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plot_stan.py
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#!/usr/bin/env python
import os
import math
import glob
import pickle
import fnmatch
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.metrics import roc_curve, auc
def autolabel(rects,labels):
"""
Attach a text label above each bar displaying its height
"""
i=0
for rect,label in zip(rects,labels):
height = rect.get_height()
plt.text(rect.get_x() + rect.get_width()/2., 1.001*height,round(label,2),ha='center', va='bottom',fontsize=8)
i+=1
def get_StanOutput_summary(workdir):
dir = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/" + workdir
all_file = sorted(os.listdir(dir))
file_dict = {}
for pkl in all_file:
if ".pickle" in pkl:
name=pkl.rsplit(".pickle")[0].rsplit("_")
file_dict[pkl] = {}
for each_value in name:
file_dict[pkl][each_value.split("-")[0]] = float(each_value.split("-")[1])
#print(name)
else:continue
file_dict_pd = pd.DataFrame(file_dict).transpose()
for each in file_dict_pd.columns:
print (each+'-unique',sorted(file_dict_pd[each].unique()))
def get_ROC_AUC(path, file_pos, file_neg, if_prob=None, if_baseline=None):
if if_prob == True and if_baseline == True:
#print ("TrueTrue")
file_pos = '_'.join(file_pos.rsplit(".pickle")[0].rsplit("_")[0:4])+'.pickle'
file_neg = '_'.join(file_neg.rsplit(".pickle")[0].rsplit("_")[0:4])+'.pickle'
prob1 = pickle.load(open(path+file_pos,"rb"))
prob2 = pickle.load(open(path+file_neg,"rb"))
fpr, tpr, _ = roc_curve([0 for i in range(len(prob1))] + [1 for i in range(len(prob2))], prob1 + prob2)
elif if_prob == True and if_baseline != True:
#print ("TrueFalse")
prob1 = pickle.load(open(path+file_pos,"rb"))
prob2 = pickle.load(open(path+file_neg,"rb"))
fpr, tpr, _ = roc_curve([1 for i in range(len(prob1))] + [0 for i in range(len(prob2))], prob1 + prob2)
else: #if_prob != True and if_baseline != True:
if if_baseline == True:
#print ("here")
file_pos = '_'.join(file_pos.rsplit(".pickle")[0].rsplit("_")[0:4])+'.pickle'
file_neg = '_'.join(file_neg.rsplit(".pickle")[0].rsplit("_")[0:4])+'.pickle'
prob1 = pickle.load(open(path+file_pos,"rb"))
prob2 = pickle.load(open(path+file_neg,"rb"))
prob1_t = [abs(x - 1) for x in prob1]
prob2_t = [abs(x - 1) for x in prob2]
fpr, tpr, _ = roc_curve([1 for i in range(len(prob1_t))] + [0 for i in range(len(prob2_t))], prob1_t + prob2_t)
return(fpr, tpr, round(auc(fpr,tpr),3))
def CompareAUC_model_baseline(label,error,theta_pos,theta_neg,if_prob=None,gene=None,hets=None,depth=None,sigma=None):
source = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/"
#workdir = "ase/new_simulation_05_error/"
#workdir = "ase/new_simulation_01_error/"
#workdir = "ase/new_simulation/"
thetas=[theta_pos,theta_neg]
if thetas.count(None) >0:
raise Exception('Both thetas could not be None. The number of None from input was {}'.format(thetas.count(None)))
var=[gene,hets,depth,sigma]
if var.count(None) !=1:
raise Exception('One variables have to be set to None. The number of None from input was {}'.format(var.count(None)))
if if_prob != True and if_prob !=None:
raise Exception('if_prob == True - means using probability, if_prob == None - means using theta')
if if_prob == True:
path_model = source + label + "/"+ error +"/output_pkl/model_prob/"
path_base = source + label + "/"+ error + "/output_pkl/baseline_prob/"
else:
path_model = source + label + "/"+ error + "/output_pkl/model_med/"
path_base = source + label + "/"+ error + "/output_pkl/baseline_theta/"
var_map_np = np.array(['g','h','d','s'])
var_fullname_map_np = np.array(['gene','hets','depth','sigma'])
full_var_map_np = np.array([gene, hets, depth, sigma])
valid_var_np = var_map_np[np.array(var) != None]
variable_var_np = var_fullname_map_np[np.array(var) == None]
fixed_var_np = var_fullname_map_np[np.array(var) != None]
valid_full_var_np = full_var_map_np[np.array(var) != None]
h=hets
s=sigma
g=gene
d=depth
all_file = sorted(os.listdir(path_model))
file_dict = {}
for pkl in all_file:
if ".pickle" in pkl:
name=pkl.rsplit(".pickle")[0].rsplit("_")
file_dict[pkl] = {}
for each_value in name:
file_dict[pkl][each_value.split("-")[0]] = float(each_value.split("-")[1])
else:continue
file_dict_pd = pd.DataFrame(file_dict).transpose()
file_dict_pd['file'] = file_dict_pd.index
pos_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta_pos)].sort_values(['d','h','g','s'])
neg_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta_neg)].sort_values(['d','h','g','s'])
current_group_pos_list = pos_pd.index
current_group_neg_list = neg_pd.index
labels= " "
var_dict = {"gene":g, "hets": h, "depth": d, "sigma": s}
for each in variable_var_np:
if each != var_fullname_map_np[np.array(var) == None][0]:
labels = each
d_group = pos_pd[var_map_np[np.array(var) == None][0]].unique()
AUC=[]
x_var = []
AUC_base=[]
x_var_base = []
for idx in range(len(current_group_pos_list)):
# model
_,_,auc12 = get_ROC_AUC(path_model, current_group_pos_list[idx],current_group_neg_list[idx],if_prob=if_prob)
AUC.append(auc12)
x_var.append(file_dict[current_group_pos_list[idx]][var_map_np[np.array(var) == None][0]])
# baseline
if if_prob == True:
current_file_pos = current_group_pos_list[idx].rsplit("_",1)[0]+".pickle"
current_file_neg = current_group_neg_list[idx].rsplit("_",1)[0]+".pickle"
else:
current_file_pos = current_group_pos_list[idx]
current_file_neg = current_group_neg_list[idx]
_,_,auc34 = get_ROC_AUC(path_base, current_file_pos,current_file_neg,if_prob=if_prob,if_baseline=True)
AUC_base.append(auc34)
x_var_base.append(file_dict[current_group_pos_list[idx]][var_map_np[np.array(var) == None][0]])
####### Plot
plt.figure(figsize=(12,8))
barWidth = 0.3
bars1 = AUC
bars2 = AUC_base
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]
# Make the plot
if "01" in path_model:
rects1=plt.bar(r1, bars1, color='teal', width=barWidth, edgecolor='white', label='%s Model with error 0.1'%(label))
rects2=plt.bar(r2, bars2, color='salmon', width=barWidth, edgecolor='white', label='%s Baseline with error 0.1'%(label))
elif "05" in path_model:
rects1=plt.bar(r1, bars1, color='darkblue', width=barWidth, edgecolor='white', label='%s Model with error 0.5'%(label))
rects2=plt.bar(r2, bars2, color='darksalmon', width=barWidth, edgecolor='white', label='%s Baseline with error 0.5'%(label))
else:
rects1=plt.bar(r1, bars1, color='lightskyblue', width=barWidth, edgecolor='white', label='%s Model with no error'%(label))
rects2=plt.bar(r2, bars2, color='lightsalmon', width=barWidth, edgecolor='white', label='%s Baseline with no error'%(label))
# Add xticks on the middle of the group bars
plt.xlabel(var_fullname_map_np[np.array(var) == None][0], fontweight='bold',fontsize=12)
plt.xticks([r + barWidth for r in range(len(bars1))], x_var)
plt.ylabel("AUC")
# Create legend & Show graphic
plt.axhline(y=1, linewidth=1, linestyle='--',color='r')
autolabel(rects1,AUC)
autolabel(rects2,AUC_base)
plt.ylim(0.5,1.2)
plt.legend(loc=2,fontsize=14)
plt.show()
def CompareAUC_3model(label,theta_pos,theta_neg,if_prob=None,gene=None,hets=None,depth=None,sigma=None):
source = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/"
thetas=[theta_pos,theta_neg]
if thetas.count(None) >0:
raise Exception('Both thetas could not be None. The number of None from input was {}'.format(thetas.count(None)))
var=[gene,hets,depth,sigma]
if var.count(None) !=1:
raise Exception('One variables have to be set to None. The number of None from input was {}'.format(var.count(None)))
if if_prob != True and if_prob !=None:
raise Exception('if_prob == True - means using probability, if_prob == None - means using theta')
if if_prob == True:
model_path = source + label + "/"+ "new_simulation/output_pkl/model_prob/"
model_path_e1 = source + label + "/"+ "new_simulation_01_error/output_pkl/model_prob/"
model_path_e5 = source + label + "/"+ "new_simulation_05_error/output_pkl/model_prob/"
else:
model_path = source + label + "/"+ "new_simulation/output_pkl/model_med/"
model_path_e1 = source + label + "/"+ "new_simulation_01_error/output_pkl/model_med/"
model_path_e5 = source + label + "/"+ "new_simulation_05_error/output_pkl/model_med/"
var_map_np = np.array(['g','h','d','s'])
var_fullname_map_np = np.array(['gene','hets','depth','sigma'])
full_var_map_np = np.array([gene, hets, depth, sigma])
valid_var_np = var_map_np[np.array(var) != None]
variable_var_np = var_fullname_map_np[np.array(var) == None]
fixed_var_np = var_fullname_map_np[np.array(var) != None]
valid_full_var_np = full_var_map_np[np.array(var) != None]
h=hets
s=sigma
g=gene
d=depth
all_file = sorted(os.listdir(model_path))
file_dict = {}
for pkl in all_file:
if ".pickle" in pkl:
name=pkl.rsplit(".pickle")[0].rsplit("_")
file_dict[pkl] = {}
for each_value in name:
file_dict[pkl][each_value.split("-")[0]] = float(each_value.split("-")[1])
else:continue
file_dict_pd = pd.DataFrame(file_dict).transpose()
file_dict_pd['file'] = file_dict_pd.index
pos_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta_pos)].sort_values(['d','h','g','s'])
neg_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta_neg)].sort_values(['d','h','g','s'])
current_group_pos_list = pos_pd.index
current_group_neg_list = neg_pd.index
labels= " "
var_dict = {"gene":g, "hets": h, "depth": d, "sigma": s}
for each in variable_var_np:
if each != var_fullname_map_np[np.array(var) == None][0]:
labels = each
d_group = pos_pd[var_map_np[np.array(var) == None][0]].unique()
AUC=[]
x_var = []
AUC_e1=[]
AUC_e5=[]
x_var_e1 = []
x_var_e5 = []
for idx in range(len(current_group_pos_list)):
current_file_pos = current_group_pos_list[idx]
current_file_neg = current_group_neg_list[idx]
# model without error, baseline without error
_,_,auc12 = get_ROC_AUC(model_path, current_group_pos_list[idx],current_group_neg_list[idx],if_prob=if_prob)
AUC.append(auc12)
x_var.append(file_dict[current_group_pos_list[idx]][var_map_np[np.array(var) == None][0]])
# model with 10% error, baseline with 10% error
_,_,auc34 = get_ROC_AUC(model_path_e1, current_file_pos,current_file_neg,if_prob=if_prob)
AUC_e1.append(auc34)
#x_var_e1.append(file_dict[current_group_pos_list[idx]][var_map_np[np.array(var) == None][0]])
# model with 50% error, baseline with 50% error
_,_,auc56 = get_ROC_AUC(model_path_e5, current_file_pos,current_file_neg,if_prob=if_prob)
AUC_e5.append(auc56)
#x_var_e5.append(file_dict[current_group_pos_list[idx]][var_map_np[np.array(var) == None][0]])
####### Plot
plt.figure(figsize=(12,8))
barWidth = 0.3
bars1 = AUC
bars2 = AUC_e1
bars3 = AUC_e5
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]
r3 = [x + barWidth for x in r2]
# Make the plot
rects1=plt.bar(r1, bars1, color='lightskyblue', width=barWidth, edgecolor='white', label='No error')
rects2=plt.bar(r2, bars2, color='teal', width=barWidth, edgecolor='white', label='10% error')
rects3=plt.bar(r3, bars3, color='darkblue', width=barWidth, edgecolor='white', label='50% error')
# Add xticks on the middle of the group bars
plt.xlabel(var_fullname_map_np[np.array(var) == None][0], fontweight='bold',fontsize=12)
plt.xticks([r + barWidth for r in range(len(bars1))], x_var)
plt.ylabel("AUC")
# Create legend & Show graphic
autolabel(rects1,AUC)
autolabel(rects2,AUC_e1)
autolabel(rects3,AUC_e5)
plt.ylim(0.5,1.2)
plt.axhline(y=1, linewidth=1, linestyle='--',color='r')
plt.legend(loc=2,fontsize=20)
plt.title("The distribution of Model AUC",fontsize=25)
plt.show()
def CompareAUC_3baseline(label,theta_pos,theta_neg, data=None,gene=None,hets=None,depth=None):
source = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/"
thetas = [theta_pos, theta_neg]
if thetas.count(None) >0:
raise Exception('Both thetas could not be None. The number of None from input was {}'.format(thetas.count(None)))
var=np.array([gene,hets,depth])
name = np.array(['gene','hets','depth'])
model_path = source + label + "/"+ "new_simulation/output_pkl/baseline_" + data +"/"
model_path_e1 = source + label + "/"+ "new_simulation_01_error/output_pkl/baseline_"+ data + "/"
model_path_e5 = source + label + "/"+ "new_simulation_05_error/output_pkl/baseline_"+ data + "/"
if_prob = None
if "prob" in data:
if_prob = True
file_pos = "g-%s_h-%s_d-%s_t-%s.pickle" %(gene, hets,depth, theta_pos)
file_neg = "g-%s_h-%s_d-%s_t-%s.pickle" %(gene, hets,depth, theta_neg)
if "None" in file_pos:
#print ("correct")
listing = glob.glob(model_path+file_pos.replace("None","*"))
#print (model_path+file_pos.replace("None","*"))
AUC = []
AUC_e1 = []
AUC_e5 = []
x_var = []
#print (listing)
for each_file in listing:
cur_pos = each_file.rsplit("/",1)[1]
cur_neg = cur_pos.replace(str(theta_pos), str(theta_neg))
_,_,auc12 = get_ROC_AUC(model_path, cur_pos,cur_neg,if_prob=if_prob,if_baseline=True)
AUC.append(auc12)
_,_,auc34 = get_ROC_AUC(model_path_e1, cur_pos,cur_neg,if_prob=if_prob,if_baseline=True)
AUC_e1.append(auc34)
_,_,auc56 = get_ROC_AUC(model_path_e5, cur_pos,cur_neg,if_prob=if_prob,if_baseline=True)
AUC_e5.append(auc56)
x_var.append(float(np.array(cur_pos.split("_")[:3])[var==None][0].split("-")[1]))
plt.figure(figsize=(12,8))
barWidth = 0.3
bars1 = AUC
bars2 = AUC_e1
bars3 = AUC_e5
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]
r3 = [x + barWidth for x in r2]
# Make the plot
rects1=plt.bar(r1, bars1, color='lightskyblue', width=barWidth, edgecolor='white', label='No error')
rects2=plt.bar(r2, bars2, color='teal', width=barWidth, edgecolor='white', label='10% error')
rects3=plt.bar(r3, bars3, color='darkblue', width=barWidth, edgecolor='white', label='50% error')
# Add xticks on the middle of the group bars
plt.xlabel(name[var==None][0])
plt.xticks([r + barWidth for r in range(len(bars1))], sorted(x_var))
plt.ylabel("AUC")
# Create legend & Show graphic
autolabel(rects1,AUC)
autolabel(rects2,AUC_e1)
autolabel(rects3,AUC_e5)
plt.ylim(0.5,1.2)
plt.axhline(y=1, linewidth=1, linestyle='--',color='r')
plt.legend(loc=2,fontsize=14)
plt.title("The distribution of Baseline AUC",fontsize=14)
plt.show()
else:
AUC = []
AUC_e1 = []
AUC_e5 = []
_,_,auc12 = get_ROC_AUC(model_path, cur_pos,cur_neg,if_prob=if_prob,if_baseline=True)
AUC.append(auc12)
_,_,auc34 = get_ROC_AUC(model_path_e1, cur_pos,cur_neg,if_prob=if_prob,if_baseline=True)
AUC_e1.append(auc34)
_,_,auc56 = get_ROC_AUC(model_path_e5, cur_pos,cur_neg,if_prob=if_prob,if_baseline=True)
AUC_e5.append(auc56)
plt.figure(figsize=(15,8))
barWidth = 0.35
bars1 = AUC
bars2 = AUC_e1
bars3 = AUC_e5
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]
r3 = [x + barWidth for x in r2]
# Make the plot
rects1=plt.bar(r1, bars1, color='lightskyblue', width=barWidth, edgecolor='white', label='No error')
rects2=plt.bar(r2, bars2, color='teal', width=barWidth, edgecolor='white', label='10% error')
rects3=plt.bar(r3, bars3, color='darkblue', width=barWidth, edgecolor='white', label='50% error')
# Add xticks on the middle of the group bars
#plt.xlabel(var_fullname_map_np[np.array(var) == None][0], fontweight='bold',fontsize=12)
#plt.xticks([r + barWidth for r in range(len(bars1))], x_var)
plt.ylabel("AUC")
# Create legend & Show graphic
autolabel(rects1,AUC)
autolabel(rects2,AUC_e1)
autolabel(rects3,AUC_e5)
plt.ylim(0.5,1.2)
plt.axhline(y=1, linewidth=1, linestyle='--',color='r')
plt.legend(loc=2,fontsize=14)
plt.title("The distribution of Baseline AUC",fontsize=14)
plt.show()
def Plot_ROC_comparison_fix2(label,workdir,theta_pos,theta_neg,if_prob=None,prob_t=None,gene=None,hets=None,depth=None,sigma=None, num = None):
source = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/"
# judge whether 2 of the 4 are None, and 2 of the 4 are not None
thetas=[theta_pos,theta_neg]
if thetas.count(None) >0:
raise Exception('Both thetas could not be None. The number of None from input was {}'.format(thetas.count(None)))
var=[gene,hets,depth,sigma]
if var.count(None)!=2:
raise Exception('Two variables have to be set to None. The number of None from input was {}'.format(var.count(None)))
# data directory is known
if if_prob == None:
path=source + label + "/" + workdir+"/output_pkl/model_med/"
else:
if prob_t == None:
path=source + label + "/" + workdir+"/output_pkl/model_prob/"
#else:
# path=source + label + "/" + workdir+"/output_pkl/model_prob2/"
var_map_np = np.array(['g','h','d','s'])
var_fullname_map_np = np.array(['gene','hets','depth','sigma'])
full_var_map_np = np.array([gene, hets, depth, sigma])
valid_var_np = var_map_np[np.array(var) != None]
variable_var_np = var_fullname_map_np[np.array(var) == None]
fixed_var_np = var_fullname_map_np[np.array(var) != None]
valid_full_var_np = full_var_map_np[np.array(var) != None]
if hets is not None:
h=hets
if sigma is not None:
s=sigma
if gene is not None:
g=gene
if depth is not None:
d=depth
all_file = sorted(os.listdir(path))
file_dict = {}
for pkl in all_file:
if ".pickle" in pkl:
name=pkl.rsplit(".pickle")[0].rsplit("_")
file_dict[pkl] = {}
for each_value in name:
file_dict[pkl][each_value.split("-")[0]] = float(each_value.split("-")[1])
else:continue
file_dict_pd = pd.DataFrame(file_dict).transpose()
file_dict_pd['file'] = file_dict_pd.index
pos_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd['t'] == theta_pos)].sort_values(['d','h','g','s'])
neg_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd['t'] == theta_neg)].sort_values(['d','h','g','s'])
d_group = pos_pd[var_map_np[np.array(var) == None][0]].unique()
#print (d_group)
if num == None:
num = 3
else:
num = int(num)
row = math.ceil(float(len(d_group))/num)
fig, axs = plt.subplots(row, num, figsize = (20,5*row))
if (row * num > len(d_group)):
for i in range(row * num - len(d_group)):
axs.flat[-1-i].set_axis_off()
xlabels = "Fixed parameters "
for i, each in enumerate(d_group):
current_group_pos_list = pos_pd[pos_pd[var_map_np[np.array(var) == None][0]] == each].index
current_group_neg_list = neg_pd[neg_pd[var_map_np[np.array(var) == None][0]] == each].index
xlabels = "Fixed parameters "
for idx in range(len(current_group_pos_list)):
fpr, tpr, roc_auc = get_ROC_AUC(path, current_group_pos_list[idx], current_group_neg_list[idx],if_prob=if_prob)
g=current_group_pos_list[idx].rsplit(".pickle")[0].rsplit("_")[0].rsplit("-")[1]
h=current_group_pos_list[idx].rsplit(".pickle")[0].rsplit("_")[1].rsplit("-")[1]
d=current_group_pos_list[idx].rsplit(".pickle")[0].rsplit("_")[2].rsplit("-")[1]
s=current_group_pos_list[idx].rsplit(".pickle")[0].rsplit("_")[4].rsplit("-")[1]
#labels=" sigma:"+str(s)
labels= " AUC: "+str(roc_auc)+" "
var_dict = {"gene":g, "hets": h, "depth": d, "sigma": s}
for each in variable_var_np:
if each != var_fullname_map_np[np.array(var) == None][0]:
labels += each+":"+var_dict[each]+' '
xlabels = "Fixed parameters "
for each in fixed_var_np:
xlabels += each+":"+var_dict[each]+' '
#axs[i].suptitle(xlabels)
axs.flat[i].set_xlabel("FPR")
axs.flat[i].set_ylabel("TPR")
axs.flat[i].set_title(var_fullname_map_np[np.array(var) == None][0]+":" + var_dict[var_fullname_map_np[np.array(var) == None][0]])
axs.flat[i].plot(fpr, tpr, label = labels)
axs.flat[i].legend()
plt.suptitle(xlabels,fontsize=20)
plt.show()
def Plot_theta_group_distri(label,workdir,theta,gene=None,hets=None,depth=None,sigma=None, if_log = None):
source = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/"
var=[gene,hets,depth,sigma]
if var.count(None) !=1:
raise Exception('One variables have to be set to None. The number of None from input was {}'.format(var.count(None)))
path_model = source + label + "/" + workdir+"/output_pkl/model_med/"
path_base = source + label + "/" + workdir+"/output_pkl/baseline_theta_pool/"
var_map_np = np.array(['g','h','d','s'])
var_fullname_map_np = np.array(['gene','hets','depth','sigma'])
full_var_map_np = np.array([gene, hets, depth, sigma])
valid_var_np = var_map_np[np.array(var) != None]
variable_var_np = var_fullname_map_np[np.array(var) == None]
fixed_var_np = var_fullname_map_np[np.array(var) != None]
valid_full_var_np = full_var_map_np[np.array(var) != None]
h=hets
s=sigma
g=gene
d=depth
all_file = sorted(os.listdir(path_model))
file_dict = {}
for pkl in all_file:
if ".pickle" in pkl:
name=pkl.rsplit(".pickle")[0].rsplit("_")
file_dict[pkl] = {}
for each_value in name:
file_dict[pkl][each_value.split("-")[0]] = float(each_value.split("-")[1])
else:continue
file_dict_pd = pd.DataFrame(file_dict).transpose()
file_dict_pd['file'] = file_dict_pd.index
pos_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta)].sort_values(['d','h','g','s'])
current_group_pos_list = pos_pd.index
labels= " "
var_dict = {"gene":g, "hets": h, "depth": d, "sigma": s}
for each in variable_var_np:
if each != var_fullname_map_np[np.array(var) == None][0]:
labels = each
d_group = pos_pd[var_map_np[np.array(var) == None][0]].unique()
theta_dist=[]
x_var = []
for idx in range(len(current_group_pos_list)):
model_rmse = get_theta_group(theta, current_group_pos_list[idx], path_model, prob=None, base=None, hue = "model" ,if_log= if_log)
theta_dist.extend(model_rmse)
base_rmse = get_theta_group(theta, current_group_pos_list[idx], path_base, prob=None, base=None, hue = "baseline",if_log= if_log)
theta_dist.extend(base_rmse)
#x_var.append(file_dict[current_group_pos_list[idx]][var_map_np[np.array(var) == None][0]])
theta_dist_pd = pd.DataFrame(theta_dist)
####### Plot
plt.figure(figsize=(12,9))
color_map = {"model":"lightskyblue", "baseline":"lightsalmon"}
sns.violinplot(x="h", y="theta", hue="hue", data=theta_dist_pd, palette=color_map, split=True)
plt.axhline(y= (math.log2(theta+1e-18) if if_log == True else theta), color='r', linestyle='-')
plt.ylabel(r'$\log_2 \theta$' if if_log == True else r'$\theta$',fontsize=20)
plt.legend(title = "Category",fontsize=14)
plt.title(r"True $\theta$ = "+ theta.__str__())
def get_theta_group(true_theta, file_name, path, prob=None, base=None, hue = None, if_log = None):
if "baseline" in path:
file_name = file_name.rsplit("_",1)[0]+".pickle"
theta_hat = pickle.load(open(path+file_name,'rb'))
h = int(file_name.split("_")[1].split("-")[1])
ret_list = []
for each in theta_hat:
ret_list.append({"h":h, "hue": hue, "theta": math.log2(each+1e-18) if if_log == True else each})
return (ret_list)
def Plot_prob_group_distri(workdir,theta,gene=None,hets=None,depth=None,sigma=None):
source = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/ase/"
var=[gene,hets,depth,sigma]
if var.count(None) !=1:
raise Exception('One variables have to be set to None. The number of None from input was {}'.format(var.count(None)))
path_model = source + workdir+"/output_pkl/model_prob/"
path_base = source + workdir+"/output_pkl/baseline_prob/"
var_map_np = np.array(['g','h','d','s'])
var_fullname_map_np = np.array(['gene','hets','depth','sigma'])
full_var_map_np = np.array([gene, hets, depth, sigma])
valid_var_np = var_map_np[np.array(var) != None]
variable_var_np = var_fullname_map_np[np.array(var) == None]
fixed_var_np = var_fullname_map_np[np.array(var) != None]
valid_full_var_np = full_var_map_np[np.array(var) != None]
h=hets
s=sigma
g=gene
d=depth
all_file = sorted(os.listdir(path_model))
file_dict = {}
for pkl in all_file:
if ".pickle" in pkl:
name=pkl.rsplit(".pickle")[0].rsplit("_")
file_dict[pkl] = {}
for each_value in name:
file_dict[pkl][each_value.split("-")[0]] = float(each_value.split("-")[1])
else:continue
file_dict_pd = pd.DataFrame(file_dict).transpose()
file_dict_pd['file'] = file_dict_pd.index
pos_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta)].sort_values(['d','h','g','s'])
current_group_pos_list = pos_pd.index
labels= " "
var_dict = {"gene":g, "hets": h, "depth": d, "sigma": s}
for each in variable_var_np:
if each != var_fullname_map_np[np.array(var) == None][0]:
labels = each
d_group = pos_pd[var_map_np[np.array(var) == None][0]].unique()
theta_dist=[]
x_var = []
for idx in range(len(current_group_pos_list)):
model_rmse = get_theta_group(theta, current_group_pos_list[idx], path_model, prob=None, base=None, hue = "model")
theta_dist.extend(model_rmse)
base_rmse = get_theta_group(theta, current_group_pos_list[idx], path_base, prob=None, base=None, hue = "baseline")
theta_dist.extend(base_rmse)
theta_dist_pd = pd.DataFrame(theta_dist)
####### Plot
plt.figure(figsize=(12,9))
color_map = {"model":"lightskyblue", "baseline":"lightsalmon"}
sns.violinplot(x="h", y="theta", hue="hue", data=theta_dist_pd, palette=color_map, split=True)
plt.ylabel("Probability",fontsize=20)
plt.legend(title = "Category",fontsize=14,loc=1)
plt.title(r"True $\theta$ = "+ theta.__str__())
def Plot_prob_group_pos_neg_hist(workdir,theta_pos,theta_neg,kind,gene=None,hets=None,depth=None,sigma=None, if_log = None):
source = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/ase/"
var=[gene,hets,depth,sigma]
path_model = source+ workdir+"/output_pkl/model_prob/"
path_base = source + workdir+"/output_pkl/baseline_prob/"
used_path = ""
used_path = (path_model if kind == 'model' else path_base)
var_map_np = np.array(['g','h','d','s'])
var_fullname_map_np = np.array(['gene','hets','depth','sigma'])
full_var_map_np = np.array([gene, hets, depth, sigma])
valid_var_np = var_map_np[np.array(var) != None]
variable_var_np = var_fullname_map_np[np.array(var) == None]
fixed_var_np = var_fullname_map_np[np.array(var) != None]
valid_full_var_np = full_var_map_np[np.array(var) != None]
h=hets
s=sigma
g=gene
d=depth
all_file = sorted(os.listdir(path_model))
file_dict = {}
for pkl in all_file:
if ".pickle" in pkl:
name=pkl.rsplit(".pickle")[0].rsplit("_")
file_dict[pkl] = {}
for each_value in name:
file_dict[pkl][each_value.split("-")[0]] = float(each_value.split("-")[1])
else:continue
file_dict_pd = pd.DataFrame(file_dict).transpose()
file_dict_pd['file'] = file_dict_pd.index
pos_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta_neg)].sort_values(['d','h','g','s'])
pos_pd = pos_pd[(pos_pd['h'] == hets) &(pos_pd['s'] == sigma)]
current_group_pos_list = pos_pd.index
theta_dist=[]
model = get_theta_group_prob(theta_pos, current_group_pos_list[0], used_path, prob=None, base=None, hue = theta_pos,if_log= if_log)
theta_dist.extend(model)
base = get_theta_group_prob(theta_neg, current_group_pos_list[0], used_path, prob=None, base=None, hue = theta_neg,if_log= if_log)
theta_dist.extend(base)
#x_var.append(file_dict[current_group_pos_list[idx]][var_map_np[np.array(var) == None][0]])
theta_dist_pd = pd.DataFrame(theta_dist)
####### Plot
plt.figure(figsize=(12,9))
#sns.violinplot(x="h", y="theta", hue="hue", data=theta_dist_pd, palette="muted", split=True)
theta_dist_pd[theta_dist_pd['hue'] == theta_pos]['theta'].hist(bins = 20, label=str(theta_pos), alpha = 0.35)
theta_dist_pd[theta_dist_pd['hue'] == theta_neg]['theta'].hist(bins = 20,label = str(theta_neg), alpha = 0.35)
#print (theta_dist_pd['theta'].describe())
#plt.axhline(y= (math.log2(theta+1e-18) if if_log == True else theta), color='r', linestyle='-')
#plt.ylabel(r'$\log_2 \theta$' if if_log == True else r'Probability',fontsize=20)
plt.legend(title = "theta",fontsize=14)
plt.xlabel(r"The probability of positive")
def get_theta_group_prob(true_theta, file_name, path, prob=None, base=None, hue = None, if_log = None):
if "baseline_prob" in path:
file_name = file_name.rsplit("_",1)[0]+".pickle"
if true_theta != 1:
file_name = file_name.replace("t-1","t-"+str(true_theta))
#print (file_name)
theta_hat = pickle.load(open(path+file_name,'rb'))
h = int(file_name.split("_")[1].split("-")[1])
ret_list = []
for each in theta_hat:
ret_list.append({"h":h, "hue": hue, "theta": math.log2(each+1e-18) if if_log == True else each})
return (ret_list)
def Plot_prob_group_pos_neg_distri(workdir,theta,theta_neg,kind,gene=None,hets=None,depth=None,sigma=None, if_log = None):
source = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/ase/"
var=[gene,hets,depth,sigma]
if var.count(None) !=1:
raise Exception('One variables have to be set to None. The number of None from input was {}'.format(var.count(None)))
path_model = source + workdir + "/output_pkl/model_prob/"
path_base = source + workdir + "/output_pkl/baseline_prob/"
used_path = ""
used_path = (path_model if kind == 'model' else path_base)
var_map_np = np.array(['g','h','d','s'])
var_fullname_map_np = np.array(['gene','hets','depth','sigma'])
full_var_map_np = np.array([gene, hets, depth, sigma])
valid_var_np = var_map_np[np.array(var) != None]
variable_var_np = var_fullname_map_np[np.array(var) == None]
fixed_var_np = var_fullname_map_np[np.array(var) != None]
valid_full_var_np = full_var_map_np[np.array(var) != None]
h=hets
s=sigma
g=gene
d=depth
all_file = sorted(os.listdir(path_model))
file_dict = {}
for pkl in all_file:
if ".pickle" in pkl:
name=pkl.rsplit(".pickle")[0].rsplit("_")
file_dict[pkl] = {}
for each_value in name:
file_dict[pkl][each_value.split("-")[0]] = float(each_value.split("-")[1])
else:continue
file_dict_pd = pd.DataFrame(file_dict).transpose()
file_dict_pd['file'] = file_dict_pd.index
pos_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta)].sort_values(['d','h','g','s'])
current_group_pos_list = pos_pd.index
labels= " "
var_dict = {"gene":g, "hets": h, "depth": d, "sigma": s}
for each in variable_var_np:
if each != var_fullname_map_np[np.array(var) == None][0]:
labels = each
d_group = pos_pd[var_map_np[np.array(var) == None][0]].unique()
theta_dist=[]
x_var = []
for idx in range(len(current_group_pos_list)):
model_rmse = get_theta_group_prob(theta, current_group_pos_list[idx], used_path, prob=None, base=None, hue = theta,if_log= if_log)
theta_dist.extend(model_rmse)
base_rmse = get_theta_group_prob(theta_neg, current_group_pos_list[idx], used_path, prob=None, base=None, hue = theta_neg,if_log= if_log)
theta_dist.extend(base_rmse)
#x_var.append(file_dict[current_group_pos_list[idx]][var_map_np[np.array(var) == None][0]])
theta_dist_pd = pd.DataFrame(theta_dist)
####### Plot
plt.figure(figsize=(12,9))
#color_map = {"model":"lightskyblue", "baseline":"lightsalmon"}
sns.violinplot(x="h", y="theta", hue="hue", data=theta_dist_pd, palette="muted", split=True)
#print (theta_dist_pd['theta'].describe())
#plt.axhline(y= (math.log2(theta+1e-18) if if_log == True else theta), color='r', linestyle='-')
plt.ylabel(r'$\log_2 \theta$' if if_log == True else r'Probability',fontsize=20)
plt.legend(title = "theta",fontsize=14)
plt.title(r"The probability of positive")
def Plot_prob_group_distri(workdir,theta,gene=None,hets=None,depth=None,sigma=None):
source = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/ase/"
var=[gene,hets,depth,sigma]
if var.count(None) !=1:
raise Exception('One variables have to be set to None. The number of None from input was {}'.format(var.count(None)))
path_model = source + workdir+"/output_pkl/model_prob/"
path_base = source + workdir+"/output_pkl/baseline_prob/"
var_map_np = np.array(['g','h','d','s'])
var_fullname_map_np = np.array(['gene','hets','depth','sigma'])
full_var_map_np = np.array([gene, hets, depth, sigma])
valid_var_np = var_map_np[np.array(var) != None]
variable_var_np = var_fullname_map_np[np.array(var) == None]
fixed_var_np = var_fullname_map_np[np.array(var) != None]
valid_full_var_np = full_var_map_np[np.array(var) != None]
h=hets
s=sigma
g=gene
d=depth
all_file = sorted(os.listdir(path_model))
file_dict = {}
for pkl in all_file:
if ".pickle" in pkl:
name=pkl.rsplit(".pickle")[0].rsplit("_")
file_dict[pkl] = {}
for each_value in name:
file_dict[pkl][each_value.split("-")[0]] = float(each_value.split("-")[1])
else:continue
file_dict_pd = pd.DataFrame(file_dict).transpose()
file_dict_pd['file'] = file_dict_pd.index
pos_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta)].sort_values(['d','h','g','s'])
current_group_pos_list = pos_pd.index
labels= " "
var_dict = {"gene":g, "hets": h, "depth": d, "sigma": s}
for each in variable_var_np:
if each != var_fullname_map_np[np.array(var) == None][0]:
labels = each
d_group = pos_pd[var_map_np[np.array(var) == None][0]].unique()
theta_dist=[]
x_var = []
for idx in range(len(current_group_pos_list)):
model_rmse = get_theta_group(theta, current_group_pos_list[idx], path_model, prob=None, base=None, hue = "model")
theta_dist.extend(model_rmse)
base_rmse = get_theta_group(theta, current_group_pos_list[idx], path_base, prob=None, base=None, hue = "baseline")
theta_dist.extend(base_rmse)
#x_var.append(file_dict[current_group_pos_list[idx]][var_map_np[np.array(var) == None][0]])
theta_dist_pd = pd.DataFrame(theta_dist)
####### Plot
plt.figure(figsize=(12,9))
color_map = {"model":"lightskyblue", "baseline":"lightsalmon"}
sns.violinplot(x="h", y="theta", hue="hue", data=theta_dist_pd, palette=color_map, split=True)
#plt.axhline(y= (math.log2(theta+1e-18) if if_log == True else theta), color='r', linestyle='-')
plt.ylabel("Probability",fontsize=20)
plt.legend(title = "Category",fontsize=14)
plt.title(r"True $\theta$ = "+ theta.__str__())
def get_ROC_AUC(path, file_pos, file_neg, if_prob=None, if_baseline=None):
#print (path, file_pos, file_neg)
if if_prob == True and if_baseline == True:
file_pos = '_'.join(file_pos.rsplit(".pickle")[0].rsplit("_")[0:4])+'.pickle'
file_neg = '_'.join(file_neg.rsplit(".pickle")[0].rsplit("_")[0:4])+'.pickle'
prob1 = pickle.load(open(path+file_pos,"rb"))
prob2 = pickle.load(open(path+file_neg,"rb"))
fpr, tpr, _ = roc_curve([0 for i in range(len(prob1))] + [1 for i in range(len(prob2))], prob1 + prob2)
elif if_prob == True and if_baseline != True:
prob1 = pickle.load(open(path+file_pos,"rb"))
prob2 = pickle.load(open(path+file_neg,"rb"))
fpr, tpr, _ = roc_curve([1 for i in range(len(prob1))] + [0 for i in range(len(prob2))], prob1 + prob2)
else: #if_prob != True and if_baseline != True:
if if_baseline == True:
file_pos = '_'.join(file_pos.rsplit(".pickle")[0].rsplit("_")[0:4])+'.pickle'
file_neg = '_'.join(file_neg.rsplit(".pickle")[0].rsplit("_")[0:4])+'.pickle'
prob1 = pickle.load(open(path+file_pos,"rb"))
prob2 = pickle.load(open(path+file_neg,"rb"))
prob1_t = [abs(x - 1) for x in prob1]
prob2_t = [abs(x - 1) for x in prob2]
fpr, tpr, _ = roc_curve([1 for i in range(len(prob1_t))] + [0 for i in range(len(prob2_t))], prob1_t + prob2_t)
return(fpr, tpr, round(auc(fpr,tpr),3))
def Plot_ROC_comparison_fix4(workdir,theta_pos=None,theta_neg=None,if_prob=None,gene=None,hets=None,depth=None,sigma=None):
source = "/data/allenlab/scarlett/software/cmdstan/examples/ase/"
# judge whether 2 of the 4 are None, and 2 of the 4 are not None
thetas=[theta_pos,theta_neg]
if thetas.count(None) >0:
raise Exception('Both thetas could not be None. The number of None from input was {}'.format(thetas.count(None)))
var=[gene,hets,depth,sigma]
if var.count(None)>0:
raise Exception('None of the variables could be set to None. The number of None from input was {}'.format(var.count(None)))
# data directory is known
h=hets
s=sigma
g=gene
d=depth
if if_prob == True:
path_model = source + workdir+"/output_pkl/model_prob/"
path_base = source + workdir+"/output_pkl/baseline_prob/"
base_file_pos = "g-%s_h-%s_d-%s_t-%g.pickle" % (g, h, d, float(theta_pos))
base_file_neg = "g-%s_h-%s_d-%s_t-%s.pickle" % (g, h, d, int(theta_neg))
else:
path_model=workdir+"model_med/"
path_base=workdir+"baseline_theta/"
base_file_pos = "g-%s_h-%s_d-%s_t-%g_s-%s.pickle" % (g, h, d, float(theta_pos),s)
base_file_neg = "g-%s_h-%s_d-%s_t-%s_s-%s.pickle" % (g, h, d, int(theta_neg),s)
model_file_pos = "g-%s_h-%s_d-%s_t-%g_s-%s.pickle" % (g, h, d, float(theta_pos), s)
model_file_neg = "g-%s_h-%s_d-%s_t-%s_s-%s.pickle" % (g, h, d, int(theta_neg), s)
plt.figure(figsize=(12,8))
p1,p2,roc12 = get_ROC_AUC(path_model, model_file_pos, model_file_neg,if_prob=if_prob)
p3,p4,roc34 = get_ROC_AUC(path_base, base_file_pos, base_file_neg,if_prob=if_prob,if_baseline=True)
plt.plot(p1,p2, label = "model" +" AUC: "+ str(roc12),color='lightskyblue',linewidth=3)
plt.plot(p3,p4, label = "baseline" +" AUC: "+ str(roc34),color='lightsalmon',linewidth=3)
plt.ylabel("ROC",fontsize=14)
plt.legend(fontsize=12)
def Plot_ROC_comparison_binomial_fix4(prefix,workdir,theta_pos=None,theta_neg=None,if_prob=None,gene=None,hets=None,depth=None):
source = prefix+"scarlett/software/cmdstan/examples/binomial/output_pkl/"
# judge whether 2 of the 4 are None, and 2 of the 4 are not None
thetas=[theta_pos,theta_neg]
if thetas.count(None) >0:
raise Exception('Both thetas could not be None. The number of None from input was {}'.format(thetas.count(None)))
var=[gene,hets,depth]
if var.count(None)>0:
raise Exception('None of the variables could be set to None. The number of None from input was {}'.format(var.count(None)))
# data directory is known
h=hets
g=gene
d=depth
base_file_pos = "g-%s_h-%s_d-%s_t-%g.pickle" % (g, h, d, float(theta_pos))
base_file_neg = "g-%s_h-%s_d-%s_t-%s.pickle" % (g, h, d, int(theta_neg))
if if_prob != True:
path1 = source + workdir+"/esti1/"
path2 = source + workdir+"/esti2/"
path3 = source + workdir+"/esti3/"
path4 = source + workdir+"/esti4/"
path5 = source + workdir+"/esti5/"
path6 = source + workdir+"/esti6/"
path7 = source + workdir+"/pooled_esti1/"
else:
path1 = source + workdir+"/prob1/"
path2 = source + workdir+"/prob2/"
path3 = source + workdir+"/prob3/"
path4 = source + workdir+"/pooled_prob1/"
path5 = None
path6 = None
path7 = None
if_prob=True
base1="esti"
base2="prob"
p11,p12,roc1 = get_ROC_AUC_binomial(path1, base_file_pos, base_file_neg,if_prob=if_prob)
p21,p22,roc2 = get_ROC_AUC_binomial(path2, base_file_pos, base_file_neg,if_prob=if_prob)
p31,p32,roc3 = get_ROC_AUC_binomial(path3, base_file_pos, base_file_neg,if_prob=if_prob)
p41,p42,roc4 = get_ROC_AUC_binomial(path4, base_file_pos, base_file_neg,if_prob=if_prob)
plt.figure(figsize=(12,8))
if "prob" in path1:
plt.plot(p11,p12, label = base2 +"1 AUC: "+ str(roc1),linewidth=3)
else:
plt.plot(p11,p12, label = base1 +"1 AUC: "+ str(roc1),linewidth=3)
if "prob" in path2:
plt.plot(p21,p22, label = base2 +"2 AUC: "+ str(roc2),linewidth=3)
else:
plt.plot(p21,p22, label = base1 +"2 AUC: "+ str(roc2),linewidth=3)
if "prob" in path3:
plt.plot(p31,p32, label = base2 +"3 AUC: "+ str(roc3),linewidth=3)
else:
plt.plot(p31,p32, label = base1 +"3 AUC: "+ str(roc3),linewidth=3)
if "prob" in path4:
plt.plot(p41,p42, label = "pooled prob1 AUC: "+ str(roc4),linewidth=3)
else:
plt.plot(p41,p42, label = base1 +"4 AUC: "+ str(roc4),linewidth=3)
if path5 != None:
p51,p52,roc5 = get_ROC_AUC_binomial(path5, base_file_pos, base_file_neg)
plt.plot(p51,p52, label = base1 +"5 AUC: "+ str(roc5),linewidth=3)
if path6 != None:
p61,p62,roc6 = get_ROC_AUC_binomial(path6, base_file_pos, base_file_neg)
plt.plot(p61,p62, label = base1 +"6 AUC: "+ str(roc6),linewidth=3)
if path7 != None:
p71,p72,roc7 = get_ROC_AUC_binomial(path7, base_file_pos, base_file_neg)
plt.plot(p71,p72, label = "pooled esti1 AUC: "+ str(roc7),linewidth=3)
plt.ylabel("ROC",fontsize=14)
plt.legend(fontsize=12)
def get_ROC_AUC_binomial(path, file_pos, file_neg, if_prob=None):
prob1 = pickle.load(open(path+file_pos,"rb"))
prob2 = pickle.load(open(path+file_neg,"rb"))
if if_prob==True:
fpr,tpr, _ = roc_curve([0 for i in range(len(prob1))] + [1 for i in range(len(prob2))], prob1 + prob2)
else:
fpr, tpr, _ = roc_curve([1 for i in range(len(prob1))] + [0 for i in range(len(prob2))], prob1 + prob2)
return(fpr, tpr, round(auc(fpr,tpr),3))
def Plot_AUC_group_bar(workdir,theta_pos,theta_neg,if_prob=None,gene=None,hets=None,depth=None,sigma=None):
source = "/data/reddylab/scarlett/1000G/software/cmdstan/examples/ase/"
thetas=[theta_pos,theta_neg]
if thetas.count(None) >0:
raise Exception('Both thetas could not be None. The number of None from input was {}'.format(thetas.count(None)))
var=[gene,hets,depth,sigma]
if var.count(None) !=1:
raise Exception('One variables have to be set to None. The number of None from input was {}'.format(var.count(None)))
if if_prob != True and if_prob !=None:
raise Exception('if_prob == True - means using probability, if_prob == None - means using theta')
if if_prob == True:
path_model = source + workdir+"/output_pkl/model_prob/"
path_base = source + workdir+"/output_pkl/baseline_prob/"
else:
path_model = source + workdir+"/output_pkl/model_med/"
path_base = source + workdir+"/output_pkl/baseline_theta/" # data directory is known
var_map_np = np.array(['g','h','d','s'])
var_fullname_map_np = np.array(['gene','hets','depth','sigma'])
full_var_map_np = np.array([gene, hets, depth, sigma])
valid_var_np = var_map_np[np.array(var) != None]
variable_var_np = var_fullname_map_np[np.array(var) == None]
fixed_var_np = var_fullname_map_np[np.array(var) != None]
valid_full_var_np = full_var_map_np[np.array(var) != None]
h=hets
s=sigma
g=gene
d=depth
all_file = sorted(os.listdir(path_model))
file_dict = {}
for pkl in all_file:
if ".pickle" in pkl:
name=pkl.rsplit(".pickle")[0].rsplit("_")
file_dict[pkl] = {}
for each_value in name:
file_dict[pkl][each_value.split("-")[0]] = float(each_value.split("-")[1])
else:continue
file_dict_pd = pd.DataFrame(file_dict).transpose()
file_dict_pd['file'] = file_dict_pd.index
pos_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta_pos)].sort_values(['d','h','g','s'])
neg_pd = file_dict_pd[(file_dict_pd[valid_var_np[0]] == valid_full_var_np[0])&(file_dict_pd[valid_var_np[1]] == valid_full_var_np[1])&(file_dict_pd[valid_var_np[2]] == valid_full_var_np[2])&(file_dict_pd['t'] == theta_neg)].sort_values(['d','h','g','s'])
current_group_pos_list = pos_pd.index
current_group_neg_list = neg_pd.index
labels= " "
var_dict = {"gene":g, "hets": h, "depth": d, "sigma": s}
for each in variable_var_np:
if each != var_fullname_map_np[np.array(var) == None][0]:
labels = each
d_group = pos_pd[var_map_np[np.array(var) == None][0]].unique()
AUC=[]
x_var = []
AUC_base=[]
x_var_base = []
for idx in range(len(current_group_pos_list)):
# model
_,_,auc12 = get_ROC_AUC(path_model, current_group_pos_list[idx],current_group_neg_list[idx],if_prob=if_prob)
AUC.append(auc12)
x_var.append(file_dict[current_group_pos_list[idx]][var_map_np[np.array(var) == None][0]])