\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, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8dc9df767f6f48149f4abef4db8f096d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Training epoch 2: 0%| | 0/500 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5c22969ca92b4a71be623364ca1eb44c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Training epoch 3: 0%| | 0/500 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "losses = trainer.train_online(epochs=3, iterations_per_epoch=500, batch_size=64)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Right after training finishes, we can inspect how the loss evolved over the training duration:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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)."
+ ]
+ }
+ ],
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diff --git a/docs/source/tutorial_notebooks/img/1HT2HT.png b/docs/source/tutorial_notebooks/img/1HT2HT.png
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