def calculate_bandwidth_correl(sample_):
import kern_density_est
optimum = {}
if likelihood_cross_validation:
if domain:
print "negative_interactions"
optimum[
"positive_interactions"] = kern_density_est.chrom_cross_validation_correlation(
prior_elements,
data_set_name,
thresholds=np.linspace(0.01, .4, 200),
classification_of_interactions="positive_interactions")
optimum[
"negative_interactions"] = kern_density_est.chrom_cross_validation_correlation(
prior_elements,
data_set_name,
thresholds=np.linspace(0.01, .4, 200),
classification_of_interactions="negative_interactions")
else:
optimum[
"positive_interactions"] = kern_density_est.chrom_cross_validation_correlation(
prior_elements,
data_set_name,
thresholds=np.linspace(0.01, .4, 200),
classification_of_interactions="positive_interactions")
optimum["negative_interactions"] = "scott"
else:
optimum[
"positive_interactions"] = kern_density_est.cross_validation(
sample_["positive_interactions"])
optimum["negative_interactions"] = "scott"
return optimum
def calculate_bandwidth_dist(sample_):
import kern_density_est
optimum = {}
if domain: print "positive_interactions"
if likelihood_cross_validation:
if domain:
print "negative_interactions"
optimum[
"positive_interactions"] = kern_density_est.chrom_cross_validation_distance(
prior_elements,
thresholds=np.linspace(0.01, .4, 200),
classification_of_interactions="positive_interactions",
weights=weights)
optimum[
"negative_interactions"] = kern_density_est.chrom_cross_validation_distance(
prior_elements,
thresholds=np.linspace(0.01, .4, 200),
classification_of_interactions="negative_interactions",
weights=weights)
else:
optimum[
"positive_interactions"] = kern_density_est.chrom_cross_validation_distance(
prior_elements,
thresholds=np.linspace(0.01, .4, 200),
classification_of_interactions="positive_interactions")
optimum["negative_interactions"] = {}
for positive_or_negative_side in [
"positive_side", "negative_side"
]:
optimum["negative_interactions"][
positive_or_negative_side] = "scott"
else:
optimum["positive_interactions"] = {}
optimum["negative_interactions"] = {}
for positive_or_negative_side in [
"positive_side", "negative_side"
]:
optimum["positive_interactions"][
positive_or_negative_side] = kern_density_est.cross_validation(
sample_[positive_or_negative_side]
["positive_interactions"])
optimum["negative_interactions"][
positive_or_negative_side] = "scott"
return optimum
def calculate_bandwidth_correl(sample_):
import kern_density_est
optimum = {}
if likelihood_cross_validation:
if domain:
print "negative_interactions"
optimum["positive_interactions"] = kern_density_est.chrom_cross_validation_correlation(prior_elements, data_set_name, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions")
optimum["negative_interactions"] = kern_density_est.chrom_cross_validation_correlation(prior_elements, data_set_name, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "negative_interactions")
else:
optimum["positive_interactions"] = kern_density_est.chrom_cross_validation_correlation(prior_elements, data_set_name, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions")
optimum["negative_interactions"] = "scott"
else:
optimum["positive_interactions"] = kern_density_est.cross_validation(sample_["positive_interactions"])
optimum["negative_interactions"] = "scott"
return optimum
def calculate_bandwidth_dist(sample_):
import kern_density_est
optimum = {}
if domain: print "positive_interactions"
if likelihood_cross_validation:
if domain:
print "negative_interactions"
optimum["positive_interactions"] = kern_density_est.chrom_cross_validation_distance(prior_elements, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions", weights = weights)
optimum["negative_interactions"] = kern_density_est.chrom_cross_validation_distance(prior_elements, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "negative_interactions", weights = weights)
else:
optimum["positive_interactions"] = kern_density_est.chrom_cross_validation_distance(prior_elements, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions")
optimum["negative_interactions"] = {}
for positive_or_negative_side in ["positive_side", "negative_side"]:
optimum["negative_interactions"][positive_or_negative_side] = "scott"
else:
optimum["positive_interactions"] = {}
optimum["negative_interactions"] = {}
for positive_or_negative_side in ["positive_side", "negative_side"]:
optimum["positive_interactions"][positive_or_negative_side] = kern_density_est.cross_validation(sample_[positive_or_negative_side]["positive_interactions"])
optimum["negative_interactions"][positive_or_negative_side] = "scott"
return optimum
def calculate_or_plot_kern(attribute_of_interaction_, sample_, l_limit, up_limit, number_of_bins, colour = ("r", "b"), weights_ = None, bandwidth_pos = None, bandwidth_neg = None):
prob_ = {}
bins_ = {}
import kern_density_est
kern_density_est.plot_atr = plot_atr_kernel
xgrid = [[],[]]
xgrid[0] = np.linspace(l_limit, up_limit, number_of_bins[0])
xgrid[1] = np.linspace(l_limit, up_limit, number_of_bins[1])
if domain:
if attribute_of_interaction_ == "distance":
#prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_scipy_gaus_weighted(sample_["positive_interactions"], colour[0], xgrid[0], weights = weights_["positive_interactions"], bandwidth = "scott", factor = None)#bandwidth_pos)
#prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_scipy_gaus_weighted(sample_["negative_interactions"], colour[1], xgrid[1], weights = weights_["negative_interactions"], bandwidth = "scott", factor = None)#bandwidth_neg)
prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_scipy_gaus_weighted(sample_["positive_interactions"], colour[0], xgrid[0], weights = weights_["positive_interactions"], bandwidth = bandwidth_pos, plot_atr = True)#bandwidth_pos)
prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_scipy_gaus_weighted(sample_["negative_interactions"], colour[1], xgrid[1], weights = weights_["negative_interactions"], bandwidth = bandwidth_neg, plot_atr = True)#bandwidth_neg)
#bandwidth_pos = kern_density_est.cross_validation(sample_["positive_interactions"])# * sample_["positive_interactions"].std(ddof=1)
#bandwidth_neg = kern_density_est.cross_validation(sample_["negative_interactions"])# * sample_["negative_interactions"].std(ddof=1)
#prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kernel_weighted_samples(sample_["positive_interactions"], colour[0], xgrid[0], weights = weights_["positive_interactions"], fft = False, bw=bandwidth_pos)
#prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kernel_weighted_samples(sample_["negative_interactions"], colour[1], xgrid[1], weights = weights_["negative_interactions"], fft = False, bw=bandwidth_neg)
else:
#kernel_ = "gaussian"
#bandwidth_pos = kern_density_est.cross_validation(sample_["positive_interactions"], kernel = kernel_) # kernel =
#bandwidth_neg = kern_density_est.cross_validation(sample_["negative_interactions"], kernel = kernel_)
#prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_sklearn_expon(sample_["positive_interactions"], colour[0], xgrid[0], bandwidth = bandwidth_pos, kernel_ = kernel_)
#prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_sklearn_expon(sample_["negative_interactions"], colour[1], xgrid[1], bandwidth = bandwidth_neg, kernel_ = kernel_)
bandwidth_pos = kern_density_est.chrom_cross_validation_correlation(prior_elements, data_set_name, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions", plot_likelihood_function = False)
bandwidth_neg = kern_density_est.chrom_cross_validation_correlation(prior_elements, data_set_name, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "negative_interactions", plot_likelihood_function = False)
prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_scipy_gaus(sample_["positive_interactions"], colour[0], xgrid[0], bandwidth = bandwidth_pos)
prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_scipy_gaus(sample_["negative_interactions"], colour[1], xgrid[1], bandwidth = bandwidth_neg)
else:
#if attribute_of_interaction_ == "distance": bandwidth_pos = optimum["distance"][ite]
#else: bandwidth_pos = optimum[data_set_name]
if attribute_of_interaction_ == "distance" and positive_or_negative_side == "negative_side": label_1, label_2 = None, None
else: label_1, label_2 = "positive interactions", "negative interactions"
if likelihood_cross_validation:
if attribute_of_interaction_ == "correlation":
bandwidth_pos = kern_density_est.chrom_cross_validation_correlation(prior_elements, data_set_name, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions", plot_likelihood_function = False)
print bandwidth_pos
prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_scipy_gaus(sample_["positive_interactions"], colour[0], xgrid[0], bandwidth = bandwidth_pos, label = label_1)
prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_scipy_gaus(sample_["negative_interactions"], colour[1], xgrid[1], bandwidth = "scott", label = label_2)
else:
bandwidth_pos = kern_density_est.cross_validation(sample_["positive_interactions"])# * sample_["positive_interactions"].std(ddof=1)
print bandwidth_pos
prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_scipy_gaus(sample_["positive_interactions"], colour[0], xgrid[0], bandwidth=bandwidth_pos, label = label_1)
prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_scipy_gaus(sample_["negative_interactions"], colour[1], xgrid[1], bandwidth="scott", label = label_2)
if use_smooth_prior_for_estimation: return prob_, bins_
else: return [[], []], [[], []]
