From ad8e35c6e690d95dcb9928f40c8b199234c9e218 Mon Sep 17 00:00:00 2001 From: Diego Marvid Date: Mon, 4 Mar 2024 16:03:18 +0000 Subject: [PATCH] init project library --- .gitignore | 3 + .vscode/settings.json | 15 +- pipeline_lib/__init__.py | 0 pipeline_lib/core/__init__.py | 3 + pipeline_lib/core/data_container.py | 287 ++ pipeline_lib/core/pipeline.py | 51 + pipeline_lib/core/steps/__init__.py | 13 + pipeline_lib/core/steps/augment.py | 16 + pipeline_lib/core/steps/base.py | 17 + pipeline_lib/core/steps/calculate_features.py | 16 + pipeline_lib/core/steps/calculate_metrics.py | 33 + pipeline_lib/core/steps/calculate_reports.py | 16 + pipeline_lib/core/steps/encode.py | 16 + .../core/steps/explainer_dashboard.py | 16 + pipeline_lib/core/steps/fit_encoders.py | 16 + pipeline_lib/core/steps/fit_model.py | 16 + pipeline_lib/core/steps/input_scaling.py | 16 + pipeline_lib/core/steps/predict.py | 16 + pipeline_lib/core/steps/tabular_split.py | 143 + pipeline_lib/core/steps/target_scaling.py | 21 + pipeline_lib/implementation/__init__.py | 0 .../implementation/tabular/__init__.py | 0 .../tabular/xgboost/__init__.py | 3 + .../tabular/xgboost/explainer_dashboard.py | 40 + .../tabular/xgboost/fit_model.py | 145 + .../implementation/tabular/xgboost/predict.py | 40 + poetry.lock | 2951 +++++++++++++++++ project_base/src.py | 2 - project_base/test.py | 25 - pyproject.toml | 28 + 30 files changed, 3932 insertions(+), 32 deletions(-) create mode 100644 pipeline_lib/__init__.py create mode 100644 pipeline_lib/core/__init__.py create mode 100644 pipeline_lib/core/data_container.py create mode 100644 pipeline_lib/core/pipeline.py create mode 100644 pipeline_lib/core/steps/__init__.py create mode 100644 pipeline_lib/core/steps/augment.py create mode 100644 pipeline_lib/core/steps/base.py create mode 100644 pipeline_lib/core/steps/calculate_features.py create mode 100644 pipeline_lib/core/steps/calculate_metrics.py create mode 100644 pipeline_lib/core/steps/calculate_reports.py create mode 100644 pipeline_lib/core/steps/encode.py create mode 100644 pipeline_lib/core/steps/explainer_dashboard.py create mode 100644 pipeline_lib/core/steps/fit_encoders.py create mode 100644 pipeline_lib/core/steps/fit_model.py create mode 100644 pipeline_lib/core/steps/input_scaling.py create mode 100644 pipeline_lib/core/steps/predict.py create mode 100644 pipeline_lib/core/steps/tabular_split.py create mode 100644 pipeline_lib/core/steps/target_scaling.py create mode 100644 pipeline_lib/implementation/__init__.py create mode 100644 pipeline_lib/implementation/tabular/__init__.py create mode 100644 pipeline_lib/implementation/tabular/xgboost/__init__.py create mode 100644 pipeline_lib/implementation/tabular/xgboost/explainer_dashboard.py create mode 100644 pipeline_lib/implementation/tabular/xgboost/fit_model.py create mode 100644 pipeline_lib/implementation/tabular/xgboost/predict.py create mode 100644 poetry.lock delete mode 100644 project_base/src.py delete mode 100644 project_base/test.py diff --git a/.gitignore b/.gitignore index b4ab01e..2a7ca1a 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,9 @@ __pycache__/ # C extensions *.so +# ignore examples folder +examples/ + # Distribution / packaging .Python build/ diff --git a/.vscode/settings.json b/.vscode/settings.json index fcb89cd..d02c230 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -3,7 +3,7 @@ // Set correct python path to venv's one // "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python", - // + //` // Very optional: type checking. Remove the line if your project doesn't really use or respect // type hints. You should give it a try, though. They're great. // @@ -25,13 +25,18 @@ // "editor.formatOnSave": true, "editor.codeActionsOnSave": { - "source.organizeImports": true + "source.organizeImports": "explicit" }, "black-formatter.importStrategy": "fromEnvironment", "isort.importStrategy": "fromEnvironment", "flake8.importStrategy": "fromEnvironment", - "isort.args": ["--settings-path", "${workspaceFolder}/pyproject.toml"], - "flake8.args": ["--config=${workspaceFolder}/.flake8"], + "isort.args": [ + "--settings-path", + "${workspaceFolder}/pyproject.toml" + ], + "flake8.args": [ + "--config=${workspaceFolder}/.flake8" + ], "editor.rulers": [ 100 // if changing line length, also do it in .flake8 and pyproject.toml's [tool.black] section ], @@ -45,4 +50,4 @@ "jupyter.interactiveWindow.textEditor.executeSelection": true, // TODO: this setting is showing a deprecation warning. Maybe we should drop it? "jupyter.generateSVGPlots": true, -} +} \ No newline at end of file diff --git a/pipeline_lib/__init__.py b/pipeline_lib/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pipeline_lib/core/__init__.py b/pipeline_lib/core/__init__.py new file mode 100644 index 0000000..3e73bca --- /dev/null +++ b/pipeline_lib/core/__init__.py @@ -0,0 +1,3 @@ +from .data_container import DataContainer # noqa: F401 +from .pipeline import Pipeline # noqa: F401 +from .steps import PipelineStep # noqa: F401 diff --git a/pipeline_lib/core/data_container.py b/pipeline_lib/core/data_container.py new file mode 100644 index 0000000..bb6bf33 --- /dev/null +++ b/pipeline_lib/core/data_container.py @@ -0,0 +1,287 @@ +"""DataContainer class for storing data used in pipeline processing.""" + +from __future__ import annotations + +import json +import logging +import pickle +import sys +from typing import Optional, Union + + +class DataContainer: + """ + A container for storing and manipulating data in a pipeline. + + Attributes + ---------- + data : dict + A dictionary to store data items. + """ + + GENERATE_CONFIGS = "generate_configs" + CLEAN_CONFIGS = "clean_configs" + SPLIT_CONFIGS = "split_configs" + TARGET_SCALING_CONFIGS = "target_scaling_configs" + RAW = "raw" + CLEAN = "clean" + TRAIN = "train" + VALIDATION = "validation" + TEST = "test" + MODEL = "model" + MODEL_CONFIGS = "model_configs" + MODEL_INPUT = "model_input" + MODEL_OUTPUT = "model_output" + METRICS = "metrics" + PREDICTIONS = "predictions" + EXPLAINER = "explainer" + TUNING_PARAMS = "tuning_params" + + def __init__(self, initial_data: Optional[dict] = None): + """ + Initialize the DataContainer with an empty dictionary or provided data. + + Parameters + ---------- + initial_data : dict, optional + Initial data to populate the container. + """ + self.data = initial_data if initial_data is not None else {} + self.logger = logging.getLogger(self.__class__.__name__) + self.logger.debug(f"{self.__class__.__name__} initialized") + + def add(self, key: str, value): + """ + Add a new item to the container. + + Parameters + ---------- + key : str + The key under which the value is stored. + value + The data to be stored. + + Returns + ------- + None + """ + self.data[key] = value + self.logger.debug(f"Data added under key: {key}") + + def get(self, key: str, default=None): + """ + Retrieve an item from the container by its key. + + Parameters + ---------- + key : str + The key of the item to retrieve. + default + The default value to return if the key is not found. Defaults to None. + + Returns + ------- + The data stored under the given key or the default value. + """ + return self.data.get(key, default) + + def __getitem__(self, key: str): + """ + Retrieve an item using bracket notation. + + Parameters + ---------- + key : str + The key of the item to retrieve. + + Returns + ------- + The data stored under the given key. + """ + return self.get(key) + + def __setitem__(self, key: str, value): + """ + Add or update an item using bracket notation. + + Parameters + ---------- + key : str + The key under which the value is stored. + value + The data to be stored. + + Returns + ------- + None + """ + self.add(key, value) + + def contains(self, key: str) -> bool: + """ + Check if the container contains an item with the specified key. + + Parameters + ---------- + key : str + The key to check in the container. + + Returns + ------- + bool + True if the key exists, False otherwise. + """ + return key in self.data + + def __contains__(self, key: str) -> bool: + """ + Enable usage of the 'in' keyword. + + Parameters + ---------- + key : str + The key to check in the container. + + Returns + ------- + bool + True if the key exists, False otherwise. + """ + return self.contains(key) + + @property + def keys(self) -> list[str]: + """ + Return the keys of the container. + + Returns + ------- + list[str] + The keys of the container. + """ + return list(self.data.keys()) + + def save(self, file_path: str, keys: Optional[Union[str, list[str]]] = None): + """ + Serialize the container data using pickle and save it to a file. + + Parameters + ---------- + file_path : str + The path of the file where the serialized data should be saved. + keys : Optional[Union[str, List[str]]], optional + The keys of the data to be saved. If None, all data is saved. + + Returns + ------- + None + """ + if isinstance(keys, str): + keys = [keys] + + data_to_save = {k: self.data[k] for k in keys} if keys else self.data + + serialized_data = pickle.dumps(data_to_save) + data_size_bytes = sys.getsizeof(serialized_data) + data_size_mb = data_size_bytes / 1048576 # Convert bytes to megabytes + + with open(file_path, "wb") as file: + file.write(serialized_data) + self.logger.info( + f"{self.__class__.__name__} serialized and saved to {file_path}. Size:" + f" {data_size_mb:.2f} MB" + ) + + @classmethod + def load(cls, file_path: str, keys: Optional[Union[str, list[str]]] = None) -> DataContainer: + """ + Load data from a file and return a new instance of DataContainer. + + Parameters + ---------- + file_path : str + The path of the file from which the serialized data should be read. + keys : Optional[Union[str, List[str]]], optional + The keys of the data to be loaded. If None, all data is loaded. + + Returns + ------- + DataContainer + A new instance of DataContainer populated with the deserialized data. + """ + with open(file_path, "rb") as file: + data = pickle.loads(file.read()) + + if isinstance(keys, str): + keys = [keys] + + if keys: + data = {k: v for k, v in data.items() if k in keys} + + new_container = cls(initial_data=data) + + if keys: + loaded_keys = set(new_container.keys) + not_loaded_keys = set(keys) - loaded_keys if keys else set() + if not_loaded_keys: + new_container.logger.warning(f"Keys without values: {not_loaded_keys}") + + new_container.logger.info(f"{cls.__name__} loaded from {file_path}") + return new_container + + def __eq__(self, other) -> bool: + """ + Compare this DataContainer with another for equality. + + Parameters + ---------- + other : DataContainer + Another DataContainer instance to compare with. + + Returns + ------- + bool + True if containers are equal, False otherwise. + """ + if isinstance(other, DataContainer): + return self.data == other.data + return False + + def __ne__(self, other) -> bool: + """ + Compare this DataContainer with another for inequality. + + Parameters + ---------- + other : DataContainer + Another DataContainer instance to compare with. + + Returns + ------- + bool + True if containers are not equal, False otherwise. + """ + return not self.__eq__(other) + + def __str__(self): + """ + Generate a user-friendly JSON string representation of the DataContainer. + + Returns + ------- + str + A JSON string describing the keys and types of contents of the DataContainer. + """ + data_summary = {key: type(value).__name__ for key, value in self.data.items()} + return json.dumps(data_summary, indent=4) + + def __repr__(self): + """ + Generate an official string representation of the DataContainer. + + Returns + ------- + str + A formal string representation of the DataContainer's state. + """ + return f"" diff --git a/pipeline_lib/core/pipeline.py b/pipeline_lib/core/pipeline.py new file mode 100644 index 0000000..3e6358c --- /dev/null +++ b/pipeline_lib/core/pipeline.py @@ -0,0 +1,51 @@ +from __future__ import annotations + +import logging +from abc import ABC, abstractmethod +from typing import Optional + +from pipeline_lib.core.data_container import DataContainer +from pipeline_lib.core.steps import PipelineStep + + +class Pipeline(ABC): + """Base class for pipelines.""" + + def __init__(self, initial_data: Optional[DataContainer] = None): + self.steps = self.define_steps() + if not all(isinstance(step, PipelineStep) for step in self.steps): + raise TypeError("All steps must be instances of PipelineStep") + self.initial_data = initial_data + + def run(self, data: Optional[DataContainer] = None) -> DataContainer: + """Run the pipeline on the given data.""" + if data is None: + if self.initial_data is None: + raise ValueError("No data given and no initial data set") + self.logger.debug("No data given, using initial data") + data = self.initial_data + + for i, step in enumerate(self.steps): + self.logger.info(f"Running {step.__class__.__name__} - {i + 1} / {len(self.steps)}") + data = step.execute(data) + return data + + @abstractmethod + def define_steps(self) -> list[PipelineStep]: + """ + Subclasses should implement this method to define their specific steps. + """ + + def init_logger(self) -> None: + """Initialize the logger.""" + self.logger = logging.getLogger(self.__class__.__name__) + self.logger.debug(f"{self.__class__.__name__} initialized") + + def __str__(self) -> str: + step_names = [f"{i + 1}. {step.__class__.__name__}" for i, step in enumerate(self.steps)] + return f"{self.__class__.__name__} with steps:\n" + "\n".join(step_names) + + def __repr__(self) -> str: + """Return an unambiguous string representation of the pipeline.""" + step_names = [f"{step.__class__.__name__}()" for step in self.steps] + return f"{self.__class__.__name__}({', '.join(step_names)})" diff --git a/pipeline_lib/core/steps/__init__.py b/pipeline_lib/core/steps/__init__.py new file mode 100644 index 0000000..9c8593c --- /dev/null +++ b/pipeline_lib/core/steps/__init__.py @@ -0,0 +1,13 @@ +from .augment import AugmentStep # noqa: F401 +from .base import PipelineStep # noqa: F401 +from .calculate_features import CalculateFeaturesStep # noqa: F401 +from .calculate_metrics import CalculateMetricsStep # noqa: F401 +from .calculate_reports import CalculateReportsStep # 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 .input_scaling import InputScalingStep # noqa: F401 +from .predict import PredictStep # noqa: F401 +from .tabular_split import TabularSplitStep # noqa: F401 +from .target_scaling import TargetScalingStep # noqa: F401 diff --git a/pipeline_lib/core/steps/augment.py b/pipeline_lib/core/steps/augment.py new file mode 100644 index 0000000..e92e029 --- /dev/null +++ b/pipeline_lib/core/steps/augment.py @@ -0,0 +1,16 @@ +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class AugmentStep(PipelineStep): + """Augment the data.""" + + def __init__(self) -> None: + """Initialize AugmentStep.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + """Execute the step.""" + self.logger.info("Augmenting data") + return data diff --git a/pipeline_lib/core/steps/base.py b/pipeline_lib/core/steps/base.py new file mode 100644 index 0000000..cfa57ce --- /dev/null +++ b/pipeline_lib/core/steps/base.py @@ -0,0 +1,17 @@ +import logging +from abc import ABC, abstractmethod + +from pipeline_lib.core.data_container import DataContainer + + +class PipelineStep(ABC): + """Base class for pipeline steps.""" + + @abstractmethod + def execute(self, data: DataContainer) -> DataContainer: + """Abstract method for executing the step.""" + + def init_logger(self) -> None: + """Initialize the logger.""" + self.logger = logging.getLogger(self.__class__.__name__) + self.logger.debug(f"{self.__class__.__name__} initialized") diff --git a/pipeline_lib/core/steps/calculate_features.py b/pipeline_lib/core/steps/calculate_features.py new file mode 100644 index 0000000..588ccc9 --- /dev/null +++ b/pipeline_lib/core/steps/calculate_features.py @@ -0,0 +1,16 @@ +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class CalculateFeaturesStep(PipelineStep): + """Calculate features.""" + + def __init__(self) -> None: + """Initialize CalculateFeaturesStep.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + """Execute the step.""" + self.logger.info("Calculating features") + return data diff --git a/pipeline_lib/core/steps/calculate_metrics.py b/pipeline_lib/core/steps/calculate_metrics.py new file mode 100644 index 0000000..29fe5f5 --- /dev/null +++ b/pipeline_lib/core/steps/calculate_metrics.py @@ -0,0 +1,33 @@ +import numpy as np +from sklearn.metrics import mean_absolute_error, mean_squared_error + +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class CalculateMetricsStep(PipelineStep): + """Calculate metrics.""" + + def __init__(self) -> None: + """Initialize CalculateMetricsStep.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + self.logger.debug("Starting metric calculation") + model_output = data[DataContainer.MODEL_OUTPUT] + model_configs = data[DataContainer.MODEL_CONFIGS] + target_column_name = model_configs.get("target") + + if target_column_name is None: + raise ValueError("Target column not found in model_configs.") + + true_values = model_output[target_column_name] + predictions = model_output[DataContainer.PREDICTIONS] + + mae = mean_absolute_error(true_values, predictions) + rmse = np.sqrt(mean_squared_error(true_values, predictions)) + + results = {"MAE": str(mae), "RMSE": str(rmse)} + data[DataContainer.METRICS] = results + return data diff --git a/pipeline_lib/core/steps/calculate_reports.py b/pipeline_lib/core/steps/calculate_reports.py new file mode 100644 index 0000000..0eee829 --- /dev/null +++ b/pipeline_lib/core/steps/calculate_reports.py @@ -0,0 +1,16 @@ +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class CalculateReportsStep(PipelineStep): + """Calculate reports.""" + + def __init__(self) -> None: + """Initialize CalculateReportsStep.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + """Execute the step.""" + self.logger.info("Calculating reports") + return data diff --git a/pipeline_lib/core/steps/encode.py b/pipeline_lib/core/steps/encode.py new file mode 100644 index 0000000..f770131 --- /dev/null +++ b/pipeline_lib/core/steps/encode.py @@ -0,0 +1,16 @@ +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class EncodeStep(PipelineStep): + """Encode the data.""" + + def __init__(self) -> None: + """Initialize EncodeStep.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + """Execute the step.""" + self.logger.info("Encoding data") + return data diff --git a/pipeline_lib/core/steps/explainer_dashboard.py b/pipeline_lib/core/steps/explainer_dashboard.py new file mode 100644 index 0000000..bd48da6 --- /dev/null +++ b/pipeline_lib/core/steps/explainer_dashboard.py @@ -0,0 +1,16 @@ +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class ExplainerDashboardStep(PipelineStep): + """Explainer Dashboard.""" + + def __init__(self) -> None: + """Initialize ExplainerDashboardStep.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + """Execute the step.""" + self.logger.info("Creating explainer dashboard.") + return data diff --git a/pipeline_lib/core/steps/fit_encoders.py b/pipeline_lib/core/steps/fit_encoders.py new file mode 100644 index 0000000..192b2cf --- /dev/null +++ b/pipeline_lib/core/steps/fit_encoders.py @@ -0,0 +1,16 @@ +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class FitEncodersStep(PipelineStep): + """Fit encoders.""" + + def __init__(self) -> None: + """Initialize FitEncodersStep.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + """Execute the step.""" + self.logger.info("Fitting encoders") + return data diff --git a/pipeline_lib/core/steps/fit_model.py b/pipeline_lib/core/steps/fit_model.py new file mode 100644 index 0000000..248c128 --- /dev/null +++ b/pipeline_lib/core/steps/fit_model.py @@ -0,0 +1,16 @@ +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class FitModelStep(PipelineStep): + """Fit the model.""" + + def __init__(self) -> None: + """Initialize FitModelStep.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + """Execute the step.""" + self.logger.info("Fitting the model") + return data diff --git a/pipeline_lib/core/steps/input_scaling.py b/pipeline_lib/core/steps/input_scaling.py new file mode 100644 index 0000000..5f04f7f --- /dev/null +++ b/pipeline_lib/core/steps/input_scaling.py @@ -0,0 +1,16 @@ +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class InputScalingStep(PipelineStep): + """Scale the input.""" + + def __init__(self) -> None: + """Initialize InputScalingStep.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + """Execute the step.""" + self.logger.info("Scaling input") + return data diff --git a/pipeline_lib/core/steps/predict.py b/pipeline_lib/core/steps/predict.py new file mode 100644 index 0000000..82bee7d --- /dev/null +++ b/pipeline_lib/core/steps/predict.py @@ -0,0 +1,16 @@ +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class PredictStep(PipelineStep): + """Obtain the predictions.""" + + def __init__(self) -> None: + """Initialize Predict Step.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + """Execute the step.""" + self.logger.info("Obtaining predictions") + return data diff --git a/pipeline_lib/core/steps/tabular_split.py b/pipeline_lib/core/steps/tabular_split.py new file mode 100644 index 0000000..72568ca --- /dev/null +++ b/pipeline_lib/core/steps/tabular_split.py @@ -0,0 +1,143 @@ +from typing import Tuple + +import pandas as pd +from sklearn.model_selection import train_test_split + +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class TabularSplitStep(PipelineStep): + """Split the data.""" + + def __init__(self) -> None: + """Initialize SplitStep.""" + self.init_logger() + + 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() + + def execute(self, data: DataContainer) -> DataContainer: + """Execute the split based on IDs.""" + self.logger.info("Splitting tabular data...") + + split_configs = data[DataContainer.SPLIT_CONFIGS] + + if split_configs is None: + self.logger.info("No split_configs found. No splitting will be performed.") + return data + + df = data[DataContainer.CLEAN] + id_column_name = split_configs.get("id_column") + if not id_column_name: + raise ValueError("ID column name must be specified in split_configs.") + + # check if both train_percentage and train_ids are provided + if "train_percentage" in split_configs and "train_ids" in split_configs: + raise ValueError( + "Both train_percentage and train_ids cannot be provided in split_configs." + ) + + # check if either train_percentage or train_ids are provided + if "train_percentage" not in split_configs and "train_ids" not in split_configs: + raise ValueError( + "Either train_percentage or train_ids must be provided in split_configs." + ) + + if "train_percentage" in split_configs: + train_percentage = split_configs.get("train_percentage") + if train_percentage is None or train_percentage <= 0 or train_percentage >= 1: + raise ValueError("train_percentage must be between 0 and 1.") + train_ids, validation_ids = self._percentage_based_id_split( + df, train_percentage, id_column_name + ) + else: + train_ids = split_configs.get("train_ids") + validation_ids = split_configs.get("validation_ids") + if not train_ids or not validation_ids: + raise ValueError( + "Both train_ids and validation_ids must be provided in split_configs unless" + " train_percentage is specified." + ) + + 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, id_column_name + ) + + 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%}" + ) + self.logger.info( + f"Number of rows in validation set: {len(validation_df)} |" + f" {validation_rows/total_rows:.2%}" + ) + + left_ids = df[~df[id_column_name].isin(train_ids + validation_ids)][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 + + return data diff --git a/pipeline_lib/core/steps/target_scaling.py b/pipeline_lib/core/steps/target_scaling.py new file mode 100644 index 0000000..f77ce13 --- /dev/null +++ b/pipeline_lib/core/steps/target_scaling.py @@ -0,0 +1,21 @@ +from pipeline_lib.core import DataContainer + +from .base import PipelineStep + + +class TargetScalingStep(PipelineStep): + """Scale the target.""" + + def __init__(self) -> None: + """Initialize TargetScalingStep.""" + self.init_logger() + + def execute(self, data: DataContainer) -> DataContainer: + target_scaling_configs = data.get(DataContainer.TARGET_SCALING_CONFIGS) + + if target_scaling_configs is None: + self.logger.info("No target scaling configs found. Skipping target scaling.") + return data + + self.logger.info("Scaling target data.") + return data diff --git a/pipeline_lib/implementation/__init__.py b/pipeline_lib/implementation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pipeline_lib/implementation/tabular/__init__.py b/pipeline_lib/implementation/tabular/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pipeline_lib/implementation/tabular/xgboost/__init__.py b/pipeline_lib/implementation/tabular/xgboost/__init__.py new file mode 100644 index 0000000..b299d0c --- /dev/null +++ b/pipeline_lib/implementation/tabular/xgboost/__init__.py @@ -0,0 +1,3 @@ +from .explainer_dashboard import XGBoostExplainerDashboardStep # noqa: F401 +from .fit_model import XGBoostFitModelStep # noqa: F401 +from .predict import XGBoostPredictStep # noqa: F401 diff --git a/pipeline_lib/implementation/tabular/xgboost/explainer_dashboard.py b/pipeline_lib/implementation/tabular/xgboost/explainer_dashboard.py new file mode 100644 index 0000000..f1b319d --- /dev/null +++ b/pipeline_lib/implementation/tabular/xgboost/explainer_dashboard.py @@ -0,0 +1,40 @@ +from explainerdashboard import RegressionExplainer + +from pipeline_lib.core import DataContainer +from pipeline_lib.core.steps import ExplainerDashboardStep + + +class XGBoostExplainerDashboardStep(ExplainerDashboardStep): + """Scale the target using Quantile Transformer.""" + + def execute(self, data: DataContainer) -> DataContainer: + self.logger.debug("Starting explainer dashboard") + + model = data.get(DataContainer.MODEL) + if model is None: + raise ValueError("Model not found in data container.") + + val_df = data.get(DataContainer.VALIDATION) + if val_df is None: + raise ValueError("Validation data not found in data container.") + + model_configs = data[DataContainer.MODEL_CONFIGS] + if model_configs is None: + raise ValueError("Model configs not found in data container.") + + target = model_configs.get("target") + if target is None: + raise ValueError("Target column not found in model_configs.") + + drop_columns = model_configs.get("drop_columns") + if drop_columns: + val_df = val_df.drop(columns=drop_columns) + + X_test = val_df.drop(columns=[target]) + y_test = val_df[target] + + explainer = RegressionExplainer(model, X_test, y_test) + + data[DataContainer.EXPLAINER] = explainer + + return data diff --git a/pipeline_lib/implementation/tabular/xgboost/fit_model.py b/pipeline_lib/implementation/tabular/xgboost/fit_model.py new file mode 100644 index 0000000..f0ad06c --- /dev/null +++ b/pipeline_lib/implementation/tabular/xgboost/fit_model.py @@ -0,0 +1,145 @@ +import time + +import optuna +import xgboost as xgb +from optuna.pruners import MedianPruner +from sklearn.metrics import mean_absolute_error + +from pipeline_lib.core import DataContainer +from pipeline_lib.core.steps import FitModelStep + + +class XGBoostFitModelStep(FitModelStep): + """Fit the model with XGBoost.""" + + def execute(self, data: DataContainer) -> DataContainer: + self.logger.debug("Starting model fitting with XGBoost") + + start_time = time.time() + + model_configs = data[DataContainer.MODEL_CONFIGS] + + target = model_configs.get("target") + + if target is None: + raise ValueError("Target column not found in model_configs.") + + drop_columns: list[str] = model_configs.get("drop_columns") + + df_train = data[DataContainer.TRAIN] + df_valid = data[DataContainer.VALIDATION] + + if drop_columns: + df_train = df_train.drop(columns=drop_columns) + df_valid = df_valid.drop(columns=drop_columns) + + # Prepare the data + X_train = df_train.drop(columns=[target]) + y_train = df_train[target] + + X_valid = df_valid.drop(columns=[target]) + y_valid = df_valid[target] + + optuna_params = model_configs.get("optuna_params") + xgb_params = model_configs.get("xgb_params") + + if optuna_params and xgb_params: + raise ValueError("Both optuna_params and xgb_params are defined. Please choose one.") + + if not optuna_params and not xgb_params: + raise ValueError( + "No parameters defined. Please define either optuna_params or xgb_params." + ) + + params = xgb_params + + if optuna_params: + params = self.optimize_with_optuna(X_train, y_train, X_valid, y_valid, optuna_params) + data[DataContainer.TUNING_PARAMS] = params + + model = xgb.XGBRegressor(**params) + + model.fit( + X_train, + y_train, + eval_set=[(X_valid, y_valid)], + early_stopping_rounds=model_configs.get("early_stopping_rounds", 100), + verbose=True, + ) + + # Save the model to the data container + data[DataContainer.MODEL] = model + + end_time = time.time() + elapsed_time = end_time - start_time + minutes = int(elapsed_time // 60) + seconds = int(elapsed_time % 60) + self.logger.info(f"XGBoost model fitting took {minutes} minutes and {seconds} seconds.") + return data + + def optimize_with_optuna(self, X_train, y_train, X_valid, y_valid, optuna_params): + def objective(trial): + # Define the search space + max_depth = optuna_params.get("max_depth", [3, 12]) + eta = optuna_params.get("eta", [1e-8, 1.0]) + subsample = optuna_params.get("subsample", [0.2, 1.0]) + colsample_bytree = optuna_params.get("colsample_bytree", [0.2, 1.0]) + min_child_weight = optuna_params.get("min_child_weight", [1, 10]) + n_estimators = optuna_params.get("n_estimators", [100, 1000]) + + param = { + "verbosity": 0, + "objective": "reg:squarederror", + "eval_metric": "mae", + "n_jobs": -1, + "max_depth": trial.suggest_int("max_depth", max_depth[0], max_depth[1]), + "eta": trial.suggest_float("eta", eta[0], eta[1], log=True), + "subsample": trial.suggest_float("subsample", subsample[0], subsample[1]), + "colsample_bytree": trial.suggest_float( + "colsample_bytree", colsample_bytree[0], colsample_bytree[1] + ), + "min_child_weight": trial.suggest_int( + "min_child_weight", min_child_weight[0], min_child_weight[1] + ), + "n_estimators": trial.suggest_int("n_estimators", n_estimators[0], n_estimators[1]), + } + + model = xgb.XGBRegressor(**param) + model.fit( + X_train, + y_train, + eval_set=[(X_valid, y_valid)], + early_stopping_rounds=optuna_params.get("early_stopping_rounds", 50), + verbose=True, + ) + preds = model.predict(X_valid) + mae = mean_absolute_error(y_valid, preds) + return mae + + def optuna_logging_callback(study, trial): + if trial.state == optuna.trial.TrialState.COMPLETE: + self.logger.info( + f"Trial {trial.number} finished with value: {trial.value} and parameters:" + f" {trial.params}. Best is trial {study.best_trial.number} with value:" + f" {study.best_value}." + ) + + optuna_trials = optuna_params.get("trials", 20) + + self.logger.info(f"Optimizing XGBoost hyperparameters with {optuna_trials} trials.") + + study_name = optuna_params.get("study_name", "xgboost_optimization") + storage = optuna_params.get("storage", "I") + + study = optuna.create_study( + direction="minimize", + study_name=study_name, + storage=storage, + pruner=MedianPruner(), + ) + + study.optimize(objective, n_trials=optuna_trials, callbacks=[optuna_logging_callback]) + + best_params = study.best_params + self.logger.info(f"Best parameters found by Optuna: {best_params}") + return best_params diff --git a/pipeline_lib/implementation/tabular/xgboost/predict.py b/pipeline_lib/implementation/tabular/xgboost/predict.py new file mode 100644 index 0000000..c8104d5 --- /dev/null +++ b/pipeline_lib/implementation/tabular/xgboost/predict.py @@ -0,0 +1,40 @@ +import pandas as pd + +from pipeline_lib.core import DataContainer +from pipeline_lib.core.steps import PredictStep + + +class XGBoostPredictStep(PredictStep): + """Obtain the predictions for XGBoost model.""" + + def execute(self, data: DataContainer) -> DataContainer: + self.logger.debug("Obtaining predictions for XGBoost model.") + + model = data[DataContainer.MODEL] + if model is None: + raise Exception("Model not trained yet.") + + model_input = data[DataContainer.CLEAN] + + if not isinstance(model_input, pd.DataFrame): + raise ValueError("model_input must be a pandas DataFrame.") + + model_configs = data.get(DataContainer.MODEL_CONFIGS) + if model_configs: + drop_columns: list[str] = model_configs.get("drop_columns") + if drop_columns: + model_input = model_input.drop(columns=drop_columns) + target = model_configs.get("target") + if target is None: + raise ValueError("Target column not found in model_configs.") + + predictions = model.predict(model_input.drop(columns=[target])) + else: + predictions = model.predict(model_input) + + predictions_df = pd.DataFrame(predictions, columns=["prediction"]) + + model_input[DataContainer.PREDICTIONS] = predictions_df + + data[DataContainer.MODEL_OUTPUT] = model_input + return data diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..5a4ca4b --- /dev/null +++ b/poetry.lock @@ -0,0 +1,2951 @@ +[[package]] +name = "alembic" +version = "1.13.1" +description = "A database migration tool for SQLAlchemy." +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +Mako = "*" +SQLAlchemy = ">=1.3.0" +typing-extensions = ">=4" + +[package.extras] +tz = ["backports.zoneinfo"] + +[[package]] +name = "ansi2html" +version = "1.9.1" +description = "Convert text with ANSI color codes to HTML or to LaTeX" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +docs = ["mkdocs", "mkdocs-material", "mkdocs-material-extensions", "mkdocstrings", "mkdocstrings-python", "pymdown-extensions"] +test = ["pytest", "pytest-cov"] + +[[package]] +name = "appnope" +version = "0.1.4" +description = "Disable App Nap on macOS >= 10.9" +category = "main" +optional = false +python-versions = ">=3.6" + +[[package]] +name = "asttokens" +version = "2.4.1" +description = "Annotate AST trees with source code positions" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +six = ">=1.12.0" + +[package.extras] +astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"] +test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"] + +[[package]] +name = "black" +version = "23.12.1" +description = "The uncompromising code formatter." +category = "dev" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +click = ">=8.0.0" +mypy-extensions = ">=0.4.3" +packaging = ">=22.0" +pathspec = ">=0.9.0" +platformdirs = ">=2" +tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""} +typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""} + +[package.extras] +colorama = ["colorama (>=0.4.3)"] +d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"] +jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"] +uvloop = ["uvloop (>=0.15.2)"] + +[[package]] +name = "blinker" +version = "1.7.0" +description = "Fast, simple object-to-object and broadcast signaling" +category = "main" +optional = false +python-versions = ">=3.8" + +[[package]] +name = "certifi" +version = "2024.2.2" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = ">=3.6" + +[[package]] +name = "cffi" +version = "1.16.0" +description = "Foreign Function Interface for Python calling C code." +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +pycparser = "*" + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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