", "
") + # Remove any HTML comments from the Markdown. They are fine to keep in the + # notebooks, but are not really useful in the MDX. + mdx = re.sub("()", "", mdx, flags=re.DOTALL) + # "\" Escape braces to make the text MDX compatible. + mdx = re.sub("([^\\\\])([{}])", "\\g<1>\\\\\\g<2>", mdx) + + # -- KaTeX -- + # Wrap '\begin{}...\end{}' in $$ for KaTeX to work. + mdx = re.sub( + "(\\\\begin\\\\{(\\w*?)\\\\}(.|\n)*?end\\\\{\\2\\\\})", "$$\\g<1>$$", mdx + ) + # Make sure $$ symbols are not escaped and include line breaks. + mdx = re.sub( + "\\\\?\\$\\\\?\\$((?:.|\n)*?)\\\\?\\$\\\\?\\$", "\n$$\n\\g<1>\n$$\n", mdx + ) + # Escaping braces causes issues in math blocks, unescape them. + mdx = re.sub( + "\\$?\\$(.|\n)*?\\$\\$?", + lambda match: match[0].replace("\\{", "{").replace("\\}", "}"), + mdx, + ) + + return mdx + + +def handle_markdown_cell( + cell: NotebookNode, + new_img_dir: Path, + lib_dir: Path, +) -> str: + """ + Handle the given Jupyter Markdown cell and convert it to MDX. + + Args: + cell (NotebookNode): Jupyter Markdown cell object. + new_img_dir (Path): Path where images are copied to for display in the + Markdown cell. + lib_dir (Path): The location for the Bean Machine library. + + Returns: + str: Transformed Markdown object suitable for inclusion in MDX. + """ + markdown = cell["source"] + + # Update image paths in the Markdown and copy them to the Markdown tutorials folder. + # Skip - Our images are base64 encoded, so we don't need to copy them to the docs + # folder. + # markdown = handle_images_found_in_markdown(markdown, new_img_dir, lib_dir) + + markdown = sanitize_mdx(markdown) + mdx = mdformat.text(markdown, options={"wrap": 88}, extensions={"myst"}) + + # We will attempt to handle inline style attributes written in HTML by converting + # them to something React can consume. This has to be done after the + # mdformat.text() step since mdformat complains about the converted styles. + mdx = transform_style_attributes(mdx) + + return f"{mdx}\n" + + +def handle_cell_input(cell: NotebookNode, language: str) -> str: + """ + Create a Markdown cell block using the given cell source, and the language. + + The given language will determine cell input syntax styles. Docusaurus uses Prism as + the syntax highlighter, https://prismjs.com. See the Docusaurus documentation for + more information on code blocks + https://docusaurus.io/docs/markdown-features/code-blocks. + + Args: + cell (NotebookNode): A notebook cell. + language (str): Language specifier for syntax highlighting. + + Returns: + str: Code block formatted Markdown string. + """ + cell_source = cell.get("source", "") + return f"```{language}\n{cell_source}\n```\n\n" + + +def handle_image( + values: List[Dict[str, Union[int, str, NotebookNode]]], +) -> List[Tuple[int, str]]: + """ + Convert embedded images to string MDX can consume. + + Args: + values (List[Dict[str, Union[int, str, NotebookNode]]]): Bokeh tagged cell + outputs. + + Returns: + List[Tuple[int, str]]: A list of tuples, where the first entry in the tuple is + the index where the output occurred from the cell, and the second entry of + the tuple is the MDX formatted string. + """ + output = [] + for value in values: + index = value["index"] + mime_type = value["mime_type"] + img = value["data"] + output.append((index, f"\n\n")) + return output + + +def handle_markdown( + values: List[Dict[str, Union[int, str, NotebookNode]]], +) -> List[Tuple[int, str]]: + """ + Convert and format Markdown for MDX. + + Args: + values (List[Dict[str, Union[int, str, NotebookNode]]]): Bokeh tagged cell + outputs. + + Returns: + List[Tuple[int, str]]: A list of tuples, where the first entry in the tuple is + the index where the output occurred from the cell, and the second entry of + the tuple is the MDX formatted string. + """ + output = [] + for value in values: + index = int(value["index"]) + markdown = str(value["data"]) + markdown = mdformat.text(markdown, options={"wrap": 88}, extensions={"myst"}) + output.append((index, f"{markdown}\n\n")) + return output + + +def handle_pandas( + values: List[Dict[str, Union[int, str, NotebookNode]]], +) -> List[Tuple[int, str]]: + """ + Handle how to display pandas DataFrames. + + There is a scoped style tag in the DataFrame output that uses the class name + `dataframe` to style the output. We will use this token to determine if a pandas + DataFrame is being displayed. + + Args: + values (List[Dict[str, Union[int, str, NotebookNode]]]): Bokeh tagged cell + outputs. + + Returns: + List[Tuple[int, str]]: A list of tuples, where the first entry in the tuple is + the index where the output occurred from the cell, and the second entry of + the tuple is the MDX formatted string. + """ + output = [] + for value in values: + index = int(value["index"]) + data = str(value["data"]) + if 'class="dataframe"' not in data: + # pd.read_html() raises an error if there's no dataframe. + continue + df = pd.read_html(io.StringIO(data), flavor="lxml") + # NOTE: The return is a list of dataframes and we only care about the first + # one. + md_df = df[0] + for column in md_df.columns: + if column.startswith("Unnamed"): + md_df.rename(columns={column: ""}, inplace=True) + # Remove the index if it is just a range, and output to markdown. + mdx = "" + if isinstance(md_df.index, pd.RangeIndex): + mdx = md_df.to_markdown(index=False) + elif not isinstance(md_df.index, pd.RangeIndex): + mdx = md_df.to_markdown() + output.append((index, f"\n{sanitize_mdx(mdx)}\n\n")) + return output + + +def handle_plain( + values: List[Dict[str, Union[int, str, NotebookNode]]], +) -> List[Tuple[int, str]]: + """ + Handle how to plain cell output should be displayed in MDX. + + Args: + values (List[Dict[str, Union[int, str, NotebookNode]]]): Bokeh tagged cell + outputs. + + Returns: + List[Tuple[int, str]]: A list of tuples, where the first entry in the tuple is + the index where the output occurred from the cell, and the second entry of + the tuple is the MDX formatted string. + """ + output = [] + for value in values: + index = int(value["index"]) + data = str(value["data"]) + data = [line.strip() for line in data.splitlines() if line] + data = [datum for datum in data if datum] + if data: + data = "\n".join([line for line in str(value["data"]).splitlines() if line]) + # Remove backticks to make the text MDX compatible. + data = data.replace("`", "") + output.append( + (index, f"
-
-
-
- );
-}
-
-module.exports = Versions;
diff --git a/setup.py b/setup.py
index 506bca41dc..37b62d358c 100644
--- a/setup.py
+++ b/setup.py
@@ -26,6 +26,11 @@
"papermill",
"pykeops",
"torchvision",
+ "mdformat",
+ "pandas",
+ "lxml",
+ "mdformat-myst",
+ "tabulate",
]
# Check for python version
@@ -70,7 +75,7 @@ def read_deps_from_file(filname):
# Read in pinned versions of the formatting tools
FMT_REQUIRES += read_deps_from_file("requirements-fmt.txt")
# Dev is test + formatting + docs generation
-DEV_REQUIRES = TEST_REQUIRES + FMT_REQUIRES + ["sphinx"]
+DEV_REQUIRES = TEST_REQUIRES + FMT_REQUIRES + ["sphinx", "sphinx-rtd-theme"]
# read in README.md as the long description
with open(os.path.join(root_dir, "README.md"), "r") as fh:
diff --git a/sphinx/source/_static/custom.css b/sphinx/source/_static/custom.css
new file mode 100644
index 0000000000..827ccb0baf
--- /dev/null
+++ b/sphinx/source/_static/custom.css
@@ -0,0 +1,3 @@
+.wy-nav-content {
+ max-width: initial;
+}
diff --git a/sphinx/source/conf.py b/sphinx/source/conf.py
index 7d9a9d3c5e..3becd29d69 100644
--- a/sphinx/source/conf.py
+++ b/sphinx/source/conf.py
@@ -83,7 +83,7 @@
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
-html_theme = "alabaster"
+html_theme = "sphinx_rtd_theme"
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
@@ -93,8 +93,9 @@
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
-# html_static_path = ["_static"]
-html_static_path = [] # for now we have no static files to track
+html_static_path = ["_static"]
+
+html_css_files = ["custom.css"]
# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
diff --git a/tutorials/GIBBON_for_efficient_batch_entropy_search.ipynb b/tutorials/GIBBON_for_efficient_batch_entropy_search/GIBBON_for_efficient_batch_entropy_search.ipynb
similarity index 99%
rename from tutorials/GIBBON_for_efficient_batch_entropy_search.ipynb
rename to tutorials/GIBBON_for_efficient_batch_entropy_search/GIBBON_for_efficient_batch_entropy_search.ipynb
index 8835b0491c..f360d22f1b 100644
--- a/tutorials/GIBBON_for_efficient_batch_entropy_search.ipynb
+++ b/tutorials/GIBBON_for_efficient_batch_entropy_search/GIBBON_for_efficient_batch_entropy_search.ipynb
@@ -34,6 +34,18 @@
"[2] [Moss, M., et al., _GIBBON: General-purpose Information-Based Bayesian Optimisation._ arXiv:2102.03324, 2020](https://arxiv.org/abs/2102.03324)\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/tutorials/Multi_objective_multi_fidelity_BO.ipynb b/tutorials/Multi_objective_multi_fidelity_BO/Multi_objective_multi_fidelity_BO.ipynb
similarity index 99%
rename from tutorials/Multi_objective_multi_fidelity_BO.ipynb
rename to tutorials/Multi_objective_multi_fidelity_BO/Multi_objective_multi_fidelity_BO.ipynb
index d2828c14c9..9bc2c9065d 100644
--- a/tutorials/Multi_objective_multi_fidelity_BO.ipynb
+++ b/tutorials/Multi_objective_multi_fidelity_BO/Multi_objective_multi_fidelity_BO.ipynb
@@ -39,6 +39,18 @@
"[3] [S. Daulton, M. Balandat, and E. Bakshy. Hypervolume Knowledge Gradient for Multi-Objective Bayesian Optimization with Partial Information. ICML, 2023.](https://proceedings.mlr.press/v202/daulton23a.html)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/tutorials/batch_mode_cross_validation.ipynb b/tutorials/batch_mode_cross_validation/batch_mode_cross_validation.ipynb
similarity index 99%
rename from tutorials/batch_mode_cross_validation.ipynb
rename to tutorials/batch_mode_cross_validation/batch_mode_cross_validation.ipynb
index 925a6d4142..883b2a048e 100644
--- a/tutorials/batch_mode_cross_validation.ipynb
+++ b/tutorials/batch_mode_cross_validation/batch_mode_cross_validation.ipynb
@@ -18,6 +18,18 @@
"Note: this tutorial aims to introduce batch-mode regression and evaluation in GPyTorch with CV as an example application. For alternative, more user-friendly functions to perform CV in Ax, see [ax.modelbridge.cross_validation](https://github.com/facebook/Ax/blob/main/ax/modelbridge/cross_validation.py). However, for larger CV tasks, it may be useful to exploit GPyTorch batch-mode, as shown in this tutorial."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/tutorials/baxus.ipynb b/tutorials/baxus/baxus.ipynb
similarity index 99%
rename from tutorials/baxus.ipynb
rename to tutorials/baxus/baxus.ipynb
index 6bd064b4e5..bb8e830c3b 100644
--- a/tutorials/baxus.ipynb
+++ b/tutorials/baxus/baxus.ipynb
@@ -17,6 +17,18 @@
"- [2]: [Branin Test Function](https://www.sfu.ca/~ssurjano/branin.html)\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/tutorials/bo_with_warped_gp.ipynb b/tutorials/bo_with_warped_gp/bo_with_warped_gp.ipynb
similarity index 99%
rename from tutorials/bo_with_warped_gp.ipynb
rename to tutorials/bo_with_warped_gp/bo_with_warped_gp.ipynb
index 36816a1fe9..349ad4f167 100644
--- a/tutorials/bo_with_warped_gp.ipynb
+++ b/tutorials/bo_with_warped_gp/bo_with_warped_gp.ipynb
@@ -28,6 +28,18 @@
"[1] [J. Snoek, K. Swersky, R. S. Zemel, R. P. Adams. Input Warping for Bayesian Optimization of Non-Stationary Functions. Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):1674-1682, 2014.](http://proceedings.mlr.press/v32/snoek14.pdf)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 2,
diff --git a/tutorials/bope.ipynb b/tutorials/bope/bope.ipynb
similarity index 99%
rename from tutorials/bope.ipynb
rename to tutorials/bope/bope.ipynb
index 9de494f77d..8986cae9d8 100644
--- a/tutorials/bope.ipynb
+++ b/tutorials/bope/bope.ipynb
@@ -36,6 +36,18 @@
"[1] [Z.J. Lin, R. Astudillo, P.I. Frazier, and E. Bakshy, Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes. AISTATS, 2022.](https://arxiv.org/abs/2203.11382)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -50,10 +62,7 @@
"loadingStatus": "loaded"
},
"requestMsgId": "9aa7b0c4-ac2c-4e47-a067-7e0797bdd89d",
- "showInput": true,
- "vscode": {
- "languageId": "python"
- }
+ "showInput": true
},
"outputs": [
{
@@ -135,10 +144,7 @@
"hidden_ranges": [],
"originalKey": "470d8e92-e1ff-4ae7-8343-3423273900f8",
"requestMsgId": "f8b170ce-5da8-40f6-aae4-1d637ea97e33",
- "showInput": true,
- "vscode": {
- "languageId": "python"
- }
+ "showInput": true
},
"outputs": [],
"source": [
@@ -196,10 +202,7 @@
"hidden_ranges": [],
"originalKey": "8a6b9736-1ba8-4914-b295-7406bec5ece6",
"requestMsgId": "43fea609-1909-4ba8-af8f-7ac3de4016dd",
- "showInput": true,
- "vscode": {
- "languageId": "python"
- }
+ "showInput": true
},
"outputs": [],
"source": [
@@ -421,10 +424,7 @@
"loadingStatus": "loaded"
},
"requestMsgId": "b824d249-226a-497e-8764-fea353877527",
- "showInput": true,
- "vscode": {
- "languageId": "python"
- }
+ "showInput": true
},
"outputs": [
{
@@ -618,10 +618,7 @@
"loadingStatus": "loaded"
},
"requestMsgId": "5791d879-9ccc-4be5-a671-b3ac431978f4",
- "showInput": true,
- "vscode": {
- "languageId": "python"
- }
+ "showInput": true
},
"outputs": [
{
diff --git a/tutorials/closed_loop_botorch_only.ipynb b/tutorials/closed_loop_botorch_only/closed_loop_botorch_only.ipynb
similarity index 99%
rename from tutorials/closed_loop_botorch_only.ipynb
rename to tutorials/closed_loop_botorch_only/closed_loop_botorch_only.ipynb
index 467d40e811..56ef65a9d5 100644
--- a/tutorials/closed_loop_botorch_only.ipynb
+++ b/tutorials/closed_loop_botorch_only/closed_loop_botorch_only.ipynb
@@ -27,6 +27,18 @@
"Since botorch assumes a maximization problem, we will attempt to maximize $-f(x)$ to achieve $\\max_{x} -f(x) = 3.32237$."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 14,
diff --git a/tutorials/compare_mc_analytic_acquisition.ipynb b/tutorials/compare_mc_analytic_acquisition/compare_mc_analytic_acquisition.ipynb
similarity index 98%
rename from tutorials/compare_mc_analytic_acquisition.ipynb
rename to tutorials/compare_mc_analytic_acquisition/compare_mc_analytic_acquisition.ipynb
index c61f34689c..ea735a8920 100644
--- a/tutorials/compare_mc_analytic_acquisition.ipynb
+++ b/tutorials/compare_mc_analytic_acquisition/compare_mc_analytic_acquisition.ipynb
@@ -12,6 +12,18 @@
"In this tutorial, we compare the analytic and MC-based EI acquisition functions and show both `scipy`- and `torch`-based optimizers for optimizing the acquisition. This tutorial highlights the modularity of botorch and the ability to easily try different acquisition functions and accompanying optimization algorithms on the same fitted model."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
diff --git a/tutorials/composite_bo_with_hogp.ipynb b/tutorials/composite_bo_with_hogp/composite_bo_with_hogp.ipynb
similarity index 99%
rename from tutorials/composite_bo_with_hogp.ipynb
rename to tutorials/composite_bo_with_hogp/composite_bo_with_hogp.ipynb
index 2497b4b864..2850e93f56 100644
--- a/tutorials/composite_bo_with_hogp.ipynb
+++ b/tutorials/composite_bo_with_hogp/composite_bo_with_hogp.ipynb
@@ -36,6 +36,18 @@
"Recently, [Maddox et al, '21](https://arxiv.org/abs/2106.12997) proposed a method for computing posterior samples from the HOGP by exploiting structure in the posterior distribution, thereby enabling its usage in BO settings. While they show that this approach allows to use composite BO on problems with tens or thousands of outputs, for scalability we consider a much smaller example here (that does not require GPU acceleration)."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -597,8 +609,8 @@
},
"indentAmount": 2,
"kernelspec": {
- "name": "python3",
- "display_name": "python3"
+ "display_name": "python3",
+ "name": "python3"
}
},
"nbformat": 4,
diff --git a/tutorials/composite_mtbo.ipynb b/tutorials/composite_mtbo/composite_mtbo.ipynb
similarity index 99%
rename from tutorials/composite_mtbo.ipynb
rename to tutorials/composite_mtbo/composite_mtbo.ipynb
index 4d3d54a2c8..eddab45457 100644
--- a/tutorials/composite_mtbo.ipynb
+++ b/tutorials/composite_mtbo/composite_mtbo.ipynb
@@ -20,6 +20,18 @@
"We will use a Multi-Task Gaussian process ([MTGP](https://papers.nips.cc/paper/2007/hash/66368270ffd51418ec58bd793f2d9b1b-Abstract.html)) with an ICM kernel to model all of the outputs in this problem. MTGPs can be easily accessed in Botorch via the `botorch.models.KroneckerMultiTaskGP` model class (for the \"block design\" case of fully observed outputs at all inputs). Given $T$ tasks (outputs) and $n$ data points, they assume that the responses, $Y \\sim \\mathbb{R}^{n \\times T},$ are distributed as $\\text{vec}(Y) \\sim \\mathcal{N}(f, D)$ and $f \\sim \\mathcal{GP}(\\mu_{\\theta}, K_{XX} \\otimes K_{T}),$ where $D$ is a (diagonal) noise term."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -494,8 +506,8 @@
},
"indentAmount": 2,
"kernelspec": {
- "name": "python3",
- "display_name": "python3"
+ "display_name": "python3",
+ "name": "python3"
}
},
"nbformat": 4,
diff --git a/tutorials/constrained_multi_objective_bo.ipynb b/tutorials/constrained_multi_objective_bo/constrained_multi_objective_bo.ipynb
similarity index 99%
rename from tutorials/constrained_multi_objective_bo.ipynb
rename to tutorials/constrained_multi_objective_bo/constrained_multi_objective_bo.ipynb
index baa2bd9de5..8fc714ff9c 100644
--- a/tutorials/constrained_multi_objective_bo.ipynb
+++ b/tutorials/constrained_multi_objective_bo/constrained_multi_objective_bo.ipynb
@@ -42,6 +42,18 @@
"Note: $q$EHVI aggressively exploits parallel hardware and is much faster when run on a GPU. See [1] for details."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/tutorials/constraint_active_search.ipynb b/tutorials/constraint_active_search/constraint_active_search.ipynb
similarity index 99%
rename from tutorials/constraint_active_search.ipynb
rename to tutorials/constraint_active_search/constraint_active_search.ipynb
index e2ca3b90a5..071cb59173 100644
--- a/tutorials/constraint_active_search.ipynb
+++ b/tutorials/constraint_active_search/constraint_active_search.ipynb
@@ -25,6 +25,18 @@
"[1]: [Malkomes et al., Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design, Proceedings of the 38th International Conference on Machine Learning, 2021](http://proceedings.mlr.press/v139/malkomes21a/malkomes21a.pdf)."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/tutorials/cost_aware_bayesian_optimization.ipynb b/tutorials/cost_aware_bayesian_optimization/cost_aware_bayesian_optimization.ipynb
similarity index 99%
rename from tutorials/cost_aware_bayesian_optimization.ipynb
rename to tutorials/cost_aware_bayesian_optimization/cost_aware_bayesian_optimization.ipynb
index 400bea2882..bfc94e48d1 100644
--- a/tutorials/cost_aware_bayesian_optimization.ipynb
+++ b/tutorials/cost_aware_bayesian_optimization/cost_aware_bayesian_optimization.ipynb
@@ -24,6 +24,19 @@
"[1]: [Lee, Eric Hans, et al. Cost-aware Bayesian Optimization. International Conference on Machine Learning, AutoML Workshop. 2020. ](https://arxiv.org/pdf/2003.10870.pdf)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0795fafc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -83,12 +96,12 @@
"class CostModel(torch.nn.Module, ABC):\n",
" \"\"\"\n",
" Simple abstract class for a cost model.\n",
- " \"\"\" \n",
- " \n",
+ " \"\"\"\n",
+ "\n",
" @abstractmethod\n",
" def forward(self, X):\n",
" pass\n",
- " \n",
+ "\n",
"\n",
"class CostModelGP(CostModel):\n",
" \"\"\"\n",
@@ -368,7 +381,7 @@
"b = torch.rand(n, 1, **tkwargs)\n",
"\n",
"# Timings varying batch size\n",
- "batch_sizes = 100 * torch.arange(4, 10, device=device) \n",
+ "batch_sizes = 100 * torch.arange(4, 10, device=device)\n",
"times_batch = []\n",
"for batch_size in batch_sizes:\n",
" model = NoisyLinearLeastSquares(A, b, batch_size=batch_size)\n",
diff --git a/tutorials/custom_acquisition.ipynb b/tutorials/custom_acquisition/custom_acquisition.ipynb
similarity index 87%
rename from tutorials/custom_acquisition.ipynb
rename to tutorials/custom_acquisition/custom_acquisition.ipynb
index 2cbf840cf8..ff95b4863d 100644
--- a/tutorials/custom_acquisition.ipynb
+++ b/tutorials/custom_acquisition/custom_acquisition.ipynb
@@ -1,35 +1,24 @@
{
- "metadata": {
- "kernelspec": {
- "display_name": "python3",
- "language": "python",
- "name": "python3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
"cells": [
{
"cell_type": "markdown",
"metadata": {
+ "code_folding": [],
"collapsed": true,
+ "hidden_ranges": [],
"originalKey": "4c6694a4-a6f8-4fc6-a9a7-4a29617406cb",
- "showInput": false,
- "code_folding": [],
- "hidden_ranges": []
+ "showInput": false
},
- "source": [
- ""
- ]
+ "source": []
},
{
"cell_type": "markdown",
"metadata": {
+ "code_folding": [],
"collapsed": true,
+ "hidden_ranges": [],
"originalKey": "c901c723-b2f3-4f75-96c0-6555954209f5",
- "showInput": false,
- "code_folding": [],
- "hidden_ranges": []
+ "showInput": false
},
"source": [
"### Upper Confidence Bound (UCB)\n",
@@ -43,40 +32,108 @@
},
{
"cell_type": "code",
+ "execution_count": 1,
"metadata": {
"collapsed": false,
+ "customOutput": null,
+ "executionStartTime": 1668651266713,
+ "executionStopTime": 1668651266719,
+ "originalKey": "bdd02e32-c993-4bc7-966a-dfa520e243d3",
"pycharm": {
"name": "#%%\n"
},
- "originalKey": "bdd02e32-c993-4bc7-966a-dfa520e243d3",
- "requestMsgId": "2364e148-21c4-4037-998e-e4352a4f4929",
- "customOutput": null,
- "executionStartTime": 1668651266713,
- "executionStopTime": 1668651266719
+ "requestMsgId": "2364e148-21c4-4037-998e-e4352a4f4929"
},
+ "outputs": [],
"source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
"import plotly.io as pio\n",
- "\n",
- "# Ax uses Plotly to produce interactive plots. These are great for viewing and analysis,\n",
- "# though they also lead to large file sizes, which is not ideal for files living in GH.\n",
- "# Changing the default to `png` strips the interactive components to get around this.\n",
- "pio.renderers.default = \"png\""
- ],
- "execution_count": 1,
- "outputs": []
+ "if 'google.colab' in sys.modules:\n",
+ " pio.renderers.default = \"colab\"\n",
+ " %pip install botorch ax\n",
+ "else:\n",
+ " # Ax uses Plotly to produce interactive plots. These are great for viewing and analysis,\n",
+ " # though they also lead to large file sizes, which is not ideal for files living in GH.\n",
+ " # Changing the default to `png` strips the interactive components to get around this.\n",
+ " pio.renderers.default = \"png\""
+ ]
},
{
"cell_type": "code",
+ "execution_count": 2,
"metadata": {
- "originalKey": "75503e22-bf3b-49d6-87a8-cb9c741e941e",
"code_folding": [],
- "hidden_ranges": [],
"collapsed": false,
- "requestMsgId": "28d53a4f-be29-4ec6-8529-e1bdc80b5adf",
+ "customOutput": null,
"executionStartTime": 1668651267039,
"executionStopTime": 1668651273452,
- "customOutput": null
+ "hidden_ranges": [],
+ "originalKey": "75503e22-bf3b-49d6-87a8-cb9c741e941e",
+ "requestMsgId": "28d53a4f-be29-4ec6-8529-e1bdc80b5adf"
},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "I1116 181426.999 _utils_internal.py:179] NCCL_DEBUG env var is set to None\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "I1116 181427.000 _utils_internal.py:188] NCCL_DEBUG is INFO from /etc/nccl.conf\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/mpmath/ctx_mp_python.py:892: SyntaxWarning:\n",
+ "\n",
+ "\"is\" with a literal. Did you mean \"==\"?\n",
+ "\n",
+ "/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/mpmath/ctx_mp_python.py:986: SyntaxWarning:\n",
+ "\n",
+ "\"is\" with a literal. Did you mean \"==\"?\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/sympy/solvers/diophantine.py:3188: SyntaxWarning:\n",
+ "\n",
+ "\"is\" with a literal. Did you mean \"==\"?\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/sympy/plotting/plot.py:520: SyntaxWarning:\n",
+ "\n",
+ "\"is\" with a literal. Did you mean \"==\"?\n",
+ "\n",
+ "/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/sympy/plotting/plot.py:540: SyntaxWarning:\n",
+ "\n",
+ "\"is\" with a literal. Did you mean \"==\"?\n",
+ "\n",
+ "/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/sympy/plotting/plot.py:553: SyntaxWarning:\n",
+ "\n",
+ "\"is\" with a literal. Did you mean \"==\"?\n",
+ "\n",
+ "/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/sympy/plotting/plot.py:560: SyntaxWarning:\n",
+ "\n",
+ "\"is\" with a literal. Did you mean \"==\"?\n",
+ "\n"
+ ]
+ }
+ ],
"source": [
"import math\n",
"from typing import Optional\n",
@@ -129,53 +186,15 @@
" * (scalarized_samples - scalarized_mean).abs()\n",
" )\n",
" return ucb_samples.max(dim=-1)[0].mean(dim=0)"
- ],
- "execution_count": 2,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "I1116 181426.999 _utils_internal.py:179] NCCL_DEBUG env var is set to None\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "I1116 181427.000 _utils_internal.py:188] NCCL_DEBUG is INFO from /etc/nccl.conf\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/mpmath/ctx_mp_python.py:892: SyntaxWarning:\n\n\"is\" with a literal. Did you mean \"==\"?\n\n/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/mpmath/ctx_mp_python.py:986: SyntaxWarning:\n\n\"is\" with a literal. Did you mean \"==\"?\n\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/sympy/solvers/diophantine.py:3188: SyntaxWarning:\n\n\"is\" with a literal. Did you mean \"==\"?\n\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/sympy/plotting/plot.py:520: SyntaxWarning:\n\n\"is\" with a literal. Did you mean \"==\"?\n\n/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/sympy/plotting/plot.py:540: SyntaxWarning:\n\n\"is\" with a literal. Did you mean \"==\"?\n\n/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/sympy/plotting/plot.py:553: SyntaxWarning:\n\n\"is\" with a literal. Did you mean \"==\"?\n\n/data/sandcastle/boxes/fbsource/buck-out/v2/gen/fbcode/f3b9a99e517e0a13/bento/kernels/__bento_kernel_axoptics__/bento_kernel_axoptics#link-tree/sympy/plotting/plot.py:560: SyntaxWarning:\n\n\"is\" with a literal. Did you mean \"==\"?\n\n"
- ]
- }
]
},
{
"cell_type": "markdown",
"metadata": {
- "originalKey": "b1d4ee8e-69e1-4914-b113-473add84f322",
- "showInput": false,
"code_folding": [],
- "hidden_ranges": []
+ "hidden_ranges": [],
+ "originalKey": "b1d4ee8e-69e1-4914-b113-473add84f322",
+ "showInput": false
},
"source": [
"Note that `qScalarizedUpperConfidenceBound` is very similar to `qUpperConfidenceBound` and only requires a few lines of new code to accomodate scalarization of multiple outputs. The `@t_batch_mode_transform` decorator ensures that the input `X` has an explicit t-batch dimension (code comments are added with shapes for clarity).\n",
@@ -197,16 +216,18 @@
},
{
"cell_type": "code",
+ "execution_count": 3,
"metadata": {
- "originalKey": "4958d0f5-cce4-4fc8-8fa2-8b9d7fe2bd5d",
+ "code_folding": [],
"collapsed": false,
- "requestMsgId": "534c2e0c-f4c0-4bb0-bdec-e44f86c0ed2b",
+ "customOutput": null,
"executionStartTime": 1668651273753,
"executionStopTime": 1668651274217,
- "code_folding": [],
"hidden_ranges": [],
- "customOutput": null
+ "originalKey": "4958d0f5-cce4-4fc8-8fa2-8b9d7fe2bd5d",
+ "requestMsgId": "534c2e0c-f4c0-4bb0-bdec-e44f86c0ed2b"
},
+ "outputs": [],
"source": [
"import torch\n",
"\n",
@@ -228,65 +249,67 @@
"\n",
"# construct the acquisition function\n",
"qSUCB = qScalarizedUpperConfidenceBound(gp, beta=0.1, weights=torch.tensor([0.1, 0.5]))"
- ],
- "execution_count": 3,
- "outputs": []
+ ]
},
{
"cell_type": "code",
+ "execution_count": 4,
"metadata": {
- "originalKey": "5070d96c-5673-4bbf-ab72-781d1eabd1bf",
"collapsed": false,
- "requestMsgId": "1a5d87ac-e176-46dd-b7b9-fb29bfd14971",
+ "customOutput": null,
"executionStartTime": 1668651274500,
"executionStopTime": 1668651274569,
- "customOutput": null
+ "originalKey": "5070d96c-5673-4bbf-ab72-781d1eabd1bf",
+ "requestMsgId": "1a5d87ac-e176-46dd-b7b9-fb29bfd14971"
},
- "source": [
- "# evaluate on single q-batch with q=3\n",
- "qSUCB(torch.rand(3, 2))"
- ],
- "execution_count": 4,
"outputs": [
{
- "output_type": "execute_result",
"data": {
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| - {'latest'} - {' (main)'} - | -
-
- pip install git+https://github.com/pytorch/botorch.git
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+ " Generation Step ... Arm Parameterizations\n",
+ "0 0 ... {'0_0': {'x1': 0.63, 'x2': 0.51}}\n",
+ "1 0 ... {'1_0': {'x1': 0.43, 'x2': 0.4}}\n",
+ "2 0 ... {'2_0': {'x1': 0.08, 'x2': 0.93}}\n",
+ "3 0 ... {'3_0': {'x1': 0.86, 'x2': 0.04}}\n",
+ "4 0 ... {'4_0': {'x1': 0.95, 'x2': 0.81}}\n",
+ "5 1 ... {'5_0': {'x1': 1.0, 'x2': 0.34}}\n",
+ "6 1 ... {'6_0': {'x1': 0.55, 'x2': 0.0}}\n",
+ "7 1 ... {'7_0': {'x1': 0.12, 'x2': 0.0}}\n",
+ "8 1 ... {'8_0': {'x1': 0.05, 'x2': 0.0}}\n",
+ "9 1 ... {'9_0': {'x1': 0.12, 'x2': 0.03}}\n",
+ "\n",
+ "[10 rows x 5 columns]"
+ ]
},
+ "execution_count": 14,
"metadata": {
"bento_obj_id": "140146254574736"
},
- "execution_count": 14
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "ax_client.generation_strategy.trials_as_df"
]
},
{
"cell_type": "markdown",
"metadata": {
- "originalKey": "ca47b562-3c01-4557-9c76-a7143a978dae",
- "showInput": false,
- "customInput": null,
"code_folding": [],
- "hidden_ranges": []
+ "customInput": null,
+ "hidden_ranges": [],
+ "originalKey": "ca47b562-3c01-4557-9c76-a7143a978dae",
+ "showInput": false
},
"source": [
"Plot the Pareto frontier.\n",
@@ -1099,18 +1250,28 @@
},
{
"cell_type": "code",
+ "execution_count": 15,
"metadata": {
- "originalKey": "5bbd1ba3-1f88-45d3-b4a9-c660a4ff9449",
- "showInput": true,
- "customInput": null,
"code_folding": [],
- "hidden_ranges": [],
"collapsed": false,
- "requestMsgId": "6357abd4-a4b5-4bcb-bf11-9d0532db6072",
- "executionStopTime": 1668651301173,
+ "customInput": null,
+ "customOutput": null,
"executionStartTime": 1668651290670,
- "customOutput": null
+ "executionStopTime": 1668651301173,
+ "hidden_ranges": [],
+ "originalKey": "5bbd1ba3-1f88-45d3-b4a9-c660a4ff9449",
+ "requestMsgId": "6357abd4-a4b5-4bcb-bf11-9d0532db6072",
+ "showInput": true
},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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AuHu/hXlzAoR73myltydCuOu2jnDXebEaAWsBwt2a0PsBhLv3W5g3J0C4581WensihLtu6wh3nRerEbAWINytCb0fQLh7v4V5cwKEe95spbcnQrjrto5w13mxGgFrAcLdmtD7AYS791uYNydAuOfNVnp7IoS7busId50XqxGwFiDcrQm9H0C4e7+FeXMChHvebKW3J0K467aOcNd5sRoBawHC3ZrQ+wGEu/dbmDcnQLjnzVZ6eyKEu27rCHedF6sRsBYg3K0JvR9AuHu/hXlzAoR73myltydCuOu2jnDXebEaAWsBwt2a0PsBhLv3W5g3J0C4581WensihLtu6wh3nRerEbAWINytCb0fQLh7v4V5cwKEe95spbcnQrjrto5w13mxGgFrAcLdmtD7AYS791uYNydAuOfNVnp7IoS7busId50XqxGwFiDcrQm9H0C4e7+FeXMChHvebKW3J0K467aOcNd5sRoBawHC3ZrQ+wGEu/dbmDcnQLjnzVZ6eyKEu27rCHedF6sRsBYg3K0JvR9AuHu/hXlzAoR73myltydCuOu2jnDXebEaAWsBwt2a0PsBhLv3W5g3J0C4581WensihLtu6wh3nRerEbAWINytCb0fQLjrtnBVc7tkMv0/Z0RliW4gq7sFCHcuhqEWINx1O0C467xYjYC1AOFuTej9AMJdt4ULVzRJZjPlvsPIKimKRHRDWR0KEO5cCEMtQLjrdoBw13mxGgFrAcLdmtD7AYS7bgs3fsd9dXN7+OThFXER6Yx13nHXeW68mnA3t+OZbgQId50j4a7zYjUC1gKEuzWh9wMId/MtXLi8UYK7ZniX3dyQcHdjxxQ3AoS7zpFw13mxGgFrAcLdmtD7AYS7+RYS7uZ2fT2Td9zdejJNL0C468wId50XqxGwFiDcrQm9H0C4m28h4W5uR7i7tWOaGwHCXedIuOu8WI2AtQDhbk3o/QDC3XwLCXdzO8LdrR3T3AgQ7jpHwl3nxWoErAUId2tC7wcQ7uZbSLib2xHubu2Y5kaAcNc5Eu46L1YjYC1AuFsTej+AcDffQsLd3I5wd2vHNDcChLvOkXDXebEaAWsBwt2a0PsBhLtuC1c2t0trRyp80nvrWsL/HFtT1v3d7fUVJVIej+qGsjoU4MOpXAhDLUC463aAcNd5sRoBawHC3ZrQ+wGEu24LF69rlab2RPikZY1t4X+OqiqRyIZfujS2tkyqS2K6oawm3LkGckKAcNdtA+Gu82I1AtYChLs1ofcDCHfdFrYn05La8JtTX1y0VjpSafnA6GqpKu2M9ZLiIonym1N1qBtW8467ERtPcihAuOswCXedF6sRsBYg3K0JvR9AuJtv4d/eXiVtybR8ZHyt1JYFvz2Vh40A4W6jx3NdCBDuOkXCXefFagRCgXUN6+WwUy6V808/Vo4/+sBeKiecPVsWvP4/kQ3vAlZXlsuff/XdcB3hzkVEuJtfA4S7uV1fzyTc3XoyTS9AuOvMCHedF6sRCAUuv+4uee7FBTLjxMP7DPfDT/mq3DL7XJk4YVwvMcKdi4hwN78GCHdzO8LdrR3T3AgQ7jpHwl3nxWoE5LkXFsjcefNl4rbjZNKEcX2G+/7HnC8/v+PrMnrEMMKda6aXAOFuflEQ7uZ2hLtbO6a5ESDcdY6Eu86L1QUukEgkZfqZs+TGWTPlZw892W+473rIDNlvj13kXy+/JsPqquWiM6bL/nt9ONTjHfcCv4hEhHA3vwYId3M7wt2tHdPcCBDuOkfCXefF6gIXmHv3fMlkMjLztGlyzZx7+wz3dDojV37nh3LQfh+Vj+++i/zfc/+Wr3zj+/LwvOtkzMhhvcK9rpIP2BXaZVVW0vmd463tnd9NziN7gT8tXC4tHSnZa/vhMqy8JPsnsrJPgaKISKy4SNoTaYQQGBSBtes7Nnkdwl3HTrjrvFhdwALvLFomF189V+6be6XE47F+w70voi9c+G055rD95IiD9+oV7l0RV8C0BXfqtZVxiQQfcl7fIZmCO3u7E/7L6yukNZmWj21TJ3WEux2miESLIlJRWiyNLZ3fk88DgYEW6PmGBeGuEyfcdV6sLmCBux94TO6452GJxYpDheaWNolGi+TEaQfJBTOO65ZpaW2X195aJB/54MTuv/a5866Vk445WA494GPcKlPA11DXqXOrjPlFwK0y5nZ9PZNvlXHryTS9AOGuMyPcdV6sRqBboOetMo88+azsOWXn8N34qZ+5UG6++hz5+O6T5f+ee1m+PPt2eeTeb8nwumrCnWuIe9wtrgHC3QKvj6cS7m49maYXINx1ZoS7zovVCPQb7vtNO0/mzD5HpkzeQf7y95fl+tvvl+Ur18j4MSPkKzM/K3vs+oHwuXw4lYuId9zNrwHC3dyOd9zd2jHNjQDhrnMk3HVerEbAWoBwtyb0fgDhbr6FhLu5HeHu1o5pbgQId50j4a7zYjUC1gKEuzWh9wMId/MtJNzN7Qh3t3ZMcyNAuOscCXedF6sRsBYg3K0JvR9AuJtvIeFubke4u7VjmhsBwl3nSLjrvFiNgLUA4W5N6P0Awt18Cwl3czvC3a0d09wIEO46R8Jd58VqBKwFCHdrQu8HEO7mW0i4m9sR7m7tmOZGgHDXORLuOi9WI2AtQLhbE3o/gHA330LC3dyOcHdrxzQ3AoS7zpFw13mxGgFrAcLdmtD7AYS7+RYS7uZ2hLtbO6a5ESDcdY6Eu86L1QhYCxDu1oTeDyDczbeQcDe3I9zd2jHNjQDhrnMk3HVerEbAWoBwtyb0fgDhbr6FhLu5HeHu1o5pbgQId50j4a7zYjUC1gKEuzWh9wMId/MtJNzN7Qh3t3ZMcyNAuOscCXedF6sRsBYg3K0JvR9AuJtvIeFubke4u7VjmhsBwl3nSLjrvFiNgLUA4W5N6P0Awt18Cwl3czvC3a0d09wIEO46R8Jd58VqBKwFCHdrQu8HEO7mW0i4m9sR7m7tmOZGgHDXORLuOi9WI2AtQLhbE3o/gHA330LC3dyOcHdrxzQ3AoS7zpFw13mxGgFrAcLdmtD7AYS7+RYS7uZ2hLtbO6a5ESDcdY6Eu86L1QhYCxDu1oTeDyDczbeQcDe3I9zd2jHNjQDhrnMk3HVerEbAWoBwtyb0fgDhbr6FhLu5HeHu1o5pbgQId50j4a7zYjUC1gKEuzWh9wMId/MtJNzN7Qh3t3ZMcyNAuOscCXedF6sRsBYg3K0JvR9AuJtvIeFubke4u7VjmhsBwl3nSLjrvFiNgLUA4W5N6P0Awt18Cwl3czvC3a0d09wIEO46R8Jd58VqBKwFCHdrQu8HEO7mW0i4m9sR7m7tmOZGgHDXORLuOi9WI2AtQLhbE3o/gHA330LC3dyOcHdrxzQ3AoS7zpFw13mxGgFrAcLdmtD7AYS7+RYS7uZ2hLtbO6a5ESDcdY6Eu86L1QhYCxDu1oTeDyDczbeQcDe3I9zd2jHNjQDhrnMk3HVerEbAWoBwtyb0fgDhbr6FhLu5HeHu1o5pbgQId50j4a7zYjUC1gKEuzWh9wMId/MtJNzN7Qh3t3ZMcyNAuOscCXedF6sRsBYg3K0JvR9AuJtvIeFubke4u7VjmhsBwl3nSLjrvFiNgLUA4W5N6P0Awt18Cwl3czvC3a0d09wIEO46R8Jd58VqBKwFCHdrQu8HEO7mW0i4m9sR7m7tmOZGgHDXORLuOi9WI2AtQLhbE3o/gHA330LC3dyOcHdrxzQ3AoS7zpFw13mxGgFrAcLdmtD7AYS7+RYS7uZ2hLtbO6a5ESDcdY6Eu86L1QhYCxDu1oTeDyDczbeQcDe3I9zd2jHNjQDhrnMk3HVerEbAWoBwtyb0fgDhbr6FhLu5HeHu1o5pbgQId50j4a7zYjUC1gKEuzWh9wMId/MtJNzN7Qh3t3ZMcyNAuOscCXedF6sRCAXWNayXw065VM4//Vg5/ugD+1Xpax3hzkVEuJtfA4S7uR3h7taOaW4ECHedI+Gu82I1AqHA5dfdJc+9uEBmnHj4ZsO9r3WEOxcR4W5+DRDu5naEu1s7prkRINx1joS7zovVCMhzLyyQufPmy8Rtx8mkCeP6Dff+1hHuXESEu/k1QLib2xHubu2Y5kaAcNc5Eu46L1YXuEAikZTpZ86SG2fNlJ899GS/4b65dYR7gV9EIkK4m18DhLu5HeHu1o5pbgQId50j4a7zYnWBC8y9e75kMhmZedo0uWbOvf2G++bW9Qz3eKyowFUL7/SHV5dIRERWN7ZLpvBO3+qMn3ljpbQmU/LRrYdJXXncahZPFikuikhlWUzWNXfAgcCgCHQk0pu8DuGuYyfcdV6sLmCBdxYtk4uvniv3zb1S4vFYv+G+pXU9w72+uqSAVQvz1Lt+WOv5P2CFqaE76z/8d5m0JFKy9/b1MryCf3Z0er1XRyIixdGIJJL8CGlryfOzE1jV2E64Z0fV5yrC3QKPpxaWwN0PPCZ33POwxGLF4Yk3t7RJNFokJ047SC6YcVw3xpbWcatMYV03fZ0tt8qYXwPcKmNu19czY9GI1FbGZWXDpjHl9lWYhkD/Arzjrrs6CHedF6sR6BboeavMI08+K3tO2Vnqh9VsotRzHeHORUS4m18DhLu5HeHu1o5pbgQId50j4a7zYjUC/Yb7ftPOkzmzz5Epk3cg3LlONitAuJtfIIS7uR3h7taOaW4ECHedI+Gu82I1AtYCvONuTej9AMLdfAsJd3M7wt2tHdPcCBDuOkfCXefFagSsBQh3a0LvBxDu5ltIuJvbEe5u7ZjmRoBw1zkS7jovViNgLUC4WxN6P4BwN99Cwt3cjnB3a8c0NwKEu86RcNd5sRoBawHC3ZrQ+wGEu/kWEu7mdoS7WzumuREg3HWOhLvOi9UIWAsQ7taE3g8g3M23kHA3tyPc3doxzY0A4a5zJNx1XqxGwFqAcLcm9H4A4W6+hYS7uR3h7taOaW4ECHedI+Gu82I1AtYChLs1ofcDCHfzLSTcze0Id7d2THMjQLjrHAl3nRerEbAWINytCb0fQLibbyHhbm5HuLu1Y5obAcJd50i467xYjYC1AOFuTej9AMLdfAsJd3M7wt2tHdPcCBDuOkfCXefFagSsBQh3a0LvBxDu5ltIuJvbEe5u7ZjmRoBw1zkS7jovViNgLUC4WxN6P4BwN99Cwt3cjnB3a8c0NwKEu86RcNd5sRoBawHC3ZrQ+wGEu/kWEu7mdoS7WzumuREg3HWOhLvOi9UIWAsQ7taE3g8g3M23kHA3tyPc3doxzY0A4a5zJNx1XqxGwFqAcLcm9H4A4W6+hYS7uR3h7taOaW4ECHedI+Gu82I1AtYChLs1ofcDCHfzLSTcze0Id7d2THMjQLjrHAl3nRerEbAWINytCb0fQLibbyHhbm5HuLu1Y5obAcJd50i467xYjYC1AOFuTej9AMLdfAsJd3M7wt2tHdPcCBDuOkfCXefFagSsBQh3a0LvBxDu5ltIuJvbEe5u7ZjmRoBw1zkS7jovViNgLUC4WxN6P4BwN99Cwt3cjnB3a8c0NwKEu86RcNd5sRoBawHC3ZrQ+wGEu/kWEu7mdoS7WzumuREg3HWOhLvOi9UIWAsQ7taE3g8g3M23kHA3tyPc3doxzY0A4a5zJNx1XqxGwFqAcLcm9H4A4W6+hYS7uR3h7taOaW4ECHedI+Gu82I1AtYChLs1ofcDCHfzLSTcze0Id7d2THMjQLjrHAl3nRerEbAWINytCb0fQLibbyHhbm5HuLu1Y5obAcJd50i467xYjYC1AOFuTej9AMLdfAsJd3M7wt2tHdPcCBDuOkfCXefFagSsBQh3a0LvBxDu5ltIuJvbEe5u7ZjmRoBw1zkS7jovViNgLUC4WxN6P4BwN9/CrnCfMLxCyuPF/Q4aXh6XoqKI+QsVyDNj0YjUVsZlZUN7gZwxp5lrAoS7bkcId50XqxGwFiDcrQm9H0C4m29hV7jXlcekpDja76CJI1TcF6wAACAASURBVKqkWBHuq5vbpbkj1e+8YeVxqSzp/wcF8zMa2mcS7kPrz6uLEO66q4Bw13mxGgFrAcLdmtD7AYS7+Rb29Y77mpYOyWQyEsR1JNL5Lrv2Hfdlja2yrjXR74GNri6T2rKY+YHn6DMJ9xzdmAI6LMJdt9mEu86L1QhYCxDu1oTeDyDczbewr3vcX1/ZJKl0RibWV0pxtMhoeEcqI8l0OnzumuZ2Wd+elOEVJVKx4V32eLRI9Q6+0UEMwZMI9yFA5yU3ESDcdRcE4a7zYjUC1gKEuzWh9wMId/MtHKhw3/iIlja0SkNbQkZXl0ptWdz8YD14JuHuwSbl+SES7roNJtx1XqxGwFqAcLcm9H4A4W6+hQMR7i0dKVnV/P6HM4N33FsSqTDag/vay2JRGVFZYn7QOfxMwj2HN6dADo1w12004a7zYjUC1gKEuzWh9wMId/MtHIhwX93SIYvXtXQfVGNbQtoSKakqDaK9WGrKY7JNbYX5QefwMwn3HN6cAjk0wl230YS7zovVCFgLEO7WhN4PINzNt3Agwn1pY5usXN8WHlR7Mi2rmtokuNu9vjIehvvIytLwtpl8fBDu+birfp0T4a7bL8Jd58VqBKwFCHdrQu8HEO7mWzgQ4b62pUOa2pPhQTW0dMh761rCb6fZelhFeKtMbXlMqkvy7xtlgvMl3M2vRZ7pRoBw1zkS7jovViMQCqxrWC+HnXKpnH/6sXL80Qf2Unl5wdtyzc33yFvvLpXRI+rk4rOOlwP2/ki4jnDnIiLcza+BgQj3jY/mrVXrZeHyRimNR2XX8XV8ONV8q3gmAlkJEO5ZMXUvItx1XqxGIBS4/Lq75LkXF8iMEw/vFe7B90lPnX6RXDjjM3LEwXvJU8++KF+efbs88/D3pCQeI9y5hoRwN78ICHdzu76eyTvubj2Zphcg3HVmhLvOi9UIyHMvLJC58+bLxG3HyaQJ43qFe1t7hzz+1PNy9KH7dGtNOWSGPDzvWhk/ZgThzjVEuFtcA4S7BV4fTyXc3XoyTS9AuOvMCHedF6sLXCCRSMr0M2fJjbNmys8eerLPcN+YKFj/0KN/lvvm/1Ee/MFsiUaLCPcCv4aC0+cdd/OLgHA3t+Mdd7d2THMjQLjrHAl3nRerC1xg7t3zw1+tPvO0aXLNnHs3G+5/+usLcu4V35VR9XUy5xvnyuSdJoR6Pe9xH1GTn98PXeCXymZPP1bc+ds9E8nO39TJI3uBJ/+7LPyO9X22rw9/s2nweGVxgyTSafnQ2BqJKX9zanN7UjIbvfzry5vk1aUNUh6Pym7bDJeth5Vnf3AeroxERKJFEUmmNlbw8EQ4ZG8EVja8/zsTgoMm3HVbR7jrvFhdwALvLFomF189V+6be6XE47EthntAlUyl5PkXFsil37xD7r/9Khk7ur5XuHdFXAHTFtyp19eUSEREVjW0bxKNBQdhcMLPvLky/I71KdsMk7oNv9U0+DBpKp2RSSOr1OH+2oomSabe/wFqRVObBL85NV4cDaN9162GSXFRsFv5+QjOrao8JmvXd+TnCXJWOSfQ8w0Lwl23RYS7zovVBSxw9wOPyR33PCyxWHGo0NzSFt76cuK0g+SCGcd1y6xe2yjP/uPV8IOpXY/PX/AtmX7kJ+SwqXtwq0wBX0Ndp86tMuYXgetbZRava5VUpvPd5paOpAThvrK5TUqiUdmuvkp2HFUl0eBt6Tx9cI97nm6sR6dFuOs2i3DXebEagW6BnrfKPPLks7LnlJ3DsD9o+kVy06yZsu8eu8jCNxfJ5867Vn5y2xUyacJ4wp1riHvcLa4Bl+GeSqdlTUui+2gWrW2RVevbwltxSouj8oHR1bLNsPz8jaldJ024W1yMPNWJAOGuYyTcdV6sRqDfcN9v2nkyZ/Y5MmXyDvKXv/9bbrrjAVmyfLXUVlfKGScfKccevl/4XL7HnYuId9zNrwGX4d6Ryshbq5q6DyZ4t31dayK8FackViQThlfKLmNrzQ/Wg2cS7h5sUp4fIuGu22DCXefFagSsBQh3a0LvBxDu5lvoMtw3fsc9nUnLwuVN0tCWCO+X5x138z3imQhoBAh3jZYI4a7zYjUC1gKEuzWh9wMId/MtdBnuGx9FRyotwezGtqREIhkpjxfzm1PNt4lnIpC1AOGeNVW4kHDXebEaAWsBwt2a0PsBhLv5FhLu5nZ9PZNbZdx6Mk0vQLjrzAh3nRerEbAWINytCb0fQLibbyHhbm5HuLu1Y5obAcJd50i467xYjYC1AOFuTej9AMLdfAsJd3M7wt2tHdPcCBDuOkfCXefFagSsBQh3a0LvBxDu5ltIuJvbEe5u7ZjmRoBw1zkS7jovViNgLUC4WxN6P4BwN99Cwt3cjnB3a8c0NwKEu86RcNd5sRoBawHC3ZrQ+wGEu/kWEu7mdoS7WzumuREg3HWOhLvOi9UIWAsQ7taE3g8g3M23kHA3tyPc3doxzY0A4a5zJNx1XqxGwFqAcLcm9H4A4W6+hYS7uR3h7taOaW4ECHedI+Gu82I1AtYChLs1ofcDCHfzLSTcze0Id7d2THMjQLjrHAl3nRerEbAWINytCb0fQLibbyHhbm5HuLu1Y5obAcJd50i467xYjYC1AOFuTej9AMLdfAsJd3M7wt2tHdPcCBDuOkfCXefFagSsBQh3a0LvBxDu5ltIuJvbEe5u7ZjmRoBw1zkS7jovViNgLUC4WxN6P4BwN99Cwt3cjnB3a8c0NwKEu86RcNd5sRoBawHC3ZrQ+wGEu/kWEu7mdoS7WzumuREg3HWOhLvOi9UIWAsQ7taE3g8g3M23kHA3tyPc3doxzY0A4a5zJNx1XqxGwFqAcLcm9H4A4W6+hYS7uR3h7taOaW4ECHedI+Gu82I1AtYChLs1ofcDCHfzLbQN99XN7ZLO9H79RCotryxtkI5kWiKRjJTHi2XX8XVSWxY3P1gPnhmLRqS2Mi4rG9o9OFoOMR8FCHfdrhLuOi9WI2AtQLhbE3o/gHA330LbcH9tRZOkM73LPZnOyFur1ktRRCQSEcLdfIt4JgIqAcJdxSWEu86L1QhYCxDu1oTeDyDczbfQNtw3fsc9+O/BY3h5XBLpDO+4m28Lz0TAWIBw19ER7jovViNgLUC4WxN6P4BwN99C23Df+JVfW9EY3jYzaUSVpDIZCWY3tiW5VcZ8e3gmAmoBwl1HRrjrvFiNgLUA4W5N6P0Awt18C3uG+5qWdnllSUMY4CMq4xItKtpkeG1ZTEZXl/X5goS7CPe4m1+LPNONAOGucyTcdV6sRsBagHC3JvR+AOFuvoWEu7ldX88k3N16Mk0vQLjrzAh3nRerEbAWINytCb0fQLibb+HmbpUZVhGTNc0J2dy77Bu/Mu+48467+ZXIM10JEO46ScJd58VqBKwFCHdrQu8HEO7mW0i4m9vxjrtbO6a5ESDcdY6Eu86L1QhYCxDu1oTeDyDczbeQcDe3I9zd2jHNjQDhrnMk3HVerEbAWoBwtyb0fgDhbr6FhLu5HeHu1o5pbgQId50j4a7zYjUC1gKEuzWh9wMId/MtJNzN7Qh3t3ZMcyNAuOscCXedF6sRsBYg3K0JvR9AuJtvIeFubke4u7VjmhsBwl3nSLjrvFiNgLUA4W5N6P0Awt18Cwl3czvC3a0d09wIEO46R8Jd58VqBKwFCHdrQu8HEO7mW0i4m9sR7m7tmOZGgHDXORLuOi9WI2AtQLhbE3o/gHA330LC3dyOcHdrxzQ3AoS7zpFw13mxeggF1je3ykOP/lneXrRM2ts7eh3JtZfNGMKjy/6lCffsrfJ1JeFuvrOEu7kd4e7WjmluBAh3nSPhrvNi9RAKnP3Vm+XNdxbLlF12kFhxca8j+cZXvjBoR7euYb0cdsqlcv7px8rxRx/Y63WD45x14zxZ+Oa7Uj+sRi45+wQ5cJ9dw3WE+6BtU86+EOFuvjWEu7kd4e7WjmluBAh3nSPhrvNi9RAK7H/M+fK7n35HystKhvAoOl/68uvukudeXCAzTjy8z3A/+rQr5LjD95eTjjlYnnn+Fblo1m3y51/dKmWlccJ9yHdv6A+AcDffA8Ld3I5wd2vHNDcChLvOkXDXebF6CAU+fdrX5Fc/+oZEIpEhPAqR515YIHPnzZeJ246TSRPG9Qr3ZColv/rdX2Tap/aV4mg0PNY9Dj9bfnHn1bL1uJGE+5DuXm68OOFuvg+Eu7kd4e7WjmluBAh3nSPhrvNi9RAKPPzEM7LwjUUy46QjpLamckiOJJFIyvQzZ8mNs2bKzx56ss9w73lgL//3LTn/qlvlyZ/fJEVFEcJ9SHYut16UcDffD8Ld3I5wd2vHNDcChLvOkXDXebF6CAUO/eyXZcXqddLRkZDSkrj0fOP9H4/dOeBHN/fu+ZLJZGTmadPkmjn3bjHc31u6Us748g1y5QWfk712+2B4fD3vcS8v6X2//oCfCC8wpAI1lTEJ/txo3frEkB6Hjy/+59dXSGsyJbtvM0zqyjtvm1uwrFGS6bTUV5bIqvXtUlcel3G15Vs8vf8sbZB0JiMfGF0jyXRG/vLGCmlo7Qj3pqIkJh/bZpgMqxj6W/O2eCIWC6JFIuWlxdLUkrSYwlMRyF6gpX3Ta41wz94uWEm467xYPYQCTz/7khQVFfV7BPvuMXlAj+6dRcvk4qvnyn1zr5R4PLbFcF/45iI5/8pb5avnnCgH7P2R7mPrGe61lbEBPW6G555A1w9rPf8HLPeONPeO6E8LV0hLR1L22n64DNsQ7q8uaQjDe2RVXFY0dcjwiriMr9tyuL+yZJ2k0iIfGtsZ7n9auFzWtSTCNwUqS4plz+2Gy/A8D/eiSETixUXSlkjl3mZzRHkp0PMNC8Jdt82Eu86L1QUscPcDj8kd9zwssVjnO+TNLW0SjRbJidMOkgtmHLeJzKIlK2TGJTdI8BWVUyZP2uTv8a0yBXwRbTh1bpUxvwa4Vcbcrq9nxqIRqa2My8qGdreDmYZAlgKEe5ZQG5YR7jovVg+ywAVX3SbnnDZNJk4YJ8F/39xjzuxzBvXoet4q88iTz8qeU3YOv/7x8xd8S44/6hPyqQP36HVMhPugblNOvhjhbr4thLu5HeHu1o5pbgQId50j4a7zYvUgC1x360/lpGMOkq3HjZLgv2/ucdm5Jw3q0fUM9/2mnSfBDw8j6+skuB+/6535roO64aqz5aB9P8qHUwd1l3LzxQh3830h3M3tCHe3dkxzI0C46xwJd50Xq4dIIPiKxeBbXI47Yn8pLysdoqNw87K84+7G0ecphLv57hHu5naEu1s7prkRINx1joS7zovVQyiwz9HnyE9v+5psu9XoITwK+5cm3O0NfZ9AuJvvIOFubke4u7VjmhsBwl3nSLjrvFg9hAK/fvwZefQPf5PDpu4pW40dKfH4pl+j+KEdJwzh0WX/0oR79lb5upJwN99Zwt3cjnB3a8c0NwKEu86RcNd5sXoIBT54wOc3++qvPnX3EB5d9i89GOH+7ppmaUmkZKu6cqno8QNO9kfKyoESINzNZQl3czvC3a2dy2ltiaS8s6Yl/GrO7YYPzS8YdHk+mlmEu0aL73HXabF6SAXWN7dKcXFUIj1/89KGoyqJ+/F96LkS7mtaOiSVzsjw8nj4G115DJ4A4a63bk+mJYj2N1eul1QmI+NqymTqTqOlpLhIXl/ZFF7LwypisqY5IbVlMRldXbbFF3ltRaOkMyKTRlSFM4P5jW1JiUQyUh4vll3H10ltWXyLc3xewNdBDs7uBW+kNLcnpTwe7fPNFMJ9cPYhH16Fd9zzYRcL4ByCD6de+PXvyTWXni41VRVen3GuhPsbK5vCXzqz/fBKiRX3/4utvMbO0YMn3PUb89TrK+T1FU2bPHFsdZkcPnks4a7n7H4G4W6Bp3jqqub27t/qO6qq9xcsEO4KzAJfSrgX+AXg0+kf+bnL5MoLT5Xdd93Jp8PudayDGe6bg1rR1Ba+2ziiskSivOM+qNdUTUXnnw41tCREMoP60t6+2HPvrJbgXfeej30njpCua7kiHpXmjpSUxYqkJot3ypc3tUkmIzKyqiT8Z+GtVeu7f4NocMvC1nXl4Tvv+fwI/tkvK4nK+tZNfw19Pp/zUJzb+vakBP8XvONeXdr/nw5zq8xQ7I5fr0m4+7VfBX20P77/d/LL3z4tu35oUueHUzf8BtMulNNO+JQXPrkT7u2SzgS3BPR/+5EXoB4eZGVpsQR3fAWxRLdnt4EvL14X3g7T87HjqGppTaQkIxkpjRVJWyId3j5TWbLlW+fWNLeH/sPK42G4L2lolfZESiQiEiuKyKjqMimNRbM7QE9XRSMiJfGotLSnPD0DPw47kUpLRzItwQ+XVYT7JpvGPe66a5hw13mxeggFjv3iVRIrjkpYPH087r/9qiE8uuxfejDDfXMfTu26VSb7I2elKwHecddL9veO++SxtdKRSoXhzTvuelfecdeb2Tyjrjwu3CqzqSDhrruiCHedF6sRsBbInXBfL8l0WurK+HCq9aYqB9RXl4Q/f65qbA9v1eCxZYFXlzb0uMc9I5KJyMcnjgjfcQ8+XFpTGpWGtpRUlURleMWWf1Fb8O1Lwc03W9eWh89/ZWlD+AHCYG9KiqOy48iqzb47uuWjzv0VxUURqSqPydr1Hbl/sB4fYVuy88OpwZ/ujOQe9012knDXXdiEu86L1UMo8Ie//KvfVw8+vHroAR8bwqPL/qVzJ9z5cGr2u+Z2JR9O1Xs2tSXloRcXSUfq/fvcx9SUyREf4sOpes33n8GHU230sn8uH07t34pwz/46ClYS7jovVg+hwN5Hzdzk1TPpjDSub5HSkriMHTVcfnPPdUN4dNm/9GCEezZHw7fKZKM0MGsIdzPXIN7/+Nryzm9Dqq+Qj4yvCwfxdZBmnsGzCHdzO80ztxTumln5tpZw1+0o4a7zYnWOCbS0tsnt8x6WCVuPlmMO2y/Hjq7vw8mVcOd73IfuciHcze35BUzmdn09k3B369nftC19j/vgHEVuvgrhrtsXwl3nxeocFfj0aV+T+T++JkePbtPDypVw9wIrTw+ScDffWMLd3I5wd2vHNDcChLvOkXDXebE6BwVaWtvlUyd9RZ5+6JYcPLreh0S4e7FNA3qQhLs5L+Fubke4u7VjmhsBwl3nSLjrvFg9hAKXXnNHr1dPJJPy8oK3ZaeJW8ut15w3hEeX/UsT7tlb5etKwt18Zwl3czvC3a0d09wIEO46R8Jd58XqIRS4+sa7e716SUlcJmw1Wo48ZB8pLysZwqPL/qUJ9+yt8nUl4W6+s4S7uR3h7taOaW4ECHedI+Gu82J1DgikUmmJRovCI0kkkhLr8RtUc+AQN3sIhHuu79DAHx/hbm5MuJvbEe5u7ZjmRoBw1zkS7jovVg+hwHtLV8olV8+Vk487RI44aK/wSO649zfyp2f+JTfOminjRtcP4dFl/9KEe/ZW+bqScDffWcLd3I5wd2vHNDcChLvOkXDXebF6CAW+eMn1MmJYrVx81nSpH1YTHsnqtY1y852/kBWr1sqd118yhEeX/UsT7tlb5etKwt18Zwl3czvC3a0d09wIEO46R8Jd58XqIRT46KFnyB9/ebPUVFVschRr1jXJISdcLP947M4hPLrsX5pwz94qX1cS7uY7S7ib2xHubu2Y5kaAcNc5Eu46L1YPocD+x5wvt117gUzeacImR/HsP16Vy667S556cM4QHl32L024Z2+VrysJd/OdJdzN7Qh3t3ZMcyNAuOscCXedF6uHUCC4n/2nD/1ejjp0Hxk/ZoSk0xl5639L5OEnnpEZJx0R/p8PD8Ldh13qPMb17QlZ05KQinhUhle4+9Yiwt38GiDcze2GMtyb2hOytiUhVSXFUlced3sSTPNagHDXbR/hrvNi9RALPP7U8/LQo3+WRUtWhEey9biRcuzh+8vB++02xEeW/csT7tlbDfXKda0dsqyxTWpKYzKmpszZ4RDu5pQ9w31JQ6u8urRBEqm0jKoqlYqSYqkti8no6i3v12srGiWdEZk0okpSmYwEsxvbkhKJZKQ8Xiy7jq+T2rL8jsxYNCK1lXFZ2dBuvilZPHNNS7usaGrPem+yGMmSPBEg3HUbSbjrvFiNgLUA4W5NOGgDCPdBo876hTYO9xVNbfL06ys3ee7Y2jL56FZ1hHuWooR7llAsGzABwl1HS7jrvFiNgLUA4W5NOGgD1rUmZFljqxQXFUm8uPN3B7h4DKvqfBd37foOyWRcTCycGQuXN0pHKiPb1VfIq0saZW1L73eKd992eFa3Yyxe1yIB/9iasvCd9wXLG6W1IyUSyUhJcVS2r6+UinhxXuMWF0WkorRYGloSA3qeiXRaEsm01JbHZHTVlv80ZEAPhuE5JUC467aDcNd5sRoBawHC3Zpw0AZ0vePu+gVrKmLhyDCWBjjck+mMtCZSEgRaWSzq+lQGfd5bq9ZLIp2RrerKZMHSJmnuSPY6hl3G14a3N23psbypLfzBaWRVSRjuwey2ZEoiIuEPalvVlUt5jv6Ct/ZkWjpSaSkpLpL4hl9It6Xz7evvR4ProiQq61t7O5rM29Jzsr2NaUtz+Pv5I0C46/aScNd5sRoBawHC3Zpw0AZ0veNeWVIswxx+OHVkbalEIiIr1nWG40A+gg/YBvfpl5cUy9gs7vseyGNxMfuf766R9lRaPjimRl5ctEbeW9u6ydjA9aNbD5MdR1Vv8eXeXNUU+k8YXhne4/6vd9dIU3sqvMe9LB6VD43J7geALb7QACxYub5dGlo7ZFh53OraLI5GpKY8JqubOgbgKN8f2dTWEX44lXfcB5TZy+GEu27bCHedF6sRsBYg3K0JB21APtzj3tSWkMUNrRL88DG+tnzQ7AbqhTa+xz2VysgjrywJ33l+/xH8JBQJvwXo8A+NDd+R7u/h84dTgz8tWNvSIfUVcamvLDXm5h53Yzqe6EiAcNdBEu46L1YjYC1AuFsTDtqAvAj39oQsXtca3vpRVbLl20cGDdfwhV5Zsk6C20R2HFUllSUxSaQy8tJ7a+W9tS2dE4P7XDY8Jo+rDe9T7+/x7ppmCZJ/69ry8B33V5Y2SHPwrTJFEt7jvuPIKqnK4pYbw1OxelpLRzK8BWpkVWn4rrvpg3A3leN5rgQId50k4a7zYjUC1gKEuzXhoA1IpoP7iDOdH06NblSElkcwmF8H2fWOu+Uh58zTN77Hvev+8wXLm2RlU1uvY9x6WIVsM6z/P2Xo8x73RCqcE/ygs3Vwj3uOfzjVl3fcg6/rDD6bUByJOP2gd85cmByIsQDhrqMj3HVerEbAWoBwtyb0fsCghnuev+MeXAz/Wdogr61o6nVd7DS6WnbazL3uvOMuMljvuHv/Dy0nMGAChLuOlnDXebEaAWsBwt2a0PsBgxrueXyPe9cvR3p9ZZM8/86aTb5hJvimlRN224Z73LfwTwvh7v2/Trw/AcJdt4WEu86L1QhYCxDu1oTeDyDczbew529ODSYF4d6eSEtTR0IaW5NSGiuSPbet32y0B8/jw6m8425+JfJMVwKEu06ScNd5sRoBawHC3ZrQ+wGDGe4dybQ0tCXCiK3O0Q9aaja0v3BPpTMyrCIma5oTku13hfsc7sH317d0pKSipFjKLb6fn3fcNVcfawdCgHDXqRLuOi9WIxAKrGtYL4edcqmcf/qxcvzRB/ap8siTz8rVN94t11z6RTn0gI91ryHcuYgGM9zzTZtwd7ujhLtbT6bpBQh3nRnhrvNiNQKhwOXX3SXPvbhAZpx4eJ/hfvcDj8k/X1ooK1evk9NOOIxw57rZRIBwN78gCHdzu76eSbi79WSaXoBw15kR7jovViMgz72wQObOmy8Ttx0nkyaM6zPcF7zxruy4/VbyxYuvl+lHfYJw57oh3B1dA4S7I8gNYwh3t55M0wsQ7jozwl3nxeoCF0gkkjL9zFly46yZ8rOHnuw33LuYTr/oO4R7gV8zfZ0+77ibXxSEu7kd77i7tWOaGwHCXedIuOu8WF3gAnPvni+ZTEZmnjZNrplzr5NwH11n/uvKC3w7sjr94LdqptIiHx5fK9Eid79EKasX72dR0YbjSKczNmMK8rm//88yae5Iyb6T6mV4RUlo8PLideFvUB1VXSLLG9ulvjIuwS9f2tJj42sjmc7IE/9ZKuuaE+FvX60qLZaPTxwh9ZWdr5G3j4hIUSQiXIub7nDXtbHLuBopjhbl7fYPxYktW7vpL0sj3HW7QLjrvFhdwALvLFomF189V+6be6XE4zFn4d4VcblE++7aFslkpNdvndz4rwe/CXFJQ5sUR0XG1Wz62ymDX8f+vzUtUhYrkm2H9/8r5zc+5yCcXl/RJMEvKN1hM780R+u0cHmjBH28w8iqnAn3UbWlEomILF/XFjrzyF7g2bdWSWsyJVO2qpOu73EPfvlS8K0ywytjsnp9QmrLYzKmuqzfoe3JtDz71kp5c+X6cM342nLZe/sR8s9Fa6SpNQj3jFTEY7LrVnVSVx7P/uA8XBkrikh1RUxWN3V4ePQDd8hd/96YNKJyUMK9NZGUd1a3SElxRLarr8r6xJY1tcna5g4ZWVXS/YNsX0/u+e/0FU1t0ppIy4iqkl7fStSWSEvwW4WDr1UdVeX+jaWePyQS7llvd7iQcNd5sbqABYIPnN5xz8MSixWHCs0tbRKNFsmJ0w6SC2Yc16eMr7fKLFjWIEFZ7jiySiJBYW54BP9jFnRmEMFBaAe/fj74H/7tR2z6PzRBuAf/Q1EWi8o2WbzzGYwP5r2xskmCN6N3GFnt7Erb+Cv/cuUdd26VMd9eF7fKPPX6ivCHxO5HRiT4ATod/BQVXOCRsN2lriIuh3xgTPjue74+uMe9753t+vfGxPrBCfe2INzXtEi8uEi2y/LNjuDIg8Be29IhIyo3H+49/52+aG1z+CdXwQ+tlSWbXt/BpilvVQAAIABJREFUV40uWtsSBn02f3Jl+88G4a4TJNx1XqxGoFug560ywdc/7jllZ6kfVtO9hnDPnXDPpUu3piIWHk5DS6IzFHlkLRD8sJhIZ2Sr2jIpj3cGR/DuYfCnKhXxaBgjwZ/01JT1/075c++skfZkqvdrboj2jf9GNBKR3bcdLsXBHwXl4SP4YbasJCrrW5N5eHb2p0S4b/mWM1tlwl0nSLjrvFiNQL/hvt+082TO7HNkyuQd5LgZX5c33lksyWRKokVFEimKyLevOEMOPWB38eF73BcsbwzPM3jHfON33Bevawk7M3hXKAil4J2eiER6RU0qlZHmhC4Egjc7+4wpy2suuKUn1x7BPcXBI3yHl4dKIJnKhNdgcNtxl2Nwm0zno7O8A96uv9fX8PaOlOrnpeB6z8Vb2lRwm1kc/DMefHaHR2+B4uDf34P5M1tEpDS4/1D5KI0WSXwzv4ir69+D9Rs+FxJ8JiSdSUvw/5f2eF4qlZb2VDr8wXjruk1vg1QeVlbLCfesmLoXEe46L1YjYC3gRbhvuFWm58kua3z/Q0WpTEbWt/cd50GQBvcRax8DEdldUdf5+bLB/F/g/s+ecNdeGe+vT6bSYXQXFwWB3rmfXXsckSDqtxzuiWS6+znZHEm+h3twexqfk970Skilg39/RcLP3AxuuWckFtWHe/DPQyyLD9FWlxaHb7Z0PYLPcAS/VbmvB7fKZPNvh8FfQ7gPvjmvWOACXoT7hnfct6otD/+0oOvxxqqm8E3NsTVlksiILF7bEn7gc0SPDzC1JVOysqld4sURGVXV/4cEN74Ugg8sLW5oCe9xH1fr7o9n3wv+lCATzCzb7Luwg3lZDquKhy2wpqmDD6cq4f+7rEE60mnZvr5KKjfcKhP8SVDww2JwL3pTW0oqSopkWHn/H6oL3m3879J14fUb/jgXkQ370Me9MiKyz8SRUl3WeXtTvj2CRqwqjXV+mw6PboGuf2+MrSkN/9R0oB8dyVR4v3rwDv+Ymuz+nRkc09rWDlnflgyvz5rS/q/RZQ2t4SlMGN7579ZVze3SnkiFr1Wx4Z+jrnNsS6TC2894x32gd91sPuFu5sazEDAW8CLc+XCq8f5m80Q+nJqNUt9rXHw4tWty1wcQh5WXyO8XLJWWjuC+903jfc8J9TJ57PufWzE/8tx8Jh9O7Xtf+HAqH07NzX9i+VaZXN0XjiuPBQj3vjd3oL5VJhcvJcLdfFcGItwnjaiS4NavYHZjWzIM+MqSqOy9XX33V06aH3FuP5Nwz4394VtlcmMffDgK3nH3YZc4xrwS8CHcg69zDO4fCO5x3PjRmuj8UF/w14N7YoNbYoIbaYIPsfLIXoBwz96q58rBCPdIJBPeJrDr+Pe/K978iHP7mYR7bu+Pq6Pr+e/09kRagn+bB/e3B9+ctPEjuG2xLZWWIomE3+U+0A8+nKoTJtx1XqxGwFrAh3C3PkkGbFaAcDe/QAh3c7u+nkm4u/Vkml6AcNeZEe46L1YjYC1AuFsTej+AcDffQsLd3I5wd2vHNDcChLvOkXDXebEaAWsBwt2a0PsBhLv5FhLu5naEu1s7prkRINx1joS7zovVCFgLEO7WhN4PINzNt5BwN7cj3N3aMc2NAOGucyTcdV6sRsBagHC3JvR+AOFuvoWEu7kd4e7WjmluBAh3nSPhrvNiNQLWAoS7NaH3Awh38y0k3M3tCHe3dkxzI0C46xwJd50XqxGwFiDcrQm9H0C4m28h4W5uR7i7tWOaGwHCXedIuOu8WI2AtQDhbk3o/QDC3XwLCXdzO8LdrR3T3AgQ7jpHwl3nxWoErAUId2tC7wcQ7uZbSLib2xHubu2Y5kaAcNc5Eu46L1YjYC1AuFsTej+AcDffQsLd3I5wd2vHNDcChLvOkXDXebEaAWsBwt2a0PsBhLv5FhLu5naEu1s7prkRINx1joS7zovVCFgLEO7WhN4PINzNt5BwN7cj3N3aMc2NAOGucyTcdV6sRsBagHC3JvR+AOFuvoWEu7kd4e7WjmluBAh3nSPhrvNiNQLWAoS7NaH3Awh38y0k3M3tCHe3dkxzI0C46xwJd50XqxGwFiDcrQm9H0C4m28h4W5uR7i7tWOaGwHCXedIuOu8WI2AtQDhbk3o/QDC3XwLCXdzO8LdrR3T3AgQ7jpHwl3nxWoErAUId2tC7wcQ7uZbSLib2xHubu2Y5kaAcNc5Eu46L1YjYC1AuFsTej+AcDffQsLd3I5wd2vHNDcChLvOkXDXebEaAWsBwt2a0PsBhLv5FhLu5naEu1s7prkRINx1joS7zovVCFgLEO7WhN4PINzNt5BwN7cj3N3aMc2NAOGucyTcdV6sRsBagHC3JvR+AOFuvoWEu7kd4e7WjmluBAh3nSPhrvNiNQLWAoS7NaH3Awh38y0k3M3tCHe3dkxzI0C46xwJd50XqxGwFiDcrQm9H0C4m28h4W5uR7i7tWOaGwHCXedIuOu8WI2AtQDhbk3o/QDC3XwLCXdzO8LdrR3T3AgQ7jpHwl3nxWoErAUId2tC7wcQ7uZbSLib2xHubu2Y5kaAcNc5Eu46L1YjYC1AuFsTej+AcDffQsLd3I5wd2vHNDcChLvOkXDXebEaAWsBwt2a0PsBhLv5FhLu5naEu1s7prkRINx1joS7zovVCFgLEO7WhN4PINzNt5BwN7cj3N3aMc2NAOGucyTcdV6sRsBagHC3JvR+AOFuvoWEu7kd4e7WjmluBAh3nSPhrvNiNQIqgXUN6+WwUy6V808/Vo4/+sDwuYS7ijAvFxPu5ttKuJvbEe5u7ZjmRoBw1zkS7jovViOgErj8urvkuRcXyIwTDyfcVXL5vZhwN99fwt3cjnB3a8c0NwKEu86RcNd5sRqBrAWee2GBzJ03XyZuO04mTRhHuGctl/8LCXfzPe4Z7k3tCXl1aaOkMxmpr4xLOi1SXFQk8eKi8EWqSoqlrjze5wu+tiJ4nsikEVWSymQkmN3YlpRIJCPl8WLZdXyd1Jb1/VzzM8itZ8aiEamtjMvKhvbcOjCOpmAECHfdVhPuOi9WI5CVQCKRlOlnzpIbZ82Unz30JOGelVrhLCLczfe6Z7ivbemQl5esCwN8RGVcokWdwd71qC2PyeiqMsK9H3LC3fxa5JluBAh3nSPhrvNiNQJZCcy9e75kMhmZedo0uWbOvZsN96ryWFYzWZQ/AlVlxeHJNLUm8+ekBulMnn5thbQmkrL7hOEyrLxEEsm0vLK0QZLpjEwaWSnF0U3DPVYUkZLiaJ9H9+qG4N95TE34/KdfWy4NrQmJREQq4sWyR/AaFSWDdGZD8zLRiEhpPCrN7amhOQBeteAEmloSm5wz4a67BAh3nRerEdiiwDuLlsnFV8+V++ZeKfF4bMvhviHitjiYBXkj0PXDWs//AcubExzAE+kM95TsPmFYGO7B479LGyWZTstOo6sl1iPcN3cory5tkHQ6I++H+wppaO2QSFEQ7jHZY9theR/uRUWRMNxb2vghcgAvW0ZvJNDzDQvCXXd5EO46L1YjsEWBux94TO6452GJxTrfVW1uaZNotEhOnHaQXDDjOL5VZouC+b+AW2XM93hzH06dWN/7HffNvRL3uItwq4z5tcgz3QgQ7jpHwl3nxWoE1AJbulVGPZAneC9AuJtvIeFubtfXMwl3t55M0wsQ7jozwl3nxWoE1AKEu5os759AuJtvMeFubke4u7VjmhsBwl3nSLjrvFiNgLUAv4DJmtD7AYS7+RYS7uZ2hLtbO6a5ESDcdY6Eu86L1QhYCxDu1oTeDyDczbeQcDe3I9zd2jHNjQDhrnMk3HVerEbAWoBwtyb0fgDhbr6FhLu5HeHu1o5pbgQId50j4a7zYjUC1gKEuzWh9wMId/MtJNzN7Qh3t3ZMcyNAuOscCXedF6sRsBYg3K0JvR9AuJtvIeFubke4u7VjmhsBwl3nSLjrvFiNgLUA4W5N6P0Awt18Cwl3czvC3a0d09wIEO46R8Jd58VqBKwFCHdrQu8HEO7mW0i4m9sR7m7tmOZGgHDXORLuOi9WI2AtQLhbE3o/gHA330LC3dyOcHdrxzQ3AoS7zpFw13mxGgFrAcLdmtD7AYS7+RYS7uZ2hLtbO6a5ESDcdY6Eu86L1QhYCxDu1oTeDyDczbeQcDe3I9zd2jHNjQDhrnMk3HVerEbAWoBwtyb0fgDhbr6FhLu5HeHu1o5pbgQId50j4a7zYjUC1gKEuzWh9wMId/MtJNzN7Qh3t3ZMcyNAuOscCXedF6sRsBYg3K0JvR9AuJtvIeFubke4u7VjmhsBwl3nSLjrvFiNgLUA4W5N6P0Awt18Cwl3czvC3a0d09wIEO46R8Jd58VqBKwFCHdrQu8HEO7mW0i4m9sR7m7tmOZGgHDXORLuOi9WI2AtQLhbE3o/gHA330LC3dyOcHdrxzQ3AoS7zpFw13mxGgFrAcLdmtD7AYS7+RYS7uZ2hLtbO6a5ESDcdY6Eu86L1QhYCxDu1oTeDyDczbeQcDe3I9zd2jHNjQDhrnMk3HVerEbAWoBwtyb0fgDhbr6FhLu5HeHu1o5pbgQId50j4a7zYjUC1gKEuzWh9wMId/MtJNzN7Qh3t3ZMcyNAuOscCXedF6sRsBYg3K0JvR9AuJtvIeFubke4u7VjmhsBwl3nSLjrvFiNgLUA4W5N6P0Awt18C23DfXVzu6Qzna8f/PfgMbw8Lol0Rl5Z2iAdybREIhkpjxfLruPrpLYsbn6wHjwzFo1IbWVcVjZ0WvBAYLAFCHedOOGu82I1AtYChLs1ofcDCHfzLbQN99dWNEk6s6HcNzqMZDojb61aL0URkUhECHfzLeKZCKgECHcVlxDuOi9WI2AtQLhbE3o/gHA330LbcN/4HfeNjyKRSvOOu/m28EwEjAUIdx0d4a7zYjUC1gKEuzWh9wMId/Mt7Ar3MTWlUhYrDge1dCTD/yyNRaUoeLtcRMbVlkl0w3/P5tU6UmkJZje2JblVJhsw1iDgSIBw10ES7jovViNgLUC4WxN6P4BwN9/CrnCvK49LSXFRv4MmjqiS4uC+lywfhDv3uGd5qbDMsQDhrgMl3HVerEbAWoBwtyb0fgDhbr6FXeG+0+gqqS7t/4Oj5bGo6kUId8JddcGw2JkA4a6jJNx1XqxGwFqAcLcm9H4A4W6+hX3d424+7f1nEu6Eu4vriBl6AcJdZ0a467xYjYC1AOFuTej9AMLdfAsJd3O7vp7J10G69WSaXoBw15kR7jovViNgLUC4WxN6P4BwN99Cwt3cjnB3a8c0NwKEu86RcNd5sRoBawHC3ZrQ+wGEu/kWEu7mdoS7WzumuREg3HWOhLvOi9UIWAsQ7taE3g8g3M23kHA3tyPc3doxzY0A4a5zJNx1XqxGwFqAcLcm9H4A4W6+hYS7uR3h7taOaW4ECHedI+Gu82I1AtYChLs1ofcDCHfzLSTcze0Id7d2THMjQLjrHAl3nRerEbAWINytCb0fQLibbyHhbm5HuLu1Y5obAcJd50i467xYXeACLy94W665+R55692lMnpEnVx81vFywN4f6aWy8M1FcvWNd8sb7yyWrcaOlOsuP0N22G58uI5wL/CLSEQId/NrgHA3tyPc3doxzY0A4a5zJNx1XqwuYIFMJiNTp18kF874jBxx8F7y1LMvypdn3y7PPPw9KYnHumXS6YwcfspX5eRjD5bPfnqq/PKRp+QnD/5eHp53LeFewNfPxqdOuJtfCIS7uR3h7taOaW4ECHedI+Gu82J1AQu0tXfI4089L0cfuk+3wpRDZoRBPn7MiO6/tmTZKjn6tCvkuUe/L5FIJPzr+x9zvvzgxi/LpAnjece9gK+hrlMn3M0vAsLd3I5wd2vHNDcChLvOkXDXebEagVAgkUjKQ4/+We6b/0d58AezJRot6pZZumKNHPm5r8rzv7ujO9wPOeESuXTmiTJ13ym9wn1D2yNbQAKj68ok2Pdla1slkymgE3dwqs++vUrakmnZdXyt1JbFHUzsHNGRTEswu7EtKZFIRsrjxbLr+DqpK3f3Gs4O1uGgWFFEairjsqqx3eFURiHQv0DPf+cR7rqrhXDXebEaAfnTX1+Qc6/4royqr5M53zhXJu80YROV4JaaI0+9PLxN5oSjD5THnnpOrrjuLrn2sjPksKl79Ar34N1XHoUl0PXDGtGu3/cnXl0qLYmU7DtphAyvKNEP6OcZ7cm0PP7qUlnX0hH+UFVZEgtfo77S3Ws4O1iXgyIiwZ8Lci26RGXW5gSWrmnd5G8T7rrrhXDXebEagVAgmUrJ8y8skEu/eYfcf/tVMnZ0/SYywYdTv3HzPbJs5Ro5aN+Pykv/eVO+dOqnZd89JnOrDNcQH061uAa4VcYCr4+nxqIRqa2My8oG3nF3K8u0bAUI92ylOtcR7jovVhewwOq1jfLsP14NP5ja9fj8Bd+S6Ud+Inwnvb9HcFtNcI/7/B9/U0bW1xLuBXwNdZ0697ibXwSEu7ldX88k3N16Mk0vQLjrzAh3nRerC1igoalZDpp+kdw0a6bsu8cuEryr/rnzrpWf3HZF+KHTR558VvacsrPUD6uRz37pG/KlU4+WvXf7kHz/nl+H77jfef0loR5fB1nAF9GGUyfcza8Bwt3cjnB3a8c0NwKEu86RcNd5sbrABf7y93/LTXc8IEuWr5ba6ko54+Qj5djD9wtV9pt2nsyZfY5MmbyD/O2f/5HZN8+T4F36yR/YTq67bIaMGF5LuBf49cM77vYXgMtwT6bTsqShLTyodDot765tkYbWhHSk0lISK5IdRlTJDiOr7Q86hyfwjnsOb06BHBrhrttowl3nxWoErAV4x92a0PsBvONuvoUuw70jlZG3VjV1H8yKpjZZ15qQtkQqDPcJwytkl7F15gfrwTMJdw82Kc8PkXDXbTDhrvNiNQLWAoS7NaH3Awh38y10Ge7pTCb8asmux1ur18uKxjZZ35GUslhUJo+pkdHV+f2tT4S7+bXIM90IEO46R8Jd58VqBKwFCHdrQu8HEO7mW+gy3IOjWNPSIal055fpB/99eWNr+J8lxVHZcWSVbDusQoqKOn+RWj4+CPd83FW/zolw1+0X4a7zYjUC1gKEuzWh9wMId/MtdB3ub6xskuSGcA+OKrhdZnlTm8SiRbJ1XbnsMq5Oigl38w3jmQhsQYBw110ihLvOi9UIWAsQ7taE3g8g3M230HW4b/yOe3BU761rkf+taeYdd/Mt4pkIqAQIdxUX3+Ou42I1AvYChLu9oe8TCHfzHXQd7j2P5K1V62Xh8kYpjUdl1/F1UlsWNz9YD57JrTIebFKeHyLhrttg3nHXebEaAWsBwt2a0PsBhLv5FhLu5nZ9PZNwd+vJNL0A4a4zI9x1XqxGwFqAcLcm9H4A4W6+hYS7uR3h7taOaW4ECHedI+Gu82I1AtYChLs1ofcDCHfzLSTcze0Id7d2THMjQLjrHAl3nRerEbAWINytCb0fQLibb+FAhHvwi5iC36IaPBavaZE3VjVJvLhIdh5TIzVlcQluJ4kVFZkfdA4/k1tlcnhzCuTQCHfdRhPuOi9WI2AtQLhbE3o/gHA338KBCPfg6x/XtnSEB9XakZJlja0SZPyY6lIpjxfLyKpSGVaenx9SJdzNr0We6UaAcNc5Eu46L1YjYC1AuFsTej+AcDffwoEI9yUNbbK0sTU8qOCXMTW0dkginZHq0lj4G1SHV8TDX8SUjw/CPR931a9zItx1+0W467xYjYC1AOFuTej9AMLdfAsHItwb2xOyZF1nuAePINxbE2mpLi0O33GvKo3JuJoy84PO4WcS7jm8OQVyaIS7bqMJd50XqxGwFiDcrQm9H0C4m2/hQIR7KpOR9mTnPe7BI7h1pqktISMqS8J73KMSkZIY97ib7xrPRKB/AcJdd3UQ7jovViNgLUC4WxN6P4BwN9/CgQj3nkeztKFVGtoSMrq6lF/AZL5VPBOBrAQI96yYuhcR7jovViNgLUC4WxN6P4BwN9/CgQr31c3t0tyRCg+sI5kOv2Um+GaZ4g3fJhN8OLWypNj8wHP0mdwqk6MbU0CHRbjrNptw13mxGgFrAcLdmtD7AYS7+RYOVLgH3ySzrjXR74GNri6T2rKY+YHn6DMJ9xzdmAI6LMJdt9mEu86L1QhYCxDu1oTeDyDczbdwoMJ94+9y7+vo4tHg3feI+YHn6DMJ9xzdmAI6LMJdt9mEu86L1QhYCxDu1oTeDyDczbdwoMLd/Ij8fibh7vf+5cPRE+66XSTcdV6sRsBagHC3JvR+AOFuvoWEu7ldX88k3N16Mk0vQLjrzAh3nRerEbAWINytCb0fQLibbyHhbm5HuLu1Y5obAcJd50i467xYjYC1AOFuTej9AMLdfAsJd3M7wt2tHdPcCBDuOkfCXefFagSsBQh3a0LvBxDu5ltIuJvbEe5u7ZjmRoBw1zkS7jovViNgLUC4WxN6P4BwN99Cwt3cjnB3a8c0NwKEu86RcNd5sRoBawHC3ZrQ+wGEu/kWEu7mdoS7WzumuREg3HWOhLvOi9UIWAsQ7taE3g8g3M23kHA3tyPc3doxzY0A4a5zJNx1XqxGwFqAcLcm9H4A4a7bwvZkWlKZTPikFxetlY5UWj4wulqqSjt/k2lJcZFEI/n3y5F0Smar+TpIMzee5U6AcNdZEu46L1YjYC1AuFsTej+AcNdt4eJ1rdLUngiftKyxLfzPUVUlEtkQ62Nry6S6pDPieegECHedF6vdCxDuOlPCXefFagSsBQh3a0LvBxDuui1c2dwurR2p8EnvrWsJ/3NsTZkUbQj3+ooSKY9HdUNZHQoQ7lwIQy1AuOt2gHDXebEaAWsBwt2a0PsBhLv5Fi5c3ijBTTM7jKzqDnfzaTyTcOcaGGoBwl23A4S7zovVCFgLEO7WhN4PINzNt5BwN7fr65mEu1tPpukFCHedGeGu82I1AtYChLs1ofcDCHfzLSTcze0Id7d2THMjQLjrHAl3nRerEbAWINytCb0fQLjrtnBVc7ts+FIZWd3cHj55eEVcRDq/SWZEZYluIKu7BXjHnYthqAUId90OEO46L1YjYC1AuFsTej+AcNdt4cIVTZLpKvc+nsr97jrPjVcT7uZ2PNONAOGucyTcdV6sLnCBlxe8LdfcfI+89e5SGT2iTi4+63g5YO+P9FJZ8Ma7MvumebJmXZOUlsTl4rOmy7577BKuI9wL/CISEcJddw1s/I57X8/kHXedJ+Fu7sUz3QsQ7jpTwl3nxeoCFgje8Zs6/SK5cMZn5IiD95Knnn1Rvjz7dnnm4e9JSXzT75A+6vNXyFmnHCWHTd1Dgoj/3HnXylMPzpHyslLCvYCvoa5TJ9y5CHJFgHfcc2UnCvc4CHfd3hPuOi9WF7BAW3uHPP7U83L0oft0K0w5ZIY8PO9aGT9mRPdfCwJ/l6lfkD//6rtSV1MV/vW9j5op9956hWy/zVjCvYCvIcKdzc81AcI913ak8I6HcNftOeGu82I1AqFAIpGUhx79s9w3/4/y4A9mSzRatInM6Rd9Rw7efzc54egD5V8vvyZf/ead8tuffFtixVHCnWuIW2W4BnJGgHDPma0o2AMh3HVbT7jrvFiNgPzpry/IuVd8V0bV18mcb5wrk3ea0Etl4ZuL5LQLvxX+SvaW1na54cqzZeq+U8J1Pe9xr6ngV7UX2mVVUVocnnJzW7LQTp3zzTGBoohISSza/Ztpc+zwOJw8FGhoTmxyVoS7bpMJd50XqxEIBZKplDz/wgK59Jt3yP23XyVjR9d3y7R3JOSIz10mX7/oVPn47pPDD7KedsG35N5bL5etx43qFe5dEQdt4Qh0/bDW83/ACkeAM80VgWhRRMpKorK+lR8ic2VP8v04er5hQbjrdpxw13mxuoAFVq9tlGf/8Wr4wdSux+cv+JZMP/IT4YdQux7/ff1/ctalN8nTD93S/de+eMn1ctQhe8tRh+zDrTIFfA11nTofTuUiyBUBbpXJlZ0o3OMg3HV7T7jrvFhdwAINTc1y0PSL5KZZM8Ovdgxuhwm+LeYnt10hkyaMl0eefFb2nLKzxOMxmfqZC+WHN35Fdtl5e1m5ep1M+8KVctcNl8gHJm1DuBfwNUS4s/m5JkC459qOFN7xEO66PSfcdV6sLnCBv/z933LTHQ/IkuWrpba6Us44+Ug59vD9QpX9pp0nc2afI1Mm7yBPP/uS3PKDX4b3twcfXD3luEPCD6oGD77HvcAvIr7HnQsghwQI9xzajAI9FMJdt/GEu86L1QhYCxDu1oTeD+BWGe+3MG9OgHDPm6309kQId93WEe46L1YjYC1AuFsTej+AcPd+C/PmBAj3vNlKb0+EcNdtHeGu82I1AtYChLs1ofcDCHfvtzBvToBwz5ut9PZECHfd1hHuOi9WI2AtQLhbE3o/gHD3fgvz5gQI97zZSm9PhHDXbR3hrvNiNQLWAoS7NaH3Awh377cwb06AcM+brfT2RAh33dYR7jovViNgLUC4WxN6P4Bw934L8+YECPe82UpvT4Rw120d4a7zYjUC1gKEuzWh9wMId++3MG9OgHDPm6309kQId93WEe46L1YjYC1AuFsTej+AcPd+C/PmBAj3vNlKb0+EcNdtHeGu82I1AtYChLs1IQMQQAABBPJEgHDXbSThrvNiNQLWAoS7NSEDEEAAAQTyRIBw120k4a7zYjUC1gKEuzUhAxBAAAEE8kSAcNdtJOGu82I1AtYChLs1IQMQQAABBPJEgHDXbSThrvNiNQIIIIAAAggggAACQyJAuA8JOy+KAAIIIIAAAggggIBOgHDXebEaAQQQQAABBBBAAIEhESDch4SdF0UAAQQQQAABBBBAQCdAuOu8WI0AAghYCdz100dk3gOPSzKVksOm7ilXnHeyRKNFVjN5MgJagY6OhOx6yAyJxYq7n3rgPruE00CWAAAJaElEQVTKTbNmakexHgEEBlGAcB9EbF4KAQQKW+Bv//yPfO07P5R5t1wmNVUVcvZXb5bDpu4hn/301MKG4ewHXWDVmgY5+rQr5Jlf3zbor80LIoCAuQDhbm7HMxFAAAGVwOyb75ExI4fJjJOOCJ/3p7++EL77fvecr6rmsBgBW4G3310a/uD42M++YzuK5yOAwCAKEO6DiM1LIYBAYQucfvF35ISjD5SD99sthAji6bQLvy1PPTinsGE4+0EX+Pd/3pRzv/ZdmbD1GHn97fdkx+22kqsuOlW23Wr0oB8LL4gAAtkLEO7ZW7ESAQQQsBI4aeY1cuYpR8p+e344nLNk2Sr59Be+Js89+n2ruTwZAa3Am+8slnt++YScOO0gmbDVaLn9noflT8+8IPN/fI12FOsRQGAQBQj3QcTmpRBAoLAFvnjJ9XLMp/YL72sPHgvfXCRnfuVG3nEv7MsiJ84++LD0boeeIY/fd4OMGlGXE8fEQSCAQG8Bwp2rAgEEEBgkgW/ecq/UVlfKzNOmha/46B/+Lg/+9mn54U1fGaQj4GUQ6BRYuXqdNDQ2y8QJ48L/P5FIykc/eYY8/dAtUldTBRMCCOSoAOGeoxvDYSGAQP4J/Ovl1+Qr3/i+3PPdy6WiokzOuOQGmX7UJ+TYw/fLv5PljHJa4K//eEW+9u0fyr23XiGjRwyT2+f9Wv7v+Zfl/tuvyunj5uAQKHQBwr3QrwDOHwEEBlVg3i8elx/89BFJJFPy6U9+XC6d+VmJRCKDegy8GAKBwI/v/53c++AT0tbWIZM/sF344dRxo+vBQQCBHBYg3HN4czg0BBBAAAEEEEAAAQS6BAh3rgUEEEAAAQQQQAABBDwQINw92CQOEQEEEEAAAQQQQAABwp1rAAEEEEAAAQQQQAABDwQIdw82iUNEAAEEEEAAAQQQQIBw5xpAAAEEEEAAAQQQQMADAcLdg036//btP8brOQ7g+CtTxiSS6ai4KInWZqQYZX6Fw2yxi2FK+iFarV9Sq/VDKb9SrpyLxlSmH9IshM1aSCpqaGvTqF250qRS6Hb2/Wzftkphu7N7n8f9d91333t9H6/XH8+++54RCRAgQIAAAQIECAh3N0CAAAECBAgQIEAgAQHhnsCSjEiAAAECBAgQIEBAuLsBAgQIECBAgAABAgkICPcElmREAgQIECBAgAABAsLdDRAgQIAAAQIECBBIQEC4J7AkIxIgQIAAAQIECBAQ7m6AAAECBAgQIECAQAICwj2BJRmRAAECBAgQIECAgHB3AwQIECBAgAABAgQSEBDuCSzJiAQIECBAgAABAgSEuxsgQIAAgSQEftnza3Qq6hdvvTI+WhU2q/GZ/+vfV+MvyC8gQCB5AeGe/Aq9AAIECPw/BP7rkD5QWRlr1m2Mi9sUxkknnvD/QPYqCRCo1QLCvVavx3AECBAgkBf4N+FeVVUVVVURxx1XDyABAgTqjIBwrzOr9EIIECBQtwXy4T5xRK94ee7S2FxeEW1bnxu575sVnBEfrVgbU0rmRreiLjH9lUUxt2RUtG7ZPJ4tfTOWLPskdu3eG4XNm8bQh7tHx0vaZlh39R4Tt15/RXz+5YbY+N2WOHDgQAzuWxxdr+kQh/9H4ViPrdvyXh0BArVFQLjXlk2YgwABAgSOKZAP6XYXtowJwx+Mxqc2jOETSrPAzkX68pXrYsi4mdG1S4fofd9t0eS0U2Lxeytiatn8mD31sTi7aZOYs+iDKHv9nfh44dSoX//4KO47Nnbs3BUvTRkchS0KYs6iD2ParAXxyZIXYvfefYd8pv5Yj61Xzzv7zpcAgZoXEO41b+w3ECBAgEA1COTDffywnnHHTVdlz7hy7bfRY+CTsWLx9Fi/YVP0GfZ0LJv3VJzVtEn2899+/yN+3bc/TmvUMPv+51174srb+8eSVydGyxYFWbi3a1MYjw+4N/t57l38rncPzcK+QYP6R4T70R7bpHGjaniFnoIAAQLHFhDuLoQAAQIEkhDIh/ucklHRvu15h4T2wlnjomLHz9F/xHPx1YezDr6eXb/sjefK5seqLzfE/v2/Zf++tWJnLCgbG23Ob5GF+w2dL40exTdnP9u2fWdce+egeH/eU9Hw5JOOCPejPTb3br4vAgQI1LSAcK9pYc9PgAABAtUikA/3eTNHZ++S5762bN0eN3YfEu+8Nik2l2+PgaOnxRfvlh78fcOfKI3vt/wYz497JM44/dTYs3dfXH5L30PC/cbOl8UDxTf9o3A/2mOFe7Ws2JMQIPA3AsLdiRAgQIBAEgL5cH9yZO8ouq5TNnP+ozKrls6M1es2HhHuuajvdU9RdCvqnD3+szXfRM9Bk4V7Ehs3JAEChwsIdzdBgAABAkkI5MP9knatYvLIPtlHWXJ/jFpZWRmlUwbH8pXrjwj3+wdMjIIzT48nhveKTT+Ux5QZb8SnX3wd0yY8Gld3bJ99VMY77kms35AECESEcHcGBAgQIJCEQP4PS58Z0y9emL04yrdtj4suKIxJIx7K4vyvwj33B6uPT3wptlb8FBe2OifGD3swXnzt7fhg+eqYMWlQTC6ZK9yT2L4hCRDICQh3d0CAAAECBAgQIEAgAQHhnsCSjEiAAAECBAgQIEBAuLsBAgQIECBAgAABAgkICPcElmREAgQIECBAgAABAsLdDRAgQIAAAQIECBBIQEC4J7AkIxIgQIAAAQIECBAQ7m6AAAECBAgQIECAQAICwj2BJRmRAAECBAgQIECAgHB3AwQIECBAgAABAgQSEBDuCSzJiAQIECBAgAABAgSEuxsgQIAAAQIECBAgkICAcE9gSUYkQIAAAQIECBAgINzdAAECBAgQIECAAIEEBIR7AksyIgECBAgQIECAAAHh7gYIECBAgAABAgQIJCAg3BNYkhEJECBAgAABAgQICHc3QIAAAQIECBAgQCABAeGewJKMSIAAAQIECBAgQEC4uwECBAgQIECAAAECCQgI9wSWZEQCBAgQIECAAAECwt0NECBAgAABAgQIEEhAQLgnsCQjEiBAgAABAgQIEBDuboAAAQIECBAgQIBAAgLCPYElGZEAAQIECBAgQICAcHcDBAgQIECAAAECBBIQEO4JLMmIBAgQIECAAAECBIS7GyBAgAABAgQIECCQgIBwT2BJRiRAgAABAgQIECAg3N0AAQIECBAgQIAAgQQEhHsCSzIiAQIECBAgQIAAgT8BIEt4uMHy+vEAAAAASUVORK5CYII="
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"from ax.plot.pareto_frontier import plot_pareto_frontier\n",
"from ax.plot.pareto_utils import compute_posterior_pareto_frontier\n",
@@ -1126,25 +1287,15 @@
" absolute_metrics=[\"branin\", \"currin\"],\n",
")\n",
"render(plot_pareto_frontier(frontier, CI_level=0.90))"
- ],
- "execution_count": 15,
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "image/png": 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"
- },
- "metadata": {}
- }
]
},
{
"cell_type": "markdown",
"metadata": {
- "originalKey": "b9127c73-b1b6-4e46-b593-43068d5015a8",
- "showInput": false,
"code_folding": [],
- "hidden_ranges": []
+ "hidden_ranges": [],
+ "originalKey": "b9127c73-b1b6-4e46-b593-43068d5015a8",
+ "showInput": false
},
"source": [
"### Appendix: Using `ScalarizedPosteriorTransform`\n",
@@ -1154,25 +1305,34 @@
},
{
"cell_type": "code",
+ "execution_count": 16,
"metadata": {
- "originalKey": "79a4b36f-4b14-4a62-9dc6-883931ceb5d3",
"code_folding": [],
- "hidden_ranges": [],
"collapsed": false,
- "requestMsgId": "490a2b3a-24f9-4b49-84f1-7a3851d5944f",
- "executionStopTime": 1668651301442,
+ "customOutput": null,
"executionStartTime": 1668651301431,
- "customOutput": null
+ "executionStopTime": 1668651301442,
+ "hidden_ranges": [],
+ "originalKey": "79a4b36f-4b14-4a62-9dc6-883931ceb5d3",
+ "requestMsgId": "490a2b3a-24f9-4b49-84f1-7a3851d5944f"
},
+ "outputs": [],
"source": [
"from botorch.acquisition.objective import ScalarizedPosteriorTransform\n",
"from botorch.acquisition.analytic import UpperConfidenceBound\n",
"\n",
"pt = ScalarizedPosteriorTransform(weights=torch.tensor([0.1, 0.5]))\n",
"SUCB = UpperConfidenceBound(gp, beta=0.1, posterior_transform=pt)"
- ],
- "execution_count": 16,
- "outputs": []
+ ]
}
- ]
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "python3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/tutorials/custom_botorch_model_in_ax.ipynb b/tutorials/custom_botorch_model_in_ax/custom_botorch_model_in_ax.ipynb
similarity index 99%
rename from tutorials/custom_botorch_model_in_ax.ipynb
rename to tutorials/custom_botorch_model_in_ax/custom_botorch_model_in_ax.ipynb
index 5f25bbc390..9c3aa59393 100644
--- a/tutorials/custom_botorch_model_in_ax.ipynb
+++ b/tutorials/custom_botorch_model_in_ax/custom_botorch_model_in_ax.ipynb
@@ -23,7 +23,26 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "import plotly.io as pio\n",
+ "if 'google.colab' in sys.modules:\n",
+ " pio.renderers.default = \"colab\"\n",
+ " %pip install botorch ax\n",
+ "else:\n",
+ " # Ax uses Plotly to produce interactive plots. These are great for viewing and analysis,\n",
+ " # though they also lead to large file sizes, which is not ideal for files living in GH.\n",
+ " # Changing the default to `png` strips the interactive components to get around this.\n",
+ " pio.renderers.default = \"png\"\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"customInput": null,
@@ -48,15 +67,8 @@
"import os\n",
"from contextlib import contextmanager, nullcontext\n",
"\n",
- "import plotly.io as pio\n",
- "\n",
"from ax.utils.testing.mock import mock_botorch_optimize_context_manager\n",
"\n",
- "# Ax uses Plotly to produce interactive plots. These are great for viewing and analysis,\n",
- "# though they also lead to large file sizes, which is not ideal for files living in GH.\n",
- "# Changing the default to `png` strips the interactive components to get around this.\n",
- "pio.renderers.default = \"png\"\n",
- "\n",
"SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n",
"NUM_EVALS = 10 if SMOKE_TEST else 30"
]
diff --git a/tutorials/custom_model.ipynb b/tutorials/custom_model/custom_model.ipynb
similarity index 99%
rename from tutorials/custom_model.ipynb
rename to tutorials/custom_model/custom_model.ipynb
index 0e69ea8c29..54de751106 100644
--- a/tutorials/custom_model.ipynb
+++ b/tutorials/custom_model/custom_model.ipynb
@@ -13,6 +13,18 @@
"This tutorial differs from the [Using a custom BoTorch model with Ax](https://botorch.org/tutorials/custom_botorch_model_in_ax) tutorial by focusing more on authoring a new model that is compatible with the BoTorch and less on integrating a custom model with Ax's `botorch_modular` API."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/tutorials/decoupled_mobo.ipynb b/tutorials/decoupled_mobo/decoupled_mobo.ipynb
similarity index 97%
rename from tutorials/decoupled_mobo.ipynb
rename to tutorials/decoupled_mobo/decoupled_mobo.ipynb
index 855f70974d..79891cd014 100644
--- a/tutorials/decoupled_mobo.ipynb
+++ b/tutorials/decoupled_mobo/decoupled_mobo.ipynb
@@ -1,33 +1,11 @@
{
- "metadata": {
- "kernelspec": {
- "name": "python3",
- "display_name": "python3",
- "language": "python",
- "isCinder": true
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.11"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
"cells": [
{
"cell_type": "markdown",
"metadata": {
"originalKey": "61330204-a407-449f-af77-fcd868546651",
- "showInput": false,
- "outputsInitialized": false
+ "outputsInitialized": false,
+ "showInput": false
},
"source": [
"## Multi-Objective BO with Decoupled Evaluations using HVKG\n",
@@ -50,8 +28,8 @@
"cell_type": "markdown",
"metadata": {
"originalKey": "f01416cb-e83e-4263-b599-b2a3221d144d",
- "showInput": false,
- "outputsInitialized": false
+ "outputsInitialized": false,
+ "showInput": false
},
"source": [
"### Set dtype and device\n",
@@ -60,54 +38,66 @@
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install dependencies if we are running in colab\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " %pip install botorch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
"metadata": {
+ "collapsed": false,
+ "customOutput": null,
"executionStartTime": 1699627780155,
"executionStopTime": 1699627787200,
"originalKey": "fc7f5adf-9bff-4fe5-8c24-5faad8b7ad01",
- "requestMsgId": "a61c88db-2b5e-4aa7-935f-ac82c1bbc845",
- "collapsed": false,
- "customOutput": null,
- "outputsInitialized": false,
"output": {
"id": "804505111685014"
- }
+ },
+ "outputsInitialized": false,
+ "requestMsgId": "a61c88db-2b5e-4aa7-935f-ac82c1bbc845"
},
- "source": [
- "import os\n",
- "\n",
- "import torch\n",
- "\n",
- "\n",
- "tkwargs = {\n",
- " \"dtype\": torch.double,\n",
- " \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
- "}\n",
- "SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")"
- ],
- "execution_count": 1,
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"I1110 064940.229 _utils_internal.py:230] NCCL_DEBUG env var is set to None\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"I1110 064940.231 _utils_internal.py:239] NCCL_DEBUG is INFO from /etc/nccl.conf\n"
]
}
+ ],
+ "source": [
+ "import os\n",
+ "\n",
+ "import torch\n",
+ "\n",
+ "\n",
+ "tkwargs = {\n",
+ " \"dtype\": torch.double,\n",
+ " \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
+ "}\n",
+ "SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"originalKey": "8d895a93-397c-4d2c-b6f5-96f589312538",
- "showInput": false,
- "outputsInitialized": false
+ "outputsInitialized": false,
+ "showInput": false
},
"source": [
"### Problem setup\n",
@@ -117,15 +107,17 @@
},
{
"cell_type": "code",
+ "execution_count": 2,
"metadata": {
+ "collapsed": false,
+ "customOutput": null,
"executionStartTime": 1699627787229,
"executionStopTime": 1699627791232,
"originalKey": "32104fee-5b27-41b3-9007-5a55d04235d3",
- "requestMsgId": "a5b45d06-0f88-4371-bde5-bff6aaff5aab",
- "collapsed": false,
- "customOutput": null,
- "outputsInitialized": false
+ "outputsInitialized": false,
+ "requestMsgId": "a5b45d06-0f88-4371-bde5-bff6aaff5aab"
},
+ "outputs": [],
"source": [
"from botorch.test_functions.multi_objective import ZDT2\n",
"from botorch.models.cost import FixedCostModel\n",
@@ -141,16 +133,14 @@
" [objective_costs[k] for k in sorted(objective_costs.keys())], **tkwargs\n",
")\n",
"cost_model = FixedCostModel(fixed_cost=objective_costs_t)"
- ],
- "execution_count": 2,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
"metadata": {
"originalKey": "fc047039-c2a9-4ea8-920e-c057547cfb11",
- "showInput": false,
- "outputsInitialized": false
+ "outputsInitialized": false,
+ "showInput": false
},
"source": [
"#### Model initialization\n",
@@ -160,15 +150,17 @@
},
{
"cell_type": "code",
+ "execution_count": 3,
"metadata": {
+ "collapsed": false,
+ "customOutput": null,
"executionStartTime": 1699627791256,
"executionStopTime": 1699627791262,
"originalKey": "3ecef619-db90-4676-8c8d-99bbf915a0fa",
- "requestMsgId": "d9e8e1fe-509f-4b25-8cf1-bcf16ebb380f",
- "collapsed": false,
- "customOutput": null,
- "outputsInitialized": false
+ "outputsInitialized": false,
+ "requestMsgId": "d9e8e1fe-509f-4b25-8cf1-bcf16ebb380f"
},
+ "outputs": [],
"source": [
"from botorch.models.gp_regression import SingleTaskGP\n",
"from botorch.models.model_list_gp_regression import ModelListGP\n",
@@ -214,16 +206,14 @@
" model = ModelListGP(*models)\n",
" mll = SumMarginalLogLikelihood(model.likelihood, model)\n",
" return mll, model"
- ],
- "execution_count": 3,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
"metadata": {
"originalKey": "6bfaef9a-3f34-4d51-9700-fbddc79eccf1",
- "showInput": false,
- "outputsInitialized": false
+ "outputsInitialized": false,
+ "showInput": false
},
"source": [
"#### Define a helper functions that performs the essential BO step for $q$NEHVI and HVKG\n",
@@ -236,15 +226,17 @@
},
{
"cell_type": "code",
+ "execution_count": 4,
"metadata": {
+ "collapsed": false,
+ "customOutput": null,
"executionStartTime": 1699627791276,
"executionStopTime": 1699627791277,
"originalKey": "758fad16-635b-4e2c-a5ae-5fa6d43ae569",
- "requestMsgId": "4305049b-e57a-4c3b-85cb-7d83bc1e3b42",
- "collapsed": false,
- "customOutput": null,
- "outputsInitialized": false
+ "outputsInitialized": false,
+ "requestMsgId": "4305049b-e57a-4c3b-85cb-7d83bc1e3b42"
},
+ "outputs": [],
"source": [
"from botorch.acquisition.multi_objective.monte_carlo import (\n",
" qNoisyExpectedHypervolumeImprovement,\n",
@@ -284,22 +276,21 @@
" new_x = unnormalize(candidates.detach(), bounds=problem.bounds)\n",
" new_obj_true = problem(new_x)\n",
" return new_x, new_obj_true"
- ],
- "execution_count": 4,
- "outputs": []
+ ]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
+ "collapsed": false,
+ "customInput": null,
+ "customOutput": null,
"executionStartTime": 1692892083921,
"executionStopTime": 1692892083976,
"originalKey": "e4fb82e5-bdc8-44e4-846c-a3ecf7620a8a",
+ "outputsInitialized": false,
"requestMsgId": "e4fb82e5-bdc8-44e4-846c-a3ecf7620a8a",
- "collapsed": false,
- "customOutput": null,
- "showInput": false,
- "customInput": null,
- "outputsInitialized": false
+ "showInput": false
},
"source": [
"### Helper Function for initializing and optimizing HVKG\n",
@@ -307,22 +298,23 @@
"Below we define the following helper functions:\n",
"1. `get_current_value` for computing the current hypervolume of the hypervolume maximizing set under the posterior mean.\n",
"2. `optimize_HVKG_and_get_obs_decoupled` to initialize and optimize HVKG to determine which design to evaluate and which objective to evaluate the design on. This method obtains the observation corresponding to that design."
- ],
- "attachments": {}
+ ]
},
{
"cell_type": "code",
+ "execution_count": 5,
"metadata": {
- "originalKey": "0f8cbe2d-1901-4f06-b10a-8ecb7cd9d71a",
- "showInput": true,
- "customInput": null,
"collapsed": false,
- "requestMsgId": "5d9f944b-8c7f-4585-bac5-4d7212b915ff",
+ "customInput": null,
+ "customOutput": null,
"executionStartTime": 1699627791283,
"executionStopTime": 1699627791294,
+ "originalKey": "0f8cbe2d-1901-4f06-b10a-8ecb7cd9d71a",
"outputsInitialized": false,
- "customOutput": null
+ "requestMsgId": "5d9f944b-8c7f-4585-bac5-4d7212b915ff",
+ "showInput": true
},
+ "outputs": [],
"source": [
"from botorch.acquisition.cost_aware import InverseCostWeightedUtility\n",
"from botorch.acquisition.multi_objective.hypervolume_knowledge_gradient import (\n",
@@ -416,35 +408,34 @@
" new_obj = problem(new_x)\n",
" new_obj = new_obj[..., eval_objective_indices]\n",
" return new_x, new_obj, eval_objective_indices"
- ],
- "execution_count": 5,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
"metadata": {
- "originalKey": "678281c4-f8a1-420e-b4be-6a4ee682a2e8",
- "showInput": false,
"customInput": null,
- "outputsInitialized": false
+ "originalKey": "678281c4-f8a1-420e-b4be-6a4ee682a2e8",
+ "outputsInitialized": false,
+ "showInput": false
},
"source": [
- "## Define function to find model-estimated Pareto set of designs under posterior mean using NSGA-II\n",
- ""
+ "## Define function to find model-estimated Pareto set of designs under posterior mean using NSGA-II\n"
]
},
{
"cell_type": "code",
+ "execution_count": 6,
"metadata": {
- "originalKey": "6f682eec-e4f4-4c3f-935a-46dfd3e7801e",
- "showInput": true,
- "customInput": null,
"collapsed": false,
- "requestMsgId": "a086a9e6-4eb3-4f5b-9e08-a5f868fa47e1",
+ "customInput": null,
"executionStartTime": 1699627791311,
"executionStopTime": 1699627791486,
- "outputsInitialized": false
+ "originalKey": "6f682eec-e4f4-4c3f-935a-46dfd3e7801e",
+ "outputsInitialized": false,
+ "requestMsgId": "a086a9e6-4eb3-4f5b-9e08-a5f868fa47e1",
+ "showInput": true
},
+ "outputs": [],
"source": [
"import numpy as np\n",
"from botorch.utils.multi_objective.box_decompositions.non_dominated import (\n",
@@ -549,16 +540,14 @@
" # compute HV\n",
" partitioning = FastNondominatedPartitioning(ref_point=problem.ref_point, Y=Y)\n",
" return partitioning.compute_hypervolume().item()"
- ],
- "execution_count": 6,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
"metadata": {
"originalKey": "f8ffd86a-f4bb-4984-9330-04ff4d62f189",
- "showInput": false,
- "outputsInitialized": false
+ "outputsInitialized": false,
+ "showInput": false
},
"source": [
"### Perform Bayesian Optimization loop with Decoupled HVKG and compared against non-decoupled $q$NEHVI\n",
@@ -572,18 +561,29 @@
},
{
"cell_type": "code",
+ "execution_count": 7,
"metadata": {
+ "collapsed": false,
+ "customOutput": null,
"executionStartTime": 1699627791496,
"executionStopTime": 1699629252322,
"originalKey": "3189ba56-cc8c-4c78-95ab-9bb702f11ecb",
- "requestMsgId": "2a400c57-e37a-4a7b-9758-81570d204538",
- "collapsed": false,
- "customOutput": null,
- "outputsInitialized": true,
"output": {
"id": "913932473488501"
- }
+ },
+ "outputsInitialized": true,
+ "requestMsgId": "2a400c57-e37a-4a7b-9758-81570d204538"
},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Initial: Hypervolume (random, qHVKG, qNEHVI) = (89.34, 89.34, 89.34)."
+ ]
+ }
+ ],
"source": [
"import time\n",
"import warnings\n",
@@ -736,24 +736,14 @@
" print(\".\", end=\"\")\n",
" iteration += 1\n",
" active_algos = {k for k, v in total_cost.items() if v < COST_BUDGET}"
- ],
- "execution_count": 7,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\nInitial: Hypervolume (random, qHVKG, qNEHVI) = (89.34, 89.34, 89.34)."
- ]
- }
]
},
{
"cell_type": "markdown",
"metadata": {
"originalKey": "72232578-0908-4292-aca4-d52197890dd6",
- "showInput": false,
- "outputsInitialized": false
+ "outputsInitialized": false,
+ "showInput": false
},
"source": [
"#### Plot the cost vs inference regret\n",
@@ -764,18 +754,41 @@
},
{
"cell_type": "code",
+ "execution_count": 21,
"metadata": {
+ "collapsed": false,
+ "customOutput": null,
"executionStartTime": 1699632303324,
"executionStopTime": 1699632303816,
"originalKey": "0187c932-1a45-45d2-82f9-9f96853eff1c",
- "requestMsgId": "ad28407c-5b74-4ace-8a6c-dd347fd5571d",
- "customOutput": null,
- "collapsed": false,
- "outputsInitialized": true,
"output": {
"id": "1029368728310675"
- }
+ },
+ "outputsInitialized": true,
+ "requestMsgId": "ad28407c-5b74-4ace-8a6c-dd347fd5571d"
},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | Generation Step | \n", + "Generation Model | \n", + "Trial Index | \n", + "Trial Status | \n", + "Arm Parameterizations | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "Sobol | \n", + "0 | \n", + "COMPLETED | \n", + "{'0_0': {'x1': 0.63, 'x2': 0.51}} | \n", + "
| 1 | \n", + "0 | \n", + "Sobol | \n", + "1 | \n", + "COMPLETED | \n", + "{'1_0': {'x1': 0.43, 'x2': 0.4}} | \n", + "
| 2 | \n", + "0 | \n", + "Sobol | \n", + "2 | \n", + "COMPLETED | \n", + "{'2_0': {'x1': 0.08, 'x2': 0.93}} | \n", + "
| 3 | \n", + "0 | \n", + "Sobol | \n", + "3 | \n", + "COMPLETED | \n", + "{'3_0': {'x1': 0.86, 'x2': 0.04}} | \n", + "
| 4 | \n", + "0 | \n", + "Sobol | \n", + "4 | \n", + "COMPLETED | \n", + "{'4_0': {'x1': 0.95, 'x2': 0.81}} | \n", + "
| 5 | \n", + "1 | \n", + "BoTorch | \n", + "5 | \n", + "COMPLETED | \n", + "{'5_0': {'x1': 1.0, 'x2': 0.34}} | \n", + "
| 6 | \n", + "1 | \n", + "BoTorch | \n", + "6 | \n", + "COMPLETED | \n", + "{'6_0': {'x1': 0.55, 'x2': 0.0}} | \n", + "
| 7 | \n", + "1 | \n", + "BoTorch | \n", + "7 | \n", + "COMPLETED | \n", + "{'7_0': {'x1': 0.12, 'x2': 0.0}} | \n", + "
| 8 | \n", + "1 | \n", + "BoTorch | \n", + "8 | \n", + "COMPLETED | \n", + "{'8_0': {'x1': 0.05, 'x2': 0.0}} | \n", + "
| 9 | \n", + "1 | \n", + "BoTorch | \n", + "9 | \n", + "COMPLETED | \n", + "{'9_0': {'x1': 0.12, 'x2': 0.03}} | \n", + "
-
- );
-}
-
-SocialFooter.propTypes = {
- config: PropTypes.object,
-};
-
-class Footer extends React.Component {
- docUrl(doc, language) {
- const baseUrl = this.props.config.baseUrl;
- const docsUrl = this.props.config.docsUrl;
- const docsPart = `${docsUrl ? `${docsUrl}/` : ''}`;
- const langPart = `${language ? `${language}/` : ''}`;
- return `${baseUrl}${docsPart}${langPart}${doc}`;
- }
-
- pageUrl(doc, language) {
- const baseUrl = this.props.config.baseUrl;
- return baseUrl + (language ? `${language}/` : '') + doc;
- }
-
- render() {
- const currentYear = new Date().getFullYear();
- return (
-
- );
- }
-}
-
-module.exports = Footer;
diff --git a/website/core/Tutorial.js b/website/core/Tutorial.js
deleted file mode 100644
index e8d409d3bb..0000000000
--- a/website/core/Tutorial.js
+++ /dev/null
@@ -1,83 +0,0 @@
-/**
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- *
- * @format
- */
-
-const React = require('react');
-
-const fs = require('fs-extra');
-const path = require('path');
-const CWD = process.cwd();
-
-const CompLibrary = require(`${CWD}/node_modules/docusaurus/lib/core/CompLibrary.js`);
-const Container = CompLibrary.Container;
-
-const TutorialSidebar = require(`${CWD}/core/TutorialSidebar.js`);
-
-function renderDownloadIcon() {
- return (
-
- );
-}
-
-class Tutorial extends React.Component {
- render() {
- const {baseUrl, tutorialID} = this.props;
-
- const htmlFile = `${CWD}/_tutorials/${tutorialID}.html`;
- const normalizedHtmlFile = path.normalize(htmlFile);
-
- return (
- Social
- -
-
-
-
-
-
-
- );
- }
-}
-
-module.exports = Tutorial;
diff --git a/website/core/TutorialSidebar.js b/website/core/TutorialSidebar.js
deleted file mode 100644
index a8140b8477..0000000000
--- a/website/core/TutorialSidebar.js
+++ /dev/null
@@ -1,92 +0,0 @@
-/**
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- *
- * @format
- */
-
-const React = require('react');
-const fs = require('fs-extra');
-const path = require('path');
-const join = path.join;
-const CWD = process.cwd();
-
-const CompLibrary = require(join(
- CWD,
- '/node_modules/docusaurus/lib/core/CompLibrary.js',
-));
-const SideNav = require(join(
- CWD,
- '/node_modules/docusaurus/lib/core/nav/SideNav.js',
-));
-
-const Container = CompLibrary.Container;
-
-const OVERVIEW_ID = 'tutorial_overview';
-
-class TutorialSidebar extends React.Component {
- render() {
- const {currentTutorialID} = this.props;
- const current = {
- id: currentTutorialID || OVERVIEW_ID,
- };
-
- const toc = [
- {
- type: 'CATEGORY',
- title: 'Tutorials',
- children: [
- {
- type: 'LINK',
- item: {
- permalink: 'tutorials/',
- id: OVERVIEW_ID,
- title: 'Overview',
- },
- },
- ],
- },
- ];
-
- const jsonFile = join(CWD, 'tutorials.json');
- const normJsonFile = path.normalize(jsonFile);
- const json = JSON.parse(fs.readFileSync(normJsonFile, {encoding: 'utf8'}));
-
- Object.keys(json).forEach(category => {
- const categoryItems = json[category];
- const items = [];
- categoryItems.map(item => {
- items.push({
- type: 'LINK',
- item: {
- permalink: `tutorials/${item.id}`,
- id: item.id,
- title: item.title,
- },
- });
- });
-
- toc.push({
- type: 'CATEGORY',
- title: category,
- children: items,
- });
- });
-
- return (
- ', + }, + { + type: 'html', + className: 'margin-horiz--sm text--bold', + value: 'Archived versions', + }, + { + href: 'https://archive.botorch.org/versions', + label: '<= 0.11.3', + }, + ], + }, + { + "href": "https://github.com/pytorch/botorch", + "className": "header-github-link", + "aria-label": "GitHub", + "position": "right" + }, + ] + }, + "image": "img/botorch.png", + "footer": { + style: 'dark', + "logo": { + alt: "Botorch", + "src": "img/meta_opensource_logo_negative.svg", + }, + links: [ + { + title: 'Docs', + items: [ + { + label: 'Introduction', + to: 'docs/introduction', + }, + { + label: 'Getting Started', + to: 'docs/getting_started', + }, + { + label: 'Tutorials', + to: 'docs/tutorials/', + }, + { + label: 'API Reference', + to: 'https://botorch-dev.readthedocs.io/', + }, + { + label: 'Paper', + href: 'https://arxiv.org/abs/1910.06403', + }, + ], + }, + { + title: 'Legal', + // Please do not remove the privacy and terms, it's a legal requirement. + items: [ + { + label: 'Privacy', + href: 'https://opensource.facebook.com/legal/privacy/', + target: '_blank', + rel: 'noreferrer noopener', + }, + { + label: 'Terms', + href: 'https://opensource.facebook.com/legal/terms/', + target: '_blank', + rel: 'noreferrer noopener', + }, + ], + }, + { + title: 'Social', + items: [ + { + label: 'GitHub', + href: 'https://github.com/pytorch/botorch', + }, + { + html: ``, + }, + ], + }, + ], + copyright: `Copyright © ${new Date().getFullYear()} Meta Platforms, Inc.`, + }, + "algolia": { + "appId": "BH4D9OD16A", + "apiKey": "207c27d819f967749142d8611de7cb19", + "indexName": "botorch" + } + } +} diff --git a/website/package.json b/website/package.json index 162b8d7789..26192f1ce2 100644 --- a/website/package.json +++ b/website/package.json @@ -1,17 +1,29 @@ { "scripts": { - "examples": "docusaurus-examples", - "start": "docusaurus-start", - "build": "docusaurus-build", - "publish-gh-pages": "docusaurus-publish", - "write-translations": "docusaurus-write-translations", - "version": "docusaurus-version", - "rename-version": "docusaurus-rename-version" + "docusaurus": "docusaurus", + "start": "docusaurus start", + "build": "docusaurus build", + "swizzle": "docusaurus swizzle", + "deploy": "docusaurus deploy", + "clear": "docusaurus clear", + "serve": "docusaurus serve", + "write-translations": "docusaurus write-translations", + "write-heading-ids": "docusaurus write-heading-ids" }, "devDependencies": { - "docusaurus": "^1.14.7" + "@docusaurus/module-type-aliases": "3.6.3", + "@docusaurus/types": "3.6.3" }, "dependencies": { - "bl": "^6.0.0" + "@docusaurus/core": "^3.6.3", + "@docusaurus/preset-classic": "^3.6.3", + "bl": "^6.0.0", + "clsx": "^1.1.1", + "plotly.js": "^2.8.1", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-plotly.js": "^2.5.1", + "rehype-katex": "7", + "remark-math": "6" } } diff --git a/website/pages/en/index.js b/website/pages/en/index.js deleted file mode 100755 index 9d7e5d151f..0000000000 --- a/website/pages/en/index.js +++ /dev/null @@ -1,280 +0,0 @@ -/** - * Copyright (c) Meta Platforms, Inc. and affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - * - * @format - */ - -const React = require('react'); - -const CompLibrary = require('../../core/CompLibrary.js'); - -const MarkdownBlock = CompLibrary.MarkdownBlock; -const Container = CompLibrary.Container; -const GridBlock = CompLibrary.GridBlock; - -const bash = (...args) => `~~~bash\n${String.raw(...args)}\n~~~`; - -class HomeSplash extends React.Component { - render() { - const { siteConfig, language = '' } = this.props; - const { baseUrl, docsUrl } = siteConfig; - const docsPart = `${docsUrl ? `${docsUrl}/` : ''}`; - const langPart = `${language ? `${language}/` : ''}`; - const docUrl = doc => `${baseUrl}${docsPart}${langPart}${doc}`; - - const SplashContainer = props => ( -
-
- );
-
- const Logo = props => (
-
-
- {props.children}
-
- 
-
- );
-
- const ProjectTitle = () => (
- - {siteConfig.tagline} -
- ); - - const PromoSection = props => ( -
-
- );
-
- const Button = props => (
-
- );
-
- return (
-
-
- {props.children}
-
-
-
-
-
-
-
-
-
-
-
- );
-
- const Features = () => (
- Get Started
--
-
-
-
Install BoTorch:
- via pip (recommended): -{bash`pip install botorch`} - via Anaconda (from the unofficial conda-forge channel): -{bash`conda install botorch -c gpytorch -c conda-forge`} -
- -
-
Fit a model:
-{modelFitCodeExample} -
- -
-
Construct an acquisition function:
-{constrAcqFuncExample} -
- -
-
Optimize the acquisition function:
-{optAcqFuncExample} -
-
-
- {[
- {
- content:
- 'Plug in new models, acquisition functions, and optimizers.',
- image: `${baseUrl}img/puzzle_pieces.svg`,
- imageAlign: 'top',
- title: 'Modular',
- },
- {
- content:
- 'Easily integrate neural network modules. Native GPU & autograd support.',
- image: `${baseUrl}img/pytorch_logo.svg`,
- imageAlign: 'top',
- title: 'Built on PyTorch',
- },
- {
- content:
- 'Support for scalable GPs via GPyTorch. Run code on multiple devices.',
- image: `${baseUrl}img/expanding_arrows.svg`,
- imageAlign: 'top',
- title: 'Scalable',
- },
- ]}
-
-
- );
-
- const Reference = () => (
- Key Features
-
-
- {papertitle}
- {paper_bibtex}
- Check out some other papers using BoTorch.
-
-
- );
-
- const Showcase = () => {
- if ((siteConfig.users || []).length === 0) {
- return null;
- }
-
- const showcase = siteConfig.users
- .filter(user => user.pinned)
- .map(user => (
-
- References
-
-
- );
- };
-
- return (
- Who is Using This?
-This project is used by all these people
-{showcase}
-
-
-
- );
- }
-}
-
-module.exports = Index;
diff --git a/website/pages/tutorials/index.js b/website/pages/tutorials/index.js
deleted file mode 100644
index d8f098e5d8..0000000000
--- a/website/pages/tutorials/index.js
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- *
- * @format
- */
-
-const React = require('react');
-
-const CWD = process.cwd();
-
-const CompLibrary = require(`${CWD}/node_modules/docusaurus/lib/core/CompLibrary.js`);
-const Container = CompLibrary.Container;
-const MarkdownBlock = CompLibrary.MarkdownBlock;
-
-const TutorialSidebar = require(`${CWD}/core/TutorialSidebar.js`);
-
-class TutorialHome extends React.Component {
- render() {
- return (
-
-
-
-
-
-
- );
- }
-}
-
-module.exports = TutorialHome;
diff --git a/website/sidebars.js b/website/sidebars.js
new file mode 100644
index 0000000000..b0da534bd1
--- /dev/null
+++ b/website/sidebars.js
@@ -0,0 +1,44 @@
+const tutorials = () => {
+ const allTutorialMetadata = require('./tutorials.json');
+ const tutorialsSidebar = [{
+ type: 'category',
+ label: 'Tutorials',
+ collapsed: false,
+ items: [
+ {
+ type: 'doc',
+ id: 'tutorials/index',
+ label: 'Overview',
+ },
+ ],
+ },];
+ for (var category in allTutorialMetadata) {
+ const categoryItems = allTutorialMetadata[category];
+ const items = [];
+ categoryItems.map(item => {
+ items.push({
+ type: 'doc',
+ label: item.title,
+ id: `tutorials/${item.id}/index`,
+ });
+ });
+
+ tutorialsSidebar.push({
+ type: 'category',
+ label: category,
+ items: items,
+ });
+ }
+ return tutorialsSidebar;
+};
+
+export default {
+ "docs": {
+ "About": ["introduction", "design_philosophy", "botorch_and_ax", "papers"],
+ "General": ["getting_started"],
+ "Basic Concepts": ["overview", "models", "posteriors", "acquisition", "optimization"],
+ "Advanced Topics": ["constraints", "objectives", "batching", "samplers"],
+ "Multi-Objective Optimization": ["multi_objective"]
+ },
+ tutorials: tutorials(),
+}
diff --git a/website/sidebars.json b/website/sidebars.json
deleted file mode 100644
index 2cd5992c75..0000000000
--- a/website/sidebars.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
- "docs": {
- "About": ["introduction", "design_philosophy", "botorch_and_ax", "papers"],
- "General": ["getting_started"],
- "Basic Concepts": ["overview", "models", "posteriors", "acquisition", "optimization"],
- "Advanced Topics": ["constraints", "objectives", "batching", "samplers"],
- "Multi-Objective Optimization": ["multi_objective"]
- }
-}
diff --git a/website/siteConfig.js b/website/siteConfig.js
deleted file mode 100644
index 8329137916..0000000000
--- a/website/siteConfig.js
+++ /dev/null
@@ -1,110 +0,0 @@
-/**
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- *
- * @format
- */
-
-// See https://docusaurus.io/docs/site-config for all the possible
-// site configuration options.
-
-// Define this so it can be easily modified in scripts (to host elsewhere)
-const baseUrl = '/';
-
-// If true, include Algolia search bar when building site
-// Note: this setting is toggled to false by publish_site.sh script, so
-// it should not be renamed without modifying that script.
-const includeAlgolia = true;
-
-// List of projects/orgs using your project for the users page.
-const users = [];
-
-const siteConfig = {
- title: 'BoTorch',
- tagline: 'Bayesian Optimization in PyTorch',
- url: 'https://botorch.org',
- baseUrl: baseUrl,
- cleanUrl: true, // No .html extensions for paths
-
- // used for publishing and more
- organizationName: 'pytorch',
- projectName: 'botorch',
-
- // Google analytics
- gaTrackingId: 'G-CXN3PGE3CC',
-
- // Use global site tags (gtag.js) to be compatible with Google Analytics 4
- gaGtag: true,
-
- // links that will be used in the header navigation bar
- headerLinks: [
- {doc: 'introduction', label: 'Docs'},
- {href: `${baseUrl}tutorials/`, label: 'Tutorials'},
- {href: `${baseUrl}api/`, label: 'API Reference'},
- {href: `${baseUrl}docs/papers`, label: 'Papers'},
- {href: 'https://github.com/pytorch/botorch', label: 'GitHub'},
- ],
-
- // add users to the website
- users,
-
- // images for header/footer and favicon
- headerIcon: 'img/botorch_logo_lockup_white.png',
- footerIcon: 'img/botorch.png',
- favicon: 'img/botorch.ico',
-
- // colors for website
- colors: {
- primaryColor: '#f29837', // orange
- secondaryColor: '#f0bc40', // yellow
- },
-
- highlight: {
- theme: 'default',
- },
-
- // custom scripts that are placed in of each page
- scripts: [
- // Github buttons
- 'https://buttons.github.io/buttons.js',
- // Copy-to-clipboard button for code blocks
- `${baseUrl}js/code_block_buttons.js`,
- 'https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.0/clipboard.min.js',
- // Mathjax for rendering math content
- `${baseUrl}js/mathjax.js`,
- 'https://cdn.jsdelivr.net/npm/mathjax@2/MathJax.js?config=TeX-AMS_SVG',
- ],
-
- // CSS sources to load
- stylesheets: [`${baseUrl}css/code_block_buttons.css`],
-
- // enable on-page navigation for the current documentation page
- onPageNav: 'separate',
-
- // enable scroll to top button a the bottom of the site
- scrollToTop: true,
-
- // if true, expand/collapse links & subcategories in sidebar
- docsSideNavCollapsible: false,
-
- // URL for editing docs
- editUrl: 'https://github.com/pytorch/botorch/edit/main/docs/',
-
- // Open Graph and Twitter card images
- ogImage: 'img/botorch.png',
- twitterImage: 'img/botorch.png',
-
- // show html docs generated by sphinx
- wrapPagesHTML: true,
-};
-
-if (includeAlgolia == true) {
- siteConfig['algolia'] = {
- apiKey: '207c27d819f967749142d8611de7cb19',
- indexName: 'botorch',
- };
-}
-
-module.exports = siteConfig;
diff --git a/website/src/components/CellOutput.jsx b/website/src/components/CellOutput.jsx
new file mode 100644
index 0000000000..434139c67e
--- /dev/null
+++ b/website/src/components/CellOutput.jsx
@@ -0,0 +1,37 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+import React from 'react';
+import {v4 as uuidv4} from 'uuid';
+
+const CellOutput = (props) => {
+ return (
+
-
-
-
-
- BoTorch Tutorials
-- The tutorials here will help you understand and use BoTorch in - your own work. They assume that you are familiar with both - Bayesian optimization (BO) and PyTorch. -
-- If you are new to BO, we recommend you start with the{' '} - Ax docs and the - following{' '} - tutorial paper. -
-- If you are new to PyTorch, the easiest way to get started is - with the{' '} - - What is PyTorch? - {' '} - tutorial. -
-- The BoTorch tutorials are grouped into the following four areas. -
--
Using BoTorch with Ax
- These tutorials give you an overview of how to leverage{' '} - Ax, a platform for sequential - experimentation, in order to simplify the management of your BO - loop. Doing so can help you focus on the main aspects of BO - (models, acquisition functions, optimization of acquisition - functions), rather than tedious loop control. See our{' '} - - Documentation - {' '} - for additional information. -Full Optimization Loops
- In some situations (e.g. when working in a non-standard setting, - or if you want to understand and control various details of the - BO loop), then you may also consider working purely in BoTorch. - The tutorials in this section illustrate this approach. -Bite-Sized Tutorials
- Rather than guiding you through full end-to-end BO loops, the - tutorials in this section focus on specific tasks that you will - encounter in customizing your BO algorithms. For instance, you - may want to{' '} - - write a custom acquisition function - {' '} - and then{' '} - - use a custom zero-th order optimizer - {' '} - to optimize it. -Advanced Usage
- Tutorials in this section showcase more advanced ways of using - BoTorch. For instance,{' '} - - this tutorial - {' '} - shows how to perform BO if your objective function is an image, - by optimizing in the latent space of a variational auto-encoder - (VAE). - - -
+ Out:
+
+ );
+};
+
+export default CellOutput;
diff --git a/website/src/components/LinkButtons.jsx b/website/src/components/LinkButtons.jsx
new file mode 100644
index 0000000000..d55f50593b
--- /dev/null
+++ b/website/src/components/LinkButtons.jsx
@@ -0,0 +1,27 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+import React from 'react';
+import Link from '@docusaurus/Link';
+import IconExternalLink from '@theme/Icon/ExternalLink'
+
+const LinkButtons = ({githubUrl, colabUrl}) => {
+ return (
+
+ {props.children.split('\n').map((line) => {
+ return (
+
+ {line}
+
+ );
+ })}
+
+
+
+ Open in GitHub
+
+ );
+};
+
+export default LinkButtons;
diff --git a/website/src/components/Plotting.jsx b/website/src/components/Plotting.jsx
new file mode 100644
index 0000000000..b838a26c93
--- /dev/null
+++ b/website/src/components/Plotting.jsx
@@ -0,0 +1,28 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+import React from 'react';
+import Loadable from 'react-loadable';
+
+const Plotly = Loadable({
+ loader: () => import(`react-plotly.js`),
+ loading: ({timedOut}) =>
+ timedOut ? (
+ Error: Loading Plotly timed out.+ ) : ( +
loading...
+ ),
+ timeout: 10000,
+});
+
+export const PlotlyFigure = React.memo(({data}) => {
+ return (
+
+
+ );
+});
diff --git a/website/src/css/customTheme.css b/website/src/css/customTheme.css
new file mode 100644
index 0000000000..8a42c3fcef
--- /dev/null
+++ b/website/src/css/customTheme.css
@@ -0,0 +1,9 @@
+:root{
+ --ifm-color-primary-lightest: #F7C38B;
+ --ifm-color-primary-lighter: #F5AE61;
+ --ifm-color-primary-light: #F4A653;
+ --ifm-color-primary: #F29837;
+ --ifm-color-primary-dark: #F08A1B;
+ --ifm-color-primary-darker: #ED820F;
+ --ifm-color-primary-darkest: #C36B0D;
+}
diff --git a/website/src/pages/index.js b/website/src/pages/index.js
new file mode 100755
index 0000000000..12a4d16a48
--- /dev/null
+++ b/website/src/pages/index.js
@@ -0,0 +1,208 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ *
+ * @format
+ */
+
+import React from 'react';
+import Link from '@docusaurus/Link';
+import Layout from "@theme/Layout";
+import useBaseUrl from '@docusaurus/useBaseUrl';
+import useDocusaurusContext from '@docusaurus/useDocusaurusContext';
+import CodeBlock from '@theme/CodeBlock';
+
+const features = [
+ {
+ content:
+ 'Plug in new models, acquisition functions, and optimizers.',
+ image: 'img/puzzle_pieces.svg',
+ title: 'Modular',
+ },
+ {
+ content:
+ 'Easily integrate neural network modules. Native GPU & autograd support.',
+ image: 'img/pytorch_logo.svg',
+ title: 'Built on PyTorch',
+ },
+ {
+ content:
+ 'Support for scalable GPs via GPyTorch. Run code on multiple devices.',
+ image: 'img/expanding_arrows.svg',
+ title: 'Scalable',
+ },
+];
+
+const Feature = ({imageUrl, title, content, image}) => {
+ const imgUrl = useBaseUrl(imageUrl);
+ return (
+
+ {imgUrl && (
+
+ );
+}
+
+const Features = () => (
+
+ 
+
+ )}
+ {image && (
+
+
+ 
+
+ )}
+ {title}
+{content}
+
+
+)
+
+const HomeSplash = () => {
+ const {siteConfig} = useDocusaurusContext();
+ return (
+ Key Features
+ {features && features.length > 0 && ( +
+ {features.map(({ title, imageUrl, content, image }) => (
+
+ )}
+
+
+ );
+}
+
+export default () => {
+ const {siteConfig} = useDocusaurusContext();
+ // Example for model fitting
+ const modelFitCodeExample = `import torch
+from botorch.models import SingleTaskGP
+from botorch.models.transforms import Normalize, Standardize
+from botorch.fit import fit_gpytorch_mll
+from gpytorch.mlls import ExactMarginalLogLikelihood
+
+train_X = torch.rand(10, 2, dtype=torch.double) * 2
+Y = 1 - torch.linalg.norm(train_X - 0.5, dim=-1, keepdim=True)
+Y = Y + 0.1 * torch.randn_like(Y) # add some noise
+
+gp = SingleTaskGP(
+ train_X=train_X,
+ train_Y=Y,
+ input_transform=Normalize(d=2),
+ outcome_transform=Standardize(m=1),
+)
+mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
+fit_gpytorch_mll(mll)`;
+ // Example for defining an acquisition function
+ const constrAcqFuncExample = `from botorch.acquisition import LogExpectedImprovement
+
+logEI = LogExpectedImprovement(model=gp, best_f=Y.max())`;
+ // Example for optimizing candidates
+ const optAcqFuncExample = `from botorch.optim import optimize_acqf
+
+bounds = torch.stack([torch.zeros(2), torch.ones(2)]).to(torch.double)
+candidate, acq_value = optimize_acqf(
+ logEI, bounds=bounds, q=1, num_restarts=5, raw_samples=20,
+)
+candidate # tensor([[0.2981, 0.2401]], dtype=torch.float64)`;
+ const papertitle = `BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization`
+ const paper_bibtex = `@inproceedings{balandat2020botorch,
+ title = {{BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization}},
+ author = {Balandat, Maximilian and Karrer, Brian and Jiang, Daniel R. and Daulton, Samuel and Letham, Benjamin and Wilson, Andrew Gordon and Bakshy, Eytan},
+ booktitle = {Advances in Neural Information Processing Systems 33},
+ year = 2020,
+ url = {http://arxiv.org/abs/1910.06403}
+}`;
+ //
+ const QuickStart = () => (
+
+ })
+
+
+ {siteConfig.tagline}
+
+
+ Introduction
+
+
+ Get started
+
+
+ Tutorials
+
+
+
+
+ );
+
+ const Reference = () => (
+ Get Started
+
+
+ -
+
-
+
Install BoTorch:
+ via pip (recommended): +pip install botorch + via Anaconda (from the unofficial conda-forge channel): +conda install botorch -c gpytorch -c conda-forge +
+ -
+
Fit a model:
+{modelFitCodeExample} +
+ -
+
Construct an acquisition function:
+{constrAcqFuncExample} +
+ -
+
Optimize the acquisition function:
+{optAcqFuncExample} +
+
+
+ );
+
+ return (
+