kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
13,880,662 | train=pd.read_csv('.. /input/train.csv',index_col=0)
test=pd.read_csv('.. /input/test.csv',index_col=0 )<feature_engineering> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv' ) | Cassava Leaf Disease Classification |
13,880,662 | test['revenue']=-99<drop_column> | def vote_in_ensemble(v1, v2, v3):
if v1 == v2:
return v1
if v2 == v3:
return v2
if v1 == v3:
return v3
return v1 | Cassava Leaf Disease Classification |
13,880,662 | train=train.drop('belongs_to_collection',axis=1)
test=test.drop('belongs_to_collection',axis=1 )<feature_engineering> | def predict_for_pretrained(model):
ds_test = generator(JPEG_PATH,np.sort(submission.image_id.values))
preds = np.argmax(model.predict(ds_test, verbose=True), axis=-1)
return preds
dense_preds = predict_for_pretrained(dense201)
inception_preds = predict_for_pretrained(inception)
efficient_net_preds = predict_for_pret... | Cassava Leaf Disease Classification |
13,880,662 | <feature_engineering><EOS> | submission["label"] = result
submission.to_csv("submission.csv", index=False ) | Cassava Leaf Disease Classification |
13,628,995 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<concatenate> | !pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git | Cassava Leaf Disease Classification |
13,628,995 | new_data=pd.concat([train,test],axis=0)
new_data.head()<count_missing_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 = 21
seed_everything(seed)
warnings.filterwarnings('ignore' ) | Cassava Leaf Disease Classification |
13,628,995 | new_data.isnull().sum()<filter> | BATCH_SIZE = 32 * REPLICAS
HEIGHT = 512
WIDTH = 512
CHANNELS = 3
N_CLASSES = 5
TTA_STEPS = 8 | Cassava Leaf Disease Classification |
13,628,995 | new_data[new_data['release_date'].isnull() ]<feature_engineering> | 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 |
13,628,995 | new_data['release_date']=new_data['release_date'].fillna('3/20/01' )<data_type_conversions> | def get_name(file_path):
parts = tf.strings.split(file_path, os.path.sep)
name = parts[-1]
return name
def decode_image(image_data):
image = tf.image.decode_jpeg(image_data, channels=3)
image = tf.cast(image, tf.float32)/ 255.0
return image
def center_crop(image):
image = tf.reshape(image, [600, 800, CHANNELS])
h, w... | Cassava Leaf Disease Classification |
13,628,995 | new_data['release_year']=pd.to_datetime(new_data['release_date'] ).dt.year
new_data['release_month']=pd.to_datetime(new_data['release_date'] ).dt.month
new_data['release_day']=pd.to_datetime(new_data['release_date'] ).dt.day<feature_engineering> | 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 |
13,628,995 | new_data['release_year'].loc[new_data['release_year']>=2018]-=100<drop_column> | 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 |
13,628,995 | new_data=new_data.drop('release_date',axis=1 )<define_variables> | 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 |
13,628,995 | weiji=[1929,1930,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,1945,1950,1951,1952,1953,1961,1962,1963,1964,1965,1966,1967,1968,1969,1971,1973,1974,1975,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,
1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2007,2008,2009,2010,2011]<feature_eng... | submission = pd.DataFrame({'image_id': image_names, 'label': test_preds})
submission.to_csv('submission.csv', index=False)
display(submission.head() ) | Cassava Leaf Disease Classification |
13,906,147 | for i in weiji:
new_data['is_'+str(i)]=new_data['release_year'].apply(lambda x:1 if x==i else 0 )<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 |
13,906,147 | new_data['homepage_fact']=new_data['homepage'].apply(lambda x: 0 if x is np.nan else 1 )<feature_engineering> | CFG = {
'vit_img_size': 384,
'tta': 3,
'valid_bs': 16,
'device': 'cuda' if torch.cuda.is_available() else 'cpu',
'vit_models': ['model_5.pt', 'model_6.pt', 'model_7.pt', 'model_8.pt']
} | Cassava Leaf Disease Classification |
13,906,147 | new_data['homepage_end']=new_data[new_data['homepage'].notna() ]['homepage'].str.findall(r'\.( [a-z]+ )(?:\/|$)' ).apply(lambda x:x[0])
new_data['homepage_end'].head()<data_type_conversions> | class DiseaseDatasetInference(torch.utils.data.Dataset):
def __init__(self, df, transform=None, opt_label=True):
self.df = df.reset_index(drop=True ).copy()
self.transform = transform
self.opt_label = opt_label
if self.opt_label:
self.data = [(row['image_id'], row['label'])for _, row in self.df.iterrows() ]
else:
self.... | Cassava Leaf Disease Classification |
13,906,147 | new_data['homepage_end']=new_data['homepage_end'].fillna('unknow' )<categorify> | def get_inference_transforms(img_size = 512):
return Compose([
CenterCrop(img_size, img_size, p=0.5),
Resize(img_size, img_size),
Transpose(p=0.5),
RandomRotate90(p=0.25),
ShiftScaleRotate(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.... | Cassava Leaf Disease Classification |
13,906,147 | page=pd.get_dummies(new_data['homepage_end'])
page.head()<drop_column> | df = pd.read_csv('/kaggle/input/cassava-leaf-disease-classification/sample_submission.csv')
PATH = '/kaggle/input/cassava-leaf-disease-classification/test_images/' | Cassava Leaf Disease Classification |
13,906,147 | new_data=new_data.drop('homepage',axis=1 )<drop_column> | test_csv = df.copy()
test_csv['image_id'] = PATH + test_csv['image_id'] | Cassava Leaf Disease Classification |
13,906,147 | new_data=new_data.drop('poster_path',axis=1 )<feature_engineering> | def inference(model, data_loader, device):
preds = []
model.eval()
test_tqdm = tq.tqdm(data_loader, total=len(data_loader), desc="Testing", position=0, leave=True)
for images in test_tqdm:
images = images.to(device)
preds.extend(model(images ).detach().cpu().numpy())
return preds | Cassava Leaf Disease Classification |
13,906,147 | new_data['len_overview']=new_data['overview'].fillna('NAN' ).apply(lambda x:len(x))
new_data['len_overview'][new_data['len_overview']==0]=1
new_data['len_overview_budget']=new_data['budget']/(new_data['len_overview']+1 )<sort_values> | class CassavaImageClassifier(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.head.in_features
self.model.head = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.model(x)
re... | Cassava Leaf Disease Classification |
13,906,147 | len_rew_sort=new_data['len_overview'].sort_values(ascending=True)
len_rew_sort.head()<define_variables> | vit_test_ds = DiseaseDatasetInference(test_csv, transform=get_inference_transforms(img_size=CFG['vit_img_size']), opt_label=False)
vit_test_loader = torch.utils.data.DataLoader(vit_test_ds, batch_size=CFG['valid_bs'], shuffle=False, pin_memory=False ) | Cassava Leaf Disease Classification |
13,906,147 | length=len(new_data['len_overview'])
m=0.1
n=0.1
arr_len_ove=[]
for i in range(1,11):
arr_len_ove.append(round(length*m))
m+=n
arr_len_ove<filter> | vit_preds = []
for vit_model_name in CFG['vit_models']:
print("Model: ", vit_model_name)
vit_model = torch.load('/kaggle/input/vit-cassava/'+vit_model_name, map_location=torch.device(CFG['device']))
with torch.no_grad() :
for i in range(CFG['tta']):
vit_preds += [inference(vit_model, vit_test_loader, CFG['device'])]
v... | Cassava Leaf Disease Classification |
13,906,147 | for i in range(10):
qu=qu_arr[i]
if i==0:
new_data['len_overview'].loc[(new_data['len_overview']<len_rew_sort.iloc[arr_len_ove[i]-1])]=qu
else:
new_data['len_overview'].loc[(new_data['len_overview']<len_rew_sort.iloc[arr_len_ove[i]-1])&(new_data['len_overview'] >qu_arr[i-1])]=qu
print(i,qu )<groupby> | vit_outcomes = pd.concat([df['image_id'], pd.DataFrame(vit_preds)], axis=1 ).sort_values(['image_id'] ) | Cassava Leaf Disease Classification |
13,906,147 | len_ove_agg=new_data.groupby('len_overview' ).revenue.aggregate(['min','max','std'] )<feature_engineering> | final_preds = vit_outcomes.drop('image_id', axis=1 ).to_numpy().argmax(1 ) | Cassava Leaf Disease Classification |
13,906,147 | new_data['geres_name']=new_data['genres'].str.findall(r''name'\s?:\s?'(\w+)'')
new_data['geres_name'].head()<drop_column> | submit = pd.DataFrame({'image_id': df['image_id'].values, 'label': final_preds})
submit.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,786,497 | new_data=new_data.drop('genres',axis=1 )<find_best_params> | test_df = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
print(test_df)
AUTOTUNE = tf.data.experimental.AUTOTUNE
GCS_PATH = '.. /input/cassava-leaf-disease-classification'
BATCH_SIZE = 16*8
IMAGE_SIZE = [512,512]
CLASSES = ["1", "2", "3", "4", "5"]
def dataset_sizes(filenames):
n =... | Cassava Leaf Disease Classification |
13,786,497 | country=new_data['production_countries'].str.findall(r'[A-Z]{2,5}')
<feature_engineering> | def to_float32(image, label):
return tf.cast(image, tf.float32), label
def decode_img(img):
img = tf.io.decode_jpeg(img, channels = 3)
img = tf.cast(img, tf.float32)/255.0
img = tf.reshape(img, [*IMAGE_SIZE, 3])
return img
def read_tfrecord(example, labeled):
if labeled:
TFREC_FORMAT = {
"image": tf.io.FixedLenFeatur... | Cassava Leaf Disease Classification |
13,786,497 | new_data['production_countries']=country
<feature_engineering> | test_ds = get_test_data(ordered=True)
test_ds = test_ds.map(to_float32)
testing_dataset = get_test_data()
testing_dataset = testing_dataset.unbatch().batch(1)
print('Computing predictions...')
test_images_ds = testing_dataset
test_images_ds = test_ds.map(lambda image, idnum: image)
prob1 = model_15.predict(test_im... | Cassava Leaf Disease Classification |
13,786,497 | <drop_column><EOS> | print('Generating submission.csv file...')
test_ids_ds = test_ds.map(lambda image, idnum: idnum ).unbatch()
test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES)) ).numpy().astype('U')
np.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='image_id,label', ... | Cassava Leaf Disease Classification |
13,431,242 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<concatenate> | package_path = '.. /input/pytorchimagemodels'
| Cassava Leaf Disease Classification |
13,431,242 | new_data=pd.concat([new_data,page],axis=1 )<feature_engineering> | import os
import random
import cv2
import timm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import albumentations as A
import albumentations.pytorch as Apy
import torch
import torchvision
from torch import nn
from torchvision import transforms
from torch.utils.data import Dataset,DataLoader
fr... | Cassava Leaf Disease Classification |
13,431,242 | new_data['production_companies']=new_data['production_companies'].str.findall(r''name'?:\s?'([A-Za-z]+)')
new_data.fillna('Unknow')
print('接下来就是地图可视化了' )<drop_column> | config = {
'fold_num': 1,
'seed': 719,
'model_arch': 'resnext50d_32x4d',
'img_size': 512,
'valid_bs': 256,
'num_workers': 4,
'accum_iter': 1,
'verbose_step': 1,
'device': 'cuda:0' if torch.cuda.is_available() else "cpu",
} | Cassava Leaf Disease Classification |
13,431,242 | new_data=new_data.drop('imdb_id',axis=1 )<feature_engineering> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
13,431,242 | new_data['spoken_languages']=new_data['spoken_languages'].str.findall(r''([a-z]{2})'' )<data_type_conversions> | 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)
return im_bgr[:, :, ::-1] | Cassava Leaf Disease Classification |
13,431,242 | new_data['production_companies']=new_data['production_companies'].fillna('unknow' )<data_type_conversions> | 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 |
13,431,242 | new_data['spoken_languages']=new_data['spoken_languages'].fillna('unknow' )<feature_engineering> | 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.fc.in_features
self.model.fc = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.model(x)
return x | Cassava Leaf Disease Classification |
13,431,242 | new_data['Keywords']=new_data['Keywords'].str.findall(r''?:\s?'([a-z]+\s?[a-z]+)'' ).fillna('unkonw' )<count_unique_values> | 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() , output_label=False)
tst_loader = torch.utils.data.DataLoader(
tes... | Cassava Leaf Disease Classification |
13,431,242 | <feature_engineering><EOS> | test['label'] = np.argmax(tst_preds, axis=1)
test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,315,962 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<drop_column> | 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 |
13,315,962 | new_data=new_data.drop('Keywords',axis=1 )<data_type_conversions> | class CFG:
debug=False
num_workers=20
model_name='resnext50_32x4d'
size=512
batch_size=32
seed=42
target_size=5
target_col='label'
n_fold=5
trn_fold=[0, 1, 2, 3, 4]
inference=True | Cassava Leaf Disease Classification |
13,315,962 | new_data['cast']=new_data['cast'].fillna('unknow' )<feature_engineering> | test = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
test.head() | Cassava Leaf Disease Classification |
13,315,962 | for i in d:
m=i[0][0]
new_data['cast_name_'+m]=new_data['cast'].apply(lambda x:1 if m in x else 0 )<count_values> | 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 |
13,315,962 | list_cast_gender=list(new_data['cast'].str.findall(r''gender'\s?:\s?(\d+)\s?'))
Counter([i for j in list_cast_gender for i in j] ).most_common()<feature_engineering> | def get_transforms(*, data):
if data == 'valid':
return A.Compose([
A.Resize(CFG.size, CFG.size),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2() ,
] ) | Cassava Leaf Disease Classification |
13,315,962 | new_data['cast_gender_0']=new_data['cast'].str.findall(r''gender'\s?:\s?(\d+)\s?' ).apply(lambda x: x.count('0'))
new_data['cast_gender_1']=new_data['cast'].str.findall(r''gender'\s?:\s?(\d+)\s?' ).apply(lambda x: x.count('1'))
new_data['cast_gender_2']=new_data['cast'].str.findall(r''gender'\s?:\s?(\d+)\s?' ).apply(la... | 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 |
13,315,962 | for g in top_cast_char:
m=g[0]
new_data['cast_char_'+m]=new_data['cast'].apply(lambda x:1 if m in x else 0 )<drop_column> | 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_dic... | Cassava Leaf Disease Classification |
13,315,962 | <feature_engineering><EOS> | 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.n... | Cassava Leaf Disease Classification |
13,376,525 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering> | import numpy as np
import pandas as pd
from tensorflow import keras | Cassava Leaf Disease Classification |
13,376,525 | new_data['overview']=new_data['overview'].fillna('' )<import_modules> | model = keras.models.Sequential()
model.add(keras.applications.Xception(input_shape=(300, 300, 3), weights=None, include_top=False))
model.add(keras.layers.GlobalAveragePooling2D())
model.add(keras.layers.Dense(5, activation='softmax'))
model.summary() | Cassava Leaf Disease Classification |
13,376,525 | from sklearn.linear_model import LinearRegression
import eli5
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
<train_on_grid> | model.load_weights(".. /input/cassava-xception-try/best_weights_xception.h5" ) | Cassava Leaf Disease Classification |
13,376,525 | <load_pretrained><EOS> | preds = []
ss = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
for image in ss.image_id:
img = keras.preprocessing.image.load_img('.. /input/cassava-leaf-disease-classification/test_images/' + image)
img = keras.preprocessing.image.img_to_array(img)
img = keras.preprocessing.image... | Cassava Leaf Disease Classification |
13,293,951 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering> | OUTPUT_DIR = './'
MODEL_DIR = '.. /input/cassava-xception-4fold/'
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 |
13,293,951 | new_data['crew_0']=new_data['crew'].fillna('' ).str.replace(',','' ).str.replace('}','' ).str.findall(''gender\S?'\s?:\s?\S?(\d+)\s?\S?' ).apply(lambda x:x.count('0'))
new_data['crew_1']=new_data['crew'].fillna('' ).str.replace(',','' ).str.replace('}','' ).str.findall(''gender\S?'\s?:\s?\S?(\d+)\s?\S?' ).apply(lambda ... | class CFG:
debug=False
num_workers=0
model_name='xception'
size=386
batch_size=32
seed=2020
target_size=5
target_col='label'
n_fold=4
trn_fold=[0, 1, 2, 3]
train=False
inference=True | Cassava Leaf Disease Classification |
13,293,951 | for i in d:
new_data['crew_depart_is_'+i[0]]=new_data['crew'].fillna('' ).apply(lambda x:1 if i[0] in x else 0)
for i in dd:
new_data['crew_job_is_'+i[0]]=new_data['crew'].fillna('' ).apply(lambda x:1 if i[0] in x else 0 )<feature_engineering> | test = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
test.head() | Cassava Leaf Disease Classification |
13,293,951 | new_data['crew']=new_data['crew'].str.replace(',','' ).str.replace('}','' ).str.replace(r''gender'\s?\S?:\s?\S?\s+'id'\s?:\s?\d+','' ).str.findall(r''department'\s?\S?:\s+\S?(\S+)\S?\s?\S?\s+'job\S?'\s?:\s?'(\D+)\S\s?\S?'name\S?'\s?:\s?'(\D+)'\s?\S?'profile_path\S?':\s+'?(\S+)\S?\s?\s?\S?' )<feature_engineering> | 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 |
13,293,951 | new_data['len_crew']=new_data['crew'].fillna('1' ).apply(lambda x:len(x))<filter> | def get_transforms(*, data):
if data == 'train':
return Compose([
RandomResizedCrop(CFG.size, CFG.size),
Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
ShiftScaleRotate(p=0.5),
Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2() ,
])
elif data == 'valid':
return Compose([... | Cassava Leaf Disease Classification |
13,293,951 | new_train=new_data.loc[np.array(train.index)]<feature_engineering> | class CustomResNext(nn.Module):
def __init__(self, model_name='xception', 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)
retur... | Cassava Leaf Disease Classification |
13,293,951 | new_train['production_countries']=new_train['production_countries'].fillna('' )<feature_engineering> | def inference(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 = model(imag... | Cassava Leaf Disease Classification |
13,293,951 | new_train['production_countries']=new_train['production_countries'].apply(lambda x:'_'.join(x))<count_values> | model = CustomResNext(CFG.model_name, pretrained=False)
states = [torch.load(f'.. /input/cassava-xception-4fold/xception_fold{fold}_best.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=Fals... | Cassava Leaf Disease Classification |
13,322,937 | count=new_train['production_countries'].value_counts()
count=count[count>5]<groupby> | print("Tensorflow version " + tf.__version__)
| Cassava Leaf Disease Classification |
13,322,937 | bud=new_train.groupby('production_countries' ).budget.mean()<sort_values> | AUTOTUNE = tf.data.experimental.AUTOTUNE
REPLICAS = strategy.num_replicas_in_sync
FILENAMES = tf.io.gfile.glob(".. /input/cassava-leaf-disease-classification" + '/test_tfrecords/ld_test*.tfrec')
BATCH_SIZE = 128 * strategy.num_replicas_in_sync
IMAGE_SIZE = [512, 512]
classes = ['0', '1', '2', '3', '4']
os.environ['PYT... | Cassava Leaf Disease Classification |
13,322,937 | bud=bud.sort_values(ascending=False)[:10]<groupby> | def decode_image(image):
image = tf.image.decode_jpeg(image, channels=3)
image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [*IMAGE_SIZE, 3])
return image
def read_labeled_tfrecord(example):
LABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"target": tf.io.FixedLenFeature([], tf... | Cassava Leaf Disease Classification |
13,322,937 | rev=new_train.groupby('production_countries' ).revenue.mean()<sort_values> | VALIDATE = False | Cassava Leaf Disease Classification |
13,322,937 | rev=rev.sort_values(ascending=False)[:10]<filter> | FOLDS=5
SEED=34
if VALIDATE:
GCS_PATH = KaggleDatasets().get_gcs_path('cassava-leaf-disease-tfrecords-512x512')
TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/*.tfrec')
AUG_TYPE = 'CUTMIXUP' | Cassava Leaf Disease Classification |
13,322,937 | new_train['production_countries'].loc[new_train['production_countries']=='ET']='Ethiopia'
along_co=new_train[new_train['production_countries'].apply(lambda x:1 if len(x)==2 else 0)==1]
along_co.head()<define_variables> | if VALIDATE:
histories = []
oof_pred = []; oof_labels = []
kfold = KFold(FOLDS, shuffle = True, random_state = SEED)
for f,(train_index, val_index)in enumerate(kfold.split(TRAINING_FILENAMES)) :
print('
print('Getting datasets...'); print('')
val_ds = get_val_dataset(list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_... | Cassava Leaf Disease Classification |
13,322,937 | count_rev=along_co[['production_countries','revenue']]<prepare_output> | if VALIDATE:
y_true = np.concatenate(oof_labels)
y_preds = np.concatenate(oof_pred)
print(classification_report(np.argmax(y_true, axis=1)if AUG_TYPE is 'CUTMIXUP' else y_true, y_preds))
print(f"OOF accuracy score: {accuracy_score(np.argmax(y_true, axis=1)if AUG_TYPE is 'CUTMIXUP' else y_true, y_preds)}" ) | Cassava Leaf Disease Classification |
13,322,937 | dd=count_rev['production_countries'].unique()
mn=pd.DataFrame(dd,columns=['address'])
mn.head()<load_pretrained> | JPEG_PATH = ".. /input/cassava-leaf-disease-classification/test_images"
JPEG_PATH_TR = ".. /input/cassava-leaf-disease-classification/train_images"
def load_image(jpeg_path, image_id):
img = cv2.imread(os.path.join(jpeg_path, image_id)) /255.0
img = cv2.resize(img,(512, 512)) [:, :, ::-1]
return img
def generator(filep... | Cassava Leaf Disease Classification |
13,322,937 | import geopandas as ge
<load_from_csv> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
tr = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv' ) | Cassava Leaf Disease Classification |
13,322,937 | world = ge.read_file(ge.datasets.get_path('naturalearth_lowres'))<import_modules> | preds_all = []
preds_model = []
for fold in range(FOLDS):
print(f"
ds_test = generator(JPEG_PATH,submission.image_id.values)
K.clear_session()
print('Loading and inferring...')
model = tf.keras.models.load_model(f'.. /input/cassava-tensorflow-starter-training/EFFNET_{fold}_34_CUTMIXUP_512_full.h5')
preds = model.pre... | Cassava Leaf Disease Classification |
13,322,937 | from mpl_toolkits.axes_grid1 import make_axes_locatable
<count_values> | submission["label"] = preds_all.mean(0 ).argmax(1)
submission.to_csv("submission.csv", index=False ) | Cassava Leaf Disease Classification |
13,280,913 | dd=count_rev['production_countries'].value_counts()
mn['values']=dd.values
mn.head()<categorify> | package_path = '.. /input/vision-transformer-pytorch/VisionTransformer-Pytorch'
sys.path.append(package_path ) | Cassava Leaf Disease Classification |
13,280,913 | mn['address'][0]='United States'
mn['address'][2]='Korea'
mn['address'][1]='India'
mn['address'][3]='Serbia'
mn['address'][4]='United Kingdom'
mn['address'][5]='France'
mn['address'][6]='New Zealand'
mn['address'][7]='Italy'
mn['address'][8]='Belgium'
mn['address'][9]='Czech Rep.'
mn['address'][11]='Russia'
mn['address... | import os
import pandas as pd
import albumentations as albu
import matplotlib.pyplot as plt
import json
import seaborn as sns
import cv2
import albumentations as albu
import numpy as np | Cassava Leaf Disease Classification |
13,280,913 | mn=mn.drop(10)
mn=mn.drop(34 )<feature_engineering> | BASE_DIR=".. /input/cassava-leaf-disease-classification/"
TRAIN_IMAGES_DIR=os.path.join(BASE_DIR,'train_images')
train_df=pd.read_csv(os.path.join(BASE_DIR,'train.csv')) | Cassava Leaf Disease Classification |
13,280,913 | mn['geometry']='unknow'<feature_engineering> | print("Count of training images {0}".format(len(os.listdir(TRAIN_IMAGES_DIR)))) | Cassava Leaf Disease Classification |
13,280,913 | for i in range(39):
if i==10 or i==34:
continue;
d=world[world['name']==mn['address'][i]]['geometry'].values[0]
mn['geometry'][i]=d<feature_engineering> | with open(f'{BASE_DIR}/label_num_to_disease_map.json', 'r')as f:
name_mapping = json.load(f)
name_mapping = {int(k): v for k, v in name_mapping.items() }
train_df["class_id"]=train_df["label"].map(name_mapping ) | Cassava Leaf Disease Classification |
13,280,913 | TS = train.loc[:,["original_title","release_date","budget","runtime","revenue"]]
TS.dropna()
TS.release_date = pd.to_datetime(TS.release_date)
TS.loc[:,"Year"] = TS["release_date"].dt.year
TS.loc[:,"Month"] = TS["release_date"].dt.month
TS = TS[TS.Year<2018]<sort_values> | import torch
import torch.nn as nn
import torchvision.models as models
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torch.optim.lr_scheduler import ReduceLROnPlateau
from sklearn.metrics import accuracy_score
from sklearn.model_selection import StratifiedKFold, GroupKFold, KFold, tr... | Cassava Leaf Disease Classification |
13,280,913 | top3 = train.sort_values(by='popularity',ascending=False)[:10]
id3=top3[['title','poster_path','revenue']]<count_values> | class CassavaDataset(Dataset):
def __init__(self,df:pd.DataFrame,imfolder:str,train:bool = True, transforms=None):
self.df=df
self.imfolder=imfolder
self.train=train
self.transforms=transforms
def __getitem__(self,index):
im_path=os.path.join(self.imfolder,self.df.iloc[index]['image_id'])
x=cv2.imread(im_path,cv2.IMRE... | Cassava Leaf Disease Classification |
13,280,913 | old_title=new_data['title'].value_counts()<prepare_x_and_y> | train, valid = train_test_split(
train_df,
test_size=0.1,
random_state=42,
stratify=train_df.label.values
)
train = train.reset_index(drop=True)
valid = valid.reset_index(drop=True)
train_targets = train.label.values
valid_targets = valid.label.values | Cassava Leaf Disease Classification |
13,280,913 | a=old_title[old_title.values==3].index
b=old_title[old_title==2].index<feature_engineering> | train_dataset=CassavaDataset(
df=train,
imfolder=TRAIN_IMAGES_DIR,
train=True,
transforms=train_augs
)
valid_dataset=CassavaDataset(
df=valid,
imfolder=TRAIN_IMAGES_DIR,
train=True,
transforms=valid_augs
) | Cassava Leaf Disease Classification |
13,280,913 | new_data['fan_pai_2']=new_data['title'].apply(lambda x:1 if x in a else 0)
new_data['pan_pai_3']=new_data['title'].apply(lambda x:1 if x in b else 0 )<filter> | train_loader = DataLoader(
train_dataset,
batch_size=16,
num_workers=4,
shuffle=True,
)
valid_loader = DataLoader(
valid_dataset,
batch_size=16,
num_workers=4,
shuffle=False,
)
| Cassava Leaf Disease Classification |
13,280,913 | new_data.loc[new_data['title'].fillna('un' ).str.contains('Planet of the Apes')]<sort_values> | def train_model(datasets, dataloaders, model, criterion, optimizer, scheduler, num_epochs, device):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs-1))
print('-' * 10)
for phase in ['train', 'valid']:
if... | Cassava Leaf Disease Classification |
13,280,913 | top10 = train.sort_values(by='revenue',ascending=False)[:10]
id10=top10[['title','poster_path','revenue']]<count_values> | datasets={'train':train_dataset,'valid':valid_dataset}
dataloaders={'train':train_loader,'valid':valid_loader}
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = VisionTransformer.from_name('ViT-B_16', num_classes=5)
optimizer = torch.optim.AdamW(model.parameters() , lr=1e-4, weight_decay=0... | Cassava Leaf Disease Classification |
13,280,913 | title_count=train['title'].value_counts()
len(title_count[title_count.values!=1] )<filter> | model.load_state_dict(torch.load('.. /input/vit-model-1/ViT-B_16.pt')) | Cassava Leaf Disease Classification |
13,280,913 | train[train['title'].str.contains('Furious')]['title']
<filter> | test_df = pd.read_csv(".. /input/cassava-leaf-disease-classification/sample_submission.csv")
image_path = ".. /input/cassava-leaf-disease-classification/test_images/"
test_targets = test_df.label.values
test_aug = albu.Compose([
albu.CenterCrop(512, 512, p=1.) ,
albu.Resize(384, 384),
albu.Normalize(
mean=[0.485, 0.4... | Cassava Leaf Disease Classification |
13,280,913 | test[test['title'].fillna('Unknow' ).str.contains('Furious')]['title']<drop_column> | predictions=[]
for imgs in test_loader:
imgs = imgs.to(device)
with torch.no_grad() :
model=model.to(device)
outputs = model(imgs)
_, predicted = torch.max(outputs, dim=1)
predicted=predicted.to('cpu')
predictions.append(predicted)
| Cassava Leaf Disease Classification |
13,280,913 | d.remove(d[7] )<feature_engineering> | test_df['label'] = np.concatenate(predictions)
| Cassava Leaf Disease Classification |
13,280,913 | <feature_engineering><EOS> | test_df.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,209,362 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering> | import math, os, re, warnings, random, glob
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.keras.layers as L
import tensorflow.keras.backend as K
from tensorflow.keras import Sequential
from kaggle_datasets import KaggleDatasets
| Cassava Leaf Disease Classification |
13,209,362 | new_data['spoken_languages_count']=new_data['spoken_languages'].apply(lambda x:len(x))<groupby> | 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 |
13,209,362 | new_data.loc[train.index].groupby('spoken_languages_count' ).revenue.median()<categorify> | BATCH_SIZE = 16 * REPLICAS
HEIGHT = 512
WIDTH = 512
CHANNELS = 3
N_CLASSES = 5
TTA_STEPS = 3
IMAGE_SIZE = [512, 512]
SEED =555
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
AUG_BATCH = BATCH_SIZE | Cassava Leaf Disease Classification |
13,209,362 | status_du=pd.get_dummies(new_data['status'] )<concatenate> | def data_augment(image, label):
image = tf.image.rot90(image,k=np.random.randint(4))
image = tf.image.random_flip_left_right(image , seed=SEED)
image= image = tf.image.random_flip_up_down(image, seed=SEED)
IMG_SIZE=IMAGE_SIZE[0]
image = tf.image.resize_with_crop_or_pad(image, IMG_SIZE + 6, IMG_SIZE + 6)
image = tf.i... | Cassava Leaf Disease Classification |
13,209,362 | new_data=pd.concat([new_data,status_du],axis=1)
<drop_column> | def get_name(file_path):
parts = tf.strings.split(file_path, os.path.sep)
name = parts[-1]
return name
def decode_image(image_data):
image = tf.image.decode_jpeg(image_data, channels=3)
image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [*IMAGE_SIZE, 3])
return image
def resize_image(image, label):
... | Cassava Leaf Disease Classification |
13,209,362 | new_data=new_data.drop(['status'],axis=1)
new_data.head()<drop_column> | 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/*.tfrec')
NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
print(f'GCS:... | Cassava Leaf Disease Classification |
13,209,362 | new_data=new_data.drop(['original_language'],axis=1 )<count_missing_values> | model_path_list = glob.glob('/kaggle/input/casavaleafclassificationdensenet201e20f3/*.h5')
model_path_list.sort()
print('Models to predict:')
print(*model_path_list, sep='
' ) | Cassava Leaf Disease Classification |
13,209,362 | sum(new_data['geres_name'].isna() )<data_type_conversions> | models = []
i = 0
for model_path in model_path_list:
print(model_path)
K.clear_session()
models.append(keras.models.load_model(model_path))
| Cassava Leaf Disease Classification |
13,209,362 | new_data['geres_name']=new_data['geres_name'].fillna('Unknow' )<feature_engineering> | print(" TTA_STEPS = {} ".format(TTA_STEPS))
if TTA_STEPS > 0:
for step in range(TTA_STEPS):
test_ds = get_test_dataset(ordered=True, tta=True)
print(f'TTA step {step+1}/{TTA_STEPS}')
test_images_ds = test_ds.map(lambda image, image_name: image)
probabilities = np.average([models[i].predict(test_images_ds)for i in ra... | Cassava Leaf Disease Classification |
13,209,362 | <groupby><EOS> | print('Generating submission.csv file...')
test_ids_ds = test_ds.map(lambda image, idnum: idnum ).unbatch()
test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES)) ).numpy().astype('U')
np.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='image_id,label', ... | Cassava Leaf Disease Classification |
13,020,876 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<drop_column> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
| Cassava Leaf Disease Classification |
13,020,876 | new_data=new_data.drop('homepage_end',axis=1 )<feature_engineering> | 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 |
13,020,876 | new_data['original_and_new']=new_data['original_and_new'].apply(lambda x: 1 if x else 0 )<define_variables> | CFG = {
'folder' : 'effnetb5512',
'fold_num': [0],
'seed': 719,
'model_arch': 'tf_efficientnet_b5_ns',
'img_size': 512,
'epochs': 21,
'train_bs': 16,
'valid_bs': 16,
'lr': 1e-4,
'num_workers': 4,
'accum_iter': 1,
'verbose_step': 1,
'device': 'cuda:0',
'tta': 5,
'used_epochs': [3,4,5],
'weights': [1,1,1]
}
CFG1 = {
'fol... | Cassava Leaf Disease Classification |
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