kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
13,685,132 | train['spoken_languages_count'] = train['spoken_languages'].apply(lambda x: len(eval(x)) if str(x)!= 'nan' else 0)
test['spoken_languages_count'] = test['spoken_languages'].apply(lambda x: len(eval(x)) if str(x)!= 'nan' else 0 )<drop_column> | BATCH_SIZE = 16 * REPLICAS
HEIGHT = 512
WIDTH = 512
CHANNELS = 3
N_CLASSES = 5
TTA_STEPS = 0
USE_REGULAR = False
USE_SCL = True | Cassava Leaf Disease Classification |
13,685,132 | train = train.drop(['spoken_languages'], axis=1)
test = test.drop(['spoken_languages'], axis=1 )<define_variables> | def data_augment(image, label):
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, dtype=tf.float32)
p_pixel_3 = tf.random.uniform([], 0, 1.0, dty... | Cassava Leaf Disease Classification |
13,685,132 | list_of_keywords = list(train['Keywords'].apply(lambda x: [i['name'] for i in eval(x)] if str(x)!= 'nan' else [] ).values )<feature_engineering> | database_base_path = '/kaggle/input/cassava-leaf-disease-classification/'
submission = pd.read_csv(f'{database_base_path}sample_submission.csv')
display(submission.head())
TEST_FILENAMES = tf.io.gfile.glob(f'{database_base_path}test_tfrecords/ld_test*.tfrec')
NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
print... | Cassava Leaf Disease Classification |
13,685,132 | train['num_Keywords'] = train['Keywords'].apply(lambda x: len(eval(x)) if str(x)!= 'nan' else 0)
train['all_Keywords'] = train['Keywords'].apply(lambda x: ' '.join(sorted([i['name'] for i in eval(x)])) if str(x)!= 'nan' else '')
top_keywords = [m[0] for m in Counter([i for j in list_of_keywords for i in j] ).most_com... | model_path_list = glob.glob('/kaggle/input/cassava-leaf-supervised-contrastive-learning/model_reg*.h5')
model_path_list.sort()
print('Models to predict:')
print(*model_path_list, sep='
')
model_path_list_scl = glob.glob('/kaggle/input/cassava-leaf-supervised-contrastive-learning/model_scl*.h5')
model_path_list_scl.... | Cassava Leaf Disease Classification |
13,685,132 | keywords_encoder = LabelEncoder()
train['all_Keywords'] = keywords_encoder.fit_transform(train['all_Keywords'])
test['all_Keywords'] = keywords_encoder.fit_transform(test['all_Keywords'] )<drop_column> | def encoder_fn(input_shape):
inputs = L.Input(shape=input_shape, name='inputs')
base_model = efn.EfficientNetB3(input_tensor=inputs,
include_top=False,
weights=None,
pooling='avg')
model = Model(inputs=inputs, outputs=base_model.outputs)
return model
def classifier_fn(input_shape, N_CLASSES, encoder, trainable=True)... | Cassava Leaf Disease Classification |
13,685,132 | train = train.drop(['Keywords'], axis=1)
test = test.drop(['Keywords'], axis=1 )<feature_engineering> | files_path = f'{database_base_path}test_images/'
test_size = len(os.listdir(files_path))
test_preds = np.zeros(( test_size, N_CLASSES))
if USE_REGULAR:
print('Inference for regular trainining models')
with strategy.scope() :
encoder = encoder_fn(( None, None, CHANNELS))
model = classifier_fn(( None, None, CHANNELS), N... | Cassava Leaf Disease Classification |
13,685,132 | <feature_engineering><EOS> | submission = pd.DataFrame({'image_id': image_names, 'label': test_preds})
submission.to_csv('submission.csv', index=False)
display(submission.head() ) | Cassava Leaf Disease Classification |
13,753,285 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<define_variables> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
sys.path.append(package_path)
DATA_DIR = '.. /input/cassava-leaf-disease-classification'
MODEL_DIR = '.. /input/efficientnet-baseline-train-amp-aug' | Cassava Leaf Disease Classification |
13,753,285 | list_of_cast_members = list(train['cast'].apply(lambda x: [i['name'] for i in x] if str(x)!= 'nan' else [] ).values )<define_variables> | 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 |
13,753,285 | top_cast_members = [m[0] for m in Counter([i for j in list_of_cast_members for i in j] ).most_common(50)]<feature_engineering> | CFG = {
'fold_num': 5,
'seed': 719,
'model_arch': 'tf_efficientnet_b4_ns',
'img_size': 512,
'epochs': 10,
'train_bs': 32,
'valid_bs': 32,
'lr': 1e-4,
'num_workers': 4,
'accum_iter': 1,
'verbose_step': 1,
'device': 'cuda:0',
'tta': 3,
'used_epochs': [6,7,8,9],
'weights': [1,1,1,1]
} | Cassava Leaf Disease Classification |
13,753,285 | for g in top_cast_members:
train['cast_member_'+g] = train['cast'].apply(lambda x: 1 if g in str(x)else 0)
for g in top_cast_members:
test['cast_member_'+g] = test['cast'].apply(lambda x: 1 if g in str(x)else 0 )<drop_column> | train = pd.read_csv(f'{DATA_DIR}/train.csv' ) | Cassava Leaf Disease Classification |
13,753,285 | train = train.drop(['cast'], axis=1)
test = test.drop(['cast'], axis=1 )<feature_engineering> | 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 = True
def get_img(path):
im_bgr = cv2.imread(path)
im_rgb = im_bgr[:, :, ::-1]
r... | Cassava Leaf Disease Classification |
13,753,285 | train['crew'] = train['crew'].apply(lambda x: eval(x)if str(x)!= 'nan' else [])
test['crew'] = test['crew'].apply(lambda x: eval(x)if str(x)!= 'nan' else [] )<define_variables> | 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__(self, i... | Cassava Leaf Disease Classification |
13,753,285 | list_of_crew_members = list(train['crew'].apply(lambda x: [i['name'] for i in x] if str(x)!= 'nan' else [] ).values )<define_variables> | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.mod... | Cassava Leaf Disease Classification |
13,753,285 | top_crew_members = [m[0] for m in Counter([i for j in list_of_crew_members for i in j] ).most_common(50)]<feature_engineering> | def inference_one_epoch(model, data_loader, device):
model.eval()
image_preds_all = []
pbar = tqdm(enumerate(data_loader), total=len(data_loader))
for step,(imgs)in pbar:
imgs = imgs.to(device ).float()
image_preds = model(imgs)
image_preds_all += [torch.softmax(image_preds, 1 ).detach().cpu().numpy() ]
image_preds_al... | Cassava Leaf Disease Classification |
13,753,285 | for g in top_crew_members:
train['crew_member_'+g] = train['crew'].apply(lambda x: 1 if g in str(x)else 0)
for g in top_crew_members:
test['crew_member_'+g] = test['crew'].apply(lambda x: 1 if g in str(x)else 0 )<drop_column> | %%time
if __name__ == '__main__':
seed_everything(CFG['seed'])
stratifiedKFold = StratifiedKFold(n_splits=CFG['fold_num'])
folds = stratifiedKFold.split(np.arange(train.shape[0]), train.label.values)
for fold,(trn_idx, val_idx)in enumerate(folds):
if fold > 0:
break
print(f'Inference fold {fold} started')
valid_ = ... | Cassava Leaf Disease Classification |
13,753,285 | <feature_engineering><EOS> | test['label'] = np.argmax(tst_preds, axis=1)
test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,684,511 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<drop_column> | !pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git | Cassava Leaf Disease Classification |
13,684,511 | train = train.drop(['homepage'], axis=1)
test = test.drop(['homepage'], axis=1 )<drop_column> | import math, re, os
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from kaggle_datasets import KaggleDatasets
from tensorflow import keras
from functools import partial
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import ModelCheckp... | Cassava Leaf Disease Classification |
13,684,511 | train = train.drop(['poster_path'], axis=1)
test = test.drop(['poster_path'], axis=1 )<drop_column> | AUTOTUNE = tf.data.experimental.AUTOTUNE
WORK_PATH = '.. /input/cassava-leaf-disease-classification'
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
IMAGE_SIZE = [512, 512]
CHANNELS = 3
CLASSES = ['0', '1', '2', '3', '4']
EPOCHS = 30 | Cassava Leaf Disease Classification |
13,684,511 | train = train.drop(['status'], axis=1)
test = test.drop(['status'], axis=1 )<feature_engineering> | def decode_image(image):
image = tf.image.decode_jpeg(image, channels=CHANNELS)
image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [*IMAGE_SIZE, 3])
return image | Cassava Leaf Disease Classification |
13,684,511 | for col in ['title', 'tagline', 'overview', 'original_title']:
train['len_' + col] = train[col].fillna('' ).apply(lambda x: len(str(x)))
train['words_' + col] = train[col].fillna('' ).apply(lambda x: len(str(x.split(' '))))
test['len_' + col] = test[col].fillna('' ).apply(lambda x: len(str(x)))
test['words_' + col] =... | def read_tfrecord(example, labeled):
tfrecord_format = {
"image": tf.io.FixedLenFeature([], tf.string),
"target": tf.io.FixedLenFeature([], tf.int64)
} if labeled else {
"image": tf.io.FixedLenFeature([], tf.string),
"image_name": tf.io.FixedLenFeature([], tf.string)
}
example = tf.io.parse_single_example(example, tf... | Cassava Leaf Disease Classification |
13,684,511 | train = train.drop(["imdb_id", "original_title", "overview", "tagline", "title"], axis=1)
test = test.drop(["imdb_id", "original_title", "overview", "tagline", "title"], axis=1 )<prepare_x_and_y> | def load_dataset(filenames, labeled=True, ordered=False):
ignore_order = tf.data.Options()
if not ordered:
ignore_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTOTUNE)
dataset = dataset.with_options(ignore_order)
dataset = dataset.map(partial(read_tfrecord,... | Cassava Leaf Disease Classification |
13,684,511 | X = train.drop(['id', 'revenue'], axis=1)
Y = np.log1p(train['revenue'])
X_test = test.drop(['id'], axis=1 )<split> | TEST_FILENAMES = tf.io.gfile.glob(WORK_PATH + '/test_tfrecords/ld_test*.tfrec' ) | Cassava Leaf Disease Classification |
13,684,511 | X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, test_size=0.1 )<choose_model_class> | def get_test_dataset(ordered=False):
dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(AUTOTUNE)
return dataset | Cassava Leaf Disease Classification |
13,684,511 | params = {'num_leaves': 30,
'min_data_in_leaf': 20,
'objective': 'regression',
'max_depth': 5,
'learning_rate': 0.01,
"boosting": "gbdt",
"feature_fraction": 0.9,
"bagging_freq": 1,
"bagging_fraction": 0.9,
"bagging_seed": 11,
"metric": 'rmse',
"lambda_l1": 0.2,
"verbosity": -1}
model = lgb.LGBMRegressor(**params, n_es... | with strategy.scope() :
base_model = EfficientNetB5(weights=None, include_top=False, input_shape =(None, None, 3), pooling='avg')
model = tf.keras.Sequential([
base_model,
tf.keras.layers.Dropout (.3),
tf.keras.layers.Dense(len(CLASSES), activation='softmax')
])
model.compile(
optimizer=tf.keras.optimizers.Adam(lr ... | Cassava Leaf Disease Classification |
13,684,511 | y_pred_valid = model.predict(X_valid)
y_pred = model.predict(X_test, num_iteration=model.best_iteration_ )<save_to_csv> | model.load_weights('.. /input/cassava-leaf-disease-tpu-efficientnetb4/EffNetB5_best_weights.h5' ) | Cassava Leaf Disease Classification |
13,684,511 | sample_submission['revenue'] = np.expm1(y_pred)
sample_submission.to_csv("submission.csv", index=False )<set_options> | def to_float32(image, label):
return tf.cast(image, tf.float32), label | Cassava Leaf Disease Classification |
13,684,511 | pd.set_option('max_columns', None)
%matplotlib inline
plt.style.use('ggplot')
stop = set(stopwords.words('english'))
py.init_notebook_mode(connected=True)
print(os.listdir(".. /input"))
<load_from_csv> | def count_data_items(filenames):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1)) for filename in filenames]
return np.sum(n)
NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES ) | Cassava Leaf Disease Classification |
13,684,511 | <load_from_csv><EOS> | test_ds = get_test_dataset(ordered=True ).map(to_float32)
print('Computing predictions...')
test_images_ds = test_ds.map(lambda image, idnum: image)
probabilities = model.predict(test_images_ds)
test_preds = np.argmax(probabilities, axis=-1)
test_ids_ds = test_ds.map(lambda image, idnum: idnum ).unbatch()
image_na... | Cassava Leaf Disease Classification |
13,460,607 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
| Cassava Leaf Disease Classification |
13,460,607 | dict_columns = ['belongs_to_collection', 'genres', 'production_companies',
'production_countries', 'spoken_languages', 'Keywords', 'cast', 'crew']
def text_to_dict(df):
for column in dict_columns:
df[column] = df[column].apply(lambda x: {} if pd.isna(x)else ast.literal_eval(x))
return df
train = text_to_dict(train)
pr... | from glob import glob
from sklearn.model_selection import GroupKFold, StratifiedKFold
import cv2
from skimage import io
import torch
from torch import nn
import os
from datetime import datetime
import time
import random
import cv2
import torchvision
from torchvision import transforms
import pandas as pd
import numpy as... | Cassava Leaf Disease Classification |
13,460,607 | for i, e in enumerate(train['belongs_to_collection'][:5]):
print(i, e )<count_values> | CFG = {
'fold_num': 10,
'seed': 719,
'model_arch': 'tf_efficientnet_b3_ns',
'img_size': 512,
'epochs': 32,
'train_bs': 28,
'valid_bs': 32,
'lr': 1e-4,
'num_workers': 4,
'accum_iter': 1,
'verbose_step': 1,
'device': 'cuda:0',
'tta': 1,
'used_epochs': [6,7,8,9],
'weights': [1,1,1,1]
} | Cassava Leaf Disease Classification |
13,460,607 | train['belongs_to_collection'].apply(lambda x: len(x)if x != {} else 0 ).value_counts()<feature_engineering> | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train.head() | Cassava Leaf Disease Classification |
13,460,607 | train['collection_name'] = train['belongs_to_collection'].apply(lambda x: x[0]['name'] if x != {} else 0)
train['has_collection'] = train['belongs_to_collection'].apply(lambda x: len(x)if x != {} else 0)
test['collection_name'] = test['belongs_to_collection'].apply(lambda x: x[0]['name'] if x != {} else 0)
test['has... | train.label.value_counts() | Cassava Leaf Disease Classification |
13,460,607 | for i, e in enumerate(train['genres'][:5]):
print(i, e )<count_values> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
13,460,607 | print('Number of genres in films')
genres_count=train['genres'].apply(lambda x: len(x)if x != {} else 0 ).value_counts()
genres_count<define_variables> | 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 |
13,460,607 | list_of_genres = list(train['genres'].apply(lambda x: [i['name'] for i in x] if x != {} else [] ).values)
list_of_genres<feature_engineering> | 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 |
13,460,607 | train['num_genres'] = train['genres'].apply(lambda x: len(x)if x != {} else 0)
train['all_genres'] = train['genres'].apply(lambda x: ' '.join(sorted([i['name'] for i in x])) if x != {} else '')
top_genres = [m[0] for m in Counter([i for j in list_of_genres for i in j] ).most_common(15)]
for g in top_genres:
train['ge... | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.mod... | Cassava Leaf Disease Classification |
13,460,607 | for i, e in enumerate(train['production_companies'][:5]):
print(i, e )<count_values> | if __name__ == '__main__':
seed_everything(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'] = li... | Cassava Leaf Disease Classification |
13,460,607 | print('Number of production companies in films')
train['production_companies'].apply(lambda x: len(x)if x != {} else 0 ).value_counts()<filter> | test['label'] = np.argmax(tst_preds, axis=1)
test.head() | Cassava Leaf Disease Classification |
13,460,607 | <count_values><EOS> | test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,273,599 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<define_variables> | !pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git | Cassava Leaf Disease Classification |
13,273,599 | for i, e in enumerate(train['production_countries'][:5]):
print(i, e )<count_values> | def seed_everything(seed=0):
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
seed = 0
seed_everything(seed)
warnings.filterwarnings('ignore' ) | Cassava Leaf Disease Classification |
13,273,599 | print('Number of production countries in films')
train['production_countries'].apply(lambda x: len(x)if x != {} else 0 ).value_counts()<count_values> | 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 |
13,273,599 | list_of_countries = list(train['production_countries'].apply(lambda x: [i['name'] for i in x] if x != {} else [] ).values)
Counter([i for j in list_of_countries for i in j] ).most_common(25 )<feature_engineering> | BATCH_SIZE = 16 * REPLICAS
HEIGHT = 512
WIDTH = 512
CHANNELS = 3
N_CLASSES = 5
TTA_STEPS = 8 | Cassava Leaf Disease Classification |
13,273,599 | train['num_countries'] = train['production_countries'].apply(lambda x: len(x)if x != {} else 0)
train['all_countries'] = train['production_countries'].apply(lambda x: ' '.join(sorted([i['name'] for i in x])) if x != {} else '')
top_countries = [m[0] for m in Counter([i for j in list_of_countries for i in j] ).most_co... | def data_augment(image, label):
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, dtype=tf.float32)
p_pixel_3 = tf.random.uniform([], 0, 1.0, dty... | Cassava Leaf Disease Classification |
13,273,599 | for i, e in enumerate(train['spoken_languages'][:5]):
print(i, e )<count_values> | database_base_path = '/kaggle/input/cassava-leaf-disease-classification/'
submission = pd.read_csv(f'{database_base_path}sample_submission.csv')
display(submission.head())
TEST_FILENAMES = tf.io.gfile.glob(f'{database_base_path}test_tfrecords/ld_test*.tfrec')
NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
print... | Cassava Leaf Disease Classification |
13,273,599 | print('Number of spoken languages in films')
train['spoken_languages'].apply(lambda x: len(x)if x != {} else 0 ).value_counts()<count_values> | model_path_list = glob.glob('/kaggle/input/cassava-leaf-disease-training-with-tpu-v2-pods/*.h5')
model_path_list.sort()
print('Models to predict:')
print(*model_path_list, sep='
' ) | Cassava Leaf Disease Classification |
13,273,599 | list_of_languages = list(train['spoken_languages'].apply(lambda x: [i['name'] for i in x] if x != {} else [] ).values)
Counter([i for j in list_of_languages for i in j] ).most_common(15 )<feature_engineering> | def model_fn(input_shape, N_CLASSES):
inputs = L.Input(shape=input_shape, name='input_image')
base_model = efn.EfficientNetB4(input_tensor=inputs,
include_top=False,
weights=None,
pooling='avg')
x = L.Dropout (.5 )(base_model.output)
output = L.Dense(N_CLASSES, activation='softmax', name='output' )(x)
model = Model... | Cassava Leaf Disease Classification |
13,273,599 | train['num_languages'] = train['spoken_languages'].apply(lambda x: len(x)if x != {} else 0)
train['all_languages'] = train['spoken_languages'].apply(lambda x: ' '.join(sorted([i['name'] for i in x])) if x != {} else '')
top_languages = [m[0] for m in Counter([i for j in list_of_languages for i in j] ).most_common(30)... | files_path = f'{database_base_path}test_images/'
test_size = len(os.listdir(files_path))
test_preds = np.zeros(( test_size, N_CLASSES))
for model_path in model_path_list:
print(model_path)
K.clear_session()
model.load_weights(model_path)
if TTA_STEPS > 0:
test_ds = get_dataset(files_path, tta=True ).repeat()
ct_steps... | Cassava Leaf Disease Classification |
13,273,599 | <count_values><EOS> | submission = pd.DataFrame({'image_id': image_names, 'label': test_preds})
submission.to_csv('submission.csv', index=False)
display(submission.head() ) | Cassava Leaf Disease Classification |
13,499,132 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
| Cassava Leaf Disease Classification |
13,499,132 | list_of_keywords = list(train['Keywords'].apply(lambda x: [i['name'] for i in x] if x != {} else [] ).values)
train['num_Keywords'] = train['Keywords'].apply(lambda x: len(x)if x != {} else 0)
train['all_Keywords'] = train['Keywords'].apply(lambda x: ' '.join(sorted([i['name'] for i in x])) if x != {} else '')
top_k... | from datetime import datetime
from glob import glob
from scipy.ndimage.interpolation import zoom
from skimage import io
from sklearn import metrics
from sklearn.metrics import log_loss
from sklearn.metrics import roc_auc_score, log_loss
from sklearn.model_selection import GroupKFold, StratifiedKFold
from torch import n... | Cassava Leaf Disease Classification |
13,499,132 | for i, e in enumerate(train['cast'][:1]):
print(i, e )<count_values> | CFG = {
'fold_num': 10,
'seed': 719,
'model_arch': 'tf_efficientnet_b3_ns',
'img_size': 512,
'epochs': 32,
'train_bs': 28,
'valid_bs': 32,
'lr': 1e-4,
'num_workers': 4,
'accum_iter': 1,
'verbose_step': 1,
'device': 'cuda:0',
'tta': 3,
'used_epochs': [6,7,8,9],
'weights': [1,1,1,1]
} | Cassava Leaf Disease Classification |
13,499,132 | print('Number of casted persons in films')
train['cast'].apply(lambda x: len(x)if x != {} else 0 ).value_counts().head(15 )<count_values> | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train.head() | Cassava Leaf Disease Classification |
13,499,132 | list_of_cast_names = list(train['cast'].apply(lambda x: [i['name'] for i in x] if x != {} else [] ).values)
Counter([i for j in list_of_cast_names for i in j] ).most_common(15 )<count_unique_values> | train.label.value_counts() | Cassava Leaf Disease Classification |
13,499,132 | list_of_cast_genders = list(train['cast'].apply(lambda x: [i['gender'] for i in x] if x != {} else [] ).values)
Counter([i for j in list_of_cast_genders for i in j] ).most_common()<count_values> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
13,499,132 | list_of_cast_characters = list(train['cast'].apply(lambda x: [i['character'] for i in x] if x != {} else [] ).values)
Counter([i for j in list_of_cast_characters for i in j] ).most_common(15 )<feature_engineering> | 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 = True
def get_img(path):
im_bgr = cv2.imread(path)
im_rgb = im_bgr[:, :, ::-1]
r... | Cassava Leaf Disease Classification |
13,499,132 | train['num_cast'] = train['cast'].apply(lambda x: len(x)if x != {} else 0)
top_cast_names = [m[0] for m in Counter([i for j in list_of_cast_names for i in j] ).most_common(15)]
for g in top_cast_names:
train['cast_name_' + g] = train['cast'].apply(lambda x: 1 if g in str(x)else 0)
train['genders_0_cast'] = train['cas... | 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 |
13,499,132 | print('Number of casted persons in films')
train['crew'].apply(lambda x: len(x)if x != {} else 0 ).value_counts().head(10 )<count_values> | 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 |
13,499,132 | list_of_crew_names = list(train['crew'].apply(lambda x: [i['name'] for i in x] if x != {} else [] ).values)
Counter([i for j in list_of_crew_names for i in j] ).most_common(15 )<count_values> | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.mod... | Cassava Leaf Disease Classification |
13,499,132 | list_of_crew_jobs = list(train['crew'].apply(lambda x: [i['job'] for i in x] if x != {} else [] ).values)
Counter([i for j in list_of_crew_jobs for i in j] ).most_common(15 )<count_values> | if __name__ == '__main__':
seed_everything(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))
valid_ = train.loc[val_idx,:].reset_ind... | Cassava Leaf Disease Classification |
13,499,132 | list_of_crew_genders = list(train['crew'].apply(lambda x: [i['gender'] for i in x] if x != {} else [] ).values)
Counter([i for j in list_of_crew_genders for i in j] ).most_common(15 )<count_values> | df_test_predict_proba_1 = pd.concat(
[test, pd.DataFrame(softmax(tst_preds, axis = 1)) ],
axis=1
).sort_values(["image_id"] ) | Cassava Leaf Disease Classification |
13,499,132 | list_of_crew_departments = list(train['crew'].apply(lambda x: [i['department'] for i in x] if x != {} else [] ).values)
Counter([i for j in list_of_crew_departments for i in j] ).most_common(14 )<feature_engineering> | variable_list = %who_ls
for _ in variable_list:
if _ is not "df_test_predict_proba_1":
del globals() [_]
%who_ls | Cassava Leaf Disease Classification |
13,499,132 | train['num_crew'] = train['crew'].apply(lambda x: len(x)if x != {} else 0)
top_crew_names = [m[0] for m in Counter([i for j in list_of_crew_names for i in j] ).most_common(15)]
for g in top_crew_names:
train['crew_name_' + g] = train['crew'].apply(lambda x: 1 if g in str(x)else 0)
train['genders_0_crew'] = train['cre... | OUTPUT_DIR = './'
MODEL_DIR = '.. /input/cassava-resnext50-32x4d-weights/'
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
TRAIN_PATH = '.. /input/cassava-leaf-disease-classification/train_images'
TEST_PATH = '.. /input/cassava-leaf-disease-classification/test_images' | Cassava Leaf Disease Classification |
13,499,132 | train['log_revenue'] = np.log1p(train['revenue'] )<feature_engineering> | class CFG:
debug=False
num_workers=8
model_name='resnext50_32x4d'
size=512
batch_size=32
seed=2020
target_size=5
target_col='label'
n_fold=5
trn_fold=[0, 1, 2, 3, 4]
inference=True | Cassava Leaf Disease Classification |
13,499,132 | train['log_budget'] = np.log1p(train['budget'])
test['log_budget'] = np.log1p(test['budget'] )<count_values> | sys.path.append('.. /input/pytorch-image-models/pytorch-image-models-master')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
warnings.filterwarnings('ignore' ) | Cassava Leaf Disease Classification |
13,499,132 | train['homepage'].value_counts().head()<import_modules> | 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 init_logger(log_file=OUTPUT_DIR+'inference.log'):
logger = getLogger(__name__)
logger.setLevel(IN... | Cassava Leaf Disease Classification |
13,499,132 | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LinearRegression
import eli5<train_model> | test = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
test.head() | Cassava Leaf Disease Classification |
13,499,132 | vectorizer = TfidfVectorizer(
sublinear_tf=True,
analyzer='word',
token_pattern=r'\w{1,}',
ngram_range=(1, 2),
min_df=5)
overview_text = vectorizer.fit_transform(train['overview'].fillna(''))
linreg = LinearRegression()
linreg.fit(overview_text, train['log_revenue'])
eli5.show_weights(linreg, vec=vectorizer, top=20,... | def get_transforms(*, data):
if data == 'valid':
return A.Compose([
A.Resize(CFG.size, CFG.size),
A.Transpose(p=0.5),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.ShiftScaleRotate(p=0.5),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
] ) | Cassava Leaf Disease Classification |
13,499,132 | test.loc[test['release_date'].isnull() == True, 'release_date'] = '01/01/98'<categorify> | class CustomResNext(nn.Module):
def __init__(self, model_name='resnext50_32x4d', pretrained=False):
super().__init__()
self.model = timm.create_model(model_name, pretrained=pretrained)
n_features = self.model.fc.in_features
self.model.fc = nn.Linear(n_features, CFG.target_size)
def forward(self, x):
x = self.model(x)... | Cassava Leaf Disease Classification |
13,499,132 | def fix_date(x):
year = x.split('/')[2]
if int(year)<= 19:
return x[:-2] + '20' + year
else:
return x[:-2] + '19' + year<data_type_conversions> | def load_state(model_path):
model = CustomResNext(CFG.model_name, pretrained=False)
try:
model.load_state_dict(torch.load(model_path)['model'], strict=True)
state_dict = torch.load(model_path)['model']
except:
state_dict = torch.load(model_path)['model']
state_dict = {
k[7:]
if k.startswith('module.')
else k: state_... | Cassava Leaf Disease Classification |
13,499,132 | train['release_date'] = train['release_date'].apply(lambda x: fix_date(x))
test['release_date'] = test['release_date'].apply(lambda x: fix_date(x))
train['release_date'] = pd.to_datetime(train['release_date'])
test['release_date'] = pd.to_datetime(test['release_date'] )<data_type_conversions> | model = CustomResNext(CFG.model_name, pretrained=False)
states = [load_state(MODEL_DIR+f'{CFG.model_name}_fold{fold}.pth')for fold in CFG.trn_fold]
test_dataset = TestDataset(test, transform=get_transforms(data='valid'))
test_loader = DataLoader(
test_dataset,
batch_size=CFG.batch_size,
shuffle=False,
num_workers=CFG... | Cassava Leaf Disease Classification |
13,499,132 | def process_date(df):
date_parts = ["year", "weekday", "month", 'weekofyear', 'day', 'quarter']
for part in date_parts:
part_col = 'release_date' + "_" + part
df[part_col] = getattr(df['release_date'].dt, part ).astype(int)
return df
train = process_date(train)
test = process_date(test )<set_options> | df_test_predict_proba_2 = pd.concat(
[test["image_id"], pd.DataFrame(softmax(predictions, axis = 1)) ],
axis=1
).sort_values(["image_id"] ) | Cassava Leaf Disease Classification |
13,499,132 | py.init_notebook_mode(connected=True)
<count_values> | submission = df_test_predict_proba_1[["image_id"]]
submission["label"] =(
df_test_predict_proba_1.drop(["image_id"], axis=1)* 0.5
+ df_test_predict_proba_2.drop(["image_id"], axis=1)* 0.5
).to_numpy().argmax(1)
| Cassava Leaf Disease Classification |
13,499,132 | train['status'].value_counts()<count_values> | submission.to_csv("submission.csv", index=False ) | Cassava Leaf Disease Classification |
13,499,132 | <drop_column><EOS> | submission.to_csv("submission.csv", index=False ) | Cassava Leaf Disease Classification |
14,015,712 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<drop_column> | !pip install.. /input/timmwhl/timm-0.3.3-py3-none-any.whl | Cassava Leaf Disease Classification |
14,015,712 |
<categorify> | import random
import os
import sys
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torchvision
import timm
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from tqdm import tqdm
import torch.nn.functional as F
import albumentations as A
from alb... | Cassava Leaf Disease Classification |
14,015,712 | for col in ['original_language', 'collection_name', 'all_genres']:
le = LabelEncoder()
le.fit(list(train[col].fillna('')) + list(test[col].fillna('')))
train[col] = le.transform(train[col].fillna('' ).astype(str))
test[col] = le.transform(test[col].fillna('' ).astype(str))<define_variables> | warnings.filterwarnings("ignore" ) | Cassava Leaf Disease Classification |
14,015,712 | train_texts = train[['title', 'tagline', 'overview', 'original_title']]
test_texts = test[['title', 'tagline', 'overview', 'original_title']]<feature_engineering> | DATA_PATH = '.. /input/cassava-leaf-disease-classification/'
bs = 16
sz = 448
TIMM_MODEL = 'resnet50' | Cassava Leaf Disease Classification |
14,015,712 | for col in ['title', 'tagline', 'overview', 'original_title']:
train['len_' + col] = train[col].fillna('' ).apply(lambda x: len(str(x)))
train['words_' + col] = train[col].fillna('' ).apply(lambda x: len(str(x.split(' '))))
train = train.drop(col, axis=1)
test['len_' + col] = test[col].fillna('' ).apply(lambda x: len... | 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 = True
SEED = 1234
seed_everything(SEED)
device = torch.device("cuda:0" if torch.... | Cassava Leaf Disease Classification |
14,015,712 | train.loc[train['id'] == 16,'revenue'] = 192864
train.loc[train['id'] == 90,'budget'] = 30000000
train.loc[train['id'] == 118,'budget'] = 60000000
train.loc[train['id'] == 149,'budget'] = 18000000
train.loc[train['id'] == 313,'revenue'] = 12000000
train.loc[train['id'] == 451,'revenue'] = 12000000
train.loc[train['id']... | class CassavaDataset(Dataset):
def __init__(self, dataframe, root_dir, transforms=None):
super().__init__()
self.dataframe = dataframe
self.root_dir = root_dir
self.transforms = transforms
def __len__(self):
return len(self.dataframe)
def get_img_bgr_to_rgb(self, path):
im_bgr = cv2.imread(path)
im_rgb = im_bgr[:, :,... | Cassava Leaf Disease Classification |
14,015,712 | X = train.drop(['id', 'revenue','production_companies'], axis=1)
y = np.log1p(train['revenue'])
X_test = test.drop(['id','production_companies'], axis=1 )<split> | def test_transforms() :
return Compose([
A.Resize(sz, sz),
A.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0, p=1.0),
ToTensorV2(p=1.0),
], p=1.) | Cassava Leaf Disease Classification |
14,015,712 | X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.1)
<create_dataframe> | class CassavaNet(nn.Module):
def __init__(self):
super().__init__()
backbone = timm.create_model(TIMM_MODEL, pretrained=False)
n_features = backbone.fc.in_features
self.backbone = nn.Sequential(*backbone.children())[:-2]
self.classifier = nn.Linear(n_features, 5)
self.pool = nn.AdaptiveAvgPool2d(( 1, 1))
def forward_... | Cassava Leaf Disease Classification |
14,015,712 | lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_valid, y_valid, reference=lgb_train)
params = {'num_leaves': 30,
'min_data_in_leaf': 20,
'objective': 'regression',
'max_depth': 5,
'learning_rate': 0.01,
"boosting": "gbdt",
"feature_fraction": 0.9,
"bagging_freq": 1,
"bagging_fraction": 0.9,
"baggin... | model = CassavaNet().to(device ) | Cassava Leaf Disease Classification |
14,015,712 | eli5.show_weights(model1, feature_filter=lambda x: x != '<BIAS>' )<choose_model_class> | def predict(model, ckpts, dataloader):
predict_list=[]
with torch.no_grad() :
for _, data in enumerate(dataloader):
avg_preds = []
for ckpt in ckpts:
model.load_state_dict(ckpt['state_dict'])
model.eval()
images, label = data.values()
images = images.to(device)
outputs, _ = model(images)
preds = F.softmax(outputs ).... | Cassava Leaf Disease Classification |
14,015,712 | n_fold = 5
folds = KFold(n_splits=n_fold, shuffle=True, random_state=42 )<split> | test_df = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
test_dir = '.. /input/cassava-leaf-disease-classification/test_images/'
test_ds = CassavaDataset(dataframe=test_df,
root_dir=test_dir,
transforms=test_transforms())
test_dl = DataLoader(test_ds, batch_size=bs,
shuffle=False, ... | Cassava Leaf Disease Classification |
14,015,712 | def train_model(X, X_test, y, params=None, folds=folds, model_type='lgb', plot_feature_importance=False, model=None):
oof = np.zeros(X.shape[0])
prediction = np.zeros(X_test.shape[0])
scores = []
feature_importance = pd.DataFrame()
for fold_n,(train_index, valid_index)in enumerate(folds.split(X)) :
print('Fold', fold... | ckpts=[]
trained_model_path = ".. /input/cassavalblsmoothingresnet50"
for path in os.listdir(trained_model_path):
ckpts.append(torch.load(os.path.join(trained_model_path, path)))
| Cassava Leaf Disease Classification |
14,015,712 | params = {'num_leaves': 30,
'min_data_in_leaf': 10,
'objective': 'regression',
'max_depth': 5,
'learning_rate': 0.01,
"boosting": "gbdt",
"feature_fraction": 0.9,
"bagging_freq": 1,
"bagging_fraction": 0.9,
"bagging_seed": 11,
"metric": 'rmse',
"lambda_l1": 0.2,
"verbosity": -1}
oof_lgb, prediction_lgb, _ = train_model... | test_predict_list=predict(model, ckpts, test_dl ) | Cassava Leaf Disease Classification |
14,015,712 | <feature_engineering><EOS> | test_df['label'] = test_predict_list
test_df[['image_id', 'label']].to_csv('submission.csv', index=False)
test_df.head() | Cassava Leaf Disease Classification |
14,102,111 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering> | import os
import tensorflow as tf
from tensorflow import keras
from keras.preprocessing.image import load_img,img_to_array,smart_resize
import matplotlib.pyplot as plt
import cv2
import pandas as pd
import json
import numpy as np
| Cassava Leaf Disease Classification |
14,102,111 | X = new_features(X)
X_test = new_features(X_test )<train_model> | model1=keras.models.load_model('.. /input/notebook841c84bbfb/IncepResNetV2.h5')
model2=keras.models.load_model('.. /input/notebook841c84bbfb/EfficientNetB_V2.h5' ) | Cassava Leaf Disease Classification |
14,102,111 | oof_lgb, prediction_lgb, _ = train_model(X, X_test, y, params=params, model_type='lgb', plot_feature_importance=True )<train_model> | train_dir='.. /input/cassava-leaf-disease-classification/train_images'
test_dir='.. /input/cassava-leaf-disease-classification/test_images'
train=pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
sample_sub=pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
sample_... | Cassava Leaf Disease Classification |
14,102,111 | xgb_params = {'eta': 0.01,
'objective': 'reg:linear',
'max_depth': 7,
'subsample': 0.8,
'colsample_bytree': 0.8,
'eval_metric': 'rmse',
'seed': 11,
'silent': True}
oof_xgb, prediction_xgb= train_model(X, X_test, y, params=xgb_params, model_type='xgb', plot_feature_importance=True )<define_variables> | def sample_df(sample_size=100):
df= train.sample(sample_size)
df=df.reset_index()
return df
dfs=sample_df(sample_size=50)
preds=[]
y_true=dfs['label']
for im_id in dfs.image_id:
img=load_img(train_dir + '/' + im_id)
img=img_to_array(img)
img=smart_resize(img,(512,512))
img=np.expand_dims(img,axis=0)
img=img/255
pr... | Cassava Leaf Disease Classification |
14,102,111 | cat_params = {'learning_rate': 0.002,
'depth': 5,
'l2_leaf_reg': 10,
'colsample_bylevel': 0.8,
'bagging_temperature': 0.2,
'od_type': 'Iter',
'od_wait': 100,
'random_seed': 11,
'allow_writing_files': False}
oof_cat, prediction_cat = train_model(X, X_test, y, params=cat_params, model_type='cat' )<create_dataframe> | sample_test=pd.DataFrame({'Prediction':preds, 'Actual':y_true})
sample_test.head(30 ) | Cassava Leaf Disease Classification |
14,102,111 | <train_on_grid><EOS> | predictions=[]
for img_id in sample_sub.image_id:
img=load_img(test_dir + '/' + img_id)
img=img_to_array(img)
img=smart_resize(img,(512,512))
img=np.expand_dims(img,axis=0)
img=img/255
lab=np.argmax(( model1.predict(img)* 0.5)+(model2.predict(img)*0.5))
predictions.append(lab)
submission=pd.DataFrame({'image_id':sa... | Cassava Leaf Disease Classification |
13,958,924 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<save_to_csv> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
| Cassava Leaf Disease Classification |
13,958,924 | sub = pd.read_csv('.. /input/sample_submission.csv')
sub['revenue'] = np.expm1(prediction_lgb)
sub.to_csv("lgb.csv", index=False)
sub['revenue'] = np.expm1(( prediction_lgb + prediction_xgb)/ 2)
sub.to_csv("blend.csv", index=False)
sub['revenue'] = np.expm1(( prediction_lgb + prediction_xgb + prediction_cat)/ 3)
... | from glob import glob
from sklearn.model_selection import GroupKFold, StratifiedKFold
import cv2
from skimage import io
import torch
from torch import nn
import os
from datetime import datetime
import time
import random
import cv2
import torchvision
from torchvision import transforms
import pandas as pd
import numpy as... | Cassava Leaf Disease Classification |
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