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Fix figure issues #320

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Fix figure issues #320

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62 changes: 34 additions & 28 deletions avae/vis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -638,6 +638,7 @@ def confidence_plot(x, y, s, suffix=None):
if len(np.unique(y)) % 2 != 0:
rows += 1
fig, ax = plt.subplots(len(np.unique(y)), sharex=True, sharey=True)
ax = np.atleast_1d(ax)
for c, cl in enumerate(np.unique(y)):
mu_cl = np.take(x, np.where(np.array(y) == cl)[0], axis=0)
var_cl = np.take(s, np.where(np.array(y) == cl)[0], axis=0)
Expand Down Expand Up @@ -667,6 +668,7 @@ def confidence_plot(x, y, s, suffix=None):
leg = fig.legend(
handles, labels, bbox_to_anchor=(1.06, 0.9)
) # , loc="upper left")
plt.tight_layout()
fig.savefig(name, bbox_extra_artists=(leg,), bbox_inches="tight")
plt.close()

Expand Down Expand Up @@ -713,6 +715,10 @@ def accuracy_plot(
else:
classes_list = np.unique(np.concatenate((y_train, ypred_train)))

#to avoid figure being too small with too little classses
fig_size = max(6, int(len(classes_list)) / 2)
font_size = max(8, int(len(classes_list) / 3) + 3)

# Compute confusion matrix
cm = confusion_matrix(y_train, ypred_train, labels=classes_list)

Expand All @@ -736,25 +742,26 @@ def accuracy_plot(
)

with plt.rc_context(
{"font.weight": "bold", "font.size": int(len(classes_list) / 3) + 3}
{"font.weight": "bold", "font.size": font_size}
):
fig, ax = plt.subplots(
figsize=(int(len(classes_list)) / 2, int(len(classes_list)) / 2)
figsize=(fig_size, fig_size)
)

disp.plot(cmap=plt.cm.Blues, ax=ax, xticks_rotation=90)

plt.tight_layout()
plt.title(
"Average accuracy at epoch {}: {:.3f}%".format(
epoch, np.mean(avg_accuracy) * 100
epoch, np.mean(avg_accuracy)
),
fontsize=10,
)

if not os.path.exists("plots"):
os.mkdir("plots")

plt.tight_layout()
plt.savefig(
f"plots/confusion_train{mode}.{settings.VIS_FORMAT}", dpi=300
)
Expand All @@ -771,7 +778,7 @@ def accuracy_plot(
plt.close()

fig, ax = plt.subplots(
figsize=(int(len(classes_list)) / 2, int(len(classes_list)) / 2)
figsize=(fig_size, fig_size)
)

dispn.plot(
Expand All @@ -787,7 +794,7 @@ def accuracy_plot(
"Average accuracy at epoch {}: {:.3f}%".format(
epoch, np.mean(avg_accuracy) * 100
),
fontsize=12,
fontsize=font_size+2,
)

plt.xlabel("Predicted label (%)")
Expand Down Expand Up @@ -848,13 +855,12 @@ def accuracy_plot(
with plt.rc_context(
{
"font.weight": "bold",
"font.size": int(len(ordered_class_eval) / 3) + 3,
"font.size": font_size,
}
):
fig, ax = plt.subplots(
figsize=(
int(len(ordered_class_eval)) / 2,
int(len(ordered_class_eval)) / 2,
fig_size,fig_size
)
)
disp_eval.plot(cmap=plt.cm.Blues, ax=ax, xticks_rotation=90)
Expand All @@ -863,13 +869,13 @@ def accuracy_plot(
"Average accuracy at epoch {}: {:.1f}%".format(
epoch, np.mean(avg_accuracy_eval) * 100
),
fontsize=12,
fontsize=font_size+2,
)
plt.savefig(figure_name + f".{settings.VIS_FORMAT}", dpi=300)
plt.close()

fig, ax = plt.subplots(
figsize=(int(len(classes_list)) / 2, int(len(classes_list)) / 2)
figsize=(fig_size,fig_size)
)

dispn_eval.plot(
Expand Down Expand Up @@ -935,6 +941,8 @@ def f1_plot(
classes_list = np.unique(np.concatenate((y_train, ypred_train)))

classes_list_eval = np.unique(np.concatenate((y_val, ypred_val)))
fig_size = max(6, int(len(classes_list)) / 2)
font_size = max(8, int(len(classes_list) / 3) + 3)

if np.setdiff1d(classes_list_eval, classes_list).size > 0:
logging.info(
Expand Down Expand Up @@ -985,13 +993,12 @@ def f1_plot(
with plt.rc_context(
{
"font.weight": "bold",
"font.size": int(len(classes_list) / 3) + 3,
"font.size": font_size,
}
):
fig, ax = plt.subplots(
figsize=(
int(len(classes_list)) / 2,
int(len(classes_list)) / 2,
fig_size,fig_size
)
)
plt.plot(classes_list, train_f1_score, label="train", marker="o")
Expand All @@ -1000,6 +1007,7 @@ def f1_plot(
plt.legend(loc="lower left")
plt.title("F1 Score at epoch {}".format(epoch))
plt.ylabel("F1 Score")
plt.tight_layout()
plt.savefig(f"plots/f1{mode}.{settings.VIS_FORMAT}", dpi=150)

if writer:
Expand Down Expand Up @@ -1852,21 +1860,17 @@ def plot_affinity_matrix(
if len(vis_format) > 0:
settings.VIS_FORMAT = vis_format

if fig_size is None:
with plt.rc_context(
{"font.weight": "bold", "font.size": int(len(all_classes) / 3) + 3}
):
fig, ax = plt.subplots(
figsize=(int(len(all_classes)) / 2, int(len(all_classes)) / 2)
)
else:
fig, ax = plt.subplots(figsize=(fig_size, fig_size))
fig_size = fig_size if fig_size is not None else max(6, int(len(all_classes)) / 2)
font_size = max(8, int(len(all_classes) / 3) + 3)

# Create the figure and gridspec
fig = plt.figure(figsize=(fig_size, fig_size))
gs = gridspec.GridSpec(1, 2, width_ratios=[9, 0.4])

# Plot the data on the left grid
ax = plt.subplot(gs[0])
ax.set_title("Affinity Matrix", fontsize=16)
ax = fig.add_subplot(gs[0])
ax2 = fig.add_subplot(gs[1])
ax.set_title("Affinity Matrix", fontsize=font_size+2)

im = ax.imshow(lookup, vmin=-1, vmax=1, cmap=plt.get_cmap("RdBu"))

Expand Down Expand Up @@ -1996,6 +2000,7 @@ def latent_space_similarity_plot(
if len(vis_format) > 0:
settings.VIS_FORMAT = vis_format


if len(classes_order) == 0:
unique_classes = np.unique(class_labels)
else:
Expand All @@ -2011,6 +2016,8 @@ def latent_space_similarity_plot(
unique_classes = classes_order

num_classes = len(unique_classes)
fig_size = fig_size if fig_size is not None else max(6,int(num_classes / 2))
font_size = max(8, int(num_classes/ 3) + 3)
cosine_sim = latent_space_similarity_mat(
latent_space,
class_labels,
Expand All @@ -2024,18 +2031,17 @@ def latent_space_similarity_plot(
with plt.rc_context(
{
"font.weight": "bold",
"font.size": int(len(unique_classes) / 3) + 3,
"font.size": font_size,
}
):
fig, ax = plt.subplots(
figsize=(
int(len(unique_classes)) / 2,
int(len(unique_classes)) / 2,
fig_size, fig_size
)
)
else:
fig, ax = plt.subplots(figsize=(fig_size, fig_size))
fig.tight_layout(pad=3)
fig.tight_layout()
plt.imshow(cosine_sim, cmap="RdBu", vmin=-1, vmax=1)
plt.colorbar(label="Average Cosine Similarity")
plt.xticks(ticks=np.arange(num_classes), labels=unique_classes)
Expand Down