Hi I am Trying to run below code:
This code perfectly working on 8gb ram and i7 processor laptop (it was slow but it worked)
Now I am trying to run this code on my new laptop with same installation of anaconda and jupyter notebook. laptop config is 16gb ram and i7 processor . But this time my notebook gets disconnected from kernel.
Here is my code:
g1 = df.groupby('i_away') att_starting_points = np.log(g1['away_team/goals'].fillna(0).mean()) g2 = df.groupby('i_home') def_starting_points = -np.log(g2['home_team/goals'].fillna(0).mean()) def_starting_points with pm.Model() as model: # global model priors: standard deviation and intercept 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) with model: trace = pm.sample(1000, tune=1000, cores=2) pm.traceplot(trace)
Even if I do 10 samples it will still crash.
Versions and main components
PyMC3 Version: PyMC3 v3.4.1
pygpu: 0.7.6-py36_0 mila-udem
theano: 1.0.2-py36_0 mila-udem
Python Version: python 3.6
Operating system: windows 10
How did you install PyMC3: (conda/pip): conda
conda install pymc3