From 53dbde2799513f6fd7ffa65fa91fadd4d5bf346b Mon Sep 17 00:00:00 2001
From: Jan Boelts <jan.boelts@mailbox.org>
Date: Wed, 4 Sep 2024 11:53:13 +0200
Subject: [PATCH] fix #1260: include points in plotting limits

---
 sbi/analysis/plot.py               |  68 ++++++++-----
 tests/plot_test.py                 |   6 +-
 tutorials/00_getting_started.ipynb | 154 +++++++++++++++++------------
 3 files changed, 138 insertions(+), 90 deletions(-)

diff --git a/sbi/analysis/plot.py b/sbi/analysis/plot.py
index 45b072cea..9dc5648bc 100644
--- a/sbi/analysis/plot.py
+++ b/sbi/analysis/plot.py
@@ -554,42 +554,66 @@ def handle_nan_infs(samples: List[np.ndarray]) -> List[np.ndarray]:
     return samples
 
 
+def convert_to_list_of_numpy(
+    arr: Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor],
+) -> List[np.ndarray]:
+    """Converts a list of torch.Tensor to a list of np.ndarray."""
+    if not isinstance(arr, list):
+        arr = ensure_numpy(arr)
+        return [arr]
+    return [ensure_numpy(a) for a in arr]
+
+
+def infer_limits(
+    samples: List[np.ndarray],
+    dim: int,
+    points: Optional[List[np.ndarray]] = None,
+    eps: float = 0.1,
+) -> List:
+    """Infer limits for the plot.
+
+    Args:
+        samples: List of samples.
+        dim: Dimension of the samples.
+        points: List of points.
+        eps: Relative margin for the limits.
+    """
+    limits = []
+    for d in range(dim):
+        min_val = min(np.min(sample[:, d]) for sample in samples)
+        max_val = max(np.max(sample[:, d]) for sample in samples)
+        if points is not None:
+            min_val = min(min_val, min(np.min(point[:, d]) for point in points))
+            max_val = max(max_val, max(np.max(point[:, d]) for point in points))
+        limits.append([min_val * (1 + eps), max_val * (1 + eps)])
+    return limits
+
+
 def prepare_for_plot(
     samples: Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor],
-    limits: Optional[Union[List, torch.Tensor, np.ndarray]],
+    limits: Optional[Union[List, torch.Tensor, np.ndarray]] = None,
+    points: Optional[
+        Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]
+    ] = None,
 ) -> Tuple[List[np.ndarray], int, torch.Tensor]:
     """
     Ensures correct formatting for samples and limits, and returns dimension
     of the samples.
     """
 
-    # Prepare samples
-    if not isinstance(samples, list):
-        samples = ensure_numpy(samples)
-        samples = [samples]
-    else:
-        samples = [ensure_numpy(sample) for sample in samples]
+    samples = convert_to_list_of_numpy(samples)
+    if points is not None:
+        points = convert_to_list_of_numpy(points)
 
-    # check if nans and infs
     samples = handle_nan_infs(samples)
 
-    # Dimensionality of the problem.
     dim = samples[0].shape[1]
 
-    # Prepare limits. Infer them from samples if they had not been passed.
-    if limits == [] or limits is None:
-        limits = []
-        for d in range(dim):
-            min = +np.inf
-            max = -np.inf
-            for sample in samples:
-                min_ = np.min(sample[:, d])
-                min = min_ if min_ < min else min
-                max_ = np.max(sample[:, d])
-                max = max_ if max_ > max else max
-            limits.append([min, max])
+    if limits is None or limits == []:
+        limits = infer_limits(samples, dim, points)
     else:
         limits = [limits[0] for _ in range(dim)] if len(limits) == 1 else limits
+
     limits = torch.as_tensor(limits)
     return samples, dim, limits
 
@@ -737,7 +761,7 @@ def pairplot(
         )
         return fig, axes
 
-    samples, dim, limits = prepare_for_plot(samples, limits)
+    samples, dim, limits = prepare_for_plot(samples, limits, points)
 
     # prepate figure kwargs
     fig_kwargs_filled = _get_default_fig_kwargs()
diff --git a/tests/plot_test.py b/tests/plot_test.py
index 8505956b5..9f21553ca 100644
--- a/tests/plot_test.py
+++ b/tests/plot_test.py
@@ -15,7 +15,7 @@
 
 
 @pytest.mark.parametrize("samples", (torch.randn(100, 1),))
-@pytest.mark.parametrize("limits", ([(-1, 1)],))
+@pytest.mark.parametrize("limits", ([(-1, 1)], None))
 def test_pairplot1D(samples, limits):
     fig, axs = pairplot(**{k: v for k, v in locals().items() if v is not None})
     assert isinstance(fig, Figure)
@@ -24,7 +24,7 @@ def test_pairplot1D(samples, limits):
 
 
 @pytest.mark.parametrize("samples", (torch.randn(100, 2),))
-@pytest.mark.parametrize("limits", ([(-1, 1)],))
+@pytest.mark.parametrize("limits", ([(-1, 1)], None))
 def test_nan_inf(samples, limits):
     samples[0, 0] = np.nan
     samples[5, 1] = np.inf
@@ -37,7 +37,7 @@ def test_nan_inf(samples, limits):
 
 @pytest.mark.parametrize("samples", (torch.randn(100, 2), [torch.randn(100, 3)] * 2))
 @pytest.mark.parametrize("points", (torch.ones(1, 3),))
-@pytest.mark.parametrize("limits", ([(-3, 3)],))
+@pytest.mark.parametrize("limits", ([(-3, 3)], None))
 @pytest.mark.parametrize("subset", (None, [0, 1]))
 @pytest.mark.parametrize("upper", ("scatter",))
 @pytest.mark.parametrize(
diff --git a/tutorials/00_getting_started.ipynb b/tutorials/00_getting_started.ipynb
index 9c44f2636..78877a79b 100644
--- a/tutorials/00_getting_started.ipynb
+++ b/tutorials/00_getting_started.ipynb
@@ -29,20 +29,12 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "\n",
     "from sbi.analysis import pairplot\n",
-    "from sbi.inference import NPE, simulate_for_sbi\n",
+    "from sbi.inference import NPE\n",
     "from sbi.utils import BoxUniform\n",
     "from sbi.utils.user_input_checks import (\n",
     "    check_sbi_inputs,\n",
@@ -103,9 +95,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have to ensure that your _simulator_ and _prior_ adhere to the requirements of `sbi` such as returning `torch.Tensor`s in a standardised shape. \n",
+    "Here, we are using this simple toy simulator. In practice, the simulator can be anything\n",
+    "that takes parameters and returns simulated data. The data simulation process is\n",
+    "decoupled from the algorithms implemented in the `sbi` package. That is, you can\n",
+    "simulate your data beforehand, e.g., on a cluster or using a different programming\n",
+    "language or environment. All that `sbi` needs is a `Tensor` of parameters `theta` and\n",
+    "corresponding simulated data `x`. And, of course, observed data `x_o`. \n",
+    "\n",
+    "However, `sbi` also offers a function to run your simulations in parallel. To that end,\n",
+    "we have to ensure that your _simulator_ and _prior_ adhere to the requirements of `sbi`\n",
+    "such as returning `torch.Tensor`s in a standardised shape. \n",
     "\n",
-    "You can do so with the `process_simulator()` and `process_prior()` functions, which prepare them appropriately. Finally, you can call `check_sbi_input()` to make sure they are consistent which each other."
+    "You can do so with the `process_simulator()` and `process_prior()` functions, which\n",
+    "prepare them appropriately. Finally, you can call `check_sbi_input()` to make sure they\n",
+    "are consistent which each other."
    ]
   },
   {
@@ -163,20 +166,6 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b66b2e7830b43b7860e65e25c89b358",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -187,7 +176,9 @@
     }
    ],
    "source": [
-    "theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=2000)\n",
+    "num_simulations = 2000\n",
+    "theta = prior.sample((num_simulations,))\n",
+    "x = simulator(theta)\n",
     "print(\"theta.shape\", theta.shape)\n",
     "print(\"x.shape\", x.shape)"
    ]
@@ -224,7 +215,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Neural network successfully converged after 158 epochs."
+      " Neural network successfully converged after 255 epochs."
      ]
     }
    ],
@@ -252,7 +243,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Posterior conditional density p(θ|x) of type DirectPosterior. It samples the posterior network and rejects samples that\n",
+      "Posterior p(θ|x) of type DirectPosterior. It samples the posterior network and rejects samples that\n",
       "            lie outside of the prior bounds.\n"
      ]
     }
@@ -294,7 +285,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " Given this observation, we can sample from the posterior $p(\\theta|x_{obs})$ and visualise the univariate and pairwise marginals for the three parameters via `analysis.pairplot()`."
+    "Given this observation, we can sample from the posterior $p(\\theta|x_{obs})$ and\n",
+    "visualise the posterior. Visualizing a three-dimensional distributions is challenging.\n",
+    "Therefore, we use `sbi`'s `pairplot` function that generates a upper triangular grid of\n",
+    "plots, showing the univariate marginals on the diagnonal and every combination of\n",
+    "pairwise marginals for the three parameters on the upper offdiagonal:"
    ]
   },
   {
@@ -304,21 +299,7 @@
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1abb306e881d4c3090c1452c37da452b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x600 with 9 Axes>"
       ]
@@ -329,21 +310,30 @@
    ],
    "source": [
     "samples = posterior.sample((10000,), x=x_obs)\n",
-    "_ = pairplot(samples, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(6, 6),labels=[r\"$\\theta_1$\", r\"$\\theta_2$\", r\"$\\theta_3$\"])"
+    "_ = pairplot(samples,\n",
+    "             limits=[[-2, 2], [-2, 2], [-2, 2]],\n",
+    "             figsize=(6, 6),\n",
+    "             labels=[r\"$\\theta_1$\", r\"$\\theta_2$\", r\"$\\theta_3$\"])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Assessing the posterior for the known $\\theta, x$ - pair "
+    "## Assessing the posterior for the known ($\\theta, x$) - pair "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For this special case, we have access to the ground-truth parameters that generated the observation. We can thus assess if the inferred distributions over the parameters match the parameters $\\theta_{true}$ we used to generate our test observation $x_{obs}$."
+    "For this special case, we have access to the ground-truth parameters that generated the\n",
+    "observation. We can thus assess if the inferred distributions over the parameters match\n",
+    "the parameters $\\theta_{true}$ we used to generate our test observation $x_{obs}$.\n",
+    "\n",
+    "Note that in general, the inferred posterior distribution is not neccessarily centered\n",
+    "on the underlying \"ground-truth\" parameters $\\theta$ because there is noise in simulator\n",
+    "and limited data. However, it should lie \"within\" the posterior."
    ]
   },
   {
@@ -353,21 +343,47 @@
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "14abd2077ac64e41a90fe42c63fbee57",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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",
       "text/plain": [
-       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+       "<Figure size 600x600 with 9 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "samples = posterior.sample((10000,), x=x_obs)\n",
+    "pairplot(samples,\n",
+    "         points=theta_true,\n",
+    "         limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(6, 6),\n",
+    "         labels=[r\"$\\theta_1$\", r\"$\\theta_2$\", r\"$\\theta_3$\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assessing the predictive performance of the posterior\n",
+    "\n",
+    "Another way to assess the quality of the posterior is checking whether parameters\n",
+    "sampled from the posterior $p(\\theta \\mid x_{obs})$ can reproduce the observation\n",
+    "$x_{obs}$ when we simulate data with them. This *posterior predictive distribution*\n",
+    "should contain on the $x_{obs}$. We can again use the `pairplot` function to\n",
+    "visualize it. \n",
+    "\n",
+    "As you can see below, in this Gaussian toy example, the posterior predictive\n",
+    "distribution is nicely centered on the data it was conditioned on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x600 with 9 Axes>"
       ]
@@ -377,22 +393,30 @@
     }
    ],
    "source": [
-    "samples = posterior.sample((10000,), x=x_obs)\n",
-    "_ = pairplot(samples, points=theta_true, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(6, 6), labels=[r\"$\\theta_1$\", r\"$\\theta_2$\", r\"$\\theta_3$\"])"
+    "theta_posterior = posterior.sample((10000,), x=x_obs)  # sample from posterior\n",
+    "x_predictive = simulator(theta_posterior)  # simulate data from posterior\n",
+    "pairplot(x_predictive,\n",
+    "         points=x_obs,  # plot with x_obs as a point\n",
+    "         figsize=(6, 6),\n",
+    "         labels=[r\"$x_1$\", r\"$x_2$\", r\"$x_3$\"]);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The log-probability should ideally indicate that the true parameters, given the corresponding observation, are more likely than a different set of randomly chosen parameters from the prior distribution. \n",
+    "Finally, we can also compare the probabilities of different parameters under the\n",
+    "posterior. The log-probability should ideally indicate that the true parameters, given\n",
+    "the corresponding observation, are more likely than a different set of randomly chosen\n",
+    "parameters from the prior distribution. \n",
     "\n",
-    "Relative to the obtained log-probabilities, we can investigate the range of log-probabilities of the parameters sampled from the posterior."
+    "Relative to the obtained log-probabilities, we can investigate the range of\n",
+    "log-probabilities of the parameters sampled from the posterior."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -402,16 +426,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "high for true theta : tensor([2.7324])\n",
-      "low for different theta : tensor([-201.8379])\n",
-      "range of posterior samples: min: tensor(-6.6965)  max : tensor(4.2212)\n"
+      "high for true theta : tensor([3.4911])\n",
+      "low for different theta : tensor([-351.0345])\n",
+      "range of posterior samples: min: tensor(-8.8757)  max : tensor(3.9791)\n"
      ]
     }
    ],