diff --git a/README.md b/README.md
index 3f1960d6..7059b9ad 100644
--- a/README.md
+++ b/README.md
@@ -7,10 +7,10 @@
**A unified interface for optimization algorithms and problems.**
-* [easy sklearn-like interface](#hyperactive-is-very-easy-to-use), [versatile and configurable](./examples/optimization_applications/search_space_example.py)
-- collection of [optimization algorithms](#overview), integrates with major [ML frameworks](#overview) such as `scikit-learn`
-- [memory-efficient](./examples/optimization_applications/memory.py) native implementations of [gradient-free optimizers](https://github.com/SimonBlanke/Gradient-Free-Optimizers)
-- unified API to popular optimization packages such as `optuna`
+Hyperactive implements a collection of optimization algorithms, accessible through a unified experiment-based
+interface that separates optimization problems from algorithms. The library provides native implementations of algorithms from the Gradient-Free-Optimizers
+package alongside direct interfaces to Optuna and scikit-learn optimizers, supporting discrete, continuous, and mixed parameter spaces.
+
@@ -46,29 +46,29 @@ pip install hyperactive
import numpy as np
# function to be maximized
-def problem(opt):
- x = opt["x"]
- y = opt["y"]
+def problem(params):
+ x = params["x"]
+ y = params["y"]
return - x ** 2 + - y ** 2
# discrete search space: dict of iterable, scikit-learn like grid space
# (valid search space types depends on optimizer)
-grid = {
+search_space = {
"x": np.arange(-1, 1, 0.01),
"y": np.arange(-1, 2, 0.1),
}
-from hyperactive.opt import HillClimbing
+from hyperactive.opt.gfo import HillClimbing
hillclimbing = HillClimbing(
- experiment=problem,
- search_space=grid,
+ search_space=search_space,
n_iter=100,
+ experiment=problem,
)
# running the hill climbing search:
-best_params = hillclimbing.run()
+best_params = hillclimbing.solve()
```
### experiment abstraction - example: scikit-learn CV experiment
@@ -79,6 +79,7 @@ best_params = hillclimbing.run()
`scikit-learn` cross-validation experiments:
```python
+import numpy as np
from hyperactive.experiment.integrations import SklearnCvExperiment
from sklearn.datasets import load_iris
from sklearn.svm import SVC
@@ -96,23 +97,25 @@ sklearn_exp = SklearnCvExperiment(
y=y,
)
-# experiments can be evaluated via "score
+# experiments can be evaluated via "score"
params = {"C": 1.0, "kernel": "linear"}
score, add_info = sklearn_exp.score(params)
# they can be used in optimizers like above
-from hyperactive.opt import HillClimbing
+from hyperactive.opt.gfo import HillClimbing
+
+search_space = {
+ "C": np.logspace(0.01, 100, num=10),
+ "kernel": ["linear", "rbf"],
+}
hillclimbing = HillClimbing(
- experiment=problem,
- search_space={
- "C": np.logspace(0.01, 100, num=10),
- "kernel": ["linear", "rbf"],
- }
+ search_space=search_space,
n_iter=100,
+ experiment=sklearn_exp,
)
-best_params = hillclimbing.run()
+best_params = hillclimbing.solve()
```
### full ML toolbox integration - example: scikit-learn
@@ -125,10 +128,11 @@ Any `hyperactive` optimizer can be combined with the ML toolbox integrations!
# 1. defining the tuned estimator:
from sklearn.svm import SVC
from hyperactive.integrations.sklearn import OptCV
-from hyperactive.opt import HillClimbing
+from hyperactive.opt.gfo import HillClimbing
-param_grid = {"kernel": ["linear", "rbf"], "C": [1, 10]}
-tuned_svc = OptCV(SVC(), HillClimbing(param_grid))
+search_space = {"kernel": ["linear", "rbf"], "C": [1, 10]}
+optimizer = HillClimbing(search_space=search_space, n_iter=20)
+tuned_svc = OptCV(SVC(), optimizer)
# 2. fitting the tuned estimator:
from sklearn.datasets import load_iris
@@ -145,6 +149,26 @@ best_params = tuned_svc.best_params_
best_estimator = tuned_svc.best_estimator_
```
+## :bulb: Key Concepts
+
+### Experiment-Based Architecture
+
+Hyperactive v5 introduces a clean separation between optimization algorithms and optimization problems through the **experiment abstraction**:
+
+- **Experiments** define *what* to optimize (the objective function and evaluation logic)
+- **Optimizers** define *how* to optimize (the search strategy and algorithm)
+
+This design allows you to:
+- Mix and match any optimizer with any experiment type
+- Create reusable experiment definitions for common ML tasks
+- Easily switch between different optimization strategies
+- Build complex optimization workflows with consistent interfaces
+
+**Built-in experiments include:**
+- `SklearnCvExperiment` - Cross-validation for sklearn estimators
+- `SktimeForecastingExperiment` - Time series forecasting optimization
+- Custom function experiments (pass any callable as experiment)
+
## Overview
@@ -177,43 +201,54 @@ Hyperactive features a collection of optimization algorithms that can be used fo
Local Search:
Global Search:
Population Methods:
Sequential Methods:
+ Optuna Backend:
+
|
Machine Learning:
|