add cross val. and lazy cross val. Pt.3

examples/cross_val.py

@@ -0,0 +1,82 @@
+import os 
+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.linear_model import ElasticNetCV
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.metrics import accuracy_score
+from time import time
+
+print(f"\n ----- Running: {os.path.basename(__file__)}... ----- \n")
+
+dataset = load_breast_cancer()
+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
+)
+
+print("\n Example 1 -----")
+
+res1 = ub.cross_val_optim(
+    X_train,
+    y_train,
+    X_test=None,
+    y_test=None,
+    model_type="lightgbm",
+    type_fit="classification",
+    scoring="accuracy",
+    n_estimators=100,
+    surrogate_obj=None,
+    cv=5,
+    n_jobs=None,
+    n_init=10,
+    n_iter=190,
+    abs_tol=1e-3,
+    verbose=2,
+    seed=123,
+)
+print(res1)
+
+print("\n Example 2 -----")
+
+res2 = ub.cross_val_optim(
+    X_train,
+    y_train,
+    X_test=X_test,
+    y_test=y_test,
+    model_type="lightgbm",
+    type_fit="classification",
+    scoring="accuracy",
+    n_estimators=100,
+    surrogate_obj=None,
+    cv=5,
+    n_jobs=None,
+    n_init=10,
+    n_iter=190,
+    abs_tol=1e-3,
+    verbose=2,
+    seed=123,
+)
+print(res2)
+
+print("\n Example 3 -----")
+
+res3 = ub.cross_val_optim(
+    X_train,
+    y_train,
+    X_test=X_test,
+    y_test=y_test,
+    model_type="lightgbm",
+    type_fit="classification",
+    scoring="accuracy",
+    n_estimators=100,
+    surrogate_obj=KernelRidge(),
+    cv=5,
+    n_jobs=None,
+    n_init=10,
+    n_iter=190,
+    abs_tol=1e-3,
+    verbose=2,
+    seed=123,
+)
+print(res3)

examples/lazy_cross_val.py

@@ -0,0 +1,34 @@
+import os 
+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.linear_model import ElasticNetCV
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.metrics import accuracy_score
+from time import time
+
+print(f"\n ----- Running: {os.path.basename(__file__)}... ----- \n")
+
+dataset = load_iris()
+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
+)
+
+res3 = ub.lazy_cross_val_optim(
+    X_train,
+    y_train,
+    X_test=X_test,
+    y_test=y_test,
+    model_type="lightgbm",
+    type_fit="classification",
+    scoring="accuracy",
+    n_estimators=100,
+    cv=5,
+    n_jobs=None,
+    n_init=10,
+    n_iter=190,
+    abs_tol=1e-3,
+    seed=123,
+)
+print(res3)