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")
def main():
RBPs = [("data/pum2.train.positive.fasta",
"data/pum2.train.negative.fasta",
"data/pum2.test.positive.fasta",
"data/pum2.test.negative.fasta",
"PUM2"),
("data/qki.train.positive.fasta",
"data/qki.train.negative.fasta",
"data/qki.test.positive.fasta",
"data/qki.test.negative.fasta",
"QKI"),
("data/igf2bp123.train.positive.fasta",
"data/igf2bp123.train.negative.fasta",
"data/igf2bp123.test.positive.fasta",
"data/igf2bp123.test.negative.fasta",
"IGF2BP123"),
("data/srsf1.train.positive.fasta",
"data/srsf1.train.negative.fasta",
"data/srsf1.test.positive.fasta",
"data/srsf1.test.negative.fasta",
"SRSF1"),
("data/taf2n.train.positive.fasta",
"data/taf2n.train.negative.fasta",
"data/taf2n.test.positive.fasta",
"data/taf2n.test.negative.fasta",
"TAF2N"),
("data/nova.train.positive.fasta",
"data/nova.train.negative.fasta",
"data/nova.test.positive.fasta",
"data/nova.test.negative.fasta",
"NOVA")]
for entry in RBPs:
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.gz", annotate=True)
utils.predict_structures(entry[1], entry[1]+".struct.gz", annotate=True)
utils.predict_structures(entry[2], entry[2]+".struct.gz", annotate=True)
utils.predict_structures(entry[3], entry[3]+".struct.gz", annotate=True)
# load data
data = Data([entry[0]+".struct.gz", entry[1]+".struct.gz"], ("ACGU", "HIMS"))
data.train_val_test_split(0.8, 0.1999) # we need to have at least one test sequence, even though we don't need it
print(data.get_summary())
# training
params = {"kernel_len": 8}
model = Model(params, data)
model.train(data)
# load and predict test data
data_test = Data([entry[2]+".struct.gz", entry[3]+".struct.gz"], ("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")
print(utils.get_performance_report(labels, predictions))
# get motifs
activations = model.get_max_activations(data_test, "all")
logos, scores = [], []
for kernel in range(model.params["kernel_num"]):
logo, score = model.visualize_kernel(activations, data_test, kernel, output_folder)
logos.append(logo)
scores.append(score)
# sort motifs by importance score
sorted_idx = [i[0] for i in sorted(enumerate(scores), key=lambda x:x[1])]
with open(output_folder+"kernel_scores.txt", "wt") as handle:
for x in sorted_idx:
print("kernel {:>3}: {:.3f}".format(x, scores[x]))
handle.write("kernel {:>3}: {:.3f}\n".format(x, scores[x]))
# save model to drive
utils.save_model(model, "{}model.pkl".format(output_folder))
def test_data_additional(self):
self.assertTrue(len(self.data_pwm.meta) == 2)
self.assertTrue(self.data_pwm.meta[0]['is_categorical'] == False)
self.assertTrue(self.data_pwm.meta[1]['is_categorical'] == True)
self.assertTrue(self.data_pwm.meta[0]['data'] == [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 16, 15, 14,
13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1
])
self.assertTrue(len(self.data_pwm.meta[1]['data']) == 32)
for x in self.data_pwm.meta[1]['data']:
self.assertTrue(sum(x) == 1)
self.assertTrue((self.data_pwm.meta[1]['data'][0] ==
self.data_pwm.meta[1]['data'][31]).all())
self.assertTrue((self.data_pwm.meta[1]['data'][13] ==
self.data_pwm.meta[1]['data'][18]).all())
addi = self.data_pwm._get_additional_data([0, 1, 15, 16], 0, 4)
self.assertTrue(len(addi) == 4)
self.assertTrue(
np.allclose(addi[0], [1, *self.data_pwm.meta[1]['data'][0]]))
self.assertTrue(
np.allclose(addi[1], [2, *self.data_pwm.meta[1]['data'][1]]))
self.assertTrue(
np.allclose(addi[2], [16, *self.data_pwm.meta[1]['data'][15]]))
self.assertTrue(
np.allclose(addi[3], [16, *self.data_pwm.meta[1]['data'][16]]))
# check position-wise additional data
self.assertTrue(len(self.data_pwm.positionwise) == 2)
self.assertTrue(
list(self.data_pwm.positionwise.keys()) == ["feat1", "feat2"])
gen = self.data_pwm._data_generator("all", 32, False, False)
dat = next(gen)
self.assertTrue(dat[1].shape == (32, 17))
self.assertTrue(dat[0].shape == (32, 10, 14))
self.assertTrue(
np.allclose(dat[0][0, :, 12],
[0.9, 0.8, 0.7, 0.9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]))
self.assertTrue(
np.allclose(dat[0][31, :, 13],
[0.1, 0.2, 0.3, 0.1, 1.0, 1.0, 0.8, 0.3, 0.2, 0.1]))
self.assertTrue(
np.allclose(dat[0][31, :, 12],
[2.1, 2.2, 2.3, 2.1, 2.0, 2.0, 2.8, 2.3, 2.2, 2.1]))
mod = Model(
{
"conv_num": 1,
"kernel_num": 2,
"kernel_len": 4,
"neuron_num": 2,
"epochs": 2
}, self.data_pwm)
mod.train(self.data_pwm, verbose=True)
predictions = mod.predict(self.data_pwm, "all")
self.assertTrue(predictions.shape == (32, 2))
# check kernel output plot for position-wise data
folder = gettempdir() + '/'
acts = mod.get_max_activations(self.data_pwm, 'all')
motif, score = mod.visualize_kernel(acts, self.data_pwm, 0, folder)
with Image.open(folder + "additional_features_kernel_0.png") as img:
self.assertTrue(img.size == (500, 1400))
remove(folder + "additional_features_kernel_0.png")
remove(folder + "motif_kernel_0.png")
remove(folder + "position_kernel_0.png")
remove(folder + "activations_kernel_0.png")
