I am using python3.6
My jupyter notebook is crashing again and again when I try to run NUTS sampling in pymc3.
My laptop has 16gb and i7 I think it should be enough. I ran same code on 8gb and i7 laptop and it worked that time. Not able to fig out what the issue is in this one.
I have generated the config file for jupyter with this command
$ jupyter notebook --generate-config
I am not able to fig out which parameter I need to modify to tackle this issue.
This is code I am using
with pm.Model() as model:
#hyperpriors
home = pm.Flat('home') #flat pdf is uninformative - means we have no idea
sd_att = pm.HalfStudentT('sd_att', nu=3, sd=2.5)
sd_def = pm.HalfStudentT('sd_def', nu=3, sd=2.5)
intercept = pm.Flat('intercept')
# team-specific model parameters
atts_star = pm.Normal("atts_star", mu=0, sd=sd_att, shape=num_teams)
defs_star = pm.Normal("defs_star", mu=0, sd=sd_def, shape=num_teams)
# To allow samples of expressions to be saved, we need to wrap them in pymc3
Deterministic objects
atts = pm.Deterministic('atts', atts_star - tt.mean(atts_star))
defs = pm.Deterministic('defs', defs_star - tt.mean(defs_star))
# Assume exponential search on home_theta and away_theta. With pymc3, need to
rely on theano.
# tt is theano.tensor.. why Sampyl may be easier to use..
home_theta = tt.exp(intercept + home + atts[home_team] + defs[away_team])
away_theta = tt.exp(intercept + atts[away_team] + defs[home_team])
# likelihood of observed data
home_points = pm.Poisson('home_points', mu=home_theta,
observed=observed_home_goals)
away_points = pm.Poisson('away_points', mu=away_theta,
observed=observed_away_goals)
Also this is the error sc:
Yes, you can use the following command after activating your environment:
jupyter notebook --NotebookApp.iopub_Data_Rate_Limit=1e10
If you need more or less memory change 1e10. By default it is 1e6.