_height,
chart_width,
Metric_Chart,
accessibility_mode,
)
# GET MAIN CHART COMPONENTS
main_chart = get_metric_bubble_chart_components(
plot_table,
metrics,
ref_group,
global_scales,
selection,
fairness_threshold,
chart_height,
chart_width,
accessibility_mode,
)
# ADD LEGEND
legend = draw_legend(global_scales, selection, chart_width)
full_chart = main_chart + legend
# FINALIZE CHART
metric_chart = (
full_chart.configure_view(strokeWidth=0)
.configure_axisLeft(
labelFontSize=Metric_Axis.label_font_size,
labelColor=Metric_Axis.label_color,
labelFont=FONT,
)
.properties(
height=chart_height,
width=chart_width,
title=f"Absolute values by {attribute.title()}",
padding=Metric_Chart.full_chart_padding,
)
.configure_title(
align="center",
baseline="middle",
font=FONT,
fontWeight=Chart_Title.font_weight,
fontSize=Chart_Title.font_size,
color=Chart_Title.font_color,
)
.resolve_scale(y="independent", size="independent")
)
return metric_chart
def plot_xy_metrics_chart(
disparity_df,
x_metric,
y_metric,
attribute,
fairness_threshold=1.25,
chart_height=None,
chart_width=None,
accessibility_mode=False,
):
"""Draws XY scatterplot, with the group_size encoded in the bubble size, based on the two metrics provided.
Additionally draws threshold rules and bands if the fairness_threshold value is not None.
:param disparity_df: a dataframe generated by the Aequitas Bias class
:type disparity_df: pandas.core.frame.DataFrame
:param x_metric: a metric to plot in the x axis
:type x_metric: str
:param y_metric: a metric to plot in the y axis
:type y_metric: str
:param attribute: an attribute to plot
:type attribute: str
:param fairness_threshold: a value for the maximum allowed disparity, defaults to 1.25
:type fairness_threshold: float, optional
:param chart_height: a value (in pixels) for the height of the chart
:type chart_height: int, optional
:param chart_width: a value (in pixels) for the width of the chart
:type chart_width: int, optional
:param accessibility_mode: a switch for the display of more accessible visual elements, defaults to False
:type accessibility_mode: bool, optional
:return: the full scatterplot chart
:rtype: Altair chart object
"""
(
plot_table,
x_metric,
y_metric,
ref_group,
global_scales,
chart_height,
chart_width,
interactive_selection_group,
) = Initializer.prepare_xy_chart(
disparity_df,
x_metric,
y_metric,
attribute,
fairness_threshold,
chart_height,
chart_width,
XY_Chart,
accessibility_mode,
)
position_scales = __get_position_scales(chart_height, chart_width)
scales = dict(global_scales, **position_scales)
# AXIS RULES
axis_rules = __draw_axis_rules(x_metric, y_metric, scales)
# TICK LABELS
tick_labels = __draw_tick_labels(scales, chart_height, chart_width)
# INITIATE CHART
chart = axis_rules + tick_labels
# THRESHOLD AND BANDS
if fairness_threshold is not None:
# REF VALUES
ref_group_index = plot_table.loc[plot_table["attribute_value"] ==
ref_group].index
x_ref_group_value = plot_table.loc[ref_group_index, x_metric].iloc[0]
y_ref_group_value = plot_table.loc[ref_group_index, y_metric].iloc[0]
# Y AXIS
if y_metric not in Validator.NON_DISPARITY_METRICS_LIST:
y_thresholds = __draw_threshold_bands(
ref_group_value=y_ref_group_value,
fairness_threshold=fairness_threshold,
main_scale=scales["y"],
aux_scale=scales["x"],
accessibility_mode=accessibility_mode,
)
chart += y_thresholds
# X AXIS
if x_metric not in Validator.NON_DISPARITY_METRICS_LIST:
x_thresholds = __draw_threshold_bands(
ref_group_value=x_ref_group_value,
fairness_threshold=fairness_threshold,
main_scale=scales["x"],
aux_scale=scales["y"],
accessibility_mode=accessibility_mode,
drawing_x=True,
)
chart += x_thresholds
# LEGEND
legend = draw_legend(scales, interactive_selection_group, chart_width)
# BUBBLES
bubbles = __draw_bubbles(
plot_table,
x_metric,
y_metric,
ref_group,
scales,
interactive_selection_group,
)
# FINISH CHART COMPOSITION
chart += legend + bubbles
# CONFIGURATION
styled_chart = (chart.configure_view(strokeWidth=0).properties(
height=chart_height,
width=chart_width,
title=
f"{y_metric.upper()} by {x_metric.upper()} on {attribute.title()}",
padding=XY_Chart.full_chart_padding,
).configure_title(
align="center",
baseline="middle",
font=FONT,
fontWeight=Chart_Title.font_weight,
fontSize=Chart_Title.font_size,
color=Chart_Title.font_color,
).configure_axis(
titleFont=FONT,
titleColor=Scatter_Axis.title_color,
titleFontSize=Scatter_Axis.title_font_size,
titleFontWeight=Scatter_Axis.title_font_weight,
titleAngle=0,
).configure_axisLeft(
titlePadding=Scatter_Axis.title_padding, ).resolve_scale(
y="independent", x="independent",
size="independent").resolve_axis(x="shared", y="shared"))
return styled_chart
def plot_concatenated_bubble_charts(
disparity_df,
metrics_list,
attribute,
fairness_threshold=1.25,
chart_height=None,
chart_width=Sizes.Concat_Chart.full_width,
accessibility_mode=False,
):
"""Draws a concatenation of the disparity bubble chart and the metric values bubble chart,
of the selected metrics for a given attribute.
:param disparity_df: a dataframe generated by the Aequitas Bias class
:type disparity_df: pandas.core.frame.DataFrame
:param metrics_list: a list of the metrics of interest
:type metrics_list: list
:param attribute: an attribute to plot
:type attribute: str
:param fairness_threshold: a value for the maximum allowed disparity, defaults to 1.25
:type fairness_threshold: float, optional
:param chart_height: a value (in pixels) for the height of the chart
:type chart_height: int, optional
:param chart_width: a value (in pixels) for the width of the chart
:type chart_width: int, optional
:param accessibility_mode: a switch for the display of more accessible visual elements, defaults to False
:type accessibility_mode: bool, optional
:return: the full disparities chart
:rtype: Altair chart object
"""
(
plot_table,
metrics,
ref_group,
global_scales,
chart_height,
chart_width,
selection,
) = Initializer.prepare_bubble_chart(
disparity_df,
metrics_list,
attribute,
fairness_threshold,
chart_height,
chart_width,
Sizes.Disparity_Chart,
accessibility_mode,
)
chart_sizes = __get_chart_sizes(chart_width)
# TITLES
disparity_title = draw_chart_title("DISPARITIES",
chart_sizes["disparity_chart_width"])
metric_title = draw_chart_title("METRICS",
chart_sizes["metric_chart_width"])
# DISPARITY CHART
disparity_chart = ((get_disparity_bubble_chart_components(
plot_table,
metrics,
ref_group,
global_scales,
selection,
fairness_threshold,
chart_height,
chart_sizes["disparity_chart_width"],
accessibility_mode,
concat_chart=True,
) + disparity_title).resolve_scale(
y="independent", size="independent").properties(
height=chart_height, width=chart_sizes["disparity_chart_width"]))
# METRIC CHART
metric_chart = (
(get_metric_bubble_chart_components(
plot_table,
metrics,
ref_group,
global_scales,
selection,
fairness_threshold,
chart_height,
chart_sizes["metric_chart_width"],
accessibility_mode,
concat_chart=True,
) + metric_title +
draw_legend(global_scales, selection,
chart_sizes["metric_chart_width"])).resolve_scale(
y="independent").properties(
height=chart_height,
width=chart_sizes["metric_chart_width"]))
full_chart = (alt.hconcat(
disparity_chart, metric_chart, bounds="flush",
spacing=20).configure_view(strokeWidth=0).configure_axisLeft(
labelFontSize=Metric_Axis.label_font_size,
labelColor=Metric_Axis.label_color,
labelFont=FONT,
))
return full_chart