examples/From_BayesFlow_1.1_to_2.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Moving from BayesFlow v1.1 to v2.0\n",
    "\n",
    "_Authors: Leona Odole, Valentin Pratz_\n",
    "\n",
    "**Note: This document is work in progress, and not all relevant changes are discussed yet. If you encounter problems during the transition, please do not hesitate to reach out.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Long-time users of BayesFlow will notice that with the update to version 2.0 many things have changed. This short guide aims to clarify some of those changes. Users familiar with the previous Quickstart guide will notice that this notebook follows a similar structure, but assumes that users are already familiar with BayesFlow. We omit many of the the mathematical explanations in favor of demonstrating the differences in workflow. For a more detailed explanation of the BayesFlow framework, users should read, for example, the Starter Notebook on Bayesian Linear Regression.\n",
    "\n",
    "Additionally to avoid confusion, similarly named objects from _BayesFlow v1.1_ will have `1.1` after their name, whereas those from _BayesFlow v2.0_ will not. Finally, a short table with a summary of the function call changes is provided at the end of the guide. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras Framework\n",
    "\n",
    "BayesFlow 2.0 looks quite different from BayesFlow 1.1 because the library was refactored to replace the old backend, TensorFlow, with the new [Keras](https://keras.io) API. Users can now choose their preferred backend among the machine learning frameworks `TensorFlow`, `JAX` and `PyTorch`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# ensure the backend is set\n",
    "import os\n",
    "if \"KERAS_BACKEND\" not in os.environ:\n",
    "    # set this to \"torch\", \"tensorflow\", or \"jax\"\n",
    "    os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
    "\n",
    "import keras\n",
    "import bayesflow as bf\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, BayesFlow 2.0 relies much more on dictionaries since parameters are now named by convention. Many objects now expect a dictionary, and parameters and data are returned as dictionaries as well. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Workflow \n",
    "\n",
    "### Priors and Likelihood Model\n",
    "\n",
    "Any BayesFlow workflow begins with simulated data which is specified with a prior and a corresponding likelihood function (simulator). While these two core components are still present, their use and naming conventions within the workflow have changed. \n",
    "\n",
    "Previously users would define a prior function and pass it to a `Prior1.1` object to sample prior values. The likelihood would also be specified via a function and passed to a `Simulator1.1` wrapper to produce observations for given parameter values. These were then combined in the `GenerativeModel1.1`.  \n",
    "\n",
    "In 2.0 we no longer make use of the `Prior1.1`, `Simulator1.1` or `GenerativeModel1.1` objects. Instead, the `Simulator` class comprises the whole functionality, taking the role of the `GenerativeModel1.1`. It directly produces joint samples from prior and likelihood, without creating separate `Prior1.1` and `Simulator1.1` objects first. The `bf.simulator.make_simulator` offers a convenient wrapper to create the appropriate simulator for different settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def theta_prior():\n",
    "    theta = np.random.normal(size=4)\n",
    "    # previously: \n",
    "    # return theta \n",
    "    return dict(theta=theta) # now we return a dictionary\n",
    "    \n",
    "\n",
    "def likelihood_model(theta, n_obs):\n",
    "    x = np.random.normal(loc=theta, size=(n_obs, theta.shape[0]))\n",
    "    return dict(x=x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously the prior and likelihood were defined as"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``` python\n",
    "prior_1 = bf.simulation.Prior(prior_fun=theta_prior)\n",
    "simulator_1 = bf.simulation.Simulator(simulator_fun=likelihood_model)\n",
    "model_1 = bf.simulation.GenerativeModel(prior=prior_1, simulator=simulator_1)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whereas the new framework directly uses the likelihood and prior functions directly in the simulator. In addition, we can define a meta function which allows us, for example, to dynamically set the number of observations per simulated batch. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def meta():\n",
    "    return dict(n_obs=1)\n",
    "\n",
    "simulator = bf.make_simulator([theta_prior, likelihood_model], meta_fn=meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then generate batches of training samples as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 1, 4)\n",
      "(500, 4)\n"
     ]
    }
   ],
   "source": [
    "sim_draws = simulator.sample(500)\n",
    "print(sim_draws[\"x\"].shape)\n",
    "print(sim_draws[\"theta\"].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adapter and Data Configuration\n",
    "\n",
    "In _BayesFlow 1.1_, data configuration and pre-processing had to be handled manually in a user-specified `configurator` function. In _BayesFlow 2.0_ we also need to specify the data configuration, but this has become much simpler, as we now offer a (semi-)invertible processing pipeline. The `bf.adapters.Adapter` class can be used to perform operations like standardization, but also for renaming and grouping variables into the format required by BayesFlow.\n",
    "\n",
    "In general, BayesFlow expects three different kinds of inputs. The `summary_variables` are variables that will be summarized by a summary network. The `inference_variables` are variables we want to learn the approximate distributions for. In the posterior approximation setting, we would provide the parameter values drawn from the prior distribution, as we want to approximate a conditional distribution $p(\\theta|x)$ of the parameters $\\theta$. The `inference_conditions` are variables which are directly passed as conditions, without going through a summary network. Examples for this are hand-crafted summary statistics, as well as context variables like the number of observations. Previously these things were inferred from the type of network used, but now they need to be defined explictly using the `adapter`. The new approach is much more explicit and extensible. It also makes it easier to change individual settings, while keeping other settings at their defaults.\n",
    "\n",
    "The example below shows an example of an adapter. After some initial processing, including the standardization of the variables, it renames the respective variables to establish the required format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "adapter = (\n",
    "    bf.adapters.Adapter()\n",
    "    .to_array()\n",
    "    .broadcast(\"n_obs\", to=\"theta\")\n",
    "    .convert_dtype(from_dtype=\"float64\", to_dtype=\"float32\")\n",
    "    .standardize(exclude=[\"n_obs\"])\n",
    "    .rename(\"x\", \"summary_variables\")\n",
    "    .rename(\"theta\", \"inference_variables\")\n",
    "    .rename(\"n_obs\", \"inference_conditions\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a full list of the adapter transforms, please refer to the documentation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Network and Inference Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in _BayesFlow 1.1_, users can specify an appropriate summary and inference network. All networks can be found in the `bf.networks` module. Summary networks inherit from the `SummaryNetwork` class. Please note that not all summary networks have been ported to the new version yet, so please refer to the API reference to see if the networks you require are already available. If a network you need is missing, please consider opening an issue so that we can prioritize its implementation. Also, please be aware that the parameters and defaults for some classes have changed, so you might need to adapt your code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_net = bf.networks.DeepSet(depth=2, summary_dim=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the inference network there are now several implemented architectures for users to choose from. They are `FlowMatching`, `ConsistencyModel`, and `CouplingFlow`. For this demonstration we use `FlowMatching`, for explanations on the different models please refer to the other examples and the API documentation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "inference_net = bf.networks.FlowMatching()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Approximator (Amortizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously the actual training and amortization was done with two different objects, an `Amortizer1.1` and a `Trainer1.1`. First, users would create an amortizer containing the summary and inference networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``` python\n",
    "# Renamed to Approximator\n",
    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net)\n",
    "\n",
    "# Defunct\n",
    "trainer = bf.trainers.Trainer(amortizer=amortizer, generative_model=gen_model)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This functionality is now handled by the `bf.approximators` module. The default approximator is the `ContinuousApproximator`, which approximates distributions for continuous variables. It takes the summary network, inference network and the data adapter as arguments. The `PointApproximator` enables the approximation of point summaries ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "approximator = bf.approximators.ContinuousApproximator(\n",
    "    summary_network=summary_net,\n",
    "    inference_network=inference_net,\n",
    "    adapter=adapter\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whereas previously a  `Trainer1.1` object was used for training, users can now directly call fit on the `Approximator`. For additional flexibility in training, the `approximator` also has two additional arguments the `learning_rate` and `optimizer`. The optimizer can be any Keras optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 1e-4\n",
    "optimizer = keras.optimizers.AdamW(learning_rate=learning_rate, clipnorm=1.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Users must then compile the `approximator` with the `optimizer` to make everything ready for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "approximator.compile(optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train the network users now only need to call fit on the `approximator`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = approximator.fit(\n",
    "    epochs=50,\n",
    "    num_batches=200,\n",
    "    batch_size=64,\n",
    "    simulator=simulator\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics \n",
    "\n",
    "Another change was made in the model diagnostics. Much of the functionality remains the same, but the naming convention has changed. For example previously users would plot losses by using `bf.diagnostics.plot_losses()`. In *BayesFlow 2.0*, we instead have all the plotting function grouped together in `bf.diagnostics.plots`. This means, for example, that the loss function is now in `bf.diagnostics.plots.loss()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = bf.diagnostics.plots.loss(\n",
    "    history=history\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was done to also incorporate other diagnostic metrics such as calibration error, posterior contraction, and root mean squared error. These functions can be found in the `bf.diagnostics.metrics` submodule. For more information please refer to the API documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Change Table \n",
    "\n",
    "| BayesFlow v1.1       | BayesFlow v2.0 usage |\n",
    "| :--------| :---------| \n",
    "| `Prior`, `Simulator` | Defunct and no longer standalone objects but incorporated into `bf.simulators.Simulator` | \n",
    "|`GenerativeModel`     | Defunct with it's functionality having been taken over by `bf.simulators.make_simulator` | \n",
    "| `training.configurator` | Functionality taken over by `bf.adapters.Adapter` | \n",
    "|`Trainer` | Functionality taken over by `fit` method of `bf.approximators.Approximator` | \n",
    "| `AmortizedPosterior`, `AmortizedLikelihood` | Functionality taken over by `ContinuousApproximator` | "
   ]
  }
 ],
 "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": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}