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Sleeping
Clement Vachet
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Parent(s):
Initial commit
Browse files- .gitattributes +36 -0
- .gitignore +162 -0
- classification/__init__.py +0 -0
- classification/classifier.py +57 -0
- main.py +27 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.gitignore
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3-journal
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instance/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# install all needed dependencies.
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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.pdm.toml
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.pdm-python
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.pdm-build/
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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.idea/
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classification/__init__.py
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classification/classifier.py
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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import joblib
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import pandas as pd
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import os
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class Classifier:
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def __init__(self):
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pass
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def train_and_save(self):
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print("\nIRIS model training...")
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iris = load_iris()
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ada = AdaBoostClassifier(n_estimators=5)
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X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.1, random_state=42)
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model = ada.fit(X_train, y_train)
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print(f"Model score: {ada.score(X_train, y_train):.3f}")
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print(f"Test Accuracy: {ada.score(X_test, y_test):.3f}")
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current_dir = os.path.dirname(os.path.abspath(__file__))
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parent_dir = os.path.dirname(current_dir)
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test_data_csv_path = os.path.join(parent_dir, "data", "test_data.csv")
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pd.concat([pd.DataFrame(X_test), pd.DataFrame(y_test, columns=['4'])], axis=1).to_csv(test_data_csv_path,
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index=False)
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model_path = os.path.join(parent_dir, "models", "model.pkl")
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joblib.dump(model, model_path)
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print(f"Model saved to {model_path}")
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def load_and_test(self, data):
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print("\nIRIS model prediction...")
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current_dir = os.path.dirname(os.path.abspath(__file__))
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parent_dir = os.path.dirname(current_dir)
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model_path = os.path.join(parent_dir, "models", "model.pkl")
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model = joblib.load(model_path)
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features = data.get('features', [])
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if not isinstance(features, list) or not all(isinstance(sample, list) for sample in features):
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raise ValueError("Input data should be a list of feature vectors.")
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# Predict the class
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predictions = model.predict(features).tolist()
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probabilities = model.predict_proba(features).tolist()
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# Map predictions to class labels
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iris_types = {0: 'setosa', 1: 'versicolor', 2: 'virginica'}
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prediction_labels = [iris_types[pred] for pred in predictions]
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return {"predictions": prediction_labels, "probabilities": probabilities}
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main.py
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from classification.classifier import Classifier
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import json
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if __name__ == "__main__":
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cls = Classifier()
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# Training
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cls.train_and_save()
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# Testing
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data = { "features": [
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[6.5, 3.0, 5.8, 2.2],
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[6.1, 2.8, 4.7, 1.2]
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]}
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results = cls.load_and_test(data)
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print("results:", results)
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# Response similar to HTTP call
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response = {
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'statusCode': 200,
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'body': json.dumps({
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'predictions': results["predictions"],
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'probabilities': results["probabilities"]
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})
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}
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print("Example Full API response: ", response)
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