{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Initialize Environment","execution_count":null},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"!pip install -q efficientnet >> /dev/null","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd, numpy as np\nfrom kaggle_datasets import KaggleDatasets\nimport tensorflow as tf, re, math\nimport tensorflow.keras.backend as K\nimport efficientnet.tfkeras as efn\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import roc_auc_score\nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Configuration\nIn order to be a proper cross validation with a meaningful overall CV score (aligned with LB score), **you need to choose the same** `IMG_SIZES`, `INC2019`, `INC2018`, and `EFF_NETS` **for each fold**. If your goal is to just run lots of experiments, then you can choose to have a different experiment in each fold. Then each fold is like a holdout validation experiment. When you find a configuration you like, you can use that configuration for all folds. \n* DEVICE - is GPU or TPU\n* SEED - a different seed produces a different triple stratified kfold split.\n* FOLDS - number of folds. Best set to 3, 5, or 15 but can be any number between 2 and 15\n* IMG_SIZES - is a Python list of length FOLDS. These are the image sizes to use each fold\n* INC2019 - This includes the new half of the 2019 competition data. The second half of the 2019 data is the comp data from 2018 plus 2017\n* INC2018 - This includes the second half of the 2019 competition data which is the comp data from 2018 plus 2017\n* BATCH_SIZES - is a list of length FOLDS. These are batch sizes for each fold. For maximum speed, it is best to use the largest batch size your GPU or TPU allows.\n* EPOCHS - is a list of length FOLDS. These are maximum epochs. Note that each fold, the best epoch model is saved and used. So if epochs is too large, it won't matter.\n* EFF_NETS - is a list of length FOLDS. These are the EfficientNets to use each fold. The number refers to the B. So a number of `0` refers to EfficientNetB0, and `1` refers to EfficientNetB1, etc.\n* WGTS - this should be `1/FOLDS` for each fold. This is the weight when ensembling the folds to predict the test set. If you want a weird ensemble, you can use different weights.\n* TTA - test time augmentation. Each test image is randomly augmented and predicted TTA times and the average prediction is used. TTA is also applied to OOF during validation.","execution_count":null},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"DEVICE = \"TPU\" #or \"GPU\"\n\n# USE DIFFERENT SEED FOR DIFFERENT STRATIFIED KFOLD\nSEED = 42\n\n# NUMBER OF FOLDS. USE 3, 5, OR 15 \nFOLDS = 3\n\n# WHICH IMAGE SIZES TO LOAD EACH FOLD\n# CHOOSE 128, 192, 256, 384, 512, 768 \n##IMG_SIZES = [384,384,384,384,384]\nIMG_SIZES = [384,384,384]\n\n# INCLUDE OLD COMP DATA? YES=1 NO=0\nINC2019 = [0,0,0,0,0]\nINC2018 = [1,1,1,1,1]\n\n# BATCH SIZE AND EPOCHS\nBATCH_SIZES = [32]*FOLDS\nEPOCHS = [12]*FOLDS\n\n# WHICH EFFICIENTNET B? TO USE\nEFF_NETS = [6,6,6,6,6]\n\n# WEIGHTS FOR FOLD MODELS WHEN PREDICTING TEST\nWGTS = [1/FOLDS]*FOLDS\n\n# TEST TIME AUGMENTATION STEPS\nTTA = 11","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if DEVICE == \"TPU\":\n    print(\"connecting to TPU...\")\n    try:\n        tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n        print('Running on TPU ', tpu.master())\n    except ValueError:\n        print(\"Could not connect to TPU\")\n        tpu = None\n\n    if tpu:\n        try:\n            print(\"initializing  TPU ...\")\n            tf.config.experimental_connect_to_cluster(tpu)\n            tf.tpu.experimental.initialize_tpu_system(tpu)\n            strategy = tf.distribute.experimental.TPUStrategy(tpu)\n            print(\"TPU initialized\")\n        except _:\n            print(\"failed to initialize TPU\")\n    else:\n        DEVICE = \"GPU\"\n\nif DEVICE != \"TPU\":\n    print(\"Using default strategy for CPU and single GPU\")\n    strategy = tf.distribute.get_strategy()\n\nif DEVICE == \"GPU\":\n    print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n    \n\nAUTO     = tf.data.experimental.AUTOTUNE\nREPLICAS = strategy.num_replicas_in_sync\nprint(f'REPLICAS: {REPLICAS}')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Step 1: Preprocess\nPreprocess has already been done and saved to TFRecords. Here we choose which size to load. We can use either 128x128, 192x192, 256x256, 384x384, 512x512, 768x768 by changing the `IMG_SIZES` variable in the preceeding code section. These TFRecords are discussed [here][1]. The advantage of using different input sizes is discussed [here][2]\n\n[1]: https://www.kaggle.com/c/siim-isic-melanoma-classification/discussion/155579\n[2]: https://www.kaggle.com/c/siim-isic-melanoma-classification/discussion/160147","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"GCS_PATH = [None]*FOLDS; GCS_PATH2 = [None]*FOLDS\nfor i,k in enumerate(IMG_SIZES):\n    GCS_PATH[i] = KaggleDatasets().get_gcs_path('melanoma-%ix%i'%(k,k))\n    GCS_PATH2[i] = KaggleDatasets().get_gcs_path('isic2019-%ix%i'%(k,k))\nfiles_train = np.sort(np.array(tf.io.gfile.glob(GCS_PATH[0] + '/train*.tfrec')))\nfiles_test  = np.sort(np.array(tf.io.gfile.glob(GCS_PATH[0] + '/test*.tfrec')))\nprint(GCS_PATH,GCS_PATH2)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Step 2: Data Augmentation","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"ROT_ = 180.0\nSHR_ = 2.0\nHZOOM_ = 8.0\nWZOOM_ = 8.0\nHSHIFT_ = 8.0\nWSHIFT_ = 8.0","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_mat(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):\n    # returns 3x3 transformmatrix which transforms indicies\n        \n    # CONVERT DEGREES TO RADIANS\n    rotation = math.pi * rotation / 180.\n    shear    = math.pi * shear    / 180.\n\n    def get_3x3_mat(lst):\n        return tf.reshape(tf.concat([lst],axis=0), [3,3])\n    \n    # ROTATION MATRIX\n    c1   = tf.math.cos(rotation)\n    s1   = tf.math.sin(rotation)\n    one  = tf.constant([1],dtype='float32')\n    zero = tf.constant([0],dtype='float32')\n    \n    rotation_matrix = get_3x3_mat([c1,   s1,   zero, \n                                   -s1,  c1,   zero, \n                                   zero, zero, one])    \n    # SHEAR MATRIX\n    c2 = tf.math.cos(shear)\n    s2 = tf.math.sin(shear)    \n    \n    shear_matrix = get_3x3_mat([one,  s2,   zero, \n                                zero, c2,   zero, \n                                zero, zero, one])        \n    # ZOOM MATRIX\n    zoom_matrix = get_3x3_mat([one/height_zoom, zero,           zero, \n                               zero,            one/width_zoom, zero, \n                               zero,            zero,           one])    \n    # SHIFT MATRIX\n    shift_matrix = get_3x3_mat([one,  zero, height_shift, \n                                zero, one,  width_shift, \n                                zero, zero, one])\n    \n    return K.dot(K.dot(rotation_matrix, shear_matrix), \n                 K.dot(zoom_matrix,     shift_matrix))\n\n\ndef transform(image, DIM=256):    \n    # input image - is one image of size [dim,dim,3] not a batch of [b,dim,dim,3]\n    # output - image randomly rotated, sheared, zoomed, and shifted\n    XDIM = DIM%2 #fix for size 331\n    \n    rot = ROT_ * tf.random.normal([1], dtype='float32')\n    shr = SHR_ * tf.random.normal([1], dtype='float32') \n    h_zoom = 1.0 + tf.random.normal([1], dtype='float32') / HZOOM_\n    w_zoom = 1.0 + tf.random.normal([1], dtype='float32') / WZOOM_\n    h_shift = HSHIFT_ * tf.random.normal([1], dtype='float32') \n    w_shift = WSHIFT_ * tf.random.normal([1], dtype='float32') \n\n    # GET TRANSFORMATION MATRIX\n    m = get_mat(rot,shr,h_zoom,w_zoom,h_shift,w_shift) \n\n    # LIST DESTINATION PIXEL INDICES\n    x   = tf.repeat(tf.range(DIM//2, -DIM//2,-1), DIM)\n    y   = tf.tile(tf.range(-DIM//2, DIM//2), [DIM])\n    z   = tf.ones([DIM*DIM], dtype='int32')\n    idx = tf.stack( [x,y,z] )\n    \n    # ROTATE DESTINATION PIXELS ONTO ORIGIN PIXELS\n    idx2 = K.dot(m, tf.cast(idx, dtype='float32'))\n    idx2 = K.cast(idx2, dtype='int32')\n    idx2 = K.clip(idx2, -DIM//2+XDIM+1, DIM//2)\n    \n    # FIND ORIGIN PIXEL VALUES           \n    idx3 = tf.stack([DIM//2-idx2[0,], DIM//2-1+idx2[1,]])\n    d    = tf.gather_nd(image, tf.transpose(idx3))\n        \n    return tf.reshape(d,[DIM, DIM,3])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def read_labeled_tfrecord(example):\n    tfrec_format = {\n        'image'                        : tf.io.FixedLenFeature([], tf.string),\n        'image_name'                   : tf.io.FixedLenFeature([], tf.string),\n        'patient_id'                   : tf.io.FixedLenFeature([], tf.int64),\n        'sex'                          : tf.io.FixedLenFeature([], tf.int64),\n        'age_approx'                   : tf.io.FixedLenFeature([], tf.int64),\n        'anatom_site_general_challenge': tf.io.FixedLenFeature([], tf.int64),\n        'diagnosis'                    : tf.io.FixedLenFeature([], tf.int64),\n        'target'                       : tf.io.FixedLenFeature([], tf.int64)\n    }           \n    example = tf.io.parse_single_example(example, tfrec_format)\n    return example['image'], example['target']\n\n\ndef read_unlabeled_tfrecord(example, return_image_name):\n    tfrec_format = {\n        'image'                        : tf.io.FixedLenFeature([], tf.string),\n        'image_name'                   : tf.io.FixedLenFeature([], tf.string),\n    }\n    example = tf.io.parse_single_example(example, tfrec_format)\n    return example['image'], example['image_name'] if return_image_name else 0\n\n \ndef prepare_image(img, augment=True, dim=256):    \n    img = tf.image.decode_jpeg(img, channels=3)\n    img = tf.cast(img, tf.float32) / 255.0\n    \n    if augment:\n        img = transform(img,DIM=dim)\n        img = tf.image.random_flip_left_right(img)\n        #img = tf.image.random_hue(img, 0.01)\n        img = tf.image.random_saturation(img, 0.7, 1.3)\n        img = tf.image.random_contrast(img, 0.8, 1.2)\n        img = tf.image.random_brightness(img, 0.1)\n                      \n    img = tf.reshape(img, [dim,dim, 3])\n            \n    return img\n\ndef count_data_items(filenames):\n    n = [int(re.compile(r\"-([0-9]*)\\.\").search(filename).group(1)) \n         for filename in filenames]\n    return np.sum(n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_dataset(files, augment = False, shuffle = False, repeat = False, \n                labeled=True, return_image_names=True, batch_size=16, dim=256):\n    \n    ds = tf.data.TFRecordDataset(files, num_parallel_reads=AUTO)\n    ds = ds.cache()\n    \n    if repeat:\n        ds = ds.repeat()\n    \n    if shuffle: \n        ds = ds.shuffle(1024*8)\n        opt = tf.data.Options()\n        opt.experimental_deterministic = False\n        ds = ds.with_options(opt)\n        \n    if labeled: \n        ds = ds.map(read_labeled_tfrecord, num_parallel_calls=AUTO)\n    else:\n        ds = ds.map(lambda example: read_unlabeled_tfrecord(example, return_image_names), \n                    num_parallel_calls=AUTO)      \n    \n    ds = ds.map(lambda img, imgname_or_label: (prepare_image(img, augment=augment, dim=dim), \n                                               imgname_or_label), \n                num_parallel_calls=AUTO)\n    \n    ds = ds.batch(batch_size * REPLICAS)\n    ds = ds.prefetch(AUTO)\n    return ds","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Step 3: Build Model","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"EFNS = [efn.EfficientNetB0, efn.EfficientNetB1, efn.EfficientNetB2, efn.EfficientNetB3, \n        efn.EfficientNetB4, efn.EfficientNetB5, efn.EfficientNetB6]\n\ndef build_model(dim=128, ef=0):\n    inp = tf.keras.layers.Input(shape=(dim,dim,3))\n    base = EFNS[ef](input_shape=(dim,dim,3),weights='imagenet',include_top=False)\n    x = base(inp)\n    x = tf.keras.layers.GlobalAveragePooling2D()(x)\n    x = tf.keras.layers.Dense(1,activation='sigmoid')(x)\n    model = tf.keras.Model(inputs=inp,outputs=x)\n    opt = tf.keras.optimizers.Adam(learning_rate=0.001)\n    loss = tf.keras.losses.BinaryCrossentropy(label_smoothing=0.05) \n    model.compile(optimizer=opt,loss=loss,metrics=['AUC'])\n    return model","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Step 4: Train Schedule","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_lr_callback(batch_size=8):\n    lr_start   = 0.000005\n    lr_max     = 0.00000125 * REPLICAS * batch_size\n    lr_min     = 0.000001\n    lr_ramp_ep = 5\n    lr_sus_ep  = 0\n    lr_decay   = 0.8\n   \n    def lrfn(epoch):\n        if epoch < lr_ramp_ep:\n            lr = (lr_max - lr_start) / lr_ramp_ep * epoch + lr_start\n            \n        elif epoch < lr_ramp_ep + lr_sus_ep:\n            lr = lr_max\n            \n        else:\n            lr = (lr_max - lr_min) * lr_decay**(epoch - lr_ramp_ep - lr_sus_ep) + lr_min\n            \n        return lr\n\n    lr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=False)\n    return lr_callback","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Train Model\nOur model will be trained for the number of FOLDS and EPOCHS you chose in the configuration above. Each fold the model with lowest validation loss will be saved and used to predict OOF and test. Adjust the variables `VERBOSE` and `DISPLOY_PLOT` below to determine what output you want displayed. The variable `VERBOSE=1 or 2` will display the training and validation loss and auc for each epoch as text. The variable `DISPLAY_PLOT` shows this information as a plot. ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"# USE VERBOSE=0 for silent, VERBOSE=1 for interactive, VERBOSE=2 for commit\nVERBOSE = 1\n\nDISPLAY_PLOT = True\n\nskf = KFold(n_splits=FOLDS,shuffle=True,random_state=SEED)\noof_pred = []; oof_tar = []; oof_val = []; oof_names = []; oof_folds = [] \npreds = np.zeros((count_data_items(files_test),1))\n\nfor fold,(idxT,idxV) in enumerate(skf.split(np.arange(15))):\n    \n    # DISPLAY FOLD INFO\n    if DEVICE=='TPU':\n        if tpu: tf.tpu.experimental.initialize_tpu_system(tpu)\n    print('#'*25); print('#### FOLD',fold+1)\n    print('#### Image Size %i with EfficientNet B%i and batch_size %i'%\n          (IMG_SIZES[fold],EFF_NETS[fold],BATCH_SIZES[fold]*REPLICAS))\n    \n    # CREATE TRAIN AND VALIDATION SUBSETS\n    files_train = tf.io.gfile.glob([GCS_PATH[fold] + '/train%.2i*.tfrec'%x for x in idxT])\n    if INC2019[fold]:\n        files_train += tf.io.gfile.glob([GCS_PATH2[fold] + '/train%.2i*.tfrec'%x for x in idxT*2+1])\n        print('#### Using 2019 external data')\n    if INC2018[fold]:\n        files_train += tf.io.gfile.glob([GCS_PATH2[fold] + '/train%.2i*.tfrec'%x for x in idxT*2])\n        print('#### Using 2018+2017 external data')\n    np.random.shuffle(files_train); print('#'*25)\n    files_valid = tf.io.gfile.glob([GCS_PATH[fold] + '/train%.2i*.tfrec'%x for x in idxV])\n    files_test = np.sort(np.array(tf.io.gfile.glob(GCS_PATH[fold] + '/test*.tfrec')))\n    \n    # BUILD MODEL\n    K.clear_session()\n    with strategy.scope():\n        model = build_model(dim=IMG_SIZES[fold],ef=EFF_NETS[fold])\n        \n    # SAVE BEST MODEL EACH FOLD\n    sv = tf.keras.callbacks.ModelCheckpoint(\n        'fold-%i.h5'%fold, monitor='val_loss', verbose=0, save_best_only=True,\n        save_weights_only=True, mode='min', save_freq='epoch')\n   \n    # TRAIN\n    print('Training...')\n    history = model.fit(\n        get_dataset(files_train, augment=True, shuffle=True, repeat=True,\n                dim=IMG_SIZES[fold], batch_size = BATCH_SIZES[fold]), \n        epochs=EPOCHS[fold], callbacks = [sv,get_lr_callback(BATCH_SIZES[fold])], \n        steps_per_epoch=count_data_items(files_train)/BATCH_SIZES[fold]//REPLICAS,\n        validation_data=get_dataset(files_valid,augment=False,shuffle=False,\n                repeat=False,dim=IMG_SIZES[fold]), #class_weight = {0:1,1:2},\n        verbose=VERBOSE\n    )\n    \n    print('Loading best model...')\n    model.load_weights('fold-%i.h5'%fold)\n    \n    # PREDICT OOF USING TTA\n    print('Predicting OOF with TTA...')\n    ds_valid = get_dataset(files_valid,labeled=False,return_image_names=False,augment=True,\n            repeat=True,shuffle=False,dim=IMG_SIZES[fold],batch_size=BATCH_SIZES[fold]*4)\n    ct_valid = count_data_items(files_valid); STEPS = TTA * ct_valid/BATCH_SIZES[fold]/4/REPLICAS\n    pred = model.predict(ds_valid,steps=STEPS,verbose=VERBOSE)[:TTA*ct_valid,] \n    oof_pred.append( np.mean(pred.reshape((ct_valid,TTA),order='F'),axis=1) )                 \n    #oof_pred.append(model.predict(get_dataset(files_valid,dim=IMG_SIZES[fold]),verbose=1))\n    \n    # GET OOF TARGETS AND NAMES\n    ds_valid = get_dataset(files_valid, augment=False, repeat=False, dim=IMG_SIZES[fold],\n            labeled=True, return_image_names=True)\n    oof_tar.append( np.array([target.numpy() for img, target in iter(ds_valid.unbatch())]) )\n    oof_folds.append( np.ones_like(oof_tar[-1],dtype='int8')*fold )\n    ds = get_dataset(files_valid, augment=False, repeat=False, dim=IMG_SIZES[fold],\n                labeled=False, return_image_names=True)\n    oof_names.append( np.array([img_name.numpy().decode(\"utf-8\") for img, img_name in iter(ds.unbatch())]))\n    \n    # PREDICT TEST USING TTA\n    print('Predicting Test with TTA...')\n    ds_test = get_dataset(files_test,labeled=False,return_image_names=False,augment=True,\n            repeat=True,shuffle=False,dim=IMG_SIZES[fold],batch_size=BATCH_SIZES[fold]*4)\n    ct_test = count_data_items(files_test); STEPS = TTA * ct_test/BATCH_SIZES[fold]/4/REPLICAS\n    pred = model.predict(ds_test,steps=STEPS,verbose=VERBOSE)[:TTA*ct_test,] \n    preds[:,0] += np.mean(pred.reshape((ct_test,TTA),order='F'),axis=1) * WGTS[fold]\n    \n    # REPORT RESULTS\n    auc = roc_auc_score(oof_tar[-1],oof_pred[-1])\n    oof_val.append(np.max( history.history['val_auc'] ))\n    print('#### FOLD %i OOF AUC without TTA = %.3f, with TTA = %.3f'%(fold+1,oof_val[-1],auc))\n    \n#     # PLOT TRAINING\n#     if DISPLAY_PLOT:\n#         plt.figure(figsize=(15,5))\n#         plt.plot(np.arange(EPOCHS[fold]),history.history['auc'],'-o',label='Train AUC',color='#ff7f0e')\n#         plt.plot(np.arange(EPOCHS[fold]),history.history['val_auc'],'-o',label='Val AUC',color='#1f77b4')\n#         x = np.argmax( history.history['val_auc'] ); y = np.max( history.history['val_auc'] )\n#         xdist = plt.xlim()[1] - plt.xlim()[0]; ydist = plt.ylim()[1] - plt.ylim()[0]\n#         plt.scatter(x,y,s=200,color='#1f77b4'); plt.text(x-0.03*xdist,y-0.13*ydist,'max auc\\n%.2f'%y,size=14)\n#         plt.ylabel('AUC',size=14); plt.xlabel('Epoch',size=14)\n#         plt.legend(loc=2)\n#         plt2 = plt.gca().twinx()\n#         plt2.plot(np.arange(EPOCHS[fold]),history.history['loss'],'-o',label='Train Loss',color='#2ca02c')\n#         plt2.plot(np.arange(EPOCHS[fold]),history.history['val_loss'],'-o',label='Val Loss',color='#d62728')\n#         x = np.argmin( history.history['val_loss'] ); y = np.min( history.history['val_loss'] )\n#         ydist = plt.ylim()[1] - plt.ylim()[0]\n#         plt.scatter(x,y,s=200,color='#d62728'); plt.text(x-0.03*xdist,y+0.05*ydist,'min loss',size=14)\n#         plt.ylabel('Loss',size=14)\n#         plt.title('FOLD %i - Image Size %i, EfficientNet B%i, inc2019=%i, inc2018=%i'%\n#                 (fold+1,IMG_SIZES[fold],EFF_NETS[fold],INC2019[fold],INC2018[fold]),size=18)\n#         plt.legend(loc=3)\n#         plt.show()  ","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Calculate OOF AUC\nThe OOF (out of fold) predictions are saved to disk. If you wish to ensemble multiple models, use the OOF to determine what are the best weights to blend your models with. Choose weights that maximize OOF CV score when used to blend OOF. Then use those same weights to blend your test predictions.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"# COMPUTE OVERALL OOF AUC\noof = np.concatenate(oof_pred); true = np.concatenate(oof_tar);\nnames = np.concatenate(oof_names); folds = np.concatenate(oof_folds)\nauc = roc_auc_score(true,oof)\nprint('Overall OOF AUC with TTA = %.3f'%auc)\n\n# SAVE OOF TO DISK\ndf_oof = pd.DataFrame(dict(\n    image_name = names, target=true, pred = oof, fold=folds))\ndf_oof.to_csv('oof.csv',index=False)\ndf_oof.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Prepare file for Kaggle submission","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"ds = get_dataset(files_test, augment=False, repeat=False, dim=IMG_SIZES[fold],\n                 labeled=False, return_image_names=True)\n\nimage_names = np.array([img_name.numpy().decode(\"utf-8\") \n                        for img, img_name in iter(ds.unbatch())])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.DataFrame(dict(image_name=image_names, target=preds[:,0]))\nsubmission = submission.sort_values('image_name') \nsubmission.to_csv('submission.csv', index=False)\nsubmission.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.hist(submission.target,bins=100)\nplt.show()","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}