├── data └── raw │ └── .gitkeep ├── models └── .gitkeep ├── .python-version ├── tests ├── test_process.py ├── test_train_model.py └── __init__.py ├── src ├── __init__.py ├── helper.py ├── process.py └── train_model.py ├── conf ├── process │ ├── process1.yaml │ └── process2.yaml ├── main.yaml └── train │ ├── train1.yaml │ └── train2.yaml ├── .pre-commit-config.yaml ├── pyproject.toml ├── README.md ├── .gitignore └── uv.lock /data/raw/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /models/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.python-version: -------------------------------------------------------------------------------- 1 | 3.11 2 | -------------------------------------------------------------------------------- /tests/test_process.py: -------------------------------------------------------------------------------- 1 | from src.process import * -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- 1 | """Source code of your project""" 2 | -------------------------------------------------------------------------------- /tests/test_train_model.py: -------------------------------------------------------------------------------- 1 | from src.train_model import * -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- 1 | # Avoid ModuleNotFoundError 2 | 3 | import sys 4 | sys.path.append('./src') -------------------------------------------------------------------------------- /conf/process/process1.yaml: -------------------------------------------------------------------------------- 1 | cols_to_drop: 2 | - free sulfur dioxide 3 | feature: quality 4 | test_size: 0.2 5 | -------------------------------------------------------------------------------- /conf/process/process2.yaml: -------------------------------------------------------------------------------- 1 | cols_to_drop: 2 | - total sulfur dioxide 3 | feature: quality 4 | test_size: 0.3 5 | -------------------------------------------------------------------------------- /conf/main.yaml: -------------------------------------------------------------------------------- 1 | defaults: 2 | - process: process1 3 | - train: train1 4 | - _self_ 5 | 6 | data: 7 | raw: data/raw/winequality-red.csv 8 | intermediate: data/intermediate 9 | 10 | model: models 11 | -------------------------------------------------------------------------------- /conf/train/train1.yaml: -------------------------------------------------------------------------------- 1 | hyperparameters: 2 | svm__kernel: 3 | - rbf 4 | svm__C: 5 | - 0.1 6 | - 1 7 | - 10 8 | svm__gamma: 9 | - 0.1 10 | - 1 11 | - 10 12 | grid_search: 13 | cv: 2 14 | scoring: accuracy 15 | verbose: 0 16 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | repos: 2 | - repo: https://github.com/charliermarsh/ruff-pre-commit 3 | rev: v0.11.6 4 | hooks: 5 | - id: ruff 6 | args: [--fix] 7 | - repo: https://github.com/pre-commit/mirrors-mypy 8 | rev: v1.15.0 9 | hooks: 10 | - id: mypy -------------------------------------------------------------------------------- /conf/train/train2.yaml: -------------------------------------------------------------------------------- 1 | hyperparameters: 2 | svm__kernel: 3 | - linear 4 | svm__C: 5 | - 0.1 6 | - 1 7 | - 10 8 | svm__gamma: 9 | - 0.1 10 | - 1 11 | - 10 12 | grid_search: 13 | cv: 2 14 | scoring: accuracy 15 | verbose: 0 16 | -------------------------------------------------------------------------------- /src/helper.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | 3 | import pandas as pd 4 | 5 | 6 | def load_data(path: str): 7 | """Load data from path""" 8 | file_path = Path(path) 9 | if file_path.suffix == ".csv": 10 | df = pd.read_csv(file_path) 11 | elif file_path.suffix == ".pkl": 12 | df = pd.read_pickle(file_path) 13 | else: 14 | raise ValueError("File format not supported. Please use a CSV or PKL file.") 15 | 16 | return df 17 | 18 | 19 | def save_data(df: pd.DataFrame, path: str, name: str): 20 | """Save data to path""" 21 | path = f"{path}/{name}.pkl" 22 | Path(path).parent.mkdir(exist_ok=True) 23 | df.to_pickle(path) 24 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "hydra-demo" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.11" 7 | dependencies = [ 8 | "hydra-core>=1.3.2", 9 | "pandas>=2.2.3", 10 | "scikit-learn>=1.6.1", 11 | ] 12 | 13 | [dependency-groups] 14 | dev = [ 15 | "pre-commit>=4.2.0", 16 | "pytest>=8.3.5", 17 | ] 18 | 19 | [tool.ruff] 20 | # Exclude a variety of commonly ignored directories. 21 | exclude = [ 22 | ".bzr", 23 | ".direnv", 24 | ".eggs", 25 | ".git", 26 | ".git-rewrite", 27 | ".hg", 28 | ".mypy_cache", 29 | ".nox", 30 | ".pants.d", 31 | ".pytype", 32 | ".ruff_cache", 33 | ".svn", 34 | ".tox", 35 | ".venv", 36 | "__pypackages__", 37 | "_build", 38 | "buck-out", 39 | "build", 40 | "dist", 41 | "node_modules", 42 | "venv", 43 | ] 44 | 45 | # Same as Black. 46 | line-length = 88 47 | 48 | [tool.ruff.lint] 49 | ignore = ["E501"] 50 | select = ["B", "C", "E", "F", "W", "B9", "I", "Q"] 51 | 52 | [tool.ruff.format] 53 | quote-style = "double" 54 | indent-style = "tab" 55 | skip-magic-trailing-comma = false 56 | 57 | [tool.ruff.lint.mccabe] 58 | max-complexity = 10 59 | 60 | [tool.mypy] 61 | ignore_missing_imports = true -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Hydra Demo 2 | 3 | [![View the Article](https://img.shields.io/badge/CodeCut-View%20the%20Article-blue)](https://codecut.ai/stop-hard-coding-in-a-data-science-project-use-configuration-files-instead/) 4 | 5 | ## Set up the environment 6 | 7 | 1. Install [uv](https://github.com/astral-sh/uv) 8 | 1. Set up the environment: 9 | 10 | ```bash 11 | uv sync 12 | ``` 13 | 14 | ## Download the data 15 | 16 | 1. Download the dataset from [Kaggle](https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009?resource=download) 17 | 18 | 2. Move the downloaded file to `data/raw/` 19 | 20 | ## Run the scripts 21 | 22 | Run the data processing script: 23 | 24 | ```bash 25 | uv run src/process.py 26 | ``` 27 | 28 | Run the model training script: 29 | 30 | ```bash 31 | uv run src/train_model.py 32 | ``` 33 | 34 | Both scripts use Hydra for configuration management. The default configurations are in the `conf/main.yaml` file. You can override any configuration parameter using the command line. For example: 35 | 36 | ```bash 37 | # Override test size in process.py 38 | uv run src/process.py process.test_size=0.3 39 | 40 | # Override hyperparameters in train_model.py 41 | uv run src/train_model.py train.hyperparameters.svm__C=10 42 | ``` 43 | 44 | To see all available configuration options, you can use the `--help` flag: 45 | 46 | ```bash 47 | # View configuration options for process.py 48 | uv run src/process.py --help 49 | 50 | # View configuration options for train_model.py 51 | uv run src/train_model.py --help 52 | ``` -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | .venv 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg' 26 | venv 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | 55 | # Sphinx documentation 56 | docs/_build/ 57 | 58 | # PyBuilder 59 | target/ 60 | 61 | # DotEnv configuration 62 | .env 63 | 64 | # Database 65 | *.db 66 | *.rdb 67 | 68 | # Pycharm 69 | .idea 70 | 71 | # VS Code 72 | .vscode/ 73 | 74 | # Spyder 75 | .spyproject/ 76 | 77 | # Jupyter NB Checkpoints 78 | .ipynb_checkpoints/ 79 | 80 | # Mac OS-specific storage files 81 | .DS_Store 82 | 83 | # vim 84 | *.swp 85 | *.swo 86 | 87 | # Mypy cache 88 | .mypy_cache/ 89 | 90 | # pytest cache 91 | .pytest_cache/ 92 | 93 | # Hydra logs 94 | outputs 95 | .hydra 96 | multirun 97 | 98 | # Models 99 | models 100 | 101 | # Data 102 | data -------------------------------------------------------------------------------- /src/process.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | from typing import Tuple 3 | 4 | import hydra 5 | import pandas as pd 6 | from omegaconf import DictConfig 7 | from sklearn.model_selection import train_test_split 8 | 9 | from helper import load_data, save_data 10 | 11 | # Ignore all future warnings 12 | warnings.simplefilter(action="ignore", category=FutureWarning) 13 | 14 | 15 | def drop_irrelevant_columns(data: pd.DataFrame, columns: list) -> pd.DataFrame: 16 | print(f"\nDropping columns: {columns}") 17 | return data.drop(columns=columns) 18 | 19 | 20 | def get_X_y(data: pd.DataFrame, feature: str) -> Tuple[pd.DataFrame, pd.Series]: 21 | """Split data into X and y""" 22 | print("\nSplitting data into X and y") 23 | print(f"Feature to predict: {feature}") 24 | X = data.drop(columns=feature) 25 | y = data[feature] 26 | return X, y 27 | 28 | 29 | def split_train_test(X: pd.DataFrame, y: pd.Series, test_size: float) -> dict: 30 | """Split data into train and test sets""" 31 | print("\nSplitting data into train and test sets") 32 | print(f"Test size: {test_size}") 33 | X_train, X_test, y_train, y_test = train_test_split( 34 | X, y, test_size=test_size, random_state=42 35 | ) 36 | return { 37 | "X_train": X_train, 38 | "X_test": X_test, 39 | "y_train": y_train, 40 | "y_test": y_test, 41 | } 42 | 43 | 44 | @hydra.main(config_path="../conf", config_name="main", version_base=None) 45 | def process_data(config: DictConfig): 46 | df = load_data(config.data.raw) 47 | processed_df = drop_irrelevant_columns(df, config.process.cols_to_drop) 48 | X, y = get_X_y(processed_df, config.process.feature) 49 | splitted_datasets = split_train_test(X, y, config.process.test_size) 50 | for name, data in splitted_datasets.items(): 51 | save_data(data, config.data.intermediate, name) 52 | 53 | 54 | if __name__ == "__main__": 55 | process_data() 56 | -------------------------------------------------------------------------------- /src/train_model.py: -------------------------------------------------------------------------------- 1 | import hydra 2 | import joblib 3 | import pandas as pd 4 | from omegaconf import DictConfig 5 | from sklearn.model_selection import GridSearchCV 6 | from sklearn.pipeline import Pipeline 7 | from sklearn.preprocessing import StandardScaler 8 | from sklearn.svm import SVC 9 | 10 | from helper import load_data 11 | 12 | 13 | def create_pipeline() -> Pipeline: 14 | print("\nCreating pipeline with:") 15 | print("- StandardScaler for feature scaling") 16 | print("- SVC (Support Vector Classifier) as the model") 17 | return Pipeline([("scaler", StandardScaler()), ("svm", SVC())]) 18 | 19 | 20 | def train_model( 21 | X_train: pd.DataFrame, 22 | y_train: pd.Series, 23 | pipeline: Pipeline, 24 | hyperparameters: dict, 25 | grid_params: dict, 26 | ) -> GridSearchCV: 27 | """Train model using GridSearchCV""" 28 | print("\nTraining model with parameters:") 29 | print(f"Hyperparameters to search: {hyperparameters}") 30 | print(f"Grid search parameters: {grid_params}") 31 | grid_search = GridSearchCV(pipeline, dict(hyperparameters), **grid_params) 32 | grid_search.fit(X_train, y_train) 33 | print(f"\nBest parameters found: {grid_search.best_params_}") 34 | print(f"Best cross-validation score: {grid_search.best_score_:.3f}") 35 | return grid_search 36 | 37 | 38 | def save_model(model, path: str): 39 | """Save model to path""" 40 | print(f"\nSaving model to: {path}/svm.pkl") 41 | joblib.dump(model, f"{path}/svm.pkl") 42 | 43 | 44 | @hydra.main(config_path="../conf", config_name="main", version_base=None) 45 | def 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