update examples --> bump v0.2.2

.gitignore

@@ -0,0 +1,113 @@
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+downloads/
+#eggs/
+#.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+#*.egg-info/
+.installed.cfg
+#*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# IDE settings
+.vscode/
+.Rproj.user
+site/
+
+# Apple files
+.DS_Store
+
+# R files
+*.Rhistory
+
+# Others 
+unifiedbooster-docs/

LICENSE

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+Copyright <2024> <T. Moudiki>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -1 +1,91 @@
 # unifiedbooster
+
+![PyPI](https://img.shields.io/pypi/v/unifiedbooster) [![PyPI - License](https://img.shields.io/pypi/l/unifiedbooster)](https://github.com/thierrymoudiki/unifiedbooster/blob/master/LICENSE) [![Downloads](https://pepy.tech/badge/unifiedbooster)](https://pepy.tech/project/unifiedbooster) 
+[![Documentation](https://img.shields.io/badge/documentation-is_here-green)](https://techtonique.github.io/unifiedbooster/)
+
+## Examples 
+
+### classification 
+
+```python
+import unifiedbooster as ub
+from sklearn.datasets import load_iris, load_breast_cancer, load_wine
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+datasets = [load_iris(), load_breast_cancer(), load_wine()]
+
+for dataset in datasets:
+
+  X, y = dataset.data, dataset.target
+  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+  # Initialize the unified regressor (example with XGBoost)
+  regressor1 = ub.GBDTClassifier(model_type='xgboost')
+  regressor2 = ub.GBDTClassifier(model_type='catboost')
+  regressor3 = ub.GBDTClassifier(model_type='lightgbm')
+
+  # Fit the model
+  regressor1.fit(X_train, y_train)
+  regressor2.fit(X_train, y_train)
+  regressor3.fit(X_train, y_train)
+
+  # Predict on the test set
+  y_pred1 = regressor1.predict(X_test)
+  y_pred2 = regressor2.predict(X_test)
+  y_pred3 = regressor3.predict(X_test)
+
+  # Evaluate the model
+  accuracy1 = accuracy_score(y_test, y_pred1)
+  accuracy2 = accuracy_score(y_test, y_pred2)
+  accuracy3 = accuracy_score(y_test, y_pred3)
+  print("-------------------------")
+  print(f"Classification Accuracy xgboost: {accuracy1:.2f}")
+  print(f"Classification Accuracy catboost: {accuracy2:.2f}")
+  print(f"Classification Accuracy lightgbm: {accuracy3:.2f}")
+```
+
+### regression 
+
+```python
+import numpy as np
+import unifiedbooster as ub
+from sklearn.datasets import fetch_california_housing, load_diabetes
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+
+
+datasets = [fetch_california_housing(), load_diabetes()]
+
+for dataset in datasets:
+
+  # Load dataset
+  X, y = dataset.data, dataset.target
+
+  # Split dataset into training and testing sets
+  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+  # Initialize the unified regressor (example with XGBoost)
+  regressor1 = ub.GBDTRegressor(model_type='xgboost')
+  regressor2 = ub.GBDTRegressor(model_type='catboost')
+  regressor3 = ub.GBDTRegressor(model_type='lightgbm')
+
+  # Fit the model
+  regressor1.fit(X_train, y_train)
+  regressor2.fit(X_train, y_train)
+  regressor3.fit(X_train, y_train)
+
+  # Predict on the test set
+  y_pred1 = regressor1.predict(X_test)
+  y_pred2 = regressor2.predict(X_test)
+  y_pred3 = regressor3.predict(X_test)
+
+  # Evaluate the model
+  mse1 = np.sqrt(mean_squared_error(y_test, y_pred1))
+  mse2 = np.sqrt(mean_squared_error(y_test, y_pred2))
+  mse3 = np.sqrt(mean_squared_error(y_test, y_pred3))
+  print("-------------------------")
+  print(f"Regression Root Mean Squared Error xgboost: {mse1:.2f}")
+  print(f"Regression Root Mean Squared Error catboost: {mse2:.2f}")
+  print(f"Regression Root Mean Squared Error lightgbm: {mse3:.2f}")
+```

examples/classification.py

@@ -1,6 +1,6 @@
 import unifiedbooster as ub
 from sklearn.datasets import load_iris
-from sklearn.model_selection import train_test_split
+from sklearn.model_selection import train_test_split, cross_val_score
 from sklearn.metrics import accuracy_score
 
 # Load dataset
@@ -10,25 +10,27 @@
 # Split dataset into training and testing sets
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
-# Initialize the unified regressor (example with XGBoost)
-regressor1 = ub.GBDTClassifier(model_type='xgboost')
-#regressor2 = ub.GBDTClassifier(model_type='catboost')
-regressor3 = ub.GBDTClassifier(model_type='lightgbm')
+# Initialize the unified clf (example with XGBoost)
+clf1 = ub.GBDTClassifier(model_type='xgboost')
+#clf2 = ub.GBDTClassifier(model_type='catboost')
+clf3 = ub.GBDTClassifier(model_type='lightgbm')
 
 # Fit the model
-regressor1.fit(X_train, y_train)
-#regressor2.fit(X_train, y_train)
-regressor3.fit(X_train, y_train)
+clf1.fit(X_train, y_train)
+#clf2.fit(X_train, y_train)
+clf3.fit(X_train, y_train)
 
 # Predict on the test set
-y_pred1 = regressor1.predict(X_test)
-#y_pred2 = regressor2.predict(X_test)
-y_pred3 = regressor3.predict(X_test)
+y_pred1 = clf1.predict(X_test)
+#y_pred2 = clf2.predict(X_test)
+y_pred3 = clf3.predict(X_test)
 
 # Evaluate the model
 accuracy1 = accuracy_score(y_test, y_pred1)
 #accuracy2 = accuracy_score(y_test, y_pred2)
 accuracy3 = accuracy_score(y_test, y_pred3)
 print(f"Classification Accuracy xgboost: {accuracy1:.2f}")
 #print(f"Classification Accuracy catboost: {accuracy2:.2f}")
-print(f"Classification Accuracy lightgbm: {accuracy3:.2f}")
+print(f"Classification Accuracy lightgbm: {accuracy3:.2f}")
+print(f"CV xgboost: {cross_val_score(clf1, X_train, y_train)}")
+print(f"CV lightgbm: {cross_val_score(clf3, X_train, y_train)}")

examples/regression.py

@@ -1,6 +1,6 @@
 import unifiedbooster as ub
 from sklearn.datasets import fetch_california_housing
-from sklearn.model_selection import train_test_split
+from sklearn.model_selection import train_test_split, cross_val_score
 from sklearn.metrics import mean_squared_error
 
 # Load dataset
@@ -10,20 +10,20 @@
 # Split dataset into training and testing sets
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
-# Initialize the unified regressor (example with XGBoost)
-regressor1 = ub.GBDTRegressor(model_type='xgboost')
-#regressor2 = ub.GBDTRegressor(model_type='catboost')
-regressor3 = ub.GBDTRegressor(model_type='lightgbm')
+# Initialize the unified regr (example with XGBoost)
+regr1 = ub.GBDTregr(model_type='xgboost')
+#regr2 = ub.GBDTregr(model_type='catboost')
+regr3 = ub.GBDTregr(model_type='lightgbm')
 
 # Fit the model
-regressor1.fit(X_train, y_train)
-#regressor2.fit(X_train, y_train)
-regressor3.fit(X_train, y_train)
+regr1.fit(X_train, y_train)
+#regr2.fit(X_train, y_train)
+regr3.fit(X_train, y_train)
 
 # Predict on the test set
-y_pred1 = regressor1.predict(X_test)
-#y_pred2 = regressor2.predict(X_test)
-y_pred3 = regressor3.predict(X_test)
+y_pred1 = regr1.predict(X_test)
+#y_pred2 = regr2.predict(X_test)
+y_pred3 = regr3.predict(X_test)
 
 # Evaluate the model
 mse1 = mean_squared_error(y_test, y_pred1)
@@ -32,3 +32,5 @@
 print(f"Regression Mean Squared Error xgboost: {mse1:.2f}")
 #print(f"Regression Mean Squared Error catboost: {mse2:.2f}")
 print(f"Regression Mean Squared Error lightgbm: {mse3:.2f}")
+print(f"CV xgboost: {cross_val_score(regr1, X_train, y_train)}")
+print(f"CV lightgbm: {cross_val_score(regr3, X_train, y_train)}")

setup.py

@@ -10,7 +10,7 @@
 
 subprocess.check_call(['pip', 'install', 'Cython'])
 
-__version__ = "0.2.1"
+__version__ = "0.2.2"
 
 here = path.abspath(path.dirname(__file__))
 

unifiedbooster/__init__.py

@@ -1,4 +1,5 @@
+from .gbdt import GBDT
 from .gbdt_classification import GBDTClassifier
 from .gbdt_regression import GBDTRegressor
 
-__all__ = ["GBDTClassifier", "GBDTRegressor"]
+__all__ = ["GBDT", "GBDTClassifier", "GBDTRegressor"]