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"cell_type": "markdown",
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"source": [
"## Tutorial 4: visualizing bees"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [],
"source": [
"# Set Plotly to render plots as interactive in notebooks (this cell will be hidden: I set \"nbsphinx\": \"hidden\" in the cell metadata)\n",
"import plotly.io as pio\n",
"pio.renderers.default = \"notebook\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this tutorial, you will learn how to use the `contourplot_bees` from the `beeoptimal.plotting` module.\n",
"As its name suggests, the function allows plotting the positions of one or more bees over the function landscape.\n",
"\n",
"For its basic usage, `contourplot_bees` requires the two following key inputs:\n",
"\n",
"- `function`: The objective function you are optimizing, provide as a `BenchmarkFunction` object.\n",
"- `bee_colony`: A list of bees to plot (e.g., optimal bee or the entire colony at a given iteration).\n",
"\n",
"Additional optional parameters can be specified to customize the plot, such as `title`, `figsize`, `bounds`, `zoom`, `bee_marker_size`, and `optimal_solution`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from beeoptimal.benchmarks import Schwefel2d\n",
"from beeoptimal.plotting import contourplot_bees\n",
"from beeoptimal import ArtificialBeeColony"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before plotting the bees, we need to optimize first our function (check the previous tutorial for details)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Running Optimization: 2%|▏ |[00:00<00:05]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Early termination: Optimization stagnated at iteration 19 / 1000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"ABC = ArtificialBeeColony(\n",
" colony_size = 100,\n",
" function = Schwefel2d.fun,\n",
" bounds = Schwefel2d.bounds\n",
" )\n",
"\n",
"ABC.optimize(\n",
" max_iters = 1000,\n",
" mutation = 'ABC/best/1',\n",
" stagnation_tol = 1e-6,\n",
" verbose = True,\n",
" random_seed = 12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One thing we might want to do is to visualize the optimal bee at the end of the optimization process:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
""
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"metadata": {},
"output_type": "display_data"
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],
"source": [
"contourplot_bees(\n",
" function = Schwefel2d,\n",
" bee_colony = [ABC.optimal_bee],\n",
" title = 'Optimal Bee after ABC optimization',\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, we might want to visualize the entire colony at a given iteration. In the example below, we visualize the colony at initialization"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
]
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"metadata": {},
"output_type": "display_data"
}
],
"source": [
"contourplot_bees(\n",
" function = Schwefel2d,\n",
" bee_colony = ABC.colony_history[0],\n",
" title = f\"{Schwefel2d.name} contourplot with bees\",\n",
" figsize = (600,600),\n",
" bounds = None,\n",
" zoom = 1.05,\n",
" bee_marker_size = 55,\n",
" optimal_solution = ABC.optimal_bee.position\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can use this function in a more sophisticated way in order to visualize the entire optimization process as animated gif."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"![](\"../_static/animated_optimization.gif\"/)
"
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from PIL import Image\n",
"from IPython.display import Image as IPImage\n",
"from io import BytesIO\n",
"\n",
"plots = []\n",
"# Adaptive step in order to have gifs with same number of frames\n",
"step = max(1, (ABC.actual_iters+1) // 50) #Note: actual_iters +1 to include initial population\n",
"for iteration in range(0,(ABC.actual_iters+1),step): \n",
" plots.append(\n",
" contourplot_bees(\n",
" function = Schwefel2d,\n",
" bee_colony = ABC.colony_history[iteration],\n",
" title = f\"{Schwefel2d.name.upper()} optimization [Iteration {iteration} / {ABC.actual_iters}]\",\n",
" optimal_solution = Schwefel2d.optimal_solution,\n",
" bounds = None,\n",
" zoom = 1.05,\n",
" bee_marker_size = 55,\n",
" figsize =(500,500)\n",
" ))\n",
"\n",
"images = []\n",
"for fig in plots:\n",
" # Save each figure to a BytesIO object in memory instead of a file\n",
" img_buf = BytesIO()\n",
" fig.write_image(img_buf, format=\"png\", scale=1.5) # Save the Plotly figure as PNG into the buffer\n",
" img_buf.seek(0) # Rewind the buffer to the start\n",
" images.append(Image.open(img_buf)) # Open the image from the buffer\n",
" \n",
"# Create the GIF\n",
"gif_path = '../_static/animated_optimization.gif'\n",
"images[0].save(gif_path, save_all=True, append_images=images[1:], duration=200, loop=0)\n",
"IPImage(url=gif_path)\n"
]
}
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