def make_gp(self, ripl):
ripl.assume('make_linear', VentureFunction(makeLinear, [t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_periodic', VentureFunction(makePeriodic, [t.NumberType(), t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_se',VentureFunction(makeSquaredExponential,[t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_noise', VentureFunction(makeNoise, [t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('a','(mem (lambda (i) (tag (quote parameter) i (uniform_continuous 0.1 8))))')
ripl.assume('sn','(tag (quote parameter) 10 (uniform_continuous 0.01 3))')
ripl.assume('sf','(mem (lambda (i) (tag (quote parameter) i (uniform_continuous 0.1 8 ))))')
ripl.assume('sf2','(mem (lambda (i) (tag (quote parameter) i(uniform_continuous 0.1 8 ))))')
ripl.assume('p','(mem (lambda (i) (tag (quote parameter) i (uniform_continuous 0.1 8))))')
ripl.assume('l','(mem (lambda (i) (tag (quote parameter) i (uniform_continuous 0.1 8))))')
ripl.assume('l2','(mem (lambda (i) (tag (quote parameter) i (uniform_continuous 0.1 8))))')
ripl.assume('lin1', "(apply_function make_linear ( a 0) )")
ripl.assume('lin2', "(apply_function make_linear ( a 1) )")
ripl.assume('lin3', "(apply_function make_linear ( a 2) )")
ripl.assume('per1', "(apply_function make_periodic ( l 3) ( p 4) ( sf 5) )")
ripl.assume('se1', "(apply_function make_se ( sf2 6) ( l2 7))")
ripl.assume('se2', "(apply_function make_se ( sf2 8) ( l2 9))")
ripl.assume('wn', "(apply_function make_noise sn)")
#### GP Structure Prior
###### for simplicity, I start with the max amount of kernel per type given
ripl.assume("max_lin","(array lin1 lin2 lin3 )")
ripl.assume("max_per","(array per1 )")
ripl.assume("max_se","(array se1 se2)")
ripl.assume("max_wn","(array wn)")
ripl.bind_foreign_sp("gp_grammar", typed_nr(Grammar(), [t.HomogeneousArrayType(t.HomogeneousArrayType(t.AnyType())),t.AnyType()], covfunctionType, min_req_args=0))
ripl.assume("cov_structure","(tag (quote grammar) 0 (gp_grammar (array max_lin max_wn max_per max_se) 2 ))")
#ripl.bind_foreign_sp("covfunc_interpreter",typed_nr(GrammarInterpreter(), [t.AnyType()], t.AnyType()))
#ripl.assume("interp","(covfunc_interpreter grammar)")
ripl.assume('gp','(tag (quote model) 0 (make_gp_part_der zero cov_structure))')
def run_cgm_experiment(patient_number):
mat_contents =scio.loadmat("../data/glucose"+patient_number)
X = mat_contents['Glucose']
X = X[np.nonzero(X)]
ripl = shortcuts.make_lite_church_prime_ripl()
ripl.bind_foreign_sp("make_gp_part_der",gp_der.makeGPSP)
ripl.bind_foreign_sp("covfunc_interpreter",typed_nr(GrammarInterpreter(), [t.AnyType()], t.AnyType()))
ripl.bind_foreign_sp("subset",typed_nr(Subset(), [t.ListType(),t.SimplexType()], t.ListType()))
ripl.assume('make_const_func', VentureFunction(makeConstFunc, [t.NumberType()], constantType))
ripl.assume('zero', "(apply_function make_const_func 0)")
ripl.assume("func_times", makeLiftedMult(lambda x1, x2: np.multiply(x1,x2)))
ripl.assume("func_plus", makeLiftedAdd(lambda x1, x2: x1 + x2))
ripl.assume('make_linear', VentureFunction(makeLinear, [t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_periodic', VentureFunction(makePeriodic, [t.NumberType(), t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_se',VentureFunction(makeSquaredExponential,[t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_rq',VentureFunction(makeRQ, [t.NumberType(), t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('a','(tag (quote parameter) 0 (log (uniform_continuous 0 5)))')
ripl.assume('sf1','(tag (quote parameter) 1 (log (uniform_continuous 0 5 )))')
ripl.assume('p',' (tag (quote parameter) 2 (log (uniform_continuous 0.01 5)))')
ripl.assume('l',' (tag (quote parameter) 3 (log (uniform_continuous 0 5)))')
ripl.assume('l1',' (tag (quote parameter) 4 (log (uniform_continuous 0 5)))')
ripl.assume('sf_rq','(tag (quote parameter) 5 (log (uniform_continuous 0 5)))')
ripl.assume('l_rq','(tag (quote parameter) 6 (log (uniform_continuous 0 5)))')
ripl.assume('alpha','(tag (quote parameter)7 (log (uniform_continuous 0 5)))')
ripl.assume('sf',' (tag (quote parameter) 8 (log (uniform_continuous 0 5)))')
ripl.assume('lin1', "(apply_function make_linear a )")
ripl.assume('per1', "(apply_function make_periodic l p sf ) ")
ripl.assume('se1', "(apply_function make_se sf1 l1)")
ripl.assume('rq', "(apply_function make_rq l_rq sf_rq alpha)")
ripl.assume('interp','covfunc_interpreter')
# Grammar
ripl.assume('cov_compo',"""
(lambda (l )
(if (lte ( size l) 1)
(first l)
(if (flip)
(apply_function func_plus (first l) (cov_compo (rest l)))
(apply_function func_times (first l) (cov_compo (rest l)))
)
))
""")
number = 4
total_perms =0
perms = []
for i in range(number):
perms.append((len(list(itertools.permutations([j for j in range(i+1)])))))
total_perms+=perms[i]
simplex = "( simplex "
for i in range(number):
simplex+=str(float(perms[i])/total_perms) + " "
simplex+=" )"
#print(' (tag (quote grammar) 0 (subset (list lin1 per1 se1 se2 rq) '+simplex + ' ))')
ripl.assume('s',' (tag (quote hyper) 0 (subset (list lin1 per1 se1 rq) '+simplex + ' ))')
ripl.assume('cov','(tag (quote hyper) 1 (cov_compo s))')
ripl.assume('gp',"""(tag (quote model) 0
(make_gp_part_der zero cov))""")
makeObservations([i for i in range(X.shape[0])],X,ripl)
ripl.infer("(repeat "+steps+" (do (mh (quote hyper) one 1) (mh (quote parameter) one 5)))")
# prediction
fig = plt.figure(figsize=(figlength,figheigth), dpi=200)
for i in range(number_curves):
xpost= np.random.uniform(0,X.shape[0]+5,200)
sampleString=genSamples(xpost,ripl)
ypost = ripl.sample(sampleString)
yp = [y_temp for (x_temp,y_temp) in sorted(zip(xpost,ypost))]
pl.plot(sorted(xpost),yp,c="red",alpha=alpha_value,linewidth=2)
#pl.locator_params(nbins=4)
#plt.axis((-2,2,-1,3))
#pl.plot(X,color='blue')
pl.scatter(range(X.shape[0]),X,color='black',marker='x',s=50,edgecolor='blue',linewidth='1.5')
non_zero_index = np.nonzero(X)
X = X[non_zero_index]
time_stamps = get_time_stamps(patient)
time_stamps=[time_stamps[i] for i in non_zero_index[0]]
pl.xlabel('Time',fontsize=font_size)
pl.ylabel('mg/dL',fontsize=font_size)
pl.xticks(fontsize=font_size)
pl.yticks(fontsize=font_size)
ax = pl.gca()
ax.set_xlim([0, X.shape[0]+5])
ax.set_ylim([np.min(X)-10,np.max(X)+10])
ticks = ax.get_xticks()
time_stamps[0]=''
ax.set_xticklabels([time_stamps[int(i)] for i in ticks[:-1]])
fig.savefig(path+'posterior_samples_patient_'+patient_number+'_'+str(number_predictive_samples)+'_'+str(number_curves)+'a_'+str(alpha_value)+'_'+exp_run+'.png', dpi=fig.dpi,bbox_inches='tight')
def makeObservations(x,y,gp_str='(gp '):
xString = genSamples(x,gp_str)
ripl.observe(xString, array(y))
def genSamples(x,gp_str='(gp '):
sampleString=gp_str+' (array '
for i in range(len(x)):
sampleString+= str(x[i]) + ' '
sampleString+='))'
#print(sampleString)
return sampleString
ripl = shortcuts.make_lite_church_prime_ripl()
ripl.bind_foreign_sp("make_gp_part_der",gp_der.makeGPSP)
ripl.bind_foreign_sp("covfunc_interpreter",typed_nr(GrammarInterpreter(), [t.AnyType()], t.AnyType()))
ripl.bind_foreign_sp("subset",typed_nr(Subset(), [t.ListType(),t.SimplexType()], t.ListType()))
ripl.assume('make_const_func', VentureFunction(makeConstFunc, [t.NumberType()], constantType))
ripl.assume('zero', "(apply_function make_const_func 0)")
ripl.assume("func_times", makeLiftedMult(lambda x1, x2: np.multiply(x1,x2)))
ripl.assume("func_plus", makeLiftedAdd(lambda x1, x2: x1 + x2))
ripl.assume('make_linear', VentureFunction(makeLinear, [t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_periodic', VentureFunction(makePeriodic, [t.NumberType(), t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_se',VentureFunction(makeSquaredExponential,[t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_rq',VentureFunction(makeRQ, [t.NumberType(), t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('a','(tag (quote parameter) 0 (log (uniform_continuous 0 5)))')
ripl.assume('sf1','(tag (quote parameter) 1 (log (uniform_continuous 0 5 )))')
def run_cgm_experiment(patient_number,exp_index):
if not glob.glob('/home/ulli/Dropbox/cgm/patient_structure_'+patient_number+'/*_'+str(exp_index)):
mat_contents =scio.loadmat("../data/glucose"+patient_number)
X = mat_contents['Glucose']
X = X[np.nonzero(X)]
if not os.path.exists('/home/ulli/Dropbox/cgm/patient_structure_'+patient_number+'/'):
os.makedirs('/home/ulli/Dropbox/cgm/patient_structure_'+patient_number+'/')
ripl = shortcuts.make_lite_church_prime_ripl()
ripl.bind_foreign_sp("make_gp_part_der",gp_der.makeGPSP)
ripl.bind_foreign_sp("covfunc_interpreter",typed_nr(GrammarInterpreter(), [t.AnyType()], t.AnyType()))
ripl.bind_foreign_sp("subset",typed_nr(Subset(), [t.ListType(),t.SimplexType()], t.ListType()))
ripl.assume('make_const_func', VentureFunction(makeConstFunc, [t.NumberType()], constantType))
ripl.assume('zero', "(apply_function make_const_func 0)")
ripl.assume("func_times", makeLiftedMult(lambda x1, x2: np.multiply(x1,x2)))
ripl.assume("func_plus", makeLiftedAdd(lambda x1, x2: x1 + x2))
ripl.assume('make_linear', VentureFunction(makeLinear, [t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_periodic', VentureFunction(makePeriodic, [t.NumberType(), t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_se',VentureFunction(makeSquaredExponential,[t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('make_rq',VentureFunction(makeRQ, [t.NumberType(), t.NumberType(), t.NumberType()], t.AnyType("VentureFunction")))
ripl.assume('a','(tag (quote parameter) 0 (log (uniform_continuous 0 5)))')
ripl.assume('sf1','(tag (quote parameter) 1 (log (uniform_continuous 0 5 )))')
ripl.assume('p',' (tag (quote parameter) 2 (log (uniform_continuous 0.01 5)))')
ripl.assume('l',' (tag (quote parameter) 3 (log (uniform_continuous 0 5)))')
ripl.assume('l1',' (tag (quote parameter) 4 (log (uniform_continuous 0 5)))')
ripl.assume('sf_rq','(tag (quote parameter) 5 (log (uniform_continuous 0 5)))')
ripl.assume('l_rq','(tag (quote parameter) 6 (log (uniform_continuous 0 5)))')
ripl.assume('alpha','(tag (quote parameter)7 (log (uniform_continuous 0 5)))')
ripl.assume('sf',' (tag (quote parameter) 8 (log (uniform_continuous 0 5)))')
ripl.assume('lin1', "(apply_function make_linear a )")
ripl.assume('per1', "(apply_function make_periodic l p sf ) ")
ripl.assume('se1', "(apply_function make_se sf1 l1)")
ripl.assume('rq', "(apply_function make_rq l_rq sf_rq alpha)")
ripl.assume('interp','covfunc_interpreter')
# Grammar
ripl.assume('cov_compo',"""
(lambda (l )
(if (lte ( size l) 1)
(first l)
(if (flip)
(apply_function func_plus (first l) (cov_compo (rest l)))
(apply_function func_times (first l) (cov_compo (rest l)))
)
))
""")
number = 4
total_perms =0
perms = []
for i in range(number):
perms.append((len(list(itertools.permutations([j for j in range(i+1)])))))
total_perms+=perms[i]
simplex = "( simplex "
for i in range(number):
simplex+=str(float(perms[i])/total_perms) + " "
simplex+=" )"
#print(' (tag (quote grammar) 0 (subset (list lin1 per1 se1 se2 rq) '+simplex + ' ))')
ripl.assume('s',' (tag (quote hyper) 0 (subset (list lin1 per1 se1 rq) '+simplex + ' ))')
ripl.assume('cov','(tag (quote hyper) 1 (cov_compo s))')
ripl.assume('gp',"""(tag (quote model) 0
(make_gp_part_der zero cov))""")
makeObservations([i for i in range(X.shape[0])],X,ripl)
ripl.infer("(repeat "+steps+" (do (mh (quote hyper) one 1) (mh (quote parameter) one 5)))")
touch('/home/ulli/Dropbox/cgm/patient_structure_'+patient_number+'/',ripl.sample('(interp cov )')+'_'+str(exp_index))
