def function() -> Column:
targets = data.targets
predictions = data.predictions
p = figure(tools=TOOLS)
residuals = np.asarray(targets) - np.asarray(predictions)
residual_hist, edges = np.histogram(residuals, bins=n_bins)
p.quad(
top=residual_hist,
bottom=0,
left=edges[:-1],
right=edges[1:],
fill_color=DARK_RED,
fill_alpha=0.5,
line_color="white",
line_alpha=0.5,
)
apply_default_style(p)
p.xaxis.axis_label = "Residual"
p.yaxis.axis_label = "Number of Occurrences"
return column(title_div(title_rows), p, sizing_mode="scale_width")
def function() -> Column:
targets = data.targets
predictions = data.predictions
p = figure(tools=TOOLS)
# Plot looks better when we use the same bin sizes for both histograms.
_, edges = np.histogram(list(targets) + list(predictions), bins=n_bins)
pred_hist, pred_edges = np.histogram(predictions, bins=edges)
np.testing.assert_allclose(pred_edges, edges)
p.quad(
top=pred_hist,
bottom=0,
left=pred_edges[:-1],
right=pred_edges[1:],
fill_color=DARK_BLUE,
fill_alpha=0.5,
line_color="white",
line_alpha=0.5,
legend_label="Predicted ",
)
target_hist, target_edges = np.histogram(targets, bins=edges)
np.testing.assert_allclose(target_edges, edges)
p.line(
x=[e for e in target_edges for _ in range(2)],
y=[0.0] + [h for h in target_hist for _ in range(2)] + [0.0],
color=GROUND_TRUTH_HISTOGRAM_ENVELOPE_COLOR,
alpha=GROUND_TRUTH_HISTOGRAM_ENVELOPE_ALPHA,
legend_label="Actual ",
)
# Increase the y range a bit to leave enough space for the legend
p.y_range.end = 1.25 * max(max(target_hist), max(pred_hist))
apply_default_style(p)
p.xaxis.axis_label = "Value"
p.yaxis.axis_label = "Number of Occurrences"
p.legend.padding = 4
p.legend.orientation = "horizontal"
return column(title_div(title_rows), p, sizing_mode="scale_width")
def _bokeh_scatter_with_histograms(
x: Floats,
y: Floats,
x_label: str,
y_label: str,
x_histogram_fill_color: Optional[str],
y_histogram_fill_color: Optional[str],
y_histogram_envelope_color: Optional[str],
title_rows: Sequence[str],
n_bins: int,
) -> Tuple[LayoutDOM, Figure]:
"""
Scatter plot with small histograms attached to the axes.
"""
assert len(x) == len(y)
# --- Create the scatter plot ---
p_scatter = figure(
tools=TOOLS,
plot_width=250,
plot_height=250,
min_border=0,
min_border_left=20,
sizing_mode="stretch_width",
)
p_scatter.scatter(
x,
y,
color=DARK_BLUE,
size=(scatter_plot_circle_size(
len(x),
biggest=3,
smallest=1,
use_smallest_when_num_points_at_least=5000,
)),
fill_alpha=SCATTER_CIRCLES_FILL_ALPHA,
line_alpha=SCATTER_CIRCLES_LINE_ALPHA,
)
p_scatter.xaxis.axis_label = x_label
p_scatter.yaxis.axis_label = y_label
# --- Compute histograms ---
hist_x, hist_x_edges = np.histogram(x, bins=n_bins)
hist_y, hist_y_edges = np.histogram(y, bins=n_bins)
# Make sure the two histograms are similarly scaled
hist_range = (-0.1, 1.05 * max(max(hist_x), max(hist_y)))
# --- Create the horizontal (ground truth) histogram, above the scatter plot ---
p_hist_x_above = figure(
plot_width=p_scatter.plot_width,
plot_height=30,
x_range=p_scatter.x_range,
y_range=hist_range,
min_border=0,
min_border_left=0,
x_axis_location=None,
y_axis_location=None,
sizing_mode="stretch_width",
tools=TOOLS,
)
p_hist_x_above.quad(
bottom=0,
left=hist_x_edges[:-1],
right=hist_x_edges[1:],
top=hist_x,
color=x_histogram_fill_color,
alpha=HISTOGRAM_ALPHA,
line_color="white",
)
# --- Create the vertical (prediction) histogram, to the right of the scatter plot ---
p_hist_y_right = figure(
plot_width=40,
plot_height=p_scatter.plot_height,
x_range=hist_range,
y_range=p_scatter.y_range,
x_axis_location=None,
y_axis_location=None,
sizing_mode="fixed",
tools=TOOLS,
)
p_hist_y_right.quad(
left=0,
bottom=hist_y_edges[:-1],
top=hist_y_edges[1:],
right=hist_y,
color=y_histogram_fill_color,
alpha=HISTOGRAM_ALPHA,
line_color="white" if y_histogram_fill_color is not None else None,
)
if y_histogram_envelope_color is not None:
p_hist_y_right.line(
x=[0.0] + [h for h in hist_y for _ in range(2)] + [0.0],
y=[e for e in hist_y_edges for _ in range(2)],
color=GROUND_TRUTH_HISTOGRAM_ENVELOPE_COLOR,
alpha=GROUND_TRUTH_HISTOGRAM_ENVELOPE_ALPHA,
)
# --- Style and layout ---
apply_default_style(p_scatter)
apply_default_style(p_hist_x_above)
apply_default_style(p_hist_y_right)
# Overwrite some of the defaults
p_hist_x_above.ygrid.grid_line_color = None
p_hist_y_right.xgrid.grid_line_color = None
grid = gridplot(
# fmt:off
[[p_hist_x_above, None], [p_scatter, p_hist_y_right]],
merge_tools=True,
toolbar_location="right",
toolbar_options={
"logo": None,
},
sizing_mode="scale_width",
# fmt:on
)
layout = column(title_div(title_rows), grid, sizing_mode="scale_width")
return layout, p_scatter