Add notebook abc model showcase, add some pretty print functions
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notebooks/abc_model_showcase.ipynb
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314
notebooks/abc_model_showcase.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 32,
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"id": "20768e45-df62-4638-ba33-b0ccf239f1aa",
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"metadata": {},
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"outputs": [],
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"source": [
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"using Revise\n",
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"using MetagraphOptimization\n",
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"using BenchmarkTools"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"id": "9df482a4-ca44-44c5-9ea7-7a2977d529be",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ABCModel()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Create a model identifier\n",
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"model = ABCModel()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "30b16872-07f7-4d47-8ff8-8c3a849c9d4e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ABC Process: 'AB->ABBB'"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Create a process in our model\n",
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"process_str = \"AB->ABBB\"\n",
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"process = parse_process(process_str, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "043bd9e2-f89a-4362-885a-8c89d4cdd76f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Graph:\n",
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" Nodes: \n",
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" Edges: 385\n",
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" Total Compute Effort: 1075.0\n",
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" Total Data Transfer: 10944.0\n",
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" Total Compute Intensity: 0.09822733918128655\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total: 280, ComputeTaskS2: 24, DataTask: 143, \n",
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" ComputeTaskU: 6, ComputeTaskSum: 1, ComputeTaskS1: 36, \n",
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" ComputeTaskV: 64, ComputeTaskP: 6"
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]
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}
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],
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"source": [
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"# Read the graph (of the same process) from a file\n",
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"graph = parse_dag(\"../input/$process_str.txt\", model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "02f01ad3-fd10-48d5-a0e0-c03dc83c80a4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Input for ABC Process: 'AB->ABBB':\n",
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" Input particles:\n",
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" A: [3.1643943055419363, 0.0, 0.0, 3.002231057221651]\n",
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" B: [3.1643943055419363, 0.0, 0.0, -3.002231057221651]\n",
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" Output Particles:\n",
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" A: [-2.256745792111763, -1.8187612014283614, 0.7623781267848754, -0.45142978877450474]\n",
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" B: [-1.2010630585601612, 0.48677829051626326, -0.3095511190586244, -0.33132683442411753]\n",
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" B: [-1.851230645629639, 1.2170428203792298, -0.6136921618583376, 0.7544823438327369]\n",
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" B: [-1.0197491147823097, 0.11494009053286842, 0.16086515413208705, 0.02827427936588541]\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Generate some random input data for our process\n",
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"input_data = gen_process_input(process)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"id": "083fb1be-ce2a-47f9-afb9-60a6fdfaed0b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"compute_ca11d460_5e25_11ee_14ab_5ddab1542812 (generic function with 1 method)"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Get the function computing the result of the process from a ProcessInput\n",
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"AB_AB3_compute = get_compute_function(graph, process)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "a40c9500-8f79-4f04-b3c5-59b72a6b7ba9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"-2.133138322948753e-12"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Actually compute a result using the generated function and the input data\n",
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"result = AB_AB3_compute(input_data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"id": "80c70010",
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"metadata": {},
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"outputs": [],
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"source": [
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"include(\"../examples/profiling_utilities.jl\")\n",
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"\n",
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"# We can also mute the graph by applying some operations to it\n",
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"reduce_all!(graph)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"id": "5b192b44",
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"metadata": {},
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"outputs": [],
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"source": [
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"# The result should be the same as before (we can use execute to save having to generate the function ourselves)\n",
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"@assert result ≈ execute(graph, process, input_data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"id": "9b2f4a3f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1000-element Vector{Float64}:\n",
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" -4.414620594828263e-10\n",
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" -6.52806527801196e-13\n",
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" -1.697445561359304e-14\n",
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" -3.0968989470125384e-10\n",
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" -4.502627629291429e-15\n",
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" -1.5151443298524234e-12\n",
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" -4.6735707896483893e-11\n",
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" -4.39957245807686e-12\n",
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" -4.9303667114098105e-12\n",
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" -7.978610716067286e-12\n",
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" ⋮\n",
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" -3.274950681363404e-15\n",
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" 3.720176763468205e-14\n",
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" -1.6979650967920612e-16\n",
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" -1.2110610922665538e-11\n",
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" -3.488772363060999e-13\n",
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" -7.743825512592736e-12\n",
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" -1.1887405783378775e-11\n",
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" -1.7657864496526309e-13\n",
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" -8.099730579187646e-12"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Now we can generate a function and use it on lots of inputs\n",
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"inputs = [gen_process_input(process) for _ in 1:1000]\n",
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"AB_AB3_reduced_compute = get_compute_function(graph, process)\n",
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"\n",
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"results = AB_AB3_reduced_compute.(inputs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"id": "d43e4ff0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"BenchmarkTools.Trial: 1338 samples with 1 evaluation.\n",
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" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m2.718 ms\u001b[22m\u001b[39m … \u001b[35m7.994 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 32.43%\n",
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" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m3.007 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
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" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m3.736 ms\u001b[22m\u001b[39m ± \u001b[32m1.156 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m5.92% ± 11.07%\n",
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"\n",
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" \u001b[39m█\u001b[39m█\u001b[39m▃\u001b[39m▂\u001b[34m▂\u001b[39m\u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m▁\u001b[39m \u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▂\u001b[39m▂\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
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" \u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[39m▇\u001b[39m▇\u001b[39m▆\u001b[39m█\u001b[32m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▆\u001b[39m▅\u001b[39m▁\u001b[39m▅\u001b[39m▄\u001b[39m▅\u001b[39m▄\u001b[39m▄\u001b[39m▁\u001b[39m▅\u001b[39m▄\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▆\u001b[39m▁\u001b[39m▄\u001b[39m▆\u001b[39m▇\u001b[39m \u001b[39m█\n",
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" 2.72 ms\u001b[90m \u001b[39m\u001b[90mHistogram: \u001b[39m\u001b[90m\u001b[1mlog(\u001b[22m\u001b[39m\u001b[90mfrequency\u001b[39m\u001b[90m\u001b[1m)\u001b[22m\u001b[39m\u001b[90m by time\u001b[39m 7.16 ms \u001b[0m\u001b[1m<\u001b[22m\n",
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"\n",
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" Memory estimate\u001b[90m: \u001b[39m\u001b[33m6.17 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m143004\u001b[39m."
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"@benchmark results = AB_AB3_compute.($inputs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"id": "e18d9546",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"BenchmarkTools.Trial: 1739 samples with 1 evaluation.\n",
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" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m2.178 ms\u001b[22m\u001b[39m … \u001b[35m 6.643 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 37.05%\n",
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" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m2.291 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
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" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m2.873 ms\u001b[22m\u001b[39m ± \u001b[32m902.626 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m6.58% ± 12.21%\n",
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"\n",
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" \u001b[39m█\u001b[39m▇\u001b[34m▄\u001b[39m\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
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" \u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▇\u001b[32m█\u001b[39m\u001b[39m█\u001b[39m▇\u001b[39m▇\u001b[39m▅\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▅\u001b[39m▆\u001b[39m▆\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▅\u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▄\u001b[39m▆\u001b[39m▅\u001b[39m▁\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▅\u001b[39m▄\u001b[39m▆\u001b[39m \u001b[39m█\n",
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" 2.18 ms\u001b[90m \u001b[39m\u001b[90mHistogram: \u001b[39m\u001b[90m\u001b[1mlog(\u001b[22m\u001b[39m\u001b[90mfrequency\u001b[39m\u001b[90m\u001b[1m)\u001b[22m\u001b[39m\u001b[90m by time\u001b[39m 5.97 ms \u001b[0m\u001b[1m<\u001b[22m\n",
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"\n",
|
||||
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m5.26 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m123004\u001b[39m."
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"@benchmark results = AB_AB3_reduced_compute.($inputs)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"id": "8bc5dece",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Julia 1.9.3",
|
||||
"language": "julia",
|
||||
"name": "julia-1.9"
|
||||
},
|
||||
"language_info": {
|
||||
"file_extension": ".jl",
|
||||
"mimetype": "application/julia",
|
||||
"name": "julia",
|
||||
"version": "1.9.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user