I have a fairly simple test data set I am trying to fit with pymc3.
The result generated by traceplot looks something like this.
Essentially the trace of all parameter look like there is a standard 'caterpillar' for 100 iterations, followed by a flat line for 750 iterations, followed by the caterpillar again.
The initial 100 iterations happen after 25,000 ADVI iterations, and 10,000 tune iterations. If I change these amounts, I randomly will/won't have these periods of unwanted stability.
I'm wondering if anyone has any advice about how I can stop this from happening - and what is causing it?
Thanks.
The full code is below. In brief, I am generating a set of 'phases' (-pi -> pi) with a corresponding set of values y = a(j)*sin(phase) + b(j)*sin(phase). a and b are drawn for each subject j at random, but are related to each other.
I then essentially try to fit this same model.
Edit: Here is a similar example, running for 25,000 iterations. Something goes wrong around iteration 20,000.
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
%matplotlib inline
np.random.seed(0)
n_draw = 2000
n_tune = 10000
n_init = 25000
init_string = 'advi'
target_accept = 0.95
##
# Generate some test data
# Just generates:
# x a vector of phases
# y a vector corresponding to some sinusoidal function of x
# subject_idx a vector corresponding to which subject x is
#9 Subjects
N_j = 9
#Each with 276 measurements
N_i = 276
sigma_y = 1.0
mean = [0.1, 0.1]
cov = [[0.01, 0], [0, 0.01]] # diagonal covariance
x_sub = np.zeros((N_j,N_i))
y_sub = np.zeros((N_j,N_i))
y_true_sub = np.zeros((N_j,N_i))
ab_sub = np.zeros((N_j,2))
tuning_sub = np.zeros((N_j,1))
sub_ix_sub = np.zeros((N_j,N_i))
for j in range(0,N_j):
aj,bj = np.random.multivariate_normal(mean, cov)
#aj = np.abs(aj)
#bj = np.abs(bj)
xj = np.random.uniform(-1,1,size = (N_i,1))*np.pi
xj = np.sort(xj)#for convenience
yj_true = aj*np.sin(xj) + bj*np.cos(xj)
yj = yj_true + np.random.normal(scale=sigma_y, size=(N_i,1))
x_sub[j,:] = xj.ravel()
y_sub[j,:] = yj.ravel()
y_true_sub[j,:] = yj_true.ravel()
ab_sub[j,:] = [aj,bj]
tuning_sub[j,:] = np.sqrt(((aj**2)+(bj**2)))
sub_ix_sub[j,:] = [j]*N_i
x = np.ravel(x_sub)
y = np.ravel(y_sub)
subject_idx = np.ravel(sub_ix_sub)
subject_idx = np.asarray(subject_idx, dtype=int)
##
# Fit model
hb1_model = pm.Model()
with hb1_model:
# Hyperpriors
hb1_mu_a = pm.Normal('hb1_mu_a', mu=0., sd=100)
hb1_sigma_a = pm.HalfCauchy('hb1_sigma_a', 4)
hb1_mu_b = pm.Normal('hb1_mu_b', mu=0., sd=100)
hb1_sigma_b = pm.HalfCauchy('hb1_sigma_b', 4)
# We fit a mixture of a sine and cosine with these two coeffieicents
# allowed to be different for each subject
hb1_aj = pm.Normal('hb1_aj', mu=hb1_mu_a, sd=hb1_sigma_a, shape = N_j)
hb1_bj = pm.Normal('hb1_bj', mu=hb1_mu_b, sd=hb1_sigma_b, shape = N_j)
# Model error
hb1_eps = pm.HalfCauchy('hb1_eps', 5)
hb1_linear = hb1_aj[subject_idx]*pm.math.sin(x) + hb1_bj[subject_idx]*pm.math.cos(x)
hb1_linear_like = pm.Normal('y', mu = hb1_linear, sd=hb1_eps, observed=y)
with hb1_model:
hb1_trace = pm.sample(draws=n_draw, tune = n_tune,
init = init_string, n_init = n_init,
target_accept = target_accept)
pm.traceplot(hb1_trace)
To partially answer my own question: After playing with this for a while, it looks like the problem might be due to the hyperprior standard deviation going to 0. I am not sure why the algorithm should get stuck there though (testing a small standard deviation can't be that uncommon...).
In any case, two solutions that seem to alleviate the problem (although they don't remove it entirely) are:
1) Add an offset to the definitions of the standard deviation. e.g.:
offset = 1e-2
hb1_sigma_a = offset + pm.HalfCauchy('hb1_sigma_a', 4)
2) Instead of using a HalfCauchy or HalfNormal for the SD prior, use a logNormal distribution set so that 0 is unlikely.
I'd look at the divergencies, as explained in notes and literature on Hamiltonian Monte Carlo, see, e.g., here and here.
with model:
np.savetxt('diverging.csv', hb1_trace['diverging'])
As a dirty solution, you can try to increase target_accept, perhaps.
Good luck!
Related
I have been using gekko to optimize a bioreactor using example 12 (https://apmonitor.com/wiki/index.php/Main/GekkoPythonOptimization) as a basis.
My model is slightly more complicated with 6 states, 7 states and 2 manipulated variables. When I run it for small values of time (t ~20), the simulation is able to converge (albeit requiring a fine time resolution (dt < 0.1). However, when I try to extend the time (e.g., t = 30), it fails quite consistently with the following error:
EXIT: Converged to a point of local infeasibility. Problem may be infeasible
I have tried the following:
Employing different solvers with m.options.SOLVER = 1,2,3
Increasing m.options.MAX_ITER to 10000
Decreasing m.options.NODES to 1 (a lower order descretization seems to help with convergence)
Supplying a reasonable initial guess to the MVs by specifying a value in the declaration:
D = m.MV(value=0.1,lb=0.0,ub=0.1). From some of the various posts, it seems this should help.
I am not too sure how to go about solving this. For a simplified model (3 states, 5 parameters and 2 MVs), gekko is able to optimize it quite well (though it fails somewhat when I try to go to large t) even though the rate constants of the simplified model are a subset of the full model.
My code is as follows:
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
#Parameters and IC
full_params = [0.027,2.12e-9,7.13e-3,168,168,0.035,1e-3]
full_x0 = [5e6,0.0,0.0,0.0,1.25e5,0.0]
mu,k1,k2,k3,k33,k4, f= full_params
#Initialize model
m = GEKKO()
#Time discretization
n_steps = 201
m.time = np.linspace(0,20,n_steps)
#Define MVs
D = m.MV(value=0.1,lb=0.0,ub=0.1)
D.STATUS = 1
D.DCOST = 0.0
Tin = m.MV(value=1e7,lb=0.0,ub=1e7)
Tin.STATUS = 1
Tin.DCOST = 0.0
#Define States
T = m.Var(value=full_x0[0])
Id = m.Var(value=full_x0[1])
Is = m.Var(value=full_x0[2])
Ic = m.Var(value=full_x0[3])
Vs = m.Var(value=full_x0[4])
Vd = m.Var(value=full_x0[5])
#Define equations
m.Equation(T.dt() == mu*T -k1*(Vs+Vd)*T + D*(Tin-T))
m.Equation(Id.dt() == k1*Vd*T -(k1*Vs -mu)*Id -D*Id)
m.Equation(Is.dt() == k1*Vs*T -(k1*Vd + k2)*Is -D*Is)
m.Equation(Ic.dt() == k1*(Vs*Id + Vd*Is) -k2*Ic -D*Ic)
m.Equation(Vs.dt() == k3*Is - (k1*(T+Id+Is+Ic) + k4 + D)*Vs)
m.Equation(Vd.dt() == k33*Ic + f*k3*Is - (k1*(T+Id+Is+Ic) + k4 + D)*Vd)
#Define objective function
J = m.Var(value=0) # objective (profit)
Jf = m.FV() # final objective
Jf.STATUS = 1
m.Connection(Jf,J,pos2="end")
m.Equation(J.dt() == D*(Vs + Vd))
m.Obj(-Jf)
m.options.IMODE = 6 # optimal control
m.options.NODES = 1 # collocation nodes
m.options.SOLVER = 3
m.options.MAX_ITER = 10000
#Solve
m.solve()
For clarity, the model equations are:
I would be grateful for any assistance e.g., how to implement the scaling of the parameters per https://apmonitor.com/do/index.php/Main/ModelInitialization. Thank you!
Try increasing the value of the final time until the solver can no-longer find a solution such as with tf=28 (successful). A plot of the solution reveals that Tin is adjusted to be zero at about the time where the solution almost fails to converge. I added a couple additional objective forms that didn't help the convergence (see Objective Method #1 and #2). The values of J, Vs, Vd are high but not unmanageable by the solver. One way to think about scaling is by changing units such as changing from kg/day to kg/s as the basis. Gekko automatically scales variables by the initial condition.
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
#Parameters and IC
full_params = [0.027,2.12e-9,7.13e-3,168,168,0.035,1e-3]
full_x0 = [5e6,0.0,0.0,0.0,1.25e5,0.0]
mu,k1,k2,k3,k33,k4, f= full_params
#Initialize model
m = GEKKO()
#Time discretization
tf = 28
n_steps = tf*10+1
m.time = np.linspace(0,tf,n_steps)
#Define MVs
D = m.MV(value=0.1,lb=0.0,ub=0.1)
D.STATUS = 1
D.DCOST = 0.0
Tin = m.MV(value=1e7,lb=0,ub=1e7)
Tin.STATUS = 1
Tin.DCOST = 0.0
#Define States
T = m.Var(value=full_x0[0])
Id = m.Var(value=full_x0[1])
Is = m.Var(value=full_x0[2])
Ic = m.Var(value=full_x0[3])
Vs = m.Var(value=full_x0[4])
Vd = m.Var(value=full_x0[5])
#Define equations
m.Equation(T.dt() == mu*T -k1*(Vs+Vd)*T + D*(Tin-T))
m.Equation(Id.dt() == k1*Vd*T -(k1*Vs -mu)*Id -D*Id)
m.Equation(Is.dt() == k1*Vs*T -(k1*Vd + k2)*Is -D*Is)
m.Equation(Ic.dt() == k1*(Vs*Id + Vd*Is) -k2*Ic -D*Ic)
m.Equation(Vs.dt() == k3*Is - (k1*(T+Id+Is+Ic) + k4 + D)*Vs)
m.Equation(Vd.dt() == k33*Ic + f*k3*Is - (k1*(T+Id+Is+Ic) + k4 + D)*Vd)
# Original Objective
if True:
J = m.Var(value=0) # objective (profit)
Jf = m.FV() # final objective
Jf.STATUS = 1
m.Connection(Jf,J,pos2="end")
m.Equation(J.dt() == D*(Vs + Vd))
m.Obj(-Jf)
# Objective Method 1
if False:
p=np.zeros_like(m.time); p[-1]=1
final = m.Param(p)
J = m.Var(value=0) # objective (profit)
m.Equation(J.dt() == D*(Vs + Vd))
m.Maximize(J*final)
# Objective Method 2
if False:
m.Maximize(D*(Vs + Vd))
m.options.IMODE = 6 # optimal control
m.options.NODES = 2 # collocation nodes
m.options.SOLVER = 3
m.options.MAX_ITER = 10000
#Solve
m.solve()
plt.figure(figsize=(10,8))
plt.subplot(3,1,1)
plt.plot(m.time,Tin.value,'r.-',label='Tin')
plt.legend(); plt.grid()
plt.subplot(3,1,2)
plt.semilogy(m.time,T.value,label='T')
plt.semilogy(m.time,Id.value,label='Id')
plt.semilogy(m.time,Is.value,label='Is')
plt.semilogy(m.time,Ic.value,label='Ic')
plt.legend(); plt.grid()
plt.subplot(3,1,3)
plt.semilogy(m.time,Vs.value,label='Vs')
plt.semilogy(m.time,Vd.value,label='Vd')
plt.semilogy(m.time,J.value,label='Objective')
plt.legend(); plt.grid()
plt.show()
Is there any type of constraint in the problem that would favor a decrease at the end? This may be the cause of the infeasibility at tf=30. Another way to get a feasible solution is to solve with m.options.TIME_STEP=20 and resolve the problem with the initial conditions from the prior solution equal to the value at time step 20.
#Solve
m.solve()
m.options.TIME_SHIFT=20
m.solve()
This way, the solution steps forward in time to optimize in parts. This strategy was used to optimize a High Altitude Long Endurance (HALE) UAV and is called Receding Horizon Control.
Martin, R.A., Gates, N., Ning, A., Hedengren, J.D., Dynamic
Optimization of High-Altitude Solar Aircraft Trajectories Under
Station-Keeping Constraints, Journal of Guidance, Control, and
Dynamics, 2018, doi: 10.2514/1.G003737.
I have some discrete data points representing a path and I want to minimize the distance between a trajectory of an object to these path points along with some other constraints. I'm trying out gekko as a tool to solve this problem and for that I made a simple problem by making data points from a parabola and a constraint to the path. My attempt to solve it is
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
import time
#path data points
x_ref = np.linspace(0, 4, num=21)
y_ref = - np.square(x_ref) + 16
#constraint for visualization purposes
x_bound = np.linspace(0, 4, num=10)
y_bound = 1.5*x_bound + 4
def distfunc(x,y,xref,yref,p):
'''
Shortest distance from (x,y) to (xref, yref)
'''
dtemp = []
for i in range(len(xref)):
d = (x-xref[i])**2+(y-yref[i])**2
dtemp.append(dtemp)
min_id = dtemp.index(min(dtemp))
if min_id == 0:
next_id = min_id+1
elif min_id == len(x_ref):
next_id = min_id-1
else:
d2 = (x-xref[min_id-1])**2+(y-yref[min_id-1])**2
d1 = (x-xref[min_id+1])**2+(y-yref[mid_id+1])**2
d_next = [d2, d1]
next_id = min_id + 2*d_next.index(min(d_next)) - 1
n1 = xref[next_id] - xref[min_id]
n2 = yref[next_id] - yref[min_id]
nnorm = p.sqrt(n1**2+n2**2)
n1 = n1 / nnorm
n2 = n2 / nnorm
difx = x-xref[min_id]
dify = y-yref[min_id]
dot = difx*n1 + dify*n2
deltax = difx - dot*n1
deltay = dify - dot*n2
return deltax**2+deltay**2
v_ref = 3
now = time.time()
p = GEKKO(remote=False)
p.time = np.linspace(0,10,21)
x = p.Var(value=0)
y = p.Var(value=16)
vx = p.Var(value=1)
vy = p.Var(value=0)
ax = p.Var(value=0)
ay = p.Var(value=0)
p.options.IMODE = 6
p.options.SOLVER = 3
p.options.WEB = 0
x_refg = p.Param(value=x_ref)
y_refg = p.Param(value=y_ref)
x_refg = p.Param(value=x_ref)
y_refg = p.Param(value=y_ref)
v_ref = p.Const(value=v_ref)
p.Obj(distfunc(x,y,x_refg,y_refg,p))
p.Obj( (p.sqrt(vx**2+vy**2) - v_ref)**2 + ax**2 + ay**2)
p.Equation(x.dt()==vx)
p.Equation(y.dt()==vy)
p.Equation(vx.dt()==ax)
p.Equation(vy.dt()==ay)
p.Equation(y>=1.5*x+4)
p.solve(disp=False, debug=True)
print(f'run time: {time.time()-now}')
plt.plot(x_ref, y_ref)
plt.plot(x_bound, y_bound)
plt.plot(x1.value,x2.value)
plt.show()
This is the result that I get. As you can see, its not exactly the solution that one should expect. For reference to a solution that you may expect, here is what I get using the cost function below
p.Obj((x-x_refg)**2 + (y-y_refg)**2 + ax**2 + ay**2)
However since what I actually wanted is the shortest distance to a path described by these points I expect the distfunc to be closer to what I want since the shortest distance is most likely to some interpolated point. So my question is twofold:
Is this the correct gekko expression/formulation for the objective function?
My other goal is solution speed so is there a more efficient way of expressing this problem for gekko?
You can't define an objective function that changes based on conditions unless you insert logical conditions that are continuously differentiable such as with the if2 or if3 function. Gekko evaluates the symbolic model once and then passes that off to an executable for solution. It only calls the Python model build once because it is compiling the model to efficient byte-code for execution. You can see the model that you created with p.open_folder(). The model file ends in the apm extension: gk_model0.apm.
Model
Constants
i0 = 3
End Constants
Parameters
p1
p2
p3
p4
End Parameters
Variables
v1 = 0
v2 = 16
v3 = 1
v4 = 0
v5 = 0
v6 = 0
End Variables
Equations
v3=$v1
v4=$v2
v5=$v3
v6=$v4
v2>=(((1.5)*(v1))+4)
minimize (((((v1-0.0)-((((((v1-0.0))*((0.2/sqrt(0.04159999999999994))))+(((v2-16.0))&
*((-0.03999999999999915/sqrt(0.04159999999999994))))))*&
((0.2/sqrt(0.04159999999999994))))))^(2))+((((v2-16.0)&
-((((((v1-0.0))*((0.2/sqrt(0.04159999999999994))))+(((v2-16.0))&
*((-0.03999999999999915/sqrt(0.04159999999999994))))))&
*((-0.03999999999999915/sqrt(0.04159999999999994))))))^(2)))
minimize (((((sqrt((((v3)^(2))+((v4)^(2))))-i0))^(2))+((v5)^(2)))+((v6)^(2)))
End Equations
End Model
One strategy is to split your problem into multiple optimization problems that are all minimal time problems where you navigate to the first way-point and then re-initialize the problem to navigate to the second way-point, and so on. If you want to preserve momentum and anticipate the turning then you'll need to use more advanced methods such as shown in the Pigeon / Eagle tracking problem (see source files) or similar to a trajectory optimization with UAVs or HALE UAVs (see references below).
Martin, R.A., Gates, N., Ning, A., Hedengren, J.D., Dynamic Optimization of High-Altitude Solar Aircraft Trajectories Under Station-Keeping Constraints, Journal of Guidance, Control, and Dynamics, 2018, doi: 10.2514/1.G003737.
Gates, N.S., Moore, K.R., Ning, A., Hedengren, J.D., Combined Trajectory, Propulsion and Battery Mass Optimization for Solar-Regenerative High-Altitude Long Endurance Unmanned Aircraft, AIAA Science and Technology Forum (SciTech), 2019.
I like to constrain the variable value u < 1 in y model. Added ub=1 to the variable definition u = m.Var(name='u', value=0, lb=-2, ub=1) but it resulted in "No soulution found" (EXIT: Converged to a point of local infeasibility. Problem may be infeasible.). I guess I have to reformulate the problem to avoid this, but I have not been able to find examples how this should be done. How do i write a proper model to avoid infeasible solutions when constraining variable values?
I hav tied to reformulate the problem by adding equation like m.Equation(u < 1) with no success.
import numpy as np
from gekko import GEKKO
import matplotlib.pyplot as pyplt
m = GEKKO(remote=False)
t = np.linspace(0, 1000, 101) # time
d = np.ones(t.shape)
d[0:10] = 0
y_delay=0
# Add data to model
m.time = t
K = m.Const(0.01, name='K')
r = m.Const(name='r', value=0) # Reference
d = m.Param(name='d', value=d) # Disturbance
y = m.Var(name='y', value=0, lb=-2, ub=2) # State variable
u = m.Var(name='u', value=0, lb=-2, ub=1) # Output
e = m.Var(name='e', value=0)
Tc = m.FV(name='Tc', value=1200, lb=60, ub=1200) # time constant
# Update variable status
Tc.STATUS = 1 # Optimizer can adjust value
Kp = m.Intermediate(1 / K * 1 / Tc, name='Kp')
Ti = m.Intermediate(4 * Tc, name='Ti')
# Model equations
m.Equations([y.dt() == K * (u-d),
e == r-y,
u.dt() == Kp*e.dt()+Kp/Ti*e])
# Model constraints
m.Equation(y < 0.5)
m.Equation(y > -0.5)
# Model objective
m.Obj(-Tc)
# options
m.options.IMODE = 6 # Problem type: 6 = Dynamic optimization
# solve
m.solve(disp=True, debug=True)
print('Tc: %6.2f [s]' % (Tc.value[-1], ))
fig1, (ax1, ax2, ax3) = pyplt.subplots(3, sharex='all')
ax1.plot(t, y.value)
ax1.set_ylabel("y", fontsize=8), ax1.grid(True, which='both')
ax2.plot(t, e.value)
ax2.set_ylabel("e", fontsize=8), ax2.grid(True, which='both')
ax3.plot(t, u.value)
ax3.plot(t, d.value)
ax3.set_ylabel("u and d", fontsize=8), ax3.grid(True, which='both')
pyplt.show()
EXIT: Converged to a point of local infeasibility. Problem may be infeasible.
An error occured.
The error code is 2
If I change the upper bound of u to 2, the optimization problem is solved as expected.
Hard constraints on variables can lead to an infeasible solution, as you observed. I recommend that you use soft constraints by specifying the variable y as a Controlled Variable and set an upper and lower set point range with SPHI and SPLO.
y = m.CV(name='y', value=0) # Controlled variable
y.STATUS = 1
y.TR_INIT = 0
y.SPHI = 0.5
y.SPLO = -0.5
I also removed the lb and ub from y and u to not give them hard bounds that can lead to the infeasibility. You also have an objective to maximize the value of Tc with m.Obj(-Tc). It goes to the maximum limit: 1200 when the solver is able to adjust the value. As you can see from the plot, the value of y exceeds the setpoint range. It may not be possible for the controller to keep it within that range. A soft constraint (objective based) approach to constraints penalizes deviations but does not lead to an infeasible solution. If you need to increase the penalty on violations of the SPHI or SPLO, the parameters WSPHI and WSPLO can be adjusted.
It appears that you have a first order dynamic model and you are trying to optimize PID parameters. If you need to model saturation of the controller output (actuator) then the if3, max3, min3 or corresponding if2, max2, min2 functions may be useful. There is more information on CV objectives and tuning in the Dynamic Optimization course.
Here is a feasible solution to your problem:
import numpy as np
from gekko import GEKKO
import matplotlib.pyplot as pyplt
m = GEKKO() # remote=False
t = np.linspace(0, 1000, 101) # time
d = np.ones(t.shape)
d[0:10] = 0
y_delay=0
# Add data to model
m.time = t
K = m.Const(0.01, name='K')
r = m.Const(name='r', value=0) # Reference
d = m.Param(name='d', value=d) # Disturbance
e = m.Var(name='e', value=0)
u = m.Var(name='u', value=0) # Output
Tc = m.FV(name='Tc', value=1200, lb=60, ub=1200) # time constant
y = m.CV(name='y', value=0) # Controlled variable
y.STATUS = 1
y.TR_INIT = 0
y.SPHI = 0.5
y.SPLO = -0.5
# Update variable status
Tc.STATUS = 1 # Optimizer can adjust value
Kp = m.Intermediate((1 / K) * (1 / Tc), name='Kp')
Ti = m.Intermediate(4 * Tc, name='Ti')
# Model equations
m.Equations([y.dt() == K * (u-d),
e == r-y,
u.dt() == Kp*e.dt()+(Kp/Ti)*e])
# Model constraints
#m.Equation(y < 0.5)
#m.Equation(y > -0.5)
# Model objective
m.Obj(-Tc)
# options
m.options.IMODE = 6 # Problem type: 6 = Dynamic optimization
m.options.SOLVER = 3
m.options.MAX_ITER = 1000
# solve
m.solve(disp=True, debug=True)
print('Tc: %6.2f [s]' % (Tc.value[-1], ))
fig1, (ax1, ax2, ax3) = pyplt.subplots(3, sharex='all')
ax1.plot(t, y.value)
ax1.plot([min(t),max(t)],[0.5,0.5],'k--')
ax1.plot([min(t),max(t)],[-0.5,-0.5],'k--')
ax1.set_ylabel("y", fontsize=8), ax1.grid(True, which='both')
ax2.plot(t, e.value)
ax2.set_ylabel("e", fontsize=8), ax2.grid(True, which='both')
ax3.plot(t, u.value)
ax3.plot(t, d.value)
ax3.set_ylabel("u and d", fontsize=8), ax3.grid(True, which='both')
pyplt.show()
Thanks for an extensive and useful answer to my question. I really appreciate it.
As you correctly observed I am trying to optimize tuning parameters for my simple control problem. I have executed your code with soft constraints, and it sure solves the feasibility issue. I also added the WSPHI/LO parameters and set their values high to have a solution within the constraints. Still, I like to have a model where the control output (“u”) is bounded [0,1]. Based on your answer I probably must add “if” or “max/min” statements in the model to avoid having a non-feasible set of equations when “u” hits the bound. Something like “if u<0, u.dt() = 0 else u.dt() = Kp*e ….”. Could it alternatively be possible to add a variable (a type slack variable) to ensure feasibility of the equation set? I will also investigate the material in the dynamic optimization course links to get a better understanding of dynamic modelling. Thanks again for guiding me in the right direction in this issue.
I would like to know why the sampler is incredibly slow when sampling step by step.
For example, if I run:
mcmc = MCMC(model)
mcmc.sample(1000)
the sampling is fast. However, if I run:
mcmc = MCMC(model)
for i in arange(1000):
mcmc.sample(1)
the sampling is slower (and the more it samples, the slower it is).
If you are wondering why I am asking this.. well, I need a step by step sampling because I want to perform some operations on the values of the variables after each step of the sampler.
Is there a way to speed it up?
Thank you in advance!
------------------ EDIT -------------------------------------------------------------
Here I present the specific problem in more details:
I have two models in competition and they are part of a bigger model that has a categorical variable functioning as a 'switch' between the two.
In this toy example, I have the observed vector 'Y', that could be explained by a Poisson or a Geometric distribution. The Categorical variable 'switch_model' selects the Geometric model when = 0 and the Poisson model when =1.
After each sample, if switch_model selects the Geometric model, I want the variables of the Poisson model NOT to be updated, because they are not influencing the likelihood and therefore they are just drifting away. The opposite is true if the switch_model selects the Poisson model.
Basically what I do at each step is to 'change' the value of the non-selected model by bringing it manually one step back.
I hope that my explanation and the commented code will be clear enough. Let me know if you need further details.
import numpy as np
import pymc as pm
import pandas as pd
import matplotlib.pyplot as plt
# OBSERVED VALUES
Y = np.array([0, 1, 2, 3, 8])
# PRIOR ON THE MODELS
pi = (0.5, 0.5)
switch_model = pm.Categorical("switch_model", p = pi)
# switch_model = 0 for Geometric, switch_model = 1 for Poisson
p = pm.Uniform('p', lower = 0, upper = 1) # Prior of the parameter of the geometric distribution
mu = pm.Uniform('mu', lower = 0, upper = 10) # Prior of the parameter of the Poisson distribution
# LIKELIHOOD
#pm.observed
def Ylike(value = Y, mu = mu, p = p, M = switch_model):
if M == 0:
out = pm.geometric_like(value+1, p)
elif M == 1:
out = pm.poisson_like(value, mu)
return out
model = pm.Model([Ylike, p, mu, switch_model])
mcmc = pm.MCMC(model)
n_samples = 5000
traces = {}
for var in mcmc.stochastics:
traces[str(var)] = np.zeros(n_samples)
bar = pm.progressbar.progress_bar(n_samples)
bar.update(0)
mcmc.sample(1, progress_bar=False)
for var in mcmc.stochastics:
traces[str(var)][0] = mcmc.trace(var)[-1]
for i in np.arange(1,n_samples):
mcmc.sample(1, progress_bar=False)
bar.update(i)
for var in mcmc.stochastics:
traces[str(var)][i] = mcmc.trace(var)[-1]
if mcmc.trace('switch_model')[-1] == 0: # Gemetric wins
traces['mu'][i] = traces['mu'][i-1] # One step back for the sampler of the Poisson parameter
mu.value = traces['mu'][i-1]
elif mcmc.trace('switch_model')[-1] == 1: # Poisson wins
traces['p'][i] = traces['p'][i-1] # One step back for the sampler of the Geometric parameter
p.value = traces['p'][i-1]
print '\n\n'
traces=pd.DataFrame(traces)
traces['mu'][traces['switch_model'] == 0] = np.nan
traces['p'][traces['switch_model'] == 1] = np.nan
print traces.describe()
traces.plot()
plt.show()
The reason this is so slow is that Python's for loops are pretty slow, especially when they are compared to FORTRAN loops (Which is what PyMC is written in basically.) If you could show more detailed code, it might be easier to see what you are trying to do and to provide faster alternative algorithms.
Actually I found a 'crazy' solution, and I have the suspect to know why it works. I would still like to get an expert opinion on my trick.
Basically if I modify the for loop in the following way, adding a 'reset of the mcmc' every 1000 loops, the sampling fires up again:
for i in np.arange(1,n_samples):
mcmc.sample(1, progress_bar=False)
bar.update(i)
for var in mcmc.stochastics:
traces[str(var)][i] = mcmc.trace(var)[-1]
if mcmc.trace('switch_model')[-1] == 0: # Gemetric wins
traces['mu'][i] = traces['mu'][i-1] # One step back for the sampler of the Poisson parameter
mu.value = traces['mu'][i-1]
elif mcmc.trace('switch_model')[-1] == 1: # Poisson wins
traces['p'][i] = traces['p'][i-1] # One step back for the sampler of the Geometric parameter
p.value = traces['p'][i-1]
if i%1000 == 0:
mcmc = pm.MCMC(model)
In practice this trick erases the traces and the database of the sampler every 1000 steps. It looks like the sampler does not like having a long database, although I do not really understand why. (of course 1000 steps is arbitrary, too short it adds too much overhead, too long it will cause the traces and database to be too long).
I find this hack a bit crazy and definitely not elegant.. does any of the experts or developers have a comment on it? Thank you!
I am interested in applying PyMC to model averaging. My goal is to estimate many linear models and average estimates across them, weighting by their posterior model probabilities. I am currently using the Bayesian Information Criterion (BIC) to approximate the likelihood of my data (therefore, my analysis is not fully Bayesian). I have successfully simulated a Markov Chain of models using one of my own scripts but I want to use PyMC because it seems like a great tool.
In my attempts thus far, I have not been forming the Markov Chain correctly. I am not visiting models with higher posterior weights more often than others. I will include the example code below. Please also see the IPython notebook here! on github for the math markup and code together.
import numpy as np
from pymc import stochastic, DiscreteMetropolis, MCMC
import statsmodels.api as sm
import pandas as pd
import random
def pack(alist, rank):
binary = [str(1) if i in alist else str(0) for i in xrange(0,rank)]
string = '0b1'+''.join(binary)
return int(string, 2)
def unpack(integer):
string = bin(integer)[3:]
return [int(i) for i in xrange(len(string)) if string[i]=='1']
def make_bma():
# Simulating Data
size = 100
rank = 20
X = 10*np.random.randn(size, rank)
error = 30*np.random.randn(size,1)
coefficients = np.array([10, 2, 2, 2, 2, 2]).reshape((6,1))
y = np.dot(sm.add_constant(X[:,:5], prepend=True), coefficients) + error
# Number of allowable regressors
predictors = [3,4,5,6,7]
#stochastic(dtype=int)
def regression_model():
def logp(value):
columns = unpack(value)
x = sm.add_constant(X[:,columns], prepend=True)
corr = np.corrcoef(x[:,1:], rowvar=0)
prior = np.linalg.det(corr)
ols = sm.OLS(y,x).fit()
posterior = np.exp(-0.5*ols.bic)*prior
return np.log(posterior)
def random():
k = np.random.choice(predictors)
columns = sorted(np.random.choice(xrange(0,rank), size=k, replace=False))
return pack(columns, rank)
class ModelMetropolis(DiscreteMetropolis):
def __init__(self, stochastic):
DiscreteMetropolis.__init__(self, stochastic)
def propose(self):
'''considers a neighborhood around the previous model,
defined as having one regressor removed or added, provided
the total number of regressors coincides with predictors
'''
# Building set of neighboring models
last = unpack(self.stochastic.value)
last_indicator = np.zeros(rank)
last_indicator[last] = 1
last_indicator = last_indicator.reshape((-1,1))
neighbors = abs(np.diag(np.ones(rank)) - last_indicator)
neighbors = neighbors[:,np.any([neighbors.sum(axis=0) == i \
for i in predictors], axis=0)]
neighbors = pd.DataFrame(neighbors)
# Drawing one model at random from the neighborhood
draw = random.choice(xrange(neighbors.shape[1]))
self.stochastic.value = pack(list(neighbors[draw][neighbors[draw]==1].index), rank)
# def step(self):
#
# logp_p = self.stochastic.logp
#
# self.propose()
#
# logp = self.stochastic.logp
#
# if np.log(random.random()) > logp_p - logp:
#
# self.reject()
return locals()
if __name__ == '__main__':
model = make_bma()
M = MCMC(model)
M.use_step_method(model['ModelMetropolis'], model['regression_model'])
M.sample(iter=5000, burn=1000, thin=1)
model_chain = M.trace("regression_model")[:]
from collections import Counter
counts = Counter(model_chain).items()
counts.sort(reverse=True, key=lambda x: x[1])
for f in counts[:10]:
columns = unpack(f[0])
print('Visits:', f[1])
print(np.array([1. if i in columns else 0 for i in range(0,M.rank)]))
print(M.coefficients.flatten())
X = sm.add_constant(M.X[:, columns], prepend=True)
corr = np.corrcoef(X[:,1:], rowvar=0)
prior = np.linalg.det(corr)
fit = sm.OLS(model['y'],X).fit()
posterior = np.exp(-0.5*fit.bic)*prior
print(fit.params)
print('R-squared:', fit.rsquared)
print('BIC', fit.bic)
print('Prior', prior)
print('Posterior', posterior)
print(" ")
It sounds like you are trying to do something akin to reversible jump MCMC, where you are sampling from the model space in addition to the parameter space(s). PyMC does not currently do rjMCMC, though it probably ought to. The trick is to account for the change in dimension when moving among models. If you do have a modest number of models, you can use an indicator function to select from the models, all of which are fit simultaneously.