kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
13,011,840 | train_set = train.iloc[:,1:]
label = train["label"]<categorify> | def get_dataset(files, augment = False, shuffle = False, repeat = False,
labeled=True, return_image_names=False, batch_size=16, dim=512):
ds = tf.data.TFRecordDataset(files, num_parallel_reads=AUTOTUNE)
ds = ds.cache()
if repeat:
ds = ds.repeat()
if shuffle:
ds = ds.shuffle(1024*8)
opt = tf.data.Options()
opt.experim... | Cassava Leaf Disease Classification |
13,011,840 | norm_train_set = train_set / 255
norm_test_set = test / 255<split> | class DataGenerator(Sequence):
def __init__(self, path, list_IDs, labels, batch_size, img_size, img_channel):
self.path = path
self.list_IDs = list_IDs
self.labels = labels
self.batch_size = batch_size
self.img_size = img_size
self.img_channel = img_channel
self.indexes = np.arange(len(self.list_IDs))
def __len__(self)... | Cassava Leaf Disease Classification |
13,011,840 | X_train, X_validate, y_train, y_validate = train_test_split(norm_train_set, label, test_size = 0.1 )<categorify> | Cassava Leaf Disease Classification | |
13,011,840 | X_train = torch.from_numpy(X_train.values.reshape(-1,1,28,28))
X_validate = torch.from_numpy(X_validate.values.reshape(-1,1,28,28))
testing_set = torch.from_numpy(norm_test_set.values.reshape(-1,1,28,28))
y_train = torch.from_numpy(y_train.values)
y_validate = torch.from_numpy(y_validate.values )<prepare_x_and_y> | test_generator = DataGenerator('.. /input/cassava-leaf-disease-classification/'+'test_images/', test_df['image_id'], test_df['label'], 1, DIM, 3 ) | Cassava Leaf Disease Classification |
13,011,840 | training_set = torch.utils.data.TensorDataset(X_train.float() , y_train)
validating_set = torch.utils.data.TensorDataset(X_validate.float() , y_validate)
testing_set = torch.utils.data.TensorDataset(testing_set.float() )<load_pretrained> |
BASE_WEIGHTS_PATH = 'https://storage.googleapis.com/keras-applications/'
WEIGHTS_HASHES = {
'b0':('902e53a9f72be733fc0bcb005b3ebbac',
'50bc09e76180e00e4465e1a485ddc09d'),
'b1':('1d254153d4ab51201f1646940f018540',
'74c4e6b3e1f6a1eea24c589628592432'),
'b2':('b15cce36ff4dcbd00b6dd88e7857a6ad',
'111f8e2ac8aa800a7a99e3239... | Cassava Leaf Disease Classification |
13,011,840 | train_loader = DataLoader(training_set, shuffle=True, batch_size = 88)
validate_loader = DataLoader(validating_set, shuffle=False, batch_size = 88)
test_set = DataLoader(testing_set, shuffle=False, batch_size = 88 )<define_search_model> | def get_model(weights='imagenet'):
inp = tf.keras.layers.Input(shape=(DIM,DIM,3))
base = EfficientNetB0(input_shape=(DIM,DIM,3),weights=None,include_top=False)
x = base(inp)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(5,activation='softmax' )(x)
model = tf.keras.Model(inputs=inp, outpu... | Cassava Leaf Disease Classification |
13,011,840 | class CNN_DigitClassifier(nn.Module):
def __init__(self):
super(CNN_DigitClassifier, self ).__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 32, 5),
nn.ReLU(inplace=True),
nn.Conv2d(32, 32, 5),
nn.ReLU(inplace=True),
nn.MaxPool2d(2,2),
nn.Dropout(0.25),
nn.Conv2d(32, 64, 3),
nn.ReLU(inplace=True),
nn.Conv2d(64, ... | skf = KFold(n_splits=FOLDS,shuffle=True,random_state=12)
for fold,(idxT,idxV)in enumerate(skf.split(np.arange(5))):
if fold==(FOLDS-1):
idxTT = idxT; idxVV = idxV
print('
print('Fold',fold,'has TRAIN:',idxT,'VALID:',idxV ) | Cassava Leaf Disease Classification |
13,011,840 | model = CNN_DigitClassifier()
optimizer = optim.RMSprop(model.parameters() , lr=0.001, alpha=0.9)
criterion = nn.CrossEntropyLoss()
lr_reduction = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=3, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0.00001)
if torch.cuda.is_av... | pred = np.zeros(( test_df.shape[0],5))
for fold,(idxT,idxV)in enumerate(skf.split(np.arange(5))):
print() ; print('
print('
print('
files_train = tf.io.gfile.glob([GCS_PATH_STRATIFICATED + '/train%.2i*.tfrec'%x for x in idxT])
files_valid = tf.io.gfile.glob([GCS_PATH_STRATIFICATED + '/train%.2i*.tfrec'%x for x in idxV... | Cassava Leaf Disease Classification |
13,011,840 | count = 0
losses = []
iteration_list = []
training_accuracy = []
validation_accuracy = []
training_loss = []
validation_loss = []<train_on_grid> | ds = get_dataset(files_test, augment=False, repeat=False, dim=IMAGE_SIZE[0],
labeled=False, return_image_names=True)
image_names = np.array([img_name.numpy().decode("utf-8")
for img, img_name in iter(ds.unbatch())] ) | Cassava Leaf Disease Classification |
13,011,840 | <compute_train_metric><EOS> | prediction = np.argmax(pred, axis=1)
test_df['label'] = prediction
test_df = test_df[["image_id","label"]]
test_df.to_csv('submission.csv',index=False)
test_df | Cassava Leaf Disease Classification |
12,991,733 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<predict_on_test> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
| Cassava Leaf Disease Classification |
12,991,733 | def prediction(data_loader):
model.eval()
test_pred = torch.LongTensor()
for batch_idx, data in enumerate(data_loader):
data = Variable(data[0])
if torch.cuda.is_available() :
data = data.cuda()
output = model(data)
pred = output.cpu().data.max(1, keepdim=True)[1]
test_pred = torch.cat(( test_pred, pred), dim=0)
ret... | 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 |
12,991,733 | test_prediction = prediction(test_set )<save_to_csv> | 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 |
12,991,733 | submission.to_csv("CNN_model_TPU_submission.csv", index = False )<load_from_csv> | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train.head() | Cassava Leaf Disease Classification |
12,991,733 | train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
train.head()<prepare_x_and_y> | train.label.value_counts() | Cassava Leaf Disease Classification |
12,991,733 | Y_train = to_categorical(train['label'].values, 10)
X_train =(train.loc[:, 'pixel0':] / 255 ).values
X_train.shape, Y_train.shape<prepare_x_and_y> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
12,991,733 | X_test =(test / 255 ).values<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 |
12,991,733 | datagener = ImageDataGenerator(
rotation_range=15,
zoom_range=0.1,
width_shift_range=0.1,
height_shift_range=0.1,
)<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 |
12,991,733 | example = X_train[6].reshape(( 1, 28, 28, 1))
label = Y_train[6]<split> | 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 |
12,991,733 |
<prepare_x_and_y> | 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_index(d... | Cassava Leaf Disease Classification |
12,991,733 | count_network = 5
size_for_network = X_train.shape[0] // count_network
X_train_list = []
X_valid_list = []
Y_train_list = []
Y_valid_list = []
for i in range(count_network):
X_train_list.append(X_train[i * size_for_network :(i + 1)* size_for_network])
Y_train_list.append(Y_train[i * size_for_network :(i + 1)* size_for... | test['label'] = np.argmax(tst_preds, axis=1)
test.head() | Cassava Leaf Disease Classification |
12,991,733 | <choose_model_class><EOS> | test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
12,985,935 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<train_model> | !pip install.. /input/pytorch-image-models/timm-0.3.1-py3-none-any.whl | Cassava Leaf Disease Classification |
12,985,935 | for i in range(count_network):
list_models[i].load_weights(f'bestmodel{i + 1}.hdf5')
print(f'Model №{i + 1}')
_, acc = list_models[i].evaluate(X_train_list[i], Y_train_list[i])
_, acc2 = list_models[i].evaluate(X_valid_list[i], Y_valid_list[i])
print()<predict_on_test> | import numpy as np
import os
import pandas as pd
from fastai.vision.all import *
import albumentations | Cassava Leaf Disease Classification |
12,985,935 | def get_predict(models, data, method_voting='soft', count_classes=10):
if method_voting == 'soft':
for_test = np.zeros(( data.shape[0], count_classes))
for i in range(len(models)) :
for_test += models[i].predict(data)
return np.argmax(for_test, axis=1)
elif method_voting == 'hard':
for_test = np.zeros(( data.shape[0]... | set_seed(999,reproducible=True ) | Cassava Leaf Disease Classification |
12,985,935 | submit = pd.DataFrame(get_predict(list_models, X_test), columns=['Label'], index=pd.read_csv('.. /input/digit-recognizer/sample_submission.csv')['ImageId'])
submit2 = pd.DataFrame(get_predict(list_models, X_test, method_voting='hard'), columns=['Label'],
index=pd.read_csv('.. /input/digit-recognizer/sample_submission.... | train_df = pd.read_csv(dataset_path/'train.csv' ) | Cassava Leaf Disease Classification |
12,985,935 | comparison = submit.join(submit2, lsuffix='_1', rsuffix='_2')
comparison.loc[~(comparison['Label_1'] == comparison['Label_2'])]<set_options> | train_df['path'] = train_df['image_id'].map(lambda x:dataset_path/'train_images'/x)
train_df = train_df.drop(columns=['image_id'])
train_df = train_df.sample(frac=1 ).reset_index(drop=True)
train_df.head(10 ) | Cassava Leaf Disease Classification |
12,985,935 | %matplotlib inline
plt.style.use('ggplot')
%matplotlib notebook
py.init_notebook_mode(connected=True)
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 100)
pd.set_option('display.max_rows', 100)
<load_from_csv> | im = Image.open(train_df['path'][1])
width, height = im.size
print(width,height ) | Cassava Leaf Disease Classification |
12,985,935 | train = pd.read_csv('.. /input/tmdb-box-office-prediction/train.csv')
test = pd.read_csv('.. /input/tmdb-box-office-prediction/test.csv')
sam_sub = pd.read_csv('.. /input/tmdb-box-office-prediction/sample_submission.csv')
print("train dataset:", train.shape,"
","test dataset: ",test.shape,"
","sample_submission data... | class AlbumentationsTransform(RandTransform):
"A transform handler for multiple `Albumentation` transforms"
split_idx,order=None,2
def __init__(self, train_aug, valid_aug): store_attr()
def before_call(self, b, split_idx):
self.idx = split_idx
def encodes(self, img: PILImage):
if self.idx == 0:
aug_img = self.train_aug... | Cassava Leaf Disease Classification |
12,985,935 | train.sort_values(by='revenue', ascending=False ).head(20)[['title','revenue','release_date']]<feature_engineering> | def get_train_aug(sz): return albumentations.Compose([
albumentations.RandomResizedCrop(sz,sz),
albumentations.Transpose(p=0.5),
albumentations.HorizontalFlip(p=0.5),
albumentations.VerticalFlip(p=0.5),
albumentations.ShiftScaleRotate(p=0.5),
albumentations.HueSaturationValue(
hue_shift_limit=0.2,
sat_shift_limit=0.2,... | Cassava Leaf Disease Classification |
12,985,935 | 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']... | def get_dls(sz,bs):
item_tfms = AlbumentationsTransform(get_train_aug(sz), get_valid_aug(sz))
batch_tfms = [Normalize.from_stats(*imagenet_stats)]
dls = ImageDataLoaders.from_df(train_df,
valid_pct=0.2,
seed=999,
label_col=0,
fn_col=1,
bs=bs,
item_tfms=item_tfms,
batch_tfms=batch_tfms)
return dls | Cassava Leaf Disease Classification |
12,985,935 | def date_features(df):
df['release_date'] = pd.to_datetime(df['release_date'])
df['release_year'] = df['release_date'].dt.year
df['release_month'] = df['release_date'].dt.month
df['release_day'] = df['release_date'].dt.day
df['release_quarter'] = df['release_date'].dt.quarter
df.drop(columns=['release_date'], inplace=... | dls = get_dls(456,16 ) | Cassava Leaf Disease Classification |
12,985,935 | train['release_year'].iloc[np.where(train['release_year']> 2019)][:10]<feature_engineering> | dls.show_batch() | Cassava Leaf Disease Classification |
12,985,935 | train['release_year']=np.where(train['release_year']> 2019, train['release_year']-100, train['release_year'])
test['release_year']=np.where(test['release_year']> 2019, test['release_year']-100, test['release_year'] )<categorify> | def create_timm_body(arch:str, pretrained=True, cut=None, n_in=3):
"Creates a body from any model in the `timm` library."
model = create_model(arch, pretrained=pretrained, num_classes=0, global_pool='')
_update_first_layer(model, n_in, pretrained)
if cut is None:
ll = list(enumerate(model.children()))
cut = next(i fo... | Cassava Leaf Disease Classification |
12,985,935 | fillna_column = {'release_year':'mode','release_month':'mode',
'release_day':'mode'}
for k,v in fillna_column.items() :
if v == 'mode':
fill = train[k].mode() [0]
else:
fill = v
print(k, ': ', fill)
train[k].fillna(value = fill, inplace = True)
test[k].fillna(value = fill, inplace = True )<data_type_conversions> | def timm_learner(dls, arch:str, loss_func=None, pretrained=True, cut=None, splitter=None,
y_range=None, config=None, n_out=None, normalize=True, **kwargs):
"Build a convnet style learner from `dls` and `arch` using the `timm` library"
if config is None: config = {}
if n_out is None: n_out = get_c(dls)
assert n_out, "`... | Cassava Leaf Disease Classification |
12,985,935 | def year_month_together(df):
year = df["release_year"].astype(int ).copy().astype(str)
month=df['release_month'].astype(int ).copy().astype(str)
day=df['release_day'].astype(int ).copy().astype(str)
df["release_date"]= month.str.cat(day.str.cat(year,sep="/"), sep ="/")
df['release_date']=pd.to_datetime(df['release_... | learn = timm_learner(dls,
'tf_efficientnet_b5_ns',
opt_func=ranger,
loss_func=LabelSmoothingCrossEntropy() ,
cbs=[GradientAccumulation(n_acc=32)],
metrics = [accuracy] ).to_native_fp16() | Cassava Leaf Disease Classification |
12,985,935 | 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)
te... | learn.lr_find() | Cassava Leaf Disease Classification |
12,985,935 | train['belongs_to_collection'].apply(lambda x: len(x)if x != {} else 0 ).value_counts()<feature_engineering> | learn.freeze()
learn.fit_flat_cos(1,1e-1, wd=0.1, cbs=[MixUp() ] ) | Cassava Leaf Disease Classification |
12,985,935 | 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... | learn.save('stage-1' ) | Cassava Leaf Disease Classification |
12,985,935 | train['collection_name'].value_counts() [1:10]<feature_engineering> | learn = learn.load('stage-1' ) | Cassava Leaf Disease Classification |
12,985,935 | 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... | learn.unfreeze()
learn.lr_find() | Cassava Leaf Disease Classification |
12,985,935 | drama=train.loc[train['genre_Drama']==1,]
comedy=train.loc[train['genre_Comedy']==1,]
action=train.loc[train['genre_Action']==1,]
thriller=train.loc[train['genre_Thriller']==1,]
text_drama = " ".join(review for review in drama.title)
text_comedy = " ".join(review for review in comedy.title)
text_action = " ".join(rev... | learn.unfreeze()
learn.fit_flat_cos(10, 1e-3,cbs=[MixUp() ,SaveModelCallback() ] ) | Cassava Leaf Disease Classification |
12,985,935 | drama_revenue=drama.groupby(['release_year'] ).mean() ['revenue']
comedy_revenue=comedy.groupby(['release_year'] ).mean() ['revenue']
action_revenue=action_revenue=action.groupby(['release_year'] ).mean() ['revenue']
thriller_revenue=thriller.groupby(['release_year'] ).mean() ['revenue']
revenue_concat = pd.concat([dra... | learn = learn.to_native_fp32() | Cassava Leaf Disease Classification |
12,985,935 | train['num_companies'] = train['production_companies'].apply(lambda x: len(x)if x != {} else 0)
train['all_production_companies'] = train['production_companies'].apply(lambda x: ' '.join(sorted([i['name'] for i in x])) if x != {} else '')
top_companies = [m[0] for m in Counter([i for j in list_of_companies for i in j... | learn.save('stage-2' ) | Cassava Leaf Disease Classification |
12,985,935 | Warner_Bros=train.loc[train['production_company_Warner Bros.']==1,]
Universal_Pictures=train.loc[train['production_company_Universal Pictures']==1,]
Twentieth_Century_Fox_Film=train.loc[train['production_company_Twentieth Century Fox Film Corporation']==1,]
Columbia_Pictures=train.loc[train['production_company_Columbia... | sample_df = pd.read_csv(dataset_path/'sample_submission.csv')
sample_df.head() | Cassava Leaf Disease Classification |
12,985,935 | 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... | _sample_df = sample_df.copy()
_sample_df['path'] = _sample_df['image_id'].map(lambda x:dataset_path/'test_images'/x)
_sample_df = _sample_df.drop(columns=['image_id'])
test_dl = dls.test_dl(_sample_df ) | Cassava Leaf Disease Classification |
12,985,935 | 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['iso_639_1'] 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_commo... | test_dl.show_batch() | Cassava Leaf Disease Classification |
12,985,935 | text_drama = " ".join(review for review in drama['Keywords'].apply(lambda x: ' '.join(sorted([i['name'] for i in x])) if x != {} else ''))
text_comedy = " ".join(review for review in comedy['Keywords'].apply(lambda x: ' '.join(sorted([i['name'] for i in x])) if x != {} else ''))
text_action = " ".join(review for review... | preds, _ = learn.tta(dl=test_dl, n=15, beta=0 ) | Cassava Leaf Disease Classification |
12,985,935 | 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_keywords = [m[0] for m in Counter([i for j in list_of_keywords for i in j] ).most_common(30)]
for g in top_keywo... | sample_df['label'] = preds.argmax(dim=-1 ).numpy() | Cassava Leaf Disease Classification |
12,985,935 | <filter><EOS> | sample_df.to_csv('submission.csv',index=False ) | Cassava Leaf Disease Classification |
13,620,114 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering> | from fastai.vision.all import * | Cassava Leaf Disease Classification |
13,620,114 | list_of_cast_genders = list(train['cast'].apply(lambda x: [i['gender'] for i in x] if x != {} else [] ).values)
list_of_cast_characters = list(train['cast'].apply(lambda x: [i['character'] for i in x] if x != {} else [] ).values)
train['genders_0'] = train['cast'].apply(lambda x: sum([1 for i in x if i['gender'] == 0... | path = Path('/kaggle/input/cassava-leaf-disease-classification/')
path.ls() | Cassava Leaf Disease Classification |
13,620,114 | list_of_crew_names = list(train['crew'].apply(lambda x: [i['name'] for i in x] if x != {} else [] ).values)
train['num_crew'] = train['crew'].apply(lambda x: len(x)if x != {} else 0)
train['all_crew'] = train['crew'].apply(lambda x: ' '.join(sorted([i['name'] for i in x])) if x != {} else '')
top_crew_names = [m[0] ... | train_df = pd.read_csv(path/'train.csv')
train_df.head()
with open(path/'label_num_to_disease_map.json')as f:
label_dict = json.load(f)
label_dict = {int(k):v for k,v in label_dict.items() }
df = train_df.set_index('image_id')
labels = df.to_dict() ['label']
def get_label(labels, x):
x = Path(x)
return labels[x.nam... | Cassava Leaf Disease Classification |
13,620,114 | crew_name_Avy_Kaufman=train.loc[train['crew_name_Avy Kaufman']==1,]
crew_name_Robert_Rodriguez=train.loc[train['crew_name_Robert Rodriguez']==1,]
crew_name_Deborah_Aquila=train.loc[train['crew_name_Deborah Aquila']==1,]
crew_name_James_Newton_Howard=train.loc[train['crew_name_James Newton Howard']==1,]
crew_name_Mary_V... | dls = get_data(labels,bs=128, presize=384)
learn = cnn_learner(dls, models.resnet50, metrics=[accuracy], pretrained=False)
test_files = get_image_files(path/'test_images')
predictions = []
for fold in range(3):
learn.load(f'/kaggle/input/resnet50/models/resnet50-full-fold_{fold}')
test_dl = dls.test_dl(test_files)
... | Cassava Leaf Disease Classification |
13,620,114 | list_of_crew_jobs = list(train['crew'].apply(lambda x: [i['job'] for i in x] if x != {} else [] ).values)
list_of_crew_genders = list(train['crew'].apply(lambda x: [i['gender'] for i in x] if x != {} else [] ).values)
list_of_crew_departments = list(train['crew'].apply(lambda x: [i['department'] for i in x] if x != {... | preds = torch.argmax(torch.mean(torch.stack(predictions), dim=0), dim=1)
test_fn = map(lambda x: x.name, test_files ) | Cassava Leaf Disease Classification |
13,620,114 | <feature_engineering><EOS> | submission = pd.DataFrame({'image_id': test_fn, 'label': preds})
submission.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,484,450 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering> | print("Tensorflow version " + tf.__version__)
| Cassava Leaf Disease Classification |
13,484,450 | def prepare(df):
df['_budget_runtime_ratio'] = df['budget']/df['runtime']
df['_budget_popularity_ratio'] = df['budget']/df['popularity']
df['_budget_year_ratio'] = df['budget']/(df['release_year']*df['release_year'])
df['_releaseYear_popularity_ratio'] = df['release_year']/df['popularity']
df['_releaseYear_popularity_... | dense201 = tf.keras.models.load_model('.. /input/trained-models/densenet201_40.h5')
inception = tf.keras.models.load_model('.. /input/trained-models/inceptionv3_40.h5')
efficient_net = tf.keras.models.load_model(
'.. /input/trained-models/efficient_netb3_40.h5',
compile=False,
custom_objects={'FixedDropout':FixedDro... | Cassava Leaf Disease Classification |
13,484,450 | train_new.to_csv("train_new.csv", index=False)
test_new.to_csv("test_new.csv", index=False )<drop_column> | JPEG_PATH = ".. /input/cassava-leaf-disease-classification/test_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(filepath, paths, batch_size=32):
i=0
print(len(paths))
while i <= len(paths):
bat... | Cassava Leaf Disease Classification |
13,484,450 | drop_columns=['homepage','imdb_id','poster_path','status','title', 'release_date','tagline', 'overview', 'original_title','all_genres','all_cast',
'original_language','collection_name','all_crew']
train_new=train_new.drop(drop_columns,axis=1)
test_new=test_new.drop(drop_columns,axis=1 )<split> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv' ) | Cassava Leaf Disease Classification |
13,484,450 | X = train_new.drop(['id', 'revenue'], axis=1)
y = np.log1p(train_new['revenue'])
X_test = test_new.drop(['id'], axis=1)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2 )<choose_model_class> | 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 |
13,484,450 | 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}
lgb_model = lgb.LGBMRegressor(**params, ... | 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 |
13,484,450 | <init_hyperparams><EOS> | submission["label"] = result
submission.to_csv("submission.csv", index=False ) | Cassava Leaf Disease Classification |
13,556,030 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<train_model> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
| Cassava Leaf Disease Classification |
13,556,030 | n_fold = 5
random_seed=2222
folds = KFold(n_splits=n_fold, shuffle=True, random_state=42)
def train_model(X, X_test, y, params=None, folds=folds, model_type='lgb', plot_feature_importance=True, model=None):
oof = np.zeros(X.shape[0])
prediction = np.zeros(X_test.shape[0])
scores = []
feature_importance = pd.DataFram... | 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 |
13,556,030 | start = time.time()
oof_lgb, prediction_lgb, _ = train_model(X, X_test, y, params=params, model_type='lgb')
end = time.time()
print("time elapsed:",end - start, "second" )<train_model> | 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 |
13,556,030 | xgb_params = {'eta': 0.01,
'objective': 'reg:linear',
'max_depth': 6,
'min_child_weight': 3,
'subsample': 0.8,
'colsample_bytree': 0.8,
'eval_metric': 'rmse',
'seed': 11,
'silent': True}
start = time.time()
oof_xgb, prediction_xgb = train_model(X, X_test, y, params=xgb_params, model_type='xgb')
end = time.time()
print... | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train.head() | Cassava Leaf Disease Classification |
13,556,030 | sam_sub['revenue'] = np.expm1(prediction_lgb)
sam_sub.to_csv("lgb.csv", index=False)
sam_sub['revenue'] = np.expm1(prediction_xgb)
sam_sub.to_csv("xgb.csv", index=False)
sam_sub['revenue'] = np.expm1(( prediction_lgb + prediction_xgb)/ 2)
sam_sub.to_csv("blend_lgb_xgb.csv", index=False )<set_options> | train.label.value_counts() | Cassava Leaf Disease Classification |
13,556,030 | warnings.filterwarnings("ignore" )<load_from_csv> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
13,556,030 | train = pd.read_csv('.. /input/tmdb-box-office-prediction/train.csv')
train.info()<load_from_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__(sel... | Cassava Leaf Disease Classification |
13,556,030 | test = pd.read_csv('.. /input/tmdb-box-office-prediction/test.csv')
test.info()<count_missing_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 |
13,556,030 | train.isna().sum()<count_missing_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 |
13,556,030 | test.isna().sum()<feature_engineering> | 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_index(d... | Cassava Leaf Disease Classification |
13,556,030 | train[['release_month','release_day','release_year']]=train['release_date'].str.split('/',expand=True ).replace(np.nan, -1 ).astype(int)
train.loc[(train['release_year'] <= 19)&(train['release_year'] < 100), "release_year"] += 2000
train.loc[(train['release_year'] > 19)&(train['release_year'] < 100), "release_year"] +... | test['label'] = np.argmax(tst_preds, axis=1)
test.head() | Cassava Leaf Disease Classification |
13,556,030 | <categorify><EOS> | test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
14,012,257 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<count_values> | !pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git | Cassava Leaf Disease Classification |
14,012,257 | train['status'].value_counts()<filter> | 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 |
14,012,257 | train.loc[train['status'] == "Rumored"][['status','revenue']]<count_values> | BATCH_SIZE = 32 * REPLICAS
HEIGHT = 512
WIDTH = 512
CHANNELS = 3
N_CLASSES = 5
TTA_STEPS = 8 | Cassava Leaf Disease Classification |
14,012,257 | test['status'].value_counts()<load_from_csv> | 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 |
14,012,257 | trainAdditionalFeatures = pd.read_csv('.. /input/tmdb-competition-additional-features/TrainAdditionalFeatures.csv')
testAdditionalFeatures = pd.read_csv('.. /input/tmdb-competition-additional-features/TestAdditionalFeatures.csv')
train = pd.merge(train, trainAdditionalFeatures, how='left', on=['imdb_id'])
test = pd.... | 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 |
14,012,257 | print("Missing rating in Train set", train['rating'].isna().sum())
print("Missing total Votes in Train set", train['totalVotes'].isna().sum())
print("")
print("Missing rating in Test set", test['rating'].isna().sum())
print("Missing total Votes in Test set", test['totalVotes'].isna().sum() )<data_type_conversions> | 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 |
14,012,257 | train['rating'] = train['rating'].fillna(1.5)
train['totalVotes'] = train['totalVotes'].fillna(6)
test['rating'] = test['rating'].fillna(1.5)
test['totalVotes'] = test['totalVotes'].fillna(6 )<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 |
14,012,257 | def prepare(df):
global json_cols
global train_dict
df[['release_month','release_day','release_year']]=df['release_date'].str.split('/',expand=True ).replace(np.nan, 0 ).astype(int)
df['release_year'] = df['release_year']
df.loc[(df['release_year'] <= 19)&(df['release_year'] < 100), "release_year"] += 2000
df.loc[(df[... | 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 |
14,012,257 | <train_model><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 |
13,901,216 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<train_model> | package_path = '.. /input/vision-transformer-pytorch/VisionTransformer-Pytorch'
sys.path.append(package_path)
| Cassava Leaf Disease Classification |
13,901,216 | def lgb_model(trn_x, trn_y, val_x, val_y, test, verbose):
params = {'objective':'regression',
'num_leaves' : 30,
'min_data_in_leaf' : 20,
'max_depth' : 9,
'learning_rate': 0.004,
'feature_fraction':0.9,
"bagging_freq": 1,
"bagging_fraction": 0.9,
'lambda_l1': 0.2,
"bagging_seed": random_seed,
"metric": 'rmse',
"random_... | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
sys.path.append(package_path ) | Cassava Leaf Disease Classification |
13,901,216 | def cat_model(trn_x, trn_y, val_x, val_y, test, verbose):
model = CatBoostRegressor(iterations=100000,
learning_rate=0.004,
depth=5,
eval_metric='RMSE',
colsample_bylevel=0.8,
random_seed = random_seed,
bagging_temperature = 0.2,
metric_period = None,
early_stopping_rounds=200
)
model.fit(trn_x, trn_y,
eval_set=(val_... | 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 |
13,901,216 | result_dict = dict()
val_pred = np.zeros(train.shape[0])
test_pred = np.zeros(test.shape[0])
final_err = 0
verbose = False
for i,(trn, val)in enumerate(fold):
print(i+1, "fold.RMSE")
trn_x = train.loc[trn, :]
trn_y = y[trn]
val_x = train.loc[val, :]
val_y = y[val]
fold_val_pred = []
fold_test_pred = []
fold_err = []... | CFG = {
'fold_num': 10,
'seed': 719,
'model_arch': 'tf_efficientnet_b3_ns',
'img_size': 384,
'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 |
13,901,216 | sub = pd.read_csv('.. /input/tmdb-box-office-prediction/sample_submission.csv')
df_sub = pd.DataFrame()
df_sub['id'] = sub['id']
df_sub['revenue'] = np.expm1(test_pred*3)
df_sub.to_csv("submission.csv", index=False )<load_from_csv> | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train.head() | Cassava Leaf Disease Classification |
13,901,216 | train = pd.read_csv('.. /input/train.csv')
test = pd.read_csv('.. /input/test.csv')
sub = pd.read_csv('.. /input/sample_submission.csv')
train.shape, test.shape, sub.shape<drop_column> | train.label.value_counts() | Cassava Leaf Disease Classification |
13,901,216 | train.drop(columns=['imdb_id', 'homepage', 'poster_path'], inplace=True)
test.drop(columns=['imdb_id', 'homepage', 'poster_path'], inplace=True )<feature_engineering> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
13,901,216 | def date_features(df):
df['release_date'] = pd.to_datetime(df['release_date'])
df['release_year'] = df['release_date'].dt.year
df['release_month'] = df['release_date'].dt.month
df['release_quarter'] = df['release_date'].dt.quarter
df['release_dow'] = df['release_date'].dt.dayofweek
df.drop(columns=['release_date'], in... | 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 |
13,901,216 | def get_dictionary(s):
try:
d = eval(s)
except:
d = {}
return d<categorify> | 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 |
13,901,216 | train.belongs_to_collection = train.belongs_to_collection.map(lambda x: len(get_dictionary(x)) ).clip(0,1)
test.belongs_to_collection = test.belongs_to_collection.map(lambda x: len(get_dictionary(x)) ).clip(0,1)
train.genres = train.genres.map(lambda x: sorted([d['id'] for d in get_dictionary(x)])).map(lambda x: ','.... | 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 |
13,901,216 | train.drop(columns=['cast', 'crew'], inplace=True)
test.drop(columns=['cast', 'crew'], inplace=True )<feature_engineering> | 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_index(d... | Cassava Leaf Disease Classification |
13,901,216 | def standard_text_features(df):
for c in ['original_title', 'title', 'tagline', 'overview']:
df[c + '_len'] = df[c].map(lambda x: len(str(x)))
df[c + '_wlen'] = df[c].map(lambda x: len(str(x ).split(' ')))
df.drop(columns=['original_title', 'title', 'tagline', 'overview'], inplace=True)
return df
train = standard_te... | test['label'] = np.argmax(tst_preds, axis=1)
test.head() | Cassava Leaf Disease Classification |
13,901,216 | <train_model><EOS> | test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,880,662 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<save_to_csv> | print("Tensorflow version " + tf.__version__)
| Cassava Leaf Disease Classification |
13,880,662 | model = ensemble.GradientBoostingRegressor(random_state=4, loss='ls', learning_rate=0.02, n_estimators=1000, max_depth=7)
model.fit(x1, y1)
print(np.sqrt(metrics.mean_squared_error(y2, model.predict(x2))))
model.fit(train[col].fillna(-1), np.log1p(train['revenue']))
pred2 = model.predict(test[col].fillna(-1))
test['r... | dense201 = tf.keras.models.load_model('.. /input/train-model-cassava/densenet201.h5')
inception = tf.keras.models.load_model('.. /input/train-model-cassava/inceptionv3.h5')
efficient_net = tf.keras.models.load_model(
'.. /input/train-model-cassava/efficient_netb3.h5',
compile=False,
custom_objects={'FixedDropout':Fi... | Cassava Leaf Disease Classification |
13,880,662 | pd.set_option('max_columns', None)
%matplotlib inline
plt.style.use('ggplot')
stop = set(stopwords.words('english'))
warnings.filterwarnings("ignore" )<load_from_csv> | JPEG_PATH = ".. /input/cassava-leaf-disease-classification/test_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(filepath, paths, batch_size=32):
i=0
print(len(paths))
while i <= len(paths):
bat... | Cassava Leaf Disease Classification |
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