run_glm.py

import numpy as np
import pymc3 as pm
import theano as t

train_new = np.load('train_new.npy')
targets = np.load('y.npy')
X = t.shared(train_new)
features = list(map(str, range(train_new.shape[1])))

with pm.Model() as logistic_model:
    # we won't estimate the intercept because the data is centered
    glm = pm.glm.GLM(X, targets, labels=features,
                     intercept=False, family='binomial')
    trace = pm.sample(3000, tune=3000, jobs=-1)

train_new.npy

y.npy