{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c52c1d0",
   "metadata": {},
   "source": [
    "# SupraLocal Interpretable Model Agnostic Explanations (SLIME)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca40dc2",
   "metadata": {},
   "source": [
    "In this demo we explain the SLIME algorithm and show you how to:\n",
    "* Use `SymbolicModel`'s SLIME functionality\n",
    "* Approximate local behavior of a black-box model \n",
    "* Make predictions with the surrogate symbolic model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f17e1e1",
   "metadata": {},
   "source": [
    "SLIME is a very similar model interpretability technique to [LIME](https://arxiv.org/pdf/1602.04938) (Local Interpretable Model Agnostic Explanations). Essentially, LIME is a technique that approximates any black box machine learning model with a linear model around a certain point. The surrogate model is fitted with Gaussian perturbed samples around the point. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b925fb1d",
   "metadata": {},
   "source": [
    "![SLIME_conceptual_pic0](../../_static/slime_conceptual_pic.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ce802a",
   "metadata": {},
   "source": [
    "SLIME extends this idea. Here, we implement the framework as presented by the [SLIME paper](https://dl.acm.org/doi/10.1145/3712255.3726721) (Fong, Motani 2025). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1769471d",
   "metadata": {},
   "source": [
    "**SLIME Algorithm**: Suppose we have a black-box model $f(x)$,\n",
    "1. We want to fit a local surrogate model $g^{*}(x) \\in \\mathcal{G}$ where $\\mathcal{S}$ is the space of closed-form analytic expressions (symbolic models) to estimate the behaviour of $f(x)$ around point $x_0$.\n",
    "2. To choose our surrogate model, we create a dataset points near $x_0$. This dataset, $\\mathcal{D}$, consists of the $J-$ nearest neighbours around $x_0$ and $N_{\\text{synthetic}}$ number of Gaussian sampled points around $x_0$ (the default variance of the Gaussian is equal to half the variance of the set of $J$ neighbours, hence we choose the size of the neighbourhood by setting $J$).\n",
    "3. We evaluate the outputs of the model on this dataset, $\\mathcal{D}$, to create the targets for our symbolic regression $\\mathcal{T}$.\n",
    "4. We choose $g^{*}(x)$ by\n",
    "\n",
    "$$\n",
    "g^*(x) = \\arg\\min_{g \\in \\mathcal{S}} \\sum_{z_i \\in \\text{synthetic}} \\pi_x(z_i) [f(z_i)-g(z_i)]^2 + M \\sum_{z_j \\in \\text{neighbours}} [f(z_j)-g(z_j)]^2\n",
    "$$\n",
    "\n",
    "where $\\pi_x(z_i) = \\exp(\\frac{-||x_0-x_i||^2}{\\sigma^2})$ is the proximity kernel weighting and $M$ is a weighting for the real neighbours."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9db230",
   "metadata": {},
   "source": [
    "SLIME has benefits over LIME as it does not restrict itself to linear models, yet does not have any reduction in interpretability of the surrogate model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030dce01",
   "metadata": {},
   "source": [
    "SymTorch's `SymbolicModel` can fit a SLIME model for you. The user can specify:\n",
    "* $J-$ nearest neighbours (`J_nn = 10`)\n",
    "* $M$ (`real_weighting = 1.0`)\n",
    "* $N_{\\text{synthetic}}$ (`num_synthetic = 0`)\n",
    "\n",
    "As well as\n",
    "\n",
    "* Nearest neighbour metric (`nn_metric = \"euclidean\"`)\n",
    "* PySR parameters (`pysr_params=None` defaults to default parameters)\n",
    "\n",
    "The model is fitted using the `.distill(SLIME = True)` method and predictions can be made using `.predict()`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "679875dc",
   "metadata": {},
   "source": [
    "![SLIME_conceptual_pic](../../_static/SLIME.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a403d7f",
   "metadata": {},
   "source": [
    "## Conceptual example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8dfe9ca",
   "metadata": {},
   "source": [
    "Let us see an example of how SLIME can be effective at analysing black box models by using a spiral dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "139942dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(7)\n",
    "\n",
    "N = 400\n",
    "theta = np.sqrt(np.random.rand(N)) * 2 * np.pi\n",
    "\n",
    "# Class A spiral\n",
    "r_a = 2 * theta + np.pi\n",
    "data_a = np.column_stack([np.cos(theta) * r_a, np.sin(theta) * r_a])\n",
    "x_a = data_a + np.random.randn(N, 2)\n",
    "y_a = np.zeros(N, dtype=int)\n",
    "\n",
    "# Class B spiral\n",
    "r_b = -2 * theta - np.pi\n",
    "data_b = np.column_stack([np.cos(theta) * r_b, np.sin(theta) * r_b])\n",
    "x_b = data_b + np.random.randn(N, 2)\n",
    "y_b = np.ones(N, dtype=int)\n",
    "\n",
    "# Combine\n",
    "X = np.vstack([x_a, x_b])\n",
    "y = np.concatenate([y_a, y_b])\n",
    "\n",
    "# Shuffle together\n",
    "idx = np.random.permutation(len(y))\n",
    "X = X[idx]\n",
    "y = y[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123c5610",
   "metadata": {},
   "source": [
    "### The dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e317a936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y==0, 0], X[y==0, 1], s=8, label=\"Class 0\")\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1], s=8, label=\"Class 1\")\n",
    "plt.xlabel(r\"$x_1$\")\n",
    "plt.ylabel(r\"$x_2$\")\n",
    "plt.legend()\n",
    "plt.title('Original dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32705895",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c884ec",
   "metadata": {},
   "source": [
    "Set up a simple two layer MLP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14ba53bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class SimpleMLP(nn.Module):\n",
    "    def __init__(self, in_dim=2, hidden_dim=64, out_dim=2):\n",
    "        super().__init__()\n",
    "        self.layer1 = nn.Linear(in_dim, hidden_dim)\n",
    "        self.layer2 = nn.Linear(hidden_dim, hidden_dim)\n",
    "        self.output = nn.Linear(hidden_dim, out_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.layer1(x))\n",
    "        x = F.relu(self.layer2(x))\n",
    "        x = self.output(x)      \n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8082679",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "X_tensor = torch.from_numpy(X).float()     \n",
    "y_tensor = torch.from_numpy(y).long()         \n",
    "\n",
    "dataset = TensorDataset(X_tensor, y_tensor)\n",
    "loader = DataLoader(dataset, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d10657",
   "metadata": {},
   "source": [
    "Set up training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20e1670d",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"mps\")\n",
    "\n",
    "model = SimpleMLP(in_dim=2, hidden_dim=64, out_dim=2).to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77972118",
   "metadata": {},
   "source": [
    "Train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ccadd98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10, loss = 0.3800\n",
      "Epoch 20, loss = 0.2161\n",
      "Epoch 30, loss = 0.0862\n",
      "Epoch 40, loss = 0.0434\n",
      "Epoch 50, loss = 0.0265\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(50):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "\n",
    "    for xb, yb in loader:\n",
    "        xb = xb.to(device)\n",
    "        yb = yb.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        logits = model(xb)\n",
    "        loss = criterion(logits, yb)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item() * xb.size(0)\n",
    "\n",
    "    epoch_loss = running_loss / len(dataset)\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f\"Epoch {epoch+1:02d}, loss = {epoch_loss:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf964e09",
   "metadata": {},
   "source": [
    "Get predictions for the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5fa53b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_func(inputs):\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        # convert numpy arrays to torch tensors\n",
    "        if isinstance(inputs, np.ndarray):\n",
    "            inputs = torch.from_numpy(inputs).float()\n",
    "        \n",
    "        # ensure tensor + move to device\n",
    "        inputs = inputs.to(device)\n",
    "\n",
    "        logits = model(inputs)\n",
    "        probs = torch.softmax(logits, dim=-1)\n",
    "        preds = probs.argmax(dim=-1).cpu().numpy()\n",
    "\n",
    "    return preds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02b7074c",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model_func(X_tensor.to(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd60306e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model accuracy on this dataset is 0.9975.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "acc = accuracy_score(y, preds)\n",
    "print(f\"The model accuracy on this dataset is {acc}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b22cdb2",
   "metadata": {},
   "source": [
    "## Interpreting the model outputs with SLIME\n",
    "\n",
    "Imagining that we don't know the true form of the data, we would want to understand how the model behaves. For complex models, it is easier to see how the model behaves around a local point of interest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf7d8984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x318175dd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(7)\n",
    "idx = np.random.randint(len(X))\n",
    "x0 = X[idx]\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1], s=8, label=\"Class 0\")\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1], s=8, label=\"Class 1\")\n",
    "\n",
    "# plot the special point on top\n",
    "plt.scatter(x0[0], x0[1], color=\"red\", s=40, label=r\"$x_0$\")\n",
    "\n",
    "plt.xlabel(r\"$x_1$\")\n",
    "plt.ylabel(r\"$x_2$\")\n",
    "plt.title(r\"Original dataset with chosen local point, $x_0$\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f66e002a",
   "metadata": {},
   "source": [
    "Let us just restrict ourself to the neighbourhood of points close to $x_0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da15591d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "nbrs = NearestNeighbors(n_neighbors=50, metric='euclidean').fit(X)\n",
    "_, indices = nbrs.kneighbors(x0.reshape(1, -1))\n",
    "neigh_idx = indices[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c6e0fef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot neighbours using the same class-specific colours\n",
    "plt.scatter(X[neigh_idx][y[neigh_idx] == 0, 0],\n",
    "            X[neigh_idx][y[neigh_idx] == 0, 1],\n",
    "            s=20, label=\"Neighbours (Class 0)\")\n",
    "\n",
    "plt.scatter(X[neigh_idx][y[neigh_idx] == 1, 0],\n",
    "            X[neigh_idx][y[neigh_idx] == 1, 1],\n",
    "            s=20, label=\"Neighbours (Class 1)\")\n",
    "\n",
    "# highlight x0\n",
    "plt.scatter(x0[0], x0[1],\n",
    "            s=60, color=\"red\", label=r\"$x_0$\")\n",
    "\n",
    "plt.xlabel(r\"$x_1$\")\n",
    "plt.ylabel(r\"$x_2$\")\n",
    "plt.legend()\n",
    "plt.title(r\"Local point $x_0$ and the 50 nearest neighbours\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16fc9041",
   "metadata": {},
   "source": [
    "We can now add synthetic points to further explore the model classifications. We sample our synthetic points from the region around $x_0$ with the half the variance as the $J-$ nearest neighbours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9fcb7a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = np.var(X[neigh_idx])\n",
    "synthetic_samples = np.random.normal(loc=x0, scale=np.sqrt(var)/2, size=(10_000, len(x0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd70b424",
   "metadata": {},
   "source": [
    "Put our synthetic samples and our real samples into the model to see how they are classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "43907da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "real_inputs = X[neigh_idx]\n",
    "\n",
    "real_preds = model_func(torch.from_numpy(real_inputs).float().to(device))\n",
    "synthetic_preds = model_func(torch.from_numpy(synthetic_samples).float().to(device))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2de7e8f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12fefaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Real and synthetic points in neighbourhood of $x_0$')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(synthetic_samples[synthetic_preds == 0, 0],\n",
    "            synthetic_samples[synthetic_preds == 0, 1],\n",
    "            s=50, facecolor=\"blue\", label=\"Synthetic samples (class 0)\")\n",
    "\n",
    "plt.scatter(synthetic_samples[synthetic_preds == 1, 0],\n",
    "            synthetic_samples[synthetic_preds == 1, 1],\n",
    "            s=50, facecolor=\"orange\", label=\"Synthetic samples (class 1)\")\n",
    "\n",
    "plt.scatter(real_inputs[real_preds == 0, 0],\n",
    "            real_inputs[real_preds == 0, 1],\n",
    "            s=50, facecolor=\"blue\", edgecolor=\"black\",\n",
    "            linewidth=1.5, label=\"Real neighbours (class 0)\"\n",
    "\n",
    ")\n",
    "\n",
    "# class 1 neighbours\n",
    "plt.scatter(real_inputs[real_preds == 1, 0],\n",
    "            real_inputs[real_preds == 1, 1],\n",
    "            s=50, facecolor=\"orange\", edgecolor=\"black\",\n",
    "            linewidth=1.5, label=\"Real neighbours (class 1)\"\n",
    ")\n",
    "\n",
    "\n",
    "# 4 — highlight x0\n",
    "plt.scatter(x0[0], x0[1],\n",
    "            s=80, color=\"red\", edgecolor=\"black\", linewidth=1.2,\n",
    "            label=r\"$x_0$\")\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(r\"$x_1$\")\n",
    "plt.ylabel(r\"$x_2$\")\n",
    "plt.title(r\"Real and synthetic points in neighbourhood of $x_0$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbcd8540",
   "metadata": {},
   "source": [
    "This is how the model classfies the points in this neighbourhood. Now we can approximate the behaviour of this model using a symbolic expression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ef48a12b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected IPython. Loading juliacall extension. See https://juliapy.github.io/PythonCall.jl/stable/compat/#IPython\n"
     ]
    }
   ],
   "source": [
    "from symtorch import SymbolicModel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138bd0cb",
   "metadata": {},
   "source": [
    "Set up the SLIME model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c43a5442",
   "metadata": {},
   "outputs": [],
   "source": [
    "slime_model = SymbolicModel(model_func, block_name = \"func\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "09d66450",
   "metadata": {},
   "outputs": [],
   "source": [
    "slime_params = {\"x\": x0,\n",
    "                \"J_nn\": 50,\n",
    "                \"num_synthetic\": 10_000,\n",
    "                \"nn_metric\": \"euclidean\"\n",
    "                }\n",
    "\n",
    "pysr_params={\n",
    "    'niterations': 1000,\n",
    "    'binary_operators': ['+', '*', '-', '/', 'cond'],\n",
    "    'unary_operators': ['sin', 'cos', 'exp', 'log', 'sqrt'],\n",
    "    'complexity_of_operators': {'sin': 3, 'cos': 3, 'exp': 3, 'log': 3, 'sqrt': 2, 'cond': 2}, \n",
    "    'constraints':{'exp': 2, 'log':2, 'sin':2, 'cos':2},\n",
    "    'verbosity': 0\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1175b15b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔍 SLIME mode: Using 10050 points (50 real + 10000 synthetic)\n",
      "   Point of interest: [8.23135402 3.41537682]\n",
      "🛠️ Running SR on output dimension 0 of 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/liz/PhD/SymTorch_project/symtorch_venv/lib/python3.11/site-packages/pysr/sr.py:2811: UserWarning: Note: it looks like you are running in Jupyter. The progress bar will be turned off.\n",
      "  warnings.warn(\n",
      "/Users/liz/PhD/SymTorch_project/symtorch_venv/lib/python3.11/site-packages/pysr/sr.py:2265: UserWarning: Note: you are running with more than 10,000 datapoints. You should consider turning on batching (https://ai.damtp.cam.ac.uk/pysr/options/#batching). You should also reconsider if you need that many datapoints. Unless you have a large amount of noise (in which case you should smooth your dataset first), generally < 10,000 datapoints is enough to find a functional form with symbolic regression. More datapoints will lower the search speed.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "💡Best equation for output 0 found to be cond((((cos(x1) * 0.04937385) + 0.5404889) * x0) + -2.906935, 1.0).\n",
      "❤️ SR on func complete.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{0: PySRRegressor.equations_ = [\n",
       " \t   pick     score                                           equation  \\\n",
       " \t0        0.000000                                          0.9994247   \n",
       " \t1        0.013140                     (-0.77621204 / x0) + 1.0939363   \n",
       " \t2        9.639104                         cond(x0 + -5.8403883, 1.0)   \n",
       " \t3        0.000001    cond(x0 + -5.84091, 0.99998724) - -1.2700881e-5   \n",
       " \t4        0.063348   cond(((x1 * 0.011053928) + x0) + -5.849988, 1.0)   \n",
       " \t5        0.061500  cond((((x1 * 0.04135067) + x0) * 1.0558497) + ...   \n",
       " \t6        0.000024  cond(((x0 * 1.0541468) + (x1 * 0.045976285)) +...   \n",
       " \t7  >>>>       inf  cond((((cos(x1) * 0.04937385) + 0.5404889) * x...   \n",
       " \t\n",
       " \t           loss  complexity  \n",
       " \t0  5.769923e-04           1  \n",
       " \t1  5.474488e-04           5  \n",
       " \t2  3.565610e-08           6  \n",
       " \t3  3.565600e-08           8  \n",
       " \t4  3.141298e-08          10  \n",
       " \t5  2.777738e-08          12  \n",
       " \t6  2.777605e-08          14  \n",
       " \t7  0.000000e+00          15  \n",
       " ]}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slime_model.distill(X, SLIME= True, slime_params= slime_params, sr_params = pysr_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f3a60e",
   "metadata": {},
   "source": [
    "This is the equation that the symbolic regression found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "16547a5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "➡️ Slime symbolic expressions for output dimension 0:\n",
      "   complexity          loss  \\\n",
      "0           1  5.769923e-04   \n",
      "1           5  5.474488e-04   \n",
      "2           6  3.565610e-08   \n",
      "3           8  3.565600e-08   \n",
      "4          10  3.141298e-08   \n",
      "5          12  2.777738e-08   \n",
      "6          14  2.777605e-08   \n",
      "7          15  0.000000e+00   \n",
      "\n",
      "                                            equation     score  \\\n",
      "0                                          0.9994247  0.000000   \n",
      "1                     (-0.77621204 / x0) + 1.0939363  0.013140   \n",
      "2                         cond(x0 + -5.8403883, 1.0)  9.639104   \n",
      "3    cond(x0 + -5.84091, 0.99998724) - -1.2700881e-5  0.000001   \n",
      "4   cond(((x1 * 0.011053928) + x0) + -5.849988, 1.0)  0.063348   \n",
      "5  cond((((x1 * 0.04135067) + x0) * 1.0558497) + ...  0.061500   \n",
      "6  cond(((x0 * 1.0541468) + (x1 * 0.045976285)) +...  0.000024   \n",
      "7  cond((((cos(x1) * 0.04937385) + 0.5404889) * x...       inf   \n",
      "\n",
      "                                        sympy_format  \\\n",
      "0                                  0.999424700000000   \n",
      "1                          1.0939363 - 0.77621204/x0   \n",
      "2  Piecewise((1.0, x0 - 5.8403883 > 0), (0.0, True))   \n",
      "3  Piecewise((0.99998724, x0 - 5.84091 > 0), (0.0...   \n",
      "4  Piecewise((1.0, x0 + x1*0.011053928 - 5.849988...   \n",
      "5  Piecewise((1.0, (x0 + x1*0.04135067)*1.0558497...   \n",
      "6  Piecewise((0.9999528, x0*1.0541468 + x1*0.0459...   \n",
      "7  Piecewise((1.0, x0*(cos(x1)*0.04937385 + 0.540...   \n",
      "\n",
      "                                       lambda_format  \n",
      "0                 PySRFunction(X=>0.999424700000000)  \n",
      "1         PySRFunction(X=>1.0939363 - 0.77621204/x0)  \n",
      "2  PySRFunction(X=>Piecewise((1.0, x0 - 5.8403883...  \n",
      "3  PySRFunction(X=>Piecewise((0.99998724, x0 - 5....  \n",
      "4  PySRFunction(X=>Piecewise((1.0, x0 + x1*0.0110...  \n",
      "5  PySRFunction(X=>Piecewise((1.0, (x0 + x1*0.041...  \n",
      "6  PySRFunction(X=>Piecewise((0.9999528, x0*1.054...  \n",
      "7  PySRFunction(X=>Piecewise((1.0, x0*(cos(x1)*0....  \n",
      "🏆 Best: cond((((cos(x1) * 0.04937385) + 0.5404889) * x0) + -2.906935, 1.0) (loss: 0.000000e+00)\n"
     ]
    }
   ],
   "source": [
    "slime_model.show_symbolic_expression(SLIME=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1dc0a93",
   "metadata": {},
   "source": [
    "which is some sort of conditional decision boundary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb305188",
   "metadata": {},
   "source": [
    "Now let's see if our SLIME model has approximated the local behaviour well..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "91a2b862",
   "metadata": {},
   "outputs": [],
   "source": [
    "slime_predictions=slime_model(synthetic_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1aa513a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "slime_labels = (slime_predictions >= 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f1654df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SLIME surrogate model predictions of synthetic data in $x_0$ neighbourhood')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    synthetic_samples[slime_labels == 0, 0],\n",
    "    synthetic_samples[slime_labels == 0, 1],\n",
    "    s=50, facecolor=\"blue\", label=\"Synthetic samples (class 0)\"\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    synthetic_samples[slime_labels == 1, 0],\n",
    "    synthetic_samples[slime_labels == 1, 1],\n",
    "    s=50, facecolor=\"orange\", label=\"Synthetic samples (class 1)\"\n",
    ")\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(r\"$x_1$\")\n",
    "plt.ylabel(r\"$x_2$\")\n",
    "plt.title(r\"SLIME surrogate model predictions of synthetic data in $x_0$ neighbourhood\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5a405692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Successfully switched func to SLIME symbolic equations for all 1 dimensions:\n",
      "   Dimension 0: cond((((cos(x1) * 0.04937385) + 0.5404889) * x0) + -2.906935, 1.0)\n",
      "   Variables: ['x0', 'x1']\n",
      "🎯 All 1 output dimensions now using SLIME symbolic equations.\n"
     ]
    }
   ],
   "source": [
    "slime_model.switch_to_symbolic(SLIME=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4d46d537",
   "metadata": {},
   "outputs": [],
   "source": [
    "func = slime_model.get_symbolic_function(SLIME=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63271ef0",
   "metadata": {},
   "source": [
    "It has done a pretty good job!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f0fe62",
   "metadata": {},
   "source": [
    "Let's visualise the SLIME decision boundary on the true dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ef16b27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Boundary of Local Symbolic Surrogate Model')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a grid covering data\n",
    "x0_min, x0_max = X[:,0].min() - 1, X[:,0].max() + 1\n",
    "x1_min, x1_max = X[:,1].min() - 1, X[:,1].max() + 1\n",
    "\n",
    "xx, yy = np.meshgrid(\n",
    "    np.linspace(x0_min, x0_max, 600),\n",
    "    np.linspace(x1_min, x1_max, 600)\n",
    ")\n",
    "\n",
    "# evaluate model\n",
    "grid = np.column_stack([xx.ravel(), yy.ravel()])   # shape (600*600, 2)\n",
    "Z = slime_model(grid)\n",
    "\n",
    "Z = np.asarray(Z).reshape(-1)   # ensure 1D\n",
    "Z = Z.reshape(xx.shape) \n",
    "\n",
    "# plot ONLY the boundary line\n",
    "plt.contour(xx, yy, Z, levels=[0.5], colors='black', linewidths=2)\n",
    "\n",
    "#plot the original data\n",
    "plt.scatter(synthetic_samples[synthetic_preds == 0, 0],\n",
    "            synthetic_samples[synthetic_preds == 0, 1],\n",
    "            s=50, facecolor=\"blue\", label=\"Synthetic samples (class 0)\")\n",
    "\n",
    "plt.scatter(synthetic_samples[synthetic_preds == 1, 0],\n",
    "            synthetic_samples[synthetic_preds == 1, 1],\n",
    "            s=50, facecolor=\"orange\", label=\"Synthetic samples (class 1)\")\n",
    "\n",
    "plt.scatter(real_inputs[real_preds == 0, 0],\n",
    "            real_inputs[real_preds == 0, 1],\n",
    "            s=50, facecolor=\"blue\", edgecolor=\"black\",\n",
    "            linewidth=1.5, label=\"Real neighbours (class 0)\")\n",
    "plt.scatter(real_inputs[real_preds == 1, 0],\n",
    "            real_inputs[real_preds == 1, 1],\n",
    "            s=50, facecolor=\"orange\", edgecolor=\"black\",\n",
    "            linewidth=1.5, label=\"Real neighbours (class 1)\")\n",
    "plt.scatter(x0[0], x0[1],\n",
    "            s=80, color=\"red\", edgecolor=\"black\", linewidth=1.2,\n",
    "            label=r\"$x_0$\")\n",
    "\n",
    "plt.xlim(2,15)\n",
    "plt.ylim(-2,10)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(r\"$x_1$\")\n",
    "plt.ylabel(r\"$x_2$\")\n",
    "\n",
    "plt.title(\"Decision Boundary of Local Symbolic Surrogate Model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efafb48d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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