{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hyperparameter optimization\n", "\n", "[![Download JupyterNotebook](https://img.shields.io/badge/Download-Notebook-orange?style=for-the-badge&logo=Jupyter)](https://raw.githubusercontent.com/ANNarchy/ANNarchy.github.io/master/notebooks/BayesianOptimization.ipynb) [![Download JupyterNotebook](https://img.shields.io/badge/Open_in-Colab-blue?style=for-the-badge&logo=Jupyter)](https://colab.research.google.com/github/ANNarchy/ANNarchy.github.io/blob/master/notebooks/BayesianOptimization.ipynb)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#!pip install ANNarchy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Most of the work in computational neuroscience is to guess the values of parameters which are not constrained by the biology. The most basic approach is to simply try out different values, run the simulation, reason about why the results are not what you want, change some parameters, run again, etc. It is very easy to get lost in this process and it requires a great deal of intuition about how the model works.\n", "\n", "If you are able to define an objective function for your model (a single number that tells how well your model performs), you can use search algorithms to find this hyperparameters automatically, at the cost of running your model multiple times.\n", "\n", "Let's take the example of a rate-coded model depending on two hyperparameters `a` and `b`, where is the objective is to have a minimal activity after 1 s of simulation (dummy example):\n", "\n", "```python\n", "import ANNarchy as ann\n", "\n", "pop = ann.Population(...)\n", "...\n", "ann.compile()\n", "\n", "def run(a, b):\n", " pop.a = a\n", " pop.b = b\n", " \n", " ann.simulate(1000.)\n", " \n", " return (pop.r)**2\n", "```\n", "\n", "**Grid search** would iterate over all possible values of the parameters to perform the search:\n", "\n", "```python\n", "min_loss = 1000.\n", "for a in np.linspace(0.0, 1.0, 100):\n", " for b in np.linspace(0.0, 1.0, 100):\n", " loss = run(a, b)\n", " if loss < min_loss:\n", " min_loss = loss\n", " a_best = a ; b_best = b\n", "```\n", "\n", "If you try 100 values for each parameters, you need 10000 simulations to find your parameters. The number of simulations explodes with the number of free parameters. Moreover, you cannot stop the search before the end, as you could miss the interesting region.\n", "\n", "**Random search** samples blindly values for the hyperparameters:\n", "\n", "```python\n", "min_loss = 1000.\n", "for _ in range(1000):\n", " a = np.random.uniform(0.0, 1.0)\n", " b = np.random.uniform(0.0, 1.0)\n", " loss = run(a, b)\n", " if loss < min_loss:\n", " min_loss = loss\n", " a_best = a ; b_best = b\n", "```\n", "\n", "If you are lucky, you may find a good solution quite early in the search, so you can stop it when the loss is below a desired threshold. The main drawback is that the search may spend a lot of time in uninteresting regions: it does not learn anything between two samples.\n", "\n", "An often much more efficient search method is **Bayesian optimization** (also called sequential model-based optimization - SMBO). It is a form of random search that updates beliefs on the hyperparameters. In short, if some parameter values do not lead to good values of the objective function in early samples, they will not be used in later samples. The search becomes more and more focused on the interesting regions of the hyperparameter space. \n", "\n", "As always with Python, there are many libraries for that, including:\n", "\n", "* `hyperopt` \n", "* `optuna` \n", "* `talos` (for keras models) \n", "\n", "This notebook demonstrates how to use `hyperopt` to find some hyperparameters of the COBA models already included in the ANNarchy examples:\n", "\n", "\n", "\n", "Additionally, we will use the `tensorboard` extension to visualize the dependency between the parameters and the objective function." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "import ANNarchy as ann\n", "from ANNarchy.extensions.tensorboard import Logger\n", "ann.clear()\n", "ann.setup(dt=0.1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compiling ... OK \n" ] } ], "source": [ "COBA = ann.Neuron(\n", " parameters=\"\"\"\n", " El = -60.0 : population\n", " Vr = -60.0 : population\n", " Erev_exc = 0.0 : population\n", " Erev_inh = -80.0 : population\n", " Vt = -50.0 : population\n", " tau = 20.0 : population\n", " tau_exc = 5.0 : population\n", " tau_inh = 10.0 : population\n", " I = 20.0 : population\n", " \"\"\",\n", " equations=\"\"\"\n", " tau * dv/dt = (El - v) + g_exc * (Erev_exc - v) + g_inh * (Erev_inh - v ) + I\n", "\n", " tau_exc * dg_exc/dt = - g_exc\n", " tau_inh * dg_inh/dt = - g_inh\n", " \"\"\",\n", " spike = \"v > Vt\",\n", " reset = \"v = Vr\",\n", " refractory = 5.0\n", ")\n", "\n", "P = ann.Population(geometry=4000, neuron=COBA)\n", "Pe = P[:3200]\n", "Pi = P[3200:]\n", "P.v = ann.Normal(-55.0, 5.0)\n", "P.g_exc = ann.Normal(4.0, 1.5)\n", "P.g_inh = ann.Normal(20.0, 12.0)\n", "\n", "Ce = ann.Projection(pre=Pe, post=P, target='exc')\n", "Ce.connect_fixed_probability(weights=0.6, probability=0.02)\n", "Ci = ann.Projection(pre=Pi, post=P, target='inh')\n", "Ci.connect_fixed_probability(weights=6.7, probability=0.02)\n", "\n", "ann.compile()\n", "\n", "m = ann.Monitor(P, ['spike'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the default parameters, the COBA network fires at around 20 Hz:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "21.389999999999997\n" ] } ], "source": [ "ann.simulate(1000.0)\n", "data = m.get('spike')\n", "fr = m.mean_fr(data)\n", "print(fr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's suppose we now want the network to fire at 30 Hz. Which parameters should we change to obtain that value?\n", "\n", "Many parameters might influence the firing rate of the network (if not all). Here, we make the assumption that the weight values for the excitatory connections (0.6) and inhibitory ones (6.7) are the most critical ones.\n", "\n", "Let's start by importing `hyperopt` (after installing it with `pip install hyperopt`):" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from hyperopt import fmin, tpe, hp, STATUS_OK" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define a `trial()` method taking values for the two hyperparameters as inputs. It starts by resetting the network, sets the excitatory and inhibitory weights to the desired value, simulates for one second, computes the mean firing rate of the population, logs the parameters and finally returns the objective function: the squared error between the recorded firing rate and 30 Hz." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logging in runs/May29_14-03-35_Juliens-MBP\n" ] } ], "source": [ "logger = Logger()\n", "\n", "def trial(args):\n", " \n", " # Retrieve the parameters\n", " w_exc = args[0]\n", " w_inh = args[1]\n", " \n", " # Reset the network\n", " ann.reset()\n", " \n", " # Set the hyperparameters\n", " Ce.w = w_exc\n", " Ci.w = w_inh\n", " \n", " # Simulate 1 second\n", " ann.simulate(1000.0)\n", "\n", " # Retrieve the spike recordings and the membrane potential\n", " spikes = m.get('spike')\n", "\n", " # Compute the population firing rate\n", " fr = m.mean_fr(spikes)\n", " \n", " # Compute a quadratic loss around 30 Hz\n", " loss = 0.001*(fr - 30.0)**2 \n", " \n", " # Log the parameters\n", " logger.add_parameters({'w_exc': w_exc, 'w_inh': w_inh},\n", " {'loss': loss, 'firing_rate': fr})\n", " \n", " return {\n", " 'loss': loss,\n", " 'status': STATUS_OK,\n", " # -- store other results like this\n", " 'fr': fr,\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check that the default parameters indeed lead to a firing rate of 20 Hz:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'loss': 0.07413210000000005, 'status': 'ok', 'fr': 21.389999999999997}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trial([0.6, 6.7])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now use `hyperopt` to find the hyperparameters making the network fire at 30 Hz.\n", "\n", "The `fmin()` function takes:\n", "\n", "* `fn`: the objective function for a set of parameters.\n", "* `space`: the search space for the hyperparameters (the prior). \n", "* `algo`: which algorithm to use, either tpe.suggest or random.suggest\n", "* `max_evals`: number of samples (simulations) to make.\n", "\n", "Here, we will sample the excitatory weights between 0.1 and 1, the inhibitory ones between 1 and 10. Of course, the smaller the range, the better. Refer to the doc of hyperopt for other sampling priors." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:14<00:00, 6.86trial/s, best loss: 8.702499999999263e-07]\n", "{'w_exc': 0.8752658685269741, 'w_inh': 6.989948034837044}\n" ] } ], "source": [ "best = fmin(\n", " fn=trial,\n", " space=[\n", " hp.uniform('w_exc', 0.1, 1.0), \n", " hp.uniform('w_inh', 1.0, 10.0)\n", " ],\n", " algo=tpe.suggest,\n", " max_evals=100)\n", "print(best)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After 100 simulations, `hyperopt` returns a set of hyperparameter values that make the network fire at 30Hz. We can check that it is true with:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'loss': 8.702499999999263e-07, 'status': 'ok', 'fr': 30.0295}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trial([best['w_exc'], best['w_inh']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are plenty of options to `hyperopt` (check Trials or the parallel search using MongoDB), but this simple example should get you started. \n", "\n", "If we start tensorboard in the default directory `runs/`, we can additionally visualize how the firing rate depends on `w_exc` and `w_inh` in the `HPARAMS` tab." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "logger.close()\n", "%load_ext tensorboard\n", "%tensorboard --logdir runs" ] }, { "attachments": { "61f5c638-3f1a-44b7-a708-411fa327846e.png": { "image/png": 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