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218 regels
7.2 KiB
218 regels
7.2 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b59dad38",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"\n",
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"class FileTypeError(TypeError):\n",
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" pass\n",
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"\n",
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"\n",
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"class ContestScore:\n",
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" # for eval(cur_info) in gen_max_cases_\n",
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" global null\n",
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" null = ''\n",
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"\n",
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" def __init__(self, ):\n",
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" self.rank_path = ''\n",
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" self.submission_path = ''\n",
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" self.problem_path = ''\n",
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" self.ranks_df = None\n",
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" self.submission_df = None\n",
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" self.problem_df = None\n",
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" self.problem_names = []\n",
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" self.problem_ids = []\n",
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" self.contest_score = None\n",
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"\n",
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" # for calculate max cases\n",
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" self.target_problem = 0\n",
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" self.gen_all_cases = True\n",
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"\n",
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" def load_data(self, rank_path, submission_path, problem_path):\n",
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" # load data\n",
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" self.ranks_df = ContestScore.read_dataframe(rank_path)\n",
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" self.submission_df = ContestScore.read_dataframe(submission_path)\n",
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" self.rank_path = rank_path\n",
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" self.submission_path = submission_path\n",
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" rank_columns = self.ranks_df.columns.tolist()\n",
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" self.problem_names = rank_columns[6:]\n",
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"\n",
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" # process ranks\n",
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" drop_data = ['User ID', 'Real Name', 'Total Submission'] + self.problem_names\n",
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" self.contest_score = self.ranks_df.drop(axis=1, columns=drop_data)\n",
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"\n",
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" # calculate max cases\n",
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" self.contest_score['通过用例数'] = self.contest_score.apply(self.gen_max_cases, axis=1)\n",
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"\n",
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" # get problem_names and problem_ids\n",
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" # calculate problem cases\n",
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" if problem_path:\n",
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" self.get_problems(problem_path)\n",
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" self.calculate()\n",
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" else:\n",
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" print(\"Please Set Problem names and ids by set_problems(names, ids) function\")\n",
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"\n",
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" # sort scores\n",
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" self.sort()\n",
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"\n",
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" def get_problems(self, problem_path):\n",
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" self.problem_df = ContestScore.read_dataframe(problem_path)\n",
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" self.problem_names = self.problem_df.title.to_list()\n",
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" self.problem_ids = self.problem_df.id.to_list()\n",
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" self.problem_path = problem_path\n",
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"\n",
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" def gen_max_cases(self, df):\n",
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" self.gen_all_cases = True\n",
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" return self.gen_max_cases_(df)\n",
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"\n",
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" def set_problems(self, problem_names, problem_ids):\n",
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" self.problem_names = problem_names\n",
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" self.problem_ids = problem_ids\n",
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"\n",
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" def calculate(self):\n",
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" # calculate max cases for certain problems\n",
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" self.gen_all_cases = False\n",
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" for index, p_id in enumerate(self.problem_ids):\n",
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" self.target_problem = p_id\n",
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" p_name = self.problem_names[index]\n",
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" self.contest_score[p_name] = self.contest_score.apply(self.gen_max_cases_problem, axis=1)\n",
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"\n",
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" def gen_max_cases_problem(self, df):\n",
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" self.gen_all_cases = False\n",
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" return self.gen_max_cases_(df)\n",
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"\n",
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" def gen_max_cases_(self, df):\n",
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" cur_max_cases = {}\n",
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" for i in range(len(self.submission_df)):\n",
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" if str(self.submission_df.iloc[i]['username']) == str(df['Username']) \\\n",
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" and (self.gen_all_cases or self.target_problem == self.submission_df.iloc[i]['problem_id']):\n",
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" try:\n",
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" cur_info = self.submission_df.iloc[i]['info']\n",
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" cur_problem_id = self.submission_df.iloc[i]['problem_id']\n",
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" cur_score = sum(list(map(lambda x: x['result'] == 0, eval(cur_info)['data'])))\n",
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"\n",
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" if not (cur_problem_id in cur_max_cases.keys()):\n",
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" cur_max_cases[cur_problem_id] = cur_score\n",
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" elif cur_score > cur_max_cases[cur_problem_id]:\n",
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" cur_max_cases[cur_problem_id] = cur_score\n",
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" except:\n",
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" continue\n",
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" return sum(cur_max_cases.values())\n",
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"\n",
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" def sort(self, by=['AC', '通过用例数', 'Total Time'], ascending=[False, False, True]):\n",
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" self.contest_score = self.contest_score.sort_values(by=by, ascending=ascending)\n",
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" return self.contest_score\n",
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"\n",
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" def save(self, path):\n",
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" self.contest_score.to_excel(path, index=False)\n",
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" return f\"File Saved at {path} as Excel\"\n",
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"\n",
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" @staticmethod\n",
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" def read_dataframe(path):\n",
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" filetype = path.split('.')[-1]\n",
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" dataframe = None\n",
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" if filetype == 'csv':\n",
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" dataframe = pd.read_csv(path)\n",
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" elif filetype == 'xlsx':\n",
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" dataframe = pd.read_excel(path)\n",
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" else:\n",
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" FileTypeError(filetype)\n",
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" return dataframe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "09049d66",
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"metadata": {},
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"outputs": [],
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"source": [
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"test = ContestScore()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "73373376",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[0m\u001b[38;5;167m机试成绩导出.ipynb\u001b[0m \u001b[38;5;78mproblem_202207021234.csv\u001b[0m\r\n",
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"\u001b[38;5;33;1mcontent-68-rank.xlsx\u001b[0m \u001b[38;5;78msubmission_202207021235.csv\u001b[0m\r\n"
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]
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}
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],
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"source": [
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"%ls"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "2b0cedc1",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'File Saved at Result.xlsx as Excel'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# load data and calculate scores , ranks automatically\n",
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"test.load_data(rank_path='./content-68-rank.xlsx', submission_path='./submission_202207021235.csv', problem_path='./problem_202207021234.csv')\n",
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"\n",
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"# retun the result by a pandas DataFrame\n",
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"test.contest_score\n",
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"\n",
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"# Save result as excel\n",
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"test.save('Result.xlsx')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "50e6428e",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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