Remove requires fitting and finalize routine

molpipeline/error_handling.py

@@ -6,11 +6,13 @@
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
+from loguru import logger
 
 from molpipeline.abstract_pipeline_elements.core import (
     ABCPipelineElement,
     InvalidInstance,
     RemovedInstance,
+    SingleInstanceTransformerMixin,
     TransformingPipelineElement,
 )
 from molpipeline.utils.molpipeline_types import AnyVarSeq, TypeFixedVarSeq
@@ -21,7 +23,7 @@
 _S = TypeVar("_S")
 
 
-class ErrorFilter(ABCPipelineElement):
+class ErrorFilter(SingleInstanceTransformerMixin, TransformingPipelineElement):
     """Collects tracks and removes error values."""
 
     element_ids: set[str]
@@ -71,7 +73,6 @@ def __init__(
         self.element_ids = element_ids
         self.filter_everything = filter_everything
         self.n_total = 0
-        self._requires_fitting = True
 
     @classmethod
     def from_element_list(
@@ -179,7 +180,11 @@ def check_removal(self, value: Any) -> bool:
             return False
         return self.filter_everything or value.element_id in self.element_ids
 
-    def fit(self, values: AnyVarSeq, labels: Any = None) -> Self:  # noqa: ARG002
+    def fit(
+        self,
+        values: AnyVarSeq,  # noqa: ARG002
+        labels: Any = None,  # noqa: ARG002
+    ) -> Self:
         """Fit to input values.
 
         Only for compatibility with sklearn Pipelines.
@@ -212,8 +217,8 @@ def fit_transform(
         ----------
         values: TypeFixedVarSeq
             Iterable to which element is fitted and which is subsequently transformed.
-        labels: Any
-            Label used for fitting. For compatibility with sklearn, not used.
+        labels: Any, optional
+            Label used for fitting.
 
         Returns
         -------
@@ -249,6 +254,9 @@ def co_transform(self, values: TypeFixedVarSeq) -> TypeFixedVarSeq:
 
         """
         if self.n_total != len(values):
+            logger.error(
+                f"Expected {self.n_total} values, but got {len(values)}",
+            )
             raise ValueError("Length of values does not match length of values in fit")
         if isinstance(values, list):
             out_list = []
@@ -288,6 +296,8 @@ def transform(self, values: TypeFixedVarSeq) -> TypeFixedVarSeq:
     def transform_single(self, value: Any) -> Any:
         """Transform a single value.
 
+        Overrides parent method to not skip InvalidInstances.
+
         Parameters
         ----------
         value: Any
@@ -688,8 +698,8 @@ def fit_transform(
         ----------
         values: TypeFixedVarSeq
             Iterable to which element is fitted and which is subsequently transformed.
-        labels: Any
-            Label used for fitting. For compatibility with sklearn, not used.
+        labels: Any, optional
+            Label used for fitting.
         **params: Any
             Additional keyword arguments. For compatibility with sklearn, not used.
 
@@ -702,47 +712,10 @@ def fit_transform(
         self.fit(values)
         return self.transform(values)
 
-    def transform_single(self, value: Any) -> Any:
-        """Transform a single value.
-
-        Parameters
-        ----------
-        value: Any
-            Value to be transformed.
-
-        Returns
-        -------
-        Any
-            Transformed value.
-
-        """
-        return self.pretransform_single(value)
-
-    def pretransform_single(self, value: Any) -> Any:
-        """Transform a single value.
-
-        Parameters
-        ----------
-        value: Any
-            Value to be transformed.
-
-        Returns
-        -------
-        Any
-            Transformed value.
-
-        """
-        if (
-            isinstance(value, RemovedInstance)
-            and value.filter_element_id == self.error_filter.uuid
-        ):
-            return self.fill_value
-        return value
-
     def transform(
         self,
         values: TypeFixedVarSeq,
-        **_params: Any,
+        **params: Any,
     ) -> TypeFixedVarSeq:
         """Transform iterable of values by removing invalid instances.
 
@@ -752,7 +725,7 @@ def transform(
         ----------
         values: TypeFixedVarSeq
             Iterable of which according invalid instances are removed.
-        **_params: Any
+        **params: Any
             Additional keyword arguments.
 
         Raises
@@ -770,12 +743,14 @@ def transform(
         if len(values) != self.error_filter.n_total - len(
             self.error_filter.error_indices,
         ):
-            expected_length = self.error_filter.n_total - len(
+            self.error_filter.n_total - len(
                 self.error_filter.error_indices,
             )
             raise ValueError(
-                "Length of values does not match length of values in fit. "
-                f"Expected: {expected_length} - Received :{len(values)}",
+                f"Length of values does not match length of values in fit. "
+                f"Expected: "
+                f"{self.error_filter.n_total - len(self.error_filter.error_indices)} "
+                f"- Received :{len(values)}",
             )
         return self.fill_with_dummy(values)
 

molpipeline/estimators/chemprop/lightning_wrapper.py

@@ -106,7 +106,7 @@ def get_trainer_path(trainer: pl.Trainer) -> str | None:
         None if the path is the current path.
 
     """
-    curr_path = str(Path().resolve())
+    curr_path = str(Path.cwd())
     trainer_path: str | None = trainer.default_root_dir
     if trainer_path == curr_path:
         trainer_path = None
@@ -139,7 +139,7 @@ def get_params_trainer(trainer: pl.Trainer) -> dict[str, Any]:
     else:
         enable_progress_model_summary = False
 
-    trainer_dict = {
+    return {
         "accelerator": get_device(trainer),
         "strategy": "auto",  # trainer.strategy, # collides with accelerator
         "devices": "auto",  # trainer._accelerator_connector._devices_flag does not work
@@ -180,7 +180,6 @@ def get_params_trainer(trainer: pl.Trainer) -> dict[str, Any]:
         "reload_dataloaders_every_n_epochs": trainer.reload_dataloaders_every_n_epochs,  # type: ignore[attr-defined]
         "default_root_dir": get_trainer_path(trainer),
     }
-    return trainer_dict
 
 
 def get_non_default_params_trainer(trainer: pl.Trainer) -> dict[str, Any]:
@@ -198,9 +197,8 @@ def get_non_default_params_trainer(trainer: pl.Trainer) -> dict[str, Any]:
 
     """
     trainer_dict = get_params_trainer(trainer)
-    non_default_values = {
+    return {
         key: value
         for key, value in trainer_dict.items()
         if key not in TRAINER_DEFAULT_PARAMS or value != TRAINER_DEFAULT_PARAMS[key]
     }
-    return non_default_values

molpipeline/estimators/murcko_scaffold_clustering.py

@@ -119,8 +119,12 @@ def _generate_pipeline(self) -> Pipeline:
             )
 
             # Directly add the error filter and replacer to the pipeline
-            pipeline_step_list.append(("no_scaffold_filter", no_scaffold_filter))
-            pipeline_step_list.append(("no_scaffold_replacer", no_scaffold_replacer))
+            pipeline_step_list.extend(
+                (
+                    ("no_scaffold_filter", no_scaffold_filter),
+                    ("no_scaffold_replacer", no_scaffold_replacer),
+                ),
+            )
         else:
             raise ValueError(
                 f"Invalid value for linear_molecules_strategy: "
@@ -139,11 +143,10 @@ def _generate_pipeline(self) -> Pipeline:
             ],
         )
 
-        cluster_pipeline = Pipeline(
+        return Pipeline(
             pipeline_step_list,
             n_jobs=self.n_jobs,
         )
-        return cluster_pipeline
 
     # pylint: disable=C0103,W0613
     @_fit_context(prefer_skip_nested_validation=True)

molpipeline/experimental/explainability/visualization/heatmaps.py

@@ -289,8 +289,7 @@ def get_color_normalizer_from_data(
 
     """
     abs_max = np.max(np.abs(values))
-    normalizer = colors.Normalize(vmin=-abs_max, vmax=abs_max)
-    return normalizer
+    return colors.Normalize(vmin=-abs_max, vmax=abs_max)
 
 
 def color_canvas(canvas: Draw.MolDraw2D, color_grid: ColorGrid) -> None:

molpipeline/experimental/explainability/visualization/utils.py

@@ -146,8 +146,7 @@ def color_tuple_to_colormap(
     half_to_one = col1 * linspace4d + col2 * (1 - linspace4d)
 
     # Creating new colormap from
-    color_map = ListedColormap(np.vstack([zero_to_half, half_to_one]))
-    return color_map
+    return ListedColormap(np.vstack([zero_to_half, half_to_one]))
 
 
 def to_png(data: bytes) -> Image.Image:
@@ -165,8 +164,7 @@ def to_png(data: bytes) -> Image.Image:
 
     """
     bio = io.BytesIO(data)
-    img = Image.open(bio)
-    return img
+    return Image.open(bio)
 
 
 def plt_to_pil(figure: plt.Figure) -> Image.Image:
@@ -186,8 +184,7 @@ def plt_to_pil(figure: plt.Figure) -> Image.Image:
     bio = io.BytesIO()
     figure.savefig(bio, format="png")
     bio.seek(0)
-    img = Image.open(bio)
-    return img
+    return Image.open(bio)
 
 
 def get_atom_coords_of_bond(

molpipeline/experimental/explainability/visualization/visualization.py

@@ -88,8 +88,7 @@ def _make_grid_from_mol(
 
     xl = list(pad(xl, padding[0]))  # Increasing size of x-axis
     yl = list(pad(yl, padding[1]))  # Increasing size of y-axis
-    v_map = ValueGrid(xl, yl, grid_resolution[0], grid_resolution[1])
-    return v_map
+    return ValueGrid(xl, yl, grid_resolution[0], grid_resolution[1])
 
 
 def _add_gaussians_for_atoms(
@@ -537,8 +536,7 @@ def structure_heatmap(
         color_limits,
     )
     figure_bytes = drawer.GetDrawingText()
-    image = to_png(figure_bytes)
-    return image
+    return to_png(figure_bytes)
 
 
 def structure_heatmap_shap(  # pylint: disable=too-many-locals

molpipeline/mol2any/mol2bool.py

@@ -46,5 +46,4 @@ def transform_single(self, value: Any) -> Any:
             Bool representation of the molecule.
 
         """
-        pre_value = self.pretransform_single(value)
-        return self.finalize_single(pre_value)
+        return self.pretransform_single(value)