Python UserLog.save方法代码示例

# 需要导入模块: from kalite.main.models import UserLog [as 别名]
# 或者: from kalite.main.models.UserLog import save [as 别名]
def generate_fake_exercise_logs(
    facility_user=None, topics=topics, start_date=datetime.datetime.now() - datetime.timedelta(days=30 * 6)
):
    """Add exercise logs for the given topics, for each of the given users.
    If no users are given, they are created.
    If no topics exist, they are taken from the list at the top of this file.

    By default, users start learning randomly between 6 months ago and now.
    """

    date_diff = datetime.datetime.now() - start_date
    exercise_logs = []
    user_logs = []

    # It's not a user: probably a list.
    # Recursive case
    if not hasattr(facility_user, "username"):
        # It's NONE :-/ generate the users first!
        if not facility_user:
            (facility_user, _, _) = generate_fake_facility_users()

        for topic in topics:
            for user in facility_user:
                (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.info(
                "# 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_attempts = probability_of(qty="attempts", user_settings=user_settings)

                attempts = int(random.random() * p_attempts * 30 + 10)  # always enough to have completed

                elog, created = ExerciseLog.objects.get_or_create(user=facility_user, exercise_id=exercise["id"])

                alogs = []

                for i in range(0, attempts):
                    alog = AttemptLog.objects.create(
                        user=facility_user, exercise_id=exercise["id"], timestamp=start_date + date_diff * i / attempts
                    )
                    alogs.append(alog)
                    if random.random() < user_settings["speed_of_learning"]:
                        alog.correct = True
                        alog.points = 10
                    alog.save()

                elog.attempts = attempts
                elog.latest_activity_timestamp = start_date + date_diff
                elog.streak_progress = sum([log.correct for log in alogs][-10:]) * 10
                elog.points = sum([log.points for log in alogs][-10:])

                elog.save()

                exercise_logs.append(elog)

                ulog = UserLog(
                    user=facility_user,
                    activity_type=1,
                    start_datetime=start_date,
                    end_datetime=start_date + date_diff,
                    last_active_datetime=start_date + date_diff,
                )
#.........这里部分代码省略.........

# 需要导入模块: from kalite.main.models import UserLog [as 别名]
# 或者: from kalite.main.models.UserLog import save [as 别名]
def generate_fake_exercise_logs(facility_user=None, topics=topics, start_date=datetime.datetime.now() - datetime.timedelta(days=30 * 6)):
    """Add exercise logs for the given topics, for each of the given users.
    If no users are given, they are created.
    If no topics exist, they are taken from the list at the top of this file.

    By default, users start learning randomly between 6 months ago and now.
    """

    date_diff = datetime.datetime.now() - start_date
    exercise_logs = []
    user_logs = []

    # It's not a user: probably a list.
    # Recursive case
    if not hasattr(facility_user, "username"):
        # It's NONE :-/ generate the users first!
        if not facility_user:
            (facility_user, _, _) = generate_fake_facility_users()

        for topic in topics:
            for user in facility_user:
                (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),
                        complete=completed,
                        completion_timestamp=date_completed,
#.........这里部分代码省略.........