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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This tutorial is generated from a [Jupyter](http://jupyter.org/) notebook that can be found [here](https://github.com/elfi-dev/notebooks). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## BOLFI"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In practice inference problems often have a complicated and computationally heavy simulator, and one simply cannot run it for millions of times. The Bayesian Optimization for Likelihood-Free Inference [BOLFI](http://jmlr.csail.mit.edu/papers/v17/15-017.html) framework is likely to prove useful in such situation: a statistical model (usually [Gaussian process](https://en.wikipedia.org/wiki/Gaussian_process), GP) is created for the discrepancy, and its minimum is inferred with [Bayesian optimization](https://en.wikipedia.org/wiki/Bayesian_optimization). This approach typically reduces the number of required simulator calls by several orders of magnitude.\n",
+ "\n",
+ "This tutorial demonstrates how to use BOLFI to do LFI in ELFI."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import scipy.stats\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "%matplotlib inline\n",
+ "%precision 2\n",
+ "\n",
+ "import logging\n",
+ "logging.basicConfig(level=logging.INFO)\n",
+ "\n",
+ "# Set an arbitrary global seed to keep the randomly generated quantities the same\n",
+ "seed = 20170703\n",
+ "np.random.seed(seed)\n",
+ "\n",
+ "import elfi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although BOLFI is best used with complicated simulators, for demonstration purposes we will use the familiar MA2 model introduced in the basic tutorial, and load it from ready-made examples:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from elfi.examples import ma2\n",
+ "model = ma2.get_model(seed_obs=seed)\n",
+ "elfi.draw(model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Fitting the surrogate model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can immediately proceed with the inference. However, when dealing with a Gaussian process, it may be beneficial to take a logarithm of the discrepancies in order to reduce the effect that high discrepancies have on the GP. (Sometimes you may want to add a small constant to avoid very negative or even -Inf distances occurring especially if it is likely that there can be exact matches between simulated and observed data.) In ELFI such transformed node can be created easily:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "log_d = elfi.Operation(np.log, model['d'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As BOLFI is a more advanced inference method, its interface is also a bit more involved as compared to for example rejection sampling. But not much: Using the same graphical model as earlier, the inference could begin by defining a Gaussian process (GP) model, for which ELFI uses the [GPy](https://sheffieldml.github.io/GPy/) library. This could be given as an `elfi.GPyRegression` object via the keyword argument `target_model`. In this case, we are happy with the default that ELFI creates for us when we just give it each parameter some `bounds` as a dictionary.\n",
+ "\n",
+ "Other notable arguments include the `initial_evidence`, which gives the number of initialization points sampled straight from the priors before starting to optimize the acquisition of points, `update_interval` which defines how often the GP hyperparameters are optimized, and `acq_noise_var` which defines the diagonal covariance of noise added to the acquired points."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bolfi = elfi.BOLFI(log_d, batch_size=5, initial_evidence=20, update_interval=10, \n",
+ " bounds={'t1':(-2, 2), 't2':(-1, 1)}, acq_noise_var=[0.1, 0.1], seed=seed)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sometimes you may have some samples readily available. You could then initialize the GP model with a dictionary of previous results by giving `initial_evidence=result.outputs`.\n",
+ "\n",
+ "The BOLFI class can now try to `fit` the surrogate model (the GP) to the relationship between parameter values and the resulting discrepancies. We'll request only 100 evidence points (including the `initial_evidence` defined above)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:elfi.methods.parameter_inference:BOLFI: Fitting the surrogate model...\n",
+ "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.4121) of the GP discrepancy mean function as a threshold\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 13.2 s, sys: 139 ms, total: 13.3 s\n",
+ "Wall time: 7.09 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time post = bolfi.fit(n_evidence=100)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "(More on the returned `BolfiPosterior` object [below](#BOLFI-Posterior).)\n",
+ "\n",
+ "Note that in spite of the very few simulator runs, fitting the model took longer than any of the previous methods. Indeed, BOLFI is intended for scenarios where the simulator takes a lot of time to run.\n",
+ "\n",
+ "The fitted `target_model` uses the GPy library, and can be investigated further:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\n",
+ "Name : GP regression\n",
+ "Objective : 92.664837723526\n",
+ "Number of Parameters : 4\n",
+ "Number of Optimization Parameters : 4\n",
+ "Updates : True\n",
+ "Parameters:\n",
+ " \u001b[1mGP_regression. \u001b[0;0m | value | constraints | priors \n",
+ " \u001b[1msum.rbf.variance \u001b[0;0m | 0.326569075912 | +ve | Ga(0.096, 1)\n",
+ " \u001b[1msum.rbf.lengthscale \u001b[0;0m | 0.552572833732 | +ve | Ga(1.3, 1) \n",
+ " \u001b[1msum.bias.variance \u001b[0;0m | 0.0878317664626 | +ve | Ga(0.024, 1)\n",
+ " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.21318627419 | +ve | "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bolfi.target_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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M/gVFETm4L5WEnSdJ2HmS9HNKAXtnZ0d8/dzx9fPA188DP3/lX09PF3JzS8hI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\nVX9+Xu5MvaE3K966hynDu7H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FUko+6XEDTlb4Xf8XaOodwMy+t3LbhrlM3vQzPw+8A18X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0hW8CCgkCQJm7NJloJOkcXlu5FwAXs+Oou/bVyH1engmfGTZB1SfLVpP2XVDTw4c5Tsww2Hw8lr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\nXOVpV8akxdlcCScPF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PvSLVSX1vDlq74tIVSrVVx74wjyDpeSs6tQ9vi48CBumjyYH3Yc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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "bolfi.plot_state();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It may be useful to see the acquired parameter values and the resulting discrepancies:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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oFVXGdKQi085IskAz1T3tlL4kHVXkw7Kn0Ma2PU16wIDrmMtTdGrxvZIWdXom\n4IckfVHSY7r/ADRUVsc6bYd72VM2avBQVp3X8cQ0RamoMqYjFZl2VtVnDiCsuqed0pekoap8mJWz\nfvaP7pzotZuyPbG6f3mKrlr8Fkkys/Pc/WSR3zGz2yQ9YchTb3L3DxUN0MyulXStJG3evLnorwEo\nSRVH8BeXOrrpcOeMa2RN0lWX1jcdKqYpSkVVdXZ8UNMXC8H0yM3pqnPaKX1JGqrKh1m5ac19ojOz\nTdmeWN2/PEVXLX6+pN+R9GhJm83s2ZJ+0t1/Kut33P0HygjQ3W+QdIMkbdu2bXBtGAAVq6LDHZY0\nXdLt95yYJtSxxDZFqYi6piORZDEKuRlF0Jekoap8mLcexiSFcpO2p5iuY05Z0Wtk3yNph6SbJcnd\n7zSz76ksKgBRKbvDjaGITPEalTqPRpNkAZSBviR+VeXDYTmr3yQ5n+0J/YpeIyt3//LAQ8O3ygLM\n7OVm9jeSni/pFjM7MOlrAUhPDNe5pHiNSpNvqwAACKOqfNjLWTMZi7vGfOAYaSh6RvbLZvYCSW5m\ns5J+WtLdk76pu39Q0gcn/X0AaYvhOpdJpyiFXumYo9EAgDINy4eXPWWj9h04rje+/8hUua73O6Fz\nPpqpaCH7ekn/VdKCpI6kWyX9u6qCAtBssVznMm5RWPatBAAAiEF/Piw718WS89E8IwtZM5uR9Bp3\nf3UN8QBoiXGKyNBnQXtSXOkYAIBx5N02R5q8mCVPomwjr5F19zVJ/6aGWADgLDHd7zWGRaoAAKjS\nqNvmxHy/dbRL0cWe/p+Z/bqZ/Ssze07vX6WRAYDyz4LWLYZFqgAAqFJeTguVf4Fhihayl0h6uqS3\nSnpX998vVxUUAPTEdBY0xZWOAQAYx7Bc149ZSIhFocWe3P2yqgMBgGFiut8rC1YAAJqul9N+9o/u\n1Jr7Wc8zCwmxKFTImtl/kfROd1/ufv9YST/r7r9YZXAAUOatespYNIoFKwAATVf1bXNiWcQRaSs6\ntfjFvSJWktz9AUkvqSYkADitd0P1hfk5maSF+Tm9/RXPHDvhxbRoFAAAsSsr/w4iH6MsRe8jO2Nm\nj3D3b0qSmc1JekR1YQEIKbYjpWWcBeXWOQCAKsWWO8tQxSwk8jHKUrSQvVHSn5jZ/+x+/2OSfq+a\nkACEVPaN0GMR06JRAIBmaWrurAL5GGUpNLXY3d8h6ZckPbX7723u/s4qAwMQRky3uykTt84BAFSl\nqbmzCuTCoj5rAAARMElEQVRjlKVQIWtmj5J0q7v/nKTfkvQIM5utNDIAQTT1SCm3zgEAVKWpubMK\n5GOUpehiT5+Q9EgzW5D0x5JeI+l3qwoKQDhNPVJa1aIVAAA0NXdWgXyMshS9Rtbc/aSZXSPpN939\nnWZ2pMrAAIRR5u1uYsOtcwAAVWhy7qwC+RhlKFzImtnzJb1a0jXdx2Zyfh5AonqJJcWVF5u4YiQA\nIH4p586UkOfRr2ghe52kPZI+6O7HzOw7JN1eXVgAQkrxSCkrRgIAQkoxd6aEPI9BRVct/lN3v6K7\nerHc/S/d/T9UGxoAFMeKkQAANBd5HoNyz8ia2Xvc/Toz+7AkH3ze3a+oLDIAUPFpRKwYCQBAcxXN\n80w/bo9RU4v/oPv/L1cdCIDmKCuJjDONaNP8nDpDkhwrRgIAkL4ieb7ouIFitxlypxa7++Hu/38q\n6bOSPtudZvyn3ccA4Ay9JNJZXpHrdBJZXOqM/VrjTCPivnQAADRXkTxfZNxQ5jgFYY28RtbMrjez\nr0o6LulzZnbCzN5cfWgAUlTmNSzjTBfmvnQAADRXkTxfZNzAtbbNMeoa2Z+RtF3Sd7n7F7uPfYek\n3zSzN7r7u2uIEUBCyrxWddzpwqwYCQBAc43K80XGDayp0Ryjzsi+RtKrekWstL5isaQfkfSjVQYG\nIE1ZReYk16oyXRgAABRVZNxQ5jgFYY0qZGfd/auDD7r7CUmz1YQEIGVlFp9MFwYAAEUVGTdwkLw5\nRq1a/K0JnwPQUr1kUdZqgEwXBgAARY0aN5Q9TkE4owrZZ5vZ14c8bpIeWUE8ABqA4hMAAMSKcUoz\n5Bay7j6T9zwAAAAAAHUbefsdAAAAAABiQiELAAAAAEjKqGtkK2Fm+yS9TOsLRt0r6cfcfTlELADS\nsLjUYWEGAAAiQ35GKKHOyH5c0jPc/VmSPidpT6A4ACRgcamjPfuPqrO8IpfUWV7Rnv1HtbjUCR0a\nAACtRX5GSEEKWXe/1d0f6n57h6QLQ8QBIA37DhzXyuraGY+trK5p34HjgSICAADkZ4QUwzWyPy7p\nY6GDABCv+5ZXxnocAABUj/yMkCorZM3sNjP7zJB/V/b9zJskPSTpxpzXudbMDpnZoRMnTlQVLoCI\nbZqfG+txANUiNwOQyM8Iq7JC1t1/wN2fMeTfhyTJzF4n6aWSXu3unvM6N7j7NnfftnHjxqrCBRCx\nXTu2aG72zNtaz83OaNeOLWc8trjU0fa9B3Xx7lu0fe9BrtEBKkJuBiAVz8+DyNcoQ6hViy+X9POS\nvtfdT4aIAUA6eqsf5q2K2FtwonetTm/Bif7fBwAA5SmSnweRr1GWIIWspF+X9AhJHzczSbrD3V8f\nKBYACdi5dSE3weUtOEFiBACgGqPy8yDyNcoSpJB19+8M8b4AmosFJwAAiB/5GmWJYdViAJgaC04A\nABA/8jXKQiELoBEmXXACAADUh3yNsoS6RhYASjXJghMAAKBe5GuUhUIWQGOMu+AEAACoH/kaZWBq\nMQAAAAAgKRSyAAAAAICkUMgCAAAAAJJCIQsAAAAASAqFLAAAAAAgKaxaDCBZi0sdlu8HAKBE5Fak\ngkIWQJIWlzras/+oVlbXJEmd5RXt2X9Ukki4AABMgNyKlDC1GECS9h04firR9qysrmnfgeOBIgIA\nIG3kVqSEQhZAku5bXhnrcQAAkI/cipRQyAJI0qb5ubEeBwAA+citSAmFLIAk7dqxRXOzM2c8Njc7\no107tgSKCACAtJFbkRIWewKQpN6iE6ysCABAOcitSAmFLIBk7dy6QHIFAKBE5FakgqnFAAAAAICk\nUMgCAAAAAJJCIQsAAAAASAqFLAAAAAAgKRSyAAAAAICkUMgCAAAAAJJCIQsAAAAASAqFLAAAAAAg\nKRSyAAAAAICkUMgCAAAAAJJCIQsAAAAASAqFLAAAAAAgKUEKWTN7m5ndZWZHzOxWM9sUIg4AAAAA\nQHpCnZHd5+7PcvdLJH1E0psDxQEAAAAASEyQQtbdv9737aMkeYg4AAAAAADpOSfUG5vZf5b0o5Ie\nlHRZqDgAAAAAAGmp7Iysmd1mZp8Z8u9KSXL3N7n7EyXdKOkNOa9zrZkdMrNDJ06cqCpcAABQELkZ\nABCauYed1WtmmyV91N2fMepnt23b5ocOHaohKgBAG5jZYXffFjqOlJGbAQBlKpqbQ61a/OS+b6+U\ndE+IOAAAAAAA6Ql1jexeM9si6WFJX5L0+kBxAAAAAAASE6SQdferQrwvAAAAACB9oe4jCwAAAADA\nRChkAQAAAABJoZAFAAAAACSFQhYAAAAAkBQKWQAAAABAUihkAQAAAABJoZAFAAAAACSFQhYAAAAA\nkBQKWQAAAABAUihkAQAAAABJoZAFAAAAACTlnNABAEC/xaWO9h04rvuWV7Rpfk67dmzRzq0LocMC\nAAAVIv9jXBSyAKKxuNTRnv1HtbK6JknqLK9oz/6jkkQyAwCgocj/mARTiwFEY9+B46eSWM/K6pr2\nHTgeKCIAAFA18j8mQSELIBr3La+M9TgAAEgf+R+ToJAFEI1N83NjPQ4AANJH/sckKGQBRGPXji2a\nm50547G52Rnt2rElUEQAAKBq5H9MgsWeAESjt6ADqxYCANAe5H9MgkIWQFR2bl0gcQEA0DLkf4yL\nqcUAAAAAgKRQyAIAAAAAkkIhCwAAAABICoUsAAAAACApFLIAAAAAgKRQyAIAAAAAkkIhCwAAAABI\nCoUsAAAAACAp5u6hYyjMzE5I+tKUL/N4SV8tIZy6pRq3lG7sxF2/VGNPNW4p3djLivtJ7r6xhNdp\nrZJyc0/bt8e6pRq3lG7sxF2/VGNvc9yFcnNShWwZzOyQu28LHce4Uo1bSjd24q5fqrGnGreUbuyp\nxo18qX6uxF2/VGMn7vqlGjtxj8bUYgAAAABAUihkAQAAAABJaWMhe0PoACaUatxSurETd/1SjT3V\nuKV0Y081buRL9XMl7vqlGjtx1y/V2Il7hNZdIwsAAAAASFsbz8gCAAAAABLW+ELWzPaZ2T1mdpeZ\nfdDM5jN+7nIzO25mXzCz3XXHOSSeV5rZMTN72MwyV/4ys78ys6NmdsTMDtUZY5YxYo+tzR9nZh83\ns893/39sxs9F0eaj2s/W/Wr3+bvM7Dkh4hxUIO7vM7MHu+17xMzeHCLOQWb2XjO738w+k/F8lO0t\nFYo91jZ/opndbmaf7fYpPz3kZ6Jtd5xt2vxQtJ+uQpH3NrMtffvRETP7upld133uejPr9D33klji\n7v7c0NwWqs0LtndmH1F3e0+Tk0f9btUKxP7qbsxHzexTZvbsvueCjYmmGU+EbPMCce/qi/kzZrZm\nZo/rPheyvSceB1XW3u7e6H+SflDSOd2v3yHpHUN+ZkbSvZK+Q9K5ku6U9LTAcT9V0hZJ/0fStpyf\n+ytJjw/dzuPGHmmbv1PS7u7Xu4dtK7G0eZH2k/QSSR+TZJKeJ+nTEWwbReL+PkkfCR3rkNi/R9Jz\nJH0m4/no2nuM2GNt8wskPaf79WMkfS6F7Zx/uZ/pVPmhaD9dUexjvXf37/hbrd8PUZKul/RzAdp8\nqtwWqs2LvG9eH1Fne0+Tk4v8bgSxv0DSY7tfv7i/n83abiKJe2huC9nm4763pJdJOhi6vbvvPdE4\nqMr2bvwZWXe/1d0f6n57h6QLh/zYcyV9wd3/0t2/JekPJV1ZV4zDuPvd7n48ZAyTKhh7dG3eff/f\n6379e5J2BoxllCLtd6Wk3/d1d0iaN7ML6g50QIyfeyHu/glJf5/zIzG2t6RCsUfJ3b/i7n/R/fob\nku6WtDDwY9G2O85WQn4I2U+P+97fL+led/9SpVGNNm2bhWrzke9bsI+owzQ5OXReHPn+7v4pd3+g\n+23WWLpu07RbyDYf971fJel9tUQ2whTjoMrau/GF7IAf1/qRgkELkr7c9/3fKExHOAmXdJuZHTaz\na0MHM4YY2/zb3f0r3a//VtK3Z/xcDG1epP1ibOOiMb2gOy3lY2b29HpCm1qM7T2OqNvczC6StFXS\npweeSr3dcba8z7RoP12Fcd/7h3X2APTfd/ez99Y1RVfT57ZQbT7W+2b0EXW19zQ5OXQfNu77X6Mz\nx9KhxkTTjCdCtnnh9zaz8yRdLummvodjGINmqX0bP6eMFwnNzG6T9IQhT73J3T/U/Zk3SXpI0o11\nxpanSNwFvNDdO2b2bZI+bmb3dI+YVKqk2GuXF3f/N+7uZpa1pHeQNm+Rv5C02d3/oXtN06KkJweO\nqemibnMze7TWE/l17v710PEgX135YUQ/PZGScoTM7FxJV0ja0/fwb0p6m9YHom+T9C6tH2CfWl25\nrew2L7G9h/URlbV3W5nZZVovZF/Y93DMY6Koc1sBL5P0SXfvPwsac3vXrhGFrLv/QN7zZvY6SS+V\n9P3enaw9oCPpiX3fX9h9rFKj4i74Gp3u//eb2Qe1fvq+8g26hNija3Mz+zszu8Ddv9KdCnF/xmsE\nafMBRdovSBuPMDKm/kLF3T9qZv/NzB7v7l+tKcZJxdjehcTc5mY2q/UB6o3uvn/IjyTb7k1VcX4o\n1E9Pqowc0fViSX/h7n/X99qnvjaz35L0kTJi7r52lbmtsjYvI+6sPqLK9h5impw8W+B3q1SoDzWz\nZ0n6bUkvdvev9R4POCaaeDxR5HcrNM57nzWrI5IxaJbat/HGTy02s8sl/bykK9z9ZMaP/bmkJ5vZ\nxd2jqD8s6ea6YpyUmT3KzB7T+1rrC1sNXUksQjG2+c2SXtv9+rWSzjpzEFGbF2m/myX9aHcVuedJ\nerBvmlYoI+M2syeYmXW/fq7W+6mvnfVK8YmxvQuJtc27Mf2OpLvd/VcyfizZdkemvH5iZD9doXHe\n+6zr2uzMa7dfrvpyx7S5LVSbF4k7s4+oub2nycmhx0NF8vJmSfslvcbdP9f3eMgx0TTjiZBtXui9\nzex8Sd+rvu0+ojFolvq3cQ+w6lWd/yR9Qevzso90//337uObJH207+deovXV7u7V+vSn0HG/XOtz\nyL8p6e8kHRiMW+urf93Z/XcshriLxh5pm/9zSX8i6fOSbpP0uJjbfFj7SXq9pNd3vzZJv9F9/qhy\nVr+OLO43dNv2Tq0vKvGC0DF343qfpK9IWu1u39ek0N4FY4+1zV+o9WmBd/X14S9Jpd35N/QznSo/\nZPXTNcU+Mkd0v3+U1gfL5w/8/h90t9G7tD6IuyCWuPNyW6g2Lxj30D4iRHsP22aL9lVZ23uN2/ao\n2H9b0gN9bXxo1HYTSdyZuS1km4+Ku/v96yT94cDvhW7vicdBVbW3dV8cAAAAAIAkNH5qMQAAAACg\nWShkAQAAAABJoZAFAAAAACSFQhYAAAAAkBQKWQAAAABAUihkgcSZ2byZ/VTf939sZstmVuXN3wEA\nQIb+3Gxml5jZn5nZMTO7y8yuDh0f0ATcfgdInJldJOkj7v6M7vffL+k8ST/p7i8NGBoAAK3Un5vN\n7F9Kcnf/vJltknRY0lPdfTlkjEDqOCMLpG+vpH9hZkfMbJ+7/4mkb4QOCgCAFjuVmyX9W3f/vCS5\n+32S7pe0MWRwQBOcEzoAAFPbLekZ7n5J6EAAAICkjNxsZs+VdK6ke4NEBTQIhSwAAABQMTO7QNIf\nSHqtuz8cOh4gdUwtBgAAACpkZv9M0i2S3uTud4SOB2gCClkgfd+Q9JjQQQAAgFNO5WYzO1fSByX9\nvrt/IGhUQIOwajHQAGb2vyQ9S9LHJD1P0lMkPVrS1yRd4+4HAoYHAEDr9OXmR0m6UNKxvqdf5+5H\nggQGNASFLAAAAAAgKUwtBgAAAAAkhUIWAAAAAJAUClkAAAAAQFIoZAEAAAAASaGQBQAAAAAkhUIW\nAAAAAJAUClkAAAAAQFIoZAEAAAAASfn/Vh3/79Cdt60AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "bolfi.plot_discrepancy();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There could be an unnecessarily high number of points at parameter bounds. These could probably be decreased by lowering the covariance of the noise added to acquired points, defined by the optional `acq_noise_var` argument for the BOLFI constructor. Another possibility could be to [add virtual derivative observations at the borders](https://arxiv.org/abs/1704.00963), though not yet implemented in ELFI."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### BOLFI Posterior"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Above, the `fit` method returned a `BolfiPosterior` object representing a BOLFI posterior (please see the [paper](http://jmlr.csail.mit.edu/papers/v17/15-017.html) for details). The `fit` method accepts a threshold parameter; if none is given, ELFI will use the minimum value of discrepancy estimate mean. Afterwards, one may request for a posterior with a different threshold:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "post2 = bolfi.extract_posterior(-1.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One can visualize a posterior directly (remember that the priors form a triangle):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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zPfz+Mjn0njdlZn6JR+MbA2Cn9OAzV8vMVoGI2DAuuWsiaz7bwPEjf3ZcoxwJ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\n1ia92x5cNE4AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "post.plot(logpdf=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sampling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, samples from the posterior can be acquired with an MCMC sampler. By default it runs 4 chains, and half of the requested samples are spent in adaptation/warmup. Note that depending on the smoothness of the GP approximation, the number of priors, their gradients etc., this may be slow."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.4121) of the GP discrepancy mean function as a threshold\n",
+ "INFO:elfi.methods.mcmc:NUTS: Performing 1000 iterations with 500 adaptation steps.\n",
+ "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
+ "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.422. After warmup 80 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n",
+ "INFO:elfi.methods.mcmc:NUTS: Performing 1000 iterations with 500 adaptation steps.\n",
+ "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
+ "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.414. After warmup 85 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n",
+ "INFO:elfi.methods.mcmc:NUTS: Performing 1000 iterations with 500 adaptation steps.\n",
+ "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
+ "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.408. After warmup 73 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n",
+ "INFO:elfi.methods.mcmc:NUTS: Performing 1000 iterations with 500 adaptation steps.\n",
+ "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
+ "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.404. After warmup 74 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "4 chains of 1000 iterations acquired. Effective sample size and Rhat for each parameter:\n",
+ "t1 1848.12533825 0.999883608451\n",
+ "t2 2060.13369699 0.999774254928\n",
+ "CPU times: user 1min 27s, sys: 1.21 s, total: 1min 28s\n",
+ "Wall time: 46.6 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time result_BOLFI = bolfi.sample(1000, info_freq=1000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The sampling algorithms may be fine-tuned with some parameters. The default [No-U-Turn-Sampler](http://jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdf) is a sophisticated algorithm, and in some cases one may get warnings about diverged proposals, which are signs that [something may be wrong and should be investigated](http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup). It is good to understand the cause of these warnings although they don't automatically mean that the results are unreliable. You could try rerunning the `sample` method with a higher target probability `target_prob` during adaptation, as its default 0.6 may be inadequate for a non-smooth posteriors, but this will slow down the sampling. \n",
+ "\n",
+ "Note also that since MCMC proposals outside the region allowed by either the model priors or GP bounds are rejected, a tight domain may lead to suboptimal overall acceptance ratio. In our MA2 case the prior defines a triangle-shaped uniform support for the posterior, making it a good example of a difficult model for the NUTS algorithm.\n",
+ "\n",
+ "Now we finally have a `Sample` object again, which has several convenience methods:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Method: BOLFI\n",
+ "Number of posterior samples: 2000\n",
+ "Number of simulations: 100\n",
+ "Threshold: -1.41\n",
+ "Posterior means: t1: 0.564, t2: 0.28"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "result_BOLFI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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4NEaiFBFO0ZYFxQ6MPfXNuGfMCl/9px3p1gy/C+BSm/2XATgy/u9WAK8BACEkH8Ar8f0D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goh5gKYBmC4lHKk3fGEEEIIIYQAjPBNCHEmY2bSdkgptwA43+T79QAujf29AsCxbo4nhBBCCCGE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bcxMEkQhIGCYSgynPMAnGBEEQBJE4KtLZAbqtI5nCsGEmTQtgBEHoEIswzBjrwRh7gDG2Ofe3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\nzgRWFNbfDnw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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "result_BOLFI.plot_traces();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The black vertical lines indicate the end of warmup, which by default is half of the number of iterations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "result_BOLFI.plot_marginals();"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [default]",
+ "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": "3.5.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/ELFI_non_python_operations.ipynb b/ELFI_non_python_operations.ipynb
deleted file mode 100644
index 30a7c93..0000000
--- a/ELFI_non_python_operations.ipynb
+++ /dev/null
@@ -1,1200 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import os\n",
- "import numpy as np\n",
- "import matplotlib\n",
- "import matplotlib.pyplot as plt\n",
- "import scipy.io as sio\n",
- "import scipy.stats as ss\n",
- "\n",
- "import elfi\n",
- "import elfi.examples\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Using other than Python operations with ELFI"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If your simulator or other operations are implemented in a programming language other than Python, you can still use ELFI. This notebook briefly demonstrates how to do this in 3 common scenarios:\n",
- "\n",
- "* External executable (written e.g. in C++ or a shell script)\n",
- "* R function\n",
- "* MATLAB function\n",
- "\n",
- "**Note:** to run some parts of this notebook you need to either compile the simulator, have R or MATLAB installed and install their respective wrapper libraries."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## External executables"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "ELFI supports using external simulators and other operations that can be called from the command-line. ELFI provides some tools to easily incorporate such operations to ELFI models. This functionality is briefly introduced in this notebook. For an introductory tutorial on ELFI, please see the ELFI_tutorial notebook. \n",
- "\n",
- "### Birth-Death-Mutation process\n",
- "\n",
- "We will consider here the Birth-Death-Mutation process simulator introduced in *Tanaka et al 2006 [1]* for the spread of Tuberculosis. The simulator outputs a count vector where each of its elements represents a \"mutation\" of the disease and the count describes how many are currently infected by that mutation. There are three rates and the population size:\n",
- "\n",
- "- $\\alpha$ - (birth rate) the rate at which any infectious host transmits the disease.\n",
- "- $\\delta$ - (death rate) the rate at which any existing infectious hosts either recovers or dies.\n",
- "- $\\tau$ - (mutation rate) the rate at which any infectious host develops a new unseen mutation of the disease within themselves.\n",
- "- $N$ - (population size) the size of the simulated infectious population\n",
- "\n",
- "It is assumed that the susceptible population is infinite, the hosts carry only one mutation of the disease and transmit that mutation onward. A more accurate description of the model can be found from the original paper or e.g. [*Lintusaari at al 2016*](https://doi.org/10.1093/sysbio/syw077) *[2]*.\n",
- "\n",
- "\n",
- "\n",
- "This simulator cannot be implemented effectively with vectorized operations so we have implemented it with C++ that handles loops efficiently. We will now reproduce Figure 6(a) in [*Lintusaari at al 2016*](https://doi.org/10.1093/sysbio/syw077) *[2]* with ELFI. Let's start by defining some constants:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Fixed model parameters\n",
- "delta = 0\n",
- "tau = 0.198\n",
- "N = 20\n",
- "\n",
- "# The zeros are to make the observed population vector have length N\n",
- "y_obs = np.array([6, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype='int16')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's build the beginning of a new model for the birth rate $\\alpha$ as the only unknown"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Prior(name='alpha', 'uniform')"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "m = elfi.ElfiModel(name='bdm')\n",
- "elfi.Prior('uniform', .005, 2, model=m, name='alpha')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Wrapping External executables\n",
- "\n",
- "We now need to wrap the executable as an ELFI node for the model. We can use `elfi.tools.external_operation` tool to wrap any executables as a Python callables (function). Let's first investigate how it works with a simple shell `echo` command:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 3., 1., 123.])"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Make an external command as an elfi operation. {0} {1} are positional arguments and {seed} a keyword argument `seed`.\n",
- "command = 'echo {0} {1} {seed}'\n",
- "echo_sim = elfi.tools.external_operation(command)\n",
- "\n",
- "# Test that `echo_sim` can now be called as a regular python function\n",
- "echo_sim(3, 1, seed=123)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The placeholders for arguments in the command string are just Python's [`format strings`](https://docs.python.org/3/library/string.html#formatstrings).\n",
- "\n",
- "Currently `echo_sim` only accepts scalar arguments. In order to work in ELFI, `echo_sim` needs to be vectorized so that we can pass to it a vector of arguments. ELFI provides a handy tool for this as well:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 1.23623071e+00, 0.00000000e+00, 8.11524093e+08],\n",
- " [ 8.41518312e-01, 0.00000000e+00, 6.34595311e+08],\n",
- " [ 7.87740154e-01, 0.00000000e+00, 1.28446951e+09]])"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Vectorize it with elfi tools\n",
- "echo_sim_vec = elfi.tools.vectorize(echo_sim)\n",
- "\n",
- "# Add it to the model\n",
- "elfi.Simulator(echo_sim_vec, m['alpha'], 0, name='sim')\n",
- "\n",
- "# Test to generate 3 simulations from it\n",
- "m['sim'].generate(3)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "So above, the first column draws from our uniform prior for $\\alpha$, the second column has constant zeros, and the last one lists the seeds provided to the command by ELFI.\n",
- "\n",
- "### More complex wrapping of external operations $-$ case BDM\n",
- "\n",
- "Lets now wrap the actual BDM simulator in place of the echo simulator. We assume the executable `bdm` is located at the same directory where this notebook is run from.\n",
- "\n",
- "**Note**: The source code for the BDM simulator comes with ELFI. You can get the directory with `elfi.examples.bdm.get_source_directory()`. Under unix-like systems it can be compiled with just typing `make` to console in the source directory. For windows systems, you need to have some C++ compiler available to compile it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "make: Entering directory '/l/lintusj1/notebooks-elfi/resources/cpp'\n",
- "g++ bdm.cpp -O -o bdm --std=c++11\n",
- "make: Leaving directory '/l/lintusj1/notebooks-elfi/resources/cpp'\n"
- ]
- }
- ],
- "source": [
- "# Get the BDM source directory\n",
- "sources_path = elfi.examples.bdm.get_sources_path()\n",
- "\n",
- "# Copy to resources folder and compile (unix-like systems)\n",
- "!cp -r $sources_path resources\n",
- "!make -C resources/cpp\n",
- "\n",
- "# Move the file in to the working directory\n",
- "!mv ./resources/cpp/bdm ."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 19., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
- " 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Test the executable (assuming we have the executable `bdm` in the working directory)\n",
- "sim = elfi.tools.external_operation('./bdm {0} {1} {2} {3} --seed {seed} --mode 1')\n",
- "sim(1, delta, tau, N, seed=123)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": true
- },
- "source": [
- "The BDM simulator is actually already internally vectorized if you provide it an input file with parameters on the rows. This is more efficient than looping in Python (`elfi.tools.vectorize`), because one simulation takes very little time and we wish to generate tens of thousands of simulations. We will also here redirect the output to a file and then read the file into a numpy array. \n",
- "\n",
- "This is just one possibility among the many to implement this. The most efficient would be to write a native Python module with C++ but it's beyond the scope of this article. So let's work through files which is a fairly common situation especially with existing software."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Assuming we have the executable `bdm` in the working directory\n",
- "command = './bdm {filename} --seed {seed} --mode 1 > {output_filename}'\n",
- "\n",
- "\n",
- "# Function to prepare the inputs for the simulator. We will create filenames and write an input file.\n",
- "def prepare_inputs(*inputs, **kwinputs):\n",
- " alpha, delta, tau, N = inputs\n",
- " meta = kwinputs['meta']\n",
- "\n",
- " # Organize the parameters to an array. The broadcasting works nicely with constant arguments here.\n",
- " param_array = np.row_stack(np.broadcast(alpha, delta, tau, N))\n",
- " \n",
- " # Prepare a unique filename for parallel settings\n",
- " filename = '{model_name}_{batch_index}_{submission_index}.txt'.format(**meta)\n",
- " np.savetxt(filename, param_array, fmt='%.4f %.4f %.4f %d')\n",
- "\n",
- " # Add the filenames to kwinputs\n",
- " kwinputs['filename'] = filename\n",
- " kwinputs['output_filename'] = filename[:-4] + '_out.txt'\n",
- " \n",
- " # Return new inputs that the command will receive\n",
- " return inputs, kwinputs\n",
- "\n",
- "\n",
- "# Function to process the result of the simulation\n",
- "def process_result(completed_process, *inputs, **kwinputs):\n",
- " output_filename = kwinputs['output_filename']\n",
- " \n",
- " # Read the simulations from the file.\n",
- " simulations = np.loadtxt(output_filename, dtype='int16')\n",
- " \n",
- " # Clean up the files after reading the data in\n",
- " os.remove(kwinputs['filename'])\n",
- " os.remove(output_filename)\n",
- " \n",
- " # This will be passed to ELFI as the result of the command\n",
- " return simulations\n",
- "\n",
- "\n",
- "# Create the python function (do not read stdout since we will work through files)\n",
- "bdm = elfi.tools.external_operation(command, \n",
- " prepare_inputs=prepare_inputs, \n",
- " process_result=process_result, \n",
- " stdout=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's replace the echo simulator with this. To create unique but informative filenames, we ask ELFI to provide the operation some meta information. That will be available under the `meta` keyword (see the `prepare_inputs` function above):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Create the simulator\n",
- "bdm_node = elfi.Simulator(bdm, m['alpha'], delta, tau, N, observed=y_obs)\n",
- "m['sim'].become(bdm_node)\n",
- "\n",
- "# Ask ELFI to provide the meta dict\n",
- "bdm_node.uses_meta = True\n",
- "\n",
- "# Draw the model\n",
- "elfi.draw(m)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[11 1 1 1 1 1 1 3 0 0 0 0 0 0 0 0 0 0 0 0]\n",
- " [ 5 3 4 1 1 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0]\n",
- " [11 3 1 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
- ]
- }
- ],
- "source": [
- "# Test it\n",
- "data = bdm_node.generate(3)\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Completing the BDM model\n",
- "\n",
- "We are now ready to finish up the BDM model. To reproduce Figure 6(a) in [*Lintusaari at al 2016*](https://doi.org/10.1093/sysbio/syw077) *[2]*, let's add different summaries and discrepancies to the model and run the inference for each of them:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Distance(name='d_sim')"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "def T1(clusters):\n",
- " clusters = np.atleast_2d(clusters)\n",
- " return np.sum(clusters > 0, 1)/np.sum(clusters, 1)\n",
- "\n",
- "def T2(clusters, n=20):\n",
- " clusters = np.atleast_2d(clusters)\n",
- " return 1 - np.sum((clusters/n)**2, axis=1)\n",
- "\n",
- "# Add the different distances to the model\n",
- "elfi.Summary(T1, bdm_node, name='T1')\n",
- "elfi.Distance('minkowski', m['T1'], p=1, name='d_T1')\n",
- "\n",
- "elfi.Summary(T2, bdm_node, name='T2')\n",
- "elfi.Distance('minkowski', m['T2'], p=1, name='d_T2')\n",
- "\n",
- "elfi.Distance('minkowski', m['sim'], p=1, name='d_sim')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "elfi.draw(m)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 4.04 s, sys: 40 ms, total: 4.08 s\n",
- "Wall time: 4.85 s\n",
- "CPU times: user 28 ms, sys: 0 ns, total: 28 ms\n",
- "Wall time: 26.5 ms\n",
- "CPU times: user 28 ms, sys: 0 ns, total: 28 ms\n",
- "Wall time: 28 ms\n"
- ]
- }
- ],
- "source": [
- "# Save parameter and simulation results in memory to speed up the later inference\n",
- "pool = elfi.OutputPool(['alpha', 'sim'])\n",
- "# Fix a seed\n",
- "seed = 20170511\n",
- "\n",
- "rej = elfi.Rejection(m, 'd_T1', batch_size=10000, pool=pool, seed=seed)\n",
- "%time T1_res = rej.sample(5000, n_sim=int(1e5))\n",
- "\n",
- "rej = elfi.Rejection(m, 'd_T2', batch_size=10000, pool=pool, seed=seed)\n",
- "%time T2_res = rej.sample(5000, n_sim=int(1e5))\n",
- "\n",
- "rej = elfi.Rejection(m, 'd_sim', batch_size=10000, pool=pool, seed=seed)\n",
- "%time sim_res = rej.sample(5000, n_sim=int(1e5))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Results after 100000 simulations. Compare to figure 6(a) in Lintusaari et al. 2016.\n"
- ]
- },
- {
- "data": {
- "image/png": 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UYEr6munAOeAMcBzoUpBcFYdfchw6dEgC0u4/dnLwr4MLd/FPP2lx90eO5JhK\nS5OyRw8pa9WSMkviYpGJPHFCxhsjd7YwkskpuWfwFob33tPyAc6dK75uFYXyEIefF40bN5Z3794t\ntpzk5ORs77t37y5PnTql9/tIKeXu3bsz7/fBBx/IDz74QKfrPvnkE/nVV19lvr9586b09/eXUkr5\n4MED2aJFC3kul3+4pZ5pK6X0BfJ9cJdSfgd8V8TPHIWeCQkJgSpwM/EmrvVcdb9QSs2d4+yspbE+\nxd69cPCgtsNPT1wsFnU6deKLNrX559l7BC/9D22mFm97PnMmLF2qhYhuyuF4VJQVgwYNIjw8nMTE\nRN566y28vb1zrPnss89YvXo1tra2NGzYkI4dO/L+++8TEBDAlClTePToEc2aNcPHxwcbGxt69OiB\ni4sLvr6+jBo1iri4OKpVq4aDgwN+fn6MGTMGc3Nzjh07BsDixYvZtm0bycnJrF+/ntatWzNnzhyu\nXLlCWFgY169f5+uvv+b48ePs2rULOzs7tm3bhulTmeW9e/fO/N7Dw0Pngm1PU79+ferXrw+AlZUV\nbdq04caNGzg65hbxrj9U8bQKyMWLFzG1M0UicarrpPuFJ09q9YinTs3hnJdS8903bJina79IpIyc\nSkBdsJ3zVbEzqGrX1s6ZN2/WfhRF+cDHxwd/f3/8/PxYtGgRUVHZvb2nTp1i48aNnDlzhl27duHn\n55c5N27cOObNm0dgYCBOTk7MnTs3cy4pKQk/Pz/ee++9zDEvLy/c3NxYs2YNAQEBmUXSateuzenT\np5k6dSoLFizIXB8aGsr+/fvZunUrY8eOpWfPngQFBWFubs6OHTsK/Ln69u1brN8NaP0A/v77b555\n5pliyyoIZfArICEhIdi21aKgnOs6637hjz9CtWpaZu1T7N6ttSScNUs7z9UXI7zG8K4T1L4Tp2X1\nFpO339YSemfO1D6kFGXPokWLaN++PR4eHoSHh+doMXjkyBEGDhyImZkZVlZW9O/fH4DY2Fju379P\n9+5aH+bx48dn1s8HGDFihM46DBkyBICOHTty9erVzPG+fftiamqKk5MTqampmRU5nZycsq17ms8/\n/xwTExPG5PJ/pTDEx8czdOhQvvnmG6rnUoFW3yiDXwG5ePEi5g7mVKtSDYcaDrpd9PChVhxtzJgc\n3cMzdveNG8OECfrVtVWrVpwzr8PvrSD1839DZGSx5FlZaR9KBw7An3/qSUlFkTl48CD79u3j2LFj\nnDlzBlcwlMr+AAAgAElEQVRX18xyycXF0lL3dp1V03cpxsbG2bpdZYwbGRlhamqamaBoZGSUZ1es\n5cuXs337dtasWVP4hMYsJCcnM3ToUMaMGZP5gVTSKINfwUhJSSE0NJSkmkm0q9MOI6Hjn3jHDq0v\n7ahROaZ27tRcJLNmQZUqelYYGNJ2CDM80e6vhzoJr7+uVez88svi66YoHrGxsdjY2GBhYcGFCxc4\nfvx4jjVdu3Zl27ZtJCYmEh8fz/btWva1tbU1NjY2HD58GIBVq1Zl7vbzw8rKiri4OP3+IOn88ccf\nzJ8/n61bt2Y2ZAGty1avXr10liOlZPLkybRp04Z33323JFTNFWXwKxhXr14lOTmZaJNonOsUwp2z\nfj3UrQvdumUbllLrNtWkCZRUzanxA8dzKRlWeVSHn3/WYj+LQdWqmmtn/37I4g5WlAF9+vQhJSWF\nNm3aMHPmTDxy6Ubv7u7OgAEDcHZ2pm/fvjg5OWFtbQ3AihUrmDFjBs7OzgQEBDBbhw3BhAkTmDJl\nCi4uLiQkJOj155k+fTpxcXF4enri4uLClClTALh161ahavQcOXKEVatWsX///sxQzZ07desCVyyK\nGt5T3JcKyywZtm/fLrFCMgf53YnvdLsoPl5Kc3Mpp03LMXXwoBal+dNPelY0C6mpqdKyn6W0+QCZ\nWsNayv79iy0zNlbK6tW1/iuVmfIclpmVjKYoDx8+lB07dswMWTQUFi9eLH///Xe9yy318sgKw+Li\nxYuQnmel84Htzp2aO2XYsBxTCxdq0S8ZhdJKAiMjI/o07kOMBfiN6gHbtsGJ3Mo16U716lqw0YYN\nEBqqHz0VJYe3tzcuLi506NCBoUOH0qFDh7JWqVBMnz6dAQMGlLUaBVJ564RWUEJCQjBvYk4CCbSr\n0063izLcOU8VtL98WbO9s2ZBSbcAnfzyZDbu3chMh5vsr11b8+Xv3l0smf/4h1ZnZ+FC+P57PSlq\nyLz9NgQE6Femi4teoqvWrl2rB2UUBaF2+BWMixcvYtnEkobVG2JjblPwBY8eaQe2Q4bAU/XKv/1W\n62g4bVoJKZuF559/HtMwUw49PE3S++/Anj3g61ssmQ0aaE8mPj6girMqFCgffkXD3t5e1viohnx5\nzcu6XbB+veak378/23B0tNaRcPx4/euYF89NfE4yB7nV/1cp69WTsnt3rZ5DMQgO1n68Tz/Vj46G\nRnnw4T969Eg+99xzMiUlpdTuuXz5ctm8eXPZvHlzuXz58szxESNGyIsXL5aaHsVF+fAVeZKYmEjE\nrQgeVH2gu/9+/XqoUweeey7b8LJl2ub/nXdKQNE8ePWFVyERlviv1Orw//WXFmpTDFq3Bk9PreRC\nHmHVihLGx8eHIUOG5NnxSt9ER0czd+5cTpw4wcmTJ5k7dy4xMTEATJ06lfnz55eKHuURZfArEFev\nXoXakEYaTnV0KKnw+LHmzhk8OJs7JyUFFi+G55+H9u1LTt+nGfDyAMRFwZ83/iRp8gTNJ5NHe8XC\nMHUqRERoP6qi9FmzZg0DBw4E4KuvvsLd3R1nZ2c++eQTQPt327p1a8aMGUObNm3w8vLi0aNHAMyc\nORNHR0ecnZ15//33dbrf7t278fT0pGbNmtjY2ODp6ckff/wBwLPPPsu+ffvyTKqq6CiDX4EICwsr\nXISOr6+WYfvyy9mG//hDM5BvvFECSuaDtbU1rlVdeSwes//GEXjvPS1ltpgRO/37a4lYP/6oJ0UV\nOpOUlERYWBgODg7s2bOHS5cucfLkSQICAvD3988slRASEsK0adMIDg6mevXq/PDDD0RFRbF582bO\nnTtHYGAgs9IbJ69ZsyYzdj3ry8vLC9CSoBo2bJipg729PTfSGyUYGRnRvHlzzlTSnpjK4FcgQkND\noQ5UMapCy1otC75g1y4tdfb557MNL12q9RxPL2lSqnj38obH8NPhn7S+hTVrFjtl1sQEXntNC/q5\nfFlPiip04t69e9RI75q2Z88e9uzZg6urKx06dODChQuZdXUaNmxI165dARg7diy+vr5YW1tjZmbG\n5MmT2bRpU2Zm65gxYwgICMjx0rVyZZ06dbLVy69MKINfgQgLC8O4gTFtbNtgamxa8AU7d0L37pCl\nJkmG62PSJC1Cp7TxGuSFuCjYfX03KRZm8Oab8PvvcO5cseS++qrmtfrpJz0pqtAJc3PzzNo5Uko+\n+uijTAN9+fJlJk+eDJCjJo0QAhMTE06ePImXlxfbt2/PLGxW0A7fzs6O8PDwTFkRERHY2dllvk9M\nTMysolnpKOppb3FfKkpH/wwYMECafGAix24aW/Diq1e18JWFC7MNz52rDYeFlZCSOtB+RHvJHOTe\ny3ulvHdPSktLKV95pdhyhw7VmrckJOhBSQOhPETp2Nvby4SEBLl7927ZqVOnzKzaiIgIGRkZKa9c\nuSIBefToUSmllJMnT5YLFiyQcXFxMjIyUkop5f3792XNmjV1ul9UVJR0cHCQ0dHRMjo6Wjo4OMio\nLB172rVrJ2/duqXnn7JkUFE6ijy5dP0SKRYpONbWoYnCrvQ+slnqeaemaqVsevfWaueUFa/2fBWS\nYKnvUqhVS3PtrF0L+ZSr1YWpU7VevEXsWaEoIr1798bX15fevXszevRoOnfujJOTE15eXplFzlq1\nasX3339PmzZtiImJYerUqcTFxdGvXz+cnZ3p1q0bCxfm2mE1BzVr1uTjjz/G3d0dd3d3Zs+eTc30\njj2RkZGYm5tTr169Evt5yzVF/aQo7kvt8PVLWlqarNqsqmQOcnPw5oIv6N9fSgeHbHHuO3Zou/sN\nG0pQUR2IjIyUDENafmIpU1JTpIyIkNLUVMo33yyW3NRUKZs2lbJXLz0pagCUhx2+v7+/HDs276fO\nK1euyLZt25aKLgsXLpQ///xzqdxLH6gdviJXIiMjeWz1GABH2wJ2+I8fa/HtL72UrbPVsmVahYWy\nLglSp04d2tKWh+Ihh68d1kJsRo7UUmbzaRpdEEZGWsXP/fvh2jU9KqzIlw4dOtCzZ09SU1PLWhVq\n1KjB+JIq+2oAKINfQQgLC4PaYCJMaGrTNP/Fhw9r4ZhZ3Dl37mh1c8aPL5vD2qeZ3H0yJMOPvumx\nlO+8o+n888/FkjtunFbyeeVKPSip0JlJkyblmXjl4ODA2bNnS0WPiRMnFqqMcUWjQIMvhGgohDgg\nhDgvhDgnhHgrlzVCCLFICHFZCBEohDCsUncVgLCwMLCFJtWbYGJUwD/ojHDMnj0zh9at03z448aV\nsKI6MnLISLgA28K2kZyaDK6u0KMHLFpUrJRZBwftx16+XLVAVFQ+dNnhpwDvSSkdAQ/gDSHE0z6D\nvkCL9Jc3oFJcSpnQ0FCoDe3q6VAhc9euHOGYK1dCx47Qtm0JKlkI6tevj2OaIwkksDdsrzb4zjsQ\nHg4bNxZL9oQJEBZW7NpsBoNUn2wGSUn83Qo0+FLKW1LK0+nfxwHBgN1TywYCK9PPFI4DNYQQ9fWu\nrSJPLl25BDbQrm4BBv/2bQgO1grMpHPuHJw+XbI174vC5O6TIQGWHFmiDfTrB82ba/WOi/GfYehQ\nrVf78uX60bM8Y2ZmRlRUlDL6BoaUkqioKMzMzPQqt1DOLCGEA+AKPJ3rbgeEZ3kfkT5266nrvdGe\nAGjUqFHhNFXky/nI89BEhwPbv/7SvvbokTm0apWWlJRLO9syZfjQ4bw37T12m+/mUfIjLEwttJru\n06fD0aOQnplZWCwtYfhwrWf7okXZHnQqHPb29kRERHBX1Yc2OMzMzLC3t9erTJ0NvhCiGrAReFtK\n+aAoN5NSLgWWAri5uakthx65Gn8VgDa12+S/8OBBsLLSfOJofvvVq7Xz2zp1SlbHwmJvb49jqiPn\nOc+2kG2MaDdC88fMmqVZ6iIafNDE+PjApk3l78lGn5iamtKkLJMqFOUKnaJ0hBCmaMZ+jZRyUy5L\nbgANs7y3Tx9TlAIJCQnEmMQgEAXX0Dl4UOtslR6pcOAA3LhRfg5rn2bi8xPhASw7sUwbsLTU6j5s\n3KjVgSgi3bpB48baYbVCUVnQJUpHAL8AwVLKvFLdtgLj0qN1PIBYKeWtPNYq9MyVK1egNtia2mJu\nmk+NkNu34cKFbO6clSvB2rpsCqXpwjCvYXAWDkYcJDohWht84w1IS4MlS4osVwjNhbVnj+qGpag8\n6LLD7wq8AjwvhAhIf70khJgihJiSvmYnEAZcBpYBpdAUT5FBRkhmixot8l+YXoo2w+A/eqS5NIYN\nAz2fDemNxo0b45jqSCqpbDyfHp3TtKl2gLt0KaQX5ioKo0drLq316/WkrEJRztElSsdXSimklM5S\nSpf0104p5RIp5ZL0NVJK+YaUspmU0klK6VfyqisyuBR6CWqBi51L/guf8t9v367lMo0eXfI6Fodx\nnuPgHvj4+TwZfPNNbWv+229FluvkBO3aaWV6FIrKgMq0rQAEXA0AE+jQqIB8t4MHNed1uv/+11+h\nfv0c3Q3LHV5eXhAEJ26f4MaD9KOhF17Q+hcuXlysEM3Ro+HIEVVqQVE5UAa/AnD+7nmggJDMyEgt\n/j7dnRMbq5XDHz48W3fDckmzZs1wEk5IJP879z9tUAgtPNPPr1gdsUaO1L7++qseFFUoyjnK4FcA\nridcBwoIyXwq/n7LFq2GWobBK+9MHDARboDPqSxunXHjNBfV4sVFltukCXTurNw6isqBMvgGjpSS\ne+IelmmWWJtZ573w4EEtvbSD5vb59Vetrswzz5SKmsVmxIgRcBbOxZwj5F6INmhlBRMnaqeut28X\nWfbo0RAYCKVUv0uhKDOUwTdwbt26RVrNNOyrFpCR99dfmfH39+7B3r3a7v6pznLllgYNGtDVuitI\nWBuUZTv+xhuQnFys3oXDhmmlk4tx/qtQGATK4Bs4GUXTWtbMJ+Hqzh04f14rmIbW8Sk11XDcORlM\n9JoIV2C5//IntWFattTShJcsgaSkIsmtW1f71ahOWIqKjjL4Bo7/JX+oCh0a5hOh85T//tdftQAX\nZ+eS10+fDBkyBONgY64/vI7/Lf8nE2++qbl0imGxvby0M+1i9kpXKMo1yuAbOH7XtJSHLi275L0o\ni//+9m0t/2rECMNx52RgY2ND74a9IRVWnVn1ZOLFF6FFi2Id3g4Zov0+VBKWoiKjDL6BExKlHWC2\nr98+70UZ8fempmzerIWtDxtWOvrpmwkjJsAFWPn3Sq0xCmgO+DfegOPHtTDNIlCvnnbEodw6ioqM\nMvgGTnhiOCbJJtSxzKPUZYb/Pt2ds2GD5s5xLKCKcnmlX79+mF80537yfXaH7n4yMWGCVlitGLv8\nYcM0l05wcPH1VCjKI8rgGzjRxtHUTKuJyMs/k6V+zt272mbfy8vw3DkZWFhYMKLjCMQjwS/+vzyZ\nsLbWGvL++qv2IVcEMtw6apevqKgog2/APHz4kGTrZBqaNcx70cGD2s63Qwe2bNGKTHp5lZqKJcLE\n8RORgZLtF7cTkxDzZGL6dC1SZ9myIslt0EArsa/8+IqKijL4BszfF/4GS2hdu3Xei7L47zds0DoE\nGlp0ztN069aN+nfqk0LKk1ILAG3aaK0bf/hBi80vAsOGQVAQhIToSVmFohyhDL4B4xuideF2b+ye\n+4I7dzSndI8eREXBn38atjsnAyMjI17t9ypEws+nfs4++eabcPMmbN5cJNlDhmhflVtHURFRBt+A\n+TvibwCec8yj3GUW//3WrVqylaG7czIYP248nAH/O/5cjLr4ZOKll7QCOUU8vLW3hy5dlFtHUTFR\nBt+ACYkOgWRwbpyHjybDf9+xIxs2aLVzOhRQQdlQaNasGe5m7pAGK8+sfDJhbKyFaPr6QkBAkWR7\necGZM3Dpkp6UVSjKCcrgGzA3Ht/A/KE5xkZ51Df+6y/o1o37D03Zu7diuHOy4j3KG8K0xihpMu3J\nxKRJYGFR5F3+0KHaV+XWUVQ0lME3YO6b3qc2tXOfvHtXK//YvTvbtmlnmBXFnZPBsGHDMD1vyq2E\nWxy6dujJhI0NjB2r1TyOiiq03EaNtCqiyuArKhrK4BsoDxIfkGKZQmOLxrkvyOK/37ABGjaETp1K\nT7/SwNrammFOw+Ax/OL3S/bJ6dO1frc//5z7xQUwbBicPg1hYXpQVKEoJxRo8IUQPkKIO0KIXKuF\nCyF6CCFiszQ4n61/NRVP43tBi9DJs8vVwYNgYcGDlm7s3q25KSqSOyeDqa9OhXOw/vx6HiY9fDLh\n5AQ9e2ohmikphZab8TSkDm8VFQlddvjLgT4FrDmcpcH5p8VXS1EQRy4eAcDdIY+QzPT4+x17THn8\nuOK5czLo2rUrje835rF8zJYLW7JPvvkmXL8OW7cWWm7jxuDurtw6iopFgQZfSnkIiC4FXRSF4MyN\nM5AK3dp2yzmZ4b9Pd+fUr6+18auICCGYPmA6xMD3R77PPtm/v+aQL+Lh7bBhWi22K1f0oKhCUQ7Q\nlw+/ixAiUAixSwjRVk8yFflwOfYyREMzh2Y5J9P994869WDnTs2dY1SBT2smjJ+A0TkjjkUe48aD\nG08mTExg2jTtaScoqNByM56K1C5fUVHQhxk4DTSSUjoDi4EteS0UQngLIfyEEH53797Vw60rL7eS\nb2Hx0AJTU9Ock3/9BRYW7LzjRmJixXXnZFC7dm361OsDAnz8fbJPvvoqmJvD//1foeU2aaLlLWzc\nqCdFFYoyptgGX0r5QEoZn/79TsBUCJFrrKCUcqmU0k1K6WZra1vcW1daklKTeGDyAFujPH6HBw9C\n166s32JKnTpaKZ2KzrsT3oWr8P2x75+0PwSoVQteew3WrNH8+YXEywtOnIDwcP3pqlCUFcU2+EKI\neiK9Nq8QolO6zMIHPyt05nL0ZTCCptWa5py8dw+Cgkjq2oMdO2DwYC35tKLTs2dP6t6sS2RyZPaY\nfID33tO+FmGXn5GEtWlTMRVUKMoBuoRlrgOOAa2EEBFCiMlCiClCiCnpS7yAs0KIM8AiYKTMtsVS\n6JvT4acBaFs3l+OSdP/9sSo9ePjQcDtbFRYjIyPe6/seJMK8ffOyTzZqpCViLVumHWgXgpYttQhP\n5cdXVAR0idIZJaWsL6U0lVLaSyl/kVIukVIuSZ//TkrZVkrZXkrpIaU8WvJqV26OXT4GgHvTXEIy\n0+Pvfw5wo1Yt6N69dHUrS16b8BomwSbsCd+TvU4+wAcfaIlYixYVWq6XFxw5Ardu6UlRhaKMqMCx\nGxWXoFtBEAOOzXNJujp4kNTOXdmyswqDB2uBKpWFGjVqMMB+AKlGqSw7/lQTlDZtYNAg+O47iIsr\nlNyhQ7U+wEWsuKxQlBuUwTdAQh+Ewj2tYmQ2oqIgKIiL9XsQH1/xo3NyY87rc+AWfHv425yTH30E\n9+/D99/nnMsHR0etD7By6ygMHWXwDYzUtFQiUyOp+qAqNjY22SfT/fcb7nbHxgaef74MFCxjnJyc\naP6gOTflTU5GnMw+6e4OL78M8+dDbKzOMoXQPjz/+qvQRwAKRblCGXwD41rsNVJFKnWN6+acPHgQ\naW7OomPuDBoEuYXoVwZmDZwFyfDxlo9zTn76KcTEwNdfF0rm0KFaP+AteWaZKBTlH2XwDYzgu8EA\nNKueS4btwYNEterKvQdVKqU7J4PRQ0ZjGWbJvjv7uJ94P/tkhw6a9V64sFClk9u3h2bNlFtHYdgo\ng29gnLtzDgCn+k7ZJ6KiIDCQQ0Y9sLaGXr1KX7fygqmpKd6u3qQZp/HZ1s9yLpg7F+LjNdeOjmS4\ndfbvh2hVWUphoCiDb2D4X/eHeGjb9KkY/HT//bJLPRgwAKpWLQPlyhFzXp+D8U1jlgUsI0daSNu2\nMHq0VlTt9m2dZQ4dqlVaLkLxTYWiXKAMvoFxLvIc3IWmTZ/Ksv3rL1KrmvNnnHuldudkUL16dV6s\n9SJxVeNYe3xtzgVz5mhtwGbr3r7BzU3L4VJuHYWhogy+ASGlJCwuLPeQzIMHCanVharVqtC7d9no\nV9741vtbeAifbP8k52Tz5lpXrJ9/1rnZeYZbZ8+eQgX5KBTlBmXwDYjb8bdJkAmIe4KGDRs+mYiO\nRgYGsiWmB/37g5lZ2elYnmju0Jy2SW0JNQnl7PVcGrbNng01a8Lbb2uZVTowdKj2YLB9u56VVShK\nAWXwDYjge1qETj3jephkTaE9dAghJTsTeih3zlPMHz4fBLyx/I2ckzY28NlnWoC9jtXRPDygQQPl\n1lEYJsrgGxAZIZnNazTPPnHwIEkm5py3cKdv3zJQrBzzUpeXqBdTj8MJh4mMicy54LXXoF07eP99\nrdZOARgZabv8P/7QAn0UCkNCGXwDIvheMOKxoE3DNtnG5cGDnBCd8exXFXPzMlKuHDO371ykmWTK\nj1NyTpqYwDffwNWr8OWXOsnz8tI+G5RbR2FoKINvQATdDkLelTRrmuXA9u5dxJkz/JH8vHLn5MFr\nL76GdZw12+5uIy4+l8JpvXppYZpffgnnzhUor2tXqFcPfvutBJRVKEoQZfANiLN3zuYMyTxwAADf\nqr2UOycPhBC84/EOqTVSefvHt3Nf9M03UL261hIxNTVfecbGWp+BnTvhwYMSUFihKCGUwTcQ7jy8\nQ/TjaIjMbvDT9v3JA1Ed2z5uVKtWhgqWc/415F9UTajK6tDVJObmq7e11Yz+8ePwww8Fyhs5Eh4/\nht9/LwFlFYoSQhl8AyEoMkj7JjJ7DH7ijj85KLszfHQlKnxfBEyMTJjkOImk+kl8vCSXomoAY8bA\niy9qZZSvXctXnocHNGwI//tfCSirUJQQyuAbCIGRgQDUSKqBtbW1NnjtGhY3Q/Gt0ot+/cpQOQPh\nP8P/g0myCYv/XszDhw9zLhAClizRvo4bl69rx8gIhg/XkrBiYvJcplCUK5TBNxCC7gRRJakKzes/\nCclM2f0nAOKFXlhYlJVmhkN1s+qMazWOx00f89E3H+W+yMFB64p16FCBxdVGjNCSsFQnLIWhoAy+\ngRB0JwjjKONs/vvItX9ym7o8OyWXZuaKXPnK6ytMUk1Ycm4J0XmVvRw3TrPms2fDqVN5ynJzg6ZN\nlVtHYTgUaPCFED5CiDtCiFxy00FoLBJCXBZCBAohOuhfzcpNaloqZ++cJeFaAq1atdIGpcTyxH4O\nV+lF7xdF2SpoQNQ0r8n4NuNJbpnMh199mPsiIeDHH6F+fc2vn0eGlRCaW+fPP1UnLIVhoMsOfznQ\nJ5/5vkCL9Jc38GPx1VJkJTQmlMSURLgNLVu2BCDx9HlqJN7m4TO9qFKljBU0ML4c8CXG0pj/Xv4v\nN2/ezH2RjQ2sWgWhoeDtnWetnZEjNVe/KrWgMAQKNPhSykNAfi0fBgIrpcZxoIYQor6+FFRkj9DJ\nMPgXvtf89y2mVOJOJ0XE1tKWCW0nkOqYyltz38p7YffuWkvEdevybHzu7KyV11+zpoSUVSj0iD58\n+HZAeJb3EeljCj0RGBmIQMDdJwY/+Y99XDFuhseIxmWsnWHyWd/PMBEmbLi3gdOnT+e98KOPoF8/\nePddOHYsx7QQMHYsHDkCYWElqLBCoQdK9dBWCOEthPATQvjdVU5PnQm6E0T15OrUqVmHGjVqEBOZ\nROtbB7jZ5gWMjctaO8OkvlV9/tHpH+AMk2dNztkVKwMjI1i5EuzttfTaO3dyLBk9Wvu6Npc+KwpF\neUIfBv8GkKU4O/bpYzmQUi6VUrpJKd1sbW31cOvKQdCdIEyjTTN3977zj2BFPHUnqFoKxWH287Ox\nFJYE1A5g/fr1eS+0sYGNG7W+wUOHaim2WWjUSPP+rF6tc1l9haJM0IfB3wqMS4/W8QBipZS39CBX\nATxMekhodGi2CJ0H/9tFMqY0e+35MtbOsLE2s2buC3OhGUz/ejoJCQl5L3Z1heXLwdcXpk7NYdnH\njoWQEPD3L1mdFYrioEtY5jrgGNBKCBEhhJgshJgihMioNbsTCAMuA8uAaSWmbSXk3N1zSCQPwx7S\nsmVLQkPB+cYubjbthqhuVdbqGTzTO02nnlk97ra/y5f/KaA88ogR8PHH8N//anV3suDlBVWqaLt8\nhaK8okuUzigpZX0ppamU0l5K+YuUcomUckn6vJRSviGlbCaldJJS+pW82pWHpyN0tn4fjhNnsR6p\n3Dn6oKpJVRb0XQD14YsdX3DhwoX8L5gzR3PrvP8+bNuWOVyjhna2u24dpKSUrM4KRVFRmbblnMDI\nQKqKqnAfWrRoyd1VfwBQY5Qy+PpilNMo3Oq6kdYrjcnT8znABe0Qd8UK6NBB2/GfOJE5NXasdqa7\nd28pKK1QFAFl8Ms5QXeCqJVWCyNhxJ07zXG7t4uHNe214G+FXjASRvw86GeEheCo+VFWrFiR/wWW\nlrBjh5aJ268fXLoEwMsvQ+3a4ONTCkorFEVAGfxyjJSSv2//jfl9cxwcHFi/Bl5gH1UG9NUCwBV6\no3299rzl8Ra4wVsL3qLAsOE6dbTGtgB9+kBkJFWqaLv833+He/dKXmeForAog1+OCY0J5X7ifZKu\nJtG0qRPX1h2lOnGYDlDunJJgbo+51DWvy4PnHjBtug6xBy1aaI1tb93Sdvrx8UyerFXQVIe3ivKI\nMvjlmFM3tEqNdwPvAsN47tEu0oxNtB6sCr1jVdWKH/r/AHVhw60N/E+XMpjPPKM1tz19GkaMoF3r\nFNzdNbeOislXlDeUwS/HnLp5CjNjMxKvJRIW9jyDq+xEPNtN672qKBEGtx7MsDbDED0F3p94c+uW\nDikl/fpp1TV37oQpU5g8SRIUBH4qXk1RzlAGvxxz6uYpmlo2hTRHRNhDWiadRQwcWNZqVWiEECzp\nv4Q6lnWI84xj8usFRO1k4O0Ns2bBL78wIfhDzM2kOrxVlDuUwS+npKalcvrWaeqm1AVew8tokzYx\naFCZ6lUZqGlek1VDVyFrS3al7GLp0qW6XfjppzB1KlUXfcWalnNYuxYePSpZXRWKwqAMfjkl+F4w\nj7DngyoAABy6SURBVJIfYXLHAniFSTU2a7HfDg5lrVqlwLOZJ292ehOegTcXvUlgYGDBFwmhtUec\nNInBgZ8y7cGX/PZbyeuqUOiKMvjllIwD2/DjbajPY1pGH4chQ8pYq8rFvBfm0bZWW1IGpDBo4iDi\n8+h8lQ0jI1i6FDl6NF/yT6I//lod3irKDcrgl1P8bvphVcWKK35DGFFlpTY4eHDZKlXJMDc15/fR\nv2NZzZIr7lfwfsNbN3++sTFixQpCXYbybsS7XP1QNYFTlA+UwS+nnLp5iuaWLjxO7MwrVuuhVSto\n06as1ap0NKvZjHXD1kF9WBe7Tnd/vokJdfatZadxf5p8NU2l3yrKBcrgl0OSUpM4E3mG2PPO2BCO\nS0yA5s5R2bVlQr+W/fhXt3+BK7zxyxv4+vrqdJ1VrSrs8/6NvaI38tVXtSqbCkUZogx+OSQoMoik\n1CSuHu1Kf2ZhlJam3DllzNyec+nVuBdpfdLoP6U/169f1+k673+YMVBu5krTF2DSpDx74yoUpYEy\n+OWQUze1A9u0cA+GGW9F2tuDm1sZa1W5MTYy5n/D/0d9q/rEvhhLv+H9eKRDzGXr1tDN04LeiVtJ\n6z8Apk+H+fNLQWOFIifK4JdDToSfQiTUonHaRXqnxSKGDVPunHJALYta/D7md0ysTQhqGcTIUSNJ\n0aH4/fTpEHrDjI0jN8DIkfDhh9orLa0UtFYonqAMfjlk34UTyBtuDEiYSRUp4ZVXylolRTpuDdz4\nsd+P0Ay2PdrG9OnTC4zcefllaNkS/vN/pshVq7UWifPnw/jxkJRUSporFMrglzvuxN8lIukcNg+6\nMTI5gOgGDcDFpazVUmRhcofJvN7xdegGPx35iS+++CLf9cbG8MEHWn21fQeMNT/+v/+tldTs3x8e\nPCglzRWVHWXwyxnf/H4QgFE1qtAFiFfROeWSRX0X0a1hN4yHGDPru1ksWbIk3/Vjx0KDBvCf/6D9\nPf/1L/jlF/jzT+jSBcLCSkdxRaVGGfxyhJTgs38/IrkaQ6/sJA2o/Y9/lLVailyoYlyFDcM3UN+6\nPuYTzZn63lR88om1r1oV3n0X9u+HU6fSBydNgt274eZN6NQJDh0qHeUVlRadDL4Qoo8QIkQIcVkI\nMTOX+R5CiFghRED6a7b+Va34HDgAkRYHaGv+LG39T3HUwgKLFi3KWi1FHtStVpfNIzcjLSU2r9sw\n2Xsyq1atynO9t7fW7HzevCyDvXppfXFr14YXXtBq8ahaDIoSokCDL4QwBr4H+gKOwCghhGMuSw9L\nKV3SX5/qWc9Kwax5N6F2CB9Ud6Duo0ecVn1ryz1uDdxY1n8ZMdYx2E+yZ8KECXn2xLWy0iJ2Nm2C\n4OAsEy1awPHj0Ls3vPkmjB4NutTtUSgKiS47/E7AZSllmJQyCfgVUEXZ9YyvLxy7dRCAF45EEA88\nevHFMtVJoRtjncfyrse7RNhF0GpUKyZMmMC3336b69q33tJ6oM9++hm4Rg3YuhW++ELroNWpE5w5\nU/LKKyoVuhh8OyA8y/uI9LGn6SKECBRC7BJCqK1pIfn0UzBrfYBG0po6W/exHnB0dy9rtRQ6Ms9z\nHp5NPQltHUr3sd15++23+eSTT3KEbNauDe+/Dxs2ZPHlZ2BkBB99BHv3QkyMZvQXLFDx+gq9oa9D\n29P8f3tnHl1Flefxz++9l30BQiAEQiBuIBA2EYLjhg2HRbttkaZFBbdWmbYR3AZbHVucVuhxBHXU\nBhoQp9sWxQWR1oOgshiWBgHZIs2SyBYIhBASAkneq9/8cR8QkeVlIRXI/ZxzT92qe1P1rZe6v7pr\n/SBVVTsC/wvMOlUmEXlARFaKyMp9+/bV0KXPf+bONWU8psNXPLutJd4jR3gd6NSpk9vSLCHi8/iY\nMWgGrRq0Yl36OgbeP5Dnn3+e+++/n7KT5to/+qgx/E89dZqT3XADrF0LAwbAE0+Yvn07i8dSA4Ri\n8HcBLSvspwSPHUdVD6lqcTD+GRAmIoknn0hVJ6tqN1Xt1qRJk2rIvnAIBEyZTk3fTkFgG7d+lcvW\nZs3Y2qABqampbsuzVIKEqAQ+v+NzvOJldfvVjHpmFFOnTqVfv34UFBQczxcXZ2Zlzp9vwilp0sR0\n9k+ZYpoCHTqYxVrl5bVzM5YLklAM/grgUhFJE5Fw4DZgdsUMItJMxEwWF5HuwfPm17TYC5G334Z1\n6+CmEV8zYDM03JXP9Ph40tPTETv//rzj4oSLmXP7HPYU7+Gb1G+YPH0ymZmZZGRk8P333x/PN3w4\npKaaHpzTTsoRgfvuMyO8ffuazzF06wYLFtTKvVguPM5q8FXVD/wOmAtkAe+r6gYRGS4iw4PZBgHr\nReQ74DXgNg3JU0T95vBh4/c6IwMON/max1aG4bRowYScHDIyMtyWZ6ki3Vt0571B77E6dzV/df7K\np3M/paCggCuvvJL3gz4PIyNhzBhYuRL+/veznDAlBT7+2NT4CwqgVy/4xS9OmupjsYSAqroSrrji\nCq3vjBmjCqqLvvHrtY81VgXd+pvfKKCffvqp2/Is1WTGuhnqHePVa6Zdo99v+1579uypgD788MNa\nWlqqfr9qt26qSUmqBw+GeNKSEtWxY1Xj4lQ9HtXbb1ddu/ac3oelbgGs1CraXbvS1iU2b4axY2HQ\nIAi0WMztC/IJRITxQaNGiAhXX3212xIt1eTXHX7NOwPfYcmOJdy/4H5mz53NyJEjee211+jRoweb\nNm1k4kTIyzMtvZCIioInn4StW+GRR+CTT6BjR/NNns8+M4NCFsvpqOqborqhPtfwHUf1+utVGzRQ\n3bVL9alpd+pRL1r2m3u1V69e2qVLF7clWmqQ99a/p94xXu3wZgfNKcjRWbNmaWJiokZGRuqrr76q\nv/2tox6P6sqVVTh5fr5pKjZpYpqLKSmqzzyjumaNedAsFxxUo4ZvDb4L/OUv5pefPFm1PFCuf+se\nqWU+jx7dvFkjIyN11KhRbku01DDzts7TBmMbaNJLSbp853LNzc3V/v37K6A9e/bXxMRyvfJKVb+/\nihcoLVX94APVfv1URcwD1qqV6ogRqh99pJqXV5O3Y3ERa/DPI3btMjX76683FbAl897SckE3D71R\nFy1apIDOmjXLbZmWc8DGvI2a9kqaRv4xUqd8O0UDgYBOmTJFGzZsqF7vUAXVF14or/6F9uwxtYqb\nblKNjDTFHFTbtFEdPFj1+efNS2Dz5mq8YSxuUR2DL+bva59u3brpypUrXbm2W6jCzTebRVZr15pP\nqKy49mIuX74N79ZsXp7+N5599ln2799PQkKC23It54B9h/cx5MMhfJn9JYPbD2bSTZM4evAoDz88\nkpkzfwXczMsvL+WRR66pmWm5paVmKtDixbB0qZkDnJ19Ij0qCi6/3IS2bU+ESy4xU4ksdQ4R+VZV\nq+bztKpviuqG+ljDHz/eVLQmTDD75atWqoJ+dGt7VVXt3bu3duzY0UWFltog4AR07OKx6h3j1VYT\nWun8rfNVVXXmzC/V59utsElvuOEXumbNmnMjoKhIdfly1alTVR95RLVPH9XU1BMtATAzgC65RHXQ\nINUXX1SdO1e1sPDc6LFUCmyXTt1nyRJVn0/1l78MjqU5ju67tpseiETnLP+blpaWanR0tI4YMcJt\nqZZaYtmOZXrJa5coz6H3zLpH80vydd68chVxNDz8bQV0yJAhunnz5toRVFysumqV6rvvqv7hD6q3\n3qp60UU/fgl06aI6apTq55+bKaKWWsca/DrOvn1m8kRammpBQfDg3/+uCvofN4brkfIjmpmZqYB+\n8MEHrmq11C4lZSX6+/m/V+8YrzZ9qalOWzVNf/9UQEF1wIAPNDo6Wr1erw4bNkw3btzojsgDB1S/\n+EL12WdVe/U6MS4QFaV6442q06aZPJZawRr8OkxpqWrv3qrh4RWm3eXlaSCxsS5v6dF7Phymqqov\nvviiAppnZ1PUS9bkrtGeU3oqz6FdJ16h3QYuVp9PdebMfB01apRGRUWpiOjAgQN14cKF6rg55bKk\nxNTwH35YtXVrY0bCwlQHDFCdOdM89JZzhjX4dZRAQHXIEPMrv/VWhYTbb9dyn0fb/RbdkLdBVVX7\n9Omj7du3d0WnpW7gOI6+s/YdTRmfojyHxj5wo8ZetkKzslTz8vL0mWee0YYNGyqgHTp00DfffFMP\nHTrktmjVFStUn3jCNGPBrAl47DFVt1okFzjW4NdBHEd15EjzC48dWyFhzhxV0Bd7R+otM25RVdXd\nu3er1+vV0aNHuyPWUqcoLi3WFxa9oA3HJijPodG/uVHfWzFfHcfRw4cP69SpU7Vr164KaFxcnD70\n0EO6fv16t2WbKZ6ffWb6/n0+8/BfdZXp8jl82G11FwzW4NcxHEf1v/7L/LojR1ZY8PjDD6pNm+q+\ntCQNewb9585/qqrq2LFjFdBNmza5J9pS5yg8WqjD33lB+Y/GynPoJRPa6itLX9Hcolx1HEeXLVum\nw4YN04iICAW0c+fOOm7cOM3OznZbuurevaovvaTatq0pCI0aqT76qJn7b6kW1uDXIQIBM9MNVO+8\n0+yrqpkK16mTOvHxes0Tidr7/3qrqmnGX3rppXrNNde4J9pSp/lq0RGN7PG2hj/UQ3kOledE/23q\nv+n/ZP6Prty1UnP35uqECRM0IyNDAQU0IyNDJ0yYoDt37nRXvOOoLlxoFnwdq/X37as6e7Zd9FVF\nqmPw7cKrGqSsDO65x3zudsQIeOUV47UOx4Ff/QpmzeIf4/+dmw6+wVfDvqJXWi8WLVrEddddx/Tp\n07nrrrvcvgVLHWXlSuPjPKzFBgY+/SFLD37Ed3uNz9uYsBh6pPSgXWI7EiWR7au3kzk3k03fboIS\n6NqlK/369aN///5kZGTg8/ncuYncXPjLX2DSJNi9G1q1Mo4B7rvPOHyxhER1Fl5Zg19D7NoFd9wB\nCxcaP9RPPmn8V6BqvFz86U8UjRvDpd43SWuUxpJ7lyAi3HXXXXz88cfk5uYSExPj9m1Y6jDHvB7m\n58PEiXDDL3eQuSOTzO2ZLN+1nO/3f09RWdFP/s7r9xIoCUCpiSfGJpLSJIXWzVuT0iSFhKgEWsa3\npFXDVlzU6CJSG6TikXP4Id3ycuOw/Y034OuvITzcVIjuvReuvz5YS7KcDmvwXWbOHLj7bjhyxFRe\n7rwzmOA4MHIkvP46+sAD3HTtDr7M/ooV968gPSmdwsJCkpOTGTp0KJMmTXLzFiznCXl5cNttxk4O\nHw4vvwzR0SZNVcktzmXLgS3sLd5L3uE89pXso/BoIfuL9rNl5xZ27N1B3qE8yqUcIkAiBY34sQ2I\nDY+lfZP2pDdNJz0pnfSm6XRM6kjj6MY1f0NZWfDmm/DXv0JhoXEDNnQoDB4M6enBWpOlItbgu0RB\ngXFEPXEidO4MM2ZAmzbBxLIy8xZ491147DFeHdiCUfMe5fX+r/NQ94cAmDRpEsOHD2f58uV0797d\ntfuwnF/4/ea5e+kluOgiYy/79g3971WVrKwsMjMz+eabb1i8ZDHZ+dnQEHxNfTS+vDHeZC+FUYUc\ndg4f/7uU+BQ6JXWic7POx7cXJ1xcM62BI0fMt/2nTzcfm3IcuOwyuPVW6N/fuIULC6v+dS4ArMF3\ngfffh9/9zjSvR440zkwiIoKJP/xgjP2CBTBuHKuH9iFjWk/6XdKPWb+ehYhQUlJC165dCQsLY+3a\ntdZ/raXSLFgADz4I//qXqfX/8Y9w8cVVO9fu3bvJzMxk6dKlrF69mlWrVnHo0CGIBU9zD03TmxKR\nGsGRBkfYz34cHMCMH3RM6kjnZp2PvwjSk9KJDouu+o3t3QuzZsHMmeYmAwGIjzeuHa+6Cnr2hCuu\nONG0qWdYg+8CkyfDtGnw5z9Dly7Bg6owdSo8+qiJv/EGq/uk8/N3f46jDmv/fS2J0YkAPPjgg0ye\nPJkvvviCPn36uHcjlvOa0lIYN85UOPx+M4709NOmclwdHMchOzv7uPH/7rvvyMrKIicnB/UqNAFJ\nFuIvi0eShZK4Eso8ZQB48JDWMI32TdvTJrENlyZcSkp8Ci3iW9AsthnxEfFEeCN+VMlRVRx18Dt+\nyp1yHHUIOAG0oADvgoWEzfuS8MVL8G0NfulTxDRv2rc3zeqWLU1ITjYvh2MhJuaCGxOwBt8FHFPB\nMc+SqqmJPP+82fbqBdOmMbt0LUM+HEJCVAJzhsyhU7NOAMycOZPBgwczevRoxo0b59YtWC4gcnNN\nF8/EiXD0KPzsZ2bG2C23mC8g1xQlJSVs2rSJrKwssrKy2LRpE9nZ2WTnZJPvz4dmmNAUpIlAAuYF\ncRJe8RLpjSRAAL/jx+/4Q7p+4mG4Ljecq/Ii6HQgnMv2+kneU4yv/DSuHUUgLs60BrxeE3y+E/HK\nHDt53+Mx5w81wInvkVaMn20fzPWioiAqCnnllXNr8EWkH/Aq4AWmqOq4k9IlmD4AKAHuVtVVZzrn\n+W7wAVO9mjMHxo+HJUugWTP4z//k4N238fKyCbyw+AW6Ne/GJ7d9QnJcMgA5OTl07tyZtm3bsnjx\nYsJsv6SlBtm71xj96dMhJ8fYur59zeyefv1MBfhcUVRURE5ODjk5OeYlEHwRbMnbQu7hXAr8BWiM\nQjgm+AAHIsMjiY2OJToqmpioGGKiYoiNjiU2xoS4mDjiYuOIjI6k3FPOUTlKYXkhOw7t4IfCH/ih\nIIeEYoeWh6BliY+2ESm0CU/mIk9jWkoDkpwoostAHMd0D/n9ZlsxVOWY4/zYSJ8pgNme/AIIdd/v\nN+McR44gRUXnzuCLiBf4F9AH2AmsAIao6sYKeQYAIzAGvwfwqqr2ONN5z3uD/+qr8Ic/nJhZMHo0\n22/tzZ/XvcUbK96gqKyIO9LvYPLPJxMdFo2q8vHHH/P444+Tn5/PmjVrSEtLc/suLBcojmOmCL/7\nrvFtvmuXOZ6WBj16QPfu0K6d8XvSsmXtTIYJBALk5eWRm5v7o7Bnzx727NlDfn4+Bw4cID8/n/z8\nfEpLS894vpiYGOLj44ltGEt483C0qVLWqIyS2BIKIws57Dkx4BwrsbSMaEladBppsWmkxqfSukFr\n0hLSiI+JJzo6mqioKKKjo4mMjMRTw91Aqorf8VMaKKXUX0pZoOx4PKCnbp2EecKICosiyhdFdFg0\nkb5IRKRaXTqhrMDoDmxR1W0AIjIDuBnYWCHPzcD/BVeBLRORhiKSrKq5VRF1PlDQIIKjva7k2xsu\nZ3ZKMV/tGM/WiQ8hCIPbD+bJq5+kfeP2bNu2jQ0bNjB+/Hg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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Load a precomputed posterior based on an analytic solution (see Lintusaari et al 2016)\n",
- "matdata = sio.loadmat('./resources/bdm.mat')\n",
- "x = matdata['likgrid'].reshape(-1)\n",
- "posterior_at_x = matdata['post'].reshape(-1)\n",
- "\n",
- "# Plot the reference\n",
- "plt.figure()\n",
- "plt.plot(x, posterior_at_x, c='k')\n",
- "\n",
- "# Plot the different curves\n",
- "for res, d_node, c in ([sim_res, 'd_sim', 'b'], [T1_res, 'd_T1', 'g'], [T2_res, 'd_T2', 'r']):\n",
- " alphas = res.outputs['alpha']\n",
- " dists = res.outputs[d_node]\n",
- " # Use gaussian kde to make the curves look nice. Note that this tends to benefit the algorithm 1 \n",
- " # a lot as it ususally has only a very few accepted samples with 100000 simulations\n",
- " kde = ss.gaussian_kde(alphas[dists<=0])\n",
- " plt.plot(x, kde(x), c=c)\n",
- " \n",
- "plt.legend(['reference', 'algorithm 1', 'algorithm 2, T1\\n(eps=0)', 'algorithm 2, T2\\n(eps=0)'])\n",
- "plt.xlim([-.2, 1.2]);\n",
- "print('Results after 100000 simulations. Compare to figure 6(a) in Lintusaari et al. 2016.')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Interfacing with R"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It is possible to run R scripts in command line for example with [Rscript](http://stat.ethz.ch/R-manual/R-devel/library/utils/html/Rscript.html). However, in Python it may be more convenient to use [rpy2](http://rpy2.readthedocs.io), which allows convenient access to the functionality of R from within Python. You can install it with `pip install rpy2`.\n",
- "\n",
- "Here we demonstrate how to calculate the summary statistics used in the ELFI tutorial (autocovariances) using R's `acf` function for the MA2 model.\n",
- "\n",
- "**Note:** See this [issue](https://github.com/ContinuumIO/anaconda-issues/issues/152) if you get a `undefined symbol: PC` error in the import after installing rpy2."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import rpy2.robjects as robj\n",
- "from rpy2.robjects import numpy2ri as np2ri\n",
- "\n",
- "# Converts numpy arrays automatically\n",
- "np2ri.activate()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's create a Python function that wraps the R commands (please see the documentation of [rpy2](http://rpy2.readthedocs.io) for details):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "\n",
- "robj.r('''\n",
- " # create a function `f`\n",
- " f <- function(x, lag=1) {\n",
- " ac = acf(x, plot=FALSE, type=\"covariance\", lag.max=lag, demean=FALSE)\n",
- " ac[['acf']][lag+1]\n",
- " }\n",
- " ''')\n",
- "\n",
- "f = robj.globalenv['f']\n",
- "\n",
- "def autocovR(x, lag=1):\n",
- " x = np.atleast_2d(x)\n",
- " apply = robj.r['apply']\n",
- " ans = apply(x, 1, f, lag=lag)\n",
- " return np.atleast_1d(ans)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 5., 23.])"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Test it\n",
- "autocovR(np.array([[1,2,3,4], [4,5,6,7]]), 1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load a ready made MA2 model:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ma2 = elfi.examples.ma2.get_model(seed_obs=4)\n",
- "elfi.draw(ma2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Replace the summaries S1 and S2 with our R autocovariance function."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Method: Rejection\n",
- "Number of posterior samples: 100\n",
- "Number of simulations: 10000\n",
- "Threshold: 0.11\n",
- "Posterior means: t1: 0.597, t2: 0.168"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Replace with R autocov\n",
- "S1 = elfi.Summary(autocovR, ma2['MA2'], 1)\n",
- "S2 = elfi.Summary(autocovR, ma2['MA2'], 2)\n",
- "ma2['S1'].become(S1)\n",
- "ma2['S2'].become(S2)\n",
- "\n",
- "# Run the inference\n",
- "rej = elfi.Rejection(ma2, 'd', batch_size=1000, seed=seed)\n",
- "rej.sample(100)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Interfacing with MATLAB"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "There are a number of options for running MATLAB (or Octave) scripts from within Python. Here, evaluating the distance is demonstrated with a MATLAB function using the official [MATLAB Python cd API](http://www.mathworks.com/help/matlab/matlab-engine-for-python.html). (Tested with MATLAB 2016b.)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import matlab.engine"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A MATLAB session needs to be started (and stopped) separately:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "eng = matlab.engine.start_matlab() # takes a while..."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Similarly as with R, we have to write a piece of code to interface between MATLAB and Python:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def euclidean_M(x, y):\n",
- " # MATLAB array initialized with Python's list\n",
- " ddM = matlab.double((x-y).tolist())\n",
- " \n",
- " # euclidean distance\n",
- " dM = eng.sqrt(eng.sum(eng.power(ddM, 2.0), 2))\n",
- " \n",
- " # Convert back to numpy array\n",
- " d = np.atleast_1d(dM).reshape(-1)\n",
- " return d"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 1.41421356, 8.77496439, 1. ])"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Test it\n",
- "euclidean_M(np.array([[1,2,3], [6,7,8], [2,2,3]]), np.array([2,2,2]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load a ready made MA2 model:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ma2M = elfi.examples.ma2.get_model(seed_obs=4)\n",
- "elfi.draw(ma2M)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "Replace the summaries S1 and S2 with our R autocovariance function."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Method: Rejection\n",
- "Number of posterior samples: 100\n",
- "Number of simulations: 10000\n",
- "Threshold: 0.111\n",
- "Posterior means: t1: 0.6, t2: 0.169"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Replace with Matlab distance implementation\n",
- "d = elfi.Distance(euclidean_M, ma2M['S1'], ma2M['S2'])\n",
- "ma2M['d'].become(d)\n",
- "\n",
- "# Run the inference\n",
- "rej = elfi.Rejection(ma2M, 'd', batch_size=1000, seed=seed)\n",
- "rej.sample(100)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, don't forget to quit the MATLAB session:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "eng.quit()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Verdict\n",
- "\n",
- "We showed here a few examples of how to incorporate non Python operations to ELFI models. There are multiple other ways to achieve the same results and even make the wrapping more efficient.\n",
- "\n",
- "Wrapping often introduces some overhead to the evaluation of the generative model. In many cases however this is not an issue since the operations are usually expensive by themselves making the added overhead insignificant."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": true
- },
- "source": [
- "### References\n",
- "- [1] Tanaka, Mark M., et al. \"Using approximate Bayesian computation to estimate\n",
- "tuberculosis transmission parameters from genotype data.\"\n",
- "Genetics 173.3 (2006): 1511-1520.\n",
- "- [2] Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander; Fundamentals and Recent Developments in Approximate Bayesian Computation. Syst Biol 2017; 66 (1): e66-e82. doi: 10.1093/sysbio/syw077"
- ]
- }
- ],
- "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": "3.5.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/ELFI_parallelization.ipynb b/ELFI_parallelization.ipynb
deleted file mode 100644
index a2f55c0..0000000
--- a/ELFI_parallelization.ipynb
+++ /dev/null
@@ -1,547 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### If this is your first encounter with ELFI, we recommend that you start with the ELFI_tutorial notebook."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Automatic parallelization with ipyparallel"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Behind the scenes, ELFI can automatically parallelize the computational inference via clients that support it. Currently ELFI has two clients:\n",
- "\n",
- "- `elfi.clients.native` (activated by default): does not parallelize but makes it easy to test and debug your code.\n",
- "- `elfi.clients.ipyparallel`: [_ipyparallel_](http://ipyparallel.readthedocs.io/) based client can parallelize both locally (using available cores) and in a distributed cluster.\n",
- "\n",
- "We will show in this notebook how to activate and use the `ipyparallel` client.\n",
- "\n",
- "\n",
- "### Turn on parallelization\n",
- "\n",
- "To activate the `ipyparallel` client in ELFI just import it with `import elfi.clients.ipyparallel`. Before you can actually run things in parallel you also need to start an `ipyparallel` cluster. Below is an example of how to start a local cluster using your CPU cores (tested in a Unix-like operating system). Please see the [ipyparallel documentation](http://ipyparallel.readthedocs.io/en/latest/intro.html) for more info and details for setting up ipyparallel clusters.\n",
- "\n",
- "### Tuning the parallelization\n",
- "\n",
- "The parallelization performance can be tuned with the keyword argument `batch_size` given for the inference methods (e.g. Rejection). Batch size tells how many \"runs\" (evaluations of the generative model) each available worker should run before sending back the results. Because of overhead involved in parallelization, batches should be large enough in computation time, but not too large in memory consumption eating all of your memory. The simulator node often outputs the largest amount of data, so you should count that at least twice the amount of output data in a batch from the simulator should easily fit into the memory. (Also remember that in a shared-memory system this precious resource is shared among the workers). \n",
- "\n",
- "**Note:** this notebook demonstrates parallelization with the rejection sampling ABC algorithm, which is an example of [embarrassingly parallel](https://en.wikipedia.org/wiki/Embarrassingly_parallel) computing: each simulation is completely independent. Such algorithms highly benefit from parallelization. Some other algorithms depend heavily on previous results making them much harder to parallelize in a meaningful way.\n",
- "\n",
- "**Note:** Even when using the default native client, Numpy may use multiple cores in its operations as well."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Starting ipcluster"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Taking advantage of ipyparallel requires that you have an Ipyparallel cluster running. You can start a local cluster with the following command (the exclamation mark is a Jupyter syntax for executing shell commands):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "!ipcluster start -n 4 --daemon"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This starts 1 Ipyparallel controller and 4 engines (workers) on a single host. The `daemon` option makes Ipyparallel run in the background, which is necessary in the notebook environment. This however means that one should remember to `stop` the ipcluster once it's no longer needed (see [bottom](#Remember-to-stop-the-ipcluster-when-done) of this notebook). \n",
- "\n",
- "One may also run the command in a separate terminal window, which is beneficial for debugging. For more options and details on Ipyparallel, please see its [documentation](http://ipyparallel.readthedocs.io)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# This is here just to ensure that ipcluster has started properly before continuing\n",
- "import time\n",
- "time.sleep(10)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Parallel inference with ELFI"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can proceed to importing ELFI. It is important that all imports and definitions are visible to all Ipyparallel engines, which may be less trivial in some distributed environments. Here it suffices that *ELFI has been installed*, or added to the `PYTHONPATH`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import elfi"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "ELFI is told to use the ipyparallel cluster by importing the client:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import elfi.clients.ipyparallel"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The MA2 model described and built in the basic ELFI tutorial is available for importing under `elfi.examples`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from elfi.examples import ma2\n",
- "model = ma2.get_model()\n",
- "\n",
- "elfi.draw(model)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Otherwise everything should be familiar, and ELFI handles everything for you regarding the parallelization. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "rej = elfi.Rejection(model, 'd', batch_size=10000)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "When running the next command, take a look at the system monitor of your operating system; it should show 4 (or whatever number you gave the `ipcluster start` command) Python processes doing heavy computation simultaneously."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 3.03 s, sys: 180 ms, total: 3.21 s\n",
- "Wall time: 14.2 s\n"
- ]
- }
- ],
- "source": [
- "%time result = rej.sample(5000, n_sim=int(5e6)) # 5 million simulations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `Result` object is also just like in the basic case:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Method: Rejection\n",
- "Number of posterior samples: 5000\n",
- "Number of simulations: 5000000\n",
- "Threshold: 0.0358\n",
- "Posterior means: t1: 0.655, t2: 0.159\n"
- ]
- }
- ],
- "source": [
- "result.summary"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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e36qqaostbvZnlnH/97uJeXYq9jZa5o6tzyrfOLz7Se8JMOerHYzv6W0+783l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\nAgeyyyzeB1cHayZF+hDo7sC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modXAnBEh3DTCsnfuyHBPfr1nJP0D3ZgY5YNWo1BWo8fV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DZe31PCttxGlqeHMjzIjeXpYVyR4M9Xt6Wz4aZUXrxqOLdPiiLMy4X9RS18cLACtUKG3W4n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\nu5IkSZKk7vwBq96CnLEp9fsAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "result.plot_pairs()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To summarize, the only thing that needed to be changed from the basic scenario was enabling the `ipyparallel` client."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Functions in Jupyter and Ipyparallel"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "All imports and definitions must be visible for all Ipyparallel engines. This means that customizing the model from within the notebook has some caveats.\n",
- "\n",
- "You can change the model with built-in functionality like this without problems:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "d2 = elfi.Distance('cityblock', model['S1'], model['S2'], p=1)\n",
- "\n",
- "rej2 = elfi.Rejection(d2, batch_size=10000)\n",
- "result2 = rej2.sample(1000, quantile=0.01)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "But let's say you want to use your very own distance:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def my_distance(x, y):\n",
- " # Note that interactively defined functions must use full module names, e.g. numpy instead of np\n",
- " return numpy.sum((x-y)**2, axis=1)\n",
- "\n",
- "d3 = elfi.Distance(my_distance, model['S1'], model['S2'])\n",
- "rej3 = elfi.Rejection(d3, batch_size=10000)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This is not automatically visible for the ipyparallel engines, the below will therefore fail:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "# This will fail if you try it!\n",
- "# result3 = rej3.sample(1000, quantile=0.01)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Ipyparallel provides a way to manually `push` the new definition to the scopes of the engines. Because `my_distance` also uses numpy, that must be imported in the engines as well:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "importing numpy on engine(s)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Get the ipyparallel client\n",
- "ipyclient = elfi.get_client().ipp_client\n",
- "\n",
- "# Import numpy in the engines (note that you cannot use \"as\" abbreviations, but must use plain imports)\n",
- "with ipyclient[:].sync_imports():\n",
- " import numpy\n",
- "\n",
- "# Then push my_distance to the engines\n",
- "ipyclient[:].push({'my_distance': my_distance})\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The above may look a bit cumbersome, but now this works:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Method: Rejection\n",
- "Number of posterior samples: 1000\n",
- "Number of simulations: 100000\n",
- "Threshold: 0.013\n",
- "Posterior means: t1: 0.654, t2: 0.163"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "rej3.sample(1000, quantile=0.01) # now this works"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "However, a simple solution to problems like this is to define your functions in external scripts (see `elfi.examples.ma2`) and have the module files be available in the folder where you run your ipyparallel engines."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Remember to stop the ipcluster when done"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2017-05-19 17:09:08.430 [IPClusterStop] Stopping cluster [pid=2398185] with [signal=]\r\n"
- ]
- }
- ],
- "source": [
- "!ipcluster stop"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.5.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/ELFI_tutorial.ipynb b/ELFI_tutorial.ipynb
deleted file mode 100644
index e958bc7..0000000
--- a/ELFI_tutorial.ipynb
+++ /dev/null
@@ -1,1790 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# ELFI tutorial\n",
- "\n",
- "This tutorial covers the basics of using ELFI, how to make models, save results for later use and run different inference algorithms. Please see also our other tutorials for parallelization and using non Python operations in ELFI models."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import scipy.stats\n",
- "import matplotlib\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "logging.basicConfig(level=logging.INFO) # logging may not show up in Jupyter without this"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Inference with ELFI: case MA(2) model\n",
- "\n",
- "The 2nd order moving average model, MA(2), is a common model used in univariate time analysis. Assuming zero mean it can be written as\n",
- "\n",
- "$$\n",
- "y_t = w_t + \\theta_1 w_{t-1} + \\theta_2 w_{t-2},\n",
- "$$\n",
- "\n",
- "where $\\theta_1, \\theta_2 \\in \\mathbb{R}$ and $(w_k)_{k\\in \\mathbb{Z}} \\sim N(0,1)$ represents an independent and identically distributed sequence of white noise."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### The observed data and the inference problem"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this notebook, our task is to infer the parameters $\\theta_1, \\theta_2$ given a sequence of 100 observations $y$ that originate from an MA(2) process. Let's define this MA(2) simulator as a Python function:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def MA2(t1, t2, n_obs=100, batch_size=1, random_state=None):\n",
- " # Make inputs 2d arrays for broadcasting with w\n",
- " t1 = np.asanyarray(t1).reshape((-1, 1))\n",
- " t2 = np.asanyarray(t2).reshape((-1, 1))\n",
- " random_state = random_state or np.random\n",
- "\n",
- " w = random_state.randn(batch_size, n_obs+2) # i.i.d. sequence ~ N(0,1)\n",
- " x = w[:, 2:] + t1*w[:, 1:-1] + t2*w[:, :-2]\n",
- " return x"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Above, `t1`, `t2`, and `n_obs` are the arguments specific to the MA2 process. The latter two are ELFI specific keyword arguments. In ELFI, operations are normally vectorized, meaning that instead of simulating a single MA2 sequence at a time, we simulate a batch of them. Vectorization is a way to make operations efficient in Python. Above we rely on numpy to carry out the vectorized calculations. The simulator is passed a `batch_size` argument by ELFI to indicate how many simulations are needed. This also implies that `t1` and `t2` are going to be vectors of scalars and that the method returns in this case a 2d array with simulations on the rows.\n",
- "\n",
- "The other keyword argument `random_state` is for generating random quantities in your simulator. It is a `numpy.RandomState` object that has all the same methods as `numpy.random` module has. Using it ensures that you will get consistent and reliable results. \n",
- "\n",
- "**Note**: there is a built-in tool (`elfi.tools.vectorize`) in ELFI to vectorize operations that cannot be otherwise vectorized. It is basically a for loop wrapper for convenience.\n",
- "\n",
- "**Important**: in order to guarantee a consistent state of pseudo-random number generation, the simulator must have `random_state` as a keyword argument for reading in a `numpy.RandomState` object.\n",
- "\n",
- "(If you *really* don't want to take advantage of these, you can wrap your function into another that accepts these keyword arguments but simply doesn't use them.)\n",
- "\n",
- "Let's now use this simulator to create toy observations. We will use parameter values $\\theta_1=0.6, \\theta_2=0.2$ as in [*Marin et al. (2012)*](http://link.springer.com/article/10.1007/s11222-011-9288-2) making comparing the figures also possible. We will then try to infer these parameter values back based on the toy observed data alone."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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Am1E0yjneZaPd6mlg0/ZqJNFIDB5ih+cfqf/+W/8j7+w//RAQ3LL6eYGxwe3kL3IavTWF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Oadh9/J/Sv3s13b5q6lFFq7w6nr/dfUpFivro8+MLlECYLp1VgerLgWai2SEZ8wlB6mNFpRgpMr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\nUJL/H0RspODM0g02AAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# true parameters\n",
- "t1_true = 0.6\n",
- "t2_true = 0.2\n",
- "\n",
- "# Set up observed data y with some random seed\n",
- "random_state = np.random.RandomState(20161130)\n",
- "y_obs = MA2(t1_true, t2_true, random_state=random_state)\n",
- "\n",
- "# Plot the observed sequence\n",
- "plt.figure(figsize=(11, 6));\n",
- "plt.plot(y_obs.ravel());\n",
- "\n",
- "# To illustrate the stochasticity, let's plot a couple of more observations with the same true parameters:\n",
- "plt.plot(MA2(t1_true, t2_true).ravel());\n",
- "plt.plot(MA2(t1_true, t2_true).ravel());"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Approximate Bayesian Computation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Standard statistical inference methods rely on the use of the *likelihood* function. Given a configuration of the parameters, the likelihood function quantifies how likely it is that these values of the parameters produced the observed data. In our simple example case above however, evaluating the likelihood is difficult due to the unobserved latent sequence (variable `w` in the simulator code). In many real world applications the likelihood function is not available or it is too expensive to evaluate preventing the use of traditional inference methods.\n",
- "\n",
- "One way to approach this problem is to use Approximate Bayesian Computation (ABC) which is a statistically based method replacing the use of the likelihood function with a simulator of the data. Loosely speaking, it is based on the intuition that similar data is likely to have been produced by similar parameters. Looking at the picture above, in essence we would keep simulating until we have found enough sequences that are similar to the observed sequence. Although the idea may appear inapplicable for the task at hand, you will soon see that it does work. For more information about ABC, please see e.g. \n",
- "\n",
- "* [Lintusaari, J., Gutmann, M. U., Dutta, R., Kaski, S., and Corander, J. (2016). Fundamentals and recent\n",
- "developments in approximate Bayesian computation. *Systematic Biology*, doi: 10.1093/sysbio/syw077.](http://sysbio.oxfordjournals.org/content/early/2016/09/07/sysbio.syw077.full.pdf)\n",
- "\n",
- "* [Marin, J.-M., Pudlo, P., Robert, C. P., and Ryder, R. J. (2012). Approximate Bayesian computational\n",
- "methods. *Statistics and Computing*, 22(6):1167–1180.](http://link.springer.com/article/10.1007/s11222-011-9288-2)\n",
- "\n",
- "* https://en.wikipedia.org/wiki/Approximate_Bayesian_computation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Defining the inference problem in ELFI"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To run this notebook, ELFI should be installed or added to your `PYTHONPATH`. Once that is done, you should be able to `import` it:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import elfi"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In ELFI, we build a generative model for the distance between simulations and the observed data. The model will be a directed acyclic graph ([DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph)), which associates each node with its parent nodes, the last node being the distance. This provides an intuitive means to describe even complex dependencies. Let's build such a model.\n",
- "\n",
- "As is usual in Bayesian statistical inference, we need to define *prior* distributions for the unknown parameters $\\theta_1, \\theta_2$. In ELFI the priors can be any of the continuous and discrete distributions available in `scipy.stats` (for custom priors, see [below](#custom_prior)). For simplicity, let's start by assuming that both parameters follow `Uniform(0, 2)`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# a node is defined by giving a distribution from scipy.stats and its parents (here constants 0 and 2)\n",
- "t1 = elfi.Prior(scipy.stats.uniform, 0, 2)\n",
- "\n",
- "# ELFI also supports giving the scipy.stats distributions as strings\n",
- "t2 = elfi.Prior('uniform', 0, 2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Next, we define the *Simulator* node by giving it the `MA2` function, and the priors as its parents. As the evaluation of this node can be compared with observations, we give them as well."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "Y = elfi.Simulator(MA2, t1, t2, observed=y_obs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "But how does one compare the simulated sequences with the observed sequence? As could be seen from the plot of just a few observed sequences, a direct pointwise comparison would probably not work very well: the three sequences look quite different although they were generated with the same parameter values. Indeed, the comparison of simulated sequences is often the most difficult (and ad hoc) part of ABC. Typically one chooses one or more summary statistics and then calculates the discrepancy between those.\n",
- "\n",
- "Here, we will apply the intuition arising from the definition of the MA(2) process, and use the autocovariances with lags 1 and 2 as the summary statistics:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def autocov(x, lag=1):\n",
- " C = np.mean(x[:,lag:] * x[:,:-lag], axis=1)\n",
- " return C"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As is familiar by now, a `Summary` node is defined by giving the autocovariance function and the simulated data (which includes the observed as well):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "S1 = elfi.Summary(autocov, Y)\n",
- "S2 = elfi.Summary(autocov, Y, 2) # the optional keyword lag is given the value 2"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here, we choose the discrepancy as the common Euclidean L2-distance. ELFI can use many common distances directly from `scipy.spatial.distance` like this:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# Finish the model with the final node that calculates the squared distance (S1_sim-S1_obs)**2 + (S2_sim-S2_obs)**2\n",
- "d = elfi.Distance('euclidean', S1, S2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "One may wish to use a distance function that is unavailable in `scipy.spatial.distance`. ELFI supports defining a custom distance/discrepancy functions as well (see the documentation for `elfi.Distance` and `elfi.Discrepancy`).\n",
- "\n",
- "Now that the inference model is defined, ELFI can visualize the DAG. __Note__ that you need the [Graphviz](http://www.graphviz.org) software as well as the `graphviz` [Python package](https://pypi.python.org/pypi/graphviz) for drawing this."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Remember to install graphviz for this to work\n",
- "elfi.draw(d) # just give it a node in the model, or the model itself (d.model)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Defining custom priors"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Although the above definition is perfectly valid, let's use the same priors as in [*Marin et al. (2012)*](http://link.springer.com/article/10.1007/s11222-011-9288-2) that guarantee that the problem will be identifiable (loosely speaking, the likelihood willl have just one mode). Marin et al. used priors for which $-2<\\theta_1<2$ with $\\theta_1+\\theta_2>-1$ and $\\theta_1-\\theta_2<1$ i.e. the parameters are sampled from a triangle (see below).\n",
- "\n",
- "In ELFI, custom distributions can be defined similar to distributions in `scipy.stats` (i.e. they need to have at least the `rvs` method implemented for the simplest algorithms). To be safe they can inherit `elfi.Distribution` which defines the methods needed. In this case we only need these for sampling, so implementing a static `rvs` method suffices. As was in the context of simulators, it is important to accept the keyword argument `random_state`, which is needed for ELFI's internal book-keeping of pseudo-random number generation. Also the `size` keyword is needed (which in the simple cases is the same as the `batch_size` in the simulator definition)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# define prior for t1 as in Marin et al., 2012 with t1 in range [-b, b]\n",
- "class CustomPrior_t1(elfi.Distribution):\n",
- " def rvs(b, size=1, random_state=None):\n",
- " u = scipy.stats.uniform.rvs(loc=0, scale=1, size=size, random_state=random_state)\n",
- " t1 = np.where(u<0.5, np.sqrt(2.*u)*b-b, -np.sqrt(2.*(1.-u))*b+b)\n",
- " return t1\n",
- "\n",
- "# define prior for t2 conditionally on t1 as in Marin et al., 2012, in range [-a, a]\n",
- "class CustomPrior_t2(elfi.Distribution):\n",
- " def rvs(t1, a, size=1, random_state=None):\n",
- " locs = np.maximum(-a-t1, t1-a)\n",
- " scales = a - locs\n",
- " t2 = scipy.stats.uniform.rvs(loc=locs, scale=scales, size=size, random_state=random_state)\n",
- " return t2"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These indeed sample from a triangle:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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NPJQ2cXExv/4W9E5/fF1sEOphr5DQfmNPCp7fmQi+6MZvKb+Wgx/O5mNHXLFK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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "t1_1000 = CustomPrior_t1.rvs(2, 1000)\n",
- "t2_1000 = CustomPrior_t2.rvs(t1_1000, 1, 1000)\n",
- "plt.scatter(t1_1000, t2_1000, s=4, edgecolor='none');\n",
- "# plt.plot([0, 2, -2, 0], [-1, 1, 1, -1], 'b') # outlines of the triangle"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's change the earlier priors to the new ones in the inference model:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "t1.become(elfi.Prior(CustomPrior_t1, 2))\n",
- "t2.become(elfi.Prior(CustomPrior_t2, t1, 1))\n",
- "\n",
- "elfi.draw(d)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note that `t2` now depends on `t1`. Yes, ELFI supports hierarchy."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Basic inference with rejection sampling"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The simplest ABC algorithm samples parameters from their prior distributions, runs the simulator with these and compares them to the observations. The samples are either accepted or rejected depending on how large the distance is. The accepted samples represent samples from the approximate posterior distribution.\n",
- "\n",
- "In ELFI, ABC methods are initialized either with a node giving the distance, or with the `ElfiModel` object and the name of the distance node. Depending on the inference method, additional arguments may be accepted or required. \n",
- "\n",
- "A common optional keyword argument, accepted by all inference methods, `batch_size` defines how many simulations are performed in each passing through the graph. In Python, doing many calculations with a single function call can potentially save a lot of CPU time, depending on the operation. For example, here we draw 10000 samples from `t1`, give them as input to `t2` and draw 10000 samples from it, use these to run 10000 simulations etc. all in just one passing through the graph and hence the overall number of function calls is reduced 10000-fold. However, this does not mean that batches should be as big as possible, since you may run out of memory, the fraction of time spent in function call overhead becomes insignificant, and many algorithms operate in multiples of `batch_size`. Furthermore, the `batch_size` is a crucial element for efficient parallelization (see the notebook on parallelization)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "rej = elfi.Rejection(d, batch_size=10000)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "After the ABC method has been initialized, samples can be drawn from it. By default, rejection sampling in ELFI works in `quantile` mode i.e. a certain quantile of the samples with smallest discrepancies is accepted. The `sample` method requires the number of output samples as a parameter. Note that the simulator is then run `(N/quantile)` times. (Alternatively, the same behavior can be achieved by saying `n_sim=1000000`.)\n",
- "\n",
- "The IPython magic command `%time` is used here to give you an idea of runtime on a typical personal computer."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "Threshold: 0.11405655544800358"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 29.7 s, sys: 544 ms, total: 30.2 s\n",
- "Wall time: 30.1 s\n"
- ]
- }
- ],
- "source": [
- "N = 10000\n",
- "\n",
- "vis = dict(xlim=[-2,2], ylim=[-1,1])\n",
- "\n",
- "# You can give the sample method a `vis` keyword to see an animation how the prior transforms towards the\n",
- "# posterior with a decreasing threshold (interactive visualization will slow it down a bit though).\n",
- "%time result = rej.sample(N, quantile=0.01, vis=vis)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `sample` method returns a `Result` object, which contains several attributes and methods. Most notably the attribute `samples` contains an OrderedDict (i.e. an ordered Python dictionary) of the posterior numpy arrays for all non-constant elfi.Priors in the model. For rejection sampling, other attributes include e.g. the `threshold`, which is the threshold value resulting in the requested quantile. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.59467042582746232"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "result.samples['t1'].mean()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `Result` object includes a convenient `summary` method:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Method: Rejection\n",
- "Number of posterior samples: 10000\n",
- "Number of simulations: 1000000\n",
- "Threshold: 0.114\n",
- "Posterior means: t1: 0.595, t2: 0.162\n"
- ]
- }
- ],
- "source": [
- "result.summary"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Rejection sampling can also be performed threshold-based so that it accepts all samples that result in a discrepancy below certain threshold. Note that since we require a fixed number of samples, there is no guarantee how many times the simulator will be run."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 2.11 s, sys: 80 ms, total: 2.19 s\n",
- "Wall time: 2.18 s\n",
- "Method: Rejection\n",
- "Number of posterior samples: 10000\n",
- "Number of simulations: 340000\n",
- "Threshold: 0.2\n",
- "Posterior means: t1: 0.59, t2: 0.168\n",
- "\n"
- ]
- }
- ],
- "source": [
- "%time result2 = rej.sample(N, threshold=0.2)\n",
- "\n",
- "print(result2) # the Result object's __str__ contains the output from summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Storing simulated values"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As the samples are already in numpy arrays, you can just say e.g. `np.savetxt('t1.txt', result.samples['t1'])` to save them. However, ELFI provides some additional functionality. You may define a *pool* for storing the simulation results (not just the accepted samples) for certain nodes:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 6.9 s, sys: 8 ms, total: 6.91 s\n",
- "Wall time: 6.91 s\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Method: Rejection\n",
- "Number of posterior samples: 10000\n",
- "Number of simulations: 1000000\n",
- "Threshold: 0.114\n",
- "Posterior means: t1: 0.594, t2: 0.163"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pool = elfi.OutputPool(['t1', 't2', 'S1', 'S2'])\n",
- "rej = elfi.Rejection(d, pool=pool)\n",
- "\n",
- "%time result3 = rej.sample(N, n_sim=1000000)\n",
- "result3"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The benefit of the pool is that you may reuse simulations without having to resimulate them. Above we saved the summaries to the pool, so we can change the distance node of the model without having to resimulate anything. Let's do that."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 936 ms, sys: 0 ns, total: 936 ms\n",
- "Wall time: 934 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Method: Rejection\n",
- "Number of posterior samples: 10000\n",
- "Number of simulations: 1000000\n",
- "Threshold: 0.143\n",
- "Posterior means: t1: 0.593, t2: 0.164"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Replace the current distance with a cityblock (manhattan) distance and recreate the inference\n",
- "d.become(elfi.Distance('cityblock', S1, S2, p=1))\n",
- "rej = elfi.Rejection(d, pool=pool)\n",
- "\n",
- "%time result4 = rej.sample(N, n_sim=1000000)\n",
- "result4"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note the significant saving in time, even though the total number of considered simulations stayed the same. \n",
- "\n",
- "We can also increase the total amount of simulations and only have to simulate the new ones:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 2.3 s, sys: 4 ms, total: 2.31 s\n",
- "Wall time: 2.31 s\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Method: Rejection\n",
- "Number of posterior samples: 10000\n",
- "Number of simulations: 1200000\n",
- "Threshold: 0.13\n",
- "Posterior means: t1: 0.594, t2: 0.163"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%time result5 = rej.sample(N, n_sim=1200000)\n",
- "result5"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Above the results were saved into a python dictionary (the pool). If you store a lot of large data to there, you will eventually \n",
- "run out of memory. Instead you can use arrays persisted to standard .npy files. ELFI makes it possible to append\n",
- "to these arrays as well."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 24 ms, sys: 0 ns, total: 24 ms\n",
- "Wall time: 23.3 ms\n"
- ]
- }
- ],
- "source": [
- "arraypool = elfi.store.ArrayPool(['t1', 't2', 'Y', 'd'], basepath='./output')\n",
- "rej = elfi.Rejection(d, pool=arraypool)\n",
- "%time result5 = rej.sample(100, threshold=0.3)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This stores the simulated data in binary `npy` format under seed-specific directory `arraypool.path`, and can be loaded with `np.load`. (**Note:** depending on your operating system, you may have to run `arraypool.flush()` before you can access the files while this notebook is open.)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "d.npy t1.npy t2.npy Y.npy\r\n"
- ]
- }
- ],
- "source": [
- "arraypool.flush()\n",
- "!ls $arraypool.path"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now lets load all the parameters `t1` that were generated with numpy:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([-0.15717118, -0.4007612 , -0.03841609, ..., 1.53443984,\n",
- " 0.74164426, 0.03774473])"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.load(arraypool.path + '/t1.npy')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can delete the files with:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "ls: cannot access './output/arraypool/2445516670': No such file or directory\r\n"
- ]
- }
- ],
- "source": [
- "arraypool.delete()\n",
- "\n",
- "!ls $arraypool.path # verify the deletion"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Visualizing the results"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instances of `Result` contain methods for some basic plotting (these are convenience methods to plotting functions defined under `elfi.visualization`).\n",
- "\n",
- "For example one can plot the marginal distributions:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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CAAAwahJXAAAARs3iTAAAMGIWTgQjrgAAAIycxBUAAIBRk7gCAAAwahJXAAAARk3iCgAA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\nkfJscqMGvV8AAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "result.plot_marginals();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Often \"pairwise relationships\" are more informative:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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L+/sT4unQ5rjn/tXX/L+NqkpORQMNzZ1b33thnB8VDc3cO2c78+4Ybt7+9cwkiz63wR4O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6iRcdmUVG6anUXhQ5s53mlh7qhYTnRayIzRYlDLyYnT0dBr47OqXnQqKXa3nzCFlJLmQep6rSwc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uTsOH45dJHGGwjx5u5WLp+ejUp+vOB7ZUcze5vNXDEjh3GZMYx9YC2BUJiqBxcfJw7L2yz4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\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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "result.plot_pairs();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note that if working in a non-interactive environment, you can use e.g. `plt.savefig('pairs.png')` after an ELFI plotting command to save the current figure to disk."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Sequential Monte Carlo ABC"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Rejection sampling is quite inefficient, as it does not learn from its history. The sequential Monte Carlo (SMC) ABC algorithm does just that by applying importance sampling: samples are *weighed* according to the resulting discrepancies and the next *population* of samples is drawn near to the previous using the weights as probabilities. \n",
- "\n",
- "For evaluating the weights, SMC ABC needs to have probability density functions for the priors. In our MA2 example the second prior is conditional on the first, which complicates matters a bit. Let's modify the prior distribution classes:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# define prior for t1 as in Marin et al., 2012 with t1 in range [-b, b]\n",
- "class CustomPrior_t1(elfi.Distribution):\n",
- " def rvs(b, size=1, random_state=None):\n",
- " u = scipy.stats.uniform.rvs(loc=0, scale=1, size=size, random_state=random_state)\n",
- " t1 = np.where(u<0.5, np.sqrt(2.*u)*b-b, -np.sqrt(2.*(1.-u))*b+b)\n",
- " return t1\n",
- " \n",
- " def pdf(x, b):\n",
- " p = 1./b - np.abs(x) / (b*b)\n",
- " p = np.where(p < 0., 0., p) # disallow values outside of [-b, b] (affects weights only)\n",
- " return p\n",
- "\n",
- " \n",
- "# define prior for t2 conditionally on t1 as in Marin et al., 2012, in range [-a, a]\n",
- "class CustomPrior_t2(elfi.Distribution):\n",
- " def rvs(t1, a, size=1, random_state=None):\n",
- " locs = np.maximum(-a-t1, t1-a)\n",
- " scales = a - locs\n",
- " t2 = scipy.stats.uniform.rvs(loc=locs, scale=scales, size=size, random_state=random_state)\n",
- " return t2\n",
- " \n",
- " def pdf(x, t1, a):\n",
- " locs = np.maximum(-a-t1, t1-a)\n",
- " scales = a - locs\n",
- " p = scipy.stats.uniform.pdf(x, loc=locs, scale=scales)\n",
- " p = np.where(scales>0., p, 0.) # disallow values outside of [-a, a] (affects weights only)\n",
- " return p\n",
- " \n",
- " \n",
- "# Redefine the priors\n",
- "t1.become(elfi.Prior(CustomPrior_t1, 2, model=t1.model))\n",
- "t2.become(elfi.Prior(CustomPrior_t2, t1, 1))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In ELFI, one can setup a SMC ABC sampler just like the Rejection sampler:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "smc = elfi.SMC(d, batch_size=10000)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For sampling, one has to define the number of output samples, the number of populations and a *schedule* i.e. a list of quantiles to use for each population. In essence, a population is just refined rejection sampling."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:elfi.methods.methods:---------------- Starting round 0 ----------------\n",
- "INFO:elfi.methods.methods:---------------- Starting round 1 ----------------\n",
- "INFO:elfi.methods.methods:---------------- Starting round 2 ----------------\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 6.12 s, sys: 156 ms, total: 6.28 s\n",
- "Wall time: 1.75 s\n"
- ]
- }
- ],
- "source": [
- "N = 1000\n",
- "schedule = [0.7, 0.2, 0.05]\n",
- "%time result_smc = smc.sample(N, schedule)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can have summaries and plots of the results just like above:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Method: SMC-ABC\n",
- "Number of posterior samples: 1000\n",
- "Number of simulations: 180000\n",
- "Threshold: 0.0491\n",
- "Posterior means for final population: t1: 0.598, t2: 0.167\n"
- ]
- }
- ],
- "source": [
- "result_smc.summary"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `Result` object returned by the SMC-ABC sampling contains also some methods for investigating the evolution of populations, e.g.:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Posterior means for population 0: t1: 0.557, t2: 0.2\n",
- "Posterior means for population 1: t1: 0.599, t2: 0.166\n",
- "Posterior means for population 2: t1: 0.598, t2: 0.167\n",
- "\n"
- ]
- }
- ],
- "source": [
- "result_smc.posterior_means_all_populations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "result_smc.plot_marginals_all_populations(bins=25, figsize=(8, 2), fontsize=12)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Obviously one still has direct access to the samples as well, which allows custom plotting:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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woSJeUVQlS/fHcPeH63jz54OlOq+4D5LF8XF3wc3FyeFTdywWPbXw7ruhWTOj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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "n_populations = len(schedule)\n",
- "fig, ax = plt.subplots(ncols=n_populations, sharex=True, sharey=True, figsize=(16,6))\n",
- "samples = [pop.samples_list for pop in result_smc.populations]\n",
- "for ii in range(n_populations):\n",
- " s = samples[ii]\n",
- " ax[ii].scatter(s[0], s[1], s=5, edgecolor='none');\n",
- " ax[ii].set_title(\"Population {}\".format(ii));\n",
- " ax[ii].plot([0, 2, -2, 0], [-1, 1, 1, -1], 'b')\n",
- "ax[0].set_xlabel(result_smc.names_list[0]);\n",
- "ax[0].set_ylabel(result_smc.names_list[1]);\n",
- "ax[0].set_xlim([-2, 2])\n",
- "ax[0].set_ylim([-1, 1]);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It can be seen that the populations iteratively concentrate more and more around the true parameter values.\n",
- "\n",
- "Note that for the later populations some of the samples lie outside allowed region. This is due to the SMC algorithm sampling near previous samples, with *near* meaning a Gaussian distribution centered around previous samples with variance as twice the weighted empirical variance. However, the outliers carry zero weight, and have no effect on the estimates."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Bayesian Optimization for Likelihood-Free Inference (BOLFI)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In practice inference problems often have a more complicated and computationally heavy simulator than the model `MA2` here, and one simply cannot run it for millions of times. The [BOLFI](http://jmlr.csail.mit.edu/papers/v17/15-017.html) framework is likely to prove useful in such situation: a statistical model (e.g. [Gaussian process](https://en.wikipedia.org/wiki/Gaussian_process), GP) is created for the discrepancy, and its minimum is inferred with [Bayesian optimization](https://en.wikipedia.org/wiki/Bayesian_optimization). This approach typically reduces the number of required simulator calls by several orders of magnitude."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "When dealing with a Gaussian process, it is advisable to take a logarithm of the discrepancies in order to reduce the effect that high discrepancies have on the GP. In ELFI such transformed node can be created easily:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "log_d = elfi.Operation(np.log, d)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As BOLFI is a more advanced inference method, its interface is also a bit more involved. But not much: Using the same graphical model as earlier, the inference could begin by defining a Gaussian process model, for which we use the [GPy](https://sheffieldml.github.io/GPy/) library. This could then be given via a keyword argument `target_model`. In this case, we are happy with the default that ELFI creates for us when we just give it each parameter some `bounds`.\n",
- "\n",
- "Other notable arguments include the `initial_evidence`, which defines the number of initialization points sampled straight from the priors before starting to optimize the acquisition of points, and `update_interval` which defines how often the target model hyperparameters are optimized."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "bolfi = elfi.BOLFI(log_d, batch_size=5, initial_evidence=20, update_interval=10, bounds=[(-2, 2), (-1, 1)])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Sometimes you may have some samples readily available. You could then initialize the GP model with a dictionary of previous results by giving `initial_evidence=result1.outputs`.\n",
- "\n",
- "The BOLFI class can now try to `fit` the surrogate model (the GP) to the relationship between parameter values and the resulting discrepancies. We'll request 200 evidence points (including the `initial_evidence` defined above)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:elfi.methods.methods:BOLFI: Fitting the surrogate model...\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 47.6 s, sys: 796 ms, total: 48.4 s\n",
- "Wall time: 14.7 s\n"
- ]
- }
- ],
- "source": [
- "%time bolfi.fit(n_evidence=200)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Running this does not return anything currently, but internally the GP is now fitted. \n",
- "\n",
- "Note that in spite of the very few simulator runs, fitting the model took longer than any of the previous methods. Indeed, BOLFI is intended for scenarios where the simulator takes a lot of time to run."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The fitted `target_model` uses the GPy libarary, which can be investigated further:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "\n",
- "Name : GP regression\n",
- "Objective : 136.28701600037343\n",
- "Number of Parameters : 4\n",
- "Number of Optimization Parameters : 4\n",
- "Updates : True\n",
- "Parameters:\n",
- " \u001b[1mGP_regression. \u001b[0;0m | value | constraints | priors\n",
- " \u001b[1msum.rbf.variance \u001b[0;0m | 0.341008706617 | +ve | \n",
- " \u001b[1msum.rbf.lengthscale \u001b[0;0m | 0.742691569995 | +ve | \n",
- " \u001b[1msum.bias.variance \u001b[0;0m | 0.423438414136 | +ve | \n",
- " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.178468369903 | +ve | "
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "bolfi.target_model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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2UR0+/+3139XnXs94Jc8bqTXW43TrWuHzKc7+4T+YP304P//uWce1feDDT3hzyw6+uvtH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\nyFD+MW0sSZk5vLZ2m0UNBwpx1d9Nziqz/NXpm8vezo5HOvdly/THmdaxC5+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W22/PusAf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\nSJIkSZIkSZJkFjL5kCRJkiRJkiTJLP4/bz4RYTUn7kYAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "bolfi.plot_state();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It may be helpful to see the acquired parameter values and the resulting discrepancies:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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dNd3/Ey/Qnh0LmZ/XqPNR816nCJ98XFru6Ja7j2U2wn0Mp/FNSw9uWus5NzvT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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "bolfi.plot_discrepancy();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note the high number of points at parameter bounds. These could probably be decreased by lowering the covariance of the noise added to acquired points, defined by the optional `acq_noise_cov` argument for the BOLFI constructor. Another possibility could be to [add virtual derivative observations at the borders](https://arxiv.org/abs/1704.00963), though not yet implemented in ELFI.\n",
- "\n",
- "We can now infer the BOLFI posterior (please see the [paper](http://jmlr.csail.mit.edu/papers/v17/15-017.html) for details). The method accepts a threshold parameter; if none is given, ELFI will use the minimum value of discrepancy estimate mean."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:elfi.methods.posteriors:Using minimum value of discrepancy estimate mean (-1.0113) as threshold\n"
- ]
- }
- ],
- "source": [
- "post = bolfi.infer_posterior()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can get estimates for *maximum a posteriori* and *maximum likelihood* easily:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "((array([ 0.55780718, 0.17812642]), array([[ 0.69314718]])),\n",
- " (array([ 0.55780722, 0.17812648]), array([[ 0.69314718]])))"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "post.MAP, post.ML"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can visualize the posterior directly:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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fBJ3GDKZ9WRs5W3CcgYF34qS3PmX2agkhGBb8AF4GP5ad/85ivEIIwRPRPblQ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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "post.plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, samples from the posterior can be acquired with an MCMC sampler (note that depending on the smoothness of the GP approximation, this may be slow):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:elfi.methods.posteriors:Using minimum value of discrepancy estimate mean (-1.0113) as threshold\n",
- "INFO:elfi.mcmc:NUTS: Performing 1000 iterations with 500 adaptation steps.\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 100/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 200/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 300/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 400/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 500/1000...\n",
- "INFO:elfi.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 600/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 700/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 800/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 900/1000...\n",
- "INFO:elfi.mcmc:NUTS: Acceptance ratio: 0.244, Diverged proposals after warmup (i.e. n_adapt=500 steps): 6\n",
- "INFO:elfi.mcmc:NUTS: Performing 1000 iterations with 500 adaptation steps.\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 100/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 200/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 300/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 400/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 500/1000...\n",
- "INFO:elfi.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 600/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 700/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 800/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 900/1000...\n",
- "INFO:elfi.mcmc:NUTS: Acceptance ratio: 0.212, Diverged proposals after warmup (i.e. n_adapt=500 steps): 6\n",
- "INFO:elfi.mcmc:NUTS: Performing 1000 iterations with 500 adaptation steps.\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 100/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 200/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 300/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 400/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 500/1000...\n",
- "INFO:elfi.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 600/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 700/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 800/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 900/1000...\n",
- "INFO:elfi.mcmc:NUTS: Acceptance ratio: 0.226, Diverged proposals after warmup (i.e. n_adapt=500 steps): 8\n",
- "INFO:elfi.mcmc:NUTS: Performing 1000 iterations with 500 adaptation steps.\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 100/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 200/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 300/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 400/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 500/1000...\n",
- "INFO:elfi.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 600/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 700/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 800/1000...\n",
- "INFO:elfi.mcmc:NUTS: Iterations performed: 900/1000...\n",
- "INFO:elfi.mcmc:NUTS: Acceptance ratio: 0.199, Diverged proposals after warmup (i.e. n_adapt=500 steps): 8\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "4 chains of 1000 iterations acquired. Effective sample size and Rhat for each parameter:\n",
- "t1 1863.04625881 1.00045708142\n",
- "t2 1977.62113509 1.00060969673\n",
- "CPU times: user 4min 15s, sys: 3.11 s, total: 4min 18s\n",
- "Wall time: 1min 4s\n"
- ]
- }
- ],
- "source": [
- "# bolfi.model.computation_context.seed = 10\n",
- "%time result_BOLFI = bolfi.sample(1000, target_prob=0.9)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The sampling algorithms may be fine-tuned with some parameters. If you get a warning about diverged proposals, something may be wrong and should be investigated. You can try rerunning the `sample` method with a higher target probability `target_prob` during adaptation, as its default 0.6 may be inadequate for a non-smooth GP, but this will slow down the sampling.\n",
- "\n",
- "Now we finally have a `Result` object again, which has several convenience methods:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Method: BOLFI\n",
- "Number of posterior samples: 2000\n",
- "Number of simulations: 200\n",
- "Threshold: -1.01\n",
- "Posterior means: t1: 0.598, t2: 0.134"
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "result_BOLFI"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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mWZnbKY6Evc/SG2EJFBQ3vDMR57yY9jX41Wtj8fv3JrlclX/pv1P7EOURzgV3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k7Xv6HA60oit7KGJLfQ3OXh2BKUUEJCG352Ubp+DzB07DLCtgnRth+rlWG0X/6vJTLh9h0b2S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\nwmZmZmZmZlYr/wMimZD4TukCewAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "result_BOLFI.plot_traces();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The black vertical lines indicate the end of warmup, which by default is half of the number of iterations."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "result_BOLFI.plot_marginals();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### That's it! See the other notebooks for more topics on e.g. using external simulators and parallelization.\n",
- "\n",
- "Full documentation can be found at http://elfi.readthedocs.io/. Limited user-support may be asked from elfi-support.at.hiit.fi, but the [Gitter chat](https://gitter.im/elfi-dev/elfi) is preferable.\n",
- "\n",
- "If you create something that you think would benefit other ELFI users, please consider opening a pull request in an appropriate [category](https://github.com/elfi-dev), like ELFI itself or the Zoo."
- ]
- }
- ],
- "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": "3.5.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/README.md b/README.md
index b6a88fc..d10b782 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,12 @@
# [ELFI](https://github.com/elfi-dev/elfi) Notebooks
-This repository contains tutorials and examples in the [Jupyter Notebook](http://jupyter.org) format. These should be maintained, so please let us know if something doesn't work with current release of ELFI.
+This repository contains tutorials and examples in the [Jupyter Notebook](http://jupyter.org) format. These should be maintained, so please raise an issue if something doesn't work.
Full documentation can be found at http://elfi.readthedocs.io/. Limited user-support may be asked from elfi-support.at.hiit.fi, but the [Gitter chat](https://gitter.im/HIIT/elfi?utm_source=share-link&utm_medium=link&utm_campaign=share-link) is preferable.
-## About running these notebooks
-
-These notebooks have been run with Python 3.5.3 using ELFI version 0.5.0.
+## Differences between versions and/or branches
+* The *master* branch of this repository reflects current functionality in the most recent release of ELFI in [PyPI](https://pypi.python.org/pypi/elfi/).
+* The *dev* branch may provide more cutting-edge functionality as introduced in the *dev* branch of ELFI's [GitHub repository](https://github.com/elfi-dev/elfi).
+* Other branches may exist temporarily (until merged to *dev*) to demonstrate functionality in respective branches in ELFI's GitHub repository.
+* Legacy versions of these notebooks are available for PyPI [releases](https://github.com/elfi-dev/notebooks/releases).
diff --git a/non_python_operations.ipynb b/non_python_operations.ipynb
new file mode 100644
index 0000000..0aa602b
--- /dev/null
+++ b/non_python_operations.ipynb
@@ -0,0 +1,1233 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This tutorial is generated from a [Jupyter](http://jupyter.org/) notebook that can be found [here](https://github.com/elfi-dev/notebooks). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using non-Python operations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If your simulator or other operations are implemented in a programming language other than Python, you can still use ELFI. This notebook briefly demonstrates how to do this in three common scenarios:\n",
+ "\n",
+ "* External executable (written e.g. in C++ or a shell script)\n",
+ "* R function\n",
+ "* MATLAB function\n",
+ "\n",
+ "Let's begin by importing some libraries that we will be using:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "import scipy.io as sio\n",
+ "import scipy.stats as ss\n",
+ "\n",
+ "import elfi\n",
+ "import elfi.examples\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ ".. note:: To run some parts of this notebook you need to either compile the simulator, have R or MATLAB installed and install their respective wrapper libraries."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## External executables\n",
+ "\n",
+ "ELFI supports using external simulators and other operations that can be called from the command-line. ELFI provides some tools to easily incorporate such operations to ELFI models. This functionality is introduced in this tutorial.\n",
+ "\n",
+ "We demonstrate here how to wrap executables as ELFI nodes. We will first use `elfi.tools.external_operation` tool to wrap executables as a Python callables (function). Let's first investigate how it works with a simple shell `echo` command:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 3., 1., 123.])"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Make an external command. {0} {1} are positional arguments and {seed} a keyword argument `seed`.\n",
+ "command = 'echo {0} {1} {seed}'\n",
+ "echo_sim = elfi.tools.external_operation(command)\n",
+ "\n",
+ "# Test that `echo_sim` can now be called as a regular python function\n",
+ "echo_sim(3, 1, seed=123)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The placeholders for arguments in the command string are just Python's [`format strings`](https://docs.python.org/3/library/string.html#formatstrings).\n",
+ "\n",
+ "Currently `echo_sim` only accepts scalar arguments. In order to work in ELFI, `echo_sim` needs to be vectorized so that we can pass to it a vector of arguments. ELFI provides a handy tool for this as well:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.78154613e+00, 0.00000000e+00, 8.49425160e+08],\n",
+ " [ 1.48064044e+00, 0.00000000e+00, 8.49425160e+08],\n",
+ " [ 1.94733396e+00, 0.00000000e+00, 8.49425160e+08]])"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Vectorize it with elfi tools\n",
+ "echo_sim_vec = elfi.tools.vectorize(echo_sim)\n",
+ "\n",
+ "# Make a simple model\n",
+ "m = elfi.ElfiModel(name='echo')\n",
+ "elfi.Prior('uniform', .005, 2, model=m, name='alpha')\n",
+ "elfi.Simulator(echo_sim_vec, m['alpha'], 0, name='echo')\n",
+ "\n",
+ "# Test to generate 3 simulations from it\n",
+ "m['echo'].generate(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So above, the first column draws from our uniform prior for $\\alpha$, the second column has constant zeros, and the last one lists the seeds provided to the command by ELFI.\n",
+ "\n",
+ "## Complex external operations $-$ case BDM\n",
+ "\n",
+ "To provide a more realistic example of external operations, we will consider the Birth-Death-Mutation (BDM) model used in [*Lintusaari at al 2016*](https://doi.org/10.1093/sysbio/syw077) *[1]*.\n",
+ "\n",
+ "### Birth-Death-Mutation process\n",
+ "\n",
+ "We will consider here the Birth-Death-Mutation process simulator introduced in *Tanaka et al 2006 [2]* for the spread of Tuberculosis. The simulator outputs a count vector where each of its elements represents a \"mutation\" of the disease and the count describes how many are currently infected by that mutation. There are three rates and the population size:\n",
+ "\n",
+ "- $\\alpha$ - (birth rate) the rate at which any infectious host transmits the disease.\n",
+ "- $\\delta$ - (death rate) the rate at which any existing infectious hosts either recovers or dies.\n",
+ "- $\\tau$ - (mutation rate) the rate at which any infectious host develops a new unseen mutation of the disease within themselves.\n",
+ "- $N$ - (population size) the size of the simulated infectious population\n",
+ "\n",
+ "It is assumed that the susceptible population is infinite, the hosts carry only one mutation of the disease and transmit that mutation onward. A more accurate description of the model can be found from the original paper or e.g. [*Lintusaari at al 2016*](https://doi.org/10.1093/sysbio/syw077) *[1]*.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ ".. For documentation\n",
+ "\n",
+ ".. image:: http://research.cs.aalto.fi/pml/software/elfi/docs/0.5/images/bdm.png\n",
+ " :width: 400 px\n",
+ " :alt: BDM model illustration from Lintusaari et al. 2016\n",
+ " :align: center"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This simulator cannot be implemented effectively with vectorized operations so we have implemented it with C++ that handles loops efficiently. We will now reproduce Figure 6(a) in [*Lintusaari at al 2016*](https://doi.org/10.1093/sysbio/syw077) *[2]* with ELFI. Let's start by defining some constants:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Fixed model parameters\n",
+ "delta = 0\n",
+ "tau = 0.198\n",
+ "N = 20\n",
+ "\n",
+ "# The zeros are to make the observed population vector have length N\n",
+ "y_obs = np.array([6, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype='int16')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's build the beginning of a new model for the birth rate $\\alpha$ as the only unknown"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Prior(name='alpha', 'uniform')"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = elfi.ElfiModel(name='bdm')\n",
+ "elfi.Prior('uniform', .005, 2, model=m, name='alpha')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "g++ bdm.cpp --std=c++0x -O -Wall -o bdm\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get the BDM source directory\n",
+ "sources_path = elfi.examples.bdm.get_sources_path()\n",
+ "\n",
+ "# Copy to resources folder and compile (unix-like systems)\n",
+ "!cp -r $sources_path resources\n",
+ "!make -C resources/cpp\n",
+ "\n",
+ "# Move the file in to the working directory\n",
+ "!mv ./resources/cpp/bdm ."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ ".. note:: The source code for the BDM simulator comes with ELFI. You can get the directory with `elfi.examples.bdm.get_source_directory()`. Under unix-like systems it can be compiled with just typing `make` to console in the source directory. For windows systems, you need to have some C++ compiler available to compile it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 19., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+ " 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Test the executable (assuming we have the executable `bdm` in the working directory)\n",
+ "sim = elfi.tools.external_operation('./bdm {0} {1} {2} {3} --seed {seed} --mode 1')\n",
+ "sim(1, delta, tau, N, seed=123)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "The BDM simulator is actually already internally vectorized if you provide it an input file with parameters on the rows. This is more efficient than looping in Python (`elfi.tools.vectorize`), because one simulation takes very little time and we wish to generate tens of thousands of simulations. We will also here redirect the output to a file and then read the file into a numpy array. \n",
+ "\n",
+ "This is just one possibility among the many to implement this. The most efficient would be to write a native Python module with C++ but it's beyond the scope of this article. So let's work through files which is a fairly common situation especially with existing software."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Assuming we have the executable `bdm` in the working directory\n",
+ "command = './bdm {filename} --seed {seed} --mode 1 > {output_filename}'\n",
+ "\n",
+ "\n",
+ "# Function to prepare the inputs for the simulator. We will create filenames and write an input file.\n",
+ "def prepare_inputs(*inputs, **kwinputs):\n",
+ " alpha, delta, tau, N = inputs\n",
+ " meta = kwinputs['meta']\n",
+ "\n",
+ " # Organize the parameters to an array. The broadcasting works nicely with constant arguments here.\n",
+ " param_array = np.row_stack(np.broadcast(alpha, delta, tau, N))\n",
+ " \n",
+ " # Prepare a unique filename for parallel settings\n",
+ " filename = '{model_name}_{batch_index}_{submission_index}.txt'.format(**meta)\n",
+ " np.savetxt(filename, param_array, fmt='%.4f %.4f %.4f %d')\n",
+ "\n",
+ " # Add the filenames to kwinputs\n",
+ " kwinputs['filename'] = filename\n",
+ " kwinputs['output_filename'] = filename[:-4] + '_out.txt'\n",
+ " \n",
+ " # Return new inputs that the command will receive\n",
+ " return inputs, kwinputs\n",
+ "\n",
+ "\n",
+ "# Function to process the result of the simulation\n",
+ "def process_result(completed_process, *inputs, **kwinputs):\n",
+ " output_filename = kwinputs['output_filename']\n",
+ " \n",
+ " # Read the simulations from the file.\n",
+ " simulations = np.loadtxt(output_filename, dtype='int16')\n",
+ " \n",
+ " # Clean up the files after reading the data in\n",
+ " os.remove(kwinputs['filename'])\n",
+ " os.remove(output_filename)\n",
+ " \n",
+ " # This will be passed to ELFI as the result of the command\n",
+ " return simulations\n",
+ "\n",
+ "\n",
+ "# Create the python function (do not read stdout since we will work through files)\n",
+ "bdm = elfi.tools.external_operation(command, \n",
+ " prepare_inputs=prepare_inputs, \n",
+ " process_result=process_result, \n",
+ " stdout=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's replace the echo simulator with this. To create unique but informative filenames, we ask ELFI to provide the operation some meta information. That will be available under the `meta` keyword (see the `prepare_inputs` function above):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create the simulator\n",
+ "bdm_node = elfi.Simulator(bdm, m['alpha'], delta, tau, N, observed=y_obs, name='sim')\n",
+ "\n",
+ "# Ask ELFI to provide the meta dict\n",
+ "bdm_node.uses_meta = True\n",
+ "\n",
+ "# Draw the model\n",
+ "elfi.draw(m)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[12 1 1 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0]\n",
+ " [18 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
+ " [19 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Test it\n",
+ "data = bdm_node.generate(3)\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Completing the BDM model\n",
+ "\n",
+ "We are now ready to finish up the BDM model. To reproduce Figure 6(a) in [*Lintusaari at al 2016*](https://doi.org/10.1093/sysbio/syw077) *[2]*, let's add different summaries and discrepancies to the model and run the inference for each of them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Distance(name='d_sim')"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def T1(clusters):\n",
+ " clusters = np.atleast_2d(clusters)\n",
+ " return np.sum(clusters > 0, 1)/np.sum(clusters, 1)\n",
+ "\n",
+ "def T2(clusters, n=20):\n",
+ " clusters = np.atleast_2d(clusters)\n",
+ " return 1 - np.sum((clusters/n)**2, axis=1)\n",
+ "\n",
+ "# Add the different distances to the model\n",
+ "elfi.Summary(T1, bdm_node, name='T1')\n",
+ "elfi.Distance('minkowski', m['T1'], p=1, name='d_T1')\n",
+ "\n",
+ "elfi.Summary(T2, bdm_node, name='T2')\n",
+ "elfi.Distance('minkowski', m['T2'], p=1, name='d_T2')\n",
+ "\n",
+ "elfi.Distance('minkowski', m['sim'], p=1, name='d_sim')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "elfi.draw(m)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 2.95 s, sys: 96.3 ms, total: 3.05 s\n",
+ "Wall time: 5.05 s\n",
+ "CPU times: user 30.4 ms, sys: 1.7 ms, total: 32.1 ms\n",
+ "Wall time: 31.9 ms\n",
+ "CPU times: user 33.8 ms, sys: 728 µs, total: 34.5 ms\n",
+ "Wall time: 34.4 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Save parameter and simulation results in memory to speed up the later inference\n",
+ "pool = elfi.OutputPool(['alpha', 'sim'])\n",
+ "# Fix a seed\n",
+ "seed = 20170511\n",
+ "\n",
+ "rej = elfi.Rejection(m, 'd_T1', batch_size=10000, pool=pool, seed=seed)\n",
+ "%time T1_res = rej.sample(5000, n_sim=int(1e5))\n",
+ "\n",
+ "rej = elfi.Rejection(m, 'd_T2', batch_size=10000, pool=pool, seed=seed)\n",
+ "%time T2_res = rej.sample(5000, n_sim=int(1e5))\n",
+ "\n",
+ "rej = elfi.Rejection(m, 'd_sim', batch_size=10000, pool=pool, seed=seed)\n",
+ "%time sim_res = rej.sample(5000, n_sim=int(1e5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Results after 100000 simulations. Compare to figure 6(a) in Lintusaari et al. 2016.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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fTvc+U6/DN2aVzgHgqRMBUspvgW9z+kNHM62zZ8/CM2o6JzJSlUIeMyab0znh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\nVlG6AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load a precomputed posterior based on an analytic solution (see Lintusaari et al 2016)\n",
+ "matdata = sio.loadmat('./resources/bdm.mat')\n",
+ "x = matdata['likgrid'].reshape(-1)\n",
+ "posterior_at_x = matdata['post'].reshape(-1)\n",
+ "\n",
+ "# Plot the reference\n",
+ "plt.figure()\n",
+ "plt.plot(x, posterior_at_x, c='k')\n",
+ "\n",
+ "# Plot the different curves\n",
+ "for res, d_node, c in ([sim_res, 'd_sim', 'b'], [T1_res, 'd_T1', 'g'], [T2_res, 'd_T2', 'r']):\n",
+ " alphas = res.outputs['alpha']\n",
+ " dists = res.outputs[d_node]\n",
+ " # Use gaussian kde to make the curves look nice. Note that this tends to benefit the algorithm 1 \n",
+ " # a lot as it ususally has only a very few accepted samples with 100000 simulations\n",
+ " kde = ss.gaussian_kde(alphas[dists<=0])\n",
+ " plt.plot(x, kde(x), c=c)\n",
+ " \n",
+ "plt.legend(['reference', 'algorithm 1', 'algorithm 2, T1\\n(eps=0)', 'algorithm 2, T2\\n(eps=0)'])\n",
+ "plt.xlim([-.2, 1.2]);\n",
+ "print('Results after 100000 simulations. Compare to figure 6(a) in Lintusaari et al. 2016.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Interfacing with R"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It is possible to run R scripts in command line for example with [Rscript](http://stat.ethz.ch/R-manual/R-devel/library/utils/html/Rscript.html). However, in Python it may be more convenient to use [rpy2](http://rpy2.readthedocs.io), which allows convenient access to the functionality of R from within Python. You can install it with `pip install rpy2`.\n",
+ "\n",
+ "Here we demonstrate how to calculate the summary statistics used in the ELFI tutorial (autocovariances) using R's `acf` function for the MA2 model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import rpy2.robjects as robj\n",
+ "from rpy2.robjects import numpy2ri as np2ri\n",
+ "\n",
+ "# Converts numpy arrays automatically\n",
+ "np2ri.activate()"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ ".. Note:: See this issue_ if you get a `undefined symbol: PC` error in the import after installing rpy2 and you are using Anaconda.\n",
+ "\n",
+ ".. _issue: https://github.com/ContinuumIO/anaconda-issues/issues/152"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's create a Python function that wraps the R commands (please see the documentation of [rpy2](http://rpy2.readthedocs.io) for details):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "robj.r('''\n",
+ " # create a function `f`\n",
+ " f <- function(x, lag=1) {\n",
+ " ac = acf(x, plot=FALSE, type=\"covariance\", lag.max=lag, demean=FALSE)\n",
+ " ac[['acf']][lag+1]\n",
+ " }\n",
+ " ''')\n",
+ "\n",
+ "f = robj.globalenv['f']\n",
+ "\n",
+ "def autocovR(x, lag=1):\n",
+ " x = np.atleast_2d(x)\n",
+ " apply = robj.r['apply']\n",
+ " ans = apply(x, 1, f, lag=lag)\n",
+ " return np.atleast_1d(ans)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 5., 23.])"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Test it\n",
+ "autocovR(np.array([[1,2,3,4], [4,5,6,7]]), 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load a ready made MA2 model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ma2 = elfi.examples.ma2.get_model(seed_obs=4)\n",
+ "elfi.draw(ma2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Replace the summaries S1 and S2 with our R autocovariance function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Method: Rejection\n",
+ "Number of posterior samples: 100\n",
+ "Number of simulations: 10000\n",
+ "Threshold: 0.111\n",
+ "Posterior means: t1: 0.599, t2: 0.177"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Replace with R autocov\n",
+ "S1 = elfi.Summary(autocovR, ma2['MA2'], 1)\n",
+ "S2 = elfi.Summary(autocovR, ma2['MA2'], 2)\n",
+ "ma2['S1'].become(S1)\n",
+ "ma2['S2'].become(S2)\n",
+ "\n",
+ "# Run the inference\n",
+ "rej = elfi.Rejection(ma2, 'd', batch_size=1000, seed=seed)\n",
+ "rej.sample(100)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Interfacing with MATLAB"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are a number of options for running MATLAB (or Octave) scripts from within Python. Here, evaluating the distance is demonstrated with a MATLAB function using the official [MATLAB Python cd API](http://www.mathworks.com/help/matlab/matlab-engine-for-python.html). (Tested with MATLAB 2016b.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import matlab.engine"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A MATLAB session needs to be started (and stopped) separately:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "eng = matlab.engine.start_matlab() # takes a while..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Similarly as with R, we have to write a piece of code to interface between MATLAB and Python:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def euclidean_M(x, y):\n",
+ " # MATLAB array initialized with Python's list\n",
+ " ddM = matlab.double((x-y).tolist())\n",
+ " \n",
+ " # euclidean distance\n",
+ " dM = eng.sqrt(eng.sum(eng.power(ddM, 2.0), 2))\n",
+ " \n",
+ " # Convert back to numpy array\n",
+ " d = np.atleast_1d(dM).reshape(-1)\n",
+ " return d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1.41421356, 8.77496439, 1. ])"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Test it\n",
+ "euclidean_M(np.array([[1,2,3], [6,7,8], [2,2,3]]), np.array([2,2,2]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load a ready made MA2 model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ma2M = elfi.examples.ma2.get_model(seed_obs=4)\n",
+ "elfi.draw(ma2M)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Replace the summaries S1 and S2 with our R autocovariance function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Method: Rejection\n",
+ "Number of posterior samples: 100\n",
+ "Number of simulations: 10000\n",
+ "Threshold: 0.113\n",
+ "Posterior means: t1: 0.602, t2: 0.178"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Replace with Matlab distance implementation\n",
+ "d = elfi.Distance(euclidean_M, ma2M['S1'], ma2M['S2'])\n",
+ "ma2M['d'].become(d)\n",
+ "\n",
+ "# Run the inference\n",
+ "rej = elfi.Rejection(ma2M, 'd', batch_size=1000, seed=seed)\n",
+ "rej.sample(100)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, don't forget to quit the MATLAB session:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "eng.quit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Verdict\n",
+ "\n",
+ "We showed here a few examples of how to incorporate non Python operations to ELFI models. There are multiple other ways to achieve the same results and even make the wrapping more efficient.\n",
+ "\n",
+ "Wrapping often introduces some overhead to the evaluation of the generative model. In many cases however this is not an issue since the operations are usually expensive by themselves making the added overhead insignificant."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "### References\n",
+ "- [1] Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander; Fundamentals and Recent Developments in Approximate Bayesian Computation. Syst Biol 2017; 66 (1): e66-e82. doi: 10.1093/sysbio/syw077\n",
+ "- [2] Tanaka, Mark M., et al. \"Using approximate Bayesian computation to estimate\n",
+ "tuberculosis transmission parameters from genotype data.\"\n",
+ "Genetics 173.3 (2006): 1511-1520.\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [default]",
+ "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": "3.5.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/parallelization.ipynb b/parallelization.ipynb
new file mode 100644
index 0000000..ebc0af1
--- /dev/null
+++ b/parallelization.ipynb
@@ -0,0 +1,512 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This tutorial is generated from a [Jupyter](http://jupyter.org/) notebook that can be found [here](https://github.com/elfi-dev/notebooks)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Parallelization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Behind the scenes, ELFI can automatically parallelize the computational inference via different clients. Currently ELFI includes three clients:\n",
+ "\n",
+ "- `elfi.clients.native` (activated by default): does not parallelize but makes it easy to test and debug your code.\n",
+ "- `elfi.clients.multiprocessing`: basic local parallelization using Python's built-in multiprocessing library\n",
+ "- `elfi.clients.ipyparallel`: [ipyparallel](http://ipyparallel.readthedocs.io/) based client that can parallelize from multiple cores up to a distributed cluster.\n",
+ "\n",
+ "A client is activated by importing the respective ELFI module. \n",
+ "\n",
+ "This tutorial shows how to activate and use the `ipyparallel` client with ELFI. For local parallelization, the `multiprocessing` client is simpler to use.\n",
+ "\n",
+ "\n",
+ "## Activating parallelization\n",
+ "\n",
+ "To activate the `ipyparallel` client in ELFI you just need to import it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import elfi\n",
+ "# This activates the parallelization with ipyparallel\n",
+ "import elfi.clients.ipyparallel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Starting a local ipcluster\n",
+ "\n",
+ "Before you can actually run things in parallel you also need to start an `ipyparallel` cluster. Below is an example of how to start a local cluster to the background using 4 CPU cores:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "!ipcluster start -n 4 --daemon\n",
+ "\n",
+ "# This is here just to ensure that ipcluster has enough time to start properly before continuing\n",
+ "import time\n",
+ "time.sleep(10)"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ ".. note:: The exclamation mark above is a Jupyter syntax for executing shell commands. You can run the same command in your terminal without the exclamation mark.\n",
+ "\n",
+ ".. tip:: Please see the [ipyparallel documentation](https://ipyparallel.readthedocs.io/en/latest/intro.html#getting-started) for more information and details for setting up and using ipyparallel clusters."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Running parallel inference"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will run parallel inference for the MA2 model introduced in the basic tutorial. A ready made model can be imported from `elfi.examples`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from elfi.examples import ma2\n",
+ "model = ma2.get_model()\n",
+ "\n",
+ "elfi.draw(model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Otherwise everything should be familiar, and ELFI handles everything for you regarding the parallelization. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "rej = elfi.Rejection(model, 'd', batch_size=10000, seed=20170530)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When running the next command, take a look at the system monitor of your operating system; it should show 4 (or whatever number you gave the `ipcluster start` command) Python processes doing heavy computation simultaneously."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 3.59 s, sys: 417 ms, total: 4 s\n",
+ "Wall time: 20.9 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time result = rej.sample(5000, n_sim=int(5e6)) # 5 million simulations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `Sample` object is also just like in the basic case:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Method: Rejection\n",
+ "Number of posterior samples: 5000\n",
+ "Number of simulations: 5000000\n",
+ "Threshold: 0.0336\n",
+ "Posterior means: t1: 0.493, t2: 0.0332\n"
+ ]
+ }
+ ],
+ "source": [
+ "result.summary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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d+2HzIT5blU5JdcudmKcNbk+gR9OHAhqNwuSEcIoq9Tw6ZydFVfUUV9fz2/Zs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jVWtgyFu7WH5XHP39TV5LeysLUl+ZQHJxPb3cbahu0nK8oJZl+02e4DVJhfT1dWDfmSoSFpq8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\nZ2kAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "result.plot_pairs()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To summarize, the only thing that needed to be changed from the basic scenario was enabling the `ipyparallel` client."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Working interactively"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "All imports and definitions must be visible to all `ipyparallel` engines. You can ensure this by writing a script file that has all the definitions in it. In a distributed setting, this file must be present in all remote workers running an `ipyparallel` engine. \n",
+ "\n",
+ "However, you may wish to experiment in an interactive session, using e.g. a jupyter notebook. `ipyparallel` makes it possible to interactively define functions for ELFI model and send them to workers. This is especially useful if you work from a jupyter notebook. We will show a few examples. More information can be found from [`ipyparallel` documentation](http://ipyparallel.readthedocs.io/). \n",
+ "\n",
+ "In interactive sessions, you can change the model with built-in functionality without problems:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "d2 = elfi.Distance('cityblock', model['S1'], model['S2'], p=1)\n",
+ "\n",
+ "rej2 = elfi.Rejection(d2, batch_size=10000)\n",
+ "result2 = rej2.sample(1000, quantile=0.01)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But let's say you want to use your very own distance function in a jupyter notebook:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def my_distance(x, y):\n",
+ " # Note that interactively defined functions must use full module names, e.g. numpy instead of np\n",
+ " return numpy.sum((x-y)**2, axis=1)\n",
+ "\n",
+ "d3 = elfi.Distance(my_distance, model['S1'], model['S2'])\n",
+ "rej3 = elfi.Rejection(d3, batch_size=10000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This function definition is not automatically visible for the `ipyparallel` engines if it is not defined in a physical file. The engines run in different processes and will not see interactively defined objects and functions. The below would therefore fail:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# This will fail if you try it!\n",
+ "# result3 = rej3.sample(1000, quantile=0.01)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ipyparallel provides a way to manually `push` the new definition to the scopes of the engines from interactive sessions. Because `my_distance` also uses `numpy`, that must be imported in the engines as well:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "importing numpy on engine(s)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get the ipyparallel client\n",
+ "ipyclient = elfi.get_client().ipp_client\n",
+ "\n",
+ "# Import numpy in the engines (note that you cannot use \"as\" abbreviations, but must use plain imports)\n",
+ "with ipyclient[:].sync_imports():\n",
+ " import numpy\n",
+ "\n",
+ "# Then push my_distance to the engines\n",
+ "ipyclient[:].push({'my_distance': my_distance});"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The above may look a bit cumbersome, but now this works:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Method: Rejection\n",
+ "Number of posterior samples: 1000\n",
+ "Number of simulations: 100000\n",
+ "Threshold: 0.0117\n",
+ "Posterior means: t1: 0.492, t2: 0.0389"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rej3.sample(1000, quantile=0.01) # now this works"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "However, a simpler solution to cases like this may be to define your functions in external scripts (see `elfi.examples.ma2`) and have the module files be available in the folder where you run your ipyparallel engines."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Remember to stop the ipcluster when done"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2017-06-21 16:06:24.007 [IPClusterStop] Stopping cluster [pid=94248] with [signal=]\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!ipcluster stop"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [default]",
+ "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": "3.5.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/post-sampling_correction.ipynb b/post-sampling_correction.ipynb
new file mode 100644
index 0000000..1b3a617
--- /dev/null
+++ b/post-sampling_correction.ipynb
@@ -0,0 +1,289 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Post-sampling correction\n",
+ "\n",
+ "Small values of the threshold parameter $\\epsilon$ produce more accurate approximations of the posterior distribution. However, this comes at a computational cost as the acceptance probability is consequently also reduced. One approach to remedy this problem is to fit regression models to estimate the approximate posterior with $\\epsilon = 0$. This is discussed in more detail in [*Lintusaari et al 2016*](https://doi.org/10.1093/sysbio/syw077) *[1]* and [*Beaumont et al 2002*](http://www.genetics.org/content/162/4/2025) *[2]*.\n",
+ "\n",
+ "Note that some more advanced ABC methods, such as BOLFI, model the discrepancy function directly and the threshold can be specified prior to sampling the approximate posterior."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import scipy.io as sio\n",
+ "import scipy.stats as ss\n",
+ "\n",
+ "from functools import partial\n",
+ "\n",
+ "import elfi\n",
+ "from elfi.examples import gauss"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will use a simple model of a univariate Gaussian with an unknown mean to illustrate posterior adjustment. The observed data is 50 data points sampled from a Gaussian distribution with a mean of 5 and a standard deviation of 1. Since we use a Gaussian prior for the mean, the posterior is also Gaussian with known parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m = gauss.get_model()\n",
+ "\n",
+ "seed = 20170616\n",
+ "n_obs = 50\n",
+ "batch_size = 100\n",
+ "mu, sigma = (5, 1)\n",
+ "\n",
+ "y_obs = gauss.Gauss(mu, sigma, n_obs=n_obs, batch_size=1,\n",
+ " random_state=np.random.RandomState(seed))\n",
+ "\n",
+ "# Hyperparameters\n",
+ "mu0, sigma0 = (7, 100)\n",
+ "\n",
+ "# Posterior parameters\n",
+ "n = y_obs.shape[1]\n",
+ "mu1 = (mu0/sigma0**2 + y_obs.sum()/sigma**2)/(1/sigma0**2 + n/sigma**2)\n",
+ "sigma1 = (1/sigma0**2 + n/sigma**2)**(-0.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We use a normal distribution with a large standard deviation $\\sigma = 100$ and a non-centered mean $\\mu = 7$ as an uninformative prior for the unknown mean. A large acceptance radius of $\\epsilon = 1$ does not produce very accurate results. The large standard deviation leads to inefficient sampling and is used for illustrative purpse here. In practice, it is common to use more informative priors."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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pkUPCOx4AgGTByiwAAFHS0t6hc376om5/9v2w32OMZCQFg1YdwfjatBFAfIm3jWER/wb7\n7xxlFgCAKHl6Y4X21wU0b2xh3wcfIdDWoVP/+3mt2lzlUTIAyc7n86m2tpZCi6ix1qq2tlY+n2/A\n5+A2YwAAomTDnjrlZKTq1AlF/XpfZnqqWto79Nhb+3T2tDKP0gFIZqNGjdLevXtVU1PjOgqSiM/n\n06hRowb8fsosAABRsq2mQeNLcvu9K7GRdOGsEXrwtd2qa2pTQXa6NwEBJK309HSNGzfOdQygX7jN\nGACAKNlW3aDxxbkDeu/Fs0eqtSOopzZWRDgVAADxiTILAEAUWGt1w+ITdfHskQN6/4yRBRpfnKPH\n39oX4WQAAMQnbjMGACAKjDFaetLHBvX+fzt3stJT+3eLMgAAiYoyCwBAFOw/3KzGlnaNL85VSsrA\nCuk5bP4EAEA3bjMGACAKHnpjt86540W1BYODOs+bOw/qjR0HI5QKAID4RZkFACAKttc0akxhtjLT\nUgd1nh88tVk//csHEUoFAED8oswCABAF22oGvpPxkcYV5WhXbWMEEgEAEN8oswAAeKwjaLX9QKNO\nLBl8mR07LEf76wIKtHVEIBkAAPGLMgsAgMf2HWpWa3swIiuzY4uyJUm7apsGfS4AAOIZZRYAAI8N\ny83Qrz4/T6dMKBr0ucYV5UiSdhzgVmMAQHJjNA8AAB7LyUzTmVNLI3KuiaV5+tMNizShdPCrvAAA\nxDPKLAAAHlu99YDSUoxOOmHYoM/lS0/VrNFDIpAKAID4xm3GAAB47GfPbdFtz0ZunM4L71frf97Y\nHbHzAQAQjyizAAB4bFtNg8aX5ETsfE9uqNBPn2PWLAAguVFmAQDw0KHGVtU2tkZkJ+OQcUXZqvK3\nqKm1PWLnBAAg3lBmAQDw0PYDDZIU0TI7tmtH450HGM8DAEhelFkAADy0rbpzhE5Ey+ywrjJby3ge\nAEDyYjdjAAA8dFH5CE0bma+RQ7Mids7Qyuzug6zMAgCSF2UWAAAP+dJTNW1EQUTPmZuZpjf//UwV\n5WZE9LwAAMQTbjMGAMBDd72wVa9sOxDx8xbnZcoYE/HzAgAQLyizAAB4pKW9Q7c9+4Fe21Yb8XOv\n2lyl7/xpU8TPCwBAvKDMAgDgkd21TeoIWo0vidzmTyHvVdbrN6/uUkML43kAAMmJMgsAgEe2Vkd+\nLE/IuK5NoHaxozEAIElRZgEA8Mi2ms4yGyqekdQ9nodZswCAJEWZBQDAI/sON2tEgU85mZEfHjC2\nKFsSs2YBAMmL0TwAAHjkR5fM9Ow7rdkZaRpdmMV3ZgEASYsyCwCAh3I9WJUNefHGTzCeBwCQtLjN\nGAAAD1TXB/S1h97Shj2HPbsGRRYAkMwoswAAeGBLVYOe2LBfjR7eBvy3D2r0uV++Jn+gzbNrAAAQ\nqyizAAB4ILSTsRczZkOaW9v1yrZa7WJHYwBAEqLMAgDggW3VDcrNTFNJXqZn1xjbNfJnBzsaAwCS\nEGUWAAAPbKtp1PjiHE+/1/qxwtCsWcosACD5UGYBAPBAaorR1BEFnl4jKyNVwwt8lFkAQFJiNA8A\nAB74zdULonKdk8YVqiA7PSrXAgAglnhWZo0xoyU9IKlUkpV0r7X2Zx855gxJf5K0o+upP1pr/9Or\nTAAAJJo7PjvbdQQAAJzw8jbjdkn/Yq2dKulkSTcYY6Ye57iXrLXlXT8UWQBA3Fu5qVKX3fOqqusD\nrqMAAJCwPCuz1toKa+26rj/XS9osaaRX1wMAIFa8s79Oa3cfUkGW97f/rtt9SJ+49QVt3Fvn+bUA\nAIglUdkAyhgzVtJsSa8f5+WFxpgNxphnjDHTenj/tcaYNcaYNTU1NR4mBQBg8LbVNGhMYbYy01I9\nv1ZuZpp2HGjU9gMNnl8LAIBY4nmZNcbkSnpU0jestf6PvLxO0sestbMk3Snp8eOdw1p7r7V2nrV2\nXnFxsbeBAQAYpG3VnWN5omFMYbaMkXawozEAIMl4WmaNMenqLLIPWmv/+NHXrbV+a21D15+flpRu\njCnyMhMAAF7qCFrtONCo8cW5UbmeLz1VIwqyGM8DAEg6npVZ0zkl/j5Jm621t/dwTFnXcTLGLOjK\nU+tVJgAAvNYQaNcpE4pUPnpI1K45tihbO2qbonY9AABigZdzZhdJulLSRmPM+q7nvilpjCRZa++R\n9BlJ1xtj2iU1S/qstdZ6mAkAAE8VZKdrxVXzo3rNT0wq0f7D7JwMAEgunpVZa+3Lkkwfx/xc0s+9\nygAAQLRZa9V101HUfOnUE6J6PQAAYkFUdjMGACBZfPOxjfrUz1+O+nWDQau2jmDUrwsAgCuUWQAA\nImhLVUNURvIcac/BJk39zko9uWF/VK8LAIBLlFkAACJo+4FGjS+JzliekNJ8n1rbg+xoDABIKpRZ\nAAAi5GBjqw42tkZtLE9IRlqKRg7N0k52NAYAJBHKLAAAEbK9pkGSNL4kumVWksYOy9HOWlZmAQDJ\ngzILAECE5PnS9bmTxmhKWX7Urz12WI52HGgUE+4AAMnCyzmzAAAklUllefrhxTOcXHvJlBIV5mSo\nPWiVnhrd0UAAALhAmQUAIEION7UqNzNNaanRv/HpjEklOmNSSdSvCwCAK9xmDABAhHxhxRu6+jdr\nnF3/UNcGVAAAJAPKLAAAEVJRF1BZfqaTa7d1BDXvB89pxcs7nFwfAIBoo8wCABABbR1B1TS0qCzf\n5+T66akpGjU0SzvY0RgAkCQoswAAREBNfYuslcoKspxlGDssRzsPUGYBAMmBMgsAQARU+gOSpLIC\nN7cZS9K4os4yy3geAEAyoMwCABABJXmZuvGcSZrsYMZsyNhh2Wps7VBNQ4uzDAAARAujeQAAiIBR\nQ7N1wydOdJrhlAnF+vElM+RLT3WaAwCAaKDMAgAQAfsONyvFSMMdfmf2xJJcnViS6+z6AABEE7cZ\nAwAQAT9+5j199t7XXMfQ5gq/3q+sdx0DAADPUWYBAIiAqrqASh2N5TnS9b9bq2V/3eI6BgAAnqPM\nAgAQAZX+gIYXuC+zZQU+VdUFXMcAAMBzlFkAAAbJWqtKf0BlMbAyW5bvU1U9ZRYAkPgoswAADNKh\npja1tgdVFgMrs6X5PlX5W5g1CwBIeJRZAAAGKTMtRbdfNkunTih2HUWl+T61tgd1qKnNdRQAADzF\naB4AAAYpJzNNl8wZ5TqGJOmsqaUaX5Kr7AxmzQIAEhtlFgCAQdpd26SahoDKRw9VaopxmmV0YbZG\nF2Y7zQAAQDRwmzEAAIP0h7V7dOk9r8bE91Rb24P6v3cqtaWKWbMAgMRGmQUAYJAq6gIqzstUWmps\nfKxe99u1empjhesYAAB4KjY+dQEAiGOV/oDKCrJcx5AkZaSlqCg3Q1X+FtdRAADwFGUWAIBBqqwL\nqCw/03WMbiV5PlX5mTULAEhslFkAAAapsi6g4TGyMitJZQU+VdZRZgEAiY3djAEAGARrre79/DwV\n5Wa4jtKtNN+nt/cedh0DAABPUWYBABgEY4wWjh/mOsZRrj99vK5eNNZ1DAAAPEWZBQBgEPYdbta6\nXYd0+qRi5fvSXceRJI0ZxpxZAEDi4zuzAAAMwmvbavXVh97SwYZW11G61dS36Dev7NSeg02uowAA\n4BnKLAAAg1DZtWtwWYHPcZIPHWho0XeeeEdv761zHQUAAM9QZgEAGITKuoCGZKfLl57qOkq30vzO\nYs14HgBAIqPMAgAwCBV1AZXlx86qrCQNzU5XRloKZRYAkNAoswAADEKVP9C9EhorjDEqzc/svgUa\nAIBExG7GAAAMwl1L56itI+g6xjHK8n2qrKPMAgASF2UWAIBBGF0Ym2Nwfv65OcrOiJ3v8QIAEGnc\nZgwAwAAdbGzVL1/crl21ja6jHKM036e8GJl7CwCAFyizAAAM0PaaBv3g6c3acSD2yuymfXX60TOb\n1dDS7joKAACeoMwCADBAsThjNmRbTYN+8bftqjjc7DoKAACeoMwCADBAoQ2WhudnOU5yrNC4IHY0\nBgAkKsosAAADVFkXkC89RflZsbefYmi1mB2NAQCJijILAMAAVfoDGl6QJWOM6yjHCM2+ra5vcZwE\nAABvxN6vkgEAiBO3XjpL/uY21zGOy5eeqoKsdB1ooMwCABITZRYAgAHypafKlx67s1xfu3mJspg1\nCwBIUNxmDADAAASDVv/55Lt6fXut6yg9osgCABIZZRYAgAE42NSqFat3aHOF33WUHj31doW+9fgm\n1zEAAPAEZRYAgAEI7RJcVhB7Y3lC3q/068HXd6m9I+g6CgAAEUeZBQBgAD4ssz7HSXpWku9T0Eq1\nja2uowAAEHGUWQAABqDC31lmh8dwmS3LZ9YsACBxUWYBABiAuqZWpaUYFeVmuo7So9Cs2Uo/ZRYA\nkHgYzQMAwAD84+IJuva08UpNMa6j9Ki0IFN5vjQ1t3a4jgIAQMRRZgEAGKCMtNi+wakkz6eN3z3H\ndQwAADwR25/CAADEqO8+8Y7+d80e1zEAAEhalFkAAAbgD2v3xvSM2ZDb//KBvv/nd13HAAAg4iiz\nAAD0U32gTQ0t7d27BceyDyrr9cIHNa5jAAAQcZRZAAD6qcof+zNmQ8oKfN15AQBIJJRZAAD6qaJr\nbms8rMyW5GeqPtCuptZ211EAAIgoyiwAAP0UaAuqKDdDwwuyXEfpU6hwV9axOgsASCyM5gEAoJ/O\nmlqqs6ae5TpGWEYNzdbE0lwF2oKuowAAEFGUWQAAEtiCcYV69p9Odx0DAICI4zZjAAD66QdPvasf\nPMW4GwAAXKLMAgDQT6u31mpbTaPrGGG76v43dOeqLa5jAAAQUdxmDABAP1X5A5o1eojrGGHbfbBJ\n2RmprmMAABBRrMwCANAPLe0dqm1s1fA4mDEbUpbvYzdjAEDCocwCANAP1f4WSfExYzakLN+nqq7c\nAAAkCsosAAD9EGjr0PSR+RozLNt1lLCV5PtUXR9QMGhdRwEAIGL4ziwAAP0woTRPf/7qqa5j9MvU\nEfn6+PgiNbd1KCeTj34AQGLgEw0AgAR30awRumjWCNcxAACIKG4zBgCgH5at2qLL733NdQwAAJIe\nZRYAgH54v7JeVf742hm4tqFFi378V/3vmj2uowAAEDGUWQAA+qGirlllcTSWR5Lys9K1v65Zew81\nu44CAEDEUGYBAOiHKn9LXI3lkaT01BQV5WaqOs5WlAEA6A1lFgCAMAWDVlX+QNytzEpSaX6mKimz\nAIAEQpkFACBMzW0d+sTkEk0fWeA6Sr+V5ftUWUeZBQAkDkbzAAAQppzMNP3y8/NcxxiQ0yYWa8/B\nJtcxAACIGM/KrDFmtKQHJJVKspLutdb+7CPHGEk/k3S+pCZJV1lr13mVCQCAZPX5hWNdRwAAIKK8\nvM24XdK/WGunSjpZ0g3GmKkfOeY8SRO6fq6VdLeHeQAAGJTfvrZL83/wnA41trqOMiDBoFUwaF3H\nAAAgIjwrs9baitAqq7W2XtJmSSM/ctinJD1gO70maYgxZrhXmQAAGIz9h5t1qLFVBVnprqP026vb\najXxlme0dvch11EAAIiIqGwAZYwZK2m2pNc/8tJISUdOcN+rYwsvAAAxoaouoNJ8n1JSjOso/VaY\nk6H2rt2YAQBIBJ6XWWNMrqRHJX3DWusf4DmuNcasMcasqampiWxAAADCVBmnY3mkztE8ktjRGACQ\nMDwts8aYdHUW2QettX88ziH7JI0+4vGorueOYq2911o7z1o7r7i42JuwAAD0odIf6C6F8aYgK12Z\naSmszAIAEoZnZbZrp+L7JG221t7ew2FPSPq86XSypDprbYVXmQAAGIyzppbqjIklrmMMiDFGZQU+\nVfpbXEcBACAivJwzu0jSlZI2GmPWdz33TUljJMlae4+kp9U5lmerOkfzfNHDPAAADMrN501xHWFQ\nlp40RoU58bmyDADAR3lWZq21L0vqdYcMa62VdINXGQAAiJT2jqCspPTUqOyd6IlrTxvvOgIAABET\nv5/IAABE0Vt7DmviLc9o9dYDrqMMWEfQqro+oM7fJQMAEN8oswAAhKHa3yJrO0fcxKv7V+/Qgh+s\nkr+53XUUAAAGjTILAEAYauo7dwEuzovf75yW5neOFapkR2MAQAKgzAIAEIbq+halpRgVZsfvymxo\nRi5lFgBXsvaVAAAgAElEQVSQCCizAACEoaa+RUW5mUpJ6XVvw5hW1rUyW1VHmQUAxD8vR/MAAJAw\nzphUogmlua5jDEroFukqVmYBAAmAMgsAQBg+OXO46wiD5ktP1c3nTda8sUNdRwEAYNAoswAAhKGi\nrlmFORnKTEt1HWVQrjudWbMAgMTAd2YBAOhDe0dQH//xX7X8+W2uowzawcZWfVBV7zoGAACDRpkF\nAKAPtY2tslYqieOxPCH/9cx7uuJXr7uOAQDAoFFmAQDoQ019i6T4njEbUpyXqQMNLeoIWtdRAAAY\nFMosAAB9qK7v3P03EVZmS/IzFbSdtxsDABDPKLMAAPSh2p84K7OhQh4q6AAAxCvKLAAAfSgfM0Q3\nnzc5Icps6D9D6NZpAADiFaN5AADow+SyfE0uy3cdIyLGF+fqJ5+ZqUllea6jAAAwKJRZAAD6sK2m\nQVnpqRoxJMt1lEEbkp2hS+eNdh0DAIBB4zZjAAD6cOMjG3TjHza4jhExG/fWaXOF33UMAAAGhTIL\nAEAfqutbVJLncx0jYr7x8Fu6869bXMcAAGBQKLMAAPTCWqvq+paE2PwppDgvs3uHZgAA4hVlFgCA\nXvib29XaHkyIGbMhJXk+1TRQZgEA8Y0yCwBAL0LzWBNpZbaka2XWWus6CgAAA0aZBQCgFyV5Pv3s\ns+WaN7bQdZSIKc7LVHNbhxpbO1xHAQBgwBjNAwBALwqy0/Wp8pGuY0TU+TOGa/rIAmWk8jttAED8\noswCANCLbTUNqqlv0YKxhUpJMa7jRMTowmyNLsx2HQMAgEHhV7IAAPTif97YrS+seEMmMXqsJCnQ\n1qGnN1Zoe02D6ygAAAwYZRYAgF5U17eoJD9TJoHabKCtQ195cJ2ef7/GdRQAAAaMMgsAQC+q/S0q\nyfO5jhFRBVnpykhL6d6pGQCAeESZBQCgFzUNLQk1Y1aSjDEqzs1UjZ9ZswCA+EWZBQCgF9X+QELN\nmA0pzstUdT1lFgAQv9jNGACAXvzqC/NVmJPuOkbEleRlamdto+sYAAAMGGUWAIBeLBhX6DqCJ24+\nf4oSZ0srAEAy4jZjAAB6UFkX0ONv7dOhxlbXUSJuXFGOxhbluI4BAMCAUWYBAOjB+j2H9Y2H12vf\n4WbXUSJuV22j7nt5hw43JV5RBwAkB8osAAA9qOkaXVOSn3gbQG2patD3/vyudtY2uY4CAMCAUGYB\nAOhBdX2LUow0LCfxymyooFf7mTULAIhPlFkAAHpQU9+iYbmZSk1JvK2SSvJ8kjrn6AIAEI8oswAA\n9KC6vkUlCThjVpKG5WbIGKnaT5kFAMQnRvMAANCDH1w8XY0t7a5jeCI9NUWF2RmqrqfMAgDiE2UW\nAIAeDC/Ich3BU0989RQNzU53HQMAgAHhNmMAAI4jGLS652/btGlfnesonhk5JEvZGfxeGwAQnyiz\nAAAcR21jq378zHtau+uQ6yieeeH9ai1btcV1DAAABoQyCwDAcVSHZswm6AZQkvT6joO6869bFAxa\n11EAAOg3yiwAAMcR2hgpNI81EZXkZaqtw+pwc5vrKAAA9BtlFgCA46jpKrPFuT7HSbxT3LXqHFqF\nBgAgnlBmAQA4jpqkWJntLOo1jOcBAMQhtjAEAOA4vnzqCbpkzkj50lNdR/FM6PvABxtbHScBAKD/\nKLMAABxHRlpKws+ZHVOYrfe+d25CF3YAQOLiNmMAAI7jVy9t15/W73Mdw1MpKYYiCwCIW5RZAACO\n44FXd2nV5mrXMTy3/PmtWvHyDtcxAADoN8osAAAfYa1VTX1L926/iexv79do5TuVrmMAANBvlFkA\nAD6ioaVdzW0d3RskJbLi/Ex2MwYAxCXKLAAAH1GdBGN5QkryKLMAgPhEmQUA4CMONbbKmA/nsCay\n4rxMNbS0q6m13XUUAAD6hdE8AAB8xLyxhdry/fNcx4iKkjyfhmSn61BTm7Iz+L8FAID4wacWAADH\nkZaaHDcvfXrOSH1m7ijXMQAA6Lfk+KQGAKAf/rhur/7zyXddx4gKY4zrCAAADAhlFgCAj3h5ywH9\nX5KMq2lu7dBXHlyrpzdWuI4CAEC/9FlmjTHDohEEAIBYUdOQHDNmJSkzLUXPvlOljfvqXEcBAKBf\nwlmZfc0Y84gx5nzDvUgAgCRQ7W9JihmzkpSSYlSUy3geAED8CafMTpR0r6QrJW0xxvzQGDPR21gA\nALhTXR9IihmzISX5md2zdQEAiBd9llnb6S/W2sslfVnSFyS9YYz5mzFmoecJAQCIovaOoDLTUjW8\nIMt1lKgpyWNlFgAQf/oczdP1ndkr1LkyWyXpq5KekFQu6RFJ47wMCABANKWlpui1by5xHSOqxpfk\nqqm1w3UMAAD6JZw5s69K+q2kv7PW7j3i+TXGmHu8iQUAAKLl5vOmuI4AAEC/hfOd2Vustd87ssga\nYy6VJGvtf3mWDAAAB17fXqtrfv2m9h5qch0FAAD0Ipwye9Nxnrs50kEAAIgFW2satOq9aqWlJM8o\n9vV7Duviu1brvUq/6ygAAIStx9uMjTHnSTpf0khjzLIjXsqX1O51MAAAXKj2t8gYaVhuhusoUfXW\n7sPad6hZk8vyXUcBACAsvX1ndr+kNZIukrT2iOfrJf2Tl6EAAHClur5FhdkZSk9NnpXZ4q6Zuuxo\nDACIJz2WWWvtBkkbjDEPWmtZiQUAJIWa+kB3uUsWRV2r0MyaBQDEk95uM/5fa+1lkt4yxtgjX1Ln\n+NmZnqcDACDKhmRnKM+X7jpGVGWmpWpIdrqq6wOuowAAELbebjP+etc/L4hGEAAAYsGtl85yHcGJ\nRScWqSzf5zoGAABh6+0244quPx6Q1GytDRpjJkqaLOmZaIQDAADRsfxzc1xHAACgX8LZ3eJFST5j\nzEhJz0q6UtKvvQwFAIALhxpb9cllL+nZdypdRwEAAH0Ip8waa22TpEsk3WWtvVTSNG9jAQAQfVX1\nAb2z36/WjqDrKFF3/+odOv0nz8ta2/fBAADEgLDKrDFmoaSlkp7qei7Vu0gAALhR7e/czbckL/m+\nO9oRtNpV2yR/MwMMAADxIZwy+3VJN0t6zFr7jjHmBEnPexsLAIDoC81ZLUmy0TzSh7Nm2dEYABAv\netvNWJJkrX1Rnd+bDT3eLulrXoYCAMCF0JzVZJszK324Gl1T36IJpXmO0wAA0Lc+y2zXDsb/Kmns\nkcdbaxd7FwsAgOgblpOhj48fppzMPj8eE05JfmhltsVxEgAAwhPOp/Ujku6R9CtJHd7GAQDAncvm\nj9Zl80e7juFEab5PZ04p1bDcDNdRAAAISzhltt1ae7fnSQAAgDO5mWn61RfmuY4BAEDYwtkA6klj\nzFeMMcONMYWhn77eZIxZYYypNsZs6uH1M4wxdcaY9V0/3+53egAAIuiSu1brh09vdh3DKUbzAADi\nRTgrs1/o+ueNRzxnJZ3Qx/t+Lennkh7o5ZiXrLUXhJEBAADPfVDVoFmjh7iO4cyXfrNGLe0d+u01\nJ7mOAgBAn8LZzXjcQE5srX3RGDN2IO8FACDamlrb1dDSnpQzZkPSU4121jKaBwAQH/q8zdgYk22M\nucUYc2/X4wnGmEitpi40xmwwxjxjjJnWS4ZrjTFrjDFrampqInRpAAA+VOXv3MW3ND/5xvKElORl\nqtpPmQUAxIdwvjN7v6RWSR/verxP0vcjcO11kj5mrZ0l6U5Jj/d0oLX2XmvtPGvtvOLi4ghcGgCA\no1V1lbjS/ORdmS3J98kfaFegjeEFAIDYF06ZHW+t/W9JbZJkrW2SZAZ7YWut31rb0PXnpyWlG2OK\nBnteAAAGIjczTZ+cMVxjCrNdR3GmOLdzVbqGWbMAgDgQzgZQrcaYLHVu+iRjzHhJg/6UM8aUSaqy\n1lpjzAJ1FuvawZ4XAICBmD6yQMuXznEdw6mpI/J15ckfU3pqOL/rBgDArXDK7HckrZQ02hjzoKRF\nkq7q603GmIcknSGpyBizt+s86ZJkrb1H0mckXW+MaZfULOmzlnkAAABHgkGrlJRB33gU16aPLND0\nkQWuYwAAEJZwdjP+izFmnaST1Xl78dettQfCeN/lfbz+c3WO7gEAwLmv/c9b2nOwSX/6x1NcR3Gq\nrSOo9g6rrIxU11EAAOhVj/cRGWPmhH4kfUxShaT9ksZ0PQcAQMKo8geSvsB1BK2mfGullj+/1XUU\nAAD61NvK7G1d//RJmidpgzpXZmdKWiNpobfRAACInip/i2aPGeI6hlOpKUZDczLYAAoAEBd6XJm1\n1n7CWvsJda7IzukajTNX0mx1jucBACAhWGtV6Q8k9ViekJK8TFXXM2sWABD7wtmucJK1dmPogbV2\nk6Qp3kUCACC66prb1NoepMyqs8zWNLAyCwCIfeHsZvy2MeZXkn7X9XippLe9iwQAQHRZK33plHEq\nH53ctxlLUkmeT+/s97uOAQBAn8Ips1+UdL2kr3c9flHS3Z4lAgAgyobmZOiWC6a6jhETzpleqnHF\nOa5jAADQp3BG8wQk/bTrBwCAhNPY0q7UFCNfenLvZixJiyeXavHkUtcxAADoUzjfmQUAIKHdv3qH\nJn9rpQJtHa6jONfeEdTeQ01qbGl3HQUAgF5RZgEASa/K36Ih2emszEp6t8KvU/7rea3eesB1FAAA\netVrmTXGpBpjbo1WGAAAXKj0B1Sax07Gkrp3dK5i1iwAIMb1WmattR2STolSFgAAnKj2B1RaQJmV\npKLcTKWmGFXVMWsWABDbwtnN+C1jzBOSHpHUGHrSWvtHz1IBABBFVf4WTSzNcx0jJqSmGBXnZqrS\nT5kFAMS2cMqsT1KtpMVHPGclUWYBAAnhutNP0LgixtGElBb4VEWZBQDEuHBG83wxGkEAAHDli4vG\nuY4QU64/fbwy0ozrGAAA9KrP3YyNMaOMMY8ZY6q7fh41xoyKRjgAALzW0NKu7TUNam0Puo4SM86d\nXsasWQBAzAtnNM/9kp6QNKLr58mu5wAAiHuvbavV4tv+pncr/K6jxIy6pja9ufMgBR8AENPCKbPF\n1tr7rbXtXT+/llTscS4AAKKiqr7zu6Gl+ZmOk8SO5zZX6dJ7XtW+w82uowAA0KNwymytMeaKrpmz\nqcaYK9S5IRQAAHGvyt8iY6TiXMpsSFnXmKJKxvMAAGJYOGX2akmXSaqUVCHpM5LYFAoAkBCq/QEV\n5WYqLTWcj8TkUJrfWWbZ0RgAEMvC2c14l6SLopAFAICoq/QHuMX4I0J/H5RZAEAs67HMGmP+zVr7\n38aYO9U5V/Yo1tqveZoMAIAo+NIpJ6iptd11jJiS50tXTkaqKimzAIAY1tvK7Oauf66JRhAAAFw4\nZUKR6wgx6Y7PztaYwmzXMQAA6FGPZdZa+6QxJlXSDGvtv0YxEwAAUdHWEdSbOw5qUlmehrEB1FHO\nmsqcWQBAbOt1twtrbYekRVHKAgBAVFX5A/rcr17Xc5urXEeJOdtqGvTsO5WuYwAA0KNwtm5cb4x5\nwhhzpTHmktCP58kAAPBYlb9FklTStXsvPvTImr264ffrFAwes20GAAAxoc/djCX51DlXdvERz1lJ\nf/QkEQAAUVLdtcFRaR5l9qPK8jPV1mF1sKlVRdyCDQCIQeGM5mGmLAAgIYV262U0z7HKCjoLfmVd\ngDILAIhJfd5mbIyZaIxZZYzZ1PV4pjHmFu+jAQDgrSp/i9JTjQpzMlxHiTmlXbdeM2sWABCrwvnO\n7C8l3SypTZKstW9L+qyXoQAAiIa/nz9a9145T8YY11FiTvfKLGUWABCjwvnObLa19o2PfNAzXR4A\nEPfGFeVoXFGO6xgxqSTPp4evPVkTSvNcRwEA4LjCWZk9YIwZr85Nn2SM+YykCk9TAQAQBSs3VWpz\nhd91jJiUmmJ00gnDuAUbABCzwimzN0j6haTJxph9kr4h6R88TQUAQBTc+MgGPfzmHtcxYtaLH9To\nmY38/hoAEJvCuc3YWmvPNMbkSEqx1tYbY8Z5HQwAAC81trSrvqW9e6MjHOuBV3dp76EmnTdjuOso\nAAAcI5yV2UclyVrbaK2t73ruD95FAgDAe6FdessKGDvTk7KCTDaAAgDErB5XZo0xkyVNk1RgjLnk\niJfyJfFrbABAXKvyt0iSSvP4SOtJWb5Ph5vaFGjrkC891XUcAACO0tttxpMkXSBpiKQLj3i+XtKX\nvQwFAIDXqus7VxxLuM24R6G/m2p/i8YMy3acBgCAo/VYZq21f5L0J2PMQmvtq1HMBACA5z4xuUSP\n37BIowuzXEeJWWX5H86apcwCAGJNON+ZvdgYk2+MSTfGrDLG1BhjrvA8GQAAHsr3pat89BBlpnH7\nbE/mjR2ql/7tE5o9ZojrKAAAHCOcMnu2tdavzluOd0o6UdKNXoYCAMBrKzdVauWmStcxYlp2RppG\nF2YrPTWc/7sAAEB0hfPplN71z09KesRaW+dhHgAAouK+l7fr/tU7XMeIefev3qH/e4fSDwCIPeGU\n2SeNMe9JmitplTGmWBL79AMA4lqVv0VlBWz+1JffvLJTT2zY7zoGAADH6LPMWmtvkvRxSfOstW2S\nGiV9yutgAAB4xVqrKn9Apexk3KfSfJ+qmTULAIhBvY3mkSQZY9IlXSHpNGOMJP1N0j0e5wIAwDN1\nzW1qaQ9SZsNQVuDTut2HXMcAAOAYfZZZSXer83uzd3U9vrLruS95FQoAAC9V+VskSaX5mY6TxL6y\nfJ+q/C2y1qrrl9oAAMSEcMrsfGvtrCMe/9UYs8GrQAAAeO3Ekly9/s0lys0M52MwuZXm+9TaHtTh\npjYNzclwHQcAgG7hfIp3GGPGW2u3SZIx5gRJHd7GAgDAO6kphluMw/S5k8Zo6cljmMcLAIg54ZTZ\nGyU9b4zZLslI+pikL3qaCgAADz3/frU27q3TVxefyK2zffClU2IBALEpnN2MV0maIOlrkr4qaZK1\n9nmvgwEA4JW/bq7WitU7KLJhaGxp13efeEcvbzngOgoAAEcJZzdjn6SvSDpFkpX0kjHmHmst+/QD\nAOJSlT+g0jxuMw5HRlqKfvPqTg3JTtcpE4pcxwEAoFs4txk/IKle0p1djz8n6beSLvUqFAAAXqry\nB1TCTsZhSU9N0bCcTFUxaxYAEGPCKbPTrbVTj3j8vDHmXa8CAQDgtSp/iyaW5rmOETfKCjJVWUeZ\nBQDElj6/MytpnTHm5NADY8xJktZ4FwkAAO8Eg1YHm1rZzbgfSvN8quyazQsAQKwIZ2V2rqRXjDG7\nux6PkfS+MWajJGutnelZOgAAIiwlxWjzf56rto6g6yhxY/gQn3YfbHIdAwCAo4RTZs/1PAUAAFGU\nmmKUmsLImXB971PT2fkZABBz+iyz1tpd0QgCAEA0rN9zWA+/uUffOHMCtxqHiSILAIhF4XxnFgCA\nhLFpX50eemO3rHWdJH5srW7QtQ+s0bv7/a6jAADQjTILAEgq1f6AUoxUlJvhOkrc6AhaPftulbbV\nNLiOAgBAN8osACCpVPlbVJSbqbRUPgLDVdo1k5dZswCAWMInOQAgqVTVB/iubD8VZKUrMy2FMgsA\niCmUWQBAUglaadTQLNcx4ooxRmUFzJoFAMSWcEbzAACQMB64eoHrCHFp6vB85WQwzggAEDsoswAA\noE93XzHXdQQAAI7CbcYAgKRRWRfQVfe/oTd3HnQdBQAADBJlFgCQNPYdbtIL79eosaXddZS485d3\nq3ThnS+rrqnNdRQAACRRZgEASaSqawMjdjPuv5b2Dm3cV6cKf7PrKAAASKLMAgCSSGi0DGW2/8q6\n/s4q6xjPAwCIDZRZAEDSqPQHlJGaoqHZ6a6jxJ3QLwCYNQsAiBWUWQBA0shOT9Os0QUyxriOEndK\n8jMlfXirNgAArjGaBwCQNL5+5gR9/cwJrmPEpcy0VJ06oUhDczJcRwEAQBJlFgAAhOm315zkOgIA\nAN24zRgAkDQuvmu1fvvaLtcxAABABFBmAQBJobGlXW/tPqyGADNmB+rOVVt07h0vuo4BAIAkyiwA\nIEmEduEtK8h0nCR+dVir9yrr1doedB0FAADKLAAgOVSGZszmMWN2oELjeWoa2NEYAOAeZRYAkBT2\nHmqWJI0amu04Sfwq6yqzlXXMmgUAuEeZBQAkhbzMNM0fO1RlBazMDlRoZTZ0yzYAAC4xmgcAkBTO\nmzFc580Y7jpGXBs5JEvnTivT0GxmzQIA3KPMAgCAsBRkp+ueK+e6jgEAgCQPbzM2xqwwxlQbYzb1\n8Loxxiwzxmw1xrxtjJnjVRYAAM6940X998r3XMdICMGgdR0BAABPvzP7a0nn9vL6eZImdP1cK+lu\nD7MAAJJYW0dQH1TVKy3FuI4S96759Zu64r7XXccAAMC7MmutfVHSwV4O+ZSkB2yn1yQNMcbwZSYA\nQMRV1gUUtOxkHAm+jFRVsJsxACAGuNzNeKSkPUc83tv1HAAAEbXnUJMkadTQLMdJ4l9Zvk+VdQFZ\ny63GAAC34mI0jzHmWmPMGmPMmpqaGtdxAABxhhmzkVOW71NzW4f8gXbXUQAASc5lmd0nafQRj0d1\nPXcMa+291tp51tp5xcXFUQkHAEgcIwqydOGsEcyYjYCS/ExJzJoFALjncjTPE5L+0RjzP5JOklRn\nra1wmAcAkKBOmVCkUyYUuY6REKYMz9dVHx+r7IxU11EAAEnOszJrjHlI0hmSiowxeyV9R1K6JFlr\n75H0tKTzJW2V1CTpi15lAQAkt0Bbh3zplK9ImFiap+9eNM11DAAAvCuz1trL+3jdSrrBq+sDABCy\n5La/6bSJRfrRJTNdR0kILe0dam7t0JDsDNdRAABJLC42gAIAYKDaO4Kq9AdUlJvpOkrCOP9nL+mb\nj210HQMAkOQoswCAhFZRF1BH0DKWJ4JGDs3WnoPNrmMAAJIcZRYAkNAYyxN5o4dmdc/uBQDAFcos\nACCh7e0qXazMRs7owmwdbmqTP9DmOgoAIIlRZgEACW1CaZ6uO+0EDS+gzEbKmMLOVe49B1mdBQC4\n43LOLAAAnisfPUTlo4e4jpFQZo4q0DfPn8ymWgAApyizAICEtudgk4blZig7g4+8SBk1NFvXnjbe\ndQwAQJLjNmMAQEL77L2v6d8f2+Q6RsLZe6hJ22saXMcAACQxyiwAIGGFZsyy+VPkXfvAWn3/qc2u\nYwAAkhhlFgCQsJgx653RhVnazQZQAACHKLMAgITFjFnvjB6arb2HmmStdR0FAJCkKLMAgITFjFnv\njC7MVqAtqJqGFtdRAABJijILAEhYs8cM1XcvnMqMWQ+MLuz8O91zsNlxEgBAsmJOAQAgYZ1YkqsT\nS3Jdx0hIs0YN0Z2Xz9a4ohzXUQAASYqVWQBAwlq/57D2sEmRJ4blZurCWSNUmJPhOgoAIElRZgEA\nCesff79Otz37vusYCWv9nsNau+ug6xgAgCRFmQUAJKT2jqAq6gLsZOyh/3zyHd36fx+4jgEASFKU\nWQBAQqr0M2PWa6MLs5k1CwBwhjILAEhIzJj13pjCbFXUNautI+g6CgAgCVFmAQAJ6cMyy8qsV0YP\nzVbQShWHA66jAACSEGUWAJCQTp1QpF9cOVcjhlBmvTIqNGv2ELcaAwCijzmzAICEVJrv0znTylzH\nSGgzRw3Ro9cv1OSyfNdRAABJiJVZAEBCWrW5Shv2HHYdI6HlZqZp7scKlZPJ78YBANFHmQUAJKTv\nPPGO7l+9w3WMhPfcu1VauanSdQwAQBLiV6kAgITDjNnouf+VHWps6dC507mlGwAQXazMAgASDjNm\n/3979x0nVX3vf/z1ndleYQvssrD0joiAKKJYsGvsxhaNRqMpRuO9SW6S3001uebelJuYeBMVu8be\n0GisxIYFEFDpvbO7bK+zOzPf3x/fgV2WRVGYPVPez8fjPM7sfA+7n90vZ875nG/rPYP6ZrFFE0CJ\niIgHlMyKiEjC0RqzvWdQQRY7m9ppDgS9DkVERJKMklkREUk4WmO29wwqcA8Mdv3NRUREeovGzIqI\nSMI5dUIJo/vnUqZkNuoGRf7Gm2taGF2S63E0IiKSTJTMiohIwslJT+GQgfleh5EUxg3I450fnkBJ\nXobXoYiISJJRN2MREUk4j83fzOsrKrwOIymkp/gp65OJ32e8DkVERJKMklkREUk4t76+mjmLt3kd\nRtJ4bMFmHnh3g9dhiIhIklEyKyIiCUVrzPa+l5fu4KH3N3kdhoiIJBklsyIiklB2rTGryZ96z8C+\nWWyuacFa63UoIiKSRJTMiohIQtmqZXl63aCCLJrbQ9Q0t3sdioiIJBElsyIiklA615hVN+PeUh5Z\na3az1poVEZFepKV5REQkoZw3uYxjRxfTNyvN61CSxqACN5txVWPA61BERCSJKJkVEZGEYoyhKCfd\n6zCSyqh+uay4+VRS/erwJSIivUdXHRERSSh//ddaHluw2eswkorPZ5TIiohIr9OVR0REEsrfP9jI\nO2t2eh1G0rnzzXX89qUVXochIiJJRMmsiIgkjGAozPa6Ns1k7IElW+p4/qPtXochIiJJRMmsiIgk\njIrGAMGw1UzGHhhUkMW2ulZCYa01KyIivUPJrIiIJIwtNS2A1pj1wqC+WXSELDsa2rwORUREkoSS\nWRERSRi1Le2k+X1qmfXAoAL3AGFTdYvHkYiISLLQ0jwiIpIwTp1QyoqbSzDG60iST3lBFqX5GbR2\nBL0ORUREkoSSWRERSSg+nzJZLwwuzObdH83yOgwREUki6mYsIiIJ479eWM5f/7XW6zBERESkFyiZ\nFRGRhPHiJ9tZsaPB6zCS1u9eWsm/PbbY6zBERCRJKJkVEZGEoDVmvVfR0Mbbq3d6HYaIiCQJJbMi\nIpIQtMas9wYVZFHZGKCtI+R1KCIikgSUzIqISELQGrPe27U8z5baVo8jERGRZKBkVkREEkIgGGZw\nYRaD1DLrmV1/+801WmtWRESiT0vziIhIQpg5qpg3vn+812EktfLCLCaX9yHFr+WRREQk+pTMioiI\nyPoWTFwAACAASURBVEHRLzeDp741w+swREQkSaibsYiIJITLZr/HbXPXeB2GiIiI9BIlsyIiEvfa\nOkK8u7aaQDDsdShJ72fPfsLFd7zrdRgiIpIElMyKiEjcW1PZRNjC6P65XoeS9MIWlm5r8DoMERFJ\nAkpmRUQk7q2ubARgdEmOx5FIeUEWjW1B6ls6vA5FREQSnJJZERGJeyt3NJHqNwwuzPY6lKS3a63Z\nzbVankdERKJLyayIiMS9sr6ZfOnQAaT6dVnz2kCtNSsiIr1ES/OIiEjcu/zIwVx+5GCvwxDcWrOn\nTSihKDfd61BERCTBKZkVEZG4FgpbAPw+43EkApCXkcpfvzLF6zBERCQJqD+WiIjEtY+21DHup/9k\n3pqdXociXTS2aQIoERGJLiWzIiIS11ZXNBEIhhnQJ9PrUCTitrlrOOyXrxAIhrwORUREEpiSWRER\niWsrKxrJSPUxqCDL61Akorwgi2DYsray2etQREQkgSmZFRGRuLaqopGR/XI1ZjaGjCnJBWDFjgaP\nIxERkUSmZFZEROLaqopGRvbP8ToM6WJoUTZpfh8rdjR6HYqIiCQwzWYsIiJxKxy2fOWIwYwtzfM6\nFOkixe9jZP8clm9Xy6yIiESPklkREYlbPp/hO7NGeh2G9OCaY4bi96kDmIiIRI+SWRERiVuVDW34\nfYbCnHSvQ5Fuzj1soNchiIhIgtMjUxERiVu3zV3Dsb/9F9Zar0ORbkJhy8odjVQ2tHkdioiIJCgl\nsyIiErdWRiZ/MkYzGceaupZ2Tvnjm8xZss3rUEREJEEpmRURkbi1uqKJUf1yvQ5DelCYk05xbrpm\nNBYRkahRMisiInFpZ1OA6uZ2RpUomY1VY0pytdasiIhEjZJZERGJS6siLX6jtMZszBpbmseqiiaC\nobDXoYiISAJSMisiInFpRL8c/ueCiRxSlu91KLIPY0pyaQ+GWb+z2etQREQkAWlpHhERiUv98jL4\n8tRBXochn+LokUXcc+XhDOiT6XUoIiKSgJTMiohIXPrXykrKC7IYVpzE3YzDIWirh9ZaaKtz+9Y6\n974/Ffxpkf2u15Gv0/MgfxCkpEU1vH65GfQbkxHVnyEiIslLyayIiMQday3feXgR50wq4+ZzJngd\nTnSEQ9Cw1SWrwTZ47ZdQuwFqN0LLzkgC2wB8wTV2jQ/yBkLfwdB3SJdtKBQOg8y+B+XXWLixlsqG\nNk47pPSgfD8REZFdoprMGmNOBf4E+IHZ1trfdCu/EvgtsDXy1l+stbOjGZOISFKyFoIB6GiJbK1u\n397laxuClAy3pWbuuU/JgPQc93UM2NHQRmNbMDEmf2qpgaqVULXC7XeudElr3SYIByHwn+64t/8I\nfQZBn8FQMNQlm123jD6R133AlwKhDgi1Q7ij83WoHUJBaKmGuo2R5HgDrH4Zmir2jKvfOBh8FAye\n4bbc/l/o13vg3Q18sL5GyayIiBx0UUtmjTF+4DbgJGALMN8YM8dau6zboY9aa6+PVhwiIkkh0OiS\nn/qt0LAlst8K9VugYZt7HWw78J+TluuSmpySnvd9Brvuq/7odvxZVdEEwKj+cbQsT3M1VC3vTFqr\nVkDlCmiu7DwmNQuKRkHpJBh3jmspfbufe5jwjcro/l3bm93/odoNULEUNs6DJY/A/Mgz5sIRkeT2\naBgyA/IH7te3HVOaxzOLt1Hf0kF+Vmr04hcRkaQTzbuNacAaa+06AGPMI8DZQPdkVkRE9ldHK+xc\nBZXLoXJZZL8c6jfveZzxQW4p5JVBySEw+jTIKnDJ0u4tE9K6fG18rvU22AodbW4fDLifGWyDQAM0\nVULjDteKt/VDt+9o2fNn+1JcQlsw1HVZ7Tuk83XBUEjLPuA/Q+eyPDGYzLbWuiS1anmX/XJoruo8\nJi0XikfDyJPdvngM9Bvjuv36ui00sOBdt4/yAwLSsqHfWLeNPs29FwrCjiWw4R3Y+A4sfRY+vN+V\nlU6CCefB+HOhT/k+v+2YyDrAK3Y0cMSwwuj+DiIiklSieWUsA7reXW0BjujhuPONMTOBVcBN1trN\n3Q8wxlwLXAtQXr7vC6aISEIJNMG2RbB1gUscK5dBzTqwkTU7/WlQNBrKp0O/q1zSmDcQ8stcS2m0\nkx9w3ZcDjS6pbdjmuq7WrI90X13v4m6r2/Pf5JRAwTA3LrOgy9Z3KGTk7dePXVnRSHFuOn2zozuB\n0T51tLnfr3otVK+JbJHXXVta03IiSespLlktHhtJWsvAGG9i/zz8KVA2xW0zbnDjeCuWwrq5sPQZ\neOWnbiub6hLbcee4/39djC11dbp8u5JZERE5uLyeAOo54GFrbcAYcx1wH3BC94OstXcAdwBMnTr1\nC850ISISw8Jh1+K6Zb5LXrcscMnrrsS171AomQATzo+0no13CWBvJKyfxhiXgGbkQdHIno9prXXJ\nbc16l4zv2q9+FZp27HlsZoHrvtp9y4vsc/qDP4UfnDqaK6YPjs7vZK0bU9qwtbOLdsP2zte166Fu\nM3tMvJRd7LrhjjoZCke6Oioe41qou7e0xjOfH0onum3Gja4ulz7ttpd+7Lby6a61dsL5kF1Ev9x0\n+malsiLSmi4iInKwGGujkxsaY6YDP7fWnhL5+kcA1tpb9nG8H6ix1uZ/2vedOnWqXbBgwcEOV0Sk\nd+3uvvm22za957rxAmTku5awgYe7Fq+yKZCdoC1agSaXHNascy2b9ZvdON/6yHjfQP3e/yY9LzLZ\nUX5k36dzAqT0PJdgG1+XvQ+IvMa6saGBBtei3H1ra3AJdqh9z59p/JFu26VuXHDhiMg23G0Zn3rp\nOiAX3e66GT963fSo/YyDYueazsS2cin4UmHsmTDlSjbmTaGkTxbpKX6voxQRkThgjFlorZ36WcdF\n85H+fGCkMWYobrbii4FLux5gjCm11m6PfHkWsDyK8YiIeCccgu1dk9d3O5PXolGuFWvQNJe8Fo5I\nrNa8T5Oe48b0lhzSc3lbQ+dEVvWboamSloadbN62nfKsdjI7GmDnare2amsthAL793NTMiE9t3PL\nyHPdtNNy3ERWeWWdY47zBkBOP9cqKftWNAKO/b7bKpbBogdgycOw9GkG9x0Ck6+ASZdBbonXkYqI\nSIKIWjJrrQ0aY64HXsItzXO3tXapMeaXwAJr7RzgBmPMWUAQqAGujFY8IiK9rn4rrHkFVr8C69/s\nTF4LR7rkdegxbmbYL7jkSVLY1YW539jdb81bVsE18xbw5DePYsrgbmuhhjpc12xrI120I/td72Fd\nwurXrLpR1X8cnHoLzPoZLH+O1vfvJvO1X2Jf/zVm9Gkw5UoYfoIeEIiIyAGJ6mAra+0LwAvd3vtp\nl9c/An4UzRhERHpNqAM2v++S19WvuK6W4MZNjj8Xhs5063Xmab3NA7GyYtdMxj2sMaskNbakZsDE\nC1ldcBI33vYEdx2yjGGbnoUVz7vzYvJXYfLlaq0VEZEvxOsJoERE4ltrHax8EVa9CGvnutZXX4qb\nBOekm2HkSW4ioHiYuTZOrK5oZEB+BrkZSlzjxch+uWyklGeLZ3LTl38DK1+AhffA3F/BG79xSwFN\n/RoMPS55uthHUyjYOcN4R7PrVp+S7pbjSsnYc68HQCISx5TMioh8Xm0NsOqf8MlTsPY1N1lQ7gAY\nf45bN3Tosfu9xIx8fisrmhhVEoPry8o+Zab5GVKUzfLtDZCS5s6V8ee4Sb8W3guLH4Llz7lxy1Ou\ncmNrc4q9Djt2WevGkm9b7CZQa9jmxpU3bHNb047OmdA/S1oODDjMTTg38HAYONWNERcRiQNKZkVE\n9kegySWwS592XYhDATc50LRrXRfisilqfe0F4bBlU3UzM0cWeR2KfE5jS/L4eGu32akLh8PJN8MJ\n/+mS2QV3w6s/g9d/BWPOgEMvhhEnqvWwYbtbc3rbIti+2O2bqzrLU7Pd+r55A2D48W6fN8B9RqVl\nQ7DNrY0cbIOOVrff9V7TDti6EObdCuGg+359yjtnUy8/AgZM1uebiMQkJbMiIvtirZu4aeG9ritx\nsBVySmDqVTD+PHezpy6RvcrnMyz66ckEgiGvQ5HPaUxJLvM31BAIhvZeoiclHQ65wG1VK2HBPfDx\nY7DsGcgqdBOmTbwYypIgqQp1wPaPYOM7btbzrR92rsdsfFA81vUAGXAYlE6C4lGdS1IdiI5WN+P6\nlvlunetN78MnT7qy4jFw+DXu4UK6ekWISOyI2jqz0aJ1ZkUk6lprYfHDrpWoerVbw3TC+S6BLT9S\nM7BKr4qbdWY/Qyhs8fs+R8IV6oA1r8KSR9zDpFDALVs18SKY+GXXJTkRdLS5ltGN81wCu/kDN84V\noGC4e2g24DC3lUxwLa29pWE7rH0d5t/pWoPTcmHSJXD4110SLSISJfu7zqySWRGRXbYuhPl3u9aI\nYKu7iZx6tRvbl5rpdXQCPPXhFj7eWs9PzxyHSfQWuohESWYPSFs9LHsWljwKG9927w2c5looR8xy\nLZTx0kuipcbNer7pPbffutCNu8dA//Ew+Ci3lR8VW8t2bVkIH9wBS59y8Q491g2zGHUq+NXRT0QO\nLiWzIiL7IxiAjx+H+bNdy0NqNky80CWxpRO9jk66+fZDH/LJtnre+P7xXofSaxIpmf3RUx9R1ieT\n608Y+cW/Sd3mSBfkZ123WHBdkYcd7xLb4SfEzlI/1sLO1bA5krhuet/19gDwpbrPmMFHuSW7Bh0B\nWQXexrs/mqpg0f3uwV/DFrfE0nE/gkmXJn4XcBHpNfubzOpRmogkp2A7LH4Q3vy9uyErHgun/851\nX8zI9zo62YeVFY2M6q8xe/FqTWUTayqbDiyZ7TMIjvl3tzVVwbq5sOY11x32kyfcMf0PgeHHQclE\nN96zaGT0e1e0NUDVCqhcBpWR/Y6PobXGlWf2dQnrpEvdvmxyfPb4yCl2f/ujbnST4r3zR3j2W25G\n6jP/F4pHex2hiCQRJbMiklxCHe6m683fQ/0m11XxrFtda45aFWJaIBhiw85mThkfQ10v5XMZU5LH\nM4u2Yq09ON3Ec4rdA6iJX4ZwGCo+cctlrXkN3vsbhDsiBxo3xrZ4jEu2ise4MZ+ZBZCaBWlZbr+v\n8fAdrdBS7boIt9Z0vq7bBJXL3dawpfP41Cz3M8ac7hLXQUe6hDqRPmP8KTD2TBh9Oix6AF75Kfx1\nBsy4EWZ+Lz4TdRGJO0pmRSQ5hDpgycPw5m/dDWjZFNeKMGJWYt1gJrD1O5sJhq1aZuPYmNJcGt8L\nsqW2lUEFWQf3m/t8rttu6UQ4+ibX+6JmHVQtdzMkV61w+zWvdklyu/GnuUQ0NcslY8GAS1yDrfs+\nvmi06yrcbwz0Gwf9xkJ+efyM4T1QPh9M+apLal/+T3jrd66F/Izfu2WVRESiSMmsiCS2UBA+esQl\nsbUb3Iygp/8eRp6kJDbO1DZ3UJKXwegSJbPxakxJHgArdjQe/GS2u5S0SII5Zs/3Q0GoXQ87V7mJ\npdqbXctrR0tka4X2yOuUDDeONavAjcvNjOy7fq3Jj5ycYjjvdjjsMnj+JngwMgP8qbfEzhhmEUk4\n+gQWkcS1eT784yY3bq30ULjkURh1ipLYODV9eCHv/XiW12HIARhTksuEsjxvg/CnuC6/RQcwblf2\nbehM+OY8ePuP8NbvXUv4qb9xSa6IyEGmZFZEEk9LDbz2C1h4H+SWwgX3wPhzlcSKeCw7PYXnv3OM\n12FItKWkw3H/AYdcAM/d6CaI2roATv1v12IuInKQJMmADhFJCtbCoofgL1Phwwdg+rfh+g9gwnlK\nZONcezDMiX94g6cXbfnsgyXmxduygPIFFQ6HK56FGd+FBXfDvadDwzavoxKRBKJkVkQSQ8UyuOd0\n1wJQMByuexNO+TWka3xlIliypY41lU1kpqpDUbx7dP4mDrv5FVrbQ16HIr3B54eTfgEX3uc+p28/\nFjbO8zoqEUkQSmZFJL61N8PLP4Hbj3Gzlp71Z/jaS1AywevI5CCat6YaY+DIYQVehyIHKD8zlbqW\nDlZXNnodivSm8efA119zDxjv+xK8f7vrTSMicgCUzIpI/Nq6EP52NMy7FQ69BK5fCJOvSJ4lMZLI\nvLU7GT8gjz5ZGm8X73bPaLxdyWzS6TcWrp0LI06CF38AT3/DzRwtIvIF6Y5PROJPOOxmyrzrZLeW\n5JX/gLP/AtmFXkcmUdDaHmLRpjpmDC/yOhQ5CMoLsshM9bNse4PXoYgXMvLh4r/DcT+Gjx6Fu092\ny6aJiHwBSmZFJL40bIcHzoZXfwZjzoBvvg1DjvY6Komi5vYgF0wdyKyx/b0ORQ4Cn88waVAf3l9f\n43Uo4hWfz812fOmjULsJ7pwF2xZ5HZWIxCElsyISP1a8AH89CrYscGNjL7wPMvt6HZVEWVFOOv91\n7iFMG6rxsoniosMHcer4EsJhjZlMaqNOceNoUzPh3i/B+je9jkhE4oySWRGJfR2t8I9/h0cugfyB\nbqbiyVdouZ0ksbaqSUlPgjnnsDJuPHEkPp/O4aRXNBKufhnyy+DB82HZHK8jEpE4omRWRGJbxVK4\n4ziYPxuO+g5c86q7+ZGk0NDWwUl/eIO/zF3jdShykDUHgnyytd7rMCQW5A2Aq16E0knw+Fdh4b1e\nRyQicULJrIjErk+ecmOpWmrgK0/Byb+ClHSvo5Je9MG6GsIWDh+iLsaJ5ifPfMIVd3+gVndxsgrg\nimdg+Cx47kZ483daukdEPpOSWRGJPeEQvPoLeOIqKD0UvvkOjJjldVTigXlrq0lP8XFYeR+vQ5GD\n7OiRRdQ0t7N0m2Y1loi0bLjkYTjky/D6zfDSj93s9SIi+5DidQAiIntoq4cnvw6rX4LJX4XTfwcp\nWls0Wc1bu5OpQ/qSker3OhQ5yI4ZWQzAm6urOGRgvsfRSMzwp8K5t0NWIbz3f9BSDWff5t4XEelG\nLbMiEjt2rnHdite+Bmf8Hr70JyWySWxnU4AVOxo5SuvLJqTi3HTGD8jjjVVVXociscbng1NvgRN+\n4taifeRSNxGgiEg3SmZFJDasfgXuPAFaa+CKZ+HwazRbcZLLSU/hnisP56xDB3gdikTJzFHFfLix\nlsa2Dq9DkVhjDMz8Hpz5R3d9ePhiaG/xOioRiTHqZiwi3rIW3vkTvPpzKJkAF/8d+pR7HZXEgIxU\nP8eP6ed1GBJFl04r54xDSslO0+2I7MPUq9zEf898C/7+Zbj0UTe2VkQEtcyKiJc62uCpr8OrP4Px\n58DXXlIiK7vd9fZ6Vu5o9DoMiaJBBVlMKMvXerPy6SZdCufdARvfgYcuhIA+F0TEUTIrIt5oqYEH\nzoGPH3fjoi64R0/bZbdtda3c/Pwy3l6z0+tQJMoWbqzhNy+u8DoMiXUTvwznz4ZN78GDF0CbZsEW\nESWzIuKFmvVw18mwdSFccLcbF6XxsdLFu2urAThqeKHHkUi0Ld3WwN/eWMuGnc1ehyKxbsL5cOE9\nsHUBPHiem/1eRJKaklkR6V1bF8JdJ0FzlZvoacL5XkckMWje2moKstMY3T/X61AkymZ2WaJH5DON\nOxsuvA+2LYb7z4HWWq8jEhEPKZkVkd6z4gW45wxIzYKrX4HBR3kdkcQgay3z1u5k+rBCjaVMAkOK\nsikvyOJNLdEj+2vsmXDRg1DxCdx/thu2IiJJScmsiPSOD+6ERy+DfmPgmleheJTXEUmMqmwMUNvS\nznR1MU4aM0cV8e7aatqDYa9DkXgx+lQ3+33lCrj/LGiu9joiEfGAklkRia5wGF7+CbzwPRh5Clz5\nD8jRciuyb/3zMljys5M5f/JAr0ORXjJzZDF5malsqdU6ovI5jDwJLnkYdq6G+74EzZowTiTZKJkV\nkejpaIMnr4Z5t8Lh18DFD2nGYtkv6Sl+MtP8XochveTEsf2Z98MTGFac43UoEm9GzIJLHoGadS6h\nbVJ3dZFkomRWRKKjtQ4ePB+WPgUn/gJO/x34lJzIpwuHLZfNfo/nlmzzOhTpRT6fwRiDtdbrUCQe\nDT8eLn3UzZR/35nQVOl1RCLSS5TMisjB17AN7jkdNr8P590JR39XS+/IfllZ0cg7a6oJaOxk0nl1\nWQVH3vIaVY0Br0OReDTsWLjscajbBPeeCY0VXkckIr1AyayIHFxVq9wasnUb4bLH3EL3IvtpXmR9\nWU3+lHz652VQ0RDg7TXqJipf0NBj4LInoH6La6Ft3OF1RCISZUpmReTg2fwB3H0yBNvcRE/DT/A6\nIokz767dyZDCLMr6ZHodivSy8QPyKMxO481VmsRHDsCQGfCVJ10PoXvPcHsRSVhKZkXk4Fj5T7jv\nLMjsC1e/DAMmeR2RxJlgKMz762qYPrzI61DEAz6f4eiRRby1uopwWGNn5QAMnu4S2sYKl9DWb/U6\nIhGJEiWzInLgPrwfHrnUrSH7tZehYJjXEUkcamgLMmNEESeM0dJNyWrmyGJ2NrWzbHuD16FIvCs/\nEi5/ys1ufO8ZULfZ64hEJAqUzIrIF2ctvPFbmPMdGHYcfPV5yCn2OiqJUwXZafzt8imcNK6/16GI\nR44ZVcTlRw4mS8syycEwaBpc8Qy01MA9p0H1Wq8jEpGDTMmsiHwxoSA8fxPM/RVMvNgti5CuNSLl\ni6ttbvc6BPFYv9wMbj5ngtablYNn4FS48jnoaIG7T4WKpV5HJCIHkZJZEfn8Ak2uW/HCe+Dom+Dc\nv4E/1euoJI5trmlhyq9eYfZb67wORTwWCluWbK6jpT3odSiSKEoPhateBF+KWzZuywKvIxKRg0TJ\nrIh8Prsm1FjzCpzxBzjx51pDVg7Y/e9uwBjDGRNLvQ5FPPb+umrOvu0d5q2p9joUSSTFo+FrL0Jm\nH7j/bFj/ptcRichBoGRWRPZf1Sq460TYuQoufhgOv9rriCQBNAeCPDJ/M6dNKKE0X0vyJLspQ/qS\nmernzdVab1YOsr5D4Kp/Qv5AePACNwu/iMQ1JbMisn82vgt3nQQdrXDl8zD6VK8jkgTx1IdbaGwL\nctWMoV6HIjEgPcXP9OGFvLKsgo5Q2OtwJNHklcKVL0D/cfDoZfDxE15HJCIHQMmsiHy2pU+7blnZ\nxXDNq1A2xeuIJIE8vnALEwfmM7m8j9ehSIz4ypHlbK9vY87ibV6HIokouxCumAODjoAnr4GF93od\nkYh8QUpmRWTfrIV5f4bHr4QBh8HVL7tuWiIH0YPXHMEfvnwoRmOvJeL40f0YU5LL04u2eh2KJKqM\nPLjsCRgxC567Ed7+X3fNE5G4kuJ1ACISo0Id8M8fwvzZMO4cOPd2SM3wOipJQHkZqeRlaDZs6WSM\n4Y7Lp9I/P93rUCSRpWW5+R+evg5e/TnUrIczfq/Z+UXiiFpmRWRvzdXwwLkukT3qBrjgHiWyctCt\nrWrijFvf4pOt9V6HIjGovDCL9BQ/HaEwVi1mEi0paXD+XXDMv8OH98FDF0BrnddRich+UjIrInva\n8QnceRxs/gDOvQNOvhl8+qiQg+++eRtYXdFE/zw9KJGerdjRwMz/mcu767RMj0SRzwezfgpn3wYb\n3oa7T4HaDV5HJSL7QXeoItJp2Ry462TXxfiqF+HQi7yOSBJUfWsHTyzcwpcOHUBxrrqSSs+GFGYT\nDFv++q+1XociyeCwr8DlT0Pjdph9ImxZ4HVEIvIZlMyKCITDMPcWeOxy6DcWrv0XDNSMxRI9jy/Y\nTEt7iKtmDPE6FIlhGal+rj56KG+t3snHW9QdXXrB0Jlw9auQlg33ngFLn/E6IhH5FEpmRZJdoAke\nvwLe+A0ceilc+Q/ILfE6KklgobDl3nkbmDakgAll+V6HIzHusiPKyc1I4a9vrPE6FEkWxaPgmteg\n9FB4/Kvw1h8007FIjNJsxiLJrHYDPHwpVC2HU/4LjvwWaHkUiTJrLd89cRQD8jVWVj5bbkYqV0wf\nzP/9ay0bq5sZXJjtdUiSDLKL3Fq0z34LXvsF7FztZjpOy/I6MhHpQsmsSLJa+gw8d4N7vWutPZFe\nkOL3ccGUgV6HIXHkqhlDmTqkgPICJRLSi1Iz4LzZUDgS3vhv2LYILrzHDccRkZigbsYiyaa9Gebc\n4LpOFQx342OVyEovWVXRyJ1vrqM5EPQ6FIkjRTnpHD+6H0Y9R6S3+Xxw/I/g8qegZSfccTx8+IC6\nHYvECCWzIslk+0dwx3Hw4f1w9E1w9ctQMMzrqCSJ3PXWen7/yko6QmGvQ5E4Y63ldy+t5A+vrPI6\nFElGw0+Ab7wNgw6HOdfDU9dCoNHrqESSnpJZkWRgLbz7fzB7FrQ1wBXPwIk/B3+q15FJEqlpbueZ\nxVs5b/JA+mSleR2OxBljDNvqWpn91jpqm9u9DkeSUW4JXP4MHP//4JMn4PZj3UNiEfGMklmRRNdU\nBQ9dCC/9CIbPgm/Og2HHeR2VJKGHP9hEIBjmqqOGeB2KxKlvHDeclvYQ9727wetQJFn5/HDsD+Cr\nz0FHi1uP9oM71e1YxCNKZkUS2ZpX4a9Hwfo34fTfwSUPQ3ah11FJEmoKBLn/3Q0cM7KIkf1zvQ5H\n4tSo/rmcOLY/987boHHX4q0hR7tux0Nnwgvfc+u0N1Z4HZVI0lEyK5KIGnfAk9fAg+dDViFcOxem\nfV3L7ohnWtqDDOqbxY2zRnodisS5bx43nLqWDh7+YJPXoUiyyy6CSx+Dk34Jq16CvxzuWmnDIa8j\nE0kaWppHJJGEgjB/Nsz9NQTbYOYP4Jh/g9RMryOTJGatpV9uBo9/Y7pmo5UDNmVwX7574kiOGl7k\ndSgibrbjGTfC6DPghX93rbSL/w5n/i8MmOR1dCIJTy2zIoli8wdw53Hwz/+AgVPhW+/BCf9Piax4\nau6KSq6+bwENbR1KZOWg+e6Joxg3II9gKEwgqFYwiQFFI9zkUOffBfVb4M7j4cX/cJMuikjUKJkV\niXfN1fDs9XDXSe71hffBV56CwuFeRyZJbv3OZm54ZBE76ttI9elyIwdXOGz5xoML+bfHlhAO1jo6\nxgAAGNFJREFUa/IdiQHGwCEXwPXzYerX4P3bXdfjpU9rgiiRKNHdhUi8CgVh4b3wlymw5GE46jtw\n/Qcw/hyNjRXPNQWCXHv/AlJ8htsvn0Jmmt/rkCTB+HyGaUML+MdH27nlxeVehyPSKbMPnPF7uOY1\nyOkHj1/p5rDQMj4iB53GzIrEm2A7fPQIvPUHqF0P5Ue5i2b/cV5HJgK4MbLff3wJa6uaeODqIxhU\nkOV1SJKgvn7MMLbVtXHnW+spzc/ka0cP9TokkU4Dp8DX58L8O2HuLXD7MTD6dJj5fSib7HV0IglB\nyaxIvOhog0UPwDt/gvrNUHooXPQgjDlTLbESU7bXt7FwYy0/Om0sM0Zokh6JHmMMPzlzHNvrW7n5\nH8sozc/gtENKvQ5LpJM/BY78Jhx6iet2/N5tcOcLMPJkN0njoMO9jlAkrimZFYl17c2w4B6Ydys0\nVcDAaW6WxBEnKomVmDSgTyYv3zST/MxUr0ORJOD3Gf508WHc8PAiyvpqwjuJUZl94Lj/cIntB3fA\nu3+Bu06E4SfAsf8B5Ud6HaFIXFIyKxKrmqth4T3w3v9BS7VbmP382TDkGCWxEpM2Vjfz5MIt3DBr\nJH2y0rwOR5JIRqqfO66YuvvrhrYO8jL0MEViUEYezPweHHEdzL8L5v0Z7j7FXeOnXQejTgG//u+K\n7C8lsyKxJBhwC68veQRWvwThoOuKdMz3oPwIr6MT2afKxjauvX8hFY1tXHJEOaX5aiETb9w2dw2P\nzN/EU9+cQXFuutfhiPQsPReO/i5M+7qbzPGdW+HRyyCrCCZeBIddBv3Hex2lSMxTMiviNWth60I3\nI/EnT0JrLeT07xxjo4uZxLBQ2PL39zfyPy+tJNAR5q4rpyqRFU/NGFHEn19fzdX3zefOK6bSPy/D\n65BE9i0tG6Z/27XKrnkVFj/kuiG/d5ubG2PSV9xyP1kFXkcqEpOMjbN1r6ZOnWoXLFjgdRgiB8Za\nqF4Dy551rbDVqyElw03mdOglMOw4N2mESIy74eFFzFmyjaOGF/KrcyYwrDjH65ASzkW3vwvAo9dN\n9ziS+PHqsgq+9dCH+H2Gr88cxnUzh5Gdrs9UiRPN1fDx4y6x3fER+NNg9Gkw/lx3f5DZ1+sIRaLO\nGLPQWjv1s47TJ7tIb2mqhHVvwLp/ua1hi3t/8AyYcQOMOxsy8r2MUGS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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Model\n",
+ "m = elfi.ElfiModel(set_current=False)\n",
+ "sim_fn = partial(gauss.Gauss, sigma=sigma, n_obs=n_obs)\n",
+ "elfi.Prior('norm', mu0, sigma0, model=m, name='mu')\n",
+ "elfi.Simulator(sim_fn, m['mu'], observed=y_obs, name='Gauss')\n",
+ "elfi.Summary(lambda x: x.mean(axis=1), m['Gauss'], name='S1')\n",
+ "elfi.Distance('euclidean', m['S1'], name='d')\n",
+ "\n",
+ "res = elfi.Rejection(m['d'], output_names=['S1'], batch_size=batch_size, seed=seed).sample(1000, threshold=1)\n",
+ "original = res.outputs['mu']\n",
+ "original = original[np.isfinite(original)] # omit non-finite values\n",
+ "\n",
+ "# Kernel density estimate for visualization\n",
+ "kde = ss.gaussian_kde(original)\n",
+ "\n",
+ "plt.figure(figsize=(16,10))\n",
+ "t = np.linspace(3, 7, 100)\n",
+ "plt.plot(t, ss.norm(loc=mu1, scale=sigma1).pdf(t), '--')\n",
+ "plt.plot(t, kde.pdf(t))\n",
+ "plt.legend(['Reference', 'Rejection sampling, $\\epsilon$=1'])\n",
+ "plt.xlabel('mu')\n",
+ "plt.ylabel('posterior density')\n",
+ "plt.axvline(x=mu);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By regressing on the differences between the sampled and observed summary statistics, we attempt to correct the posterior sample with a linear model. The posterior adjustment is done with the `adjust_posterior` function. By default this performs a linear adjustment on all the parameters in the `Sample` object, but the adjusted parameters can also be chosen using the `parameter_names` argument. Other regression models, instead of a linear one, can be specified with the `adjustment` keyword argument."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "adj = elfi.adjust_posterior(sample=res, model=m, summary_names=['S1'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Similarly to other ABC algorithms in ELFI, post-processing produces a `Result` object. As can be seen, the linear correction improves the posterior approximation of this model compared to rejection sampling alone."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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llFkAABzWm8pI5Z2q8+XneVlJaqzKldmO3p15GxMAgFJCmQUAwGFvdu6QKUur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\n364dUSZqHgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "kde_adj = ss.gaussian_kde(adj.outputs['mu'])\n",
+ "\n",
+ "plt.figure(figsize=(16,10))\n",
+ "t = np.linspace(2, 8, 100)\n",
+ "plt.plot(t, ss.norm(loc=mu1, scale=sigma1).pdf(t), '--')\n",
+ "plt.plot(t, kde.pdf(t))\n",
+ "plt.plot(t, kde_adj(t))\n",
+ "plt.legend(['Reference', 'Rejection sampling, $\\epsilon$=1', 'Adjusted posterior'])\n",
+ "plt.xlabel('mu')\n",
+ "plt.ylabel('posterior density')\n",
+ "plt.axvline(x=mu);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Multiple parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Posterior adjustment can also be used with models having multiple parameters. In this case, each scalar parameter has their own regression model that is used to adjust them. Non-scalar parameters are not supported.\n",
+ "\n",
+ "To illustrate multiple parameter models, we use the moving average model contained in the examples folder. We use the values $t_1 = 0.6$ and $t_2 = 0.2$ to generate the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from elfi.examples import ma2\n",
+ "\n",
+ "seed = 20170511\n",
+ "threshold = 0.2\n",
+ "batch_size = 1000\n",
+ "n_samples = 500\n",
+ "\n",
+ "m2 = ma2.get_model(n_obs=100, true_params=[0.6, 0.2], seed_obs=seed)\n",
+ "rej2 = elfi.Rejection(m2, m2['d'], output_names=['S1', 'S2'], batch_size=batch_size, seed=seed)\n",
+ "res2 = rej2.sample(n_samples, threshold=threshold)\n",
+ "\n",
+ "adj2 = elfi.adjust_posterior(model=m2, sample=res2, parameter_names=['t1', 't2'],\n",
+ " summary_names=['S1', 'S2'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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l7qZ8nhjTnT/ckUlIkPM3ym8d5M+f7uzNiOQo/uuj3ewpPOvcAgwDbnkJDD/4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\nREREREREEs7/A2q/50OJFVy8AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(16,10))\n",
+ "t = np.linspace(0, 1.25, 100)\n",
+ "plt.plot(t, ss.gaussian_kde(res2.outputs['t1'])(t))\n",
+ "plt.plot(t, ss.gaussian_kde(adj2.outputs['t1'])(t))\n",
+ "plt.legend(['Rejection sampling, $\\epsilon$=0.2', 'Adjusted posterior'])\n",
+ "plt.xlabel('t1')\n",
+ "plt.ylabel('posterior density')\n",
+ "plt.axvline(x=0.6);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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v4CHDMKJ+NeZaoMPFr/uA/zgxj4hIzVaYZ7tT2HGk7c6hOFernhDQUEuNRURE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dhhzrT1Gf44eB5boBGq1jkhtXINVK11tqvWQai147jMwc4X2aiznXXBWOc5sBo2LCIikpg8STB0\nqrOccTsEW6BilTP6audHzvLh407vbbu0Qsgu7brkDHXWaQU9fh9ubpqPKWk+pgzP7fJ+KBxhR10r\nW6qb2VzVzObqJrZUNbOttoVV2+upbAzs9WdlJnspyvRTkOGnMMNPQYaPgkw/Bel+CjP95Gf4yEv1\n4VIPryQYhVn5TKy1VDe1sbaikVXb61m1o55V2+tZW9FAMOxcJUz2uhldnMH1Jw3j2Ogz19J8Mfqn\n1tYE1Ruhej1UrYfqDR1Lw46O44zLGVaUd5jzmJy8kc52/ihIzo5N7SIiItI3vMkwcIKztItEoHaT\nE2wr10DNJqjZDJvfhRVPAp1GN3r8kDEQMgftvWRE1z3Us+txuxick8LgnBROGLH3/kAoTHldgO11\nLeyoa2F7bSs76lrYWddKeX2AVTucwLvn4EyPyzAg3UdBNOwWZvi7BN6CDB+FmcmxO6cT2Qf9a5S9\nWGupampjU2UTm6qa2VzVxMbKJjZXNbOpqmn3NPMAeWlJjC7O5OTDBzC6KIPRxRmU5qb27b0bLTVO\nYK3ZGA2qmzq2OwdWgNR859E4w09zrqbmHeYsOcPAoynyRUREJMrlcs4PcobtvS8UgNqt0YC70VnX\nb4O6bbB+XvT8Y4+0mJIbDbiDnSUrus4c5MyzkZLbI727Po+bktwUSnL3H55D4QiVjW3srG+lPLq0\nh92KhlY27Gpiwfoq6jud87VL93kozHQCblGmn8LM5OjaeV2clRwfI/CkX1CY7WciEUtNs/PLa2dd\nKzvqnPX26BW79vdaguHdP+N2GQZlJzMkN5XxJVmU5qYydEAqY4ozyE/390HRYecLomZT16U6Glhb\na7sen1boBNVhp0LuMMgZ3vFl5M/o/XpFRETk0ObxQd4IZ9mXUJsTaOvKossWJ+jWbXVuZ1o/D4JN\nXX/Gm+qE2qwSyB7ijBjrvJ2c1XPlu127A+mBNLeFqKgP7A69nc8dd9S3sqZ8FxUNe/fypvk8u4Nt\n8e57eJMpzvRTlOWEX79Xt2vJF6cwm+CstTS3haluanOW5jaqG9uobAxEF2d7V4OzXd0UILLHLxy3\ny1CQ7qMw08+oogxOOyKf4qxkhualUpqXysCsZJI8vTirn7XQWOHMMli3xVnXbukIrbVbIdIxox/G\n7VzNzC6FsRdA9tBoWI3et5KU2nu1ioiIiByMJ8kJodlD9r3fWmdkWd1WJ+zWdj7/2QxbFkCgvuvP\n+DOdUNsebrNLO70ucYZK97CUJA+leR5K8/Z/bhUMR9jVEOgypLnzeuX2Oiob2/b6uewU7+4Jqop2\nT1jlDGvOjw51TvN54muSUIk7CrNxJBKxNARC1Da3UdMcpKa5zdluCu5+r7bF2a5qbKOm2QmwgVBk\nn3+e3+siL81HXpqPQdnJHD04K/o6KXo1zrkylpfm691hwYFGqN/u9K7Wb48uZU5Ird3i/CLvPOES\nOPepZpdC0VEw+tyuky1kDAK3/umKiIhIgjIGUnKcpeiovfdb64w8q9kMtZu7rnd9Cmtf2/vcKTU/\nOnS501DmzEEdw5mTs3t8kioAr9sV7YFNZsJ+sntrMLx7JOCOaNDdEe3lLatpYfGmGupagnv9XEqS\nm4IMP/npPvKj69y0JHJSkshJ7bpk+L0JOYFVOBggUF9JW0MlbY1VhBuribTWYQMNEGhwzqPbGnAF\nGnAFm3AHG3CFWjGREC4bxBUJRrdDuCJBXDYENoI1HqzLHV1Hl+g2Lg/umY+QWjgy1v/5X1ivJgJj\nzAzg94AbeMhae8ce+33ALGACUAXMtNZu6s2aelswHKGhNURDa5D6lhD1rUHqW4LRtfO6riVIbXN0\n3RKkrrmN2hbnuD17TdsZ48xEl52SRFaKl+IsP2OKM8iJfqCzU5PITXXWOSlJ5KX7SE1y997VrHAI\nmqugqQKadkHjrq7bjeUdwTVQt/fPp+Q6VxHzR8Fh0zuG0mSVOL90+/AB5iIiIiJxxRgnfCZnQ/HR\ne++PRJzzrs4ht26L01FQvgrWvLJ32PWmOLMupxdBeoFzW1Z6pyWtwDk/S84Gd8/e8+r3uimNjvjb\nn6ZAiJ31rVRE79t17uUNUB59b0VZLeX1rbQG992J43YZslO8pPu9pPrcpPk8pPk8pEbX7dt+rwuP\ny4XX48LrMnjdLjxuQ5Lbhcftwu1y/vdaIGIt1jojIdtfhyOWtlCEtnCEYHTdFooQDAahrR53oA53\nay3eQA3eQA2+YB2+YC3JoXpSw3WkhutJjzSQTgOZtoFUEyAF2N8dzm3WTQMpNFk/jaTQQDIB66UN\nHyFSCeGmDQ8h6yaEmyBuIrhwE8FDCA8RPCaMh67L0MYwQ79ow8aBXguzxhg3cA9wBlAGLDbGvGit\nXdXpsOuAGmvtCGPMpcBvgJm9VVNvs9Yy6icvd3n2155cBtL9XrJTvGQme8lMSWJITgpZKV6ykr1k\nJHvJSkkiO6VjnZ2SREay94v1nloL4SCE25wl2Axtzc6U8+3bwaboutkZ2tJS61wVbKmF1rpO27XQ\nWs9eExsAuJOcK4NpAyB3uDPlfUaxM8NfRrGzpBeDtw/utRURERE5FLlcHSG05Ni991vrdDrUbone\ns7vVuWe3cSc0lDszNDe81vXRQ535MpxQm5IDydEe5ORsp7MhKa1jnZQKvjTn2bxJqc5QZ4/Pmd3Z\nnRRddy9upPo8DB+QxvABaQc8rqUtvPu2uupm5xa66qbg7nVjIERTIERja4jtta00tTmvG1pDu0cz\nGiIkEcJHGz6C+EwQH0H8BPETINW0kkorqaaVFFpJo5WU6HtptJBhmsk0TWTQTIZpIoMmMkzLfmuO\n4KLJlUaTO4MWXyatniIqvKMoS8ok6Msi4ssi7M/efQHD+DNxJadjfOl4k5LxelwkuV0keVwUuF24\nXQZjwGU6rQETfW1tR/AORyyhiCUciRCKWEJh572iwkOj46g3e2YnA+ustRsAjDFPAOcCncPsucBt\n0e1ngD8aY4y1e95GnhiMMfxr8Cw8hPC6XXhdLrxu5yZ7r9vgcbnwuMCJpJYud8s3W2cB530b6brQ\n/l50HQk5EyNFQmDD0e3o60goGlwDzgQE4TZn+7Ny+5zJBvxZzjqtAPIOd7aTsyF1AKTlO+v2AOvL\n6JUhLCIiIiLSTcZAap6zDDxm/8cFGp3RdA07nHVztXMvb3M1tFR3rKs3OOtAQ/S89LPU4uoIt+4k\niA5zxeXqtO0Bl9s5FhM9l+y0bv9vwpCMZaCNMLD9nLjLObLtOBduX1whSAqCJ4SNhCEcwIT3vof3\nYKxxEfGmEfGmYv1Zzj3MyUOc4JmSjU3OxCRnO++n5Ha5CODyZ5HucnFoxMf40pthdiCwtdPrMmDP\nS0e7j7HWhowxdUAuUNn5IGPMDcANACUlJb1Vb48YSVk0PHZ6c69w1/VD2WW7/VAT/UAbE113WjDO\nL4X2D77L47zf+bXb64RRj6/TdvSXiNvnXDlLSnWGnCSlODPoJaVEX6c6V9x6YSIBEREREYkTvjRn\nyR3eveOtdYYvBxqdXt22xuh2E7Q1QLA12pkScI4LtTnr9vfCbXt3wNjOr8N0dPh07vjp9N7uwNvp\nPLnz6/ZzYZc3ek7cEZiNy7N3z3H76/a119/R0+xL290DbTx+3MagOZjjS0LMomOtfQB4AGDixInx\n3Wv7tXdiXYGIiIiISM8zxuns8CYDA2JdjQi9+LwVtgGDO70eFH1vn8cYYzxAJs5EUCIiIiIiIiL7\n1ZthdjEw0hgz1BiTBFwKvLjHMS8CV0e3LwLeSNT7ZUVERERERKTv9Now4+g9sN8EXsF5NM+frbUr\njTE/B5ZYa18EHgZmG2PWAdU4gVdERERERETkgHr1nllr7Rxgzh7v/bTTditwcW/WICIiIiIiIoee\n3hxmLCIiIiIiItIrFGZFREREREQk4SjMioiIiIiISMJRmBUREREREZGEozArIiIiIiIiCUdhVkRE\nRERERBKOwqyIiIiIiIgkHIVZERERERERSTgKsyIiIiIiIpJwFGZFREREREQk4SjMioiIiIiISMJR\nmBUREREREZGEozArIiIiIiIiCUdhVkRERERERBKOwqyIiIiIiIgkHIVZERERERERSTgKsyIiIiIi\nIpJwFGZFREREREQk4SjMioiIiIiISMIx1tpY1/CZGGN2AZtjXUc/lQdUxroI6Ta1V2JReyUetVli\nUXslFrVXYlF7JZZEaK8h1toBBzso4cKsxI4xZom1dmKs65DuUXslFrVX4lGbJRa1V2JReyUWtVdi\nOZTaS8OMRUREREREJOEozIqIiIiIiEjCUZiVz+KBWBcgn4naK7GovRKP2iyxqL0Si9orsai9Essh\n0166Z1ZEREREREQSjnpmRUREREREJOEozMp+GWNyjDGvGWPWRtfZBzg2wxhTZoz5Y1/WKB26017G\nmKONMQuMMSuNMSuMMTNjUWt/ZoyZYYz51Bizzhhz6z72+4wxT0b3LzLGlPZ9ldKuG+31XWPMqujn\naa4xZkgs6pQOB2uzTsddaIyxxphDYkbPRNWd9jLGXBL9nK00xjze1zVKh278TiwxxswzxiyL/l48\nKxZ1isMY82djTIUx5uP97DfGmD9E23OFMeaYvq7xi1KYlQO5FZhrrR0JzI2+3p9fAPP7pCrZn+60\nVzNwlbV2DDAD+F9jTFYf1tivGWPcwD3AmcBo4DJjzOg9DrsOqLHWjgDuBn7Tt1VKu2621zJgorX2\nSOAZ4Ld9W6V01s02wxiTDnwbWNS3FUpn3WkvY8xI4IfACdHvru/0eaECdPvz9V/AU9ba8cClwL19\nW6Xs4RGc8739ORMYGV1uAO7rg5p6lMKsHMi5wKPR7UeB8/Z1kDFmAlAAvNpHdcm+HbS9rLVrrLVr\no9vbgQrgoA+klh4zGVhnrd1grW0DnsBpt846t+MzwOnGGNOHNUqHg7aXtXaetbY5+nIhMKiPa5Su\nuvMZA+cC7G+A1r4sTvbSnfa6HrjHWlsDYK2t6OMapUN32ssCGdHtTGB7H9Yne7DWzgeqD3DIucAs\n61gIZBljivqmup6hMCsHUmCt3RHd3okTWLswxriA3wG39GVhsk8Hba/OjDGTgSRgfW8XJrsNBLZ2\nel0WfW+fx1hrQ0AdkNsn1cmeutNenV0H/KtXK5KDOWibRYfRDbbWvtSXhck+deczdhhwmDHmXWPM\nQmPMgXqZpHd1p71uA64wxpQBc4Bv9U1p8jl91u+5uOOJdQESW8aY14HCfez6cecX1lprjNnX1Ndf\nB+ZYa8vUedT7eqC92v+cImA2cLW1NtKzVYr0P8aYK4CJwMmxrkX2L3oB9i7gmhiXIt3nwRkCeQrO\nyIf5xphx1tramFYl+3MZ8Ii19nfGmCnAbGPMWJ1rSG9RmO3nrLXT9rfPGFNujCmy1u6Ihp99De2Z\nAkw1xnwdSAOSjDGN1toD3V8rn1MPtBfGmAzgJeDH0SEl0ne2AYM7vR4UfW9fx5QZYzw4w7Sq+qY8\n2UN32gtjzDScC0onW2sDfVSb7NvB2iwdGAu8Gb0AWwi8aIw5x1q7pM+qlHbd+YyVAYustUFgozFm\nDU64Xdw3JUon3Wmv64jeo2mtXWCM8QN57OecRGKuW99z8UzDjOVAXgSujm5fDbyw5wHW2suttSXW\n2lKcocazFGRj5qDtZYxJAp7Haadn+rA2cSwGRhpjhkbb4lKcduusczteBLxh9UDwWDloexljxgP3\nA+foXr64cMA2s9bWWWvzrLWl0e+thThtpyAbG935nfh3nF5ZjDF5OMOON/RlkbJbd9prC3A6gDFm\nFOAHdvVplfJZvAhcFZ3V+DigrtMtawlBYVYO5A7gDGPMWmBa9DXGmInGmIdiWpnsS3fa6xLgJOAa\nY8yH0eXo2JTb/0Tvgf0m8AqwGmfGx5XGmJ8bY86JHvYwkGuMWQd8lwPPIi69qJvtdSfOqJSno5+n\nPU/spA91s80kTnSzvV4Bqowxq4B5wPestRqtEgPdbK+bgeuNMcuBvwHX6IJs7H0pYrUAAAG2SURB\nVBhj/gYsAA43ziM0rzPG3GSMuSl6yByci0PrgAdxbh9MKEb/vkRERERERCTRqGdWREREREREEo7C\nrIiIiIiIiCQchVkRERERERFJOAqzIiIiIiIiknAUZkVERERERCThKMyKiIjEEWNMljHm69Hto40x\nC4wxK40xK4wxM2Ndn4iISLzQo3lERETiiDGmFPintXasMeYwwFpr1xpjioGlwChrbW0saxQREYkH\n6pkVERGJL3cAw40xHwLXW2vXAlhrtwMVwIBYFiciIhIvPLEuQERERLq4FRhrrT2685vGmMlAErA+\nJlWJiIjEGYVZERGROGeMKQJmA1dbayOxrkdERCQeaJixiIhIHDPGZAAvAT+21i6MdT0iIiLxQmFW\nREQkvjQA6QDGmCTgeWCWtfaZmFYlIiISZzSbsYiISJwxxjwOHAmkAoOAlZ12X2Ot/TAmhYmIiMQR\nhVkRERERERFJOBpmLCIiIiIiIglHYVZEREREREQSjsKsiIiIiIiIJByFWREREREREUk4CrMiIiIi\nIiKScBRmRUREREREJOEozIqIiIiIiEjCUZgVERERERGRhPP/BD9gWrVm8aEAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(16,10))\n",
+ "t = np.linspace(-0.5, 1, 100)\n",
+ "plt.plot(t, ss.gaussian_kde(res2.outputs['t2'])(t))\n",
+ "plt.plot(t, ss.gaussian_kde(adj2.outputs['t2'])(t))\n",
+ "plt.legend(['Rejection sampling, $\\epsilon$=0.2', 'Adjusted posterior'])\n",
+ "plt.xlabel('t2')\n",
+ "plt.ylabel('posterior density')\n",
+ "plt.axvline(x=0.2);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# References\n",
+ "\n",
+ "- [1] Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander; Fundamentals and Recent Developments in Approximate Bayesian Computation. Syst Biol 2017; 66 (1): e66-e82. doi: 10.1093/sysbio/syw077\n",
+ "- [2] Beaumont M.A., Zhang W., Balding, D.J. 2002. Approximate Bayesian computation in population genetics. Genetics 162:2025–2035"
+ ]
+ }
+ ],
+ "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": "3.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/quickstart.ipynb b/quickstart.ipynb
new file mode 100644
index 0000000..1c33067
--- /dev/null
+++ b/quickstart.ipynb
@@ -0,0 +1,316 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Quickstart\n",
+ "\n",
+ "First ensure you have [installed](http://elfi.readthedocs.io/en/stable/installation.html) Python 3.5 (or greater) and ELFI. After installation you can start using ELFI:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import elfi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "ELFI includes an easy to use generative modeling syntax, where the generative model is specified as a directed acyclic graph (DAG). Let’s create two prior nodes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "mu = elfi.Prior('uniform', -2, 4)\n",
+ "sigma = elfi.Prior('uniform', 1, 4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The above would create two prior nodes, a uniform distribution from -2 to 2 for the mean `mu` and another uniform distribution from 1 to 5 for the standard deviation `sigma`. All distributions from `scipy.stats` are available.\n",
+ "\n",
+ "For likelihood-free models we typically need to define a simulator and summary statistics for the data. As an example, lets define the simulator as 30 draws from a Gaussian distribution with a given mean and standard deviation. Let's use mean and variance as our summaries:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import scipy.stats as ss\n",
+ "import numpy as np\n",
+ "\n",
+ "def simulator(mu, sigma, batch_size=1, random_state=None):\n",
+ " mu, sigma = np.atleast_1d(mu, sigma)\n",
+ " return ss.norm.rvs(mu[:, None], sigma[:, None], size=(batch_size, 30), random_state=random_state)\n",
+ "\n",
+ "def mean(y):\n",
+ " return np.mean(y, axis=1)\n",
+ "\n",
+ "def var(y):\n",
+ " return np.var(y, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let’s now assume we have some observed data `y0` (here we just create some with the simulator):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 3.7990926 1.49411834 0.90999905 2.46088006 -0.10696721 0.80490023\n",
+ " 0.7413415 -5.07258261 0.89397268 3.55462229 0.45888389 -3.31930036\n",
+ " -0.55378741 3.00865492 1.59394854 -3.37065996 5.03883749 -2.73279084\n",
+ " 6.10128027 5.09388631 1.90079255 -1.7161259 3.86821266 0.4963219\n",
+ " 1.64594033 -2.51620566 -0.83601666 2.68225112 2.75598375 -6.02538356]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Set the generating parameters that we will try to infer\n",
+ "mean0 = 1\n",
+ "std0 = 3\n",
+ "\n",
+ "# Generate some data (using a fixed seed here)\n",
+ "np.random.seed(20170525) \n",
+ "y0 = simulator(mean0, std0)\n",
+ "print(y0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we have all the components needed. Let’s complete our model by adding the simulator, the observed data, summaries and a distance to our model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Add the simulator node and observed data to the model\n",
+ "sim = elfi.Simulator(simulator, mu, sigma, observed=y0)\n",
+ "\n",
+ "# Add summary statistics to the model\n",
+ "S1 = elfi.Summary(mean, sim)\n",
+ "S2 = elfi.Summary(var, sim)\n",
+ "\n",
+ "# Specify distance as euclidean between summary vectors (S1, S2) from simulated and\n",
+ "# observed data\n",
+ "d = elfi.Distance('euclidean', S1, S2)\n",
+ "\n",
+ "# Plot the complete model (requires graphviz)\n",
+ "elfi.draw(d)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can try to infer the true generating parameters `mean0` and `std0` above with any of ELFI’s inference methods. Let’s use ABC Rejection sampling and sample 1000 samples from the approximate posterior using threshold value 0.5:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Method: Rejection\n",
+ "Number of posterior samples: 1000\n",
+ "Number of simulations: 120000\n",
+ "Threshold: 0.492\n",
+ "Posterior means: mu: 0.748, sigma: 3.1\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "rej = elfi.Rejection(d, batch_size=10000, seed=30052017)\n",
+ "res = rej.sample(1000, threshold=.5)\n",
+ "print(res)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's plot also the marginal distributions for the parameters:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "res.plot_marginals()\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [default]",
+ "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": "3.5.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorial.ipynb b/tutorial.ipynb
new file mode 100644
index 0000000..1ef6e54
--- /dev/null
+++ b/tutorial.ipynb
@@ -0,0 +1,1430 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This tutorial is generated from a [Jupyter](http://jupyter.org/) notebook that can be found [here](https://github.com/elfi-dev/notebooks). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# ELFI tutorial\n",
+ "\n",
+ "This tutorial covers the basics of using ELFI, i.e. how to make models, save results for later use and run different inference algorithms.\n",
+ "\n",
+ "Let's begin by importing libraries that we will use and specify some settings."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import scipy.stats\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "%matplotlib inline\n",
+ "%precision 2\n",
+ "\n",
+ "import logging\n",
+ "logging.basicConfig(level=logging.INFO)\n",
+ "\n",
+ "# Set an arbitrary global seed to keep the randomly generated quantities the same\n",
+ "np.random.seed(20170530)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Inference with ELFI: case MA(2) model\n",
+ "\n",
+ "Throughout this tutorial we will use the 2nd order moving average model MA(2) as an example. MA(2) is a common model used in univariate time series analysis. Assuming zero mean it can be written as\n",
+ "\n",
+ "$$\n",
+ "y_t = w_t + \\theta_1 w_{t-1} + \\theta_2 w_{t-2},\n",
+ "$$\n",
+ "\n",
+ "where $\\theta_1, \\theta_2 \\in \\mathbb{R}$ and $(w_k)_{k\\in \\mathbb{Z}} \\sim N(0,1)$ represents an independent and identically distributed sequence of white noise."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The observed data and the inference problem"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this tutorial, our task is to infer the parameters $\\theta_1, \\theta_2$ given a sequence of 100 observations $y$ that originate from an MA(2) process. Let's define the MA(2) simulator as a Python function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def MA2(t1, t2, n_obs=100, batch_size=1, random_state=None):\n",
+ " # Make inputs 2d arrays for numpy broadcasting with w\n",
+ " t1 = np.asanyarray(t1).reshape((-1, 1))\n",
+ " t2 = np.asanyarray(t2).reshape((-1, 1))\n",
+ " random_state = random_state or np.random\n",
+ "\n",
+ " w = random_state.randn(batch_size, n_obs+2) # i.i.d. sequence ~ N(0,1)\n",
+ " x = w[:, 2:] + t1*w[:, 1:-1] + t2*w[:, :-2]\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Above, `t1`, `t2`, and `n_obs` are the arguments specific to the MA2 process. The latter two, `batch_size` and `random_state` are ELFI specific keyword arguments. The `batch_size` argument tells how many simulations are needed. The `random_state` argument is for generating random quantities in your simulator. It is a `numpy.RandomState` object that has all the same methods as `numpy.random` module has. It is used for ensuring consistent results and handling random number generation in parallel settings.\n",
+ "\n",
+ "### Vectorization\n",
+ "\n",
+ "What is the purpose of the `batch_size` argument? In ELFI, operations are vectorized, meaning that instead of simulating a single MA2 sequence at a time, we simulate a batch of them. A vectorized function takes vectors as inputs, and computes the output for each element in the vector. Vectorization is a way to make operations efficient in Python. Above we rely on numpy to carry out the vectorized calculations. \n",
+ "\n",
+ "In this case the arguments `t1` and `t2` are going to be vectors of length `batch_size` and the method returns a 2d array with the simulations on the rows. Notice that for convenience, the funtion also works with scalars that are first converted to vectors."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ ".. note:: there is a built-in tool (`elfi.tools.vectorize`) in ELFI to vectorize operations that are not vectorized. It is basically a for loop wrapper.\n",
+ "\n",
+ ".. Important:: in order to guarantee a consistent state of pseudo-random number generation, the simulator must have `random_state` as a keyword argument for reading in a `numpy.RandomState` object."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's now use this simulator to create toy observations. We will use parameter values $\\theta_1=0.6, \\theta_2=0.2$ as in [*Marin et al. (2012)*](http://link.springer.com/article/10.1007/s11222-011-9288-2) and then try to infer these parameter values back based on the toy observed data alone."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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odOLpfruntQJg2QPySTROFkXVQEjSEQjWV4g5TmOuCoSnSTSOgsZ4GuAx1hhF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gtbvetV0ob3NG63OphKjyEjZHDOlHABmpME4wa17fAOwxRClcaxdByxbh0KwyIZz4X+rGlaqErd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7nBH7CLa5t2nqa/zume/CoDPgrpm71uHI+o+7Zu6SS9TJfLKnQo33NPIp7kF1Gjk+\na/87jZFMRDHg+0z0DIq0iJ1eIRo5N4/ejJdNv2xgncZ24U4jL0/X+3fySE4jn6A+F0rCoCMYdQ9W\naPpGRIjGdWDfhBtOiwFnVhKKn+9GeXopIQV7b8LyNMCid35z+Td1N1oUigV8f+77ODR+CB5L54Kq\nNxKHJkol6kQu0ReicdDL05x+dxr5m/xycrnmcyfCbAhGlKdLXLPlGvzVbX8Fg675qtFmwGFywGF0\nYCmxBD3Rw2NWv+Z6qnoaz4eSmPTZYNALydJrxBlYBwgh2BqwYy6oLBpTuQJs5uYvNG6TW3UjzEJ8\nAQadYdPe9R4YO4BMIYMnLz+p+pjHlh5DMBXcNNmMSlgNVnmKOp6N93SogQ/CyCHjAxruzen3nsZ6\nWY0nIidgM9gw7hxf78MSbGD4jYjf4q8bN+Wx8vI0dxoTqusDBeuLEI3rxEwd0ZjMtl6eTuVTyBQy\nNZ9bii9h1D7atzlw3eb64eth1ptxeEG9r/FrJ74Gp9GJWyduXccj6z/unGZT1EeDR3sq1JwmJ/I0\nj1CardgbeKdxA0xPA8pZjScjJ7HDu2PTXl8ErcH7Ghs99w16HZwWg5zVyIK9hWjsB8Qrfp2YDdix\nuJpCOleo+VwyW4C1xfI0UFq9Vs5CYmFT9jNyLAYLbhi+QbWv8cjiETx84WG87aq3wazf3GupeIk6\nT/M9L08DJWdr0EVjvzuNavunKaU4GT4p+hkFTcNvRLRUwDw2I6LJLMKJLOKZvOrOacH6IkTjOjEb\nsINS4Hw4WfFxSmnLPY18BVl1X2Myl8RcdA4TjonWD3gA2D+2H2dXz2IpvlTx8Vwxh798/C8x6ZzE\nm/e8uUdH1z/wEjXQ25KwLBoTgysaHUaH/P/93tNoNVjhNrtrnMalxBJiuZiYnBY0Db8RqZfRyPHa\nTIgkczgXYu+ZojzdHwjRuE7wqIDqEnW2UEShSGFvsacRqF0l+MALDyCWi+E1O17T4tEOBmrROw8c\nfwBnV8/iwzd+eNO7jBw+Pd7rnkag5GwNYuSO1WCFgRjgNDlh1Bt7fTgNUQr45usDxRCMoFnKexob\n4bGx/dPzIfaeKZzG/kCIxnViJqAsGlNZVq62ttjTCFQ6jYlcAl94/gs4MH4A+4b2tXq4A8FW91YM\n24YrStTBVBB/+9u/xcHxg3jxxIt7eHT9xcHxgwhYA5h09i7gnJduB7k8TQiB0+Tse5eRM2IfqSlP\n88lpsT5Q0CxcNGpzGo2IpnKYDyVBCDDpG7zrwUZEZAOsE26rEX67CeeqRGNSEo2tlKd5ZEG5aHzg\nhQcQzUTxvn3va+NoBwNCCA6OH8SPz/0Y+WIeBp0Bf/3UXyNdSOPDN3540+yZ1oLVYMWPXvsjGHW9\nc7/Ky9NGnXFgo0scJkff9zNyhm3DeG7luYqPnYicwKRzUqwPFDTNjHsGOqLDrHu24WM9ViMiCeY0\njrmtMBuaf48UdJ7BvCr3KbMBO86qiMZ2BmG4aIxn4/jC81/AofFD2Du0t82jHQz2j+3Ht059C88F\nn4OBGPDg6Qfxtj1v03TR2myY9K3tlO0UXDSG0iH5uT2I3DRyk+y49DvDNhbwnSlk5FaOU5FTojQt\naIlJ5yR+8rqfYMiqZRDGhLV0HmeDIm6nnxCicR2ZCdjxy6od1MlsHgBga2GNoM1gg4EY5J7Grxz/\nClYzq3jfNcJl5Lxo9EXQER0eWXgEjy4+ioA1gHfte1evD0uggNPkBAEBBR3I0jTnP+//z70+BM3w\nrMblxDImXZNI5pKYX5vHPbP39PjIBBuVLTZtaxb5KsEXlmJ47fWbe6iznxA9jevIbMCO5VgG8Uxe\n/hh3Gu0tOI2EELjMLqxmVhHLxvDFY1/EbRO3YU9gT8eOeaPjNruxN7AX//z8P+O54HP4wPUfEGW1\nPkVHdHCY2HTxIIvGjQSfdr2UZBPUZ6JnQEGF0yjoOnyVYLZQFE5jHyFE4zoyKw3DlPc1ptooTwOl\nVYJfPv5lxLIxvOea97R/oAPGgfEDSBfS2De0D6/Y+opeH46gDrxEPYjbYDYiIzbmNPLYnRMRaX2g\nT4hGQXfhqwQBYEaIxr5BiMZ1hMfunAuVRGNpEKa1TgG3yY2L8Yv40vNfwksmX4Ld/t3tH+iAcdf0\nXRh3jOMjL/qI2GDR53DRKJzG/oCXEvkE9YnwCdiNdow7xPpAQXfx2ko91lM+UR3qF0RP4zoyE2B3\nS3MrJdGYkHsaW3caf3HxFwCA917z3jaPcDDZ6tmKH772h70+DIEGhGjsL2xGG1wml+w0noycxA6P\nWB8o6D7lolGUp/sH8cpfR2wmA0ZcFsyFOlueBoCXTr0UV/qubP8gBYIewgO+BzHYe6PCsxoppTgV\nOSU2wQjWBbdUnh5ymltafiHoDkI0rjOzAXtFwHdpEKa1F4XX7AUAvHvfu9s/OIGgx/CAb+E09g98\nK8xiYhGxXEwMwQjWBZfFAL2OYNonbiD7CSHf15mZgB0/PFrahZzK5kEIYDG2pt/ftOtNuH74enH3\nLxgIRHm6/xi2D+P50PM4GRbrAwXrByEEW5xmbN/iaPxgwbohROM6szVgRySZQzSZhcdmQjJbgNWo\nb3k7yahjFKOO0Q4fpUDQG4Ro7D9GbCMIp8N4Lsg2wwjRKFgvPv/WGxFwmHt9GIIyRHl6naneQZ3I\nFloeghEIBg0RudN/8KzGRxYewaRzUvSbCtaNXaMuDDmFaOwnhGhcZ+SsRmkYJpXNtxy3IxAMGnwQ\nRjiN/QPfCnM8fBw7vaINRiDYzAjRuM5M+WzQkVLsTlI4jQKBjDwIYxSisV8o35MtQr0Fgs2NEI3r\njMmgw7jXirlQEgCQyhVajtsRCAYNUZ7uPypEo+hnFAg2NUI09oDZgANzwTgA4TQKBOXs8O7AXTN3\n4frh63t9KAIJHvANQJSnBYJNjmim6wGzfhuemo+AUopEJg+vTbgqAgHAehk/++LP9vowBFUM24dR\noAWMOcZ6fSgCgaCHCNHYA2YDdsQzeazEM0jlCrCbhdMoEAj6lz3+PZhwTIj1gQLBJkeIxh7AY3fO\nBZOiPC0QCPqej+//OChorw9DIBD0GCEae8DWAEu4PxdMIJUtwGoUp0EgEPQvhBAQtLaAQCAQDA6i\n1tADxjwWGPUEZ4MJJLN54TQKBAKBQCDoe4Ro7AEGvQ6TPhteuLSGIoWI3BEIBAKBQND3CNHYI7YG\n7Di2uAYAsAvRKBAIBAKBoM8RorFHzPjtWI5lAECsERQIBAKBQND3CNHYI2aH7PL/i/K0QCAQCASC\nfkeIxh4x6y+JRjEIIxAIBAKBoN8RorFHlDuNojwtEAgEAoGg32lLNBJCPkEIeZYQ8gwh5MeEELFj\nSiPDTgssRvbPL5xGgUAgEAgE/U67TuNnKKVXU0qvAfAQgI924Jg2BTodwYxUohaiUSAQCAQCQb/T\nlmiklK6V/dUOiD1TzTArrRMUgzACgUAgEAj6nbab6QghnwTwZgCrAF7S9hFtIvgOatHTKBAIBAKB\noN9p6DQSQn5KCDmq8N+rAIBS+hFK6SSArwD4wzrf552EkCcJIU+urKx07jfYwLzm2nG8/eAsvDZj\nrw9FIBAIBAKBoC6E0s5UlAkhUwC+Tym9qtFjb7jhBvrkk0925OcKBAKBQCAQCFqHEPIbSukNjR7X\n7vT0jrK/vgrAC+18P4FAIBAIBAJBf9JuM92nCSE7ARQBzAN4d/uHJBAIBAKBQCDoN9oSjZTS13bq\nQAQCgUAgEAgE/YvYCCMQCAQCgUAgaIgQjQKBQCAQCASChgjRKBAIBAKBQCBoiBCNAoFAIBAIBIKG\nCNEoEAgEAoFAIGiIEI0CgUAgEAgEgoYI0SgQCAQCgUAgaIgQjQKBQCAQCASChgjRKBAIBAKBQCBo\niBCNAoFAIBAIBIKGEErp+v9QQlbAdlWvFwEAwXX8eYLOI87hxkacv42POIcbH3EONzbdPH/TlNKh\nRg/qiWhcbwghT1JKb+j1cQhaR5zDjY04fxsfcQ43PuIcbmz64fyJ8rRAIBAIBAKBoCFCNAoEAoFA\nIBAIGrJZROPf9/oABG0jzuHGRpy/jY84hxsfcQ43Nj0/f5uip1EgEAgEAoFA0B6bxWkUCAQCgUAg\nELTBQItGQsjdhJAThJDThJA/7fXxCBpDCJkkhDxMCDlGCHmeEPJ+6eM+QshPCCGnpD+9vT5WgTqE\nED0h5GlCyEPS38X520AQQjyEkG8SQl4ghBwnhNwizuHGghDyJ9I19Cgh5AFCiEWcw/6GEPJ5Qsgy\nIeRo2cdUzxkh5M8kfXOCEHLXehzjwIpGQogewOcAvBzAbgC/SwjZ3dujEmggD+A/Ukp3A7gZwPuk\n8/anAH5GKd0B4GfS3wX9y/sBHC/7uzh/G4u/BvBDSumVAPaBnUtxDjcIhJBxAH8M4AZK6VUA9ADe\nCHEO+50vALi76mOK50x6X3wjgD3S1/xPSfd0lYEVjQBuAnCaUnqWUpoF8DUAr+rxMQkaQCldopQ+\nJf1/DOzNahzs3H1RetgXAby6N0coaAQhZALAKwD8Q9mHxfnbIBBC3ABuBfCPAEApzVJKoxDncKNh\nAGAlhBgA2AAsQpzDvoZS+ksA4aoPq52zVwH4GqU0QymdA3AaTPd0lUEWjeMALpT9/aL0McEGgRAy\nA+BaAI8BGKaULkmfugRguEeHJWjMfwPwYQDFso+J87dxmAWwAuCfpBaDfyCE2CHO4YaBUroA4LMA\nzgNYArBKKf0xxDnciKids55onEEWjYINDCHEAeBbAP4DpXSt/HOUjfyLsf8+hBByL4BlSulv1B4j\nzl/fYwBwHYC/pZReCyCBqjKmOIf9jdT39iqwG4AxAHZCyO+VP0acw41HP5yzQRaNCwAmy/4+IX1M\n0OcQQoxggvErlNJ/lT58mRAyKn1+FMByr45PUJcDAF5JCDkH1hJyOyHkyxDnbyNxEcBFSulj0t+/\nCSYixTncOLwUwByldIVSmgPwrwD2Q5zDjYjaOeuJxhlk0fgEgB2EkFlCiAmsYfTfenxMggYQQghY\nL9VxSulflX3q3wC8Rfr/twD4znofm6AxlNI/o5ROUEpnwF5zP6eU/h7E+dswUEovAbhACNkpfegO\nAMcgzuFG4jyAmwkhNumaegdYf7g4hxsPtXP2bwDeSAgxE0JmAewA8Hi3D2agw70JIfeA9VfpAXye\nUvrJHh+SoAGEkIMAfgXgOZR64v4vsL7GfwEwBWAewP2U0uqGYUEfQQi5DcAHKaX3EkL8EOdvw0AI\nuQZskMkE4CyAt4GZDOIcbhAIIX8B4A1giRRPA3g7AAfEOexbCCEPALgNQADAZQAfA/AgVM4ZIeQj\nAH4f7Bz/B0rpD7p+jIMsGgUCgUAgEAgEnWGQy9MCgUAgEAgEgg4hRKNAIBAIBAKBoCFCNAoEAoFA\nIBAIGiJEo0AgEAgEAoGgIUI0CgQCgUAgEAgaIkSjQCAQCAQCgaAhQjQKBAKBQCAQCBoiRKNAIBAI\nBAKBoCH/Px7UVLepMxaMAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# true parameters\n",
+ "t1_true = 0.6\n",
+ "t2_true = 0.2\n",
+ "\n",
+ "y_obs = MA2(t1_true, t2_true)\n",
+ "\n",
+ "# Plot the observed sequence\n",
+ "plt.figure(figsize=(11, 6));\n",
+ "plt.plot(y_obs.ravel());\n",
+ "\n",
+ "# To illustrate the stochasticity, let's plot a couple of more observations with the same true parameters:\n",
+ "plt.plot(MA2(t1_true, t2_true).ravel());\n",
+ "plt.plot(MA2(t1_true, t2_true).ravel());"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Approximate Bayesian Computation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Standard statistical inference methods rely on the use of the *likelihood* function. Given a configuration of the parameters, the likelihood function quantifies how likely it is that values of the parameters produced the observed data. In our simple example case above however, evaluating the likelihood is difficult due to the unobserved latent sequence (variable `w` in the simulator code). In many real world applications the likelihood function is not available or it is too expensive to evaluate preventing the use of traditional inference methods.\n",
+ "\n",
+ "One way to approach this problem is to use Approximate Bayesian Computation (ABC) which is a statistically based method replacing the use of the likelihood function with a simulator of the data. Loosely speaking, it is based on the intuition that similar data is likely to have been produced by similar parameters. Looking at the picture above, in essence we would keep simulating until we have found enough sequences that are similar to the observed sequence. Although the idea may appear inapplicable for the task at hand, you will soon see that it does work. For more information about ABC, please see e.g. \n",
+ "\n",
+ "* [Lintusaari, J., Gutmann, M. U., Dutta, R., Kaski, S., and Corander, J. (2016). Fundamentals and recent\n",
+ "developments in approximate Bayesian computation. *Systematic Biology*, doi: 10.1093/sysbio/syw077.](http://sysbio.oxfordjournals.org/content/early/2016/09/07/sysbio.syw077.full.pdf)\n",
+ "\n",
+ "* [Marin, J.-M., Pudlo, P., Robert, C. P., and Ryder, R. J. (2012). Approximate Bayesian computational\n",
+ "methods. *Statistics and Computing*, 22(6):1167–1180.](http://link.springer.com/article/10.1007/s11222-011-9288-2)\n",
+ "\n",
+ "* https://en.wikipedia.org/wiki/Approximate_Bayesian_computation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Defining the model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "ELFI includes an easy to use generative modeling syntax, where the generative model is specified as a directed acyclic graph ([DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph)). This provides an intuitive means to describe rather complex dependencies conveniently. Often the target of the generative model is a distance between the simulated and observed data. To start creating our model, we will first import ELFI:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import elfi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As is usual in Bayesian statistical inference, we need to define *prior* distributions for the unknown parameters $\\theta_1, \\theta_2$. In ELFI the priors can be any of the continuous and discrete distributions available in `scipy.stats` (for custom priors, see [below](#Custom-priors)). For simplicity, let's start by assuming that both parameters follow `Uniform(0, 2)`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# a node is defined by giving a distribution from scipy.stats together with any arguments (here 0 and 2)\n",
+ "t1 = elfi.Prior(scipy.stats.uniform, 0, 2)\n",
+ "\n",
+ "# ELFI also supports giving the scipy.stats distributions as strings\n",
+ "t2 = elfi.Prior('uniform', 0, 2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we define the *simulator* node with the `MA2` function above, and give the priors to it as arguments. This means that the parameters for the simulations will be drawn from the priors. Because we have the observed data available for this node, we provide it here as well:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "Y = elfi.Simulator(MA2, t1, t2, observed=y_obs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But how does one compare the simulated sequences with the observed sequence? Looking at the plot of just a few observed sequences above, a direct pointwise comparison would probably not work very well: the three sequences look quite different although they were generated with the same parameter values. Indeed, the comparison of simulated sequences is often the most difficult (and ad hoc) part of ABC. Typically one chooses one or more summary statistics and then calculates the discrepancy between those.\n",
+ "\n",
+ "Here, we will apply the intuition arising from the definition of the MA(2) process, and use the autocovariances with lags 1 and 2 as the summary statistics:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def autocov(x, lag=1):\n",
+ " C = np.mean(x[:,lag:] * x[:,:-lag], axis=1)\n",
+ " return C"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As is familiar by now, a `Summary` node is defined by giving the autocovariance function and the simulated data (which includes the observed as well):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "S1 = elfi.Summary(autocov, Y)\n",
+ "S2 = elfi.Summary(autocov, Y, 2) # the optional keyword lag is given the value 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, we choose the discrepancy as the common Euclidean L2-distance. ELFI can use many common distances directly from `scipy.spatial.distance` like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Finish the model with the final node that calculates the squared distance (S1_sim-S1_obs)**2 + (S2_sim-S2_obs)**2\n",
+ "d = elfi.Distance('euclidean', S1, S2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One may wish to use a distance function that is unavailable in `scipy.spatial.distance`. ELFI supports defining a custom distance/discrepancy functions as well (see the documentation for `elfi.Distance` and `elfi.Discrepancy`).\n",
+ "\n",
+ "Now that the inference model is defined, ELFI can visualize the model as a DAG. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "elfi.draw(d) # just give it a node in the model, or the model itself (d.model)"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ ".. note:: You will need the Graphviz_ software as well as the graphviz `Python package`_ (https://pypi.python.org/pypi/graphviz) for drawing this. The software is already installed in many unix-like OS.\n",
+ "\n",
+ ".. _Graphviz: http://www.graphviz.org\n",
+ ".. _`Python package`: https://pypi.python.org/pypi/graphviz"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Modifying the model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although the above definition is perfectly valid, let's use the same priors as in [*Marin et al. (2012)*](http://link.springer.com/article/10.1007/s11222-011-9288-2) that guarantee that the problem will be identifiable (loosely speaking, the likelihood willl have just one mode). Marin et al. used priors for which $-2<\\theta_1<2$ with $\\theta_1+\\theta_2>-1$ and $\\theta_1-\\theta_2<1$ i.e. the parameters are sampled from a triangle (see below).\n",
+ "\n",
+ "### Custom priors\n",
+ "\n",
+ "In ELFI, custom distributions can be defined similar to distributions in `scipy.stats` (i.e. they need to have at least the `rvs` method implemented for the simplest algorithms). To be safe they can inherit `elfi.Distribution` which defines the methods needed. In this case we only need these for sampling, so implementing a static `rvs` method suffices. As was in the context of simulators, it is important to accept the keyword argument `random_state`, which is needed for ELFI's internal book-keeping of pseudo-random number generation. Also the `size` keyword is needed (which in the simple cases is the same as the `batch_size` in the simulator definition)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# define prior for t1 as in Marin et al., 2012 with t1 in range [-b, b]\n",
+ "class CustomPrior_t1(elfi.Distribution):\n",
+ " def rvs(b, size=1, random_state=None):\n",
+ " u = scipy.stats.uniform.rvs(loc=0, scale=1, size=size, random_state=random_state)\n",
+ " t1 = np.where(u<0.5, np.sqrt(2.*u)*b-b, -np.sqrt(2.*(1.-u))*b+b)\n",
+ " return t1\n",
+ "\n",
+ "# define prior for t2 conditionally on t1 as in Marin et al., 2012, in range [-a, a]\n",
+ "class CustomPrior_t2(elfi.Distribution):\n",
+ " def rvs(t1, a, size=1, random_state=None):\n",
+ " locs = np.maximum(-a-t1, t1-a)\n",
+ " scales = a - locs\n",
+ " t2 = scipy.stats.uniform.rvs(loc=locs, scale=scales, size=size, random_state=random_state)\n",
+ " return t2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These indeed sample from a triangle:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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1OsWLp6MtQr2d4O/mgMemh+LJmQPhbKfdVubuaIMAdwf4umjWcLmZ6bXlj2EY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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "t1_1000 = CustomPrior_t1.rvs(2, 1000)\n",
+ "t2_1000 = CustomPrior_t2.rvs(t1_1000, 1, 1000)\n",
+ "plt.scatter(t1_1000, t2_1000, s=4, edgecolor='none');\n",
+ "# plt.plot([0, 2, -2, 0], [-1, 1, 1, -1], 'b') # outlines of the triangle"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's change the earlier priors to the new ones in the inference model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "t1.become(elfi.Prior(CustomPrior_t1, 2))\n",
+ "t2.become(elfi.Prior(CustomPrior_t2, t1, 1))\n",
+ "\n",
+ "elfi.draw(d)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that `t2` now depends on `t1`. Yes, ELFI supports hierarchy."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Inference with rejection sampling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The simplest ABC algorithm samples parameters from their prior distributions, runs the simulator with these and compares them to the observations. The samples are either accepted or rejected depending on how large the distance is. The accepted samples represent samples from the approximate posterior distribution.\n",
+ "\n",
+ "In ELFI, ABC methods are initialized either with a node giving the distance, or with the `ElfiModel` object and the name of the distance node. Depending on the inference method, additional arguments may be accepted or required. \n",
+ "\n",
+ "A common optional keyword argument, accepted by all inference methods, `batch_size` defines how many simulations are performed in each passing through the graph. \n",
+ "\n",
+ "Another optional keyword is the seed. This ensures that the outcome will be always the same for the same data and model. If you leave it out, a random seed will be taken."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "seed = 20170530\n",
+ "rej = elfi.Rejection(d, batch_size=10000, seed=seed)"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ ".. note:: In Python, doing many calculations with a single function call can potentially save a lot of CPU time, depending on the operation. For example, here we draw 10000 samples from `t1`, pass them as input to `t2`, draw 10000 samples from `t2`, and then use these both to run 10000 simulations and so forth. All this is done in one passing through the graph and hence the overall number of function calls is reduced 10000-fold. However, this does not mean that batches should be as big as possible, since you may run out of memory, the fraction of time spent in function call overhead becomes insignificant, and many algorithms operate in multiples of `batch_size`. Furthermore, the `batch_size` is a crucial element for efficient parallelization (see the notebook on parallelization)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After the ABC method has been initialized, samples can be drawn from it. By default, rejection sampling in ELFI works in `quantile` mode i.e. a certain quantile of the samples with smallest discrepancies is accepted. The `sample` method requires the number of output samples as a parameter. Note that the simulator is then run `(N/quantile)` times. (Alternatively, the same behavior can be achieved by saying `n_sim=1000000`.)\n",
+ "\n",
+ "The IPython magic command `%time` is used here to give you an idea of runtime on a typical personal computer. We will turn interactive visualization on so that if you run this on a notebook you will see the posterior forming from a prior distribution. In this case most of the time is spent in drawing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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JjwIXJK+RtFDS9IgwSScCFwK31dz/p5J2SHoIOA+4ujPVNjOzVmXWRdZIRLxA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\nAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Threshold: 0.116859716394976"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 2.2 s, sys: 182 ms, total: 2.38 s\n",
+ "Wall time: 2.39 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "N = 1000\n",
+ "\n",
+ "vis = dict(xlim=[-2,2], ylim=[-1,1])\n",
+ "\n",
+ "# You can give the sample method a `vis` keyword to see an animation how the prior transforms towards the\n",
+ "# posterior with a decreasing threshold (interactive visualization will slow it down a bit though).\n",
+ "%time result = rej.sample(N, quantile=0.01, vis=vis)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `sample` method returns a `Sample` object, which contains several attributes and methods. Most notably the attribute `samples` contains an `OrderedDict` (i.e. an ordered Python dictionary) of the posterior numpy arrays for all the model parameters (`elfi.Prior`s in the model). For rejection sampling, other attributes include e.g. the `threshold`, which is the threshold value resulting in the requested quantile. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.56"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "result.samples['t1'].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `Sample` object includes a convenient `summary` method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Method: Rejection\n",
+ "Number of posterior samples: 1000\n",
+ "Number of simulations: 100000\n",
+ "Threshold: 0.117\n",
+ "Posterior means: t1: 0.556, t2: 0.219\n"
+ ]
+ }
+ ],
+ "source": [
+ "result.summary"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Rejection sampling can also be performed with using a threshold or total number of simulations. Let's define here threshold. This means that all draws from the prior for which the generated distance is below the threshold will be accepted as samples. Note that the simulator will run as long as it takes to generate the requested number of samples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 215 ms, sys: 51.8 ms, total: 267 ms\n",
+ "Wall time: 269 ms\n",
+ "Method: Rejection\n",
+ "Number of posterior samples: 1000\n",
+ "Number of simulations: 40000\n",
+ "Threshold: 0.185\n",
+ "Posterior means: t1: 0.555, t2: 0.223\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time result2 = rej.sample(N, threshold=0.2)\n",
+ "\n",
+ "print(result2) # the Sample object's __str__ contains the output from summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Storing simulated data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As the samples are already in numpy arrays, you can just say e.g. `np.save('t1_data.npy', result.samples['t1'])` to save them. However, ELFI provides some additional functionality. You may define a *pool* for storing all outputs of any node in the model (not just the accepted samples). Let's save all outputs for `t1`, `t2`, `S1` and `S2` in our model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 6.14 s, sys: 102 ms, total: 6.24 s\n",
+ "Wall time: 6.38 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Method: Rejection\n",
+ "Number of posterior samples: 1000\n",
+ "Number of simulations: 1000000\n",
+ "Threshold: 0.036\n",
+ "Posterior means: t1: 0.56, t2: 0.227"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pool = elfi.OutputPool(['t1', 't2', 'S1', 'S2'])\n",
+ "rej = elfi.Rejection(d, pool=pool)\n",
+ "\n",
+ "%time result3 = rej.sample(N, n_sim=1000000)\n",
+ "result3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The benefit of the pool is that you may reuse simulations without having to resimulate them. Above we saved the summaries to the pool, so we can change the distance node of the model without having to resimulate anything. Let's do that."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 848 ms, sys: 12.1 ms, total: 860 ms\n",
+ "Wall time: 895 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Method: Rejection\n",
+ "Number of posterior samples: 1000\n",
+ "Number of simulations: 1000000\n",
+ "Threshold: 0.0447\n",
+ "Posterior means: t1: 0.56, t2: 0.227"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Replace the current distance with a cityblock (manhattan) distance and recreate the inference\n",
+ "d.become(elfi.Distance('cityblock', S1, S2, p=1))\n",
+ "rej = elfi.Rejection(d, pool=pool)\n",
+ "\n",
+ "%time result4 = rej.sample(N, n_sim=1000000)\n",
+ "result4"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note the significant saving in time, even though the total number of considered simulations stayed the same. \n",
+ "\n",
+ "We can also continue the inference by increasing the total number of simulations and only have to simulate the new ones:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.96 s, sys: 29.4 ms, total: 1.99 s\n",
+ "Wall time: 2.02 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Method: Rejection\n",
+ "Number of posterior samples: 1000\n",
+ "Number of simulations: 1200000\n",
+ "Threshold: 0.0409\n",
+ "Posterior means: t1: 0.56, t2: 0.23"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%time result5 = rej.sample(N, n_sim=1200000)\n",
+ "result5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Above the results were saved into a python dictionary. If you store a lot of data to dictionaries, you will eventually run out of memory. Instead you can save the outputs to standard numpy .npy files:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 25.6 ms, sys: 2.58 ms, total: 28.2 ms\n",
+ "Wall time: 29.3 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "arraypool = elfi.store.ArrayPool(['t1', 't2', 'Y', 'd'], basepath='./output')\n",
+ "rej = elfi.Rejection(d, pool=arraypool)\n",
+ "%time result5 = rej.sample(100, threshold=0.3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This stores the simulated data in binary `npy` format under `arraypool.path`, and can be loaded with `np.load`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['d.npy', 't1.npy', 't2.npy', 'Y.npy']"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Let's flush the outputs to disk (alternatively you can close the pool) so that we can read them\n",
+ "# while we still have the arraypool open.\n",
+ "arraypool.flush()\n",
+ "\n",
+ "import os\n",
+ "os.listdir(arraypool.path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now lets load all the parameters `t1` that were generated with numpy:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.51, 0.09, 0.72, ..., -1.23, 0.02, -0.66])"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.load(arraypool.path + '/t1.npy')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can delete the files with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No such file or directory\n"
+ ]
+ }
+ ],
+ "source": [
+ "arraypool.delete()\n",
+ "\n",
+ "# verify the deletion\n",
+ "try:\n",
+ " os.listdir(arraypool.path)\n",
+ " \n",
+ "except FileNotFoundError:\n",
+ " print(\"No such file or directory\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualizing the results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Instances of `Sample` contain methods for some basic plotting (these are convenience methods to plotting functions defined under `elfi.visualization`).\n",
+ "\n",
+ "For example one can plot the marginal distributions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "result.plot_marginals();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Often \"pairwise relationships\" are more informative:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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X3YWPr43lrz0nuef7XTaPmfnGOn7ffYJP1x/nlx3WlbGTckqZ/Ooac7/iP/ee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2Oszj0woTsAN3+xj9kDw7h+qPFe0NYTRTyxKoGkV6ZxUzORqnvGRaLR6YnLKGd0d3+r57QHF0cl\n0HaZcU55Pa6OttedUVJ+bFUCtw7vwoBO3gCtBrsAgztIKfi7eSM65DzNOZpbyTexGTw1PRqlBHtT\nS+gR7IG/e1O2uqu/W7s+G0/8dARXRyVvXtPfNCZJEivnj6RHcMf77QoEAoFAIBD827loJc2yLOuA\nB4GNwAlglSzLxyRJuleSpHsv1r4Efx+yy+p4dOVhU+muPVz50W6+3ZcJQIS/G2E+Lm0eU1ilZsxb\n21m2K417xkXSrZnA06ToIFNZ7Pngj6P5FFU3lTjfOborw7v5mh5P6xPEjqcnWBVXOpJdyX/+d9Cs\nn7c5JTUapr+/k5zyJqudlrY79vLAhO7cOdq2h+0ZPrxxEE9M60lWaR3f7E1n7+mSs7peS9KKa3h7\ng+1e5I7gp7hs4jObysonRQey5fFxJsuol9YeY1ty232/Gp1RmMoaj0yK4t5xkRbj/cK9zEq1BQKB\nQCAQCAQdw0Xt4ZVl+U9ZlnvIshwpy/LrjWNLZVleamXtHfZYEgkuXd7fksJr65pEuhUKycJapi0W\nXz+QqwaEAnDN4HAm9Axs85iSGg0hXk7MGhhmMTcmyp+YCF8rR3UM729J4Uh2penxlN5BZmW5kiSZ\nZRSbM6yrL0denoqro/VCDQ9nFbMHhZlsfJILqhj79nbyKzvG4/V0cQ3ltQ2mx2+uP0FuRT1eLg7c\n+tV+NiYVUlRl2/e2JavjcziYYd3HuEqts+nhuy+t1Oxzc7YkZFeQXlJreqxQSIT7NGWoNz92GdfH\ndLJ2qBkv/pbEU6uPWp3rFuBul3VRYZWaVQez21x3ockqrWNNQsvOE4FAIBAIBIK/L3930SrBv4iR\n3fy4rEeTqMmHW04xJsq/XZmvfuFerVq+WKNPqBe/PzTWrK/2QrH58XFM6R101sfnV6j5YmcaOiuC\nVU4qJfeOizQFxM6Nr6NG23rP7PG8KirqGlpdA/D06qP8LzbD6tzqe0ex/D/DmD3I8iaCLRJzKjiR\nX8XDPxw2y3qDsU/4s1utW60pFRKqdt4YacmzPx+lX5gX1w4Jt7mmLQujMzw2pQfPTO9pepyYU8mw\n17dQo7GeibdGSmE13+zNsHv9hSIpr5IfDmRd7G0IBAKBQCAQ2I0IeAUXFINBtlnuObxFwDuws7dd\n2bCOZOWBLNYdzbN7fUJ2Bavjc0yP1Vo9qw5mo28U0Yo9XWpX8Hg2yLLM70fz2H6yiP6vbCKuWXZU\nqzOwIclcuTrC3409z04kwr/11/SxHxP4+VDbWbzPbx3CFf1DAKNH7sBwb0Z08wNgZ0ox72w8yawl\ne6zaJVnjlVl9mTM4HJVSQmFncAlGi6RnZ0S3ua6+Qc8bf56gSm2piFxYpea9LSl2X9MWP8fn8M7G\nFLPMcGSgGwsujzbzBG6LsVEB/PlIkxBZeW2Dze9NS9qymdqTWsLRnAq799Kcy/uFsHL+yLM6ViAQ\nCAQCgeBiIAJewQXl811pXP9ZrF1rbxrWmYGN4kcXimq1jtp2ZOJOF9XwZ2Ieizcbg6W8inoWbUim\nsEpNjUbH0z8fYftJ232fsizz9Z50SmvsK/1dn5jP1Pd2GK9VqebDrad4/ep+LJk7mL5hXqZ1y/dl\ncO+KQ6SV1PDbYWPw+suhHLPgW2+Q0ekNZJfVIcvGoHTFvkz0soE7R0WY1uVX1pNmpZx40/FCbvvy\nAADH8qp47tdEU1Cml2U8XRy4akCohY/tgfQy3rLRj+vmpGLx9QNtlnGfC2qtnqTcShb9eYJThdVm\nc5/OHcLQCF+ySlvvca7R6Bj/3+02/ZSjgtwZFelnNubqqOLqQbYzx/Zw74p43t9yqs11SbmVjFy0\nleJq25+n34/ksT1Z+KEKBAKBQCD4d9BqykGSJE8gQJbl0y3G+8uybL1JTSBohetjOnFZ1Ll7MVap\ntXg6O3TAjsxprvjcFoezyvl0x2lemNmLv04aA4hwH1fqGnT8FJfN5zvTSHhpKol5lVSrtfx4MJsr\n+oea+fxq9TKr4nIY3NkHPzuCvCERPjw+xVguKwG6xqB1Qs9AcsrriM8oZ2hXX+4a3ZXewZ7M+ngP\nBlkmIaucskav1z6hxsD4ld+PUVSlYcuJQv531zBGd/dnRt9geoV4sDW5CBcHJcO6+vLR1lSKazR8\ncZt5SfF1Q8KZ1MvYIz2sqy+HX5pqmmut11Wj01stwW7JmoRccsrrTQrWtqhr0JFdVs/Xe9LZmlzE\nrqcnUFilJtzH1SQ4BeDj5sj3d4/g4R8OU98YmH+09RTH8qu4e0xXXByUtJVYdnVQ8p+x3Qi3IYbW\nP9yb/uEdf5Pm/RsH4mZHhrhvmBfrHxlr0/cYYNGc/jbnBAKBQHBpEvHsHx1ynoxFMzvkPALB3wmb\nf0FJknQ98D5QJEmSA3CHLMsHG6e/AQaf/+0J/mn4ujmaRJTOlqM5FcxesoeNj15ml59qe6lSa8ks\nqaNfuFer6yL83LhjVATjewYyvlEcy1GlIO6FKagUEmN7BOCgUvDAd4d4YmoP/kjMZ1Bnbxwbx96/\ncSBBns6sf8S6h641Aj2cmd7X6NUa6u3Cwecnm7KhSbmVfLQ9lazVdSS+MpXh3fx4dVZfvF0dKK9r\nMGUZd58qYVhXX+4e2w2NTs+zM6Lp4mcswfVzd8LP3YlX1x1nfVI+k3sFkVxQxQ/zLW1/VEoFgR6t\n9z3/HJ/D+J4BpmC+RqNj/vJ4VswbxqGscvQGmaE2RMGcVMpWrZDOsDYhj4+2pfLe9QOp0eiQgOnv\n72LBjGiq1FoenNjkofxTfDYVdQ30a8yGD+7iw4GMMo7kVPLf6wa0eS2FQuJWO2yuOpoQr7bVxs8Q\nHWy/L7BAIBAIBALBP53WUgbPAUNkWc6XJGkY8K0kSQtkWf4VY3LpX09SbiWyTJuBkaBj+etkMT2D\nPegeeHa+pbIsM+9/cTw6uYfV9+7Gz/ZRUKXm0ItTzMaLqtVkl9UzpIvRO9bHzdGqx++ZXs0zHrPb\nnxyPs4OSa4cYs551DTr6h3vZDOaOZFcQ5uNiV1lv8zXT+4YwtXcw6aW1JqGv2YPCGPHGVl6b3Rcw\n9trOW36Qb/8z3GagCfDsjGg8nR24ZlAYBuSzssyRZZkl21MJ8XJmVHcn9qSWEOjhxNJbh9A3zIvF\nm1LQ6AwMjfBFrdWjbKHKfSawt8WGpHwcVQquj+nEjL4heLk6MKzR0mnz45eRU17Psl3pVNQ18OD3\nh/nvdf0pqFCTVVaH3iCjUkqM7u5v8jHedKyA4/lVPDr53D2Xm1NUpUatNdDZzxW1Vo8sn/E/buJo\nTgWRAe52ZXEFAoFAIBAIBPbTWg+vUpblfABZlg8AE4AXJEl6GLBPheYfzo8Hs/kxTiiWXmgemtid\ntQ+OsaqaqzfI7D7VuverJElEh3jg5WK9JPqBCZH8785hFuN/HM1n4dpj7d6vs4N5cOPqqGLB5b3w\nsFGS/dyviaxNsF84qzkKhURko49w7OlS/jpZxKuz+xITYQy+3ZxUHHl5Ku9sPMmmYwU2z+OgVPDI\n5CiqNTqL0vG1R/J4+IfDbe5FkiS2PTmeUd39ySqtY3lsBluTixjSxYd7v43ntlERLLyqD2DsUX19\n3XFyK8wtk349nMMPB7LYfLyQBb8cxWCQ2ZlSjCzLnCyo4VRhDQqFhJer+R7DfVwZ0c2PZbfH4KRS\n0ivEA1dHFQ9NiuKvpyZYVXV2UCpIKawmtajaYu5s+H5/Fr8dzuWTv07zf+uMn5vnfk1kwS+W3SD/\n+V8cm45bvh9zl+3rMC/ji01JjcZu4S2BQCAQCASCjqK1gLdakqTIMw8ag9/xwCygz3ne1yXBq7P7\n8trsfhd7G/8aiqrUvLPxJAYZm/68Jwuqueubg5TYEIEqr23g2k/3Mnd4Fzr7uVpdM7N/qNXM752j\nu/LbA6PP/gnYya/3j+bO0RHtOuZ0cY1FsHgwo4y9p0uZ0jvIZNWUlFvJ06uPcu2QcLtKX5/86YiZ\nCjVAZIAbY6L87d6b3iAz9f0dTIwO4p7LuqFSSHTxc8NZ1fQevnxlH7zdHLnty/1mx2p1MhqtHn93\nRyID3EkvrWXe/+LIr1TzyOQo7hkX2fJyAKSX1HI8zygs5eKo5PmZvW3e4DjDhOhAGnQGkgs6JuBt\n0Olp0Bl47vJefHSTsQPk6WnRPGNFUfqvJ8dbFbaa0DOQcG/rn9NLjTu/PsgXO9Mu9jYEAoFAIBD8\ny5DOqLNaTEjSAKAOmCfL8jPNxh2BZFmW7Vf3ucDExMTIcXFxF3sbgg4mpbCahWuP8fWdQ1stsVVr\n9RZZ1eZzH29LZf64bjZFr1bH5zC8qy+dfC9coJFdVndO15u/PI5QbxdTxtQWqUXVrNiX1ea6khoN\nvx7KZe7wzjg7KC2Ulm3xxc40vtydzu5nzLOoacU1TP9gF5/fOsTU73yG+7+LZ+7wLsRE+FBRpyWo\nDT9ka+/vJ3+lUl7bwPMzewPw6rrj5FfW88ncIa2eK7eingB3JxxV/xzBerVWT25FvSnT/3chr6Ie\nLxeHS7JsW5KkeFmWrRtBC+xG/DZfWnSUCJLg0kKIVgkuFdrz22zzrzxZlo/IsnwKmNJivAGoPbct\nCgTw1E9HWLEv0+71PYI8+P7uEW32k9oKds/MPTmtZ6sKzz/FZdu0nWkPd359gA+2pPDR1lM8svIw\nlXWW/q9gDELHvr2d9OL2fa12nSpme7LR8ujjmwfzwsxeNte+tu44X+5Op3ugB3UNOpJyK7nrm4Ps\nSDG3p9mbWsLsJXvIr1Dz+9E8VEqF3cEuQK9QDwI8HNG3uJHWLcCdn+4ZSa8QTwvLnH5h3gR4OOGk\nUlJe10Dzm3BpxTXEtijptfb+DurkY/IA/uFAFqMiffn4JktdPZ3eYHb+az/dy8+HcizWnU92phSz\n5XjheTv/2oQ8bv5i33k7/9kS6u1ySQa7AoFAIBAILm1sBrySJN0nSVIi0FOSpKPN/ksHjly4LQr+\nLqSX1HLfingadG1bytjD+J6B9O8gwa9F65NZtqtjyiV/vGckU/u0LpjUnGq1lqU7TqPVG8gpb/Jx\nnd43GC8XB4qrNSia9RurtXq+25+JTm9g96kSFq1PJtDDib1p7evVTMiqIC6zDDCqQ1vrSwX442ge\nq+Oz6dRopeOkUqJSSuSW13M0u8JsbdcAN+YMCadfuBdrHxxjynzWNxh7LxdvOklSbqXNkvEx3QP4\n/aGxFjclbvgslhs/38db65N5dd1xs7lRkX7kVtRTXtvA5R/sYkdKMTd9vo+iajXf7M3g5i/2k9ei\nXBugsk7La+uOU9+gZ2SkH5N6BQFQWKWmtFZrNVC/7asDfLC1yc929X2jmDP43Dxy20tibiVHcira\nXmiFgko18Y3vuS3mDAnnj4ftV/4WCAQCgUAg+CfT2u3274H1wJvAs83Gq2VZbv0vLsE/EkeVAn93\nJ9qR8GuVmf1Dzuq4l9ckcV1MJ/qGNQXLAzt52RSBOhviM8voF+ZtV6lrSU0DaxLyGNLZh+s+i2Xf\ngkkEezlzw9DOVtfnV6r5ZPtppvUJJsTbmRHd/HhlVl8C7FBlLqxSU9egp6u/Gw9NimpzPcDe06X0\nDPZkYrSxlPjVRsXmx6ZEERngTmmNMSD3cXMkxMvFqu3O5MU7eGRyFKnFNeTuSSeztI7V940C4O0N\nyYT5uDB3uG27np7BHtwyogsTogOprGsgKbfS9P7FppWSXlzLhJ6BxC6YhLuTitjTpbg6qlh4ZR8m\nRQdyKKsc/xalx3VaHScLq2nQGcxUj1tTWX7u8l74uRv7mT/bcZqiag0vXtHbrtexo2jLV7glsiwz\na8lunFRKRkX6sTu1lJ8bX3trKBWSXQrfAoFAIBAIBP8GbPbwXsqIPqF/Ns//msi0PsF0C3Aj3Kep\n77VGo+PHg9ncPrKLzWynLQ5mlNEjyKjcrNHpGfDKJr64LYaxUQE2j9mbWkLXADczj9TUomq6B3a8\nNzAY+00f/zEBdycVX94xFID9aaWU1jZweb+mmwcfbT1FXYOe1Ydy2P7keJNN0mifIhcAACAASURB\nVJt/nmBa32CTXdIZHvj+EC4OSt6x4UPb56UN9A/34pnp0by7OYUPbhyEVm8w9dr+ejgHf3enVl8r\nMJYTJ+ZWkpRXxde709n25HjT3Jbjhby4JonYBZMsjiuu1jDxnb/49YFRdr+2CdkVeLs4EOHvZjae\nlFvJy2uSmD8uEmeVkmqNliv6h9p1zo6kWq0lt6Lebs/cT/86TXJBFQuv7IOXi0O7yswF547o4e0Y\nxG/zpYXo4f13Inp4BZcKHdLDKxBcKDQ6Pfd/F09GiX09rK9f3Y9tyUW8tMbcIqiwSs0PB7Ko1bTf\n+uTRlQlsPWHsq3RSKYl7YQqxp0v5X2yG1fX5lfXc8uV+Vh4wt6WyFZAl5lS2agNUVmvMep7hvc0p\nZJU2lUdvOV7I7lPFaPUGlsxt6k09nl/FwYwyajU6EnOMx4d6u9A71JPnLo82BbsAGp0BvcHyBtfL\nVxoznAWVarR6y3L1e8ZFcs+4SEK8Xegb5oWXi4OZsNTVg8LbDHa1egPxmeXc8Nk+rh4YalFyGxPh\nw6uz+loctyOlGLVWT+Ir0+wOdgsq1by9Idlqb25FnRatQcbbxYHfj+ax8dj566Vtjd8S8rj323i7\n1983PpIPbhyEj5ujCHZbISG7wuI7KRAIBAKB4N+NCHgFFx2FJOHr5ohDO5Ryn50RzUc3DTIbiwxw\nZ8vj4yw8We3hr6fGc02zXk53JxV7UktZEWtdVCvIw5mPbx7MQxPNy4rzKur5NjbDYn1cZhkbrQS8\nGSW15JTXsfJAFk+vNvqz7kwpZnV8NqW1TX2yvyXkUt+g55f7R5uJNt05uisvX9mHNQm53PH1fnam\nFDNnSDhXDgjl6kHhaHR6k7DVwqv6MDTC1/h8TxaxZHsqAJ7ODtRqdFTVaxnwyiYOZph3LDw8KYre\nIZ4EeTrzzPRolM0Crq/3pJNaVAMYe5OXbE/liVXmLf6JOZX0W7iRnsEeHHxhMs4OSrMSZABvV0cm\n9w6yeH0+2Z5qIazVFkt3nMbVUcUTU3tazI2J8mftg2MY3s2Pp6f15PnLe5n23pJDWeWkFLZtUWQw\nyBzLq2xzXXPmDuvM7w+NadcxgrbJLK3lUFb5xd6GQCAQCASCvxEi4BVcdByUCl6b3Y8wb5e2Fzfi\n7KDsUMXXXaeKWbEvw2xszYOj2fz4OKvrFQqJy/uFWJROZ5TU8svhXKrVWkYv2mYKhG4a1pmNxwo5\nkN4UTH649RSPr0rgP9/EcSirHA8nJV/sSkOhkLhxaGcGNSs9/vjmwdwxuis1Gh1LtqdaCIct2Z5K\nvdbAnV8fQKNrCt6O51XxwPeHePKnI3y1O900rtXLaBrP4eyg5NNbhtAj2INPbxlC/3AvFm86ySd/\npVKj0bErpZhhb2zlxs9jza7ZoDOwJ7WEnPI69qeVMuCVTQzv6svsQeYlwtEhHnw6dwjero78FJfN\nlR/tblP4rFqt5dmfj/LZrUO4xUpPcWs8P7MXH988yOqcTm9g+vs7ic8sI9DTmWAvZ348mMXU93Za\nrPtmTwa/HMpt83pxmeXM+ngP1WrrKtzWUCgkDqSXsS+t1O5jBG0za2AYb19rvTRfIBAIBALBvxPh\nESH4x2IwyGgNBps2RjUaHe9vTkEvy5wsqOZkQTW3jIiw69wrD2Txy+EcRnT148qBoXy3L4vnLu/F\nqO7+/NrdH1mWeXhSdyL83MgoqSXC342PbhpE/3AvCqvUOCgVxGeW8/jUnnTzd6Vea+Db2EyW783g\ns1tjbApSldc28GdiPjcN64yvypEajY74zHJuGtaZb/Zm8MbV/cye76DOPhx9eSpbThQR6GkUMsos\nrcVJpeDxKZbiTuN6GEuT+4UbBbv+OJrHh1tTWXrLELLLmkrONx8v5MmfjnDk5amAMfj9+s6hxDRm\nkJvjoFQwoVEw66qBoXy2I41Vcdlmgex7m1OoqGvg2Rm9yC2vwyAbn+vhrAq8XB0s+o5bw0GpwJYz\nlUqpYO6ILnTxa+rt3XO6lMm9jPuLyyjjvu8OEezpxO8P2ad0PKyrL7ELJrVbNC32dCm+7o4mOyWB\nQCAQCAQCQccjRKsEfyuu/mQPt4+MYPagMJtr5ny6l2sGh9lUBV68OYUQL2fKahvYeqKQX+4fbfNa\nPYI8mDM4nKERPjToDHy5J531iQU8MCGSomoNaxPyTGrEzckuq+NITjnf7MnkiSk9WbY7jY9vHkyV\nWmvW33qqsJop7+1k9zMTTAJbdy+PI9DDidev7md1X7IssyGpgCm9g9oU39qeXMRjqxJY//BYErIr\nmNHPXPn6tXXH8XVz5Ks9Gex8ejyujir+tzeDzccLWTFvOGDsrz2aU4Gbk4qoQA+zkmVZlqlr0OPm\npKKuQcfLa46RkF3Bd/OGc7q4FoMs8+vhHG4dEcGATt6t7vUMOeV1Jt/dM8RnlpNcUIXeIPP+llM4\nKhVM6hXIvrRSRnf35/+s9PfaIrmgirlf7Gfdw2Oob9DTLcC91fVbjhcS5OlMv3AvSms0rIrLZmSk\nPwPtfD4XgqM5FYR4uRDgIdSXLwZCtKpjEL/NlxZCtOrfiRCtElwqtOe3+aIGvJIkTQc+AJTAMlmW\nF7WYnws8A0hANXCfLMttegCLH9VLh1Vx2agUkql/dtOxAvqEebVa3rzrVDHdAtxtrvkpLht/Dyf6\nhnpRXK2hd6h1Jdy04hp83RzxdjXa1NQ36Lnx81im9w3mqoHGgDuzpJZR3f3tei41Gh39F25kWp9g\nPr1lCAnZFXy5O50npvQwUwuuUmtRKSRcHY0FFiU1Gm74LJZv7hxGeV0Dj/2YQH6lmlX3jKBvmDHo\n2nqiEEmCidGWfa56g2wKUouq1Pi6OZoC5a/3pBPh58aWE4XcOTrCTPipqFpNYaWGGo2O278+gGyQ\nuXVkBMO7+TKtTzDF1Rr+TMzn9lERAJTWaLhnRTyjI/14ZFIPFAqJ535JZE1CLlN6B/H+jdbLiO0h\ntaiGyYt38NeT43ByUKI3yOj0MjqDod2q12qtng1JBVTVa1m2O52dT0+wWPPm+hP0C/OyqtBcrdaS\nU15PrxD7FJTPcCyvkgAPJwI9nDmYUYaDUtFhQfOsJXu4vG8w94yL7JDzCdqHCHg7BvHbfGkhAt5/\nJyLgFVwqtOe3+aKVNEuSpASWAFOAHOCgJElrZVk+3mxZOjBOluVySZJmAJ8Dwy/8bgXnC7VWb5ZR\nnNon2Gy+sk7L0dwKMxXgthSBr4vpZPr/1jJiLTN/Lo5K1jxoLiTUPKjW6Q1klNbRPdB6xtDdScV/\nrx1AYZUaADdHJaHezmw8VkBSXpVJZMuzRemrl4sDNw3rjL+7E25OKm4d0YUZfYMZ/dZ21j08huhg\nT5Jyq1AqrAe8zV+/Kz/ezeNTepg8gO8c3RWA7/ZnciLf3DLph/1Z/G9vBgtn9WVEV1/euX4AP8Xl\nUFlv7EXNKqs1lR4/+uNhatU6Vt9rnu1+YGJ3rhkcRkyELzUaHcv3ZtAzxJ1J0ebvIxj7jDVaPYO6\n+DC2u79Z9rp7oDsHnptEYLPsOBhthB5dedgimJ763g4WzOhlKpVujrODktmDwtDpDTa9noM8nPnh\nQBZRgR70DDYPqH87nMtXezLY3sw2qS3WHc3j0ZUJ3Dqyi0lEzN3JgW9jM+kW4GaX967eICPLstWs\n/up7R6IS6swCgUAgEAgE7eZiilYNA1JlWU6TZbkBWAnMar5AluW9siyfkdzcB4Qj+Edx28gIm6XJ\nALtTS0zqxS358WAWT69uM+Hfbhp0Bu7/Lp604hqz8e0ni7nio11WrX3OMGdIOPc3BjdRQR4smNGL\ny3oEcO2Qpo/uzpRixry1jfoGo7iUg1LBvLHdcHFU4uvmyB2ju3Iws5y3r+1Pj8YA9ZHJUTzYTBG6\nvkHPA98fIrei3uz6q+4ZabUcfNntQ7lygHk2864xXRnbI4BQb2cmRgcS6OHMAxO6c33jDYMhXXz5\n4+GxKBUSAzt5k1ZSS8uKkDBvF1Pfbm55PR9tT+U/38SbCWedoW+YFxH+btz7bTynimos5lsGuwBO\nKgW+bpY3LR6Y0J0+Ya1nYFVKBX7u1m943Dk6gtKaBv46WWQa0+j0ZJcZraAettFDbYvJvYJYOX8E\nC2YYVZ9fm92PZ2dEM3tQqKkvui1eXpvEoz8mWJ1zUCqQJGPAm19Zj6GVz2BHk1pUzRUf7aJGo7tg\n1xQIBAKBQCDoKC5mwBsGZDd7nNM4Zov/AOttTUqSNF+SpDhJkuKKi9tnYyI4f/x1soiJ7/511sfP\n7B/C3mcnWp3rGezJ6FbKjY/lVTLglU1U1tmvngugkMDPzQnHFjZJk3sF8teTE8wyqnqDsYe1NdXh\nXiGeZkHPxmMFODso2HLCti/vqrgcFJJk4bmaXFDF/OVxGGQZd0eVRdavi5+bTZGulng4O/DBjYOI\n6eLLHY2ZYFv8Z0w3/npqginoskbPYA+OvDSVHU+Nt7qHcT0CuGZwOIkLp9ldLhwV5MFLjT7BzZk1\nMIxAD2fSS2rPyndVkiRuGxlhVnL866FcrlsaS41G3+6A0tlBSUyEr8VnZmxUAH3DvOw6x/yxkTw6\nufVAW5ZlJr6zg03HL5x/cIC7MzP7heJiSwlMIGgF8dssEAgEgovNJaHSLEnSBIwBr03jSlmWP8dY\n8kxMTMw/T4nrEqVvmBePtDNbllVax/70UlNpsq0ga2An71Z7JCMD3Hltdl88Xdr3MVcpFbw62yiS\npDfI7EwpZnzPACRJItjLPAtZWqvh1XUnGBDu3aY40rJdaRzOrmDx9QM4nFXO/d8dZmJ0kFV7peV3\nDQNg/vI4okM8TYrKbo4qOvu64uqo5K1r+7d6vcScSnqFeJBaXEOtRseQLpYKyu2lvkHP4exyRkUa\nbzQcy6skOtgTpULi+s9imTu8M7MG2r5vVVmvJbuszu4gsC1Si2r4IzGfG4d1No1p9QZUCqnV4Bzg\n5uGdzR7PGRLO+J6BFu/xhaKzn2ubayRJ4o+Hx9DZt+21HYWXqwP3jRe9w4KzQ/w2CwQCgeBiczEz\nvLlAp2aPwxvHzJAkqT+wDJgly7IwrbzE8Hd3MguAfjucy7f7Mls9Jrmgip8P5ZzztZ0dlFw5INQi\n8EktqmHpjtN2nSO9pIZ7VsRzsqCa6z+LpaBSbTYf6OHMoRenmAW7toTgxkT5c92QcJxUSkZ08+fQ\ni1OoqNdaLRU9nFVOfGY594zrhqezik//Mu731i/3M6q7n81gLvZ0KfGZ5ai1euYs3cu+tDLWJuTx\n5a50q+ub06Az8NmO09Q1mO9n+8kiUgqrAdiXXso9y+PRG2TqGnTMXrLH5C18x6gIC/ugugYd7248\nyS+N7+fq+Bwe/P5Qm3vZm1rCrCV72lw3pXcQ3/6nqa2/qFrNNZ/sZemOtDaPbYmDUnFOwa5aa1nG\nfT7oFuDepnq3QCAQCAQCgcDIxfyr6SAQJUlSV0mSHIEbgbXNF0iS1Bn4BbhVluWUi7BHQQejM8jo\n9bbLf8EoXLVy/kirc/GZZQx/Y8s5BRdFVWriMsrbXgh0D/QgaeE0uvi5MaSLD25O5mWdaxJyqVIb\nS6a3JReSXVbHrV8e4L3NKSQXVKHVG0wBbXSwJ5EB7iZRKID7vzvEsl2Wwdm6o/k8s/oojkol4T6u\nplLZp6ZFsyulhB/2Z/LjQfNS3u0ni3jw+0OsistGlmH/gkmMifLnjlERbDhWwIn8KhauPcbGY+al\n1CU1Gqa/v5MHvo9ndXwOpTUNZvOrDmaz61QJABN6BhL34mSUjSrTe5+dxMhIo4/s5f1C2HqikJ0p\nTWWLT60+yp7TJXi5GIW6ErLK6d+p7exu1wA3rhvSdst+y9Lj1/84gb+7I3MGt9YdAScLqlkem9Hm\n+e2lvkHPwP/bxP60v+89uXc3nWzzZpNAIBAIBALBP42LVtIsy7JOkqQHgY0YbYm+kmX5mCRJ9zbO\nLwVeAvyATxozWjphDXFpc60dQUxr9Ajy4LnLe+HcSj9hUm4lX+5O570bBlqdH9Xd32Q11KAz8PaG\nZO4dH4nBIFNep7VQ7T0TbD4zPdpsvEFnYNH6ZEK8XBjW1ZelO9LoHuhGYm4lT07ryYwPdnFjTCdO\nFdWw+r5R7DpVzMK1x5jZP5THp/Tg852nuXdcN4ZF+KLVG3BolrV78Yre6A3HcFBJTO/bpHg8MtKP\nB78/xBX9Qyiu0ZjUmAGigz14dkY0b204yYhuvlw9yPhaB3o688v9o4kO9iDcxwVfN0fUWr3pNXR3\nUjGuRwAZJbX89sBotiYX4eakwtfNaNf06S1DzJ73in1ZXDUglCq1lsjGzPanf53msh7+FFVrzISi\nXpjZC6Uk4dGoTP3ilb2RsF1qXFHXgLerIyFeLtwywraY2RlGv7UND2cVmx4bB8DrV/dDAtycVMRl\nlJGYW2lSqm5OdlkdX+5Ox91JZbLEOhdcHJV8dftQBnY2lti/8FsiswaGMTTi3MvIO4rIAHe8XR3a\nXigQCAQCgUDwD+Ki1sXJsvynLMs9ZFmOlGX59caxpY3BLrIsz5Nl2UeW5YGN/4lg9yJSWqMxla9e\nLDycHVrtEQWjsu+ZjGJbaPUGUopqqNPoWbEvk4Vrj9m9F0eVgtgFkxjW1RjUPDChOxsSC1h22xAG\ndvIm9tlJPD09mv9eNwCAw1kVTOwZyAMTIjmaU8Hx/GokJO5dEc9HW09ZnH/hVX04llvF3cubfCt9\n3Rz56o4Ywn1dLbLgIV4uzB4Uxs3DOuHv5mSmlDywkzeSJDFvbDc6+bjSb+FGU5mys4OSBZf34rPb\nYnBzUvHOxpMk5VaanbuuQce938aTWVbLb4dz2Xu6hEnv7iCvUSX6VGE1pTUNPD09misHhCLLMj8c\nyGLXqRJmfrSbAf+3iVqNjkAPZ5tWUeuO5DH8ja0WAmB6g4zWRlXACzN78eyMphsR7k4qU090cbWG\n9JJas/VVai0fbDnFZT0CuG9cZKt+z+1lVHd/k1iXm5MKx/NUdvzsz0ctsvRn+OFAFvetiLc6N3tQ\nGON7Wto4XWhqNbpWlc4FAoFAIBAIOhLRCCawm83HC3nu18SLvY02iQryYOFVfexa6+akYvldw+js\n58qjk3vwzV1Dz/q6gzp7s/CqPny8/TRJORUs2Z6Ks4OSrv5uAIzu7seh7AocFArWJuRhMMhsPl7I\nm9f0s5qFXJOQS055HVf2DzHrHXZzcqBGrSO7rI4ZH+yirLapBLmuQc+25CLmfxvHrpQSq/sM9nLm\n6zuG0T3Anad+OsLuU+brdj49gctaWOkoJAlfd0dcHJT8/tAYZg0MY9fTEwhtDBgX3zDQ7JityYW8\ntzmFUC9n3prTj+V3DbMqznWGpNxKHl55mGW3xXCqqJqDGU03Vl5dd5yHvj9s9biZ/UOtehMDzOgX\nwv/N6ms2VlGrZdvJIsrrGrhiQCjDu/nZ3BPAn4n5Z1U+v2BGLwa0Iqh2LvQI8iCo0cLpdHENT68+\nYirtHtzZh6ta2E/93bjpi31299CfLVuOF3Ld0r3n9RoCgUAgEAguDS4JlWbB34Mbh3VmQvTFzxB1\nNH8m5hMT4UOghzNOirO3XvF0dmBqn2ASsitRKiXyK+vRGQwYK/Zh5YFs3ByVKBQSL1zRm/SSWj7Z\nnkpXf3eUComE7Aq2nijkiak9AYjPKCfI0xk3JxXj/rudY69MQ6VUMKyrL8O6+lKr0XHVgFDcmwWS\nXi4OrHt4LNVqramM2Bpjoowl3R7OKvIq6vjlUA4ZJbU83njtljg7KHnj6n4Wz/d0cY2prLk5iTlV\nXDsknP3pZcwb263NjHufUE9+uX80Azt589+NyRRUakzlwPPGdkWtbb3v2146+7my5oHRvPBbIoVV\nGr64zXbRSGW9lmd/Psr3d49oU1X6pTVJ+Ls7tdu/92y4a4z5zZHmydKewR4WJfl/N969boDNLH9H\n0TPYw+QnLRD824h49o+LvQWBQCD4WyEyvAK7OZRVzqhF29rta3uudLT6rVZv4PavDnCywFjS+97m\nFA5nVXTIuRetTwagV4gXy24fahZ0XjkglEea+ax29Xfjv9cNMPn61jfoTdnanSlF/H40jz5hnozv\nGciqe0ZSWmsuJuXmpGLOkDDWHc2z2Ie1YPc/3xxkc6N/65mSUm9XRzYdL8THzZFAT2cMBpkFvySS\nVlzT5nP9dl8Gj/+YYHXusSk9uOeySGJPl1JVr6WgUs1bG5Jt+ttKkmSymHpqWjTXxYSTVVoHQLiP\nK90DW7d8ai+PTu7B/81qvQrAy8WBowun2WWhNLlXUKue0OfKsbxKq+ORAe68c90AC7/mvzNRQR54\nuzqe12t08nU12ZoJBAKBQCD4dyMCXoHdDAz3Zs0Do/FqQ/imrkFn0YfZnLyKeoveytYY/sZWmz2L\nZ4NSkugd6mny5938+Dim9Qk2W6PW6tnRTG3YXq4aGMqVA0Kszl3WI8CqF+77W1K4e/lBtp8s4omp\nPVm8OYXtJ4u5fVQE4T6uLPjlKH8czePRlZbB5Yn8arvLQyf2CqRBp+ftDcmMf2c7AA9PimLZ7UMZ\nHenPz4dySMqrRKPVY7BirdSgM5hZKN07LpIV84ZbrDuDl6sDq+8bRSdfV/5v3TG2Jxehawx4v9iZ\nxup429ZTH29LZcOxfLMxWZbZnlzUrv7PynotMz/cZfF583d3IsTL/v7dpNxKJi/eQX2D9Zsvxve2\nyZIpPrOMl9ckoWtDkdweUouqmfnhbnIb+6VLazRUqy/sTSeBQCAQCASCSxUR8ArsRqGQ7Mp23bfi\nEG9vSLY5v3THaRatP9HqORKyK1h5wGi788VtMYyN6rjsmUIh8cz0aB5ZmcD6xHyraw5llXP/ingz\n4Sd7GNzZh0EtvGjb4rIeAQyL8MNBKVFR10Ds6RKenNqTRyf3AGRk2RiYLpk72Oy4wio1lfVak0Kx\nNdRavSlAnDu8C8kF1RRWq3l7zgCzdQ5Kiam9gwnxcmHxDQPpHmgsi61Sa4nPNPbTvrvppJkgkkqp\nwMPZgfLaBp5efYRHVx42s0qqbQyOC6vU+Lg68vrV/UyK186OSpwdbP/zs2LecOZfFtni+Wq4Z0W8\n3TdLKuu1HMoqY2b/EPzcLTOKPxzI4lSjcBfApmMFjHpzqymwPF1cYwpYO/m4cuuILq3ueemO08xd\ntg+AhWuPs/1kMfrGGwcL1x5jS2N2vb10D/TgwHOTTAJbj686wrubhEubQCAQCAQCgT1IspVMzqVO\nTEyMHBcX1/ZCwXkhs7QWV0eVzT49rd6AQZZNirbWWJOQy57UEt6+doDFnFqr5+dDOdwQ0wnVOSjh\nrknIZXBnHzr5ulqdL6pSs/FYAbkVau4bF2mW2V6TkMv+9DKLvlaArNI6lu/LQKsz8PzM3qYgr0aj\nI6WwmsF2BMQrD2SRX6nmsSk9bK7ZfLyQxZtTWP/IWJtrbv5iH4M7+/DkNOu9uW3xy6Ec3t2Uwp5n\nJ1Jao6FWo6ezn/nrVVKj4aU1SYyNCqB3iCcDOnmz93QJd359kCMvT2V7chEfbkttdZ/20tK+qTXW\nJ+az8Pdj7H9uMgAphdV8uSudt67tD8D85XFcMziM6X2NGfmxb20jJsKXRXP64ahU0Puljbx3w0Az\nW6jWyC6ro6BKzdAIX6rVWpxUStN7/9mO0wzu4tMhNkUlNRqcVIpWe7QFHYskSfHCJeDcEb/NFwbR\nwys4FzIWzbzYWxAI7KI9v81CtErQblKLqnFSKW0Gil383Fo93p6AZdbAMJv2Q0u2p/JTXA5TegcR\n6OFsMV9R18CYt7azcn7rYkNt2RtlltaybHc6wZ7OVGu0ZgFvuI8rtRrr2d/Pdp5mbUIe4b4uaHR6\nU9Cz5Xghb64/YQrAWiPQ06nNvsyKugYu62Ge+V57JI9wb2cGN5ZOL7yqD4s3nWRHSjH9w7y4+pM9\nfHXHUCRJws1RSaCn5evXnGsGhzOzvzEg9HN3ws9KK62/uxOfzDX36h0a4ct384bj7KBkRr8QZvQL\n4WhOBYm5lcwd3ra/ri3sDXYBEnMr+foOc9VtqdlL+nkLwaotT4wzuwmz/cnxBHnaL67UydfV9J1o\nGYzeMy7S2iFnhb/7+RV8EggEAoFAIPgnIUqaBe3mnY0pfLk7/aJcW6c38NfJYpbMHWw12AWjENN/\nr+2PWqvnps/32RRKao34zDLu/CaO9Y+M5cd7RhLuYx7cD+niw83DO1s99v9m9WXfcxO5fWQEzg5N\nAdTsQWHseGqCzWvqDTKP/5hAalENE6OD2lSZDfV2oZt/080Fg0HmjT9OMOfTWJOPbo8gD6JDPCmq\nVuPhrOLO0V0J9nLmld+P8fnOtDZfB8AUBKYV13DPt3Es3pRCeQsBrZY4KBXEtMhmZpfVcyjTXBxs\nX1op2WV1du2jveRV1FPfTPCsR5AHi+b0Z01CLo+vsuyHbllxEOzljCSdvRjUu5tOkphjXWzqn4ha\nq2d1fM5Zfd8EAoFAIBAIzhci4BW0m49uHsSLV/Q+r9eorNeSWmSpFKxSKpjUK9AiSEorrmHqeztM\nCtIz+oUQ4u3CqEi/dinYrjuaR0VdA31CvXjvhoG4Ora/CEKpkCir1fL+llOU1hgDw2/2pPP5ztNm\nAXBLJMDJQYG92x3d3Z8bhjYF3QqFxL7nJrH72Ylmme1OPq68v/kUKqWC20dF4OqoYuktQ3hmRrTd\nz8lgkKnX6qlv0PPr4Ww+3HbK7mPPMLN/CO9eb16i/sGWUxaCZEm5lXzWAT6t7984yKpIWDd/d3oH\ne1o9prJey6L1yR2iDF5craG2Qdf2wn8IWWV1vLUhmcp6IaglEAgEAoHg74MIeAXtxkGpMFnpnC++\n25/Joz8etjrn5+ZoUlg+Q6CnM9fHdMLNqSmgDPN2wVGlYEOSpTCV3iCzISnfLBslyzJv/plMYm4l\nzg5KpvQOsnu/b64/YSaA1cnXlX3PTSLYy9m0vyBPZ6rUWovs6BlRKYVCGuxrbwAAIABJREFU4s1r\n+tPNiq+tNWo0Oqv2QWfEjc5w1cBQtj1pLmzl7KA0lQefKqwmuaDKbL6yTktOedNNhV8P53L7VwdB\nkhjfMxBXx7P3K27OD/NHMG9sN7OxkhqNyTLqDNPf38m25CbRJ71BJqNRvCqvop6Fa4+h0xuQZZl9\naSUm4SlrlNc1sGhDMt/GZrB4s7n4U61GR0J2eYcEvIvm9GdEN79zPs+lQo8gDw4+PxlvVwd+isum\n7l8U7AsEAoFAIPj7IgJewd+S+WO78cPdI6zO3ToygonR5sGou5OKeWO7WYhYyRjLZlvauOSU1/HY\nj0fIq2wKjCRJYs+zExkbFdDu/Qa4O+HpYltE6PJ+IcwaGMai9ck8/fNR03h8Zjn9Fm40Ba/adtjY\n/Hgwm/nfxre6prJOS7+FG0nOr7a55pu9GXyx07xE/dMdp3nypyOmxzP7h/D93cNZftcwXp3dj6em\nWc8Orz2Sx57UErv2r9HpefzHBIts/fiegSy+YaDpcXJBFT2C3Okb2pS13pZcxPQPdqI3yKi1evIq\n6tHLMstjM7nz6zh+PWTb8mh0d398XB3IKa/Hv4V6c6i3CyvnjzzvPrH/ZKo1Ot7dlEJGyfkpVRcI\nBAKBQCBoD0K0SvC3RKVU4HEOCsxnmDM4jJkf7ubKAWFmPqld/Nw4/n/TkCSJ/25MZmJ0oNXyV3u5\nPqYT8VnlAGxIyuePxAI+ummQxbpnpkej0xuIzywjvaSWzNI6ltw8GHcnFSM/3sMb1/TjygGhZsf8\nFJfNoM4+dA80z/zePrILcwa3Lrzl5erAp7cM4ZO/UrlrdFeGW8k4vn51P5qrtWt0etYn5lNe15SJ\ndnZQUlytwc1JRZi3CwaDzBe70hjfIwBvN0eCGsWvjuVVEurlwujudtpISeZCUtYoqtJgkDET2JoU\nHciWx8ehVEh0C3A3CVDNHhjGoM7e9A/3tnk+pUIidsGkc1L4FtjG09mBfc9NutjbEAgEAoFAIABE\nhldwiVJR18CHW0+ZfFLPIMsyx/KahIKW7kijZ7CHWbB7hrjMcsa+vY0ajY4G3dkL7egNMsPf2Mrd\n/4ujqEpNZ183lBI8vfqIxVovFwf83J14d1MKsadLkSSJCdGBgFEleGa/EItjNiQVcDyvknVH88xK\nsFVKhV2ZyAk9A+kb6mXmRfvh1lNmZczNxZkclQrmDAnnpSt7M+z1LcSeLiW9pJZbv9zPb42Z0wa9\ngT8S83n9zxPMXbafBp3xfVgwoxe3j4poc09gFIlafP1AC0GwllzWI4CPbzb3IFYoJNNxzYN1L1cH\nq8HuE6uO8MJviRzJrqCuQSeCXYFAIBAIBIJ/CSLDK7gkKattYFtyEXeN6Yp7s+AlIbuCaz7Zy4Hn\nJxPg4cTjU3rY7MfsEejBI5N6cO2QcIu5+Mwy7vz6IPufm4xLG/2qSoXErSO74KxS4KRS8v6WFHIr\n6pl/WTebx3xvpVw7yIZF0Jd3DOV0cQ2zPt7DwE7exGeU4+niYAqUbfH1nnRqNToenBjFQ5OizOZS\ni2psesJKksTDjeudHZT0CfPE1UHJRzcNZkajJ215XQOpRTV8dNNAPtuRToPeYLJfasmZGxB9Qm1b\nRJ3h5i/28eCE7oyyN0MMzF6yhwh/N96a09+mKNi8sV1RKSRu+mI/L17Rq01LKoFAIBAI/o10lI+z\n8PMV/J0QaQ7BJUm3AHd+e2A0D35/iDWHc03jibmVhHq7EOBh9Cp1c1LhZ8O31MvVwWqwC9A7xIv/\nXjegzWD3DEO6+NDZz40qtZbCKjVzh3exGlTFZ5abZSRb8lNcNjtTismvrKeiWUlxZIA7Sa9MI6u0\njsd/OsKKfZlt7inM28WmV/KHNw1iZKRlebPBIFOjMYoN1Wh0HMurQqWQUCkVzOwfYlK8DvJw5t3r\nBjCuRyCr7h2Ju5P5vbP1ifnc9tUBAL7bn8V3+7Os7uNAepnZ6zGpV5DNPdvikUlR7DpVQlYr9ka9\nQjyJCvJgx1Pjrb4vhsZeYGuU1GiYu2wfRdVqjuVV8uth8/5gvUHmQHpZu/YsEAgEAoFAILgwiIBX\ncEnSoDOwN7WE+Mxy/kzKp7YxSLtxaGd+vm+U3ecpr22w6inr4qhkWp9gu88zrU8w1w4Jx8VRSbVG\nx5hmGcoT+VXMXx5HSkE11y3dS0qhpbLyGT7YeoqX1iTxzM+JfLg11WJ+VHd/Dr80hS/vGNrmnqb2\nCW53JvPbfZnM+ng3AHUaHfGZ5dQ1WAaCCoXEjH4hNkuDewZ7cFVjL/IbV/fjjav7meZqNDqGvb6F\nLccLuemLfWSUNgWq/xnTtV0B75bjheRW1HPoxSn0CPJoc72bk/WilmW707huaazVORcHJb1DPHFx\nUHIiv5rtycVm8yfyq7j5i32U1mjs3rdAIBAIBAKB4MIgSpoFHU52WV27s3TtJTG3gju/Ocj2J8dz\nw+ex5JTX0zPYA0eVwmQFZA//t+44BlnmgxstBaY+3naKaX2CibIjkKrR6Bj39nZem92XW4Z3wd+j\nqV92VVw2h7MqcHJQ8PHNg+kZbP18q+NzWPvgaJxUSnQGGafGEuGE7Ar2pZVy77hIwCgKdLac6bW1\nVX589eAwhnU1ljoHejqz6p6Rdp33j6P5jO7uh7uTiis+2s3rV/e1mT13c1Ty7IxoRnX3I+GlKXi0\n8Xy+3pNOhJ8rKoWCPxLzefnKPqbMe7VGS3ndufu+zhkcblNoy81JxfMzjb7T1w4Jt3hefcO8SHh5\nqkWWWyAQCAQCgUBw8RF/oQk6lOSCKqa/v4vYBRMJ8XJp+4CzZEgXX468PBVnByW7np541udZeFUf\no3eRFY7nVzGkiy9RdtjxujkqiQpy5831J/jmzmG4OjZ9tV6+sg8vzuzNtuQiXvgticutCFOptXre\n25xCZIAbgzqbC2yV1zaQWVprNlZcrWH6+zt57vJezLERWFrjhd8S0egMVgN8MAbTniFNAei6I3kg\nwRX9Q62uB6No1Bt/nmDRnH6MjQpg7oguRPi52VwvSRLXDLbc85bjhUQFudOlxbFltQ2otXre3nCS\nidEB6JuVQF89yP7nrjfIPPnTER6c2J3IFl7Hfu5ONkvf7UEEuwKB4FzpqN5JgUAgEJgjtdZPeN4v\nLknTgQ8AJbBMluVFLealxvnLgTrgDlmWD7V13piYGDkuLu487FhgD2nFNXRrEVD8HdAbZE7kV9E3\nrG3xpLNBo9Xz+KojPDSpO9HBnlbXGAyyqQ/2XNAbZB78/hA6vcwXt8dYzJfUaNDpZYtsd055HQYD\ndPaznYFfdTCbkZF+dPJ1ZeSbW1E0+hOfb+Yu28eV/UO5cVhnq/PF1RpTb7a91Gh01Gl0BHo6ozfI\nPP9rIveOiyTC33ZA/k+jsk7L0z8f4bXZ/dr9+gmMSJIUL8uy5RdN0C7Eb3PriIBX8E9CiFYJzjft\n+W2+aD28kiQpgSXADKA3cJMkSb1bLJsBRDX+Nx/49IJuUnBW/B2DXYC4jDJmL9lDjUaHWqtn6Y7T\nqLV69AaZl9Ykkd0oenS2N4GcHJQsmTvYZrALmILdugYd72w8aeo9bklRtbrVaykVEp/eMsRqsAvw\n7qYUFq49ZjEe7uNqEexW1mu55pM9ZDX20q6OzyGlsBqA2AWTWg12H1+VwM6UYpvz7eG7eSNsBrvA\nWQVrH207xcMrDwPG12zRnP5tBrt1DTquW7qX08W2e60vJZRKCV83R1QdcKPlbNDpDWf9nRIIBAKB\nQCA4Vy6maNUwIFWW5TRZlhuAlcCsFmtmActlI/sAb0mSLOtBBZcEF/uP3uHd/Nj33CTcnVRU1Gn5\n7XAumaW15FfWU1KjoaHR0/fapbEs25V2XvdSq9GzL63UasCrN8iMfWs7D35/yNRz2xqXf7CLDUkF\nZmMvXdGbd64fYNdeXByUjI0KwMvVAb1B5sd7RjCpV9t13KlFNXT1c8PXralf+a+TRXwbm2G27o0/\nT/DAd8bCjO0ni1h7JM+ufXUEj0yKYkkLD9+2cFQqGBnpj48dHseXAu5OKt68pj8+bhfu+Xy87RTr\nE/MBuHt5HG9vPGk2fyC9DK2+7c+2QCAQCAQCwblyMQPeMCC72eOcxrH2rgFAkqT5kiTFSZIUV1zc\nMRknQcchyzJDXtvC9uSii7oP/8Y+zWAvZzY8ehnLYzN55ffjfDJ3iKmvc8GMaGb2P3/3VdRaPQEe\nTqy+bxSBVrx3lQqJUG9nDqaXUd9olZNRUsu25ELTmiXbU00BxQMTujOos7fZOVwclTb7SrV6A1tP\nNJ3LUaXgsSk98HJx4OEfDvPK78fteh7XfxZLuK8L+9PLuPPrA+xIKaaiTktRtbla8Q1DO3HvuEjS\nS2o5lltJamP2uC1OFlTz8pokCipbz3a3hqujbVuq5td57tdE02OVUsHjU3qYAvkNSfkUVwsF5vbg\nqFKYFLyfnh7NLSO6mObqGnTc8uV+DmWWX6ztCS4g4rdZIBAIBBebf4wtkSzLn8uyHCPLckxAQMDF\n3o6gBZIk8e51A4j5//buPD6q8twD+O+ZTEIWshCyEJJAIIQlRWQJuyJCcAHvRVuqVAX12irWtepV\nFNtqe63Yxeq9LkitltpSVyyoVMoiLmyyCgSEhFUgkLCFJJCQ5bl/zBCzzGTOTCZzkjO/7+eTDzNz\n3jnznJfknPeZ9z3vm9HJc+EAmjWpH/7QqCc0JyO+1Sbc2rD/JC5+6t91a9268+btw7HsocsQG+GY\nQGr1nhN4Y9X+uu2hIVI3PHrSgBQku0icL9h88FSDNWbzj5Xh7vmbXC6jc9/4LHSNC0fuc5+huLQS\n892snwsASx4Yg8kXpyKhYxjC7DbU1Nbi2kGpeOiKPg3Kna+uRVJMB/xi4XacqajGg422u3O+uhab\nDp7GTa+tNVS+OdsPlzRY17i+WlVUN9Pb+Lslu7AxAMnZC8vysSTvqOeC7cAdYzIxIdsxSqBfSgxS\n4777e4oMs2PzzydgeM+m60CT9fDaTEREZjNzatHDANLrPU9zvuZtGfKTT7Yfxef5xQ3WTPWny/sm\ntcp+W6L+bMrunD1fjbe++hbTRnZHqJt1Z40oOVuF0BAbOnawY1XB8Qbr/L68sgD5x8rwxxsGAnDc\na1vfjcO74cbh393feseYTI+f98aqfchK6og739yIF6YOQq4zAcnuGoO8p65CiIt7Ovt0iUZ8VBj6\ndonBnuIy/HXNfkwdmu5yoq0L99ROHpja7Hq/v/pwB4b2iMfcaTmwhxi/j/SitFi8O2MkTrlJVL3x\ns7e34Mbh3XDb6B5NtvVLicFvp7gf/r38obEe9//x1kIs23ms7v/PFx1CbQj1on7aM3frIRMRERH5\nm5k9vOsBZIlIDxEJAzAVwKJGZRYBmC4OIwCUqGphoAMNFkkxHZCV5N8Jp46cPufX/Zmh6Ewl/rbu\nAEormu+V9eT9TYfwyHtb8aNh3TBn5R4s3laIU+WOZO6y3on4/mD3SeO58zXYfNB9L+PxskoMe3pZ\ng4mWCksqUHKuGutm5dYluxe4SnYvSIzugDG9EzGiZ2d88sCYFs8qPe+/huGB8VmICAvx+guDa19a\nhQ37W9a7ure4DPuOlyG3X+t94dK9cySGZsS3aB8zLsvEuL4G1sCygC/yi3Hjn1rec09ERETkiWkJ\nr6pWA7gHwBIAOwG8o6p5IjJDRGY4iy0GsBdAAYA/AfipKcEGicHdOrnsAfPV9sMlGP3simZnHK6o\nqsGaPSf89pkl56oMlSutaFruw6+P4NNdTe8xzkiIwoqHxjaYnMkXt47KwAd3j8INQ9Nx04ju+M3i\nnfj60GkAwPe6xuLSLPfD/VZ8U4Tb/rIe/9x8GJtcJL6dIsPw8JV9GgwdfXxiP0wakGL6GrFPfZiH\nmQu2+vTeh67og+E94rF6z3EUlvj25UlG5yj85bbhTXrN/al/amyDHnhqXvf4qAYjHIiIiIhai6n3\n8KrqYlXtraqZqvq087U5qjrH+VhV9W7n9otUlQv4tSP9U2Px7wfGICna/f2lX+07iR/PW++XGVuX\n7jiGUc8sR21t87NBnz1fjSG/XoZ1exsm2vnHSrH/eHmL43DHZhNEhtmRHh+JKUPS8OWj4zC2j7Fe\nx0kDUrDq0XFYt+8Edh9tOOlTRVUN7n9rM0ZldkZ4aIjP8Z0oq8S+Vjj+6SMzcOso375ImZCdjKSY\ncDzxwXbc/No6n/ZhswkuyUqAY1nv1vHm2gNYu9d/X9y0Z5sOnsKUV1Y3+3fYrXMkbhmVEbigiIiI\nKGhZZtIqapuykqOb3T6mdyI2/nxCi+6N/W5fCfjHHSM8DsGNDLNj/k+GY3D3hhNoPXhFH7/2cDfn\n+jlrsHibd6PzF2w6hB2FpS7Xqo0Ks8Nuc9ThkdPncNGTS3DghCN5feZfOw0lsq99uQ+z6s1W7C99\nukQju6v7tYmNeP3WoQ0SpNpaxahnluPL/OMtjM4/9haX4dgZ32eT9sXCLYfR6/HFOHTqbEA/15OU\n2HBMyE5u8VB4IiIiIn/gzCFkupb0StbXwR6CAWlxngvCMRNzS1TX1OLyP6zE76dc7NNsszeN6IaL\nUmNRW6soLqtsdpblC3Kzk5Hp4h7r8NAQPDtlQN3z5JhwPH3dRUiNi4CqYv/xcpQZuP/4wQm9UVVT\nizdW7QOAuuS/6EwFTp2tQp8uzX950ZoyEqKQkRBV99xmE8y8ui/eXn8QXePC0TOxI/YfL68rsyTv\nKBZsOoRXp+W43edzS3fjRFklnvbDJG2//I/vtXgf3hrWIx7XXJyCzlHNL7sUaCmxEbjzMs+TqhER\nEREFAnt4ybJUFS99WoCiVuh5s4fY8LPc3oaSwG9Pnm1y/+nkgalIj4/EkryjGPf7lYY+MyU2AqMy\nEzyWq1VFZmIU7CE2iAhenZaDi9JiPb4vNMSGyDA74qPCGtyv/Le1B/DkojxDMQbSpAFdERlmh4gg\n/1gpxv5+JQ47J0nrkRCFMb2bXwJlXN8kTLqo9dZbduVoSUXd+sktlRIbgedvGISIMP98YURERERk\nRezhJcuqqlF8+k0RLuudiCQDPaje+v7gNI9lSs5V4dlPvkHHDnbM/sGAJtsnZCdj0b2X+DWuLwuO\nY8abG7HtySsRZm/+O621e0/g893FeOSqvnWvNV5i6IHc3qiqbfk91kZtPXTaUE99iE0a9GyvfHhs\n3aRdvZOj0dvDcPqB6cZGA/jTxgOn8MpnBbg6wIk2ERERUbBiDy+1ayfKKvH9l1e5nME3zG7De3eN\nQv9Uz72b9c36YBs2HjjZ4tg+212M4b9Zhmeuuwi/mtzfZRl7iA2Zif5dCuryPklYPXOcx2QXcPQG\n13iY5MtmE3SwN+xFPFV+HqNnr0BBUambd/nm25NnMfmlVdhbXIZXVu7B0x/vMPze+kOevVFaUeVx\nojNfLNxyGN8cPdPgtUkDUvDRvZd6tZ+qmloUFJV5LkhERERETTDhpTbBU9LlTlQHOy7plYDo8FC/\nxRLVwe71JFr7j5fj4ImGkweN7NkZb90xEtERoYaST3/q3NHYfZ2jMhPw2MR+Xu8/JiIUd1/eC6lx\n/l3qJz0+Ehtm5aJnYkcMTI/D8B6u748+cvoczlf7p9f52pdW4a9r9nv1noqqGpw7XwMAKCgqw0Pv\nfN0kaV6SdxTbD59x9XavLMk7iuteWtXi/RAREREFIya81CZMnbsGr6zc4/X7wkND8OAVffy61uzj\nE/sZnvzqgheW5+PFT/MbvBZmtzU7bPbONzdg44Gma+q2pqLSimZ7ryurazDxhS+w1bk+sDshNkFc\nZChed05wdUFFVQ0mPPcZvv62+fd/e/JsXcLYWFWNYseRMxiZ2Rm52ckuy0x5ZTXe23io7vm2QyU+\n9za/fNMQfH+I5+Hp9T314Q5MmbMaL67IhwgQGtJ0RuKXbxqCKV7ut7GPtxbiwIlyLH/oMq/eV1BU\nhkueXYGT5edb9PlERERE7R3v4aU24fGJ/ZASG+FyW1llNaprahEXGeZye1vw2ykD0DjlKSw5h1pF\n3X2ljfVJjkZcZNOe6T8u3Y0f5qQhrZN/e08BYPHWQry36VCTYbUFRaXoGheBiNAQTBmS5jbm+uw2\naZLodbDbMH1kd3SLbz72W9/4CtcM6IrocDt+fGnPBtvmf3UQG/afxPyfjHD7/nfvGoXEer3Yc7/Y\ni+ToDnjimmyPcTfmy+zTP8vNwvKdxxBqD0FmYkeX92f7Q4hzOLm396CnxkXgrrGZiI3w38gHIiIi\novZIVP1/75rZcnJydMOGDWaHQX7y839ux5HT5/DnW4eaHYpXHn73a1TV1OKFqYMMv6e2VnHLG1/h\nv6/s43Uv8wXz1x3E0IxObtdArqlVhDRaI/XS367AnWMycfOI7j59preKSytRUFSK3y3ZhffvGgWR\n7+KprVXUqDYZVr754Cmkx0ciwZnoHjp1Fv/cfBj3jMsKSMwUvERko6q6X+OKDOG1uXkZMz82OwQi\nv9k/e5LZIZDFeXNt5pBmavMevqJPq/Wgtab/ubY/nnUT9/GySpev22yCN28f7nOyCwCf7irC3uPl\nLreVnK3CkdNNJ/j68J5L8KNh3bz+rCv/+DlW7iry+n2J0R0wMjMBC346ukGyCzjqwNU91LM+2I4P\nvz5S97yotBJr9p5AIL60e+rDPCyq99lERERE1D5wSDO1ebEuhv26M/fzPdiw/xTmTje/MyY81PX6\nqJsOnsIP56zBpicmeHVsRv2pmWOf+8UerN93Cu/MGImT5ecRHe6YoMvX4eJ3XtYT2V1jfA3VKwvv\nGd0gER7crRP+/mP3w569tWDTIby9/lu8fefIJtt6JnZEcrSxicA82XzwFLp3jmqw1jEREZGV+GvE\nAnuKyR+Y8JKljOubhOwU75Yham3bD5fg5wu34+07RjomskqLw8K7R7dKsuvJfeOzUHHeMbvx9a+u\nwdSh6U3uofWGkbWI/cXbmbO9NTQjHpFhrk+J0/w41PvR97di2ojumDYyw2/7JCIiIiLXmPCSpfRK\nikavJO8nIWpNyTHhuCK7S90ETzabeL02sL90sIfUran72vQcJLrotXzu37uQm53comHV7VF6fCTS\nPUy25Q8f3XtpwJepIiIiIgpWbHURtbLE6A64a2xmk3tVzZaREIUoF8s5FZdVorzS9ZJBnmw7VIKj\nJRUtDc2QymrfYjQbk10iIiKiwGHLiyiIfby1EDe/tq7ueXFpJW4c1h0jMzt7va+CojLc99ZmvLPh\nW3+G6NLqPccx6FdLUVHVPpNeIiIiIgoMJrwUtM5X1/r83pdXFuCBtzb7MRpzfK9rDH4wJLXu+YJN\nh/DEwu0+7Wv/8XJkdI7EveN6+Ss8twZ364THru6LDn7oLT18+hyKS13Pmt1SFVU1AZlFmoiIiIhc\nY8JLQWlvcRn6P7nE5RI9RoztnYQpQ9J9eu/017/CxgOnfHqvv2UkROG6Qd9NPHXHmJ5418UsxUbk\nZifjjduGBWTo9pmKKvxyUR52Fpa2eF9PLcrD88t2+yGqpia/uArzVu9vlX0TERERkWectIqCUkbn\nKLx68xCkxIb79P6WLMWT070Tkvy0xI2/iQjC7N4nrDe8ugbXDUrFVDdr+ZZWVCE63LdZqfcUlyEl\nNrzBDMpJ0eHY8MQEvyzt88cbBiLE5t0xF5VW4JcL8/DslAGIaea4/nD9xUjrFNHSEImIiIjIR6b0\n8IpIvIgsFZF857+dXJRJF5FPRWSHiOSJyP1mxErWZLMJLu+bZMpEUveNzwrIbMCBNOOyTIzuleBy\nW2HJOQz81VLsPuZbb+xtb6zH+5sON3ndX+vYRnWwu10z2R27zYbYiFCEePj96Z8a6/Max0RERETU\ncmYNaZ4JYLmqZgFY7nzeWDWAh1Q1G8AIAHeLSHYAYyQigy7vm+Q2iU+JjcA/fjICWUkdfdr3wrtH\n40Y3PcdmiY8Kw+wfDHA5yzURERERtR1mJbyTAcxzPp4H4NrGBVS1UFU3OR+XAtgJILVxOSJq+4b1\niPe5N71TVJjXQ46JiIiIiADzEt5kVS10Pj4KILm5wiKSAWAQgHXNlLlDRDaIyIbi4mJ/xUlkSaqK\n7YdL/La/8spqlFVW+21/RGQNvDYTEZHZWi3hFZFlIrLdxc/k+uXUsWaH23U7RKQjgPcBPKCqZ9yV\nU9W5qpqjqjmJiYl+Ow4KHjsLz2D4b5ah5FyV2aG0ut3HyvAfL36JoyUVftnfLxbm4bEF2/yyLwA4\nffa83/ZlprPn+SUABTdem4mIyGytdgOaqua62yYix0QkRVULRSQFQJGbcqFwJLt/V9UFrRQqEQCg\ne+dIPHxFH8SEW/++zD5dorF+Vi4SOvpntujHJvZFrYf1ZiuqarDraCkuTo9rtlxNrWLkMyvwfz8a\nhNzsZgd/tGlbvj2N6+eswfpZuYiN9G2GaiJq+zJmfmx2CERE1AyzWvaLANwCYLbz34WNC4jjhr8/\nA9ipqs8FNjwKRpFhdvwwx7e1ddsjfyW7Rve1clcR/vu9rdj25JXNlguxCd6dMRK9k6P9FZ5Hqur3\nGbv7d43BvP8axmSXiIjIR/76Qmn/7El+2Q+1T2bdwzsbwAQRyQeQ63wOEekqIoudZUYDmAZgnIhs\ncf5MNCdcImqpq/qnYPXMcYbK9k+NRZg9MKen/12ej+mvf9XgtTMVVaitbb7H2hN7iA0jMzu3aB9E\nRERE1DKm9PCq6gkA4128fgTAROfjLwFwalYiL5wsP48rn/8c8388HFkB7CE1Kjq87fV2XjcoFZdm\nNVxD+D//70vcfmlPTBvR3aSoiIiIiMgfrH+zIlEQiYsIxaNX9XW7Ji41lR4f2aS+5kwbgrROrEMi\nIiKi9o4JL5GF2GyCKUPSzA6j3evbJcbsEIiIiMhPeC9wcDPrHl4iIiIiIiKiVsUeXqJ2aN/xckSE\nhqBLbLjZoRAREREFBfYUt0/s4SVqh57+eCfmfLbH7DCIiIiIiNqJIwSWAAAJ9ElEQVQ09vAStUMv\n3jgIITZOYk5ERERE1BwmvETtUHhoiNkheK2mVvHpN0UY3y8JIkzWichc/hqaSEREbRuHNBNRQOw7\nXo57/rEJx85Umh0KEREREQUJ9vASUUD0SuqIvKeu4lBsIiIiIgoY9vASUcAw2SUiIiKiQGLCS0RE\nRERERJbEhJeIiIiIiIgsiffwElG7o6pYuuMYxvVNgj2E39sRBRPOrkxE5OCP8+H+2ZP8EEnbxoSX\niNqdY2cqcd9bm/HRvZeiV1JHs8MhogAKhsYZEZERPB8aw4SXiNqdLrHh2PHUVbBxEiwiIiIiagbH\nAhJRu8Rkl4iIiIg8YcJLRERERERElsSEl4iIiIiIiCzJlIRXROJFZKmI5Dv/7dRM2RAR2SwiHwUy\nRiIiIiIiImrfzOrhnQlguapmAVjufO7O/QB2BiQqIiIiIiIisgyzEt7JAOY5H88DcK2rQiKSBmAS\ngNcCFBcRERERERFZhFkJb7KqFjofHwWQ7Kbc8wAeAVDraYcicoeIbBCRDcXFxX4Kk4iIiHzFazMR\nEZmt1RJeEVkmIttd/EyuX05VFYC6eP81AIpUdaORz1PVuaqao6o5iYmJ/jkIIiIi8hmvzUREZDZx\n5JsB/lCRXQDGqmqhiKQAWKmqfRqVeQbANADVAMIBxABYoKo3G9h/MYAD9V5KAHDcX/FbHOvKONaV\nMawn41hXxgWyrrqrKrO1FnJxbW6OVf8WeFztj1WPzarHBVj32HhcDRm+NpuV8P4OwAlVnS0iMwHE\nq+ojzZQfC+BhVb3Gx8/boKo5vkUbXFhXxrGujGE9Gce6Mo51ZW1W/f/lcbU/Vj02qx4XYN1j43H5\nzqx7eGcDmCAi+QBync8hIl1FZLFJMREREREREZGF2M34UFU9AWC8i9ePAJjo4vWVAFa2emBERERE\nRERkGWb18AbaXLMDaEdYV8axroxhPRnHujKOdWVtVv3/5XG1P1Y9NqseF2DdY+Nx+ciUe3iJiIiI\niIiIWluw9PASERERERFRkGHCS0RERERERJZkmYRXRK4SkV0iUuBc6qjxdhGR/3Vu3yoig82Isy0w\nUFc3Oetom4isFpGLzYizLfBUV/XKDRWRahGZEsj42hIjdSUiY0Vki4jkichngY6xrTDwNxgrIh+K\nyNfOurrNjDjNJiKvi0iRiGx3s53ndYsQkXgRWSoi+c5/OzVTNkRENovIR4GM0RdGjktE0kXkUxHZ\n4fx7v9+MWI2wclvLqm0jq7ZjrNzmsGobwdRruqq2+x8AIQD2AOgJIAzA1wCyG5WZCOBfAATACADr\nzI67DdfVKACdnI+vZl25r6t65VYAWAxgitlxt9W6AhAHYAeAbs7nSWbH3Ybr6nEAzzofJwI4CSDM\n7NhNqKsxAAYD2O5mO8/rFvkB8FsAM52PZ174/XdT9kEA8wF8ZHbc/jguACkABjsfRwPY7epaY/aP\nldtaVm0bWbUdY+U2h5XbCGZe063SwzsMQIGq7lXV8wDeAjC5UZnJAP6qDmsBxIlISqADbQM81pWq\nrlbVU86nawGkBTjGtsLI7xUA3AvgfQBFgQyujTFSVzcCWKCqBwFAVYO1vozUlQKIFhEB0BGOi1l1\nYMM0n6p+Dsexu8PzunVMBjDP+XgegGtdFRKRNACTALwWoLhayuNxqWqhqm5yPi4FsBNAasAiNM7K\nbS2rto2s2o6xcpvDsm0EM6/pVkl4UwF8W+/5ITS9WBgpEwy8rYfb4fi2JRh5rCsRSQVwHYBXAhhX\nW2Tk96o3gE4islJENorI9IBF17YYqasXAfQDcATANgD3q2ptYMJrV3het45kVS10Pj4KINlNuecB\nPAKgvfw9GD0uAICIZAAYBGBd64blEyu3tazaNrJqO8bKbY5gbiO02vnD7o+dkDWJyOVwnNQvMTuW\nNux5AI+qaq3jizZqhh3AEADjAUQAWCMia1V1t7lhtUlXAtgCYByATABLReQLVT1jblhEvhORZQC6\nuNg0q/4TVVURabJmoohcA6BIVTeKyNjWidJ7LT2uevvpCEcv2wP8W2+7LNg2smo7xsptDrYRvGSV\nhPcwgPR6z9Ocr3lbJhgYqgcRGQDHkLGrVfVEgGJra4zUVQ6At5wXiQQAE0WkWlX/GZgQ2wwjdXUI\nwAlVLQdQLiKfA7gYjvvVgomRuroNwGx13NRSICL7APQF8FVgQmw3eF5vR1Q11902ETkmIimqWugc\nwuZq+OFoAP8pIhMBhAOIEZG/qerNrRSyIX44LohIKBzJ7t9VdUErhdpSVm5rWbVtZNV2jJXbHMHc\nRmi184dVhjSvB5AlIj1EJAzAVACLGpVZBGC6cwawEQBK6g0zCiYe60pEugFYAGCaRb4J85XHulLV\nHqqaoaoZAN4D8NM2fpFoLUb+BhcCuERE7CISCWA4HPeqBRsjdXUQjm+lISLJAPoA2BvQKNsHntet\nYxGAW5yPb4HjfNGAqj6mqmnO8+1UACvMTnYN8Hhczvvw/gxgp6o+F8DYvGXltpZV20ZWbcdYuc0R\nzG2EVjt/WKKHV1WrReQeAEvgmN3sdVXNE5EZzu1z4Jh5biKAAgBn4fh2JOgYrKtfAOgM4GXnN37V\nqppjVsxmMVhXBGN1pao7ReQTAFvhuP/uNVV1OTW9lRn8vfo1gL+IyDY4Zit8VFWPmxa0SUTkHwDG\nAkgQkUMAfgkgFOB53YJmA3hHRG4HcADA9QAgIl3hOFdMNDO4FjByXKMBTAOwTUS2ON/3uKouNiNg\nd6zc1rJq28iq7Rgrtzms3EYw85oujt5wIiIiIiIiImuxypBmIiIiIiIiogaY8BIREREREZElMeEl\nIiIiIiIiS2LCS0RERERERJbEhJeIiIiIiIgsiQkvUZAQkTgR+Wm955+IyGkR+cjMuIiIiIJV/Wuz\niAwUkTUikiciW0XkBrPjI7ICLktEFCREJAPAR6ra3/l8PIBIAHeq6jUmhkZERBSU6l+bRaQ3AFXV\nfOdazRsB9FPV02bGSNTesYeXKHjMBpApIltE5HequhxAqdlBERERBbG6azOAn6hqPgCo6hEARQAS\nzQyOyArsZgdARAEzE0B/VR1odiBEREQEwM21WUSGAQgDsMeUqIgshAkvEREREVEbISIpAN4EcIuq\n1podD1F7xyHNRERERERtgIjEAPgYwCxVXWt2PERWwISXKHiUAog2OwgiIiKqU3dtFpEwAB8A+Kuq\nvmdqVEQWwlmaiYKIiMwHMADAvwCMANAXQEcAJwDcrqpLTAyPiIgo6NS7NkcBSAOQV2/zraq6xZTA\niCyCCS8RERERERFZEoc0ExERERERkSUx4SUiIiIiIiJLYsJLRERERERElsSEl4iIiIiIiCyJCS8R\nERERERFZEhNeIiIiIiIisiQmvERERERERGRJ/w9YcQx6t1e5VQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "result.plot_pairs();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that if working in a non-interactive environment, you can use e.g. `plt.savefig('pairs.png')` after an ELFI plotting command to save the current figure to disk."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sequential Monte Carlo ABC"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Rejection sampling is quite inefficient, as it does not learn from its history. The sequential Monte Carlo (SMC) ABC algorithm does just that by applying importance sampling: samples are *weighed* according to the resulting discrepancies and the next *population* of samples is drawn near to the previous using the weights as probabilities. \n",
+ "\n",
+ "For evaluating the weights, SMC ABC needs to be able to compute the probability density of the generated parameters. In our MA2 example we used custom priors, so we have to specify a `pdf` function by ourselves. If we used standard priors, this step would not be needed. Let's modify the prior distribution classes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# define prior for t1 as in Marin et al., 2012 with t1 in range [-b, b]\n",
+ "class CustomPrior_t1(elfi.Distribution):\n",
+ " def rvs(b, size=1, random_state=None):\n",
+ " u = scipy.stats.uniform.rvs(loc=0, scale=1, size=size, random_state=random_state)\n",
+ " t1 = np.where(u<0.5, np.sqrt(2.*u)*b-b, -np.sqrt(2.*(1.-u))*b+b)\n",
+ " return t1\n",
+ " \n",
+ " def pdf(x, b):\n",
+ " p = 1./b - np.abs(x) / (b*b)\n",
+ " p = np.where(p < 0., 0., p) # disallow values outside of [-b, b] (affects weights only)\n",
+ " return p\n",
+ "\n",
+ " \n",
+ "# define prior for t2 conditionally on t1 as in Marin et al., 2012, in range [-a, a]\n",
+ "class CustomPrior_t2(elfi.Distribution):\n",
+ " def rvs(t1, a, size=1, random_state=None):\n",
+ " locs = np.maximum(-a-t1, t1-a)\n",
+ " scales = a - locs\n",
+ " t2 = scipy.stats.uniform.rvs(loc=locs, scale=scales, size=size, random_state=random_state)\n",
+ " return t2\n",
+ " \n",
+ " def pdf(x, t1, a):\n",
+ " locs = np.maximum(-a-t1, t1-a)\n",
+ " scales = a - locs\n",
+ " p = scipy.stats.uniform.pdf(x, loc=locs, scale=scales)\n",
+ " p = np.where(scales>0., p, 0.) # disallow values outside of [-a, a] (affects weights only)\n",
+ " return p\n",
+ " \n",
+ " \n",
+ "# Redefine the priors\n",
+ "t1.become(elfi.Prior(CustomPrior_t1, 2, model=t1.model))\n",
+ "t2.become(elfi.Prior(CustomPrior_t2, t1, 1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Run SMC ABC\n",
+ "\n",
+ "In ELFI, one can setup a SMC ABC sampler just like the Rejection sampler:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "smc = elfi.SMC(d, batch_size=10000, seed=seed)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For sampling, one has to define the number of output samples, the number of populations and a *schedule* i.e. a list of quantiles to use for each population. In essence, a population is just refined rejection sampling."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:elfi.methods.parameter_inference:---------------- Starting round 0 ----------------\n",
+ "INFO:elfi.methods.parameter_inference:---------------- Starting round 1 ----------------\n",
+ "INFO:elfi.methods.parameter_inference:---------------- Starting round 2 ----------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.36 s, sys: 241 ms, total: 1.6 s\n",
+ "Wall time: 1.62 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "N = 1000\n",
+ "schedule = [0.7, 0.2, 0.05]\n",
+ "%time result_smc = smc.sample(N, schedule)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can have summaries and plots of the results just like above:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Method: SMC\n",
+ "Number of posterior samples: 1000\n",
+ "Number of simulations: 190000\n",
+ "Threshold: 0.0492\n",
+ "Posterior means for final population: t1: 0.552, t2: 0.205\n"
+ ]
+ }
+ ],
+ "source": [
+ "result_smc.summary"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `Sample` object returned by the SMC-ABC sampling contains also some methods for investigating the evolution of populations, e.g.:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Posterior means for population 0: t1: 0.547, t2: 0.232\n",
+ "Posterior means for population 1: t1: 0.559, t2: 0.23\n",
+ "Posterior means for population 2: t1: 0.552, t2: 0.205\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "result_smc.posterior_means_all_populations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ ""
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "result_smc.plot_marginals_all_populations(bins=25, figsize=(8, 2), fontsize=12)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Obviously one still has direct access to the samples as well, which allows custom plotting:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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777rxlJnN0mvr7rwTatSQtk4IZ5GcDN9+qzvFQc6fyFPmSquta98e2raVIlzC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MxbtmzVwH00wsu5kyBU6fVtMtEYFatVxn+jlzYOdO39FEa/ZsN0OkGRQRMcZ97vnkE1i92nc0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\nIpLV/j+Y70cbg50sOAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "n_populations = len(schedule)\n",
+ "fig, ax = plt.subplots(ncols=n_populations, sharex=True, sharey=True, figsize=(16,6))\n",
+ "samples = [pop.samples_list for pop in result_smc.populations]\n",
+ "for ii in range(n_populations):\n",
+ " s = samples[ii]\n",
+ " ax[ii].scatter(s[0], s[1], s=5, edgecolor='none');\n",
+ " ax[ii].set_title(\"Population {}\".format(ii));\n",
+ " ax[ii].plot([0, 2, -2, 0], [-1, 1, 1, -1], 'b')\n",
+ "ax[0].set_xlabel(result_smc.names_list[0]);\n",
+ "ax[0].set_ylabel(result_smc.names_list[1]);\n",
+ "ax[0].set_xlim([-2, 2])\n",
+ "ax[0].set_ylim([-1, 1]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It can be seen that the populations iteratively concentrate more and more around the true parameter values.\n",
+ "\n",
+ "Note that for the later populations some of the samples lie outside allowed region. This is due to the SMC algorithm sampling near previous samples, with *near* meaning a Gaussian distribution centered around previous samples with variance as twice the weighted empirical variance. However, the outliers carry zero weight, and have no effect on the estimates."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's it! See the other documentation for more advanced topics on e.g. BOLFI, external simulators and parallelization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [default]",
+ "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": "3.5.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}