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make_nn_config.py
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make_nn_config.py
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#!/usr/bin/env python
import cPickle
import os
import numpy as np
from random import sample
class TargetRegions(object):
def __init__(self, tsv, already_sorted=False):
assert(os.stat(tsv).st_size != 0), "Empty regions file"
self.region_array = np.loadtxt(tsv,
usecols=(0, 1),
dtype=np.int32)
if len(self.region_array.shape) == 1:
a = np.empty([1, 2], dtype=np.int32)
a[0] = self.region_array
self.region_array = a
if not already_sorted:
self.region_array = np.sort(self.region_array, axis=1)
###############################################################################
j = {
"experiment_name": "two motif test",
"hidden_dim": [50, 10],
"model_type": "ReLUthreeLayer",
"sites": []
}
motifs = sample([747, 354, 147, 796, 289, 363, 755, 626, 813, 653, 525, 80, 874], 2)
for m in motifs:
m_list = [m]
d = dict()
d['motif_start_position'] = [m_list, m_list, m_list]
d['title'] = str(m)
j['sites'].append(d)
cPickle.dump(j, open("./configs/motif_test.pkl", 'w'))
###############################################################################
j = {
"experiment_name": "individual cytosine motif classification",
"hidden_dim": [50, 10],
"model_type": "ReLUthreeLayer",
"sites": []
}
for m in [747, 354, 147, 796, 289, 363, 755, 626, 813, 653, 525, 80, 874]:
m_list = [m]
d = dict()
d['motif_start_position'] = [m_list, m_list, m_list]
d['title'] = str(m)
j['sites'].append(d)
cPickle.dump(j, open("./configs/indivCytosineZymo.pkl", 'w'))
###############################################################################
j = {
"experiment_name": "individual cytosine motif classification 4 Layer",
"hidden_dim": [50, 100, 50],
"model_type": "fourLayer",
"sites": []
}
for m in [747, 354, 147, 796, 289, 363, 755, 626, 813, 653, 525, 80, 874]:
m_list = [m]
d = dict()
d['motif_start_position'] = [m_list, m_list, m_list]
d['title'] = str(m)
j['sites'].append(d)
cPickle.dump(j, open("./configs/indivCytosineZymo_4L.pkl", 'w'))
###############################################################################
j = {
"experiment_name": "individual cytosine motif classification ConvNet",
"hidden_dim": 10,
"model_type": "ConvNet3",
"extra_args": {
"batch_size": 2,
"n_filters": [10],
"n_channels": [1],
"data_shape": [3, 6],
"filter_shape": [1, 3],
"poolsize": (2, 2),
},
"sites": []
}
for m in [747, 354, 147, 796, 289, 363, 755, 626, 813, 653, 525, 80, 874]:
m = [m]
d = dict()
d['motif_start_position'] = [m, m, m]
d['title'] = str(m)
j['sites'].append(d)
cPickle.dump(j, open("./configs/indivCytosineZymo_conv.pkl", 'w'))
###############################################################################
j = {
"experiment_name": "indvidual null motif classification",
"hidden_dim": [50, 10],
"model_type": "ReLUthreeLayer",
"sites": []
}
for m in [62, 87, 218, 295, 371, 383, 457, 518, 740, 785, 805, 842, 866]:
m_list = [m]
d = dict()
d['motif_start_position'] = [m_list, m_list, m_list]
d['title'] = str(m)
j['sites'].append(d)
cPickle.dump(j, open("./configs/indivNullZymo.pkl", 'w'))
###############################################################################
j = {
"experiment_name": "All cytosine motifs classification",
"hidden_dim": 10,
"model_type": "ConvNet3",
"extra_args": {
"batch_size": 2,
"n_filters": [10],
"data_shape": [3, 6],
"filter_shape": [1, 3],
"poolsize": (2, 2),
},
"sites": []
}
for m in [[747, 354, 147, 796, 289, 363, 755, 626, 813, 653, 525, 80, 874]]:
d = dict()
d['motif_start_position'] = [m, m, m]
d['title'] = "all_zymo_cytosine"
j['sites'].append(d)
cPickle.dump(j, open("./configs/all_zymo_cytosine_conv.pkl", 'w'))
###############################################################################
j = {
"experiment_name": "all cytosine motifs classification",
"hidden_dim": [100, 100],
"model_type": "ReLUthreeLayer",
"sites": []
}
for m in [[747, 354, 147, 796, 289, 363, 755, 626, 813, 653, 525, 80, 874]]:
d = dict()
d['motif_start_position'] = [m, m, m]
d['title'] = "all_zymo_motifs"
j['sites'].append(d)
cPickle.dump(j, open("./configs/all_zymo_motifs.pkl", 'w'))
###############################################################################
j = {
"experiment_name": "all null motifs classification",
"hidden_dim": [100, 100],
"model_type": "ReLUthreeLayer",
"sites": []
}
for m in [[62, 87, 218, 295, 371, 383, 457, 518, 740, 785, 805, 842, 866]]:
d = dict()
d['motif_start_position'] = [m, m, m]
d['title'] = "all_zymo_null"
j['sites'].append(d)
cPickle.dump(j, open("./configs/all_zymo_null.pkl", 'w'))
###############################################################################
ecoli_positive_A = TargetRegions("./regions/ecoli_positive_A.tsv")
ecoli_positive_T = TargetRegions("./regions/ecoli_positive_T.tsv")
ecoli_negative_A = TargetRegions("./regions/ecoli_null_A.tsv")
ecoli_negative_T = TargetRegions("./regions/ecoli_null_T.tsv")
pos_A_list = [x[0] for x in ecoli_positive_A.region_array]
pos_T_list = [x[0] for x in ecoli_positive_T.region_array]
neg_A_list = [x[0] for x in ecoli_negative_A.region_array]
neg_T_list = [x[0] for x in ecoli_negative_T.region_array]
j = {
"experiment_name": "ecoli CCAGG",
"hidden_dim": [500, 500],
"model_type": "ReLUthreeLayer",
"sites": []
}
d = dict()
d['motif_start_position'] = [pos_A_list, neg_A_list]
d['title'] = "ecoli_CCAGG"
j['sites'].append(d)
cPickle.dump(j, open("./configs/ecoli_CCAGG.pkl", 'w'))
###############################################################################
j = {
"experiment_name": "ecoli CCTGG",
"hidden_dim": [100, 100],
"model_type": "ReLUthreeLayer",
"sites": []
}
d = dict()
d['motif_start_position'] = [pos_T_list, neg_T_list]
d['title'] = "ecoli_CCTGG"
j['sites'].append(d)
cPickle.dump(j, open("./configs/ecoli_CCTGG.pkl", 'w'))
###############################################################################
ecoli_pos_intersect_A = TargetRegions("./regions/ecoli_positive_intersect_A.tsv")
ecoli_neg_intersect_A = TargetRegions("./regions/ecoli_negative_intersect_A.tsv")
pos_int_A_list = [x[0] for x in ecoli_pos_intersect_A.region_array]
neg_int_A_list = [x[0] for x in ecoli_neg_intersect_A.region_array]
j = {
"experiment_name": "ecoli CCAGG - intersect",
"hidden_dim": [100, 100, 100],
"model_type": "fourLayer",
"sites": []
}
d = dict()
d['motif_start_position'] = [pos_int_A_list, neg_int_A_list]
d['title'] = "ecoli_CCAGG_intersection"
j['sites'].append(d)
cPickle.dump(j, open("./configs/ecoli_intersect_CCAGG.pkl", 'w'))
###############################################################################