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CSHMM_train.py
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CSHMM_train.py
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import numpy as np
import argparse
from cvxpy import *
#import random
import progressbar
from collections import defaultdict
from scipy.stats import spearmanr
import time
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.cluster import KMeans,SpectralClustering
from scipy.spatial import distance
from numpy import inf
import pickle
import multiprocessing as mp
import sys
def load_data_2(file_name,max_gene):
print 'loading data......'
lines=open(file_name).readlines()
#print lines
head=''
cell_names=lines[0].replace('\n','').split('\t')[1:-1]
cell_times=np.array(map(int,map(float,lines[1].replace('\n','').split('\t')[1:-1])))
cell_labels=[]
gene_exps=[]
gene_names=[]
for i,name in enumerate(cell_names):
splits=name.split('_')
cell_lab = splits[1]
if 'Day' in cell_lab:
cell_lab='NA'
cell_labels.append(cell_lab)
for line in lines[2:]:
line=line.replace('\n','')
splits=line.split('\t')[:-1]
gene_names.append(splits[0])
gene_exp=map(float,splits[1:])
gene_exps.append(gene_exp)
cell_exps=np.transpose(np.array(gene_exps))
gene_names=np.array(gene_names)
rm_col=np.all(cell_exps<0.1,axis=0)#remove all < 0.1 genes
n_cell,n_gene = cell_exps.shape
for j in range(n_gene):
if np.count_nonzero(cell_exps[:,j])<n_cell/4: #remove the gene that express in less than 25% of cells
rm_col[j]=True
cell_exps=cell_exps[:,~rm_col]
gene_names=gene_names[~rm_col]
cell_exps=np.log2(cell_exps+1)
n_cell,n_gene = cell_exps.shape
if n_gene>max_gene: #select the top [max_gene] genes by variance
cell_exps_var=np.var(cell_exps,axis=0)
sort_index = np.argsort(-cell_exps_var)
select_gene=sort_index[:max_gene]
cell_exps=cell_exps[:,select_gene]
gene_names=gene_names[select_gene]
n_cell,n_gene = cell_exps.shape
print n_cell, ' cell loaded with ',n_gene,' selected'
print np.unique(np.array(cell_labels),return_counts=True)
return cell_names,cell_times,cell_labels,cell_exps,gene_names
def load_data(file_name,max_gene):
print 'loading data......'
lines=open(file_name).readlines()
head=lines[0].replace('\n','')
cell_names=[]
cell_day=[]
cell_labels=[]
cell_exps=[]
gene_names=np.array(head.split('\t')[3:])
for line in lines[1:]:
line=line.replace('\n','')
splits=line.split('\t')
cell_name=splits[0]
day=int(splits[1])
label=splits[2]
gene_exp=splits[3:]
cell_names.append(cell_name)
cell_day.append(day)
cell_labels.append(label)
cell_exps.append(map(float,gene_exp))
cell_exps=np.array(cell_exps)
n_cell,n_gene = cell_exps.shape
if n_gene>max_gene: #select the top [max_gene] genes by variance
cell_exps_var=np.var(cell_exps,axis=0)
sort_index = np.argsort(-cell_exps_var)
select_gene=sort_index[:max_gene]
cell_exps=cell_exps[:,select_gene]
gene_names=gene_names[select_gene]
n_cell,n_gene = cell_exps.shape
n_cell,n_gene = cell_exps.shape
print n_cell, ' cell loaded with ',n_gene,' genes selected'
return cell_names,cell_day,cell_labels,cell_exps,gene_names
def init_var_Jun(init_file,cell_names,cell_times,cell_exps,cell_labels):
print 'initializing parameters and hidden variable with Juns model structure......'
st_line=open(init_file).readlines()[0].replace('\n','').split('\t')
c_line=open(init_file).readlines()[1].replace('\n','').split('\t')
n_path=len(st_line)+1
n_state=n_path+1
n_cell,n_gene = cell_exps.shape
path_info=[]
adj_mat=np.zeros((n_state,n_state))
adj_mat[0,1]=1
for i in range(n_path):
path_info.append(defaultdict(lambda:[]))
path_info[0]['Sp_idx']=0
path_info[0]['level']=0
for i in range(n_path):
path_info[i]['Sc_idx']=i+1
path_info[i]['ID']=i
for line in st_line:
pa,pb = map(int,line.split(' '))
path_info[pa]['child_path'].append(pb)
path_info[pb]['parent_path']=pa
path_info[pb]['Sp_idx']=path_info[pa]['Sc_idx']
path_info[pb]['level']=path_info[pa]['level']+1
adj_mat[path_info[pb]['Sp_idx'],pb+1]=1
for i in range(n_state):
adj_sum=np.sum(adj_mat[i])
if adj_sum>0:
adj_mat[i,:]/=adj_sum
g_param=np.zeros((n_state,n_gene))
sigma_param=np.ones(n_gene)
K_param=np.random.sample((n_path,n_gene))*K_param_range
A=adj_mat
cell_path=np.zeros(n_cell,dtype=int)
#cell_names=cell_names.tolist()
print cell_names
for line in c_line:
cn,p=line.split(' ')
p=int(p)
if cn in cell_names:
cell_path[cell_names.index(cn)]=p
cell_time=np.random.sample((n_cell,))
model={}
model['g_param']=g_param
model['sigma_param']=sigma_param
model['K_param']=K_param
model['trans_mat']=adj_mat
model['path_info']=path_info
hid_var={}
hid_var['cell_time']=cell_time
hid_var['cell_ori_time']=cell_times
hid_var['cell_path']=cell_path
hid_var['cell_labels']=np.array(cell_labels)
optimize_w_nz(model,hid_var,cell_exps)
path_trans_prob=compute_path_trans_log_prob(adj_mat,path_info)
model['path_trans_prob']=path_trans_prob
return model,hid_var
def save_model(file_name,model,hid_var):
print 'saving model to file: ',file_name
with open(file_name, 'wb') as handle:
out_dict={}
out_dict['model']=model
out_dict['hid_var']=hid_var
pickle.dump(out_dict, handle)
def load_model(file_name):
print 'loading model from file: ',file_name
with open(file_name, 'rb') as handle:
out_dict = pickle.load(handle)
return out_dict['model'],out_dict['hid_var']
def optimize_w_nz(model,hid_var,cell_exps):
print 'M-step: optimizing w param......'
path_info=model['path_info']
cell_path=hid_var['cell_path']
cell_time=hid_var['cell_time']
K_param=model['K_param']
g_param=model['g_param']
n_cell,n_gene=cell_exps.shape
n_state=g_param.shape[0]
n_path=n_state-1
sigma_param=model['sigma_param']
w_nz=np.ones((n_path,n_gene)) #non zero ratio for each gene in each path (wpj)
if optimize_w:
w_nz=np.zeros((n_path,n_gene)) #non zero ratio for each gene in each path (wpj)
if progress_bar:
bar = progressbar.ProgressBar(maxval=n_path*n_gene, \
widgets=[' [', progressbar.Timer(), '] ',progressbar.Bar('=','[',']'),' ',progressbar.Percentage(),' (', progressbar.ETA(), ') '] )
bar.start()
w_split=n_split
path_gene_w_table=np.zeros((n_path,n_gene,w_split))
for p in range(n_path):
Sp_idx=path_info[p]['Sp_idx']
Sc_idx=path_info[p]['Sc_idx']
g_a=g_param[Sp_idx]
g_b=g_param[Sc_idx]
p_idx=(cell_path==p)
cell_exps_p=cell_exps[p_idx]
cell_time_p=cell_time[p_idx]
for j in range(n_gene):
x_js=cell_exps_p[:,j]
mu_x_js=g_b[j]+(g_a[j]-g_b[j])*np.exp(-K_param[p,j]*cell_time_p)
tmp=(x_js-mu_x_js)**2./(2.*sigma_param[j]**2.)
prob2 = np.where(x_js!=0.,0.,drop_out_param)
prob1= np.exp(-tmp)/(sigma_param[j])/np.sqrt(2.*np.pi)
for ws in range(1,w_split+1):
w=1/float(w_split)*ws
mix_prob=w*prob1+(1-w)*prob2
sum_log_prob=np.sum(np.log(mix_prob))
path_gene_w_table[p,j,ws-1]=sum_log_prob
max_ws=np.argmax(path_gene_w_table[p,j,:])+1
max_w=1/float(w_split)*max_ws
w_nz[p,j]=max_w
#print 'max_w: ',max_w
if progress_bar:
bar.update(p*n_gene+j+1)
if progress_bar:
bar.finish()
model['w_nz']=w_nz
def compute_path_trans_log_prob(trans_mat,path_info):
ret=[]
for i,p in enumerate(path_info):
mult = 1
now=p
while(True):
Sp=now['Sp_idx']
Sc=now['Sc_idx']
if Sp==0:
break
mult*=trans_mat[Sp,Sc]
now=path_info[now['parent_path']]
ret.append(mult)
with np.errstate(divide='ignore'):
ret=np.log(np.array(ret))
return ret
def calc_cell_exp_prob(p,t,model,x_i):
path_info=model['path_info']
path_trans_prob=model['path_trans_prob']
g_param=model['g_param']
sigma_param=model['sigma_param']
K_param=model['K_param']
w_nz=model['w_nz']
Sp_idx=path_info[p]['Sp_idx']
Sc_idx=path_info[p]['Sc_idx']
g_a=g_param[Sp_idx]
g_b=g_param[Sc_idx]
mu_x_i=g_b+(g_a-g_b)*np.exp(-K_param[p]*t)
tmp=(x_i-mu_x_i)**2./(2.*sigma_param**2.)+np.log((sigma_param*np.sqrt(2.*np.pi)) )
prob2 = np.where(x_i!=0.,0.,drop_out_param)
mix_prob=w_nz[p]*np.exp(-tmp)+(1-w_nz[p])*prob2
log_mix_prob=np.log(mix_prob)
ret=np.sum(log_mix_prob)+path_trans_prob[p]
return ret
def log_likelihood(model,hid_var,cell_exps):
ret=0.
path_info=model['path_info']
cell_path=hid_var['cell_path']
cell_time=hid_var['cell_time']
g_param=model['g_param']
K_param=model['K_param']
n_state,n_gene = g_param.shape
n_path=n_state-1
n_cell=cell_exps.shape[0]
for i in range(n_path):
s_a=path_info[i]['Sp_idx']
s_b=path_info[i]['Sc_idx']
delta_g=g_param[s_a]-g_param[s_b]
ret+=-lamb*np.sum(np.fabs(delta_g))
for i in range(n_cell):
p=cell_path[i]
t=cell_time[i]
x_i=cell_exps[i,:]
ret+=calc_cell_exp_prob(p,t,model,x_i)
return ret
def model_score(model,hid_var,cell_exps,method):
print 'calculating ',method,' score......'
n_cell=cell_exps.shape[0]
g_param=model['g_param']
n_state,n_gene = g_param.shape
k=n_gene * n_state * 3 - n_gene # g_param: G*S, K_param: G*P = G*(S-1), sigma_param: G, w_nz: G*(S-1)
if not optimize_w:
k=n_gene * n_state * 2 # g_param: G*S, K_param: G*P = G*(S-1), sigma_param: G, w_nz: G*(S-1)
ll2= 2*log_likelihood(model,hid_var,cell_exps)
BIC_score = ll2 - np.log(n_cell)*k
AIC_score = ll2 - 2*k
GIC2_score = ll2 - k**(1/3.)*k
GIC3_score = ll2 - 2*np.log(k)*k
GIC4_score = ll2 - 2*(np.log(k)+np.log(np.log(k)))*k
GIC5_score = ll2 - np.log(np.log(n_cell))*np.log(k)*k
GIC6_score = ll2 - np.log(n_cell)*np.log(k)*k
if method=='BIC':
return 'BIC= ', BIC_score
if method=='AIC':
return 'AIC= ', AIC_score
if method=='ALL':
return '(AIC,BIC,G2,G3,G4,G5,G6)=', (AIC_score,BIC_score,GIC2_score,GIC3_score,GIC4_score,GIC5_score,GIC6_score)
def optimize_transition_prob(model,hid_var):
trans_mat=model['trans_mat']
path_info=model['path_info']
cell_path=hid_var['cell_path']
new_trans_mat=np.zeros(trans_mat.shape)
for i in range(cell_path.shape[0]):
p=cell_path[i]
Sp=path_info[p]['Sp_idx']
Sc=path_info[p]['Sc_idx']
new_trans_mat[Sp,Sc]+=1
sum_vector=np.sum(new_trans_mat,axis=1)
for i in range(new_trans_mat.shape[0]):
if sum_vector[i]>0:
new_trans_mat[i,:]/=sum_vector[i]
model['trans_mat']=new_trans_mat
path_trans_prob=compute_path_trans_log_prob(new_trans_mat,path_info)
model['path_trans_prob']=path_trans_prob
return
def assign_path_and_time(model,hid_var,cell_exps):
print 'E-step: assigning new path and time for cell......'
n_path=model['K_param'].shape[0]
n_cell=cell_exps.shape[0]
if progress_bar:
bar = progressbar.ProgressBar(maxval=n_cell, \
widgets=[' [', progressbar.Timer(), '] ',progressbar.Bar('=','[',']'),' ',progressbar.Percentage(),' (', progressbar.ETA(), ') '] )
bar.start()
time_split=n_split
path_time_table=np.zeros((n_path,time_split+1))
cell_path=hid_var['cell_path']
cell_time=hid_var['cell_time']
cell_ori_time=hid_var['cell_ori_time']
if n_anchor:
anchor=defaultdict(lambda:defaultdict(lambda:[]))
for i in range(n_cell):
p=cell_path[i]
t=cell_time[i]
prob=calc_cell_exp_prob(p,t,model,cell_exps[i,:])
anchor[p]['cell_index'].append(i)
anchor[p]['cell_prob'].append(prob)
anchor_cell=np.array([-1])
for p in range(n_path):
cell_index=np.array(anchor[p]['cell_index'])
cell_prob=np.array(anchor[p]['cell_prob'])
anchor_p= cell_index[np.argsort(-cell_prob)[:n_anchor]]
anchor_cell=np.union1d(anchor_cell,anchor_p)
print 'anchor cell: ', anchor_cell
for i in range(n_cell):
if progress_bar:
bar.update(i+1)
if n_anchor and i in anchor_cell:
continue
for p in range(n_path):
for t_sp in range(time_split+1):
t=t_sp/float(time_split)
path_time_table[p,t_sp]=calc_cell_exp_prob(p,t,model,cell_exps[i,:])
max_time=np.argmax(path_time_table,axis=1) #max_time for every path
max_prob=np.max(path_time_table,axis=1) #prob of every path with max_time
new_path = np.argmax(max_prob)
ori_prob= np.exp(max_prob-np.max(max_prob))
norm_prob=ori_prob/np.sum(ori_prob)
valid_idx=np.array(range(n_path))
sample_prob=norm_prob[valid_idx]/np.sum(norm_prob[valid_idx])
sample = np.random.multinomial(1,sample_prob)
for index,s in enumerate(sample):
if s==1:
sampled_path=valid_idx[index]
break
new_path=valid_idx[np.argmax(max_prob[valid_idx])]
new_time=max_time[new_path]/float(time_split)
cell_path[i]=new_path
if assign_by_prob_sampling:
cell_path[i]=sampled_path
cell_time[i]=new_time
hid_var['cell_time']=cell_time
hid_var['cell_path']=cell_path
if progress_bar:
bar.finish()
return
def optimize_K_param(model,hid_var,cell_exps):
print 'M-step: optimizing K param......'
K_param=model['K_param']
new_K_param=np.zeros(K_param.shape)
w_nz=model['w_nz']
n_path,n_gene=K_param.shape
cell_path=hid_var['cell_path']
cell_time=hid_var['cell_time']
path_info=model['path_info']
g_param=model['g_param']
sigma_param=model['sigma_param']
k_split=n_split
path_gene_k_table=np.zeros((n_path,n_gene,k_split))
if progress_bar:
bar = progressbar.ProgressBar(maxval=n_path*n_gene, \
widgets=[' [', progressbar.Timer(), '] ',progressbar.Bar('=','[',']'),' ',progressbar.Percentage(),' (', progressbar.ETA(), ') '] )
bar.start()
count=0
for p in range(n_path):
Sp_idx=path_info[p]['Sp_idx']
Sc_idx=path_info[p]['Sc_idx']
g_a=g_param[Sp_idx]
g_b=g_param[Sc_idx]
p_idx=(cell_path==p)
cell_exps_p=cell_exps[p_idx]
cell_time_p=cell_time[p_idx]
for j in range(n_gene):
x_js=cell_exps_p[:,j]
for ks in range(1,k_split+1):
k=K_param_range/float(k_split)*ks
mu_x_js=g_b[j]+(g_a[j]-g_b[j])*np.exp(-k*cell_time_p)
tmp=((x_js-mu_x_js)**2./(2.*sigma_param[j]**2.)+np.log((sigma_param[j]*np.sqrt(2.*np.pi)) ))
prob2 = np.where(x_js!=0.,0.,drop_out_param)
mix_prob=w_nz[p,j]*np.exp(-tmp)+(1-w_nz[p,j])*prob2
sum_log_prob=np.sum(np.log(mix_prob))
path_gene_k_table[p,j,ks-1]=sum_log_prob
max_ks=np.argmax(path_gene_k_table[p,j,:])+1
max_k=K_param_range/float(k_split)*max_ks
K_param[p,j]=max_k
count+=1
if progress_bar:
bar.update(count)
if progress_bar:
bar.finish()
def optimize_sigma_param(model,hid_var,cell_exps):
print 'M-step: optimizing sigma param......'
cell_path=hid_var['cell_path']
cell_time=hid_var['cell_time']
path_info=model['path_info']
g_param=model['g_param']
n_cell,n_gene=cell_exps.shape
new_sigma_param=np.zeros(n_gene)
K_param=model['K_param']
if progress_bar:
bar = progressbar.ProgressBar(maxval=n_gene, \
widgets=[' [', progressbar.Timer(), '] ',progressbar.Bar('=','[',']'),' ',progressbar.Percentage(),' (', progressbar.ETA(), ') '] )
bar.start()
for i in range(n_cell):
p=cell_path[i]
Sp_idx=path_info[p]['Sp_idx']
Sc_idx=path_info[p]['Sc_idx']
g_a=g_param[Sp_idx]
g_b=g_param[Sc_idx]
t=cell_time[i]
x_i=cell_exps[i]
mu_x_i=g_b+(g_a-g_b)*np.exp(-K_param[p]*t)
new_sigma_param+=(x_i-mu_x_i)**2
if progress_bar:
bar.update(i)
new_sigma_param=(new_sigma_param/float(n_cell))**0.5
new_sigma_param=np.where(new_sigma_param<1,1,new_sigma_param)
model['sigma_param']=new_sigma_param
if progress_bar:
bar.finish()
def optimize_g_param_cvx(model,hid_var,cell_exps):
print 'M-step: optimizing g param with CVX......'
path_info=model['path_info']
cell_path=hid_var['cell_path']
cell_time=hid_var['cell_time']
K_param=model['K_param']
g_param=model['g_param']
n_cell,n_gene=cell_exps.shape
n_state=g_param.shape[0]
sigma_param=model['sigma_param']
A2=np.zeros((n_state-1,n_state))
_,path_count=np.unique(hid_var['cell_path'],return_counts=True)
path_nz_g=check_diff_gene(model)
for index in range(n_state-1):
path=path_info[index]
Sp_idx=path['Sp_idx']
Sc_idx=path['Sc_idx']
A2[index,Sp_idx]=1
A2[index,Sc_idx]=-1
if lamb_data_mult=='N':
A2[index,:]*=path_count[index] # multiply by N
if lamb_data_mult=='sqrtN':
A2[index,:]*=np.sqrt(path_count[index]) # multiply by sqrt(N)
if lamb_data_mult=='logN':
A2[index,:]*=np.log(path_count[index]) # multiply by log(N)
if lamb_ratio_mult=='sqrtR':
A2[index,:]*=np.sqrt(path_nz_g[index])
if lamb_ratio_mult=='R':
A2[index,:]*=path_nz_g[index]
A2*=lamb
if progress_bar:
bar = progressbar.ProgressBar(maxval=n_gene, \
widgets=[' [', progressbar.Timer(), '] ',progressbar.Bar('=','[',']'),' ',progressbar.Percentage(),' (', progressbar.ETA(), ') '] )
bar.start()
for j in range(n_gene):
A1=np.zeros((n_cell,n_state))
Xjs=np.zeros(n_cell)
sigma_j=sigma_param[j]
for i in range(n_cell):
p=cell_path[i]
Sp_idx=path_info[p]['Sp_idx']
Sc_idx=path_info[p]['Sc_idx']
t=cell_time[i]
x_ij=cell_exps[i,j]
w_ij=np.exp(-K_param[p,j]*t)
A1[i,Sp_idx]=w_ij
A1[i,Sc_idx]=1-w_ij
Xjs[i]=x_ij
g_js = Variable(n_state)
objective = Minimize(sum_squares(0.5*(A1*g_js-Xjs)/sigma_j)+pnorm(A2*g_js,1))
constraints=[]
prob = Problem(objective, constraints)
result = prob.solve(solver=SCS)
g_param[:,j]=g_js.value.flatten()
if progress_bar:
bar.update(j)
if progress_bar:
bar.finish()
def check_diff_gene(model):
path_info=model['path_info']
g_param=model['g_param']
n_gene=g_param.shape[1]
path_nz_g={}
for index,path in enumerate(path_info):
Sp_idx=path['Sp_idx']
Sc_idx=path['Sc_idx']
g_a=g_param[Sp_idx]
g_b=g_param[Sc_idx]
g_abs_diff=np.fabs(g_a-g_b)
g_abs_diff_nz=np.where(g_abs_diff<1e-1,0,g_abs_diff)
nz_count= len(g_abs_diff_nz[np.nonzero(g_abs_diff_nz)])
nz_ratio = nz_count/float(n_gene)
if verbose:
print 'path: ', index, ' nz_ratio: ', nz_ratio
path_nz_g[index]=nz_ratio
return path_nz_g
def show_cell_time(hid_var):
cell_path = hid_var['cell_path']
cell_labels = hid_var['cell_labels']
cell_time=hid_var['cell_time']
cell_ori_time=np.array(hid_var['cell_ori_time'])
paths=np.unique(cell_path)
for p in paths:
print '----------path: ',p,'-------------'
print 'time\tlabel\t assigned_time'
p_idx=(cell_path==p)
cell_time_p=np.around(cell_time[p_idx],decimals=2)
cell_labels_p=cell_labels[p_idx]
cell_ori_time_p=cell_ori_time[p_idx]
sort_idx=np.argsort(cell_time_p)
for lab,ori_t,t in zip(cell_labels_p[sort_idx],cell_ori_time_p[sort_idx], cell_time_p[sort_idx]):
print ori_t,'\t', lab, ' \t' , t
def compute_ARI_confuss_mat(hid_var,n_path):
cell_path = hid_var['cell_path']
cell_labels = hid_var['cell_labels']
unique_paths=np.unique(cell_path)
unique_labels={}
cell_label_num=[]
ARI_ans=[]
ARI_pred=[]
head=[]
for i,lab in enumerate(cell_labels):
if lab in unique_labels.keys():
cell_label_num.append(unique_labels[lab])
else:
ID=len(unique_labels.keys())
unique_labels[lab]=ID
cell_label_num.append(unique_labels[lab])
head.append(lab)
if lab!='NA':
ARI_ans.append(unique_labels[lab])
ARI_pred.append(cell_path[i])
confuss_mat=np.zeros((n_path,len(unique_labels)))
for i,num in enumerate(cell_label_num):
confuss_mat[cell_path[i],num]+=1
print 'confussion matrix:'
print head
print confuss_mat
ARI= adjusted_rand_score(ARI_ans, ARI_pred)
print 'ARI: ',ARI
return confuss_mat,ARI
def load_adj_mat(file_name):
return np.load(file_name)
def path_distance(pa,pb,cell_exps,cell_path):
pa_center = np.average(cell_exps[cell_path==pa],axis=0)
pb_center = np.average(cell_exps[cell_path==pb],axis=0)
return 1-spearmanr(pa_center,pb_center)[0]
def adjust_model_structure(model,hid_var,cell_exps):
print 'adjusting model structure '
path_info=model['path_info']
cell_path=hid_var['cell_path']
cell_time=hid_var['cell_time']
n_path=len(path_info)
valid_parent=sorted(list(set(np.unique(hid_var['cell_path']).tolist())))
level_path=defaultdict(lambda:[])
for i,p in enumerate(path_info):
p['child_path']=[]
if i not in valid_parent:
print 'zero path: ', i
continue
level_path[p['level']].append(i)
def getKey(item):
return item['level']
childs = sorted([x for x in path_info], key = getKey)
for p in childs:
ID=p['ID']
if ID not in valid_parent or ID ==0:
continue
level= p['level']
new_parent_list=level_path[level-1]
if len(new_parent_list)==1:
new_parent = new_parent_list[0]
print str(new_parent) + ' -> '+str(ID)
else:
par_distance = []
for par in new_parent_list:
p['Sp_idx']=path_info[par]['Sc_idx']
p_idx=(cell_path==ID)
cell_exps_p=cell_exps[p_idx]
cell_time_p=cell_time[p_idx]
s=0
for i in range(cell_exps_p.shape[0]):
s+=calc_cell_exp_prob(ID,cell_time_p[i],model,cell_exps_p[i,:])
par_distance.append(s)
#par_distance = [path_distance(ID,x,cell_exps,cell_path) for x in new_parent_list]
new_parent = new_parent_list[np.argmax(par_distance)]
print str(new_parent) + ' -> '+str(ID)
p['parent_path']=new_parent
new_p=path_info[new_parent]
p['Sp_idx']=new_p['Sc_idx']
new_p['child_path'].append(ID)
p['level']=new_p['level']+1
def optimize_likelihood(cell_exps, model, hid_var, model_name,store_model=True):
for out_it in range(1,n_iteration+1):
prev_path=np.array(hid_var['cell_path'],copy=True)
print 'training iteration: ', out_it
sys.stdout.flush()
print 'cell paths: ',np.unique(hid_var['cell_path'],return_counts=True)
score = model_score(model,hid_var,cell_exps,method='ALL')
print 'model score: ',score
optimize_g_param_cvx(model,hid_var,cell_exps)
print 'after M-step g_param full log-likelihood: ',log_likelihood(model,hid_var,cell_exps)
sys.stdout.flush()
#check_diff_gene(model)
optimize_sigma_param(model,hid_var,cell_exps)
print 'after M-step sigma_param full log-likelihood: ',log_likelihood(model,hid_var,cell_exps)
optimize_K_param(model,hid_var,cell_exps)
print 'after M-step K_param full log-likelihood: ',log_likelihood(model,hid_var,cell_exps)
#assign_path_and_time(model,hid_var,cell_exps)
assign_path_and_time(model,hid_var,cell_exps)
print 'after E-step full log-likelihood: ',log_likelihood(model,hid_var,cell_exps)
adjust_model_structure(model,hid_var,cell_exps)
sys.stdout.flush()
optimize_w_nz(model,hid_var,cell_exps)
print 'after setting w_nz full log-likelihood: ',log_likelihood(model,hid_var,cell_exps)
optimize_transition_prob(model,hid_var)
#print 'opt trans mat full log-likelihood: ',log_likelihood(model,hid_var,cell_exps)
path_trans_prob=compute_path_trans_log_prob(model['trans_mat'],model['path_info'])
model['path_trans_prob']=path_trans_prob
print 'after setting trans_prob full log-likelihood: ',log_likelihood(model,hid_var,cell_exps)
n_path=len(model['path_info'])
compute_ARI_confuss_mat(hid_var,n_path)
if verbose:
show_cell_time(hid_var)
if np.array_equal(prev_path,hid_var['cell_path']):
print 'path assignment the same as previous iteration, stop training.'
#if out_it % 10 ==0:
if store_model:
save_model(model_name+'_it'+str(out_it)+'.pickle',model, hid_var)
#model,hid_var=load_model(model_name)
sys.stdout.flush()
print 'maximum training iteration reached.'
#print 'after M-step g_param full log-likelihood: ',log_likelihood(model,hid_var,cell_exps)
#optimize_g_param_close(model,hid_var,cell_exps)
#print 'after M-step g_param full log-likelihood (close): ',log_likelihood(model,hid_var,cell_exps)
def cv_split_idx(cell_day,n_fold=5):
cell_day = np.array(cell_day)
unique_day=np.unique(cell_day)
n_cell=cell_day.shape[0]
batch=n_cell/n_fold
fold_idx=np.zeros(n_cell)
cell_day_dict={}
for ud in unique_day:
ud_idx=np.where(cell_day==ud)[0]
ud_count=ud_idx.shape[0]
np.random.shuffle(ud_idx)
batch=ud_count/n_fold
for i in range(n_fold):
fold_idx[ud_idx[batch*i:batch*(i+1)]]=i+1
if batch*(i+1)<ud_count:
fold_idx[ud_idx[batch*i:]]=i+1
return np.array(fold_idx,dtype=int)
if __name__=='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-d',"--data_file", help="specify the data file, if not specified then a default training data file will be used")
parser.add_argument('-dt',"--data_file_testing", help="specify the testing data file and output best interation for testing, if not specifed then the model will not do testing.", default = None)
parser.add_argument('-st',"--structure_file", help="specify the structure file, if not specified then a default structure file will be used")
parser.add_argument('-seed',"--random_seed", help="specify the random seed, default is 0", type=int,default=0)
parser.add_argument('-ni',"--n_iteration", help="specify the number of training iteration, default is 10", type=int,default=10)
parser.add_argument('-k',"--k_param_range", help="specify the range of K parameter, default is 5", type=int,default=5)
parser.add_argument('-ns',"--n_split", help="specify the number of splits in learning K and assign cell time, default is 10", type=int,default=10)
parser.add_argument('-na',"--n_anchor", help="specify the number of anchor cells to remain in each path during training, default is 0", type=int,default=0)
parser.add_argument('-ng',"--n_gene", help="specify the maximum number of genes used in training, default is 1000", type=int,default=1000)
parser.add_argument('-lamb',"--lamb", help="specify the regularizing parameter for L1 sparsity, default is 1", type=float,default=1)
#parser.add_argument('-dop',"--drop_out_param", help="specify the drop-out parameter, default is 0.1", type=float,default=0.1, help=argparse.SUPPRESS)
parser.add_argument('-dop',"--drop_out_param", type=float,default=0.1, help=argparse.SUPPRESS)
parser.add_argument('-ps',"--assign_by_prob_sampling", help="specify the whether to use multinomial sampling in path assignment, default is 1", type=int,choices=[0,1],default=1)
#parser.add_argument('-opt_w',"--optimize_w", help="specify the whether to optimize the w parameter in drop-out event, default is 0", type=int,choices=[0,1],default=0, help=argparse.SUPPRESS)
parser.add_argument('-opt_w',"--optimize_w", type=int,choices=[0,1],default=0, help=argparse.SUPPRESS)
#parser.add_argument('-ci',"--cluster_init", help="specify the whether to use k-means clustering as initialization of path assignment, default is 0", type=int,choices=[0,1],default=0, help=argparse.SUPPRESS)
parser.add_argument('-ci',"--cluster_init", type=int,choices=[0,1],default=0, help=argparse.SUPPRESS)
#parser.add_argument('-ldm',"--lamb_data_mult", help="specify the multiplier of lambda data parameter, default is logN",choices=['1','sqrtN','logN','N'],default='log(N)')
#parser.add_argument('-lrm',"--lamb_ratio_mult", help="specify the multiplier of lambda ratio parameter, default is sqrtR",choices=['1','sqrtR','R'],default='sqrt(r)')
# parser.add_argument('-ldm',"--lamb_data_mult", help="specify the multiplier of lambda data parameter, default is 1",choices=['1','sqrtN','logN','N'],default='1', help=argparse.SUPPRESS)
# parser.add_argument('-lrm',"--lamb_ratio_mult", help="specify the multiplier of lambda ratio parameter, default is 1",choices=['1','sqrtR','R'],default='1', help=argparse.SUPPRESS)
# parser.add_argument('-pc',"--path_constraint", help="specify the whether to apply path constraint in training, default is 0", type=int,choices=[0,1],default=0, help=argparse.SUPPRESS)
# parser.add_argument('-pg',"--progress_bar", help="specify the whether to show progress_bar in training, default is 1", type=int,choices=[0,1],default=0, help=argparse.SUPPRESS)
parser.add_argument('-ldm',"--lamb_data_mult",choices=['1','sqrtN','logN','N'],default='1', help=argparse.SUPPRESS)
parser.add_argument('-lrm',"--lamb_ratio_mult",choices=['1','sqrtR','R'],default='1', help=argparse.SUPPRESS)
parser.add_argument('-pc',"--path_constraint", type=int,choices=[0,1],default=0, help=argparse.SUPPRESS)
parser.add_argument('-pg',"--progress_bar", type=int,choices=[0,1],default=0, help=argparse.SUPPRESS)
parser.add_argument('-mn',"--model_name", help="specify the model_name",default = None)
parser.add_argument('-cv',"--cross_validation", help="specify whether to use 5-fold cross_validation, 0 means not, default is 0", type=int, choices=[0,1],default=0)
args=parser.parse_args()
print args
data_file='data/treutlein2014'
if args.data_file is not None:
data_file=args.data_file
splits=data_file.split('/')
#structure_file=splits[0]+'/init_cluster_'+splits[1]+'.txt'
if args.structure_file is not None:
structure_file=args.structure_file
else:
structure_file=splits[0]+'/trained_cluster_'+splits[1]+'.txt'
verbose=1
np.random.seed(args.random_seed)
n_iteration=args.n_iteration
n_split=args.n_split
K_param_range=args.k_param_range
n_anchor=args.n_anchor
lamb=args.lamb
max_gene=args.n_gene
drop_out_param=args.drop_out_param
assign_by_prob_sampling=args.assign_by_prob_sampling
optimize_w=args.optimize_w
path_constraint=args.path_constraint
cluster_init=args.cluster_init
lamb_data_mult=args.lamb_data_mult
lamb_ratio_mult=args.lamb_ratio_mult
progress_bar=args.progress_bar
cv = args.cross_validation
if args.model_name is not None:
model_name=args.model_name
else:
model_name = 'model/model_'+splits[1]+'_ns_'+str(n_split)+'_lamb_'+str(lamb)+'_ng_'+str(max_gene)+'_cv_'+str(cv)
verbose=0
if args.data_file_testing is not None:
verbose = 0
cell_names_train,cell_day_train,cell_labels_train,cell_exps_train,gene_names=load_data(data_file,max_gene)
cell_names_test,cell_day_test,cell_labels_test,cell_exps_test,gene_names=load_data(args.data_file_testing,max_gene)
model,hid_var_train = init_var_Jun(structure_file,cell_names_train,cell_day_train,cell_exps_train,cell_labels_train)
_,hid_var_test = init_var_Jun(structure_file,cell_names_test,cell_day_test,cell_exps_test,cell_labels_test)
max_it=args.n_iteration
#max_it=2
max_test_ll=-float('inf')
for it in range(1,max_it+1):
n_iteration = 1
assign_by_prob_sampling=args.assign_by_prob_sampling
optimize_likelihood(cell_exps_train, model, hid_var_train,model_name,store_model=False)
assign_by_prob_sampling=False
assign_path_and_time(model,hid_var_test,cell_exps_test)
train_ll = log_likelihood(model,hid_var_train,cell_exps_train)
test_ll = log_likelihood(model,hid_var_test,cell_exps_test)
print 'iteration:\t ', it, '\t train_LL:\t ', np.around(train_ll,2), '\t test_ll: \t', np.around(test_ll,2)
if test_ll > max_test_ll:
max_test_ll = test_ll
count = 0
best_it = it
else:
count+=1
if count>1:
break
print 'best_test_it: ', best_it, '\t max_test_ll: ', max_test_ll
print best_it
#best_its.append(best_it)
#best_test_lls.append(max_test_ll)
#print 'best_its: ', best_its
#print 'best_test_lls: ', best_test_lls
#print 'mean_best_its: ',np.mean(best_its)
#print 'mean_best_test_lls: ',np.mean(best_test_lls)
#print 'training all data with the best it:', int(np.rint(np.mean(best_its)))
sys.exit(0)
if args.cross_validation:
cell_names,cell_day,cell_labels,cell_exps,gene_names=load_data(data_file,max_gene)
n_fold = 5
cv_idx = cv_split_idx(cell_day=cell_day,n_fold=n_fold)
best_its=[]
best_test_lls=[]
verbose = 0
for i in range(1,n_fold+1):
print 'fold: ', i
test_idx = cv_idx==i
train_idx = cv_idx!=i
cell_day_test = np.array(cell_day)[test_idx]
cell_exps_test = np.array(cell_exps)[test_idx]
cell_labels_test = np.array(cell_labels)[test_idx]
cell_names_test = np.array(cell_names)[test_idx]
cell_day_train = np.array(cell_day)[train_idx]
cell_exps_train = np.array(cell_exps)[train_idx]
cell_labels_train = np.array(cell_labels)[train_idx]
cell_names_train = np.array(cell_names)[train_idx]
model,hid_var_train = init_var_Jun(structure_file,cell_names_train.tolist(),cell_day_train,cell_exps_train,cell_labels_train)
_,hid_var_test = init_var_Jun(structure_file,cell_names_test.tolist(),cell_day_test,cell_exps_test,cell_labels_test)
#model,hid_var_train = init_var(adj_mat,cell_day_train,cell_exps_train,cell_labels_train)
#_,hid_var_test = init_var(adj_mat,cell_day_test,cell_exps_test,cell_labels_test,testing=True)
#train_ll = log_likelihood(model,hid_var_train,cell_exps_train)
#test_ll = log_likelihood(model,hid_var_test,cell_exps_test)
#print 'initial training full log-likelihood: ',train_ll
#print 'initial testing full log-likelihood: ',test_ll
#print 'iteration:\t ', 0, '\t train_LL:\t ', np.around(train_ll,2), '\t test_ll: \t', np.around(test_ll,2)
#compute_ARI_confuss_mat(hid_var_train)
n_iteration = 1
max_it=args.n_iteration
max_test_ll=-float('inf')
count=0
best_it = 0
for it in range(1,max_it):
assign_by_prob_sampling=args.assign_by_prob_sampling
optimize_likelihood(cell_exps_train, model, hid_var_train,model_name,store_model=False)
assign_by_prob_sampling=False
assign_path_and_time(model,hid_var_test,cell_exps_test)
train_ll = log_likelihood(model,hid_var_train,cell_exps_train)
test_ll = log_likelihood(model,hid_var_test,cell_exps_test)
print 'iteration:\t ', it, '\t train_LL:\t ', np.around(train_ll,2), '\t test_ll: \t', np.around(test_ll,2)
if test_ll > max_test_ll:
max_test_ll = test_ll
count = 0
best_it = it
else:
count+=1
if count>1:
break
print 'best_test_it: ', best_it, '\t max_test_ll: ', max_test_ll
best_its.append(best_it)
best_test_lls.append(max_test_ll)
print 'best_its: ', best_its
print 'best_test_lls: ', best_test_lls
print 'mean_best_its: ',np.mean(best_its)
print 'mean_best_test_lls: ',np.mean(best_test_lls)
print 'training all data with the best it:', int(np.rint(np.mean(best_its)))
verbose = 1
n_iteration= int(np.rint(np.mean(best_its)))
#model,hid_var = init_var(adj_mat,cell_day,cell_exps,cell_labels)
model,hid_var = init_var_Jun(structure_file,cell_names,cell_day,cell_exps,cell_labels)
#print 'initial full log-likelihood: ',log_likelihood(model,hid_var,cell_exps)
n_path=len(model['path_info'] )
compute_ARI_confuss_mat(hid_var,n_path)
optimize_likelihood(cell_exps, model, hid_var,model_name)
else:
cell_names,cell_day,cell_labels,cell_exps,gene_names=load_data(data_file,max_gene)
n_cell=len(cell_names)
model,hid_var = init_var_Jun(structure_file,cell_names,cell_day,cell_exps,cell_labels)
n_path=len(model['path_info'] )
compute_ARI_confuss_mat(hid_var,n_path)
optimize_likelihood(cell_exps, model, hid_var,model_name)