def __create_tooltip_variables(attribute_df, metric, fairness_threshold):
""" Creates disparity explanation and formatted group size tooltip variables. """
# PARITY TEST EXPLANATION
attribute_df[
f"tooltip_parity_test_explanation_{metric}"] = attribute_df.apply(
lambda row: get_tooltip_text_parity_test_explanation(
row[f"{metric}_parity_result"],
metric,
fairness_threshold,
),
axis=1,
)
# DISPARITY EXPLANATION
ref_group = attribute_df["ref_group_value"].iloc[0]
attribute_df[
f"tooltip_disparity_explanation_{metric}"] = attribute_df.apply(
lambda row: get_tooltip_text_disparity_explanation(
row[f"{metric}_disparity_scaled"],
row["attribute_value"],
metric,
ref_group,
),
axis=1,
)
# FORMATTED GROUP SIZE
attribute_df["tooltip_group_size"] = attribute_df.apply(
lambda row: get_tooltip_text_group_size(row["group_size"], row[
"total_entities"]),
axis=1,
)
def __draw_bubbles(
plot_table,
metrics,
ref_group,
scales,
selection,
):
"""Draws the bubbles for all metrics."""
# X AXIS GRIDLINES
axis_values = [0.25, 0.5, 0.75]
x_axis = alt.Axis(
values=axis_values, ticks=False, domain=False, labels=False, title=None
)
# COLOR
bubble_color_encoding = alt.condition(
selection,
alt.Color("attribute_value:N", scale=scales["color"], legend=None),
alt.value(Bubble.color_faded),
)
# CHART INITIALIZATION
bubble_centers = alt.Chart().mark_point()
bubble_areas = alt.Chart().mark_circle()
plot_table["tooltip_group_size"] = plot_table.apply(
lambda row: get_tooltip_text_group_size(
row["group_size"], row["total_entities"]
),
axis=1,
)
# LAYERING THE METRICS
for metric in metrics:
# TOOLTIP
plot_table[f"tooltip_disparity_explanation_{metric}"] = plot_table.apply(
lambda row: get_tooltip_text_disparity_explanation(
row[f"{metric}_disparity_scaled"],
row["attribute_value"],
metric,
ref_group,
),
axis=1,
)
bubble_tooltip_encoding = [
alt.Tooltip(field="attribute_value", type="nominal", title="Group"),
alt.Tooltip(field="tooltip_group_size", type="nominal", title="Group Size"),
alt.Tooltip(
field=f"tooltip_disparity_explanation_{metric}",
type="nominal",
title="Disparity",
),
alt.Tooltip(
field=f"{metric}",
type="quantitative",
format=".2f",
title=f"{metric}".upper(),
),
]
# BUBBLE CENTERS
trigger_centers = alt.selection_multi(empty="all", fields=["attribute_value"])
bubble_centers += (
alt.Chart(plot_table)
.transform_calculate(metric_variable=f"'{metric.upper()}'")
.mark_point(filled=True, size=Bubble.center_size)
.encode(
x=alt.X(f"{metric}:Q", scale=scales["x"], axis=x_axis),
y=alt.Y("metric_variable:N", scale=scales["y"], axis=no_axis()),
tooltip=bubble_tooltip_encoding,
color=bubble_color_encoding,
shape=alt.Shape(
"attribute_value:N", scale=scales["shape"], legend=None
),
)
.add_selection(trigger_centers)
)
# BUBBLE AREAS
trigger_areas = alt.selection_multi(empty="all", fields=["attribute_value"])
bubble_areas += (
alt.Chart(plot_table)
.mark_circle(opacity=Bubble.opacity)
.transform_calculate(metric_variable=f"'{metric.upper()}'")
.encode(
x=alt.X(f"{metric}:Q", scale=scales["x"], axis=x_axis),
y=alt.Y("metric_variable:N", scale=scales["y"], axis=no_axis()),
tooltip=bubble_tooltip_encoding,
color=bubble_color_encoding,
size=alt.Size("group_size:Q", legend=None, scale=scales["bubble_size"]),
)
.add_selection(trigger_areas)
)
return bubble_areas + bubble_centers