bugfixes

VERSION

@@ -1 +1 @@
-1.4.1
+1.4.2

docs/source/conf.py

@@ -24,8 +24,8 @@
 project = 'f3dasm'
 author = 'Martin van der Schelling'
 copyright = '2022, Martin van der Schelling'
-version = '1.4.1'
-release = '1.4.1'
+version = '1.4.2'
+release = '1.4.2'
 
 
 # -- General configuration ----------------------------------------------------

src/f3dasm/__init__.py

@@ -37,7 +37,7 @@
 # =============================================================================
 
 
-__version__ = '1.4.1'
+__version__ = '1.4.2'
 
 
 # Log welcome message and the version of f3dasm

src/f3dasm/_src/datageneration/datagenerator.py

@@ -38,18 +38,19 @@ def store(object: Any, name: str, to_disk: bool) -> None:
  def job_number(self) -> int:
  ...
 
+
 class DataGenerator:
  """Base class for a data generator"""
 
- def pre_process(self, **kwargs) -> None:
+ def pre_process(self, experiment_sample: ExperimentSample, **kwargs) -> None:
  """Function that handles the pre-processing"""
  ...
 
  def execute(self, **kwargs) -> None:
  """Function that calls the FEM simulator the pre-processing"""
  raise NotImplementedError("No execute function implemented!")
 
- def post_process(self, **kwargs) -> None:
+ def post_process(self, experiment_sample: ExperimentSample, **kwargs) -> None:
  """Function that handles the post-processing"""
  ...
 

src/f3dasm/_src/datageneration/functions/function_factory.py

@@ -4,6 +4,8 @@
 # Modules
 # =============================================================================
 
+from __future__ import annotations
+
 from typing import Any, Dict, Optional
 
 from ...design.domain import Domain
@@ -19,20 +21,26 @@
 #
 # =============================================================================
 
-FUNTION_MAPPING: Dict[str, DataGenerator] = {
- f.name.lower().replace(' ', '').replace('-', '').replace('_', ''): f for f in _FUNCTIONS}
+FUNCTION_MAPPING: Dict[str, DataGenerator] = {
+ f.name.lower().replace(' ', '').replace('-', '').replace('_', '').replace('.', ''): f for f in _FUNCTIONS}
 
 
-def datagenerator_factory(data_generator: str, domain: Domain,
+def datagenerator_factory(data_generator: str, domain: Domain | int,
  kwargs: Optional[Dict[str, Any]] = None) -> DataGenerator:
 
+ if isinstance(domain, int):
+ dim = domain
+
+ else:
+ dim = len(domain)
+
  if kwargs is None:
  kwargs = {}
 
- filtered_name = data_generator.lower().replace(' ', '').replace('-', '').replace('_', '')
+ filtered_name = data_generator.lower().replace(' ', '').replace('-', '').replace('_', '').replace('.', '')
 
- if filtered_name in FUNTION_MAPPING:
- return FUNTION_MAPPING[filtered_name](dimensionality=len(domain), **kwargs)
+ if filtered_name in FUNCTION_MAPPING:
+ return FUNCTION_MAPPING[filtered_name](dimensionality=dim, **kwargs)
 
  else:
  raise KeyError(f"Unknown data generator: {data_generator}")

src/f3dasm/_src/design/domain.py

@@ -148,10 +148,7 @@ def from_yaml(cls: Type[Domain], yaml: DictConfig) -> Domain:
  Domain
  Domain object
  """
- args = {}
- for space, params in yaml.items():
- args[space] = {name: instantiate(param, _convert_="all") for name, param in params.items()}
- return cls(**args)
+ return cls({name: instantiate(param, _convert_="all") for name, param in yaml.items()})
 
  @classmethod
  def from_dataframe(cls, df: pd.DataFrame) -> Domain:
@@ -171,9 +168,17 @@ def from_dataframe(cls, df: pd.DataFrame) -> Domain:
  space = {}
  for name, type in df.dtypes.items():
  if type == 'float64':
+ if float(df[name].min()) == float(df[name].max()):
+ space[name] = ConstantParameter(value=float(df[name].min()))
+ continue
+
  space[name] = ContinuousParameter(lower_bound=float(
  df[name].min()), upper_bound=float(df[name].max()))
  elif type == 'int64':
+ if int(df[name].min()) == int(df[name].max()):
+ space[name] = ConstantParameter(value=int(df[name].min()))
+ continue
+
  space[name] = DiscreteParameter(lower_bound=int(df[name].min()), upper_bound=int(df[name].max()))
  else:
  space[name] = CategoricalParameter(df[name].unique().tolist())

src/f3dasm/_src/experimentdata/experimentdata.py

@@ -182,7 +182,7 @@ def from_file(cls: Type[ExperimentData], filename: str = 'experimentdata') -> Ex
  ExperimentData object containing the loaded data.
  """
  try:
- return cls._from_file_attempt(filename)
+ return cls._from_file_attempt(Path(filename))
  except FileNotFoundError:
  try:
  filename_with_path = Path(get_original_cwd()) / filename
@@ -239,21 +239,21 @@ def from_yaml(cls, config: DictConfig) -> ExperimentData:
  # Option 2: Sample from the domain
  elif 'from_sampling' in config.experimentdata:
  domain = Domain.from_yaml(config.domain)
- return cls.from_sampling(sampler=config.sampler, domain=domain,
+ return cls.from_sampling(sampler=config.experimentdata.from_sampling.sampler, domain=domain,
  n_samples=config.experimentdata.from_sampling.n_samples,
  seed=config.experimentdata.from_sampling.seed,
- filename=config.experimentdata.from_sampling.filename)
+ filename=config.experimentdata.name)
 
  else:
  return cls(**config)
 
  @classmethod
- def _from_file_attempt(cls: Type[ExperimentData], filename: str) -> ExperimentData:
+ def _from_file_attempt(cls: Type[ExperimentData], filename: Path) -> ExperimentData:
  """Attempt to create an ExperimentData object from .csv and .json files.
 
  Parameters
  ----------
- filename : str
+ filename : Path
  Name of the file, excluding suffix.
 
  Returns
@@ -268,7 +268,8 @@ def _from_file_attempt(cls: Type[ExperimentData], filename: str) -> ExperimentDa
  """
  try:
  return cls(domain=Path(f"{filename}{DOMAIN_SUFFIX}"), input_data=Path(f"{filename}{INPUT_DATA_SUFFIX}"),
- output_data=Path(f"{filename}{OUTPUT_DATA_SUFFIX}"), jobs=Path(f"{filename}{JOBS_SUFFIX}"))
+ output_data=Path(f"{filename}{OUTPUT_DATA_SUFFIX}"), jobs=Path(f"{filename}{JOBS_SUFFIX}"),
+ filename=filename.name)
  except FileNotFoundError:
  raise FileNotFoundError(f"Cannot find the files from {filename}.")
 

src/f3dasm/_src/run_optimization.py

@@ -13,10 +13,9 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
-from pathos.helpers import mp
-
 from f3dasm.design import Domain
 from f3dasm.optimization import Optimizer
+from pathos.helpers import mp
 
 # Locals
 from .datageneration.datagenerator import DataGenerator
@@ -85,16 +84,13 @@ def to_xarray(self) -> xr.Dataset:
  xarr.attrs['realization_seeds']: List[int] = list(self.seeds)
 
  # Benchmark functions
- xarr.attrs['function_seed']: int = self.kwargs['seed']
+ xarr.attrs['function_seed']: int = self.func.seed
  xarr.attrs['function_name']: str = self.data_generator
- xarr.attrs['function_noise']: str = self.kwargs['noise']
+ xarr.attrs['function_noise']: str = self.func.noise
  xarr.attrs['function_dimensionality']: int = len(self.data[0].domain)
 
  # Global minimum function
- func = datagenerator_factory(data_generator=self.data_generator,
- domain=self.data[0].domain, kwargs=self.kwargs)
-
- _, g = func.get_global_minimum(d=func.dimensionality)
+ _, g = self.func.get_global_minimum(d=self.func.dimensionality)
  xarr.attrs['function_global_minimum']: float = float(np.array(g if not isinstance(g, list) else g[0])[0, 0])
  return xarr