threshold=0.5,
window=20,
num_cols=8,
figsize=(30, 5))
outfile = os.path.join(save_path, name + '.pdf')
fig.savefig(outfile,
format='pdf',
dpi=200,
bbox_inches='tight')
plt.close()
# clip filters about motif to reduce false-positive Tomtom matches
W_clipped = utils.clip_filters(W, threshold=0.5, pad=3)
output_file = os.path.join(save_path, name + '.meme')
utils.meme_generate(W_clipped, output_file)
# predict test sequences and calculate performance metrics
predictions = model.predict(x_test)
mean_vals, std_vals = metrics.calculate_metrics(
y_test, predictions, 'binary')
trial_roc_mean.append(mean_vals[1])
trial_roc_std.append(std_vals[1])
trial_pr_mean.append(mean_vals[2])
trial_pr_std.append(std_vals[2])
f.write(
"%s\t%.3f+/-%.3f\t%.3f+/-%.3f\n" %
(base_name, np.mean(trial_roc_mean), np.std(trial_roc_mean),
np.mean(trial_pr_mean), np.std(trial_pr_mean)))
def update(self, loss, prediction, label):
scores = metrics.calculate_metrics(label, prediction, self.objective)
self.add_loss(loss)
self.add_metrics(scores)
