Source code for deepmd.entrypoints.test

"""Test trained DeePMD model."""
import logging
from pathlib import Path
from typing import TYPE_CHECKING, List, Dict, Optional, Tuple

import numpy as np
from deepmd import DeepPotential
from deepmd.common import expand_sys_str
from deepmd.utils import random as dp_random
from deepmd.utils.data import DeepmdData
from deepmd.utils.weight_avg import weighted_average

if TYPE_CHECKING:
    from deepmd.infer import DeepDipole, DeepPolar, DeepPot, DeepWFC
    from deepmd.infer.deep_eval import DeepTensor

__all__ = ["test"]

log = logging.getLogger(__name__)


[docs]def test( *, model: str, system: str, set_prefix: str, numb_test: int, rand_seed: Optional[int], shuffle_test: bool, detail_file: str, atomic: bool, **kwargs, ): """Test model predictions. Parameters ---------- model : str path where model is stored system : str system directory set_prefix : str string prefix of set numb_test : int munber of tests to do rand_seed : Optional[int] seed for random generator shuffle_test : bool whether to shuffle tests detail_file : Optional[str] file where test details will be output atomic : bool whether per atom quantities should be computed Raises ------ RuntimeError if no valid system was found """ all_sys = expand_sys_str(system) if len(all_sys) == 0: raise RuntimeError("Did not find valid system") err_coll = [] siz_coll = [] # init random seed if rand_seed is not None: dp_random.seed(rand_seed % (2 ** 32)) # init model dp = DeepPotential(model) for cc, system in enumerate(all_sys): log.info("# ---------------output of dp test--------------- ") log.info(f"# testing system : {system}") # create data class tmap = dp.get_type_map() if dp.model_type == "ener" else None data = DeepmdData(system, set_prefix, shuffle_test=shuffle_test, type_map=tmap) if dp.model_type == "ener": err = test_ener( dp, data, system, numb_test, detail_file, atomic, append_detail=(cc != 0), ) elif dp.model_type == "dipole": err = test_dipole(dp, data, numb_test, detail_file, atomic) elif dp.model_type == "polar": err = test_polar(dp, data, numb_test, detail_file, atomic=atomic) elif dp.model_type == "global_polar": # should not appear in this new version log.warning("Global polar model is not currently supported. Please directly use the polar mode and change loss parameters.") err = test_polar(dp, data, numb_test, detail_file, atomic=False) # YWolfeee: downward compatibility log.info("# ----------------------------------------------- ") err_coll.append(err) avg_err = weighted_average(err_coll) if len(all_sys) != len(err_coll): log.warning("Not all systems are tested! Check if the systems are valid") if len(all_sys) > 1: log.info("# ----------weighted average of errors----------- ") log.info(f"# number of systems : {len(all_sys)}") if dp.model_type == "ener": print_ener_sys_avg(avg_err) elif dp.model_type == "dipole": print_dipole_sys_avg(avg_err) elif dp.model_type == "polar": print_polar_sys_avg(avg_err) elif dp.model_type == "global_polar": print_polar_sys_avg(avg_err) elif dp.model_type == "wfc": print_wfc_sys_avg(avg_err) log.info("# ----------------------------------------------- ")
def rmse(diff: np.ndarray) -> np.ndarray: """Calculate average root mean square error. Parameters ---------- diff: np.ndarray difference Returns ------- np.ndarray array with normalized difference """ return np.sqrt(np.average(diff * diff)) def save_txt_file( fname: Path, data: np.ndarray, header: str = "", append: bool = False ): """Save numpy array to test file. Parameters ---------- fname : str filename data : np.ndarray data to save to disk header : str, optional header string to use in file, by default "" append : bool, optional if true file will be appended insted of overwriting, by default False """ flags = "ab" if append else "w" with fname.open(flags) as fp: np.savetxt(fp, data, header=header) def test_ener( dp: "DeepPot", data: DeepmdData, system: str, numb_test: int, detail_file: Optional[str], has_atom_ener: bool, append_detail: bool = False, ) -> Tuple[List[np.ndarray], List[int]]: """Test energy type model. Parameters ---------- dp : DeepPot instance of deep potential data: DeepmdData data container object system : str system directory numb_test : int munber of tests to do detail_file : Optional[str] file where test details will be output has_atom_ener : bool whether per atom quantities should be computed append_detail : bool, optional if true append output detail file, by default False Returns ------- Tuple[List[np.ndarray], List[int]] arrays with results and their shapes """ data.add("energy", 1, atomic=False, must=False, high_prec=True) data.add("force", 3, atomic=True, must=False, high_prec=False) data.add("virial", 9, atomic=False, must=False, high_prec=False) if dp.has_efield: data.add("efield", 3, atomic=True, must=True, high_prec=False) if has_atom_ener: data.add("atom_ener", 1, atomic=True, must=True, high_prec=False) if dp.get_dim_fparam() > 0: data.add( "fparam", dp.get_dim_fparam(), atomic=False, must=True, high_prec=False ) if dp.get_dim_aparam() > 0: data.add("aparam", dp.get_dim_aparam(), atomic=True, must=True, high_prec=False) test_data = data.get_test() natoms = len(test_data["type"][0]) nframes = test_data["box"].shape[0] numb_test = min(nframes, numb_test) coord = test_data["coord"][:numb_test].reshape([numb_test, -1]) box = test_data["box"][:numb_test] if dp.has_efield: efield = test_data["efield"][:numb_test].reshape([numb_test, -1]) else: efield = None if not data.pbc: box = None atype = test_data["type"][0] if dp.get_dim_fparam() > 0: fparam = test_data["fparam"][:numb_test] else: fparam = None if dp.get_dim_aparam() > 0: aparam = test_data["aparam"][:numb_test] else: aparam = None ret = dp.eval( coord, box, atype, fparam=fparam, aparam=aparam, atomic=has_atom_ener, efield=efield, ) energy = ret[0] force = ret[1] virial = ret[2] energy = energy.reshape([numb_test, 1]) force = force.reshape([numb_test, -1]) virial = virial.reshape([numb_test, 9]) if has_atom_ener: ae = ret[3] av = ret[4] ae = ae.reshape([numb_test, -1]) av = av.reshape([numb_test, -1]) rmse_e = rmse(energy - test_data["energy"][:numb_test].reshape([-1, 1])) rmse_f = rmse(force - test_data["force"][:numb_test]) rmse_v = rmse(virial - test_data["virial"][:numb_test]) rmse_ea = rmse_e / natoms rmse_va = rmse_v / natoms if has_atom_ener: rmse_ae = rmse( test_data["atom_ener"][:numb_test].reshape([-1]) - ae.reshape([-1]) ) # print ("# energies: %s" % energy) log.info(f"# number of test data : {numb_test:d} ") log.info(f"Energy RMSE : {rmse_e:e} eV") log.info(f"Energy RMSE/Natoms : {rmse_ea:e} eV") log.info(f"Force RMSE : {rmse_f:e} eV/A") log.info(f"Virial RMSE : {rmse_v:e} eV") log.info(f"Virial RMSE/Natoms : {rmse_va:e} eV") if has_atom_ener: log.info(f"Atomic ener RMSE : {rmse_ae:e} eV") if detail_file is not None: detail_path = Path(detail_file) pe = np.concatenate( ( np.reshape(test_data["energy"][:numb_test], [-1, 1]), np.reshape(energy, [-1, 1]), ), axis=1, ) save_txt_file( detail_path.with_suffix(".e.out"), pe, header="%s: data_e pred_e" % system, append=append_detail, ) pf = np.concatenate( ( np.reshape(test_data["force"][:numb_test], [-1, 3]), np.reshape(force, [-1, 3]), ), axis=1, ) save_txt_file( detail_path.with_suffix(".f.out"), pf, header="%s: data_fx data_fy data_fz pred_fx pred_fy pred_fz" % system, append=append_detail, ) pv = np.concatenate( ( np.reshape(test_data["virial"][:numb_test], [-1, 9]), np.reshape(virial, [-1, 9]), ), axis=1, ) save_txt_file( detail_path.with_suffix(".v.out"), pv, header=f"{system}: data_vxx data_vxy data_vxz data_vyx data_vyy " "data_vyz data_vzx data_vzy data_vzz pred_vxx pred_vxy pred_vxz pred_vyx " "pred_vyy pred_vyz pred_vzx pred_vzy pred_vzz", append=append_detail, ) return { "rmse_ea" : (rmse_ea, energy.size), "rmse_f" : (rmse_f, force.size), "rmse_va" : (rmse_va, virial.size), } def print_ener_sys_avg(avg: Dict[str,float]): """Print errors summary for energy type potential. Parameters ---------- avg : np.ndarray array with summaries """ log.info(f"Energy RMSE/Natoms : {avg['rmse_ea']:e} eV") log.info(f"Force RMSE : {avg['rmse_f']:e} eV/A") log.info(f"Virial RMSE/Natoms : {avg['rmse_va']:e} eV") def run_test(dp: "DeepTensor", test_data: dict, numb_test: int): """Run tests. Parameters ---------- dp : DeepTensor instance of deep potential test_data : dict dictionary with test data numb_test : int munber of tests to do Returns ------- [type] [description] """ nframes = test_data["box"].shape[0] numb_test = min(nframes, numb_test) coord = test_data["coord"][:numb_test].reshape([numb_test, -1]) box = test_data["box"][:numb_test] atype = test_data["type"][0] prediction = dp.eval(coord, box, atype) return prediction.reshape([numb_test, -1]), numb_test, atype def test_wfc( dp: "DeepWFC", data: DeepmdData, numb_test: int, detail_file: Optional[str], ) -> Tuple[List[np.ndarray], List[int]]: """Test energy type model. Parameters ---------- dp : DeepPot instance of deep potential data: DeepmdData data container object numb_test : int munber of tests to do detail_file : Optional[str] file where test details will be output Returns ------- Tuple[List[np.ndarray], List[int]] arrays with results and their shapes """ data.add( "wfc", 12, atomic=True, must=True, high_prec=False, type_sel=dp.get_sel_type() ) test_data = data.get_test() wfc, numb_test, _ = run_test(dp, test_data, numb_test) rmse_f = rmse(wfc - test_data["wfc"][:numb_test]) log.info("# number of test data : {numb_test:d} ") log.info("WFC RMSE : {rmse_f:e} eV/A") if detail_file is not None: detail_path = Path(detail_file) pe = np.concatenate( ( np.reshape(test_data["wfc"][:numb_test], [-1, 12]), np.reshape(wfc, [-1, 12]), ), axis=1, ) np.savetxt( detail_path.with_suffix(".out"), pe, header="ref_wfc(12 dofs) predicted_wfc(12 dofs)", ) return { 'rmse' : (rmse_f, wfc.size) } def print_wfc_sys_avg(avg): """Print errors summary for wfc type potential. Parameters ---------- avg : np.ndarray array with summaries """ log.info(f"WFC RMSE : {avg['rmse']:e} eV/A") def test_polar( dp: "DeepPolar", data: DeepmdData, numb_test: int, detail_file: Optional[str], *, atomic: bool, ) -> Tuple[List[np.ndarray], List[int]]: """Test energy type model. Parameters ---------- dp : DeepPot instance of deep potential data: DeepmdData data container object numb_test : int munber of tests to do detail_file : Optional[str] file where test details will be output global_polar : bool wheter to use glovbal version of polar potential Returns ------- Tuple[List[np.ndarray], List[int]] arrays with results and their shapes """ data.add( "polarizability" if not atomic else "atomic_polarizability", 9, atomic=atomic, must=True, high_prec=False, type_sel=dp.get_sel_type(), ) test_data = data.get_test() polar, numb_test, atype = run_test(dp, test_data, numb_test) sel_type = dp.get_sel_type() sel_natoms = 0 for ii in sel_type: sel_natoms += sum(atype == ii) # YWolfeee: do summation in global polar mode if not atomic: polar = np.sum(polar.reshape((polar.shape[0],-1,9)),axis=1) rmse_f = rmse(polar - test_data["polarizability"][:numb_test]) rmse_fs = rmse_f / np.sqrt(sel_natoms) rmse_fa = rmse_f / sel_natoms else: rmse_f = rmse(polar - test_data["atomic_polarizability"][:numb_test]) log.info(f"# number of test data : {numb_test:d} ") log.info(f"Polarizability RMSE : {rmse_f:e}") if not atomic: log.info(f"Polarizability RMSE/sqrtN : {rmse_fs:e}") log.info(f"Polarizability RMSE/N : {rmse_fa:e}") log.info(f"The unit of error is the same as the unit of provided label.") if detail_file is not None: detail_path = Path(detail_file) pe = np.concatenate( ( np.reshape(test_data["polarizability"][:numb_test], [-1, 9]), np.reshape(polar, [-1, 9]), ), axis=1, ) np.savetxt( detail_path.with_suffix(".out"), pe, header="data_pxx data_pxy data_pxz data_pyx data_pyy data_pyz data_pzx " "data_pzy data_pzz pred_pxx pred_pxy pred_pxz pred_pyx pred_pyy pred_pyz " "pred_pzx pred_pzy pred_pzz", ) return { "rmse" : (rmse_f, polar.size) } def print_polar_sys_avg(avg): """Print errors summary for polar type potential. Parameters ---------- avg : np.ndarray array with summaries """ log.info(f"Polarizability RMSE : {avg['rmse']:e} eV/A") def test_dipole( dp: "DeepDipole", data: DeepmdData, numb_test: int, detail_file: Optional[str], atomic: bool, ) -> Tuple[List[np.ndarray], List[int]]: """Test energy type model. Parameters ---------- dp : DeepPot instance of deep potential data: DeepmdData data container object numb_test : int munber of tests to do detail_file : Optional[str] file where test details will be output atomic : bool whether atomic dipole is provided Returns ------- Tuple[List[np.ndarray], List[int]] arrays with results and their shapes """ data.add( "dipole" if not atomic else "atomic_dipole", 3, atomic=atomic, must=True, high_prec=False, type_sel=dp.get_sel_type() ) test_data = data.get_test() dipole, numb_test, atype = run_test(dp, test_data, numb_test) sel_type = dp.get_sel_type() sel_natoms = 0 for ii in sel_type: sel_natoms += sum(atype == ii) # do summation in atom dimension if not atomic: dipole = np.sum(dipole.reshape((dipole.shape[0], -1, 3)),axis=1) rmse_f = rmse(dipole - test_data["dipole"][:numb_test]) rmse_fs = rmse_f / np.sqrt(sel_natoms) rmse_fa = rmse_f / sel_natoms else: rmse_f = rmse(dipole - test_data["atomic_dipole"][:numb_test]) log.info(f"# number of test data : {numb_test:d}") log.info(f"Dipole RMSE : {rmse_f:e}") if not atomic: log.info(f"Dipole RMSE/sqrtN : {rmse_fs:e}") log.info(f"Dipole RMSE/N : {rmse_fa:e}") log.info(f"The unit of error is the same as the unit of provided label.") if detail_file is not None: detail_path = Path(detail_file) pe = np.concatenate( ( np.reshape(test_data["dipole"][:numb_test], [-1, 3]), np.reshape(dipole, [-1, 3]), ), axis=1, ) np.savetxt( detail_path.with_suffix(".out"), pe, header="data_x data_y data_z pred_x pred_y pred_z", ) return { 'rmse' : (rmse_f, dipole.size) } def print_dipole_sys_avg(avg): """Print errors summary for dipole type potential. Parameters ---------- avg : np.ndarray array with summaries """ log.info(f"Dipole RMSE : {avg['rmse']:e} eV/A")