{ "cells": [ { "cell_type": "markdown", "id": "11c8f5b7", "metadata": {}, "source": [ "# Shap-BPT v/s Shap-AA on ImagNet dataset using ResNet50 model" ] }, { "cell_type": "markdown", "id": "5eaec3be", "metadata": {}, "source": [ "This notebook provides a minimal, self-contained demonstration of the `shap_bpt` library for pixel-wise explanation of image classifiers.\n", "We load a pretrained model (like ResNet-50), define a masking-based black-box prediction function, specify simple background (baseline) images as replacement values, and use `shap_bpt` to compute Owen values with the BPT and the AA methods.\n", "The goal is to clearly illustrate the essential workflow for generating and visualizing explanations.\n", "Generated explanations are evaluated using simple response-based curves." ] }, { "cell_type": "markdown", "id": "a3d5ed9a-5521-46a5-9de6-7796f8398de8", "metadata": {}, "source": [ "## Imports & library versions" ] }, { "cell_type": "code", "execution_count": 1, "id": "b442c596", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "shap_bpt version: 1.0\n" ] } ], "source": [ "# Core scientific / plotting libraries\n", "import cv2\n", "import json\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# PyTorch + torchvision, image processing\n", "import torch\n", "from torchvision import transforms\n", "from skimage.filters import gaussian\n", "\n", "# ShapBPT\n", "import shap_bpt as shap_bpt\n", "print('shap_bpt version:',shap_bpt.__version__)" ] }, { "cell_type": "markdown", "id": "80651a69-4b93-4661-aefe-b0e116fab44f", "metadata": {}, "source": [ "## Device configuration (CPU / GPU / MPS)" ] }, { "cell_type": "code", "execution_count": 2, "id": "1f435678-0959-4726-b68c-f0aa61774dad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using device: mps\n" ] } ], "source": [ "use_cuda = torch.cuda.is_available()\n", "use_mps = ('mps' in dir(torch.backends)) and torch.backends.mps.is_available()\n", "torch.manual_seed(12345)\n", "\n", "if use_cuda: device = torch.device(\"cuda\")\n", "elif use_mps: device = torch.device(\"mps\")\n", "else: device = torch.device(\"cpu\")\n", "print('Using device:', device)" ] }, { "cell_type": "markdown", "id": "e63d8db9", "metadata": {}, "source": [ "## Utilities" ] }, { "cell_type": "code", "execution_count": 3, "id": "e51cb768", "metadata": {}, "outputs": [], "source": [ "def np_softmax(x):\n", " e_x = np.exp(x - np.max(x))\n", " return e_x / e_x.sum()" ] }, { "cell_type": "markdown", "id": "50f97c16-34e9-41a0-b31a-d9141a167bf1", "metadata": {}, "source": [ "## Black-box model and preprocessing" ] }, { "cell_type": "code", "execution_count": 4, "id": "d5e8b217-aee1-48bc-9c2c-38caef78c7a1", "metadata": {}, "outputs": [], "source": [ "from torchvision.models import resnet50, ResNet50_Weights\n", "model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2).to(device)\n", "\n", "# from torchvision.models import vit_b_16\n", "# model = vit_b_16(weights='IMAGENET1K_V1').to(device)\n", "\n", "# # # SWIN-ViT from timm\n", "# from torchvision.models import swin_t\n", "# model = swin_t(weights='IMAGENET1K_V1').to(device)\n", "\n", "# import timm\n", "# model = timm.create_model('vit_base_patch16_224', pretrained=True).to(device)\n", "\n", "# from torchvision.models import vgg16\n", "# model = vgg16(weights='IMAGENET1K_V1').to(device)\n", "\n", "\n", "model.eval() # we are only doing inference\n", "# Preprocessing\n", "mean = torch.tensor([0.485, 0.456, 0.406])\n", "std = torch.tensor([0.229, 0.224, 0.225])\n", "preprocess = transforms.Compose( # HWC [0,1] -> CHW normalized tensor\n", " [transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]\n", ")\n", "normalize = transforms.Normalize(mean=mean, std=std)\n", "\n", "# Black-box prediction function: takes a batch of tensors and returns logits (NumPy)\n", "def bb_predict(x):\n", " with torch.no_grad():\n", " # return np_softmax(model(x), dim=1).cpu().detach().numpy()\n", " return model(x).cpu().detach().numpy()" ] }, { "cell_type": "markdown", "id": "92e2aada-47d6-4f04-871c-c75d67733b3c", "metadata": {}, "source": [ "## Load ImageNet class names" ] }, { "cell_type": "code", "execution_count": 5, "id": "919d55de", "metadata": {}, "outputs": [], "source": [ "# Load ImageNet class names (id -> human-readable label)\n", "with open('imagenet_class_index.json') as file:\n", " class_names = [v[1] for v in json.load(file).values()]" ] }, { "cell_type": "markdown", "id": "eef019e0", "metadata": {}, "source": [ "## Helper to prepare one image (image-specific backgrounds & masking)" ] }, { "cell_type": "code", "execution_count": 6, "id": "d2c2a699", "metadata": {}, "outputs": [], "source": [ "def prepare_image(image_path: str, target_size=(224, 224), seed: int = 0):\n", " \"\"\"\n", " Loads an image, builds image-specific replacement backgrounds and a\n", " masking-based black-box wrapper nu_masked for shap_bpt.\n", "\n", " Returns a dict with all objects needed for explanation.\n", " \"\"\"\n", " # --- Load and resize the image (BGR -> RGB) ---\n", " image_bgr = cv2.imread(image_path, cv2.IMREAD_COLOR)\n", " if image_bgr is None:\n", " raise FileNotFoundError(f\"Could not read image: {image_path}\")\n", " image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n", " image_rgb = cv2.resize(image_rgb, target_size)\n", " image_np = image_rgb.astype(np.uint8) # HWC, uint8, [0..255]\n", "\n", " H, W, _ = image_np.shape\n", "\n", " # --- Preprocess for ResNet-50 ---\n", " image_tensor = preprocess(\n", " image_np.astype(np.float32) / 255.0\n", " ).unsqueeze(0).to(device) # shape (1,3,H,W)\n", "\n", " # --- Image-specific replacement backgrounds ---\n", " # Black, gray, white\n", " bkgnd0 = np.full_like(image_np, 0)\n", " bkgnd1 = np.full_like(image_np, 127)\n", " bkgnd2 = np.full_like(image_np, 255)\n", "\n", " # Strongly blurred original\n", " bkgnd3 = (gaussian(image_np, sigma=8, channel_axis=-1) * 255).astype(np.uint8)\n", "\n", " # Smoothed random noise (same size as the image)\n", " rng = np.random.RandomState(seed)\n", " noise = rng.normal(loc=128, scale=128, size=image_np.shape)\n", " noise = np.clip(noise, 0, 255).astype(np.uint8)\n", " bkgnd4 = (gaussian(noise, sigma=2.0, channel_axis=-1) * 255).astype(np.uint8)\n", "\n", " # Stack backgrounds into one array\n", " background_image_set = np.stack([bkgnd0, bkgnd1, bkgnd2, bkgnd3, bkgnd4], axis=0)\n", "\n", " # Preprocess all backgrounds with the same transform as the main image\n", " background_tensors = torch.cat([preprocess(bkgnd.astype(np.float32) / 255.0).unsqueeze(0)\n", " for bkgnd in background_image_set\n", " ], dim=0).to(device) # shape (B,3,H,W)\n", "\n", " # --- Image-specific masking-based prediction function ---\n", " def nu_masked(masks: np.ndarray) -> np.ndarray:\n", " \"\"\"\n", " masks: Boolean NumPy array of shape (N, H, W)\n", " True = keep original pixel\n", " False = replace with background pixel\n", "\n", " Returns: (N, num_classes) NumPy array of averaged logits.\n", " \"\"\"\n", " N, Hm, Wm = masks.shape\n", " assert (Hm, Wm) == (H, W), \"Mask size must match image size.\"\n", "\n", " B = background_tensors.shape[0]\n", "\n", " # Convert masks to a tensor and broadcast to (B*N, 3, H, W)\n", " masks_t = torch.from_numpy(masks).bool().to(device) # (N,H,W)\n", " masks_t = masks_t.view(N, 1, H, W).repeat(1, 3, 1, 1) # (N,3,H,W)\n", " masks_t = masks_t.unsqueeze(2).repeat(1, 1, B, 1, 1) # (N,3,B,H,W)\n", " masks_t = masks_t.view(B * N, 3, H, W) # (B*N,3,H,W)\n", "\n", " # Tile original image and backgrounds to match masks\n", " Xf = image_tensor.repeat(B * N, 1, 1, 1) # (B*N,3,H,W)\n", " Xb = background_tensors.repeat(N, 1, 1, 1) # (B*N,3,H,W)\n", "\n", " # Apply masks: True -> keep original pixel, False -> background pixel\n", " X = torch.where(masks_t, Xf, Xb)\n", "\n", " # Use the global, image-agnostic black-box function\n", " logits = bb_predict(X) # (B*N, num_classes)\n", "\n", " # Average over backgrounds\n", " logits = logits.reshape(N, B, -1) # (N,B,num_classes)\n", " return logits.mean(axis=1) # (N,num_classes)\n", "\n", " # --- Predict the class for the full (unmasked) image ---\n", " logits_full = bb_predict(image_tensor)[0] # (num_classes,)\n", " probs_full = np_softmax(logits_full)\n", " pred_class = int(np.argmax(probs_full))\n", "\n", " print(\n", " f\"{image_path}: predicted as '{class_names[pred_class]}' \"\n", " f\"with probability {probs_full[pred_class]:.4f}\"\n", " )\n", "\n", " return {\n", " \"image_path\": image_path,\n", " \"image_np\": image_np,\n", " \"image_tensor\": image_tensor,\n", " \"background_image_set\": background_image_set,\n", " \"background_tensors\": background_tensors,\n", " \"nu_masked\": nu_masked,\n", " \"logits_full\": logits_full,\n", " \"probs_full\": probs_full,\n", " \"predicted_class\": pred_class,\n", " }" ] }, { "cell_type": "markdown", "id": "849a0082", "metadata": {}, "source": [ "## Load image and visualize input and replacement values" ] }, { "cell_type": "code", "execution_count": 20, "id": "4e5939df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flamingo4.png: predicted as 'flamingo' with probability 0.5045\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = prepare_image(\"flamingo4.png\")\n", "\n", "fig,ax = plt.subplots(1,1+len(data[\"background_image_set\"]), \n", " figsize=(2*(1+len(data[\"background_image_set\"])), 2))\n", "ax[0].imshow(data[\"image_np\"])\n", "ax[0].set_title('Input')\n", "ax[0].set_xticks([]) ; ax[0].set_yticks([])\n", "for i,img in enumerate(data[\"background_image_set\"]):\n", " ax[i+1].imshow(img.astype(np.uint8))\n", " ax[i+1].set_title(f'Replacement {i}')\n", " ax[i+1].set_xticks([]) ; ax[i+1].set_yticks([])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "7781805b-33b1-4850-9d33-8c90674b51d2", "metadata": {}, "source": [ "## Configure explanation parameters" ] }, { "cell_type": "code", "execution_count": 8, "id": "4a49d27d-4fb0-464f-8ab5-f67e605503d2", "metadata": {}, "outputs": [], "source": [ "# How many black-box evaluations we are willing to pay for the explanation\n", "MAX_EVALS_BUDGET = 100\n", "\n", "# Batch size for evaluations within shap_bpt\n", "batch_size = 4\n", "\n", "# Number of top classes to explain (here: explain top-4 classes)\n", "num_explained_classes = 4" ] }, { "cell_type": "code", "execution_count": 9, "id": "07ee26a1", "metadata": {}, "outputs": [], "source": [ "# Create the shap_bpt explainer using our masking-based black-box function\n", "explainer = shap_bpt.Explainer(data[\"nu_masked\"], data[\"image_np\"],\n", " num_explained_classes=num_explained_classes, verbose=True)" ] }, { "cell_type": "markdown", "id": "12681553", "metadata": {}, "source": [ "## Run shap_bpt with method = \"BPT\" (Binary Partition Tree)" ] }, { "cell_type": "code", "execution_count": 10, "id": "5749509a-99b9-400a-937a-b24aec81b732", "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2fc60152eafe4c769c54cbfd4bda7278", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/100 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "shap_bpt.plot_owen_values(explainer, shap_values_bpt, class_names)" ] }, { "cell_type": "markdown", "id": "a844b032", "metadata": {}, "source": [ "## Sanity check: Owen values sum to nu(S) − nu(∅)" ] }, { "cell_type": "code", "execution_count": 12, "id": "9e0431a3-8794-4b93-ac7d-1f55928a482b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Expected Shapley explanation: 6.482598960399628\n", "Computed Shapley explanation: 6.482598960399629\n" ] } ], "source": [ "print('Expected Shapley explanation: ', explainer.base_f_S[0] - explainer.base_f_0[0])\n", "print('Computed Shapley explanation: ', np.sum(shap_values_bpt[0]))" ] }, { "cell_type": "markdown", "id": "4ad926be-5377-423e-853d-d91f86d3ecf7", "metadata": {}, "source": [ "## Run shap_bpt with method = \"AA\" (Axis-Aligned partitions)" ] }, { "cell_type": "code", "execution_count": 13, "id": "4db55be3-d7ba-4f99-ae58-00ea2ae88471", "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b509f3d873654327b4f012ee005f4a86", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/100 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "shap_bpt.plot_owen_values(explainer, [shap_values_aa,shap_values_bpt], \n", " class_names, names=['AxisAligned','BPT'])" ] }, { "cell_type": "code", "execution_count": 15, "id": "e2994334-8df7-4684-a62c-1cd17fdfb684", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Expected Shapley explanation: 6.482598960399628\n", "Computed Shapley explanation: 6.482598960399627\n" ] } ], "source": [ "print('Expected Shapley explanation: ', explainer.base_f_S[0] - explainer.base_f_0[0])\n", "print('Computed Shapley explanation: ', np.sum(shap_values_aa[0]))" ] }, { "cell_type": "markdown", "id": "297b1c84", "metadata": {}, "source": [ "## Evaluation with response-based scores\n", "Using Insertion (AUC+) and Deletion (AUC-) scores from (Petsiuk, Das and Saenko 2018)" ] }, { "cell_type": "code", "execution_count": 16, "id": "051c7b27-f3ac-4b8e-9ada-4c10d963cfa2", "metadata": {}, "outputs": [], "source": [ "def saliency_to_auc(nu, heatmap, f_S, f_0, predicted_cls, batch_size=4, method='del', num_samples=101, \n", " rule='trapezoid'):\n", " assert isinstance(heatmap, np.ndarray)\n", " assert len(heatmap.shape)==2 and np.issubdtype(heatmap.dtype, np.floating)\n", "\n", " xs, ys, ms, masks, qs = [], [], [], [], []\n", " for i, value in enumerate(np.linspace(start=1.0, stop=0.0, num=num_samples)):\n", " if method=='del':\n", " epsilon = (1 if value==0.0 else 0)\n", " q = (np.quantile(heatmap, q=value) - epsilon)\n", " m = heatmap <= q\n", " nx = (1.0 - np.sum(m) / m.size)\n", " elif method=='ins':\n", " epsilon = (1 if value==1.0 else 0)\n", " q = (np.quantile(heatmap, q=value) + epsilon)\n", " m = heatmap >= q\n", " nx = (np.sum(m) / m.size)\n", " else:\n", " raise Exception()\n", " \n", " # add a new datapoint on the curve\n", " if len(xs)==0 or nx != xs[-1]: \n", " assert m.dtype==bool and len(m.shape)==2\n", " xs.append(nx)\n", " masks.append(m)\n", " ms.append(np.sum(heatmap[m]))\n", " qs.append(q)\n", "\n", " # evaluate the characteristic function\n", " if len(masks) >= batch_size or (len(masks)>0 and i==(num_samples-1)):\n", " y = nu(np.array(masks))[:, predicted_cls]\n", " ys.extend(y)\n", " masks = []\n", "\n", " assert len(masks)==0 \n", " xs, ys = np.array(xs), np.array(ys)\n", " assert(len(xs) == len(ys))\n", "\n", " # compute considering under/over shoots\n", " if f_S > f_0:\n", " overshoot_max = np.maximum(0, ys - f_S) # overshoot for values exceeding the maximum f(S)\n", " overshoot_min = np.maximum(0, f_0 - ys) # overshoot for values below the minimum f(0)\n", " else: # f(S) < f(0)\n", " overshoot_max = np.maximum(0, ys - f_0) # overshoot for values exceeding the maximum f(0)\n", " overshoot_min = np.maximum(0, f_S - ys) # overshoot for values below the minimum f(S)\n", "\n", " # clip ys, no oveshoots\n", " y_clipped = np.clip(ys, min(f_S, f_0), max(f_S, f_0))\n", " # adjust ys with the overshoot. Clip it inside the admitted range\n", " y_adjusted = np.clip(ys - 2*overshoot_max + 2*overshoot_min, min(f_S, f_0), max(f_S, f_0))\n", "\n", " # rebase to f(0)\n", " if f_S > f_0:\n", " flipped = False\n", " ys = ys - f_0 \n", " y_clipped = y_clipped - f_0 \n", " y_adjusted = y_adjusted - f_0\n", " else: # f(S) < f(0)\n", " flipped = True\n", " ys = f_0 - ys \n", " y_clipped = f_0 - y_clipped \n", " y_adjusted = f_0 - y_adjusted\n", "\n", " # rescaling\n", " ys_rescaled = ys / abs(f_S - f_0)\n", " y_clipped_rescaled = y_clipped / abs(f_S - f_0)\n", " y_adjusted_rescaled = y_adjusted / abs(f_S - f_0)\n", "\n", " auc, auc_r, auc_mae, auc_mse, auc_adj, auc_adjr, auc_clip, auc_clipr = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0\n", "\n", " curve_range = range(1, len(xs)) if rule=='trapezoid' else range(len(xs))\n", "\n", " # compute the area under the curve with the midpoint Riemann sum (i.e. the trapezoidal rule)\n", " for i in curve_range:\n", " if rule=='trapezoid':\n", " delta_x = abs(xs[i] - xs[i-1])\n", " assert delta_x > 0\n", " y_mid = 0.5*(ys[i-1] + ys[i])\n", " y_r_mid = 0.5*(ys_rescaled[i-1] + ys_rescaled[i])\n", " err_mid = y_mid - 0.5*(ms[i-1] - ms[i])\n", " y_clip_mid = 0.5*(y_clipped[i-1] + y_clipped[i])\n", " y_clipr_mid = 0.5*(y_clipped_rescaled[i-1] + y_clipped_rescaled[i])\n", " y_adj_mid = 0.5*(y_adjusted[i-1] + y_adjusted[i])\n", " y_adjr_mid = 0.5*(y_adjusted_rescaled[i-1] + y_adjusted_rescaled[i])\n", " else: # rectangles\n", " delta_x = 1.0/num_samples if i==len(xs)-1 else abs(xs[i+1] - xs[i])\n", " assert delta_x > 0\n", " y_mid = ys[i]\n", " y_r_mid = ys_rescaled[i]\n", " err_mid = y_mid - ms[i]\n", " y_clip_mid = y_clipped[i]\n", " y_clipr_mid = y_clipped_rescaled[i]\n", " y_adj_mid = y_adjusted[i]\n", " y_adjr_mid = y_adjusted_rescaled[i]\n", "\n", "\n", " auc += abs(delta_x * y_mid) # base * height\n", " auc_r += abs(delta_x * y_r_mid) # base * height\n", " auc_mae += abs(delta_x * err_mid) # base * height\n", " auc_mse += abs(delta_x * (err_mid**2)) # base * height^2\n", " auc_clip += abs(delta_x * y_clip_mid)\n", " auc_clipr += abs(delta_x * y_clipr_mid)\n", " auc_adj += abs(delta_x * y_adj_mid)\n", " auc_adjr += abs(delta_x * y_adjr_mid)\n", "\n", " return {'xs':xs, 'ms':ms, 'qs':qs, \n", " 'f_0':f_0, 'f_S':f_S, 'flipped':flipped, \n", " 'ys':ys, 'ysr':ys_rescaled,\n", " 'y_clip':y_clipped, 'y_clipr':y_clipped_rescaled, \n", " 'y_adj':y_adjusted, 'y_adjr':y_adjusted_rescaled, \n", " 'method':method, 'predicted_cls':predicted_cls,\n", " 'auc':auc, 'auc_r':auc_r,\n", " 'auc_mae':auc_mae, 'auc_mse':auc_mse, 'auc_rmse':np.sqrt(auc_mse), \n", " 'auc_clip':auc_clip, 'auc_clipr':auc_clipr,\n", " 'auc_adj':auc_adj, 'auc_adjr':auc_adjr}" ] }, { "cell_type": "code", "execution_count": 17, "id": "8aee8dc4-2454-4256-8816-af6c296c6ac5", "metadata": {}, "outputs": [], "source": [ "predicted_cls = explainer.output_indexes[0]\n", "f_S = explainer.base_f_S[0]\n", "f_0 = explainer.base_f_0[0]\n", "aucD_aa = saliency_to_auc(data[\"nu_masked\"], shap_values_aa[0], f_S, f_0, predicted_cls, method='del')\n", "aucD_bpt = saliency_to_auc(data[\"nu_masked\"], shap_values_bpt[0], f_S, f_0, predicted_cls, method='del')\n", "aucI_aa = saliency_to_auc(data[\"nu_masked\"], shap_values_aa[0], f_S, f_0, predicted_cls, method='ins')\n", "aucI_bpt = saliency_to_auc(data[\"nu_masked\"], shap_values_bpt[0], f_S, f_0, predicted_cls, method='ins')" ] }, { "cell_type": "code", "execution_count": 18, "id": "f16c729d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,axes = plt.subplots(1,2, figsize=(9,3), sharex=True)\n", "for i in range(2):\n", " ax = axes.flat[i]\n", " if i==0: # AUC+\n", " title='$\\\\mathit{AUC}^{+}$'\n", " Xa,Ya,Ma,La = aucI_aa['xs'], aucI_aa['y_clipr'], aucI_aa['ms'], aucI_aa['auc_clipr']\n", " Xb,Yb,Mb,Lb = aucI_bpt['xs'], aucI_bpt['y_clipr'], aucI_bpt['ms'], aucI_bpt['auc_clipr']\n", " elif i==1: # AUC-\n", " title='$\\\\mathit{AUC}^{-}$'\n", " Xa,Ya,Ma,La = aucD_aa['xs'], aucD_aa['y_clipr'], aucD_aa['ms'], aucD_aa['auc_clipr']\n", " Xb,Yb,Mb,Lb = aucD_bpt['xs'], aucD_bpt['y_clipr'], aucD_bpt['ms'], aucD_bpt['auc_clipr']\n", "\n", " Sa, Sb = ('*', '') if La=1 else 'lower right')\n", " ax.set_title(title, fontsize=16)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "46e39d8b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "metal11", "language": 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