def measure_rbp(entry):
import os
from time import time
from pysster import utils
output_folder = entry[4] + "_pysster/"
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
start = time()
# predict secondary structures
utils.predict_structures(entry[0], entry[0] + ".struct", annotate=True)
utils.predict_structures(entry[1], entry[1] + ".struct", annotate=True)
utils.predict_structures(entry[2], entry[2] + ".struct", annotate=True)
utils.predict_structures(entry[3], entry[3] + ".struct", annotate=True)
from pysster.Data import Data
from pysster.Model import Model
# load data
data = Data([entry[0] + ".struct", entry[1] + ".struct"], ("ACGU", "HIMS"))
data.train_val_test_split(
0.8, 0.1999
) # we need to have at least one test sequence, even though we have a separate test object
# training
params = {"kernel_len": 8}
model = Model(params, data)
model.train(data)
# load and predict test data
data_test = Data([entry[2] + ".struct", entry[3] + ".struct"],
("ACGU", "HIMS"))
predictions = model.predict(data_test, "all")
stop = time()
print("{}, time in seconds: {}".format(entry[4], stop - start))
# performance evaluation
labels = data_test.get_labels("all")
utils.plot_roc(labels, predictions, output_folder + "roc.pdf")
utils.plot_prec_recall(labels, predictions, output_folder + "prec.pdf")
# get motifs
activations = model.get_max_activations(data_test, "all")
_ = model.visualize_all_kernels(activations, data_test, output_folder)
# save model to drive
utils.save_model(model, "{}model.pkl".format(output_folder))
class Test_Model(unittest.TestCase):
def setUp(self):
folder = dirname(__file__)
file_name = folder + "/data/rna.fasta"
self.data = Data(file_name, ("ACGU", "()."))
self.params = {
"conv_num": 1,
"kernel_num": 3,
"kernel_len": 5,
"neuron_num": 2,
"epochs": 3
}
self.m1 = Model(self.params, self.data, seed=2)
self.m2 = Model(self.params, self.data, seed=13)
self.m3 = Model(self.params, self.data, seed=2)
def test_model_init(self):
self.assertTrue(self.m1.params["conv_num"] == 1)
self.assertTrue(self.m1.params["kernel_num"] == 3)
self.assertTrue(self.m1.params["kernel_len"] == 5)
self.assertTrue(self.m1.params["neuron_num"] == 2)
self.assertTrue(self.m1.params["activation"] == "sigmoid")
self.assertTrue(self.m1.model.layers[2].get_weights()[0].shape == (5,
12,
3))
self.assertTrue(
np.allclose(self.m1.model.layers[2].get_weights()[0],
self.m3.model.layers[2].get_weights()[0]))
self.assertFalse(
np.allclose(self.m2.model.layers[2].get_weights()[0],
self.m3.model.layers[2].get_weights()[0]))
self.assertTrue(
np.allclose(self.m1.model.layers[6].get_weights()[0],
self.m3.model.layers[6].get_weights()[0]))
self.assertFalse(
np.allclose(self.m2.model.layers[6].get_weights()[0],
self.m3.model.layers[6].get_weights()[0]))
def test_model_train_predict(self):
for obj in [self.m1, self.m2, self.m3]:
obj.train(self.data, verbose=False)
predictions = obj.predict(self.data, "test")
self.assertTrue(predictions.shape == (3, 3))
self.assertTrue((predictions > 0.49).all())
self.assertTrue((predictions < 0.51).all())
predictions = obj.predict(self.data, "all")
self.assertTrue(predictions.shape == (20, 3))
self.assertTrue((predictions > 0.49).all())
self.assertTrue((predictions < 0.51).all())
self.assertTrue(
np.allclose(self.m1.model.layers[2].get_weights()[0],
self.m3.model.layers[2].get_weights()[0],
atol=0.001))
self.assertFalse(
np.allclose(self.m2.model.layers[2].get_weights()[0],
self.m3.model.layers[2].get_weights()[0],
atol=0.001))
self.assertTrue(
np.allclose(self.m1.model.layers[6].get_weights()[0],
self.m3.model.layers[6].get_weights()[0],
atol=0.001))
self.assertFalse(
np.allclose(self.m2.model.layers[6].get_weights()[0],
self.m3.model.layers[6].get_weights()[0],
atol=0.001))
def test_model_get_max_activations(self):
acts = self.m1.get_max_activations(self.data, 'test')
self.assertTrue(acts['activations'].shape == (3, 3))
self.assertTrue(acts['labels'].shape == (3, 3))
self.assertTrue(acts['group'] == 'test')
def test_model_visualize_kernel(self):
acts = self.m1.get_max_activations(self.data, 'all')
folder = gettempdir() + '/'
# individual kernels
for kernel in range(self.params['kernel_num']):
motif, score = self.m1.visualize_kernel(acts, self.data, kernel,
folder)
self.assertTrue(
isfile(folder + "motif_kernel_{}.png".format(kernel)))
self.assertTrue(
isfile(folder + "position_kernel_{}.png".format(kernel)))
self.assertTrue(
isfile(folder + "activations_kernel_{}.png".format(kernel)))
remove(folder + "motif_kernel_{}.png".format(kernel))
remove(folder + "position_kernel_{}.png".format(kernel))
remove(folder + "activations_kernel_{}.png".format(kernel))
self.assertTrue(isinstance(motif, tuple))
self.assertTrue(isinstance(motif[0], Motif))
self.assertTrue(np.isclose(score, 0) or score > 0)
# all kernels
motifs = self.m1.visualize_all_kernels(acts, self.data, folder)
self.assertTrue(len(motifs) == 3)
for x in range(3):
self.assertTrue(isinstance(motifs[x], tuple))
self.assertTrue(isinstance(motifs[x][0], Motif))
for kernel in range(self.params['kernel_num']):
self.assertTrue(
isfile(folder + "motif_kernel_{}.png".format(kernel)))
self.assertTrue(
isfile(folder + "position_kernel_{}.png".format(kernel)))
self.assertTrue(
isfile(folder + "activations_kernel_{}.png".format(kernel)))
remove(folder + "motif_kernel_{}.png".format(kernel))
remove(folder + "position_kernel_{}.png".format(kernel))
remove(folder + "activations_kernel_{}.png".format(kernel))
self.assertTrue(isfile(folder + "summary.html"))
remove(folder + "summary.html")
def test_model_plot_clustering(self):
acts = self.m1.get_max_activations(self.data, 'test')
self.m1.plot_clustering(acts, gettempdir() + "/clust.png")
self.assertFalse(isfile(gettempdir() + "/clust.png"))
def test_model_optimized_inputs(self):
self.m1.visualize_optimized_inputs(self.data,
self.m1.model.layers[2].name,
gettempdir() + "/test.png")
self.m1.visualize_optimized_inputs(self.data,
self.m1.model.layers[2].name,
gettempdir() + "/test2.png",
nodes=[0])
with Image.open(gettempdir() + "/test.png") as img:
self.assertTrue(img.size == (1998, 1128))
with Image.open(gettempdir() + "/test2.png") as img:
self.assertTrue(img.size == (1998, 376))
remove(gettempdir() + "/test.png")
remove(gettempdir() + "/test2.png")
