update docstrings

Techtonique · Oct 6, 2024 · 07c8b1c · 07c8b1c
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diff --git a/Makefile b/Makefile
@@ -32,7 +32,7 @@ clean-build: ## remove build artifacts
 	rm -fr build/	
 	rm -fr .eggs/
 	find . -name '*.egg-info' -exec rm -fr {} +
-	find . -name '*.egg' -exec rm -f {} +
+	find . -name '*.egg' -exec rm -rf {} +
 	find . -name '*.so' -exec rm -f {} +
 	find . -name '*.c' -exec rm -f {} +	
 

diff --git a/mlsauce-docs/mlsauce.html b/mlsauce-docs/mlsauce.html
diff --git a/mlsauce-docs/mlsauce/_config.html b/mlsauce-docs/mlsauce/_config.html
diff --git a/mlsauce-docs/mlsauce/adaopt.html b/mlsauce-docs/mlsauce/adaopt.html
diff --git a/mlsauce-docs/mlsauce/booster.html b/mlsauce-docs/mlsauce/booster.html
diff --git a/mlsauce-docs/mlsauce/datasets.html b/mlsauce-docs/mlsauce/datasets.html
diff --git a/mlsauce-docs/mlsauce/datasets/dowload.html b/mlsauce-docs/mlsauce/datasets/dowload.html
diff --git a/mlsauce-docs/mlsauce/demo.html b/mlsauce-docs/mlsauce/demo.html
diff --git a/mlsauce-docs/mlsauce/elasticnet.html b/mlsauce-docs/mlsauce/elasticnet.html
diff --git a/mlsauce-docs/mlsauce/lasso.html b/mlsauce-docs/mlsauce/lasso.html
diff --git a/mlsauce-docs/mlsauce/lazybooster.html b/mlsauce-docs/mlsauce/lazybooster.html
diff --git a/mlsauce-docs/mlsauce/nonconformist.html b/mlsauce-docs/mlsauce/nonconformist.html
diff --git a/mlsauce-docs/mlsauce/predictioninterval.html b/mlsauce-docs/mlsauce/predictioninterval.html
diff --git a/mlsauce-docs/mlsauce/ridge.html b/mlsauce-docs/mlsauce/ridge.html
diff --git a/mlsauce-docs/mlsauce/setup.html b/mlsauce-docs/mlsauce/setup.html
diff --git a/mlsauce-docs/mlsauce/stump.html b/mlsauce-docs/mlsauce/stump.html
diff --git a/mlsauce-docs/mlsauce/utils.html b/mlsauce-docs/mlsauce/utils.html
diff --git a/mlsauce-docs/mlsauce/utils/get_beta.html b/mlsauce-docs/mlsauce/utils/get_beta.html
diff --git a/mlsauce-docs/search.js b/mlsauce-docs/search.js
diff --git a/mlsauce.egg-info/PKG-INFO b/mlsauce.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: mlsauce
-Version: 0.20.2
+Version: 0.20.3
 Summary: Miscellaneous Statistical/Machine Learning tools
 Maintainer: T. Moudiki
 Maintainer-email: [email protected]

diff --git a/mlsauce/adaopt/_adaoptc.c b/mlsauce/adaopt/_adaoptc.c
diff --git a/mlsauce/adaopt/_adaoptc.cpython-311-darwin.so b/mlsauce/adaopt/_adaoptc.cpython-311-darwin.so
diff --git a/mlsauce/adaopt/_adaoptc.cpython-312-x86_64-linux-gnu.so b/mlsauce/adaopt/_adaoptc.cpython-312-x86_64-linux-gnu.so
diff --git a/mlsauce/booster/_booster_classifier.py b/mlsauce/booster/_booster_classifier.py
@@ -83,6 +83,204 @@ class LSBoostClassifier(BaseEstimator, ClassifierMixin):
             distribution of weights for constructing the model's hidden layer;
             currently 'uniform', 'gaussian'
 
+    Examples:
+
+        ```python
+        import numpy as np
+        from sklearn.datasets import load_digits, load_breast_cancer, load_wine, load_iris
+        from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
+        from sklearn.tree import DecisionTreeRegressor
+        from sklearn.kernel_ridge import KernelRidge
+        from time import time
+        from os import chdir
+        from sklearn import metrics
+        import os
+
+        import mlsauce as ms
+
+        print("\n")
+        print("GenericBoosting Decision tree -----")
+        print("\n")
+
+        print("\n")
+        print("breast_cancer data -----")
+
+        # data 1
+        breast_cancer = load_breast_cancer()
+        X = breast_cancer.data
+        y = breast_cancer.target
+        # split data into training test and test set
+        np.random.seed(15029)
+        X_train, X_test, y_train, y_test = train_test_split(X, y,
+                                                            test_size=0.2)
+
+        clf = DecisionTreeRegressor()
+        clf2 = KernelRidge()
+
+        obj = ms.GenericBoostingClassifier(clf, tolerance=1e-2)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(obj.score(X_test, y_test))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+
+        obj = ms.GenericBoostingClassifier(clf, tolerance=1e-2, n_clusters=2)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(obj.score(X_test, y_test))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+
+
+        # data 2
+        print("\n")
+        print("wine data -----")
+
+        wine = load_wine()
+        Z = wine.data
+        t = wine.target
+        np.random.seed(879423)
+        X_train, X_test, y_train, y_test = train_test_split(Z, t,
+                                                            test_size=0.2)
+
+        obj = ms.GenericBoostingClassifier(clf)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(obj.score(X_test, y_test))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+
+        obj = ms.GenericBoostingClassifier(clf, n_clusters=3)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(obj.score(X_test, y_test))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+
+        # data 3
+        print("\n")
+        print("iris data -----")
+
+        iris = load_iris()
+        Z = iris.data
+        t = iris.target
+        np.random.seed(734563)
+        X_train, X_test, y_train, y_test = train_test_split(Z, t,
+                                                            test_size=0.2)
+
+
+        obj = ms.GenericBoostingClassifier(clf)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(obj.score(X_test, y_test))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+
+
+        print("\n")
+        print("GenericBoosting  KRR -----")
+        print("\n")
+
+        obj = ms.GenericBoostingClassifier(clf2, tolerance=1e-2)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(obj.score(X_test, y_test))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+
+        obj = ms.GenericBoostingClassifier(clf2, tolerance=1e-2, n_clusters=2)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(obj.score(X_test, y_test))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+
+
+        # data 2
+        print("\n")
+        print("wine data -----")
+
+        wine = load_wine()
+        Z = wine.data
+        t = wine.target
+        np.random.seed(879423)
+        X_train, X_test, y_train, y_test = train_test_split(Z, t,
+                                                            test_size=0.2)
+
+        obj = ms.GenericBoostingClassifier(clf2)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(obj.score(X_test, y_test))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+
+        obj = ms.GenericBoostingClassifier(clf2, n_clusters=3)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(obj.score(X_test, y_test))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+
+        # data 3
+        print("\n")
+        print("iris data -----")
+
+        iris = load_iris()
+        Z = iris.data
+        t = iris.target
+        np.random.seed(734563)
+        X_train, X_test, y_train, y_test = train_test_split(Z, t,
+                                                            test_size=0.2)
+
+
+        obj = ms.GenericBoostingClassifier(clf2)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(obj.score(X_test, y_test))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+    ```
+
     """
 
     def __init__(
@@ -302,7 +500,7 @@ def predict_proba(self, X, **kwargs):
                 self.obj, np.asarray(X, order="C")
             )
         except ValueError:
-            pass 
+            pass
 
 
 class GenericBoostingClassifier(LSBoostClassifier):

diff --git a/mlsauce/booster/_booster_regressor.py b/mlsauce/booster/_booster_regressor.py
@@ -93,6 +93,58 @@ class LSBoostRegressor(BaseEstimator, RegressorMixin):
             distribution of weights for constructing the model's hidden layer;
             either 'uniform' or 'gaussian'
 
+    Examples:
+
+        ```python
+        import subprocess
+        import sys
+        import os
+
+        subprocess.check_call([sys.executable, "-m", "pip", "install", "matplotlib"])
+
+        import mlsauce as ms
+        import numpy as np
+        import matplotlib.pyplot as plt
+        from sklearn.datasets import load_diabetes
+        from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
+        from sklearn.tree import DecisionTreeRegressor
+        from time import time
+        from os import chdir
+        from sklearn import metrics
+
+        regr = DecisionTreeRegressor()
+
+        diabetes = load_diabetes()
+        X = diabetes.data
+        y = diabetes.target
+        # split data into training test and test set
+        np.random.seed(15029)
+        X_train, X_test, y_train, y_test = train_test_split(X, y,
+                                                            test_size=0.2)
+
+        obj = ms.GenericBoostingRegressor(regr, col_sample=0.9, row_sample=0.9)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(np.sqrt(np.mean(np.square(obj.predict(X_test) - y_test))))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+
+        obj = ms.GenericBoostingRegressor(regr, col_sample=0.9, row_sample=0.9, n_clusters=2)
+        print(obj.get_params())
+        start = time()
+        obj.fit(X_train, y_train)
+        print(time()-start)
+        start = time()
+        print(np.sqrt(np.mean(np.square(obj.predict(X_test) - y_test))))
+        print(time()-start)
+
+        print(obj.obj['loss'])
+        ```
+
     """
 
     def __init__(
@@ -251,7 +303,7 @@ def fit(self, X, y, **kwargs):
                 obj=self.base_model,
             )
         except ValueError:
-            pass 
+            pass
 
         self.n_estimators = self.obj["n_estimators"]
 
@@ -329,7 +381,7 @@ def predict(self, X, level=95, method=None, **kwargs):
                 self.obj, np.asarray(X, order="C")
             )
         except ValueError:
-            pass 
+            pass
 
 
 class GenericBoostingRegressor(LSBoostRegressor):