VisibleDeprecationWarning in weight class

[ipcamit]

Line 177 in kliff.dataset.weight module is giving the following error in Python > 3.9, and error in Python >3.10

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.

originating here:

    def _compute_weight_one_property(data_norm, property_weight_params, property_type):
        c1, c2 = property_weight_params
->        sigma = np.linalg.norm([c1, (c2 * data_norm)])
        weight = 1 / sigma
...

@yonatank93 I am currently adapting it for new workflow, can you please tell what is the expected behavior of this, so I can modify it accordingly?

Does this captures your intent correctly? :

    c1_arr = np.ones_like(data_norm) * c1
    c2_arr = data_norm * c2
    sigma = np.linalg.norm(np.vstack((c1_arr, c2_arr)), axis=0) 

Both functions give identical results.

But as per the equation defined in MagnitudeInverseWeight here: https://kliff.readthedocs.io/en/latest/modules/dataset.html#magnitude-inverse-weight

I think it should be:

    sigma = np.linalg.norm([c1, c2 * np.linalg.norm(data_norm)]) 

[yonatank93]

Hi @ipcamit.
I think we should modify L177 as

sigma = np.array([np.linalg.norm(c1, c2 * dn) for dn in data_norm])

The equation in the documentation is for per data point.
And, for example, if the configuration has 3 atoms in it, the function _compute_weight_one_property will return 3 numbers, one corresponding to each atom.

If we apply this change, I think we also need to apply additional minor change, for example to L146-L148

self._energy_weight = self._compute_weight_one_property(
        [energy_norm], self._weight_params["energy_weight_params"], "energy"
)[0]

and to L165-L167

self._stress_weight = self._compute_weight_one_property(
        [stress_norm], self._weight_params["stress_weight_params"], "stress"
)[0]

Note that I haven't tested these suggestions.

The reason is that for a configuration with N atoms, self._forces_weight needs to be a 1D array with 3N elements, and self._energy_weight and self._stress_weight each needs to be a float.

[mjwen]

@yonatank93 you seem updated the Weight class in the just merged PR. Is the issue reported by @ipcamit resolved there?

[yonatank93]

Yes, I did. I think it is resolved.

[mjwen]

Also, we need to update the Weight class such that the input shape of forces_weight and stress_weight do not need to be a float. For example, the force weight need to accommodate for three cases

• a float. It will be multiple by all (N, 3) force components, where N is the number of atoms
• 1d array with shape (N,). It will be broadcasted into a shape of (N, 3) and then multiple the forces. Physically, this enables using a different weight for each atom
• 2d array with shape (N,3). This will be directly multiplied by the forces, enabling the use of a different weight for each individual component

Since @ipcamit is adapting for the new workflow, we do not need to modify it now. But once the new stuff is functioning, we need to get back to this.