kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
14,660,386 | def feature_creator(df_train, df_test):
df_train['Fare_cat'] = pd.qcut(df_train['Fare'], 7)
df_test['Fare_cat'] = pd.qcut(df_test['Fare'], 7)
df_train['Fare_cat'] = LabelEncoder().fit_transform(df_train['Fare_cat'])
df_test['Fare_cat'] = LabelEncoder().fit_transform(df_test['Fare_cat'])
df_train['Age_cat'] = pd.cut... | if __name__ == "__main__":
seeder(CFG["seed"])
folds = StratifiedKFold(n_splits=CFG['fold_num'] ).split(np.arange(train.shape[0]), train.label.values)
for fold,(trn_idx, val_idx)in enumerate(folds):
if fold > 0:
break
print('Inference fold {} started'.format(fold))
test = pd.DataFrame()
test['image_id'] = list(os.lis... | Cassava Leaf Disease Classification |
14,660,386 | X_train, y_train, X_test = feature_creator(train_imputed.copy() , test_imputed.copy())
y_train = y_train.astype(int )<choose_model_class> | test['label'] = np.argmax(tst_preds, axis=1)
test.head() | Cassava Leaf Disease Classification |
14,660,386 | <define_search_space><EOS> | test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
14,648,510 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<count_missing_values> | import numpy as np
import pandas as pd
from collections import OrderedDict
import os
| Cassava Leaf Disease Classification |
14,648,510 | pd.DataFrame(grid.cv_results_)['mean_test_score'].isna().sum()<categorify> | ! pip install.. /input/timm-package/timm-0.1.26-py3-none-any.whl | Cassava Leaf Disease Classification |
14,648,510 | <feature_engineering><EOS> | HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
IAASharpen, IAAEmboss, RandomBrightnessCon... | Cassava Leaf Disease Classification |
14,644,507 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<categorify> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv' ) | Cassava Leaf Disease Classification |
14,644,507 | X_test_fe = fe.transform(X_test.copy() )<split> | CFG = {
'normalize_mean':[0.42984136, 0.49624753, 0.3129598],
'normalize_std':[0.21417203, 0.21910103, 0.19542212],
'device': 'cuda:0',
'fold_num': 5,
'seed': 42,
'valid_bs': 32,
'num_workers': 4,
'model_arch': ['tf_efficientnet_b4_ns',
'tf_efficientnet_b4_ns',
'tf_efficientnet_b4_ns',
'tf_efficientnet_b4_ns',
'tf_effi... | Cassava Leaf Disease Classification |
14,644,507 | def learning_curve_plotter(Model, X, y, params_1, params_2, step=50):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
plt.figure(figsize=(16, 7))
for i,(name, params)in enumerate([params_1, params_2]):
train_score = []
val_score = []
plt.subplot(1, 2, i+1)
for j in range(100,... | def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.cuda.manual_seed_all(seed)
def get_img(path):
im_bgr = cv2.imread(p... | Cassava Leaf Disease Classification |
14,644,507 | param_grid_logreg = {'penalty':['elasticnet'],
'C':0.01 * np.arange(100),
'l1_ratio':0.1 * np.arange(10),
'solver':['saga']}<choose_model_class> | class CassavaDataset(Dataset):
def __init__(
self, df, data_root, transforms=None, output_label=True
):
super().__init__()
self.df = df.reset_index(drop=True ).copy()
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def __len__(self):
return self.df.shape[0]
def __getitem__(sel... | Cassava Leaf Disease Classification |
14,644,507 | grid_logreg = GridSearchCV(LogisticRegression() , param_grid_logreg,
cv=RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=42),
scoring='accuracy', verbose=2, n_jobs=-1 )<train_model> | HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
IAASharpen, IAAEmboss, RandomBrightnessCon... | Cassava Leaf Disease Classification |
14,644,507 | grid_logreg.fit(X_train_fe, y_train )<define_search_space> | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained, num_classes=5)
def forward(self, x):
x = self.model(x)
return x | Cassava Leaf Disease Classification |
14,644,507 | params_logreg = {'C': 0.28, 'l1_ratio': 0.9, 'penalty': 'elasticnet', 'solver': 'saga'}<define_search_space> | seed_everything(CFG['seed'])
tst_preds = []
device = torch.device(CFG['device'])
test = pd.DataFrame()
test['image_id'] = list(os.listdir('.. /input/cassava-leaf-disease-classification/test_images/'))
for i, sub_model in enumerate(CFG['used_epochs']):
if "vit" not in sub_model:
test_ds = CassavaDataset(test, '.. /inp... | Cassava Leaf Disease Classification |
14,644,507 | param_grid_knn = {'n_neighbors':np.arange(50),
'weights':['uniform'],
'algorithm':['ball_tree'],
'leaf_size':np.arange(1, 40, 2)}<choose_model_class> | test['label'] = np.argmax(tst_preds, axis=1)
test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
14,562,043 | grid_knn = GridSearchCV(KNeighborsClassifier() , param_grid_knn,
cv=RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=42),
scoring='accuracy', verbose=2, n_jobs=-1 )<train_model> | !mkdir -p /tmp/pip/cache/
!cp.. /input/omegaconf/PyYAML-5.4b2-cp38-cp38-manylinux1_x86_64.whl /tmp/pip/cache/
!cp.. /input/omegaconf/omegaconf-2.0.5-py3-none-any.whl /tmp/pip/cache/
!cp.. /input/omegaconf/typing_extensions-3.7.4.3-py3-none-any.whl /tmp/pip/cache/
!pip install --no-index --find-links /tmp/pip/cache/ ome... | Cassava Leaf Disease Classification |
14,562,043 | grid_knn.fit(X_train_fe, y_train )<choose_model_class> | sys.path.append('.. /input/timm-pytorch-image-models/pytorch-image-models-master')
sys.path.append(".. /input/cleanlab/")
warnings.filterwarnings('ignore')
| Cassava Leaf Disease Classification |
14,562,043 | params_knn = {'algorithm': 'ball_tree', 'leaf_size': 1, 'n_neighbors': 7, 'weights': 'uniform'}<define_search_space> | mean, std =(0.485, 0.456, 0.406),(0.229, 0.224, 0.225)
def get_transforms(img_size=(512, 512)) :
transformations = Compose([
PadIfNeeded(min_height=img_size[0], min_width=img_size[1]),
CenterCrop(img_size[0], img_size[1]),
Normalize(mean=mean, std=std, max_pixel_value=255.0, p=1.0),
ToTensorV2(p=1.0),
], p=1.0)
retur... | Cassava Leaf Disease Classification |
14,562,043 | param_grid_svc = {'C':[0.001, 0.01, 0.1, 1, 5],
'kernel':['rbf'],
'gamma':0.01 * np.arange(100),
'probability':[True]}<choose_model_class> | def create_model(model_name: str,
pretrained: bool,
num_classes: int,
in_chans: int):
model = timm.create_model(model_name=model_name,
pretrained=pretrained,
num_classes=num_classes,
in_chans=in_chans)
return model | Cassava Leaf Disease Classification |
14,562,043 | grid_svc = GridSearchCV(SVC() , param_grid_svc, cv=RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=42),
scoring='accuracy', verbose=2, n_jobs=-1 )<train_model> | def get_state_dict_from_checkpoint(log_dir, fold_num):
ckpt_path = glob(os.path.join(log_dir, f'checkpoints/*fold{fold_num}*.ckpt')) [0]
state_dict = pl_load(ckpt_path, map_location='cpu')
if 'state_dict' in state_dict:
state_dict = state_dict['state_dict']
did_distillation = False
state_dict = OrderedDict(( k.replace... | Cassava Leaf Disease Classification |
14,562,043 | grid_svc.fit(X_train_fe, y_train )<init_hyperparams> | class LitTester(pl.LightningModule):
def __init__(self, network_cfg, state_dict):
super(LitTester, self ).__init__()
self.model = create_model(**network_cfg)
self.model.load_state_dict(state_dict)
self.model.eval()
def forward(self, x):
x = self.model(x)
return x
def test_step(self, batch, batch_idx):
score = torch.... | Cassava Leaf Disease Classification |
14,562,043 | params_svc = {'C': 1, 'gamma': 0.09, 'kernel': 'rbf', 'probability': True}<define_search_space> | eff_b0_cfg_s =
eff_b0_cfg = OmegaConf.create(eff_b0_cfg_s ) | Cassava Leaf Disease Classification |
14,562,043 | param_grid_random = {'n_estimators':[300, 500, 1000],
'max_depth':[5, 9],
'max_samples':[0.5, 0.7, 0.9],
'max_features':[0.5, 0.7, 0.9],
'min_samples_split':[2, 5, 8]
}<choose_model_class> | name = '14-10-36'
cfg = eff_b0_cfg
do_predict = True
do_submit = True
img_dir = '.. /input/cassava-leaf-disease-merged/train/'
label_path = '.. /input/cassava-leaf-disease-merged/merged.csv'
log_dir = os.path.join('.. /input/cassava-public-ckpt', name)
n_folds = len(glob(os.path.join(log_dir, 'checkpoints/*.ckpt')))
... | Cassava Leaf Disease Classification |
14,562,043 | grid_random = GridSearchCV(RandomForestClassifier() , param_grid_random,
cv=RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=42),
scoring='accuracy', verbose=2, n_jobs=-1 )<train_model> | seed_everything(42)
label_df = pd.read_csv(label_path)
if 'fold' not in label_df.columns:
skf = StratifiedKFold(n_splits=5, shuffle=True)
label_df.loc[:, 'fold'] = 0
for fold_num,(train_index, val_index)in enumerate(skf.split(X=label_df.index, y=label_df.label.values)) :
label_df.loc[label_df.iloc[val_index].index, ... | Cassava Leaf Disease Classification |
14,562,043 | grid_random.fit(X_train_fe, y_train )<init_hyperparams> | if do_submit:
sub = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
infer = pl.Trainer(gpus=1)
test_dataset = TestDataset('.. /input/cassava-leaf-disease-classification/test_images',
sub,
img_size=cfg.img_size)
test_dataloader = DataLoader(test_dataset,
batch_size=cfg.batch_size,
n... | Cassava Leaf Disease Classification |
14,562,043 | params_random = {'max_depth': 5, 'max_features': 0.5, 'max_samples': 0.9,
'min_samples_split': 8, 'n_estimators': 300}<define_search_space> | label_df = label_df.sort_values(by='image_id', ascending=1)
pred_df = pred_df.sort_values(by='image_id', ascending=1)
ids, labels = label_df.image_id.values, label_df.label.values
preds = np.array([literal_eval(pred)if isinstance(pred, str)else pred for pred in pred_df.label.values])
print(f'total {len(ids)} images'... | Cassava Leaf Disease Classification |
14,562,043 | param_grid_gradient = {'max_depth':[3, 4],
'n_estimators':[300, 400, 500],
'learning_rate':[0.01, 0.03, 0.05],
'subsample':[0.5, 0.7],
'max_features':[0.5, 0.7],
}<choose_model_class> | s = labels
psx = preds
K = len(np.unique(s))
thresholds = [np.mean(psx[:,k][s == k])for k in range(K)]
thresholds = np.asarray(thresholds)
confident_joint = np.zeros(( K, K), dtype = int)
for i, row in enumerate(psx):
s_label = s[i]
confident_bins = row >= thresholds - 1e-6
num_confident_bins = sum(confident_bins)
i... | Cassava Leaf Disease Classification |
14,562,043 | grid_gradient = GridSearchCV(GradientBoostingClassifier() , param_grid_gradient,
cv=RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=42),
scoring='accuracy', verbose=2, n_jobs=-1 )<train_model> | total_idx = np.arange(len(ids))
clean_idx = np.array([idx for idx in total_idx if idx not in label_errors_idx])
guesses = np.stack(noise_masks_per_class ).argmax(axis=0)
guesses[clean_idx] = labels[clean_idx]
clean_ids = ids[clean_idx]
clean_labels = labels[clean_idx]
clean_guesses = guesses[clean_idx]
noisy_ids = id... | Cassava Leaf Disease Classification |
14,562,043 | grid_gradient.fit(X_train_fe, y_train )<init_hyperparams> | all_data = pd.DataFrame({'image_id': ids,
'given_label': labels,
'guess_label': guesses})
all_data['is_noisy'] =(all_data.given_label != all_data.guess_label)
all_data['max_prob'] = preds.max(axis=1 ) | Cassava Leaf Disease Classification |
14,562,043 | <define_search_space><EOS> | class_colors = np.array(['
num2class = [f'{idx}-{elem}' for idx, elem in enumerate(num2class)] | Cassava Leaf Disease Classification |
14,485,909 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<choose_model_class> | with open('.. /input/train-weights-optimization/best_weights.json', 'r')as f:
weights_dict = json.load(f)
weights_dict | Cassava Leaf Disease Classification |
14,485,909 | grid_xgb = GridSearchCV(XGBClassifier() , param_grid_xgb,
cv=RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=42),
scoring='accuracy', verbose=2, n_jobs=-1 )<train_model> | normal_configs = []
tta_configs = []
normal_model_dirs = []
tta_model_dirs = []
for model_dir in weights_dict.keys() :
assert len(glob.glob(f'{model_dir}/*.yml')) ==1
config_path = glob.glob(f'{model_dir}/*.yml')[0]
with open(config_path)as f:
config = yaml.load(f)
if 'valid_augmentation' in config['tag'].keys() :
tta... | Cassava Leaf Disease Classification |
14,485,909 | grid_xgb.fit(X_train_fe, y_train )<init_hyperparams> | def get_score(y_true, y_pred):
return accuracy_score(y_true, y_pred)
@contextmanager
def timer(name):
t0 = time.time()
LOGGER.info(f'[{name}] start')
yield
LOGGER.info(f'[{name}] done in {time.time() - t0:.0f} s.')
def seed_torch(seed=42):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(s... | Cassava Leaf Disease Classification |
14,485,909 | params_xgb = {'colsample_bylevel': 0.7, 'learning_rate': 0.03, 'max_depth': 3,
'n_estimators': 400, 'reg_lambda': 15, 'subsample': 0.5}<choose_model_class> | test = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
test.head() | Cassava Leaf Disease Classification |
14,485,909 | logreg = LogisticRegression(**params_logreg)
svc = SVC(**params_svc)
knn = KNeighborsClassifier(**params_knn)
rfc = RandomForestClassifier(**params_random)
gradient = GradientBoostingClassifier(**params_gradient)
xgb = XGBClassifier(**params_xgb)
estimators = [('logreg', logreg),('knn', knn),('svc', svc),('rfc', ... | class TestDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.file_names = df['image_id'].values
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
file_name = self.file_names[idx]
file_path = f'{TEST_PATH}/{file_name}'
image = cv2.imread(file_path)
i... | Cassava Leaf Disease Classification |
14,485,909 | y_preds = logreg.fit(X_train_fe, y_train ).predict(X_test_fe )<predict_on_test> | def _get_augmentations(aug_list, cfg):
process = []
for aug in aug_list:
if aug == 'Resize':
process.append(Resize(cfg['size'], cfg['size']))
elif aug == 'RandomResizedCrop':
process.append(RandomResizedCrop(cfg['size'], cfg['size']))
elif aug == 'CenterCrop':
process.append(CenterCrop(CFG['size'], CFG['size']))
elif a... | Cassava Leaf Disease Classification |
14,485,909 | y_preds = svc.fit(X_train_fe, y_train ).predict(X_test_fe )<predict_on_test> | def get_transforms(*, aug_list, cfg):
return Compose(
_get_augmentations(aug_list, cfg)
) | Cassava Leaf Disease Classification |
14,485,909 | y_preds = knn.fit(X_train_fe, y_train ).predict(X_test_fe )<train_model> | class CustomModel(nn.Module):
def __init__(self, model_name, target_size, pretrained=False):
super().__init__()
self.model = timm.create_model(model_name, pretrained=pretrained)
if hasattr(self.model, 'classifier'):
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, target_siz... | Cassava Leaf Disease Classification |
14,485,909 | y_preds = rfc.fit(X_train_fe, y_train ).predict(X_test_fe )<train_model> | def inference_normal(model, states, test_loader, device):
model.to(device)
tk0 = tqdm(enumerate(test_loader), total=len(test_loader))
probs = []
for i,(images)in tk0:
images = images.to(device)
avg_preds = []
for state in states:
model.load_state_dict(state['model'])
model.eval()
with torch.no_grad() :
y_preds = mod... | Cassava Leaf Disease Classification |
14,485,909 | y_preds = gradient.fit(X_train_fe, y_train ).predict(X_test_fe )<predict_on_test> | def main(config, model_dir):
INFO = config['info']
TAG = config['tag']
CFG = config['cfg']
CFG['train'] = False
CFG['inference'] = True
inference_batch_size = 64
seed_torch(seed=CFG['seed'])
model = CustomModel(TAG['model_name'], CFG['target_size'], pretrained=False)
states = [torch.load(path)for path in glob.glob(f'... | Cassava Leaf Disease Classification |
14,485,909 | y_preds = xgb.fit(X_train_fe, y_train ).predict(X_test_fe )<create_dataframe> | predictions_list = []
model_dir_list = []
for config, model_dir in zip(normal_configs, normal_model_dirs):
predictions_list.append(main(config, model_dir))
model_dir_list.append(model_dir)
for config, model_dir in zip(tta_configs, tta_model_dirs):
predictions_list.append(main_tta(config, model_dir))
model_dir_list.app... | Cassava Leaf Disease Classification |
14,485,909 | submission = pd.DataFrame({'PassengerId':test.index,
'Survived':y_preds} )<save_to_csv> | predictions = np.zeros(predictions_list[0].shape, dtype=predictions_list[0].dtype)
for i, key in zip(range(len(predictions_list)) , model_dir_list):
predictions += predictions_list[i] * weights_dict[key]
test['label'] = predictions.argmax(1)
test[['image_id', 'label']].to_csv(OUTPUT_DIR+'submission.csv', index=False)... | Cassava Leaf Disease Classification |
14,477,820 | submission.to_csv('submission.csv', index=False )<save_to_csv> | !mkdir -p /tmp/pip/cache/
!cp.. /input/omegaconf/PyYAML-5.4b2-cp38-cp38-manylinux1_x86_64.whl /tmp/pip/cache/
!cp.. /input/omegaconf/omegaconf-2.0.5-py3-none-any.whl /tmp/pip/cache/
!cp.. /input/omegaconf/typing_extensions-3.7.4.3-py3-none-any.whl /tmp/pip/cache/
!pip install --no-index --find-links /tmp/pip/cache/ ome... | Cassava Leaf Disease Classification |
14,477,820 | submission.to_csv('submission.csv', index=False )<load_from_csv> | sys.path.append('.. /input/timm-pytorch-image-models/pytorch-image-models-master')
sys.path.append(".. /input/cleanlab/")
warnings.filterwarnings('ignore')
| Cassava Leaf Disease Classification |
14,477,820 | pd.read_csv('submission.csv' )<import_modules> | mean, std =(0.485, 0.456, 0.406),(0.229, 0.224, 0.225)
def get_transforms(img_size=(512, 512)) :
transformations = Compose([
PadIfNeeded(min_height=img_size[0], min_width=img_size[1]),
CenterCrop(img_size[0], img_size[1]),
Normalize(mean=mean, std=std, max_pixel_value=255.0, p=1.0),
ToTensorV2(p=1.0),
], p=1.0)
retur... | Cassava Leaf Disease Classification |
14,477,820 | from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from mlxtend.feature_selection import SequentialFeatureSelector as SFS<load_from_csv> | def create_model(model_name: str,
pretrained: bool,
num_classes: int,
in_chans: int):
model = timm.create_model(model_name=model_name,
pretrained=pretrained,
num_classes=num_classes,
in_chans=in_chans)
return model | Cassava Leaf Disease Classification |
14,477,820 | train_data = pd.read_csv("/kaggle/input/titanic/train.csv")
train_data.head()<load_from_csv> | def get_state_dict_from_checkpoint(log_dir, fold_num):
ckpt_path = glob(os.path.join(log_dir, f'checkpoints/*fold{fold_num}*.ckpt')) [0]
state_dict = pl_load(ckpt_path, map_location='cpu')
if 'state_dict' in state_dict:
state_dict = state_dict['state_dict']
did_distillation = False
state_dict = OrderedDict(( k.replace... | Cassava Leaf Disease Classification |
14,477,820 | test_data = pd.read_csv("/kaggle/input/titanic/test.csv")
test_data.head()<data_type_conversions> | class LitTester(pl.LightningModule):
def __init__(self, network_cfg, state_dict):
super(LitTester, self ).__init__()
self.model = create_model(**network_cfg)
self.model.load_state_dict(state_dict)
self.model.eval()
def forward(self, x):
x = self.model(x)
return x
def test_step(self, batch, batch_idx):
score = torch.... | Cassava Leaf Disease Classification |
14,477,820 | train_data['Age'] = train_data['Age'].fillna(train_data.Age.mean())
test_data['Age'] = test_data['Age'].fillna(test_data.Age.mean())
test_data['Fare'] = test_data['Fare'].fillna(test_data.Fare.mean() )<count_values> | eff_b0_cfg_s =
eff_b0_cfg = OmegaConf.create(eff_b0_cfg_s ) | Cassava Leaf Disease Classification |
14,477,820 | train_data["Embarked"].value_counts()<categorify> | name = '14-10-36'
cfg = eff_b0_cfg
do_predict = True
do_submit = False
img_dir = '.. /input/cassava-leaf-disease-merged/train/'
label_path = '.. /input/cassava-leaf-disease-merged/merged.csv'
log_dir = os.path.join('.. /input/cassava-public-ckpt', name)
n_folds = len(glob(os.path.join(log_dir, 'checkpoints/*.ckpt')))
... | Cassava Leaf Disease Classification |
14,477,820 | train_data = train_data.fillna({"Embarked": "S"} )<drop_column> | seed_everything(42)
label_df = pd.read_csv(label_path)
if 'fold' not in label_df.columns:
skf = StratifiedKFold(n_splits=5, shuffle=True)
label_df.loc[:, 'fold'] = 0
for fold_num,(train_index, val_index)in enumerate(skf.split(X=label_df.index, y=label_df.label.values)) :
label_df.loc[label_df.iloc[val_index].index, ... | Cassava Leaf Disease Classification |
14,477,820 | train_data.drop(['Name','Ticket','Cabin'], axis = 1, inplace = True)
test_data.drop(['Name','Ticket','Cabin'], axis = 1, inplace = True )<count_missing_values> | if do_submit:
sub = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
infer = pl.Trainer(gpus=1)
test_dataset = TestDataset('.. /input/cassava-leaf-disease-classification/test_images',
sub,
img_size=cfg.img_size)
test_dataloader = DataLoader(test_dataset,
batch_size=cfg.batch_size,
n... | Cassava Leaf Disease Classification |
14,477,820 | train_data.isnull().sum()
test_data.isnull().sum()<categorify> | label_df = label_df.sort_values(by='image_id', ascending=1)
pred_df = pred_df.sort_values(by='image_id', ascending=1)
ids, labels = label_df.image_id.values, label_df.label.values
preds = np.array([literal_eval(pred)if isinstance(pred, str)else pred for pred in pred_df.label.values])
print(f'total {len(ids)} images'... | Cassava Leaf Disease Classification |
14,477,820 | train_data = pd.get_dummies(train_data, columns=["Sex"])
train_data = pd.get_dummies(train_data, columns=["Embarked"])
test_data = pd.get_dummies(test_data, columns=["Sex"])
test_data = pd.get_dummies(test_data, columns=["Embarked"] )<prepare_x_and_y> | s = labels
psx = preds
K = len(np.unique(s))
thresholds = [np.mean(psx[:,k][s == k])for k in range(K)]
thresholds = np.asarray(thresholds)
confident_joint = np.zeros(( K, K), dtype = int)
for i, row in enumerate(psx):
s_label = s[i]
confident_bins = row >= thresholds - 1e-6
num_confident_bins = sum(confident_bins)
i... | Cassava Leaf Disease Classification |
14,477,820 | X = train_data
X = train_data.drop("Survived",axis=1)
y = train_data["Survived"]<split> | total_idx = np.arange(len(ids))
clean_idx = np.array([idx for idx in total_idx if idx not in label_errors_idx])
guesses = np.stack(noise_masks_per_class ).argmax(axis=0)
guesses[clean_idx] = labels[clean_idx]
clean_ids = ids[clean_idx]
clean_labels = labels[clean_idx]
clean_guesses = guesses[clean_idx]
noisy_ids = id... | Cassava Leaf Disease Classification |
14,477,820 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)
X_train.shape, X_test.shape<train_model> | all_data = pd.DataFrame({'image_id': ids,
'given_label': labels,
'guess_label': guesses})
all_data['is_noisy'] =(all_data.given_label != all_data.guess_label)
all_data['max_prob'] = preds.max(axis=1 ) | Cassava Leaf Disease Classification |
14,477,820 | <compute_test_metric><EOS> | class_colors = np.array(['
num2class = [f'{idx}-{elem}' for idx, elem in enumerate(num2class)] | Cassava Leaf Disease Classification |
14,460,402 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<save_to_csv> | !pip install -U -q.. /input/resnest/resnest-0.0.5-py3-none-any.whl | Cassava Leaf Disease Classification |
14,460,402 | features = ['Pclass', 'Age', 'SibSp', 'Fare', 'Sex_male', 'Embarked_S']
X = train_data[features]
X_test = test_data[features]
model = RandomForestClassifier(n_estimators=250, max_depth=5, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)
output = pd.DataFrame({'PassengerId': test_data.PassengerId, ... | HorizontalFlip, VerticalFlip, Transpose, RandomResizedCrop, Compose, Normalize, ShiftScaleRotate, CenterCrop, Resize, RandomResizedCrop
)
| Cassava Leaf Disease Classification |
14,460,402 | sub_path = ".. /input/chemistry-models"
all_files = os.listdir(sub_path)
all_files<feature_engineering> | import timm | Cassava Leaf Disease Classification |
14,460,402 | concat_sub['m_max'] = concat_sub.iloc[:, 1:].max(axis=1)
concat_sub['m_min'] = concat_sub.iloc[:, 1:].min(axis=1)
concat_sub['m_median'] = concat_sub.iloc[:, 1:].median(axis=1 )<define_variables> | CFG = {
'seed': 1337,
'img_size': 512,
'bs': 32,
'num_workers': 4,
'tta': 4,
} | Cassava Leaf Disease Classification |
14,460,402 | cutoff_lo = 0.8
cutoff_hi = 0.2<feature_engineering> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
14,460,402 | rank = np.tril(concat_sub.iloc[:,1:ncol].corr().values,-1)
m_gmean = 0
n = 8
while rank.max() >0:
mx = np.unravel_index(rank.argmax() , rank.shape)
m_gmean += n*(np.log(concat_sub.iloc[:, mx[0]+1])+ np.log(concat_sub.iloc[:, mx[1]+1])) /2
rank[mx] = 0
n += 1<feature_engineering> | def seed_everything(seed: int):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False | Cassava Leaf Disease Classification |
14,460,402 | concat_sub['m_mean'] = np.exp(m_gmean/(n-1)**2 )<save_to_csv> | def get_img(path):
return cv2.imread(path)[:, :, ::-1] | Cassava Leaf Disease Classification |
14,460,402 | concat_sub['scalar_coupling_constant'] = concat_sub['m_mean']
concat_sub[['id', 'scalar_coupling_constant']].to_csv('stack_mean.csv',
index=False, float_format='%.6f' )<save_to_csv> | seed_everything(CFG['seed'] ) | Cassava Leaf Disease Classification |
14,460,402 | concat_sub['scalar_coupling_constant'] = concat_sub['m_median']
concat_sub[['id', 'scalar_coupling_constant']].to_csv('stack_median.csv',
index=False, float_format='%.6f' )<save_to_csv> | class CassavaDataset(Dataset):
def __init__(self, df, data_root, transforms=None):
super().__init__()
self.df = df.reset_index(drop=True ).copy()
self.transforms = transforms
self.data_root = data_root
def __len__(self):
return self.df.shape[0]
def __getitem__(self, index: int):
img = get_img(f"{self.data_root}/{self.d... | Cassava Leaf Disease Classification |
14,460,402 | concat_sub['scalar_coupling_constant'] = np.where(np.all(concat_sub.iloc[:,1:7] > cutoff_lo, axis=1), 1,
np.where(np.all(concat_sub.iloc[:,1:7] < cutoff_hi, axis=1),
0, concat_sub['m_median']))
concat_sub[['id', 'scalar_coupling_constant']].to_csv('stack_pushout_median.csv',
index=False, float_format='%.6f' )<feature_e... | class CassvaClassifierV1(nn.Module):
def __init__(self, n_classes: int = 5, dropout: float =.5):
super().__init__()
self.backbone = resnest50_fast_4s2x40d(pretrained=False)
self.pool = nn.AdaptiveAvgPool2d(1)
self.dropout = nn.Dropout(p=dropout)
self.emb_size: int = 2048
self.classifier = nn.Linear(self.emb_size, n_... | Cassava Leaf Disease Classification |
14,460,402 | concat_sub['scalar_coupling_constant'] = np.where(np.all(concat_sub.iloc[:,1:7] > cutoff_lo, axis=1),
concat_sub['m_max'],
np.where(np.all(concat_sub.iloc[:,1:7] < cutoff_hi, axis=1),
concat_sub['m_min'],
concat_sub['m_mean']))
concat_sub[['id', 'scalar_coupling_constant']].to_csv('stack_minmax_mean.csv',
index=False, ... | class CassvaClassifierV2(nn.Module):
def __init__(self, n_classes: int = 5):
super().__init__()
self.model = timm.create_model('tf_efficientnet_b4_ns', pretrained=False)
self.model.classifier = nn.Linear(self.model.classifier.in_features, n_classes)
def forward(self, x):
return self.model(x ) | Cassava Leaf Disease Classification |
14,460,402 | concat_sub['scalar_coupling_constant'] = np.where(np.all(concat_sub.iloc[:,1:7] > cutoff_lo, axis=1),
concat_sub['m_max'],
np.where(np.all(concat_sub.iloc[:,1:7] < cutoff_hi, axis=1),
concat_sub['m_min'],
concat_sub['m_median']))
concat_sub[['id', 'scalar_coupling_constant']].to_csv('stack_minmax_median.csv',
index=Fal... | class CassvaClassifierV3(nn.Module):
def __init__(self, n_classes: int = 5):
super().__init__()
self.model = timm.create_model('tf_efficientnet_b3_ns', pretrained=False)
self.model.classifier = nn.Linear(self.model.classifier.in_features, n_classes)
def forward(self, x):
return self.model(x ) | Cassava Leaf Disease Classification |
14,460,402 | concat_sub['scalar_coupling_constant'] = concat_sub['mol0'].rank(method ='min')+ concat_sub['mol1'].rank(method ='min')+ concat_sub['mol2'].rank(method ='min')
concat_sub['scalar_coupling_constant'] =(concat_sub['scalar_coupling_constant']-concat_sub['scalar_coupling_constant'].min())/(concat_sub['scalar_coupling_cons... | def inference_one_epoch(model, data_loader, device):
model.eval()
image_preds_all = []
for imgs in data_loader:
image_preds = model(imgs.to(device ).float())
image_preds_all += [torch.softmax(image_preds, 1 ).cpu().numpy() ]
return np.concatenate(image_preds_all, axis=0 ) | Cassava Leaf Disease Classification |
14,460,402 | one = pd.read_csv('.. /input/champs-blending-tutorial/1.csv')
two = pd.read_csv('.. /input/champs-blending-tutorial/2.csv')
three = pd.read_csv('.. /input/champs-blending-tutorial/3.csv')
submission = pd.DataFrame()
submission['id'] = one.id
submission['scalar_coupling_constant'] =(0.40*one.scalar_coupling_constant)... | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu' ) | Cassava Leaf Disease Classification |
14,460,402 | !pip install tensorflow-gpu==2.0a0<import_modules> | def get_models(model_paths, recipe: str):
models = []
for model_path in model_paths:
model_name: str = model_path.split('/')[-1]
n_folds: int = int([x[-1] for x in model_name.split('-')if x.startswith('fold')][0])
n_epochs: int = int([x[6:] for x in model_name.split('-')if x.startswith('epochs')][0])
if not model_nam... | Cassava Leaf Disease Classification |
14,460,402 | print(tf.__version__ )<set_options> | test = pd.DataFrame()
test['image_id'] = list(os.listdir('.. /input/cassava-leaf-disease-classification/test_images/'))
test_ds = CassavaDataset(test, '.. /input/cassava-leaf-disease-classification/test_images/', transforms=get_inference_transforms())
tst_loader = torch.utils.data.DataLoader(
test_ds,
batch_size=CFG[... | Cassava Leaf Disease Classification |
14,460,402 | tf.test.is_gpu_available(
cuda_only=False,
min_cuda_compute_capability=None
)
<load_pretrained> | recipe_list = [
'effnetb4-cutmix-fmix-',
'effnetb4-fcl-',
'resnest50_fast_4s2x40d-cutmix-fmix-',
'resnest50_fast_4s2x40d-fcl-',
'resnest50_fast_4s2x40d-fmix-cutmix-',
] | Cassava Leaf Disease Classification |
14,460,402 | datadir = ".. /input/"
nodes_train = np.load(datadir + "champs-basic-graph/nodes_train.npz")['arr_0']
in_edges_train = np.load(datadir + "champs-basic-graph/in_edges_train.npz")['arr_0']
out_edges_train = np.load(datadir + "champs-basic-graph/out_edges_train.npz")['arr_0']
nodes_test = np.load(datadir + "champs-basic-g... | preds = []
with torch.no_grad() :
for recipe in recipe_list:
models = get_models(model_paths, recipe)
preds_per_recipe = np.mean(
[
np.mean([inference_one_epoch(model, tst_loader, device)for _ in range(CFG['tta'])], axis=0)
for model in models
], axis=0
)
preds.append(preds_per_recipe)
del models | Cassava Leaf Disease Classification |
14,460,402 | nodes_train, in_edges_train, out_labels = shuffle(nodes_train, in_edges_train, out_labels )<choose_model_class> | tst_preds = weights[0] * preds[0] + weights[1] * preds[1] + weights[2] * preds[2] + weights[3] * preds[3] + weights[4] * preds[4]
| Cassava Leaf Disease Classification |
14,460,402 | class Message_Passer_NNM(tf.keras.layers.Layer):
def __init__(self, node_dim):
super(Message_Passer_NNM, self ).__init__()
self.node_dim = node_dim
self.nn = tf.keras.layers.Dense(units=self.node_dim*self.node_dim, activation = tf.nn.relu)
def call(self, node_j, edge_ij):
A = self.nn(edge_ij)
A = tf.reshape(A, [-1, s... | test['label'] = np.argmax(tst_preds, axis=1 ) | Cassava Leaf Disease Classification |
14,460,402 | <choose_model_class><EOS> | test.to_csv('submission.csv', index=False)
test | Cassava Leaf Disease Classification |
14,353,935 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<choose_model_class> | !pip install.. /input/validators
!cp -R.. /input/vit-keras/vit_keras./ | Cassava Leaf Disease Classification |
14,353,935 | class Edge_Regressor(tf.keras.layers.Layer):
def __init__(self, intermediate_dim):
super(Edge_Regressor, self ).__init__()
self.concat_layer = tf.keras.layers.Concatenate()
self.hidden_layer_1 = tf.keras.layers.Dense(units=intermediate_dim, activation=tf.nn.relu)
self.hidden_layer_2 = tf.keras.layers.Dense(units=inter... | Flatten,GlobalAveragePooling2D,BatchNormalization, Activation
print(tf.__version__ ) | Cassava Leaf Disease Classification |
14,353,935 | class MP_Layer(tf.keras.layers.Layer):
def __init__(self, state_dim):
super(MP_Layer, self ).__init__(self)
self.message_passers = Message_Passer_NNM(node_dim = state_dim)
self.message_aggs = Message_Agg()
self.update_functions = Update_Func_GRU(state_dim = state_dim)
self.state_dim = state_dim
def call(self, nodes,... | try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print(f'Running on TPU {tpu.master() }')
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
strategy = tf.distr... | Cassava Leaf Disease Classification |
14,353,935 | adj_input = tf.keras.Input(shape=(None,), name='adj_input')
nod_input = tf.keras.Input(shape=(None,), name='nod_input')
class MPNN(tf.keras.Model):
def __init__(self, out_int_dim, state_dim, T):
super(MPNN, self ).__init__(self)
self.T = T
self.embed = tf.keras.layers.Dense(units=state_dim, activation=tf.nn.relu)
s... | SEED = 100
DEBUG = False
WANDB = False
VALIDATION_SIZE = 0.2
BATCH_SIZE = 8 *REPLICAS
LEARNING_RATE = 3e-5 * REPLICAS
EPOCHS=40
MODEL_NAME = "VitL16"
N_FOLDS = 5
TTA = False
N_TTA = 3
T_1 = 0.2
T_2 = 1.2
SMOOTH_FRACTION = 0.01
N_ITER = 5
HEIGHT = 512
WIDTH = 512
HEIGHT_RS = 512
WIDTH_RS = 512
CHANNELS = 3
N_CLASSES = 5... | Cassava Leaf Disease Classification |
14,353,935 | def mse(orig , preds):
mask = tf.where(tf.equal(orig, 0), orig, tf.ones_like(orig))
nums = tf.boolean_mask(orig, mask)
preds = tf.boolean_mask(preds, mask)
reconstruction_error = tf.reduce_mean(tf.square(tf.subtract(nums, preds)))
return reconstruction_error<compute_test_metric> | def transform_rotation(image, height, rotation):
DIM = height
XDIM = DIM%2
rotation = rotation * tf.random.uniform([1],dtype='float32')
rotation = math.pi * rotation / 180.
c1 = tf.math.cos(rotation)
s1 = tf.math.sin(rotation)
one = tf.constant([1],dtype='float32')
zero = tf.constant([0],dtype='float32')
rotation... | Cassava Leaf Disease Classification |
14,353,935 | def log_mse(orig , preds):
mask = tf.where(tf.equal(orig, 0), orig, tf.ones_like(orig))
nums = tf.boolean_mask(orig, mask)
preds = tf.boolean_mask(preds, mask)
reconstruction_error = tf.math.log(tf.reduce_mean(tf.square(tf.subtract(nums, preds))))
return reconstruction_error<compute_test_metric> | def data_augment(image, label):
p_rotation = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
p_spatial = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
p_rotate = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
p_pixel_1 = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
p_pixel_2 = tf.random.uniform([], 0, 1.0, dt... | Cassava Leaf Disease Classification |
14,353,935 | def mae(orig , preds):
mask = tf.where(tf.equal(orig, 0), orig, tf.ones_like(orig))
nums = tf.boolean_mask(orig, mask)
preds = tf.boolean_mask(preds, mask)
reconstruction_error = tf.reduce_mean(tf.abs(tf.subtract(nums, preds)))
return reconstruction_error<compute_test_metric> | copyfile(src = ".. /input/bitempered-logistic-loss-tensorflow-v2/bi_tempered_loss.py", dst = ".. /working/loss.py")
| Cassava Leaf Disease Classification |
14,353,935 | def log_mae(orig , preds):
mask = tf.where(tf.equal(orig, 0), orig, tf.ones_like(orig))
nums = tf.boolean_mask(orig, mask)
preds = tf.boolean_mask(preds, mask)
reconstruction_error = tf.math.log(tf.reduce_mean(tf.abs(tf.subtract(nums, preds))))
return reconstruction_error<init_hyperparams> | with strategy.scope() :
class BiTemperedLogisticLoss(tf.keras.losses.Loss):
def __init__(self, t1, t2, lbl_smth, n_iter):
super(BiTemperedLogisticLoss, self ).__init__()
self.t1 = t1
self.t2 = t2
self.lbl_smth = lbl_smth
self.n_iter = n_iter
def call(self, y_true, y_pred):
return bi_tempered_logistic_loss(y_pred, y_tru... | Cassava Leaf Disease Classification |
14,353,935 | learning_rate = 0.001
def step_decay(epoch):
initial_lrate = learning_rate
drop = 0.1
epochs_drop = 20.0
lrate = initial_lrate * np.power(drop,
np.floor(( epoch)/epochs_drop))
tf.print("Learning rate: ", lrate)
return lrate
lrate = tf.keras.callbacks.LearningRateScheduler(step_decay)
stop_early = tf.keras.callbacks.E... | def get_vit_model(weights = None):
if weights == None:
return vit.vit_l16(
image_size=HEIGHT,
activation='softmax',
pretrained=False,
include_top=True,
pretrained_top=False,
classes = 5,
)
else:
return vit.vit_l16(
image_size=HEIGHT,
activation='softmax',
pretrained=False,
include_top=True,
pretrained_top=False,
cl... | Cassava Leaf Disease Classification |
14,353,935 | mpnn = MPNN(out_int_dim = 512, state_dim = 128, T = 4)
mpnn.compile(opt, log_mae, metrics = [mae, log_mse] )<define_variables> | files_path = '.. /input/cassava-leaf-disease-classification/test_images'
TEST_FILENAMES = tf.io.gfile.glob('.. /input/cassava-leaf-disease-classification/test_tfrecords/*')
model_path_list = [
'.. /input/cassava-leaf-vit-models/ViTB16_best_fold_0__v3_.h5',
'.. /input/cassava-leaf-vit-models/ViTB16_best_fold_0_v4_.h5',... | Cassava Leaf Disease Classification |
14,353,935 | <split><EOS> | test_preds = np.argmax(test_preds, axis=-1)
image_names = [img_name.numpy().decode('utf-8')for img, img_name in iter(test_ds.unbatch())]
submission = pd.DataFrame({'image_id': image_names, 'label': test_preds})
submission.to_csv('submission.csv', index=False)
display(submission.head() ) | Cassava Leaf Disease Classification |
15,025,111 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<train_model> | import pandas as pd
import numpy as np
import cv2
from glob import glob
import sklearn
from sklearn.model_selection import GroupKFold, StratifiedKFold
from sklearn.metrics import roc_auc_score, log_loss
from sklearn import metrics
from sklearn.metrics import log_loss
from skimage import io
import os
from datetime impor... | Cassava Leaf Disease Classification |
15,025,111 | mpnn.fit({'adj_input' : in_edges_train[:train_size], 'nod_input': nodes_train[:train_size]}, y = out_labels[:train_size], batch_size = batch_size, epochs = epochs,
callbacks = [lrate, stop_early], use_multiprocessing = True, initial_epoch = 0, verbose = 2,
validation_data =({'adj_input' : in_edges_train[train_size:], '... | CFG = {
'fold_num': 12,
'seed': 719,
'model_arch': 'tf_efficientnet_b3_ns',
'img_size': 384,
'epochs': 120,
'train_bs': 28,
'valid_bs': 32,
'lr': 1e-2,
'num_workers': 5,
'accum_iter': 1,
'verbose_step': 2,
'device': 'cuda:0',
'tta': 10,
'used_epochs': [6,7,8,9],
'weights': [1,1,1,1]
} | Cassava Leaf Disease Classification |
15,025,111 | preds = mpnn.predict({'adj_input' : in_edges_test, 'nod_input': nodes_test} )<save_model> | def all_seed(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True | Cassava Leaf Disease Classification |
15,025,111 | np.save("preds_kernel.npy" , preds )<load_from_csv> | def get_img(path):
im_bgr = cv2.imread(path)
im_rgb = im_bgr[:, :, ::-1]
return im_rgb | Cassava Leaf Disease Classification |
15,025,111 | train = pd.read_csv(datadir + "champs-scalar-coupling/train.csv")
test = pd.read_csv(datadir + "champs-scalar-coupling/test.csv")
test_group = test.groupby('molecule_name')
scale_min = train['scalar_coupling_constant'].min()
scale_max = train['scalar_coupling_constant'].max()
scale_mid =(scale_max + scale_min)/2
sca... | img = get_img('.. /input/cassava-leaf-disease-classification/train_images/1000015157.jpg')
plt.imshow(img)
plt.show() | Cassava Leaf Disease Classification |
15,025,111 | def make_outs(test_group, preds):
i = 0
x = np.array([])
for test_gp, preds in zip(test_group, preds):
if(not i%1000):
print(i)
gp = test_gp[1]
x = np.append(x,(preds[gp['atom_index_0'].values, gp['atom_index_1'].values] + preds[gp['atom_index_1'].values, gp['atom_index_0'].values])/2.0)
i = i+1
return x<normalizati... | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv' ) | Cassava Leaf Disease Classification |
15,025,111 | out_unscaled = make_outs(test_group, preds )<save_to_csv> | train.label.value_counts() | Cassava Leaf Disease Classification |
15,025,111 | test['scalar_coupling_constant'] = out_unscaled
test['scalar_coupling_constant'] = test['scalar_coupling_constant']*scale_norm + scale_mid
test[['id','scalar_coupling_constant']].to_csv('submission.csv', index=False )<load_from_csv> | sample_submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
sample_submission.head() | Cassava Leaf Disease Classification |
15,025,111 | sub1 = pd.read_csv('.. /input/lgb-public-kernels-plus-more-features/sub_lgb_model_individual.csv')
sub2 = pd.read_csv('.. /input/staking-and-stealing-like-a-molecule/submission.csv')
sample = pd.read_csv('.. /input/champs-scalar-coupling/sample_submission.csv' )<save_to_csv> | class CassavaDataset(Dataset):
def __init__(self, df, data_root, transforms = None, output_label = True):
super().__init__()
self.df = df.reset_index(drop = True ).copy()
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def __len__(self):
return self.df.shape[0]
def __getitem... | Cassava Leaf Disease Classification |
15,025,111 | sample['scalar_coupling_constant'] =(0.6*sub2['scalar_coupling_constant'] + 0.4*sub1['scalar_coupling_constant'])
sample.to_csv('stackers_blend.csv', index=False )<load_from_csv> | HorizontalFlip, VerticalFlip, Transpose, ShiftScaleRotate,
HueSaturationValue,RandomResizedCrop, RandomBrightnessContrast,
Compose, Normalize, Cutout, CoarseDropout, CenterCrop, Resize
)
| Cassava Leaf Disease Classification |
15,025,111 | pd.options.display.float_format = '{:,}'.format
train_df = pd.read_csv(".. /input/champs-scalar-coupling/train.csv")
test_df = pd.read_csv(".. /input/champs-scalar-coupling/test.csv")
structures = pd.read_csv(".. /input/champs-scalar-coupling/structures.csv")
yuk = pd.read_csv(".. /input/submolecularyukawapotential/... | def get_train_transforms() :
return Compose([
RandomResizedCrop(CFG['img_size'], CFG['img_size']),
Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
ShiftScaleRotate(p=0.5),
HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.5),
RandomBrightnessContrast(brightness_limit=... | Cassava Leaf Disease Classification |
15,025,111 | target = train_df.pop('scalar_coupling_constant')
features = [col for col in train_df.columns if col != 'id' and col != 'scalar_coupling_constant']
all_data = pd.concat([train_df[features], test_df[features]], axis=0, sort=False)
all_data[['molecule_name', 'atom_index_0', 'atom_index_1']].to_csv("all_data_index.csv",... | def get_inference_transforms() :
return Compose([
RandomResizedCrop(CFG['img_size'], CFG['img_size']),
Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.5),
RandomBrightnessContrast(brightness_limit=(-0.1,0.1), contrast_... | Cassava Leaf Disease Classification |
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