def _predict_single(net, driver_id, options):
""" Make predictions for given driver.
Assumes output-2 of the net is the positive class
"""
x_var = T.ftensor3('x_var') # symbolic var
net_output = get_output(net, x_var, deterministic=True) # dropout inactive
output_fn = theano_function(inputs=[x_var], outputs=net_output)
driver_data = load_driver(join(options.datapath, str(driver_id)), options, make_train_val=False)
probs = output_fn(driver_data['x'])
output = sorted(np.vstack((np.asarray(driver_data['trip_ids']), probs[:, 1])).transpose().tolist(),
key=lambda x: x[0])
output = [[str(driver_id) + '_' + str(int(trip_id)), str(p)] for trip_id, p in output]
return output
def logp_forw(out_vars, vars, shared):
"""Compile Theano function of the model and the input and output variables.
Parameters
----------
out_vars: List
containing :class:`pymc3.Distribution` for the output variables
vars: List
containing :class:`pymc3.Distribution` for the input variables
shared: List
containing :class:`theano.tensor.Tensor` for depended shared data
"""
out_list, inarray0 = join_nonshared_inputs(out_vars, vars, shared)
f = theano_function([inarray0], out_list[0])
f.trust_input = True
return f
