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View the Project on GitHub ngthanhtrung23/ACM_Notebook_new
import sys input = sys.stdin.readline import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import lightgbm as lgb def main(): # K = dimension of input vectors # T = specific to this problem # N = number of training data # M = number of test data # U = number of dimensions # Note that in this problem, input vectors are sparse, so K < U K, T, N, M, U = map(int, input().split()) feat_cols = [f'user_{i}' for i in range(U)] # Read training data train_df = np.full((N, U+1), -1) for i in range(N): tups = input().split() for tup in tups: x, a = map(int, tup.split(':')) train_df[i][x] = a # Read training labels for i in range(N): train_df[i][U] = int(input()) # Setup training DF train_df = pd.DataFrame(train_df, columns = feat_cols + ['label']) # Read test data test_df = np.full((M, U), -1) for i in range(M): tups = input().split() for tup in tups: x, a = map(int, tup.split(':')) test_df[i][x] = a # Setup test DF test_df = pd.DataFrame(test_df, columns = feat_cols) # train train_df, val_df = train_test_split(train_df, test_size=0.2) train_X, train_y = train_df[feat_cols], train_df[['label']] val_X, val_y = val_df[feat_cols], val_df[['label']] test_X = test_df del train_df, val_df, test_df params = { 'boosting_type': 'gbdt', 'objective': 'regression', 'metric': 'l2', 'learning_rate': 0.005, 'subsample': 1, 'colsample_bytree': 0.2, 'reg_alpha': 3, 'reg_lambda': 1, 'n_estimators': 2000, 'verbose': 1, 'max_depth': -1, 'seed':100, 'force_col_wise': True, 'verbose': -1, } clf = lgb.train(params, train_set = lgb.Dataset(train_X, train_y), valid_sets = [lgb.Dataset(val_X, val_y)], verbose_eval = False, ) # predict y_test = clf.predict(test_X, num_iteration=clf.best_iteration) for y in y_test: print(max(min(round(y), T), 0)) if __name__ == '__main__': main()
Traceback (most recent call last): File "/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/onlinejudge_verify/documentation/build.py", line 71, in _render_source_code_stat bundled_code = language.bundle(stat.path, basedir=basedir, options={'include_paths': [basedir]}).decode() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/onlinejudge_verify/languages/python.py", line 96, in bundle raise NotImplementedError NotImplementedError