Datasets:

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vikp

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clean_notebooks

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ERROR: type should be string, got "https://www.kaggle.com/zhangyang/bldcv0708091?scriptVersionId=16901388\n\n# params\n\n```\ndbg = False\nif dbg:\n dbgsz = 500\n\nPRFX = 'CV070816' \np_o = f'../output/{PRFX}'\nSEED = 111\n\nBS = 128\nSZ = 224\nFP16 = False\n```\n\n# setup\n\n```\nimport fastai\nprint('fastai.__version__: ', fastai.__version__)\n\nimport random \nimport numpy as np\nimport torch\nimport os\n\ndef set_torch_seed(seed=SEED):\n os.environ['PYTHONHASHSEED'] = str(seed)\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n \n if torch.cuda.is_available(): \n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed) \n torch.backends.cudnn.deterministic = True \n torch.backends.cudnn.benchmark = False\n\nset_torch_seed()\n\nfrom fastai import *\nfrom fastai.vision import *\nfrom fastai.callbacks import *\n\nimport scipy as sp\nfrom sklearn.metrics import cohen_kappa_score\n\ndef quadratic_weighted_kappa(y1, y2):\n return cohen_kappa_score(y1, y2, weights='quadratic')\n```\n\n# preprocess\n\n```\nimg2grd = []\n\np = '../input/aptos2019-blindness-detection'\npp = Path(p)\ntrain = pd.read_csv(pp/'train.csv')\ntest = pd.read_csv(pp/'test.csv')\nlen_blnd = len(train)\nlen_blnd_test = len(test)\n\nimg2grd_blnd = [(f'{p}/train_images/{o[0]}.png',o[1]) for o in train.values]\n\nlen_blnd, len_blnd_test\nimg2grd += img2grd_blnd\ndisplay(len(img2grd))\ndisplay(Counter(o[1] for o in img2grd).most_common())\np = '../input/diabetic-retinopathy-detection'\npp = Path(p)\n\ntrain=pd.read_csv(pp/'trainLabels.csv')\nimg2grd_diab_train=[(f'../input/diabetic-retinopathy-detection/train_images/{o[0]}.jpeg',o[1]) for o in train.values]\nimg2grd += img2grd_diab_train\ndisplay(len(img2grd))\ndisplay(Counter(o[1] for o in img2grd).most_common())\n\n# test=pd.read_csv(pp/'retinopathy_solution.csv')\n# img2grd_diab_test=[(f'../input/diabetic-retinopathy-detection/test_images/{o[0]}.jpeg',o[1]) for o in test.values]\n# img2grd += img2grd_diab_test\n# display(len(img2grd))\n# display(Counter(o[1] for o in img2grd).most_common())\np = '../input/IDRID/B. Disease Grading'\npp = Path(p)\n\ntrain=pd.read_csv(pp/'2. Groundtruths/a. IDRiD_Disease Grading_Training Labels.csv')\nimg2grd_idrid_train=[(f'../input/IDRID/B. Disease Grading/1. Original Images/a. Training Set/{o[0]}.jpg',o[1]) for o in train.values]\nimg2grd += img2grd_idrid_train\ndisplay(len(img2grd))\ndisplay(Counter(o[1] for o in img2grd).most_common())\n\ntest=pd.read_csv(pp/'2. Groundtruths/b. IDRiD_Disease Grading_Testing Labels.csv')\nimg2grd_idrid_test=[(f'../input/IDRID/B. Disease Grading/1. Original Images/b. Testing Set/{o[0]}.jpg',o[1]) for o in test.values]\nimg2grd += img2grd_idrid_test\ndisplay(len(img2grd))\ndisplay(Counter(o[1] for o in img2grd).most_common())\nif not np.all([Path(o[0]).exists() for o in img2grd]): print('Some files are missing!!!')\ndf = pd.DataFrame(img2grd)\ndf.columns = ['fnm', 'target']\n\ndf.shape\nset_torch_seed()\nidx_blnd_train = np.where(df.fnm.str.contains('aptos2019-blindness-detection/train_images'))[0]\nidx_val = np.random.choice(idx_blnd_train, int(len_blnd*0.50), replace=False)\ndf['is_val']=False\ndf.loc[idx_val, 'is_val']=True\n\nif dbg:\n df=df.head(dbgsz)\n```\n\n# dataset\n\n```\n%%time\ntfms = get_transforms()\n\ndef get_data(sz, bs):\n src = (ImageList.from_df(df=df,path='./',cols='fnm') \n .split_from_df(col='is_val') \n .label_from_df(cols='target', \n label_cls=FloatList)\n )\n\n data= (src.transform(tfms, size=sz) #Data augmentation\n .databunch(bs=bs) #DataBunch\n .normalize(imagenet_stats) #Normalize \n )\n return data\n\nbs = BS \nsz = SZ\nset_torch_seed()\ndata = get_data(sz, bs)\n```\n\n# model\n\n```\n%%time\n# Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /tmp/.cache/torch/checkpoints/resnet50-19c8e357.pth\n\n# Making pretrained weights work without needing to find the default filename\nif not os.path.exists('/tmp/.cache/torch/checkpoints/'):\n os.makedirs('/tmp/.cache/torch/checkpoints/')\n!cp '../input/pytorch-vision-pretrained-models/resnet50-19c8e357.pth' '/tmp/.cache/torch/checkpoints/resnet50-19c8e357.pth'\nlearn = cnn_learner(data, \n base_arch = models.resnet50, \n path=p_o)\nlearn.loss = MSELossFlat\n\nif FP16: learn = learn.to_fp16()\nlearn.freeze()\nlen(learn.data.train_dl)\n%%time\nlearn.lr_find(start_lr=1e-4)\nlearn.recorder.plot(suggestion=True)\nset_torch_seed()\nlearn.fit_one_cycle(15, max_lr=5e-3, callbacks=[SaveModelCallback(learn, name='bestmodel_frozen')])\nlearn.recorder.plot_losses()\n# learn.recorder.plot_metrics()\nlearn.save('mdl-frozen')\n!nvidia-smi\nlearn.unfreeze()\n%%time\nlearn.lr_find()\nlearn.recorder.plot(suggestion=True)\nset_torch_seed()\nlearn.fit_one_cycle(10, max_lr=slice(5e-7,5e-5), callbacks=[SaveModelCallback(learn, name='bestmodel_finetune')])\nlearn.recorder.plot_losses()\n# learn.recorder.plot_metrics()\nlearn.save('mdl')\n!nvidia-smi\n```\n\n# validate and thresholding\n\n```\nlearn = learn.to_fp32()\n%%time\nset_torch_seed()\npreds_val, y_val = learn.get_preds(ds_type=DatasetType.Valid)\npreds_val = preds_val.numpy().squeeze()\ny_val= y_val.numpy()\nnp.save(f'{p_o}/preds_val.npy', preds_val)\nnp.save(f'{p_o}/y_val.npy', y_val)\n# https://www.kaggle.com/c/petfinder-adoption-prediction/discussion/88773#latest-515044\n# We used OptimizedRounder given by hocop1. https://www.kaggle.com/c/petfinder-adoption-prediction/discussion/76107#480970\n# put numerical value to one of bins\ndef to_bins(x, borders):\n for i in range(len(borders)):\n if x <= borders[i]:\n return i\n return len(borders)\n\nclass Hocop1OptimizedRounder(object):\n def __init__(self):\n self.coef_ = 0\n\n def _loss(self, coef, X, y, idx):\n X_p = np.array([to_bins(pred, coef) for pred in X])\n ll = -quadratic_weighted_kappa(y, X_p)\n return ll\n\n def fit(self, X, y):\n coef = [1.5, 2.0, 2.5, 3.0]\n golden1 = 0.618\n golden2 = 1 - golden1\n ab_start = [(1, 2), (1.5, 2.5), (2, 3), (2.5, 3.5)]\n for it1 in range(10):\n for idx in range(4):\n # golden section search\n a, b = ab_start[idx]\n # calc losses\n coef[idx] = a\n la = self._loss(coef, X, y, idx)\n coef[idx] = b\n lb = self._loss(coef, X, y, idx)\n for it in range(20):\n # choose value\n if la > lb:\n a = b - (b - a) * golden1\n coef[idx] = a\n la = self._loss(coef, X, y, idx)\n else:\n b = b - (b - a) * golden2\n coef[idx] = b\n lb = self._loss(coef, X, y, idx)\n self.coef_ = {'x': coef}\n\n def predict(self, X, coef):\n X_p = np.array([to_bins(pred, coef) for pred in X])\n return X_p\n\n def coefficients(self):\n return self.coef_['x']\n# https://www.kaggle.com/c/petfinder-adoption-prediction/discussion/76107#480970\nclass AbhishekOptimizedRounder(object):\n def __init__(self):\n self.coef_ = 0\n\n def _kappa_loss(self, coef, X, y):\n X_p = np.copy(X)\n for i, pred in enumerate(X_p):\n if pred < coef[0]:\n X_p[i] = 0\n elif pred >= coef[0] and pred < coef[1]:\n X_p[i] = 1\n elif pred >= coef[1] and pred < coef[2]:\n X_p[i] = 2\n elif pred >= coef[2] and pred < coef[3]:\n X_p[i] = 3\n else:\n X_p[i] = 4\n\n ll = quadratic_weighted_kappa(y, X_p)\n return -ll\n\n def fit(self, X, y):\n loss_partial = partial(self._kappa_loss, X=X, y=y)\n initial_coef = [0.5, 1.5, 2.5, 3.5]\n self.coef_ = sp.optimize.minimize(loss_partial, initial_coef, method='nelder-mead')\n\n def predict(self, X, coef):\n X_p = np.copy(X)\n for i, pred in enumerate(X_p):\n if pred < coef[0]:\n X_p[i] = 0\n elif pred >= coef[0] and pred < coef[1]:\n X_p[i] = 1\n elif pred >= coef[1] and pred < coef[2]:\n X_p[i] = 2\n elif pred >= coef[2] and pred < coef[3]:\n X_p[i] = 3\n else:\n X_p[i] = 4\n return X_p\n\n def coefficients(self):\n return self.coef_['x']\ndef bucket(preds_raw, coef = [0.5, 1.5, 2.5, 3.5]):\n preds = np.zeros(preds_raw.shape)\n for i, pred in enumerate(preds_raw):\n if pred < coef[0]:\n preds[i] = 0\n elif pred >= coef[0] and pred < coef[1]:\n preds[i] = 1\n elif pred >= coef[1] and pred < coef[2]:\n preds[i] = 2\n elif pred >= coef[2] and pred < coef[3]:\n preds[i] = 3\n else:\n preds[i] = 4\n return preds\noptnm2coefs = {'simple': [0.5, 1.5, 2.5, 3.5]}\n%%time\nset_torch_seed()\noptR = Hocop1OptimizedRounder()\noptR.fit(preds_val, y_val)\noptnm2coefs['hocop1'] = optR.coefficients()\n%%time\nset_torch_seed()\noptR = AbhishekOptimizedRounder()\noptR.fit(preds_val, y_val)\noptnm2coefs['abhishek'] = optR.coefficients()\noptnm2coefs\noptnm2preds_val_grd = {k: bucket(preds_val, coef) for k,coef in optnm2coefs.items()}\noptnm2qwk = {k: quadratic_weighted_kappa(y_val, preds) for k,preds in optnm2preds_val_grd.items()}\noptnm2qwk\nCounter(y_val).most_common()\npreds_val_grd = optnm2preds_val_grd['simple'].squeeze()\npreds_val_grd.mean()\nCounter(preds_val_grd).most_common()\nlist(zip(preds_val_grd, y_val))[:10]\n(preds_val_grd== y_val.squeeze()).mean()\npickle.dump(optnm2qwk, open(f'{p_o}/optnm2qwk.p', 'wb'))\npickle.dump(optnm2preds_val_grd, open(f'{p_o}/optnm2preds_val_grd.p', 'wb'))\npickle.dump(optnm2coefs, open(f'{p_o}/optnm2coefs.p', 'wb'))\n```\n\n# testing\n\n```\ndf_test = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')\ndf_test.head()\nlearn.data.add_test(\n ImageList.from_df(df_test,\n '../input/aptos2019-blindness-detection',\n folder='test_images',\n suffix='.png'))\n%%time\nset_torch_seed()\npreds_tst, _ = learn.get_preds(ds_type=DatasetType.Test)\npreds_tst = preds_tst.numpy().squeeze()\nnp.save(f'{p_o}/preds_tst.npy', preds_tst)\ndef bucket(preds_raw, coef = [0.5, 1.5, 2.5, 3.5]):\n preds = np.zeros(preds_raw.shape)\n for i, pred in enumerate(preds_raw):\n if pred < coef[0]:\n preds[i] = 0\n elif pred >= coef[0] and pred < coef[1]:\n preds[i] = 1\n elif pred >= coef[1] and pred < coef[2]:\n preds[i] = 2\n elif pred >= coef[2] and pred < coef[3]:\n preds[i] = 3\n else:\n preds[i] = 4\n return preds\ncoef = optnm2coefs['simple']\npreds_tst_grd = bucket(preds_tst, coef)\npreds_tst_grd.squeeze()\nCounter(preds_tst_grd.squeeze()).most_common()\n```\n\n## submit\n\n```\nsubm = pd.read_csv(\"../input/aptos2019-blindness-detection/test.csv\")\nsubm['diagnosis'] = preds_tst_grd.squeeze().astype(int)\nsubm.head()\nsubm.diagnosis.value_counts()\nsubm.to_csv(f\"{p_o}/submission.csv\", index=False)\n```\n\n"