def mds_and_plot(model):
data = DataSet()
x, y, data_list = data.get_test_frames('train')
custom_model = Model(inputs=model.input,
outputs=model.get_layer('dense_1').output)
y_pred = custom_model.predict(x)
mds = MDS()
mds.fit(y_pred)
a = mds.embedding_
mark = ['or', 'ob', 'og', 'oy', 'ok', '+r', 'sr', 'dr', '<r', 'pr']
color = 0
j = 0
for item in y:
index = 0
for i in item:
if i == 1:
break
index = index + 1
plt.plot([a[j:j + 1, 0]], [a[j:j + 1, 1]], mark[index], markersize=5)
print(index)
j += 1
plt.show()
def test_rnn(src, model):
data = DataSet(src)
x, y, data_list = data.get_test_frames('train')
s = time.clock()
y_pred = model.predict(x)
e = time.clock()
print(e - s)
y_pred[y_pred < 0.7] = 0
y_pred[y_pred >= 0.7] = 1
print(metrics.precision_score(y, y_pred, average='micro', zero_division=0))
print(metrics.precision_score(y, y_pred, average='macro', zero_division=0))
print(metrics.recall_score(y, y_pred, average='micro', zero_division=0))
print(metrics.recall_score(y, y_pred, average='macro', zero_division=0))
print(metrics.f1_score(y, y_pred, average='weighted', zero_division=0))