def executor():
import config_variables
from prepare_interactions_clean import un_string
import MAP_function_clean as MAP_
import classifiers_clean
import itertools
mode = config_variables.mode
one_sided_or_two_sided = config_variables.one_sided_or_two_sided
domain = config_variables.domain
np = config_variables.np
chroms_to_infer = config_variables.chroms_to_infer
chroms_in_prior = config_variables.chroms_in_prior
f1 = open("MAP_performance_{0}_{1}_p_{2}_i_{3}_d{4}".format(mode, one_sided_or_two_sided, "-".join(chroms_in_prior[[0,-1]]), ",".join(chroms_to_infer), domain), "w")
stuff = [1,2,3,4]
dict_option_ = {0: 'Pol2_2012-03', 1: 'Pol2', 2: 'H2AZ', 3: 'ER', 4: 'H3K4me3', 5: '2012-03_RNA', 6: 'RNA'}
combinations = []
for L in range(0, len(stuff)+1):
for subset in itertools.combinations(stuff, L):
if len(subset): combinations += [list(subset)]
combinations = np.array(combinations)
sensitivity_match_MAP = {}
sensitivity_match_MAP["correl_dist"] = {}
sensitivity_match_MAP["correl"] = {}
sensitivity_match_MAP["dist"] = {}
for domain_atr in ["outside_domain", "within_domain"]:
sensitivity_match_MAP["correl_dist"][domain_atr] = {}
sensitivity_match_MAP["correl"][domain_atr] = {}
sensitivity_match_MAP["dist"][domain_atr] = {}
total_number_of_interacting_enhancers = {}
for option_ in combinations:
comb = ",".join([dict_option_[el] for el in option_])
if option_ == combinations[-1]: comb = "All"
print comb
f1.write(comb + "\n" )
match_MAP_correl_dist = {}
infered_elements_correl_dist = {}
MAP_probabilites_correl_dist = {}
match_MAP_correl = {}
infered_elements_correl = {}
MAP_probabilites_correl = {}
match_MAP_dist = {}
infered_elements_dist = {}
MAP_probabilites_dist = {}
posterior_correl_dist_true, posterior_correl_dist_false = classifiers_clean.posterior_producer([0], option_)
posterior_correl_true, posterior_correl_false = classifiers_clean.posterior_producer([], option_)
posterior_dist_true, posterior_dist_false = classifiers_clean.posterior_producer([0], [])
for chrom in chroms_to_infer:
# classifiers_clean takes value of mode from config_variables on it's own
i_s_f, j_s_f = MAP_.positive_negative_interactions_for_MAP(chrom)
match_MAP_correl_dist[chrom], infered_elements_correl_dist[chrom], MAP_probabilites_correl_dist[chrom] = MAP_.MAP(posterior_correl_dist_true, posterior_correl_dist_false, chrom, i_s_f, j_s_f)
match_MAP_correl[chrom], infered_elements_correl[chrom], MAP_probabilites_correl[chrom] = MAP_.MAP(posterior_correl_true, posterior_correl_false, chrom, i_s_f, j_s_f)
match_MAP_dist[chrom], infered_elements_dist[chrom], MAP_probabilites_dist[chrom] = MAP_.MAP(posterior_dist_true, posterior_dist_false, chrom, i_s_f, j_s_f)
match_MAP_correl_dist_total = list(itertools.chain.from_iterable([match_MAP_correl_dist[chrom_] for chrom_ in chroms_to_infer]))
match_MAP_correl_total = list(itertools.chain.from_iterable([match_MAP_correl[chrom_] for chrom_ in chroms_to_infer]))
match_MAP_dist_total = list(itertools.chain.from_iterable([match_MAP_dist[chrom_] for chrom_ in chroms_to_infer]))
f1.write("correl_dist:" + str(np.sum(match_MAP_correl_dist_total)/float(len(match_MAP_correl_dist_total))) + "\n"+ "\n")
f1.write("dist:" + str(np.sum(match_MAP_dist_total)/float(len(match_MAP_dist_total))) + "\n"+ "\n")
f1.write("correl:" + str(np.sum(match_MAP_correl_total)/float(len(match_MAP_correl_total))) + "\n" + "\n")
print "correl_dist:", np.sum(match_MAP_correl_dist_total)/float(len(match_MAP_correl_dist_total))
print "dist:", np.sum(match_MAP_dist_total)/float(len(match_MAP_dist_total))
print "correl:", np.sum(match_MAP_correl_total)/float(len(match_MAP_correl_total))
sensitivity_match_MAP["correl_dist"][",".join(np.array(option_, str))] = np.sum(match_MAP_correl_dist_total)/float(len(match_MAP_correl_dist_total))
sensitivity_match_MAP["correl"][",".join(np.array(option_, str))] = np.sum(match_MAP_correl_total)/float(len(match_MAP_correl_total))
sensitivity_match_MAP["dist"][",".join(np.array(option_, str))] = np.sum(match_MAP_dist_total)/float(len(match_MAP_dist_total))
def get_MAPS_for_domain_non_domain_interacting_enhancers(domain_atr):
chrom_interacting_enhancers_pro = config_variables.chrom_interacting_enhancers_pro
from prepare_interactions_clean import un_string
interacting_non_intracting_mask = {}
total_number_of_interacting_enhancers = 0
for chrom___ in chroms_to_infer:
mask = np.in1d(np.unique(un_string(config_variables.chr_interactions_dict_pro_enh[chrom___])[:,1]), chrom_interacting_enhancers_pro[chrom___])
if domain_atr == "outside_domain":
interacting_non_intracting_mask[chrom___] = np.invert(mask)
elif domain_atr == "within_domain":
interacting_non_intracting_mask[chrom___] = mask
total_number_of_interacting_enhancers += np.sum(interacting_non_intracting_mask[chrom___])
match_MAP_correl_dist_total = list(itertools.chain.from_iterable([match_MAP_correl_dist[chrom_][interacting_non_intracting_mask[chrom_]] for chrom_ in chroms_to_infer]))
match_MAP_correl_total = list(itertools.chain.from_iterable([match_MAP_correl[chrom_][interacting_non_intracting_mask[chrom_]] for chrom_ in chroms_to_infer]))
match_MAP_dist_total = list(itertools.chain.from_iterable([match_MAP_dist[chrom_][interacting_non_intracting_mask[chrom_]] for chrom_ in chroms_to_infer]))
sensitivity_match_MAP_correl_dist = np.sum(match_MAP_correl_dist_total)/float(len(match_MAP_correl_dist_total))
sensitivity_match_MAP_correl = np.sum(match_MAP_correl_total)/float(len(match_MAP_correl_total))
sensitivity_match_MAP_dist = np.sum(match_MAP_dist_total)/float(len(match_MAP_dist_total))
return sensitivity_match_MAP_correl_dist, sensitivity_match_MAP_correl, sensitivity_match_MAP_dist, total_number_of_interacting_enhancers
for domain_atr in ["outside_domain", "within_domain"]:
[sensitivity_match_MAP["correl_dist"][domain_atr][",".join(np.array(option_, str))],
sensitivity_match_MAP["correl"][domain_atr][",".join(np.array(option_, str))],
sensitivity_match_MAP["dist"][domain_atr][",".join(np.array(option_, str))],
total_number_of_interacting_enhancers[domain_atr]] = get_MAPS_for_domain_non_domain_interacting_enhancers(domain_atr)
config_variables.total_number_of_interacting_enhancers = total_number_of_interacting_enhancers
f1.close()
return MAP_probabilites_correl_dist, infered_elements_correl_dist, match_MAP_correl_dist, sensitivity_match_MAP
def executor(posterior, selected_combinations, type_of_models):
import config_variables
from prepare_interactions_clean import un_string
import MAP_function_clean as MAP_
import classifiers_clean
import itertools
mode = config_variables.mode
one_sided_or_two_sided = config_variables.one_sided_or_two_sided
domain = config_variables.domain
np = config_variables.np
chroms_to_infer = config_variables.chroms_to_infer
chroms_in_prior = config_variables.chroms_in_prior
results_folder = config_variables.results_folder
MoG_classificator = config_variables.MoG_classificator
dict_option_ = config_variables.dict_option
#f1 = open(results_folder + "MAP_performance_{0}_{1}_p_{2}_i_{3}_d{4}".format(mode, one_sided_or_two_sided, "-".join(chroms_in_prior[[0,-1]]), ",".join(chroms_to_infer), domain), "w")
def get_MAPS_for_domain_non_domain_interacting_enhancers(domain_atr):
chrom_interacting_enhancers_pro = config_variables.chrom_interacting_enhancers_pro
from prepare_interactions_clean import un_string
interacting_non_intracting_mask = {}
#total_number_of_interacting_enhancers = 0
if config_variables.disentagled_features_validation:
chr_interactions_dict_pro_enh = config_variables.chr_interactions_dict_pro_enh_TSS
else:
chr_interactions_dict_pro_enh = config_variables.chr_interactions_dict_pro_enh
for chrom___ in chroms_to_infer:
mask = np.in1d(np.unique(un_string(chr_interactions_dict_pro_enh[chrom___])[:,1]), chrom_interacting_enhancers_pro[chrom___])
if domain_atr == "outside_domain":
interacting_non_intracting_mask[chrom___] = np.invert(mask)
elif domain_atr == "within_domain":
interacting_non_intracting_mask[chrom___] = mask
#total_number_of_interacting_enhancers += np.sum(interacting_non_intracting_mask[chrom___])
return interacting_non_intracting_mask
sensitivity_match_MAP = {}
match_MAP = {}
infered_elements = {}
MAP_probabilites = {}
#total_number_of_interacting_enhancers = {}
probabilities_for_promoters_of_interacting_enhancers = {}
for type_of_model in type_of_models:
sensitivity_match_MAP[type_of_model] = {}
match_MAP[type_of_model] = {}
infered_elements[type_of_model] = {}
MAP_probabilites[type_of_model] = {}
probabilities_for_promoters_of_interacting_enhancers[type_of_model] = {}
for domain_atr in ["outside_domain", "within_domain", "unsplit"]:
sensitivity_match_MAP[type_of_model][domain_atr] = {}
for type_of_model in type_of_models:
for chrom in chroms_to_infer:
# classifiers_clean takes value of mode from config_variables on it's own
i_s_f, j_s_f = MAP_.positive_negative_interactions_for_MAP(chrom)
if type_of_model in ["dist", "MOG_dist"]:
match_MAP[type_of_model][chrom], infered_elements[type_of_model][chrom], MAP_probabilites[type_of_model][chrom], probabilities_for_promoters_of_interacting_enhancers[type_of_model][chrom] = MAP_.MAP(posterior["positive_interactions"][type_of_model], posterior["negative_interactions"][type_of_model], chrom, i_s_f, j_s_f)
elif type_of_model in ["correl", "correl_dist", "MOG_correl_dist"]:
match_MAP[type_of_model][chrom], infered_elements[type_of_model][chrom], MAP_probabilites[type_of_model][chrom] = {}, {}, {}
for option_ in selected_combinations:
comb = "_".join([dict_option_[el] for el in option_])
print comb
match_MAP[type_of_model][chrom][comb], infered_elements[type_of_model][chrom][comb], MAP_probabilites[type_of_model][chrom][comb], probabilities_for_promoters_of_interacting_enhancers[type_of_model][chrom] = MAP_.MAP(posterior["positive_interactions"][type_of_model][comb], posterior["negative_interactions"][type_of_model][comb], chrom, i_s_f, j_s_f)
for type_of_model in type_of_models:
if type_of_model in ["dist", "MOG_dist"]:
match_of_total_number_of_predictions = np.sum([np.sum(match_MAP[type_of_model][chrom_]) for chrom_ in chroms_to_infer])
total_number_of_predictions = float(np.sum([len(match_MAP[type_of_model][chrom_]) for chrom_ in chroms_to_infer]))
sensitivity_match_MAP[type_of_model]["unsplit"] = match_of_total_number_of_predictions/total_number_of_predictions
for domain_atr in ["outside_domain", "within_domain"]:
interacting_non_intracting_mask = get_MAPS_for_domain_non_domain_interacting_enhancers(domain_atr)
match_of_total_number_of_predictions = np.sum([np.sum(match_MAP[type_of_model][chrom_][interacting_non_intracting_mask[chrom_]]) for chrom_ in chroms_to_infer])
total_number_of_predictions = float(np.sum([len(match_MAP[type_of_model][chrom_][interacting_non_intracting_mask[chrom_]]) for chrom_ in chroms_to_infer]))
sensitivity_match_MAP[type_of_model][domain_atr] = match_of_total_number_of_predictions/total_number_of_predictions
elif type_of_model in ["correl", "correl_dist", "MOG_correl_dist"]:
for option_ in selected_combinations:
comb = "_".join([dict_option_[el] for el in option_])
match_of_total_number_of_predictions = np.sum([np.sum(match_MAP[type_of_model][chrom_][comb]) for chrom_ in chroms_to_infer])
total_number_of_predictions = float(np.sum([len(match_MAP[type_of_model][chrom_][comb]) for chrom_ in chroms_to_infer]))
sensitivity_match_MAP[type_of_model]["unsplit"][comb] = match_of_total_number_of_predictions/total_number_of_predictions
for domain_atr in ["outside_domain", "within_domain"]:
interacting_non_intracting_mask = get_MAPS_for_domain_non_domain_interacting_enhancers(domain_atr)
match_of_total_number_of_predictions = np.sum([np.sum(match_MAP[type_of_model][chrom_][comb][interacting_non_intracting_mask[chrom_]]) for chrom_ in chroms_to_infer])
total_number_of_predictions = float(np.sum([len(match_MAP[type_of_model][chrom_][comb][interacting_non_intracting_mask[chrom_]]) for chrom_ in chroms_to_infer]))
sensitivity_match_MAP[type_of_model][domain_atr][comb] = match_of_total_number_of_predictions/total_number_of_predictions
#f1.close()
return match_MAP, sensitivity_match_MAP, MAP_probabilites, infered_elements, probabilities_for_promoters_of_interacting_enhancers
def executor(posterior, selected_combinations, type_of_models):
import config_variables
from prepare_interactions_clean import un_string
import MAP_function_clean as MAP_
import classifiers_clean
import itertools
mode = config_variables.mode
one_sided_or_two_sided = config_variables.one_sided_or_two_sided
domain = config_variables.domain
np = config_variables.np
chroms_to_infer = config_variables.chroms_to_infer
chroms_in_prior = config_variables.chroms_in_prior
results_folder = config_variables.results_folder
MoG_classificator = config_variables.MoG_classificator
dict_option_ = config_variables.dict_option
#f1 = open(results_folder + "MAP_performance_{0}_{1}_p_{2}_i_{3}_d{4}".format(mode, one_sided_or_two_sided, "-".join(chroms_in_prior[[0,-1]]), ",".join(chroms_to_infer), domain), "w")
def get_MAPS_for_domain_non_domain_interacting_enhancers(domain_atr):
chrom_interacting_enhancers_pro = config_variables.chrom_interacting_enhancers_pro
from prepare_interactions_clean import un_string
interacting_non_intracting_mask = {}
#total_number_of_interacting_enhancers = 0
if config_variables.disentagled_features_validation:
chr_interactions_dict_pro_enh = config_variables.chr_interactions_dict_pro_enh_TSS
else:
chr_interactions_dict_pro_enh = config_variables.chr_interactions_dict_pro_enh
for chrom___ in chroms_to_infer:
mask = np.in1d(
np.unique(
un_string(chr_interactions_dict_pro_enh[chrom___])[:, 1]),
chrom_interacting_enhancers_pro[chrom___])
if domain_atr == "outside_domain":
interacting_non_intracting_mask[chrom___] = np.invert(mask)
elif domain_atr == "within_domain":
interacting_non_intracting_mask[chrom___] = mask
#total_number_of_interacting_enhancers += np.sum(interacting_non_intracting_mask[chrom___])
return interacting_non_intracting_mask
sensitivity_match_MAP = {}
match_MAP = {}
infered_elements = {}
MAP_probabilites = {}
#total_number_of_interacting_enhancers = {}
probabilities_for_promoters_of_interacting_enhancers = {}
for type_of_model in type_of_models:
sensitivity_match_MAP[type_of_model] = {}
match_MAP[type_of_model] = {}
infered_elements[type_of_model] = {}
MAP_probabilites[type_of_model] = {}
probabilities_for_promoters_of_interacting_enhancers[
type_of_model] = {}
for domain_atr in ["outside_domain", "within_domain", "unsplit"]:
sensitivity_match_MAP[type_of_model][domain_atr] = {}
for type_of_model in type_of_models:
for chrom in chroms_to_infer:
# classifiers_clean takes value of mode from config_variables on it's own
i_s_f, j_s_f = MAP_.positive_negative_interactions_for_MAP(chrom)
if type_of_model in ["dist", "MOG_dist"]:
match_MAP[type_of_model][chrom], infered_elements[
type_of_model][chrom], MAP_probabilites[type_of_model][
chrom], probabilities_for_promoters_of_interacting_enhancers[
type_of_model][chrom] = MAP_.MAP(
posterior["positive_interactions"]
[type_of_model],
posterior["negative_interactions"]
[type_of_model], chrom, i_s_f, j_s_f)
elif type_of_model in ["correl", "correl_dist", "MOG_correl_dist"]:
match_MAP[type_of_model][chrom], infered_elements[
type_of_model][chrom], MAP_probabilites[type_of_model][
chrom] = {}, {}, {}
for option_ in selected_combinations:
comb = "_".join([dict_option_[el] for el in option_])
print comb
match_MAP[type_of_model][chrom][comb], infered_elements[
type_of_model][chrom][comb], MAP_probabilites[
type_of_model][chrom][
comb], probabilities_for_promoters_of_interacting_enhancers[
type_of_model][chrom] = MAP_.MAP(
posterior["positive_interactions"]
[type_of_model][comb],
posterior["negative_interactions"]
[type_of_model][comb], chrom, i_s_f,
j_s_f)
for type_of_model in type_of_models:
if type_of_model in ["dist", "MOG_dist"]:
match_of_total_number_of_predictions = np.sum([
np.sum(match_MAP[type_of_model][chrom_])
for chrom_ in chroms_to_infer
])
total_number_of_predictions = float(
np.sum([
len(match_MAP[type_of_model][chrom_])
for chrom_ in chroms_to_infer
]))
sensitivity_match_MAP[type_of_model][
"unsplit"] = match_of_total_number_of_predictions / total_number_of_predictions
for domain_atr in ["outside_domain", "within_domain"]:
interacting_non_intracting_mask = get_MAPS_for_domain_non_domain_interacting_enhancers(
domain_atr)
match_of_total_number_of_predictions = np.sum([
np.sum(match_MAP[type_of_model][chrom_][
interacting_non_intracting_mask[chrom_]])
for chrom_ in chroms_to_infer
])
total_number_of_predictions = float(
np.sum([
len(match_MAP[type_of_model][chrom_][
interacting_non_intracting_mask[chrom_]])
for chrom_ in chroms_to_infer
]))
sensitivity_match_MAP[type_of_model][
domain_atr] = match_of_total_number_of_predictions / total_number_of_predictions
elif type_of_model in ["correl", "correl_dist", "MOG_correl_dist"]:
for option_ in selected_combinations:
comb = "_".join([dict_option_[el] for el in option_])
match_of_total_number_of_predictions = np.sum([
np.sum(match_MAP[type_of_model][chrom_][comb])
for chrom_ in chroms_to_infer
])
total_number_of_predictions = float(
np.sum([
len(match_MAP[type_of_model][chrom_][comb])
for chrom_ in chroms_to_infer
]))
sensitivity_match_MAP[type_of_model]["unsplit"][
comb] = match_of_total_number_of_predictions / total_number_of_predictions
for domain_atr in ["outside_domain", "within_domain"]:
interacting_non_intracting_mask = get_MAPS_for_domain_non_domain_interacting_enhancers(
domain_atr)
match_of_total_number_of_predictions = np.sum([
np.sum(match_MAP[type_of_model][chrom_][comb][
interacting_non_intracting_mask[chrom_]])
for chrom_ in chroms_to_infer
])
total_number_of_predictions = float(
np.sum([
len(match_MAP[type_of_model][chrom_][comb][
interacting_non_intracting_mask[chrom_]])
for chrom_ in chroms_to_infer
]))
sensitivity_match_MAP[type_of_model][domain_atr][
comb] = match_of_total_number_of_predictions / total_number_of_predictions
#f1.close()
return match_MAP, sensitivity_match_MAP, MAP_probabilites, infered_elements, probabilities_for_promoters_of_interacting_enhancers
