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Xboost_cat.py
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Xboost_cat.py
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import numpy as np
import DataIO
import Preprocessing
import pandas as pd
from catboost import CatBoostClassifier, Pool
from tensorflow.keras.utils import to_categorical
from sklearn.metrics import log_loss
from sklearn.model_selection import StratifiedKFold, train_test_split
def train_stacking(X, y, X_submmit, params, n_split):
pass
def train_catboost(X, y, X_submmit, params, n_splits, categorical):
folds = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=756353)
outcomes = []
sub = np.zeros((X_submmit.shape[0], 3))
for n_fold, (train_index, val_index) in enumerate(folds.split(X, y)):
X_train, X_val = X.iloc[train_index, :], X.iloc[val_index, :]
y_train, y_val = y.iloc[train_index, :], y.iloc[val_index, :]
train_data = Pool(X_train, y_train, cat_features=categorical)
test_data = Pool(X_val, y_val, cat_features=categorical)
clf = CatBoostClassifier(allow_writing_files=False)
clf.fit(train_data, eval_set=test_data, early_stopping_rounds=5000, verbose=1000)
predictions = clf.predict_proba(X_val)
logloss = log_loss(to_categorical(y_val), predictions)
outcomes.append(logloss)
print(f"FOLD {n_fold} : logloss:{logloss}")
sub += clf.predict_proba(X_submmit)
mean_outcome = np.mean(outcomes)
my_submission = sub / folds.n_splits
print("Mean:{}".format(mean_outcome))
submission = pd.read_csv('./data/sample_submission.csv')
submission.loc[:, 1:] = my_submission
submission.to_csv(f'./data/submission/kfold_6/{n_splits}_{mean_outcome}_xgboost.csv', index=False)
def train_catboost_one(X, y, X_submmit, params):
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, stratify=y, random_state=678988)
clf = CatBoostClassifier(**params)
clf.fit(X_train, y_train, eval_set=(X_val, y_val), early_stopping_rounds=5000)
predictions = clf.predict_proba(X_val)
logloss = log_loss(to_categorical(y_val), predictions)
print(f"logloss: {logloss}")
predictions = clf.predict_proba(X_submmit)
submission = pd.read_csv('./data/sample_submission.csv')
submission.loc[:, 1:] = predictions
submission.to_csv(f'./data/submission/one_{logloss}_xgboost.csv', index=False)
def main():
dataio = DataIO.DataReadWrite()
preprocesser = Preprocessing.PreprocesserGBoost()
df = dataio.read_csv_to_df('train.csv')
df_test = dataio.read_csv_to_df('test.csv')
# 모든 데이터가 중복 되는 열 제거
# df = df.drop_duplicates(df.columns)
# 클래스 분리
# y = df.loc[:, ['credit']]
# df = df.drop(['credit'], axis=1)
# 데이터 전처리
X, y, numerical_columns, categorical_columns = preprocesser.data_preprocess_2_comb(df.drop_duplicates(), 'train')
X_submmit, numerical_submmit, categorical_submmit = preprocesser.data_preprocess_2_comb(df_test, 'test')
X = X.reset_index()
X = X.drop(['index'], axis=1)
y = y.reset_index()
y = y.drop(['index'], axis=1)
# param = {
# 'task_type': 'CPU',
# 'random_seed': 1234,
# 'thread_count': 12,
# 'iterations': 40000,
# }
# 1번
# one_0.7056862464826119_xgboost.csv
# param = {
# 'objective': 'MultiClass',
# 'depth': 14,
# 'learning_rate': 0.06259226791856165,
# 'grow_policy': 'Lossguide',
# 'bootstrap_type': 'Bayesian',
# 'l2_leaf_reg': 22,
# 'task_type': 'CPU',
# 'random_seed': 1234,
# 'thread_count': 12,
# 'bagging_temperature': 1.5476071404273228,
# 'max_leaves': 62,
# 'iterations': 40000,
# }
# param 2
# two_0.7027083784935696_xgboost.csv
# param = {
# 'objective': 'MultiClass',
# 'depth': 10,
# 'learning_rate': 0.06576219655285793,
# 'grow_policy': 'Lossguide',
# 'bootstrap_type': 'Bernoulli',
# 'l2_leaf_reg': 5,
# 'task_type': 'CPU',
# 'random_seed': 1234,
# 'thread_count': 12,
# 'subsample': 0.692762589836913,
# 'max_leaves': 56,
# 'iterations': 40000,
# }
# param 4
# param = {
# 'objective': 'MultiClass',
# 'depth': 9,
# 'learning_rate': 0.03,
# 'grow_policy': 'Lossguide',
# 'bootstrap_type': 'Bernoulli',
# 'l2_leaf_reg': 2,
# 'task_type': 'GPU',
# 'random_state': 1234,
# 'subsample': 0.86129349174007,
# 'max_leaves': 41,
# 'iterations': 80000,
# }
#prarm 5
# param = {
# 'objective': 'MultiClass',
# 'depth': 14,
# 'learning_rate': 0.015,
# 'grow_policy': 'Lossguide',
# 'bootstrap_type': 'Bernoulli',
# 'l2_leaf_reg': 3,
# 'task_type': 'GPU',
# 'random_state': 1234,
# 'subsample': 0.8500607447093872,
# 'max_leaves': 51,
# 'iterations': 80000,
# 'allow_writing_files': False
# }
# param6
param = {
'objective': 'MultiClass',
'depth': 13,
'learning_rate': 0.015,
'grow_policy': 'Lossguide',
'bootstrap_type': 'Bernoulli',
'l2_leaf_reg': 1,
'task_type': 'GPU',
'random_state': 1234,
'subsample': 0.7415073673257514,
'max_leaves': 60,
'iterations': 80000,
'allow_writing_files': False
}
# Mean Encoding
# param 3
# param = {
# 'objective': 'MultiClass',
# 'depth': 13,
# 'learning_rate': 0.013196422959834992,
# 'grow_policy': 'SymmetricTree',
# 'bootstrap_type': 'Bayesian',
# 'l2_leaf_reg': 38,
# 'task_type': 'GPU',
# 'random_seed': 1234,
# 'thread_count': 1024,
# 'bagging_temperature': 0.09182176591772706,
# 'boosting_type': 'Plain',
# 'iterations': 40000,
# }
# train_catboost_one(X, y, X_submmit, param)
print(len(X.columns.tolist()))
print(len(categorical_columns))
print(len(numerical_columns))
for i in range(10, 20):
print(f"KFold: {i}")
train_catboost(X, y, X_submmit, param, i, categorical_columns)
if __name__ == '__main__':
main()