Error when running NNP.train( ) about maml HOT 8 CLOSED

@YunxingZuo it seems that there are no validation data inputs for the NNP training, but the codes try to find validation errors from outputs. Could you please check that? THANKS!

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Essentially the underlying n2p2 use the keyword "test_fraction" to split the data inputs into training and validation set. In maml, the default value is set to 0 as all the data inputs are used for training. A separate test dataset is used for evaluation. @Akram-Ibrahim Can you provide a snippet of code/data for us to reproduce the error you met? The version information of n2p2 and maml are appreciated.

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@YunxingZuo I'm using maml 0.0.2. For n2p2, I've cloned the n2p2 github repository a week ago.
Here is a snippet of my code/data.
snippet.zip

Thank you for the help!

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@Akram-Ibrahim Please update the maml package and run the code again. The error is due to the minor change of the new n2p2 output format. They changed the keyword "FORCES" to "FORCE". Now the NNPotential module is compatible with both new and old versions of n2p2.

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@YunxingZuo Thank you so much. However, I've tried updating the maml package with pip but I still get the version 0.0.2 and the error is still occurring with the NNPotential.

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This is strange... Could you run the following snippet of code and paste the file "training_output"? Thanks.

import numpy as np
import pandas as pd
from pymatgen import Structure
import subprocess
from pymatgen.io.vasp.outputs import Vasprun
import os
import json
from maml.utils import pool_from, convert_docs, check_structures_forces_stresses

# load the training pymatgen structures
with open('structures.json', 'r') as f:
    d = json.load(f)

train_structures = [Structure.from_dict(x) for x in d]  

# load the training energies
with open('energies.json', 'r') as f:
    d= json.load(f)
    
train_energies = [x for x in d] 

# load the training forces 
with open('forces.json', 'r') as f:
    d= json.load(f)
    
train_forces = [x for x in d] 

# train potential step by step
train_structures, train_forces, train_stresses = check_structures_forces_stresses(
    train_structures, train_forces, None)
train_pool = pool_from(train_structures, train_energies, train_forces, train_stresses)
atoms_filename = "input.data"
_ = nnp.write_cfgs(filename=atoms_filename, cfg_pool=train_pool)
output = "training_output"
nnp.write_input(cutoff_type=1,
          r_etas = [0.5, 2.0], a_etas = [0.5, 2.0],
          r_shift=[0.0],
          zetas=[1.0, 4.0],
          r_cut=4.2, hidden_layers=[2, 2], epochs=3)

p_scaling = subprocess.Popen(["nnp-scaling", "100"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout, stderr = p_scaling.communicate()

p_train = subprocess.Popen(["nnp-train"], stdout=open(output, "w"), stderr=subprocess.PIPE)
stdout, stderr = p_train.communicate()

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@Akram-Ibrahim @YunxingZuo I just did a release. Version 0.0.4 should be available now via pypi

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@chc273 @YunxingZuo Thanks a lot. I've updated the maml package now and the 0.0.4 has been loaded and the error doesn't appear anymore.

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