diff --git a/OJ-BE/Dockerfile b/OJ-BE/Dockerfile index f66305b..3a84b6c 100644 --- a/OJ-BE/Dockerfile +++ b/OJ-BE/Dockerfile @@ -11,7 +11,7 @@ RUN apk add --update --no-cache build-base nginx openssl curl unzip supervisor j pip install --no-cache-dir -r /app/deploy/requirements.txt && \ apk del build-base --purge -RUN curl -L $(curl -s https://api.github.com/repos/QingdaoU/OnlineJudgeFE/releases/latest | grep /dist.zip | cut -d '"' -f 4) -o dist.zip && \ +RUN curl -L $(curl -s https://api.github.com/repos/Sejong-Creative-Semester2021/Sejong.creative.semester.2021/releases/latest | grep /dist.zip | cut -d '"' -f 4) -o dist.zip && \ unzip dist.zip && \ rm dist.zip diff --git a/OJ-BE/aicontest/views/oj.py b/OJ-BE/aicontest/views/oj.py index 92775d8..d954fe0 100644 --- a/OJ-BE/aicontest/views/oj.py +++ b/OJ-BE/aicontest/views/oj.py @@ -15,7 +15,7 @@ import json import csv # import pandas as pd -# import numpy as np +import numpy as np # import sklearn import logging @@ -88,7 +88,8 @@ def get(self, request): logger.info('problem_data={}'.format(problem_data)) return self.success(problem_data) except AIProblem.DoesNotExist: - return self.error("Problem does not exist") + # return self.error("Problem does not exist-aicontest-views-oj.py2") + return None logger.info('problem get continue') # limit = request.GET.get("limit") # if not limit: @@ -219,98 +220,98 @@ def post(self, request): 2,13 3,346 """ - # y_true = np.array(np.loadtxt(os.path.join(settings.SOLUTION_DIR, csv.solution_id, "solution.csv"), delimiter=",", dtype=np.float32, skiprows=1, usecols = (1,))) - # y_pred = np.array(np.loadtxt(os.path.join(settings.PREDICT_DIR, predict_id, "predict.csv"), delimiter=",", dtype=np.float32, skiprows=1, usecols = (1,))) - # logger.info("y_true={}".format(str(y_true))) - # logger.info("y_pred={}".format(str(y_pred))) + y_true = np.array(np.loadtxt(os.path.join(settings.SOLUTION_DIR, csv.solution_id, "solution.csv"), delimiter=",", dtype=np.float32, skiprows=1, usecols = (1,))) + y_pred = np.array(np.loadtxt(os.path.join(settings.PREDICT_DIR, predict_id, "predict.csv"), delimiter=",", dtype=np.float32, skiprows=1, usecols = (1,))) + logger.info("y_true={}".format(str(y_true))) + logger.info("y_pred={}".format(str(y_pred))) - # eval_type = csv.eval_type - # logger.info("eval_type={}".format(eval_type)) - - # if eval_type == 'mse': - # logger.info("if mse in") - # y_score = np.square(np.subtract(y_true, y_pred)).mean() - # logger.info("y_score={}".format(str(y_score))) - - # if eval_type == 'acc': - # logger.info("if acc in") - # y_score = (y_true == y_pred).mean() - # logger.info("y_score={}".format(str(y_score))) - - # if eval_type == 'rmse': - # logger.info("if rmse in") - # y_score = np.sqrt(np.mean(np.subtract(y_true, y_pred)**2)) + eval_type = csv.eval_type + logger.info("eval_type={}".format(eval_type)) + + if eval_type == 'mse': + logger.info("if mse in") + y_score = np.square(np.subtract(y_true, y_pred)).mean() + logger.info("y_score={}".format(str(y_score))) + + if eval_type == 'acc': + logger.info("if acc in") + y_score = (y_true == y_pred).mean() + logger.info("y_score={}".format(str(y_score))) + + if eval_type == 'rmse': + logger.info("if rmse in") + y_score = np.sqrt(np.mean(np.subtract(y_true, y_pred)**2)) - # if eval_type == 'auc': - # logger.info("if auc in") - - # if eval_type == 'mean_f_score': - # logger.info("if mean_f_score in") - # # accuracy = np.mean(np.equal(y_true, y_pred)) - # right = np.sum(y_true * y_pred == 1) - # precision = right / np.sum(y_pred) - # recall = right / np.sum(y_true) - # f1 = 2 * precision*recall/(precision+recall) - # y_score = f1 - # logger.info("if mean_f_score out") - - # if eval_type == 'mae': - # logger.info("if mae in") - # y_score = np.mean(np.abs(np.subtract(y_true, y_pred))) - - # if eval_type == 'f_beta': - # logger.info("if f_beta in") - - # if eval_type == 'logloss': - # logger.info("if logloss in") - # def my_logloss(answer_array, proba_array): - # # 출처 https://velog.io/@skyepodium/logloss-%EC%95%8C%EC%95%84%EB%B3%B4%EA%B8%B0 - # # 0이면 무한대 값이 나오기 때문에 0에 가까운 값으로 치환해줍니다. - # MIN_VALUE = 1e-15 - # # array의 크기를 가져옵니다. - # size = answer_array.shape[0] - # # 반복문을 사용해서 logloss의 합을 계산합니다. - # logloss_sum = 0 - # # zip함수로 묶으면 함께 순회할 수 있습니다. - # for answer, arr in zip(answer_array, proba_array): - # proba = arr[answer - 1] - # # 0이면 무한대 값이 나오기 때문에 0에 가까운 값으로 치환해줍니다. - # if proba <= MIN_VALUE: - # proba = MIN_VALUE - # # 음의 로그함수에 넣어서 logloss 계산 - # logloss_sum += -np.log(proba) - # # logloss의 평균 계산 - # result = logloss_sum / size - # # 반환 - # return result - # y_score = my_logloss(y_true, y_pred) - - # if eval_type == 'rmsle': - # logger.info("if rmsle in") - # def rmsle(predicted_values, actual_values): - # # 예측값과 실제 값에 1을 더하고 로그를 씌워준다. - # log_predict = np.log(predicted_values + 1) - # log_actual = np.log(actual_values + 1) - # # 위에서 계산한 예측값에서 실제값을 빼주고 제곱을 해준다. - # difference = log_predict - log_actual - # # difference = (log_predict - log_actual) ** 2 - # difference = np.square(difference) - # # 평균을 낸다. - # mean_difference = difference.mean() - # # 다시 루트를 씌운다. - # score = np.sqrt(mean_difference) - # return score - # y_score = rmsle(y_true, y_pred) - - # if eval_type == 'map@k': - # logger.info("if map@k in") + if eval_type == 'auc': + logger.info("if auc in") + + if eval_type == 'mean_f_score': + logger.info("if mean_f_score in") + # accuracy = np.mean(np.equal(y_true, y_pred)) + right = np.sum(y_true * y_pred == 1) + precision = right / np.sum(y_pred) + recall = right / np.sum(y_true) + f1 = 2 * precision*recall/(precision+recall) + y_score = f1 + logger.info("if mean_f_score out") + + if eval_type == 'mae': + logger.info("if mae in") + y_score = np.mean(np.abs(np.subtract(y_true, y_pred))) + + if eval_type == 'f_beta': + logger.info("if f_beta in") + + if eval_type == 'logloss': + logger.info("if logloss in") + def my_logloss(answer_array, proba_array): + # 출처 https://velog.io/@skyepodium/logloss-%EC%95%8C%EC%95%84%EB%B3%B4%EA%B8%B0 + # 0이면 무한대 값이 나오기 때문에 0에 가까운 값으로 치환해줍니다. + MIN_VALUE = 1e-15 + # array의 크기를 가져옵니다. + size = answer_array.shape[0] + # 반복문을 사용해서 logloss의 합을 계산합니다. + logloss_sum = 0 + # zip함수로 묶으면 함께 순회할 수 있습니다. + for answer, arr in zip(answer_array, proba_array): + proba = arr[answer - 1] + # 0이면 무한대 값이 나오기 때문에 0에 가까운 값으로 치환해줍니다. + if proba <= MIN_VALUE: + proba = MIN_VALUE + # 음의 로그함수에 넣어서 logloss 계산 + logloss_sum += -np.log(proba) + # logloss의 평균 계산 + result = logloss_sum / size + # 반환 + return result + y_score = my_logloss(y_true, y_pred) + + if eval_type == 'rmsle': + logger.info("if rmsle in") + def rmsle(predicted_values, actual_values): + # 예측값과 실제 값에 1을 더하고 로그를 씌워준다. + log_predict = np.log(predicted_values + 1) + log_actual = np.log(actual_values + 1) + # 위에서 계산한 예측값에서 실제값을 빼주고 제곱을 해준다. + difference = log_predict - log_actual + # difference = (log_predict - log_actual) ** 2 + difference = np.square(difference) + # 평균을 낸다. + mean_difference = difference.mean() + # 다시 루트를 씌운다. + score = np.sqrt(mean_difference) + return score + y_score = rmsle(y_true, y_pred) + + if eval_type == 'map@k': + logger.info("if map@k in") - # os.remove(tmp_file) + os.remove(tmp_file) - # y_score = float(y_score) + y_score = float(y_score) - # logger.info("float change - y_score={}".format(y_score)) - y_score = 55 + logger.info("float change - y_score={}".format(y_score)) + # y_score = 55 return self.success({"predictId": predict_id, "solutionId": csv.solution_id, "info": info, "y_score": y_score}) @@ -458,7 +459,7 @@ def put(self, request): logger.info('problem={}'.format(problem)) # ensure_created_by(problem, request.user) except AIProblem.DoesNotExist: - return self.error("Problem does not exist") + return self.error("Problem does not exist-aicontest-views-oj.py") # _id = data["_id"] # Display ID # if not _id: diff --git a/OJ-BE/deploy/requirements.txt b/OJ-BE/deploy/requirements.txt index 00f7d92..faf455f 100644 --- a/OJ-BE/deploy/requirements.txt +++ b/OJ-BE/deploy/requirements.txt @@ -30,4 +30,4 @@ XlsxWriter==1.1.5 django-dramatiq==0.5.0 dramatiq==1.3.0 django-dbconn-retry==0.1.5 -# numpy \ No newline at end of file +numpy \ No newline at end of file diff --git a/OJ-BE/problem/views/oj.py b/OJ-BE/problem/views/oj.py index 534fa60..abe0ecf 100644 --- a/OJ-BE/problem/views/oj.py +++ b/OJ-BE/problem/views/oj.py @@ -56,7 +56,7 @@ def get(self, request): self._add_problem_status(request, problem_data) return self.success(problem_data) except Problem.DoesNotExist: - return self.error("Problem does not exist") + return self.error("Problem does not exist-problem-views-oj.py-2") limit = request.GET.get("limit") if not limit: @@ -103,7 +103,7 @@ def get(self, request): contest=self.contest, visible=True) except Problem.DoesNotExist: - return self.error("Problem does not exist.") + return self.error("Problem does not exist-problem-views-oj.py-1") if self.contest.problem_details_permission(request.user): problem_data = ProblemSerializer(problem).data self._add_problem_status(request, [problem_data, ]) diff --git a/OJ-FE/src/pages/oj/views/aicontest/AIContest.vue b/OJ-FE/src/pages/oj/views/aicontest/AIContest.vue index bcff206..fd40c5f 100644 --- a/OJ-FE/src/pages/oj/views/aicontest/AIContest.vue +++ b/OJ-FE/src/pages/oj/views/aicontest/AIContest.vue @@ -7,7 +7,7 @@
{{problem.title}}
- {{joinText}} + {{joinText}}
@@ -479,6 +479,7 @@ console.log('gap2', gap) if (gap < 0) { this.beforeStart = true + this.joinText = '참여 불가' } }, submit () { diff --git a/OJ-FE/src/pages/oj/views/aicontest/AIContestList.vue b/OJ-FE/src/pages/oj/views/aicontest/AIContestList.vue index 5707890..7741c7e 100644 --- a/OJ-FE/src/pages/oj/views/aicontest/AIContestList.vue +++ b/OJ-FE/src/pages/oj/views/aicontest/AIContestList.vue @@ -16,7 +16,7 @@ - +
- {{problem.title}} + {{problem.title}} {{problem.created_by.username}}

{{problem.start_time | localtime('YYYY-M-D')}} - {{problem.end_time | localtime('YYYY-M-D')}}

- - - - D-{{getDuration(problem.start_time, problem.end_time)}} + 입장하기 + @@ -208,43 +200,11 @@ this.getProblemList() this.getClassProblemList() }, - // 추가 부분 - getDuration (startTime, endTime) { - // console.log(time.duration(startTime, endTime)) - // this.currentTime = new Date() - var year = endTime.slice(0, 4) - var month = endTime.slice(5, 7) - var day = endTime.slice(8, 10) - // console.log('endTime', endTime) - // console.log(year, month, day) - var Dday = new Date(year, month - 1, day) - var nowTime = Date.now() - var gap = nowTime - Dday.getTime() - // console.log('gap1', gap) - // console.log('nowTime', nowTime) - // console.log('Dday', Dday) - var result = Math.floor(gap / (1000 * 60 * 60 * 24)) * -1 - // console.log('result', result) - if (result === 0) { - result = 'D-day' - } - if (result > 0) { // 평상시 상황 - result = 'D-' + result - } - if (result < 0) { // dday 지난 경우 - result = '종료' - } - // 지금 시간이 대회 시작 날짜보다 이전이면... - var startDay = new Date(startTime.slice(0, 4), startTime.slice(5, 7) - 1, startTime.slice(8, 10)) - console.log('nowTime', nowTime) - console.log('startDay.getTime()', startDay.getTime()) - gap = nowTime - startDay.getTime() - var result2 = Math.floor(gap / (1000 * 60 * 60 * 24)) * -1 - console.log('gap2', gap) - if (gap < 0) { - result = 'OPEN D-' + result2 - } - return result + modalButtonClick (problemID, problemPwd) { + console.log('modal button') + this.isModalViewed = true + this.modalProblemID = problemID + this.modalProblemPwd = problemPwd }, goProblem (problemID) { this.$router.push({name: 'aiproblem-general-details', params: {problemID: problemID}}) @@ -405,10 +365,15 @@ margin-top: -20px; margin-left: 10px; } + // .problem-title{ + // font-size: 23px; + // font-weight: 800; + // color: #3399ff; + // } .problem-title{ font-size: 23px; font-weight: 800; - color: #3399ff; + color: #2d2762; } .problem-subtitle{ font-size: 16px;