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+ "title": {
+ "text": "Factor Effect Plot for Concentration"
+ },
+ "width": 600,
+ "xaxis": {
+ "title": {
+ "text": "Concentration"
+ }
+ },
+ "yaxis": {
+ "title": {
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+ },
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ ],
+ "source": [
+ "factor_plot(campaign.optimizer, feature_id=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Optimize new experiment suggestions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from obsidian.objectives import Objective_Sequence, Feature_Objective, Scalar_WeightedSum"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note: Objectives can be passed directly to an `Optimizer`, or set using `campaign.set_objective()` after which the `Campaign` will automatically use the objective durign `campaign.suggest()`. At any time, the objective can be re-set to a new objective, or deleted using `campaign.clear_objective()`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "penalize_enz_loading = Feature_Objective(X_space, indices=[2], coeff=[-5])\n",
+ "add_objectives = Scalar_WeightedSum(1)\n",
+ "\n",
+ "campaign.set_objective(objective=Objective_Sequence([penalize_enz_loading, add_objectives]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\kevin\\miniconda3\\envs\\obsidian-dev\\lib\\site-packages\\botorch\\optim\\optimize.py:564: RuntimeWarning:\n",
+ "\n",
+ "Optimization failed in `gen_candidates_scipy` with the following warning(s):\n",
+ "[OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]\n",
+ "Trying again with a new set of initial conditions.\n",
+ "\n",
+ "c:\\Users\\kevin\\miniconda3\\envs\\obsidian-dev\\lib\\site-packages\\botorch\\optim\\optimize.py:564: RuntimeWarning:\n",
+ "\n",
+ "Optimization failed on the second try, after generating a new set of initial conditions.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "X_suggest, eval_suggest = campaign.suggest(m_batch = 3, optim_sequential = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " Temperature Concentration Enzyme Product (pred) Product lb \\\n",
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+ "execution_count": 11,
+ "metadata": {},
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+ ],
+ "source": [
+ "df_suggest = pd.concat([X_suggest, eval_suggest], axis=1)\n",
+ "df_suggest"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note: We can examine the output of various objectives within the sequence by passing them directly to `optimizer.evaluate`. Here, we can explicitly see the balance of Objective 1 (product response) and Objective 2 (cost penalty) before they are combined in the weighted sum."
+ ]
+ },
+ {
+ "cell_type": "code",
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+ " Product (pred) Product lb Product ub f(Product) Objective 1 \\\n",
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+ "1 -1.380605 4.775616 False \n",
+ "2 -0.301376 4.775616 True "
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+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "campaign.optimizer.evaluate(X_suggest, objective=penalize_enz_loading)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Collect data at new suggestions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " -1.214936 | \n",
+ " -0.977403 | \n",
+ " NEI | \n",
+ " 1.823846 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 0.748942 | \n",
+ " 100.907522 | \n",
+ " 0.276121 | \n",
+ " -79.689998 | \n",
+ " 1 | \n",
+ " -2.047438 | \n",
+ " -70.506235 | \n",
+ " -99.886332 | \n",
+ " -41.126134 | \n",
+ " -0.532599 | \n",
+ " -46.057986 | \n",
+ " -0.977403 | \n",
+ " NEI | \n",
+ " 1.823846 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " -10.000000 | \n",
+ " 53.696260 | \n",
+ " 0.060275 | \n",
+ " 87.065807 | \n",
+ " 1 | \n",
+ " 1.469179 | \n",
+ " 76.435947 | \n",
+ " 46.268528 | \n",
+ " 106.603372 | \n",
+ " 1.615183 | \n",
+ " -1.893481 | \n",
+ " -0.977403 | \n",
+ " NEI | \n",
+ " 1.823846 | \n",
+ "
\n",
+ " \n",
+ "
\n",
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+ "text/plain": [
+ " Temperature Concentration Enzyme Product Iteration \\\n",
+ "Observation ID \n",
+ "8 24.000000 115.000000 0.256500 -166.801349 0 \n",
+ "9 -4.000000 31.000000 0.082500 87.425397 0 \n",
+ "10 -1.366459 23.701565 0.027559 100.139209 1 \n",
+ "11 0.748942 100.907522 0.276121 -79.689998 1 \n",
+ "12 -10.000000 53.696260 0.060275 87.065807 1 \n",
+ "\n",
+ " Objective 1 Product (pred) Product lb Product ub \\\n",
+ "Observation ID \n",
+ "8 -3.222598 NaN NaN NaN \n",
+ "9 1.363311 NaN NaN NaN \n",
+ "10 1.823846 82.545038 52.602369 112.487712 \n",
+ "11 -2.047438 -70.506235 -99.886332 -41.126134 \n",
+ "12 1.469179 76.435947 46.268528 106.603372 \n",
+ "\n",
+ " f(Product) aq Value aq Value (joint) aq Method \\\n",
+ "Observation ID \n",
+ "8 NaN NaN NaN NaN \n",
+ "9 NaN NaN NaN NaN \n",
+ "10 1.704477 -1.214936 -0.977403 NEI \n",
+ "11 -0.532599 -46.057986 -0.977403 NEI \n",
+ "12 1.615183 -1.893481 -0.977403 NEI \n",
+ "\n",
+ " Objective 1 (max) (iter) \n",
+ "Observation ID \n",
+ "8 1.363311 \n",
+ "9 1.363311 \n",
+ "10 1.823846 \n",
+ "11 1.823846 \n",
+ "12 1.823846 "
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_iter1 = pd.DataFrame(simulator.simulate(X_suggest), columns = ['Product'])\n",
+ "Z_iter1 = pd.concat([X_suggest, y_iter1, eval_suggest], axis=1)\n",
+ "campaign.add_data(Z_iter1)\n",
+ "campaign.data.tail()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Repeat as desired"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GP model has been fit to data with a train-score of: 1 for response: Product\n",
+ "GP model has been fit to data with a train-score of: 1 for response: Product\n",
+ "GP model has been fit to data with a train-score of: 1 for response: Product\n"
+ ]
+ }
+ ],
+ "source": [
+ "for iter in range(3):\n",
+ " campaign.fit()\n",
+ " X_suggest, eval_suggest = campaign.suggest(m_batch=3)\n",
+ " y_iter = pd.DataFrame(simulator.simulate(X_suggest))\n",
+ " Z_iter = pd.concat([X_suggest, y_iter, eval_suggest], axis=1)\n",
+ " campaign.add_data(Z_iter)"
+ ]
+ },
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+ "
"
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+ "source": [
+ "campaign.clear_objective()\n",
+ "optim_progress(campaign)"
+ ]
+ }
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+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
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+}
diff --git a/demo/Joint optimization with constant param.ipynb b/demo/Joint optimization with constant param.ipynb
deleted file mode 100644
index 8174785..0000000
--- a/demo/Joint optimization with constant param.ipynb
+++ /dev/null
@@ -1,280 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import obsidian\n",
- "obsidian.__version__"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import plotly.express as px"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from obsidian.parameters import ParamSpace, Param_Categorical, Param_Ordinal, Param_Continuous\n",
- "from obsidian.experiment import ExpDesigner"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Set up parameter space and initialize a design"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "params = [\n",
- " Param_Continuous('Temperature', -10, 30),\n",
- " Param_Continuous('Concentration', 10, 150),\n",
- " Param_Continuous('Enzyme', 0.01, 0.30),\n",
- " ]\n",
- "\n",
- "X_space = ParamSpace(params)\n",
- "designer = ExpDesigner(X_space, seed=0)\n",
- "X0 = designer.initialize(10, 'LHS')\n",
- "\n",
- "X0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Collect results (e.g. from a simulation)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from obsidian.experiment import Simulator\n",
- "from obsidian.experiment.benchmark import shifted_parab\n",
- "\n",
- "simulator = Simulator(X_space, shifted_parab, name='Yield', eps=0.05)\n",
- "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='Temperature', y='Yield', kind='scatter', figsize=(4,3))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Define the Target"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from obsidian.parameters import Target\n",
- "target = Target('Yield', aim='max')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create a campaign to track optimization"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from obsidian.campaign import Campaign"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "my_campaign = Campaign(X_space,target)\n",
- "my_campaign.add_data(Z0)\n",
- "my_campaign.data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Fit an optimizer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "my_campaign.fit()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Make new experiment suggestions"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Jointly optimize a large number of experiments, but do so while holding one of the parameters constant.\n",
- "#### Note that the constant value is still optimized, the constraint simply keeps the experimental range low."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from obsidian.constraints import InConstraint_ConstantDim"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "X_suggest, eval_suggest = my_campaign.optimizer.suggest(m_batch=12, nleq_constraints=[InConstraint_ConstantDim(X_space, dim=0)], optim_sequential=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pd.concat([X_suggest, eval_suggest], axis=1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Collect data at new suggestions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "y_iter1 = pd.DataFrame(simulator.simulate(X_suggest), columns = ['Yield'])\n",
- "Z_iter1 = pd.concat([X_suggest, y_iter1, eval_suggest], axis=1)\n",
- "my_campaign.add_data(Z_iter1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Repeat as desired"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for iter in range(3):\n",
- " my_campaign.fit()\n",
- " X_suggest, eval_suggest = my_campaign.optimizer.suggest(m_batch=3)\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)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig = px.scatter(my_campaign.data, x=my_campaign.data.index, y='Yield', color='aq Value')\n",
- "fig.update_layout(height=300, width=400, template='ggplot2')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "my_campaign.data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.14"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/_static/tutorials/Cost-penalized custom objective.html b/docs/_static/tutorials/Cost-penalized custom objective.html
new file mode 100644
index 0000000..7fdce6b
--- /dev/null
+++ b/docs/_static/tutorials/Cost-penalized custom objective.html
@@ -0,0 +1,8673 @@
+
+
+
+
+
+
Cost-penalized custom objective
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
obsidian version: 0.8.0
+
+
+
+
+
+
+
+
+
+
+
+
+
Out[3]:
+
+
+
+
+
+
+ |
+Temperature |
+Concentration |
+Enzyme |
+
+
+
+
+| 0 |
+16.0 |
+17.0 |
+0.1985 |
+
+
+| 1 |
+0.0 |
+129.0 |
+0.1695 |
+
+
+| 2 |
+8.0 |
+59.0 |
+0.2855 |
+
+
+| 3 |
+12.0 |
+143.0 |
+0.1115 |
+
+
+| 4 |
+-8.0 |
+87.0 |
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GP model has been fit to data with a train-score of: 1 for response: Product
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+
c:\Users\kevin\miniconda3\envs\obsidian-dev\lib\site-packages\botorch\optim\optimize.py:564: RuntimeWarning:
+
+Optimization failed in `gen_candidates_scipy` with the following warning(s):
+[OptimizationWarning('Optimization failed within `scipy.optimize.minimize` with status 2 and message ABNORMAL_TERMINATION_IN_LNSRCH.')]
+Trying again with a new set of initial conditions.
+
+c:\Users\kevin\miniconda3\envs\obsidian-dev\lib\site-packages\botorch\optim\optimize.py:564: RuntimeWarning:
+
+Optimization failed on the second try, after generating a new set of initial conditions.
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+
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+
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+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
GP model has been fit to data with a train-score of: 1 for response: Product
+GP model has been fit to data with a train-score of: 1 for response: Product
+GP model has been fit to data with a train-score of: 1 for response: Product
+
+
+
+
+
+
+
+
diff --git a/docs/stubs/tutorials.rst b/docs/stubs/tutorials.rst
index 8d2820b..1491b73 100644
--- a/docs/stubs/tutorials.rst
+++ b/docs/stubs/tutorials.rst
@@ -7,6 +7,7 @@ Tutorials
../../stubs/tutorials/Simple single objective.rst
../../stubs/tutorials/Constrained multi-output min-max.rst
+ ../../stubs/tutorials/Cost-penalized custom objective.rst
Indices
~~~~~~~~~~~~~~~~~~
diff --git a/docs/stubs/tutorials/Cost-penalized custom objective.rst b/docs/stubs/tutorials/Cost-penalized custom objective.rst
new file mode 100644
index 0000000..fad0824
--- /dev/null
+++ b/docs/stubs/tutorials/Cost-penalized custom objective.rst
@@ -0,0 +1,6 @@
+
+Cost-Penalized Custom Objective
+===============================
+
+.. raw:: html
+ :file: ../../_static/tutorials/Cost-penalized custom objective.html
\ No newline at end of file
From a7445229cc917205256586f8ecd9ebde58e10c90 Mon Sep 17 00:00:00 2001
From: Kevin Stone
Date: Wed, 14 Aug 2024 23:53:58 -0400
Subject: [PATCH 10/10] Deleted cell outputs for demos
---
demo/Cost-penalized custom objective.ipynb | 4490 +-------------------
1 file changed, 30 insertions(+), 4460 deletions(-)
diff --git a/demo/Cost-penalized custom objective.ipynb b/demo/Cost-penalized custom objective.ipynb
index e11485e..12cc60a 100644
--- a/demo/Cost-penalized custom objective.ipynb
+++ b/demo/Cost-penalized custom objective.ipynb
@@ -2,17 +2,9 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "obsidian version: 0.8.0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import obsidian\n",
"print(f'obsidian version: ' + obsidian.__version__)\n",
@@ -52,7 +44,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -62,119 +54,9 @@
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{
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- ],
- "sequentialminus": [
- [
- 0,
- "rgb(20,44,66)"
- ],
- [
- 1,
- "rgb(90,179,244)"
- ]
- ]
- },
- "colorway": [
- "#F8766D",
- "#A3A500",
- "#00BF7D",
- "#00B0F6",
- "#E76BF3"
- ],
- "font": {
- "color": "rgb(51,51,51)"
- },
- "geo": {
- "bgcolor": "white",
- "lakecolor": "white",
- "landcolor": "rgb(237,237,237)",
- "showlakes": true,
- "showland": true,
- "subunitcolor": "white"
- },
- "hoverlabel": {
- "align": "left"
- },
- "hovermode": "closest",
- "paper_bgcolor": "white",
- "plot_bgcolor": "rgb(237,237,237)",
- "polar": {
- "angularaxis": {
- "gridcolor": "white",
- "linecolor": "white",
- "showgrid": true,
- "tickcolor": "rgb(51,51,51)",
- "ticks": "outside"
- },
- "bgcolor": "rgb(237,237,237)",
- "radialaxis": {
- "gridcolor": "white",
- "linecolor": "white",
- "showgrid": true,
- "tickcolor": "rgb(51,51,51)",
- "ticks": "outside"
- }
- },
- "scene": {
- "xaxis": {
- "backgroundcolor": "rgb(237,237,237)",
- "gridcolor": "white",
- "gridwidth": 2,
- "linecolor": "white",
- "showbackground": true,
- "showgrid": true,
- "tickcolor": "rgb(51,51,51)",
- "ticks": "outside",
- "zerolinecolor": "white"
- },
- "yaxis": {
- "backgroundcolor": "rgb(237,237,237)",
- "gridcolor": "white",
- "gridwidth": 2,
- "linecolor": "white",
- "showbackground": true,
- "showgrid": true,
- "tickcolor": "rgb(51,51,51)",
- "ticks": "outside",
- "zerolinecolor": "white"
- },
- "zaxis": {
- "backgroundcolor": "rgb(237,237,237)",
- "gridcolor": "white",
- "gridwidth": 2,
- "linecolor": "white",
- "showbackground": true,
- "showgrid": true,
- "tickcolor": "rgb(51,51,51)",
- "ticks": "outside",
- "zerolinecolor": "white"
- }
- },
- "shapedefaults": {
- "fillcolor": "black",
- "line": {
- "width": 0
- },
- "opacity": 0.3
- },
- "ternary": {
- "aaxis": {
- "gridcolor": "white",
- "linecolor": "white",
- "showgrid": true,
- "tickcolor": "rgb(51,51,51)",
- "ticks": "outside"
- },
- "baxis": {
- "gridcolor": "white",
- "linecolor": "white",
- "showgrid": true,
- "tickcolor": "rgb(51,51,51)",
- "ticks": "outside"
- },
- "bgcolor": "rgb(237,237,237)",
- "caxis": {
- "gridcolor": "white",
- "linecolor": "white",
- "showgrid": true,
- "tickcolor": "rgb(51,51,51)",
- "ticks": "outside"
- }
- },
- "xaxis": {
- "automargin": true,
- "gridcolor": "white",
- "linecolor": "white",
- "showgrid": true,
- "tickcolor": "rgb(51,51,51)",
- "ticks": "outside",
- "title": {
- "standoff": 15
- },
- "zerolinecolor": "white"
- },
- "yaxis": {
- "automargin": true,
- "gridcolor": "white",
- "linecolor": "white",
- "showgrid": true,
- "tickcolor": "rgb(51,51,51)",
- "ticks": "outside",
- "title": {
- "standoff": 15
- },
- "zerolinecolor": "white"
- }
- }
- },
- "title": {
- "text": "Optimization Results"
- },
- "width": 500,
- "xaxis": {
- "title": {
- "text": "Experiment"
- }
- },
- "yaxis": {
- "title": {
- "text": "Product"
- }
- }
- }
- },
- "text/html": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"campaign.clear_objective()\n",
"optim_progress(campaign)"
-
+