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teng_ml/tracker/epoch_tracker.py
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@ -111,28 +111,26 @@ class EpochTracker:
""" """
@param model_dir: Optional. If given, save to model_dir as svg @param model_dir: Optional. If given, save to model_dir as svg
""" """
fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, layout="tight", figsize=(6, 6)) fig, ax = plt.subplots(nrows=3, ncols=1, sharex=True, layout="tight")
ax[0].plot(self.epochs, self.accuracies, color="red") ax[0].plot(self.epochs, self.accuracies, color="red")
ax[0].set_ylabel("Accuracy") ax[0].set_ylabel("Accuracy")
ax[0].grid("minor")
ax[1].plot(self.epochs, self.learning_rate, color="green") ax[1].plot(self.epochs, self.learning_rate, color="green")
ax[1].set_ylabel("Learning Rate") ax[1].set_ylabel("Learning Rate")
ax[1].grid("minor")
# ax[2].plot(self.epochs, self.loss, color="blue") ax[2].plot(self.epochs, self.loss, color="blue")
# ax[2].set_ylabel("Loss") ax[2].set_ylabel("Loss")
fig.suptitle(title) fig.suptitle(title)
ax[-1].set_xlabel("Epoch") ax[2].set_xlabel("Epoch")
plt.tight_layout() plt.tight_layout()
if model_dir is not None: if model_dir is not None:
fig.savefig(f"{model_dir}/{name}.svg") fig.savefig(f"{model_dir}/{name}.svg")
return fig, ax return fig, ax
def plot_predictions(self, title="Predictions per Label", ep=-1, model_dir=None, name="img_training_predictions", empty_zero=True): def plot_predictions(self, title="Predictions per Label", ep=-1, model_dir=None, name="img_training_predictions"):
""" """
@param model_dir: Optional. If given, save to model_dir as svg @param model_dir: Optional. If given, save to model_dir as svg
@param ep: Epoch, defaults to last @param ep: Epoch, defaults to last
@ -143,23 +141,8 @@ class EpochTracker:
N = len(self.labels) N = len(self.labels)
label_names = self.labels.get_labels() label_names = self.labels.get_labels()
# print(label_names)
replace = {
"cloth": "fabric",
"foam": "foam_PDMS_pure",
"foil": "bubble_wrap",
"rigid_foam": "foam_PE",
"fabric_PP": "fabric",
"foam_PDMS_white": "foam_PDMS_pure",
"foam_PDMS_black": "foam_PEDOT",
"bubble_wrap_PE": "bubble_wrap",
}
label_names = [ replace[label] if label in replace else label for label in label_names ]
if len(label_names) > 6: fig, ax = plt.subplots(layout="tight")
fig, ax = plt.subplots(layout="tight", figsize=(7, 6))
else:
fig, ax = plt.subplots(layout="tight", figsize=(6, 5))
im = ax.imshow(normalized_predictions, cmap='Blues') # cmap='BuPu', , norm=colors.PowerNorm(1./2.) im = ax.imshow(normalized_predictions, cmap='Blues') # cmap='BuPu', , norm=colors.PowerNorm(1./2.)
ax.set_xticks(np.arange(N)) ax.set_xticks(np.arange(N))
ax.set_yticks(np.arange(N)) ax.set_yticks(np.arange(N))
@ -172,21 +155,14 @@ class EpochTracker:
for i in range(1, N): for i in range(1, N):
ax.axhline(i-0.5, color='black', linewidth=1) ax.axhline(i-0.5, color='black', linewidth=1)
# for i in range(1, N):
# ax.axvline(i-0.5, color='#bbb', linewidth=1)
# rotate the x-axis labels for better readability # rotate the x-axis labels for better readability
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
# create annotations # create annotations
for i in range(N): for i in range(N):
for j in range(N): for j in range(N):
val = round(normalized_predictions[i, j], 2) text = ax.text(j, i, round(normalized_predictions[i, j], 2),
if empty_zero and val == 0: continue ha="center", va="center", color="black")
color = "black"
if normalized_predictions[i, j] >= 0.6: color = "white"
text = ax.text(j, i, val,
ha="center", va="center", color=color)
# add colorbar # add colorbar
cbar = ax.figure.colorbar(im, ax=ax) cbar = ax.figure.colorbar(im, ax=ax)