add generate and clean steps to core

.gitignore

@@ -6,6 +6,10 @@ __pycache__/
 # C extensions
 *.so
 
+*.joblib
+*.bin
+*.json
+
 # ignore examples folder
 examples/
 

pipeline_lib/core/__init__.py

@@ -1,3 +1,2 @@
 from .data_container import DataContainer  # noqa: F401
-from .pipeline import Pipeline  # noqa: F401
 from .steps import PipelineStep  # noqa: F401

pipeline_lib/core/step_registry.py

@@ -3,7 +3,7 @@
 import os
 import pkgutil
 
-from pipeline_lib.core.steps import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class StepClassNotFoundError(Exception):

pipeline_lib/core/steps/__init__.py

@@ -3,10 +3,12 @@
 from .calculate_features import CalculateFeaturesStep  # noqa: F401
 from .calculate_metrics import CalculateMetricsStep  # noqa: F401
 from .calculate_reports import CalculateReportsStep  # noqa: F401
+from .clean import CleanStep  # noqa: F401
 from .encode import EncodeStep  # noqa: F401
 from .explainer_dashboard import ExplainerDashboardStep  # noqa: F401
 from .fit_encoders import FitEncodersStep  # noqa: F401
 from .fit_model import FitModelStep  # noqa: F401
+from .generate import GenerateStep  # noqa: F401
 from .input_scaling import InputScalingStep  # noqa: F401
 from .predict import PredictStep  # noqa: F401
 from .tabular_split import TabularSplitStep  # noqa: F401

pipeline_lib/core/steps/augment.py

@@ -1,8 +1,7 @@
 from typing import Optional
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class AugmentStep(PipelineStep):

pipeline_lib/core/steps/base.py

@@ -2,7 +2,7 @@
 from abc import ABC, abstractmethod
 from typing import Optional
 
-from pipeline_lib.core import DataContainer
+from pipeline_lib.core.data_container import DataContainer
 
 
 class PipelineStep(ABC):

pipeline_lib/core/steps/calculate_features.py

@@ -1,8 +1,7 @@
 from typing import Optional
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class CalculateFeaturesStep(PipelineStep):

pipeline_lib/core/steps/calculate_metrics.py

@@ -2,8 +2,7 @@
 from sklearn.metrics import mean_absolute_error, mean_squared_error
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class CalculateMetricsStep(PipelineStep):

pipeline_lib/core/steps/calculate_reports.py

@@ -1,8 +1,7 @@
 from typing import Optional
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class CalculateReportsStep(PipelineStep):

pipeline_lib/core/steps/clean.py

@@ -0,0 +1,69 @@
+from typing import Optional
+
+from pipeline_lib.core import DataContainer
+from pipeline_lib.core.steps.base import PipelineStep
+
+
+class CleanStep(PipelineStep):
+    def __init__(
+        self,
+        fill_missing: Optional[dict] = None,
+        remove_outliers: Optional[dict] = None,
+        convert_dtypes: Optional[dict] = None,
+    ):
+        self.init_logger()
+        self.fill_missing = fill_missing
+        self.remove_outliers = remove_outliers
+        self.convert_dtypes = convert_dtypes
+
+    def execute(self, data: DataContainer) -> DataContainer:
+        self.logger.info("Cleaning tabular data...")
+
+        df = data[DataContainer.RAW]
+
+        if self.fill_missing:
+            for column, fill_value in self.fill_missing.items():
+                if column in df.columns:
+                    df[column].fillna(fill_value, inplace=True)
+                    self.logger.info(
+                        f"Filled missing values in column '{column}' with {fill_value}"
+                    )
+                else:
+                    self.logger.warning(f"Column '{column}' not found in the DataFrame")
+
+        if self.remove_outliers:
+            for column, method in self.remove_outliers.items():
+                if column in df.columns:
+                    if method == "clip":
+                        q1 = df[column].quantile(0.25)
+                        q3 = df[column].quantile(0.75)
+                        iqr = q3 - q1
+                        lower_bound = q1 - (1.5 * iqr)
+                        upper_bound = q3 + (1.5 * iqr)
+                        df[column] = df[column].clip(lower=lower_bound, upper=upper_bound)
+                        self.logger.info(f"Clipped outliers in column '{column}'")
+                    elif method == "drop":
+                        q1 = df[column].quantile(0.25)
+                        q3 = df[column].quantile(0.75)
+                        iqr = q3 - q1
+                        lower_bound = q1 - (1.5 * iqr)
+                        upper_bound = q3 + (1.5 * iqr)
+                        outliers = (df[column] < lower_bound) | (df[column] > upper_bound)
+                        df = df[~outliers]
+                        self.logger.info(f"Dropped outliers in column '{column}'")
+                    else:
+                        self.logger.warning(f"Unsupported outlier removal method '{method}'")
+                else:
+                    self.logger.warning(f"Column '{column}' not found in the DataFrame")
+
+        if self.convert_dtypes:
+            for column, dtype in self.convert_dtypes.items():
+                if column in df.columns:
+                    df[column] = df[column].astype(dtype)
+                    self.logger.info(f"Converted column '{column}' to {dtype}")
+                else:
+                    self.logger.warning(f"Column '{column}' not found in the DataFrame")
+
+        data[DataContainer.CLEAN] = df
+
+        return data

pipeline_lib/core/steps/encode.py

@@ -1,8 +1,7 @@
 from typing import Optional
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class EncodeStep(PipelineStep):

pipeline_lib/core/steps/explainer_dashboard.py

@@ -1,7 +1,7 @@
 from explainerdashboard import RegressionExplainer
 
 from pipeline_lib.core import DataContainer
-from pipeline_lib.core.steps import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class ExplainerDashboardStep(PipelineStep):

pipeline_lib/core/steps/fit_encoders.py

@@ -1,8 +1,7 @@
 from typing import Optional
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class FitEncodersStep(PipelineStep):

pipeline_lib/core/steps/fit_model.py

@@ -1,8 +1,7 @@
 from typing import Optional
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class FitModelStep(PipelineStep):

pipeline_lib/core/steps/generate.py

@@ -0,0 +1,65 @@
+import os
+from enum import Enum
+
+import pandas as pd
+
+from pipeline_lib.core import DataContainer
+from pipeline_lib.core.steps.base import PipelineStep
+
+
+class FileType(Enum):
+    CSV = ".csv"
+    PARQUET = ".parquet"
+
+
+class GenerateStep(PipelineStep):
+    def __init__(self, path: str, **kwargs):
+        self.init_logger()
+        self.file_path = path
+        self.kwargs = kwargs
+
+    def execute(self, data: DataContainer) -> DataContainer:
+        self.logger.info(f"Generating data from file: {self.file_path}")
+
+        if not os.path.exists(self.file_path):
+            raise FileNotFoundError(f"File not found: {self.file_path}")
+
+        file_type = self._infer_file_type()
+
+        if file_type == FileType.CSV:
+            df = self._read_csv()
+        elif file_type == FileType.PARQUET:
+            df = self._read_parquet()
+        else:
+            raise ValueError(f"Unsupported file type: {file_type}")
+
+        data[DataContainer.RAW] = df
+
+        self.logger.info(f"Generated DataFrame with shape: {df.shape}")
+
+        return data
+
+    def _infer_file_type(self) -> FileType:
+        _, file_extension = os.path.splitext(self.file_path)
+        file_extension = file_extension.lower()
+
+        try:
+            return FileType(file_extension)
+        except ValueError:
+            raise ValueError(f"Unsupported file extension: {file_extension}")
+
+    def _read_csv(self) -> pd.DataFrame:
+        kwargs = self.kwargs.copy()
+        index_col = kwargs.pop("index", None)
+        df = pd.read_csv(self.file_path, **kwargs)
+        if index_col is not None:
+            df.set_index(index_col, inplace=True)
+        return df
+
+    def _read_parquet(self) -> pd.DataFrame:
+        kwargs = self.kwargs.copy()
+        index_col = kwargs.pop("index", None)
+        df = pd.read_parquet(self.file_path, **kwargs)
+        if index_col is not None:
+            df.set_index(index_col, inplace=True)
+        return df

pipeline_lib/core/steps/input_scaling.py

@@ -1,8 +1,7 @@
 from typing import Optional
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class InputScalingStep(PipelineStep):

pipeline_lib/core/steps/predict.py

@@ -1,8 +1,7 @@
 from typing import Optional
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class PredictStep(PipelineStep):

pipeline_lib/core/steps/tabular_split.py

@@ -1,8 +1,7 @@
 from sklearn.model_selection import train_test_split
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class TabularSplitStep(PipelineStep):

pipeline_lib/core/steps/target_scaling.py

@@ -1,8 +1,7 @@
 from typing import Optional
 
 from pipeline_lib.core import DataContainer
-
-from .base import PipelineStep
+from pipeline_lib.core.steps.base import PipelineStep
 
 
 class TargetScalingStep(PipelineStep):

pipeline_lib/implementation/tabular/xgboost/fit_model.py

@@ -40,6 +40,11 @@ def __init__(
 
         self.xgb_params = xgb_params
         self.optuna_params = optuna_params
+
+        if save_path:
+            if not save_path.endswith(".joblib"):
+                raise ValueError("Only joblib format is supported for saving the model.")
+
         self.save_path = save_path
 
     def execute(self, data: DataContainer) -> DataContainer:
@@ -90,15 +95,9 @@ def execute(self, data: DataContainer) -> DataContainer:
         # Save the model to the data container
         data[DataContainer.MODEL] = model
 
-        # save model to disk
-        save_path = self.save_path
-
-        if save_path:
-            if not save_path.endswith(".joblib"):
-                raise ValueError("Only joblib format is supported for saving the model.")
-            self.logger.info(f"Saving the model to {save_path}")
-            dump(model, save_path)
-
+        if self.save_path:
+            self.logger.info(f"Saving the model to {self.save_path}")
+            dump(model, self.save_path)
         return data
 
     def optimize_with_optuna(self, X_train, y_train, X_valid, y_valid, optuna_params):