{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Master MD2SL\n# Exam of Hands on Lab\n# Simple CNN classifier for blood clot origin\n# Francesco Caso","metadata":{}},{"cell_type":"markdown","source":"\n\nThe following notebook is a simple application of some methods learned during the course \"Hands on Python for Data Science\" of the 2nd level master degree in Data Science and Statistical Learning. Namely, I used a simple Convolutional Neural Network on blood clots' images in order to classify them by their origin.\n\nAlthough the task was just to apply some of the methods learned, I've found interesting to try to face a real competition (without expecting too much from my first one).\n\nThe main issue of this competition is the pre-proccessing part. We are given a very big dataset and because of this, it's difficult to work outside of Kaggle: it would require to download a huge dataset. Furthermore, each element of the dataset is a WSI, i.e. a Whole Slide Image: these are very high resolution images of a whole glass slide, such are those used by pathologists. The use of WSI leads to applying very specific libraries developed for handling such data. \n\nWARNING: the main purpose of the notebook is to show the understanding of the methods learned during the aforementioned course. Having just started working with image data, I looked at the work of other participants at the competition; I changed their code and utilized only those parts of the code which I understood. Nonetheless, I cited everyone. In the hypothesis I forgot someone, please let me know. Thanks!\n","metadata":{}},{"cell_type":"markdown","source":"# Imports","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-09-20T07:10:34.734815Z","iopub.execute_input":"2022-09-20T07:10:34.735823Z","iopub.status.idle":"2022-09-20T07:10:34.986368Z","shell.execute_reply.started":"2022-09-20T07:10:34.735686Z","shell.execute_reply":"2022-09-20T07:10:34.985072Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\n\nfrom tqdm.notebook import tqdm # shows charging bars\n\nfrom openslide import OpenSlide # very important for WSI\n\nfrom pathlib import Path\n\nfrom PIL import Image\nImage.MAX_IMAGE_PIXELS = None #usually PIL uses a max size to avoid DOS attacks but our images are very big\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nfrom torchvision.transforms import ToTensor # this should be useful to turn tif into a tensor\n\nimport cv2\nimport tifffile as tifi\n\nfrom torch.utils.data import Dataset, DataLoader\n\n#import for pytorch\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.utils.data\nimport torch.nn.functional as F\nimport torchvision\nfrom torchvision import transforms\nfrom PIL import Image, ImageFile\n\nfrom sklearn.metrics import roc_auc_score, accuracy_score, recall_score, roc_curve, make_scorer, RocCurveDisplay, average_precision_score, precision_score, f1_score, confusion_matrix","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:10:37.077535Z","iopub.execute_input":"2022-09-20T07:10:37.077996Z","iopub.status.idle":"2022-09-20T07:10:40.32547Z","shell.execute_reply.started":"2022-09-20T07:10:37.077942Z","shell.execute_reply":"2022-09-20T07:10:40.324167Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# First look at the data","metadata":{}},{"cell_type":"code","source":"for img_path in sorted(Path(\"../input/mayo-clinic-strip-ai/train\").glob(\"*.tif\"))[:10]:\n    img = Image.open(img_path) # uso PIL\n    img.thumbnail((2048, 2048)) #le immagini sono troppo grandi devo fare un thumbnail(miniatura)\n    plt.figure(figsize=(12, 12))\n    plt.title(f\"{img_path.stem}\") # stemming sul path per avere solo il patient_id\n    plt.imshow(img)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T09:04:36.068658Z","iopub.execute_input":"2022-09-20T09:04:36.069208Z","iopub.status.idle":"2022-09-20T09:04:47.506912Z","shell.execute_reply.started":"2022-09-20T09:04:36.069165Z","shell.execute_reply":"2022-09-20T09:04:47.505435Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's import the data in some DataFrames","metadata":{}},{"cell_type":"code","source":"train_csv = pd.read_csv(\"../input/mayo-clinic-strip-ai/train.csv\")\ntest_csv = pd.read_csv(\"../input/mayo-clinic-strip-ai/test.csv\")\nother_csv = pd.read_csv(\"../input/mayo-clinic-strip-ai/other.csv\")\nss = pd.read_csv(\"../input/mayo-clinic-strip-ai/sample_submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:10:41.587301Z","iopub.execute_input":"2022-09-20T07:10:41.588884Z","iopub.status.idle":"2022-09-20T07:10:41.627311Z","shell.execute_reply.started":"2022-09-20T07:10:41.588834Z","shell.execute_reply":"2022-09-20T07:10:41.62581Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's take a look at the DataFrames","metadata":{}},{"cell_type":"code","source":"\nprint('='*10)\nprint('train')\n# print shape\nprint(train_csv.shape)\n# print first few lines\nprint(train_csv.head())\n\nprint('='*10)\nprint('test')\n# print shape\nprint(test_csv.shape)\n# print first few lines\nprint(test_csv.head())","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:10:43.40144Z","iopub.execute_input":"2022-09-20T07:10:43.401926Z","iopub.status.idle":"2022-09-20T07:10:43.419175Z","shell.execute_reply.started":"2022-09-20T07:10:43.401883Z","shell.execute_reply":"2022-09-20T07:10:43.418037Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Resizing these images is far from trivial. A naive approach would require to use too much memory leading the notebook to crush (e.g. the infamous WSI number 329...). So I tried many approaches. Nonetheless, in my opinion the best and most ingeniuous one was proposed by another partecipant.","metadata":{}},{"cell_type":"markdown","source":"The following resize and stitching method has been taken from \"Handling Large Images - tile resize and stitch\" by Saurabh Sawhney  ","metadata":{}},{"cell_type":"markdown","source":"The function used perform better if we enhance the csv dataframe with more information","metadata":{}},{"cell_type":"code","source":"cols = train_csv.columns\n# Getting image paths into the df\ntrain_csv['image_path'] = train_csv.image_id.apply(lambda x: os.path.join(\"../input/mayo-clinic-strip-ai/train/\", x+\".tif\"))\ndef enhance_df(df):\n    df[\"image_size\"]   = df.image_path.apply(lambda x: Image.open(x).size)\n    df[\"image_pixels\"] = df[\"image_size\"].apply(lambda x: int(x[0]*int(x[1])))    \n    df[\"image_width\"]  = df[\"image_size\"].apply(lambda x: int(x[0]))\n    df[\"image_height\"] = df[\"image_size\"].apply(lambda x: int(x[1]))\n    df[\"aspect_ratio\"] = df[\"image_width\"]/df[\"image_height\"]\n    return df\ntrain_csv=enhance_df(train_csv)\nprint('enhanced train dataframe ready')\n\n# The largest and smallest images\ndisplay(train_csv[train_csv.image_pixels==train_csv.image_pixels.max()])\ndisplay(train_csv[train_csv.image_pixels==train_csv.image_pixels.min()])","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:10:46.900358Z","iopub.execute_input":"2022-09-20T07:10:46.900783Z","iopub.status.idle":"2022-09-20T07:11:06.009209Z","shell.execute_reply.started":"2022-09-20T07:10:46.900749Z","shell.execute_reply":"2022-09-20T07:11:06.008043Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\ndef tile_resize_stich(row, horizontal_size=4000, cutoff_size=3500000000, tiles_per_side=5, show=False, debug=False):\n    '''\n    Break up the large image, resize individual tiles, put them back together\n    Keep horizontal size at the same size specified for general resizing.\n    '''\n    def demarc(): print('='*100)\n    \n    # get the metadata -----------------------------------------------------------        \n    image_path=row.image_path\n    image_width=row.image_width\n    image_height=row.image_height\n    \n    # Show the original image, if req'd ------------------------------------------\n    if show and row.image_pixels<cutoff_size:\n        orig_image = np.array(cv2.imread(row.image_path))\n        orig_image = cv2.cvtColor(orig_image, cv2.COLOR_RGB2BGR)        \n        print('Original Image')\n        plt.imshow(orig_image); plt.show()\n        demarc()\n        \n    # What should be the tile size? ----------------------------------------------    \n    # Before  resizing -----------\n    tile_size=(int(image_width/tiles_per_side),int(image_height/tiles_per_side))\n    # After resizing -------------\n    h_size=int(horizontal_size/tiles_per_side) # target horizontal size of each tile after resizing\n    v_size=int(h_size/row.aspect_ratio)        # target vertical size of each tile after resizing, to maintain aspect ratio\n\n    # Let's make tiles from this -------------------------------------------------\n    slide=OpenSlide(image_path) # using OpenSlide to get access to the image    \n    if debug:\n        print(f'Original image_width {image_width}   image_height {image_height}')\n        print(f'individual tile_size before resizing: {tile_size}')\n    tiles=[]\n    big_tile_number=1\n    for v in tqdm(range(0,image_height-tile_size[1]+1,tile_size[1])): # The +1 is just to manage the last step in the range\n        for h in range(0,image_width-tile_size[0]+1,tile_size[0]):\n            if debug: print('processing big tile', big_tile_number)\n            image = slide.read_region((h,v),0, tile_size)  # reading a tile_size area of the image, starting at h,v,position.\n            image = np.array(image)\n            image = cv2.resize(image, dsize=(h_size,v_size), interpolation=cv2.INTER_NEAREST) # INTER_CUBIC takes far longer\n            tiles.append(image)\n            big_tile_number+=1\n            if debug: print('Tile shape:', image.shape)\n            \n    # Showing off the tiles in a grid structure ----------------------------------        \n    if show:\n        print('showing tiles')\n        fig, ax = plt.subplots(nrows = tiles_per_side,ncols = tiles_per_side, figsize = (6,6/row.aspect_ratio))\n        for i,t in enumerate(tiles):\n            x_grid=int(i/tiles_per_side)            \n            y_grid=i%tiles_per_side\n            ax[x_grid,y_grid].imshow(t)\n            ax[x_grid,y_grid].axis('off')\n        fig.tight_layout()\n        plt.show()\n        \n    # Stitching it all up --------------------------------------------------------\n    stitched = np.array(Image.new('RGBA', (h_size*tiles_per_side, v_size*tiles_per_side)))\n    if debug:\n        print('Beginning the stitching process...')\n        print('First, a placeholder image of shape', stitched.shape)\n    for pos, individual_tile in enumerate(tiles):\n            x_grid=pos%tiles_per_side\n            y_grid=int(pos/tiles_per_side)\n            if debug: print(f'GRID POSITIONS: {x_grid}, {y_grid}')\n            stitched[\n                     y_grid*v_size:y_grid*v_size+v_size,\n                     x_grid*h_size:x_grid*h_size+h_size,                \n                    :] = individual_tile\n    # Showing off the stitching process -----------------------------------------                \n            if show: \n                plt.imshow(stitched)\n                plt.show()\n                demarc()\n    # All done ------------------------------------------------------------------              \n    return stitched","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:11:06.011102Z","iopub.execute_input":"2022-09-20T07:11:06.011445Z","iopub.status.idle":"2022-09-20T07:11:06.16556Z","shell.execute_reply.started":"2022-09-20T07:11:06.011414Z","shell.execute_reply":"2022-09-20T07:11:06.164389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_check = True\nfor index in range(450):\n        row=train_csv.loc[index]\n        print('='*100)\n        image = tile_resize_stich(row, tiles_per_side=5)\n        save_paths = [\"../test/\", \"../train/\"]\n        save_img_path = save_paths[train_check] \n        os.makedirs(save_img_path, exist_ok = True) \n        torch.save(image , os.path.join(save_img_path, train_csv.iloc[index].image_id ))","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:10:57.857605Z","iopub.execute_input":"2022-09-20T08:10:57.858561Z","iopub.status.idle":"2022-09-20T08:13:52.862208Z","shell.execute_reply.started":"2022-09-20T08:10:57.858515Z","shell.execute_reply":"2022-09-20T08:13:52.860949Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(os.listdir('../train'))","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.172856Z","iopub.execute_input":"2022-09-20T07:40:11.173283Z","iopub.status.idle":"2022-09-20T07:40:11.182867Z","shell.execute_reply.started":"2022-09-20T07:40:11.173246Z","shell.execute_reply":"2022-09-20T07:40:11.181587Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_check = True\npaths = [\"../test/\", \"../train/\"]\n\nfor index in range(0, 10):\n    img_path = paths[train_check]  + train_csv.iloc[index].image_id \n    img = torch.load(img_path)\n    img = Image.fromarray(img)\n    print(type(img))\n    #img = Image.open(img_path) # uso PIL\n    img.thumbnail((2048, 2048)) #le immagini sono troppo grandi devo fare un thumbnail(miniatura)\n    plt.figure(figsize=(12, 12))\n    plt.imshow(img)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:44:39.325408Z","iopub.execute_input":"2022-09-20T08:44:39.325874Z","iopub.status.idle":"2022-09-20T08:44:41.619419Z","shell.execute_reply.started":"2022-09-20T08:44:39.325832Z","shell.execute_reply":"2022-09-20T08:44:41.618099Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Class Imbalance","metadata":{}},{"cell_type":"markdown","source":"Some of these ideas has been taken from '[Class Imbalance]->Weighted Binary Cross Entropy' by Parth Dhameliya","metadata":{}},{"cell_type":"code","source":"def compute_class_freqs(labels):\n    \n    labels = np.array(labels)\n    \n    N = labels.shape[0]\n    \n    positive_frequencies = np.sum(labels,axis = 0) / N\n    negative_frequencies = 1 - positive_frequencies\n    \n    return positive_frequencies, negative_frequencies","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.185024Z","iopub.execute_input":"2022-09-20T07:40:11.185446Z","iopub.status.idle":"2022-09-20T07:40:11.194905Z","shell.execute_reply.started":"2022-09-20T07:40:11.18541Z","shell.execute_reply":"2022-09-20T07:40:11.193702Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train = []\nfor index in range(400):\n    y_train.append({\"CE\" : 0, \"LAA\": 1}[train_csv.iloc[index].label])","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.196368Z","iopub.execute_input":"2022-09-20T07:40:11.197274Z","iopub.status.idle":"2022-09-20T07:40:11.21463Z","shell.execute_reply.started":"2022-09-20T07:40:11.197231Z","shell.execute_reply":"2022-09-20T07:40:11.213589Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"freq_pos, freq_neg = compute_class_freqs(y_train)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.21602Z","iopub.execute_input":"2022-09-20T07:40:11.216436Z","iopub.status.idle":"2022-09-20T07:40:11.229086Z","shell.execute_reply.started":"2022-09-20T07:40:11.216404Z","shell.execute_reply":"2022-09-20T07:40:11.228033Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"freq_pos","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.232293Z","iopub.execute_input":"2022-09-20T07:40:11.233514Z","iopub.status.idle":"2022-09-20T07:40:11.243008Z","shell.execute_reply.started":"2022-09-20T07:40:11.233471Z","shell.execute_reply":"2022-09-20T07:40:11.242104Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"freq_neg","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.244621Z","iopub.execute_input":"2022-09-20T07:40:11.245235Z","iopub.status.idle":"2022-09-20T07:40:11.257057Z","shell.execute_reply.started":"2022-09-20T07:40:11.245197Z","shell.execute_reply":"2022-09-20T07:40:11.25624Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.DataFrame({\"Targets\": ['0'], \"Label\": [\"Negative\"], \"Value\": freq_neg})\ndf = df.append({\"Targets\": '1', \"Label\": \"Positive\", \"Value\": freq_pos}, ignore_index=True)\nsns.barplot(x=\"Targets\", y=\"Value\" ,data=df)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.258457Z","iopub.execute_input":"2022-09-20T07:40:11.259756Z","iopub.status.idle":"2022-09-20T07:40:11.496939Z","shell.execute_reply.started":"2022-09-20T07:40:11.259714Z","shell.execute_reply":"2022-09-20T07:40:11.49576Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pos_weights = freq_neg\nneg_weights = freq_pos\npos_contribution = freq_pos * pos_weights \nneg_contribution = freq_neg * neg_weights","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.498468Z","iopub.execute_input":"2022-09-20T07:40:11.498934Z","iopub.status.idle":"2022-09-20T07:40:11.505882Z","shell.execute_reply.started":"2022-09-20T07:40:11.498875Z","shell.execute_reply":"2022-09-20T07:40:11.504637Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\ndf = pd.DataFrame({\"Targets\": ['0'], \"Label\": [\"Negative\"], \"Value\": neg_contribution})\ndf = df.append({\"Targets\": '1', \"Label\": \"Positive\", \"Value\": pos_contribution}, ignore_index=True)\nf = sns.barplot(x=\"Targets\", y=\"Value\" ,data=df)\n\n","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.507114Z","iopub.execute_input":"2022-09-20T07:40:11.508245Z","iopub.status.idle":"2022-09-20T07:40:11.682433Z","shell.execute_reply.started":"2022-09-20T07:40:11.508195Z","shell.execute_reply":"2022-09-20T07:40:11.681472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class W_BCEWithLogitsLoss(torch.nn.Module):\n    \n    def __init__(self, w_p = None, w_n = None):\n        super(W_BCEWithLogitsLoss, self).__init__()\n        \n        self.w_p = w_p\n        self.w_n = w_n\n        \n    def forward(self, logits, labels, epsilon = 1e-7):\n        # it used to compute the sigmoid of the logits/raw values \n        # but my model outputs the probabilities so I throw that passage\n        \n        ps = logits.squeeze()\n        \n        loss_pos = -1 * torch.mean(self.w_p * labels * torch.log(ps + epsilon))\n        loss_neg = -1 * torch.mean(self.w_n * (1-labels) * torch.log((1-ps) + epsilon))\n        \n        loss = loss_pos + loss_neg\n        \n        return loss","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.68358Z","iopub.execute_input":"2022-09-20T07:40:11.684802Z","iopub.status.idle":"2022-09-20T07:40:11.693553Z","shell.execute_reply.started":"2022-09-20T07:40:11.684761Z","shell.execute_reply":"2022-09-20T07:40:11.692247Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Dataset and Dataloader","metadata":{}},{"cell_type":"code","source":"class MayoDataset(Dataset):\n    def __init__(self, df):\n        self.df = df\n        self.istrain = 'label' in df.columns # serve per il path dopo\n        \n    def __getitem__(self, index):\n        paths = [\"../test/\", \"../train/\"]\n        img_path = paths[self.istrain] + self.df.iloc[index].image_id\n        img = torch.load(img_path)\n        #img = cv2.resize(img, (512, 512)).transpose(2, 0, 1)# resize because to big and change to appropriate order\n        img = cv2.resize(img, (512, 512))\n        image = ToTensor()(img)\n        \n        if(self.istrain):\n            label = {\"CE\" : 0.0, \"LAA\": 1.0}[self.df.iloc[index].label] # I use a dictionary\n            \n        return image, label\n    \n    def __len__(self):\n        return len(self.df)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.696995Z","iopub.execute_input":"2022-09-20T07:40:11.697415Z","iopub.status.idle":"2022-09-20T07:40:11.708478Z","shell.execute_reply.started":"2022-09-20T07:40:11.697379Z","shell.execute_reply":"2022-09-20T07:40:11.707256Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df_train = train_csv[:400]\ntrain_df_val = train_csv[400:450]","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:11.709939Z","iopub.execute_input":"2022-09-20T07:40:11.710351Z","iopub.status.idle":"2022-09-20T07:40:11.7185Z","shell.execute_reply.started":"2022-09-20T07:40:11.710318Z","shell.execute_reply":"2022-09-20T07:40:11.717435Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"batch_size = 50\ntrain_ds = MayoDataset(train_df_train) # chiamo la mia classe dataset per creare il training dataset\ntrain_dl = DataLoader(\n    train_ds, batch_size=batch_size, shuffle=True) # il train_dataset è uno degli input del dataloader\n\nval_ds = MayoDataset(train_df_val) # chiamo la mia classe dataset per creare il validation dataset\nval_dl = DataLoader(\n    val_ds, batch_size=batch_size) # il validation_dataset è uno degli input del dataloader","metadata":{"execution":{"iopub.status.busy":"2022-09-20T09:00:06.729534Z","iopub.execute_input":"2022-09-20T09:00:06.730022Z","iopub.status.idle":"2022-09-20T09:00:06.737223Z","shell.execute_reply.started":"2022-09-20T09:00:06.729957Z","shell.execute_reply":"2022-09-20T09:00:06.735978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Training and Validation","metadata":{}},{"cell_type":"code","source":"class CNN(nn.Module):\n    def __init__(self):\n        super(CNN, self).__init__()\n\n        # Convolution 1\n        self.cnn1 = nn.Conv2d(in_channels=4, out_channels=16,\n                              kernel_size=3, stride=1, padding=0) #16 filtri kernel -> 3x3; stride-> si sposta di uno\n        self.relu1 = nn.ReLU() \n\n        # Max pool 1\n        self.maxpool1 = nn.MaxPool2d(kernel_size=2) # maxpooling 2x2\n\n        # Convolution 2\n        self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32,\n                              kernel_size=3, stride=1, padding=0) #32 filtri 3x3 si sposta di 1 e non ho padding\n        self.relu2 = nn.ReLU()\n\n        # Max pool 2\n        self.maxpool2 = nn.MaxPool2d(kernel_size=2) # maxpooling 2x2\n\n        # Fully connected 1\n        self.fc1 = nn.Linear(508032, 100) # layer lineare da tantissime feature a 10^4\n        \n        self.relu3 = nn.ReLU()\n        \n        # Fully connected 2\n        self.fc2 = nn.Linear(100, 1) # devo predire 2 classi\n        \n        # Logit transform for probability\n        self.lgt = nn.Sigmoid() # preferisco avere una probabilità in uscita\n        \n        self.double()\n\n    def forward(self, x, to_print=False): # to_print è una sorta di verbose\n        # Set 1\n        if to_print:\n          print('INPUT',x.shape)\n        out = self.cnn1(x) # primo layer conv\n        if to_print:\n          print('CNN1',out.shape)\n        out = self.relu1(out)\n        out = self.maxpool1(out) # maxpool\n        if to_print:\n          print('MAXPOOL1',out.shape)\n\n        # Set 2\n        out = self.cnn2(out) # secondo layer conv\n        if to_print:\n          print('CNN2',out.shape)\n\n        out = self.relu2(out)\n\n        out = self.maxpool2(out) # maxpool\n        if to_print:\n          print(\"after the 2nd maxpool:{} \".format(out.shape))\n        # Flatten\n        out = out.view(out.size(0), -1)\n        if to_print:\n          print(\"after the flatten:{} \".format(out.shape))\n        out = self.fc1(out) # primo layer lineare\n        \n        out = self.relu3(out)\n        \n        out = self.fc2(out) # secondo layer lineare\n        \n        out = self.lgt(out) # sigmoide per trasformare in [0, 1]\n        if to_print:\n          print('FINAL',out.shape)\n\n        return out","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:40:48.032038Z","iopub.execute_input":"2022-09-20T07:40:48.032515Z","iopub.status.idle":"2022-09-20T07:40:48.048269Z","shell.execute_reply.started":"2022-09-20T07:40:48.032475Z","shell.execute_reply":"2022-09-20T07:40:48.047176Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"First 10 images of validation","metadata":{}},{"cell_type":"code","source":"for img_path in sorted(Path(\"../input/mayo-clinic-strip-ai/train\").glob(\"*.tif\"))[400:410]:\n    img = Image.open(img_path) # uso PIL\n    img.thumbnail((2048, 2048)) #le immagini sono troppo grandi devo fare un thumbnail(miniatura)\n    plt.figure(figsize=(12, 12))\n    plt.title(f\"{img_path.stem}\") # stemming sul path per avere solo il patient_id\n    plt.imshow(img)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T09:07:36.315451Z","iopub.execute_input":"2022-09-20T09:07:36.316675Z","iopub.status.idle":"2022-09-20T09:08:15.771624Z","shell.execute_reply.started":"2022-09-20T09:07:36.316619Z","shell.execute_reply":"2022-09-20T09:08:15.769442Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"First 10 images in validation processed","metadata":{}},{"cell_type":"code","source":"for batch in val_dl:\n        optimizer.zero_grad() # clear gradients for next train\n        inputs, targets = batch \n        inputs = inputs.to(device)\n        \n        for i in range (10):\n            plt.imshow(  inputs[i][0:3].permute(1, 2, 0)  )\n\n            plt.show()\n\n            plt.imshow(inputs[i][1])\n            plt.show()\n            plt.imshow(inputs[i][2])\n            plt.show()\n            plt.imshow(inputs[i][3])\n            plt.show()\n            \n        break","metadata":{"execution":{"iopub.status.busy":"2022-09-20T09:08:15.776032Z","iopub.execute_input":"2022-09-20T09:08:15.776602Z","iopub.status.idle":"2022-09-20T09:08:24.682336Z","shell.execute_reply.started":"2022-09-20T09:08:15.776531Z","shell.execute_reply":"2022-09-20T09:08:24.681374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## from the notes of 'Hands on Python for Data Science' \n\ndef train(model, optimizer, loss_fn, train_loader, val_loader, epochs=20, device=\"cpu\",to_print=False):\n    for epoch in range(1, epochs+1):\n        training_loss = 0.0\n        valid_loss = 0.0\n        model.train() # training mode\n        for batch in train_loader:\n            optimizer.zero_grad() # clear gradients for next train\n            inputs, targets = batch \n            inputs = inputs.to(device)\n            inputs.shape()\n            targets = targets.to(device)\n            output = model(inputs.double(),to_print)\n            loss = loss_fn(output, targets.unsqueeze(1)) # output e targets non venivano con la stessa shape\n            loss.backward() # backpropagation, compute gradients\n            optimizer.step() # apply gradients\n            training_loss += loss.data.item() * inputs.size(0)\n            # print(training_loss,loss.data.item(),inputs.size(0))\n            if to_print:\n              break\n        training_loss /= len(train_loader.dataset)\n        if to_print:\n          break\n        with torch.no_grad(): # disables gradient calculation (more memory :) )\n          model.eval()\n          num_correct = 0 \n          num_examples = 0\n          for batch in val_loader:\n              inputs, targets = batch\n              inputs = inputs.to(device)\n              output = model(inputs.double())\n              targets = targets.to(device)\n              loss = loss_fn(output,targets.unsqueeze(1)) \n              valid_loss += loss.data.item() * inputs.size(0)\n              correct = torch.eq(torch.max(F.softmax(output, dim=1), dim=1)[1], targets)\n              num_correct += torch.sum(correct).item()\n              num_examples += correct.shape[0]\n          valid_loss /= len(val_loader.dataset)\n\n        print('Epoch: {}, Training Loss: {:.4f}, Validation Loss: {:.4f}, accuracy = {:.4f}'.format(epoch, training_loss,\n        valid_loss, num_correct / num_examples))","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:23:59.737452Z","iopub.execute_input":"2022-09-20T08:23:59.738008Z","iopub.status.idle":"2022-09-20T08:23:59.753406Z","shell.execute_reply.started":"2022-09-20T08:23:59.737954Z","shell.execute_reply":"2022-09-20T08:23:59.752086Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cnn = CNN()","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:24:04.456267Z","iopub.execute_input":"2022-09-20T08:24:04.456722Z","iopub.status.idle":"2022-09-20T08:24:05.658157Z","shell.execute_reply.started":"2022-09-20T08:24:04.456674Z","shell.execute_reply":"2022-09-20T08:24:05.656826Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#optimizer = optim.SGD(cnn.parameters(),lr=0.001)\noptimizer = optim.Adam(cnn.parameters(), lr=0.001)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:24:10.622734Z","iopub.execute_input":"2022-09-20T08:24:10.623208Z","iopub.status.idle":"2022-09-20T08:24:10.679481Z","shell.execute_reply.started":"2022-09-20T08:24:10.623159Z","shell.execute_reply":"2022-09-20T08:24:10.678486Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#loss = nn.CrossEntropyLoss()\n#loss = nn.BCELoss()\n#loss = nn.BCEWithLogitsLoss()\nloss = W_BCEWithLogitsLoss(pos_weights, neg_weights)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:24:21.511513Z","iopub.execute_input":"2022-09-20T08:24:21.512024Z","iopub.status.idle":"2022-09-20T08:24:21.518145Z","shell.execute_reply.started":"2022-09-20T08:24:21.511982Z","shell.execute_reply":"2022-09-20T08:24:21.516856Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if torch.cuda.is_available():\n  device = torch.device('cuda')\nelse:\n  device = torch.device('cpu')\n\ncnn.to(device)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:24:45.408203Z","iopub.execute_input":"2022-09-20T08:24:45.40867Z","iopub.status.idle":"2022-09-20T08:24:45.419105Z","shell.execute_reply.started":"2022-09-20T08:24:45.408633Z","shell.execute_reply":"2022-09-20T08:24:45.417757Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"sistema: {}\".format(device))","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:24:47.73619Z","iopub.execute_input":"2022-09-20T08:24:47.736648Z","iopub.status.idle":"2022-09-20T08:24:47.742168Z","shell.execute_reply.started":"2022-09-20T08:24:47.73661Z","shell.execute_reply":"2022-09-20T08:24:47.741225Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train(cnn, optimizer,loss, train_dl,val_dl, epochs=10, device=device)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:24:58.464087Z","iopub.execute_input":"2022-09-20T08:24:58.465446Z","iopub.status.idle":"2022-09-20T08:27:31.771062Z","shell.execute_reply.started":"2022-09-20T08:24:58.465383Z","shell.execute_reply":"2022-09-20T08:27:31.769283Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Final Evaluation","metadata":{}},{"cell_type":"code","source":"# Function that returns the threshold corresponding to the closest point to (0,1) on the ROC curve \ndef find_best_th_roc(ytrain, temp_ytrain_hat):\n    fpr_train, tpr_train, th_roc_train = roc_curve(ytrain, temp_ytrain_hat)\n    best_point_roc_x = np.array([0] * fpr_train.shape[0])\n    #print('best_point_roc_x', best_point_roc_x)\n    best_point_roc_y = np.array([1] * tpr_train.shape[0])\n    #print('best_point_roc_y', best_point_roc_y)\n    temp_x = (fpr_train - best_point_roc_x)\n    temp_y = (tpr_train - best_point_roc_y)\n    temp_sqrt = np.sqrt(np.square(temp_x) + np.square(temp_y))\n    index_min_temp_sqrt = np.argmin(temp_sqrt)\n    best_th_roc = th_roc_train[index_min_temp_sqrt]\n    RocCurveDisplay.from_predictions(ytrain, temp_ytrain_hat)\n    plt.scatter(fpr_train[th_roc_train == best_th_roc], tpr_train[th_roc_train == best_th_roc])\n    plt.legend(['ROC curve', 'Best thr'])\n    plt.show()\n    return best_th_roc","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:27:38.155016Z","iopub.execute_input":"2022-09-20T08:27:38.155471Z","iopub.status.idle":"2022-09-20T08:27:38.1648Z","shell.execute_reply.started":"2022-09-20T08:27:38.155435Z","shell.execute_reply":"2022-09-20T08:27:38.16336Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Function that returns the classifier performance metrics\ndef clf_metrics(y, y_score, y_pred, phase):\n    AUROC = roc_auc_score(y, y_score)\n    ACC = accuracy_score(y, y_pred)\n    SENS = recall_score(y, y_pred, pos_label=1)\n    SPEC = recall_score(y, y_pred, pos_label=0)\n    AVE_PREC = average_precision_score(y, y_score)\n    PREC = precision_score(y, y_pred)\n    F1 = f1_score(y, y_pred)\n    print('### ' + phase + ' set performances with the best threshold ###')\n    print('AUROC', AUROC)\n    print('Accuracy', ACC)\n    print('Sensitivity', SENS)\n    print('Specificity', SPEC)\n    print('Average precision', AVE_PREC)\n    print('Precision', PREC) #TP\n    print('F1 score', F1)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:27:39.713216Z","iopub.execute_input":"2022-09-20T08:27:39.713639Z","iopub.status.idle":"2022-09-20T08:27:39.7224Z","shell.execute_reply.started":"2022-09-20T08:27:39.713604Z","shell.execute_reply":"2022-09-20T08:27:39.720987Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train = []\nfor index in range(400):\n    y_train.append({\"CE\" : 0, \"LAA\": 1}[train_csv.iloc[index].label])","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:52:28.463037Z","iopub.execute_input":"2022-09-20T07:52:28.463429Z","iopub.status.idle":"2022-09-20T07:52:28.479104Z","shell.execute_reply.started":"2022-09-20T07:52:28.463395Z","shell.execute_reply":"2022-09-20T07:52:28.477848Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_val = []\nfor index in range(400, 450):\n    y_val.append({\"CE\" : 0, \"LAA\": 1}[train_csv.iloc[index].label])","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:52:28.480632Z","iopub.execute_input":"2022-09-20T07:52:28.481604Z","iopub.status.idle":"2022-09-20T07:52:28.497606Z","shell.execute_reply.started":"2022-09-20T07:52:28.481555Z","shell.execute_reply":"2022-09-20T07:52:28.496438Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# come richiamare la predizione\ny_pred_train = []\nwith torch.no_grad():\n    cnn.eval() \n    for X_batch, _ in train_dl:\n        X_batch = X_batch.to(device)\n        y_test_pred = cnn(X_batch.double())\n        y_pred_train.append(y_test_pred.cpu().numpy())\ny_pred_train = [a.squeeze().tolist() for a in y_pred_train]","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:52:28.499084Z","iopub.execute_input":"2022-09-20T07:52:28.499646Z","iopub.status.idle":"2022-09-20T07:54:09.030983Z","shell.execute_reply.started":"2022-09-20T07:52:28.499609Z","shell.execute_reply":"2022-09-20T07:54:09.029606Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_train = [item for sublist in y_pred_train for item in sublist]","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:54:09.032703Z","iopub.execute_input":"2022-09-20T07:54:09.036356Z","iopub.status.idle":"2022-09-20T07:54:09.041928Z","shell.execute_reply.started":"2022-09-20T07:54:09.036302Z","shell.execute_reply":"2022-09-20T07:54:09.040719Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_train","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:54:09.043205Z","iopub.execute_input":"2022-09-20T07:54:09.043633Z","iopub.status.idle":"2022-09-20T07:54:09.05708Z","shell.execute_reply.started":"2022-09-20T07:54:09.043603Z","shell.execute_reply":"2022-09-20T07:54:09.056021Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# come richiamare la predizione\ny_pred_val = []\nwith torch.no_grad():\n    cnn.eval() \n    for X_batch, _ in val_dl:\n        X_batch = X_batch.to(device)\n        y_test_pred = cnn(X_batch.double())\n        y_pred_val.append(y_test_pred.cpu().numpy())\ny_pred_val = [a.squeeze().tolist() for a in y_pred_val]","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:54:09.058638Z","iopub.execute_input":"2022-09-20T07:54:09.058979Z","iopub.status.idle":"2022-09-20T07:54:22.393934Z","shell.execute_reply.started":"2022-09-20T07:54:09.058936Z","shell.execute_reply":"2022-09-20T07:54:22.392623Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_val = [item for sublist in y_pred_val for item in sublist]","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:54:22.39817Z","iopub.execute_input":"2022-09-20T07:54:22.398603Z","iopub.status.idle":"2022-09-20T07:54:22.403065Z","shell.execute_reply.started":"2022-09-20T07:54:22.398565Z","shell.execute_reply":"2022-09-20T07:54:22.402239Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_val","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:54:22.404221Z","iopub.execute_input":"2022-09-20T07:54:22.404694Z","iopub.status.idle":"2022-09-20T07:54:22.415989Z","shell.execute_reply.started":"2022-09-20T07:54:22.404662Z","shell.execute_reply":"2022-09-20T07:54:22.415235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"best_th_pr = find_best_th_roc(y_train, y_pred_train) # threshold computed on training data\nprint('Best thr', best_th_pr)\ny_train_pred = (y_pred_train >= best_th_pr).astype(int)\nclf_metrics(y_train, y_pred_train, y_train_pred, 'TRAINING')","metadata":{"execution":{"iopub.status.busy":"2022-09-20T08:28:03.313794Z","iopub.execute_input":"2022-09-20T08:28:03.314264Z","iopub.status.idle":"2022-09-20T08:28:03.510255Z","shell.execute_reply.started":"2022-09-20T08:28:03.314226Z","shell.execute_reply":"2022-09-20T08:28:03.508785Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Plot the training set confusion matrix\nCM = confusion_matrix(y_train, y_train_pred) # (tn, fp, fn, tp)\nplt.figure()\nax = sns.heatmap(CM, annot=True, annot_kws={\"size\": 18}, fmt='g') # font size\nplt.xlabel('Predicted class')\nplt.ylabel('Actual class')\nplt.title('TRAINING set confusion matrix')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:54:22.697908Z","iopub.execute_input":"2022-09-20T07:54:22.698398Z","iopub.status.idle":"2022-09-20T07:54:22.940986Z","shell.execute_reply.started":"2022-09-20T07:54:22.698352Z","shell.execute_reply":"2022-09-20T07:54:22.939732Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Compute performance metrics on the validation set (with best threshold computed on training data)\nprint()\ny_pred = (y_pred_val >= best_th_pr).astype(int)\nclf_metrics(y_val, y_pred_val, y_pred, 'VALIDATION')\n\n# Plot the validation set ROC curve\nRocCurveDisplay.from_predictions(y_val, y_pred_val)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:54:22.942417Z","iopub.execute_input":"2022-09-20T07:54:22.943648Z","iopub.status.idle":"2022-09-20T07:54:23.140349Z","shell.execute_reply.started":"2022-09-20T07:54:22.943611Z","shell.execute_reply":"2022-09-20T07:54:23.139128Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Plot the validation set confusion matrix\nCM = confusion_matrix(y_val, y_pred) # (tn, fp, fn, tp)\nplt.figure()\nax = sns.heatmap(CM, annot=True, annot_kws={\"size\": 18}, fmt='g') # font size\nplt.xlabel('Predicted class')\nplt.ylabel('Actual class')\nplt.title('VALIDATION set confusion matrix')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:54:23.141875Z","iopub.execute_input":"2022-09-20T07:54:23.142253Z","iopub.status.idle":"2022-09-20T07:54:23.378194Z","shell.execute_reply.started":"2022-09-20T07:54:23.142219Z","shell.execute_reply":"2022-09-20T07:54:23.376905Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![class_metric_tab.png](attachment:857a49f7-9214-4267-899c-6de5b61e881e.png)","metadata":{},"attachments":{"857a49f7-9214-4267-899c-6de5b61e881e.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# Future Directions","metadata":{}},{"cell_type":"markdown","source":"**Background removal**","metadata":{}},{"cell_type":"markdown","source":"This code-blocks are taken from 'Mayo Clinic - Removing Background via Seam Carving' by YU4U","metadata":{}},{"cell_type":"code","source":"# My fork version enables us to terminate removing seams if the energy of the seam becomes greater than the threshold value\n# Orignal implementation: https://github.com/li-plus/seam-carving\n! pip install git+https://github.com/yu4u/seam-carving.git","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:08:16.326109Z","iopub.execute_input":"2022-09-20T07:08:16.326534Z","iopub.status.idle":"2022-09-20T07:08:33.535892Z","shell.execute_reply.started":"2022-09-20T07:08:16.326498Z","shell.execute_reply":"2022-09-20T07:08:33.534393Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from pathlib import Path\nfrom PIL import Image\nImage.MAX_IMAGE_PIXELS = None\nimport matplotlib.pyplot as plt\nimport seam_carving\nseam_carving.carve.MAX_MEAN_ENERGY = 10.0","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:08:33.538394Z","iopub.execute_input":"2022-09-20T07:08:33.538794Z","iopub.status.idle":"2022-09-20T07:08:33.550933Z","shell.execute_reply.started":"2022-09-20T07:08:33.538757Z","shell.execute_reply":"2022-09-20T07:08:33.549752Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# WARNING: it crushes!!!!\n#for img_path in sorted(Path(\"../input/mayo-clinic-strip-ai/train\").glob(\"*.tif\"))[328:330]:\n #   img = Image.open(img_path)\n #   img.thumbnail((2048, 2048))\n #   dst = seam_carving.resize(img, (100, 100))\n #   fig = plt.figure(figsize=(12, 12))\n #   ax = fig.add_subplot(1, 2, 1)\n #   ax.set_title(f\"{img_path.stem} original\")\n #   ax.imshow(img)\n #   ax = fig.add_subplot(1, 2, 2)\n #   ax.set_title(f\"{img_path.stem} resized\")\n #   ax.imshow(dst)\n #   fig.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:08:33.552414Z","iopub.execute_input":"2022-09-20T07:08:33.55291Z","iopub.status.idle":"2022-09-20T07:08:33.563786Z","shell.execute_reply.started":"2022-09-20T07:08:33.552877Z","shell.execute_reply":"2022-09-20T07:08:33.562626Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for img_path in sorted(Path(\"../input/mayo-clinic-strip-ai/train\").glob(\"*.tif\"))[0:1]:\n    img = Image.open(img_path)\n    img.thumbnail((2048, 2048))\n    dst = seam_carving.resize(img, (100, 100))\n    fig = plt.figure(figsize=(12, 12))\n    ax = fig.add_subplot(1, 2, 1)\n    ax.set_title(f\"{img_path.stem} original\")\n    ax.imshow(img)\n    ax = fig.add_subplot(1, 2, 2)\n    ax.set_title(f\"{img_path.stem} resized\")\n    ax.imshow(dst)\n    fig.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-20T07:09:14.88914Z","iopub.execute_input":"2022-09-20T07:09:14.890183Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Balancing the dataset and preventing leakage**","metadata":{}}]}