diff --git a/.gitignore b/.gitignore index 9b5efb9..3bd232d 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,93 @@ -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 - Comprehensive ignoring of data files and directories +data/ +data/* +*/data/* +*.csv +*.json +*.jsonl +*.parquet +*.feather +*.arrow +*.hdf5 +*.h5 +*.npz +*.npy +*.tar +*.zip +*.gz +*.bz2 +*.xz +*.7z +*.txt +!requirements.txt +!environment.txt + +# 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/README.md b/README.md index 50e1dd5..6224773 100644 --- a/README.md +++ b/README.md @@ -1,48 +1,89 @@ -# bio_ml_handler +# EZFlow: A Machine Learning Framework -A data handler for bioinformatics machine learning tasks, including data loading, processing, and model handling. +

+ 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 . +``` + +### Development Installation -2. Install the package with pip: +```bash +# Install in development mode from source +git clone https://github.com/Alcray/ezflow.git +cd ezflow +pip install -e . +``` - ```bash - pip install . - ``` +## Quick Start -Or, install directly from GitHub: +### Creating a New Project - ```bash - pip install git+https://github.com/Alcray/BioML.git - ``` +```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 +``` + +## Data Management + +EZFlow works with data stored in the `data/` directory. This directory is included in `.gitignore` to prevent uploading datasets to GitHub. When using the framework, place your datasets in this directory, and they will be automatically used by the dataset classes. + +## 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 22560aa..0000000 Binary files a/bio_ml_handler/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/bio_ml_handler/__pycache__/data_handler.cpython-310.pyc b/bio_ml_handler/__pycache__/data_handler.cpython-310.pyc deleted file mode 100644 index fc6ea74..0000000 Binary files a/bio_ml_handler/__pycache__/data_handler.cpython-310.pyc and /dev/null differ diff --git a/bio_ml_handler/data_handler.py b/bio_ml_handler/data_handler.py deleted file mode 100644 index 3db5a4a..0000000 --- a/bio_ml_handler/data_handler.py +++ /dev/null @@ -1,257 +0,0 @@ -import os -import pandas as pd -import numpy as np -import json -from rdkit import Chem -from rdkit.Chem import AllChem -from sklearn.linear_model import LogisticRegression -from sklearn.metrics import average_precision_score - -class BioMLDataHandler: - def __init__(self, data_path: str = 'data', split_data_path: str = 'split_data'): - """ - Initializes the data handler and loads datasets from specified directories. - - Parameters: - data_path (str): Path to the 'data' directory containing train.csv and test.csv. - split_data_path (str): Path to the 'split_data' directory containing train_split.csv and val_split.csv. - """ - self.data_path = data_path - self.split_data_path = split_data_path - - self.train = None - self.test = None - self.train_split = None - self.val_split = None - self.model = None - - # Load datasets - self.load_data() - - def load_data(self): - """ - Loads data from the specified directories into the handler. - """ - try: - # Load the main train and test datasets - self.train = pd.read_csv(os.path.join(self.data_path, 'train.csv')) - self.test = pd.read_csv(os.path.join(self.data_path, 'test.csv')) - print("Main train and test datasets loaded successfully.") - - # Load the train and validation splits - self.train_split = pd.read_csv(os.path.join(self.split_data_path, 'train_split.csv')) - self.val_split = pd.read_csv(os.path.join(self.split_data_path, 'val_split.csv')) - print("Train and validation splits loaded successfully.") - except FileNotFoundError as e: - print(f"Error loading data: {e}") - raise - - @staticmethod - def generate_fingerprint(smiles: str, radius=2, n_bits=2048) -> np.ndarray: - """ - Converts a SMILES string to an ECFP4 fingerprint. - - Parameters: - smiles (str): SMILES representation of a molecule. - radius (int): Radius for Morgan fingerprint. 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/data/sample_submission.csv b/data/sample_submission.csv deleted file mode 100644 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-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 -250,O=C(O)c1ccc(CN2CCC(CN3CCC(Oc4ccc(Cl)c(Cl)c4)CC3)CC2)cc1 -251,O=C(NC1CC1c1ccccc1)N1CCC(n2ccnn2)CC1 -252,Cc1cc(F)ccc1OC1CCN(CC2CCN([C@@H](Cc3ccc(F)cc3)C(=O)O)CC2)CC1 -253,COc1cnc(-c2cccc3c2C[C@H](NC(=O)c2ccc(COCC(F)(F)F)nc2)CO3)cn1 -254,Fc1ccc2cc(CO[C@@H]3CCNC3)ccc2c1 -255,O=C(Nc1cc(Cl)ccc1Cl)NS(=O)(=O)c1ccc(OCCCCN2CCCCC2)cc1 -256,CN1CCN(c2ccc(NC(=O)c3cc(N(C)Cc4ccc(C(F)(F)F)cc4)ncn3)cc2)CC1 -257,Cc1ccc(CN2[C@@H]3CC[C@H]2C[C@H](Oc2cccc(C(N)=O)c2)C3)cc1 -258,CCN(C(=O)Cc1ccc(S(C)(=O)=O)cc1)C1CCN(CC[C@@H](c2cc(Cl)cc(Cl)c2)C2CCN(S(C)(=O)=O)CC2)CC1 -259,CN1Cc2c(N)cccc2C(c2ccccc2)C1 -260,N#C[C@H]1CC[C@H](N2C(=O)c3ccccc3C2C(=O)NCc2ccc(OC(F)(F)F)cc2)CC1 -261,COc1ccc(-c2cc(-c3ccc(C(=O)N4CCN(C)CC4)cc3)cnc2N)cn1 -262,CC(=O)NCCc1ccccc1-c1onc([C@H]2CNCC[C@]2(OCC(O)CO)c2ccc(F)c(F)c2)c1Br -263,CCCCS(=O)(=O)N1Cc2ccc(/C=C/C(=O)NO)cc2C1 -264,O=C(/C=C/c1ccc2c(c1)CN(C(=O)Cc1ccccc1)C2)NO -265,CC(C)CN(C(=O)c1ccccc1-c1ccccc1)[C@H]1CCNC1 -266,Cc1n[nH]c(C)c1[C@H](C)NC(=O)COc1cc(C(F)(F)F)c2c(-c3ccccc3)nn(C)c2n1 -267,NC(=O)c1nnc(N[C@@H]2CCCC[C@@H]2N)nc1Nc1cccc(-n2nccn2)c1 -268,CCc1nc2ccc3c(c2s1)CCN(CCCSc1nnc(-c2ocnc2C)n1C)CC3 -269,Cc1cccnc1CN1CCC2(CC1)C(=O)N(c1ccc(-c3cnc(=O)[nH]c3)cc1)C(=O)N2c1cc(=O)[nH]cn1 -270,Cc1ncoc1-c1nnc(SCCCN2CCC3(c4ccc(C(F)(F)F)cc4)CC3C2)n1C -271,O=C(Nc1cc(Cl)ccc1Cl)NS(=O)(=O)c1ccc(OCCCCN2CCCC2)cc1 -272,O[C@]1(c2ccc(F)c(F)c2)CCNC[C@@H]1c1onc(-c2ccccc2Cl)c1Br -273,Cc1ccc(C#Cc2cccc(C#Cc3ccc(C)cc3)[n+]2C)cc1 -274,CNc1nc(NCC(F)(F)F)c2sc(-c3ccc(C(F)(F)F)cc3)cc2n1 -275,COc1ccccc1Oc1ccccc1CN1CCC2(CC1)CCN(C(=O)c1ccccc1C(=O)O)CC2 -276,CCC1=C(C)CC(C(=O)NCCc2ccc(S(=O)(=O)NC(=O)NC3CCC(C)CC3)cc2)C1=O -277,O=C(NC1CCN(Cc2ccc3c(c2)OCO3)CC1)c1cc(=O)c2cc(F)c(Cl)cc2o1 -278,CN(C)C(=O)N[C@H]1CC[C@@H](CN2[C@@H]3CC[C@H]2C[C@@H](Oc2cccc(C(N)=O)c2)C3)CC1 -279,O=C(Nc1ccc(C(F)(F)F)cc1)NS(=O)(=O)c1ccc(OCCCN2CCCC2)cc1 -280,O=c1[nH]ccc2cc(O[C@H]3CCCNC3)ccc12 -281,C[C@@H]1CCCN1CCc1ccc2nc(-c3cccs3)ccc2c1 -282,C[C@@H]1C(=O)NN=C2COc3cc(C(F)(F)F)c(NC4CNC4)cc3N21 -283,O=C(NC1CCN(Cc2ccc3c(c2)OCO3)CC1)c1cc(=O)c2cc(Cl)c(Cl)cc2o1 -284,CC(C)(C)NC(=O)C1c2ccccc2C(=O)N1Cc1cc(F)ccc1-c1ccc(F)cc1 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-COc1cccc(-c2c(C(=O)N3CCCCC3CO)cc3ccccn23)c1,0 -Cc1nncn1Cc1cn(-c2ccc(Cl)cc2)nn1,1 -CN(CCCC(=O)NCCO)C(=O)c1ccc2c(c1)c1c(n2C)CC[C@@H](C2CCOCC2)C1,0 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..1543c53 --- /dev/null +++ b/ezflow/examples/iris_example.py @@ -0,0 +1,151 @@ +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 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 +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) + + # 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).""" + 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 more comprehensive experiment with the Iris dataset using cross-validation.""" + + # Create directories + Path("experiments").mkdir(exist_ok=True) + Path("models").mkdir(exist_ok=True) + + # Initialize dataset + dataset = IrisDataset(data_dir=".") + dataset.load_data() + dataset.preprocess() + + # 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": 50, + "max_depth": 3, + "random_state": 42 + }) + ]) + + # Create experiment config + config = ExperimentConfig( + name="iris_classification_cv", + model_params={ + "n_estimators": 50, + "max_depth": 3 + }, + dataset_params={ + "n_splits": 5 + }, + training_params={ + "random_state": 42 + } + ) + + # Initialize experiment tracker + tracker = ExperimentTracker( + experiment_name="iris_example_cv", + artifacts_dir="experiments" + ) + + # Run experiment with cross-validation + with tracker: + # Start run + tracker.start_run(config) + + # Perform cross-validation + cv = KFold(n_splits=5, shuffle=True, random_state=42) + cv_scores = cross_val_score(pipeline.pipeline, X, y, cv=cv) + + # Train final model on full dataset + pipeline.fit(X, y) + + # Make predictions + y_pred = pipeline.predict(X) + + # Calculate and log metrics + tracker.log_metrics({ + "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("\nCross-validation Results:") + print("-" * 50) + print(f"Mean CV Accuracy: {cv_scores.mean():.4f} (+/- {cv_scores.std() * 2:.4f})") + print("\nFinal Model Results:") + print("-" * 50) + print(classification_report(y, y_pred, target_names=dataset.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 b/similarities_data/similarity2test.csv deleted file mode 100644 index 5d33aeb..0000000 --- a/similarities_data/similarity2test.csv +++ /dev/null @@ -1,4217 +0,0 @@ -smiles,activity,similarity -CC(C)n1c(N2CCOC(CN)C2)nc2c(C#N)c(Br)c(Br)cc21,1,0.10093730717157433 -N#Cc1ccc2nc(N)n(CC(O)c3cccc(Cl)c3Cl)c2c1,0,0.11412041063184553 -C=C(c1ccc(NC(N)=O)cc1)c1ccc2c(c1)Nc1ccccc1S2,0,0.12193201366054035 -COc1ccc(CC(=O)Nc2cc3c(cc2[N+](=O)[O-])OC(C)(C)[C@H](O)[C@H]3NC2CC2)cc1,0,0.11916440246704292 -CC(=O)Nc1ccc2c(c1)N(c1cc(NC3CC3)n3ncc(C#N)c3n1)CC2,0,0.09662849263934513 -O=C1c2cccc3c2[C@H](CCC3)CN1[C@@H]1CN2CCC1CC2,1,0.09021886524214374 -COc1cccc(-c2c(C(=O)N3CCCCC3CO)cc3ccccn23)c1,0,0.10959041656178017 -CC(C)[C@@H](CN1CCC(c2ccccc2)CC1)NC(=O)[C@H]1Cc2ccc(O)cc2CN1,1,0.12321971573890282 -FC(F)Oc1cccc(-c2cnn(Cc3cc[nH]n3)c2)c1,1,0.13796961838924618 -C[C@@H]1Cc2c([nH]c3ccccc23)[C@@H](c2nccc(OCCNCCCF)c2Cl)N1CC(F)(F)F,1,0.12388584376650902 -Cn1cc(-c2cnc3ccc4ccc(NS(=O)(=O)N5CC(F)C5)cc4c(=O)c3c2)cn1,0,0.11305641727568891 -OC(c1cc(-c2ncnc3cc(N4CCOCC4)ccc23)c(F)cc1Cl)c1nccn2ccnc12,0,0.12775867127834048 -Cc1c(OCCNCCCF)ncc(F)c1[C@@H]1c2[nH]c3ccccc3c2C[C@@H](C)N1C[C@H](C)C(=O)O,0,0.10579699098058758 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