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| Original file line number | Diff line number | Diff line change |
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| @@ -1 +1,2 @@ | ||
| from .regressor import CrossLgbRegression | ||
| from .regressor import CrossLgbRegression, CrossXgbRegression | ||
| from .classifier import CrossLgbBiClassifier, CrossXgbBiClassifier |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,257 @@ | ||
| import datetime | ||
| from time import time | ||
| import lightgbm as lgb | ||
| import numpy as np | ||
| import pandas as pd | ||
| from sklearn.metrics import roc_auc_score | ||
| from sklearn.model_selection import KFold | ||
| from ..util import log | ||
| from sklearn.model_selection import train_test_split | ||
| import optuna | ||
| from optuna.samplers import TPESampler | ||
| import xgboost as xgb | ||
| from sklearn.preprocessing import StandardScaler | ||
| from pytorch_tabnet.tab_model import TabNetClassifier | ||
|
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| class CrossTabnetBiClassifier(object): | ||
| pass | ||
| # https://pypi.org/project/pytorch-tabnet/ | ||
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| class CrossXgbBiClassifier(object): | ||
| def __init__(self, params=None, n_fold=10): | ||
| self.models = [] | ||
| self.scaler = None | ||
| self.feature_importances_ = pd.DataFrame() | ||
| self.n_fold = n_fold | ||
| self.params_ = { | ||
| 'eta': 0.01, | ||
| 'max_depth': 5, | ||
| 'subsample': 0.6, | ||
| 'n_estimators': 1700, | ||
| 'reg_alpha': 40, | ||
| 'reg_lambda': 18, | ||
| 'min_child_weight': 16, | ||
| 'tree_method': 'gpu_hist' | ||
| } | ||
| if params is not None: | ||
| self.params_ = params | ||
|
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| def get_params(self): | ||
| return self.params_ | ||
|
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| def set_params(self, params): | ||
| self.params_ = params | ||
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| def optuna_tuning(self, X, y): | ||
| X_train, X_valid, y_train, y_valid = train_test_split(X, y, stratify=y, test_size=0.4) | ||
| def objective(trial): | ||
| param_grid = { | ||
| 'max_depth': trial.suggest_int('max_depth', 4, 15), | ||
| 'subsample': trial.suggest_discrete_uniform('subsample', 0.6, 1.0, 0.1), | ||
| 'n_estimators': trial.suggest_int('n_estimators', 500, 2000, 100), | ||
| 'eta': 0.01, | ||
| 'reg_alpha': trial.suggest_int('reg_alpha', 1, 50), | ||
| 'reg_lambda': trial.suggest_int('reg_lambda', 5, 100), | ||
| 'min_child_weight': trial.suggest_int('min_child_weight', 5, 20), | ||
| } | ||
| reg = xgb.XGBClassifier(tree_method='gpu_hist', **param_grid) | ||
| reg.fit(X_train, y_train, | ||
| eval_set=[(X_valid, y_valid)], eval_metric='auc', | ||
| verbose=False) | ||
| return roc_auc_score(y_valid, reg.predict(X_valid)) | ||
|
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| train_time = 1 * 10 * 60 # h * m * s | ||
| study = optuna.create_study(direction='maximize', sampler=TPESampler(), study_name='XGBClassifier') | ||
| study.optimize(objective, timeout=train_time) | ||
|
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| log(f'Number of finished trials: {len(study.trials)}') | ||
| log('Best trial:') | ||
| trial = study.best_trial | ||
|
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| log(f'\tValue: {trial.value}') | ||
| log('\tParams: ') | ||
| for key, value in trial.params.items(): | ||
| log('\t\t{}: {}'.format(key, value)) | ||
|
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| self.params_ = trial.params | ||
| self.params_['eta'] = 0.01 | ||
| self.params_['tree_method'] = 'gpu_hist' | ||
|
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| def fit(self, X, y, tuning=True): | ||
| log(X.shape) | ||
| self.feature_importances_['feature'] = X.columns | ||
| self.scaler = StandardScaler() | ||
| X = self.scaler.fit_transform(X) | ||
|
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| if tuning: | ||
| log("[+]tuning params") | ||
| self.optuna_tuning(X, y) | ||
|
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| folds = KFold(n_splits=self.n_fold, shuffle=True) | ||
| AUCs = [] | ||
|
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| for fold_n, (train_index, valid_index) in enumerate(folds.split(X)): | ||
|
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| start_time = time() | ||
| print('Training on fold {}'.format(fold_n + 1)) | ||
| X_train, y_train = X[train_index], y.iloc[train_index] | ||
| X_valid, y_valid = X[valid_index], y.iloc[valid_index] | ||
| model = xgb.XGBClassifier(**self.params_) | ||
| model.fit(X_train, y_train, | ||
| eval_set=[(X_valid, y_valid)], | ||
| eval_metric='auc', verbose=False) | ||
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| self.models.append(model) | ||
| self.feature_importances_['fold_{}'.format(fold_n + 1)] = model.feature_importances_ | ||
| val = model.predict(X[valid_index]) | ||
| auc_ = roc_auc_score(y.iloc[valid_index], val) | ||
| print('AUC: {}'.format(auc_)) | ||
| AUCs.append(auc_) | ||
| print('Fold {} finished in {}'.format(fold_n + 1, str(datetime.timedelta( | ||
| seconds=time() - start_time)))) | ||
| log(f'Average KFold AUC: {np.mean(AUCs)}') | ||
| self.feature_importances_['average'] = self.feature_importances_[ | ||
| [x for x in self.feature_importances_.columns if x != "feature"]].mean(axis=1) | ||
| self.feature_importances_ = self.feature_importances_.sort_values(by="average", ascending=False) | ||
| self.feature_importances_.index = range(len(self.feature_importances_)) | ||
|
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| def predict(self, test): | ||
| test = self.scaler.transform(test) | ||
| for idx, model in enumerate(self.models): | ||
| if idx == 0: | ||
| result = model.predict(test) | ||
| else: | ||
| result += model.predict(test) | ||
| result /= self.n_fold | ||
| return result | ||
|
|
||
| class CrossLgbBiClassifier(object): | ||
| def __init__(self, params=None, n_fold=5): | ||
| self.models = [] | ||
| self.feature_importances_ = pd.DataFrame() | ||
| self.n_fold = n_fold | ||
| self.params_ = { | ||
| 'objective': 'binary', | ||
| 'metric': 'auc', | ||
| 'boosting_type': 'gbdt', | ||
| 'learning_rate': 0.01, | ||
| 'num_leaves': 2 ** 5, | ||
| 'bagging_fraction': 0.95, | ||
| 'bagging_freq': 1, | ||
| 'bagging_seed': 66, | ||
| 'feature_fraction': 0.7, | ||
| 'feature_fraction_seed': 66, | ||
| 'max_bin': 100, | ||
| 'max_depth': 5, | ||
| 'verbose': -1 | ||
| } | ||
| if params is not None: | ||
| self.params_ = params | ||
| self.Early_Stopping_Rounds = 150 | ||
| self.N_round = 5000 | ||
| self.Verbose = 100 | ||
|
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| def get_params(self): | ||
| return self.params_ | ||
|
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| def set_params(self, params): | ||
| self.params_ = params | ||
|
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| def optuna_tuning(self, X, y): | ||
| X_train, X_valid, y_train, y_valid = train_test_split(X, y, stratify=y, test_size=0.2, random_state=42) | ||
|
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||
| def objective(trial): | ||
| param_grid = { | ||
| 'num_leaves': trial.suggest_int('num_leaves', 2 ** 3, 2 ** 9), | ||
| 'num_boost_round': trial.suggest_int('num_boost_round', 100, 8000), | ||
| 'max_depth': trial.suggest_int('max_depth', 3, 9), | ||
| 'objective': 'binary', | ||
| 'metric': 'auc', | ||
| 'boosting_type': 'gbdt', | ||
| 'learning_rate': 0.01, | ||
| 'bagging_fraction': 0.95, | ||
| 'bagging_freq': 1, | ||
| 'bagging_seed': 66, | ||
| 'feature_fraction': 0.7, | ||
| 'feature_fraction_seed': 66, | ||
| 'max_bin': 100, | ||
| 'verbose': -1 | ||
| } | ||
| trn_data = lgb.Dataset(X_train, label=y_train, categorical_feature="") | ||
| val_data = lgb.Dataset(X_valid, label=y_valid, categorical_feature="") | ||
| clf = lgb.train(param_grid, trn_data, valid_sets=[trn_data, val_data], verbose_eval=False, | ||
| early_stopping_rounds=self.Early_Stopping_Rounds) | ||
| pred_val = clf.predict(X_valid) | ||
| auc_ = roc_auc_score(y_valid, pred_val) | ||
|
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| return auc_ | ||
|
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| train_time = 1 * 10 * 60 # h * m * s | ||
| study = optuna.create_study(direction='maximize', sampler=TPESampler(), study_name='LgbClassifier') | ||
| study.optimize(objective, timeout=train_time) | ||
|
|
||
| log(f'Number of finished trials: {len(study.trials)}') | ||
| log('Best trial:') | ||
| trial = study.best_trial | ||
|
|
||
| log(f'\tValue: {trial.value}') | ||
| log('\tParams: ') | ||
| for key, value in trial.params.items(): | ||
| log('\t\t{}: {}'.format(key, value)) | ||
|
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| self.params_['num_leaves'] = trial.params['num_leaves'] | ||
| self.params_['max_depth'] = trial.params['max_depth'] | ||
| self.N_round = trial.params['num_boost_round'] | ||
|
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| def fit(self, X, y, Early_Stopping_Rounds=None, N_round=None, Verbose=None, tuning=True): | ||
| log(X.shape) | ||
|
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| if tuning: | ||
| log("[+]tuning params") | ||
| self.optuna_tuning(X, y) | ||
|
|
||
| if Early_Stopping_Rounds is not None: | ||
| self.Early_Stopping_Rounds = Early_Stopping_Rounds | ||
| if N_round is not None: | ||
| self.N_round = N_round | ||
| if Verbose is not None: | ||
| self.Verbose = Verbose | ||
|
|
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| folds = KFold(n_splits=self.n_fold, shuffle=True, random_state=889) | ||
| AUCs = [] | ||
| self.feature_importances_['feature'] = X.columns | ||
|
|
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| for fold_n, (train_index, valid_index) in enumerate(folds.split(X)): | ||
|
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| start_time = time() | ||
| print('Training on fold {}'.format(fold_n + 1)) | ||
|
|
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| trn_data = lgb.Dataset(X.iloc[train_index], | ||
| label=y.iloc[train_index], categorical_feature="") | ||
| val_data = lgb.Dataset(X.iloc[valid_index], | ||
| label=y.iloc[valid_index], categorical_feature="") | ||
| model = lgb.train(self.params_, trn_data, num_boost_round=self.N_round, valid_sets=[trn_data, val_data], | ||
| verbose_eval=self.Verbose, | ||
| early_stopping_rounds=self.Early_Stopping_Rounds) | ||
| self.models.append(model) | ||
| self.feature_importances_['fold_{}'.format(fold_n + 1)] = model.feature_importance() | ||
| val = model.predict(X.iloc[valid_index]) | ||
| auc_ = roc_auc_score(y.iloc[valid_index], val) | ||
| print('AUC: {}'.format(auc_)) | ||
| AUCs.append(auc_) | ||
| print('Fold {} finished in {}'.format(fold_n + 1, str(datetime.timedelta( | ||
| seconds=time() - start_time)))) | ||
| self.feature_importances_['average'] = self.feature_importances_[ | ||
| [x for x in self.feature_importances_.columns if x != "feature"]].mean(axis=1) | ||
| self.feature_importances_ = self.feature_importances_.sort_values(by="average", ascending=False) | ||
| self.feature_importances_.index = range(len(self.feature_importances_)) | ||
|
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| def predict(self, test): | ||
| for idx, model in enumerate(self.models): | ||
| if idx == 0: | ||
| result = model.predict(test) / self.n_fold | ||
| else: | ||
| result += model.predict(test) / self.n_fold | ||
| return result |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,6 @@ | ||
| from autox import AutoX | ||
| path = '../input/tabular-playground-series-aug-2021' | ||
| autox = AutoX(target = 'loss', train_name = 'train.csv', test_name = 'test.csv', | ||
| id = ['id'], path = path) | ||
| sub = autox.get_submit() | ||
| sub.to_csv("submission.csv", index = False) |