import neralbinder.neuralbindhelpers.plot_helper as plot_helper
from neuralbinder.deepomics import neuralnetwork as nn
from neuralbinder.deepomics import utils, fit
#---------------------------------------------------------------------------------------
models = ['clip_conv_net', 'clip_residualbind']
ss_types = ['seq', 'pu']
window = 200
# dataset path
dataset_path = '/media/peter/storage/encode_eclip/eclip_datasets'
# set results path
results_path = helper.make_directory('../../results', 'encode_eclip')
saliency_results_path = utils.make_directory(results_path, 'saliency_ensemble')
# get list of .h5 files in dataset path
file_names = helper.get_file_names(dataset_path)
# loop through models
for model in models:
# model results path
model_path = helper.make_directory(results_path, model)
saliency_model_path = helper.make_directory(saliency_results_path, model)
# loop through secondary structure types
for ss_type in ss_types:
from neuralbinder.deepomics import neuralnetwork as nn
from neuralbinder.deepomics import utils, fit, metrics, saliency, visualize, init
#---------------------------------------------------------------------------------------
num_saliencies = [200]
num_filters = 5
# different deep learning models to try out
models = ['affinity_conv_net', 'affinity_residualbind', 'affinity_all_conv_net']
normalize_method = 'log_norm' # 'clip_norm'
ss_types = ['seq', 'pu']
data_path = neuralbinder.get_datasets('rnacompete2009.h5')
trained_path = '../../results/RNAcompete_2009'
results_path = utils.make_directory(trained_path, 'motifs')
#---------------------------------------------------------------------------------------
# classifier model
def classifier_model(input_shape, output_shape, num_filters):
# create model
layer1 = {'layer': 'input',
'input_shape': input_shape
}
layer2 = {'layer': 'conv1d',
'num_filters': num_filters,
'filter_size': 11,
'activation': 'sigmoid',
'W': init.HeUniform(),
from neuralbinder.deepomics import neuralnetwork as nn
from neuralbinder.deepomics import utils, fit
#---------------------------------------------------------------------------------------
num_epochs = 100
batch_size = 100
# different deep learning models to try out
models = ['affinity_conv_net', 'affinity_residualbind', 'affinity_all_conv_net']
normalize_method = 'log_norm' # 'clip_norm'
ss_types = ['seq', 'pu']
data_path = neuralbinder.get_datasets('rnacompete2013.h5')
trained_path = '../../results/RNAcompete_2013'
results_path = utils.make_directory(trained_path, 'saliency')
#---------------------------------------------------------------------------------------
# get list of rnacompete experiments
experiments = helper.get_experiments_hdf5(data_path)
# loop over different secondary structure contexts
for ss_type in ss_types:
print('input data: ' + ss_type)
sstype_path = os.path.join(trained_path, normalize_method+'_'+ss_type)
# directory to save parameters of saliency
saliency_sstype_path = helper.make_directory(results_path, normalize_method+'_'+ss_type)
saliency_sstype_path = helper.make_directory(saliency_sstype_path, 'ensemble')