split step_registry to new class

pipeline_lib/__init__.py

@@ -1,4 +1,6 @@
 from .core.pipeline import Pipeline
 
-Pipeline.auto_register_steps_from_package("pipeline_lib.core.steps")
-Pipeline.auto_register_steps_from_package("pipeline_lib.implementation.tabular.xgboost")
+Pipeline.step_registry.auto_register_steps_from_package("pipeline_lib.core.steps")
+Pipeline.step_registry.auto_register_steps_from_package(
+    "pipeline_lib.implementation.tabular.xgboost"
+)

pipeline_lib/core/pipeline.py

@@ -1,13 +1,11 @@
 from __future__ import annotations
 
-import importlib
 import json
 import logging
-import os
-import pkgutil
 from typing import Optional
 
 from pipeline_lib.core.data_container import DataContainer
+from pipeline_lib.core.step_registry import StepRegistry
 from pipeline_lib.core.steps import PipelineStep
 
 
@@ -16,89 +14,18 @@ class Pipeline:
 
     _step_registry = {}
     logger = logging.getLogger("Pipeline")
+    step_registry = StepRegistry()
 
     def __init__(self, initial_data: Optional[DataContainer] = None):
         self.steps = []
         self.initial_data = initial_data
         self.save_path = None
         self.load_path = None
 
-    @classmethod
-    def register_step(cls, step_class):
-        """Register a step class using its class name."""
-        step_name = step_class.__name__
-        if not issubclass(step_class, PipelineStep):
-            raise ValueError(f"{step_class} must be a subclass of PipelineStep")
-        cls._step_registry[step_name] = step_class
-
-    @classmethod
-    def get_step_class(cls, step_name):
-        """Retrieve a step class by name."""
-        if step_name in cls._step_registry:
-            return cls._step_registry[step_name]
-        else:
-            raise ValueError(f"Step class '{step_name}' not found in registry.")
-
     def add_steps(self, steps: list[PipelineStep]):
         """Add steps to the pipeline."""
         self.steps.extend(steps)
 
-    @classmethod
-    def auto_register_steps_from_package(cls, package_name):
-        """
-        Automatically registers all step classes found within a specified package.
-        """
-        package = importlib.import_module(package_name)
-        prefix = package.__name__ + "."
-        for importer, modname, ispkg in pkgutil.walk_packages(package.__path__, prefix):
-            module = importlib.import_module(modname)
-            for name in dir(module):
-                attribute = getattr(module, name)
-                if (
-                    isinstance(attribute, type)
-                    and issubclass(attribute, PipelineStep)
-                    and attribute is not PipelineStep
-                ):
-                    cls.register_step(attribute)
-
-    @classmethod
-    def load_and_register_custom_steps(cls, custom_steps_path: str) -> None:
-        """
-        Dynamically loads and registers step classes found in the specified directory.
-
-        This method scans a specified directory for Python files (excluding __init__.py),
-        dynamically imports these files as modules, and registers all classes derived from
-        PipelineStep found within these modules.
-
-        Parameters
-        ----------
-        custom_steps_path : str
-            The path to the directory containing custom step implementation files.
-        """
-        Pipeline.logger.debug(f"Loading custom steps from: {custom_steps_path}")
-        for filename in os.listdir(custom_steps_path):
-            if filename.endswith(".py") and not filename.startswith("__"):
-                filepath = os.path.join(custom_steps_path, filename)
-                module_name = os.path.splitext(filename)[0]
-                spec = importlib.util.spec_from_file_location(module_name, filepath)
-                module = importlib.util.module_from_spec(spec)
-
-                try:
-                    spec.loader.exec_module(module)
-                    Pipeline.logger.debug(f"Successfully loaded module: {module_name}")
-
-                    for attribute_name in dir(module):
-                        attribute = getattr(module, attribute_name)
-                        if (
-                            isinstance(attribute, type)
-                            and issubclass(attribute, PipelineStep)
-                            and attribute is not PipelineStep
-                        ):
-                            Pipeline.register_step(attribute)
-                            Pipeline.logger.debug(f"Registered step class: {attribute_name}")
-                except Exception as e:
-                    Pipeline.logger.error(f"Failed to load module: {module_name}. Error: {e}")
-
     def run(self) -> DataContainer:
         """Run the pipeline on the given data."""
 
@@ -125,7 +52,7 @@ def from_json(cls, path: str) -> Pipeline:
 
         custom_steps_path = config.get("custom_steps_path")
         if custom_steps_path:
-            Pipeline.load_and_register_custom_steps(custom_steps_path)
+            cls.step_registry.load_and_register_custom_steps(custom_steps_path)
 
         pipeline = Pipeline()
 
@@ -142,7 +69,7 @@ def from_json(cls, path: str) -> Pipeline:
                 f"Creating step {step_type} with parameters: \n {json.dumps(parameters, indent=4)}"
             )
 
-            step_class = Pipeline.get_step_class(step_type)
+            step_class = cls.step_registry.get_step_class(step_type)
             step = step_class(**parameters)
             steps.append(step)
 

pipeline_lib/core/step_registry.py

@@ -0,0 +1,73 @@
+import importlib
+import logging
+import os
+import pkgutil
+
+from pipeline_lib.core.steps import PipelineStep
+
+
+class StepRegistry:
+    """A helper class for managing the registry of pipeline steps."""
+
+    def __init__(self):
+        self._step_registry = {}
+        self.logger = logging.getLogger(StepRegistry.__name__)
+
+    def register_step(self, step_class):
+        """Register a step class using its class name."""
+        step_name = step_class.__name__
+        if not issubclass(step_class, PipelineStep):
+            raise ValueError(f"{step_class} must be a subclass of PipelineStep")
+        self._step_registry[step_name] = step_class
+
+    def get_step_class(self, step_name):
+        """Retrieve a step class by name."""
+        if step_name in self._step_registry:
+            return self._step_registry[step_name]
+        else:
+            raise ValueError(f"Step class '{step_name}' not found in registry.")
+
+    def auto_register_steps_from_package(self, package_name):
+        """
+        Automatically registers all step classes found within a specified package.
+        """
+        package = importlib.import_module(package_name)
+        prefix = package.__name__ + "."
+        for importer, modname, ispkg in pkgutil.walk_packages(package.__path__, prefix):
+            module = importlib.import_module(modname)
+            for name in dir(module):
+                attribute = getattr(module, name)
+                if (
+                    isinstance(attribute, type)
+                    and issubclass(attribute, PipelineStep)
+                    and attribute is not PipelineStep
+                ):
+                    self.register_step(attribute)
+
+    def load_and_register_custom_steps(self, custom_steps_path: str) -> None:
+        """
+        Dynamically loads and registers step classes found in the specified directory.
+        """
+        self.logger.debug(f"Loading custom steps from: {custom_steps_path}")
+        for filename in os.listdir(custom_steps_path):
+            if filename.endswith(".py") and not filename.startswith("__"):
+                filepath = os.path.join(custom_steps_path, filename)
+                module_name = os.path.splitext(filename)[0]
+                spec = importlib.util.spec_from_file_location(module_name, filepath)
+                module = importlib.util.module_from_spec(spec)
+
+                try:
+                    spec.loader.exec_module(module)
+                    self.logger.debug(f"Successfully loaded module: {module_name}")
+
+                    for attribute_name in dir(module):
+                        attribute = getattr(module, attribute_name)
+                        if (
+                            isinstance(attribute, type)
+                            and issubclass(attribute, PipelineStep)
+                            and attribute is not PipelineStep
+                        ):
+                            self.register_step(attribute)
+                            self.logger.debug(f"Registered step class: {attribute_name}")
+                except Exception as e:
+                    self.logger.error(f"Failed to load module: {module_name}. Error: {e}")

pipeline_lib/core/steps/calculate_metrics.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 import numpy as np
 from sklearn.metrics import mean_absolute_error, mean_squared_error
 

pipeline_lib/core/steps/tabular_split.py

@@ -1,6 +1,3 @@
-from typing import Optional, Tuple
-
-import pandas as pd
 from sklearn.model_selection import train_test_split
 
 from pipeline_lib.core import DataContainer
@@ -11,118 +8,36 @@
 class TabularSplitStep(PipelineStep):
     """Split the data."""
 
-    def __init__(
-        self,
-        train_percentage: float,
-        id_column: str,
-        train_ids: Optional[list[str]] = None,
-        validation_ids: Optional[list[str]] = None,
-    ) -> None:
+    def __init__(self, train_percentage: float) -> None:
         """Initialize SplitStep."""
         self.init_logger()
         self.train_percentage = train_percentage
-        self.id_column_name = id_column
-        self.train_ids = train_ids
-        self.validation_ids = validation_ids
-
-    def _id_based_split(
-        self,
-        df: pd.DataFrame,
-        train_ids: list[str],
-        validation_ids: list[str],
-        id_column_name: str,
-    ) -> Tuple[pd.DataFrame, pd.DataFrame]:
-        """
-        Splits the DataFrame into training and validation sets based on specified IDs.
-
-        Parameters
-        ----------
-        df : pd.DataFrame
-            The DataFrame to split.
-        train_ids : List[str]
-            List of IDs for the training set.
-        validation_ids : List[str]
-            List of IDs for the validation set.
-        id_column_name : str
-            The name of the column in df that contains the IDs.
-
-        Returns
-        -------
-        Tuple[pd.DataFrame, pd.DataFrame]
-            A tuple containing the training set and the validation set.
-        """
-        train_df = df[df[id_column_name].isin(train_ids)]
-        validation_df = df[df[id_column_name].isin(validation_ids)]
-        return train_df, validation_df
-
-    def _percentage_based_id_split(
-        self, df: pd.DataFrame, train_percentage: float, id_column_name: str
-    ) -> Tuple[list[str], list[str]]:
-        """
-        Splits the unique IDs into training and validation sets based on specified percentages.
 
-        Parameters
-        ----------
-        df : pd.DataFrame
-            The DataFrame containing the IDs.
-        train_percentage : float
-            The percentage of IDs to include in the training set.
-        id_column_name : str
-            The name of the column containing the IDs.
-
-        Returns
-        -------
-        Tuple[List[str], List[str]]
-            A tuple containing lists of training and validation IDs.
-        """
-        unique_ids = df[id_column_name].unique()
-        train_ids, validation_ids = train_test_split(
-            unique_ids, train_size=train_percentage, random_state=42
-        )
-        return train_ids.tolist(), validation_ids.tolist()
+        if self.train_percentage <= 0 or self.train_percentage >= 1:
+            raise ValueError("train_percentage must be between 0 and 1.")
 
     def execute(self, data: DataContainer) -> DataContainer:
-        """Execute the split based on IDs."""
+        """Execute the random train-validation split."""
         self.logger.info("Splitting tabular data...")
 
         df = data[DataContainer.CLEAN]
 
-        if self.train_percentage:
-            if (
-                self.train_percentage is None
-                or self.train_percentage <= 0
-                or self.train_percentage >= 1
-            ):
-                raise ValueError("train_percentage must be between 0 and 1.")
-            train_ids, validation_ids = self._percentage_based_id_split(
-                df, self.train_percentage, self.id_column_name
-            )
-
-        self.logger.info(f"Number of train ids: {len(train_ids)}")
-        self.logger.info(f"Number of validation ids: {len(validation_ids)}")
-
-        train_df, validation_df = self._id_based_split(
-            df, train_ids, validation_ids, self.id_column_name
+        train_df, validation_df = train_test_split(
+            df, train_size=self.train_percentage, random_state=42
         )
 
         train_rows = len(train_df)
         validation_rows = len(validation_df)
         total_rows = train_rows + validation_rows
 
         self.logger.info(
-            f"Number of rows in training set: {len(train_df)} | {train_rows/total_rows:.2%}"
+            f"Number of rows in training set: {train_rows} | {train_rows/total_rows:.2%}"
         )
         self.logger.info(
-            f"Number of rows in validation set: {len(validation_df)} |"
+            f"Number of rows in validation set: {validation_rows} |"
             f" {validation_rows/total_rows:.2%}"
         )
 
-        left_ids = df[~df[self.id_column_name].isin(train_ids + validation_ids)][
-            self.id_column_name
-        ].unique()
-        self.logger.info(f"Number of IDs left from total df: {len(left_ids)}")
-        self.logger.debug(f"IDs left from total df: {left_ids}")
-
         data[DataContainer.TRAIN] = train_df
         data[DataContainer.VALIDATION] = validation_df
 

pipeline_lib/implementation/tabular/xgboost/fit_model.py

@@ -1,13 +1,12 @@
 import time
+from typing import Optional
 
 import optuna
 import xgboost as xgb
 from joblib import dump
 from optuna.pruners import MedianPruner
 from sklearn.metrics import mean_absolute_error
 
-from typing import Optional
-
 from pipeline_lib.core import DataContainer
 from pipeline_lib.core.steps import FitModelStep
 

pipeline_lib/implementation/tabular/xgboost/predict.py

@@ -1,9 +1,10 @@
+from typing import Optional
+
 import pandas as pd
 from joblib import load
 
 from pipeline_lib.core import DataContainer
 from pipeline_lib.core.steps import PredictStep
-from typing import Optional
 
 
 class XGBoostPredictStep(PredictStep):