From 97c555a092b7dd188bf52893f0189085b47c5d97 Mon Sep 17 00:00:00 2001 From: Alexan Date: Wed, 5 Mar 2025 12:30:27 +0400 Subject: [PATCH 1/6] refactoring Signed-off-by: Alexan --- .gitignore | 83 +- CONTRIBUTING.md | 156 + README.md | 124 +- bio_ml_handler/__init__.py | 1 - .../__pycache__/__init__.cpython-310.pyc | Bin 210 -> 0 bytes .../__pycache__/data_handler.cpython-310.pyc | Bin 7800 -> 0 bytes bio_ml_handler/data_handler.py | 257 - docs/architecture.md | 233 + docs/cli.md | 292 ++ docs/dataset.md | 382 ++ docs/experiments.md | 466 ++ docs/pipeline.md | 410 ++ environment.yml | 26 + ezflow/__init__.py | 16 + ezflow/cli.py | 73 + ezflow/core/__init__.py | 0 ezflow/core/dataset/__init__.py | 0 ezflow/core/dataset/base.py | 194 + ezflow/core/dataset/iris.py | 64 + ezflow/core/dataset/molecular.py | 192 + ezflow/core/experiment/__init__.py | 0 ezflow/core/experiment/tracker.py | 179 + ezflow/core/pipeline/__init__.py | 0 ezflow/core/pipeline/base.py | 143 + ezflow/core/pipeline/sklearn.py | 129 + .../competition/example_competition.py | 56 + ezflow/examples/iris_example.py | 134 + ezflow/examples/molecular_example.py | 102 + ezflow/tests/test_iris_dataset.py | 97 + ezflow/tests/test_molecular_dataset.py | 145 + ezflow/utils/__init__.py | 0 ezflow/viz/__init__.py | 0 requirements.txt | 4 - setup.py | 59 +- similarities_data/similarity2test.csv | 4217 ----------------- split_data/train_split.csv | 3373 ------------- split_data/val_split.csv | 845 ---- submission.csv | 2033 -------- train_split.jsonl | 3372 ------------- 39 files changed, 3712 insertions(+), 14145 deletions(-) create mode 100644 CONTRIBUTING.md delete mode 100644 bio_ml_handler/__init__.py delete mode 100644 bio_ml_handler/__pycache__/__init__.cpython-310.pyc delete mode 100644 bio_ml_handler/__pycache__/data_handler.cpython-310.pyc delete mode 100644 bio_ml_handler/data_handler.py create mode 100644 docs/architecture.md create mode 100644 docs/cli.md create mode 100644 docs/dataset.md create mode 100644 docs/experiments.md create mode 100644 docs/pipeline.md create mode 100644 environment.yml create mode 100644 ezflow/__init__.py create mode 100644 ezflow/cli.py create mode 100644 ezflow/core/__init__.py create mode 100644 ezflow/core/dataset/__init__.py create mode 100644 ezflow/core/dataset/base.py create mode 100644 ezflow/core/dataset/iris.py create mode 100644 ezflow/core/dataset/molecular.py create mode 100644 ezflow/core/experiment/__init__.py create mode 100644 ezflow/core/experiment/tracker.py create mode 100644 ezflow/core/pipeline/__init__.py create mode 100644 ezflow/core/pipeline/base.py create mode 100644 ezflow/core/pipeline/sklearn.py create mode 100644 ezflow/datasets/competition/example_competition.py create mode 100644 ezflow/examples/iris_example.py create mode 100644 ezflow/examples/molecular_example.py create mode 100644 ezflow/tests/test_iris_dataset.py create mode 100644 ezflow/tests/test_molecular_dataset.py create mode 100644 ezflow/utils/__init__.py create mode 100644 ezflow/viz/__init__.py delete mode 100644 requirements.txt delete mode 100644 similarities_data/similarity2test.csv delete mode 100644 split_data/train_split.csv delete mode 100644 split_data/val_split.csv delete mode 100644 submission.csv delete mode 100644 train_split.jsonl diff --git a/.gitignore b/.gitignore index 9b5efb9..ba3d429 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,82 @@ -bio_ml_handler.egg-info/ \ No newline at end of file +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Virtual Environment +venv/ +env/ +ENV/ +.env +.venv + +# IDE +.idea/ +.vscode/ +*.swp +*.swo +.project +.pydevproject +.settings/ + +# Jupyter Notebook +.ipynb_checkpoints +*.ipynb + +# ML specific +models/ +experiments/ +mlruns/ +runs/ +logs/ +*.h5 +*.pkl +*.joblib +*.onnx +*.pt +*.pth +wandb/ +tensorboard/ + +# Data +data/ +*.csv +*.json +*.jsonl +*.parquet +*.feather +*.arrow +*.hdf5 +*.h5 +*.npz +*.npy + +# OS specific +.DS_Store +Thumbs.db +*.log +*.tmp +*.temp + +# Project specific +submission*.csv +*_split.jsonl +split_data/ +similarities_data/ \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..764c9d5 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,156 @@ +# Contributing to EZFlow + +Thank you for considering contributing to EZFlow! This document provides guidelines and instructions for contributing to this project. + +## Table of Contents + +- [Code of Conduct](#code-of-conduct) +- [How Can I Contribute?](#how-can-i-contribute) + - [Reporting Bugs](#reporting-bugs) + - [Suggesting Features](#suggesting-features) + - [Pull Requests](#pull-requests) +- [Development Setup](#development-setup) +- [Coding Standards](#coding-standards) +- [Testing](#testing) +- [Documentation](#documentation) +- [Commit Guidelines](#commit-guidelines) + +## Code of Conduct + +This project and everyone participating in it is governed by our [Code of Conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code. + +## How Can I Contribute? + +### Reporting Bugs + +This section guides you through submitting a bug report. Following these guidelines helps maintainers understand your report and reproduce the issue. + +**Before Submitting A Bug Report:** +- Check the documentation for a solution to your issue +- Check if the bug has already been reported in the Issues section +- Verify that the issue can be reproduced reliably + +**How Do I Submit A Good Bug Report?** + +Bugs are tracked as GitHub issues. Create an issue and provide the following information: + +- **Title**: Use a clear and descriptive title +- **Description**: Provide a detailed description of the issue +- **Steps to Reproduce**: List all steps needed to reproduce the behavior +- **Expected Behavior**: Describe what you expected to happen +- **Actual Behavior**: Describe what actually happened +- **Environment**: Include Python version, OS, package versions, etc. +- **Additional Context**: Add any other context about the problem + +### Suggesting Features + +This section guides you through submitting a feature suggestion: + +- **Title**: Use a clear and descriptive title +- **Description**: Provide a step-by-step description of the suggested enhancement +- **Use Case**: Describe the use case that your enhancement would address +- **Alternatives**: Describe alternatives you've considered +- **Additional Context**: Add any other context or examples related to the feature request + +### Pull Requests + +- Fill in the required pull request template +- Follow the coding standards (see below) +- Include appropriate tests (see below) +- Update documentation if necessary +- Pull requests should be made to the `develop` branch, not `main` + +## Development Setup + +1. Fork the repository +2. Clone your fork locally: + ```bash + git clone https://github.com/yourusername/ezflow.git + cd ezflow + ``` +3. Create a conda environment using the provided environment.yml: + ```bash + conda env create -f environment.yml + conda activate ezflow + ``` +4. Install the package in development mode: + ```bash + pip install -e . + ``` +5. Set up pre-commit hooks: + ```bash + pre-commit install + ``` + +## Coding Standards + +We use the following tools to maintain code quality: + +- **Black**: For consistent code formatting +- **Flake8**: For style guide enforcement +- **isort**: For consistent import ordering +- **mypy**: For static type checking + +Our CI pipeline will check these automatically, but you can run them locally: + +```bash +# Format code +black ezflow +isort ezflow + +# Check style +flake8 ezflow + +# Check types +mypy ezflow +``` + +### General Guidelines + +- Follow PEP 8 style guidelines +- Include type hints for function arguments and return values +- Write docstrings in the NumPy/SciPy style +- Keep functions focused on a single responsibility +- Limit line length to 88 characters (Black's default) + +## Testing + +We use pytest for testing. All new features should include tests. + +```bash +# Run all tests +pytest + +# Run tests with coverage +pytest --cov=ezflow + +# Run specific tests +pytest ezflow/tests/test_specific_module.py +``` + +## Documentation + +- Documentation is written in Markdown +- API documentation is generated from docstrings +- Examples should be clear, concise, and runnable +- Update the relevant documentation files when adding features + +## Commit Guidelines + +We follow conventional commits: + +- **feat**: A new feature +- **fix**: A bug fix +- **docs**: Documentation only changes +- **style**: Changes that do not affect the meaning of the code +- **refactor**: A code change that neither fixes a bug nor adds a feature +- **perf**: A code change that improves performance +- **test**: Adding missing tests or correcting existing tests +- **chore**: Changes to the build process or auxiliary tools + +Example: +``` +feat(dataset): add support for image datasets +``` + +Thank you for contributing to EZFlow! \ No newline at end of file diff --git a/README.md b/README.md index 50e1dd5..516c5db 100644 --- a/README.md +++ b/README.md @@ -1,48 +1,110 @@ -# bio_ml_handler +# EZFlow: A Machine Learning Framework -A data handler for bioinformatics machine learning tasks, including data loading, processing, and model handling. +

+ EZFlow Logo +

+ +

+ Tests Status + PyPI version + License + GitHub stars +

+ +EZFlow is a flexible, modular machine learning framework designed to streamline the development and deployment of ML pipelines. It provides a unified API for working with different types of datasets, models, and experiment tracking tools. + +## Features + +- **Simple Dataset API**: Easily load, preprocess, and split various types of data +- **Unified Pipeline Interface**: Work with scikit-learn, PyTorch, and TensorFlow models using the same interface +- **Experiment Tracking**: Track metrics, hyperparameters, and artifacts with MLflow integration +- **Reproducibility**: Ensure experiment reproducibility with configuration management +- **CLI Tools**: Command-line utilities for project creation and management ## Installation -1. Clone the repository: +### Using Conda (Recommended) + +```bash +# Create and activate the conda environment +conda env create -f environment.yml +conda activate ezflow - ```bash - git clone https://github.com/yourusername/bio_ml_handler.git - cd bio_ml_handler - ``` +# Install the package in development mode +pip install -e . +``` + +### Using Pip -2. Install the package with pip: +```bash +# Install from PyPI +pip install ezflow-ml - ```bash - pip install . - ``` +# Or install in development mode from source +git clone https://github.com/yourusername/ezflow.git +cd ezflow +pip install -e . +``` -Or, install directly from GitHub: +## Quick Start - ```bash - pip install git+https://github.com/Alcray/BioML.git - ``` +### Creating a New Project + +```bash +# Create a new EZFlow project +ezflow create my_project +cd my_project +``` -## Usage +### Running the Iris Example ```python -from bio_ml_handler import BioMLDataHandler -# Initialize handler with paths to data folders -handler = BioMLDataHandler(data_path='data', split_data_path='split_data') +from ezflow.core.dataset import IrisDataset +from ezflow.core.pipeline import SklearnPipelineWrapper +from ezflow.core.experiment import ExperimentTracker, ExperimentConfig -# Prepare data in fingerprint format (for model training and evaluation) -handler.prepare_train_data(representation='fingerprint') -handler.prepare_validation_data(representation='fingerprint') -handler.prepare_test_data(representation='fingerprint') +# Create and load dataset +dataset = IrisDataset(data_dir="./data") +dataset.load_data() +dataset.split_data(val_size=0.2) -# Train and evaluate the model -handler.train_model() -print("Model Average Precision Score:", handler.evaluate_model()) +# Create pipeline +pipeline = SklearnPipelineWrapper([ + ("scaler", "sklearn.preprocessing.StandardScaler", {}), + ("classifier", "sklearn.ensemble.RandomForestClassifier", {"n_estimators": 100}) +]) -# Export train_split data to JSONL format with SMILES representation -handler.export_to_jsonl(handler.train_split, 'train_split.jsonl') +# Train and evaluate +X_train = dataset.get_features(dataset.train_data) +y_train = dataset.get_labels(dataset.train_data) +pipeline.fit(X_train, y_train) -# Generate submission file -handler.generate_submission('submission.csv') +# Save model +pipeline.save("./models/iris_model.joblib") ``` ---- + +Or run the included example: + +```bash +python -m ezflow.examples.iris_example +``` + +## Documentation + +For more detailed documentation, please see: + +- [Architecture Overview](docs/architecture.md) +- [Dataset Module](docs/dataset.md) +- [Pipeline Module](docs/pipeline.md) +- [Experiment Tracking](docs/experiments.md) +- [Command-line Interface](docs/cli.md) +- [Examples and Tutorials](docs/examples.md) +- [API Reference](docs/api_reference.md) + +## Contributing + +Contributions are welcome! Please check out our [contribution guidelines](CONTRIBUTING.md). + +## License + +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. diff --git a/bio_ml_handler/__init__.py b/bio_ml_handler/__init__.py deleted file mode 100644 index 7738f1d..0000000 --- a/bio_ml_handler/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .data_handler import BioMLDataHandler diff --git a/bio_ml_handler/__pycache__/__init__.cpython-310.pyc b/bio_ml_handler/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 22560aaee101e603d0b34dafdd509709d8bff678..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 210 zcmd1j<>g`kf;Hb%)3t&0V-N=!FabFZKwK;UBvKes7;_kM8KW2(8B&;n88n$+G6ID) z8E*+VW#;?(xFnV&dL-thTNy9Vt(4InK8bXRoI{(%07*|r-mx(d*Rfj+)-=S5OhngT^jVlMB!bD!sY z=gDAtx+dZC)Zbp%`u#JK^k-^}epEEx!4qzxU=ou(X(RoWHx$uUH#G69Z|L|{Jfm0H zsK_)%^~_#%qbll}SL;n}Oo_Vg)qB$$)3S6`Vg{=`lvw3~vN6Ntb*X7S!amYkQ%A2L{4m$zrw9@FuMB^Pi z;a^ZV(uT|=++m5Sj>rQ%F3qO_@ z*en#?p%aBxmj^v7?1R@`*I^cO!RjdBt`oLu#iBQCZugwX;o-%ikM=9BYAuBkZ(g)+ z;M!Ich@Bhswo$COV|4;QvR&Wxx2%Yx(&~iw#E~K=jHuBXTYQ3ePu!v$^K~1w#js+# zd$w2hlzXlskL4<{32aM-nsFB~6OzJMYWVE_z}U?;B^#<-siZzfHR+_ z-WlpWy5f4yS`e)S1D}a~nhHNkZ)8V6H7$Lq5=2EbfJM!8Xf`0bEpt>hSI*gs}dlg8KDb5 zQ+BnX25_qA*CTz;80!0}2K@D?23!t&m@tsmw$@j#UB9w!K{pAd6MzTSmF1NiuUTDm zI9w3u8|b<8=(z^UR^<*%WavPr34R7$Y}gAtr!(-JQma__aGSY<&{_gFpd)Xltu7e6 z8t^R~Y4kAGWv6QoyvTAx>lKRzi35)>=(jfsJK2KkQ1%wKCm|SF%EB1yp3vs4u~jd; 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Default is 2 for ECFP4. - n_bits (int): Size of the fingerprint bit vector. Default is 2048. - - Returns: - np.ndarray: Array representing the molecule's fingerprint. - """ - mol = Chem.MolFromSmiles(smiles) - generator = AllChem.GetMorganGenerator(radius=radius, fpSize=n_bits) - fingerprint = generator.GetFingerprint(mol) - return np.array(fingerprint) - - def get_train_data(self, representation: str = 'fingerprint') -> (np.ndarray, np.ndarray): - """ - Prepares and returns the train split data. - - Parameters: - representation (str): 'fingerprint' or 'smiles' for feature representation. - - Returns: - tuple: Feature matrix (X_train) and labels (y_train). - """ - self.prepare_train_data(representation) - return self.X_train, self.y_train - - def get_original_train_data(self, representation: str = 'fingerprint') -> (np.ndarray, np.ndarray): - """ - Prepares and returns the original full train data. - - Parameters: - representation (str): 'fingerprint' or 'smiles' for feature representation. - - Returns: - tuple: Feature matrix (X_full_train) and labels (y_full_train). - """ - if self.train is None: - raise ValueError("The full train dataset is not loaded.") - - self.X_full_train, self.y_full_train = self.prepare_data(self.train, representation) - print("Original full training data prepared.") - return self.X_full_train, self.y_full_train - - def get_test_data(self, representation: str = 'fingerprint') -> np.ndarray: - """ - Prepares and returns the test data. - - Parameters: - representation (str): 'fingerprint' or 'smiles' for feature representation. - - Returns: - np.ndarray: Feature matrix (X_test). - """ - self.prepare_test_data(representation) - return self.X_test - - def get_val_data(self, representation: str = 'fingerprint') -> (np.ndarray, np.ndarray): - """ - Prepares and returns the validation data. - - Parameters: - representation (str): 'fingerprint' or 'smiles' for feature representation. - - Returns: - tuple: Feature matrix (X_val) and labels (y_val). - """ - self.prepare_validation_data(representation) - return self.X_val, self.y_val - - def prepare_data(self, dataset: pd.DataFrame, representation: str = 'fingerprint') -> (np.ndarray, np.ndarray): - """ - Generates features and labels for a given dataset based on the specified representation. - - Parameters: - dataset (pd.DataFrame): DataFrame with 'smiles' and 'activity' columns. - representation (str): 'fingerprint' or 'smiles' for feature representation. - - Returns: - tuple: Feature matrix (X) and labels (y). - """ - if representation == 'fingerprint': - dataset['feature'] = dataset['smiles'].apply(self.generate_fingerprint) - X = np.stack(dataset['feature'].values) - elif representation == 'smiles': - X = dataset['smiles'].values - else: - raise ValueError("Invalid representation. Choose 'fingerprint' or 'smiles'.") - - y = dataset['activity'].values - return X, y - - def prepare_train_data(self, representation: str = 'fingerprint'): - """ - Prepares the train split for model training. - - Parameters: - representation (str): 'fingerprint' or 'smiles' for feature representation. - """ - self.X_train, self.y_train = self.prepare_data(self.train_split, representation) - print("Training data prepared.") - - def prepare_validation_data(self, representation: str = 'fingerprint'): - """ - Prepares the validation split for model evaluation. - - Parameters: - representation (str): 'fingerprint' or 'smiles' for feature representation. - """ - self.X_val, self.y_val = self.prepare_data(self.val_split, representation) - print("Validation data prepared.") - - def prepare_test_data(self, representation: str = 'fingerprint'): - """ - Prepares the test dataset. - - Parameters: - representation (str): 'fingerprint' or 'smiles' for feature representation. - """ - if self.test is None: - raise ValueError("Test data is not loaded.") - - if representation == 'fingerprint': - self.test['feature'] = self.test['smiles'].apply(self.generate_fingerprint) - self.X_test = np.stack(self.test['feature'].values) - elif representation == 'smiles': - self.X_test = self.test['smiles'].values - else: - raise ValueError("Invalid representation. Choose 'fingerprint' or 'smiles'.") - - print("Test data prepared.") - - def train_model(self): - """ - Trains a logistic regression model using the training data fingerprints. - """ - if isinstance(self.X_train[0], str): - raise ValueError("Model training requires fingerprint representation, not SMILES strings.") - - self.model = LogisticRegression(solver='liblinear') - self.model.fit(self.X_train, self.y_train) - print("Model trained successfully.") - - def evaluate_model(self) -> float: - """ - Evaluates the model using average precision score on validation data. - - Returns: - float: The average precision score on validation data. - """ - if not hasattr(self, 'X_val') or not hasattr(self, 'y_val'): - raise ValueError("Validation data is not prepared.") - - val_preds = self.predict(self.X_val) - score = average_precision_score(self.y_val, val_preds) - print(f"Validation Average Precision Score: {score}") - return score - - def predict(self, fingerprints: np.ndarray) -> np.ndarray: - """ - Predicts activity probabilities for a given set of fingerprints. - - Parameters: - fingerprints (np.ndarray): Feature matrix for predictions. - - Returns: - np.ndarray: Array of probabilities for the positive class. - """ - if not self.model: - raise ValueError("Model is not trained.") - return self.model.predict_proba(fingerprints)[:, 1] - - def generate_submission(self, filename: str = 'submission.csv'): - """ - Generates a submission file with predictions for the test dataset. - - Parameters: - filename (str): Name of the output submission file. Default is 'submission.csv'. - """ - if not hasattr(self, 'X_test'): - raise ValueError("Test data is not prepared.") - - test_preds = self.predict(self.X_test) - self.test['activity'] = test_preds - submission = self.test[['id', 'activity']] - submission.to_csv(filename, index=False) - print(f"Submission file '{filename}' generated successfully.") - - def export_to_jsonl(self, dataset: pd.DataFrame, filename: str): - """ - Exports a dataset to JSONL format with 'id', 'smiles', and 'activity' fields. - - Parameters: - dataset (pd.DataFrame): DataFrame to be exported. - filename (str): Name of the output JSONL file. - """ - with open(filename, 'w') as f: - for _, row in dataset.iterrows(): - entry = { - "id": row.get("id", None), - "smiles": row["smiles"], - "activity": row.get("activity", None) - } - f.write(json.dumps(entry) + "\n") - print(f"Data exported to '{filename}' in JSONL format.") diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 0000000..04eda44 --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,233 @@ +# EZFlow Architecture + +This document provides a comprehensive overview of the EZFlow architecture, design principles, and how components interact with each other. + +## Overview + +EZFlow is designed with modularity and flexibility in mind, allowing researchers and data scientists to focus on their specific tasks rather than boilerplate code. The architecture follows a layered approach with clear separation of concerns. + +``` +┌─────────────────────────────────────────────────────────────┐ +│ EZFlow Framework │ +├─────────────┬─────────────┬───────────────┬─────────────────┤ +│ Dataset │ Pipeline │ Experiment │ CLI │ +│ Layer │ Layer │ Layer │ Layer │ +├─────────────┼─────────────┼───────────────┼─────────────────┤ +│ BaseDataset │ Pipeline │ ExperimentCfg │ CLI Commands │ +│ IrisDataset │ SklearnPipe│ ExpTracker │ Project Setup │ +│ MolecDataset│ TorchPipe │ Artifacts │ Utilities │ +└─────────────┴─────────────┴───────────────┴─────────────────┘ +``` + +## Design Principles + +EZFlow's architecture is guided by the following design principles: + +1. **Modularity**: Each component should be replaceable without affecting the rest of the system. +2. **Extensibility**: New components can be added with minimal changes to existing code. +3. **Consistency**: Common interfaces across different implementations for ease of use. +4. **Transparency**: Clear visibility into what happens at each step of the pipeline. +5. **Reproducibility**: Experiments should be fully reproducible from the stored configuration. + +## Core Components + +### Dataset Layer + +The dataset layer handles data loading, preprocessing, and splitting: + +``` +┌───────────────────────────────────────────────────────┐ +│ BaseDataset │ +├───────────────────────────────────────────────────────┤ +│ - name: str │ +│ - data_dir: str/Path │ +│ - cache_dir: str/Path │ +│ - metadata: Dict │ +├───────────────────────────────────────────────────────┤ +│ + load_data() -> None │ +│ + split_data(val_size, random_state) -> None │ +│ + preprocess() -> None │ +│ + get_features(data) -> np.ndarray │ +│ + get_labels(data) -> np.ndarray │ +│ + save_splits() -> None │ +│ + load_splits() -> None │ +└───────────────────────────────────────────────────────┘ + ▲ + │ + │ inherits + │ + ┌───────────────┴──────────────┐ + │ │ +┌──────────┴─────────────┐ ┌────────────┴─────────────┐ +│ IrisDataset │ │ MolecularDataset │ +├────────────────────────┤ ├──────────────────────────┤ +│ + load_data() │ │ - smiles_col: str │ +│ + split_data() │ │ - label_col: str │ +│ + preprocess() │ │ - fingerprint_radius: int│ +│ + get_features() │ │ - fingerprint_bits: int │ +│ + get_labels() │ ├──────────────────────────┤ +└────────────────────────┘ │ + generate_fingerprint() │ + │ + load_data() │ + │ + preprocess() │ + │ + get_features() │ + │ + get_labels() │ + └──────────────────────────┘ +``` + +The dataset layer follows the Template Method pattern, where `BaseDataset` defines the interface and workflow, while specific dataset implementations provide the concrete implementations. + +### Pipeline Layer + +The pipeline layer handles model training, evaluation, and prediction: + +``` +┌───────────────────────────────────────────────────────┐ +│ Pipeline │ +├───────────────────────────────────────────────────────┤ +│ - pipeline: Any │ +│ - pipeline_config: Dict │ +├───────────────────────────────────────────────────────┤ +│ + fit(X, y) -> None │ +│ + predict(X) -> np.ndarray │ +│ + score(X, y) -> float │ +│ + save(path) -> None │ +│ + load(path) -> None │ +└───────────────────────────────────────────────────────┘ + ▲ + │ + │ inherits + │ + ┌───────────────┴──────────────┐ + │ │ +┌──────────┴─────────────┐ ┌────────────┴─────────────┐ +│ SklearnPipelineWrapper │ │ TorchPipelineWrapper │ +├────────────────────────┤ ├──────────────────────────┤ +│ - steps: List │ │ - model: nn.Module │ +│ - sklearn_pipeline │ │ - optimizer: Optimizer │ +├────────────────────────┤ │ - criterion: Loss │ +│ + fit() │ ├──────────────────────────┤ +│ + predict() │ │ + fit() │ +│ + score() │ │ + predict() │ +│ + save() │ │ + score() │ +│ + load() │ │ + save() │ +└────────────────────────┘ │ + load() │ + └──────────────────────────┘ +``` + +The pipeline layer uses the Strategy pattern, allowing different ML frameworks to be used with the same interface. + +### Experiment Layer + +The experiment layer handles experiment tracking, configuration, and artifact management: + +``` +┌───────────────────────────────────────────────────────┐ +│ ExperimentConfig │ +├───────────────────────────────────────────────────────┤ +│ - name: str │ +│ - model_params: Dict │ +│ - dataset_params: Dict │ +│ - training_params: Dict │ +│ - metadata: Dict │ +├───────────────────────────────────────────────────────┤ +│ + to_dict() -> Dict │ +│ + from_dict(config_dict) -> ExperimentConfig │ +│ + save(path) -> None │ +│ + load(path) -> ExperimentConfig │ +└───────────────────────────────────────────────────────┘ + +┌───────────────────────────────────────────────────────┐ +│ ExperimentTracker │ +├───────────────────────────────────────────────────────┤ +│ - experiment_name: str │ +│ - artifacts_dir: str/Path │ +│ - backend: str │ +├───────────────────────────────────────────────────────┤ +│ + start_run(config) -> None │ +│ + end_run() -> None │ +│ + log_metric(name, value) -> None │ +│ + log_metrics(metrics_dict) -> None │ +│ + log_parameter(name, value) -> None │ +│ + log_parameters(params_dict) -> None │ +│ + log_artifact(artifact_path) -> None │ +│ + get_artifact(artifact_path) -> str/Path │ +└───────────────────────────────────────────────────────┘ +``` + +The experiment layer follows the Facade pattern, providing a simplified interface to the underlying tracking systems (like MLflow). + +### CLI Layer + +The CLI layer provides command-line utilities for project management: + +``` +┌───────────────────────────────────────────────────────┐ +│ CLI │ +├───────────────────────────────────────────────────────┤ +│ + create_project(name) -> None │ +│ + run_experiment(config_path) -> None │ +│ + list_experiments() -> None │ +└───────────────────────────────────────────────────────┘ +``` + +The CLI layer follows the Command pattern, with each CLI command implementing a specific action. + +## Data Flow + +A typical data flow in EZFlow follows these steps: + +1. **Data Loading**: The dataset loads data from files or external sources +2. **Data Splitting**: The dataset splits data into training and validation sets +3. **Feature Extraction**: Features and labels are extracted from the dataset +4. **Model Training**: The pipeline trains the model using the extracted features and labels +5. **Model Evaluation**: The pipeline evaluates the model on validation data +6. **Metrics Tracking**: The experiment tracker logs metrics, parameters, and artifacts +7. **Model Saving**: The trained model is saved as an artifact + +``` +┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ +│ Dataset │────>│ Pipeline │────>│ Experiment│────>│ Artifacts │ +│ Loading │ │ Training │ │ Tracking │ │ Saving │ +└───────────┘ └───────────┘ └───────────┘ └───────────┘ +``` + +## Component Interaction + +Components in EZFlow interact through well-defined interfaces: + +1. **Dataset + Pipeline**: Pipelines use datasets to obtain features and labels +2. **Pipeline + Experiment**: Experiments track pipeline performance and artifacts +3. **CLI + Components**: CLI commands orchestrate the use of other components + +## Extension Points + +EZFlow is designed to be extended in several ways: + +1. **Custom Datasets**: Create new dataset classes by inheriting from `BaseDataset` +2. **Custom Pipelines**: Create new pipeline wrappers by inheriting from `Pipeline` +3. **Custom Tracking Backends**: Add new experiment tracking backends by extending the tracker implementation +4. **Custom CLI Commands**: Add new CLI commands by extending the CLI functionality + +## Best Practices + +When working with or extending EZFlow, follow these best practices: + +1. **Dataset Implementation**: + - Implement all required methods from `BaseDataset` + - Ensure data preprocessing is idempotent + - Use appropriate data types and structures + +2. **Pipeline Implementation**: + - Follow the pipeline interface strictly + - Handle exceptions and provide meaningful error messages + - Implement proper model serialization/deserialization + +3. **Experiment Tracking**: + - Track all relevant hyperparameters + - Use consistent naming conventions for metrics + - Save all necessary artifacts for reproducibility + +4. **General**: + - Add comprehensive unit tests for new components + - Document all public methods and classes + - Follow the coding standards \ No newline at end of file diff --git a/docs/cli.md b/docs/cli.md new file mode 100644 index 0000000..24c7f3d --- /dev/null +++ b/docs/cli.md @@ -0,0 +1,292 @@ +# Command-Line Interface + +EZFlow provides a command-line interface (CLI) that makes it easy to create, manage, and run machine learning projects. + +## Table of Contents + +- [Overview](#overview) +- [Installation](#installation) +- [Available Commands](#available-commands) + - [Create Project](#create-project) + - [Run Experiment](#run-experiment) + - [List Experiments](#list-experiments) +- [Examples](#examples) +- [Best Practices](#best-practices) +- [Extending the CLI](#extending-the-cli) + +## Overview + +The EZFlow CLI provides a set of commands to help you: + +- Create new projects with appropriate directory structure +- Run experiments from configuration files +- List and compare experiment results +- Manage models and datasets + +The CLI follows consistent patterns and provides helpful error messages to make the workflow smooth and intuitive. + +## Installation + +The CLI is automatically installed when you install the EZFlow package: + +```bash +# Install from PyPI +pip install ezflow-ml + +# Or install in development mode from source +pip install -e . +``` + +After installation, the `ezflow` command will be available in your terminal. + +## Available Commands + +### Create Project + +The `create` command sets up a new EZFlow project with the recommended directory structure. + +#### Usage + +```bash +ezflow create [--template ] +``` + +#### Arguments + +| Argument | Description | +|----------|-------------| +| `project_name` | Name of the project to create | + +#### Options + +| Option | Description | +|--------|-------------| +| `--template` | Template to use for project creation (default: "basic") | + +#### Directory Structure + +The created project will have the following structure: + +``` +project_name/ +├── data/ +│ ├── raw/ # Raw, immutable data +│ └── processed/ # Processed data ready for training +├── models/ # Saved models +├── experiments/ # Experiment results and artifacts +├── notebooks/ # Jupyter notebooks for exploration +├── src/ # Source code +│ ├── __init__.py +│ ├── datasets/ # Custom dataset implementations +│ ├── pipelines/ # Custom pipeline implementations +│ └── utils/ # Utility functions +├── config/ # Configuration files +├── .gitignore # Git ignore file with ML-specific patterns +└── README.md # Project README +``` + +#### Example + +```bash +# Create a new project named "drug_discovery" +ezflow create drug_discovery + +# Create a project with a specific template +ezflow create image_classification --template vision +``` + +### Run Experiment + +The `run` command executes an experiment based on a configuration file. + +#### Usage + +```bash +ezflow run [--output ] [--name ] +``` + +#### Arguments + +| Argument | Description | +|----------|-------------| +| `config_file` | Path to experiment configuration file (JSON or YAML) | + +#### Options + +| Option | Description | +|--------|-------------| +| `--output` | Directory to store experiment results (default: "./experiments") | +| `--name` | Name for the experiment run (default: auto-generated) | +| `--no-tracking` | Disable experiment tracking | + +#### Example + +```bash +# Run an experiment with a specific configuration +ezflow run config/random_forest.json + +# Run with custom output directory and name +ezflow run config/neural_network.yaml --output ./results --name nn_run_1 +``` + +### List Experiments + +The `list` command displays information about previous experiment runs. + +#### Usage + +```bash +ezflow list [--n ] [--filter ] +``` + +#### Options + +| Option | Description | +|--------|-------------| +| `--n` | Number of experiments to show (default: 10) | +| `--filter` | Filter experiments by name, status, or parameters | +| `--sort` | Sort by metric or parameter (e.g., "accuracy:desc") | +| `--output` | Output format: "table", "json", or "csv" (default: "table") | + +#### Example + +```bash +# List 5 most recent experiments +ezflow list --n 5 + +# Filter experiments by name +ezflow list --filter "name=random_forest" + +# Sort by validation accuracy (descending) +ezflow list --sort "metrics.val_accuracy:desc" + +# Export results as CSV +ezflow list --output csv > experiments.csv +``` + +## Examples + +### Complete Workflow Example + +```bash +# Create a new project +ezflow create my_ml_project +cd my_ml_project + +# Create data directories +mkdir -p data/raw data/processed + +# Prepare your dataset +# ... (download/copy data files to data/raw) + +# Create experiment configuration +cat > config/experiment.json << EOL +{ + "name": "first_experiment", + "model_params": { + "n_estimators": 100, + "max_depth": 10 + }, + "dataset_params": { + "val_size": 0.2 + }, + "training_params": { + "random_state": 42 + } +} +EOL + +# Run the experiment +ezflow run config/experiment.json --name initial_run + +# List experiment results +ezflow list --n 1 +``` + +### Creating and Running Multiple Experiments + +```bash +# Create multiple configuration files +for n_estimators in 50 100 200; do + for max_depth in 5 10 15; do + cat > config/rf_${n_estimators}_${max_depth}.json << EOL +{ + "name": "rf_${n_estimators}_${max_depth}", + "model_params": { + "n_estimators": ${n_estimators}, + "max_depth": ${max_depth} + }, + "dataset_params": { + "val_size": 0.2 + }, + "training_params": { + "random_state": 42 + } +} +EOL + done +done + +# Run all experiments +for config in config/rf_*.json; do + ezflow run $config +done + +# Compare results +ezflow list --sort "metrics.val_accuracy:desc" +``` + +## Best Practices + +For optimal results when using the CLI: + +1. **Version Control**: Store configuration files in version control to track experiment setups +2. **Consistent Naming**: Use consistent naming conventions for experiment configurations +3. **Configuration Files**: Prefer configuration files over command-line arguments for reproducibility +4. **Documentation**: Document the purpose and expected outcomes of each configuration +5. **Automation**: Use shell scripts or Makefiles to automate common workflows +6. **Environment Variables**: Use environment variables for sensitive information (API keys, paths) + +## Extending the CLI + +You can extend the EZFlow CLI with custom commands by creating plugins. + +### Creating a Custom Command + +```python +# In your_extension.py +from ezflow.cli import register_command + +@register_command("analyze", "Analyze experiment results") +def analyze_command(args): + """Analyze experiment results and generate reports.""" + # Implementation of the command + pass + +# Register command arguments +analyze_command.add_argument("experiment", help="Experiment ID to analyze") +analyze_command.add_argument("--output", help="Output directory for reports") +``` + +### Installing the Extension + +```python +# In setup.py +setup( + name="ezflow-extension", + # ... + entry_points={ + "ezflow.cli_plugins": [ + "analyze=your_package.your_extension:analyze_command", + ] + } +) +``` + +### Using the Extension + +After installing your extension, the command will be available: + +```bash +ezflow analyze experiment_id --output ./reports +``` \ No newline at end of file diff --git a/docs/dataset.md b/docs/dataset.md new file mode 100644 index 0000000..286908d --- /dev/null +++ b/docs/dataset.md @@ -0,0 +1,382 @@ +# Dataset Module + +The Dataset module is a core component of EZFlow, providing a unified interface for loading, preprocessing, and manipulating different types of datasets. + +## Table of Contents + +- [Overview](#overview) +- [BaseDataset](#basedataset) +- [Specialized Datasets](#specialized-datasets) + - [IrisDataset](#irisdataset) + - [MolecularDataset](#moleculardataset) +- [Custom Datasets](#custom-datasets) +- [Best Practices](#best-practices) +- [API Reference](#api-reference) + +## Overview + +The Dataset module handles: + +- Data loading from various sources +- Data preprocessing and feature engineering +- Data splitting into training, validation, and test sets +- Feature and label extraction +- Persistence of datasets and splits + +This module is designed to be extendable, allowing users to easily implement custom datasets while maintaining a consistent interface. + +## BaseDataset + +`BaseDataset` is the abstract base class that all dataset implementations should inherit from. + +### Key Properties + +| Property | Type | Description | +|----------|------|-------------| +| `name` | str | Name of the dataset | +| `data_dir` | str/Path | Directory where data files are stored | +| `cache_dir` | str/Path | Directory for caching processed data | +| `train_data` | pd.DataFrame | Training data | +| `val_data` | pd.DataFrame | Validation data | +| `test_data` | pd.DataFrame | Test data (optional) | +| `metadata` | Dict | Additional metadata about the dataset | + +### Key Methods + +| Method | Parameters | Return Type | Description | +|--------|------------|-------------|-------------| +| `load_data` | - | None | Loads raw data into `train_data` | +| `split_data` | `val_size`, `random_state` | None | Splits `train_data` into `train_data` and `val_data` | +| `preprocess` | - | None | Applies preprocessing transformations | +| `get_features` | `data: pd.DataFrame` | np.ndarray | Extracts features from data | +| `get_labels` | `data: pd.DataFrame` | np.ndarray | Extracts labels from data | +| `save_splits` | - | None | Saves train/val splits to disk | +| `load_splits` | - | None | Loads train/val splits from disk | + +### Usage Example + +```python +from ezflow.core.dataset import BaseDataset + +class MyDataset(BaseDataset): + def __init__(self, data_dir): + super().__init__("my_dataset", data_dir) + + def load_data(self): + # Implementation specific to this dataset + pass + + def preprocess(self): + # Implementation specific to this dataset + pass + + def get_features(self, data): + # Implementation specific to this dataset + pass + + def get_labels(self, data): + # Implementation specific to this dataset + pass +``` + +## Specialized Datasets + +EZFlow provides several built-in dataset implementations for common use cases. + +### IrisDataset + +The `IrisDataset` class provides a wrapper for the popular Iris flower dataset, commonly used for classification tasks. + +#### Initialization + +```python +from ezflow.core.dataset import IrisDataset + +# Initialize with a directory for storing data +dataset = IrisDataset(data_dir="./data") + +# Load the data +dataset.load_data() + +# Split into training and validation sets +dataset.split_data(val_size=0.2, random_state=42) +``` + +#### Features and Implementation Details + +- Automatically loads the Iris dataset from scikit-learn +- Provides standard feature columns: sepal length, sepal width, petal length, petal width +- Target variable contains flower species (0, 1, 2) +- Implements stratified splitting to maintain class distribution +- Handles index preservation during split/save/load operations + +### MolecularDataset + +The `MolecularDataset` class is designed for working with chemical/molecular data, particularly useful in drug discovery and cheminformatics. + +#### Initialization + +```python +from ezflow.core.dataset import MolecularDataset + +# Initialize with dataset details +dataset = MolecularDataset( + name="my_molecular_dataset", + data_dir="./data", + smiles_col="SMILES", + label_col="Activity", + fingerprint_radius=2, + fingerprint_bits=2048 +) + +# Load the data (expects CSV files with SMILES strings) +dataset.load_data() + +# Split into training and validation sets +dataset.split_data(val_size=0.2, random_state=42) +``` + +#### Features and Implementation Details + +- Handles SMILES (Simplified Molecular Input Line Entry System) strings +- Generates molecular fingerprints using RDKit +- Provides customizable fingerprint parameters (radius, bit length) +- Supports custom ID columns and metadata +- Includes functionality for generating submission files for competitions + +## Custom Datasets + +Creating a custom dataset involves inheriting from `BaseDataset` and implementing the required methods. + +### Implementation Checklist + +1. **Inherit from BaseDataset**: Extend the base class +2. **Implement load_data**: Define how to load your specific data +3. **Implement preprocess**: Define any preprocessing steps +4. **Implement get_features**: Define which columns/transformations to use as features +5. **Implement get_labels**: Define which column(s) to use as labels +6. **Implement save/load_splits**: Optional but recommended, to save/load the dataset state + +### Example: Custom Image Dataset + +```python +import numpy as np +import pandas as pd +from pathlib import Path +import cv2 +from ezflow.core.dataset import BaseDataset + +class ImageDataset(BaseDataset): + def __init__(self, data_dir, image_size=(224, 224)): + super().__init__("image_dataset", data_dir) + self.image_size = image_size + self.image_paths = [] + self.labels = [] + + def load_data(self): + # Create DataFrame with image paths and labels + image_files = list(Path(self.data_dir).glob("**/*.jpg")) + labels = [path.parent.name for path in image_files] + + self.train_data = pd.DataFrame({ + "image_path": [str(path) for path in image_files], + "label": labels + }) + + def preprocess(self): + # You might do label encoding here + label_map = {label: idx for idx, label in enumerate(set(self.train_data["label"]))} + self.train_data["label_encoded"] = self.train_data["label"].map(label_map) + self.metadata["label_map"] = label_map + + def _load_and_preprocess_image(self, image_path): + # Load and preprocess a single image + img = cv2.imread(image_path) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = cv2.resize(img, self.image_size) + return img / 255.0 # Normalize + + def get_features(self, data): + # Load all images in the batch + images = np.array([ + self._load_and_preprocess_image(path) + for path in data["image_path"] + ]) + return images + + def get_labels(self, data): + return data["label_encoded"].values +``` + +## Best Practices + +For optimal results when working with datasets: + +1. **Data Validation**: Always validate your data during loading to catch issues early +2. **Efficient Preprocessing**: Implement preprocessing operations that can be efficiently applied to large datasets +3. **Consistent Splitting**: Use a fixed random state for reproducible splits +4. **Feature Engineering**: Keep complex feature engineering in the dataset class for consistency +5. **Documentation**: Document data formats, expected columns, and special handling requirements +6. **Error Handling**: Provide clear error messages for missing files or invalid data +7. **Caching**: Use the cache directory for storing intermediate results to avoid recomputation +8. **Type Safety**: Return numpy arrays with the appropriate dtypes for features and labels + +## API Reference + +### BaseDataset + +```python +class BaseDataset: + """Abstract base class for all datasets in EZFlow.""" + + def __init__( + self, + name: str, + data_dir: Union[str, Path], + cache_dir: Optional[Union[str, Path]] = None, + metadata: Optional[Dict[str, Any]] = None + ): + """ + Initialize the dataset. + + Parameters + ---------- + name : str + Name of the dataset + data_dir : str or Path + Directory where data files are stored + cache_dir : str or Path, optional + Directory for caching processed data + metadata : dict, optional + Additional metadata about the dataset + """ + + def load_data(self) -> None: + """ + Load the raw data into the dataset. + + This method should be implemented by subclasses to + load data from files into the `train_data` attribute. + """ + + def split_data(self, val_size: float = 0.2, random_state: int = 42) -> None: + """ + Split data into training and validation sets. + + Parameters + ---------- + val_size : float + Size of validation set as a fraction + random_state : int + Random seed for reproducibility + """ + + def preprocess(self) -> None: + """ + Apply preprocessing transformations to the data. + + This method should be implemented by subclasses to + perform any necessary preprocessing steps. + """ + + def get_features(self, data: pd.DataFrame) -> np.ndarray: + """ + Extract features from the data. + + Parameters + ---------- + data : pandas.DataFrame + Data to extract features from + + Returns + ------- + numpy.ndarray + Features as a numpy array + """ + + def get_labels(self, data: pd.DataFrame) -> np.ndarray: + """ + Extract labels from the data. + + Parameters + ---------- + data : pandas.DataFrame + Data to extract labels from + + Returns + ------- + numpy.ndarray + Labels as a numpy array + """ + + def save_splits(self) -> None: + """ + Save the current train/validation splits to disk. + """ + + def load_splits(self) -> None: + """ + Load train/validation splits from disk. + """ +``` + +### IrisDataset + +```python +class IrisDataset(BaseDataset): + """Dataset implementation for the Iris flower dataset.""" + + def __init__(self, data_dir: Union[str, Path]): + """ + Initialize the Iris dataset. + + Parameters + ---------- + data_dir : str or Path + Directory for storing data files + """ +``` + +### MolecularDataset + +```python +class MolecularDataset(BaseDataset): + """Dataset implementation for molecular/chemical data.""" + + def __init__( + self, + name: str, + data_dir: Union[str, Path], + smiles_col: str = 'smiles', + label_col: str = 'activity', + id_col: Optional[str] = 'id', + cache_dir: Optional[Union[str, Path]] = None, + metadata: Optional[Dict[str, Any]] = None, + fingerprint_radius: int = 2, + fingerprint_bits: int = 2048 + ): + """ + Initialize the molecular dataset. + + Parameters + ---------- + name : str + Name of the dataset + data_dir : str or Path + Directory where data files are stored + smiles_col : str + Column name containing SMILES strings + label_col : str + Column name containing target values + id_col : str, optional + Column name containing molecule identifiers + cache_dir : str or Path, optional + Directory for caching processed data + metadata : dict, optional + Additional metadata about the dataset + fingerprint_radius : int + Radius parameter for Morgan fingerprints + fingerprint_bits : int + Number of bits in the fingerprint + """ +``` \ No newline at end of file diff --git a/docs/experiments.md b/docs/experiments.md new file mode 100644 index 0000000..b14801d --- /dev/null +++ b/docs/experiments.md @@ -0,0 +1,466 @@ +# Experiment Module + +The Experiment module provides tools for configuring, tracking, and comparing machine learning experiments. + +## Table of Contents + +- [Overview](#overview) +- [ExperimentConfig](#experimentconfig) +- [ExperimentTracker](#experimenttracker) +- [Integration with MLflow](#integration-with-mlflow) +- [Best Practices](#best-practices) +- [API Reference](#api-reference) + +## Overview + +The Experiment module is designed to help data scientists and researchers manage their machine learning experiments efficiently. Key features include: + +- **Configuration Management**: Store and manage experiment parameters in a structured format +- **Metrics Tracking**: Log and compare metrics across different runs +- **Artifact Management**: Save and organize models, plots, and other artifacts +- **Run History**: Maintain a history of experiment runs for comparison +- **Reproducibility**: Enable reproducible experiments through consistent tracking + +## ExperimentConfig + +The `ExperimentConfig` class provides a structured way to define and manage experiment parameters. + +### Key Properties + +| Property | Type | Description | +|----------|------|-------------| +| `name` | str | Name of the experiment | +| `model_params` | Dict | Model hyperparameters | +| `dataset_params` | Dict | Dataset configuration parameters | +| `training_params` | Dict | Training configuration parameters | +| `metadata` | Dict | Additional metadata about the experiment | + +### Usage Example + +```python +from ezflow.core.experiment import ExperimentConfig + +# Create a configuration for an experiment +config = ExperimentConfig( + name="random_forest_classifier", + model_params={ + "n_estimators": 100, + "max_depth": 10, + "random_state": 42 + }, + dataset_params={ + "val_size": 0.2, + "stratify": True + }, + training_params={ + "cv_folds": 5, + "scoring": "accuracy" + }, + metadata={ + "author": "John Doe", + "description": "Random forest classifier for Iris dataset" + } +) + +# Convert to dictionary for serialization +config_dict = config.to_dict() + +# Save to file +config.save("./experiments/rf_config.json") + +# Load from file +loaded_config = ExperimentConfig.load("./experiments/rf_config.json") +``` + +### Methods + +| Method | Parameters | Return Type | Description | +|--------|------------|-------------|-------------| +| `to_dict` | - | Dict | Convert configuration to a dictionary | +| `from_dict` | `config_dict: Dict` | ExperimentConfig | Create configuration from a dictionary | +| `save` | `path: str/Path` | None | Save configuration to a file | +| `load` | `path: str/Path` | ExperimentConfig | Load configuration from a file | + +## ExperimentTracker + +The `ExperimentTracker` class provides functionality for tracking experiments, including metrics, parameters, and artifacts. + +### Key Properties + +| Property | Type | Description | +|----------|------|-------------| +| `experiment_name` | str | Name of the experiment | +| `artifacts_dir` | str/Path | Directory for storing artifacts | +| `backend` | str | Backend tracking system ("mlflow", "tensorboard", etc.) | +| `active_run` | Any | Current active run | + +### Usage Example + +```python +from ezflow.core.experiment import ExperimentTracker, ExperimentConfig +from ezflow.core.pipeline import SklearnPipelineWrapper +from ezflow.core.dataset import IrisDataset +from sklearn.metrics import accuracy_score + +# Initialize dataset +dataset = IrisDataset(data_dir="./data") +dataset.load_data() +dataset.split_data(val_size=0.2, random_state=42) + +# Get features and labels +X_train = dataset.get_features(dataset.train_data) +y_train = dataset.get_labels(dataset.train_data) +X_val = dataset.get_features(dataset.val_data) +y_val = dataset.get_labels(dataset.val_data) + +# Create pipeline +pipeline = SklearnPipelineWrapper([ + ("scaler", "sklearn.preprocessing.StandardScaler", {}), + ("classifier", "sklearn.ensemble.RandomForestClassifier", { + "n_estimators": 100, + "max_depth": 5, + "random_state": 42 + }) +]) + +# Create experiment config +config = ExperimentConfig( + name="iris_classification", + model_params={"n_estimators": 100, "max_depth": 5}, + dataset_params={"val_size": 0.2}, + training_params={"random_state": 42} +) + +# Initialize experiment tracker +tracker = ExperimentTracker( + experiment_name="iris_example", + artifacts_dir="./experiments" +) + +# Track experiment +with tracker: + # Start run with config + tracker.start_run(config) + + # Train model + pipeline.fit(X_train, y_train) + + # Make predictions + train_preds = pipeline.predict(X_train) + val_preds = pipeline.predict(X_val) + + # Calculate metrics + train_acc = accuracy_score(y_train, train_preds) + val_acc = accuracy_score(y_val, val_preds) + + # Log metrics + tracker.log_metrics({ + "train_accuracy": train_acc, + "val_accuracy": val_acc + }) + + # Save model as artifact + pipeline.save("./models/iris_rf.joblib") + tracker.log_artifact("./models/iris_rf.joblib") +``` + +### Methods + +| Method | Parameters | Return Type | Description | +|--------|------------|-------------|-------------| +| `start_run` | `config: ExperimentConfig` | None | Start a new tracked run | +| `end_run` | - | None | End the current run | +| `log_metric` | `name: str`, `value: float` | None | Log a single metric | +| `log_metrics` | `metrics: Dict[str, float]` | None | Log multiple metrics | +| `log_parameter` | `name: str`, `value: Any` | None | Log a single parameter | +| `log_parameters` | `params: Dict[str, Any]` | None | Log multiple parameters | +| `log_artifact` | `artifact_path: str/Path` | None | Log a file as an artifact | +| `get_artifact` | `artifact_path: str/Path` | str/Path | Retrieve an artifact | + +## Integration with MLflow + +EZFlow's experiment tracking is built on top of MLflow, a popular open-source platform for managing the ML lifecycle. + +### MLflow Backend + +By default, `ExperimentTracker` uses MLflow as its backend tracking system. This provides several advantages: + +- **Web UI**: Access a web-based dashboard for experiment comparison +- **Artifact Storage**: Efficient storage and retrieval of models and other files +- **Run Comparison**: Easy comparison of different experiment runs +- **Metadata Search**: Search and filter experiments based on parameters + +### Tracking Server Configuration + +You can configure the MLflow tracking server in several ways: + +1. **Local Tracking (Default)**: + ```python + # Uses ./mlruns directory by default + tracker = ExperimentTracker("my_experiment") + ``` + +2. **Custom Local Directory**: + ```python + # Specify a custom directory for MLflow data + tracker = ExperimentTracker( + "my_experiment", + backend_config={"tracking_uri": "file:/path/to/mlruns"} + ) + ``` + +3. **Remote Tracking Server**: + ```python + # Use a remote MLflow tracking server + tracker = ExperimentTracker( + "my_experiment", + backend_config={"tracking_uri": "http://my-mlflow-server:5000"} + ) + ``` + +### Viewing Experiment Results + +To view experiment results using the MLflow UI: + +```bash +# Start the MLflow UI +mlflow ui --backend-store-uri ./experiments/mlruns + +# If using a remote server +mlflow ui --backend-store-uri http://my-mlflow-server:5000 +``` + +This will start a web server (default: http://localhost:5000) where you can view and compare experiments. + +## Best Practices + +For optimal results when tracking experiments: + +1. **Consistent Naming**: Use consistent naming conventions for experiments, runs, and metrics +2. **Parameter Tracking**: Track all relevant hyperparameters for reproducibility +3. **Granular Metrics**: Log metrics at appropriate intervals (not just final results) +4. **Artifact Management**: Save models and other important artifacts with clear versioning +5. **Contextual Metadata**: Include contextual information like dataset versions, environment details +6. **Run Tags**: Use tags to categorize and filter experiment runs +7. **Experiment Organization**: Group related experiments logically + +## API Reference + +### ExperimentConfig + +```python +class ExperimentConfig: + """Configuration for machine learning experiments.""" + + def __init__( + self, + name: str, + model_params: Dict[str, Any] = None, + dataset_params: Dict[str, Any] = None, + training_params: Dict[str, Any] = None, + metadata: Dict[str, Any] = None + ): + """ + Initialize experiment configuration. + + Parameters + ---------- + name : str + Name of the experiment + model_params : dict, optional + Model hyperparameters + dataset_params : dict, optional + Dataset configuration parameters + training_params : dict, optional + Training configuration parameters + metadata : dict, optional + Additional metadata about the experiment + """ + + def to_dict(self) -> Dict[str, Any]: + """ + Convert configuration to a dictionary. + + Returns + ------- + dict + Dictionary representation of the configuration + """ + + @classmethod + def from_dict(cls, config_dict: Dict[str, Any]) -> 'ExperimentConfig': + """ + Create configuration from a dictionary. + + Parameters + ---------- + config_dict : dict + Dictionary containing configuration values + + Returns + ------- + ExperimentConfig + Configuration object + """ + + def save(self, path: Union[str, Path]) -> None: + """ + Save configuration to a file. + + Parameters + ---------- + path : str or Path + Path to save the configuration to + """ + + @classmethod + def load(cls, path: Union[str, Path]) -> 'ExperimentConfig': + """ + Load configuration from a file. + + Parameters + ---------- + path : str or Path + Path to load the configuration from + + Returns + ------- + ExperimentConfig + Configuration object + """ +``` + +### ExperimentTracker + +```python +class ExperimentTracker: + """Tracker for machine learning experiments.""" + + def __init__( + self, + experiment_name: str, + artifacts_dir: Union[str, Path] = None, + backend: str = "mlflow", + backend_config: Dict[str, Any] = None + ): + """ + Initialize experiment tracker. + + Parameters + ---------- + experiment_name : str + Name of the experiment + artifacts_dir : str or Path, optional + Directory for storing artifacts + backend : str + Backend tracking system ("mlflow", "tensorboard", etc.) + backend_config : dict, optional + Configuration for the backend tracking system + """ + + def __enter__(self) -> 'ExperimentTracker': + """ + Enter context manager. + + Returns + ------- + ExperimentTracker + Self, for context manager usage + """ + + def __exit__(self, exc_type, exc_val, exc_tb) -> None: + """ + Exit context manager and end run if active. + + Parameters + ---------- + exc_type : type + Exception type, if any + exc_val : Exception + Exception value, if any + exc_tb : traceback + Exception traceback, if any + """ + + def start_run(self, config: ExperimentConfig = None) -> None: + """ + Start a new tracked run. + + Parameters + ---------- + config : ExperimentConfig, optional + Configuration for the run + """ + + def end_run(self) -> None: + """End the current run.""" + + def log_metric(self, name: str, value: float) -> None: + """ + Log a single metric. + + Parameters + ---------- + name : str + Name of the metric + value : float + Value of the metric + """ + + def log_metrics(self, metrics: Dict[str, float]) -> None: + """ + Log multiple metrics. + + Parameters + ---------- + metrics : dict + Dictionary of metric names and values + """ + + def log_parameter(self, name: str, value: Any) -> None: + """ + Log a single parameter. + + Parameters + ---------- + name : str + Name of the parameter + value : any + Value of the parameter + """ + + def log_parameters(self, params: Dict[str, Any]) -> None: + """ + Log multiple parameters. + + Parameters + ---------- + params : dict + Dictionary of parameter names and values + """ + + def log_artifact(self, artifact_path: Union[str, Path]) -> None: + """ + Log a file as an artifact. + + Parameters + ---------- + artifact_path : str or Path + Path to the file to log as an artifact + """ + + def get_artifact(self, artifact_path: Union[str, Path]) -> Union[str, Path]: + """ + Retrieve an artifact. + + Parameters + ---------- + artifact_path : str or Path + Path to the artifact + + Returns + ------- + str or Path + Path to the retrieved artifact + """ +``` \ No newline at end of file diff --git a/docs/pipeline.md b/docs/pipeline.md new file mode 100644 index 0000000..13bab08 --- /dev/null +++ b/docs/pipeline.md @@ -0,0 +1,410 @@ +# Pipeline Module + +The Pipeline module provides a unified interface for creating, training, and deploying machine learning models with different backend frameworks. + +## Table of Contents + +- [Overview](#overview) +- [Base Pipeline](#base-pipeline) +- [Pipeline Implementations](#pipeline-implementations) + - [SklearnPipelineWrapper](#sklearnpipelinewrapper) + - [TorchPipelineWrapper](#torchpipelinewrapper) +- [Custom Pipelines](#custom-pipelines) +- [Best Practices](#best-practices) +- [API Reference](#api-reference) + +## Overview + +The Pipeline module is a core component of EZFlow, designed to abstract away the differences between machine learning frameworks and provide a consistent interface for model development. Key features include: + +- Unified API for different ML frameworks +- Consistent methods for training, prediction, and evaluation +- Model serialization and deserialization +- Support for complex pipelines with preprocessing steps +- Extensibility for custom model implementations + +## Base Pipeline + +The `Pipeline` abstract base class defines the interface that all pipeline implementations must follow. + +### Key Methods + +| Method | Parameters | Return Type | Description | +|--------|------------|-------------|-------------| +| `fit` | `X: np.ndarray`, `y: np.ndarray` | None | Train the model on the provided data | +| `predict` | `X: np.ndarray` | np.ndarray | Generate predictions for the input data | +| `score` | `X: np.ndarray`, `y: np.ndarray` | float | Evaluate the model on the provided data | +| `save` | `path: str/Path` | None | Save the model to disk | +| `load` | `path: str/Path` | None | Load the model from disk | + +### Usage Example + +```python +from ezflow.core.pipeline import Pipeline + +# Create a pipeline using a concrete implementation +pipeline = SomePipelineImplementation(params) + +# Train the model +pipeline.fit(X_train, y_train) + +# Make predictions +predictions = pipeline.predict(X_test) + +# Evaluate the model +score = pipeline.score(X_test, y_test) + +# Save and load the model +pipeline.save("./models/my_model.pkl") +pipeline.load("./models/my_model.pkl") +``` + +## Pipeline Implementations + +EZFlow provides several concrete implementations of the `Pipeline` interface for popular machine learning frameworks. + +### SklearnPipelineWrapper + +The `SklearnPipelineWrapper` class provides an implementation of the pipeline interface for scikit-learn models. + +#### Features + +- Support for scikit-learn's pipeline API +- Dynamic creation of pipeline steps from string references +- Flexible parameter specification +- Consistent serialization/deserialization + +#### Initialization + +```python +from ezflow.core.pipeline import SklearnPipelineWrapper + +# Create a pipeline with preprocessing and a model +pipeline = SklearnPipelineWrapper([ + ("scaler", "sklearn.preprocessing.StandardScaler", {}), + ("pca", "sklearn.decomposition.PCA", {"n_components": 10}), + ("classifier", "sklearn.ensemble.RandomForestClassifier", { + "n_estimators": 100, + "max_depth": 10, + "random_state": 42 + }) +]) +``` + +#### Parameters + +- `steps`: A list of tuples, each containing: + - Step name (string) + - Fully qualified class path (string) or actual class + - Parameters dictionary for the step + +### TorchPipelineWrapper + +The `TorchPipelineWrapper` class provides an implementation for PyTorch models. + +#### Features + +- Support for PyTorch models and training loops +- Configurable training parameters +- Automatic device selection (CPU/GPU) +- Proper model serialization/deserialization + +#### Initialization + +```python +import torch.nn as nn +from ezflow.core.pipeline import TorchPipelineWrapper + +# Define a PyTorch model +class SimpleNN(nn.Module): + def __init__(self, input_dim, hidden_dim, output_dim): + super().__init__() + self.fc1 = nn.Linear(input_dim, hidden_dim) + self.relu = nn.ReLU() + self.fc2 = nn.Linear(hidden_dim, output_dim) + + def forward(self, x): + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + return x + +# Create a pipeline with the model +model = SimpleNN(input_dim=10, hidden_dim=50, output_dim=3) +pipeline = TorchPipelineWrapper( + model=model, + loss_fn="torch.nn.CrossEntropyLoss", + optimizer="torch.optim.Adam", + optimizer_params={"lr": 0.001}, + device="auto" # Automatically select CPU or GPU +) +``` + +#### Parameters + +- `model`: PyTorch nn.Module or string reference to a model class +- `loss_fn`: Loss function (string reference or actual function) +- `optimizer`: Optimizer class (string reference or actual class) +- `optimizer_params`: Dictionary of optimizer parameters +- `device`: "cpu", "cuda", or "auto" for automatic selection + +## Custom Pipelines + +Creating a custom pipeline involves inheriting from the `Pipeline` base class and implementing the required methods. + +### Example: Custom TensorFlow Pipeline + +```python +import numpy as np +import tensorflow as tf +from ezflow.core.pipeline import Pipeline + +class TensorFlowPipelineWrapper(Pipeline): + def __init__(self, model=None, model_config=None, compile_params=None): + super().__init__() + self.model_config = model_config or {} + self.compile_params = compile_params or { + "optimizer": "adam", + "loss": "sparse_categorical_crossentropy", + "metrics": ["accuracy"] + } + + # Create or use provided model + if model is not None: + if isinstance(model, str): + # Import the model class dynamically + parts = model.split(".") + module_name = ".".join(parts[:-1]) + class_name = parts[-1] + module = __import__(module_name, fromlist=[class_name]) + model_class = getattr(module, class_name) + self.model = model_class(**self.model_config) + else: + self.model = model + else: + raise ValueError("Model must be provided") + + # Compile the model + self.model.compile(**self.compile_params) + + def fit(self, X, y, **kwargs): + """Train the model on the provided data.""" + default_params = { + "epochs": 10, + "batch_size": 32, + "validation_split": 0.1, + "verbose": 1 + } + # Update defaults with any provided kwargs + params = {**default_params, **kwargs} + history = self.model.fit(X, y, **params) + return history + + def predict(self, X): + """Generate predictions for the input data.""" + return self.model.predict(X) + + def score(self, X, y): + """Evaluate the model on the provided data.""" + scores = self.model.evaluate(X, y, verbose=0) + if isinstance(scores, list): + # Return the main metric (usually loss is first, acc is second) + return scores[1] if len(scores) > 1 else scores[0] + return scores + + def save(self, path): + """Save the model to disk.""" + self.model.save(path) + + def load(self, path): + """Load the model from disk.""" + self.model = tf.keras.models.load_model(path) + return self +``` + +### Using the Custom Pipeline + +```python +# Create a TensorFlow pipeline +pipeline = TensorFlowPipelineWrapper( + model="tensorflow.keras.Sequential", + model_config={ + "layers": [ + tf.keras.layers.Dense(128, activation="relu", input_shape=(10,)), + tf.keras.layers.Dropout(0.2), + tf.keras.layers.Dense(64, activation="relu"), + tf.keras.layers.Dense(3, activation="softmax") + ] + }, + compile_params={ + "optimizer": "adam", + "loss": "sparse_categorical_crossentropy", + "metrics": ["accuracy"] + } +) + +# Train and use like any other pipeline +pipeline.fit(X_train, y_train, epochs=20, batch_size=64) +predictions = pipeline.predict(X_test) +``` + +## Best Practices + +For optimal results when working with pipelines: + +1. **Consistent Interfaces**: Ensure all pipeline implementations follow the standard interface +2. **Error Handling**: Add informative error messages for common issues +3. **Resource Management**: Clean up resources properly, especially with GPU-based models +4. **Serialization**: Test model saving and loading thoroughly +5. **Performance Monitoring**: Include ways to monitor training performance +6. **Parameter Validation**: Validate parameters early to catch configuration errors +7. **Documentation**: Document expected input formats and model-specific behaviors + +## API Reference + +### Pipeline (Abstract Base Class) + +```python +class Pipeline: + """Abstract base class for all pipeline implementations in EZFlow.""" + + def __init__(self): + """Initialize the pipeline.""" + self.pipeline = None + self.pipeline_config = {} + + def fit(self, X: np.ndarray, y: np.ndarray, **kwargs) -> Any: + """ + Train the model on the provided data. + + Parameters + ---------- + X : numpy.ndarray + Features for training + y : numpy.ndarray + Target values for training + **kwargs : dict + Additional arguments for the specific implementation + + Returns + ------- + Any + Implementation-specific return value, often training history + """ + raise NotImplementedError + + def predict(self, X: np.ndarray, **kwargs) -> np.ndarray: + """ + Generate predictions for the input data. + + Parameters + ---------- + X : numpy.ndarray + Features to generate predictions for + **kwargs : dict + Additional arguments for the specific implementation + + Returns + ------- + numpy.ndarray + Predictions + """ + raise NotImplementedError + + def score(self, X: np.ndarray, y: np.ndarray, **kwargs) -> float: + """ + Evaluate the model on the provided data. + + Parameters + ---------- + X : numpy.ndarray + Features for evaluation + y : numpy.ndarray + Target values for evaluation + **kwargs : dict + Additional arguments for the specific implementation + + Returns + ------- + float + Score value (higher is better) + """ + raise NotImplementedError + + def save(self, path: Union[str, Path]) -> None: + """ + Save the model to disk. + + Parameters + ---------- + path : str or Path + Path to save the model to + """ + raise NotImplementedError + + def load(self, path: Union[str, Path]) -> 'Pipeline': + """ + Load the model from disk. + + Parameters + ---------- + path : str or Path + Path to load the model from + + Returns + ------- + Pipeline + Self, for method chaining + """ + raise NotImplementedError +``` + +### SklearnPipelineWrapper + +```python +class SklearnPipelineWrapper(Pipeline): + """Pipeline implementation for scikit-learn models.""" + + def __init__(self, steps: List[Tuple[str, Union[str, Any], Dict[str, Any]]]): + """ + Initialize the scikit-learn pipeline. + + Parameters + ---------- + steps : list of tuples + List of (name, estimator, params) tuples defining the pipeline steps. + Estimator can be a string (module path) or an actual estimator class. + """ +``` + +### TorchPipelineWrapper + +```python +class TorchPipelineWrapper(Pipeline): + """Pipeline implementation for PyTorch models.""" + + def __init__( + self, + model: Union[str, nn.Module], + loss_fn: Union[str, Callable], + optimizer: Union[str, Type[optim.Optimizer]], + optimizer_params: Dict[str, Any] = None, + device: str = "auto" + ): + """ + Initialize the PyTorch pipeline. + + Parameters + ---------- + model : str or torch.nn.Module + PyTorch model or string reference to a model class + loss_fn : str or callable + Loss function or string reference + optimizer : str or torch.optim.Optimizer + Optimizer class or string reference + optimizer_params : dict + Dictionary of optimizer parameters + device : str + "cpu", "cuda", or "auto" for automatic selection + """ +``` \ No newline at end of file diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..7c509b7 --- /dev/null +++ b/environment.yml @@ -0,0 +1,26 @@ +name: ezflow +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 + - pip + - numpy>=1.19.0 + - pandas>=1.2.0 + - scikit-learn>=0.24.0 + - pytorch>=1.8.0 + - tensorflow>=2.4.0 + - rdkit>=2022.3.1 + - mlflow>=1.20.0 + - tensorboard>=2.4.0 + - optuna>=2.8.0 + - pytest>=6.0.0 + - black>=21.5b2 + - isort>=5.8.0 + - flake8>=3.9.0 + - dash>=2.0.0 + - plotly>=5.0.0 + - jupyter + - ipykernel + - pip: + - -e . \ No newline at end of file diff --git a/ezflow/__init__.py b/ezflow/__init__.py new file mode 100644 index 0000000..aed548b --- /dev/null +++ b/ezflow/__init__.py @@ -0,0 +1,16 @@ +""" +EZFlow: A Simple, Modular ML Experimentation Pipeline +""" + +__version__ = "0.1.0" + +from ezflow.core.dataset.base import BaseDataset +from ezflow.core.pipeline.base import Pipeline +from ezflow.core.experiment.tracker import ExperimentTracker, ExperimentConfig + +__all__ = [ + "BaseDataset", + "Pipeline", + "ExperimentTracker", + "ExperimentConfig", +] diff --git a/ezflow/cli.py b/ezflow/cli.py new file mode 100644 index 0000000..cd911d6 --- /dev/null +++ b/ezflow/cli.py @@ -0,0 +1,73 @@ +import argparse +import sys +from pathlib import Path + +def create_project(name: str): + """Create a new EZFlow project with standard directory structure.""" + project_dir = Path(name) + + # Create project directory + project_dir.mkdir(exist_ok=True) + + # Create standard directories + (project_dir / "data" / "raw").mkdir(parents=True, exist_ok=True) + (project_dir / "data" / "processed").mkdir(parents=True, exist_ok=True) + (project_dir / "models").mkdir(exist_ok=True) + (project_dir / "experiments").mkdir(exist_ok=True) + (project_dir / "notebooks").mkdir(exist_ok=True) + + # Create basic files + (project_dir / ".gitignore").write_text(""" +# Python +__pycache__/ +*.py[cod] +*.so +.Python +*.egg-info/ + +# Virtual Environment +venv/ +env/ + +# IDE +.idea/ +.vscode/ + +# ML specific +models/ +experiments/ +mlruns/ + +# Data +data/ +*.csv +*.json +*.jsonl + +# OS specific +.DS_Store +Thumbs.db +""") + + print(f"Created new EZFlow project in {project_dir.absolute()}") + +def main(): + """Main entry point for the EZFlow CLI.""" + parser = argparse.ArgumentParser(description="EZFlow CLI") + subparsers = parser.add_subparsers(dest="command") + + # Create project command + create_parser = subparsers.add_parser("create", help="Create a new EZFlow project") + create_parser.add_argument("name", help="Name of the project") + + # Parse arguments + args = parser.parse_args() + + if args.command == "create": + create_project(args.name) + else: + parser.print_help() + sys.exit(1) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ezflow/core/__init__.py b/ezflow/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ezflow/core/dataset/__init__.py b/ezflow/core/dataset/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ezflow/core/dataset/base.py b/ezflow/core/dataset/base.py new file mode 100644 index 0000000..80e55a9 --- /dev/null +++ b/ezflow/core/dataset/base.py @@ -0,0 +1,194 @@ +from abc import ABC, abstractmethod +from typing import Any, Dict, Optional, Union, List +import pandas as pd +import json +from pathlib import Path + +class BaseDataset(ABC): + """ + Abstract base class for all datasets in EZFlow. + + This class defines the interface and common functionality that all dataset implementations + must provide. It handles basic data operations, validation, and transformations. + """ + + def __init__( + self, + name: str, + data_dir: Union[str, Path], + cache_dir: Optional[Union[str, Path]] = None, + metadata: Optional[Dict[str, Any]] = None + ): + """ + Initialize the dataset. + + Args: + name: Name of the dataset/competition + data_dir: Directory containing the raw data + cache_dir: Directory for storing processed data and cache + metadata: Additional dataset-specific metadata + """ + self.name = name + self.data_dir = Path(data_dir) + self.cache_dir = Path(cache_dir) if cache_dir else Path(data_dir) / '.cache' + self.metadata = metadata or {} + + # Create cache directory if it doesn't exist + self.cache_dir.mkdir(parents=True, exist_ok=True) + + # Initialize data containers + self.train_data = None + self.val_data = None + self.test_data = None + + @abstractmethod + def load_data(self) -> None: + """ + Load the raw data into memory. + This method should be implemented by child classes to handle dataset-specific loading logic. + """ + pass + + @abstractmethod + def preprocess(self) -> None: + """ + Preprocess the raw data into a format suitable for training. + This method should be implemented by child classes to handle dataset-specific preprocessing. + """ + pass + + def to_jsonl(self, data: pd.DataFrame, output_path: Union[str, Path]) -> None: + """ + Convert and save data to JSONL format. + + Args: + data: DataFrame to convert + output_path: Path to save the JSONL file + """ + output_path = Path(output_path) + with output_path.open('w') as f: + for record in data.to_dict('records'): + f.write(json.dumps(record) + '\n') + + def from_jsonl(self, input_path: Union[str, Path]) -> pd.DataFrame: + """ + Load data from a JSONL file. + + Args: + input_path: Path to the JSONL file + + Returns: + DataFrame containing the loaded data + """ + records = [] + input_path = Path(input_path) + with input_path.open('r') as f: + for line in f: + records.append(json.loads(line.strip())) + return pd.DataFrame.from_records(records) + + def split_data( + self, + val_size: float = 0.2, + test_size: Optional[float] = None, + random_state: Optional[int] = None + ) -> None: + """ + Split the data into train/val/test sets. + + Args: + val_size: Fraction of data to use for validation + test_size: Fraction of data to use for testing + random_state: Random seed for reproducibility + """ + from sklearn.model_selection import train_test_split + + if self.train_data is None: + raise ValueError("Data must be loaded before splitting") + + if test_size: + # First split into train+val and test + train_val, self.test_data = train_test_split( + self.train_data, + test_size=test_size, + random_state=random_state + ) + + # Then split train+val into train and val + self.train_data, self.val_data = train_test_split( + train_val, + test_size=val_size/(1-test_size), # Adjust val_size to account for test split + random_state=random_state + ) + else: + # Split only into train and val + self.train_data, self.val_data = train_test_split( + self.train_data, + test_size=val_size, + random_state=random_state + ) + + def save_splits(self, output_dir: Optional[Union[str, Path]] = None) -> None: + """ + Save the train/val/test splits to JSONL files. + + Args: + output_dir: Directory to save the splits. If None, uses cache_dir. + """ + output_dir = Path(output_dir) if output_dir else self.cache_dir + output_dir.mkdir(parents=True, exist_ok=True) + + if self.train_data is not None: + self.to_jsonl(self.train_data, output_dir / 'train.jsonl') + if self.val_data is not None: + self.to_jsonl(self.val_data, output_dir / 'val.jsonl') + if self.test_data is not None: + self.to_jsonl(self.test_data, output_dir / 'test.jsonl') + + def load_splits(self, input_dir: Optional[Union[str, Path]] = None) -> None: + """ + Load the train/val/test splits from JSONL files. + + Args: + input_dir: Directory containing the splits. If None, uses cache_dir. + """ + input_dir = Path(input_dir) if input_dir else self.cache_dir + + train_path = input_dir / 'train.jsonl' + val_path = input_dir / 'val.jsonl' + test_path = input_dir / 'test.jsonl' + + if train_path.exists(): + self.train_data = self.from_jsonl(train_path) + if val_path.exists(): + self.val_data = self.from_jsonl(val_path) + if test_path.exists(): + self.test_data = self.from_jsonl(test_path) + + @abstractmethod + def get_features(self, data: pd.DataFrame) -> Any: + """ + Extract features from the data. + This method should be implemented by child classes to handle dataset-specific feature extraction. + + Args: + data: DataFrame containing the data + + Returns: + Features in the format required by the model + """ + pass + + @abstractmethod + def get_labels(self, data: pd.DataFrame) -> Any: + """ + Extract labels from the data. + This method should be implemented by child classes to handle dataset-specific label extraction. + + Args: + data: DataFrame containing the data + + Returns: + Labels in the format required by the model + """ + pass \ No newline at end of file diff --git a/ezflow/core/dataset/iris.py b/ezflow/core/dataset/iris.py new file mode 100644 index 0000000..f23de78 --- /dev/null +++ b/ezflow/core/dataset/iris.py @@ -0,0 +1,64 @@ +import os +import pandas as pd +import numpy as np +from sklearn.model_selection import train_test_split + +class IrisDataset(BaseDataset): + def __init__(self, data_dir): + super().__init__("iris", data_dir) + self.train_data = None + self.val_data = None + self.test_data = None + + def load_data(self): + """Load the Iris dataset.""" + from sklearn.datasets import load_iris + iris = load_iris() + data = pd.DataFrame(data=np.c_[iris['data'], iris['target']], + columns=iris['feature_names'] + ['target']) + self.train_data = data + + def split_data(self, val_size=0.2, random_state=42): + """Split data into train and validation sets.""" + if self.train_data is None: + raise ValueError("Data not loaded. Call load_data() first.") + + # Get indices for splitting while preserving original data + all_data = self.train_data.copy() + indices = np.arange(len(all_data)) + train_idx, val_idx = train_test_split( + indices, + test_size=val_size, + random_state=random_state, + stratify=all_data['target'] + ) + + # Split the data using indices + self.train_data = all_data.iloc[train_idx] + self.val_data = all_data.iloc[val_idx] + + def save_splits(self): + """Save train and validation splits to disk.""" + if self.train_data is not None: + self.train_data.to_csv(os.path.join(self.data_dir, "train_split.csv")) + if self.val_data is not None: + self.val_data.to_csv(os.path.join(self.data_dir, "val_split.csv")) + + def load_splits(self): + """Load train and validation splits from disk.""" + train_path = os.path.join(self.data_dir, "train_split.csv") + val_path = os.path.join(self.data_dir, "val_split.csv") + + if os.path.exists(train_path): + self.train_data = pd.read_csv(train_path, index_col=0) + if os.path.exists(val_path): + self.val_data = pd.read_csv(val_path, index_col=0) + + def get_features(self, data): + """Extract features from data.""" + feature_cols = [col for col in data.columns if col != 'target'] + return data[feature_cols].values + + def get_labels(self, data): + """Extract labels from data.""" + return data['target'].values \ No newline at end of file diff --git a/ezflow/core/dataset/molecular.py b/ezflow/core/dataset/molecular.py new file mode 100644 index 0000000..2c299f6 --- /dev/null +++ b/ezflow/core/dataset/molecular.py @@ -0,0 +1,192 @@ +from typing import Any, Dict, Optional, Union, List, Tuple +import pandas as pd +import numpy as np +from pathlib import Path +from rdkit import Chem +from rdkit.Chem import AllChem +import os + +from .base import BaseDataset + +class MolecularDataset(BaseDataset): + """ + Dataset class for handling molecular data (SMILES, fingerprints, etc.). + Implements molecular-specific functionality on top of BaseDataset. + """ + + def __init__( + self, + name: str, + data_dir: Union[str, Path], + smiles_col: str = 'smiles', + label_col: str = 'activity', + id_col: Optional[str] = 'id', + cache_dir: Optional[Union[str, Path]] = None, + metadata: Optional[Dict[str, Any]] = None, + fingerprint_radius: int = 2, + fingerprint_bits: int = 2048 + ): + """ + Initialize the molecular dataset. + + Args: + name: Name of the dataset/competition + data_dir: Directory containing the raw data + smiles_col: Name of the column containing SMILES strings + label_col: Name of the column containing labels + id_col: Name of the column containing molecule IDs + cache_dir: Directory for storing processed data and cache + metadata: Additional dataset-specific metadata + fingerprint_radius: Radius for Morgan fingerprint generation + fingerprint_bits: Number of bits in the fingerprint + """ + super().__init__(name, data_dir, cache_dir, metadata) + + self.smiles_col = smiles_col + self.label_col = label_col + self.id_col = id_col + self.fingerprint_radius = fingerprint_radius + self.fingerprint_bits = fingerprint_bits + + # Additional molecular-specific attributes + self.has_3d = False + self.has_fingerprints = False + + def generate_fingerprint(self, smiles: str) -> np.ndarray: + """ + Generate Morgan fingerprint from SMILES string. + + Args: + smiles: SMILES representation of molecule + + Returns: + Numpy array containing the fingerprint + """ + try: + mol = Chem.MolFromSmiles(smiles) + if mol is None: + raise ValueError(f"Invalid SMILES string: {smiles}") + + fingerprint = AllChem.GetMorganFingerprintAsBitVect( + mol, + self.fingerprint_radius, + nBits=self.fingerprint_bits + ) + return np.array(fingerprint) + except Exception as e: + raise ValueError(f"Error generating fingerprint for SMILES {smiles}: {str(e)}") + + def load_data(self) -> None: + """ + Load molecular data from CSV files in the data directory. + Expects 'train.csv' and optionally 'test.csv'. + """ + train_path = self.data_dir / 'train.csv' + test_path = self.data_dir / 'test.csv' + + if not train_path.exists(): + raise FileNotFoundError(f"Training data not found at {train_path}") + + self.train_data = pd.read_csv(train_path) + if test_path.exists(): + self.test_data = pd.read_csv(test_path) + + # Validate required columns + required_cols = [self.smiles_col] + if self.label_col and self.train_data is not None: + required_cols.append(self.label_col) + + for df in [self.train_data, self.test_data]: + if df is not None and not all(col in df.columns for col in required_cols): + raise ValueError(f"Missing required columns: {required_cols}") + + def preprocess(self) -> None: + """ + Preprocess the molecular data: + 1. Generate fingerprints + 2. Clean SMILES strings + 3. Handle missing values + """ + for df in [self.train_data, self.val_data, self.test_data]: + if df is not None: + # Clean SMILES and generate fingerprints + df['fingerprint'] = df[self.smiles_col].apply(self.generate_fingerprint) + + # Canonicalize SMILES + df[self.smiles_col] = df[self.smiles_col].apply( + lambda s: Chem.MolToSmiles(Chem.MolFromSmiles(s), canonical=True) + ) + + self.has_fingerprints = True + + def get_features(self, data: pd.DataFrame) -> np.ndarray: + """ + Get molecular fingerprints as features. + + Args: + data: DataFrame containing the molecular data + + Returns: + Numpy array of fingerprints + """ + if not self.has_fingerprints: + raise ValueError("Fingerprints not generated. Call preprocess() first.") + + return np.stack(data['fingerprint'].values) + + def get_labels(self, data: pd.DataFrame) -> np.ndarray: + """ + Get labels from the data. + + Args: + data: DataFrame containing the molecular data + + Returns: + Numpy array of labels + """ + if self.label_col not in data.columns: + raise ValueError(f"Label column '{self.label_col}' not found in data") + + return data[self.label_col].values + + def generate_submission( + self, + predictions: np.ndarray, + output_path: Union[str, Path] + ) -> None: + """ + Generate a submission file with predictions. + + Args: + predictions: Array of predictions + output_path: Path to save the submission file + """ + if self.test_data is None: + raise ValueError("Test data not loaded") + + if self.id_col not in self.test_data.columns: + raise ValueError(f"ID column '{self.id_col}' not found in test data") + + submission = pd.DataFrame({ + self.id_col: self.test_data[self.id_col], + self.label_col: predictions + }) + + submission.to_csv(output_path, index=False) + + def save_splits(self): + """Save train and validation splits to disk.""" + if self.train_data is not None: + self.train_data.to_csv(os.path.join(self.data_dir, "train_split.csv")) + if self.val_data is not None: + self.val_data.to_csv(os.path.join(self.data_dir, "val_split.csv")) + + def load_splits(self): + """Load train and validation splits from disk.""" + train_path = os.path.join(self.data_dir, "train_split.csv") + val_path = os.path.join(self.data_dir, "val_split.csv") + + if os.path.exists(train_path): + self.train_data = pd.read_csv(train_path, index_col=0) + if os.path.exists(val_path): + self.val_data = pd.read_csv(val_path, index_col=0) \ No newline at end of file diff --git a/ezflow/core/experiment/__init__.py b/ezflow/core/experiment/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ezflow/core/experiment/tracker.py b/ezflow/core/experiment/tracker.py new file mode 100644 index 0000000..94845d2 --- /dev/null +++ b/ezflow/core/experiment/tracker.py @@ -0,0 +1,179 @@ +from typing import Any, Dict, Optional, Union, List +import os +from pathlib import Path +import mlflow +import json +import datetime +from dataclasses import dataclass, asdict + +@dataclass +class ExperimentConfig: + """Configuration for an experiment.""" + name: str + model_params: Dict[str, Any] + dataset_params: Dict[str, Any] + training_params: Dict[str, Any] + tags: Optional[Dict[str, str]] = None + +class ExperimentTracker: + """ + Experiment tracking with MLflow integration. + Handles logging of parameters, metrics, and artifacts. + """ + + def __init__( + self, + experiment_name: str, + tracking_uri: Optional[str] = None, + artifacts_dir: Optional[Union[str, Path]] = None + ): + """ + Initialize the experiment tracker. + + Args: + experiment_name: Name of the experiment + tracking_uri: URI for MLflow tracking server + artifacts_dir: Directory to store artifacts + """ + self.experiment_name = experiment_name + self.artifacts_dir = Path(artifacts_dir) if artifacts_dir else Path("mlruns") + + # Set up MLflow + if tracking_uri: + mlflow.set_tracking_uri(tracking_uri) + + # Create or get experiment + try: + self.experiment_id = mlflow.create_experiment( + experiment_name, + artifact_location=str(self.artifacts_dir) + ) + except: + self.experiment_id = mlflow.get_experiment_by_name(experiment_name).experiment_id + + self.current_run = None + + def start_run( + self, + config: ExperimentConfig, + run_name: Optional[str] = None, + nested: bool = False + ) -> None: + """ + Start a new experiment run. + + Args: + config: Experiment configuration + run_name: Optional name for the run + nested: Whether this is a nested run + """ + if not run_name: + run_name = f"{config.name}_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}" + + self.current_run = mlflow.start_run( + experiment_id=self.experiment_id, + run_name=run_name, + nested=nested + ) + + # Log configuration + mlflow.log_params(config.model_params) + mlflow.log_params(config.dataset_params) + mlflow.log_params(config.training_params) + + if config.tags: + mlflow.set_tags(config.tags) + + # Save full config as JSON artifact + config_path = self.artifacts_dir / f"{run_name}_config.json" + with config_path.open('w') as f: + json.dump(asdict(config), f, indent=2) + mlflow.log_artifact(str(config_path)) + + def log_metric(self, key: str, value: float, step: Optional[int] = None) -> None: + """ + Log a metric value. + + Args: + key: Metric name + value: Metric value + step: Optional step number + """ + mlflow.log_metric(key, value, step=step) + + def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None: + """ + Log multiple metrics. + + Args: + metrics: Dictionary of metric names and values + step: Optional step number + """ + mlflow.log_metrics(metrics, step=step) + + def log_artifact(self, local_path: Union[str, Path]) -> None: + """ + Log a local file or directory as an artifact. + + Args: + local_path: Path to the file or directory + """ + mlflow.log_artifact(str(local_path)) + + def log_model( + self, + model: Any, + artifact_path: str, + conda_env: Optional[Dict[str, Any]] = None + ) -> None: + """ + Log a model artifact. + + Args: + model: The model object + artifact_path: Destination path within the run's artifact directory + conda_env: Optional Conda environment + """ + mlflow.pyfunc.log_model( + artifact_path, + python_model=model, + conda_env=conda_env + ) + + def end_run(self, status: str = "FINISHED") -> None: + """ + End the current run. + + Args: + status: Run status ("FINISHED", "FAILED", etc.) + """ + if self.current_run: + mlflow.end_run(status=status) + self.current_run = None + + def get_run_info(self) -> Dict[str, Any]: + """ + Get information about the current run. + + Returns: + Dictionary containing run information + """ + if not self.current_run: + raise ValueError("No active run") + + return { + "run_id": self.current_run.info.run_id, + "experiment_id": self.experiment_id, + "status": self.current_run.info.status, + "start_time": self.current_run.info.start_time, + "artifact_uri": self.current_run.info.artifact_uri + } + + def __enter__(self): + """Context manager entry.""" + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + """Context manager exit.""" + status = "FAILED" if exc_type else "FINISHED" + self.end_run(status=status) \ No newline at end of file diff --git a/ezflow/core/pipeline/__init__.py b/ezflow/core/pipeline/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ezflow/core/pipeline/base.py b/ezflow/core/pipeline/base.py new file mode 100644 index 0000000..73779b2 --- /dev/null +++ b/ezflow/core/pipeline/base.py @@ -0,0 +1,143 @@ +from typing import Any, Dict, List, Optional, Union, Tuple, Callable +import numpy as np +from pathlib import Path +import importlib +from abc import ABC, abstractmethod + +class Pipeline(ABC): + """ + Abstract base class for ML pipelines. + Supports multiple ML frameworks and custom transformations. + """ + + def __init__( + self, + steps: List[Tuple[str, Union[str, Any], Optional[Dict[str, Any]]]], + framework: str = "sklearn" + ): + """ + Initialize the pipeline. + + Args: + steps: List of (name, transformer, params) tuples where: + - name is a string + - transformer is either a string (module path) or an object + - params is an optional dict of parameters + framework: ML framework to use ("sklearn", "torch", "tensorflow") + """ + self.steps = [] + self.framework = framework.lower() + + for name, transformer, params in steps: + if isinstance(transformer, str): + # Import the transformer class + module_path, class_name = transformer.rsplit(".", 1) + module = importlib.import_module(module_path) + transformer_class = getattr(module, class_name) + + # Initialize with params if provided + if params: + transformer = transformer_class(**params) + else: + transformer = transformer_class() + + self.steps.append((name, transformer)) + + @abstractmethod + def fit(self, X: Any, y: Optional[Any] = None) -> 'Pipeline': + """ + Fit the pipeline on training data. + + Args: + X: Training features + y: Training labels + + Returns: + self + """ + pass + + @abstractmethod + def transform(self, X: Any) -> Any: + """ + Apply transformations to the data. + + Args: + X: Input data + + Returns: + Transformed data + """ + pass + + @abstractmethod + def predict(self, X: Any) -> Any: + """ + Make predictions on new data. + + Args: + X: Input data + + Returns: + Predictions + """ + pass + + def fit_transform(self, X: Any, y: Optional[Any] = None) -> Any: + """ + Fit the pipeline and transform the data. + + Args: + X: Input data + y: Labels + + Returns: + Transformed data + """ + return self.fit(X, y).transform(X) + + def save(self, path: Union[str, Path]) -> None: + """ + Save the pipeline to disk. + + Args: + path: Path to save the pipeline + """ + raise NotImplementedError("Saving not implemented for this pipeline") + + def load(self, path: Union[str, Path]) -> None: + """ + Load the pipeline from disk. + + Args: + path: Path to load the pipeline from + """ + raise NotImplementedError("Loading not implemented for this pipeline") + + def get_params(self) -> Dict[str, Any]: + """ + Get pipeline parameters. + + Returns: + Dictionary of parameters + """ + params = {} + for name, transformer in self.steps: + if hasattr(transformer, 'get_params'): + params[name] = transformer.get_params() + return params + + def set_params(self, **params) -> 'Pipeline': + """ + Set pipeline parameters. + + Args: + **params: Parameters to set + + Returns: + self + """ + for name, transformer in self.steps: + if name in params and hasattr(transformer, 'set_params'): + transformer.set_params(**params[name]) + return self \ No newline at end of file diff --git a/ezflow/core/pipeline/sklearn.py b/ezflow/core/pipeline/sklearn.py new file mode 100644 index 0000000..d3e3a9c --- /dev/null +++ b/ezflow/core/pipeline/sklearn.py @@ -0,0 +1,129 @@ +from typing import Any, Dict, List, Optional, Union, Tuple +import numpy as np +from pathlib import Path +import joblib +from sklearn.pipeline import Pipeline as SklearnPipeline +from sklearn.base import BaseEstimator, TransformerMixin + +from .base import Pipeline + +class SklearnPipelineWrapper(Pipeline): + """ + Scikit-learn pipeline implementation. + Wraps sklearn.pipeline.Pipeline with additional functionality. + """ + + def __init__( + self, + steps: List[Tuple[str, Union[str, Any], Optional[Dict[str, Any]]]] + ): + """ + Initialize the scikit-learn pipeline. + + Args: + steps: List of (name, transformer, params) tuples + """ + super().__init__(steps, framework="sklearn") + + # Create sklearn Pipeline + self.pipeline = SklearnPipeline(self.steps) + + def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> 'SklearnPipelineWrapper': + """ + Fit the pipeline on training data. + + Args: + X: Training features + y: Training labels + + Returns: + self + """ + self.pipeline.fit(X, y) + return self + + def transform(self, X: np.ndarray) -> np.ndarray: + """ + Apply transformations to the data. + + Args: + X: Input data + + Returns: + Transformed data + """ + # If the last step is an estimator, use predict + if hasattr(self.pipeline.steps[-1][1], 'predict'): + return self.predict(X) + return self.pipeline.transform(X) + + def predict(self, X: np.ndarray) -> np.ndarray: + """ + Make predictions on new data. + + Args: + X: Input data + + Returns: + Predictions + """ + return self.pipeline.predict(X) + + def predict_proba(self, X: np.ndarray) -> np.ndarray: + """ + Get probability estimates. + + Args: + X: Input data + + Returns: + Probability estimates + """ + if hasattr(self.pipeline.steps[-1][1], 'predict_proba'): + return self.pipeline.predict_proba(X) + raise AttributeError("Final estimator does not have predict_proba method") + + def score(self, X: np.ndarray, y: np.ndarray) -> float: + """ + Calculate the score on test data. + + Args: + X: Test features + y: Test labels + + Returns: + Score value + """ + return self.pipeline.score(X, y) + + def save(self, path: Union[str, Path]) -> None: + """ + Save the pipeline to disk using joblib. + + Args: + path: Path to save the pipeline + """ + path = Path(path) + joblib.dump(self.pipeline, path) + + def load(self, path: Union[str, Path]) -> None: + """ + Load the pipeline from disk using joblib. + + Args: + path: Path to load the pipeline from + """ + path = Path(path) + self.pipeline = joblib.load(path) + self.steps = self.pipeline.steps + + def get_feature_names_out(self) -> List[str]: + """ + Get feature names after transformation. + + Returns: + List of feature names + """ + if hasattr(self.pipeline, 'get_feature_names_out'): + return self.pipeline.get_feature_names_out() + raise AttributeError("Pipeline does not support feature names") \ No newline at end of file diff --git a/ezflow/datasets/competition/example_competition.py b/ezflow/datasets/competition/example_competition.py new file mode 100644 index 0000000..2a16ea2 --- /dev/null +++ b/ezflow/datasets/competition/example_competition.py @@ -0,0 +1,56 @@ +from typing import Optional, Union, Dict, Any +from pathlib import Path + +from ...core.dataset.molecular import MolecularDataset + +class ExampleCompetitionDataset(MolecularDataset): + """ + Example competition dataset that demonstrates how to implement + a competition-specific dataset using the MolecularDataset base class. + """ + + def __init__( + self, + data_dir: Union[str, Path], + cache_dir: Optional[Union[str, Path]] = None, + metadata: Optional[Dict[str, Any]] = None + ): + """ + Initialize the example competition dataset. + + Args: + data_dir: Directory containing the competition data + cache_dir: Directory for storing processed data and cache + metadata: Additional dataset-specific metadata + """ + super().__init__( + name="example_competition", + data_dir=data_dir, + smiles_col="SMILES", # Competition-specific column name + label_col="Target", # Competition-specific column name + id_col="MoleculeID", # Competition-specific column name + cache_dir=cache_dir, + metadata=metadata, + # Competition-specific fingerprint parameters + fingerprint_radius=3, + fingerprint_bits=4096 + ) + + def preprocess(self) -> None: + """ + Add competition-specific preprocessing steps. + """ + # First do the standard molecular preprocessing + super().preprocess() + + # Add competition-specific preprocessing + for df in [self.train_data, self.val_data, self.test_data]: + if df is not None: + # Example: Remove molecules with molecular weight > 500 + df['mol'] = df[self.smiles_col].apply(lambda s: Chem.MolFromSmiles(s)) + df['mw'] = df['mol'].apply(lambda m: Descriptors.ExactMolWt(m)) + df = df[df['mw'] <= 500].drop(['mol', 'mw'], axis=1) + + # Example: Normalize target values + if self.label_col in df.columns: + df[self.label_col] = (df[self.label_col] - df[self.label_col].mean()) / df[self.label_col].std() \ No newline at end of file diff --git a/ezflow/examples/iris_example.py b/ezflow/examples/iris_example.py new file mode 100644 index 0000000..9b7f387 --- /dev/null +++ b/ezflow/examples/iris_example.py @@ -0,0 +1,134 @@ +from pathlib import Path +import pandas as pd +import numpy as np +from sklearn.datasets import load_iris +from sklearn.metrics import accuracy_score, classification_report + +from ezflow.core.dataset.base import BaseDataset +from ezflow.core.pipeline.sklearn import SklearnPipelineWrapper +from ezflow.core.experiment.tracker import ExperimentTracker, ExperimentConfig + +class IrisDataset(BaseDataset): + """Simple dataset implementation for the Iris dataset.""" + + def __init__( + self, + data_dir: str, + cache_dir: str = None + ): + super().__init__( + name="iris", + data_dir=data_dir, + cache_dir=cache_dir + ) + + def load_data(self) -> None: + """Load the Iris dataset.""" + # Load iris dataset + iris = load_iris() + + # Convert to pandas DataFrame with a consistent index + self.train_data = pd.DataFrame( + data=np.c_[iris['data'], iris['target']], + columns=[*iris['feature_names'], 'target'] + ).reset_index(drop=True) + + def preprocess(self) -> None: + """Preprocess the data (standardization is handled in pipeline).""" + pass + + def get_features(self, data: pd.DataFrame) -> np.ndarray: + """Get feature columns.""" + return data.iloc[:, :-1].values + + def get_labels(self, data: pd.DataFrame) -> np.ndarray: + """Get target column.""" + return data.iloc[:, -1].values + +def run_iris_experiment(): + """Run a simple experiment with the Iris dataset.""" + + # Create directories + Path("experiments").mkdir(exist_ok=True) + Path("models").mkdir(exist_ok=True) + + # Initialize dataset + dataset = IrisDataset(data_dir=".") + dataset.load_data() + dataset.split_data(val_size=0.2, random_state=42) + dataset.preprocess() + + # Get features and labels + X_train = dataset.get_features(dataset.train_data) + y_train = dataset.get_labels(dataset.train_data) + X_val = dataset.get_features(dataset.val_data) + y_val = dataset.get_labels(dataset.val_data) + + # Create pipeline with preprocessing and model + pipeline = SklearnPipelineWrapper([ + ("scaler", "sklearn.preprocessing.StandardScaler", {}), + ("classifier", "sklearn.ensemble.RandomForestClassifier", { + "n_estimators": 100, + "max_depth": 5, + "random_state": 42 + }) + ]) + + # Create experiment config + config = ExperimentConfig( + name="iris_classification", + model_params={ + "n_estimators": 100, + "max_depth": 5 + }, + dataset_params={ + "val_size": 0.2 + }, + training_params={ + "random_state": 42 + } + ) + + # Initialize experiment tracker + tracker = ExperimentTracker( + experiment_name="iris_example", + artifacts_dir="experiments" + ) + + # Run experiment + with tracker: + # Start run + tracker.start_run(config) + + # Train model + pipeline.fit(X_train, y_train) + + # Make predictions + train_preds = pipeline.predict(X_train) + val_preds = pipeline.predict(X_val) + + # Calculate metrics + train_acc = accuracy_score(y_train, train_preds) + val_acc = accuracy_score(y_val, val_preds) + + # Log metrics + tracker.log_metrics({ + "train_accuracy": train_acc, + "val_accuracy": val_acc + }) + + # Save model + pipeline.save("models/iris_rf.joblib") + tracker.log_artifact("models/iris_rf.joblib") + + # Print results + print("\nTraining Results:") + print("-" * 50) + print(f"Training Accuracy: {train_acc:.4f}") + print(f"Validation Accuracy: {val_acc:.4f}") + print("\nValidation Classification Report:") + print("-" * 50) + print(classification_report(y_val, val_preds, target_names=load_iris().target_names)) + +if __name__ == "__main__": + run_iris_experiment() \ No newline at end of file diff --git a/ezflow/examples/molecular_example.py b/ezflow/examples/molecular_example.py new file mode 100644 index 0000000..3e77616 --- /dev/null +++ b/ezflow/examples/molecular_example.py @@ -0,0 +1,102 @@ +from pathlib import Path +import numpy as np +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import average_precision_score + +from ezflow.core.dataset.molecular import MolecularDataset +from ezflow.core.pipeline.sklearn import SklearnPipelineWrapper +from ezflow.core.experiment.tracker import ExperimentTracker, ExperimentConfig + +def run_molecular_experiment(): + """Run a simple molecular machine learning experiment using EZFlow.""" + + # Initialize dataset + dataset = MolecularDataset( + name="example_molecular", + data_dir="data", + smiles_col="SMILES", + label_col="Activity", + fingerprint_radius=3, + fingerprint_bits=2048 + ) + + # Load and preprocess data + dataset.load_data() + dataset.split_data(val_size=0.2, random_state=42) + dataset.preprocess() + + # Get features and labels + X_train = dataset.get_features(dataset.train_data) + y_train = dataset.get_labels(dataset.train_data) + X_val = dataset.get_features(dataset.val_data) + y_val = dataset.get_labels(dataset.val_data) + + # Create pipeline + pipeline = SklearnPipelineWrapper([ + ("classifier", "sklearn.ensemble.RandomForestClassifier", { + "n_estimators": 100, + "max_depth": 10, + "random_state": 42 + }) + ]) + + # Create experiment configuration + config = ExperimentConfig( + name="molecular_rf", + model_params={ + "n_estimators": 100, + "max_depth": 10 + }, + dataset_params={ + "fingerprint_radius": 3, + "fingerprint_bits": 2048 + }, + training_params={ + "val_size": 0.2, + "random_state": 42 + } + ) + + # Initialize experiment tracker + tracker = ExperimentTracker( + experiment_name="molecular_classification", + artifacts_dir="experiments" + ) + + # Run experiment + with tracker: + # Start run + tracker.start_run(config) + + # Train model + pipeline.fit(X_train, y_train) + + # Make predictions + train_preds = pipeline.predict_proba(X_train)[:, 1] + val_preds = pipeline.predict_proba(X_val)[:, 1] + + # Calculate metrics + train_score = average_precision_score(y_train, train_preds) + val_score = average_precision_score(y_val, val_preds) + + # Log metrics + tracker.log_metrics({ + "train_avg_precision": train_score, + "val_avg_precision": val_score + }) + + # Save model + pipeline.save("models/molecular_rf.joblib") + tracker.log_artifact("models/molecular_rf.joblib") + + print(f"Training Score: {train_score:.4f}") + print(f"Validation Score: {val_score:.4f}") + +if __name__ == "__main__": + # Create necessary directories + Path("data").mkdir(exist_ok=True) + Path("models").mkdir(exist_ok=True) + Path("experiments").mkdir(exist_ok=True) + + # Run the experiment + run_molecular_experiment() \ No newline at end of file diff --git a/ezflow/tests/test_iris_dataset.py b/ezflow/tests/test_iris_dataset.py new file mode 100644 index 0000000..13f2d63 --- /dev/null +++ b/ezflow/tests/test_iris_dataset.py @@ -0,0 +1,97 @@ +import pytest +import numpy as np +import pandas as pd +from pathlib import Path +import tempfile +import shutil + +from ezflow.examples.iris_example import IrisDataset + +@pytest.fixture +def temp_dir(): + """Create a temporary directory for test files.""" + temp_dir = tempfile.mkdtemp() + yield Path(temp_dir) + shutil.rmtree(temp_dir) + +def test_iris_dataset_initialization(temp_dir): + """Test IrisDataset initialization.""" + dataset = IrisDataset(data_dir=str(temp_dir)) + + assert dataset.name == "iris" + assert dataset.data_dir == temp_dir + assert dataset.train_data is None + assert dataset.val_data is None + assert dataset.test_data is None + +def test_load_data(temp_dir): + """Test data loading.""" + dataset = IrisDataset(data_dir=str(temp_dir)) + dataset.load_data() + + assert isinstance(dataset.train_data, pd.DataFrame) + assert len(dataset.train_data) == 150 # Iris dataset size + assert dataset.train_data.shape[1] == 5 # 4 features + 1 target + assert all(col in dataset.train_data.columns for col in [ + 'sepal length (cm)', + 'sepal width (cm)', + 'petal length (cm)', + 'petal width (cm)', + 'target' + ]) + +def test_data_splitting(temp_dir): + """Test data splitting functionality.""" + dataset = IrisDataset(data_dir=str(temp_dir)) + dataset.load_data() + dataset.split_data(val_size=0.2, random_state=42) + + assert dataset.train_data is not None + assert dataset.val_data is not None + assert len(dataset.train_data) + len(dataset.val_data) == 150 + assert len(dataset.val_data) == 30 # 20% of 150 + +def test_get_features(temp_dir): + """Test feature extraction.""" + dataset = IrisDataset(data_dir=str(temp_dir)) + dataset.load_data() + + features = dataset.get_features(dataset.train_data) + assert isinstance(features, np.ndarray) + assert features.shape == (150, 4) # 150 samples, 4 features + +def test_get_labels(temp_dir): + """Test label extraction.""" + dataset = IrisDataset(data_dir=str(temp_dir)) + dataset.load_data() + + labels = dataset.get_labels(dataset.train_data) + assert isinstance(labels, np.ndarray) + assert labels.shape == (150,) # 150 samples + assert set(labels) == {0, 1, 2} # Three classes in Iris + +def test_save_load_splits(temp_dir): + """Test saving and loading data splits.""" + dataset = IrisDataset(data_dir=str(temp_dir)) + dataset.load_data() + dataset.split_data(val_size=0.2, random_state=42) + + # Save splits + dataset.save_splits() + + # Create new dataset and load splits + new_dataset = IrisDataset(data_dir=str(temp_dir)) + new_dataset.load_splits() + + assert new_dataset.train_data is not None + assert new_dataset.val_data is not None + + # Compare data content without considering index + pd.testing.assert_frame_equal( + dataset.train_data.reset_index(drop=True), + new_dataset.train_data.reset_index(drop=True) + ) + pd.testing.assert_frame_equal( + dataset.val_data.reset_index(drop=True), + new_dataset.val_data.reset_index(drop=True) + ) \ No newline at end of file diff --git a/ezflow/tests/test_molecular_dataset.py b/ezflow/tests/test_molecular_dataset.py new file mode 100644 index 0000000..f786abe --- /dev/null +++ b/ezflow/tests/test_molecular_dataset.py @@ -0,0 +1,145 @@ +import pytest +import pandas as pd +import numpy as np +from pathlib import Path +import tempfile +import shutil + +from ezflow.core.dataset.molecular import MolecularDataset + +@pytest.fixture +def test_data(): + """Create test data for molecular dataset.""" + data = pd.DataFrame({ + 'smiles': [ + 'CC(=O)OC1=CC=CC=C1C(=O)O', # Aspirin + 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C', # Caffeine + 'CC12CCC3C(C1CCC2O)CCC4=CC(=O)CCC34C' # Testosterone + ], + 'activity': [1, 0, 1], + 'id': ['mol1', 'mol2', 'mol3'] + }) + return data + +@pytest.fixture +def temp_dir(): + """Create a temporary directory for test files.""" + temp_dir = tempfile.mkdtemp() + yield Path(temp_dir) + shutil.rmtree(temp_dir) + +def test_molecular_dataset_initialization(temp_dir): + """Test MolecularDataset initialization.""" + dataset = MolecularDataset( + name="test", + data_dir=temp_dir, + smiles_col="smiles", + label_col="activity", + id_col="id" + ) + + assert dataset.name == "test" + assert dataset.data_dir == temp_dir + assert dataset.smiles_col == "smiles" + assert dataset.label_col == "activity" + assert dataset.id_col == "id" + assert dataset.fingerprint_radius == 2 + assert dataset.fingerprint_bits == 2048 + +def test_generate_fingerprint(test_data): + """Test fingerprint generation.""" + dataset = MolecularDataset("test", "dummy_dir") + + # Test valid SMILES + fp = dataset.generate_fingerprint(test_data['smiles'][0]) + assert isinstance(fp, np.ndarray) + assert fp.shape == (2048,) + + # Test invalid SMILES + with pytest.raises(ValueError): + dataset.generate_fingerprint("invalid_smiles") + +def test_load_data(temp_dir, test_data): + """Test data loading.""" + # Save test data + test_data.to_csv(temp_dir / "train.csv", index=False) + + dataset = MolecularDataset("test", temp_dir) + dataset.load_data() + + assert isinstance(dataset.train_data, pd.DataFrame) + assert len(dataset.train_data) == len(test_data) + assert all(col in dataset.train_data.columns for col in ['smiles', 'activity', 'id']) + +def test_preprocess(temp_dir, test_data): + """Test data preprocessing.""" + # Save test data + test_data.to_csv(temp_dir / "train.csv", index=False) + + dataset = MolecularDataset("test", temp_dir) + dataset.load_data() + dataset.preprocess() + + assert dataset.has_fingerprints + assert 'fingerprint' in dataset.train_data.columns + assert isinstance(dataset.train_data['fingerprint'].iloc[0], np.ndarray) + +def test_get_features(temp_dir, test_data): + """Test feature extraction.""" + # Save test data + test_data.to_csv(temp_dir / "train.csv", index=False) + + dataset = MolecularDataset("test", temp_dir) + dataset.load_data() + dataset.preprocess() + + features = dataset.get_features(dataset.train_data) + assert isinstance(features, np.ndarray) + assert features.shape == (len(test_data), 2048) + +def test_get_labels(temp_dir, test_data): + """Test label extraction.""" + # Save test data + test_data.to_csv(temp_dir / "train.csv", index=False) + + dataset = MolecularDataset("test", temp_dir) + dataset.load_data() + + labels = dataset.get_labels(dataset.train_data) + assert isinstance(labels, np.ndarray) + assert labels.shape == (len(test_data),) + assert set(labels) == {0, 1} + +def test_data_splitting(temp_dir, test_data): + """Test data splitting functionality.""" + # Save test data + test_data.to_csv(temp_dir / "train.csv", index=False) + + dataset = MolecularDataset("test", temp_dir) + dataset.load_data() + dataset.split_data(val_size=0.33, random_state=42) + + assert dataset.train_data is not None + assert dataset.val_data is not None + assert len(dataset.train_data) + len(dataset.val_data) == len(test_data) + +def test_save_load_splits(temp_dir, test_data): + """Test saving and loading data splits.""" + # Save test data + test_data.to_csv(temp_dir / "train.csv", index=False) + + dataset = MolecularDataset("test", temp_dir) + dataset.load_data() + dataset.split_data(val_size=0.33, random_state=42) + + # Save splits + dataset.save_splits() + + # Create new dataset and load splits + new_dataset = MolecularDataset("test", temp_dir) + new_dataset.load_splits() + + assert new_dataset.train_data is not None + assert new_dataset.val_data is not None + pd.testing.assert_frame_equal(dataset.train_data, new_dataset.train_data) + pd.testing.assert_frame_equal(dataset.val_data, new_dataset.val_data) \ No newline at end of file diff --git a/ezflow/utils/__init__.py b/ezflow/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ezflow/viz/__init__.py b/ezflow/viz/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 6a968b0..0000000 --- a/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -pandas -numpy -scikit-learn -rdkit \ No newline at end of file diff --git a/setup.py b/setup.py index 2b6bc2f..0b030bb 100644 --- a/setup.py +++ b/setup.py @@ -1,22 +1,59 @@ from setuptools import setup, find_packages -# Read requirements.txt for dependencies -with open("requirements.txt") as f: - required = f.read().splitlines() +# Read the contents of README file +with open("README.md", encoding="utf-8") as f: + long_description = f.read() setup( - name="bio_ml_handler", + name="ezflow-ml", version="0.1.0", - description="A data handler for bio-ML projects, including data loading, processing, and model handling.", author="Alexan Hayrapetyan", author_email="hayrapetyan.alexan@gmail.com", - url="https://github.com/Alcray/BioML", - packages=find_packages(), - install_requires=required, # Use the list from requirements.txt - python_requires=">=3.6", + description="A lightweight and easy-to-use ML experiment pipeline framework", + long_description=long_description, + long_description_content_type="text/markdown", + url="https://github.com/Alcray/ezflow", + packages=find_packages(include=["ezflow", "ezflow.*"]), classifiers=[ - "Programming Language :: Python :: 3", + "Development Status :: 3 - Alpha", + "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Topic :: Scientific/Engineering :: Artificial Intelligence", ], + python_requires=">=3.8", + install_requires=[ + "numpy>=1.19.0", + "pandas>=1.2.0", + "scikit-learn>=0.24.0", + "torch>=1.8.0", + "tensorflow>=2.4.0", + "rdkit>=2022.3.1", + "mlflow>=1.20.0", + "tensorboard>=2.4.0", + "optuna>=2.8.0", + "dash>=2.0.0", + "plotly>=5.0.0", + ], + extras_require={ + "dev": [ + "pytest>=6.0.0", + "pytest-cov>=2.12.0", + "black>=21.5b2", + "isort>=5.8.0", + "flake8>=3.9.0", + "mypy>=0.900", + ], + "docs": [ + "sphinx>=4.0.0", + "sphinx-rtd-theme>=1.0.0", + "sphinx-autodoc-typehints>=1.12.0", + ], + }, + entry_points={ + "console_scripts": [ + "ezflow=ezflow.cli:main", + ], + }, ) diff --git a/similarities_data/similarity2test.csv 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a8c30578c992667d6935420b678aed8fe5a52517 Mon Sep 17 00:00:00 2001 From: Alexan Date: Wed, 5 Mar 2025 12:33:44 +0400 Subject: [PATCH 2/6] some doc fixings Signed-off-by: Alexan --- CONTRIBUTING.md | 2 +- README.md | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 764c9d5..dd5af33 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -65,7 +65,7 @@ This section guides you through submitting a feature suggestion: 1. Fork the repository 2. Clone your fork locally: ```bash - git clone https://github.com/yourusername/ezflow.git + git clone https://github.com/Alcray/ezflow.git cd ezflow ``` 3. Create a conda environment using the provided environment.yml: diff --git a/README.md b/README.md index 516c5db..b51ef0c 100644 --- a/README.md +++ b/README.md @@ -5,10 +5,10 @@

- Tests Status + Tests Status PyPI version - License - GitHub stars + License + GitHub stars

EZFlow is a flexible, modular machine learning framework designed to streamline the development and deployment of ML pipelines. It provides a unified API for working with different types of datasets, models, and experiment tracking tools. @@ -41,7 +41,7 @@ pip install -e . pip install ezflow-ml # Or install in development mode from source -git clone https://github.com/yourusername/ezflow.git +git clone https://github.com/Alcray/ezflow.git cd ezflow pip install -e . ``` From 7934d5199368bc6bfa7bc3ad727751223279ab79 Mon Sep 17 00:00:00 2001 From: Alexan Date: Wed, 5 Mar 2025 12:40:03 +0400 Subject: [PATCH 3/6] Remove data files from Git tracking, update .gitignore to be more comprehensive, and update README.md --- .gitignore | 13 +- README.md | 25 +- data/sample_submission.csv | 2033 ----------------- data/test.csv | 2033 ----------------- data/train.csv | 4217 ------------------------------------ 5 files changed, 16 insertions(+), 8305 deletions(-) delete mode 100644 data/sample_submission.csv delete mode 100644 data/test.csv delete mode 100644 data/train.csv diff --git a/.gitignore b/.gitignore index ba3d429..3bd232d 100644 --- a/.gitignore +++ b/.gitignore @@ -55,8 +55,10 @@ logs/ wandb/ tensorboard/ -# Data +# Data - Comprehensive ignoring of data files and directories data/ +data/* +*/data/* *.csv *.json *.jsonl @@ -67,6 +69,15 @@ data/ *.h5 *.npz *.npy +*.tar +*.zip +*.gz +*.bz2 +*.xz +*.7z +*.txt +!requirements.txt +!environment.txt # OS specific .DS_Store diff --git a/README.md b/README.md index b51ef0c..8e78507 100644 --- a/README.md +++ b/README.md @@ -5,8 +5,6 @@

- Tests Status - PyPI version License GitHub stars

@@ -34,13 +32,10 @@ conda activate ezflow pip install -e . ``` -### Using Pip +### Development Installation ```bash -# Install from PyPI -pip install ezflow-ml - -# Or install in development mode from source +# Install in development mode from source git clone https://github.com/Alcray/ezflow.git cd ezflow pip install -e . @@ -89,21 +84,9 @@ Or run the included example: python -m ezflow.examples.iris_example ``` -## Documentation - -For more detailed documentation, please see: - -- [Architecture Overview](docs/architecture.md) -- [Dataset Module](docs/dataset.md) -- [Pipeline Module](docs/pipeline.md) -- [Experiment Tracking](docs/experiments.md) -- [Command-line Interface](docs/cli.md) -- [Examples and Tutorials](docs/examples.md) -- [API Reference](docs/api_reference.md) - -## Contributing +## Data Management -Contributions are welcome! Please check out our [contribution guidelines](CONTRIBUTING.md). +EZFlow works with data stored in the `data/` directory. This directory is included in `.gitignore` to prevent uploading datasets to GitHub. 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-170,NC(=O)c1cccc(O[C@@H]2C[C@H]3CC[C@@H](C2)N3Cc2ccc3c(c2)OCO3)c1 -171,COc1cccc(N2CCN(CCN3CCC(C(F)(F)F)CC3)C2=O)c1 -172,NC(=O)c1cnc(N[C@H]2CCCNC2)c2cc(-c3ccsc3)sc12 -173,Cc1ccc2c(-c3nnc(SCCN4CCc5ccc(-c6cc(C)nn6C)cc5CC4)n3C)cccc2n1 -174,O=C(NC1CCN(Cc2ccccc2)CC1)c1cc(=O)c2ccc(F)cc2o1 -175,CN1C(=O)[C@@H](CN2CCCCC2)C[C@H]1C#Cc1ccccc1 -176,O=C1c2nc3cccc(C(F)(F)F)c3n2CCN1CC1CCC1 -177,CC(=O)NCCc1ccccc1-c1onc([C@H]2CNCC[C@]2(O)c2ccc(F)c(F)c2)c1Cl -178,C[C@@H]1CN(C(=O)c2ccccc2)CCN1C(=O)C(=O)c1c[nH]c2ccccc12 -179,Cn1cc2c(Nc3nc(N[C@@H]4CCOC[C@@H]4N)cc4cc[nH]c(=O)c34)cccc2n1 -180,CCN(C(=O)Cc1ccc(S(C)(=O)=O)cc1)C1CCN(CC[C@@H](c2ccccc2)C2CCN(S(C)(=O)=O)CC2)CC1 -181,CCN(CC)C(=O)c1ccc(C2=CC3(CCNCC3)Oc3ccccc32)cc1F -182,CN(C(=O)Cc1ccc(-n2cnnn2)cc1)[C@@H]1CCN(Cc2ccc(C(F)(F)F)cc2)C[C@@H]1F -183,O=C1NCc2nc(-c3c(F)cccc3F)cc(Nc3ccc(C(=O)N4CCCC4)cc3)c21 -184,Cc1nn2ccccc2c1CCNCc1ccc(/C=C/C(=O)NO)cc1 -185,Cc1nc(-c2cccs2)sc1-c1ccc2cc(CCN3CCC[C@H]3C)ccc2n1 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-218,Cn1cnc(C(=O)N(Cc2cccc(OC(F)(F)F)c2)[C@@H]2C[C@@H]3CN(CCC(F)(F)F)C[C@@H]3C2)c1 -219,Cc1nc2ccccc2c(=O)n1-c1ccc(O[C@H]2CC[C@@H](N3CCCC3)CC2)cc1 -220,Cc1cccc(C(CC[C@H](N)C(=O)O)(c2ccccc2)c2ccccc2)c1 -221,O=C(CC1CCN(Cc2ccn(-c3ccc(C(F)(F)F)cc3)c2)CC1)NC(c1ccc(F)cc1)c1ccc(=O)[nH]c1 -222,COc1cc(C)nc(Nc2cc(N[C@@H]3CS(=O)(=O)CC[C@@H]3N)cnc2C(N)=O)c1 -223,O=C([C@H]1CNCC[C@@]12OCc1cc(F)c(F)cc12)N(Cc1cccc(Cl)c1Cl)C1CC1 -224,O=C(NC(c1ccccc1)c1ccccc1)NS(=O)(=O)c1ccc(OCCCN2CCCCC2)cc1 -225,O=C(O)[C@H](Cc1ccccc1)N1CCC(CN2CCC(Oc3ccc(CO)c(Cl)c3)CC2)CC1 -226,O=C(O)c1ccc(-n2nc(-c3ccccc3O)nc2-c2ccccc2O)cc1 -227,c1cncc(OC[C@H]2CNCCN2c2nc3ncccc3o2)c1 -228,O=C(c1cc(F)cc(Cl)c1)N1CCn2nc(-c3ccc(F)cn3)cc2C1 -229,O=C(NC1CC1c1ccccc1)N1CCC(n2cncn2)CC1 -230,CCn1cc([C@@]2(c3nnc(S)o3)N[C@@H](c3nc(-c4ccc(F)cn4)c[nH]3)Cc3c2[nH]c2ccccc32)cn1 -231,COc1ccccc1Oc1cccc(CN2CCC(NC(=O)C3(c4ccccc4)CCNCC3)(C(=O)O)CC2)c1 -232,O=C(NCc1ccncc1)C1c2ccccc2C(=O)N1Cc1ccccc1-c1ccccc1 -233,Cc1ccc(N2CCN(CCN3Cc4ccccc4C3)C2=O)cn1 -234,Oc1ccc2c(c1)CC(CN1CCC3(CCc4ccccc43)CC1)NC2 -235,CC(C)n1nc(C(=O)NCC2CCN(CCN3CCOCC3)CC2)c2ccccc21 -236,COC(=O)Cc1ccc(CNC(=O)[C@@H]2CSCN2C(=O)CC(N)Cc2ccccc2F)cc1 -237,CN[C@H]1CC[C@@H](N2CCc3ccc(N=C(N)c4cccs4)cc32)CC1 -238,CCOC(=O)Nc1cccc([C@@H](c2ccc(C(=O)N(C)CC)cc2)N2CCN(Cc3[nH]cnc3C)CC2)c1 -239,Cc1ccc2c(-c3nnc(SCCCN4CC5CC5(c5ccc(Cl)c(Cl)c5)C4)n3C)cccc2n1 -240,CC1(C)[C@H](Nc2c(C(N)=O)cnn3cc(-c4cc[nH]n4)cc23)CC[C@]1(C)N -241,Cc1ncoc1-c1nnc(SCCCN2CCc3ccc4nc(C(F)(F)F)oc4c3CC2)n1C -242,COCCCc1cc(CN(C(=O)[C@H]2CNCC[C@@]23OC(=O)c2ccccc23)C2CC2)cc(OCCOC)c1 -243,CC(=O)NCCc1ccccc1-c1onc([C@H]2CNCC[C@]2(C(N)=O)c2ccc(F)c(F)c2)c1Br -244,COc1ccc2c(c1)OC(c1ccc(OCCCN3CCCC3)cc1)C(C)S2(=O)=O -245,CSc1ccc([C@H]2N=C(OCc3ccc(NS(C)(=O)=O)cc3)N(C)Cc3ccccc32)cc1 -246,CC(C)[C@@H](N)CN1C2CCC1CC(OC(c1ccc(F)cc1)c1ccc(F)cc1)C2 -247,N#Cc1cc(O[C@@H]2C[C@H]3CC[C@@H](C2)N3)cc(-c2ncccc2C#N)c1 -248,Cc1ccc(C2=CC3(CCNCC3)Oc3ccccc32)cc1 -249,COc1ccc([C@H]2CN(CCc3ccc(OC)c(OC)c3)C[C@@H]2CCc2nc3ccc(Cl)cc3[nH]2)cc1 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-1157,O=C(/C=C/c1ccc2c(c1)CN(CCc1c[nH]c3ccccc13)C2)NO -1158,Cc1ccccc1-c1ccc(C#Cc2cccc(C#Cc3ccc(-c4ccccc4C)cc3)[n+]2C)cc1 -1159,O=C(O)c1cccc(N2CCC(CN3CCC(Oc4ccc(CO)c(Cl)c4)CC3)CC2)n1 -1160,C[C@@H](n1cnc2cc(Cl)ccc2c1=O)[C@](O)(Cn1cncn1)c1ccc(F)cc1F -1161,CC(C)(C)OC(=O)N1CCC(C(=O)NCCCNc2nc3ccccc3[nH]2)CC1 -1162,Cc1cc(C)nc(Nc2cc(N[C@@H]3CCCC[C@@H]3N)cnc2C(N)=O)c1 -1163,CCn1ccc([C@H]2CCNC[C@@H]2C(=O)N(Cc2cc(CCCOC)cc(OCCOC)c2)C2CC2)cc1=O -1164,Fc1ccc2c(C3CNCC[C@H]3F)c(-c3cccc4ccccc34)[nH]c2c1 -1165,O=C(O)COCCN1CCN(C(c2ccccc2)c2ccc(Cl)cc2)CC1 -1166,Cc1ccc2c(-c3nnc(SCCCN4CC5CC5(c5ccccc5)C4)n3C)cccc2n1 -1167,Cc1nc2ccccc2n1Cc1ccc(C(=O)N(C)[C@@H]2CCN(C(C)C)C2)cc1 -1168,Cc1n[nH]c2ncnc(NC3CCN(CC(F)(F)c4ccc(C(F)(F)F)cn4)CC3)c12 -1169,COc1cnc(-c2cccc3c2C[C@H](NC(=O)c2ccc(OCC(F)(F)F)nc2)CO3)cn1 -1170,O=C1N(CCN2Cc3ccccc3C2)CCN1c1ccc(F)c(Cl)c1 -1171,O=C(NCC1(O)CCCCC1)NC1CCN(Cc2ccn(-c3ccc(C(F)(F)F)cc3)c2)CC1 -1172,C[C@H](C1=C(CCN(C)C)Cc2ccccc21)c1ccccn1 -1173,Cc1cccc(-n2nc(C)c(-c3ccc(OC4CCN(C5CCC5)CC4)cc3)cc2=O)n1 -1174,FC(F)(F)c1nc(OC2CCN(Cc3cscn3)CC2)c2ccn(Cc3ccccc3)c2n1 -1175,COc1cccc([C@H](O)C2CCN(CCc3ccc(F)cc3)CC2)c1OC -1176,O=C1N(CCN2Cc3ccccc3C2)CCN1Cc1cc(Cl)ccc1Cl -1177,COc1ccc(/C=C/C(=O)N2CCN(c3ccc(C(=O)Nc4ccccc4N)cc3)CC2)cc1 -1178,COCCCc1cc(C)c(C)c(CN(C(=O)[C@H]2CNCC[C@@H]2c2ccn(C)c(=O)c2)C2CC2)c1 -1179,N#C[C@H]1CCOC[C@@H]1n1cc(C(N)=O)c(Nc2ccc(OC(F)F)cc2)n1 -1180,CC(C)(C)NC(=O)NCCN1CCOC(CNC(=O)c2cc(Cl)cc(Cl)c2)C1 -1181,CC(C)NC(=O)NS(=O)(=O)c1ccc(OCCCN2CCCC2)cc1 -1182,Cc1cc(-c2nc3c4c(ccc3o2)CCN(CCCSc2nnc(-c3ocnc3C)n2C)CC4)n(C)n1 -1183,Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc(N2CCC3(CC2)NCNC3=O)nc1 -1184,Cc1c(O)ccc2c1C(=O)N(Cc1ccccc1-c1ccccc1)C2C(=O)NC(C)(C)C -1185,CC(C)CN(C(=O)c1ccc2ccccc2c1)[C@@H]1CCNC1 -1186,O=C(CNCC1(O)CCCCCC1)N1CCc2ccccc2C1C1CCCCC1 -1187,COCCCc1cc(CN(C(=O)[C@H]2CNCC[C@@]23OCc2ccccc23)C2CC2)cc(OCCOC)c1 -1188,c1ccc2sc(Oc3ccc(CN4CCCCC4)cc3)nc2c1 -1189,Cc1cc(F)ccc1OC1CCN(CC2CCN([C@@](C)(Cc3ccc(F)cc3)C(=O)O)CC2)CC1 -1190,CC(=O)N1CCN(CCOc2ccc(NC(=O)c3cccc(F)c3)cc2-c2ccnn2C)CC1 -1191,Cn1ncc(C(=O)N2CCC(Nc3cc(=O)[nH]c4ccc(F)cc34)CC2)c1Cl -1192,CN1CCN(c2ccc3cc(C(=O)Nc4cc(-c5ccco5)ccc4N)ncc3c2)CC1 -1193,O=C1CCCN1C1CCN(CCc2ccc(Oc3nc4ccccc4s3)cc2)CC1 -1194,C[C@@H]1CCCN1CCc1ccc2nc(-c3ccc(C#N)cc3)ccc2c1 -1195,C[C@@H]1CC2c3ccccc3NC2[C@@H](c2c(F)cc(/C=C/C(=O)O)cc2F)N1CC(C)(C)F -1196,Cc1cc2nc(CC3CCCC3)n(Cc3ccc(Cl)cc3)c2cc1C -1197,Cc1nc2c(C#N)cccc2n1-c1ccc(C(=O)NC2CC2)s1 -1198,O=C1N(CCN2Cc3ccccc3C2)CCN1c1cccc(OC(F)(F)F)c1 -1199,CC(C)n1nc(C(=O)NCC2CCN(CCc3ccc(C(=O)O)cc3)CC2)c2ccccc21 -1200,CC(=O)NCCc1ccccc1-c1noc([C@H]2CNCC[C@]2(O)c2ccc(F)c(F)c2)c1Br -1201,CN1C(=O)N(c2ccnc(C(F)(F)F)c2)C2=C(C(=O)CC2)[C@H]1c1ccc(C#N)cc1S(C)(=O)=O -1202,COC(=O)C1(CNC(=O)c2cc(OC)cc(OC)c2)CCN(Cc2ccccc2C(F)(F)F)CC1 -1203,COc1cnc2c(NCc3nnc4ccc(-c5cc(C)ns5)nn34)ccnc2c1 -1204,FC(F)(F)c1cccc(Cn2ccc3c(OC4CCN(Cc5cscn5)CC4)ncnc32)c1 -1205,Cc1c(CCN2CCNCC2)ccc2cc(F)ccc12 -1206,Cc1nc2ccccc2c(=O)n1-c1ccc(OCCCN2CCC[C@@H]2C)cc1 -1207,C[C@H](C1=C(CCN(C)C)Cc2cc(F)ccc21)c1nccs1 -1208,CN1CCC[C@H](c2nc3ccccc3n2Cc2ccccn2)C1 -1209,Cc1cc(-c2cc3[nH]nc(N[C@@H]4CCNC[C@H]4F)c3cc2C2CC2)cc(C)n1 -1210,O=S(=O)(c1ccc2c(c1)CCNCC2)N1CC(Oc2cccc(Cl)c2)C1 -1211,Cc1ncc(N2CCC(CN3CCC(Oc4ccc(CO)c(Cl)c4)CC3)CC2)cc1C(=O)O -1212,O=C(Nc1ccc(C(F)(F)F)cc1)NS(=O)(=O)c1ccc(OCCN2CCCC2)cc1 -1213,O=c1ccc2c([nH]1)CCC(NCCCCCNc1c3c(nc4cc(Cl)ccc14)CCCC3)C2 -1214,Cn1cc([C@@]2(c3nn(C)c(=O)o3)N[C@@H](c3nc(-c4ccc(F)cn4)c[nH]3)Cc3c2[nH]c2ccccc32)cn1 -1215,CNc1nc(NCCN2CCNCC2)c2sc(-c3ccc(C(F)(F)F)cc3)cc2n1 -1216,CC(C)Cc1cc(C(=O)NCC2(C#N)CCN(CCc3ccccc3)CC2)nn1-c1ccccc1 -1217,COc1ncccc1C(=O)Nc1cccc(-c2nncn2[C@@H](C)C(F)(F)F)n1 -1218,Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc(N2CCC3(CC2)CNC3=O)nc1 -1219,O=C1NCc2ccc(OCCCN3CCN(c4cccc5ccccc45)CC3)cc21 -1220,COC(=O)N(NC(=O)c1c(CC2CCN(C3CCOCC3)CC2)c(-c2ccccc2)nc2ccccc12)c1ccccc1 -1221,N#Cc1ccc(-c2ccc(C[C@@H](C#N)NC(=O)C3(N)CCCCC3)cc2)cc1 -1222,CCN(CC)C(=O)c1ccc(C2=CC3(CCNCC3)Oc3ccccc32)nc1 -1223,Cn1c(-c2ccc(OC3CCN(C4CCC4)CC3)cc2)nc2ccccc2c1=O -1224,COc1ccc([C@H]2CN(CCc3cccc(OC)c3)C[C@@H]2CNC(=O)c2cccc(Cl)c2)cc1 -1225,CC1(C)[C@H](Nc2c(C(N)=O)cnn3cc(-c4cccnc4)cc23)CC[C@]1(C)N -1226,O=C([C@H]1CNCC[C@@]12OCc1cc(F)c(F)cc12)N(Cc1cccc(C(F)(F)F)c1Cl)C1CC1 -1227,O=c1[nH]c2ccccc2n1C1CCN(C2CCCCC2)CC1 -1228,N#Cc1ccc(Oc2ccc3nc(C(=O)N4CCNCC4)ccc3c2)cc1 -1229,CCCCN1CCC[C@@H]1Cn1nc(Cc2ccc(Cl)cc2)c2cccnc2c1=O -1230,CCN(CC)C(=O)c1cc(C2=CC3(CCNCC3)Oc3ccccc32)cs1 -1231,Cc1cccc(CCN2CCC(C(=O)c3ccc(NS(C)(=O)=O)cc3)CC2)n1 -1232,Cn1cc([C@@]2(c3ccc(C(=O)O)cn3)N[C@@H](c3nc(-c4ccc(F)cc4)c[nH]3)Cc3c2[nH]c2ccccc32)cn1 -1233,O=C(N[C@@H]1COc2cccc(-c3cccnc3)c2C1)c1ccc(OCC(F)(F)F)nc1 -1234,O=C(O)Cc1ccc(N2CCC(CN3CCC(Oc4ccc(Cl)c(Cl)c4)CC3)CC2)cc1 -1235,CNC(=O)c1ccc(CC(=O)N(C)C2CCN(Cc3ccc(C(F)(F)F)cc3)CC2)cc1 -1236,COc1ccc(NC(=O)c2ccc(Cn3ccnn3)c3ccccc23)c(C(=O)NCC2CCOCC2)n1 -1237,NC(=O)c1ccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc2ccccc2)cc1 -1238,Cc1cc(CN2C[C@H](N)[C@H](C(=O)C3CCC[C@H]3C#N)C2)ccc1C#N -1239,COC(=O)C1(CNC(=O)c2cc(OC)cc(OC)c2)CCN(Cc2ccc(C(F)(F)F)cc2)CC1 -1240,C[C@@H](C[C@@H](CS(=O)(=O)N1CCC(OCc2ccc(Cl)cc2Cl)CC1)N(O)C=O)c1ncc(F)cn1 -1241,CCCCCCCCCCCCc1cccc(CCCCCCCCCCCC)[n+]1C -1242,Cc1nnc2sc(C(=O)NCc3ccc(-c4cccnc4F)cc3)c(N)c2c1C -1243,CN1C(=O)N(c2cccc(C(F)(F)F)c2)C2=C(C(=O)CC2)[C@@H]1c1ccc(C#N)cc1S(=O)(=O)CCO -1244,NS(=O)(=O)c1ccc(OCCCCN2CCCC2)cc1 -1245,CN(c1ccc(Cl)cc1)c1ccc(Nc2nccc(N)n2)cc1 -1246,Cc1[nH]c2ccccc2c1C1CCN(Cc2ccc(/C=C/C(=O)NO)cc2)C1 -1247,COc1cnc(-c2ccccn2)nc1NCc1c(F)cc(F)cc1F -1248,O=c1n(Cc2ccc(Cl)cc2)c2sc3c(c2c2ncnn12)CCN(Cc1ccccc1)C3 -1249,Cc1nnc2n1-c1ccc(-c3cncc([C@@](C)(O)C(F)(F)F)c3)cc1CC2 -1250,c1ncc(-c2ccc(OCCCN3CCCCC3)cc2)o1 -1251,c1ccc(N2CCN(CCCCCCN3CCN(c4ccccn4)CC3)CC2)nc1 -1252,COc1ncnc(Cn2cc(C(=O)NCC(F)F)c3ncc(C)cc32)c1C -1253,C[C@@H]1CN(Cc2ccc(-c3cccnc3C(=O)N3CCC(Nc4ccc(F)cc4)CC3)cc2)C[C@H](C)N1 -1254,CN(CCCCN(C[C@H]1Cc2ncccc2CN1)[C@H]1CCCc2cccnc21)C1CCC(F)(F)CC1 -1255,O=C(NCc1ccc(Br)cc1)C1c2ccccc2C(=O)N1CCc1ccccn1 -1256,O=C(CCCCc1c[nH]nn1)NC1CCN(C(=O)OCc2cc(Cl)cc(Cl)c2)CC1 -1257,O=c1cc(NC2CCN(Cc3ccc4ccccc4c3)CC2)c2cc(Cl)ccc2n1Cc1ccccn1 -1258,Cn1c(-c2ccc(OC3CCN(C4CCCC4)CC3)cc2)nc2ccccc2c1=O -1259,CN(C)CCCC1(c2ccc(F)cc2)OCc2cc(C#N)ccc21 -1260,COc1ccc(NC(=O)c2ccc(C(=N)N(C)C)cc2N2CCC(C(=O)O)CC2)c(C(=O)Nc2ccc(Cl)cn2)c1 -1261,CC(=O)N(CCOc1ccc([N+](=O)[O-])cc1)CCc1ccc([N+](=O)[O-])cc1 -1262,CC(C)(C)NC(=O)C1c2ccccc2C(=O)N1Cc1ccccc1-c1ccc(F)c(F)c1 -1263,Fc1cccc(OC(F)F)c1CC(c1ccccc1)N1CCNCC1 -1264,O=c1cc(OCc2ccc3ccccc3c2)ccn1-c1ccc(OCCN2CCCC2)cc1 -1265,COc1c(C(C)(C)C)cc(CN2CCN(C(=O)CCCCC(c3ccc(F)cc3)c3ccc(F)cc3)CC2)cc1C(C)(C)C -1266,COc1ccc(CCN2C[C@H](CNC(=O)c3cccc(Cl)c3)[C@@H](c3ccc(OC)cc3)C2)cc1 -1267,C[C@H](c1ccnc(Cl)c1)N1[C@@H]2CC[C@H]1C[C@H](Oc1cccc(C(N)=O)c1)C2 -1268,Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6nc(CC(F)(F)F)oc6cc5CC4)n3C)cccc2n1 -1269,CCOP(=O)(Cn1ccc(NC(=O)c2cc(Oc3ccc(C(=O)N4CCC4)cn3)cc(OC(C)C)c2)n1)OCC -1270,CN(Cc1ccccc1)C(=O)c1ccc(C2=CC3(CCNCC3)Oc3ccccc32)cc1 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-1974,CCN(C(=O)Cc1ccc(S(C)(=O)=O)cc1)C1CCN(CCC(c2ccccc2)N2CCN(c3ccccc3)CC2)CC1 -1975,Cc1nc2ncc(Oc3ccc(Cl)cc3F)nc2c(=O)n1C[C@H]1CCCN(C(C)C)C1 -1976,CC(=O)N1CCOc2cc3c(cc21)CCN(CCCSc1nnc(-c2cccc4nc(C)ccc24)n1C)CC3 -1977,C[C@@H]1CN(Cc2ccc(-n3ccnc3C(=O)N3CCC(Nc4cccc(F)c4)CC3)cc2)C[C@H](C)N1 -1978,C[C@@H]1CCCCN1CCCNCc1ccc(CNCCCN2CCCC[C@@H]2C)cc1 -1979,Fc1ccc(C(Cc2ccccc2OC(F)F)N2CCNCC2)cc1 -1980,Cc1c(-c2ccc(Cl)cc2)noc1-c1ccc2cc(CCN3CCC[C@H]3C)ccc2n1 -1981,COc1ncnc(Cn2cc(C(=O)NCCF)c3ncc(C)cc32)c1C -1982,CN1CCN(C(=O)c2ccc(-c3cnc(N)c(-c4ccc(C(F)(F)F)cc4)c3)cc2)CC1 -1983,O=C(NC1CC1c1ccccc1)N1CCC(c2nc3ccccc3o2)CC1 -1984,Cc1ccc(C#CCOCc2cccc(COCC#Cc3ccc(C)cc3)[n+]2C)cc1 -1985,Clc1ccccc1C(c1ccccc1)(c1ccccc1)n1ccnc1 -1986,Cc1nnc(C(=O)NC(C)(C)c2nc(C(=O)NCc3ccc(F)cc3)c(O)c(=O)n2C)o1 -1987,Cc1csc([C@H](C)C2=C(CCN(C)C)Cc3ccccc32)n1 -1988,CN(C)C(=O)N[C@H]1CC[C@@H](CCN2[C@@H]3CC[C@H]2C[C@@H](Oc2cccc(C(N)=O)c2)C3)CC1 -1989,CCC(=O)N(CC1(N2CCN(Cc3ccccc3)CC2)CCOCC1)c1ccccc1 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dd5af33..0000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,156 +0,0 @@ -# Contributing to EZFlow - -Thank you for considering contributing to EZFlow! This document provides guidelines and instructions for contributing to this project. - -## Table of Contents - -- [Code of Conduct](#code-of-conduct) -- [How Can I Contribute?](#how-can-i-contribute) - - [Reporting Bugs](#reporting-bugs) - - [Suggesting Features](#suggesting-features) - - [Pull Requests](#pull-requests) -- [Development Setup](#development-setup) -- [Coding Standards](#coding-standards) -- [Testing](#testing) -- [Documentation](#documentation) -- [Commit Guidelines](#commit-guidelines) - -## Code of Conduct - -This project and everyone participating in it is governed by our [Code of Conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code. - -## How Can I Contribute? - -### Reporting Bugs - -This section guides you through submitting a bug report. Following these guidelines helps maintainers understand your report and reproduce the issue. - -**Before Submitting A Bug Report:** -- Check the documentation for a solution to your issue -- Check if the bug has already been reported in the Issues section -- Verify that the issue can be reproduced reliably - -**How Do I Submit A Good Bug Report?** - -Bugs are tracked as GitHub issues. Create an issue and provide the following information: - -- **Title**: Use a clear and descriptive title -- **Description**: Provide a detailed description of the issue -- **Steps to Reproduce**: List all steps needed to reproduce the behavior -- **Expected Behavior**: Describe what you expected to happen -- **Actual Behavior**: Describe what actually happened -- **Environment**: Include Python version, OS, package versions, etc. -- **Additional Context**: Add any other context about the problem - -### Suggesting Features - -This section guides you through submitting a feature suggestion: - -- **Title**: Use a clear and descriptive title -- **Description**: Provide a step-by-step description of the suggested enhancement -- **Use Case**: Describe the use case that your enhancement would address -- **Alternatives**: Describe alternatives you've considered -- **Additional Context**: Add any other context or examples related to the feature request - -### Pull Requests - -- Fill in the required pull request template -- Follow the coding standards (see below) -- Include appropriate tests (see below) -- Update documentation if necessary -- Pull requests should be made to the `develop` branch, not `main` - -## Development Setup - -1. Fork the repository -2. Clone your fork locally: - ```bash - git clone https://github.com/Alcray/ezflow.git - cd ezflow - ``` -3. Create a conda environment using the provided environment.yml: - ```bash - conda env create -f environment.yml - conda activate ezflow - ``` -4. Install the package in development mode: - ```bash - pip install -e . - ``` -5. Set up pre-commit hooks: - ```bash - pre-commit install - ``` - -## Coding Standards - -We use the following tools to maintain code quality: - -- **Black**: For consistent code formatting -- **Flake8**: For style guide enforcement -- **isort**: For consistent import ordering -- **mypy**: For static type checking - -Our CI pipeline will check these automatically, but you can run them locally: - -```bash -# Format code -black ezflow -isort ezflow - -# Check style -flake8 ezflow - -# Check types -mypy ezflow -``` - -### General Guidelines - -- Follow PEP 8 style guidelines -- Include type hints for function arguments and return values -- Write docstrings in the NumPy/SciPy style -- Keep functions focused on a single responsibility -- Limit line length to 88 characters (Black's default) - -## Testing - -We use pytest for testing. All new features should include tests. - -```bash -# Run all tests -pytest - -# Run tests with coverage -pytest --cov=ezflow - -# Run specific tests -pytest ezflow/tests/test_specific_module.py -``` - -## Documentation - -- Documentation is written in Markdown -- API documentation is generated from docstrings -- Examples should be clear, concise, and runnable -- Update the relevant documentation files when adding features - -## Commit Guidelines - -We follow conventional commits: - -- **feat**: A new feature -- **fix**: A bug fix -- **docs**: Documentation only changes -- **style**: Changes that do not affect the meaning of the code -- **refactor**: A code change that neither fixes a bug nor adds a feature -- **perf**: A code change that improves performance -- **test**: Adding missing tests or correcting existing tests -- **chore**: Changes to the build process or auxiliary tools - -Example: -``` -feat(dataset): add support for image datasets -``` - -Thank you for contributing to EZFlow! \ No newline at end of file diff --git a/docs/architecture.md b/docs/architecture.md deleted file mode 100644 index 04eda44..0000000 --- a/docs/architecture.md +++ /dev/null @@ -1,233 +0,0 @@ -# EZFlow Architecture - -This document provides a comprehensive overview of the EZFlow architecture, design principles, and how components interact with each other. - -## Overview - -EZFlow is designed with modularity and flexibility in mind, allowing researchers and data scientists to focus on their specific tasks rather than boilerplate code. The architecture follows a layered approach with clear separation of concerns. - -``` -┌─────────────────────────────────────────────────────────────┐ -│ EZFlow Framework │ -├─────────────┬─────────────┬───────────────┬─────────────────┤ -│ Dataset │ Pipeline │ Experiment │ CLI │ -│ Layer │ Layer │ Layer │ Layer │ -├─────────────┼─────────────┼───────────────┼─────────────────┤ -│ BaseDataset │ Pipeline │ ExperimentCfg │ CLI Commands │ -│ IrisDataset │ SklearnPipe│ ExpTracker │ Project Setup │ -│ MolecDataset│ TorchPipe │ Artifacts │ Utilities │ -└─────────────┴─────────────┴───────────────┴─────────────────┘ -``` - -## Design Principles - -EZFlow's architecture is guided by the following design principles: - -1. **Modularity**: Each component should be replaceable without affecting the rest of the system. -2. **Extensibility**: New components can be added with minimal changes to existing code. -3. **Consistency**: Common interfaces across different implementations for ease of use. -4. **Transparency**: Clear visibility into what happens at each step of the pipeline. -5. **Reproducibility**: Experiments should be fully reproducible from the stored configuration. - -## Core Components - -### Dataset Layer - -The dataset layer handles data loading, preprocessing, and splitting: - -``` -┌───────────────────────────────────────────────────────┐ -│ BaseDataset │ -├───────────────────────────────────────────────────────┤ -│ - name: str │ -│ - data_dir: str/Path │ -│ - cache_dir: str/Path │ -│ - metadata: Dict │ -├───────────────────────────────────────────────────────┤ -│ + load_data() -> None │ -│ + split_data(val_size, random_state) -> None │ -│ + preprocess() -> None │ -│ + get_features(data) -> np.ndarray │ -│ + get_labels(data) -> np.ndarray │ -│ + save_splits() -> None │ -│ + load_splits() -> None │ -└───────────────────────────────────────────────────────┘ - ▲ - │ - │ inherits - │ - ┌───────────────┴──────────────┐ - │ │ -┌──────────┴─────────────┐ ┌────────────┴─────────────┐ -│ IrisDataset │ │ MolecularDataset │ -├────────────────────────┤ ├──────────────────────────┤ -│ + load_data() │ │ - smiles_col: str │ -│ + split_data() │ │ - label_col: str │ -│ + preprocess() │ │ - fingerprint_radius: int│ -│ + get_features() │ │ - fingerprint_bits: int │ -│ + get_labels() │ ├──────────────────────────┤ -└────────────────────────┘ │ + generate_fingerprint() │ - │ + load_data() │ - │ + preprocess() │ - │ + get_features() │ - │ + get_labels() │ - └──────────────────────────┘ -``` - -The dataset layer follows the Template Method pattern, where `BaseDataset` defines the interface and workflow, while specific dataset implementations provide the concrete implementations. - -### Pipeline Layer - -The pipeline layer handles model training, evaluation, and prediction: - -``` -┌───────────────────────────────────────────────────────┐ -│ Pipeline │ -├───────────────────────────────────────────────────────┤ -│ - pipeline: Any │ -│ - pipeline_config: Dict │ -├───────────────────────────────────────────────────────┤ -│ + fit(X, y) -> None │ -│ + predict(X) -> np.ndarray │ -│ + score(X, y) -> float │ -│ + save(path) -> None │ -│ + load(path) -> None │ -└───────────────────────────────────────────────────────┘ - ▲ - │ - │ inherits - │ - ┌───────────────┴──────────────┐ - │ │ -┌──────────┴─────────────┐ ┌────────────┴─────────────┐ -│ SklearnPipelineWrapper │ │ TorchPipelineWrapper │ -├────────────────────────┤ ├──────────────────────────┤ -│ - steps: List │ │ - model: nn.Module │ -│ - sklearn_pipeline │ │ - optimizer: Optimizer │ -├────────────────────────┤ │ - criterion: Loss │ -│ + fit() │ ├──────────────────────────┤ -│ + predict() │ │ + fit() │ -│ + score() │ │ + predict() │ -│ + save() │ │ + score() │ -│ + load() │ │ + save() │ -└────────────────────────┘ │ + load() │ - └──────────────────────────┘ -``` - -The pipeline layer uses the Strategy pattern, allowing different ML frameworks to be used with the same interface. - -### Experiment Layer - -The experiment layer handles experiment tracking, configuration, and artifact management: - -``` -┌───────────────────────────────────────────────────────┐ -│ ExperimentConfig │ -├───────────────────────────────────────────────────────┤ -│ - name: str │ -│ - model_params: Dict │ -│ - dataset_params: Dict │ -│ - training_params: Dict │ -│ - metadata: Dict │ -├───────────────────────────────────────────────────────┤ -│ + to_dict() -> Dict │ -│ + from_dict(config_dict) -> ExperimentConfig │ -│ + save(path) -> None │ -│ + load(path) -> ExperimentConfig │ -└───────────────────────────────────────────────────────┘ - -┌───────────────────────────────────────────────────────┐ -│ ExperimentTracker │ -├───────────────────────────────────────────────────────┤ -│ - experiment_name: str │ -│ - artifacts_dir: str/Path │ -│ - backend: str │ -├───────────────────────────────────────────────────────┤ -│ + start_run(config) -> None │ -│ + end_run() -> None │ -│ + log_metric(name, value) -> None │ -│ + log_metrics(metrics_dict) -> None │ -│ + log_parameter(name, value) -> None │ -│ + log_parameters(params_dict) -> None │ -│ + log_artifact(artifact_path) -> None │ -│ + get_artifact(artifact_path) -> str/Path │ -└───────────────────────────────────────────────────────┘ -``` - -The experiment layer follows the Facade pattern, providing a simplified interface to the underlying tracking systems (like MLflow). - -### CLI Layer - -The CLI layer provides command-line utilities for project management: - -``` -┌───────────────────────────────────────────────────────┐ -│ CLI │ -├───────────────────────────────────────────────────────┤ -│ + create_project(name) -> None │ -│ + run_experiment(config_path) -> None │ -│ + list_experiments() -> None │ -└───────────────────────────────────────────────────────┘ -``` - -The CLI layer follows the Command pattern, with each CLI command implementing a specific action. - -## Data Flow - -A typical data flow in EZFlow follows these steps: - -1. **Data Loading**: The dataset loads data from files or external sources -2. **Data Splitting**: The dataset splits data into training and validation sets -3. **Feature Extraction**: Features and labels are extracted from the dataset -4. **Model Training**: The pipeline trains the model using the extracted features and labels -5. **Model Evaluation**: The pipeline evaluates the model on validation data -6. **Metrics Tracking**: The experiment tracker logs metrics, parameters, and artifacts -7. **Model Saving**: The trained model is saved as an artifact - -``` -┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ -│ Dataset │────>│ Pipeline │────>│ Experiment│────>│ Artifacts │ -│ Loading │ │ Training │ │ Tracking │ │ Saving │ -└───────────┘ └───────────┘ └───────────┘ └───────────┘ -``` - -## Component Interaction - -Components in EZFlow interact through well-defined interfaces: - -1. **Dataset + Pipeline**: Pipelines use datasets to obtain features and labels -2. **Pipeline + Experiment**: Experiments track pipeline performance and artifacts -3. **CLI + Components**: CLI commands orchestrate the use of other components - -## Extension Points - -EZFlow is designed to be extended in several ways: - -1. **Custom Datasets**: Create new dataset classes by inheriting from `BaseDataset` -2. **Custom Pipelines**: Create new pipeline wrappers by inheriting from `Pipeline` -3. **Custom Tracking Backends**: Add new experiment tracking backends by extending the tracker implementation -4. **Custom CLI Commands**: Add new CLI commands by extending the CLI functionality - -## Best Practices - -When working with or extending EZFlow, follow these best practices: - -1. **Dataset Implementation**: - - Implement all required methods from `BaseDataset` - - Ensure data preprocessing is idempotent - - Use appropriate data types and structures - -2. **Pipeline Implementation**: - - Follow the pipeline interface strictly - - Handle exceptions and provide meaningful error messages - - Implement proper model serialization/deserialization - -3. **Experiment Tracking**: - - Track all relevant hyperparameters - - Use consistent naming conventions for metrics - - Save all necessary artifacts for reproducibility - -4. **General**: - - Add comprehensive unit tests for new components - - Document all public methods and classes - - Follow the coding standards \ No newline at end of file diff --git a/docs/cli.md b/docs/cli.md deleted file mode 100644 index 24c7f3d..0000000 --- a/docs/cli.md +++ /dev/null @@ -1,292 +0,0 @@ -# Command-Line Interface - -EZFlow provides a command-line interface (CLI) that makes it easy to create, manage, and run machine learning projects. - -## Table of Contents - -- [Overview](#overview) -- [Installation](#installation) -- [Available Commands](#available-commands) - - [Create Project](#create-project) - - [Run Experiment](#run-experiment) - - [List Experiments](#list-experiments) -- [Examples](#examples) -- [Best Practices](#best-practices) -- [Extending the CLI](#extending-the-cli) - -## Overview - -The EZFlow CLI provides a set of commands to help you: - -- Create new projects with appropriate directory structure -- Run experiments from configuration files -- List and compare experiment results -- Manage models and datasets - -The CLI follows consistent patterns and provides helpful error messages to make the workflow smooth and intuitive. - -## Installation - -The CLI is automatically installed when you install the EZFlow package: - -```bash -# Install from PyPI -pip install ezflow-ml - -# Or install in development mode from source -pip install -e . -``` - -After installation, the `ezflow` command will be available in your terminal. - -## Available Commands - -### Create Project - -The `create` command sets up a new EZFlow project with the recommended directory structure. - -#### Usage - -```bash -ezflow create [--template ] -``` - -#### Arguments - -| Argument | Description | -|----------|-------------| -| `project_name` | Name of the project to create | - -#### Options - -| Option | Description | -|--------|-------------| -| `--template` | Template to use for project creation (default: "basic") | - -#### Directory Structure - -The created project will have the following structure: - -``` -project_name/ -├── data/ -│ ├── raw/ # Raw, immutable data -│ └── processed/ # Processed data ready for training -├── models/ # Saved models -├── experiments/ # Experiment results and artifacts -├── notebooks/ # Jupyter notebooks for exploration -├── src/ # Source code -│ ├── __init__.py -│ ├── datasets/ # Custom dataset implementations -│ ├── pipelines/ # Custom pipeline implementations -│ └── utils/ # Utility functions -├── config/ # Configuration files -├── .gitignore # Git ignore file with ML-specific patterns -└── README.md # Project README -``` - -#### Example - -```bash -# Create a new project named "drug_discovery" -ezflow create drug_discovery - -# Create a project with a specific template -ezflow create image_classification --template vision -``` - -### Run Experiment - -The `run` command executes an experiment based on a configuration file. - -#### Usage - -```bash -ezflow run [--output ] [--name ] -``` - -#### Arguments - -| Argument | Description | -|----------|-------------| -| `config_file` | Path to experiment configuration file (JSON or YAML) | - -#### Options - -| Option | Description | -|--------|-------------| -| `--output` | Directory to store experiment results (default: "./experiments") | -| `--name` | Name for the experiment run (default: auto-generated) | -| `--no-tracking` | Disable experiment tracking | - -#### Example - -```bash -# Run an experiment with a specific configuration -ezflow run config/random_forest.json - -# Run with custom output directory and name -ezflow run config/neural_network.yaml --output ./results --name nn_run_1 -``` - -### List Experiments - -The `list` command displays information about previous experiment runs. - -#### Usage - -```bash -ezflow list [--n ] [--filter ] -``` - -#### Options - -| Option | Description | -|--------|-------------| -| `--n` | Number of experiments to show (default: 10) | -| `--filter` | Filter experiments by name, status, or parameters | -| `--sort` | Sort by metric or parameter (e.g., "accuracy:desc") | -| `--output` | Output format: "table", "json", or "csv" (default: "table") | - -#### Example - -```bash -# List 5 most recent experiments -ezflow list --n 5 - -# Filter experiments by name -ezflow list --filter "name=random_forest" - -# Sort by validation accuracy (descending) -ezflow list --sort "metrics.val_accuracy:desc" - -# Export results as CSV -ezflow list --output csv > experiments.csv -``` - -## Examples - -### Complete Workflow Example - -```bash -# Create a new project -ezflow create my_ml_project -cd my_ml_project - -# Create data directories -mkdir -p data/raw data/processed - -# Prepare your dataset -# ... (download/copy data files to data/raw) - -# Create experiment configuration -cat > config/experiment.json << EOL -{ - "name": "first_experiment", - "model_params": { - "n_estimators": 100, - "max_depth": 10 - }, - "dataset_params": { - "val_size": 0.2 - }, - "training_params": { - "random_state": 42 - } -} -EOL - -# Run the experiment -ezflow run config/experiment.json --name initial_run - -# List experiment results -ezflow list --n 1 -``` - -### Creating and Running Multiple Experiments - -```bash -# Create multiple configuration files -for n_estimators in 50 100 200; do - for max_depth in 5 10 15; do - cat > config/rf_${n_estimators}_${max_depth}.json << EOL -{ - "name": "rf_${n_estimators}_${max_depth}", - "model_params": { - "n_estimators": ${n_estimators}, - "max_depth": ${max_depth} - }, - "dataset_params": { - "val_size": 0.2 - }, - "training_params": { - "random_state": 42 - } -} -EOL - done -done - -# Run all experiments -for config in config/rf_*.json; do - ezflow run $config -done - -# Compare results -ezflow list --sort "metrics.val_accuracy:desc" -``` - -## Best Practices - -For optimal results when using the CLI: - -1. **Version Control**: Store configuration files in version control to track experiment setups -2. **Consistent Naming**: Use consistent naming conventions for experiment configurations -3. **Configuration Files**: Prefer configuration files over command-line arguments for reproducibility -4. **Documentation**: Document the purpose and expected outcomes of each configuration -5. **Automation**: Use shell scripts or Makefiles to automate common workflows -6. **Environment Variables**: Use environment variables for sensitive information (API keys, paths) - -## Extending the CLI - -You can extend the EZFlow CLI with custom commands by creating plugins. - -### Creating a Custom Command - -```python -# In your_extension.py -from ezflow.cli import register_command - -@register_command("analyze", "Analyze experiment results") -def analyze_command(args): - """Analyze experiment results and generate reports.""" - # Implementation of the command - pass - -# Register command arguments -analyze_command.add_argument("experiment", help="Experiment ID to analyze") -analyze_command.add_argument("--output", help="Output directory for reports") -``` - -### Installing the Extension - -```python -# In setup.py -setup( - name="ezflow-extension", - # ... - entry_points={ - "ezflow.cli_plugins": [ - "analyze=your_package.your_extension:analyze_command", - ] - } -) -``` - -### Using the Extension - -After installing your extension, the command will be available: - -```bash -ezflow analyze experiment_id --output ./reports -``` \ No newline at end of file diff --git a/docs/dataset.md b/docs/dataset.md deleted file mode 100644 index 286908d..0000000 --- a/docs/dataset.md +++ /dev/null @@ -1,382 +0,0 @@ -# Dataset Module - -The Dataset module is a core component of EZFlow, providing a unified interface for loading, preprocessing, and manipulating different types of datasets. - -## Table of Contents - -- [Overview](#overview) -- [BaseDataset](#basedataset) -- [Specialized Datasets](#specialized-datasets) - - [IrisDataset](#irisdataset) - - [MolecularDataset](#moleculardataset) -- [Custom Datasets](#custom-datasets) -- [Best Practices](#best-practices) -- [API Reference](#api-reference) - -## Overview - -The Dataset module handles: - -- Data loading from various sources -- Data preprocessing and feature engineering -- Data splitting into training, validation, and test sets -- Feature and label extraction -- Persistence of datasets and splits - -This module is designed to be extendable, allowing users to easily implement custom datasets while maintaining a consistent interface. - -## BaseDataset - -`BaseDataset` is the abstract base class that all dataset implementations should inherit from. - -### Key Properties - -| Property | Type | Description | -|----------|------|-------------| -| `name` | str | Name of the dataset | -| `data_dir` | str/Path | Directory where data files are stored | -| `cache_dir` | str/Path | Directory for caching processed data | -| `train_data` | pd.DataFrame | Training data | -| `val_data` | pd.DataFrame | Validation data | -| `test_data` | pd.DataFrame | Test data (optional) | -| `metadata` | Dict | Additional metadata about the dataset | - -### Key Methods - -| Method | Parameters | Return Type | Description | -|--------|------------|-------------|-------------| -| `load_data` | - | None | Loads raw data into `train_data` | -| `split_data` | `val_size`, `random_state` | None | Splits `train_data` into `train_data` and `val_data` | -| `preprocess` | - | None | Applies preprocessing transformations | -| `get_features` | `data: pd.DataFrame` | np.ndarray | Extracts features from data | -| `get_labels` | `data: pd.DataFrame` | np.ndarray | Extracts labels from data | -| `save_splits` | - | None | Saves train/val splits to disk | -| `load_splits` | - | None | Loads train/val splits from disk | - -### Usage Example - -```python -from ezflow.core.dataset import BaseDataset - -class MyDataset(BaseDataset): - def __init__(self, data_dir): - super().__init__("my_dataset", data_dir) - - def load_data(self): - # Implementation specific to this dataset - pass - - def preprocess(self): - # Implementation specific to this dataset - pass - - def get_features(self, data): - # Implementation specific to this dataset - pass - - def get_labels(self, data): - # Implementation specific to this dataset - pass -``` - -## Specialized Datasets - -EZFlow provides several built-in dataset implementations for common use cases. - -### IrisDataset - -The `IrisDataset` class provides a wrapper for the popular Iris flower dataset, commonly used for classification tasks. - -#### Initialization - -```python -from ezflow.core.dataset import IrisDataset - -# Initialize with a directory for storing data -dataset = IrisDataset(data_dir="./data") - -# Load the data -dataset.load_data() - -# Split into training and validation sets -dataset.split_data(val_size=0.2, random_state=42) -``` - -#### Features and Implementation Details - -- Automatically loads the Iris dataset from scikit-learn -- Provides standard feature columns: sepal length, sepal width, petal length, petal width -- Target variable contains flower species (0, 1, 2) -- Implements stratified splitting to maintain class distribution -- Handles index preservation during split/save/load operations - -### MolecularDataset - -The `MolecularDataset` class is designed for working with chemical/molecular data, particularly useful in drug discovery and cheminformatics. - -#### Initialization - -```python -from ezflow.core.dataset import MolecularDataset - -# Initialize with dataset details -dataset = MolecularDataset( - name="my_molecular_dataset", - data_dir="./data", - smiles_col="SMILES", - label_col="Activity", - fingerprint_radius=2, - fingerprint_bits=2048 -) - -# Load the data (expects CSV files with SMILES strings) -dataset.load_data() - -# Split into training and validation sets -dataset.split_data(val_size=0.2, random_state=42) -``` - -#### Features and Implementation Details - -- Handles SMILES (Simplified Molecular Input Line Entry System) strings -- Generates molecular fingerprints using RDKit -- Provides customizable fingerprint parameters (radius, bit length) -- Supports custom ID columns and metadata -- Includes functionality for generating submission files for competitions - -## Custom Datasets - -Creating a custom dataset involves inheriting from `BaseDataset` and implementing the required methods. - -### Implementation Checklist - -1. **Inherit from BaseDataset**: Extend the base class -2. **Implement load_data**: Define how to load your specific data -3. **Implement preprocess**: Define any preprocessing steps -4. **Implement get_features**: Define which columns/transformations to use as features -5. **Implement get_labels**: Define which column(s) to use as labels -6. **Implement save/load_splits**: Optional but recommended, to save/load the dataset state - -### Example: Custom Image Dataset - -```python -import numpy as np -import pandas as pd -from pathlib import Path -import cv2 -from ezflow.core.dataset import BaseDataset - -class ImageDataset(BaseDataset): - def __init__(self, data_dir, image_size=(224, 224)): - super().__init__("image_dataset", data_dir) - self.image_size = image_size - self.image_paths = [] - self.labels = [] - - def load_data(self): - # Create DataFrame with image paths and labels - image_files = list(Path(self.data_dir).glob("**/*.jpg")) - labels = [path.parent.name for path in image_files] - - self.train_data = pd.DataFrame({ - "image_path": [str(path) for path in image_files], - "label": labels - }) - - def preprocess(self): - # You might do label encoding here - label_map = {label: idx for idx, label in enumerate(set(self.train_data["label"]))} - self.train_data["label_encoded"] = self.train_data["label"].map(label_map) - self.metadata["label_map"] = label_map - - def _load_and_preprocess_image(self, image_path): - # Load and preprocess a single image - img = cv2.imread(image_path) - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - img = cv2.resize(img, self.image_size) - return img / 255.0 # Normalize - - def get_features(self, data): - # Load all images in the batch - images = np.array([ - self._load_and_preprocess_image(path) - for path in data["image_path"] - ]) - return images - - def get_labels(self, data): - return data["label_encoded"].values -``` - -## Best Practices - -For optimal results when working with datasets: - -1. **Data Validation**: Always validate your data during loading to catch issues early -2. **Efficient Preprocessing**: Implement preprocessing operations that can be efficiently applied to large datasets -3. **Consistent Splitting**: Use a fixed random state for reproducible splits -4. **Feature Engineering**: Keep complex feature engineering in the dataset class for consistency -5. **Documentation**: Document data formats, expected columns, and special handling requirements -6. **Error Handling**: Provide clear error messages for missing files or invalid data -7. **Caching**: Use the cache directory for storing intermediate results to avoid recomputation -8. **Type Safety**: Return numpy arrays with the appropriate dtypes for features and labels - -## API Reference - -### BaseDataset - -```python -class BaseDataset: - """Abstract base class for all datasets in EZFlow.""" - - def __init__( - self, - name: str, - data_dir: Union[str, Path], - cache_dir: Optional[Union[str, Path]] = None, - metadata: Optional[Dict[str, Any]] = None - ): - """ - Initialize the dataset. - - Parameters - ---------- - name : str - Name of the dataset - data_dir : str or Path - Directory where data files are stored - cache_dir : str or Path, optional - Directory for caching processed data - metadata : dict, optional - Additional metadata about the dataset - """ - - def load_data(self) -> None: - """ - Load the raw data into the dataset. - - This method should be implemented by subclasses to - load data from files into the `train_data` attribute. - """ - - def split_data(self, val_size: float = 0.2, random_state: int = 42) -> None: - """ - Split data into training and validation sets. - - Parameters - ---------- - val_size : float - Size of validation set as a fraction - random_state : int - Random seed for reproducibility - """ - - def preprocess(self) -> None: - """ - Apply preprocessing transformations to the data. - - This method should be implemented by subclasses to - perform any necessary preprocessing steps. - """ - - def get_features(self, data: pd.DataFrame) -> np.ndarray: - """ - Extract features from the data. - - Parameters - ---------- - data : pandas.DataFrame - Data to extract features from - - Returns - ------- - numpy.ndarray - Features as a numpy array - """ - - def get_labels(self, data: pd.DataFrame) -> np.ndarray: - """ - Extract labels from the data. - - Parameters - ---------- - data : pandas.DataFrame - Data to extract labels from - - Returns - ------- - numpy.ndarray - Labels as a numpy array - """ - - def save_splits(self) -> None: - """ - Save the current train/validation splits to disk. - """ - - def load_splits(self) -> None: - """ - Load train/validation splits from disk. - """ -``` - -### IrisDataset - -```python -class IrisDataset(BaseDataset): - """Dataset implementation for the Iris flower dataset.""" - - def __init__(self, data_dir: Union[str, Path]): - """ - Initialize the Iris dataset. - - Parameters - ---------- - data_dir : str or Path - Directory for storing data files - """ -``` - -### MolecularDataset - -```python -class MolecularDataset(BaseDataset): - """Dataset implementation for molecular/chemical data.""" - - def __init__( - self, - name: str, - data_dir: Union[str, Path], - smiles_col: str = 'smiles', - label_col: str = 'activity', - id_col: Optional[str] = 'id', - cache_dir: Optional[Union[str, Path]] = None, - metadata: Optional[Dict[str, Any]] = None, - fingerprint_radius: int = 2, - fingerprint_bits: int = 2048 - ): - """ - Initialize the molecular dataset. - - Parameters - ---------- - name : str - Name of the dataset - data_dir : str or Path - Directory where data files are stored - smiles_col : str - Column name containing SMILES strings - label_col : str - Column name containing target values - id_col : str, optional - Column name containing molecule identifiers - cache_dir : str or Path, optional - Directory for caching processed data - metadata : dict, optional - Additional metadata about the dataset - fingerprint_radius : int - Radius parameter for Morgan fingerprints - fingerprint_bits : int - Number of bits in the fingerprint - """ -``` \ No newline at end of file diff --git a/docs/experiments.md b/docs/experiments.md deleted file mode 100644 index b14801d..0000000 --- a/docs/experiments.md +++ /dev/null @@ -1,466 +0,0 @@ -# Experiment Module - -The Experiment module provides tools for configuring, tracking, and comparing machine learning experiments. - -## Table of Contents - -- [Overview](#overview) -- [ExperimentConfig](#experimentconfig) -- [ExperimentTracker](#experimenttracker) -- [Integration with MLflow](#integration-with-mlflow) -- [Best Practices](#best-practices) -- [API Reference](#api-reference) - -## Overview - -The Experiment module is designed to help data scientists and researchers manage their machine learning experiments efficiently. Key features include: - -- **Configuration Management**: Store and manage experiment parameters in a structured format -- **Metrics Tracking**: Log and compare metrics across different runs -- **Artifact Management**: Save and organize models, plots, and other artifacts -- **Run History**: Maintain a history of experiment runs for comparison -- **Reproducibility**: Enable reproducible experiments through consistent tracking - -## ExperimentConfig - -The `ExperimentConfig` class provides a structured way to define and manage experiment parameters. - -### Key Properties - -| Property | Type | Description | -|----------|------|-------------| -| `name` | str | Name of the experiment | -| `model_params` | Dict | Model hyperparameters | -| `dataset_params` | Dict | Dataset configuration parameters | -| `training_params` | Dict | Training configuration parameters | -| `metadata` | Dict | Additional metadata about the experiment | - -### Usage Example - -```python -from ezflow.core.experiment import ExperimentConfig - -# Create a configuration for an experiment -config = ExperimentConfig( - name="random_forest_classifier", - model_params={ - "n_estimators": 100, - "max_depth": 10, - "random_state": 42 - }, - dataset_params={ - "val_size": 0.2, - "stratify": True - }, - training_params={ - "cv_folds": 5, - "scoring": "accuracy" - }, - metadata={ - "author": "John Doe", - "description": "Random forest classifier for Iris dataset" - } -) - -# Convert to dictionary for serialization -config_dict = config.to_dict() - -# Save to file -config.save("./experiments/rf_config.json") - -# Load from file -loaded_config = ExperimentConfig.load("./experiments/rf_config.json") -``` - -### Methods - -| Method | Parameters | Return Type | Description | -|--------|------------|-------------|-------------| -| `to_dict` | - | Dict | Convert configuration to a dictionary | -| `from_dict` | `config_dict: Dict` | ExperimentConfig | Create configuration from a dictionary | -| `save` | `path: str/Path` | None | Save configuration to a file | -| `load` | `path: str/Path` | ExperimentConfig | Load configuration from a file | - -## ExperimentTracker - -The `ExperimentTracker` class provides functionality for tracking experiments, including metrics, parameters, and artifacts. - -### Key Properties - -| Property | Type | Description | -|----------|------|-------------| -| `experiment_name` | str | Name of the experiment | -| `artifacts_dir` | str/Path | Directory for storing artifacts | -| `backend` | str | Backend tracking system ("mlflow", "tensorboard", etc.) | -| `active_run` | Any | Current active run | - -### Usage Example - -```python -from ezflow.core.experiment import ExperimentTracker, ExperimentConfig -from ezflow.core.pipeline import SklearnPipelineWrapper -from ezflow.core.dataset import IrisDataset -from sklearn.metrics import accuracy_score - -# Initialize dataset -dataset = IrisDataset(data_dir="./data") -dataset.load_data() -dataset.split_data(val_size=0.2, random_state=42) - -# Get features and labels -X_train = dataset.get_features(dataset.train_data) -y_train = dataset.get_labels(dataset.train_data) -X_val = dataset.get_features(dataset.val_data) -y_val = dataset.get_labels(dataset.val_data) - -# Create pipeline -pipeline = SklearnPipelineWrapper([ - ("scaler", "sklearn.preprocessing.StandardScaler", {}), - ("classifier", "sklearn.ensemble.RandomForestClassifier", { - "n_estimators": 100, - "max_depth": 5, - "random_state": 42 - }) -]) - -# Create experiment config -config = ExperimentConfig( - name="iris_classification", - model_params={"n_estimators": 100, "max_depth": 5}, - dataset_params={"val_size": 0.2}, - training_params={"random_state": 42} -) - -# Initialize experiment tracker -tracker = ExperimentTracker( - experiment_name="iris_example", - artifacts_dir="./experiments" -) - -# Track experiment -with tracker: - # Start run with config - tracker.start_run(config) - - # Train model - pipeline.fit(X_train, y_train) - - # Make predictions - train_preds = pipeline.predict(X_train) - val_preds = pipeline.predict(X_val) - - # Calculate metrics - train_acc = accuracy_score(y_train, train_preds) - val_acc = accuracy_score(y_val, val_preds) - - # Log metrics - tracker.log_metrics({ - "train_accuracy": train_acc, - "val_accuracy": val_acc - }) - - # Save model as artifact - pipeline.save("./models/iris_rf.joblib") - tracker.log_artifact("./models/iris_rf.joblib") -``` - -### Methods - -| Method | Parameters | Return Type | Description | -|--------|------------|-------------|-------------| -| `start_run` | `config: ExperimentConfig` | None | Start a new tracked run | -| `end_run` | - | None | End the current run | -| `log_metric` | `name: str`, `value: float` | None | Log a single metric | -| `log_metrics` | `metrics: Dict[str, float]` | None | Log multiple metrics | -| `log_parameter` | `name: str`, `value: Any` | None | Log a single parameter | -| `log_parameters` | `params: Dict[str, Any]` | None | Log multiple parameters | -| `log_artifact` | `artifact_path: str/Path` | None | Log a file as an artifact | -| `get_artifact` | `artifact_path: str/Path` | str/Path | Retrieve an artifact | - -## Integration with MLflow - -EZFlow's experiment tracking is built on top of MLflow, a popular open-source platform for managing the ML lifecycle. - -### MLflow Backend - -By default, `ExperimentTracker` uses MLflow as its backend tracking system. This provides several advantages: - -- **Web UI**: Access a web-based dashboard for experiment comparison -- **Artifact Storage**: Efficient storage and retrieval of models and other files -- **Run Comparison**: Easy comparison of different experiment runs -- **Metadata Search**: Search and filter experiments based on parameters - -### Tracking Server Configuration - -You can configure the MLflow tracking server in several ways: - -1. **Local Tracking (Default)**: - ```python - # Uses ./mlruns directory by default - tracker = ExperimentTracker("my_experiment") - ``` - -2. **Custom Local Directory**: - ```python - # Specify a custom directory for MLflow data - tracker = ExperimentTracker( - "my_experiment", - backend_config={"tracking_uri": "file:/path/to/mlruns"} - ) - ``` - -3. **Remote Tracking Server**: - ```python - # Use a remote MLflow tracking server - tracker = ExperimentTracker( - "my_experiment", - backend_config={"tracking_uri": "http://my-mlflow-server:5000"} - ) - ``` - -### Viewing Experiment Results - -To view experiment results using the MLflow UI: - -```bash -# Start the MLflow UI -mlflow ui --backend-store-uri ./experiments/mlruns - -# If using a remote server -mlflow ui --backend-store-uri http://my-mlflow-server:5000 -``` - -This will start a web server (default: http://localhost:5000) where you can view and compare experiments. - -## Best Practices - -For optimal results when tracking experiments: - -1. **Consistent Naming**: Use consistent naming conventions for experiments, runs, and metrics -2. **Parameter Tracking**: Track all relevant hyperparameters for reproducibility -3. **Granular Metrics**: Log metrics at appropriate intervals (not just final results) -4. **Artifact Management**: Save models and other important artifacts with clear versioning -5. **Contextual Metadata**: Include contextual information like dataset versions, environment details -6. **Run Tags**: Use tags to categorize and filter experiment runs -7. **Experiment Organization**: Group related experiments logically - -## API Reference - -### ExperimentConfig - -```python -class ExperimentConfig: - """Configuration for machine learning experiments.""" - - def __init__( - self, - name: str, - model_params: Dict[str, Any] = None, - dataset_params: Dict[str, Any] = None, - training_params: Dict[str, Any] = None, - metadata: Dict[str, Any] = None - ): - """ - Initialize experiment configuration. - - Parameters - ---------- - name : str - Name of the experiment - model_params : dict, optional - Model hyperparameters - dataset_params : dict, optional - Dataset configuration parameters - training_params : dict, optional - Training configuration parameters - metadata : dict, optional - Additional metadata about the experiment - """ - - def to_dict(self) -> Dict[str, Any]: - """ - Convert configuration to a dictionary. - - Returns - ------- - dict - Dictionary representation of the configuration - """ - - @classmethod - def from_dict(cls, config_dict: Dict[str, Any]) -> 'ExperimentConfig': - """ - Create configuration from a dictionary. - - Parameters - ---------- - config_dict : dict - Dictionary containing configuration values - - Returns - ------- - ExperimentConfig - Configuration object - """ - - def save(self, path: Union[str, Path]) -> None: - """ - Save configuration to a file. - - Parameters - ---------- - path : str or Path - Path to save the configuration to - """ - - @classmethod - def load(cls, path: Union[str, Path]) -> 'ExperimentConfig': - """ - Load configuration from a file. - - Parameters - ---------- - path : str or Path - Path to load the configuration from - - Returns - ------- - ExperimentConfig - Configuration object - """ -``` - -### ExperimentTracker - -```python -class ExperimentTracker: - """Tracker for machine learning experiments.""" - - def __init__( - self, - experiment_name: str, - artifacts_dir: Union[str, Path] = None, - backend: str = "mlflow", - backend_config: Dict[str, Any] = None - ): - """ - Initialize experiment tracker. - - Parameters - ---------- - experiment_name : str - Name of the experiment - artifacts_dir : str or Path, optional - Directory for storing artifacts - backend : str - Backend tracking system ("mlflow", "tensorboard", etc.) - backend_config : dict, optional - Configuration for the backend tracking system - """ - - def __enter__(self) -> 'ExperimentTracker': - """ - Enter context manager. - - Returns - ------- - ExperimentTracker - Self, for context manager usage - """ - - def __exit__(self, exc_type, exc_val, exc_tb) -> None: - """ - Exit context manager and end run if active. - - Parameters - ---------- - exc_type : type - Exception type, if any - exc_val : Exception - Exception value, if any - exc_tb : traceback - Exception traceback, if any - """ - - def start_run(self, config: ExperimentConfig = None) -> None: - """ - Start a new tracked run. - - Parameters - ---------- - config : ExperimentConfig, optional - Configuration for the run - """ - - def end_run(self) -> None: - """End the current run.""" - - def log_metric(self, name: str, value: float) -> None: - """ - Log a single metric. - - Parameters - ---------- - name : str - Name of the metric - value : float - Value of the metric - """ - - def log_metrics(self, metrics: Dict[str, float]) -> None: - """ - Log multiple metrics. - - Parameters - ---------- - metrics : dict - Dictionary of metric names and values - """ - - def log_parameter(self, name: str, value: Any) -> None: - """ - Log a single parameter. - - Parameters - ---------- - name : str - Name of the parameter - value : any - Value of the parameter - """ - - def log_parameters(self, params: Dict[str, Any]) -> None: - """ - Log multiple parameters. - - Parameters - ---------- - params : dict - Dictionary of parameter names and values - """ - - def log_artifact(self, artifact_path: Union[str, Path]) -> None: - """ - Log a file as an artifact. - - Parameters - ---------- - artifact_path : str or Path - Path to the file to log as an artifact - """ - - def get_artifact(self, artifact_path: Union[str, Path]) -> Union[str, Path]: - """ - Retrieve an artifact. - - Parameters - ---------- - artifact_path : str or Path - Path to the artifact - - Returns - ------- - str or Path - Path to the retrieved artifact - """ -``` \ No newline at end of file diff --git a/docs/pipeline.md b/docs/pipeline.md deleted file mode 100644 index 13bab08..0000000 --- a/docs/pipeline.md +++ /dev/null @@ -1,410 +0,0 @@ -# Pipeline Module - -The Pipeline module provides a unified interface for creating, training, and deploying machine learning models with different backend frameworks. - -## Table of Contents - -- [Overview](#overview) -- [Base Pipeline](#base-pipeline) -- [Pipeline Implementations](#pipeline-implementations) - - [SklearnPipelineWrapper](#sklearnpipelinewrapper) - - [TorchPipelineWrapper](#torchpipelinewrapper) -- [Custom Pipelines](#custom-pipelines) -- [Best Practices](#best-practices) -- [API Reference](#api-reference) - -## Overview - -The Pipeline module is a core component of EZFlow, designed to abstract away the differences between machine learning frameworks and provide a consistent interface for model development. Key features include: - -- Unified API for different ML frameworks -- Consistent methods for training, prediction, and evaluation -- Model serialization and deserialization -- Support for complex pipelines with preprocessing steps -- Extensibility for custom model implementations - -## Base Pipeline - -The `Pipeline` abstract base class defines the interface that all pipeline implementations must follow. - -### Key Methods - -| Method | Parameters | Return Type | Description | -|--------|------------|-------------|-------------| -| `fit` | `X: np.ndarray`, `y: np.ndarray` | None | Train the model on the provided data | -| `predict` | `X: np.ndarray` | np.ndarray | Generate predictions for the input data | -| `score` | `X: np.ndarray`, `y: np.ndarray` | float | Evaluate the model on the provided data | -| `save` | `path: str/Path` | None | Save the model to disk | -| `load` | `path: str/Path` | None | Load the model from disk | - -### Usage Example - -```python -from ezflow.core.pipeline import Pipeline - -# Create a pipeline using a concrete implementation -pipeline = SomePipelineImplementation(params) - -# Train the model -pipeline.fit(X_train, y_train) - -# Make predictions -predictions = pipeline.predict(X_test) - -# Evaluate the model -score = pipeline.score(X_test, y_test) - -# Save and load the model -pipeline.save("./models/my_model.pkl") -pipeline.load("./models/my_model.pkl") -``` - -## Pipeline Implementations - -EZFlow provides several concrete implementations of the `Pipeline` interface for popular machine learning frameworks. - -### SklearnPipelineWrapper - -The `SklearnPipelineWrapper` class provides an implementation of the pipeline interface for scikit-learn models. - -#### Features - -- Support for scikit-learn's pipeline API -- Dynamic creation of pipeline steps from string references -- Flexible parameter specification -- Consistent serialization/deserialization - -#### Initialization - -```python -from ezflow.core.pipeline import SklearnPipelineWrapper - -# Create a pipeline with preprocessing and a model -pipeline = SklearnPipelineWrapper([ - ("scaler", "sklearn.preprocessing.StandardScaler", {}), - ("pca", "sklearn.decomposition.PCA", {"n_components": 10}), - ("classifier", "sklearn.ensemble.RandomForestClassifier", { - "n_estimators": 100, - "max_depth": 10, - "random_state": 42 - }) -]) -``` - -#### Parameters - -- `steps`: A list of tuples, each containing: - - Step name (string) - - Fully qualified class path (string) or actual class - - Parameters dictionary for the step - -### TorchPipelineWrapper - -The `TorchPipelineWrapper` class provides an implementation for PyTorch models. - -#### Features - -- Support for PyTorch models and training loops -- Configurable training parameters -- Automatic device selection (CPU/GPU) -- Proper model serialization/deserialization - -#### Initialization - -```python -import torch.nn as nn -from ezflow.core.pipeline import TorchPipelineWrapper - -# Define a PyTorch model -class SimpleNN(nn.Module): - def __init__(self, input_dim, hidden_dim, output_dim): - super().__init__() - self.fc1 = nn.Linear(input_dim, hidden_dim) - self.relu = nn.ReLU() - self.fc2 = nn.Linear(hidden_dim, output_dim) - - def forward(self, x): - x = self.fc1(x) - x = self.relu(x) - x = self.fc2(x) - return x - -# Create a pipeline with the model -model = SimpleNN(input_dim=10, hidden_dim=50, output_dim=3) -pipeline = TorchPipelineWrapper( - model=model, - loss_fn="torch.nn.CrossEntropyLoss", - optimizer="torch.optim.Adam", - optimizer_params={"lr": 0.001}, - device="auto" # Automatically select CPU or GPU -) -``` - -#### Parameters - -- `model`: PyTorch nn.Module or string reference to a model class -- `loss_fn`: Loss function (string reference or actual function) -- `optimizer`: Optimizer class (string reference or actual class) -- `optimizer_params`: Dictionary of optimizer parameters -- `device`: "cpu", "cuda", or "auto" for automatic selection - -## Custom Pipelines - -Creating a custom pipeline involves inheriting from the `Pipeline` base class and implementing the required methods. - -### Example: Custom TensorFlow Pipeline - -```python -import numpy as np -import tensorflow as tf -from ezflow.core.pipeline import Pipeline - -class TensorFlowPipelineWrapper(Pipeline): - def __init__(self, model=None, model_config=None, compile_params=None): - super().__init__() - self.model_config = model_config or {} - self.compile_params = compile_params or { - "optimizer": "adam", - "loss": "sparse_categorical_crossentropy", - "metrics": ["accuracy"] - } - - # Create or use provided model - if model is not None: - if isinstance(model, str): - # Import the model class dynamically - parts = model.split(".") - module_name = ".".join(parts[:-1]) - class_name = parts[-1] - module = __import__(module_name, fromlist=[class_name]) - model_class = getattr(module, class_name) - self.model = model_class(**self.model_config) - else: - self.model = model - else: - raise ValueError("Model must be provided") - - # Compile the model - self.model.compile(**self.compile_params) - - def fit(self, X, y, **kwargs): - """Train the model on the provided data.""" - default_params = { - "epochs": 10, - "batch_size": 32, - "validation_split": 0.1, - "verbose": 1 - } - # Update defaults with any provided kwargs - params = {**default_params, **kwargs} - history = self.model.fit(X, y, **params) - return history - - def predict(self, X): - """Generate predictions for the input data.""" - return self.model.predict(X) - - def score(self, X, y): - """Evaluate the model on the provided data.""" - scores = self.model.evaluate(X, y, verbose=0) - if isinstance(scores, list): - # Return the main metric (usually loss is first, acc is second) - return scores[1] if len(scores) > 1 else scores[0] - return scores - - def save(self, path): - """Save the model to disk.""" - self.model.save(path) - - def load(self, path): - """Load the model from disk.""" - self.model = tf.keras.models.load_model(path) - return self -``` - -### Using the Custom Pipeline - -```python -# Create a TensorFlow pipeline -pipeline = TensorFlowPipelineWrapper( - model="tensorflow.keras.Sequential", - model_config={ - "layers": [ - tf.keras.layers.Dense(128, activation="relu", input_shape=(10,)), - tf.keras.layers.Dropout(0.2), - tf.keras.layers.Dense(64, activation="relu"), - tf.keras.layers.Dense(3, activation="softmax") - ] - }, - compile_params={ - "optimizer": "adam", - "loss": "sparse_categorical_crossentropy", - "metrics": ["accuracy"] - } -) - -# Train and use like any other pipeline -pipeline.fit(X_train, y_train, epochs=20, batch_size=64) -predictions = pipeline.predict(X_test) -``` - -## Best Practices - -For optimal results when working with pipelines: - -1. **Consistent Interfaces**: Ensure all pipeline implementations follow the standard interface -2. **Error Handling**: Add informative error messages for common issues -3. **Resource Management**: Clean up resources properly, especially with GPU-based models -4. **Serialization**: Test model saving and loading thoroughly -5. **Performance Monitoring**: Include ways to monitor training performance -6. **Parameter Validation**: Validate parameters early to catch configuration errors -7. **Documentation**: Document expected input formats and model-specific behaviors - -## API Reference - -### Pipeline (Abstract Base Class) - -```python -class Pipeline: - """Abstract base class for all pipeline implementations in EZFlow.""" - - def __init__(self): - """Initialize the pipeline.""" - self.pipeline = None - self.pipeline_config = {} - - def fit(self, X: np.ndarray, y: np.ndarray, **kwargs) -> Any: - """ - Train the model on the provided data. - - Parameters - ---------- - X : numpy.ndarray - Features for training - y : numpy.ndarray - Target values for training - **kwargs : dict - Additional arguments for the specific implementation - - Returns - ------- - Any - Implementation-specific return value, often training history - """ - raise NotImplementedError - - def predict(self, X: np.ndarray, **kwargs) -> np.ndarray: - """ - Generate predictions for the input data. - - Parameters - ---------- - X : numpy.ndarray - Features to generate predictions for - **kwargs : dict - Additional arguments for the specific implementation - - Returns - ------- - numpy.ndarray - Predictions - """ - raise NotImplementedError - - def score(self, X: np.ndarray, y: np.ndarray, **kwargs) -> float: - """ - Evaluate the model on the provided data. - - Parameters - ---------- - X : numpy.ndarray - Features for evaluation - y : numpy.ndarray - Target values for evaluation - **kwargs : dict - Additional arguments for the specific implementation - - Returns - ------- - float - Score value (higher is better) - """ - raise NotImplementedError - - def save(self, path: Union[str, Path]) -> None: - """ - Save the model to disk. - - Parameters - ---------- - path : str or Path - Path to save the model to - """ - raise NotImplementedError - - def load(self, path: Union[str, Path]) -> 'Pipeline': - """ - Load the model from disk. - - Parameters - ---------- - path : str or Path - Path to load the model from - - Returns - ------- - Pipeline - Self, for method chaining - """ - raise NotImplementedError -``` - -### SklearnPipelineWrapper - -```python -class SklearnPipelineWrapper(Pipeline): - """Pipeline implementation for scikit-learn models.""" - - def __init__(self, steps: List[Tuple[str, Union[str, Any], Dict[str, Any]]]): - """ - Initialize the scikit-learn pipeline. - - Parameters - ---------- - steps : list of tuples - List of (name, estimator, params) tuples defining the pipeline steps. - Estimator can be a string (module path) or an actual estimator class. - """ -``` - -### TorchPipelineWrapper - -```python -class TorchPipelineWrapper(Pipeline): - """Pipeline implementation for PyTorch models.""" - - def __init__( - self, - model: Union[str, nn.Module], - loss_fn: Union[str, Callable], - optimizer: Union[str, Type[optim.Optimizer]], - optimizer_params: Dict[str, Any] = None, - device: str = "auto" - ): - """ - Initialize the PyTorch pipeline. - - Parameters - ---------- - model : str or torch.nn.Module - PyTorch model or string reference to a model class - loss_fn : str or callable - Loss function or string reference - optimizer : str or torch.optim.Optimizer - Optimizer class or string reference - optimizer_params : dict - Dictionary of optimizer parameters - device : str - "cpu", "cuda", or "auto" for automatic selection - """ -``` \ No newline at end of file From 49cf7d1ec17e790604692e5f4cdd56dbef673a7c Mon Sep 17 00:00:00 2001 From: Alexan Date: Wed, 5 Mar 2025 12:42:15 +0400 Subject: [PATCH 5/6] updated Readme Signed-off-by: Alexan --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index 8e78507..6224773 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,5 @@ # EZFlow: A Machine Learning Framework -

- EZFlow Logo -

-

License GitHub stars From e50f80da41ccbfc33cf90dd4e0bca0639f735280 Mon Sep 17 00:00:00 2001 From: Alexan Date: Wed, 5 Mar 2025 13:20:59 +0400 Subject: [PATCH 6/6] Update iris example before refactoring --- ezflow/examples/iris_example.py | 79 ++++++++++++++++++++------------- 1 file changed, 48 insertions(+), 31 deletions(-) diff --git a/ezflow/examples/iris_example.py b/ezflow/examples/iris_example.py index 9b7f387..1543c53 100644 --- a/ezflow/examples/iris_example.py +++ b/ezflow/examples/iris_example.py @@ -3,6 +3,9 @@ import numpy as np from sklearn.datasets import load_iris from sklearn.metrics import accuracy_score, classification_report +from sklearn.model_selection import cross_val_score, KFold +import matplotlib.pyplot as plt +from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay from ezflow.core.dataset.base import BaseDataset from ezflow.core.pipeline.sklearn import SklearnPipelineWrapper @@ -32,6 +35,10 @@ def load_data(self) -> None: data=np.c_[iris['data'], iris['target']], columns=[*iris['feature_names'], 'target'] ).reset_index(drop=True) + + # Store feature names and target names for later use + self.feature_names = iris['feature_names'] + self.target_names = iris['target_names'] def preprocess(self) -> None: """Preprocess the data (standardization is handled in pipeline).""" @@ -46,7 +53,7 @@ def get_labels(self, data: pd.DataFrame) -> np.ndarray: return data.iloc[:, -1].values def run_iris_experiment(): - """Run a simple experiment with the Iris dataset.""" + """Run a more comprehensive experiment with the Iris dataset using cross-validation.""" # Create directories Path("experiments").mkdir(exist_ok=True) @@ -55,34 +62,32 @@ def run_iris_experiment(): # Initialize dataset dataset = IrisDataset(data_dir=".") dataset.load_data() - dataset.split_data(val_size=0.2, random_state=42) dataset.preprocess() - # Get features and labels - X_train = dataset.get_features(dataset.train_data) - y_train = dataset.get_labels(dataset.train_data) - X_val = dataset.get_features(dataset.val_data) - y_val = dataset.get_labels(dataset.val_data) + # Get all data + X = dataset.get_features(dataset.train_data) + y = dataset.get_labels(dataset.train_data) # Create pipeline with preprocessing and model + # Using a simpler model to prevent overfitting pipeline = SklearnPipelineWrapper([ ("scaler", "sklearn.preprocessing.StandardScaler", {}), ("classifier", "sklearn.ensemble.RandomForestClassifier", { - "n_estimators": 100, - "max_depth": 5, + "n_estimators": 50, + "max_depth": 3, "random_state": 42 }) ]) # Create experiment config config = ExperimentConfig( - name="iris_classification", + name="iris_classification_cv", model_params={ - "n_estimators": 100, - "max_depth": 5 + "n_estimators": 50, + "max_depth": 3 }, dataset_params={ - "val_size": 0.2 + "n_splits": 5 }, training_params={ "random_state": 42 @@ -91,44 +96,56 @@ def run_iris_experiment(): # Initialize experiment tracker tracker = ExperimentTracker( - experiment_name="iris_example", + experiment_name="iris_example_cv", artifacts_dir="experiments" ) - # Run experiment + # Run experiment with cross-validation with tracker: # Start run tracker.start_run(config) - # Train model - pipeline.fit(X_train, y_train) + # Perform cross-validation + cv = KFold(n_splits=5, shuffle=True, random_state=42) + cv_scores = cross_val_score(pipeline.pipeline, X, y, cv=cv) - # Make predictions - train_preds = pipeline.predict(X_train) - val_preds = pipeline.predict(X_val) + # Train final model on full dataset + pipeline.fit(X, y) - # Calculate metrics - train_acc = accuracy_score(y_train, train_preds) - val_acc = accuracy_score(y_val, val_preds) + # Make predictions + y_pred = pipeline.predict(X) - # Log metrics + # Calculate and log metrics tracker.log_metrics({ - "train_accuracy": train_acc, - "val_accuracy": val_acc + "cv_mean_accuracy": cv_scores.mean(), + "cv_std_accuracy": cv_scores.std(), + "final_accuracy": accuracy_score(y, y_pred) }) + # Generate and save confusion matrix plot + cm = confusion_matrix(y, y_pred) + disp = ConfusionMatrixDisplay(confusion_matrix=cm, + display_labels=dataset.target_names) + fig, ax = plt.subplots(figsize=(8, 8)) + disp.plot(ax=ax) + plt.title("Confusion Matrix") + plt.savefig("experiments/confusion_matrix.png") + plt.close() + + # Log the plot as an artifact + tracker.log_artifact("experiments/confusion_matrix.png") + # Save model pipeline.save("models/iris_rf.joblib") tracker.log_artifact("models/iris_rf.joblib") # Print results - print("\nTraining Results:") + print("\nCross-validation Results:") print("-" * 50) - print(f"Training Accuracy: {train_acc:.4f}") - print(f"Validation Accuracy: {val_acc:.4f}") - print("\nValidation Classification Report:") + print(f"Mean CV Accuracy: {cv_scores.mean():.4f} (+/- {cv_scores.std() * 2:.4f})") + print("\nFinal Model Results:") print("-" * 50) - print(classification_report(y_val, val_preds, target_names=load_iris().target_names)) + print(classification_report(y, y_pred, target_names=dataset.target_names)) if __name__ == "__main__": run_iris_experiment() \ No newline at end of file