"""Select models from the top clusters.
This module selects a number of models from a number of clusters. The
selection is based on the score of the models within the clusters.
In the standard HADDOCK analysis, the top 4 models of the top 10 clusters
are shown. In case seletopclusts is run after a sampling module, we can
keep a few models from all the clusters to have more diversity at the
refinement stage(s).
"""
from pathlib import Path
from haddock import log
from haddock.core.defaults import MODULE_DEFAULT_YAML
from haddock.core.typing import Any, FilePath
from haddock.modules import BaseHaddockModule
from haddock.modules.analysis.seletopclusts.seletopclusts import (
select_top_clusts_models,
write_selected_models,
)
RECIPE_PATH = Path(__file__).resolve().parent
DEFAULT_CONFIG = Path(RECIPE_PATH, MODULE_DEFAULT_YAML)
[docs]
class HaddockModule(BaseHaddockModule):
"""Haddock Module for 'seletopclusts'."""
name = RECIPE_PATH.name
def __init__(self,
order: int,
path: Path,
*ignore: Any,
init_params: FilePath = DEFAULT_CONFIG,
**everything: Any) -> None:
super().__init__(order, path, init_params)
[docs]
@classmethod
def confirm_installation(cls) -> None:
"""Confirm if module is installed."""
return
def _run(self) -> None:
"""Execute the module's protocol."""
# Check parameters validity
if self.params["top_models"] <= 0:
_msg = "top_models must be either > 0 or nan."
self.finish_with_error(_msg)
if not isinstance(self.params["top_clusters"], int):
_msg = "top_clusters must be an integer."
self.finish_with_error(_msg)
# Retrieve list of previous models
models_to_select = self.previous_io.retrieve_models()
# Check if cluster info is accessible
if any([mdl.clt_rank is None for mdl in models_to_select]):
_msg = (
"Impossible to obtain cluster information. Please consider "
"running a clustering method prior to this module."
)
self.finish_with_error(_msg)
# Make model selection
selected_models, _notes = select_top_clusts_models(
self.params["sortby"],
models_to_select,
self.params["top_clusters"],
self.params["top_models"],
)
# Log notes
for note in _notes:
log.info(note)
# dump the models to disk and change their attributes
renamed_models = write_selected_models(
"seletopclusts.txt",
selected_models,
self.path,
)
# Make these new models the output of this module
self.output_models = renamed_models
# Export outputs
self.export_io_models()