v4.1.0

What's Changed

• Switch to official ruff gh action by @joshdavham in #66
• Add linear damping by @joshdavham in #67
• Fuzz more intervals by @joshdavham in #68

Full Changelog: v4.0.0...v4.1.0

v4.0.0

Py-FSRS v4.0.0

This release makes some major changes to the API, the required version of python and a couple of other changes.

At a high level:

• Py-FSRS now requires python 3.10+
• The scheduler can now randomly "fuzz" calculated review intervals
• The scheduler can now set explicit learning and relearning steps
• Card objects no longer have a New state (new cards now start off in the Learning state)
• Card objects now have different attributes
• The scheduler has been renamed from FSRS to Scheduler
• The repeat method has been replaced in favor of the review_card method

Upgrade from v3 -> v4

To upgrade, first make sure you're using python 3.10 or later.

Next, you'll want to review the updated README to get familiar with the new API, which should hopefully be mostly straightforward.

The tricky part will be in updating previous v3 cards to v4.

This was the old class schema for v3 Cards:

class Card:
    """
    Represents a flashcard in the FSRS system.

    Attributes:
        due (datetime): The date and time when the card is due next.
        stability (float): Core FSRS parameter used for scheduling.
        difficulty (float): Core FSRS parameter used for scheduling.
        elapsed_days (int): The number of days since the card was last reviewed.
        scheduled_days (int): The number of days until the card is due next.
        reps (int): The number of times the card has been reviewed in its history.
        lapses (int): The number of times the card has been lapsed in its history.
        state (State): The card's current learning state.
        last_review (datetime): The date and time of the card's last review.
    """

    due: datetime
    stability: float
    difficulty: float
    elapsed_days: int
    scheduled_days: int
    reps: int
    lapses: int
    state: State
    last_review: datetime

and here's the new class schema for v4 Cards:

class Card:
    """
    Represents a flashcard in the FSRS system.

    Attributes:
        card_id (int): The id of the card. Defaults to the epoch miliseconds of when the card was created.
        state (State): The card's current learning state.
        step (int | None): The card's current learning or relearning step or None if the card is in the Review state.
        stability (float | None): Core mathematical parameter used for future scheduling.
        difficulty (float | None): Core mathematical parameter used for future scheduling.
        due (datetime): The date and time when the card is due next.
        last_review (datetime | None): The date and time of the card's last review.
    """

    card_id: int
    state: State
    step: int | None
    stability: float | None
    difficulty: float | None
    due: datetime
    last_review: datetime | None

To upgrade your v3 cards to v4 cards, do the following:

• Set card_id to a unique integer value. Unless otherwise specified, new v4 cards will be set to the epoch miliseconds of when the card was created, but you are free to set it to anything else.
• If your card's state was set to New, convert it to Learning and set its stability and difficulty values to None
• If your card was in the New, Learning or Relearning state, set its step to 0, otherwise, if the v3 card was in the Review state, set its step to None
• Remove the following attributes from your v3 cards (these were removed since they don't affect scheduling)
  • elapsed_days
  • scheduled_days
  • reps
  • lapses
• If your v3 card previously didn't have a last_review attribute, create one and set it to None

Now as for upgrading your v3ReviewLog objects to v4 objects, this will likely require some custom migration on your part. Apologies for any inconvenience.

In either case, this was the old v3 ReviewLog class schema:

class ReviewLog:
    """
    Represents the log entry of Card that has been reviewed.
    Attributes:
        rating (Rating): The rating given to the card during the review.
        scheduled_days (int): The number of days until the card is due next.
        elapsed_days (int): The number of days since the card was last reviewed.
        review (datetime): The date and time of the review.
        state (State): The learning state of the card before the review.
    """

    rating: Rating
    scheduled_days: int
    elapsed_days: int
    review: datetime
    state: State

and here's the new v4 ReviewLog schema:

class ReviewLog:
    """
    Represents the log entry of a Card object that has been reviewed.

    Attributes:
        card (Card): Copy of the card object that was reviewed.
        rating (Rating): The rating given to the card during the review.
        review_datetime (datetime): The date and time of the review.
        review_duration (int | None): The number of miliseconds it took to review the card or None if unspecified.
    """

    card: Card
    rating: Rating
    review_datetime: datetime
    review_duration: int | None

Upgrading to the new scheduler should be simple enough, just review the new README and compare it to how you were previously using the scheduler.

What's Changed

• Major update and refactor by @joshdavham in #64

Full Changelog: v3.1.0...v4.0.0

v3.1.0

What's Changed

• remove wildcard imports by @joshdavham in #53
• Add unit test of memory state by @L-M-Sherlock in #54
• add more type annotation by @joshdavham in #55
• Add file that summarizes the FSRS scheduler by @joshdavham in #57
• Add docstrings by @joshdavham in #58
• Update ALGORITHM.md by @ishiko732 in #59
• compute retrievability for all states by @joshdavham in #61

Full Changelog: v3.0.0...v3.1.0

v3.0.0

Py-FSRS v3.0.0

This release implements the new FSRS-5 scheduler algorithm.

FSRS-5 now uses 19 parameters instead of 17, so if you were previously setting your own custom parameters you must do one of the following to upgrade:

1. Optimize again with an FSRS-5 optimizer to get a new set of FSRS-5 weights
   • You can choose from the following three currently available optimizers: fsrs-optimizer, fsrs-rs or fsrs-browser
   • Additionally, you can also wait till the next Anki release when the FSRS-5 optimizer will be included as a new feature
2. Keep your previous weights, but set the two new parameters equal to 0 (w[17]=0, w[18]=0)
3. Or simply revert to using default parameters for now

What's Changed

• modify readme and contributing for Ruff addition+ run ruff formatter by @joshdavham in #50
• mypy contributing by @joshdavham in #51
• FSRS-5 by @joshdavham in #52

Full Changelog: v2.5.1...v3.0.0

v2.5.1

What's Changed

• use ruff by @asukaminato0721 in #47
• Create mypy.yml + fix mypy warning by @asukaminato0721 in #48
• Cast integer and Float values when reading from dict by @lomenzel in #49

New Contributors

• @asukaminato0721 made their first contribution in #47
• @lomenzel made their first contribution in #49

Full Changelog: v2.5.0...v2.5.1

v2.5.0

What's Changed

• update README.md by @joshdavham in #44
• small cleanup by @joshdavham in #45
• add custom args to FSRS class by @joshdavham in #46

Full Changelog: v2.4.0...v2.5.0

v2.4.0

What's Changed

• feat/add review_card function by @joshdavham in #43

Full Changelog: v2.3.0...v2.4.0

v2.3.0

What's Changed

• add a CONTRIBUTING.md page by @joshdavham in #40
• make ReviewLog objects JSON-serializable by @joshdavham in #42

Full Changelog: v2.2.1...v2.3.0

v2.2.1

What's Changed

• fix/fsrs4.5-parameter-weights by @joshdavham in #39

Full Changelog: v2.2.0...v2.2.1

v2.2.0

What's Changed

• Use lower case for attribute in SchedulingInfo by @vanicat in #36
• update README.md by @joshdavham in #37
• add default argument to repeat function by @joshdavham in #38

New Contributors

• @vanicat made their first contribution in #36

Full Changelog: v2.1.1...v2.2.0