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 figure() -> Figure:
p = plotting.figure(
x_range=(-0.5, -0.5 + len(class_names)),
y_range=(-0.5, -0.5 + len(class_names)),
plot_height=350,
plot_width=350,
tools=TOOLS,
toolbar_location=TOOLBAR_LOCATION,
match_aspect=True,
)
def noise() -> np.ndarray:
return (np.random.beta(1, 1, size=len(y_true)) - 0.5) * 0.6
p.scatter(
x=y_true + noise(),
y=y_pred + noise(),
size=scatter_plot_circle_size(
num_points=len(y_true),
biggest=4.0,
smallest=1.0,
use_smallest_when_num_points_at_least=5000,
),
color=DARK_BLUE,
fill_alpha=SCATTER_CIRCLES_FILL_ALPHA,
line_alpha=SCATTER_CIRCLES_LINE_ALPHA,
)
add_title_rows(p, title_rows)
apply_default_style(p)
p.xaxis.axis_label = "Ground Truth"
p.yaxis.axis_label = "Prediction"
arange = np.arange(len(class_names))
p.xaxis.ticker = arange
p.yaxis.ticker = arange
p.xaxis.major_label_overrides = {i: name for i, name in enumerate(class_names)}
p.yaxis.major_label_overrides = {i: name for i, name in enumerate(class_names)}
p.xaxis.major_label_orientation = x_label_rotation
p.yaxis.major_label_orientation = y_label_rotation
# grid between classes, not at classes
p.xgrid.ticker = arange[0:-1] + 0.5
p.ygrid.ticker = arange[0:-1] + 0.5
p.xgrid.grid_line_width = 3
p.ygrid.grid_line_width = 3
# prevent panning to empty regions
p.x_range.bounds = (-0.5, -0.5 + len(class_names))
p.y_range.bounds = (-0.5, -0.5 + len(class_names))
return p
def figure() -> Figure:
source = ColumnDataSource(
data={
"FPR": fpr,
"TPR": tpr,
"threshold": thresholds,
"specificity": 1.0 - fpr,
})
p = plotting.figure(
plot_height=400,
plot_width=350,
tools=TOOLS,
toolbar_location=TOOLBAR_LOCATION,
# toolbar_location=None, # hides entire toolbar
match_aspect=True,
)
p.xaxis.axis_label = "FPR"
p.yaxis.axis_label = "TPR"
add_title_rows(p, title_rows)
apply_default_style(p)
curve = p.line(x="FPR",
y="TPR",
line_width=2,
color=DARK_BLUE,
source=source)
p.line(
x=[0.0, 1.0],
y=[0.0, 1.0],
line_alpha=0.75,
color="grey",
line_dash="dotted",
)
p.add_tools(
HoverTool(
# make sure there is no tool tip for the diagonal baseline
renderers=[curve],
tooltips=[
("TPR", "@TPR"),
("FPR", "@FPR"),
("Sensitivity", "@TPR"),
("Specificity", "@specificity"),
("Threshold", "@threshold"),
],
# display a tooltip whenever the cursor is vertically in line with a glyph
mode="vline",
))
return p
def figure() -> Figure:
p = plotting.figure(
x_range=class_names,
plot_height=350,
plot_width=350,
tools=TOOLS,
toolbar_location=TOOLBAR_LOCATION,
)
# class distribution in prediction
p.vbar(
x=class_names,
top=np.histogram(y_pred,
bins=bins,
weights=sample_weights,
density=normalize)[0],
width=0.85,
color=DARK_BLUE,
alpha=HISTOGRAM_ALPHA,
legend_label="Prediction",
)
# class distribution in ground truth
p.vbar(
x=class_names,
top=np.histogram(y_true,
bins=bins,
weights=sample_weights,
density=normalize)[0],
width=0.85,
alpha=0.6,
legend_label="Ground Truth",
fill_color=None,
line_color="black",
line_width=2.5,
)
add_title_rows(p, title_rows)
apply_default_style(p)
p.yaxis.axis_label = ("Fraction of Instances"
if normalize else "Number of Instances")
p.xaxis.major_label_orientation = x_label_rotation
p.xgrid.grid_line_color = None
# prevent panning to empty regions
p.x_range.bounds = (-0.5, 0.5 + len(class_names))
return p
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 figure() -> Figure:
p = plotting.figure(
plot_height=400,
plot_width=350,
x_range=(-0.05, 1.05),
y_range=(-0.05, 1.05),
tools=TOOLS,
toolbar_location=TOOLBAR_LOCATION,
# match_aspect=True,
)
source = ColumnDataSource(data={
"precision": precision,
"recall": recall,
"threshold": thresholds
})
# reminder: tpr == recall == sensitivity
p.line(x="recall", y="precision", line_width=2, source=source)
add_title_rows(p, title_rows)
apply_default_style(p)
p.xaxis.axis_label = "Recall"
p.yaxis.axis_label = "Precision"
p.add_tools(
HoverTool(
tooltips=[
("Precision", "@precision"),
("Recall", "@recall"),
("Threshold", "@threshold"),
],
# display a tooltip whenever the cursor is vertically in line with a glyph
mode="vline",
))
return p
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
def figure() -> Figure:
# ----- bokeh plot -----
p = plotting.figure(
plot_height=400,
plot_width=350,
x_range=(-0.05, 1.05),
y_range=(-0.05, 1.05),
tools=TOOLS,
toolbar_location=TOOLBAR_LOCATION,
# match_aspect=True,
)
source = ColumnDataSource(
data={key: np.array(lst)
for key, lst in chart_data.items()})
accuracy_line = p.line(
x="automation_rate",
y="accuracy",
line_width=2,
color=DARK_BLUE,
source=source,
legend_label="Accuracy",
)
p.line(
x="automation_rate",
y="threshold",
line_width=2,
color="grey",
source=source,
legend_label="Threshold",
)
# make sure something is visible if lines consist of just a single point
p.scatter(
x=source.data["automation_rate"][[0, -1]],
y=source.data["accuracy"][[0, -1]],
)
p.scatter(
x=source.data["automation_rate"][[0, -1]],
y=source.data["threshold"][[0, -1]],
color="grey",
)
add_title_rows(p, title_rows)
apply_default_style(p)
p.xaxis.axis_label = "Automation Rate"
p.legend.location = "bottom_left"
p.add_tools(
HoverTool(
renderers=[accuracy_line],
tooltips=[
("Accuracy", "@accuracy"),
("Threshold", "@threshold"),
("Automation Rate", "@automation_rate"),
],
# display a tooltip whenever the cursor is vertically in line with a glyph
mode="vline",
))
return p
def figure() -> Figure:
source = ColumnDataSource(
data={
"predicted": np.array(predicted),
"actual": np.array(actual),
"count": np.array(count),
"normalized": np.array(normalized),
"normalized_by_pred": np.array(normalized_by_pred),
"normalized_by_true": np.array(normalized_by_true),
})
p = plotting.figure(tools=TOOLS,
x_range=class_names,
y_range=class_names)
mapper = LinearColorMapper(palette="Viridis256", low=0.0, high=1.0)
p.rect(
x="actual",
y="predicted",
width=0.95,
height=0.95,
source=source,
fill_color={
"field": "normalized_by_true",
"transform": mapper
},
line_width=0,
line_color="black",
)
p.xaxis.axis_label = "Ground Truth"
p.yaxis.axis_label = "Prediction"
p.xaxis.major_label_orientation = x_label_rotation
p.yaxis.major_label_orientation = y_label_rotation
p.add_tools(
HoverTool(tooltips=[
("Predicted", "@predicted"),
("Ground truth", "@actual"),
("Count", "@count"),
("Normalized", "@normalized"),
("Normalized by prediction", "@normalized_by_pred"),
("Normalized by ground truth", "@normalized_by_true"),
]))
color_bar = ColorBar(
color_mapper=mapper,
major_label_text_font_size=FONT_SIZE,
ticker=BasicTicker(desired_num_ticks=10),
formatter=PrintfTickFormatter(format="%.1f"),
label_standoff=5,
border_line_color=None,
location=(0, 0),
)
p.add_layout(color_bar, "right")
add_title_rows(p, title_rows)
apply_default_style(p)
return p