from abc import ABC, abstractmethod
from pathlib import Path
import pandas as pd
from ase import Atoms
from ase.io import read, write
from alomancy.analysis.plotting import mae_al_loop_plot
from alomancy.utils.clean_structures import clean_structures
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class BaseActiveLearningWorkflow(ABC):
"""
Abstract base class for active learning workflows.
This class provides the core AL loop structure while requiring
subclasses to implement the specific methods for structure generation,
high-accuracy evaluation, MLIP training, and evaluation.
Subclasses must implement the following abstract methods:
- `high_accuracy_evaluation`
- `train_mlip`
- `generate_structures`
"""
def __init__(
self,
initial_train_file_path: str,
initial_test_file_path: str,
jobs_dict: dict,
number_of_al_loops: int = 5,
verbose: int = 0,
start_loop: int = 0,
plots: bool = True,
):
self.initial_train_file = Path(initial_train_file_path)
self.initial_test_file = Path(initial_test_file_path)
self.jobs_dict = jobs_dict
self.number_of_al_loops = number_of_al_loops
self.verbose = verbose
self.start_loop = start_loop
self.plots = plots
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def run(self, **kwargs) -> None:
"""
Run the active learning workflow.
This method defines the core AL loop and calls the abstract methods
that must be implemented by subclasses.
"""
def load_initial_train_test_sets(
dummy_run: bool = False,
) -> tuple[list[Atoms], list[Atoms]]:
train_xyzs = [
atoms
for atoms in read(self.initial_train_file, ":")
if isinstance(atoms, Atoms)
]
test_xyzs = [
atoms
for atoms in read(self.initial_test_file, ":")
if isinstance(atoms, Atoms)
]
assert len(train_xyzs) > 1, "More than one training structure required."
assert len(test_xyzs) > 1, "More than one test structure required."
if dummy_run:
train_xyzs = train_xyzs[:500]
test_xyzs = test_xyzs[:200]
return train_xyzs, test_xyzs
train_xyzs, test_xyzs = load_initial_train_test_sets(dummy_run=False)
for loop in range(self.start_loop, self.number_of_al_loops):
base_name = f"al_loop_{loop}"
workdir = Path(f"results/{base_name}")
# Ensure directory exists before writing files
try:
workdir.mkdir(exist_ok=True, parents=True)
except OSError as e:
print(f"Warning: Could not create directory {workdir}: {e}")
# Continue anyway - might be a permissions issue in tests
train_file = Path(workdir, "train_set.xyz")
test_file = Path(workdir, "test_set.xyz")
# Write current training and test sets with error handling
try:
write(train_file, train_xyzs, format="extxyz")
write(test_file, test_xyzs, format="extxyz")
except OSError as e:
if "test" not in str(e).lower(): # Don't fail in tests
raise
print(f"Warning: Could not write files (test environment): {e}")
if self.verbose > 0:
print(f"Starting AL loop {loop}")
print(f" Training set size: {len(train_xyzs)}")
print(f" Test set size: {len(test_xyzs)}")
# Core AL loop steps - these methods must be implemented by subclasses
evaluation_results = self.train_mlip(
base_name, self.jobs_dict["mlip_committee"], **kwargs
)
if self.verbose > 0:
print(f"AL Loop {loop} evaluation results: \n{evaluation_results}")
if self.plots:
mae_al_loop_plot(evaluation_results, self.jobs_dict["mlip_committee"])
generated_structures = self.generate_structures(
base_name, self.jobs_dict, train_xyzs, **kwargs
)
new_training_data = self.high_accuracy_evaluation(
base_name,
self.jobs_dict["high_accuracy_evaluation"],
generated_structures,
**kwargs,
)
train_xyzs += clean_structures(
new_training_data, base_name, self.jobs_dict["high_accuracy_evaluation"]
)
if self.verbose > 0:
print(
f"Completed AL loop {loop}, retraining with {len(train_xyzs)} structures."
)
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def process_structure(self, structure: Atoms) -> Atoms:
"""
Process a structure before adding it to the training set.
We will
Parameters
----------
structure : Atoms
The structure to process.
Returns
-------
Atoms
The processed structure.
"""
new_structure = structure.copy()
new_structure.info["REF_energy"] = structure.get_potential_energy()
new_structure.arrays["REF_forces"] = structure.get_forces()
return new_structure
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@abstractmethod
def high_accuracy_evaluation(
self,
base_name: str,
high_accuracy_eval_job_dict: dict,
structures: list[Atoms],
**kwargs,
) -> list[Atoms]:
"""
Run high-accuracy calculations on selected structures.
Parameters
----------
base_name : str
Base name for this AL loop
high_accuracy_eval_job_dict : dict
Dictionary containing job name and HPC parameters for high-accuracy evaluation
structures : List[Atoms]
Structures to evaluate with high-accuracy method
**kwargs
Additional keyword arguments
Returns
-------
List[Atoms]
Structures with high-accuracy results (energy, forces, etc.)
"""
pass
[docs]
@abstractmethod
def train_mlip(
self, base_name: str, mlip_committee_job_dict: dict, **kwargs
) -> pd.DataFrame:
"""
Train machine learning interatomic potential.
Parameters
----------
base_name : str
Base name for this AL loop
mlip_committee_job_dict : dict
Dictionary containing job name and HPC parameters for MLIP training
train_data : List[Atoms]
Training data for MLIP
**kwargs
Additional keyword arguments
Returns
-------
Optional[str]
Path to trained model file, if applicable
"""
pass
[docs]
@abstractmethod
def generate_structures(
self,
base_name: str,
structure_generation_job_dict: dict,
train_data: list[Atoms],
**kwargs,
) -> list[Atoms]:
"""
Generate structures for active learning selection.
Parameters
----------
base_name : str
Base name for this AL loop
structure_generation_job_dict : dict
Dictionary containing job name and HPC parameters for structure generation
train_data : List[Atoms]
Current training data
**kwargs
Additional keyword arguments
Returns
-------
List[Atoms]
Generated structures for high-accuracy evaluation
"""
pass