pip install git+https://github.com/ju-ki/my_pipeline
Set up environment for tabular competition
from jukijuki .utils .logger import Logger
from jukijuki .utils .timer import Timer
from jukijuki .utils .util import create_folder , seed_everything
from jukijuki .validation .SturgesRuleStratifiedKFold import sturges_skf
from jukijuki .tabular .util import AbstractBaseBlock , WrapperBlock , run_blocks
from jukijuki .tabular .feature_engine import LabelEncodingBlock , CountEncodingBlock , AggregationBlock , OneHotEncodingBlock , CrossCategoricalFeatureBlock
from jukijuki .gb_model .model_lgbm import MyLGBMModel
from jukijuki .gb_model .model_xgboost import MyXGBModel
from jukijuki .gb_model .model_cat import MyCatModel
class Config :
competition_name = "hogehoge"
exp_name = "hoge"
target_col = "target"
seed = 42
n_fold = 5
create_folder (Config )
seed_everything (Config .seed )
logger = Logger (Config .log_dir , Config .exp_name )
Set up environment for image competition
from jukijuki .image .util import get_file_path
from jukijuki .utils .timer import Timer
from jukijuki .utils .logger import Logger
from jukijuki .utils .EarlyStopping import EarlyStopping
from jukijuki .utils .util import create_folder , seed_everything , get_device
from jukijuki .pytorch_model .util import get_optimizer , get_scheduler
class Config :
apex = False
competition_name = "hogehoge"
exp_name = "hoge"
target_col = "target"
batch_size = 32
num_workers = 4
size = 224
epochs = 8
model_name = "resnet34d"
optimizer_name = "AdamW"
scheduler = "CosineAnnealingLR"
T_max = epochs
lr = 1e-4
min_lr = 1e-6
weight_decay = 1e-6
gradient_accumulation_steps = 1
max_grad_norm = 1000
n_fold = 5
trn_fold = [0 , 1 , 2 , ,3 , 4 ]
seed = 42
target_size = 1
TRAIN = True
INFERENCE = False
DEBUG = True
create_folder (Config )
seed_everything (Config .seed )
device = get_device ()
logger = Logger (Config .log_dir , Config .exp_name )
Set up environment for nlp competition
from jukijuki .nlp .util import get_tokenizer , get_max_lengths
from jukijuki .utils .timer import Timer
from jukijuki .utils .logger import Logger
from jukijuki .utils .EarlyStopping import EarlyStopping
from jukijuki .utils .util import create_folder , seed_everything , get_device
from jukijuki .nlp .pooler import AttentionPoolingV1 , MeanPoolingV1
from jukijuki .pytorch_model .util import get_optimizer , get_scheduler
class Config :
apex = False
competition_name = "hogehoge"
exp_name = "hoge"
target_col = "target"
sentence_col = "hoge"
batch_size = 32
num_workers = 4
max_len = 250
epochs = 8
model_name = "roberta-base"
pool_name = "attention"
optimizer_name = "AdamW"
scheduler = "cosine"
T_max = epochs
lr = 1e-4
min_lr = 1e-6
weight_decay = 1e-6
gradient_accumulation_steps = 1
max_grad_norm = 1000
n_fold = 5
trn_fold = [0 , 1 , 2 , 3 , 4 ]
seed = 42
target_size = 1
batch_scheduler = True
TRAIN = True
INFERENCE = False
DEBUG = True
create_folder (Config )
seed_everything (Config .seed )
device = get_device ()
tokenizer = get_tokenizer (Config )
logger = Logger (Config .log_dir , Config .exp_name )