def dump_dicts(self):
"""
Dump the model evaluation dictionaries
"""
p_train = paths.get_plot_evaluation_path_for_model(self.model.get_root_path(), "train_dict.pkl")
pkl.dump(self.eval_train, open(p_train, "wb"))
p_test = paths.get_plot_evaluation_path_for_model(self.model.get_root_path(), "test_dict.pkl")
pkl.dump(self.eval_test, open(p_test, "wb"))
p_val = paths.get_plot_evaluation_path_for_model(self.model.get_root_path(), "validation_dict.pkl")
pkl.dump(self.eval_validation, open(p_val, "wb"))
def dump_dicts(self):
"""
Dump the model evaluation dictionaries
"""
p_train = paths.get_plot_evaluation_path_for_model(self.model.get_root_path(), "train_dict.pkl")
pkl.dump(self.eval_train, open(p_train, "wb"))
p_test = paths.get_plot_evaluation_path_for_model(self.model.get_root_path(), "test_dict.pkl")
pkl.dump(self.eval_test, open(p_test, "wb"))
p_val = paths.get_plot_evaluation_path_for_model(self.model.get_root_path(), "validation_dict.pkl")
pkl.dump(self.eval_validation, open(p_val, "wb"))
def plot_eval(self, eval_dict, labels, path_extension=""):
"""
Plot the loss function in a overall plot and a zoomed plot.
:param path_extension: If the plot should be saved in an incremental way.
"""
def plot(x, y, fit, label):
sns.regplot(np.array(x),
np.array(y),
fit_reg=fit,
label=label,
scatter_kws={"s": 5})
plt.clf()
plt.subplot(211)
idx = np.array(eval_dict.values()[0]).shape[0]
x = np.array(eval_dict.values())
for i in range(idx):
plot(eval_dict.keys(), x[:, i], False, labels[i])
plt.legend()
plt.subplot(212)
for i in range(idx):
plot(eval_dict.keys()[-int(len(x) * 0.25):],
x[-int(len(x) * 0.25):][:, i], True, labels[i])
plt.xlabel('Epochs')
plt.savefig(
paths.get_plot_evaluation_path_for_model(
self.model.get_root_path(), path_extension + ".png"))
def plot_eval(self, eval_dict, labels, path_extension=""):
"""
Plot the loss function in a overall plot and a zoomed plot.
:param path_extension: If the plot should be saved in an incremental way.
"""
def plot(x, y, fit, label):
sns.regplot(np.array(x), np.array(y), fit_reg=fit, label=label, scatter_kws={"s": 5})
plt.clf()
plt.subplot(211)
idx = np.array(eval_dict.values()[0]).shape[0]
x = np.array(eval_dict.values())
for i in range(idx):
plot(eval_dict.keys(), x[:, i], False, labels[i])
plt.legend()
plt.subplot(212)
for i in range(idx):
plot(eval_dict.keys()[-int(len(x) * 0.25):], x[-int(len(x) * 0.25):][:, i], True, labels[i])
plt.xlabel('Epochs')
plt.savefig(paths.get_plot_evaluation_path_for_model(self.model.get_root_path(), path_extension+".png"))
