Initial commit

deepks/tools/geom_optim.py

+#!/usr/bin/env python
+#SBATCH -N 1
+#SBATCH -c 20
+#SBATCH -t 24:00:00
+#SBATCH --mem=8G
+
+import time
+import numpy as np
+from deepks.utils import load_yaml
+from deepks.scf.scf import DSCF
+from pyscf import gto, lib
+try:
+    from pyscf.geomopt.berny_solver import optimize
+except ImportError:
+    from pyscf.geomopt.geometric_solver import optimize
+
+
+def run_optim(mol, model=None, proj_basis=None, scf_args={}, conv_args={}):
+    cf = DSCF(mol, model, proj_basis=proj_basis).set(**scf_args)
+    mol_eq = optimize(cf, **conv_args)
+    return mol_eq
+
+def dump_xyz(filename, mol):
+    coords = mol.atom_coords(unit="Angstrom").reshape(-1,3)
+    elems = mol.elements
+    with open(filename, 'w') as fp:
+        fp.write(f"{mol.natm}\n\n")
+        for x, e in zip(coords, elems):
+            fp.write("%s %.18g %.18g %.18g\n" % (e, x[0], x[1], x[2]))
+
+
+if __name__ == "__main__":
+    import argparse
+    import os
+    parser = argparse.ArgumentParser(description="Calculate and save mp2 energy and mo_coeffs for given xyz files.")
+    parser.add_argument("files", nargs="+", help="input xyz files")
+    parser.add_argument("-m", "--model-file", help="file of the trained model")
+    parser.add_argument("-d", "--dump-dir", help="dir of dumped files, default is same dir as xyz file")
+    parser.add_argument("-B", "--basis", default="ccpvdz", type=str, help="basis used to do the calculation")
+    parser.add_argument("-P", "--proj_basis", help="basis set used to project dm, must match with model") 
+    parser.add_argument("-C", "--charge", default=0, type=int, help="net charge of the molecule")
+    parser.add_argument("-v", "--verbose", default=1, type=int, help="output calculation information")
+    parser.add_argument("-S", "--suffix", help="suffix added to the saved xyz")
+    parser.add_argument("--scf-input", help="yaml file to specify scf arguments")
+    parser.add_argument("--conv-input", help="yaml file to specify convergence arguments")
+    args = parser.parse_args()
+    
+    if args.verbose:
+        print(f"starting calculation with OMP threads: {lib.num_threads()}",
+              f"and max memory: {lib.param.MAX_MEMORY}")
+
+    if args.dump_dir is not None:
+        os.makedirs(args.dump_dir, exist_ok = True)
+    for fn in args.files:
+        tic = time.time()
+        mol = gto.M(atom=fn, basis=args.basis, verbose=args.verbose, charge=args.charge, parse_arg=False)
+        model = args.model_file
+        scf_args = {}
+        if args.scf_input is not None:
+            argdict = load_yaml(args.scf_input)
+            if "scf_args" in argdict:
+                scf_args = argdict["scf_args"]
+                if model is None and "model" in argdict:
+                    model = argdict["model"]
+            else:
+                scf_args = argdict
+        conv_args = load_yaml(args.conv_input) if args.conv_input is not None else {}
+        mol_eq = run_optim(mol, model, args.proj_basis, scf_args, conv_args)
+        suffix = args.suffix
+        if args.dump_dir is None:
+            dump_dir = os.path.dirname(fn)
+            if not suffix:
+                suffix = "eq"
+        else:
+            dump_dir = args.dump_dir
+        dump = os.path.join(dump_dir, os.path.splitext(os.path.basename(fn))[0])
+        if suffix:
+            dump += f".{suffix}"
+        dump_xyz(dump+".xyz", mol_eq)
+        if args.verbose:
+            print(fn, f"done, time = {time.time()-tic}")
\ No newline at end of file

deepks/tools/num_hessian.py

+#!/usr/bin/env python
+#SBATCH -N 1
+#SBATCH -c 20
+#SBATCH -t 24:00:00
+#SBATCH --mem=8G
+
+import time
+import numpy as np
+from deepks.utils import load_yaml
+from deepks.scf.scf import DSCF
+from pyscf import gto, lib
+
+BOHR = 0.52917721092
+
+def finite_difference(f, x, delta=1e-6):
+    in_shape = x.shape
+    y0 = f(x)
+    out_shape = y0.shape
+    res = np.empty(in_shape + out_shape)
+    for idx in np.ndindex(*in_shape):
+        diff = np.zeros(in_shape)
+        diff[idx] += delta
+        y1 = f(x+diff)
+        res[idx] = (y1-y0) / delta
+    return res
+
+def calc_deriv(mol, model=None, proj_basis=None, **scfargs):
+    cf = DSCF(mol, model, proj_basis=proj_basis).run(**scfargs)
+    if not cf.converged:
+        raise RuntimeError("SCF not converged!")
+    ff = cf.nuc_grad_method().run()
+    return ff.de
+
+def make_closure(mol, model=None, proj_basis=None, **scfargs):
+    refmol = mol
+    def cc2de(coords):
+        tic = time.time()
+        mol = refmol.set_geom_(coords, inplace=False, unit="Bohr")
+        de = calc_deriv(mol, model, proj_basis, **scfargs)
+        if mol.verbose > 1:
+            print(f"step time = {time.time()-tic}")
+        return de
+    return cc2de
+    # scanner is not very stable. We construct new scf objects every time
+    # scanner = DSCF(mol.set(unit="Bohr"), model).set(**scfargs).nuc_grad_method().as_scanner()
+    # return lambda m: scanner(m)[-1]
+
+def calc_hessian(mol, model=None, delta=1e-6, proj_basis=None, **scfargs):
+    cc2de = make_closure(mol, model, proj_basis, **scfargs)
+    cc0 = mol.atom_coords(unit="Bohr")
+    hess = finite_difference(cc2de, cc0, delta).transpose((0,2,1,3))
+    return hess
+
+
+if __name__ == "__main__":
+    import argparse
+    import os
+    parser = argparse.ArgumentParser(description="Calculate and save mp2 energy and mo_coeffs for given xyz files.")
+    parser.add_argument("files", nargs="+", help="input xyz files")
+    parser.add_argument("-m", "--model-file", help="file of the trained model")
+    parser.add_argument("-d", "--dump-dir", help="dir of dumped files, default is same dir as xyz file")
+    parser.add_argument("-D", "--delta", default=1e-6, type=float, help="numerical difference step size")
+    parser.add_argument("-B", "--basis", default="ccpvdz", type=str, help="basis used to do the calculation")
+    parser.add_argument("-P", "--proj_basis", help="basis set used to project dm, must match with model") 
+    parser.add_argument("-C", "--charge", default=0, type=int, help="net charge of the molecule")
+    parser.add_argument("-U", "--unit", default="Angstrom", help="choose length unit (Bohr or Angstrom)")
+    parser.add_argument("-v", "--verbose", default=1, type=int, help="output calculation information")
+    parser.add_argument("--scf-input", help="yaml file to specify scf arguments")
+    args = parser.parse_args()
+    
+    if args.verbose:
+        print(f"starting calculation with OMP threads: {lib.num_threads()}",
+              f"and max memory: {lib.param.MAX_MEMORY}")
+
+    if args.dump_dir is not None:
+        os.makedirs(args.dump_dir, exist_ok = True)
+    for fn in args.files:
+        tic = time.time()
+        mol = gto.M(atom=fn, basis=args.basis, verbose=args.verbose, charge=args.charge, parse_arg=False)
+        model = args.model_file
+        scfargs = {}
+        if args.scf_input is not None:
+            argdict = load_yaml(args.scf_input)
+            if "scf_args" in argdict:
+                scfargs = argdict["scf_args"]
+                if model is None and "model" in argdict:
+                    model = argdict["model"]
+            else:
+                scfargs = argdict
+        hess = calc_hessian(mol, model, args.delta, args.proj_basis, **scfargs)
+        if not args.unit.upper().startswith(("B", "AU")):
+            hess /= BOHR**2
+        if args.dump_dir is None:
+            dump_dir = os.path.dirname(fn)
+        else:
+            dump_dir = args.dump_dir
+        dump = os.path.join(dump_dir, os.path.splitext(os.path.basename(fn))[0])
+        np.save(dump+".hessian.npy", hess)
+        if args.verbose:
+            print(fn, f"done, time = {time.time()-tic}")
\ No newline at end of file

deepks/utils.py

+import os
+import shutil
+from glob import glob
+from pathlib import Path
+import ruamel.yaml as yaml
+import numpy as np
+from collections.abc import Mapping
+from itertools import chain
+
+
+QCDIR = os.path.dirname(os.path.realpath(__file__))
+
+
+# below are basis set settings
+
+_zeta = 1.5**np.array([17,13,10,7,5,3,2,1,0,-1,-2,-3])
+_coef = np.diag(np.ones(_zeta.size)) - np.diag(np.ones(_zeta.size-1), k=1)
+_table = np.concatenate([_zeta.reshape(-1,1), _coef], axis=1)
+DEFAULT_BASIS = [[0, *_table.tolist()], [1, *_table.tolist()], [2, *_table.tolist()]]
+DEFAULT_SYMB = "Ne"
+
+def load_basis(basis):
+    if basis is None:
+        return DEFAULT_BASIS
+    elif isinstance(basis, np.ndarray) and basis.ndim == 2:
+        return [[ll, *basis.tolist()] for ll in range(3)]
+    elif not isinstance(basis, str):
+        return basis
+    elif basis.endswith(".npy"):
+        table = np.load(basis)
+        return [[ll, *table.tolist()] for ll in range(3)]
+    elif basis.endswith(".npz"):
+        all_tables = np.load(basis)
+        return [[int(name.split("_L")[-1]) if "_L" in name else ii, *table.tolist()] 
+                for ii, (name, table) in enumerate(all_tables.items())]
+    else:
+        from pyscf import gto
+        symb = DEFAULT_SYMB
+        if "@" in basis:
+            basis, symb = basis.split("@")
+        return gto.basis.load(basis, symb=symb)
+
+
+def save_basis(file, basis):
+    """Save the basis to npz file from internal format of pyscf"""
+    tables = {f"arr_{i}_L{l}":np.array(b) for i, (l,*b) in enumerate(basis)}
+    np.savez(file, **tables)
+
+
+def get_shell_sec(basis):
+    if not isinstance(basis, (list, tuple)):
+        basis = load_basis(basis)
+    shell_sec = []
+    for l, c0, *cr in basis:
+        nb = c0 if isinstance(c0, int) else (len(c0)-1)
+        shell_sec.extend([2*l+1] * nb)
+    return shell_sec
+    
+
+# below are argument chekcing utils
+
+def check_list(arg, nullable=True):
+    # make sure the argument is a list
+    if arg is None:
+        if nullable:
+            return []
+        else:
+            raise TypeError("arg cannot be None")
+    if not isinstance(arg, (list, tuple, np.ndarray)):
+        return [arg]
+    return arg
+
+
+def check_array(arr, nullable=True):
+    if arr is None:
+        if nullable:
+            return arr
+        else:
+            raise TypeError("arg cannot be None")
+    if isinstance(arr, str):
+        return load_array(arr)
+    else:
+        return np.array(arr)
+
+
+def flat_file_list(file_list, filter_func=lambda p: True):
+    # make sure file list contains desired files
+    # flat all wildcards and files contains other files (once)
+    # if no satisfied files, return empty list
+    file_list = check_list(file_list)
+    file_list = sorted(sum([glob(p) for p in file_list], []))
+    new_list = []
+    for p in file_list:
+        if filter_func(p):
+            new_list.append(p)
+        else:
+            with open(p) as f:
+                sub_list = f.read().splitlines()
+                sub_list = sorted(sum([glob(p) for p in sub_list], []))
+                new_list.extend(sub_list)
+    return new_list
+
+
+def load_dirs(path_list):
+    return flat_file_list(path_list, os.path.isdir)
+
+def load_xyz_files(file_list):
+    return flat_file_list(file_list, is_xyz)
+
+def load_sys_paths(sys_list):
+    return flat_file_list(sys_list, lambda p: os.path.isdir(p) or is_xyz(p))
+
+def is_xyz(p):
+    return os.path.splitext(p)[1] == '.xyz'
+
+
+def deep_update(o, u=(), **f):
+    """Recursively update a dict.
+
+    Subdict's won't be overwritten but also updated.
+    """
+    if not isinstance(o, Mapping):
+        return u
+    kvlst = chain(u.items() if isinstance(u, Mapping) else u, 
+                  f.items())
+    for k, v in kvlst:
+        if isinstance(v, Mapping):
+            o[k] = deep_update(o.get(k, {}), v)
+        else:
+            o[k] = v
+    return o
+
+
+# below are file loading utils
+
+def load_yaml(file_path):
+    with open(file_path, 'r') as fp:
+        res = yaml.safe_load(fp)
+    return res
+
+
+def save_yaml(data, file_path):
+    dirname = os.path.dirname(file_path)
+    if not os.path.exists(dirname):
+        os.makedirs(dirname)
+    with open(file_path, 'w') as fp:
+        yaml.safe_dump(data, fp)
+
+
+def load_array(file):
+    ext = os.path.splitext(file)[-1]
+    if "npy" in ext:
+        return np.load(file)
+    elif "npz" in ext:
+        raise NotImplementedError
+    else:
+        try:
+            arr = np.loadtxt(file)
+        except ValueError:
+            arr = np.loadtxt(file, dtype=str)
+        return arr
+
+
+def parse_xyz(filename):
+    with open(filename) as fp:
+        natom = int(fp.readline())
+        comments = fp.readline().strip()
+        atom_str = fp.readlines()
+    atom_list = [a.split() for a in atom_str]
+    elements = [a[0] for a in atom_list]
+    coords = np.array([a[1:] for a in atom_list], dtype=float)
+    return natom, comments, elements, coords
+
+
+def load_elem_table(filename):
+    elem_list, elem_const = np.loadtxt(filename).T
+    elem_list = elem_list.round().astype(int)
+    return elem_list, elem_const
+
+
+def save_elem_table(filename, elem_table):
+    np.savetxt(filename, np.stack(elem_table).T, fmt=["%i", "%.16f"])
+    
+
+# below are path related utils
+
+def get_abs_path(p):
+    if p is None:
+        return None
+    else:
+        return Path(p).absolute()
+
+
+def get_sys_name(p):
+    if p.endswith(os.path.sep):
+        return p.rstrip(os.path.sep)
+    if p.endswith(".xyz"):
+        return p[:-4]
+    return p
+
+
+def get_with_prefix(p, base=None, prefer=None, nullable=False):
+    """
+    Get file path by searching its prefix.
+    If `base` is a directory, equals to get "base/p*".
+    Otherwise, equals to get "base.p*".
+    Only one result will be return. 
+    If more than one match, give the first one with suffix in `prefer`.
+    """
+    if not base:
+        base = "./"
+    if os.path.isdir(base):
+        pattern = os.path.join(base, p)
+    else:
+        pattern = f"{base.rstrip('.')}.{p}"
+    matches = glob(pattern + "*")
+    if len(matches) == 1:
+        return matches[0]
+    prefer = check_list(prefer)
+    for suffix in prefer:
+        if pattern+suffix in matches:
+            return pattern+suffix
+    if nullable:
+        return None
+    raise FileNotFoundError(f"{pattern}* not exists or has more than one matches")
+
+    
+def link_file(src, dst, use_abs=False):
+    src, dst = Path(src), Path(dst)
+    assert src.exists(), f'{src} does not exist'
+    src_path = os.path.abspath(src) if use_abs else os.path.relpath(src, dst.parent)
+    if not dst.exists():
+        if not dst.parent.exists():
+            os.makedirs(dst.parent)
+        os.symlink(src_path, dst)
+    elif not os.path.samefile(src, dst):
+        os.remove(dst)
+        os.symlink(src_path, dst)
+
+
+def copy_file(src, dst):
+    src, dst = Path(src), Path(dst)
+    assert src.exists(), f'{src} does not exist'
+    if not dst.exists():
+        if not dst.parent.exists():
+            os.makedirs(dst.parent)
+        shutil.copy2(src, dst)
+    elif not os.path.samefile(src, dst):
+        os.remove(dst)
+        shutil.copy2(src, dst)
+
+
+def create_dir(dirname, backup=False):
+    dirname = Path(dirname)
+    if not dirname.exists():
+        os.makedirs(dirname)
+    elif backup and dirname != Path('.'):
+        os.makedirs(dirname.parent, exist_ok=True)
+        counter = 0
+        bckname = str(dirname) + f'.bck.{counter:03d}'
+        while os.path.exists(bckname):
+            counter += 1
+            bckname = str(dirname) + f'.bck.{counter:03d}'
+        dirname.rename(bckname)
+        os.makedirs(dirname)
+    else:
+        assert dirname.is_dir(), f'{dirname} is not a dir'
+

examples/iterate/combined.yaml

+# all arguments are flatten into this file
+# they can also be splitted into separate files and referenced here
+
+# number of iterations to do, can be set to zero for deephf training
+n_iter: 5
+
+# training and testing systems
+systems_train: # can also be files that containing system paths
+  - ../system/batch/set.0[0-5]* # support glob
+  - ../system/batch/set.060
+  - ../system/batch/set.061
+  - ../system/batch/set.062
+
+systems_test: # if empty, use the last system of training set
+  - ../system/batch/set.063
+  
+# directory setting
+workdir: "."
+share_folder: "share" # folder that stores all other settings
+
+# scf settings
+scf_input: # can also be specified by a separete file
+  basis: ccpvdz
+  # this is for force training
+  dump_fields: [e_base, e_tot, dm_eig, conv, f_base, f_tot, grad_vx, l_f_delta, l_e_delta]
+  verbose: 1
+  mol_args:
+    incore_anyway: True
+  scf_args:
+    conv_tol: 1e-6
+    conv_tol_grad: 1e-2
+    level_shift: 0.1
+    diis_space: 20
+    conv_check: false # pyscf conv_check has bug
+
+scf_machine: 
+  sub_size: 5 # 5 systems will be in one task, default is 1
+  group_size: 2 # 2 tasks will be gathered into one group and submitted together
+  ingroup_parallel: 2 # this will set numb_node to 2 in resources
+  dispatcher: 
+    context: local
+    batch: slurm
+    remote_profile: null # use lazy local
+  resources:
+    numb_node: 2 # parallel in two nodes
+    time_limit: '24:00:00'
+    cpus_per_task: 8
+    mem_limit: 8
+    envs:
+      PYSCF_MAX_MEMORY: 8000 # increase from 4G to 8G
+  sub_res: # resources for each sub task
+    cpus_per_task: 8
+  python: "python" # use python in path
+
+# train settings
+train_input:
+  # model_args is ignored, since this is used as restart
+  data_args: 
+    batch_size: 16
+    group_batch: 1
+    extra_label: true
+    conv_filter: true
+    conv_name: conv
+  preprocess_args:
+    preshift: false # restarting model already shifted. Will not recompute shift value
+    prescale: false # same as above
+    prefit_ridge: 1e1
+    prefit_trainable: false
+  train_args: 
+    decay_rate: 0.5
+    decay_steps: 1000
+    display_epoch: 100
+    force_factor: 0.1
+    n_epoch: 5000
+    start_lr: 0.0001
+
+train_machine: 
+  dispatcher: 
+    context: local
+    batch: slurm
+    remote_profile: null # use lazy local
+  resources:
+    time_limit: '24:00:00'
+    cpus_per_task: 4
+    numb_gpu: 1
+    mem_limit: 8
+  python: "python" # use python in path
+
+# init settings
+init_model: false # do not use existing model in share_folder/init/model.pth
+
+init_scf: 
+  basis: ccpvdz
+  # this is for pure energy training
+  dump_fields: [e_base, e_tot, dm_eig, conv, l_e_delta]
+  verbose: 1
+  mol_args:
+    incore_anyway: True
+  scf_args:
+    conv_tol: 1e-8
+    conv_check: false # pyscf conv_check has bug
+
+init_train: 
+  model_args: # necessary as this is init training
+    hidden_sizes: [200, 200, 200]
+    output_scale: 100
+    use_resnet: true
+    actv_fn: mygelu
+  data_args: 
+    batch_size: 16
+    group_batch: 1
+  preprocess_args:
+    preshift: true
+    prescale: false
+    prefit_ridge: 1e1
+    prefit_trainable: false
+  train_args: 
+    decay_rate: 0.96
+    decay_steps: 500
+    display_epoch: 100
+    n_epoch: 50000
+    start_lr: 0.0003
+
+# other settings
+cleanup: false
+strict: true

examples/iterate/splitted/args.yaml

+# all value here are defaults parameters
+# except for `scf_machine` which shows grouping and ingroup parallelization
+n_iter: 5
+systems_train: null # use share_folder/systems_train.raw (check exist)
+systems_test: null # use share_folder/systems_test.raw
+workdir: "."
+share_folder: "share" # folder that contains all other settings
+# scf parameters
+scf_input: true # use share_folder/scf_input.yaml (check exist)
+scf_machine: 
+  sub_size: 5 # 5 systems will be in one task, default is 1
+  group_size: 2 # 2 tasks will be gathered into one group and submitted together
+  ingroup_parallel: 2 # 2 tasks in one group submission can run toghther
+# train parameters
+train_input: true # use share_folder/train_input.yaml (check exist)
+train_machine: 
+  resources: # add 1 gpu
+    numb_gpu: 1
+# init parameters
+init_model: false # do not use existing model in share_folder/init/model.pth
+init_scf: true # use share_folder/init_scf.yaml (check exist)
+init_train: true # use share_folder/init_train.yaml (check exist)
+# other settings
+cleanup: false
+strict: true

examples/iterate/splitted/share/init_scf.yaml

+basis: ccpvdz
+model_file: null
+dump_fields: [e_base, e_tot, dm_eig, conv, l_e_delta]
+verbose: 1
+mol_args:
+  incore_anyway: True
+scf_args:
+  conv_tol: 1e-8
+  conv_check: false
+#penalty_terms:
+#  - type: coulomb
+#    required_labels: dm
+#    strength: 1
+#    random: true    

examples/iterate/splitted/share/init_train.yaml

+model_args:
+  hidden_sizes: [100, 100, 100]
+  output_scale: 100
+  use_resnet: true
+  actv_fn: mygelu
+data_args: 
+  batch_size: 16
+  group_batch: 1
+  e_name: l_e_delta
+preprocess_args:
+  preshift: true
+  prescale: false
+  prefit_ridge: 1e1
+  prefit_trainable: false
+train_args: 
+  ckpt_file: model.pth
+  decay_rate: 0.96
+  decay_steps: 500
+  display_epoch: 100
+  n_epoch: 50000
+  start_lr: 0.0001
+train_paths:
+  - data_train/* 
+test_paths:
+  - data_test/* 

examples/iterate/splitted/share/scf_input.yaml

+basis: ccpvdz
+model_file: model.pth
+dump_fields: [e_base, e_tot, dm_eig, conv, f_base, f_tot, grad_vx, l_f_delta, l_e_delta]
+verbose: 1
+mol_args:
+  incore_anyway: True
+scf_args:
+  conv_tol: 1e-6
+  conv_tol_grad: 1e-2
+  level_shift: 0.1
+  diis_space: 20
+  conv_check: false
+#penalty_terms:
+#  - type: coulomb
+#    required_labels: dm
+#    strength: 1
+#    random: true    

examples/iterate/splitted/share/systems_test.raw

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examples/iterate/splitted/share/systems_train.raw

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examples/iterate/splitted/share/train_input.yaml

+model_args:
+  hidden_sizes: [100, 100, 100] 
+  output_scale: 100
+  use_resnet: true
+  actv_fn: mygelu
+data_args: 
+  batch_size: 16
+  group_batch: 1
+  e_name: l_e_delta
+  f_name: l_f_delta
+  extra_label: true
+  conv_filter: true
+preprocess_args:
+  preshift: false 
+  prescale: false
+  prefit_ridge: 1e1
+  prefit_trainable: false
+train_args: 
+  ckpt_file: model.pth
+  decay_rate: 0.5
+  decay_steps: 1000
+  display_epoch: 100
+  force_factor: 0.1
+  n_epoch: 5000
+  start_lr: 0.0001
+train_paths:
+  - data_train/* 
+test_paths:
+  - data_test/*

examples/legacy/iter_linear/run.py

+import os
+import sys
+import torch
+import numpy as np
+import pyscf
+from pyscf import gto
+from sklearn import linear_model
+
+sys.path.append(os.path.dirname(os.path.realpath(__file__)) + '/../../')
+from deepks.scf.scf import DSCF
+from deepks.scf.run import build_mol, solve_mol
+
+def get_linear_model(weig, wec):
+#     too_small = weig.reshape(-1,108).std(0) < 1e-3
+    wreg = linear_model.Ridge(1e-7, tol=1e-9)
+    wreg.fit(weig.sum(1)[:], wec[:])
+    linear = torch.nn.Linear(108,1).double()
+    linear.weight.data[:] = torch.from_numpy(wreg.coef_)
+    linear.bias.data[:] = torch.tensor(wreg.intercept_ / 3)
+    model = lambda x: linear(x).sum(1)
+    return model
+
+def get_linear_model_normed(weig, wec, stdmin=1e-3):
+#     too_small = weig.reshape(-1,108).std(0) < 1e-3
+    input_scale = weig.reshape(-1,108).std(0).clip(stdmin)
+    t_input_scale = torch.from_numpy(input_scale)
+    weig /= input_scale
+    wreg = linear_model.Ridge(1e-7, tol=1e-9)
+    wreg.fit(weig.sum(1)[:], wec[:])
+    linear = torch.nn.Linear(108,1).double()
+    linear.weight.data[:] = torch.from_numpy(wreg.coef_)
+    linear.bias.data[:] = torch.tensor(wreg.intercept_ / 3)
+    model = lambda x: linear(x / t_input_scale).sum(1)
+    return model
+
+nmol = 1000
+ntrain = 900
+niter = 10
+
+mol_list = [build_mol(f'../path/to/data/water/geometry/{i:0>5}.xyz') for i in range(nmol)]
+ehfs = np.load('../path/to/data/water/rproj_mb2/e_hf.npy').reshape(-1)[:nmol]
+wene = np.loadtxt('../path/to/data/water/energy.dat', usecols=(1,2,3,4))[:nmol]
+erefs = wene[:,3]
+ecfs = ehfs
+ecs = erefs - ehfs
+ceigs = np.load('../../../data/tom_miller/water/rproj_mb2/dm_eig.npy')[:nmol]
+model = get_linear_model(ceigs[:ntrain], ecs[:ntrain])
+
+os.makedirs('dump', exist_ok=True)
+np.save('dump/000.ehfs.npy', ehfs)
+np.save('dump/000.ecfs.npy', ecfs)
+np.save('dump/000.ceigs.npy', ceigs)
+np.save('dump/000.ecs.npy', ecs)
+np.save('dump/000.convs.npy', np.ones(ehfs.shape, dtype=bool))
+
+for i in range(1, niter+1):
+    oldecfs, oldceigs, oldehfs = ecfs, ceigs, ehfs
+    oldecs = ecs
+    oldmodel = model
+    
+    results = [solve_mol(mol, model) for mol in mol_list]
+    meta, ehfs, ecfs, cdms, ceigs, convs = map(np.array, zip(*results))
+    ecs = erefs - ehfs
+    model = get_linear_model(ceigs[:ntrain], ecs[:ntrain])
+    
+    print((ecfs - erefs).mean(), np.abs(ecfs - erefs).mean())
+    
+    np.save(f'dump/{i:0>3}.ehfs.npy', ehfs)
+    np.save(f'dump/{i:0>3}.ecfs.npy', ecfs)
+    np.save(f'dump/{i:0>3}.ceigs.npy', ceigs)
+    np.save(f'dump/{i:0>3}.ecs.npy', ecs)
+    np.save(f'dump/{i:0>3}.convs.npy', convs)

examples/legacy/iter_nn_local/run.py

+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import sys
+import numpy as np
+
+# sys.path.append('/path/to/source')
+import deepks
+from deepks.model.train import main as train_main
+from deepks.scf.run import main as scf_main
+from deepks.utils import load_yaml
+from deepks.task.task import PythonTask
+from deepks.task.workflow import Sequence, Iteration
+
+from pathlib import Path
+import shutil
+
+def collect_data(nmol, ntrain):
+    ecf = np.load('results/e_tot.npy')
+    assert ecf.size == nmol
+    eref = np.load('e_ref.npy')
+
+    err = eref.reshape(-1) - ecf.reshape(-1)
+    convs = np.load("results/conv.npy").reshape(-1)
+    print(f'converged calculation: {np.sum(convs)} / {nmol} = {np.sum(convs) / nmol:.3f}')
+    print(f'mean error: {err.mean()}')
+    print(f'mean absolute error: {np.abs(err).mean()}')
+    print(f'mean absolute error after shift: {np.abs(err - err[:ntrain].mean()).mean()}')
+    print(f'  training: {np.abs(err[:ntrain] - err[:ntrain].mean()).mean()}')
+    print(f'  testing: {np.abs(err[ntrain:] - err[:ntrain].mean()).mean()}')
+
+    ehf = np.load('results/e_base.npy')
+    np.save('results/l_e_delta.npy', eref - ehf)
+
+    dd = ['dm_eig.npy', 'l_e_delta.npy']
+    os.makedirs('train', exist_ok=True)
+    os.makedirs('test', exist_ok=True)
+    for d in dd:
+        np.save(f"train/{d}", np.load(f'results/{d}')[:ntrain])
+    for d in dd:
+        np.save(f"test/{d}", np.load(f'results/{d}')[ntrain:])
+    shutil.copy('results/system.raw', 'train')
+    shutil.copy('results/system.raw', 'test')
+    Path('train_paths.raw').write_text(str(Path('train').absolute()))
+    Path('test_paths.raw').write_text(str(Path('test').absolute()))
+
+
+niter = 5
+nmol = 1500
+ntrain = 1000
+ntest = 500
+
+train_input = load_yaml('share/train_input.yaml')
+scf_input = load_yaml('share/scf_input.yaml')
+
+task_train = PythonTask(train_main, call_kwargs=train_input,
+                        outlog='log.train',
+                        workdir='00.train',
+                        link_prev_files=['train_paths.raw', 'test_paths.raw'])
+
+task_scf = PythonTask(scf_main, call_kwargs=scf_input,
+                      outlog='log.scf',
+                      workdir='01.scf',
+                      link_prev_files=['model.pth'],
+                      share_folder='share', link_share_files=['mol_files.raw'])
+
+task_data = PythonTask(collect_data, call_args=[nmol, ntrain],
+                       outlog='log.data',
+                       workdir='02.data',
+                       link_prev_files=['results'],
+                       share_folder='share', link_share_files=['e_ref.npy'])
+
+seq = Sequence([task_train, task_scf, task_data])
+iterate = Iteration(seq, niter, init_folder='share/init', record_file='RECORD')
+
+if Path('RECORD').exists():
+    iterate.restart()
+else:
+    iterate.run()

examples/legacy/iter_nn_local/run_res.py

+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import sys
+import numpy as np
+
+# sys.path.append('/path/to/source')
+import deepks
+from deepks.model.train import main as train_main
+from deepks.model.test import main as train_test
+from deepks.scf.run import main as scf_main
+from deepks.scf.stats import collect_data_grouped
+from deepks.utils import load_yaml
+from deepks.task.task import PythonTask
+from deepks.task.workflow import Sequence, Iteration
+
+
+niter = 5
+nmol = 1500
+ntrain = 1000
+ntest = 500
+
+train_input = load_yaml('share/train_input.yaml')
+scf_input = load_yaml('share/scf_input.yaml')
+train_idx = np.arange(ntrain)
+
+task_scf = PythonTask(scf_main, call_kwargs=scf_input,
+                      outlog='log.scf',
+                      workdir='00.scf',
+                      link_prev_files=['model.pth'],
+                      share_folder='share', link_share_files=['mol_files.raw'])
+
+task_data = PythonTask(collect_data_grouped, call_args=[train_idx],
+                       outlog='log.data',
+                       workdir='01.data',
+                       link_prev_files=['model.pth', "results"],
+                       share_folder='share', link_share_files=['e_ref.npy'])
+
+task_train = PythonTask(train_main, call_args=["old_model.pth"], call_kwargs=train_input,
+                        outlog='log.train',
+                        workdir='02.train',
+                        link_prev_files=[('model.pth', 'old_model.pth'),
+                                         'train_paths.raw', 'test_paths.raw'])
+
+seq = Sequence([task_scf, task_data, task_train])
+iterate = Iteration(seq, niter, init_folder='share/init', record_file='RECORD')
+
+if os.path.exists('RECORD'):
+    iterate.restart()
+else:
+    iterate.run()

examples/legacy/iter_nn_local/share/init/test_paths.raw

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examples/legacy/iter_nn_local/share/init/train_paths.raw

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examples/legacy/iter_nn_local/share/scf_input.yaml

+basis: ccpvdz
+conv_tol: 1e-7
+xyz_files: [mol_files.raw]
+model_file: model.pth
+dump_dir: results
+dump_fields: [e_base, e_tot, dm_eig, conv]
+group: true 
+verbose: 1