{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Using Weights and Biases for automated Hyperparameter Search\n\nIn this notebook, I'll be demonstrating the [Weights and Biases library](https://docs.wandb.com/)'s automated hyperparameter sweep functionality on FasterRCNN. W&B is integrated within Kaggle and can be installed using pip and imported using \"import wandb\".\n\nOnly a small subset of the data will be used for training and validation to avoid GPU out of memory error due to Kaggle's memory limit. This is only to demonstrate how to integrate W&B and you are free to modify the code to suit your purpose or run it locally on another machine.\n\nIn this example, we are trying to find the best combination of epochs, batch size, and learning rate to minimize the training loss. You can set what metric to optimize for, we will just use training loss as an example. Other metrics/scores such as the Average Precision Score will also be logged for the validation set (technically the Mean Average Precision Score if evaluated on entire set, see this kernel: https://www.kaggle.com/pestipeti/competition-metric-details-script).\n\nDocs for W&B hyperparameter sweeps: https://docs.wandb.com/sweeps\nW&B for Kaggle: https://www.wandb.com/kaggle\n\nOriginal FasterRCNN notebooks (credits to @pestipeti):\n\nhttps://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-train\n\nhttps://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-inference\n\n","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"![Screenshot_79.png](attachment:Screenshot_79.png)","attachments":{"Screenshot_79.png":{"image/png":"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"}},"execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# Setup","execution_count":null},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport cv2\nimport os\nimport re\nimport gc\n\nfrom PIL import Image\n\nimport albumentations as A\nfrom albumentations.pytorch.transforms import ToTensorV2\n\nimport torch\nimport torchvision\n\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision.models.detection import FasterRCNN\nfrom torchvision.models.detection.rpn import AnchorGenerator\n\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.utils.data.sampler import SequentialSampler\n\nfrom matplotlib import pyplot as plt\n\nDIR_INPUT = '/kaggle/input/global-wheat-detection'\nDIR_TRAIN = f'{DIR_INPUT}/train'\nDIR_TEST = f'{DIR_INPUT}/test'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Install + import Weights and Biases\n!pip install --upgrade wandb\nimport wandb","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.read_csv(f'{DIR_INPUT}/train.csv')\ntrain_df.shape\ntrain_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Extract the bbox column and explode it into x,y,w,h\ntrain_df['x'] = -1\ntrain_df['y'] = -1\ntrain_df['w'] = -1\ntrain_df['h'] = -1\n\ndef expand_bbox(x):\n    r = np.array(re.findall(\"([0-9]+[.]?[0-9]*)\", x))\n    if len(r) == 0:\n        r = [-1, -1, -1, -1]\n    return r\n\ntrain_df[['x', 'y', 'w', 'h']] = np.stack(train_df['bbox'].apply(lambda x: expand_bbox(x)))\ntrain_df.drop(columns=['bbox'], inplace=True)\ntrain_df['x'] = train_df['x'].astype(np.float)\ntrain_df['y'] = train_df['y'].astype(np.float)\ntrain_df['w'] = train_df['w'].astype(np.float)\ntrain_df['h'] = train_df['h'].astype(np.float)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_ids = train_df['image_id'].unique()\n#Original: 3373 total images divided into 2708 for training, 665 for validation\n#valid_ids = image_ids[-665:]\n#train_ids = image_ids[:-665]\n\n#Select smaller set: 677 for training, 166 for validation (1/4 size original) to avoid any CUDA out of memory issues\ntrain_ids = image_ids[:677]\nvalid_ids = image_ids[677:677+166]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(image_ids.shape)\nprint(valid_ids.shape)\nprint(train_ids.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#take the bounding boxes and put them into their respective category (train, valid) by id \nvalid_df = train_df[train_df['image_id'].isin(valid_ids)]\ntrain_df = train_df[train_df['image_id'].isin(train_ids)]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"valid_df.shape, train_df.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class WheatDataset(Dataset):\n\n    def __init__(self, dataframe, image_dir, transforms=None):\n        super().__init__()\n\n        self.image_ids = dataframe['image_id'].unique() #all images\n        self.df = dataframe\n        self.image_dir = image_dir\n        self.transforms = transforms\n\n    def __getitem__(self, index: int):\n\n        image_id = self.image_ids[index]\n        records = self.df[self.df['image_id'] == image_id]\n        \n        #load image and normalize image pixels to 0-1\n        image = cv2.imread(f'{self.image_dir}/{image_id}.jpg', cv2.IMREAD_COLOR)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)\n        image /= 255.0\n        \n        boxes = records[['x', 'y', 'w', 'h']].values\n        #turn each bounding box into format [x_start, y_start, x_end, y_end]\n        boxes[:, 2] = boxes[:, 0] + boxes[:, 2]\n        boxes[:, 3] = boxes[:, 1] + boxes[:, 3]\n        \n        #calculate areas of each bounding box\n        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n        area = torch.as_tensor(area, dtype=torch.float32)\n\n        # there is only one class\n        labels = torch.ones((records.shape[0],), dtype=torch.int64)\n        \n        # suppose all instances are not crowd\n        iscrowd = torch.zeros((records.shape[0],), dtype=torch.int64)\n        \n        target = {}\n        target['boxes'] = boxes\n        target['labels'] = labels #all boxes are labelled as 1 (wheat head)\n        # target['masks'] = None\n        target['image_id'] = torch.tensor([index])\n        target['area'] = area\n        target['iscrowd'] = iscrowd\n        \n        #apply transformations to this image\n        if self.transforms:\n            sample = {\n                'image': image,\n                'bboxes': target['boxes'],\n                'labels': labels\n            }\n            sample = self.transforms(**sample)\n            image = sample['image']\n            \n            target['boxes'] = torch.stack(tuple(map(torch.tensor, zip(*sample['bboxes'])))).permute(1, 0)\n\n        return image, target, image_id\n\n    def __len__(self) -> int:\n        return self.image_ids.shape[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Albumentations (pretty much image data augmentation)\ndef get_train_transform():\n    return A.Compose([\n        A.Flip(0.5),\n        ToTensorV2(p=1.0)\n    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})\n\ndef get_valid_transform():\n    return A.Compose([\n        ToTensorV2(p=1.0)\n    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})\n\n#Collate function\ndef collate_fn(batch):\n    return tuple(zip(*batch))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#IOU Metric Calculation\nfrom collections import namedtuple\nfrom typing import List, Union\n\nBox = namedtuple('Box', 'xmin ymin xmax ymax')\n\n\ndef calculate_iou(gt: List[Union[int, float]],\n                  pred: List[Union[int, float]],\n                  form: str = 'pascal_voc') -> float:\n    \"\"\"Calculates the IoU.\n    \n    Args:\n        gt: List[Union[int, float]] coordinates of the ground-truth box\n        pred: List[Union[int, float]] coordinates of the prdected box\n        form: str gt/pred coordinates format\n            - pascal_voc: [xmin, ymin, xmax, ymax]\n            - coco: [xmin, ymin, w, h]\n    Returns:\n        IoU: float Intersection over union (0.0 <= iou <= 1.0)\n    \"\"\"\n    if form == 'coco':\n        bgt = Box(gt[0], gt[1], gt[0] + gt[2], gt[1] + gt[3])\n        bpr = Box(pred[0], pred[1], pred[0] + pred[2], pred[1] + pred[3])\n    else:\n        bgt = Box(gt[0], gt[1], gt[2], gt[3])\n        bpr = Box(pred[0], pred[1], pred[2], pred[3])\n        \n\n    overlap_area = 0.0\n    union_area = 0.0\n\n    # Calculate overlap area\n    dx = min(bgt.xmax, bpr.xmax) - max(bgt.xmin, bpr.xmin)\n    dy = min(bgt.ymax, bpr.ymax) - max(bgt.ymin, bpr.ymin)\n\n    if (dx > 0) and (dy > 0):\n        overlap_area = dx * dy\n\n    # Calculate union area\n    union_area = (\n            (bgt.xmax - bgt.xmin) * (bgt.ymax - bgt.ymin) +\n            (bpr.xmax - bpr.xmin) * (bpr.ymax - bpr.ymin) -\n            overlap_area\n    )\n\n    return overlap_area / union_area","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#MAP Calculation\ndef find_best_match(gts, predd, threshold=0.5, form='pascal_voc'):\n    \"\"\"Returns the index of the 'best match' between the\n    ground-truth boxes and the prediction. The 'best match'\n    is the highest IoU. (0.0 IoUs are ignored).\n    \n    Args:\n        gts: Coordinates of the available ground-truth boxes\n        pred: Coordinates of the predicted box\n        threshold: Threshold\n        form: Format of the coordinates\n        \n    Return:\n        Index of the best match GT box (-1 if no match above threshold)\n    \"\"\"\n    best_match_iou = -np.inf\n    best_match_idx = -1\n    \n    for gt_idx, ggt in enumerate(gts):\n        iou = calculate_iou(ggt, predd, form=form)\n        \n        if iou < threshold:\n            continue\n        \n        if iou > best_match_iou:\n            best_match_iou = iou\n            best_match_idx = gt_idx\n\n    return best_match_idx\n\n\ndef calculate_precision(preds_sorted, gt_boxes, threshold=0.5, form='coco'):\n    \"\"\"Calculates precision per at one threshold.\n    \n    Args:\n        preds_sorted: \n    \"\"\"\n    tp = 0\n    fp = 0\n    fn = 0\n\n    fn_boxes = []\n\n    for pred_idx, pred in enumerate(preds_sorted):\n        best_match_gt_idx = find_best_match(gt_boxes, pred, threshold=threshold, form=form)\n\n        if best_match_gt_idx >= 0:\n            # True positive: The predicted box matches a gt box with an IoU above the threshold.\n            tp += 1\n\n            # Remove the matched GT box\n            gt_boxes = np.delete(gt_boxes, best_match_gt_idx, axis=0)\n\n        else:\n            # No match\n            # False positive: indicates a predicted box had no associated gt box.\n            fn += 1\n            fn_boxes.append(pred)\n\n    # False negative: indicates a gt box had no associated predicted box.\n    fp = len(gt_boxes)\n    precision = tp / (tp + fp + fn)\n    return precision, fn_boxes, gt_boxes\n\n\ndef calculate_image_precision(preds_sorted, gt_boxes, thresholds=(0.5), form='coco', debug=False):\n    \n    n_threshold = len(thresholds)\n    image_precision = 0.0\n    \n    for threshold in thresholds:\n        precision_at_threshold, _, _ = calculate_precision(preds_sorted,\n                                                           gt_boxes,\n                                                           threshold=threshold,\n                                                           form=form\n                                                          )\n        if debug:\n            print(\"@{0:.2f} = {1:.4f}\".format(threshold, precision_at_threshold))\n\n        image_precision += precision_at_threshold / n_threshold\n    \n    return image_precision","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Train and Test Functions","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\ndef train(args, model, device, train_data_loader, optimizer, epoch, iteration):\n    print(\"Epoch: \", epoch)\n    \n    #Train Loop\n    for batch_idx, (images, targets, image_ids) in enumerate(train_data_loader):\n        torch.cuda.empty_cache()\n        \n        #Train and calculate loss\n        model.train()\n        images = list(image.to(device) for image in images)\n        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n        \n        loss_dict = model(images, targets)\n        \n        losses = sum(loss for loss in loss_dict.values())\n        loss_value = losses.item()\n\n        optimizer.zero_grad()\n        losses.backward()\n        optimizer.step()\n        if batch_idx % args.log_interval == 0:\n            #Log the train loss and iteration\n            wandb.log({\"train_loss\": loss_value, \"iteration_train\": iteration})\n        \n        iteration += 1 #iteration increases for every batch\n        \n    \n    return iteration #returns the updated new iteration\n            ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#[xmin, ymin, xmax, ymax] => [x, y, width, height]\ndef convert_boxes_format(boxes):\n    boxes[:, 2] = boxes[:, 2] - boxes[:, 0]\n    boxes[:, 3] = boxes[:, 3] - boxes[:, 1]\n    return boxes\n\nclass_id_to_label = {\n    1: \"wheat\"\n}\n\ndef test(args, model, device, test_data_loader, epoch, iteration):\n    model.eval()\n    \n    epoch_precision_score = []\n    for batch_idx, (images, targets, image_ids) in enumerate(test_data_loader):\n        images = list(image.to(device) for image in images)\n        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n\n        outputs = model(images)\n        \n        #Calculate Precision Score Per Batch\n        batch_precision_score = 0\n        for i, image in enumerate(images):\n            \n            #predicted bounding boxes\n            boxes = outputs[i]['boxes'].data.cpu().numpy()\n            boxes = convert_boxes_format(boxes)\n            \n            scores = outputs[i]['scores'].data.cpu().numpy()\n            \n            #ground truth bounding boxes\n            gt_boxes = targets[i][\"boxes\"].cpu().numpy()\n            gt_boxes = convert_boxes_format(gt_boxes)\n\n            image_id = image_ids[i]\n\n            # Sort highest confidence -> lowest confidence\n            preds_sorted_idx = np.argsort(scores)[::-1]\n            preds_sorted = boxes[preds_sorted_idx]\n\n            iou_thresholds = [x for x in np.arange(0.5, 0.76, 0.05)]\n            image_precision = calculate_image_precision(preds_sorted, gt_boxes,\n                                            thresholds=iou_thresholds,\n                                            form='coco', debug=True)\n            \n            print(\"Average Precision of image: {0:.4f}\".format(image_precision))\n            batch_precision_score +=  image_precision\n        \n        batch_precision_score = batch_precision_score / len(images) #average precision score for the batch\n        epoch_precision_score.append(batch_precision_score)\n        \n        if batch_idx % args.log_interval == 0:\n            #logs the precision score per batch and also the iteration\n            wandb.log({\"batch_score_validation\": batch_precision_score, \"iteration_validation\": iteration})\n            \n        if batch_idx % args.image_log_interval == 0:\n            #Log some images with bounding boxes (log 3 images)\n            for i, image in enumerate(images[:3]): \n\n                image_id = image_ids[i]\n                scores = outputs[i]['scores'].data.cpu().numpy().astype(np.float64)\n\n                #predicted bounding boxes\n                boxes = outputs[i]['boxes'].data.cpu().numpy().astype(np.float64)\n                predicted_boxes = []\n                for b_i, box in enumerate(boxes):\n                    print(type(box[0]))\n                    box_data = {\"position\" : {\n                          \"minX\" : box[0],\n                          \"maxX\" : box[2],\n                          \"minY\" : box[1],\n                          \"maxY\" : box[3] \n                        },\n                      \"class_id\" : 1, #wheat\n                      \"box_caption\" : \"wheat: (%.3f)\" % (scores[b_i]),\n                      \"domain\": \"pixel\",\n                      \"scores\" : { \"score\" : scores[b_i] }\n                    }\n                    predicted_boxes.append(box_data)\n                    \n                print(\"predicted boxes \", predicted_boxes)\n                print()\n\n                #ground truth bounding boxes\n                gt_boxes = targets[i][\"boxes\"].cpu().numpy().astype(np.float64)\n                target_boxes = [] \n                for b_i, box in enumerate(gt_boxes):\n                    print(type(box[0]))\n                    box_data = {\"position\" : {\n                          \"minX\" : box[0],\n                          \"maxX\" : box[2],\n                          \"minY\" : box[1],\n                          \"maxY\" : box[3] \n                        },\n                                \"domain\": \"pixel\",\n                      \"box_caption\" : \"ground_truth\"\n                    }\n                    target_boxes.append(box_data)\n                \n                print(\"target boxes: \", target_boxes)\n                \n                image = image.permute(1,2,0).cpu().numpy().astype(np.float64)\n                \n                #create object and log\n                img = wandb.Image(image, boxes = \n                                  {\"predictions\": \n                                   {\"box_data\": predicted_boxes,\n                                    \"class_labels\" : class_id_to_label}\n                                   })\n                \n                wandb.log({\"bounding_boxes\": img})\n            \n        iteration += 1\n    \n    epoch_precision_score = sum(epoch_precision_score) / len(epoch_precision_score) \n    wandb.log({\"epoch_score_validation\": batch_precision_score, \"epoch\": epoch})\n    return iteration\n        \n        \n    \n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Load dataset\ntrain_dataset = WheatDataset(train_df, DIR_TRAIN, get_train_transform())\nvalid_dataset = WheatDataset(valid_df, DIR_TRAIN, get_valid_transform())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Define Sweep Configuration and Run\n","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"#Hyperparameter sweep configuration\nsweep_config = {\n    'method': 'grid', #grid, random (random will continue running until you terminate it or specify a target field for metric)\n    'metric': {\n      'name': 'train_loss',\n      'goal': 'minimize'   \n    },\n    'parameters': {\n        'epochs': {\n            'values': [2, 4]\n        },\n        'batch_size': {\n            'values': [4, 8]\n        },\n        'lr': {\n            'values': [1e-3, 5e-4] \n        }\n    }\n}\n\n#initialize the sweep\n#Running this line will require you to provide an authentication token provided by your W&B account\n#sweep_id = wandb.sweep(sweep_config, project=\"GWD-fasterRCNN\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def run():\n    \n    config_default = {         # Initialize config\n    \"batch_size\": 8,          # input batch size for training (default: 64)\n    \"test_batch_size\": 8,    # input batch size for testing (default: 1000)\n    \"epochs\": 2,             # number of epochs to train (default: 10)\n    \"lr\": 0.005,               # learning rate (default: 0.01)\n    \"momentum\": 0.9,          # SGD momentum (default: 0.5) \n    \"no_cuda\": False,         # disables CUDA training\n    \"seed\": 42,               # random seed (default: 42)\n    \"log_interval\": 1,      #how many batches to wait before logging in train/test loops\n    \"image_log_interval\": 10,\n    \"decay\": 0.0005\n    }\n    wandb.init(config=config_default, project=\"GWD-fasterRCNN\")\n    config = wandb.config\n    \n    # load a model; pre-trained on COCO\n    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n    num_classes = 2  # wheat (1)....or not wheat (0)\n    # get number of input features for the classifier\n    in_features = model.roi_heads.box_predictor.cls_score.in_features\n    # replace the pre-trained head with a new one\n    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n\n    use_cuda = not config[\"no_cuda\"] and torch.cuda.is_available()\n    device = torch.device('cuda') if use_cuda else torch.device('cpu')\n\n    # Set random seeds and deterministic pytorch for reproducibility\n    # random.seed(config.seed)       # python random seed\n    torch.manual_seed(config[\"seed\"]) # pytorch random seed\n    np.random.seed(config[\"seed\"]) # numpy random seed\n    torch.backends.cudnn.deterministic = True\n\n\n    #Create dataloaders\n    train_data_loader = DataLoader(\n        train_dataset,\n        batch_size=config[\"batch_size\"],\n        shuffle=False,\n        num_workers=4,\n        collate_fn=collate_fn\n    )\n\n    valid_data_loader = DataLoader(\n        valid_dataset,\n        batch_size=config[\"test_batch_size\"],\n        shuffle=False,\n        num_workers=4,\n        collate_fn=collate_fn\n    )\n\n\n    model.to(device)\n    params = [p for p in model.parameters() if p.requires_grad]\n    optimizer = torch.optim.SGD(params, lr=config[\"lr\"], momentum=config[\"momentum\"], weight_decay=config[\"decay\"])\n    \n    ####\n    #wandb.watch(model, log=\"all\") #this line only needs to be run once to hook W&B to your model (comment out for future runs)\n    ####\n    \n    iteration_train = 0\n    iteration_test = 0\n    for epoch in range(1, config[\"epochs\"] + 1):\n        #Runs training and returns the new training iteration counter\n        #iteration_train = train(config, model, device, train_data_loader, optimizer, epoch, iteration_train)\n        #Runs testing/valiadtion and returns the new test iteration counter\n        iteration_test = test(config, model, device, valid_data_loader, epoch, iteration_test)\n        print(\"Iteration Train: \", iteration_train)\n        print(\"Iteration Test: \", iteration_test)\n\n    torch.save(model.state_dict(), 'fasterrcnn_resnet50_fpn.pth')\n    wandb.save('fasterrcnn_resnet50_fpn.pth')\n    \n    #Clear memory\n    del model\n    del train_data_loader\n    del valid_data_loader\n    gc.collect()\n    torch.cuda.empty_cache()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Run sweep\n#wandb.agent(sweep_id, run)\nimport gc\ngc.collect()\nrun()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Hyperparameter sweep results\nWill produce 2x2x2=8 runs (because in the sweep config, there are 2 values being tested for each of epochs, batch size, and lr)\n\nhttps://app.wandb.ai/kshen/GWD-fasterRCNN/sweeps/l5fgi1d4\n","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"Weights and Biases provides visualizations to compare different HP configurations.\n\n![Screenshot_84.png](attachment:Screenshot_84.png)","attachments":{"Screenshot_84.png":{"image/png":"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"}},"execution_count":null},{"metadata":{},"cell_type":"markdown","source":"Train Loss over 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"}},"execution_count":null},{"metadata":{},"cell_type":"markdown","source":"\n\nBy logging other metrics (such as the Average Precision Score per batch: batch_score_validation) we can see that certain runs/configurations can produce better scores (the charts here aren't conclusive or accurate as it's trained/tested on a small subset):\n\n![Screenshot_82.png](attachment:Screenshot_82.png)","attachments":{"Screenshot_82.png":{"image/png":"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"}},"execution_count":null}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 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