From d10e8c1c9394be3043236c829bea37a37e66239f Mon Sep 17 00:00:00 2001 From: elseml <60779710+elseml@users.noreply.github.com> Date: Mon, 8 May 2023 14:46:40 +0200 Subject: [PATCH 1/6] Add model comparison tutorial notebooks --- README.md | 2 + .../Hierarchical_Model_Comparison_MPT.ipynb | 958 ++++++++++++++ .../Model_Comparison_MPT.ipynb | 1114 +++++++++++++++++ docs/source/tutorial_notebooks/img/1HT2HT.png | Bin 0 -> 113595 bytes 4 files changed, 2074 insertions(+) create mode 100644 docs/source/tutorial_notebooks/Hierarchical_Model_Comparison_MPT.ipynb create mode 100644 docs/source/tutorial_notebooks/Model_Comparison_MPT.ipynb create mode 100644 docs/source/tutorial_notebooks/img/1HT2HT.png diff --git a/README.md b/README.md index 02670724c..169f289ae 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,8 @@ For starters, check out some of our walk-through notebooks: 2. [Principled Bayesian workflow for cognitive models](docs/source/tutorial_notebooks/LCA_Model_Posterior_Estimation.ipynb) 3. [Posterior estimation for ODEs](docs/source/tutorial_notebooks/Linear_ODE_system.ipynb) 4. [Posterior estimation for SIR-like models](docs/source/tutorial_notebooks/Covid19_Initial_Posterior_Estimation.ipynb) +5. [Model comparison for cognitive models](docs/source/tutorial_notebooks/Model_Comparison_MPT.ipynb) +6. [Hierarchical model comparison for cognitive models](docs/source/tutorial_notebooks/Model_Comparison_MPT.ipynb) ## Project Documentation diff --git a/docs/source/tutorial_notebooks/Hierarchical_Model_Comparison_MPT.ipynb b/docs/source/tutorial_notebooks/Hierarchical_Model_Comparison_MPT.ipynb new file mode 100644 index 000000000..72f1d0ae7 --- /dev/null +++ b/docs/source/tutorial_notebooks/Hierarchical_Model_Comparison_MPT.ipynb @@ -0,0 +1,958 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Amortized Hierarchical Model Comparison Workflow for Cognitive Modeling\n", + "by Lasse Elsemüller" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 2: Hierarchical Model Comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from functools import partial\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "from scipy import stats\n", + "\n", + "import bayesflow as bf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Confirm that GPU is available for training\n", + "tf.config.list_physical_devices(\"GPU\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction \n", + "\n", + "This is the second part of the tutorial series covering amortized model comparison with BayesFlow! The general workflow, the scenario and the cognitive models were introduced in [part 1](./Model_Comparison_MPT.ipynb) and are assumed to be known, so here we will focus on the new elements introduced when comparing hierarchical models.\n", + "\n", + "In [part 1](./Model_Comparison_MPT.ipynb), we only conducted model comparison for a single participant at a time. Let us now consider all participants and their nested observations simultaneously in our model comparison!" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generative Model Definition" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To extend our MPT models to hierarchical ones, we need to introduce a superordinate level that encodes our assumptions about the relationships between individuals. We use the most popular hierarchical MPT framework, the latent-trait approach by [Klauer (2010)](https://link.springer.com/article/10.1007/s11336-009-9141-0). Here, we replace our non-hierarchical Beta priors by a multivariate normal distribution, which allows us to model correlations between our parameters. We afterwards use the cumulative distribution function of the standard normal distribution, $\\Phi$, to transform from the real-line to probabilities. Let's write out our new model components explicitly with $m \\in M$ denoting the participants:" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{align}\n", + "\\left[ \\begin{array}{l} d_m' \\\\ g_m' \\end{array} \\right] \n", + "&\\sim \\mathcal{N} \\left( \n", + "\\left[\\begin{array}{l} \\mu_{d} \\\\ \\mu_{g} \\end{array} \\right], \\Sigma\n", + "\\right) \\text{ for } m=1,\\dots,M\\\\\n", + "d_m &= \\Phi(d_m') \\text{ for } m=1,\\dots,M\\\\\n", + "g_m &= \\Phi(g_m') \\text{ for } m=1,\\dots,M\\\\\n", + "\\end{align}\n", + "$$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hyperpriors and Priors" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have to define hyperpriors for the parameters of the multivariate normal prior distribution. For the covariance matrix $\\Sigma$, the latent-trait approach employs a scaled inverse Wishart distribution. The $Q$ parameter controls the correlation between our parameters $d$ and $g$, while the variances are determined jointly with the scaling parameters $\\lambda$.\n", + "\n", + "$$\n", + "\\begin{align}\n", + "\\mu_{d} &\\sim \\mathcal{N}(0, 0.25) \\\\\n", + "\\mu_{g} &\\sim \\mathcal{N}(0, 0.25) \\\\\n", + "Q &\\sim InvWishart(\\mathbb{I}, 10)\\\\\n", + "\\lambda_p &\\sim \\textrm{Uniform}(0, 3) \\text{ for } p= d', g'\\\\\n", + "\\Sigma &= \\textrm{Diag}(\\lambda_p) Q \\textrm{Diag}(\\lambda_p)\\\\\n", + "\\end{align}\n", + "$$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we choose our priors to reflect our belief that the hierarchical models should generate data patterns similar to their non-hierarchical counterparts. Remember that Bayesian model comparison penalizes predictive flexibility and expects you to encode your theoretical assumptions in all parts of your models. Therefore, using flat/very weakly informative priors as you may do in parameter estimation won't give you the results you are looking for here. \n", + "\n", + "Things can get a little confusing for these hierarchical model formulations, so let's have a look at the role of our prior choices:\n", + "- $\\mu_d$ and $\\mu_g$: A zero-centered normal distribution on the probit scale translates to participant mean values centered around 0.5 on the probability scale.\n", + "- $Q$: An inverse Wishart distribution with an identity scale matrix centers the expected correlations between $d$ and $g$ at 0, while the 10 degrees of freedom encode our belief that high correlations are rather unlikely (see below for a visualization).\n", + "- $\\lambda$: We keep the values of this auxiliary scaling parameter rather low to limit the amount of variability that we introduce into our models." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q = stats.invwishart.rvs(df=10, scale=np.identity(2), size=5000)\n", + "corrs = Q[:, 0, 1] / (np.sqrt(Q[:, 0, 0] * Q[:, 1, 1]))\n", + "f, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "sns.histplot(corrs, kde=True, color=\"#8f2727\", alpha=0.9, ax=ax)\n", + "ax.set_title(\"Inverse Wishart Correlation Prior\")\n", + "sns.despine(ax=ax)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now follow the same steps as in [part 1](./Model_Comparison_MPT.ipynb) to translate our prior into code:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "PARAM_NAMES = [r\"$\\mu_d$\", r\"$\\mu_g$\", r\"$\\Sigma_{00}$\", r\"$\\Sigma_{01}$\", r\"$\\Sigma_{10}$\", r\"$\\Sigma_{11}$\"]\n", + "RNG = np.random.default_rng(2023)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def hierarchical_prior_fun(rng=None):\n", + " \"Samples a random parameter configuration from the hierarchical prior distribution.\"\n", + "\n", + " if rng is None:\n", + " rng = np.random.default_rng()\n", + "\n", + " mu_d = rng.normal(0, 0.25)\n", + " mu_g = rng.normal(0, 0.25)\n", + " Q = stats.invwishart.rvs(df=10, scale=np.identity(2), random_state=rng)\n", + " lambdas = rng.uniform(0, 3, size=2)\n", + " sigma = np.matmul(np.matmul(np.diag(lambdas), Q), np.diag(lambdas))\n", + " return np.concatenate([np.r_[mu_d, mu_g], sigma.flatten()])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'prior_draws': array([[ 0.33133578, -0.15124493, 0.19373801, -0.0171947 , -0.0171947 ,\n", + " 1.20265298]]),\n", + " 'batchable_context': None,\n", + " 'non_batchable_context': None}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prior = bf.simulation.Prior(prior_fun=hierarchical_prior_fun, param_names=PARAM_NAMES)\n", + "prior(batch_size=1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Simulators" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, it is important to stress again our new definition of a data set: In [part 1](./Model_Comparison_MPT.ipynb), we analyzed participants separately, so each data set contained a single participant. With hierarchical models, we can now take all of our experimental data simultaneously into consideration, so each data set contains several participants with nested observations each. We could also have other hierarchical models, such as students nested into classes or employees nested into organizations, so we refer to the higher order units with the general term *groups*. For this tutorial, we consider a scenario with 50 participants performing 100 trials each.\n", + "\n", + "We continue our known workflow by first specifying simulator functions, then creating our generative models with the ``GenerativeModel`` wrapper and finally combining them with the ``MultiGenerativeModel`` wrapper." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "N_GROUPS = 50\n", + "N_OBS = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "def hierarchical_mpt_simulator(theta, model, num_groups, num_obs, rng=None, *args):\n", + " \"\"\"Simulates data from a hierarchical 1HT or 2HT MPT model, assuming equal proportions of old and new stimuli.\n", + "\n", + " Parameters\n", + " ----------\n", + " theta : np.ndarray of shape (num_parameters)\n", + " Contains draws from the prior distribution for each parameter.\n", + " model : str, either \"1HT\" or \"2HT\"\n", + " Decides the model to generate data from.\n", + " num_obs : int\n", + " The number of groups (participants).\n", + " num_obs : int\n", + " The number of observations (trials) per group.\n", + "\n", + " Returns\n", + " -------\n", + " X : np.ndarray of shape (num_groups, num_obs, 3)\n", + " The generated data set. Contains two columns:\n", + " 1. Stimulus type (0=\"new\", 1=\"old\")\n", + " 2. Response (0=\"new\", 1=\"old\")\n", + " \"\"\"\n", + "\n", + " if rng is None:\n", + " rng = np.random.default_rng()\n", + "\n", + " obs_per_condition = int(np.ceil(num_obs / 2))\n", + "\n", + " mu_d, mu_g = theta[:2]\n", + " sigma = np.reshape(theta[2:], (2, 2))\n", + "\n", + " # Draw vectors containing individual parameters and transform to probabilities\n", + " params = rng.multivariate_normal([mu_d, mu_g], sigma, size=num_groups)\n", + " d = stats.norm.cdf(params[:, 0])\n", + " g = stats.norm.cdf(params[:, 1])\n", + "\n", + " # Compute category probabilities per model\n", + " if model == \"1HT\":\n", + " p_11 = d + (1 - d) * g\n", + " p_10 = (1 - d) * (1 - g)\n", + " p_01 = g\n", + " p_00 = 1 - g\n", + "\n", + " if model == \"2HT\":\n", + " p_11 = d + (1 - d) * g\n", + " p_10 = (1 - d) * (1 - g)\n", + " p_01 = (1 - d) * g\n", + " p_00 = d + (1 - d) * (1 - g)\n", + "\n", + " # Assert that category probabilities sum to 1\n", + " assert np.all(np.isclose((p_11 + p_10, p_01 + p_00), 1)), \"Category probabilities do not sum to 1!\"\n", + "\n", + " # Create vectors of stimulus types\n", + " stims_single = np.repeat([[1, 0]], repeats=obs_per_condition, axis=1) # For 1 participant\n", + " stims_data_set = np.repeat(stims_single, repeats=num_groups, axis=0) # For all participants\n", + "\n", + " # Simulate responses\n", + " resp_1 = rng.binomial(n=1, p=p_11, size=(obs_per_condition, num_groups)).T\n", + " resp_0 = rng.binomial(n=1, p=p_01, size=(obs_per_condition, num_groups)).T\n", + " resp = np.concatenate((resp_1, resp_0), axis=1)\n", + "\n", + " # Create final data set\n", + " data = np.stack((stims_data_set, resp), axis=2)\n", + "\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Performing 2 pilot runs with the 1HT model...\n", + "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 6)\n", + "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 50, 100, 2)\n", + "INFO:root:No optional prior non-batchable context provided.\n", + "INFO:root:No optional prior batchable context provided.\n", + "INFO:root:No optional simulation non-batchable context provided.\n", + "INFO:root:No optional simulation batchable context provided.\n", + "INFO:root:Performing 2 pilot runs with the 2HT model...\n", + "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 6)\n", + "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 50, 100, 2)\n", + "INFO:root:No optional prior non-batchable context provided.\n", + "INFO:root:No optional prior batchable context provided.\n", + "INFO:root:No optional simulation non-batchable context provided.\n", + "INFO:root:No optional simulation batchable context provided.\n" + ] + } + ], + "source": [ + "model_1ht = bf.simulation.GenerativeModel(\n", + " prior=prior,\n", + " simulator=partial(hierarchical_mpt_simulator, model=\"1HT\", num_groups=N_GROUPS, num_obs=N_OBS),\n", + " name=\"1HT\",\n", + " simulator_is_batched=False,\n", + ")\n", + "\n", + "model_2ht = bf.simulation.GenerativeModel(\n", + " prior=prior,\n", + " simulator=partial(hierarchical_mpt_simulator, model=\"2HT\", num_groups=N_GROUPS, num_obs=N_OBS),\n", + " name=\"2HT\",\n", + " simulator_is_batched=False,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We added the 'group'-dimension to our data sets, so we now have our data in a 4-dimensional format with the shape (number of data sets, number of groups/participants, number of observations, number of variables):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of data batch: (5, 50, 100, 2)\n", + "First 3 rows of first 2 participants in first data set:\n", + "[[[1 1]\n", + " [1 1]\n", + " [1 0]]\n", + "\n", + " [[1 0]\n", + " [1 1]\n", + " [1 1]]]\n" + ] + } + ], + "source": [ + "model_output = model_1ht(batch_size=5)\n", + "print(\"Shape of data batch:\", model_output[\"sim_data\"].shape)\n", + "print(\"First 3 rows of first 2 participants in first data set:\")\n", + "print(model_output[\"sim_data\"][0, :2, :3, :])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "meta_model = bf.simulation.MultiGenerativeModel([model_1ht, model_2ht])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prior Predictive Checks" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The interplay between parameters on several levels adds more complexity to the behavior of hierarchical models. Therefore, prior predictive or pushfoward checks become even more crucial for inspecting whether the chosen parametrization matches one's expectations. Our simulated data sets now contain ``NUM_GROUPS`` participants and ``NUM_OBS`` observations each. If we want to simulate 1000 participants as in [part 1](./Model_Comparison_MPT.ipynb), we now need only 20 simulations from a model, as each data set contains 50 participants. Afterwards, we calculate the hit rates and false alarm rates for each simulated participant as before and plot them. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# 1. Data simulation\n", + "sim_pfcheck_1ht = model_1ht(batch_size=20)\n", + "sim_pfcheck_2ht = model_2ht(batch_size=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# 2. Summary statistics\n", + "def get_rates(sim_data):\n", + " \"\"\"Get the hit rate and false alarm rate per participant for each data set in a batch\n", + " of hierarchical data sets simulating binary decision (recognition) tasks.\n", + " Assumes first half of data to cover old items and second half to cover new items.\"\"\"\n", + "\n", + " obs_per_condition = int(np.ceil(sim_data.shape[-2] / 2))\n", + " hit_rates = np.mean(sim_data[..., :obs_per_condition, 1], axis=2)\n", + " fa_rates = np.mean(sim_data[..., obs_per_condition:, 1], axis=2)\n", + "\n", + " return hit_rates, fa_rates\n", + "\n", + "\n", + "rates_1htm = get_rates(sim_pfcheck_1ht[\"sim_data\"])\n", + "rates_2htm = get_rates(sim_pfcheck_2ht[\"sim_data\"])\n", + "rates = [rates_1htm, rates_2htm]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 3a. Plot rates across all data sets\n", + "fig = plt.figure(constrained_layout=True, figsize=(8, 6))\n", + "subfigs = fig.subfigures(nrows=2, ncols=1)\n", + "model_names = [\"Hierarchical 1HT MPT Model\", \"Hierarchical 2HT MPT Model\"]\n", + "num_bins = 20\n", + "bins = np.linspace(0.0, 1.0, num_bins + 1)\n", + "\n", + "for row, subfig in enumerate(subfigs):\n", + " subfig.suptitle(model_names[row], fontsize=18)\n", + " axs = subfig.subplots(nrows=1, ncols=2)\n", + " sns.histplot(rates[row][0].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=axs[0]).set(\n", + " title=\"Hit Rates\"\n", + " )\n", + " sns.histplot(rates[row][1].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=axs[1]).set(\n", + " title=\"False Alarm Rates\"\n", + " )\n", + "sns.despine()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we plot our 1000 participants over all data sets, we see simular patterns as in [part 1](./Model_Comparison_MPT.ipynb). Let's additionally take a look at the patterns within the simulated data sets to ensure that the variability between data sets is reasonable:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.12.2'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sns.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 3b. Plot rates for each data set\n", + "fig = plt.figure(constrained_layout=True, figsize=(8, 6))\n", + "subfigs = fig.subfigures(nrows=2, ncols=1)\n", + "palette = [\"#8f2727\"] * rates_1htm[0].shape[0]\n", + "\n", + "for row, subfig in enumerate(subfigs):\n", + " subfig.suptitle(model_names[row], fontsize=18)\n", + " axs = subfig.subplots(nrows=1, ncols=2)\n", + " [ax.set_xlim([0, 1]) for ax in axs]\n", + " sns.kdeplot(rates[row][0].T, alpha=0.5, palette=palette, legend=False, ax=axs[0]).set(title=\"Hit Rates\")\n", + " sns.kdeplot(rates[row][1].T, alpha=0.5, palette=palette, legend=False, ax=axs[1]).set(title=\"False Alarm Rates\")\n", + "sns.despine()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe some variability but not completely opposing patterns between our simulated data sets. This matches the expectations that we would have when collecting multiple data sets in the field." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining, Training & Validating the Neural Approximator" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We adapt the neural network architecture to this new symmetry by changing only a single part, the summary network. We now use a ``HierarchicalNetwork`` which we pass one summary network for each level. The majority of hierarchical models in cognitive modeling assume IID data on all levels, so we simply use one ``DeepSet`` network for each level. If would have, for instance, temporal dependencies within each participant, we would exchange the first network to one that is specialized on time series data, such as a ``TimeSeriesTransformer``.\n", + "\n", + "After this adjustment, all subsequent elements of the training and validation process stay the same as in [part 1](./Model_Comparison_MPT.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "summary_net = bf.summary_networks.HierarchicalNetwork([bf.networks.DeepSet(), bf.networks.DeepSet()])\n", + "inference_net = bf.inference_networks.PMPNetwork(num_models=2)\n", + "amortizer = bf.amortizers.AmortizedModelComparison(inference_net, summary_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Performing a consistency check with provided components...\n", + "INFO:root:Done.\n" + ] + } + ], + "source": [ + "trainer = bf.trainers.Trainer(amortizer=amortizer, generative_model=meta_model)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: Online learning will be a bit slow to due slow data generation, but still worth it due to amortization. :-)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9a80c0c876d948e58dd6074095605ea2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training epoch 1: 0%| | 0/100 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "diag_plot = bf.diagnostics.plot_losses(train_losses=losses)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate some validation data in a list to avoid memory troubles during evaluation\n", + "sim_data = [trainer.configurator(meta_model(50)) for _ in range(20)]\n", + "\n", + "# Get true indices and predicted PMPs from the trained network\n", + "sim_indices = np.concatenate([s[\"model_indices\"] for s in sim_data])\n", + "\n", + "# Estimate model probs in a loop\n", + "model_probs = np.concatenate([amortizer.posterior_probs(s) for s in sim_data])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cal_curves = bf.diagnostics.plot_calibration_curves(sim_indices, model_probs)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = bf.diagnostics.plot_confusion_matrix(sim_indices, model_probs)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our neural network quickly learned to discriminate between the two hierarchical models and shows excellent performance when validated on simulated data. The calibration curves look a bit shaky, but the marginal bin histograms tell us that this is due to the majority of the predicted probabilities being close to 0 or 1, leaving the middle ('uncertain') bins quite abandonded." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Network Application" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As in [part 1](./Model_Comparison_MPT.ipynb), we apply our trained model to a synthetic data set from the 2HT model. We again redefine our simulator with fixed random seeds for reproducible results:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 50, 100, 2)\n" + ] + } + ], + "source": [ + "prior_fixed = bf.simulation.Prior(\n", + " prior_fun=partial(hierarchical_prior_fun, rng=np.random.default_rng(2023)), param_names=PARAM_NAMES\n", + ")\n", + "fake_data_generator = bf.simulation.GenerativeModel(\n", + " prior=prior_fixed,\n", + " simulator=partial(\n", + " hierarchical_mpt_simulator, model=\"2HT\", num_groups=N_GROUPS, num_obs=N_OBS, rng=np.random.default_rng(2023)\n", + " ),\n", + " skip_test=True,\n", + " simulator_is_batched=False,\n", + ")\n", + "\n", + "fake_data = fake_data_generator(batch_size=1)[\"sim_data\"]\n", + "print(fake_data.shape)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can inspect our simulated data set by looking at hit and false alarm rates for each of our 50 participants. This is best done visually:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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WK+rVqyf69u1bbl4TQoh58+YJAGLNmjXl7vPee+8JAOLLL78UQpQ9Kr+iObFhw4aib9++pc5RXg4u7991+vTpAoBIT08vN24h/h6VX5Z9+/YJlUolhg8fLoSo+HskxPX/vuzZs0c8+uijIjo6Wmi1WlG7dm1xzz33iPfee8+9z8SJE0WnTp1ERESE+99s3Lhx4vLly9e9HiqbJMQNJpIkIiIiIvIBjsonIiIiIkVgYUpEREREisDClIiIiIgUgYUpERERESkCC1MiIiIiUgQWpkRERESkCH5dmAohYLFYwBmviIgqh3mUiJTErwvT7OxsmEwmZGdnyx0KEZFfYh4lIiXx68KUiIiIiAIHC1MiIiIiUgQWpkRERESkCCxMiYiIiEgRWJgSERERkSKwMCUiIiIiRWBhSkSkUL/++iv69++PunXrQpIkrFmzpsTrQgjMmDEDdevWhdFoRPfu3XHgwAF5giUiqgIsTImIFCo3Nxdt27bFO++8U+brr732GubOnYt33nkHycnJqF27Nu677z7OSUpEfksjdwBERFS2Bx54AA888ECZrwkhMG/ePEyZMgUPPfQQAGDp0qWoVasWli1bhhEjRvgyVCKiKsHClIgUIz09HWazucL72+12aLVaj85hMplQs2ZNT0NTnFOnTuHChQvo1auXe5ter0e3bt3w22+/lVuYWq1WWK1W93OLxeL1WG+Gp78TQOC8x0TVEQtTIlKE9PR0NImNhSUnp8LHqCQJLg/XeA8LCcHxkyf9vnC5cOECAKBWrVoltteqVQupqanlHpeYmIiEhASvxlZVKvM7AQTOe0xUHbEwJSJFMJvNsOTkYFx8PKL0+hvuf8JiQdKxYxgdF4doo7FC58iwWvHm/v0wm80BU7RIklTiuRCi1LarTZo0CePHj3c/t1gsiImJ8Vp8N8PT3wkgMN9jouqEhSkRKUqUXl+hQjOjoAAAEKnTVbgwDSS1a9cGUNhyWqdOHff2S5culWpFvZper4e+gkWeUlT0d4KI/B9H5RMR+aHGjRujdu3a2LBhg3ubzWbD5s2b0aVLFxkjIyKqPLaYEhEpVE5ODo4fP+5+furUKezevRuRkZFo0KABxo4di9mzZ6Np06Zo2rQpZs+ejaCgIDz++OMyRk1EVHksTImIFGrHjh3o0aOH+3nxvaFDhw7FkiVL8NJLLyE/Px/PP/88MjMz0blzZ6xfvx6hoaFyhUxEdFNYmBIRKVT37t0hrjPrgCRJmDFjBmbMmOG7oIiIvIj3mBIRERGRIrAwJSIiIiJFYGFKRERERIrAwpSIiIiIFIGFKREREREpAgtTIiIiIlIEFqZEREREpAgsTImIiIhIEViYEhEREZEisDAlIiIiIkVgYUpEREREisDClIiIiIgUgYUpERERESmCrIWpw+HA1KlT0bhxYxiNRsTGxuKVV16By+WSMywiIiIikoFGzpPPmTMH7733HpYuXYpWrVphx44dGD58OEwmE8aMGSNnaERERETkY7IWpr///jsGDBiAvn37AgAaNWqE5cuXY8eOHXKGRUREREQykLUr/84778TGjRtx9OhRAMCePXuwdetW9OnTR86wiIiIiEgGsraYTpgwAWazGS1atIBarYbT6cSsWbPwj3/8o8z9rVYrrFar+7nFYvFVqERERETkZbK2mK5cuRKffvopli1bhp07d2Lp0qV4/fXXsXTp0jL3T0xMhMlkcj9iYmJ8HDEREREReYushel//vMfTJw4EY899hhat26NIUOGYNy4cUhMTCxz/0mTJsFsNrsff/31l48jJiIiIiJvkbUrPy8vDypVydpYrVaXO12UXq+HXq/3RWhERERE5GOyFqb9+/fHrFmz0KBBA7Rq1Qq7du3C3Llz8eSTT8oZFhERERHJQNbCdP78+Xj55Zfx/PPP49KlS6hbty5GjBiBadOmyRkWEREREclA1sI0NDQU8+bNw7x58+QMg4iIiIgUQNbBT0RERERExViYEhEREZEisDAlIiIiIkVgYUpEREREisDClIiIiIgUgYUpERERESkCC1MiIiIiUgQWpkRERESkCCxMiYiIiEgRWJgSEfkxh8OBqVOnonHjxjAajYiNjcUrr7wCl8sld2hERB6TdUlSIiK6OXPmzMF7772HpUuXolWrVtixYweGDx8Ok8mEMWPGyB0eEZFHWJgSEfmx33//HQMGDEDfvn0BAI0aNcLy5cuxY8cOmSMjIvIcu/KJiPzYnXfeiY0bN+Lo0aMAgD179mDr1q3o06dPmftbrVZYLJYSDyIipWCLKRGRH5swYQLMZjNatGgBtVoNp9OJWbNm4R//+EeZ+ycmJiIhIcHHURIRVQxbTImI/NjKlSvx6aefYtmyZdi5cyeWLl2K119/HUuXLi1z/0mTJsFsNrsff/31l48jJiIqH1tMiYj82H/+8x9MnDgRjz32GACgdevWSE1NRWJiIoYOHVpqf71eD71e7+swiYgqhC2mRER+LC8vDypVyVSuVqs5XRQR+SW2mBJVM+np6TCbzR4dYzKZULNmTS9FRDejf//+mDVrFho0aIBWrVph165dmDt3Lp588km5QyMi8hgLU6JqJD09HU1iY2HJyfHouLCQEBw/eZLFqQLNnz8fL7/8Mp5//nlcunQJdevWxYgRIzBt2jS5QyMi8hgLU6JqxGw2w5KTg3Hx8Yiq4H2GGVYr3ty/H2azmYWpAoWGhmLevHmYN2+e3KEQEd00FqZE1VCUXo9oo1HuMIiIiErg4CciIiIiUgQWpkRERESkCCxMiYiIiEgRWJgSERERkSKwMCUiIiIiRWBhSkRERESKwMKUiIiIiBSBhSkRERERKQILUyIiIiJSBBamRERERKQILEyJiIiISBFYmBIRERGRIrAwJSIiIiJF0MgdABER+Z/09HSYzWaPjjGZTKhZs6aXIiKiQMDClIiIPJKeno4msbGw5OR4dFxYSAiOnzzJ4pSIysXClIiIPGI2m2HJycG4+HhE6fUVOibDasWb+/fDbDazMCWicrEwJSKiSonS6xFtNModBhEFENkHP6WlpeGf//wnoqKiEBQUhHbt2iElJUXusIiIiIjIx2RtMc3MzETXrl3Ro0cPfP/994iOjsaJEycQHh4uZ1hEREREJANZC9M5c+YgJiYGSUlJ7m2NGjWSLyAiIiIiko2sXflr165Fp06d8MgjjyA6Ohrt27fHhx9+KGdIRERERCQTWVtMT548iYULF2L8+PGYPHky/vzzT4wePRp6vR5PPPFEqf2tViusVqv7ucVi8WW4RNVaamqqR/tzzkoiIvKUrIWpy+VCp06dMHv2bABA+/btceDAASxcuLDMwjQxMREJCQm+DpOoWsu12yEB6Nmzp0fHcc5KIiLylKyFaZ06dRAXF1diW8uWLfHll1+Wuf+kSZMwfvx493OLxYKYmBivxkhU3RU4nRAARsfFVXhqIM5ZSURElSFrYdq1a1ccOXKkxLajR4+iYcOGZe6v1+uhr+BkzkRUtSJ1Os5ZSUREXiXr4Kdx48Zh+/btmD17No4fP45ly5bhgw8+wMiRI+UMi4iIiIhkIGtheuutt+Krr77C8uXLER8fj//+97+YN28eBg8eLGdYRERERCQD2Zck7devH/r16yd3GETkJUII2HJz4bLbodbpoA0KgiRJcodFREQKJHthSkSBRQgBtcOB/4uOxq+jR+PHCxdgz8tzv64LCUFU06ao27EjmvTsidpt2kBSyb46MhERKQALUyKqEsLlgj0vD/aCAhiEwN0REbCcPFlqP1tODs7v2oXzu3Yh5aOPENG4MTo9/TS018zQQURE1Q8LUyK6KUII2HNzYc/Pd29zSRJ+u3IFo199Fa27d0dIrVpQ6/VwFBTAcvYs0g8fxqnNm3Fq0yZknjqFDVOmILhePTThqH8iomqNhSkRVZrTZoM1OxvC5QIAqDQaaIOCcMXpxJeHD+PVu+5CROPG7v11wcGo0bw5ajRvjpYDBsCak4P9K1ciJSkJuWlpGNmgAewFBRAGA+9DJSKqhnhjFxF5rHhAU4HZDOFyQVKpoA8LgyE8HBq9HqhgUakPCUHHp57CE+vWoWHfvnAJAa3DgYKsLLicTi9fReBIS0vDP//5T0RFRSEoKAjt2rVDSkqK3GEREXmMLaZE5BEhBKwWC5w2GwBAYzBAFxx8UwOYDGFhaP3cc5j8/vt4pn59uIqKU4PJBJWGaep6MjMz0bVrV/To0QPff/89oqOjceLECYSHh8sdWqVdOXkSx9evx/Ft2/Bs/frQFxTADkCj13OgHFGAq1TGj42NRXJyMqKiokpsz8rKQocOHXCyjAEPROT/hMuFArMZLocDAKALDYXWYKiyn384Nxf5QUEItlohnE4UmM1+WZz6MkfOmTMHMTExSEpKcm9r1KhRlf18XyrIysLWN97AgS+/dN8e0jw4GHA4YMvJgS03F1qjkVOOEQWwSmX706dPw1lGN5vVakVaWtpNB0VEyiMJgfysLAinE5AkGEwmqLXaKj+PUKlgDA93d+cXmM0whIdDpVZX+bm8xZc5cu3atejduzceeeQRbN68GfXq1cPzzz+Pf/3rX2Xub7VaYbVa3c8tFkuVxnMjqampZW7PSUtDckICcs+dAwDU7NgRmthYvPbWWxjUsCH0LheE0wl7Xh6cNhv0YWGy/k6kp6fDbDZ7dIzJZELNmjW9FBFRYPCoMF27dq37/3/88UeYTCb3c6fTiY0bN/rtN3UiKl+wWl3YiikEJJXK662YkkoFQ3i4uxAuMJthDA9XfDeuHDny5MmTWLhwIcaPH4/Jkyfjzz//xOjRo6HX6/HEE0+U2j8xMREJCQlVGkNF5NrtkAD07Nmz1Gs1tVqMbtAAIRoNrtjt+PT8eZw6csT9+kCtFuFBQXBarbDm5Mh+q0d6ejqaxMbCkpPj0XFhISE4fvIki1Oi6/DoEz1w4EAAgCRJGDp0aInXtFotGjVqhDfeeKPKgiMi+UlCYET9+lAXF6U+ar0sLoALiopTq8UCvcmk6C5cOXKky+VCp06dMHv2bABA+/btceDAASxcuLDMwnTSpEkYP368+7nFYkFMTEyVxlSWAqcTAsDouDhEXz0tmMsFY34+VELAqVJBbzLhqYgIAMAJiwVJx47BJQQkSYLGYIBKqy0cdOd0Ij8rC8bwcJ8Xp2azGZacHIyLj0eUXl+hYzKsVry5fz/MZjMLU6Lr8OjT7Cq656dx48ZITk5GjRo1vBIUESmDEAJRLheCDAa4AASbTD7tPlWp1dCbTCjIzITTboc9Lw+64GCfnd9TcuTIOnXqIO6axQlatmyJL7/8ssz99Xo99BUsprwhUqdzF6ZCiMJ7lou+9IRGRCDsqlbxjIKCUser1OrCWz2K7nWW81aPKL2+ZJFNRDetUv1ip06dYlFKVA3YcnMRJATsLhfydDpZuk3VGg10oaEAAHteHhxFswEomS9zZNeuXXHkqm5vADh69CgaNmzok/PfDEd+Plx2OwDAYDJV+FaN4tZ0Sa2GcLlgtVgghPBmqETkI5X+K7Nx40Zs3LgRly5dcrcSFFu8ePFNB0ZE8nJYrXAUreb02fnz+L8mTWSLRWswwGW3w1FQAFt2NtRFXb1K5qscOW7cOHTp0gWzZ8/Go48+ij///BMffPABPvjggyo7hze4nE7YcnMBALqQEI+/9BQXp/mZmXAVjdrXF32BISL/VakW04SEBPTq1QsbN27E5cuXkZmZWeJBRP7N5XTCmp0NALBIEvZ4OMjDG3QhIX+3kCkgnuvxZY689dZb8dVXX2H58uWIj4/Hf//7X8ybNw+DBw+u0vNUNVvRe6jSaqGp5JRjKrUahrAwAICjoAD2Mrr+ici/VKrF9L333sOSJUswZMiQqo6HiGRWPIE+hIBKo0GWQrpIJUmCPjQUBVlZcFqtMCp4hL6vc2S/fv3Qr18/n5yrKjhsNvcCDfqQkJsa0KbW6aANCoI9Lw+27GxIQUFVFSYRyaBSmd1ms6FLly5VHQsRKYCtaDoeSBL0YWEVXl7UF9RaLbRFg00iXC7oFBTb1Zgjr0MI2Iu68DVGY5Xct6wNCoKqaE5dfUEBlPlbQUQVUanC9Omnn8ayZcuqOhYikpnTZoOjqDtU7gnMy6MtWv5UA6DXNSsrKQVzZPk0Lpf7i4+uilo3i1vTIUlQu1zo5gf3IBNR2Sr1VbWgoAAffPABfvrpJ7Rp0wbaa1Z/mTt3bpUER0S+I4Rw31eqMRig0elkjqhskiRBFxICq8WC7pGRyLtmYJESMEeWTQJgKBqFrzUaq3TBBJVaDV1wMGw5OehTowZy/voLkHHAHhFVTqUK071796Jdu3YAgP3795d4TcmTXxNR+Ww5ORAuFySVStFzhQKARq9HpiQhCH8XOkrCHFm2lsHBUAsBSJL7loyqpDEYkJefDy2A3fPmoc3ddyuy1Z+IylepwvSXX36p6jiISEYluvBDQxW/9CcAZKlU0Dsc0LpccNpsUCuohZc5smzdi7rYtQaDV37HJEmCTa+HPTsbOHIEu5YsQcennqry8xCR9yj/rw8ReZUQwj39ksZgUFSBdz0OScK2rCwAhQsBcIJ1ZdMKgabBwRAoHPTkLUKlwtfp6QCA3+fPR9aZM147FxFVvUq1mPbo0eO63VE///xzpQMiIt+y5+dDOJ2Fg1EU3oV/rfUZGbgrIgIuhwNOq7XS82FWNebI0kKL7gW2q9Ve717/w2zGyF69kLF3LzZOm4aHkpKq9S0URP6kUoVp8b1Txex2O3bv3o39+/dj6NChVREXEfmAy+mEPS8PQNEE9n7QhX+1XKcTVo0GBocDtrw8qPV6RRQgzJElCZcLwUUt2jYfLWvb5oUXsOWFF/DX9u048OWXiH/4YZ+cl4huTqUyxJtvvlnm9hkzZiBH4SuyENHfbLm57on0NXq93OFUilWjgcHphHA6FdNqyhxZkqNobtG0ggKE+Oj9Ca5TB3eMHo0tr72GX+fMQaO77kJIrVo+OTcRVV6VNo/885//rNI1oInIe5w2G5xWK4Ci1lIFtDRWylUjvG15eYq+17Q65kghhHup0N/NZp8u2ND+iSdQKz4etuxs/DJzps/OS0SVV6WF6e+//w6DAloriOgGhChsLUXRgKdr5tn0N1qjEZCkwlbToqUulag65kiXwwHhdMIFIMVi8em5VRoNes6aBZVGgxMbNuDYjz/69PxE5LlKdeU/9NBDJZ4LIXD+/Hns2LEDL7/8cpUERkTeo3Y4/l59x88GPJVFUqmgNRoL10vPzYVap5O1BZg58m/F05DlSRIKZFgMoWbz5uj0r3/hz4UL8cvMmYi5/XYYTCafx0FEFVOpwtR0zYdapVKhefPmeOWVV9CrV68qCYyIvEMtSdAVtSpW9eo7ctIaje4ZBpw2m6z3zDJHFhJCuAvTXBl/z2577jkc+/FHZJ48iS2vvYb7Zs2SLRYiur5KFaZJSUlVHQcR+cid4eFQCVHYylhFa5UrgaRSQWswwJ6fD3t+vqyFKXNkoeJ7mCW1GlYZ49DodLhv5kx8PngwDnz5JZr37YsGXbrIGBERleem5u1ISUnBoUOHIEkS4uLi0L59+6qKi4i8wJ6Tg/uiogAA2qAg/x3wVA5NUaupy26H026X/d7Z6p4jHUWFqUavB2S+97duhw5o+/jj2PPZZ/hp2jQMWbvWL76Ypaenw2w2e3SMyWRCzZo1vRQRkXdVqjC9dOkSHnvsMWzatAnh4eEQQsBsNqNHjx5YsWIFPxBECnVi9WoEq9VwqVSKmFapqqnUamj0ejisVtjz82UrTJkjC+cuLR6IpoTCFAC6jhuHkz//DMvZs/j97bdx98SJcod0Xenp6WgSGwuLh1OMhYWE4PjJk9Xi94wCT6UK0xdeeAEWiwUHDhxAy5YtAQAHDx7E0KFDMXr0aCxfvrxKgySim5efmYlT33wDALDJPDjImzRGIxxWK5xWK1xOp9dXGSoLc+TfraUqjQYqH02qfyO6kBDck5CAr595Brs+/hjN+vRB7TZt5A6rXGazGZacHIyLj0dUBW9NybBa8eb+/TCbzSxMyS9VKlv88MMP+Omnn9wJFwDi4uLw7rvvVqsb+4n8ScrixXDm5+OvggJEBsBI/PKotVqotFq47HY48vOhCwnxeQzMkdd04ytI47vvRosHH8ThtWuxftIk/GPVKsV36Ufp9YgumquXKNBVapiky+WCtowuMq1WC5cM04EQ0fXlZWRg96efAgB+vHzZp5Ocy6F4wn17QYEsE+5X9xzpcjrhstsBAGqFFaYA0G3SJATXrIkrJ05w4n0ihalUYXrPPfdgzJgxOHfunHtbWloaxo0bh3vvvbfKgiOiqpGyaBEc+fkwNW2KA0UT6wcytU4HSa0GrpquyJeqe44sHo2v0mpluZXiRowREbj/9dchqVQ4uHo1Dq5ZI3dIRFSkUoXpO++8g+zsbDRq1Ai33HILmjRpgsaNGyM7Oxvz58+v6hiJ6Cbkpqdjz7JlAIBmjz8uczS+IUkStEWDu+z5+T5vNa3uOVKp3fhXi+ncGZ1HjgQA/JyQgEuHDskcEREBlbzHNCYmBjt37sSGDRtw+PBhCCEQFxeHnj17VnV8RHSTdnz0ERwFBajdti2iO3WSOxyf0RgMsOXmFi6H6XD4dIR+dc6RrqJ/b6Cw5VrJbnv2WZzbuRNntm3D2mefxWOff46QWrXkDouoWvOoxfTnn39GXFwcLEXrHd9333144YUXMHr0aNx6661o1aoVtmzZ4pVAichzuZcuYe+KFQCA20eNCtiR+GWRrpoSy56f75NzMkfCPUWUSqNRZDf+1VRqNfq8+SYiYmORc/Ei1j73HOx5eXKHRVSteVSYzps3D//6178QFhZW6jWTyYQRI0Zg7ty5lQokMTERkiRh7NixlTqeiEpLWbwYTqsVddq1Q8M775Q7HJ8rLkydViuEDwYdeTNH+ovibnwlDnoqiyEsDAPffx/GiAhcOngQX7M4JZKVR4Xpnj17cP/995f7eq9evZCSkuJxEMnJyfjggw/QRsHzyRH5m/zMTOz7/HMAhWuFV6fW0mJqrdY9h6bdB4OgvJUj/YVwudyj8TUK78a/mikmBg8uXAhdcDDO/vEH1owYAVs1GCRIpEQeFaYXL14scwqUYhqNBunp6R4FkJOTg8GDB+PDDz9ERESER8cSUfn2fPYZ7Hl5qNGiBRrdfbfc4cimuNXUkZ8PeHkQlDdypD9xFHXjS2q1YibVr6g67dph4EcfQRccjLTkZKx+8knkBvB7RaRUHhWm9erVw759+8p9fe/evahTp45HAYwcORJ9+/atFoMCiHzFlpuL3Z98AgC49V//qpatpcU0BgMgSRAuF9ROp1fP5Y0c6U+cfjAa/3rqtm+P/1u8GHqTCRf27MHyRx5BWgC3cBMpkUdfafv06YNp06bhgQcegOGadbbz8/Mxffp09OvXr8I/b8WKFdi5cyeSk5MrtL/VaoW1KPEBcA8wIKKS9q9ahQKzGaYGDdC0d2/Z4khNTfXKvp6QJAkavR6OggJoikaLe0tV50h/IoRwD3zyl/tLy1KnbVs8tnIl1j73HDJPncIXQ4agw/Dh6Pzcc7KsIkZU3XhUmE6dOhWrV69Gs2bNMGrUKDRv3hySJOHQoUN499134XQ6MWXKlAr9rL/++gtjxozB+vXrSyXw8iQmJiIhIcGTkImqHYfNhp1JSQCATk8/LUuXaq7dDgmoVE+IN1ZG0hiNcBQUQO1wINiLI8WrMkf6m+KiVFKpFD8a/0YiGjXCY6tWYdPMmTi0Zg1SFi3CobVr0enJJxH/yCNyh0cU0Dz6i1WrVi389ttveO655zBp0iT3pNWSJKF3795YsGABalVwDriUlBRcunQJHTt2dG9zOp349ddf8c4778BqtUJ9TXKbNGkSxo8f735usVgQExPjySUQBbzDa9ci5+JFBEdHo+XAgbLEUOB0QgAYHRdX4TW+T1gsSDp2DC4v3Aeq1mig0mjgcjjQsYwR81WlKnOkv3FeNRo/EG4d0YeEoPerr6Jp797YnJgI85kz+HXOHPz21luo2bEjOoSGQnK5IIQIiOslUgqPm1IaNmyIdevWITMzE8ePH4cQAk2bNvV44NK9995b6l6s4cOHo0WLFpgwYUKpohQA9Ho99H7cRUTkbS6nEzs+/BAA0GHYMNlHRkfqdBUuTDO8PGpeYzDAlpODziaTV1eCqqoc6U+EEO6BT3L/zlW12B490KBrVxz++mukJCUh8+RJnN+2DUPq1gXy8pCXnw+VWg1JpYKkKhy2IUkSIEl//1elgsrhQE2t1t2yTERlq3QfX0REBG699dZKnzg0NBTx8fEltgUHByMqKqrUdiKqmOMbNiArNRV6kwmtH31U7nAURaPXw5qTg7p6PbKOHgWaNvXq+W42R/oTl8NROOOBJEHlwxW2fEWj0yH+kUfQ6uGHkX7wIP5YvhybPv0U9Q0GSEK4V7q6HiOAybGx+P6hh/B7w4ao064d6nXqhFt69oQxgL+0EHnKo1H5RKRcQggkf/ABAKDd4MEcqHENSaWCQ6NBisXinkKKqoZ70JNOF9Dd2pIkIbpVK7QcNgxzU1ORFxwMQ0QE9GFh0IWEQBscDG1QEDRGIzQGA9R6PdQ6XeFtJCoVCorun85KTcWhr7/GTy+/jA/vugtrnnkGpzZv9mpLPpG/UNREc5s2bZI7BCK/lbp1K9IPHoTGaES7IUPkDkeRbHo9Pj1/HtMbNpQ7FK9ITEzE5MmTMWbMGMybN89n5726MK1WJAlqjQaowADDS/n5mJqSgv07diA4Lw/ndu7Eqc2bkX7wIE7/+itO//orolu1wh1jxqBxNZ53mIgtpkQBori1tPWjj7JrsDwB3Jon1wp6LqfT3ZUdaPeXeoPeZEKju+5ClzFjMHj1ajyxbh06DB8ObVAQLh04gK+feQbrxo9H7uXLcodKJAsWpkQB4NyuXUhLToZKq0WH4cPlDod8TM4V9IpbS1UajXvwD1VcZGws7p4wAU9u3IgOw4dDUqtxdN06fNy3Ly7t2CF3eEQ+p6iufCKqnB1FraUtH3wQobVryxwN+drVK+jNnDnzuvtW9UIlgdCNn56eDrPZXOH9vbEYhDEiAndPmIAW/fphw8svI/3gQfyZkICekZFeX0qXSElYmBL5uctHj+LkL78AkoSOTz0ldzjkY56uoFelC5UEwGpP6enpaBIbC0tOjsfHemMxiOhWrTBoxQpsmjkT+z//HH1r1oTdaoUwGgN6YBlRMRamRH6ueN7Spr16ITI2VuZoyJcqs4JeVS5UonY6Afj3ak9msxmWnByMi49HVAWLa28uBgEU3qvb85VXIEVHY/f8+dA6HLBaLNCHhbE4pYDHwpTIj5nPnsWRdesAALc+84zM0ZCvVWYFvapcqKS4MA2EaaKi9HrFLAZRrOH992PspEl4sl49OG02FqdULbAwJfJjKYsWQTidaNC1K6JbtZI7HPKxyqygV5XURaPx/fn+UqXbn5MDq8EAQ0EBnDYbbNnZ0IWGsjilgMXClMhP5V66hANffgkAuG3ECJmjITnIuYJeLZ0OqqKubBam3uXUaKAPC4PVYoHDaoWkUnEBDQpYnNuDyE/tXLoUTpsNddq3R71qsvQlKUdccDAAQK3VsvXOBzR6PXShoQAAe34+7Pn5MkdE5B1sMSXyQwVmM/YuXw6gsLWUhQEV89UKenFFLXb+OhrfH2kNBginE/a8PNhycqDSaKDWauUOi6hKscWUyA/t+ewz2PPyUKN5czTq1k3ucKiaseXkoHHRQCF24/uWNijI/W9utVjgKhqARhQoWJgS+Rlbbi52LV0KoHAkPltLydfSU1KgliS4/HiaKH8lSRL0oaGQ1GoIlwvW7GwITsBPAYSFKZGf2b9qFQrMZoQ3bIim998vdzhUDRUvlelgUSoLSaWCISwMAOCy22HPy5M5IqKqw8KUyI84bDbsTEoCAHR6+mm2VpHPuZxOXEpJAVA4WpzkodJo/h4MlZcHp90uc0REVYOFKZEfOfz118i5eBHB0dFoMWCA3OFQNeRyONB00CDsy86GS8U/IXLS6PXuwWdWiwXCC0ukEvkaswqRn3A5ndjx0UcAgI5PPgkNB52QDDR6PWIHDMDic+cA3t8sK0mSoA8JgaRSFd5vmpMjd0hEN42FKZGfOPLtt8hKTYUhPBzxjzwidzhEpACSSgV90f2mTqvVvRoXkb/iDUJU7aSnp8NsNnt0jMlkQs2aNb0UUaHrxeVyOrFl3jwAQKMBA3Dm/HkAgN1uh9aDeQxTU1NvOk4iUha1Vgut0Qh7fj50ViuCeIsF+TEWplStpKeno0lsLCwednmFhYTg+MmTXitObxTXrWFheLxOHeQ4HBgwZQpskycDAFSSBFclpopx8V40ooCiDQ6Gw2aDyunE/0VHyx0OUaWxMKVqxWw2w5KTg3Hx8Yiq4Io1GVYr3ty/H2az2WuF6XXjEgLGvDxACOiCgjCtQwcAwAmLBUnHjmF0XByiiyY7v5HiYypTzBKRchXPb5qflYVOJhMu/PEHmjRpIndYRB5jYUrVUpReX+FizpfKisteUACbEIAkITwszD2hfkZBAQAgUqer8LUUH0NEgUet1cKu1UJnt2PfO++gU9++MISHyx0WkUd4IwqRggkhYM/NBVC4FCFXeSKi67HrdLhotcKamYlf58yROxwij7EwJVIwR0FB4dyEkgStAlt4iUhhJAnLL1wAJAkHv/oKqVu3yh0RkUdYmBIplBDCvdSgjq2lRFRBqQUFaNSvHwBg4/TpsBX1uhD5A95jSqRQjvx8CJcLkkoFDVtLiQKCJ1O23cz0bkHdu8P4xx+wpKXh+4QEtHrmmXL39cV0eEQVxcKUSIGEywVbUWsp7y0l8n+5djskAD179vT4WE+mdys+T+9+/dA8KAjPxsTgxNdfY8z8+UgtZ/Cjt6fDI/IEC1MiBbLn5wNCQFKroTEY5A6HiG5SgdMJAXh9erdrz2MvKIDW4cDoxo2RbzSWWkbWF9PhEXmChSmRwriczr/vLQ0OZmspUQDx1fRuxecRej3yrlyByuVCeNFcyERKxsFPRApTXJSqNBqodTqZoyEifyapVNCHhgIozC0uh0PmiIiuj4UpkYKonE44ilpJdCEhbC0lopum1uncX3Kt2dkQXPmNFIyFKZFCSAB0VisAQK3XQ63VyhsQEQUESZKgCwkBJAkuhwOO/Hy5QyIqFwtTIoXoGBYGddHoW11wsMzREFEgUanV7rxiy82Fy+mUOSKisrEwJVIAe24u+heNiNUGB0OlVsscEREFGo3BAFVRTwy79EmpWJgSKcDRZcsQptHAxaVHichLJElyD4Ry2e3u+9mJlISFKZHMLuzdi1PffAMAsOn1HPBERF5zbZe+5MHk/US+wMKUSEYOqxXrJ08GXC7ssFjg1HBqYSLyLo3RCJVGAwjhHnBJpBQsTIlktP2dd3Dl+HHow8Px1cWLcodDRNWAJEnQFXXpa5xOtAkJkTkior+xMCWSyYV9+5CyaBEAoPXIkchjlxoR+Yhao4G2aBWo/1erFmzZ2TJHRFRI1sI0MTERt956K0JDQxEdHY2BAwfiyJEjcoZE5BP2vDz8OGEChMuF5n37ovYdd8gdEhFVM9qgILgkCWEaDQ4VfUkmkpushenmzZsxcuRIbN++HRs2bIDD4UCvXr2Qm5srZ1hEXvfLf/+LzJMnERwdje5Tp8odDhFVQ5IkwWowwCUE/vrpJ6Ru2yZ3SESQdaTFDz/8UOJ5UlISoqOjkZKSgrvvvlumqIi86+CaNTj41VeQVCo88PrrMEZEABkZcodFRNWQS63Gtqws3BURgY3TpmHIN9+4u/iJ5KCoIcBmsxkAEBkZWebrVqsV1qtGEFosFp/ERb6Rnp7u/h2oKJPJhJpFE9P7g4v79mHj9OkAgNtHjUL9226TOSIiqu6+S09Hr2bNYElLw5bXX8c906bJHRJVY4opTIUQGD9+PO68807Ex8eXuU9iYiISEhJ8HBn5Qnp6OprExsKSk+PRcWEhITh+8qRfFKe5ly7hm1Gj4LRa0ahbN9w6YoTcIRERwSoE2rzwAv6YNg17ly1Dgy5d0KRnT7nDompKMYXpqFGjsHfvXmzdurXcfSZNmoTx48e7n1ssFsTExPgiPPIys9kMS04OxsXHI0qvr9AxGVYr3ty/H2azWfGFqS0nB2uffx45Fy8i8pZb8MAbb3DZUaoSiYmJWL16NQ4fPgyj0YguXbpgzpw5aN68udyhkR+p2aEDOgwfjp1JSdgwZQpqtWqF0Dp15A6LqiFFTBf1wgsvYO3atfjll19Qv379cvfT6/UICwsr8aDAEqXXI9porNCjogWs3Bw2G74ZNQoX9++HMSIC/d99F3rOG0hVhINIqap0HTcOteLjYTWb8f2LL8LlcMgdElVDshamQgiMGjUKq1evxs8//4zGjRvLGQ5RlXPYbFg3diz+2r4d2qAgDPzgA0Q0aiR3WBRAfvjhBwwbNgytWrVC27ZtkZSUhDNnziAlJUXu0MjPqHU6PDB3LnTBwTiXkoLt77wjd0hUDcnalT9y5EgsW7YMX3/9NUJDQ3HhwgUAhQNajEajnKER3TR7fj6+GTUKZ7Ztg1qnQ/9330Wt1q3lDosCHAeRFkpNTa3S/aqL8AYNcE9CAn548UX8+d57iI6PL/N+0+owWJXkIWthunDhQgBA9+7dS2xPSkrCsGHDfB8QURXJTU/HN6NG4cKePdAGBeHBBQsQc/vtcodFAY6DSIFcux0SgJ4eDt5xceU1txb9+uHCnj3Y/ckn+PGllxC5ahUib7nF/Xp1GKxK8pG1MBVCyHl6Iq+4uG8fvnnhBeRcuAC9yYQBCxeibocOcodF1QAHkQIFTicEgNFxcYiuQM/bCYsFSceOwcW/RyXc9dJLSD98GGnJyfhm5Eg8tmoV9KGhAAJ/sCrJSzGj8on8ncvpRMqiRfj97bfhcjgQERuLAQsXIrxhQ7lDo2qgeBDpr7/+esNBpHo/GTh4MyJ1ugoVphkFBT6Ixv+otVr0nTcPyx9+GJmnT+P7f/8bDy5YAJXm77KheLAqUVVSxKh8In93Yd8+rBw0CNvmzoXL4UDT3r0xaMUKFqXkdRxESt4SFBWFfvPnQ2Mw4PSvv+LnhAT2dJLXscWU6CZkHD+OPxYswNHvvweEgC4kBN0mTULcQw9BkiS5w6NqgINIyZtqxcfjgblz8e2oUdi/ahVC69ZF1H33yR0WBTAWpkQeclitOL15M/atWoXUrVuBohaElgMG4M4XX0Qw758iH+IgUvK2W+65B91ffhm/JCTg97feQlt+6SYvYmFKdCNCoK5ej9PffYcjp07h9K+/wp6X5365yX33ofPIkajZooWMQVJ1xa5V8oW2//gHss+dw44PP8Set99Gu6KBUERVjYUpVWtCCEAICJfr70fRc5fTCeF0IsjhwH8aNcL+opYpAAiOjkbLAQMQ/8gjCG/QQMYrICLyja7jxiE/MxMHvvgC/6xTB3auDEVewMKUApI9Px9ZZ84gOy0NlnPnkH3uHHIvX0ZGWhrGNmgAY25u4ZKNFWhtkgAUuFyo37EjmnTtioZ33YXarVtDUnHsIBFVH5JKhZ6vvALzlSs4+/PPUBUUwKHTQVMNZnkg32FhSn7PWVCA1G3bcGHvXlw+fBiXjxxBZmpquUVnQ6Ox9GuSBEmlKnwU/79aDZVajUyHA1N37cLRdevQpEkTH1wREZEySSoV2o4Zg6/XrEHHsDBYLRYgNBQag0Hu0ChAsDAlvyOEgMvhgNZqxQsxMfjhsccgyuhSMphMCKtXD6H16iGsbl0ER0fDYrVizIQJeLxJE0QaDIWtnpJ03RH0wuUC7+IjIiokqdVYdv482kZGQuNwwJqdDSEEtJwFgqoAC1PyC0IIuOx2OKxWOG02CJcLOgCxQUEQDgdCatdGvU6dEN2yJWo0b44aLVoguEaNUj/n+PHj2D9yJFxqdYmJoomIqOJcAKx6PQwaDRwFBbDl5EC4XNAGBXGqPLop/MtMiuZyOOAoKICjoKDk6GNJgkOtxhdnz+L9b79Fm65dmQyJiHxJkqALCYGkUsGelwd7Xl5ho0FICPMxVRoLU1IcIYS7GHVd3UUvSdDo9VDr9VBrtUgvKMAfZjOCa9dmEiQikoEkSdAFB0NSqWDLySnM204nDGFhHCBKlcLClBTDpNFAa7Ui75rR8mqdDhqDAWqdjgUoEZECaY1GSCoVrNnZcNntyM/KgiEsjLdMkcf4G0OyEkLg/K5dSFm4EC/HxkJttwMoHPmpMRqhLR6gREREiqbR66FSq1FgNkM4ncjPyoI+NJTTSZFHWJiSLBw2G459/z12ffwxLh04AABQSxKcKhWCQkLYOkpE5IdUGg2MEREoMJvhcjhgtVjgMhoBNjBQBbEwJZ/KvXQJe1eswL6VK5GXkQGgsKu+brduGP3hh3g+Ph5h/HZNROS3JJUKhvBw2HJz4cjPhz0/HwaVCqFqtdyhkR9gYUpeV9xdv/vTT3F8/Xr3gKaQWrXQ5vHH0frRR5GWkYFz77wjc6RERFQVJEmCPiQEaq0W1uxsqF0u/LtRI1zeu5cLldB1sTD1c+np6TCbzR4dY7fbodVqPTrGZDKhZs2aHh1z4exZHPr2W5z+5huYT5xwb4+Ii0Pjfv1Qu0sXqDQapGVkIDU11aOffTVPjr2Z8xARkWeK7zvNNZth0miwfcoUuP76C7ePHMmBUVQm/lb4sfT0dDSJjYUlJ8ej41SSBFcF1oi/WlhICI6fPFmh4jTrzBkkf/wx/ly6FMFFXTd2lws7s7OxJTMTaUeOAF99VeaxLperwjHl2u2QAPTs2bPCx1TmPEREVHkqjQb5RiP2paXh9vBw/LlwIf76/Xfc//rrMNWvL3d4pDAsTP2Y2WyGJScH4+LjEVXB+zJPWCxIOnYMo+PiEF3B5eMyrFa8uX8/zGZzuYWpPT8fx9evx/4vvkBacjIAIFithgOAS6eDXatFfFgY4uvVu25cnhTMBU4nBODRtVTmPEREgc7rPU+ShJUXL+KR8eNxfsUKnN+9G588+CDajBqFunffXe5hlemtI//GwjQAROn1FS8yCwoAAJE6XYWPKY/L6URacjKOfPcdjn7/PWzFLbeShJodOmDO2rUY1KwZooOCKhxXZXhyLTdzHiKiQOOrnqfi8zw8YQIiNBoMqVsXjQHsfO01LJwyBasvXoStjAYDT3rrKDCwMCWPCJcLaSkpOPr99zj+44/ukfUAEFa/Plr9v/+HuIEDcTE3F/uWLcMgTvlERKRYvup5KnUeIWCz2aC129HZZMKt4eGwGgxwXTVyvyK9dRR4WJjSjQmBFkFB2LdwIX7580/kpqe7XzKYTGjSqxea9+2L+rfd5p4M/+Lx43JFS0REHvJVz1OJ8wQFwWmzwZqdDZXLBWN+PrRBQdAGBXEe62qMhSmVyeV0wmmzwWmzIchmw4iYGKR+9x0AQBcaiiY9e6JZnz6Iuf12qD0c4U9ERAQUzmNtjIiANScHTqsV9rw8OG026END5Q6NZMLClAAUzjXqcjjcxWjxXKMAIAHIstvRpn9/tBswAA26doVGp5MvWCIiChiSSgVDWBgcVius2dlwORzIz8yERqcD202rHxam1ZhwueC02eAoKkZxzf1CKo0Gap0OFiGQcOQIjo0ahVhOjExERF6g0euh0mhgy8kpbDW12fBc/fqwejhXN/k3FqbViBACWiFwX2Qkgq1W5OXnl9xBkqDW6aDW6Qq/qRbdL+q6dj8iIiIvUKnV0IeFwVFQAGtODoLUamgqMLMLBQ4WptWAw2qF02qF02ZDHSFQp2ZNoGiaD5Va7S5GVVotbzgnIiJZSZIErdGILJcLH586hREcx1CtsDCtBpxWKxxWKwDABWB/djaaREYiKjQUqqum5iAiIlIKoVLhks0mdxjkYyq5AyDvUxsM0BiNMJhMOKtWI+ncOdg1GhalREREpChsMa0GNEX3jAIA2FVPRERECsUWUyIiIiJSBBamRERERKQILEyJiIiISBFYmBIRERGRIrAwJSIiIiJFYGFKRERERIrAwpSIiIiIFEH2wnTBggVo3LgxDAYDOnbsiC1btsgdEhGR32EuJaJAIGthunLlSowdOxZTpkzBrl27cNddd+GBBx7AmTNn5AyLiMivMJcSUaCQtTCdO3cunnrqKTz99NNo2bIl5s2bh5iYGCxcuFDOsIiI/ApzKREFCtmWJLXZbEhJScHEiRNLbO/Vqxd+++23Mo+xWq2wWq3u52azGQBgsVg8OveVK1eQmZnp0TGSJEEI4dVjPN3/r7/+AgCcy8tDgdNZoWMu5ecDAM7n58NRwfNcKfo3P3jwILKzs70SW2Xi8tUxlbl+wLP301fvZSAdczPvZXZ2tsd5IzQ0FJICl/T1NJdWRR4t/hwEwu+rUuPy1TFKjQvwTe6tzP48BoiIiEBkZKRH5wAqmEeFTNLS0gQAsW3bthLbZ82aJZo1a1bmMdOnTxcA+OCDDz58/jCbzb5IjR7zNJcyj/LBBx9yPSqSR2VrMS12beUshCi3mp40aRLGjx/vfu5yuXDlyhVERUVVuCXDYrEgJiYGf/31F8LCwiofuALwWpQrkK6H11IoNDTUS1FVjYrmUubRkgLpWoDAuh5eizJ5O4/KVpjWqFEDarUaFy5cKLH90qVLqFWrVpnH6PV66PX6EtvCw8Mrdf6wsDC//+UoxmtRrkC6Hl6LMnmaS5lHyxZI1wIE1vXwWpTJW9ci2+AnnU6Hjh07YsOGDSW2b9iwAV26dJEpKiIi/8JcSkSBRNau/PHjx2PIkCHo1KkT7rjjDnzwwQc4c+YMnn32WTnDIiLyK8ylRBQoZC1MBw0ahIyMDLzyyis4f/484uPjsW7dOjRs2NBr59Tr9Zg+fXqprix/xGtRrkC6Hl6L8vk6lwbSv2MgXQsQWNfDa1Emb1+LJISHcwoQEREREXmB7EuSEhEREREBLEyJiIiISCFYmBIRERGRIrAwJSIiIiJFCMjCdMGCBWjcuDEMBgM6duyILVu2XHf/zZs3o2PHjjAYDIiNjcV7773no0hvzJNrWb16Ne677z7UrFkTYWFhuOOOO/Djjz/6MNrr8/R9KbZt2zZoNBq0a9fOuwF6wNNrsVqtmDJlCho2bAi9Xo9bbrkFixcv9lG0N+bp9Xz22Wdo27YtgoKCUKdOHQwfPhwZGRk+irZ8v/76K/r374+6detCkiSsWbPmhsco+fMvJ+ZR5lFfCKRcyjxaRZ//KlmsWUFWrFghtFqt+PDDD8XBgwfFmDFjRHBwsEhNTS1z/5MnT4qgoCAxZswYcfDgQfHhhx8KrVYrvvjiCx9HXpqn1zJmzBgxZ84c8eeff4qjR4+KSZMmCa1WK3bu3OnjyEvz9FqKZWVlidjYWNGrVy/Rtm1b3wR7A5W5lgcffFB07txZbNiwQZw6dUr88ccfpdY2l4un17NlyxahUqnEW2+9JU6ePCm2bNkiWrVqJQYOHOjjyEtbt26dmDJlivjyyy8FAPHVV19dd38lf/7lxDzKPOoLgZRLmUer7vMfcIXpbbfdJp599tkS21q0aCEmTpxY5v4vvfSSaNGiRYltI0aMELfffrvXYqwoT6+lLHFxcSIhIaGqQ/NYZa9l0KBBYurUqWL69OmKSaieXsv3338vTCaTyMjI8EV4HvP0ev73v/+J2NjYEtvefvttUb9+fa/FWBkVSahK/vzLiXm0JOZR7wikXMo8WnWf/4DqyrfZbEhJSUGvXr1KbO/Vqxd+++23Mo/5/fffS+3fu3dv7NixA3a73Wux3khlruVaLpcL2dnZiIyM9EaIFVbZa0lKSsKJEycwffp0b4dYYZW5lrVr16JTp0547bXXUK9ePTRr1gwvvvgi8vPzfRHydVXmerp06YKzZ89i3bp1EELg4sWL+OKLL9C3b19fhFyllPr5lxPzaEnMo94RSLmUebRqP/+yrvx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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rates = get_rates(fake_data)\n", + "f, ax = plt.subplots(1, 2, figsize=(8, 3))\n", + "sns.histplot(rates[0].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=ax[0]).set(title=\"Hit Rates\")\n", + "sns.histplot(rates[1].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=ax[1]).set(\n", + " title=\"False Alarm Rates\"\n", + ")\n", + "sns.despine()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As in [part 1](./Model_Comparison_MPT.ipynb), our data set contains many participants with low false alarm rates, which are unlikely under the 1HT model. Let's see the evidence contained in our simulated data:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embeddings = summary_net(fake_data)\n", + "preds = inference_net.posterior_probs(embeddings)[0]\n", + "preds" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bayes_factor12 = preds[0] / preds[1]\n", + "bayes_factor12" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our Bayesian model comparison reveals clear evidence for the 2HT model, much more decisive than in [part 1](./Model_Comparison_MPT.ipynb). While the unambiguousness of the results depends of course on the model specification and the randomly simulated data, our model comparison now contains 50 times more data by considering all participants simultaneously!" + ] + } + ], + "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": "3.10.10" + }, + "toc": { + "base_numbering": "1", + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": true, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "165px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "ee0d8b6520eaafdd1f9814dabe622906f4f270f1207107f9cbd03103451f6c10" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/tutorial_notebooks/Model_Comparison_MPT.ipynb b/docs/source/tutorial_notebooks/Model_Comparison_MPT.ipynb new file mode 100644 index 000000000..a074ac1cf --- /dev/null +++ b/docs/source/tutorial_notebooks/Model_Comparison_MPT.ipynb @@ -0,0 +1,1114 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Amortized Model Comparison Workflow for Cognitive Modeling\n", + "by Lasse Elsemüller" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 1: Non-Hierarchical Model Comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from functools import partial\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "from scipy import stats\n", + "\n", + "import bayesflow as bf" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Introduction

\n", + "\n", + "This tutorial series contains workflows for comparing competing probabilistic models via posterior model probabilities (PMPs) or Bayes Factors (BFs) with BayesFlow. We start with non-hierarchical model comparison in this tutorial (part 1), while [part 2](./Hierarchical_Model_Comparison_MPT.ipynb) will look at the modifications that allow us to compare hierarchical models. To keep the content concise, the focus will be on the model comparison steps themselves. For a comprehensive overview of the different functionalities BayesFlow has to offer, see the [\"Principled Amortized Bayesian Workflow for Cognitive Modeling\"](./LCA_Model_Posterior_Estimation.ipynb) tutorial notebook." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generative Model Definition" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we will compare simple Multinomial Processing Tree (MPT) models. They are a popular class of stochastic models in cognitive psychology aiming to explain observed categorical decision data by a branched structure of discrete latent processes. We embed the tutorial within the scenario of an old-new-recognition task. In this task, participants memorize a list of stimulus items (e.g., words) and indicate in a subsequent phase whether a presented stimulus was shown before ('old' decision) or is a distractor item ('new' decision)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More specifically, we compare two classic MPT models: The basic one-high-threshold (1HT) model and the popular two-high-threshold (2HT) model.\n", + "\n", + "The 1HT model can be considered as the simplest model formulation: For old items, it assumes that participants either recognize an item or if they do not, guess whether it is old and new. For new items, it assumes that participant directly initiate a guessing process.\n", + "\n", + "The 2HT model extends the process assumed for new items by proposing a similar process as for new items, such that participants either recognize a stimulus as new directly and only if they do not enter the guessing process. This model frequently explains categorical decision data much better than the 1HT model.\n", + "\n", + "For further information on MPT models and the 1HT and 2HT instantiations see [Erdfelder et al. (2009)](https://psycnet.apa.org/record/2009-21670-002)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By traversing the branches of the trees, we obtain the equations for each outcome category. For these equations, we encode 'old' items as 1 and 'new' items as 0. Further, the first index of the response probabilities indicates the stimulus type and the second the response. Thus, $p_{11}$ stands for the probability to correctly recognize a previously presented stimulus, while $p_{01}$ stands for a false alarm (identifying a distractor item as 'old').\n", + "\n", + "In order to make the 2HT model identifiable, we follow the convention of assuming equal probabilities for recognizing old items and identifying new items." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One-high-threshold (1HT) MPT model:\n", + "\n", + "$$\n", + "\\begin{align}\n", + "p_{11} &= d + (1-d)*g \\\\\n", + "p_{10} &= (1-d)*(1-g) \\\\\n", + "p_{01} &= g \\\\\n", + "p_{00} &= (1-g) \\\\\n", + "x &\\sim \\textrm{Multinomial}(p_{11}, p_{10}, p_{01}, p_{00})\n", + "\\end{align}\n", + "$$\n", + "\n", + "\n", + "Two-high-threshold (2HT) MPT model:\n", + "\n", + "$$\n", + "\\begin{align}\n", + "p_{11} &= d + (1-d)*g \\\\\n", + "p_{10} &= (1-d)*(1-g) \\\\\n", + "p_{01} &= (1-d)*g \\\\\n", + "p_{00} &= d + (1-d)*(1-g) \\\\\n", + "x &\\sim \\textrm{Multinomial}(p_{11}, p_{10}, p_{01}, p_{00})\n", + "\\end{align}\n", + "$$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Priors" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our models only have two parameters: $d$ for recognition and $g$ for guessing. As both parameters represent probabilities, we choose moderately informative beta priors with 2 for both shape parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "PARAM_NAMES = [r\"$d$\", r\"$g$\"]\n", + "RNG = np.random.default_rng(2023)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def prior_fun(rng=None):\n", + " \"Samples a random parameter configuration from the prior distribution.\"\n", + " if rng is None:\n", + " rng = np.random.default_rng()\n", + "\n", + " d = rng.beta(a=2, b=2)\n", + " g = rng.beta(a=2, b=2)\n", + " return np.r_[d, g]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The BayesFlow ``Prior`` wrapper provides us further utilities for inspecting our chosen parameter prior:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "prior = bf.simulation.Prior(prior_fun=prior_fun, param_names=PARAM_NAMES)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can sample from the constructed prior, with the argument ``batch_size`` governing the number of draws. For instance, calling the prior with ``batch_size=5`` will return a dictionary, containing, among others, an entry ``prior_draws`` which holds 5 random draws from the prior in the form of a $5 \\times 2$ matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'prior_draws': array([[0.20412946, 0.57868044],\n", + " [0.30434277, 0.54419832],\n", + " [0.32941418, 0.74166574],\n", + " [0.96095506, 0.57423711],\n", + " [0.52095118, 0.15191819]]),\n", + " 'batchable_context': None,\n", + " 'non_batchable_context': None}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prior(batch_size=5)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note, that the prior also returned some other stuff, which allows for more flexible priors (e.g., parametric priors or prior sensitivity analysis). To inspect whether our chosen prior is sensible, we can conduct some prior predictive checks in the parameter space. The simplest one is to simply visualize our prior draws:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f = prior.plot_prior2d(n_samples=500)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that our beta priors symmetrically concentrate the probability mass around .5, but still consider more extreme parameter values possible." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating Simulators" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we translate the model equations above into a simulator from which we can generate simulated observational data. Since both models are nested, we can use a single simulator function. For non-nested models, we would construct one function for each computational model.\n", + "\n", + "We will apply BayesFlow to the trial-level data, as this is much more instructive and generalizes to other applications, noting that traditional MPT models use aggregated data. We therefore do not directly implement the multinomial likelihood stated above (which would results in a single row per participant) but decompose it into Bernoulli draws to generate as many rows per participant as trials. As our binary category probabilities add up to 1, we only need the probabilities for old responses, $p_{11}$ and $p_{01}$.\n", + "\n", + "One could additionally add context variables here to include varying trial numbers for instance (see the [\"Principled Amortized Bayesian Workflow for Cognitive Modeling\"](https://github.com/stefanradev93/BayesFlow/blob/master/docs/source/tutorial_notebooks/LCA_Model_Posterior_Estimation.ipynb) tutorial)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "N_OBS = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def mpt_simulator(theta, model, num_obs, rng=None, *args):\n", + " \"\"\"Simulates data from a 1HT or 2HT MPT model, assuming equal proportions of old and new stimuli.\n", + "\n", + " Parameters\n", + " ----------\n", + " theta : np.ndarray of shape (num_parameters)\n", + " Contains draws from the prior distribution for each parameter.\n", + " model : str, either \"1HT\" or \"2HT\"\n", + " Decides the model to generate data from.\n", + " num_obs : int\n", + " The number of observations (trials).\n", + "\n", + " Returns\n", + " -------\n", + " X : np.ndarray of shape (num_obs, 2)\n", + " The generated data set. Contains two columns:\n", + " 1. Stimulus type (0=\"new\", 1=\"old\")\n", + " 2. Response (0=\"new\", 1=\"old\")\n", + " \"\"\"\n", + " if rng is None:\n", + " rng = np.random.default_rng()\n", + "\n", + " obs_per_condition = int(np.ceil(num_obs / 2))\n", + "\n", + " # Compute category probabilities per model\n", + " d, g = theta\n", + "\n", + " if model == \"1HT\":\n", + " p_11 = d + (1 - d) * g\n", + " p_01 = g\n", + "\n", + " if model == \"2HT\":\n", + " p_11 = d + (1 - d) * g\n", + " p_01 = (1 - d) * g\n", + "\n", + " # Create a vector of stimulus types\n", + " stims = np.repeat([[1, 0]], repeats=obs_per_condition, axis=1).T\n", + "\n", + " # Simulate responses\n", + " resp_old_items = rng.binomial(n=1, p=p_11, size=obs_per_condition)[:, np.newaxis]\n", + " resp_new_items = rng.binomial(n=1, p=p_01, size=obs_per_condition)[:, np.newaxis]\n", + " resp = np.concatenate((resp_old_items, resp_new_items), axis=0)\n", + "\n", + " # Create final data set\n", + " data = np.concatenate((stims, resp), axis=1)\n", + "\n", + " return data" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now pass our custom prior and simulator functions to the ``GenerativeModel`` wrapper. Here, we use the ``partial`` function to provide the arguments for each model. If you provided context variables before, you could use a wrapper for your simulator function beforehand. In this case, specifying ``simulator_is_batched`` would not be necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Performing 2 pilot runs with the 1HT model...\n", + "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 2)\n", + "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 100, 2)\n", + "INFO:root:No optional prior non-batchable context provided.\n", + "INFO:root:No optional prior batchable context provided.\n", + "INFO:root:No optional simulation non-batchable context provided.\n", + "INFO:root:No optional simulation batchable context provided.\n", + "INFO:root:Performing 2 pilot runs with the 2HT model...\n", + "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 2)\n", + "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 100, 2)\n", + "INFO:root:No optional prior non-batchable context provided.\n", + "INFO:root:No optional prior batchable context provided.\n", + "INFO:root:No optional simulation non-batchable context provided.\n", + "INFO:root:No optional simulation batchable context provided.\n" + ] + } + ], + "source": [ + "model_1ht = bf.simulation.GenerativeModel(\n", + " prior=prior_fun,\n", + " simulator=partial(mpt_simulator, model=\"1HT\", num_obs=N_OBS),\n", + " name=\"1HT\",\n", + " simulator_is_batched=False,\n", + ")\n", + "\n", + "model_2ht = bf.simulation.GenerativeModel(\n", + " prior=prior_fun,\n", + " simulator=partial(mpt_simulator, model=\"2HT\", num_obs=N_OBS),\n", + " name=\"2HT\",\n", + " simulator_is_batched=False,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now inspect all the components contained in our finished generative models by calling them:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws', 'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n" + ] + } + ], + "source": [ + "model_output = model_1ht(batch_size=5)\n", + "print(model_output.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of data batch: (5, 100, 2)\n", + "First 3 rows in first data set:\n", + "[[1 1]\n", + " [1 1]\n", + " [1 1]]\n" + ] + } + ], + "source": [ + "print(\"Shape of data batch:\", model_output[\"sim_data\"].shape)\n", + "print(\"First 3 rows in first data set:\")\n", + "print(model_output[\"sim_data\"][0, :3, :])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a last step that is specific to model comparison, we combine all generative models using the ``MultiGenerativeModel`` wrapper. This is necessary because during the training process, we want to generate data from not just one, but all candidate models. The wrapper assumes the common case of equal prior model probabilities, but we could also supply other probabilities via the ``model_probs`` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "generative_models = bf.simulation.MultiGenerativeModel([model_1ht, model_2ht])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prior Predictive Checks" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we fully implemented the generative models as simulators, we can conduct the final model building step by checking the faithfulness of the resulting data patterns. For this, we implement prior predictive checks on the data level in three steps: \n", + "\n", + "1. Simulate a large number of data sets (= participants) from each model\n", + "2. Compute meaningful summary statistics (here: hit rates and false-alarms rates) for each model \n", + "3. Plot the resulting data summaries for each model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# 1. Data simulation\n", + "sim_pfcheck_1ht = model_1ht(batch_size=1000)\n", + "sim_pfcheck_2ht = model_2ht(batch_size=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# 2. Summary statistics\n", + "def get_rates(sim_data):\n", + " \"\"\"Get the hit rate and false alarm rate for each data set (= participant) in a batch of data\n", + " sets simulating binary decision (recognition) tasks.\n", + " Assumes first half of data to cover old items and second half to cover new items.\"\"\"\n", + "\n", + " obs_per_condition = int(np.ceil(sim_data.shape[-2] / 2))\n", + " hit_rates = np.mean(sim_data[:, :obs_per_condition, 1], axis=-1)\n", + " fa_rates = np.mean(sim_data[:, obs_per_condition:, 1], axis=-1)\n", + "\n", + " return hit_rates, fa_rates\n", + "\n", + "\n", + "rates_1htm = get_rates(sim_pfcheck_1ht[\"sim_data\"])\n", + "rates_2htm = get_rates(sim_pfcheck_2ht[\"sim_data\"])\n", + "rates = [rates_1htm, rates_2htm]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 3. Plot rates across all data sets\n", + "fig = plt.figure(constrained_layout=True, figsize=(8, 6))\n", + "subfigs = fig.subfigures(nrows=2, ncols=1)\n", + "model_names = [\"1HT MPT Model\", \"2HT MPT Model\"]\n", + "num_bins = 20\n", + "bins = np.linspace(0.0, 1.0, num_bins + 1)\n", + "\n", + "for row, subfig in enumerate(subfigs):\n", + " subfig.suptitle(model_names[row], fontsize=18)\n", + " axs = subfig.subplots(nrows=1, ncols=2)\n", + " sns.histplot(rates[row][0].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=axs[0]).set(\n", + " title=\"Hit Rates\"\n", + " )\n", + " sns.histplot(rates[row][1].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=axs[1]).set(\n", + " title=\"False Alarm Rates\"\n", + " )\n", + "sns.despine()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unsurprisingly, we observe similar hit rates for both models, as they assume the same latent processes for old items. Their difference in assumptions concerning new items manifests in the false alarm rates: The symmetric beta prior on the $g$ parameter directly translates into false alarm rates around ~.5 for the 1HT model. For the 2HT model, the additional recognition stage set before the guessing process lowers the false alarm rate to ~.25." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining the Neural Approximator" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We assured the faithfulness of our simulator and can move on to building a neural approximator for the Bayesian model comparison task. Our first network is a summary network that reduces the dimensionality of our data.[^1] We assume our data to be independent and identically distributed (iid) and thus choose a ``DeepSet`` network that is aligned to this probabilistic symmetry.\n", + "\n", + "[^1]: This is admittedly a slight overkill for our very simple models, since we could compute perfect summary statistics directly here." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "summary_net = bf.summary_networks.DeepSet()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we choose the inference network for our current inference task. For model comparison, we select the ``PMPNetwork`` which approximates posterior model probabilities (that we could subsequently transform into Bayes factors if desired)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "inference_net = bf.inference_networks.PMPNetwork(num_models=2)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we use the ``AmortizedModelComparison`` wrapper to connect the two networks." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "amortizer = bf.amortizers.AmortizedModelComparison(inference_net, summary_net)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining the Configurator" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use a configurator to mediate between the simulators and the amortizer containing the networks. It transforms data into a suitable format for the neural networks, which are here two elements: The simulated data sets and the indices of the generating model for each data set. For this, we will simply use the ``DefaultModelComparisonConfigurator`` which is automatically initialized by the trainer instance (see below). We will also use the configurator later on, when validating the trained network, for convenient data transformations." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining the Trainer" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can reward ourselves for our hard work and bring all previous elements of our workflow together. We pass them to the ``Trainer`` class, which handles all aspects of the training process for us. If desired, we could also pass it a ``checkpoint_path`` where it regularly saves the trained network so we can reuse it. The consistency check assures us that there should be no major bugs preventing in our training workflow from simulating the data to updating the network weights." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Performing a consistency check with provided components...\n", + "INFO:root:Done.\n" + ] + } + ], + "source": [ + "trainer = bf.trainers.Trainer(\n", + " amortizer=amortizer,\n", + " generative_model=generative_models,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``summary`` function gives us a quick overview of the network component sizes:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"amortized_model_comparison\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " pmp_network (PMPNetwork) multiple 9154 \n", + " \n", + " deep_set (DeepSet) multiple 67466 \n", + " \n", + "=================================================================\n", + "Total params: 76,620\n", + "Trainable params: 76,620\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "amortizer.summary()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training Phase" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simple simulators are extremly fast, so we can use online training (simulating the data on the fly during training). Here, we use 3 epochs with 500 iterations each and a batch size of 64 simulations. This means that we use $3 \\times 500 \\times 64 = 96000$ unique simulations in total for training our neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "52ba1fc1399f4c6cb967246f56abf873", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training epoch 1: 0%| | 0/500 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "diag_plot = bf.diagnostics.plot_losses(train_losses=losses)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the network picked up the important parts of the task very fast and plateaued afterwards, which indicates that we have had more than enough training steps." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Network Validation" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ability of our amortized networks to quickly process thousands of simulated data sets opens up new possibilities for validating our method prior to applying it. Let's first simulate some data from our models and use the configurator to quickly transform it:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate some validation data\n", + "sim_data = generative_models(1000)\n", + "\n", + "# Use the configurator to transform the data structure\n", + "sim_data_transformed = trainer.configurator(sim_data)\n", + "\n", + "# Get true indices and predicted PMPs from the trained network\n", + "sim_indices = sim_data_transformed[\"model_indices\"]\n", + "sim_preds = amortizer(sim_data_transformed)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first ask the most important question: Do our approximated PMPs correspond to some ground-truth? We can approach this question by looking at the _calibration_. It measures the closeness of the PMPs to the true underlying probabilities of our simulated data.\n", + "\n", + "We assess it with ``plot_calibration_curves``, which provides us with three important pieces of information for each model:\n", + "1. The calibration curve, where we bin the predicted PMPs and contrast the bin means with the true probability for the respective model in each bin\n", + "2. The marginal histogram of the bins, which tells us how stable the calibration curve is by showing the fraction of predictions in each bin\n", + "3. The expected calibration error (ECE), a numerical measure of the calibration curve's divergence that takes the binning distribution into account" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cal_curves = bf.diagnostics.plot_calibration_curves(true_models=sim_indices, pred_models=sim_preds)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe a close alignment of the calibration curve to the diagonal without systematic over- or underconfidence. The ECE being close 0 also confirms that our neural approximator produces highly calibrated PMPs.\n", + "We can further inspect our approximator by examing the confusion matrix for our simulated data sets:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bf.diagnostics.plot_confusion_matrix(true_models=sim_indices, pred_models=sim_preds)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that in ~75% of simulated data sets the underlying model is correctly detected. By increasing the training duration and/or size of the neural networks, we could check whether our classifier is performing suboptimally or we already reached the upper bound performance that our sparse data allow for. The excellent calibration that we observed before suggests the second option here. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Network Application" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can apply our trained network to our observed data. To demonstrate this, we simulate some data from the 2HT model. We quickly redefine our generating process with a fixed random seed to obtain reproducible outcomes:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 100, 2)\n" + ] + } + ], + "source": [ + "fixed_rng = np.random.default_rng(2023)\n", + "prior_fixed = bf.simulation.Prior(prior_fun=partial(prior_fun, rng=fixed_rng), param_names=PARAM_NAMES)\n", + "fake_data_generator = bf.simulation.GenerativeModel(\n", + " prior=prior_fixed,\n", + " simulator=partial(mpt_simulator, model=\"2HT\", num_obs=N_OBS, rng=fixed_rng),\n", + " skip_test=True,\n", + " simulator_is_batched=False,\n", + ")\n", + "\n", + "fake_data = fake_data_generator(batch_size=1)[\"sim_data\"]\n", + "print(fake_data.shape)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulated data already has the required (number of data sets, number of observations, number of variables) shape, so we can directly proceed and have a look at the hit rate and the false alarm rate of our fake participant:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0.88]), array([0.02]))" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_rates(fake_data)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we see very low false alarm rate that the 1HT model struggles to explain, so we would expect our neural approximator to assign higher evidence to the 2HT model." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embeddings = summary_net(fake_data)\n", + "preds = inference_net.posterior_probs(embeddings)[0]\n", + "preds" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the PMPs are in favor of the 2HT model. We assumed equal prior model probabilities, so the transformation of these results into a Bayes factor is straightforward:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bayes_factor12 = preds[0] / preds[1]\n", + "bayes_factor12" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Corresponding to the PMPs, the Bayes factor assigns higher evidence to the 2HT model. Despite only having 100 binary observations at hand, the data are so untypical for the 1HT that the Bayes factor reflects the data being ~20 times more likely under the 2HT model compared to the 1HT model." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations, you now know how to conduct amortized Bayesian model comparison with BayesFlow! When you feel ready to find out how to compare hierarchical models, continue with [part 2](./Hierarchical_Model_Comparison_MPT.ipynb)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.10" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": true, + "skip_h1_title": true, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "465.455px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": 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z8u=9w^tOQBpQPk|2KT9A;8p%looQpkBnO7YmJ@;Ua5toD{+`*MR|sa~y>pWVeG zRw$nqTK(R}t{bC!XSzsgR~@)(*wy=9KJ0b`FKRl(s#fAF2c6_*DV~eVB~M$mt%?qA m_5$z!|M@>h0V7(o{Cc3mwI`#!jrJZ8`q~UkG!|M?#*8>#33 literal 0 HcmV?d00001 From d01fefcd1677d2d3ad650b31ee4b77c2726cb9c6 Mon Sep 17 00:00:00 2001 From: elseml <60779710+elseml@users.noreply.github.com> Date: Mon, 8 May 2023 14:56:22 +0200 Subject: [PATCH 2/6] Update README links --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 169f289ae..7507a956c 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ For starters, check out some of our walk-through notebooks: 3. [Posterior estimation for ODEs](docs/source/tutorial_notebooks/Linear_ODE_system.ipynb) 4. [Posterior estimation for SIR-like models](docs/source/tutorial_notebooks/Covid19_Initial_Posterior_Estimation.ipynb) 5. [Model comparison for cognitive models](docs/source/tutorial_notebooks/Model_Comparison_MPT.ipynb) -6. [Hierarchical model comparison for cognitive models](docs/source/tutorial_notebooks/Model_Comparison_MPT.ipynb) +6. [Hierarchical model comparison for cognitive models](docs/source/tutorial_notebooks/Hierarchical_Model_Comparison_MPT.ipynb) ## Project Documentation From e201a822accd1c3b8106bb7b2ed7eaa5204a2752 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lasse=20Elsem=C3=BCller?= Date: Mon, 8 May 2023 16:48:34 +0200 Subject: [PATCH 3/6] Allow for rotating labels in confusion matrix --- bayesflow/diagnostics.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/bayesflow/diagnostics.py b/bayesflow/diagnostics.py index 86bee4645..260ccd53c 100644 --- a/bayesflow/diagnostics.py +++ b/bayesflow/diagnostics.py @@ -1107,6 +1107,8 @@ def plot_confusion_matrix( fig_size=(5, 5), title_fontsize=18, tick_fontsize=12, + xtick_rotation=None, + ytick_rotation=None, normalize=True, cmap=None, title=True, @@ -1124,9 +1126,13 @@ def plot_confusion_matrix( fig_size : tuple or None, optional, default: (5, 5) The figure size passed to the ``matplotlib`` constructor. Inferred if ``None`` title_fontsize : int, optional, default: 18 - The font size of the axis label texts. + The font size of the title text. tick_fontsize : int, optional, default: 12 - The font size of the axis label texts. + The font size of the axis label and model name texts. + xtick_rotation: int, optional, default: None + Rotation of x-axis tick labels (helps with long model names). + ytick_rotation: int, optional, default: None + Rotation of y-axis tick labels (helps with long model names). normalize : bool, optional, default: True A flag for normalization of the confusion matrix. If True, each row of the confusion matrix is normalized to sum to 1. @@ -1165,7 +1171,11 @@ def plot_confusion_matrix( ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0])) ax.set_xticklabels(model_names, fontsize=tick_fontsize) + if xtick_rotation: + plt.xticks(rotation=xtick_rotation, ha="right") ax.set_yticklabels(model_names, fontsize=tick_fontsize) + if ytick_rotation: + plt.yticks(rotation=ytick_rotation) ax.set_xlabel("Predicted model", fontsize=tick_fontsize) ax.set_ylabel("True model", fontsize=tick_fontsize) From fb9b229a74cfd0aab47d3a5e5c5e574db897b8b3 Mon Sep 17 00:00:00 2001 From: elseml <60779710+elseml@users.noreply.github.com> Date: Tue, 9 May 2023 09:55:02 +0200 Subject: [PATCH 4/6] Add model comparison section --- README.md | 72 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 71 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 7507a956c..01f00258a 100644 --- a/README.md +++ b/README.md @@ -179,7 +179,73 @@ preprint, available for free at: https://arxiv.org/abs/2112.08866 ## Model Comparison -Example coming soon... +BayesFlow can not only be used for parameter estimation, but also to approximate Bayesian model comparison via posterior model probabilities or Bayes factors. + +Let's extend the minimal example from before with a second model $M_2$ that we want to compare with our original model $M_1$: + +```python +def simulator(theta, n_obs=50, scale=1.0): + return np.random.default_rng().normal(loc=theta, scale=scale, size=(n_obs, theta.shape[0])) + +def prior_m1(D=2, mu=0., sigma=1.0): + return np.random.default_rng().normal(loc=mu, scale=sigma, size=D) + +def prior_m2(D=2, mu=2., sigma=1.0): + return np.random.default_rng().normal(loc=mu, scale=sigma, size=D) +``` + +We create both models as before and use a `MultiGenerativeModel` wrapper to combine them in a `meta_model`: + +```python +model_m1 = bf.simulation.GenerativeModel(prior_m1, simulator, simulator_is_batched=False) +model_m2 = bf.simulation.GenerativeModel(prior_m2, simulator, simulator_is_batched=False) +meta_model = bf.simulation.MultiGenerativeModel([model_m1, model_m2]) +``` + +Next, we construct our neural network with a `PMPNetwork` for approximating posterior model probabilities: + +```python +summary_net = bf.networks.DeepSet() +probability_net = bf.networks.PMPNetwork(num_models=2) +amortizer = bf.amortizers.AmortizedModelComparison(probability_net, summary_net) +``` + +We combine all previous steps with a `Trainer` instance and train the neural approximator: + +```python +trainer = bf.trainers.Trainer(amortizer=amortizer, generative_model=meta_model) +losses = trainer.train_online(epochs=3, iterations_per_epoch=100, batch_size=32) +``` + +Let's simulate data sets from our models to check our networks' performance: + +```python +sim_data = trainer.configurator(meta_model(5000)) +sim_indices = sim_data["model_indices"] +``` + +When feeding the data to our trained network, we almost immediately obtain posterior model probabilities for each of the 5000 data sets: + +```python +sim_preds = amortizer(sim_data) +sim_preds[0,:] +``` + +How good are these predicted probabilities? We can have a look at the calibration: + +```python +cal_curves = bf.diagnostics.plot_calibration_curves(sim_indices, sim_preds) +``` + +Our approximator shows excellent calibration, with an expected calibration error (ECE) close to 0 and most predicted probabilities being certain of the model underlying a data set. We can further assess patterns of misclassification with a confusion matrix: + +```python +conf_matrix = bf.diagnostics.plot_confusion_matrix(sim_indices, sim_preds) +``` + +For the vast majority of simulated data sets, the generating model is correctly detected. With these diagnostic results backing us up, we can safely apply our trained network to empirical data. + +BayesFlow is also able to conduct model comparison for hierarchical models. See this [tutorial notebook](docs/source/tutorial_notebooks/Hierarchical_Model_Comparison_MPT.ipynb) for an introduction to the associated workflow. ### References and Further Reading @@ -192,6 +258,10 @@ doi:10.1109/TNNLS.2021.3124052 available for free at: https://arxiv.org/abs/2004 Bayesian Model Comparison. ArXiv preprint, available for free at: https://arxiv.org/abs/2210.07278 +- Elsemüller, L., Schnuerch, M., Bürkner, P. C., & Radev, S. T. (2023). A Deep +Learning Method for Comparing Bayesian Hierarchical Models. ArXiv preprint, +available for free at: https://arxiv.org/abs/2301.11873 + ## Likelihood emulation Example coming soon... From 81a7155261eb5eeae057a9a6e4c17975809c96d8 Mon Sep 17 00:00:00 2001 From: elseml <60779710+elseml@users.noreply.github.com> Date: Tue, 9 May 2023 10:12:57 +0200 Subject: [PATCH 5/6] Add diagnostic plots --- README.md | 7 +++++-- img/showcase_calibration_curves.png | Bin 0 -> 185612 bytes img/showcase_confusion_matrix.png | Bin 0 -> 79162 bytes 3 files changed, 5 insertions(+), 2 deletions(-) create mode 100644 img/showcase_calibration_curves.png create mode 100644 img/showcase_confusion_matrix.png diff --git a/README.md b/README.md index 01f00258a..6797d4c33 100644 --- a/README.md +++ b/README.md @@ -228,7 +228,6 @@ When feeding the data to our trained network, we almost immediately obtain poste ```python sim_preds = amortizer(sim_data) -sim_preds[0,:] ``` How good are these predicted probabilities? We can have a look at the calibration: @@ -237,12 +236,16 @@ How good are these predicted probabilities? We can have a look at the calibratio cal_curves = bf.diagnostics.plot_calibration_curves(sim_indices, sim_preds) ``` -Our approximator shows excellent calibration, with an expected calibration error (ECE) close to 0 and most predicted probabilities being certain of the model underlying a data set. We can further assess patterns of misclassification with a confusion matrix: + + +Our approximator shows excellent calibration, with the calibration curve being closely aligned to the diagonal, an expected calibration error (ECE) near 0 and most predicted probabilities being certain of the model underlying a data set. We can further assess patterns of misclassification with a confusion matrix: ```python conf_matrix = bf.diagnostics.plot_confusion_matrix(sim_indices, sim_preds) ``` + + For the vast majority of simulated data sets, the generating model is correctly detected. With these diagnostic results backing us up, we can safely apply our trained network to empirical data. BayesFlow is also able to conduct model comparison for hierarchical models. 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z++a!m4+!v13+esqPGxPJJfXrl3M#0|3Ak>Fk{&aDiD*EnPXG(k)fIvhAx!6pNJnj7 zq^&}w33c&51LI&P9NIGGTD>+#GAEk9eUc6?8s#tsf4m00tPGx~NFQb$1?|6#IzFX@ zN&~wr5^-E~*5mAB2fRpV%+<`|2)GkmZ z4Am_sIDc0ma?M8~8HP<)hI*7pXGC2fNGpavNeu(OJZkiuiBGl=kS~8sF1D@(@ye*e z-_2=vEOgp?AcXoTJtq0;IKW}_BY^pLAt;35<6SP^*@4cPu2nro;jrV1c1JsexH`o9 zA}ZvsbZmkZqx8o|zYuzS;Gw-WA?(EFKwy0ia3ukv3}p8mgOU;NKA`k!Y+d*J`JXv1Y?p9{{m~ Date: Tue, 9 May 2023 10:18:29 +0200 Subject: [PATCH 6/6] Rescale confusion matrix --- README.md | 2 +- img/showcase_confusion_matrix.png | Bin 79162 -> 72967 bytes 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6797d4c33..d26f2405b 100644 --- a/README.md +++ b/README.md @@ -244,7 +244,7 @@ Our approximator shows excellent calibration, with the calibration curve being c conf_matrix = bf.diagnostics.plot_confusion_matrix(sim_indices, sim_preds) ``` - + For the vast majority of simulated data sets, the generating model is correctly detected. 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