From 074efa0dba76218728239e197064a7cda1feb892 Mon Sep 17 00:00:00 2001
From: Jan <jan.boelts@mailbox.org>
Date: Thu, 12 Dec 2024 16:37:59 +0100
Subject: [PATCH] update notebook, bugfixes

---
 .../potentials/likelihood_based_potential.py  |   4 +-
 .../Example_01_DecisionMakingModel.ipynb      | 190 +++++++++---------
 2 files changed, 95 insertions(+), 99 deletions(-)

diff --git a/sbi/inference/potentials/likelihood_based_potential.py b/sbi/inference/potentials/likelihood_based_potential.py
index 20a831899..f82d8443c 100644
--- a/sbi/inference/potentials/likelihood_based_potential.py
+++ b/sbi/inference/potentials/likelihood_based_potential.py
@@ -134,12 +134,12 @@ def conditioned_potential(
             theta: Tensor, x_o: Optional[Tensor] = None, track_gradients: bool = True
         ) -> Tensor:
             assert (
-                len(dims_to_sample) == theta.shape[1] - condition.shape[1]
+                len(dims_to_sample) == theta.shape[1]
             ), "dims_to_sample must match the number of parameters to sample."
             theta_without_condition = theta[:, dims_to_sample]
 
             return _log_likelihood_with_iid_condition(
-                x=x_o or self.x_o,
+                x=x_o if x_o is not None else self.x_o,
                 theta_without_condition=theta_without_condition,
                 condition=condition,
                 estimator=self.likelihood_estimator,
diff --git a/tutorials/Example_01_DecisionMakingModel.ipynb b/tutorials/Example_01_DecisionMakingModel.ipynb
index fcfa10ced..ee56f3585 100644
--- a/tutorials/Example_01_DecisionMakingModel.ipynb
+++ b/tutorials/Example_01_DecisionMakingModel.ipynb
@@ -87,16 +87,14 @@
     "import torch\n",
     "from pyro.distributions import InverseGamma\n",
     "from torch import Tensor\n",
-    "from torch.distributions import Beta, Binomial, Categorical, Gamma\n",
+    "from torch.distributions import Beta, Binomial, Gamma, Distribution\n",
+    "from typing import Union\n",
     "\n",
     "from sbi.analysis import pairplot\n",
     "from sbi.inference import MNLE, MCMCPosterior\n",
     "from sbi.inference.potentials.base_potential import BasePotential\n",
-    "from sbi.inference.potentials.likelihood_based_potential import (\n",
-    "    MixedLikelihoodBasedPotential,\n",
-    ")\n",
-    "from sbi.utils import MultipleIndependent, mcmc_transform\n",
-    "from sbi.utils.conditional_density_utils import ConditionedPotential\n",
+    "from sbi.inference.potentials.likelihood_based_potential import LikelihoodBasedPotential\n",
+    "from sbi.utils import BoxUniform, MultipleIndependent, mcmc_transform\n",
     "from sbi.utils.metrics import c2st\n",
     "from sbi.utils.torchutils import atleast_2d"
    ]
@@ -127,15 +125,29 @@
     "    return torch.cat((rts, choices), dim=1)\n",
     "\n",
     "\n",
-    "# The potential function defines the ground truth likelihood and allows us to\n",
-    "# obtain reference posterior samples via MCMC.\n",
     "class BinomialGammaPotential(BasePotential):\n",
-    "\n",
-    "    def __init__(self, prior, x_o, concentration_scaling=1.0, device=\"cpu\"):\n",
+    "    \"\"\"Binomial-Gamma potential for mixed data.\"\"\"\n",
+    "\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        prior: Distribution,\n",
+    "        x_o: Tensor,\n",
+    "        concentration_scaling: Union[Tensor, float] = 1.0,\n",
+    "        device=\"cpu\",\n",
+    "    ):\n",
     "        super().__init__(prior, x_o, device)\n",
+    "\n",
+    "        # concentration_scaling needs to be a float or match the batch size\n",
+    "        if isinstance(concentration_scaling, Tensor):\n",
+    "            num_trials = x_o.shape[0]\n",
+    "            assert concentration_scaling.shape[0] == num_trials\n",
+    "\n",
+    "            # Reshape to match convention (batch_size, num_trials, *event_shape)\n",
+    "            concentration_scaling = concentration_scaling.reshape(1, num_trials, -1)\n",
+    "\n",
     "        self.concentration_scaling = concentration_scaling\n",
     "\n",
-    "    def __call__(self, theta, track_gradients: bool = True):\n",
+    "    def __call__(self, theta: Tensor, track_gradients: bool = True) -> Tensor:\n",
     "        theta = atleast_2d(theta)\n",
     "\n",
     "        with torch.set_grad_enabled(track_gradients):\n",
@@ -143,11 +155,12 @@
     "\n",
     "        return iid_ll + self.prior.log_prob(theta)\n",
     "\n",
-    "    def iid_likelihood(self, theta):\n",
+    "    def iid_likelihood(self, theta: Tensor) -> Tensor:\n",
     "        batch_size = theta.shape[0]\n",
     "        num_trials = self.x_o.shape[0]\n",
     "        theta = theta.reshape(batch_size, 1, -1)\n",
     "        beta, rho = theta[:, :, :1], theta[:, :, 1:]\n",
+    "\n",
     "        # vectorized\n",
     "        logprob_choices = Binomial(probs=rho).log_prob(\n",
     "            self.x_o[:, 1:].reshape(1, num_trials, -1)\n",
@@ -216,7 +229,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8070275b9eac45d1991d5be41935c145",
+       "model_id": "271465813a4f4e2aab0fce77c0520ed5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -269,13 +282,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Neural network successfully converged after 65 epochs."
+      " Neural network successfully converged after 70 epochs."
      ]
     }
    ],
    "source": [
     "# Training data\n",
-    "num_simulations = 20000\n",
+    "num_simulations = 10000\n",
     "# For training the MNLE emulator we need to define a proposal distribution, the prior is\n",
     "# a good choice.\n",
     "proposal = prior\n",
@@ -284,7 +297,7 @@
     "\n",
     "# Train MNLE and obtain MCMC-based posterior.\n",
     "trainer = MNLE()\n",
-    "estimator = trainer.append_simulations(theta, x).train(training_batch_size=1000)"
+    "estimator = trainer.append_simulations(theta, x).train()"
    ]
   },
   {
@@ -295,7 +308,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1a7792c605404a11a586681fcd3c0a32",
+       "model_id": "b6672a4116a642ea9064dd37f4cc0a33",
        "version_major": 2,
        "version_minor": 0
       },
@@ -328,7 +341,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x600 with 4 Axes>"
       ]
@@ -390,7 +403,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb02120c58a54d029953b4c589f24eca",
+       "model_id": "59ce5b5d56ac40f9ab6a8ba4a6f62dee",
        "version_major": 2,
        "version_minor": 0
       },
@@ -404,7 +417,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1cd3bc58ca8e4a21b1df2812fad8bf45",
+       "model_id": "5f53b93e15914db098b7d8f67786eb27",
        "version_major": 2,
        "version_minor": 0
       },
@@ -430,7 +443,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x600 with 4 Axes>"
       ]
@@ -477,7 +490,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "c2st between true and MNLE posterior: 0.593\n"
+      "c2st between true and MNLE posterior: 0.5605\n"
      ]
     }
    ],
@@ -515,16 +528,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Define a proposal that contains both, priors for the parameters and a discrte\n",
+    "# prior over experimental conditions.\n",
+    "proposal = MultipleIndependent(\n",
+    "    [\n",
+    "        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n",
+    "        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n",
+    "        BoxUniform(torch.tensor([0.0]), torch.tensor([1.0])),\n",
+    "    ],\n",
+    "    validate_args=False,\n",
+    ")\n",
+    "\n",
     "# define a simulator wrapper in which the experimental condition are contained\n",
     "# in theta and passed to the simulator.\n",
-    "def sim_wrapper(theta):\n",
+    "def sim_wrapper(theta_and_conditions):\n",
     "    # simulate with experiment conditions\n",
     "    return mixed_simulator(\n",
     "        # we assume the first two parameters are beta and rho\n",
-    "        theta=theta[:, :2],\n",
+    "        theta=theta_and_conditions[:, :2],\n",
     "        # we treat the third concentration parameter as an experimental condition\n",
-    "        # add 1 to deal with 0 values from Categorical distribution\n",
-    "        concentration_scaling=theta[:, 2:] + 1,\n",
+    "        concentration_scaling=theta_and_conditions[:, 2:],\n",
     "    )"
    ]
   },
@@ -534,17 +557,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Define a proposal that contains both, priors for the parameters and a discrte\n",
-    "# prior over experimental conditions.\n",
-    "proposal = MultipleIndependent(\n",
-    "    [\n",
-    "        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n",
-    "        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n",
-    "        Categorical(probs=torch.ones(1, 3)),  # 3 discrete conditions\n",
-    "    ],\n",
-    "    validate_args=False,\n",
-    ")\n",
-    "\n",
     "# Simulated data\n",
     "num_simulations = 10000\n",
     "num_samples = 1000\n",
@@ -554,10 +566,13 @@
     "\n",
     "# simulate observed data and define ground truth parameters\n",
     "num_trials = 10\n",
-    "theta_o = proposal.sample((1,))\n",
-    "theta_o[0, 2] = 2.0  # set condition to 2 as in original simulator.\n",
-    "# NOTE: we use the same experimental condition for all trials.\n",
-    "x_o = sim_wrapper(theta_o.repeat(num_trials, 1))"
+    "# draw one ground truth parameter\n",
+    "theta_o = proposal.sample((1,))[:, :2]\n",
+    "# draw num_trials many different conditions\n",
+    "conditions = proposal.sample((num_trials,))[:, 2:]\n",
+    "# Theta is repeated for each trial, conditions are different for each trial.\n",
+    "theta_and_conditions_o = torch.cat((theta_o.repeat(num_trials, 1), conditions), dim=1)\n",
+    "x_o = sim_wrapper(theta_and_conditions_o)"
    ]
   },
   {
@@ -589,7 +604,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ad169fdca3da40649e6e1c329460e355",
+       "model_id": "bd0bec9c116a43eaa7783e3627485dfe",
        "version_major": 2,
        "version_minor": 0
       },
@@ -599,6 +614,26 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[14], line 21\u001b[0m\n\u001b[1;32m      8\u001b[0m prior_transform \u001b[38;5;241m=\u001b[39m mcmc_transform(prior)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# We can now use the PotentialFunctionProvider to obtain a ground-truth\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m# posterior via MCMC.\u001b[39;00m\n\u001b[1;32m     12\u001b[0m true_posterior_samples \u001b[38;5;241m=\u001b[39m \u001b[43mMCMCPosterior\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m    \u001b[49m\u001b[43mBinomialGammaPotential\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx_o\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconcentration_scaling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconditions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     17\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     18\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtheta_transform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_transform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     19\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproposal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmcmc_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m---> 21\u001b[0m \u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshow_progress_bars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/qode/sbi/sbi/inference/posteriors/mcmc_posterior.py:318\u001b[0m, in \u001b[0;36mMCMCPosterior.sample\u001b[0;34m(self, sample_shape, x, method, thin, warmup_steps, num_chains, init_strategy, init_strategy_parameters, init_strategy_num_candidates, mcmc_parameters, mcmc_method, sample_with, num_workers, mp_context, show_progress_bars)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(track_gradients):\n\u001b[1;32m    317\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice_np\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice_np_vectorized\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 318\u001b[0m         transformed_samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_slice_np_mcmc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    319\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    320\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpotential_function\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpotential_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    321\u001b[0m \u001b[43m            \u001b[49m\u001b[43minitial_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m            \u001b[49m\u001b[43mthin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m    323\u001b[0m \u001b[43m            \u001b[49m\u001b[43mwarmup_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwarmup_steps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m    324\u001b[0m \u001b[43m            \u001b[49m\u001b[43mvectorized\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mslice_np_vectorized\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m            \u001b[49m\u001b[43minterchangeable_chains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    326\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    327\u001b[0m \u001b[43m            \u001b[49m\u001b[43mshow_progress_bars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_progress_bars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    329\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhmc_pyro\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnuts_pyro\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    330\u001b[0m         transformed_samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pyro_mcmc(\n\u001b[1;32m    331\u001b[0m             num_samples\u001b[38;5;241m=\u001b[39mnum_samples,\n\u001b[1;32m    332\u001b[0m             potential_function\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpotential_,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    339\u001b[0m             mp_context\u001b[38;5;241m=\u001b[39mmp_context,\n\u001b[1;32m    340\u001b[0m         )\n",
+      "File \u001b[0;32m~/qode/sbi/sbi/inference/posteriors/mcmc_posterior.py:753\u001b[0m, in \u001b[0;36mMCMCPosterior._slice_np_mcmc\u001b[0;34m(self, num_samples, potential_function, initial_params, thin, warmup_steps, vectorized, interchangeable_chains, num_workers, init_width, show_progress_bars)\u001b[0m\n\u001b[1;32m    751\u001b[0m num_samples_ \u001b[38;5;241m=\u001b[39m ceil((num_samples \u001b[38;5;241m*\u001b[39m thin) \u001b[38;5;241m/\u001b[39m num_chains)\n\u001b[1;32m    752\u001b[0m \u001b[38;5;66;03m# Run mcmc including warmup\u001b[39;00m\n\u001b[0;32m--> 753\u001b[0m samples \u001b[38;5;241m=\u001b[39m \u001b[43mposterior_sampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwarmup_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnum_samples_\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    754\u001b[0m samples \u001b[38;5;241m=\u001b[39m samples[:, warmup_steps:, :]  \u001b[38;5;66;03m# discard warmup steps\u001b[39;00m\n\u001b[1;32m    755\u001b[0m samples \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mfrom_numpy(samples)  \u001b[38;5;66;03m# chains x samples x dim\u001b[39;00m\n",
+      "File \u001b[0;32m~/qode/sbi/sbi/samplers/mcmc/slice_numpy.py:462\u001b[0m, in \u001b[0;36mSliceSamplerVectorized.run\u001b[0;34m(self, num_samples)\u001b[0m\n\u001b[1;32m    455\u001b[0m         sc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnext_param\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate([\n\u001b[1;32m    456\u001b[0m             sc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m][: sc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morder\u001b[39m\u001b[38;5;124m\"\u001b[39m][sc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi\u001b[39m\u001b[38;5;124m\"\u001b[39m]]],\n\u001b[1;32m    457\u001b[0m             [sc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcxi\u001b[39m\u001b[38;5;124m\"\u001b[39m]],\n\u001b[1;32m    458\u001b[0m             sc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m][sc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morder\u001b[39m\u001b[38;5;124m\"\u001b[39m][sc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi\u001b[39m\u001b[38;5;124m\"\u001b[39m]] \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m :],\n\u001b[1;32m    459\u001b[0m         ])\n\u001b[1;32m    461\u001b[0m params \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mstack([sc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnext_param\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m sc \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mvalues()])\n\u001b[0;32m--> 462\u001b[0m log_probs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_prob_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    464\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_chains):\n\u001b[1;32m    465\u001b[0m     sc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate[c]\n",
+      "File \u001b[0;32m~/qode/sbi/sbi/inference/posteriors/mcmc_posterior.py:738\u001b[0m, in \u001b[0;36mMCMCPosterior._slice_np_mcmc.<locals>.multi_obs_potential\u001b[0;34m(params)\u001b[0m\n\u001b[1;32m    736\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmulti_obs_potential\u001b[39m(params):\n\u001b[1;32m    737\u001b[0m     \u001b[38;5;66;03m# Params are of shape (num_chains * num_obs, event).\u001b[39;00m\n\u001b[0;32m--> 738\u001b[0m     all_potentials \u001b[38;5;241m=\u001b[39m \u001b[43mpotential_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Shape: (num_chains, num_obs)\u001b[39;00m\n\u001b[1;32m    739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m all_potentials\u001b[38;5;241m.\u001b[39mflatten()\n",
+      "File \u001b[0;32m~/qode/sbi/sbi/utils/potentialutils.py:44\u001b[0m, in \u001b[0;36mtransformed_potential\u001b[0;34m(theta, potential_fn, theta_transform, device, track_gradients)\u001b[0m\n\u001b[1;32m     41\u001b[0m theta \u001b[38;5;241m=\u001b[39m theta_transform\u001b[38;5;241m.\u001b[39minv(transformed_theta)  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m     42\u001b[0m log_abs_det \u001b[38;5;241m=\u001b[39m theta_transform\u001b[38;5;241m.\u001b[39mlog_abs_det_jacobian(theta, transformed_theta)\n\u001b[0;32m---> 44\u001b[0m posterior_potential \u001b[38;5;241m=\u001b[39m \u001b[43mpotential_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrack_gradients\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrack_gradients\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m posterior_potential_transformed \u001b[38;5;241m=\u001b[39m posterior_potential \u001b[38;5;241m-\u001b[39m log_abs_det\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m posterior_potential_transformed\n",
+      "Cell \u001b[0;32mIn[2], line 47\u001b[0m, in \u001b[0;36mBinomialGammaPotential.__call__\u001b[0;34m(self, theta, track_gradients)\u001b[0m\n\u001b[1;32m     44\u001b[0m theta \u001b[38;5;241m=\u001b[39m atleast_2d(theta)\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(track_gradients):\n\u001b[0;32m---> 47\u001b[0m     iid_ll \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miid_likelihood\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m iid_ll \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior\u001b[38;5;241m.\u001b[39mlog_prob(theta)\n",
+      "Cell \u001b[0;32mIn[2], line 65\u001b[0m, in \u001b[0;36mBinomialGammaPotential.iid_likelihood\u001b[0;34m(self, theta)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# vectorized\u001b[39;00m\n\u001b[1;32m     58\u001b[0m logprob_choices \u001b[38;5;241m=\u001b[39m Binomial(probs\u001b[38;5;241m=\u001b[39mrho)\u001b[38;5;241m.\u001b[39mlog_prob(\n\u001b[1;32m     59\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx_o[:, \u001b[38;5;241m1\u001b[39m:]\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m1\u001b[39m, num_trials, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     60\u001b[0m )\n\u001b[1;32m     62\u001b[0m logprob_rts \u001b[38;5;241m=\u001b[39m \u001b[43mInverseGamma\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     63\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconcentration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcentration_scaling\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeta\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m---> 65\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx_o\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     67\u001b[0m joint_likelihood \u001b[38;5;241m=\u001b[39m (logprob_choices \u001b[38;5;241m+\u001b[39m logprob_rts)\u001b[38;5;241m.\u001b[39msqueeze()\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m joint_likelihood\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mSize([theta\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx_o\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]])\n",
+      "File \u001b[0;32m~/miniconda3/envs/sbi-dev/lib/python3.12/site-packages/torch/distributions/transformed_distribution.py:176\u001b[0m, in \u001b[0;36mTransformedDistribution.log_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    169\u001b[0m     log_prob \u001b[38;5;241m=\u001b[39m log_prob \u001b[38;5;241m-\u001b[39m _sum_rightmost(\n\u001b[1;32m    170\u001b[0m         transform\u001b[38;5;241m.\u001b[39mlog_abs_det_jacobian(x, y),\n\u001b[1;32m    171\u001b[0m         event_dim \u001b[38;5;241m-\u001b[39m transform\u001b[38;5;241m.\u001b[39mdomain\u001b[38;5;241m.\u001b[39mevent_dim,\n\u001b[1;32m    172\u001b[0m     )\n\u001b[1;32m    173\u001b[0m     y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m    175\u001b[0m log_prob \u001b[38;5;241m=\u001b[39m log_prob \u001b[38;5;241m+\u001b[39m _sum_rightmost(\n\u001b[0;32m--> 176\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_dist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m, event_dim \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbase_dist\u001b[38;5;241m.\u001b[39mevent_shape)\n\u001b[1;32m    177\u001b[0m )\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m log_prob\n",
+      "File \u001b[0;32m~/miniconda3/envs/sbi-dev/lib/python3.12/site-packages/torch/distributions/gamma.py:86\u001b[0m, in \u001b[0;36mGamma.log_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_args:\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_sample(value)\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m     85\u001b[0m     torch\u001b[38;5;241m.\u001b[39mxlogy(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconcentration, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrate)\n\u001b[0;32m---> 86\u001b[0m     \u001b[38;5;241m+\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxlogy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcentration\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrate \u001b[38;5;241m*\u001b[39m value\n\u001b[1;32m     88\u001b[0m     \u001b[38;5;241m-\u001b[39m torch\u001b[38;5;241m.\u001b[39mlgamma(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconcentration)\n\u001b[1;32m     89\u001b[0m )\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
     }
    ],
    "source": [
@@ -617,8 +652,7 @@
     "    BinomialGammaPotential(\n",
     "        prior,\n",
     "        x_o,\n",
-    "        concentration_scaling=float(theta_o[0, 2])\n",
-    "        + 1.0,  # add one because the sim_wrapper adds one (see above)\n",
+    "        concentration_scaling=conditions,\n",
     "    ),\n",
     "    theta_transform=prior_transform,\n",
     "    proposal=prior,\n",
@@ -642,6 +676,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/janteusen/qode/sbi/sbi/inference/trainers/base.py:271: UserWarning: Z-scoring these simulation outputs resulted in 4 unique datapoints. Before z-scoring, it had been 19864. This can occur due to numerical inaccuracies when the data covers a large range of values. Consider either setting `z_score_x=False` (but beware that this can be problematic for training the NN) or exclude outliers from your dataset. Note: if you have already set `z_score_x=False`, this warning will still be displayed, but you can ignore it.\n",
+      "  warn_if_zscoring_changes_data(x)\n",
       "/Users/janteusen/qode/sbi/sbi/neural_nets/factory.py:205: UserWarning: The mixed neural likelihood estimator assumes that x contains continuous data in the first n-1 columns (e.g., reaction times) and categorical data in the last column (e.g., corresponding choices). If this is not the case for the passed `x` do not use this function.\n",
       "  return model_builders[model](batch_x=batch_x, batch_y=batch_theta, **kwargs)\n"
      ]
@@ -650,12 +686,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Neural network successfully converged after 60 epochs."
+      " Neural network successfully converged after 102 epochs."
      ]
     }
    ],
    "source": [
-    "trainer = MNLE(proposal)\n",
+    "from sbi.neural_nets import likelihood_nn\n",
+    "\n",
+    "estimator_builder = likelihood_nn(model=\"mnle\", z_score_x=None)\n",
+    "trainer = MNLE(proposal, estimator_builder)\n",
     "estimator = trainer.append_simulations(theta, x).train()"
    ]
   },
@@ -678,31 +717,11 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 3])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "theta_o.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "54115a1a0f534028b377fa5aa4661dc4",
+       "model_id": "4bfa2e454bdd44d192ee790ecd90d35f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -716,16 +735,8 @@
    ],
    "source": [
     "# We define the potential function for the complete, unconditional MNLE-likelihood\n",
-    "potential_fn = MixedLikelihoodBasedPotential(estimator, proposal, x_o)\n",
-    "\n",
-    "# Then we use the potential to construct the conditional potential function.\n",
-    "# Here, we tell the constructor to condition on the last dimension (index 2) by\n",
-    "# passing dims_to_sample=[0, 1].\n",
-    "conditioned_potential_fn = ConditionedPotential(\n",
-    "    potential_fn,\n",
-    "    condition=theta_o,\n",
-    "    dims_to_sample=[0, 1],\n",
-    ")\n",
+    "potential_fn = LikelihoodBasedPotential(estimator, proposal, x_o)\n",
+    "conditioned_potential_fn = potential_fn.condition_on(conditions, dims_to_sample=[0, 1])\n",
     "\n",
     "# Using this potential function, we can now obtain conditional samples.\n",
     "mnle_posterior = MCMCPosterior(\n",
@@ -739,20 +750,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Finally, we can compare the ground truth conditional posterior with the\n",
     "# MNLE-conditional posterior.\n",
@@ -779,7 +779,8 @@
     "    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n",
     "    frameon=False,\n",
     "    fontsize=12,\n",
-    ");"
+    ");\n",
+    "print(\"c2st between true and MNLE posterior:\", c2st(true_posterior_samples, conditional_samples).item())"
    ]
   },
   {
@@ -792,7 +793,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.13 ('sbi')",
+   "display_name": "sbi-dev",
    "language": "python",
    "name": "python3"
   },
@@ -807,11 +808,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.12.4"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "9ef9b53a5ce850816b9705a866e49207a37a04a71269aa157d9f9ab944ea42bf"
-   }
   }
  },
  "nbformat": 4,