Merge branch 'master' of https://github.com/SimonBlanke/Hyperactive

CODE_OF_CONDUCT.md

@@ -0,0 +1,128 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+simon.blanke@yahoo.com.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

README.md

@@ -177,7 +177,7 @@ Hyperactive features a collection of optimization algorithms that can be used fo
           </ul>
         <a><b>Data Collection:</b></a>
           <ul>
-            <li><a href="./examples/optimization_applications/meta_data_collection.py">Meta-data Collection</a></li>
+            <li><a href="./examples/optimization_applications/meta_data_collection.py">Search Data Collection</a></li>
             <li><a href="./examples/optimization_applications/meta_optimization.py">Meta Optimization</a></li>
             <li><a href="./examples/optimization_applications/meta_learning.py">Meta Learning</a></li>
           </ul>
@@ -959,17 +959,33 @@ A combination between simulated annealing and random search.
 <summary><b> Read this before opening a bug-issue </b></summary>
 
 <br>
+
+- <b>Are you sure the bug is located in Hyperactive? </b>
 
-Are you sure the bug is located in Hyperactive?
-
-Look at the error message from the command line. <b>If</b> one of the last messages look like this:
-   - File "/.../gradient_free_optimizers/...", line ...
-
-<b>Then</b> you should post the bug report in: 
-   - https://github.com/SimonBlanke/Gradient-Free-Optimizers
+  The error might be located in the optimization-backend. 
+  Look at the error message from the command line. <b>If</b> one of the last messages look like this:
+     - File "/.../gradient_free_optimizers/...", line ...
 
-<b>Otherwise</b> you can post the bug report in Hyperactive
+  <b>Then</b> you should post the bug report in: 
+     - https://github.com/SimonBlanke/Gradient-Free-Optimizers
 
+  <br>Otherwise</b> you can post the bug report in Hyperactive
+
+- <b>Do you have the correct Hyperactive version? </b>
+
+  Every major version update (e.g. v2.2 -> v3.0) the API of Hyperactive changes.
+  Check which version of Hyperactive you have. If your major version is older you have two options:
+
+  <b>Recommended:</b> You could just update your Hyperactive version with:
+  ```bash
+  pip install hyperactive --upgrade
+  ```
+  This way you can use all the new documentation and examples from the current repository.
+
+  Or you could continue using the old version and use an old repository branch as documentation.
+  You can do that by selecting the corresponding branch. (top right of the repository. The default is "master" or "main")
+  So if your major version is older (e.g. v2.1.0) you can select the 2.x.x branch to get the old repository for that version.
+
 </details>
 
 

examples/optimization_applications/meta_optimization.py

@@ -1,40 +1,40 @@
 import numpy as np
-from sklearn.datasets import load_breast_cancer
-from hyperactive import Hyperactive
-
-from gradient_free_optimizers import EvolutionStrategyOptimizer
-
-data = load_breast_cancer()
-X, y = data.data, data.target
+from hyperactive import Hyperactive, BayesianOptimizer
 
+from gradient_free_optimizers import RandomRestartHillClimbingOptimizer
 
 def meta_opt(opt_para):
     scores = []
 
-    for i in range(25):
+    for i in range(33):
 
-        def sphere_function(para):
-            loss = []
-            for key in para.keys():
-                loss.append(para[key] * para[key])
+        def ackley_function(para):
+            x = para["x"]
+            y = para["y"]
+            loss1 = - 20 * np.exp(-0.2 * np.sqrt(0.5 * (x * x + y * y)))
+            loss2 = -np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
+            loss3 = np.exp(1)
+            loss4 = 20
 
-            return -np.array(loss).sum()
+            loss = loss1 + loss2 + loss3 + loss4
 
-        dim_size = np.arange(-10, 10, 0.01)
+            return -loss
+
+        dim_size = np.arange(-6, 6, 0.01)
 
         search_space = {
-            "x1": dim_size,
-            "x2": dim_size,
+            "x": dim_size,
+            "y": dim_size,
         }
 
-        opt = EvolutionStrategyOptimizer(
+        opt = RandomRestartHillClimbingOptimizer(
             search_space,
-            mutation_rate=opt_para["mutation_rate"],
-            crossover_rate=opt_para["crossover_rate"],
-            initialize={"random": opt_para["individuals"]},
+            epsilon=opt_para["epsilon"],
+            n_neighbours=opt_para["n_neighbours"],
+            n_iter_restart=opt_para["n_iter_restart"],
         )
         opt.search(
-            sphere_function,
+            ackley_function,
             n_iter=100,
             random_state=i,
             verbosity=False,
@@ -46,12 +46,14 @@ def sphere_function(para):
 
 
 search_space = {
-    "individuals": list(range(2, 11)),
-    "mutation_rate": list(np.arange(0, 1, 0.1)),
-    "crossover_rate": list(np.arange(0, 1, 0.1)),
+    "epsilon": list(np.arange(0.01, 0.1, 0.01)),
+    "n_neighbours": list(range(1, 10)),
+    "n_iter_restart": list(range(2, 12)),
 }
 
 
+optimizer = BayesianOptimizer()
+
 hyper = Hyperactive()
-hyper.add_search(meta_opt, search_space, n_iter=50)
+hyper.add_search(meta_opt, search_space, n_iter=120, optimizer=optimizer)
 hyper.run()

examples/optimization_applications/progress_visualization.py

@@ -1,3 +1,9 @@
+"""
+Optimization runs are often like a black box. The information provided in the command line is often sparse.
+The Hyperactive ProgressBoard enables the visualization of the optimization progress during the run. I tried
+to make its usage as convenient as possible. You only have to initialize the ProgressBoard class and pass it to 
+.add_search(...). Hyperactive will do the rest!
+"""
 from sklearn.model_selection import cross_val_score
 from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
 from sklearn.datasets import load_boston
@@ -58,7 +64,7 @@ def model_rfr(opt):
 # progress_board2 = ProgressBoard()
 """
 uncomment the line above and pass progress_board2 
-to one .add_search(...) to open two browser tabs at the same time
+to one .add_search(...) to open an additional browser tabs at the same time
 """
 
 
@@ -76,7 +82,7 @@ def model_rfr(opt):
     search_space_rfr,
     n_iter=200,
     n_jobs=4,
-    progress_board=progress_board1,
+    progress_board=progress_board1, # optionally put progress_board2 here
 )
-# a terminal will open, which opens a dashboard in your browser
+# an additional terminal will open, which opens a dashboard in your browser
 hyper.run()

examples/optimization_applications/search_space_example.py

@@ -1,9 +1,9 @@
 """
-Hyperactive is very versatile, because of it can handle not just numerical or 
+Hyperactive is very versatile, because it can handle not just numerical or 
 string variables in the search space, but also functions. This enables many 
 possibilities for more complex optimization applications. Neural architecture search,
 feature engineering, ensemble optimization and many other applications are 
-only possible or much easier, if you can put functions in the search space.
+only possible or much easier if you can put functions in the search space.
 """
 
 from hyperactive import Hyperactive
@@ -44,7 +44,7 @@ def list3():
 
 
 def objective_function(para):
-    # score must be a single number
+    # score must be a number
     score = 1
     return score