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Merge pull request #7 from MindFoundry/1.3.4
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Introduce Cyclical parameters, upgrade to 1.3.4
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@csymeonides-mf
csymeonides-mf committed Oct 30, 2018
2 parents b63327d + 78968f3 commit c6ab905
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2 changes: 1 addition & 1 deletion Dockerfile
View file
Expand Up @@ -11,6 +11,6 @@ COPY --chown=1000 . ${HOME}
WORKDIR ${HOME}

RUN pip install -r requirements.txt
RUN Rscript -e "options(unzip = 'internal'); devtools::install_github('MindFoundry/optaas-r-client@1.3.3')"
RUN Rscript -e "options(unzip = 'internal'); devtools::install_github('MindFoundry/optaas-r-client@1.3.4')"

CMD ["jupyter", "notebook", "--ip", "0.0.0.0"]
135 changes: 135 additions & 0 deletions notebooks/R/07. Cyclical Parameters.ipynb
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@@ -0,0 +1,135 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# OPTaaS Cyclical Parameters\n",
"\n",
"### <span style=\"color:red\">Note:</span> To run this notebook, you need an API Key. You can get one <a href=\"mailto:charles.brecque@mindfoundry.ai\">here</a>.\n",
"\n",
"A new flag on `FloatParameter` now allows you to specify that the parameter is **cyclical** (aka *circular* or *periodic*). OPTaaS will select values from a period starting from the `minimum` (inclusive) and ending at the `maximum` (exclusive). Values near the minimum and maximum will be considered to be close, as if they were on a circle.\n",
"\n",
"**Note:** If you use any Cyclical parameters in your task, all your parameters must be Floats, Constants or Groups (other types are not currently supported), and none of them can be `optional`.\n",
"\n",
"As a simple example, let's optimize `cos(x)` for x in the range `[0, 2π)`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connect to OPTaaS using your API Key"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"library(optaas.client)\n",
"\n",
"client <- OPTaaSClient$new(\"https://optaas.mindfoundry.ai\", \"Your OPTaaS API Key\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define your task"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"task <- client$create_task(\n",
" title=\"Cyclical Example\",\n",
" parameters=list(FloatParameter('x', minimum=0, maximum=1, cyclical=TRUE))\n",
")\n",
"\n",
"scoring_function <- function(x) {\n",
" cos(x)\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run your Task"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] \"Running Cyclical Example for 10 iterations\"\n",
"[1] \"Iteration: 1 Score: 0.877582561890373\"\n",
"[1] \"Iteration: 2 Score: 0.731688868873821\"\n",
"[1] \"Iteration: 3 Score: 0.968912421710645\"\n",
"[1] \"Iteration: 4 Score: 0.930507621912314\"\n",
"[1] \"Iteration: 5 Score: 0.640996858163325\"\n",
"[1] \"Iteration: 6 Score: 0.810963119505218\"\n",
"[1] \"Iteration: 7 Score: 0.992197667229329\"\n",
"[1] \"Iteration: 8 Score: 0.982473313101255\"\n",
"[1] \"Iteration: 9 Score: 0.772834946152472\"\n",
"[1] \"Iteration: 10 Score: 0.591805075092477\"\n",
"[1] \"Task Completed\"\n",
"[1] \"Best Score: 0.9922\"\n",
"[1] \"with configuration:\"\n",
"$x\n",
"[1] 0.125\n",
"\n"
]
}
],
"source": [
"best_result <- task$run(scoring_function=scoring_function, number_of_iterations=10)\n",
"\n",
"print(paste(\"Best Score:\", best_result$score))\n",
"print(\"with configuration:\")\n",
"print(best_result$configuration$values)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.5.1"
},
"nav_menu": {},
"toc": {
"navigate_menu": true,
"number_sections": false,
"sideBar": true,
"threshold": 6,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
169 changes: 169 additions & 0 deletions notebooks/python/12. Cyclical Parameters.ipynb
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@@ -0,0 +1,169 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# OPTaaS Cyclical Parameters\n",
"\n",
"### <span style=\"color:red\">Note:</span> To run this notebook, you need an API Key. You can get one <a href=\"mailto:charles.brecque@mindfoundry.ai\">here</a>.\n",
"\n",
"A new flag on `FloatParameter` now allows you to specify that the parameter is **cyclical** (aka *circular* or *periodic*). OPTaaS will select values from a period starting from the `minimum` (inclusive) and ending at the `maximum` (exclusive). Values near the minimum and maximum will be considered to be close, as if they were on a circle.\n",
"\n",
"**Note:** If you use any Cyclical parameters in your task, all your parameters must be Floats, Constants or Groups (other types are not currently supported), and none of them can be `optional`.\n",
"\n",
"As a simple example, let's optimize `cos(x)` for x in the range `[0, 2π)`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connect to OPTaaS using your API Key"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from mindfoundry.optaas.client.client import OPTaaSClient\n",
"\n",
"client = OPTaaSClient('https://optaas.mindfoundry.ai', '<Your OPTaaS API Key>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define your task"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from math import cos, pi\n",
"\n",
"from mindfoundry.optaas.client.parameter import FloatParameter\n",
"\n",
"def scoring_function(x):\n",
" return cos(x)\n",
"\n",
"x = FloatParameter(\"x\", minimum=0, maximum=2 * pi, cyclical=True)\n",
"\n",
"task = client.create_task(\n",
" title='Cyclical Example',\n",
" parameters=[x],\n",
" initial_configurations=1\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run your Task"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running task \"Cyclical Example\" for 10 iterations\n",
"(no score threshold set)\n",
"\n",
"Iteration: 0 Score: -1.0\n",
"Configuration: {'x': 3.141592653589793}\n",
"\n",
"Iteration: 1 Score: -0.5181244988793857\n",
"Configuration: {'x': 2.115453031608477}\n",
"\n",
"Iteration: 2 Score: -0.41413695380966903\n",
"Configuration: {'x': 1.997790746187629}\n",
"\n",
"Iteration: 3 Score: 0.4477296432117673\n",
"Configuration: {'x': 1.1065716756360366}\n",
"\n",
"Iteration: 4 Score: 0.9964383191318358\n",
"Configuration: {'x': 6.198760226305232}\n",
"\n",
"Iteration: 5 Score: 0.5181244988794008\n",
"Configuration: {'x': 5.257045685198288}\n",
"\n",
"Iteration: 6 Score: 0.9962804147395031\n",
"Configuration: {'x': 0.08627738331880817}\n",
"\n",
"Iteration: 7 Score: 0.9971137826607362\n",
"Configuration: {'x': 0.07599482592806261}\n",
"\n",
"Iteration: 8 Score: 0.4141369532186448\n",
"Configuration: {'x': 5.139383399128098}\n",
"\n",
"Iteration: 9 Score: -0.4491755260657931\n",
"Configuration: {'x': 4.246546660377198}\n",
"\n",
"Task Completed\n",
"\n"
]
},
{
"data": {
"text/plain": [
"{ 'configuration': {'type': 'exploitation', 'values': {'x': 0.07599482592806261}},\n",
" 'score': 0.9971137826607362,\n",
" 'user_defined_data': None}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"task.run(scoring_function, max_iterations=10)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.4"
},
"nav_menu": {},
"toc": {
"navigate_menu": true,
"number_sections": false,
"sideBar": true,
"threshold": 6,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
2 changes: 1 addition & 1 deletion requirements.txt
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@@ -1,4 +1,4 @@
scikit-learn>=0.19.2
pandas>=0.23.4
matplotlib>=2.2.3
mindfoundry-optaas-client==1.3.3
mindfoundry-optaas-client==1.3.4

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