Python ExerciseLog.full_clean方法代码示例

# 需要导入模块: from main.models import ExerciseLog [as 别名]
# 或者: from main.models.ExerciseLog import full_clean [as 别名]
    def handle(self, *args, **options):
        if settings.CENTRAL_SERVER:
            raise CommandError("Don't run this on the central server!!  Data not linked to any zone on the central server is BAD.")

        facility = Facility(name="Wilson Elementary")
        facility.save()
        group1 = FacilityGroup(facility=facility, name="Class 4E")
        group1.full_clean()
        group1.save()
        group2 = FacilityGroup(facility=facility, name="Class 5B")
        group2.full_clean()
        group2.save()
        facilityusers = []
        
        for i in range(0,10):
            newuser1 = FacilityUser(facility=facility, username=usernames[i], first_name=firstnames[i], last_name=lastnames[i], group=group1)
            if settings.DEBUG:
                newuser1.set_password(hashed_password = hashed_blah)#"blah")
            else:
                newuser1.set_password("blah")
            newuser1.full_clean()
            newuser1.save()
            facilityusers.append(newuser1)
            newuser2 = FacilityUser(facility=facility, username=usernames[i+10], first_name=firstnames[i+10], last_name=lastnames[i+10], group=group2)
            if settings.DEBUG:
                newuser2.set_password(hashed_password = hashed_blah)#"blah")
            else:
                newuser2.set_password("blah")
            newuser2.full_clean()
            newuser2.save()
            facilityusers.append(newuser2)
        
        for topic in topics:
            exercises = get_topic_exercises(topic_id=topic)
            exercises_a = [random.random() for i in range(len(exercises))]
            exercises_b = [float(i) / len(exercises) for i in range(len(exercises))]
            for i, user in enumerate(facilityusers):
                for j, exercise in enumerate(exercises):
                    sig = sigmoid(proficiency[i], exercises_a[j], exercises_b[j])
                    if random.random() < 0.05 * (1-sig) and j > 2:
                        break
                    if random.random() < 0.15:
                        continue
                    attempts = random.random() * 40 + 10
                    streak_progress = max(10, min(100, 10 * random.random() + 10 * attempts * sig))
                    print int(attempts), int(streak_progress), user.first_name, exercise["name"]
                    log = ExerciseLog(user=user, exercise_id=exercise["name"], attempts=attempts, streak_progress=streak_progress)
                    log.full_clean()
                    log.save()

# 需要导入模块: from main.models import ExerciseLog [as 别名]
# 或者: from main.models.ExerciseLog import full_clean [as 别名]
 def handle(self, *args, **options):
     facility = Facility(name="Wilson Elementary")
     facility.save()
     group1 = FacilityGroup(facility=facility, name="Class 4E")
     group1.full_clean()
     group1.save()
     group2 = FacilityGroup(facility=facility, name="Class 5B")
     group2.full_clean()
     group2.save()
     facilityusers = []
     
     for i in range(0,10):
         newuser1 = FacilityUser(facility=facility, username=usernames[i], first_name=firstnames[i], last_name=lastnames[i], group=group1)
         newuser1.set_password("blah")
         newuser1.full_clean()
         newuser1.save()
         facilityusers.append(newuser1)
         newuser2 = FacilityUser(facility=facility, username=usernames[i+10], first_name=firstnames[i+10], last_name=lastnames[i+10], group=group2)
         newuser2.set_password("blah")
         newuser2.full_clean()
         newuser2.save()
         facilityusers.append(newuser2)
     
     for topic in topics:
         exercises = json.load(open("./static/data/topicdata/" + topic + ".json","r"))
         exercises = sorted(exercises, key = lambda k: (k["h_position"], k["v_position"]))
         exercises_a = [random.random() for i in range(len(exercises))]
         exercises_b = [float(i) / len(exercises) for i in range(len(exercises))]
         for i, user in enumerate(facilityusers):
             for j, exercise in enumerate(exercises):
                 sig = sigmoid(proficiency[i], exercises_a[j], exercises_b[j])
                 if random.random() < 0.05 * (1-sig) and j > 2:
                     break
                 if random.random() < 0.15:
                     continue
                 attempts = random.random() * 40 + 10
                 streak_progress = max(10, min(100, 10 * random.random() + 10 * attempts * sig))
                 print int(attempts), int(streak_progress), user.first_name, exercise["name"]
                 log = ExerciseLog(user=user, exercise_id=exercise["name"], attempts=attempts, streak_progress=streak_progress)
                 log.full_clean()
                 log.save()

# 需要导入模块: from main.models import ExerciseLog [as 别名]
# 或者: from main.models.ExerciseLog import full_clean [as 别名]

#.........这里部分代码省略.........
                (elogs, ulogs) = generate_fake_exercise_logs(facility_user=user, topics=[topic], start_date=start_date)
                exercise_logs.append(elogs)
                user_logs.append(ulogs)

    # Actually generate!
    else:
        # Get (or create) user type
        try:
            user_settings = json.loads(facility_user.notes)
        except:
            user_settings = sample_user_settings()
            facility_user.notes = json.dumps(user_settings)
            facility_user.save()
        date_diff_started = datetime.timedelta(seconds=datediff(date_diff, units="seconds") * user_settings["time_in_program"])  # when this user started in the program, relative to NOW

        for topic in topics:
            # Get all exercises related to the topic
            exercises = get_topic_exercises(topic_id=topic)

            # Problem:
            #   Not realistic for students to have lots of unfinished exercises.
            #   If they start them, they tend to get stuck, right?
            #
            # So, need to make it more probable that they will finish an exercise,
            #   and less probable that they start one.
            #
            # What we need is P(streak|started), not P(streak)

            # Probability of doing any particular exercise
            p_exercise = probability_of(qty="exercise", user_settings=user_settings)
            logging.debug("# exercises: %d; p(exercise)=%4.3f, user settings: %s\n" % (len(exercises), p_exercise, json.dumps(user_settings)))

            # of exercises is related to
            for j, exercise in enumerate(exercises):
                if random.random() > p_exercise:
                    continue

                # Probability of completing this exercise, and .. proportion of attempts
                p_completed = probability_of(qty="completed", user_settings=user_settings)
                p_attempts = probability_of(qty="attempts", user_settings=user_settings)

                attempts = int(random.random() * p_attempts * 30 + 10)  # always enough to have completed
                completed = (random.random() < p_completed)
                if completed:
                    streak_progress = 100
                else:
                    streak_progress = max(0, min(90, random.gauss(100 * user_settings["speed_of_learning"], 20)))
                    streak_progress = int(floor(streak_progress / 10.)) * 10
                points = streak_progress / 10 * 12 if completed else 0  # only get points when you master.

                # Choose a rate of exercises, based on their effort level and speed of learning.
                #   Compute the latest possible start time.
                #   Then sample a start time between their start time
                #   and the latest possible start_time
                rate_of_exercises = 0.66 * user_settings["effort_level"] + 0.33 * user_settings["speed_of_learning"]  # exercises per day
                time_for_attempts = min(datetime.timedelta(days=rate_of_exercises * attempts), date_diff_started)  # protect with min
                time_delta_completed = datetime.timedelta(seconds=random.randint(int(datediff(time_for_attempts, units="seconds")), int(datediff(date_diff_started, units="seconds"))))
                date_completed = datetime.datetime.now() - time_delta_completed

                # Always create new
                logging.info("Creating exercise log: %-12s: %-25s (%d points, %d attempts, %d%% streak on %s)" % (
                    facility_user.first_name,
                    exercise["name"],
                    points,
                    attempts,
                    streak_progress,
                    date_completed,
                ))
                try:
                    elog = ExerciseLog.objects.get(user=facility_user, exercise_id=exercise["name"])
                except ExerciseLog.DoesNotExist:
                    elog = ExerciseLog(
                        user=facility_user,
                        exercise_id=exercise["name"],
                        attempts=int(attempts),
                        streak_progress=streak_progress,
                        points=int(points),
                        completion_timestamp=date_completed,
                        completion_counter=datediff(date_completed, start_date, units="seconds"),
                    )
                    elog.full_clean()
                    elog.save()   # TODO(bcipolli): bulk saving of logs

                    # For now, make all attempts on an exercise into a single UserLog.
                    seconds_per_attempt = 10 * (1 + user_settings["speed_of_learning"] * random.random())
                    time_to_navigate = 15 * (0.5 + random.random())  #between 7.5s and 22.5s
                    time_to_logout = 5 * (0.5 + random.random()) # between 2.5 and 7.5s
                    ulog = UserLog(
                        user=facility_user,
                        activity_type=1,
                        start_datetime = date_completed - datetime.timedelta(seconds=int(attempts * seconds_per_attempt + time_to_navigate)),
                        end_datetime = date_completed + datetime.timedelta(seconds=time_to_logout),
                        last_active_datetime = date_completed,
                    )
                    ulog.full_clean()
                    ulog.save()
                    user_logs.append(ulog)
                exercise_logs.append(elog)

    return (exercise_logs, user_logs)