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Add more Wiki pages and Tutorials #26

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Aug 15, 2024
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Add more Wiki pages and Tutorials #26

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6f2e883
fix the demo ipynb files and remove outputs to reduce file sizes. Out…
xuyuting Aug 14, 2024
033b461
Improved reactivity of readme to light/dark theme
kstone40 Aug 15, 2024
d5401e3
Upgraded settings for parity_plot
kstone40 Aug 15, 2024
cee5d62
Fixed hoverdata for non-numeric params
kstone40 Aug 15, 2024
bfe94d1
enable auto adjust table width for the markdown files in docs/wiki
xuyuting Aug 15, 2024
94b2b03
template of surrogate model and acquisition function user guide, to b…
xuyuting Aug 15, 2024
8ac2bbc
Completed first two tutorial notebooks and HTML
kstone40 Aug 15, 2024
c162df3
Bug fix for optim_progress with n_response
kstone40 Aug 15, 2024
5132d3e
Added a third demo, removed constant_input demo for now
kstone40 Aug 15, 2024
a744522
Deleted cell outputs for demos
kstone40 Aug 15, 2024
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134 changes: 63 additions & 71 deletions demo/Constrained multi objective.ipynb → demo/Constrained multi-output min-max.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -7,34 +7,47 @@
"outputs": [],
"source": [
"import obsidian\n",
"obsidian.__version__"
"print(f'obsidian version: ' + obsidian.__version__)\n",
"\n",
"import pandas as pd\n",
"import plotly.express as px\n",
"import plotly.io as pio\n",
"pio.renderers.default = \"plotly_mimetype+notebook\""
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import plotly.express as px"
"## Introduction"
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"from obsidian.parameters import ParamSpace, Param_Continuous\n",
"from obsidian.experiment import ExpDesigner"
"In this tutorial, we will see how to use _obsidian_ for multi-output optimization. To demonstrate the versatility of the approach, we will seek to maximize one response while minimizing the other.\n",
"\n",
"$$\\underset{X}{argmax} HV\\left(+f\\left(y_1\\right) -f\\left(y_2\\right)\\right)$$\n",
"\n",
"Furthermore, we will apply a linear constraint on the input variables; requiring that the $X_1 + X_2 \\leq 6 $."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set up parameter space and initialize a design"
"## Set up parameter space and initialize a design"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from obsidian import Campaign, Target, ParamSpace, BayesianOptimizer\n",
"from obsidian.parameters import Param_Continuous"
]
},
{
Expand All @@ -49,8 +62,12 @@
" ]\n",
"\n",
"X_space = ParamSpace(params)\n",
"designer = ExpDesigner(X_space, seed=0)\n",
"X0 = designer.initialize(4, 'LHS')\n",
"target = [\n",
" Target('Response 1', aim='max'),\n",
" Target('Response 2', aim='min')\n",
"]\n",
"campaign = Campaign(X_space, target, seed=0)\n",
"X0 = campaign.designer.initialize(4, 'LHS')\n",
"\n",
"X0"
]
Expand All @@ -59,7 +76,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Collect results (e.g. from a simulation)"
"## Collect results (e.g. from a simulation)"
]
},
{
Expand All @@ -75,23 +92,15 @@
"y0 = simulator.simulate(X0)\n",
"Z0 = pd.concat([X0, y0], axis=1)\n",
"\n",
"Z0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Z0.plot(x='Response 1', y='Response 2', kind='scatter', figsize=(4,3))"
"campaign.add_data(Z0)\n",
"campaign.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a campaign to track optimization"
"### Fit an optimizer and visualize results"
]
},
{
Expand All @@ -100,7 +109,7 @@
"metadata": {},
"outputs": [],
"source": [
"from obsidian.campaign import Campaign"
"campaign.fit()"
]
},
{
Expand All @@ -109,25 +118,23 @@
"metadata": {},
"outputs": [],
"source": [
"my_campaign = Campaign(X_space)\n",
"my_campaign.add_data(Z0)\n",
"my_campaign.data"
"from obsidian.plotting import surface_plot, optim_progress"
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"### Fit an optimizer"
"surface_plot(campaign.optimizer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"from obsidian.parameters import Target"
"## Optimize new experiment suggestions"
]
},
{
Expand All @@ -136,29 +143,16 @@
"metadata": {},
"outputs": [],
"source": [
"target = [\n",
" Target('Response 1', aim='max'),\n",
" Target('Response 2', aim='min')\n",
"]\n",
"\n",
"my_campaign.set_target(target)\n",
"my_campaign.fit()\n"
"from obsidian.constraints import InConstraint_Generic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Make new experiment suggestions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from obsidian.constraints import InConstraint_Generic"
"_Note:_ It is a good idea to balance a set of acquisition functions with those that prefer design-space exploration. This helps to ensure that the optimizer is not severely misled by deficiencies in the dataset, particularly for small data. It also helps to ascertain a global optimum.\n",
"\n",
"A simple choice is __Space Filling (SF)__ although __Negative Integrated Posterior Variance (NIPV)__ is available for single-output optimizations; and there are various other acquisiiton functions whose hyperparameters can be tuned to manage the \"explore-exploit\" balance."
]
},
{
Expand All @@ -167,13 +161,9 @@
"metadata": {},
"outputs": [],
"source": [
"# # X1 + X2 >= 2\n",
"# optim_kwargs = {'m_batch':2, 'acquisition':[{'qNEHVI':{'ref_point':[-350,0]}}], 'ineq_constraints':[InConstraint_Generic(X_space, indices=[0,1], coeff=[1,1], rhs=2)]}\n",
"\n",
"# X1 + X2 <= 6 aka -X1 - X2 >= -6\n",
"optim_kwargs = {'m_batch':2, 'acquisition':[{'qNEHVI':{'ref_point':[-350,0]}}], 'ineq_constraints':[InConstraint_Generic(X_space, indices=[0,1], coeff=[-1,-1], rhs=-6)]}\n",
"\n",
"X_suggest, eval_suggest = my_campaign.optimizer.suggest(**optim_kwargs)"
"X_suggest, eval_suggest = campaign.optimizer.suggest(acquisition = [{'NEHVI':{'ref_point':[-350, -20]}}, 'SF'],\n",
" # X1 + X2 <= 6, written as -X1 - X2 >= -6\n",
" ineq_constraints = [InConstraint_Generic(X_space, indices=[0,1], coeff=[-1,-1], rhs=-6)])"
]
},
{
Expand All @@ -189,7 +179,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Collect data at new suggestions"
"## Collect data at new suggestions"
]
},
{
Expand All @@ -200,15 +190,15 @@
"source": [
"y_iter1 = pd.DataFrame(simulator.simulate(X_suggest))\n",
"Z_iter1 = pd.concat([X_suggest, y_iter1, eval_suggest], axis=1)\n",
"my_campaign.add_data(Z_iter1)\n",
"my_campaign.data"
"campaign.add_data(Z_iter1)\n",
"campaign.data.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Repeat as desired"
"## Repeat as desired"
]
},
{
Expand All @@ -218,11 +208,12 @@
"outputs": [],
"source": [
"for iter in range(5):\n",
" my_campaign.fit()\n",
" X_suggest, eval_suggest = my_campaign.optimizer.suggest(**optim_kwargs)\n",
" campaign.fit()\n",
" X_suggest, eval_suggest = campaign.optimizer.suggest(acquisition = [{'NEHVI':{'ref_point':[-350, -20]}}, 'SF'],\n",
" ineq_constraints = [InConstraint_Generic(X_space, indices=[0,1], coeff=[-1,-1], rhs=-6)])\n",
" y_iter = pd.DataFrame(simulator.simulate(X_suggest))\n",
" Z_iter = pd.concat([X_suggest, y_iter, eval_suggest], axis=1)\n",
" my_campaign.add_data(Z_iter)"
" campaign.add_data(Z_iter)"
]
},
{
Expand All @@ -231,8 +222,7 @@
"metadata": {},
"outputs": [],
"source": [
"fig = px.scatter(my_campaign.data, x='Response 1', y='Response 2', color='Iteration')\n",
"fig.update_layout(height=300, width=400, template='ggplot2')"
"optim_progress(campaign)"
]
},
{
Expand All @@ -241,15 +231,17 @@
"metadata": {},
"outputs": [],
"source": [
"my_campaign.data"
"surface_plot(campaign.optimizer, response_id = 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"surface_plot(campaign.optimizer, response_id = 1)"
]
}
],
"metadata": {
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