def main_submit(): start_time = time.time() train = read_train_data(nrows=None) test = read_test_data() train, test = process_data(train, test) X = train.drop(['ID_code', 'target'], axis=1) y = train['target'] X_test = test.drop(['ID_code'], axis=1) oof, predictions, scores, feature_importance = train_model( X, X_test, y, params, n_fold=10, plot_feature_importance=True, model_type='lgb_sklearn') str_metric_score = metric + '_0' + str( int(scores['auc_score'].iloc[0] * 10000)) submit(test, predictions, str_metric_score) comment = 'add 5 max min feature before standard scale' storage_src(str_metric_score, scores, feature_importance, comment) elapsed_time = time.time() - start_time print(elapsed_time)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'drop main and second Type, main state East West, States_Federal, metadata_crop, VideoAmt' storage_src(str_metric_score, score, second_score, feature_score, comment, adoption_shuffle)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'remove img height and width feature' storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'test feature importance' storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, scores, feature_score): submit(submission, str_metric_score) comment = 'Mixed_Breed change' storage_src(str_metric_score, scores, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'coefficient not change' storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'drop main state State,' storage_src(str_metric_score, score, second_score, feature_score, comment, adoption_shuffle)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'State_factorize to dummies' storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'new hyper parameter,' storage_src(str_metric_score, score, second_score, feature_score, comment, adoption_shuffle)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'factrize stateID' storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'remove size_feature' storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, scores, feature_score): submit(submission, str_metric_score) comment = 'add 5 max min feature before standard scale' storage_src(str_metric_score, scores, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'metadata breed 2(1)' storage_src(str_metric_score, score, second_score, feature_score, comment, adoption_shuffle)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'main state East West, States_Federal,' storage_src(str_metric_score, score, second_score, feature_score, comment, adoption_shuffle)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'metrics mlogloss' storage_src(str_metric_score, score, second_score, feature_score, comment)
def storage_process(submission, str_metric_score, score, second_score, feature_score): submit(submission, str_metric_score) comment = 'shaffle_adoptionspeed' storage_src(str_metric_score, score, second_score, feature_score, comment, adoption_shuffle)