add example with multiple different optimization algos. in parallel

examples/optimization_applications/multiple_different_optimizers.py

@@ -0,0 +1,85 @@
+import numpy as np
+
+from sklearn.model_selection import cross_val_score
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.datasets import load_breast_cancer
+
+from hyperactive import Hyperactive
+from hyperactive.optimizers import (
+    HillClimbingOptimizer,
+    RandomRestartHillClimbingOptimizer,
+)
+
+data = load_breast_cancer()
+X, y = data.data, data.target
+
+
+def model_rfc(opt):
+    rfc = RandomForestClassifier(
+        n_estimators=opt["n_estimators"],
+        criterion=opt["criterion"],
+        max_features=opt["max_features"],
+        min_samples_split=opt["min_samples_split"],
+        min_samples_leaf=opt["min_samples_leaf"],
+        bootstrap=opt["bootstrap"],
+    )
+    scores = cross_val_score(rfc, X, y, cv=3)
+
+    return scores.mean()
+
+
+def model_gbc(opt):
+    gbc = GradientBoostingClassifier(
+        n_estimators=opt["n_estimators"],
+        learning_rate=opt["learning_rate"],
+        max_depth=opt["max_depth"],
+        min_samples_split=opt["min_samples_split"],
+        min_samples_leaf=opt["min_samples_leaf"],
+        subsample=opt["subsample"],
+        max_features=opt["max_features"],
+    )
+    scores = cross_val_score(gbc, X, y, cv=3)
+
+    return scores.mean()
+
+
+search_space_rfc = {
+    "n_estimators": list(range(10, 200, 10)),
+    "criterion": ["gini", "entropy"],
+    "max_features": list(np.arange(0.05, 1.01, 0.05)),
+    "min_samples_split": list(range(2, 21)),
+    "min_samples_leaf": list(range(1, 21)),
+    "bootstrap": [True, False],
+}
+
+
+search_space_gbc = {
+    "n_estimators": list(range(10, 200, 10)),
+    "learning_rate": [1e-3, 1e-2, 1e-1, 0.5, 1.0],
+    "max_depth": list(range(1, 11)),
+    "min_samples_split": list(range(2, 21)),
+    "min_samples_leaf": list(range(1, 21)),
+    "subsample": list(np.arange(0.05, 1.01, 0.05)),
+    "max_features": list(np.arange(0.05, 1.01, 0.05)),
+}
+
+optimizer1 = HillClimbingOptimizer()
+optimizer2 = RandomRestartHillClimbingOptimizer()
+
+
+hyper = Hyperactive()
+hyper.add_search(
+    model_rfc,
+    search_space_rfc,
+    n_iter=50,
+    optimizer=optimizer1,
+)
+hyper.add_search(
+    model_gbc,
+    search_space_gbc,
+    n_iter=50,
+    optimizer=optimizer2,
+    n_jobs=2,
+)
+hyper.run(max_time=5)