This repo needs tests, and to be hooked into circle-ci.

Needs bokeh:

dwave-ocean-sdk==2.1.1

jupyter
jupyter_contrib_nbextensions==0.5.1
autopep8==1.3.2
bokeh==0.12.15

On colab, I copy over the file dwave-examples/factoring-notebook/blob/master/01-factoring-overview.ipynb from your github, though the open menu.

I insert a code cell at the top with:

!pip install dwave-ocean-sdk
!pip install dwavebinarycsp[maxgap]

(the second line is suggested by an error if the notebook is run without it)

I restart the runtime from the cell on colab, as per instructions from pip.

When I run the rest of the notebook, it hangs on:
and_bqm = dbc.stitch(and_csp)

With the following messages:

VisibleDeprecationWarning Traceback (most recent call last)
in ()
----> 1 and_bqm = dbc.stitch(and_csp)
2 and_bqm.remove_offset()
3
4 print("Linear coefficients:\n\t{}".format(
5 {key: round(val, 2) for key, val in and_bqm.linear.items()}))

10 frames
/usr/local/lib/python3.7/dist-packages/dwavebinarycsp/compilers/stitcher.py in stitch(csp, min_classical_gap, max_graph_size)
180 # try to use the penaltymodel ecosystem
181 try:
--> 182 pmodel = pm.get_penalty_model(spec)
183 except pm.ImpossiblePenaltyModel:
184 # hopefully adding more variables will make it possible

/usr/local/lib/python3.7/dist-packages/penaltymodel/core/interface.py in get_penalty_model(specification)
71 for factory in iter_factories():
72 try:
---> 73 pm = factory(specification)
74 except ImpossiblePenaltyModel as e:
75 # information about impossible models should be propagated

/usr/local/lib/python3.7/dist-packages/penaltymodel/lp/interface.py in get_penalty_model(specification)
57 linear_energy_ranges=specification.ising_linear_ranges,
58 quadratic_energy_ranges=quadratic_ranges,
---> 59 min_classical_gap=specification.min_classical_gap)
60 except ValueError:
61 raise pm.exceptions.FactoryException("Specification is for too large of a model")

/usr/local/lib/python3.7/dist-packages/penaltymodel/lp/generation.py in generate_bqm(graph, table, decision_variables, linear_energy_ranges, quadratic_energy_ranges, min_classical_gap, catch_warnings)
176 try:
177 result = linprog(cost_weights.flatten(), A_eq=noted_matrix, b_eq=noted_bound,
--> 178 A_ub=unnoted_matrix, b_ub=unnoted_bound, bounds=bounds)
179 except (OptimizeWarning, LinAlgWarning) as e:
180 raise ValueError('Penaltymodel-lp has a bad matrix')

/usr/local/lib/python3.7/dist-packages/scipy/optimize/_linprog.py in linprog(c, A_ub, b_ub, A_eq, b_eq, bounds, method, callback, options, x0)
552 x, status, message, iteration = _linprog_ip(
553 c, c0=c0, A=A, b=b, callback=callback,
--> 554 postsolve_args=postsolve_args, **solver_options)
555 elif meth == 'revised simplex':
556 x, status, message, iteration = _linprog_rs(

/usr/local/lib/python3.7/dist-packages/scipy/optimize/_linprog_ip.py in _linprog_ip(c, c0, A, b, callback, postsolve_args, maxiter, tol, disp, alpha0, beta, sparse, lstsq, sym_pos, cholesky, pc, ip, permc_spec, **unknown_options)
1123 lstsq, sym_pos, cholesky,
1124 pc, ip, permc_spec, callback,
-> 1125 postsolve_args)
1126
1127 return x, status, message, iteration

/usr/local/lib/python3.7/dist-packages/scipy/optimize/_linprog_ip.py in _ip_hsd(A, b, c, c0, alpha0, beta, maxiter, disp, tol, sparse, lstsq, sym_pos, cholesky, pc, ip, permc_spec, callback, postsolve_args)
753 d_x, d_y, d_z, d_tau, d_kappa = _get_delta(
754 A, b, c, x, y, z, tau, kappa, gamma, eta,
--> 755 sparse, lstsq, sym_pos, cholesky, pc, ip, permc_spec)
756
757 if ip: # initial point

/usr/local/lib/python3.7/dist-packages/scipy/optimize/_linprog_ip.py in _get_delta(A, b, c, x, y, z, tau, kappa, gamma, eta, sparse, lstsq, sym_pos, cholesky, pc, ip, permc_spec)
319
320 # [4] 8.12 and "Let alpha be the maximal possible step..." before 8.23
--> 321 alpha = _get_step(x, d_x, z, d_z, tau, d_tau, kappa, d_kappa, 1)
322 if ip: # initial point - see [4] 4.4
323 gamma = 10

/usr/local/lib/python3.7/dist-packages/scipy/optimize/_linprog_ip.py in _get_step(x, d_x, z, d_z, tau, d_tau, kappa, d_kappa, alpha0)
372 alpha_z = alpha0 * np.min(z[i_z] / -d_z[i_z]) if np.any(i_z) else 1
373 alpha_kappa = alpha0 * kappa / -d_kappa if d_kappa < 0 else 1
--> 374 alpha = np.min([1, alpha_x, alpha_tau, alpha_z, alpha_kappa])
375 return alpha
376

<array_function internals> in amin(*args, **kwargs)

/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py in amin(a, axis, out, keepdims, initial, where)
2829 """
2830 return _wrapreduction(a, np.minimum, 'min', axis, None, out,
-> 2831 keepdims=keepdims, initial=initial, where=where)
2832
2833

/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
85 return reduction(axis=axis, out=out, **passkwargs)
86
---> 87 return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
88
89

VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray

** I get identical errors using dwavebinarycsp.stitch on my own PC as well.

Is this a code issue, or human error on my part?

Thanks!

D-Wave Systems Inc

Should be

D-Wave Systems Inc.

Currently we pin to bokeh===0.12.15, it would be nice to update to the latest, 2.2.2.
However, 2.2.2 does not support Python 3.5. The previous version, which does support 3.5, is 1.4.0. Unfortunately, between 1.4.0 and 2.2.2 there is a deprecation that affects this JN: "Importing from_networkx from bokeh.models.graphs is deprecated. Import from bokeh.plotting instead."
So better to wait till after 3.5 is no longer supported.

See dwavesystems/dwave-ocean-sdk#91

I have the code change here for when we do: https://github.com/JoelPasvolsky/factoring-notebook/tree/bokeh