{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hyper-study\n", "\n", "The time series models built with the help of *bayesloop* are called [hierarchical models](https://en.wikipedia.org/wiki/Bayesian_hierarchical_modeling), since the parameters of the observation model are in turn controlled by hyper-parameters that are possibly included in the transition model. In the [previous section](hyperparameteroptimization.ipynb), we optimized two hyper-parameters of a serially combined transition model: the slope of the decrease in coal mining disasters from 1885 to 1895, and the magnitude of the fluctuations afterwards. While the optimization routine yields the most probable values of these hyper-parameters, one might also be interested in the uncertainty tied to these *\"optimal\"* values. *bayesloop* therefore provides the `HyperStudy` class that allows to compute the full distribution of hyper-parameters by defining a discrete grid of hyper-parameter values.\n", "\n", "While the `HyperStudy` instance can be configured just like a standard `Study` instance, one may supply not only single hyper-parameter values, but also lists/arrays of values (*Note:* It will fall back to the standard fit method if only one combination of hyper-parameter values is supplied). Here, we test a range on hyper-parameter values by supplying regularly spaced hyper-parameter values using the `cint` function (one can of course also use similar functions like [numpy.linspace](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html)). In the example below, we return to the serial transition model used [here](hyperparameteroptimization.ipynb#Global-optimization) and compute a two-dimensional distribution of the two hyper-parameters `slope` (20 steps from -0.4 to 0.0) and `sigma` (20 steps from 0.0 to 0.8).\n", "\n", "After running the `fit` method for all value-tuples of the hyper-grid, the model evidence values of the individual fits are used as weights to compute weighted average parameter distributions. These average parameter distributions allow to assess the temporal evolution of the model parameters, explicitly considering the uncertainty of the hyper-parameters. However, in case one is only interested in the hyper-parameter distribution, setting the keyword argument `evidence_only=True` of the `fit` method shortens the computation time but skips the evaluation of parameter distributions.\n", "\n", "Finally, *bayesloop* provides several methods to plot the results of the hyper-study. To display the joint distribution of two hyper-parameters, choose `get_joint_hyper_parameter_distribution` (or shorter: `get_joint_hyper_parameter_distribution`). The method automatically computes the [marginal distribution](https://en.wikipedia.org/wiki/Marginal_distribution) for the two specified hyper-parameters and returns three arrays, two for the hyper-parameters values and one with the corresponding probability values. Here, the first argument represents a list of two hyper-parameter names. If the keyword argument `plot=True` is set, a visualization is created using the `bar3d` function from the [mpl_toolkits.mplot3d](http://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html) module." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:17:04.854287Z", "iopub.status.busy": "2026-04-27T19:17:04.853912Z", "iopub.status.idle": "2026-04-27T19:17:18.627536Z", "shell.execute_reply": "2026-04-27T19:17:18.627092Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", " --> Hyper-study\n", "+ Successfully imported example data.\n", "+ Observation model: Poisson. Parameter(s): ['accident_rate']\n", "+ Transition model: Serial transition model. Hyper-Parameter(s): ['slope', 'sigma', 't_1', 't_2']\n", "+ Set hyper-prior(s): ['uniform', 'uniform', 'uniform', 'uniform']\n", "+ Started new fit.\n", " + 400 analyses to run.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "013e45586dae419787d9db1240ae7584", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/400 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt # plotting\n", "plt.style.use('seaborn-v0_8-whitegrid') # plot styling\n", "\n", "import numpy as np\n", "import bayesloop as bl\n", "\n", "S = bl.HyperStudy()\n", "S.load_example_data()\n", "\n", "L = bl.om.Poisson('accident_rate', bl.oint(0, 6, 1000))\n", "\n", "T = bl.tm.SerialTransitionModel(bl.tm.Static(),\n", " bl.tm.BreakPoint('t_1', 1885),\n", " bl.tm.Deterministic(lambda t, slope=bl.cint(-0.4, 0, 20): slope*t, \n", " target='accident_rate'),\n", " bl.tm.BreakPoint('t_2', 1895),\n", " bl.tm.GaussianRandomWalk('sigma', bl.cint(0, 0.8, 20), \n", " target='accident_rate'))\n", "S.set(L, T)\n", "\n", "S.fit()\n", "S.get_joint_hyper_parameter_distribution(['slope', 'sigma'], plot=True, color=[0.1, 0.8, 0.1])\n", "\n", "plt.xlim([-0.5, 0.1])\n", "plt.ylim([-0.1, 0.9]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is important to note here that the log$_{10}$ evidence of the averaged hyper-study model, **-73.48**, is smaller than the optimized-model value of **-72.69** obtained in a previous analysis [here](hyperparameteroptimization.ipynb#Global-optimization). There, we optimized the hyper-parameter values and assumed that these optimal values are not subject to uncertainty, therefore over-estimating the model evidence. In contrast, the hyper-study explicitly considers the uncertainty tied to the hyper-parameter values.\n", "\n", "While the joint distribution of two hyper-parameters may uncover possible correlations between the two quantities, the 3D plot is often difficult to integrate into existing figures. To plot the marginal distribution of a single hyper-parameter in a simple 2D histogram/bar plot, use the `plot` method, just as for the parameters of the observation model:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:17:18.628713Z", "iopub.status.busy": "2026-04-27T19:17:18.628581Z", "iopub.status.idle": "2026-04-27T19:17:18.697707Z", "shell.execute_reply": "2026-04-27T19:17:18.697357Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,4))\n", "plt.subplot(121)\n", "S.plot('slope', color='g', alpha=.8);\n", "\n", "plt.subplot(122)\n", "S.plot('sigma', color='g', alpha=.8);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the temporal evolution of the model parameter may be displayed using, again, the `plot` method:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:17:18.698799Z", "iopub.status.busy": "2026-04-27T19:17:18.698722Z", "iopub.status.idle": "2026-04-27T19:17:18.793951Z", "shell.execute_reply": "2026-04-27T19:17:18.793611Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 4))\n", "plt.bar(S.raw_timestamps, S.raw_data, align='center', facecolor='r', alpha=.5)\n", "S.plot('accident_rate')\n", "plt.xlim([1851, 1962])\n", "plt.xlabel('year');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In principle, a `HyperStudy` could also be used to test different time stamps for change- and break-points. However, in a transition model with several change- or break-points, the correct order has to be maintained. Since a `HyperStudy` fits all possible combinations of hyper-parameter values, we need another type of study that takes care of the correct order of change-/break-points, the `ChangepointStudy` class. It is introduced in [the next tutorial](changepointstudy.ipynb)." ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.12.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "013e45586dae419787d9db1240ae7584": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_b42c622ca56347baa5329fd8e2107cfa", 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