def __init__(self, cmap, vmin, vmax=None, label=True, label_position=None,
label_rotation=None,
clipmin=None, clipmax=None, orientation='horizontal',
unit=None, contours=(), width=None, ticks=None, threshold=None,
ticklocation='auto', background='white', tight=True,
h=None, w=None, *args, **kwargs):
# get Colormap
if isinstance(cmap, np.ndarray):
if threshold is not None:
raise NotImplementedError("threshold parameter with cmap=array")
if cmap.max() > 1:
cmap = cmap / 255.
cm = mpl.colors.ListedColormap(cmap, 'LUT')
else:
cm = mpl.cm.get_cmap(cmap)
# prepare layout
if orientation == 'horizontal':
if h is None and w is None:
h = 1
ax_aspect = 4
elif orientation == 'vertical':
if h is None and w is None:
h = 4
ax_aspect = 0.3
else:
raise ValueError("orientation=%s" % repr(orientation))
layout = Layout(1, ax_aspect, 2, tight, False, h, w, *args, **kwargs)
EelFigure.__init__(self, cm.name, layout)
ax = self._axes[0]
# translate between axes and data coordinates
if isinstance(vmin, Normalize):
norm = vmin
else:
vmin, vmax = fix_vlim_for_cmap(vmin, vmax, cm.name)
norm = Normalize(vmin, vmax)
# value ticks
if ticks is False:
ticks = ()
tick_labels = None
elif isinstance(ticks, dict):
tick_dict = ticks
ticks = sorted(tick_dict)
tick_labels = [tick_dict[t] for t in ticks]
else:
tick_labels = None
if orientation == 'horizontal':
axis = ax.xaxis
contour_func = ax.axhline
else:
axis = ax.yaxis
contour_func = ax.axvline
if label is True:
if unit:
label = unit
else:
label = cm.name
elif not label:
label = ''
# show only part of the colorbar
if clipmin is not None or clipmax is not None:
if isinstance(norm, SymmetricNormalize):
raise NotImplementedError(
"clipmin or clipmax with SymmetricNormalize")
boundaries = norm.inverse(np.linspace(0, 1, cm.N + 1))
if clipmin is None:
start = None
else:
start = np.digitize(clipmin, boundaries, True)
if clipmax is None:
stop = None
else:
stop = np.digitize(clipmax, boundaries) + 1
boundaries = boundaries[start:stop]
else:
boundaries = None
colorbar = ColorbarBase(ax, cm, norm, boundaries=boundaries,
orientation=orientation,
ticklocation=ticklocation, ticks=ticks,
label=label)
# fix tick location
if isinstance(norm, SymmetricNormalize) and ticks is not None:
tick_norm = Normalize(norm.vmin, norm.vmax, norm.clip)
axis.set_ticks(tick_norm(ticks))
# unit-based tick-labels
if unit and tick_labels is None:
formatter, label = find_axis_params_data(unit, label)
tick_labels = tuple(map(formatter, colorbar.get_ticks()))
if tick_labels is not None:
if clipmin is not None:
tick_labels = [l for l, t in zip(tick_labels, ticks) if t >= clipmin]
axis.set_ticklabels(tick_labels)
# label position/rotation
if label_position is not None:
axis.set_label_position(label_position)
if label_rotation is not None:
axis.label.set_rotation(label_rotation)
if orientation == 'vertical':
if (label_rotation + 10) % 360 < 20:
axis.label.set_va('center')
elif orientation == 'vertical' and len(label) <= 3:
axis.label.set_rotation(0)
axis.label.set_va('center')
self._contours = [contour_func(c, c='k') for c in contours]
self._draw_hooks.append(self.__fix_alpha)
self._draw_hooks.append(self.__update_bar_tickness)
self._background = background
self._colorbar = colorbar
self._orientation = orientation
self._width = width
self._show()
def heatmap_plot(df,
filename=None,
average=True,
group_by="accuracy",
str_format="{:.2%}",
box="max",
print_flag=True,
time_heatmap=False):
"""Generate heatmap plot"""
df_grouped = group_df(df,
average=average,
group_by=group_by,
ascending=box == "min")
v_names = list(df_grouped.columns)
if average:
avg = df_grouped[df_grouped.index == "Average"].iloc[0]
v_names.sort(
key=lambda x: round(avg[x], 4) * (1 if box == "max" else -1))
df_grouped = df_grouped[v_names]
c_names = list(df_grouped.index)
min_value = min(df[group_by])
max_value = max(df[group_by])
ratio = (len(c_names) / len(v_names)) / 2
figsize = (len(v_names) * 0.8 + 3.1, 2 + ratio * len(v_names) * 0.8)
fig, ax = plt.subplots(1, 1, figsize=figsize)
fig.subplots_adjust(wspace=0.2)
plt.title(group_by)
cmap = cm.get_cmap("Greens", 512)
cb_cmap = im_cmap = ListedColormap(cmap(np.linspace(0.0, 0.5, 256)))
if box == "min":
im_cmap = ListedColormap(cmap(np.linspace(0.5, 0.0, 256)))
ax.imshow(df_grouped, cmap=im_cmap, aspect="auto")
if isinstance(str_format, str):
str_formater = str_format.format
else:
str_formater = str_format
# Create colorbar
if min_value != max_value:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size=0.2, pad=0.2)
ticks = np.linspace(min_value, max_value, 10)
if box == "min":
ticks = ticks[::-1]
norm = Normalize(vmin=0, vmax=9, clip=False)
cbar = ColorbarBase(cax, cmap=cb_cmap, ticks=range(10), norm=norm)
cbar.ax.set_yticklabels(
[str_formater(ticks[i]) for i in cbar.get_ticks()])
xticks = np.arange(len(v_names))
ax.set_xticks(xticks)
ax.set_yticks(np.arange(len(c_names)))
ax.set_yticklabels([x.split(".")[-1] for x in c_names])
ax.set_xticklabels(v_names)
ax.tick_params(top=False, bottom=True, labeltop=False, labelbottom=True)
plt.setp(ax.get_xticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor")
for (i, c_name) in enumerate(c_names):
for (j, v_name) in enumerate(v_names):
value = df_grouped[df_grouped.index == c_name][v_name].values[0]
value = str_formater(value)
ax.text(j, i, value, ha="center", va="center", color="black")
if print_flag and "flag" in df.columns and "Average" not in [
c_name, v_name
]:
row = df[(df.c_name == c_name) & (df.v_name == v_name)]
if row.empty:
continue
flag = row.iloc[0].flag
if flag:
ax.text(j + 0.4,
i - 0.25,
flag,
ha="center",
va="center",
color="black")
ax.set_xticks(np.arange(len(v_names)) - 0.5, minor=True)
ax.set_yticks(np.arange(len(c_names)) - 0.5, minor=True)
ax.grid(True, which="minor", linestyle="-", color="w")
ax.tick_params(which="minor", length=0.0)
if average:
x_avg = v_names.index("Average")
y_avg = c_names.index("Average")
plot_lines(ax,
v=[x_avg - 0.5, x_avg + 0.5],
h=[y_avg - 0.5, y_avg + 0.5],
color="red")
box_value = max(df[group_by]) if box == "max" else min(df[group_by])
box_row = df[df[group_by] == box_value].iloc[0]
x, y = v_names.index(box_row.v_name), c_names.index(box_row.c_name)
plot_box(ax,
left=x - 0.5,
right=x + 0.5,
bottom=y - 0.5,
top=y + 0.5,
color="b")
fig.tight_layout()
if filename is None:
filename = f"{len(v_names)}_{len(c_names)}"
plt.savefig(f"plot/heatmap_{filename}.png")
logging.info("Saved 'plot/heatmap_%s.png'", filename)
if average:
ax.fill_between(np.arange(-0.5, x_avg + 1, 0.5),
-0.5,
len(c_names) - 0.5,
facecolor="black",
alpha=0.2)
ax.fill_between(np.arange(x_avg + 0.5, len(v_names), 0.5),
y_avg - 0.5,
len(c_names) - 0.5,
facecolor="black",
alpha=0.2)
plt.savefig(f"plot/heatmap_{filename}_2.png")
logging.info("Saved 'plot/heatmap_%s_2.png'", filename)
if group_by == "accuracy" and time_heatmap:
df["total_time"] = df.apply(
lambda row: row.train_time + row.predict_time, axis=1)
heatmap_plot(df,
filename=f"{filename}_train_time",
average=average,
print_flag=False,
group_by="train_time",
str_format=partial(time_format, decimals=2),
box="min")
heatmap_plot(df,
filename=f"{filename}_predict_time",
average=average,
print_flag=False,
group_by="predict_time",
str_format=partial(time_format, decimals=3),
box="min")
heatmap_plot(df,
filename=f"{filename}_total_time",
average=average,
print_flag=False,
group_by="total_time",
str_format=partial(time_format, decimals=2),
box="min")
def __init__(self, cmap, vmin, vmax=None, label=True, label_position=None,
label_rotation=None,
clipmin=None, clipmax=None, orientation='horizontal',
unit=None, contours=(), width=None, ticks=None, threshold=None,
ticklocation='auto', background='white', tight=True,
h=None, w=None, *args, **kwargs):
# get Colormap
if isinstance(cmap, np.ndarray):
if threshold is not None:
raise NotImplementedError("threshold parameter with cmap=array")
if cmap.max() > 1:
cmap = cmap / 255.
cm = mpl.colors.ListedColormap(cmap, 'LUT')
else:
cm = mpl.cm.get_cmap(cmap)
# prepare layout
if orientation == 'horizontal':
if h is None and w is None:
h = 1
ax_aspect = 4
elif orientation == 'vertical':
if h is None and w is None:
h = 4
ax_aspect = 0.3
else:
raise ValueError("orientation=%s" % repr(orientation))
layout = Layout(1, ax_aspect, 2, tight, False, h, w, *args, **kwargs)
EelFigure.__init__(self, cm.name, layout)
ax = self._axes[0]
# translate between axes and data coordinates
if isinstance(vmin, Normalize):
norm = vmin
else:
vmin, vmax = fix_vlim_for_cmap(vmin, vmax, cm.name)
norm = Normalize(vmin, vmax)
# value ticks
if ticks is False:
ticks = ()
tick_labels = None
elif isinstance(ticks, dict):
tick_dict = ticks
ticks = sorted(tick_dict)
tick_labels = [tick_dict[t] for t in ticks]
else:
tick_labels = None
if orientation == 'horizontal':
axis = ax.xaxis
contour_func = ax.axhline
else:
axis = ax.yaxis
contour_func = ax.axvline
if label is True:
if unit:
label = unit
else:
label = cm.name
elif not label:
label = ''
# show only part of the colorbar
if clipmin is not None or clipmax is not None:
if isinstance(norm, SymmetricNormalize):
raise NotImplementedError(
"clipmin or clipmax with SymmetricNormalize")
boundaries = norm.inverse(np.linspace(0, 1, cm.N + 1))
if clipmin is None:
start = None
else:
start = np.digitize(clipmin, boundaries, True)
if clipmax is None:
stop = None
else:
stop = np.digitize(clipmax, boundaries) + 1
boundaries = boundaries[start:stop]
else:
boundaries = None
colorbar = ColorbarBase(ax, cm, norm, boundaries=boundaries,
orientation=orientation,
ticklocation=ticklocation, ticks=ticks,
label=label)
# fix tick location
if isinstance(norm, SymmetricNormalize) and ticks is not None:
tick_norm = Normalize(norm.vmin, norm.vmax, norm.clip)
axis.set_ticks(tick_norm(ticks))
# unit-based tick-labels
if unit and tick_labels is None:
formatter, label = find_axis_params_data(unit, label)
tick_labels = tuple(map(formatter, colorbar.get_ticks()))
if tick_labels is not None:
if clipmin is not None:
tick_labels = [l for l, t in zip(tick_labels, ticks) if t >= clipmin]
axis.set_ticklabels(tick_labels)
# label position/rotation
if label_position is not None:
axis.set_label_position(label_position)
if label_rotation is not None:
axis.label.set_rotation(label_rotation)
if orientation == 'vertical':
if (label_rotation + 10) % 360 < 20:
axis.label.set_va('center')
elif orientation == 'vertical' and len(label) <= 3:
axis.label.set_rotation(0)
axis.label.set_va('center')
self._contours = [contour_func(c, c='k') for c in contours]
self._draw_hooks.append(self.__fix_alpha)
self._draw_hooks.append(self.__update_bar_tickness)
self._background = background
self._colorbar = colorbar
self._orientation = orientation
self._width = width
self._show()
