Revert "Merge branch 'dev' into tutorial-updates"

This reverts commit dafd4c1, reversing
changes made to 2645e9d.

Author: sgbaird

.gitignore

@@ -54,6 +54,3 @@ venv/*
 .conda*/
 .python-version
 reports/~$honegumi-logo.pptx
-docs/curriculum/assignments/Assignment_1_SOBO.ipynb
-docs/curriculum/assignments/Assignment_2_MOBO.ipynb
-src/honegumi/core/honegumi-pyodide-refactor-backup.html.jinja

.pre-commit-config.yaml

@@ -18,13 +18,13 @@ repos:
   - id: mixed-line-ending
     args: ['--fix=auto']  # replace 'auto' with 'lf' to enforce Linux/Mac line endings or 'crlf' for Windows
 
-# # If you want to automatically "modernize" your Python code:
-# - repo: https://github.com/asottile/pyupgrade
-#   rev: v3.9.0
-#   hooks:
-#   - id: pyupgrade
-#     args: ['--py37-plus']
-#     exclude: ^tests/generated_scripts/
+# If you want to automatically "modernize" your Python code:
+- repo: https://github.com/asottile/pyupgrade
+  rev: v3.9.0
+  hooks:
+  - id: pyupgrade
+    args: ['--py37-plus']
+    exclude: ^tests/generated_scripts/
 
 # # If you want to avoid flake8 errors due to unused vars or imports:
 # Failing due to

CONTRIBUTING.md

@@ -103,45 +103,19 @@ python3 -m http.server --directory 'docs/_build/html'
 
 ## Code Contributions
 
-For a high-level roadmap of Honegumi's development, see https://github.com/sgbaird/honegumi/discussions/2. Honegumi uses Python, Javascript, Jinja2, pytest, and GitHub actions to automate the generation, testing, and deployment of templates with a focus on Bayesian optimization packages. As of 2024-06-18, only [Meta's Ax Platform](https://ax.dev) is supported. The plumbing and logic that creates this is thorough and scalable.
+For a high-level roadmap of Honegumi's development, see https://github.com/sgbaird/honegumi/discussions/2. Honegumi uses Python, Javascript, Jinja2, pytest, and GitHub actions to automate the generation, testing, and deployment of templates with a focus on Bayesian optimization packages. As of 2023-08-21, only a single package ([Meta's Ax Platform](https://ax.dev) for a small set of features. However, the plumbing and logic that creates this is thorough and scalable. I focused first on getting all the pieces together before scaling up to many features (and thus slowing down the development cycle).
 
-Here are some ways you can help with the https://github.com/sgbaird/honegumi/blob/main/
+Here are some ways you can help with the project:
 1. Use the tool and let us know what you think 😉
 2. [Provide feedback](https://github.com/sgbaird/honegumi/discussions/2) on the overall organization, logic, and workflow of the project
 3. Extend the Ax features to additional options (i.e., additional rows and options within rows) via direct edits to [ax/main.py.jinja](https://github.com/sgbaird/honegumi/blob/main/src/honegumi/ax/main.py.jinja)
-4.Extend the [`honegumi.html.jinja`](https://github.com/sgbaird/honegumi/blob/main/src/honegumi/core/honegumi.html.jinja) and [`main.py.jinja`](https://github.com/sgbaird/honegumi/blob/main/src/honegumi/ax/main.py.jinja) templates (make sure to run [`generate_scripts.py`](https://github.com/sgbaird/honegumi/blob/main/scripts/generate_scripts.py) after changes). See below for more information.
-1. Extend Honegumi to additional platforms such as BoFire, Atlas, or BayBE
-2. Spread the word about the tool
+4. Improve the `honegumi.html` and `honegumi.ipynb` templates (may also need to update `generate_scripts.py`). See below for more information.
+5. Extend Honegumi to additional platforms such as BoFire or Atlas
+6. Spread the word about the tool
 
-For those unfamiliar with Jinja2, see the Google Colab tutorial: [_A Gentle Introduction to Jinja2_](https://colab.research.google.com/github/sgbaird/honegumi/blob/main/notebooks/1.0-sgb-gentle-introduction-jinja.ipynb). The main template file for Meta's Adaptive Experimentation (Ax) Platform is [`ax/main.py.jinja`](https://github.com/sgbaird/honegumi/blob/main/src/honegumi/ax/main.py.jinja). The main file that interacts with this template is at [`scripts/generate_scripts.py`](https://github.com/sgbaird/honegumi/blob/main/scripts/generate_scripts.py). The generated scripts are [available on GitHub](https://github.com/sgbaird/honegumi/blob/main/docs/generated_scripts/ax). Each script is tested [via `pytest`](https://github.com/sgbaird/honegumi/blob/main/tests/) and [GitHub Actions](https://github.com/sgbaird/honegumi/actions/workflows/ci.yml) to ensure it can run error-free. Finally, the results are passed to [core/honegumi.html.jinja](https://github.com/sgbaird/honegumi/blob/main/src/honegumi/core/honegumi.html.jinja) and [core/honegumi.ipynb.jinja](https://github.com/sgbaird/honegumi/blob/main/src/honegumi/core/honegumi.ipynb.jinja) to create the scripts and notebooks, respectively.
+For those unfamiliar with Jinja2, see the Google Colab tutorial: [_A Gentle Introduction to Jinja2_](https://colab.research.google.com/github/sgbaird/honegumi/blob/main/notebooks/1.0-sgb-gentle-introduction-jinja.ipynb). The main template file for Meta's Adaptive Experimentation (Ax) Platform is [`ax/main.py.jinja`](https://github.com/sgbaird/honegumi/blob/main/src/honegumi/ax/main.py.jinja). The main file that interacts with this template is at [`scripts/generate_scripts.py`](https://github.com/sgbaird/honegumi/blob/main/scripts/generate_scripts.py). The generated scripts are [available on GitHub](https://github.com/sgbaird/honegumi/tree/main/docs/generated_scripts/ax). Each script is tested [via `pytest`](https://github.com/sgbaird/honegumi/tree/main/tests) and [GitHub Actions](https://github.com/sgbaird/honegumi/actions/workflows/ci.yml) to ensure it can run error-free. Finally, the results are passed to [core/honegumi.html.jinja](https://github.com/sgbaird/honegumi/blob/main/src/honegumi/core/honegumi.html.jinja) and [core/honegumi.ipynb.jinja](https://github.com/sgbaird/honegumi/blob/main/src/honegumi/core/honegumi.ipynb.jinja) to create the scripts and notebooks, respectively.
 
-```{figure} _static/honegumi-mermaid.png
-Behind-the-scenes flowchart for Honegumi.
-```
-
-```{evalrst}
-flowchart TD
-    A[main.py.jinja Template] -->|Used by| B[generate_scripts.py]
-    B -->|Generates| C[.py Files]
-    B -->|Generates| D[_test.py Files]
-    B -->|Generates| E[.ipynb Files]
-    B -->|Generates| F[honegumi.html]
-    D -->|Tested via| G[GitHub Actions running pytest]
-    G -->|If Tests Pass| H[Documentation]
-    F -->|Included in| H
-
-    click A href "https://github.com/sgbaird/honegumi/blob/main/src/honegumi/ax/main.py.jinja" "main.py.jinja Template"
-    click B href "https://github.com/sgbaird/honegumi/blob/main/scripts/generate_scripts.py" "generate_scripts.py"
-    click C href "https://github.com/sgbaird/honegumi/blob/main/docs/generated_scripts/ax" ".py Files"
-    click D href "https://github.com/sgbaird/honegumi/blob/main/tests/" "_test.py Files"
-    click E href "https://github.com/sgbaird/honegumi/blob/main/src/honegumi/core/honegumi.ipynb.jinja" ".ipynb Files"
-    click F href "https://github.com/sgbaird/honegumi/blob/main/src/honegumi/core/honegumi.html.jinja" "honegumi.html"
-    click G href "https://github.com/sgbaird/honegumi/actions/workflows/ci.yml" "GitHub Actions"
-    click H href "https://github.com/sgbaird/honegumi/blob/main/docs/generated_scripts/ax" "Documentation"
-```
-
-````{tip}
-If you are committing some of the generated scripts or notebooks on Windows, you will [likely need to run the following command](https://stackoverflow.com/questions/22575662/filename-too-long-in-git-for-windows) in a terminal (e.g., git bash) as an administrator to avoid an `lstat(...) Filename too long` error:
+NOTE: If you are committing some of the generated scripts or notebooks on Windows, you will [likely need to run this command](https://stackoverflow.com/questions/22575662/filename-too-long-in-git-for-windows) in a terminal (e.g., git bash) as an administrator to avoid an `lstat(...) Filename too long` error:
 
 ```bash
 git config --system core.longpaths true
@@ -161,7 +135,6 @@ To only commit non-generated files, you can add all files and reset the generate
 git add .
 git reset docs/generated_scripts docs/generated_notebooks tests/generated_scripts
 ```
-````
 
 ## Project Organization
 

docs/_static/honegumi-mermaid.png

@@ -0,0 +1,53 @@
+import numpy as np
+from ax.service.ax_client import AxClient, ObjectiveProperties
+
+obj1_name = "branin"
+obj2_name = "branin_swapped"
+
+
+def branin_moo(x1, x2):
+    y = float(
+        (x2 - 5.1 / (4 * np.pi**2) * x1**2 + 5.0 / np.pi * x1 - 6.0) ** 2
+        + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x1)
+        + 10
+    )
+
+    # second objective has x1 and x2 swapped
+    y2 = float(
+        (x1 - 5.1 / (4 * np.pi**2) * x2**2 + 5.0 / np.pi * x2 - 6.0) ** 2
+        + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x2)
+        + 10
+    )
+
+    return {obj1_name: y, obj2_name: y2}
+
+
+ax_client = AxClient()
+
+ax_client.create_experiment(
+    parameters=[
+        {"name": "x1", "type": "range", "bounds": [-5.0, 10.0]},
+        {"name": "x2", "type": "range", "bounds": [0.0, 10.0]},
+    ],
+    objectives={
+        obj1_name: ObjectiveProperties(minimize=True),
+        obj2_name: ObjectiveProperties(minimize=True),
+    },
+)
+
+
+batch_size = 2
+
+
+for _ in range(19):
+
+    parameterizations, optimization_complete = ax_client.get_next_trials(batch_size)
+    for trial_index, parameterization in list(parameterizations.items()):
+        # extract parameters
+        x1 = parameterization["x1"]
+        x2 = parameterization["x2"]
+
+        results = branin_moo(x1, x2)
+        ax_client.complete_trial(trial_index=trial_index, raw_data=results)
+
+pareto_results = ax_client.get_pareto_optimal_parameters()

docs/generated_scripts/ax/objective-multi+model-Default+custom_gen-False+existing_data-False+sum_constraint-False+order_constraint-False+linear_constraint-False+composition_constraint-False+categorical-False+custom_threshold-False+synchrony-batch.py

@@ -0,0 +1,50 @@
+import numpy as np
+from ax.service.ax_client import AxClient, ObjectiveProperties
+
+obj1_name = "branin"
+obj2_name = "branin_swapped"
+
+
+def branin_moo(x1, x2):
+    y = float(
+        (x2 - 5.1 / (4 * np.pi**2) * x1**2 + 5.0 / np.pi * x1 - 6.0) ** 2
+        + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x1)
+        + 10
+    )
+
+    # second objective has x1 and x2 swapped
+    y2 = float(
+        (x1 - 5.1 / (4 * np.pi**2) * x2**2 + 5.0 / np.pi * x2 - 6.0) ** 2
+        + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x2)
+        + 10
+    )
+
+    return {obj1_name: y, obj2_name: y2}
+
+
+ax_client = AxClient()
+
+ax_client.create_experiment(
+    parameters=[
+        {"name": "x1", "type": "range", "bounds": [-5.0, 10.0]},
+        {"name": "x2", "type": "range", "bounds": [0.0, 10.0]},
+    ],
+    objectives={
+        obj1_name: ObjectiveProperties(minimize=True),
+        obj2_name: ObjectiveProperties(minimize=True),
+    },
+)
+
+
+for _ in range(19):
+
+    parameterization, trial_index = ax_client.get_next_trial()
+
+    # extract parameters
+    x1 = parameterization["x1"]
+    x2 = parameterization["x2"]
+
+    results = branin_moo(x1, x2)
+    ax_client.complete_trial(trial_index=trial_index, raw_data=results)
+
+pareto_results = ax_client.get_pareto_optimal_parameters()

docs/generated_scripts/ax/objective-multi+model-Default+custom_gen-False+existing_data-False+sum_constraint-False+order_constraint-False+linear_constraint-False+composition_constraint-False+categorical-False+custom_threshold-False+synchrony-single.py

@@ -0,0 +1,53 @@
+import numpy as np
+from ax.service.ax_client import AxClient, ObjectiveProperties
+
+obj1_name = "branin"
+obj2_name = "branin_swapped"
+
+
+def branin_moo(x1, x2):
+    y = float(
+        (x2 - 5.1 / (4 * np.pi**2) * x1**2 + 5.0 / np.pi * x1 - 6.0) ** 2
+        + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x1)
+        + 10
+    )
+
+    # second objective has x1 and x2 swapped
+    y2 = float(
+        (x1 - 5.1 / (4 * np.pi**2) * x2**2 + 5.0 / np.pi * x2 - 6.0) ** 2
+        + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x2)
+        + 10
+    )
+
+    return {obj1_name: y, obj2_name: y2}
+
+
+ax_client = AxClient()
+
+ax_client.create_experiment(
+    parameters=[
+        {"name": "x1", "type": "range", "bounds": [-5.0, 10.0]},
+        {"name": "x2", "type": "range", "bounds": [0.0, 10.0]},
+    ],
+    objectives={
+        obj1_name: ObjectiveProperties(minimize=True, threshold=25.0),
+        obj2_name: ObjectiveProperties(minimize=True, threshold=15.0),
+    },
+)
+
+
+batch_size = 2
+
+
+for _ in range(19):
+
+    parameterizations, optimization_complete = ax_client.get_next_trials(batch_size)
+    for trial_index, parameterization in list(parameterizations.items()):
+        # extract parameters
+        x1 = parameterization["x1"]
+        x2 = parameterization["x2"]
+
+        results = branin_moo(x1, x2)
+        ax_client.complete_trial(trial_index=trial_index, raw_data=results)
+
+pareto_results = ax_client.get_pareto_optimal_parameters()

docs/generated_scripts/ax/objective-multi+model-Default+custom_gen-False+existing_data-False+sum_constraint-False+order_constraint-False+linear_constraint-False+composition_constraint-False+categorical-False+custom_threshold-True+synchrony-batch.py

@@ -0,0 +1,50 @@
+import numpy as np
+from ax.service.ax_client import AxClient, ObjectiveProperties
+
+obj1_name = "branin"
+obj2_name = "branin_swapped"
+
+
+def branin_moo(x1, x2):
+    y = float(
+        (x2 - 5.1 / (4 * np.pi**2) * x1**2 + 5.0 / np.pi * x1 - 6.0) ** 2
+        + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x1)
+        + 10
+    )
+
+    # second objective has x1 and x2 swapped
+    y2 = float(
+        (x1 - 5.1 / (4 * np.pi**2) * x2**2 + 5.0 / np.pi * x2 - 6.0) ** 2
+        + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x2)
+        + 10
+    )
+
+    return {obj1_name: y, obj2_name: y2}
+
+
+ax_client = AxClient()
+
+ax_client.create_experiment(
+    parameters=[
+        {"name": "x1", "type": "range", "bounds": [-5.0, 10.0]},
+        {"name": "x2", "type": "range", "bounds": [0.0, 10.0]},
+    ],
+    objectives={
+        obj1_name: ObjectiveProperties(minimize=True, threshold=25.0),
+        obj2_name: ObjectiveProperties(minimize=True, threshold=15.0),
+    },
+)
+
+
+for _ in range(19):
+
+    parameterization, trial_index = ax_client.get_next_trial()
+
+    # extract parameters
+    x1 = parameterization["x1"]
+    x2 = parameterization["x2"]
+
+    results = branin_moo(x1, x2)
+    ax_client.complete_trial(trial_index=trial_index, raw_data=results)
+
+pareto_results = ax_client.get_pareto_optimal_parameters()

docs/generated_scripts/ax/objective-multi+model-Default+custom_gen-False+existing_data-False+sum_constraint-False+order_constraint-False+linear_constraint-False+composition_constraint-False+categorical-False+custom_threshold-True+synchrony-single.py

@@ -0,0 +1,69 @@
+import numpy as np
+from ax.service.ax_client import AxClient, ObjectiveProperties
+
+obj1_name = "branin"
+obj2_name = "branin_swapped"
+
+
+def branin_moo(x1, x2, c1):
+    y = float(
+        (x2 - 5.1 / (4 * np.pi**2) * x1**2 + 5.0 / np.pi * x1 - 6.0) ** 2
+        + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x1)
+        + 10
+    )
+
+    # add a made-up penalty based on category
+    penalty_lookup = {"A": 1.0, "B": 0.0, "C": 2.0}
+    y += penalty_lookup[c1]
+
+    # second objective has x1 and x2 swapped
+    y2 = float(
+        (x1 - 5.1 / (4 * np.pi**2) * x2**2 + 5.0 / np.pi * x2 - 6.0) ** 2
+        + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x2)
+        + 10
+    )
+
+    # add a made-up penalty based on category
+    penalty_lookup = {"A": 0.0, "B": 2.0, "C": 1.0}
+    y2 += penalty_lookup[c1]
+
+    return {obj1_name: y, obj2_name: y2}
+
+
+ax_client = AxClient()
+
+ax_client.create_experiment(
+    parameters=[
+        {"name": "x1", "type": "range", "bounds": [-5.0, 10.0]},
+        {"name": "x2", "type": "range", "bounds": [0.0, 10.0]},
+        {
+            "name": "c1",
+            "type": "choice",
+            "is_ordered": False,
+            "values": ["A", "B", "C"],
+        },
+    ],
+    objectives={
+        obj1_name: ObjectiveProperties(minimize=True),
+        obj2_name: ObjectiveProperties(minimize=True),
+    },
+)
+
+
+batch_size = 2
+
+
+for _ in range(21):
+
+    parameterizations, optimization_complete = ax_client.get_next_trials(batch_size)
+    for trial_index, parameterization in list(parameterizations.items()):
+        # extract parameters
+        x1 = parameterization["x1"]
+        x2 = parameterization["x2"]
+
+        c1 = parameterization["c1"]
+
+        results = branin_moo(x1, x2, c1)
+        ax_client.complete_trial(trial_index=trial_index, raw_data=results)
+
+pareto_results = ax_client.get_pareto_optimal_parameters()