How to handle pymc3 form Deterministic variables

I have been working on creating a hierarchical model of some psychophysical behavioral data in pymc3. I'm incredibly impressed with things in general, but while trying to get up to speed with Theano and pymc3, I have a model that mostly works, but has a couple of problems.

The code is structured to match the parameterized version of Weibull for seven datasets. Each study is modeled as a binary Bernoulli result, and the thresholds (thact output as y values ​​that are used to match a Gaussian function in height, width, and height (a, c, and d in a typical Gaussian).

Using parameterized Weibull seems to work well and is now hierarchical for the Weibull tilt, while the thresholds fit separately for each piece of data. However - the output I get from k and y_est leads me to believe that they may not be the correct size, and unlike the probability distributions, it doesn't look like I can specify the shape (unless there is an anal way of doing this I haven't found - although from what I've read, the defining figure in theanano is tricky).

Ultimately, I would like to use y_est to estimate the height or width of a gaussian, however the output right now leads to an incredible mess, which I think arises with the size problems in y_est and k. Any help would be fantastic - the code below should simulate some data followed by the model. The model does a great job with every single rapids and gets slopes, but falls apart when it deals with the rest.

Thanks for watching - I'm still impressed with pymc3!

EDIT: Ok, so the form output using y_est.tag.test_value.shape looks like this:

y_est.tag.test_value.shape
(101, 7)
k.tag.test_value.shape
(7,)

      

        
        
        
      

    

I think this is where I ran into difficulties, although it may be poorly constructed on my part. k is well-formed (one k value per unique_xval). y_est outputs the full dataset (101x7) instead of one score (one y_est per unique_xval) for each difficulty level. Is there a way to indicate that y_est receives specific subsets of df_y_vals to manage this?

#Import necessary modules and define our weibull function
import numpy as np
import pylab as pl    
from scipy.stats import bernoulli

#x stimulus intensity
#g chance (0.5 for 2AFC)
# m slope
# t threshold
# a performance level defining threshold 
def weib(x,g,a,m,t):
    k=-np.log(((1-a)/(1-g))**(1/t))
    return 1- (1-g)*np.exp(- (k*x/t)**m);

#Output values from weibull function
xit=101
xvals=np.linspace(0.05,1,xit)
out_weib=weib(xvals, 0.5, 0.8, 3, 0.6)

#Okay, fitting the perfect output of a Weibull should be easy, contaminate         with some noise
#Slope of 3, threshold of 0.6


#How about 5% noise!

noise=0.05*np.random.randn(np.size(out_weib))
out=out_weib+noise

#Let make this more like a typical experiment - 
#i.e. no percent correct, just one or zero
#Randomly pick based on the probability at each point whether they got the trial right or wrong
trial=np.zeros_like(out)
for i in np.arange(out.size):
    p=out_weib[i]
    trial[i] = bernoulli.rvs(p)

#Iterate for 6 sets of data, similar slope (from a normal dist), different thresh (output from gaussian)
#Gauss parameters=

true_gauss_height = 0.3
true_gauss_width = 0.01
true_gauss_elevation = 0.2

#What thresholds will we get then? 6 discrete points along that gaussian, from 0 to 180 degree mask

x_points=[0, 30, 60, 90, 120, 150, 180]

x_points=np.asarray(x_points)
gauss_points=true_gauss_height*np.exp(-    ((x_points**2)/2*true_gauss_width**2))+true_gauss_elevation

import pymc as pm2
import pymc3 as pm
import pandas as pd

slopes=pm2.rnormal(3, 3, size=7)
out_weib=np.zeros([xvals.size,x_points.size])

for i in np.arange(x_points.size):
    out_weib[:,i]=weib(xvals, 0.5, 0.8, slopes[i], gauss_points[i])

#Let make this more like a typical experiment - i.e. no percent correct, just one or zero
#Randomly pick based on the probability at each point whether they got the trial right or wrong
trials=np.zeros_like(out_weib)

for i in np.arange(len(trials)):
    for ii in np.arange(gauss_points.size):
        p=out_weib[i,ii]
        trials[i,ii] = bernoulli.rvs(p)

#Let make that data into a DataFrame for pymc3
y_vals=np.tile(xvals, [7, 1])

df_correct = pd.DataFrame(trials, columns=x_points)
df_y_vals = pd.DataFrame(y_vals.T, columns=x_points)
unique_xvals=x_points

import theano as th

with pm.Model() as hierarchical_model:
    # Hyperpriors for group node
    mu_slope = pm.Normal('mu_slope', mu=3, sd=1)
    sigma_slope = pm.Uniform('sigma_slope', lower=0.1, upper=2)

#Priors for the overall gaussian function - 3 params, the height of the gaussian
#Width, and elevation

gauss_width = pm.HalfNormal('gauss_width', sd=1)
gauss_elevation = pm.HalfNormal('gauss_elevation', sd=1)

slope = pm.Normal('slope', mu=mu_slope, sd=sigma_slope,     shape=unique_xvals.size)

thresh=pm.Uniform('thresh', upper=1, lower=0.1, shape=unique_xvals.size)

k = -th.tensor.log(((1-0.8)/(1-0.5))**(1/thresh))
y_est=1-(1-0.5)*th.tensor.exp(-(k*df_y_vals/thresh)**slope)

#We want our model to predict either height or width...height would be easier.
#Our Gaussian function has y values estimated by y_est as the 82% thresholds
#and Xvals based on where each of those psychometrics were taken.
#height_est=pm.Deterministic('height_est', (y_est/(th.tensor.exp((-unique_xvals**2)/2*gauss_width)))+gauss_elevation)
height_est = pm.Deterministic('height_est', (y_est-gauss_elevation)*th.tensor.exp((unique_xvals**2)/2*gauss_width**2))

#Define likelihood as Bernoulli for each binary trial
likelihood = pm.Bernoulli('likelihood',p=y_est, shape=unique_xvals.size, observed=df_correct)

#Find start
start=pm.find_MAP()
step=pm.NUTS(state=start)
#Do MCMC
trace = pm.sample(5000, step, njobs=1, progressbar=True) # draw 5000 posterior samples using NUTS sampling