def draw_svm(x_expr, predictions,actual, f = 0, names = None):
fig = plt.figure(f)
nx = len(x_expr)
thr = .05
pcols = map(lambda x : x <= -thr and 'blue' or \
x >= thr and 'red' or \
'black',
predictions)
ycols = map(lambda x : x <= -thr and 'blue' or \
x >= thr and 'red' or \
'black',
actual)
mcols = map(lambda x, y: y == x and 'none' or \
'black', pcols, ycols)
pcount = 0
pmax = (power(nx,2) - nx) /2
for i in range(nx):
for j in range(nx):
ax_r = [float(i)/nx, float(j)/nx,float(1)/nx,float(1)/nx]
ax = fig.add_axes(ax_r,frameon = False)
rsml = 20
rbig = rsml*2.5
ax.scatter( x_expr[i],
x_expr[j],
rsml,
color = pcols
)
ax.scatter(x_expr[i],
x_expr[j],
rbig,
edgecolor = ycols,
color = 'none')
scatter_errors = False
if scatter_errors:
ax.scatter(x_expr[i],
x_expr[j],
rbig*2,
color = mcols,
zorder = -100,
edgecolor = 'none')
myplots.hideaxes(ax)
if names:
namearr = [names[i],names[j]]
else:
namearr= [str(i),str(j)]
if names: alpha = 1.0
else: alpha = .6
myplots.maketitle(ax,' vs '.join(namearr), alpha = alpha)
def test():
nhc = 2
ntg = 2
ntf_s = 2
max_tfu = 2
gagd = GAGD(nhc,ntg,ntf_s, [ max_tfu for i in range(ntg) ] )
xs, ys = sd.synth_data(ntg,max_tfu,ntf_s)
g, ga = gagd.sample_genome()
gagd.init_net()
gagd.make_cxns_from_genome(g)
net = gagd.mynn.net
f = plt.figure(0)
f.clear()
ax = f.add_subplot(121)
myplots.draw_pb(ax,net)
myplots.hideaxes(ax)
myplots.maketitle(ax,'GANN')
gagd.set_data(xs.T,ys.T)
gagd.set_trainer()
gagd.train()
return
def run( method ='identity',index = 0, reset = 0,
nxmax = 100 ,
binary_x = False, binary_y = False,
expression = 'time' ,
cluster_idx = 0,
lrn = 'tree',
showall = False,
tgonly = False,
randomize_tfs = False,
ctfs = 5,
ctgs = 5,
cofs = 1,
do_normalize_cluster = True,
cluster_tfs = True,
verbose_expr_labels = False,
ctype = False):
'''
sush2.run:
run a selected learning algorithm for a cluster.
KEYWORDS:
index [0]: select a tf/target to model from the cluster
method ['identity']: a membership method
multi [False]: meaningless
nxmax [3]: max cluster members
binary_x: model x data as binary
binary_y: model y data as binary
expression ['time']: which expression series to use
cluster_idx: not yet implemented
reset
'''
#Data assembly:
#
#1: Grab a list of genes of interest and
# corresponding expression vectors
#
trg_kidxs = nu.net_trg_keyidxs()
tf_kidxs = nu.net_tf_keyidxs()
#
#retrieve the list of trg/tf names present in a given cluster.
#note that at the moment, these are fake functions that just give back
#a little list of trgs and all of their associated TFs
#
#--CLUSTERS USED--
cands = get_candidates(10,ctfs,ctgs)
cidx = cands[cofs]
trg_ssnames = get_trg_ss(cluster = cidx )
tf_ssnames = get_tf_ss(cluster = cidx , trgnames = trg_ssnames)
if cluster_tfs:
tf_ssnames = get_tf_ss(cluster = cidx , trgnames = trg_ssnames)
else:
tgs, tfs = nu.parse_net()
tg_specific = trg_ssnames[cluster_idx]
trg_tfs = tgs[tg_specific]['tfs']
tf_ssnames = trg_tfs
if randomize_tfs:
r =np.random.random_integers(0,len(tf_kidxs.keys()),len(tf_ssnames))
tf_ssnames = []
print 'Randomizing TFs'
for i in r:
tf_ssnames.append(tf_kidxs.keys()[i])
trg_ssidxs = array([trg_kidxs[name] for name in trg_ssnames])
tf_ssidxs = array([tf_kidxs[name] for name in tf_ssnames])
#
#2: Project expression data onto membership vectors
#
#--EXPR CLUSTERING--
#4: Grab a list of 'membership vectors' which
# translate genes to x and y in the machine learning problem
# data merging has not yet been implemented but should be quite simple
#
x_memberships = get_membership(tf_ssnames, method = method)
y_memberships = get_membership(trg_ssnames, method = method)
if do_normalize_cluster:
exprtype = 'clustered'
else:
exprtype = 'standard'
if exprtype == 'standard':
all_expr = non_normal_cluster_expr(trg_ssnames, tf_ssnames,ctype = ctype)
else:
all_expr = normalize_cluster_expr(trg_ssnames, tf_ssnames,ctype = ctype)
tg_expr, tf_expr = all_expr
x_expr = array((tf_expr)).T
y_expr = array((tg_expr)).T
show_clustered_expr(y_expr,x_expr, trg_ssnames, tf_ssnames,fig = 8)
nx, npertg = shape(x_expr)
x_all, y_all = fold_expr(x_expr, y_expr)
nx, nt_folded = shape(x_all)
train_idxs, test_idxs = [],[]
nt = npertg
if ctype:
nt -= 4
tginds = range(cluster_idx *npertg,(cluster_idx*npertg)+npertg)
cinds = []
for i in range(nt_folded):
if (divmod(i,npertg))[1] >= npertg - 4:
cinds.append(i)
for i in range(nt_folded):
if ctype:
if i in cinds and i in tginds:
test_idxs.append(i)
else:
if i in tginds[:-4]:
test_idxs.append(i)
if tgonly:
if i in tginds[:-4]:
train_idxs.append(i)
else:
if not (i in tginds) and not (i in cinds):
train_idxs.append(i)
print 'N_TRAIN' , len(train_idxs)
expr_fig = 0
draw_expr(x_expr, y_expr, expr_fig = expr_fig)
if lrn =='svm':
model = learn_svm( x_all, y_all,
train_idxs = train_idxs,
test_idxs = test_idxs,
binary_x = binary_x,
binary_y = binary_y)
predictions = run_svm((x_all.T)[test_idxs].T , y_all[test_idxs], model)
if lrn in ['knn','tree','forest']:
#pred = myrf.run_tree(x_all,y_all, train_idxs, test_idxs)
#raise Exception()
all_ex = myrf.get_ex(x_all,y_all)
train_ex = all_ex.getitems([int(x) for x in train_idxs])
test_ex = all_ex.getitems([int(x) for x in test_idxs])
#test_ex = myrf.examples_from_inds(x_all,y_all,test_idxs)
#cl_ex = myrf.examples_from_inds(x_all,y_all,cl_idxs)
model = myrf.OLearn(lrn, train_ex, test_ex = test_ex)
predictions = model.predictions(test_ex)
if lrn == 'nn':
nhc = 2
ntg = 2
ntf_s = 2
max_tfu = 2
gf = sf.genfann(nhc,ntg,ntf_s, [ max_tfu for i in range(ntg) ] )
xs, ys = sf.synth_data(ntg,max_tfu,ntf_s)
g, ga = gf.sample_genome()
gf.init_net()
gf.make_cxns_from_genome(g)
#gf.net_from_cxns(hidden_cxns,output_cxns)
net = gf.mynn.net
f = plt.figure(0)
f.clear()
ax = f.add_subplot(121)
myplots.draw_pb(ax,net)
myplots.hideaxes(ax)
myplots.maketitle(ax,'GANN')
gf.set_data(xs.T,ys.T)
gf.set_trainer()
gf.train()
ax2 = f.add_subplot(122)
myplots.draw_pb(ax2,net)
myplots.hideaxes(ax2)
myplots.maketitle(ax2,'GANN')
return
raise Exception()
raise Exception()
#igrps = [ arange(2)+2*i for i in range(3) ]
#igrps = [
raise Exception()
gf.train()
raise Exception()
#gagd.MyFANN(x_all.T,y_all[newaxis,:].T,train_idxs)
actual = y_all[test_idxs]
showall = True
if showall:
if verbose_expr_labels:
names = tf_ssnames
else:
names = None
draw_svm(x_all[:,test_idxs],actual, predictions, f = expr_fig,names = names)
print predictions
print actual
if ctype:
forstring = 'CL Data'
else:
forstring = 'TS Data'
namestr = trg_ssnames[cluster_idx]
subt = 'TFs: '+','.join(tf_ssnames)
if randomize_tfs:
title = 'Random TF Predictions ' + forstring + ', ' +namestr
fnum = 5
else:
if cluster_tfs:
title = 'Network Cluster TF Predictions'+ forstring + ', ' +namestr
else:
title = 'Network UnClustered TF Predictions'+ forstring + ', ' +namestr
fnum = 6
msecov = draw_prediction(predictions,actual,fig=fnum,
title = title,
subt = ','.join(tf_ssnames))
print msecov
return msecov
