def graph_error_bar(restraints, x_axis, y_axis, error_bar, diff):
df, header = load()
xs, ys, errors = [], [], []
for r in restraints:
filt = filter_data(df, header, r)
x1, y1, _ = extract_plotable_data(filt, header, x_axis, y_axis)
x2, y2, _ = extract_plotable_data(filt, header, x_axis, error_bar)
y1, x1, max_len1 = multi_average(x1, y1, 'sample')
y2, x2, max_len2 = multi_average(x2, y2, 'sample')
# add desired data.
xs.append(x1[-1])
ys.append(y1[-1])
errors.append(y2[-1])
# xs, ys, errors
plot_seaborn_bar(xs, ys, errors, x_axis, y_axis, diff)
def main(restraints, x_axis, y_axis, one_graph=False):
df, header = load()
filt = filter_data(df, header, restraints)
xs, ys, hs = extract_plotable_data(filt, header, x_axis, y_axis)
if not (one_graph):
for x, y, h in zip(xs, ys, hs):
plot(x, y, x_axis, y_axis, h, header, one_graph)
else:
plot(xs, ys, x_axis, y_axis, hs, header, one_graph)
return None
def overlay_graph(restraints, y_name, y_axis1, y_axis2, config='single'):
""" Same restraints, different variables (i.e. token_auc + entropy). """
df, header = load()
filt = filter_data(df, header, restraints)
x1, y1, h1 = extract_plotable_data(filt, header, 'epoch', y_axis1)
x2, y2, h2 = extract_plotable_data(filt, header, 'epoch', y_axis2)
if config == 'single':
y1 = y1[0]
y2 = y2[0]
x1 = x1[0]
x2 = x2[0]
elif config == 'avg':
y1 = list(np.mean(y1, axis=0))
y2 = list(np.mean(y2, axis=0))
diff = [y_axis1] * len(x1) + [y_axis2] * len(x2)
x1 = x1 + x2
y1 = y1 + y2
plot_seaborn(x1, y1, diff, y_axis1 + " vs. " + y_axis2, 'epochs',
'combined')
return None
def overlay_graph_diff(restraints, y_name, x_axis, y_axis, config='single'):
""" Different restraints, same variables. Y_name should be an array (len 2) of names. """
df, header = load()
filts, xs, ys, hs = [], [], [], []
for r in restraints:
filt = filter_data(df, header, r)
x, y, h = extract_plotable_data(filt, header, x_axis, y_axis)
filts.append(filt)
xs.append(x)
ys.append(y)
hs.append(h)
if config == 'single':
xs = [x[0] for x in xs]
ys = [y[0] for y in ys]
hs = [h[0] for h in hs]
elif config == 'avg':
new_xs, new_ys = [], []
for x, y in zip(xs, ys):
ty, tx, max_len = multi_average(x, y)
new_xs.append(tx)
new_ys.append(ty)
xs, ys = new_xs, new_ys
diff = [
item for sublist in [[y_name[i]] * len(xs[i]) for i in range(len(xs))]
for item in sublist
]
diff_name = 'Experiment Variants'
fx = [] # final x, final y
fy = []
for x, y in zip(xs, ys):
fx.extend(x)
fy.extend(y)
plot_seaborn(fx, fy, diff, diff_name, x_axis, y_axis)
def negative_epoch_graph(restraints,
diff,
y_name,
y_axis1,
y_axis2,
config='avg'):
df, header = load()
xs, ys, splits, dashed, colors = [], [], [], [], []
color_order = [
(0.00392156862745098, 0.45098039215686275, 0.6980392156862745),
(0.00392156862745098, 0.45098039215686275, 0.6980392156862745),
(0.00784313725490196, 0.6196078431372549, 0.45098039215686275),
(0.00784313725490196, 0.6196078431372549, 0.45098039215686275),
]
i = 0
c_order = 0
for r in restraints:
filt = filter_data(df, header, r)
x1, y1, _ = extract_plotable_data(filt, header, 'epoch', y_axis1)
x2, y2, _ = extract_plotable_data(filt, header, 'epoch', y_axis2)
if config == 'avg' or config == 'sample':
if len(y1) != 0:
y1, x1, max_len1 = multi_average(x1, y1, config)
y2, x2, max_len2 = multi_average(x2, y2, config)
# change value of x1 and x2
x1 = list(np.asarray(x1) - len(x1))
x2 = list(np.asarray(x2) - 1)
# connect the lines
x1.append(0.0)
y1.append(y2[0])
# set up data
x = x1 + x2
y = y1 + y2
splits += [i] * (max_len1 + 1) + [i] * max_len2
dashed.append(True)
colors += [color_order[c_order]]
i += 1
c_order += 1
else:
y, x, max_len = multi_average(x2, y2, config)
x = list(np.asarray(x) - 1)
splits += [i] * max_len
dashed.append(False)
colors += [color_order[c_order]]
i += 1
c_order += 1
# add data
xs.append(x)
ys.append(y)
fx = [] # final x, final y
fy = []
for x, y in zip(xs, ys):
fx.extend(x)
fy.extend(y)
plot_seaborn_neg(fx, fy, diff, splits, dashed, colors, 'epoch', y_axis1)
return None